def PerformLikelihoodAnalysis(self):
print "\nPerforming likelihood analysis on position: ra=%s, dec=%s" % (
self.xref, self.yref)
# Wait a random amount of time between 1 and 5 minutes before starting in order to not crash the asf/nsf disks at SLAC
waitTime = random.random() * 300
time.sleep(waitTime)
# Defind the scratch directory
JobID = os.environ.get('LSB_JOBID')
Username = getpass.getuser()
ScratchDirectory = "/scratch/%s/%s/" % (Username, JobID)
# Define the pfile directory
if JobID == 'None':
PFILESDirectory = "%s/pfiles_%s/" % (self.outdir, self.binNumber)
else:
PFILESDirectory = "%s/pfiles/" % ScratchDirectory
# Create the output directory if it doesn't already exist
if (os.path.isdir(self.outdir) == False):
print "\n >> Creating Directory: " + self.outdir
cmd = "mkdir " + self.outdir
os.system(cmd)
# Define where to save the results
likelihoodResults = '%s/likelihoodResults_bin%s.txt' % (self.outdir,
self.binNumber)
# Remove any pre-existing pfiles
if (os.path.isdir(PFILESDirectory) == True):
cmd = "rm -r %s" % PFILESDirectory
os.system(cmd)
# Set the new pfiles directory
SetPfilesDirectory(PFILESDirectory)
# Make a copy of the source model
xmlModelWithPutativeSource = '%s/ModelSource_bin%s.xml' % (
self.outdir, self.binNumber)
cmd = "cp " + self.srcmdl + " " + xmlModelWithPutativeSource
print cmd
os.system(cmd)
# Add a putative point source at the requested location
# AddCandidateSource(self.xref, self.yref, xmlModelWithPutativeSource)
ModifySourceModel(xmlModelWithPutativeSource, self.xref, self.yref)
# # Import the necessary gtapps
# gtlike = GtApp('gtlike')
# # Run the likelihood analysis
# print '\nPerforming the likelihood fit:'
# gtlike.run(statistic=self.statistic,
# scfile=self.scfile,
# evfile=self.evfile,
# expmap=self.expmap,
# expcube=self.expcube,
# srcmdl=xmlModelWithPutativeSource,
# irfs=self.irfs,
# optimizer=self.optimizer,
# results=likelihoodResults,
# plot='no',
# save='yes')
# Setup the unbinned likelihood object
print '\nPerforming the likelihood fit:'
try:
obs = UnbinnedObs(self.evfile,
self.scfile,
expMap=self.expmap,
expCube=self.expcube,
irfs=self.irfs)
# Define the likelihood object
#like = UnbinnedAnalysis(obs,xmlModelWithPutativeSource,optimizer=self.optimizer)
like = UnbinnedAnalysis(obs,
xmlModelWithPutativeSource,
optimizer='MINUIT')
# Setup the likelihood parameters
Source = 'CandidateSource'
Integral = like.par_index(Source, 'Integral')
Index = like.par_index(Source, 'Index')
LowerLimit = like.par_index(Source, 'LowerLimit')
UpperLimit = like.par_index(Source, 'UpperLimit')
# Setup the likelihood bounds
like[Integral].setScale(1e-3)
like[Index].setBounds(-5, -0.5)
# like[LowerLimit] = emin
# like[UpperLimit] = emax
# Perform the likelihood fit
#optObject = pyLike.NewMinuit(like.logLike)
#like.fit(verbosity=0,covar=True,tol=0.02,optObject=optObject)
like.fit(verbosity=1,
covar=True,
tol=1e-10,
optimizer='MINUIT',
optObject=None)
# Extract the best fit index
IndexValue = like[Index].value()
IndexError = like[Index].error()
# Extract the best fit flux
FluxValue = like.flux(Source, emin=100, emax=3e5)
FluxError = like.fluxError(Source, emin=100, emax=3e5)
# Extract likelihood fit results
print '\nLikelihood Results:'
print like.model[Source]
print "TS = %s" % like.Ts(Source)
print "Flux = %s +/- %s" % (FluxValue, FluxError)
print "Index = %s +/- %s" % (IndexValue, IndexError)
# Save the xml file
like.writeXml(xmlFile=xmlModelWithPutativeSource)
except Exception, message:
print traceback.format_exc()
like.tol
like.tolType
like.tol = 0.0001
if optimizerType=='Minuit':
likeobj = pyLike.Minuit(like.logLike)
elif optimizerType=='NewMinuit':
likeobj = pyLike.NewMinuit(like.logLike)
else:
print "Error: Bad Optimizer. Exiting"
exit()
like.fit(verbosity=0,covar=True,optObject=likeobj) # Warning: This takes VERY long ~ 30 minutes
# Get all the TS values of the sources in the model
sourceDetails = {}
for source in like.sourceNames():
sourceDetails[source] = like.Ts(source)
# Output for data
os.system('mkdir results')
printDictionaryToFile(sourceDetails,'TSValues_preDelete.txt')
# N.B A source with a TS value less than 0 should never happen unless the minimization failed. Remove that source and try fitting again and check the return code again.
