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File indexing completed on 2024-04-06 12:07:16

 #include "DQM/EcalMonitorClient/interface/MLClient.h"
 
 #include "CondFormats/EcalObjects/interface/EcalDQMStatusHelper.h"
 
 #include "DQM/EcalCommon/interface/EcalDQMCommonUtils.h"
 
 #include "FWCore/ParameterSet/interface/ParameterSet.h"
 
 #include "PhysicsTools/ONNXRuntime/interface/ONNXRuntime.h"
 
 #include "DQM/EcalCommon/interface/MESetNonObject.h"
 
 #include <fstream>
 #include <string>
 
 using namespace cms::Ort;
 
 namespace ecaldqm {
   MLClient::MLClient() : DQWorkerClient() { qualitySummaries_.insert("MLQualitySummary"); }
 
   void MLClient::setParams(edm::ParameterSet const& _params) {
     EBThreshold_ = _params.getUntrackedParameter<double>("EBThreshold");
     EEpThreshold_ = _params.getUntrackedParameter<double>("EEpThreshold");
     EEmThreshold_ = _params.getUntrackedParameter<double>("EEmThreshold");
     EB_PUcorr_slope_ = _params.getUntrackedParameter<double>("EB_PUcorr_slope");
     EB_PUcorr_intercept_ = _params.getUntrackedParameter<double>("EB_PUcorr_intercept");
     EEp_PUcorr_slope_ = _params.getUntrackedParameter<double>("EEp_PUcorr_slope");
     EEp_PUcorr_intercept_ = _params.getUntrackedParameter<double>("EEp_PUcorr_intercept");
     EEm_PUcorr_slope_ = _params.getUntrackedParameter<double>("EEm_PUcorr_slope");
     EEm_PUcorr_intercept_ = _params.getUntrackedParameter<double>("EEm_PUcorr_intercept");
 
     if (!onlineMode_) {
       MEs_.erase(std::string("MLQualitySummary"));
       MEs_.erase(std::string("EventsperMLImage"));
       sources_.erase(std::string("PU"));
       sources_.erase(std::string("NumEvents"));
       sources_.erase(std::string("DigiAllByLumi"));
       sources_.erase(std::string("AELoss"));
       sources_.erase(std::string("BadTowerCount"));
       sources_.erase(std::string("BadTowerCountNorm"));
     }
   }
 
   void MLClient::producePlots(ProcessType) {
     if (!onlineMode_)
       return;
     nbadtowerEB = 0;
     nbadtowerEE = 0;
 
     using namespace std;
     MESet& meMLQualitySummary(MEs_.at("MLQualitySummary"));
     MESet& meEventsperMLImage(MEs_.at("EventsperMLImage"));
 
     MESetNonObject const& sPU(static_cast<MESetNonObject&>(sources_.at("PU")));
     MESetNonObject const& sNumEvents(static_cast<MESetNonObject&>(sources_.at("NumEvents")));
 
     //Get the no.of events and the PU per LS calculated in OccupancyTask
     int nEv = sNumEvents.getFloatValue();
     double pu = sPU.getFloatValue();
 
     //Do not compute ML quality if PU is non existent.
     if (pu <= 0.) {
       return;
     }
     uint32_t mask(1 << EcalDQMStatusHelper::PEDESTAL_ONLINE_HIGH_GAIN_RMS_ERROR |
                   1 << EcalDQMStatusHelper::PHYSICS_BAD_CHANNEL_WARNING |
                   1 << EcalDQMStatusHelper::PHYSICS_BAD_CHANNEL_ERROR);
 
     //////////////// ML Data Preprocessing //////////////////////////////////
     //Inorder to feed the data into the ML model we apply some preprocessing.
     //We use the Digi Occupancy per Lumisection as the input source.
     //The model was trained on each occupancy plot having 500 events.
     //In apprehension of the low luminosity in the beginning of Run3, where in online DQM
     //the no.of events per LS could be lower than 500, we sum the occupancies over a fixed no.of lumisections as a running sum,
     //and require that the total no.of events on this summed occupancy to be atleast 200.
     //(This no.of LS and the no.of events are parameters which would require tuning later)
     //This summed occupancy is now the input image, which is then corrected for PileUp(PU) dependence and
     //change in no.of events, which are derived from training.
     //The input image is also padded by replicating the top and bottom rows so as to prevent the "edge effect"
     //wherein the ML model's learning degrades near the edge of the data set it sees.
     //This padding is then removed during inference on the model output.
 
