def main(args):
# Container for all distributions to be drawn
systematics_mm = Systematics("shapes_mm_recoil_2016.root",
num_threads=args.num_threads,
find_unique_objects=True)
# Era
era = Run2016(args.datasets)
# Channels and processes
# yapf: disable
directory = args.directory
zptm_path = "/portal/ekpbms1/home/akhmet/workdir/FriendTreeProductionMain/CMSSW_10_2_14/src/ZPtMReweighting_workdir/ZPtMReweighting_collected/"
mm = MM()
mm_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, mm, friend_directory=[])),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, mm, friend_directory=[zptm_path])),
"ZL" : Process("ZL", ZLEstimation (era, directory, mm, friend_directory=[zptm_path])),
"TTT" : Process("TTT", TTTEstimation (era, directory, mm, friend_directory=[])),
"TTL" : Process("TTL", TTLEstimation (era, directory, mm, friend_directory=[])),
"VVT" : Process("VVT", VVTEstimation (era, directory, mm, friend_directory=[])),
"VVL" : Process("VVL", VVLEstimation (era, directory, mm, friend_directory=[])),
"W" : Process("W", WEstimation (era, directory, mm, friend_directory=[])),
}
mm_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, mm,
[mm_processes[process] for process in ["ZTT", "ZL", "W", "TTT", "TTL", "VVT", "VVL"]],
mm_processes["data"], friend_directory=[], extrapolation_factor=2.0))
# Variables and categories
binning = yaml.load(open(args.binning))
mm_categories = []
variable_bins = {
"njets" : [0, 1, 2],
"ptvis" : [0, 10, 20, 30, 50],
}
variable_names = [
"metParToZ", "metPerpToZ",
"puppimetParToZ", "puppimetPerpToZ",
# "recoilParToZ", "recoilPerpToZ",
# "puppirecoilParToZ", "puppirecoilPerpToZ",
]
for njets_bin in range(len(variable_bins["njets"])):
for pt_bin in range(len(variable_bins["ptvis"])):
name = "njets_bin_%s_vs_ptvis_bin_%s"%(str(njets_bin),str(pt_bin))
category_njets = ""
category_pt = ""
if njets_bin == (len(variable_bins["njets"]) - 1):
category_njets = "njets >= %s"%str(variable_bins["njets"][njets_bin])
else:
category_njets = "njets == %s"%str(variable_bins["njets"][njets_bin])
if pt_bin == (len(variable_bins["ptvis"]) - 1):
category_pt = "ptvis > %s"%str(variable_bins["ptvis"][pt_bin])
else:
category_pt= "ptvis > %s && ptvis <= %s"%(str(variable_bins["ptvis"][pt_bin]),str(variable_bins["ptvis"][pt_bin+1]))
print category_njets, category_pt
cuts = Cuts(
Cut(category_njets,"njets_category"),
Cut(category_pt,"ptvis_category"),
Cut("m_vis > 70 && m_vis < 110","z_peak")
)
for v in variable_names:
mm_categories.append(
Category(
name,
mm,
cuts,
variable=Variable(v,VariableBinning(binning["control"]["mm"][v]["bins"]), expression=binning["control"]["mm"][v]["expression"])))
# Nominal histograms
for process, category in product(mm_processes.values(), mm_categories):
systematics_mm.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
# Produce histograms
systematics_mm.produce()
def main(args):
# Container for all distributions to be drawn
systematics_mm = Systematics("shapes_mm_recoilunc_2016.root",
num_threads=args.num_threads,
find_unique_objects=True)
# Era
era = Run2016(args.datasets)
# Channels and processes
# yapf: disable
directory = args.directory
mm = MM()
mm_processes = {
"ZL" : Process("ZL", ZLEstimation (era, directory, mm, friend_directory=[])),
}
# Variables and categories
binning = yaml.load(open(args.binning))
mm_categories = []
variable_bins = {
"njets" : [0, 1, 2],
"genbosonpt" : [0, 10, 20, 30, 50],
}
variable_names = [
"recoilParToZ",
"puppirecoilParToZ",
]
for njets_bin in range(len(variable_bins["njets"])):
for pt_bin in range(len(variable_bins["genbosonpt"])):
name = "njets_bin_%s_vs_ptvis_bin_%s"%(str(njets_bin),str(pt_bin))
category_njets = ""
category_pt = ""
if njets_bin == (len(variable_bins["njets"]) - 1):
category_njets = "njets >= %s"%str(variable_bins["njets"][njets_bin])
else:
category_njets = "njets == %s"%str(variable_bins["njets"][njets_bin])
if pt_bin == (len(variable_bins["genbosonpt"]) - 1):
