Dumpers/BinaryDumpers/options/allen.py

@@ -217,7 +217,7 @@ else:
         for entry in args.tags.split('|'):
             build, t = entry.split(':')
             tags[build] = t.split(',')
-            dddb_tag, conddb_tag = tags['dd4hep' if UseDD4Hep else 'detdesc']
+        dddb_tag, conddb_tag = tags['dd4hep' if UseDD4Hep else 'detdesc']
     else:
         dddb_tag, conddb_tag = args.tags.split(',')
 

Dumpers/BinaryDumpers/tests/qmtest/lhcb_ODQV_plot.qmt

@@ -46,7 +46,8 @@ expectedFiles=["fileCanvas.pdf", "IPforwardCanvas.pdf",
         "longForwardCanvas.pdf", "longMatchingCanvas.pdf",
         "occupancyCanvas.pdf", "PIDCanvas.pdf",
         "PIDkinCanvas.pdf", "PVcanvas.pdf",
-        "PVcovCanvas.pdf", "veloCanvas.pdf"]
+        "PVcovCanvas.pdf", "PVdistCanvas.pdf",
+	"IPresolutionCanvas.pdf", "veloCanvas.pdf"]
 
 for file in expectedFiles:
     if not os.path.exists(file):

Rec/Allen/tests/refs/allen_event_loop.ref

@@ -322,8 +322,8 @@ fit_v0_secondary_vertices              INFO 1D histograms in directory "fit_v0_s
  | number_of_svs                                 | "NSVs"                                        |      10000 |      0.854 | 1.8782     |     5.8281 |     76.047 |
 gather_selections                      INFO 1D histograms in directory "gather_selections" : 2
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
- | line_passes                                   | "line passes"                                 |      17371 |     79.602 | 17.669     |   -0.79901 |   -0.33106 |
- | line_rates                                    | "line rates"                                  |        834 |     67.189 | 28.489     |   -0.66155 |    -1.1836 |
+ | line_passes                                   | "line passes"                                 |      17371 |     80.809 | 18.607     |   -0.75667 |   -0.52873 |
+ | line_rates                                    | "line rates"                                  |        834 |     68.386 | 29.423     |   -0.64559 |    -1.2252 |
 is_muon                                INFO 1D histograms in directory "is_muon" : 3
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
  | n_muons                                       | "# muons"                                     |     458878 |    0.10947 | 0.31223    |     2.5015 |     4.2576 |

Rec/Allen/tests/refs/allen_event_loop.ref.detdesc

@@ -322,8 +322,8 @@ fit_v0_secondary_vertices              INFO 1D histograms in directory "fit_v0_s
  | number_of_svs                                 | "NSVs"                                        |      10000 |     0.8616 | 1.8936     |     5.7952 |     74.373 |
 gather_selections                      INFO 1D histograms in directory "gather_selections" : 2
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
- | line_passes                                   | "line passes"                                 |      17411 |     79.535 | 17.770     |   -0.81572 |   -0.26522 |
- | line_rates                                    | "line rates"                                  |        860 |     67.085 | 28.595     |   -0.65966 |    -1.1917 |
+ | line_passes                                   | "line passes"                                 |      17411 |     80.741 | 18.707     |   -0.77161 |   -0.47002 |
+ | line_rates                                    | "line rates"                                  |        860 |      68.28 | 29.529     |   -0.64379 |    -1.2325 |
 is_muon                                INFO 1D histograms in directory "is_muon" : 3
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
  | n_muons                                       | "# muons"                                     |     461023 |    0.10963 | 0.31243    |     2.4989 |     4.2447 |

Rec/Allen/tests/refs/allen_event_loop.ref.x86_64_v3-detdesc-opt

@@ -325,8 +325,8 @@ fit_v0_secondary_vertices              INFO 1D histograms in directory "fit_v0_s
  | number_of_svs                                 | "NSVs"                                        |      10000 |      0.864 | 1.8964     |     5.7826 |     74.022 |
 gather_selections                      INFO 1D histograms in directory "gather_selections" : 2
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
- | line_passes                                   | "line passes"                                 |      17434 |     79.473 | 17.856     |   -0.82602 |   -0.22905 |
- | line_rates                                    | "line rates"                                  |        866 |     66.982 | 28.602     |   -0.64588 |     -1.218 |
+ | line_passes                                   | "line passes"                                 |      17434 |     80.678 | 18.791     |   -0.78078 |   -0.43713 |
+ | line_rates                                    | "line rates"                                  |        866 |     68.173 | 29.538     |   -0.63037 |    -1.2576 |
 is_muon                                INFO 1D histograms in directory "is_muon" : 3
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
  | n_muons                                       | "# muons"                                     |     461016 |    0.10963 | 0.31243    |      2.499 |     4.2448 |

