def analyze_data_function(data, parameters):
ret = Results()
num_events = data["num_events"]
muons = data["Muon"]
mu_pt = nplib.sqrt(muons.Px**2 + muons.Py**2)
muons.attrs_data["pt"] = mu_pt
mask_events = nplib.ones(muons.numevents(), dtype=nplib.bool)
mask_muons_passing_pt = muons.pt > parameters["muons_ptcut"]
num_muons_event = kernels.sum_in_offsets(backend, muons.offsets,
mask_muons_passing_pt,
mask_events, muons.masks["all"],
nplib.int8)
mask_events_dimuon = num_muons_event == 2
#get the leading muon pt in events that have exactly two muons
inds = nplib.zeros(num_events, dtype=nplib.int32)
leading_muon_pt = kernels.get_in_offsets(backend, muons.offsets, muons.pt,
inds, mask_events_dimuon,
mask_muons_passing_pt)
#compute a weighted histogram
weights = nplib.ones(num_events, dtype=nplib.float32)
bins = nplib.linspace(0, 300, 101, dtype=nplib.float32)
hist_muons_pt = Histogram(*kernels.histogram_from_vector(
backend, leading_muon_pt[mask_events_dimuon],
weights[mask_events_dimuon], bins))
#save it to the output
ret["hist_leading_muon_pt"] = hist_muons_pt
return ret
def load_hist(hist_dict):
return Histogram.from_dict({
"edges":
np.array(hist_dict["edges"]),
"contents":
np.array(hist_dict["contents"]),
"contents_w2":
np.array(hist_dict["contents_w2"]),
})
def fill_histograms_several(hists, systematic_name, histname_prefix, variables,
mask, weights, use_cuda):
this_worker = get_worker_wrapper()
NUMPY_LIB = this_worker.NUMPY_LIB
backend = this_worker.backend
all_arrays = []
all_bins = []
num_histograms = len(variables)
for array, varname, bins in variables:
if (len(array) != len(variables[0][0]) or len(array) != len(mask)
or len(array) != len(weights["nominal"])):
raise Exception(
"Data array {0} is of incompatible size".format(varname))
all_arrays += [array]
all_bins += [bins]
max_bins = max([b.shape[0] for b in all_bins])
stacked_array = NUMPY_LIB.stack(all_arrays, axis=0)
stacked_bins = np.concatenate(all_bins)
nbins = np.array([len(b) for b in all_bins])
nbins_sum = np.cumsum(nbins)
nbins_sum = np.insert(nbins_sum, 0, [0])
for weight_name, weight_array in weights.items():
if use_cuda:
nblocks = 32
out_w = NUMPY_LIB.zeros((len(variables), nblocks, max_bins),
dtype=NUMPY_LIB.float32)
out_w2 = NUMPY_LIB.zeros((len(variables), nblocks, max_bins),
dtype=NUMPY_LIB.float32)
backend.fill_histogram_several[nblocks, 1024](
stacked_array,
weight_array,
mask,
stacked_bins,
NUMPY_LIB.array(nbins),
NUMPY_LIB.array(nbins_sum),
out_w,
out_w2,
)
cuda.synchronize()
out_w = out_w.sum(axis=1)
out_w2 = out_w2.sum(axis=1)
out_w = NUMPY_LIB.asnumpy(out_w)
out_w2 = NUMPY_LIB.asnumpy(out_w2)
else:
out_w = NUMPY_LIB.zeros((len(variables), max_bins),
dtype=NUMPY_LIB.float32)
out_w2 = NUMPY_LIB.zeros((len(variables), max_bins),
dtype=NUMPY_LIB.float32)
backend.fill_histogram_several(
stacked_array,
weight_array,
mask,
stacked_bins,
nbins,
nbins_sum,
out_w,
out_w2,
)
out_w_separated = [
out_w[i, 0:nbins[i] - 1] for i in range(num_histograms)
]
out_w2_separated = [
out_w2[i, 0:nbins[i] - 1] for i in range(num_histograms)
]
for ihist in range(num_histograms):
hist_name = histname_prefix + variables[ihist][1]
bins = variables[ihist][2]
target_histogram = Histogram(out_w_separated[ihist],
out_w2_separated[ihist], bins)
target = {weight_name: target_histogram}
update_histograms_systematic(hists, hist_name, systematic_name,
target)
def analyze_data(data,
sample,
NUMPY_LIB=None,
parameters={},
samples_info={},
is_mc=True,
lumimask=None,
cat=False,
DNN=False,
DNN_model=None,
jets_met_corrected=True):
#Output structure that will be returned and added up among the files.
