void Consolidator::findTruePctErrors( ErrorCalculator &e_obj,int ma_snp_ends, bool holdOut,int window,float ma_threshold, float empirical_ma_threshold )
{
for(int i=0;i<person_count;i++)
{
for(int j=i;j<person_count;j++)
{
for(int l=0;l<m_trueMatches[i][j].size();l++)
{
if(m_trueMatches[i][j][l].end==-1)
{
continue;
}
//handle moving averages calculation
//
int t1 = m_trueMatches[ i ][ j ][ l ].start +
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.25;
int t2 = m_trueMatches[ i ][ j ][ l ].end -
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.25;
//now we have the positions of the first and last 25% of the truly ibd SH
//all that's left to do is to pass them into the moving averages function, and obtain the max ma
//then store that in a vector, sort them, and find the xth percentile of that vector. That will be
//the ma that we use later
//for that "finalErrors" parameters, need to get the number of errors along the truly IBD SH first...
vector<vector<int> > trueErrors=e_obj.checkErrors( i, j, t1, t2);
vector<int>finalTrueErrors=e_obj.getFinalErrors( trueErrors );
//handles MA calculations
std::vector<float> av;
float current_max;
if(empirical_ma_threshold < 0.0){
av = e_obj.getTrueMovingAverages(finalTrueErrors,t1,t2,window);
current_max = av[0];
for(int q = 1; q < av.size(); q++){
if(av[q] > current_max){
current_max = av[q];
}
}
e_obj.addMaxAverage(current_max);
}
//
int temp1 = m_trueMatches[ i ][ j ][ l ].start +
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.15; //Should probably stop doing this
int temp2 = m_trueMatches[ i ][ j ][ l ].end -
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.15;
int start =0, end =0, fend = ( temp2 -temp1 ) ;
//since we are using MOL at this point, this will pick out a random SH from the set of non-truly IBD SH
//and use that length to define the region over which we find PIE. Unless you are changing something with MOL,
//don't ever read this next block
int randPers1, randPers2, pos;
randPers1 = std::rand() % person_count;
randPers2 = std::rand() % person_count;
if( randPers1 > randPers2 )
{
randPers1 = randPers1 + randPers2;
randPers2 = randPers1 - randPers2;
randPers1 = randPers1 - randPers2;
}
while( m_matches[ randPers1 ][ randPers2 ].size() <= 0 )
{
randPers1 = std::rand() % person_count;
randPers2 = std::rand() % person_count;
if( randPers1 > randPers2 )
{
randPers1 = randPers1 + randPers2;
randPers2 = randPers1 - randPers2;
randPers1 = randPers1 - randPers2;
}
}
pos = std::rand() % m_matches[ randPers1 ][ randPers2 ].size();
int len = m_matches[ randPers1 ][ randPers2 ][ pos ].end
- m_matches[ randPers1 ][ randPers2 ][ pos ].start;
if( len >= fend || len <= 0)
{
continue;
}
temp1 = temp1;
temp2 = temp1 + len;
//end crazy MOL stuff
vector<vector<int> > errors=e_obj.checkErrors( i, j, temp1, temp2);
vector<int>finalErrors=e_obj.getFinalErrors( errors );
float per_err = e_obj.getThreshold(finalErrors,temp1,temp2);//overload
m_errors.push_back( per_err );
if( holdOut )
{
float oppHom = ( e_obj.getOppHomThreshold( i, j, temp1, temp2 ) ) / ( temp2 -temp1 );
m_holdOutErrors.push_back( oppHom );
}
}
}
}
vector<float>maxes;
float cutoff = empirical_ma_threshold;
if(empirical_ma_threshold < 0.0){
maxes = e_obj.getMaxAverages();
std::sort(maxes.begin(),maxes.end());
e_obj.setMaxAverage(maxes);
cutoff = e_obj.getXthPercentile(ma_threshold);
}
e_obj.setCutoff(cutoff);//set the actual threshold to be used when calculating MA in all other SH
