def ChangeModel(comp, 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"""
# if approximated Gamma is outside of bounds set it to limit
Gamma_min=-5
Gamma_max=0.5
Gamma = min(max(Gamma_min,Gamma),Gamma_max)
Eav = utils.GetE0(E1, E2)
spectrum = comp.logLike.getSource(name).getSrcFuncs()['Spectrum']
spectrum.getParam('Prefactor').setBounds(1e-5,1e5)
spectrum.getParam('Prefactor').setScale(utils.fluxScale(Pref))
spectrum.getParam('Prefactor').setValue(utils.fluxNorm(Pref))
spectrum.getParam('Index').setBounds(Gamma_min,Gamma_max)
spectrum.getParam('Index').setValue(Gamma)
spectrum.getParam('Index').setFree(False)
spectrum.getParam('Scale').setValue(Eav)
spectrum.getParam('Scale').setBounds(20,3e6)
return comp
def ChangeModel(comp, 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"""
# if approximated Gamma is outside of bounds set it to limit
Gamma_min = -5
Gamma_max = 0.5
Gamma = min(max(Gamma_min, Gamma), Gamma_max)
Eav = utils.GetE0(E1, E2)
spectrum = comp.logLike.getSource(name).getSrcFuncs()['Spectrum']
spectrum.getParam('Prefactor').setBounds(1e-5, 1e5)
spectrum.getParam('Prefactor').setScale(utils.fluxScale(Pref))
spectrum.getParam('Prefactor').setValue(utils.fluxNorm(Pref))
spectrum.getParam('Index').setBounds(Gamma_min, Gamma_max)
spectrum.getParam('Index').setValue(Gamma)
spectrum.getParam('Index').setFree(0)
spectrum.getParam('Scale').setValue(Eav)
spectrum.getParam('Scale').setBounds(20, 3e6)
return comp
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))
Gamma_min = -5
Gamma_max = 0
# if approximated Gamma is outside of bounds set it to limit
if Gamma < Gamma_min:
Gamma = Gamma_min
elif Gamma > Gamma_max:
Gamma = Gamma_max
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam(
'Index').setBounds(Gamma_min, Gamma_max)
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
def VariabilityIndex(self):
"""Compute the variability index as in the 2FLG catalogue. (see Nolan et al, 2012)"""
LcOutPath = self.LCfolder + self.config['target']['name']
utils._log('Computing Variability index ')
self.config['Spectrum']['FitsGeneration'] = 'no'
try :
ResultDicDC = utils.ReadResult(self.generalconfig)
except :
self.warning("No results file found; please run enrico_sed first.")
return
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 :
self._errorReading("Fail reading the config file ",i)
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 = GenAnalysisObjects(CurConfig,verbose=0)#be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
parameters = dict()
parameters['Index'] = -2.
parameters['alpha'] = +2.
parameters['Index1'] = -2.
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
try:
utils.FreezeParams(Fit, self.srcname, key, parameters[key])
except:
continue
LogL1.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
for key in ["norm","Prefactor","Integral"]:
try:
utils.FreezeParams(Fit,self.srcname,key, utils.fluxNorm(ResultsDicDC[key]))
except:
continue
LogL0.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
del Fit #Clean memory
plt.figure()
plt.xlabel("Time")
plt.ylabel("Log(Like) Variability")
plt.errorbar(Time,LogL0,fmt='o',color='black',ls='None')
plt.savefig(LcOutPath+"_VarIndex.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
self.info("Variability index calculation")
print "\t TSvar = ",2*(sum(LogL1)-sum(LogL0))
print "\t NDF = ",len(LogL0)-1
print "\t Chi2 prob = ",1 - chi2.cdf(2*(sum(LogL1)-sum(LogL0)),len(LogL0)-1)
print
def VariabilityIndex(self):
"""Compute the variability index as in the 2FGL catalogue. (see Nolan et al, 2012)"""
LcOutPath = self.LCfolder + self.config['target']['name']
utils._log('Computing Variability index ')
self.config['Spectrum']['FitsGeneration'] = 'no'
try :
ResultDicDC = utils.ReadResult(self.generalconfig)
except :
self.warning("No results file found; please run enrico_sed first.")
return
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 :
self._errorReading("Fail reading the config file ",i)
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 = GenAnalysisObjects(CurConfig,verbose=0)#be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
parameters = dict()
parameters['Index'] = -2.
parameters['alpha'] = +2.
parameters['Index1'] = -2.
