#include <MTT0PatternRecognition.h>

Collaboration diagram for MuonCalib::MTT0PatternRecognition:

Public Member Functions
	MTT0PatternRecognition ()
	Default constructor.
bool	Initialize (TH1F hist, const T0MTSettings settings)
	Initialize class - returns true if pattern recognition was successfull.
double	GetBackground () const
	get the background level
double	GetHeight () const
	get height
double	GetEstimatedT0 () const
	get estimated t0
double	GetFitRangeMin () const
	get fit range
double	GetFitRangeMax () const
	get fit range
bool	GetError () const
	return error flag

Private Member Functions
bool	estimate_background (TH1F *hist, double scale_min)
	estimates the background level
double	estimate_height (TH1F *hist)
	estimates the height of the spectrum.

Private Attributes
const T0MTSettingsT0 *	m_settings
	settings
bool	m_draw_debug_graph
double	m_background
	background level
double	m_height
	height
double	m_fit_min
	fit range
double	m_fit_max
double	m_t0_est
	t0 estimate
VariableBinwidthHistogram	m_vbh
	Variable binwidth histogram.
bool	m_error
	error flag

Detailed Description

Definition at line 35 of file MTT0PatternRecognition.h.

Constructor & Destructor Documentation

◆ MTT0PatternRecognition()

MuonCalib::MTT0PatternRecognition::MTT0PatternRecognition ( )

inline

Default constructor.

Definition at line 39 of file MTT0PatternRecognition.h.

                                        :
            m_settings(NULL),
            m_draw_debug_graph(false),
            m_background(0.),
            m_height(0.),
            m_fit_min(0.),
            m_fit_max(0.),
            m_t0_est(0.),
            m_error(false) {}

Member Function Documentation

◆ estimate_background()

bool MuonCalib::MTT0PatternRecognition::estimate_background	(	TH1F *	hist,
		double	scale_min )

private

estimates the background level

Parameters

hist	input histogram
scale_min	lower end of the scale region as returned from estimate_height()

Definition at line 19 of file MTT0PatternRecognition.cxx.

                                                                                 {
        // Get first bin in input histogram which is not empty.     This avoids underestimation of the background rate if the range of
        // the input histogram exceeds the TDC-range
        int min(-1);
        for (int i = 1; i <= hist->GetNbinsX(); i++) {
            if (hist->GetBinContent(i) > 0) {
                min = i;
                break;
            }
        }
        if (min == -1) {
            MsgStream log(Athena::getMessageSvc(), "MTT0PattternRecognition");
            log << MSG::WARNING << "estimate_background() - No hits in input histogram!" << endmsg;
            m_error = true;
            return false;
        }
        double maxx = scale_min - 40;
        int max = hist->FindBin(maxx);
        // we want 10 bins minimum for the background estimate
        if (max - min < m_settings->MinBackgroundBins()) {
            min = max - 128;
            if (min < 0) min = 0;
        }
        // if there are still not enough bins for the background estimate we have a problem
        if (max - min < m_settings->MinBackgroundBins()) {
            MsgStream log(Athena::getMessageSvc(), "MTT0PattternRecognition");
            log << MSG::WARNING << "estimate_background() - Rising edge is to close to lower histogram range!" << endmsg;
            m_error = true;
            return false;
        }
        // calculate average bin content
        m_background = 0.0;
        double back_squared = 0.0;
        double n_bins = 0.0;
        double referenceChi2 = 0.0;
        for (int i = min; i < max; i++) {
            n_bins++;
            m_background += hist->GetBinContent(i);
            back_squared += hist->GetBinContent(i) * hist->GetBinContent(i);
            if (n_bins == m_settings->MinBackgroundBins()) {
                //coverity[DIVIDE_BY_ZERO:FALSE]
                double bac = m_background / n_bins;
                //coverity[DIVIDE_BY_ZERO:FALSE]
                referenceChi2 = 2 * (back_squared / n_bins - bac * bac);
            }
            if (n_bins > m_settings->MinBackgroundBins()) {
                //coverity[DIVIDE_BY_ZERO:FALSE]
                double bac = m_background / n_bins;
                //coverity[DIVIDE_BY_ZERO:FALSE]
                double chi2 = 2 * (back_squared / n_bins - bac * bac);
                if (chi2 > 5 * referenceChi2) break;
            }
        }
        if (n_bins == 0){
          //messaging should be revisited in this whole class
          MsgStream log(Athena::getMessageSvc(), "MTT0PattternRecognition");
          log << MSG::WARNING << "n_bins is zero" << endmsg;
          m_background =0.;       
        } else {
          m_background /= n_bins;
        }
        // store lower edge of fit range
        m_fit_min = hist->GetBinCenter(min);
        m_t0_est = 0.5 * (scale_min + hist->GetBinCenter(max));
        // mark selected range
        if (m_draw_debug_graph) {
            (new TLine(hist->GetBinCenter(min), 0, hist->GetBinCenter(min), hist->GetMaximum()))->Write("t0_back_left");
            (new TLine(hist->GetBinCenter(max), 0, hist->GetBinCenter(max), hist->GetMaximum()))->Write("t0_back_right");
        }
 
