diff --git a/data_results/PrCheckerB_dp.root b/data_results/PrCheckerB_dp.root
new file mode 100644
index 0000000..d8e63cb
Binary files /dev/null and b/data_results/PrCheckerB_dp.root differ
diff --git a/electron_main.py b/electron_main.py
index 5450576..57936c9 100644
--- a/electron_main.py
+++ b/electron_main.py
@@ -185,8 +185,8 @@ if args.matching_weights and not args.residuals:
         only_electrons=True,
         filter_seeds=True,
         outdir="nn_electron_training",
-        n_train_signal=100e3,
-        n_train_bkg=100e3,
+        n_train_signal=150e3,
+        n_train_bkg=150e3,
         n_test_signal=10e3,
         n_test_bkg=10e3,
     )
diff --git a/moore_options/get_ghost_data.py b/moore_options/get_ghost_data.py
index 6e98d50..9dbc501 100644
--- a/moore_options/get_ghost_data.py
+++ b/moore_options/get_ghost_data.py
@@ -53,9 +53,6 @@ elif decay == "both":
 options.dddb_tag = "dddb-20210617"
 options.conddb_tag = "sim-20210617-vc-md100"
 
-
-# options.geometry_version = "run3/trunk" #"run3/before-rich1-geom-update-26052022"
-# options.conditions_version = "master"
 options.simulation = True
 
 
diff --git a/moore_options/get_resolution_and_eff_data.py b/moore_options/get_resolution_and_eff_data.py
index 6a05f38..5550eda 100644
--- a/moore_options/get_resolution_and_eff_data.py
+++ b/moore_options/get_resolution_and_eff_data.py
@@ -39,7 +39,7 @@ decay = "B"
 
 options.evt_max = -1
 
-options.ntuple_file = f"data/resolutions_and_effs_{decay}_thesis.root"
+options.ntuple_file = f"data/resolutions_and_effs_{decay}_elec_sig_def_bkg.root"
 options.input_type = "ROOT"
 
 
diff --git a/moore_options/get_resolution_and_eff_data2.py b/moore_options/get_resolution_and_eff_data2.py
new file mode 100644
index 0000000..ae6d536
--- /dev/null
+++ b/moore_options/get_resolution_and_eff_data2.py
@@ -0,0 +1,168 @@
+# flake8: noqa
+
+
+"""
+This set of options is used for reconstruction development purposes,
+and assumes that the input contains MCHits (i.e. is of `Exended`
+DST/digi type).
+author:     Furkan Cetin
+date:       10/2023
+Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py
+"""
+
+from Moore import options, run_reconstruction
+
+from Moore.config import Reconstruction
+from PyConf.Algorithms import PrKalmanFilter
+from PyConf.Tools import TrackMasterExtrapolator
+import glob
+
+from RecoConf.mc_checking import (
+    check_track_resolution,
+    check_tracking_efficiency,
+    get_mc_categories,
+    get_hit_type_mask,
+    make_links_lhcbids_mcparticles_tracking_system,
+    make_links_tracks_mcparticles,
+    get_track_checkers,
+)
+from RecoConf.core_algorithms import make_unique_id_generator
+from RecoConf.hlt2_tracking import make_hlt2_tracks
+from RecoConf.hlt1_tracking import (
+    make_VeloClusterTrackingSIMD_hits,
+    make_PrStorePrUTHits_hits,
+    make_PrStoreSciFiHits_hits,
+    get_global_materiallocator,
+)
+
+decay = "BJpsi"
+
+options.evt_max = -1
+
+options.ntuple_file = f"data/resolutions_and_effs_{decay}_elec_sig_def_bkg.root"
+options.input_type = "ROOT"
+
+
+if decay == "B":
+    options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi")
+elif decay == "BJpsi":
+    options.input_files = glob.glob("/auto/data/guenther/Bd_JpsiKst_ee/*.xdigi")
+elif decay == "D":
+    options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi")
+elif decay == "test2":
+    options.input_files = [
