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CaloGPUClusterAndCellDataMonitor Class Reference

Places (matched) cluster and cell properties in monitored variables to enable plotting with the Athena THistSvc instead of the custom solution that was being used previously. Its histograms can be configured as in a MonitoringTool. More...

#include <CaloGPUClusterAndCellDataMonitor.h>

Inheritance diagram for CaloGPUClusterAndCellDataMonitor:
Collaboration diagram for CaloGPUClusterAndCellDataMonitor:

Classes

struct  pair_to_plot
 
struct  per_tool_storage
 
struct  sample_comparisons_holder
 

Public Member Functions

 CaloGPUClusterAndCellDataMonitor (const std::string &type, const std::string &name, const IInterface *parent)
 
virtual StatusCode initialize () override
 
virtual ~CaloGPUClusterAndCellDataMonitor ()=default
 
virtual StatusCode update_plots_start (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr) const override
 
virtual StatusCode update_plots_end (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr) const override
 
virtual StatusCode update_plots (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, const CaloClusterCollectionProcessor *tool) const override
 
virtual StatusCode update_plots (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, const CaloRecGPU::EventDataHolder &event_data, const ICaloClusterGPUInputTransformer *tool) const override
 
virtual StatusCode update_plots (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, const CaloRecGPU::EventDataHolder &event_data, const CaloClusterGPUProcessor *tool) const override
 
virtual StatusCode update_plots (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, const CaloRecGPU::EventDataHolder &event_data, const ICaloClusterGPUOutputTransformer *tool) const override
 
virtual StatusCode finalize_plots () const override
 

Private Member Functions

StatusCode initialize_plotted_variables ()
 
StatusCode add_data (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments, const std::string &tool_name) const
 
StatusCode add_combination (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const int index_1, const int index_2, const std::string &prefix, const bool match_in_energy, const bool match_without_shared, const bool match_perfectly) const
 
bool filter_tool_by_name (const std::string &tool_name) const
 Returns true if this tool should be plotted for. More...
 
StatusCode convert_to_GPU_data_structures (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments) const
 
StatusCode compactify_clusters (const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments) const
 Remove invalid clusters, reorder by ET and update the tags accordingly. More...
 
StatusCode match_clusters (sample_comparisons_holder &sch, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::CellInfoArr &cell_info, const CaloRecGPU::CellStateArr &cell_state_1, const CaloRecGPU::CellStateArr &cell_state_2, const CaloRecGPU::ClusterInfoArr &cluster_info_1, const CaloRecGPU::ClusterInfoArr &cluster_info_2, const CaloRecGPU::ClusterMomentsArr &, const CaloRecGPU::ClusterMomentsArr &, const bool match_in_energy, const bool match_without_shared) const
 
StatusCode match_clusters_perfectly (sample_comparisons_holder &sch, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::CellInfoArr &cell_info, const CaloRecGPU::CellStateArr &cell_state_1, const CaloRecGPU::CellStateArr &cell_state_2, const CaloRecGPU::ClusterInfoArr &cluster_info_1, const CaloRecGPU::ClusterInfoArr &cluster_info_2, const CaloRecGPU::ClusterMomentsArr &, const CaloRecGPU::ClusterMomentsArr &, const bool match_without_shared) const
 

Private Attributes

Gaudi::Property< float > m_termThreshold {this, "CellThreshold", 0., "Cell (terminal) threshold (in units of noise Sigma)"}
 Cell (terminal) threshold to use for cluster matching. More...
 
Gaudi::Property< float > m_growThreshold {this, "NeighborThreshold", 2., "Neighbor (grow) threshold (in units of noise Sigma)"}
 Neighbor (growing) threshold to use for cluster matching. More...
 
Gaudi::Property< float > m_seedThreshold {this, "SeedThreshold", 4., "Seed threshold (in units of noise Sigma)"}
 Seed threshold to use for cluster matching. More...
 
SG::ReadHandleKey< CaloCellContainer > m_cellsKey {this, "CellsName", "", "Name(s) of Cell Containers"}
 vector of names of the cell containers to use as input. More...
 
ToolHandle< GenericMonitoringTool > m_moniTool { this, "MonitoringTool", "", "Monitoring tool" }
 Monitoring tool. More...
 
Gaudi::Property< std::vector< SimpleSingleTool > > m_toolsToPlot {this, "ToolsToPlot", {}, "Tools to be plotted individually"}
 Tools to plot individually. More...
 
Gaudi::Property< std::vector< SimpleToolPair > > m_pairsToPlot {this, "PairsToPlot", {}, "Pairs of tools to be compared and plotted"}
 Pairs of tools to compare. More...
 
Gaudi::Property< MatchingOptions > m_matchingOptions {this, "ClusterMatchingParameters", {}, "Parameters for the cluster matching algorithm"}
 Option for adjusting the parameters for the cluster matching algorithm. More...
 
double m_min_similarity = 0.5
 Parameters for the cluster matching algorithm, for easier access. More...
 
double m_seed_weight = 5000.
 
double m_grow_weight = 250.
 
double m_terminal_weight = 10.
 
const CaloCell_ID * m_calo_id {nullptr}
 Pointer to Calo ID Helper. More...
 
std::map< std::string, int > m_toolsToCheckFor
 Map of the strings corresponding to all the tools that will be relevant for plotting (individually or in comparisons) to the index that will be used to identify the tool within the plotter. More...
 
std::map< std::string, std::string > m_toolToIdMap
 Maps tools to their respective identifying prefix for variables. More...
 
int m_numToolsToKeep = 0
 The number of tools that will actually need to be kept in memory for combined plotting. More...
 
std::vector< pair_to_plot > m_toolCombinations
 
std::map< std::string, std::atomic< size_t > > m_numClustersPerTool ATLAS_THREAD_SAFE
 Counts the total number of clusters per tool. More...
 
size_t m_numEvents = 0
 Counts the number of events. More...
 
CaloRecGPU::Helpers::separate_thread_holder< std::vector< per_tool_storage > > m_storageHolder ATLAS_THREAD_SAFE
 Stores the intermediate results needed for tool-level matching. More...
 
std::vector< bool > m_clusterPropertiesToDo
 Control which properties will actually be calculated and stored. More...
 
std::vector< bool > m_comparedClusterPropertiesToDo
 
std::vector< bool > m_extraComparedClusterPropertiesToDo
 
std::vector< bool > m_cellPropertiesToDo
 
std::vector< bool > m_comparedCellPropertiesToDo
 
std::vector< bool > m_cellTypesToDo
 
std::vector< bool > m_comparedCellTypesToDo
 
std::vector< bool > m_extraThingsToDo
 
bool m_doCells = false
 If no properties are asked for, skip the relevant loops entirely... More...
 
bool m_doClusters = false
 
bool m_doCombinedCells = false
 
bool m_doCombinedClusters = false
 
std::atomic< bool > m_plottedVariablesInitialized
 A flag to signal that the variables to be monitored have been detected based on the booked histograms. More...
 
std::mutex m_mutex
 This mutex is locked to ensure only one thread detects the monotired variables. More...
 

Detailed Description

Places (matched) cluster and cell properties in monitored variables to enable plotting with the Athena THistSvc instead of the custom solution that was being used previously. Its histograms can be configured as in a MonitoringTool.

Author
Nuno Fernandes nuno..nosp@m.dos..nosp@m.santo.nosp@m.s.fe.nosp@m.rnand.nosp@m.es@c.nosp@m.ern.c.nosp@m.h
Date
18 March 2023

Definition at line 40 of file CaloGPUClusterAndCellDataMonitor.h.

Constructor & Destructor Documentation

◆ CaloGPUClusterAndCellDataMonitor()

CaloGPUClusterAndCellDataMonitor::CaloGPUClusterAndCellDataMonitor ( const std::string &  type,
const std::string &  name,
const IInterface *  parent 
)

Definition at line 25 of file CaloGPUClusterAndCellDataMonitor.cxx.

25  :
26  base_class(type, name, parent),
27  m_plottedVariablesInitialized(false)
28 {
29 }

◆ ~CaloGPUClusterAndCellDataMonitor()

virtual CaloGPUClusterAndCellDataMonitor::~CaloGPUClusterAndCellDataMonitor ( )
virtualdefault

Member Function Documentation

◆ add_combination()

StatusCode CaloGPUClusterAndCellDataMonitor::add_combination ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const int  index_1,
const int  index_2,
const std::string &  prefix,
const bool  match_in_energy,
const bool  match_without_shared,
const bool  match_perfectly 
) const
private

Definition at line 1842 of file CaloGPUClusterAndCellDataMonitor.cxx.

