def main():
options = {
# The main root TQSampleFolder name
"master_sample_name": "samples",
# Where the ntuples are located
"ntuple_path": "/nfs-7/userdata/phchang/WWW_babies/WWW_v1.2.3/skim/",
# Path to the config file that defines how the samples should be organized
"sample_config_path": "samples.cfg",
# The samples with "priority" (defined in sample_config_pat) values satisfying the following condition is looped over
"priority_value": ">0",
# The samples with "priority" (defined in sample_config_pat) values satisfying the following condition is NOT looped over
"exclude_priority_value": "<-1",
# N-cores
"ncore": 4,
# TQCuts config file
"cuts": "cuts.cfg",
# Histogram config file
"histo": "histo.cfg",
# Eventlist histogram
"eventlist": "eventlist.cfg",
# Custom observables (dictionary)
"customobservables": {},
# Custom observables (dictionary)
"output_dir": "outputs/"
}
# First generate cuts.cfg file
generate_cuts_config()
# Analyze
loop(options)
# Create plots and tables
samples = TQSampleFolder.loadSampleFolder("outputs/output.root:samples")
bkg_path = [
("WWW", "/sig/www"),
("WHWWW", "/sig/whwww"),
]
sig_path = [("WZ", "/bkg/WZ")]
autoplot(samples,
bkg_path=bkg_path,
sig_path=sig_path,
options={
"remove_underflow": True,
"signal_scale": "auto"
})
autotable(samples,
"yield",
bkg_path=bkg_path,
sig_path=sig_path,
options={"cuts": "cuts.cfg"})
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQXSecParser, TQCut, TQAnalysisSampleVisitor, TQSampleInitializer, TQCutflowAnalysisJob, TQCutflowPrinter, TQHistoMakerAnalysisJob
from rooutil import plottery_wrapper as p
from plottery import plottery as ply
try:
tqsamplefolderpath = sys.argv[1]
except:
tqsamplefolderpath = "output.root"
ROOT.gROOT.SetBatch(True)
samples = TQSampleFolder.loadSampleFolder(tqsamplefolderpath + ":samples")
samples_up = TQSampleFolder.loadSampleFolder("output_up.root:samples")
samples_dn = TQSampleFolder.loadSampleFolder("output_dn.root:samples")
output_plot_dir = "plots"
doW = False
docombinedqcdel = True
testsample = "/top"
testsamplename = "t#bar{t}"
testsamplelegendname = "t#bar{t}"
if doW:
testsample = "/W/HT"
#testsample = "/W"
testsamplename = "W"
def compute_fake_factor_1d(th1):
for ix in xrange(0, th1.GetNbinsX() + 2):
frnom = th1.GetBinContent(ix)
frerr = th1.GetBinError(ix)
fr = E(frnom, frerr)
if fr.val != 0 and fr.val != 1:
ff = fr / (E(1., 0.) - fr)
else:
ff = E(0., 0.)
th1.SetBinContent(ix, ff.val)
th1.SetBinError(ix, ff.err)
ROOT.gROOT.SetBatch(True)
samples = TQSampleFolder.loadSampleFolder("output.root:samples")
qcdloosemu = samples.getHistogram("/qcd/mu",
"OneMuLoose/lep_ptcorrcoarse_vs_etacoarse")
qcdtightmu = samples.getHistogram("/qcd/mu",
"OneMuTight/lep_ptcorrcoarse_vs_etacoarse")
qcdloosemu.Print("all")
qcdtightmu.Print("all")
qcdlooseel = samples.getHistogram("/qcd/el",
"OneElLoose/lep_ptcorrcoarse_vs_etacoarse")
qcdtightel = samples.getHistogram("/qcd/el",
"OneElTight/lep_ptcorrcoarse_vs_etacoarse")
qcdlooseelEM = samples.getHistogram(
"/qcd/el/EM", "OneElLoose/lep_ptcorrcoarse_vs_etacoarse")
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQXSecParser, TQCut, TQAnalysisSampleVisitor, TQSampleInitializer, TQCutflowAnalysisJob, TQCutflowPrinter, TQHistoMakerAnalysisJob
from rooutil import plottery_wrapper as p
from plottery import plottery as ply
from rooutil.syncfiles.pyfiles.errors import E
ROOT.gROOT.SetBatch(True)
samples_cn = TQSampleFolder.loadSampleFolder("output.root:samples")
samples_up = TQSampleFolder.loadSampleFolder("output_up.root:samples")
samples_dn = TQSampleFolder.loadSampleFolder("output_dn.root:samples")
output_plot_dir = "plots"
#_________________________________________________________
def compute_fake_factor_1d(th1):
for ix in xrange(0, th1.GetNbinsX()+2):
frnom = th1.GetBinContent(ix)
frerr = th1.GetBinError(ix)
fr = E(frnom, frerr)
if fr.val != 0 and fr.val != 1:
ff = fr / (E(1., 0.) - fr)
else:
ff = E(0., 0.)
