class ClassImageDeconvMachine():
def __init__(self, Gain=0.3,
MaxMinorIter=100,
NCPU=1, #psutil.cpu_count()
CycleFactor=2.5,
FluxThreshold=None,
RMSFactor=3,
PeakFactor=0,
PrevPeakFactor=0,
GD=None,
SearchMaxAbs=1,
ModelMachine=None,
NFreqBands=1,
RefFreq=None,
MainCache=None,
IdSharedMem="",
ParallelMode=True,
CacheFileName="HMPBasis",
**kw # absorb any unknown keywords arguments into this
):
"""
ImageDeconvMachine constructor. Note that this should be called pretty much when setting up the imager,
before APP workers are started, because the object registers APP handlers.
"""
self.IdSharedMem=IdSharedMem
self.SearchMaxAbs=SearchMaxAbs
self._ModelImage = None
self.MaxMinorIter = MaxMinorIter
self.NCPU = NCPU
self.Chi2Thr = 10000
self._MaskArray = None
self.GD = GD
self.SubPSF = None
self.MultiFreqMode = NFreqBands > 1
self.NFreqBands = NFreqBands
self.RefFreq = RefFreq
self.FluxThreshold = FluxThreshold
self.CycleFactor = CycleFactor
self.RMSFactor = RMSFactor
self.PeakFactor = PeakFactor
self.PrevPeakFactor = PrevPeakFactor
self.CacheFileName=CacheFileName
self.GainMachine=ClassGainMachine.get_instance()
self.ModelMachine = None
self.PSFServer = None
if ModelMachine is not None:
self.updateModelMachine(ModelMachine)
self.PSFHasChanged=False
self._previous_initial_peak = None
self.maincache = MainCache
# reset overall iteration counter
self._niter = 0
self.facetcache=None
self._MaskArray=None
self.MaskMachine=None
self.ParallelMode=ParallelMode
if self.ParallelMode:
APP.registerJobHandlers(self)
# we are in a worker
if not self.ParallelMode:
numexpr.set_num_threads(NCPU)
# peak finding mode.
# "normal" searches for peak in mean dirty image
# "sigma" searches for peak in mean_dirty/noise_map (setNoiseMap will have been called)
# "weighted" searched for peak in mean_dirty*weight
self._peakMode = "normal"
self.CurrentNegMask=None
self._NoiseMap=None
self._PNRStop=None # in _peakMode "sigma", provides addiitonal stopping criterion
if self.GD["HMP"]["PeakWeightImage"]:
print>> log, " Reading peak weighting image %s" % self.GD["HMP"]["PeakWeightImage"]
img = image(self.GD["HMP"]["PeakWeightImage"]).getdata()
_, _, nx, ny = img.shape
# collapse freq and pol axes
img = img.sum(axis=1).sum(axis=0).T[::-1].copy()
self._peakWeightImage = img.reshape((1,1,ny,nx))
self._peakMode = "weighted"
self._prevPeak = None
def setNCPU(self,NCPU):
self.NCPU=NCPU
numexpr.set_num_threads(NCPU)
def __del__ (self):
if type(self.facetcache) is shared_dict.SharedDict:
self.facetcache.delete()
def updateMask(self,Mask):
nx,ny=Mask.shape
self._MaskArray = np.zeros((1,1,nx,ny),np.bool8)
self._MaskArray[0,0,:,:]=Mask[:,:]
def setMaskMachine(self,MaskMachine):
self.MaskMachine=MaskMachine
def resetCounter(self):
self._niter = 0
def updateModelMachine(self, ModelMachine):
if ModelMachine.DicoSMStacked["Type"] not in ("MSMF", "HMP"):
raise ValueError("ModelMachine Type should be HMP")
if ModelMachine.RefFreq != self.RefFreq:
raise ValueError("RefFreqs should be equal")
self.ModelMachine = ModelMachine
if self.PSFServer is not None:
for iFacet in range(self.PSFServer.NFacets):
self.DicoMSMachine[iFacet].setModelMachine(self.ModelMachine)
def GiveModelImage(self,*args): return self.ModelMachine.GiveModelImage(*args)
def setSideLobeLevel(self,SideLobeLevel,OffsetSideLobe):
self.SideLobeLevel=SideLobeLevel
self.OffsetSideLobe=OffsetSideLobe
def SetPSF(self, DicoVariablePSF, quiet=False):
self.PSFServer=ClassPSFServer(self.GD)
self.PSFServer.setDicoVariablePSF(DicoVariablePSF,NormalisePSF=True, quiet=quiet)
self.PSFServer.setRefFreq(self.RefFreq)
self.DicoVariablePSF=DicoVariablePSF
#self.NChannels=self.DicoDirty["NChannels"]
def Init(self, PSFVar, PSFAve, approx=False, cache=None, facetcache=None, **kwargs):
"""
Init method. This is called after the first round of gridding: PSFs and such are available.
ModelMachine must be set by now.
facetcache: dict of basis functions. If supplied, then InitMSMF is not called.
cache: cache the basis functions. If None, GD["Cache"]["HMP"] setting is used
"""
# close the solutions dump, in case it was opened by a previous HMP instance
ClassMultiScaleMachine.CleanSolutionsDump.close()
self.SetPSF(PSFVar)
self.setSideLobeLevel(PSFAve[0], PSFAve[1])
if cache is None:
cache = self.GD["Cache"]["HMP"]
self.InitMSMF(approx=approx, cache=cache, facetcache=facetcache)
## OMS: why is this needed? self.RefFreq is set from self.ModelMachine in the first place
# self.ModelMachine.setRefFreq(self.RefFreq)
try: # LB - this is needed because sometimes kwargs["DegridFreqs"] is an array already
AllDegridFreqs = []
for i in kwargs["DegridFreqs"].keys():
AllDegridFreqs.append(kwargs["DegridFreqs"][i])
DegridFreqs = np.unique(np.asarray(AllDegridFreqs).flatten())
except:
DegridFreqs = kwargs["DegridFreqs"]
self.ModelMachine.setFreqMachine(kwargs["GridFreqs"], DegridFreqs)
def Reset(self):
print>>log, "resetting HMP machine"
self.DicoMSMachine = {}
if type(self.facetcache) is shared_dict.SharedDict and self.facetcache.is_writeable():
print>> log, "deleting HMP facet cache"
self.facetcache.delete()
self.facetcache = None
def setNoiseMap(self, NoiseMap, PNRStop=10):
"""Sets the noise map. The mean dirty will be divided by the noise map before peak finding.
If PNRStop is set, an additional stopping criterion (peak-to-noisemap) will be applied.
Peaks are reported in units of sigmas.
If PNRStop is not set, NoiseMap is treated as simply an (inverse) weighting that will bias
peak selection in the minor cycle. In this mode, peaks are reported in units of flux.