//
std::sort( m_errors.begin(), m_errors.end() );
std::sort( m_holdOutErrors.begin(), m_holdOutErrors.end() );
std::string str = " \n No of elements in error check are: "
+ NumberToString( m_errors.size() );
str = str + " \n No of elements in hold error check are: "
+ NumberToString( m_holdOutErrors.size() );
e_obj.log( str );
}
//overloaded version
void Consolidator::performTrim(ErrorCalculator& e_obj,int window,
int ma_snp_ends, float ma_threshold,
int min_snp,float min_cm,
float per_err_threshold, string option,
float hThreshold, bool holdOut,float empirical_threshold, float empirical_pie_threshold,float cut_value,float cm_cut_value)
{
int removed1 =0, removed2 = 0, removed3 = 0, removed4 = 0;
int not_removed = 0;
int total_count = global_initial;
bool wrongOption = false;
if((cut_value >= 0.0) && (cm_cut_value >= 0.0) ){
cerr << "ERROR: You have specified both a cut value percentage and a cM cut value, but only one is allowed. Please try again." << endl;
exit(1);
}
//8/14/14
//Adding header for output
cout << "id1" << "\t" << "id2" << "\t" << "marker_id1" << "\t" << "marker_id2" << "\t" << "snp_length" << "\t" << "cm_length" << "\t" << "start/end" << "\t" << "pie" << "\t" << "ma_max" << "\t" << "random_pie" << "\t" << "random_ma_max" << "\t" << "errors" << "\t" << "moving_averages" << endl;
for(int i=0;i<person_count;i++)
{
for(int j=i;j<person_count;j++)
{
for(int l=0;l<m_matches[i][j].size();l++)
{
total_count++;
if(m_matches[i][j][l].end==-1)
{
continue;
}
if(i == j){
continue; //ignore same person matches
}
int pers1 = i, pers2 = j;
//cerr << "For this snp, before applying the cut argument, the length is: " << ((m_matches[i][j][l].end - m_matches[i][j][l].start)) << " SNPs" << endl;
if((m_matches[i][j][l].end - m_matches[i][j][l].start) < min_snp){
//reduced argument forces an initial drop due to SNPS
continue;
}
//reduced argument check for mincm
if(e_obj.isInitialCmDrop(m_matches[i][j][l].start,m_matches[i][j][l].end,min_cm)){
continue;
}
int temp1,temp2;
if(cut_value >= 0.0){
//remove first and last 25% by default, this can also be a user-specified value
float new_cut_value = (1 - cut_value) * 0.5; //cut it in half for each side
temp1 = m_matches[ i ][ j ][ l ].start +
(int)(( m_matches[ i ][ j ][ l ].end -
m_matches[ i ][ j ][ l ].start ) * new_cut_value);
temp2 = m_matches[ i ][ j ][ l ].end -
(int)(( m_matches[ i ][ j ][ l ].end -
m_matches[ i ][ j ][ l ].start ) * new_cut_value);
} else {
//check that cm arg does not exceed the SH's cM length
float new_cm_length = e_obj.newCmLength(m_matches[i][j][l].start, m_matches[i][j][l].end,cm_cut_value); //change that cut_value parameter.
new_cm_length = (new_cm_length / 2.0);
if(new_cm_length <= 0.0){
cerr << "Error: The cM length that you specified was larger than one or more of the SH lengths." << endl;
exit(1);
}
float new_cm_start = e_obj.adjustCmLength(m_matches[i][j][l].start, new_cm_length);
float new_cm_end = e_obj.adjustCmLength(m_matches[i][j][l].end, ((-1.0) * new_cm_length) );
if(new_cm_start >= new_cm_end){
cerr << "ERROR: start value in cM greater than end value. Exiting..." << endl;
exit(1);
}
temp1 = e_obj.snpFinder(new_cm_start,m_matches[i][j][l].start,m_matches[i][j][l].end,0);
temp2 = e_obj.snpFinder(new_cm_end,m_matches[i][j][l].start,m_matches[i][j][l].end,1);
}
//we need to calculate for both this person and a random person.
