def __init__(self,infile):
self.infile = infile
try :
self.config = get_config(infile)
except :
config = ConfigObj(indent_type='\t')
config['out'] = os.getcwd()
self.config = get_config(config)
try :
ftmp=open(self.config['file']['xml'],'r')
ftmp.close()
except :
os.system('touch '+self.config['file']['xml'])
self.window = gtk.Window(gtk.WINDOW_TOPLEVEL)
self.window.connect("delete_event", self.delete)
self.window.set_border_width(10)
table = gtk.Table(3,6,False)
self.window.add(table)
self.notebloc = gtk.Notebook()
self.notebloc.set_tab_pos(gtk.POS_LEFT)
table.attach(self.notebloc, 0,6,0,1)
self.notebloc.show()
self._addMainOption()
self._addFiles()
self._addTargetSpace()
self._addAnalysis()
self._addEnergyTime()
self._addSpectrum()
self._addUL()
self._addLC()
# self._addFoldedLC()
self._addAppFoldedLC()
# self._addEbin()
self._addTSMap()
self._addfindsrcsrcprob()
self._addPlot()
self.notebloc.set_current_page(0)
ReloadButton = gtk.Button("Reload conf file")
ReloadButton.connect("clicked", self.reload, "")
table.attach(ReloadButton, 1,2,1,2)
ReloadButton.show()
SaveButton = gtk.Button("Save file")
SaveButton.connect("clicked", self.save, "")
table.attach(SaveButton, 3,4,1,2)
SaveButton.show()
CloseButton = gtk.Button("Close")
CloseButton.connect("clicked", self.delete)
table.attach(CloseButton, 4,5,1,2)
CloseButton.show()
table.show()
self.window.show()
def run(infile):
from enrico import utils
from enrico import energybin
from enrico.config import get_config
"""Run an entire Fermi analysis (spectrum) by reading a config file"""
config = get_config(infile)
folder = config['out']
utils.create_dir(folder)
FitRunner,Fit = GenAnalysisObjects(config)
# create all the fit files and run gtlike
FitRunner.PerformFit(Fit)
Result = FitRunner.GetAndPrintResults(Fit)#Get and dump the target specific results
utils.DumpResult(Result, config)
#plot the SED and model map if possible and asked
if config['Spectrum']['ResultPlots'] == 'yes' :
from enrico.constants import SpectrumPath
utils.create_dir("%s/%s/" %(config['out'],SpectrumPath))
if float(config['UpperLimit']['TSlimit']) < Fit.Ts(config['target']['name']):
FitRunner.ComputeSED(Fit)
outXml = utils._dump_xml(config)
if config['Spectrum']['SummedLike'] != 'yes': # the possiblity of making the model map is checked inside the function
FitRunner.ModelMap(outXml)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
energybin.RunEbin(folder,Nbin,Fit,FitRunner)
del(Result)
del(FitRunner)
def run(infile):
"""Run an entire Fermi analysis (spectrum) by reading a config file"""
config = get_config(infile)
folder = config['out']
os.system('mkdir -p ' + folder)
FitRunner,Fit = GenAnalysisObjects(config)
# create all the fit files and run gtlike
FitRunner.PerformFit(Fit)
Result = FitRunner.GetAndPrintResults(Fit)#Get and dump the target specific results
if config['verbose'] == 'yes' :
utils.GetFluxes(Fit,FitRunner.obs.Emin,FitRunner.obs.Emax) #print the flux of all the sources
utils.DumpResult(Result, config)
#plot the SED and model map if possible and asked
if config['Spectrum']['ResultPlots'] == 'yes' :
from enrico.constants import SpectrumPath
os.system("mkdir -p "+config['out'] + '/'+SpectrumPath+'/')
if float(config['UpperLimit']['TSlimit']) < Fit.Ts(config['target']['name']):
FitRunner.ComputeSED(Fit)
outXml = utils._dump_xml(config)
if config['Spectrum']['SummedLike'] != 'yes': # the possiblity of making the model map is checked inside the function
FitRunner.ModelMap(outXml)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
energybin.RunEbin(folder,Nbin,Fit,FitRunner)
def run(infile):
"""Run an entire Fermi analysis (spectrum) by reading a config file"""
config = get_config(infile)
folder = config['out']
os.system('mkdir -p ' + folder)
FitRunner, Fit = GenAnalysisObjects(config)
# create all the fit files and run gtlike
FitRunner.PerformFit(Fit)
Result = FitRunner.GetAndPrintResults(
Fit) #Get and dump the target specific results
if config['verbose'] == 'yes':
utils.GetFluxes(Fit, FitRunner.obs.Emin,
FitRunner.obs.Emax) #print the flux of all the sources
utils.DumpResult(Result, config)
#plot the SED and model map if possible and asked
if config['Spectrum']['ResultPlots'] == 'yes':
from enrico.constants import SpectrumPath
os.system("mkdir -p " + config['out'] + '/' + SpectrumPath + '/')
if float(config['UpperLimit']['TSlimit']) < Fit.Ts(
config['target']['name']):
FitRunner.ComputeSED(Fit)
outXml = utils._dump_xml(config)
if config['Spectrum'][
'SummedLike'] != 'yes': # the possiblity of making the model map is checked inside the function
FitRunner.ModelMap(outXml)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
energybin.RunEbin(folder, Nbin, Fit, FitRunner)
def run(infile):
from enrico import utils
from enrico import energybin
from enrico.config import get_config
from enrico import Loggin
mes = Loggin.Message()
"""Run an entire Fermi analysis (spectrum) by reading a config file"""
config = get_config(infile)
folder = config['out']
utils.create_dir(folder)
FitRunner,Fit = GenAnalysisObjects(config)
# create all the fit files and run gtlike
FitRunner.PerformFit(Fit)
#plot the SED and model map if possible and asked
if float(config['UpperLimit']['TSlimit']) < Fit.Ts(config['target']['name']):
if config['Spectrum']['ResultPlots'] == 'yes':
from enrico.constants import SpectrumPath
utils.create_dir("%s/%s/" %(config['out'],SpectrumPath))
sedresult = FitRunner.ComputeSED(Fit,dump=True)
else:
sedresult = FitRunner.ComputeSED(Fit,dump=False)
# Update the energy scale to decorrelation energy
mes.info('Setting the decorrelation energy as new Scale for the spectral parameters')
spectrum = Fit[FitRunner.obs.srcname].funcs['Spectrum']
modeltype = spectrum.genericName()
if Fit.model.srcs[FitRunner.obs.srcname].spectrum().genericName()=="PowerLaw":
varscale = "Scale"
if Fit.model.srcs[FitRunner.obs.srcname].spectrum().genericName()=="PowerLaw2":
varscale = None
elif Fit.model.srcs[FitRunner.obs.srcname].spectrum().genericName()=="PLSuperExpCutoff":
varscale = "Scale"
elif Fit.model.srcs[FitRunner.obs.srcname].spectrum().genericName()=="LogParabola":
varscale = "Eb"
elif Fit.model.srcs[FitRunner.obs.srcname].spectrum().genericName()=="BrokenPowerLaw":
varscale = "Eb"
if varscale is not None:
spectrum.getParam(varscale).setValue(sedresult.decE)
FitRunner.PerformFit(Fit)
if config['Spectrum']['ResultPlots'] == 'yes' :
outXml = utils._dump_xml(config)
if config['Spectrum']['SummedLike'] != 'yes':
# the possiblity of making the model map is checked inside the function
FitRunner.ModelMap(outXml)
#Get and dump the target specific results
Result = FitRunner.GetAndPrintResults(Fit)
utils.DumpResult(Result, config)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
energybin.RunEbin(folder,Nbin,Fit,FitRunner)
del(Result)
del(FitRunner)
def run_analysis(self):
import shutil
from enrico import environ
from enrico.config import get_config
from enrico.appertureLC import AppLC
from enrico.submit import call
from enrico.constants import AppLCPath
infile = self.configfile
config = get_config(infile)
self.remove_prev_dir()
verbose("-> Running the analysis for %s" %(analysis.name))
