ProcessBankSplitFullTimeTask.cpp

Go to the documentation of this file.

// Mantid Repository : https://github.com/mantidproject/mantid
//
// Copyright © 2025 ISIS Rutherford Appleton Laboratory UKRI,
//   NScD Oak Ridge National Laboratory, European Spallation Source,
//   Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
// SPDX - License - Identifier: GPL - 3.0 +
 
#include "MantidDataHandling/AlignAndFocusPowderSlim/ProcessBankSplitFullTimeTask.h"
#include "MantidAPI/Run.h"
#include "MantidDataHandling/AlignAndFocusPowderSlim/ProcessEventsTask.h"
#include "MantidKernel/Logger.h"
#include "MantidKernel/ParallelMinMax.h"
#include "MantidKernel/TimeSeriesProperty.h"
#include "MantidKernel/Timer.h"
#include "MantidNexus/H5Util.h"
#include "tbb/parallel_for.h"
#include "tbb/parallel_reduce.h"
 
#include <ranges>
 
namespace Mantid::DataHandling::AlignAndFocusPowderSlim {
 
namespace {
 
// Logger for this class
auto g_log = Kernel::Logger("ProcessBankSplitFullTimeTask");
 
} // namespace
 
ProcessBankSplitFullTimeTask::ProcessBankSplitFullTimeTask(
    std::vector<std::string> &bankEntryNames, H5::H5File &h5file, std::shared_ptr<NexusLoader> loader,
    std::vector<int> &workspaceIndices, std::vector<SpectraProcessingData> &processingDatas,
    const BankCalibrationFactory &calibFactory, const size_t events_per_chunk, const size_t grainsize_event,
    const std::map<Mantid::Types::Core::DateAndTime, int> &splitterMap,
    std::shared_ptr<std::vector<Types::Core::DateAndTime>> pulse_times, std::shared_ptr<API::Progress> &progress)
    : ProcessBankTaskBase(bankEntryNames, loader, calibFactory), m_h5file(h5file), m_workspaceIndices(workspaceIndices),
      m_processingDatas(processingDatas), m_events_per_chunk(events_per_chunk), m_splitterMap(splitterMap),
      m_grainsize_event(grainsize_event), m_pulse_times(pulse_times), m_progress(progress) {}
 
void ProcessBankSplitFullTimeTask::operator()(const tbb::blocked_range<size_t> &range) const {
  auto entry = m_h5file.openGroup("entry"); // type=NXentry
  for (size_t bank_index = range.begin(); bank_index < range.end(); ++bank_index) {
    const auto &bankName = this->bankName(bank_index);
    // empty bank names indicate spectra to skip; control should never get here, but just in case
    if (bankName.empty()) {
      continue;
    }
    Kernel::Timer timer;
    g_log.debug() << bankName << " start" << std::endl;
 
    // open the bank
    auto event_group = entry.openGroup(bankName); // type=NXevent_data
 
    // skip empty dataset
    auto tof_SDS = event_group.openDataSet(NxsFieldNames::TIME_OF_FLIGHT);
    const int64_t total_events = static_cast<size_t>(tof_SDS.getSpace().getSelectNpoints());
    if (total_events == 0) {
      m_progress->report();
      continue;
    }
 
    std::unique_ptr<std::vector<uint64_t>> event_index = std::make_unique<std::vector<uint64_t>>();
    auto eventRanges = this->getEventIndexRanges(event_group, total_events, &event_index);
 
    // get handle to the data
    auto detID_SDS = event_group.openDataSet(NxsFieldNames::DETID);
    std::string tof_unit;
    Nexus::H5Util::readStringAttribute(tof_SDS, "units", tof_unit);
    // now the calibration for the output group can be created
    // which detectors go into the current group - assumes ouput spectrum number is one more than workspace index
    const auto calibrations = this->getCalibrations(tof_unit, bank_index);
 
    // declare arrays once so memory can be reused
    auto event_detid = std::make_unique<std::vector<uint32_t>>();       // uint32 for ORNL nexus file
    auto event_time_of_flight = std::make_unique<std::vector<float>>(); // float for ORNL nexus files
    auto pulse_times_idx = std::make_unique<std::vector<size_t>>();     // index into pulse_times for every event
 
    // read parts of the bank at a time until all events are processed
    while (!eventRanges.empty()) {
      // Create offsets and slab sizes for the next chunk of events.
      // This will read at most m_events_per_chunk events from the file
      // and will split the ranges if necessary for the next iteration.
      std::vector<size_t> offsets;
      std::vector<size_t> slabsizes;
 
      size_t total_events_to_read = 0;
      // Process the event ranges until we reach the desired number of events to read or run out of ranges
      while (!eventRanges.empty() && total_events_to_read < m_events_per_chunk) {
        // Get the next event range from the stack
        auto eventRange = eventRanges.top();
        eventRanges.pop();
 
        size_t range_size = eventRange.second - eventRange.first;
        size_t remaining_chunk = m_events_per_chunk - total_events_to_read;
 
        // If the range size is larger than the remaining chunk, we need to split it
        if (range_size > remaining_chunk) {
          // Split the range: process only part of it now, push the rest back for later
          offsets.push_back(eventRange.first);
          slabsizes.push_back(remaining_chunk);
          total_events_to_read += remaining_chunk;
          // Push the remainder of the range back to the front for next iteration
          eventRanges.emplace(eventRange.first + remaining_chunk, eventRange.second);
          break;
        } else {
          offsets.push_back(eventRange.first);
          slabsizes.push_back(range_size);
          total_events_to_read += range_size;
          // Continue to next range
        }
      }
 