1850 {
1851 
1852  //Note: Part of the work here is superfluous in the case
1853  // where we are monitoring the tools individually too,
1854  // but in the most generic case that is not guaranteed.
1855  // Partially wasted work, but it's cleaner than the alternative...
1856 
1857  std::vector<per_tool_storage> & store_vec = m_storageHolder.get_for_thread();
1858 
1859  const CaloRecGPU::CellInfoArr & cell_info_1 = store_vec[index_1].cell_info;
1860  const CaloRecGPU::CellStateArr & cell_state_1 = store_vec[index_1].cell_state;
1861  const CaloRecGPU::ClusterInfoArr & clusters_1 = store_vec[index_1].clusters;
1862  const CaloRecGPU::ClusterMomentsArr & moments_1 = store_vec[index_1].moments;
1863 
1864  const CaloRecGPU::CellInfoArr & cell_info_2 = store_vec[index_2].cell_info;
1865  const CaloRecGPU::CellStateArr & cell_state_2 = store_vec[index_2].cell_state;
1866  const CaloRecGPU::ClusterInfoArr & clusters_2 = store_vec[index_2].clusters;
1867  const CaloRecGPU::ClusterMomentsArr & moments_2 = store_vec[index_2].moments;
1868 
1869  sample_comparisons_holder sch;
1870 
1871  if (match_perfectly)
1872  {
1873  ATH_CHECK( match_clusters_perfectly(sch, constant_data, cell_info_1, cell_state_1, cell_state_2,
1874  clusters_1, clusters_2, moments_1, moments_2, match_without_shared) );
1875  }
1876  else
1877  {
1878  ATH_CHECK( match_clusters(sch, constant_data, cell_info_1, cell_state_1, cell_state_2,
1879  clusters_1, clusters_2, moments_1, moments_2, match_in_energy, match_without_shared) );
1880  }
1881 
1882  std::unordered_map<std::string, std::vector<double>> cluster_properties, cell_properties;
1883 
1884  std::unordered_map<std::string, long long int> cell_counts;
1885 
1886  std::vector<double> ref_size_vec(clusters_1.number, 0.), test_size_vec(clusters_2.number, 0.),
1887  ref_weighted_size_vec(clusters_1.number, 0.), test_weighted_size_vec(clusters_2.number, 0.),
1888  ref_diff_cells(clusters_1.number, 0.), test_diff_cells(clusters_2.number, 0.),
1889  ref_diff_cells_weight(clusters_1.number, 0.), test_diff_cells_weight(clusters_2.number, 0.);
1890  //We can store integers up to 2^53 on a double...
1891 
1892  long long int same_energy_1 = 0, same_energy_2 = 0,
1893  same_abs_energy_1 = 0, same_abs_energy_2 = 0,
1894  same_snr_1 = 0, same_snr_2 = 0,
1895  same_abs_snr_1 = 0, same_abs_snr_2 = 0,
1896  same_cluster_cells_count = 0, diff_cluster_cells_count = 0;
1897 
1898  std::set<double> energies_1, energies_2, snrs_1, snrs_2;
1899 
1900  if (m_doCombinedCells)
1901  {
1902  for (int cell = 0; cell < NCaloCells; ++cell)
1903  {
1904  if (!cell_info_1.is_valid(cell) || !cell_info_2.is_valid(cell))
1905  {
1906  continue;
1907  }
1908 
1909  apply_to_multi_class([&](const auto & prop, const size_t i)
1910  {
1911  if (m_comparedCellPropertiesToDo[i])
1912  {
1913  const auto prop_1 = prop.get_property(constant_data, cell_info_1, cell_state_1, clusters_1, moments_1, cell);
1914  const auto prop_2 = prop.get_property(constant_data, cell_info_2, cell_state_2, clusters_2, moments_2, cell);
1915 
1916  cell_properties[prop.name() + "_ref"].push_back(prop_1);
1917  cell_properties[prop.name() + "_test"].push_back(prop_2);
1918 
1919  cell_properties["delta_" + prop.name()].push_back(prop_2 - prop_1);
1920  cell_properties["delta_" + prop.name() + "_rel_ref"].push_back((prop_2 - prop_1) / protect_from_zero(std::abs(prop_1)));
1921  cell_properties["delta_" + prop.name() + "_rel_test"].push_back((prop_2 - prop_1) / protect_from_zero(std::abs(prop_2)));
1922  }
1923  }, BasicCellProperties{});
1924 
1925  apply_to_multi_class([&](const auto & prop, const size_t i)
1926  {
1927  if (m_comparedCellTypesToDo[i])
1928  {
1929  const auto is_1 = prop.is_type(constant_data, cell_info_1, cell_state_1, clusters_1, moments_1, cell);
1930  const auto is_2 = prop.is_type(constant_data, cell_info_2, cell_state_2, clusters_2, moments_2, cell);
1931 
1932  cell_counts["num_" + prop.name() + "_cells_ref"] += is_1;
1933  cell_counts["num_" + prop.name() + "_cells_test"] += is_2;
1934  cell_counts["delta_num_" + prop.name() + "_cells"] += is_2 - is_1;
1935  }
1936  }, BasicCellTypes{});
1937 
1938  const float this_energy_1 = cell_info_1.energy[cell];
1939  const float this_energy_2 = cell_info_2.energy[cell];
1940 
1941  if (m_extraThingsToDo[SameECellsCombined])
1942  {
1943 
1944  if (energies_1.count(this_energy_1))
1945  {
1946  ++same_energy_1;
1947  ++same_abs_energy_1;
1948  }
1949  else if (energies_1.count(-this_energy_1))
1950  {
1951  ++same_abs_energy_1;
1952  }
1953  energies_1.insert(this_energy_1);
1954 
1955  if (energies_2.count(this_energy_2))
1956  {
1957  ++same_energy_2;
1958  ++same_abs_energy_2;
1959  }
1960  else if (energies_2.count(-this_energy_2))
1961  {
1962  ++same_abs_energy_2;
1963  }
1964  energies_2.insert(this_energy_2);
1965  }
1966 
1967  if (m_extraThingsToDo[SameSNRCellsCombined])
1968  {
1969  const float this_snr_1 = this_energy_1 / protect_from_zero(constant_data.m_cell_noise->get_noise(cell, cell_info_1.gain[cell]));
1970 
1971  if (snrs_1.count(this_snr_1))
1972  {
1973  ++same_snr_1;
1974  ++same_abs_snr_1;
1975  }
1976  else if (snrs_1.count(-this_snr_1))
1977  {
1978  ++same_abs_snr_1;
1979  }
1980  snrs_1.insert(this_snr_1);
1981 
1982 
1983  const float this_snr_2 = this_energy_2 / protect_from_zero(constant_data.m_cell_noise->get_noise(cell, cell_info_2.gain[cell]));
1984 
1985  if (snrs_2.count(this_snr_2))
1986  {
1987  ++same_snr_2;
1988  ++same_abs_snr_2;
1989  }
1990  else if (snrs_2.count(-this_snr_2))
1991  {
1992  ++same_abs_snr_2;
1993  }
1994  snrs_2.insert(this_snr_2);
1995  }
1996 
1997  if (m_extraThingsToDo[DiffCells] || m_extraThingsToDo[ClusterComparedSize])
1998  {
1999 
2000  const ClusterTag ref_tag = cell_state_1.clusterTag[cell];
2001  const ClusterTag test_tag = cell_state_2.clusterTag[cell];
2002  int ref_c1 = -3, ref_c2 = -3, test_c1 = -3, test_c2 = -3;
2003  if (ref_tag.is_part_of_cluster())
2004  {
2005  ref_c1 = ref_tag.cluster_index();
2006  ref_c2 = ref_tag.is_shared_between_clusters() ? ref_tag.secondary_cluster_index() : -2;
2007  }
2008 
2009  if (test_tag.is_part_of_cluster())
2010  {
2011  test_c1 = test_tag.cluster_index();
2012  test_c2 = test_tag.is_shared_between_clusters() ? test_tag.secondary_cluster_index() : -2;
2013  }
2014 
2015  const int match_1 = test_c1 < 0 ? -1 : sch.t2r(test_c1);
2016  const int match_2 = test_c2 < 0 ? -1 : sch.t2r(test_c2);
2017 
2018  const float ref_rev_weight = float_unhack(ref_tag.secondary_cluster_weight());
2019  const float test_rev_weight = float_unhack(test_tag.secondary_cluster_weight());
2020 
2021  const float ref_weight = 1.0f - ref_rev_weight;
2022  const float test_weight = 1.0f - test_rev_weight;
2023 
2024  bool cell_is_diff = false;
2025 
2026  if (ref_c1 >= 0)
2027  {
2028  ref_size_vec[ref_c1] += 1;
2029  ref_weighted_size_vec[ref_c1] += ref_weight;
2030 #if CALORECGPU_DISABLE_STRICT_MATCHING
2031  if (!(ref_c1 == match_1 || ref_c1 == match_2 || (match_1 < 0 || match_2 < 0)))
2032 #else
2033  if (!(ref_c1 == match_1 || ref_c1 == match_2))
2034 #endif
2035  {
2036  cell_is_diff = true;
2037  ref_diff_cells[ref_c1] += 1;
2038  ref_diff_cells_weight[ref_c1] += ref_weight;
2039  }
2040  }
2041 
2042  if (ref_c2 >= 0)
2043  {
2044  ref_size_vec[ref_c2] += 1;
2045  ref_weighted_size_vec[ref_c2] += ref_rev_weight;
2046 #if CALORECGPU_DISABLE_STRICT_MATCHING
2047  if (!(ref_c2 == match_1 || ref_c2 == match_2 || (match_1 < 0 || match_2 < 0)))
2048 #else
2049  if (!(ref_c2 == match_1 || ref_c2 == match_2))
2050 #endif
2051  {
2052  cell_is_diff = true;
2053  ref_diff_cells[ref_c2] += 1;
2054  ref_diff_cells_weight[ref_c2] += ref_rev_weight;
2055  }
2056  }
2057 
2058  if (test_c1 >= 0)
2059  {
2060  test_size_vec[test_c1] += 1;
2061  test_weighted_size_vec[test_c1] += test_weight;
2062 #if CALORECGPU_DISABLE_STRICT_MATCHING
2063  if (match_1 >= 0 && !(match_1 == ref_c1 || match_1 == ref_c2 || (ref_c1 < 0 || ref_c2 < 0)))
2064 #else
2065  if (match_1 < 0 || !(match_1 == ref_c1 || match_1 == ref_c2))
2066 #endif
2067  {
2068  cell_is_diff = true;
2069  test_diff_cells[test_c1] += 1;
2070  test_diff_cells_weight[test_c1] += test_weight;
2071  }
2072  }
2073 
2074  if (test_c2 >= 0)
2075  {
2076  test_size_vec[test_c2] += 1;
2077  test_weighted_size_vec[test_c2] += test_rev_weight;
2078 #if CALORECGPU_DISABLE_STRICT_MATCHING
2079  if (match_2 >= 0 && !(match_2 == ref_c1 || match_2 == ref_c2 || (ref_c1 < 0 || ref_c2 < 0)))
2080 #else
2081  if (match_2 < 0 || !(match_2 == ref_c1 || match_2 == ref_c2))
2082 #endif
2083  {
2084  cell_is_diff = true;
2085  test_diff_cells[test_c2] += 1;
2086  test_diff_cells_weight[test_c2] += test_rev_weight;
2087  }
2088  }
2089 
2090  if (!cell_is_diff && (ref_c1 >= 0 || ref_c2 >= 0 || test_c1 >= 0 || test_c2 >= 0))
2091  {
2092  ++same_cluster_cells_count;
2093  }
2094  else if (cell_is_diff)
2095  {
2096  char message_buffer[256];
2097  snprintf(message_buffer, 256,
2098  "%7d | %18f || %6d | %6d | %6d | %6d || %6d | %6d || %016llX | %016llX",
2099  cell, this_energy_1 / protect_from_zero(constant_data.m_cell_noise->get_noise(cell, cell_info_1.gain[cell])),
2100  ref_c1, ref_c2, test_c1, test_c2,
2101  match_1, match_2,
2102  static_cast<tag_type>(ref_tag), static_cast<tag_type>(test_tag));
2103  ATH_MSG_DEBUG(message_buffer);
2104  ++diff_cluster_cells_count;
2105  }
2106 
2107  }
2108  }
2109  }
2110 
2111  if (m_doCombinedClusters)
2112  {
2113 
2114  for (int cluster = 0; cluster < clusters_1.number; ++cluster)
2115  {
2116  const int match = sch.r2t(cluster);
2117  if (match < 0)
2118  //The cluster isn't matched.
2119  {
2120  continue;
2121  }
2122 
2123  apply_to_multi_class([&](const auto & prop, const size_t i)
2124  {
2125  if (m_comparedClusterPropertiesToDo[i])
2126  {
2127  const auto prop_1 = prop.get_property(constant_data, cell_info_1, cell_state_1, clusters_1, moments_1, cluster);
2128  const auto prop_2 = prop.get_property(constant_data, cell_info_2, cell_state_2, clusters_2, moments_2, match);
2129 
2130  cluster_properties[prop.name() + "_ref"].push_back(prop_1);
2131  cluster_properties[prop.name() + "_test"].push_back(prop_2);
2132 
2133  cluster_properties["delta_" + prop.name()].push_back(prop_2 - prop_1);
2134  cluster_properties["delta_" + prop.name() + "_rel_ref"].push_back((prop_2 - prop_1) / protect_from_zero(std::abs(prop_1)));
2135  cluster_properties["delta_" + prop.name() + "_rel_test"].push_back((prop_2 - prop_1) / protect_from_zero(std::abs(prop_2)));
2136  }
2137  }, BasicClusterProperties{});
2138 
2139  apply_to_multi_class([&](const auto & prop, const size_t i)
2140  {
2141  if (m_extraComparedClusterPropertiesToDo[i])
2142  {
2143  cluster_properties[prop.name()].push_back(prop.get_property(constant_data, cell_info_1, cell_state_1, clusters_1, moments_1, cluster,
2144  cell_info_2, cell_state_2, clusters_2, moments_2, match));
2145  }
2146  }, ComparedClusterProperties{});
2147 
2148  if (m_extraThingsToDo[ClusterComparedSize])
2149  {
2150  cluster_properties["size_ref"].push_back(ref_size_vec[cluster]);
2151  cluster_properties["size_test"].push_back(test_size_vec[match]);
2152  cluster_properties["delta_size"].push_back(ref_size_vec[cluster] - test_size_vec[match]);
2153  cluster_properties["delta_size_rel_ref"].push_back((ref_size_vec[cluster] - test_size_vec[match]) / protect_from_zero(ref_size_vec[cluster]));
2154  cluster_properties["delta_size_rel_test"].push_back((ref_size_vec[cluster] - test_size_vec[match]) / protect_from_zero(test_size_vec[match]));
2155 
2156  cluster_properties["weighted_size_ref"].push_back(ref_weighted_size_vec[cluster]);
2157  cluster_properties["weighted_size_test"].push_back(test_weighted_size_vec[match]);
2158  cluster_properties["delta_weighted_size"].push_back(ref_weighted_size_vec[cluster] - test_weighted_size_vec[match]);
2159  cluster_properties["delta_weighted_size_rel_ref"].push_back((ref_weighted_size_vec[cluster] - test_weighted_size_vec[match]) / protect_from_zero(ref_weighted_size_vec[cluster]));
2160  cluster_properties["delta_weighted_size_rel_test"].push_back((ref_weighted_size_vec[cluster] - test_weighted_size_vec[match]) / protect_from_zero(test_weighted_size_vec[match]));
2161  }
2162 
2163  if (m_extraThingsToDo[DiffCells])
2164  {
2165  cluster_properties["diff_cells_ref"].push_back(ref_diff_cells[cluster]);
2166  cluster_properties["diff_cells_ref_rel_size"].push_back(ref_diff_cells[cluster] / protect_from_zero(ref_size_vec[cluster]));
2167  cluster_properties["diff_cells_test"].push_back(test_diff_cells[match]);
2168  cluster_properties["diff_cells_test_rel_size"].push_back(test_diff_cells[match] / protect_from_zero(test_size_vec[match]));
2169  cluster_properties["diff_cells"].push_back(ref_diff_cells[cluster] + test_diff_cells[match]);
2170 
2171  cluster_properties["weighted_diff_cells_ref"].push_back(ref_diff_cells_weight[cluster]);
2172  cluster_properties["weighted_diff_cells_ref_rel_size"].push_back(ref_diff_cells_weight[cluster] / protect_from_zero(ref_weighted_size_vec[cluster]));
2173  cluster_properties["weighted_diff_cells_test"].push_back(test_diff_cells_weight[match]);
2174  cluster_properties["weighted_diff_cells_test_rel_size"].push_back(test_diff_cells_weight[match] / protect_from_zero(test_weighted_size_vec[match]));
2175  cluster_properties["weighted_diff_cells"].push_back(ref_diff_cells_weight[cluster] + test_diff_cells_weight[match]);
2176  }
2177  }
2178  }
2179 
2180  using coll_type = decltype(Monitored::Collection("", std::declval<std::vector<double> &>()));
2181  using scalar_type = decltype(Monitored::Scalar("", std::declval<long long int>()));
2182 
2183  std::vector<coll_type> collections;
2184  std::vector<scalar_type> count_scalars;
2185  std::vector<std::reference_wrapper<Monitored::IMonitoredVariable>> cluster_group, cell_group, counts_group;
2186 
2187  collections.reserve(cluster_properties.size() + cell_properties.size());
2188  count_scalars.reserve(cell_counts.size() + 6 * 3);
2189  cluster_group.reserve(cluster_properties.size());
2190  cell_group.reserve(cell_properties.size());
2191  counts_group.reserve(cell_counts.size() + 3 + 6 * 3);
2192 
2193  auto add_count_vars = [&](const std::string & name, const long long int ref_num, const long long int test_num)
2194  {
2195  count_scalars.emplace_back(Monitored::Scalar(prefix + "_" + name + "_ref", ref_num));
2196  counts_group.push_back(std::ref(count_scalars.back()));
2197 
2198  count_scalars.emplace_back(Monitored::Scalar(prefix + "_" + name + "_test", test_num));
2199  counts_group.push_back(std::ref(count_scalars.back()));
2200 
2201  count_scalars.emplace_back(Monitored::Scalar(prefix + "_delta_" + name, test_num - ref_num));
2202  counts_group.push_back(std::ref(count_scalars.back()));
2203  };
2204 
2205  add_count_vars("num_clusters", clusters_1.number, clusters_2.number);
2206  add_count_vars("num_unmatched_clusters", sch.ref_unmatched(), sch.test_unmatched());
2207 
2208  add_count_vars("num_same_E_cells", same_energy_1, same_energy_2);
2209  add_count_vars("num_same_abs_E_cells", same_abs_energy_1, same_abs_energy_2);
2210  add_count_vars("num_same_SNR_cells", same_snr_1, same_snr_2);
2211  add_count_vars("num_same_abs_SNR_cells", same_abs_snr_1, same_abs_snr_2);
2212 
2213  auto mon_total_unmatched = Monitored::Scalar(prefix + "_num_unmatched_clusters", sch.ref_unmatched() + sch.test_unmatched());
2214  auto mon_same_cluster_cell = Monitored::Scalar(prefix + "_same_cluster_cells", same_cluster_cells_count);
2215  auto mon_diff_cluster_cell = Monitored::Scalar(prefix + "_diff_cluster_cells", diff_cluster_cells_count);
2216 
2217  counts_group.push_back(std::ref(mon_total_unmatched));
2218  counts_group.push_back(std::ref(mon_same_cluster_cell));
2219  counts_group.push_back(std::ref(mon_diff_cluster_cell));
2220 
2221  for (const auto & k_v : cluster_properties)
2222  {
2223  collections.emplace_back(Monitored::Collection(prefix + "_cluster_" + k_v.first, k_v.second));
2224  cluster_group.push_back(std::ref(collections.back()));
2225  }
2226 
2227  for (const auto & k_v : cell_properties)
2228  {
2229  collections.emplace_back(Monitored::Collection(prefix + "_cell_" + k_v.first, k_v.second));
2230  cell_group.push_back(std::ref(collections.back()));
2231  }
2232 
2233  for (const auto & k_v : cell_counts)
2234  {
2235  count_scalars.emplace_back(Monitored::Scalar(prefix + "_" + k_v.first, k_v.second));
2236  counts_group.push_back(std::ref(count_scalars.back()));
2237  }
2238 
2239  auto monitor_clusters = Monitored::Group(m_moniTool, cluster_group);
2240  auto monitor_cells = Monitored::Group(m_moniTool, cell_group);
2241  auto monitor_counts = Monitored::Group(m_moniTool, counts_group);
2242 
2243  return StatusCode::SUCCESS;
2244 }

◆ add_data()

StatusCode CaloGPUClusterAndCellDataMonitor::add_data ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &  cell_info,
const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &  cell_state,
const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &  clusters,
const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &  moments,
const std::string &  tool_name 
) const
private

Definition at line 1656 of file CaloGPUClusterAndCellDataMonitor.cxx.

