casagui.apps._interactiveclean

implementation of the InteractiveClean application for interactive control of tclean

Module Contents

Classes

InteractiveClean

InteractiveClean(...) implements interactive clean using Bokeh
class casagui.apps._interactiveclean.InteractiveClean(vis, imagename, field='', spw='', timerange='', uvrange='', antenna='', scan='', observation='', intent='', datacolumn='corrected', imsize=[int(100)], cell=[], phasecenter='', stokes='I', startmodel='', specmode='mfs', reffreq='', nchan=int(-1), start='', width='', outframe='LSRK', veltype='radio', restfreq=[], interpolation='linear', perchanweightdensity=True, gridder='standard', wprojplanes=int(1), mosweight=True, psterm=False, wbawp=True, conjbeams=False, usepointing=False, pointingoffsetsigdev=[], pblimit=float(0.2), deconvolver='hogbom', scales=[], nterms=int(2), smallscalebias=float(0.0), restoringbeam=[], pbcor=False, weighting='natural', robust=float(0.5), npixels=int(0), niter=int(0), gain=float(0.1), threshold=float(0.0), nsigma=float(0.0), cycleniter=int(-1), cyclefactor=float(1.0), minpsffraction=float(0.05), maxpsffraction=float(0.8), nmajor=int(-1), usemask='user', mask='', pbmask=float(0.0), sidelobethreshold=float(3.0), noisethreshold=float(5.0), lownoisethreshold=float(1.5), negativethreshold=float(0.0), smoothfactor=float(1.0), minbeamfrac=float(0.3), cutthreshold=float(0.01), growiterations=int(75), dogrowprune=True, minpercentchange=float(-1.0), verbose=False, fastnoise=True, savemodel='none', parallel=False)
InteractiveClean(…) implements interactive clean using Bokeh

tclean —- Radio Interferometric Image Reconstruction

Form images from visibilities and reconstruct a sky model. This task handles continuum images and spectral line cubes, supports outlier fields, contains standard clean based algorithms along with algorithms for multi-scale and wideband image reconstruction, widefield imaging correcting for the w-term, full primary-beam imaging and joint mosaic imaging (with heterogeneous array support for ALMA).

——— parameter descriptions ———————————————

vis Name(s) of input visibility file(s)

default: none; example: vis=’ngc5921.ms’

vis=[‘ngc5921a.ms’,’ngc5921b.ms’]; multiple MSes

field 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 l 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 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 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 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 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 Observation ID range

default: ‘’ (all) example: observation=’1~5’

intent Scan Intent(s)

default: ‘’ (all) example: intent=’TARGET_SOURCE’ example: intent=’TARGET_SOURCE1,TARGET_SOURCE2’ example: intent=’TARGET_POINTING*’

datacolumn Data column to image (data or observed, corrected)

default:’corrected’ ( If ‘corrected’ does not exist, it will use ‘data’ instead )

imagename 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)

TipInclude a directory name in ‘imagename’ for all

output images to be sent there instead of the current working directory : imagename=’mydir/try’

TipRestarting 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 tclean 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 tclean docs for ‘restart’).

NoteAll 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 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 Cell size

example: cell=[‘0.5arcsec,’0.5arcsec’] or cell=[‘1arcmin’, ‘1arcmin’] cell = ‘1arcsec’ is equivalent to [‘1arcsec’,’1arcsec’]

phasecenter 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 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’

NoteDue 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.

NoteThe ‘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.

startmodel 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.

[ NoteIf 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 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.

(NoteEven 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 associate with the output images, thus the image spectral frame will be labelled “Undefined”.

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 no “SOURCE” frame defined in CASA, the frame in the image will be labelled “REST” (but do note the velocity of a given line reported may be different from the rest frame velocity if the emission region is moving w.r.t the systemic velocity frame of the source)

reffreq 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 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 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’. channels in ‘spw’. Since the integer number in ‘start’ represents the data channel number, when the channel number is used along with the spectral window id selection in ‘spw’, ‘start’ specified as an integer should be carefully set otherwise it may result in the blank image channels if the ‘start’ channel (i.e. absolute channel number) is outside of the channel range specified in ‘spw’. In such a case, ‘start’ can be left as a default (=’’) to ensure matching with the data spectral channel selection. 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 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 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 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 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 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 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 or for natural and radial weighting schemes. 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 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 7x7 uv pixel support size

[ This mode can also be invoked using ‘ft’ or ‘gridft’ ]

wprojectW-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. (In our current version of tclean, this

combination runs only with parallel=False.

mosaicA-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’ ]

awprojectA-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.

VLAUses 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)

ALMASimilar 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 tclean 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 tclean. ( Currently this will work only for non-parallel runs )

—— Note on PB masks :

In tclean, 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 tclean 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 tclean 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.

wprojplanes 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.

mosweight 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. psterm 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.