converganceCheckOne="Zero?: %d" %(likeobj.getRetCode())
converganceCheckTwo="NA"
print converganceCheckOne # This value should be zero, refit using the method below if not
if not likeobj.getRetCode() ==0 or eMin<100000: # If energy is too high, just don't refit because it causes problems
print colors.OKBLUE+"Minimization Failed. Removing sources with TS < 9.0 and refitting!"
def main(NAME, RA, DEC, TSTART, TSTOP, EMIN, EMAX, SC, ROIu, xml):
ROIue = float(ROIu) + 10
os.system('ls -1 *PH*.fits > %s_events.list' % (NAME))
my_apps.filter['evclass'] = 128
my_apps.filter['evtype'] = 3
my_apps.filter['ra'] = RA
my_apps.filter['dec'] = DEC
my_apps.filter['rad'] = ROIu
my_apps.filter['emin'] = EMIN
my_apps.filter['emax'] = EMAX
my_apps.filter['zmax'] = 90
my_apps.filter['tmin'] = TSTART
my_apps.filter['tmax'] = TSTOP
my_apps.filter['infile'] = '@%s_events.list' % (NAME)
my_apps.filter['outfile'] = '%s_filtered.fits' % (NAME)
my_apps.filter.run()
# maketime
my_apps.maketime['scfile'] = SC
my_apps.maketime['filter'] = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
my_apps.maketime['roicut'] = 'no'
my_apps.maketime['evfile'] = '%s_filtered.fits' % (NAME)
my_apps.maketime['outfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.maketime.run()
#
my_apps.counts_map['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.counts_map['scfile'] = SC
my_apps.counts_map['outfile'] = '%s_CountMap.fits' % (NAME)
# my_apps.counts_map.run()
#
my_apps.expCube['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.expCube['scfile'] = SC
my_apps.expCube['zmax'] = 90
my_apps.expCube['outfile'] = 'expCube.fits'
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube.run()
my_apps.expMap['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.expMap['scfile'] = SC
my_apps.expMap['expcube'] = 'expCube.fits'
my_apps.expMap['outfile'] = 'expMap.fits'
my_apps.expMap['irfs'] = 'CALDB'
my_apps.expMap['srcrad'] = ROIue
my_apps.expMap['nlong'] = 120
my_apps.expMap['nlat'] = 120
my_apps.expMap['nenergies'] = 20
my_apps.expMap.run()
# sara xml model
roiname = '%s_filtered_gti.fits' % NAME
if float(xml) == 0:
xml_creator_P8_v1.main(NAME, float(RA), float(DEC), float(EMIN), float(EMAX), 15)
xmlmodelname = '%s_model.xml' % NAME
my_apps.diffResps['evfile'] = '%s_filtered_gti.fits' % (NAME)
my_apps.diffResps['scfile'] = SC
my_apps.diffResps['srcmdl'] = xmlmodelname
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
xmlfitname = '%s_fit1.xml' % NAME
obs = UnbinnedObs(roiname, SC, expMap='expMap.fits', expCube='expCube.fits', irfs='CALDB')
# like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='MINUIT')
like1 = UnbinnedAnalysis(obs, xmlmodelname, optimizer='NewMinuit')
likeobj = pyLike.NewMinuit(like1.logLike)
like1.fit(verbosity=0, optObject=likeobj)
print likeobj.getRetCode()
sourceDetails = {}
for source in like1.sourceNames():
sourceDetails[source] = like1.Ts(source)
for source, TS in sourceDetails.iteritems():
if (TS < 2):
print "Deleting...", source, " TS = ", TS