        return true;
    }

◆ estimate_height()

double MuonCalib::MTT0PatternRecognition::estimate_height ( TH1F * hist )

private

estimates the height of the spectrum.

It returns the lower end of the scale region

Parameters

hist	input histogram

Definition at line 96 of file MTT0PatternRecognition.cxx.

                                                             {
        // smooth histogram
        if (!m_vbh.Smooth(hist->GetBinWidth(1))) {
            m_error = true;
            return -1;
        }
        if (!m_vbh.Smooth(hist->GetBinWidth(1) / 2)) {
            m_error = true;
            return -1;
        }
        // draw debug graphs
        if (m_draw_debug_graph) {
            m_vbh.DenistyGraph()->Write("t0_density");
            m_vbh.BinWidthGraph()->Write("t0_binwidth");
            m_vbh.BinContentGraph()->Write("t0_bin_content");
            m_vbh.DiffDensityGraph()->Write("t0_diff_density");
            m_vbh.DiffDensityGraph()->Write("t0_diffbinwidth");
        }
        // get the range in which the smallest, and second smallest bins are
        double lower = m_vbh.GetSortedBin(0).Width();
        double left(9e9), right(-9e9);
        //  bool smallest_found(false);
        for (unsigned int i = 0; i < m_vbh.GetNumberOfBins(); i++) {
            if (m_vbh.GetSortedBin(i).Left() < left) left = m_vbh.GetSortedBin(i).Left();
            if (m_vbh.GetSortedBin(i).Right() > right) right = m_vbh.GetSortedBin(i).Right();
            // NOTE: For greater numerical stability m_vbh.GetSortedBin(i).Width() is considered greater only if it is greater by
            // hist->GetBinWidth(1) / 100.0. I have seen differences in Binwidth in the order of 1e-13. On the other hand, due to the
            // binning of the input histogram, e real difference in the binwidth of the VBH n*hist->GetBinWidth(1).
            if ((m_vbh.GetSortedBin(i).Width() - lower) > hist->GetBinWidth(1) / 100.0) {
                // reqire minimum distance between bins, and minimum number if significant bins
                if (right - left > 50 && i > 20) break;
                lower = m_vbh.GetSortedBin(i).Width();
            }
        }
        // mark selected range
        if (m_draw_debug_graph) {
            (new TLine(left, 0, left, hist->GetMaximum()))->Write("t0_height_left");
            (new TLine(right, 0, right, hist->GetMaximum()))->Write("t0_height_right");
        }
        // calcularte mean bin content of input histogram in range
        int bleft(hist->FindBin(left)), bright(hist->FindBin(right));
        double nbins = 0.0;
        m_height = 0.0;
        for (int i = bleft; i <= bright; i++) {
            m_height += hist->GetBinContent(i);
            nbins++;
        }
        if (nbins < 1) {
            MsgStream log(Athena::getMessageSvc(), "MTT0PattternRecognition");
            log << MSG::WARNING << "estimate_height() - top region is too small! left=" << left << " right=" << right << endmsg;
            m_error = true;
            return -1;
        }
        m_height /= nbins;
        m_fit_max = right;
        return left;
    }

◆ GetBackground()

double MuonCalib::MTT0PatternRecognition::GetBackground ( ) const

inline

get the background level

Definition at line 64 of file MTT0PatternRecognition.h.

