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/*
* See header file for a description of this class.
*
* \author G. Cerminara - INFN Torino
*/
#include "DTOccupancyClusterBuilder.h"
#include "FWCore/MessageLogger/interface/MessageLogger.h"
#include "TCanvas.h"
#include "TH2F.h"
#include <algorithm>
#include <sstream>
#include <iostream>
using namespace std;
using namespace edm;
DTOccupancyClusterBuilder::DTOccupancyClusterBuilder() : maxMean(-1.), maxRMS(-1.) {}
DTOccupancyClusterBuilder::~DTOccupancyClusterBuilder() {}
void DTOccupancyClusterBuilder::addPoint(const DTOccupancyPoint& point) {
// loop over points already stored
for (set<DTOccupancyPoint>::const_iterator pt = thePoints.begin(); pt != thePoints.end(); ++pt) {
theDistances[(*pt).distance(point)] = make_pair(*pt, point);
}
thePoints.insert(point);
}
void DTOccupancyClusterBuilder::buildClusters() {
while (buildNewCluster()) {
if (thePoints.size() <= 1)
break;
}
// build single point clusters with the remaining points
for (set<DTOccupancyPoint>::const_iterator pt = thePoints.begin(); pt != thePoints.end(); ++pt) {
DTOccupancyCluster clusterCandidate(*pt);
theClusters.push_back(clusterCandidate);
// store the range for building the histograms later
if (clusterCandidate.maxMean() > maxMean)
maxMean = clusterCandidate.maxMean();
if (clusterCandidate.maxRMS() > maxRMS)
maxRMS = clusterCandidate.maxRMS();
}
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< " # of valid clusters: " << theClusters.size() << endl;
sortClusters();
}
void DTOccupancyClusterBuilder::drawClusters(std::string canvasName) {
int nBinsX = 100;
int nBinsY = 100;
int colorMap[12] = {632, 600, 800, 400, 820, 416, 432, 880, 616, 860, 900, 920};
TCanvas* canvas = new TCanvas(canvasName.c_str(), canvasName.c_str());
canvas->cd();
for (vector<DTOccupancyCluster>::const_iterator cluster = theClusters.begin(); cluster != theClusters.end();
++cluster) {
stringstream stream;
stream << canvasName << "_" << cluster - theClusters.begin();
string histoName = stream.str();
TH2F* histo = (*cluster).getHisto(histoName,
nBinsX,
0,
maxMean + 3 * maxMean / 100.,
nBinsY,
0,
maxRMS + 3 * maxRMS / 100.,
colorMap[cluster - theClusters.begin()]);
if (cluster == theClusters.begin())
histo->Draw("box");
else
histo->Draw("box,same");
}
}
std::pair<DTOccupancyPoint, DTOccupancyPoint> DTOccupancyClusterBuilder::getInitialPair() {
return theDistances.begin()->second;
}
void DTOccupancyClusterBuilder::computePointToPointDistances() {
theDistances.clear();
for (set<DTOccupancyPoint>::const_iterator pt_i = thePoints.begin(); pt_i != thePoints.end(); ++pt_i) { // i loopo
for (set<DTOccupancyPoint>::const_iterator pt_j = thePoints.begin(); pt_j != thePoints.end(); ++pt_j) { // j loop
if (*pt_i != *pt_j) {
theDistances[pt_i->distance(*pt_j)] = make_pair(*pt_i, *pt_j);
}
}
}
}
void DTOccupancyClusterBuilder::computeDistancesToCluster(const DTOccupancyCluster& cluster) {
theDistancesFromTheCluster.clear();
for (set<DTOccupancyPoint>::const_iterator pt = thePoints.begin(); pt != thePoints.end(); ++pt) {
theDistancesFromTheCluster[cluster.distance(*pt)] = *pt;
}
}
bool DTOccupancyClusterBuilder::buildNewCluster() {
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< "--------- New Cluster Candidate ----------------------" << endl;
pair<DTOccupancyPoint, DTOccupancyPoint> initialPair = getInitialPair();
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< " Initial Pair: " << endl
<< " point1: mean " << initialPair.first.mean() << " rms " << initialPair.first.rms() << endl
<< " point2: mean " << initialPair.second.mean() << " rms " << initialPair.second.rms() << endl;
DTOccupancyCluster clusterCandidate(initialPair.first, initialPair.second);
if (clusterCandidate.isValid()) {
// remove already used pair
thePoints.erase(initialPair.first);
thePoints.erase(initialPair.second);
if (!thePoints.empty()) {
computeDistancesToCluster(clusterCandidate);
while (clusterCandidate.addPoint(theDistancesFromTheCluster.begin()->second)) {
thePoints.erase(theDistancesFromTheCluster.begin()->second);
if (thePoints.empty())
break;
computeDistancesToCluster(clusterCandidate);
}
}
} else {
return false;
}
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< " # of layers: " << clusterCandidate.nPoints() << " avrg. mean: " << clusterCandidate.averageMean()
<< " avrg. rms: " << clusterCandidate.averageRMS() << endl;
theClusters.push_back(clusterCandidate);
// store the range for building the histograms later
if (clusterCandidate.maxMean() > maxMean)
maxMean = clusterCandidate.maxMean();
if (clusterCandidate.maxRMS() > maxRMS)
maxRMS = clusterCandidate.maxRMS();
computePointToPointDistances();
return true;
}
void DTOccupancyClusterBuilder::sortClusters() {
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder") << " sorting" << endl;
sort(theClusters.begin(), theClusters.end(), clusterIsLessThan);
// we save the detid of the clusters which are not the best one
for (vector<DTOccupancyCluster>::const_iterator cluster = ++(theClusters.begin()); cluster != theClusters.end();
++cluster) { // loop over clusters skipping the first
set<DTLayerId> clusterLayers = (*cluster).getLayerIDs();
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< " # layers in the cluster: " << clusterLayers.size() << endl;
theProblematicLayers.insert(clusterLayers.begin(), clusterLayers.end());
}
LogTrace("DTDQM|DTMonitorClient|DTOccupancyTest|DTOccupancyClusterBuilder")
<< " # of problematic layers: " << theProblematicLayers.size() << endl;
}
DTOccupancyCluster DTOccupancyClusterBuilder::getBestCluster() const { return theClusters.front(); }
bool DTOccupancyClusterBuilder::isProblematic(DTLayerId layerId) const {
if (theProblematicLayers.find(layerId) != theProblematicLayers.end()) {
return true;
}
return false;
}
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