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plot_config_ggF.py
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plot_config_ggF.py
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# from samples_HHbbmm import *
import varial
import sys
import ROOT as R
import array
import yaml
from Htautau import *
import argparse
# import wrappers
''' usage:
python run_plotting_with_varial.py plot_config_ggF.py input_path plot_tag &
'''
####################
# General Settings #
####################
parser = argparse.ArgumentParser(description='Plot btag variables from ROOT files with weights.')
parser.add_argument('dummy_input', type=str, nargs='+', help='Place holder, dummy argument')
parser.add_argument('--input_path', type=str, help='Path to the folder containing ROOT files')
parser.add_argument('--mass', type=str, help='mass of signal to plot')
parser.add_argument('--era', type=str, default='2022postEE', help='input era, 2022postEE or 2022EE')
parser.add_argument('--channel', type=str, default='mt', help='decay channel, mt, et, tt, em')
parser.add_argument('--region', type=str, default='nob', help='btag selection, nob, btag')
parser.add_argument('--PNN', type=int, default=0, help='run PNN score or not')
parser.add_argument('--signal_type', type=str, default='ggH', help='ggH or bbH or ggH_bbH')
args = parser.parse_args()
input_path= args.input_path
mass = args.mass
PNN = args.PNN
channel_name = args.channel
era = args.era
signal_type = args.signal_type
region = args.region
# input_path = sys.argv[3]
# mass = int(sys.argv[4])
# PNN = int(sys.argv[5])
# channel_name = str(sys.argv[6])
# era = sys.argv[7]
# run_fakes = int(sys.argv[8])
# region = sys.argv[9]
# run_dy = True
if channel_name == "et":
channel_index = 1
elif channel_name == "mt":
channel_index = 2
else:
channel_index = 1
embedding_SF = {"2016preVFPmt": 1.15,"2016preVFPet": 1.15,"2016postVFPmt": 0.75,"2016postVFPet": 1.05,"2017et": 0.9,"2017mt": 0.9,"2018et": 0.85,"2018mt": 1 }
luminosities = {
"2016preVFP": 15.1158e3,
"2016postVFP": 10.0093e3,
"2017": 41.5e3,
"2018": 59.8e3,
"2022EE":7.875e3,
"2022postEE": 26.337e3 }
# 2022 golden json
# C 4.953
# D 2.922
# E 5.672
# F 17.610
# G 3.055
# postEE 26.337
if era in luminosities:
lumi = luminosities[era]
else:
print("Wrong year provided")
sys.exit(-10)
print(mass, sys.argv[4],"===================================")
samples_name = 'sample_database/datasets_plotting.yaml'
samples_f = open(samples_name, "r")
samples_list = yaml.load(samples_f, Loader = yaml.Loader)
# 'embedding',
stacking_order = [
'fakes',
'Z->tautau',
'Z->ll',
'ttbar',
'wjets',
'Single H', ###
'other',
]
if int(mass) > 200:
scale =10
elif int(mass) >= 100:
scale = 200
else:
scale =1000
sample_colors = {
# 'embedding' : 838,
# 'electroweak_boson' : 804,
# 'electroweak_boson' : 804,
# 'dyjets' : 814,
'fakes' : 617,
'Z->ll' : 814,
'Z->tautau' : 864,
'ttbar' : 400,
'wjets' : 632,
'Single H': 838,
'other' : 920,
"%s * bbH %s" %(scale,mass) : 416,
"%s * ggH %s" %(scale,mass) : 632,