+        "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi"
+    ]
+elif decay == "test":
+    options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
+
+options.dddb_tag = "dddb-20210617"
+options.conddb_tag = "sim-20210617-vc-md100"
+options.simulation = True
+options.output_level = 3
+
+
+def run_tracking_resolution():
+    tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
+    fitted_forward_tracks = PrKalmanFilter(
+        Input=tracks["Forward"]["Pr"],
+        MaxChi2=2.8,
+        MaxChi2PreOutlierRemoval=20,
+        HitsVP=make_VeloClusterTrackingSIMD_hits(),
+        HitsUT=make_PrStorePrUTHits_hits(),
+        HitsFT=make_PrStoreSciFiHits_hits(),
+        ReferenceExtrapolator=TrackMasterExtrapolator(
+            MaterialLocator=get_global_materiallocator(),
+        ),
+        InputUniqueIDGenerator=make_unique_id_generator(),
+    ).OutputTracks
+
+    links_to_lhcbids = make_links_lhcbids_mcparticles_tracking_system()
+    links_to_forward = make_links_tracks_mcparticles(
+        InputTracks=tracks["Forward"],
+        LinksToLHCbIDs=links_to_lhcbids,
+    )
+    links_to_match = make_links_tracks_mcparticles(
+        InputTracks=tracks["Match"],
+        LinksToLHCbIDs=links_to_lhcbids,
+    )
+    links_to_best = make_links_tracks_mcparticles(
+        InputTracks=tracks["BestLong"],
+        LinksToLHCbIDs=links_to_lhcbids,
+    )
+    links_to_seed = make_links_tracks_mcparticles(
+        InputTracks=tracks["Seed"],
+        LinksToLHCbIDs=links_to_lhcbids,
+    )
+
+    res_checker_forward = check_track_resolution(tracks["Forward"], suffix="Forward")
+    res_checker_best_long = check_track_resolution(
+        tracks["BestLong"],
+        suffix="BestLong",
+    )
+    res_checker_best_forward = check_track_resolution(
+        dict(v1=fitted_forward_tracks),
+        suffix="BestForward",
+    )
+    res_checker_seed = check_track_resolution(
+        tracks["Seed"],
+        suffix="Seed",
+    )
+
+    eff_checker_forward = check_tracking_efficiency(
+        "Forward",
+        tracks["Forward"],
+        links_to_forward,
+        links_to_lhcbids,
+        get_mc_categories("Forward"),
+        get_hit_type_mask("Forward"),
+    )
+    eff_checker_match = check_tracking_efficiency(
+        "Match",
+        tracks["Match"],
+        links_to_match,
+        links_to_lhcbids,
+        get_mc_categories("Match"),
+        get_hit_type_mask("Match"),
+    )
+    eff_checker_best_long = check_tracking_efficiency(
+        "BestLong",
+        tracks["BestLong"],
+        links_to_best,
+        links_to_lhcbids,
+        get_mc_categories("BestLong"),
+        get_hit_type_mask("BestLong"),
+    )
+    eff_checker_seed = check_tracking_efficiency(
+        "Seed",
+        tracks["Seed"],
+        links_to_seed,
+        links_to_lhcbids,
+        get_mc_categories("Seed"),
+        get_hit_type_mask("Seed"),
+    )
+
+    # types_and_locations_for_checkers = {
+    #     "Forward": tracks["Forward"],
+    #     "Seed": tracks["Seed"],
+    #     "Match": tracks["Match"],
+    # }
+
+    # data = []
+    # data += get_track_checkers(types_and_locations_for_checkers)
+
+    data = [
+        res_checker_forward,
+        res_checker_best_long,
+        res_checker_best_forward,
+        res_checker_seed,
+        eff_checker_forward,
+        eff_checker_match,
+        eff_checker_best_long,