#Should be relatively small.
ret = Results()
muons = data["Muon"]
electrons = data["Electron"]
scalars = data["eventvars"]
jets = data["Jet"]
nEvents = muons.numevents()
indices = {}
indices["leading"] = NUMPY_LIB.zeros(nEvents, dtype=NUMPY_LIB.int32)
indices["subleading"] = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.int32)
mask_events = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.bool)
# apply event cleaning and PV selection
flags = [
"Flag_goodVertices", "Flag_globalSuperTightHalo2016Filter",
"Flag_HBHENoiseFilter", "Flag_HBHENoiseIsoFilter",
"Flag_EcalDeadCellTriggerPrimitiveFilter", "Flag_BadPFMuonFilter",
"Flag_BadChargedCandidateFilter", "Flag_ecalBadCalibFilter"
]
if not is_mc:
flags.append("Flag_eeBadScFilter")
for flag in flags:
mask_events = mask_events & scalars[flag]
mask_events = mask_events & (scalars["PV_npvsGood"] > 0)
#mask_events = vertex_selection(scalars, mask_events)
# apply object selection for muons, electrons, jets
good_muons, veto_muons = lepton_selection(muons, parameters["muons"])
good_electrons, veto_electrons = lepton_selection(electrons,
parameters["electrons"])
good_jets = jet_selection(jets, muons,
(veto_muons | good_muons), parameters["jets"],
jets_met_corrected) & jet_selection(
jets, electrons,
(veto_electrons | good_electrons),
parameters["jets"], jets_met_corrected)
bjets = good_jets & (
getattr(jets, parameters["btagging algorithm"]) >
parameters["btagging WP"][parameters["btagging algorithm"]])
# apply basic event selection -> individual categories cut later
nleps = NUMPY_LIB.add(
ha.sum_in_offsets(muons, good_muons, mask_events, muons.masks["all"],
NUMPY_LIB.int8),
ha.sum_in_offsets(electrons, good_electrons, mask_events,
electrons.masks["all"], NUMPY_LIB.int8))
nMuons = ha.sum_in_offsets(muons, good_muons, mask_events,
muons.masks["all"], NUMPY_LIB.int8)
nElectrons = ha.sum_in_offsets(electrons, good_electrons, mask_events,
electrons.masks["all"], NUMPY_LIB.int8)
lepton_veto = NUMPY_LIB.add(
ha.sum_in_offsets(muons, veto_muons, mask_events, muons.masks["all"],
NUMPY_LIB.int8),
ha.sum_in_offsets(electrons, veto_electrons, mask_events,
electrons.masks["all"], NUMPY_LIB.int8))
njets = ha.sum_in_offsets(jets, good_jets, mask_events, jets.masks["all"],
NUMPY_LIB.int8)
btags = ha.sum_in_offsets(jets, bjets, mask_events, jets.masks["all"],
NUMPY_LIB.int8)
if jets_met_corrected:
#met = (scalars["MET_pt_nom"] > 20)
met = (scalars["METFixEE2017_pt_nom"] > 20)
else:
met = (scalars["MET_pt"] > 20)
# trigger logic
# needs update for different years!
trigger_el = (scalars["HLT_Ele35_WPTight_Gsf"]
| scalars["HLT_Ele28_eta2p1_WPTight_Gsf_HT150"]) & (
nleps == 1) & (nElectrons == 1)
trigger_mu = (scalars["HLT_IsoMu27"]) & (nleps == 1) & (nMuons == 1)
if not is_mc:
if "SingleMuon" in sample:
trigger_el = NUMPY_LIB.zeros(nEvents, dtype=NUMPY_LIB.bool)
if "SingleElectron" in sample:
trigger_mu = NUMPY_LIB.zeros(nEvents, dtype=NUMPY_LIB.bool)
mask_events = mask_events & (trigger_el | trigger_mu)
mask_events = mask_events & (nleps == 1) & (lepton_veto == 0) & (
njets >= 4) & (btags >= 2) & met
### calculation of all needed variables
var = {}
var["njets"] = njets
var["btags"] = btags
var["nleps"] = nleps
if jets_met_corrected: pt_label = "pt_nom"
else: pt_label = "pt"
variables = [
("jet", jets, good_jets, "leading", [pt_label, "eta"]),
("bjet", jets, bjets, "leading", [pt_label, "eta"]),
]
# special role of lepton
var["leading_lepton_pt"] = NUMPY_LIB.maximum(
ha.get_in_offsets(muons.pt, muons.offsets, indices["leading"],
mask_events, good_muons),
ha.get_in_offsets(electrons.pt, electrons.offsets, indices["leading"],
mask_events, good_electrons))
var["leading_lepton_eta"] = NUMPY_LIB.maximum(
ha.get_in_offsets(muons.eta, muons.offsets, indices["leading"],
mask_events, good_muons),
ha.get_in_offsets(electrons.eta, electrons.offsets, indices["leading"],
mask_events, good_electrons))
# all other variables
for v in variables:
calculate_variable_features(v, mask_events, indices, var)
#synch
#mask = (scalars["event"] == 2895765)
# calculate weights for MC samples
weights = {}
weights["nominal"] = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.float32)
if is_mc:
weights["nominal"] = weights["nominal"] * scalars[
