def CheckNpred(self, Npred, Flux, FluxErr, detected_indices):
"""check if the errors are well computed using the Npred/sqrt(Npred) vs Flux/FluxErr relation
and print corresponding point which failled"""
_, TgrNpred = plotting.PlotNpred(Npred[detected_indices],
Flux[detected_indices],
FluxErr[detected_indices])
func = ROOT.TF1("func", "pol1", np.min(np.sqrt(Npred)),
np.max(np.sqrt(Npred)))
TgrNpred.Fit(func, "Q")
for i in xrange(len(Flux)):
if Flux[i] / FluxErr[i] > 2 * func.Eval(sqrt(Npred[i])):
self._errorReading("problem in errors calculation for", i)
print "Flux +/- error = ", Flux[i], " +/- ", FluxErr[i]
print "V(Npred) = ", sqrt(Npred[i])
print
func.SetLineColor(15)
func.SetLineStyle(2)
return func
def _PlotLC(self, folded=False):
root_style.RootStyle() #Nice plot style
self.info("Reading files produced by enrico")
LcOutPath = self.LCfolder + self.config['target']['name']
#Result are stored into list. This allow to get rid of the bin which failled
Time = []
TimeErr = []
Flux = []
FluxErr = []
Index = []
IndexErr = []
Cutoff = []
CutoffErr = []
FluxForNpred = []
FluxErrForNpred = []
Npred = []
Npred_detected_indices = []
TS = []
# Find name used for index parameter
if (self.config['target']['spectrum'] == 'PowerLaw'
or self.config['target']['spectrum'] == 'PowerLaw2'):
IndexName = 'Index'
CutoffName = None
elif (self.config['target']['spectrum'] == 'PLExpCutoff'
or self.config['target']['spectrum'] == 'PLSuperExpCutoff'):
IndexName = 'Index1'
CutoffName = 'Cutoff'
CutoffErrName = 'dCutoff'
IndexErrName = 'd' + IndexName
Nfail = 0
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 config file", i)
Nfail += 1
continue
#Update the time and time error array
Time.append((ResultDic.get("tmax") + ResultDic.get("tmin")) / 2.)
TimeErr.append(
(ResultDic.get("tmax") - ResultDic.get("tmin")) / 2.)
#Check is an ul have been computed. The error is set to zero for the TGraph.
if ResultDic.has_key('Ulvalue'):
Flux.append(ResultDic.get("Ulvalue"))
FluxErr.append(0)
Index.append(ResultDic.get(IndexName))
IndexErr.append(0)
else:
Flux.append(ResultDic.get("Flux"))
FluxErr.append(ResultDic.get("dFlux"))
Index.append(ResultDic.get(IndexName))
IndexErr.append(ResultDic.get(IndexErrName))
if CutoffName is not None:
Cutoff.append(ResultDic.get(CutoffName))
CutoffErr.append(ResultDic.get(CutoffErrName))
FluxErrForNpred.append(ResultDic.get("dFlux"))
FluxForNpred.append(ResultDic.get("Flux"))
#Get the Npred and TS values
Npred.append(ResultDic.get("Npred"))
TS.append(ResultDic.get("TS"))
if (CurConfig['LightCurve']['TSLightCurve'] < float(
ResultDic.get("TS"))):
Npred_detected_indices.append(i - Nfail)
#change the list into np array
TS = np.array(TS)
Npred = np.array(Npred)
Npred_detected = Npred[Npred_detected_indices]
Time = np.array(Time)
TimeErr = np.array(TimeErr)
Flux = np.array(Flux)
FluxErr = np.array(FluxErr)
Index = np.array(Index)
IndexErr = np.array(IndexErr)
Cutoff = np.array(Cutoff)
CutoffErr = np.array(CutoffErr)
FluxForNpred = np.array(FluxForNpred)
FluxErrForNpred = np.array(FluxErrForNpred)
#Plots the diagnostic plots is asked
# Plots are : Npred vs flux
# TS vs Time
if self.config['LightCurve']['DiagnosticPlots'] == 'yes':
fittedFunc = self.CheckNpred(
Npred, FluxForNpred, FluxErrForNpred,
Npred_detected_indices) #check the errors calculation
gTHNpred, TgrNpred = plotting.PlotNpred(Npred, FluxForNpred,
FluxErrForNpred)
CanvNpred = _GetCanvas()
gTHNpred.Draw()
TgrNpred.Draw('zP')
_, TgrNpred_detected = plotting.PlotNpred(
Npred_detected, Flux[Npred_detected_indices],
FluxErrForNpred[Npred_detected_indices])
TgrNpred_detected.SetLineColor(2)
TgrNpred_detected.SetMarkerColor(2)
TgrNpred_detected.Draw('zP')
fittedFunc.Draw("SAME")
CanvNpred.Print(LcOutPath + "_Npred.png")
CanvNpred.Print(LcOutPath + "_Npred.eps")
CanvNpred.Print(LcOutPath + "_Npred.C")
gTHTS, TgrTS = plotting.PlotTS(Time, TimeErr, TS)
CanvTS = _GetCanvas()
gTHTS.Draw()
TgrTS.Draw('zP')
CanvTS.Print(LcOutPath + '_TS.png')
CanvTS.Print(LcOutPath + '_TS.eps')
CanvTS.Print(LcOutPath + '_TS.C')
# Plot the LC itself. This function return a TH2F for a nice plot
# a TGraph and a list of TArrow for the ULs
if folded:
phase = np.linspace(0, 1, self.Nbin + 1)
Time = (phase[1:] + phase[:-1]) / 2.
