def ToSM(self):
Osm=reformat.reformat(self.Fits,LastSlash=False)
SM=ClassSM.ClassSM(Osm,ReName=True,DoREG=True,SaveNp=True,FromExt=self.Cat)
SM.MakeREG()
#SM.MakeREG()
#SM.Finalise()
SM.Save()
def main(options=None):
if options == None:
f = open("last_MakePModel.obj", 'rb')
options = pickle.load(f)
Osm = options.Osm
Pfact = float(options.Pfact)
DoPlot = (options.DoPlot == "1")
imname = options.RestoredImage
if Osm == "":
Osm = reformat.reformat(imname, LastSlash=False)
print >> log, "Fitting sources in %s" % (imname)
im = image(imname)
PMaj = None
try:
PMaj = (im.imageinfo()["restoringbeam"]["major"]["value"])
PMin = (im.imageinfo()["restoringbeam"]["minor"]["value"])
PPA = (im.imageinfo()["restoringbeam"]["positionangle"]["value"])
PMaj *= Pfact
PMin *= Pfact
except:
print >> log, ModColor.Str(" No psf seen in header")
pass
if options.PSF != "":
m0, m1, pa = options.PSF.split(',')
PMaj, PMin, PPA = float(m0), float(m1), float(pa)
PMaj *= Pfact
PMin *= Pfact
if PMaj != None:
print >> log, "Using psf (maj,min,pa)=(%6.2f, %6.2f, %6.2f) (mult. fact.=%6.2f)" % (
PMaj, PMin, PPA, Pfact)
else:
print >> log, ModColor.Str("No psf info could be gotten from anywhere")
print >> log, ModColor.Str(
" use PSF keyword to tell what the psf is or is not")
exit()
ToSig = (1. / 3600.) * (np.pi / 180.) / (2. * np.sqrt(2. * np.log(2)))
PMaj *= ToSig
PMin *= ToSig
PPA *= np.pi / 180
b = im.getdata()[0, 0, :, :]
#b=b[3000:4000,3000:4000]#[120:170,300:370]
c = im.coordinates()
incr = np.abs(c.dict()["direction0"]["cdelt"][0])
print >> log, "Psf Size Sigma_(Maj,Min) = (%5.1f,%5.1f) pixels" % (
PMaj / incr, PMin / incr)
nx, _ = b.shape
Nr = 10000
indx, indy = np.int64(np.random.rand(Nr) * nx), np.int64(
np.random.rand(Nr) * nx)
StdResidual = np.std(b[indx, indy])
MaskName = options.MaskName
CasaMaskImage = image(MaskName)
MaskImage = CasaMaskImage.getdata()[0, 0, :, :]
snr = None
Boost = None
Islands = ClassIslands.ClassIslands(b,
T=snr,
Boost=Boost,
DoPlot=DoPlot,
MaskImage=MaskImage)
Islands.FindAllIslands()
Islands.Noise = StdResidual
CFit = ClassFitIslands.ClassFitIslands(Islands, NCPU=options.NCPU)
#sourceList=CFit.FitSerial((PMin,PMaj,PPA),incr,StdResidual)
sourceList = CFit.FitParallel((PMin, PMaj, PPA), incr, StdResidual)
xlist = []
ylist = []
slist = []
Cat = np.zeros((50000, ),
dtype=[('ra', np.float), ('dec', np.float), ('I', np.float),
('Gmaj', np.float), ('Gmin', np.float),
('Gangle', np.float)])
Cat = Cat.view(np.recarray)
isource = 0
for Dico in sourceList:
if type(Dico) == list: continue
for iCompDico in sorted(Dico.keys()):
CompDico = Dico[iCompDico]
if CompDico["SM"] > 5: continue
if CompDico["Sm"] > 5: continue
i = CompDico["l"]
j = CompDico["m"]
s = CompDico["s"]
xlist.append(i)
ylist.append(j)
slist.append(s)
f, d, dec, ra = im.toworld((0, 0, i, j))
Cat.ra[isource] = ra
Cat.dec[isource] = dec
Cat.I[isource] = s
Cat.Gmin[isource] = CompDico["Sm"] * (
incr / ToSig / 3600.) * np.pi / 180 / (2. *
np.sqrt(2. * np.log(2)))
Cat.Gmaj[isource] = CompDico["SM"] * (
incr / ToSig / 3600.) * np.pi / 180 / (2. *
np.sqrt(2. * np.log(2)))
Cat.Gangle[isource] = -CompDico["PA"] + np.pi / 2
isource += 1
Cat = Cat[Cat.ra != 0].copy()
Islands.FittedComps = (xlist, ylist, slist)
Islands.plot()
SM = ClassSM.ClassSM(
Osm, ReName=True, DoREG=True, SaveNp=True,
FromExt=Cat) #,NCluster=NCluster,DoPlot=DoPlot,ClusterMethod=CMethod)
#SM=ClassSM.ClassSM(Osm,ReName=True,SaveNp=True,DoPlot=DoPlot,FromExt=Cat)