1663 {
1664  m_numClustersPerTool[tool_name].fetch_add(clusters->number);
1665 
1666  const std::string prefix = m_toolToIdMap.at(tool_name);
1667 
1668  const int index = m_toolsToCheckFor.at(tool_name);
1669 
1670  if (index >= 0 && m_numToolsToKeep > 0)
1671  {
1672  std::vector<per_tool_storage> & store_vec = m_storageHolder.get_for_thread();
1673  store_vec[index].cell_info = *cell_info;
1674  store_vec[index].cell_state = *cell_state;
1675  store_vec[index].clusters = *clusters;
1676  store_vec[index].moments = *moments;
1677  }
1678 
1679  if (prefix != "")
1680  //Tools that are not meant to be plotted individually
1681  //have the empty string as a prefix.
1682  {
1683  std::unordered_map<std::string, std::vector<double>> cluster_properties, cell_properties;
1684 
1685  std::unordered_map<std::string, long long int> cell_counts;
1686 
1687  cluster_properties["size"].resize(clusters->number, 0.);
1688  cluster_properties["weighted_size"].resize(clusters->number, 0.);
1689 
1690  long long int same_energy = 0, same_abs_energy = 0, same_snr = 0, same_abs_snr = 0;
1691 
1692  std::set<double> energies, snrs;
1693 
1694  if (m_doCells)
1695  {
1696 
1697  for (int cell = 0; cell < NCaloCells; ++cell)
1698  {
1699  if (!cell_info->is_valid(cell))
1700  {
1701  continue;
1702  }
1703 
1704  apply_to_multi_class([&](const auto & prop, const size_t i)
1705  {
1706  if (m_cellPropertiesToDo[i])
1707  {
1708  cell_properties[prop.name()].push_back(prop.get_property(constant_data, *cell_info, *cell_state, *clusters, *moments, cell));
1709  }
1710  }, BasicCellProperties{});
1711 
1712  apply_to_multi_class([&](const auto & prop, const size_t i)
1713  {
1714  if (m_cellTypesToDo[i])
1715  {
1716  cell_counts[prop.name()] += prop.is_type(constant_data, *cell_info, *cell_state, *clusters, *moments, cell);
1717  }
1718  }, BasicCellTypes{});
1719 
1720  const float this_energy = cell_info->energy[cell];
1721 
1722  if (m_extraThingsToDo[SameECells])
1723  {
1724 
1725  if (energies.count(this_energy))
1726  {
1727  ++same_energy;
1728  ++same_abs_energy;
1729  }
1730  else if (energies.count(-this_energy))
1731  {
1732  ++same_abs_energy;
1733  }
1734  energies.insert(this_energy);
1735  }
1736 
1737  if (m_extraThingsToDo[SameSNRCells])
1738  {
1739 
1740  const float this_snr = this_energy / protect_from_zero(constant_data.m_cell_noise->get_noise(cell, cell_info->gain[cell]));
1741 
1742  if (snrs.count(this_snr))
1743  {
1744  ++same_snr;
1745  ++same_abs_snr;
1746  }
1747  else if (snrs.count(-this_snr))
1748  {
1749  ++same_abs_snr;
1750  }
1751  snrs.insert(this_snr);
1752  }
1753 
1754  if (m_extraThingsToDo[ClusterSize])
1755  {
1756  const ClusterTag tag = cell_state->clusterTag[cell];
1757  const float weight = float_unhack(tag.secondary_cluster_weight());
1758  if (tag.is_part_of_cluster())
1759  {
1760  cluster_properties["size"][tag.cluster_index()] += 1;
1761  cluster_properties["weighted_size"][tag.cluster_index()] += 1.0f - weight;
1762  if (tag.is_shared_between_clusters())
1763  {
1764  cluster_properties["size"][tag.secondary_cluster_index()] += 1;
1765  cluster_properties["weighted_size"][tag.secondary_cluster_index()] += weight;
1766  }
1767  }
1768  }
1769  }
1770  }
1771 
1772  if (m_doClusters)
1773  {
1774  for (int cluster = 0; cluster < clusters->number; ++cluster)
1775  {
1776  apply_to_multi_class([&](const auto & prop, const size_t i)
1777  {
1778  if (m_clusterPropertiesToDo[i])
1779  {
1780  cluster_properties[prop.name()].push_back(prop.get_property(constant_data, *cell_info, *cell_state, *clusters, *moments, cluster));
1781  }
1782  }, BasicClusterProperties{});
1783  }
1784  }
1785 
1786  using coll_type = decltype(Monitored::Collection("", std::declval<std::vector<double> &>()));
1787  using scalar_type = decltype(Monitored::Scalar("", std::declval<long long int>()));
1788 
1789  std::vector<coll_type> collections;
1790  std::vector<scalar_type> count_scalars;
1791  std::vector<std::reference_wrapper<Monitored::IMonitoredVariable>> cluster_group, cell_group, counts_group;
1792 
1793  collections.reserve(cluster_properties.size() + cell_properties.size());
1794  count_scalars.reserve(cell_counts.size());
1795  cluster_group.reserve(cluster_properties.size());
1796  cell_group.reserve(cell_properties.size());
1797  counts_group.reserve(cell_counts.size() + 5);
1798 
1799  auto mon_clus_num = Monitored::Scalar(prefix + "_num_clusters", clusters->number);
1800  auto mon_same_energy = Monitored::Scalar(prefix + "_num_same_E_cells", same_energy);
1801  auto mon_same_abs_energy = Monitored::Scalar(prefix + "_num_same_abs_E_cells", same_abs_energy);
1802  auto mon_same_snr = Monitored::Scalar(prefix + "_num_same_SNR_cells", same_snr);
1803  auto mon_same_abs_snr = Monitored::Scalar(prefix + "_num_same_abs_SNR_cells", same_abs_snr);
1804 
1805  counts_group.push_back(std::ref(mon_clus_num));
1806  counts_group.push_back(std::ref(mon_same_energy));
1807  counts_group.push_back(std::ref(mon_same_abs_energy));
1808  counts_group.push_back(std::ref(mon_same_snr));
1809  counts_group.push_back(std::ref(mon_same_abs_snr));
1810 
1811  //If we're not doing these plots,
1812  //we're still saving,
1813  //which is slightly inefficient, but.. let's not complicate.
1814 
1815  for (const auto & k_v : cluster_properties)
1816  {
1817  collections.emplace_back(Monitored::Collection(prefix + "_cluster_" + k_v.first, k_v.second));
1818  cluster_group.push_back(std::ref(collections.back()));
1819  }
1820 
1821  for (const auto & k_v : cell_properties)
1822  {
1823  collections.emplace_back(Monitored::Collection(prefix + "_cell_" + k_v.first, k_v.second));
1824  cell_group.push_back(std::ref(collections.back()));
1825  }
1826 
1827  for (const auto & k_v : cell_counts)
1828  {
1829  count_scalars.emplace_back(Monitored::Scalar(prefix + "_num_" + k_v.first + "_cells", k_v.second));
1830  counts_group.push_back(std::ref(count_scalars.back()));
1831  }
1832 
1833  auto monitor_clusters = Monitored::Group(m_moniTool, cluster_group);
1834  auto monitor_cells = Monitored::Group(m_moniTool, cell_group);
1835  auto monitor_counts = Monitored::Group(m_moniTool, counts_group);
1836 
1837  }
1838  return StatusCode::SUCCESS;
1839 }

◆ compactify_clusters()

StatusCode CaloGPUClusterAndCellDataMonitor::compactify_clusters ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &  cell_info,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &  cell_state,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &  clusters,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &  moments 
) const
private

Remove invalid clusters, reorder by ET and update the tags accordingly.

Definition at line 480 of file CaloGPUClusterAndCellDataMonitor.cxx.

486 {
487  std::map<int, int> tag_map;
488 
489  std::vector<int> cluster_order(clusters->number);
490 
491  std::iota(cluster_order.begin(), cluster_order.end(), 0);
492 
493  std::sort(cluster_order.begin(), cluster_order.end(), [&](const int a, const int b)
494  {
495  if (clusters->seedCellID[a] < 0)
496  {
497  return false;
498  //This means that clusters with no cells
499  //(marked as invalid) always compare lower,
500  //so they appear in the end.
501  }
502  else if (clusters->seedCellID[b] < 0)
503  {
504  return true;
505  }
506  return clusters->clusterEt[a] > clusters->clusterEt[b];
507  } );
508 
509  int real_cluster_numbers = clusters->number;
510 
511  for (size_t i = 0; i < cluster_order.size(); ++i)
512  {
513  const int this_id = cluster_order[i];
514  if (clusters->seedCellID[this_id] < 0)
515  {
516  tag_map[this_id] = -1;
517  --real_cluster_numbers;
518  }
519  else
520  {
521  tag_map[this_id] = i;
522  }
523  }
524 
525  const Helpers::CPU_object<ClusterInfoArr> temp_clusters(clusters);
526  const Helpers::CPU_object<ClusterMomentsArr> temp_moments(moments);
527 
528  clusters->number = real_cluster_numbers;
529 
530  for (int i = 0; i < temp_clusters->number; ++i)
531  {
532  clusters->clusterEnergy[i] = temp_clusters->clusterEnergy[cluster_order[i]];
533  clusters->clusterEt[i] = temp_clusters->clusterEt[cluster_order[i]];
534  clusters->clusterEta[i] = temp_clusters->clusterEta[cluster_order[i]];
535  clusters->clusterPhi[i] = temp_clusters->clusterPhi[cluster_order[i]];
536  clusters->seedCellID[i] = temp_clusters->seedCellID[cluster_order[i]];
537  for (int j = 0; j < NumSamplings; ++j)
538  {
539  moments->energyPerSample[j][i] = temp_moments->energyPerSample[j][cluster_order[i]];
540  moments->maxEPerSample[j][i] = temp_moments->maxEPerSample[j][cluster_order[i]];
541  moments->maxPhiPerSample[j][i] = temp_moments->maxPhiPerSample[j][cluster_order[i]];
542  moments->maxEtaPerSample[j][i] = temp_moments->maxEtaPerSample[j][cluster_order[i]];
543  moments->etaPerSample[j][i] = temp_moments->etaPerSample[j][cluster_order[i]];
544  moments->phiPerSample[j][i] = temp_moments->phiPerSample[j][cluster_order[i]];
545  }
546  moments->time[i] = temp_moments->time[cluster_order[i]];
547  moments->firstPhi[i] = temp_moments->firstPhi[cluster_order[i]];
548  moments->firstEta[i] = temp_moments->firstEta[cluster_order[i]];
549  moments->secondR[i] = temp_moments->secondR[cluster_order[i]];
550  moments->secondLambda[i] = temp_moments->secondLambda[cluster_order[i]];
551  moments->deltaPhi[i] = temp_moments->deltaPhi[cluster_order[i]];
552  moments->deltaTheta[i] = temp_moments->deltaTheta[cluster_order[i]];
553  moments->deltaAlpha[i] = temp_moments->deltaAlpha[cluster_order[i]];
554  moments->centerX[i] = temp_moments->centerX[cluster_order[i]];
555  moments->centerY[i] = temp_moments->centerY[cluster_order[i]];
556  moments->centerZ[i] = temp_moments->centerZ[cluster_order[i]];
557  moments->centerMag[i] = temp_moments->centerMag[cluster_order[i]];
558  moments->centerLambda[i] = temp_moments->centerLambda[cluster_order[i]];
559  moments->lateral[i] = temp_moments->lateral[cluster_order[i]];
560  moments->longitudinal[i] = temp_moments->longitudinal[cluster_order[i]];
561  moments->engFracEM[i] = temp_moments->engFracEM[cluster_order[i]];
562  moments->engFracMax[i] = temp_moments->engFracMax[cluster_order[i]];
563  moments->engFracCore[i] = temp_moments->engFracCore[cluster_order[i]];
564  moments->firstEngDens[i] = temp_moments->firstEngDens[cluster_order[i]];
565  moments->secondEngDens[i] = temp_moments->secondEngDens[cluster_order[i]];
566  moments->isolation[i] = temp_moments->isolation[cluster_order[i]];
567  moments->engBadCells[i] = temp_moments->engBadCells[cluster_order[i]];
568  moments->nBadCells[i] = temp_moments->nBadCells[cluster_order[i]];
569  moments->nBadCellsCorr[i] = temp_moments->nBadCellsCorr[cluster_order[i]];
570  moments->badCellsCorrE[i] = temp_moments->badCellsCorrE[cluster_order[i]];
571  moments->badLArQFrac[i] = temp_moments->badLArQFrac[cluster_order[i]];
572  moments->engPos[i] = temp_moments->engPos[cluster_order[i]];
573  moments->significance[i] = temp_moments->significance[cluster_order[i]];
574  moments->cellSignificance[i] = temp_moments->cellSignificance[cluster_order[i]];
575  moments->cellSigSampling[i] = temp_moments->cellSigSampling[cluster_order[i]];
576  moments->avgLArQ[i] = temp_moments->avgLArQ[cluster_order[i]];
577  moments->avgTileQ[i] = temp_moments->avgTileQ[cluster_order[i]];
578  moments->engBadHVCells[i] = temp_moments->engBadHVCells[cluster_order[i]];
579  moments->nBadHVCells[i] = temp_moments->nBadHVCells[cluster_order[i]];
580  moments->PTD[i] = temp_moments->PTD[cluster_order[i]];
581  moments->mass[i] = temp_moments->mass[cluster_order[i]];
582  moments->EMProbability[i] = temp_moments->EMProbability[cluster_order[i]];
583  moments->hadWeight[i] = temp_moments->hadWeight[cluster_order[i]];
584  moments->OOCweight[i] = temp_moments->OOCweight[cluster_order[i]];
585  moments->DMweight[i] = temp_moments->DMweight[cluster_order[i]];
586  moments->tileConfidenceLevel[i] = temp_moments->tileConfidenceLevel[cluster_order[i]];
587  moments->secondTime[i] = temp_moments->secondTime[cluster_order[i]];
588  for (int j = 0; j < NumSamplings; ++j)
589  {
590  moments->nCellSampling[j][i] = temp_moments->nCellSampling[j][cluster_order[i]];
591  }
592  moments->nExtraCellSampling[i] = temp_moments->nExtraCellSampling[cluster_order[i]];
593  moments->numCells[i] = temp_moments->numCells[cluster_order[i]];
594  moments->vertexFraction[i] = temp_moments->vertexFraction[cluster_order[i]];
595  moments->nVertexFraction[i] = temp_moments->nVertexFraction[cluster_order[i]];
596  moments->etaCaloFrame[i] = temp_moments->etaCaloFrame[cluster_order[i]];
597  moments->phiCaloFrame[i] = temp_moments->phiCaloFrame[cluster_order[i]];
598  moments->eta1CaloFrame[i] = temp_moments->eta1CaloFrame[cluster_order[i]];
599  moments->phi1CaloFrame[i] = temp_moments->phi1CaloFrame[cluster_order[i]];
600  moments->eta2CaloFrame[i] = temp_moments->eta2CaloFrame[cluster_order[i]];
601  moments->phi2CaloFrame[i] = temp_moments->phi2CaloFrame[cluster_order[i]];
602  moments->engCalibTot[i] = temp_moments->engCalibTot[cluster_order[i]];
603  moments->engCalibOutL[i] = temp_moments->engCalibOutL[cluster_order[i]];
604  moments->engCalibOutM[i] = temp_moments->engCalibOutM[cluster_order[i]];
605  moments->engCalibOutT[i] = temp_moments->engCalibOutT[cluster_order[i]];
606  moments->engCalibDeadL[i] = temp_moments->engCalibDeadL[cluster_order[i]];
607  moments->engCalibDeadM[i] = temp_moments->engCalibDeadM[cluster_order[i]];
608  moments->engCalibDeadT[i] = temp_moments->engCalibDeadT[cluster_order[i]];
609  moments->engCalibEMB0[i] = temp_moments->engCalibEMB0[cluster_order[i]];
610  moments->engCalibEME0[i] = temp_moments->engCalibEME0[cluster_order[i]];
611  moments->engCalibTileG3[i] = temp_moments->engCalibTileG3[cluster_order[i]];
612  moments->engCalibDeadTot[i] = temp_moments->engCalibDeadTot[cluster_order[i]];
613  moments->engCalibDeadEMB0[i] = temp_moments->engCalibDeadEMB0[cluster_order[i]];
614  moments->engCalibDeadTile0[i] = temp_moments->engCalibDeadTile0[cluster_order[i]];
615  moments->engCalibDeadTileG3[i] = temp_moments->engCalibDeadTileG3[cluster_order[i]];
616  moments->engCalibDeadEME0[i] = temp_moments->engCalibDeadEME0[cluster_order[i]];
617  moments->engCalibDeadHEC0[i] = temp_moments->engCalibDeadHEC0[cluster_order[i]];
618  moments->engCalibDeadFCAL[i] = temp_moments->engCalibDeadFCAL[cluster_order[i]];
619  moments->engCalibDeadLeakage[i] = temp_moments->engCalibDeadLeakage[cluster_order[i]];
620  moments->engCalibDeadUnclass[i] = temp_moments->engCalibDeadUnclass[cluster_order[i]];
621  moments->engCalibFracEM[i] = temp_moments->engCalibFracEM[cluster_order[i]];
622  moments->engCalibFracHad[i] = temp_moments->engCalibFracHad[cluster_order[i]];
623  moments->engCalibFracRest[i] = temp_moments->engCalibFracRest[cluster_order[i]];
624  }
625 
626  for (int i = 0; i < NCaloCells; ++i)
627  {
628  if (!cell_info->is_valid(i))
629  {
630  continue;
631  }
632  const ClusterTag this_tag = cell_state->clusterTag[i];
633  if (!this_tag.is_part_of_cluster())
634  {
635  cell_state->clusterTag[i] = ClusterTag::make_invalid_tag();
636  }
637  else if (this_tag.is_part_of_cluster())
638  {
639  const int old_idx = this_tag.cluster_index();
640  const int new_idx = tag_map[old_idx];
641  const int old_idx2 = this_tag.is_shared_between_clusters() ? this_tag.secondary_cluster_index() : -1;
642  const int new_idx2 = old_idx2 >= 0 ? tag_map[old_idx2] : -1;
643  if (new_idx < 0 && new_idx2 < 0)
644  {
645  cell_state->clusterTag[i] = ClusterTag::make_invalid_tag();
646  }
647  else if (new_idx < 0)
648  {
649  cell_state->clusterTag[i] = ClusterTag::make_tag(new_idx2);
650  }
651  else if (new_idx2 < 0)
652  {
653  cell_state->clusterTag[i] = ClusterTag::make_tag(new_idx);
654  }
655  else
656  {
657  cell_state->clusterTag[i] = ClusterTag::make_tag(new_idx, this_tag.secondary_cluster_weight(), new_idx2);
658  }
659  }
660  }
661 
662  return StatusCode::SUCCESS;
663 }

◆ convert_to_GPU_data_structures()

StatusCode CaloGPUClusterAndCellDataMonitor::convert_to_GPU_data_structures ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &  cell_info,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &  cell_state,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &  clusters,
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &  moments 
) const
private

Definition at line 264 of file CaloGPUClusterAndCellDataMonitor.cxx.

