def tofnalhist(obj):
if not isinstance(obj, Histogram):
raise TypeError("cannot convert {0} to a fnal_column_analysis_tools histogram".format(type(obj).__name__))
axes = []
sparse_binning = {}
dense_map = {}
dense_shape = []
for i, ax in enumerate(obj.axis):
if isinstance(ax.binning, CategoryBinning):
sparse_binning[i] = ax.binning.categories
if ax.binning.loc_overflow.value < BinLocation.nonexistent.value:
sparse_binning[i].insert(0, "")
elif ax.binning.loc_overflow.value > BinLocation.nonexistent.value:
sparse_binning[i].append("")
new_ax = hist.Cat(ax.expression, ax.title)
new_ax._categories = sparse_binning[i]
axes.append(new_ax)
elif isinstance(ax.binning, RegularBinning):
dense_map[i] = [-numpy.inf, ..., numpy.inf, numpy.nan]
dense_shape.append(ax.binning.num+3)
axes.append(hist.Bin(ax.expression,
ax.title,
ax.binning.num,
ax.binning.interval.low,
ax.binning.interval.high
))
elif isinstance(ax.binning, EdgesBinning):
dense_map[i] = [-numpy.inf, ..., numpy.inf, numpy.nan]
dense_shape.append(ax.binning.edges.size+2)
axes.append(hist.Bin(ax.expression,
ax.title,
ax.binning.edges
))
else:
raise TypeError("unable to convert axes of type {0} to fnalhist axes".format(type(ax).__name__))
hout = hist.Hist(obj.title, *axes)
if isinstance(obj.counts, WeightedCounts):
hout._init_sumw2()
walk_shape = [len(s) for s in sparse_binning.values()]
walk_indices = numpy.unravel_index(numpy.arange(numpy.prod(walk_shape)), walk_shape)
for walk_index in zip(*walk_indices):
insert_index = tuple(v[k] for k,v in zip(walk_index, sparse_binning.values()))
extract_index = [None]*len(axes)
for k,v in dense_map.items():
extract_index[k] = v
for k,v in zip(sparse_binning.keys(), walk_index):
extract_index[k] = v
extract_index = tuple(extract_index)
if isinstance(obj.counts, UnweightedCounts):
hout._sumw[insert_index] = obj.counts[extract_index]
elif isinstance(obj.counts, WeightedCounts):
counts = obj.counts[extract_index]
hout._sumw[insert_index] = counts['sumw']
hout._sumw2[insert_index] = counts['sumw2']
return hout
def __init__(self, columns=[]):
self._columns = columns
dataset_axis = hist.Cat("dataset", "Primary dataset")
mass_axis = hist.Bin("mass", r"$m_{\mu\mu}$ [GeV]", 30000, 0.25, 300)
pt_axis = hist.Bin("pt", r"$p_{T}$ [GeV]", 30000, 0.25, 300)
self._accumulator = processor.dict_accumulator({
'mass':
hist.Hist("Counts", dataset_axis, mass_axis),
'pt':
hist.Hist("Counts", dataset_axis, pt_axis),
'cutflow':
processor.defaultdict_accumulator(int),
})
def calc(self, physics_objects, dataset_name):
electrons = physics_objects["Electron"]
ele = electrons[(electrons.pt > 20) & (np.abs(electrons.eta) < 2.5) &
(electrons.cutBased >= 4)]
muons = physics_objects["Muon"]
mu = muons[(muons.pt > 20) & (np.abs(muons.eta) < 2.4) &
(muons.tightId > 0)]
# Just to demonstrate broadcast variables
weights_eval = self.nonevent_data.value
electrons['SF'] = weights_eval["eleScaleFactor_TightId_POG"](
electrons.eta, electrons.pt)
ee = ele.distincts()
mm = mu.distincts()
em = ele.cross(mu)
dileptons = {}
dileptons['ee'] = ee[(ee.i0.pdgId * ee.i1.pdgId == -11 * 11)
& (ee.i0.pt > 25)]
dileptons['mm'] = mm[(mm.i0.pdgId * mm.i1.pdgId == -13 * 13)]
dileptons['em'] = em[(em.i0.pdgId * em.i1.pdgId == -11 * 13)]
channels = {}
channels['ee'] = (ee.counts == 1) & (mu.counts == 0)
channels['mm'] = (mm.counts == 1) & (ele.counts == 0)
channels['em'] = (em.counts == 1) & (ele.counts == 1) & (mu.counts
== 1)
# dupe = np.zeros(Muon_pt.size, dtype=bool)
tot = 0
isRealData = True
for channel, cut in channels.items():
zcands = dileptons[channel][cut]
# dupe |= cut
tot += cut.sum()
weight = np.array(1.)
