# LINE
linemask = spave.create_mask([2900,3150])
curr_max = spave.stats('max', linemask) # IF[2] = 1.275
curr_sum = spave.stats('sum', linemask) # IF[2] = 90.350
curr_median = spave.stats('median', linemask) # IF[2] = 0.231
curr_mean = spave.stats('mean', linemask) # IF[2] = 0.360
# Chans bchan echan Xmin Xmax Ymin Ymax
# 251 2900 3150 2900.0 3150.0 -0.20952 1.2829
# Mean Median RMS Variance Area
# 0.36219 0.23209 0.39342 0.15478 90.909
#
# Fitting
spave.set_unit('channel') # set units to channel # chan
sd.plotter.plot(spave) # plot spectrum
f = sd.fitter()
msk = spave.create_mask([2900,3150]) # create region around line # gregion,[2900,3150]
f.set_function(gauss=1) # set a single gaussian component # ngauss,1
f.set_scan(spave, msk) # set the data and region for the fitter
#f.set_gauss_parameters(1.2,3100.,100.) # set initial guess # gparamvalues,0,[1.2,3100.,100.]
f.fit() # auto fit # gauss
f.plot(residual=True) # plot residual
f.get_parameters() # retrieve fit parameters #
# 0: peak = 1.054 K , centre = 3037.182 channel, FWHM = 71.654 channel
# area = 80.382 K channel
#
# Regression values of CASA 2.3(#6654)+ASAP 2.2.0(#1448)
# on 64bit REL5.2 (2008/12/01)
# 0: peak = 1.110 K , centre = 3037.176 channel, FWHM = 71.676 channel
# area = 84.727 K channel
def __fit(self, scantab=None):
# initialize fitter
self.fitter = sd.fitter()
if abs(self.plotlevel) > 0:
self.__init_plot()
dbw = 1.0
if scantab is None:
scantab = self.scan
current_unit = scantab.get_unit()
kw = {'thescan': scantab}
if len(self.defaultmask) > 0: kw['mask'] = self.defaultmask
self.fitter.set_scan(**kw)
firstplot = True
for irow in range(scantab.nrow()):
casalog.post("start row %d" % (irow))
# check for FLAGROW
if scantab._getflagrow(irow):
casalog.post("Row %d is flagged. skip fitting " % irow)
self.fitparams.append([[0, 0, 0]])
self.result['nfit'] += [-1]
continue
numlines = self.nlines[irow] if isinstance(self.nlines,list) \
else self.nlines
if numlines == 0:
self.fitparams.append([[0, 0, 0]])
self.result['nfit'] += [-1]
self.__warn_fit_failed(scantab, irow, 'No lines detected.')
continue
if (self.fitmode == 'auto'):
# Auto mode - one comp per line region
# Overwriting user-supplied nfit
numfit = numlines
comps = [1 for i in xrange(numlines)]
else:
# Get number of things to fit from nfit list
comps = self.nfit if isinstance(self.nfit,
list) else [self.nfit]
# Drop extra over numlines
numfit = min(len(comps), numlines)
ncomps = sum(comps)
casalog.post("Will fit %d components in %d regions" %
(ncomps, numfit))
if numfit <= 0:
self.fitparams.append([[0, 0, 0]])
self.result['nfit'] += [-1]
self.__warn_fit_failed(scantab, irow, 'Fit failed.')
