#!/usr/bin/env python
# coding: utf-8

# # df105_WBosonAnalysis
# 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.
# 
# See the [corresponding spec json file](https://github.com/root-project/root/blob/master/tutorials/analysis/dataframe/df105_WBosonAnalysis.json).
# 
# 
# 
# 
# **Author:** Stefan Wunsch (KIT, CERN)  
# <i><small>This notebook tutorial was automatically generated with <a href= "https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Wednesday, May 14, 2025 at 04:42 PM.</small></i>

# In[1]:


import ROOT
import sys
import json
import argparse
import os


# Argument parsing

# In[2]:


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)")
if 'df105_WBosonAnalysis.py' in sys.argv[0]:
    # Script
    args = parser.parse_args()
else:
    # Notebook
    args = parser.parse_args(args=[])

if args.b: ROOT.gROOT.SetBatch(True)
if args.t: ROOT.EnableImplicitMT()

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.

# In[3]:


files = json.load(open(os.path.join(ROOT.gROOT.GetTutorialsDir(), "analysis/dataframe/df105_WBosonAnalysis.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

# In[4]:


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

# In[5]:


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

# In[6]:


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.

# In[7]:


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

# In[8]:


ROOT.gROOT.SetStyle("ATLAS")


# Create canvas

# In[9]:


c = ROOT.TCanvas("c", "", 600, 600)
c.SetTickx(0)
c.SetTicky(0)
c.SetLogy()


# Draw stack with MC contributions

# In[10]:


stack = ROOT.THStack()
for h, color in zip(
        [singletop, diboson, ttbar, zjets, wjets],
        [(0.82, 0.94, 0.76), (0.76, 0.54, 0.57), (0.61, 0.6, 0.8), (0.97, 0.81, 0.41), (0.87, 0.35, 0.42)]):
    h.SetLineWidth(1)
    h.SetLineColor("black")
    h.SetFillColor(ROOT.TColor.GetColor(*color))
    # From Python 3.11 onward, implicit conversion of the tuple works too:
    # h.SetFillColor(color)
    stack.Add(h)
stack.Draw("HIST")
stack.GetXaxis().SetLabelSize(0.04)
stack.GetXaxis().SetTitleSize(0.045)
stack.GetXaxis().SetTitleOffset(1.3)
stack.GetXaxis().SetTitle("m_{T}^{W#rightarrow l#nu} [GeV]")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetLabelSize(0.04)
stack.GetYaxis().SetTitleSize(0.045)
stack.SetMaximum(1e10 * args.lumi_scale)
stack.SetMinimum(1)


# Draw data

# In[11]:


data.SetMarkerStyle(20)
data.SetMarkerSize(1.2)
data.SetLineWidth(2)
data.SetLineColor("black")
data.Draw("E SAME")


# Add legend

# In[12]:


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")
legend.Draw()


# Add ATLAS label

# In[13]:


text = ROOT.TLatex()
text.SetNDC()
text.SetTextFont(72)
text.SetTextSize(0.045)
text.DrawLatex(0.21, 0.86, "ATLAS")
text.SetTextFont(42)
text.DrawLatex(0.21 + 0.16, 0.86, "Open Data")
text.SetTextSize(0.04)
text.DrawLatex(0.21, 0.80, "#sqrt{{s}} = 13 TeV, {:.2f} fb^{{-1}}".format(lumi * args.lumi_scale / 1000.0))


# Save the plot

# In[14]:


c.SaveAs("df105_WBosonAnalysis.png")
print("Saved figure to df105_WBosonAnalysis.png")


# Draw all canvases 

# In[15]:


from ROOT import gROOT 
gROOT.GetListOfCanvases().Draw()