Rf 4 0 2_Datahandling

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

Author: Wouter Verkerke
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, January 19, 2022 at 10:21 AM.

In [1]:
%%cpp -d
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooCategory.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "TFile.h"
In [2]:
%%cpp -d
// This is a workaround to make sure the namespace is used inside functions
using namespace RooFit;

Wve add reduction by range

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

In [3]:
RooRealVar x("x", "x", -10, 10);
RooRealVar y("y", "y", 0, 40);
RooCategory c("c", "c");
c.defineType("Plus", +1);
c.defineType("Minus", -1);
RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt

Basic operations on unbinned datasets

Roodataset is an unbinned dataset (a collection of points in n-dimensional space)

In [4]:
RooDataSet d("d", "d", RooArgSet(x, y, c));

Unlike rooabsargs (rooabspdf,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

In [5]:
Int_t i;
for (i = 0; i < 1000; i++) {
   x = i / 50 - 10;
   y = sqrt(1.0 * i);
   c.setLabel((i % 2) ? "Plus" : "Minus");

   // We must explicitly refer to x,y,c here to pass the values because
   // d is not linked to them (as explained above)
   d.add(RooArgSet(x, y, c));
}
d.Print("v");
cout << endl;
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

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

In [6]:
const RooArgSet *row = d.get();
row->Print("v");
cout << endl;
  1) 0x7f9169b93e00 RooRealVar:: x = 9  L(-10 - 10)  "x"
  2) 0x7f9169c43290 RooRealVar:: y = 31.607  L(0 - 40)  "y"
  3) 0x7f9169bafc70 RooCategory:: c = Plus(idx = 1)
  "c"

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 [7]:
d.get(900)->Print("v");
cout << endl;
  1) 0x7f9169b93e00 RooRealVar:: x = 8  L(-10 - 10)  "x"
  2) 0x7f9169c43290 RooRealVar:: y = 30  L(0 - 40)  "y"
  3) 0x7f9169bafc70 RooCategory:: c = Minus(idx = -1)
  "c"

Reducing, appending and merging

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

In [8]:
cout << endl << ">> d1 has only columns x,c" << endl;
RooDataSet *d1 = (RooDataSet *)d.reduce(RooArgSet(x, c));
d1->Print("v");

cout << endl << ">> d2 has only column y" << endl;
RooDataSet *d2 = (RooDataSet *)d.reduce(RooArgSet(y));
d2->Print("v");

cout << endl << ">> d3 has only the points with y>5.17" << endl;
RooDataSet *d3 = (RooDataSet *)d.reduce("y>5.17");
d3->Print("v");

cout << endl << ">> d4 has only columns x,c for data points with y>5.17" << endl;
RooDataSet *d4 = (RooDataSet *)d.reduce(RooArgSet(x, c), "y>5.17");
d4->Print("v");
>> d1 has only columns x,c
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

>> d2 has only column y
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  y = 31.607  L(0 - 40)  "y"

>> d3 has only the points with y>5.17
[#1] INFO:InputArguments -- The formula y>5.17 claims to use the variables (x,y,c) but only (y) seem to be in use.
  inputs:         y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

>> d4 has only columns x,c for data points with y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

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

In [9]:
cout << endl << ">> merge d2(y) with d1(x,c) to form d1(x,c,y)" << endl;
d1->merge(d2);
d1->Print("v");
>> merge d2(y) with d1(x,c) to form d1(x,c,y)
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"

The append() function addes two datasets row-wise

In [10]:
cout << endl << ">> append data points of d3 to d1" << endl;
d1->append(*d3);
d1->Print("v");
>> append data points of d3 to d1
DataStore d (d)
  Contains 1973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"

Operations on binned datasets

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

In [11]:
cout << ">> construct dh (binned) from d(unbinned) but only take the x and y dimensions," << endl
     << ">> the category 'c' will be projected in the filling process" << endl;
>> construct dh (binned) from d(unbinned) but only take the x and y dimensions,
>> 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 [12]:
x.setBins(10);
y.setBins(10);
RooDataHist dh("dh", "binned version of d", RooArgSet(x, y), d);
dh.Print("v");

RooPlot *yframe = y.frame(Bins(10), Title("Operations on binned datasets"));
dh.plotOn(yframe); // plot projection of 2D binned data on y
DataStore dh (binned version of d)
  Contains 100 entries
  Observables: 
    1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 38  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 100 bins with a total weight of 1000
  Observables:     1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 38  L(0 - 40) B(10)  "y"

Examine the statistics of a binned dataset

In [13]:
cout << ">> number of bins in dh   : " << dh.numEntries() << endl;
cout << ">> sum of weights in dh   : " << dh.sum(kFALSE) << endl;
cout << ">> integral over histogram: " << dh.sum(kTRUE) << endl; // accounts for bin volume
>> number of bins in dh   : 100
>> sum of weights in dh   : 1000
>> integral over histogram: 8000

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

In [14]:
x = 0.3;
y = 20.5;
cout << ">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) " << endl;
cout << " bin center:" << endl;
dh.get(RooArgSet(x, y))->Print("v");         // load bin center coordinates in internal buffer
cout << " weight = " << dh.weight() << endl; // return weight of last loaded coordinates
>> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) 
 bin center:
  1) 0x7f9169c5f890 RooRealVar:: x = 1  L(-10 - 10) B(10)  "x"
  2) 0x7f9169e43100 RooRealVar:: y = 22  L(0 - 40) B(10)  "y"
 weight = 76

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 [15]:
cout << ">> Creating 1-dimensional projection on y of dh for bins with x>0" << endl;
RooDataHist *dh2 = (RooDataHist *)dh.reduce(y, "x>0");
dh2->Print("v");
>> Creating 1-dimensional projection on y of dh for bins with x>0
DataStore dh (binned version of d)
  Contains 10 entries
  Observables: 
    1)  y = 38  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 10 bins with a total weight of 500
  Observables:     1)  y = 38  L(0 - 40) B(10)  "y"

Add dh2 to yframe and redraw

In [16]:
dh2->plotOn(yframe, LineColor(kRed), MarkerColor(kRed));
[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 500 will supercede previous event count of 1000 for normalization of PDF projections

Saving and loading from file

Datasets can be persisted with root i/o

In [17]:
cout << endl << ">> Persisting d via ROOT I/O" << endl;
TFile f("rf402_datahandling.root", "RECREATE");
d.Write();
f.ls();
>> Persisting d via ROOT I/O
TFile**		rf402_datahandling.root	
 TFile*		rf402_datahandling.root	
  KEY: RooDataSet	d;1	d
  KEY: TProcessID	ProcessID0;1	83a61048-7911-11ec-9d1b-942c8a89beef

To read back in future session:

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

In [18]:
new TCanvas("rf402_datahandling", "rf402_datahandling", 600, 600);
gPad->SetLeftMargin(0.15);
yframe->GetYaxis()->SetTitleOffset(1.4);
yframe->Draw();

Draw all canvases

In [19]:
%jsroot on
gROOT->GetListOfCanvases()->Draw()