th1.SetBinContent(ix, ff.val)
th1.SetBinError(ix, ff.err)
#___________________________________________________________________________
def main(model="", mass=""):
samples = TQSampleFolder.loadSampleFolder("outputs/output.root:samples")
samples_jec_up = TQSampleFolder.loadSampleFolder(
"outputs/output_jec_up.root:samples")
samples_jec_dn = TQSampleFolder.loadSampleFolder(
"outputs/output_jec_dn.root:samples")
options = {
# Signal name and TQSampleFolder path
"sig":
("www", "/sig" if model == "" else "/bsm/{}/{}".format(model, mass)),
# Background names and TQSampelFolder paths
"bkgs": [
("lostlep", "/typebkg/lostlep/[ttZ+WZ+Other]"),
("fake", "/fake"),
("vbsww", "/typebkg/?/VBSWW"),
("ttw", "/typebkg/?/ttW"),
("photon", "/typebkg/photon/[ttZ+WZ+Other]"),
("qflip", "/typebkg/qflip/[ttZ+WZ+Other]"),
("prompt", "/typebkg/prompt/[ttZ+WZ+Other]"
if model == "" else "/typebkg/prompt/[ttZ+WZ+Other]+sig"),
],
# Data TQSampleFolder paths
"data":
"/data",
# Counter names for getting yields
"bins": [
"SRSSeeFull",
"SRSSemFull",
"SRSSmmFull",
"SideSSeeFull",
"SideSSemFull",
"SideSSmmFull",
"SR0SFOSFull",
"SR1SFOSFull",
"SR2SFOSFull",
#"SRNj1SSeeFull",
#"SRNj1SSemFull",
#"SRNj1SSmmFull",
],
# TQSampleFolder object
"nominal_sample":
samples,
# Control regions
# The control regions will normalize the counts
# The systematics
"control_regions": {
("SRSSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSeeFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SRSSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSemFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SRSSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSmmFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSeeFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSemFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SideSSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCRSSmmFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SR1SFOSFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCR1SFOSFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
("SR2SFOSFull", "/typebkg/lostlep/[ttZ+WZ+Other]"):
("WZCR2SFOSFull",
"/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"
),
#("SRNj1SSeeFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSeeFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
#("SRNj1SSemFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSemFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
#("SRNj1SSmmFull", "/typebkg/lostlep/[ttZ+WZ+Other]") : ("WZCRNj1SSmmFull" , "/data-typebkg/qflip-typebkg/photon-typebkg/prompt-typebkg/fakes-typebkg/lostlep/VBSWW-typebkg/lostlep/ttW-sig"),
},
# Weight variation systematics that are saved in the "nominal_sample" TQSampleFolder
# The nomenclature of the coutner names must be <BIN_COUNTER><SYSTS>Up and <BIN_COUNTER><SYSTS>Down
# The keyword are the systematics and then the items list the processes to apply the systematics
"systematics": [
("LepSF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("TrigSF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("BTagLF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("BTagHF", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("Pileup", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
]
}),
("FakeRateEl", {
"procs_to_apply": ["fake"]
}),
("FakeRateMu", {
"procs_to_apply": ["fake"]
}),
("FakeClosureEl", {
"procs_to_apply": ["fake"]
}),
("FakeClosureMu", {
"procs_to_apply": ["fake"]
}),
("PDF", {
