From 5cc47e7c18de4cc385e5fd4ab66c0b0405f966a2 Mon Sep 17 00:00:00 2001 From: Andres Rios Tascon Date: Thu, 21 Nov 2024 10:42:33 -0800 Subject: [PATCH 1/2] Add pT Runtime Toggle + pT Toggle for Geometry Inputs Co-authored-by: Gavin Niendorf --- .../plugins/alpaka/LSTModulesDevESProducer.cc | 11 ++++- RecoTracker/LST/plugins/alpaka/LSTProducer.cc | 11 ++++- RecoTracker/LSTCore/BuildFile.xml | 1 - RecoTracker/LSTCore/interface/Common.h | 5 --- RecoTracker/LSTCore/interface/LSTESData.h | 2 +- RecoTracker/LSTCore/interface/alpaka/Common.h | 1 - RecoTracker/LSTCore/interface/alpaka/LST.h | 4 +- RecoTracker/LSTCore/src/LSTESData.cc | 25 +++++------ RecoTracker/LSTCore/src/alpaka/LST.cc | 13 +++--- .../LSTCore/src/alpaka/LSTEvent.dev.cc | 18 +++++--- RecoTracker/LSTCore/src/alpaka/LSTEvent.h | 11 ++++- RecoTracker/LSTCore/src/alpaka/MiniDoublet.h | 38 +++++++++++------ .../LSTCore/src/alpaka/PixelQuintuplet.h | 10 +++-- RecoTracker/LSTCore/src/alpaka/PixelTriplet.h | 34 ++++++++++----- RecoTracker/LSTCore/src/alpaka/Quintuplet.h | 42 ++++++++++++------- RecoTracker/LSTCore/src/alpaka/Segment.h | 30 ++++++++----- RecoTracker/LSTCore/src/alpaka/Triplet.h | 36 ++++++++++------ RecoTracker/LSTCore/standalone/.gitignore | 1 + RecoTracker/LSTCore/standalone/LST/Makefile | 18 ++++---- RecoTracker/LSTCore/standalone/Makefile | 13 +++--- RecoTracker/LSTCore/standalone/bin/lst.cc | 11 ++++- .../standalone/bin/lst_make_tracklooper | 14 ++----- .../standalone/code/core/AnalysisConfig.h | 3 ++ .../LSTCore/standalone/code/core/trkCore.cc | 4 +- 24 files changed, 225 insertions(+), 131 deletions(-) diff --git a/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc b/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc index d0e103b1e315b..7152da9ed13c7 100644 --- a/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc +++ b/RecoTracker/LST/plugins/alpaka/LSTModulesDevESProducer.cc @@ -13,16 +13,23 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { class LSTModulesDevESProducer : public ESProducer { + private: + std::string ptCutLabel_; + public: - LSTModulesDevESProducer(edm::ParameterSet const& iConfig) : ESProducer(iConfig) { setWhatProduced(this); } + LSTModulesDevESProducer(edm::ParameterSet const& iConfig) + : ESProducer(iConfig), ptCutLabel_(iConfig.getParameter("ptCutLabel")) { + setWhatProduced(this, ptCutLabel_); + } static void fillDescriptions(edm::ConfigurationDescriptions& descriptions) { edm::ParameterSetDescription desc; + desc.add("ptCutLabel", "0.8"); descriptions.addWithDefaultLabel(desc); } std::unique_ptr> produce(TrackerRecoGeometryRecord const& iRecord) { - return lst::loadAndFillESHost(); + return lst::loadAndFillESHost(ptCutLabel_); } }; diff --git a/RecoTracker/LST/plugins/alpaka/LSTProducer.cc b/RecoTracker/LST/plugins/alpaka/LSTProducer.cc index 7eb6c57ade05c..4b83dd3693624 100644 --- a/RecoTracker/LST/plugins/alpaka/LSTProducer.cc +++ b/RecoTracker/LST/plugins/alpaka/LSTProducer.cc @@ -28,8 +28,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { LSTProducer(edm::ParameterSet const& config) : lstPixelSeedInputToken_{consumes(config.getParameter("pixelSeedInput"))}, lstPhase2OTHitsInputToken_{consumes(config.getParameter("phase2OTHitsInput"))}, - lstESToken_{esConsumes()}, + lstESToken_{esConsumes(edm::ESInputTag("", config.getParameter("ptCutLabel")))}, verbose_(config.getParameter("verbose")), + ptCut_(config.getParameter("ptCut")), nopLSDupClean_(config.getParameter("nopLSDupClean")), tcpLSTriplets_(config.getParameter("tcpLSTriplets")), lstOutputToken_{produces()} {} @@ -43,6 +44,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { lst_.run(event.queue(), verbose_, + static_cast(ptCut_), &lstESDeviceData, pixelSeeds.px(), pixelSeeds.py(), @@ -78,6 +80,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { desc.add("pixelSeedInput", edm::InputTag{"lstPixelSeedInputProducer"}); desc.add("phase2OTHitsInput", edm::InputTag{"lstPhase2OTHitsInputProducer"}); desc.add("verbose", false); + desc.add("ptCut", 0.8); + desc.add("ptCutLabel", "0.8"); desc.add("nopLSDupClean", false); desc.add("tcpLSTriplets", false); descriptions.addWithDefaultLabel(desc); @@ -87,7 +91,10 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { edm::EDGetTokenT lstPixelSeedInputToken_; edm::EDGetTokenT lstPhase2OTHitsInputToken_; device::ESGetToken, TrackerRecoGeometryRecord> lstESToken_; - const bool verbose_, nopLSDupClean_, tcpLSTriplets_; + const bool verbose_; + const double ptCut_; + const bool nopLSDupClean_; + const bool tcpLSTriplets_; edm::EDPutTokenT lstOutputToken_; lst::LST lst_; diff --git a/RecoTracker/LSTCore/BuildFile.xml b/RecoTracker/LSTCore/BuildFile.xml index a58a1898046ae..8dd2b885bf1b4 100644 --- a/RecoTracker/LSTCore/BuildFile.xml +++ b/RecoTracker/LSTCore/BuildFile.xml @@ -2,7 +2,6 @@ - diff --git a/RecoTracker/LSTCore/interface/Common.h b/RecoTracker/LSTCore/interface/Common.h index f65ca7a50d867..32337d8f409ae 100644 --- a/RecoTracker/LSTCore/interface/Common.h +++ b/RecoTracker/LSTCore/interface/Common.h @@ -20,11 +20,6 @@ namespace lst { // Named types for LST objects enum LSTObjType { T5 = 4, pT3 = 5, pT5 = 7, pLS = 8 }; -// If a compile time flag does not define PT_CUT, default to 0.8 (GeV) -#ifndef PT_CUT - constexpr float PT_CUT = 0.8f; -#endif - constexpr unsigned int max_blocks = 80; constexpr unsigned int max_connected_modules = 40; diff --git a/RecoTracker/LSTCore/interface/LSTESData.h b/RecoTracker/LSTCore/interface/LSTESData.h index 45887d3cb1fea..bfa10186f8f2e 100644 --- a/RecoTracker/LSTCore/interface/LSTESData.h +++ b/RecoTracker/LSTCore/interface/LSTESData.h @@ -40,7 +40,7 @@ namespace lst { pixelMapping(pixelMappingIn) {} }; - std::unique_ptr> loadAndFillESHost(); + std::unique_ptr> loadAndFillESHost(std::string& ptCutLabel); } // namespace lst diff --git a/RecoTracker/LSTCore/interface/alpaka/Common.h b/RecoTracker/LSTCore/interface/alpaka/Common.h index 7a1feabfcf076..642d21e5484ba 100644 --- a/RecoTracker/LSTCore/interface/alpaka/Common.h +++ b/RecoTracker/LSTCore/interface/alpaka/Common.h @@ -49,7 +49,6 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float k2Rinv1GeVf = (2.99792458e-3 * 3.8) / 2; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kR1GeVf = 1. / (2.99792458e-3 * 3.8); ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kSinAlphaMax = 0.95; - ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float ptCut = PT_CUT; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kDeltaZLum = 15.0; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kPixelPSZpitch = 0.15; ALPAKA_STATIC_ACC_MEM_GLOBAL constexpr float kStripPSZpitch = 2.4; diff --git a/RecoTracker/LSTCore/interface/alpaka/LST.h b/RecoTracker/LSTCore/interface/alpaka/LST.h index 40d912de3f291..5fe369b9cd22b 100644 --- a/RecoTracker/LSTCore/interface/alpaka/LST.h +++ b/RecoTracker/LSTCore/interface/alpaka/LST.h @@ -17,6 +17,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { void run(Queue& queue, bool verbose, + const float ptCut, LSTESData const* deviceESData, std::vector const& see_px, std::vector const& see_py, @@ -63,7 +64,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { std::vector const& ph2_detId, std::vector const& ph2_x, std::vector const& ph2_y, - std::vector const& ph2_z); + std::vector const& ph2_z, + const float ptCut); void getOutput(LSTEvent& event); diff --git a/RecoTracker/LSTCore/src/LSTESData.cc b/RecoTracker/LSTCore/src/LSTESData.cc index 66163d39beb2e..8c2197ac76fa8 100644 --- a/RecoTracker/LSTCore/src/LSTESData.cc +++ b/RecoTracker/LSTCore/src/LSTESData.cc @@ -43,21 +43,22 @@ namespace { void loadMapsHost(lst::MapPLStoLayer& pLStoLayer, lst::EndcapGeometry& endcapGeometry, lst::TiltedGeometry& tiltedGeometry, - lst::ModuleConnectionMap& moduleConnectionMap) { + lst::ModuleConnectionMap& moduleConnectionMap, + std::string& ptCutLabel) { // Module orientation information (DrDz or phi angles) - auto endcap_geom = - get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt0.8/endcap_orientation.bin"); - auto tilted_geom = get_absolute_path_after_check_file_exists( - geometryDataDir() + "/data/OT800_IT615_pt0.8/tilted_barrel_orientation.bin"); + auto endcap_geom = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + + ptCutLabel + "/endcap_orientation.bin"); + auto tilted_geom = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + + ptCutLabel + "/tilted_barrel_orientation.bin"); // Module connection map (for line segment building) - auto mappath = get_absolute_path_after_check_file_exists( - geometryDataDir() + "/data/OT800_IT615_pt0.8/module_connection_tracing_merged.bin"); + auto mappath = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel + + "/module_connection_tracing_merged.bin"); endcapGeometry.load(endcap_geom); tiltedGeometry.load(tilted_geom); moduleConnectionMap.load(mappath); - auto pLSMapDir = geometryDataDir() + "/data/OT800_IT615_pt0.8/pixelmap/pLS_map"; + auto pLSMapDir = geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel + "/pixelmap/pLS_map"; const std::array connects{ {"_layer1_subdet5", "_layer2_subdet5", "_layer1_subdet4", "_layer2_subdet4"}}; std::string path; @@ -78,7 +79,7 @@ namespace { } } // namespace -std::unique_ptr> lst::loadAndFillESHost() { +std::unique_ptr> lst::loadAndFillESHost(std::string& ptCutLabel) { uint16_t nModules; uint16_t nLowerModules; unsigned int nPixels; @@ -87,7 +88,7 @@ std::unique_ptr> lst::loadAndFillESHost() TiltedGeometry tiltedGeometry; PixelMap pixelMapping; ModuleConnectionMap moduleConnectionMap; - ::loadMapsHost(pLStoLayer, endcapGeometry, tiltedGeometry, moduleConnectionMap); + ::loadMapsHost(pLStoLayer, endcapGeometry, tiltedGeometry, moduleConnectionMap, ptCutLabel); auto endcapGeometryDev = std::make_shared(endcapGeometry.nEndCapMap, cms::alpakatools::host()); @@ -98,8 +99,8 @@ std::unique_ptr> lst::loadAndFillESHost() endcapGeometry.geoMapPhi_buf.data(), endcapGeometry.nEndCapMap * sizeof(float)); - auto path = - get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt0.8/sensor_centroids.bin"); + auto path = get_absolute_path_after_check_file_exists(geometryDataDir() + "/data/OT800_IT615_pt" + ptCutLabel + + "/sensor_centroids.bin"); auto modulesBuffers = lst::loadModulesFromFile(pLStoLayer, path.c_str(), nModules, diff --git a/RecoTracker/LSTCore/src/alpaka/LST.cc b/RecoTracker/LSTCore/src/alpaka/LST.cc index 3c1638677eab2..b2fe87e3a2917 100644 --- a/RecoTracker/LSTCore/src/alpaka/LST.cc +++ b/RecoTracker/LSTCore/src/alpaka/LST.cc @@ -75,7 +75,8 @@ void LST::prepareInput(std::vector const& see_px, std::vector const& ph2_detId, std::vector const& ph2_x, std::vector const& ph2_y, - std::vector const& ph2_z) { + std::vector const& ph2_z, + float const ptCut) { in_trkX_.clear(); in_trkY_.clear(); in_trkZ_.clear(); @@ -132,7 +133,7 @@ void LST::prepareInput(std::vector const& see_px, float eta = p3LH.eta(); float ptErr = see_ptErr[iSeed]; - if ((ptIn > 0.8 - 2 * ptErr)) { + if ((ptIn > ptCut - 2 * ptErr)) { XYZVector r3LH(see_stateTrajGlbX[iSeed], see_stateTrajGlbY[iSeed], see_stateTrajGlbZ[iSeed]); XYZVector p3PCA(see_px[iSeed], see_py[iSeed], see_pz[iSeed]); XYZVector r3PCA(calculateR3FromPCA(p3PCA, see_dxy[iSeed], see_dz[iSeed])); @@ -149,7 +150,7 @@ void LST::prepareInput(std::vector const& see_px, if (ptIn >= 2.0) pixtype = PixelType::kHighPt; - else if (ptIn >= (0.8 - 2 * ptErr) and ptIn < 2.0) { + else if (ptIn >= (ptCut - 2 * ptErr) and ptIn < 2.0) { if (pixelSegmentDeltaPhiChange >= 0) pixtype = PixelType::kLowPtPosCurv; else @@ -255,6 +256,7 @@ void LST::getOutput(LSTEvent& event) { void LST::run(Queue& queue, bool verbose, + float const ptCut, LSTESData const* deviceESData, std::vector const& see_px, std::vector const& see_py, @@ -277,7 +279,7 @@ void LST::run(Queue& queue, std::vector const& ph2_z, bool no_pls_dupclean, bool tc_pls_triplets) { - auto event = LSTEvent(verbose, queue, deviceESData); + auto event = LSTEvent(verbose, ptCut, queue, deviceESData); prepareInput(see_px, see_py, see_pz, @@ -296,7 +298,8 @@ void LST::run(Queue& queue, ph2_detId, ph2_x, ph2_y, - ph2_z); + ph2_z, + ptCut); event.addHitToEvent(in_trkX_, in_trkY_, in_trkZ_, in_hitId_, in_hitIdxs_); event.addPixelSegmentToEvent(in_hitIndices_vec0_, diff --git a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc index be6c2b88b73c8..3b647273303b2 100644 --- a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc +++ b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc @@ -381,7 +381,8 @@ void LSTEvent::createMiniDoublets() { hitsDC_->const_view(), miniDoubletsDC_->view(), miniDoubletsDC_->view(), - rangesDC_->const_view()); + rangesDC_->const_view(), + ptCut_); WorkDiv1D const addMiniDoubletRangesToEventExplicit_workDiv = createWorkDiv({1}, {1024}, {1}); @@ -427,7 +428,8 @@ void LSTEvent::createSegmentsWithModuleMap() { miniDoubletsDC_->const_view(), segmentsDC_->view(), segmentsDC_->view(), - rangesDC_->const_view()); + rangesDC_->const_view(), + ptCut_); WorkDiv1D const addSegmentRangesToEventExplicit_workDiv = createWorkDiv({1}, {1024}, {1}); @@ -537,7 +539,8 @@ void LSTEvent::createTriplets() { tripletsDC_->view(), rangesDC_->const_view(), index_gpu_buf.data(), - nonZeroModules); + nonZeroModules, + ptCut_); WorkDiv1D const addTripletRangesToEventExplicit_workDiv = createWorkDiv({1}, {1024}, {1}); @@ -822,7 +825,8 @@ void LSTEvent::createPixelTriplets() { pixelTripletsDC_->view(), connectedPixelSize_dev_buf.data(), connectedPixelIndex_dev_buf.data(), - nInnerSegments); + nInnerSegments, + ptCut_); #ifdef WARNINGS auto nPixelTriplets_buf = cms::alpakatools::make_host_buffer(queue_); @@ -904,7 +908,8 @@ void LSTEvent::createQuintuplets() { quintupletsDC_->view(), quintupletsDC_->view(), rangesDC_->const_view(), - nEligibleT5Modules); + nEligibleT5Modules, + ptCut_); Vec3D const threadsPerBlockDupQuint{1, 16, 16}; Vec3D const blocksPerGridDupQuint{max_blocks, 1, 1}; @@ -1065,7 +1070,8 @@ void LSTEvent::createPixelQuintuplets() { connectedPixelSize_dev_buf.data(), connectedPixelIndex_dev_buf.data(), nInnerSegments, - rangesDC_->const_view()); + rangesDC_->const_view(), + ptCut_); Vec3D const threadsPerBlockDupPix{1, 16, 16}; Vec3D const blocksPerGridDupPix{1, max_blocks, 1}; diff --git a/RecoTracker/LSTCore/src/alpaka/LSTEvent.h b/RecoTracker/LSTCore/src/alpaka/LSTEvent.h index 59f249aa9405f..02f1decef916b 100644 --- a/RecoTracker/LSTCore/src/alpaka/LSTEvent.h +++ b/RecoTracker/LSTCore/src/alpaka/LSTEvent.h @@ -36,6 +36,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { class LSTEvent { private: Queue& queue_; + const float ptCut_; std::array n_minidoublets_by_layer_barrel_{}; std::array n_minidoublets_by_layer_endcap_{}; @@ -81,8 +82,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { public: // Constructor used for CMSSW integration. Uses an external queue. - LSTEvent(bool verbose, Queue& q, const LSTESData* deviceESData) + LSTEvent(bool verbose, const float pt_cut, Queue& q, const LSTESData* deviceESData) : queue_(q), + ptCut_(pt_cut), nModules_(deviceESData->nModules), nLowerModules_(deviceESData->nLowerModules), nPixels_(deviceESData->nPixels), @@ -90,7 +92,12 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { modules_(*deviceESData->modules), pixelMapping_(*deviceESData->pixelMapping), endcapGeometry_(*deviceESData->endcapGeometry), - addObjects_(verbose) {} + addObjects_(verbose) { + if (pt_cut < 0.6f) { + throw std::invalid_argument("Minimum pT cut must be at least 0.6 GeV. Provided value: " + + std::to_string(pt_cut)); + } + } void initSync(); // synchronizes, for standalone usage void resetEventSync(); // synchronizes, for standalone usage void wait() const { alpaka::wait(queue_); } diff --git a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h index 0a0abff8b6986..210d7201fd800 100644 --- a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h +++ b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h @@ -157,8 +157,13 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } template - ALPAKA_FN_ACC ALPAKA_FN_INLINE float dPhiThreshold( - TAcc const& acc, float rt, ModulesConst modules, uint16_t moduleIndex, float dPhi = 0, float dz = 0) { + ALPAKA_FN_ACC ALPAKA_FN_INLINE float dPhiThreshold(TAcc const& acc, + float rt, + ModulesConst modules, + uint16_t moduleIndex, + const float ptCut, + float dPhi = 0, + float dz = 0) { // ================================================================= // Various constants // ================================================================= @@ -403,7 +408,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float xUpper, float yUpper, float zUpper, - float rtUpper) { + float rtUpper, + const float ptCut) { dz = zLower - zUpper; const float dzCut = modules.moduleType()[lowerModuleIndex] == PS ? 2.f : 10.f; const float sign = ((dz > 0) - (dz < 0)) * ((zLower > 0) - (zLower < 0)); @@ -415,8 +421,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float miniCut = 0; miniCut = modules.moduleLayerType()[lowerModuleIndex] == Pixel - ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex) - : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex); + ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, ptCut) + : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, ptCut); // Cut #2: dphi difference // Ref to original code: https://github.com/slava77/cms-tkph2-ntuple/blob/184d2325147e6930030d3d1f780136bc2dd29ce6/doubletAnalysis.C#L3085 @@ -530,7 +536,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float xUpper, float yUpper, float zUpper, - float rtUpper) { + float rtUpper, + const float ptCut) { // There are series of cuts that applies to mini-doublet in a "endcap" region // Cut #1 : dz cut. The dz difference can't be larger than 1cm. (max separation is 4mm for modules in the endcap) // Ref to original code: https://github.com/slava77/cms-tkph2-ntuple/blob/184d2325147e6930030d3d1f780136bc2dd29ce6/doubletAnalysis.C#L3093 @@ -603,8 +610,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float miniCut = 0; miniCut = modules.moduleLayerType()[lowerModuleIndex] == Pixel - ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, dPhi, dz) - : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, dPhi, dz); + ? dPhiThreshold(acc, rtLower, modules, lowerModuleIndex, ptCut, dPhi, dz) + : dPhiThreshold(acc, rtUpper, modules, lowerModuleIndex, ptCut, dPhi, dz); if (alpaka::math::abs(acc, dPhi) >= miniCut) return false; @@ -641,7 +648,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float xUpper, float yUpper, float zUpper, - float rtUpper) { + float rtUpper, + const float ptCut) { if (modules.subdets()[lowerModuleIndex] == Barrel) { return runMiniDoubletDefaultAlgoBarrel(acc, modules, @@ -664,7 +672,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { xUpper, yUpper, zUpper, - rtUpper); + rtUpper, + ptCut); } else { return runMiniDoubletDefaultAlgoEndcap(acc, modules, @@ -687,7 +696,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { xUpper, yUpper, zUpper, - rtUpper); + rtUpper, + ptCut); } } @@ -699,7 +709,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { HitsRangesConst hitsRanges, MiniDoublets mds, MiniDoubletsOccupancy mdsOccupancy, - ObjectRangesConst ranges) const { + ObjectRangesConst ranges, + const float ptCut) const { auto const globalThreadIdx = alpaka::getIdx(acc); auto const gridThreadExtent = alpaka::getWorkDiv(acc); @@ -754,7 +765,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { xUpper, yUpper, zUpper, - rtUpper); + rtUpper, + ptCut); if (success) { int totOccupancyMDs = alpaka::atomicAdd( acc, &mdsOccupancy.totOccupancyMDs()[lowerModuleIndex], 1u, alpaka::hierarchy::Threads{}); diff --git a/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h b/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h index 08feb0dfe3384..eccb641f35b43 100644 --- a/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h +++ b/RecoTracker/LSTCore/src/alpaka/PixelQuintuplet.h @@ -571,7 +571,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& quintupletRadius, float& centerX, float& centerY, - unsigned int pixelSegmentArrayIndex) { + unsigned int pixelSegmentArrayIndex, + const float ptCut) { unsigned int t5InnerT3Index = quintuplets.tripletIndices()[quintupletIndex][0]; unsigned int t5OuterT3Index = quintuplets.tripletIndices()[quintupletIndex][1]; @@ -594,6 +595,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { rzChiSquaredTemp, rPhiChiSquaredTemp, rPhiChiSquaredInwardsTemp, + ptCut, false)) return false; @@ -715,7 +717,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int* connectedPixelSize, unsigned int* connectedPixelIndex, unsigned int nPixelSegments, - ObjectRangesConst ranges) const { + ObjectRangesConst ranges, + const float ptCut) const { auto const globalBlockIdx = alpaka::getIdx(acc); auto const globalThreadIdx = alpaka::getIdx(acc); auto const gridBlockExtent = alpaka::getWorkDiv(acc); @@ -770,7 +773,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { quintupletRadius, centerX, centerY, - static_cast(i_pLS)); + static_cast(i_pLS), + ptCut); if (success) { unsigned int totOccupancyPixelQuintuplets = alpaka::atomicAdd( acc, &pixelQuintuplets.totOccupancyPixelQuintuplets(), 1u, alpaka::hierarchy::Threads{}); diff --git a/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h b/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h index a8be90fff5227..211d58bffb37d 100644 --- a/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h +++ b/RecoTracker/LSTCore/src/alpaka/PixelTriplet.h @@ -27,7 +27,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex); + unsigned int fourthMDIndex, + const float ptCut); template ALPAKA_FN_ACC ALPAKA_FN_INLINE bool runTripletDefaultAlgoPPEE(TAcc const& acc, ModulesConst modules, @@ -43,7 +44,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex); + unsigned int fourthMDIndex, + const float ptCut); ALPAKA_FN_ACC ALPAKA_FN_INLINE void addPixelTripletToMemory(MiniDoubletsConst mds, SegmentsConst segments, @@ -121,7 +123,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { uint16_t outerInnerLowerModuleIndex, uint16_t outerOuterLowerModuleIndex, unsigned int innerSegmentIndex, - unsigned int outerSegmentIndex) { + unsigned int outerSegmentIndex, + const float ptCut) { short outerInnerLowerModuleSubdet = modules.subdets()[outerInnerLowerModuleIndex]; short outerOuterLowerModuleSubdet = modules.subdets()[outerOuterLowerModuleIndex]; @@ -147,7 +150,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } else if (outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) { return runTripletDefaultAlgoPPEE(acc, modules, @@ -163,7 +167,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } return false; } @@ -683,6 +688,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& rzChiSquared, float& rPhiChiSquared, float& rPhiChiSquaredInwards, + const float ptCut, bool runChiSquaredCuts = true) { //run pT4 compatibility between the pixel segment and inner