def cmodel(cid, nam, _f, _fOut, out_ws, diag, year, convention="BU"):
# Some setup
_fin = _f.Get("category_%s" % cid)
_wspace = _fin.Get("wspace_%s" % cid)
# ############################ USER DEFINED ###########################################################
# First define the nominal transfer factors (histograms of signal/control, usually MC
# note there are many tools available inside include/diagonalize.h for you to make
# special datasets/histograms representing these and systematic effects
# example below for creating shape systematic for photon which is just every bin up/down 30%
metname = 'mjj' # Observable variable name
target = _fin.Get(
"signal_ewkzjets"
) # define monimal (MC) of which process this config will model
controlmc = _fin.Get(
"Zmm_ewkzll") # defines Zmm MC of which process will be controlled by
controlmc_e = _fin.Get(
"Zee_ewkzll") # defines Zmm MC of which process will be controlled by
controlmc_w = _fin.Get("signal_ewkwjets")
controlmc_g = _fin.Get("gjets_ewkgjets")
# Create the transfer factors and save them (not here you can also create systematic variations of these
# transfer factors (named with extention _sysname_Up/Down
ZmmScales = target.Clone()
ZmmScales.SetName("ewk_zmm_weights_%s" % cid)
ZmmScales.Divide(controlmc)
_fOut.WriteTObject(ZmmScales) # always write out to the directory
ZeeScales = target.Clone()
ZeeScales.SetName("ewk_zee_weights_%s" % cid)
ZeeScales.Divide(controlmc_e)
_fOut.WriteTObject(ZeeScales) # always write out to the directory
WZScales = target.Clone()
WZScales.SetName("ewk_w_weights_%s" % cid)
WZScales.Divide(controlmc_w)
_fOut.WriteTObject(WZScales) # always write out to the directory
PhotonScales = target.Clone()
PhotonScales.SetName("ewk_photon_weights_%s" % cid)
PhotonScales.Divide(controlmc_g)
_fOut.WriteTObject(PhotonScales)
my_function(_wspace, _fin, _fOut, cid, diag, year)
#######################################################################################################
_bins = [
] # take bins from some histogram, can choose anything but this is easy
for b in range(target.GetNbinsX() + 1):
_bins.append(target.GetBinLowEdge(b + 1))
# Here is the important bit which "Builds" the control region, make a list of control regions which
# are constraining this process, each "Channel" is created with ...
# (name,_wspace,out_ws,cid+'_'+model,TRANSFERFACTORS)
# the second and third arguments can be left unchanged, the others instead must be set
# TRANSFERFACTORS are what is created above, eg WScales
CRs = [
Channel("ewk_dimuon",
_wspace,
out_ws,
cid + '_' + model,
ZmmScales,
convention=convention),
Channel("ewk_dielectron",
_wspace,
out_ws,
cid + '_' + model,
ZeeScales,
convention=convention),
Channel("ewk_wjetssignal",
_wspace,
out_ws,
cid + '_' + model,
WZScales,
convention=convention),
Channel("ewk_photon",
_wspace,
out_ws,
cid + '_' + model,
PhotonScales,
convention=convention)
]
CRs[2].add_nuisance('CMS_veto{YEAR}_t'.format(YEAR=year), -0.01)
CRs[2].add_nuisance('CMS_veto{YEAR}_m'.format(YEAR=year), -0.02)
CRs[2].add_nuisance('CMS_veto{YEAR}_e'.format(YEAR=year), -0.03)
# Get the JES/JER uncertainty file for transfer factors
# Read the split uncertainties from there
fjes = get_jes_jer_source_file_for_tf(category='vbf')
jet_variations = get_jes_variations(fjes, year, proc='ewk')
for var in jet_variations:
add_variation(
WZScales, fjes,
'znunu_over_wlnu{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_w_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
WZScales, fjes, 'znunu_over_wlnu{YEAR}_ewk_{VARIATION}Down'.format(
YEAR=year - 2000, VARIATION=var),
"ewk_w_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[2].add_nuisance_shape(var, _fOut)
add_variation(
ZmmScales, fjes,
'znunu_over_zmumu{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_zmm_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
ZmmScales, fjes,
'znunu_over_zmumu{YEAR}_ewk_{VARIATION}Down'.format(YEAR=year -
2000,
VARIATION=var),
"ewk_zmm_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[0].add_nuisance_shape(var, _fOut)
add_variation(
ZeeScales, fjes,
'znunu_over_zee{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_zee_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
ZeeScales, fjes,
'znunu_over_zee{YEAR}_ewk_{VARIATION}Down'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_zee_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[1].add_nuisance_shape(var, _fOut)
add_variation(
PhotonScales, fjes,
'znunu_over_gjets{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_photon_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
PhotonScales, fjes,
'znunu_over_gjets{YEAR}_ewk_{VARIATION}Down'.format(YEAR=year -
2000,
VARIATION=var),
"ewk_photon_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[3].add_nuisance_shape(var, _fOut)