# We will always try to run it in parallel,
# either with python multiproc or with external torque pbs.
if config['Submit'] == 'no':
global fqueue
fqueue.put(infile)
else:
enricodir = environ.DIRS.get('ENRICO_DIR')
fermidir = environ.DIRS.get('FERMI_DIR')
cmd = enricodir+"/enrico/appertureLC.py %s" %infile
LCoutfolder = config['out']+"/"+AppLCPath
os.system("mkdir -p "+LCoutfolder)
prefix = LCoutfolder +"/"+ config['target']['name'] + "_AppertureLightCurve"
scriptname = prefix+"_Script.sh"
JobLog = prefix + "_Job.log"
JobName = "LC_%s" %self.name
call(cmd, enricodir, fermidir, scriptname, JobLog, JobName)
verbose("--> Job sent successfully")
def AppLC(infile):
'''Main function of the apperture photometrie Lightcurve script. Read the config file and run the analysis'''
ROOT.gROOT.SetBatch(ROOT.kTRUE) #Batch mode
enricodir = environ.DIRS.get('ENRICO_DIR')
fermidir = environ.DIRS.get('FERMI_DIR')
config = get_config(infile)
folder = config['out']
#Create a subfolder name LightCurve
LCoutfolder = folder + "/" + AppLCPath
os.system("mkdir -p " + LCoutfolder)
#Change the ROI to the desired radius in degree, legacy 1 deg.
try:
config['space']['rad'] = config['AppLC']['rad']
except NameError:
config['space']['rad'] = 1
Nbins = config['AppLC']['NLCbin'] #Number of bins
#Get The time bin
dt = (config['time']['tmax'] - config['time']['tmin']) / Nbins #sec
Obs = Observation(LCoutfolder, config, tag="")
if config['AppLC']["FitsGeneration"] == "yes":
_log('gtselect', 'Select data from library') #run gtselect
Obs.FirstCut()
Obs.SelectEvents()
_log('gtmktime',
'Update the GTI and cut data based on ROI') #run gtdiffresp
Obs.MkTime()
#Binning from data or using a fix bin size
if config['AppLC']['binsFromData'] == "no":
_log('gtbin', 'bin the data into a light-curve using fixe time bin'
) #run gtbin
print "Use a dt of %2.2e seconds" % (dt)
Obs.GtLCbin(dt=dt)
else:
spfile = pyfits.open(Obs.eventfile)
diff = spfile[1].data.field(9)[1:-1] - spfile[1].data.field(9)[:-2]
dt = np.min(
diff
) / 2. ##Compute the delta T as being the min delta t between 2 events divided by 2
timefile = LCoutfolder + "/Timebin.txt"
MakeTimebinFile(Obs, timefile)
_log('gtbindef', 'define de bins') #run gtbindef
Obs.GtBinDef(timefile)
_log('gtbin',
'bin the data into a light-curve using bins based on data'
) #run gtbin
Obs.GtLCbin(dt=0)
_log('gtexposure', 'compute the exposure') #run gtexposure
Obs.GtExposure()
#Get Some usefull value here. This allow PlotAppLC to be call independently
Nbins = config['AppLC']['NLCbin'] #Number of bins
#Plot the results and dump into ascii files
PlotAppLC(Nbins, LCoutfolder, Obs.lcfile)
def __init__(self, config):
super(LightCurve, self).__init__()
Loggin.Message.__init__(self)
ROOT.gROOT.SetBatch(ROOT.kTRUE) #Batch mode
self.config = get_config(config)
self.generalconfig = get_config(config)
print(self.generalconfig)
#Read the config
self.srcname = self.config['target']['name'] #src name
self.Tag = self.config['file']['tag']
self.tmin = self.config['time']['tmin']
self.tmax = self.config['time']['tmax']
self.submit = self.config['Submit']
# One point of the LC will be computed as a spectrum plot.
# enrico_sed will be used
# Do fits files will be generated
self.config['Spectrum']['FitsGeneration'] = self.config['LightCurve'][
'FitsGeneration']
#Froze the Spectral index at a value of self.config['LightCurve']['SpectralIndex'] (no effect if 0)
self.config['Spectrum']['FrozenSpectralIndex'] = self.config[
'LightCurve']['SpectralIndex']
#TS limit. Compute an UL if the TS is below TSLightCurve
self.config['UpperLimit']['TSlimit'] = self.config['LightCurve'][
'TSLightCurve']
self.folder = self.config['out']
#No plot, no bin in energy, no decE optimization, Normal UL
self.config['Spectrum']['ResultPlots'] = 'no'
self.config['Ebin']['NumEnergyBins'] = 0
self.config['energy']['decorrelation_energy'] = 'no'
self.config['UpperLimit']['envelope'] = 'no'
#No submition. Submission will be directly handle by this soft
self.config['Submit'] = 'no'
# self.config['verbose'] ='no' #Be quiet
# Try to speed-up the analysis by reusing the evt file from the main analysis
self._RecycleEvtCoarse()
self.configfile = [
] #All the config file in the disk are stored in a list
def __init__(self, config, parent_filename=""):
super(LightCurve,self).__init__()
Loggin.Message.__init__(self)
self.parent_filename = os.path.abspath(parent_filename)
self.config = get_config(config)
self.generalconfig = get_config(config)
print(self.generalconfig)
#Read the config
self.srcname = self.config['target']['name'] #src name
self.Tag = self.config['file']['tag']
self.tmin = self.config['time']['tmin']
self.tmax = self.config['time']['tmax']
self.submit = self.config['Submit']