segment, and between the pixel and outer segment of the triplet uint16_t pixelModuleIndex = segments.innerLowerModuleIndices()[pixelSegmentIndex]; @@ -703,7 +709,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { lowerModuleIndex, middleModuleIndex, pixelSegmentIndex, - triplets.segmentIndices()[tripletIndex][0])) + triplets.segmentIndices()[tripletIndex][0], + ptCut)) return false; //pixel segment vs outer segment of triplet @@ -717,7 +724,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { middleModuleIndex, upperModuleIndex, pixelSegmentIndex, - triplets.segmentIndices()[tripletIndex][1])) + triplets.segmentIndices()[tripletIndex][1], + ptCut)) return false; } @@ -835,7 +843,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { PixelTriplets pixelTriplets, unsigned int* connectedPixelSize, unsigned int* connectedPixelIndex, - unsigned int nPixelSegments) const { + unsigned int nPixelSegments, + const float ptCut) const { auto const globalBlockIdx = alpaka::getIdx(acc); auto const globalThreadIdx = alpaka::getIdx(acc); auto const gridBlockExtent = alpaka::getWorkDiv(acc); @@ -911,7 +920,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { centerY, rzChiSquared, rPhiChiSquared, - rPhiChiSquaredInwards); + rPhiChiSquaredInwards, + ptCut); if (success) { float phi = @@ -1076,7 +1086,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { float dPhi, betaIn, betaOut, pt_beta, zLo, zHi, zLoPointed, zHiPointed, dPhiCut, betaOutCut; bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS); @@ -1334,7 +1345,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { float dPhi, betaIn, betaOut, pt_beta, rtLo, rtHi, dPhiCut, betaOutCut; bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS); diff --git a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h index 24ce2d1d53e22..ffc0386d18746 100644 --- a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h +++ b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h @@ -1211,7 +1211,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { bool isPS_InLo = (modules.moduleType()[innerInnerLowerModuleIndex] == PS); bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS); @@ -1453,7 +1454,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { bool isPS_InLo = (modules.moduleType()[innerInnerLowerModuleIndex] == PS); bool isPS_OutLo = (modules.moduleType()[outerInnerLowerModuleIndex] == PS); @@ -1698,7 +1700,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { float rt_InLo = mds.anchorRt()[firstMDIndex]; float rt_InOut = mds.anchorRt()[secondMDIndex]; float rt_OutLo = mds.anchorRt()[thirdMDIndex]; @@ -1923,7 +1926,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int firstMDIndex, unsigned int secondMDIndex, unsigned int thirdMDIndex, - unsigned int fourthMDIndex) { + unsigned int fourthMDIndex, + const float ptCut) { short innerInnerLowerModuleSubdet = modules.subdets()[innerInnerLowerModuleIndex]; short innerOuterLowerModuleSubdet = modules.subdets()[innerOuterLowerModuleIndex]; short outerInnerLowerModuleSubdet = modules.subdets()[outerInnerLowerModuleIndex]; @@ -1944,7 +1948,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Barrel and outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) { return runQuintupletDefaultAlgoBBEE(acc, @@ -1960,7 +1965,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Barrel and outerInnerLowerModuleSubdet == Barrel and outerOuterLowerModuleSubdet == Endcap) { return runQuintupletDefaultAlgoBBBB(acc, @@ -1976,7 +1982,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } else if (innerInnerLowerModuleSubdet == Barrel and innerOuterLowerModuleSubdet == Endcap and outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) { return runQuintupletDefaultAlgoBBEE(acc, @@ -1992,7 +1999,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } else if (innerInnerLowerModuleSubdet == Endcap and innerOuterLowerModuleSubdet == Endcap and outerInnerLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) { return runQuintupletDefaultAlgoEEEE(acc, @@ -2008,7 +2016,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex); + fourthMDIndex, + ptCut); } return false; @@ -2036,7 +2045,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& rzChiSquared, float& chiSquared, float& nonAnchorChiSquared, - bool& tightCutFlag) { + bool& tightCutFlag, + const float ptCut) { unsigned int firstSegmentIndex = triplets.segmentIndices()[innerTripletIndex][0]; unsigned int secondSegmentIndex = triplets.segmentIndices()[innerTripletIndex][1]; unsigned int thirdSegmentIndex = triplets.segmentIndices()[outerTripletIndex][0]; @@ -2070,7 +2080,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, thirdMDIndex, - fourthMDIndex)) + fourthMDIndex, + ptCut)) return false; if (not runQuintupletAlgoSelector(acc, @@ -2086,7 +2097,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { firstMDIndex, secondMDIndex, fourthMDIndex, - fifthMDIndex)) + fifthMDIndex, + ptCut)) return false; float x1 = mds.anchorX()[firstMDIndex]; @@ -2342,7 +2354,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { Quintuplets quintuplets, QuintupletsOccupancy quintupletsOccupancy, ObjectRangesConst ranges, - uint16_t nEligibleT5Modules) const { + uint16_t nEligibleT5Modules, + const float ptCut) const { auto const globalThreadIdx = alpaka::getIdx(acc); auto const gridThreadExtent = alpaka::getWorkDiv(acc); @@ -2397,7 +2410,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { rzChiSquared, chiSquared, nonAnchorChiSquared, - tightCutFlag); + tightCutFlag, + ptCut); if (success) { int totOccupancyQuintuplets = alpaka::atomicAdd( diff --git a/RecoTracker/LSTCore/src/alpaka/Segment.h b/RecoTracker/LSTCore/src/alpaka/Segment.h index fc885e9d66afe..f91d176687f5d 100644 --- a/RecoTracker/LSTCore/src/alpaka/Segment.h +++ b/RecoTracker/LSTCore/src/alpaka/Segment.h @@ -116,7 +116,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { uint16_t innerLowerModuleIndex, uint16_t outerLowerModuleIndex, unsigned int innerMDIndex, - unsigned int outerMDIndex) { + unsigned int outerMDIndex, + const float ptCut) { float sdMuls = (modules.subdets()[innerLowerModuleIndex] == Barrel) ? kMiniMulsPtScaleBarrel[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut : kMiniMulsPtScaleEndcap[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut; @@ -299,7 +300,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& dPhiMax, float& dPhiChange, float& dPhiChangeMin, - float& dPhiChangeMax) { + float& dPhiChangeMax, + const float ptCut) { float sdMuls = (modules.subdets()[innerLowerModuleIndex] == Barrel) ? kMiniMulsPtScaleBarrel[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut : kMiniMulsPtScaleEndcap[modules.layers()[innerLowerModuleIndex] - 1] * 3.f / ptCut; @@ -357,7 +359,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerLowerModuleIndex, outerLowerModuleIndex, innerMDIndex, - outerMDIndex); + outerMDIndex, + ptCut); float innerMDAlpha = mds.dphichanges()[innerMDIndex]; float outerMDAlpha = mds.dphichanges()[outerMDIndex]; @@ -389,7 +392,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& dPhiMax, float& dPhiChange, float& dPhiChangeMin, - float& dPhiChangeMax) { + float& dPhiChangeMax, + const float ptCut) { float xIn, yIn, zIn, rtIn, xOut, yOut, zOut, rtOut; xIn = mds.anchorX()[innerMDIndex]; @@ -470,7 +474,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerLowerModuleIndex, outerLowerModuleIndex, innerMDIndex, - outerMDIndex); + outerMDIndex, + ptCut); float innerMDAlpha = mds.dphichanges()[innerMDIndex]; float outerMDAlpha = mds.dphichanges()[outerMDIndex]; @@ -502,7 +507,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& dPhiMax, float& dPhiChange, float& dPhiChangeMin, - float& dPhiChangeMax) { + float& dPhiChangeMax, + const float ptCut) { if (modules.subdets()[innerLowerModuleIndex] == Barrel and modules.subdets()[outerLowerModuleIndex] == Barrel) { return runSegmentDefaultAlgoBarrel(acc, modules, @@ -516,7 +522,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { dPhiMax, dPhiChange, dPhiChangeMin, - dPhiChangeMax); + dPhiChangeMax, + ptCut); } else { return runSegmentDefaultAlgoEndcap(acc, modules, @@ -530,7 +537,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { dPhiMax, dPhiChange, dPhiChangeMin, - dPhiChangeMax); + dPhiChangeMax, + ptCut); } } @@ -542,7 +550,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { MiniDoubletsOccupancyConst mdsOccupancy, Segments segments, SegmentsOccupancy segmentsOccupancy, - ObjectRangesConst ranges) const { + ObjectRangesConst ranges, + const float ptCut) const { auto const globalBlockIdx = alpaka::getIdx(acc); auto const blockThreadIdx = alpaka::getIdx(acc); auto const gridBlockExtent = alpaka::getWorkDiv(acc); @@ -595,7 +604,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { dPhiMax, dPhiChange, dPhiChangeMin, - dPhiChangeMax)) { + dPhiChangeMax, + ptCut)) { unsigned int totOccupancySegments = alpaka::atomicAdd(acc, &segmentsOccupancy.totOccupancySegments()[innerLowerModuleIndex], diff --git a/RecoTracker/LSTCore/src/alpaka/Triplet.h b/RecoTracker/LSTCore/src/alpaka/Triplet.h index 9192edbd9a186..a57d70f0f5238 100644 --- a/RecoTracker/LSTCore/src/alpaka/Triplet.h +++ b/RecoTracker/LSTCore/src/alpaka/Triplet.h @@ -141,7 +141,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& rtOut, unsigned int innerSegmentIndex, float& betaIn, - float& betaInCut) { + float& betaInCut, + const float ptCut) { bool isPSIn = (modules.moduleType()[innerInnerLowerModuleIndex] == PS); bool isPSOut = (modules.moduleType()[outerOuterLowerModuleIndex] == PS); @@ -238,7 +239,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int innerSegmentIndex, unsigned int outerSegmentIndex, float& betaIn, - float& betaInCut) { + float& betaInCut, + const float ptCut) { bool isPSIn = (modules.moduleType()[innerInnerLowerModuleIndex] == PS); bool isPSOut = (modules.moduleType()[outerOuterLowerModuleIndex] == PS); @@ -356,7 +358,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int innerSegmentIndex, unsigned int outerSegmentIndex, float& betaIn, - float& betaInCut) { + float& betaInCut, + const float ptCut) { float rtIn = mds.anchorRt()[firstMDIndex]; float rtMid = mds.anchorRt()[secondMDIndex]; rtOut = mds.anchorRt()[thirdMDIndex]; @@ -478,7 +481,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { unsigned int innerSegmentIndex, unsigned int outerSegmentIndex, float& betaIn, - float& betaInCut) { + float& betaInCut, + const float ptCut) { short innerInnerLowerModuleSubdet = modules.subdets()[innerInnerLowerModuleIndex]; short middleLowerModuleSubdet = modules.subdets()[middleLowerModuleIndex]; short outerOuterLowerModuleSubdet = modules.subdets()[outerOuterLowerModuleIndex]; @@ -499,7 +503,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { rtOut, innerSegmentIndex, betaIn, - betaInCut); + betaInCut, + ptCut); } else if (innerInnerLowerModuleSubdet == Barrel and middleLowerModuleSubdet == Barrel and outerOuterLowerModuleSubdet == Endcap) { return passPointingConstraintBBE(acc, @@ -518,7 +523,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerSegmentIndex, outerSegmentIndex, betaIn, - betaInCut); + betaInCut, + ptCut); } else if (innerInnerLowerModuleSubdet == Barrel and middleLowerModuleSubdet == Endcap and outerOuterLowerModuleSubdet == Endcap) { return passPointingConstraintBBE(acc, @@ -537,7 +543,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerSegmentIndex, outerSegmentIndex, betaIn, - betaInCut); + betaInCut, + ptCut); } @@ -558,7 +565,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerSegmentIndex, outerSegmentIndex, betaIn, - betaInCut); + betaInCut, + ptCut); } return false; // failsafe } @@ -612,7 +620,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { float& betaInCut, float& circleRadius, float& circleCenterX, - float& circleCenterY) { + float& circleCenterY, + const float ptCut) { //this cut reduces the number of candidates by a factor of 4, i.e., 3 out of 4 warps can end right here! if (segments.mdIndices()[innerSegmentIndex][1] != segments.mdIndices()[outerSegmentIndex][0]) return false; @@ -647,7 +656,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { innerSegmentIndex, outerSegmentIndex, betaIn, - betaInCut)) + betaInCut, + ptCut)) return false; float x1 = mds.anchorX()[firstMDIndex]; @@ -672,7 +682,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { TripletsOccupancy tripletsOccupancy, ObjectRangesConst ranges, uint16_t* index_gpu, - uint16_t nonZeroModules) const { + uint16_t nonZeroModules, + const float ptCut) const { auto const globalThreadIdx = alpaka::getIdx(acc); auto const gridThreadExtent = alpaka::getWorkDiv(acc); @@ -719,7 +730,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { betaInCut, circleRadius, circleCenterX, - circleCenterY); + circleCenterY, + ptCut); if (success) { unsigned int totOccupancyTriplets = diff --git a/RecoTracker/LSTCore/standalone/.gitignore b/RecoTracker/LSTCore/standalone/.gitignore index 29e86cb6b932a..3d27afd0c4469 100644 --- a/RecoTracker/LSTCore/standalone/.gitignore +++ b/RecoTracker/LSTCore/standalone/.gitignore @@ -9,6 +9,7 @@ plots_*/ scripts/moduleconnection*.txt *.root .make.log* +performance* bin/doAnalysis bin/lst bin/lst_cuda diff --git a/RecoTracker/LSTCore/standalone/LST/Makefile b/RecoTracker/LSTCore/standalone/LST/Makefile index ee6f82ecccde1..8aed3e58ccb09 100644 --- a/RecoTracker/LSTCore/standalone/LST/Makefile +++ b/RecoTracker/LSTCore/standalone/LST/Makefile @@ -88,31 +88,31 @@ CUTVALUEFLAG = CUTVALUEFLAG_FLAGS = -DCUT_VALUE_DEBUG %_cpu.o: ../../src/alpaka/%.dev.cc - $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ + $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ %_cuda.o: ../../src/alpaka/%.dev.cc - $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ + $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ %_rocm.o: ../../src/alpaka/%.dev.cc - $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ + $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ %_cpu.o: ../../src/alpaka/%.cc - $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ + $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ %_cuda.o: ../../src/alpaka/%.cc - $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ + $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ %_rocm.o: ../../src/alpaka/%.cc - $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ + $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ %_cpu.o: ../../src/%.cc - $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ + $(COMPILE_CMD_CPU) $(CXXFLAGS_CPU) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CPU) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CPU) $< -o $@ %_cuda.o: ../../src/%.cc - $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ + $(COMPILE_CMD_CUDA) $(CXXFLAGS_CUDA) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_CUDA) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_CUDA) $< -o $@ %_rocm.o: ../../src/%.cc - $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(PTCUTFLAG) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ + $(COMPILE_CMD_ROCM) $(CXXFLAGS_ROCM) $(ROOTINCLUDE) $(CUTVALUEFLAG) $(LSTWARNINGSFLAG) $(CMSSW_WERRORS_ROCM) $(T5CUTFLAGS) $(ALPAKAINCLUDE) $(ALPAKABACKEND_ROCM) $< -o $@ $(LIB_CPU): $(CCOBJECTS_CPU) $(LSTOBJECTS_CPU) $(LD_CPU) $(SOFLAGS_CPU) $^ -o $@ diff --git a/RecoTracker/LSTCore/standalone/Makefile b/RecoTracker/LSTCore/standalone/Makefile index b98df31df1b5e..18ec73db8d975 100644 --- a/RecoTracker/LSTCore/standalone/Makefile +++ b/RecoTracker/LSTCore/standalone/Makefile @@ -25,7 +25,6 @@ ALPAKA_CUDA = -DALPAKA_ACC_GPU_CUDA_ENABLED -DALPAKA_HOST_ONLY -DALPAKA_DISABLE_ ALPAKA_ROCM = -DALPAKA_ACC_GPU_HIP_ENABLED -DALPAKA_HOST_ONLY -DALPAKA_DISABLE_VENDOR_RNG -D__HIP_PLATFORM_HCC__ -D__HIP_PLATFORM_AMD__ -DALPAKA_DEFAULT_HOST_MEMORY_ALIGNMENT=128 EXTRAFLAGS = -ITMultiDrawTreePlayer -Wunused-variable -lTMVA -lEG -lGenVector -lXMLIO -lMLP -lTreePlayer -fopenmp DOQUINTUPLET = -PTCUTFLAG = CUTVALUEFLAG = CUTVALUEFLAG_FLAGS = -DCUT_VALUE_DEBUG @@ -47,20 +46,20 @@ cutvalue_primitive: rooutil efficiency $(EXES) bin/lst_cpu: LSTLIB=-llst_cpu bin/lst_cpu: bin/lst_cpu.o $(OBJECTS_CPU) - $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) -o $@ + $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) -o $@ bin/lst_cuda: LSTLIB=-llst_cuda bin/lst_cuda: bin/lst_cuda.o $(OBJECTS_CUDA) - $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(LDFLAGS_CUDA) -o $@ + $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(LDFLAGS_CUDA) -o $@ bin/lst_rocm: LSTLIB=-llst_rocm bin/lst_rocm: bin/lst_rocm.o $(OBJECTS_ROCM) - $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(LDFLAGS_ROCM) -o $@ + $(CXX) $(LDFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $^ $(ROOTLIBS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(LDFLAGS_ROCM) -o $@ %_cpu.o: %.cc rooutil - $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) $< -c -o $@ + $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CPU) $< -c -o $@ %_cuda.o: %.cc rooutil - $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(CUDAINCLUDE) $< -c -o $@ + $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_CUDA) $(CUDAINCLUDE) $< -c -o $@ %_rocm.o: %.cc rooutil - $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(PTCUTFLAG) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(ROCMINCLUDE) $< -c -o $@ + $(CXX) $(CXXFLAGS) $(EXTRAFLAGS) $(INCLUDEFLAGS) $(ALPAKAFLAGS) $(CUTVALUEFLAG) $(PRIMITIVEFLAG) $(DOQUINTUPLET) $(ALPAKA_ROCM) $(ROCMINCLUDE) $< -c -o $@ rooutil: $(MAKE) -C code/rooutil/ diff --git a/RecoTracker/LSTCore/standalone/bin/lst.cc b/RecoTracker/LSTCore/standalone/bin/lst.cc index 369680bc4309e..17eb4a54628d6 100644 --- a/RecoTracker/LSTCore/standalone/bin/lst.cc +++ b/RecoTracker/LSTCore/standalone/bin/lst.cc @@ -53,6 +53,7 @@ int main(int argc, char **argv) { cxxopts::value()->default_value("trackingNtuple/tree"))( "o,output", "Output file name", cxxopts::value())( "N,nmatch", "N match for MTV-like matching", cxxopts::value()->default_value("9"))( + "p,ptCut", "Min pT cut In GeV", cxxopts::value()->default_value("0.8"))( "n,nevents", "N events to loop over", cxxopts::value()->default_value("-1"))( "x,event_index", "specific event index to process", cxxopts::value()->default_value("-1"))( "g,pdg_id", "The simhit pdgId match option", cxxopts::value()->default_value("0"))( @@ -145,6 +146,10 @@ int main(int argc, char **argv) { } } + //_______________________________________________________________________________ + // --ptCut + ana.ptCut = result["ptCut"].as(); + //_______________________________________________________________________________ // --nmatch ana.nmatch_threshold = result["nmatch"].as(); @@ -308,7 +313,9 @@ void run_lst() { // Load various maps used in the lst reconstruction TStopwatch full_timer; full_timer.Start(); - auto hostESData = lst::loadAndFillESHost(); + // Determine which maps to use based on given pt cut for standalone. + std::string ptCutString = (ana.ptCut >= 0.8) ? "0.8" : "0.6"; + auto hostESData = lst::loadAndFillESHost(ptCutString); auto deviceESData = cms::alpakatools::CopyToDevice>::copyAsync(queues[0], *hostESData.get()); float timeForMapLoading = full_timer.RealTime() * 1000; @@ -388,7 +395,7 @@ void run_lst() { full_timer.Start(); std::vector events; for (int s = 0; s < ana.streams; s++) { - LSTEvent *event = new LSTEvent(ana.verbose >= 2, queues[s], &deviceESData); + LSTEvent *event = new LSTEvent(ana.verbose >= 2, ana.ptCut, queues[s], &deviceESData); events.push_back(event); } float timeForEventCreation = full_timer.RealTime() * 1000; diff --git a/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper b/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper index 7686b3df42bf5..0c3c9be329e91 100755 --- a/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper +++ b/RecoTracker/LSTCore/standalone/bin/lst_make_tracklooper @@ -28,7 +28,6 @@ usage() echo " -G GPU (CUDA) backend (Compile for CUDA)" echo " -R ROCm backend (Compile for ROCm)" echo " -A All backends (Compile for all backends, including ROCm)" - echo " -P PT Cut Value (In GeV, Default is 0.8, Works only for standalone version of code)" echo " -w Warning mode (Print extra warning outputs)" echo exit @@ -47,7 +46,6 @@ while getopts ":cxgsmdp3NCGRA2ehwP:" OPTION; do R) ROCMBACKEND=true;; A) ALLBACKENDS=true;; w) PRINTWARNINGS=true;; - P) PTCUTVALUE=$OPTARG;; h) usage;; :) usage;; esac @@ -64,7 +62,6 @@ if [ -z ${CUDABACKEND} ]; then CUDABACKEND=false; fi if [ -z ${ROCMBACKEND} ]; then ROCMBACKEND=false; fi if [ -z ${ALLBACKENDS} ]; then ALLBACKENDS=false; fi if [ -z ${PRINTWARNINGS} ]; then PRINTWARNINGS=false; fi -if [ -z ${PTCUTVALUE} ]; then PTCUTVALUE=0.8; fi # Default to only CPU and CUDA backends if [ "${CPUBACKEND}" == false ] && [ "${CUDABACKEND}" == false ] && [ "${ROCMBACKEND}" == false ]; then @@ -101,7 +98,6 @@ echo " CPUBACKEND : ${CPUBACKEND}" | tee -a ${LOG} echo " CUDABACKEND : ${CUDABACKEND}" | tee -a ${LOG} echo " ROCMBACKEND : ${ROCMBACKEND}" | tee -a ${LOG} echo " PRINTWARNINGS : ${PRINTWARNINGS}" | tee -a ${LOG} -echo " PTCUTVALUE : ${PTCUTVALUE} GeV" | tee -a ${LOG} echo "" | tee -a ${LOG} echo " (cf. Run > sh $(basename $0) -h to see all options)" | tee -a ${LOG} echo "" | tee -a ${LOG} @@ -159,8 +155,6 @@ if $PRINTWARNINGS; then PRINTWARNINGSOPT="LSTWARNINGSFLAG=-DWARNINGS" fi -PTCUTOPT="PTCUTFLAG=-DPT_CUT=${PTCUTVALUE}" - if [ -z "${MAXMAKETHREADS}" ]; then MAXMAKETHREADS=32 fi @@ -177,9 +171,9 @@ echo "-------------------------------------------------------------------------- echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 if $SHOWLOG; then - (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} ${PTCUTOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) 2>&1 | tee -a ${LOG} + (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) 2>&1 | tee -a ${LOG} else - (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} ${PTCUTOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) >> ${LOG} 2>&1 + (cd LST && make clean && make ${BACKENDOPT} ${PRINTWARNINGSOPT} -j ${MAXMAKETHREADS} ${MAKETARGET} && cd -) >> ${LOG} 2>&1 fi if ([[ "$BACKENDOPT" == *"all"* ]] || [[ "$BACKENDOPT" == *"cpu"* ]]) && [ ! -f LST/liblst_cpu.so ]; then @@ -214,9 +208,9 @@ echo "-------------------------------------------------------------------------- echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 echo "---------------------------------------------------------------------------------------------" >> ${LOG} 2>&1 if $SHOWLOG; then - make EXES="${EXES}" ${TRACKLOOPERTARGET} ${PTCUTOPT} -j ${MAXMAKETHREADS} 2>&1 | tee -a ${LOG} + make EXES="${EXES}" ${TRACKLOOPERTARGET} -j ${MAXMAKETHREADS} 2>&1 | tee -a ${LOG} else - make EXES="${EXES}" ${TRACKLOOPERTARGET} ${PTCUTOPT} -j ${MAXMAKETHREADS} >> ${LOG} 2>&1 + make EXES="${EXES}" ${TRACKLOOPERTARGET} -j ${MAXMAKETHREADS} >> ${LOG} 2>&1 fi if ([[ "$BACKENDOPT" == *"all"* ]] || [[ "$BACKENDOPT" == *"cpu"* ]]) && [ ! -f bin/lst_cpu ]; then diff --git a/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h b/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h index 8608bc95ed2fa..6d0da61bf2395 100644 --- a/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h +++ b/RecoTracker/LSTCore/standalone/code/core/AnalysisConfig.h @@ -46,6 +46,9 @@ class AnalysisConfig { // pt binning options int ptbound_mode; + // pt cut + float ptCut; + // pdg id of the particles to compute efficincies on int pdg_id; diff --git a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc index ffb2e7de205ac..50c5a2e2df61b 100644 --- a/RecoTracker/LSTCore/standalone/code/core/trkCore.cc +++ b/RecoTracker/LSTCore/standalone/code/core/trkCore.cc @@ -701,7 +701,7 @@ void addInputsToLineSegmentTrackingPreLoad(std::vector> &out_ float eta = p3LH.Eta(); float ptErr = trk.see_ptErr()[iSeed]; - if ((ptIn > PT_CUT - 2 * ptErr)) { + if ((ptIn > ana.ptCut - 2 * ptErr)) { TVector3 r3LH(trk.see_stateTrajGlbX()[iSeed], trk.see_stateTrajGlbY()[iSeed], trk.see_stateTrajGlbZ()[iSeed]); TVector3 p3PCA(trk.see_px()[iSeed], trk.see_py()[iSeed], trk.see_pz()[iSeed]); TVector3 r3PCA(calculateR3FromPCA(p3PCA, trk.see_dxy()[iSeed], trk.see_dz()[iSeed])); @@ -722,7 +722,7 @@ void addInputsToLineSegmentTrackingPreLoad(std::vector> &out_ PixelType pixtype = PixelType::kInvalid; if (ptIn >= 2.0) { pixtype = PixelType::kHighPt; - } else if (ptIn >= (PT_CUT - 2 * ptErr) and ptIn < 2.0) { + } else if (ptIn >= (ana.ptCut - 2 * ptErr) and ptIn < 2.0) { if (pixelSegmentDeltaPhiChange >= 0) { pixtype = PixelType::kLowPtPosCurv; } else { From 8f56da489ef770f193f286879f62532f426d425e Mon Sep 17 00:00:00 2001 From: Andres Rios Tascon Date: Fri, 22 Nov 2024 12:11:46 -0800 Subject: [PATCH 2/2] Add Low pT Occupancies Co-authored-by: Gavin Niendorf --- .../LSTCore/src/alpaka/LSTEvent.dev.cc | 15 +- RecoTracker/LSTCore/src/alpaka/MiniDoublet.h | 114 ++-- RecoTracker/LSTCore/src/alpaka/Quintuplet.h | 81 +-- RecoTracker/LSTCore/src/alpaka/Segment.h | 92 +-- RecoTracker/LSTCore/src/alpaka/Triplet.h | 89 +-- .../occupancy/compute_occupancies.ipynb | 586 ++++++++++++++++++ 6 files changed, 755 insertions(+), 222 deletions(-) create mode 100644 RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb diff --git a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc index 3b647273303b2..c9c6d5c051b27 100644 --- a/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc +++ b/RecoTracker/LSTCore/src/alpaka/LSTEvent.dev.cc @@ -202,7 +202,8 @@ void LSTEvent::addPixelSegmentToEvent(std::vector const& hitIndice createMDArrayRangesGPU_workDiv, CreateMDArrayRangesGPU{}, modules_.const_view(), - rangesDC_->view()); + rangesDC_->view(), + ptCut_); auto nTotalMDs_buf_h = cms::alpakatools::make_host_buffer(queue_); auto nTotalMDs_buf_d = cms::alpakatools::make_device_view(queue_, rangesOccupancy.nTotalMDs()); @@ -233,7 +234,8 @@ void LSTEvent::addPixelSegmentToEvent(std::vector const& hitIndice CreateSegmentArrayRanges{}, modules_.const_view(), rangesDC_->view(), - miniDoubletsDC_->const_view()); + miniDoubletsDC_->const_view(), + ptCut_); auto rangesOccupancy = rangesDC_->view(); auto nTotalSegments_view_h = cms::alpakatools::make_host_view(nTotalSegments_); @@ -346,7 +348,8 @@ void LSTEvent::createMiniDoublets() { createMDArrayRangesGPU_workDiv, CreateMDArrayRangesGPU{}, modules_.const_view(), - rangesDC_->view()); + rangesDC_->view(), + ptCut_); auto nTotalMDs_buf_h = cms::alpakatools::make_host_buffer(queue_); auto nTotalMDs_buf_d = cms::alpakatools::make_device_view(queue_, rangesOccupancy.nTotalMDs()); @@ -454,7 +457,8 @@ void LSTEvent::createTriplets() { CreateTripletArrayRanges{}, modules_.const_view(), rangesDC_->view(), - segmentsDC_->const_view()); + segmentsDC_->const_view(), + ptCut_); // TODO: Why are we pulling this back down only to put it back on the device in a new struct? auto rangesOccupancy = rangesDC_->view(); @@ -857,7 +861,8 @@ void LSTEvent::createQuintuplets() { CreateEligibleModulesListForQuintuplets{}, modules_.const_view(), tripletsDC_->const_view(), - rangesDC_->view()); + rangesDC_->view(), + ptCut_); auto nEligibleT5Modules_buf = cms::alpakatools::make_host_buffer(queue_); auto nTotalQuintuplets_buf = cms::alpakatools::make_host_buffer(queue_); diff --git a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h index 210d7201fd800..7373b2790bb48 100644 --- a/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h +++ b/RecoTracker/LSTCore/src/alpaka/MiniDoublet.h @@ -772,7 +772,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { acc, &mdsOccupancy.totOccupancyMDs()[lowerModuleIndex], 1u, alpaka::hierarchy::Threads{}); if (totOccupancyMDs >= (ranges.miniDoubletModuleOccupancy()[lowerModuleIndex])) { #ifdef WARNINGS - printf("Mini-doublet excess alert! Module index = %d\n", lowerModuleIndex); + printf( + "Mini-doublet excess alert! Module index = %d, Occupancy = %d\n", lowerModuleIndex, totOccupancyMDs); #endif } else { int mdModuleIndex = @@ -802,9 +803,38 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } }; + // Helper function to determine eta bin for occupancies + ALPAKA_FN_ACC ALPAKA_FN_INLINE int getEtaBin(const float module_eta) { + if (module_eta < 0.75f) + return 0; + else if (module_eta < 1.5f) + return 1; + else if (module_eta < 2.25f) + return 2; + else if (module_eta < 3.0f) + return 3; + return -1; + } + + // Helper function to determine category number for occupancies + ALPAKA_FN_ACC ALPAKA_FN_INLINE int getCategoryNumber(const short module_layers, + const short module_subdets, + const short module_rings) { + if (module_subdets == Barrel) { + return (module_layers <= 3) ? 0 : 1; + } else if (module_subdets == Endcap) { + if (module_layers <= 2) { + return (module_rings >= 11) ? 2 : 3; + } else { + return (module_rings >= 8) ? 2 : 3; + } + } + return -1; + } + struct CreateMDArrayRangesGPU { template - ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, ObjectRanges ranges) const { + ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, ObjectRanges ranges, const float ptCut) const { // implementation is 1D with a single block static_assert(std::is_same_v, "Should be Acc1D"); ALPAKA_ASSERT_ACC((alpaka::getWorkDiv(acc)[0] == 1)); @@ -819,67 +849,43 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } alpaka::syncBlockThreads(acc); + // Occupancy matrix for 0.8 GeV pT Cut + constexpr int p08_occupancy_matrix[4][4] = { + {49, 42, 37, 41}, // category 0 + {100, 100, 0, 0}, // category 1 + {0, 16, 19, 0}, // category 2 + {0, 14, 20, 25} // category 3 + }; + + // Occupancy matrix for 0.6 GeV pT Cut, 99.99% + constexpr int p06_occupancy_matrix[4][4] = { + {60, 57, 54, 48}, // category 0 + {259, 195, 0, 0}, // category 1 + {0, 23, 28, 0}, // category 2 + {0, 25, 25, 33} // category 3 + }; + + // Select the appropriate occupancy matrix based on ptCut + const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix; + for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) { short module_rings = modules.rings()[i]; short module_layers = modules.layers()[i]; short module_subdets = modules.subdets()[i]; float module_eta = alpaka::math::abs(acc, modules.eta()[i]); - int category_number; - if (module_layers <= 3 && module_subdets == 5) - category_number = 0; - else if (module_layers >= 4 && module_subdets == 5) - category_number = 1; - else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11) - category_number = 2; - else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8) - category_number = 2; - else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10) - category_number = 3; - else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7) - category_number = 3; - else - category_number = -1; - - int eta_number; - if (module_eta < 0.75f) - eta_number = 0; - else if (module_eta < 1.5f) - eta_number = 1; - else if (module_eta < 2.25f) - eta_number = 2; - else if (module_eta < 3.0f) - eta_number = 3; - else - eta_number = -1; - - int occupancy; - if (category_number == 0 && eta_number == 0) - occupancy = 49; - else if (category_number == 0 && eta_number == 1) - occupancy = 42; - else if (category_number == 0 && eta_number == 2) - occupancy = 37; - else if (category_number == 0 && eta_number == 3) - occupancy = 41; - else if (category_number == 1) - occupancy = 100; - else if (category_number == 2 && eta_number == 1) - occupancy = 16; - else if (category_number == 2 && eta_number == 2) - occupancy = 19; - else if (category_number == 3 && eta_number == 1) - occupancy = 14; - else if (category_number == 3 && eta_number == 2) - occupancy = 20; - else if (category_number == 3 && eta_number == 3) - occupancy = 25; - else { - occupancy = 0; + int category_number = getCategoryNumber(module_layers, module_subdets, module_rings); + int eta_number = getEtaBin(module_eta); + + int occupancy = 0; + if (category_number != -1 && eta_number != -1) { + occupancy = occupancy_matrix[category_number][eta_number]; + } #ifdef WARNINGS + else { printf("Unhandled case in createMDArrayRangesGPU! Module index = %i\n", i); -#endif } +#endif unsigned int nTotMDs = alpaka::atomicAdd(acc, &nTotalMDs, occupancy, alpaka::hierarchy::Threads{}); diff --git a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h index ffc0386d18746..8cd888a5fa59a 100644 --- a/RecoTracker/LSTCore/src/alpaka/Quintuplet.h +++ b/RecoTracker/LSTCore/src/alpaka/Quintuplet.h @@ -2418,7 +2418,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { acc, &quintupletsOccupancy.totOccupancyQuintuplets()[lowerModule1], 1u, alpaka::hierarchy::Threads{}); if (totOccupancyQuintuplets >= ranges.quintupletModuleOccupancy()[lowerModule1]) { #ifdef WARNINGS - printf("Quintuplet excess alert! Module index = %d\n", lowerModule1); + printf("Quintuplet excess alert! Module index = %d, Occupancy = %d\n", + lowerModule1, + totOccupancyQuintuplets); #endif } else { int quintupletModuleIndex = alpaka::atomicAdd( @@ -2478,7 +2480,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, TripletsOccupancyConst tripletsOccupancy, - ObjectRanges ranges) const { + ObjectRanges ranges, + const float ptCut) const { // implementation is 1D with a single block static_assert(std::is_same_v, "Should be Acc1D"); ALPAKA_ASSERT_ACC((alpaka::getWorkDiv(acc)[0] == 1)); @@ -2495,6 +2498,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } alpaka::syncBlockThreads(acc); + // Occupancy matrix for 0.8 GeV pT Cut + constexpr int p08_occupancy_matrix[4][4] = { + {336, 414, 231, 146}, // category 0 + {0, 0, 0, 0}, // category 1 + {0, 0, 0, 0}, // category 2 + {0, 0, 191, 106} // category 3 + }; + + // Occupancy matrix for 0.6 GeV pT Cut, 99.99% + constexpr int p06_occupancy_matrix[4][4] = { + {325, 237, 217, 176}, // category 0 + {0, 0, 0, 0}, // category 1 + {0, 0, 0, 0}, // category 2 + {0, 0, 129, 180} // category 3 + }; + + // Select the appropriate occupancy matrix based on ptCut + const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix; + for (int i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) { // Condition for a quintuple to exist for a module // TCs don't exist for layers 5 and 6 barrel, and layers 2,3,4,5 endcap @@ -2512,55 +2534,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { int nEligibleT5Modules = alpaka::atomicAdd(acc, &nEligibleT5Modulesx, 1, alpaka::hierarchy::Threads{}); - int category_number; - if (module_layers <= 3 && module_subdets == 5) - category_number = 0; - else if (module_layers >= 4 && module_subdets == 5) - category_number = 1; - else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11) - category_number = 2; - else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8) - category_number = 2; - else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10) - category_number = 3; - else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7) - category_number = 3; - else - category_number = -1; - - int eta_number; - if (module_eta < 0.75f) - eta_number = 0; - else if (module_eta < 1.5f) - eta_number = 1; - else if (module_eta < 2.25f) - eta_number = 2; - else if (module_eta < 3.0f) - eta_number = 3; - else - eta_number = -1; - - int occupancy; - if (category_number == 0 && eta_number == 0) - occupancy = 336; - else if (category_number == 0 && eta_number == 1) - occupancy = 414; - else if (category_number == 0 && eta_number == 2) - occupancy = 231; - else if (category_number == 0 && eta_number == 3) - occupancy = 146; - else if (category_number == 3 && eta_number == 1) - occupancy = 0; - else if (category_number == 3 && eta_number == 2) - occupancy = 191; - else if (category_number == 3 && eta_number == 3) - occupancy = 106; - else { - occupancy = 0; + int category_number = getCategoryNumber(module_layers, module_subdets, module_rings); + int eta_number = getEtaBin(module_eta); + + int occupancy = 0; + if (category_number != -1 && eta_number != -1) { + occupancy = occupancy_matrix[category_number][eta_number]; + } #ifdef WARNINGS + else { printf("Unhandled case in createEligibleModulesListForQuintupletsGPU! Module index = %i\n", i); -#endif } +#endif int nTotQ = alpaka::atomicAdd(acc, &nTotalQuintupletsx, occupancy, alpaka::hierarchy::Threads{}); ranges.quintupletModuleIndices()[i] = nTotQ; diff --git a/RecoTracker/LSTCore/src/alpaka/Segment.h b/RecoTracker/LSTCore/src/alpaka/Segment.h index f91d176687f5d..fa8ff2d61a008 100644 --- a/RecoTracker/LSTCore/src/alpaka/Segment.h +++ b/RecoTracker/LSTCore/src/alpaka/Segment.h @@ -613,7 +613,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { alpaka::hierarchy::Threads{}); if (static_cast(totOccupancySegments) >= ranges.segmentModuleOccupancy()[innerLowerModuleIndex]) { #ifdef WARNINGS - printf("Segment excess alert! Module index = %d\n", innerLowerModuleIndex); + printf("Segment excess alert! Module index = %d, Occupancy = %d\n", + innerLowerModuleIndex, + totOccupancySegments); #endif } else { unsigned int segmentModuleIdx = alpaka::atomicAdd( @@ -644,10 +646,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { struct CreateSegmentArrayRanges { template - ALPAKA_FN_ACC void operator()(TAcc const& acc, - ModulesConst modules, - ObjectRanges ranges, - MiniDoubletsConst mds) const { + ALPAKA_FN_ACC void operator()( + TAcc const& acc, ModulesConst modules, ObjectRanges ranges, MiniDoubletsConst mds, const float ptCut) const { // implementation is 1D with a single block static_assert(std::is_same_v, "Should be Acc1D"); ALPAKA_ASSERT_ACC((alpaka::getWorkDiv(acc)[0] == 1)); @@ -662,6 +662,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } alpaka::syncBlockThreads(acc); + // Occupancy matrix for 0.8 GeV pT Cut + constexpr int p08_occupancy_matrix[4][4] = { + {572, 300, 183, 62}, // category 0 + {191, 128, 0, 0}, // category 1 + {0, 107, 102, 0}, // category 2 + {0, 64, 79, 85} // category 3 + }; + + // Occupancy matrix for 0.6 GeV pT Cut, 99.9% + constexpr int p06_occupancy_matrix[4][4] = { + {936, 351, 256, 61}, // category 0 + {1358, 763, 0, 0}, // category 1 + {0, 210, 268, 0}, // category 2 + {0, 60, 97, 96} // category 3 + }; + + // Select the appropriate occupancy matrix based on ptCut + const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix; + for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) { if (modules.nConnectedModules()[i] == 0) { ranges.segmentModuleIndices()[i] = nTotalSegments; @@ -674,63 +693,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { short module_subdets = modules.subdets()[i]; float module_eta = alpaka::math::abs(acc, modules.eta()[i]); - int category_number; - if (module_layers <= 3 && module_subdets == 5) - category_number = 0; - else if (module_layers >= 4 && module_subdets == 5) - category_number = 1; - else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11) - category_number = 2; - else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8) - category_number = 2; - else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10) - category_number = 3; - else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7) - category_number = 3; - else - category_number = -1; - - int eta_number; - if (module_eta < 0.75f) - eta_number = 0; - else if (module_eta < 1.5f) - eta_number = 1; - else if (module_eta < 2.25f) - eta_number = 2; - else if (module_eta < 3.0f) - eta_number = 3; - else - eta_number = -1; - - int occupancy; - if (category_number == 0 && eta_number == 0) - occupancy = 572; - else if (category_number == 0 && eta_number == 1) - occupancy = 300; - else if (category_number == 0 && eta_number == 2) - occupancy = 183; - else if (category_number == 0 && eta_number == 3) - occupancy = 62; - else if (category_number == 1 && eta_number == 0) - occupancy = 191; - else if (category_number == 1 && eta_number == 1) - occupancy = 128; - else if (category_number == 2 && eta_number == 1) - occupancy = 107; - else if (category_number == 2 && eta_number == 2) - occupancy = 102; - else if (category_number == 3 && eta_number == 1) - occupancy = 64; - else if (category_number == 3 && eta_number == 2) - occupancy = 79; - else if (category_number == 3 && eta_number == 3) - occupancy = 85; - else { - occupancy = 0; + int category_number = getCategoryNumber(module_layers, module_subdets, module_rings); + int eta_number = getEtaBin(module_eta); + + int occupancy = 0; + if (category_number != -1 && eta_number != -1) { + occupancy = occupancy_matrix[category_number][eta_number]; + } #ifdef WARNINGS + else { printf("Unhandled case in createSegmentArrayRanges! Module index = %i\n", i); -#endif } +#endif int nTotSegs = alpaka::atomicAdd(acc, &nTotalSegments, occupancy, alpaka::hierarchy::Threads{}); ranges.segmentModuleIndices()[i] = nTotSegs; diff --git a/RecoTracker/LSTCore/src/alpaka/Triplet.h b/RecoTracker/LSTCore/src/alpaka/Triplet.h index a57d70f0f5238..1a5f5a2c2a7f1 100644 --- a/RecoTracker/LSTCore/src/alpaka/Triplet.h +++ b/RecoTracker/LSTCore/src/alpaka/Triplet.h @@ -742,7 +742,9 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { if (static_cast(totOccupancyTriplets) >= ranges.tripletModuleOccupancy()[innerInnerLowerModuleIndex]) { #ifdef WARNINGS - printf("Triplet excess alert! Module index = %d\n", innerInnerLowerModuleIndex); + printf("Triplet excess alert! Module index = %d, Occupancy = %d\n", + innerInnerLowerModuleIndex, + totOccupancyTriplets); #endif } else { unsigned int tripletModuleIndex = alpaka::atomicAdd( @@ -781,7 +783,8 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { ALPAKA_FN_ACC void operator()(TAcc const& acc, ModulesConst modules, ObjectRanges ranges, - SegmentsOccupancyConst segmentsOccupancy) const { + SegmentsOccupancyConst segmentsOccupancy, + const float ptCut) const { // implementation is 1D with a single block static_assert(std::is_same_v, "Should be Acc1D"); ALPAKA_ASSERT_ACC((alpaka::getWorkDiv(acc)[0] == 1)); @@ -796,6 +799,25 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { } alpaka::syncBlockThreads(acc); + // Occupancy matrix for 0.8 GeV pT Cut + constexpr int p08_occupancy_matrix[4][4] = { + {543, 235, 88, 46}, // category 0 + {755, 347, 0, 0}, // category 1 + {0, 0, 0, 0}, // category 2 + {0, 38, 46, 39} // category 3 + }; + + // Occupancy matrix for 0.6 GeV pT Cut, 99.9% + constexpr int p06_occupancy_matrix[4][4] = { + {1146, 544, 216, 83}, // category 0 + {1032, 275, 0, 0}, // category 1 + {0, 0, 0, 0}, // category 2 + {0, 115, 110, 76} // category 3 + }; + + // Select the appropriate occupancy matrix based on ptCut + const auto& occupancy_matrix = (ptCut < 0.8f) ? p06_occupancy_matrix : p08_occupancy_matrix; + for (uint16_t i = globalThreadIdx[0]; i < modules.nLowerModules(); i += gridThreadExtent[0]) { if (segmentsOccupancy.nSegments()[i] == 0) { ranges.tripletModuleIndices()[i] = nTotalTriplets; @@ -808,63 +830,18 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE::lst { short module_subdets = modules.subdets()[i]; float module_eta = alpaka::math::abs(acc, modules.eta()[i]); - int category_number; - if (module_layers <= 3 && module_subdets == 5) - category_number = 0; - else if (module_layers >= 4 && module_subdets == 5) - category_number = 1; - else if (module_layers <= 2 && module_subdets == 4 && module_rings >= 11) - category_number = 2; - else if (module_layers >= 3 && module_subdets == 4 && module_rings >= 8) - category_number = 2; - else if (module_layers <= 2 && module_subdets == 4 && module_rings <= 10) - category_number = 3; - else if (module_layers >= 3 && module_subdets == 4 && module_rings <= 7) - category_number = 3; - else - category_number = -1; - - int eta_number; - if (module_eta < 0.75f) - eta_number = 0; - else if (module_eta < 1.5f) - eta_number = 1; - else if (module_eta < 2.25f) - eta_number = 2; - else if (module_eta < 3.0f) - eta_number = 3; - else - eta_number = -1; - - int occupancy; - if (category_number == 0 && eta_number == 0) - occupancy = 543; - else if (category_number == 0 && eta_number == 1) - occupancy = 235; - else if (category_number == 0 && eta_number == 2) - occupancy = 88; - else if (category_number == 0 && eta_number == 3) - occupancy = 46; - else if (category_number == 1 && eta_number == 0) - occupancy = 755; - else if (category_number == 1 && eta_number == 1) - occupancy = 347; - else if (category_number == 2 && eta_number == 1) - occupancy = 0; - else if (category_number == 2 && eta_number == 2) - occupancy = 0; - else if (category_number == 3 && eta_number == 1) - occupancy = 38; - else if (category_number == 3 && eta_number == 2) - occupancy = 46; - else if (category_number == 3 && eta_number == 3) - occupancy = 39; - else { - occupancy = 0; + int category_number = getCategoryNumber(module_layers, module_subdets, module_rings); + int eta_number = getEtaBin(module_eta); + + int occupancy = 0; + if (category_number != -1 && eta_number != -1) { + occupancy = occupancy_matrix[category_number][eta_number]; + } #ifdef WARNINGS + else { printf("Unhandled case in createTripletArrayRanges! Module index = %i\n", i); -#endif } +#endif ranges.tripletModuleOccupancy()[i] = occupancy; unsigned int nTotT = alpaka::atomicAdd(acc, &nTotalTriplets, occupancy, alpaka::hierarchy::Threads{}); diff --git a/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb b/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb new file mode 100644 index 0000000000000..246d12b11e8c5 --- /dev/null +++ b/RecoTracker/LSTCore/standalone/analysis/occupancy/compute_occupancies.