     //Get the histogram of the input digi occupancy per lumisection.
     TH2F* hEEmDigiMap((sources_.at("DigiAllByLumi")).getME(0)->getTH2F());
     TH2F* hEbDigiMap((sources_.at("DigiAllByLumi")).getME(1)->getTH2F());
     TH2F* hEEpDigiMap((sources_.at("DigiAllByLumi")).getME(2)->getTH2F());
 
     size_t nEBTowers = nEBEtaTowers * nEBPhiTowers;  //Each EB occupancy map is of size 34x72 towers
     size_t nEETowers = nEEEtaTowers * nEEPhiTowers;  //Each EE occupancy map is of size 20x20 towers
 
     //Vectors to feed into the ML network
     std::vector<float> ebOccMap1dCumulPad;
     std::vector<float> eemOccMap1dCumulPad;
     std::vector<float> eepOccMap1dCumulPad;
 
     //Array to store occupancy maps
     std::valarray<float> ebOccMap1d(nEBTowers);
     std::valarray<float> eemOccMap1d(nEETowers);
     std::valarray<float> eepOccMap1d(nEETowers);
 
     //Store the values from the input histogram into the array
     //to do preprocessing
     for (int i = 0; i < hEbDigiMap->GetNbinsY(); i++) {  //NbinsY = 34, NbinsX = 72
       for (int j = 0; j < hEbDigiMap->GetNbinsX(); j++) {
         int bin = hEbDigiMap->GetBin(j + 1, i + 1);
         int k = (i * nEBPhiTowers) + j;
         ebOccMap1d[k] = hEbDigiMap->GetBinContent(bin);
       }
     }
     ebOccMap1dQ.push_back(ebOccMap1d);  //Queue which stores input occupancy maps for nLS lumis
 
     for (int i = 0; i < hEEpDigiMap->GetNbinsY(); i++) {  //NbinsY = 20, NbinsX = 20
       for (int j = 0; j < hEEpDigiMap->GetNbinsX(); j++) {
         int bin = hEEpDigiMap->GetBin(j + 1, i + 1);
         int k = (i * nEEPhiTowers) + j;
         eemOccMap1d[k] = hEEmDigiMap->GetBinContent(bin);
         eepOccMap1d[k] = hEEpDigiMap->GetBinContent(bin);
       }
     }
 
     //Queue which stores input occupancy maps for nLS lumis
     eemOccMap1dQ.push_back(eemOccMap1d);
     eepOccMap1dQ.push_back(eepOccMap1d);
 
     NEventQ.push_back(nEv);  //Queue which stores the no.of events per LS for nLS lumis
 
     if (NEventQ.size() < nLS) {
       return;  //Should have nLS lumis to add the occupancy over.
     }
     if (NEventQ.size() > nLS) {
       NEventQ.pop_front();  //Keep only nLS consecutive LS. Pop the first one if size greater than nLS
     }
     if (ebOccMap1dQ.size() > nLS) {
       ebOccMap1dQ.pop_front();  //Same conditon for the input occupancy maps.
       eemOccMap1dQ.pop_front();
       eepOccMap1dQ.pop_front();
     }
 
     int TNum = 0;
     for (size_t i = 0; i < nLS; i++) {
       TNum += NEventQ[i];  //Total no.of events over nLS lumis
     }
 
     if (TNum < 400) {
       return;  //The total no.of events should be atleast 400 over nLS for meaningful statistics
     }
     //Fill the ME to monitor the trend of the total no.of events in each input image to the ML model
     meEventsperMLImage.fill(getEcalDQMSetupObjects(), EcalBarrel, double(timestamp_.iLumi), double(TNum));
 