zMassHist = self.accumulators["zMass"]
zMass = hist.Hist(
"Events",
zMassHist.dataset_axis,
zMassHist.channel_cat_axis,
hist.Bin("mass", "$m_{\ell\ell}$ [GeV]", 120, 0, 120),
)
zMass.fill(dataset=dataset_name,
channel=channel,
mass=zcands.mass.flatten(),
weight=weight.flatten())
zMassHist.accumulator.add(zMass)
return np.zeros(electrons.pt.size)
def compute_zpeak(dataset,
nElectron,
Electron_pt,
Electron_eta,
Electron_phi,
Electron_mass,
Electron_cutBased,
Electron_pdgId,
Electron_pfRelIso03_all,
nMuon,
Muon_pt,
Muon_eta,
Muon_phi,
Muon_mass,
Muon_tightId,
Muon_pdgId,
Muon_pfRelIso04_all):
global hists, non_event_data
tic = time.time()
electrons = JaggedCandidateArray.candidatesfromcounts(
nElectron.array,
pt=Electron_pt.array[0].base,
eta=Electron_eta.array[0].base,
phi=Electron_phi.array[0].base,
mass=Electron_mass.array[0].base,
cutBased=Electron_cutBased.array[0].base,
pdgId=Electron_pdgId.array[0].base,
pfRelIso03_all=Electron_pfRelIso03_all.array[0].base,
)
ele = electrons[(electrons.pt > 20) &
(np.abs(electrons.eta) < 2.5) &
(electrons.cutBased >= 4)]
# Just to demonstrate broadcast variables
weights_eval = non_event_data.value
electrons['SF'] = weights_eval["eleScaleFactor_TightId_POG"](
electrons.eta,
electrons.pt)
muons = JaggedCandidateArray.candidatesfromcounts(
nMuon.values,
pt=Muon_pt.array[0].base,
eta=Muon_eta.array[0].base,
phi=Muon_phi.array[0].base,
mass=Muon_mass.array[0].base,
tightId=Muon_tightId.array[0].base,
pdgId=Muon_pdgId.array[0].base,
pfRelIso04_all=Muon_pfRelIso04_all.array[0].base,
)
mu = muons[(muons.pt > 20) &
(np.abs(muons.eta) < 2.4) &
(muons.tightId > 0)]
ee = ele.distincts()
mm = mu.distincts()
em = ele.cross(mu)
dileptons = {}
dileptons['ee'] = ee[
(ee.i0.pdgId * ee.i1.pdgId == -11 * 11) & (ee.i0.pt > 25)]
dileptons['mm'] = mm[(mm.i0.pdgId * mm.i1.pdgId == -13 * 13)]
dileptons['em'] = em[(em.i0.pdgId * em.i1.pdgId == -11 * 13)]
channels = {}
channels['ee'] = (ee.counts == 1) & (mu.counts == 0)
channels['mm'] = (mm.counts == 1) & (ele.counts == 0)
channels['em'] = (em.counts == 1) & (ele.counts == 1) & (
mu.counts == 1)
dupe = np.zeros(Muon_pt.size, dtype=bool)
tot = 0
isRealData = True
for channel, cut in channels.items():
zcands = dileptons[channel][cut]
dupe |= cut
tot += cut.sum()
weight = np.array(1.)
