rf211_paramconv.C File Reference

Detailed Description

Addition and convolution: working with a pdf with a convolution operator in terms of a parameter

This tutorial requires FFT3 to be enabled.

 
#include "RooRealVar.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooGenericPdf.h"
#include "RooFormulaVar.h"
#include "RooFFTConvPdf.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH2.h"
using namespace RooFit;
 
void rf211_paramconv()
{
   // S e t u p   c o m p o n e n t   p d f s
   // ---------------------------------------
 
   // Gaussian g(x ; mean,sigma)
   RooRealVar x("x", "x", -10, 10);
   RooRealVar mean("mean", "mean", -3, 3);
   RooRealVar sigma("sigma", "sigma", 0.5, 0.1, 10);
   RooGaussian modelx("gx", "gx", x, mean, sigma);
 
   // Block function in mean
   RooRealVar a("a", "a", 2, 1, 10);
   RooGenericPdf model_mean("model_mean", "abs(mean)<a", RooArgList(mean, a));
 
   // Convolution in mean parameter model = g(x,mean,sigma) (x) block(mean)
   x.setBins(1000, "cache");
   mean.setBins(50, "cache");
   RooFFTConvPdf model("model", "model", mean, modelx, model_mean);
 
   // Configure convolution to construct a 2-D cache in (x,mean)
   // rather than a 1-d cache in mean that needs to be recalculated
   // for each value of x
   model.setCacheObservables(x);
   model.setBufferFraction(1.0);
 
   // Integrate model over mean projModel = Int model dmean
   RooAbsPdf *projModel = model.createProjection(mean);
 
   // Generate 1000 toy events
   RooDataHist *d = projModel->generateBinned(x, 1000);
 
   // Fit pdf to toy data
   projModel->fitTo(*d, Verbose(), PrintLevel(-1));
 
   // Plot data and fitted pdf
   RooPlot *frame = x.frame(Bins(25));
   d->plotOn(frame);
   projModel->plotOn(frame);
 
   // Make 2d histogram of model(x;mean)
   TH1 *hh = model.createHistogram("hh", x, Binning(50), YVar(mean, Binning(50)), ConditionalObservables(mean));
   hh->SetTitle("histogram of model(x|mean)");
   hh->SetLineColor(kBlue);
 
   // Draw frame on canvas
   TCanvas *c = new TCanvas("rf211_paramconv", "rf211_paramconv", 800, 400);
   c->Divide(2);
   c->cd(1);
   gPad->SetLeftMargin(0.15);
   frame->GetYaxis()->SetTitleOffset(1.4);
   frame->Draw();
   c->cd(2);
   gPad->SetLeftMargin(0.20);
   hh->GetZaxis()->SetTitleOffset(2.5);
   hh->Draw("surf");
}

 
[#1] INFO:Eval -- RooRealVar::setRange(mean) new range named 'refrange_fft_model' created with bounds [-3,3]
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x564b05461000 with pdf gx_CONV_model_mean_CACHE_Obs[mean,x]_NORM_mean for nset (mean) with code 0
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x564b056569b0 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x564b05664a40 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1 from preexisting content.
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for a: using 0.5
[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for sigma: using 0.2
 
prevFCN = 2171.275755  a=2.012, sigma=0.5, [#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
 