continue
# Fit the line using numfit gaussians or lorentzians
# Assume the nfit list matches maskline
self.fitter.set_function(**{self.fitfunc: ncomps})
if (self.doguess):
# in auto mode, linelist will be detemined for each spectra
# otherwise, linelist will be the same for all spectra
if current_unit != 'channel':
xx = scantab._getabcissa(irow)
dbw = abs(xx[1] - xx[0])
self.__initial_guess(scantab, dbw, numfit, comps, irow)
else:
# No guesses
casalog.post("Fitting lines without starting guess")
# Now fit
self.fitter.fit(row=irow)
fstat = self.fitter.get_parameters()
# Check for convergence
goodfit = (len(fstat['errors']) > 0)
if (goodfit):
self.__update_params(ncomps)
else:
# Did not converge
self.result['nfit'] += [-ncomps]
self.fitparams.append([[0, 0, 0]])
self.__warn_fit_failed(scantab, irow, 'Fit failed to converge')
# plot
if (irow < 16 and abs(self.plotlevel) > 0):
self.__plot(irow, goodfit, firstplot)
firstplot = False
# LINE
linemask = spave.create_mask([2900, 3150])
curr_max = spave.stats('max', linemask) # IF[2] = 1.275
curr_sum = spave.stats('sum', linemask) # IF[2] = 90.350
curr_median = spave.stats('median', linemask) # IF[2] = 0.231
curr_mean = spave.stats('mean', linemask) # IF[2] = 0.360
# Chans bchan echan Xmin Xmax Ymin Ymax
# 251 2900 3150 2900.0 3150.0 -0.20952 1.2829
# Mean Median RMS Variance Area
# 0.36219 0.23209 0.39342 0.15478 90.909
#
# Fitting
spave.set_unit('channel') # set units to channel # chan
sd.plotter.plot(spave) # plot spectrum
f = sd.fitter()
msk = spave.create_mask(
[2900, 3150]) # create region around line # gregion,[2900,3150]
f.set_function(gauss=1) # set a single gaussian component # ngauss,1
f.set_scan(spave, msk) # set the data and region for the fitter
#f.set_gauss_parameters(1.2,3100.,100.) # set initial guess # gparamvalues,0,[1.2,3100.,100.]
f.fit() # auto fit # gauss
f.plot(residual=True) # plot residual
f.get_parameters() # retrieve fit parameters #
# 0: peak = 1.054 K , centre = 3037.182 channel, FWHM = 71.654 channel
# area = 80.382 K channel
#
# Regression values of CASA 2.3(#6654)+ASAP 2.2.0(#1448)
# on 64bit REL5.2 (2008/12/01)
# 0: peak = 1.110 K , centre = 3037.176 channel, FWHM = 71.676 channel
# area = 84.727 K channel
def __execute_baseline(self):
#print "calibration begins..."
#select the totalpower data for antenna1,2=1 (DV01, vertex antenna)
# use generic pylab
if type(self.antid) == list and len(self.antid) > 2:
#print "Assume the antenna selection is all..."
casalog.post( "Assume the antenna selection is all..." )
self.open_table(self.infile)
ndat = 0
pl.ioff()
# determine the size of processing data
subtb = self.table.query('any(ANTENNA1==%s && ANTENNA2==%s)' % (self.antid,self.antid))
nr = subtb.nrows()
#nr = len(data)*len(data[0])
#ndatcol = numpy.zeros((npol,nr),dtype=numpy.float)
ndatcol = subtb.getcol(self.datacol)
(l,m,n) = ndatcol.shape
# m (= nchan) should be 1 since the task is specifically designed for total power data
ndatcol.reshape(l,n)
for np in range(len(self.selcorrtypeind)):
pl.figure(np+1)
pl.clf()
pl.subplot(311)
#pl.title(infile+' Ant:'+str(antlist.values()))
pl.ylabel(self.datacol,fontsize='smaller')
symbols=['b.','c.']
#print "Arranging data by scans..."
