def VariabilityIndex(self):
LcOutPath = self.LCfolder + self.config['target']['name']
utils._log('Computing Variability index ')
self.config['Spectrum']['FitsGeneration'] = 'no'
# ValueDC = self.GetDCValue()
ResultDicDC = utils.ReadResult(self.config)
LogL1 = []
LogL0 = []
Time = []
for i in xrange(self.Nbin):
CurConfig = get_config(self.configfile[i])
#Read the result. If it fails, it means that the bins has not bin computed. A warning message is printed
try :
ResultDic = utils.ReadResult(CurConfig)
except :
print "WARNING : fail reading the config file : ",CurConfig
print "Job Number : ",i
print "Please have a look at this job log file"
continue
# LogL1.append(ResultDic.get("log_like"))
#Update the time and time error array
Time.append((ResultDic.get("tmax")+ResultDic.get("tmin"))/2.)
##############################################################
# Compute the loglike value using the DC flux or prefactor
##############################################################
# Create one obs instance
CurConfig['Spectrum']['FitsGeneration'] = 'no'
_,Fit = GenAnalysisObject(CurConfig,verbose=0)#be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
utils.FreezeParams(Fit, self.srcname, 'Index', -self.config['LightCurve']['SpectralIndex'])
LogL1.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
Model_type = Fit.model.srcs[self.srcname].spectrum().genericName()
if (Model_type == 'PowerLaw') :
utils.FreezeParams(Fit, self.srcname, 'Prefactor', utils.fluxNorm(ResultDicDC['Prefactor']))
if (Model_type == 'PowerLaw2') :
utils.FreezeParams(Fit, self.srcname, 'Integral', utils.fluxNorm(ResultDicDC['Integral']))
LogL0.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
Can = _GetCanvas()
TgrDC = ROOT.TGraph(len(Time),np.array(Time),np.array(LogL0))
TgrDC.Draw("ALP*")
TgrDC = ROOT.TGraph(len(Time),np.array(Time),np.array(LogL0))
TgrDC.SetMarkerColor(2)
TgrDC.Draw("PL*")
#Save the canvas in the LightCurve subfolder
Can.Print(LcOutPath+'_VarIndex.eps')
Can.Print(LcOutPath+'_VarIndex.C')
print
print "TSvar = ",2*(sum(LogL1)-sum(LogL0))
print "NDF = ",len(LogL0)-1
print "Chi2 prob = ",ROOT.TMath.Prob(2*(sum(LogL1)-sum(LogL0)),len(LogL0)-1)
def ChangeModel(Fit, E1, E2, name, Pref, Gamma):
"""Change the spectral model of a source called name
to allow a fit between E1 and E2
If the spectral model is PowerLaw, the prefactor is updated
if not the model is change to PowerLaw.
The index is frozen in all cases"""
Eav = utils.GetE0(E1, E2)
# Set Parameters
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setBounds(1e-5,1e5)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setScale(utils.fluxScale(Pref))
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setValue(utils.fluxNorm(Pref))
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setBounds(-5,0)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setValue(Gamma)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setFree(0)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Scale').setValue(Eav)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Scale').setBounds(20,3e6)
return Fit
