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Merged
ddobrigk merged 1 commit into from
Jul 29, 2025
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Please consider the following formatting changes to #12277 #457

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6 changes: 3 additions & 3 deletions Common/TableProducer/PID/pidTPC.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -298,9 +298,9 @@
std::vector<float> createNetworkPrediction(C const& collisions, T const& tracks, B const& bcs, const size_t size)
{

std::vector<float> network_prediction;

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[name/function-variable] 

Use lowerCamelCase for names of functions and variables.

auto start_network_total = std::chrono::high_resolution_clock::now();

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[name/function-variable] 

Use lowerCamelCase for names of functions and variables.
if (autofetchNetworks) {
const auto& bc = bcs.begin();
// Initialise correct TPC response object before NN setup (for NCl normalisation)
Expand Down Expand Up @@ -343,10 +343,10 @@
}

// Defining some network parameters
int input_dimensions = network.getNumInputNodes();

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[name/function-variable] 

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int output_dimensions = network.getNumOutputNodes();

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[name/function-variable] 

Use lowerCamelCase for names of functions and variables.
const uint64_t track_prop_size = input_dimensions * size;

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[name/function-variable] 

Use lowerCamelCase for names of functions and variables.
const uint64_t prediction_size = output_dimensions * size;

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[name/function-variable] 

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network_prediction = std::vector<float>(prediction_size * 9); // For each mass hypotheses
const float nNclNormalization = response->GetNClNormalization();
Expand All @@ -358,7 +358,7 @@

// Filling a std::vector<float> to be evaluated by the network
// Evaluation on single tracks brings huge overhead: Thus evaluation is done on one large vector
for (int i = 0; i < 9; i++) { // Loop over particle number for which network correction is used

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[magic-number] 

Avoid magic numbers in expressions. Assign the value to a clearly named variable or constant.
for (auto const& trk : tracks) {
if (!trk.hasTPC()) {
continue;
Expand All @@ -374,7 +374,7 @@
track_properties[counter_track_props + 3] = o2::track::pid_constants::sMasses[i];
track_properties[counter_track_props + 4] = trk.has_collision() ? collisions.iteratorAt(trk.collisionId()).multTPC() / 11000. : 1.;
track_properties[counter_track_props + 5] = std::sqrt(nNclNormalization / trk.tpcNClsFound());
if (input_dimensions == 7 && networkVersion == "2") {

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track_properties[counter_track_props + 6] = trk.has_collision() ? collisions.iteratorAt(trk.collisionId()).ft0cOccupancyInTimeRange() / 60000. : 1.;
}
counter_track_props += input_dimensions;
Expand Down Expand Up @@ -435,10 +435,10 @@
}

float nSigma = -999.f;
int multTPC = 0;
if(trk.has_collision()){
int multTPC = 0;
if (trk.has_collision()) {
auto collision = collisions.rawIteratorAt(trk.collisionId());
multTPC = collision.multTPC();
multTPC = collision.multTPC();
}
float bg = trk.tpcInnerParam() / o2::track::pid_constants::sMasses[pid]; // estimated beta-gamma for network cutoff
if (useNetworkCorrection && speciesNetworkFlags[pid] && trk.has_collision() && bg > networkBetaGammaCutoff) {
Expand All @@ -447,10 +447,10 @@
// For now only the option 2: sigma will be used. The other options are kept if there would be demand later on
if (network.getNumOutputNodes() == 1) { // Expected mean correction; no sigma correction
nSigma = (tpcSignal - network_prediction[count_tracks + tracksForNet_size * pid] * expSignal) / expSigma;
} else if (network.getNumOutputNodes() == 2) { // Symmetric sigma correction

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expSigma = (network_prediction[2 * (count_tracks + tracksForNet_size * pid) + 1] - network_prediction[2 * (count_tracks + tracksForNet_size * pid)]) * expSignal;
nSigma = (tpcSignal / expSignal - network_prediction[2 * (count_tracks + tracksForNet_size * pid)]) / (network_prediction[2 * (count_tracks + tracksForNet_size * pid) + 1] - network_prediction[2 * (count_tracks + tracksForNet_size * pid)]);
} else if (network.getNumOutputNodes() == 3) { // Asymmetric sigma corection

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[magic-number] 

Avoid magic numbers in expressions. Assign the value to a clearly named variable or constant.
if (tpcSignal / expSignal >= network_prediction[3 * (count_tracks + tracksForNet_size * pid)]) {
expSigma = (network_prediction[3 * (count_tracks + tracksForNet_size * pid) + 1] - network_prediction[3 * (count_tracks + tracksForNet_size * pid)]) * expSignal;
nSigma = (tpcSignal / expSignal - network_prediction[3 * (count_tracks + tracksForNet_size * pid)]) / (network_prediction[3 * (count_tracks + tracksForNet_size * pid) + 1] - network_prediction[3 * (count_tracks + tracksForNet_size * pid)]);
Expand Down
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