Rec/Allen/tests/refs/allen_event_loop.ref.x86_64_v3-opt

@@ -325,8 +325,8 @@ fit_v0_secondary_vertices              INFO 1D histograms in directory "fit_v0_s
  | number_of_svs                                 | "NSVs"                                        |      10000 |     0.8562 | 1.8803     |      5.817 |     75.771 |
 gather_selections                      INFO 1D histograms in directory "gather_selections" : 2
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
- | line_passes                                   | "line passes"                                 |      17401 |     79.505 | 17.804     |   -0.81668 |   -0.26439 |
- | line_rates                                    | "line rates"                                  |        844 |     66.636 | 28.666     |   -0.61853 |    -1.2546 |
+ | line_passes                                   | "line passes"                                 |      17401 |      80.71 | 18.741     |   -0.77234 |   -0.46878 |
+ | line_rates                                    | "line rates"                                  |        844 |     67.816 | 29.604     |   -0.60315 |    -1.2932 |
 is_muon                                INFO 1D histograms in directory "is_muon" : 3
  | ID                                            | Title                                         |      #     |     Mean   |    RMS     |  Skewness  |  Kurtosis  |
  | n_muons                                       | "# muons"                                     |     458865 |    0.10948 | 0.31224    |     2.5015 |     4.2573 |

configuration/python/AllenConf/HLT1.py

@@ -1283,8 +1283,10 @@ def setup_hlt1_node(enablePhysics=True,
             with_ut=with_ut,
             with_muon=with_muon,
             tracking_type=TrackingType.FORWARD)
-        node = make_dq_node(reconstructed_objects,
-                            reconstructed_objects_forward, line_algorithms)
+        node = make_dq_node(
+            reconstructed_objects,
+            reconstructed_objects_forward,
+            prefilters=beam_beam_filter)
         return node
 
     if not withMCChecking:

configuration/python/AllenConf/hlt1_reconstruction.py

@@ -409,15 +409,14 @@ def make_composite_node_with_gec(alg_name,
 
 def make_dq_node(reconstructed_matching,
                  reconstructed_forward,
-                 line_algorithms,
-                 methods=["forward", "matching", "occupancy", "pv", "velo"]):
+                 methods=["forward", "matching", "occupancy", "pv", "velo"],
+                 prefilters=[]):
     # N.B. if more 'methods' are added to the ODQV later, make sure to update Allen/Dumpers/BinaryDumpers/tests/qmtest/lhcb_ODQV.qmt line 35
 
     nodes = [
         data_quality_node(
             reconstructed_forward if method == "forward" else
-            reconstructed_matching, line_algorithms, method)
-        for method in methods
+            reconstructed_matching, method, prefilters) for method in methods
     ]
 
     return CompositeNode(
@@ -427,15 +426,14 @@ def make_dq_node(reconstructed_matching,
         force_order=False)
 
 
-def data_quality_node(reconstructed_objects=None,
-                      line_algorithms=None,
-                      method=""):
+def data_quality_node(reconstructed_objects=None, method="", prefilters=[]):
 
     validators = []
     if method in ["forward", "matching"]:
         validators = [
             CompositeNode(
-                f"data_quality_validation_{method}", [
+                f"data_quality_validation_{method}",
+                prefilters + [
                     data_quality_validation_long(
                         reconstructed_objects["long_tracks"],
                         reconstructed_objects["long_track_particles"],
@@ -447,7 +445,8 @@ def data_quality_node(reconstructed_objects=None,
     elif method == "occupancy":
         validators = [
             CompositeNode(
-                f"data_quality_validation_{method}", [
+                f"data_quality_validation_{method}",
+                prefilters + [
                     data_quality_validation_occupancy(
                         f"data_quality_validation_{method}")
                 ],
@@ -457,7 +456,8 @@ def data_quality_node(reconstructed_objects=None,
     elif method == "pv":
         validators = [
             CompositeNode(
-                f"data_quality_validation_{method}", [
+                f"data_quality_validation_{method}",
+                prefilters + [
                     data_quality_validation_pv(
                         reconstructed_objects["long_tracks"],
                         f"data_quality_validation_{method}")
@@ -468,7 +468,8 @@ def data_quality_node(reconstructed_objects=None,
     elif method == "velo":
         validators = [
             CompositeNode(
-                f"data_quality_validation_{method}", [
+                f"data_quality_validation_{method}",
+                prefilters + [
                     data_quality_validation_velo(
                         reconstructed_objects["long_tracks"],
                         f"data_quality_validation_{method}")

configuration/python/AllenConf/validators.py

@@ -457,6 +457,7 @@ def data_quality_validation_occupancy(name="data_quality_validator"):
     number_of_events = initialize_number_of_events()
 
