diff --git a/DataModel/Channel.h b/DataModel/Channel.h index 3c44cb804..90f127f38 100644 --- a/DataModel/Channel.h +++ b/DataModel/Channel.h @@ -101,10 +101,12 @@ class Channel : public SerialisableObject{ ar & hv_card; ar & hv_channel; ar & status; - ar & channelType; + if(version > 0) ar & channelType; } } }; +BOOST_CLASS_VERSION(Channel, 1) + #endif diff --git a/DataModel/LAPPDPulse.h b/DataModel/LAPPDPulse.h index 497ae30cb..5ab8253e5 100755 --- a/DataModel/LAPPDPulse.h +++ b/DataModel/LAPPDPulse.h @@ -68,12 +68,16 @@ class LAPPDPulse : public Hit{ ar & halfHeightTime; ar & halfEndTime; ar & baseline; - ar & pulseFollowTime; - ar & pulseFollowCharge; + if(version>0){ + ar & pulseFollowTime; + ar & pulseFollowCharge; + } } } }; +BOOST_CLASS_VERSION(LAPPDPulse, 1) + /* Derived classes, if there's a reason to have them. So far...not really class TDCHit : public Hit { diff --git a/UserTools/ANNIEEventTreeMaker/ANNIEEventTreeMaker.cpp b/UserTools/ANNIEEventTreeMaker/ANNIEEventTreeMaker.cpp index 9b772b15e..65e64a530 100644 --- a/UserTools/ANNIEEventTreeMaker/ANNIEEventTreeMaker.cpp +++ b/UserTools/ANNIEEventTreeMaker/ANNIEEventTreeMaker.cpp @@ -670,6 +670,18 @@ bool ANNIEEventTreeMaker::Execute() bool ANNIEEventTreeMaker::Finalise() { + if (MCTruth_fill) { + delete fTrueNeutCapVtxX; + delete fTrueNeutCapVtxY; + delete fTrueNeutCapVtxZ; + delete fTrueNeutCapNucleus; + delete fTrueNeutCapTime; + delete fTrueNeutCapGammas; + delete fTrueNeutCapE; + delete fTrueNeutCapGammaE; + delete fTruePrimaryPdgs; + } + Log("ANNIEEventTreeMaker Tool: Got " + std::to_string(processedEvents) + " events", 0, ANNIEEventTreeMakerVerbosity); fOutput_tfile->cd(); fANNIETree->Write(); diff --git a/UserTools/EBLAPPD/EBLAPPD.cpp b/UserTools/EBLAPPD/EBLAPPD.cpp index 4658d96d1..e78ca5a18 100644 --- a/UserTools/EBLAPPD/EBLAPPD.cpp +++ b/UserTools/EBLAPPD/EBLAPPD.cpp @@ -276,7 +276,7 @@ bool EBLAPPD::LoadLAPPDData() bool EBLAPPD::Matching(int targetTrigger, int matchToTrack) { - cout << "\033[1;34m******* EBLAPPD : Matching *******\033[0m" << endl; + Log("\033[1;34m******* EBLAPPD : Matching *******\033[0m", v_message, verbosityEBLAPPD); Log("EBLAPPD: Matching LAPPD data with target trigger " + std::to_string(targetTrigger) + " in track " + std::to_string(matchToTrack), v_message, verbosityEBLAPPD); std::map>> GroupedTriggersInTotal; // each map is a group of triggers, with the key is the target trigger word @@ -308,7 +308,7 @@ bool EBLAPPD::Matching(int targetTrigger, int matchToTrack) int matchedTrack = 0; int matchedIndex = 0; - for (std::pair>> pair : GroupedTriggersInTotal) + for (const std::pair>>& pair : GroupedTriggersInTotal) { int TrackTriggerWord = pair.first; if (matchedNumberInTrack.find(TrackTriggerWord) == matchedNumberInTrack.end()) @@ -318,13 +318,13 @@ bool EBLAPPD::Matching(int targetTrigger, int matchToTrack) // Log("EBLAPPD: Skipping TrackTriggerWord " + std::to_string(TrackTriggerWord), v_debug, verbosityEBLAPPD); continue; } - vector> GroupedTriggers = pair.second; + const vector>& GroupedTriggers = pair.second; for (int j = 0; j < GroupedTriggers.size(); j++) { - map groupedTrigger = GroupedTriggers.at(j); + const map& groupedTrigger = GroupedTriggers.at(j); // itearte over all the grouped triggers, if the value is target trigger, then calculate the time difference - for (std::pair p : groupedTrigger) + for (const std::pair& p : groupedTrigger) { if (matchToAllTriggers || p.second == targetTrigger) { @@ -379,4 +379,4 @@ bool EBLAPPD::Matching(int targetTrigger, int matchToTrack) } return true; -} \ No newline at end of file +} diff --git a/UserTools/EBLoadRaw/EBLoadRaw.cpp b/UserTools/EBLoadRaw/EBLoadRaw.cpp index bb3c53fd6..68ae0deae 100644 --- a/UserTools/EBLoadRaw/EBLoadRaw.cpp +++ b/UserTools/EBLoadRaw/EBLoadRaw.cpp @@ -336,6 +336,10 @@ std::vector EBLoadRaw::OrganizeRunParts(std::string FileList) OrganizedFiles.push_back(SortingVector.at(j).second); } } + else + { + std::cerr << "EBLoadRaw::OrganizeRunParts: Failed to open file list " << FileList << std::endl; + } // print the OrganizedFiles for (int i = 0; i < (int)OrganizedFiles.size(); i++) { diff --git a/UserTools/EBMRD/EBMRD.cpp b/UserTools/EBMRD/EBMRD.cpp index c00047144..fbbaa483b 100644 --- a/UserTools/EBMRD/EBMRD.cpp +++ b/UserTools/EBMRD/EBMRD.cpp @@ -45,17 +45,17 @@ bool EBMRD::Execute() Log("EBMRD: Current buffer size is " + std::to_string(MRDEventsBuffer.size()), v_message, verbosityEBMRD); // loop the MRDEvents, save every event to MRDEventsBuffer if it's not already in the buffer int newLoadedEvents = 0; - for (std::pair>> p : MRDEvents) + for (const std::pair>>& p : MRDEvents) { - uint64_t MTCtime = p.first; - std::vector> WaveMap = p.second; + const uint64_t MTCtime = p.first; + const std::vector>& WaveMap = p.second; // if find the MTCtime in the PairedMRDTimeStamps, then skip if (PairedMRDTimeStamps.size() > 0) { bool skip = false; - for (std::pair> pair : PairedMRDTimeStamps) + for (const std::pair>& pair : PairedMRDTimeStamps) { - for (uint64_t t : pair.second) + for (const uint64_t t : pair.second) { if (t == MTCtime) { @@ -135,7 +135,7 @@ bool EBMRD::Finalise() bool EBMRD::Matching(int targetTrigger, int matchToTrack) { - cout << "\033[1;34m******* EBMRD : Matching *******\033[0m" << endl; + Log("\033[1;34m******* EBMRD : Matching *******\033[0m", v_message, verbosityEBMRD); std::map>> GroupedTriggersInTotal; // each map is a group of triggers, with the key is the target trigger word m_data->CStore.Get("GroupedTriggersInTotal", GroupedTriggersInTotal); @@ -148,12 +148,12 @@ bool EBMRD::Matching(int targetTrigger, int matchToTrack) // in each group of trigger, find the target trigger word and it's time // fine the minimum time difference, if smaller than matchTolerance_ns, then save the time to PairedCTCTimeStamps and PairedMRDTimeStamps int loopNum = 0; - for (std::pair>> mrdpair : MRDEventsBuffer) + for (const std::pair>>& mrdpair : MRDEventsBuffer) { if (verbosityEBMRD > 11) cout << "******************EBMRD: new MRD event: " << loopNum << endl; - uint64_t MTCtime = mrdpair.first; - std::vector> WaveMap = mrdpair.second; + const uint64_t MTCtime = mrdpair.first; + const std::vector>& WaveMap = mrdpair.second; // set minDT to 5 min uint64_t minDT = 5 * 60 * 1e9; uint64_t minDTTrigger = 0; @@ -161,7 +161,7 @@ bool EBMRD::Matching(int targetTrigger, int matchToTrack) uint32_t matchedTrigWord = 0; int matchedTrack = 0; int matchedIndex = 0; - for (std::pair>> pair : GroupedTriggersInTotal) + for (const std::pair>>& pair : GroupedTriggersInTotal) { int TrackTriggerWord = pair.first; if (TrackTriggerWord != matchToTrack && !matchToAllTriggers) @@ -172,13 +172,13 @@ bool EBMRD::Matching(int targetTrigger, int matchToTrack) if (matchedNumberInTrack.find(TrackTriggerWord) == matchedNumberInTrack.end()) matchedNumberInTrack.emplace(TrackTriggerWord, 0); - vector> groupedTriggers = pair.second; + const vector>& groupedTriggers = pair.second; for (int i = 0; i < groupedTriggers.size(); i++) { - map groupedTrigger = groupedTriggers.at(i); + const map& groupedTrigger = groupedTriggers.at(i); // itearte over all the grouped triggers, if the value is target trigger, then calculate the time difference - for (std::pair p : groupedTrigger) + for (const std::pair& p : groupedTrigger) { if (matchToAllTriggers || p.second == targetTrigger) { @@ -234,4 +234,4 @@ bool EBMRD::Matching(int targetTrigger, int matchToTrack) } return true; -} \ No newline at end of file +} diff --git a/UserTools/EBSaver/EBSaver.cpp b/UserTools/EBSaver/EBSaver.cpp index c7c79421d..8621243ac 100644 --- a/UserTools/EBSaver/EBSaver.cpp +++ b/UserTools/EBSaver/EBSaver.cpp @@ -64,20 +64,10 @@ bool EBSaver::Initialise(std::string configfile, DataModel &data) m_data->CStore.Get("AmBeTriggerMain", AmBeTriggerMain); m_data->CStore.Get("PPSMain", PPSMain); - InProgressHits = new std::map> *>; - InProgressChkey = new std::map>; - InProgressRecoADCHits = new std::map>>>; - InProgressRecoADCHitsAux = new std::map>>>; - InProgressHitsAux = new std::map> *>; - FinishedRawAcqSize = new std::map>>; - - RWMRawWaveforms = new std::map>; - BRFRawWaveforms = new std::map>; - if (saveBeamInfo) { Log("EBSaver: saveBeamInfo is true, loading Beam Info", v_message, verbosityEBSaver); - LoadBeamInfo(); + return LoadBeamInfo(); } return true; @@ -1304,16 +1294,25 @@ void EBSaver::BuildEmptyLAPPDData() ANNIEEvent->Set("LAPPDTS_PPSMissing", LAPPDTS_PPSMissing); } -void EBSaver::LoadBeamInfo() +bool EBSaver::LoadBeamInfo() { TFile *file = new TFile(beamInfoFileName.c_str(), "READ"); + + if (file->IsZombie()) { + delete file; + Log("EBSaver: Failed to load beam info from file with name: " + beamInfoFileName, v_error, verbosityEBSaver); + return false; + } + TTree *tree; file->GetObject("BeamTree", tree); if (!tree) { - cout << "EBSaver: Failed to load beam info from file with name: " << beamInfoFileName << endl; - return; + file->Close(); + delete file; + Log("EBSaver: Failed to load beam info from file with name: " + beamInfoFileName, v_error, verbosityEBSaver); + return false; } // copy from IFBeamDBInterfaceV2.cpp @@ -1401,7 +1400,9 @@ void EBSaver::LoadBeamInfo() Log("EBSaver: Finished loading beam info from " + beamInfoFileName, v_message, verbosityEBSaver); - return; + file->Close(); + delete file; + return true; } bool EBSaver::SaveBeamInfo(uint64_t TriggerTime) diff --git a/UserTools/EBSaver/EBSaver.h b/UserTools/EBSaver/EBSaver.h index 9a78b2ac1..70dc6a96d 100644 --- a/UserTools/EBSaver/EBSaver.h +++ b/UserTools/EBSaver/EBSaver.h @@ -59,7 +59,7 @@ class EBSaver : public Tool void BuildEmptyMRDData(); void BuildEmptyLAPPDData(); - void LoadBeamInfo(); + bool LoadBeamInfo(); private: int saveRunNumber; diff --git a/UserTools/Factory/Factory.cpp b/UserTools/Factory/Factory.cpp index 9e07812dc..77380424d 100644 --- a/UserTools/Factory/Factory.cpp +++ b/UserTools/Factory/Factory.cpp @@ -186,7 +186,6 @@ if (tool=="ProcessedLAPPDFilter") ret=new ProcessedLAPPDFilter; if (tool=="BeamQuality") ret=new BeamQuality; if (tool=="FitRWMWaveform") ret=new FitRWMWaveform; if (tool=="LAPPDLoadTXT") ret=new LAPPDLoadTXT; -if (tool=="LAPPDBSCharging") ret=new LAPPDBSCharging; if (tool=="MuonFitter") ret=new MuonFitter; if (tool=="BackTracker") ret=new BackTracker; if (tool=="PrintDQ") ret=new PrintDQ; diff --git a/UserTools/LAPPDBSCharging/LAPPDBSCharging.cpp b/UserTools/LAPPDBSCharging/LAPPDBSCharging.cpp deleted file mode 100644 index 878aa77a8..000000000 --- a/UserTools/LAPPDBSCharging/LAPPDBSCharging.cpp +++ /dev/null @@ -1,28 +0,0 @@ -#include "LAPPDBSCharging.h" - -LAPPDBSCharging::LAPPDBSCharging():Tool(){} - - -bool LAPPDBSCharging::Initialise(std::string configfile, DataModel &data){ - - /////////////////// Useful header /////////////////////// - if(configfile!