PulseFitWithShape.cc

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/* 
 *  \class PulseFitWithShape
 *
 *  \author: Julie Malcles - CEA/Saclay
 */

#include "CalibCalorimetry/EcalLaserAnalyzer/interface/PulseFitWithShape.h"

#include <iostream>
#include "TMath.h"
#include <cmath>

//ClassImp(PulseFitWithShape)

// Constructor...
PulseFitWithShape::PulseFitWithShape() {
  fNsamples = 0;
  fNsamplesShape = 0;
  fNum_samp_bef_max = 0;
  fNum_samp_after_max = 0;
  fNoise = 0.0;
}

// Destructor
PulseFitWithShape::~PulseFitWithShape() {}

// Initialisation

void PulseFitWithShape::init(int n_samples,
                             int samplb,
                             int sampla,
                             int niter,
                             int n_samplesShape,
                             const std::vector<double> &shape,
                             double nois) {
  fNsamples = n_samples;
  fNsamplesShape = n_samplesShape;
  fNb_iter = niter;
  fNum_samp_bef_max = samplb;
  fNum_samp_after_max = sampla;

  if (fNsamplesShape < fNum_samp_bef_max + fNum_samp_after_max + 1) {
    std::cout << "PulseFitWithShape::init: Error! Configuration samples in fit greater than total number of samples!"
              << std::endl;
  }

  for (int i = 0; i < fNsamplesShape; i++) {
    pshape.push_back(shape[i]);
    dshape.push_back(0.0);
  }

  fNoise = nois;
  return;
}

// Compute the amplitude using as input the Crystaldata

double PulseFitWithShape::doFit(double *adc, double *cova) {
  // xpar = fit paramaters
  //     [0] = signal amplitude
  //     [1] = residual pedestal
  //     [2] = clock phase

  bool useCova = true;
  if (cova == nullptr)
    useCova = false;

  double xpar[3];
  double chi2;

  fAmp_fitted_max = 0.;
  fTim_fitted_max = 0.;

  // for now don't fit pedestal

  xpar[1] = 0.0;

  // Sample noise. If the cova matrix is defined, use it :

  double noise = fNoise;
  //if(cova[0] > 0.) noise=1./sqrt(cova[0]);

  xpar[0] = 0.;
  xpar[2] = 0.;

  // first locate max:

  int imax = 0;
  double amax = 0.0;
  for (int i = 0; i < fNsamples; i++) {
    if (adc[i] > amax) {
      amax = adc[i];
      imax = i;
    }
  }

  // Shift pulse shape and calculate its derivative:

  double qms = 0.;
  int ims = 0;

  // 1) search for maximum

  for (int is = 0; is < fNsamplesShape; is++) {
    if (pshape[is] > qms) {
      qms = pshape[is];
      ims = is;
    }

    // 2) compute shape derivative :

    if (is < fNsamplesShape - 2)
      dshape[is] = (pshape[is + 2] - pshape[is]) * 12.5;
    else
      dshape[is] = dshape[is - 1];
  }

  // 3) compute pol2 max

  double sq1 = pshape[ims - 1];
  double sq2 = pshape[ims];
  double sq3 = pshape[ims + 1];

  double a2 = (sq3 + sq1) / 2.0 - sq2;
  double a1 = sq2 - sq1 + a2 * (1 - 2 * ims);

  double t_ims = 0;
  if (a2 != 0)
    t_ims = -a1 / (2.0 * a2);

  // Starting point of the fit : qmax and tmax given by a
  // P2 fit on 3 max samples.

  double qm = 0.;
  int im = 0;

  int nsamplef = fNum_samp_bef_max + fNum_samp_after_max + 1;  // number of samples used in the fit
  int nsampleo = imax - fNum_samp_bef_max;                     // first sample number = sample max-fNum_samp_bef_max

  for (int is = 0; is < nsamplef; is++) {
    if (adc[is + nsampleo] > qm) {
      qm = adc[is + nsampleo];
      im = nsampleo + is;
    }
  }

  double tm;
  if (qm > 5. * noise) {
    if (im >= nsamplef + nsampleo)
      im = nsampleo + nsamplef - 1;
    double q1 = adc[im - 1];
    double q2 = adc[im];
    double q3 = adc[im + 1];
    double y2 = (q1 + q3) / 2. - q2;
    double y1 = q2 - q1 + y2 * (1 - 2 * im);
    double y0 = q2 - y1 * (double)im - y2 * (double)(im * im);
    tm = -y1 / 2. / y2;
    xpar[0] = y0 + y1 * tm + y2 * tm * tm;
    xpar[2] = (double)ims / 25. - tm;
  }

  double chi2old = 999999.;
  chi2 = 99999.;
  int nsfit = nsamplef;
  int iloop = 0;
  int nloop = fNb_iter;
  if (qm <= 5 * noise)
    nloop = 1;  // Just one iteration for very low signal

  std::vector<double> resi(fNsamples, 0.0);

