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):
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(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)
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.create_dir("%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)
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)
# Make energy bins by running a *new* analysis
Nbin = config['Ebin']['NumEnergyBins']
energybin.RunEbin(folder,Nbin,Fit,FitRunner,sedresult)
del(sedresult)
del(Result)
del(FitRunner)
