def PrepareEbin(Fit, FitRunner, sedresult=None):
""" Prepare the computation of spectral point in energy bins by
i) removing the weak sources (TS<1) # not true
ii) updating the config file (option and energy)
and save it in a new ascii file
iii) changing the spectral model and saving it in a new xml file.
A list of the ascii files is returned"""
mes = Loggin.Message()
NEbin = int(FitRunner.config['Ebin']['NumEnergyBins'])
config = FitRunner.config
config['verbose'] = 'no' #Be quiet
#Replace the evt file with the fits file produced before
#in order to speed up the production of the fits files
config['file']['event'] = FitRunner.obs.eventcoarse
#update the config to allow the fit in energy bins
config['UpperLimit']['envelope'] = 'no'
config['Ebin']['NumEnergyBins'] = '0' #no new bin in energy!
config['target']['redshift'] = '0' #Disable EBL correction
config['out'] = FitRunner.config['out'] + '/' + EbinPath + str(NEbin)
config['Spectrum']['ResultPlots'] = 'no' #no SED plot/modelmap
#copy the chose of the user for the enery bin computing
config['Spectrum']['FitsGeneration'] = config['Ebin']['FitsGeneration']
config['UpperLimit']['TSlimit'] = config['Ebin']['TSEnergyBins']
tag = FitRunner.config['file']['tag']
lEmax = np.log10(float(FitRunner.config['energy']['emax']))
lEmin = np.log10(float(FitRunner.config['energy']['emin']))
utils._log("Preparing submission of fit into energy bins")
print(" Emin = ", float(FitRunner.config['energy']['emin']), " Emax = ",
float(FitRunner.config['energy']['emax']), " Nbins = ", NEbin)
if config['Ebin']['DistEbins'] in ['TS', 'mix'] and sedresult != None:
# Make the bins equispaced in sum(SED/SEDerr) - using the butterfly
ipo = 0
iTS = sedresult.SED / sedresult.Err
TScumula = 0
TSperbin = 1. * sum(iTS) / NEbin
ener = [10**lEmin]
while ipo < len(sedresult.E) - 1:
TScumula += iTS[ipo]
if TScumula / TSperbin > 1:
ener.append(sedresult.E[ipo])
TScumula -= TSperbin
ipo += 1
ener.append(10**lEmax)
ener = np.array(ener)
# intermediate approach (between both TS-spaced and logE spaced)
if config['Ebin']['DistEbins'] == 'mix':
ener = 0.5 * (ener + np.logspace(lEmin, lEmax, NEbin + 1))
else:
# Make the bins equispaced in logE (standard)
ener = np.logspace(lEmin, lEmax, NEbin + 1)
os.system("mkdir -p " + config['out'])
paramsfile = []
srcname = FitRunner.config['target']['name']
if config['UpperLimit']['TSlimit'] > Fit.Ts(srcname):
utils._log('Re-optimize', False)
print "An upper limit has been computed. The fit need to be re-optmized"
Fit.optimize(0)
Pref = utils.ApproxPref(Fit, ener, srcname)
Gamma = utils.ApproxGamma(Fit, ener, srcname)
Model_type = Fit.model.srcs[srcname].spectrum().genericName()
# if the model is not PowerLaw : change the model
if not (Model_type == 'PowerLaw'):
for comp in Fit.components:
comp.logLike.getSource(srcname).setSpectrum(
"PowerLaw") #Change model
config['target']['spectrum'] = "PowerLaw"
xmltag_list = [""] #handle summed like analysis
if config['ComponentAnalysis']['FrontBack'] == "yes":
xmltag_list = ["_FRONT", "_BACK"]
mes.info("Splitting Front/Back events")
elif config['ComponentAnalysis']['PSF'] == "yes":
xmltag_list = ["_PSF0", "_PSF1", "_PSF2"]
mes.info("Splitting PSF events")
elif config['ComponentAnalysis']['EDISP'] == "yes":
xmltag_list = ["_EDISP0", "_EDISP1", "_EDISP2", "_EDISP3"]
mes.info("Splitting EDISP events")
for ibin in xrange(NEbin): #Loop over the energy bins
E = utils.GetE0(ener[ibin + 1], ener[ibin])
mes.info("Submitting # " + str(ibin) + " at energy " + str(E))
#Update the model for the bin
for comp, xmltag in zip(Fit.components, xmltag_list):
NewFitObject = ChangeModel(comp, ener[ibin], ener[ibin + 1],
srcname, Pref[ibin], Gamma[ibin])
Xmlname = (config['out'] + "/" + srcname + "_" + str(ibin) +
xmltag + ".xml")
NewFitObject.writeXml(Xmlname) # dump the corresponding xml file
config['file']['xml'] = Xmlname.replace(xmltag, "")
#update the energy bounds
config['energy']['emin'] = str(ener[ibin])
config['energy']['emax'] = str(ener[ibin + 1])
config['energy']['decorrelation_energy'] = "no"
# Change the spectral index to follow the Estimated Gamma
# if approximated Gamma is outside of bounds set it to limit
Gamma_min = -5
