def ConvolveGaussianParallel(shareddict,
field_in,
field_out,
CellSizeRad=None,
GaussPars=[(0., 0., 0.)],
Normalise=False):
"""Convolves images held in a dict, using APP.
"""
Ain0 = shareddict[field_in]
nch, npol, _, _ = Ain0.shape
Aout = shareddict[field_out]
# single channel? Handle serially
if nch == 1:
return ConvolveGaussian(shareddict, field_in, field_out, 0,
CellSizeRad, GaussPars[0], None, Normalise)
jobid = "convolve:%s:%s:" % (field_in, field_out)
for ch in range(nch):
sd_rw = shareddict.readwrite()
APP.runJob(jobid + str(ch),
_convolveSingleGaussianFFTW_noret,
args=(sd_rw, field_in, field_out, ch, CellSizeRad,
GaussPars[ch], None, Normalise))
APP.awaitJobResults(jobid + "*") #, progress="Convolving")
return Aout
def Interpol(self):
APP.startWorkers()
if "TEC" in self.DicoFile0["SmoothMode"]:
TECArray = NpShared.ToShared("%sTECArray" % IdSharedMem,
self.DicoFile0["SolsTEC"])
CPhaseArray = NpShared.ToShared("%sCPhaseArray" % IdSharedMem,
self.DicoFile0["SolsCPhase"])
nt, nd, na = TECArray.shape
iJob = 0
for it in range(nt):
APP.runJob("InterpolTECTime_%d" % iJob,
self.InterpolTECTime,
args=(it, )) #,serial=True)
iJob += 1
workers_res = APP.awaitJobResults("InterpolTECTime*",
progress="Interpol TEC")
iJob = 0
for it in range(nt):
APP.runJob("InterpolAmpTime_%d" % iJob,
self.InterpolAmpTime,
args=(it, )) #,serial=True)
iJob += 1
workers_res = APP.awaitJobResults("InterpolAmpTime*",
progress="Interpol Amp")
# APP.terminate()
APP.shutdown()
Multiprocessing.cleanupShm()
def giveWeigthParallel(self):
self.DATA = shared_dict.attach(self.DictName)
self.na = self.DATA["na"]
for A0 in range(0, self.na):
for A1 in range(A0 + 1, self.na):
APP.runJob("giveWeigthChunk:%d:%d" % (A0, A1),
self.giveWeigthChunk,
args=(A0, A1,
self.DATA.readwrite())) #,serial=True)
APP.awaitJobResults("giveWeigthChunk:*", progress="CalcWeight")
def giveWeigthParallel(self):
self.DATA = shared_dict.attach(self.DictName)
nrow, nch, npol = self.DATA["data"].shape
self.na = self.DATA["na"]
ntu = self.DATA["times_unique"].size
self.DATA["Wa"] = np.zeros((self.na, ntu), np.float32)
for A0 in range(self.na):
APP.runJob("giveWeigthAnt:%d" % (A0),
self.giveWeigthAnt,
args=(A0, )) #,serial=True)
APP.awaitJobResults("giveWeigthAnt:*", progress="CalcWeight")
def StackAll(self):
while self.iCurrentMS<self.nMS:
if self.LoadNextMS()=="NotRead": continue
print("Making dynamic spectra...", file=log)
# for iTime in range(self.NTimes):
# APP.runJob("Stack_SingleTime:%d"%(iTime),
# self.Stack_SingleTime,
# args=(iTime,))#,serial=True)
# APP.awaitJobResults("Stack_SingleTime:*", progress="Append MS %i"%self.DicoDATA["iMS"])
FF=self.DicoGrids["DomainEdges_Freq"]
TT=self.DicoGrids["DomainEdges_Time"]
for iTime in range(TT.size-1):
for iFreq in range(FF.size-1):
#print(iTime,iFreq)
APP.runJob("Stack_SingleTime:%d:%d"%(iTime,iFreq),
self.Stack_SingleTime,
args=(iTime,iFreq))#,serial=True)
APP.awaitJobResults("Stack_SingleTime:*", progress="Append MS %i"%self.DicoDATA["iMS"])
FOut="%s.Weights.npy"%self.DicoDATA["MSName"]
self.DicoDATA.reload()
# print(self.DicoDATA["WOUT"])
#self.DicoDATA["WOUT"]/=np.median(self.DicoDATA["WOUT"])
w=self.DicoDATA["WOUT"]
w/=np.median(w)
w[w<=0]=0
w[w>2.]=2
log.print(" saving weights as %s"%FOut)
np.save(FOut,self.DicoDATA["WOUT"])
# import pylab
# pylab.clf()
# pylab.hist(w[:,0].ravel())
# pylab.draw()
# pylab.show()
#np.save(FOut,self.DicoGrids["GridSTD"])
# for iTime in range(self.NTimes):
# self.Stack_SingleTime(iTime)
self.Finalise()
def StackAll(self):
while self.iCurrentMS < self.nMS:
if self.LoadNextMS() == "NotRead": continue
print("Making dynamic spectra...", file=log)
for iTime in range(self.NTimes):
APP.runJob("Stack_SingleTime:%d" % (iTime),
self.Stack_SingleTime,
args=(iTime, )) #,serial=True)
APP.awaitJobResults("Stack_SingleTime:*",
progress="Append MS %i" % self.DicoDATA["iMS"])
# for iTime in range(self.NTimes):
# self.Stack_SingleTime(iTime)
self.Finalise()
def StackAll(self):
while self.iCurrentMS < self.nMS:
if self.LoadNextMS() == "NotRead": continue
print("Making dynamic spectra...", file=log)
for iTime in range(self.NTimes):
APP.runJob("Stack_SingleTime:%d" % (iTime),
self.Stack_SingleTime,
args=(iTime, )) #,serial=True)
APP.awaitJobResults("Stack_SingleTime:*",
progress="Append MS %i" % self.DicoDATA["iMS"])
FOut = "%s.Weights.npy" % self.DicoDATA["MSName"]
log.print(" saving weights as %s" % FOut)
self.DicoDATA.reload()
# print(self.DicoDATA["WOUT"])
self.DicoDATA["WOUT"] /= np.median(self.DicoDATA["WOUT"])
np.save(FOut, self.DicoDATA["WOUT"])
# for iTime in range(self.NTimes):
# self.Stack_SingleTime(iTime)
self.Finalise()
def giveDicoInitIndiv(self,
ListIslands,
ModelImage,
DicoDirty,
ListDoIsland=None):
DicoInitIndiv = shared_dict.create("DicoInitIsland")
ParmDict = shared_dict.create("InitSSDModelHMP")
ParmDict["ModelImage"] = ModelImage
ParmDict["GridFreqs"] = self.GridFreqs
ParmDict["DegridFreqs"] = self.DegridFreqs
# ListBigIslands=[]
# ListSmallIslands=[]
# ListDoBigIsland=[]
# ListDoSmallIsland=[]
# NParallel=0
# for iIsland,Island in enumerate(ListIslands):
# if len(Island)>self.GD["SSDClean"]["ConvFFTSwitch"]:
# ListBigIslands.append(Island)
# ListDoBigIsland.append(ListDoIsland[iIsland])
# if ListDoIsland or ListDoIsland[iIsland]:
# NParallel+=1
# else:
# ListSmallIslands.append(Island)
# ListDoSmallIsland.append(ListDoIsland[iIsland])
# print>>log,"Initialise big islands (parallelised per island)"
# pBAR= ProgressBar(Title="Init islands")
# pBAR.render(0, NParallel)
# nDone=0
# for iIsland,Island in enumerate(ListBigIslands):
# if not ListDoIsland or ListDoBigIsland[iIsland]:
# subdict = DicoInitIndiv.addSubdict(iIsland)
# # APP.runJob("InitIsland:%d" % iIsland, self._initIsland_worker,
# # args=(subdict.writeonly(), iIsland, Island,
# # self.DicoVariablePSF.readonly(), DicoDirty.readonly(),
