#include <TruncMean.h>

Public Member Functions
void	CalcTruncMeanProfile (const std::vector< float > &rr_v, const std::vector< float > &dq_v, std::vector< float > &dq_trunc_v, const float &nsigma=1)
	Given residual range and dq vectors return truncated local dq. Input vectors are assumed to be match pair-wise (nth entry in rr_v corresponds to nth entry in dq_v vector). Input rr_v values are also assumed to be ordered: monotonically increasing or decreasing. For every dq value a truncated linear dq value is calculated as follows: 0) all dq values within a rr range set by the class variable _rad are selected. 1) the median and rms of these values is calculated. 2) the subset of local dq values within the range [median-rms, median+rms] is selected. 3) the resulting local truncated dq is the average of this truncated subset. std::vector<float> rr_v -> vector of x-axis coordinates (i.e. position for track profile) std::vector<float> dq_v -> vector of measured values for which truncated profile is requested (i.e. charge profile of a track) std::vector<float> dq_trunc_v -> passed by reference -> output stored here float nsigma -> optional parameter, number of sigma to keep around RMS for TM calculation. More...

float	CalcIterativeTruncMean (std::vector< float > v, const size_t &nmin, const size_t &nmax, const size_t &currentiteration, const size_t &lmin, const float &convergencelimit, const float &nsigma, const float &oldmed=kINVALID_FLOAT)
	Iteratively calculate the truncated mean of a distribution std::vector<float> v -> vector of values for which truncated mean is asked size_t nmin -> minimum number of iterations to converge on truncated mean size_t nmax -> maximum number of iterations to converge on truncated mean size_t lmin -> minimum number of entries in vector before exiting and returning current value size_t currentiteration -> current iteration float convergencelimit -> fractional difference between successive iterations under which the iteration is completed, provided nmin iterations have occurred. nsigma -> number of sigma around the median value to keep when the distribution is trimmed. More...

void	setRadius (const float &rad)
	Set the smearing radius over which to take hits for truncated mean computaton. More...

Private Member Functions
float	Mean (const std::vector< float > &v)

float	Median (const std::vector< float > &v)

float	RMS (const std::vector< float > &v)

Private Attributes
double	_rad

Detailed Description

The truncated mean class allows to compute the following quantities 1) the truncated mean profile of an ordered vector of values, such as the charge profile along a particle's track. To create such a profile use the function CalcTruncMeanProfile() 2) Get the truncated mean value of a distribution. This function iteratively hones in on the truncated mean of a distribution by updating the mean and cutting the tails after each iteration. For this functionality use CalcIterativeTruncMean() doxygen documentation!

Definition at line 33 of file larana/larana/TruncatedMean/Algorithm/TruncMean.h.

Member Function Documentation

float TruncMean::CalcIterativeTruncMean	(	std::vector< float >	v,
		const size_t &	nmin,
		const size_t &	nmax,
		const size_t &	currentiteration,
		const size_t &	lmin,
		const float &	convergencelimit,
		const float &	nsigma,
		const float &	oldmed = `kINVALID_FLOAT`
	)

Iteratively calculate the truncated mean of a distribution std::vector<float> v -> vector of values for which truncated mean is asked size_t nmin -> minimum number of iterations to converge on truncated mean size_t nmax -> maximum number of iterations to converge on truncated mean size_t lmin -> minimum number of entries in vector before exiting and returning current value size_t currentiteration -> current iteration float convergencelimit -> fractional difference between successive iterations under which the iteration is completed, provided nmin iterations have occurred. nsigma -> number of sigma around the median value to keep when the distribution is trimmed.

Definition at line 7 of file larana/larana/TruncatedMean/Algorithm/TruncMean.cxx.

 {
 
   auto const& mean = Mean(v);
   auto const& med  = Median(v);
   auto const& rms  = RMS(v);
 
   // if the vector length is below the lower limit -> return
   if (v.size() < lmin)
     return mean;
 
   // if we have passed the maximum number of iterations -> return
   if (currentiteration >= nmax)
     return mean;
 
   // if we passed the minimum number of iterations and the mean is close enough to the old value
   float fracdiff = fabs(med-oldmed) / oldmed;
   if ( (currentiteration >= nmin) && (fracdiff < convergencelimit) )
     return mean;
 