# One point of the LC will be computed as a spectrum plot.
# enrico_sed will be used
# Do fits files will be generated
#self.config['target']['spectrum'] = 'PowerLaw' # simplify the spectrum
self.config['Spectrum']['FitsGeneration'] = self.config['LightCurve']['FitsGeneration']
#Freeze the Spectral index at a value of self.config['LightCurve']['SpectralIndex'] (no effect if 0)
self.config['Spectrum']['FrozenSpectralIndex'] = self.config['LightCurve']['SpectralIndex']
if (self.config['LightCurve']['SpectralIndex'] != 0):
self.config['UpperLimit']['SpectralIndex'] = self.config['LightCurve']['SpectralIndex']
#TS limit. Compute an UL if the TS is below TSLightCurve
self.config['UpperLimit']['TSlimit'] = self.config['LightCurve']['TSLightCurve']
self.folder = self.config['out']
# Do not create plots
self.config['Spectrum']['ResultPlots'] = 'no' # no
self.config['Spectrum']['ResultParentPlots'] = 'no' # no
self.config['Ebin']['NumEnergyBins'] = 0
self.config['energy']['decorrelation_energy'] = 'yes' # no
self.config['UpperLimit']['envelope'] = 'no'
# Submission will be directly handle by this soft
self.config['Submit'] = 'no'
# self.config['verbose'] ='no' #Be quiet
# Speed-up the analysis by reusing the evt file from the main analysis
self._RecycleEvtCoarse()
self.configfile = [] #All the config file in the disk are stored in a list
def AppLC(infile):
'''Main function of the apperture photometrie Lightcurve script. Read the config file and run the analysis'''
ROOT.gROOT.SetBatch(ROOT.kTRUE) #Batch mode
enricodir = environ.DIRS.get('ENRICO_DIR')
fermidir = environ.DIRS.get('FERMI_DIR')
config = get_config(infile)
folder = config['out']
#Create a subfolder name LightCurve
LCoutfolder = folder+"/"+AppLCPath
utils.mkdir_p(LCoutfolder)
#Change the ROI to the desired radius in degree, legacy 1 deg.
try: config['space']['rad'] = config['AppLC']['rad']
except NameError: config['space']['rad'] = 1
Nbins = config['AppLC']['NLCbin']#Number of bins
#Get The time bin
dt = (config['time']['tmax']-config['time']['tmin'])/Nbins #sec
Obs = Observation(LCoutfolder, config, tag="")
if config['AppLC']["FitsGeneration"] == "yes":
_log('gtselect', 'Select data from library')#run gtselect
Obs.FirstCut()
Obs.SelectEvents()
_log('gtmktime', 'Update the GTI and cut data based on ROI')#run gtdiffresp
Obs.MkTime()
#Binning from data or using a fix bin size
if config['AppLC']['binsFromData'] == "no":
_log('gtbin', 'bin the data into a light-curve using fixe time bin')#run gtbin
print "Use a dt of %2.2e seconds"%(dt)
Obs.GtLCbin(dt = dt)
else:
spfile=fits.open(Obs.eventfile)
diff = spfile[1].data.field(9)[1:-1]-spfile[1].data.field(9)[:-2]
dt = np.min(diff)/2. ##Compute the delta T as being the min delta t between 2 events divided by 2
timefile = LCoutfolder+"/Timebin.txt"
MakeTimebinFile(Obs,timefile)
_log('gtbindef', 'define de bins')#run gtbindef
Obs.GtBinDef(timefile)
_log('gtbin', 'bin the data into a light-curve using bins based on data')#run gtbin
Obs.GtLCbin(dt = 0)
_log('gtexposure', 'compute the exposure')#run gtexposure
Obs.GtExposure()
#Get Some usefull value here. This allow PlotAppLC to be call independently
Nbins = config['AppLC']['NLCbin']#Number of bins
#Plot the results and dump into ascii files
PlotAppLC(Nbins,LCoutfolder,Obs.lcfile)
def __init__(self, config, parent_filename=""):
super(LightCurve,self).__init__()
Loggin.Message.__init__(self)
self.parent_filename = os.path.abspath(parent_filename)
self.config = get_config(config)
self.generalconfig = get_config(config)
print(self.generalconfig)
#Read the config
self.srcname = self.config['target']['name'] #src name
self.Tag = self.config['file']['tag']
self.tmin = self.config['time']['tmin']
self.tmax = self.config['time']['tmax']
self.submit = self.config['Submit']