     decoded_scifi = decode_scifi()
+    scifi_xz_seeds = make_seeding_XZ_tracks(decoded_scifi)
     decoded_muon = decode_muon()
     decoded_calo = decode_calo()
     ecal_clusters = make_ecal_clusters(
@@ -477,6 +478,7 @@ def data_quality_validation_occupancy(name="data_quality_validator"):
             "dev_offsets_estimated_input_size"],
         dev_offsets_velo_tracks_t=velo_tracks["dev_offsets_all_velo_tracks"],
         dev_scifi_hit_offsets_t=decoded_scifi["dev_scifi_hit_offsets"],
+        dev_scifi_seedsXZ_t=scifi_xz_seeds['seed_xz_number_of_tracks'],
         dev_ecal_clusters_offsets_t=ecal_clusters["dev_ecal_cluster_offsets"])
 
 

configuration/python/AllenSequences/hlt1_pp_odqv.py

@@ -23,5 +23,9 @@ hlt1_node = setup_hlt1_node(
     with_ut=False,
     # tracking_type is set to matching, but will output both matching and forward
     tracking_type=TrackingType.MATCHING,
+    with_lumi=False,
+    enableBGI=False,
+    withSMOG2=False,
+    enablePhysics=True,
     with_calo=True)
 generate(hlt1_node)

host/validators/include/DataQualityValidator.h

@@ -104,6 +104,7 @@ namespace data_quality_validator_occupancy {
     DEVICE_INPUT(dev_offsets_velo_tracks_t, unsigned) dev_offsets_velo_tracks;
     DEVICE_INPUT(dev_scifi_hit_offsets_t, unsigned) dev_scifi_hit_offsets;
     DEVICE_INPUT(dev_ecal_clusters_offsets_t, unsigned) dev_ecal_clusters_offsets;
+    DEVICE_INPUT(dev_scifi_seedsXZ_t, unsigned) dev_scifi_seedsXZ;
   };
 
   struct data_quality_validator_occupancy_t : public HostAlgorithm, Parameters {

host/validators/src/DataQualityValidator.cpp

@@ -100,6 +100,7 @@ void data_quality_validator_pv::data_quality_validator_pv_t::output_monitor(
   // --> PVs
   auto tree = handler.tree("PVs");
   auto eventTree = handler.tree("PV_event");
+  auto PVpairsTree = handler.tree("PV_pairs");
   const auto PVs = make_host_buffer<dev_multi_fit_vertices_t>(arguments, context);
   const auto n_pvs = make_host_buffer<dev_number_of_multi_fit_vertices_t>(arguments, context);
   const auto event_list = make_host_buffer<dev_event_list_t>(arguments, context);
@@ -120,7 +121,15 @@ void data_quality_validator_pv::data_quality_validator_pv_t::output_monitor(
   handler.branch(tree, "cov22", pv_cov[2][2]);
 
   int nPVs;
+  float PVdistance_min, PVdistance_max, PVdistance_mean;
   handler.branch(eventTree, "n_pvs", nPVs);
+  handler.branch(eventTree, "PVdistance_min", PVdistance_min);
+  handler.branch(eventTree, "PVdistance_max", PVdistance_max);
+  handler.branch(eventTree, "PVdistance_mean", PVdistance_mean);
+
+  // Fill with the Delta Z distance of each pair of PVs
+  float PVdelta_z;
+  handler.branch(PVpairsTree, "PVdelta_z", PVdelta_z);
 
   for (unsigned i = 0; i < event_list.size(); ++i) {
     const auto evnum = event_list[i];
@@ -131,6 +140,19 @@ void data_quality_validator_pv::data_quality_validator_pv_t::output_monitor(
     // something like dev_pv_offsets
     const unsigned pv_offset = evnum * PV::max_number_vertices;
 