"procs_to_apply": ["www"]
}),
("AlphaS", {
"procs_to_apply": ["www"]
}),
("Qsq", {
"procs_to_apply": ["www"]
}),
("JEC", {
"procs_to_apply": [
"www", "vbsww", "ttw", "photon", "qflip", "prompt",
"lostlep"
],
"syst_samples": {
"Up": samples_jec_up,
"Down": samples_jec_dn
}
}),
],
"statistical":
["www", "vbsww", "ttw", "photon", "qflip", "prompt", "fake"],
"flat_systematics": [
("VBSWWXSec", ["vbsww"], "1.2", ""),
("ttWXSec", ["ttw"], "1.2", ""),
("VBSWWVRSyst", ["vbsww"], "1.22", ""),
("ttWVRSyst", ["ttw"], "1.18", ""),
("QFlipVRSyst", ["qflip"], "1.5", ""),
("PhotonVRSyst", ["photon"], "1.5", ""),
("LostLepMll3LModeling", ["lostlep"], "1.082", "SFOS"),
("LostLepMllSSModeling", ["lostlep"], "1.053", "SS"),
("LostLepMjjModeling", ["lostlep"], "1.049", "SS"),
("LumSyst", ["vbsww", "ttw", "photon", "qflip", "prompt",
"www"], "1.025", ""),
],
}
return qutils.make_counting_experiment_statistics_data_card(options)
#!/bin/env python
import os
import sys
import ROOT
from QFramework import TQSampleFolder, TQEventlistPrinter, TQTaggable
from rooutil import plottery_wrapper as p
ROOT.gROOT.SetBatch(True)
path = "eventlists/"
filename = sys.argv[1]
samples = TQSampleFolder.loadSampleFolder("{}:samples".format(filename))
printer = TQEventlistPrinter(samples)
printer.addCut("SRSSeeFull")
printer.addCut("SRSSemFull")
printer.addCut("SRSSmmFull")
printer.addCut("SideSSeeFull")
printer.addCut("SideSSemFull")
printer.addCut("SideSSmmFull")
printer.addCut("SR0SFOSFull")
printer.addCut("SR1SFOSFull")
printer.addCut("SR2SFOSFull")
printer.addCut("WZCRSSeeFull")
printer.addCut("WZCRSSemFull")
printer.addCut("WZCRSSmmFull")
printer.addCut("WZCR1SFOSFull")
printer.addCut("WZCR2SFOSFull")
#printer.addProcess("/sig/whwww")
printer.addProcess("/typebkg/lostlep/WZ")
def main(transformation, p, batch_index):
ott_parser = OTTParser()
transformation = ott_parser.parse_file(transformation)
transformation.set_root(transformation)
batch_index = str(batch_index) or "all"
transformation.compile({"-batch-index": batch_index})
if p:
print transformation
for input in transformation.get("input"):
sf = False
print "Open: %s" % input.attributes['path']
if "sample-folder" in input.attributes:
input_file = TQSampleFolder.loadSampleFolder(
"%s:%s" %
(input.attributes['path'], input.attributes['sample-folder']))
sf = True
else:
input_file = ROOT.TFile.Open(input.attributes['path'])
for output in transformation.get("output"):
output_file = ROOT.TFile.Open(output.attributes['path'],
"RECREATE")
for TH1F in output.get("TH1F"):
output_file.cd()
min = float(TH1F.get("min")[0].content)
max = float(TH1F.get("max")[0].content)
destination = TH1F.get("destination")[0].content
bins = TH1F.get("bin")
token = destination.rsplit("/", 1)
if len(token) == 1:
token = "", token[0]
if not output_file.Get(token[0]):
output_file.mkdir(token[0])
output_file.cd(token[0])
destination_name = token[1]
print " TH1F(\"%s\", \"\", %d, %g, %g) -> %s %s" % \
(destination_name, len(bins), min, max,
output.attributes['path'], destination)
histogram = ROOT.TH1F(destination_name, "", len(bins), min,
max)
for i, bin in enumerate(bins):
histogram.SetBinContent(i + 1, float(bin.content))
histogram.Write()
for duplicate in output.get("duplicate"):
output_file.cd()
source = duplicate.get("source")[0].content
destination = duplicate.get("destination")[0].content
token = destination.rsplit("/", 1)
if len(token) == 1:
token = "", token[0]
if not output_file.Get(token[0]):
