def efficiency_uncertainty(sample, selection, prong, category):
category = category.replace(
'tau_numberOfVertices', 'number_of_good_vertices')
#total = sample.events(Cut('trueTau1_nProng==%d' % prong) & category)
total = sample.events(Cut('tau1_numTrack==%d' % prong) & category)
passing_high = 0.
passing_low = 0.
cut = Cut('tau1_numTrack==%d' % prong) & category
for weight, event in sample.iter(cut):
pt = event.tau1_fourvect.Pt()
high_score, low_score = uncertainty(
event.tau1_BDTJetScore,
pt,
event.tau1_numTrack,
event.number_of_good_vertices)
selection_cut = selection.Eval(pt)
if high_score > selection_cut:
passing_high += weight
if low_score > selection_cut:
passing_low += weight
return passing_high / total, passing_low / total
import numpy as np
from matplotlib import pyplot as plt
from tauid_uncertainty import efficiency, efficiency_uncertainty
pt = 40000
for prong in tauid.PRONGS:
loose = selection('loose', prong, 3).Eval(pt)
medium = selection('medium', prong, 3).Eval(pt)
tight = selection('tight', prong, 3).Eval(pt)
scores = np.linspace(0.5, 1., 1000, endpoint=True)
high = []
low = []
for score in scores:
high_score, low_score = tauid.uncertainty(score, pt, prong, 3)
high.append(high_score - score)
low.append(low_score - score)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(scores, low, high, facecolor='yellow', linewidth=0)
ax.vlines([loose, medium, tight], [-.5, -.5, -.5], [.5, .5, .5],
color='k',
linestyles='--')
ax.axhline(0., 0., 1.)
ax.set_xlim(scores[0], scores[-1])
ax.set_ylim(min(low) * 1.2, max(high) * 1.2)
# working point labels
for wp, loc in (('loose', loose), ('medium', medium), ('tight', tight)):
plt.text(loc + 0.005,
import numpy as np
from matplotlib import pyplot as plt
from tauid_uncertainty import efficiency, efficiency_uncertainty
pt = 40000
for prong in tauid.PRONGS:
loose = selection('loose', prong, 3).Eval(pt)
medium = selection('medium', prong, 3).Eval(pt)
tight = selection('tight', prong, 3).Eval(pt)
scores = np.linspace(0.5, 1., 1000, endpoint=True)
high = []
low = []
for score in scores:
high_score, low_score = tauid.uncertainty(score, pt, prong, 3)
high.append(high_score - score)
low.append(low_score - score)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill_between(scores, low, high, facecolor='yellow', linewidth=0)
ax.vlines([loose, medium, tight], [-.5, -.5, -.5], [.5, .5, .5], color='k',
linestyles='--')
ax.axhline(0., 0., 1.)
ax.set_xlim(scores[0], scores[-1])
ax.set_ylim(min(low)*1.2, max(high)*1.2)
# working point labels
for wp, loc in (('loose', loose), ('medium', medium), ('tight', tight)):
plt.text(loc+0.005, ax.get_ylim()[1] / 2., wp,
ha='left', va='center', color='black', rotation=90)
