Invert and deconvolve images with selected algorithm
The clean task has many options:
1) Make 'dirty' image and 'dirty' beam (psf)
2) Multi-frequency-continuum images or spectral channel imaging
3) Full Stokes imaging
4) Mosaicking of several pointings
5) Multi-scale cleaning
6) Widefield cleaning
7) Interactive clean boxing
8) Use starting model (eg from single dish)
vis -- Name(s) of input visibility file(s)
default: none;
example: vis='ngc5921.ms'
vis=['ngc5921a.ms','ngc5921b.ms']; multiple MSes
imagename -- Pre-name of output images:
default: none; example: imagename='m2'
output images are:
m2.image; cleaned and restored image
With or without primary beam correction
m2.psf; point-spread function (dirty beam)
m2.flux; relative sky sensitivity over field
m2.flux.pbcoverage; relative pb coverage over field
(gets created only for ft='mosaic')
m2.model; image of clean components
m2.residual; image of residuals
m2.interactive.mask; image containing clean regions
To include outlier fields:
imagename=['n5921','outlier1','outlier2']
outlierfile --- Text file name which contains image names, sizes, field
centers (See 'HINTS ON CLEAN WITH FLANKING FIELDS' below
for the format of this outlier file.)
field -- 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 -- 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
selectdata -- Other data selection parameters
default: True
>>> selectdata=True expandable parameters
See help par.selectdata for more on these
timerange -- Select data based on time range:
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 units 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
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
Check 'go listobs' to insure the scan numbers are in order.
observation -- Observation ID range.
default: '' (all)
example: observation='1~5'
mode -- Frequency Specification:
NOTE: Channels deselected with spw parameter will contain all
zeros.
See examples below.
default: 'mfs'
mode = 'mfs' means produce one image from all
specified data.
mode = 'channel'; Use with nchan, start, width to specify
output image cube.
mode = 'velocity', channels are specified in velocity.
mode = 'frequency', channels are specified in frequency.
>>> mode='mfs' expandable parameters
Make a continuum image from the selected frequency
channels/range using Multi-frequency synthesis
algorithm for wide-band narrow field imaging.
mode='mfs' examples:
spw = '0,1'; mode = 'mfs'
will produce one image made from all channels in spw
0 and 1
spw='0:5~28^2'; mode = 'mfs'
will produce one image made with channels
(5,7,9,...,25,27)
nterms -- Number of Taylor terms to be used to model the
frequency dependence of the sky emission. nterms=1 is
equivalent to assuming no frequency dependence.
nterms>1 runs the MS-MFS algorithm, and the choice of nterms
should depend on the expected shape and SNR of the spectral
structure, across the chosen bandwidth. Output images
represent taylor-coefficients of the sky spectrum
(images with file-name extensions of tt0,tt1,etc).
A spectral index map is also computed as the ratio of the
first two terms (following the convention of I(nu) = I(ref_nu) x (nu/nu_0)^alpha).
Additionally, a spectral-index error image is made
by treating taylor-coefficient residuals as errors, and propagating
them through the division used to compute spectral-index.
It is meant to be a guide to which parts of the spectral-index
image to trust, and the values may not always represent a
statistically-correct error.
For more details about this algorithm, please refer to
"A multi-scale multi-frequency deconvolution algorithm for synthesis
imaging in radio interferometry", Rau and Cornwell, AA, Volume 532, 2011
** Note that the software implementation of the MS-MFS algorithm
for nterms>1 currently does not allow combination with
mosaics, and pbcor.**
reffreq -- The reference frequency (for nterms>1) about which
the Taylor expansion is done. reffreq='' defaults to the
middle frequency of the selected range.
>>> mode='channel', 'velocity', and 'frequency' expandable parameters
nchan -- Total number of channels in the output image.
Example: nchan=100.
Default: -1; Automatically selects enough channels to cover
data selected by 'spw' consistent with 'start' and 'width'.
It is often easiest to leave nchan at the default value.
start -- First channel, velocity, or frequency.