category_pt = "genbosonpt > %s"%str(variable_bins["genbosonpt"][pt_bin])
else:
category_pt= "genbosonpt > %s && genbosonpt <= %s"%(str(variable_bins["genbosonpt"][pt_bin]),str(variable_bins["genbosonpt"][pt_bin+1]))
print category_njets, category_pt
cuts = Cuts(
Cut(category_njets,"njets_category"),
Cut(category_pt,"ptvis_category"),
Cut("m_vis > 70 && m_vis < 110","z_peak")
)
for v in variable_names:
mm_categories.append(
Category(
name,
mm,
cuts,
variable=Variable("relative_%s"%v,ConstantBinning(400,-20.0,20.0), expression="-%s/genbosonpt"%v)))
# Nominal histograms
for process, category in product(mm_processes.values(), mm_categories):
systematics_mm.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
# Produce histograms
systematics_mm.produce()
def main(args):
# Container for all distributions to be drawn
systematics_mm = Systematics("counts_zptm_2018.root",
num_threads=args.num_threads,
find_unique_objects=True)
# Era
era = Run2018(args.datasets)
# Channels and processes
# yapf: disable
directory = args.directory
mm = MM()
mm_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, mm, friend_directory=[])),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, mm, friend_directory=[])),
"ZL" : Process("ZL", ZLEstimation (era, directory, mm, friend_directory=[])),
"TTT" : Process("TTT", TTTEstimation (era, directory, mm, friend_directory=[])),
"TTL" : Process("TTL", TTLEstimation (era, directory, mm, friend_directory=[])),
"VVT" : Process("VVT", VVTEstimation (era, directory, mm, friend_directory=[])),
"VVL" : Process("VVL", VVLEstimation (era, directory, mm, friend_directory=[])),
"W" : Process("W", WEstimation (era, directory, mm, friend_directory=[])),
}
mm_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, mm,
[mm_processes[process] for process in ["ZTT", "ZL", "W", "TTT", "TTL", "VVT", "VVL"]],
mm_processes["data"], friend_directory=[], extrapolation_factor=2.0))
# Variables and categories
mm_categories = []
variable_bins = {
"m_vis" : [50, 100, 200, 500, 1000],
"ptvis" : [0, 10, 20, 30, 40, 50, 100, 150, 200, 300, 400, 1000],
}
for mass_bin in range(len(variable_bins["m_vis"]) - 1):
for pt_bin in range(len(variable_bins["ptvis"]) - 1):
name = "%s_bin_%s_vs_%s_bin_%s"%("m_vis",str(mass_bin),"ptvis",str(pt_bin))
cuts = Cuts(Cut("(m_vis > %s && m_vis < %s) && (ptvis > %s && ptvis < %s)"%(str(variable_bins["m_vis"][mass_bin]),str(variable_bins["m_vis"][mass_bin+1]),str(variable_bins["ptvis"][pt_bin]),str(variable_bins["ptvis"][pt_bin+1])),"zptm_category"))
mm_categories.append(
Category(
name,
mm,
cuts,
variable=None))
# Nominal histograms
for process, category in product(mm_processes.values(), mm_categories):
#if process.name in ["ZTT","ZLL"]:
# process.estimation_method.get_weights().remove("zPtReweightWeight")
systematics_mm.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
# Produce histograms
systematics_mm.produce()
def main(args):
# Container for all distributions to be drawn
systematics_mt = Systematics("shapes_mt_2017.root",
num_threads=args.num_threads,
find_unique_objects=True)
systematics_et = Systematics("shapes_et_2017.root",
num_threads=args.num_threads,
find_unique_objects=True)
systematics_tt = Systematics("shapes_tt_2017.root",
num_threads=args.num_threads,
find_unique_objects=True)
systematics_em = Systematics("shapes_em_2017.root",
num_threads=args.num_threads,
find_unique_objects=True)
systematics_mm = Systematics("shapes_mm_2017.root",
num_threads=args.num_threads,
find_unique_objects=True)
# Era
era = Run2017(args.datasets)
# Channels and processes
# yapf: disable
directory = args.directory
et_friend_directory = args.et_friend_directory
mt_friend_directory = args.mt_friend_directory
tt_friend_directory = args.tt_friend_directory
em_friend_directory = args.em_friend_directory
mm_friend_directory = args.mm_friend_directory
ff_friend_directory = args.fake_factor_friend_directory
mt = MT()