# ############################ USER DEFINED ###########################################################
# Add systematics in the following, for normalisations use name, relative size (0.01 --> 1%)
# for shapes use add_nuisance_shape with (name,_fOut)
# note, the code will LOOK for something called NOMINAL_name_Up and NOMINAL_name_Down, where NOMINAL=WScales.GetName()
# these must be created and writted to the same dirctory as the nominal (fDir)
# Bin by bin nuisances to cover statistical uncertainties ...
do_stat_unc(ZmmScales,
proc='ewk_zmm',
region='ewk_dimuonCR',
CR=CRs[0],
cid=cid,
outfile=_fOut)
do_stat_unc(ZeeScales,
proc='ewk_zee',
region='ewk_dielectronCR',
CR=CRs[1],
cid=cid,
outfile=_fOut)
do_stat_unc(WZScales,
proc='ewk_w',
region='ewk_wzCR',
CR=CRs[2],
cid=cid,
outfile=_fOut)
do_stat_unc(PhotonScales,
proc='ewk_photon',
region='ewk_photonCR',
CR=CRs[3],
cid=cid,
outfile=_fOut)
#######################################################################################################
CRs[2].add_nuisance_shape("ZnunuWJets_EWK_renscale_vbf", _fOut)
CRs[2].add_nuisance_shape("ZnunuWJets_EWK_facscale_vbf", _fOut)
CRs[2].add_nuisance_shape("ZnunuWJets_EWK_pdf_vbf", _fOut)
for b in range(target.GetNbinsX()):
CRs[2].add_nuisance_shape(
"ewk_ewk_%s_bin%d" % (re.sub("_201(\d)", "", cid), b), _fOut)
CRs[3].add_nuisance_shape("Photon_EWK_renscale_vbf", _fOut)
CRs[3].add_nuisance_shape("Photon_EWK_facscale_vbf", _fOut)
CRs[3].add_nuisance_shape("Photon_EWK_pdf_vbf", _fOut)
for b in range(target.GetNbinsX()):
CRs[3].add_nuisance_shape(
"ewkphoton_ewk_%s_bin%d" % (re.sub("_201(\d)", "", cid), b), _fOut)
#######################################################################################################
cat = Category(model,
cid,
nam,
_fin,
_fOut,
_wspace,
out_ws,
_bins,
metname,
target.GetName(),
CRs,
diag,
convention=convention)
cat.setDependant("qcd_zjets", "ewkqcd_signal")
# Return of course
return cat
def cmodel(cid, nam, _f, _fOut, out_ws, diag, year):
# Some setup
_fin = _f.Get("category_%s" % cid)
_wspace = _fin.Get("wspace_%s" % cid)
# ############################ USER DEFINED ###########################################################
# First define the nominal transfer factors (histograms of signal/control, usually MC
# note there are many tools available inside include/diagonalize.h for you to make
# special datasets/histograms representing these and systematic effects
# example below for creating shape systematic for photon which is just every bin up/down 30%
metname = "met" # Observable variable name
gvptname = "genBosonPt" # Weights are in generator pT
target = _fin.Get(
"signal_zjets"
) # define monimal (MC) of which process this config will model
controlmc = _fin.Get(
"Zmm_zll") # defines Zmm MC of which process will be controlled by
controlmc_photon = _fin.Get(
"gjets_gjets"
) # defines Gjets MC of which process will be controlled by
controlmc_e = _fin.Get(
"Zee_zll") # defines Zmm MC of which process will be controlled by
controlmc_w = _fin.Get("signal_wjets")
# Create the transfer factors and save them (not here you can also create systematic variations of these
# transfer factors (named with extention _sysname_Up/Down
ZmmScales = target.Clone()
ZmmScales.SetName("zmm_weights_%s" % cid)
ZmmScales.Divide(controlmc)
_fOut.WriteTObject(ZmmScales) # always write out to the directory
ZeeScales = target.Clone()
ZeeScales.SetName("zee_weights_%s" % cid)
ZeeScales.Divide(controlmc_e)
_fOut.WriteTObject(ZeeScales) # always write out to the directory
WZScales = target.Clone()
WZScales.SetName("w_weights_%s" % cid)
WZScales.Divide(controlmc_w)
_fOut.WriteTObject(WZScales) # always write out to the directory
my_function(_wspace, _fin, _fOut, cid, diag)
PhotonScales = _fOut.Get("photon_weights_%s" % cid)
#######################################################################################################
_bins = [
] # take bins from some histogram, can choose anything but this is easy
for b in range(target.GetNbinsX() + 1):
_bins.append(target.GetBinLowEdge(b + 1))