   // if reached here it means we have to go on for another iteration
 
   // cutoff tails of distribution surrounding the mean
   // use erase-remove : https://en.wikipedia.org/wiki/Erase%E2%80%93remove_idiom
   // https://stackoverflow.com/questions/17270837/stdvector-removing-elements-which-fulfill-some-conditions
   v.erase( std::remove_if( v.begin(), v.end(),
                            [med,nsigma,rms](const float& x) { return ( (x < (med-nsigma*rms)) || (x > (med+nsigma*rms)) ); }), // lamdda condition for events to be removed
            v.end());
 
   return CalcIterativeTruncMean(v, nmin, nmax, lmin, currentiteration+1, convergencelimit, nsigma, med);
 }

void TruncMean::CalcTruncMeanProfile	(	const std::vector< float > &	rr_v,
		const std::vector< float > &	dq_v,
		std::vector< float > &	dq_trunc_v,
		const float &	nsigma = `1`
	)

Given residual range and dq vectors return truncated local dq. Input vectors are assumed to be match pair-wise (nth entry in rr_v corresponds to nth entry in dq_v vector). Input rr_v values are also assumed to be ordered: monotonically increasing or decreasing. For every dq value a truncated linear dq value is calculated as follows: 0) all dq values within a rr range set by the class variable _rad are selected. 1) the median and rms of these values is calculated. 2) the subset of local dq values within the range [median-rms, median+rms] is selected. 3) the resulting local truncated dq is the average of this truncated subset. std::vector<float> rr_v -> vector of x-axis coordinates (i.e. position for track profile) std::vector<float> dq_v -> vector of measured values for which truncated profile is requested (i.e. charge profile of a track) std::vector<float> dq_trunc_v -> passed by reference -> output stored here float nsigma -> optional parameter, number of sigma to keep around RMS for TM calculation.

Definition at line 43 of file larana/larana/TruncatedMean/Algorithm/TruncMean.cxx.

 {
 
   // how many points to sample
   int Nneighbor = (int)(_rad * 3 * 2);
 
   dq_trunc_v.clear();
   dq_trunc_v.reserve( rr_v.size() );
 
   int Nmax = dq_v.size()-1;
 
   for (size_t n=0; n < dq_v.size(); n++) {
 
     // current residual range
     float rr = rr_v.at(n);
 
     int nmin = n - Nneighbor;
     int nmax = n + Nneighbor;
 
     if (nmin < 0) nmin = 0;
     if (nmax > Nmax) nmax = Nmax;
 
     // vector for local dq values
     std::vector<float> dq_local_v;
 
     for (int i=nmin; i < nmax; i++) {
 
       float dr = rr - rr_v[i];
       if (dr < 0) dr *= -1;
 
       if (dr > _rad) continue;
 
       dq_local_v.push_back( dq_v[i] );
 
     }// for all ticks we want to scan
 
     if (dq_local_v.size() == 0) {
       dq_trunc_v.push_back( dq_v.at(n) );
       continue;
     }
 
     // calculate median and rms
     float median = Median(dq_local_v);
     float rms    = RMS(dq_local_v);
 
     float truncated_dq = 0.;
     int npts = 0;
     for (auto const& dq : dq_local_v) {
       if ( ( dq < (median+rms * nsigma) ) && ( dq > (median-rms * nsigma) ) ){
         truncated_dq += dq;
         npts += 1;
       }
     }
 
     dq_trunc_v.push_back( truncated_dq / npts );
   }// for all values
 
   return;
 }

float TruncMean::Mean ( const std::vector< float > & v )

private

Definition at line 104 of file larana/larana/TruncatedMean/Algorithm/TruncMean.cxx.

 {
 
   float mean = 0.;
   for (auto const& n : v) mean += n;
   mean /= v.size();
 
   return mean;
 }

float TruncMean::Median ( const std::vector< float > & v )

private

Definition at line 114 of file larana/larana/TruncatedMean/Algorithm/TruncMean.cxx.

 {
 
   if (v.size() == 1) return v[0];
 
   std::vector<float> vcpy = v;
 
   std::sort(vcpy.begin(), vcpy.end());
 
   float median = vcpy[ vcpy.size() / 2 ];
 
   return median;
 }

float TruncMean::RMS ( const std::vector< float > & v )

private

Definition at line 128 of file larana/larana/TruncatedMean/Algorithm/TruncMean.cxx.

 {
 
   float avg = 0.;
   for (auto const& val : v) avg += val;
   avg /= v.size();
   float rms = 0.;
   for (auto const& val : v) rms += (val-avg)*(val-avg);
   rms = sqrt( rms / ( v.size() -  1 ) );
 
   return rms;
 }