# # ParmDict.readonly(), self.InitMachine.DeconvMachine.facetcache.readonly(),self.NCPU),serial=True)
# self._initIsland_worker(subdict, iIsland, Island,
# self.DicoVariablePSF, DicoDirty,
# ParmDict, self.InitMachine.DeconvMachine.facetcache,
# self.NCPU)
# pBAR.render(nDone+1, NParallel)
# nDone+=1
# # APP.awaitJobResults("InitIsland:*", progress="Init islands")
# print>>log,"Initialise small islands (parallelised over islands)"
# for iIsland,Island in enumerate(ListSmallIslands):
# if not ListDoIsland or ListDoSmallIsland[iIsland]:
# subdict = DicoInitIndiv.addSubdict(iIsland)
# APP.runJob("InitIsland:%d" % iIsland, self._initIsland_worker,
# args=(subdict.writeonly(), iIsland, Island,
# self.DicoVariablePSF.readonly(), DicoDirty.readonly(),
# ParmDict.readonly(), self.InitMachine.DeconvMachine.facetcache.readonly(),1))
# APP.awaitJobResults("InitIsland:*", progress="Init islands")
# DicoInitIndiv.reload()
print >> log, "Initialise islands (parallelised over islands)"
if not self.GD["GAClean"]["ParallelInitHMP"]:
pBAR = ProgressBar(Title=" Init islands")
for iIsland, Island in enumerate(ListIslands):
if not ListDoIsland or ListDoIsland[iIsland]:
subdict = DicoInitIndiv.addSubdict(iIsland)
self._initIsland_worker(
subdict, iIsland, Island, self.DicoVariablePSF,
DicoDirty, ParmDict,
self.InitMachine.DeconvMachine.facetcache, 1)
pBAR.render(iIsland, len(ListIslands))
else:
for iIsland, Island in enumerate(ListIslands):
if not ListDoIsland or ListDoIsland[iIsland]:
subdict = DicoInitIndiv.addSubdict(iIsland)
APP.runJob("InitIsland:%d" % iIsland,
self._initIsland_worker,
args=(subdict.writeonly(), iIsland, Island,
self.DicoVariablePSF.readonly(),
DicoDirty.readonly(), ParmDict.readonly(),
self.InitMachine.DeconvMachine.facetcache.
readonly(), 1))
APP.awaitJobResults("InitIsland:*", progress="Init islands")
DicoInitIndiv.reload()
ParmDict.delete()
return DicoInitIndiv
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 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", "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
centralFacet = self.PSFServer.DicoVariablePSF["CentralFacet"]
if approx:
print >> log, "HMP approximation mode: using PSF of central facet (%d)" % centralFacet
self.PSFServer.setFacet(centralFacet)
MSMachine = ClassMultiScaleMachine.ClassMultiScaleMachine(
self.GD,
self.facetcache.addSubdict(0),
self.GainMachine,
NFreqBands=self.NFreqBands)
MSMachine.setModelMachine(self.ModelMachine)
MSMachine.setSideLobeLevel(self.SideLobeLevel, self.OffsetSideLobe)
MSMachine.SetFacet(centralFacet)
MSMachine.SetPSF(self.PSFServer) # ThisPSF,ThisMeanPSF)
MSMachine.FindPSFExtent(verbose=True)
MSMachine.MakeMultiScaleCube(verbose=True)
MSMachine.MakeBasisMatrix()
for iFacet in xrange(self.PSFServer.NFacets):
self.DicoMSMachine[iFacet] = MSMachine
else:
# if no facet cache, init in parallel
if self.facetcache is None:
self.facetcache = shared_dict.create(self.CacheFileName)
# breakout = False
for iFacet in xrange(self.PSFServer.NFacets):