mt_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"EMB" : Process("EMB", ZTTEmbeddedEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ZJ" : Process("ZJ", ZJEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"TTJ" : Process("TTJ", TTJEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"VVJ" : Process("VVJ", VVJEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"W" : Process("W", WEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ggH" : Process("ggH125", ggHEstimation ("ggH125", era, directory, mt, friend_directory=mt_friend_directory)),
"qqH" : Process("qqH125", qqHEstimation ("qqH125", era, directory, mt, friend_directory=mt_friend_directory)),
"VH" : Process("VH125", VHEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"WH" : Process("WH125", WHEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ZH" : Process("ZH125", ZHEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"ttH" : Process("ttH125", ttHEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
"HWW" : Process("HWW", HWWEstimation (era, directory, mt, friend_directory=mt_friend_directory)),
}
mt_processes["FAKES"] = Process("jetFakes", NewFakeEstimationLT(era, directory, mt, [mt_processes[process] for process in ["ZTT", "ZL", "TTT", "TTL", "VVT", "VVL"]], mt_processes["data"], friend_directory=mt_friend_directory+[ff_friend_directory]))
mt_processes["FAKESEMB"] = Process("jetFakesEMB", NewFakeEstimationLT(era, directory, mt, [mt_processes[process] for process in ["EMB", "ZL", "TTL", "VVL"]], mt_processes["data"], friend_directory=mt_friend_directory+[ff_friend_directory]))
mt_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, mt,
[mt_processes[process] for process in ["ZTT", "ZL", "ZJ", "W", "TTT", "TTJ", "TTL", "VVT", "VVJ", "VVL"]],
mt_processes["data"], friend_directory=mt_friend_directory, extrapolation_factor=1.00))
mt_processes["QCDEMB"] = Process("QCDEMB", QCDEstimation_SStoOS_MTETEM(era, directory, mt,
[mt_processes[process] for process in ["EMB", "ZL", "ZJ", "W", "TTJ", "TTL", "VVJ", "VVL"]],
mt_processes["data"], friend_directory=mt_friend_directory, extrapolation_factor=1.00))
et = ET()
et_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, et, friend_directory=et_friend_directory)),
"EMB" : Process("EMB", ZTTEmbeddedEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ZJ" : Process("ZJ", ZJEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, et, friend_directory=et_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, et, friend_directory=et_friend_directory)),
"TTJ" : Process("TTJ", TTJEstimation (era, directory, et, friend_directory=et_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, et, friend_directory=et_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, et, friend_directory=et_friend_directory)),
"VVJ" : Process("VVJ", VVJEstimation (era, directory, et, friend_directory=et_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, et, friend_directory=et_friend_directory)),
"W" : Process("W", WEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ggH" : Process("ggH125", ggHEstimation ("ggH125", era, directory, et, friend_directory=et_friend_directory)),
"qqH" : Process("qqH125", qqHEstimation ("qqH125", era, directory, et, friend_directory=et_friend_directory)),
"VH" : Process("VH125", VHEstimation (era, directory, et, friend_directory=et_friend_directory)),
"WH" : Process("WH125", WHEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ZH" : Process("ZH125", ZHEstimation (era, directory, et, friend_directory=et_friend_directory)),
"ttH" : Process("ttH125", ttHEstimation (era, directory, et, friend_directory=et_friend_directory)),
"HWW" : Process("HWW", HWWEstimation (era, directory, et, friend_directory=et_friend_directory)),
}
et_processes["FAKES"] = Process("jetFakes", NewFakeEstimationLT(era, directory, et, [et_processes[process] for process in ["ZTT", "ZL", "TTT", "TTL", "VVT", "VVL"]], et_processes["data"], friend_directory=et_friend_directory+[ff_friend_directory]))