# One point of the LC will be computed as a spectrum plot.
# enrico_sed will be used
# Do fits files will be generated
#self.config['target']['spectrum'] = 'PowerLaw' # simplify the spectrum
self.config['Spectrum']['FitsGeneration'] = self.config['LightCurve']['FitsGeneration']
#Freeze the Spectral index at a value of self.config['LightCurve']['SpectralIndex'] (no effect if 0)
self.config['Spectrum']['FrozenSpectralIndex'] = self.config['LightCurve']['SpectralIndex']
#TS limit. Compute an UL if the TS is below TSLightCurve
self.config['UpperLimit']['TSlimit'] = self.config['LightCurve']['TSLightCurve']
self.folder = self.config['out']
# Do not create plots
self.config['Spectrum']['ResultPlots'] = 'no' # no
self.config['Spectrum']['ResultParentPlots'] = 'no' # no
self.config['Ebin']['NumEnergyBins'] = 0
self.config['energy']['decorrelation_energy'] = 'yes' # no
self.config['UpperLimit']['envelope'] = 'no'
# Submission will be directly handle by this soft
self.config['Submit'] = 'no'
# self.config['verbose'] ='no' #Be quiet
# Speed-up the analysis by reusing the evt file from the main analysis
self._RecycleEvtCoarse()
self.configfile = [] #All the config file in the disk are stored in a list
def RunEbin(folder,Nbin,Fit,FitRunner):
if int(Nbin) > 0:
configfiles = PrepareEbin(Fit, FitRunner)
ind = 0
enricodir = environ.DIRS.get('ENRICO_DIR')
fermidir = environ.DIRS.get('FERMI_DIR')
for conf in configfiles:
pathconf = folder + "/"+ EbinPath + str(Nbin) +"/" + conf
Newconfig = get_config(pathconf)
cmd = enricodir+"/enrico/RunGTlike.py "+pathconf
if Newconfig['Submit'] == 'no' : #run directly
os.system(cmd)
else : #submit a job to a cluster
prefix = Newconfig['out'] + "/"+ EbinPath + str(ind)
scriptname = prefix + "_Script.sh"
JobLog = prefix + "_Job.log"
JobName = (Newconfig['target']['name'] + "_" +
Newconfig['analysis']['likelihood'] +
"_Ebin_" + str(ind) + "_" + Newconfig['file']['tag'])
call(cmd, enricodir, fermidir, scriptname, JobLog, JobName)# submition
ind+=1
def FindSrc(infile):
config = get_config(infile)
folder = config['out']
Obs = Observation(folder, config)
utils._log('SUMMARY: ')
Obs.printSum()
FitRunner = FitMaker(Obs, config)
if config["findsrc"]["FitsGeneration"]== "yes":
config['analysis']['likelihood'] = 'unbinned'
FitRunner.GenerateFits()
FitRunner._log('gtfindsrc', 'Optimize source position')
os.system("rm "+utils._dump_findsrcout(config))
Obs.FindSource()
try:
update_reg(config)
except:
pass
def __init__(self, infile):
self.config = get_config(infile)
self.folder = self.config['out']
os.system("mkdir -p "+self.folder+"/TestModel")
convtype = self.config['analysis']['convtype']
if self.config['Spectrum']['SummedLike'] == 'yes':
Obs1 = Observation(self.folder, self.config, convtype=0, tag="FRONT")
Obs2 = Observation(self.folder, self.config, convtype=1, tag="BACK")
FitRunnerfront = FitMaker(Obs1, self.config)
FitRunnerback = FitMaker(Obs2, self.config)
FitRunnerfront.CreateLikeObject()
FitRunnerback.CreateLikeObject()
self.Fit = SummedLikelihood.SummedLikelihood()
else:
Obs = Observation(self.folder, self.config, convtype, tag="")
FitRunner = FitMaker(Obs, self.config)##Class
self.Fit = FitRunner.CreateLikeObject()
# Store the results in a dictionnary
self.Results = {}
self.Results["PowerLaw"] = 0
self.Results["LogParabola"] = 0
self.Results["PLSuperExpCutoff"] = 0
src.getSrcFuncs()['Spectrum'].getParam('Prefactor').setBounds(1e-5,1e5)
src.getSrcFuncs()['Spectrum'].getParam('Prefactor').setScale(1e-9)
src.getSrcFuncs()['Spectrum'].getParam('Index').setBounds(-5,0)
src.getSrcFuncs()['Spectrum'].getParam('Scale').setValue(300)
src.getSrcFuncs()['Spectrum'].getParam('Scale').setBounds(1e-5,1e5)
return src
if __name__ == '__main__':
try:
infile = sys.argv[1]
except:
print('FATAL: Config file not found.')
sys.exit(1)
from enrico.config import get_config
config = get_config(infile)
TSmap = TSMap(config,infile)
if len(sys.argv)== 6 :
if TSmap.config['TSMap']['method'] == 'row' :
TSmap.FitOneRow(float(sys.argv[2]),int(sys.argv[4]))
else :
TSmap.FitOnePixel(float(sys.argv[2]),float(sys.argv[3]),int(sys.argv[4]),int(sys.argv[5]))
else :
print "Wrong number of arguments"
sys.exit(1)
def _PlotLC(self,folded=False):
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 = []
# FluxErrChi2 = []
Index = []
IndexErr = []
Cutoff = []
CutoffErr = []
FluxForNpred = []
# FluxErrForNpred = []
Npred = []
Npred_detected_indices = []
TS = []
uplim = []
# Find name used for index parameter
if ((self.config['target']['spectrum'] == 'PowerLaw' or
self.config['target']['spectrum'] == 'PowerLaw2') and
self.config['target']['redshift'] == 0):
IndexName = 'Index'
CutoffName = None
elif (self.config['target']['spectrum'] == 'PLExpCutoff' or
self.config['target']['spectrum'] == 'PLSuperExpCutoff'):
IndexName = 'Index1'
CutoffName = 'Cutoff'
CutoffErrName = 'dCutoff'
else:
IndexName = 'alpha'
CutoffName = None
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)
if ResultDic == {}:
raise(ValueError)
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') :
uplim.append(1)
Flux.append(ResultDic.get("Ulvalue"))
# FluxErr.append(0)
# FluxErrChi2.append(ResultDic.get("dFlux"))
# Index.append(ResultDic.get(IndexName))
# IndexErr.append(0)
else :
uplim.append(0)
Flux.append(ResultDic.get("Flux"))
FluxErr.append(ResultDic.get("dFlux"))
# FluxErrChi2.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.asarray(Npred)
Npred_detected = np.asarray(Npred[Npred_detected_indices])
Time = np.asarray(Time)
TimeErr = np.asarray(TimeErr)
Flux = np.asarray(Flux)
FluxErr = np.asarray(FluxErr)
# Index = np.array(Index)
# IndexErr = np.array(IndexErr)
# Cutoff = np.array(Cutoff)
# CutoffErr = np.array(CutoffErr)
FluxForNpred = np.asarray(FluxForNpred)
# FluxErrForNpred = np.array(FluxErrForNpred)
uplim = np.asarray(uplim,dtype=bool)
