def ChangeModel(comp, E1, E2, name, Pref, Gamma):
"""Change the spectral model of a source called name
to allow a fit between E1 and E2
If the spectral model is PowerLaw, the prefactor is updated
if not the model is change to PowerLaw.
The index is frozen in all cases"""
# if approximated Gamma is outside of bounds set it to limit
Gamma_min = -5
Gamma_max = 0.5
Gamma = min(max(Gamma_min, Gamma), Gamma_max)
Eav = utils.GetE0(E1, E2)
spectrum = comp.logLike.getSource(name).getSrcFuncs()['Spectrum']
spectrum.getParam('Prefactor').setBounds(1e-5, 1e5)
spectrum.getParam('Prefactor').setScale(utils.fluxScale(Pref))
spectrum.getParam('Prefactor').setValue(utils.fluxNorm(Pref))
spectrum.getParam('Index').setBounds(Gamma_min, Gamma_max)
spectrum.getParam('Index').setValue(Gamma)
spectrum.getParam('Index').setFree(0)
spectrum.getParam('Scale').setValue(Eav)
spectrum.getParam('Scale').setBounds(20, 3e6)
return comp
def ChangeModel(Fit, E1, E2, name, Pref, Gamma):
"""Change the spectral model of a source called name
to allow a fit between E1 and E2
If the spectral model is PowerLaw, the prefactor is updated
if not the model is change to PowerLaw.
The index is frozen in all cases"""
Eav = utils.GetE0(E1, E2)
# Set Parameters
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setBounds(1e-5,1e5)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setScale(utils.fluxScale(Pref))
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Prefactor').setValue(utils.fluxNorm(Pref))
Gamma_min=-5
Gamma_max=0
# if approximated Gamma is outside of bounds set it to limit
if Gamma < Gamma_min:
Gamma = Gamma_min
elif Gamma > Gamma_max:
Gamma = Gamma_max
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setBounds(Gamma_min,Gamma_max)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setValue(Gamma)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Index').setFree(0)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Scale').setValue(Eav)
Fit.logLike.getSource(name).getSrcFuncs()['Spectrum'].getParam('Scale').setBounds(20,3e6)
return Fit
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']
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, 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
def WriteXml(lib, doc, srclist, config):
from enrico import Loggin
mes = Loggin.Message()
"""Fill and write the library of sources into an XML file"""
emin = config['energy']['emin']
emax = config['energy']['emax']
# Check if it is an energy bin, so we won't take the pivot energy from the catalog
is_energy_bin = 1 if 'Ebin' in config['file']['tag'] else 0
Galname = "GalDiffModel"
Isoname = "IsoDiffModel"
#test if the user provides diffuse files. if not use the default one
if config['model']['diffuse_gal_dir'] == "":
Gal_dir = env.DIFFUSE_DIR
else:
Gal_dir = config['model']['diffuse_gal_dir']
if config['model']['diffuse_iso_dir'] == "":
Iso_dir = env.DIFFUSE_DIR
else:
Iso_dir = config['model']['diffuse_iso_dir']
if config['model']['diffuse_gal'] == "":
Gal = Gal_dir + "/" + env.DIFFUSE_GAL
else:
Gal = Gal_dir + "/" + config['model']['diffuse_gal']
if config['model']['diffuse_iso'] == "":
Iso = utils.GetIso(config["event"]["evclass"],
config["event"]["evtype"])
try:
if not (os.path.isfile(Iso)):
raise IOError
except IOError:
mes.warning(
"Cannot find Iso file %s, please have a look. Switching to default one"