int randpers1;
int randpers2;
//for now, let's enable randomness by default
randpers1 = std::rand() % e_obj.getNoOfPersons();
randpers2 = std::rand() % e_obj.getNoOfPersons();
if( randpers1 > randpers2 )
{
randpers1 = randpers1 + randpers2;
randpers2 = randpers1 - randpers2;
randpers1 = randpers1 - randpers2;
}
//we now have pers1,2 and randpers1,2
vector<vector<int> > errors=e_obj.checkErrors(pers1, pers2, temp1, temp2);
vector<int>finalErrors=e_obj.getFinalErrors(errors);
vector<vector<int> > random_errors = e_obj.checkErrors(randpers1,randpers2,temp1,temp2);
vector<int>random_final_errors = e_obj.getFinalErrors(random_errors);
vector<float>movingAverages;
vector<float>random_moving_averages;
float threshold;
movingAverages = e_obj.getMovingAverages(finalErrors,temp1,temp2,window);
random_moving_averages = e_obj.getMovingAverages(random_final_errors,temp1,temp2,window);
if(empirical_threshold < 0.0){
threshold = e_obj.getCutoff(); //this is the empirical average threshold for moving averages
} else {
threshold = empirical_threshold;
}
float per_err = e_obj.getThreshold(finalErrors,temp1,temp2);
float random_per_err = e_obj.getThreshold(random_final_errors,temp1,temp2);
//find maximum of moving averages
int max_pos = 0;
int rmax_pos = 0;
float max_ma = 0.0;
float rmax_ma = 0.0;
for(int z = 1; z < movingAverages.size();z++){
if(movingAverages[z] > movingAverages[max_pos]){
max_pos = z;
}
}
max_ma = movingAverages[max_pos];
for(int c = 1; c < random_moving_averages.size(); c++){
if(random_moving_averages[c] > random_moving_averages[rmax_pos]){
rmax_pos = c;
}
}
rmax_ma = random_moving_averages[rmax_pos];
if( (option.compare("Error1") == 0 ) || (option.compare("ErrorRandom1") == 0) || (option.compare("Error") == 0) ){
not_removed++;
e_obj.errorOutput(i,j,temp1,temp2,min_snp,min_cm,movingAverages,finalErrors,per_err,temp1,temp2,per_err,max_ma,random_per_err,rmax_ma);
continue;
}//end error1
}//l
}//j
}//i
ma_drop_str = "No of matches removed due to length of trimming by moving averages: " + NumberToString( removed2 );
pie_drop_str = "No of matches removed due to percentage error: " + NumberToString( removed1 );
}//endperftrim()
void Consolidator::findTrueSimplePctErrors( ErrorCalculator &e_obj, float PIElength, bool holdOut,int window, float ma_threshold, float empirical_ma_threshold )
{
for(int i=0;i<person_count;i++)
{
for(int j=i;j<person_count;j++)
{
for(int l=0;l<m_trueMatches[i][j].size();l++)
{
if(m_trueMatches[i][j][l].end==-1)
{
continue;
}
//-------------------------------------------------------------------------------------------------
int t1 = m_trueMatches[ i ][ j ][ l ].start +
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.25;
int t2 = m_trueMatches[ i ][ j ][ l ].end -
( m_trueMatches[ i ][ j ][ l ].end -
m_trueMatches[ i ][ j ][ l ].start ) * 0.25;
/*What do these two functions do? Is this necessary for being able to find errors, or
is it only useful for MA calculations?*/
vector<vector<int> > trueErrors=e_obj.checkErrors( i, j, t1, t2);
vector<int>finalTrueErrors=e_obj.getFinalErrors( trueErrors );
/*x*/
//This section handles finding the maximum moving averages amongst trulyIBD segments
std::vector<float> av;
float current_max;
if(empirical_ma_threshold < 0.0){
av = e_obj.getTrueMovingAverages(finalTrueErrors,t1,t2,window);
current_max = av[0];
for(int q = 1; q < av.size(); q++){
if(av[q] > current_max){
current_max = av[q];
}
}
e_obj.addMaxAverage(current_max);
}
//------------------------------------------------------------------------------
int temp1 = m_trueMatches[i][j][l].start;
int temp2 = m_trueMatches[i][j][l].end;
float startCM = e_obj.getCMDistance( temp1 );
float endCM = e_obj.getCMDistance( temp2 );
float mid1CM = startCM + ( endCM - startCM ) / 2 - PIElength / 2;
float mid2CM = startCM + ( endCM - startCM ) / 2 + PIElength / 2;
while( e_obj.getCMDistance( temp1 ) <= mid1CM || e_obj.getCMDistance( temp2 ) >=mid2CM )
{
if( e_obj.getCMDistance( temp1 ) <= mid1CM )
{
++temp1;
}
if( e_obj.getCMDistance( temp2 ) >=mid2CM )
{
--temp2;
}
}
/*Here they are again.
