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# casalog.post("Model visibilities may not have been saved in the MS even though you have asked for it. Please check the logger for the phrases 'Run (Last) Major Cycle' and '" + wstr +"'. If these do not appear, then please save the model via a separate tclean run with niter=0,calcres=F,calcpsf=F. It will pick up the existing model from disk and save/predict it. Reason for this : For performance reasons model visibilities are saved only in the last major cycle. If the X button on the interactive GUI is used to terminate a run before this automatically detected 'last' major cycle, the model isn't written. However, a subsequent tclean run as described above will predict and save the model. ","WARN")import osimport mathimport shutilimport stringimport timeimport reimport copyfrom typing import TYPE_CHECKINGfrom casatools import ( synthesisimager, synthesisdeconvolver, synthesisnormalizer, iterbotsink, ctsys, table, image,)from casatasks import casalogfrom casatasks.private.imagerhelpers.summary_minor import SummaryMinorif TYPE_CHECKING: from casatasks.private.imagerhelpers.input_parameters import ImagerParametersctsys_hostinfo = ctsys.hostinfo_tb = table()_ia = image()"""A set of helper functions for tclean.Summary..."""#############################################class PySynthesisImager: def __init__(self,params: 'ImagerParameters'): ################ Tools self.initDefaults() # Check all input parameters, after partitioning setup. # Selection Parameters. Dictionary of dictionaries, indexed by 'ms0','ms1',... self.allselpars = params.getSelPars() # Imaging/Deconvolution parameters. Same for serial and parallel runs self.alldecpars = params.getDecPars() self.allimpars = params.getImagePars() self.allgridpars = params.getGridPars() self.allnormpars = params.getNormPars() self.weightpars = params.getWeightPars() # Iteration parameters self.iterpars = params.getIterPars() ## Or just params.iterpars # CFCache params self.cfcachepars = params.getCFCachePars() ## Number of fields ( main + outliers ) self.NF = len(self.allimpars.keys()) self.stopMinor = {} ##[0]*self.NF for immod in range(0, self.NF): self.stopMinor[str(immod)] = 1.0 ## Number of nodes. This gets set for parallel runs ## It can also be used serially to process the major cycle in pieces.