sdintimaging

sdintimaging(vis, usedata='sdint', sdimage='', sdpsf='', sdgain=1.0, dishdia='', selectdata=True, field='', spw='', timerange='', uvrange='', antenna='', scan='', observation='', intent='', datacolumn='corrected', imagename='', imsize=100, cell='"1arcsec"', phasecenter='', stokes='I', projection='SIN', startmodel='', specmode='mfs', reffreq='', nchan=-1, start='', width='', outframe='LSRK', veltype='radio', restfreq='', interpolation='linear', perchanweightdensity=True, gridder='standard', facets=1, psfphasecenter='', wprojplanes=1, vptable='', mosweight=True, aterm=True, psterm=False, wbawp=True, cfcache='', usepointing=False, computepastep=360.0, rotatepastep=360.0, pointingoffsetsigdev='', pblimit=0.2, deconvolver='hogbom', scales='', nterms=2, smallscalebias=0.0, restoration=True, restoringbeam='', pbcor=False, weighting='natural', robust=0.5, noise='1.0Jy', npixels=0, uvtaper=[''], niter=0, gain=0.1, threshold=0.0, nsigma=0.0, cycleniter=-1, cyclefactor=1.0, minpsffraction=0.05, maxpsffraction=0.8, interactive=False, fullsummary=False, nmajor=-1, usemask='user', mask='', pbmask=0.0, sidelobethreshold=3.0, noisethreshold=5.0, lownoisethreshold=1.5, negativethreshold=0.0, smoothfactor=1.0, minbeamfrac=0.3, cutthreshold=0.01, growiterations=75, dogrowprune=True, minpercentchange=-1.0, verbose=False, fastnoise=True, restart=True, calcres=True, calcpsf=True)[source]

Form images from interferometric visibilities and single dish image to reconstruct a sky model by joint deconvolution.

[Description] [Examples] [Development] [Details]

Parameters
  • usedata (string=’sdint’) - Output image type(int, sd, sdint)

    usedata = sd
    • sdimage (string=’’) - Input single dish image

    • sdpsf ({string, double}=’’) - Input single dish PSF image

    • sdgain (double=1.0) - A factor or gain to adjust single dish flux scale

    usedata = sdint
    • sdimage (string=’’) - Input single dish image

    • sdpsf ({string, double}=’’) - Input single dish PSF image

    • sdgain (double=1.0) - A factor or gain to adjust single dish flux scale

  • vis ({string, stringVec}) - Name of input visibility file(s)

  • selectdata (bool=True) - Enable data selection parameters

    selectdata = True
    • field ({string, stringVec}=’’) - field(s) to select

    • spw ({string, stringVec}=’’) - spw(s)/channels to select

    • timerange ({string, stringVec}=’’) - Range of time to select from data

    • uvrange ({string, stringVec}=’’) - Select data within uvrange

    • antenna ({string, stringVec}=’’) - Select data based on antenna/baseline

    • scan ({string, stringVec}=’’) - Scan number range

    • observation ({string, int}=’’) - Observation ID range

    • intent ({string, stringVec}=’’) - Scan Intent(s)

  • datacolumn (string=’corrected’) - Data column to image(data,corrected)

  • imagename ({int, string, stringVec}=’’) - Pre-name of output images

  • imsize ({int, intVec}=100) - Number of pixels

  • cell ({int, double, intVec, doubleVec, string, stringVec}=‘“1arcsec”’) - Cell size

  • phasecenter ({int, string}=’’) - Phase center of the image

  • stokes (string=’I’) - Stokes Planes to make

  • projection (string=’SIN’) - Coordinate projection

  • startmodel (string=’’) - Name of starting model image

  • specmode (string=’mfs’) - Spectral definition mode (mfs,cube,cubedata, cubesource)

    specmode = mfs
    • reffreq (string=’’) - Reference frequency

  • nchan (int=-1) - Number of channels in the output image

  • start (string=’’) - First channel (e.g. start=3,start='1.1GHz',start='15343km/s')

  • width (string=’’) - Channel width (e.g. width=2,width='0.1MHz',width='10km/s')

  • outframe (string=’LSRK’) - Spectral reference frame in which to interpret 'start' and 'width'

  • veltype (string=’radio’) - Velocity type (radio, z, ratio, beta, gamma, optical)

  • restfreq (stringVec=’’) - List of rest frequencies

  • interpolation (string=’linear’) - Spectral interpolation (nearest,linear,cubic)

  • perchanweightdensity (bool=True) - whether to calculate weight density per channel in Briggs style weighting or not

  • gridder (string=’standard’) - Gridding options (standard, wproject, widefield, mosaic, awproject)

    gridder = standard
    • vptable (string=’’) - Name of Voltage Pattern table

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = widefield
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • facets (int=1) - Number of facets on a side

    • vptable (string=’’) - Name of Voltage Pattern table

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = wproject
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • vptable (string=’’) - Name of Voltage Pattern table

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = wprojectft
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • vptable (string=’’) - Name of Voltage Pattern table

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = mosaic
    • vptable (string=’’) - Name of Voltage Pattern table

    • usepointing (bool=False) - The parameter makes the gridder utilize the pointing table phase directions while computing the residual image.

    • mosweight (bool=True) - Indepently weight each field in a mosaic

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    • psfphasecenter ({int, string}=’’) - optional direction to calculate psf for mosaic (default is image phasecenter)

    gridder = mosaicft
    • vptable (string=’’) - Name of Voltage Pattern table

    • usepointing (bool=False) - The parameter makes the gridder utilize the pointing table phase directions while computing the residual image.

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    • psfphasecenter ({int, string}=’’) - optional direction to calculate psf for mosaic (default is image phasecenter)

    gridder = ftmosaic
    • vptable (string=’’) - Name of Voltage Pattern table

    • usepointing (bool=False) - The parameter makes the gridder utilize the pointing table phase directions while computing the residual image.

    • mosweight (bool=True) - Indepently weight each field in a mosaic

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = imagemosaic
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • vptable (string=’’) - Name of Voltage Pattern table

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    gridder = awproject
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • psterm (bool=False) - Use prolate spheroidal during gridding

    • aterm (bool=True) - Use aperture illumination functions during gridding

    • cfcache (string=’’) - Convolution function cache directory name

    • computepastep (double=360.0) - Parallactic angle interval after the AIFs are recomputed (deg)

    • rotatepastep (double=360.0) - Parallactic angle interval after which the nearest AIF is rotated (deg)

    • pointingoffsetsigdev ({intVec, doubleVec}=’’) - Pointing offset threshold to determine heterogeneity of pointing corrections for the AWProject gridder

    • wbawp (bool=True) - Use wideband A-terms

    • mosweight (bool=True) - Indepently weight each field in a mosaic

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    • usepointing (bool=False) - The parameter makes the gridder utilize the pointing table phase directions while computing the residual image.

    gridder = awprojectft
    • wprojplanes (int=1) - Number of distinct w-values for convolution functions

    • psterm (bool=False) - Use prolate spheroidal during gridding

    • aterm (bool=True) - Use aperture illumination functions during gridding

    • cfcache (string=’’) - Convolution function cache directory name

    • computepastep (double=360.0) - Parallactic angle interval after the AIFs are recomputed (deg)

    • rotatepastep (double=360.0) - Parallactic angle interval after which the nearest AIF is rotated (deg)

    • pointingoffsetsigdev ({intVec, doubleVec}=’’) - Pointing offset threshold to determine heterogeneity of pointing corrections for the AWProject gridder

    • wbawp (bool=True) - Use wideband A-terms

    • mosweight (bool=True) - Indepently weight each field in a mosaic

    • pblimit (double=0.2) - PB gain level at which to cut off normalizations

    • usepointing (bool=False) - The parameter makes the gridder utilize the pointing table phase directions while computing the residual image.

  • deconvolver (string=’hogbom’) - Minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes,asp)

    deconvolver = multiscale
    • scales ({intVec, floatVec}=’’) - List of scale sizes (in pixels) for multi-scale algorithms

    • smallscalebias (double=0.0) - Biases the scale selection when using multi-scale or mtmfs deconvolvers

    deconvolver = mtmfs
    • scales ({intVec, floatVec}=’’) - List of scale sizes (in pixels) for multi-scale algorithms

    • nterms (int=2) - Number of Taylor coefficients in the spectral model

    • smallscalebias (double=0.0) - Biases the scale selection when using multi-scale or mtmfs deconvolvers

  • restoration (bool=True) - Do restoration steps (or not)

    restoration = True
    • restoringbeam ({string, stringVec}=’’) - Restoring beam shape to use. Default is the PSF main lobe

    • pbcor (bool=False) - Apply PB correction on the output restored image

  • weighting (string=’natural’) - Weighting scheme (natural,uniform,briggs, briggsabs[experimental])

    weighting = natural
    • uvtaper (stringVec=[‘’]) - uv-taper on outer baselines in uv-plane

    weighting = briggs
    • robust (double=0.5) - Robustness parameter

    • npixels (int=0) - Number of pixels to determine uv-cell size

    • uvtaper (stringVec=[‘’]) - uv-taper on outer baselines in uv-plane

    weighting = briggsabs
    • robust (double=0.5) - Robustness parameter

    • noise (variant=’1.0Jy’)

    • npixels (int=0) - Number of pixels to determine uv-cell size

    • uvtaper (stringVec=[‘’]) - uv-taper on outer baselines in uv-plane

  • niter (int=0) - Maximum number of iterations

    niter != 0
    • gain (double=0.1) - Loop gain

    • threshold (double=0.0) - Stopping threshold

    • nsigma (double=0.0) - Multiplicative factor for rms-based threshold stopping

    • cycleniter (int=-1) - Maximum number of minor-cycle iterations

    • cyclefactor (double=1.0) - Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.