# 'diboson' : 596,
# 'ggh_hbb' : 596,
# 'ggh_htautau' : 838,
# 'singletop' : 920,
# 'ttbarFake' : 596,
# 'vbf_htautau' : 814,
# 'rem_hbb': 615,
}
varial.settings.max_num_processes = 80
varial.settings.rootfile_postfixes += [ '.png'] #'.pdf'
varial.settings.stacking_order = stacking_order
# try to find output on disk and don't run a step if present
enable_reuse_step = True
#################
# Plot Settings #
#################
name = sys.argv[2]
# 'DR-ttbar-Run2-fixbtag_nprebtagjets_newgensum'
weight_dict_mt = {
"2022postEE": 'Xsec * {0} * puweight * genWeight/genEventSumW * btag_weight * FF_weight *id_wgt_tau_vsJet_Medium_2 * iso_wgt_mu_1 *trg_wgt_ditau_crosstau_2 * id_wgt_tau_vsMu_Tight_2 * id_wgt_mu_1'.format(lumi), #
"2022EE": 'Xsec * {0} * puweight * genWeight/genEventSumW * btag_weight * FF_weight * id_wgt_tau_vsJet_Medium_2 * iso_wgt_mu_1 * trg_wgt_ditau_crosstau_2 * id_wgt_tau_vsMu_Tight_2 * id_wgt_mu_1'.format(lumi), #
"2018": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_mu_1 * id_wgt_tau_vsEle_VVLoose_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_Tight_2 * iso_wgt_mu_1 * btag_weight * puweight '.format(lumi),
"2017": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_mu_1 * id_wgt_tau_vsEle_VVLoose_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_Tight_2 * iso_wgt_mu_1 * btag_weight * puweight '.format(lumi),
"2016": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_mu_1 * id_wgt_tau_vsEle_VVLoose_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_Tight_2 * iso_wgt_mu_1 * btag_weight * puweight '.format(lumi),
}
weight_dict_et = {
"2022postEE": 'Xsec * {0}* puweight * genWeight/genEventSumW * id_wgt_tau_vsEle_Tight_2 * btag_weight * FF_weight * id_wgt_tau_vsJet_Medium_2 * id_wgt_ele_wpTight * trg_wgt_ditau_crosstau_2 * trg_wgt_single_ele30 ' .format(lumi),
"2022EE": 'Xsec * {0}* puweight * genWeight/genEventSumW * id_wgt_tau_vsEle_Tight_2 * * btag_weight * FF_weight * id_wgt_tau_vsJet_Medium_2 * id_wgt_ele_wpTight * trg_wgt_ditau_crosstau_2 * trg_wgt_single_ele30 ' .format(lumi),
"2018": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_tau_vsEle_Tight_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_VLoose_2 * btag_weight * puweight '.format(lumi),
"2017": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_tau_vsEle_Tight_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_VLoose_2 * btag_weight * puweight '.format(lumi),
"2016": 'Xsec * {0} * genWeight/genEventSumW * id_wgt_tau_vsEle_Tight_2 * id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsMu_VLoose_2 * btag_weight * puweight '.format(lumi),
}
weight_dict_tt = {
"2022postEE": 'Xsec * {0}* puweight * genWeight/genEventSumW * btag_weight *id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsJet_Medium_1 * FF_weight * trg_wgt_ditau_crosstau_1 *trg_wgt_ditau_crosstau_2 '.format(lumi),
"2022EE": 'Xsec * {0}* puweight * genWeight/genEventSumW * btag_weight *id_wgt_tau_vsJet_Medium_2 * id_wgt_tau_vsJet_Medium_1 * FF_weight * trg_wgt_ditau_crosstau_1 *trg_wgt_ditau_crosstau_2 '.format(lumi),
}
weight_dict_em = {
## id_wgt_tau_vsEle_VVLoose_1 * id_wgt_tau_vsEle_VVLoose_2 * id_wgt_tau_vsMu_VLoose_1 * id_wgt_tau_vsMu_VLoose_2 * id_wgt_tau_vsJet_Medium_1 * id_wgt_tau_vsJet_Medium_2
## id_wgt_tau_vsEle_VVLoose_1 * id_wgt_tau_vsEle_VVLoose_2 * id_wgt_tau_vsMu_VLoose_1 * id_wgt_tau_vsMu_VLoose_2 * id_wgt_tau_vsJet_Medium_1 * id_wgt_tau_vsJet_Medium_2
"2022postEE": '(Xsec * genWeight * {0} / genEventSumW)* (trg_wgt_single_ele30 * trg_single_ele30 > 0 + 1 * trg_single_ele30 < 1 ) * id_wgt_ele_wpTight * id_wgt_mu_2 * btag_weight * puweight * (trg_wgt_single_mu24 * trg_single_mu24 > 0 + 1 * trg_single_mu24 < 1 )' .format(lumi) ,
"2022EE": '(Xsec * genWeight * {0} / genEventSumW)* (trg_wgt_single_ele30 * trg_single_ele30 > 0 + 1 * trg_single_ele30 < 1 ) * id_wgt_ele_wpTight * id_wgt_mu_2 * btag_weight * puweight * (trg_wgt_single_mu24 * trg_single_mu24 > 0 + 1 * trg_single_mu24 < 1 )' .format(lumi),
}
if channel_name == "et":
weight_dict = weight_dict_et
elif channel_name == "mt":
weight_dict = weight_dict_mt
elif channel_name == "tt":
weight_dict = weight_dict_tt
elif channel_name == "em":
weight_dict = weight_dict_em
else:
sys.exit()
weight = weight_dict[era]
plot_vars = {}
mt_tot_b = [30, 60, 80, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450, 500]
nob_tight_mT = [0,50.0,60.0,70.0,80.0,90.0,100.0,110.0,120.0,130.0,140.0,150.0,160.0,170.0,180.0,190.0,200.0,225.0,250.0,275.0,300.0,325.0,350.0,400.0,450.0,500.0,600.0,700.0,800.0,900.0,1100.0,1300.0,2100.0,5000.0]
m_fastmtt = [0,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200,220,240,260,300]
met = [0,10,20,30,40,50,60,70,80,90,120,140,200,400]
pt = [30,40,50,60,70,80,90,100,120,140,200,350,500, 700, 1000]
plot_vars.update({
'eta_1' : ('eta_1',';eta_{1};NEvents',30,-2.3,2.3),
'eta_2' : ('eta_2',';eta_{2};NEvents',30,-2.3,2.3),
'eta_fastmtt' : ('eta_fastmtt',';eta_{#tau#tau};NEvents',20,-10,10),
'mTdileptonMET' : ('mTdileptonMET',';mTdileptonMET;NEvents',20,0,200),
'm_fastmtt' : ('m_fastmtt', '; m^{#tau#tau};NEvents', 30, 0, 500),
'm_vis' : ('m_vis', '; m^{vis};NEvents', 30, 0, 300),
'met' : ('met',';met;NEvents',40,0,400),
'metSumEt' : ('metSumEt',';metSumEt;NEvents',20,0,200),
'metphi' : ('metphi',';metphi;NEvents',80,-4,4),
'mt_tot': ('mt_tot', ';m_{T}^{tot} [GeV];NEvents', 50, 0, 500),
'mt_1' : ('mt_1',';mt_{1};NEvents',50,0,200),
'mt_2' : ('mt_2',';mt_{2};NEvents',50,0,200),
'phi_1' : ('phi_1',';phi_{1};NEvents',20,-3.14,3.14),
'phi_2' : ('phi_2',';phi_{2};NEvents',20,-3.14,3.14),
'pt_1' : ('pt_1',';pt_{1};NEvents',20,20,220),
'pt_2' : ('pt_2',';pt_{2};NEvents',20,20,220),
'pt_fastmtt' : ('pt_fastmtt', ';p_{T}^{#tau#tau} ;NEvents', 50, 0, 500),
'pt_tt' : ('pt_tt', ';pt_{tt} ;NEvents', 40, 0, 400),
'pt_vis' : ('pt_vis', ';pt_{vis} ;NEvents', 30, 0, 300),
'pzetamissvis' : ('pzetamissvis',';D_{#zeta};NEvents',25,-150,100),
'deltaR_ditaupair' : ('deltaR_ditaupair', ';#deltaR [GeV];NEvents',25 , 0, 5),
})
if PNN:
plot_vars = {
'mt_tot': ('mt_tot', ';m_{T}^{tot} [GeV];NEvents', 50, 0, 500),
'm_fastmtt' : ('m_fastmtt', '; m^{#tau#tau};NEvents', 30, 0, 500),
'pt_1' : ('pt_1',';pt_{1};NEvents',20,20,220),