et_processes["FAKESEMB"] = Process("jetFakesEMB", NewFakeEstimationLT(era, directory, et, [et_processes[process] for process in ["EMB", "ZL", "TTL", "VVL"]], et_processes["data"], friend_directory=et_friend_directory+[ff_friend_directory]))
et_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, et,
[et_processes[process] for process in ["ZTT", "ZL", "ZJ", "W", "TTT", "TTJ", "TTL", "VVT", "VVJ", "VVL"]],
et_processes["data"], friend_directory=et_friend_directory, extrapolation_factor=1.00))
et_processes["QCDEMB"] = Process("QCDEMB", QCDEstimation_SStoOS_MTETEM(era, directory, et,
[et_processes[process] for process in ["EMB", "ZL", "ZJ", "W", "TTJ", "TTL", "VVJ", "VVL"]],
et_processes["data"], friend_directory=et_friend_directory, extrapolation_factor=1.00))
tt = TT()
tt_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"EMB" : Process("EMB", ZTTEmbeddedEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ZJ" : Process("ZJ", ZJEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"TTJ" : Process("TTJ", TTJEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"VVJ" : Process("VVJ", VVJEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"W" : Process("W", WEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ggH" : Process("ggH125", ggHEstimation ("ggH125", era, directory, tt, friend_directory=tt_friend_directory)),
"qqH" : Process("qqH125", qqHEstimation ("qqH125", era, directory, tt, friend_directory=tt_friend_directory)),
"VH" : Process("VH125", VHEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"WH" : Process("WH125", WHEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ZH" : Process("ZH125", ZHEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"ttH" : Process("ttH125", ttHEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
"HWW" : Process("HWW", HWWEstimation (era, directory, tt, friend_directory=tt_friend_directory)),
}
tt_processes["FAKESEMB"] = Process("jetFakesEMB", NewFakeEstimationTT(era, directory, tt, [tt_processes[process] for process in ["EMB", "ZL", "TTL", "VVL"]], tt_processes["data"], friend_directory=tt_friend_directory+[ff_friend_directory]))
tt_processes["FAKES"] = Process("jetFakes", NewFakeEstimationTT(era, directory, tt, [tt_processes[process] for process in ["ZTT", "ZL", "TTT", "TTL", "VVT", "VVL"]], tt_processes["data"], friend_directory=tt_friend_directory+[ff_friend_directory]))
tt_processes["QCD"] = Process("QCD", QCDEstimation_ABCD_TT_ISO2(era, directory, tt,
[tt_processes[process] for process in ["ZTT", "ZL", "ZJ", "W", "TTT", "TTJ", "TTL", "VVT", "VVJ", "VVL"]],
tt_processes["data"], friend_directory=tt_friend_directory))
tt_processes["QCDEMB"] = Process("QCDEMB", QCDEstimation_ABCD_TT_ISO2(era, directory, tt,
[tt_processes[process] for process in ["EMB", "ZL", "ZJ", "W", "TTJ", "TTL", "VVJ", "VVL"]],
tt_processes["data"], friend_directory=tt_friend_directory))
em = EM()
em_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, em, friend_directory=em_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, em, friend_directory=em_friend_directory)),
"EMB" : Process("EMB", ZTTEmbeddedEstimation (era, directory, em, friend_directory=em_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, em, friend_directory=em_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, em, friend_directory=em_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, em, friend_directory=em_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, em, friend_directory=em_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, em, friend_directory=em_friend_directory)),
"W" : Process("W", WEstimation (era, directory, em, friend_directory=em_friend_directory)),
"ggH" : Process("ggH125", ggHEstimation ("ggH125", era, directory, em, friend_directory=em_friend_directory)),
"qqH" : Process("qqH125", qqHEstimation ("qqH125", era, directory, em, friend_directory=em_friend_directory)),
"VH" : Process("VH125", VHEstimation (era, directory, em, friend_directory=em_friend_directory)),
"WH" : Process("WH125", WHEstimation (era, directory, em, friend_directory=em_friend_directory)),