zMassHist = hists["zMass"]["accumulator"]
zMass = hist.Hist("Events", hists["zMass"]["dataset_axis"],
hists["zMass"]["channel_cat_axis"],
hist.Bin("mass", "$m_{\ell\ell}$ [GeV]", 120, 0, 120),
)
zMass.fill(dataset=dataset[0], channel=channel,
mass=zcands.mass.flatten(),
weight=weight.flatten())
zMassHist.add(zMass)
dt = time.time() - tic
return pd.Series(np.ones(Electron_pt.size) * dt/Electron_pt.size)
def addInPlace(self, val1, val2):
val1 += val2
return val1
# Create a histogram accumulator for ZMass
hists = OrderedDict()
dataset_axis = hist.Cat("dataset", "DAS name")
channel_cat_axis = hist.Cat("channel", "dilepton flavor")
hists['zMass'] = {
"accumulator":
spark.sparkContext.accumulator(
hist.Hist("Events", dataset_axis, channel_cat_axis,
hist.Bin("mass", "$m_{\ell\ell}$ [GeV]", 120, 0, 120),
),
FLNAL_Hist_AccumulatorParam()
),
"dataset_axis": hist.Cat("dataset", "DAS name"),
"channel_cat_axis": hist.Cat("channel", "dilepton flavor")
}
# Create a broadcast variable for the non-event data
weightsext = lookup_tools.extractor()
correctionDescriptions = open("newCorrectionFiles.txt").readlines()
weightsext.add_weight_sets(correctionDescriptions)
weightsext.finalize()
weights_eval = weightsext.make_evaluator()
non_event_data = spark.sparkContext.broadcast(weights_eval)
def test_hist():
counts, test_eta, test_pt = dummy_jagged_eta_pt()
h_nothing = hist.Hist("empty inside")
assert h_nothing.sparse_dim() == h_nothing.dense_dim() == 0
assert h_nothing.values() == {}
h_regular_bins = hist.Hist("regular joe", hist.Bin("x", "x", 20, 0, 200),
hist.Bin("y", "why", 20, -3, 3))
h_regular_bins.fill(x=test_pt, y=test_eta)
nentries = np.sum(counts)
assert h_regular_bins.sum(
"x", "y",
overflow='all').values(sumw2=True)[()] == (nentries, nentries)
# bin x=2, y=10 (when overflow removed)
count_some_bin = np.sum((test_pt >= 20.) & (test_pt < 30.)
& (test_eta >= 0.) & (test_eta < 0.3))
assert h_regular_bins.project("x",
slice(20,
30)).values()[()][10] == count_some_bin
assert h_regular_bins.project("y",
slice(0,
0.3)).values()[()][2] == count_some_bin
h_reduced = h_regular_bins[10:, -.6:]
# bin x=1, y=2
assert h_reduced.project("x", slice(20,
30)).values()[()][2] == count_some_bin
assert h_reduced.project("y", slice(0,
0.3)).values()[()][1] == count_some_bin
h_reduced.fill(x=23, y=0.1)
assert h_reduced.project("x",
slice(20,
30)).values()[()][2] == count_some_bin + 1
assert h_reduced.project("y",
slice(0,
0.3)).values()[()][1] == count_some_bin + 1
animal = hist.Cat("animal", "type of animal")
vocalization = hist.Cat("vocalization",
"onomatopoiea is that how you spell it?")
h_cat_bins = hist.Hist("I like cats", animal, vocalization)
h_cat_bins.fill(animal="cat", vocalization="meow", weight=2.)
h_cat_bins.fill(animal="dog",
vocalization="meow",
weight=np.array([-1., -1., -5.]))
h_cat_bins.fill(animal="dog", vocalization="woof", weight=100.)