="") m_variables.Initialise(configfile); // loading config file - //m_variables.Print(); - - m_data= &data; //assigning transient data pointer - ///////////////////////////////////////////////////////////////// - - return true; -} - - -bool LAPPDBSCharging::Execute(){ - - return true; -} - - -bool LAPPDBSCharging::Finalise(){ - - return true; -} diff --git a/UserTools/LAPPDBSCharging/LAPPDBSCharging.h b/UserTools/LAPPDBSCharging/LAPPDBSCharging.h deleted file mode 100644 index f8c90801c..000000000 --- a/UserTools/LAPPDBSCharging/LAPPDBSCharging.h +++ /dev/null @@ -1,39 +0,0 @@ -#ifndef LAPPDBSCharging_H -#define LAPPDBSCharging_H - -#include -#include - -#include "Tool.h" - - -/** - * \class LAPPDBSCharging - * - * This is a blank template for a Tool used by the script to generate a new custom tool. Please fill out the description and author information. -* -* $Author: B.Richards $ -* $Date: 2019/05/28 10:44:00 $ -* Contact: b.richards@qmul.ac.uk -*/ -class LAPPDBSCharging: public Tool { - - - public: - - LAPPDBSCharging(); ///< Simple constructor - bool Initialise(std::string configfile,DataModel &data); ///< Initialise Function for setting up Tool resources. @param configfile The path and name of the dynamic configuration file to read in. @param data A reference to the transient data class used to pass information between Tools. - bool Execute(); ///< Execute function used to perform Tool purpose. - bool Finalise(); ///< Finalise function used to clean up resources. - - - private: - - - - - -}; - - -#endif diff --git a/UserTools/LAPPDBSCharging/README.md b/UserTools/LAPPDBSCharging/README.md deleted file mode 100644 index af939269b..000000000 --- a/UserTools/LAPPDBSCharging/README.md +++ /dev/null @@ -1,20 +0,0 @@ -# LAPPDBSCharging - -LAPPDBSCharging - -## Data - -Describe any data formats LAPPDBSCharging creates, destroys, changes, or analyzes. E.G. - -**RawLAPPDData** `map>>` -* Takes this data from the `ANNIEEvent` store and finds the number of peaks - - -## Configuration - -Describe any configuration variables for LAPPDBSCharging. - -``` -param1 value1 -param2 value2 -``` diff --git a/UserTools/LAPPDTimeAlignment/LAPPDTimeAlignment.cpp b/UserTools/LAPPDTimeAlignment/LAPPDTimeAlignment.cpp index 8185d3bbe..95b0f8a32 100644 --- a/UserTools/LAPPDTimeAlignment/LAPPDTimeAlignment.cpp +++ b/UserTools/LAPPDTimeAlignment/LAPPDTimeAlignment.cpp @@ -305,6 +305,7 @@ double LAPPDTimeAlignment::Tfit(std::vector *wf) } } firstcross = false; + delete edge; } } else @@ -364,6 +365,7 @@ double LAPPDTimeAlignment::Tfit(std::vector *wf) */ firstcross = false; + delete edge; } } } diff --git a/UserTools/LAPPDTreeMaker/LAPPDTreeMaker.cpp b/UserTools/LAPPDTreeMaker/LAPPDTreeMaker.cpp index 3091f508f..f4e5cec6b 100644 --- a/UserTools/LAPPDTreeMaker/LAPPDTreeMaker.cpp +++ b/UserTools/LAPPDTreeMaker/LAPPDTreeMaker.cpp @@ -171,7 +171,6 @@ bool LAPPDTreeMaker::Initialise(std::string configfile, DataModel &data) fGroupedTrigger->Branch("gTrigType", &groupedTriggerType, "gTrigType/I"); fGroupedTrigger->Branch("gTrigNum", &TriggerGroupNumInThisEvent, "gTrigNum/I"); - TriggerWordMap = new std::map>; m_data->Stores["ANNIEEvent"]->Header->Get("AnnieGeometry", _geom); EventNumber = 0; @@ -369,7 +368,10 @@ bool LAPPDTreeMaker::Finalise() fTimeStamp->Write(); fTrigger->Write(); fGroupedTrigger->Write(); + file->Close(); + delete file; + return true; } @@ -1090,4 +1092,4 @@ void LAPPDTreeMaker::LoadLAPPDMapInfo() { cout << "LAPPDTreeMaker::LoadLAPPDMapInfo, no LAPPDDataMap found" << endl; } -} \ No newline at end of file +} diff --git a/UserTools/Unity.h b/UserTools/Unity.h index 8a03c4e7b..ebdfbfea9 100644 --- a/UserTools/Unity.h +++ b/UserTools/Unity.h @@ -194,7 +194,6 @@ #include "BeamQuality.h" #include "FitRWMWaveform.h" #include "LAPPDLoadTXT.h" -#include "LAPPDBSCharging.h" #include "MuonFitter.h" #include "BackTracker.h" #include "PrintDQ.h" diff --git a/configfiles/RingCounting/RC_files_to_load.txt b/configfiles/RingCounting/RC_files_to_load.txt index ba0ee1c19..888a106a6 100644 --- a/configfiles/RingCounting/RC_files_to_load.txt +++ b/configfiles/RingCounting/RC_files_to_load.txt @@ -2,6 +2,6 @@ singleEntry.csv singleEntry.csv singleEntry.csv singleEntry.csv -/annie/app/users/dschmid/ToolAnalysisDev/R1613p1_CNNImage_charge.csv +R1613p1_CNNImage_charge.csv singleEntry.csv R1613p1_CNNImage_charge.csv diff --git a/configfiles/RingCounting/RC_model_v1_0_0.model/keras_metadata.pb b/configfiles/RingCounting/RC_model_v1_0_0.model/keras_metadata.pb deleted file mode 100644 index 39da4b4f8..000000000 --- a/configfiles/RingCounting/RC_model_v1_0_0.model/keras_metadata.pb +++ /dev/null @@ -1,38 +0,0 @@ - -¯Èroot"_tf_keras_network*ŒÈ{"name": "model_23", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_23", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 16, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "charge"}, "name": "charge", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "conv2d_40", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_40", "inbound_nodes": [[["charge", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_50", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}, "name": "dropout_50", "inbound_nodes": [[["conv2d_40", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_40", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_40", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}}}, "name": "activation_40", "inbound_nodes": [[["dropout_50", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_30", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_30", "inbound_nodes": [[["activation_40", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_41", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_41", "inbound_nodes": [[["max_pooling2d_30", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_51", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_51", "inbound_nodes": [[["conv2d_41", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_40", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_40", "inbound_nodes": [[["dropout_51", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_41", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_41", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}}}, "name": "activation_41", "inbound_nodes": [[["batch_normalization_40", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_31", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_31", "inbound_nodes": [[["activation_41", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_42", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_42", "inbound_nodes": [[["max_pooling2d_31", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_52", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_52", "inbound_nodes": [[["conv2d_42", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_41", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_41", "inbound_nodes": [[["dropout_52", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_42", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_42", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}}}, "name": "activation_42", "inbound_nodes": [[["batch_normalization_41", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_32", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_32", "inbound_nodes": [[["activation_42", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_43", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_43", "inbound_nodes": [[["max_pooling2d_32", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_53", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_53", "inbound_nodes": [[["conv2d_43", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_43", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_43", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}}}, "name": "activation_43", "inbound_nodes": [[["dropout_53", 0, 0, {}]]]}, {"class_name": "Flatten", "config": {"name": "flatten_10", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_10", "inbound_nodes": [[["activation_43", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_30", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_30", "inbound_nodes": [[["flatten_10", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_42", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_42", "inbound_nodes": [[["dense_30", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_31", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_31", "inbound_nodes": [[["batch_normalization_42", 0, 0, {}]]]}, {"class_name": "Dropout", "config": {"name": "dropout_54", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_54", "inbound_nodes": [[["dense_31", 0, 0, {}]]]}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_43", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_43", "inbound_nodes": [[["dropout_54", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_32", "trainable": true, "dtype": "float32", "units": 2, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_32", "inbound_nodes": [[["batch_normalization_43", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "pred", "trainable": true, "dtype": "float32", "activation": "softmax"}, "name": "pred", "inbound_nodes": [[["dense_32", 0, 0, {}]]]}], "input_layers": [["charge", 0, 0]], "output_layers": [["pred", 0, 0]]}, "shared_object_id": 60, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 10, 16, 1]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 16, 1]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 10, 16, 1]}, "float32", "charge"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 10, 16, 1]}, "float32", "charge"]}, "keras_version": "2.7.