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
try:
utils.FreezeParams(Fit, self.srcname, key, parameters[key])
except:
continue
LogL1.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
for key in ["norm","Prefactor","Integral"]:
try:
utils.FreezeParams(Fit,self.srcname,key, utils.fluxNorm(ResultsDicDC[key]))
except:
continue
LogL0.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
del Fit #Clean memory
plt.figure()
plt.xlabel("Time")
plt.ylabel("Log(Like) Variability")
plt.errorbar(Time,LogL0,fmt='o',color='black',ls='None')
plt.xlim(xmin=max(plt.xlim()[0],1.02*min(Time)-0.02*max(Time)),
xmax=min(plt.xlim()[1],1.02*max(Time)-0.02*min(Time)))
# Move the offset to the axis label
ax = plt.gca()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
offset_factor = int(np.mean(np.log10(np.abs(ax.get_ylim()))))
if (offset_factor != 0):
ax.set_yticklabels([float(round(k,5)) \
for k in ax.get_yticks()*10**(-offset_factor)])
ax.yaxis.set_label_text(ax.yaxis.get_label_text() +\
r" [${\times 10^{%d}}$]" %offset_factor)
# Secondary axis with MJD
mjdaxis = ax.twiny()
mjdaxis.set_xlim([utils.met_to_MJD(k) for k in ax.get_xlim()])
mjdaxis.set_xlabel(r"Time (MJD)")
mjdaxis.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter(useOffset=False))
plt.setp( mjdaxis.xaxis.get_majorticklabels(), rotation=15 )
plt.tight_layout()
plt.savefig(LcOutPath+"_VarIndex.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
self.info("Variability index calculation")
print "\t TSvar = ",2*(sum(LogL1)-sum(LogL0))
print "\t NDF = ",len(LogL0)-1
print "\t Chi2 prob = ",1 - chi2.cdf(2*(sum(LogL1)-sum(LogL0)),len(LogL0)-1)
print
def VariabilityIndex(self):
"""Compute the variability index as in the 2FLG catalogue. (see Nolan et al, 2012)"""
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 = GenAnalysisObjects(CurConfig,verbose=0)#be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
utils.FreezeParams(Fit, self.srcname, 'Index', -2)
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']))
del Fit #Clean memory
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 "\t TSvar = ",2*(sum(LogL1)-sum(LogL0))
print "\t NDF = ",len(LogL0)-1
print "\t Chi2 prob = ",ROOT.TMath.Prob(2*(sum(LogL1)-sum(LogL0)),len(LogL0)-1)
print
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))
Gamma_min=-5
Gamma_max=0
# if approximated Gamma is outside of bounds set it to limit
if Gamma < Gamma_min:
Gamma = Gamma_min
elif Gamma > Gamma_max:
Gamma = Gamma_max
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setBounds(Gamma_min,Gamma_max)
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
def VariabilityIndex(self):
"""Compute the variability index as in the 2FLG catalogue. (see Nolan et al, 2012)"""
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:
self._errorReading("fail reading the config file ", i)
# 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")) / 2.0)
##############################################################
# Compute the loglike value using the DC flux or prefactor
##############################################################
# Create one obs instance
CurConfig["Spectrum"]["FitsGeneration"] = "no"
_, Fit = GenAnalysisObjects(CurConfig, verbose=0) # be quiet
Fit.ftol = float(self.config["fitting"]["ftol"])
# Spectral index management!
self.info("Spectral index frozen to a value of 2")
utils.FreezeParams(Fit, self.srcname, "Index", -2)
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"]))
del Fit # Clean memory
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")
self.info("Variability index calculation")
print "\t TSvar = ", 2 * (sum(LogL1) - sum(LogL0))
print "\t NDF = ", len(LogL0) - 1
print "\t Chi2 prob = ", ROOT.TMath.Prob(2 * (sum(LogL1) - sum(LogL0)), len(LogL0) - 1)
print
def VariabilityIndex(self):
"""Compute the variability index as in the 2FGL catalogue. (see Nolan et al, 2012)"""
LcOutPath = self.LCfolder + self.config['target']['name']
utils._log('Computing Variability index ')
self.config['Spectrum']['FitsGeneration'] = 'no'
try :
ResultDicDC = utils.ReadResult(self.generalconfig)
except :
self.warning("No results file found; please run enrico_sed first.")
return
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 :
self._errorReading("Fail reading the config file ",i)
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 = GenAnalysisObjects(CurConfig,verbose=0)#be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
parameters = dict()
parameters['Index'] = -2.
parameters['alpha'] = +2.
parameters['Index1'] = -2.