    • minpsffraction (double=0.05) - PSF fraction that marks the max depth of cleaning in the minor cycle

    • maxpsffraction (double=0.8) - PSF fraction that marks the minimum depth of cleaning in the minor cycle

    • interactive (bool=False) - Modify masks and parameters at runtime

    • fullsummary (bool=False) - Return dictionary with complete convergence history

    • calcres (bool=True) - Calculate initial residual image

    • calcpsf (bool=True) - Calculate PSF

  • nmajor (int=-1) - Maximum number of major cycles to evaluate

  • usemask (string=’user’) - Type of mask(s) for deconvolution: user, pb, or auto-multithresh

    usemask = user
    • mask ({string, stringVec}=’’) - Mask (a list of image name(s) or region file(s) or region string(s) )

    • pbmask (double=0.0) - primary beam mask

    usemask = pb
    • pbmask (double=0.0) - primary beam mask

    usemask = auto-multithresh
    • pbmask (double=0.0) - primary beam mask

    • sidelobethreshold (double=3.0) - sidelobethreshold * the max sidelobe level * peak residual

    • noisethreshold (double=5.0) - noisethreshold * rms in residual image + location(median)

    • lownoisethreshold (double=1.5) - lownoisethreshold * rms in residual image + location(median)

    • negativethreshold (double=0.0) - negativethreshold * rms in residual image + location(median)

    • smoothfactor (double=1.0) - smoothing factor in a unit of the beam

    • minbeamfrac (double=0.3) - minimum beam fraction for pruning

    • cutthreshold (double=0.01) - threshold to cut the smoothed mask to create a final mask

    • growiterations (int=75) - number of binary dilation iterations for growing the mask

    • dogrowprune (bool=True) - Do pruning on the grow mask

    • minpercentchange (double=-1.0) - minimum percentage change in mask size (per channel plane) to trigger updating of mask by automask

    • verbose (bool=False) - True: print more automasking information in the logger

  • fastnoise (bool=True) - True: use the faster (old) noise calculation. False: use the new improved noise calculations

  • restart (bool=True) - True : Re-use existing images. False : Increment imagename

  • calcres (bool=True) - Calculate initial residual image

  • calcpsf (bool=True) - Calculate PSF

Returns

summary (dict) - convergence history of the run, when interactive=1/0 instead of True/False

Description

The sdintimaging task allows joint reconstruction of wideband single dish and interferometer data.

Warning

The experience with joint reconstruction of wideband single dish and interferometer data with sdintimaging in CASA is limited. Please pay close attention to the description of tested usage modes in the CASA Docs chapter page on Joint Single Dish and Interferometer Image Reconstruction

Warning

There is a Known Issues for sdintimaging: gridder=’awproject’ is not yet available. It will be enabled in a future release.

Interferometer data are gridded into an image cube (and corresponding PSF). The single dish image and PSF cubes are combined with the interferometer cubes in a feathering step. The joint image and PSF cubes then form inputs to any deconvolution algorithm (in either cube or mfs/mtmfs modes). Model images from the deconvolution algorithm are translated back to model image cubes prior to subtraction from both the single dish image cube as well as the interferometer data to form a new pair of residual image cubes to be feathered in the next iteration. In the case of mosaic imaging, primary beam corrections are performed per channel of the image cube, followed by a multiplication by a common primary beam, prior to deconvolution. Therefore, for mosaic imaging, this task always implements conjbeams=True and normtype=’flatnoise’.

|image1|

A more detailed description of the underlying algorithm, as well as results from its testing, can be found on the CASA Docs chapter page on Joint Single Dish and Interferometer Image Reconstruction. Note that the above diagram shows only the ‘mtmfs’ variant. Cube deconvolution proceeds directly with the cubes in the green box above, without the extra conversion back and forth to the multi-term basis. Primary beam handling is also not shown in this diagram, but full details (via pseudocode) are available in the reference publication.

Task Specification : sdintimaging

The sdintimaging task shares a significant number of parameters with the tclean task. In the description below, parameters that are specific to sdintimaging are listed with full details, but all others will reference the existing tclean parameter documentation.

Multiple MSes

  • Multiple MSes given as a list in vis is supported as in tclean. The details on the conformance checks that are performed on the list of the MSes are summarized in the CASA Docs on Combining Datasets.

Examples

To run sdintimaging with automatic SD-PSF generation, n-sigma stopping thresholds, a pb-based mask at the 0.3 gain level, and no other deconvolution masks (interactive=False). Use the helper function shown below to extract frequency information from the sd cube to supply as input to sdintimaging. Note that the sdimage cube must contain per-plane restoring beams.

from sdint_helper import \*
sdintlib = SDINT_helper()
sdintlib.setup_cube_params(sdcube='M100_TP')
   Output : Shape of SD cube : [90 90  1 70]
   Coordinate ordering : ['Direction', 'Direction', 'Stokes',
   'Spectral']
   nchan = 70
   start = 114732899312.0Hz
   width = -1922516.74324Hz
   Found 70 per-plane restoring beams#
   (For specmode='mfs' in sdintimaging, please remember to set
   'reffreq' to a value within the freq range of the cube)
   Returned Dict : {'nchan': 70, 'start': '114732899312.0Hz',
   'width': '-1922516.74324Hz'}

sdintimaging(usedata="sdint", sdimage="../M100_TP",
             sdpsf="",sdgain=3.0, dishdia=12.0, vis="../M100_12m_7m",
             imagename="try_sdint_niter5k", imsize=1000, cell="0.5arcsec",
             phasecenter="J2000 12h22m54.936s +15d48m51.848s", stokes="I",
             specmode="cube", reffreq="", nchan=70,
             start="114732899312.0Hz", width="-1922516.74324Hz",
             outframe="LSRK", veltype="radio", restfreq="115.271201800GHz",
             interpolation="linear", perchanweightdensity=True, 
             gridder="mosaic", mosweight=True,
             pblimit=0.2, deconvolver="multiscale", scales=[0, 5, 10, 15, 20],
             smallscalebias=0.0, pbcor=False, weighting="briggs",
             robust=0.5, niter=5000, gain=0.1, threshold=0.0, nsigma=3.0,
             interactive=False, usemask="user", mask="", pbmask=0.3)

For test-results using these parameters, and for additional test-results, see the CASA Docs chapter page on Joint Single Dish and Interferometeric Image Reconstruction.

Development

This page gives an overview of the code design and future development work that needs to be done. Detailed information on the algorithm can be found on the chapter page on Joint Single Dish and Interferometer Image Reconstruction, while a description of the sdintimaging task and associated parameters can be found on the sdintimaging task pages.

Code Design

The sdintimaging task is implemented using the PySynthesisImager module in CASA.

Core algorithm implementation: sdint_imager.py and sdint_helper.py

sdint_imager contains main setup fuctions using PySnthesisImager: setup_imager, setup_deconvolver, setup_sdimaging as well as main joint imaging alogrithm (do_reconstruct). The sdint_helper provides helper functions such as feathering of sd + int, single dish residual calculation, primary beam manipulation, checks for consistency between SD and INT cube coordinate systems, etc.

As shown in the diagram at the top of this page, a feathering step is inserted in between major and minor cycles to combine SD residual and interferometer residual images as well as PSFs before deconvolution. Apart from this, standard major/minor cycle iterations are performed and most imaging modes of task tclean are preserved. However, only the above documented subset of modes have been tested.

Future work

The following is a list of features that are either not available yet or currently untested with the sdintimaging task (or known bugs):

  • Use of task_deconvolve for sd only.

  • Fully test and characterize ‘int-only’ as a wideband mosaic option

  • Add the ability to specify only the SD image cube and have the interferometer cube coordinate system be generated to match it.

  • Improve how task feather works on cubes with per-plane restoring beams

  • Understand why the feather step results in NaNs if the pblimit is set to a negative value for joint mosaic imaging of the INT data.

  • Stop the ‘imageregrid’ warnings for every single run, which are given even if the SD cell size and beam are compatible.

  • Add tools to check the relative flux densities of single-dish and interferometer visibility data to verify the results of joint deconvolution and other combination techniques.

  • Check if restoration can happen with niter=0.

  • Use sdint_helper:: setup_cube_params() to autogenerate nchan/start/width and then remove some parameters from the sdintimaging task interface, and check for validity of the input Single Dish image and PSF cubes

  • If it is not possible to run ‘imregrid’, provide guidance to users on what to do.

  • Connect to tsdimaging internally for ALMA data.