like1.deleteSource(source)
like1.fit(verbosity=0, optObject=likeobj)
print "0 is converged", likeobj.getRetCode()
like1.logLike.writeXml(xmlfitname)
numl = search(NAME, xmlfitname)
numlg = str(numl + 3)
os.system("sed '" + numlg + "," + numlg + " s/free=\"1\"/free=\"0\"/' " + xmlfitname + " > xml_sed.xml ")
inputs = likeInput(like1, NAME, model="xml_sed.xml", nbins=6, phCorr=1.0)
inputs.plotBins()
inputs.fullFit(CoVar=True)
sed = likeSED(inputs)
sed.getECent()
sed.fitBands()
sed.Plot()
result = like1.model[NAME]
TS = like1.Ts(NAME)
flux = like1.flux(NAME, emin=100)
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg = like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME, emin=100)
like1.plot()
fitsedname = '%s_6bins_likeSEDout.fits' % NAME
sedtool(fitsedname)
print NAME, " TS=", TS
print result
if float(xml) == 1:
xmlmodelname = '%s_model.xml' % NAME
xmlfitname = '%s_fit1.xml' % NAME
obs = UnbinnedObs(roiname, SC, expMap='expMap.fits', expCube='expCube.fits', irfs='CALDB')
# like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='MINUIT')
like1 = UnbinnedAnalysis(obs, xmlmodelname, optimizer='NewMinuit')
likeobj = pyLike.NewMinuit(like1.logLike)
like1.fit(verbosity=0, optObject=likeobj)
print likeobj.getRetCode()
sourceDetails = {}
for source in like1.sourceNames():
sourceDetails[source] = like1.Ts(source)
for source, TS in sourceDetails.iteritems():
if (TS < 2):
print "Deleting...", source, " TS = ", TS
like1.deleteSource(source)
like1.fit(verbosity=0, optObject=likeobj)
print "0 is converged", likeobj.getRetCode()
like1.logLike.writeXml(xmlfitname)
numl = search(NAME, xmlfitname)
numlg = str(numl + 3)
os.system("sed '" + numlg + "," + numlg + " s/free=\"1\"/free=\"0\"/' " + xmlfitname + " > xml_sed.xml ")
inputs = likeInput(like1, NAME, model="xml_sed.xml", nbins=6, phCorr=1.0)
inputs.plotBins()
inputs.fullFit(CoVar=True)
sed = likeSED(inputs)
sed.getECent()
sed.fitBands()
sed.Plot()
result = like1.model[NAME]
TS = like1.Ts(NAME)
flux = like1.flux(NAME, emin=100)
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg = like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME, emin=100)
like1.plot()
fitsedname = '%s_6bins_likeSEDout.fits' % NAME
sedtool(fitsedname)
print NAME, " TS=", TS
print result
like.tol
like.tolType
like.tol = 0.0001
if optimizerType=='Minuit':
likeobj = pyLike.Minuit(like.logLike)
elif optimizerType=='NewMinuit':
likeobj = pyLike.NewMinuit(like.logLike)
else:
print "Error: Bad Optimizer. Exiting"
exit()
like.fit(verbosity=0,covar=True,optObject=likeobj) # Warning: This takes VERY long ~ 1 hour
# Get all the TS values of the sources in the model
sourceDetails = {}
for source in like.sourceNames():
sourceDetails[source] = like.Ts(source)
# Output for data
os.system('mkdir results')
printDictionaryToFile(sourceDetails,'TSValues_preDelete.txt')
# N.B A source with a TS value less than 0 should never happen unless the minimization failed. Remove that source and try fitting again and check the return code again.
