def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
xmlmodel = thisCommand.getParValue('xmlmodel')
tsltcube = thisCommand.getParValue('tsltcube')
tsexpomap = thisCommand.getParValue('tsexpomap')
tsmap = thisCommand.getParValue('tsmap')
step = float(thisCommand.getParValue('step'))
side = thisCommand.getParValue('side')
if (side == 'auto'):
side = None
# showmodelimage = thisCommand.getParValue('showmodelimage')
# optimize = thisCommand.getParValue('optimizeposition')
# tsmin = float(thisCommand.getParValue('tsmin'))
# skymap = thisCommand.getParValue('skymap')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
origra = float(dataHandling._getParamFromXML(xmlmodel, 'RA'))
origdec = float(dataHandling._getParamFromXML(xmlmodel, 'DEC'))
sourceName = dataHandling._getParamFromXML(xmlmodel, 'OBJECT')
#Verify that TS map, if provided, is compatible with the position in the XML
if (tsexpomap is not None and tsexpomap != ''):
if (os.path.exists(tsexpomap)):
header = pyfits.getheader(tsexpomap)
ra, dec = (float(header.get('CRVAL1')),
float(header.get('CRVAL2')))
angdist = getAngularDistance(origra, origdec, ra, dec)
if (angdist > 0.1):
print(
"Provided exposure map has a different center. Will compute it again."
)
tsexpomap = None
else:
print(
"Provided exposure map does not exist. Will compute it again.")
tsexpomap = None
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
tsmap = LATdata.makeTSmap(xmlmodel, sourceName, step, side, tsmap,
tsltcube, tsexpomap)
tsltcube = LATdata.livetimeCube
tsexpomap = LATdata.exposureMap
ra, dec, tsmax = dataHandling.findMaximumTSmap(tsmap, tsexpomap)
print(
"\nCoordinates of the maximum of the TS map in the allowed region (TS = %.1f):"
% (tsmax))
print("(R.A., Dec.) = (%6.3f, %6.3f)\n" % (ra, dec))
print("Distance from ROI center = %6.3f\n\n" %
(getAngularDistance(origra, origdec, ra, dec)))
if (figure is not None):
from GtBurst import aplpy
#Display the TS map
figure.clear()
tsfig = aplpy.FITSFigure(tsmap, convention='calabretta', figure=figure)
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
tsfig.show_colorscale(cmap='gist_heat', aspect='auto')
tsfig.show_markers([ra], [dec],
edgecolor='green',
facecolor='none',
marker='o',
s=10,
alpha=0.5)
# Modify the tick labels for precision and format
tsfig.tick_labels.set_xformat('ddd.dd')
tsfig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
tsfig.show_grid()
figure.canvas.draw()
pass
return 'tsmap', tsmap, 'tsmap_ra', ra, 'tsmap_dec', dec, 'tsmap_maxTS', tsmax, 'tsltcube', tsltcube, 'tsexpomap', tsexpomap
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
tsmin = float(thisCommand.getParValue('tsmin'))
fixphindex = thisCommand.getParValue('fixphindex')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
sedtxt = thisCommand.getParValue('sedtxt')
energybins = thisCommand.getParValue('energybins')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
try:
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(xmlmodel,tsmin=20,expomap=expomap,ltcube=ltcube)
except GtBurstException as gt:
raise gt
except:
raise
#Now produce a pha1 file (just to compute the total exposure)
pha1file = LATdata.binByEnergy(2)
totalExposure = pyfits.getheader(pha1file,'SPECTRUM').get("EXPOSURE")
print("\nTotal exposure: %s s" %(totalExposure))
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel,'IRF')
ra = dataHandling._getParamFromXML(xmlmodel,'RA')
dec = dataHandling._getParamFromXML(xmlmodel,'DEC')
sourceName = dataHandling._getParamFromXML(xmlmodel,'OBJECT')
if(irf is None):
print("\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing...")