Parameter Details

Detailed descriptions of each function parameter

usedata (string='sdint') - Output image type: ‘int’ - use interferometric data only;
‘sd’ - use single dish data only;
‘sdint’ - use both single dish and interferic data
sdimage (string='') - Input single dish image
This single dish Image cube must contain images per frequency channel (blanked for empty
or flagged channels).
If the associated sdpsf parameter is set to an empty string to signal an automatic
calculation of the SD PSF cube, this SD image cube must contain per-plane
restoringbeams that represent the effect SDbeam per frequency.
sdpsf ({string, double}='') - Input single dish PSF image.
This single dish PSF cube must contain the effective SD beam in the center of the image,
for each frequency channel, normalized to peak 1. The coordinate system should ideally
be the same as the SD image cube and contain per-plane restoringbeams that represent the
effect SD beam per frequency.
If the sdpsf is set to a blank string (sdpsf=””) an approximate PSF cube will be automatically
calculated internally by using per-plane restoring-beam information from the regridded sdimage
to evaluate 2D Gaussians.
In the future, we will provide an option to auto-generate Airy disk beams derived from
the specified dish diameter.
sdgain (double=1.0) - A factor or gain to adjust single dish flux scale (to use in feather stage)
dishdia (double='') - Effective dish diameter of the SD telescope (meters)
vis ({string, stringVec}) - Name(s) of input visibility file(s)
default: none;
example: vis=’ngc5921.ms’
vis=[‘ngc5921a.ms’,’ngc5921b.ms’]; multiple MSes
selectdata (bool=True) - Enable data selection parameters.
field ({string, stringVec}='') - Select fields to image or mosaic. Use field id(s) or name(s).
[‘go listobs’ to obtain the list id’s or names]
default: ‘’= all fields
If field string is a non-negative integer, it is assumed to
be a field index otherwise, it is assumed to be a
field name
field=’0~2’; field ids 0,1,2
field=’0,4,5~7’; field ids 0,4,5,6,7
field=’3C286,3C295’; field named 3C286 and 3C295
field = ‘3,4C*’; field id 3, all names starting with 4C
For multiple MS input, a list of field strings can be used:
field = [‘0~2’,’0~4’]; field ids 0-2 for the first MS and 0-4
for the second
field = ‘0~2’; field ids 0-2 for all input MSes
spw ({string, stringVec}='') - Select spectral window/channels
NOTE: channels de-selected here will contain all zeros if
selected by the parameter mode subparameters.
default: ‘’=all spectral windows and channels
spw=’0~2,4’; spectral windows 0,1,2,4 (all channels)
spw=’0:5~61’; spw 0, channels 5 to 61
spw=’<2’; spectral windows less than 2 (i.e. 0,1)
spw=’0,10,3:3~45’; spw 0,10 all channels, spw 3,
channels 3 to 45.
spw=’0~2:2~6’; spw 0,1,2 with channels 2 through 6 in each.
For multiple MS input, a list of spw strings can be used:
spw=[‘0’,’0~3’]; spw ids 0 for the first MS and 0-3 for the second
spw=’0~3’ spw ids 0-3 for all input MS
spw=’3:10~20;50~60’ for multiple channel ranges within spw id 3
spw=’3:10~20;50~60,4:0~30’ for different channel ranges for spw ids 3 and 4
spw=’0:0~10,1:20~30,2:1;2;3’; spw 0, channels 0-10,
spw 1, channels 20-30, and spw 2, channels, 1,2 and 3
spw=’1~4;6:15~48’ for channels 15 through 48 for spw ids 1,2,3,4 and 6
timerange ({string, stringVec}='') - Range of time to select from data
default: ‘’ (all); examples,
timerange = ‘YYYY/MM/DD/hh:mm:ss~YYYY/MM/DD/hh:mm:ss’
Note: if YYYY/MM/DD is missing date defaults to first
day in data set
timerange=’09:14:0~09:54:0’ picks 40 min on first day
timerange=’25:00:00~27:30:00’ picks 1 hr to 3 hr
30min on NEXT day
timerange=’09:44:00’ pick data within one integration
of time
timerange=’> 10:24:00’ data after this time
For multiple MS input, a list of timerange strings can be
used:
timerange=[‘09:14:0~09:54:0’,’> 10:24:00’]
timerange=’09:14:0~09:54:0’’; apply the same timerange for
all input MSes
uvrange ({string, stringVec}='') - Select data within uvrange (default unit is meters)
default: ‘’ (all); example:
uvrange=’0~1000klambda’; uvrange from 0-1000 kilo-lambda
uvrange=’> 4klambda’;uvranges greater than 4 kilo lambda
For multiple MS input, a list of uvrange strings can be
used:
uvrange=[‘0~1000klambda’,’100~1000klamda’]
uvrange=’0~1000klambda’; apply 0-1000 kilo-lambda for all
input MSes
antenna ({string, stringVec}='') - Select data based on antenna/baseline
default: ‘’ (all)
If antenna string is a non-negative integer, it is
assumed to be an antenna index, otherwise, it is
considered an antenna name.
antenna=’5&6’; baseline between antenna index 5 and
index 6.
antenna=’VA05&VA06’; baseline between VLA antenna 5
and 6.
antenna=’5&6;7&8’; baselines 5-6 and 7-8
antenna=’5’; all baselines with antenna index 5
antenna=’05’; all baselines with antenna number 05
(VLA old name)
antenna=’5,6,9’; all baselines with antennas 5,6,9
index number
For multiple MS input, a list of antenna strings can be
used:
antenna=[‘5’,’5&6’];
antenna=’5’; antenna index 5 for all input MSes
antenna=’!DV14’; use all antennas except DV14
scan ({string, stringVec}='') - Scan number range
default: ‘’ (all)
example: scan=’1~5’
For multiple MS input, a list of scan strings can be used:
scan=[‘0~100’,’10~200’]
scan=’0~100; scan ids 0-100 for all input MSes
observation ({string, int}='') - Observation ID range
default: ‘’ (all)
example: observation=’1~5’
intent ({string, stringVec}='') - Scan Intent(s)
default: ‘’ (all)
example: intent=’TARGET_SOURCE’
example: intent=’TARGET_SOURCE1,TARGET_SOURCE2’
example: intent=’TARGET_POINTING*’
datacolumn (string='corrected') - Data column to image (data or observed, corrected)
default:’corrected’
( If ‘corrected’ does not exist, it will use ‘data’ instead )
imagename ({int, string, stringVec}='') - Pre-name of output images
example : imagename=’try’
Output images will be (a subset of) :
try.psf - Point spread function
try.residual - Residual image
try.image - Restored image
try.model - Model image (contains only flux components)
try.sumwt - Single pixel image containing sum-of-weights.
(for natural weighting, sensitivity=1/sqrt(sumwt))
try.pb - Primary beam model (values depend on the gridder used)
Widefield projection algorithms (gridder=mosaic,awproject) will
compute the following images too.
try.weight - FT of gridded weights or the
un-normalized sum of PB-square (for all pointings)
Here, PB = sqrt(weight) normalized to a maximum of 1.0
For multi-term wideband imaging, all relevant images above will
have additional .tt0,.tt1, etc suffixes to indicate Taylor terms,
plus the following extra output images.
try.alpha - spectral index
try.alpha.error - estimate of error on spectral index
try.beta - spectral curvature (if nterms > 2)
Tip : Include a directory name in ‘imagename’ for all
output images to be sent there instead of the
current working directory : imagename=’mydir/try’
Tip : Restarting an imaging run without changing ‘imagename’
implies continuation from the existing model image on disk.
- If ‘startmodel’ was initially specified it needs to be set to “”
for the restart run (or sdintimaging will exit with an error message).
- By default, the residual image and psf will be recomputed
but if no changes were made to relevant parameters between
the runs, set calcres=False, calcpsf=False to resume directly from
the minor cycle without the (unnecessary) first major cycle.
To automatically change ‘imagename’ with a numerical
increment, set restart=False (see sdintimaging docs for ‘restart’).
Note : All imaging runs will by default produce restored images.
For a niter=0 run, this will be redundant and can optionally
be turned off via the ‘restoration=T/F’ parameter.
imsize ({int, intVec}=100) - Number of pixels
example:
imsize = [350,250]
imsize = 500 is equivalent to [500,500]

To take proper advantage of internal optimized FFT routines, the
number of pixels must be even and factorizable by 2,3,5 only.
To find the nearest optimal imsize to that desired by the user, please use the following tool method:
from casatools import synthesisutils
su = synthesisutils()
su.getOptimumSize(345)
Output : 360
cell ({int, double, intVec, doubleVec, string, stringVec}='"1arcsec"') - Cell size
example: cell=[‘0.5arcsec,’0.5arcsec’] or
cell=[‘1arcmin’, ‘1arcmin’]
cell = ‘1arcsec’ is equivalent to [‘1arcsec’,’1arcsec’]
phasecenter ({int, string}='') - Phase center of the image (string or field id); if the phasecenter is the name known major solar system object (‘MERCURY’, ‘VENUS’, ‘MARS’, ‘JUPITER’, ‘SATURN’, ‘URANUS’, ‘NEPTUNE’, ‘PLUTO’, ‘SUN’, ‘MOON’) or is an ephemerides table then that source is tracked and the background sources get smeared. There is a special case, when phasecenter=’TRACKFIELD’, which will use the ephemerides or polynomial phasecenter in the FIELD table of the MS’s as the source center to track.
example: phasecenter=6
phasecenter=’J2000 19h30m00 -40d00m00’
phasecenter=’J2000 292.5deg -40.0deg’
phasecenter=’J2000 5.105rad -0.698rad’
phasecenter=’ICRS 13:05:27.2780 -049.28.04.458’
phasecenter=’myComet_ephem.tab’
phasecenter=’MOON’
phasecenter=’TRACKFIELD’
stokes (string='I') - Stokes Planes to make
default=’I’; example: stokes=’IQUV’;
Options: ‘I’,’Q’,’U’,’V’,’IV’,’QU’,’IQ’,’UV’,’IQUV’,’RR’,’LL’,’XX’,’YY’,’RRLL’,’XXYY’,’pseudoI’
Note : Due to current internal code constraints, if any correlation pair
is flagged, by default, no data for that row in the MS will be used.
So, in an MS with XX,YY, if only YY is flagged, neither a
Stokes I image nor an XX image can be made from those data points.
In such a situation, please split out only the unflagged correlation into
a separate MS.
Note : The ‘pseudoI’ option is a partial solution, allowing Stokes I imaging
when either of the parallel-hand correlations are unflagged.
The remaining constraints shall be removed (where logical) in a future release.
projection (string='SIN') - Coordinate projection
Examples : SIN, NCP
A list of supported (but untested) projections can be found here :
startmodel (string='') - Name of starting model image
The contents of the supplied starting model image will be
copied to the imagename.model before the run begins.
example : startmodel = ‘singledish.im’
For deconvolver=’mtmfs’, one image per Taylor term must be provided.
example : startmodel = [‘try.model.tt0’, ‘try.model.tt1’]
startmodel = [‘try.model.tt0’] will use a starting model only
for the zeroth order term.
startmodel = [‘’,’try.model.tt1’] will use a starting model only
for the first order term.
This starting model can be of a different image shape and size from
what is currently being imaged. If so, an image regrid is first triggered
to resample the input image onto the target coordinate system.
A common usage is to set this parameter equal to a single dish image
Negative components in the model image will be included as is.
[ Note : If an error occurs during image resampling/regridding,
please try using task imregrid to resample the starting model
image onto a CASA image with the target shape and
coordinate system before supplying it via startmodel ]
specmode (string='mfs') - Spectral definition mode (mfs,cube,cubedata, cubesource)
mode=’mfs’ : Continuum imaging with only one output image channel.
(mode=’cont’ can also be used here)
mode=’cube’ : Spectral line imaging with one or more channels
Parameters start, width,and nchan define the spectral
coordinate system and can be specified either in terms
of channel numbers, frequency or velocity in whatever
spectral frame is specified in ‘outframe’.
All internal and output images are made with outframe as the
base spectral frame. However imaging code internally uses the fixed
spectral frame, LSRK for automatic internal software
Doppler tracking so that a spectral line observed over an
extended time range will line up appropriately.
Therefore the output images have additional spectral frame conversion
layer in LSRK on the top the base frame.
(Note : Even if the input parameters are specified in a frame
other than LSRK, the viewer still displays spectral
axis in LSRK by default because of the conversion frame
layer mentioned above. The viewer can be used to relabel
the spectral axis in any desired frame - via the spectral
reference option under axis label properties in the
data display options window.)