print "Zero?: %d" %(likeobj.getRetCode()) # This value should be zero, refit using the method below if not
# Delete useing like.deleteSource('_2FGLJ1625.2-0020')
# Protip, you can really simplify the model by removing sources with TS levels below 9 (about 3 sigma)
for source,TS in sourceDetails.iteritems():
print source, TS
if (TS < 9.0):
print "Deleting..."
def runFermiTools(Name, RA, DEC, minEnergy, maxEnergy, SCFile, radius, binsz,
TSTART, TSTOP, Evfile, bins, zmax, evclass, evtype, TSul,
NMtol, lc_bin_num, runMRM):
print "Working on bin " + str(lc_bin_num) + " for the light curve."
f = FermiObject()
"""
Following steps execute Fermi Tool gtselect
"""
print('\nWorking on file.')
print('Cutting file to fit desired parameters . . .\n')
f._setEvclass(evclass)
f._setEvtype(evtype)
f._setRa(RA)
f._setDec(DEC)
f._setRad(radius)
f._setEmin(minEnergy)
f._setEmax(maxEnergy)
f._setZmax(zmax)
f._setTmin(TSTART)
f._setTmax(TSTOP)
f._setInfile(Evfile)
f._setOutfile(Name + '_gtselect' + str(lc_bin_num) + '_lc.fits')
f.amonSelect()
print(
'File cuts have been made. Now making cuts for GTI using spacecraft file.'
)
"""
Following steps execute Fermi Tool gtmktime
"""
f._setScfile(SCFile)
f._setRoicut('no')
f._setEvfile(Name + '_gtselect' + str(lc_bin_num) + '_lc.fits')
f._setOutfile(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits')
###############################################
# Filter expression #
Filter = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
###############################################
f._setFilter(Filter)
print('Working on file ' + str(f.getOutfile()) + '. . .')
f.amonTime()
print('File cuts have been made.')
print('Using XML model from whole dataset.\n Moving on to gtltcube.')
print "Now working on ltcube file using gtltcube\n"
my_apps.expCube['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.expCube['scfile'] = SCFile
my_apps.expCube['outfile'] = Name + '_ltcube' + str(
lc_bin_num) + '_lc.fits'
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube['phibins'] = 0
my_apps.expCube['zmax'] = zmax
my_apps.expCube['chatter'] = 0
my_apps.expCube.run()
print "\nltcube complete.\nMoving to compute exposure map with gtexpmap.\n"
my_apps.expMap['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.expMap['scfile'] = SCFile
my_apps.expMap['expcube'] = Name + '_ltcube' + str(lc_bin_num) + '_lc.fits'
my_apps.expMap['outfile'] = Name + '_expMap' + str(lc_bin_num) + '_lc.fits'
my_apps.expMap['irfs'] = 'CALDB'
my_apps.expMap['srcrad'] = radius + 10
my_apps.expMap['nlong'] = 4 * (radius + 10)
my_apps.expMap['nlat'] = 4 * (radius + 10)
ebin = int(10 * log10(maxEnergy / minEnergy))
print "There are " + str(ebin) + " energy bans."
my_apps.expMap['nenergies'] = ebin
my_apps.expMap.run()
print "Finnished making exposure map.\n"
print "Calcualting the diffuse response for photons in this bin."
my_apps.diffResps['evfile'] = Name + '_gtmktime' + str(
lc_bin_num) + '_lc.fits'
my_apps.diffResps['scfile'] = SCFile
my_apps.diffResps['srcmdl'] = Name + '_output_model.xml'
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
print "Finished calculating diffuse response. Now moving to conduct a UNBINNED likelihood analysis."