"""
self._NoiseMap = NoiseMap
self._PNRStop = PNRStop
self._peakMode = "sigma"
def _initMSM_handler(self, fcdict, sfdict, psfdict, iFacet, SideLobeLevel, OffsetSideLobe, verbose):
# init PSF server from PSF shared dict
self.SetPSF(psfdict, quiet=True)
MSMachine = self._initMSM_facet(iFacet,fcdict,sfdict,SideLobeLevel,OffsetSideLobe,verbose=verbose)
del MSMachine
def _initMSM_facet(self, iFacet, fcdict, sfdict, SideLobeLevel, OffsetSideLobe, MSM0=None, verbose=False):
"""initializes MSM for one facet"""
self.PSFServer.setFacet(iFacet)
MSMachine = ClassMultiScaleMachine.ClassMultiScaleMachine(self.GD, fcdict, self.GainMachine, NFreqBands=self.NFreqBands)
MSMachine.setModelMachine(self.ModelMachine)
MSMachine.setSideLobeLevel(SideLobeLevel, OffsetSideLobe)
MSMachine.SetFacet(iFacet)
MSMachine.SetPSF(self.PSFServer) # ThisPSF,ThisMeanPSF)
MSMachine.FindPSFExtent(verbose=verbose) # only print to log for central facet
if MSM0 is not None:
MSMachine.CopyListScales(MSM0)
else:
MSMachine.MakeListScales(verbose=verbose, scalefuncs=sfdict)
MSMachine.MakeMultiScaleCube()
MSMachine.MakeBasisMatrix()
return MSMachine
def InitMSMF(self, approx=False, cache=True, facetcache=None):
"""Initializes MSMF basis functions. If approx is True, then uses the central facet's PSF for
all facets.
Populates the self.facetcache dict, unless facetcache is supplied
"""
self.DicoMSMachine = {}
valid = True
if facetcache is not None:
print>> log, "HMP basis functions pre-initialized"
self.facetcache = facetcache
else:
cachehash = dict(
[(section, self.GD[section]) for section in (
"Data", "Beam", "Selection", "Freq",
"Image", "Facets", "Weight", "RIME","DDESolutions",
"Comp", "CF",
"HMP")])
cachepath, valid = self.maincache.checkCache(self.CacheFileName, cachehash, reset=not cache or self.PSFHasChanged)
# do not use cache in approx mode
if approx or not cache:
valid = False
if valid:
print>>log,"Initialising HMP basis functions from cache %s"%cachepath
self.facetcache = shared_dict.create(self.CacheFileName)
self.facetcache.restore(cachepath)
else:
self.facetcache = None
init_cache = self.facetcache is None
if init_cache:
self.facetcache = shared_dict.create(self.CacheFileName)
# in any mode, start by initializing a MS machine for the central facet. This will precompute the scale
# functions
centralFacet = self.PSFServer.DicoVariablePSF["CentralFacet"]
self.DicoMSMachine[centralFacet] = MSM0 = \
self._initMSM_facet(centralFacet,
self.facetcache.addSubdict(centralFacet) if init_cache else self.facetcache[centralFacet],
None, self.SideLobeLevel, self.OffsetSideLobe, verbose=True)
if approx:
print>>log, "HMP approximation mode: using PSF of central facet (%d)" % centralFacet
for iFacet in xrange(self.PSFServer.NFacets):
self.DicoMSMachine[iFacet] = MSM0
elif (self.GD["Facets"]["NFacets"]==1)&(not self.GD["DDESolutions"]["DDSols"]):
self.DicoMSMachine[0] = MSM0
else:
# if no facet cache, init in parallel
if init_cache:
for iFacet in xrange(self.PSFServer.NFacets):
if iFacet != centralFacet:
fcdict = self.facetcache.addSubdict(iFacet)
if self.ParallelMode:
args=(fcdict.writeonly(), MSM0.ScaleFuncs.readonly(), self.DicoVariablePSF.readonly(),
iFacet, self.SideLobeLevel, self.OffsetSideLobe, False)
APP.runJob("InitHMP:%d"%iFacet, self._initMSM_handler,
args=args)
else:
self.DicoMSMachine[iFacet] = \
self._initMSM_facet(iFacet, fcdict, None,
self.SideLobeLevel, self.OffsetSideLobe, MSM0=MSM0, verbose=False)
if self.ParallelMode:
APP.awaitJobResults("InitHMP:*", progress="Init HMP")
self.facetcache.reload()
# t = ClassTimeIt.ClassTimeIt()
# now reinit from cache (since cache was computed by subprocesses)
for iFacet in xrange(self.PSFServer.NFacets):
if iFacet not in self.DicoMSMachine:
self.DicoMSMachine[iFacet] = \
self._initMSM_facet(iFacet, self.facetcache[iFacet], None,
self.SideLobeLevel, self.OffsetSideLobe, MSM0=MSM0, verbose=False)
# write cache to disk, unless in a mode where we explicitly don't want it
if facetcache is None and not valid and cache and not approx:
try:
#MyPickle.DicoNPToFile(facetcache,cachepath)
#cPickle.dump(facetcache, file(cachepath, 'w'), 2)
print>>log," saving HMP cache to %s"%cachepath
self.facetcache.save(cachepath)
#self.maincache.saveCache("HMPMachine")
self.maincache.saveCache(self.CacheFileName)
self.PSFHasChanged=False
print>>log," HMP init done"
except:
print>>log, traceback.format_exc()
print >>log, ModColor.Str(
"WARNING: HMP cache could not be written, see error report above. Proceeding anyway.")
def SetDirty(self, DicoDirty):#,DoSetMask=True):
# if len(PSF.shape)==4:
# self.PSF=PSF[0,0]
# else:
# self.PSF=PSF
self.DicoDirty = DicoDirty
# self.DicoPSF=DicoPSF
# self.DicoVariablePSF=DicoVariablePSF
for iFacet in xrange(self.PSFServer.NFacets):
MSMachine = self.DicoMSMachine[iFacet]
MSMachine.SetDirty(DicoDirty)
# self._PSF=self.MSMachine._PSF
self._CubeDirty = MSMachine._Dirty
self._MeanDirty = MSMachine._MeanDirty
# vector of per-band overall weights -- starts out as N,1 in the dico, so reshape
W = np.float32(self.DicoDirty["WeightChansImages"])
self._band_weights = W.reshape(W.size)[:, np.newaxis, np.newaxis, np.newaxis]
if self._peakMode is "sigma":
print>>log,"Will search for the peak in the SNR-weighted dirty map"
a, b = self._MeanDirty, self._NoiseMap.reshape(self._MeanDirty.shape)
self._PeakSearchImage = numexpr.evaluate("a/b")
elif self._peakMode is "weighted":
print>>log,"Will search for the peak in the weighted dirty map"
a, b = self._MeanDirty, self._peakWeightImage
self._PeakSearchImage = numexpr.evaluate("a*b")
else:
print>>log,"Will search for the peak in the unweighted dirty map"