output_file.mkdir(token[0])
output_file.cd(token[0])
destination_name = token[1]
if sf:
cut = duplicate.get("cut")[0].content
histogram_name = duplicate.get("histogram")[0].content
histogram = input_file.getHistogram(
ROOT.TString(source),
ROOT.TString("%s/%s" % (cut, histogram_name)))
if not histogram:
continue
print " %s %s/%s -> %s %s" % \
(source, cut, histogram_name,
output.attributes['path'], destination)
clone = histogram.Clone(destination_name)
clone.Write()
# todo
output_file.Close()
def main(model="sm", mass0=-1, mass1=-1):
# Print the model name and mass points
print model, mass0, mass1
# Suffis that will be attached to output file names for bookkeeping
suffix = make_suffix(model, mass0, mass1)
# Create directory where the outputs will be
makedir("statinputs")
# Open input files
filename = "output_sf_applied.root"
samples = TQSampleFolder.loadSampleFolder("{}:samples".format(filename))
samples_jec_up = TQSampleFolder.loadSampleFolder("output_jec_up.root:samples")
samples_jec_dn = TQSampleFolder.loadSampleFolder("output_jec_dn.root:samples")
samples_gen_met = TQSampleFolder.loadSampleFolder("output_gen_met.root:samples")
# Set the histogram name to perform the fit on (we use the 9 bin histogram
histname = "{SRSSeeFull,SRSSemFull,SRSSmmFull,SideSSeeFull,SideSSemFull,SideSSmmFull,SR0SFOSFull,SR1SFOSFull,SR2SFOSFull}"
# We have 8 categories for the fit
processes = [ "vbsww" , "ttw" , "lostlep" , "photon" , "qflip" , "prompt" , "fake" , "www" ,]
#######
# NOTE "www" means "signal" - i.e. for whsusy model www = whsusy and www is included in prompt
#######
# Set the diectionary of the paths where we will retrieve the histograms from
sampledirpaths = {
"vbsww" : "/typebkg/?/VBSWW",
"ttw" : "/typebkg/?/ttW",
"lostlep" : "/typebkg/lostlep/[ttZ+WZ+Other]",
"photon" : "/typebkg/photon/[ttZ+WZ+Other]",
"qflip" : "/typebkg/qflip/[ttZ+WZ+Other]",
"prompt" : "/typebkg/prompt/[ttZ+WZ+Other]" if model == "sm" else "/typebkg/prompt/[ttZ+WZ+Other]+sig" if model != "whwww" else "/typebkg/prompt/[ttZ+WZ+Other]+sig/www",
"fake" : "/fake",
"www" : get_sigmodel_path(model, mass0, mass1),
}
# Create output file
ofile = ROOT.TFile("statinputs/hist_{}.root".format(suffix), "recreate")
ofile.cd()
# Array of numbers where we will aggregate some results for nice tables
rates = {}
# Write histograms
for process in processes:
#print process, histname
# Get nominal histogram
h_nom = samples.getHistogram(sampledirpaths[process], histname).Clone(process)
# If lost lepton get the nominal number directly from the AN Table 13
if process == "lostlep": h_nom = set_to_lostlep_nominal_hist(h_nom)
# If whsusy model with signal then get the average of the two histogram
#if model == "whsusy" and process == "www": set_to_average_and_write_genmet_syst_hist(h_nom, samples_gen_met.getHistogram(sampledirpaths[process], histname).Clone(process))
# Write nominal histogram
#h_nom.Write()
mask_bins(h_nom).Write()
# Save the total number that will be used to output to datacards
rates[process] = h_nom.Integral()
# Nominal histogram errors are needed to be varied one by one to create an effective uncorrelated histograms
if process != "lostlep" and process != "fake": write_nominal_stat_variations(h_nom, process)
# Write systematic histograms that are from weight variations
for systvar in systvars:
# Some process or some variations do not need to be written
if do_not_write_syst_hist(process, systvar, model):
continue