For mode='channel'; This selects the channel index number
from the MS (0 based) that you want to correspond to the
first channel of the output cube. The output cube will be
in frequency space with the first channel having the
frequency of the MS channel selected by start. start=0
refers to the first channel in the first selected spw, even
if that channel is de-selected in the spw parameter.
Channels de-selected by the spw parameter will be filled with
zeros if included by the start parameter. For example,
spw=3~8:3~100 and start=2 will produce a cube that starts on
the third channel (recall 0 based) of spw index 3, and the
first channel will be blank.
example:start=5
For mode='velocity' or 'frequency': default='';
starts at first input channel of first input spw
examples: start='5.0km/s', or start='22.3GHz'.
width -- Output channel width
For mode='channel', default=1; >1 indicates channel averaging
example: width=4.
For mode= 'velocity' or 'frequency', default=''; width of
first input channel, or more precisely, the difference
in frequencies between the first two selected channels.
-- For example if channels 1 and 3 are selected with spw,
then the default width will be the difference between their
frequencies, and not the width of channel 1.
-- Similarly, if the selected data has uneven channel-spacing,
the default width will be picked from the first two selected
channels. In this case, please specify the desired width.
When specifying the width, one must give units
examples: width='1.0km/s', or width='24.2kHz'.
Setting width>0 gives channels of increasing frequency for
mode='frequency', and increasing velocity for mode='velocity'.
interpolation -- Interpolation type for spectral gridding onto
the uv-plane. Options: 'nearest', 'linear', or 'cubic'.
default = 'linear'
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.
For mode='channel', please use 'nearest'.
chaniter -- specify how spectral CLEAN is performed,
default: chaniter=False;
example: chaniter=True; step through channels
outframe -- For mode='velocity', 'frequency', or 'channel':
velocity reference frame of output image
Options: '','LSRK','LSRD','BARY','GEO','TOPO','GALACTO',
'LGROUP','CMB'
default: ''; same as input data
example: frame='bary' for Barycentric frame
veltype -- for mode='velocity' gives the velocity definition
Options: 'radio','optical'
default: 'radio'
NOTE: the viewer always defaults to displaying the 'radio'
frame, but that can be changed in the position tracking
pull down.
mode='channel' examples:
spw = '0'; mode = 'channel': nchan=3; start=5; width=4
will produce an image with 3 output planes
plane 1 contains data from channels (5+6+7+8)
plane 2 contains data from channels (9+10+11+12)
plane 3 contains data from channels (13+14+15+16)
spw = '0:0~63^3'; mode='channel'; nchan=21; start = 0;
width = 1
will produce an image with 20 output planes
Plane 1 contains data from channel 0
Plane 2 contains date from channel 2
Plane 21 contains data from channel 61
spw = '0:0~40^2'; mode = 'channel'; nchan = 3; start =
5; width = 4
will produce an image with three output planes
plane 1 contains channels (5,7)
plane 2 contains channels (13,15)
plane 3 contains channels (21,23)
psfmode -- method of PSF calculation to use during minor cycles:
default: 'clark': Options: 'clark','clarkstokes', 'hogbom'
'clark' use smaller beam (faster, usually good enough);
for stokes images clean components peaks are searched
in the I^2+Q^2+U^2+V^2 domain
'clarkstokes' locate clean components independently in
each stokes image
'hogbom' full-width of image (slower, better for poor
uv-coverage)
Note: psfmode will also be used to clean if imagermode = ''
imagermode -- Advanced imaging e.g. mosaic or Cotton-Schwab clean
default: imagermode='csclean': Options: '', 'csclean', 'mosaic'
'' => psfmode cleaning algorithm used
NOTE: imagermode 'mosaic' (and/or) any gridmode not blank
(and/or) nterms>1 : will always use CS style clean.
>>> gridmode='' expandable parameters
The default value of '' has no effect.
>>> gridmode='widefield' expandable parameters
Apply corrections for non-coplanar effects during imaging
using the W-Projection algorithm (Cornwell et al. IEEE JSTSP
(2008)) or faceting or a combination of the two.
wprojplanes is the number of pre-computed w-planes used for
the W-Projection algorithm. wprojplanes=1 disables
correction for non-coplanar effects.
facets is the number of facets on each side of the image
(i.e. the total number of facets is 'facets x facets').