'pt_2' : ('pt_2',';pt_{2};NEvents',20,20,220),
'mt_1' : ('mt_1',';mt_{1};NEvents',50,0,200),
'mt_2' : ('mt_2',';mt_{2};NEvents',50,0,200),
}
if int(mass) < 250:
plot_vars.update({
'PNN_%s' %mass : ('PNN_%s' %mass, ';PNN %s [GeV];NEvents' %mass, 20,0,1), })
else:
plot_vars.update({
'dxy_1' : ('dxy_1',';dxy_{1};NEvents',25,-0.05,0.05),
'dxy_2' : ('dxy_2',';dxy_{2};NEvents',25,-0.1,0.1),
'dz_1' : ('dz_1',';dz_{1};NEvents',25,-0.2,0.2),
'dz_2' : ('dz_2',';dz_{2};NEvents',25,-0.2,0.2),
'iso_1' : ('iso_1',';iso_{1};NEvents',20,0,3),
'iso_2' : ('iso_2',';iso_{2};NEvents',20,0,3),
'mass_1' : ('mass_1',';mass_{1};NEvents',30,0,0.1),
'mass_2' : ('mass_2',';mass_{2};NEvents',30,0,3),
'njets' : ('njets',';njets;NEvents',10,0,10),
'phi_fastmtt' : ('phi_fastmtt',';phi_{#tau#tau};NEvents',20,-3.14,3.14),
})
#######################################
# Samples, Selections, and Categories #
#######################################
"""
Xsec: dictionary with key: nick name, value: dictionary of 'xsec', 'era', 'nevents', 'nfiles', 'sample_type'
"""
y_bounds = (100,10e6)
treename = 'ntuple'
input_pattern = [input_path + '/%s*root']
def get_samples(channel, signal_overlay=True, **kwargs):
sf_lumi = 1.
sf_zjb = kwargs.get('sf_zjb', 1.)
##########################################
samples = {}
## for the first run, set rerun = True
for nick in samples_list:
# print(nick)
try:
xsec = samples_list[nick]['xsec']
except:
xsec = 0
sample_type = samples_list[nick]['sample_type']
if os.path.exists(input_path):
if sample_type == 'fakes':
if region == "SR":
samples["FF_Combined"] = ['1', 1, sample_type , ["FF_Combined"] , 0 ]
elif region == "DR_QCD":
samples["FF_QCD"] = ['1', 1, sample_type , ["FF_QCD"] , 0 ]
elif region == "DR_W":
samples["FF_W"] = ['1', 1, sample_type , ["FF_W"] , 0 ]
elif region == "DR_ttbar":
samples["FF_ttbar"] = ['1', 1, sample_type , ["FF_ttbar"] , 0 ]
elif 'vbf' in sample_type or 'ggh_hbb' in sample_type or 'ggh' in sample_type or ("H" in nick and "SUSY" not in nick ) or 'bbHto' in nick:
if "2HDM" not in nick:
pass
elif PNN and "M-%s_2HDM" %mass in nick:
if 'bbH' in signal_type and 'bbHto' in nick:
samples[nick] = ['%s'%scale, 1 , "%s * bbH %s" %(scale, mass) , [nick] , 0 ]
if 'ggH' in signal_type and 'GluGlu' in nick:
samples[nick] = ['%s'%scale , 1 , "%s * ggH %s" %(scale, mass) , [nick] , 0 ]
else:
continue
elif sample_type == 'ttbar':
continue
elif 'dyjets_ee' in sample_type or 'dyjets_mumu' in sample_type:
# samples[nick] = ['1* (gen_match_2 == {0})'.format(channel_index), 1 , "Z->ll" , [nick] , 0 ]
samples[nick] = ['1', 1 , "Z->ll" , [nick] , 0 ]
elif 'dyjets_tautau' in sample_type:
samples[nick] = ['1', 1 , "Z->tautau" , [nick] , 0 ]
else:
if 'data' not in sample_type:
if 'electroweak_boson' in sample_type or 'diboson' in sample_type or 'singletop' in sample_type :
samples[nick] = ['1', 1., 'other' , [nick] , 0 ]
elif 'wjets' in sample_type:
samples[nick] = ['1', 1., 'wjets' , [nick] , 0 ]
else:
print("sample type: ", sample_type, "sample name: ", nick)
samples[nick] = ['1', 1., sample_type , [nick] , 0 ]
ttbar_list=[]