else:
dataHandling._writeParamIntoXML(outfilelike,IRF=irf,OBJECT=sourceName,RA=ra,DEC=dec)
pass
#Make a copy of the output XML file and freeze all parameters except the source
tree = ET.parse(outfilelike)
root = tree.getroot()
phIndex = -2.0
for source in root.findall('source'):
if(source.get('name')!=sourceName):
#Freeze all parameters
for spectrum in source.findall('spectrum'):
for param in spectrum.findall('parameter'):
param.set('free',"%s" % 0)
pass
pass
else:
if(fixphindex.lower()!='no' and fixphindex!=False):
#Fix photon index
for spectrum in source.findall('spectrum'):
for param in spectrum.findall('parameter'):
if(param.get('name')=='Index'):
if(fixphindex.lower()!='prefit'):
try:
value = float(fixphindex)
except:
raise ValueError("The value for the photon index (%s) is not a float. Value not recognized." % fixphindex)
print("\n\nFixing photon index to the provided value (%s) \n\n" %(value))
param.set('value',"%s" % value)
else:
print("\n\nFixing photon index to the best fit value on the whole energy range\n\n")
param.set('free',"%s" % 0)
phIndex = float(param.get('value'))
pass
pass
pass
pass
f = open("__temporary_XML.xml",'w+')
tree.write(f)
f.write('''<!-- OBJECT=%s -->
<!-- DEC=%s -->
<!-- RA=%s -->
<!-- IRF=%s -->\n''' %(sourceName,ra,dec,irf))
f.close()
#Now for each energy band, make a likelihood and compute the flux
f = pyfits.open(eventfile)
#Take the list in inverse order so I will rebin at low energies, instead that at high energies
energies = numpy.array(sorted(f['EVENTS'].data.ENERGY)[::-1])
totalInputCounts = len(energies)
f.close()
if(energybins is not None):
energyBoundaries = map(lambda x:float(x),energybins.split(','))
else:
energyBoundaries = LikelihoodComponent.optimizeBins(LATdata.like1,energies,sourceName,minTs=tsmin,minEvt=3)
print("\nEnergy boundaries:")
for i,ee1,ee2 in zip(range(len(energyBoundaries)-1),energyBoundaries[:-1],energyBoundaries[1:]):
print("%02i: %10s - %10s" %(i+1,ee1,ee2))
pass
print("\n")
print("\nNumber of energy bins: %s\n" %(len(energyBoundaries)-1))
fluxes = numpy.zeros(len(energyBoundaries)-1)
fluxes_errors = numpy.zeros(len(energyBoundaries)-1)
phfluxes = numpy.zeros(len(energyBoundaries)-1)
phfluxes_errors = numpy.zeros(len(energyBoundaries)-1)
TSs = numpy.zeros(len(energyBoundaries)-1)
phIndexes = numpy.zeros(len(energyBoundaries)-1)
totalCounts = 0
for i,e1,e2 in zip(range(len(fluxes)),energyBoundaries[:-1],energyBoundaries[1:]):
thisLATdata = dataHandling.LATData(eventfile,rspfile,ft2file,root="SED_%s-%s" %(e1,e2))
#Further cut in the energy range for this Band
#Remove the version _vv from the name of the irf
cleanedIrf = "_".join(LATdata.irf.split("_")[:-1])
outf,thisCounts = thisLATdata.performStandardCut(LATdata.ra,LATdata.dec,LATdata.rad,cleanedIrf,
LATdata.tmin,LATdata.tmax,e1,e2,180,gtmktime=False,roicut=False)
totalCounts += thisCounts
#Perform the likelihood analysis
outfilelike, sources = thisLATdata.doUnbinnedLikelihoodAnalysis("__temporary_XML.xml",
tsmin=tsmin,
dogtdiffrsp=False,
expomap=expomap,
ltcube=ltcube)
source = filter(lambda x:x.name==sourceName,sources)[0]
if(source.flux.find("<")>=0):
#This is an upper limit
source.flux = float(source.flux.split("<")[1].strip())
source.fluxError = -1
source.photonFlux = float(source.photonFlux.split("<")[1].strip())
source.photonFluxError = -1
pass
fluxes[i] = source.flux
fluxes_errors[i] = source.fluxError
phfluxes[i] = source.photonFlux
phfluxes_errors[i] = source.photonFluxError
TSs[i] = float(source.TS)
phIndexes[i] = float(source.photonIndex)
pass
if(totalCounts!=totalInputCounts):
raise RuntimeError("We have losted somewhere %s counts!" %(totalInputCounts-totalCounts))
pass
#Now compute the SED points
MeV2Erg = 1.60217657e-6
ee1 = numpy.array(energyBoundaries[:-1])
ee2 = numpy.array(energyBoundaries[1:])
ee = (ee1+ee2)/2.0
de = (ee2-ee1)