# Here is the important bit which "Builds" the control region, make a list of control regions which
# are constraining this process, each "Channel" is created with ...
# (name,_wspace,out_ws,cid+'_'+model,TRANSFERFACTORS)
# the second and third arguments can be left unchanged, the others instead must be set
# TRANSFERFACTORS are what is created above, eg WScales
CRs = [
Channel("photon", _wspace, out_ws, cid + '_' + model, PhotonScales),
Channel("dimuon", _wspace, out_ws, cid + '_' + model, ZmmScales),
Channel("dielectron", _wspace, out_ws, cid + '_' + model, ZeeScales),
Channel("wjetssignal", _wspace, out_ws, cid + '_' + model, WZScales)
]
# ############################ USER DEFINED ###########################################################
# Add systematics in the following, for normalisations use name, relative size (0.01 --> 1%)
# for shapes use add_nuisance_shape with (name,_fOut)
# note, the code will LOOK for something called NOMINAL_name_Up and NOMINAL_name_Down, where NOMINAL=WScales.GetName()
# these must be created and writted to the same dirctory as the nominal (fDir)
if "monov" in cid:
tag = "_monov"
else:
tag = ""
do_stat_unc(PhotonScales, "photon", cid, "photonCR", CRs[0], _fOut)
do_stat_unc(ZmmScales, "zmm", cid, "dimuonCR", CRs[1], _fOut)
do_stat_unc(ZeeScales, "zee", cid, "dielectronCR", CRs[2], _fOut)
do_stat_unc(WZScales, "w", cid, "wzCR", CRs[3], _fOut)
## Here now adding the trigger uncertainty
fztoz_trig = r.TFile.Open("sys/all_trig_2017.root") # 250 - 1400 binning
# We want to correlate experimental uncertainties between the loose and tight regions.
cid_corr = re.sub("(loose|tight)", "", cid)
add_variation(PhotonScales, fztoz_trig, "trig_sys_down" + tag,
"photon_weights_%s_mettrig_%s_Down" % (cid, year), _fOut)
add_variation(PhotonScales, fztoz_trig, "trig_sys_up" + tag,
"photon_weights_%s_mettrig_%s_Up" % (cid, year), _fOut)
CRs[0].add_nuisance_shape("mettrig_%s" % year, _fOut)
# Take the square of the uncertainty because we are going from zero to two leptons
add_variation(ZmmScales, fztoz_trig, "trig_sys_sqr_down" + tag,
"zmm_weights_%s_mettrig_%s_Down" % (cid, year), _fOut)
add_variation(ZmmScales, fztoz_trig, "trig_sys_sqr_up" + tag,
"zmm_weights_%s_mettrig_%s_Up" % (cid, year), _fOut)
CRs[1].add_nuisance_shape("mettrig_%s" % year, _fOut)
## Here now adding the trigger uncertainty
add_variation(ZeeScales, fztoz_trig, "trig_sys_down" + tag,
"zee_weights_%s_mettrig_%s_Down" % (cid, year), _fOut)
add_variation(ZeeScales, fztoz_trig, "trig_sys_up" + tag,
"zee_weights_%s_mettrig_%s_Up" % (cid, year), _fOut)
CRs[2].add_nuisance_shape("mettrig_%s" % year, _fOut)
#######################################################################################################
CRs[0].add_nuisance_shape("qcd", _fOut)
CRs[0].add_nuisance_shape("qcdshape", _fOut)
CRs[0].add_nuisance_shape("qcdprocess", _fOut)
CRs[0].add_nuisance_shape("ewk", _fOut)
CRs[0].add_nuisance_shape("sudakovZ", _fOut)
CRs[0].add_nuisance_shape("sudakovG", _fOut)
CRs[0].add_nuisance_shape("nnlomissZ", _fOut)
CRs[0].add_nuisance_shape("nnlomissG", _fOut)
CRs[0].add_nuisance_shape("cross", _fOut)
CRs[0].add_nuisance_shape("pdf", _fOut)
CRs[3].add_nuisance_shape("wqcd", _fOut)
CRs[3].add_nuisance_shape("wqcdshape", _fOut)
CRs[3].add_nuisance_shape("wqcdprocess", _fOut)
CRs[3].add_nuisance_shape("wewk", _fOut)
CRs[3].add_nuisance_shape("sudakovZ", _fOut)
CRs[3].add_nuisance_shape("sudakovW", _fOut)
CRs[3].add_nuisance_shape("nnlomissZ", _fOut)
CRs[3].add_nuisance_shape("nnlomissW", _fOut)
CRs[3].add_nuisance_shape("wcross", _fOut)
CRs[3].add_nuisance_shape("wpdf", _fOut)
#######################################################################################################
cat = Category(model, cid, nam, _fin, _fOut, _wspace, out_ws, _bins,
metname, target.GetName(), CRs, diag)
# Return of course
return cat
def cmodel(cid, nam, _f, _fOut, out_ws, diag, year, convention="BU"):
# Some setup
_fin = _f.Get("category_%s" % cid)
_wspace = _fin.Get("wspace_%s" % cid)
# ############################ USER DEFINED ###########################################################
# First define the nominal transfer factors (histograms of signal/control, usually MC
# note there are many tools available inside include/diagonalize.h for you to make
# special datasets/histograms representing these and systematic effects
# but for now this is just kept simple
processName = "WJets" # Give a name of the process being modelled
metname = 'mjj' # Observable variable name
targetmc = _fin.Get(
"signal_ewkwjets"
) # define monimal (MC) of which process this config will model
controlmc = _fin.Get("Wmn_ewkwjets") # defines in / out acceptance
controlmc_e = _fin.Get("Wen_ewkwjets") # defines in / out acceptance
# Create the transfer factors and save them (not here you can also create systematic variations of these
# transfer factors (named with extention _sysname_Up/Down
WScales = targetmc.Clone()
WScales.SetName("ewk_wmn_weights_%s" % cid)
WScales.Divide(controlmc)
_fOut.WriteTObject(WScales)
WScales_e = targetmc.Clone()
WScales_e.SetName("ewk_wen_weights_%s" % cid)
WScales_e.Divide(controlmc_e)
_fOut.WriteTObject(WScales_e)
#######################################################################################################
_bins = [
] # take bins from some histogram, can choose anything but this is easy
for b in range(targetmc.GetNbinsX() + 1):
_bins.append(targetmc.GetBinLowEdge(b + 1))