"genWeight"] * parameters["lumi"] * samples_info[sample][
"XS"] / samples_info[sample]["ngen_weight"]
# pu corrections
#pu_weights = compute_pu_weights(parameters["pu_corrections_target"], weights["nominal"], scalars["Pileup_nTrueInt"], scalars["PV_npvsGood"])
pu_weights = compute_pu_weights(parameters["pu_corrections_target"],
weights["nominal"],
scalars["Pileup_nTrueInt"],
scalars["Pileup_nTrueInt"])
weights["nominal"] = weights["nominal"] * pu_weights
var["pu_weights"] = pu_weights
# lepton SF corrections
electron_weights = compute_lepton_weights(
electrons, (electrons.deltaEtaSC + electrons.eta), electrons.pt,
mask_events, good_electrons, evaluator,
["el_triggerSF", "el_recoSF", "el_idSF"])
muon_weights = compute_lepton_weights(
muons, muons.pt, NUMPY_LIB.abs(muons.eta), mask_events, good_muons,
evaluator, ["mu_triggerSF", "mu_isoSF", "mu_idSF"])
weights[
"nominal"] = weights["nominal"] * muon_weights * electron_weights
# btag SF corrections
btag_weights = compute_btag_weights(jets, mask_events, good_jets,
parameters["btag_SF_target"],
jets_met_corrected,
parameters["btagging algorithm"])
var["btag_weights"] = btag_weights
weights["nominal"] = weights["nominal"] * btag_weights
#in case of data: check if event is in golden lumi file
if not is_mc and not (lumimask is None):
mask_lumi = lumimask(scalars["run"], scalars["luminosityBlock"])
mask_events = mask_events & mask_lumi
#evaluate DNN
if DNN:
DNN_pred = evaluate_DNN(jets, good_jets, electrons, good_electrons,
muons, good_muons, scalars, mask_events,
nEvents, DNN, DNN_model)
# in case of tt+jets -> split in ttbb, tt2b, ttb, ttcc, ttlf
processes = {}
if sample.startswith("TTTo"): #Changed for TTV samples
ttCls = scalars["genTtbarId"] % 100
processes["ttbb"] = mask_events & (ttCls >= 53) & (ttCls <= 56)
processes["tt2b"] = mask_events & (ttCls == 52)
processes["ttb"] = mask_events & (ttCls == 51)
processes["ttcc"] = mask_events & (ttCls >= 41) & (ttCls <= 45)
ttHF = ((ttCls >= 53) &
(ttCls <= 56)) | (ttCls == 52) | (ttCls == 51) | (
(ttCls >= 41) & (ttCls <= 45))
processes["ttlf"] = mask_events & NUMPY_LIB.invert(ttHF)
else:
processes["unsplit"] = mask_events
for p in processes.keys():
mask_events_split = processes[p]
# Categories
categories = {}
categories["sl_jge4_tge2"] = mask_events_split
categories["sl_jge4_tge3"] = mask_events_split & (btags >= 3)
categories["sl_jge4_tge4"] = mask_events_split & (btags >= 4)
categories["sl_j4_tge3"] = mask_events_split & (njets
== 4) & (btags >= 3)
categories["sl_j5_tge3"] = mask_events_split & (njets
== 5) & (btags >= 3)
categories["sl_jge6_tge3"] = mask_events_split & (njets >= 6) & (btags
>= 3)
categories["sl_j4_t3"] = mask_events_split & (njets == 4) & (btags
== 3)
categories["sl_j4_tge4"] = mask_events_split & (njets
== 4) & (btags >= 4)
categories["sl_j5_t3"] = mask_events_split & (njets == 5) & (btags
== 3)
categories["sl_j5_tge4"] = mask_events_split & (njets
== 5) & (btags >= 4)
categories["sl_jge6_t3"] = mask_events_split & (njets >= 6) & (btags
== 3)
categories["sl_jge6_tge4"] = mask_events_split & (njets >= 6) & (btags
>= 4)
#print("sl_j4_t3", scalars["event"][categories["sl_j4_t3"]], len(scalars["event"][categories["sl_j4_t3"]]))
#print("sl_j5_t3", scalars["event"][categories["sl_j5_t3"]], len(scalars["event"][categories["sl_j5_t3"]]))
#print("sl_jge6_t3", scalars["event"][categories["sl_jge6_t3"]], len(scalars["event"][categories["sl_jge6_t3"]]))
#print("sl_j4_tge4", scalars["event"][categories["sl_j4_tge4"]], len(scalars["event"][categories["sl_j4_tge4"]]))
#print("sl_j5_tge4", scalars["event"][categories["sl_j5_tge4"]], len(scalars["event"][categories["sl_j5_tge4"]]))
#print("sl_jge6_tge4", scalars["event"][categories["sl_jge6_tge4"]], len(scalars["event"][categories["sl_jge6_tge4"]]))
if not isinstance(cat, list):
cat = [cat]
for c in cat:
cut = categories[c]
cut_name = c
if p == "unsplit":
if "Run" in sample:
name = "data" + "_" + cut_name
else:
name = samples_info[sample]["process"] + "_" + cut_name
else:
name = p + "_" + cut_name
# create histograms filled with weighted events
for k in var.keys():
if not k in histogram_settings.keys():
raise Exception(
"please add variable {0} to definitions_analysis.py".