TimeErr = (phase[1:] - phase[:-1]) / 2.
gTHLC, TgrLC, ArrowLC = plotting.PlotFoldedLC(
Time, TimeErr, Flux, FluxErr)
gTHIndex, TgrIndex, ArrowIndex = plotting.PlotFoldedLC(
Time, TimeErr, Index, IndexErr)
if CutoffName is not None:
gTHCutoff, TgrCutoff, ArrowCutoff = plotting.PlotFoldedLC(
Time, TimeErr, Cutoff, CutoffErr)
else:
gTHLC, TgrLC, ArrowLC = plotting.PlotLC(Time, TimeErr, Flux,
FluxErr)
gTHIndex, TgrIndex, ArrowIndex = plotting.PlotLC(
Time, TimeErr, Index, IndexErr)
if CutoffName is not None:
gTHCutoff, TgrCutoff, ArrowCutoff = plotting.PlotFoldedLC(
Time, TimeErr, Cutoff, CutoffErr)
### plot and save the flux LC
CanvLC = ROOT.TCanvas()
gTHLC.Draw()
TgrLC.Draw('zP')
#plot the ul as arrow
for i in xrange(len(ArrowLC)):
ArrowLC[i].Draw()
# compute Fvar and probability of being cst
self.info("Flux vs Time: infos")
self.FitWithCst(TgrLC)
self.Fvar(Flux, FluxErr)
#Save the canvas in the LightCurve subfolder
CanvLC.Print(LcOutPath + '_LC.png')
CanvLC.Print(LcOutPath + '_LC.eps')
CanvLC.Print(LcOutPath + '_LC.C')
### plot and save the Index LC
CanvIndex = ROOT.TCanvas()
gTHIndex.Draw()
TgrIndex.Draw('zP')
#plot the ul as arrow
for i in xrange(len(ArrowIndex)):
ArrowIndex[i].Draw()
#Save the canvas in the LightCurve subfolder
if self.config["LightCurve"]["SpectralIndex"] == 0:
self.info("Index vs Time")
self.FitWithCst(TgrIndex)
CanvIndex.Print(LcOutPath + '_Index.png')
CanvIndex.Print(LcOutPath + '_Index.eps')
CanvIndex.Print(LcOutPath + '_Index.C')
if len(Cutoff) > 0:
### plot and save the Cutoff LC
CanvCutoff = ROOT.TCanvas()
gTHCutoff.Draw()
TgrCutoff.Draw('zP')
#plot the ul as arrow
for i in xrange(len(ArrowCutoff)):
ArrowCutoff[i].Draw()
print "Cutoff vs Time: infos"
self.FitWithCst(TgrCutoff)
CanvCutoff.Print(LcOutPath + '_Cutoff.png')
CanvCutoff.Print(LcOutPath + '_Cutoff.eps')
CanvCutoff.Print(LcOutPath + '_Cutoff.C')
#Dump into ascii
lcfilename = LcOutPath + "_results.dat"
self.info("Write to Ascii file : " + lcfilename)
WriteToAscii(Time, TimeErr, Flux, FluxErr, Index, IndexErr, Cutoff,
CutoffErr, TS, Npred, lcfilename)
if self.config["LightCurve"]['ComputeVarIndex'] == 'yes':
self.VariabilityIndex()
def _PlotLC(self, folded=False):
root_style.RootStyle() #Nice plot style
print "Reading files produced by enrico"
LcOutPath = self.LCfolder + self.config['target']['name']
#Result are stored into list. This allow to get rid of the bin which failled
Time = []
TimeErr = []
Flux = []
FluxErr = []
FluxForNpred = []
FluxErrForNpred = []
Npred = []
Npred_detected_indices = []
TS = []
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 config file", i)
continue
#Update the time and time error array
Time.append((ResultDic.get("tmax") + ResultDic.get("tmin")) / 2.)