If wprojplanes>1, W-Projection is done for each facet.
>>> gridmode='aprojection' expandable parameters
Corrects for the (E)VLA time-varying PB effects
including polarization squint using the A-Projection
algorithm (Bhatnagar et al., AandA, 487, 419 (2008)).
This can optinally include w-projection also.
wprojplanes is the number of pre-computed w-planes used
for W-Projection algorithm. wprojplanes=1 diables
correction for non-coplanar effects.
cfcache is the name of the directory to store the
convolution functions and weighted sensitivty pattern
function.
painc (in degrees) is the Parallactic Angle increment
used to compute the convolution functions.
>>> imagermode='mosaic' expandable parameter(s):
Make a mosaic of the different pointings (uses csclean style
too)
mosweight -- Individually weight the fields of the mosaic
default: False; example: mosweight=True
This can be useful if some of your fields are more
sensitive than others (i.e. due to time spent
on-source); this parameter will give more weight to
higher sensitivity fields in the overlap regions.
ftmachine -- Gridding method for the mosaic;
Options: 'ft' (standard interferometric gridding), 'sd'
(standard single dish),
and 'mosaic' (grid using PB as convolution function).
default: 'mosaic';
ONLY if imagermode='mosaic' is chosen and
ftmachine='mosaic', is heterogeneous imaging (CARMA, ALMA)
possible using the right convolution of primary beams for
each baseline.
scaletype -- Controls scaling of pixels in the image plane.
(controls what is seen if interactive=True)
It does *not* affect the scaling of the *final* image -
that is done by pbcor.
default='SAULT'; example: scaletype='PBCOR'
Options: 'PBCOR','SAULT'
'SAULT' when interactive=True shows the residual
with constant noise across the mosaic.
Can also be achieved by setting pbcor=False.
'PBCOR' uses the SAULT scaling scheme for
deconvolution, but if interactive=True shows the
primary beam corrected image during interactive.
cyclefactor -- Controls the threshhold at which the
deconvolution cycle will pause to degrid and subtract the
model from the visibilities.
With poor PSFs, reconcile often (cyclefactor=4 or 5) for
reliability.
With good PSFs, use cyclefactor = 1.5 to 2.0 for speed.
Note: threshold = cyclefactor * max sidelobe * max residual
default: 1.5; example: cyclefactor=4
cyclespeedup -- The major cycle threshold doubles in this
number of iterations.
Default: -1 (no doubling)
Example: cyclespeedup=3
Try cyclespeedup = 50 to speed up cleaning.
flatnoise -- Controls whether searching for clean components
is done in a constant noise residual image (True) or in an
optimal signal-to-noise residual image (False) when
ftmosaic='mosaic' is chosen.
default=True
>>> imagermode='csclean' expandable parameter(s):
Image using the Cotton-Schwab algorithm in between major cycles
cyclefactor -- See above, under imagermode='mosaic'.
cyclespeedup -- See above, under imagermode='mosaic'.
multiscale -- set of scales to use in deconvolution. If set,
cleans with several resolutions using Hogbom clean. The
scale sizes are in units of cellsize. So if
cell='2arcsec', a multiscale scale=10 => 20arcsec. The
first scale is recommended to be 0 (point), we suggest the
second be on the order of synthesized beam, the third 3-5
times the synthesized beam, etc.. Avoid making the largest
scale too large relative to the image width or the scale of
the lowest measured spatial frequency. For example, if the
synthesized beam is 10" FWHM and cell=2", try
multiscale = [0,5,15].
default: multiscale=[] (standard CLEAN with psfmode algorithm,
no multi-scale).
Example: multiscale = [0,5,15]
>>> multiscale expandable parameter(s):
negcomponent -- Stop component search when the largest scale
has found this number of negative components;
-1 means continue component search even if the largest
component is negative. default: -1; example: negcomponent=50
smallscalebias -- A bias toward smaller scales.
The peak flux found at each scale is weighted by
a factor = 1 - smallscalebias*scale/max_scale, so
that Fw = F*factor.