*/
vector<vector<int> > errors=e_obj.checkErrors( i, j, temp1, temp2);
vector<int>finalErrors=e_obj.getFinalErrors( errors );
// float per_err = e_obj.getThreshold(finalErrors,temp1, temp2, 0 );
float per_err = e_obj.getThreshold(finalErrors,temp1,temp2); //overload!
m_errors.push_back( per_err );
/*x*/
}//end for(l)
}//end for(j)
}//end for(i)
//this section actually handles the sorting of the max averages, and the setting of the user supplied percentile.
vector<float>maxes;
float cutoff = empirical_ma_threshold; //assume the user wanted to supply a value. This value will be overwritten shortly if they did not.
if(empirical_ma_threshold < 0.0){
maxes = e_obj.getMaxAverages();
std::sort(maxes.begin(),maxes.end());
e_obj.setMaxAverage(maxes);
cutoff = e_obj.getXthPercentile(ma_threshold); //<-make that an actual user input value
}
e_obj.setCutoff(cutoff);//set the actual threshold to be used when calculating MA in all other SH
if(empirical_ma_threshold < 0.0){
ma_thresh_str = "User supplied ma-threshold is: " + NumberToString(ma_threshold);
emp_ma_thresh_str = "Moving Averages will be tested usign the empirical threshold: " + NumberToString(cutoff);
} else {
emp_ma_thresh_str = "Moving Averages will be tested usign the empirical threshold: " + NumberToString(cutoff);
}
//----------------------------------------
std::sort( m_errors.begin(), m_errors.end() );
std::sort( m_holdOutErrors.begin(), m_holdOutErrors.end() );
ibg_str = "No of segments deemed to be IBD for finding empirical error threshold "
+ NumberToString( m_errors.size() );
}//end ftspe
//overloaded version
void Consolidator::performTrim(ErrorCalculator& e_obj,int window,
int ma_snp_ends, float ma_threshold,
int min_snp,float min_cm,
float per_err_threshold, std::string option,
float hThreshold, bool holdOut,float empirical_threshold, float empirical_pie_threshold,int extendSnp)//<piyush> added the param int EXTENDSNP for calculating moving window avg)
{
int removed1 =0, removed2 = 0, removed3 = 0, removed4 = 0;
int not_removed = 0;
int total_count = global_initial;
bool wrongOption = false;
float per_err_threshold1;
if(empirical_pie_threshold >= 0.0){
per_err_threshold1 = empirical_pie_threshold;
} else {
per_err_threshold1 = getPctErrThreshold( per_err_threshold );
}
std::stringstream sstr;
sstr << std::fixed << std::setprecision(10) << per_err_threshold1;
std::string per_err_value = sstr.str();
emp_pie_thresh_str = "empirical pie threshold is : " + per_err_value + " \n";
float hThreshold1 = 0;
if( holdOut )
{
hThreshold1 = getHoldOutThreshold( hThreshold );
}
per_err_threshold = per_err_threshold1;
hThreshold = hThreshold1;
for(int i=0;i<person_count;i++)
{
for(int j=i;j<person_count;j++)
{
for(int l=0;l<m_matches[i][j].size();l++)
{
total_count++;
if(m_matches[i][j][l].end==-1)
{
continue;
}
int temp1=m_matches[i][j][l].start;
//cout<<"temp1 start begin= "<<temp1<<endl;
int temp2=m_matches[i][j][l].end;
//cout<<"temp2 end begin= "<<temp2<<endl;
if (extendSnp != 0)
{
/*<piyush1>*/
if(temp1-extendSnp <0)
{
temp1=0;
//cout<<"New value of temp1= "<<temp1<<endl;
}
else
{
temp1=m_matches[i][j][l].start-extendSnp;
//cout<<"New value of temp1= "<<temp1<<endl;
}
if(temp2+extendSnp > 4443)// change this constant
{
temp2=m_matches[i][j][l].end;
//cout<<"New value of temp2= "<<temp2<<endl;
}
else
{
temp2=m_matches[i][j][l].end+extendSnp;
//cout<<"New value of temp2= "<<temp2<<endl;
}
/*till here*/
/*cout<<"temp1 start after= "<<temp1<<endl;
cout<<"temp2 end after= "<<temp2<<endl;*/
//cout<<"perform trim temp1= "<<temp1<<endl;
//cout<<"perform trim temp2= "<<temp2<<endl;
}
int pers1 = i, pers2 = j;
if( option.compare( "ErrorRandom1" ) == 0 || option.compare( "ErrorRandom2" ) == 0 || option.compare( "ErrorRandom3" ) == 0 )
{
pers1 = std::rand() % e_obj.getNoOfPersons();