     //Array to hold the sum of inputs, which make atleast 400 events.
     std::valarray<float> ebOccMap1dCumul(0., nEBTowers);
     std::valarray<float> eemOccMap1dCumul(0., nEETowers);
     std::valarray<float> eepOccMap1dCumul(0., nEETowers);
 
     //Sum the input arrays of nLS.
     for (size_t i = 0; i < ebOccMap1dQ.size(); i++) {
       ebOccMap1dCumul += ebOccMap1dQ[i];
       eemOccMap1dCumul += eemOccMap1dQ[i];
       eepOccMap1dCumul += eepOccMap1dQ[i];
     }
 
     //Applying PU correction derived from training
     ebOccMap1dCumul = ebOccMap1dCumul / (EB_PUcorr_slope_ * pu + EB_PUcorr_intercept_);
     eemOccMap1dCumul = eemOccMap1dCumul / (EEm_PUcorr_slope_ * pu + EEm_PUcorr_intercept_);
     eepOccMap1dCumul = eepOccMap1dCumul / (EEp_PUcorr_slope_ * pu + EEp_PUcorr_intercept_);
 
     //Scaling up to match input dimensions.
     ebOccMap1dCumul = ebOccMap1dCumul * (nEBEtaTowers * nEBPhiTowers);
     eemOccMap1dCumul = eemOccMap1dCumul * nEEEtaTowers * nEEPhiTowers;  //(nEETowersPad * nEETowersPad);
     eepOccMap1dCumul = eepOccMap1dCumul * nEEEtaTowers * nEEPhiTowers;  //(nEETowersPad * nEETowersPad);
 
     //Correction for no.of events in each input image as originally model trained with 500 events per image
     ebOccMap1dCumul = ebOccMap1dCumul * (500. / TNum);
     eemOccMap1dCumul = eemOccMap1dCumul * (500. / TNum);
     eepOccMap1dCumul = eepOccMap1dCumul * (500. / TNum);
 
     std::vector<std::vector<float>> ebOccMap2dCumul(nEBEtaTowers, std::vector<float>(nEBPhiTowers, 0.));
     //Convert 1dCumul to 2d
     for (size_t i = 0; i < nEBEtaTowers; i++) {
       for (size_t j = 0; j < nEBPhiTowers; j++) {
         int k = (i * nEBPhiTowers) + j;
         ebOccMap2dCumul[i][j] = ebOccMap1dCumul[k];
       }
     }
 
     std::vector<float> pad_top;
     std::vector<float> pad_bottom;
     std::vector<float> pad_left;
     std::vector<float> pad_right;
 
     pad_top = ebOccMap2dCumul[0];
     pad_bottom = ebOccMap2dCumul[ebOccMap2dCumul.size() - 1];
 
     ebOccMap2dCumul.insert(ebOccMap2dCumul.begin(), pad_top);
     ebOccMap2dCumul.push_back(pad_bottom);
 
     //// Endcaps ///
     std::vector<std::vector<float>> eemOccMap2dCumul(nEEEtaTowers, std::vector<float>(nEEPhiTowers, 0.));
     std::vector<std::vector<float>> eepOccMap2dCumul(nEEEtaTowers, std::vector<float>(nEEPhiTowers, 0.));
 
     for (size_t i = 0; i < nEEEtaTowers; i++) {
       for (size_t j = 0; j < nEEPhiTowers; j++) {
         int k = (i * nEEPhiTowers) + j;
         eemOccMap2dCumul[i][j] = eemOccMap1dCumul[k];
         eepOccMap2dCumul[i][j] = eepOccMap1dCumul[k];
       }
     }
 