for i in range(0, 11):
for v in ['TTtoLNu2Q', 'TTto2L2Nu', 'TTto4Q']:
ttbar_list.append('{0}_{1}'.format(i, v),)
samples.update({
'ttbar': ['1', 1 , 'ttbar' , ['TTtoLNu2Q', 'TTto2L2Nu', 'TTto4Q'] + ttbar_list , 0 ],
'Data': ["1", 1, 'Data', ['SingleMuon_Run201', 'Tau_Run201', 'EGamma_Run201', 'SingleElectron_Run201', 'DoubleMuon_Run201','MuonEG_Run2', "Muon_Sep", "EGamma_Sep", "SingleMuon_Sep", "Tau_Sep", "Data", "MuonEG_Sep", "Run2022" ],0],
})
return samples
Htautau = Htautau_selections()
if channel_name == "mt":
lepton_selection = combinecut( Htautau.mt_triggers_selections[era],Htautau.muon_selections,Htautau.lepton_veto,Htautau.mt_tau_selections[era])
anti_selection = combinecut( Htautau.mt_triggers_selections[era],Htautau.muon_selections,Htautau.lepton_veto,"(id_tau_vsMu_Loose_2 > 0 && id_tau_vsJet_Medium_2 < 1 && id_tau_vsEle_VVLoose_2 > 0 && pt_2 > 30 ) ")
elif channel_name == "et":
lepton_selection = combinecut( Htautau.et_triggers_selections[era],Htautau.electron_selections,Htautau.lepton_veto,Htautau.et_tau_selections[era])
anti_selection = combinecut( Htautau.et_triggers_selections[era],Htautau.electron_selections,Htautau.lepton_veto, "(id_tau_vsMu_VLoose_2 > 0 && id_tau_vsEle_Tight_2 > 0 && id_tau_vsJet_Medium_2 <1 && pt_2 > 30 )")
elif channel_name == "tt":
lepton_selection = combinecut( Htautau.tt_triggers_selections[era],Htautau.lepton_veto, Htautau.tt_leadingtau_selections, Htautau.tt_secondtau_selections)
anti_selection = combinecut( Htautau.tt_triggers_selections[era],Htautau.lepton_veto, Htautau.tt_secondtau_selections, "(id_tau_vsJet_Medium_1 <1 && dz_1 < 0.2 && pt_1 > 40 && eta_1 < 2.1 && eta_1 > -2.1 && id_tau_vsEle_VVLoose_1 > 0 &&id_tau_vsMu_VLoose_1 > 0 )")
elif channel_name == "em":
lepton_selection = combinecut(Htautau.em_electron_selection, Htautau.em_muon_selection, Htautau.lepton_veto, Htautau.em_triggers_selections[era])
the_samples_dict = get_samples(
channel='Htt',
signal_overlay=True,
sf_zjb = 1.0,
)
regions = {
# 'nob': '1> 0 '
"nob" :Htautau.nob,
"btag" : Htautau.btag,
"nob_PNN0p8" : combinecut( "PNN_100 > 0.8", Htautau.nob),
"btag_PNN0p8" : combinecut("PNN_100 > 0.8", Htautau.btag),
# # "nob_tight_mT" : combinecut(Htautau.nob, Htautau.tight_mT,lepton_selection, Htautau.W_true_only, Htautau.opposite_sign,"mt_1 < 40"),
# "nob_AntiID" : combinecut( Htautau.nob, anti_selection, Htautau.W_true_only, Htautau.ttbar_true_only,Htautau.opposite_sign, "mt_1 < 70") if (channel_name != 'tt' and channel_name != 'em') else combinecut( Htautau.nob, anti_selection, Htautau.W_true_only, Htautau.opposite_sign),
# "btag_AntiID" : combinecut( Htautau.btag, anti_selection, Htautau.W_true_only,Htautau.ttbar_true_only, Htautau.opposite_sign, "mt_1 < 70") if (channel_name != 'tt' and channel_name != 'em') else combinecut( Htautau.btag, anti_selection, Htautau.W_true_only, Htautau.opposite_sign),
}
selections = [ combinecut (lepton_selection, Htautau.W_true_only, Htautau.opposite_sign )]
if (channel_name == 'mt') or (channel_name =='et'):
for i in regions:
regions[i] = combinecut(regions[i], Htautau.ttbar_true_only, "mt_1 < 70")
if channel_name == 'et' and era =="2022postEE":
for i in regions:
regions[i] = combinecut(regions[i], '( ! (phi_2>1.8 && phi_2< 2.7 && eta_2 > 1.5 && eta_2<2.2) )')
if region == "SR":
pass
elif region == "DR_QCD":
regions["DR_QCD"] = combinecut(Htautau.DR_QCD_tt, lepton_selection)
regions["AntiDR_QCD"] = combinecut(Htautau.DR_QCD_tt, anti_selection)
regions["DR_QCD_btag"] = combinecut(Htautau.btag, Htautau.DR_QCD_tt, lepton_selection)
elif region == "DR_W":
regions["DR_W"] = combinecut(Htautau.DR_W, lepton_selection, Htautau.W_true_only)
regions["AntiDR_W"] = combinecut(Htautau.DR_W, anti_selection, Htautau.W_true_only)
#regions["DR_W_btag"] = combinecut(Htautau.btag, Htautau.DR_W_tt ,anti_selection)
elif region == "DR_ttbar":
regions["DR_ttbar"] = combinecut(Htautau.DR_ttbar, lepton_selection,Htautau.ttbar_true_only)
regions["AntiDR_ttbar"] = combinecut(Htautau.DR_ttbar, anti_selection,Htautau.ttbar_true_only)
#regions["DR_ttbar_btag"] = combinecut(Htautau.btag, Htautau.DR_ttbar_tt ,anti_selection)
else:
print("wrong region provided!! region supported: SR, DR_QCD,DR_ttbar, DR_W ")
sys.exit(0)
print( "printing cuts applied: ",regions['nob_PNN0p8'] , "for channels: ", channel_name )
the_category_dict = {
'Htautau': [regions, selections, plot_vars],
}
# yields_f_tmp = open('sample_database/datayields.yaml', 'r')
# yields = yaml.safe_load(yields_f_tmp)
# bins = yields['bins']
# Blinding the Signal Region
def additional_input_hook(wrps):
@varial.history.track_history
def blind_in_HmmWin(w):
if w.legend == 'Data': # and w.in_file_path.startswith('Hmm_win'):
if 'm_fastmtt' in w.name or 'PNN' in w.name:
print('BLINDING Data in %s' % w.in_file_path)
for i in xrange(w.histo.GetNbinsX() -9, w.histo.GetNbinsX() + 1):
w.histo.SetBinContent(i, 0.)
w.histo.SetBinError(i, 0.)
if 'mt_tot' in w.name: #
if args.mass >= 200:
for i in xrange(6, w.histo.GetNbinsX() + 1):
w.histo.SetBinContent(i, 0.)
w.histo.SetBinError(i, 0.)
else:
for i in xrange(3, 11):
w.histo.SetBinContent(i, 0.)
w.histo.SetBinError(i, 0.)
return w
def getyields(w):
region = ["nob_loose","nob_tight","btag_tight","btag_loose"]
if "mt_tot" in w.name and ("ALL" not in w.in_file_path):
for r in region:
yields_l = []
if r in w.in_file_path:
os.system('rm ' + r+'datayields.yaml')
w_copy = w.histo.Rebin(len(bins[r])-1, 'w_copy'+r, array.array('d', bins[r]))
for i in xrange(1,w_copy.GetNbinsX() + 1):
# print(' 'combinecut(i, w_copy.GetBinContent(i), w_copy.GetXaxis().GetBinLowEdge(i), w.in_file_path))
yields_l.append(w_copy.GetBinContent(i))
a = {}
a[r] = yields_l
os.system('echo ' + r + ': ' + str(a[r]) + ' >> ' + r+'datayields.yaml')
# with open(r+'datayields.yaml' , 'w') as file:
# yaml.safe_dump(yields, file)
return w
def rebin(w):
# if "mt_tot" in w.name:
# w.histo = w.histo.Rebin(len(mt_tot_b)-1, 'rebinned_mt_tot', array.array('d', mt_tot_b))
# if "mt_" in w.name or "mT" in w.name:
# w.histo = w.histo.Rebin(len(mt)-1, 'rebinned_mt', array.array('d', mt))
if "met" in w.name:
w.histo = w.histo.Rebin(len(met)-1, 'rebinned_met{0}'.format(w.name), array.array('d', met))