fcdict = self.facetcache.addSubdict(iFacet)
if self.ParallelMode:
args = (fcdict.writeonly(),
self.DicoVariablePSF.readonly(), iFacet,
self.SideLobeLevel, self.OffsetSideLobe,
centralFacet)
APP.runJob("InitHMP:%d" % iFacet,
self._initMSM_handler,
args=args)
else:
args = (fcdict, self.DicoVariablePSF, iFacet,
self.SideLobeLevel, self.OffsetSideLobe,
centralFacet)
self._initMSM_handler(*args)
# import pdb;
# pdb.set_trace()
# if breakout:
# raise RuntimeError("exiting")
if self.ParallelMode:
APP.awaitJobResults("InitHMP:*", progress="Init HMP")
self.facetcache.reload()
# t = ClassTimeIt.ClassTimeIt()
for iFacet in xrange(self.PSFServer.NFacets):
self.PSFServer.setFacet(iFacet)
MSMachine = ClassMultiScaleMachine.ClassMultiScaleMachine(
self.GD,
self.facetcache[iFacet],
self.GainMachine,
NFreqBands=self.NFreqBands)
MSMachine.setModelMachine(self.ModelMachine)
MSMachine.setSideLobeLevel(self.SideLobeLevel,
self.OffsetSideLobe)
MSMachine.SetFacet(iFacet)
MSMachine.SetPSF(self.PSFServer) # ThisPSF,ThisMeanPSF)
MSMachine.FindPSFExtent(
verbose=(iFacet == centralFacet
)) # only print to log for central facet
MSMachine.MakeMultiScaleCube(verbose=(iFacet == centralFacet))
MSMachine.MakeBasisMatrix()
self.DicoMSMachine[iFacet] = MSMachine
# 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 InterpolParallel(self):
Sols0 = self.Sols
nt, nch, na, nd, _, _ = Sols0.G.shape
log.print(" #Times: %i" % nt)
log.print(" #Channels: %i" % nch)
log.print(" #Antennas: %i" % na)
log.print(" #Directions: %i" % nd)
# APP.terminate()
# APP.shutdown()
# Multiprocessing.cleanupShm()
APP.startWorkers()
iJob = 0
# for iAnt in [49]:#range(na):
# for iDir in [0]:#range(nd):
if "TEC" in self.InterpMode:
#APP.runJob("FitThisTEC_%d"%iJob, self.FitThisTEC, args=(208,)); iJob+=1
self.TECArray = NpShared.ToShared(
"%sTECArray" % IdSharedMem, np.zeros((nt, nd, na), np.float32))
self.CPhaseArray = NpShared.ToShared(
"%sCPhaseArray" % IdSharedMem,
np.zeros((nt, nd, na), np.float32))
for it in range(nt):
# for iDir in range(nd):
APP.runJob("FitThisTEC_%d" % iJob,
self.FitThisTEC,
args=(it, )) #,serial=True)
iJob += 1
workers_res = APP.awaitJobResults("FitThisTEC*",
progress="Fit TEC")
if "Amp" in self.InterpMode:
for iAnt in range(na):
for iDir in range(nd):
APP.runJob("FitThisAmp_%d" % iJob,
self.FitThisAmp,
args=(iAnt, iDir)) #,serial=True)
iJob += 1
workers_res = APP.awaitJobResults("FitThisAmp*",
progress="Smooth Amp")
if "PolyAmp" in self.InterpMode:
for iDir in range(nd):
APP.runJob("FitThisPolyAmp_%d" % iJob,
self.FitThisPolyAmp,
args=(iDir, ))
iJob += 1
workers_res = APP.awaitJobResults("FitThisPolyAmp*",
progress="Smooth Amp")
if "Clip" in self.InterpMode:
for iDir in range(nd):
APP.runJob("ClipThisDir_%d" % iJob,
self.ClipThisDir,
args=(iDir, ),
serial=True)
iJob += 1
workers_res = APP.awaitJobResults("ClipThisDir*",
progress="Clip Amp")
#APP.terminate()
APP.shutdown()
Multiprocessing.cleanupShm()