+        eff_checker_seed,
+    ]
+
+    return Reconstruction("run_tracking_debug", data)
+
+
+run_reconstruction(options, run_tracking_resolution)
diff --git a/parameterisations/train_matching_ghost_mlps_electron.py b/parameterisations/train_matching_ghost_mlps_electron.py
index 9752539..02f6681 100644
--- a/parameterisations/train_matching_ghost_mlps_electron.py
+++ b/parameterisations/train_matching_ghost_mlps_electron.py
@@ -105,20 +105,20 @@ def train_matching_ghost_mlp(
     dataloader.AddVariable("distX", "F")
     dataloader.AddVariable("distY", "F")
     dataloader.AddVariable("dSlope", "F")
-    # dataloader.AddVariable("dSlopeY", "F")
+    dataloader.AddVariable("dSlopeY", "F")
     # dataloader.AddVariable("zmag", "F")
-    dataloader.AddVariable("eta", "F")
+    # dataloader.AddVariable("eta", "F")
     # dataloader.AddVariable("dEta", "F")
+    # dataloader.AddVariable("meanEta", "F")
     # dataloader.AddVariable("eta_scifi", "F")
 
-
-
     dataloader.AddSignalTree(signal_tree, 1.0)
     dataloader.AddBackgroundTree(bkg_tree, 1.0)
 
     # these cuts are also applied in the algorithm
     preselectionCuts = ROOT.TCut(
         # "chi2<30 && distX<500 && distY<500 && dSlope<2.0 && dSlopeY<0.15",  #### ganz raus für elektronen
+        # "eta>2 && eta<5 && eta_scifi>2 && eta_scifi<5"
     )
     dataloader.PrepareTrainingAndTestTree(
         preselectionCuts,
diff --git a/scripts/PrCheckerEfficiency.py b/scripts/PrCheckerEfficiency.py
index a8bd7e1..8381090 100644
--- a/scripts/PrCheckerEfficiency.py
+++ b/scripts/PrCheckerEfficiency.py
@@ -236,6 +236,11 @@ def get_eff(eff, hist, tf, histoName, label, var):
             numerator = findRootObjByName(tf[lab], numeratorName)
         except:
             numerator = denominator
+        # except:
+        #     denominator = findRootObjByName(
+        #         tf[lab], denominatorName.replace("dP_", "P_")
+        #     )
+        #     numerator = findRootObjByName(tf[lab], numeratorName.replace("dP_", "P_"))
 
         if numerator.GetEntries() == 0 or denominator.GetEntries() == 0:
             continue
diff --git a/thesis/TMVA_stuff.ipynb b/thesis/TMVA_stuff.ipynb
index a1db68f..1866e93 100644
--- a/thesis/TMVA_stuff.ipynb
+++ b/thesis/TMVA_stuff.ipynb
@@ -18,16 +18,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "train_tree = uproot.open({\n",
-    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
+    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\":\n",
     "    \"MatchNNDataSet/TrainTree\"\n",
     "})\n",
     "test_tree = uproot.open({\n",
-    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
+    "    \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\":\n",
     "    \"MatchNNDataSet/TestTree\"\n",
     "})\n",
     "train_array = train_tree.arrays()\n",
@@ -36,12 +36,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1200x900 with 1 Axes>"
       ]
@@ -55,49 +55,57 @@
     "train_sig = train_array[train_array.classID == 0]\n",
     "test_bkg = test_array[test_array.classID == 1]\n",
     "test_sig = test_array[test_array.classID == 0]\n",
-    "plt.hist(train_sig.matching_mlp,\n",
-    "         bins=50,\n",
-    "         alpha=0.5,\n",
-    "         density=True,\n",
-    "         color=\"#107E7D\",\n",
-    "         label=\"training sample, true pairs\")\n",
-    "plt.hist(train_bkg.matching_mlp,\n",
-    "         bins=50,\n",