271 {
272  SG::ReadHandle<CaloCellContainer> cell_collection(m_cellsKey, ctx);
273  if ( !cell_collection.isValid() )
274  {
275  ATH_MSG_ERROR( " Cannot retrieve CaloCellContainer: " << cell_collection.name() );
276  return StatusCode::FAILURE;
277  }
278 
279  ret_info.allocate();
280 
281  if (cluster_collection != nullptr)
282  {
283  ret_state.allocate();
284  ret_clusts.allocate();
285  ret_moments.allocate();
286  }
287 
288  for (int i = 0; i < NCaloCells; ++i)
289  {
290  ret_info->energy[i] = 0;
291  ret_info->gain[i] = GainConversion::invalid_gain();
292  ret_info->time[i] = 0;
293  ret_info->qualityProvenance[i] = 0;
294 
295  if (cluster_collection != nullptr)
296  {
297  ret_state->clusterTag[i] = ClusterTag::make_invalid_tag();
298  }
299  }
300 
301  for (CaloCellContainer::const_iterator iCells = cell_collection->begin(); iCells != cell_collection->end(); ++iCells)
302  {
303  const CaloCell * cell = (*iCells);
304 
305  const int index = m_calo_id->calo_cell_hash(cell->ID());
306 
307  const float energy = cell->energy();
308 
309  const unsigned int gain = GainConversion::from_standard_gain(cell->gain());
310 
311  ret_info->energy[index] = energy;
312  ret_info->gain[index] = gain;
313  ret_info->time[index] = cell->time();
314  ret_info->qualityProvenance[index] = QualityProvenance{cell->quality(), cell->provenance()};
315 
316  }
317 
318  if (cluster_collection != nullptr)
319  {
320  const auto cluster_end = cluster_collection->end();
321  auto cluster_iter = cluster_collection->begin();
322 
323  for (int cluster_number = 0; cluster_iter != cluster_end; ++cluster_iter, ++cluster_number )
324  {
325  const xAOD::CaloCluster * cluster = (*cluster_iter);
326  const CaloClusterCellLink * cell_links = cluster->getCellLinks();
327  if (!cell_links)
328  {
329  ATH_MSG_ERROR("Can't get valid links to CaloCells (CaloClusterCellLink)!");
330  return StatusCode::FAILURE;
331  }
332 
333  ret_clusts->clusterEnergy[cluster_number] = cluster->e();
334  ret_clusts->clusterEt[cluster_number] = cluster->et();
335  ret_clusts->clusterEta[cluster_number] = cluster->eta();
336  ret_clusts->clusterPhi[cluster_number] = cluster->phi();
337  ret_clusts->seedCellID[cluster_number] = m_calo_id->calo_cell_hash(cluster->cell_begin()->ID());
338  for (int i = 0; i < NumSamplings; ++i)
339  {
340  ret_moments->energyPerSample[i][cluster_number] = cluster->eSample((CaloSampling::CaloSample) i);
341  ret_moments->maxEPerSample[i][cluster_number] = cluster->energy_max((CaloSampling::CaloSample) i);
342  ret_moments->maxPhiPerSample[i][cluster_number] = cluster->phimax((CaloSampling::CaloSample) i);
343  ret_moments->maxEtaPerSample[i][cluster_number] = cluster->etamax((CaloSampling::CaloSample) i);
344  ret_moments->etaPerSample[i][cluster_number] = cluster->etaSample((CaloSampling::CaloSample) i);
345  ret_moments->phiPerSample[i][cluster_number] = cluster->phiSample((CaloSampling::CaloSample) i);
346  ret_moments->nCellSampling[i][cluster_number] = cluster->numberCellsInSampling((CaloSampling::CaloSample) i);
347  }
348  ret_moments->time[cluster_number] = cluster->time();
349  ret_moments->firstPhi[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::FIRST_PHI);
350  ret_moments->firstEta[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::FIRST_ETA);
351  ret_moments->secondR[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::SECOND_R);
352  ret_moments->secondLambda[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::SECOND_LAMBDA);
353  ret_moments->deltaPhi[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::DELTA_PHI);
354  ret_moments->deltaTheta[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::DELTA_THETA);
355  ret_moments->deltaAlpha[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::DELTA_ALPHA);
356  ret_moments->centerX[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CENTER_X);
357  ret_moments->centerY[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CENTER_Y);
358  ret_moments->centerZ[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CENTER_Z);
359  ret_moments->centerMag[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CENTER_MAG);
360  ret_moments->centerLambda[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CENTER_LAMBDA);
361  ret_moments->lateral[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::LATERAL);
362  ret_moments->longitudinal[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::LONGITUDINAL);
363  ret_moments->engFracEM[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_FRAC_EM);
364  ret_moments->engFracMax[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_FRAC_MAX);
365  ret_moments->engFracCore[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_FRAC_CORE);
366  ret_moments->firstEngDens[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::FIRST_ENG_DENS);
367  ret_moments->secondEngDens[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::SECOND_ENG_DENS);
368  ret_moments->isolation[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ISOLATION);
369  ret_moments->engBadCells[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_BAD_CELLS);
370  ret_moments->nBadCells[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::N_BAD_CELLS);
371  ret_moments->nBadCellsCorr[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::N_BAD_CELLS_CORR);
372  ret_moments->badCellsCorrE[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::BAD_CELLS_CORR_E);
373  ret_moments->badLArQFrac[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::BADLARQ_FRAC);
374  ret_moments->engPos[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_POS);
375  ret_moments->significance[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::SIGNIFICANCE);
376  ret_moments->cellSignificance[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CELL_SIGNIFICANCE);
377  ret_moments->cellSigSampling[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::CELL_SIG_SAMPLING);
378  ret_moments->avgLArQ[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::AVG_LAR_Q);
379  ret_moments->avgTileQ[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::AVG_TILE_Q);
380  ret_moments->engBadHVCells[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_BAD_HV_CELLS);
381  ret_moments->nBadHVCells[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::N_BAD_HV_CELLS);
382  ret_moments->PTD[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::PTD);
383  ret_moments->mass[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::MASS);
384  ret_moments->EMProbability[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::EM_PROBABILITY);
385  ret_moments->hadWeight[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::HAD_WEIGHT);
386  ret_moments->OOCweight[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::OOC_WEIGHT);
387  ret_moments->DMweight[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::DM_WEIGHT);
388  ret_moments->tileConfidenceLevel[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::TILE_CONFIDENCE_LEVEL);
389  ret_moments->secondTime[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::SECOND_TIME);
390  ret_moments->nExtraCellSampling[cluster_number] = cluster->numberCellsInSampling(CaloSampling::EME2, true);
391  ret_moments->numCells[cluster_number] = cluster->numberCells();
392  ret_moments->vertexFraction[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::VERTEX_FRACTION);
393  ret_moments->nVertexFraction[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::NVERTEX_FRACTION);
394  ret_moments->etaCaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ETACALOFRAME);
395  ret_moments->phiCaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::PHICALOFRAME);
396  ret_moments->eta1CaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ETA1CALOFRAME);
397  ret_moments->phi1CaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::PHI1CALOFRAME);
398  ret_moments->eta2CaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ETA2CALOFRAME);
399  ret_moments->phi2CaloFrame[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::PHI2CALOFRAME);
400  ret_moments->engCalibTot[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_TOT);
401  ret_moments->engCalibOutL[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_OUT_L);
402  ret_moments->engCalibOutM[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_OUT_M);
403  ret_moments->engCalibOutT[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_OUT_T);
404  ret_moments->engCalibDeadL[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_L);
405  ret_moments->engCalibDeadM[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_M);
406  ret_moments->engCalibDeadT[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_T);
407  ret_moments->engCalibEMB0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_EMB0);
408  ret_moments->engCalibEME0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_EME0);
409  ret_moments->engCalibTileG3[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_TILEG3);
410  ret_moments->engCalibDeadTot[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_TOT);
411  ret_moments->engCalibDeadEMB0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_EMB0);
412  ret_moments->engCalibDeadTile0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_TILE0);
413  ret_moments->engCalibDeadTileG3[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_TILEG3);
414  ret_moments->engCalibDeadEME0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_EME0);
415  ret_moments->engCalibDeadHEC0[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_HEC0);
416  ret_moments->engCalibDeadFCAL[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_FCAL);
417  ret_moments->engCalibDeadLeakage[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_LEAKAGE);
418  ret_moments->engCalibDeadUnclass[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_DEAD_UNCLASS);
419  ret_moments->engCalibFracEM[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_FRAC_EM);
420  ret_moments->engCalibFracHad[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_FRAC_HAD);
421  ret_moments->engCalibFracRest[cluster_number] = cluster->getMomentValue(xAOD::CaloCluster::ENG_CALIB_FRAC_REST);
422  for (auto it = cell_links->begin(); it != cell_links->end(); ++it)
423  {
424  const int cell_ID = m_calo_id->calo_cell_hash(it->ID());
425  const float weight = it.weight();
426 
427  uint32_t weight_as_int = 0;
428  std::memcpy(&weight_as_int, &weight, sizeof(float));
429  //On the platforms we expect to be running this, it should be fine.
430  //Still UB.
431  //With C++20, we could do that bit-cast thing.
432 
433  if (weight_as_int == 0)
434  {
435  weight_as_int = 1;
436  //Subnormal,
437  //but just to distinguish from
438  //a non-shared cluster.
439  }
440 
441  const ClusterTag other_tag = ret_state->clusterTag[cell_ID];
442 
443  const int other_index = other_tag.is_part_of_cluster() ? other_tag.cluster_index() : -1;
444 
445  if (other_index < 0)
446  {
447  if (weight < 0.5f)
448  {
449  ret_state->clusterTag[cell_ID] = ClusterTag::make_tag(cluster_number, weight_as_int, 0);
450  }
451  else
452  {
453  ret_state->clusterTag[cell_ID] = ClusterTag::make_tag(cluster_number);
454  }
455  }
456  else if (weight > 0.5f)
457  {
458  ret_state->clusterTag[cell_ID] = ClusterTag::make_tag(cluster_number, other_tag.secondary_cluster_weight(), other_index);
459  }
460  else if (weight == 0.5f)
461  //Unlikely, but...
462  {
463  const int max_cluster = cluster_number > other_index ? cluster_number : other_index;
464  const int min_cluster = cluster_number > other_index ? other_index : cluster_number;
465  ret_state->clusterTag[cell_ID] = ClusterTag::make_tag(max_cluster, weight_as_int, min_cluster);
466  }
467  else /*if (weight < 0.5f)*/
468  {
469  ret_state->clusterTag[cell_ID] = ClusterTag::make_tag(other_index, weight_as_int, cluster_number);
470  }
471  }
472  }
473 
474  ret_clusts->number = cluster_collection->size();
475  }
476 
477  return StatusCode::SUCCESS;
478 }

◆ filter_tool_by_name()

bool CaloGPUClusterAndCellDataMonitor::filter_tool_by_name ( const std::string &  tool_name) const
private

Returns true if this tool should be plotted for.

Definition at line 254 of file CaloGPUClusterAndCellDataMonitor.cxx.

256 {
257  ATH_MSG_DEBUG("Checking : '" << tool_name << "': " << m_toolsToCheckFor.count(tool_name));
258  return m_toolsToCheckFor.count(tool_name) > 0;
259 }

◆ finalize_plots()

StatusCode CaloGPUClusterAndCellDataMonitor::finalize_plots ( ) const
overridevirtual

Definition at line 100 of file CaloGPUClusterAndCellDataMonitor.cxx.

101 {
102  //Well, not do plots, just monitor the number of events and the total number of clusters...
103 
104  auto mon_num_events = Monitored::Scalar("num_events", m_numEvents);
105 
106  for (const auto & k_v : m_toolToIdMap)
107  {
108  auto mon_num_clust = Monitored::Scalar(k_v.second + "_num_total_clusters", m_numClustersPerTool.at(k_v.first).load());
109  }
110 
111  return StatusCode::SUCCESS;
112 }

◆ initialize()

StatusCode CaloGPUClusterAndCellDataMonitor::initialize ( )
overridevirtual

Definition at line 31 of file CaloGPUClusterAndCellDataMonitor.cxx.

32 {
33  ATH_CHECK( m_cellsKey.initialize() );
34 
35  ATH_CHECK( detStore()->retrieve(m_calo_id, "CaloCell_ID") );
36 
37  const std::string this_name = this->name();
38 
39  const std::string algorithm_name_prefix = this_name.substr(0, this_name.rfind('.'));
40  //This is so we take into account the fact that tools
41  //are prefixed with the parent algorithm's name.
42 
43  auto final_string = [& algorithm_name_prefix](const std::string & unpref_str) -> std::string
44  {
45  return algorithm_name_prefix + "." + unpref_str;
46  };
47 
48  const MatchingOptions opts = m_matchingOptions;
49 
50  m_min_similarity = opts.min_similarity;
51  m_seed_weight = opts.seed_w;
52  m_grow_weight = opts.grow_w;
53  m_terminal_weight = opts.term_w;
54 
55  for (const auto & tool : m_toolsToPlot)
56  {
57  const std::string tool_name = final_string(tool.tool);
58  m_toolToIdMap[tool_name] = tool.plot_id;
59  m_toolsToCheckFor[tool_name] = -1;
60  }
61 
62  auto add_tool_from_pair = [this](const std::string & name) -> int
63  {
64  if (!m_toolsToCheckFor.count(name))
65  {
66  m_toolsToCheckFor[name] = m_numToolsToKeep;
67  m_toolToIdMap[name] = "";
68  return m_numToolsToKeep++;
69  }
70  else
71  {
72  const int current = m_toolsToCheckFor[name];
73  if (current >= 0)
74  {
75  return current;
76  }
77  else
78  {
79  m_toolsToCheckFor[name] = m_numToolsToKeep;
80  return m_numToolsToKeep++;
81  }
82  }
83  };
84 
85  for (const auto & pair : m_pairsToPlot)
86  {
87  const int first_index = add_tool_from_pair(final_string(pair.tool_ref));
88  const int second_index = add_tool_from_pair(final_string(pair.tool_test));
89  m_toolCombinations.emplace_back(pair_to_plot{first_index, second_index, pair.plot_id,
90  pair.match_in_energy,
91  pair.match_without_shared,
92  pair.match_perfectly});
93  }
94 
95  ATH_CHECK( m_moniTool.retrieve() );
96 
97  return StatusCode::SUCCESS;
98 }

◆ initialize_plotted_variables()

StatusCode CaloGPUClusterAndCellDataMonitor::initialize_plotted_variables ( )
private

Definition at line 1512 of file CaloGPUClusterAndCellDataMonitor.cxx.

