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df105.py File Reference

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namespace  df105
 

Detailed Description

The W boson mass analysis from the ATLAS Open Data release of 2020, with RDataFrame.

This tutorial is the analysis of the W boson mass taken from the ATLAS Open Data release in 2020 (http://opendata.atlas.cern/release/2020/documentation/). The data was recorded with the ATLAS detector during 2016 at a center-of-mass energy of 13 TeV. W bosons are produced frequently at the LHC and are an important background to studies of Standard Model processes, for example the Higgs boson analyses.

The analysis is translated to a RDataFrame workflow processing up to 60 GB of simulated events and data. By default the analysis runs on a preskimmed dataset to reduce the runtime. The full dataset can be used with the –full-dataset argument and you can also run only on a fraction of the original dataset using the argument –lumi-scale.

This macro is replica of tutorials/dataframe/df105_WBosonAnalysis.py, but with usage of ROOT7 graphics Run macro with python3 -i df105.py command to get interactive canvas

import ROOT
import json
import argparse
import os
from ROOT.Experimental import RCanvas, RText, RAttrText, RAttrFont, RPadPos, TObjectDrawable
# Argument parsing
parser = argparse.ArgumentParser()
parser.add_argument("--lumi-scale", type=float, default=0.001,
help="Run only on a fraction of the total available 10 fb^-1 (only usable together with --full-dataset)")
parser.add_argument("--full-dataset", action="store_true", default=False,
help="Use the full dataset (use --lumi-scale to run only on a fraction of it)")
parser.add_argument("-b", action="store_true", default=False, help="Use ROOT batch mode")
parser.add_argument("-t", action="store_true", default=False, help="Use implicit multi threading (for the full dataset only possible with --lumi-scale 1.0)")
args = parser.parse_args()
if args.b: ROOT.gROOT.SetWebDisplay("batch")
if not args.full_dataset: lumi_scale = 0.001 # The preskimmed dataset contains only 0.01 fb^-1
else: lumi_scale = args.lumi_scale
lumi = 10064.0
print('Run on data corresponding to {:.2f} fb^-1 ...'.format(lumi * lumi_scale / 1000.0))
if args.full_dataset: dataset_path = "root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22"
else: dataset_path = "root://eospublic.cern.ch//eos/root-eos/reduced_atlas_opendata/w"
# Create a ROOT dataframe for each dataset
# Note that we load the filenames from the external json file placed in the same folder than this script.
files = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "df105.json")))
processes = files.keys()
df = {}
xsecs = {}
sumws = {}
samples = []
for p in processes:
for d in files[p]:
# Construct the dataframes
folder = d[0] # Folder name
sample = d[1] # Sample name
xsecs[sample] = d[2] # Cross-section
sumws[sample] = d[3] # Sum of weights
num_events = d[4] # Number of events
samples.append(sample)
df[sample] = ROOT.RDataFrame("mini", "{}/1lep/{}/{}.1lep.root".format(dataset_path, folder, sample))
# Scale down the datasets if requested
if args.full_dataset and lumi_scale < 1.0:
df[sample] = df[sample].Range(int(num_events * lumi_scale))
# Select events for the analysis
# Just-in-time compile custom helper function performing complex computations
ROOT.gInterpreter.Declare("""
bool GoodElectronOrMuon(int type, float pt, float eta, float phi, float e, float trackd0pv, float tracksigd0pv, float z0)
{
ROOT::Math::PtEtaPhiEVector p(pt / 1000.0, eta, phi, e / 1000.0);
if (abs(z0 * sin(p.theta())) > 0.5) return false;
if (type == 11 && abs(eta) < 2.46 && (abs(eta) < 1.37 || abs(eta) > 1.52)) {
if (abs(trackd0pv / tracksigd0pv) > 5) return false;
return true;
}
if (type == 13 && abs(eta) < 2.5) {
if (abs(trackd0pv / tracksigd0pv) > 3) return false;
return true;
}
return false;
}
""")
for s in samples:
# Select events with a muon or electron trigger and with a missing transverse energy larger than 30 GeV
df[s] = df[s].Filter("trigE || trigM")\
.Filter("met_et > 30000")
# Find events with exactly one good lepton
df[s] = df[s].Define("good_lep", "lep_isTightID && lep_pt > 35000 && lep_ptcone30 / lep_pt < 0.1 && lep_etcone20 / lep_pt < 0.1")\
.Filter("ROOT::VecOps::Sum(good_lep) == 1")
# Apply additional cuts in case the lepton is an electron or muon
df[s] = df[s].Define("idx", "ROOT::VecOps::ArgMax(good_lep)")\
.Filter("GoodElectronOrMuon(lep_type[idx], lep_pt[idx], lep_eta[idx], lep_phi[idx], lep_E[idx], lep_trackd0pvunbiased[idx], lep_tracksigd0pvunbiased[idx], lep_z0[idx])")
# Apply luminosity, scale factors and MC weights for simulated events
for s in samples:
if "data" in s:
df[s] = df[s].Define("weight", "1.0")
else:
df[s] = df[s].Define("weight", "scaleFactor_ELE * scaleFactor_MUON * scaleFactor_LepTRIGGER * scaleFactor_PILEUP * mcWeight * {} / {} * {}".format(xsecs[s], sumws[s], lumi))
# Compute transverse mass of the W boson using the lepton and the missing transverse energy and make a histogram
ROOT.gInterpreter.Declare("""
float ComputeTransverseMass(float met_et, float met_phi, float lep_pt, float lep_eta, float lep_phi, float lep_e)
{
ROOT::Math::PtEtaPhiEVector met(met_et, 0, met_phi, met_et);
ROOT::Math::PtEtaPhiEVector lep(lep_pt, lep_eta, lep_phi, lep_e);
return (met + lep).Mt() / 1000.0;
}
""")
histos = {}
for s in samples:
df[s] = df[s].Define("mt_w", "ComputeTransverseMass(met_et, met_phi, lep_pt[idx], lep_eta[idx], lep_phi[idx], lep_E[idx])")
histos[s] = df[s].Histo1D(ROOT.RDF.TH1DModel(s, "mt_w", 24, 60, 180), "mt_w", "weight")
# Run the event loop and merge histograms of the respective processes
# RunGraphs allows to run the event loops of the separate RDataFrame graphs
# concurrently. This results in an improved usage of the available resources
# if each separate RDataFrame can not utilize all available resources, e.g.,
# because not enough data is available.
ROOT.RDF.RunGraphs([histos[s] for s in samples])
def merge_histos(label):
h = None
for i, d in enumerate(files[label]):
t = histos[d[1]].GetValue()
if i == 0: h = t.Clone()
else: h.Add(t)
h.SetNameTitle(label, label)
return h
data = merge_histos("data")
wjets = merge_histos("wjets")
zjets = merge_histos("zjets")
ttbar = merge_histos("ttbar")
diboson = merge_histos("diboson")
singletop = merge_histos("singletop")
# Create the plot
# Set styles
ROOT.gROOT.SetStyle("ATLAS")
# Create canvas and configure frame with axis attributes
c = RCanvas.Create("df105_WBosonAnalysis")
frame = c.AddFrame()
frame.margins.top = 0.05
frame.margins.right = 0.05
frame.margins.left = 0.16
frame.margins.bottom = 0.16
# c.SetTickx(0)
# c.SetTicky(0)
frame.x.min = 60
frame.x.max = 180
frame.x.title.value = "m_{T}^{W#rightarrow l#nu} [GeV]"
frame.x.title.size = 0.045
frame.x.title.offset = 0.01
frame.x.labels.size = 0.04
frame.y.min = 1
frame.y.max = 1e10*args.lumi_scale
frame.y.log = 10
frame.y.title.value = "Events"
frame.y.title.size = 0.045
frame.y.labels.size = 0.04
# instruct RFrame to draw axes
frame.drawAxes = True
# Draw stack with MC contributions
stack = ROOT.THStack()
for h, color in zip(
[singletop, diboson, ttbar, zjets, wjets],
[(208, 240, 193), (195, 138, 145), (155, 152, 204), (248, 206, 104), (222, 90, 106)]):
h.SetLineWidth(1)
h.SetLineColor(1)
h.SetFillColor(ROOT.TColor.GetColor(*color))
stack.Add(h)
c.Add[TObjectDrawable]().Set(stack, "HIST SAME")
# Draw data
data.SetMarkerStyle(20)
data.SetMarkerSize(1.2)
data.SetLineWidth(2)
data.SetLineColor(ROOT.kBlack)
c.Add[TObjectDrawable]().Set(data, "E SAME")
# Add TLegend while histograms packed in the THStack
legend = ROOT.TLegend(0.60, 0.65, 0.92, 0.92)
legend.SetTextFont(42)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.04)
legend.SetTextAlign(32)
legend.AddEntry(data, "Data" ,"lep")
legend.AddEntry(wjets, "W+jets", "f")
legend.AddEntry(zjets, "Z+jets", "f")
legend.AddEntry(ttbar, "t#bar{t}", "f")
legend.AddEntry(diboson, "Diboson", "f")
legend.AddEntry(singletop, "Single top", "f")
c.Add[TObjectDrawable]().Set(legend)
# Add ATLAS label
lbl1 = c.Add[RText](RPadPos(0.05, 0.88), "ATLAS")
lbl1.onFrame = True
lbl1.text.font = RAttrFont.kArialBoldOblique
lbl1.text.size = 0.04
lbl1.text.align = RAttrText.kLeftBottom
lbl2 = c.Add[RText](RPadPos(0.05 + 0.20, 0.88), "Open Data")
lbl2.onFrame = True
lbl2.text.font = RAttrFont.kArial
lbl2.text.size = 0.04
lbl2.text.align = RAttrText.kLeftBottom
lbl3 = c.Add[RText](RPadPos(0.05, 0.82), "#sqrt{{s}} = 13 TeV, {:.2f} fb^{{-1}}".format(lumi * args.lumi_scale / 1000.0))
lbl3.onFrame = True
lbl3.text.font = RAttrFont.kArial
lbl3.text.size = 0.03
lbl3.text.align = RAttrText.kLeftBottom
# show canvas finally
c.SetSize(600, 600)
c.Show()
# Save the plot
c.SaveAs("df105.png")
print("Saved figure to df105.png")
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition: RDataFrame.hxx:40
RVec< T > Filter(const RVec< T > &v, F &&f)
Create a new collection with the elements passing the filter expressed by the predicate.
Definition: RVec.hxx:2052
void RunGraphs(std::vector< RResultHandle > handles)
Trigger the event loop of multiple RDataFrames concurrently.
Definition: RDFHelpers.cxx:23
void EnableImplicitMT(UInt_t numthreads=0)
Enable ROOT's implicit multi-threading for all objects and methods that provide an internal paralleli...
Definition: TROOT.cxx:527
A struct which stores the parameters of a TH1D.
Definition: HistoModels.hxx:27
Ta Range(0, 0, 1, 1)
Date
March 2020
Authors
Stefan Wunsch (KIT, CERN) Sergey Linev (GSI)

Definition in file df105.py.