def get_effs_per_track_and_p_station(tracks, data, event_ids, stations='12'):
tracks_eff_before = pandas.DataFrame(columns=['Eff', 'Momentum'])
for event_id in event_ids:
if stations == '12':
labels = tracks[event_id]['labels12']
event = data[data.EventID == event_id]
event = event[(event.StatNb == 1) + (event.StatNb == 2)]
elif stations == '34':
labels = tracks[event_id]['labels34']
event = data[data.EventID == event_id]
event = event[(event.StatNb == 3) + (event.StatNb == 4)]
true_labels = event.Label.values
trm = TracksReconstractionMetrics(0.2)
trm.fit(labels, event)
eff = trm.efficiencies_
for num, lab in enumerate(numpy.unique(labels[labels != -1])):
valid_labs = true_labels[labels == lab]
unique, counts = numpy.unique(valid_labs[valid_labs != -1],
return_counts=True)
if len(unique) == 0:
continue
true_lab = unique[counts == counts.max()][0]
track = event[true_labels == true_lab]
p_one = numpy.sqrt(track.Px.values**2 + track.Py.values**2 +
track.Pz.values**2).mean()
eff_one = eff[num]
tracks_eff_before.loc[len(tracks_eff_before)] = [eff_one, p_one]
return tracks_eff_before
def get_effs_per_track_and_p_station(tracks, data, event_ids, stations='12'):
tracks_eff_before = pandas.DataFrame(columns=['Eff', 'Momentum'])
for event_id in event_ids:
if stations == '12':
labels = tracks[event_id]['labels12']
event = data[data.EventID == event_id]
event = event[(event.StatNb == 1) + (event.StatNb == 2)]
elif stations == '34':
labels = tracks[event_id]['labels34']
event = data[data.EventID == event_id]
event = event[(event.StatNb == 3) + (event.StatNb == 4)]
true_labels = event.Label.values
trm = TracksReconstractionMetrics(0.2)
trm.fit(labels, event)
eff = trm.efficiencies_
for num, lab in enumerate(numpy.unique(labels[labels != -1])):
valid_labs = true_labels[labels == lab]
unique, counts = numpy.unique(valid_labs[valid_labs!=-1], return_counts=True)
if len(unique) == 0:
continue
true_lab = unique[counts == counts.max()][0]
track = event[true_labels == true_lab]
p_one = numpy.sqrt(track.Px.values**2+track.Py.values**2+track.Pz.values**2).mean()
eff_one = eff[num]
tracks_eff_before.loc[len(tracks_eff_before)] = [eff_one, p_one]
return tracks_eff_before
def get_eff_value(params):
event_id, data, tracks, clf = params
# Get an event
event = data[data.EventID == event_id]
event12 = event[(event.StatNb == 1) + (event.StatNb == 2)]
event34 = event[(event.StatNb == 3) + (event.StatNb == 4)]
labels12 = tracks[event_id]['labels12']
tracks_params12 = tracks[event_id]['params12']
labels34 = tracks[event_id]['labels34']
tracks_params34 = tracks[event_id]['params34']
# Quality metrics of the reconstruction
trm12 = TracksReconstractionMetrics(0.2, n_tracks=2)
trm12.fit(labels12, event12)
# Quality metrics of the reconstruction
trm34 = TracksReconstractionMetrics(0.2, n_tracks=2)
trm34.fit(labels34, event34)
# Combination of the tracks before and after the magnet
if clf == None:
comb = Combinator()
else:
comb = SuperCombinator(classifier=clf)
comb.combine(tracks_params12, tracks_params34)
# Quality of the combination
cq = CombinatorQuality()
cq.fit(labels12, labels34, comb.tracks_combinations_, comb.charges_,
comb.inv_momentums_, event12, event34)
# Overall quality
line = numpy.array([
1, (trm12.recognition_efficiency_y_ == 1) * 1,
(trm12.recognition_efficiency_stereo_ >= 1.) * 1,
(trm12.recognition_efficiency_ == 1) * 1,
(trm34.recognition_efficiency_y_ == 1) * 1,
(trm34.recognition_efficiency_stereo_ >= 1.) * 1,
(trm34.recognition_efficiency_ == 1) * 1, (cq.n_combined_ >= 2) * 1,
(cq.reco_eff_ == 1) * 1
]).cumprod()
return line[-1]
def get_eff_value(params):
event_id, data, tracks, clf = params
# Get an event
event = data[data.EventID == event_id]
event12 = event[(event.StatNb == 1) + (event.StatNb == 2)]
event34 = event[(event.StatNb == 3) + (event.StatNb == 4)]
labels12 = tracks[event_id]['labels12']
tracks_params12 = tracks[event_id]['params12']
labels34 = tracks[event_id]['labels34']
tracks_params34 = tracks[event_id]['params34']
# Quality metrics of the reconstruction
trm12 = TracksReconstractionMetrics(0.2, n_tracks=2)
trm12.fit(labels12, event12)
# Quality metrics of the reconstruction
trm34 = TracksReconstractionMetrics(0.2, n_tracks=2)
trm34.fit(labels34, event34)
# Combination of the tracks before and after the magnet
if clf == None:
comb = Combinator()
else:
comb = SuperCombinator(classifier=clf)
comb.combine(tracks_params12, tracks_params34)
# Quality of the combination
cq = CombinatorQuality()
cq.fit(labels12, labels34, comb.tracks_combinations_, comb.charges_, comb.inv_momentums_, event12, event34)
# Overall quality
line = numpy.array([1,
(trm12.recognition_efficiency_y_ == 1)*1,
(trm12.recognition_efficiency_stereo_ >= 1.)*1,
(trm12.recognition_efficiency_ == 1)*1,
(trm34.recognition_efficiency_y_ == 1)*1,
(trm34.recognition_efficiency_stereo_ >= 1.)*1,
(trm34.recognition_efficiency_ == 1)*1,
(cq.n_combined_ >= 2)*1,
(cq.reco_eff_ == 1)*1]).cumprod()
return line[-1]