def scale_factors(channel, cut, apply_signal_cut=True, mt_cut=False):
tchan = fit_sfs[channel][0]
top = fit_sfs[channel][1]
wzjets = fit_sfs[channel][2]
if "2j1t" not in cut:
tchan = 1
top = 1
wzjets = 1
qcd = top
if apply_signal_cut:
if "mva_loose" in cut:
qcd = qcd * qcd_cut_SF["mva"]["loose"][channel]
elif "cutbased_final" in cut:
qcd = qcd * qcd_cut_SF["cutbased"]["final"][channel]
if mt_cut is False:
qcd = qcd * qcd_mt_fit_sfs[channel]
else:
qcd = qcd * load_qcd_sf(channel, mt_cut)
ret = [
(["tchan"], tchan, -1),
(["TTJets", "tWchan", "schan"], top, -1),
(["qcd"], qcd, -1),
(["WJets", "diboson", "DYJets"], wzjets, -1),
]
return ret
def scale_factors(channel, cut, apply_signal_cut=True, mt_cut=False):
tchan, tchan_err = fit_sfs[channel][0]
top, top_err = fit_sfs[channel][1]
wzjets, wzjets_err = fit_sfs[channel][2]
if "2j1t" not in cut:
tchan = 1
top = 1
wzjets = 1
qcd = top
if apply_signal_cut:
if "mva_loose" in cut:
qcd = qcd * qcd_cut_SF['mva']['loose'][channel]
elif "cutbased_final" in cut:
qcd = qcd * qcd_cut_SF['cutbased']['final'][channel]
if mt_cut is False:
qcd_sf, qcd_err = qcd_mt_fit_sfs[channel]
else:
qcd_sf, qcd_err = load_qcd_sf(channel, mt_cut, do_uncertainty=True)
qcd = qcd * qcd_sf
# Calculate the total error in the QCD scale factor from the error in
# the MET fit (qcd_err) and the BDT fit (top_err) assuming uncorrelate
# errors.
rel_qcd_err = qcd_err / qcd_sf
rel_top_err = top_err / top
tot_rel_qcd_err = math.sqrt(sum([x**2 for x in [rel_top_err, rel_qcd_err]]))
tot_qcd_err = tot_rel_qcd_err * qcd
ret = [
(['tchan'], tchan, tchan_err),
(['TTJets', 'tWchan', 'schan'], top, top_err),
(['qcd'], qcd, tot_qcd_err),
(['WJets', 'diboson', 'DYJets'], wzjets, wzjets_err)
]
return ret
from plots.common.tdrstyle import tdrstyle
from plots.fit_scale_factors import fitpars_process
from plots.qcd_scale_factors import load_qcd_sf, qcd_cut_SF
from plots.vars import varnames
from plots.common.histogram import calc_int_err
import os, copy, math
import numpy as np
channels_pretty = {"mu":"Muon", "ele":"Electron"}
#Determined from the QCD fit
qcd_mt_fit_sfs = dict()
qcd_mt_fit_sfs['mu'] = load_qcd_sf('mu', 50, do_uncertainty=True)
qcd_mt_fit_sfs['ele'] = load_qcd_sf('ele', 45, do_uncertainty=True)
#Determined as the ratio of the integral of anti-iso data after full selection / full selection minus MVA
#See qcd_scale_factors.ipynb for determination
qcd_loose_to_MVA_sfs = qcd_cut_SF['mva']['loose']
fit_sfs = dict()
fit_sfs['mu'] = [x[1:] for x in fitpars_process['final_2j1t_mva']['mu']]
fit_sfs['ele'] = [x[1:] for x in fitpars_process['final_2j1t_mva']['ele']]
# FIXME: load from file
# final_fit/results/ele__mva_BDT_with_top_mass_C_eta_lj_el_pt_mt_el_pt_bj_mass_bj_met_mass_lj.txt
# final_fit/results/mu__mva_BDT_with_top_mass_eta_lj_C_mu_pt_mt_mu_met_mass_bj_pt_bj_mass_lj.txt
Rab = 0.289989 #tchan top
from plots.common.cross_sections import lumis
from plots.common.tdrstyle import tdrstyle
from plots.fit_scale_factors import fitpars_process
from plots.qcd_scale_factors import load_qcd_sf, qcd_cut_SF
from plots.vars import varnames
from plots.common.histogram import calc_int_err
import os
channels_pretty = {"mu": "Muon", "ele": "Electron"}
# Determined from the QCD fit
qcd_mt_fit_sfs = dict()
qcd_mt_fit_sfs["mu"] = load_qcd_sf("mu", 50)
qcd_mt_fit_sfs["ele"] = load_qcd_sf("ele", 45)
# Determined as the ratio of the integral of anti-iso data after full selection / full selection minus MVA
# See qcd_scale_factors.ipynb for determination
qcd_loose_to_MVA_sfs = qcd_cut_SF["mva"]["loose"]
fit_sfs = dict()
fit_sfs["mu"] = [x[1] for x in fitpars_process["final_2j1t_mva"]["mu"]]
fit_sfs["ele"] = [x[1] for x in fitpars_process["final_2j1t_mva"]["ele"]]
def scale_factors(channel, cut, apply_signal_cut=True, mt_cut=False):
tchan = fit_sfs[channel][0]
top = fit_sfs[channel][1]
wzjets = fit_sfs[channel][2]