TimeErr.append(
(ResultDic.get("tmax") - ResultDic.get("tmin")) / 2.)
#Check is an ul have been computed. The error is set to zero for the TGraph.
if ResultDic.has_key('Ulvalue'):
Flux.append(ResultDic.get("Ulvalue"))
FluxErr.append(0)
else:
Flux.append(ResultDic.get("Flux"))
FluxErr.append(ResultDic.get("dFlux"))
FluxErrForNpred.append(ResultDic.get("dFlux"))
FluxForNpred.append(ResultDic.get("Flux"))
#Get the Npred and TS values
Npred.append(ResultDic.get("Npred"))
TS.append(ResultDic.get("TS"))
if (CurConfig['LightCurve']['TSLightCurve'] < float(
ResultDic.get("TS"))):
Npred_detected_indices.append(i)
#change the list into np array
TS = np.array(TS)
Npred = np.array(Npred)
Npred_detected = Npred[Npred_detected_indices]
Time = np.array(Time)
TimeErr = np.array(TimeErr)
Flux = np.array(Flux)
FluxErr = np.array(FluxErr)
FluxForNpred = np.array(FluxForNpred)
FluxErrForNpred = np.array(FluxErrForNpred)
fittedFunc = self.CheckNpred(
Npred, FluxForNpred, FluxErrForNpred,
Npred_detected_indices) #check the errors calculation
#Plots the diagnostic plots is asked
# Plots are : Npred vs flux
# TS vs Time
if self.config['LightCurve']['DiagnosticPlots'] == 'yes':
gTHNpred, TgrNpred = plotting.PlotNpred(Npred, FluxForNpred,
FluxErrForNpred)
CanvNpred = _GetCanvas()
gTHNpred.Draw()
TgrNpred.Draw('zP')
_, TgrNpred_detected = plotting.PlotNpred(
Npred_detected, Flux[Npred_detected_indices],
FluxErrForNpred[Npred_detected_indices])
TgrNpred_detected.SetLineColor(2)
TgrNpred_detected.SetMarkerColor(2)
TgrNpred_detected.Draw('zP')
fittedFunc.Draw("SAME")
CanvNpred.Print(LcOutPath + "_Npred.eps")
CanvNpred.Print(LcOutPath + "_Npred.C")
gTHTS, TgrTS = plotting.PlotTS(Time, TimeErr, TS)
CanvTS = _GetCanvas()
gTHTS.Draw()
TgrTS.Draw('zP')
CanvTS.Print(LcOutPath + '_TS.eps')
CanvTS.Print(LcOutPath + '_TS.C')
# Plot the LC itself. This function return a TH2F for a nice plot
# a TGraph and a list of TArrow for the ULs
if folded:
phase = np.linspace(0, 1, self.Nbin + 1)
Time = (phase[1:] + phase[:-1]) / 2.
TimeErr = (phase[1:] - phase[:-1]) / 2.
gTHLC, TgrLC, ArrowLC = plotting.PlotFoldedLC(
Time, TimeErr, Flux, FluxErr)
else:
gTHLC, TgrLC, ArrowLC = plotting.PlotLC(Time, TimeErr, Flux,
FluxErr)
CanvLC = ROOT.TCanvas()
gTHLC.Draw()
TgrLC.Draw('zP')
#plot the ul as arrow
for i in xrange(len(ArrowLC)):
ArrowLC[i].Draw()
# compute Fvar and probability of being cst
self.FitWithCst(TgrLC)
self.Fvar(Flux, FluxErr)
#Save the canvas in the LightCurve subfolder
CanvLC.Print(LcOutPath + '_LC.eps')
CanvLC.Print(LcOutPath + '_LC.C')
#Dump into ascii
lcfilename = LcOutPath + "_results.dat"
print "Write to Ascii file : ", lcfilename
WriteToAscii(Time, TimeErr, Flux, FluxErr, TS, Npred, lcfilename)
if self.config["LightCurve"]['ComputeVarIndex'] == 'yes':
self.VariabilityIndex()