casalog.post( "Arranging data by scans..." )
data = []
flag = []
ndat0 = 0
ndat = 0
if self.selnpol == 1: np = self.selpol
pl.title(self.infile.rstrip('/')+' Ant:'+self.antnameused+' '+self.corrtypestr[np])
casalog.post( "select %s data..." % self.corrtypestr[np] )
for i in xrange(self.nscan):
idata = []
iflag = []
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
# may be need to iterate on each antenna
# identify 'scan' by STATE ID
#selsubtb=tb.query('any(ANTENNA1==%s && ANTENNA2==%s) && SCAN_NUMBER==%s && STATE_ID==%s' % (antid,antid,scans[i],subscans[j]))
selsubtb = self.table.query('any(ANTENNA1==%s && ANTENNA2==%s) && SCAN_NUMBER==%s && STATE_ID==%s' % (self.antid,self.antid,self.scans[i],self.subscans[i][j]))
datcol = selsubtb.getcol(self.datacol)
flgcol = selsubtb.getcol("FLAG") # only channel flag is needed to be hanled in MS
selsubtb.close()
if self.npol > 1 and self.selnpol == 1:
#casalog.post( "select %s data..." % corrtypestr[selpol] )
rdatcol = datcol[self.selpol].real
flagcell = flgcol[self.selpol]
else:
rdatcol = datcol[self.selcorrtypeind[np]].real
flagcell = flgcol[self.selpol]
(m,n) = rdatcol.shape
if flagcell.shape != (m,n):
raise Exception, "Data conformation error. Shape of FLAG and %s differs." % self.datacol
rdatcol = rdatcol.reshape(n)
flagcell = flagcell.reshape(n)
#data.append(rdatcol)
idata.append(rdatcol)
iflag.append(flagcell)
ndat0 = ndat
ndat += len(rdatcol)
data.append(idata)
flag.append(iflag)
if abs(self.plotlevel) > 0:
pl.xlim(0,ndat)
t1 = pl.getp(pl.gca(), 'xticklabels')
t2 = pl.getp(pl.gca(), 'yticklabels')
pl.setp((t1,t2), fontsize='smaller')
mdat = 0
for i in xrange(self.nscan):
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
mdat0 = mdat
mdat += len(data[i][j])
masked_data = numpy.ma.masked_array(data[i][j], flag[i][j])
#pl.plot(xrange(mdat0,mdat),data[i][j],symbols[subscans[j]%2])
#pl.plot(xrange(mdat0,mdat), data[i][j], symbols[self.subscans[i][j]%2])
pl.plot(xrange(mdat0,mdat), masked_data, symbols[self.subscans[i][j]%2])
ax = pl.gca()
if i == 1:
leg = ax.legend(('even subscan no.', 'odd subscan no.'), numpoints=1, handletextsep=0.01)
for t in leg.get_texts():
t.set_fontsize('small')
pl.ion()
pl.plot()
#pl.draw()
### Calibration ############################################
# Do a zero-th order calibration by subtracting a baseline #
# from each scan to take out atmospheric effect. #
# The baseline fitting range specified by masklist must be #
# given and is the same for all the scans. #
############################################################
f = sd.fitter()
f.set_function(lpoly = self.blpoly)
#print "Processing %s %s scans" % (corrtypestr[np], nscan)
casalog.post( "Processing %s %s scans" % (self.corrtypestr[np], self.nscan) )
cdat = numpy.array([])
pl.subplot(312)
pl.ioff()
for i in xrange(self.nscan):
casalog.post( "Processing Scan#=%s" % i )
#for j in xrange(nsubscan):
for j in xrange(self.nsubscan[i]):
masks = numpy.zeros(len(data[i][j]), dtype=numpy.int)
if self.left_mask >= len(data[i][j]) \
or self.right_mask >= len(data[i][j]):
msg = "Too large mask. All data will be used for baseline subtraction.\n The fitting may not be correct since it might confuse signal component as a background..."
casalog.post(msg, "WARN")
else:
msg = "Subtracting baselines, set masks for fitting at row ranges: [0,%s] and [%s,%s] " % (self.left_mask-1, len(masks)-self.right_mask, len(masks)-1)
casalog.post(msg, "INFO")
#Use edge channels for fitting excluding flagged integrations.
#masks[:self.left_mask] = True
#masks[-self.right_mask:] = True
masks[:self.left_mask] = (flag[i][j][:self.left_mask]==False)
masks[-self.right_mask:] = (flag[i][j][-self.right_mask:]==False)
x = xrange(len(data[i][j]))
if self.plotlevel > 1:
pl.cla()
pl.ioff()
pl.title('scan %s subscan %s'%(self.scans[i],self.subscans[i][j]))
#pl.plot(x, data[i][j], 'b')
pl.plot(x, numpy.ma.masked_array(data[i][j], flag[i][j]), 'b')
f.set_data(x, data[i][j], mask=masks)
f.fit()
#f.plot(residual=True, components=[1], plotparms=True)
fitd=f.get_fit()
data[i][j] = data[i][j]-fitd
if self.plotlevel > 1:
pl.ion()
pl.plot(xrange(len(fitd)), fitd, 'r-')
pl.draw()
pl.cla()
pl.ioff()
pl.title('scan %s subscan %s'%(self.scans[i], self.subscans[i][j]))
pl.ion()
#pl.plot(x, data[i][j], 'g')
pl.plot(x, numpy.ma.masked_array(data[i][j], flag[i][j]), 'g')
pl.draw()
if interactive: check=raw_input('Hit return for Next\n')
cdat = numpy.concatenate([cdat,data[i][j]])
ndatcol[np] = cdat
del data
subtb.close()
self.close_table()
self.open_table(self.infile, nomodify=False)