64{ return m_background; }

◆ GetError()

bool MuonCalib::MTT0PatternRecognition::GetError ( ) const

inline

return error flag

Definition at line 79 of file MTT0PatternRecognition.h.

79{ return m_error; }

◆ GetEstimatedT0()

double MuonCalib::MTT0PatternRecognition::GetEstimatedT0 ( ) const

inline

get estimated t0

Definition at line 70 of file MTT0PatternRecognition.h.

70{ return m_t0_est; }

◆ GetFitRangeMax()

double MuonCalib::MTT0PatternRecognition::GetFitRangeMax ( ) const

inline

get fit range

Definition at line 76 of file MTT0PatternRecognition.h.

76{ return m_fit_max; }

◆ GetFitRangeMin()

double MuonCalib::MTT0PatternRecognition::GetFitRangeMin ( ) const

inline

get fit range

Definition at line 73 of file MTT0PatternRecognition.h.

73{ return m_fit_min; }

◆ GetHeight()

double MuonCalib::MTT0PatternRecognition::GetHeight ( ) const

inline

get height

Definition at line 67 of file MTT0PatternRecognition.h.

67{ return m_height; }

◆ Initialize()

bool MuonCalib::MTT0PatternRecognition::Initialize	(	TH1F *	hist,
		const T0MTSettings *	settings )

inline

Initialize class - returns true if pattern recognition was successfull.

Parameters

hist	Histogram which is to be fitted

Definition at line 53 of file MTT0PatternRecognition.h.

                                                                         {
            m_settings = settings->T0Settings();
            m_draw_debug_graph = settings->DrawDebugGraphs();
            m_error = !m_vbh.Initialize(hist, m_settings->VBHBinContent(), m_settings->MaxBinWidth());
            double scale_min = 0.;
            if (!m_error) scale_min = estimate_height(hist);
            if (!m_error) estimate_background(hist, scale_min);
            return !m_error;
        }

Member Data Documentation

◆ m_background

double MuonCalib::MTT0PatternRecognition::m_background

private

background level

Definition at line 88 of file MTT0PatternRecognition.h.

◆ m_draw_debug_graph

bool MuonCalib::MTT0PatternRecognition::m_draw_debug_graph

private

Definition at line 85 of file MTT0PatternRecognition.h.

◆ m_error

bool MuonCalib::MTT0PatternRecognition::m_error

private

error flag

Definition at line 103 of file MTT0PatternRecognition.h.

◆ m_fit_max

double MuonCalib::MTT0PatternRecognition::m_fit_max

private

Definition at line 94 of file MTT0PatternRecognition.h.

◆ m_fit_min

double MuonCalib::MTT0PatternRecognition::m_fit_min

private

fit range

Definition at line 94 of file MTT0PatternRecognition.h.

◆ m_height

double MuonCalib::MTT0PatternRecognition::m_height

private

height

Definition at line 91 of file MTT0PatternRecognition.h.

◆ m_settings

const T0MTSettingsT0* MuonCalib::MTT0PatternRecognition::m_settings

private

settings

Definition at line 84 of file MTT0PatternRecognition.h.

◆ m_t0_est

double MuonCalib::MTT0PatternRecognition::m_t0_est

private

t0 estimate

Definition at line 97 of file MTT0PatternRecognition.h.

◆ m_vbh

VariableBinwidthHistogram MuonCalib::MTT0PatternRecognition::m_vbh

private

Variable binwidth histogram.

Definition at line 100 of file MTT0PatternRecognition.h.

The documentation for this class was generated from the following files:

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ MTT0PatternRecognition()

Member Function Documentation

◆ estimate_background()

◆ estimate_height()

◆ GetBackground()

◆ GetError()

◆ GetEstimatedT0()

◆ GetFitRangeMax()

◆ GetFitRangeMin()

◆ GetHeight()

◆ Initialize()

Member Data Documentation

◆ m_background

◆ m_draw_debug_graph

◆ m_error

◆ m_fit_max

◆ m_fit_min

◆ m_height

◆ m_settings

◆ m_t0_est

◆ m_vbh