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_23", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 16, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "charge"}, "name": "charge", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Conv2D", "config": {"name": "conv2d_40", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_40", "inbound_nodes": [[["charge", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "Dropout", "config": {"name": "dropout_50", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}, "name": "dropout_50", "inbound_nodes": [[["conv2d_40", 0, 0, {}]]], "shared_object_id": 4}, {"class_name": "Activation", "config": {"name": "activation_40", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_40", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 5}}, "name": "activation_40", "inbound_nodes": [[["dropout_50", 0, 0, {}]]], "shared_object_id": 6}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_30", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_30", "inbound_nodes": [[["activation_40", 0, 0, {}]]], "shared_object_id": 7}, {"class_name": "Conv2D", "config": {"name": "conv2d_41", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_41", "inbound_nodes": [[["max_pooling2d_30", 0, 0, {}]]], "shared_object_id": 10}, {"class_name": "Dropout", "config": {"name": "dropout_51", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_51", "inbound_nodes": [[["conv2d_41", 0, 0, {}]]], "shared_object_id": 11}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_40", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 12}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 13}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 15}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_40", "inbound_nodes": [[["dropout_51", 0, 0, {}]]], "shared_object_id": 16}, {"class_name": "Activation", "config": {"name": "activation_41", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_41", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 17}}, "name": "activation_41", "inbound_nodes": [[["batch_normalization_40", 0, 0, {}]]], "shared_object_id": 18}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_31", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_31", "inbound_nodes": [[["activation_41", 0, 0, {}]]], "shared_object_id": 19}, {"class_name": "Conv2D", "config": {"name": "conv2d_42", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 20}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 21}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_42", "inbound_nodes": [[["max_pooling2d_31", 0, 0, {}]]], "shared_object_id": 22}, {"class_name": "Dropout", "config": {"name": "dropout_52", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_52", "inbound_nodes": [[["conv2d_42", 0, 0, {}]]], "shared_object_id": 23}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_41", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 25}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 26}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 27}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_41", "inbound_nodes": [[["dropout_52", 0, 0, {}]]], "shared_object_id": 28}, {"class_name": "Activation", "config": {"name": "activation_42", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_42", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 29}}, "name": "activation_42", "inbound_nodes": [[["batch_normalization_41", 0, 0, {}]]], "shared_object_id": 30}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_32", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_32", "inbound_nodes": [[["activation_42", 0, 0, {}]]], "shared_object_id": 31}, {"class_name": "Conv2D", "config": {"name": "conv2d_43", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 32}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 33}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_43", "inbound_nodes": [[["max_pooling2d_32", 0, 0, {}]]], "shared_object_id": 34}, {"class_name": "Dropout", "config": {"name": "dropout_53", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_53", "inbound_nodes": [[["conv2d_43", 0, 0, {}]]], "shared_object_id": 35}, {"class_name": "Activation", "config": {"name": "activation_43", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_43", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 36}}, "name": "activation_43", "inbound_nodes": [[["dropout_53", 0, 0, {}]]], "shared_object_id": 37}, {"class_name": "Flatten", "config": {"name": "flatten_10", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "name": "flatten_10", "inbound_nodes": [[["activation_43", 0, 0, {}]]], "shared_object_id": 38}, {"class_name": "Dense", "config": {"name": "dense_30", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 39}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 40}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_30", "inbound_nodes": [[["flatten_10", 0, 0, {}]]], "shared_object_id": 41}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_42", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 42}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 43}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 44}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 45}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_42", "inbound_nodes": [[["dense_30", 0, 0, {}]]], "shared_object_id": 46}, {"class_name": "Dense", "config": {"name": "dense_31", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 47}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 48}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_31", "inbound_nodes": [[["batch_normalization_42", 0, 0, {}]]], "shared_object_id": 49}, {"class_name": "Dropout", "config": {"name": "dropout_54", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "name": "dropout_54", "inbound_nodes": [[["dense_31", 0, 0, {}]]], "shared_object_id": 50}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_43", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 51}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 52}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 53}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 54}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "batch_normalization_43", "inbound_nodes": [[["dropout_54", 0, 0, {}]]], "shared_object_id": 55}, {"class_name": "Dense", "config": {"name": "dense_32", "trainable": true, "dtype": "float32", "units": 2, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 56}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 57}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_32", "inbound_nodes": [[["batch_normalization_43", 0, 0, {}]]], "shared_object_id": 58}, {"class_name": "Activation", "config": {"name": "pred", "trainable": true, "dtype": "float32", "activation": "softmax"}, "name": "pred", "inbound_nodes": [[["dense_32", 0, 0, {}]]], "shared_object_id": 59}], "input_layers": [["charge", 0, 0]], "output_layers": [["pred", 0, 0]]}}, "training_config": {"loss": "binary_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 62}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Adam", "config": {"name": "Adam", "learning_rate": 9.999999747378752e-05, "decay": 0.0, "beta_1": 0.8999999761581421, "beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2 -„ root.layer-0"_tf_keras_input_layer*Ô{"class_name": "InputLayer", "name": "charge", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 16, 1]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 10, 16, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "charge"}}2 -ô root.layer_with_weights-0"_tf_keras_layer*½ {"name": "conv2d_40", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_40", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["charge", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 1}}, "shared_object_id": 63}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10, 16, 1]}}2 -­ root.layer-2"_tf_keras_layer*ƒ{"name": "dropout_50", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_50", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv2d_40", 0, 0, {}]]], "shared_object_id": 4}2 -È root.layer-3"_tf_keras_layer*ž{"name": "activation_40", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_40", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_40", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 5}}, "inbound_nodes": [[["dropout_50", 0, 0, {}]]], "shared_object_id": 6}2 -ã root.layer-4"_tf_keras_layer*¹{"name": "max_pooling2d_30", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_30", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["activation_40", 0, 0, {}]]], "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 64}}2 -€ -root.layer_with_weights-1"_tf_keras_layer*É {"name": "conv2d_41", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_41", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 8}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 9}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_30", 0, 0, {}]]], "shared_object_id": 10, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 65}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 8, 64]}}2 -® root.layer-6"_tf_keras_layer*„{"name": "dropout_51", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_51", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv2d_41", 0, 0, {}]]], "shared_object_id": 11}2 -Ä root.layer_with_weights-2"_tf_keras_layer* {"name": "batch_normalization_40", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_40", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 12}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 13}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 15}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_51", 0, 0, {}]]], "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 128}}, "shared_object_id": 66}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 8, 128]}}2 -Ö  root.layer-8"_tf_keras_layer*¬{"name": "activation_41", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_41", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_41", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 17}}, "inbound_nodes": [[["batch_normalization_40", 0, 0, {}]]], "shared_object_id": 18}2 -ä - root.layer-9"_tf_keras_layer*º{"name": "max_pooling2d_31", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_31", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["activation_41", 0, 0, {}]]], "shared_object_id": 19, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 67}}2 -„ - root.layer_with_weights-3"_tf_keras_layer*Í {"name": "conv2d_42", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_42", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 20}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 21}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_31", 0, 0, {}]]], "shared_object_id": 22, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 68}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2, 4, 128]}}2 -¯  root.layer-11"_tf_keras_layer*„{"name": "dropout_52", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_52", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv2d_42", 0, 0, {}]]], "shared_object_id": 23}2 -Ä  root.layer_with_weights-4"_tf_keras_layer* {"name": "batch_normalization_41", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_41", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 