h_cat_bins.fill(animal="dog", vocalization="ruff")
assert h_cat_bins.values()[("cat", "meow")] == 2.
assert h_cat_bins.values(sumw2=True)[("dog", "meow")] == (-7., 27.)
assert h_cat_bins.project(
"vocalization",
["woof", "ruff"]).values(sumw2=True)[("dog", )] == (101., 10001.)
height = hist.Bin("height", "height [m]", 10, 0, 5)
h_mascots_1 = hist.Hist(
"fermi mascot showdown",
animal,
vocalization,
height,
# weight is a reserved keyword
hist.Bin("mass", "weight (g=9.81m/s**2) [kg]",
np.power(10.,
np.arange(5) - 1)),
)
adult_bison_h = np.random.normal(loc=2.5, scale=0.2, size=40)
adult_bison_w = np.random.normal(loc=700, scale=100, size=40)
h_mascots_1.fill(animal="bison",
vocalization="huff",
height=adult_bison_h,
mass=adult_bison_w)
goose_h = np.random.normal(loc=0.4, scale=0.05, size=1000)
goose_w = np.random.normal(loc=7, scale=1, size=1000)
h_mascots_1.fill(animal="goose",
vocalization="honk",
height=goose_h,
mass=goose_w)
crane_h = np.random.normal(loc=1, scale=0.05, size=4)
crane_w = np.random.normal(loc=10, scale=1, size=4)
h_mascots_1.fill(animal="crane",
vocalization="none",
height=crane_h,
mass=crane_w)
h_mascots_2 = h_mascots_1.copy()
h_mascots_2.clear()
baby_bison_h = np.random.normal(loc=.5, scale=0.1, size=20)
baby_bison_w = np.random.normal(loc=200, scale=10, size=20)
baby_bison_cutefactor = 2.5 * np.ones_like(baby_bison_w)
h_mascots_2.fill(animal="bison",
vocalization="baa",
height=baby_bison_h,
mass=baby_bison_w,
weight=baby_bison_cutefactor)
h_mascots_2.fill(animal="fox", vocalization="none", height=1., mass=30.)
h_mascots = h_mascots_1 + h_mascots_2
assert h_mascots.project("vocalization",
"h*").sum("height", "mass",
"animal").values()[()] == 1040.
species_class = hist.Cat("species_class",
"where the subphylum is vertibrates")
classes = {
'birds': ['goose', 'crane'],
'mammals': ['bison', 'fox'],
}
h_species = h_mascots.group(species_class, "animal", classes)
assert set(h_species.project("vocalization").values().keys()) == set([
('birds', ), ('mammals', )
])
nbirds_bin = np.sum((goose_h >= 0.5) & (goose_h < 1) & (goose_w > 10)
& (goose_w < 100))
nbirds_bin += np.sum((crane_h >= 0.5) & (crane_h < 1) & (crane_w > 10)
& (crane_w < 100))
assert h_species.project("vocalization").values()[(
'birds', )][1, 2] == nbirds_bin
tally = h_species.sum("mass", "height", "vocalization").values()
assert tally[('birds', )] == 1004.
assert tally[('mammals', )] == 91.
h_species.scale({"honk": 0.1, "huff": 0.9}, axis="vocalization")
h_species.scale(5.)
tally = h_species.sum("mass", height, vocalization).values(sumw2=True)
assert tally[('birds', )] == (520., 350.)
