def FitOnePixel(self,ra,dec,i,j) :
"""Run a evaluation of the pixel (i,j) corresponding to position (ra,dec)"""
outXml = utils._dump_xml(self.config)
folder = self.config['out']
_,Fit = GenAnalysisObjects(self.config,xmlfile=outXml) #get the Fit object
src = GetSrc(Fit,ra,dec) # get the Source object at postion ra dec
if self.config['TSMap']['RemoveTarget'] : # remove the target is asked
Fit.deleteSource(self.config['target']['name'])
if self.config['TSMap']['Re-Fit']: # reoptimze before is asked
Fit.fit(0,optimizer=self.config['fitting']['optimizer'])
for par in xrange(Fit.logLike.getNumParams()): # freeze all the source parameters
Fit[par].setFree(0)
Fit.addSource(src)# add a spurious source
# dump the *new* xml file
Fit.writeXml(self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml")
# a new Fit object with the new xml file is needed.
# just changing the position of the spurious source does not work
_,TSFit = GenAnalysisObjects(self.config,xmlfile=self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml") #get the Fit object
TSFit.fit(0,optimizer=self.config['fitting']['optimizer'])
# save the result
fsave = open(self._PixelFile(i,j),'w')
fsave.write(str(ra)+"\t"+str(dec)+"\t"+
str(TSFit.Ts("Spurious"))+"\t"+
str(TSFit.logLike.value()))
fsave.close()
def 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 PerformFit(self, Fit, writeXml = True):
"""Run gtlile tool. First it run gtlike with the DRNMGB optimizer
and the user optimizer after. A dictionnay is return with all
the releveant results"""
self._log('gtlike', 'Run likelihood analysis')
try:
Fit.fit(1, optimizer="DRMNGB") #first try to run gtlike to approche the minimum
except:
pass #first try to run gtlike to approche the minimum
# Now the precise fit will be done
#change the fit tolerance to the one given by the user
Fit.ftol = float(self.config['fitting']['ftol'])
#fit with the user optimizer and ask gtlike to compute the covariance matrix
self.log_like = Fit.fit(1,covar=True, optimizer=self.config['fitting']['optimizer'])
#fit with the user optimizer and ask gtlike to compute the covariance matrix
print Fit
# remove source with TS<min_source_TS (default=1)
# to be sure that MINUIT will converge
try: self.config['fitting']['min_source_TS']
except KeyError: self.config['fitting']['min_source_TS'] = 1.
self.RemoveWeakSources(Fit,\
self.config['fitting']['min_source_TS'])
if writeXml :
Fit.writeXml(utils._dump_xml(self.config))
self.success("Fit with gtlike preformed")
def FitOnePixel(self,ra,dec,i,j) :
"""Run a evaluation of the pixel (i,j) corresponding to position (ra,dec)"""
outXml = utils._dump_xml(self.config)
folder = self.config['out']
_,Fit = GenAnalysisObjects(self.config,xmlfile=outXml) #get the Fit object
src = GetSrc(Fit,ra,dec) # get the Source object at postion ra dec
if self.config['TSMap']['RemoveTarget'] : # remove the target is asked
Fit.deleteSource(self.config['target']['name'])
if self.config['TSMap']['Re-Fit']: # reoptimze before is asked
Fit.fit(0,optimizer=self.config['fitting']['optimizer'])
for par in xrange(Fit.logLike.getNumParams()): # freeze all the source parameters
Fit[par].setFree(0)
Fit.addSource(src)# add a spurious source
# dump the *new* xml file
Fit.writeXml(self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml")
