def fix_dico_shape(fulldico, outdico, NPixOut):
# dico_model = 'image_full_ampphase_di_m.NS.DicoModel'
# save_dico = dico_model.replace('.DicoModel', '.restricted.DicoModel')
# NPixOut = 10000
dico = MyPickle.Load(fulldico)
NPix = dico['ModelShape'][-1]
NPix0, _ = EstimateNpix(float(NPix), Padding=1)
if NPix != NPix0:
raise ValueError("NPix != NPix0")
logger.info("Changing image size: %i -> %i pixels" % (NPix, NPixOut))
xc0 = NPix // 2
xc1 = NPixOut // 2
dx = xc0 - xc1
DCompOut = {}
for k, v in dico.items():
if k == 'Comp':
DCompOut['Comp'] = {}
continue
DCompOut[k] = v
DCompOut["Type"] = "SSD"
N, M, _, _ = dico['ModelShape']
DCompOut['ModelShape'] = [N, M, NPixOut, NPixOut]
for (x0, y0) in dico['Comp'].keys():
x1 = x0 - dx
y1 = y0 - dx
c0 = (x1 >= 0) & (x1 < NPixOut)
c1 = (y1 >= 0) & (y1 < NPixOut)
if c0 & c1:
logger.info("Mapping (%i,%i)->(%i,%i)" % (x0, y0, x1, y1))
DCompOut['Comp'][(x1, y1)] = dico['Comp'][(x0, y0)]
logger.info("Saving in {}".format(outdico))
MyPickle.Save(DCompOut, outdico)
def Cluster(self):
l, m = self.radec2lm(self.Cat.ra, self.Cat.dec)
S = self.Cat.S.copy()
PolyList = None
self.BigPolygon = []
PolyList = []
if self.AvoidPolygons != "":
print("Reading polygon file: %s" % self.AvoidPolygons, file=log)
PolyList += MyPickle.Load(self.AvoidPolygons)
if len(PolyList) > 0:
LPoly = []
inside = np.zeros((l.size, ), np.float32)
for iPolygon, Poly in enumerate(PolyList):
ra, dec = Poly.T
lp, mp = self.radec2lm(ra, dec)
Poly[:, 0] = lp
Poly[:, 1] = mp
P = Polygon.Polygon(Poly)
if P.area() > self.BigPolygonSize:
self.BigPolygon.append(Poly)
for ip in range(l.size):
if P.isInside(l[ip], m[ip]):
inside[ip] = 1
# l=l[inside==0]
# m=m[inside==0]
# S=S[inside==0]
print("There are %i big polygons" % len(self.BigPolygon), file=log)
if self.CentralRadius > 0:
print("Create central polygon with radius %f degrees" %
self.CentralRadius,
file=log)
Rad = self.CentralRadius * np.pi / 180
th = np.arange(0, 2. * np.pi, 2. * np.pi / 100)
lp = np.cos(th) * Rad
mp = np.sin(th) * Rad
Poly = np.zeros((lp.size, 2), np.float32)
Poly[:, 0] = lp
Poly[:, 1] = mp
PolyList += [Poly]
CC = Sky.ClassClusterDEAP.ClassCluster(l,
m,
S,
nNode=self.NCluster,
NGen=self.NGen,
NPop=self.NPop,
DoPlot=self.DoPlot,
PolyCut=self.PolyCut,
NCPU=self.NCPU,
BigPolygon=self.BigPolygon)
CC.setAvoidPolygon(PolyList)
xyNodes, self.LPolygon = CC.Cluster()
nNodes = xyNodes.size // 2
xc, yc = xyNodes.reshape((2, nNodes))
self.xcyc = xc, yc
def main(args=None, messages=[]):
if args is None:
args = MyPickle.Load(SaveFile)
MSList = expandMSList(args.ms)
MSList = [mstuple[0] for mstuple in MSList]