pers2 = std::rand() % e_obj.getNoOfPersons();
if( pers1 > pers2 )
{
pers1 = pers1 + pers2;
pers2 = pers1 - pers2;
pers1 = pers1 - pers2;
}
}
std::vector<std::vector<int> > errors=e_obj.checkErrors(pers1, pers2, temp1, temp2);
std::vector<int>finalErrors=e_obj.getFinalErrors(errors);//<piyush for errors>
//cout<<"finalErrors size= "<<finalErrors.size()<<endl;
/*Inject implied error at start/end of SH here*/
std::vector<int>::iterator it;
it = finalErrors.begin(); //go to the start of the vector
if(finalErrors[0] != 1){
finalErrors.insert(it,1); //inject an error at position 1, if not already there
}
/*End inject implied error section*/
std::vector<int>trimPositions;
std::vector<float>movingAverages;
float threshold;
if( (e_obj.isInitialCmDrop(temp1,temp2,min_cm)) || ((temp2-temp1) < min_snp) ){ //initial drop. Don't calculate MA
trimPositions.push_back(temp1);
trimPositions.push_back(temp2);
trimPositions.push_back(1);
}else{
movingAverages = e_obj.getMovingAverages(finalErrors,temp1,temp2,window,extendSnp);//<piyush> get moving averages are calculated from this part
if(empirical_threshold < 0.0){
threshold = e_obj.getCutoff();
} else {
threshold = empirical_threshold;
}
trimPositions = e_obj.getTrimPositions(movingAverages,temp1,temp2,threshold,min_cm);
}
//-----------------
int beforeTrimStart = temp1;
int beforeTrimEnd = temp2;
m_matches[i][j][l].end = temp2 = temp1+trimPositions[1];
m_matches[i][j][l].start = temp1 = temp1+trimPositions[0];
int del0 = trimPositions[0];
int del1 = trimPositions[1];
float per_err = e_obj.getThreshold(finalErrors,del0,del1,ma_snp_ends);
//add new weighted option
/*
For this new option, we only output SH that are not dropped. So, the output is finalOutput + weighted column.
*/
if( (option.compare("weightedOutput") == 0) || (option.compare("weightedOutputBP") == 0) ){
int snp1 = 0, snp2 = 0, hlength = 0;
float noOfOppHom = 0;
if( holdOut )
{
snp1 = e_obj.getNewSnp( temp1 );
snp2 = e_obj.getNewSnp( temp2 );
hlength = snp2 - snp1;
if( hlength <= 0 )
{
hlength = 1;
}
noOfOppHom = e_obj.getOppHomThreshold( pers1, pers2, m_matches[i][j][l].start, m_matches[i][j][l].end );
}
if( ( (beforeTrimEnd - beforeTrimStart) < min_snp) || ( (trimPositions.size() == 3) && (trimPositions[2] == 1) ) ){
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
removed4++;
continue;
}
if( (( temp2-temp1 ) < min_snp) || (trimPositions.size() == 3) ){ //removed2 a tpos.size of 3 indicates trimming due ot cM
removed2++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( per_err > per_err_threshold){
removed1++;
continue;
}
if( holdOut && hThreshold < ( noOfOppHom ) / hlength ){
removed3++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
} //removed3
not_removed++;
m_weighted_sh.push_back(Weighted_SH(temp1,temp2,i,j)); //build the vector of SH that passed
continue;
}//end weghtedOutput
/*Add new finalErrorsOutput*/
if( (option.compare("finalErrorsOutput") == 0) ){
int snp1 = 0, snp2 = 0, hlength = 0;
float noOfOppHom = 0;
if( holdOut )
{
snp1 = e_obj.getNewSnp( temp1 );
snp2 = e_obj.getNewSnp( temp2 );
hlength = snp2 - snp1;
if( hlength <= 0 )
{
hlength = 1;
}
noOfOppHom = e_obj.getOppHomThreshold( pers1, pers2, m_matches[i][j][l].start, m_matches[i][j][l].end );
}
if( ( (beforeTrimEnd - beforeTrimStart) < min_snp) || ( (trimPositions.size() == 3) && (trimPositions[2] == 1) ) ){
std::vector<float>movingAverages;
temp1 = beforeTrimStart;
temp2 = beforeTrimEnd;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
removed4++;
continue;
}
if( (( temp2-temp1 ) < min_snp) || (trimPositions.size() == 3) ){ //removed2 a tpos.size of 3 indicates trimming due ot cM
removed2++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( per_err > per_err_threshold){
removed1++;
continue;
}
if( holdOut && hThreshold < ( noOfOppHom ) / hlength ){
