# Rf 1 0 2_Dataimport¶

'BASIC FUNCTIONALITY' RooFit tutorial macro #102 Importing data from ROOT TTrees and THx histograms

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 09:49 AM.

In [ ]:
import ROOT
from array import array

def makeTH1():

# Create ROOT ROOT.TH1 filled with a Gaussian distribution

hh = ROOT.TH1D("hh", "hh", 25, -10, 10)
for i in range(100):
hh.Fill(ROOT.gRandom.Gaus(0, 3))
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])
tree.Branch("x", px, "x/D")
tree.Branch("y", py, "y/D")
for i in range(100):
px[0] = ROOT.gRandom.Gaus(0, 3)
py[0] = ROOT.gRandom.Uniform() * 30 - 15
tree.Fill()
return tree

###### #¶

Importing ROOT histograms

## Import ROOT TH1 into a RooDataHist¶

Create a ROOT TH1 histogram

In [ ]:
hh = makeTH1()


Declare observable x

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


Create a binned dataset that imports contents of ROOT.TH1 and associates its contents to observable 'x'

In [ ]:
dh = ROOT.RooDataHist("dh", "dh", [x], Import=hh)


## Plot and fit a RooDataHist¶

Make plot of binned dataset showing Poisson error bars (ROOT.RooFit default)

In [ ]:
frame = x.frame(Title="Imported ROOT.TH1 with Poisson error bars")
dh.plotOn(frame)


Fit a Gaussian p.d.f to the data

In [ ]:
mean = ROOT.RooRealVar("mean", "mean", 0, -10, 10)
sigma = ROOT.RooRealVar("sigma", "sigma", 3, 0.1, 10)
gauss = ROOT.RooGaussian("gauss", "gauss", x, mean, sigma)
gauss.fitTo(dh)
gauss.plotOn(frame)


## Plot and fit a RooDataHist with internal errors¶

If histogram has custom error (i.e. its contents is does not originate from a Poisson process but e.g. is a sum of weighted events) you can data with symmetric 'sum-of-weights' error instead (same error bars as shown by ROOT)

In [ ]:
frame2 = x.frame(Title="Imported ROOT.TH1 with internal errors")
dh.plotOn(frame2, DataError=ROOT.RooAbsData.SumW2)
gauss.plotOn(frame2)


Please note that error bars shown (Poisson or SumW2) are for visualization only, the are NOT used in a maximum likelihood fit

A (binned) ML fit will ALWAYS assume the Poisson error interpretation of data (the mathematical definition of likelihood does not take any external definition of errors). Data with non-unit weights can only be correctly fitted with a chi^2 fit (see rf602_chi2fit.py)

## Importing ROOT TTrees¶

Import ROOT TTree into a RooDataSet

In [ ]:
tree = makeTTree()


Define 2nd observable y

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


Construct unbinned dataset importing tree branches x and y matching between branches and ROOT.RooRealVars is done by name of the branch/RRV

Note that ONLY entries for which x,y have values within their allowed ranges as defined in ROOT.RooRealVar x and y are imported. Since the y values in the import tree are in the range [-15,15] and RRV y defines a range [-10,10] this means that the ROOT.RooDataSet below will have less entries than the ROOT.TTree 'tree'

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


## Use ascii import/export for datasets¶

In [ ]:
def write_dataset(ds, filename):
# Write data to output stream
outstream = ROOT.std.ofstream(filename)
# Optionally, adjust the stream here (e.g. std::setprecision)
ds.write(outstream)
outstream.close()

write_dataset(ds, "rf102_testData.txt")


Read data from input stream. The variables of the dataset need to be supplied to the RooDataSet::read() function.

In [ ]:
print("\n-----------------------\nReading data from ASCII")
"rf102_testData.txt",
[x, y],  # variables to be read. If the file has more fields, these are ignored.
"D",  # Prints if a RooFit message stream listens for debug messages. Use Q for quiet.
)

print("\nOriginal data, line 20:")
ds.get(20).Print("V")



## Plot data set with multiple binning choices¶

Print number of events in dataset

In [ ]:
ds.Print()


Print unbinned dataset with default frame binning (100 bins)

In [ ]:
frame3 = y.frame(Title="Unbinned data shown in default frame binning")
ds.plotOn(frame3)


Print unbinned dataset with custom binning choice (20 bins)

In [ ]:
frame4 = y.frame(Title="Unbinned data shown with custom binning")
ds.plotOn(frame4, Binning=20)

frame5 = y.frame(Title="Unbinned data read back from ASCII file")
ds.plotOn(frame5, Binning=20)


Draw all frames on a canvas

In [ ]:
c = ROOT.TCanvas("rf102_dataimport", "rf102_dataimport", 800, 800)
c.Divide(3, 2)
c.cd(1)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
c.cd(2)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
c.cd(4)
frame3.GetYaxis().SetTitleOffset(1.4)
frame3.Draw()
c.cd(5)
frame4.GetYaxis().SetTitleOffset(1.4)
frame4.Draw()
c.cd(6)

from ROOT import gROOT