gaussian_plus_argus.inl

/*----------------------------------------------------------------------------
 *
 *   Copyright (C) 2016 - 2023 Antonio Augusto Alves Junior
 *
 *   This file is part of Hydra Data Analysis Framework.
 *
 *   Hydra is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   Hydra is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with Hydra.  If not, see <http://www.gnu.org/licenses/>.
 *
 *---------------------------------------------------------------------------*/

/*
 * gaussian_plus_argus.inl
 *
 *  Created on: 20/12/2017
 *      Author: Antonio Augusto Alves Junior
 */

#ifndef GAUSSIANPLUSARGUS_H_
#define GAUSSIANPLUSARGUS_H_


/**
 * \example gaussian_plus_argus.inl
 *
 */


#include <iostream>
#include <assert.h>
#include <time.h>
#include <chrono>
#include <random>
#include <algorithm>

//command line
#include <tclap/CmdLine.h>

//this lib
#include <hydra/device/System.h>
#include <hydra/Function.h>
#include <hydra/Lambda.h>
#include <hydra/FunctorArithmetic.h>
#include <hydra/Random.h>
#include <hydra/LogLikelihoodFCN.h>
#include <hydra/Parameter.h>
#include <hydra/UserParameters.h>
#include <hydra/Pdf.h>
#include <hydra/AddPdf.h>
#include <hydra/Filter.h>
#include <hydra/DenseHistogram.h>
#include <hydra/functions/Gaussian.h>
#include <hydra/functions/ArgusShape.h>
#include <hydra/Placeholders.h>

//Minuit2
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/MnPrint.h"
#include "Minuit2/MnMigrad.h"
#include "Minuit2/MnMinimize.h"

/*-------------------------------------
 * Include classes from ROOT to fill
 * and draw histograms and plots.
 *-------------------------------------
 */
#ifdef _ROOT_AVAILABLE_

#include <TROOT.h>
#include <TH1D.h>
#include <TApplication.h>
#include <TCanvas.h>

#endif //_ROOT_AVAILABLE_


using namespace ROOT::Minuit2;
using namespace hydra::placeholders;
using namespace hydra::arguments;

declarg( _X, double)

int main(int argv, char** argc)
{
    size_t nentries = 0;

    try {

        TCLAP::CmdLine cmd("Command line arguments for ", '=');

        TCLAP::ValueArg<size_t> EArg("n", "number-of-events","Number of events", true, 10e6, "size_t");
        cmd.add(EArg);

        // Parse the argv array.
        cmd.parse(argv, argc);

        // Get the value parsed by each arg.
        nentries = EArg.getValue();

    }
    catch (TCLAP::ArgException &e)  {
        std::cerr << "error: " << e.error() << " for arg " << e.argId()
                                                        << std::endl;
    }

    //-----------------
    // some definitions
    double min   =  5.20;
    double max   =  5.30;


    //===========================
    //fit model gaussian + argus

    //Gaussian
    hydra::Parameter  mean  = hydra::Parameter::Create().Name("Mean").Value( 5.28).Error(0.0001).Limits(5.25,5.29);
    hydra::Parameter  sigma = hydra::Parameter::Create().Name("Sigma").Value(0.0026).Error(0.0001).Limits(0.0024,0.0028);

    //gaussian function evaluating on the first argument
    auto Signal_PDF = hydra::make_pdf( hydra::Gaussian<_X>(mean, sigma),
            hydra::AnalyticalIntegral<hydra::Gaussian<_X>>(min, max));

    //-------------------------------------------
    //Argus
    //parameters
    auto  m0     = hydra::Parameter::Create().Name("M0").Value(5.291).Error(0.0001).Limits(5.28, 5.3);
    auto  slope  = hydra::Parameter::Create().Name("Slope").Value(-20.0).Error(0.0001).Limits(-30.0, -10.0);
    auto  power  = hydra::Parameter::Create().Name("Power").Value(0.5).Fixed();

    //gaussian function evaluating on the first argument
    auto Background_PDF = hydra::make_pdf( hydra::ArgusShape<_X>(m0, slope, power),
            hydra::AnalyticalIntegral<hydra::ArgusShape<_X>>(min, max));

    //------------------
    //yields
    hydra::Parameter N_Signal("N_Signal"        ,500, 100, 100 , nentries) ;
    hydra::Parameter N_Background("N_Background",2000, 100, 100 , nentries) ;

    //make model
    auto model = hydra::add_pdfs( {N_Signal, N_Background}, Signal_PDF, Background_PDF);
    model.SetExtended(1);

    //===========================

        double B0_mass    = 5.27955;   // B0 mass
        double Jpsi_mass  = 3.0969;    // J/psi mass
        double K_mass     = 0.493677;  // K+ mass
        double pi_mass    = 0.13957061;// pi mass