D = ClassDynSpecMS(ListMSName=MSList,
ColName=args.data,
ModelName=args.model,
SolsName=args.sols,
UVRange=args.uv,
FileCoords=args.srclist,
Radius=args.rad,
NOff=args.noff,
Image=args.image,
SolsDir=args.SolsDir,
NCPU=args.NCPU)
if D.NDirSelected == 0:
return
D.StackAll()
SaveMachine = ClassSaveResults.ClassSaveResults(D)
SaveMachine.WriteFits()
SaveMachine.PlotSpec()
SaveMachine.tarDirectory()
def GiveMMFromFile(self,FileName=None):
"""
Initialise a model machine from a file
Input:
FileName = The file to read
"""
if FileName is not None:
DicoSMStacked = MyPickle.Load(FileName)
return self.GiveMMFromDico(DicoSMStacked)
else:
return self.GiveMMFromDico()
def GiveInitialisedMMFromFile(self, FileName):
"""
Initialise a model machine from a file
Input:
FileName = The file to read
"""
DicoSMStacked = MyPickle.Load(FileName)
if self.GD is None:
self.GD = DicoSMStacked["GD"]
MM = self.GiveMMFromDico(DicoSMStacked)
MM.FromDico(DicoSMStacked)
return MM
def testMF_DATA():
Dico = MyPickle.Load("SaveTest")
Dirty = Dico["Dirty"]
PSF = Dico["PSF"]
FreqsInfo = Dico["FreqsInfo"]
IslandBestIndiv = Dico["IslandBestIndiv"]
ListPixData = Dico["ListPixData"]
ListPixParms = Dico["ListPixParms"]
GD = Dico["GD"]
FacetID = Dico["FacetID"]
IdSharedMem = Dico["IdSharedMem"]
iIsland = Dico["iIsland"]
#GD["GAClean"]["GASolvePars"]=["S","Alpha","GSig"]
#GD["GAClean"]["GASolvePars"]=["S","Alpha"]
nch = FreqsInfo["MeanJonesBand"][FacetID].size
WeightMeanJonesBand = FreqsInfo["MeanJonesBand"][FacetID].reshape(
(nch, 1, 1, 1))
WeightMueller = WeightMeanJonesBand.ravel()
WeightMuellerSignal = WeightMueller * FreqsInfo["WeightChansImages"].ravel(
)
# IncreaseIslandMachine=ClassIncreaseIsland.ClassIncreaseIsland()
# ListPixData=IncreaseIslandMachine.IncreaseIsland(ListPixData,dx=20)
# IncreaseIslandMachine=ClassIncreaseIsland.ClassIncreaseIsland()
# ListPixData=IncreaseIslandMachine.IncreaseIsland(ListPixData,dx=5)
#IslandBestIndiv=np.zeros((len(GD["GAClean"]["GASolvePars"])*len(
CEv = ClassEvolveGA(Dirty,
PSF,
FreqsInfo,
ListPixParms=ListPixParms,
ListPixData=ListPixData,
GD=GD,
IslandBestIndiv=IslandBestIndiv,
WeightFreqBands=WeightMuellerSignal,
iIsland=iIsland,
IdSharedMem=IdSharedMem)
CEv.main()
def Cluster(self):
l,m=self.radec2lm(self.Cat.ra,self.Cat.dec)
S=self.Cat.S.copy()
PolyList=None
if self.AvoidPolygons!="":
print>>log,"Reading polygon file: %s"%self.AvoidPolygons
self.BigPolygon=[]
PolyList=MyPickle.Load(self.AvoidPolygons)
LPoly=[]
inside=np.zeros((l.size,),np.float32)
for iPolygon,Poly in enumerate(PolyList):
ra,dec=Poly.T
lp,mp=self.radec2lm(ra,dec)