# Write the systvariation histograms
#samples.getHistogram(sampledirpaths[process], histname.replace("Full", "Full" + systvar)).Clone(process + "_" + systvar).Write()
mask_bins(samples.getHistogram(sampledirpaths[process], histname.replace("Full", "Full" + systvar)).Clone(process + "_" + systvar)).Write()
# JEC systematic histograms need to be called from a different sample output
if process != "fake" and process != "lostlep":
#samples_jec_up.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECUp").Write()
#samples_jec_dn.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECDown").Write()
mask_bins(samples_jec_up.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECUp")).Write()
mask_bins(samples_jec_dn.getHistogram(sampledirpaths[process], histname).Clone(process + "_JECDown")).Write()
# Lost lepton has special treatment
if process == "lostlep":
#write_lostlep_stat_variations(h_nom)
#write_lostlep_syst_variations(h_nom)
write_lostlep_CRstat_variations(h_nom)
write_lostlep_TFstat_variations(h_nom)
write_lostlep_TFsyst_variations(h_nom)
write_lostlep_Mjjsyst_variations(h_nom)
write_lostlep_MllSSsyst_variations(h_nom)
write_lostlep_Mll3lsyst_variations(h_nom)
# WWW signal theory systematics
if model == "sm":
if process == "www":
write_www_theory_syst_variations(h_nom)
# Fake has AR statistics
if process == "fake":
write_fake_ARstat_variations(h_nom)
# Write data histogram
h_data = samples.getHistogram("/typebkg", histname).Clone("data_obs")
for i in xrange(0,h_data.GetNbinsX()+2):
h_data.SetBinContent(i, int(h_data.GetBinContent(i)))
#h_data.Write()
mask_bins(h_data).Write()
datacard="""imax 1 number of bins
jmax * number of processes
kmax * number of nuisance parameters
----------------------------------------------------------------------------------------------------------------------------------
shapes * * statinputs/hist_{}.root $PROCESS $PROCESS_$SYSTEMATIC
----------------------------------------------------------------------------------------------------------------------------------
bin SR
observation {:.1f}
----------------------------------------------------------------------------------------------------------------------------------
bin SR SR SR SR SR SR SR SR
process 0 1 2 3 4 5 6 7
process www fake photon lostlep qflip prompt ttw vbsww
rate {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f} {:<6.3f}
----------------------------------------------------------------------------------------------------------------------------------
JEC shape 1 - 1 - - 1 1 1
LepSF shape 1 - 1 - - 1 1 1
TrigSF shape 1 - 1 - - 1 1 1
BTagHF shape 1 - 1 - - 1 1 1
BTagLF shape 1 - 1 - - 1 1 1
Pileup shape 1 - 1 - - 1 1 1
FakeRateEl shape - 1 - - - - - -
FakeRateMu shape - 1 - - - - - -
FakeClosureEl shape - 1 - - - - - -
FakeClosureMu shape - 1 - - - - - -
LostLepSyst shape - - - 1 - - - -
MjjModeling shape - - - 1 - - - -
MllSSModeling shape - - - 1 - - - -
Mll3lModeling shape - - - 1 - - - -
SigXSec lnN 1.06 - - - - - - -
LumSyst lnN 1.025 - 1.025 - 1.025 1.025 1.025 1.025
vbsww_xsec lnN - - - - - - - 1.20
vbsww_validation lnN - - - - - - - 1.22
ttw_xsec lnN - - - - - - 1.20 -
ttw_validation lnN - - - - - - 1.18 -
photon_syst lnN - - 1.50 - - - - -
qflip_syst lnN - - - - 1.50 - - -
www_stat_in_ee shape 1 - - - - - - -
www_stat_in_em shape 1 - - - - - - -
www_stat_in_mm shape 1 - - - - - - -
www_stat_out_ee shape 1 - - - - - - -
www_stat_out_em shape 1 - - - - - - -
www_stat_out_mm shape 1 - - - - - - -