-    "         alpha=0.5,\n",
-    "         density=True,\n",
-    "         color=\"#F05342\",\n",
-    "         label=\"training sample, wrong pairs\")\n",
-    "mplhep.histplot(np.histogram(np.array(test_sig.matching_mlp), 50),\n",
-    "                histtype=\"errorbar\",\n",
-    "                density=True,\n",
-    "                yerr=True,\n",
-    "                color=\"#107E7D\",\n",
-    "                marker=\"^\",\n",
-    "                markersize=7,\n",
-    "                label=\"test sample, true pairs\")\n",
-    "mplhep.histplot(np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
-    "                histtype=\"errorbar\",\n",
-    "                density=True,\n",
-    "                yerr=True,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"test sample, wrong pairs\")\n",
+    "plt.hist(\n",
+    "    train_sig.matching_mlp,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "plt.hist(\n",
+    "    train_bkg.matching_mlp,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
+    "mplhep.histplot(\n",
+    "    np.histogram(np.array(test_sig.matching_mlp), 50),\n",
+    "    histtype=\"errorbar\",\n",
+    "    density=True,\n",
+    "    yerr=True,\n",
+    "    color=\"#107E7D\",\n",
+    "    marker=\"^\",\n",
+    "    markersize=7,\n",
+    "    label=\"test sample, true pairs\",\n",
+    ")\n",
+    "mplhep.histplot(\n",
+    "    np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
+    "    histtype=\"errorbar\",\n",
+    "    density=True,\n",
+    "    yerr=True,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"test sample, wrong pairs\",\n",
+    ")\n",
     "plt.xlabel(\"neural network response\")\n",
     "plt.ylabel(\"Number of tracks (normalised)\")\n",
     "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
     "plt.legend(loc=\"upper center\")\n",
-    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_response.pdf\",\n",
+    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_response.pdf\",\n",
     "            format=\"PDF\")\n",
     "# plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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O48h1XWWzWSUSialyoddqNdVqNf/Hunq9PvLy/VU7tl5u54d4KQ3WNUgc5v4Nvwdh9aGgfRwAAAAA7kOQeELDl3EGvex5uNy4S6nnpX/G80Ozg4dTTDiOo2q1qmw2q1gspmw2u5Q2A7hfsViUZVnK5XJrvShVo9FQIpHQ7u6uisWiisWi8vm8ms3mRPX0f2ZlMhm1Wi0VCgVJv/qM86zSsfUWK5vmSpZxP/y5rrvwxetmNWp2bBj7N+o9CKMPTdLHAQAAAGAcgsQTevXq1cDjoPkNhy9NnWTBoWl5qSYSicTYdg4vxDeKNyspn8/Ps4nARhpelC2ocYtzrZNsNqtaraZKpeL/O2maCentrOTh2aWlUsmvywv0Sat1bL3P66Cfx4Zh+EHLk5OTe9MQZLPZia9Yuc+0fbzfqHaOqjeM/Rv1HoTRhybp4wAAAAAwDkHiCY1bpGac4XKL/uLWn2riyZMnY8uapqlKpaJSqfTgDKhqtcolrMAU+n+o6Z/9N8kl8l6AbDiv+dnZmX9/OEDmvWaa/KTjAn2Lqjefz8txnLnNxBzObSy9TQkgDaYLmuXYTsOr67PPPnuwTD/LsuQ4jgqFwkTHyFtw1XVdJZPJgT5o27aSyaQSicRAnfl8XslkMnDaoXn0celX/18Ov3f9V7wMX9kyzf4FMcl7sOzz0xO0jwMAAADAOASJJzSvnIeLXlDmxYsX/v0gM/ByuZwKhYIqlYparZZub29VKpVGzkC2bdv/AgogOG8W49nZmcrlstLp9MBnive5cF9aBS9tjG3bisfjymazisfjA4GgbDbr5/KVBn+QGvW545Ub9dnm/W1UMHRR9b548UK2bSsWi/n50ZPJpL8YV7lcnujzs1wu37lSwlsEtP+zcZpj67Vj3P5Lo68cCbLYWrFYHEgXYFmWstmscrmcHxS9b5vDbcpkMgOLmyWTScViMcViMSWTSR0eHg7UWa/XVa1WZdv2RIugzdrHpV/NzrUsS8lk0n8v3n//fb9+y7IUi8X8uifdv6AmeQ8WcX4GOV5B+zgAAAAAjEOQeELTBneHg63zuBR3HG9m0TQzp6S37S0UCrq9vVWtVrvTfsuyVC6X59FUX7vd1sHBwcy38/PzubYLmJdKpaJEIiHXdXVxcaFisahUKqVyuTxwiXq1Wh25cGSlUlEul5NhGLq5udHu7q6azaYymcxAwCqfz8t1XeXz+YHz9OjoyH/s/djjfabV63V/Fq+