mode=’cubedata’ : Spectral line imaging with one or more channels
There is no internal software Doppler tracking so
a spectral line observed over an extended time range
may be smeared out in frequency. There is strictly
no valid spectral frame with which to label the output
images, but they will list the frame defined in the MS.
mode=’cubesource’: Spectral line imaging while
tracking moving source (near field or solar system
objects). The velocity of the source is accounted
and the frequency reported is in the source frame.
As there is not SOURCE frame defined,
the frame reported will be REST (as it may not be
in the rest frame emission region may be
moving w.r.t the systemic velocity frame)
reffreq (string='') - Reference frequency of the output image coordinate system
Example : reffreq=’1.5GHz’ as a string with units.
By default, it is calculated as the middle of the selected frequency range.
For deconvolver=’mtmfs’ the Taylor expansion is also done about
this specified reference frequency.
nchan (int=-1) - Number of channels in the output image
For default (=-1), the number of channels will be automatically determined
based on data selected by ‘spw’ with ‘start’ and ‘width’.
It is often easiest to leave nchan at the default value.
example: nchan=100
start (string='') - First channel (e.g. start=3,start='1.1GHz',start='15343km/s')
of output cube images specified by data channel number (integer),
velocity (string with a unit), or frequency (string with a unit).
Default:’’; The first channel is automatically determined based on
the ‘spw’ channel selection and ‘width’.
When the channel number is used along with the channel selection
in ‘spw’ (e.g. spw=’0:6~100’),
‘start’ channel number is RELATIVE (zero-based) to the selected
channels in ‘spw’. So for the above example,
start=1 means that the first image channel is the second selected
data channel, which is channel 7.
For specmode=’cube’, when velocity or frequency is used it is
interpreted with the frame defined in outframe. [The parameters of
the desired output cube can be estimated by using the ‘transform’
functionality of ‘plotms’]
examples: start=’5.0km/s’; 1st channel, 5.0km/s in outframe
start=’22.3GHz’; 1st channel, 22.3GHz in outframe
width (string='') - Channel width (e.g. width=2,width='0.1MHz',width='10km/s') of output cube images
specified by data channel number (integer), velocity (string with a unit), or
or frequency (string with a unit).
Default:’’; data channel width
The sign of width defines the direction of the channels to be incremented.
For width specified in velocity or frequency with ‘-’ in front gives image channels in
decreasing velocity or frequency, respectively.
For specmode=’cube’, when velocity or frequency is used it is interpreted with
the reference frame defined in outframe.
examples: width=’2.0km/s’; results in channels with increasing velocity
width=’-2.0km/s’; results in channels with decreasing velocity
width=’40kHz’; results in channels with increasing frequency
width=-2; results in channels averaged of 2 data channels incremented from
high to low channel numbers
outframe (string='LSRK') - Spectral reference frame in which to interpret 'start' and 'width'
Options: ‘’,’LSRK’,’LSRD’,’BARY’,’GEO’,’TOPO’,’GALACTO’,’LGROUP’,’CMB’
example: outframe=’bary’ for Barycentric frame
REST – Rest frequency
LSRD – Local Standard of Rest (J2000)
– as the dynamical definition (IAU, [9,12,7] km/s in galactic coordinates)
LSRK – LSR as a kinematical (radio) definition
– 20.0 km/s in direction ra,dec = [270,+30] deg (B1900.0)
BARY – Barycentric (J2000)
GEO — Geocentric
TOPO – Topocentric
GALACTO – Galacto centric (with rotation of 220 km/s in direction l,b = [90,0] deg.
LGROUP – Local group velocity – 308km/s towards l,b = [105,-7] deg (F. Ghigo)
CMB – CMB velocity – 369.5km/s towards l,b = [264.4, 48.4] deg (F. Ghigo)
DEFAULT = LSRK
veltype (string='radio') - Velocity type (radio, z, ratio, beta, gamma, optical)
For start and/or width specified in velocity, specifies the velocity definition
Options: ‘radio’,’optical’,’z’,’beta’,’gamma’,’optical’
NOTE: the viewer always defaults to displaying the ‘radio’ frame,
but that can be changed in the position tracking pull down.
The different types (with F = f/f0, the frequency ratio), are:
Z = (-1 + 1/F)
RATIO = (F) *
RADIO = (1 - F)
OPTICAL == Z
BETA = ((1 - F2)/(1 + F2))
GAMMA = ((1 + F2)/2F) *
RELATIVISTIC == BETA (== v/c)
DEFAULT == RADIO
Note that the ones with an ‘*’ have no real interpretation
(although the calculation will proceed) if given as a velocity.
restfreq (stringVec='') - List of rest frequencies or a rest frequency in a string.
Specify rest frequency to use for output image.
Currently it uses the first rest frequency in the list for translation of
velocities. The list will be stored in the output images.
Default: []; look for the rest frequency stored in the MS, if not available,
use center frequency of the selected channels
examples: restfreq=[‘1.42GHz’]
restfreq=’1.42GHz’
interpolation (string='linear') - Spectral interpolation (nearest,linear,cubic)
Interpolation rules to use when binning data channels onto image channels
and evaluating visibility values at the centers of image channels.
Note : ‘linear’ and ‘cubic’ interpolation requires data points on both sides of
each image frequency. Errors are therefore possible at edge channels, or near
flagged data channels. When image channel width is much larger than the data
channel width there is nothing much to be gained using linear or cubic thus
not worth the extra computation involved.
perchanweightdensity (bool=True) - When calculating weight density for Briggs
style weighting in a cube, this parameter
determines whether to calculate the weight
density for each channel independently
(the default, True)
or a common weight density for all of the selected
data. This parameter has no
meaning for continuum (specmode=’mfs’)
imaging but for cube imaging
perchanweightdensity=True is a recommended
option that provides more uniform
sensitivity per channel for cubes, but with
generally larger psfs than the
perchanweightdensity=False (prior behavior)
option. When using Briggs style weight with
perchanweightdensity=True, the imaging weight
density calculations use only the weights of
data that contribute specifically to that
channel. On the other hand, when
perchanweightdensity=False, the imaging
weight density calculations sum all of the
weights from all of the data channels
selected whose (u,v) falls in a given uv cell
on the weight density grid. Since the
aggregated weights, in any given uv cell,
will change depending on the number of
channels included when imaging, the psf
calculated for a given frequency channel will
also necessarily change, resulting in
variability in the psf for a given frequency
channel when perchanweightdensity=False. In
general, perchanweightdensity=False results
in smaller psfs for the same value of
robustness compared to
perchanweightdensity=True, but the rms noise
as a function of channel varies and increases
toward the edge channels;
perchanweightdensity=True provides more
uniform sensitivity per channel for
cubes. This may make it harder to find
estimates of continuum when
perchanweightdensity=False. If you intend to
image a large cube in many smaller subcubes
and subsequently concatenate, it is advisable
to use perchanweightdensity=True to avoid
surprisingly varying sensitivity and psfs
across the concatenated cube.
gridder (string='standard') - Gridding options (standard, wproject, widefield, mosaic, awproject)
The following options choose different gridding convolution
functions for the process of convolutional resampling of the measured
visibilities onto a regular uv-grid prior to an inverse FFT.
Model prediction (degridding) also uses these same functions.
Several wide-field effects can be accounted for via careful choices of
convolution functions. Gridding (degridding) runtime will rise in
proportion to the support size of these convolution functions (in uv-pixels).
standard : Prolate Spheroid with 3x3 uv pixel support size
[ This mode can also be invoked using ‘ft’ or ‘gridft’ ]
wproject : W-Projection algorithm to correct for the widefield
non-coplanar baseline effect. [Cornwell et.al 2008]
wprojplanes is the number of distinct w-values at
which to compute and use different gridding convolution
functions (see help for wprojplanes).
Convolution function support size can range
from 5x5 to few 100 x few 100.
[ This mode can also be invoked using ‘wprojectft’ ]
widefield : Facetted imaging with or without W-Projection per facet.
A set of facets x facets subregions of the specified image
are gridded separately using their respective phase centers
(to minimize max W). Deconvolution is done on the joint
full size image, using a PSF from the first subregion.
wprojplanes=1 : standard prolate spheroid gridder per facet.
wprojplanes > 1 : W-Projection gridder per facet.
nfacets=1, wprojplanes > 1 : Pure W-Projection and no facetting
nfacets=1, wprojplanes=1 : Same as standard,ft,gridft
A combination of facetting and W-Projection is relevant only for
very large fields of view.
mosaic : A-Projection with azimuthally symmetric beams without
sidelobes, beam rotation or squint correction.
Gridding convolution functions per visibility are computed
from FTs of PB models per antenna.
This gridder can be run on single fields as well as mosaics.
VLA : PB polynomial fit model (Napier and Rots, 1982)
EVLA : PB polynomial fit model (Perley, 2015)
ALMA : Airy disks for a 10.7m dish (for 12m dishes) and
6.25m dish (for 7m dishes) each with 0.75m
blockages (Hunter/Brogan 2011). Joint mosaic
imaging supports heterogeneous arrays for ALMA.
Typical gridding convolution function support sizes are
between 7 and 50 depending on the desired
accuracy (given by the uv cell size or image field of view).
[ This mode can also be invoked using ‘mosaicft’ or ‘ftmosaic’ ]
awproject : A-Projection with azimuthally asymmetric beams and
including beam rotation, squint correction,
conjugate frequency beams and W-projection.
[Bhatnagar et.al, 2008]
Gridding convolution functions are computed from
aperture illumination models per antenna and optionally
combined with W-Projection kernels and a prolate spheroid.
This gridder can be run on single fields as well as mosaics.