1513 {
1514  const std::vector<std::string> histo_strings = m_moniTool->histogramService()->getHists();
1515  //Small problem: other histograms with matching names.
1516  //Mitigated by the fact that we use cell_<property> and cluster_<property>...
1517 
1518  m_clusterPropertiesToDo.resize(BasicClusterProperties::size(), false);
1519  m_comparedClusterPropertiesToDo.resize(BasicClusterProperties::size(), false);
1520  m_extraComparedClusterPropertiesToDo.resize(ComparedClusterProperties::size(), false);
1521  m_cellPropertiesToDo.resize(BasicCellProperties::size(), false);
1522  m_comparedCellPropertiesToDo.resize(BasicCellProperties::size(), false);
1523  m_cellTypesToDo.resize(BasicCellTypes::size(), false);
1524  m_comparedCellTypesToDo.resize(BasicCellTypes::size(), false);
1525  m_extraThingsToDo.resize(ExtraThingsSize, false);
1526 
1527  auto string_contains = [](const std::string & container, const std::string & contained) -> bool
1528  {
1529  return container.find(contained) != std::string::npos;
1530  };
1531 
1532  auto search_lambda = [&](const auto & prop, const size_t count, bool & check,
1533  const std::string & str, std::vector<bool> & to_do,
1534  const std::string & prefix = "", const std::string & suffix = "")
1535  {
1536  if (string_contains(str, prefix + prop.name() + suffix))
1537  {
1538  to_do[count] = true;
1539  check = true;
1540  }
1541  };
1542 
1543  for (const auto & str : histo_strings)
1544  {
1545  bool found = false;
1546 
1547  apply_to_multi_class(search_lambda, BasicCellProperties{}, found, str, m_cellPropertiesToDo, "_cell_");
1548  apply_to_multi_class(search_lambda, BasicCellTypes{}, found, str, m_cellTypesToDo, "_", "_cells");
1549 
1550  if (found)
1551  {
1552  m_doCells = true;
1553  }
1554 
1555  found = false;
1556 
1557  apply_to_multi_class(search_lambda, BasicClusterProperties{}, found, str, m_clusterPropertiesToDo, "_cluster_");
1558 
1559  if (found)
1560  {
1561  m_doClusters = true;
1562  }
1563 
1564  found = false;
1565 
1566  apply_to_multi_class(search_lambda, BasicCellProperties{}, found, str, m_comparedCellPropertiesToDo, "_cell_delta_");
1567  apply_to_multi_class(search_lambda, BasicCellProperties{}, found, str, m_comparedCellPropertiesToDo, "_cell_", "_ref");
1568  apply_to_multi_class(search_lambda, BasicCellProperties{}, found, str, m_comparedCellPropertiesToDo, "_cell_", "_test");
1569  apply_to_multi_class(search_lambda, BasicCellTypes{}, found, str, m_comparedCellTypesToDo, "num_ref_", "_cells");
1570  apply_to_multi_class(search_lambda, BasicCellTypes{}, found, str, m_comparedCellTypesToDo, "num_test_", "_cells");
1571  apply_to_multi_class(search_lambda, BasicCellTypes{}, found, str, m_comparedCellTypesToDo, "delta_", "_cells");
1572 
1573  if (found)
1574  {
1575  m_doCombinedCells = true;
1576  }
1577 
1578  found = false;
1579 
1580  apply_to_multi_class(search_lambda, BasicClusterProperties{}, found, str, m_comparedClusterPropertiesToDo, "_cluster_delta_", "");
1581  apply_to_multi_class(search_lambda, BasicClusterProperties{}, found, str, m_comparedClusterPropertiesToDo, "_cluster_", "_ref");
1582  apply_to_multi_class(search_lambda, BasicClusterProperties{}, found, str, m_comparedClusterPropertiesToDo, "_cluster_", "_test");
1583  apply_to_multi_class(search_lambda, ComparedClusterProperties{}, found, str, m_extraComparedClusterPropertiesToDo);
1584 
1585  if (found)
1586  {
1587  m_doCombinedClusters = true;
1588  }
1589 
1590  if ( string_contains(str, "cluster_size_ref") ||
1591  string_contains(str, "cluster_size_test") ||
1592  string_contains(str, "cluster_delta_size") ||
1593  string_contains(str, "cluster_weighted_size_ref") ||
1594  string_contains(str, "cluster_weighted_size_test") ||
1595  string_contains(str, "cluster_delta_weighted_size") )
1596  {
1597  m_extraThingsToDo[ClusterComparedSize] = true;
1598  m_doCombinedCells = true;
1599  m_doCombinedClusters = true;
1600  }
1601  else if ( string_contains(str, "cluster_size") ||
1602  string_contains(str, "cluster_weighted_size") )
1603  {
1604  m_extraThingsToDo[ClusterSize] = true;
1605  m_doCells = true;
1606  m_doClusters = true;
1607  }
1608 
1609  if (string_contains(str, "cluster_diff_cells"))
1610  {
1611  m_extraThingsToDo[DiffCells] = true;
1612  m_doCombinedCells = true;
1613  m_doCombinedClusters = true;
1614  }
1615 
1616  if ( string_contains(str, "_num_same_E_cells_ref") ||
1617  string_contains(str, "_num_same_E_cells_test") ||
1618  string_contains(str, "delta_num_same_E_cells") ||
1619  string_contains(str, "_num_same_abs_E_cells_ref") ||
1620  string_contains(str, "_num_same_abs_E_cells_test") ||
1621  string_contains(str, "delta_num_same_abs_E_cells") )
1622  {
1623  m_extraThingsToDo[SameECellsCombined] = true;
1624  m_doCombinedCells = true;
1625  }
1626  else if ( string_contains(str, "_num_same_E_cells") ||
1627  string_contains(str, "_num_same_abs_E_cells") )
1628  {
1629  m_extraThingsToDo[SameECells] = true;
1630  m_doCells = true;
1631  }
1632 
1633  if ( string_contains(str, "_num_same_SNR_cells_ref") ||
1634  string_contains(str, "_num_same_SNR_cells_test") ||
1635  string_contains(str, "delta_num_same_SNR_cells") ||
1636  string_contains(str, "_num_same_abs_SNR_cells_ref") ||
1637  string_contains(str, "_num_same_abs_SNR_cells_test") ||
1638  string_contains(str, "delta_num_same_abs_SNR_cells") )
1639  {
1640  m_extraThingsToDo[SameSNRCellsCombined] = true;
1641  m_doCombinedCells = true;
1642  }
1643  else if ( string_contains(str, "_num_same_SNR_cells") ||
1644  string_contains(str, "_num_same_abs_SNR_cells") )
1645  {
1646  m_extraThingsToDo[SameSNRCells] = true;
1647  m_doCells = true;
1648  }
1649  }
1650 
1651  return StatusCode::SUCCESS;
1652 
1653 }

◆ match_clusters()

StatusCode CaloGPUClusterAndCellDataMonitor::match_clusters ( sample_comparisons_holder &  sch,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const CaloRecGPU::CellInfoArr &  cell_info,
const CaloRecGPU::CellStateArr &  cell_state_1,
const CaloRecGPU::CellStateArr &  cell_state_2,
const CaloRecGPU::ClusterInfoArr &  cluster_info_1,
const CaloRecGPU::ClusterInfoArr &  cluster_info_2,
const CaloRecGPU::ClusterMomentsArr &  ,
const CaloRecGPU::ClusterMomentsArr &  ,
const bool  match_in_energy,
const bool  match_without_shared 
) const
private

Definition at line 716 of file CaloGPUClusterAndCellDataMonitor.cxx.

727 {
728  sch.r2t_table.clear();
729  sch.r2t_table.resize(cluster_info_1.number, -1);
730 
731  sch.t2r_table.clear();
732  sch.t2r_table.resize(cluster_info_2.number, -1);
733 
734  std::vector<double> similarity_map(cluster_info_1.number * cluster_info_2.number, 0.);
735 
736  std::vector<double> ref_normalization(cluster_info_1.number, 0.);
737  std::vector<double> test_normalization(cluster_info_2.number, 0.);
738 
739  for (int i = 0; i < NCaloCells; ++i)
740  {
741  const ClusterTag ref_tag = cell_state_1.clusterTag[i];
742  const ClusterTag test_tag = cell_state_2.clusterTag[i];
743 
744  if (!cell_info.is_valid(i))
745  {
746  continue;
747  }
748 
749  double SNR = 0.00001;
750 
751  if (!cell_info.is_bad(*(constant_data.m_geometry), i))
752  {
753  const int gain = cell_info.gain[i];
754 
755  const double cellNoise = constant_data.m_cell_noise->get_noise(i, gain);
756  if (std::isfinite(cellNoise) && cellNoise > 0.0f)
757  {
758  SNR = std::abs(cell_info.energy[i] / cellNoise);
759  }
760  }
761 
762  const double quantity = ( match_in_energy ? std::abs(cell_info.energy[i]) : SNR );
763  const double weight = (quantity + 1e-8) *
764  ( SNR > m_seedThreshold ? (match_in_energy ? 1000 : m_seed_weight) :
765  (
766  SNR > m_growThreshold ? (match_in_energy ? 950 : m_grow_weight) :
767  (
768  SNR > m_termThreshold ? (match_in_energy ? 900 : m_terminal_weight) : (match_in_energy ? 100 : 0)
769  )
770  )
771  );
772  int ref_c1 = -1, ref_c2 = -1, test_c1 = -1, test_c2 = -1;
773 
774  if (ref_tag.is_part_of_cluster())
775  {
776  if (match_without_shared && ref_tag.is_shared_between_clusters())
777  {
778  continue;
779  }
780  ref_c1 = ref_tag.cluster_index();
781  ref_c2 = ref_tag.is_shared_between_clusters() ? ref_tag.secondary_cluster_index() : ref_c1;
782  }
783 
784  if (test_tag.is_part_of_cluster())
785  {
786  if (match_without_shared && test_tag.is_shared_between_clusters())
787  {
788  continue;
789  }
790  test_c1 = test_tag.cluster_index();
791  test_c2 = test_tag.is_shared_between_clusters() ? test_tag.secondary_cluster_index() : test_c1;
792  }
793 
794  float ref_rev_cw = float_unhack(ref_tag.secondary_cluster_weight());
795  float test_rev_cw = float_unhack(test_tag.secondary_cluster_weight());
796 
797  float ref_cw = 1.0f - ref_rev_cw;
798  float test_cw = 1.0f - test_rev_cw;
799 
800  if (ref_c1 >= int(cluster_info_1.number) || ref_c2 >= int(cluster_info_1.number) ||
801  test_c1 >= int(cluster_info_2.number) || test_c2 >= int(cluster_info_2.number) )
802  {
803  ATH_MSG_DEBUG( "Error in matches: " << i << " " << ref_c1 << " " << ref_c2 << " "
804  << test_c1 << " " << test_c2 << " ("
805  << cluster_info_1.number << " | " << cluster_info_2.number << ")" );
806  continue;
807  }
808 
809  if (ref_c1 >= 0 && test_c1 >= 0)
810  {
811  similarity_map[test_c1 * cluster_info_1.number + ref_c1] += weight * ref_cw * test_cw;
812  similarity_map[test_c1 * cluster_info_1.number + ref_c2] += weight * ref_rev_cw * test_cw;
813  similarity_map[test_c2 * cluster_info_1.number + ref_c1] += weight * ref_cw * test_rev_cw;
814  similarity_map[test_c2 * cluster_info_1.number + ref_c2] += weight * ref_rev_cw * test_rev_cw;
815  }
816  if (ref_c1 >= 0)
817  {
818  ref_normalization[ref_c1] += weight * ref_cw * ref_cw;
819  ref_normalization[ref_c2] += weight * ref_rev_cw * ref_rev_cw;
820  }
821  if (test_c1 >= 0)
822  {
823  test_normalization[test_c1] += weight * test_cw * test_cw;
824  test_normalization[test_c2] += weight * test_rev_cw * test_rev_cw;
825  }
826  }
827 
828  for (int testc = 0; testc < cluster_info_2.number; ++testc)
829  {
830  const double test_norm = test_normalization[testc] + double(test_normalization[testc] == 0.);
831  for (int refc = 0; refc < cluster_info_1.number; ++refc)
832  {
833  const double ref_norm = ref_normalization[refc] + double(ref_normalization[refc] == 0.);
834  similarity_map[testc * cluster_info_1.number + refc] /= std::sqrt(ref_norm * test_norm);
835  }
836  }
837 
838  //In essence, the Gale-Shapley Algorithm
839 
840  std::vector<std::vector<int>> sorted_GPU_matches;
841 
842  sorted_GPU_matches.reserve(cluster_info_2.number);
843 
844  for (int testc = 0; testc < cluster_info_2.number; ++testc)
845  {
846  std::vector<int> sorter(cluster_info_1.number);
847  std::iota(sorter.begin(), sorter.end(), 0);
848 
849  std::sort(sorter.begin(), sorter.end(),
850  [&](const int a, const int b)
851  {
852  const double a_weight = similarity_map[testc * cluster_info_1.number + a];
853  const double b_weight = similarity_map[testc * cluster_info_1.number + b];
854  return a_weight > b_weight;
855  }
856  );
857 
858  size_t wanted_size = 0;
859 
860  for (; wanted_size < sorter.size(); ++wanted_size)
861  {
862  const double match_weight = similarity_map[testc * cluster_info_1.number + sorter[wanted_size]];
863  if (match_weight < m_min_similarity)
864  {
865  break;
866  }
867  }
868 
869  //Yeah, we could do a binary search for best worst-case complexity,
870  //but we are expecting 1~2 similar clusters and the rest garbage,
871  //so we're expecting only 1~2 iterations.
872  //This actually means all that sorting is way way overkill,
873  //but we must make sure in the most general case that this works...
874 
875  sorter.resize(wanted_size);
876 
877  sorted_GPU_matches.push_back(sorter);
878  }
879 
880  int num_iter = 0;
881 
882  constexpr int max_iter = 32;
883 
884  std::vector<double> matched_weights(cluster_info_1.number, -1.);
885 
886  std::vector<size_t> skipped_matching(cluster_info_2.number, 0);
887 
888  for (int stop_counter = 0; stop_counter < cluster_info_2.number && num_iter < max_iter; ++num_iter)
889  {
890  stop_counter = 0;
891  for (int testc = 0; testc < int(sorted_GPU_matches.size()); ++testc)
892  {
893  if (skipped_matching[testc] < sorted_GPU_matches[testc].size())
894  {
895  const int match_c = sorted_GPU_matches[testc][skipped_matching[testc]];
896  const double match_weight = similarity_map[testc * cluster_info_1.number + match_c];
897  if (match_weight >= m_min_similarity && match_weight > matched_weights[match_c])
898  {
899  const int prev_match = sch.r2t_table[match_c];
900  if (prev_match >= 0)
901  {
902  ++skipped_matching[prev_match];
903  --stop_counter;
904  }
905  sch.r2t_table[match_c] = testc;
906  matched_weights[match_c] = match_weight;
907  ++stop_counter;
908  }
909  else
910  {
911  ++skipped_matching[testc];
912  }
913  }
914  else
915  {
916  ++stop_counter;
917  }
918  }
919  }
920 
921  sch.unmatched_ref_list.clear();
922  sch.unmatched_test_list.clear();
923 
924  for (size_t i = 0; i < sch.r2t_table.size(); ++i)
925  {
926  const int match = sch.r2t_table[i];
927  if (match < 0)
928  {
929  sch.unmatched_ref_list.push_back(i);
930  }
931  else
932  {
933  sch.t2r_table[match] = i;
934  }
935  }
936 
937  for (size_t i = 0; i < sch.t2r_table.size(); ++i)
938  {
939  if (sch.t2r_table[i] < 0)
940  {
941  sch.unmatched_test_list.push_back(i);
942  }
943  }
944 
945  {
946  char message_buffer[256];
947  snprintf(message_buffer, 256,
948  "%2d: %5d / %5d || %5d / %5d || %3d || %5d | %5d || %5d",
949  num_iter,
950  int(sch.r2t_table.size()) - int(sch.unmatched_ref_list.size()), int(sch.r2t_table.size()),
951  int(sch.t2r_table.size()) - int(sch.unmatched_test_list.size()), int(sch.t2r_table.size()),
952  int(sch.r2t_table.size()) - int(sch.t2r_table.size()),
953  int(sch.unmatched_ref_list.size()),
954  int(sch.unmatched_test_list.size()),
955  int(sch.unmatched_ref_list.size()) - int(sch.unmatched_test_list.size())
956  );
957  ATH_MSG_INFO(message_buffer);
958  }
959 
960  return StatusCode::SUCCESS;
961 
962 }

◆ match_clusters_perfectly()

StatusCode CaloGPUClusterAndCellDataMonitor::match_clusters_perfectly ( sample_comparisons_holder &  sch,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const CaloRecGPU::CellInfoArr &  cell_info,
const CaloRecGPU::CellStateArr &  cell_state_1,
const CaloRecGPU::CellStateArr &  cell_state_2,
const CaloRecGPU::ClusterInfoArr &  cluster_info_1,
const CaloRecGPU::ClusterInfoArr &  cluster_info_2,
const CaloRecGPU::ClusterMomentsArr &  ,
const CaloRecGPU::ClusterMomentsArr &  ,
const bool  match_without_shared 
) const
private

Definition at line 964 of file CaloGPUClusterAndCellDataMonitor.cxx.

