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Introduce 2 modules to filter FatJets and apply JERC to them using latest version of CMSJMECalculator #46

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Introduce 2 modules to filter FatJets and apply JERC to them using latest version of CMSJMECalculator #46

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fad6344
add FatJetSel.py and FatJMECalculatorRun3.py
ttedeschi Oct 15, 2024
4835dea
fix DR values for FatJetSel and FatJMECalculatorRun3
ttedeschi Oct 28, 2024
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304 changes: 304 additions & 0 deletions mkShapesRDF/processor/modules/FatJMECalculatorRun3.py
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import ROOT
from mkShapesRDF.processor.framework.module import Module


class FatJMECalculator(Module):
"""
This module calculates the JES/JER for fatjet objects and stores the nominal values and the variations (up/down) in the output tree.
"""

def __init__(
self,
jsonFile,
JEC_era,
JER_era,
jes_unc,
jsonFileSmearingTool,
jet_object,
jsonFileSubjet,
JEC_era_subjet,
subjet_object,
do_JER=True,
store_nominal=True,
store_variations=True,
):
"""
JMECalculator module

Parameters
----------
jsonFile : str
path to FatJet json file for JEC and JER
JEC_era : str
FatJet JEC era to use
JER_era : str
FatJet JER era to use
JER_era : list
list of JER uncertainties
jsonFileSmearingTool : str
path to json file to smearing tool
jet_object : str
FatJet algorithm to consider (e.g. ``AK8PFPuppi``)
jsonFileSubjet: str
path to SubJet json file for JEC and JER
JEC_era_subjet: str
SubJet JEC era to use
subjet_object: str
SubJet algorithm to consider (e.g. ``AK4PFPuppi``)
do_JER : bool, optional, default: ``True``
Whether to calculate JER
store_nominal : bool, optional, default: ``True``
Whether to store the nominal values (corrected or smeared)
store_variations : bool, optional
Whether to store the variations (up/down) for JES/JER
"""
super().__init__("JMECalculator")
self.json = jsonFile
self.JEC_era = JEC_era
self.JER_era = JER_era
self.jsonSubJet = jsonFileSubjet
self.JEC_era_subjet = JEC_era_subjet
self.jsonFileSmearingTool = jsonFileSmearingTool
self.jet_object = jet_object
self.jes_unc = jes_unc
self.subjet_object = subjet_object
self.do_JER = do_JER
self.store_nominal = store_nominal
self.store_variations = store_variations

def runModule(self, df, values):
ROOT.gInterpreter.Declare(
"""
using namespace ROOT;

RVecU revertIndicesMask(RVecU sortedIndices, uint size){
auto tmp = ROOT::VecOps::Range(size);
RVecU r {};

for (uint i = 0; i < tmp.size(); i++){
for (uint j = 0; j < sortedIndices.size(); j++){
if (tmp[i] == sortedIndices[j]){
r.push_back(j);
}
}
}
return r;

}
"""
)

from CMSJMECalculators import loadJMESystematicsCalculators
loadJMESystematicsCalculators()

jsonFile = self.json
jetAlgo = self.jet_object
jecTag = self.JEC_era
jerTag = ""
jes_unc = self.jes_unc
jsonFileSmearingTool = self.jsonFileSmearingTool
jecLevel = "L1L2L3Res"
jsonFileSubjet = self.jsonSubJet
jetAlgoSubjet = self.subjet_object
jecTagSubjet = self.JEC_era_subjet
jecLevelSubjet = jecLevel
ROOT.gROOT.ProcessLine("std::vector<string> jesUnc{}")
jesUnc = getattr(ROOT, "jesUnc")
for jes_var in jes_unc:
jesUnc.push_back(jes_var)
addHEM = "false"
smearingTool= "JERSmear"
maxDR = 0.2
maxDPT = 3

if self.do_JER:
jerTag = self.JER_era

ROOT.gROOT.ProcessLine(f"FatJetVariationsCalculator myFatJetVariationsCalculator = FatJetVariationsCalculator::create(\"{jsonFile}\", \"{jetAlgo}\", \"{jecTag}\", \"{jecLevel}\", {jesUnc}, {addHEM}, \"{jerTag}\", \"{jsonFileSmearingTool}\", \"{smearingTool}\", false, true, {maxDR}, {maxDPT}, \"{jsonFileSubjet}\", \"{jetAlgoSubjet}\", \"{jecTagSubjet}\", \"{jecLevelSubjet}\");")
calc = getattr(ROOT, "myFatJetVariationsCalculator")
jesSources = calc.available()
jesSources = calc.available()[1:][::2]
jesSources = [str(source).replace('up', '') for source in jesSources]
print(jesSources)

# list of columns to be passed to myJetVarCal produce
cols = []

# nre reco jet coll
JetColl = "newFatJet"

df = df.Define("newFatJet_pt", "CleanFatJet_pt")
df = df.Define("newFatJet_eta", "CleanFatJet_eta")
df = df.Define("newFatJet_phi", "CleanFatJet_phi")
df = df.Define("newFatJet_jetIdx", "CleanFatJet_jetIdx")

cols.append(f"{JetColl}_pt")
cols.append(f"{JetColl}_eta")
cols.append(f"{JetColl}_phi")
cols.append("CleanFatJet_mass")
cols.append(f"Take(FatJet_rawFactor, {JetColl}_jetIdx)")
cols.append(f"Take(FatJet_area, {JetColl}_jetIdx)")
cols.append(f"Take(FatJet_msoftdrop, {JetColl}_jetIdx)")
cols.append(f"Take(FatJet_subJetIdx1, {JetColl}_jetIdx)")
cols.append(f"Take(FatJet_subJetIdx2, {JetColl}_jetIdx)")

cols.append("SubJet_pt")
cols.append("SubJet_eta")
cols.append("SubJet_phi")
cols.append("SubJet_mass")
cols.append("SubJet_rawFactor")


cols.append("FatJet_jetId")