obs = UnbinnedObs(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
SCFile,
expMap=Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
expCube=Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
irfs='P8R2_SOURCE_V6')
analysis = UnbinnedAnalysis(obs,
Name + '_output_model.xml',
optimizer='NewMinuit')
likeObj = pyLike.NewMinuit(analysis.logLike)
analysis.tol = NMtol
LIKE = analysis.fit(verbosity=0, covar=True, optObject=likeObj)
fit = likeObj.getRetCode()
print "Likelihood has converged whith Code " + str(likeObj.getRetCode())
Flux = analysis.flux(Name, emin=minEnergy, emax=maxEnergy)
Ferr = analysis.fluxError(Name, emin=minEnergy, emax=maxEnergy)
MeVtoErg = 1.602e-6
ef = analysis.energyFlux(Name, minEnergy, maxEnergy) * MeVtoErg
ef_err = analysis.energyFluxError(Name, minEnergy, maxEnergy) * MeVtoErg
UL = False
TSUM = TSTART + TSTOP
TMID = TSUM / 2
limit = Flux
if analysis.Ts(Name) < TSul:
UL = True
limit, results = IUL.calc_int(analysis,
Name,
cl=0.90,
emin=minEnergy,
emax=maxEnergy)
#Do second likelihood with constant flux to calculate the TS variability
obsC = UnbinnedObs(Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
SCFile,
expMap=Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
expCube=Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
irfs='P8R2_SOURCE_V6')
analysisC = UnbinnedAnalysis(obsC,
Name + '_var_model.xml',
optimizer='NewMinuit')
likeObjC = pyLike.NewMinuit(analysisC.logLike)
analysisC.tol = NMtol
LIKEC = analysisC.fit(verbosity=0, covar=True, optObject=likeObjC)
#Run gtselect to make smaller data fits file to compute the exposure, set to 3 degrees around source of interest
f._setRad(3)
f._setInfile(Evfile)
f._setOutfile(Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits')
print "Creating file " + Name + "_gtselect_exposure.fits"
f.amonSelect()
#Run gtmaketime on this small region
f._setEvfile(Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits')
f._setOutfile(Name + '_gtmktime' + str(lc_bin_num) + '_exposure.fits')
print('Working on file ' + str(f.getOutfile()))
f.amonTime()
my_apps.evtbin['algorithm'] = 'LC'
my_apps.evtbin['evfile'] = f.getOutfile()
my_apps.evtbin['outfile'] = Name + '_LC' + str(
lc_bin_num) + '_exposure.fits'
my_apps.evtbin['scfile'] = f.getScfile()
my_apps.evtbin['tbinalg'] = 'LIN'
my_apps.evtbin['tstart'] = f.getTmin()
my_apps.evtbin['tstop'] = f.getTmax()
my_apps.evtbin['dtime'] = TSTOP - TSTART
my_apps.evtbin.run()
yes = subprocess.call([
'gtexposure', Name + '_LC' + str(lc_bin_num) + '_exposure.fits',
f.getScfile(), 'P8R2_SOURCE_V6', Name + '_output_model.xml', Name
])
if yes == 0:
print "Exposure map has been created"
else:
print "Subprocessing failed. Unable to create exposure map with gtexposure."
print "Time bin complete."
hdulist = pyfits.open(Name + '_LC' + str(lc_bin_num) + '_exposure.fits')
tbdata = hdulist[1].data
z = tbdata['EXPOSURE']
exp = z[0]
################################################################
# This portion prints to the text file #
################################################################
f = open("lc_output.txt", "a")
f.write(
str(Flux) + ',' + str(Ferr) + ',' + str(ef) + ',' + str(ef_err) + ',' +
str(limit) + ',' + str(analysis.Ts(Name)) + ',' + str(UL) + ',' +
str(TMID) + ',' + str(exp) + ',' + str(LIKE) + ',' + str(LIKEC) + '\n')
f.close()
print "Likelihood analysis on this band is complete."
yes = subprocess.call([
'rm', Name + '_gtselect' + str(lc_bin_num) + '_lc.fits',
Name + '_gtmktime' + str(lc_bin_num) + '_lc.fits',
Name + '_cmap' + str(lc_bin_num) + '_lc.fits',
Name + '_ccube' + str(lc_bin_num) + '_lc.fits',
Name + '_ltcube' + str(lc_bin_num) + '_lc.fits',
Name + '_expMap' + str(lc_bin_num) + '_lc.fits',
Name + '_LC' + str(lc_bin_num) + '_exposure.fits',
Name + '_srcmaps' + str(lc_bin_num) + '_lc.fits',
Name + '_gtselect' + str(lc_bin_num) + '_exposure.fits',
Name + '_gtmktime' + str(lc_bin_num) + '_exposure.fits'
])
if yes == 0:
print 'Files for bin have been deleted'
else:
print "Subprocessing failed. Unable to delete files for bin."
