casa / casa6 / a3f52b7c17d

Commits

Srikrishna Sekhar authored a3f52b7c17d 29 Jan 2024

CAS-14184 : Fixed masking params

Some auto-multithresh parameters were missing from the _gclean input,
added those in.

The order of masking was also incorrect, the auto-mask growing was
happening _prior_ to deconvolution rather than after. This has also been
fixed.

casatasks/src/private/imagerhelpers/_gclean.py

Modified

-186
+        else:
-187
+            if isinstance(self._threshold, str):
-188
+                if "mJy" in self._threshold:
-189
+                    self._threshold = float(self._threshold.replace("mJy", "")) / 1e3
-190
+                elif "uJy" in self._threshold:
-191
+                    self._threshold = float(self._threshold.replace("uJy", "")) / 1e6
-192
+                elif "Jy" in self._threshold:
-193
+                    self._threshold = float(self._threshold.replace("Jy", ""))
-194
-195
--
+    def __init__( self, vis, imagename, field='', spw='', timerange='', uvrange='', antenna='', scan='', observation='', intent='', datacolumn='corrected',
--
+                  imsize=[100], cell=[ ], phasecenter='', stokes='I', startmodel='', specmode='cube', reffreq='', nchan=-1, start='', width='',
--
+                  outframe='LSRK', veltype='radio', restfreq='', interpolation='linear', perchanweightdensity=True, gridder='standard', wprojplanes=int(1),
--
+                  mosweight=True, psterm=False, wbawp=True, conjbeams=False, usepointing=False, pointingoffsetsigdev=[  ], pblimit=0.2, deconvolver='hogbom',
--
+                  smallscalebias=0.0, niter=0, threshold='0.1Jy', nsigma=0.0, cycleniter=-1, nmajor=1, cyclefactor=1.0, minpsffraction=0.05, maxpsffraction=0.8,
--
+                  scales=[], restoringbeam='', pbcor=False, nterms=int(2), weighting='natural', robust=float(0.5), npixels=0, gain=float(0.1),
--
+                  sidelobethreshold=3.0, noisethreshold=5.0, lownoisethreshold=1.5, negativethreshold=0.0, minbeamfrac=0.3, growiterations=75, dogrowprune=True,
--
+                  minpercentchange=-1.0, fastnoise=True, savemodel='none', usemask='user', mask='', parallel=False,
--
+                  history_filter=lambda index, arg, history_value: history_value ):
-+
+    def __init__( self, vis, imagename, field='', spw='', timerange='', uvrange='', antenna='', scan='', observation='', intent='', datacolumn='corrected', imsize=[100], cell=[ ],
-+
+                 phasecenter='', stokes='I', startmodel='', specmode='cube', reffreq='', nchan=-1, start='', width='', outframe='LSRK', veltype='radio', restfreq='', interpolation='linear',
-+
+                 perchanweightdensity=True, gridder='standard', wprojplanes=int(1), mosweight=True, psterm=False, wbawp=True, conjbeams=False, usepointing=False, pointingoffsetsigdev=[  ],
-+
+                 pblimit=0.2, deconvolver='hogbom', smallscalebias=0.0, niter=0, threshold='0.1Jy', nsigma=0.0, cycleniter=-1, nmajor=1, cyclefactor=1.0, minpsffraction=0.05,
-+
+                 maxpsffraction=0.8, scales=[], restoringbeam='', pbcor=False, nterms=int(2), weighting='natural', robust=float(0.5), npixels=0, gain=float(0.1), pbmask=0.2, sidelobethreshold=3.0,
-+
+                 noisethreshold=5.0, lownoisethreshold=1.5, negativethreshold=0.0, smoothfactor=1.0, minbeamfrac=0.3, cutthreshold=0.01, growiterations=75, dogrowprune=True,
-+
+                 minpercentchange=-1.0, verbose=False, fastnoise=True, savemodel='none', usemask='user', mask='', parallel=False, history_filter=lambda index, arg, history_value: history_value ):
-+
-204
+        self._vis = vis
-205
+        self._imagename = imagename
-206
+        self._imsize = imsize
-207
+        self._cell = cell
-208
+        self._phasecenter = phasecenter
-209
+        self._stokes = stokes
-210
+        self._startmodel = startmodel
-211
+        self._specmode = specmode
-212
+        self._reffreq = reffreq
-213
+        self._nchan = nchan
-251
+        self._uvrange = uvrange
-252
+        self._antenna = antenna
-253
+        self._scan = scan
-254
+        self._observation = observation
-255
+        self._intent = intent
-256
+        self._datacolumn = datacolumn
-257
+        self._weighting = weighting
-258
+        self._robust = robust
-259
+        self._npixels = npixels
-260
+        self._gain = gain
-+
+        self._pbmask = pbmask
-262
+        self._sidelobethreshold = sidelobethreshold
-263
+        self._noisethreshold = noisethreshold
-264
+        self._lownoisethreshold = lownoisethreshold
-265
+        self._negativethreshold = negativethreshold