+    // If there's 1 or fewer PVs, there's no distance between them!
+    // - set a dummy number for this
+    if (nPVs <= 1) {
+      PVdistance_max = -99.f;
+      PVdistance_min = -99.f;
+      PVdistance_mean = -99.f;
+    }
+    else { // Otherwise reset the values ready for the upcoming loop
+      PVdistance_mean = 0.f;
+      PVdistance_max = 0.f;
+      PVdistance_min = std::numeric_limits<float>::infinity();
+    }
+
     for (int i_vertex = 0; i_vertex < nPVs; i_vertex++) {
       const auto pv = PVs[i_vertex + pv_offset];
       pv_x = pv.position.x;
@@ -146,6 +168,33 @@ void data_quality_validator_pv::data_quality_validator_pv_t::output_monitor(
       pv_cov[2][1] = pv.cov21;
       pv_cov[2][2] = pv.cov22;
       tree->Fill();
+
+      // Now compare this vertex to all previous ones and calculate their distances
+      if (nPVs > 1) {
+        for (int j = 0; j < i_vertex; ++j) {
+          const auto otherVertex = PVs[j + pv_offset];
+          const float delta_x = otherVertex.position.x - pv_x;
+          const float delta_y = otherVertex.position.y - pv_y;
+          const float delta_z = otherVertex.position.z - pv_z;
+          const float delta_pos = std::sqrt(delta_x * delta_x + delta_y * delta_y + delta_z * delta_z);
+
+          if (delta_pos > PVdistance_max) {
+            PVdistance_max = delta_pos;
+          }
+          if (delta_pos < PVdistance_min) {
+            PVdistance_min = delta_pos;
+          }
+          PVdistance_mean += delta_pos;
+
+          // Fill with each pair of points
+          PVdelta_z = delta_z;
+          PVpairsTree->Fill();
+        }
+      }
+    }
+    // finally, divide by nPVs to for the mean
+    if (nPVs > 1) {
+      PVdistance_mean /= nPVs;
     }
     eventTree->Fill();
   }
@@ -173,14 +222,16 @@ void data_quality_validator_occupancy::data_quality_validator_occupancy_t::outpu
   // --> Occupancy
   auto eventTree = handler.tree("occupancy");
   const auto scifi_tracks_offsets = make_host_buffer<dev_scifi_hit_offsets_t>(arguments, context);
+  const auto scifi_seeds = make_host_buffer<dev_scifi_seedsXZ_t>(arguments, context);
   const auto velo_offsets_eis = make_host_buffer<dev_velo_offsets_estimated_input_size_t>(arguments, context);
   const auto event_velo_tracks_offsets = make_host_buffer<dev_offsets_velo_tracks_t>(arguments, context);
   const auto ecal_clusters = make_host_buffer<dev_ecal_clusters_offsets_t>(arguments, context);
   const auto muon_offsets = make_host_buffer<dev_station_ocurrences_offset_t>(arguments, context);
   const auto event_list = make_host_buffer<dev_event_list_t>(arguments, context);
 
-  int n_scifi_hits, n_velo_hits, n_velo_tracks, n_ecal_clusters, n_muon_hits;
+  int n_scifi_hits, n_scifi_xz_seeds, n_velo_hits, n_velo_tracks, n_ecal_clusters, n_muon_hits;
   handler.branch(eventTree, "n_scifi_hits", n_scifi_hits);
+  handler.branch(eventTree, "n_scifi_xz_seeds", n_scifi_xz_seeds);
   handler.branch(eventTree, "n_velo_hits", n_velo_hits);
   handler.branch(eventTree, "n_velo_tracks", n_velo_tracks);
   handler.branch(eventTree, "n_ecal_clusters", n_ecal_clusters);
@@ -194,6 +245,7 @@ void data_quality_validator_occupancy::data_quality_validator_occupancy_t::outpu
     const auto evnum = event_list[i];
     SciFi::ConstHitCount scifi_hit_count {scifi_tracks_offsets.data(), evnum};
     n_scifi_hits = scifi_hit_count.event_number_of_hits();
+    n_scifi_xz_seeds = scifi_seeds.data()[evnum];
 
     const unsigned* module_pair_hit_start = velo_offsets_eis.data() + evnum * Velo::Constants::n_module_pairs;
     const unsigned event_hit_start = module_pair_hit_start[0];

scripts/DataQualityPlot_Overlay.cc

@@ -46,6 +46,16 @@ TH1* draw(
   const Int_t colourIndex,
   std::map<TVirtualPad*, TH1*>& originalHists);
 
+void makeIPplot(
+  TTree* tree,
+  TString var,
+  TVirtualPad* pad,
+  const TString& fileName,
+  const bool first,
+  const Int_t colourIndex,
+  std::map<TVirtualPad*, TH1*>& originalHists,
+  const bool forward);
+
 std::pair<TColor*, Int_t> GetColorAndLineStyle(Int_t index);
 