Poly[:,0]=lp
Poly[:,1]=mp
P=Polygon.Polygon(Poly)
if P.area()>self.BigPolygonSize:
self.BigPolygon.append(Poly)
for ip in range(l.size):
if P.isInside(l[ip],m[ip]):
inside[ip]=1
l=l[inside==0]
m=m[inside==0]
S=S[inside==0]
print>>log,"There are %i big polygons"%len(self.BigPolygon)
CC=Sky.ClassClusterDEAP.ClassCluster(l,m,S,nNode=self.NCluster,
NGen=self.NGen,
NPop=self.NPop,
DoPlot=self.DoPlot,
PolyCut=self.PolyCut,
NCPU=self.NCPU,
BigPolygon=self.BigPolygon)
CC.setAvoidPolygon(PolyList)
xyNodes,self.LPolygon=CC.Cluster()
nNodes=xyNodes.size/2
xc,yc=xyNodes.reshape((2,nNodes))
self.xcyc=xc,yc
def test():
P = ClassPrint.ClassPrint()
Obj, ValObj = MyPickle.Load("test")
#return Obj
#ValObj,_=Obj.parse_args()
#return ValObj
LGroups = Obj.option_groups
for Group in LGroups:
print(Group.title)
option_list = Group.option_list
for o in option_list:
lopt = o._long_opts[0]
oname = lopt.split("--")[-1]
V = getattr(ValObj, oname)
if V != "":
P.Print(oname, V)
# strName=%s
# print " "oname,V
print()
def main(args=None, messages=[]):
if args is None:
args = MyPickle.Load(SaveFile)
MSList=None
if args.ms:
MSList=expandMSList(args.ms)
MSList=[mstuple[0] for mstuple in MSList]
D = ClassDynSpecMS(ListMSName=MSList,
ColName=args.data, ModelName=args.model,
SolsName=args.sols,
ColWeights=args.WeightCol,
UVRange=args.uv,
FileCoords=args.srclist,
Radius=args.rad,
NOff=args.noff,
ImageI=args.imageI,
ImageV=args.imageV,
SolsDir=args.SolsDir,NCPU=args.NCPU,
BaseDirSpecs=args.BaseDirSpecs,
BeamModel=args.BeamModel,
BeamNBand=args.BeamNBand)
if D.NDirSelected==0:
return
if D.Mode=="Spec": D.StackAll()
SaveMachine=ClassSaveResults.ClassSaveResults(D)
if D.Mode=="Spec":
SaveMachine.WriteFits()
SaveMachine.PlotSpec()
SaveMachine.SaveCatalog()
SaveMachine.tarDirectory()
else:
SaveMachine.SaveCatalog()
SaveMachine.PlotSpec(Prefix="_replot")
def FromFile(self,FileName):
print>>log, "Reading dico model from file %s"%FileName
self.DicoModel=MyPickle.Load(FileName)
self.FromDico(self.DicoModel)
def FromFile(self, FileName):
print >> log, "Reading dico model from %s" % FileName
self.DicoSMStacked = MyPickle.Load(FileName)
self.FromDico(self.DicoSMStacked)
def main(OP=None, messages=[]):
if OP is None:
OP = MyPickle.Load(SaveFile)
print("Using settings from %s, then command line."%SaveFile)
DicoConfig = OP.DicoConfig
ImageName = DicoConfig["Output"]["Name"]
if not ImageName:
raise Exceptions.UserInputError("--Output-Name not specified, can't continue.")
if not DicoConfig["Data"]["MS"]:
raise Exceptions.UserInputError("--Data-MS not specified, can't continue.")