def run(Name, Ra, Dec, minEnergy, maxEnergy, SCFile, ulTS, NMtol,evclass,model):
if evclass == 512:
irf = "P8R2_ULTRACLEAN_V6"
elif evclass == 128:
irf = "P8R2_SOURCE_V6"
elif evclass == 256:
irf = "P8R2_CLEAN_V6"
elif evclass == 1024:
irf = "P8R2_ULTRACLEANVETO_V6"
print "This is multiUBLike.\nPlease make sure that you have added a model for your source with the name: " + str(Name)
print "Calcualting the diffuse response for photons in this bin."
my_apps.diffResps['evfile'] = '../' + Name + '_gtmktime.fits'
my_apps.diffResps['scfile'] = SCFile
my_apps.diffResps['srcmdl'] = model
my_apps.diffResps['irfs'] = irf
my_apps.diffResps.run()
print "Finished calculating diffuse response. Now moving to conduct a UNBINNED likelihood analysis."
obs = UnbinnedObs('../' + Name + '_gtmktime.fits', SCFile,expMap= '../' + Name + '_expMap.fits',expCube= '../' + Name + '_ltcube.fits', irfs=irf)
analysis = UnbinnedAnalysis(obs,model,optimizer='NewMinuit')
likeObj = pyLike.NewMinuit(analysis.logLike)
analysis.tol = NMtol
lkl = analysis.fit(verbosity=0,covar=True,optObject=likeObj)
analysis.writeXml( Name + '_output_model.xml')
fit = likeObj.getRetCode()
print "Likelihood has converged whith Code " + str(likeObj.getRetCode())
multiLike.printResults(analysis,Name, minEnergy,maxEnergy)
print "Fit has likelihood: " + str(lkl)
print "\nThe TS is below the threshold, calculating 95% confidence-level Bayesian upper limit."
limit,results = IUL.calc_int(analysis,Name,cl=0.95,emin=minEnergy, emax=maxEnergy)
print "Bayesian upper limit: " + str(limit) + " photons/cm^2/s"
#Array that returns the results of the unbinned analysis
#log-likelihood,flux,flux_err,test statisitc
Return = [lkl,analysis.flux(Name, emin=minEn, emax=maxEn),analysis.fluxError(Name, emin=minEn, emax=maxEn),limit,analysis.Ts(Name,reoptimize=False)]
return Return
def main(NAME,RA,DEC,TSTART,TSTOP,EMIN,EMAX,Np, path, ROIu):
#outdir = os.environ["FERMI_TMPLATAREA"]
gtliketxt=open("%s/%s_gtlike.txt"%(path,Np),'w')
gtsedtxt=open("%s/%s_sed.txt"%(path,Np),'w')
SCC='%s_SC00.fits'%(Np)
SC=path+SCC
Npp=path+Np
print SC
ROIue=float(ROIu)+10
os.system("ls -1 '"+Npp+"'_PH*.fits > %s/%s_events.list" %(path,Np))
# os.system('ls -1 'Np'+'PH*.fits > %s/%s_events.list' %(path,Np)
my_apps.filter['evclass'] = 128
my_apps.filter['evtype'] = 3
# my_apps.filter['evclsmin'] = 3
# my_apps.filter['evclsmax'] = 4
my_apps.filter['ra'] = RA
my_apps.filter['dec'] = DEC
my_apps.filter['rad'] = ROIu
my_apps.filter['emin'] = EMIN
my_apps.filter['emax'] = EMAX
my_apps.filter['zmax'] = 90
my_apps.filter['tmin'] = TSTART
my_apps.filter['tmax'] = TSTOP
my_apps.filter['infile'] = '@%s/%s_events.list' %(path,Np)
my_apps.filter['outfile'] = '%s/%s_filtered.fits'%(path,Np)
my_apps.filter.run()
# maketime
my_apps.maketime['scfile'] = SC
my_apps.maketime['filter'] = '(DATA_QUAL>0)&&(LAT_CONFIG==1)'
