def elbert_pmodels(slice_val=1.,
kind='conv nu_mu',
hadr='DPMJET-III-3.0.6',
prpl='ice_allm97_step_1',
corr_only=False):
pmodels = [(pm.HillasGaisser2012, 'H3a', 'H3a'),
(pm.PolyGonato, False, 'poly-gonato'),
(pm.GaisserHonda, None, 'GH')]
echoice = exthp.corr if corr_only else exthp.passrates
label = extlabel(corr_only) + ' {} {}'.format(tex(kind), tex(slice_val))
if slice_val > 1:
cths = np.linspace(0, 1, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(cths))
plt.plot(cths, echoice(kind)(slice_val, emu, cths), 'k--', label=label)
else:
ens = np.logspace(2, 9, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(slice_val))
plt.plot(ens, echoice(kind)(ens, emu, slice_val), 'k--', label=label)
for pmodel in pmodels:
pr = fn(slice_val)(slice_val,
kind,
pmodel[:2],
hadr,
prpl=prpl,
corr_only=corr_only,
label='{} {} {}'.format(pmodel[2], tex(kind),
tex(slice_val)))
plt.legend()
plt.tight_layout(0.3)
def elbert_only(slice_val=1., kind='conv nu_mu'):
plt.figure()
if 'nu_e' in kind:
echoices = [exthp.passrates]
names = [r'$\mathcal{P}_{\rm pass}^{\rm uncor, GJKvS}$']
else:
echoices = [exthp.corr, exthp.passrates]
names = [extlabel(True), extlabel(False)]
if slice_val > 1:
cths = np.linspace(0, 1, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(cths))
for echoice, name in zip(echoices, names):
plt.plot(cths,
echoice(kind)(slice_val, emu, cths),
'--',
label=name)
else:
ens = np.logspace(2, 9, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(slice_val))
for echoice, name in zip(echoices, names):
plt.plot(ens, echoice(kind)(ens, emu, slice_val), '--', label=name)
plt.title(r'{}, {}'.format(tex(kind), tex(slice_val)))
plt.ylim(0., 1.)
plt.ylabel(r'Passing fraction')
plt.xlim(10**3, 10**7)
plt.xscale('log')
plt.xlabel(r'$E_\nu$ [GeV]')
plt.legend()
plt.tight_layout(0.3)
def elbert(slice_val=1.,
kind='conv nu_mu',
pmodel=(pm.GaisserHonda, None),
prpl='ice_allm97_step_1',
corr_only=False):
hadrs = ['DPMJET-III-3.0.6', 'SIBYLL2.3c']
echoice = exthp.corr if corr_only else exthp.passrates
if slice_val > 1:
cths = np.linspace(0, 1, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(cths))
plt.plot(cths,
echoice(kind)(slice_val, emu, cths),
'k--',
label='Analytic approx. {} {}'.format(tex(kind),
tex(slice_val)))
else:
ens = np.logspace(2, 9, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(slice_val))
plt.plot(ens,
echoice(kind)(ens, emu, slice_val),
'k--',
label='Analytic approx. {} {}'.format(tex(kind),
tex(slice_val)))
for hadr in hadrs:
fn(slice_val)(slice_val,
kind,
pmodel,
hadr,
prpl=prpl,
corr_only=corr_only,
label='{} {} {}'.format(hadr, tex(kind), tex(slice_val)))
plt.legend()
plt.tight_layout(0.3)
def fig_extsv():
kinds = ['conv_nue', 'pr_nue', 'conv_numu', 'pr_numu']
cos_ths = [0.25, 0.85]
ens = np.logspace(2, 9, 100)
useexts = [False, True]
labels = ['This work', 'GJKvS']
for kind in kinds:
plt.figure()