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import uproot\n", + "import numpy as np\n", + "\n", + "def load_root_file(file_path, branches=None, print_branches=False):\n", + " all_branches = {}\n", + " with uproot.open(file_path) as file:\n", + " tree = file[\"tree\"]\n", + " # Load all ROOT branches into array if not specified\n", + " if branches is None:\n", + " branches = tree.keys()\n", + " # Option to print the branch names\n", + " if print_branches:\n", + " print(\"Branches:\", branches)\n", + " # Each branch is added to the dictionary\n", + " for branch in branches:\n", + " all_branches[branch] = tree[branch].array(library=\"np\")\n", + " return all_branches\n", + "\n", + "# Branches relevant to the occupancy selections\n", + "mod_occ_branches = ['module_layers', 'module_subdets', 'module_rings', 'module_eta',\n", + " 'md_occupancies', 'sg_occupancies', 't3_occupancies', 't5_occupancies']\n", + "\n", + "# Root file generated with compile -d option turned on to generate relevant occupancy branches\n", + "file_path = \"occ_1000_p06.root\"\n", + "branches = load_root_file(file_path, mod_occ_branches)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "events = np.shape(branches['module_layers'])[0]\n", + "\n", + "module_layers = np.concatenate(branches['module_layers'])\n", + "module_subdets = np.concatenate(branches['module_subdets'])\n", + "module_rings = np.concatenate(branches['module_rings'])\n", + "module_eta = np.abs(np.concatenate(branches['module_eta']))\n", + "\n", + "category_numbers = np.full_like(module_layers, -1)\n", + "\n", + "# Different category masks\n", + "mask1 = (module_layers <= 3) & (module_subdets == 5)\n", + "mask2 = (module_layers >= 4) & (module_subdets == 5)\n", + "mask3 = (module_layers <= 2) & (module_subdets == 4) & (module_rings >= 11)\n", + "mask4 = (module_layers >= 3) & (module_subdets == 4) & (module_rings >= 8)\n", + "mask5 = (module_layers <= 2) & (module_subdets == 4) & (module_rings <= 10)\n", + "mask6 = (module_layers >= 3) & (module_subdets == 4) & (module_rings <= 7)\n", + "\n", + "category_numbers[mask1] = 0\n", + "category_numbers[mask2] = 1\n", + "category_numbers[mask3 | mask4] = 2\n", + "category_numbers[mask5 | mask6] = 3\n", + "\n", + "eta_numbers = np.full_like(module_eta, -1)\n", + "\n", + "# Different eta masks\n", + "eta_numbers[module_eta < 0.75] = 0\n", + "eta_numbers[(module_eta >= 0.75) & (module_eta < 1.5)] = 1\n", + "eta_numbers[(module_eta >= 1.5) & (module_eta < 2.25)] = 2\n", + "eta_numbers[(module_eta >= 2.25) & (module_eta < 3)] = 3\n", + "\n", + "# Split the arrays back into event-wise lists\n", + "split_indices = np.cumsum([len(x) for x in branches['module_layers'][:-1]])\n", + "\n", + "category_numbers_split = np.split(category_numbers, split_indices)\n", + "eta_numbers_split = np.split(eta_numbers, split_indices)\n", + "\n", + "# Add category number and eta number branches\n", + "branches['category_number'] = np.array(category_numbers_split, dtype=object)\n", + "branches['eta_number'] = np.array(eta_numbers_split, dtype=object)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "font = {'size' : 20}\n", + "\n", + "matplotlib.rc('font', **font)\n", + "\n", + "def plot_histogram(data, title, xlabel, ylabel, occ_percentile=None):\n", + " plt.figure(figsize=(10, 6))\n", + " plt.hist(data, bins=50, edgecolor='black', alpha=0.7)\n", + " plt.title(title)\n", + " plt.xlabel(xlabel)\n", + " plt.ylabel(ylabel)\n", + " plt.grid(True)\n", + " plt.yscale('log')\n", + " # Plotting a vertical line at the occupancy value\n", + " if occ_percentile is not None:\n", + " non_zero_data = data[data > 0]\n", + " percentile_value = np.percentile(non_zero_data, occ_percentile)\n", + " plt.axvline(percentile_value, color='red', linestyle='dashed', linewidth=1, label=f'{occ_percentile}th percentile: {percentile_value:.0f}')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_occupancies(branches, occupancy_variables, occ_percentiles, plot=False):\n", + " cat_eta_combinations = [(cat, eta) for cat in range(4) for eta in range(4)]\n", + "\n", + " for var, percentile in zip(occupancy_variables, occ_percentiles):\n", + " for cat, eta in cat_eta_combinations:\n", + " data_to_plot = [\n", + " occupancy for sublist_cat, sublist_eta, sublist_occ in zip(branches['category_number'], branches['eta_number'], branches[var])\n", + " for c, e, occupancy in zip(sublist_cat, sublist_eta, sublist_occ) if c == cat and e == eta\n", + " ]\n", + " data_to_plot = np.array(data_to_plot)\n", + " non_zero_data = data_to_plot[data_to_plot > 0]\n", + " if non_zero_data.any():\n", + " if plot:\n", + " plot_histogram(data_to_plot, f'{var} for Category {cat} and Eta {eta}', 'Occupancy', 'Frequency', percentile)\n", + " else:\n", + " percentile_value = np.percentile(non_zero_data, percentile)\n", + " print(f'{var} for Category {cat} and Eta {eta} - {percentile}th percentile: {percentile_value:.0f}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "md_occupancies for Category 0 and Eta 0 - 99.99th percentile: 60\n", + "md_occupancies for Category 0 and Eta 1 - 99.99th percentile: 57\n", + "md_occupancies for Category 0 and Eta 2 - 99.99th percentile: 54\n", + "md_occupancies for Category 0 and Eta 3 - 99.99th percentile: 48\n", + "md_occupancies for Category 1 and Eta 0 - 99.99th percentile: 259\n", + "md_occupancies for Category 1 and Eta 1 - 99.99th percentile: 195\n", + "md_occupancies for Category 2 and Eta 1 - 99.99th percentile: 23\n", + "md_occupancies for Category 2 and Eta 2 - 99.99th percentile: 28\n", + "md_occupancies for Category 3 and Eta 1 - 99.99th percentile: 25\n", + "md_occupancies for Category 3 and Eta 2 - 99.99th percentile: 25\n", + "md_occupancies for Category 3 and Eta 3 - 99.99th percentile: 33\n", + "sg_occupancies for Category 0 and Eta 0 - 99.9th percentile: 936\n", + "sg_occupancies for Category 0 and Eta 1 - 99.9th percentile: 351\n", + "sg_occupancies for Category 0 and Eta 2 - 99.9th percentile: 256\n", + "sg_occupancies for Category 0 and Eta 3 - 99.9th percentile: 61\n", + "sg_occupancies for Category 1 and Eta 0 - 99.9th percentile: 1358\n", + "sg_occupancies for Category 1 and Eta 1 - 99.9th percentile: 763\n", + "sg_occupancies for Category 2 and Eta 1 - 99.9th percentile: 210\n", + "sg_occupancies for Category 2 and Eta 2 - 99.9th percentile: 268\n", + "sg_occupancies for Category 3 and Eta 1 - 99.9th percentile: 60\n", + "sg_occupancies for Category 3 and Eta 2 - 99.9th percentile: 97\n", + "sg_occupancies for Category 3 and Eta 3 - 99.9th percentile: 96\n", + "t3_occupancies for Category 0 and Eta 0 - 99.9th percentile: 1146\n", + "t3_occupancies for Category 0 and Eta 1 - 99.9th percentile: 544\n", + "t3_occupancies for Category 0 and Eta 2 - 99.9th percentile: 216\n", + "t3_occupancies for Category 0 and Eta 3 - 99.9th percentile: 83\n", + "t3_occupancies for Category 1 and Eta 0 - 99.9th percentile: 1032\n", + "t3_occupancies for Category 1 and Eta 1 - 99.9th percentile: 275\n", + "t3_occupancies for Category 3 and Eta 1 - 99.9th percentile: 115\n", + "t3_occupancies for Category 3 and Eta 2 - 99.9th percentile: 110\n", + "t3_occupancies for Category 3 and Eta 3 - 99.9th percentile: 76\n", + "t5_occupancies for Category 0 and Eta 0 - 99.99th percentile: 325\n", + "t5_occupancies for Category 0 and Eta 1 - 99.99th percentile: 237\n", + "t5_occupancies for Category 0 and Eta 2 - 99.99th percentile: 217\n", + "t5_occupancies for Category 0 and Eta 3 - 99.99th percentile: 176\n", + "t5_occupancies for Category 3 and Eta 2 - 99.99th percentile: 129\n", + "t5_occupancies for Category 3 and Eta 3 - 99.99th percentile: 180\n" + ] + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACEAMgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiq02o2VvLJFNdRRyRx+a6swBVM43H2zQBZorLk1e1uobiKyuVklSMPlD0DYwQfoR+Yqe4hS3t5JpLudERdzMX6AdT0oA5uHxlfeTHPPpcXlvDHNthnLORJG7qACo5/dkH6itPw7rM+qx6lNM1vIsEyrGLV/MXBhR8BsDJyxq5ZG01CDz7S+lliB27lfuO3Skn0SG4kjdru/jKdBDdPGD9QpAP40BuYVp40luzAnkWcRmKnznuT5SBk3bGbb/rB02/j7VP/AMJeEh+0TpaxwmaSIqZ/ni2rIR5gx8pJjwB79++/9hUgjzpsE5xu7/lR9hU5zNNzyfm6/pQByq+Om3Tb7WABFOFWcl1OyNgXGPlUmTGc/wA+JbTxlcXUcUw06PyR5azbZtzbmlki+TAwwzHnr0Nb0elQW8j+U8y+cxeT5/vHAHP5VP8AYgOk8/8A31/9agDmJfGM8Vlb3ohgmE0DP5cE4ZUJeFRvYgYK+Yc9On5XdL8Ty32qW1ncW0NuZ4S67ZxKWYZz93IAwM84zn14ra+wrgjzpuevzdf0oFiqnImmBxjIbt6dKAOWh8UahaXTRXiQSmW7kVAH2HYJ/JCxjb8zD7xyfT14vS67fP4YtdSKQWhupYlMgYyLBE5ALtkDkZ+gyM8VtG0G7iWc7T13jg1HFaSeXiSac/8AbQf4UAc9eeLHsLq30+3mh1F5UOLkMo+Y79nyj7wymDt/SoIfHF0EQPp8VwUtBPI8E4G8+WX+QHkjjacZwc+ldV9jVRnzJgFHXeBgUz7LDIAizynchK7ZQDtPUjHb3FAHOjxvKJDGbezkKSsheK5LLLjyuIjt+Z/3vT/Z9+LUPii7ZrFZbCMHUJClqElLfdfDbvl4IQM//ASK0bPS7HT4PJtpJEQSHOZ9xLn1JyS1StpkEl5HO0k7TwqQh877gbqcdOcYz9aAIJNRaLxhb6e17F5U9nJILc7QwdWQAjuchm49qy7zxk8Gs3WnQWccrRYWNjNty3mRIQwwSB+9BBxzjjNdALLJV2kmLgfe3jP54pfsfJPmT5PU7xQBzUvi27e+tLNYre3kN1HDLulyZMzPG3lgryP3ZJPGNw9Kbqev6lbanrqQTxf6FCWghdo8EiEPyv3zyT04/WunNnyCZJ8jod44pi2sUkhKzSNIvBIkBI6j+hoAytP8U/a/Eg0gxwuDEW86GXI3KqE8ddpDjDdOKtaPqT3Wsa1ZSXkU/wBlnURqu0MilAdpx6EkZNW2tVj2/vpUJIUfvAM+wpvlQRsW+0upLbCfNUEt6fWgDRoqi8bQywFZpjmUKys2QRg0UAXqx9T0R9Rvhcfa/LRYfLVBHkht6uGzn1ReMetbFUrnVrKzneGeVkkSLzSPLY5XIXg4wTkgYHPI45oAoyaRb2GjSKoV5iP3k20KzkvuP4ZPT6VZ1yNRoV+QOfIf+VQyarbahb3EMG84jEgZlwGGecd+DwQcYNWNd/5AF/8A9cH/AJU47omWzMfwMqvoDFhk+e/8hXS+Un92uU8CXtqNLazNxF9pM7kRbhuxgHpXXVdb+IyKP8OPoM8pP7tHlJ/dp9FZmpE0SeYny+tO8pP7tDf61PoafQAzyk/u0eUn92n0UAReWDkK7Jg/w49PemrCcf6+X9P8KmKqTyAfwqvaKDFyAT7ikUm7Db+yW90y4s3JYSxsnLYzn3A/pXPWPhO4sb20u0ukaWHzJHz0d28zC4AGEHmHpjOBxXV7F/uj8qNi/wB0flTJOOufBDTm7KXIi86T93g58pTvy33eWPmHryMD5quaf4Ylsb9LlbkKUfja7/Mm+ViCCcZPmD1xtrpdi/3R+VGxf7o/KgDkJPDmsXRuH/tKS3V7h3EaTtl03ybcnnbgMuAo/hGfa1J4cv8AE3l6nIHlSUCVpXzEzMxDKM4PBVfbbxXRRIvlJ8o6DtT9i/3R+VAHJy+E7yeyeKTUZGZk2bXmdlxh+O3G5kOcZwtLL4X1B5/Nj1Jog0gZkjdlJXdIQN2Cfl3jHHaur2L/AHR+VGxf7o/KgDI1HTLi6s7SOMW5lhu0n3SszYCvu4JyckcfjWSPB8sUs0kE8aOZ1eAlj+4A3fMMAbmO4ghs9Bya6qRF2j5R94dven7F/uj8qAKlyo86A4584f8AoJop90AGtsDH74fyNFAFmsu/0SLUbwXEtzOuIvKVE2gD5lcNyM5DKp6446Velu7eCWOKaeOOSXIjV2AL49PWql5rNtYXLwTJNuEPmqVXIf5gu0c/eyyj8aAIbjTbez0SSCNd3Qs7AFnO7cSSB6kn8al1yNBoN+Qig+Q/OPaqj63b6jDcQxxyoqwq/mSptGdxBXnjIIwfereuOp0G/AYE+Q/f2px3RMtmeaeFLuC18SRy3cyRwh3BaQ4H+rr1iPyJokljEbxuAysoBBB6GvDQrfaBwf8AX+n/AEzr1TwjrsGp6XFbpFLE1rBGrGTGG4xxz7V2YqH2kceEnpyM6Hyo/wC4v5UeVH/cX8qPMT++v50eYn99fzriO4Y0cfmJ8i9+1P8AKj/uL+VMaRPMT51796f5if31/OgA8qP+4v5UeVH/AHF/KjzE/vr+dHmJ/fX86ADyo/7i/lVe0jTyvuL+VWPMT++v51XtHTyvvr+YpdSvsljyo/7i/lR5Uf8AcX8qPMT++v50eYn99fzpkh5Uf9xfyo8qP+4v5UeYn99fzo8xP76/nQAyKOPyk+Reg7U/yo/7i/lTIpE8pPnXoO9P8xP76/nQAeVH/cX8qPKj/uL+VHmJ/fX86PMT++v50AMkjj2j5F+8O3vT/Kj/ALi/lTJJE2j51+8O/vT/ADE/vr+dAFa6RFe2IVQfOHQexopbp1L2wDAnzh0PsaKAMXxQqyy2lu9ol6siviB1d1DZXEjIoIZV9x1IxWhNoFhO6M6zYjg8hFEzAKuQRjnqCqnPXgVk+MY5hLp9wkSNHEZPMdoWfywQPmJVGwAcHHGcda17/WotPumglt5mPkiVGXbhyXVNo565ZeuBz1oAW6s4bTQ3toVxGgGAxySd2SST1JPOaXXQP7Bv+P8Alg/8qpNrK31tOBC8UIiDb2/hfftKHtnI7E1c1yRToV+AeTA/8qcd0TLZnjIP+kD/AK+P/adOsb6exaKeGRgY4432hyA2G6HFAVvtA4/5b/8AtOoQjfZxx/ywT/0KvYdmrM8dXWqPYfDuux6xZhpnt0ut7Awo/OB3wea2sD0FeGwyT2935kEkkT+cw3RsVONnqK9N8N+KrTUrSKGdvInSONf30gzKSMZHryP1rzq1Dk1jsejQr86tLc6NgPNTgdDTsD0FRtInmp83rTvNT+9XOdI7A9BRgegpvmp/eo81P71ADsD0FV7MDyegqbzU/vVBaSp5X3hS6lfZLOB6CjA9BTfNT+9R5qf3qZI7A9BRgegpvmp/eo81P71ABEB5ScDoKdgegqOKVPKT5uwp3mp/eoAdgegowPQU3zU/vUean96gAkA2jgfeH86dgegqOSVNo+b+Ifzp3mp/eoAhugN9tx/y2H8jRTbl1Z7YA8+cP5GigDnPGgDT2C+dHGdspUsittICtvO6N+AAeOCSRW9NotlcyLLdK88oh8ku8hGVyD0GBnIByB1A9KyNfgu7rW7If2dLNbwfMJFhilUE98OchgVHI7Ma0NS1ttOvDAbUODAJI2EmNzGRU2kY4GXXnnvxQBPd20Nror28CBIkACqD7ijXf+QBf/8AXB/5Vnrq0t/ZXEjwNFAEAyFJ/eByrAN0YZHXA61e1xwdBvxhv9Q/8J9Kcd0TL4WeMj/j4H/Xx/7TqEf8e4/64J/6FU4U/aByP9f6/wDTOoQp+zjkf6hO4/vV7FzxrEo/4+B/13b/ANAqJGKxIykqwihwQcEfNUwU/aByP9e3f/YqJVPkryP9VF3H96gLM7Lw74yax8u1vwWt0eRRKNzyZ5I716Ja3cF5Ak1vIroyhhg8gEZGfSvD1U7xyP8AXv39jU2nanqGlQ/6DdtBuiiLbNvJyRzn61y1cMpaxOuliXFWlqe4UV59p/j+S2s0hu7aS6uA7gy+Yqggc9h6cV3cFys1vHKFcb1DY2njIzXHOnKG52wqRn8JNVe0/wBVUvmD0b/vk1BaSDyujf8AfJrPqa/ZLVFM8wejf98mjzB6N/3yaZI+imeYPRv++TR5g9G/75NABF/qk+gp9RRSDyk4boP4TTvMHo3/AHyaAH0UzzB6N/3yaPMHo3/fJoAJPuj/AHh/On1FJINo4b7w/hPrTvMHo3/fJoAhu/v23/XYfyNFNuXBe2GG/wBcOqkdjRQBbqrNptjczmeezgllMZiLvGCSh6rn09qtVg6vrsum6g1uBbsDbeam5yGU+YqEt/sjfn/gJoA0L6COLSXhijVI0UBUUYAAI4AFN13/AJAF/wD9cH/lWdFqNze6XPczxYt2jUIY8FS4dlYrzkqcKRnsav64+dCvxtYfuH5/CnHdEy+FnjI/4+B/18f+06hH/HuP+uCf+hVOF/0gfMP9f/7TqEL/AKOPmX/UJ/6FXsXPGsSj/j4H/Xdv/QKiX/Ur/wBcYf8A0Kpgv+kD5h/r2/8AQKiVf3K/Mv8Aqov/AEKgLEq/6wf9d3/kagX/AFC/9cYf/QqsKvzj5h/r3/kahVf3C/Mv+pi/9CoCw8f60f8AXWT+VXdM1zUdKKPa3LhQsLGNiSrc45H0qmF/ej5h/rJP5UxV/dr8w+5B/wChUpJS0Y4txd0em6P48sbmNY9Rb7PcfPufbiM4OOOSen9a6SwdZLZXQgqwBBHcEV4ii/vV+Yfel/nW4fGGqvpkVtBJFbRiGJSYgdxycZz24HauOphveXId1LFe4+fy/U9corz+z8cLYaFbx5a8vg0gk812GACT97Bz2Fb/AIY8Qz63azzXEMcZjZQPLJxgjPOa55UpRV2tDojVhJpJ6nQ0UwSZGQrEe1G//Yb8qzNAi/1SfQU+oon/AHSfI3Qdqdv/ANhvyoAfRTN/+w35Ub/9hvyoAJPuj/eH86fUUj/KPkb7w7e9O3/7DflQBDd/ftv+uw/kaKbctl7YbWH74dfoaKALdRtBE772iQvjbuKjOPT6VJXPazrN1Yam1tDJEQbTzQpjJMZ8xVLHB5AVmOP9mgDV1FB/ZsqqMAAYA7cio9d/5AF//wBcH/lWVBPqVxps17dlXt5EARFXbn5gAwBHGcFup+8PStPXGY6FfgoQPIfnI9Kcd0TLZnjI/wCPgf8AXx/7TqEf8e4/64J/6FU4A+0D5v8Alv6f9M6hAH2cfN/ywTt/tV7FzxrEo/4+B/13b/0Col/1K/8AXGH/ANCqYAfaB83/AC3bt/sVEoHkr8w/1UXb/aoCxKv+sH/Xd/5GoF/1C/8AXGH/ANCqwoG8fN/y3ft7GoVA8hfmH+pi7f7VFwHj/Wj/AK6yfypi/wCrX/cg/wDQqkAHmj5v+Wknb2pigeWvzD7kHb/aoAcn+tX/AHpf51HH/wAe0f8A1zh/9CNSqB5i/N/FL296jiA+zx/MP9XD2/2jSe5SXuv5fqO/i/GatG11l7XRbrTBAjJcmEs5bleR2/Cs/A3fe7zdqQAcfMP+WPb3oaUtxRbjqjt/A2s3paazkkD2tvBK8ce0DBD+uM9zUFr4nvtY8V2Jy9tEGjUwxysVbJOcjiuPIAU/Pj5Z/X+9SsBvPzd4f51k6MW2+5sq8kkux7Db+ILOa5ubKHzDcWsbM4ZcA7eDg/U1maF42g1S5W0uYDDcu4VAmWUgjPJ7d64vw/q9vo9zdS3AkcSwzoNgyc7ge9VdGvYtO1qC7l3NHHJGSFHPII/rWH1dWZv9Zd4/ier6trlnoohN2ZB5pIXYm7pjP860q8S1G8ju9WuL1Wfy3aaQBuoG8V63pGtQazbPPaxShEfYd+Ac4B9fesqtHkimbUq3PJovyfdH+8P50+opGbaP3Z+8O49azpfEFtDrcektFN9okAIIA28gnrn2rFJvY2bS3Lt39+2/67D+RoptyzF7bKEfvh3HoaKQy3RRRQBHPClxC0UgJRhg4ODWTrUSQ6eYybiY3DrAIzOUBLHHJwcD8K2qiuLeC7gaG4hSWJvvI6hgfwNAHM2HhHw9f2FtfR2cyrOizqGnfI3L9fQ4qf8A4QPw/t2/ZJMbQv8Ar36D8a6NVVEVEUKqjAAGABS1ftJ92R7OHZHOf8INoG7d9llzu3f69+uMetNPgTQAhC2khIUAA3DjpyB1rpaQgMpUjIIwRR7Sfdh7OHZHmz2vh+G3hupdJnVBH50ypeMSpYuox/e5RvTGRWxpfhPQb+2kL6fJDJE/kuguncAryMHIyOR2rpYtH02ARCKwtkEIYR7YgNm7rj0zVi3toLOBYLaGOGJeiRqFA/AUe0n3Yezh2Rg/8ILoGc/ZZc5J/wBe/U9e9J/wgnh8DH2WXGFH+vft0710lUdUv2sIYTHGryTzLCm9tqgnPLHB44/E4Hej2k+7D2cOyOc1XwpoemWD3aWDSlCTsN3IpYnsvXLE4AHcmsaXT9AgkkhXR52OWEKtesMrEZN2f7uDG2Oucjkduh/t+w1ezAutLM/l3Ai2koy+f0G0sRxg5DYAwfXim3mr6DG0qSaUs0krkyp5UeWKGQZbJAOPKfH196PaT7h7OHZEkPgrw9PBHMlrNtkXeMzvn5hk96f/AMIJ4f8A+fWXt/y3ft070Hxpp4aVIoLmXyjk7FXBUK7FhkjgeU4x1yMVaHia1xExgnCTzGGBsL+9cNtwBnI7nnHCk0e0n3Yezh2RV/4QTw//AM+sv8X/AC3fv171j63oWhaNNCH0qaWOYYVxdSD51BKqe3b1z14IBrvKrTafZXM3nT2kMsmwpueME7T1HPbk/nR7Sfdh7OHZHC2GkaBeX0dm2lzKTI9vJIbxjiXDlgBxlT5Z+bjtxXQf8ILoGc/ZZeoP+vft071tRadZQSxyw2kEckaeWjLGAVX0B9Ks0e0n3Yezh2Rzf/CCeH8Y+yy4wR/r36Hr3rRsdCs9NhaKzM8SM24gTMcnGO59hWnRSc5PRsahFapHGSeJ7WS6u4Lc3EotZCWZbsZKKAWbGDznIA74zkVa0u30/WdSe+a3njuolSSOU3DElG3BSR0B4bI5x610Eun2U775rSCR9wfc0YJ3DofqKfBaW9qZDbwRReYxd9ihdzHucdTSTa2G0nuMFmgkR2kmcodwDSEjNFWaKQwooooAKKKKACiiigAooooAKKKKACmSwxXETRTRpJG33kdQQfqDRRQBC2n2TghrO3YFBGcxKcqOi9Og9Kjm0jTp5FeWxt2YP5mTGOWwRk+vDHrRRQBKLGzV3cWsAaQkuRGMsTkc+vU/maa2m2bXEc/2dBJHIZQVGMuQV3EDqcEjJ9aKKALVFFFABRRRQAUUUUAFFFFABRRRQB//2Q==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACEAMgDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iisPVtdl02/a3EcDKbfzULSEEN5ip83HC/ODn2NAG5RXOW2rajdWlxdSwLFamPbFJC24lg20keoJyQcdMetadwqW9vJNJc3SIi7mYt0A6npQBj3/ix7LU7qyMFuGjZFjZ5+MMyKXcgEIAX6Hnp2ORaj8RM/hJNb8iPc4HyCX5Bl9u4tj7o6k46A1LHHp+tWUhhurmaCQ7HaORlJI7ZGDSW+g2lpJG0E2oxpGuxYhcv5YGMfczt/T3oDczYvGMkizN9lt/8AR4JJSonybna0i/uePmH7vOfRhUx8ViF4YpjZSPN5Pltb3G5JC83lkKcc7QQT9e1bP2VeP3lz8vTnp9KalqmPv3HBOOen6UAco3xCdNI+2G0tncxxyqsdzlcMjsUZiAA4CdP9oVdk8YXEbSMbGIQlnWF2mIxteNS0ny/Kv7zJIzgKa27fTbe1to7aD7QkMahUQHgADAqX7Mv/AD1ufzoA5u98YXNjO0a20V0zMArJcKsX+rVyFcgcnPGfQ+lX7fxFNdw6yqxQR3NijNGnmbxj5tpYjjnb0ByO/vfuLcJCwEtwAEJHIwCOnaphaqM4kuRnk4PWgdtLnKx+ML6ztPKuIIbq6jhy377a7v5SybioXiM7tufWtjVtRura60+2mvYNOSaOR5bkgModduEBbgZ3MeecIcVpG2XOfMuc9OtD2hZCDJcH6uDQI5e78Z3AvJ7O1W1JiaPFxLJhComjjkLKOVH7zIJ4wM8ipU8aTyTTp9ghiRbjyFlmuVVUIcrmQDJUHGRxzkCujNmDnLznPX5xzUF1p1tdRPbzzXGJBlgs+xiB3yCDigDnV8eStZC5FhCSbbzRCLg7yfIMu4Db/q+Nm717Vt2mtXUmunR7i0RLiNPOkdHJQREAKwJAyS25cf7BNWIbK2traFIZJI4FVY4gJBjb0AFEWmwx3lxOslx9olC7287JKjIUew+907k0AVNI1SS81rU4Uvorq0t/lyAoZJATuUAckAYGT3yOxxjweOri7hka2sIHZCzZafCmMRGQHgEhsDGD0zz6V1IsgrFlecE9SGAJpRZ46SXA/wCBigDE03xJNqfiWO0UQxW/lTkxb8y5RowCwx8oO4kc8gis2x8SanJFHK11bMX1DyJBM6BY490g4C/Mp+VR83eus+yEPuEtwPU7xTFtYWZwkshb+MB1z17/AIg/lQBneH/Ev9vG/QRpD9nCskgcMCrhsEj+Ejacg8/SrPhjUW1TQ47iS6iuZBJLG0seMNtdgOBwOAD+NWmt0jxunmTecDMgG4n+dMEMSRny7iRRu28SKBu9PrQBo0VQZWhmgxLOcyBWDNkEYNFAF+ontbeSUyvBE0hXZvZATt9M+ntUtYera7Lpt+1uEgZTbeam6QghvMVMt/sjfnPsaANDUY1XS5EVQFUABQOAARUeu/8AIAv/APrg/wDKsyLUby90+e5nhX7KyAIY8Ebw+04OcsCRkHjjHWtHXHJ0K/Gxh+4fk/SnHdEy+FmR4EkT+wmTeu/z3O3PPQV1NeK6RqL6Rq63kSRySCVlw5I6x163pmpx6jYwzoUZ3jV3WNw20kZx1rfEU3GXN3McNUUoKPVF+mR9G/3jRvP9xv0psbnDfI33j6VznQS0Uzef7jfpRvP9xv0oAiuv9U//AFzb+lWKq3Tnyn+R/wDVt/Sp95/uN+lLqP7IrdPxH86Y0THJ8+Qe3H+FO356xt+lMkI8p/3R+6ewpgnYcsbKwJmdvY4/wrB1nwyNW1RbrzNieUVkUNgyHY6gdOB+8J6846Gt7I/55H8hRkf88j+QoBu5zE/hS4uNBj057pN6vI4mHBQyF9+BjHAcBeB07VLYeGJbK/W5S52lJMja7/Mm+RiGGcZIkA7/AHa6LI/55H8hTEI3P+6P3vQegoEcxN4d1i6e6f8AtKS3V7h2EaTvmRN7lcnkJgFcBR259rL+Hb/Evl6nIHlSYea0r5iZmYqyjODgFVwem3iuhyP+eR/IUZH/ADyP5CgDlZfCd7PZtDJqUhLRmPa8zsuMScdsjLIc4z8lOm8L6jJL5septFucFkidlJUNIQN2Cfl8wY47V0rEeYn7o9+wp+R/zyP5CgDJ1TSri/sIbUC3ZfO3TeYzElMk4VjkgnjnsMgY4xnXnhe6uzejfaRpNcySoApIUPGELY4+cY3A+pP1rp8j/nkfyFNkI8tv3R6HsKAIrn/WQf8AXcf+gmikuD81qAhH74fyNFAFyontoJJTI8EbSFdm5kBO30z6Ukt3bwSxxTTxxyS5EauwBfHp61javr0um6i1sptiDa+aodiGU+YqFm5+6A2f+AnmgDT1FFGmSIqgKAAAo6AEVHrv/IAv/wDrg/8AKsuLUL+9sJrq4ii+xPEBGYjks4cqW5/hPBHPStLXGJ0K/Gxh+4fn8Kcd0TL4WeMj/j4H/Xx/7TrT8N69PocqGMxLBIkRmLoWO0MQcY9iazQv+kD5h/r/AP2nUIX/AEcfMv8AqE/9Cr15JSVmePCTi00e6WN/balbC4tJfMiJK7tpHI68Gpo+jf7xryjw5r76PqX7+e4e03uPIjbIyVz0JAr0vTtTtdRg821kWUHDEKykrnkZweK8yrSdN+R6tKqqkb9S/RTN5/55t+lG8/8APNv0rI1Irr/VP/1zb+lWKq3Tnyn+Rv8AVt6e1T7z/wA82/Sl1K+yPpsv+qf/AHTSbz/zzb9KbI58p/kb7p9KZJLRTN5/55t+lG8/882/SgB9MT7z/wC9/QUbz/zzb9Kajnc/yN9729BQBLRTN5/55t+lG8/882/SgAb/AFqfjT6iZz5ifI3f0p28/wDPNv0oAfTZP9W/0NJvP/PNv0psjny2+Ruh9KAI7rra/wDXUfyNFNuGJa1G1h+9HX6GigDG8UKsstpbvaJerIr4gdXdQ2VxIyKCGVfcdSMVvRWkEMaRxxKFRAi55IX0yea5vxjHMJdPuEiRo4jJ5jtCz+WCB8xKo2ADg44zjrVvWNel03UWtlNsQbXzVDkhlPmKhZufugNn/gJ5oA1NSUf2bKoHAA4A9xUWu/8AIAv/APrg/wDKsyK/vb3TpLuaNTavEANmMFw5XcvOdpAB5/WtHXGJ0G/Gxh+4fnj0+tOO6Jlszxkf8fA/6+P/AGnUI/49x/1wT/0KpwB9oHzD/X+//POoQB9nHzD/AFCev96vYPGsSj/j4H/Xdv8A0Cr+h63daNNC8U0iwFIGmjQA7xkjHPtmqIA+0D5h/r29f7lR4Hkp8w/1MPr/AHqUkpKzKi3HVHtWla1Z6vbLLA4VmLDynYbxg46AmtGvD7G7uNPvVntJ/Kl81xuC5OMH1Fen+GvE0et2gDRss0aR7izL85I6gfhXn1qDhqtj0aNdVNHubN3/AKp/+ubf0qxVW6c+U/yN/q27j296sb2/55t+n+Nc3U6vsjqbL/qn/wB00b2/55t+n+NMkdvKf9233T6f40ySWim72/55t+n+NG9v+ebfp/jQA6mJ95/97+gpd7f882/T/GmI53P+7b73t6D3oAlopu9v+ebfp/jRvb/nm36f40AI3+tT8afUTMfMT923f0/xp+9v+ebfp/jQA6myf6t/oaN7f882/T/GmSO3lt+7bofT/GgCO662v/XUfyNFNuGJa1yjD96OTj0NFAHOeNAGnsF86OM7ZSpZFbaQFbed0b8AA8cEkiuqSBVRQ/7xwu0yOBub64Fc5r8F3da3ZD+zpZreD5hIsMUqgnvhzkMCo5HZjU+sa7Np2pNbRvbEG181VcHch8xVLNzyoDFsYH3Tz6AGrqS502VQOABwPqKi13/kAX//AFwf+VZkN7fXmmTXdygNu8ahAmMO28jeoPIBGCMk9a0dcYnQb8bGH7h+ePSnHdEy2Z4yP+Pgf9fH/tOoR/x7j/rgn/oVTgD7QPmH+v8Af/nnUIA+zj5h/qE9f71eweNYlH/HwP8Aru3/AKBUf/LFP+uMP/oVSgD7QPmH+vb1/uVHgeSnzD/Uw+v96gLaD1/1q/8AXd//AEE0yCV4PKnibbLHHCyMByp3dakUDzV+Yf69/X0NRKo8hfmH+pi9f71DBHd6T43T7M1tqrPuUSr9pPO49QNqjjj+Vd/HIk0ayRtuRgGB9Qa8IcDDfMP9ZJ6/3TWroXiG80EYtjE0TCEyRsPvckde1cdTDJtuB3UsS1FKZ7JTZf8AVP8A7prJ0rxBb6hp0VzK0MLuWBj85eMEjvg9vStKSQmFiEYgqecj/GuNpp2Z2Jpq6JqKZvb/AJ5t+Y/xo3t/zzb8x/jSGPpifef/AHv6Cje3/PNvzH+NNR23P+7b73qPQe9AEtFM3t/zzb8x/jRvb/nm35j/ABoAG/1qfjT6iZ28xP3bd/T/ABp29v8Anm35j/GgB9Nk/wBW/wBDSb2/55t+Y/xpsjt5bfu26HuP8aAI7rra/wDXUfyNFNuGJa1yjD96OTj0NFAFujFFc/rGuz6dqbW0clsQbXzQrA7kPmKpY88qAxbGB908+gBq6mN2nTD2H8xUWu/8gC//AOuD/wAqzYb29u9KmurmMNBJEgTbgAsHYFgDzgjYwyTWhrjMdCvwUIHkPzkelOO6Jlszxkf8fA/6+P8A2nUI/wCPcf8AXBP/AEKpwB9oHzD/AF/p/wBM6hAH2cfMP9Qnb/ar2LnjWJR/x8D/AK7t/wCgVH/yxT/rjD/6FUoA+0D5h/r27f7FR4Hkp8w/1MPb/aoDoPX/AFq/9d3/APQTUK/6hf8ArjD/AOhVOoHmr8w/179vY1EoHkL8w/1MXb/aouFhzdG/66Sf+gmmr/q1/wByD/0KnuBh/mH+sk7f7JpqgbF+Yfcg7f7VLqyn8KFVQZRkD70vb3rvvD3jdJbdLPUVWNjHGkTRrhQCMfMSfUVwageYvzD70vb3pqAbU+Yfcg7f7VRUpxmrMqlUlTd0e7xSxzRiSKRZEPRlOQfxp9eWaF4yuNHhW1khjmtU83aqjawIbPXn1Pam6p4xu7zVIb2zaS1EYiHlGQsjZY8kDAPB/SuL6tPmsd/1mHLc9Vpifef/AHv6Cs7Qr+41HRba6nVTLIG3FBgcMR0z7VfRm3P+7P3vUegrBqzsbp3V0S0Uzc3/ADzP5ijc3/PM/mKQwb/Wp+NPqJmbzE/dnv3FO3N/zzP5igB9Nk/1b/Q0m5v+eZ/MU2Rm8tv3Z6HuKAI7rra/9dR/I0U24Zi1rlCP3o5yPQ0UAW6KK57WdZurDU2toZIiDaeaFMZJjPmKpY4PICsxx/s0Aa2pjOnTD2H8xUWu/wDIAv8A/rg/8qyoLjUrjTZr27KtbyIAiKNufmADAEcZwW6n7w9K09cZjoV+ChA8h+cj0px3RMtmeMj/AI+B/wBfH/tOoR/x7j/rgn/oVTgD7QPm/wCW/p/0zqEAfZx83/LBO3+1XsXPGsSj/j4H/Xdv/QKj/wCWKf8AXGH/ANCqUAfaB83/AC3bt/sVHgeSnzD/AFMPb/aoDoPX/Wr/ANd3/wDQTUK/6hf+uMP/AKFU6geavzf8t37exqJQPIX5h/qYu3+1RcLDm6N/10k/9BNNX/Vr/uQf+hU9wMN83/LSTt/smmqBsX5v4IO3+1SW7Kfwocn+sX/el/nTU+6n+5B/6FT1A8xfm/il7e9NQDanzD7kHb/apslIP4vxmpB2/wC2P86dgbvvd5u1IAOPmH/LHt70BY7O61S80zwZpD2dy0JJl37MEkAk966E+KoptAvdRsY2Z4Cg2zrgEtgdjXlRVdrHcPuz9vet201a2tfD1/pziQzXBhKsB8o5HX8q5Z0Fp6nVCu9V5Hpuh6g+qaPb3kqoskgO5U6DDEf0rRryLwrrVroWoS3Nysjq0coxGuTw4PfFdh/wsPSc48i76qPuL36d656lCSk+VaHTTrxcVzPU6pv9an40+qNlfpqNrb3cMbiOVSV3YBq3ub/nmfzFYPQ6E7j6bJ/q3+hpNzf88z+YrKuNftotXXSWim+0yLwQBt5BPXPtTSb2E2luX7rra/8AXUfyNFNuGYta5Qj96Ocj0NFIZbooooAjmhS4haKQZRhg4OKydaiSHTzGTcTG4dYBGZygJY45ODgfhW1UVxbwXcDQ3EKSxN95HUMD+BoA5mw8I+Hr+wtr6OzmVZ0WdQ075G5fr6HFT/8ACB+H9u37JJjaF/179B+NdGqqiKiKFVRgADAApav2k+7I9nDsjj7/AMKaHZT2eNPkkW4uRGzfanBQlTzjv0x2rCjtPDxVQdInwY0m5vG4gyu0+7DePl+vJr0a5srW8MRubeOYxOJI96g7GHQj0NRDSdOG3FhbDbJ5w/dLw/8Ae6dfej2k+7D2cOyMj/hBtAyD9llyGLf69+p/Go5PAugpCxSzclUwA1y4BxyATniunpksUc8LwyoskcilXRhkMDwQR6Ue0n3Yezh2R5tNa+H4YhPLpFwIlRncpeOQ27zArLnG5WETHPHDA49NzTPCWg31qzvYSRSRyGF0F07AFGI4ORkfhXSJpOnJ5e2xtx5SlE/dj5VPUD0ByfzqC9nj0Wyt4bO2hUSTLDEmfLjUtk5JA4HXtySPWj2k+4ezh2RnjwLoAOfssuck/wCvfv171hanoWiWGpJp66TKzzoot5PtkigkMoAJIwMbs8bunI5Fb9p4shubcv8AY5S6zC3IR0KtLnopJGRjkMQAR78VWvtZ0LzZTNpYnml3JKDHGSQhkzkk/wDTFj+Ao9pPuw9nDsjK0fSPDmsXKJHpk8cckbyRu105JwE3gjPH+sGOTnB6Vt/8IJ4f/wCfWXt/y3ft071AnifRYLq4ktdPkMygB5IokUtGFc5ySMgCJhjrkAYrRHia1xExgnCTzGGBsL+9cNtwBnI7nnHCk0e0n3Yezh2Rga/oPhjQbJJ57R2Mknlqhu2TO4/Mck9hk/p3rMa18PqXJ0mdvk+0D/TG/wBQuef975T8vv1r0mWGOeMxyxrIh6qwyDVY6TpxzmwtuZfOP7peX/vdOvvR7Sfdh7OHZGP/AMIJ4f8A+fWX+L/lu/fr3pk3gjQo4ZJEsppHVdwQXD5YqOB1rp6RlV0KMAysMEHoRR7Sfdh7OHZHDw65a6fZoq211DFaofOiW5YmJj5hCYZQSf3ZyeMZHUV0mmt9vt3d2nikjleKRBOWAZTg4OBkfhVlNJ06Py9lhbL5YZUxEvyhuoHHfJz9TU9vbwWkCw28KRRL91EUKB+AqCyP7GP+e9x/38NcnqEumx+I5h9muWvYjHDHO07rulYKAo4K4AkBJ+vBrtaqzabY3EryzWcEkkieW7tGCWX0J9KabWwmk9zC0nWYtSv4I/JuAuWCs8+7EiorMMY5GH4Pf06UVvxWFnBMJobWGOUIIw6RgEIOi59OBxRSGWKKKKACiiigAooooAKKKKACiiigApksMVxE0U0aSRt95HUEH6g0UUAQtp9k4Iazt2BQRnMSnKjovToPSo5tI06eRXlsbdmD+ZkxjlsEZPrwx60UUASixs1d3FrAGkJLkRjLE5HPr1P5mmtptm1xHP8AZ0EkchlBUYy5BXcQOpwSMn1oooAtUUUUAFFFFABRRRQAUUUUAFFFFAH/2Q==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/jpeg": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "occupancy_variables = ['md_occupancies', 'sg_occupancies', 't3_occupancies', 't5_occupancies']\n", + "percents = [99.99, 99.9, 99.9, 99.99]\n", + "compute_occupancies(branches, occupancy_variables, occ_percentiles=percents, plot=False) # To print\n", + "compute_occupancies(branches, occupancy_variables, occ_percentiles=percents, plot=True) # To plot" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}