-+
+        self._smoothfactor = smoothfactor,
-267
+        self._minbeamfrac = minbeamfrac
-+
+        self._cutthreshold = cutthreshold
-269
+        self._growiterations = growiterations
-270
+        self._dogrowprune = dogrowprune
-271
+        self._minpercentchange = minpercentchange
-+
+        self._verbose = verbose
-273
+        self._fastnoise = fastnoise
-274
+        self._savemodel = savemodel
-275
+        self._parallel = parallel
-276
+        self._usemask = usemask
-277
-278
+        ###
-279
+        ### 'self._mask' always contains the mask as supplied by the user while 'self._effective_mask' is
-280
+        ### the mask currently in play as interactive clean progresses. When the user has supplied a mask,
-281
+        ### it should be the same as 'self._mask' but when the mask is managed internally by iclean/gclean
-282
+        ### the two will diverge.
-438
+                                               field=self._field, spw=self._spw, timerange=self._timerange, uvrange=self._uvrange, antenna=self._antenna,
-439
+                                               scan=self._scan, observation=self._observation, intent=self._intent, datacolumn=self._datacolumn,
-440
+                                               weighting=self._weighting, robust=self._robust, npixels=self._npixels, interactive=False, niter=0,
-441
+                                               gain=self._gain, calcres=True, calcpsf=True, restoration=False, parallel=self._parallel, fullsummary=True)
-442
-443
-444
+                    deconv_ret = self._deconvolve(imagename=self._imagename, startmodel=self._startmodel,
-445
+                                                  deconvolver=self._deconvolver, restoration=False,
-446
+                                                  threshold=self._threshold, niter=0,
-447
+                                                  nsigma=self._nsigma, fullsummary=True, fastnoise=self._fastnoise, usemask=self._usemask,
--
+                                                  mask=self._mask, noisethreshold=self._noisethreshold)
-+
+                                                  mask=self._mask, pbmask=self._pbmask, sidelobethreshold=self._sidelobethreshold, noisethreshold=self._noisethreshold,
-+
+                                                  lownoisethreshold=self._lownoisethreshold, negativethreshold=self._negativethreshold, smoothfactor=self._smoothfactor,
-+
+                                                  minbeamfrac=self._minbeamfrac, cutthreshold=self._cutthreshold, growiterations=self._growiterations,
-+
+                                                  dogrowprune=self._dogrowprune, minpercentchange=self._minpercentchange, verbose=self._verbose)
-452
-453
+                    self.current_imdict.returndict = self.current_imdict.merge(tclean_ret, deconv_ret)
-454
+                    self.global_imdict.returndict = self.current_imdict.returndict
-455
-456
+                    ## Initial call where niterleft and nmajorleft are same as original input values.
-457
+                    self.hasit, self.stopdescription = self.global_imdict.has_converged(self._niter, self._threshold, self._nmajor)
-458
-459
+                    self.current_imdict.returndict['stopcode'] = self.hasit
-460
+                    self.current_imdict.returndict['stopDescription'] = self.stopdescription
-461
+                    self._major_done = 0
-476
+                    #self.current_imdict.returndict['stopDescription'] = self.stopdescription
-477
-478
+                    # Has not, i.e., not converged
-479
+                    if self.hasit ==0 :
-480
+                        use_cycleniter, cyclethreshold = self._calc_deconv_controls(self.current_imdict, self._niter, self._threshold, self._cycleniter)
-481
-482
+                        print("No convergence, running deconvolve + tclean")
-483
+                        # Run the minor cycle
-484
+                        deconv_ret = self._deconvolve(imagename=self._imagename, startmodel=self._startmodel,
-485
+                                                  deconvolver=self._deconvolver, restoration=False,
--
+                                                  threshold=cyclethreshold, niter=use_cycleniter, gain=self._gain, usemask=self._usemask,
--
+                                                  nsigma=self._nsigma, fullsummary=True, fastnoise=self._fastnoise, noisethreshold=self._noisethreshold)
-+
+                                                  threshold=cyclethreshold, niter=use_cycleniter, gain=self._gain, fullsummary=True)