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 Use frequency dependent A-terms

Scale aperture illumination functions appropriately with frequency when gridding and combining data from multiple channels.

conjbeams Use conjugate frequency for wideband A-terms

While gridding data from one frequency channel, choose a convolution function from a ‘conjugate’ frequency such that the resulting baseline primary beam is approximately constant across frequency. For a system in which the primary beam scales with frequency, this step will eliminate instrumental spectral structure from the measured data and leave only the sky spectrum for the minor cycle to model and reconstruct [Bhatnagar et al., ApJ, 2013].

As a rough guideline for when this is relevant, a source at the half power point of the PB at the center frequency will see an artificial spectral index of -1.4 due to the frequency dependence of the PB [Sault and Wieringa, 1994]. If left uncorrected during gridding, this spectral structure must be modeled in the minor cycle (using the mtmfs algorithm) to avoid dynamic range limits (of a few hundred for a 2:1 bandwidth). This works for specmode=’mfs’ and its value is ignored for cubes

usepointing 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.

pointingoffsetsigdev 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.

NoteThe 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 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.

NoteThis 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.

NoteTo 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’ for normalization purposes. So, for example, pick pblimit=-0.1 (and not pblimit=-1). A tclean 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.

NoteAn existing internal T/F mask may be removed from an image as
follows (without needing to re-run tclean itself).

ia.open(‘test.image’); ia.maskhandler(op=’set’, name=’’); ia.done()

deconvolver 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.

hogbomAn 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

clarkAn 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.)

multiscaleMultiScale 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

mtmfsMulti-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

memMaximum 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).

    (NoteThis MEM implementation is not very robust.

    Improvements will be made in the future.)

aspAdaptive Scale Pixel algorithm [Bhatnagar and Cornwell, 2004]

  • Define a set of initial scales defined as 0, W, 2W 4W and 8W where W is a 2D Gaussian fitting width to the PSF

  • Smooth the residual image by a Gaussian beam at initial scales

  • Search for the global peak (F) among these smoothed residual images

  • form an active Aspen set: amplitude(F), amplitude location(x,y)

  • Optimize the Aspen set by minimizing the objective function RI-Aspen*PSF, where RI is the residual image and * is the convulition operation.

  • Compute the model image and update the residual image

  • Repeat from step 2

    (Note : This is an experimental version of the ASP algorithm.)

scales 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 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.

(NoteIn 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 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.

restoringbeam ize 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.

NoteFor 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 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.

NoteStand-alone PB-correction can be triggered by re-running

tclean 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 )

NoteMulti-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 Weighting scheme (natural,uniform,briggs,superuniform,radial, briggsabs, briggsbwtaper)

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=7 is the default (when the

parameter ‘npixels’ is set to 0 with ‘superuniform’)

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 of Dan Briggs’ thesis (http://www.aoc.nrao.edu/dissertations/dbriggs)

robust 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.)

npixels 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, for ‘superuniform’

weighting, if npixels=0 it will be forced to 6 (or a box of -3pixels to +3pixels) to cover 7 pixels on a side.

niter 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.

NoteIteration 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.

NoteAdditional 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 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 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’

NoteA ‘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 tclean is run with interactive=True.

nsigma 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 tclean behavior without this feature. The top level parameter, fastnoise is relevant for the rms noise calculation which is used to determine the threshold.

The parameter ‘nsigma’ may be an int, float, or a double.

cycleniter 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.

Notecycleniter 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 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 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 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.

nmajor The nmajor parameter limits the number of minor and major cycle sets

that tclean executes. It is defined as the number of major cycles after the initial set of minor cycle iterations. In other words, 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 tclean to stop in fewer than this number of major cycles. If tclean 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 tclean 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.

usemask 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-multithreshauto-masking by multiple thresholds for deconvolution
subparameterssidelobethreshold, 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 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, tclean 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.

[ NoteIf 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

NoteMask 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.

NoteWork is in progress to generate more flexible masking options and

enable more controls.

pbmask Sub-parameter for usemask: primary beam mask

Examplespbmask=0.0 (default, no pb mask)

pbmask=0.2 (construct a mask at the 0.2 pb gain level)

sidelobethreshold Sub-parameter for “auto-multithresh”: mask threshold based on sidelobe levels: sidelobethreshold * max_sidelobe_level * peak residual noisethreshold 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 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 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 Sub-parameter for “auto-multithresh”: smoothing factor in a unit of the beam minbeamfrac Sub-parameter for “auto-multithresh”: minimum beam fraction in size to prune masks smaller than mimbeamfrac * beam

<=0.0 : No pruning

cutthreshold Sub-parameter for “auto-multithresh”: threshold to cut the smoothed mask to create a final mask: cutthreshold * peak of the smoothed mask growiterations Sub-parameter for “auto-multithresh”: Maximum number of iterations to perform using binary dilation for growing the mask dogrowprune Experimental sub-parameter for “auto-multithresh”: Do pruning on the grow mask minpercentchange 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 he 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 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.

savemodel Options to save model visibilities (none, virtual, modelcolumn)

Often, model visibilities must be created and saved in the MS to be later used for self-calibration (or to just plot and view them).

noneDo not save any model visibilities in the MS. The MS is opened

in readonly mode.

