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", )
py = array("d", )
pz = array("d", )
pi = array("i", )
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 = ROOT.gRandom.Gaus(0, 3)
py = ROOT.gRandom.Uniform() * 30 - 15
pz = ROOT.gRandom.Gaus(0, 5)
pi = i % 3
tree.Fill()

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")
c.defineType("SampleA")
c.defineType("SampleB")
c.defineType("SampleC")

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})
dh.Print()

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

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)
ds.Print()

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")
ds2.Print()

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)
ds3.Print()

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)
ds4.Print()

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(
"dsABC",
"dsABC",
{x, y},
ROOT.RooFit.Import("SampleA", dsA),
ROOT.RooFit.Import("SampleB", dsB),
Index=c,
Import=("SampleC", dsC),
)

dsABC.Print()

Draw all canvases

In [ ]:
from ROOT import gROOT
gROOT.GetListOfCanvases().Draw()