removed3++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
} //removed3
not_removed++;
e_obj.finalErrorsOutput(i,j,temp1,temp2,min_cm,per_err);// <piyush>this is where the final output is written is called at
continue;
}//end finalErrorsOutput
if( (option.compare("FullPlusDropped") == 0) ){
int snp1 = 0, snp2 = 0, hlength = 0;
float noOfOppHom = 0;
if( holdOut )
{
snp1 = e_obj.getNewSnp( temp1 );
snp2 = e_obj.getNewSnp( temp2 );
hlength = snp2 - snp1;
if( hlength <= 0 )
{
hlength = 1;
}
noOfOppHom = e_obj.getOppHomThreshold( pers1, pers2, m_matches[i][j][l].start, m_matches[i][j][l].end );
}
if( ( (beforeTrimEnd - beforeTrimStart) < min_snp) || ( (trimPositions.size() == 3) && (trimPositions[2] == 1) ) ){
std::vector<float>movingAverages;
temp1 = beforeTrimStart;
temp2 = beforeTrimEnd;
e_obj.fullPlusDroppedOutput(i,j,temp1,temp2,min_snp,min_cm,finalErrors,per_err,1);//standardize the error codes
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
removed4++;
continue;
}
if( (( temp2-temp1 ) < min_snp) || (trimPositions.size() == 3) ){ //removed2 a tpos.size of 3 indicates trimming due ot cM
e_obj.fullPlusDroppedOutput(i,j,temp1,temp2,min_snp,min_cm,finalErrors,per_err,2);
removed2++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( per_err > per_err_threshold){
e_obj.fullPlusDroppedOutput(i,j,temp1,temp2,min_snp,min_cm,finalErrors,per_err,3);
removed1++;
continue;
}
if( holdOut && hThreshold < ( noOfOppHom ) / hlength ){
e_obj.fullPlusDroppedOutput(i,j,temp1,temp2,min_snp,min_cm,finalErrors,per_err,4);
removed3++;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
} //removed3
not_removed++;
e_obj.finalOutPut(i,j,temp1,temp2,min_cm);
continue;
} //end FullPlusDropped
//Calculate Error1
if( (option.compare("Error1") == 0 ) || (option.compare("ErrorRandom1") == 0) || (option.compare("Error") == 0) ){
if( ( (beforeTrimEnd - beforeTrimStart) < min_snp) || ( (trimPositions.size() == 3) && (trimPositions[2] == 1) ) ){ //dropped before trimming
//don't bother printing out ma for this one. But go back and change it so that it doesn't actually calc it
std::vector<float>movingAverages;//null
//trying something special in this case. This can be removed once idrops aren't being trimmed
//test code
temp1 = beforeTrimStart;
temp2 = beforeTrimEnd;
//
e_obj.errorOutput(i,j,temp1,temp2,min_snp,min_cm,movingAverages,finalErrors,per_err,temp1,temp2,beforeTrimStart,beforeTrimEnd,1);
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
removed4++; //seems ok
continue;
}
if( (( temp2-temp1 ) < min_snp) || ((trimPositions.size() == 3) && (trimPositions[2] == 2) ) ) //dropped after trimming
{
e_obj.errorOutput(i,j,temp1,temp2,min_snp,min_cm,movingAverages,finalErrors,per_err,temp1,temp2,beforeTrimStart,beforeTrimEnd,2);
++removed2;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( per_err > per_err_threshold ) //dropped due to pie
{
e_obj.errorOutput(i,j,temp1,temp2,min_snp,min_cm,movingAverages,finalErrors,per_err,temp1,temp2,beforeTrimStart,beforeTrimEnd,3);
++removed1;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
not_removed++;
e_obj.errorOutput(i,j,temp1,temp2,min_snp,min_cm,movingAverages,finalErrors,per_err,temp1,temp2,beforeTrimStart,beforeTrimEnd,0);//no drop
continue;
}//end error1
int snp1 = 0, snp2 = 0, hlength = 0;
float noOfOppHom = 0;
if( holdOut )
{
snp1 = e_obj.getNewSnp( temp1 );
snp2 = e_obj.getNewSnp( temp2 );
hlength = snp2 - snp1;
if( hlength <= 0 )
{
hlength = 1;
}
noOfOppHom = e_obj.getOppHomThreshold( pers1, pers2, m_matches[i][j][l].start, m_matches[i][j][l].end );
}
//update drop order 2/26/14
if( (( temp2-temp1 ) < min_snp) || (trimPositions.size() == 3) )
{
++removed2;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( per_err > per_err_threshold )
{
++removed1;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
//probably not removed?