 template<typename... FILENAMES>
@@ -81,6 +91,7 @@ void DataQualityPlot_Overlay(FILENAMES... files)
   std::map<TString, TCanvas*> canvases = {
     {"PVcanvas", new TCanvas("PVcanvas", "PVcanvas", 900, 600)},
     {"PVcovCanvas", new TCanvas("PVcovCanvas", "PVcovCanvas", 900, 900)},
+    {"PVdistCanvas", new TCanvas("PVdistCanvas", "PVdistCanvas", 600, 600)},
     {"longMatchingCanvas", new TCanvas("longMatchingCanvas", "longMatchingCanvas", 900, 900)},
     {"longForwardCanvas", new TCanvas("longForwardCanvas", "longForwardCanvas", 900, 900)},
     {"kalmanCovCanvas", new TCanvas("kalmanCovCanvas", "kalmanCovCanvas", 1200, 1200)},
@@ -90,6 +101,7 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     {"PIDkinCanvas", new TCanvas("PIDkinCanvas", "PIDkinCanvas", 1200, 600)},
     {"IPmatchingCanvas", new TCanvas("IPmatchingCanvas", "IPmatchingCanvas", 600, 600)},
     {"IPforwardCanvas", new TCanvas("IPforwardCanvas", "IPforwardCanvas", 600, 600)},
+    {"IPresolutionCanvas", new TCanvas("IPresolutionCanvas", "IPresolutionCanvas", 600, 600)},
     {"fileCanvas", new TCanvas("fileCanvas", "fileCanvas", 600, 600)}};
   for (auto& [name, canvas] : canvases) {
     canvas->Divide(canvas->GetWindowWidth() / 300, canvas->GetWindowHeight() / 300);
@@ -113,11 +125,11 @@ void DataQualityPlot_Overlay(FILENAMES... files)
 
     canvas = canvases["PVcanvas"];
 
-    std::vector<Var> PVcanvasVars = {Var("n_pvs", "data_quality_validation_pv/PV_event", 0, 8),
-                                     Var("pv_nTracks", "data_quality_validation_pv/PVs", 0, 100),
-                                     Var("pv_y:pv_x", "data_quality_validation_pv/PVs", 0.8, 1.4, 0.35, 0.65),
-                                     Var("pv_x", "data_quality_validation_pv/PVs", 0.8, 1.4),
-                                     Var("pv_y", "data_quality_validation_pv/PVs", 0.35, 0.65),
+    std::vector<Var> PVcanvasVars = {Var("n_pvs", "data_quality_validation_pv/PV_event", 0, 12),
+                                     Var("pv_nTracks", "data_quality_validation_pv/PVs", 0, 20),
+                                     Var("pv_y:pv_x", "data_quality_validation_pv/PVs", 0.8, 1.5, -0.1, 0.5),
+                                     Var("pv_x", "data_quality_validation_pv/PVs", 0.8, 1.5),
+                                     Var("pv_y", "data_quality_validation_pv/PVs", -0.1, 0.5),
                                      Var("pv_z", "data_quality_validation_pv/PVs", -100, 100)};
 
     for (size_t i = 0; i < PVcanvasVars.size(); ++i) {
@@ -131,8 +143,19 @@ void DataQualityPlot_Overlay(FILENAMES... files)
         if (first) {
           legends["mu"] = new TLegend(0.6, 0.7, 0.9, 0.9);
         }
-        Float_t mean = hist->GetMean();
-        legends["mu"]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
+        hist->ResetStats();
+        // make a poisson shape, following
+        // https://root-forum.cern.ch/t/fitting-a-poisson-distribution-to-a-histogram/12078/2
+        TF1* f1 = new TF1(
+          "f1",
+          "[0]*TMath::Power(([1]/[2]),(x/[2]))*(TMath::Exp(-([1]/[2])))/TMath::Gamma((x/[2])+1.)",
+          var.xmin,
+          var.xmax);
+        f1->SetParameters(1, 1, 1); // you MUST set non-zero initial values for parameters
+        hist->Fit("f1", "RQ");      // "R" = fit between "xmin" and "xmax" of the "f1"
+        Float_t mean = f1->GetParameter(1);
+        legends["mu"]->AddEntry(hist, Form("#mu_{poisson} = %.2f", mean), "l");
+        delete f1;
 
         if (fileName == fileList.back()) {
           legends["mu"]->Draw();
@@ -161,11 +184,43 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       draw(tree, var, pad, fileName, first, colourIndex, originalHists);
     }
 
+    std::cout << " complete!" << std::endl;
+    std::cout << "Generating PV distance canvas ...";
+
+    canvas = canvases["PVdistCanvas"];
+
+    std::vector<Var> PVdistCanvasVars = {Var("PVdistance_min", "data_quality_validation_pv/PV_event", 0, 200),
+                                         Var("PVdistance_max", "data_quality_validation_pv/PV_event", 0, 300),
+                                         Var("PVdistance_mean", "data_quality_validation_pv/PV_event", 0, 200),
+                                         Var("PVdelta_z", "data_quality_validation_pv/PV_pairs", -6, 6)};
+
+    for (size_t i = 0; i < PVdistCanvasVars.size(); ++i) {
+      TVirtualPad* pad = canvas->cd(i + 1);
+      Var var = PVdistCanvasVars[i];
+      TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
+
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+
+      if (i < 3) {
+        if (first) {
+          legends[var.varName] = new TLegend(0.5, 0.6, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t mean = hist->GetMean();
+        Float_t lessThan2PVs = 1.f * tree->GetEntries(var.varName + "<0") / tree->GetEntries();
+        legends[var.varName]->AddEntry(hist, Form("#mu = %.2f; <2 PV rate =  %.2f%%", mean, lessThan2PVs * 100), "l");
+
+        if (fileName == fileList.back()) {
+          legends[var.varName]->Draw();
+        }
+      }
+    }
+
     std::cout << " complete!" << std::endl;
     std::cout << "Generating long tracks canvas (matching) ...";
 