# rho
cols.append("Rho_fixedGridRhoFastjetAll") #in Run2 is fixedGridRhoFastjetAll

cols.append(f"Take(FatJet_genJetAK8Idx, {JetColl}_jetIdx)")
cols.append("event")
# gen jet coll
cols.append("GenJetAK8_pt")
cols.append("GenJetAK8_eta")
cols.append("GenJetAK8_phi")
cols.append("GenJetAK8_mass")

df = df.Define("jetVars", f'myFatJetVariationsCalculator.produce({", ".join(cols)})')
if self.store_nominal:
df = df.Define("CleanFatJet_pt", "jetVars.pt(0)")
df = df.Define("CleanFatJet_mass", "jetVars.mass(0)")
df = df.Define("CleanFatJet_msoftdrop", "jetVars.msoftdrop(0)")
df = df.Define(
"CleanFatJet_sorting",
"ROOT::VecOps::Reverse(ROOT::VecOps::Argsort(CleanFatJet_pt))",
)

df = df.Define("CleanFatJet_pt", "Take( CleanFatJet_pt, CleanFatJet_sorting)")
df = df.Define("CleanFatJet_eta", "Take( CleanFatJet_eta, CleanFatJet_sorting)")
df = df.Define("CleanFatJet_phi", "Take( CleanFatJet_phi, CleanFatJet_sorting)")
df = df.Define("CleanFatJet_mass", "Take( CleanFatJet_mass, CleanFatJet_sorting)")
df = df.Define("CleanFatJet_msoftdrop", "Take( CleanFatJet_msoftdrop, CleanFatJet_sorting)")
df = df.Define("CleanFatJet_jetIdx", "Take( CleanFatJet_jetIdx, CleanFatJet_sorting)")

else:
df = df.Define(
"CleanFatJet_sorting",
"Range(CleanFatJet_pt.size())",
)

if self.store_variations:
for i, source in enumerate(jesSources):
variations_pt = []
variations_jetIdx = []
variations_mass = []
variations_msoftdrop = []
variations_phi = []
variations_eta = []
for j, tag in enumerate(["up", "down"]):
variation_pt = f"jetVars.pt({2*i+1+j})"
variation_mass = f"jetVars.mass({2*i+1+j})"
variation_msoftdrop = f"jetVars.msoftdrop({2*i+1+j})"

df = df.Define(
f"tmp_CleanFatJet_pt__JES_{source}_{tag}",
variation_pt,
)
df = df.Define(
f"tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting",
f"ROOT::VecOps::Reverse(ROOT::VecOps::Argsort(tmp_CleanFatJet_pt__JES_{source}_{tag}))",
)
variations_pt.append(
f"Take(tmp_CleanFatJet_pt__JES_{source}_{tag}, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)"
)

df = df.Define(
f"CleanFatJet_cleanFatJetIdx_preJES_{source}_{tag}",
f"tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting",
)

variations_jetIdx.append(
f"Take({JetColl}_jetIdx, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)",
)

df = df.Define(
f"tmp_CleanFatJet_mass__JES_{source}_{tag}",
f"Take({variation_mass}, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)",
)
variations_mass.append(f"tmp_CleanFatJet_mass__JES_{source}_{tag}")

df = df.Define(
f"tmp_CleanFatJet_msoftdrop__JES_{source}_{tag}",
f"Take({variation_msoftdrop}, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)",
)
variations_msoftdrop.append(f"tmp_CleanFatJet_msoftdrop__JES_{source}_{tag}")

variations_phi.append(
f"Take({JetColl}_phi, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)"
)
variations_eta.append(
f"Take({JetColl}_eta, tmp_CleanFatJet_pt__JES_{source}_{tag}_sorting)"
)



tags = ["up", "do"]
df = df.Vary(
"CleanFatJet_pt",
"ROOT::RVec<ROOT::RVecF>{"
+ variations_pt[0]
+ ", "
+ variations_pt[1]
+ "}",
tags,
source,
)

df = df.Vary(
"CleanFatJet_jetIdx",
"ROOT::RVec<ROOT::RVecI>{" + variations_jetIdx[0]
+ ", " + variations_jetIdx[1]
+ "}",
tags,
source,
)

df = df.Vary(
"CleanFatJet_mass",
"ROOT::RVec<ROOT::RVecF>{" + variations_mass[0]
+ ", " + variations_mass[1]
+ "}",
tags,
source,
)

df = df.Vary(
"CleanFatJet_phi",
"ROOT::RVec<ROOT::RVecF>{" + variations_phi[0]
+ ", " + variations_phi[1]
+ "}",
tags,
source,
)

df = df.Vary(
"CleanFatJet_eta",
"ROOT::RVec<ROOT::RVecF>{" + variations_eta[0]
+ ", " + variations_eta[1]
+ "}",
tags,
source,
)

df = df.Vary(
"CleanFatJet_msoftdrop",
"ROOT::RVec<ROOT::RVecF>{" + variations_msoftdrop[0]
+ ", " + variations_msoftdrop[1]
+ "}",
tags,
source,
)

df = df.DropColumns("tmp_*")

df = df.DropColumns("jetVars*")
df = df.DropColumns("CleanFatJet_sorting")
return df
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