prevFCN = 2171.275755  a=1.988, 
prevFCN = 2171.275755  a=2.121, 
prevFCN = 2171.861215  a=1.886, 
prevFCN = 2172.184717  a=2.012, 
prevFCN = 2171.275755  a=1.988, 
prevFCN = 2171.275755  a=2, sigma=0.5047, 
prevFCN = 2171.286528  sigma=0.4953, 
prevFCN = 2171.267762  sigma=0.5029, 
prevFCN = 2171.281998  sigma=0.4971, 
prevFCN = 2171.270547  a=2.012, sigma=0.5, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.012, 
prevFCN = 2171.275755  a=2.121, 
prevFCN = 2171.861215  a=1.886, 
prevFCN = 2172.184717  a=2.012, 
prevFCN = 2171.275755  a=1.988, 
prevFCN = 2171.275755  a=2.121, 
prevFCN = 2171.861215  a=1.886, 
prevFCN = 2172.184717  a=2, sigma=0.5029, 
prevFCN = 2171.281998  sigma=0.4971, 
prevFCN = 2171.270547  a=2.013, sigma=0.4843, 
prevFCN = 2171.259881  a=2.009, sigma=0.4884, 
prevFCN = 2171.260992  a=2.025, sigma=0.4843, 
prevFCN = 2171.259881  a=2.001, 
prevFCN = 2171.259881  a=2.134, 
prevFCN = 2171.692149  a=1.898, 
prevFCN = 2172.378568  a=2.025, 
prevFCN = 2171.259881  a=2.001, 
prevFCN = 2171.259881  a=2.013, sigma=0.4871, 
prevFCN = 2171.26042  sigma=0.4815, 
prevFCN = 2171.260367  sigma=0.4843, 
prevFCN = 2171.259881  a=2.025, 
prevFCN = 2171.259881  a=2.001, 
prevFCN = 2171.259881  a=2.134, 
prevFCN = 2171.692149  a=1.898, 
prevFCN = 2172.378568  a=2.013, sigma=0.4871, 
prevFCN = 2171.26042  sigma=0.4815, 
prevFCN = 2171.260367  a=2.015, sigma=0.4843, 
prevFCN = 2171.259881  a=2.011, 
prevFCN = 2171.259881  a=2.013, sigma=0.4848, 
prevFCN = 2171.259907  sigma=0.4837, 
prevFCN = 2171.259896  a=2.134, sigma=0.4871, 
prevFCN = 2171.720718  a=2.065, sigma=0.4512, 
prevFCN = 2171.332894  a=2.03, sigma=0.473, 
prevFCN = 2171.26812  a=2.02, sigma=0.4794, 
prevFCN = 2171.261381  a=2.016, sigma=0.482, 
prevFCN = 2171.260198  a=2.014, sigma=0.4832, 
prevFCN = 2171.25995  a=2.014, sigma=0.4837, 
prevFCN = 2171.259895  a=2.013, sigma=0.484, 
prevFCN = 2171.259883  a=2.013, sigma=0.4841, 
prevFCN = 2171.259881  a=2.013, sigma=0.4842, 
prevFCN = 2171.25988  a=2.025, 
prevFCN = 2171.25988  a=2.001, 
prevFCN = 2171.25988  a=2.134, 
prevFCN = 2171.691427  a=1.898, 
prevFCN = 2172.379556  a=2.025, 
prevFCN = 2171.25988  a=2.001, 
prevFCN = 2171.25988  a=2.013, sigma=0.487, 
prevFCN = 2171.260398  sigma=0.4814, 
prevFCN = 2171.260398  sigma=0.4842, 
prevFCN = 2171.25988  a=2.025, 
prevFCN = 2171.25988  a=2.001, 
prevFCN = 2171.25988  a=2.134, 
prevFCN = 2171.691427  a=1.898, 
prevFCN = 2172.379556  a=2.013, sigma=0.487, 
prevFCN = 2171.260398  sigma=0.4814, 
prevFCN = 2171.260398  a=2.015, sigma=0.4842, 
prevFCN = 2171.25988  a=2.011, 
prevFCN = 2171.25988  a=2.013, sigma=0.4848, 
prevFCN = 2171.259901  sigma=0.4836, 
prevFCN = 2171.259901  sigma=0.4843, 
prevFCN = 2171.259881  sigma=0.4841, 
prevFCN = 2171.259881  a=2.134, sigma=0.487, 
prevFCN = 2171.720107  a=2.013, sigma=0.4842, [#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x564b05666010 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x564b05666010 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x for nset (x) with code 1 from preexisting content.

Date

April 2009

Author

Wouter Verkerke

Definition in file rf211_paramconv.C.