     std::vector<Var> longVars = {
-      Var("n_long_tracks", "data_quality_validation_matching/long_tracks_event", 0, 30),
+      Var("n_long_tracks", "data_quality_validation_matching/long_tracks_event", 1, 140),
       Var("qop", "data_quality_validation_matching/long_track_particles", -4e-4, 4e-4),
       Var("pt", "data_quality_validation_matching/long_track_particles", 0, 5000),
       Var("tx", "data_quality_validation_matching/long_track_particles", -0.12, 0.12),
@@ -185,7 +240,19 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       }
       TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
 
-      draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      if (i == 0) {
+        if (first) {
+          legends["nLongTracks_Matching"] = new TLegend(0.6, 0.7, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t mean = hist->GetMean();
+        legends["nLongTracks_Matching"]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
+
+        if (fileName == fileList.back()) {
+          legends["nLongTracks_Matching"]->Draw();
+        }
+      }
     }
 
     std::cout << " complete!" << std::endl;
@@ -203,7 +270,19 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       var.forward = true;
       TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
 
-      draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      if (i == 0) {
+        if (first) {
+          legends["nLongTracks_Forward"] = new TLegend(0.6, 0.7, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t mean = hist->GetMean();
+        legends["nLongTracks_Forward"]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
+
+        if (fileName == fileList.back()) {
+          legends["nLongTracks_Forward"]->Draw();
+        }
+      }
     }
 
     std::cout << " complete!" << std::endl;
@@ -231,7 +310,7 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     std::cout << "Generating velo canvas ...";
 
     std::vector<Var> veloVars = {Var("n_velo_hits", "data_quality_validation_occupancy/occupancy", 0, 3000),
-                                 Var("n_velo_tracks", "data_quality_validation_occupancy/occupancy", -0.5, 249.5),
+                                 Var("n_velo_tracks", "data_quality_validation_occupancy/occupancy", -0.5, 1000),
                                  Var("n_hits_per_track", "data_quality_validation_velo/velo_states", 0, 16),
                                  Var("tx", "data_quality_validation_velo/velo_states", -0.3, 0.3),
                                  Var("ty", "data_quality_validation_velo/velo_states", -0.3, 0.3),
@@ -252,15 +331,16 @@ void DataQualityPlot_Overlay(FILENAMES... files)
 
       TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
 
-      if (i == 2) {
+      if (i == 1 or i == 2) {
         if (first) {
-          legends["nHitsPerTrack"] = new TLegend(0.6, 0.7, 0.9, 0.9);
+          legends[var.varName] = new TLegend(0.6, 0.7, 0.9, 0.9);
         }
+        hist->ResetStats();
         Float_t mean = hist->GetMean();
-        legends["nHitsPerTrack"]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
+        legends[var.varName]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
 
         if (fileName == fileList.back()) {
-          legends["nHitsPerTrack"]->Draw();
+          legends[var.varName]->Draw();
         }
       }
     }
@@ -269,11 +349,12 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     std::cout << "Generating occupancy canvas ...";
 
     std::vector<Var> occupancyVars = {
-      Var("n_velo_hits", "data_quality_validation_occupancy/occupancy", 0, 3000),
-      Var("n_scifi_hits", "data_quality_validation_occupancy/occupancy", 0, 6000),
-      Var("n_ecal_clusters", "data_quality_validation_occupancy/occupancy", 0, 60),
-      Var("n_muon_hits", "data_quality_validation_occupancy/occupancy", 0, 400),
-      Var("n_scifi_hits:n_velo_hits", "data_quality_validation_occupancy/occupancy", 0, 3000, 0, 6000),
+      Var("n_velo_hits", "data_quality_validation_occupancy/occupancy", 1, 30000),
+      Var("n_scifi_hits", "data_quality_validation_occupancy/occupancy", 1, 60000),
+      Var("n_scifi_xz_seeds", "data_quality_validation_occupancy/occupancy", 1, 800),
+      Var("n_ecal_clusters", "data_quality_validation_occupancy/occupancy", 1, 600),
+      Var("n_muon_hits", "data_quality_validation_occupancy/occupancy", 1, 4000),
+      Var("n_scifi_hits:n_velo_hits", "data_quality_validation_occupancy/occupancy", 1, 3000, 1, 6000),
     };
 
     canvas = canvases["occupancyCanvas"];
@@ -283,8 +364,23 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       Var var = occupancyVars[i];
       TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
 