format(k))
hist = Histogram(*ha.histogram_from_vector(
var[k][cut], weights["nominal"][cut],
NUMPY_LIB.linspace(histogram_settings[k][0],
histogram_settings[k][1],
histogram_settings[k][2])))
ret["hist_{0}_{1}".format(name, k)] = hist
if DNN:
if DNN == "mass_fit":
hist_DNN = Histogram(*ha.histogram_from_vector(
DNN_pred[cut], weights["nominal"][cut],
NUMPY_LIB.linspace(0., 300., 30)))
hist_DNN_zoom = Histogram(*ha.histogram_from_vector(
DNN_pred[cut], weights["nominal"][cut],
NUMPY_LIB.linspace(0., 170., 30)))
else:
hist_DNN = Histogram(*ha.histogram_from_vector(
DNN_pred[cut], weights["nominal"][cut],
NUMPY_LIB.linspace(0., 1., 16)))
ret["hist_{0}_DNN".format(name)] = hist_DNN
ret["hist_{0}_DNN_zoom".format(name)] = hist_DNN_zoom
#TODO: implement JECs
## To display properties of a single event
#evts = [5991859]
#mask = NUMPY_LIB.zeros_like(mask_events)
#for iev in evts:
# mask |= (scalars["event"] == iev)
##import pdb
##pdb.set_trace()
#print("mask", mask)
#print('nevt', scalars["event"][mask])
#print('pass sel', mask_events[mask])
#print('nleps', nleps[mask])
#print('njets', njets[mask])
##print('met', scalars['MET_pt_nom'][mask])
##print('lep_pt', leading_lepton_pt[mask])
##print('jet_pt', leading_jet_pt[mask])
##print('lep_eta', leading_lepton_eta[mask])
#print('pu_weight', pu_weights[mask])
#print('btag_weight', btag_weights[mask])
#print('lep_weight', muon_weights[mask] * electron_weights[mask])
#print('nevents', np.count_nonzero(mask_events))
#np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
#for evt in evts:
# evt_idx = NUMPY_LIB.where( scalars["event"] == evt )[0][0]
# start = jets.offsets[evt_idx]
# stop = jets.offsets[evt_idx+1]
# print(f'!!! EVENT {evt} !!!')
# print(f'njets good {njets[evt_idx]}, total {stop-start}')
# #print('jets mask', nonbjets[start:stop])
# print('jets pt', jets.pt_nom[start:stop])
# print('jets eta', jets.eta[start:stop])
# print('jets btag', getattr(jets, parameters["btagging algorithm"])[start:stop])
# print('jet Id', jets.jetId[start:stop]),
# print('jet puId', jets.puId[start:stop])
return ret
def get_histogram(data, weights, bins):
return Histogram(*ha.histogram_from_vector(data, weights, bins))
def analyze_data_function(data, parameters):
ret = Results()
ha = parameters["ha"]
num_events = data["num_events"]
lep = data["Lep"]
lep.hepaccelerate_backend = ha
lep.attrs_data["pt"] = lep.lep_pt
lep.attrs_data["eta"] = lep.lep_eta
lep.attrs_data["phi"] = lep.lep_phi
lep.attrs_data["charge"] = lep.lep_charge
lep.attrs_data["type"] = lep.lep_type
lep_mass = np.zeros_like(lep["pt"], dtype=nplib.float32)
lep_mass = np.where(lep["type"] == 11, 0.511, lep_mass)
lep_mass = np.where(lep["type"] == 13, 105.65837, lep_mass)
lep.attrs_data["mass"] = lep_mass
mask_events = nplib.ones(lep.numevents(), dtype=nplib.bool)
lep_ele = lep["type"] == 11
lep_muon = lep["type"] == 13
ele_Iso = np.logical_and(