my_apps.maketime['roicut'] = 'no'
my_apps.maketime['evfile'] = '%s/%s_filtered.fits' %(path,Np)
my_apps.maketime['outfile'] = '%s/%s_filtered_gti.fits' %(path,Np)
my_apps.maketime.run()
#
# my_apps.counts_map['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
# my_apps.counts_map['scfile'] = SC
# my_apps.counts_map['outfile'] = '%s/%s_CountMap.fits'%(path,Np)
# my_apps.counts_map.run()
#
my_apps.expCube['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.expCube['scfile'] = SC
my_apps.expCube['zmax'] = 90
my_apps.expCube['outfile'] = '%s/%s_expCube.fits' %(path,Np)
my_apps.expCube['dcostheta'] = 0.025
my_apps.expCube['binsz'] = 1
my_apps.expCube.run()
my_apps.expMap['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.expMap['scfile'] = SC
my_apps.expMap['expcube'] ='%s/%s_expCube.fits' %(path,Np)
my_apps.expMap['outfile'] ='%s/%s_expMap.fits' %(path,Np)
# my_apps.expMap['irfs'] ='P7REP_SOURCE_V15'
my_apps.expMap['irfs'] ='CALDB'
my_apps.expMap['srcrad'] = ROIue
my_apps.expMap['nlong'] =120
my_apps.expMap['nlat'] =120
my_apps.expMap['nenergies'] =20
my_apps.expMap.run()
#sara xml model
roiname='%s/%s_filtered_gti.fits' %(path,Np)
xml_creator_P7_v1.main(path,NAME,float(RA),float(DEC),float(EMIN), float(EMAX), 20,Np)
xmlmodelname='%s/%s_model.xml' %(path,Np)
my_apps.diffResps['evfile'] = '%s/%s_filtered_gti.fits'%(path,Np)
my_apps.diffResps['scfile'] = SC
my_apps.diffResps['srcmdl'] = xmlmodelname
my_apps.diffResps['irfs'] = 'CALDB'
my_apps.diffResps.run()
xmlfitname='%s/%s_fit1.xml' %(path,Np)
expMapFile='%s/%s_expMap.fits' %(path,Np)
expCubeFile='%s/%s_expCube.fits' %(path,Np)
obs = UnbinnedObs(roiname,SC ,expMap=expMapFile,expCube=expCubeFile,irfs='CALDB')
like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='NewMinuit')
like1.fit(verbosity=0)
like1.logLike.writeXml(xmlfitname)
# numl=search(NAME,xmlfitname)
# numlg=str(numl+3)
# os.system("sed '"+numlg+","+numlg+" s/free=\"1\"/free=\"0\"/' "+xmlfitname+ " > xml_sed.xml ")
# inputs=likeInput(like1,NAME,model="xml_sed.xml",nbins=9,phCorr=1.0)
#low_edges = [200.,914.61,1955.87,8944.27,19127.05,40902.61]
#high_edges = [427.69,1955.87,8944.27,19127.05,40902.61,187049.69]
#centers = [0.2767, 1.265, 5.787, 12.37, 26.46, 86.60]
#inputs.customBins(low_edges,high_edges)
# inputs.plotBins()
# inputs.fullFit(CoVar=True)
# sed = likeSED(inputs)
# sed.getECent()
# sed.fitBands()
# sed.Plot()
result=like1.model[NAME]
TS=like1.Ts(NAME)
# I = like1.model[NAME].funcs['Spectrum'].getParam('Integral').value()
flux = like1.flux(NAME,emin=100)
# flux=I*1e-9
gamma = like1.model[NAME].funcs['Spectrum'].getParam('Index').value()
cov_gg =like1.model[NAME].funcs['Spectrum'].getParam('Index').error()
# cov_II = like1.model[NAME].funcs['Spectrum'].getParam('Integral').error()
flux_err = like1.fluxError(NAME,emin=100)
