# Create kernel PDF from the generated sample
# If the number of variables passed to the constructor is larger by 1 than the phase
# space dimensionality, the last variables is considered as the weight
kde = BinnedKernelDensity("KernelPDF",
phsp,
ntuple, # Input ntuple
"x", # Variable to use
"w", # Weight variable
1000, # Number of bins
0.1, # Kernel width
0, # Approximation PDF (0 for flat approximation)
100000 # Sample size for MC convolution (0 for binned convolution)
)
# Write binned PDF into a file
kde.writeToFile("WeightedTuple_bins.root")
# That's it. Now fill some control histograms and plot them.
kernel_hist = TH1F("kernel", "Kernel PDF", 200, -1.5, 1.5)
kde.project(kernel_hist)
uniform_hist.Write()
kernel_hist.Write()
gStyle.SetOptStat(0)
canvas = TCanvas("canvas", "WeightedTuple", 400, 400)
uniform_hist.Draw()
kernel_hist.Scale( uniform_hist.GetSumOfWeights() / kernel_hist.GetSumOfWeights() )
kernel_hist.SetLineColor(2)
# Generate 10000 toys according to the uniform PDF
uniform.generate(ntuple, 10000)
# Create kernel PDF from the generate sample
kde = BinnedKernelDensity("KernelPDF",
phsp,
ntuple, # Input ntuple
"x", # Variable to use
1000, # Number of bins
0.2, # Kernel width
0, # Approximation PDF (0 for flat approximation)
100000 # Sample size for MC convolution (0 for binned convolution)
)
# Write binned PDF into a file
kde.writeToFile("OneDimPdfBins.root")
uniform_hist = TH1F("unform", "PDF", 200, -1.5, 1.5)
kernel_hist = TH1F("kernel", "Kernel PDF", 200, -1.5, 1.5)
uniform.project(uniform_hist)
kde.project(kernel_hist)
gStyle.SetOptStat(0)
canvas = TCanvas("canvas", "OneDimPdf", 400, 400)
uniform_hist.Draw()
kernel_hist.Scale( uniform_hist.GetSumOfWeights() / kernel_hist.GetSumOfWeights() )
kernel_hist.SetLineColor(2)
kernel_hist.Draw("same")
50000 # Sample size for MC integration
)
# Create kernel PDF from the generated sample
kde = BinnedKernelDensity("KernelPDF",
phsp, # Phase space
ntuple, # Input ntuple
"x","y", # Variables to use
200,200, # Numbers of bins
0.4, 0.4,# Kernel widths
poly, # Approximation PDF (0 for flat approximation)
50000 # Sample size for MC convolution (0 for binned convolution)
)
# Write binned PDF into a file
kde.writeToFile("DalitzPdf_bins.root")
# That's it. Now fill some histograms and show the results.
true_hist = TH2F("true", "True PDF", 100, phsp.lowerLimit(0), phsp.upperLimit(0),
100, phsp.lowerLimit(1), phsp.upperLimit(1))
poly_hist = TH2F("poly", "Polynomial PDF", 100, phsp.lowerLimit(0), phsp.upperLimit(0),
100, phsp.lowerLimit(1), phsp.upperLimit(1))
kernel_hist = TH2F("kernel", "Kernel PDF", 100, phsp.lowerLimit(0), phsp.upperLimit(0),
100, phsp.lowerLimit(1), phsp.upperLimit(1))
true_kpi = TH1F("true_kpi", "M^{2}(K_{S}#pi) slice", 100, phsp.lowerLimit(0), phsp.upperLimit(0) )
poly_kpi = TH1F("poly_kpi", "M^{2}(K_{S}#pi) slice", 100, phsp.lowerLimit(0), phsp.upperLimit(0) )
kernel_kpi = TH1F("kernel_kpi", "M^{2}(K_{S}#pi) slice", 100, phsp.lowerLimit(0), phsp.upperLimit(0) )
true_pipi = TH1F("true_pipi", "M^{2}(#pi#pi) slice", 100, phsp.lowerLimit(1), phsp.upperLimit(1) )
poly_pipi = TH1F("poly_pipi", "M^{2}(#pi#pi) slice", 100, phsp.lowerLimit(1), phsp.upperLimit(1) )
# Generate 50000 toys according to the "true" PDF
truepdf.generate(ntuple, 50000)
# Create kernel PDF from the generated sample. Use polynomial shape as an approximation PDF
kde = BinnedKernelDensity("KernelPDF",
phsp, # Phase space
ntuple, # Input ntuple
"x","y", # Variables to use
200,200, # Numbers of bins
0.2, 0.2, # Kernel widths
approxpdf, # Approximation PDF (0 for flat approximation)
100000 # Sample size for MC convolution (0 for binned convolution)
)
# Write binned PDF into a file
kde.writeToFile("ParamPhsp_bins.root")
# That's it. Now fill some histograms and show the results.
true_hist = TH2F("true", "True PDF", 100, phsp.lowerLimit(0), phsp.upperLimit(0),
100, phsp.lowerLimit(1), phsp.upperLimit(1))
kernel_hist = TH2F("kernel", "Kernel PDF", 100, phsp.lowerLimit(0), phsp.upperLimit(0),
100, phsp.lowerLimit(1), phsp.upperLimit(1))
true_kpi = TH1F("true_kpi", "X slice", 100, phsp.lowerLimit(0), phsp.upperLimit(0) )
kernel_kpi = TH1F("kernel_kpi", "X slice", 100, phsp.lowerLimit(0), phsp.upperLimit(0) )
true_pipi = TH1F("true_pipi", "Y slice", 100, phsp.lowerLimit(1), phsp.upperLimit(1) )
kernel_pipi = TH1F("kernel_pipi", "Y slice", 100, phsp.lowerLimit(1), phsp.upperLimit(1) )
# Coordinates in the phase space where we will create the 1D slices.
from ROOT import std, Double
