TAMakerProtoDUNEBSMWindowAlgorithm.hpp

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#ifndef TRIGGERALGS_PROTODUNEBSMWINDOW_TRIGGERACTIVITYMAKERBSMWINDOW_HPP_
#define TRIGGERALGS_PROTODUNEBSMWINDOW_TRIGGERACTIVITYMAKERBSMWINDOW_HPP_
 
#include "detchannelmaps/TPCChannelMap.hpp"
#include "triggeralgs/TriggerActivityFactory.hpp"
#include "triggeralgs/Types.hpp"
#include "triggeralgs/ProtoDUNEBSMWindow/ProtoDUNEBSMWindow.hpp"
#include "triggeralgs/ProtoDUNEBSMWindow/CompiledModelInterface.hpp"
#include "triggeralgs/ProtoDUNEBSMWindow/DetectorPlaneMap.hpp"
#include "triggeralgs/ProtoDUNEBSMWindow/PDVDEffectiveChannelMap.hpp"
 
#include <vector>
#include <algorithm>
 
namespace triggeralgs {
class TAMakerProtoDUNEBSMWindowAlgorithm : public TriggerActivityMaker
{
 
public:
  void process(const TriggerPrimitive& input_tp, std::vector<TriggerActivity>& output_ta); 
  void configure(const nlohmann::json &config);
 
  ~TAMakerProtoDUNEBSMWindowAlgorithm() override = default;
 
private:
  // Function to handle XGBoost classification
  // Returns true for signal and false for cosmic
  bool compute_treelite_classification();
 
  TriggerActivity construct_ta() const;
  
  // The current time window of TPs
  ProtoDUNEBSMWindow m_current_window;
 
  timestamp_t m_last_pred_time = 0;
  uint64_t m_primitive_count = 0;
 
  // Possible to do batch predictions with XGBoost
  // For now just keep at 1
  const int nbatch = 1;
  // XGBoost takes a row-major flat array
  std::vector<float> flat_batched_inputs;
  // row-major input for Entry objects used for compiled model
  std::vector<Entry> flat_batched_Entries;
 
  // Configurable parameters.
  uint32_t m_adc_threshold_induction = 12000000;
  float m_bdt_threshold = 0.999;
  std::string m_channel_map_name = "PD2VDTPCChannelMap";
  // End of configurable parameters
  
  // Constant parameters defined by XGBoost model training
  // Currently both PD-HD and PD-VD XGBoost models have been trained
  // on data filtered to have an ADC sum > 200k ADC
  const uint32_t m_adc_threshold_collection = 200000;
  const timestamp_t m_window_length = 20000;
  // End constant parameters
 
  // Define time binning
  timestamp_t m_bin_length = 2000;
  // Number of bins fixed by model training
  const int m_num_timebins = 10;
  // Number of bins fixed by model training
  const int m_num_chanbins = 10;
 
  // Geometry information for binning
  std::shared_ptr<dunedaq::detchannelmaps::TPCChannelMap> channelMap; 
  // First channel in each plane and number of channels
  // in a plane not in channel map by default, so add 
  // a struct to access these values
  DetectorPlaneMap m_det_plane_map;
  // In PD-VD want to work with effective offline channel
  // rather than the true offline channel. Have an object that helps
  // to do this. It prevents gaps in channel vs time images
  std::unique_ptr<PDVDEffectiveChannelMap> m_pdvd_eff_channel_mapper = nullptr;
  // If in NP02 and using a PD-VD channel map, set this to true
  bool m_pdvd_map = true;
  // Only use the XGBoost model if we are looking at collection plane TPs
  bool m_collection_plane = false;
  // first and last channel on the plane
  channel_t m_first_channel;
  // Number of channels on the plane (will be no. effective channels for PD-VD)
  channel_t m_n_channels_on_plane;
 
  // Compiled treelite model interface
  std::unique_ptr<CompiledModelInterface> m_compiled_model_interface;
 
};
} // namespace triggeralgs
 
#endif // TRIGGERALGS_PROTODUNEBSMWINDOW_TRIGGERACTIVITYMAKERBSMWINDOW_HPP_