self._PeakSearchImage = self._MeanDirty
# ########################################
# op=lambda x: np.abs(x)
# AA=op(self._PeakSearchImage)
# _,_,xx, yx = np.where(AA==np.max(AA))
# print "--!!!!!!!!!!!!!!!!!!!!!!",xx, yx
# W=np.float32(self.DicoDirty["WeightChansImages"])
# W=W/np.sum(W)
# print "W",W
# print np.sum(self._CubeDirty[:,0,xx,yx].ravel()*W.ravel()),self._MeanDirty[0,0,xx,yx]
# #print np.mean(self._CubeDirty[:,0,xx,yx])
# print "--!!!!!!!!!!!!!!!!!!!!!!"
# ########################################
NPixStats = self.GD["Deconv"]["NumRMSSamples"]
if NPixStats>0:
self.IndStats=np.int64(np.linspace(0,self._PeakSearchImage.size-1,NPixStats))
# self._MeanPSF=self.MSMachine._MeanPSF
NPSF = self.PSFServer.NPSF
#_,_,NPSF,_=self._PSF.shape
_, _, NDirty, _ = self._CubeDirty.shape
off = (NPSF-NDirty)/2
self.DirtyExtent = (off, off+NDirty, off, off+NDirty)
# if self._ModelImage is None:
# self._ModelImage=np.zeros_like(self._CubeDirty)
# if DoSetMask:
# if self._MaskArray is None:
# self._MaskArray=np.zeros(self._MeanDirty.shape,dtype=np.bool8)
# else:
# maskshape = (1,1,NDirty,NDirty)
# # check for mask shape
# if maskshape != self._MaskArray.shape:
# ma0 = self._MaskArray
# _,_,nx,ny = ma0.shape
# def match_shapes (n1,n2):
# if n1<n2:
# return slice(None), slice((n2-n1)/2,(n2-n1)/2+n1)
# elif n1>n2:
# return slice((n1-n2)/2,(n1-n2)/2+n2), slice(None)
# else:
# return slice(None), slice(None)
# sx1, sx2 = match_shapes(NDirty, nx)
# sy1, sy2 = match_shapes(NDirty, ny)
# self._MaskArray = np.zeros(maskshape, dtype=np.bool8)
# self._MaskArray[0,0,sx1,sy1] = ma0[0,0,sx2,sy2]
# print>>log,ModColor.Str("WARNING: reshaping mask image from %dx%d to %dx%d"%(nx, ny, NDirty, NDirty))
# print>>log,ModColor.Str("Are you sure you supplied the correct cleaning mask?")
def GiveEdges(self,(xc0,yc0),N0,(xc1,yc1),N1):
M_xc=xc0
M_yc=yc0
NpixMain=N0
F_xc=xc1
F_yc=yc1
NpixFacet=N1
## X
M_x0=M_xc-NpixFacet/2
x0main=np.max([0,M_x0])
dx0=x0main-M_x0
x0facet=dx0
M_x1=M_xc+NpixFacet/2
x1main=np.min([NpixMain-1,M_x1])
dx1=M_x1-x1main
x1facet=NpixFacet-dx1
x1main+=1
## Y
M_y0=M_yc-NpixFacet/2
y0main=np.max([0,M_y0])
dy0=y0main-M_y0
y0facet=dy0
M_y1=M_yc+NpixFacet/2
y1main=np.min([NpixMain-1,M_y1])
dy1=M_y1-y1main
y1facet=NpixFacet-dy1
y1main+=1
Aedge=[x0main,x1main,y0main,y1main]
Bedge=[x0facet,x1facet,y0facet,y1facet]
return Aedge,Bedge
class ClassImageDeconvMachine():
def __init__(
self,
Gain=0.3,
MaxMinorIter=100,
NCPU=1, #psutil.cpu_count()
CycleFactor=2.5,
FluxThreshold=None,
RMSFactor=3,
PeakFactor=0,
PrevPeakFactor=0,
GD=None,
SearchMaxAbs=1,
ModelMachine=None,
NFreqBands=1,
RefFreq=None,
MainCache=None,
IdSharedMem="",
ParallelMode=True,
CacheFileName="HMPBasis",
**kw # absorb any unknown keywords arguments into this
):
"""
ImageDeconvMachine constructor. Note that this should be called pretty much when setting up the imager,
before APP workers are started, because the object registers APP handlers.
"""
self.IdSharedMem = IdSharedMem
self.SearchMaxAbs = SearchMaxAbs
self._ModelImage = None
self.MaxMinorIter = MaxMinorIter
self.NCPU = NCPU
self.Chi2Thr = 10000
self._MaskArray = None
self.GD = GD
self.SubPSF = None
self.MultiFreqMode = NFreqBands > 1
self.NFreqBands = NFreqBands
self.RefFreq = RefFreq
self.FluxThreshold = FluxThreshold
self.CycleFactor = CycleFactor
self.RMSFactor = RMSFactor
self.PeakFactor = PeakFactor
self.PrevPeakFactor = PrevPeakFactor
self.CacheFileName = CacheFileName
self.GainMachine = ClassGainMachine.get_instance()
self.ModelMachine = None
self.PSFServer = None
if ModelMachine is not None:
self.updateModelMachine(ModelMachine)
self.PSFHasChanged = False
self._previous_initial_peak = None
self.maincache = MainCache
# reset overall iteration counter
self._niter = 0
self.facetcache = None
self._MaskArray = None
self.MaskMachine = None
self.ParallelMode = ParallelMode
if self.ParallelMode:
APP.registerJobHandlers(self)
# we are in a worker
if not self.ParallelMode:
numexpr.set_num_threads(NCPU)
# peak finding mode.
# "normal" searches for peak in mean dirty image
# "sigma" searches for peak in mean_dirty/noise_map (setNoiseMap will have been called)
# "weighted" searched for peak in mean_dirty*weight
self._peakMode = "normal"
self.CurrentNegMask = None
self._NoiseMap = None
self._PNRStop = None # in _peakMode "sigma", provides addiitonal stopping criterion
if self.GD["HMP"]["PeakWeightImage"]:
print(" Reading peak weighting image %s" %
self.GD["HMP"]["PeakWeightImage"],
file=log)
img = image(self.GD["HMP"]["PeakWeightImage"]).getdata()
_, _, nx, ny = img.shape
# collapse freq and pol axes
img = img.sum(axis=1).sum(axis=0).T[::-1].copy()
self._peakWeightImage = img.reshape((1, 1, ny, nx))
self._peakMode = "weighted"
self._prevPeak = None
def setNCPU(self, NCPU):
self.NCPU = NCPU
numexpr.set_num_threads(NCPU)
def __del__(self):
if shared_dict is not None and type(
self.facetcache) is shared_dict.SharedDict:
self.facetcache.delete()
def updateMask(self, Mask):
nx, ny = Mask.shape
self._MaskArray = np.zeros((1, 1, nx, ny), np.bool8)
self._MaskArray[0, 0, :, :] = Mask[:, :]
def setMaskMachine(self, MaskMachine):
self.MaskMachine = MaskMachine
def resetCounter(self):
self._niter = 0
def updateModelMachine(self, ModelMachine):
if ModelMachine.DicoSMStacked["Type"] not in ("MSMF", "HMP"):
raise ValueError("ModelMachine Type should be HMP")
if ModelMachine.RefFreq != self.RefFreq:
raise ValueError("RefFreqs should be equal")
self.ModelMachine = ModelMachine
if self.PSFServer is not None:
for iFacet in range(self.PSFServer.NFacets):
self.DicoMSMachine[iFacet].setModelMachine(self.ModelMachine)
def GiveModelImage(self, *args):
return self.ModelMachine.GiveModelImage(*args)
def setSideLobeLevel(self, SideLobeLevel, OffsetSideLobe):
self.SideLobeLevel = SideLobeLevel
self.OffsetSideLobe = OffsetSideLobe
def SetPSF(self, DicoVariablePSF, quiet=False):
self.PSFServer = ClassPSFServer(self.GD)
self.PSFServer.setDicoVariablePSF(DicoVariablePSF,
NormalisePSF=True,
quiet=quiet)
self.PSFServer.setRefFreq(self.RefFreq)
self.DicoVariablePSF = DicoVariablePSF
#self.NChannels=self.DicoDirty["NChannels"]
def Init(self,
PSFVar,
PSFAve,
approx=False,
cache=None,
facetcache=None,
**kwargs):
"""
Init method. This is called after the first round of gridding: PSFs and such are available.