-487
-488
+                        # Run the major cycle
-489
+                        tclean_ret = self._tclean( vis=self._vis, imagename=self._imagename, imsize=self._imsize, cell=self._cell,
-490
+                                           phasecenter=self._phasecenter, stokes=self._stokes, specmode=self._specmode, reffreq=self._reffreq,
-491
+                                           gridder=self._gridder, wprojplanes=self._wprojplanes, mosweight=self._mosweight, psterm=self._psterm,
-492
+                                           wbawp=self._wbawp, conjbeams=self._conjbeams, usepointing=self._usepointing, interpolation=self._interpolation,
-493
+                                           perchanweightdensity=self._perchanweightdensity, nchan=self._nchan, start=self._start,
-494
+                                           width=self._width, veltype=self._veltype, restfreq=self._restfreq, outframe=self._outframe,
-495
+                                           pointingoffsetsigdev=self._pointingoffsetsigdev, pblimit=self._pblimit, deconvolver=self._deconvolver,
-496
+                                           smallscalebias=self._smallscalebias, cyclefactor=self._cyclefactor, scales=self._scales,
-523
+                            self.hasit, self.stopdescription = self.global_imdict.has_converged(self._niter, self._threshold, self._nmajor, masksum=0)
-524
+                        else:
-525
+                            self.hasit, self.stopdescription = self.global_imdict.has_converged(self._niter, self._threshold, self._nmajor)
-526
-527
-528
+                    self.global_imdict.returndict['stopcode'] = self.hasit
-529
+                    self.global_imdict.returndict['stopDescription'] = self.stopdescription
-530
-531
+                    if not self.hasit:
-532
+                        # If we haven't converged, run deconvolve to update the mask
--
+                        self._deconvolve(imagename=self._imagename, niter=0, deconvolver=self._deconvolver, usemask=self._usemask, restoration=False)
-+
+                        #print("no convergence, updating mask")
-+
-+
+                        self._deconvolve(imagename=self._imagename, startmodel=self._startmodel, deconvolver=self._deconvolver, restoration=False, threshold=self._threshold, niter=0,
-+
+                                         nsigma=self._nsigma, fullsummary=True, fastnoise=self._fastnoise, usemask=self._usemask, mask=self._mask, pbmask=self._pbmask,
-+
+                                         sidelobethreshold=self._sidelobethreshold, noisethreshold=self._noisethreshold, lownoisethreshold=self._lownoisethreshold,
-+
+                                         negativethreshold=self._negativethreshold, smoothfactor=self._smoothfactor, minbeamfrac=self._minbeamfrac, cutthreshold=self._cutthreshold,
-+
+                                         growiterations=self._growiterations, dogrowprune=self._dogrowprune, minpercentchange=self._minpercentchange, verbose=self._verbose)
-+
-541
--
+                #print('\nGLOBAL returndict : ',self.global_imdict.returndict)
-542
+                if len(self.global_imdict.returndict) > 0 and 'summaryminor' in self.global_imdict.returndict and sum(map(len,self.global_imdict.returndict['summaryminor'].values())) > 0:
--
+                    # self.current_imdict only contains the latest tclean/deconvolve results
--
+                    # Passing in self.global_imdict will pull out the cumulative results everytime, breaking the convergence plot.
-543
+                    self._convergence_result = ( self.global_imdict.returndict['stopDescription'] if 'stopDescription' in self.global_imdict.returndict else '',
-544
+                                                 self.global_imdict.returndict['stopcode'] if 'stopcode' in self.global_imdict.returndict else 0,
-545
+                                                 self._major_done,
-546
+                                                 self._nmajor,
-547
+                                                 self._niter,
-548
+                                                 self.__add_per_major_items( self.global_imdict.returndict,
-549
+                                                                             self._convergence_result[5]['major'],
-550
+                                                                             self.__update_convergence()))
-551
+                else:
-552
+                    self._convergence_result = ( f'tclean returned an empty result',

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