     // EE - //
     pad_top.clear();
     pad_bottom.clear();
     pad_left.clear();
     pad_right.clear();
 
     pad_top = eemOccMap2dCumul[0];
     pad_bottom = eemOccMap2dCumul[eemOccMap2dCumul.size() - 1];
 
     eemOccMap2dCumul.insert(eemOccMap2dCumul.begin(), pad_top);
     eemOccMap2dCumul.push_back(pad_bottom);
 
     for (auto& row : eemOccMap2dCumul) {
       pad_left.push_back(row[0]);
       pad_right.push_back(row[row.size() - 1]);
     }
 
     std::size_t Lindex = 0;
     std::size_t Rindex = 0;
 
     for (auto& row : eemOccMap2dCumul) {
       row.insert(row.begin(), pad_left[Lindex++]);
       row.insert(row.end(), pad_right[Rindex++]);
     }
 
     // EE + //
     pad_top.clear();
     pad_bottom.clear();
 
     pad_top = eepOccMap2dCumul[0];
     pad_bottom = eepOccMap2dCumul[eepOccMap2dCumul.size() - 1];
 
     eepOccMap2dCumul.insert(eepOccMap2dCumul.begin(), pad_top);
     eepOccMap2dCumul.push_back(pad_bottom);
 
     for (auto& row : eepOccMap2dCumul) {
       pad_left.push_back(row[0]);
       pad_right.push_back(row[row.size() - 1]);
     }
 
     Lindex = 0;
     Rindex = 0;
 
     for (auto& row : eepOccMap2dCumul) {
       row.insert(row.begin(), pad_left[Lindex++]);
       row.insert(row.end(), pad_right[Rindex++]);
     }
 
     //The pre-processed input is now fed into the 1D input tensor vector which will go into the ML model
     for (auto& row : ebOccMap2dCumul) {
       ebOccMap1dCumulPad.insert(ebOccMap1dCumulPad.end(), row.begin(), row.end());
     }
 
     for (auto& row : eemOccMap2dCumul) {
       eemOccMap1dCumulPad.insert(eemOccMap1dCumulPad.end(), row.begin(), row.end());
     }
 
     for (auto& row : eepOccMap2dCumul) {
       eepOccMap1dCumulPad.insert(eepOccMap1dCumulPad.end(), row.begin(), row.end());
     }
 
     ///// Model Inference //////
     //An Autoencoder (AE) network with resnet architecture is used here which is trained on
     //certified good data (EB digi occupancy) from Run 2018 data.
     //On giving an input occupancy map, the encoder part of the AE compresses and reduces the input data, learning its features,
     //and the decoder reconstructs the data from the encoded form into a representation as close to the original input as possible.
     //We then compute the Mean squared error (MSE) between the input and output image, also called the Reconstruction Loss,
     //calculated at a tower by tower basis.
     //Thus, given an anomalous tower the loss should be significantly higher than the loss with respect to good towers, which the model
     //has already seen --> anomaly detection.
     //When calculating the loss we also apply a response correction by dividing each input and output image with the average occupancy from
     //all 2018 data (also to be tuned),to accommodate the difference in response of crystals in different regions of the Ecal Barrel
     //Further each loss map from each input image is then multiplied by the last N loss maps,
     ///so that real anomalies which persist with time are enhanced and fluctuations are suppressed.
     //A quality threshold is then applied on this time multiplied loss map, to mark them as GOOD or BAD,
     //after which it is stored as a quality summary ME.
 
     ///ONNX model running///
     std::string instanceName{"AE-DQM-inference"};
     std::string modelFilepath = edm::FileInPath("DQM/EcalMonitorClient/data/onnxModels/resnet.onnx").fullPath();
 
     Ort::SessionOptions sessionOptions;
     sessionOptions.SetIntraOpNumThreads(1);
     Ort::Env env(OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING, instanceName.c_str());
     Ort::Session session(env, modelFilepath.c_str(), sessionOptions);
 
     Ort::AllocatorWithDefaultOptions allocator;
 
     // Strings returned by session.GetInputNameAllocated are temporary, need to copy them before they are deallocated
     std::string inputName{session.GetInputNameAllocated(0, allocator).get()};
 
     Ort::TypeInfo inputTypeInfo = session.GetInputTypeInfo(0);
     auto inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();
 
     std::vector<int64_t> inputDims = inputTensorInfo.GetShape();
 
     std::string outputName{session.GetOutputNameAllocated(0, allocator).get()};
 