ModelMachine must be set by now.
facetcache: dict of basis functions. If supplied, then InitMSMF is not called.
cache: cache the basis functions. If None, GD["Cache"]["HMP"] setting is used
"""
# close the solutions dump, in case it was opened by a previous HMP instance
ClassMultiScaleMachine.CleanSolutionsDump.close()
self.SetPSF(PSFVar)
self.setSideLobeLevel(PSFAve[0], PSFAve[1])
if cache is None:
cache = self.GD["Cache"]["HMP"]
self.InitMSMF(approx=approx, cache=cache, facetcache=facetcache)
## OMS: why is this needed? self.RefFreq is set from self.ModelMachine in the first place
# self.ModelMachine.setRefFreq(self.RefFreq)
def Reset(self):
print("resetting HMP machine", file=log)
self.DicoMSMachine = {}
if type(self.facetcache
) is shared_dict.SharedDict and self.facetcache.is_writeable():
print("deleting HMP facet cache", file=log)
self.facetcache.delete()
self.facetcache = None
def setNoiseMap(self, NoiseMap, PNRStop=10):
"""Sets the noise map. The mean dirty will be divided by the noise map before peak finding.
If PNRStop is set, an additional stopping criterion (peak-to-noisemap) will be applied.
Peaks are reported in units of sigmas.
If PNRStop is not set, NoiseMap is treated as simply an (inverse) weighting that will bias
peak selection in the minor cycle. In this mode, peaks are reported in units of flux.
"""
self._NoiseMap = NoiseMap
self._PNRStop = PNRStop
self._peakMode = "sigma"
def _initMSM_handler(self, fcdict, sfdict, psfdict, iFacet, SideLobeLevel,
OffsetSideLobe, verbose):
# init PSF server from PSF shared dict
self.SetPSF(psfdict, quiet=True)
MSMachine = self._initMSM_facet(iFacet,
fcdict,
sfdict,
SideLobeLevel,
OffsetSideLobe,
verbose=verbose)
del MSMachine
def _initMSM_facet(self,
iFacet,
fcdict,
sfdict,
SideLobeLevel,
OffsetSideLobe,
MSM0=None,
verbose=False):
"""initializes MSM for one facet"""
self.PSFServer.setFacet(iFacet)
MSMachine = ClassMultiScaleMachine.ClassMultiScaleMachine(
self.GD, fcdict, self.GainMachine, NFreqBands=self.NFreqBands)
MSMachine.setModelMachine(self.ModelMachine)
MSMachine.setSideLobeLevel(SideLobeLevel, OffsetSideLobe)
MSMachine.SetFacet(iFacet)
MSMachine.SetPSF(self.PSFServer) # ThisPSF,ThisMeanPSF)
MSMachine.FindPSFExtent(
verbose=verbose) # only print to log for central facet
if MSM0 is not None:
MSMachine.CopyListScales(MSM0)
else:
MSMachine.MakeListScales(verbose=verbose, scalefuncs=sfdict)
MSMachine.MakeMultiScaleCube()
MSMachine.MakeBasisMatrix()
return MSMachine
def InitMSMF(self, approx=False, cache=True, facetcache=None):
"""Initializes MSMF basis functions. If approx is True, then uses the central facet's PSF for
all facets.
Populates the self.facetcache dict, unless facetcache is supplied
"""
self.DicoMSMachine = {}
valid = True
if facetcache is not None:
print("HMP basis functions pre-initialized", file=log)
self.facetcache = facetcache
else:
cachehash = dict([
(section, self.GD[section])
for section in ("Data", "Beam", "Selection", "Freq", "Image",
"Facets", "Weight", "RIME", "DDESolutions",
"Comp", "CF", "HMP")
])
cachepath, valid = self.maincache.checkCache(self.CacheFileName,
cachehash,
reset=not cache
or self.PSFHasChanged)
# do not use cache in approx mode
if approx or not cache:
valid = False
if valid:
print("Initialising HMP basis functions from cache %s" %
cachepath,
file=log)
self.facetcache = shared_dict.create(self.CacheFileName)
self.facetcache.restore(cachepath)
else:
self.facetcache = None
init_cache = self.facetcache is None
if init_cache:
self.facetcache = shared_dict.create(self.CacheFileName)
# in any mode, start by initializing a MS machine for the central facet. This will precompute the scale
# functions
centralFacet = self.PSFServer.DicoVariablePSF["CentralFacet"]
self.DicoMSMachine[centralFacet] = MSM0 = \
self._initMSM_facet(centralFacet,
self.facetcache.addSubdict(centralFacet) if init_cache else self.facetcache[centralFacet],
None, self.SideLobeLevel, self.OffsetSideLobe, verbose=True)
if approx:
print("HMP approximation mode: using PSF of central facet (%d)" %
centralFacet,
file=log)
for iFacet in range(self.PSFServer.NFacets):
self.DicoMSMachine[iFacet] = MSM0
elif (self.GD["Facets"]["NFacets"]
== 1) & (not self.GD["DDESolutions"]["DDSols"]):
self.DicoMSMachine[0] = MSM0
else:
# if no facet cache, init in parallel
if init_cache:
for iFacet in range(self.PSFServer.NFacets):
if iFacet != centralFacet:
fcdict = self.facetcache.addSubdict(iFacet)
if self.ParallelMode:
args = (fcdict.writeonly(),
MSM0.ScaleFuncs.readonly(),
self.DicoVariablePSF.readonly(), iFacet,
self.SideLobeLevel, self.OffsetSideLobe,
False)
APP.runJob("InitHMP:%d" % iFacet,
self._initMSM_handler,
args=args)
else:
self.DicoMSMachine[iFacet] = \
self._initMSM_facet(iFacet, fcdict, None,
self.SideLobeLevel, self.OffsetSideLobe, MSM0=MSM0, verbose=False)
if self.ParallelMode:
APP.awaitJobResults("InitHMP:*", progress="Init HMP")
self.facetcache.reload()
# t = ClassTimeIt.ClassTimeIt()
# now reinit from cache (since cache was computed by subprocesses)
for iFacet in range(self.PSFServer.NFacets):
if iFacet not in self.DicoMSMachine:
self.DicoMSMachine[iFacet] = \
self._initMSM_facet(iFacet, self.facetcache[iFacet], None,
self.SideLobeLevel, self.OffsetSideLobe, MSM0=MSM0, verbose=False)
# write cache to disk, unless in a mode where we explicitly don't want it
if facetcache is None and not valid and cache and not approx:
try:
#MyPickle.DicoNPToFile(facetcache,cachepath)
#cPickle.dump(facetcache, open(cachepath, 'w'), 2)
print(" saving HMP cache to %s" % cachepath, file=log)
self.facetcache.save(cachepath)
#self.maincache.saveCache("HMPMachine")
self.maincache.saveCache(self.CacheFileName)
self.PSFHasChanged = False
print(" HMP init done", file=log)
except:
print(traceback.format_exc(), file=log)
print(ModColor.Str(
"WARNING: HMP cache could not be written, see error report above. Proceeding anyway."