Typically the values range from 0.2 to 1.0.
default: 0.6
imsize -- Image size in pixels (x, y). DOES NOT HAVE TO BE A POWER
OF 2 (but has to be even and factorizable to 2,3,5,7 only).
default = [256,256]; example: imsize=[350,350]
imsize = 500 is equivalent to [500,500]
If include outlier fields, e.g., [[400,400],[100,100]] or
use outlierfile.
Avoid odd-numbered imsize.
cell -- Cell size (x,y)
default= '1.0arcsec';
example: cell=['0.5arcsec,'0.5arcsec'] or
cell=['1arcmin', '1arcmin']
cell = '1arcsec' is equivalent to ['1arcsec','1arcsec']
NOTE:cell = 2.0 => ['2arcsec', '2arcsec']
phasecenter -- direction measure or fieldid for the mosaic center
default: '' => first field selected ;
example: phasecenter=6
phasecenter='J2000 19h30m00 -40d00m00'
phasecenter='J2000 292.5deg -40.0deg'
phasecenter='J2000 5.105rad -0.698rad'
If include outlier fields,
e.g. ['J2000 19h30m00 -40d00m00',J2000 19h25m00 -38d40m00']
or use outlierfile.
restfreq -- Specify rest frequency to use for output image
default='' Occasionally it is necessary to set this (for
example some VLA spectral line data). For example for
NH_3 (1,1) put restfreq='23.694496GHz'
stokes -- Stokes parameters to image
default='I'; example: stokes='IQUV';
Options: 'I','Q','U','V','IV','QU','IQ','UV','IQU','IUV','IQUV','RR','LL','XX','YY','RRLL','XXYY'
niter -- Maximum number iterations,
if niter=0, then no CLEANing is done ("invert" only).
(niter=0 can be used instead of the 'ft' task to predict/save a model)
default: 500; example: niter=5000
gain -- Loop gain for CLEANing
default: 0.1; example: gain=0.5
threshold -- Flux level at which to stop CLEANing
default: '0.0mJy';
example: threshold='2.3mJy' (always include units)
threshold = '0.0023Jy'
threshold = '0.0023Jy/beam' (okay also)
interactive -- use interactive clean (with GUI viewer)
default: interactive=False
example: interactive=True
interactive clean allows the user to build the cleaning
mask interactively using the viewer. The viewer will
appear every npercycle interation, but modify as needed
The final interactive mask is saved in the file
imagename_interactive.mask. The initial masks use the
union of mask and cleanbox (see below).
>>> interactive=True expandable parameters
npercycle -- this is the number of iterations between each
interactive update of the mask. It is important to modify
this number interactively during the cleaning, starting with
a low number like 20, but then increasing as more extended
emission is encountered.
mask -- Specification of cleanbox(es), mask image(s), primary beam
coverage level, and/or region(s) to be used for CLEANing.
CLEAN tends to perform better, and is less likely to diverge,
if the CLEAN component placement is limited by a mask to where
real emission is expected to be. As long as the image has the
same shape (size), mask images (e.g. from a previous interactive
session) can be used for a new execution. NOTE: the initial
clean mask actually used is the union of what is specified in mask
and <imagename>.mask
default: [] or '' : no masking; Possible specification types:
(a) Cleanboxes, specified using the CASA region format
(http://casaguides.nrao.edu/index.php?title=CASA_Region_Format)
Example : mask='box [ [ 100pix , 130pix] , [120pix, 150pix ] ]'
mask='circle [ [ 120pix , 40pix] ,6pix ]'
mask='circle[[19h58m52.7s,+40d42m06.04s ], 30.0arcsec]'
If used with a spectral cube, it will apply to all channels.
Multiple regions may be specified as a list of pixel ranges.
Example : mask= ['circle [ [ 120pix , 40pix] ,6pix ]',
'box [ [ 100pix , 130pix] , [120pix, 150pix ] ]' ]
(b) Filename with cleanbox shapes defined using the CASA region format.