lep_ele,
np.logical_and(lep.lep_ptcone30 / lep.pt < 0.15,
lep.lep_etcone20 / lep.pt < 0.20))
muon_Iso = np.logical_and(
lep_muon,
np.logical_and(lep.lep_ptcone30 / lep.pt < 0.15,
lep.lep_etcone20 / lep.pt < 0.30))
pass_iso = np.logical_or(ele_Iso, muon_Iso)
lep.attrs_data["pass_iso"] = pass_iso
num_lep_event = kernels.sum_in_offsets(
backend,
lep.offsets,
lep.masks["all"],
mask_events,
lep.masks["all"],
nplib.int8,
)
mask_events_4lep = num_lep_event == 4
lep_attrs = ["pt", "eta", "phi", "charge", "type", "mass",
"pass_iso"] #, "ptcone30", "etcone20"]
lep0 = lep.select_nth(0,
mask_events_4lep,
lep.masks["all"],
attributes=lep_attrs)
lep1 = lep.select_nth(1,
mask_events_4lep,
lep.masks["all"],
attributes=lep_attrs)
lep2 = lep.select_nth(2,
mask_events_4lep,
lep.masks["all"],
attributes=lep_attrs)
lep3 = lep.select_nth(3,
mask_events_4lep,
lep.masks["all"],
attributes=lep_attrs)
mask_event_sumchg_zero = (lep0["charge"] + lep1["charge"] +
lep2["charge"] + lep3["charge"] == 0)
sum_lep_type = lep0["type"] + lep1["type"] + lep2["type"] + lep3["type"]
all_pass_iso = (lep0["pass_iso"] & lep1["pass_iso"] & lep2["pass_iso"]
& lep3["pass_iso"])
mask_event_sum_lep_type = np.logical_or(
(sum_lep_type == 44),
np.logical_or((sum_lep_type == 48), (sum_lep_type == 52)))
mask_events = mask_events & mask_event_sumchg_zero & mask_events_4lep & mask_event_sum_lep_type & all_pass_iso
mask_lep1_passing_pt = lep1["pt"] > parameters["leading_lep_ptcut"]
mask_lep2_passing_pt = lep2["pt"] > parameters["lep_ptcut"]
mask_events = mask_events & mask_lep1_passing_pt & mask_lep2_passing_pt
l0 = to_cartesian(lep0)
l1 = to_cartesian(lep1)
l2 = to_cartesian(lep2)
l3 = to_cartesian(lep3)
llll = {k: l0[k] + l1[k] + l2[k] + l3[k] for k in ["px", "py", "pz", "e"]}
llll_sph = to_spherical(llll)
llll_sph["mass"] = llll_sph["mass"] / 1000. # Convert to GeV
#import pdb;pdb.set_trace();
# compute a weighted histogram
weights = nplib.ones(num_events, dtype=nplib.float32)
## Add xsec weights based on sample name
if parameters["is_mc"]:
weights = data['eventvars']['mcWeight'] * data['eventvars'][
'scaleFactor_PILEUP'] * data['eventvars']['scaleFactor_ELE'] * data[
'eventvars']['scaleFactor_MUON'] * data['eventvars'][
'scaleFactor_LepTRIGGER']
info = infofile.infos[parameters["sample"]]
weights *= (lumi * 1000 * info["xsec"]) / (info["sumw"] *
info["red_eff"])
bins = nplib.linspace(110, 150, 11, dtype=nplib.float32)
hist_m4lep = Histogram(*kernels.histogram_from_vector(
backend,
llll_sph["mass"][mask_events],
weights[mask_events],
bins,
))
# save it to the output
ret["hist_m4lep"] = hist_m4lep
return ret
def analyze_data(data, sample, NUMPY_LIB=None, parameters={}, samples_info={}, is_mc=True, lumimask=None, cat=False, boosted=False, DNN=False, DNN_model=None):
#Output structure that will be returned and added up among the files.
#Should be relatively small.