# Here is the important bit which "Builds" the control region, make a list of control regions which
# are constraining this process, each "Channel" is created with ...
# (name,_wspace,out_ws,cid+'_'+model,TRANSFERFACTORS)
# the second and third arguments can be left unchanged, the others instead must be set
# TRANSFERFACTORS are what is created above, eg WScales
CRs = [
Channel("ewk_singlemuon",
_wspace,
out_ws,
cid + '_' + model,
WScales,
convention=convention),
Channel("ewk_singleelectron",
_wspace,
out_ws,
cid + '_' + model,
WScales_e,
convention=convention),
]
# Get the JES/JER unlcertainty file for transfer factors
# Read the split uncertainties from there
fjes = get_jes_jer_source_file_for_tf(category='vbf')
jet_variations = get_jes_variations(fjes, year, proc='ewk')
for var in jet_variations:
add_variation(
WScales, fjes,
'wlnu_over_wmunu{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_wmn_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
WScales, fjes, 'wlnu_over_wmunu{YEAR}_ewk_{VARIATION}Down'.format(
YEAR=year - 2000, VARIATION=var),
"ewk_wmn_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[0].add_nuisance_shape(var, _fOut)
add_variation(
WScales_e, fjes,
'wlnu_over_wenu{YEAR}_ewk_{VARIATION}Up'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_wen_weights_%s_%s_Up" % (cid, var), _fOut)
add_variation(
WScales_e, fjes,
'wlnu_over_wenu{YEAR}_ewk_{VARIATION}Down'.format(YEAR=year - 2000,
VARIATION=var),
"ewk_wen_weights_%s_%s_Down" % (cid, var), _fOut)
CRs[1].add_nuisance_shape(var, _fOut)
for c in CRs:
c.add_nuisance('CMS_veto{YEAR}_t'.format(YEAR=year), 0.01)
c.add_nuisance('CMS_veto{YEAR}_m'.format(YEAR=year), 0.02)
c.add_nuisance('CMS_veto{YEAR}_e'.format(YEAR=year), 0.03)
# ############################ USER DEFINED ###########################################################
# Add systematics in the following, for normalisations use name, relative size (0.01 --> 1%)
# for shapes use add_nuisance_shape with (name,_fOut)
# note, the code will LOOK for something called NOMINAL_name_Up and NOMINAL_name_Down, where NOMINAL=WScales.GetName()
# these must be created and writted to the same dirctory as the nominal (fDir)
do_stat_unc(WScales,
proc='ewk_wmn',
region='ewk_singlemuon',
CR=CRs[0],
cid=cid,
outfile=_fOut)
do_stat_unc(WScales_e,
proc='ewk_wen',
region='ewk_singleelectron',
CR=CRs[1],
cid=cid,
outfile=_fOut)
#######################################################################################################
cat = Category(model,
cid,
nam,
_fin,
_fOut,
_wspace,
out_ws,
_bins,
metname,
targetmc.GetName(),
CRs,
diag,
convention=convention)
cat.setDependant(
"ewk_zjets", "ewk_wjetssignal"
) # Can use this to state that the "BASE" of this is already dependant on another process
# EG if the W->lv in signal is dependant on the Z->vv and then the W->mv is depenant on W->lv, then
# give the arguments model,channel name from the config which defines the Z->vv => W->lv map!
# Return of course
return cat
def cmodel(cid,nam,_f,_fOut, out_ws, diag, year):
filler = {
"YEAR" : year,
"CID" : cid,
"CHANNEL" : "monojet" if "monojet" in cid else "monov"
}
# Some setup
_fin = _f.Get("category_%s"%cid)
_wspace = _fin.Get("wspace_%s"%cid)
# ############################ USER DEFINED ###########################################################
# First define the nominal transfer factors (histograms of signal/control, usually MC
# note there are many tools available inside include/diagonalize.h for you to make
# special datasets/histograms representing these and systematic effects
# example below for creating shape systematic for photon which is just every bin up/down 30%
metname = "met" # Observable variable name
gvptname = "genBosonPt" # Weights are in generator pT
target = _fin.Get("signal_zjets") # define monimal (MC) of which process this config will model
controlmc = _fin.Get("Zmm_zll") # defines Zmm MC of which process will be controlled by
controlmc_photon = _fin.Get("gjets_gjets") # defines Gjets MC of which process will be controlled by
controlmc_e = _fin.Get("Zee_zll") # defines Zmm MC of which process will be controlled by
controlmc_w = _fin.Get("signal_wjets")
# Save the original spectra
WSpectrum = controlmc_w.Clone("w_spectrum_%s_"%cid)
PhotonSpectrum = controlmc_photon.Clone("photon_spectrum_%s_"%cid)
ZvvSpectrum = target.Clone("zvv_spectrum_%s_"%cid)
_fOut.WriteTObject( WSpectrum )
_fOut.WriteTObject( PhotonSpectrum )
_fOut.WriteTObject( ZvvSpectrum )
# Create the transfer factors and save them (not here you can also create systematic variations of these
# transfer factors (named with extention _sysname_Up/Down
ZmmScales = target.Clone("zmm_weights_%s"%cid)
ZmmScales.Divide(controlmc)
_fOut.WriteTObject(ZmmScales)
ZeeScales = target.Clone("zee_weights_%s"%cid);
ZeeScales.Divide(controlmc_e)
_fOut.WriteTObject(ZeeScales)
WZScales = target.Clone("w_weights_%s"%cid);
WZScales.Divide(controlmc_w)
_fOut.WriteTObject(WZScales)
PhotonScales = target.Clone("photon_weights_%s"%cid);
PhotonScales.Divide(controlmc_photon)
_fOut.WriteTObject(PhotonScales)
#######################################################################################################
_bins = [] # take bins from some histogram, can choose anything but this is easy
for b in range(target.GetNbinsX()+1):
_bins.append(target.GetBinLowEdge(b+1))