),
file=log)
def SetDirty(self, DicoDirty): #,DoSetMask=True):
# if len(PSF.shape)==4:
# self.PSF=PSF[0,0]
# else:
# self.PSF=PSF
self.DicoDirty = DicoDirty
# self.DicoPSF=DicoPSF
# self.DicoVariablePSF=DicoVariablePSF
for iFacet in range(self.PSFServer.NFacets):
MSMachine = self.DicoMSMachine[iFacet]
MSMachine.SetDirty(DicoDirty)
# self._PSF=self.MSMachine._PSF
self._CubeDirty = MSMachine._Dirty
self._MeanDirty = MSMachine._MeanDirty
# vector of per-band overall weights -- starts out as N,1 in the dico, so reshape
W = np.float32(self.DicoDirty["WeightChansImages"])
self._band_weights = W.reshape(W.size)[:, np.newaxis, np.newaxis,
np.newaxis]
if self._peakMode is "sigma":
print("Will search for the peak in the SNR-weighted dirty map",
file=log)
a, b = self._MeanDirty, self._NoiseMap.reshape(
self._MeanDirty.shape)
self._PeakSearchImage = numexpr.evaluate("a/b")
elif self._peakMode is "weighted":
print("Will search for the peak in the weighted dirty map",
file=log)
a, b = self._MeanDirty, self._peakWeightImage
self._PeakSearchImage = numexpr.evaluate("a*b")
else:
print("Will search for the peak in the unweighted dirty map",
file=log)
self._PeakSearchImage = self._MeanDirty
# ########################################
# op=lambda x: np.abs(x)
# AA=op(self._PeakSearchImage)
# _,_,xx, yx = np.where(AA==np.max(AA))
# print "--!!!!!!!!!!!!!!!!!!!!!!",xx, yx
# W=np.float32(self.DicoDirty["WeightChansImages"])
# W=W/np.sum(W)
# print "W",W
# print np.sum(self._CubeDirty[:,0,xx,yx].ravel()*W.ravel()),self._MeanDirty[0,0,xx,yx]
# #print np.mean(self._CubeDirty[:,0,xx,yx])
# print "--!!!!!!!!!!!!!!!!!!!!!!"
# ########################################
NPixStats = self.GD["Deconv"]["NumRMSSamples"]
if NPixStats > 0:
self.IndStats = np.int64(
np.linspace(0, self._PeakSearchImage.size - 1, NPixStats))
# self._MeanPSF=self.MSMachine._MeanPSF
NPSF = self.PSFServer.NPSF
#_,_,NPSF,_=self._PSF.shape
_, _, NDirty, _ = self._CubeDirty.shape
off = (NPSF - NDirty) // 2
self.DirtyExtent = (off, off + NDirty, off, off + NDirty)
# if self._ModelImage is None:
# self._ModelImage=np.zeros_like(self._CubeDirty)
# if DoSetMask:
# if self._MaskArray is None:
# self._MaskArray=np.zeros(self._MeanDirty.shape,dtype=np.bool8)
# else:
# maskshape = (1,1,NDirty,NDirty)
# # check for mask shape
# if maskshape != self._MaskArray.shape:
# ma0 = self._MaskArray
# _,_,nx,ny = ma0.shape
# def match_shapes (n1,n2):
# if n1<n2:
# return slice(None), slice((n2-n1)/2,(n2-n1)/2+n1)
# elif n1>n2:
# return slice((n1-n2)/2,(n1-n2)/2+n2), slice(None)
# else:
# return slice(None), slice(None)
# sx1, sx2 = match_shapes(NDirty, nx)
# sy1, sy2 = match_shapes(NDirty, ny)
# self._MaskArray = np.zeros(maskshape, dtype=np.bool8)
# self._MaskArray[0,0,sx1,sy1] = ma0[0,0,sx2,sy2]
# print>>log,ModColor.Str("WARNING: reshaping mask image from %dx%d to %dx%d"%(nx, ny, NDirty, NDirty))
# print>>log,ModColor.Str("Are you sure you supplied the correct cleaning mask?")
def GiveEdges(self, xc0, yc0, N0, xc1, yc1, N1):
M_xc = xc0
M_yc = yc0
NpixMain = N0
F_xc = xc1
F_yc = yc1
NpixFacet = N1
## X
M_x0 = M_xc - NpixFacet // 2
x0main = np.max([0, M_x0])
dx0 = x0main - M_x0
x0facet = dx0
M_x1 = M_xc + NpixFacet // 2
x1main = np.min([NpixMain - 1, M_x1])
dx1 = M_x1 - x1main
x1facet = NpixFacet - dx1
x1main += 1
## Y
M_y0 = M_yc - NpixFacet // 2
y0main = np.max([0, M_y0])
dy0 = y0main - M_y0
y0facet = dy0
M_y1 = M_yc + NpixFacet // 2
y1main = np.min([NpixMain - 1, M_y1])
dy1 = M_y1 - y1main
y1facet = NpixFacet - dy1
y1main += 1
Aedge = [x0main, x1main, y0main, y1main]
Bedge = [x0facet, x1facet, y0facet, y1facet]
return Aedge, Bedge
def SubStep(self, dx, dy, LocalSM):
_, npol, _, _ = self._MeanDirty.shape
x0, x1, y0, y1 = self.DirtyExtent
xc, yc = dx, dy
#NpixFacet=self.SubPSF.shape[-1]
#PSF=self.CubePSFScales[iScale]
N0 = self._MeanDirty.shape[-1]
N1 = LocalSM.shape[-1]
# PSF=PSF[N1/2-1:N1/2+2,N1/2-1:N1/2+2]
# N1=PSF.shape[-1]
#Aedge,Bedge=self.GiveEdges(xc,yc,N0,N1/2,N1/2,N1)
N0x, N0y = self._MeanDirty.shape[-2::]
Aedge, Bedge = GiveEdgesDissymetric(xc, yc, N0x, N0y, N1 // 2, N1 // 2,
N1, N1)
#_,n,n=self.PSF.shape
# PSF=self.PSF.reshape((n,n))
# print "Fpol00",Fpol
factor = -1. # Fpol[0,0,0]*self.Gain
# print "Fpol01",Fpol
nch, npol, nx, ny = LocalSM.shape
# print Fpol[0,0,0]
# print Aedge
# print Bedge
#print>>log, " Removing %f Jy at (%i %i) (peak of %f Jy)"%(Fpol[0,0,0]*self.Gain,dx,dy,Fpol[0,0,0])
# PSF=self.PSF[0]
x0d, x1d, y0d, y1d = Aedge
x0p, x1p, y0p, y1p = Bedge
# nxPSF=self.CubePSFScales.shape[-1]
# x0,x1=nxPSF/2-self.SupWeightWidth,nxPSF/2+self.SupWeightWidth+1
# y0,y1=nxPSF/2-self.SupWeightWidth,nxPSF/2+self.SupWeightWidth+1
# x0p=x0+x0p
# x1p=x0+x1p
# y0p=y0+y0p
# y1p=y0+y1p
# Bedge=x0p,x1p,y0p,y1p
# # import pylab
# pylab.clf()
# ax=pylab.subplot(1,3,1)
# vmin,vmax=self._CubeDirty.min(),self._CubeDirty.max()
# pylab.imshow(self._MeanDirty[0,0,x0d:x1d,y0d:y1d],interpolation="nearest",vmin=vmin,vmax=vmax)
# pylab.colorbar()
# pylab.subplot(1,3,2,sharex=ax,sharey=ax)
# pylab.imshow(np.mean(LocalSM,axis=0)[0,x0p:x1p,y0p:y1p],interpolation="nearest",vmin=vmin,vmax=vmax)
# pylab.colorbar()
# pylab.draw()
# #print "Fpol02",Fpol
# # NpParallel.A_add_B_prod_factor((self.Dirty),LocalSM,Aedge,Bedge,factor=float(factor),NCPU=self.NCPU)
# <<<<<<< HEAD
# self._CubeDirty[:,:,x0d:x1d,y0d:y1d] -= LocalSM[:,:,x0p:x1p,y0p:y1p]
# =======
# self._CubeDirty[:,:,x0d:x1d,y0d:y1d] -= LocalSM[:,:,x0p:x1p,y0p:y1p]
cube, sm = self._CubeDirty[:, :, x0d:x1d,
y0d:y1d], LocalSM[:, :, x0p:x1p, y0p:y1p]
# if self.DoPlot:
# AA0=cube[0,0,:,:].copy()
# vmin,vmax=np.min(AA0),np.max(AA0)
# AA1=sm[0,0,:,:].copy()
# import pylab
# pylab.clf()
# pylab.subplot(1,3,1)
# pylab.imshow(AA0,interpolation="nearest")
# pylab.colorbar()
# pylab.subplot(1,3,2)
# pylab.imshow(AA1,interpolation="nearest")
# pylab.colorbar()
# pylab.subplot(1,3,3)
# pylab.imshow((AA0-AA1),interpolation="nearest")
# pylab.colorbar()
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
numexpr.evaluate('cube-sm', out=cube, casting="unsafe")
#a-=b
if self._MeanDirty is not self._CubeDirty:
### old code, got MeanDirty out of alignment with CubeDirty somehow
## W=np.float32(self.DicoDirty["WeightChansImages"])
## self._MeanDirty[0,:,x0d:x1d,y0d:y1d]-=np.sum(LocalSM[:,:,x0p:x1p,y0p:y1p]*W.reshape((W.size,1,1,1)),axis=0)
meanimage = self._MeanDirty[:, :, x0d:x1d, y0d:y1d]
# cube.mean(axis=0, out=meanimage) should be a bit faster, but we experienced problems with some numpy versions,
# see https://github.com/cyriltasse/DDFacet/issues/325
# So use array copy instead (which makes an intermediate array)
if cube.shape[0] > 1:
meanimage[...] = (cube * self._band_weights).sum(axis=0)
# cube.mean(axis=0, out=meanimage)
else:
meanimage[0, ...] = cube[0, ...]
