mutex added

Author: warunawickramasingha

Framework/Algorithms/inc/MantidAlgorithms/FindPeaksConvolve.h

@@ -66,7 +66,7 @@ class MANTID_ALGORITHMS_DLL FindPeaksConvolve : public API::Algorithm {
   bool m_centreBins;
   Eigen::VectorXd m_pdf;
   std::vector<std::string> m_intermediateWsNames;
-  std::mutex mtx;
+  std::mutex m_mtx;
 
   void init() override;
   void exec() override;

Framework/Algorithms/src/FindPeaksConvolve.cpp

@@ -188,54 +188,70 @@ void FindPeaksConvolve::performConvolution(const size_t dataIndex) {
             << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
   const auto specNum{m_specNums[dataIndex]};
   const HistogramData::HistogramX *xData{&m_inputDataWS->x(specNum)};
-  std::cout << "performConvolution 2" << std::endl; // TODO remove
+  std::cout << "performConvolution 2"
+            << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
   std::pair<const size_t, const bool> kernelBinCount{getKernelBinCount(xData)};
-  std::cout << "performConvolution 3" << std::endl; // TODO remove
+  std::cout << "performConvolution 3"
+            << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
   if (kernelBinCount.second) {
     g_log.error("The kernel size for spectrum " + std::to_string(m_specNums[dataIndex]) +
                 " exceeds the range of the x axis. Please reduce the peak extent.");
   } else {
-    std::cout << "performConvolution 4" << std::endl; // TODO remove
+    std::cout << "performConvolution 4"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const Tensor1D kernel{createKernel(static_cast<int>(kernelBinCount.first))};
-    std::cout << "performConvolution 5" << std::endl; // TODO remove
+    std::cout << "performConvolution 5"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const auto binCount{m_inputDataWS->getNumberBins(specNum)};
     // Edge handling is performed by padding the input data with 0 values. Each convolution requires a padding of kernel
     // size + 1. The 1st conv is performed with a kernel of size n, the second size n/2. The resultant pad is split
     // either side of the data.
     const double paddingSize = (std::ceil(static_cast<double>(kernelBinCount.first) * 1.5) - 2) / 2;
-    std::cout << "performConvolution 6" << std::endl; // TODO remove
+    std::cout << "performConvolution 6"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const TensorMap_const yData{&m_inputDataWS->y(specNum).front(), binCount};
-    std::cout << "performConvolution 7" << std::endl; // TODO remove
+    std::cout << "performConvolution 7"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     Eigen::array<std::pair<double, double>, 1> paddings{
         std::make_pair(std::ceil(paddingSize), std::floor(paddingSize))};
-    std::cout << "performConvolution 8" << std::endl; // TODO remove
+    std::cout << "performConvolution 8"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const auto yData_padded{yData.pad(paddings)};
-    std::cout << "performConvolution 9" << std::endl; // TODO remove
+    std::cout << "performConvolution 9"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const Eigen::array<ptrdiff_t, 1> dims({0});
-    std::cout << "performConvolution 10" << std::endl; // TODO remove
+    std::cout << "performConvolution 10"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const Tensor1D yConvOutput{yData_padded.convolve(kernel, dims)};
-    std::cout << "performConvolution 11" << std::endl; // TODO remove
+    std::cout << "performConvolution 11"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
 
     const auto eData{TensorMap_const{&m_inputDataWS->e(specNum).front(), binCount}.pad(paddings)};
     const Tensor1D eConvOutput{eData.square().convolve(kernel.square(), dims).sqrt()};
-    std::cout << "performConvolution 12" << std::endl; // TODO remove
+    std::cout << "performConvolution 12"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const Tensor1D smoothKernel{
         createSmoothKernel(static_cast<size_t>(std::ceil(static_cast<double>(kernelBinCount.first) / 2.0)))};
-    std::cout << "performConvolution 13" << std::endl; // TODO remove
+    std::cout << "performConvolution 13"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     Tensor1D iOverSig{(yConvOutput / eConvOutput).unaryExpr([](double val) { return std::isfinite(val) ? val : 0.0; })};
-    std::cout << "performConvolution 14" << std::endl; // TODO remove
+    std::cout << "performConvolution 14"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     const Tensor1D iOverSigConvOutput{iOverSig.convolve(smoothKernel, dims)};
-    std::cout << "performConvolution 15" << std::endl; // TODO remove
+    std::cout << "performConvolution 15"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
 
     extractPeaks(dataIndex, iOverSigConvOutput, xData, yData, kernelBinCount.first / 2);
 
-    std::cout << "performConvolution 16" << std::endl; // TODO remove
+    std::cout << "performConvolution 16"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
     if (m_createIntermediateWorkspaces) {
       auto wsNames = createIntermediateWorkspaces(dataIndex, kernel, iOverSigConvOutput, xData);
-      std::lock_guard<std::mutex> lock(mtx);
+      std::lock_guard<std::mutex> lock(m_mtx);
       m_intermediateWsNames.insert(m_intermediateWsNames.end(), wsNames.cbegin(), wsNames.cend());
     }
-    std::cout << "performConvolution 17" << std::endl; // TODO remove
+    std::cout << "performConvolution 17"
+              << " by thread " << std::this_thread::get_id() << std::endl; // TODO remove
   }
 }
 
@@ -332,6 +348,7 @@ void FindPeaksConvolve::storePeakResults(const size_t dataIndex,
     if (peakCount > m_maxPeakCount) {
       m_maxPeakCount = peakCount;
     }
+    std::lock_guard<std::mutex> lock(m_mtx);
     m_peakResults[dataIndex] = std::move(peakCentres);
   }
 }
@@ -367,6 +384,7 @@ size_t FindPeaksConvolve::findPeakInRawData(const int xIndex, const TensorMap_co
 
 void FindPeaksConvolve::generateNormalPDF(const int peakExtentBinNumber) {
   if (m_pdf.size() == 0) {
+    std::lock_guard<std::mutex> lock(m_mtx);
     m_pdf.resize(peakExtentBinNumber);
     boost::math::normal_distribution<> dist(0.0,
                                             peakExtentBinNumber / 2.0); // assures 2 stddevs in the resultant vector