Data and categories: tools for manipulation of (un)binned datasets

**Author:** Clemens Lange, Wouter Verkerke (C++ version)

*This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 10:02 AM.*

In [ ]:

```
from __future__ import print_function
import ROOT
import math
```

WVE Add reduction by range

Binned (RooDataHist) and unbinned datasets (RooDataSet) share many properties and inherit from a common abstract base class (RooAbsData), provides an interface for all operations that can be performed regardless of the data format

In [ ]:

```
x = ROOT.RooRealVar("x", "x", -10, 10)
y = ROOT.RooRealVar("y", "y", 0, 40)
c = ROOT.RooCategory("c", "c")
c.defineType("Plus", +1)
c.defineType("Minus", -1)
```

ROOT.RooDataSet is an unbinned dataset (a collection of points in N-dimensional space)

In [ ]:

```
d = ROOT.RooDataSet("d", "d", {x, y, c})
```

Unlike ROOT.RooAbsArgs (ROOT.RooAbsPdf, ROOT.RooFormulaVar,....) datasets are not attached to the variables they are constructed from. Instead they are attached to an internal clone of the supplied set of arguments

Fill d with dummy values

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```
for i in range(1000):
x.setVal(i / 50 - 10)
y.setVal(math.sqrt(1.0 * i))
if i % 2:
c.setLabel("Plus")
else:
c.setLabel("Minus")
# We must explicitly refer to x,y, here to pass the values because
# d is not linked to them (as explained above)
if i < 3:
print(x, y, c)
print(type(x))
d.add({x, y, c})
d.Print("v")
print("")
```

The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c) supplied in the constructor

In [ ]:

```
row = d.get()
row.Print("v")
print("")
```

Get with an argument loads a specific data point in row and returns a pointer to row argset. get() always returns the same pointer, unless an invalid row number is specified. In that case a null ptr is returned

In [ ]:

```
d.get(900).Print("v")
print("")
```

The reduce() function returns a dataset which is a subset of the original

In [ ]:

```
print("\n >> d1 has only columns x,c")
d1 = d.reduce({x, c})
d1.Print("v")
print("\n >> d2 has only column y")
d2 = d.reduce({y})
d2.Print("v")
print("\n >> d3 has only the points with y>5.17")
d3 = d.reduce("y>5.17")
d3.Print("v")
print("\n >> d4 has only columns x, for data points with y>5.17")
d4 = d.reduce({x, c}, "y>5.17")
d4.Print("v")
```

The merge() function adds two data set column-wise

In [ ]:

```
print("\n >> merge d2(y) with d1(x,c) to form d1(x,c,y)")
d1.merge(d2)
d1.Print("v")
```

The append() function addes two datasets row-wise

In [ ]:

```
print("\n >> append data points of d3 to d1")
d1.append(d3)
d1.Print("v")
```

A binned dataset can be constructed empty, an unbinned dataset, or from a ROOT native histogram (TH1,2,3)

In [ ]:

```
print(">> construct dh (binned) from d(unbinned) but only take the x and y dimensions, ")
print(">> the category 'c' will be projected in the filling process")
```

The binning of real variables (like x,y) is done using their fit range 'get/setRange()' and number of specified fit bins 'get/setBins()'. Category dimensions of binned datasets get one bin per defined category state

In [ ]:

```
x.setBins(10)
y.setBins(10)
dh = ROOT.RooDataHist("dh", "binned version of d", {x, y}, d)
dh.Print("v")
yframe = y.frame(Bins=10, Title="Operations on binned datasets")
dh.plotOn(yframe) # plot projection of 2D binned data on y
```

Examine the statistics of a binned dataset

In [ ]:

```
print(">> number of bins in dh : ", dh.numEntries())
print(">> sum of weights in dh : ", dh.sum(False))
```

accounts for bin volume

In [ ]:

```
print(">> integral over histogram: ", dh.sum(True))
```

Locate a bin from a set of coordinates and retrieve its properties

In [ ]:

```
x.setVal(0.3)
y.setVal(20.5)
print(">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:")
```

load bin center coordinates in internal buffer

In [ ]:

```
dh.get({x, y}).Print("v")
print(" weight = ", dh.weight()) # return weight of last loaded coordinates
```

Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset

All reduce() methods are interfaced in RooAbsData. All reduction techniques demonstrated on unbinned datasets can be applied to binned datasets as well.

In [ ]:

```
print(">> Creating 1-dimensional projection on y of dh for bins with x>0")
dh2 = dh.reduce({y}, "x>0")
dh2.Print("v")
```

Add dh2 to yframe and redraw

In [ ]:

```
dh2.plotOn(yframe, LineColor="r", MarkerColor="r")
```

Datasets can be persisted with ROOT I/O

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```
print("\n >> Persisting d via ROOT I/O")
f = ROOT.TFile("rf402_datahandling.root", "RECREATE")
d.Write()
f.ls()
```

To read back in future session:

ROOT.TFile f("rf402_datahandling.root") d = (ROOT.RooDataSet*) f.FindObject("d")

In [ ]:

```
c = ROOT.TCanvas("rf402_datahandling", "rf402_datahandling", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
yframe.GetYaxis().SetTitleOffset(1.4)
yframe.Draw()
c.SaveAs("rf402_datahandling.png")
```

Draw all canvases

In [ ]:

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