     Ort::TypeInfo outputTypeInfo = session.GetOutputTypeInfo(0);
     auto outputTensorInfo = outputTypeInfo.GetTensorTypeAndShapeInfo();
 
     std::vector<int64_t> outputDims = outputTensorInfo.GetShape();
 
     size_t TensorSize = nEBEtaTowersPad * nEBPhiTowers;
     std::vector<float> ebRecoOccMap1dPad(TensorSize);  //To store the output reconstructed occupancy
 
     std::vector<const char*> inputNames{inputName.c_str()};
     std::vector<const char*> outputNames{outputName.c_str()};
     std::vector<Ort::Value> inputTensors;
     std::vector<Ort::Value> outputTensors;
 
     Ort::MemoryInfo memoryInfo =
         Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
     inputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, ebOccMap1dCumulPad.data(), TensorSize, inputDims.data(), inputDims.size()));
 
     outputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, ebRecoOccMap1dPad.data(), TensorSize, outputDims.data(), outputDims.size()));
 
     session.Run(Ort::RunOptions{nullptr},
                 inputNames.data(),
                 inputTensors.data(),
                 1,
                 outputNames.data(),
                 outputTensors.data(),
                 1);
 
     //Endcaps
     // EE- //
 
     inputDims.clear();
     outputDims.clear();
     inputNames.clear();
     outputNames.clear();
     inputTensors.clear();
     outputTensors.clear();
 
     modelFilepath = edm::FileInPath("DQM/EcalMonitorClient/data/onnxModels/EEm_resnet2018.onnx").fullPath();
 
     Ort::Session EEm_session(env, modelFilepath.c_str(), sessionOptions);
 
     inputName = EEm_session.GetInputNameAllocated(0, allocator).get();
 
     inputTypeInfo = EEm_session.GetInputTypeInfo(0);
     auto EEm_inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();
 
     inputDims = EEm_inputTensorInfo.GetShape();
 
     outputName = EEm_session.GetOutputNameAllocated(0, allocator).get();
 
     //Ort::TypeInfo
     outputTypeInfo = EEm_session.GetOutputTypeInfo(0);
     auto EEm_outputTensorInfo = outputTypeInfo.GetTensorTypeAndShapeInfo();
 
     outputDims = EEm_outputTensorInfo.GetShape();
 
     size_t EE_TensorSize = nEETowersPad * nEETowersPad;
     std::vector<float> eemRecoOccMap1dPad(EE_TensorSize);  //To store the output reconstructed occupancy
 
     inputNames.push_back(inputName.c_str());
     outputNames.push_back(outputName.c_str());
 
     //Ort::MemoryInfo
     memoryInfo = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
     inputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, eemOccMap1dCumulPad.data(), EE_TensorSize, inputDims.data(), inputDims.size()));
 
     outputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, eemRecoOccMap1dPad.data(), EE_TensorSize, outputDims.data(), outputDims.size()));
 
     EEm_session.Run(Ort::RunOptions{nullptr},
                     inputNames.data(),
                     inputTensors.data(),
                     1,
                     outputNames.data(),
                     outputTensors.data(),
                     1);
 
     // EE+ //
     inputDims.clear();
     outputDims.clear();
     inputNames.clear();
     outputNames.clear();
     inputTensors.clear();
     outputTensors.clear();
 
     modelFilepath = edm::FileInPath("DQM/EcalMonitorClient/data/onnxModels/EEp_resnet2018.onnx").fullPath();
 
     Ort::Session EEp_session(env, modelFilepath.c_str(), sessionOptions);
 
     inputName = EEp_session.GetInputNameAllocated(0, allocator).get();
 
     inputTypeInfo = EEp_session.GetInputTypeInfo(0);
     auto EEp_inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();
 
     inputDims = EEp_inputTensorInfo.GetShape();
 
     outputName = EEp_session.GetOutputNameAllocated(0, allocator).get();
 
     outputTypeInfo = EEp_session.GetOutputTypeInfo(0);
 
     auto EEp_outputTensorInfo = outputTypeInfo.GetTensorTypeAndShapeInfo();
 
     outputDims = EEp_outputTensorInfo.GetShape();
 
     std::vector<float> eepRecoOccMap1dPad(EE_TensorSize);  //To store the output reconstructed occupancy
 
     inputNames.push_back(inputName.c_str());
     outputNames.push_back(outputName.c_str());
 