#Plots the diagnostic plots is asked
# Plots are : Npred vs flux
# TS vs Time
if self.config['LightCurve']['DiagnosticPlots'] == 'yes' and len(Npred)>0:
#plot Npred vs flux
plt.figure()
NdN = np.asarray(Npred) /np.sqrt(Npred)
FdF = np.asarray(FluxForNpred) / (np.asarray(FluxErr) + 1e-20)
plt.errorbar(NdN, FdF,fmt='+',color='black')
if len(Npred_detected)>2:
NdN = np.asarray(Npred_detected) /np.sqrt(Npred_detected)
FdF = np.asarray(FluxForNpred[Npred_detected_indices]) / (np.asarray(FluxErr[Npred_detected_indices]) + 1e-20)
plt.errorbar(NdN, FdF,fmt='+',color='red')
popt,_ = scipy.optimize.curve_fit(pol1, NdN, FdF, p0=[0,1])#, sigma=dydata)
for i in xrange(len(FluxForNpred)):
if FluxForNpred[i]/FluxErr[i]>2*pol1(sqrt(Npred[i]),popt[0],popt[1]):
self._errorReading("problem in errors calculation for",i)
print "Flux +/- error = ",FluxForNpred[i]," +/- ",FluxErr[i]
print "V(Npred) = ",sqrt(Npred[i])
print
plt.plot(np.array([0,max(NdN)]),pol1(np.array([0,max(NdN)]),popt[0],popt[1]),'--',color='black')
plt.xlabel(r"${\rm Npred/\sqrt{Npred}}$")
plt.ylabel(r"${\rm Flux/\Delta Flux}$")
plt.savefig(LcOutPath+"_Npred.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
else :
print "No Npred Plot produced"
#plot TS vs Time
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"Test Statistic")
plt.errorbar(x=Time,y=TS,xerr=TimeErr,fmt='+',color='black',ls='None')
plt.ylim(ymin=min(TS)*0.8,ymax=max(TS)*1.2)
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+"_TS.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
# 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)
# xmin = min(Time) - max(TimeErr) * 10
# xmax = max(Time) + max(TimeErr) * 10
# ymin = min(Flux) - max(FluxErr) * 1.3
# ymax = max(Flux) + max(FluxErr) * 1.3
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Flux\ (photon\ cm^{-2}\ s^{-1})}$")
# plt.ylim(ymin=ymin,ymax=ymax)
# plt.xlim(xmin=xmin,xmax=xmax)
#plt.errorbar(Time,Flux,xerr=TimeErr,yerr=FluxErr,i
# fmt='o',color='black',ls='None',uplims=uplim)
plot_errorbar_withuls(Time,TimeErr,TimeErr,Flux,FluxErr,FluxErr,
uplim,bblocks=True)
plt.ylim(ymin=max(plt.ylim()[0],np.percentile(Flux[~uplim],1)*0.1),
ymax=min(plt.ylim()[1],np.percentile(Flux[~uplim],99)*2.0))
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+"_LC.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
if self.config["LightCurve"]["SpectralIndex"] == 0 :
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Index}$")
Index = np.asarray(Index)
IndexErr = np.asarray(IndexErr)
uplimIndex = uplim #+ Index<0.55
plot_errorbar_withuls(Time[~uplimIndex],
TimeErr[~uplimIndex],
TimeErr[~uplimIndex],
Index[~uplimIndex],
IndexErr[~uplimIndex],
IndexErr[~uplimIndex],
uplimIndex[~uplimIndex],
bblocks=True)
plt.ylim(ymin=max(plt.ylim()[0],np.percentile(Index[~uplimIndex],1)*0.1),
ymax=min(plt.ylim()[1],np.percentile(Index[~uplimIndex],99)*2.0))
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+"_Index.png", dpi=150,
facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
# compute Fvar and probability of being cst
self.info("Flux vs Time: infos")
self.FitWithCst(Time,Flux,FluxErr)
self.Fvar(Flux,FluxErr)
# ### 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(Time,Index,IndexErr)
# CanvIndex.Print(LcOutPath+'_Index.png')
# CanvIndex.Print(LcOutPath+'_Index.eps')
# CanvIndex.Print(LcOutPath+'_Index.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 PlotDataPoints(config,pars):
"""Collect the data points/UL and generate a TGraph for the points
and a list of TArrow for the UL. All is SED format"""
#Preparation + declaration of arrays
arrows = []
NEbin = int(config['Ebin']['NumEnergyBins'])
lEmax = np.log10(float(config['energy']['emax']))
lEmin = np.log10(float(config['energy']['emin']))
Epoint = np.zeros(NEbin)
EpointErrp = np.zeros(NEbin)
EpointErrm = np.zeros(NEbin)
Fluxpoint = np.zeros(NEbin)
FluxpointErrp = np.zeros(NEbin)
FluxpointErrm = np.zeros(NEbin)
ener = np.logspace(lEmin, lEmax, NEbin + 1)
mes = Loggin.Message()
mes.info("Save Ebin results in ",pars.PlotName+".Ebin.dat")
dumpfile = open(pars.PlotName+".Ebin.dat",'w')
dumpfile.write("# Energy (MeV)\tEmin (MeV)\tEmax (MeV)\tE**2. dN/dE (erg.cm-2s-1)\tGaussianError\tMinosNegativeError\tMinosPositiveError\n")
from enrico.constants import EbinPath
for i in xrange(NEbin):#Loop over the energy bins
E = int(pow(10, (np.log10(ener[i + 1]) + np.log10(ener[i])) / 2))
filename = (config['out'] + '/'+EbinPath+str(NEbin)+'/' + config['target']['name'] +
"_" + str(i) + ".conf")
try:#read the config file of each data points
CurConf = get_config(filename)
mes.info("Reading "+filename)
results = utils.ReadResult(CurConf)
except:
mes.warning("cannot read the Results of energy "+ str(E))
continue
#fill the energy arrays
Epoint[i] = E
EpointErrm[i] = E - results.get("Emin")
EpointErrp[i] = results.get("Emax") - E
dprefactor = 0
#Compute the flux or the UL (in SED format)
if results.has_key('Ulvalue'):
PrefUl = utils.Prefactor(results.get("Ulvalue"),results.get("Index"),
results.get("Emin"),results.get("Emax"),E)
Fluxpoint[i] = MEV_TO_ERG * PrefUl * Epoint[i] ** 2
arrows.append(ROOT.TArrow(Epoint[i], Fluxpoint[i], Epoint[i],
Fluxpoint[i] * 0.5, 0.02, "|>"))
else : #Not an UL : compute points + errors
Fluxpoint[i] = MEV_TO_ERG * results.get("Prefactor") * Epoint[i] ** 2
dprefactor = results.get("dPrefactor")
try:
down = abs(results.get("dPrefactor-"))
up = results.get("dPrefactor+")
if down==0 or up ==0 :
mes.error("cannot get Error value")
FluxpointErrp[i] = MEV_TO_ERG * up * Epoint[i] ** 2
FluxpointErrm[i] = MEV_TO_ERG * down * Epoint[i] ** 2
except:
try:
err = MEV_TO_ERG * dprefactor * Epoint[i] ** 2
FluxpointErrp[i] = err
FluxpointErrm[i] = err
except:
pass
mes.info("Energy bins results")
print "Energy = ",Epoint[i]
