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test_wf.py
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test_wf.py
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import pickle, os, sys, numpy as np
from coffea import processor
import awkward as ak
import hist as Hist
from functools import partial
import gc
import os, psutil
import coffea
from coffea.analysis_tools import Weights
from BTVNanoCommissioning.utils.correction import (
load_lumi,
load_SF,
JME_shifts,
Roccor_shifts,
puwei,
met_filters,
eleSFs,
muSFs,
btagSFs,
jmar_sf,
add_ps_weight,
add_pdf_weight,
add_scalevar_7pt,
add_scalevar_3pt,
top_pT_reweighting,
)
from BTVNanoCommissioning.helpers.func import (
mT,
flatten,
normalize,
make_p4,
#defaultdict_accumulator,
update,
)
class NanoProcessor(processor.ProcessorABC):
# Define histograms
def __init__(self, cfg):
self.cfg = cfg
self._year = self.cfg.dataset["year"]
self._campaign = self.cfg.dataset["campaign"]
self.systematics = self.cfg.systematic
self._met_filters = met_filters[self._campaign]
self._lumiMasks = load_lumi(self.cfg.weights_config["lumiMask"])
self.SF_map = load_SF(
self.cfg.dataset["campaign"],
self.cfg.weights_config,
self.systematics["weights"],
)
syst_axis = Hist.axis.StrCategory([], name="syst", growth=True)
pt_axis = Hist.axis.Regular(50, 0, 300, name="pt", label=" $p_{T}$ [GeV]")
eta_axis = Hist.axis.Regular(25, -2.5, 2.5, name="eta", label=" $\eta$")
phi_axis = Hist.axis.Regular(30, -3, 3, name="phi", label="$\phi$")
self.make_output = lambda: {
"cutflow": processor.defaultdict_accumulator(
# # we don't use a lambda function to avoid pickle issues
partial(processor.defaultdict_accumulator, int)
),
"sumw": 0,
"ele_eta": Hist.Hist(syst_axis, eta_axis, Hist.storage.Weight()),
"mu_eta": Hist.Hist(syst_axis, eta_axis, Hist.storage.Weight()),
"ele_phi": Hist.Hist(syst_axis, phi_axis, Hist.storage.Weight()),
"mu_phi": Hist.Hist(syst_axis, phi_axis, Hist.storage.Weight()),
"ele_pt": Hist.Hist(syst_axis, pt_axis, Hist.storage.Weight()),
"mu_pt": Hist.Hist(syst_axis, pt_axis, Hist.storage.Weight()),
"jet_pt": Hist.Hist(syst_axis, pt_axis, Hist.storage.Weight()),
"met_pt": Hist.Hist(syst_axis, pt_axis, Hist.storage.Weight()),
}
@property
def accumulator(self):
return self._accumulator
def process(self, events):
isRealData = not hasattr(events, "genWeight")
dataset = events.metadata["dataset"]
shifts = []
if "JME" in self.SF_map.keys():
shifts = JME_shifts(
shifts,
self.SF_map,
events,
self.cfg.dataset["campaign"],
isRealData,
self.systematics["JERC"],
)
else:
shifts = [
({"Jet": events.Jet, "MET": events.MET, "Muon": events.Muon}, None)
]
if "roccor" in self.SF_map.keys():
shifts = Roccor_shifts(
shifts, self.SF_map, events, isRealData, self.systematics["roccor"]
)
else:
shifts[0][0]["Muon"] = events.Muon
return processor.accumulate(
self.process_shift(update(events, collections), name)
for collections, name in shifts
)
def process_shift(self, events, shift_name):
dataset = events.metadata["dataset"]
isRealData = not hasattr(events, "genWeight")
output = self.make_output()
# if self._export_array: output_array = ak.Array({shift_name:ak.A({})})
req_lumi = np.ones(len(events), dtype="bool")
if isRealData:
req_lumi = self._lumiMasks(events.run, events.luminosityBlock)
# #############Selections
event_mu = events.Muon
musel = (
(event_mu.pt > 15)
& (abs(event_mu.eta) < 2.4)
& (event_mu.mvaId >= 3)
& (event_mu.pfRelIso04_all < 0.15)
& (abs(event_mu.dxy) < 0.05)
& (abs(event_mu.dz) < 0.1)
)
event_mu = event_mu[musel]
# ## Electron cuts
# # electron twiki: https://twiki.cern.ch/twiki/bin/viewauth/CMS/CutBasedElectronIdentificationRun2
event_e = events.Electron
elesel = (
(event_e.pt > 15)
& (abs(event_e.eta) < 2.5)
& (event_e.mvaFall17V2Iso_WP90 == 1)
& (abs(event_e.dxy) < 0.05)
& (abs(event_e.dz) < 0.1)
)
event_e = event_e[elesel]
event_jet = events.Jet[
(events.Jet.pt > 20)
& (abs(events.Jet.eta) <= 2.4)
& ((events.Jet.puId > 6) | (events.Jet.pt > 50))
& (events.Jet.jetId > 5)
]
event_sel = (
(ak.count(event_jet.pt, axis=-1) == 1)
& (ak.count(event_e.pt, axis=-1) == 1)
& (ak.count(event_mu.pt, axis=-1) == 1)
)
weights = Weights(len(events[event_sel]), storeIndividual=True)
event_e = event_e[event_sel]
event_mu = event_mu[event_sel]
event_jet = event_jet[event_sel]
eleSFs(event_e, self.SF_map, weights, syst=self.systematics["weights"])
muSFs(event_mu, self.SF_map, weights, syst=self.systematics["weights"])
btagSFs(
event_jet,
self.SF_map,
weights,
"DeepJetC",
syst=self.systematics["weights"],
)
jmar_sf(event_jet, self.SF_map, weights, syst=self.systematics["weights"])
if shift_name is None:
systematics = ["noweight", "nominal"] + list(weights.variations)
else:
systematics = [shift_name]
# for histname, h in output.items():
for syst in systematics:
if syst in weights.variations:
weight = weights.weight(modifier=syst)
elif "noweight" == syst:
weight = np.ones_like(weights.weight())
else:
weight = weights.weight()
output["ele_pt"].fill(syst=syst, pt=event_e[:, 0].pt, weight=weight)
output["mu_pt"].fill(syst=syst, pt=event_mu[:, 0].pt, weight=weight)
output["jet_pt"].fill(syst=syst, pt=event_jet[:, 0].pt, weight=weight)
output["met_pt"].fill(syst=syst, pt=events[event_sel].MET.pt, weight=weight)
return {dataset: output}
def postprocess(self, accumulator):
return accumulator