  while (std::fabs(chi2old - chi2) > 0.1 && iloop < nloop) {
    iloop++;
    chi2old = chi2;

    double c = 0.;
    double y1 = 0.;
    double s1 = 0.;
    double s2 = 0.;
    double ys1 = 0.;
    double sp1 = 0.;
    double sp2 = 0.;
    double ssp = 0.;
    double ysp = 0.;

    for (int is = 0; is < nsfit; is++) {
      const int iis = is + nsampleo;

      double t1 = (double)iis + xpar[2];
      double xbin = t1 * 25.;
      int ibin1 = (int)xbin;

      if (ibin1 < 0)
        ibin1 = 0;

      double amp1, amp11, amp12, der1, der11, der12;

      if (ibin1 <= fNsamplesShape - 2) {  // Interpolate shape values to get the right number :

        int ibin2 = ibin1 + 1;
        double xfrac = xbin - ibin1;
        amp11 = pshape[ibin1];
        amp12 = pshape[ibin2];
        amp1 = (1. - xfrac) * amp11 + xfrac * amp12;
        der11 = dshape[ibin1];
        der12 = dshape[ibin2];
        der1 = (1. - xfrac) * der11 + xfrac * der12;

      } else {  // Or extraoplate if we are outside the array :

        amp1 = pshape[fNsamplesShape - 1] + dshape[fNsamplesShape - 1] * (xbin - double(fNsamplesShape - 1)) / 25.;
        der1 = dshape[fNsamplesShape - 1];
      }

      if (useCova) {  // Use covariance matrix:
        for (int js = 0; js < nsfit; js++) {
          int jjs = js;
          jjs += nsampleo;

          t1 = (double)jjs + xpar[2];
          xbin = t1 * 25.;
          ibin1 = (int)xbin;
          if (ibin1 < 0)
            ibin1 = 0;
          if (ibin1 > fNsamplesShape - 2)
            ibin1 = fNsamplesShape - 2;
          int ibin2 = ibin1 + 1;
          double xfrac = xbin - ibin1;
          amp11 = pshape[ibin1];
          amp12 = pshape[ibin2];
          double amp2 = (1. - xfrac) * amp11 + xfrac * amp12;
          der11 = dshape[ibin1];
          der12 = dshape[ibin2];
          double der2 = (1. - xfrac) * der11 + xfrac * der12;
          c = c + cova[js * fNsamples + is];
          y1 = y1 + adc[iis] * cova[js * fNsamples + is];
          s1 = s1 + amp1 * cova[js * fNsamples + is];
          s2 = s2 + amp1 * amp2 * cova[js * fNsamples + is];
          ys1 = ys1 + adc[iis] * amp2 * cova[js * fNsamples + is];
          sp1 = sp1 + der1 * cova[is * fNsamples + js];
          sp2 = sp2 + der1 * der2 * cova[js * fNsamples + is];
          ssp = ssp + amp1 * der2 * cova[js * fNsamples + is];
          ysp = ysp + adc[iis] * der2 * cova[js * fNsamples + is];
        }
      } else {  // Don't use covariance matrix:
        c++;
        y1 = y1 + adc[iis];
        s1 = s1 + amp1;
        s2 = s2 + amp1 * amp1;
        ys1 = ys1 + amp1 * adc[iis];
        sp1 = sp1 + der1;
        sp2 = sp2 + der1 * der1;
        ssp = ssp + der1 * amp1;
        ysp = ysp + der1 * adc[iis];
      }
    }
    xpar[0] = (ysp * ssp - ys1 * sp2) / (ssp * ssp - s2 * sp2);
    xpar[2] += (ysp / xpar[0] / sp2 - ssp / sp2);

    for (int is = 0; is < nsfit; is++) {
      const int iis = is + nsampleo;

      double t1 = (double)iis + xpar[2];
      double xbin = t1 * 25.;
      int ibin1 = (int)xbin;
      if (ibin1 < 0)
        ibin1 = 0;

      if (ibin1 < 0)
        ibin1 = 0;
      if (ibin1 > fNsamplesShape - 2)
        ibin1 = fNsamplesShape - 2;
      int ibin2 = ibin1 + 1;
      double xfrac = xbin - ibin1;
      double amp11 = xpar[0] * pshape[ibin1];
      double amp12 = xpar[0] * pshape[ibin2];
      double amp1 = xpar[1] + (1. - xfrac) * amp11 + xfrac * amp12;
      resi[iis] = adc[iis] - amp1;
    }

    chi2 = 0.;
    for (int is = 0; is < nsfit; is++) {
      const int iis = is + nsampleo;

      if (useCova) {
        for (int js = 0; js < nsfit; js++) {
          int jjs = js;
          jjs += nsampleo;
          chi2 += resi[iis] * resi[jjs] * cova[js * fNsamples + is];
        }

      } else
        chi2 += resi[iis] * resi[iis];
    }
  }

  fAmp_fitted_max = xpar[0];
  fTim_fitted_max = (double)t_ims / 25. - xpar[2];

  return chi2;
}