www_stat_0sfos shape 1 - - - - - - -
www_stat_1sfos shape 1 - - - - - - -
www_stat_2sfos shape 1 - - - - - - -
fake_ARstat_in_ee shape - 1 - - - - - -
fake_ARstat_in_em shape - 1 - - - - - -
fake_ARstat_in_mm shape - 1 - - - - - -
fake_ARstat_out_ee shape - 1 - - - - - -
fake_ARstat_out_em shape - 1 - - - - - -
fake_ARstat_out_mm shape - 1 - - - - - -
fake_ARstat_0sfos shape - 1 - - - - - -
fake_ARstat_1sfos shape - 1 - - - - - -
fake_ARstat_2sfos shape - 1 - - - - - -
photon_stat_in_ee shape - - 1 - - - - -
photon_stat_in_em shape - - 1 - - - - -
photon_stat_in_mm shape - - 1 - - - - -
photon_stat_out_ee shape - - 1 - - - - -
photon_stat_out_em shape - - 1 - - - - -
photon_stat_out_mm shape - - 1 - - - - -
photon_stat_0sfos shape - - 1 - - - - -
photon_stat_1sfos shape - - 1 - - - - -
photon_stat_2sfos shape - - 1 - - - - -
lostlep_stat_in_ee shape - - - 1 - - - -
lostlep_stat_in_em shape - - - 1 - - - -
lostlep_stat_in_mm shape - - - 1 - - - -
lostlep_stat_out_ee shape - - - 1 - - - -
lostlep_stat_out_em shape - - - 1 - - - -
lostlep_stat_out_mm shape - - - 1 - - - -
lostlep_stat_0sfos shape - - - 1 - - - -
lostlep_stat_1sfos shape - - - 1 - - - -
lostlep_stat_2sfos shape - - - 1 - - - -
qflip_stat_in_ee shape - - - - 1 - - -
qflip_stat_in_em shape - - - - 1 - - -
qflip_stat_in_mm shape - - - - 1 - - -
qflip_stat_out_ee shape - - - - 1 - - -
qflip_stat_out_em shape - - - - 1 - - -
qflip_stat_out_mm shape - - - - 1 - - -
qflip_stat_0sfos shape - - - - 1 - - -
qflip_stat_1sfos shape - - - - 1 - - -
qflip_stat_2sfos shape - - - - 1 - - -
prompt_stat_in_ee shape - - - - - 1 - -
prompt_stat_in_em shape - - - - - 1 - -
prompt_stat_in_mm shape - - - - - 1 - -
prompt_stat_out_ee shape - - - - - 1 - -
prompt_stat_out_em shape - - - - - 1 - -
prompt_stat_out_mm shape - - - - - 1 - -
prompt_stat_0sfos shape - - - - - 1 - -
prompt_stat_1sfos shape - - - - - 1 - -
prompt_stat_2sfos shape - - - - - 1 - -
ttw_stat_in_ee shape - - - - - - 1 -
ttw_stat_in_em shape - - - - - - 1 -
ttw_stat_in_mm shape - - - - - - 1 -
ttw_stat_out_ee shape - - - - - - 1 -
ttw_stat_out_em shape - - - - - - 1 -
ttw_stat_out_mm shape - - - - - - 1 -
ttw_stat_0sfos shape - - - - - - 1 -
ttw_stat_1sfos shape - - - - - - 1 -
ttw_stat_2sfos shape - - - - - - 1 -
vbsww_stat_in_ee shape - - - - - - - 1
vbsww_stat_in_em shape - - - - - - - 1
vbsww_stat_in_mm shape - - - - - - - 1
vbsww_stat_out_ee shape - - - - - - - 1
vbsww_stat_out_em shape - - - - - - - 1
vbsww_stat_out_mm shape - - - - - - - 1
vbsww_stat_0sfos shape - - - - - - - 1
vbsww_stat_1sfos shape - - - - - - - 1
vbsww_stat_2sfos shape - - - - - - - 1
lostlep_CRstat_ee shape - - - 1 - - - -
lostlep_CRstat_em shape - - - 1 - - - -
lostlep_CRstat_mm shape - - - 1 - - - -
lostlep_CRstat_0sfos shape - - - 1 - - - -
lostlep_CRstat_1sfos shape - - - 1 - - - -
lostlep_CRstat_2sfos shape - - - 1 - - - -
""".format(suffix, h_data.Integral(), rates["www"], rates["fake"], rates["photon"], rates["lostlep"], rates["qflip"], rates["prompt"], rates["ttw"], rates["vbsww"])
if model == "sm":
datacard += """SigPDF shape 1 - - - - - - -
SigQsq shape 1 - - - - - - -
SigAlpha shape 1 - - - - - - -
"""
if model == "whsusy":
datacard += """ISR shape 1 - - - - - - -
Qsq shape 1 - - - - - - -
PDF shape 1 - - - - - - -
AlphaS shape 1 - - - - - - -
GenMET shape 1 - - - - - - -
"""
f = open('statinputs/datacard_{}.txt'.format(suffix), 'w')
f.write(datacard)
f.close()
ofile.Close()