Model visibilities can be predicted in a separate step by restarting tclean with niter=0,savemodel=virtual or modelcolumn and not changing any image names so that it finds the .model on disk (or by changing imagename and setting startmodel to the original imagename).

virtualIn the last major cycle, save the image model and state of the

gridder used during imaging within the SOURCE subtable of the MS. Images required for de-gridding will also be stored internally. All future references to model visibilities will activate the (de)gridder to compute them on-the-fly. This mode is useful when the dataset is large enough that an additional model data column on disk may be too much extra disk I/O, when the gridder is simple enough that on-the-fly recomputing of the model visibilities is quicker than disk I/O. For e.g. that gridder=’awproject’ does not support virtual model.

modelcolumnIn the last major cycle, save predicted model visibilities

in the MODEL_DATA column of the MS. This mode is useful when the de-gridding cost to produce the model visibilities is higher than the I/O required to read the model visibilities from disk. This mode is currently required for gridder=’awproject’. This mode is also required for the ability to later pull out model visibilities from the MS into a python array for custom processing.

Note 1The imagename.model image on disk will always be constructed

if the minor cycle runs. This savemodel parameter applies only to model visibilities created by de-gridding the model image.

Note 2It is possible for an MS to have both a virtual model

as well as a model_data column, but under normal operation, the last used mode will get triggered. Use the delmod task to clear out existing models from an MS if confusion arises.

Note 3: when parallel=True, use savemodel=’none’; Other options are not yet ready

for use in parallel. If model visibilities need to be saved (virtual or modelcolumn): please run tclean in serial mode with niter=0; after the parallel run

parallel Run major cycles in parallel (this feature is experimental)

Parallel tclean will run only if casa has already been started using mpirun. Please refer to HPC documentation for details on how to start this on your system.

Example : mpirun -n 3 -xterm 0 which casa

Continuum Imaging :

  • Data are partitioned (in time) into NProc pieces

  • Gridding/iFT is done separately per partition

  • Images (and weights) are gathered and then normalized

  • One non-parallel minor cycle is run

  • Model image is scattered to all processes

  • Major cycle is done in parallel per partition

Cube Imaging :

  • Data and Image coordinates are partitioned (in freq) into NProc pieces

  • Each partition is processed independently (major and minor cycles)

  • All processes are synchronized at major cycle boundaries for convergence checks

  • At the end, cubes from all partitions are concatenated along the spectral axis

Note 1Iteration control for cube imaging is independent per partition.
  • There is currently no communication between them to synchronize

    information such as peak residual and cyclethreshold. Therefore, different chunks may trigger major cycles at different levels.

(Proper synchronization of iteration control is work in progress.)

RETURNS void

——— examples ———————————————————–

Please refer to the CASAdocs pages for the task tclean for examples.

async serve()

This function is intended for developers who would like to embed interactive clean as a part of a larger GUI. This embedded use of interactive clean is not currently supported and would require the addition of parameters to this function as well as changes to the interactive clean implementation. However, this function does expose the asyncio.Future that is used to signal completion of the interactive cleaning operation, and it provides the coroutines which must be managed by asyncio to make the interactive clean GUI responsive.

Example

Create iclean object, process events and retrieve result:

ic = iclean( vis='refim_point_withline.ms', imagename='test', imsize=512,
             cell='12.0arcsec', specmode='cube', interpolation='nearest', ... )
async def process_events( ):
    async with ic.serve( ) as state:
        await state[0]

asyncio.run(process_events( ))
print( "Result:", ic.result( ) )
Return type:

(asyncio.Future, dictionary of coroutines)

result()

If InteractiveClean had a return value, it would be filled in as part of the GUI dialog between Python and JavaScript and this function would return it

masks()

Retrieves the masks which were used with interactive clean.

Returns:

  • The standard casagui cube region dictionary which contains two elements

  • masks and polys.

  • The value of the masks element is a dictionary that is indexed by

  • tuples of (stokes,chan) and the value of each element is a list

  • whose elements describe the polygons drawn on the channel represented

  • by (stokes,chan). Each polygon description in this list has a

  • polygon index (p) and a x/y translation (d).

  • The value of the polys element is a dictionary that is indexed by

  • polygon indexes. The value of each polygon index is a dictionary containing

  • type (whose value is either 'rect' or 'poly) and geometry

  • (whose value is a dictionary containing 'xs' and 'ys' (which are

  • the x and y coordinates that define the polygon).

  • This can be converted to other formats with casagui.utils.convert_masks.

history()

Retrieves the commands used during the interactive clean session.

Return type:

list[str] tclean calls made during the interactive clean session.