assert tally[('mammals', )] == (435.,
25 * (40 * (0.9**2) + 20 * (2.5**2) + 1))
assert h_species.axis("vocalization") is vocalization
assert h_species.axis("height") is height
assert h_species.project("vocalization", "h*").axis("height") is height
tall_class = hist.Cat("tall_class", "species class (species above 1m)")
mapping = {
'birds': (['goose', 'crane'], slice(1., None)),
'mammals': (['bison', 'fox'], slice(1., None)),
}
h_tall = h_mascots.group(tall_class, (animal, height), mapping)
tall_bird_count = np.sum(goose_h >= 1.) + np.sum(crane_h >= 1)
assert h_tall.sum("mass",
"vocalization").values()[('birds', )] == tall_bird_count
tall_mammal_count = np.sum(adult_bison_h >= 1.) + np.sum(
baby_bison_h >= 1) + 1
assert h_tall.sum(
"mass", "vocalization").values()[('mammals', )] == tall_mammal_count
#!/usr/bin/env python
import uproot, uproot_methods
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
from Builder import Initialize
from fnal_column_analysis_tools import hist
hists = {
'recoil': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("recoil","Hadronic Recoil",[250.0, 280.0, 310.0, 340.0, 370.0, 400.0, 430.0, 470.0, 510.0, 550.0, 590.0, 640.0, 690.0, 740.0, 790.0, 840.0, 900.0, 960.0, 1020.0, 1090.0, 1160.0, 1250.0])),
'mindphi': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("mindphi","Min dPhi(MET,AK4s)",15,0,6.28)),
'j1pt': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("j1pt","AK4 Leading Jet Pt",50,30,500)),
'fj1pt': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("fj1pt","AK15 Leading Jet Pt",50,200,700)),
'njets': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("njets","AK4 Number of Jets",6,0,5)),
'nfjets': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("nfjets","AK15 Number of Jets",4,0,3)),
'fjmass': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("fjmass","AK15 Jet Mass",50,20,250)),
'TvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("TvsQCD","TvsQCD",15,0,1)),
'WvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("WvsQCD","WvsQCD",15,0,1)),
'ZvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZvsQCD","ZvsQCD",15,0,1)),
'VvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("VvsQCD","VvsQCD",15,0,1)),
'ZHbbvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZHbbvsQCD","ZHbbvsQCD",15,0,1)),
'ZHccvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZHccvsQCD","ZHccvsQCD",15,0,1)),
'WcqvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("WcqvsQCD","WcqvsQCD",15,0,1)),
'WqqvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("WqqvsQCD","WqqvsQCD",15,0,1)),
'ZbbvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZbbvsQCD","ZbbvsQCD",15,0,1)),
'ZccvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZccvsQCD","ZccvsQCD",15,0,1)),
'ZqqvsQCD': hist.Hist("Events", hist.Cat("dataset", "Primary dataset"), hist.Cat("region", "Region"), hist.Bin("ZqqvsQCD","ZqqvsQCD",15,0,1))
}
samples = {
"iszeroL":('ZJets','WJets','DY','TT_TuneCUETP8M2T4','ST_t-channel','ST_tW','WW_TuneCUETP8M1','WZ_TuneCUETP8M1','ZZ_TuneCUETP8M1','QCD','VH_HToBB','WminusH','WplusH','ttHTobb','GluGluHToBB','VBFHToBB','MET'),
""" dazsle gghbb analysis plots """
from fnal_column_analysis_tools import hist
from fnal_column_analysis_tools.hist import plot
from copy import deepcopy
dataset = hist.Cat("dataset", "Primary dataset")
systematic = hist.Cat("systematic", "Systematic Variation")
gencat = hist.Bin("ak8_isHadronicV", "Matched", [-1,0,1,2,3,9,10,11])
# one can relabel intervals, although process mapping obviates this
titles = ["Data","QCD", "V(light) matched", "V(c) matched", "V(b) matched", "Top W(ud)+b", "Top W(cs)+b"]
for i,v in enumerate(gencat.identifiers()):
setattr(v, 'label', titles[i])
jetpt = hist.Bin("ak8_pt", "Jet $p_T$", [450, 500, 550, 600, 675, 800, 1000])
jetpt_coarse = hist.Bin("ak8_pt", "Jet $p_T$", [450, 800])
jetmass = hist.Bin("ak8_msd", "Jet $m_{sd}$", 23, 40, 201)
jetmass_coarse = hist.Bin("ak8_msd", "Jet $m_{sd}$", [40, 100, 140, 200])
jetrho = hist.Bin("jetrho", r"Jet $\rho$", 13, -6, -2.1)
doubleb = hist.Bin("ak8_deepdoubleb", "Double-b", 20, 0., 1)
doublec = hist.Bin("ak8_deepdoublec", "Double-c", 20, 0., 1.)
