fit_gaussian.C

/*----------------------------------------------------------------------------
 *
 *   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/>.
 *
 *---------------------------------------------------------------------------*/

/*
 * fit_gaussian.C
 *
 *  Created on: 23/03/2018
 *      Author: Antonio Augusto Alves Junior
 */

/*!
 * \example fit_gaussian.C
 */

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


/**
 *
 */
#ifndef HYDRA_HOST_SYSTEM
#define HYDRA_HOST_SYSTEM CPP
#endif

#ifndef HYDRA_DEVICE_SYSTEM
#define HYDRA_DEVICE_SYSTEM TBB
#endif

//this lib
#include <hydra/device/System.h>
#include <hydra/Function.h>
#include <hydra/LogLikelihoodFCN.h>
#include <hydra/Parameter.h>
#include <hydra/UserParameters.h>
#include <hydra/Pdf.h>
#include <hydra/functions/Gaussian.h>
#include <hydra/DenseHistogram.h>
#include <hydra/multivector.h>
#include <hydra/Zip.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.
 *-------------------------------------
 */
#include <TROOT.h>
#include <TH1D.h>
#include <TApplication.h>
#include <TCanvas.h>

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

declarg(xvar, double)
/**
 *
 * This macro generates data and fit a Gaussian distribution in parallel.
 *
 * \note Call this macro in ROOT compile mode:
 * ```
 * root [0] .L fit_gaussian.C+
 * root [1] fit_gaussian(50000000)
 * ```
 *
 * \param nentries Number of points to generate.
 */
void fit_gaussian(size_t nentries=1000000 )
{


    //-----------------
    // some definitions
    double min   = -6.0;
    double max   =  6.0;

    //Parameters for X direction
    auto xmean  = hydra::Parameter::Create("X-mean" ).Value(0.0).Error(0.0001).Limits(-1.0, 1.0);
    auto xsigma = hydra::Parameter::Create("X-sigma").Value(1.0).Error(0.0001).Limits(0.01, 1.5);
    //Gaussian distribution for X direction
    auto xgauss = hydra::Gaussian<xvar>(xmean, xsigma);
    //Model for X direction
    auto xmodel = hydra::make_pdf(xgauss, hydra::AnalyticalIntegral< hydra::Gaussian<xvar> >(min, max) );


    //------------------------
    TH1D* hist_xvar = new TH1D("hist_xvar", "X-axis", 100, min, max);

    TH1D* hist_fit_xvar = new TH1D("hist_fit_xvar", "X-axis", 100, min, max);



    //begin raii scope
    {


        //1D device buffer
        hydra::device::vector<xvar> dataset(nentries);
        //-------------------------------------------------------

        //gaussian range


        hydra::copy(hydra::random_range(xgauss, 159753, nentries ), dataset);

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

        auto xfcn   = hydra::make_loglikehood_fcn(xmodel, dataset);

        //-------------------------------------------------------
        //fit

        ROOT::Minuit2::MnPrint::SetGlobalLevel(3);
        hydra::Print::SetLevel(hydra::WARNING);

        //minimization strategy
        MnStrategy strategy(2);

        //create Migrad minimizer
        MnMigrad xmigrad(xfcn, xfcn.GetParameters().GetMnState() ,  strategy);

        //print parameters before fitting
        std::cout << xfcn.GetParameters().GetMnState() << std::endl;

        //Minimize and profile the time
        auto start = std::chrono::high_resolution_clock::now();

        FunctionMinimum xminimum = FunctionMinimum( xmigrad(std::numeric_limits<unsigned int>::max(), 5));

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

        std::chrono::duration<double, std::milli> elapsed = stop - start;

        //print minuit result
        std::cout << " minimum: " << xminimum << std::endl;

        //time
        std::cout << "-----------------------------------------"<<std::endl;
        std::cout << "| Time (ms) ="<< elapsed.count()    <<std::endl;
        std::cout << "-----------------------------------------"<<std::endl;

        hydra::DenseHistogram<double, 1, hydra::device::sys_t>Hist_Data(100, min, max );

        Hist_Data.Fill( dataset );



        //draw data
        for(size_t i=0;  i<100; i++){
            hist_xvar->SetBinContent(i+1, Hist_Data.GetBinContent(i)  );
        }

        //draw fitted function
        hist_fit_xvar->Sumw2();
        for (size_t i=0 ; i<=100 ; i++) {
            double x = hist_fit_xvar->GetBinCenter(i);
             hist_fit_xvar->SetBinContent(i, xfcn.GetPDF()(x) );
        }

        hist_fit_xvar->Scale(hist_xvar->Integral()/hist_fit_xvar->Integral() );


    }//end raii scope


    //draw histograms
    TCanvas* canvas = new TCanvas("canvas_d" ,"Distributions - Device", 500, 500);
    hist_xvar->Draw("hist");
    hist_fit_xvar->Draw("histsameC");
    hist_fit_xvar->SetLineColor(2);

}