974 {
975  sch.r2t_table.clear();
976  sch.r2t_table.resize(cluster_info_1.number, -1);
977 
978  sch.t2r_table.clear();
979  sch.t2r_table.resize(cluster_info_2.number, -1);
980 
981  std::vector<char> match_possibilities(cluster_info_1.number * cluster_info_2.number, 1);
982 
983  for (int i = 0; i < NCaloCells; ++i)
984  {
985  const ClusterTag ref_tag = cell_state_1.clusterTag[i];
986  const ClusterTag test_tag = cell_state_2.clusterTag[i];
987 
988  if (!cell_info.is_valid(i))
989  {
990  continue;
991  }
992 
993  int ref_c1 = -1, ref_c2 = -1, test_c1 = -1, test_c2 = -1;
994 
995  if (ref_tag.is_part_of_cluster())
996  {
997  if (match_without_shared && ref_tag.is_shared_between_clusters())
998  {
999  continue;
1000  }
1001  ref_c1 = ref_tag.cluster_index();
1002  ref_c2 = ref_tag.is_shared_between_clusters() ? ref_tag.secondary_cluster_index() : -1;
1003  }
1004 
1005  if (test_tag.is_part_of_cluster())
1006  {
1007  if (match_without_shared && test_tag.is_shared_between_clusters())
1008  {
1009  continue;
1010  }
1011  test_c1 = test_tag.cluster_index();
1012  test_c2 = test_tag.is_shared_between_clusters() ? test_tag.secondary_cluster_index() : -1;
1013  }
1014 
1015  for (int refc = 0; refc < cluster_info_1.number; ++refc)
1016  {
1017  if (refc == ref_c1 || refc == ref_c2)
1018  {
1019  continue;
1020  }
1021 
1022  if (test_c1 >= 0)
1023  {
1024  match_possibilities[test_c1 * cluster_info_1.number + refc] = 0;
1025  }
1026  if (test_c2 >= 0)
1027  {
1028  match_possibilities[test_c2 * cluster_info_1.number + refc] = 0;
1029  }
1030  }
1031 
1032  for (int testc = 0; testc < cluster_info_2.number; ++testc)
1033  {
1034  if (testc == test_c1 || testc == test_c2)
1035  {
1036  continue;
1037  }
1038 
1039  if (ref_c1 >= 0)
1040  {
1041  match_possibilities[testc * cluster_info_1.number + ref_c1] = 0;
1042  }
1043  if (ref_c2 >= 0)
1044  {
1045  match_possibilities[testc * cluster_info_1.number + ref_c2] = 0;
1046  }
1047  }
1048 
1049  }
1050 
1051  for (int testc = 0; testc < cluster_info_2.number; ++testc)
1052  {
1053  for (int refc = 0; refc < cluster_info_1.number; ++refc)
1054  {
1055  if (match_possibilities[testc * cluster_info_1.number + refc] > 0)
1056  {
1057  sch.r2t_table[refc] = testc;
1058  sch.t2r_table[testc] = refc;
1059  }
1060  }
1061  }
1062 
1063  for (int refc = 0; refc < cluster_info_1.number; ++refc)
1064  {
1065  if (sch.r2t_table[refc] < 0)
1066  {
1067  sch.unmatched_ref_list.push_back(refc);
1068  }
1069  }
1070 
1071  for (int testc = 0; testc < cluster_info_2.number; ++testc)
1072  {
1073  if (sch.t2r_table[testc] < 0)
1074  {
1075  sch.unmatched_test_list.push_back(testc);
1076  }
1077  }
1078 
1079  {
1080  char message_buffer[256];
1081  snprintf(message_buffer, 256,
1082  "%2d: %5d / %5d || %5d / %5d || %3d || %5d | %5d || %5d",
1083  0,
1084  int(sch.r2t_table.size()) - int(sch.unmatched_ref_list.size()), int(sch.r2t_table.size()),
1085  int(sch.t2r_table.size()) - int(sch.unmatched_test_list.size()), int(sch.t2r_table.size()),
1086  int(sch.r2t_table.size()) - int(sch.t2r_table.size()),
1087  int(sch.unmatched_ref_list.size()),
1088  int(sch.unmatched_test_list.size()),
1089  int(sch.unmatched_ref_list.size()) - int(sch.unmatched_test_list.size())
1090  );
1091  ATH_MSG_INFO(message_buffer);
1092  }
1093 
1094  return StatusCode::SUCCESS;
1095 
1096 }

◆ update_plots() [1/4]

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr,
const CaloClusterCollectionProcessor *  tool 
) const
overridevirtual

Definition at line 167 of file CaloGPUClusterAndCellDataMonitor.cxx.

171 {
172  if (filter_tool_by_name(tool->name()))
173  {
174  Helpers::CPU_object<CellInfoArr> cell_info;
175  Helpers::CPU_object<CellStateArr> cell_state;
176  Helpers::CPU_object<ClusterInfoArr> clusters;
177  Helpers::CPU_object<ClusterMomentsArr> moments;
178 
179  ATH_CHECK( convert_to_GPU_data_structures(ctx, constant_data, cluster_collection_ptr,
180  cell_info, cell_state, clusters, moments ) );
181 
182  return add_data(ctx, constant_data, cell_info, cell_state, clusters, moments, tool->name());
183  }
184  else
185  {
186  return StatusCode::SUCCESS;
187  }
188 }

◆ update_plots() [2/4]

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr,
const CaloRecGPU::EventDataHolder &  event_data,
const CaloClusterGPUProcessor *  tool 
) const
overridevirtual

Definition at line 207 of file CaloGPUClusterAndCellDataMonitor.cxx.

212 {
213  if (filter_tool_by_name(tool->name()))
214  {
215  Helpers::CPU_object<CellInfoArr> cell_info = event_data.m_cell_info_dev;
216  Helpers::CPU_object<CellStateArr> cell_state = event_data.m_cell_state_dev;
217  Helpers::CPU_object<ClusterInfoArr> clusters = event_data.m_clusters_dev;
218  Helpers::CPU_object<CaloRecGPU::ClusterMomentsArr> moments = event_data.m_moments_dev;
219 
220  ATH_CHECK( compactify_clusters(ctx, constant_data, cell_info, cell_state, clusters, moments) );
221 
222  return add_data(ctx, constant_data, cell_info, cell_state, clusters, moments, tool->name());
223  }
224  else
225  {
226  return StatusCode::SUCCESS;
227  }
228 }

◆ update_plots() [3/4]

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr,
const CaloRecGPU::EventDataHolder &  event_data,
const ICaloClusterGPUInputTransformer *  tool 
) const
overridevirtual

Definition at line 190 of file CaloGPUClusterAndCellDataMonitor.cxx.

195 {
196  if (filter_tool_by_name(tool->name()))
197  {
198  return add_data(ctx, constant_data, event_data.m_cell_info_dev, event_data.m_cell_state_dev,
199  event_data.m_clusters_dev, event_data.m_moments_dev, tool->name());
200  }
201  else
202  {
203  return StatusCode::SUCCESS;
204  }
205 }

◆ update_plots() [4/4]

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr,
const CaloRecGPU::EventDataHolder &  event_data,
const ICaloClusterGPUOutputTransformer *  tool 
) const
overridevirtual

Definition at line 230 of file CaloGPUClusterAndCellDataMonitor.cxx.

235 {
236  if (filter_tool_by_name(tool->name()))
237  {
238  Helpers::CPU_object<CellInfoArr> cell_info;
239  Helpers::CPU_object<CellStateArr> cell_state;
240  Helpers::CPU_object<ClusterInfoArr> clusters;
241  Helpers::CPU_object<CaloRecGPU::ClusterMomentsArr> moments;
242 
243  ATH_CHECK( convert_to_GPU_data_structures(ctx, constant_data, cluster_collection_ptr,
244  cell_info, cell_state, clusters, moments ) );
245 
246  return add_data(ctx, constant_data, cell_info, cell_state, clusters, moments, tool->name());
247  }
248  else
249  {
250  return StatusCode::SUCCESS;
251  }
252 }

◆ update_plots_end()

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots_end ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr 
) const
overridevirtual

Definition at line 139 of file CaloGPUClusterAndCellDataMonitor.cxx.

142 {
143  ATH_MSG_INFO("");
144 
145  for (const auto & combination : m_toolCombinations)
146  {
147  if (combination.index_ref < 0 || combination.index_test < 0)
148  {
149  ATH_MSG_WARNING("Invalid tool combination, please check your configuration! " << combination.prefix);
150  continue;
151  }
152  ATH_CHECK( add_combination(ctx, constant_data, combination.index_ref, combination.index_test, combination.prefix,
153  combination.match_in_energy, combination.match_without_shared, combination.match_perfectly) );
154  }
155 
156  ATH_MSG_INFO("");
157 
158  if (m_numToolsToKeep > 0)
159  {
160  m_storageHolder.release_one();
161  //Release the tool storage.
162  }
163 
164  return StatusCode::SUCCESS;
165 }

◆ update_plots_start()

StatusCode CaloGPUClusterAndCellDataMonitor::update_plots_start ( const EventContext &  ctx,
const CaloRecGPU::ConstantDataHolder &  constant_data,
const xAOD::CaloClusterContainer *  cluster_collection_ptr 
) const
overridevirtual

Definition at line 114 of file CaloGPUClusterAndCellDataMonitor.cxx.

117 {
118  if (!m_plottedVariablesInitialized.load())
119  {
120  std::lock_guard<std::mutex> lock_guard(m_mutex);
121  if (!m_plottedVariablesInitialized.load())
122  {
123  CaloGPUClusterAndCellDataMonitor * dhis ATLAS_THREAD_SAFE = const_cast<CaloGPUClusterAndCellDataMonitor *>(this);
124  //We have the mutex.
125  //It's safe.
126  ATH_CHECK( dhis->initialize_plotted_variables() );
127  m_plottedVariablesInitialized.store(true);
128  }
129  }
130  if (m_numToolsToKeep > 0)
131  {
132  m_storageHolder.get_one().resize(m_numToolsToKeep);
133  }
134  //Allocate a vector of data holders for this thread and resize it to the necessary size.
135 
136  return StatusCode::SUCCESS;
137 }

Member Data Documentation

◆ ATLAS_THREAD_SAFE [1/2]

std::map<std::string, std::atomic<size_t> > m_numClustersPerTool CaloGPUClusterAndCellDataMonitor::ATLAS_THREAD_SAFE
mutableprivate

Counts the total number of clusters per tool.

Definition at line 275 of file CaloGPUClusterAndCellDataMonitor.h.

◆ ATLAS_THREAD_SAFE [2/2]

CaloRecGPU::Helpers::separate_thread_holder<std::vector<per_tool_storage> > m_storageHolder CaloGPUClusterAndCellDataMonitor::ATLAS_THREAD_SAFE
mutableprivate

Stores the intermediate results needed for tool-level matching.

Definition at line 290 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_calo_id

const CaloCell_ID* CaloGPUClusterAndCellDataMonitor::m_calo_id {nullptr}
private

Pointer to Calo ID Helper.

Definition at line 247 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_cellPropertiesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_cellPropertiesToDo
private

Definition at line 296 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_cellsKey

SG::ReadHandleKey<CaloCellContainer> CaloGPUClusterAndCellDataMonitor::m_cellsKey {this, "CellsName", "", "Name(s) of Cell Containers"}
private

vector of names of the cell containers to use as input.

Definition at line 204 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_cellTypesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_cellTypesToDo
private

Definition at line 297 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_clusterPropertiesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_clusterPropertiesToDo
private

Control which properties will actually be calculated and stored.

In principle, should be automagically filled based on the booked histograms.

Definition at line 295 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_comparedCellPropertiesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_comparedCellPropertiesToDo
private

Definition at line 297 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_comparedCellTypesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_comparedCellTypesToDo
private

Definition at line 298 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_comparedClusterPropertiesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_comparedClusterPropertiesToDo
private

Definition at line 295 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_doCells

bool CaloGPUClusterAndCellDataMonitor::m_doCells = false
private

If no properties are asked for, skip the relevant loops entirely...

Definition at line 302 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_doClusters

bool CaloGPUClusterAndCellDataMonitor::m_doClusters = false
private

Definition at line 302 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_doCombinedCells

bool CaloGPUClusterAndCellDataMonitor::m_doCombinedCells = false
private

Definition at line 302 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_doCombinedClusters

bool CaloGPUClusterAndCellDataMonitor::m_doCombinedClusters = false
private

Definition at line 302 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_extraComparedClusterPropertiesToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_extraComparedClusterPropertiesToDo
private

Definition at line 296 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_extraThingsToDo

std::vector<bool> CaloGPUClusterAndCellDataMonitor::m_extraThingsToDo
private

Definition at line 298 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_grow_weight

double CaloGPUClusterAndCellDataMonitor::m_grow_weight = 250.
private

Definition at line 242 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_growThreshold

Gaudi::Property<float> CaloGPUClusterAndCellDataMonitor::m_growThreshold {this, "NeighborThreshold", 2., "Neighbor (grow) threshold (in units of noise Sigma)"}
private

Neighbor (growing) threshold to use for cluster matching.

Definition at line 194 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_matchingOptions

Gaudi::Property<MatchingOptions> CaloGPUClusterAndCellDataMonitor::m_matchingOptions {this, "ClusterMatchingParameters", {}, "Parameters for the cluster matching algorithm"}
private

Option for adjusting the parameters for the cluster matching algorithm.

Definition at line 230 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_min_similarity

double CaloGPUClusterAndCellDataMonitor::m_min_similarity = 0.5
private

Parameters for the cluster matching algorithm, for easier access.

Definition at line 242 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_moniTool

ToolHandle< GenericMonitoringTool > CaloGPUClusterAndCellDataMonitor::m_moniTool { this, "MonitoringTool", "", "Monitoring tool" }
private

Monitoring tool.

Definition at line 208 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_mutex

std::mutex CaloGPUClusterAndCellDataMonitor::m_mutex
mutableprivate

This mutex is locked to ensure only one thread detects the monotired variables.

Definition at line 313 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_numEvents

size_t CaloGPUClusterAndCellDataMonitor::m_numEvents = 0
private

Counts the number of events.

Definition at line 278 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_numToolsToKeep

int CaloGPUClusterAndCellDataMonitor::m_numToolsToKeep = 0
private

The number of tools that will actually need to be kept in memory for combined plotting.

Definition at line 261 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_pairsToPlot

Gaudi::Property< std::vector<SimpleToolPair> > CaloGPUClusterAndCellDataMonitor::m_pairsToPlot {this, "PairsToPlot", {}, "Pairs of tools to be compared and plotted"}
private

Pairs of tools to compare.

Definition at line 225 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_plottedVariablesInitialized

std::atomic<bool> CaloGPUClusterAndCellDataMonitor::m_plottedVariablesInitialized
mutableprivate

A flag to signal that the variables to be monitored have been detected based on the booked histograms.

Definition at line 308 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_seed_weight

double CaloGPUClusterAndCellDataMonitor::m_seed_weight = 5000.
private

Definition at line 242 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_seedThreshold

Gaudi::Property<float> CaloGPUClusterAndCellDataMonitor::m_seedThreshold {this, "SeedThreshold", 4., "Seed threshold (in units of noise Sigma)"}
private

Seed threshold to use for cluster matching.

Definition at line 199 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_terminal_weight

double CaloGPUClusterAndCellDataMonitor::m_terminal_weight = 10.
private

Definition at line 242 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_termThreshold

Gaudi::Property<float> CaloGPUClusterAndCellDataMonitor::m_termThreshold {this, "CellThreshold", 0., "Cell (terminal) threshold (in units of noise Sigma)"}
private

Cell (terminal) threshold to use for cluster matching.

Definition at line 189 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_toolCombinations

std::vector<pair_to_plot> CaloGPUClusterAndCellDataMonitor::m_toolCombinations
private

Definition at line 272 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_toolsToCheckFor

std::map<std::string, int> CaloGPUClusterAndCellDataMonitor::m_toolsToCheckFor
private

Map of the strings corresponding to all the tools that will be relevant for plotting (individually or in comparisons) to the index that will be used to identify the tool within the plotter.

(Indices of -1 signal tools that are only plotted individually, no need to keep them.)

Definition at line 255 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_toolsToPlot

Gaudi::Property<std::vector<SimpleSingleTool> > CaloGPUClusterAndCellDataMonitor::m_toolsToPlot {this, "ToolsToPlot", {}, "Tools to be plotted individually"}
private

Tools to plot individually.

Warning
If a tool appears more than once with different identifiers, the last one is used.

Definition at line 220 of file CaloGPUClusterAndCellDataMonitor.h.

◆ m_toolToIdMap

std::map<std::string, std::string> CaloGPUClusterAndCellDataMonitor::m_toolToIdMap
private

Maps tools to their respective identifying prefix for variables.

Definition at line 258 of file CaloGPUClusterAndCellDataMonitor.h.

