Rf 5 0 1_Simultaneouspdf¶
Organization and simultaneous fits: using simultaneous pdfs to describe simultaneous fits to multiple datasets
Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Friday, May 13, 2022 at 09:33 AM.
import ROOT
Create model for physics sample¶
Create observables
x = ROOT.RooRealVar("x", "x", -8, 8)
Construct signal pdf
mean = ROOT.RooRealVar("mean", "mean", 0, -8, 8)
sigma = ROOT.RooRealVar("sigma", "sigma", 0.3, 0.1, 10)
gx = ROOT.RooGaussian("gx", "gx", x, mean, sigma)
Construct background pdf
a0 = ROOT.RooRealVar("a0", "a0", -0.1, -1, 1)
a1 = ROOT.RooRealVar("a1", "a1", 0.004, -1, 1)
px = ROOT.RooChebychev("px", "px", x, [a0, a1])
Construct composite pdf
f = ROOT.RooRealVar("f", "f", 0.2, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "model", [gx, px], [f])
Create model for control sample¶
Construct signal pdf. NOTE that sigma is shared with the signal sample model
mean_ctl = ROOT.RooRealVar("mean_ctl", "mean_ctl", -3, -8, 8)
gx_ctl = ROOT.RooGaussian("gx_ctl", "gx_ctl", x, mean_ctl, sigma)
Construct the background pdf
a0_ctl = ROOT.RooRealVar("a0_ctl", "a0_ctl", -0.1, -1, 1)
a1_ctl = ROOT.RooRealVar("a1_ctl", "a1_ctl", 0.5, -0.1, 1)
px_ctl = ROOT.RooChebychev("px_ctl", "px_ctl", x, [a0_ctl, a1_ctl])
Construct the composite model
f_ctl = ROOT.RooRealVar("f_ctl", "f_ctl", 0.5, 0.0, 1.0)
model_ctl = ROOT.RooAddPdf("model_ctl", "model_ctl", [gx_ctl, px_ctl], [f_ctl])
Generate events for both samples¶
Generate 1000 events in x and y from model
data = model.generate({x}, 100)
data_ctl = model_ctl.generate({x}, 2000)
Create index category and join samples¶
Define category to distinguish physics and control samples events
sample = ROOT.RooCategory("sample", "sample")
sample.defineType("physics")
sample.defineType("control")
Construct combined dataset in (x,sample)
combData = ROOT.RooDataSet(
"combData",
"combined data",
{x},
ROOT.RooFit.Index(sample),
ROOT.RooFit.Import("physics", data),
ROOT.RooFit.Import("control", data_ctl),
)
Construct a simultaneous pdf in (x, sample)¶
Construct a simultaneous pdf using category sample as index
simPdf = ROOT.RooSimultaneous("simPdf", "simultaneous pdf", sample)
Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(model, "physics")
simPdf.addPdf(model_ctl, "control")
Perform a simultaneous fit¶
Perform simultaneous fit of model to data and model_ctl to data_ctl
simPdf.fitTo(combData)
Plot model slices on data slices¶
Make a frame for the physics sample
frame1 = x.frame(Bins=30, Title="Physics sample")
Plot all data tagged as physics sample
combData.plotOn(frame1, Cut="sample==sample::physics")
Plot "physics" slice of simultaneous pdf. NB: You must project the sample index category with data using ProjWData as a RooSimultaneous makes no prediction on the shape in the index category and can thus not be integrated NB2: The sampleSet must be named. It will not work to pass this as a temporary because python will delete it. The same holds for fitTo() and plotOn() below.
sampleSet = {sample}
simPdf.plotOn(frame1, Slice=(sample, "physics"), Components="px", ProjWData=(sampleSet, combData), LineStyle="--")
The same plot for the control sample slice
frame2 = x.frame(Bins=30, Title="Control sample")
combData.plotOn(frame2, Cut="sample==sample::control")
simPdf.plotOn(frame2, Slice=(sample, "control"), ProjWData=(sampleSet, combData))
simPdf.plotOn(frame2, Slice=(sample, "control"), Components="px_ctl", ProjWData=(sampleSet, combData), LineStyle="--")
c = ROOT.TCanvas("rf501_simultaneouspdf", "rf501_simultaneouspdf", 800, 400)
c.Divide(2)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
c.SaveAs("rf501_simultaneouspdf.png")
Draw all canvases
from ROOT import gROOT
gROOT.GetListOfCanvases().Draw()
