# An introductional notebook to HEP analysis in Python

In this notebook you can find an easy set of commands that show some basic computing techniques commonly used in High Energy Physics (HEP) analyzes.

It also shows how to create an histogram, fill it and draw it. Moreover it is an introduction to ROOT too. The final output is a plot with the number of leptons.

Check the description of the varibles inside the dataset at the end of this notebook

All done with less that 15 lines of code!

## An introduction to the ATLAS public datasets

This is a notebook using the ROOT Prompt kernel that using c++ language, is intended to show the internal content and the way to call and interact with the datasets released by the ATLAS experiment with focus in Education and Training activities:

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### We use data recorded (simulated) by the ATLAS detector (experiment)

where physics objects can be represented as below

### Cell #1

First of all ROOT is imported to read the files in the _.root_ data format. A _.root_ file consists of a tree having branches and leaves.

At this point you could also import further programs that contain other formulas that you maybe use more often. But here we don't import other programs to keep it simple.

In [1]:
import ROOT

Welcome to JupyROOT 6.08/04


### Cell #2

In order to activate the interactive visualisation of the histogram that is later created we can use the JSROOT magic:

In [2]:
%jsroot on


### Cell #3

Next we have to open the data that we want to analyze. It is stored in a _*.root_ file that consists of a tree having branches and leaves.

As you can see, we are reading the data directly from the source! but you can read the file locally too.

In [3]:
f = ROOT.TFile.Open("http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root")
## f = ROOT.TFile.Open("mc_105986.ZZ.root")


### Cell #4

The next step is to define a tree named (we called _tree_) to get the data out of the _*.root_ file, that is into a tree called _mini_:

In [4]:
tree = f.Get("mini")


### Cell #5

After the data is opened we create a canvas on which we can draw a histogram. If we do not have a canvas we cannot see our histogram at the end. Its name is _Canvas_ and its header is _a first way to plot a variable_. The two following arguments define the width and the height of the canvas.

In [5]:
canvas = ROOT.TCanvas("Canvas","a first way to plot a variable",800,600)


### Cell #6

Now we define a histogram that will later be placed on this canvas. Its name is _variable_ and the header of the histogram is _Example plot: Number of leptons_. The three following arguments indicate that this histogram contains 4 so called bins which have a range from 0 to 4.

In [6]:
hist = ROOT.TH1F("variable","Example plot: Number of leptons",4,0,4)


### Cell #7

The following lines are a loop that goes over the data that is stored in the tree and fills the histogram _h_ that we already defined. In this first notebook we don't do any cuts to keep it simple. Accordingly the loop fills the histogram for each event stored in the tree. After the program has looped over all the data it prints the word

Done!.

In [7]:
for event in tree:
hist.Fill(tree.lep_n)

print "Done!"

Done!


### Cell #8

After filling the histogram we want to see the results of the analysis. First we draw the histogram on the canvas and then the canvas on which the histogram lies:

In [8]:
hist.Draw()
canvas.Draw()


### Cell #9

...

In [9]:
scale = hist.Integral()
hist.Scale(1/scale)


### Cell #10

...

In [10]:
hist.Draw()
canvas.Draw()


### Description of the Variables inside the _mini_ tree in the ATLAS Open Data samples

# variable branchname type description
01 runNumber int runNumber
02 eventNumber int eventNumber
03 channelNumber int channelNumber
04 lbNumber int lbNumber
05 rndRunNumber int randomized run number mimicking run number distribution in data
06 mu float average interactions per bunch crossing
07 mcWeight float weight of an MC event
08 pvxp_n int number of primary vertices
09 isGoodEvent int summary of diverse quality flags like hfor
10 scaleFactor float overall scale factor for the preselected event
11 trigE bool boolean whether a standard trigger has fired in the egamma stream
12 trigM bool boolean whether a standard trigger has fired in the muon stream
13 passGRL bool signifies whether event passes the GRL may be put in isGoodEvent
14 hasGoodVertex bool signifies whether the event has at least one good vertex
15 lep_n int number of preselected leptons
16 lep_truthMatched vector boolean indicating whether the lepton is matched to a truth lepton
17 lep_trigMatched vector boolean signifying whether the lepton is the one triggering the event
18 lep_pt vector transverse momentum of the lepton
19 lep_eta vector pseudo-rapidity of the lepton
20 lep_phi vector azimuthal angle of the lepton
21 lep_E vector energy of the lepton
22 lep_z0 vector z-coordinate of the track associated to the lepton wrt. the primary vertex
23 lep_charge vector charge of the lepton
24 lep_isTight vector boolean indicating whether the lepton is of tight quality
25 lep_flag vector bitmask implementing object cuts of the top group
26 lep_type vector number signifying the lepton type (e, mu, tau) of the lepton
27 lep_ptcone30 vector ptcone30 isolation for the lepton
28 lep_etcone20 vector etcone20 isolation for the lepton
28 lep_trackd0pvunbiased vector d0 of the track associated to the lepton at the point of closest approach (p.o.a.)
29 lep_tracksigd0pvunbiased vector d0 signifcance of the track associated to the lepton at the p.o.a.
30 met_et float Transverse energy of the missing momentum vector
31 met_phi float Azimuthal angle of the missing momentum vector
32 jet_n int number of selected jets
33 jet_pt vector transverse momentum of the jet
34 jet_eta vector pseudorapidity of the jet
35 jet_phi vector azimuthal angle of the jet
36 jet_E vector energy of the jet
37 jet_m vector invariant mass of the jet
38 jet_jvf vector JetVertexFraction of the jet
39 jet_flag vector bitmask implementing object cuts of the top group
40 jet_trueflav vector true flavor of the jet
41 jet_truthMatched vector information whether the jet matches a jet on truth level
42 jet_SV0 vector SV0 weight of the jet
43 jet_MV1 vector MV1 weight of the jet
44 scaleFactor_BTAG float scalefactor for btagging
45 scaleFactor_ELE float scalefactor for electron efficiency
46 scaleFactor_JVFSF float scalefactor for jet vertex fraction
47 scaleFactor_MUON float scalefactor for muon efficiency
48 scaleFactor_PILEUP float scalefactor for pileup reweighting
49 scaleFactor_TRIGGER float scalefactor for trigger
50 scaleFactor_ZVERTEX float scalefactor for z-vertex reweighting