print "E**2. dN/dE = ",Fluxpoint[i]," + ",FluxpointErrp[i]," - ",FluxpointErrm[i]
#Save the data point in a ascii file
dumpfile.write(str(Epoint[i])+"\t"+str(results.get("Emin"))+"\t"+str( results.get("Emax"))+"\t"+str(Fluxpoint[i])+"\t"+str( MEV_TO_ERG * dprefactor * Epoint[i] ** 2)+"\t"+str(FluxpointErrm[i])+"\t"+str(FluxpointErrp[i])+"\n")
#create a TGraph for the points
tgpoint = ROOT.TGraphAsymmErrors(NEbin, Epoint, Fluxpoint, EpointErrm,
EpointErrp, FluxpointErrm, FluxpointErrp)
tgpoint.SetMarkerStyle(20)
dumpfile.close()
return tgpoint, arrows
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()
def PlotLC(self):
'''Plot a lightcurve which have been generated previously'''
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 = []
self.PrepareLC()#Get the config file
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
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()
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 _PlotLC(self,folded=False):
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 = []
# FluxErrChi2 = []
Index = []
IndexErr = []
Cutoff = []
CutoffErr = []
FluxForNpred = []
# FluxErrForNpred = []
Npred = []
Npred_detected_indices = []
TS = []
uplim = []
# 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'
else:
IndexName = 'alpha'
CutoffName = None
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)
if ResultDic == {}:
raise(ValueError)
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') :
uplim.append(1)
Flux.append(ResultDic.get("Ulvalue"))
# FluxErr.append(0)
# FluxErrChi2.append(ResultDic.get("dFlux"))
# Index.append(ResultDic.get(IndexName))
# IndexErr.append(0)
else :
uplim.append(0)
Flux.append(ResultDic.get("Flux"))
FluxErr.append(ResultDic.get("dFlux"))
# FluxErrChi2.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.asarray(Npred)
Npred_detected = np.asarray(Npred[Npred_detected_indices])
Time = np.asarray(Time)
TimeErr = np.asarray(TimeErr)
Flux = np.asarray(Flux)
FluxErr = np.asarray(FluxErr)
# Index = np.array(Index)
# IndexErr = np.array(IndexErr)
# Cutoff = np.array(Cutoff)
# CutoffErr = np.array(CutoffErr)
FluxForNpred = np.asarray(FluxForNpred)
# FluxErrForNpred = np.array(FluxErrForNpred)
uplim = np.asarray(uplim,dtype=bool)
#Plots the diagnostic plots is asked
# Plots are : Npred vs flux
# TS vs Time
if self.config['LightCurve']['DiagnosticPlots'] == 'yes' and len(Npred)>0:
#plot Npred vs flux
plt.figure()
NdN = np.asarray(Npred) /np.sqrt(Npred)
FdF = np.asarray(FluxForNpred) / (np.asarray(FluxErr) + 1e-20)
plt.errorbar(NdN, FdF,fmt='+',color='black')
if len(Npred_detected)>0:
NdN = np.asarray(Npred_detected) /np.sqrt(Npred_detected)
FdF = np.asarray(FluxForNpred[Npred_detected_indices]) / (np.asarray(FluxErr[Npred_detected_indices]) + 1e-20)
plt.errorbar(NdN, FdF,fmt='+',color='red')
popt,_ = scipy.optimize.curve_fit(pol1, NdN, FdF, p0=[0,1])#, sigma=dydata)
for i in xrange(len(FluxForNpred)):
if FluxForNpred[i]/FluxErr[i]>2*pol1(sqrt(Npred[i]),popt[0],popt[1]):
self._errorReading("problem in errors calculation for",i)
print "Flux +/- error = ",FluxForNpred[i]," +/- ",FluxErr[i]
print "V(Npred) = ",sqrt(Npred[i])
print
plt.plot(np.array([0,max(NdN)]),pol1(np.array([0,max(NdN)]),popt[0],popt[1]),'--',color='black')
plt.xlabel(r"${\rm Npred/\sqrt{Npred}}$")
plt.ylabel(r"${\rm Flux/\Delta Flux}$")
plt.savefig(LcOutPath+"_Npred.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
else :
print "No Npred Plot produced"
#plot TS vs Time
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"Test Statistic")
plt.errorbar(x=Time,y=TS,xerr=TimeErr,fmt='+',color='black',ls='None')
plt.ylim(ymin=min(TS)*0.8,ymax=max(TS)*1.2)
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 = plt.twiny()
mjdaxis.set_xlim([utils.met_to_MJD(k) for k in ax.get_xlim()])
mjdaxis.set_xlabel(r"Time (MJD)")
plt.tight_layout()
plt.savefig(LcOutPath+"_TS.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
# 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)
# xmin = min(Time) - max(TimeErr) * 10
# xmax = max(Time) + max(TimeErr) * 10
# ymin = min(Flux) - max(FluxErr) * 1.3
# ymax = max(Flux) + max(FluxErr) * 1.3
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Flux\ (photon\ cm^{-2}\ s^{-1})}$")
# plt.ylim(ymin=ymin,ymax=ymax)
# plt.xlim(xmin=xmin,xmax=xmax)
#plt.errorbar(Time,Flux,xerr=TimeErr,yerr=FluxErr,fmt='o',color='black',ls='None',uplims=uplim)
plot_errorbar_withuls(Time,TimeErr,TimeErr,Flux,FluxErr,FluxErr,uplim,bblocks=True)
plt.ylim(ymin=max(plt.ylim()[0],np.percentile(Flux[~uplim],1)*0.1),
ymax=min(plt.ylim()[1],np.percentile(Flux[~uplim],99)*2.0))
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 = plt.twiny()
mjdaxis.set_xlim([utils.met_to_MJD(k) for k in ax.get_xlim()])
mjdaxis.set_xlabel(r"Time (MJD)")
plt.tight_layout()
plt.savefig(LcOutPath+"_LC.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
if self.config["LightCurve"]["SpectralIndex"] == 0 :
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Index}$")
Index = np.asarray(Index)
IndexErr = np.asarray(IndexErr)
uplimIndex = uplim + Index<0.55
plot_errorbar_withuls(Time[~uplimIndex],TimeErr[~uplimIndex],TimeErr[~uplimIndex],
Index[~uplimIndex],IndexErr[~uplimIndex],IndexErr[~uplimIndex],
uplimIndex[~uplimIndex],bblocks=True)
plt.ylim(ymin=max(plt.ylim()[0],np.percentile(Index[~uplimIndex],1)*0.1),ymax=min(plt.ylim()[1],np.percentile(Index[~uplimIndex],99)*2.0))
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)))
plt.savefig(LcOutPath+"_Index.png", dpi=150, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1,
frameon=None)
# compute Fvar and probability of being cst
self.info("Flux vs Time: infos")
self.FitWithCst(Time,Flux,FluxErr)
self.Fvar(Flux,FluxErr)
