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 GetAndPrintResults(self, Fit):
"""Get and print some useful results. Also contruct a dictonnary and fill it with results"""
if self.config['verbose'] == 'yes':
self._log('Results', 'Print results of the fit')
Result = {}
if self.config['verbose'] == 'yes':
print Fit.model, "\n"
self.info("Results for the Fit")
# Print src name, Npred and TS for source with TS > 5
print "Source Name\tNpred\tTS"
#TODO
#for src in Fit.model.srcNames:
#if Fit.Ts(src) > 5:
# print src, "\t%2.3f\t%2.3f" % (Fit.NpredValue(src), Fit.Ts(src))
# fill the dictonnary with some values
Result['Optimizer'] = self.config['fitting']['optimizer']
Result['Npred'] = Fit.NpredValue(self.obs.srcname)
Result['TS'] = Fit.Ts(self.obs.srcname)
if self.config['verbose'] == 'yes':
print "Values and (MINOS) errors for " + self.obs.srcname
print "TS : ", Fit.Ts(self.obs.srcname)
# Get the python object 'Spectrum' for the source of interest
spectrum = Fit[self.obs.srcname].funcs['Spectrum']
# Get the names of the parameters for the source of interest
ParName = spectrum.paramNames
#Get the model type and fill the dictonnary
stype = Fit.model.srcs[self.obs.srcname].spectrum().genericName()
Result['ModelType'] = stype
Result['log_like'] = Fit.logLike.value()
#Add the energy information to the result dictionnary
Result['Emin'] = self.obs.Emin
Result['Emax'] = self.obs.Emax
#Add the time information to the result dictionnary
Result['tmin'] = self.config['time']['tmin']
Result['tmax'] = self.config['time']['tmax']
Result['SrcName'] = self.obs.srcname
Result['Flux'] = Fit.flux(self.obs.srcname, self.obs.Emin,
self.obs.Emax)
Result['dFlux'] = Fit.fluxError(self.obs.srcname, self.obs.Emin,
self.obs.Emax)
for par in ParName: #Loop over the parameters and get value, error and scale
ParValue = spectrum.getParam(par).value()
ParError = spectrum.getParam(par).error()
Scale = spectrum.getParam(par).getScale()
Result[par] = ParValue * Scale
Result['d' + par] = ParError * Scale
if ParError > 0: # Compute MINOS errors for relevent parameters Fit.Ts(self.obs.srcname) > 5 and
try:
MinosErrors = Fit.minosError(self.obs.srcname, par)
if self.config['verbose'] == 'yes':
print(
par +
" : %2.2f +/- %2.2f [ %2.2f, + %2.2f ] %2.0e" %
(ParValue, ParError, MinosErrors[0],
MinosErrors[1], Scale))
Result.update(
{'d' + par + '-': MinosErrors[0] * Scale})
Result.update(
{'d' + par + '+': MinosErrors[1] * Scale})
except:
if self.config['verbose'] == 'yes':
print(
par + " : %2.2f +/- %2.2f %2.0e" %
(ParValue, ParError, Scale))
else:
if self.config['verbose'] == 'yes':
print(par + " : %2.2f %2.0e" % (ParValue, Scale))
try: # get covariance matrix
if self.config['verbose'] == 'yes':
utils.GetCovar(self.obs.srcname, Fit)
except:
pass #if the covariance matrix has not been computed
if self.config['verbose'] == 'yes':
utils.GetFluxes(Fit, self.obs.Emin,
self.obs.Emax) #print the flux of all the sources
#Compute an UL if the source is too faint
if float(self.config['UpperLimit']['TSlimit']) > Fit.Ts(
self.obs.srcname):
if self.config['UpperLimit']['envelope'] == 'yes':
self.EnvelopeUL(Fit)
else:
Ulval = self.ComputeUL(Fit)
Result['Ulvalue'] = Ulval
return Result #Return the dictionnary