widefield – Wide-field imaging and deconvolution with selected algorithm – imaging task
Description
This is the main wide-field imaging/deconvolution task. It uses the wprojection method for a large field of view, can make many facets, and can include outlier fields. Several deconvolution algorithms are supported. Interactive cleaning is also supported
Parameters
Parameter |
Default |
Description |
|---|---|---|
vis |
|
|
imagename |
|
|
outlierfile |
|
|
field |
|
|
spw |
|
|
selectdata |
|
|
timerange |
|
|
uvrange |
|
|
antenna |
|
|
scan |
|
|
mode |
|
|
niter |
|
|
gain |
|
|
threshold |
|
|
psfmode |
|
|
ftmachine |
|
|
facets |
|
|
wprojplanes |
|
|
multiscale |
|
|
negcomponent |
|
|
interactive |
|
|
mask |
|
|
nchan |
|
|
start |
|
|
width |
|
|
imsize |
|
|
cell |
|
|
phasecenter |
|
|
restfreq |
|
|
stokes |
|
|
weighting |
|
|
robust |
|
|
npixels |
|
|
noise |
|
|
cyclefactor |
|
|
cyclespeedup |
|
|
npercycle |
|
|
uvtaper |
|
|
outertaper |
|
|
innertaper |
|
|
restoringbeam |
|
|
calready |
|
Parameter Explanations
vis
numpy.array( [ ] )
name of input visibility file
imagename
''
Pre-name of output images
outlierfile
''
Text file with image names, sizes, centers
field
''
Field Name
spw
''
Spectral windows:channels: '' is all
selectdata
False
Other data selection parameters
timerange
''
Range of time to select from data
uvrange
''
Select data within uvrange
antenna
''
Select data based on antenna/baseline
scan
''
scan number range
mode
'mfs'
Type of selection (mfs, channel, velocity, frequency)
niter
int(500)
Maximum number of iterations
gain
float(0.1)
Loop gain for cleaning
threshold
'0.0Jy'
Flux level to stop cleaning. Must include units
psfmode
'clark'
Algorithm to use (clark, hogbom)
ftmachine
''
Gridding method for the image (wproject, ft)
facets
int(3)
Number of facets along each axis in main image only
wprojplanes
int(64)
Number of planes to use in wprojection convolutiuon function
multiscale
numpy.array( [ int() ] )
set deconvolution scales (pixels), default: multiscale=[]
negcomponent
int(0)
Stop cleaning if the largest scale finds this number of neg components
interactive
False
use interactive clean (with GUI viewer)
mask
numpy.array( [ ] )
cleanbox(es), mask image(s), and/or region(s)
nchan
int(1)
Number of channels (planes) in output image
start
int(0)
First channel in input to use
width
int(1)
Number of input channels to average
imsize
numpy.array( [ int(256),int(256) ] )
Image size in pixels (nx,ny), single value okay
cell
{'value': float(1.01.0), 'unit': 'arcsec'}
The image cell size in arcseconds [x,y], single value okay.
phasecenter
''
Field Identififier or direction of the image phase center
restfreq
''
rest frequency to assign to image (see help)
stokes
'I'
Stokes params to image (I,IV,QU,IQUV,RR,LL,XX,YY,RRLL,XXYY)
weighting
'natural'
Weighting to apply to visibilities
robust
float(0.0)
Briggs robustness parameter
npixels
int(0)
number of pixels to determine cell size for superuniform or briggs weighting
noise
'1.0Jy'
noise parameter for briggs abs mode weighting
cyclefactor
float(1.5)
Threshold for minor/major cycles (see pdoc)
cyclespeedup
int(-1)
Cycle threshold doubles in this number of iterations
npercycle
int(100)
Number of iterations before interactive masking prompt
uvtaper
False
Apply additional uv tapering of visibilities.
outertaper
numpy.array( [ '' ] )
uv-taper on outer baselines in uv-plane
innertaper
numpy.array( [ ] )
uv-taper in center of uv-plane
restoringbeam
numpy.array( [ ] )
Output Gaussian restoring beam for CLEAN image
calready
False
Create scratch columns and store model visibilities so that selfcal can be run after clean