# Here is the important bit which "Builds" the control region, make a list of control regions which
# are constraining this process, each "Channel" is created with ...
# (name,_wspace,out_ws,cid+'_'+model,TRANSFERFACTORS)
# the second and third arguments can be left unchanged, the others instead must be set
# TRANSFERFACTORS are what is created above, eg WScales
CRs = [
Channel("photon",_wspace,out_ws,cid+'_'+model,PhotonScales)
,Channel("dimuon",_wspace,out_ws,cid+'_'+model,ZmmScales)
,Channel("dielectron",_wspace,out_ws,cid+'_'+model,ZeeScales)
,Channel("wjetssignal",_wspace,out_ws,cid+'_'+model,WZScales)
]
# my_function(_wspace,_fin,_fOut,cid,diag, filler)
# ############################ USER DEFINED ###########################################################
# Add systematics in the following, for normalisations use name, relative size (0.01 --> 1%)
# for shapes use add_nuisance_shape with (name,_fOut)
# note, the code will LOOK for something called NOMINAL_name_Up and NOMINAL_name_Down, where NOMINAL=WScales.GetName()
# these must be created and writted to the same dirctory as the nominal (fDir)
if "monov" in cid:
tag = "_monov"
else:
tag = ""
do_stat_unc(PhotonScales, "photon", cid, "photonCR", CRs[0],_fOut)
do_stat_unc(ZmmScales, "zmm", cid, "dimuonCR", CRs[1],_fOut)
do_stat_unc(ZeeScales, "zee", cid, "dielectronCR", CRs[2],_fOut)
do_stat_unc(WZScales, "w", cid, "wzCR", CRs[3],_fOut)
## Here now adding the trigger uncertainty
fztoz_trig = r.TFile.Open("sys/all_trig_2017.root") # 250 - 1400 binning
# We want to correlate experimental uncertainties between the loose and tight regions.
cid_corr = re.sub("(loose|tight)","",cid)
# Take the square of the uncertainty because we are going from zero to two leptons
add_variation(ZmmScales,fztoz_trig,"trig_sys_sqr_down"+tag,"zmm_weights_%s_mettrig_%s_Down"%(cid,year), _fOut)
add_variation(ZmmScales,fztoz_trig,"trig_sys_sqr_up"+tag,"zmm_weights_%s_mettrig_%s_Up"%(cid,year), _fOut)
CRs[1].add_nuisance_shape("mettrig_%s"%year,_fOut, functype='quadratic')
#######################################################################################################
ftheo = ROOT.TFile("sys/vjets_reco_theory_unc.root")
# Theory uncertainties on Z/Gamma
for variation, name in [
('d1k', 'qcd'),
('d2k', 'qcdshape'),
('d3k', 'qcdprocess'),
('d1kappa', 'ewk'),
('d2kappa_g', 'nnlomissG'),
('d2kappa_z', 'nnlomissZ'),
('d3kappa_g', 'sudakovG'),
('d3kappa_z', 'sudakovZ'),
('mix', 'cross')
]:
# Flip nuisance directions for compatibility with 2016
invert = variation in ['d2kappa','d3kappa']
for direction in 'up', 'down':
add_variation(
PhotonScales,
ftheo,
"{CHANNEL}_z_over_g_{VARIATION}_{DIR}".format(VARIATION=variation,DIR=direction,**filler),
"photon_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert=invert
)
CRs[0].add_nuisance_shape(name, _fOut, functype='quadratic')
# Theory uncertainties on Z/W
for variation, name in [
('d1k', 'wqcd'),
('d2k', 'wqcdshape'),
('d3k', 'wqcdprocess'),
('d1kappa', 'wewk'),
('d2kappa_w', 'nnlomissW'),
('d2kappa_z', 'nnlomissZ'),
('d3kappa_w', 'sudakovW'),
('d3kappa_z', 'sudakovZ'),
('mix', 'wcross')
]:
# Flip nuisance directions for compatibility with 2016
invert = variation in ['d1k','d3k','d1kappa','d2kappa_w','d3kappa_w']
for direction in 'up', 'down':
add_variation(
WZScales,
ftheo,
"{CHANNEL}_z_over_w_{VARIATION}_{DIR}".format(VARIATION=variation,DIR=direction,**filler),
"w_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert=invert
)
CRs[3].add_nuisance_shape(name, _fOut, functype='quadratic')
# Mistag nuisances
fmistag = ROOT.TFile("sys/mistag_sf_variations.root")
region_wp = determine_region_wp(cid)
for sf_wp in 'loose','tight':
scale, flip = mistag_scale_and_flip(sf_wp, region_wp)