3aJw3G9MLFDqOI9u2F1Kv5/nz5/7njeu6/jYty1K9XlexWNSjR48CfRbH43FlMhlls1l/QbF0Oq1EInEn6DbNsfUCc5ZlKZ/P+/tVLpd1cnIy8Brv+FiWde/xGVapVNRsNhWPx7Wzs6NisahSqXRn5qjjOMrn8wOpD/rfE4+XuiOVSskwDBmGoVQqpUajcafOVCo1kG/3oZRE/W2epY9LUqFQUKlUkmma/oKqtVpNhUJBu7u7ymQyqtVq6vV6A/+/TbJ/QQV9D7yy8zo/gx6vSfr4qjo/P5/L2KDdboe9KwAAAECkxXq9Xi/sRqySWCw28Djo4fMCAx7vi+siuK6rnZ0dSW8vwfUWsZm1zqOjo4FLeA3D0O3t7cx1Hxwc6Pr6euZ6PH/8x3+s73znOwN/+/DHHwd67dOvfTC3dgCYnGVZqtVqKpVKchxHNzc3fkD15uZGrVZL9XpdqVRq6sBf1O3s7Mh1XT/oGLZ4PO7PTN0UUXsPNtl3vvMd/cmf/Mnc6tvf39fV1dXc6sPq88ahi+4bP7/4y0Dl3n3y+wtrAwAACMeyxhuzeifsBqwawzDmkioi6IJ305g01UQQhmGo2WwO5Hp0XVeWZc1tIaCtrS3t7e3NXM/29vYcWgNg2er1uk5OTvwfn+4LSubz+YH8tFgcx3FkGMZGBYgRLdvb29rf35+5nna7rW63O4cWAQAAAOuJIPGEdnd35xIk3t3dnb0x9/Bm15mmObfFnzzPnz8fWLiu0WjMLUi8t7cX6V9UACyO67rKZrOBgpHkRV8OLyVCrVYLuynYYKenpzo9PZ25nnlftQQAAACsG3IST2jaGcDDgeVFzSR2Xdef6buIBWsSicRAUHheC/kB2GxennbbtkfmqfXU63VdXFysdQqARS9sGtTZ2Zlqtdrcf2xcBVF5DwAAAABgWQgST+jw8HDgcdAvksML1fUvSDNP/akmnjx5spBtMIMPwLyZpun/sJXNZrWzs6NsNqt8Pu8vyrWzs6NXr16t9czWarXq369UKqH+EFcqlRaaGimqovQeAAAAAMCyECSeUH+qBSn4TNpWqzXweF4pGoZ5wZNF5pDsn1W2yLQZADZLrVZTrVbzPx/r9bosy5L09sep169fq1QqhdnEhYrH48rn8/5j27YVj8fv/L+DxeE9AAAAALCpyEk8oeGZxI7jBArG9s84NgxjIZfvegvJSVropdj9bd/EWWYAFieTySwkVc4qGP4xEcvHewAAAABgUxEknlAikZBhGH7Q99WrV4ECGpeXl/794UDzvCwj1YQ0uC+kngAAAMCqa7fbOjg4GFtmXgspAgCA9XF+fq7z8/OxZdrt9pJaMxuCxFM4Pj72cxZ6i8Q9pL9csVhcSLuWkWpCGpxptai0GQAAAMCydLtdXV9fjy3T6XSW1BoAALAqOp3Og2OIVUGQeAr5fN4PEnvpHcbpL2Oa5kICq/2pJo6Pj+def796vS5JKhQKC90OAAAAsAxbW1va29sbW2Z7e3tJrQEAAKtie3tb+/v7Y8u02211u90ltWh6BImnkEgklEql/KBsvV4fm3LCm+ErPTyLuFqtqtVqKZ/PT5S3uD/VRDabDfy6SdXrdTmOI8Mw9OzZs4VtBwAAAFiWvb09XV1dhd0MAACwYoKkozo4OFiJ2cZbYTdgVVUqFf/+2dnZveVc1/VnHadSqbELyqXTaeXzeZXLZcXj8YHF7h7Sn2pikpnKlmVpZ2dHsVhM6XR6bPoMx3F0cnIiSXr58iWL1gEAAAAAAABrgJnEUzJNU7VaTdlsVrZtq1wuj0y/cHR0JOlt8LZ/RvEow6krXrx4MTao7OlPNTFpKotareYHoy3LUjKZVC6XGwiCe89ls1nt7u6q2WxONMsZ6+XnF38ZdhMW6t0nvx92E4CpOI4jy7JUq9XUaDTCbg4AAAAAYIUwk3gGmUxGjUZDhmGoWCz6AWMvaJtMJmXbthKJhF6/fv3gzNvh54MGYvtTTeTz+Yn2YVRqimq1qp2dHWWzWeXzeSWTSaXTaeVyOQLEwIqp1+va2dlRuVxeiXoxOdu2lU6nFY/Hlc/nA+XKx2bivAUAAABwH4LEM0qlUrq9vVWpVJLjODo6OvIDrLu7u6rVamo2m4FSM9RqNZmmKcMwVCgUAs8K7p+hPOlM4lQqpVarpVwu52/bY9u2bm5u9OzZM38fSTEBzMZ13YlSyczq7OxMruveuTogqvVicolEQo1Gg/cCD+K8BQAAAHAf0k3MSaFQGJluYhJewHZSs15WbJomXxiBJclmsyqVSkokEkvZXqlUUqlUmvgqg7DqxfS4ygMP4bwFAAAAcB+CxACwJMVicempAFKp1MRXGIRZL4DF4bzFQ2zbluM4chxHiUQiUH8pl8tqNBq6vLyU67oyTVOJREL5fH7q/raIOgEAADAe6SYAYAnq9Tp5QAEAkeK6rsrlspLJpGKxmI6OjvTq1SslEgkdHh6Ofa1lWdrZ2VGxWJT0Nv1Zq9VSqVTyc6Wn0+mJUiwtok4AAAAEQ5AYABasXq/r5OTEf+zlLt/Z2RkoZ1mWstmsqtWqpLcLUcZiMaXT6Tv1pdNpZbNZpdNpJZNJ/zXDHMdRsVhUMpm885zruqpWq0qn0/7rq9Wq4vG4YrGYv/jmMuv1lMtlfzG2ZDKpfD6ver0+cWDAdV1ls1n/Fo/H/QXeRpnk2NbrdWWzWT+Y4QUwYrHYncXBHMdRPp/Xzs6O/57ety9eG7ztFovFgdfNMhvddV3l8/mB92IeP17Ytu23MRaLKR6P++0sl8t3nus/pq7r+sctHo8PHJeg54S3He89i8fjA8dw1HGISh+VZnvP5/V5IAU73pOeU4gm77OgPyDbaDT8NShSqdTYdSgsy/I/x3K5nBqNhlKplEzTVCaTUavVUiKR8BdyDnJeLKJOAAAABBfr9Xq9sBuBzXZwcKDr62vt7+/r6upqIdv48McfByr39GsfLGT76+TnF38ZdhMW6t0nv7+Qel3X9YPCzWZzICdxuVxWpVKR4ziSpEqlolqt5l9mK0mtVkumafoBo9vbW/8LfLFYVLlcVqlU8nOj27Y9kN7CMAzd3t7627QsS8Vi0Q+ElUolvXr1So7j6PDwUJZl+e3p39ai6u3nBbCazaakXwVjhwX578sLXnnHxXVdJZNJJRKJgUU/JQU+tpZlqVQq+ccgl8vJMAxZlqVUKuUHIKW37+Xu7q6KxaIfdKnX6/6l3N4+jjq2pVJJlUpl4Nh7+tvj8QIs9x0b773xAkBe4NqyrDttmUb/9mu1mjKZzMC+ee+rF/jp550f3rkxyTlh27ay2ezAe+q6rk5OTlSv15VKpVSr1fzjGKU+Out7Pq/Pg0mO9yTnVJQsY6yxKqrVqorFov/eVioV5XK5wK93XVePHj