The awproject gridder is current not supported in the sdintimaging task.
This feature will be added in the near future.
VLA : Uses ray traced model (VLA and EVLA) including feed
leg and subreflector shadows, off-axis feed location
(for beam squint and other polarization effects), and
a Gaussian fit for the feed beams (Ref: Brisken 2009)
ALMA : Similar ray-traced model as above (but the correctness
of its polarization properties remains un-verified).
Typical gridding convolution function support sizes are
between 7 and 50 depending on the desired
accuracy (given by the uv cell size or image field of view).
When combined with W-Projection they can be significantly larger.
[ This mode can also be invoked using ‘awprojectft’ ]
imagemosaic : (untested implementation)
Grid and iFT each pointing separately and combine the
images as a linear mosaic (weighted by a PB model) in
the image domain before a joint minor cycle.
VLA/ALMA PB models are same as for gridder=’mosaicft’
—— Notes on PB models :
(1) Several different sources of PB models are used in the modes
listed above. This is partly for reasons of algorithmic flexibility
and partly due to the current lack of a common beam model
repository or consensus on what beam models are most appropriate.
(2) For ALMA and gridder=’mosaic’, ray-traced (TICRA) beams
are also available via the vpmanager tool.
For example, call the following before the sdintimaging run.
vp.setpbimage(telescope=”ALMA”,
compleximage=’/home/casa/data/trunk/alma/responses/ALMA_0_DV__0_0_360_0_45_90_348.5_373_373_GHz_ticra2007_VP.im’,
antnames=[‘DV’+’%02d’%k for k in range(25)])
vp.saveastable(‘mypb.tab’)
Then, supply vptable=’mypb.tab’ to sdintimaging.
—— Note on PB masks :
In sdintimaging, A-Projection gridders (mosaic and awproject) produce a
.pb image and use the ‘pblimit’ subparameter to decide normalization
cutoffs and construct an internal T/F mask in the .pb and .image images.
However, this T/F mask cannot directly be used during deconvolution
(which needs a 1/0 mask). There are two options for making a pb based
deconvolution mask.
– Run sdintimaging with niter=0 to produce the .pb, construct a 1/0 image
with the desired threshold (using ia.open(‘newmask.im’);
ia.calc(‘iif(“xxx.pb”>0.3,1.0,0.0)’);ia.close() for example),
and supply it via the ‘mask’ parameter in a subsequent run
(with calcres=F and calcpsf=F to restart directly from the minor cycle).
– Run sdintimaging with usemask=’pb’ for it to automatically construct
a 1/0 mask from the internal T/F mask from .pb at a fixed 0.2 threshold.
—– Making PBs for gridders other than mosaic,awproject
After the PSF generation, a PB is constructed using the same
models used in gridder=’mosaic’ but just evaluated in the image
domain without consideration to weights.
facets (int=1) - Number of facets on a side
A set of (facets x facets) subregions of the specified image
are gridded separately using their respective phase centers
(to minimize max W). Deconvolution is done on the joint
full size image, using a PSF from the first subregion/facet.
psfphasecenter ({int, string}='') - For mosaic use psf centered on this
optional direction. You may need to use
this if for example the mosaic does not
have any pointing in the center of the
image. Another reason; as the psf is
approximate for a mosaic, this may help
to deconvolve a non central bright source
well and quickly.
example:
psfphasecenter=6 #center psf on field 6
psfphasecenter=’J2000 19h30m00 -40d00m00’
psfphasecenter=’J2000 292.5deg -40.0deg’
psfphasecenter=’J2000 5.105rad -0.698rad’
psfphasecenter=’ICRS 13:05:27.2780 -049.28.04.458’
wprojplanes (int=1) - Number of distinct w-values at which to compute and use different
gridding convolution functions for W-Projection
An appropriate value of wprojplanes depends on the presence/absence
of a bright source far from the phase center, the desired dynamic
range of an image in the presence of a bright far out source,
the maximum w-value in the measurements, and the desired trade off
between accuracy and computing cost.
As a (rough) guide, VLA L-Band D-config may require a
value of 128 for a source 30arcmin away from the phase
center. A-config may require 1024 or more. To converge to an
appropriate value, try starting with 128 and then increasing
it if artifacts persist. W-term artifacts (for the VLA) typically look
like arc-shaped smears in a synthesis image or a shift in source
position between images made at different times. These artifacts
are more pronounced the further the source is from the phase center.
There is no harm in simply always choosing a large value (say, 1024)
but there will be a significant performance cost to doing so, especially
for gridder=’awproject’ where it is combined with A-Projection.
wprojplanes=-1 is an option for gridder=’widefield’ or ‘wproject’
in which the number of planes is automatically computed.
vptable (string='') - VP table saved via the vpmanager
vptable=”” : Choose default beams for different telescopes
ALMA : Airy disks
EVLA : old VLA models.
Other primary beam models can be chosen via the vpmanager tool.
Step 1 : Set up the vpmanager tool and save its state in a table
vp.setpbpoly(telescope=’EVLA’, coeff=[1.0, -1.529e-3, 8.69e-7, -1.88e-10])
vp.saveastable(‘myvp.tab’)
Step 2 : Supply the name of that table in sdintimaging.
sdintimaging(….., vptable=’myvp.tab’,….)
Please see the documentation for the vpmanager for more details on how to
choose different beam models. Work is in progress to update the defaults
for EVLA and ALMA.
Note : AWProjection currently does not use this mechanism to choose
beam models. It instead uses ray-traced beams computed from
parameterized aperture illumination functions, which are not
available via the vpmanager. So, gridder=’awproject’ does not allow
the user to set this parameter.
mosweight (bool=True) - When doing Brigg’s style weighting (including uniform) to perform the weight density calculation for each field indepedently if True. If False the weight density is calculated from the average uv distribution of all the fields.
aterm (bool=True) - Use aperture illumination functions during gridding
This parameter turns on the A-term of the AW-Projection gridder.
Gridding convolution functions are constructed from aperture illumination
function models of each antenna.
psterm (bool=False) - Include the Prolate Spheroidal (PS) funtion as the anti-aliasing
operator in the gridding convolution functions used for gridding.
Setting this parameter to true is necessary when aterm is set to
false. It can be set to false when aterm is set to true, though
with this setting effects of aliasing may be there in the image,
particularly near the edges.
When set to true, the .pb images will contain the fourier transform
of the of the PS funtion. The table below enumarates the functional
effects of the psterm, aterm and wprojplanes settings. PB referes to
the Primary Beam and FT() refers to the Fourier transform operation.
Operation aterm psterm wprojplanes Contents of the .pb image
———————————————————————-
AW-Projection True True >1 FT(PS) x PB
False PB
A-Projection True True 1 FT(PS) x PB
False PB
W-Projection False True >1 FT(PS)
Standard False True 1 FT(PS)
wbawp (bool=True) - Use frequency dependent A-terms
Scale aperture illumination functions appropriately with frequency
when gridding and combining data from multiple channels.
cfcache (string='') - Convolution function cache directory name
Name of a directory in which to store gridding convolution functions.
This cache is filled at the beginning of an imaging run. This step can be time
consuming but the cache can be reused across multiple imaging runs that
use the same image parameters (cell size, image size , spectral data
selections, wprojplanes, wbawp, psterm, aterm). The effect of the wbawp,
psterm and aterm settings is frozen-in in the cfcache. Using an existing cfcache
made with a different setting of these parameters will not reflect the current
settings.
In a parallel execution, the construction of the cfcache is also parallelized
and the time to compute scales close to linearly with the number of compute
cores used. With the re-computation of Convolution Functions (CF) due to PA
rotation turned-off (the computepastep parameter), the total number of in the
cfcache can be computed as [No. of wprojplanes x No. of selected spectral windows x 4]
By default, cfcache = imagename + ‘.cf’
usepointing (bool=False) - The usepointing flag informs the gridder that it should utilize the pointing table
to use the correct direction in which the antenna is pointing with respect to the pointing phasecenter.
computepastep (double=360.0) - Parallactic angle interval after the AIFs are recomputed (deg)
This parameter controls the accuracy of the aperture illumination function
used with AProjection for alt-az mount dishes where the AIF rotates on the
sky as the synthesis image is built up. Once the PA in the data changes by
the given interval, AIFs are re-computed at the new PA.
A value of 360.0 deg (the default) implies no re-computation due to PA rotation.
AIFs are computed for the PA value of the first valid data received and used for
all of the data.
rotatepastep (double=360.0) - Parallactic angle interval after which the nearest AIF is rotated (deg)
Instead of recomputing the AIF for every timestep’s parallactic angle,
the nearest existing AIF is used and rotated
after the PA changed by rotatepastep value.
A value of 360.0 deg (the default) disables rotation of the AIF.
For example, computepastep=360.0 and rotatepastep=5.0 will compute
the AIFs at only the starting parallactic angle and all other timesteps will
use a rotated version of that AIF at the nearest 5.0 degree point.
pointingoffsetsigdev ({intVec, doubleVec}='') - Corrections for heterogenous and time-dependent pointing
offsets via AWProjection are controlled by this parameter.
It is a vector of 2 ints or doubles each of which is interpreted
in units of arcsec. Based on the first threshold, a clustering
algorithm is applied to entries from the POINTING subtable
of the MS to determine how distinct antenna groups for which
the pointing offset must be computed separately. The second
number controls how much a pointing change across time can
be ignored and after which an antenna rebinning is required.
Note : The default value of this parameter is [], due a programmatic constraint.
If run with this value, it will internally pick [600,600] and exercise the
option of using large tolerances (10arcmin) on both axes. Please choose
a setting explicitly for runs that need to use this parameter.
Note : This option is available only for gridder=’awproject’ and usepointing=True and
and has been validated primarily with VLASS on-the-fly mosaic data
where POINTING subtables have been modified after the data are recorded.
Examples of parameter usage :
[100.0,100.0] : Pointing offsets of 100 arcsec or less are considered
small enough to be ignored. Using large values for both
indicates a homogeneous array.

[10.0, 100.0] : Based on entries in the POINTING subtable, antennas
are grouped into clusters based on a 10arcsec bin size.
All antennas in a bin are given a pointing offset calculated
as the average of the offsets of all antennas in the bin.
On the time axis, offset changes upto 100 arcsec will be ignored.
[10.0,10.0] : Calculate separate pointing offsets for each antenna group
(with a 10 arcsec bin size). As a function of time, recalculate
the antenna binning if the POINTING table entries change by
more than 10 arcsec w.r.to the previously computed binning.