% Iso)
Iso = Iso_dir + "/" + env.DIFFUSE_ISO_SOURCE
else:
Iso = Iso_dir + "/" + config['model']['diffuse_iso']
#add diffuse sources
addDiffusePL(lib, Iso, free=1, value=1.0, max=10.0, min=1.0, name=Isoname)
addGalprop(lib,
Gal,
free=1,
value=1.0,
scale=1.0,
max=10.0,
min=.010,
name=Galname)
print "Iso model file ", Iso
print "Galactic model file ", Gal
yesnodict = {}
for y in ['yes', True, 'true', 1, 1.0, '1', '1.0']:
yesnodict[y] = 1
for n in ['no', False, 'false', 0, 0.0, '0', '0.0']:
yesnodict[n] = 0
try:
ebldict = {}
ebldict['tau_norm'] = 1.0
ebldict['free_redshift'] = 0 # NOTE:ToDo
ebldict['free_tau_norm'] = yesnodict[config['target']['fit_tau']]
ebldict['redshift'] = float(config['target']['redshift'])
ebldict['model'] = int(config['target']['ebl_model'])
if ebldict['redshift'] < 1.e-3:
ebldict = None
except NameError:
ebldict = None
# loop over the list of sources and add it to the library
for i in xrange(len(srclist)):
name = srclist[i].get('name')
if (name == config['target']['name']):
ebl = ebldict
else:
ebl = None
ra = srclist[i].get('ra')
dec = srclist[i].get('dec')
free = srclist[i].get('IsFree')
freeshape = srclist[i].get('IsFreeShape')
spectype = srclist[i].get('SpectrumType')
extendedName = srclist[i].get('ExtendedName')
# Check the spectrum model
if spectype.strip() == "PowerLaw":
if (ebl == None):
energyscale = utils.GetE0(
emin, emax) if is_energy_bin else srclist[i].get('scale')
addPSPowerLaw1(lib,
name,
ra,
dec,
"None",
emin=emin,
emax=emax,
eflux=energyscale,
flux_free=free,
flux_value=srclist[i].get('flux'),
index_free=freeshape,
index_value=srclist[i].get('index'),
extendedName=extendedName)
if (ebl != None):
addPSLogparabola(lib,
name,
ra,
dec,
ebl,
emin=emin,
emax=emax,
norm_free=free,
norm_value=srclist[i].get('flux'),
alpha_free=freeshape,
alpha_value=abs(srclist[i].get('index')),
beta_free=0,
beta_min=0,
beta_max=0,
beta_value=0,
extendedName=extendedName)
elif spectype.strip() == "PowerLaw2":
addPSPowerLaw2(lib,
name,
ra,
dec,
ebl,
emin=emin,
emax=emax,
flux_free=free,
flux_value=srclist[i].get('flux'),
index_free=freeshape,
index_value=srclist[i].get('index'),
extendedName=extendedName)
elif spectype.strip() == "LogParabola":
addPSLogparabola(lib,
name,
ra,
dec,
ebl,
enorm=srclist[i].get('scale'),
emin=emin,
emax=emax,
norm_free=free,
norm_value=srclist[i].get('flux'),
alpha_free=freeshape,
alpha_value=abs(srclist[i].get('index')),
beta_free=freeshape,
beta_value=srclist[i].get('beta'),
extendedName=extendedName)
elif spectype.strip(
) == "PLExpCutoff" or spectype == "PLSuperExpCutoff" or spectype == "PLSuperExpCutoff2":
addPSPLSuperExpCutoff(lib,
name,
ra,
dec,
ebl,
emin=emin,
emax=emax,
eflux=srclist[i].get('scale'),
flux_free=free,
flux_value=srclist[i].get('flux'),
index1_free=freeshape,
index1_value=srclist[i].get('index'),
cutoff_free=freeshape,
cutoff_value=srclist[i].get('cutoff'),
extendedName=extendedName)
elif spectype.strip() == "BrokenPowerLaw":
addPSBrokenPowerLaw2(lib,
name,
ra,
dec,
ebl,
emin=emin,
emax=emax,
flux_value=1.6,
flux_scale=1e-6,
extendedName=extendedName)
else:
print('Warning!!!, unknown model %s' % spectype.strip())
folder = config['out']
utils.mkdir_p(folder)
output = config['file']['xml']
mes.info("write the Xml file in " + output)
open(output, 'w').write(doc.toprettyxml(' ')) #save it
