def FitOnePixel(self,ra,dec,i,j) :
"""Run a evaluation of the pixel (i,j) corresponding to position (ra,dec)"""
outXml = utils._dump_xml(self.config)
folder = self.config['out']
_,Fit = GenAnalysisObjects(self.config,xmlfile=outXml) #get the Fit object
src = GetSrc(Fit,ra,dec) # get the Source object at postion ra dec
if self.config['TSMap']['RemoveTarget'] : # remove the target is asked
Fit.deleteSource(self.config['target']['name'])
if self.config['TSMap']['Re-Fit']: # reoptimze before is asked
Fit.fit(0,optimizer=self.config['fitting']['optimizer'])
for par in xrange(Fit.logLike.getNumParams()): # freeze all the source parameters
Fit[par].setFree(0)
Fit.addSource(src)# add a spurious source
# dump the *new* xml file
Fit.writeXml(self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml")
# a new Fit object with the new xml file is needed.
# just changing the position of the spurious source does not work
_,TSFit = GenAnalysisObjects(self.config,xmlfile=self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml") #get the Fit object
TSFit.fit(0,optimizer=self.config['fitting']['optimizer'])
# save the result
fsave = open(self._PixelFile(i,j),'w')
fsave.write(str(ra)+"\t"+str(dec)+"\t"+
str(TSFit.Ts("Spurious"))+"\t"+
str(TSFit.logLike.value()))
fsave.close()
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 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 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'
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