3y00Dct56G4ziKx+OS3qY6GbeGxiLqDGpZfSPo+GlR4xAAABCeVRmLEiRG6LyTZWtrS3t7e2PLnp6e6vT0dOJtECSeH4LE0xkXJB5+3ltMMpVKqVwuq9VqqVKp+EG44QCP96V5+O/S25mA1Wp15HOStLOzI9d1ZRiGarWaH7zr/8I+Kji1qHq9ANdwkDGbzaper8s0TT8wN26Wm/Q2EJLP5/3glseyLD8I1v+3SY9t/wzP4fb2Hx8vSDlcp6Q7bZOkWCzm79/Lly/9vuIF47xA3qj9ui9I7G1zuO/197tCoaBSqTTmiD4sHo/LcZyRdXnP3RfgLhaLA4HqIOeEJL/MqH7ovUejgklR6KOead7zeX8eBDnek5xTy3R+fq7z8/OxZdrttrrdbuQH5ovm9V/p7fvcaDQmXvSy/7PvoQCzd148VHYRdQZFkBgAACzaqgSJSTeByOh2u7q+vh5763Q6YTcTWAjDMPyAUv/iUoVCwQ+GeYa/0O/u7kp6G+QZvvz2vsvKh+vK5XIDszsNw/DzUY6a0bWoer0v/sP76F3K7gXLggTfvPq9Oj3e5cvj2u0Zd2z736/+YKEkf1ap67p6/vz5nW2Mmik67NmzZwMBXcMwBgKdkwR0i8WiDMO48+NE/9/uS1EwCe99GlWX99xwf/b+NpyuIMg54c2GvC9I5B0jy7Lu9IMo9NFh07zn8/o8CHK8pzmnlqHT6Tw4fuh2u6G1Lyqy2awfIDYMQ81mc+L3zXGcgfQnx8fHY8s/efLEv++ltVhGnQAAAJgcQWJExtbWlvb398fetre3w24msHD3BXJSqZRardadtABeUEqSbm5uBp7zAkbT8IIH/fUvul7vb8P70R/EG37uPt5s3WKxeCeQMBxsm+XYjgoGPnR8vOdHHQOvvlH1mqbpBxEvLy/HbqOflyvXyx/bf/MC1aOCipPygrWu697Jo+sFGB3HGQiOe2XHzQa875zwgpWPHz8e+XwikfD728XFRcC9WF4f9Uzzni/y8+C+4z3JObVM29vbD44ftrY2e8ibTqcHgvvNZnOqHzL63+eH8hZLGvgBzXXdOz8wLKpOAAAATG6zR8yIlL29PV1dXY29TZNqAlgn/bO+vDyokwS/VsW4GbZBZ77lcjk/uFYulwcWU3uo3kUf22mCMx4vGDkqgDmKdyy9oOLwrdfr+bdZ2iW93S8vgNM/Y9jL0+y1vf+5Fy9e3MlRHFSQY+C9rw8tRDepefTRoO57z5f9eTDpObUsp6enD44fHkpntc6q1erA+1SpVKbuo/1XCQxfmXCf/m2N6p+LqBMAAACTI0gMACumWq0qHo/r5uZGtVpNz549C7tJc+PNJh1OSeDNcB1O6/CQZrPpv8ZxHKXTaWWz2XtnzK7CsX3//fcnKu/NJg0aVJ6Vl3bBm70svT2umUzGnzH44sULv3ypVJrqOI+bMdvPCyZNOrv3PvPuo0GMe8+X3WcnPacQLm+BSk8ikZg6h+/wDyP3zeAf1h/4HZ71u4g6AQAAMB2CxACwQtLptPL5vBqNxsyL9USRt1iW4zjKZrNyHEeu6yqbzSqRSEy1MFatVlOtVvODhfV6feTl9Kt2bL28uQ8Zl9piEfovF/eCwWdnZ36+XdM0/UvEvQBR0NmD/fpnEo7KHezx2jKv2b2L6KNBDb/nYfXZoOcUwuflR/fM8iPC8KzxoOfUcLn+wPAi6gQAAMB0CBIDwIooFot+7tYwF4latEajoUQiod3dXT/3aT6fv5N79SH9QYNMJqNWq6VCoSDpbcC0/xLnVTq2XkB0msuyx82481JCzEP/bNt6va7Dw0O/vf0L2I1asG4S3r6NS3ngBceDBtWDmFcfDWrUex5Gn53knEL4hvN/96eDmcarV68GHgdNTzM8E74/t/Yi6gQAAMB0CBIDwJJNe9m7F+DzFo9aV9lsVrVaTZVKxf93msBGpVK5M3u2VCr5dfXPPl2lY+sFRIMGVw3D8IOIJycn96YFyGazU+cGHua1zbZtnZycDCxy5gWQLctStVqdaQZs/3bumyntBcmGF1qbxbz6aFCj3vMw+uwk5xTCN5wSxTu/vfzV8XhcsVhMOzs7SiaTKpfLY9OGjMuJPc5wuf5+sog6AQAAMB2CxACwBP2zo/pndk2SAsALLg8v0nN2dubfH/6C771mmnyh44LZi6o3n8/LcZy5pgYYlk6nJQ3mvpzl2E7Dq+uzzz57sEw/y7LkOI4KhcJEx8jLBey6rpLJ5EAftG1byWTSTwXhyefzSiaTU+X7NE3TD0jt7u4OBJ8Nw/AfZzKZmRbL6z8O3j7284LHkx6vZfbRfpO858v+PPAEPacQvuGZ3bu7u0omk8pms6rX6/7/P67ryrZtFYtF7ezs3HvOzytlTX//W0SdAAAAmA5BYgBYEi8wdnZ2pnK5rHQ6PfAF2fuSe98l68fHx5LeBr7i8bg/E6w/MJPNZv08qdLg7KpRX6K9cqO+qHt/GxUwW1S9L168kG3b/uy2eDyuZDLpL4710Ey3YeVy+U4qglqtpkQiMTDzc5pj67Vj3P5Loy+DDrKYXLFYHAjyWJalbDarXC43MiA6qi95MpnMwGJjyWRSsVhMsVhMyWRSh4eHA3XW63VVq1XZtj31omTerNdRbfWee2g29EPnhPQ29YNpmqpWqyqXy/7fvZzBDx2vsPtov0ne80V8HgQ53kHPKYTLy5Xd78WLF8rn87q9vVWv11Ov11Or1bozm98LIg+btl8P/xDUf14tos5ptdttHRwczHw7Pz+fuS0AACAazs/P5zI+aLfbYe9KIASJAWBJKpWKEomEXNfVxcWFisWiUqmUyuXywCXj1WpV+Xz+zpf0SqWiXC4nwzB0c3Oj3d1dNZtNZTKZgQBSPp+X67rK5/MDQbOjoyP/sW3bSqfT/hf0er3uz5D0gmvebFMvUOjlt1xEvZ7nz5/7X/5d1/W3aVmW6vW6isWiHj16FCiwEI/HlclklM1m/QW+0um0EonEnSDYNMfWC5RZlqV8Pu/vV7lc1snJycBrvONjWda9x2dYpVJRs9lUPB7Xzs6OisWiSqXSnZmcjuMon88PBFz73xOPlxbBW1jOm9HbaDRGXpben/92XM7f+2QyGaVSqZGBQ++5+9JbBD0npLezllutlkqlkhqNhh+0zefzft7jflHqo8OCvude2Xl9HgQ93pOcUwjX8GeKYRh6/fq132c8pmmOPE/GpaaZ1SLqnUed3W5X19fXM986nc7sOwQAACKh0+nMZXzQ7XbD3pVAYr1erxd2I7DZDg4OdH19rf39fV1dXS1kGx/++ONA5Z5+7YOFbB9AMJZlqVarqVQqyXEc3dzc+F/+b25u1Gq1VK/XlUqlRgbO1sHOzo5c1/WDgGGLx+P+TFEspo9G7T1fR8