The documentation for this class was generated from the following files:
CaloGPUClusterAndCellDataMonitor::m_cellPropertiesToDo
std::vector< bool > m_cellPropertiesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:296
xAOD::CaloCluster_v1::CENTER_MAG
@ CENTER_MAG
Cluster Centroid ( )
Definition: CaloCluster_v1.h:138
xAOD::CaloCluster_v1::SECOND_R
@ SECOND_R
Second Moment in .
Definition: CaloCluster_v1.h:126
python.PyKernel.retrieve
def retrieve(aClass, aKey=None)
Definition: PyKernel.py:110
AllowedVariables::e
e
Definition: AsgElectronSelectorTool.cxx:37
fillPileUpNoiseLumi.current
current
Definition: fillPileUpNoiseLumi.py:52
CaloRecGPU::CellInfoArr::gain
unsigned char gain[NCaloCells]
Definition: EventInfoDefinitions.h:190
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_UNCLASS
@ ENG_CALIB_DEAD_UNCLASS
Attached Calibration Hit energy in dead material in unclassified areas of the detector.
Definition: CaloCluster_v1.h:249
xAOD::CaloCluster_v1::FIRST_PHI
@ FIRST_PHI
First Moment in .
Definition: CaloCluster_v1.h:124
xAOD::CaloCluster_v1::OOC_WEIGHT
@ OOC_WEIGHT
Out-of-cluster weight (E_ooc/E_w)
Definition: CaloCluster_v1.h:178
xAOD::CaloCluster_v1::ENG_FRAC_CORE
@ ENG_FRAC_CORE
Energy fraction of the sum of the hottest cells in each sampling.
Definition: CaloCluster_v1.h:145
CaloRecGPU::ClusterInfoArr::number
int number
Definition: EventInfoDefinitions.h:329
DataModel_detail::const_iterator
Const iterator class for DataVector/DataList.
Definition: DVLIterator.h:82
TrigDefs::Group
Group
Properties of a chain group.
Definition: GroupProperties.h:13
hotSpotInTAG.suffix
string suffix
Definition: hotSpotInTAG.py:186
ReadCellNoiseFromCool.cell
cell
Definition: ReadCellNoiseFromCool.py:53
xAOD::CaloCluster_v1::VERTEX_FRACTION
@ VERTEX_FRACTION
Vertex fraction of this cluster wrt.
Definition: CaloCluster_v1.h:187
CaloRecGPU::ClusterTag::secondary_cluster_index
constexpr int32_t secondary_cluster_index() const
Definition: TagDefinitions.h:253
ATH_MSG_INFO
#define ATH_MSG_INFO(x)
Definition: AthMsgStreamMacros.h:31
xAOD::CaloCluster_v1::MASS
@ MASS
cell based mass i.e. the mass of the 4-vector sum of all massless positive energetic cells
Definition: CaloCluster_v1.h:175
xAOD::CaloCluster_v1::EM_PROBABILITY
@ EM_PROBABILITY
Classification probability to be em-like.
Definition: CaloCluster_v1.h:176
CaloGPUClusterAndCellDataMonitor::m_extraThingsToDo
std::vector< bool > m_extraThingsToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:298
CaloRecGPU::ClusterTag::is_shared_between_clusters
constexpr bool is_shared_between_clusters() const
Definition: TagDefinitions.h:273
xAOD::CaloCluster_v1::CENTER_X
@ CENTER_X
Cluster Centroid ( )
Definition: CaloCluster_v1.h:134
xAOD::CaloCluster_v1::ENG_BAD_HV_CELLS
@ ENG_BAD_HV_CELLS
Total em-scale energy of cells with bad HV in this cluster.
Definition: CaloCluster_v1.h:170
xAOD::uint32_t
setEventNumber uint32_t
Definition: EventInfo_v1.cxx:127
CaloRecGPU::EventDataHolder::m_moments_dev
CaloRecGPU::Helpers::CUDA_object< CaloRecGPU::ClusterMomentsArr > m_moments_dev
Definition: DataHolders.h:91
SG::ReadHandle
Definition: StoreGate/StoreGate/ReadHandle.h:67
index
Definition: index.py:1
CaloGPUClusterAndCellDataMonitor::m_min_similarity
double m_min_similarity
Parameters for the cluster matching algorithm, for easier access.
Definition: CaloGPUClusterAndCellDataMonitor.h:242
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_LEAKAGE
@ ENG_CALIB_DEAD_LEAKAGE
Attached Calibration Hit energy in dead material behind calorimeters.
Definition: CaloCluster_v1.h:246
xAOD::CaloCluster_v1::ETA2CALOFRAME
@ ETA2CALOFRAME
Eta of sampling 2 in the calo frame (for egamma)
Definition: CaloCluster_v1.h:194
xAOD::CaloCluster_v1::AVG_LAR_Q
@ AVG_LAR_Q
Sum(E_cell_LAr^2 Q_cell_LAr)/Sum(E_cell_LAr^2)
Definition: CaloCluster_v1.h:166
xAOD::CaloCluster_v1::ETA1CALOFRAME
@ ETA1CALOFRAME
Eta of sampling 1 in the calo frame (for egamma)
Definition: CaloCluster_v1.h:192
CaloCondBlobAlgs_fillNoiseFromASCII.gain
gain
Definition: CaloCondBlobAlgs_fillNoiseFromASCII.py:110
CaloRecGPU::Helpers::SimpleHolder
Holds one objects of type \T in memory context Context.
Definition: Calorimeter/CaloRecGPU/CaloRecGPU/Helpers.h:1067
CaloRecGPU::EventDataHolder::m_cell_state_dev
CaloRecGPU::Helpers::CUDA_object< CaloRecGPU::CellStateArr > m_cell_state_dev
Definition: DataHolders.h:89
CaloGPUClusterAndCellDataMonitor::m_comparedClusterPropertiesToDo
std::vector< bool > m_comparedClusterPropertiesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:295
skel.it
it
Definition: skel.GENtoEVGEN.py:407
CaloGPUClusterAndCellDataMonitor::m_plottedVariablesInitialized
std::atomic< bool > m_plottedVariablesInitialized
A flag to signal that the variables to be monitored have been detected based on the booked histograms...
Definition: CaloGPUClusterAndCellDataMonitor.h:308
CaloGPUClusterAndCellDataMonitor::m_cellTypesToDo
std::vector< bool > m_cellTypesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:297
CaloRecGPU::EventDataHolder::m_clusters_dev
CaloRecGPU::Helpers::CUDA_object< CaloRecGPU::ClusterInfoArr > m_clusters_dev
Definition: DataHolders.h:90
xAOD::CaloCluster_v1::ENG_CALIB_TILEG3
@ ENG_CALIB_TILEG3
Calibration Hit energy inside the cluster scintillator.
Definition: CaloCluster_v1.h:225
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_T
@ ENG_CALIB_DEAD_T
Attached Calibration Hit energy in dead material with tight matching (Angle < 0.3).
Definition: CaloCluster_v1.h:217
CaloGPUClusterAndCellDataMonitor::match_clusters
StatusCode match_clusters(sample_comparisons_holder &sch, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::CellInfoArr &cell_info, const CaloRecGPU::CellStateArr &cell_state_1, const CaloRecGPU::CellStateArr &cell_state_2, const CaloRecGPU::ClusterInfoArr &cluster_info_1, const CaloRecGPU::ClusterInfoArr &cluster_info_2, const CaloRecGPU::ClusterMomentsArr &, const CaloRecGPU::ClusterMomentsArr &, const bool match_in_energy, const bool match_without_shared) const
Definition: CaloGPUClusterAndCellDataMonitor.cxx:716
xAOD::CaloCluster_v1::ENG_CALIB_OUT_M
@ ENG_CALIB_OUT_M
Attached Calibration Hit energy outside clusters but inside the calorimeter with medium matching (Ang...
Definition: CaloCluster_v1.h:203
xAOD::CaloCluster_v1::CENTER_LAMBDA
@ CENTER_LAMBDA
Shower depth at Cluster Centroid.
Definition: CaloCluster_v1.h:139
CaloRecGPU::CellInfoArr::energy
float energy[NCaloCells]
Definition: EventInfoDefinitions.h:189
xAOD::CaloCluster_v1::ENG_CALIB_EMB0
@ ENG_CALIB_EMB0
Calibration Hit energy inside the cluster barrel presampler.
Definition: CaloCluster_v1.h:221
postInclude.sorter
sorter
Definition: postInclude.SortInput.py:23
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_TILE0
@ ENG_CALIB_DEAD_TILE0
Attached Calibration Hit energy in dead material between EMB3 and TILE0.
Definition: CaloCluster_v1.h:233
CaloRecGPU::CellStateArr::clusterTag
tag_type clusterTag[NCaloCells]
Definition: EventInfoDefinitions.h:324
CaloRecGPU::NumSamplings
constexpr int NumSamplings
Definition: BaseDefinitions.h:44
xAOD::CaloCluster_v1::ENG_FRAC_MAX
@ ENG_FRAC_MAX
Energy fraction of hottest cell.
Definition: CaloCluster_v1.h:143
CaloRecGPU::ConstantDataHolder::m_geometry
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::GeometryArr > m_geometry
Definition: DataHolders.h:24
CaloGPUClusterAndCellDataMonitor::add_data
StatusCode add_data(const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments, const std::string &tool_name) const
Definition: CaloGPUClusterAndCellDataMonitor.cxx:1656
xAOD::CaloCluster_v1::PHI1CALOFRAME
@ PHI1CALOFRAME
Phi of sampling 1 in the calo frame (for egamma)
Definition: CaloCluster_v1.h:193
CaloGPUClusterAndCellDataMonitor::m_numEvents
size_t m_numEvents
Counts the number of events.
Definition: CaloGPUClusterAndCellDataMonitor.h:278
python.CaloAddPedShiftConfig.type
type
Definition: CaloAddPedShiftConfig.py:42
CaloGPUClusterAndCellDataMonitor::m_terminal_weight
double m_terminal_weight
Definition: CaloGPUClusterAndCellDataMonitor.h:242
XMLtoHeader.count
count
Definition: XMLtoHeader.py:85
CaloGPUClusterAndCellDataMonitor::m_seedThreshold
Gaudi::Property< float > m_seedThreshold
Seed threshold to use for cluster matching.
Definition: CaloGPUClusterAndCellDataMonitor.h:199
CaloRecGPU::ClusterTag
Definition: TagDefinitions.h:222
CaloRecGPU::EventDataHolder::m_cell_info_dev
CaloRecGPU::Helpers::CUDA_object< CaloRecGPU::CellInfoArr > m_cell_info_dev
Definition: DataHolders.h:88
Monitored::Collection
ValuesCollection< T > Collection(std::string name, const T &collection)
Declare a monitored (double-convertible) collection.
Definition: MonitoredCollection.h:38
CaloGPUClusterAndCellDataMonitor::m_toolsToCheckFor
std::map< std::string, int > m_toolsToCheckFor
Map of the strings corresponding to all the tools that will be relevant for plotting (individually or...
Definition: CaloGPUClusterAndCellDataMonitor.h:255
xAOD::CaloCluster_v1::SECOND_LAMBDA
@ SECOND_LAMBDA
Second Moment in .
Definition: CaloCluster_v1.h:127
dqt_zlumi_pandas.weight
int weight
Definition: dqt_zlumi_pandas.py:189
xAOD::CaloCluster_v1::PTD
@ PTD
relative spread of pT of constiuent cells = sqrt(n)*RMS/Mean
Definition: CaloCluster_v1.h:173
xAOD::CaloCluster_v1::CENTER_Z
@ CENTER_Z
Cluster Centroid ( )
Definition: CaloCluster_v1.h:136
CaloGPUClusterAndCellDataMonitor::m_doCombinedCells
bool m_doCombinedCells
Definition: CaloGPUClusterAndCellDataMonitor.h:302
python.setupRTTAlg.size
int size
Definition: setupRTTAlg.py:39
CaloRecGPU::ClusterTag::cluster_index
constexpr int32_t cluster_index() const
Definition: TagDefinitions.h:243
xAOD::CaloCluster_v1::SECOND_ENG_DENS
@ SECOND_ENG_DENS
Second Moment in E/V.
Definition: CaloCluster_v1.h:147
CaloGPUClusterAndCellDataMonitor::m_grow_weight
double m_grow_weight
Definition: CaloGPUClusterAndCellDataMonitor.h:242
CaloRecGPU::CellStateArr
Definition: EventInfoDefinitions.h:323
xAOD::CaloCluster_v1
Description of a calorimeter cluster.
Definition: CaloCluster_v1.h:62
xAOD::CaloCluster_v1::DM_WEIGHT
@ DM_WEIGHT
Dead-material weight (E_dm/E_ooc)
Definition: CaloCluster_v1.h:179
CaloGPUClusterAndCellDataMonitor::m_growThreshold
Gaudi::Property< float > m_growThreshold
Neighbor (growing) threshold to use for cluster matching.
Definition: CaloGPUClusterAndCellDataMonitor.h:194
CaloGPUClusterAndCellDataMonitor::m_toolToIdMap
std::map< std::string, std::string > m_toolToIdMap
Maps tools to their respective identifying prefix for variables.
Definition: CaloGPUClusterAndCellDataMonitor.h:258
CaloRecGPU::tag_type
TagBase::carrier tag_type
Definition: TagDefinitions.h:325
xAOD::CaloCluster_v1::HAD_WEIGHT
@ HAD_WEIGHT
Hadronic weight (E_w/E_em)
Definition: CaloCluster_v1.h:177
ATH_MSG_ERROR
#define ATH_MSG_ERROR(x)
Definition: AthMsgStreamMacros.h:33
CaloGPUClusterAndCellDataMonitor::m_moniTool
ToolHandle< GenericMonitoringTool > m_moniTool
Monitoring tool.
Definition: CaloGPUClusterAndCellDataMonitor.h:208
ParticleGun_FastCalo_ChargeFlip_Config.energy
energy
Definition: ParticleGun_FastCalo_ChargeFlip_Config.py:78
columnar::ContainerId::cluster
@ cluster
CaloGPUClusterAndCellDataMonitor::m_calo_id
const CaloCell_ID * m_calo_id
Pointer to Calo ID Helper.
Definition: CaloGPUClusterAndCellDataMonitor.h:247
lumiFormat.i
int i
Definition: lumiFormat.py:85
CaloSampling::CaloSample
CaloSample
Definition: Calorimeter/CaloGeoHelpers/CaloGeoHelpers/CaloSampling.h:22
ATH_MSG_DEBUG
#define ATH_MSG_DEBUG(x)
Definition: AthMsgStreamMacros.h:29
xAOD::CaloCluster_v1::CELL_SIG_SAMPLING
@ CELL_SIG_SAMPLING
CaloSample of the cell with the largest |E|/sig.
Definition: CaloCluster_v1.h:164
xAOD::CaloCluster_v1::ENG_CALIB_TOT
@ ENG_CALIB_TOT
Calibration Hit energy inside the cluster.
Definition: CaloCluster_v1.h:198
CaloGPUClusterAndCellDataMonitor::m_cellsKey
SG::ReadHandleKey< CaloCellContainer > m_cellsKey
vector of names of the cell containers to use as input.
Definition: CaloGPUClusterAndCellDataMonitor.h:204
python.CaloAddPedShiftConfig.str
str
Definition: CaloAddPedShiftConfig.py:42
CaloGPUClusterAndCellDataMonitor::m_clusterPropertiesToDo
std::vector< bool > m_clusterPropertiesToDo
Control which properties will actually be calculated and stored.
Definition: CaloGPUClusterAndCellDataMonitor.h:295
xAOD::CaloCluster_v1::ENG_CALIB_FRAC_EM
@ ENG_CALIB_FRAC_EM
Calibration Hit energy inside the cluster caused by e/gamma/pi0.
Definition: CaloCluster_v1.h:251
xAOD::CaloCluster_v1::ISOLATION
@ ISOLATION
Energy weighted fraction of non-clustered perimeter cells.
Definition: CaloCluster_v1.h:149
xAOD::CaloCluster_v1::ENG_CALIB_FRAC_REST
@ ENG_CALIB_FRAC_REST
Calibration Hit energy inside the cluster caused by other particles.
Definition: CaloCluster_v1.h:256
checkCorrelInHIST.prefix
dictionary prefix
Definition: checkCorrelInHIST.py:391
CaloRecGPU::ClusterInfoArr
Definition: EventInfoDefinitions.h:328
CaloGPUClusterAndCellDataMonitor::m_mutex
std::mutex m_mutex
This mutex is locked to ensure only one thread detects the monotired variables.
Definition: CaloGPUClusterAndCellDataMonitor.h:313
test_pyathena.parent
parent
Definition: test_pyathena.py:15
CaloRecGPU::ConstantDataHolder::m_cell_noise
CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellNoiseArr > m_cell_noise