Example: mask='mycleanbox.txt'
The file 'mycleanbox.txt' contains :
box [ [ 100pix , 130pix ] , [ 120pix, 150pix ] ]
circle [ [ 150pix , 150pix] ,10pix ]
rotbox [ [ 60pix , 50pix ] , [ 30pix , 30pix ] , 30deg ]
(c) Filename for image mask. Example: mask='myimage.mask'
Multiple mask files may be specified.
example : mask=[ 'mask1.mask', 'mask2.mask' ]
(d) Filename for region specification (e.g. from viewer).
Example: mask='myregion.rgn'
(e) Combinations of the above options.
Example: mask=['mycleanbox.txt', 'myimage.mask',
'myregion.rgn','circle [ [ 120pix , 40pix] ,6pix ]']
(f) Threshold on primary-beam.
A number between 0 and 1, used as a threshhold of primary
beam coverage. The primary beam coverage map (imagename +
'.flux(.pbcoverage)') will be made and the CLEAN component
placement will be limited to where it is > the number.
(g) True or False.
True: like (f), but use minpb as the number.
False: go maskless (and expect trouble).
(For masks for multiple fields, please see 'HINTS ON CLEAN WITH FLANKING FIELDS' below)
uvtaper -- Apply additional uv tapering of the visibilities.
default: uvtaper=False; example: uvtaper=True
>>> uvtaper=True expandable parameters
outertaper -- uv-taper on outer baselines in uv-plane
[bmaj, bmin, bpa] taper Gaussian scale in uv or
angular units. NOTE: the on-sky FWHM in arcsec is roughly
the uv taper/200 (klambda).
default: outertaper=[]; no outer taper applied
example: outertaper=['5klambda'] circular taper
FWHM=5 kilo-lambda
outertaper=['5klambda','3klambda','45.0deg']
outertaper=['10arcsec'] on-sky FWHM 10 arcseconds
outertaper=['300.0'] default units are meters
in aperture plane
innertaper -- uv-taper in center of uv-plane
[bmaj,bmin,bpa] Gaussian scale at which taper falls to
zero at uv=0
default: innertaper=[]; no inner taper applied
NOT YET IMPLEMENTED
modelimage -- Name of model image(s) to initialize cleaning. If
multiple images, then these will be added together to
form initial staring model NOTE: these are in addition
to any initial model in the <imagename>.model image file
default: '' (none); example: modelimage='orion.model'
modelimage=['orion.model','sdorion.image'] Note: if the
units in the image are Jy/beam as in a single-dish
image, then it will be converted to Jy/pixel as in a
model image, using the restoring beam in the image
header.
When nterms>1, a one-to-one mapping is done between images
in this list and Taylor-coefficients. If more than nterms
images are specified, only the first nterms are used.
It is valid to supply fewer than nterms model images.
Example : Supply an estimate of the continuum flux from a
previous imaging run.
weighting -- Weighting to apply to visibilities:
default='natural'; example: weighting='uniform';
Options: 'natural','uniform','briggs',
'superuniform','briggsabs','radial'
>>> Weighting expandable parameters
For weighting='briggs' and 'briggsabs'
robust -- Brigg's robustness parameter
default=0.0; example: robust=0.5;
Options: -2.0 to 2.0; -2 (uniform)/+2 (natural)
For weighting='briggsabs'
noise -- noise parameter to use for Briggs "abs"
weighting
example noise='1.0mJy'
npixels -- uv-cell area used for weight calculation
example npixels=1
Default = 0
superuniform: 0 Means 3x3 cells for weighting
the cell weight is proportional to the weight of
the 3x3 cells centered on it.
superuniform = F means 1x1 cell for averaging weights.
briggs/briggsabs: 0 is similar to 1x1 cell weight.
1 may? be similar to 3X3 cells.
