# print(eff_isocut)
# print(eff_presel)
ratio.append(eff/eff_presel)
deep_thr.append(thr)
eff_iso.append(eff_isocut/eff_presel)
print(ratio)
print(deep_thr)
print(eff_iso)
# load taus from HLT physics dataset
dataset_rates = Dataset(fileName_rates, treeName_in, treeName_gen)
taus_rates = dataset_rates.get_taus()
tau_1_rates = taus_rates[0]
tau_2_rates = taus_rates[1]
Nev_den = len(dataset_rates.get_gen_events())
# Pt_thr_list = np.linspace(30, 101, 35)
Pt_thr_list = pt_bins
rate_list = []
yerr_rate = np.zeros((2, len(Pt_thr_list)))
for i, Pt_thr in enumerate(Pt_thr_list):
tau_1_mask = deepTau_selection_ptdep(tau_1_rates, pt_bins, deep_thr) & reco_tau_selection(tau_1_rates, minPt=Pt_thr)
tau_2_mask = deepTau_selection_ptdep(tau_2_rates, pt_bins, deep_thr) & reco_tau_selection(tau_2_rates, minPt=Pt_thr)
Nev_num = ak.sum(ditau_selection(tau_1_mask, tau_2_mask))
rate, rate_err_low, rate_err_up = compute_rate_witherr(Nev_num, Nev_den, is_MC=False, L1rate=L1rate_bm)
rate_list.append(rate)
yerr_rate[0][i] = rate_err_low
yerr_rate[1][i] = rate_err_up
# print(rate_list)
with PdfPages(plot_path + "optimization_" + plot_name + ".pdf") as pdf:
original_taus = dataset_eff.get_taus(apply_selection=False)
# get sample for rate computation
print("Loading sample for rate")
if args.qcd:
# get QCD sample
QCD_taus_list = []
QCD_xs_list = []
QCD_den_list = []
with open(QCD_fileJson, "r") as json_file:
samples = json.load(json_file)
for key, value in samples.items():
data = Dataset(data_path + value[0], treeName_in, treeName_gen)
QCD_taus_list.append(data.get_taus())
QCD_xs_list.append(value[1])
QCD_den_list.append(len(data.get_gen_events()))
print(QCD_xs_list)
else:
# get HLT physics sample
dataset_rates = Dataset(data_path + fileName_rates, treeName_in,
treeName_gen)
taus_rates = dataset_rates.get_taus()
Nev_den = len(dataset_rates.get_gen_events())
with PdfPages(plot_path + 'eff_vs_rate_{}.pdf'.format(plot_name)) as pdf:
print("Getting reasonable thresholds")
thr_list = np.linspace(0.0, 1.0, num=100)
for Pt_thr in Pt_thr_list: