def createSamples(mode, analysis_dir, total_weight, qcd_from_same_sign, w_qcd_mssm_method, r_qcd_os_ss):
hist_dict = {}
sample_dict = {}
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir, mode=mode)
if mode == 'mssm_control' or not 'mssm' in mode:
all_samples = [s for s in all_samples if not 'ggH' in s.name and not 'bbH' in s.name]
sample_dict['all_samples'] = all_samples
if qcd_from_same_sign and not w_qcd_mssm_method:
samples_qcdfromss = [s for s in all_samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
samples_ss = [s for s in samples_ss if not s.is_signal]
for sample in samples_ss:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
qcd = HistogramCfg(name='QCD', var=None, cfgs=samples_ss, cut=None, total_scale=r_qcd_os_ss, lumi=int_lumi, weight=total_weight)
samples_qcdfromss.append(qcd)
sample_dict['samples_qcdfromss'] = samples_qcdfromss
if w_qcd_mssm_method:
sample_dict['samples_mssm_method'] = createQCDWHistograms(samples, hist_dict, int_lumi, weight=total_weight, r_qcd_os_ss=r_qcd_os_ss)
return sample_dict, hist_dict
def createSamples(mode, analysis_dir, optimisation=False):
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tt',
mode=mode,
ztt_cut='(l2_gen_match == 5 && l1_gen_match == 5)',
zl_cut=
'(l1_gen_match < 6 && l2_gen_match < 6 && !(l1_gen_match == 5 && l2_gen_match == 5))',
zj_cut='(l2_gen_match == 6 || l1_gen_match == 6)',
signal_scale=1. if optimisation else 20.)
return all_samples, samples
# cuts.append(Cut('inclusivemt40', inc_cut + '&& l1_charge != l2_charge && mt<40'))
# MSSM Categories
cuts.append(
Cut('nobtag',
inc_cut + '&& l1_charge != l2_charge && n_bjets==0 && mt<30'))
# cuts.append(Cut('btag', inc_cut + '&& l1_charge != l2_charge && n_bjets>=1 && n_jets<=1 && mt<30'))
# cuts.append(Cut('inclusivemtgr40', inc_cut + '&& l1_charge != l2_charge && mt>40'))
# cuts.append(Cut('SS', inc_cut + '&& l1_charge == l2_charge'))
# cuts.append(Cut('SS_muantiiso', inc_cut.replace('l1_reliso05<0.1', 'l1_reliso05>0.2') + '&& l1_charge == l2_charge'))
# cuts.append(Cut('SSmt40', inc_cut + '&& l1_charge == l2_charge && mt<40'))
# cuts.append(Cut('SShighmt40', inc_cut + '&& l1_charge == l2_charge && mt>40'))
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir)
def createDefaultGroups(plot):
plot.Group('VV', [
'VVTo2L2Nu', 'ZZTo2L2Q', 'WWTo1L1Nu2Q', 'WZTo1L3Nu', 'ZZTo4L',
'WZTo3L', 'WZTo2L2Q', 'WZTo1L1Nu2Q', 'T_tWch', 'TBar_tWch',
'TToLeptons_tch_powheg', 'TBarToLeptons_tch_powheg'
])
plot.Group('ZTT', [
'ZTT', 'ZTT1Jets', 'ZTT2Jets', 'ZTT3Jets', 'ZTT4Jets', 'ZTTM150',
'ZTTM10'
])
plot.Group(
'ZJ',
['ZJ', 'ZJ1Jets', 'ZJ2Jets', 'ZJ3Jets', 'ZJ4Jets', 'ZJM150', 'ZJM10'])
# cuts['bveto_lowdeta'] = inc_cut + '&& l1_charge != l2_charge && n_bjets==0 && delta_eta_l1_l2<2.5'
#cuts['inclusive'] = inc_cut + '&& l1_charge != l2_charge && mvis <= 40 && n_bjets >= 1'
cuts = cuts.copy()
print cuts
qcd_from_same_sign = True
analysis_dir = '/data1/steggema/em/06092016/MuEleRealMCPlusTau'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir, channel='em', ztt_cut='(l1_gen_match>2 && l2_gen_match>3)', zl_cut='l2_gen_match==99',zj_cut='l2_gen_match==99', data2016G=data2016G)
if qcd_from_same_sign:
samples_qcdfromss = [s for s in samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
scale = 2.0
for sample in samples_ss:
sample.scale = scale
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -scale
qcd = HistogramCfg(name='QCD', var=None, cfgs=samples_ss, cut=inc_cut, lumi=lumi)
'T_tWch':'T_tWch',
'VVTo2L2Nu':'VVTo2L2Nu',
'W':'W',
'WWTo1L1Nu2Q':'WWTo1L1Nu2Q',
'WZTo1L1Nu2Q':'WZTo1L1Nu2Q',
'WZTo1L3Nu':'WZTo1L3Nu',
'WZTo2L2Q':'WZTo2L2Q',
'WZTo3L':'WZTo3L',
'Z':'ZL',
'ZZTo2L2Q':'ZZTo2L2Q',
}
int_lumi = 2094.2 # from Alexei's email
analysis_dir = '/afs/cern.ch/user/s/steggema/work/public/mt/151215/'
tree_prod_name = 'H2TauTauTreeProducerTauMu'
data_dir = analysis_dir
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir, tree_prod_name=tree_prod_name)
for sample in all_samples:
setSumWeights(sample, directory='MCWeighter')
mcRescalings = []
for sample in mcSamples:
cmgName = cmgSampleTranslation[sample]
mcRescalings.append(int_lumi*sampleDict[cmgName].xsec/sampleDict[cmgName].sumweights)
######################################
### define plot information
plotInfos = [PlotInfo(), PlotInfo(), PlotInfo()]
## Data
plotInfos[0].markerStyle = 20
'l2_mt',
'pzeta_disc',
'pt_l1l2'
])
split_var = train_vars[0]
clf0 = joblib.load('GradientBoostingClassifier_clf_stau_mtsum200_0.pkl')
clf1 = joblib.load('GradientBoostingClassifier_clf_stau_mtsum200_1.pkl')
out_dict = {}
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tt',
mode='susy',
ztt_cut='(l2_gen_match == 5 && l1_gen_match == 5)',
zl_cut=
'(l1_gen_match < 6 && l2_gen_match < 6 && !(l1_gen_match == 5 && l2_gen_match == 5))',
zj_cut='(l2_gen_match == 6 || l1_gen_match == 6)',
signal_scale=1.)
trees_done = []
multicat = False
for sample in all_samples:
name = sample.name
print 'Sample', name
file_name = '/'.join(
[sample.ana_dir, sample.dir_name, sample.tree_prod_name, 'tree.root'])
# cuts['OS_PU_0bjet_mZ'] = inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && mvis>70 && mvis<100 && n_bjets==0'
# cuts['OS_PU_0bjet_vetoZ'] = inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && mvis>70 && (mvis>100 || mvis<80) && n_bjets==0'
# cuts['OS_PU_2bjet'] = inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && n_bjets==2'
# cuts['OS_PU_2bjet_vetoZ'] = inc_cut + '&& l1_charge != l2_charge && abs(l1_eta) < 2.1 && n_bjets==2 && (mvis>100 || mvis<80)'
qcd_from_same_sign = False
# -> Command line
analysis_dir = '/data1/steggema/mm/300616/'
tree_prod_name = 'H2TauTauTreeProducerMuMu'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir,
channel='mm',
ztt_cut='(l1_gen_match == 4 && l2_gen_match == 4)',
zl_cut='(l1_gen_match == 2 && l2_gen_match == 2)',
zj_cut=
'(l1_gen_match != l2_gen_match || (l1_gen_match != 4 && l1_gen_match != 2))'
)
if qcd_from_same_sign:
samples_qcdfromss = [s for s in samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
scale = 1.06
for sample in samples_ss:
sample.scale = scale
if sample.name != 'Data':
# Subtract background from data
sample.scale = -scale
def createSamples(mode, analysis_dir, total_weight, qcd_from_same_sign,
w_qcd_mssm_method, r_qcd_os_ss):
hist_dict = {}
sample_dict = {}
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir)
if mode == 'mssm_control' or not 'mssm' in mode:
all_samples = [
s for s in all_samples
if not 'ggH' in s.name and not 'bbH' in s.name
]
sample_dict['all_samples'] = all_samples
if qcd_from_same_sign and not w_qcd_mssm_method:
samples_qcdfromss = [s for s in all_samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
samples_ss = [s for s in samples_ss if not s.is_signal]
for sample in samples_ss:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
qcd = HistogramCfg(name='QCD',
var=None,
cfgs=samples_ss,
cut=None,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=total_weight)
samples_qcdfromss.append(qcd)
sample_dict['samples_qcdfromss'] = samples_qcdfromss
if w_qcd_mssm_method:
w_names = ['W', 'W1Jets', 'W2Jets', 'W3Jets', 'W4Jets']
samples_non_w = [
s for s in all_samples
if s.name != 'QCD' and s.name not in w_names and not s.is_signal
]
samples_non_w_ss = copy.deepcopy(samples_non_w)
samples_signal = [s for s in all_samples if s.is_signal]
samples_w = copy.deepcopy(
[s for s in all_samples if s.name in w_names])
samples_w_ss = copy.deepcopy(samples_w)
# To calculate OS/SS ratio in inclusive W selection
samples_w_incl_os = copy.deepcopy(samples_w)
samples_w_incl_ss = copy.deepcopy(samples_w)
# To calculate W scale factor
samples_w_highmt_os = copy.deepcopy(samples_w)
# Build a high MT region: OS - non-W/QCD OS - (SS - non-W/QCD SS)
samples_non_w_highmt_os = copy.deepcopy(samples_non_w)
samples_non_w_highmt_ss = copy.deepcopy(samples_non_w)
for sample in samples_non_w_highmt_os:
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -1.
for sample in samples_non_w_highmt_ss:
if sample.name != 'data_obs':
sample.scale = -1.
for sample in samples_non_w_ss:
if sample.name != 'data_obs':
sample.scale = -1.
var_norm = VariableCfg(name='_norm_',
drawname='1.',
binning={
'nbinsx': 5,
'xmin': -1.5,
'xmax': 3.5
},
unit='',
xtitle='Normalisation')
hist_dict['stacknow_highmt_os'] = HistogramCfg(
name='HighMTOS',
var=var_norm,
cfgs=samples_non_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['stacknow_highmt_ss'] = HistogramCfg(
name='HighMTSS',
var=var_norm,
cfgs=samples_non_w_highmt_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_incl_os'] = HistogramCfg(name='WInclOS',
var=var_norm,
cfgs=samples_w_incl_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_incl_ss'] = HistogramCfg(name='WInclSS',
var=var_norm,
cfgs=samples_w_incl_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_highmt_os'] = HistogramCfg(name='WHighMTOS',
var=var_norm,
cfgs=samples_w_highmt_os,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets'] = HistogramCfg(name='W',
var=None,
cfgs=samples_w,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['wjets_ss'] = HistogramCfg(name='WSS',
var=None,
cfgs=samples_w_ss,
cut=None,
lumi=int_lumi,
weight=total_weight)
hist_dict['qcd'] = HistogramCfg(name='QCD',
var=None,
cfgs=samples_non_w_ss +
[hist_dict['wjets_ss']],
cut=None,
total_scale=r_qcd_os_ss,
lumi=int_lumi,
weight=total_weight)
sample_dict['samples_mssm_method'] = samples_non_w + [
hist_dict['wjets'], hist_dict['qcd']
] + samples_signal
return sample_dict, hist_dict
from CMGTools.H2TauTau.proto.plotter.Samples import createSampleLists
from CMGTools.H2TauTau.proto.plotter.HistCreator import setSumWeights
import pickle
analysis_dir = '/afs/cern.ch/work/j/jsauvan/public/HTauTau/Trees/mt/151215/'
tree_prod_name = 'H2TauTauTreeProducerTauMu'
data_dir = analysis_dir
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir, tree_prod_name=tree_prod_name)
for sample in all_samples:
setSumWeights(sample, directory='MCWeighter')
mc_info = {}
mc_info['Directory'] = analysis_dir
for sample in all_samples:
if sample.name!='data_obs':
mc_info[sample.name] = {'XSec':sample.xsec, 'SumWeights':sample.sumweights}
pickle.dump(mc_info, open('mc_info.pck', 'wb'))
'T_tWch':'T_tWch',
#'VVTo2L2Nu':'VVTo2L2Nu',
'W':'W',
'WWTo1L1Nu2Q':'WWTo1L1Nu2Q',
#'WZTo1L1Nu2Q':'WZTo1L1Nu2Q',
'WZTo1L3Nu':'WZTo1L3Nu',
#'WZTo2L2Q':'WZTo2L2Q',
#'WZTo3LNu':'WZTo3LNu',
'Z':'ZL',
'ZZTo4L':'ZZTo4L',
}
int_lumi = 2094.2 # from Alexei's email
analysis_dir = '/afs/cern.ch/work/j/jsauvan/public/HTauTau/Trees/mm/v20160220/'
tree_prod_name = 'H2TauTauTreeProducerMuMu'
data_dir = analysis_dir
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir, channel='mm')
for sample in all_samples:
#setSumWeights(sample, directory='MCWeighter')
setSumWeights(sample)
mcRescalings = []
for sample in mcSamples:
cmgName = cmgSampleTranslation[sample]
mcRescalings.append(int_lumi*sampleDict[cmgName].xsec/sampleDict[cmgName].sumweights)
######################################
### define plot information
plotInfos = [PlotInfo(), PlotInfo(), PlotInfo()]
## Data
from CMGTools.H2TauTau.proto.plotter.categories_TauTau import inc_sig_tau1_iso, inc_sig_tau2_iso, inc_sig_no_iso
from CMGTools.H2TauTau.proto.plotter.categories_common import cat_J1, cat_VBF
from CMGTools.H2TauTau.proto.plotter.HistCreator import createHistogram, setSumWeights
from CMGTools.H2TauTau.proto.plotter.HistDrawer import HistDrawer
from CMGTools.H2TauTau.proto.plotter.Variables import all_vars, getVars
from CMGTools.H2TauTau.proto.plotter.Samples import createSampleLists
from CMGTools.H2TauTau.proto.plotter.qcdEstimation import qcd_estimation
from CMGTools.H2TauTau.proto.plotter.cut import Cut
int_lumi = 2240. # from Alexei's email
tree_prod_name = 'H2TauTauTreeProducerTauTau'
analysis_dir = '/afs/cern.ch/work/m/manzoni/diTau2015/CMSSW_7_6_3/src/CMGTools/H2TauTau/cfgPython/tt/tt_14march2016'
total_weight = 'weight'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir, channel='tt')
myCut = namedtuple('myCut', ['name', 'cut'])
cuts = []
# categories, do not include charge and iso cuts
inc_cut = inc_sig_no_iso
jet1_cut = inc_sig_no_iso & Cut(cat_J1)
vbf_cut = inc_sig_no_iso & Cut(cat_VBF)
# iso and charge cuts, need to have them explicitly for the QCD estimation
iso_cut = inc_sig_tau1_iso & inc_sig_tau2_iso
max_iso_cut = Cut(
'l1_byIsolationMVArun2v1DBoldDMwLT > 3.5 && l2_byIsolationMVArun2v1DBoldDMwLT > 3.5'
)
iso_sideband_cut = (~iso_cut) & max_iso_cut
# inv_cuts = {}
# for cut in cuts:
# new_cuts[cut+'invmu'] = cuts[cut].replace('l1_reliso05<0.1', 'l1_reliso05>0.1')
# new_cuts[cut+'invtau'] = cuts[cut].replace('l2_byCombinedIsolationDeltaBetaCorr3Hits>1.5', 'l2_byCombinedIsolationDeltaBetaCorr3Hits<1.5')
# # cuts = inv_cuts
# cuts = cuts.copy()
# cuts.update(inv_cuts)
# cuts.update(new_cuts)
qcd_from_same_sign = True
analysis_dir = '/afs/cern.ch/work/s/steggema/public/et/23112015/'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
tree_prod_name='H2TauTauTreeProducerTauEle')
samples = samples_mc + samples_data
if qcd_from_same_sign:
samples_qcdfromss = [s for s in samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
scale = 1.06
for sample in samples_ss:
sample.scale = scale
if sample.name != 'data_obs':
# Subtract background from data
sample.scale = -scale
'l2_reliso05>0.15')
new_cuts[cut + 'inve'] = cuts[cut].replace('l1_reliso05<0.15',
'l1_reliso05>0.15')
cuts = cuts.copy()
#cuts.update(new_cuts)
print cuts
qcd_from_same_sign = True
analysis_dir = '/afs/cern.ch/user/y/ytakahas/work/public/forHTT/em'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
tree_prod_name='H2TauTauTreeProducerMuEle',
ztt_cut='(l1_gen_match>2 && l2_gen_match>3)',
zl_cut='l2_gen_match==99',
zj_cut='l2_gen_match==99')
if qcd_from_same_sign:
samples_qcdfromss = [s for s in samples if s.name != 'QCD']
samples_ss = copy.deepcopy(samples_qcdfromss)
# import pdb; pdb.set_trace()
scale = 1.06
for sample in samples_ss:
sample.scale = scale
if sample.name != 'data_obs':
# Subtract background from data
import copy
import ROOT
from Efficiency2DPlots import Efficiency2DPlot, PlotInfo
from HistCreator import createHistograms, removeNegativeValues2D, checkFractionSums2D
from CMGTools.H2TauTau.proto.plotter.PlotConfigs import HistogramCfg, VariableCfg, BasicHistogramCfg
from CMGTools.H2TauTau.proto.plotter.Samples import createSampleLists
from CMGTools.H2TauTau.proto.plotter.HistCreator import setSumWeights
int_lumi = 2094.2 # from Alexei's email
analysis_dir = '/afs/cern.ch/work/j/jsauvan/public/HTauTau/Trees/mt/151215/'
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(analysis_dir=analysis_dir)
for sample in all_samples:
setSumWeights(sample, directory='MCWeighter')
## templates for histogram and file names
histo_version = 'v_2_2016-02-09'
histo_base_dir = '/afs/cern.ch/work/j/jsauvan/Projects/Htautau_Run2/Histos/StudyFakeRate/MuTau_Signal/'
histo_file_template_name = histo_base_dir+'/{SAMPLE}/'+histo_version+'/h2tau_MuTau_{SAMPLE}.root'
histo_template_name = 'hFakeRate_{SEL}_{VAR}'
# samples to be used
Name = "Name"
DirName = "DirName"
HistoDir = 'HistoDir'
XSection = "XSection"
from CMGTools.H2TauTau.proto.plotter.HistCreator import setSumWeights
from CMGTools.H2TauTau.proto.plotter.helper_methods import plotDataOverMCEff
# total_weight = 'weight * ' + getPUWeight()
only_stack = False
total_weight = 'weight/weight_njet/weight_vertex'
analysis_dir = '/data1/steggema/TauFRMuWeights/'
int_lumi = 2301.
samples_mc, samples_data, samples, all_samples, sampleDict = createSampleLists(
analysis_dir=analysis_dir,
channel='tau_fr',
ztt_cut='(tau_gen_match == 5)',
zl_cut='(tau_gen_match>=0 && tau_gen_match < 5)',
zj_cut='(tau_gen_match == 6 || tau_gen_match<0)')
cuts = {}
inc_cut = '!veto_dilepton && !veto_thirdlepton && !veto_otherlepton && muon_reliso05<0.1 && muon_muonid_medium>0.5 && muon_pt>19. && abs(jet_eta)<2.3'
samples = [
s for s in all_samples if s.name != 'TBar_tWch' and not 'SUSY' in s.name
]
samples_mc = [s for s in samples if s.name != 'data_obs']
for s in samples:
if s.name not in [
