def effective_area(frame, model, generator):
mctree = frame["I3MCTree"]
primary = mctree.primaries[0]
muon = mctree.get_daughters(primary)[0]
bundle = MuonGun.BundleConfiguration([MuonGun.BundleEntry(0, muon.energy)])
area = 1 / generator.generated_events(primary, bundle)
frame["MCMuon"] = muon
frame["MuonEffectiveArea"] = dataclasses.I3Double(area)
weighter = MuonGun.WeightCalculator(model, generator)
weight = weighter(primary, bundle)
frame["MuonWeight"] = dataclasses.I3Double(weight)
return True
e = numpy.logspace(1, 7, 101)
target = MuonGun.load_model('Hoerandel5_atmod12_SIBYLL')
# target = MuonGun.load_model('GaisserH4a_atmod12_SIBYLL')
generators = [
('200k $E^{-2}$ 5-component CORSIKA', 2e5 * hard),
('300k $E^{-2.6}$ 5-component CORSIKA', (300e3) * soft),
('200k unweighted CORSIKA', 2e5 * unweighted),
('1k MuonGun (100k muons each)', 1e3 * gun),
('total', 2e5 * hard + 300e3 * soft + 2e5 * unweighted + 1e3 * gun),
]
fig = pylab.figure(figsize=(6, 4))
fig.subplots_adjust(bottom=0.15)
annum = 365 * 24 * 3600
for label, generator in generators:
weighter = MuonGun.WeightCalculator(target, generator)
pylab.plot(e,
1. / (get_weight(weighter, e, scale=True) * annum),
label=label)
pylab.loglog()
pylab.legend(loc='lower right', prop=dict(size='x-small'))
pylab.ylabel('Single-muon livetime [years]')
pylab.xlabel('Muon energy at sampling surface [GeV]')
pylab.grid()
pylab.savefig(args.outfile)
book_weights(infiles, outfile)
try:
import tables, pylab, numpy
hdf = tables.openFile(outfile)
# We can get pre-calculated weights calcuated by the icetray module
muon_energy = hdf.root.Muon.cols.energy[:]
pre_weights = hdf.root.MuonWeight.cols.value[:]
# or make a freestanding WeightCalculator with a different flux model
model = MuonGun.load_model('GaisserH4a_atmod12_SIBYLL')
generator = harvest_generators(infiles)
weighter = MuonGun.WeightCalculator(model, generator)
# and use the bundle axis and its radius/energy distribution to calculate a weight
axis = hdf.root.MCPrimary.read()
bundle = hdf.root.BundleParameters.read()
post_weights = weighter(axis['x'], axis['y'], axis['z'], axis['zenith'],
axis['azimuth'], bundle['multiplicity'],
bundle['energy'], bundle['radius'])
pylab.hist(muon_energy,
bins=numpy.logspace(4, 7, 101),
log=True,
histtype='bar',
label='unweighted (counts)')
pylab.hist(muon_energy,
weights=post_weights,