doublecvb = hist.Bin("ak8_deepdoublecvb", "Double-cvb", 20, 0., 1.)
doubleb_coarse = [1., 0.93, 0.92, 0.89, 0.85, 0.7]
doubleb_coarse = hist.Bin("ak8_deepdoubleb", "Double-b", doubleb_coarse[::-1])
doublec_coarse = [0.87, 0.84, 0.83, 0.79, 0.69, 0.58]
doublec_coarse = hist.Bin("ak8_deepdoublec", "Double-c", doublec_coarse[::-1])
doublecvb_coarse = [0.93, 0.91, 0.86, 0.76, 0.6, 0.17, 0.12]
doublecvb_coarse = hist.Bin("ak8_deepdoublecvb", "Double-cvb", doublecvb_coarse[::-1])
n2ddt_coarse = hist.Bin("ak8_N2sdb1_ddt", "N2 DDT", [0.])
def __init__(self, corrections, debug=False):
self._corrections = corrections
self._debug = debug
dataset_axis = hist.Cat("dataset", "Primary dataset")
gencat_axis = hist.Bin("AK8Puppijet0_isHadronicV", "V matching index",
[0, 1, 2, 3, 9, 10, 11])
jetpt_axis = hist.Bin("AK8Puppijet0_pt", r"Jet $p_T$",
[450, 500, 550, 600, 675, 800, 1200])
jetmass_axis = hist.Bin("AK8Puppijet0_msd", r"Jet $m_{sd}$", 23, 40,
201)
jetpt_coarse_axis = hist.Bin("AK8Puppijet0_pt", r"Jet $p_T$",
[450, 1200])
jetmass_coarse_axis = hist.Bin("AK8Puppijet0_msd", r"Jet $m_{sd}$",
[40, 103, 152, 201])
jetrho_axis = hist.Bin("ak8jet_rho", r"Jet $\rho$", 13, -6, -2.1)
doubleb_axis = hist.Bin("AK8Puppijet0_deepdoubleb", "Double-b", 20, 0.,
1)
doublec_axis = hist.Bin("AK8Puppijet0_deepdoublec", "Double-c", 20, 0.,
1.)
doublecvb_axis = hist.Bin("AK8Puppijet0_deepdoublecvb", "Double-cvb",
20, 0., 1.)
doubleb_wps = [1., 0.9, 0.89, 0.85, 0.7]
doubleb_coarse_axis = hist.Bin("AK8Puppijet0_deepdoubleb", "Double-b",
doubleb_wps[::-1])
doublec_wps = [0.87, 0.84, 0.83, 0.79, 0.69]
doublec_coarse_axis = hist.Bin("AK8Puppijet0_deepdoublec", "Double-c",
doublec_wps[::-1])
doublecvb_wps = [0.93, 0.91, 0.6, 0.2, 0.17]
doublecvb_coarse_axis = hist.Bin("AK8Puppijet0_deepdoublecvb",
"Double-cvb", doublecvb_wps[::-1])
hists = processor.dict_accumulator()
hist.Hist.DEFAULT_DTYPE = 'f' # save some space by keeping float bin counts instead of double
hists['sumw'] = processor.dict_accumulator(
) # the defaultdict_accumulator is broken :<
hists['jetpt_preselection'] = hist.Hist(
"Events",
dataset_axis,
gencat_axis,
hist.Bin("AK8Puppijet0_pt", "Jet $p_T$", 100, 300, 1300),
)
hists['jeteta_preselection'] = hist.Hist(
"Events",
dataset_axis,
gencat_axis,
hist.Bin("AK8Puppijet0_eta", r"Jet $\eta$", 50, -3, 3),
)
hists['jetpt_muoncontrol'] = hist.Hist(
"Events",
dataset_axis,
gencat_axis,
hist.Bin("AK8Puppijet0_pt", "Jet $p_T$", 100, 300, 1300),
)
hists['muonpt_muoncontrol'] = hist.Hist(
"Events",
dataset_axis,
gencat_axis,
hist.Bin("vmuoLoose0_pt", "Leading muon $p_T$", 100, 0, 1000),
)
hists['muoneta_muoncontrol'] = hist.Hist(
"Events",
dataset_axis,
gencat_axis,
