from higgstautau import tauid
from higgstautau.tauid.p851 import selection, nvtx_to_category, CATEGORIES
from higgstautau.tauid.common import LEVELS, PRONGS
from higgstautau.tauid import EFFIC_UNCERT_2011 as EFFIC_UNCERT
from samples import MC_TauID
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.)
from higgstautau import tauid
from higgstautau.tauid.p851 import selection, nvtx_to_category, CATEGORIES
from higgstautau.tauid.common import LEVELS, PRONGS
from higgstautau.tauid import EFFIC_UNCERT_2011 as EFFIC_UNCERT
from samples import MC_TauID
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='--')
prong, category)
print curr_effic
if prev_effic is not None and abs(curr_effic - prev_effic) < min_change:
break
if curr_effic > target:
a = mid
else:
b = mid
prev_effic = curr_effic
print efficiency(ztautau, selection + curr_shift, prong, category)
print list(curr_shift.y())
print list((selection + curr_shift).y())
print "=" * 20
return curr_shift
loose = selection('loose', prong, cat_str)
medium = selection('medium', prong, cat_str)
tight = selection('tight', prong, cat_str)
shift_loose_low = medium - loose
shift_loose_high = 0. - loose
shift_medium_low = tight - medium
shift_medium_high = loose - medium
shift_tight_low = 1. - tight
shift_tight_high = medium - tight
uncert_loose = EFFIC_UNCERT['loose'][prong]
uncert_medium = EFFIC_UNCERT['medium'][prong]
uncert_tight = EFFIC_UNCERT['tight'][prong]