# ### 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(Time,Index,IndexErr)
# CanvIndex.Print(LcOutPath+'_Index.png')
# CanvIndex.Print(LcOutPath+'_Index.eps')
# CanvIndex.Print(LcOutPath+'_Index.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 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'
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
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 _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")) / 2.0)
TimeErr.append((ResultDic.get("tmax") - ResultDic.get("tmin")) / 2.0)
# 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]) / 2.0
TimeErr = (phase[1:] - phase[:-1]) / 2.0
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 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 write_config(self):
verbose("-> Write config file for %s" %(self.name))
import enrico
from enrico.config import get_config
# Create object
config = ConfigObj(indent_type='\t')
mes = Loggin.Message()
config['out'] = self.output+'/%s_%s/'%(self.name,self.band)
config['Submit'] = 'no'
# target
config['target'] = {}
config['target']['name'] = self.name
config['target']['ra'] = str("%.4f" %self.RA)
config['target']['dec'] = str("%.4f" %self.DEC)
config['target']['redshift'] = '0'
config['target']['spectrum'] = 'PowerLaw'
# space
config['space'] = {}
config['space']['xref'] = config['target']['ra']
config['space']['yref'] = config['target']['dec']
config['space']['rad'] = self.roi
# files
#basepath = "/".join(enrico.__path__[0].split("/")[0:-1])
#datapath = basepath+"/Data/download/"
config['file'] = {}
#config['file']['spacecraft'] = str("%s/lat_spacecraft_merged.fits" %datapath)
#config['file']['event'] = str("%s/event.list" %datapath)
config['file']['spacecraft'] = self.SCfile
config['file']['event'] = self.PHfile
config['file']['tag'] = 'fast'
# time
config['time'] = {}
self.calculate_times()
config['time']['tmin'] = self.timemin
config['time']['tmax'] = self.timemax
# energy
config['energy'] = {}
if (self.band=='lo'):
config['energy']['emin'] = self.energymin
config['energy']['emax'] = self.energycut
elif (self.band=='hi'):
config['energy']['emin'] = self.energycut
config['energy']['emax'] = self.energymax
else:
config['energy']['emin'] = self.energymin
config['energy']['emax'] = self.energymax
config['energy']['enumbins_per_decade'] = 5
# event class
config['event'] = {}
config['event']['irfs'] = 'CALDB'
config['event']['evclass'] = '64' #'128' #64=transients, 128=source
config['event']['evtype'] = '3'
# analysis
config['analysis'] = {}
config['analysis']['zmax'] = 100
# Validate
verbose(config)
# Add the rest of the values
config = get_config(config)
# Tune the remaining variables
config['AppLC']['index'] = self.spindex
config['AppLC']['NLCbin'] = int(24*self.timewindow/self.binsize+0.5)
config['AppLC']['rad'] = self.roi
# Write config file
self.configfile = str("%s/%s_%sE.conf" %(self.output,self.name,self.band))
with open(self.configfile,'w') as f:
config.write(f)
src.getSrcFuncs()['Spectrum'].getParam('Scale').setValue(300)
src.getSrcFuncs()['Spectrum'].getParam('Scale').setBounds(1e-5,1e5)
return src
if __name__ == '__main__':
try:
infile = sys.argv[1]
except:
from enrico import Loggin
mes = Loggin.Message()
mes.error('Config file not found.')
from enrico.config import get_config
config = get_config(infile)
TSmap = TSMap(config,infile)
if len(sys.argv)== 6 :
if TSmap.config['TSMap']['method'] == 'row' :
TSmap.FitOneRow(float(sys.argv[2]),int(sys.argv[4]))
else :
TSmap.FitOnePixel(float(sys.argv[2]),float(sys.argv[3]),int(sys.argv[4]),int(sys.argv[5]))
else :
from enrico import Loggin
mes = Loggin.Message()
mes.error("Wrong number of arguments")
def _PlotLC(self, folded=False):
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 = []
# FluxErrChi2 = []
Index = []
IndexErr = []
Cutoff = []
CutoffErr = []
FluxForNpred = []
# FluxErrForNpred = []
Npred = []
Npred_detected_indices = []
TS = []
uplim = []
# Find name used for index parameter
if ((self.config['target']['spectrum'] == 'PowerLaw'
or self.config['target']['spectrum'] == 'PowerLaw2')
and self.config['target']['redshift'] == 0):
IndexName = 'Index'
CutoffName = None
elif (self.config['target']['spectrum'] == 'PLExpCutoff'
or self.config['target']['spectrum'] == 'PLSuperExpCutoff'):
IndexName = 'Index1'
CutoffName = 'Cutoff'
CutoffErrName = 'dCutoff'
else:
IndexName = 'alpha'
CutoffName = None
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)
if ResultDic == {}:
raise (ValueError)
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'):
uplim.append(1)
Flux.append(ResultDic.get("Ulvalue"))
# FluxErr.append(0)
# FluxErrChi2.append(ResultDic.get("dFlux"))
# Index.append(ResultDic.get(IndexName))
# IndexErr.append(0)
else:
uplim.append(0)
Flux.append(ResultDic.get("Flux"))
FluxErr.append(ResultDic.get("dFlux"))
# FluxErrChi2.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['BayesianBlocks']['TSLightCurve'] < float(
ResultDic.get("TS"))):
Npred_detected_indices.append(i - Nfail)
# #change the list into np array
# TS = np.array(TS)
Npred = np.asarray(Npred)
Npred_detected = np.asarray(Npred[Npred_detected_indices])
Time = np.asarray(Time)
TimeErr = np.asarray(TimeErr)
Flux = np.asarray(Flux)
FluxErr = np.asarray(FluxErr)
# Index = np.array(Index)
# IndexErr = np.array(IndexErr)
# Cutoff = np.array(Cutoff)
# CutoffErr = np.array(CutoffErr)
FluxForNpred = np.asarray(FluxForNpred)
# FluxErrForNpred = np.array(FluxErrForNpred)
uplim = np.asarray(uplim, dtype=bool)
#Plots the diagnostic plots is asked
# Plots are : Npred vs flux
# TS vs Time
if self.config['BayesianBlocks']['DiagnosticPlots'] == 'yes' and len(
Npred) > 0:
#plot Npred vs flux
plt.figure()
NdN = np.asarray(Npred) / np.sqrt(Npred)
FdF = np.asarray(FluxForNpred) / (np.asarray(FluxErr) + 1e-20)
plt.errorbar(NdN, FdF, fmt='+', color='black')
if len(Npred_detected) > 2:
NdN = np.asarray(Npred_detected) / np.sqrt(Npred_detected)
FdF = np.asarray(FluxForNpred[Npred_detected_indices]) / (
np.asarray(FluxErr[Npred_detected_indices]) + 1e-20)
plt.errorbar(NdN, FdF, fmt='+', color='red')
popt, _ = scipy.optimize.curve_fit(pol1, NdN, FdF,
p0=[0, 1]) #, sigma=dydata)
for i in xrange(len(FluxForNpred)):
if FluxForNpred[i] / FluxErr[i] > 2 * pol1(
sqrt(Npred[i]), popt[0], popt[1]):
self._errorReading("problem in errors calculation for",
i)
print "Flux +/- error = ", FluxForNpred[
i], " +/- ", FluxErr[i]
print "V(Npred) = ", sqrt(Npred[i])
print
plt.plot(np.array([0, max(NdN)]),
pol1(np.array([0, max(NdN)]), popt[0], popt[1]),
'--',
color='black')
plt.xlabel(r"${\rm Npred/\sqrt{Npred}}$")
plt.ylabel(r"${\rm Flux/\Delta Flux}$")
plt.savefig(LcOutPath + "_Npred.png",
dpi=150,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None)
else:
print "No Npred Plot produced"
#plot TS vs Time
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"Test Statistic")
plt.errorbar(x=Time,
y=TS,
xerr=TimeErr,
fmt='+',
color='black',
ls='None')
plt.ylim(ymin=min(TS) * 0.8, ymax=max(TS) * 1.2)
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 + "_TS.png",
dpi=150,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None)
if len(Time) > 0:
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Flux\ (photon\ cm^{-2}\ s^{-1})}$")
plot_bayesianblocks(Time - TimeErr, Time + TimeErr, Flux, FluxErr,
FluxErr, uplim)
plt.ylim(ymin=max(plt.ylim()[0],
np.percentile(Flux[~uplim], 1) * 0.1),
ymax=min(plt.ylim()[1],
np.percentile(Flux[~uplim], 99) * 2.0))
plt.xlim(xmin=max(
plt.xlim()[0],
1.02 * min(Time - TimeErr) - 0.02 * max(Time + TimeErr)),
xmax=min(
plt.xlim()[1], 1.02 * max(Time + TimeErr) -
0.02 * min(Time - TimeErr)))
# 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 + "_LC.png",
dpi=150,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None)
else:
print "[BayesianBlocks] Warning : No valid data"
if self.config["BayesianBlocks"]["SpectralIndex"] == 0:
if len(Time[~uplimIndex]) > 0:
plt.figure()
plt.xlabel(r"Time (s)")
plt.ylabel(r"${\rm Index}$")
Index = np.asarray(Index)
IndexErr = np.asarray(IndexErr)
uplimIndex = uplim #+ Index<0.55
plot_bayesianblocks(Time[~uplimIndex] - TimeErr[~uplimIndex],
Time[~uplimIndex] + TimeErr[~uplimIndex],
Index[~uplimIndex], IndexErr[~uplimIndex],
IndexErr[~uplimIndex],
uplimIndex[~uplimIndex])
plt.ylim(ymin=max(plt.ylim()[0],
np.percentile(Index[~uplimIndex], 1) * 0.1),
ymax=min(plt.ylim()[1],
np.percentile(Index[~uplimIndex], 99) * 2.0))
plt.xlim(xmin=max(
plt.xlim()[0],
1.02 * min(Time - TimeErr) - 0.02 * max(Time + TimeErr)),
xmax=min(
plt.xlim()[1], 1.02 * max(Time + TimeErr) -
0.02 * min(Time - TimeErr)))
# 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 + "_Index.png",
dpi=150,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False,
bbox_inches=None,
pad_inches=0.1,
frameon=None)
else:
print "[BayesianBlocks] Warning : No valid data"
#Dump into ascii
lcfilename = LcOutPath + "_results.dat"
self.info("Write to Ascii file : " + lcfilename)
lightcurve.WriteToAscii(Time, TimeErr, Flux, FluxErr, Index, IndexErr,
Cutoff, CutoffErr, TS, Npred, lcfilename)
def run(infile):
from enrico import utils
from enrico import energybin
from enrico.config import get_config
from enrico import Loggin
mes = Loggin.Message()
"""Run an entire Fermi analysis (spectrum) by reading a config file"""
config = get_config(infile)
folder = config['out']
utils.mkdir_p(folder)
FitRunner, Fit = GenAnalysisObjects(config)
# create all the fit files and run gtlike
FitRunner.PerformFit(Fit)
sedresult = None
#plot the SED and model map if possible and asked
if float(config['UpperLimit']['TSlimit']) < Fit.Ts(
config['target']['name']):
if config['Spectrum']['ResultPlots'] == 'yes':
from enrico.constants import SpectrumPath
utils.mkdir_p("%s/%s/" % (config['out'], SpectrumPath))
sedresult = FitRunner.ComputeSED(Fit, dump=True)
else:
sedresult = FitRunner.ComputeSED(Fit, dump=False)
if (config['energy']['decorrelation_energy'] == 'yes'):
#Update the energy scale to decorrelation energy
mes.info(
'Setting the decorrelation energy as new Scale for the spectral parameters'
)
spectrum = Fit[FitRunner.obs.srcname].funcs['Spectrum']
modeltype = spectrum.genericName()
genericName = Fit.model.srcs[
FitRunner.obs.srcname].spectrum().genericName()
varscale = None
if genericName == "PowerLaw2":
varscale = None
elif genericName in [
"PowerLaw", "PLSuperExpCutoff",
"EblAtten::PLSuperExpCutoff"
]:
varscale = "Scale"
elif genericName in ["LogParabola","EblAtten::LogParabola", \
"BrokenPowerLaw", "EblAtten::BrokenPowerLaw"]:
varscale = "Eb"
if varscale is not None:
spectrum.getParam(varscale).setValue(sedresult.decE)
FitRunner.PerformFit(Fit)
#Get and dump the target specific results
Result = FitRunner.GetAndPrintResults(Fit)
utils.DumpResult(Result, config)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
FitRunner.config['file']['parent_config'] = infile
if config['Spectrum']['ResultParentPlots'] == "yes":
plot_sed_fromconfig(get_config(config['file']['parent_config']),
ignore_missing_bins=True)
if config['Spectrum']['ResultPlots'] == 'yes':
outXml = utils._dump_xml(config)
# the possibility of making the model map is checked inside the function
FitRunner.ModelMap(outXml)
if Nbin > 0:
FitRunner.config['Spectrum']['ResultParentPlots'] = "yes"
plot_sed_fromconfig(get_config(infile), ignore_missing_bins=True)
energybin.RunEbin(folder, Nbin, Fit, FitRunner, sedresult)
del (sedresult)
del (Result)
del (FitRunner)
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', -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']))
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 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 _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()