def predictKernelPolCluster(self,
DicoData,
SM,
iDirection=None,
ApplyJones=None,
ApplyTimeJones=None,
Noise=None):
DicoData = NpShared.DicoToShared("%sDicoMemChunk" % (self.IdMemShared),
DicoData,
DelInput=False)
if ApplyTimeJones != None:
ApplyTimeJones = NpShared.DicoToShared("%sApplyTimeJones" %
(self.IdMemShared),
ApplyTimeJones,
DelInput=False)
nrow, nch, _ = DicoData["data"].shape
RowList = np.int64(np.linspace(0, nrow, self.NCPU + 1))
row0 = RowList[0:-1]
row1 = RowList[1::]
work_queue = multiprocessing.Queue()
result_queue = multiprocessing.Queue()
workerlist = []
NCPU = self.NCPU
NpShared.DelArray("%sPredictData" % (self.IdMemShared))
PredictArray = NpShared.SharedArray.create(
"%sPredictData" % (self.IdMemShared),
DicoData["data"].shape,
dtype=DicoData["data"].dtype)
for ii in range(NCPU):
W = WorkerPredict(work_queue,
result_queue,
self.IdMemShared,
SM=SM,
DoSmearing=self.DoSmearing,
BeamAtFacet=self._BeamAtFacet)
workerlist.append(W)
workerlist[ii].start()
NJobs = row0.size
for iJob in range(NJobs):
ThisJob = row0[iJob], row1[iJob]
work_queue.put(ThisJob)
while int(result_queue.qsize()) < NJobs:
time.sleep(0.1)
continue
for ii in range(NCPU):
workerlist[ii].shutdown()
workerlist[ii].terminate()
workerlist[ii].join()
return PredictArray
def GiveCovariance(self,
DicoDataIn,
ApplyTimeJones,
SM,
Mode="DDECovariance"):
DicoData = NpShared.DicoToShared("%sDicoMemChunk" % (self.IdMemShared),
DicoDataIn,
DelInput=False)
if ApplyTimeJones != None:
ApplyTimeJones = NpShared.DicoToShared("%sApplyTimeJones" %
(self.IdMemShared),
ApplyTimeJones,
DelInput=False)
nrow, nch, _ = DicoData["data"].shape
RowList = np.int64(np.linspace(0, nrow, self.NCPU + 1))
row0 = RowList[0:-1]
row1 = RowList[1::]
work_queue = multiprocessing.Queue()
result_queue = multiprocessing.Queue()
workerlist = []
NCPU = self.NCPU
# NpShared.DelArray("%sCorrectedData"%(self.IdMemShared))
# CorrectedData=NpShared.SharedArray.create("%sCorrectedData"%(self.IdMemShared),DicoData["data"].shape,dtype=DicoData["data"].dtype)
# CorrectedData=
for ii in range(NCPU):
W = WorkerPredict(work_queue,
result_queue,
self.IdMemShared,
Mode=Mode,
DoSmearing=self.DoSmearing,
SM=SM,
BeamAtFacet=self._BeamAtFacet)
workerlist.append(W)
workerlist[ii].start()
NJobs = row0.size
for iJob in range(NJobs):
ThisJob = row0[iJob], row1[iJob]
work_queue.put(ThisJob)
while int(result_queue.qsize()) < NJobs:
time.sleep(0.1)
continue
for ii in range(NCPU):
workerlist[ii].shutdown()
workerlist[ii].terminate()
workerlist[ii].join()
DicoDataIn["W"] = DicoData["W"]