# if "pt_2" in w.name or "pt_1" in w.name:
# w.histo = w.histo.Rebin(len(pt)-1, 'rebinned_{0}'.format(w.name), array.array('d', pt))
# if "mt_tot" in w.name:
# w.histo = w.histo.Rebin(2)
# if "m_fastmtt" in w.name and "zoom" not in w.name:
# w.histo = w.histo.Rebin(len(m_fastmtt)-1, 'rebinned_m_fastmtt', array.array('d', m_fastmtt))
# if "mt_tot_rebinned" in w.name:
# w.histo = w.histo.Rebin(len(nob_tight_mT)-1, 'rebinned_mt_tot', array.array('d', nob_tight_mT))
if "mt_tot" in w.name:
w.histo = w.histo.Rebin(len(nob_tight_mT)-1, 'mt_tot', array.array('d', nob_tight_mT))
if "PNN_500_rebinned" in w.name:
rebinned_h = w.histo.Rebin(len(PNN_500) -1, 'rebinned_mt_tot', array.array('d', PNN_500))
# h_PNN_500 = R.TH1F("h1", "Original Histogram", w.histo.GetNbinsX(), 0, w.histo.GetNbinsX())
w.histo =w.histo.Rebin(w.histo.GetNbinsX()/ len(PNN_500))
for i in xrange(rebinned_h.GetNbinsX() + 1):
w.histo.SetBinContent(i, rebinned_h.GetBinContent(i))
w.histo.SetBinError(i, rebinned_h.GetBinError(i))
if "PNN_1000_rebinned" in w.name:
rebinned_h = w.histo.Rebin(len(PNN_1000) -1, 'rebinned_mt_tot', array.array('d', PNN_1000))
# h_PNN_1000 = R.TH1F("h1", "Original Histogram", w.histo.GetNbinsX(), 0, w.histo.GetNbinsX())
w.histo =w.histo.Rebin(w.histo.GetNbinsX()/ len(PNN_1000))
for i in xrange(rebinned_h.GetNbinsX() + 1):
w.histo.SetBinContent(i, rebinned_h.GetBinContent(i))
w.histo.SetBinError(i, rebinned_h.GetBinError(i))
if "PNN_100_rebinned" in w.name:
rebinned_h = w.histo.Rebin(len(PNN_100) -1, 'rebinned_mt_tot', array.array('d', PNN_100))
# h_PNN_100 = R.TH1F("h1", "Original Histogram", w.histo.GetNbinsX(), 0, w.histo.GetNbinsX())
w.histo =w.histo.Rebin(w.histo.GetNbinsX()/ len(PNN_100))
for i in xrange(rebinned_h.GetNbinsX() + 1):
w.histo.SetBinContent(i, rebinned_h.GetBinContent(i))
w.histo.SetBinError(i, rebinned_h.GetBinError(i))
if "PNN_200_rebinned" in w.name:
rebinned_h = w.histo.Rebin(len(PNN_200) -1, 'rebinned_mt_tot', array.array('d', PNN_200))
# h_PNN_200 = R.TH1F("h1", "Original Histogram", w.histo.GetNbinsX(), 0, w.histo.GetNbinsX())
w.histo =w.histo.Rebin(w.histo.GetNbinsX()/ len(PNN_200))
for i in xrange(rebinned_h.GetNbinsX() + 1):
w.histo.SetBinContent(i, rebinned_h.GetBinContent(i))
w.histo.SetBinError(i, rebinned_h.GetBinError(i))
return w
def scale_by_binwidth(w):
for i in xrange(w.histo.GetNbinsX() + 1):
w.histo.SetBinContent(i, w.histo.GetBinContent(i)/w.histo.GetBinWidth(i) )
w.histo.SetBinError(i, w.histo.GetBinError(i)/w.histo.GetBinWidth(i))
# w.histo.GetYaxis().SetTitle("")
return w
def setylimit(w):
for i in xrange(w.histo.GetNbinsX() + 1):
# w.histo.GetYaxis().SetRangeUser(0.1,10e5 )
w.histo.SetMinimum(10)
return w
# print('+++++++++++++++++ ' , yields , '+++++++++++++++++ ')
# wrps = (getyields(w) for w in wrps)
# if not use_all_signal:
wrps = (rebin(w) for w in wrps)
wrps = (blind_in_HmmWin(w) for w in wrps)
# wrps = (setylimit(w) for w in wrps)
# wrps = (scale_by_binwidth(w) for w in wrps)
# for w in wrps:
# w.y_bounds = (1, 10e5)
return wrps