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Jet Flavour data pre-processing and inference #224
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@@ -228,7 +565,8 @@ getRP2TRK_phi0_tanlambda_cov(ROOT::VecOps::RVec<edm4hep::ReconstructedParticleDa | |||
for (auto & p: in) { | |||
if (p.tracks_begin<tracks.size()) | |||
result.push_back(tracks.at(p.tracks_begin).covMatrix[11]); | |||
else result.push_back(std::nan("")); | |||
//else result.push_back(std::nan("")); |
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//else result.push_back(std::nan("")); |
@@ -216,7 +552,8 @@ getRP2TRK_phi0_z0_cov(ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData> in, | |||
for (auto & p: in) { | |||
if (p.tracks_begin<tracks.size()) | |||
result.push_back(tracks.at(p.tracks_begin).covMatrix[7]); | |||
else result.push_back(std::nan("")); | |||
//else result.push_back(std::nan("")); |
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//else result.push_back(std::nan("")); |
@@ -204,7 +539,8 @@ getRP2TRK_phi0_omega_cov(ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData> | |||
for (auto & p: in) { | |||
if (p.tracks_begin<tracks.size()) | |||
result.push_back(tracks.at(p.tracks_begin).covMatrix[4]); | |||
else result.push_back(std::nan("")); | |||
//else result.push_back(std::nan("")); |
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//else result.push_back(std::nan("")); |
@@ -192,7 +526,8 @@ getRP2TRK_d0_tanlambda_cov(ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData | |||
for (auto & p: in) { | |||
if (p.tracks_begin<tracks.size()) | |||
result.push_back(tracks.at(p.tracks_begin).covMatrix[10]); | |||
else result.push_back(std::nan("")); | |||
//else result.push_back(std::nan("")); |
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//else result.push_back(std::nan("")); |
hello @selvaggi
|
This PR provides an example for to produce cluster jets, compute jet constituent observables needed for flavour tagging and build a jet based tree in two steps (
stage1.py
andstage2.cpp
). The jet-based tree is later used for training the model with @hqucms' Weaver. The model is exported intoONNX
and used for inference as showed inanalysis_inference.py
This PR bulld upon and superseeds #188.
Credits: @hqucms, @forthommel , @ADV99