not_removed++;
if( option.compare("MovingAverages")==0 ) //make this ma2
{
if( holdOut)
{
e_obj.middleHoldOutPut(i,j,temp1,temp2, min_snp,min_cm,movingAverages,trimPositions,per_err, noOfOppHom, hlength );
}
else
{
e_obj.middleOutPut(i,j,temp1,temp2, min_snp, min_cm,movingAverages, trimPositions,per_err );
}
continue;
}
if(option.compare("Error2")==0 || option.compare( "ErrorRandom2" ) == 0)
{
if( holdOut)
{
e_obj.middleHoldOutPut(i,j,temp1,temp2, min_snp, min_cm, finalErrors, trimPositions, per_err, noOfOppHom, hlength );
}
else
{
e_obj.middleOutPut(i,j,temp1,temp2, min_snp, min_cm, finalErrors, trimPositions, per_err);
}
continue;
}
if ( holdOut && hThreshold < ( noOfOppHom ) / hlength )
{
++removed3;
m_matches[i][j][l].start= m_matches[i][j][l].end=-1;
continue;
}
if( option.compare("Error3")==0 || option.compare( "ErrorRandom3" ) == 0 )
{
e_obj.middleHoldOutPut(i,j,temp1,temp2, min_snp, min_cm, finalErrors, trimPositions, per_err, noOfOppHom, hlength );
}
}//l
}//j
}//i
/*ENTERING TESTING AREA DEC 4th 2014*/
/*****************************
******************************/
/*Now, let's handle weighted output if need be*/
if( option.compare("weightedOutput") == 0 ){
float snp_average_count = 0.0;
int start_position;
int end_position;
int genome_length;
if(isUserSuppliedWeights()){ //the user has supplied their own weights.
//in this case, the min and max values correspond to the number of lines in the input file,
//since each line represents a snp. So the min is always 0, and the max is always the number of lines-1.
start_position = 0;
end_position = user_supplied_snp_weights.size() - 1;
}else {
start_position = find_genome_min();
end_position = find_genome_max();
}//end else
genome_length = (end_position - start_position)+1;
genome_vector.resize(genome_length,0);
if(isUserSuppliedWeights()){
for(int i = 0; i < user_supplied_snp_weights.size(); i++){
update_genome(i,user_supplied_snp_weights[i]);
}
}else{
/*This next for loop adds one to each snp in a SH. Bypass it if the user gives a files of weights*/
for(int i = 0; i < m_weighted_sh.size(); i++){
update_genome(m_weighted_sh[i].snp1, m_weighted_sh[i].snp2);
}
}
//this part is next...will probably need to add stuff to that weighted object...