if scale == 0:
continue
# Gamma uncertainty on Z/gamma ratio
for variation_index in range(2):
name = 'CMS_eff{YEAR}_vmistag_g_stat_{SF_WP}_{INDEX}'.format(INDEX=variation_index, SF_WP=sf_wp, **filler)
for direction in 'up','down':
add_variation(
PhotonScales,
fmistag,
'g_{SF_WP}_{YEAR}_{CHANNEL}_var{INDEX}_{DIR}'.format(INDEX=variation_index,DIR=direction, SF_WP=sf_wp, **filler),
"photon_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert = not flip,
scale=scale
)
CRs[0].add_nuisance_shape(name, _fOut, functype='quadratic')
# Z uncertainty on Z/gamma ratio
for variation_index in range(2):
name = 'CMS_eff{YEAR}_vmistag_z_stat_{SF_WP}_{INDEX}'.format(INDEX=variation_index, SF_WP=sf_wp, **filler)
for direction in 'up','down':
add_variation(
PhotonScales,
fmistag,
'z_{SF_WP}_{YEAR}_{CHANNEL}_var{INDEX}_{DIR}'.format(INDEX=variation_index,DIR=direction, SF_WP=sf_wp, **filler),
"photon_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert=flip,
scale=scale
)
CRs[0].add_nuisance_shape(name, _fOut, functype='quadratic')
# Z uncertainty on Z/W ratio
for variation_index in range(2):
name = 'CMS_eff{YEAR}_vmistag_z_stat_{SF_WP}_{INDEX}'.format(INDEX=variation_index, SF_WP=sf_wp, **filler)
for direction in 'up','down':
add_variation(
WZScales,
fmistag,
'z_{SF_WP}_{YEAR}_{CHANNEL}_var{INDEX}_{DIR}'.format(INDEX=variation_index, DIR=direction, SF_WP=sf_wp, **filler),
"w_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert=flip,
scale=scale
)
CRs[3].add_nuisance_shape(name, _fOut, functype='quadratic')
# W uncertainty on Z/W ratio
for variation_index in range(2):
name = 'CMS_eff{YEAR}_vmistag_w_stat_{SF_WP}_{INDEX}'.format(INDEX=variation_index, SF_WP=sf_wp, **filler)
for direction in 'up','down':
add_variation(
WZScales,
fmistag,
'w_{SF_WP}_{YEAR}_{CHANNEL}_var{INDEX}_{DIR}'.format(INDEX=variation_index, SF_WP=sf_wp, DIR=direction, **filler),
"w_weights_{CID}_{NAME}_{DIR}".format(NAME=name,DIR=direction.capitalize(),**filler),
_fOut,
invert=not flip,
scale=scale
)
CRs[3].add_nuisance_shape(name, _fOut, functype='quadratic')
# PDF uncertainties
fpdf = ROOT.TFile("sys/tf_pdf_unc.root")
for direction in 'up', 'down':
add_variation(
PhotonScales,
fpdf,
"{CHANNEL}_z_over_g_pdf_{YEAR}_{DIR}".format(DIR=direction,**filler),
"photon_weights_{CID}_z_over_g_pdf_{DIR}".format(DIR=direction.capitalize(), **filler),
_fOut
)
add_variation(
ZmmScales,
fpdf,
"{CHANNEL}_z_over_zmm_pdf_{YEAR}_{DIR}".format(DIR=direction,**filler),
"zmm_weights_{CID}_z_over_z_pdf_{DIR}".format(DIR=direction.capitalize(), **filler),
_fOut
)
add_variation(
ZeeScales,
fpdf,
"{CHANNEL}_z_over_zee_pdf_{YEAR}_{DIR}".format(DIR=direction,**filler),
"zee_weights_{CID}_z_over_z_pdf_{DIR}".format(DIR=direction.capitalize(), **filler),
_fOut
)
add_variation(
WZScales,
fpdf,
"{CHANNEL}_z_over_w_pdf_{YEAR}_{DIR}".format(DIR=direction,**filler),
"w_weights_{CID}_z_over_w_pdf_{DIR}".format(DIR=direction.capitalize(), **filler),
_fOut
)
CRs[0].add_nuisance_shape("z_over_g_pdf",_fOut, functype='quadratic')
CRs[1].add_nuisance_shape("z_over_z_pdf",_fOut, functype='quadratic')
CRs[2].add_nuisance_shape("z_over_z_pdf",_fOut, functype='quadratic')
CRs[3].add_nuisance_shape("z_over_w_pdf",_fOut, functype='quadratic')
#######################################################################################################
# Veto uncertainties
ftauveto = r.TFile.Open("sys/tau_veto_unc.root")
fmuveto = r.TFile.Open("sys/muon_veto_unc.root")
felveto = r.TFile.Open("sys/ele_veto_unc.root")
# The transfer factor here is Z(SR) / W(SR)
# -> Invert the veto shapes relative to W_constraints, where the TF is W(SR) / W(CR)
add_variation(WZScales, felveto, "ele_id_veto_sys_{CHANNEL}_up_{YEAR}".format(**filler), "w_weights_%s_eveto_id_%s_Up"%(cid, year), _fOut,invert=True)