#Use the photon index of the total fit to compute the mean energy
pow = numpy.power
meanEnergies = numpy.array(map(lambda x:computeMeanEnergy(x[0],x[1],x[2]),zip(phIndexes,ee1,ee2)))
nuFnu = phfluxes / de * pow(meanEnergies,2.0) * MeV2Erg
nuFnuError = phfluxes_errors / de * pow(meanEnergies,2.0) * MeV2Erg
#Print the results of the SED
fw = open(sedtxt,'w+')
fw.write("#Energy_min Energy_Max Flux Flux_error PhotonFlux PhotonFluxError TS nuFnu_energy nuFnu_energy_negerr nuFnu_energy_poserr nuFnu_value nuFnu_value_error\n")
fw.write("#MeV MeV erg/cm2/s erg/cm2/s ph/cm2/s ph/cm2/s - MeV MeV MeV erg/cm2/s erg/cm2/s\n")
fw.write("#Total exposure: %s s\n" %(totalExposure))
for e1,e2,f,fe,ph,phe,ts,ne,nee1,nee2,nuF,nuFe in zip(energyBoundaries[:-1],energyBoundaries[1:],fluxes,fluxes_errors,phfluxes,phfluxes_errors,TSs,meanEnergies,meanEnergies-ee1,ee2-meanEnergies,nuFnu,nuFnuError):
fw.write("%s %s %s %s %s %s %s %s %s %s %s %s\n" %(e1,e2,f,fe,ph,phe,ts,ne,nee1,nee2,nuF,nuFe))
print("%10s - %10s MeV -> %g +/- %g erg/cm2/s, %g +/- %g ph/cm2/s, TS = %s" %(e1,e2,f,fe,ph,phe,ts))
pass
fw.close()
if(figure is not None):
#Display the SED
if(os.environ.get('DISPLAY') is None):
os.environ.set('DISPLAY','/dev/null')
import matplotlib
matplotlib.use('Agg')
pass
import matplotlib.pyplot as plt
try:
figure.clear()
except:
print("Creating new figure...")
try:
figure = plt.figure()
except:
plt.switch_backend('Agg')
figure = plt.figure()
pass
sub = figure.add_subplot(111)
sub.errorbar(meanEnergies,nuFnu,xerr=[meanEnergies-ee1,ee2-meanEnergies],yerr=nuFnuError,fmt='.')
sub.set_xlabel("Energy (MeV)")
sub.set_ylabel("Flux (erg/cm2/s)")
sub.loglog()
sub.set_xlim(0.9*min(ee1),1.1*max(ee2))
figure.canvas.draw()
figure.savefig("%s.png" % ".".join(sedtxt.split(".")[0:-1]))
pass
return 'sedtxt', sedtxt
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('eventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
ra = thisCommand.getParValue('ra')
dec = thisCommand.getParValue('dec')
rad = thisCommand.getParValue('rad')
irf = thisCommand.getParValue('irf')
zmax = thisCommand.getParValue('zmax')
tstart = thisCommand.getParValue('tstart')
tstop = thisCommand.getParValue('tstop')
emin = thisCommand.getParValue('emin')
emax = thisCommand.getParValue('emax')
skybinsize = thisCommand.getParValue('skybinsize')
outfile = thisCommand.getParValue('skymap')
strategy = thisCommand.getParValue('strategy')
thetamax = float(thisCommand.getParValue('thetamax'))
allowEmpty = _yesOrNoToBool(thisCommand.getParValue('allowEmpty'))
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
global lastDisplay
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
if (strategy.lower() == "time"):
#gtmktime cut
filteredFile, nEvents = LATdata.performStandardCut(ra,
dec,
rad,
irf,
tstart,
tstop,
emin,
emax,
zmax,
thetamax,
True,
strategy='time')
elif (strategy.lower() == "events"):
#no gtmktime cut, Zenith cut applied directly to the events
filteredFile, nEvents = LATdata.performStandardCut(ra,
dec,
rad,
irf,
tstart,
tstop,
emin,
emax,
zmax,
thetamax,
True,
strategy='events')
pass
LATdata.doSkyMap(outfile, skybinsize)
#Now open the output file and get exposure and total number of events
skymap = pyfits.open(outfile)
totalNumberOfEvents = numpy.sum(skymap[0].data)
totalTime = numpy.sum(skymap['GTI'].data.field('STOP') -
skymap['GTI'].data.field('START'))
skymap.close()
print("\nTotal number of events in the counts map: %s" %
(totalNumberOfEvents))
print("Total time in Good Time Intervals: %s" % (totalTime))
if ((totalTime == 0) and allowEmpty == False):
raise GtBurstException(
2, "Your filter resulted in zero exposure. \n\n" +
" Loose your cuts, or enlarge the time interval. You might want to "
+
" check also the navigation plots (in the Tools menu) to make sure "
+
" that the ROI is within the LAT FOV in the desired time interval."