ret = Results()
muons = data["Muon"]
electrons = data["Electron"]
scalars = data["eventvars"]
jets = data["Jet"]
nEvents = muons.numevents()
mask_events = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.bool)
# apply event cleaning, PV selection and trigger selection
flags = [
"Flag_goodVertices", "Flag_globalSuperTightHalo2016Filter", "Flag_HBHENoiseFilter", "Flag_HBHENoiseIsoFilter", "Flag_EcalDeadCellTriggerPrimitiveFilter", "Flag_BadPFMuonFilter", "Flag_BadChargedCandidateFilter", "Flag_ecalBadCalibFilter"]
if not is_mc:
flags.append("Flag_eeBadScFilter")
for flag in flags:
mask_events = mask_events & scalars[flag]
if args.year.startswith('2016'):
trigger = (scalars["HLT_Ele27_WPTight_Gsf"] | scalars["HLT_IsoMu24"] | scalars["HLT_IsoTkMu24"])
else:
trigger = (scalars["HLT_Ele35_WPTight_Gsf"] | scalars["HLT_Ele28_eta2p1_WPTight_Gsf_HT150"] | scalars["HLT_IsoMu27"])
mask_events = mask_events & trigger
mask_events = mask_events & (scalars["PV_npvsGood"]>0)
#mask_events = vertex_selection(scalars, mask_events)
# apply object selection for muons, electrons, jets
good_muons, veto_muons = lepton_selection(muons, parameters["muons"])
good_electrons, veto_electrons = lepton_selection(electrons, parameters["electrons"])
good_jets = jet_selection(jets, muons, (veto_muons | good_muons), parameters["jets"]) & jet_selection(jets, electrons, (veto_electrons | good_electrons) , parameters["jets"])
bjets = good_jets & (getattr(jets, parameters["btagging algorithm"]) > parameters["btagging WP"])
# apply basic event selection -> individual categories cut later
nleps = NUMPY_LIB.add(ha.sum_in_offsets(muons, good_muons, mask_events, muons.masks["all"], NUMPY_LIB.int8), ha.sum_in_offsets(electrons, good_electrons, mask_events, electrons.masks["all"], NUMPY_LIB.int8))
lepton_veto = NUMPY_LIB.add(ha.sum_in_offsets(muons, veto_muons, mask_events, muons.masks["all"], NUMPY_LIB.int8), ha.sum_in_offsets(electrons, veto_electrons, mask_events, electrons.masks["all"], NUMPY_LIB.int8))
njets = ha.sum_in_offsets(jets, good_jets, mask_events, jets.masks["all"], NUMPY_LIB.int8)
btags = ha.sum_in_offsets(jets, bjets, mask_events, jets.masks["all"], NUMPY_LIB.int8)
met = (scalars["MET_pt"] > 20)
# apply basic event definition (inverted for boosted analysis)
if boosted:
mask_events = mask_events & (nleps == 1) & (lepton_veto == 0) & NUMPY_LIB.invert( (njets >= 4) & (btags >=2) ) & met
else:
mask_events = mask_events & (nleps == 1) & (lepton_veto == 0) & (njets >= 4) & (btags >=2) & met
### check overlap between AK4 and AK8 jets: if (based on tau32 and tau21) the AK8 jet is a t/H/W candidate remove the AK4 jet, otherwise remove the AK8 jet
if boosted:
fatjets = data["FatJet"]
genparts = data["GenPart"]
# get fatjets
good_fatjets = jet_selection(fatjets, muons, (veto_muons | good_muons), parameters["fatjets"]) & jet_selection(fatjets, electrons, (veto_electrons | good_electrons), parameters["fatjets"])
bfatjets = good_fatjets & (fatjets.btagHbb > parameters["bbtagging WP"])
fatjets.tau32 = NUMPY_LIB.divide(fatjets.tau3, fatjets.tau2)
fatjets.tau21 = NUMPY_LIB.divide(fatjets.tau2, fatjets.tau1)
jets_to_keep = ha.mask_overlappingAK4(jets, good_jets, fatjets, good_fatjets, 1.2, tau32cut=parameters["fatjets"]["tau32cut"], tau21cut=parameters["fatjets"]["tau21cut"])
non_overlapping_fatjets = ha.mask_deltar_first(fatjets, good_fatjets, jets, good_jets, 1.2)
good_jets &= jets_to_keep
good_fatjets &= non_overlapping_fatjets | (fatjets.tau32 < parameters["fatjets"]["tau32cut"]) | (fatjets.tau21 < parameters["fatjets"]["tau21cut"]) #we keep fat jets which are not overlapping, or if they are either a top or W/H candidate
top_candidates = (fatjets.tau32 < parameters["fatjets"]["tau32cut"])
WH_candidates = (fatjets.tau32 > tau32cut) & (fatjets.tau21 < parameters["fatjets"]["tau21cut"])
bjets = good_jets & (jets.btagDeepB > parameters["btagging WP"])
njets = ha.sum_in_offsets(jets, good_jets, mask_events, jets.masks["all"], NUMPY_LIB.int8)
btags = ha.sum_in_offsets(jets, bjets, mask_events, jets.masks["all"], NUMPY_LIB.int8)
