Examples

The following examples, to be expanded, highlight modes and options that the tclean task supports.

The examples below are written as scripts that may be copied and pasted to get started with the basic parameters needed for a particular operation. When writing scripts, it is advised that the interactive task interface be used to view lists of sub-parameters that are relevant only to the operations being performed. For example, setting specmode='cube' and running inp() will list parameters that are relevant to spectral coordinate definition, or setting niter to a number greater than zero (niter=100) followed by inp() will list iteration control parameters.

Note that all runs of tclean need the following parameters ( vis, imagename, imsize, cell ).

By default, tclean will run with niter=0, making the PSF, a primary beam, the initial dirty (or residual) image and a restored version of the image.

Imaging and Deconvolution iterations

Using Hogbom CLEAN on a single MFS image.

tclean(vis='test.ms', imagename='try1', imsize=100, cell='10.0arcsec', specmode='mfs',
deconvolver='hogbom', gridder='standard', weighting='natural', niter=100 )

Using Multi-scale CLEAN on a Cube Mosaic image.

tclean(vis='test.ms', imagename='try1', imsize=100, cell='10.0arcsec'

specmode='cube', nchan=10, start='1.0GHz', width='10MHz',
deconvolver='multiscale', scales=[0,3,10,30],

gridder='mosaic', pblimit=0.1,

weighting='natural', niter=100 )

Using W-Projection with Multi-Term MFS wideband imaging.

tclean(vis='test.ms', imagename='try1', imsize=100, cell='10.0arcsec',
deconvolver='mtmfs', reffreq='1.5GHz', nterms=2,

gridder='wproject', wprojplanes=64,

weighting='natural', niter=100 )

Scripting using PySynthesisImager

PySynthesisImager (LINK) is a python application built on top of the synthesis tools (LINK). The operations of the tclean task can be replicated using the following python script. Subsets of the script can thus be chosen, and extra external methods can be inserted in between as desired. After each stage, images are saved on disk. Therefore, any modifications done to the images in between steps will be honored. agerParameters

## (1) Import the python application layer

from imagerhelpers.imager_base import PySynthesisImager
from imagerhelpers.input_parameters import ImagerParameters

## (2) Set up Input Parameters
## - List all parameters that you need here
## - Defaults will be assumed for unspecified parameters
## - Nearly all parameters are identical to that in the task. Please look at the
## list of parameters under __init__ using " help ImagerParameters " )

paramList = ImagerParameters(
msname ='DataTest/point.ms',
field='',
spw='',
imagename='try2',
imsize=100,
cell='10.0arcsec',
specmode='mfs',
gridder='standard',
weighting='briggs',
niter=100,
deconvolver='hogbom'
)

## (3) Construct the PySynthesisImager object, with all input parameters

imager = PySynthesisImager(params=paramList)

## (4) Initialize various modules.
## - Pick only the modules you will need later on. For example, to only make
## the PSF, there is no need for the deconvolver or iteration control modules.

## Initialize modules major cycle modules

imager.initializeImagers()
imager.initializeNormalizers()
imager.setWeighting()

## Init minor cycle modules

imager.initializeDeconvolvers()
imager.initializeIterationControl()

## (5) Make the initial images

imager.makePSF()
imager.makePB()
imager.runMajorCycle() # Make initial dirty / residual image

## (6) Make the initial clean mask
imager.hasConverged()
imager.updateMask()

## (7) Run the iteration loops
while ( not imager.hasConverged() ):
    imager.runMinorCycle()
    imager.runMajorCycle()
    imager.updateMask()

## (8) Finish up

retrec=imager.getSummary();
imager.restoreImages()
imager.pbcorImages()

## (9) Close tools.

imager.deleteTools()

For model prediction (i.e. to only save an input model in preparation for self-calibration, for example), use the following in step (5). The name of the input model is either assumed to be .model (or its multi-term equivalent) or should be specified via the startmodel parameter in step (2).

imager.predictModel() # Step (5)

For major cycle parallelization for continuum imaging (specmode='mfs'), replace steps (1) and (3) with the following

from imagerhelpers.imager_parallel_continuum import PyParallelContSynthesisImager # Step (1)

imager = PyParallelContSynthesisImager(params=paramList) # Step (3)

For parallelization of both the major and minor cycles for Cube imaging, replace steps (1) and (3) with the following, and include a virtual concanenation call at the end. ( However, note that for parallel Cube imaging, if you would like to replace the minor cycle with your own code (for example), you would have to go one layer deeper. For this, please contact our team for assistance. )

from imagerhelpers.imager_parallel_cube import PyParallelCubeSynthesisImager # Step (1)

imager = PyParallelCubeSynthesisImager(params=paramList) # Step (3)

imager.concatImages(type='virtualcopy') # Step (8)