25}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 26}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 27}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_52", 0, 0, {}]]], "shared_object_id": 28, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"3": 256}}, "shared_object_id": 69}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2, 4, 256]}}2 -× root.layer-13"_tf_keras_layer*¬{"name": "activation_42", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_42", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_42", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 29}}, "inbound_nodes": [[["batch_normalization_41", 0, 0, {}]]], "shared_object_id": 30}2 -å root.layer-14"_tf_keras_layer*º{"name": "max_pooling2d_32", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_32", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["activation_42", 0, 0, {}]]], "shared_object_id": 31, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 70}}2 -„ -root.layer_with_weights-5"_tf_keras_layer*Í {"name": "conv2d_43", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_43", "trainable": true, "dtype": "float32", "filters": 256, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 32}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 33}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_32", 0, 0, {}]]], "shared_object_id": 34, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 256}}, "shared_object_id": 71}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 2, 256]}}2 -¯ root.layer-16"_tf_keras_layer*„{"name": "dropout_53", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_53", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "inbound_nodes": [[["conv2d_43", 0, 0, {}]]], "shared_object_id": 35}2 -Ë root.layer-17"_tf_keras_layer* {"name": "activation_43", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_43", "trainable": true, "dtype": "float32", "activation": {"class_name": "ReLU", "config": {"name": "re_lu_43", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 36}}, "inbound_nodes": [[["dropout_53", 0, 0, {}]]], "shared_object_id": 37}2 -Ì root.layer-18"_tf_keras_layer*¡{"name": "flatten_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_10", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["activation_43", 0, 0, {}]]], "shared_object_id": 38, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 72}}2 -þroot.layer_with_weights-6"_tf_keras_layer*Ç{"name": "dense_30", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_30", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 39}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 40}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["flatten_10", 0, 0, {}]]], "shared_object_id": 41, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 512}}, "shared_object_id": 73}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 512]}}2 -¼ root.layer_with_weights-7"_tf_keras_layer*… {"name": "batch_normalization_42", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_42", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 42}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 43}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 44}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 45}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dense_30", 0, 0, {}]]], "shared_object_id": 46, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 128}}, "shared_object_id": 74}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2 -Šroot.layer_with_weights-8"_tf_keras_layer*Ó{"name": "dense_31", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_31", "trainable": true, "dtype": "float32", "units": 128, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 47}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 48}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_42", 0, 0, {}]]], "shared_object_id": 49, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 75}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2 -® root.layer-22"_tf_keras_layer*ƒ{"name": "dropout_54", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_54", "trainable": true, "dtype": "float32", "rate": 0.4, "noise_shape": null, "seed": null}, "inbound_nodes": [[["dense_31", 0, 0, {}]]], "shared_object_id": 50}2 -¾ root.layer_with_weights-9"_tf_keras_layer*‡ {"name": "batch_normalization_43", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "BatchNormalization", "config": {"name": "batch_normalization_43", "trainable": true, "dtype": "float32", "axis": [1], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 51}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 52}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 53}, "moving_variance_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 54}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["dropout_54", 0, 0, {}]]], "shared_object_id": 55, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 2, "max_ndim": null, "min_ndim": null, "axes": {"1": 128}}, "shared_object_id": 76}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2 -‰root.layer_with_weights-10"_tf_keras_layer*Ñ{"name": "dense_32", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_32", "trainable": true, "dtype": "float32", "units": 2, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 56}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 57}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["batch_normalization_43", 0, 0, {}]]], "shared_object_id": 58, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 77}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2 - root.layer-25"_tf_keras_layer*ä{"name": "pred", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "pred", "trainable": true, "dtype": "float32", "activation": "softmax"}, "inbound_nodes": [[["dense_32", 0, 0, {}]]], "shared_object_id": 59}2 -+root.layer-3.activation"_tf_keras_layer*Û{"name": "re_lu_40", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ReLU", "config": {"name": "re_lu_40", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 5}2 -‘Groot.layer-8.activation"_tf_keras_layer*Ü{"name": "re_lu_41", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ReLU", "config": {"name": "re_lu_41", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 17}2 -’croot.layer-13.activation"_tf_keras_layer*Ü{"name": "re_lu_42", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ReLU", "config": {"name": "re_lu_42", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 29}2 -’vroot.layer-17.activation"_tf_keras_layer*Ü{"name": "re_lu_43", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "ReLU", "config": {"name": "re_lu_43", "trainable": true, "dtype": "float32", "max_value": null, "negative_slope": 0.0, "threshold": 0.0}, "shared_object_id": 36}2 -ºÂroot.keras_api.metrics.0"_tf_keras_metric*‚{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 78}2 -íÃroot.keras_api.metrics.1"_tf_keras_metric*µ{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 62}2 \ No newline at end of file diff --git a/configfiles/RingCounting/RC_model_v1_0_0.model/saved_model.pb b/configfiles/RingCounting/RC_model_v1_0_0.model/saved_model.pb deleted file mode 100644 index a9c2cce3b..000000000 Binary files a/configfiles/RingCounting/RC_model_v1_0_0.model/saved_model.pb and /dev/null differ diff --git a/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.data-00000-of-00001 b/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.data-00000-of-00001 deleted file mode 100644 index b4ad9836c..000000000 Binary files a/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.data-00000-of-00001 and /dev/null differ diff --git a/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.index b/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.index deleted file mode 100644 index 35c795f17..000000000 Binary files a/configfiles/RingCounting/RC_model_v1_0_0.model/variables/variables.index and /dev/null differ diff --git a/configfiles/RingCounting/RingCountingConfig b/configfiles/RingCounting/RingCountingConfig index 36c0c9cf8..c88bf7927 100644 --- a/configfiles/RingCounting/RingCountingConfig +++ b/configfiles/RingCounting/RingCountingConfig @@ -15,7 +15,7 @@ files_to_load configfiles/RingCounting/RC_files_to_load.txt # Model version -> check documentation version 1_0_0 # Folder containing models -model_path /exp/annie/app/users/dschmid/RingCountingStore/models/ +model_path /pnfs/annie/persistent/reconstruction/RingCounting/RingCountingStore/models/ # Masked PMTs (to 0) config -> check documentation pmt_mask november_22 # Output file diff --git a/offsetFit_MultipleLAPPD.cpp b/offsetFit_MultipleLAPPD.cpp deleted file mode 100644 index 769023467..000000000 --- a/offsetFit_MultipleLAPPD.cpp +++ /dev/null @@ -1,1272 +0,0 @@ -// This is a script for offset fitting, use it on the output LAPPDTree.root -// This script require all LAPPD events appear in sequence, and all CTC events appear in sequence -/* procedure: - 1. load LAPPDTree.root (TODO: or use LAPPDTree_runNumber.root) - 2. load TimeStamp tree, get run number and part file number - 3. for each unique run number and part file number, do fit: - 4. in this file, for each LAPPD ID: - 5. loop TimeStamp tree - load data timestamp - load data beamgate - load PPS for board 0 and board 1 - 6. loop Trig (or GTrig) tree - load CTC PPS (word = 32) - load target trigger word - - 7. Find the number of resets in LAPPD PPS: - There must be at least one event in each reset. - Based on the event index order, only fit before the reset which doesn't have data event. - Save the data event and PPS index order. - - 8. Use the target trigger word, fit the offset - 9. save the offset for this LAPPD ID, run number, part file number, index, reset number. - 10. Print info to txt file - -Laser trigger word: 47 -Undelayed beam trigger word: 14 - -root -l -q 'offsetFit_MultipleLAPPD.cpp("LAPPDTree.root", 14, 1, 10, 0)' -root -l -q 'offsetFit_MultipleLAPPD.cpp("LAPPDTree.root", 47, 0, 10, 0)' -*/ -#include -#include -#include -#include -#include -#include -#include "TFile.h" -#include "TTree.h" -#include "TH2D.h" -#include "TH1D.h" -#include "TString.h" - -vector> fitInThisReset( - const std::vector &LAPPDDataTimeStampUL, - const std::vector &LAPPDDataBeamgateUL, - const std::vector &LAPPD_PPS, - const int fitTargetTriggerWord, - const std::vector &CTCTrigger, - const std::vector &CTCPPS, - const ULong64_t PPSDeltaT) -{ - cout << "***************************************" << endl; - cout << "Fitting in this reset with:" << endl; - cout << "LAPPDDataTimeStampUL size: " << LAPPDDataTimeStampUL.size() << endl; - cout << "LAPPDDataBeamgateUL size: " << LAPPDDataBeamgateUL.size() << endl; - cout << "LAPPD_PPS size: " << LAPPD_PPS.size() << endl; - cout << "CTCTrigger size: " << CTCTrigger.size() << endl; - cout << "CTCPPS size: " << CTCPPS.size() << endl; - cout << "PPSDeltaT: " << PPSDeltaT << endl; // in ps - // for this input PPS, fit an offset - // return the offset and other information in order - // precedure: - // 1. check drift - // 2. shift the timestmap and beamgate based on drift - // 3. fit the offset - - std::vector PPSInterval_ACDC; - - for (int i = 1; i < LAPPD_PPS.size(); i++) - { - - double diff = static_cast(LAPPD_PPS[i] - LAPPD_PPS[i - 1]); - cout< 2 microseconds - cout<<"PPSInterval_ACDC["< 2) - { - h->Fill(PPSInterval_ACDC[i]); // fill in microseconds - cout << "Fill histogram: PPSInterval_ACDC[" << i << "]: " << PPSInterval_ACDC[i] << endl; - } - } - - TF1 *gausf = new TF1("gausf", "gaus", 0, 1E3); - h->Fit(gausf, "Q"); // Q for quiet mode - ULong64_t drift = static_cast(gausf->GetParameter(1) * 1E3 * 1E3); // drift in ps - ULong64_t trueInterval = PPSDeltaT - drift; - std::cout << "Gaussian Drift in ps is " << drift << std::endl; - std::cout << "True PPS interval in ps is " << trueInterval << std::endl; - delete gausf; - delete h; - // initialize variables need for fitting - int orphanCount = 0; // count the number that how many data event timestamp doesn't matched to a target trigger within an interval. - std::map>> DerivationMap; - cout << "LAPPD_PPS.size() " << LAPPD_PPS.size() << " CTCPPS.size() " << CTCPPS.size() << endl; - for (int i = 0; i < LAPPD_PPS.size(); i++) - { - if (i > 5) - { - if (i % static_cast(LAPPD_PPS.size() / 5) == 0) - cout << "Fitting PPS " << i << " of " << LAPPD_PPS.size() << endl; - } - ULong64_t LAPPD_PPS_ns = LAPPD_PPS.at(i) / 1000; - ULong64_t LAPPD_PPS_truncated_ps = LAPPD_PPS.at(i) % 1000; - - for (int j = 0; j < CTCPPS.size(); j++) - { - - vector diffSum; - ULong64_t offsetNow_ns = 0; - if (drift == 0) - { - offsetNow_ns = CTCPPS.at(j) - LAPPD_PPS_ns; - } - else - { - double LAPPD_PPS_ns_dd = static_cast(LAPPD_PPS_ns); - double driftScaling = LAPPD_PPS_ns_dd / (trueInterval / 1000); - ULong64_t totalDriftedClock = static_cast(drift * driftScaling / 1000); - offsetNow_ns = CTCPPS.at(j) - (LAPPD_PPS_ns + totalDriftedClock); - if (i < 3 && j < 3) - cout<<"totalDriftedClock in ns = "< notOrphanIndex; - vector ctcPairedIndex; - vector ctcOrphanPairedIndex; - - for (int lappdb = 0; lappdb < LAPPDDataBeamgateUL.size(); lappdb++) - { - ULong64_t TS_ns = LAPPDDataTimeStampUL.at(lappdb) / 1000; - ULong64_t TS_truncated_ps = LAPPDDataTimeStampUL.at(lappdb) % 1000; - // if fit for the undelayed beam trigger, use BG - if (fitTargetTriggerWord == 14) - { - TS_ns = LAPPDDataBeamgateUL.at(lappdb) / 1000; - TS_truncated_ps = LAPPDDataBeamgateUL.at(lappdb) % 1000; - } - - ULong64_t driftCorrectionForTS = 0; - if (drift != 0) - { - double TS_ns_dd = static_cast(TS_ns); - double driftScaling = TS_ns_dd / (trueInterval / 1000); - driftCorrectionForTS = static_cast(drift * driftScaling / 1000); - if(lappdb<3 && i<3 && j<3) - cout<::max(); - bool useThisValue = true; - int minPairIndex = 0; - string reason = "none"; - Long64_t first_derivation = 0; - int minIndex = 0; - double useValue = true; - - // find the best match of target trigger to this TS, in ns level. - for (int ctcb = 0; ctcb < CTCTrigger.size(); ctcb++) - { - ULong64_t CTCTrigger_ns = CTCTrigger.at(ctcb); - Long64_t diff = CTCTrigger_ns - DriftCorrectedTS_ns; - if (diff < 0) - diff = -diff; - - if (diff < minMatchDiff) - { - minMatchDiff = diff; - minPairIndex = ctcb; - } - - Long64_t LastBound = diff - minMatchDiff; - if (LastBound < 0) - LastBound = -LastBound; - Long64_t FirstBound = diff - first_derivation; - if (FirstBound < 0) - FirstBound = -FirstBound; - - // save the dt for the first matching to determin the matching position in range. - if (ctcb == 0) - first_derivation = diff; - - if (ctcb == CTCTrigger.size() - 1 && LastBound / 1E9 < 0.01) - { - useValue = false; - reason = "When matching the last TS, this diff is too close to (or even is) the minMatchDiff at index " + std::to_string(minPairIndex) + " in " + std::to_string(CTCTrigger.size()) + ". All LAPPD TS is out of the end of CTC trigger range"; - } - if (ctcb == CTCTrigger.size() - 1 && FirstBound / 1E9 < 0.01) - { - useValue = false; - reason = "When matching the last TS, this diff is too close to (or even is) the first_derivation at index " + std::to_string(minPairIndex) + " in " + std::to_string(CTCTrigger.size()) + ". All LAPPD TS is out of the beginning of CTC trigger range"; - } - } - - if (useValue) - { - diffSum.push_back(minMatchDiff); - double minAllowedDiff = 0; - double maxAllowedDiff = 100E3; - if (fitTargetTriggerWord == 14) - { - minAllowedDiff = 322E3; - maxAllowedDiff = 326E3; - } - if (minMatchDiff > maxAllowedDiff || minMatchDiff < minAllowedDiff) - { - // TODO: adjust the limit for laser. - orphanCount += 1; - ctcOrphanPairedIndex.push_back(minPairIndex); - } - else - { - notOrphanIndex.push_back(lappdb); - ctcPairedIndex.push_back(minPairIndex); - } - } - else - { - orphanCount += 1; - } - } - - double mean_dev = 0; - for (int k = 0; k < diffSum.size(); k++) - { - mean_dev += diffSum[k]; - } - if (diffSum.size() > 0) - mean_dev = mean_dev / diffSum.size(); - - double mean_dev_noOrphan = 0; - for (int k = 0; k < notOrphanIndex.size(); k++) - { - if (fitTargetTriggerWord == 14) - { - if (diffSum.at(k) > 322E3 && diffSum.at(k) < 326E3) - mean_dev_noOrphan += diffSum[notOrphanIndex[k]]; - } - else - { - mean_dev_noOrphan += diffSum[notOrphanIndex[k]]; - } - } - if ((diffSum.size() - orphanCount) != 0) - { - mean_dev_noOrphan = mean_dev_noOrphan / (diffSum.size() - orphanCount); - } - else - { - mean_dev_noOrphan = -1; // if all timestamps are out of the range, set it to -1 - } - - bool increMean_dev = false; - int maxAttempts = 1000; - int attemptCount = 0; - - if (mean_dev > 0) - { - int increament_dev = 0; - while (true) - { - // TODO - auto iter = DerivationMap.find(mean_dev); - if (iter == DerivationMap.end() || iter->second.empty()) - { - vector Info = {i, j, orphanCount, static_cast(mean_dev_noOrphan * 1000), increament_dev}; - DerivationMap[mean_dev].push_back(Info); - DerivationMap[mean_dev].push_back(notOrphanIndex); - DerivationMap[mean_dev].push_back(ctcPairedIndex); - DerivationMap[mean_dev].push_back(ctcOrphanPairedIndex); - break; - } - else - { - increament_dev += 1; - mean_dev += 0.001; // if the mean_dev is already in the map, increase it by 1ps - attemptCount += 1; - if (attemptCount > maxAttempts) - break; - } - } - } - } - } - // finish matching, found the minimum mean_dev in the map, extract the matching information - double min_mean_dev = std::numeric_limits::max(); - int final_i = 0; - int final_j = 0; - int gotOrphanCount = 0; - double gotMin_mean_dev_noOrphan = 0; - double increament_times = 0; - vector final_notOrphanIndex; - vector final_ctcPairedIndex; - vector final_ctcOrphanIndex; - for (const auto &minIter : DerivationMap) - { - if (minIter.first > 10 && minIter.first < min_mean_dev) - { - min_mean_dev = minIter.first; - final_i = minIter.second[0][0]; - final_j = minIter.second[0][1]; - gotOrphanCount = minIter.second[0][2]; - gotMin_mean_dev_noOrphan = static_cast(minIter.second[0][3] / 1000); - increament_times = minIter.second[0][4]; - final_notOrphanIndex = minIter.second[1]; - final_ctcPairedIndex = minIter.second[2]; - final_ctcOrphanIndex = minIter.second[3]; - } - } - - ULong64_t final_offset_ns = 0; - ULong64_t final_offset_ps_negative = 0; - if (drift == 0) - { - final_offset_ns = CTCPPS.at(final_j) - (LAPPD_PPS.at(final_i) / 1000); - final_offset_ps_negative = LAPPD_PPS.at(final_i) % 1000; - } - else - { - ULong64_t LAPPD_PPS_ns = LAPPD_PPS.at(final_i) / 1000; - ULong64_t LAPPD_PPS_truncated_ps = LAPPD_PPS.at(final_i) % 1000; - double driftScaling = static_cast(LAPPD_PPS_ns) / (trueInterval / 1000); // this is the same drift scaling as in the matching loop - ULong64_t totalDriftedClock = static_cast(drift * driftScaling / 1000); - cout< TimeStampRaw; - vector BeamGateRaw; - vector TimeStamp_ns; - vector BeamGate_ns; - vector TimeStamp_ps; - vector BeamGate_ps; - vector EventIndex; - vector EventDeviation_ns; - vector CTCTriggerIndex; - vector CTCTriggerTimeStamp_ns; - vector BeamGate_correction_tick; - vector TimeStamp_correction_tick; - vector PPS_tick_correction; // if timestamp falls in between of PPS i and i+1, corrected timestamp = timestamp + PPS_tick_correction[i] - vector LAPPD_PPS_missing_ticks; - vector LAPPD_PPS_interval_ticks; - - vector BG_PPSBefore; - vector BG_PPSAfter; - vector BG_PPSDiff; - vector BG_PPSMiss; - vector TS_PPSBefore; - vector TS_PPSAfter; - vector TS_PPSDiff; - vector TS_PPSMiss; - - vector TS_driftCorrection_ns; - vector BG_driftCorrection_ns; - - // calculate the missing ticks for each LAPPD PPS - PPS_tick_correction.push_back(0); - LAPPD_PPS_missing_ticks.push_back(0); - LAPPD_PPS_interval_ticks.push_back(0); - ULong64_t intervalTicks = 320000000 * (PPSDeltaT / 1000 / 1E9); - for (int i = 1; i < LAPPD_PPS.size(); i++) - { - ULong64_t thisPPS = LAPPD_PPS.at(i) / 3125; - ULong64_t prevPPS = LAPPD_PPS.at(i - 1) / 3125; - long long thisInterval = thisPPS - prevPPS; - long long thisMissingTicks = intervalTicks - thisInterval; - LAPPD_PPS_interval_ticks.push_back(thisInterval); - - // if the difference between this PPS and previous PPS is > intervalTicks-20 and < intervalTicks+5, - // the missed value is thisInterval - intervalTicks - // push this value plus the sum of previous missing ticks to the vector - // else just push the sum of previous missing ticks - long long sumOfPreviousMissingTicks = 0; - for (int j = 0; j < LAPPD_PPS_missing_ticks.size(); j++) - { - sumOfPreviousMissingTicks += LAPPD_PPS_missing_ticks.at(j); - } - - if (thisMissingTicks > -20 && thisMissingTicks < 20) - { - LAPPD_PPS_missing_ticks.push_back(thisMissingTicks); - PPS_tick_correction.push_back(thisMissingTicks + sumOfPreviousMissingTicks); - } - else - { - // some time one pps might be wired, but the combination of two is ok. - bool combined = false; - // if there are missing PPS, interval is like 22399999990 % 3200000000 = 3199999990 - long long pInterval = thisInterval % intervalTicks; - long long missingPTicks = 0; - if (intervalTicks - pInterval < 30) - missingPTicks = intervalTicks - pInterval; - else if (pInterval < 30) - missingPTicks = -pInterval; - ////// - if (missingPTicks == 1 || missingPTicks == -1) - { - cout << "Found missing tick is " << missingPTicks << ",continue." << endl; - LAPPD_PPS_missing_ticks.push_back(0); - PPS_tick_correction.push_back(sumOfPreviousMissingTicks); - continue; - } - - if (missingPTicks != 0) - { - LAPPD_PPS_missing_ticks.push_back(missingPTicks); - PPS_tick_correction.push_back(missingPTicks + sumOfPreviousMissingTicks); - combined = true; - cout << "Pushing PPS correction " << i << ", this PPS interval tick is " << thisInterval << ", missing ticks: " << thisMissingTicks << ", push missing " << LAPPD_PPS_missing_ticks.at(i) << ", push correction " << PPS_tick_correction.at(i) << endl; - continue; - } - - // if one PPS is not recoreded correctly, like one interval is 1574262436, followed by a 4825737559 - // then 1574262436 + 4825737559 = 6399999995 = 3200000000 * 2 - 5 - long long nextInterval = 0; - if (i < LAPPD_PPS.size() - 1) - { - nextInterval = LAPPD_PPS.at(i + 1) / 3125 - thisPPS; - long long combinedMissingTicks = intervalTicks - (nextInterval + thisInterval) % intervalTicks; - if (combinedMissingTicks > -30 && combinedMissingTicks < 30) - { - LAPPD_PPS_missing_ticks.push_back(combinedMissingTicks); - PPS_tick_correction.push_back(combinedMissingTicks + sumOfPreviousMissingTicks); - combined = true; - cout << "Pushing PPS correction " << i << ", this PPS interval tick is " << thisInterval << ", missing ticks: " << thisMissingTicks << ", push missing " << LAPPD_PPS_missing_ticks.at(i) << ", push correction " << PPS_tick_correction.at(i) << endl; - continue; - } - } - - if (!combined) - { - LAPPD_PPS_missing_ticks.push_back(0); - PPS_tick_correction.push_back(sumOfPreviousMissingTicks); - } - } - cout << "Pushing PPS correction " << i << ", this PPS interval tick is " << thisInterval << ", missing ticks: " << thisMissingTicks << ", push missing " << LAPPD_PPS_missing_ticks.at(i) << ", push correction " << PPS_tick_correction.at(i) << endl; - } - - // loop all data events, plus the offset, save the event time and beamgate time - // fing the closest CTC trigger, also save all information - cout << "Start saving results. PPS size: " << LAPPD_PPS.size() << ", beamgate size: " << LAPPDDataBeamgateUL.size() << endl; - cout << "First PPS: " << LAPPD_PPS.at(0) / 3125 << ", Last PPS: " << LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 << endl; - cout << "First BG: " << LAPPDDataBeamgateUL.at(0) / 3125 << ", Last BG: " << LAPPDDataBeamgateUL.at(LAPPDDataBeamgateUL.size() - 1) / 3125 << endl; - cout << "First TS: " << LAPPDDataTimeStampUL.at(0) / 3125 << ", Last TS: " << LAPPDDataTimeStampUL.at(LAPPDDataTimeStampUL.size() - 1) / 3125 << endl; - - for (int l = 0; l < LAPPDDataTimeStampUL.size(); l++) - { - ULong64_t TS_ns = LAPPDDataTimeStampUL.at(l) / 1000; - ULong64_t TS_truncated_ps = LAPPDDataTimeStampUL.at(l) % 1000; - ULong64_t BG_ns = LAPPDDataBeamgateUL.at(l) / 1000; - ULong64_t BG_truncated_ps = LAPPDDataBeamgateUL.at(l) % 1000; - ULong64_t driftCorrectionForTS = 0; - ULong64_t driftCorrectionForBG = 0; - if (drift != 0) - { - - double driftScaling = static_cast(TS_ns) / (trueInterval / 1000); - driftCorrectionForTS = static_cast(drift/ 1000 * driftScaling ); - double driftScalingBG = static_cast(BG_ns) / (trueInterval / 1000); - driftCorrectionForBG = static_cast(drift/ 1000 * driftScalingBG ); - cout<<"drift = "< LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125) - { - BeamGate_correction_tick.push_back(PPS_tick_correction.at(LAPPD_PPS.size() - 1) + 1000); - cout << "BGraw = " << LAPPDDataBeamgateUL.at(l) / 3125 << ", pps = " << LAPPD_PPS.at(LAPPD_PPS.size() - 1) << ", pps/3125 = " << LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 << endl; - BGFound = true; - } - if (!BGFound && LAPPDDataBeamgateUL.at(l) / 3125 < LAPPD_PPS.at(0) / 3125) - { - BeamGate_correction_tick.push_back(0 + 1000); - cout << "BGraw less than pps0" << endl; - BGFound = true; - } - if (!TSFound || !BGFound) - { - cout << "Error: PPS not found for event " << l << ", TSFound: " << TSFound << ", BGFound: " << BGFound << endl; - } - /* - cout << "******Found result:" << endl; - cout << "LAPPDDataTimeStampUL.at(" << l << "): " << LAPPDDataTimeStampUL.at(l)/3125 << endl; - cout << "LAPPDDataBeamgateUL.at(" << l << "): " << LAPPDDataBeamgateUL.at(l)/3125 << endl; - cout << "TS_ns: " << TS_ns << endl; - cout << "BG_ns: " << BG_ns << endl; - cout << "final_offset_ns: " << final_offset_ns << endl; - cout << "drift: " << drift << endl; - cout << "TS driftscaling: " << TS_ns / trueInterval / 1000 << endl; - cout << "DriftCorrectedTS_ns: " << DriftCorrectedTS_ns << endl; - cout << "TS_truncated_ps: " << TS_truncated_ps << endl; - cout << "DriftCorrectedBG_ns: " << DriftCorrectedBG_ns << endl; - cout << "BG_truncated_ps: " << BG_truncated_ps << endl; - cout << "Found min_mean_dev_match: " << min_mean_dev_match << endl; - cout << "MatchedIndex: " << matchedIndex << endl; - cout << "CTCTrigger.at(" << matchedIndex << "): " << CTCTrigger.at(matchedIndex) << endl; - cout << "BeamGate_correction_tick at " << l << ": " << BeamGate_correction_tick.at(l) << endl; - cout << "TimeStamp_correction_tick at " << l << ": " << TimeStamp_correction_tick.at(l) << endl; - */ - - // for this LAPPDDataBeamgateUL.at(l), in the LAPPD_PPS vector, find it's closest PPS before and after, and also calculate the time difference between them, and the missing tick between them. - // save as BG_PPSBefore_tick, BG_PPSAfter_tick, BG_PPSDiff_tick, BG_PPSMissing_tick - // Do the same thing for LAPPDDataTimeStampUL.at(l), save as TS_PPSBefore_tick, TS_PPSAfter_tick, TS_PPSDiff_tick, TS_PPSMissing_tick - - ULong64_t BG_PPSBefore_tick = 0; - ULong64_t BG_PPSAfter_tick = 0; - ULong64_t BG_PPSDiff_tick = 0; - ULong64_t BG_PPSMissing_tick = 0; - - ULong64_t TS_PPSBefore_tick = 0; - ULong64_t TS_PPSAfter_tick = 0; - ULong64_t TS_PPSDiff_tick = 0; - ULong64_t TS_PPSMissing_tick = 0; - // if the first PPS is later than beamgate, then set before = 0, after is the first, diff is the first, missing is the first - - cout << "Start finding PPS before and after the beamgate" << endl; - - if ((LAPPD_PPS.at(0) / 3125 > LAPPDDataBeamgateUL.at(l) / 3125) || (LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 < LAPPDDataBeamgateUL.at(l) / 3125)) - { - if (LAPPD_PPS.at(0) / 3125 > LAPPDDataBeamgateUL.at(l) / 3125) - { - BG_PPSBefore_tick = 0; - BG_PPSAfter_tick = LAPPD_PPS.at(0) / 3125; - BG_PPSDiff_tick = LAPPD_PPS.at(0) / 3125; - BG_PPSMissing_tick = LAPPD_PPS.at(0) / 3125; - cout << "First PPS is later than beamgate, before is 0, after is the first, diff is the first, missing is the first" << endl; - } - // if the last PPS is earlier than beamgate, then set before is the last, after = 0, diff is the last, missing is the last - if (LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 < LAPPDDataBeamgateUL.at(l) / 3125) - { - BG_PPSBefore_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - BG_PPSAfter_tick = 0; - BG_PPSDiff_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - BG_PPSMissing_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - cout << "Last PPS is earlier than beamgate, before is the last, after is 0, diff is the last, missing is the last" << endl; - } - } - else - { - // if the first PPS is earlier than beamgate, and the last PPS is later than beamgate, then find the closest PPS before and after - for (int i = 0; i < LAPPD_PPS.size() - 1; i++) - { - if (LAPPD_PPS.at(i) / 3125 < LAPPDDataBeamgateUL.at(l) / 3125 && LAPPD_PPS.at(i + 1) / 3125 > LAPPDDataBeamgateUL.at(l) / 3125) - { - BG_PPSBefore_tick = LAPPD_PPS.at(i) / 3125; - BG_PPSAfter_tick = LAPPD_PPS.at(i + 1) / 3125; - BG_PPSDiff_tick = LAPPD_PPS.at(i + 1) / 3125 - LAPPD_PPS.at(i) / 3125; - ULong64_t DiffTick = (LAPPD_PPS.at(i + 1) - LAPPD_PPS.at(i)) / 3125 - 1000; - BG_PPSMissing_tick = 3200000000 - DiffTick; - cout << "Found PPS before and after the beamgate, before: " << BG_PPSBefore_tick << ", after: " << BG_PPSAfter_tick << ", diff: " << BG_PPSDiff_tick << ", missing: " << BG_PPSMissing_tick << endl; - break; - } - } - } - - // do the samething for timestamp - cout << "Start finding PPS before and after the timestamp" << endl; - - if ((LAPPD_PPS.at(0) / 3125 > LAPPDDataTimeStampUL.at(l) / 3125) || (LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 < LAPPDDataTimeStampUL.at(l) / 3125)) - { - if (LAPPD_PPS.at(0) / 3125 > LAPPDDataTimeStampUL.at(l) / 3125) - { - TS_PPSBefore_tick = 0; - TS_PPSAfter_tick = LAPPD_PPS.at(0) / 3125; - TS_PPSDiff_tick = LAPPD_PPS.at(0) / 3125; - TS_PPSMissing_tick = LAPPD_PPS.at(0) / 3125; - cout << "First PPS is later than timestamp, before is 0, after is the first, diff is the first, missing is the first" << endl; - } - if (LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125 < LAPPDDataTimeStampUL.at(l) / 3125) - { - TS_PPSBefore_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - TS_PPSAfter_tick = 0; - TS_PPSDiff_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - TS_PPSMissing_tick = LAPPD_PPS.at(LAPPD_PPS.size() - 1) / 3125; - cout << "Last PPS is earlier than timestamp, before is the last, after is 0, diff is the last, missing is the last" << endl; - } - } - else - { - for (int i = 0; i < LAPPD_PPS.size() - 1; i++) - { - if (LAPPD_PPS.at(i) / 3125 < LAPPDDataTimeStampUL.at(l) / 3125 && LAPPD_PPS.at(i + 1) / 3125 > LAPPDDataTimeStampUL.at(l) / 3125) - { - TS_PPSBefore_tick = LAPPD_PPS.at(i) / 3125; - TS_PPSAfter_tick = LAPPD_PPS.at(i + 1) / 3125; - TS_PPSDiff_tick = LAPPD_PPS.at(i + 1) / 3125 - LAPPD_PPS.at(i) / 3125; - ULong64_t DiffTick = (LAPPD_PPS.at(i + 1) - LAPPD_PPS.at(i)) / 3125 - 1000; - TS_PPSMissing_tick = 3200000000 - DiffTick; - cout << "Found PPS before and after the timestamp, before: " << TS_PPSBefore_tick << ", after: " << TS_PPSAfter_tick << ", diff: " << TS_PPSDiff_tick << ", missing: " << TS_PPSMissing_tick << endl; - break; - } - } - } - - TimeStampRaw.push_back(LAPPDDataTimeStampUL.at(l) / 3125); - BeamGateRaw.push_back(LAPPDDataBeamgateUL.at(l) / 3125); - TimeStamp_ns.push_back(DriftCorrectedTS_ns); - BeamGate_ns.push_back(DriftCorrectedBG_ns); - TimeStamp_ps.push_back(TS_truncated_ps); - BeamGate_ps.push_back(BG_truncated_ps); - EventIndex.push_back(l); - EventDeviation_ns.push_back(min_mean_dev_match); - // to get ps, should minus the TS_truncated_ps - CTCTriggerIndex.push_back(matchedIndex); - CTCTriggerTimeStamp_ns.push_back(CTCTrigger.at(matchedIndex)); - - BG_PPSBefore.push_back(BG_PPSBefore_tick); - BG_PPSAfter.push_back(BG_PPSAfter_tick); - BG_PPSDiff.push_back(BG_PPSDiff_tick); - BG_PPSMiss.push_back(BG_PPSMissing_tick); - TS_PPSBefore.push_back(TS_PPSBefore_tick); - TS_PPSAfter.push_back(TS_PPSAfter_tick); - TS_PPSDiff.push_back(TS_PPSDiff_tick); - TS_PPSMiss.push_back(TS_PPSMissing_tick); - - TS_driftCorrection_ns.push_back(driftCorrectionForTS); - BG_driftCorrection_ns.push_back(driftCorrectionForBG); - } - ULong64_t totalEventNumber = LAPPDDataTimeStampUL.size(); - ULong64_t gotOrphanCount_out = gotOrphanCount; - ULong64_t gotMin_mean_dev_noOrphan_out = gotMin_mean_dev_noOrphan; - ULong64_t increament_times_out = increament_times; - ULong64_t min_mean_dev_out = min_mean_dev; - ULong64_t final_i_out = final_i; - ULong64_t final_j_out = final_j; - ULong64_t drift_out = drift; - - vector FitInfo = {final_offset_ns, final_offset_ps_negative, gotOrphanCount_out, gotMin_mean_dev_noOrphan_out, increament_times_out, min_mean_dev_out, final_i_out, final_j_out, totalEventNumber, drift_out}; - - vector> Result = {FitInfo, TimeStampRaw, BeamGateRaw, TimeStamp_ns, BeamGate_ns, TimeStamp_ps, BeamGate_ps, EventIndex, EventDeviation_ns, CTCTriggerIndex, CTCTriggerTimeStamp_ns, BeamGate_correction_tick, TimeStamp_correction_tick, LAPPD_PPS_interval_ticks, BG_PPSBefore, BG_PPSAfter, BG_PPSDiff, BG_PPSMiss, TS_PPSBefore, TS_PPSAfter, TS_PPSDiff, TS_PPSMiss, TS_driftCorrection_ns, BG_driftCorrection_ns}; - return Result; -} - -vector> fitInPartFile(TTree *lappdTree, TTree *triggerTree, int runNumber, int subRunNumber, int partFileNumber, int LAPPD_ID, int fitTargetTriggerWord, bool triggerGrouped, int intervalInSecond) -{ - cout << "***************************************" << endl; - cout << "Fitting in run " << runNumber << ", sub run " << subRunNumber << ", part file " << partFileNumber << " for LAPPD ID " << LAPPD_ID << endl; - - vector LAPPD_PPS0; - vector LAPPD_PPS1; - vector LAPPDDataTimeStampUL; - vector LAPPDDataBeamgateUL; - - vector CTCTargetTimeStamp; - vector CTCPPSTimeStamp; - - int LAPPD_ID_inTree; - int runNumber_inTree; - int subRunNumber_inTree; - int partFileNumber_inTree; - - ULong64_t ppsTime0; - ULong64_t ppsTime1; - ULong64_t LAPPDTimeStampUL; - ULong64_t LAPPDBeamgateUL; - ULong64_t LAPPDDataTimestamp; - ULong64_t LAPPDDataBeamgate; - double LAPPDDataTimestampFloat; - double LAPPDDataBeamgateFloat; - - vector *CTCTriggerWord = nullptr; - ULong64_t CTCTimeStamp; - vector *groupedTriggerWords = nullptr; - vector *groupedTriggerTimestamps = nullptr; - - lappdTree->SetBranchAddress("RunNumber", &runNumber_inTree); - lappdTree->SetBranchAddress("SubRunNumber", &subRunNumber_inTree); - lappdTree->SetBranchAddress("PartFileNumber", &partFileNumber_inTree); - lappdTree->SetBranchAddress("LAPPD_ID", &LAPPD_ID_inTree); - lappdTree->SetBranchAddress("LAPPDDataTimeStampUL", &LAPPDTimeStampUL); - lappdTree->SetBranchAddress("LAPPDDataBeamgateUL", &LAPPDBeamgateUL); - lappdTree->SetBranchAddress("LAPPDDataTimestamp", &LAPPDDataTimestamp); - lappdTree->SetBranchAddress("LAPPDDataBeamgate", &LAPPDDataBeamgate); - lappdTree->SetBranchAddress("LAPPDDataTimestampFloat", &LAPPDDataTimestampFloat); - lappdTree->SetBranchAddress("LAPPDDataBeamgateFloat", &LAPPDDataBeamgateFloat); - lappdTree->SetBranchAddress("ppsTime0", &ppsTime0); - lappdTree->SetBranchAddress("ppsTime1", &ppsTime1); - - // triggerTree->Print(); - triggerTree->SetBranchAddress("RunNumber", &runNumber_inTree); - triggerTree->SetBranchAddress("SubRunNumber", &subRunNumber_inTree); - triggerTree->SetBranchAddress("PartFileNumber", &partFileNumber_inTree); - if (triggerGrouped) - { - triggerTree->SetBranchAddress("gTrigWord", &groupedTriggerWords); - triggerTree->SetBranchAddress("gTrigTime", &groupedTriggerTimestamps); - } - else - { - triggerTree->SetBranchAddress("CTCTriggerWord", &CTCTriggerWord); - triggerTree->SetBranchAddress("CTCTimeStamp", &CTCTimeStamp); - } - - int l_nEntries = lappdTree->GetEntries(); - int t_nEntries = triggerTree->GetEntries(); - int repeatedPPSNumber0 = -1; - int repeatedPPSNumber1 = -1; - - // 5. loop TimeStamp tree - for (int i = 0; i < l_nEntries; i++) - { - lappdTree->GetEntry(i); - // if(i<100) - // cout<<"LAPPD_ID_inTree: "<(LAPPDTimeStampUL * 1000 / 8 * 25) / 1E9 / 1000 << ", Beamgate: " << static_cast(LAPPDBeamgateUL * 1000 / 8 * 25) / 1E9 / 1000 << endl; - LAPPDDataTimeStampUL.push_back(LAPPDTimeStampUL * 1000 / 8 * 25); - LAPPDDataBeamgateUL.push_back(LAPPDBeamgateUL * 1000 / 8 * 25); - /* - cout << "LAPPDDataTimestamp: " << LAPPDDataTimestamp << ", LAPPDDataBeamgate: " << LAPPDDataBeamgate << endl; - cout << "LAPPDDataTimestampFloat: " << LAPPDDataTimestampFloat << ", LAPPDDataBeamgateFloat: " << LAPPDDataBeamgateFloat << endl; - ULong64_t ULTS = LAPPDTimeStampUL*1000/8*25; - double DTS = static_cast(LAPPDDataTimestamp); - double plusDTS = DTS + LAPPDDataTimestampFloat; - ULong64_t ULBG = LAPPDBeamgateUL*1000/8*25; - double DBG = static_cast(LAPPDDataBeamgate); - double plusDBG = DBG + LAPPDDataBeamgateFloat; - cout << std::fixed << " ULTS: " << ULTS << ", DTS: " << DTS << ", plusDTS: " << plusDTS << endl; - cout << std::fixed << " ULBG: " << ULBG << ", DBG: " << DBG << ", plusDBG: " << plusDBG << endl; - cout << endl; - */ - } - else if (LAPPDTimeStampUL == 0) - { - // cout << "ppsTime0: " << ppsTime0 << ", ppsTime1: " << ppsTime1 << endl; - if (LAPPD_PPS0.size() == 0) - LAPPD_PPS0.push_back(ppsTime0 * 1000 / 8 * 25); - if (LAPPD_PPS1.size() == 0) - LAPPD_PPS1.push_back(ppsTime1 * 1000 / 8 * 25); - if (LAPPD_PPS0.size() > 0 && ppsTime0 * 1000 / 8 * 25 != LAPPD_PPS0.at(LAPPD_PPS0.size() - 1)) - LAPPD_PPS0.push_back(ppsTime0 * 1000 / 8 * 25); - else - repeatedPPSNumber0 += 1; - if (LAPPD_PPS1.size() > 0 && ppsTime1 * 1000 / 8 * 25 != LAPPD_PPS1.at(LAPPD_PPS1.size() - 1)) - LAPPD_PPS1.push_back(ppsTime1 * 1000 / 8 * 25); - else - repeatedPPSNumber1 += 1; - } - } - } - cout << "repeatedPPSNumber0: " << repeatedPPSNumber0 << ", loaded PPS0 size: " << LAPPD_PPS0.size() << endl; - cout << "repeatedPPSNumber1: " << repeatedPPSNumber1 << ", loaded PPS1 size: " << LAPPD_PPS1.size() << endl; - - // 6. loop Trig (or GTrig) tree - for (int i = 0; i < t_nEntries; i++) - { - triggerTree->GetEntry(i); - if (runNumber_inTree == runNumber && subRunNumber_inTree == subRunNumber && partFileNumber_inTree == partFileNumber) - { - // cout<<"triggerGrouped: "<size(); j++) - { - // cout<<"At j = "<at(j)<<", fitTargetTriggerWord: "<at(j) == fitTargetTriggerWord) - CTCTargetTimeStamp.push_back(groupedTriggerTimestamps->at(j)); - if (groupedTriggerWords->at(j) == 32) - CTCPPSTimeStamp.push_back(groupedTriggerTimestamps->at(j)); - } - } - else - { - for (int j = 0; j < CTCTriggerWord->size(); j++) - { - if (CTCTriggerWord->at(j) == fitTargetTriggerWord) - CTCTargetTimeStamp.push_back(CTCTimeStamp); - if (CTCTriggerWord->at(j) == 32) - CTCPPSTimeStamp.push_back(CTCTimeStamp); - } - } - } - } - cout << "Vector for partfile " << partFileNumber << " for LAPPD ID " << LAPPD_ID << " loaded." << endl; - cout << "LAPPDDataTimeStampUL in ps size: " << LAPPDDataTimeStampUL.size() << endl; - cout << "LAPPDDataBeamgateUL in ps size: " << LAPPDDataBeamgateUL.size() << endl; - cout << "LAPPD_PPS0 size: " << LAPPD_PPS0.size() << endl; - cout << "LAPPD_PPS1 size: " << LAPPD_PPS1.size() << endl; - cout << "CTCTargetTimeStamp size: " << CTCTargetTimeStamp.size() << endl; - cout << "CTCPPSTimeStamp size: " << CTCPPSTimeStamp.size() << endl; - // 7. Find the number of resets in LAPPD PPS: - int LAPPDDataFitStopIndex = 0; - int resetNumber = 0; - // first check is there a reset, if no, set the LAPPDDataFitStopIndex to the size of LAPPDDataTimeStampUL - // if all PPS in this part file was increamented, then there is no reset - for (int i = 1; i < LAPPD_PPS0.size(); i++) - { - if (LAPPD_PPS0[i] < LAPPD_PPS0[i - 1]) - { - resetNumber += 1; - cout << "For LAPPD ID " << LAPPD_ID << ", run number " << runNumber << ", sub run number " << subRunNumber << ", part file number " << partFileNumber << ", reset " - << " found at PPS_ACDC0 index " << i << endl; - break; - } - } - for (int i = 1; i < LAPPD_PPS1.size(); i++) - { - if (LAPPD_PPS1[i] < LAPPD_PPS1[i - 1]) - { - resetNumber += 1; - cout << "For LAPPD ID " << LAPPD_ID << ", run number " << runNumber << ", sub run number " << subRunNumber << ", part file number " << partFileNumber << ", reset " - << " found at PPS_ACDC1 index " << i << endl; - break; - } - } - if (resetNumber == 0) - cout << "For LAPPD ID " << LAPPD_ID << ", run number " << runNumber << ", sub run number " << subRunNumber << ", part file number " << partFileNumber << ", no reset found." << endl; - - // if reset found, loop the timestamp in order to find the LAPPDDataFitStopIndex - // - if (resetNumber != 0) - { - for (int i = 1; i < LAPPDDataTimeStampUL.size(); i++) - { - if (LAPPDDataTimeStampUL[i] < LAPPDDataTimeStampUL[i - 1]) - { - LAPPDDataFitStopIndex = i - 1; - break; - } - } - // TODO: extend this to later offsets - } - // 8. Use the target trigger word, fit the offset - // TODO: fit for each reset - vector> ResultTotal; - if (LAPPDDataTimeStampUL.size() == LAPPDDataBeamgateUL.size()) - { - if (LAPPD_PPS0.size() == 0 || LAPPD_PPS1.size() == 0) - { - cout << "Error: PPS0 or PPS1 is empty, return empty result." << endl; - return ResultTotal; - } - vector> ResultACDC0 = fitInThisReset(LAPPDDataTimeStampUL, LAPPDDataBeamgateUL, LAPPD_PPS0, fitTargetTriggerWord, CTCTargetTimeStamp, CTCPPSTimeStamp, intervalInSecond * 1E9 * 1000); - vector> ResultACDC1 = fitInThisReset(LAPPDDataTimeStampUL, LAPPDDataBeamgateUL, LAPPD_PPS1, fitTargetTriggerWord, CTCTargetTimeStamp, CTCPPSTimeStamp, intervalInSecond * 1E9 * 1000); - - // 9. save the offset for this LAPPD ID, run number, part file number, index, reset number. - - cout << "Fitting in part file " << partFileNumber << " for LAPPD ID " << LAPPD_ID << " done." << endl; - - // Combine ResultACDC0 and ResultACDC1 to ResultTotal - for (int i = 0; i < ResultACDC0.size(); i++) - { - ResultTotal.push_back(ResultACDC0[i]); - } - for (int i = 0; i < ResultACDC1.size(); i++) - { - ResultTotal.push_back(ResultACDC1[i]); - } - } - return ResultTotal; -} - -void offsetFit_MultipleLAPPD(string fileName, int fitTargetTriggerWord, bool triggerGrouped, int intervalInSecond, int processPFNumber) -{ - // 1. load LAPPDTree.root - const string file = fileName; - TFile *f = new TFile(file.c_str(), "READ"); - if (!f->IsOpen()) - { - std::cerr << "Error: cannot open file " << file << std::endl; - return; - } - - std::cout << "Opened file " << file << std::endl; - - std::ofstream outputOffset("offset.txt"); - - outputOffset << "runNumber" - << "\t" - << "subRunNumber" - << "\t" - << "partFileNumber" - << "\t" - << "resetNumber" - << "\t" - << "LAPPD_ID" - << "\t" - << "offset_ACDC0_ns" - << "\t" - << "offset_ACDC1_ns" - << "\t" - << "offset_ACDC0_ps_negative" - << "\t" - << "offset_ACDC1_ps_negative" - << "\t" - << "gotOrphanCount_ACDC0" - << "\t" - << "gotOrphanCount_ACDC1" - << "\t" - << "EventNumInThisPartFile" - << "\t" - << "min_mean_dev_noOrphan_ACDC0" - << "\t" - << "min_mean_dev_noOrphan_ACDC1" - << "\t" - << "increament_times_ACDC0" - << "\t" - << "increament_times_ACDC1" - << "\t" - << "min_mean_dev_ACDC0" - << "\t" - << "min_mean_dev_ACDC1" - << std::endl; - - // 2. load TimeStamp tree, get run number and part file number - TTree *TSTree = (TTree *)f->Get("TimeStamp"); - TTree *CTCTree = (TTree *)f->Get("Trig"); - TTree *GCTCTree = (TTree *)f->Get("GTrig"); - int runNumber; - int subRunNumber; - int partFileNumber; - int LAPPD_ID; - ULong64_t ppsCount0; - TSTree->SetBranchAddress("RunNumber", &runNumber); - TSTree->SetBranchAddress("SubRunNumber", &subRunNumber); - TSTree->SetBranchAddress("PartFileNumber", &partFileNumber); - TSTree->SetBranchAddress("LAPPD_ID", &LAPPD_ID); - TSTree->SetBranchAddress("ppsCount0", &ppsCount0); - std::vector> loopInfo; - int nEntries = TSTree->GetEntries(); - for (int i = 0; i < nEntries; i++) - { - TSTree->GetEntry(i); - // if this entry is PPS event, continue. - if (ppsCount0 != 0) - continue; - vector info = {runNumber, subRunNumber, partFileNumber, LAPPD_ID}; - // find if this info is already in loopInfo - bool found = std::find_if(loopInfo.begin(), loopInfo.end(), [&info](const std::vector &vec) - { - return vec == info; // compare vec and info - }) != loopInfo.end(); // if find_if doesn't return end，found - - if (!found) - loopInfo.push_back(info); - } - - // 3. for each unique run number and part file number - // 4. in this file, for each LAPPD ID - // do fit: - std::map>> ResultMap; - std::vector>::iterator it; - int pfNumber = 0; - for (it = loopInfo.begin(); it != loopInfo.end(); it++) - { - int runNumber = (*it)[0]; - int subRunNumber = (*it)[1]; - int partFileNumber = (*it)[2]; - int LAPPD_ID = (*it)[3]; - if (processPFNumber != 0) - { - if (pfNumber >= processPFNumber) - break; - } - - vector> Result; - if (!triggerGrouped) - { // cout<<"trigger not grouped"<> Result = {FitInfo, TimeStampRaw, BeamGateRaw, TimeStamp_ns, BeamGate_ns, TimeStamp_ps, BeamGate_ps, EventIndex, EventDeviation_ns, CTCTriggerIndex, CTCTriggerTimeStamp_ns,BeamGate_correction_tick,TimeStamp_correction_tick}; - vector FitInfo = {final_offset_ns, final_offset_ps_negative, gotOrphanCount, gotMin_mean_dev_noOrphan, increament_times, min_mean_dev, final_i, final_j, totalEventNumber}; - vector TimeStampRaw; - vector BeamGateRaw; - vector TimeStamp_ns; - vector BeamGate_ns; - vector TimeStamp_ps; - vector BeamGate_ps; - vector EventIndex; - vector EventDeviation_ns; - vector CTCTriggerIndex; - vector CTCTriggerTimeStamp_ns; - vector BeamGate_correction_tick; - vector TimeStamp_correction_tick; - */ - // Loop the ResultMap, save the result to a root tree in a root file - cout << "Start saving the result to root file and txt file..." << endl; - TFile *fOut = new TFile("offsetFitResult.root", "RECREATE"); - TTree *tOut = new TTree("Events", "Events"); - int runNumber_out; - int subRunNumber_out; - int partFileNumber_out; - int LAPPD_ID_out; - ULong64_t EventIndex; - ULong64_t EventNumberInThisPartFile; - ULong64_t final_offset_ns_0; - ULong64_t final_offset_ns_1; - ULong64_t final_offset_ps_negative_0; - ULong64_t final_offset_ps_negative_1; - double drift0; - double drift1; - ULong64_t gotOrphanCount_0; - ULong64_t gotOrphanCount_1; - ULong64_t gotMin_mean_dev_noOrphan_0; - ULong64_t gotMin_mean_dev_noOrphan_1; - ULong64_t increament_times_0; - ULong64_t increament_times_1; - ULong64_t min_mean_dev_0; - ULong64_t min_mean_dev_1; - ULong64_t TimeStampRaw; - ULong64_t BeamGateRaw; - ULong64_t TimeStamp_ns; - ULong64_t BeamGate_ns; - ULong64_t TimeStamp_ps; - ULong64_t BeamGate_ps; - ULong64_t EventDeviation_ns_0; - ULong64_t EventDeviation_ns_1; - ULong64_t CTCTriggerIndex; - ULong64_t CTCTriggerTimeStamp_ns; - long long BGMinusTrigger_ns; - long long BGCorrection_tick; - long long TSCorrection_tick; - ULong64_t LAPPD_PPS_interval_ticks; - ULong64_t BG_PPSBefore_tick; - ULong64_t BG_PPSAfter_tick; - ULong64_t BG_PPSDiff_tick; - ULong64_t BG_PPSMissing_tick; - ULong64_t TS_PPSBefore_tick; - ULong64_t TS_PPSAfter_tick; - ULong64_t TS_PPSDiff_tick; - ULong64_t TS_PPSMissing_tick; - - ULong64_t TS_driftCorrection_ns; - ULong64_t BG_driftCorrection_ns; - - tOut->Branch("runNumber", &runNumber_out, "runNumber/I"); - tOut->Branch("subRunNumber", &subRunNumber_out, "subRunNumber/I"); - tOut->Branch("partFileNumber", &partFileNumber_out, "partFileNumber/I"); - tOut->Branch("LAPPD_ID", &LAPPD_ID_out, "LAPPD_ID/I"); - tOut->Branch("EventIndex", &EventIndex, "EventIndex/l"); - tOut->Branch("EventNumInThisPF", &EventNumberInThisPartFile, "EventNumInThisPF/l"); - tOut->Branch("final_offset_ns_0", &final_offset_ns_0, "final_offset_ns_0/l"); - tOut->Branch("final_offset_ns_1", &final_offset_ns_1, "final_offset_ns_1/l"); - tOut->Branch("final_offset_ps_negative_0", &final_offset_ps_negative_0, "final_offset_ps_negative_0/l"); - tOut->Branch("final_offset_ps_negative_1", &final_offset_ps_negative_1, "final_offset_ps_negative_1/l"); - tOut->Branch("drift0", &drift0, "drift0/D"); - tOut->Branch("drift1", &drift1, "drift1/D"); - tOut->Branch("gotOrphanCount_0", &gotOrphanCount_0, "gotOrphanCount_0/l"); - tOut->Branch("gotOrphanCount_1", &gotOrphanCount_1, "gotOrphanCount_1/l"); - tOut->Branch("gotMin_mean_dev_noOrphan_0", &gotMin_mean_dev_noOrphan_0, "gotMin_mean_dev_noOrphan_0/l"); - tOut->Branch("gotMin_mean_dev_noOrphan_1", &gotMin_mean_dev_noOrphan_1, "gotMin_mean_dev_noOrphan_1/l"); - tOut->Branch("increament_times_0", &increament_times_0, "increament_times_0/l"); - tOut->Branch("increament_times_1", &increament_times_1, "increament_times_1/l"); - tOut->Branch("min_mean_dev_0", &min_mean_dev_0, "min_mean_dev_0/l"); - tOut->Branch("min_mean_dev_1", &min_mean_dev_1, "min_mean_dev_1/l"); - tOut->Branch("TimeStampRaw", &TimeStampRaw, "TimeStampRaw/l"); - tOut->Branch("BeamGateRaw", &BeamGateRaw, "BeamGateRaw/l"); - tOut->Branch("TimeStamp_ns", &TimeStamp_ns, "TimeStamp_ns/l"); - tOut->Branch("BeamGate_ns", &BeamGate_ns, "BeamGate_ns/l"); - tOut->Branch("TimeStamp_ps", &TimeStamp_ps, "TimeStamp_ps/l"); - tOut->Branch("BeamGate_ps", &BeamGate_ps, "BeamGate_ps/l"); - tOut->Branch("EventDeviation_ns_0", &EventDeviation_ns_0, "EventDeviation_ns_0/l"); - tOut->Branch("EventDeviation_ns_1", &EventDeviation_ns_1, "EventDeviation_ns_1/l"); - tOut->Branch("CTCTriggerIndex", &CTCTriggerIndex, "CTCTriggerIndex/l"); - tOut->Branch("CTCTriggerTimeStamp_ns", &CTCTriggerTimeStamp_ns, "CTCTriggerTimeStamp_ns/l"); - tOut->Branch("BGMinusTrigger_ns", &BGMinusTrigger_ns, "BGMinusTrigger_ns/L"); - tOut->Branch("BGCorrection_tick", &BGCorrection_tick, "BGCorrection_tick/l"); - tOut->Branch("TSCorrection_tick", &TSCorrection_tick, "TSCorrection_tick/l"); - tOut->Branch("LAPPD_PPS_interval_ticks", &LAPPD_PPS_interval_ticks, "LAPPD_PPS_interval_ticks/l"); - tOut->Branch("BG_PPSBefore_tick", &BG_PPSBefore_tick, "BG_PPSBefore_tick/l"); - tOut->Branch("BG_PPSAfter_tick", &BG_PPSAfter_tick, "BG_PPSAfter_tick/l"); - tOut->Branch("BG_PPSDiff_tick", &BG_PPSDiff_tick, "BG_PPSDiff_tick/l"); - tOut->Branch("BG_PPSMissing_tick", &BG_PPSMissing_tick, "BG_PPSMissing_tick/l"); - tOut->Branch("TS_PPSBefore_tick", &TS_PPSBefore_tick, "TS_PPSBefore_tick/l"); - tOut->Branch("TS_PPSAfter_tick", &TS_PPSAfter_tick, "TS_PPSAfter_tick/l"); - tOut->Branch("TS_PPSDiff_tick", &TS_PPSDiff_tick, "TS_PPSDiff_tick/l"); - tOut->Branch("TS_PPSMissing_tick", &TS_PPSMissing_tick, "TS_PPSMissing_tick/l"); - tOut->Branch("TS_driftCorrection_ns", &TS_driftCorrection_ns, "TS_driftCorrection_ns/l"); - tOut->Branch("BG_driftCorrection_ns", &BG_driftCorrection_ns, "BG_driftCorrection_ns/l"); - - std::ofstream outputEvents("outputEvents.txt"); - for (auto it = ResultMap.begin(); it != ResultMap.end(); it++) - { - string key = it->first; - vector> Result = it->second; - - if (Result.size() == 0) - continue; - - runNumber_out = std::stoi(key.substr(0, key.find("_"))); - subRunNumber_out = std::stoi(key.substr(key.find("_") + 1, key.find("_", key.find("_") + 1) - key.find("_") - 1)); - partFileNumber_out = std::stoi(key.substr(key.find("_", key.find("_") + 1) + 1, key.find("_", key.find("_", key.find("_") + 1) + 1) - key.find("_", key.find("_") + 1) - 1)); - LAPPD_ID_out = std::stoi(key.substr(key.find("_", key.find("_", key.find("_") + 1) + 1) + 1, key.size() - key.find("_", key.find("_", key.find("_") + 1) + 1) - 1)); - final_offset_ns_0 = Result[0][0]; - final_offset_ns_1 = Result[24][0]; - final_offset_ps_negative_0 = Result[0][1]; - final_offset_ps_negative_1 = Result[24][1]; - gotOrphanCount_0 = Result[0][2]; - gotOrphanCount_1 = Result[24][2]; - gotMin_mean_dev_noOrphan_0 = Result[0][3]; - gotMin_mean_dev_noOrphan_1 = Result[24][3]; - increament_times_0 = Result[0][4]; - increament_times_1 = Result[24][4]; - min_mean_dev_0 = Result[0][5]; - min_mean_dev_1 = Result[24][5]; - EventNumberInThisPartFile = Result[0][8]; - drift0 = Result[0][9]; - drift1 = Result[24][9]; - - // 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 - // vector> Result = {FitInfo, TimeStampRaw, BeamGateRaw, TimeStamp_ns, BeamGate_ns, TimeStamp_ps, BeamGate_ps, EventIndex, EventDeviation_ns, CTCTriggerIndex, CTCTriggerTimeStamp_ns, BeamGate_correction_tick, TimeStamp_correction_tick, LAPPD_PPS_interval_ticks, BG_PPSBefore, BG_PPSAfter, BG_PPSDiff, BG_PPSMiss, TS_PPSBefore, TS_PPSAfter, TS_PPSDiff, TS_PPSMiss}; - // any Result[x] , if x>13, x = x + 8 - for (int j = 0; j < Result[1].size(); j++) - { - long long BGTdiff = Result[4][j] - Result[10][j] - 325250; - // cout<<"BGTDiff: "<Fill(); - } - outputOffset << runNumber_out << "\t" << subRunNumber_out << "\t" << partFileNumber_out << "\t" << 0 << "\t" << LAPPD_ID_out << "\t" << final_offset_ns_0 << "\t" << final_offset_ns_1 << "\t" << final_offset_ps_negative_0 << "\t" << final_offset_ps_negative_1 << "\t" << gotOrphanCount_0 << "\t" << gotOrphanCount_1 << "\t" << EventNumberInThisPartFile << "\t" << gotMin_mean_dev_noOrphan_0 << "\t" << gotMin_mean_dev_noOrphan_1 << "\t" << increament_times_0 << "\t" << increament_times_1 << "\t" << min_mean_dev_0 << "\t" << min_mean_dev_1 << std::endl; - } - outputOffset.close(); - fOut->cd(); - tOut->Write(); - fOut->Close(); - cout << "Result saved." << endl; -}