# a new Fit object with the new xml file is needed.
# just changing the position of the spurious source does not work
_,TSFit = GenAnalysisObjects(self.config,xmlfile=self.tsfolder+"/model_"+str(ra)+"_"+str(dec)+".xml") #get the Fit object
TSFit.fit(0,optimizer=self.config['fitting']['optimizer'])
# save the result
fsave = open(self._PixelFile(i,j),'w')
fsave.write(str(ra)+"\t"+str(dec)+"\t"+
str(TSFit.Ts("Spurious"))+"\t"+
str(TSFit.logLike.value()))
fsave.close()
def FindSource(self):
"""Run the gtfindsrc tool"""
outfile = utils._dump_findsrcout(self.Configuration)
if (self.clobber == "no" and os.path.isfile(outfile)):
#print("File exists and clobber is False")
return (0)
findsrc = GtApp('gtfindsrc', 'Likelihood')
findsrc['evfile'] = self.mktimefile
findsrc['scfile'] = self.ft2
if self.irfs != 'CALDB':
findsrc['evtype'] = self.Configuration['event']['evtype']
else:
findsrc['evtype'] = 'INDEF'
findsrc['irfs'] = self.irfs
findsrc['expcube'] = self.Cubename
findsrc['expmap'] = self.Mapname
findsrc['srcmdl'] = utils._dump_xml(self.Configuration)
findsrc['coordsys'] = self.Configuration['space']['coordsys']
findsrc['target'] = self.srcname
findsrc['optimizer'] = self.Configuration["fitting"]["optimizer"]
findsrc['ftol'] = self.Configuration["fitting"]["ftol"]
findsrc['clobber'] = self.clobber
findsrc['reopt'] = self.Configuration["findsrc"]["Refit"]
findsrc['outfile'] = outfile
findsrc.run()
def PerformFit(self, Fit, writeXml = True):
"""Run gtlile tool. First it run gtlike with the DRNMGB optimizer
and the user optimizer after. A dictionnay is return with all
the releveant results"""
self._log('gtlike', 'Run likelihood analysis')
# TODO fix this part
# try:
# Fit.fit(0,covar=False, optimizer="DRMNGB") #first try to run gtlike to approche the minimum
# except:
# self.warning("First FIT did not converge with DRMNGB, trying DRMNFB")
# try:
# Fit.fit(0, optimizer="DRMNFB")
# except:
# self.warning("First FIT did not converge with DRMNFB either")
# Now the precise fit will be done
#change the fit tolerance to the one given by the user
Fit.ftol = float(self.config['fitting']['ftol'])
#fit with the user optimizer and ask gtlike to compute the covariance matrix
self.log_like = Fit.fit(0,covar=True, optimizer=self.config['fitting']['optimizer'])
#fit with the user optimizer and ask gtlike to compute the covariance matrix
# remove source with TS<min_source_TS (default=1)
# to be sure that MINUIT will converge
try: self.config['fitting']['min_source_TS']
except KeyError: self.config['fitting']['min_source_TS'] = 1.
self.RemoveWeakSources(Fit,\
self.config['fitting']['min_source_TS'])
if writeXml :
Fit.writeXml(utils._dump_xml(self.config))
self.success("Fit with gtlike preformed")
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 FindSource(self):
"""Run the gtfindsrc tool"""
outfile = utils._dump_findsrcout(self.Configuration)
if (self.clobber=="no" and os.path.isfile(outfile)):
#print("File exists and clobber is False")
return(0)
findsrc = GtApp('gtfindsrc', 'Likelihood')
findsrc['evfile'] = self.mktimefile
findsrc['scfile'] = self.ft2
if self.irfs != 'CALDB':
findsrc['evtype']= self.Configuration['event']['evtype']
else :
findsrc['evtype']= 'INDEF'
findsrc['irfs'] = self.irfs
findsrc['expcube'] = self.Cubename
findsrc['expmap'] = self.Mapname
findsrc['srcmdl'] = utils._dump_xml( self.Configuration)
findsrc['coordsys'] = self.Configuration['space']['coordsys']
findsrc['target'] = self.srcname
findsrc['optimizer'] = self.Configuration["fitting"]["optimizer"]
findsrc['ftol'] = self.Configuration["fitting"]["ftol"]
findsrc['clobber'] = self.clobber
findsrc['reopt'] = self.Configuration["findsrc"]["Refit"]
findsrc['outfile'] = outfile
findsrc.run()
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 FindSource(self):
"""Run the gtfindsrc tool"""
findsrc = GtApp('gtfindsrc', 'Likelihood')
findsrc['evfile'] = self.eventfile
findsrc['scfile'] = self.ft2
findsrc['irfs'] = self.irfs
findsrc['expcube'] = self.Cubename
findsrc['expmap'] = self.Mapname
findsrc['srcmdl'] = utils._dump_xml( self.Configuration)
findsrc['coordsys'] = self.Configuration['space']['coordsys']
findsrc['target'] = self.srcname
findsrc['optimizer'] = self.Configuration["fitting"]["optimizer"]
findsrc['ftol'] = self.Configuration["fitting"]["ftol"]
findsrc['clobber'] = self.clobber
findsrc['reopt'] = self.Configuration["findsrc"]["Refit"]
findsrc['outfile'] = utils._dump_findsrcout(self.Configuration)
findsrc.run()
def PerformFit(self, Fit, writeXml = True):
"""Run gtlile tool. First it run gtlike with the DRNMGB optimizer
and the user optimizer after. A dictionnay is return with all
the releveant results"""
self._log('gtlike', 'Run likelihood analysis')
# TODO fix this part
# Change the fit tolerance to the one given by the user
Fit.ftol = float(self.config['fitting']['ftol'])