"ZH" : Process("ZH125", ZHEstimation (era, directory, em, friend_directory=em_friend_directory)),
"ttH" : Process("ttH125", ttHEstimation (era, directory, em, friend_directory=em_friend_directory)),
"HWW" : Process("HWW", HWWEstimation (era, directory, em, friend_directory=em_friend_directory)),
}
em_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, em, [em_processes[process] for process in ["ZTT", "ZL", "W", "TTT", "VVT", "VVL"]], em_processes["data"], extrapolation_factor=1.0, qcd_weight = Weight("em_qcd_extrap_up_Weight","qcd_weight")))
em_processes["QCDEMB"] = Process("QCDEMB", QCDEstimation_SStoOS_MTETEM(era, directory, em, [em_processes[process] for process in ["EMB", "ZL", "W", "VVL", "TTL"]], em_processes["data"], extrapolation_factor=1.0, qcd_weight = Weight("em_qcd_extrap_up_Weight","qcd_weight")))
mm = MM()
mm_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"W" : Process("W", WEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
}
mm_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, mm,
[mm_processes[process] for process in ["ZTT", "ZL", "W", "TTT", "TTL", "VVT", "VVL"]],
mm_processes["data"], friend_directory=mm_friend_directory, extrapolation_factor=2.0))
# Variables and categories
binning = yaml.load(open(args.binning))
mt_categories = []
et_categories = []
tt_categories = []
em_categories = []
mm_categories = []
variable_names = [
# "m_vis", "ptvis",
# "DiTauDeltaR",
# "m_sv", "pt_sv", "eta_sv",
# "m_sv_puppi", "pt_sv_puppi", "eta_sv_puppi",
# "m_fastmtt", "pt_fastmtt", "eta_fastmtt",
# "m_fastmtt_puppi", "pt_fastmtt_puppi", "eta_fastmtt_puppi",
"ME_ggh", "ME_vbf", "ME_z2j_1", "ME_z2j_2",
"ME_q2v1", "ME_q2v2", "ME_costheta1", "ME_costheta2", "ME_costhetastar", "ME_phi", "ME_phi1",
"ME_ggh_vs_Z", "ME_vbf_vs_Z", "ME_vbf_vs_ggh",
# "pt_1", "pt_2", "eta_1", "eta_2",
# "mjj", "jdeta", "dijetpt",
# "njets", "jpt_1", "jpt_2", "jeta_1", "jeta_2",
# "nbtag", "bpt_1", "bpt_2", "beta_1", "beta_2",
# "met", "mt_1", "mt_2", "pt_tt", "pZetaMissVis", "pt_ttjj", "mt_tot", "mTdileptonMET",
# "puppimet", "mt_1_puppi", "mt_2_puppi", "pt_tt_puppi", "pZetaPuppiMissVis", "pt_ttjj_puppi", "mt_tot_puppi", "mTdileptonMET_puppi",
# "NNrecoil_pt", "nnmet", "mt_1_nn", "mt_2_nn", "pt_tt_nn", "pZetaNNMissVis", "pt_ttjj_nn", "mt_tot_nn", "mTdileptonMET_nn",
"metParToZ", "metPerpToZ",
"puppimetParToZ", "puppimetPerpToZ",
]
if "mt" in args.channels:
variables = [Variable(v,VariableBinning(binning["control"]["mt"][v]["bins"]), expression=binning["control"]["mt"][v]["expression"]) for v in variable_names]
cuts = Cuts()
for name, var in zip(variable_names, variables):
mt_categories.append(
Category(
name,
mt,
cuts,
variable=var))
if "et" in args.channels:
variables = [Variable(v,VariableBinning(binning["control"]["et"][v]["bins"]), expression=binning["control"]["et"][v]["expression"]) for v in variable_names]
cuts = Cuts()
for name, var in zip(variable_names, variables):
et_categories.append(
Category(
name,
et,
cuts,
variable=var))
if "tt" in args.channels:
variables = [Variable(v,VariableBinning(binning["control"]["tt"][v]["bins"]), expression=binning["control"]["tt"][v]["expression"]) for v in variable_names]
cuts = Cuts()
for name, var in zip(variable_names, variables):
tt_categories.append(
Category(
name,
tt,
cuts,
variable=var))
if "em" in args.channels:
variables = [Variable(v,VariableBinning(binning["control"]["em"][v]["bins"]), expression=binning["control"]["em"][v]["expression"]) for v in variable_names]
cuts = Cuts()
for name, var in zip(variable_names, variables):
em_categories.append(
Category(
name,
em,
cuts,
variable=var))
if "mm" in args.channels:
variables = [Variable(v,VariableBinning(binning["control"]["mm"][v]["bins"]), expression=binning["control"]["mm"][v]["expression"]) for v in variable_names]
variables.append(Variable("m_vis_high",ConstantBinning(95,50.0,1000.0),expression="m_vis"))
variable_names.append("m_vis_high")