[1.0, 1.0] : Tight tolerances will imply a fully heterogenous situation where
each antenna gets its own pointing offset. Also, time-dependent
offset changes greater than 1 arcsec will trigger recomputes of
the phase gradients. This is the most general situation and is also
the most expensive option as it constructs and uses separate
phase gradients for all baselines and timesteps.
For VLASS 1.1 data with two kinds of pointing offsets, the recommended
setting is [ 30.0, 30.0 ].
For VLASS 1.2 data with only the time-dependent pointing offsets, the
recommended setting is [ 300.0, 30.0 ] to turn off the antenna grouping
but to retain the time dependent corrections required from one timestep
to the next.
pblimit (double=0.2) - PB gain level at which to cut off normalizations
Divisions by .pb during normalizations have a cut off at a .pb gain
level given by pblimit. Outside this limit, image values are set to zero.
Additionally, by default, an internal T/F mask is applied to the .pb, .image and
.residual images to mask out (T) all invalid pixels outside the pblimit area.
Note : This internal T/F mask cannot be used as a deconvolution mask.
To do so, please follow the steps listed above in the Notes for the
‘gridder’ parameter.
Note : To prevent the internal T/F mask from appearing in anything other
than the .pb and .image.pbcor images, ‘pblimit’ can be set to a
negative number. The absolute value will still be used as a valid ‘pblimit’.
A sdintimaging restart using existing output images on disk that already
have this T/F mask in the .residual and .image but only pblimit set
to a negative value, will remove this mask after the next major cycle.
deconvolver (string='hogbom') - Name of minor cycle algorithm (hogbom,clark,multiscale,mtmfs,mem,clarkstokes,asp)
Each of the following algorithms operate on residual images and psfs
from the gridder and produce output model and restored images.
Minor cycles stop and a major cycle is triggered when cyclethreshold
or cycleniter are reached. For all methods, components are picked from
the entire extent of the image or (if specified) within a mask.
hogbom : An adapted version of Hogbom Clean [Hogbom, 1974]
- Find the location of the peak residual
- Add this delta function component to the model image
- Subtract a scaled and shifted PSF of the same size as the image
from regions of the residual image where the two overlap.
- Repeat
clark : An adapted version of Clark Clean [Clark, 1980]
- Find the location of max(I^2+Q^2+U^2+V^2)
- Add delta functions to each stokes plane of the model image
- Subtract a scaled and shifted PSF within a small patch size
from regions of the residual image where the two overlap.
- After several iterations trigger a Clark major cycle to subtract
components from the visibility domain, but without de-gridding.
- Repeat
( Note : ‘clark’ maps to imagermode=’’ in the old clean task.
‘clark_exp’ is another implementation that maps to
imagermode=’mosaic’ or ‘csclean’ in the old clean task
but the behavior is not identical. For now, please
use deconvolver=’hogbom’ if you encounter problems. )
clarkstokes : Clark Clean operating separately per Stokes plane
(Note : ‘clarkstokes_exp’ is an alternate version. See above.)
multiscale : MultiScale Clean [Cornwell, 2008]
- Smooth the residual image to multiple scale sizes
- Find the location and scale at which the peak occurs
- Add this multiscale component to the model image
- Subtract a scaled,smoothed,shifted PSF (within a small
patch size per scale) from all residual images
- Repeat from step 2
mtmfs : Multi-term (Multi Scale) Multi-Frequency Synthesis [Rau and Cornwell, 2011]
- Smooth each Taylor residual image to multiple scale sizes
- Solve a NTxNT system of equations per scale size to compute
Taylor coefficients for components at all locations
- Compute gradient chi-square and pick the Taylor coefficients
and scale size at the location with maximum reduction in
chi-square
- Add multi-scale components to each Taylor-coefficient
model image
- Subtract scaled,smoothed,shifted PSF (within a small patch size
per scale) from all smoothed Taylor residual images
- Repeat from step 2
mem : Maximum Entropy Method [Cornwell and Evans, 1985]
- Iteratively solve for values at all individual pixels via the
MEM method. It minimizes an objective function of
chi-square plus entropy (here, a measure of difference
between the current model and a flat prior model).
(Note : This MEM implementation is not very robust.
Improvements will be made in the future.)
asp : ASP Clean [Bhatnagar and Cornwell, 2004]
scales ({intVec, floatVec}='') - List of scale sizes (in pixels) for multi-scale and mtmfs algorithms.
–> scales=[0,6,20]
This set of scale sizes should represent the sizes
(diameters in units of number of pixels)
of dominant features in the image being reconstructed.
The smallest scale size is recommended to be 0 (point source),
the second the size of the synthesized beam and the third 3-5
times the synthesized beam, etc. For example, if the synthesized
beam is 10” FWHM and cell=2”,try scales = [0,5,15].
For numerical stability, the largest scale must be
smaller than the image (or mask) size and smaller than or
comparable to the scale corresponding to the lowest measured
spatial frequency (as a scale size much larger than what the
instrument is sensitive to is unconstrained by the data making
it harder to recovery from errors during the minor cycle).
nterms (int=2) - Number of Taylor coefficients in the spectral model
- nterms=1 : Assume flat spectrum source
- nterms=2 : Spectrum is a straight line with a slope
- nterms=N : A polynomial of order N-1
From a Taylor expansion of the expression of a power law, the
spectral index is derived as alpha = taylorcoeff_1 / taylorcoeff_0
Spectral curvature is similarly derived when possible.
The optimal number of Taylor terms depends on the available
signal to noise ratio, bandwidth ratio, and spectral shape of the
source as seen by the telescope (sky spectrum x PB spectrum).
nterms=2 is a good starting point for wideband EVLA imaging
and the lower frequency bands of ALMA (when fractional bandwidth
is greater than 10%) and if there is at least one bright source for
which a dynamic range of greater than few 100 is desired.
Spectral artifacts for the VLA often look like spokes radiating out from
a bright source (i.e. in the image made with standard mfs imaging).
If increasing the number of terms does not eliminate these artifacts,
check the data for inadequate bandpass calibration. If the source is away
from the pointing center, consider including wide-field corrections too.
(Note : In addition to output Taylor coefficient images .tt0,.tt1,etc
images of spectral index (.alpha), an estimate of error on
spectral index (.alpha.error) and spectral curvature (.beta,
if nterms is greater than 2) are produced.
- These alpha, alpha.error and beta images contain
internal T/F masks based on a threshold computed
as peakresidual/10. Additional masking based on
.alpha/.alpha.error may be desirable.
- .alpha.error is a purely empirical estimate derived
from the propagation of error during the division of
two noisy numbers (alpha = xx.tt1/xx.tt0) where the
‘error’ on tt1 and tt0 are simply the values picked from
the corresponding residual images. The absolute value
of the error is not always accurate and it is best to interpret
the errors across the image only in a relative sense.)
smallscalebias (double=0.0) - A numerical control to bias the scales when using multi-scale or mtmfs algorithms.
The peak from each scale’s smoothed residual is
multiplied by ( 1 - smallscalebias * scale/maxscale )
to increase or decrease the amplitude relative to other scales,
before the scale with the largest peak is chosen.
Smallscalebias can be varied between -1.0 and 1.0.
A score of 0.0 gives all scales equal weight (default).
A score larger than 0.0 will bias the solution towards smaller scales.
A score smaller than 0.0 will bias the solution towards larger scales.
The effect of smallscalebias is more pronounced when using multi-scale relative to mtmfs.
restoration (bool=True) - Restore the model image.
Construct a restored image : imagename.image by convolving the model
image with a clean beam and adding the residual image to the result.
If a restoringbeam is specified, the residual image is also
smoothed to that target resolution before adding it in.
If a .model does not exist, it will make an empty one and create
the restored image from the residuals ( with additional smoothing if needed ).
With algorithm=’mtmfs’, this will construct Taylor coefficient maps from
the residuals and compute .alpha and .alpha.error.
restoringbeam ({string, stringVec}='') - Restoring beam shape/size to use.
- restoringbeam=’’ or [‘’]
A Gaussian fitted to the PSF main lobe (separately per image plane).
- restoringbeam=’10.0arcsec’
Use a circular Gaussian of this width for all planes
- restoringbeam=[‘8.0arcsec’,’10.0arcsec’,’45deg’]
Use this elliptical Gaussian for all planes
- restoringbeam=’common’
Automatically estimate a common beam shape/size appropriate for
all planes.
Note : For any restoring beam different from the native resolution
the model image is convolved with the beam and added to
residuals that have been convolved to the same target resolution.
pbcor (bool=False) - Apply PB correction on the output restored image
A new image with extension .image.pbcor will be created from
the evaluation of .image / .pb for all pixels above the specified pblimit.
Note : Stand-alone PB-correction can be triggered by re-running
sdintimaging with the appropriate imagename and with
niter=0, calcpsf=False, calcres=False, pbcor=True, vptable=’vp.tab’
( where vp.tab is the name of the vpmanager file.
See the inline help for the ‘vptable’ parameter )
Note : Multi-term PB correction that includes a correction for the
spectral index of the PB has not been enabled for the 4.7 release.
Please use the widebandpbcor task instead.
( Wideband PB corrections are required when the amplitude of the
brightest source is known accurately enough to be sensitive
to the difference in the PB gain between the upper and lower
end of the band at its location. As a guideline, the artificial spectral
index due to the PB is -1.4 at the 0.5 gain level and less than -0.2
at the 0.9 gain level at the middle frequency )
weighting (string='natural') - Weighting scheme (natural,uniform,briggs,superuniform,radial, briggsabs)
During gridding of the dirty or residual image, each visibility value is
multiplied by a weight before it is accumulated on the uv-grid.
The PSF’s uv-grid is generated by gridding only the weights (weightgrid).
weighting=’natural’ : Gridding weights are identical to the data weights
from the MS. For visibilities with similar data weights,
the weightgrid will follow the sample density
pattern on the uv-plane. This weighting scheme
provides the maximum imaging sensitivity at the
expense of a possibly fat PSF with high sidelobes.
It is most appropriate for detection experiments
where sensitivity is most important.
weighting=’uniform’ : Gridding weights per visibility data point are the
original data weights divided by the total weight of
all data points that map to the same uv grid cell :
‘ data_weight / total_wt_per_cell ‘.
The weightgrid is as close to flat as possible resulting
in a PSF with a narrow main lobe and suppressed
sidelobes. However, since heavily sampled areas of
the uv-plane get down-weighted, the imaging
sensitivity is not as high as with natural weighting.
It is most appropriate for imaging experiments where
a well behaved PSF can help the reconstruction.
weighting=’briggs’ : Gridding weights per visibility data point are given by
‘data_weight / ( A * total_wt_per_cell + B ) ‘ where
A and B vary according to the ‘robust’ parameter.
robust = -2.0 maps to A=1,B=0 or uniform weighting.
robust = +2.0 maps to natural weighting.
(robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
Robust/Briggs weighting generates a PSF that can
vary smoothly between ‘natural’ and ‘uniform’ and
allow customized trade-offs between PSF shape and
imaging sensitivity.
weighting=’briggsabs’ : Experimental option.
Same as Briggs except the formula is different A=
robust*robust and B is dependent on the
noise per visibility estimated. Giving noise=’0Jy’
is a not a reasonable option.
In this mode (or formula) robust values
from -2.0 to 0.0 only make sense (2.0 and
-2.0 will get the same weighting)
weighting=’superuniform’ : This is similar to uniform weighting except that
the total_wt_per_cell is replaced by the
total_wt_within_NxN_cells around the uv cell of
interest. ( N = subparameter ‘npixels’ )
This method tends to give a PSF with inner
sidelobes that are suppressed as in uniform
weighting but with far-out sidelobes closer to
natural weighting. The peak sensitivity is also
closer to natural weighting.
weighting=’radial’ : Gridding weights are given by ‘ data_weight * uvdistance ‘
This method approximately minimizes rms sidelobes
for an east-west synthesis array.
weighting=’briggsbwtaper’ : A modified version of Briggs weighting for cubes where an inverse uv taper,
which is proportional to the fractional bandwidth of the entire cube,
is applied per channel. The objective is to modify cube (perchanweightdensity = True)
imaging weights to have a similar density to that of the continuum imaging weights.
This is currently an experimental weighting scheme being developed for ALMA.

