mosaic – Create a multi-field deconvolved image with selected algorithm – imaging task

Description

Form images from visibilities. Handles continuum and spectral line cubes.

Parameters

Title
Parameter	Default	Description
vis	`''`
imagename	`''`
mode	`'mfs'`
alg	`'clark'`
imsize	`numpy.array( [ int(256),int(256) ] )`
cell	`{'value': float(1.01.0), 'unit': 'arcsec'}`
phasecenter	`''`
stokes	`'I'`
niter	`int(500)`
gain	`float(0.1)`
threshold	`float(0.0)`
mask	`numpy.array( [ ] )`
cleanbox	`{ }`
nchan	`int(1)`
start	`int(0)`
width	`int(1)`
field	`''`
spw	`''`
timerange	`''`
restfreq	`''`
sdimage	`''`
modelimage	`''`
weighting	`'natural'`
mosweight	`False`
rmode	`'norm'`
robust	`float(0.0)`
ftmachine	`'mosaic'`
cyclefactor	`float(1.5)`
cyclespeedup	`int(-1)`
scaletype	`'SAULT'`
minpb	`float(0.1)`
sigma	`{'value': float(0.001), 'unit': 'Jy'}`
targetflux	`{'value': float(1.0), 'unit': 'Jy'}`
constrainflux	`False`
prior	`numpy.array( [ ] )`
negcomponent	`int(2)`
scales	`numpy.array( [ int(0),int(3),int(10) ] )`
npercycle	`int(100)`
npixels	`int(0)`
noise	`{'value': float(0.0), 'unit': 'Jy'}`

Parameter Explanations

vis

''

name of input visibility file

imagename

''

Pre-name of output images

mode

'mfs'

Type of selection (mfs, channel, velocity, frequency)

alg

'clark'

Algorithm to use (clark, hogbom, multiscale)

imsize

numpy.array( [ int(256),int(256) ] )

Image size in pixels (nx,ny), symmetric for single value

cell

{'value': float(1.01.0), 'unit': 'arcsec'}

The image cell size in arcseconds [x,y].

phasecenter

''

Field Identififier or direction of the image phase center

stokes

'I'

Stokes params to image (I,IV,QU,IQUV,RR,LL,XX,YY,RRLL,XXYY)

niter

int(500)

Maximum number of iterations

gain

float(0.1)

Loop gain for cleaning

threshold

float(0.0)

Flux level to stop cleaning (unit mJy assumed)

mask

numpy.array( [ ] )

Set of mask images used in cleaning

cleanbox

{ }

clean box regions or file name or 'interactive'

nchan

int(1)

Number of channels in output image

start

int(0)

Start channel

width

int(1)

Channel width (value > 1 indicates channel averaging)

field

''

Field Name

spw

''

Spectral windows:channels: '' is all

timerange

''

Range of time to select from data

restfreq

''

rest frequency to use in image

sdimage

''

Input single dish image to use for model

modelimage

''

Name of output(/input) model image

weighting

'natural'

Weighting to apply to visibilities

mosweight

False

Individually weight the fields of the mosaic

rmode

'norm'

Robustness mode (for Briggs weightting)

robust

float(0.0)

Briggs robustness parameter

ftmachine

'mosaic'

Gridding method for the image

cyclefactor

float(1.5)

Threshold for minor/major cycles (see pdoc)

cyclespeedup

int(-1)

Cycle threshold doubles in this number of iterations

scaletype

'SAULT'

Controls scaling of pixels in the image plane

minpb

float(0.1)

Minimum PB level to use

sigma

{'value': float(0.001), 'unit': 'Jy'}

Target image sigma

targetflux

{'value': float(1.0), 'unit': 'Jy'}

Target flux for final image

constrainflux

False

Constrain image to match target flux

prior

numpy.array( [ ] )

Name of MEM prior images

negcomponent

int(2)

Stop the component search when the largest scale has found this number of negative components

scales

numpy.array( [ int(0),int(3),int(10) ] )

resolutions in pixel units

npercycle

int(100)

Number of iterations before interactive masking prompt

npixels

int(0)

number of pixels to determine cell size for superuniform or briggs weighting

noise

{'value': float(0.0), 'unit': 'Jy'}

noise parameter for briggs weighting when rmode='abs'