Gamma_max = 0.5
config['UpperLimit']['SpectralIndex'] = -min(
max(Gamma_min, Gamma[ibin]), Gamma_max)
config['file']['tag'] = tag + '_Ebin' + str(NEbin) + '_' + str(ibin)
filename = config['target']['name'] + "_" + str(ibin) + ".conf"
paramsfile.append(filename)
config.write(open(config['out'] + '/' + paramsfile[ibin],
'w')) #save the config file in a ascii file
return paramsfile
def PrepareEbin(Fit, FitRunner):
""" Prepare the computation of spectral point in energy bins by
i) removing the weak sources (TS<1) # not true
ii) updating the config file (option and energy)
and save it in a new ascii file
iii) changing the spectral model and saving it in a new xml file.
A list of the ascii files is returned"""
NEbin = int(FitRunner.config['Ebin']['NumEnergyBins'])
config = FitRunner.config
config['verbose'] = 'no' #Be quiet
#Replace the evt file with the fits file produced before
#in order to speed up the production of the fits files
config['file']['event'] = FitRunner.obs.eventcoarse
#update the config to allow the fit in energy bins
config['UpperLimit']['envelope'] = 'no'
config['Ebin']['NumEnergyBins'] = '0' #no new bin in energy!
config['target']['redshift'] = '0' #Disable EBL correction
config['out'] = FitRunner.config['out'] + '/' + EbinPath + str(NEbin)
config['Spectrum']['ResultPlots'] = 'no' #no SED plot/modelmap
#copy the chose of the user for the enery bin computing
config['Spectrum']['FitsGeneration'] = config['Ebin']['FitsGeneration']
config['UpperLimit']['TSlimit'] = config['Ebin']['TSEnergyBins']
tag = FitRunner.config['file']['tag']
lEmax = np.log10(float(FitRunner.config['energy']['emax']))
lEmin = np.log10(float(FitRunner.config['energy']['emin']))
utils._log("Preparing submission of fit into energy bins")
print(" Emin = ", float(FitRunner.config['energy']['emin']), " Emax = ",
float(FitRunner.config['energy']['emax']), " Nbins = ", NEbin)
ener = np.logspace(lEmin, lEmax, NEbin + 1)
os.system("mkdir -p " + config['out'])
paramsfile = []
srcname = FitRunner.config['target']['name']
if config['UpperLimit']['TSlimit'] > Fit.Ts(srcname):
utils._log('Re-optimize', False)
print "An upper limit has been computed. The fit need to be re-optmized"
Fit.optimize(0)
Pref = utils.ApproxPref(Fit, ener, srcname)
Gamma = utils.ApproxGamma(Fit, ener, srcname)
Model_type = Fit.model.srcs[srcname].spectrum().genericName()
# if the model is not PowerLaw : change the model
if not (Model_type == 'PowerLaw'):
for comp in Fit.components:
comp.logLike.getSource(srcname).setSpectrum(
"PowerLaw") #Change model
config['target']['spectrum'] = "PowerLaw"
for ibin in xrange(NEbin): #Loop over the energy bins
E = utils.GetE0(ener[ibin + 1], ener[ibin])
from enrico import Loggin
mes = Loggin.Message()
mes.info("Submition # " + str(ibin) + " at energy " + str(E))
#Update the model for the bin
NewFitObject = ChangeModel(Fit, ener[ibin], ener[ibin + 1], srcname,
Pref[ibin], Gamma[ibin])
Xmlname = (config['out'] + "/" + srcname + "_" + str(ibin) + ".xml")
NewFitObject.writeXml(Xmlname) # dump the corresponding xml file
config['file']['xml'] = Xmlname
#update the energy bounds
config['energy']['emin'] = str(ener[ibin])
config['energy']['emax'] = str(ener[ibin + 1])
config['energy']['decorrelation_energy'] = "no"
# Change the spectral index to follow the Estimated Gamma
# if approximated Gamma is outside of bounds set it to limit
Gamma_min = -5
Gamma_max = 0.5
config['UpperLimit']['SpectralIndex'] = -min(
max(Gamma_min, Gamma[ibin]), Gamma_max)
config['file']['tag'] = tag + '_Ebin' + str(NEbin) + '_' + str(ibin)
filename = config['target']['name'] + "_" + str(ibin) + ".conf"
paramsfile.append(filename)
config.write(open(config['out'] + '/' + paramsfile[ibin],
'w')) #save the config file in a ascii file
return paramsfile
def PrepareEbin(Fit, FitRunner, sedresult=None):
""" Prepare the computation of spectral point in energy bins by
i) removing the weak sources (TS<1) # not true
ii) updating the config file (option and energy)
and save it in a new ascii file
iii) changing the spectral model and saving it in a new xml file.