     //Ort::MemoryInfo
     memoryInfo = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
     inputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, eepOccMap1dCumulPad.data(), EE_TensorSize, inputDims.data(), inputDims.size()));
 
     outputTensors.push_back(Ort::Value::CreateTensor<float>(
         memoryInfo, eepRecoOccMap1dPad.data(), EE_TensorSize, outputDims.data(), outputDims.size()));
 
     EEp_session.Run(Ort::RunOptions{nullptr},
                     inputNames.data(),
                     inputTensors.data(),
                     1,
                     outputNames.data(),
                     outputTensors.data(),
                     1);
 
     ///Inference on the output from the model///
     //2D Loss map to store tower by tower loss between the output (reconstructed) and input occupancies,
     //Have same dimensions as the occupancy plot
     std::valarray<std::valarray<float>> EBlossMap2d(std::valarray<float>(nEBPhiTowers), nEBEtaTowers);
     std::valarray<std::valarray<float>> EEmlossMap2d(std::valarray<float>(nEEPhiTowers), nEEEtaTowers);
     std::valarray<std::valarray<float>> EEplossMap2d(std::valarray<float>(nEEPhiTowers), nEEEtaTowers);
 
     //1D val arrays to store row wise information corresponding to the reconstructed, input and average occupancies, and loss.
     //and to do element wise (tower wise) operations on them to calculate the MSE loss between the reco and input occupancy.
     std::valarray<float> EBrecoOcc1d(0., nEBPhiTowers);
     std::valarray<float> EBinputOcc1d(0., nEBPhiTowers);
     std::valarray<float> EBavgOcc1d(0., nEBPhiTowers);
     std::valarray<float> EBloss_;
 
     std::valarray<float> EEmrecoOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEminputOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEmavgOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEmloss_;
 
     std::valarray<float> EEprecoOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEpinputOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEpavgOcc1d(0., nEEPhiTowers);
     std::valarray<float> EEploss_;
 
     std::string EBOccpath =
         edm::FileInPath("DQM/EcalMonitorClient/data/MLAvgOccupancy/EB_avgocc_Run2022_500ev.dat").fullPath();
     std::ifstream inFile;
     double val;
     inFile.open((EBOccpath).c_str());
     while (inFile) {
       inFile >> val;
       if (inFile.eof())
         break;
       EBavgOcc.push_back(val);
     }
     inFile.close();
 
     std::string EEmOccpath =
         edm::FileInPath("DQM/EcalMonitorClient/data/MLAvgOccupancy/EEm_avgocc_Run2022_500ev.dat").fullPath();
     inFile.open((EEmOccpath).c_str());
     while (inFile) {
       inFile >> val;
       if (inFile.eof())
         break;
       EEmavgOcc.push_back(val);
     }
     inFile.close();
 
     std::string EEpOccpath =
         edm::FileInPath("DQM/EcalMonitorClient/data/MLAvgOccupancy/EEp_avgocc_Run2022_500ev.dat").fullPath();
     inFile.open((EEpOccpath).c_str());
     while (inFile) {
       inFile >> val;
       if (inFile.eof())
         break;
       EEpavgOcc.push_back(val);
     }
     inFile.close();
 
     //Loss calculation
     //Ignore the top and bottom replicated padded rows when doing inference
     //by making index i run over (1,35) instead of (0,36) for EB, and over (1,21) for EE
 