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 100000. # set the cutoff to be high
for key in parameters.keys():
try:
utils.FreezeParams(Fit, self.srcname, key, parameters[key])
except:
continue
LogL1.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
for key in ["norm","Prefactor","Integral"]:
try:
utils.FreezeParams(Fit,self.srcname,key, utils.fluxNorm(ResultsDicDC[key]))
except:
continue
LogL0.append(-Fit.fit(0,optimizer=CurConfig['fitting']['optimizer']))
del Fit #Clean memory
plt.figure()
plt.xlabel("Time")
plt.ylabel("Log(Like) Variability")
plt.errorbar(Time,LogL0,fmt='o',color='black',ls='None')
plt.xlim(xmin=max(plt.xlim()[0],1.02*min(Time)-0.02*max(Time)),
xmax=min(plt.xlim()[1],1.02*max(Time)-0.02*min(Time)))
# Move the offset to the axis label
ax = plt.gca()
ax.get_yaxis().get_major_formatter().set_useOffset(False)
offset_factor = int(np.mean(np.log10(np.abs(ax.get_ylim()))))
if (offset_factor != 0):
ax.set_yticklabels([float(round(k,5)) \
for k in ax.get_yticks()*10**(-offset_factor)])
ax.yaxis.set_label_text(ax.yaxis.get_label_text() +\
r" [${\times 10^{%d}}$]" %offset_factor)
# Secondary axis with MJD
mjdaxis = ax.twiny()
mjdaxis.set_xlim([utils.met_to_MJD(k) for k in ax.get_xlim()])
mjdaxis.set_xlabel(r"Time (MJD)")
mjdaxis.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter(useOffset=False))
plt.setp( mjdaxis.xaxis.get_majorticklabels(), rotation=15 )
plt.tight_layout()
plt.savefig(LcOutPath+"_VarIndex.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
self.info("Variability index calculation")
print "\t TSvar = ",2*(sum(LogL1)-sum(LogL0))
print "\t NDF = ",len(LogL0)-1
print "\t Chi2 prob = ",1 - chi2.cdf(2*(sum(LogL1)-sum(LogL0)),len(LogL0)-1)
print
def VariabilityIndex(self):
"""Compute the variability index as in the 2FLG catalogue. (see Nolan et al, 2012)"""
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:
self._errorReading("fail reading the config file ", i)
# 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 = GenAnalysisObjects(CurConfig, verbose=0) #be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
self.info("Spectral index frozen to a value of 2")
utils.FreezeParams(Fit, self.srcname, 'Index', -2)
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']))
del Fit #Clean memory
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')
self.info("Variability index calculation")
print "\t TSvar = ", 2 * (sum(LogL1) - sum(LogL0))
print "\t NDF = ", len(LogL0) - 1
print "\t Chi2 prob = ", ROOT.TMath.Prob(2 * (sum(LogL1) - sum(LogL0)),
len(LogL0) - 1)
print
def VariabilityIndex(self):
"""Compute the variability index as in the 2FLG catalogue. (see Nolan et al, 2012)"""
LcOutPath = self.LCfolder + self.config['target']['name']
utils._log('Computing Variability index ')
self.config['Spectrum']['FitsGeneration'] = 'no'
try:
ResultDicDC = utils.ReadResult(self.generalconfig)
except:
self.warning("No results file found; please run enrico_sed first.")
return
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:
self._errorReading("fail reading the config file ", i)
# 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 = GenAnalysisObjects(CurConfig, verbose=0) #be quiet
Fit.ftol = float(self.config['fitting']['ftol'])
#Spectral index management!
parameters = dict()
parameters['Index'] = -2.
parameters['alpha'] = +2.
parameters['Index1'] = -2.
parameters['beta'] = 0
parameters['Index2'] = 2.
parameters['Cutoff'] = 30000. # set the cutoff to be high
for key in parameters.keys():
try:
utils.FreezeParams(Fit, self.srcname, key, parameters[key])
except:
continue
LogL1.append(
-Fit.fit(0, optimizer=CurConfig['fitting']['optimizer']))
for key in ["norm", "Prefactor", "Integral"]:
try:
utils.FreezeParams(Fit, self.srcname, key,
utils.fluxNorm(ResultsDicDC[key]))
except:
continue
LogL0.append(
-Fit.fit(0, optimizer=CurConfig['fitting']['optimizer']))
del Fit #Clean memory
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')
self.info("Variability index calculation")
print "\t TSvar = ", 2 * (sum(LogL1) - sum(LogL0))
print "\t NDF = ", len(LogL0) - 1
print "\t Chi2 prob = ", ROOT.TMath.Prob(2 * (sum(LogL1) - sum(LogL0)),
len(LogL0) - 1)
print