For more details on weighting please see Chapter3
robust (double=0.5) - Robustness parameter for Briggs weighting.
robust = -2.0 maps to uniform weighting.
robust = +2.0 maps to natural weighting.
(robust=0.5 is equivalent to robust=0.0 in AIPS IMAGR.)
noise (variant='1.0Jy') - noise parameter for briggs abs mode weighting
npixels (int=0) - Number of pixels to determine uv-cell size for super-uniform weighting
(0 defaults to -/+ 3 pixels)
npixels – uv-box used for weight calculation
a box going from -npixel/2 to +npixel/2 on each side
around a point is used to calculate weight density.
npixels=2 goes from -1 to +1 and covers 3 pixels on a side.
npixels=0 implies a single pixel, which does not make sense for
superuniform weighting. Therefore, if npixels=0 it will
be forced to 6 (or a box of -3pixels to +3pixels) to cover
7 pixels on a side.
uvtaper (stringVec=['']) - uv-taper on outer baselines in uv-plane
Apply a Gaussian taper in addition to the weighting scheme specified
via the ‘weighting’ parameter. Higher spatial frequencies are weighted
down relative to lower spatial frequencies to suppress artifacts
arising from poorly sampled areas of the uv-plane. It is equivalent to
smoothing the PSF obtained by other weighting schemes and can be
specified either as a Gaussian in uv-space (eg. units of lambda)
or as a Gaussian in the image domain (eg. angular units like arcsec).
uvtaper = [bmaj, bmin, bpa]
NOTE: the on-sky FWHM in arcsec is roughly the uv taper/200 (klambda).
default: uvtaper=[]; no Gaussian taper applied
example: uvtaper=[‘5klambda’] circular taper
FWHM=5 kilo-lambda
uvtaper=[‘5klambda’,’3klambda’,’45.0deg’]
uvtaper=[‘10arcsec’] on-sky FWHM 10 arcseconds
uvtaper=[‘300.0’] default units are lambda
in aperture plane
niter (int=0) - Maximum number of iterations
A stopping criterion based on total iteration count.
Currently the parameter type is defined as an integer therefore the integer value
larger than 2147483647 will not be set properly as it causes an overflow.
Iterations are typically defined as the selecting one flux component
and partially subtracting it out from the residual image.
niter=0 : Do only the initial major cycle (make dirty image, psf, pb, etc)
niter larger than zero : Run major and minor cycles.
Note : Global stopping criteria vs major-cycle triggers
In addition to global stopping criteria, the following rules are
used to determine when to terminate a set of minor cycle iterations
and trigger major cycles [derived from Cotton-Schwab Clean, 1984]
‘cycleniter’ : controls the maximum number of iterations per image
plane before triggering a major cycle.
‘cyclethreshold’ : Automatically computed threshold related to the
max sidelobe level of the PSF and peak residual.
Divergence, detected as an increase of 10% in peak residual from the
minimum so far (during minor cycle iterations)
The first criterion to be satisfied takes precedence.
Note : Iteration counts for cubes or multi-field images :
For images with multiple planes (or image fields) on which the
deconvolver operates in sequence, iterations are counted across
all planes (or image fields). The iteration count is compared with
‘niter’ only after all channels/planes/fields have completed their
minor cycles and exited either due to ‘cycleniter’ or ‘cyclethreshold’.
Therefore, the actual number of iterations reported in the logger
can sometimes be larger than the user specified value in ‘niter’.
For example, with niter=100, cycleniter=20,nchan=10,threshold=0,
a total of 200 iterations will be done in the first set of minor cycles
before the total is compared with niter=100 and it exits.
Note : Additional global stopping criteria include
- no change in peak residual across two major cycles
- a 50% or more increase in peak residual across one major cycle
gain (double=0.1) - Loop gain
Fraction of the source flux to subtract out of the residual image
for the CLEAN algorithm and its variants.
A low value (0.2 or less) is recommended when the sky brightness
distribution is not well represented by the basis functions used by
the chosen deconvolution algorithm. A higher value can be tried when
there is a good match between the true sky brightness structure and
the basis function shapes. For example, for extended emission,
multiscale clean with an appropriate set of scale sizes will tolerate
a higher loop gain than Clark clean (for example).
threshold (double=0.0) - Stopping threshold (number in units of Jy, or string)
A global stopping threshold that the peak residual (within clean mask)
across all image planes is compared to.
threshold = 0.005 : 5mJy
threshold = ‘5.0mJy’
Note : A ‘cyclethreshold’ is internally computed and used as a major cycle
trigger. It is related what fraction of the PSF can be reliably
used during minor cycle updates of the residual image. By default
the minor cycle iterations terminate once the peak residual reaches
the first sidelobe level of the brightest source.
‘cyclethreshold’ is computed as follows using the settings in
parameters ‘cyclefactor’,’minpsffraction’,’maxpsffraction’,’threshold’ :
psf_fraction = max_psf_sidelobe_level * ‘cyclefactor’
psf_fraction = max(psf_fraction, ‘minpsffraction’);
psf_fraction = min(psf_fraction, ‘maxpsffraction’);
cyclethreshold = peak_residual * psf_fraction
cyclethreshold = max( cyclethreshold, ‘threshold’ )
If nsigma is set (>0.0), the N-sigma threshold is calculated (see
the description under nsigma), then cyclethreshold is further modified as,
cyclethreshold = max( cyclethreshold, nsgima_threshold )
‘cyclethreshold’ is made visible and editable only in the
interactive GUI when sdintimaging is run with interactive=True.
nsigma (double=0.0) - Multiplicative factor for rms-based threshold stopping
N-sigma threshold is calculated as nsigma * rms value per image plane determined
from a robust statistics. For nsigma > 0.0, in a minor cycle, a maximum of the two values,
the N-sigma threshold and cyclethreshold, is used to trigger a major cycle
(see also the descreption under ‘threshold’).
Set nsigma=0.0 to preserve the previous sdintimaging behavior without this feature.
The top level parameter, fastnoise is relevant for the rms noise calculation which is used
to determine the threshold.
cycleniter (int=-1) - Maximum number of minor-cycle iterations (per plane) before triggering
a major cycle
For example, for a single plane image, if niter=100 and cycleniter=20,
there will be 5 major cycles after the initial one (assuming there is no
threshold based stopping criterion). At each major cycle boundary, if
the number of iterations left over (to reach niter) is less than cycleniter,
it is set to the difference.
Note : cycleniter applies per image plane, even if cycleniter x nplanes
gives a total number of iterations greater than ‘niter’. This is to
preserve consistency across image planes within one set of minor
cycle iterations.
cyclefactor (double=1.0) - Scaling on PSF sidelobe level to compute the minor-cycle stopping threshold.
Please refer to the Note under the documentation for ‘threshold’ that
discussed the calculation of ‘cyclethreshold’
cyclefactor=1.0 results in a cyclethreshold at the first sidelobe level of
the brightest source in the residual image before the minor cycle starts.
cyclefactor=0.5 allows the minor cycle to go deeper.
cyclefactor=2.0 triggers a major cycle sooner.
minpsffraction (double=0.05) - PSF fraction that marks the max depth of cleaning in the minor cycle
Please refer to the Note under the documentation for ‘threshold’ that
discussed the calculation of ‘cyclethreshold’
For example, minpsffraction=0.5 will stop cleaning at half the height of
the peak residual and trigger a major cycle earlier.
maxpsffraction (double=0.8) - PSF fraction that marks the minimum depth of cleaning in the minor cycle
Please refer to the Note under the documentation for ‘threshold’ that
discussed the calculation of ‘cyclethreshold’
For example, maxpsffraction=0.8 will ensure that at least the top 20
percent of the source will be subtracted out in the minor cycle even if
the first PSF sidelobe is at the 0.9 level (an extreme example), or if the
cyclefactor is set too high for anything to get cleaned.
interactive (bool=False) - Modify masks and parameters at runtime
interactive=True will trigger an interactive GUI at every major cycle
boundary (after the major cycle and before the minor cycle).
Options for runtime parameter modification are :
Interactive clean mask : Draw a 1/0 mask (appears as a contour) by hand.
If a mask is supplied at the task interface or if
automasking is invoked, the current mask is
displayed in the GUI and is available for manual
editing.
Note : If a mask contour is not visible, please
check the cursor display at the bottom of
GUI to see which parts of the mask image
have ones and zeros. If the entire mask=1
no contours will be visible.
Operation buttons : – Stop execution now (restore current model and exit)
– Continue on until global stopping criteria are reached
without stopping for any more interaction
– Continue with minor cycles and return for interaction
after the next major cycle.
Iteration control : – max cycleniter : Trigger for the next major cycle
The display begins with
[ min( cycleniter, niter - itercount ) ]
and can be edited by hand.
– iterations left : The display begins with [niter-itercount ]
and can be edited to increase or
decrease the total allowed niter.
– threshold : Edit global stopping threshold
– cyclethreshold : The display begins with the
automatically computed value
(see Note in help for ‘threshold’),
and can be edited by hand.
All edits will be reflected in the log messages that appear
once minor cycles begin.
fullsummary (bool=False) - Return dictionary with complete convergence history
fullsummary=True: A full version of the summary dictionary is returned.
Keys include ‘iterDone’,’peakRes’,’modelFlux’,’cycleThresh’ that record the
convergence state at the end of each set of minor cycle iterations
separately for each image plane (i.e. channel/stokes) being
deconvolved. Additional keys report the convergence state at the
start of minor cycle iterations, stopping criteria that triggered major
cycles, and a processor ID per channel, for parallel cube runs.
fullsummary=False (default): A shorten version of the summary dictionary is returned
with only ‘iterDone’,’peakRes’,’modelFlux’, and ‘cycleThresh’.
Detailed information about the return dictionary fields may be found
at CASA Docs > Synthesis Imaging > Iteration Control > Returned Dictionary.
nmajor (int=-1) - The nmajor parameter limits the number of minor and major cycle sets
that sdintimaging executes. It is defined as the number of major cycles after the
initial set of minor cycle iterations. The count of nmajor does
not include the initial residual calculation that occurs when calcres=True.
A setting of nmajor=-1 implies no limit (default -1).
A setting of nmajor=0 implies nothing other than the initial residual calculation
A setting of nmajor>0 imples that nmajor sets of minor and major cycles will
be done in addition to the initial residual calculation.