# create directory if it exists
dirname = os.path.dirname(ImageName)
if not os.path.exists(dirname) and not dirname == "":
os.mkdir(dirname)
# setup logging
logger.logToFile(ImageName + ".log", append=DicoConfig["Log"]["Append"])
global log
log = logger.getLogger("DDFacet")
# disable colors and progressbars if requested
ModColor.silent = SkyModel.Other.ModColor.silent = \
progressbar.ProgressBar.silent = \
DicoConfig["Log"]["Boring"]
if messages:
if not DicoConfig["Log"]["Boring"]:
#os.system('clear')
logo.print_logo()
for msg in messages:
print(msg, file=log)
print("Checking system configuration:", file=log)
# check for SHM size
ram_size = os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES')
shm_stats = os.statvfs('/dev/shm')
shm_size = shm_stats.f_bsize * shm_stats.f_blocks
shm_relsize = shm_size / float(ram_size)
shm_avail = shm_stats.f_bsize * shm_stats.f_bavail / float(ram_size)
if shm_relsize < 0.6:
print(ModColor.Str("""WARNING: max shared memory size is only {:.0%} of total RAM size.
This can cause problems for large imaging jobs. A setting of 90% is recommended for
DDFacet and killMS. If your processes keep failing with SIGBUS or "bus error" messages,
it is most likely for this reason. You can change the memory size by running
$ sudo mount -o remount,size=90% /dev/shm
To make the change permanent, edit /etc/defaults/tmps, and add a line saying "SHM_SIZE=90%".
""".format(shm_relsize)), file=log)
else:
print(" Max shared memory size is {:.0%} of total RAM size; {:.0%} currently available".format(shm_relsize, shm_avail), file=log)
try:
output = subprocess.check_output(["/sbin/sysctl", "vm.max_map_count"],universal_newlines=True)
except Exception:
print(ModColor.Str("""WARNING: /sbin/sysctl vm.max_map_count failed. Unable to check this setting."""), file=log)
max_map_count = None
else:
max_map_count = int(output.strip().rsplit(" ", 1)[-1])
if max_map_count is not None:
if max_map_count < 500000:
print(ModColor.Str("""WARNING: sysctl vm.max_map_count = {}.
This may be too little for large DDFacet and killMS jobs. If you get strange "file exists"
errors on /dev/shm, them try to bribe, beg or threaten your friendly local sysadmin into
setting vm.max_map_count=1000000 in /etc/sysctl.conf.
""".format(max_map_count)), file=log)
else:
print(" sysctl vm.max_map_count = {}".format(max_map_count), file=log)
# check for memory lock limits
import resource
msoft, mhard = resource.getrlimit(resource.RLIMIT_MEMLOCK)
if msoft >=0 or mhard >=0:
print(ModColor.Str("""WARNING: your system has a limit on memory locks configured.
This may possibly slow down DDFacet performance. You can try removing the limit by running
$ ulimit -l unlimited
If this gives an "operation not permitted" error, you can try to bribe, beg or threaten
your friendly local sysadmin into doing
# echo "* - memlock unlimited" >> /etc/security/limits.conf
"""), file=log)
if DicoConfig["Debug"]["Pdb"] == "always":
print("--Debug-Pdb=always: unexpected errors will be dropped into pdb", file=log)
Exceptions.enable_pdb_on_error(ModColor.Str("DDFacet has encountered an unexpected error. Dropping you into pdb for a post-mortem.\n" +
"(This is because you're running with --Debug-Pdb set to 'always'.)"))
elif DicoConfig["Debug"]["Pdb"] == "auto" and not DicoConfig["Log"]["Boring"]:
print("--Debug-Pdb=auto and not --Log-Boring: unexpected errors will be dropped into pdb", file=log)
Exceptions.enable_pdb_on_error(ModColor.Str("DDFacet has encountered an unexpected error. Dropping you into pdb for a post-mortem.\n" +
"(This is because you're running with --Debug-Pdb set to 'auto' and --Log-Boring is off.)"))
