rf203_ranges.py

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## \file
## \ingroup tutorial_roofit
## \notebook
## Addition and convolution: fitting and plotting in sub ranges
##
## \macro_image
## \macro_code
## \macro_output
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
from __future__ import print_function
import ROOT
 
# Set up model
# ---------------------
 
# Construct observables x
x = ROOT.RooRealVar("x", "x", -10, 10)
 
# Construct gaussx(x,mx,1)
mx = ROOT.RooRealVar("mx", "mx", 0, -10, 10)
gx = ROOT.RooGaussian("gx", "gx", x, mx, 1.0)
 
# px = 1 (flat in x)
px = ROOT.RooPolynomial("px", "px", x)
 
# model = f*gx + (1-f)px
f = ROOT.RooRealVar("f", "f", 0.0, 1.0)
model = ROOT.RooAddPdf("model", "model", [gx, px], [f])
 
# Generated 10000 events in (x,y) from pdf model
modelData = model.generate({x}, 10000)
 
# Fit full range
# ---------------------------
 
# Fit pdf to all data
r_full = model.fitTo(modelData, Save=True, PrintLevel=-1)
 
# Fit partial range
# ----------------------------------
 
# Define "signal" range in x as [-3,3]
x.setRange("signal", -3, 3)
 
# Fit pdf only to data in "signal" range
r_sig = model.fitTo(modelData, Save=True, Range="signal", PrintLevel=-1)
 
# Plot/print results
# ---------------------------------------
 
# Make plot frame in x and add data and fitted model
frame = x.frame(Title="Fitting a sub range")
modelData.plotOn(frame)
model.plotOn(frame, Range="Full", LineColor="r", LineStyle="--")  # Add shape in full ranged dashed
model.plotOn(frame)  # By default only fitted range is shown
 
# Print fit results
print("result of fit on all data ")
r_full.Print()
print("result of fit in in signal region (note increased error on signal fraction)")
r_sig.Print()
 
# Draw frame on canvas
c = ROOT.TCanvas("rf203_ranges", "rf203_ranges", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
 
c.SaveAs("rf203_ranges.png")