     MESet const& sAEReco(sources_.at("AEReco"));
     TH2F* hEBRecoMap2d(sAEReco.getME(1)->getTH2F());
 
     for (int i = 1; i < nEBEtaTowersPad - 1; i++) {
       for (int j = 0; j < nEBPhiTowers; j++) {
         int k = (i * nEBPhiTowers) + j;
         int bin_ = hEBRecoMap2d->GetBin(j + 1, i);
         EBrecoOcc1d[j] = ebRecoOccMap1dPad[k];
         EBinputOcc1d[j] = ebOccMap1dCumulPad[k];
         EBavgOcc1d[j] = EBavgOcc[k];
         double content = ebRecoOccMap1dPad[k];
         hEBRecoMap2d->SetBinContent(bin_, content);
       }
       //Calculate the MSE loss = (output-input)^2, with avg response correction
       EBloss_ = std::pow((EBrecoOcc1d / EBavgOcc1d - EBinputOcc1d / EBavgOcc1d), 2);
       EBlossMap2d[i - 1] = (EBloss_);
     }
 
     TH2F* hEEmRecoMap2d(sAEReco.getME(0)->getTH2F());
     TH2F* hEEpRecoMap2d(sAEReco.getME(2)->getTH2F());
 
     for (int i = 1; i < nEETowersPad - 1; i++) {
       for (int j = 0; j < nEEPhiTowers; j++) {
         int k = (i * nEETowersPad) + j + 1;
         int bin_ = hEEmRecoMap2d->GetBin(j + 1, i);
 
         EEmrecoOcc1d[j] = eemRecoOccMap1dPad[k];
         EEminputOcc1d[j] = eemOccMap1dCumulPad[k];
         EEmavgOcc1d[j] = EEmavgOcc[k];
         double EEmcontent = eemRecoOccMap1dPad[k];
         hEEmRecoMap2d->SetBinContent(bin_, EEmcontent);
 
         EEprecoOcc1d[j] = eepRecoOccMap1dPad[k];
         EEpinputOcc1d[j] = eepOccMap1dCumulPad[k];
         EEpavgOcc1d[j] = EEpavgOcc[k];
         double EEpcontent = eepRecoOccMap1dPad[k];
         hEEpRecoMap2d->SetBinContent(bin_, EEpcontent);
       }
       //Calculate the MSE loss = (output-input)^2, with avg response correction
       EEmloss_ = std::pow((EEmrecoOcc1d / EEmavgOcc1d - EEminputOcc1d / EEmavgOcc1d), 2);
       EEmlossMap2d[i - 1] = (EEmloss_);
 
       EEploss_ = std::pow((EEprecoOcc1d / EEpavgOcc1d - EEpinputOcc1d / EEpavgOcc1d), 2);
       EEplossMap2d[i - 1] = (EEploss_);
     }
 
     //Store each loss map from the output in the queue
     EBlossMap2dQ.push_back(EBlossMap2d);
     EEmlossMap2dQ.push_back(EEmlossMap2d);
     EEplossMap2dQ.push_back(EEplossMap2d);
 
     //Keep exactly nLSloss loss maps to multiply
     if (EBlossMap2dQ.size() > nLSloss) {
       EBlossMap2dQ.pop_front();
       EEmlossMap2dQ.pop_front();
       EEplossMap2dQ.pop_front();
     }
     if (EBlossMap2dQ.size() < nLSloss) {  //Exit if there are not nLSloss loss maps
       return;
     }
 
     //To hold the final multiplied loss
     std::valarray<std::valarray<float>> EBlossMap2dMult(std::valarray<float>(1., nEBPhiTowers), nEBEtaTowers);
     std::valarray<std::valarray<float>> EEmlossMap2dMult(std::valarray<float>(1., nEEPhiTowers), nEEEtaTowers);
     std::valarray<std::valarray<float>> EEplossMap2dMult(std::valarray<float>(1., nEEPhiTowers), nEEEtaTowers);
 
     //Multiply together the last nLSloss loss maps
     //So that real anomalies which persist with time are enhanced and fluctuations are suppressed.
     for (size_t i = 0; i < EBlossMap2dQ.size(); i++) {
       EBlossMap2dMult *= EBlossMap2dQ[i];
       EEmlossMap2dMult *= EEmlossMap2dQ[i];
       EEplossMap2dMult *= EEplossMap2dQ[i];
     }
 
     //Fill the AELoss ME with the values of this time multiplied loss map
     //MESet const& sAELoss(sources_.at("AELoss"));
     MESet& sAELoss(sources_.at("AELoss"));
 