snp_average_count = average_snp_count();
for(int i = 0; i < m_weighted_sh.size(); i++){
m_weighted_sh[i].snp_weight = update_snp_weight(m_weighted_sh[i].snp1, m_weighted_sh[i].snp2);
}
for(int i = 0; i < m_weighted_sh.size(); i++){
m_weighted_sh[i].final_weight = ( snp_average_count / (m_weighted_sh[i].snp_weight));
e_obj.weightedOutput(m_weighted_sh[i].per1, m_weighted_sh[i].per2, m_weighted_sh[i].snp1, m_weighted_sh[i].snp2, m_weighted_sh[i].final_weight);
}
}
if (option.compare("weightedOutputBP") == 0){
//begin new test code section here: Dec 4th 2014
int genome_length = e_obj.getGenomeBPLength();
float adjusted_genome_length = genome_length / 1000.0; //L using kbp for now
int genome_min = e_obj.getMinimumBP(); std::cout<<"genome_min= "<<genome_min<<std::endl;
int genome_max = e_obj.getMaximumBP(); std::cout<<"genome_max= "<<genome_max<<std::endl;
int genome_size_snps = (find_genome_max() - find_genome_min())+1; //used for genome_vector
float wprime_numerator = 0.0; //This is Ci / L
float total_sh_length_sum = 0.0;
float w2prime_denominator = 0.0;
genome_vector.resize(genome_size_snps,0); //resize and zero out the genome. shit that needs to be snps.
//update all of the snp counts in the genome. This looks fine.
for(int i = 0; i < m_weighted_sh.size(); i++){
update_genome(m_weighted_sh[i].snp1, m_weighted_sh[i].snp2);
}
//calculate the w' numerator by summing up all of the snp counts and dividing by the genome length.
//WARNING: This can cause wprime_numerator to overflow. Currently using kbp units to avoid this, but
//this needs to be addressed.
for(int i = 0; i < genome_vector.size(); i++){
wprime_numerator += genome_vector[i] / adjusted_genome_length;
}
//Calculate w' for each SH.
for(int i = 0; i < m_weighted_sh.size(); i++){
float wprime_denominator = 0.0;
m_weighted_sh[i].mbp_length = (e_obj.getSHBPLength(m_weighted_sh[i].snp1, m_weighted_sh[i].snp2)/1000.0);
wprime_denominator = get_snps_over_range(m_weighted_sh[i].snp1, m_weighted_sh[i].snp2, m_weighted_sh[i].mbp_length);
m_weighted_sh[i].wprime = wprime_numerator / wprime_denominator;
}
//This is the total length of all SH. This can probably overflow as well...ugh.
for(int i = 0; i < m_weighted_sh.size(); i++){
total_sh_length_sum += m_weighted_sh[i].mbp_length;
}
//Calculate the w2prime denominator - this value is a constant
for(int i = 0; i < m_weighted_sh.size(); i++){
float temp = m_weighted_sh[i].mbp_length * m_weighted_sh[i].wprime;
w2prime_denominator += temp / total_sh_length_sum;
}
//Calculate and output w2' for each SH
for(int i = 0; i < m_weighted_sh.size(); i++){
m_weighted_sh[i].w2prime = (m_weighted_sh[i].wprime) / w2prime_denominator;
e_obj.weightedOutput(m_weighted_sh[i].per1, m_weighted_sh[i].per2, m_weighted_sh[i].snp1, m_weighted_sh[i].snp2, m_weighted_sh[i].w2prime);
}
}
/*End weighted output*/
/*END TESTING AREA DEC 4th 2014*/
/*****************************
******************************/
ma_drop_str = "No of matches removed due to length of trimming by moving averages: " + NumberToString( removed2 );
pie_drop_str = "No of matches removed due to percentage error: " + NumberToString( removed1 );
if(holdOut){
// str = str+ " \n No of matches removed due hold out ped file checking: "+ NumberToString( removed3 );
}
//begin log output
std::string parameter_string_1 = "\n\n**********Parameters used in program**********\n";
e_obj.log(parameter_string_1);
e_obj.log(emp_ma_thresh_str); //keep
e_obj.log(emp_pie_thresh_str);//keep
parameter_string_1 = "**********************************************\n\n";
e_obj.log(parameter_string_1);
std::string total_count_str = "The total number of SH in the input file was: " + NumberToString(total_count);
e_obj.log(total_count_str);
e_obj.log(consolidated_str);
e_obj.log(initial_drop_str);
// e_obj.log(ibg_str);
e_obj.log(ma_drop_str);
e_obj.log(pie_drop_str);
final_sh_str = "Total number of SH that were not dropped is: " + NumberToString(not_removed);
e_obj.log(final_sh_str);
}//end performTrim