help='Detector configuration [IT73|IT81]')
p.add_argument(
'-o',
'--outdir',
dest='outdir',
default='/home/jbourbeau/public_html/figures/ShowerLLH/qualitycuts',
help='Output directory')
p.add_argument('-b',
'--bintype',
dest='bintype',
default='logdist',
choices=['standard', 'nozenith', 'logdist'],
help='Option for a variety of preset bin values')
args = p.parse_args()
sim_reco = load_sim(config=args.config, bintype=args.bintype)
standard_mask = sim_reco['cuts']['llh']
eff_area, eff_area_error, energy_midpoints = get_effective_area(
sim_reco, standard_mask, args.bintype)
# Plot effective area
fig, ax = plt.subplots()
ax.errorbar(energy_midpoints, eff_area, yerr=eff_area_error, marker='.')
intercept = line_fit(eff_area, eff_area_error, energy_midpoints)
print('Maximum effective area = {}'.format(intercept))
high_energy_mask = (energy_midpoints >= 10**6.2)
high_energies = energy_midpoints[high_energy_mask]
ax.plot([high_energies[0], high_energies[-1]], [intercept, intercept],
marker='None',
linestyle='-',
color='k')
'--classifier',
dest='classifier',
default='RF',
choices=['RF', 'KN'],
help='Option to specify classifier used')
p.add_argument('--outdir',
dest='outdir',
default='/home/jbourbeau/public_html/figures/composition',
help='Output directory')
args = p.parse_args()
# Throughout this code, X will represent features while y will represent
# class labels
# Load and preprocess training data
df = load_sim()
feature_list = np.array([
'reco_log_energy', 'ShowerPlane_cos_zenith', 'InIce_log_charge',
'NChannels', 'NStations', 'reco_radius', 'reco_InIce_containment',
'log_s125'
])
num_features = len(feature_list)
X_train_std, X_test_std, y_train, y_test = get_train_test_sets(
df, feature_list)
pipeline = get_pipeline(args.classifier)
train_sizes, train_scores, test_scores =\
learning_curve(estimator=pipeline,
X=X_train_std,
y=y_train,
p.add_argument('-e',
'--energy',
dest='energy',
default='MC',
choices=['MC', 'reco'],
help='Option for a variety of preset bin values')
p.add_argument('--extended',
dest='extended',
default=False,
action='store_true',
help='Use extended energy range')
args = p.parse_args()
checkdir(args.outdir + '/')
# Import ShowerLLH sim reconstructions and cuts to be made
df, cut_dict = load_sim(return_cut_dict=True)
selection_mask = np.array([True] * len(df))
standard_cut_keys = [
'reco_exists', 'reco_zenith', 'reco_IT_containment',
'IceTopMaxSignalInEdge', 'IceTopMaxSignal', 'NChannels',
'LF_InIce_containment'
]
# standard_cut_keys = ['reco_exists', 'reco_zenith', 'reco_IT_containment',
# 'IceTopMaxSignalInEdge', 'IceTopMaxSignal', 'NChannels', 'InIce_containment']
for key in standard_cut_keys:
selection_mask *= cut_dict[key]
df = df[selection_mask]
MC_proton_mask = (df.MC_comp == 'P')
MC_iron_mask = (df.MC_comp == 'Fe')