add_variation(WZScales, felveto, "ele_id_veto_sys_{CHANNEL}_down_{YEAR}".format(**filler), "w_weights_%s_eveto_id_%s_Down"%(cid, year), _fOut,invert=True)
CRs[3].add_nuisance_shape("eveto_id_%s"%year,_fOut, functype='quadratic')
add_variation(WZScales, felveto, "ele_reco_veto_sys_{CHANNEL}_up_{YEAR}".format(**filler), "w_weights_%s_eveto_reco_%s_Up"%(cid, year), _fOut,invert=True)
add_variation(WZScales, felveto, "ele_reco_veto_sys_{CHANNEL}_down_{YEAR}".format(**filler), "w_weights_%s_eveto_reco_%s_Down"%(cid, year), _fOut,invert=True)
CRs[3].add_nuisance_shape("eveto_reco_%s"%year,_fOut, functype='quadratic')
add_variation(WZScales, ftauveto, "tau_id_veto_sys_{CHANNEL}_up_{YEAR}".format(**filler), "w_weights_%s_tauveto_%s_Up"%(cid, year), _fOut,invert=True)
add_variation(WZScales, ftauveto, "tau_id_veto_sys_{CHANNEL}_down_{YEAR}".format(**filler), "w_weights_%s_tauveto_%s_Down"%(cid, year), _fOut,invert=True)
CRs[3].add_nuisance_shape("tauveto_%s"%year,_fOut, functype='quadratic')
add_variation(WZScales, fmuveto, "muon_id_veto_sys_{CHANNEL}_up_{YEAR}".format(**filler), "w_weights_%s_muveto_id_%s_Up"%(cid, year), _fOut,invert=True)
add_variation(WZScales, fmuveto, "muon_id_veto_sys_{CHANNEL}_down_{YEAR}".format(**filler), "w_weights_%s_muveto_id_%s_Down"%(cid, year), _fOut,invert=True)
CRs[3].add_nuisance_shape("muveto_id_%s"%year,_fOut, functype='quadratic')
add_variation(WZScales, fmuveto, "muon_iso_veto_sys_{CHANNEL}_up_{YEAR}".format(**filler), "w_weights_%s_muveto_iso_%s_Up"%(cid, year), _fOut,invert=True)
add_variation(WZScales, fmuveto, "muon_iso_veto_sys_{CHANNEL}_down_{YEAR}".format(**filler), "w_weights_%s_muveto_iso_%s_Down"%(cid, year), _fOut,invert=True)
CRs[3].add_nuisance_shape("muveto_iso_%s"%year,_fOut, functype='quadratic')
# Prefiring uncertainty
# The shape in the input file is just one histogram to be used for up/down
# -> need to invert for one variation
# Note that the "invert" argument is the other way round for electrons and muons
# To take into account the anticorrelation between them
if year == 2017:
fpref = r.TFile.Open("sys/pref_unc.root")
add_variation(ZmmScales, fpref, "{CHANNEL}_pref_unc_z_over_mm".format(**filler), "zmm_weights_%s_prefiring_Up"%cid, _fOut)
add_variation(ZmmScales, fpref, "{CHANNEL}_pref_unc_z_over_mm".format(**filler), "zmm_weights_%s_prefiring_Down"%cid, _fOut,invert=True)
CRs[1].add_nuisance_shape("prefiring",_fOut, functype='quadratic')
add_variation(ZeeScales, fpref, "{CHANNEL}_pref_unc_z_over_ee".format(**filler), "zee_weights_%s_prefiring_Up"%cid, _fOut,invert=True)
add_variation(ZeeScales, fpref, "{CHANNEL}_pref_unc_z_over_ee".format(**filler), "zee_weights_%s_prefiring_Down"%cid, _fOut)
CRs[2].add_nuisance_shape("prefiring",_fOut, functype='quadratic')
fphotonid = r.TFile.Open("sys/photon_id_unc.root")
add_variation(PhotonScales, fphotonid, "{CHANNEL}_{YEAR}_photon_id_up".format(**filler), "photon_weights_%s_CMS_eff%s_pho_Up"%(cid, year), _fOut, invert=True, scale=2.0)
add_variation(PhotonScales, fphotonid, "{CHANNEL}_{YEAR}_photon_id_dn".format(**filler), "photon_weights_%s_CMS_eff%s_pho_Down"%(cid, year), _fOut, invert=True, scale=2.0)
CRs[0].add_nuisance_shape("CMS_eff{YEAR}_pho".format(**filler),_fOut, functype='quadratic')
add_variation(PhotonScales, fphotonid, "{CHANNEL}_{YEAR}_photon_id_extrap_up".format(**filler), "photon_weights_%s_CMS_eff%s_pho_extrap_Up"%(cid, year), _fOut, invert=True, scale=2.0)
add_variation(PhotonScales, fphotonid, "{CHANNEL}_{YEAR}_photon_id_extrap_dn".format(**filler), "photon_weights_%s_CMS_eff%s_pho_extrap_Down"%(cid, year), _fOut, invert=True, scale=2.0)