plt.title(titling[kind])
for idx, useext in enumerate(useexts):
for cos_th in cos_ths:
if useext:
emu = extsv.minimum_muon_energy(extsv.overburden(cos_th))
plt.plot(ens,
exthp.passrates(kind)(ens, emu, cos_th),
linestyles[idx])
else:
clabel = r'$\cos \theta_z = {}$'.format(
cos_th) if idx == 0 else None
plots.fn(cos_th)(cos_th, kind, label=clabel)
plt.axvline(np.nan,
color='k',
linestyle=linestyles[idx],
label=labels[idx])
plt.gca().set_prop_cycle(None)
plt.legend()
plt.tight_layout(0.3)
save('fig/extsv_{}.eps'.format(kind))
def corsika(cos_theta_bin=-1,
kind='conv nu_mu',
pmodel=(pm.HillasGaisser2012, 'H3a'),
hadr='SIBYLL2.3',
density=('CORSIKA', ('SouthPole', 'December')),
prpl='ice_allm97_step_1',
corsika_file='eff_maxmu',
plot_nuveto_lines=False,
plot_legacy_veto_lines=False):
if isinstance(cos_theta_bin, list):
[
corsika(cth, kind, pmodel, hadr, density, prpl, corsika_file,
plot_nuveto_lines, plot_legacy_veto_lines)
for cth in cos_theta_bin
]
return
cfile = pickle.load(open(
resource_filename('nuVeto',
os.path.join('/data/corsika',
corsika_file + '.pkl')), 'rb'),
encoding='latin1')
fraction = 'eff' in corsika_file
eff, elow, eup, xedges, yedges = cfile[mceq_categ_format(kind)]
cos_theta = centers(yedges)[cos_theta_bin]
clean = eff[:, cos_theta_bin] > 0
xcenters = 10**centers(xedges)[clean]
pr = plt.errorbar(xcenters,
eff[:, cos_theta_bin][clean],
xerr=np.asarray(
list(
zip(xcenters - 10**xedges[:-1][clean],
10**xedges[1:][clean] - xcenters))).T,
yerr=np.asarray(
list(
zip(elow[:, cos_theta_bin][clean],
eup[:, cos_theta_bin][clean]))).T,
label='CORSIKA {} {}'.format(tex(kind), tex(cos_theta)),
fmt='.')
if plot_legacy_veto_lines and fraction:
ens = np.logspace(2, 9, 100)
emu = extsv.minimum_muon_energy(extsv.overburden(cos_theta))
plt.plot(ens,
exthp.passrates(kind)(ens, emu, cos_theta),
'k--',
label='Analytic approx. {} {}'.format(tex(kind),
tex(cos_theta)))
if plot_nuveto_lines:
pr_enu(cos_theta,
kind,
pmodel=pmodel,
hadr=hadr,
prpl=prpl,
density=density,
fraction=fraction,
label='{} {} {}'.format(hadr, tex(kind), tex(cos_theta)),
color=pr[0].get_color())
plt.legend()
def test_elbert(cth):
ens = np.logspace(2,8.9,50)
mine = np.asarray(
[passing(en, cth, kind='conv nu_mu', hadr='DPMJET-III-3.0.6',
pmodel=(pm.GaisserHonda, None), prpl=None, corr_only=True) for en in ens])
emu = extsv.minimum_muon_energy(extsv.overburden(cth))
theirs = exthp.corr('conv nu_mu')(ens, emu, cth)
print('test_elbert', cth)
assert np.all(np.abs(theirs-mine)<0.022)
def test_elbert():
ens = np.logspace(2, 9, 50)
cths = [0.1, 0.3, 0.8]
for cth in cths:
mine = np.asarray([
passing(en,
cth,
kind='conv_numu',
hadr='DPMJET-III',
pmodel=(pm.GaisserHonda, None),
prpl=None,
corr_only=True) for en in ens
])
emu = extsv.minimum_muon_energy(extsv.overburden(cth))
theirs = exthp.corr('conv_numu')(ens, emu, cth)
assert np.all(np.abs(theirs - mine) < 0.02)