clean – Invert and deconvolve images with selected algorithm – imaging task

Description

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

Parameters

Title

Parameter

Default

Description

vis

''

imagename

''

outlierfile

''

field

''

spw

''

selectdata

True

timerange

''

uvrange

''

antenna

''

scan

''

observation

''

intent

''

mode

'mfs'

resmooth

False

gridmode

''

wprojplanes

int(-1)

facets

int(1)

cfcache

'cfcache.dir'

rotpainc

float(5.0)

painc

float(360.0)

aterm

True

psterm

False

mterm

True

wbawp

False

conjbeams

True

epjtable

''

interpolation

'linear'

niter

int(500)

gain

float(0.1)

threshold

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

psfmode

'clark'

imagermode

'csclean'

ftmachine

'mosaic'

mosweight

False

scaletype

'SAULT'

multiscale

numpy.array( [ int(0) ] )

negcomponent

int(-1)

smallscalebias

float(0.6)

interactive

False

mask

numpy.array( [  ] )

nchan

int(-1)

start

int(0)

width

int(1)

outframe

''

veltype

'radio'

imsize

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

cell

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

phasecenter

''

restfreq

''

stokes

'I'

weighting

'natural'

robust

float(0.0)

uvtaper

False

outertaper

numpy.array( [ '' ] )

innertaper

numpy.array( [  ] )

modelimage

''

restoringbeam

numpy.array( [  ] )

pbcor

False

minpb

float(0.2)

usescratch

False

noise

'1.0Jy'

npixels

int(0)

npercycle

int(100)

cyclefactor

float(1.5)

cyclespeedup

int(-1)

nterms

int(1)

reffreq

''

chaniter

False

flatnoise

True

allowchunk

False

Parameter Explanations

vis

''

Name of input visibility file

imagename

''

Pre-name of output images

outlierfile

''

Text file with image names, sizes, centers for outliers

field

''

Field Name or id

spw

''

Spectral windows e.g. '0~3', '' is all

selectdata

True

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

observation

''

Observation ID range

intent

''

Scan Intent(s)

mode

'mfs'

Spectral gridding type (mfs, channel, velocity, frequency)

resmooth

False

Re-restore the cube image to a common beam when True

gridmode

''

Gridding kernel for FFT-based transforms, default='' None

wprojplanes

int(-1)

Number of w-projection planes for convolution; -1 => automatic determination

facets

int(1)

Number of facets along each axis (main image only)

cfcache

'cfcache.dir'

Convolution function cache directory

rotpainc

float(5.0)

Parallactic angle increment (degrees) for OTF A-term rotation

painc

float(360.0)

Parallactic angle increment (degrees) for computing A-term

aterm

True

Switch-on the A-Term?

psterm

False

Switch-on the PS-Term?

mterm

True

Switch-on the M-Term?

wbawp

False

Trigger the wide-band A-Projection algorithm?

conjbeams

True

Use frequency conjugate beams in WB A-Projection algorithm?

epjtable

''

Table of EP-Jones parameters

interpolation

'linear'

Spectral interpolation (nearest, linear, cubic).

niter

int(500)

Maximum number of iterations

gain

float(0.1)

Loop gain for cleaning

threshold

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

Flux level to stop cleaning, must include units: '1.0mJy'

psfmode

'clark'

Method of PSF calculation to use during minor cycles

imagermode

'csclean'

Options: 'csclean' or 'mosaic', '', uses psfmode

ftmachine

'mosaic'

Gridding method for the image

mosweight

False

Individually weight the fields of the mosaic

scaletype

'SAULT'

Controls scaling of pixels in the image plane. default='SAULT'; example: scaletype='PBCOR' Options: 'PBCOR','SAULT'

multiscale

numpy.array( [ int(0) ] )

Deconvolution scales (pixels); [] = standard clean

negcomponent

int(-1)

Stop cleaning if the largest scale finds this number of neg components

smallscalebias

float(0.6)

a bias to give more weight toward smaller scales

interactive

False

Use interactive clean (with GUI viewer)

mask

numpy.array( [  ] )

Cleanbox(es), mask image(s), region(s), or a level

nchan

int(-1)

Number of channels (planes) in output image; -1 = all

start

int(0)

start of output spectral dimension

width

int(1)

width of output spectral channels

outframe

''

default spectral frame of output image

veltype

'radio'

velocity definition (radio, optical, true)

imsize

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

x and y image size in pixels. Single value: same for both

cell

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

x and y cell size(s). Default unit arcsec.

phasecenter

''

Image center: direction or field index

restfreq

''

Rest frequency to assign to image (see help)

stokes

'I'

Stokes params to image (eg I,IV,IQ,IQUV)

weighting

'natural'

Weighting of uv (natural, uniform, briggs, …)

robust

float(0.0)

Briggs robustness parameter

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 (not implemented)

modelimage

''

Name of model image(s) to initialize cleaning

restoringbeam

numpy.array( [  ] )

Output Gaussian restoring beam for CLEAN image

pbcor

False

Output primary beam-corrected image

minpb

float(0.2)

Minimum PB level to use

usescratch

False

True if to save model visibilities in MODEL_DATA column

noise

'1.0Jy'

noise parameter for briggs abs mode weighting

npixels

int(0)

number of pixels for superuniform or briggs weighting

npercycle

int(100)

Clean iterations before interactive prompt (can be changed)

cyclefactor

float(1.5)

Controls how often major cycles are done. (e.g. 5 for frequently)

cyclespeedup

int(-1)

Cycle threshold doubles in this number of iterations

nterms

int(1)

Number of Taylor coefficients to model the sky frequency dependence

reffreq

''

Reference frequency (nterms > 1),'' uses central data-frequency

chaniter

False

Clean each channel to completion (True), or all channels each cycle (False)

flatnoise

True

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)

allowchunk

False

Divide large image cubes into channel chunks for deconvolution