doc/master/rf608__fitresultaspdf_8py.html

import ROOT


# Create model and dataset

# -----------------------------------------------


# Observable

x = ROOT.RooRealVar("x", "x", -20, 20)


# Model (intentional strong correlations)

mean = ROOT.RooRealVar("mean", "mean of g1 and g2", 0, -1, 1)

sigma_g1 = ROOT.RooRealVar("sigma_g1", "width of g1", 2)

g1 = ROOT.RooGaussian("g1", "g1", x, mean, sigma_g1)


sigma_g2 = ROOT.RooRealVar("sigma_g2", "width of g2", 4, 3.0, 5.0)

g2 = ROOT.RooGaussian("g2", "g2", x, mean, sigma_g2)


frac = ROOT.RooRealVar("frac", "frac", 0.5, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "model", [g1, g2], [frac])


# Generate 1000 events

data = model.generate({x}, 1000)


# Fit model to data

# ----------------------------------


r = model.fitTo(data, Save=True, PrintLevel=-1)


# Create MV Gaussian pdf of fitted parameters

# ------------------------------------------------------------------------------------


parabPdf = r.createHessePdf({frac, mean, sigma_g2})


# Some exercises with the parameter pdf

# -----------------------------------------------------------------------------


# Generate 100K points in the parameter space, from the MVGaussian pdf

d = parabPdf.generate({mean, sigma_g2, frac}, 100000)


# Sample a 3-D histogram of the pdf to be visualized as an error

# ellipsoid using the GLISO draw option

hh_3d = parabPdf.createHistogram("mean,sigma_g2,frac", 25, 25, 25)

hh_3d.SetFillColor("kBlue")


# Project 3D parameter pdf down to 3 permutations of two-dimensional pdfs

# The integrations corresponding to these projections are performed analytically

# by the MV Gaussian pdf

pdf_sigmag2_frac = parabPdf.createProjection({mean})

pdf_mean_frac = parabPdf.createProjection({sigma_g2})

pdf_mean_sigmag2 = parabPdf.createProjection({frac})


# Make 2D plots of the 3 two-dimensional pdf projections

hh_sigmag2_frac = pdf_sigmag2_frac.createHistogram("sigma_g2,frac", 50, 50)

hh_mean_frac = pdf_mean_frac.createHistogram("mean,frac", 50, 50)

hh_mean_sigmag2 = pdf_mean_sigmag2.createHistogram("mean,sigma_g2", 50, 50)

hh_mean_frac.SetLineColor("kBlue")

hh_sigmag2_frac.SetLineColor("kBlue")

hh_mean_sigmag2.SetLineColor("kBlue")


# Draw the 'sigar'

ROOT.gStyle.SetCanvasPreferGL(True)

ROOT.gStyle.SetPalette(1)

c1 = ROOT.TCanvas("rf608_fitresultaspdf_1", "rf608_fitresultaspdf_1", 600, 600)

hh_3d.Draw("gliso")


c1.SaveAs("rf608_fitresultaspdf_1.png")


# Draw the 2D projections of the 3D pdf

c2 = ROOT.TCanvas("rf608_fitresultaspdf_2", "rf608_fitresultaspdf_2", 900, 600)

c2.Divide(3, 2)

c2.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

hh_mean_sigmag2.GetZaxis().SetTitleOffset(1.4)

hh_mean_sigmag2.Draw("surf3")

c2.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

hh_sigmag2_frac.GetZaxis().SetTitleOffset(1.4)

hh_sigmag2_frac.Draw("surf3")

c2.cd(3)

ROOT.gPad.SetLeftMargin(0.15)

hh_mean_frac.GetZaxis().SetTitleOffset(1.4)

hh_mean_frac.Draw("surf3")


# Draw the distributions of parameter points sampled from the pdf

tmp1 = d.createHistogram(mean, sigma_g2, 50, 50)

tmp2 = d.createHistogram(sigma_g2, frac, 50, 50)

tmp3 = d.createHistogram(mean, frac, 50, 50)


c2.cd(4)

ROOT.gPad.SetLeftMargin(0.15)

tmp1.GetZaxis().SetTitleOffset(1.4)

tmp1.Draw("lego3")

c2.cd(5)

ROOT.gPad.SetLeftMargin(0.15)

tmp2.GetZaxis().SetTitleOffset(1.4)

tmp2.Draw("lego3")

c2.cd(6)

ROOT.gPad.SetLeftMargin(0.15)

tmp3.GetZaxis().SetTitleOffset(1.4)

tmp3.Draw("lego3")


c2.SaveAs("rf608_fitresultaspdf_2.png")

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358