bbtags = ha.sum_in_offsets(fatjets, bfatjets, mask_events, fatjets.masks["all"], NUMPY_LIB.int8)
ntop_candidates = ha.sum_in_offsets(fatjets, top_candidates, mask_events, fatjets.masks["all"], NUMPY_LIB.int8)
nWH_candidates = ha.sum_in_offsets(fatjets, WH_candidates, mask_events, fatjets.masks["all"], NUMPY_LIB.int8)
### 2 fat jets from H and W, 2 b jets from the tops
#mask_events &= (nWH_candidates > 1) & (btags > 1)
### 1 top candidate and 1 H candidate, and 1 b jet from the leptonic top
mask_events &= (ntop_candidates > 0) & (nWH_candidates > 0) & (btags > 0)
### calculation of all needed variables
var = {}
var["njets"] = njets
var["btags"] = btags
var["nleps"] = nleps
if boosted:
higgs = (genparts.pdgId == 25) & (genparts.status==62)
tops = ( (genparts.pdgId == 6) | (genparts.pdgId == -6) ) & (genparts.status==62)
var["nfatjets"] = ha.sum_in_offsets(fatjets, good_fatjets, mask_events, fatjets.masks["all"], NUMPY_LIB.int8)
var["ntop_candidates"] = ha.sum_in_offsets(fatjets, tops, mask_events, fatjets.masks["all"], NUMPY_LIB.int8)
indices = {}
indices["leading"] = NUMPY_LIB.zeros(nEvents, dtype=NUMPY_LIB.int32)
indices["subleading"] = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.int32)
if boosted:
indices["inds_WHcandidates"] = ha.index_in_offsets(fatjets.btagHbb, fatjets.offsets, 1, mask_events, WH_candidates)
variables = [
("jet", jets, good_jets, "leading", ["pt", "eta"]),
("bjet", jets, bjets, "leading", ["pt", "eta"]),
]
if boosted:
variables += [
("fatjet", fatjets, good_fatjets, "leading",["pt", "eta", "mass", "msoftdrop", "tau32", "tau21"]),
("fatjet", fatjets, good_fatjets, "subleading",["pt", "eta", "mass", "msoftdrop", "tau32", "tau21"]),
("top_candidate", fatjets, top_candidates, "leading", ["pt", "eta", "mass", "msoftdrop", "tau32", "tau21"]),
("WH_candidate", fatjets, WH_candidates, "inds_WHcandidates", ["pt", "eta", "mass", "msoftdrop", "tau32", "tau21"]),
("higgs", genparts, higgs, "leading", ["pt", "eta"]),
("tops", genparts, tops, "leading", ["pt", "eta"])
]
# special role of lepton
var["leading_lepton_pt"] = NUMPY_LIB.maximum(ha.get_in_offsets(muons.pt, muons.offsets, indices["leading"], mask_events, good_muons), ha.get_in_offsets(electrons.pt, electrons.offsets, indices["leading"], mask_events, good_electrons))
var["leading_lepton_eta"] = NUMPY_LIB.maximum(ha.get_in_offsets(muons.eta, muons.offsets, indices["leading"], mask_events, good_muons), ha.get_in_offsets(electrons.eta, electrons.offsets, indices["leading"], mask_events, good_electrons))
# all other variables
for v in variables:
calculate_variable_features(v, mask_events, indices, var)
# calculate weights for MC samples
weights = {}
weights["nominal"] = NUMPY_LIB.ones(nEvents, dtype=NUMPY_LIB.float32)
if is_mc:
weights["nominal"] = weights["nominal"] * scalars["genWeight"] * parameters["lumi"] * samples_info[sample]["XS"] / samples_info[sample]["ngen_weight"]
# pu corrections
pu_weights = compute_pu_weights(parameters["pu_corrections_target"], weights["nominal"], scalars["Pileup_nTrueInt"], scalars["PV_npvsGood"])
weights["nominal"] = weights["nominal"] * pu_weights
# lepton SF corrections
electron_weights = compute_lepton_weights(electrons, electrons.pt, (electrons.deltaEtaSC + electrons.eta), mask_events, good_electrons, evaluator, ["el_triggerSF", "el_recoSF", "el_idSF"])
muon_weights = compute_lepton_weights(muons, muons.pt, NUMPY_LIB.abs(muons.eta), mask_events, good_muons, evaluator, ["mu_triggerSF", "mu_isoSF", "mu_idSF"])
weights["nominal"] = weights["nominal"] * muon_weights * electron_weights
# btag SF corrections
btag_weights = compute_btag_weights(jets, mask_events, good_jets, evaluator)
weights["nominal"] = weights["nominal"] * btag_weights
#in case of data: check if event is in golden lumi file
if not is_mc and not (lumimask is None):
mask_lumi = lumimask(scalars["run"], scalars["luminosityBlock"])
mask_events = mask_events & mask_lumi
#evaluate DNN
if DNN:
DNN_pred = evaluate_DNN(jets, good_jets, electrons, good_electrons, muons, good_muons, scalars, mask_events, DNN, DNN_model)