sYa0SJF+j3FAqFkTPq++Xz+YGZ7MOv8fqpJ+hXCO/HMU8mk1GtVltYnZPy+sbW1pb29vamqqPf6empTk9P7/z95xd/Gej17z75/ZnbAAAA5uP8/HwuVwm12211u93Ij0XfCbsBAABIb2d0npyc6Pb2VpLuDUrm8/m5LkKG+zmOI8MwCBD/N/RRrIrhhdyC5IseTndSLpcHgsS7u7tzmbG7u7u70Dqntbe3F+kvbQAAYPnu+/F3Ut6P0lFHugkAQOhc11U2mw20GJiXLgCL5aUomHZ23rqhj2KVDAdegywQaZrmnR8++he5nHaRyXFtWUSdAAAAmA5BYgBA6LxFh2zbHpl31lOv13VxcbHWl+QvKg/opM7OzlSr1QIFRTfBIvtoVN5zrI/+HNOTODw8HHjcn9t4+Lmg/XZ4Ubn+ti2iTgAAAEyHIDEAIHSmafoLnGWzWe3s7CibzSqfz/uLZO3s7OjVq1drPbO1/1LvSqUyckG7ZSmVSszO67OoPhql9xzrY/jcDRp8HQ629gdjk8nkwHNB++pw6ov+BSsXUScAAACmQ5AYABAJtVpNtVrN/7Jfr9dlWZYkKZ1O6/Xr1w8uvLTK4vH4wEJTtm0rHo/fCaIgPPPuo7znWJRxM4LHGQ4u9+f6nbbO/gC1YRgDVycsok4AAABMh4XrAACRkclk/Nmam2Z4ZhyiaZ59lPccizKcW3javtYffE0kEjIMww/Qvnr1KtC5cHl56d8fDgovok4AAABMh5nEAAAAwJrpT8HgzXh/yHBaiuFg8/HxsX+/f1G7cfrLFYvFO88vok4AAABMjiAxAAAAsGb6g6eO4wTKS9w/43hUnt/+9ChBAs/9ZUzTXFqdAAAAmBxBYkRGu93WwcHB2Nv5+XnYzQQAABFyfn7+4Pih3W6H3cylS6VSA+kizs7OHnxNfwB2VH7tRCIxEJSt1+tj6+tfxPG+Gb+LqBMAAACTI0iMyOh2u7q+vh5763Q6YTcTAABESKfTeXD80O12w25mKPoDquVyeezCcJZl+c/ncrk7qSY8lUrFvz8u8Oy6rqrVqqS3AetcLndv2UXUCQAAgMkQJEZkbG1taX9/f+xte3s77GYCAIAI2d7efnD8sLW1mUPeRCIxEIBNp9Mj0064ruunfRh+zTDTNP3gs23bKpfLI8sdHR1JkgzDGAhWL6tOAAAATGYzR8yIpL29PV1dXY29nZ6eht1MAAAQIaenpw+OH/b29sJuZmhyuZwajYYMw5DjOHr06JHK5bJs2/YDso8ePZLjOMrlcmo2mw/Wmclk/DqLxaKy2axs25brurIsS8lkUrZtK5FI6PXr1zIMI5Q6AQAAEBxBYgAAAGCNpVIp3d7eqlKp6PDwUGdnZ0omkzo6OtLFxYVyuZxardbYGcT31VkqleQ4jo6OjrSzs6NsNqvd3V3VajU1m82JgrmLqBMAAADBvBN2AwAAAAAsXi6Xm3se30KhoEKhEPk6AQAAMB4ziQEAAAAAAABggxEkBgAAAAAAAIANRpAYAAAAAAAAADYYQWIAAAAAAAAA2GAEiQEAAAAAAABggxEkBgAAAAAAAIAN9k7YDQAAAACAMLXbbR0cHIwtc3p6qtPT0yW1CAAArILz83Odn5+PLdNut5fUmtkQJAYAAACw0brdrq6vr8eW6XQ6S2oNAABYFZ1O58ExxKogSAwAAABgo21tbWlvb29sme3t7SW1BgAArIrt7W3t7++PLdNut9XtdpfUoukRJAYAAACw0fb29nR1dRV2MwAAwIoJko7q4OBgJWYbs3AdAAAAAAAAAGwwgsQAAAAAAAAAsMEIEgMAAAAAAADABiNIDAAAAAAAAAAbjIXrEBntdlsHBwdjywRJCA4AADbH+fm5zs/Px5Zpt9tLag0AAACwmggSIzK63e6Dqz12Op0ltQYAAKyCTqezEqtFAwAAAFFGkBiRsbW1pb29vbFltre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",
       "text/plain": [
        "<Figure size 1400x1500 with 6 Axes>"
       ]
@@ -109,115 +117,140 @@
    "source": [
     "fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
     "# 0,0\n",
-    "axes[0, 0].hist(train_sig.chi2,\n",
-    "                bins=50,\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[0, 0].hist(train_bkg.chi2,\n",
-    "                bins=50,\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[0, 0].hist(\n",
+    "    train_sig.chi2,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[0, 0].hist(\n",
+    "    train_bkg.chi2,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
-    "axes[0, 0].legend(prop={'size': 20})\n",
+    "axes[0, 0].legend(prop={\"size\": 20})\n",
     "# 1,0\n",
-    "axes[1, 0].hist(train_sig.distX,\n",
-    "                bins=50,\n",
-    "                range=(0, 100),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[1, 0].hist(train_bkg.distX,\n",
-    "                bins=50,\n",
-    "                range=(0, 100),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[1, 0].hist(\n",
+    "    train_sig.distX,\n",
+    "    bins=50,\n",
+    "    range=(0, 100),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[1, 0].hist(\n",
+    "    train_bkg.distX,\n",
+    "    bins=50,\n",
+    "    range=(0, 100),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