      // log the event ranges being processed
      g_log.debug(toLogString(bankName, total_events_to_read, offsets, slabsizes));
 
      if (total_events_to_read == 0) {
        continue; // nothing to do
      }
 
      // load detid and tof at the same time
      this->loadEvents(detID_SDS, tof_SDS, offsets, slabsizes, event_detid, event_time_of_flight);
 
      pulse_times_idx->resize(total_events_to_read);
      // get the pulsetime of every event, event_index maps the first event of each pulse
      size_t pos = 0;
      auto event_index_it = event_index->cbegin();
      for (size_t i = 0; i < offsets.size(); ++i) {
        const size_t off = offsets[i];
        const size_t slab = slabsizes[i];
        for (size_t j = 0; j < slab; ++j) {
          const uint64_t global_idx = static_cast<uint64_t>(off + j);
          // find pulse: event_index holds the first event index of each pulse
          while (event_index_it != event_index->cend() && *event_index_it <= global_idx) {
            ++event_index_it;
          }
          size_t pulse_idx = 0;
          if (event_index_it != event_index->cbegin()) {
            pulse_idx = static_cast<size_t>(std::distance(event_index->cbegin(), event_index_it) - 1);
          }
          (*pulse_times_idx)[pos++] = pulse_idx;
        }
      }
 
      // Loop over all output spectra / groups
      tbb::parallel_for(
          tbb::blocked_range<size_t>(0, m_processingDatas[0].counts.size()),
          [&](const tbb::blocked_range<size_t> &output_range) {
            for (size_t output_index = output_range.begin(); output_index < output_range.end();
                 ++output_index) { // loop over targets
              const auto calibration = calibrations.at(output_index);
              tbb::parallel_for(
                  tbb::blocked_range<size_t>(0, m_workspaceIndices.size()), [&](const tbb::blocked_range<size_t> &r) {
                    for (size_t idx = r.begin(); idx != r.end(); ++idx) {
                      int i = m_workspaceIndices[idx];
 
                      // Precompute indices for this target
                      std::vector<size_t> indices;
                      auto splitter_it = m_splitterMap.cbegin();
                      for (size_t k = 0; k < event_detid->size(); ++k) {
                        // Calculate the full time at sample: full_time = pulse_time + (tof * correctionFactor), where
                        // correctionFactor is either scale_at_sample[detid] or 1.0
                        const double correctionFactor =
                            calibration.value_scale_at_sample(static_cast<detid_t>((*event_detid)[k]));
 
                        const auto tof_in_nanoseconds =
                            static_cast<int64_t>(static_cast<double>((*event_time_of_flight)[k]) * correctionFactor);
                        const auto pulsetime = (*m_pulse_times)[(*pulse_times_idx)[k]];
                        const Mantid::Types::Core::DateAndTime full_time = pulsetime + tof_in_nanoseconds;
                        // Linear search for pulsetime in splitter map, assume pulsetime and splitter map are both
                        // sorted. This is the starting point for the full_time search so we need to subtract some time
                        // (66.6ms) to ensure we don't skip it when adding the tof. Advance splitter_it until it points
                        // to the first element greater than (pulsetime - offset). This gives us a reasonable starting
                        // point for the full_time search.
                        while (splitter_it != m_splitterMap.end() &&
                               splitter_it->first <= pulsetime - static_cast<int64_t>(PULSETIME_OFFSET)) {
                          ++splitter_it;
                        }
 
                        // Now, starting at splitter_it, find full_time (TOFs will be unsorted)
                        // Advance until we find the first element > full_time, then step back to get the greatest key
                        // <= full_time.
                        auto full_time_it = splitter_it;
                        while (full_time_it != m_splitterMap.end() && full_time_it->first <= full_time) {
                          ++full_time_it;
                        }
 
                        // If there is no element <= full_time then full_time_it will be begin(); otherwise step back to
                        // the element that is <= full_time.
                        if (full_time_it == m_splitterMap.begin()) {
                          // no splitter entry <= full_time; skip this event
                        } else {
                          --full_time_it;
                          if (full_time_it != m_splitterMap.end() && full_time_it->second == i) {
                            indices.push_back(k);
                          }
                        }
                      }
 
                      auto event_id_view_for_target =
                          indices | std::views::transform([&event_detid](const auto &k) { return (*event_detid)[k]; });
                      auto event_tof_view_for_target =
                          indices | std::views::transform(
                                        [&event_time_of_flight](const auto &k) { return (*event_time_of_flight)[k]; });
 
                      ProcessEventsTask task(&event_id_view_for_target, &event_tof_view_for_target, &calibration,
                                             m_processingDatas[i].binedges[output_index]);
 
                      const tbb::blocked_range<size_t> range_info(0, indices.size(), m_grainsize_event);
                      tbb::parallel_reduce(range_info, task);
 
                      // Accumulate results into shared y_temp to combine local histograms
                      // Use atomic fetch_add to accumulate results into shared vectors
                      for (size_t j = 0; j < m_processingDatas[i].counts[output_index].size(); ++j) {
                        m_processingDatas[i].counts[output_index][j].fetch_add(task.y_temp[j],
                                                                               std::memory_order_relaxed);
                      }
                    }
                  });
            }
          });
    }
    g_log.debug() << bankName << " stop" << timer << std::endl;
    m_progress->report();
  }
}
}; // namespace Mantid::DataHandling::AlignAndFocusPowderSlim