# flux_err=cov_II*1e-9
e=1000.0
a=1
b=1.e-18
lenergy_bin=log10(double(EMIN))+(log10(double(EMAX))-log10(double(EMIN)))/2
energy_bin=pow(10,lenergy_bin)
freq=2.42e22*energy_bin/100.0
ums = 1.-gamma
conv=ums*pow(energy_bin,(-gamma))/(pow(double(EMAX),ums)-pow(double(EMIN),ums))*6.62e-2*(energy_bin/100.0)
# conv is in Jy
# now convert in nufnu erg/cm2/s
convjy=conv*freq*1.e-23
nufnu=flux*convjy
b=flux_err*convjy
err_log=log10((nufnu+b)/nufnu)
#cout<<freq<<" "<<a<<" "<<nufnu<<" "<<b<<endl;
#cout<<log10(freq)<<" "<<log10(a)<<" "<<log10(nufnu)<<" "<<err_log<<endl;
date_start=computeDate(float(TSTART))
date_stop=computeDate(float(TSTOP))
# like1.plot()
# fitsedname='%s_9bins_likeSEDout.fits' %NAME
# sedtool(fitsedname)
print NAME, " TS=", TS
# print result
# print like1.model
print "spectral index= ", gamma, " +/-", cov_gg
print " Flux=", flux, "+/-", flux_err
print "freq", freq, " nuFnu=", nufnu, b,
# print "'UL': ", results_ul, err
gtliketxt.write(NAME)
gtliketxt.write(" RA=")
gtliketxt.write(RA)
gtliketxt.write(" DEC= ")
gtliketxt.write(DEC)
gtliketxt.write(" TS= ")
gtliketxt.write(str(TS))
gtliketxt.write("\n")
gtliketxt.write(" Time Interval (MJD) ")
gtliketxt.write(str(date_start))
gtliketxt.write(" ")
gtliketxt.write(str(date_stop))
gtliketxt.write("\n ")
gtliketxt.write("Flux ")
if TS <25:
obs = UnbinnedObs(roiname,SC ,expMap=expMapFile,expCube=expCubeFile,irfs='CALDB')
like1 = UnbinnedAnalysis(obs,xmlmodelname,optimizer='NewMinuit')
like1.fit(verbosity=0)
ul=UpperLimits(like1)
UL=ul[NAME].compute(emin=double(EMIN),emax=double(EMAX))
results_ul=UL[1]*1E-9
err=0
print "'UL': ", results_ul, err
gamma_ul=2.0
ums_ul = 1.-gamma_ul
conv_ul=ums_ul*pow(energy_bin,(-gamma_ul))/(pow(double(EMAX),ums_ul)-pow(double(EMIN),ums_ul))*6.62e-2*(energy_bin/100.0)
convjy_ul=conv_ul*freq*1.e-23
nufnu_ul=results_ul*convjy_ul
b=err*convjy_ul
print "freq", freq, "0 nuFnu=", nufnu_ul, b,
gtliketxt.write(str(results_ul))
gtliketxt.write(" 0 ")
#gtliketxt.write(err)
gtsedtxt.write(str(freq))
gtsedtxt.write(" | 0 ")
gtsedtxt.write(" | ")
gtsedtxt.write(str(nufnu_ul))
gtsedtxt.write(" | ")
gtsedtxt.write(str(b))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_start))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_stop))
gtsedtxt.write(" | ")
gtsedtxt.write(" UL ")
gtsedtxt.write(" | ")
else:
gtliketxt.write(str(flux))
gtliketxt.write(" ")
gtliketxt.write(str(flux_err))
gtliketxt.write("\n")
gtliketxt.write("Spectral Index = ")
gtliketxt.write(str(gamma))
gtliketxt.write(" ")
gtliketxt.write(str(cov_gg))
gtsedtxt.write(" ")
gtsedtxt.write(str(freq))
gtsedtxt.write(" | 0 ")
gtsedtxt.write(" | ")
gtsedtxt.write(str(nufnu))
gtsedtxt.write(" | ")
gtsedtxt.write(str(b))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_start))
gtsedtxt.write(" | ")
gtsedtxt.write(str(date_stop))
gtsedtxt.write(" | ")