If the major cycle limit is reached, stopcode 9 will be returned. Other stopping
criteria (such as threshold) could cause sdintimaging to stop in fewer than this
number of major cycles. If sdintimaging reaches another stopping criteria, first
or at the same time as nmajor, then that stopcode will be returned instead.

Note however that major cycle ids in the log messages as well as in the return
dictionary do begin with 1 for the initial residual calculation, when it exists.
Example 1 : A run with ‘nmajor=5’ and ‘calcres=True’ will iterate for
5 major cycles (not counting the initial residual calculation). But, the return
dictionary will show ‘nmajordone:6’. If ‘calcres=False’, then the return
dictionary will show ‘nmajordone:5’.
Example 2 : For both the following cases, there will be a printout in the logs
“Running Major Cycle 1” and the return value will include “nmajordone: 1”,
however there is a difference in the purpose of the major cycle and the
number of minor cycles executed:
Case 1; nmajor=0, calcres=True: The major cycle done is for the creation
of the residual, and no minor cycles are executed.
Case 2; nmajor=1, calcres=False: The major cycle is done as part of the
major/minor cycle loop, and 1 minor cycle will be executed.
Note : For sdintimaging, the ‘nmajor’ parameter is not implemented for
usedata=’sdonly’. Other stopping criteria still apply as usual.
usemask (string='user') - Type of mask(s) to be used for deconvolution
user: (default) mask image(s) or user specified region file(s) or string CRTF expression(s)
subparameters: mask, pbmask
pb: primary beam mask
subparameter: pbmask
Example: usemask=”pb”, pbmask=0.2
Construct a mask at the 0.2 pb gain level.
(Currently, this option will work only with
gridders that produce .pb (i.e. mosaic and awproject)
or if an externally produced .pb image exists on disk)
auto-multithresh : auto-masking by multiple thresholds for deconvolution
subparameters : sidelobethreshold, noisethreshold, lownoisethreshold, negativethrehsold, smoothfactor,
minbeamfrac, cutthreshold, pbmask, growiterations, dogrowprune, minpercentchange, verbose
Additional top level parameter relevant to auto-multithresh: fastnoise
if pbmask is >0.0, the region outside the specified pb gain level is excluded from
image statistics in determination of the threshold.


Note: By default the intermediate mask generated by automask at each deconvolution cycle
is over-written in the next cycle but one can save them by setting
the environment variable, SAVE_ALL_AUTOMASKS=”true”.
(e.g. in the CASA prompt, os.environ[‘SAVE_ALL_AUTOMASKS’]=”true” )
The saved CASA mask image name will be imagename.mask.autothresh#, where
# is the iteration cycle number.
mask ({string, stringVec}='') - Mask (a list of image name(s) or region file(s) or region string(s)

The name of a CASA image or region file or region string that specifies
a 1/0 mask to be used for deconvolution. Only locations with value 1 will
be considered for the centers of flux components in the minor cycle.
If regions specified fall completely outside of the image, sdintimaging will throw an error.
Manual mask options/examples :
mask=’xxx.mask’ : Use this CASA image named xxx.mask and containing
ones and zeros as the mask.
If the mask is only different in spatial coordinates from what is being made
it will be resampled to the target coordinate system before being used.
The mask has to have the same shape in velocity and Stokes planes
as the output image. Exceptions are single velocity and/or single
Stokes plane masks. They will be expanded to cover all velocity and/or
Stokes planes of the output cube.
[ Note : If an error occurs during image resampling or
if the expected mask does not appear, please try
using tasks ‘imregrid’ or ‘makemask’ to resample
the mask image onto a CASA image with the target
shape and coordinates and supply it via the ‘mask’
parameter. ]
mask=’xxx.crtf’ : A text file with region strings and the following on the first line
( #CRTFv0 CASA Region Text Format version 0 )
This is the format of a file created via the viewer’s region
tool when saved in CASA region file format.
mask=’circle[[40pix,40pix],10pix]’ : A CASA region string.
mask=[‘xxx.mask’,’xxx.crtf’, ‘circle[[40pix,40pix],10pix]’] : a list of masks

Note : Mask images for deconvolution must contain 1 or 0 in each pixel.
Such a mask is different from an internal T/F mask that can be
held within each CASA image. These two types of masks are not
automatically interchangeable, so please use the makemask task
to copy between them if you need to construct a 1/0 based mask
from a T/F one.
Note : Work is in progress to generate more flexible masking options and
enable more controls.
pbmask (double=0.0) - Sub-parameter for usemask: primary beam mask
Examples : pbmask=0.0 (default, no pb mask)
pbmask=0.2 (construct a mask at the 0.2 pb gain level)
sidelobethreshold (double=3.0) - Sub-parameter for “auto-multithresh”: mask threshold based on sidelobe levels: sidelobethreshold * max_sidelobe_level * peak residual
noisethreshold (double=5.0) - Sub-parameter for “auto-multithresh”: mask threshold based on the noise level: noisethreshold * rms + location (=median)
The rms is calculated from MAD with rms = 1.4826*MAD.
lownoisethreshold (double=1.5) - Sub-parameter for “auto-multithresh”: mask threshold to grow previously masked regions via binary dilation: lownoisethreshold * rms in residual image + location (=median)
The rms is calculated from MAD with rms = 1.4826*MAD.
negativethreshold (double=0.0) - Sub-parameter for “auto-multithresh”: mask threshold for negative features: -1.0* negativethreshold * rms + location(=median)
The rms is calculated from MAD with rms = 1.4826*MAD.
smoothfactor (double=1.0) - Sub-parameter for “auto-multithresh”: smoothing factor in a unit of the beam
minbeamfrac (double=0.3) - Sub-parameter for “auto-multithresh”: minimum beam fraction in size to prune masks smaller than mimbeamfrac * beam
<=0.0 : No pruning
cutthreshold (double=0.01) - Sub-parameter for “auto-multithresh”: threshold to cut the smoothed mask to create a final mask: cutthreshold * peak of the smoothed mask
growiterations (int=75) - Sub-parameter for “auto-multithresh”: Maximum number of iterations to perform using binary dilation for growing the mask
dogrowprune (bool=True) - Experimental sub-parameter for “auto-multithresh”: Do pruning on the grow mask
minpercentchange (double=-1.0) - If the change in the mask size in a particular channel is less than minpercentchange, stop masking that channel in subsequent cycles. This check is only applied when noise based threshold is used and when the previous clean major cycle had a cyclethreshold value equal to the clean threshold. Values equal to -1.0 (or any value less than 0.0) will turn off this check (the default). Automask will still stop masking if the current channel mask is an empty mask and the noise threshold was used to determine the mask.
verbose (bool=False) - If it is set to True, the summary of automasking at the end of each automasking process
is printed in the logger. Following information per channel will be listed in the summary.
chan: channel number
masking?: F - stop updating automask for the subsequent iteration cycles
RMS: robust rms noise
peak: peak in residual image
thresh_type: type of threshold used (noise or sidelobe)
thresh_value: the value of threshold used
N_reg: number of the automask regions
N_pruned: number of the automask regions removed by pruning
N_grow: number of the grow mask regions
N_grow_pruned: number of the grow mask regions removed by pruning
N_neg_pix: number of pixels for negative mask regions
Note that for a large cube, extra logging may slow down the process.
fastnoise (bool=True) - Only relevant when automask (user=’multi-autothresh’) and/or n-sigma stopping threshold (nsigma>0.0) are/is used. If it is set to True, a simpler but faster noise calucation is used.
In this case, the threshold values are determined based on classic statistics (using all
unmasked pixels for the calculations).

If it is set to False, the new noise calculation
method is used based on pre-existing mask.

Case 1: no exiting mask
Calculate image statistics using Chauvenet algorithm

Case 2: there is an existing mask
Calculate image statistics by classical method on the region
outside the mask and inside the primary beam mask.
In all cases above RMS noise is calculated from MAD.
restart (bool=True) - Restart using existing images (and start from an existing model image)
or automatically increment the image name and make a new image set.
True : Re-use existing images. If imagename.model exists the subsequent
run will start from this model (i.e. predicting it using current gridder
settings and starting from the residual image). Care must be taken
when combining this option with startmodel. Currently, only one or
the other can be used.
startmodel=’’, imagename.model exists :
- Start from imagename.model
startmodel=’xxx’, imagename.model does not exist :
- Start from startmodel
startmodel=’xxx’, imagename.model exists :
- Exit with an error message requesting the user to pick
only one model. This situation can arise when doing one
run with startmodel=’xxx’ to produce an output
imagename.model that includes the content of startmodel,
and wanting to restart a second run to continue deconvolution.
Startmodel should be set to ‘’ before continuing.
If any change in the shape or coordinate system of the image is
desired during the restart, please change the image name and
use the startmodel (and mask) parameter(s) so that the old model
(and mask) can be regridded to the new coordinate system before starting.
False : A convenience feature to increment imagename with ‘_1’, ‘_2’,
etc as suffixes so that all runs of sdintimaging are fresh starts (without
having to change the imagename parameter or delete images).
This mode will search the current directory for all existing
imagename extensions, pick the maximum, and adds 1.
For imagename=’try’ it will make try.psf, try_2.psf, try_3.psf, etc.
This also works if you specify a directory name in the path :
imagename=’outdir/try’. If ‘./outdir’ does not exist, it will create it.
Then it will search for existing filenames inside that directory.
If outlier fields are specified, the incrementing happens for each
of them (since each has its own ‘imagename’). The counters are
synchronized across imagefields, to make it easier to match up sets
of output images. It adds 1 to the ‘max id’ from all outlier names
on disk. So, if you do two runs with only the main field
(imagename=’try’), and in the third run you add an outlier with
imagename=’outtry’, you will get the following image names
for the third run : ‘try_3’ and ‘outtry_3’ even though
‘outry’ and ‘outtry_2’ have not been used.
calcres (bool=True) - Calculate initial residual image
This parameter controls what the first major cycle does.
calcres=False with niter greater than 0 will assume that
a .residual image already exists and that the minor cycle can
begin without recomputing it.
calcres=False with niter=0 implies that only the PSF will be made
and no data will be gridded.
calcres=True requires that calcpsf=True or that the .psf and .sumwt
images already exist on disk (for normalization purposes).
Usage example : For large runs (or a pipeline scripts) it may be
useful to first run sdintimaging with niter=0 to create
an initial .residual to look at and perhaps make
a custom mask for. Imaging can be resumed
without recomputing it.
calcpsf (bool=True) - Calculate PSF
This parameter controls what the first major cycle does.
calcpsf=False will assume that a .psf image already exists
and that the minor cycle can begin without recomputing it.