# ## this is slower:
# self._MeanDirty[0,:,x0d:x1d,y0d:y1d] = self._CubeDirty[:,:,x0d:x1d,y0d:y1d].mean(axis=0)
# np.save("_MeanDirty",self._MeanDirty)
# np.save("_CubeDirty",self._CubeDirty)
# stop
if self._peakMode is "sigma":
a, b = self._MeanDirty[:, :, x0d:x1d,
y0d:y1d], self._NoiseMap[:, :, x0d:x1d,
y0d:y1d]
numexpr.evaluate("a/b",
out=self._PeakSearchImage[:, :, x0d:x1d, y0d:y1d])
elif self._peakMode is "weighted":
a, b = self._MeanDirty[:, :, x0d:x1d,
y0d:y1d], self._peakWeightImage[:, :,
x0d:x1d,
y0d:y1d]
numexpr.evaluate("a*b",
out=self._PeakSearchImage[:, :, x0d:x1d, y0d:y1d])
# pylab.subplot(1,3,3,sharex=ax,sharey=ax)
# pylab.imshow(self._MeanDirty[0,0,x0d:x1d,y0d:y1d],interpolation="nearest",vmin=vmin,vmax=vmax)#,vmin=vmin,vmax=vmax)
# pylab.colorbar()
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
# # print Aedge
# #unc print Bedge
# # print self.Dirty[0,x0d:x1d,y0d:y1d]
def Plot(self):
import pylab
pylab.clf()
pylab.subplot(1, 3, 1)
pylab.imshow(self._CubeDirty[0, 0])
pylab.colorbar()
pylab.subplot(1, 3, 2)
pylab.imshow(self._CubeDirty[1, 0])
pylab.colorbar()
pylab.draw()
pylab.show()
def updateRMS(self):
_, npol, npix, _ = self._MeanDirty.shape
NPixStats = self.GD["Deconv"]["NumRMSSamples"]
if NPixStats:
#self.IndStats=np.int64(np.random.rand(NPixStats)*npix**2)
self.IndStats = np.int64(
np.linspace(0, self._PeakSearchImage.size - 1, NPixStats))
else:
self.IndStats = slice(None)
self.RMS = np.std(np.real(
self._PeakSearchImage.ravel()[self.IndStats]))
def setMask(self, Mask):
self.CurrentNegMask = Mask
def Deconvolve(self, ch=0, UpdateRMS=True):
"""
Runs minor cycle over image channel 'ch'.
initMinor is number of minor iteration (keeps continuous count through major iterations)
Nminor is max number of minor iteration
Returns tuple of: return_code,continue,updated
where return_code is a status string;
continue is True if another cycle should be executed;
update is True if model has been updated (note update=False implies continue=False)
"""
if self._niter >= self.MaxMinorIter:
return "MaxIter", False, False
_, npol, npix, _ = self._MeanDirty.shape
xc = (npix) // 2
# m0,m1=self._CubeDirty.min(),self._CubeDirty.max()
# pylab.clf()
# pylab.subplot(1,2,1)
# pylab.imshow(self.Dirty[0],interpolation="nearest",vmin=m0,vmax=m1)
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
DoAbs = int(self.GD["Deconv"]["AllowNegative"])
print(" Running minor cycle [MinorIter = %i/%i, SearchMaxAbs = %i]" %
(self._niter, self.MaxMinorIter, DoAbs),
file=log)
if UpdateRMS: self.updateRMS()
RMS = self.RMS
self.GainMachine.SetRMS(RMS)
Fluxlimit_RMS = self.RMSFactor * RMS
#print "startmax",self._MeanDirty.shape,self._MaskArray.shape
if self.CurrentNegMask is not None:
print(" using externally defined Mask (self.CurrentNegMask)",
file=log)
CurrentNegMask = self.CurrentNegMask
elif self.MaskMachine:
print(" using MaskMachine Mask", file=log)
CurrentNegMask = self.MaskMachine.CurrentNegMask
elif self._MaskArray is not None:
print(" using externally defined Mask (self._MaskArray)",
file=log)
CurrentNegMask = self._MaskArray
else:
print(" not using a mask", file=log)
CurrentNegMask = None
x, y, MaxDirty = NpParallel.A_whereMax(self._PeakSearchImage,
NCPU=self.NCPU,
DoAbs=DoAbs,
Mask=CurrentNegMask)
# ThisFlux is evaluated against stopping criteria. In weighted mode, use the true flux. Else use sigma value.
ThisFlux = self._MeanDirty[
0, 0, x, y] if self._peakMode is "weighted" else MaxDirty
if DoAbs:
ThisFlux = abs(ThisFlux)
# in weighted or noisemap mode, look up the true max as well
trueMaxDirty = MaxDirty if self._peakMode is "normal" else ThisFlux
# return condition indicating cleaning is to be continued
cont = True
CondPeak = (self._previous_initial_peak is not None)
CondDiverge = False
if self._previous_initial_peak is not None:
CondDiverge = (abs(ThisFlux) >
self.GD["HMP"]["MajorStallThreshold"] *
self._previous_initial_peak)
CondPeakType = (self._peakMode != "sigma")
if CondPeak and CondDiverge and CondPeakType:
print(ModColor.Str(
"STALL! dirty image peak %10.6g Jy, was %10.6g at previous major cycle."