CRs[0].add_nuisance_shape("CMS_eff{YEAR}_pho_extrap".format(**filler),_fOut, functype='quadratic')
felectronid = r.TFile.Open("sys/ele_id_unc.root")
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_id_stat_up".format(**filler), "zee_weights_%s_CMS_eff%s_e_stat_Up"%(cid, year), _fOut, invert=True, scale=2.0)
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_id_stat_dn".format(**filler), "zee_weights_%s_CMS_eff%s_e_stat_Down"%(cid, year), _fOut, invert=True, scale=2.0)
CRs[2].add_nuisance_shape("CMS_eff{YEAR}_e_stat".format(**filler),_fOut, functype='quadratic')
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_id_syst_up".format(**filler), "zee_weights_%s_CMS_eff%s_e_syst_Up"%(cid, year), _fOut, invert=True, scale=2.0)
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_id_syst_dn".format(**filler), "zee_weights_%s_CMS_eff%s_e_syst_Down"%(cid, year), _fOut, invert=True, scale=2.0)
CRs[2].add_nuisance_shape("CMS_eff{YEAR}_e_syst".format(**filler),_fOut, functype='quadratic')
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_reco_up".format(**filler), "zee_weights_%s_CMS_eff%s_e_reco_Up"%(cid, year), _fOut, invert=True)
add_variation(ZeeScales, felectronid, "{CHANNEL}_{YEAR}_2e_reco_dn".format(**filler), "zee_weights_%s_CMS_eff%s_e_reco_Down"%(cid, year), _fOut, invert=True)
CRs[2].add_nuisance_shape("CMS_eff{YEAR}_e_reco".format(**filler),_fOut, functype='quadratic')
# JES uncertainties
fjes = get_jes_jer_source_file_for_tf(category='monojet')
jet_variations = get_jes_variations(fjes, year)
print "VARIATIONS"
print jet_variations
for var in jet_variations:
add_variation(WZScales, fjes, 'znunu_over_wlnu{YEAR}_qcd_{VARIATION}Up'.format(YEAR=year-2000, VARIATION=var), "w_weights_%s_%s_Up"%(cid, var), _fOut)
add_variation(WZScales, fjes, 'znunu_over_wlnu{YEAR}_qcd_{VARIATION}Down'.format(YEAR=year-2000, VARIATION=var), "w_weights_%s_%s_Down"%(cid, var), _fOut)
CRs[3].add_nuisance_shape(var,_fOut, functype='quadratic')
add_variation(ZmmScales, fjes, 'znunu_over_zmumu{YEAR}_qcd_{VARIATION}Up'.format(YEAR=year-2000, VARIATION=var), "zmm_weights_%s_%s_Up"%(cid, var), _fOut)
add_variation(ZmmScales, fjes, 'znunu_over_zmumu{YEAR}_qcd_{VARIATION}Down'.format(YEAR=year-2000, VARIATION=var), "zmm_weights_%s_%s_Down"%(cid, var), _fOut)
CRs[1].add_nuisance_shape(var,_fOut, functype='quadratic')
add_variation(ZeeScales, fjes, 'znunu_over_zee{YEAR}_qcd_{VARIATION}Up'.format(YEAR=year-2000, VARIATION=var), "zee_weights_%s_%s_Up"%(cid, var), _fOut)
add_variation(ZeeScales, fjes, 'znunu_over_zee{YEAR}_qcd_{VARIATION}Down'.format(YEAR=year-2000, VARIATION=var), "zee_weights_%s_%s_Down"%(cid, var), _fOut)
CRs[2].add_nuisance_shape(var,_fOut, functype='quadratic')
add_variation(PhotonScales, fjes, 'znunu_over_gjets{YEAR}_qcd_{VARIATION}Up'.format(YEAR=year-2000, VARIATION=var), "photon_weights_%s_%s_Up"%(cid, var), _fOut)
add_variation(PhotonScales, fjes, 'znunu_over_gjets{YEAR}_qcd_{VARIATION}Down'.format(YEAR=year-2000, VARIATION=var), "photon_weights_%s_%s_Down"%(cid, var), _fOut)
CRs[0].add_nuisance_shape(var,_fOut, functype='quadratic')
# Photon scale
fphotonscale = ROOT.TFile("sys/photon_scale_unc.root")
var = "photon_scale_%s"%year
add_variation(
PhotonScales,
fphotonscale,
'photon_pt_scale_{CHANNEL}_0.02_up'.format(**filler),
"photon_weights_%s_%s_Up"%(cid, var),
_fOut,
invert=True,
scale=0.5
)
add_variation(
PhotonScales,
fphotonscale,
'photon_pt_scale_{CHANNEL}_0.02_dn'.format(**filler),
"photon_weights_%s_%s_Down"%(cid, var),
_fOut,
invert=True,
scale=0.5
)
CRs[0].add_nuisance_shape(var, _fOut, functype='quadratic')
cat = Category(model,cid,nam,_fin,_fOut,_wspace,out_ws,_bins,metname,target.GetName(),CRs,diag)
# Return of course
return cat