A list of the ascii files is returned"""
mes = Loggin.Message()
NEbin = int(FitRunner.config['Ebin']['NumEnergyBins'])
config = FitRunner.config
config['verbose'] = 'no' #Be quiet
#Replace the evt file with the fits file produced before
#in order to speed up the production of the fits files
config['file']['event'] = FitRunner.obs.eventcoarse
#update the config to allow the fit in energy bins
config['UpperLimit']['envelope'] = 'no'
config['Ebin']['NumEnergyBins'] = '0' #no new bin in energy!
config['target']['redshift'] = '0' #Disable EBL correction
config['out'] = FitRunner.config['out'] + '/' + EbinPath + str(NEbin)
config['Spectrum']['ResultPlots'] = 'no' #no SED plot/modelmap
#copy the chose of the user for the enery bin computing
config['Spectrum']['FitsGeneration'] = config['Ebin']['FitsGeneration']
config['UpperLimit']['TSlimit'] = config['Ebin']['TSEnergyBins']
tag = FitRunner.config['file']['tag']
Emax = float(FitRunner.config['energy']['emax'])
Emin = float(FitRunner.config['energy']['emin'])
lEmax = np.log10(Emax)
lEmin = np.log10(Emin)
utils._log("Preparing submission of fit into energy bins")
print("Emin = {0} MeV".format(Emin), "Emax = {0} MeV".format(Emax),
"Nbins = {0}".format(NEbin))
ener = utils.string_to_list(config['Ebin']['DistEbins'])
if ener is None:
if (config['ComponentAnalysis']['FGL4'] == 'yes'
or config['Ebin']['DistEbins'] == 'FGL4'):
ener = np.asarray([50, 1e2, 3e2, 1e3, 3e3, 1e4, 3e4, 3e5])
NEbin = len(ener) - 1
elif config['Ebin']['DistEbins'] in ['TS', 'mix'
] and sedresult != None:
# Make the bins equispaced in sum(SED/SEDerr) - using the butterfly
ipo = 0
iTS = sedresult.SED / sedresult.Err
TScumula = 0
TSperbin = 1. * sum(iTS) / NEbin
ener = [10**lEmin]
while ipo < len(sedresult.E) - 1:
TScumula += iTS[ipo]
if TScumula / TSperbin > 1:
ener.append(sedresult.E[ipo])
TScumula -= TSperbin
ipo += 1
ener.append(10**lEmax)
ener = np.array(ener)
# intermediate approach (between both TS-spaced and logE spaced)
if config['Ebin']['DistEbins'] == 'mix':
ener = 0.5 * (ener + np.logspace(lEmin, lEmax, NEbin + 1))
else:
# Make the bins equispaced in logE (standard)
ener = np.logspace(lEmin, lEmax, NEbin + 1)