% (ThisFlux, self._previous_initial_peak),
col="red"),
file=log)
print(ModColor.Str("This will be the last major cycle"), file=log)
cont = False
self._previous_initial_peak = abs(ThisFlux)
#x,y,MaxDirty=NpParallel.A_whereMax(self._MeanDirty.copy(),NCPU=1,DoAbs=DoAbs,Mask=self._MaskArray.copy())
#A=self._MeanDirty.copy()
#A.flat[:]=np.arange(A.size)[:]
#x,y,MaxDirty=NpParallel.A_whereMax(A,NCPU=1,DoAbs=DoAbs)
#print "max",x,y
#stop
# print>>log,"npp: %d %d %g"%(x,y,MaxDirty)
# xy = ma.argmax(ma.masked_array(abs(self._MeanDirty), self._MaskArray))
# x1, y1 = xy/npix, xy%npix
# MaxDirty1 = abs(self._MeanDirty[0,0,x1,y1])
# print>>log,"argmax: %d %d %g"%(x1,y1,MaxDirty1)
Fluxlimit_Peak = ThisFlux * self.PeakFactor
# if previous peak is not set (i.e. first major cycle), use current dirty image peak instead
Fluxlimit_PrevPeak = (self._prevPeak if self._prevPeak is not None else
ThisFlux) * self.PrevPeakFactor
Fluxlimit_Sidelobe = (
(self.CycleFactor - 1.) / 4. * (1. - self.SideLobeLevel) +
self.SideLobeLevel) * ThisFlux if self.CycleFactor else 0
mm0, mm1 = self._PeakSearchImage.min(), self._PeakSearchImage.max()
# work out upper peak threshold
StopFlux = max(Fluxlimit_Peak, Fluxlimit_RMS, Fluxlimit_Sidelobe,
Fluxlimit_Peak, Fluxlimit_PrevPeak, self.FluxThreshold)
print(
" Dirty image peak = %10.6g Jy [(min, max) = (%.3g, %.3g) Jy]"
% (trueMaxDirty, mm0, mm1),
file=log)
if self._peakMode is "sigma":
print(" in sigma units = %10.6g" % MaxDirty,
file=log)
elif self._peakMode is "weighted":
print(" weighted peak flux is = %10.6g Jy" % MaxDirty,
file=log)
print(
" RMS-based threshold = %10.6g Jy [rms = %.3g Jy; RMS factor %.1f]"
% (Fluxlimit_RMS, RMS, self.RMSFactor),
file=log)
print(
" Sidelobe-based threshold = %10.6g Jy [sidelobe = %.3f of peak; cycle factor %.1f]"
% (Fluxlimit_Sidelobe, self.SideLobeLevel, self.CycleFactor),
file=log)
print(" Peak-based threshold = %10.6g Jy [%.3f of peak]" %
(Fluxlimit_Peak, self.PeakFactor),
file=log)
print(
" Previous peak-based thr = %10.6g Jy [%.3f of previous minor cycle peak]"
% (Fluxlimit_PrevPeak, self.PrevPeakFactor),
file=log)
print(" Absolute threshold = %10.6g Jy" %
(self.FluxThreshold),
file=log)
print(" Stopping flux = %10.6g Jy [%.3f of peak ]" %
(StopFlux, StopFlux / ThisFlux),
file=log)
rms = RMS
# MaxModelInit=np.max(np.abs(self.ModelImage))
# Fact=4
# self.BookKeepShape=(npix/Fact,npix/Fact)
# BookKeep=np.zeros(self.BookKeepShape,np.float32)
# NPixBook,_=self.BookKeepShape
# FactorBook=float(NPixBook)/npix
T = ClassTimeIt.ClassTimeIt()
T.disable()
# #print x,y
# print>>log, "npp: %d %d %g"%(x,y,ThisFlux)
# xy = ma.argmax(ma.masked_array(abs(self._MeanDirty), self._MaskArray))
# x, y = xy/npix, xy%npix
# ThisFlux = abs(self._MeanDirty[0,0,x,y])
# print>> log, "argmax: %d %d %g"%(x, y, ThisFlux)
if ThisFlux < StopFlux:
print(ModColor.Str(
" Initial maximum peak %10.6g Jy below threshold, we're done here"
% ThisFlux,
col="green"),
file=log)
return "FluxThreshold", False, False
# self._MaskArray.fill(1)
# self._MaskArray.fill(0)
#self._MaskArray[np.abs(self._MeanDirty) > Fluxlimit_Sidelobe]=0
# DoneScale=np.zeros((self.MSMachine.NScales,),np.float32)
PreviousMaxFlux = 1e30
# pBAR= ProgressBar('white', width=50, block='=', empty=' ',Title="Cleaning ", HeaderSize=20,TitleSize=30)
# pBAR.disable()
self.GainMachine.SetFluxMax(ThisFlux)
# pBAR.render(0,"g=%3.3f"%self.GainMachine.GiveGain())
PreviousFlux = ThisFlux
divergence_factor = 1 + max(self.GD["HMP"]["AllowResidIncrease"], 0)
xlast, ylast, Flast = None, None, None
def GivePercentDone(ThisMaxFlux):
fracDone = 1. - (ThisMaxFlux - StopFlux) / (MaxDirty - StopFlux)
return max(int(round(100 * fracDone)), 100)
x0 = y0 = None
try:
for i in range(self._niter + 1, self.MaxMinorIter + 1):
self._niter = i
# x,y,ThisFlux=NpParallel.A_whereMax(self.Dirty,NCPU=self.NCPU,DoAbs=1)
x, y, peak = NpParallel.A_whereMax(self._PeakSearchImage,
NCPU=self.NCPU,
DoAbs=DoAbs,
Mask=CurrentNegMask)
if self.GD["HMP"]["FractionRandomPeak"] is not None:
op = lambda x: x
if DoAbs: op = lambda x: np.abs(x)
_, _, indx, indy = np.where(
(op(self._PeakSearchImage) >= peak *
self.GD["HMP"]["FractionRandomPeak"])
& np.logical_not(CurrentNegMask))
ii = np.int64(np.random.rand(1)[0] * indx.size)
x, y = indx[ii], indy[ii]
peak = op(self._PeakSearchImage[0, 0, x, y])
ThisFlux = float(self._MeanDirty[
0, 0, x, y] if self._peakMode is "weighted" else peak)
if DoAbs:
ThisFlux = abs(ThisFlux)
if xlast is not None:
if x == xlast and y == ylast and np.abs(
(Flast - peak) / Flast) < 1e-6:
print(ModColor.Str(
" [iter=%i] peak of %.3g Jy stuck" %
(i, ThisFlux),
col="red"),
file=log)
return "Stuck", False, True
xlast = x
ylast = y
Flast = peak
#x,y=self.PSFServer.SolveOffsetLM(self._MeanDirty[0,0],x,y); ThisFlux=self._MeanDirty[0,0,x,y]
self.GainMachine.SetFluxMax(ThisFlux)
# #x,y=1224, 1994
# print x,y,ThisFlux
# x,y=np.where(np.abs(self.Dirty[0])==np.max(np.abs(self.Dirty[0])))
# ThisFlux=self.Dirty[0,x,y]
# print x,y,ThisFlux
# stop
T.timeit("max0")
if np.abs(ThisFlux) > divergence_factor * np.abs(PreviousFlux):
print(ModColor.Str(
" [iter=%i] peak of %.3g Jy diverging w.r.t. floor of %.3g Jy "
% (i, ThisFlux, PreviousFlux),
col="red"),
file=log)
return "Diverging", False, True
fluxgain = np.abs(ThisFlux - self._prevPeak) / abs(
ThisFlux) if self._prevPeak is not None else 1e+99
if x == x0 and y == y0 and fluxgain < 1e-6:
print(ModColor.Str(
" [iter=%i] stalled at peak of %.3g Jy, x=%d y=%d" %
(i, ThisFlux, x, y),
col="red"),
file=log)
return "Stalled", False, True