-      var.logy = true;
-      draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      if (i != 5) {
+        var.logy = true;
+      }
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+
+      if (i < 4) {
+        if (first) {
+          legends[var.varName] = new TLegend(0.6, 0.7, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t mean = hist->GetMean();
+        legends[var.varName]->AddEntry(hist, Form("#mu = %.2f", mean), "l");
+
+        if (fileName == fileList.back()) {
+          legends[var.varName]->Draw();
+        }
+      }
     }
 
     std::cout << " complete!" << std::endl;
@@ -339,7 +435,6 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     for (size_t i = 0; i < PIDkinVars.size(); ++i) {
       TVirtualPad* pad = canvas->cd(i + 1);
       Var var = PIDkinVars[i];
-      TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
       TLegend* leg = nullptr;
       leg = new TLegend();
       if (i < 4) {
@@ -357,6 +452,7 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       else {
         var.cut = "is_electron==1";
       }
+      TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
       draw(tree, var, pad, fileName, first, colourIndex, originalHists);
       leg->SetTextColor(2);
       if (fileName == fileList.front()) {
@@ -367,8 +463,8 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     std::cout << " complete!" << std::endl;
     std::cout << "Generating IP canvas (matching)...";
 
-    std::vector<Var> IPVars = {Var("ip_x", "data_quality_validation_matching/long_track_particles", -4, 4),
-                               Var("ip_y", "data_quality_validation_matching/long_track_particles", -4, 4),
+    std::vector<Var> IPVars = {Var("ip_x", "data_quality_validation_matching/long_track_particles", -0.5, 0.5),
+                               Var("ip_y", "data_quality_validation_matching/long_track_particles", -0.5, 0.5),
                                Var("ip_chi2", "data_quality_validation_matching/long_track_particles", -1, 1000),
                                Var("chi2", "data_quality_validation_matching/long_track_particles", 0, 100)};
     IPVars[2].logy = true;
@@ -380,7 +476,19 @@ void DataQualityPlot_Overlay(FILENAMES... files)
       Var var = IPVars[i];
       TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
 
-      draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      if (i < 2) {
+        if (first) {
+          legends[var.varName + "_matching"] = new TLegend(0.6, 0.7, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t width = hist->GetRMS();
+        legends[var.varName + "_matching"]->AddEntry(hist, Form("#sigma = %.4f", width), "l");
+
+        if (fileName == fileList.back()) {
+          legends[var.varName + "_matching"]->Draw();
+        }
+      }
     }
 
     std::cout << " complete!" << std::endl;
@@ -391,10 +499,56 @@ void DataQualityPlot_Overlay(FILENAMES... files)
     for (size_t i = 0; i < IPVars.size(); ++i) {
       TVirtualPad* pad = canvas->cd(i + 1);
       Var var = IPVars[i];
+      var.treeName.ReplaceAll("matching", "forward");
       TTree* tree = dynamic_cast<TTree*>(file->Get(var.treeName));
       var.forward = true;
 
-      draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      TH1* hist = draw(tree, var, pad, fileName, first, colourIndex, originalHists);
+      if (i < 2) {
+        if (first) {
+          legends[var.varName + "_forward"] = new TLegend(0.6, 0.7, 0.9, 0.9);
+        }
+        hist->ResetStats();
+        Float_t width = hist->GetRMS();
+        legends[var.varName + "_forward"]->AddEntry(hist, Form("#sigma = %.4f", width), "l");
+
+        if (fileName == fileList.back()) {
+          legends[var.varName + "_forward"]->Draw();
+        }
+      }
+    }
+
+    std::cout << " complete!" << std::endl;
+    std::cout << "Generating IP resolution canvas ...";
+
+    std::vector<TString> IPresoVars = {"ip_x", "ip_x", "ip_y", "ip_y"};
+    std::vector<TString> Trees = {"data_quality_validation_matching/long_track_particles",
+                                  "data_quality_validation_forward/long_track_particles",
+                                  "data_quality_validation_matching/long_track_particles",
+                                  "data_quality_validation_forward/long_track_particles"};
+
+    canvas = canvases["IPresolutionCanvas"];
+
+    for (size_t i = 0; i < IPresoVars.size(); ++i) {
+      TVirtualPad* pad = canvas->cd(i + 1);
+      TString var = IPresoVars[i];
+      TTree* tree = dynamic_cast<TTree*>(file->Get(Trees[i]));
+
+      TLegend* leg = new TLegend();
+      const bool forward = Trees[i].Contains("forward");
+      if (forward) {
+        leg->SetHeader("Forward", "C");
+      }
+      else {
+        leg->SetHeader("Matching", "C");
+      }
+
+      makeIPplot(tree, var, pad, fileName, first, colourIndex, originalHists, forward);
+
+      leg->SetTextColor(2);
+      if (fileName == fileList.back()) {
+        leg->Draw();
+      }
     }
 