# print current options
OP.Print(dest=log)
# enable memory logging
logger.enableMemoryLogging(DicoConfig["Log"]["Memory"])
# get rid of old shm arrays from previous runs
Multiprocessing.cleanupStaleShm()
# initialize random seed from config if set, or else from system time
if DicoConfig["Misc"]["RandomSeed"] is not None:
DicoConfig["Misc"]["RandomSeed"]=int(DicoConfig["Misc"]["RandomSeed"])
print("random seed=%d (explicit)" % DicoConfig["Misc"]["RandomSeed"], file=log)
else:
DicoConfig["Misc"]["RandomSeed"] = int(time.time())
print("random seed=%d (automatic)" % DicoConfig["Misc"]["RandomSeed"], file=log)
np.random.seed(DicoConfig["Misc"]["RandomSeed"])
# init NCPU for different bits of parallelism
ncpu = int(DicoConfig["Parallel"]["NCPU"] or psutil.cpu_count())
DicoConfig["Parallel"]["NCPU"]=ncpu
_pyArrays.pySetOMPNumThreads(ncpu)
NpParallel.NCPU_global = ModFFTW.NCPU_global = ncpu
numexpr.set_num_threads(ncpu)
print("using up to %d CPUs for parallelism" % ncpu, file=log)
# write parset
OP.ToParset("%s.parset"%ImageName)
Mode = DicoConfig["Output"]["Mode"]
# init semaphores, as they're needed for weight calculation too
ClassFacetMachine.ClassFacetMachine.setup_semaphores(DicoConfig)
# data machine initialized for all cases except PSF-only mode
# psf machine initialized for all cases except Predict-only mode
Imager = ClassDeconvMachine.ClassImagerDeconv(GD=DicoConfig,
BaseName=ImageName,
predict_only=(Mode == "Predict" or Mode == "Subtract"),
data=(Mode != "PSF"),
psf=(Mode != "Predict" and Mode != "Dirty" and Mode != "Subtract"),
readcol=(Mode != "Predict" and Mode != "PSF"),
deconvolve=("Clean" in Mode))
Imager.Init()
# Imager.testDegrid()
# stop
if "Predict" in Mode or "Subtract" in Mode:
Imager.GivePredict()
if "Clean" in Mode:
Imager.main()
elif "Dirty" in Mode:
sparsify = DicoConfig["Comp"]["Sparsification"]
if sparsify and isinstance(sparsify, list):
sparsify = sparsify[0]
Imager.GiveDirty(psf="PSF" in Mode, sparsify=sparsify)
elif "PSF" in Mode:
sparsify = DicoConfig["Comp"]["Sparsification"]
if sparsify and isinstance(sparsify, list):
sparsify = sparsify[0]
Imager.MakePSF(sparsify=sparsify)
elif "RestoreAndShift" == Mode:
Imager.RestoreAndShift()
def testMO_DATA():
Dico= MyPickle.Load("SaveTest")
Dirty=Dico["Dirty"]
PSF=Dico["PSF"]
PSF2=np.squeeze(PSF)
ListPixData=Dico["ListPixData"]
FreqsInfo=Dico["FreqsInfo"]
FreqsInfo=Dico["FreqsInfo"]
IslandBestIndiv=Dico["IslandBestIndiv"]
ListPixParms=ListPixData
ListSquarePix=Dico["ListSquarePix"]
ThisPixList=ListPixData
ThisSquarePixList=ListSquarePix
GD=Dico["GD"]
FacetID=Dico["FacetID"]
nch=FreqsInfo["MeanJonesBand"][FacetID].size
WeightMeanJonesBand=FreqsInfo["MeanJonesBand"][FacetID].reshape((nch,1,1,1))
WeightMueller=WeightMeanJonesBand.ravel()
WeightMuellerSignal=WeightMueller*FreqsInfo["WeightChansImages"].ravel()
#IncreaseIslandMachine=ClassIncreaseIsland.ClassIncreaseIsland()
#ListPixData=IncreaseIslandMachine.IncreaseIsland(ListPixData,dx=5)
# 0) Load Island info (center and square data)
ListSquarePix_Center=ListSquarePix['IslandCenter']
ListSquarePix_Data=ListSquarePix['IslandSquareData']
ListSquarePix_Mask=ListSquarePix['IslandSquareMask']
orixisland,oriyisland=ListSquarePix_Data.shape # size of the square postage stamp around island
ListSquarePix_Data=PSF2
xisland,yisland=ListSquarePix_Data.shape # size of the square postage stamp around island
