def makeCardFromHist(out_cache,
hist_name,
nonprompt_scale=1,
signal_scale=1,
bkg_scale=1,
overflow='all',
ext='',
systematics=True):
print("Writing cards using histogram:", hist_name)
card_dir = os.path.expandvars('$TWHOME/data/cards/')
if not os.path.isdir(card_dir):
os.makedirs(card_dir)
data_card = card_dir + hist_name + ext + '_card.txt'
shape_file = card_dir + hist_name + ext + '_shapes.root'
histogram = out_cache[hist_name].copy()
#histogram = histogram.rebin('mass', bins[hist_name]['bins'])
# scale some processes
scales = {
'ttbar': nonprompt_scale,
'topW_v2': signal_scale,
'TTW': bkg_scale, # only scale the most important backgrounds
'TTZ': bkg_scale,
'TTH': bkg_scale,
}
histogram.scale(scales, axis='dataset')
## making a histogram for pseudo observation. this hurts, but rn it seems to be the best option
data_counts = np.asarray(
np.round(
histogram[notdata].integrate('dataset').values(
overflow=overflow)[()], 0), int)
data_hist = histogram['topW_v2']
data_hist.clear()
data_hist_bins = data_hist.axes()[1]
for i, edge in enumerate(data_hist_bins.edges(overflow=overflow)):
if i >= len(data_counts): break
for y in range(data_counts[i]):
data_hist.fill(**{
'dataset': 'data',
data_hist_bins.name: edge + 0.0001
})
other_sel = re.compile('(TTTT|diboson|DY|rare)')
##observation = hist.export1d(histogram['pseudodata'].integrate('dataset'), overflow=overflow)
#observation = hist.export1d(data_hist['data'].integrate('dataset'), overflow=overflow)
observation = hist.export1d(histogram[notdata].integrate('dataset'),
overflow=overflow)
tw = hist.export1d(histogram['topW_v2'].integrate('dataset'),
overflow=overflow)
ttw = hist.export1d(histogram['TTW'].integrate('dataset'),
overflow=overflow)
ttz = hist.export1d(histogram['TTZ'].integrate('dataset'),
overflow=overflow)
tth = hist.export1d(histogram['TTH'].integrate('dataset'),
overflow=overflow)
rare = hist.export1d(histogram[other_sel].integrate('dataset'),
overflow=overflow)
nonprompt = hist.export1d(histogram['ttbar'].integrate('dataset'),
overflow=overflow)
fout = uproot3.recreate(shape_file)
fout["signal"] = tw
fout["nonprompt"] = nonprompt
fout["ttw"] = ttw
fout["ttz"] = ttz
fout["tth"] = tth
fout["rare"] = rare
fout["data_obs"] = observation
fout.close()
# Get the total yields to write into a data card
totals = {}
totals['signal'] = histogram['topW_v2'].integrate('dataset').values(
overflow=overflow)[()].sum()
totals['ttw'] = histogram['TTW'].integrate('dataset').values(
overflow=overflow)[()].sum()
totals['ttz'] = histogram['TTZ'].integrate('dataset').values(
overflow=overflow)[()].sum()
totals['tth'] = histogram['TTH'].integrate('dataset').values(
overflow=overflow)[()].sum()
totals['rare'] = histogram['rare'].integrate('dataset').values(
overflow=overflow)[()].sum()
totals['nonprompt'] = histogram['ttbar'].integrate('dataset').values(
overflow=overflow)[()].sum()
##totals['observation'] = histogram['pseudodata'].integrate('dataset').values(overflow=overflow)[()].sum()
#totals['observation'] = int(sum(data_hist['data'].sum('dataset').values(overflow=overflow)[()]))
totals['observation'] = histogram[notdata].integrate('dataset').values(
overflow=overflow)[()].sum()
print("{:30}{:.2f}".format("Signal expectation:", totals['signal']))
print("{:30}{:.2f}".format("Non-prompt background:", totals['nonprompt']))
print("{:30}{:.2f}".format(
"t(t)X(X)/rare background:",
totals['ttw'] + totals['ttz'] + totals['tth'] + totals['rare']))
print("{:30}{:.2f}".format("Observation:", totals['observation']))
# set up the card
card = dataCard()
card.reset()
card.setPrecision(3)
# add the uncertainties (just flat ones for now)
card.addUncertainty('lumi', 'lnN')
card.addUncertainty('ttw_norm', 'lnN')
card.addUncertainty('ttz_norm', 'lnN')
card.addUncertainty('tth_norm', 'lnN')
card.addUncertainty('rare_norm', 'lnN')
card.addUncertainty('fake', 'lnN')
# add the single bin
card.addBin('Bin0', ['ttw', 'ttz', 'tth', 'rare', 'nonprompt'], 'Bin0')
card.specifyExpectation('Bin0', 'signal', totals['signal'])
card.specifyExpectation('Bin0', 'ttw', totals['ttw'])
card.specifyExpectation('Bin0', 'ttz', totals['ttz'])
card.specifyExpectation('Bin0', 'tth', totals['tth'])
card.specifyExpectation('Bin0', 'rare', totals['rare'])
card.specifyExpectation('Bin0', 'nonprompt', totals['nonprompt'])
# set uncertainties
if systematics:
card.specifyUncertainty('ttw_norm', 'Bin0', 'ttw', 1.15)
card.specifyUncertainty('ttz_norm', 'Bin0', 'ttz', 1.10)
card.specifyUncertainty('tth_norm', 'Bin0', 'tth', 1.20)
card.specifyUncertainty('rare_norm', 'Bin0', 'rare', 1.20)
card.specifyUncertainty('fake', 'Bin0', 'nonprompt', 1.25)
card.specifyFlatUncertainty('lumi', 1.03)
## observation
#card.specifyObservation('Bin0', int(round(totals['observation'],0)))
card.specifyObservation('Bin0', totals['observation'])
print("Done.\n")
return card.writeToFile(data_card, shapeFile=shape_file)
'''
from Tools.helpers import export1d
year = 2018
card_SR_ideal_noSyst = makeCardFromHist('mjj_max_tight',
nonprompt_scale=1,
signal_scale=1,
bkg_scale=1,
overflow='all',
ext='_idealNoSyst',
systematics=False)
card = dataCard()
import mplhep
plt.style.use(mplhep.style.CMS)
plt.figure()
plt.plot(results_SR['r'][1:],
results_SR['deltaNLL'][1:] * 2,
label=r'Expected ($M_{jj}$) SR',
c='black') #, linewidths=2)
plt.xlabel(r'$r$')
plt.ylabel(r'$-2\Delta ln L$')
plt.legend()