     std::cout << " complete!" << std::endl;
@@ -492,7 +646,7 @@ TH1* draw(
     Int_t nBins = 100;
     if (var.varName.BeginsWith("n_") or var.varName == "nPVs") {
       nBins = var.xmax - var.xmin;
-      if (nBins > 100) {
+      if (nBins > 200) {
         nBins = 100;
       }
     }
@@ -542,6 +696,67 @@ TH1* draw(
   return hist;
 }
 /*-------------------------------------------------------------------------*/
+void makeIPplot(
+  TTree* tree,
+  TString var,
+  TVirtualPad* pad,
+  const TString& fileName,
+  const bool first,
+  const Int_t colourIndex,
+  std::map<TVirtualPad*, TH1*>& originalHists,
+  const bool forward)
+{
+  // These bin edges match the TDR Fig.30
+  std::vector<Float_t> pTbinEdges = {0.0f, 0.4f, 0.6f, 0.8f, 1.0f, 1.2f, 1.4f, 1.6f, 1.8f, 2.0f};
+  if (forward) {
+    pTbinEdges = {0.0f, 0.4f, 0.6f, 0.8f, 1.0f, 1.25f};
+  }
+  TString name =
+    Form("%s_canvas_%s_%s_%s", var.Data(), tree->GetName(), fileName.Data(), forward ? "forward" : "matching");
+  name.ReplaceAll("/", "_");
+  name.ReplaceAll(".root", "");
+  TH1F* IPplot = new TH1F(
+    name.Data(),
+    Form(";1/p_{T} [c/GeV];IP_{%s} resolution [#mum]", var == "ip_x" ? "x" : "y"),
+    pTbinEdges.size() - 1,
+    pTbinEdges.data());
+  for (size_t i_ptBin = 0; i_ptBin < pTbinEdges.size() - 1; ++i_ptBin) {
+    // find the IPs resolutions and fill the IPplot hist
+    TH1F IPcalculationHist("IPcalculationHist", "", 60, -0.1, 0.1);
+
+    TCut cut = Form("1000/pt > %f && 1000/pt <= %f", pTbinEdges[i_ptBin], pTbinEdges[i_ptBin + 1]);
+    // cut.Print();
+    tree->Draw(Form("%s >> IPcalculationHist", var.Data()), cut, "GOFF");
+    if (IPcalculationHist.Integral(1, 60) > 0) {
+      TFitResultPtr fit = IPcalculationHist.Fit("gaus", "SQ", "", -0.1, 0.1);
+      IPplot->SetBinContent(i_ptBin + 1, fit->Value(2) * 1000);
+      IPplot->SetBinError(i_ptBin + 1, fit->Error(2) * 1000);
+    }
+    else {
+      IPplot->SetBinContent(i_ptBin + 1, 0.f);
+      IPplot->SetBinError(i_ptBin + 1, 0.f);
+    }
+  }
+  if (not first) {
+    const Double_t yMax = IPplot->GetBinContent(IPplot->GetMaximumBin());
+    const Double_t factor = 1.1;
+    if (yMax * factor > originalHists[pad]->GetMaximum()) {
+      originalHists[pad]->SetMaximum(yMax * factor);
+    }
+  }
+  auto [colour, style] = GetColorAndLineStyle(colourIndex);
+  IPplot->SetLineColor(colour->GetNumber());
+  IPplot->SetLineStyle(style);
+
+  if (first) {
+    originalHists[pad] = IPplot;
+  }
+  TString drawOption = Form("%s", first ? "E1" : "SAME");
+  IPplot->Draw(drawOption.Data());
+  IPplot->SetMarkerStyle(0);
+  return;
+}
+/*-------------------------------------------------------------------------*/
 std::pair<TColor*, Int_t> GetColorAndLineStyle(Int_t index)
 {
   static map<std::array<Int_t, 3>, TColor*> cache;
@@ -579,7 +794,7 @@ std::pair<TColor*, Int_t> GetColorAndLineStyle(Int_t index)
 void DataQualityPlot_Overlay()
 {
   TString isQMtest = std::getenv("QMTTEST_NAME");
-  if (isQMtest == "lhcb_ODQV_plot") {
+  if (isQMtest.Contains("lhcb_ODQV_plot")) {
     DataQualityPlot_Overlay("allen_odqv_qmtest.root");
   }
   else {