print(xisland)
# 1) Shape PSF and Dirty to have even number of pixels (required by Moresane)
# DEAL WITH SQUARE DATA OF ISLAND IF UNEVEN
# Crop PSF to the island postage stamp
#PSFCrop=CropPSF(PSF,xisland)
#PSF2=CropPSF(PSF2,71)
# MORESANE requires even sized images ==> Padding by one row and one column
cropped_square_data_to_even = False
if xisland % 2 != 0:
# PSFCrop_even = np.zeros((xisland+1, xisland+1))
# PSFCrop_even[:-1, :-1] = np.squeeze(PSFCrop)
Dirty_even = np.zeros((xisland - 1, xisland - 1))
Dirty_even[:, :] = ListSquarePix_Data[:-1, :-1]
cropped_square_data_to_even = True
else:
Dirty_even = ListSquarePix_Data
# make it even by removing one line and one column (usually outside of the interesting island region)
# xbigdirty,ybigdirty=np.squeeze(Dirty).shape
# if xbigdirty % 2 != 0:
# Dirty_even=np.zeros((xbigdirty-1,xbigdirty-1))
# Dirty_even[:,:]=np.squeeze(Dirty)[:-1,:-1]
xbigpsf,ybigpsf=PSF2.shape
cropped_square_psf_to_even = False
if xbigpsf % 2 != 0:
PSF2_even=np.zeros((xbigpsf-1,xbigpsf-1))
PSF2_even[:,:]=PSF2[:-1,:-1]
cropped_square_data_to_even = True
else:
PSF2_even=PSF2
# 2) Run the actual MinorCycle algo
DictMoresaneParms=GD['MORESANE']
Moresane=ClassMoresane(Dirty_even,PSF2_even,DictMoresaneParms,GD=GD)
Model_Square=Moresane.main()
# 3) Apply Island mask to model to get rid of regions outside the island.
cropped_square_to_even = False
if cropped_square_data_to_even: # then restore the model to its original uneven dimension
Model_Square_uneven = np.zeros((xisland, xisland))
Model_Square_uneven[:-1, :-1] = Model_Square
Model_Square = Model_Square_uneven
if cropped_square_psf_to_even: # restore original PSF size
PSF_uneven=PSF2
Model_Square=CropPSF(Model_Square, orixisland)
Model_Square *= ListSquarePix_Mask # masking outside the island
# 4) Convert back to Island format ( "S" and ThisPixList )
NewModel, NewThisPixList = SquareIslandtoIsland(Model_Square, ThisSquarePixList, ThisPixList)
Model = NewModel
ThisPixList = NewThisPixList
return Model
def main(OP=None, messages=[]):
if OP is None:
OP = MyPickle.Load(SaveFile)
print "Using settings from %s, then command line." % SaveFile
DicoConfig = OP.DicoConfig
ImageName = DicoConfig["Output"]["Name"]
if not ImageName:
raise Exceptions.UserInputError(
"--Output-Name not specified, can't continue.")
if not DicoConfig["Data"]["MS"]:
raise Exceptions.UserInputError(
"--Data-MS not specified, can't continue.")
# create directory if it exists
dirname = os.path.dirname(ImageName)
if not os.path.exists(dirname) and not dirname == "":
os.mkdir(dirname)
# setup logging
MyLogger.logToFile(ImageName + ".log", append=DicoConfig["Log"]["Append"])
global log
log = MyLogger.getLogger("DDFacet")
# disable colors and progressbars if requested
ModColor.silent = SkyModel.Other.ModColor.silent = \
progressbar.ProgressBar.silent = \
DicoConfig["Log"]["Boring"]
if messages:
if not DicoConfig["Log"]["Boring"]:
#os.system('clear')
logo.print_logo()
for msg in messages:
print >> log, msg
if DicoConfig["Debug"]["Pdb"] == "always":
print >> log, "--Debug-Pdb=always: unexpected errors will be dropped into pdb"
Exceptions.enable_pdb_on_error(
ModColor.Str(
"DDFacet has encountered an unexpected error. Dropping you into pdb for a post-mortem.\n"
+
"(This is because you're running with --Debug-Pdb set to 'always'.)"