hist.Bin("vmuoLoose0_eta", r"Leading muon $\eta$", 50, -3, 3),
)
hists['jetpt_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis,
hist.Bin("AK8Puppijet0_pt", "Jet $p_T$", 100, 300, 1300))
hists['sculpt_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_axis, jetmass_axis,
doubleb_coarse_axis, doublec_coarse_axis, doublecvb_coarse_axis)
hists['tagtensor_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis, doubleb_axis, doublec_axis, doublecvb_axis)
hists['opposite_ak8_n3sdb1_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis,
hist.Bin("opposite_ak8_n3sdb1", r"Jet $N_{3,sd}^{\beta=1}$", 40,
0.5, 3))
hists['opposite_ak8_tau32_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis,
hist.Bin("opposite_ak8_tau32", r"Jet $\tau_{32}$", 40, 0, 1))
hists['opposite_ak8_msd_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis,
hist.Bin("opposite_ak8_msd", r"Jet $\m_{sd}$", 40, 50, 200))
hists['njets_ak4_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis,
hist.Bin("nAK4PuppijetsPt30", "Number AK4 Jets", 8, 0, 8))
hists['nminus1_antiak4btagMediumOppHem_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis,
hist.Bin("opposite_ak4_leadingDeepCSV",
r"Max(DeepCSV) (of $\leq4$ leading)", 40, 0, 1))
hists['nminus1_pfmet_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetpt_coarse_axis,
jetmass_coarse_axis, doubleb_coarse_axis,
hist.Bin("pfmet", r"PF $p_{T}^{miss}$", 40, 0, 200))
hists['nminus1_n2ddtPass_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetmass_coarse_axis,
doubleb_coarse_axis,
hist.Bin("ak8jet_n2ddt", r"Jet $N_{2,DDT}^{\beta=1}$", 40, -.25,
.25))
hists['nminus1_ak4btagMediumDR08_muoncontrol'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetmass_coarse_axis,
doubleb_coarse_axis,
hist.Bin("ak4_leadingDeepCSV_dR08",
r"Max(DeepCSV) ($\DeltaR(ak4, ak8)>0.8$)", 40, 0, 1))
hists['nminus1_muonDphiAK8_muoncontrol'] = hist.Hist(
"Events", dataset_axis, gencat_axis, jetmass_coarse_axis,
doubleb_coarse_axis,
hist.Bin("muon_dphi", r"$\Delta\phi(\mu, j)$", 40, 0, np.pi))
hists['templates_signalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis,
hist.Cat("systematic", "Systematic"), jetpt_axis, jetmass_axis,
doubleb_coarse_axis)
hists['templates_muoncontrol'] = hist.Hist(
"Events", dataset_axis, gencat_axis,
hist.Cat("systematic", "Systematic"), jetpt_axis, jetmass_axis,
doubleb_coarse_axis)
hists['templates_hCCsignalregion'] = hist.Hist(
"Events", dataset_axis, gencat_axis,
hist.Cat("systematic", "Systematic"), jetpt_axis, jetmass_axis,
doublec_coarse_axis)
hists['templates_hCCmuoncontrol'] = hist.Hist(
"Events", dataset_axis, gencat_axis,
hist.Cat("systematic", "Systematic"), jetpt_axis, jetmass_axis,
doublec_coarse_axis)
self._accumulator = hists