progressive_cleaning_movie.py

import os
# ## (1) Import the python application layer

from imagerhelpers.imager_base import PySynthesisImager
from imagerhelpers.input_parameters import ImagerParameters
try:
    from tasks import exportfits
    from taskinit import iatool
    ia = iatool()
except ImportError:
    from casatasks import exportfits
    from casatools import image
    ia = image()

def my_tclean(vis, imagename, *args, **kwargs):

    # (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 " )

    msname = vis

    kwargs['loopgain'] = kwargs.pop('gain', 0.1)
    kwargs['timestr'] = kwargs.pop('timerange', '')
    kwargs['dopbcorr'] = kwargs.pop('pbcor', '')
    kwargs['uvdist'] = kwargs.pop('uvrange', '')
    kwargs['scalebias'] = kwargs.pop('smallscalebias', '')
    kwargs['cfcache'] = kwargs.pop('cfcache', '')
    kwargs['nchan'] = kwargs.pop('nchan', -1) or -1
    kwargs['nterms'] = kwargs.pop('nterms', 2) or 2
    kwargs['facets'] = kwargs.pop('facets', 1) or 1
    kwargs['chanchunks'] = kwargs.pop('chanchunks', -1) or -1

    kwargs.pop('calcpsf', None)
    kwargs.pop('psfphasecenter', None)
    kwargs.pop('calcres', None)
    kwargs.pop('restoration', None)
    kwargs.pop('intent', None)
    kwargs.pop('observation', None)
    kwargs.pop('selectdata', None)

    for key in kwargs.keys():
        if kwargs[key] is None:
            kwargs.pop(key)

    paramList = ImagerParameters(msname=msname, imagename=imagename, **kwargs)

    # (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

    exportfits(imagename+".image.tt0", imagename+".image.tt0.dirty.fits", overwrite=True)
    exportfits(imagename+".residual.tt0", imagename+".residual.tt0.dirty.fits", overwrite=True)
    exportfits(imagename+".model.tt0", imagename+".model.tt0.dirty.fits", overwrite=True)

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

    majornumber = 0
    minornumber = 0

    # (7) Run the iteration loops
    while not imager.hasConverged():
        print("Beginning major cycle = {0}".format(majornumber))
        imager.runMinorCycle()
        minornumber += 1
        imager.runMajorCycle()
        imager.restoreImages()
        majornumber += 1
        imager.updateMask()
        exportfits(imagename+".image.tt0", imagename+".image.tt0.major{0}.fits".format(majornumber), overwrite=True)
        exportfits(imagename+".residual.tt0", imagename+".residual.tt0.major{0}.fits".format(majornumber), overwrite=True)
        exportfits(imagename+".model.tt0", imagename+".model.tt0.major{0}.fits".format(majornumber), overwrite=True)
        #ia.open(imagename+".image.tt0")

        print(imagename+".image.tt0.major{0}.fits".format(majornumber))
        assert os.path.exists(imagename+".image.tt0.major{0}.fits".format(majornumber))

    # (8) Finish up

    imager.restoreImages()
    imager.pbcorImages()
    # don't do this: it tries to load vis tools, which is illegal
    #retrec = imager.getSummary()

    assert os.path.exists(imagename+".image.tt0.major1.fits".format(1))

    # (9) Close tools.

    imager.deleteTools()

    #return retrec

tclean_synthesisimager.py

# ## (1) Import the python application layer

from imagerhelpers.imager_base import PySynthesisImager
from imagerhelpers.input_parameters import ImagerParameters
try:
    from tasks import exportfits
except ImportError:
    from casatasks import exportfits

def tclean(vis, imagename, *args, **kwargs):

    # (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 " )

    msname = vis

    kwargs['loopgain'] = kwargs.pop('gain', 0.1)
    kwargs['timestr'] = kwargs.pop('timerange', '')
    kwargs['dopbcorr'] = kwargs.pop('pbcor', '')
    kwargs['uvdist'] = kwargs.pop('uvrange', '')
    kwargs['scalebias'] = kwargs.pop('smallscalebias', '')
    kwargs['cfcache'] = kwargs.pop('cfcache', '')
    kwargs['nchan'] = kwargs.pop('nchan', -1) or -1
    kwargs['nterms'] = kwargs.pop('nterms', 2) or 2
    kwargs['facets'] = kwargs.pop('facets', 1) or 1
    kwargs['chanchunks'] = kwargs.pop('chanchunks', -1) or -1

    kwargs.pop('calcpsf', None)
    kwargs.pop('psfphasecenter', None)
    kwargs.pop('calcres', None)
    kwargs.pop('restoration', None)
    kwargs.pop('intent', None)
    kwargs.pop('observation', None)
    kwargs.pop('selectdata', None)

    for key in kwargs.keys():
        if kwargs[key] is None:
            kwargs.pop(key)

    paramList = ImagerParameters(msname=msname, imagename=imagename, **kwargs)

    # (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()

    majornumber = 0
    minornumber = 0

    # (7) Run the iteration loops
    while not imager.hasConverged():
        imager.runMinorCycle()
        exportfits(imagename+".image.tt0", imagename+".image.tt0.minor{0}.fits".format(minornumber), overwrite=True)
        exportfits(imagename+".residual.tt0", imagename+".residual.tt0.minor{0}.fits".format(minornumber), overwrite=True)
        minornumber += 1
        imager.runMajorCycle()
        exportfits(imagename+".image.tt0", imagename+".image.tt0.major{0}.fits".format(majornumber), overwrite=True)
        exportfits(imagename+".residual.tt0", imagename+".residual.tt0.minor{0}.fits".format(minornumber), overwrite=True)
        exportfits(imagename+".model.tt0", imagename+".model.tt0.minor{0}.fits".format(minornumber), overwrite=True)
        majornumber += 1
        imager.updateMask()

    # (8) Finish up

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

    # (9) Close tools.

    imager.deleteTools()

    return retrec