Definition: DataHolders.h:26
CaloGPUClusterAndCellDataMonitor::m_matchingOptions
Gaudi::Property< MatchingOptions > m_matchingOptions
Option for adjusting the parameters for the cluster matching algorithm.
Definition: CaloGPUClusterAndCellDataMonitor.h:231
xAOD::CaloCluster_v1::DELTA_PHI
@ DELTA_PHI
Angular shower axis deviation ( ) from IP-to-Center.
Definition: CaloCluster_v1.h:129
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_FCAL
@ ENG_CALIB_DEAD_FCAL
Attached Calibration Hit energy in dead material before FCAL, between FCAL and HEC.
Definition: CaloCluster_v1.h:244
ATH_CHECK
#define ATH_CHECK
Definition: AthCheckMacros.h:40
CaloGPUClusterAndCellDataMonitor::m_doClusters
bool m_doClusters
Definition: CaloGPUClusterAndCellDataMonitor.h:302
hist_file_dump.f
f
Definition: hist_file_dump.py:141
CaloRecGPU::NCaloCells
constexpr int NCaloCells
Definition: BaseDefinitions.h:13
SG::VarHandleKey::initialize
StatusCode initialize(bool used=true)
If this object is used as a property, then this should be called during the initialize phase.
Definition: AthToolSupport/AsgDataHandles/Root/VarHandleKey.cxx:103
xAOD::double
double
Definition: CompositeParticle_v1.cxx:159
xAOD::CaloCluster_v1::FIRST_ENG_DENS
@ FIRST_ENG_DENS
First Moment in E/V.
Definition: CaloCluster_v1.h:146
CaloGPUClusterAndCellDataMonitor::m_extraComparedClusterPropertiesToDo
std::vector< bool > m_extraComparedClusterPropertiesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:296
xAOD::CaloCluster_v1::ENG_BAD_CELLS
@ ENG_BAD_CELLS
Total em-scale energy of bad cells in this cluster.
Definition: CaloCluster_v1.h:151
CaloRecGPU::CellInfoArr
Definition: EventInfoDefinitions.h:188
CaloGPUClusterAndCellDataMonitor::m_comparedCellTypesToDo
std::vector< bool > m_comparedCellTypesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:298
xAOD::CaloCluster_v1::TILE_CONFIDENCE_LEVEL
@ TILE_CONFIDENCE_LEVEL
Confidence Level of a tile calorimeter cluster to be noise.
Definition: CaloCluster_v1.h:181
python.PyKernel.detStore
detStore
Definition: PyKernel.py:41
xAOD::CaloCluster_v1::ENG_CALIB_EME0
@ ENG_CALIB_EME0
Calibration Hit energy inside the cluster endcap presampler.
Definition: CaloCluster_v1.h:223
xAOD::CaloCluster_v1::ENG_FRAC_EM
@ ENG_FRAC_EM
Energy fraction in EM calorimeters.
Definition: CaloCluster_v1.h:142
CaloGPUClusterAndCellDataMonitor::filter_tool_by_name
bool filter_tool_by_name(const std::string &tool_name) const
Returns true if this tool should be plotted for.
Definition: CaloGPUClusterAndCellDataMonitor.cxx:254
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_M
@ ENG_CALIB_DEAD_M
Attached Calibration Hit energy in dead material with medium matching (Angle < 0.5).
Definition: CaloCluster_v1.h:214
name
std::string name
Definition: Control/AthContainers/Root/debug.cxx:240
CaloGPUClusterAndCellDataMonitor::compactify_clusters
StatusCode compactify_clusters(const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments) const
Remove invalid clusters, reorder by ET and update the tags accordingly.
Definition: CaloGPUClusterAndCellDataMonitor.cxx:480
plotBeamSpotMon.b
b
Definition: plotBeamSpotMon.py:77
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_HEC0
@ ENG_CALIB_DEAD_HEC0
Attached Calibration Hit energy in dead material between EME3 and HEC0.
Definition: CaloCluster_v1.h:241
CaloGPUClusterAndCellDataMonitor::m_doCells
bool m_doCells
If no properties are asked for, skip the relevant loops entirely...
Definition: CaloGPUClusterAndCellDataMonitor.h:302
AtlCoolConsole.tool
tool
Definition: AtlCoolConsole.py:453
checkTriggerxAOD.found
found
Definition: checkTriggerxAOD.py:328
CaloRecGPU::ClusterMomentsArr
Definition: EventInfoDefinitions.h:342
xAOD::CaloCluster_v1::AVG_TILE_Q
@ AVG_TILE_Q
Sum(E_cell_Tile^2 Q_cell_Tile)/Sum(E_cell_Tile^2)
Definition: CaloCluster_v1.h:168
LArNewCalib_Delay_OFC_Cali.check
check
Definition: LArNewCalib_Delay_OFC_Cali.py:289
xAOD::CaloCluster_v1::PHICALOFRAME
@ PHICALOFRAME
Phi in the calo frame (for egamma)
Definition: CaloCluster_v1.h:191
CaloRecGPU::ClusterTag::is_part_of_cluster
constexpr bool is_part_of_cluster() const
Definition: TagDefinitions.h:233
CaloGPUClusterAndCellDataMonitor::m_termThreshold
Gaudi::Property< float > m_termThreshold
Cell (terminal) threshold to use for cluster matching.
Definition: CaloGPUClusterAndCellDataMonitor.h:189
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_TILEG3
@ ENG_CALIB_DEAD_TILEG3
Attached Calibration Hit energy in dead material before scintillator.
Definition: CaloCluster_v1.h:235
CaloGPUClusterAndCellDataMonitor::match_clusters_perfectly
StatusCode match_clusters_perfectly(sample_comparisons_holder &sch, const CaloRecGPU::ConstantDataHolder &constant_data, const CaloRecGPU::CellInfoArr &cell_info, const CaloRecGPU::CellStateArr &cell_state_1, const CaloRecGPU::CellStateArr &cell_state_2, const CaloRecGPU::ClusterInfoArr &cluster_info_1, const CaloRecGPU::ClusterInfoArr &cluster_info_2, const CaloRecGPU::ClusterMomentsArr &, const CaloRecGPU::ClusterMomentsArr &, const bool match_without_shared) const
Definition: CaloGPUClusterAndCellDataMonitor.cxx:964
xAOD::CaloCluster_v1::FIRST_ETA
@ FIRST_ETA
First Moment in .
Definition: CaloCluster_v1.h:125
MatchingOptions
Definition: CaloGPUClusterAndCellDataMonitorOptions.h:355
xAOD::CaloCluster_v1::DELTA_THETA
@ DELTA_THETA
Angular shower axis deviation ( ) from IP-to-Center.
Definition: CaloCluster_v1.h:131
DeMoScan.index
string index
Definition: DeMoScan.py:364
CaloGPUClusterAndCellDataMonitor::m_numToolsToKeep
int m_numToolsToKeep
The number of tools that will actually need to be kept in memory for combined plotting.
Definition: CaloGPUClusterAndCellDataMonitor.h:261
a
TList * a
Definition: liststreamerinfos.cxx:10
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_TOT
@ ENG_CALIB_DEAD_TOT
Attached Calibration Hit energy in dead material.
Definition: CaloCluster_v1.h:227
python.CaloAddPedShiftConfig.int
int
Definition: CaloAddPedShiftConfig.py:45
CaloCell
Data object for each calorimeter readout cell.
Definition: CaloCell.h:57
CaloGPUClusterAndCellDataMonitor::add_combination
StatusCode add_combination(const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const int index_1, const int index_2, const std::string &prefix, const bool match_in_energy, const bool match_without_shared, const bool match_perfectly) const
Definition: CaloGPUClusterAndCellDataMonitor.cxx:1842
xAOD::CaloCluster_v1::SIGNIFICANCE
@ SIGNIFICANCE
Cluster significance.
Definition: CaloCluster_v1.h:160
std::sort
void sort(typename std::reverse_iterator< DataModel_detail::iterator< DVL > > beg, typename std::reverse_iterator< DataModel_detail::iterator< DVL > > end, const Compare &comp)
Specialization of sort for DataVector/List.
Definition: DVL_algorithms.h:623
ATH_MSG_WARNING
#define ATH_MSG_WARNING(x)
Definition: AthMsgStreamMacros.h:32
ref
const boost::regex ref(r_ef)
xAOD::CaloCluster_v1::PHI2CALOFRAME
@ PHI2CALOFRAME
Phi of sampling 2 in the calo frame (for egamma)
Definition: CaloCluster_v1.h:195
xAOD::CaloCluster_v1::N_BAD_HV_CELLS
@ N_BAD_HV_CELLS
number of cells with bad HV
Definition: CaloCluster_v1.h:171
CaloRecGPU::QualityProvenance
Definition: EventInfoDefinitions.h:116
RunTileMonitoring.clusters
clusters
Definition: RunTileMonitoring.py:133
if
if(febId1==febId2)
Definition: LArRodBlockPhysicsV0.cxx:567
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_L
@ ENG_CALIB_DEAD_L
Attached Calibration Hit energy in dead material with loose matching (Angle < 1.0).
Definition: CaloCluster_v1.h:211
CaloRecGPU::CellInfoArr::is_valid
constexpr bool is_valid(const int cell) const
Definition: EventInfoDefinitions.h:316
xAOD::CaloCluster_v1::LATERAL
@ LATERAL
Normalized lateral moment.
Definition: CaloCluster_v1.h:140
CaloGPUClusterAndCellDataMonitor::m_seed_weight
double m_seed_weight
Definition: CaloGPUClusterAndCellDataMonitor.h:242
CaloGPUClusterAndCellDataMonitor
Places (matched) cluster and cell properties in monitored variables to enable plotting with the Athen...
Definition: CaloGPUClusterAndCellDataMonitor.h:42
xAOD::CaloCluster_v1::SECOND_TIME
@ SECOND_TIME
Second moment of cell time distribution in cluster.
Definition: CaloCluster_v1.h:183
str
Definition: BTagTrackIpAccessor.cxx:11
xAOD::CaloCluster_v1::DELTA_ALPHA
@ DELTA_ALPHA
Angular shower axis deviation ( ) from IP-to-Center.
Definition: CaloCluster_v1.h:133
xAOD::CaloCluster_v1::ENG_CALIB_FRAC_HAD
@ ENG_CALIB_FRAC_HAD
Calibration Hit energy inside the cluster caused by charged pi+ and pi-.
Definition: CaloCluster_v1.h:254
xAOD::CaloCluster_v1::CELL_SIGNIFICANCE
@ CELL_SIGNIFICANCE
Cell significance = E/sig of the cell with the largest |E|/sig.
Definition: CaloCluster_v1.h:162
xAOD::CaloCluster_v1::ENG_CALIB_OUT_L
@ ENG_CALIB_OUT_L
Attached Calibration Hit energy outside clusters but inside the calorimeter with loose matching (Angl...
Definition: CaloCluster_v1.h:199
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_EME0
@ ENG_CALIB_DEAD_EME0
Attached Calibration Hit energy in dead material before EME0, between EME0 and EME1.
Definition: CaloCluster_v1.h:238
CaloCondBlobAlgs_fillNoiseFromASCII.tag
string tag
Definition: CaloCondBlobAlgs_fillNoiseFromASCII.py:24
xAOD::CaloCluster_v1::ENG_CALIB_OUT_T
@ ENG_CALIB_OUT_T
Attached Calibration Hit energy outside clusters but inside the calorimeter with tight matching (Angl...
Definition: CaloCluster_v1.h:207
CaloGPUClusterAndCellDataMonitor::m_toolCombinations
std::vector< pair_to_plot > m_toolCombinations
Definition: CaloGPUClusterAndCellDataMonitor.h:272
xAOD::CaloCluster_v1::BAD_CELLS_CORR_E
@ BAD_CELLS_CORR_E
Energy of bad cells with energy density average correction applied.
Definition: CaloCluster_v1.h:156
Monitored::Scalar
Declare a monitored scalar variable.
Definition: MonitoredScalar.h:34
CaloRecGPU::CellInfoArr::is_bad
static constexpr bool is_bad(const bool is_tile, const QualityProvenance qp, const bool treat_L1_predicted_as_good=false)
GPU version of CaloBadCellHelper::isBad.
Definition: EventInfoDefinitions.h:199
athena.opts
opts
Definition: athena.py:88
xAOD::CaloCluster_v1::ETACALOFRAME
@ ETACALOFRAME
Eta in the calo frame (for egamma)
Definition: CaloCluster_v1.h:190
xAOD::CaloCluster_v1::N_BAD_CELLS_CORR
@ N_BAD_CELLS_CORR
Number of bad cells with energy density average correction applied.
Definition: CaloCluster_v1.h:154
xAOD::CaloCluster_v1::LONGITUDINAL
@ LONGITUDINAL
Normalized longitudinal moment.
Definition: CaloCluster_v1.h:141
CaloGPUClusterAndCellDataMonitor::ATLAS_THREAD_SAFE
std::map< std::string, std::atomic< size_t > > m_numClustersPerTool ATLAS_THREAD_SAFE
Counts the total number of clusters per tool.
Definition: CaloGPUClusterAndCellDataMonitor.h:275
xAOD::CaloCluster_v1::NVERTEX_FRACTION
@ NVERTEX_FRACTION
slightly updated vertex fraction more pile up independent (similar to nJVF)
Definition: CaloCluster_v1.h:188
xAOD::CaloCluster_v1::N_BAD_CELLS
@ N_BAD_CELLS
number of bad cells
Definition: CaloCluster_v1.h:152
CaloGPUClusterAndCellDataMonitor::convert_to_GPU_data_structures
StatusCode convert_to_GPU_data_structures(const EventContext &ctx, const CaloRecGPU::ConstantDataHolder &constant_data, const xAOD::CaloClusterContainer *cluster_collection_ptr, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellInfoArr > &cell_info, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::CellStateArr > &cell_state, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterInfoArr > &clusters, CaloRecGPU::Helpers::CPU_object< CaloRecGPU::ClusterMomentsArr > &moments) const
Definition: CaloGPUClusterAndCellDataMonitor.cxx:264
xAOD::CaloCluster_v1::CENTER_Y
@ CENTER_Y
Cluster Centroid ( )
Definition: CaloCluster_v1.h:135
xAOD::CaloCluster_v1::ENG_CALIB_DEAD_EMB0
@ ENG_CALIB_DEAD_EMB0
Attached Calibration Hit energy in dead material before EMB0, between EMB0 and EMB1.
Definition: CaloCluster_v1.h:230
constants.EME2
int EME2
Definition: Calorimeter/CaloClusterCorrection/python/constants.py:56
CaloRecGPU::ClusterTag::secondary_cluster_weight
constexpr int32_t secondary_cluster_weight() const
Definition: TagDefinitions.h:263
CaloGPUClusterAndCellDataMonitor::m_toolsToPlot
Gaudi::Property< std::vector< SimpleSingleTool > > m_toolsToPlot
Tools to plot individually.
Definition: CaloGPUClusterAndCellDataMonitor.h:221
CaloGPUClusterAndCellDataMonitor::m_doCombinedClusters
bool m_doCombinedClusters
Definition: CaloGPUClusterAndCellDataMonitor.h:302
match
bool match(std::string s1, std::string s2)
match the individual directories of two strings
Definition: hcg.cxx:356
xAOD::CaloCluster_v1::ENG_POS
@ ENG_POS
Total positive Energy of this cluster.
Definition: CaloCluster_v1.h:159
CaloGPUClusterAndCellDataMonitor::m_comparedCellPropertiesToDo
std::vector< bool > m_comparedCellPropertiesToDo
Definition: CaloGPUClusterAndCellDataMonitor.h:297
CaloGPUClusterAndCellDataMonitor::m_pairsToPlot
Gaudi::Property< std::vector< SimpleToolPair > > m_pairsToPlot
Pairs of tools to compare.
Definition: CaloGPUClusterAndCellDataMonitor.h:226
xAOD::CaloCluster_v1::BADLARQ_FRAC
@ BADLARQ_FRAC
Energy fraction of LAr cells with quality larger than a given cut.
Definition: CaloCluster_v1.h:158
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