#ifdef _ROOT_AVAILABLE_

    TH1D    hist_data("data"    , "Gaussian + ARGUS", 100, min, max);
    TH1D    hist_fit("fit"      , "Gaussian + ARGUS", 100, min, max);
    TH1D    hist_signal("signal", "Gaussian + ARGUS", 100, min, max);
    TH1D    hist_background("background"  , "Gaussian + ARGUS", 100, min, max);

    TH1D    hist_test("test"    , "WignerD", 100, pow(K_mass+pi_mass,2), pow(B0_mass-Jpsi_mass,2));


#endif //_ROOT_AVAILABLE_

    //scope begin
    {

        //1D device buffer
        hydra::device::vector<double>  data(nentries);

        //-------------------------------------------------------
        // Generate data
        auto range = hydra::sample(data, min, max, model.GetFunctor());

        std::cout<< std::endl<< "Generated data:"<< std::endl;
        for(size_t i=0; i< 10; i++)
            std::cout << "[" << i << "] :" << range[i] << std::endl;

        std::cout<< std::endl<< "data size :"<< range.size() << std::endl;

        //make model and fcn
        auto fcn = hydra::make_loglikehood_fcn( model, range );

        //-------------------------------------------------------
        //fit
        ROOT::Minuit2::MnPrint::SetGlobalLevel(3);
        //hydra::Print::SetLevel(hydra::WARNING);
        //minimization strategy
        MnStrategy strategy(2);

        // create Migrad minimizer
        MnMigrad migrad_d(fcn, fcn.GetParameters().GetMnState() ,  strategy);

        std::cout<<fcn.GetParameters().GetMnState()<<std::endl;

        // ... Minimize and profile the time

        auto start_d = std::chrono::high_resolution_clock::now();

        FunctionMinimum minimum_d =  FunctionMinimum(migrad_d(50000, 50));

        auto end_d = std::chrono::high_resolution_clock::now();

        std::chrono::duration<double, std::milli> elapsed_d = end_d - start_d;

        // output
        std::cout<< "Minimum: " << minimum_d << std::endl;

        //time
        std::cout << "-----------------------------------------"<<std::endl;
        std::cout << "| [Fit] GPU Time (ms) ="<< elapsed_d.count() <<std::endl;
        std::cout << "-----------------------------------------"<<std::endl;

        //--------------------------------------------
        hydra::DenseHistogram<double, 1, hydra::device::sys_t> Hist_Data(100, min, max);
        Hist_Data.Fill( range.begin(), range.end() );




#ifdef _ROOT_AVAILABLE_


        //data
        for(size_t i=0;  i<100; i++)
            hist_data.SetBinContent(i+1, Hist_Data.GetBinContent(i));

        //fit
        for (size_t i=0 ; i<=100 ; i++) {
            double x = hist_fit.GetBinCenter(i);
            hist_fit.SetBinContent(i, fcn.GetPDF()(x) );
        }
        hist_fit.Scale(hist_data.Integral()/hist_fit.Integral() );

        //signal component
        auto   signal          = fcn.GetPDF().PDF(_0);
        double signal_fraction = fcn.GetPDF().Coefficient(0)/fcn.GetPDF().GetCoefSum();
        for (size_t i=0 ; i<=100 ; i++) {
            double x = hist_signal.GetBinCenter(i);
            hist_signal.SetBinContent(i, signal(x) );
        }
        hist_signal.Scale(hist_data.Integral()*signal_fraction/hist_signal.Integral());

        //signal component
        auto   background          = fcn.GetPDF().PDF(_1);
        double background_fraction = fcn.GetPDF().Coefficient(1)/fcn.GetPDF().GetCoefSum();
        for (size_t i=0 ; i<=100 ; i++) {
            double x = hist_background.GetBinCenter(i);
            hist_background.SetBinContent(i, background(x) );
        }
        hist_background.Scale(hist_data.Integral()*background_fraction/hist_background.Integral());




#endif //_ROOT_AVAILABLE_

    }//scope end


#ifdef _ROOT_AVAILABLE_

    TApplication *myapp=new TApplication("myapp",0,0);

    //draw histograms
    TCanvas canvas_d("canvas_d" ,"Distributions - Device", 500, 500);

    hist_data.Draw("e0");
    hist_data.SetStats(0);
    hist_data.SetLineColor(1);
    hist_data.SetLineWidth(2);

    hist_fit.Draw("histsameC");
    hist_fit.SetStats(0);
    hist_fit.SetLineColor(4);

    hist_signal.Draw("histsameC");
    hist_signal.SetStats(0);
    hist_signal.SetLineColor(3);

    hist_background.Draw("histsameC");
    hist_background.SetStats(0);
    hist_background.SetLineColor(2);
    hist_background.SetLineStyle(2);

    hist_fit.Draw("histsameC");
    hist_data.Draw("e0same");


    myapp->Run();

#endif //_ROOT_AVAILABLE_

    return 0;

}



#endif /* GAUSSIANPLUSARGUS_H_ */