# in case of tt+jets -> split in ttbb, tt2b, ttb, ttcc, ttlf
processes = {}
if sample.startswith("TT"):
ttCls = scalars["genTtbarId"]%100
processes["ttbb"] = mask_events & (ttCls >=53) & (ttCls <=56)
processes["tt2b"] = mask_events & (ttCls ==52)
processes["ttb"] = mask_events & (ttCls ==51)
processes["ttcc"] = mask_events & (ttCls >=41) & (ttCls <=45)
ttHF = ((ttCls >=53) & (ttCls <=56)) | (ttCls ==52) | (ttCls ==51) | ((ttCls >=41) & (ttCls <=45))
processes["ttlf"] = mask_events & NUMPY_LIB.invert(ttHF)
else:
processes["unsplit"] = mask_events
for p in processes.keys():
mask_events_split = processes[p]
# Categories
categories = {}
if not boosted:
categories["sl_jge4_tge2"] = mask_events_split
categories["sl_jge4_tge3"] = mask_events_split & (btags >=3)
categories["sl_j4_tge3"] = mask_events_split & (njets ==4) & (btags >=3)
categories["sl_j5_tge3"] = mask_events_split & (njets ==5) & (btags >=3)
categories["sl_jge6_tge3"] = mask_events_split & (njets >=6) & (btags >=3)
categories["sl_j4_t3"] = mask_events_split & (njets ==4) & (btags ==3)
categories["sl_j4_tge4"] = mask_events_split & (njets ==4) & (btags >=4)
categories["sl_j5_t3"] = mask_events_split & (njets ==5) & (btags ==3)
categories["sl_j5_tge4"] = mask_events_split & (njets ==5) & (btags >=4)
categories["sl_jge6_t3"] = mask_events_split & (njets >=6) & (btags ==3)
categories["sl_jge6_tge4"] = mask_events_split & (njets >=6) & (btags >=4)
if not isinstance(cat, list):
cat = [cat]
for c in cat:
cut = categories[c]
cut_name = c
if p=="unsplit":
if "Run" in sample:
name = "data" + "_" + cut_name
else:
name = samples_info[sample]["process"] + "_" + cut_name
else:
name = p + "_" + cut_name
# create histograms filled with weighted events
for k in var.keys():
if not k in histogram_settings.keys():
raise Exception("please add variable {0} to config_analysis.py".format(k))
hist = Histogram(*ha.histogram_from_vector(var[k][cut], weights["nominal"][cut], NUMPY_LIB.linspace(histogram_settings[k][0], histogram_settings[k][1], histogram_settings[k][2])))
ret["hist_{0}_{1}".format(name, k)] = hist
if DNN:
if DNN.endswith("multiclass"):
class_pred = NUMPY_LIB.argmax(DNN_pred, axis=1)
for n, n_name in zip([0,1,2,3,4,5], ["ttH", "ttbb", "tt2b", "ttb", "ttcc", "ttlf"]):
node = (class_pred == n)
DNN_node = DNN_pred[:,n]
hist_DNN = Histogram(*ha.histogram_from_vector(DNN_node[(cut & node)], weights["nominal"][(cut & node)], NUMPY_LIB.linspace(0.,1.,16)))
ret["hist_{0}_DNN_{1}".format(name, n_name)] = hist_DNN
hist_DNN_ROC = Histogram(*ha.histogram_from_vector(DNN_node[(cut & node)], weights["nominal"][(cut & node)], NUMPY_LIB.linspace(0.,1.,1000)))
ret["hist_{0}_DNN_ROC_{1}".format(name, n_name)] = hist_DNN_ROC
else:
hist_DNN = Histogram(*ha.histogram_from_vector(DNN_pred[cut], weights["nominal"][cut], NUMPY_LIB.linspace(0.,1.,16)))
ret["hist_{0}_DNN".format(name)] = hist_DNN
hist_DNN_ROC = Histogram(*ha.histogram_from_vector(DNN_pred[cut], weights["nominal"][cut], NUMPY_LIB.linspace(0.,1.,1000)))
ret["hist_{0}_DNN_ROC".format(name)] = hist_DNN_ROC
#TODO: implement JECs
return ret
def divide(h1,h2): contents = h1.contents/h2.contents contents_w2 = h1.contents_w2/h2.contents_w2 edges = h1.edges return Histogram(contents, contents_w2, edges)
import numpy as np
from hepaccelerate.utils import Histogram, Results
from glob import glob
import json,os,argparse
from pdb import set_trace
flist = glob('results/201*/v12/met20_btagDDBvL086/nominal/btagEfficiencyMaps/out_btagEfficiencyMaps_*json')
def divide(h1,h2):
contents = h1.contents/h2.contents
contents_w2 = h1.contents_w2/h2.contents_w2
edges = h1.edges
return Histogram(contents, contents_w2, edges)
for fn in flist:
with open(fn) as f:
data = json.load(f)
for h in data:
data[h] = Histogram( *data[h].values() )
for flav in ['b','l','lc']:
for var in ['central','updown']:
data[f'eff_flav{flav}_{var}'] = divide( data[f'btags_flav{flav}_{var}'], data[f'total_flav{flav}_{var}'] )
ret = Results(data)
ret.save_json(fn)