if np.abs(ThisFlux) < np.abs(PreviousFlux):
PreviousFlux = ThisFlux
self._prevPeak = ThisFlux
x0, y0 = x, y
ThisPNR = ThisFlux / rms
if ThisFlux <= (StopFlux if StopFlux is not None else 0.0) or \
ThisPNR <= (self._PNRStop if self._PNRStop is not None else 0.0):
rms = np.std(
np.real(self._PeakSearchImage.ravel()[self.IndStats]))
# pBAR.render(100,"peak %.3g"%(ThisFlux,))
if ThisFlux <= StopFlux:
print(ModColor.Str(
" [iter=%i] peak of %.3g Jy lower than stopping flux, PNR %.3g"
% (i, ThisFlux, ThisFlux / rms),
col="green"),
file=log)
elif ThisPNR <= self._PNRStop:
print(ModColor.Str(
" [iter=%i] PNR of %.3g lower than stopping PNR, peak of %.3g Jy"
% (i, ThisPNR, ThisFlux),
col="green"),
file=log)
cont = cont and ThisFlux > self.FluxThreshold
if not cont:
print(ModColor.Str(
" [iter=%i] absolute flux threshold of %.3g Jy has been reached, PNR %.3g"
% (i, self.FluxThreshold, ThisFlux / rms),
col="green",
Bold=True),
file=log)
# DoneScale*=100./np.sum(DoneScale)
# for iScale in range(DoneScale.size):
# print>>log," [Scale %i] %.1f%%"%(iScale,DoneScale[iScale])
# stop deconvolution if hit absolute treshold; update model
return "MinFluxRms", cont, True
# if (i>0)&((i%1000)==0):
# print>>log, " [iter=%i] Peak residual flux %f Jy" % (i,ThisFlux)
# if (i>0)&((i%100)==0):
# PercentDone=GivePercentDone(ThisFlux)
# pBAR.render(PercentDone,"peak %.3g i%d"%(ThisFlux,self._niter))
rounded_iter_step = 1 if i < 10 else (10 if i < 200 else (
100 if i < 2000 else 1000))
# min(int(10**math.floor(math.log10(i))), 10000)
if i >= 10 and i % rounded_iter_step == 0:
# if self.GD["Debug"]["PrintMinorCycleRMS"]:
rms = np.std(
np.real(self._PeakSearchImage.ravel()[self.IndStats]))
if self._peakMode is "weighted":
print(
" [iter=%i] peak residual %.3g, gain %.3g, rms %g, PNR %.3g (weighted peak %.3g at x=%d y=%d)"
% (i, ThisFlux, fluxgain, rms, ThisFlux / rms,
peak, x, y),
file=log)
else:
print(
" [iter=%i] peak residual %.3g, gain %.3g, rms %g, PNR %.3g (at x=%d y=%d)"
%
(i, ThisFlux, fluxgain, rms, ThisFlux / rms, x, y),
file=log)
# else:
# print >>log, " [iter=%i] peak residual %.3g" % (
# i, ThisFlux)
ClassMultiScaleMachine.CleanSolutionsDump.flush()
nch, npol, _, _ = self._CubeDirty.shape
Fpol = np.float32((self._CubeDirty[:, :, x, y].reshape(
(nch, npol, 1, 1))).copy())
#print "Fpol",Fpol
dx = x - xc
dy = y - xc
T.timeit("stuff")
# iScale=self.MSMachine.FindBestScale((x,y),Fpol)
self.PSFServer.setLocation(x, y)
PSF = self.PSFServer.GivePSF()
MSMachine = self.DicoMSMachine[self.PSFServer.iFacet]
LocalSM = MSMachine.GiveLocalSM(x, y, Fpol)
T.timeit("FindScale")
# print iScale
# if iScale=="BadFit": continue
# box=50
# x0,x1=x-box,x+box
# y0,y1=y-box,y+box
# x0,x1=0,-1
# y0,y1=0,-1
# pylab.clf()
# pylab.subplot(1,2,1)
# pylab.imshow(self.Dirty[0][x0:x1,y0:y1],interpolation="nearest",vmin=mm0,vmax=mm1)
# #pylab.subplot(1,3,2)
# #pylab.imshow(self.MaskArray[0],interpolation="nearest",vmin=0,vmax=1,cmap="gray")
# # pylab.subplot(1,2,2)
# # pylab.imshow(self.ModelImage[0][x0:x1,y0:y1],interpolation="nearest",cmap="gray")
# #pylab.imshow(PSF[0],interpolation="nearest",vmin=0,vmax=1)
# #pylab.colorbar()
# CurrentGain=self.GainMachine.GiveGain()
CurrentGain = np.float32(self.GD["Deconv"]["Gain"])
numexpr.evaluate('LocalSM*CurrentGain', out=LocalSM)
self.SubStep(x, y, LocalSM)
T.timeit("SubStep")
# pylab.subplot(1,2,2)
# pylab.imshow(self.Dirty[0][x0:x1,y0:y1],interpolation="nearest",vmin=mm0,vmax=mm1)#,vmin=m0,vmax=m1)
# #pylab.imshow(PSF[0],interpolation="nearest",vmin=0,vmax=1)
# #pylab.colorbar()
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
# ######################################
# ThisComp=self.ListScales[iScale]
# Scale=ThisComp["Scale"]
# DoneScale[Scale]+=1
# if ThisComp["ModelType"]=="Delta":
# for pol in range(npol):
# self.ModelImage[pol,x,y]+=Fpol[pol,0,0]*self.Gain
# elif ThisComp["ModelType"]=="Gaussian":
# Gauss=ThisComp["Model"]
# Sup,_=Gauss.shape
# x0,x1=x-Sup/2,x+Sup/2+1
# y0,y1=y-Sup/2,y+Sup/2+1
# _,N0,_=self.ModelImage.shape
# Aedge,Bedge=self.GiveEdges(x,y,N0,Sup/2,Sup/2,Sup)
# x0d,x1d,y0d,y1d=Aedge
# x0p,x1p,y0p,y1p=Bedge
# for pol in range(npol):
# self.ModelImage[pol,x0d:x1d,y0d:y1d]+=Gauss[x0p:x1p,y0p:y1p]*pol[pol,0,0]*self.Gain
# else:
# stop
T.timeit("End")
except KeyboardInterrupt:
rms = np.std(np.real(self._PeakSearchImage.ravel()[self.IndStats]))
print(ModColor.Str(
" [iter=%i] minor cycle interrupted with Ctrl+C, peak flux %.3g, PNR %.3g"
% (self._niter, ThisFlux, ThisFlux / rms)),
file=log)
# DoneScale*=100./np.sum(DoneScale)
# for iScale in range(DoneScale.size):
# print>>log," [Scale %i] %.1f%%"%(iScale,DoneScale[iScale])
return "MaxIter", False, True # stop deconvolution but do update model
rms = np.std(np.real(self._PeakSearchImage.ravel()[self.IndStats]))
print(ModColor.Str(
" [iter=%i] Reached maximum number of iterations, peak flux %.3g, PNR %.3g"
% (self._niter, ThisFlux, ThisFlux / rms)),
file=log)
# DoneScale*=100./np.sum(DoneScale)
# for iScale in range(DoneScale.size):
# print>>log," [Scale %i] %.1f%%"%(iScale,DoneScale[iScale])
return "MaxIter", False, True # stop deconvolution but do update model
def Update(self, DicoDirty, **kwargs):
"""
Method to update attributes from ClassDeconvMachine
"""
# Update image dict
self.SetDirty(DicoDirty)
def ToFile(self, fname):
"""
Write model dict to file
"""
self.ModelMachine.ToFile(fname)
def FromFile(self, fname):
"""
Read model dict from file SubtractModel
"""
self.ModelMachine.FromFile(fname)
def FromDico(self, DicoName):
"""
Read in model dict
"""
self.ModelMachine.FromDico(DicoName)