# Fit with DRMNGB/DRMNFB (as recommended in gtlike fhelp) optimizer to
# obtain initial parameters close to the minimum. Then switch optimizer.
try:
self.log_like = Fit.fit(verbosity=0, covar=False, optimizer='DRMNGB', optObject=None)
except Exception as exc:
self.warning('Exception while running DRMNGB, {0}'.format(str(exc)))
try:
self.log_like = Fit.fit(verbosity=0, covar=False, optimizer='DRMNFB', optObject=None)
except:
self.warning('Exception while running DRMNFB, {0}'.format(str(exc)))
if self.config['verbose'] == 'yes' :
print('Fit output with DRMNGB/DRMNFB: {0}'.format(self.log_like))
# 2nd precise fit with the user optimizer and ask gtlike to compute the covariance matrix
self.log_like = Fit.fit(verbosity=0,covar=True, optimizer=self.config['fitting']['optimizer'])
if self.config['verbose'] == 'yes' :
print('Fit output with {1}: {0}'.format(self.log_like,self.config['fitting']['optimizer']))
# remove source with TS<min_source_TS (default=1)
# to be sure that MINUIT will converge
try: self.config['fitting']['min_source_TS']
except KeyError: self.config['fitting']['min_source_TS'] = 1.
self.RemoveWeakSources(Fit,\
self.config['fitting']['min_source_TS'])
if writeXml :
Fit.writeXml(utils._dump_xml(self.config))
self.success("Fit with gtlike performed")
def FindSource(self):
"""Run the gtfindsrc tool"""
findsrc = GtApp('gtfindsrc', 'Likelihood')
findsrc['evfile'] = self.eventfile
findsrc['scfile'] = self.ft2
if self.Configuration['event']['irfs'] != 'CALDB':
findsrc['evtype'] = self.Configuration['event']['evtype']
else:
findsrc['evtype'] = 'INDEF'
findsrc['irfs'] = self.irfs
findsrc['expcube'] = self.Cubename
findsrc['expmap'] = self.Mapname
findsrc['srcmdl'] = utils._dump_xml(self.Configuration)
findsrc['coordsys'] = self.Configuration['space']['coordsys']
findsrc['target'] = self.srcname
findsrc['optimizer'] = self.Configuration["fitting"]["optimizer"]
findsrc['ftol'] = self.Configuration["fitting"]["ftol"]
findsrc['clobber'] = self.clobber
findsrc['reopt'] = self.Configuration["findsrc"]["Refit"]
findsrc['outfile'] = utils._dump_findsrcout(self.Configuration)
findsrc.run()
def PerformFit(self, Fit):
"""Run gtlile tool. First it run gtlike with the DRNMGB optimizer
and the user optimizer after. A dictionnay is return with all
the releveant results"""
self._log('gtlike', 'Run likelihood analysis')
try:
Fit.fit(0) #first try to run gtlike to approche the minimum
except:
pass
# Now the precise fit will be done
#change the fit tolerance to the one given by the user
Fit.ftol = float(self.config['fitting']['ftol'])
#fit with the user optimizer and ask gtlike to compute the covariance matrix
self.log_like = Fit.fit(
0, covar=True, optimizer=self.config['fitting']['optimizer']
) #fit with the user optimizer and ask gtlike to compute the covariance matrix
self.RemoveWeakSources(
Fit) #remove source with TS<1 to be sure that MINUIT will converge
Fit.writeXml(utils._dump_xml(self.config))
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)