     "axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
     "                      va=\"bottom\",\n",
     "                      ha=\"center\")\n",
     "# 0,1\n",
-    "axes[0, 1].hist(train_sig.teta2,\n",
-    "                bins=50,\n",
-    "                range=(0.0, 0.02),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[0, 1].hist(train_bkg.teta2,\n",
-    "                bins=50,\n",
-    "                range=(0.0, 0.02),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[0, 1].hist(\n",
+    "    train_sig.teta2,\n",
+    "    bins=50,\n",
+    "    range=(0.0, 0.02),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[0, 1].hist(\n",
+    "    train_bkg.teta2,\n",
+    "    bins=50,\n",
+    "    range=(0.0, 0.02),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
     "# 1,1\n",
-    "axes[1, 1].hist(train_sig.distY,\n",
-    "                bins=50,\n",
-    "                range=(0, 100),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[1, 1].hist(train_bkg.distY,\n",
-    "                bins=50,\n",
-    "                range=(0, 100),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[1, 1].hist(\n",
+    "    train_sig.distY,\n",
+    "    bins=50,\n",
+    "    range=(0, 100),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[1, 1].hist(\n",
+    "    train_bkg.distY,\n",
+    "    bins=50,\n",
+    "    range=(0, 100),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
     "# 2,0\n",
-    "axes[2, 0].hist(train_sig.dSlope,\n",
-    "                bins=50,\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[2, 0].hist(train_bkg.dSlope,\n",
-    "                bins=50,\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[2, 0].hist(\n",
+    "    train_sig.dSlope,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[2, 0].hist(\n",
+    "    train_bkg.dSlope,\n",
+    "    bins=50,\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
     "# 2,1\n",
-    "axes[2, 1].hist(train_sig.dSlopeY,\n",
-    "                bins=50,\n",
-    "                range=(0, 0.02),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#107E7D\",\n",
-    "                label=\"training sample, true pairs\")\n",
-    "axes[2, 1].hist(train_bkg.dSlopeY,\n",
-    "                bins=50,\n",
-    "                range=(0, 0.02),\n",
-    "                alpha=0.5,\n",
-    "                density=True,\n",
-    "                log=False,\n",
-    "                color=\"#F05342\",\n",
-    "                label=\"training sample, wrong pairs\")\n",
+    "axes[2, 1].hist(\n",
+    "    train_sig.dSlopeY,\n",
+    "    bins=50,\n",
+    "    range=(0, 0.02),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#107E7D\",\n",
+    "    label=\"training sample, true pairs\",\n",
+    ")\n",
+    "axes[2, 1].hist(\n",
+    "    train_bkg.dSlopeY,\n",
+    "    bins=50,\n",
+    "    range=(0, 0.02),\n",
+    "    alpha=0.5,\n",
+    "    density=True,\n",
+    "    log=False,\n",
+    "    color=\"#F05342\",\n",
+    "    label=\"training sample, wrong pairs\",\n",
+    ")\n",
     "axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
-    "plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_variables.pdf\",\n",
-    "            format=\"PDF\")\n",
+    "plt.savefig(\n",
+    "    \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\",\n",
+    "    format=\"PDF\")\n",
     "# plt.show()"
    ]
   },