# 1. Remove bins that are out of the range covered by the data
# 2. Limit the bin extend to the range covered by the data.
# Get elements strictly above threshold +1 element to the left for the left side
# Get elements strictly below limit +1 element to the right side.
# example. [1,2,3,4,5] -> if Emin=3.4, Emax=3.9 we want to keep [3.4,3.9].
ener = np.asarray(ener)
print("Energy bins (before energy cuts): {0}".format(str(ener)))
if len(ener) == 0:
print("** Warning: energy bin array is empty")
return (None)
available_left = ener > Emin # In the example FFFTT -> [4,5]
for k, use in enumerate(available_left[:-1]):
if not use and available_left[k + 1]:
available_left[k] = True # In the example FFTTT -> [3,5]
available_right = ener < Emax # In the example TTTFF -> [1,3]
for k, use in enumerate(available_right[1:]):
if not use and available_right[k]:
available_right[k + 1] = True # In the example TTTTF -> [1,4]
available = available_left * available_right
ener = ener[available] # In the example FFTTF -> [3,4]
# Limit the range to the real energies that are covered by our data
# If the energy bins are well placed this should not do anything.
ener[0] = np.max([Emin, ener[0]])
ener[-1] = np.min([Emax, ener[-1]])
NEbin = len(ener) - 1
print("Energy bins (after energy cuts): {0}".format(str(ener)))
if len(ener) == 0:
print("** Warning: energy bin array is empty")
return (None)
utils.mkdir_p(config['out'])
paramsfile = []
srcname = FitRunner.config['target']['name']
try:
TSsrc = Fit.Ts(srcname)
except RuntimeError:
TSsrc = 0
if config['UpperLimit']['TSlimit'] > TSsrc:
utils._log('Re-optimize', False)
print "An upper limit has been computed. The fit need to be re-optimized"
Fit.optimize(0)
Pref = utils.ApproxPref(Fit, ener, srcname)
Gamma = utils.ApproxGamma(Fit, ener, srcname)
Model_type = Fit.model.srcs[srcname].spectrum().genericName()
# if the model is not PowerLaw : change the model
if not (Model_type == 'PowerLaw'):
for comp in Fit.components:
comp.logLike.getSource(srcname).setSpectrum(
"PowerLaw") #Change model
config['target']['spectrum'] = "PowerLaw"
xmltag_list = [""] #handle summed like analysis
if config['ComponentAnalysis']['FrontBack'] == "yes":
xmltag_list = ["_FRONT", "_BACK"]
mes.info("Splitting Front/Back events")
elif config['ComponentAnalysis']['PSF'] == "yes":
xmltag_list = ["_PSF0", "_PSF1", "_PSF2", "_PSF3"]
mes.info("Splitting PSF events")
elif config['ComponentAnalysis']['EDISP'] == "yes":
xmltag_list = ["_EDISP0", "_EDISP1", "_EDISP2", "_EDISP3"]
mes.info("Splitting EDISP events")
elif config['ComponentAnalysis']['FGL4'] == "yes":
from catalogComponents import evtnum, energybins, pixelsizes
xmltag_list = []
for ebin_i in energybins:
for k, evt in enumerate(evtnum):
#if pixelsizes[ebin_i][k] > 0:
try:
xmltag_list.append("_{0}_En{1}".format(
utils.typeirfs[k], ebin_i))
except KeyError:
continue
for ibin in xrange(NEbin): #Loop over the energy bins
E = utils.GetE0(ener[ibin + 1], ener[ibin])
mes.info("Submitting # " + str(ibin) + " at energy " + str(E))
#Update the model for the bin
for comp, xmltag in zip(Fit.components, xmltag_list):
NewFitObject = ChangeModel(comp, ener[ibin], ener[ibin + 1],
srcname, Pref[ibin], Gamma[ibin])
Xmlname = (config['out'] + "/" + srcname + "_" + str(ibin) +
xmltag + ".xml")
NewFitObject.writeXml(Xmlname) # dump the corresponding xml file
config['file']['xml'] = Xmlname.replace(xmltag, "")
#update the energy bounds
config['energy']['emin'] = str(ener[ibin])
config['energy']['emax'] = str(ener[ibin + 1])
config['energy']['decorrelation_energy'] = "no"
# Change the spectral index to follow the Estimated Gamma
# if approximated Gamma is outside of bounds set it to limit
Gamma_min = -5
Gamma_max = -0.501
Gamma_bin = -max(min(Gamma_max, Gamma[ibin]), Gamma_min)
config['Spectrum']['FrozenSpectralIndex'] = Gamma_bin
config['UpperLimit']['SpectralIndex'] = Gamma_bin
config['file']['tag'] = tag + '_Ebin' + str(NEbin) + '_' + str(ibin)
filename = config['target']['name'] + "_" + str(ibin) + ".conf"
paramsfile.append(filename)
config.write(open(config['out'] + '/' + filename,
'w')) #save the config file in a ascii file
return paramsfile