)
displ = None
if (figure != None):
if (lastDisplay != None):
lastDisplay.unbind()
pass
from GtBurst.InteractiveFt1Display import InteractiveFt1Display
lastDisplay = InteractiveFt1Display(filteredFile, outfile, figure, ra,
dec)
pass
return 'skymap', outfile, 'filteredeventfile', filteredFile, 'irf', irf, 'eventDisplay', lastDisplay
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
showmodelimage = thisCommand.getParValue('showmodelimage')
optimize = thisCommand.getParValue('optimizeposition')
spectralfiles = thisCommand.getParValue('spectralfiles')
tsmin = float(thisCommand.getParValue('tsmin'))
skymap = thisCommand.getParValue('skymap')
liketype = thisCommand.getParValue('liketype')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
try:
if (liketype == 'unbinned'):
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(
xmlmodel, tsmin, expomap=expomap, ltcube=ltcube)
else:
#Generation of spectral files and optimization of the position is
#not supported yet for binned analysis
if (spectralfiles == 'yes'):
print(
"\nWARNING: you specified spectralfiles=yes, but the generation of spectral files is not supported for binned analysis\n"
)
spectralfiles = 'no'
if (optimize == 'yes'):
print(
"\nWARNING: you specified optimize=yes, but position optimization is not supported for binned analysis\n"
)
optimize = 'no'
outfilelike, sources = LATdata.doBinnedLikelihoodAnalysis(
xmlmodel, tsmin, expomap=expomap, ltcube=ltcube)
except GtBurstException as gt:
raise gt
except:
raise
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel, 'IRF')
ra = dataHandling._getParamFromXML(xmlmodel, 'RA')
dec = dataHandling._getParamFromXML(xmlmodel, 'DEC')
name = dataHandling._getParamFromXML(xmlmodel, 'OBJECT')
try:
grb = filter(lambda x: x.name.lower().find(name.lower()) >= 0,
sources)[0]
grb_TS = grb.TS
except:
#A model without GRB
print("\nWarning: no GRB in the model!")
grb_TS = -1
pass
if (irf == None):
print(
"\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing..."
)
else:
dataHandling._writeParamIntoXML(outfilelike,
IRF=irf,
OBJECT=name,
RA=ra,
DEC=dec)
pass
if (spectralfiles == 'yes'):
phafile, rspfile, bakfile = LATdata.doSpectralFiles(outfilelike)
pass
localizationMessage = ''
bestra = ''
bestdec = ''
poserr = ''
distance = ''
if (optimize == 'yes'):
sourceName = name
#If the TS for the source is above tsmin, optimize its position
#grb = filter(lambda x:x.name.lower().find(sourceName.lower())>=0,sources)[0]
if (math.ceil(grb_TS) >= tsmin):
try:
bestra, bestdec, poserr = LATdata.optimizeSourcePosition(
outfilelike, sourceName)
except:
raise GtBurstException(
207,
"gtfindsrc execution failed. Were the source detected in the likelihood step?"