cuts = Cuts()
for name, var in zip(variable_names, variables):
mm_categories.append(
Category(
name,
mm,
cuts,
variable=var))
mm_categories.append(
Category(
name+"_peak",
mm,
Cuts(Cut("m_vis > 70 && m_vis < 110","m_vis_peak")),
variable=var))
# Nominal histograms
if "mt" in args.channels:
for process, category in product(mt_processes.values(), mt_categories):
systematics_mt.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
if "et" in args.channels:
for process, category in product(et_processes.values(), et_categories):
systematics_et.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
if "tt" in args.channels:
for process, category in product(tt_processes.values(), tt_categories):
systematics_tt.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
if "em" in args.channels:
for process, category in product(em_processes.values(), em_categories):
systematics_em.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
if "mm" in args.channels:
for process, category in product(mm_processes.values(), mm_categories):
systematics_mm.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
# Produce histograms
if "mt" in args.channels: systematics_mt.produce()
if "et" in args.channels: systematics_et.produce()
if "tt" in args.channels: systematics_tt.produce()
if "em" in args.channels: systematics_em.produce()
if "mm" in args.channels: systematics_mm.produce()
def main(args):
# Container for all distributions to be drawn
systematics_mm = Systematics("fitrecoil_mm_2016.root", num_threads=args.num_threads, find_unique_objects=True)
# Era
era = Run2016(args.datasets)
# Channels and processes
# yapf: disable
directory = args.directory
mm_friend_directory = args.mm_friend_directory
mm = MM()
mm_processes = {
"data" : Process("data_obs", DataEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"ZTT" : Process("ZTT", ZTTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"ZL" : Process("ZL", ZLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"TTT" : Process("TTT", TTTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"TTL" : Process("TTL", TTLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"VVT" : Process("VVT", VVTEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"VVL" : Process("VVL", VVLEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
"W" : Process("W", WEstimation (era, directory, mm, friend_directory=mm_friend_directory)),
}
mm_processes["QCD"] = Process("QCD", QCDEstimation_SStoOS_MTETEM(era, directory, mm,
[mm_processes[process] for process in ["ZTT", "ZL", "W", "TTT", "TTL", "VVT", "VVL"]],
mm_processes["data"], friend_directory=mm_friend_directory, extrapolation_factor=2.0))
# Variables and categories
mm_categories = []
variable_names = [
# "met", "metphi",
# "puppimet", "puppimetphi",
"metParToZ", "metPerpToZ",
"puppimetParToZ", "puppimetPerpToZ",
# "recoilParToZ", "recoilPerpToZ",
# "puppirecoilParToZ", "puppirecoilPerpToZ",
]
variables = [Variable(v,ConstantBinning(25,-100.0,100.0)) for v in variable_names]
cuts = [
Cut("njets == 0", "0jet"),
Cut("njets == 1", "1jet"),
Cut("njets >= 2", "ge2jet"),
]
for cut in cuts:
for var in variables:
mm_categories.append(
Category(
cut.name,
mm,
Cuts(Cut("m_vis > 70 && m_vis < 110","m_vis_peak"), cut),
variable=var))
for process, category in product(mm_processes.values(), mm_categories):
systematics_mm.add(
Systematic(
category=category,
process=process,
analysis="smhtt",
era=era,
variation=Nominal(),
mass="125"))
# Recoil correction unc
recoil_resolution_variations = create_systematic_variations(
"CMS_htt_boson_reso_met_Run2016", "metRecoilResolution",
DifferentPipeline)
recoil_response_variations = create_systematic_variations(
"CMS_htt_boson_scale_met_Run2016", "metRecoilResponse",
DifferentPipeline)
for variation in recoil_resolution_variations + recoil_response_variations:
systematics_mm.add_systematic_variation(
variation=variation,
process=mm_processes["ZL"],
channel=mm,
era=era)
# Produce histograms
systematics_mm.produce()