# put the corrected data to CORRECTED_DATA column in MS
# assuming the data for the vertex are stored in every other
# row starting from 2nd row for the vertex antenna....
# For the alcatel startrow=0, for len(antid) >1,
# it assumes all the data to be corrected.
if type(self.antid) == int:
startrow = self.antid
rowincr = self.nant
else:
startrow = 0
rowincr = 1
casalog.post( "Storing the corrected data to %s column in the MS..."%self.datacol )
casalog.post( "Note that %s will be overwritten"%self.datacol )
cdatm = ndatcol.reshape(self.npol,1,len(cdat))
self.table.putcol(self.datacol, cdatm, startrow=startrow, rowincr=rowincr)
self.close_table()
# calibration end
#
# plot corrected data
if abs(self.plotlevel) > 0:
casalog.post( "plotting corrected data..." )
self.__plot(subplot=313, title=False, ylabel='CORRECTED_DATA')
def __fit(self, scantab=None):
# initialize fitter
self.fitter = sd.fitter()
if abs(self.plotlevel) > 0:
self.__init_plot()
dbw = 1.0
if scantab is None:
scantab = self.scan
current_unit = scantab.get_unit()
kw = {'thescan':scantab}
if len(self.defaultmask) > 0: kw['mask'] = self.defaultmask
self.fitter.set_scan(**kw)
firstplot = True
for irow in range(scantab.nrow()):
casalog.post( "start row %d" % (irow) )
# check for FLAGROW
if scantab._getflagrow(irow):
casalog.post( "Row %d is flagged. skip fitting " % irow)
self.fitparams.append([[0,0,0]])
self.result['nfit']+=[-1]
continue
numlines = self.nlines[irow] if isinstance(self.nlines,list) \
else self.nlines
if numlines == 0:
self.fitparams.append([[0,0,0]])
self.result['nfit']+=[-1]
self.__warn_fit_failed(scantab,irow,'No lines detected.')
continue
if ( self.fitmode == 'auto'):
# Auto mode - one comp per line region
# Overwriting user-supplied nfit
numfit = numlines
comps = [1 for i in xrange(numlines)]
else:
# Get number of things to fit from nfit list
comps = self.nfit if isinstance(self.nfit,list) else [self.nfit]
# Drop extra over numlines
numfit = min(len(comps),numlines)
ncomps = sum(comps)
casalog.post( "Will fit %d components in %d regions" % (ncomps, numfit) )
if numfit <= 0:
self.fitparams.append([[0,0,0]])
self.result['nfit']+=[-1]
self.__warn_fit_failed(scantab,irow,'Fit failed.')
continue
# Fit the line using numfit gaussians or lorentzians
# Assume the nfit list matches maskline
self.fitter.set_function(**{self.fitfunc:ncomps})
if ( self.doguess ):
# in auto mode, linelist will be detemined for each spectra
# otherwise, linelist will be the same for all spectra
if current_unit != 'channel':
xx = scantab._getabcissa(irow)
dbw = abs(xx[1]-xx[0])
self.__initial_guess(scantab,dbw,numfit,comps,irow)
else:
# No guesses
casalog.post( "Fitting lines without starting guess" )
# Now fit
self.fitter.fit(row=irow)
fstat = self.fitter.get_parameters()
# Check for convergence
goodfit = ( len(fstat['errors']) > 0 )
if ( goodfit ):
self.__update_params(ncomps)
else:
# Did not converge
self.result['nfit'] += [-ncomps]
self.fitparams.append([[0,0,0]])
self.__warn_fit_failed(scantab,irow,'Fit failed to converge')
# plot
if (irow < 16 and abs(self.plotlevel) > 0):
self.__plot(irow, goodfit, firstplot)
firstplot = False