SM.MakeREG()
SM.Save()
def main(options=None):
if options == None:
f = open("last_MakePModel.obj", 'rb')
options = pickle.load(f)
Boost = int(options.Boost)
#CMethod=int(options.CMethod)
#NCluster=int(options.NCluster)
Osm = options.Osm
Pfact = float(options.Pfact)
DoPlot = (options.DoPlot == "1")
imname = options.im
snr = float(options.snr)
if Osm == "":
Osm = reformat.reformat(imname, LastSlash=False)
im = image(imname)
PMaj = None
try:
PMaj = (im.imageinfo()["restoringbeam"]["major"]["value"])
PMin = (im.imageinfo()["restoringbeam"]["minor"]["value"])
PPA = (im.imageinfo()["restoringbeam"]["positionangle"]["value"])
PMaj *= Pfact
PMin *= Pfact
except:
print ModColor.Str(" No psf seen in header")
pass
if options.PSF != "":
m0, m1, pa = options.PSF.split(',')
PMaj, PMin, PPA = float(m0), float(m1), float(pa)
PMaj *= Pfact
PMin *= Pfact
if PMaj != None:
print ModColor.Str(
" - Using psf (maj,min,pa)=(%6.2f, %6.2f, %6.2f) (mult. fact.=%6.2f)"
% (PMaj, PMin, PPA, Pfact),
col='green',
Bold=False)
else:
print ModColor.Str(" - No psf info could be gotten from anywhere")
print ModColor.Str(
" use PSF keyword to tell what the psf is or is not")
exit()
ToSig = (1. / 3600.) * (np.pi / 180.) / (2. * np.sqrt(2. * np.log(2)))
PMaj *= ToSig
PMin *= ToSig
PPA *= np.pi / 180
b = im.getdata()[0, 0, :, :]
#b=b[3000:4000,3000:4000]#[120:170,300:370]
c = im.coordinates()
incr = np.abs(c.dict()["direction0"]["cdelt"][0])
print ModColor.Str(" - Psf Size Sigma_(Maj,Min) = (%5.1f,%5.1f) pixels" %
(PMaj / incr, PMin / incr),
col="green",
Bold=False)
Islands = ClassIslands.ClassIslands(b, snr, Boost=Boost, DoPlot=DoPlot)
Islands.FindAllIslands()
ImOut = np.zeros_like(b)
pBAR = ProgressBar('white', block='=', empty=' ', Title="Fit islands")
#print "ion"
#import pylab
#pylab.ion()
sourceList = []
for i in range(len(Islands.ListX)):
comment = 'Isl %i/%i' % (i + 1, len(Islands.ListX))
pBAR.render(int(100 * float(i + 1) / len(Islands.ListX)), comment)
xin, yin, zin = np.array(Islands.ListX[i]), np.array(
Islands.ListY[i]), np.array(Islands.ListS[i])
xm = int(np.sum(xin * zin) / np.sum(zin))
ym = int(np.sum(yin * zin) / np.sum(zin))
#Fit=ClassFit(xin,yin,zin,psf=(PMaj/incr,PMin/incr,PPA),noise=Islands.Noise[xm,ym])
Fit = ClassFit(xin,
yin,
zin,
psf=(PMaj / incr, PMin / incr, PPA),
noise=Islands.Noise[xm, ym],
FreePars=["l", "m", "s"])
sourceList.append(Fit.DoAllFit())
Fit.PutFittedArray(ImOut)
Islands.FitIm = ImOut
xlist = []
ylist = []
slist = []
Cat = np.zeros((50000, ),
dtype=[('ra', np.float), ('dec', np.float), ('s', np.float),
('Gmaj', np.float), ('Gmin', np.float),
('PA', np.float)])
Cat = Cat.view(np.recarray)
isource = 0
for Dico in sourceList:
for iCompDico in sorted(Dico.keys()):
CompDico = Dico[iCompDico]
i = CompDico["l"]
j = CompDico["m"]
s = CompDico["s"]
xlist.append(i)
ylist.append(j)
slist.append(s)
f, d, dec, ra = im.toworld((0, 0, i, j))
Cat.ra[isource] = ra
Cat.dec[isource] = dec
Cat.s[isource] = s
Cat.Gmin[isource] = CompDico["Sm"] * incr / ToSig
Cat.Gmaj[isource] = CompDico["SM"] * incr / ToSig
Cat.PA[isource] = CompDico["PA"]
isource += 1
Cat = Cat[Cat.ra != 0].copy()
Islands.FittedComps = (xlist, ylist, slist)
Islands.plot()
SM = ClassSM.ClassSM(
Osm, ReName=True, DoREG=True, SaveNp=True,
FromExt=Cat) #,NCluster=NCluster,DoPlot=DoPlot,ClusterMethod=CMethod)
#SM=ClassSM.ClassSM(Osm,ReName=True,SaveNp=True,DoPlot=DoPlot,FromExt=Cat)
SM.MakeREG()
SM.Save()