Rf 4 0 1_Importttreethx

'DATA AND CATEGORIES' RooFit tutorial macro #401

Overview of advanced option for importing data from ROOT ROOT.TTree and ROOT.THx histograms Basic import options are demonstrated in rf102_dataimport.py

Author: 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 10:01 AM.

In [ ]:
import ROOT
from array import array

def makeTH1(name, mean, sigma):
    """Create ROOT TH1 filled with a Gaussian distribution."""

    hh = ROOT.TH1D(name, name, 100, -10, 10)
    for i in range(1000):
        hh.Fill(ROOT.gRandom.Gaus(mean, sigma))

    return hh

def makeTTree():
    """Create ROOT ROOT.TTree filled with a Gaussian distribution in x and a uniform distribution in y."""

    tree = ROOT.TTree("tree", "tree")
    px = array("d", [0])
    py = array("d", [0])
    pz = array("d", [0])
    pi = array("i", [0])
    tree.Branch("x", px, "x/D")
    tree.Branch("y", py, "y/D")
    tree.Branch("z", pz, "z/D")
    tree.Branch("i", pi, "i/I")
    for i in range(100):
        px[0] = ROOT.gRandom.Gaus(0, 3)
        py[0] = ROOT.gRandom.Uniform() * 30 - 15
        pz[0] = ROOT.gRandom.Gaus(0, 5)
        pi[0] = i % 3

    return tree

Import multiple TH1 into a RooDataHist

Create thee ROOT ROOT.TH1 histograms

In [ ]:
hh_1 = makeTH1("hh1", 0, 3)
hh_2 = makeTH1("hh2", -3, 1)
hh_3 = makeTH1("hh3", +3, 4)

Declare observable x

In [ ]:
x = ROOT.RooRealVar("x", "x", -10, 10)

Create category observable c that serves as index for the ROOT histograms

In [ ]:
c = ROOT.RooCategory("c", "c")

Create a binned dataset that imports contents of all ROOT.TH1 mapped by index category c

In [ ]:
dh = ROOT.RooDataHist("dh", "dh", [x], Index=c, Import={"SampleA": hh_1, "SampleB": hh_2, "SampleC": hh_3})

dh2 = ROOT.RooDataHist("dh", "dh", [x], Index=c, Import={"SampleA": hh_1, "SampleB": hh_2, "SampleC": hh_3})

Importing a ROOT TTree into a RooDataSet with cuts

In [ ]:
tree = makeTTree()

Define observables y,z

In [ ]:
y = ROOT.RooRealVar("y", "y", -10, 10)
z = ROOT.RooRealVar("z", "z", -10, 10)

Import only observables (y,z)

In [ ]:
ds = ROOT.RooDataSet("ds", "ds", {x, y}, Import=tree)

Import observables (x,y,z) but only event for which (y+z<0) is ROOT.True Import observables (x,y,z) but only event for which (y+z<0) is ROOT.True

In [ ]:
ds2 = ROOT.RooDataSet("ds2", "ds2", {x, y, z}, Import=tree, Cut="y+z<0")

Importing integer ROOT TTree branches

Import integer tree branch as ROOT.RooRealVar

In [ ]:
i = ROOT.RooRealVar("i", "i", 0, 5)
ds3 = ROOT.RooDataSet("ds3", "ds3", {i, x}, Import=tree)

Define category i

In [ ]:
icat = ROOT.RooCategory("i", "i", {"State0": 0, "State1": 1})

Import integer tree branch as ROOT.RooCategory (only events with i==0 and i==1 will be imported as those are the only defined states)

In [ ]:
ds4 = ROOT.RooDataSet("ds4", "ds4", {icat, x}, Import=tree)

Import multiple RooDataSets into a RooDataSet

Create three ROOT.RooDataSets in (y,z)

In [ ]:
dsA = ds2.reduce({x, y}, "z<-5")
dsB = ds2.reduce({x, y}, "abs(z)<5")
dsC = ds2.reduce({x, y}, "z>5")

Create a dataset that imports contents of all the above datasets mapped by index category c

In [ ]:
dsABC = ROOT.RooDataSet(
    {x, y},
    ROOT.RooFit.Import("SampleA", dsA),
    ROOT.RooFit.Import("SampleB", dsB),
    Import=("SampleC", dsC),


Draw all canvases

In [ ]:
from ROOT import gROOT