def EnvelopeUL(self, Fit):
"""Compute the envelope UL. An UL is computed for different index and the maximum is taken at each energy.
This is usefull when the source index is not know or can not be constrain by theoritical argument
The index range form 1.5 to 2.5"""
import IntegralUpperLimit
import UpperLimits
self._log('EnvelopeUL', 'Compute upper limit envelope')
PhIndex = Fit.par_index(self.obs.srcname, 'Index')
Nbp = 20 #Make Nbp computations
Npgraph = 100 #The graph has Npgraph points
ener = np.logspace(np.log10(self.obs.Emin), np.log10(self.obs.Emax),
Npgraph) #the array containing the energy
Ulenv = np.array(Npgraph * [0.]) #the array containing the UL value
for i in xrange(Nbp):
indx = -1.5 - i / (Nbp - 1.)
utils.FreezeParams(Fit, self.srcname, PhIndex, indx)
#Use either the profile or the integral method
self.info("Methode used: " + self.config['UpperLimit']['Method'])
if self.config['UpperLimit']['Method'] == "Profile":
ul = UpperLimits.UpperLimits(Fit)
source_ul = ul[self.obs.srcname]
ul_val, _ = source_ul.compute(emin=self.obs.Emin,
emax=self.obs.Emax,
delta=2.71 / 2)
if self.config['UpperLimit']['Method'] == "Integral":
ul_val, _ = IntegralUpperLimit.calc_int(Fit,
self.obs.srcname,
verbosity=0)
self.success("Upper Limits calculated")
print "Index = ", indx, " UL = ", ul_val #small print
for j in xrange(Npgraph):
model_name = Fit.model.srcs[
self.obs.srcname].spectrum().genericName()
#compute the DNDE value. The computation change is
#the model is PowerLaw or PowerLaw2
#Note : Other model are not taken into account
#and no UL will be computed
if model_name == 'PowerLaw2':
newUl = ul_val * (indx + 1) * pow(ener[j], indx + 2) \
/ (pow(self.obs.Emax, indx + 1) - pow(self.obs.Emin, indx + 1))
elif model_name == 'PowerLaw':
IdEScale = utils.getParamIndx(Fit, self.obs.srcname,
'Scale')
Escale = Fit[IdEScale].value()
newUl = ul_val * pow(ener[j] / Escale,
indx + 2) * Escale**2 * 1.6022e-6
Ulenv[j] = max(Ulenv[j], newUl)
print
self.info("Result of the UL envelope")
for j in xrange(Npgraph):
print ener[j], " ", Ulenv[j]
def ComputeUL(self, Fit):
"""Compute an Upper Limit using either the profil or integral method
See the ST cicerone for more information on the 2 method"""
self._log('UpperLimit', 'Compute upper Limit')
#Index given by the user
self.info("Assumed index is "+str(self.config['UpperLimit']['SpectralIndex']))
parameters = dict()
parameters['Index'] = -float(self.config['UpperLimit']['SpectralIndex'])
parameters['alpha'] = +float(self.config['UpperLimit']['SpectralIndex'])
parameters['Index1'] = -float(self.config['UpperLimit']['SpectralIndex'])
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.obs.srcname, key, parameters[key])
except:
continue
import scipy.stats
cl = float(self.config['UpperLimit']['cl'])
delta = 0.5*scipy.stats.chi2.isf(1-2*(cl-0.5), 1)
if self.config['UpperLimit']['Method'] == "Profile": #The method is Profile
if Fit.Ts(self.obs.srcname)<2 :
self.warning("TS of the source is very low, better to use Integral method")
import UpperLimits
ulobject = UpperLimits.UpperLimits(Fit)
ul, _ = ulobject[self.obs.srcname].compute(emin=self.obs.Emin,
emax=self.obs.Emax,delta=delta)
#delta=2.71 / 2)
self.info("Upper limit using Profile method: ")
print ulobject[self.obs.srcname].results
self.warning("Be sure to have enough photons to validate the gaussian assumption")
if self.config['UpperLimit']['Method'] == "Integral": #The method is Integral
import IntegralUpperLimit
ul, _ = IntegralUpperLimit.calc_int(Fit, self.obs.srcname, cl=cl,
verbosity=0,emin=self.obs.Emin,
emax=self.obs.Emax)
print "Upper limit using Integral method: ", ul
self.warning("Be sure to have enough photons to validate the gaussian assumption")
if self.config['UpperLimit']['Method'] == "Poisson": #The method is Poisson
ul = self.PoissonUL(Fit)
print "Upper limit using Poisson statistic: ", ul
print "This is an ul on the integral flux in ph/cm2/s"
return ul #Return the result. This is an ul on the integral flux in ph/cm2/s
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'] = 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