)
else:
localizationMessage += "\nNew localization from gtfindsrc:\n\n"
localizationMessage += "(R.A., Dec) = (%6.3f, %6.3f)\n" % (
bestra, bestdec)
localizationMessage += "68 %s containment radius = %6.3f\n" % (
'%', poserr)
localizationMessage += "90 %s containment radius = %6.3f\n" % (
'%', 1.41 * poserr)
distance = getAngularDistance(float(ra), float(dec),
float(bestra), float(bestdec))
localizationMessage += "Distance from initial position = %6.3f\n\n" % (
distance)
localizationMessage += "NOTE: this new localization WILL NOT be used by default. If you judge"
localizationMessage += " it is a better localization than the one you started with, update the"
localizationMessage += " coordinates yourself and re-run the likelihood\n"
pass
pass
if (figure != None and skymap != None and showmodelimage == 'yes'):
#Now produce the binned exposure map (needed in order to display the fitted model as an image)
modelmapfile = LATdata.makeModelSkyMap(outfilelike)
#Display point sources in the image, and report in the table
#all 2FGL sources with TS > 9 + always the GRB, independently of its TS
detectedSources = []
grbFlux = 1e-13
for src in sources:
weight = 'bold'
if (src.type == 'PointSource'):
if (src.TS > 4):
detectedSources.append(src)
if (src.name.find('2FGL') < 0):
#GRB
grbFlux = src.flux
pass
pass
pass
#Display the counts map
from GtBurst import aplpy
import matplotlib.pyplot as plt
figure.clear()
orig = aplpy.FITSFigure(skymap,
convention='calabretta',
figure=figure,
subplot=[0.1, 0.1, 0.45, 0.7])
vmax = max(pyfits.open(skymap)[0].data.flatten())
nEvents = numpy.sum(pyfits.open(skymap)[0].data)
telapsed = pyfits.open(eventfile)[0].header['TSTOP'] - pyfits.open(
eventfile)[0].header['TSTART']
orig.set_tick_labels_font(size='small')
orig.set_axis_labels_font(size='small')
orig.show_colorscale(cmap='gist_heat',
vmin=0.1,
vmax=max(vmax, 0.11),
stretch='log',
aspect='auto')
# Modify the tick labels for precision and format
orig.tick_labels.set_xformat('ddd.dd')
orig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
orig.show_grid()
#Renormalize the modelmapfile to the flux of the grb
f = pyfits.open(modelmapfile, 'update')
f[0].data = f[0].data / numpy.max(f[0].data) * nEvents / telapsed
print(numpy.max(f[0].data))
f.close()
img = aplpy.FITSFigure(modelmapfile,
convention='calabretta',
figure=figure,
subplot=[0.55, 0.1, 0.4, 0.7])
img.set_tick_labels_font(size='small')
img.set_axis_labels_font(size='small')
#vmax = max(pyfits.open(modelmapfile)[0].data.flatten())
img.show_colorscale(cmap='gist_heat', aspect='auto', stretch='log')
for src in detectedSources:
img.add_label(float(src.ra),
float(src.dec),
"%s\n(ts = %i)" % (src.name, int(math.ceil(src.TS))),
relative=False,
weight=weight,
color='green',
size='small')
pass
# Modify the tick labels for precision and format
img.tick_labels.set_xformat('ddd.dd')
img.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
img.show_grid()
#ax = figure.add_axes([0.1,0.72,0.85,0.25],frame_on=False)
#ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
#col_labels =['Source Name','TS','Energy flux','Photon index']
#table_vals = map(lambda x:[x.name,"%i" %(int(math.ceil(x.TS))),
# "%s +/- %s" % (x.flux,x.fluxError),
# "%s +/- %s" % (x.photonIndex,x.photonIndexError)],detectedSources)
#
#if(len(table_vals)>0):
# the_table = ax.table(cellText=table_vals,
# colLabels=col_labels,
# loc='upper center')
figure.canvas.draw()
figure.savefig("likelihood_results.png")
pass
if (figure != None):
#Assume we have an X server running
#Now display the results
likemsg = "Log(likelihood) = %s" % (LATdata.logL)
displayResults(
figure.canvas._tkcanvas, LATdata.resultsStrings + "\n" + likemsg +
"\n" + localizationMessage)
print(localizationMessage)
return 'likexmlresults', outfilelike, 'TS', grb_TS, 'bestra', bestra, 'bestdec', bestdec, 'poserr', poserr, 'distance', distance, 'sources', sources