     TH2F* hEBLossMap2dMult(sAELoss.getME(1)->getTH2F());
 
     for (int i = 0; i < hEBLossMap2dMult->GetNbinsY(); i++) {
       for (int j = 0; j < hEBLossMap2dMult->GetNbinsX(); j++) {
         int bin_ = hEBLossMap2dMult->GetBin(j + 1, i + 1);
         double content = EBlossMap2dMult[i][j];
         hEBLossMap2dMult->SetBinContent(bin_, content);
       }
     }
 
     TH2F* hEEmLossMap2dMult(sAELoss.getME(0)->getTH2F());
     TH2F* hEEpLossMap2dMult(sAELoss.getME(2)->getTH2F());
 
     for (int i = 0; i < hEEmLossMap2dMult->GetNbinsY(); i++) {
       for (int j = 0; j < hEEmLossMap2dMult->GetNbinsX(); j++) {
         int bin_ = hEEmLossMap2dMult->GetBin(j + 1, i + 1);
 
         double EEmcontent = EEmlossMap2dMult[i][j];
         hEEmLossMap2dMult->SetBinContent(bin_, EEmcontent);
 
         double EEpcontent = EEplossMap2dMult[i][j];
         hEEpLossMap2dMult->SetBinContent(bin_, EEpcontent);
       }
     }
 
     ///////////////////// ML Quality Summary /////////////////////
     //Apply the quality threshold on the time multiplied loss map stored in the ME AELoss
     //If anomalous, the tower entry will have a large loss value. If good, the value will be close to zero.
 
     MESet& meBadTowerCount(sources_.at("BadTowerCount"));
     MESet& meBadTowerCountNorm(sources_.at("BadTowerCountNorm"));
     MESet& meTrendMLBadTower(MEs_.at("TrendMLBadTower"));
 
     LScount++;
 
     MESet::const_iterator dAEnd(sAELoss.end(GetElectronicsMap()));
     for (MESet::const_iterator dItr(sAELoss.beginChannel(GetElectronicsMap())); dItr != dAEnd;
          dItr.toNextChannel(GetElectronicsMap())) {
       DetId id(dItr->getId());
 
       bool doMaskML(meMLQualitySummary.maskMatches(id, mask, statusManager_, GetTrigTowerMap()));
 
       float entries(dItr->getBinContent());
 
       int quality(doMaskML ? kMGood : kGood);
       float MLThreshold;
 
       if (id.subdetId() == EcalEndcap) {
         EEDetId eeid(id);
         if (eeid.zside() > 0)
           MLThreshold = EEpThreshold_;
         else
           MLThreshold = EEmThreshold_;
       } else {
         MLThreshold = EBThreshold_;
       }
 
       //If a trigger tower entry is greater than the ML threshold, set it to Bad quality, otherwise Good.
       if (entries > MLThreshold) {
         quality = doMaskML ? kMBad : kBad;
         meBadTowerCount.fill(getEcalDQMSetupObjects(), id);
         if (id.subdetId() == EcalEndcap)
           nbadtowerEE++;
         else
           nbadtowerEB++;
       }
       //Fill the quality summary with the quality of the given tower id.
       meMLQualitySummary.setBinContent(getEcalDQMSetupObjects(), id, double(quality));
 
       double badtowcount(meBadTowerCount.getBinContent(getEcalDQMSetupObjects(), id));
       meBadTowerCountNorm.setBinContent(getEcalDQMSetupObjects(), id, double(badtowcount / LScount));
     }  // ML Quality Summary
 
     meTrendMLBadTower.fill(getEcalDQMSetupObjects(), EcalBarrel, double(timestamp_.iLumi), double(nbadtowerEB));
     meTrendMLBadTower.fill(getEcalDQMSetupObjects(), EcalEndcap, double(timestamp_.iLumi), double(nbadtowerEE));
 
   }  // producePlots()
 
   DEFINE_ECALDQM_WORKER(MLClient);
 }  // namespace ecaldqm

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