))
elif DicoConfig["Debug"][
"Pdb"] == "auto" and not DicoConfig["Log"]["Boring"]:
print >> log, "--Debug-Pdb=auto and not --Log-Boring: unexpected errors will be dropped into pdb"
Exceptions.enable_pdb_on_error(
ModColor.Str(
"DDFacet has encountered an unexpected error. Dropping you into pdb for a post-mortem.\n"
+
"(This is because you're running with --Debug-Pdb set to 'auto' and --Log-Boring is off.)"
))
# print current options
OP.Print(dest=log)
# enable memory logging
MyLogger.enableMemoryLogging(DicoConfig["Log"]["Memory"])
# get rid of old shm arrays from previous runs
Multiprocessing.cleanupStaleShm()
# initialize random seed from config if set, or else from system time
if DicoConfig["Misc"]["RandomSeed"] is not None:
print >> log, "random seed=%d (explicit)" % DicoConfig["Misc"][
"RandomSeed"]
else:
DicoConfig["Misc"]["RandomSeed"] = int(time.time())
print >> log, "random seed=%d (automatic)" % DicoConfig["Misc"][
"RandomSeed"]
np.random.seed(DicoConfig["Misc"]["RandomSeed"])
# init NCPU for different bits of parallelism
ncpu = DicoConfig["Parallel"]["NCPU"] or psutil.cpu_count()
DicoConfig["Parallel"]["NCPU"] = ncpu
_pyArrays.pySetOMPNumThreads(ncpu)
NpParallel.NCPU_global = ModFFTW.NCPU_global = ncpu
numexpr.set_num_threads(ncpu)
print >> log, "using up to %d CPUs for parallelism" % ncpu
# write parset
OP.ToParset("%s.parset" % ImageName)
Mode = DicoConfig["Output"]["Mode"]
# init semaphores, as they're needed for weight calculation too
ClassFacetMachine.ClassFacetMachine.setup_semaphores(DicoConfig)
# data machine initialized for all cases except PSF-only mode
# psf machine initialized for all cases except Predict-only mode
Imager = ClassDeconvMachine.ClassImagerDeconv(
GD=DicoConfig,
BaseName=ImageName,
predict_only=(Mode == "Predict" or Mode == "Subtract"),
data=(Mode != "PSF"),
psf=(Mode != "Predict" and Mode != "Dirty" and Mode != "Subtract"),
readcol=(Mode != "Predict" and Mode != "PSF"),
deconvolve=("Clean" in Mode))
Imager.Init()
# Imager.testDegrid()
# stop
if "Predict" in Mode or "Subtract" in Mode:
Imager.GivePredict()
if "Clean" in Mode:
Imager.main()
elif "Dirty" in Mode:
sparsify = DicoConfig["Comp"]["Sparsification"]
if sparsify and isinstance(sparsify, list):
sparsify = sparsify[0]
Imager.GiveDirty(psf="PSF" in Mode, sparsify=sparsify)
elif "PSF" in Mode:
sparsify = DicoConfig["Comp"]["Sparsification"]
if sparsify and isinstance(sparsify, list):
sparsify = sparsify[0]
Imager.MakePSF(sparsify=sparsify)
elif "RestoreAndShift" == Mode:
Imager.RestoreAndShift()
def test2():
Obj, ValObj = MyPickle.Load("test")
PrintOptParse(Obj, ValObj, RejectGroup=["CohJones"])
def FromFile(self,FileName):
print("Reading dico model from file %s"%FileName, file=log)
self.DicoSMStacked=MyPickle.Load(FileName)
self.FromDico(self.DicoSMStacked)