Only npixels 0 or 1 recommended
restoringbeam -- Output Gaussian restoring beam for CLEAN image
[bmaj, bmin, bpa] elliptical Gaussian restoring beam
default units are in arc-seconds for bmaj,bmin, degrees
for bpa default: restoringbeam=[]; Use PSF calculated
from dirty beam.
example: restoringbeam=['10arcsec'] circular Gaussian
FWHM 10 arcseconds example:
restoringbeam=['10.0','5.0','45.0deg'] 10"x5"
at 45 degrees
pbcor -- Output primary beam-corrected image
If pbcor=False, the final output image is NOT corrected for
the PB pattern (particularly important for mosaics), and
therefore is not "flux correct". Correction can also be
done after the fact using immath to divide
<imagename>.image by the <imagename>.flux image.
default: pbcor=False; output un-corrected image
example: pbcor=True; output pb-corrected image (masked outside
minpb)
minpb -- Minimum PB level to use for pb-correction and pb-based masking.
default=0.2;
example: minpb=0.01
When imagermode is *not* 'mosaic' :
minpb is applied to the flux image (sensitivity-weighted pb).
minpb is used to create a mask, only when pbcor=True
When imagermode='mosaic' :
minpb is applied to the flux.pbcoverage image
(mosaic pb with equal weight per pointing)
minpb is always used to create a mask (regardless of
pbcor=True/False)
usescratch -- if True will create scratch columns if they are
not there. And after clean completes the predicted model
visibility is from the clean components are written to the ms. This increases
the ms size by the data volume. if False then the model is saved in the ms
header and the calculation of the visibilities is done on the fly when using
calibration or plotms. Use True if you want to access the moedl visibilities
in python, say.
allowchunk -- Partition the image cube by channel-chunks.
default=False;
False: Major cycle grids all channels. Minor cycle steps
through all channels before the next major cycle.
True: Major and minor cycles are performed one chunk
at a time, and output images cubes are concatenated.
async -- Run asynchronously
default = False; do not run asychronously
======================================================================
HINTS ON CLEAN WITH FLANKING FIELDS
There are two ways of specifying multi-field images for clean.
(a) Task parameters are used to define the first(main) field.
A text file containing definitions of all additional fields is supplied
to the 'outlierfile' task parameter.
This outlier file must contain the following parameters per field
Required : imagename, imsize, phasecenter
Optional : mask, modelimage
The parameter set for each field must begin with 'imagename'.
Parameters can be listed in a single line or span multiple lines.
Example : Three fields.
- Task Inputs :
imagename = 'M1_0'
outlierfile='outlier.txt'
imsize = [1024,1024]
phasecenter = 'J2000 13h27m20.98 43d26m28.0'
- Contents of outlier file 'outlier.txt':
imagename = 'M1_1'
imsize = [128,128]
phasecenter = 'J2000 13h30m52.159 43d23m08.02'
mask = ['out1.mask', 'circle[[40pix,40pix],5pix]' ]
modelimage = 'out1.model'
imagename = 'M1_2'
imsize = [128,128]
phasecenter = 'J2000 13h24m08.16 43d09m48.0'
In this example, the first field 'M1_0' is defined using
main task parameters. The next two 'M1_1' and 'M1_2'
are listed in the file 'newoutlier.txt'. A mask and modelimage
has been supplied only for the second field (M1_1). Fields
with unspecified masks will use the full field for cleaning.
(b) Specify all fields as lists for each task parameter :
Parameters that support lists for multi-field specification :
'imagename', 'imsize', 'phasecenter','mask','modelimage'
Example : Three fields (same as above)
imagename = ['M1_0','M1_1','M1_2]
imsize = [[1024,1024],[128,128],[128,128]]
phasecenter = ['J2000 13h27m20.98 43d26m28.0',
'J2000 13h30m52.159 43d23m08.02',
'J2000 13h24m08.16 43d09m48.0']
mask=[[''], ['out1.mask','circle[[40pix,40pix],5pix]'],['']]
modelimage=[[''],['out1.model'],['']]
Note : All lists must have the same length.
In the examples for both (a) and (b), the following images will be made:
M1_0.image, M1_1.image, M1_2.image cleaned images
M1.0.model, M1_1.model, M1_2.model model images
M1.0.residual, M1_1.residual, M1_2.residual residual images
Note : The old AIPS-style outlier-file and boxfile formats have been deprecated.
However, due to user-requests, they will continue be supported
in CASA 3.4. Note that the old outlier file format does not support
the specification of modelimage and mask for each field.
The new format is more complete, and less ambiguous, so please
consider updating your scripts.
1.8.0