7.model_mlp_pairwise.multiparticle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c07744a",
   "metadata": {},
   "source": [
    "# Pairwise MLP approach\n",
    "\n",
    "Get tracksters from a certain neighbourhood.\n",
    "\n",
    "Train a NN to decide whether two tracksters should be joined.\n",
    "\n",
    "Neighbourhood:\n",
    "- get links from ticlNtuplizer/graph\n",
    "- convert the tracksters into some latent space and predict a link between them\n",
    "\n",
    "Graph:\n",
    "- linked_inners\n",
    "    - nodes linked to the given tracksters within its cone\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3247338",
   "metadata": {},
   "source": [
    "## MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81ffdcce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "from reco.training import precision_recall_curve, roc_auc\n",
    "\n",
    "from torch.utils.data import random_split, DataLoader\n",
    "from reco.loss import QualityFocalLoss\n",
    "\n",
    "from reco.datasetPU import TracksterPairs\n",
    "\n",
    "\n",
    "ds_name = \"MultiParticle\"\n",
    "\n",
    "data_root = \"data\"\n",
    "raw_dir = f\"/Users/ecuba/data/{ds_name}\"\n",
    "\n",
    "# data_root = \"/mnt/ceph/users/ecuba/processed\"\n",
    "# raw_dir = f\"/mnt/ceph/users/ecuba/{ds_name}\"\n",
    "\n",
    "# writer = SummaryWriter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2dc29a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cpu\n"
     ]
    }
   ],
   "source": [
    "# CUDA Setup\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "219c077d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2523941, 63])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = TracksterPairs(\n",
    "    ds_name,\n",
    "    data_root,\n",
    "    raw_dir,\n",
    "    N_FILES=10,\n",
    "    radius=30,\n",
    "    bigT_e_th=10,\n",
    ")\n",
    "\n",
    "ds.x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75c3ffff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2523941, 63])\n",
      "Positive: 463040\n",
      "Negative: 2060901\n"
     ]
    }
   ],
   "source": [
    "print(ds.x.shape)\n",
    "print(\"Positive:\", int((ds.y >= 0.5).type(torch.int).sum()))\n",
    "print(\"Negative:\", int((ds.y < 0.5).type(torch.int).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84179ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset balance: 0.1834591180086136\n"
     ]
    }
   ],
   "source": [
    "balance =  float(sum(ds.y > 0.5) / len(ds.y))\n",
    "print(\"dataset balance:\", balance) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "972263a2-610a-4ca9-8805-b2c6830aa83b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: 1000000, Test samples: 20000\n"
     ]
    }
   ],
   "source": [
    "ds_size = len(ds)\n",
    "test_set_size = ds_size // 10\n",
    "train_set_size = ds_size - test_set_size\n",
    "train_set, test_set, rest = random_split(ds, [1000000, 20000, ds_size - 1020000])\n",
    "print(f\"Train samples: {len(train_set)}, Test samples: {len(test_set)}\")\n",
    "\n",
    "train_dl = DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "test_dl = DataLoader(test_set, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d3700ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_obj = QualityFocalLoss(gamma=2)\n",
    "\n",
    "def train(model, opt, loader, loss_obj):\n",
    "    epoch_loss = 0\n",
    "    for batch, labels in loader:\n",
    "        # reset optimizer and enable training mode\n",
    "        opt.zero_grad()\n",
    "        model.train()\n",
    "\n",
    "        # move data to the device\n",
    "        batch = batch.to(device)\n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # get the prediction tensor\n",
    "        z = model(batch).reshape(-1)\n",
    "\n",
    "        # compute the loss\n",
    "        loss = loss_obj(z, labels)        \n",
    "        epoch_loss += loss\n",
    "\n",
    "        # back-propagate and update the weight\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "\n",
    "    return float(epoch_loss)\n",
    "\n",
    "@torch.no_grad()\n",
    "def test(model, data):\n",
    "    total = 0\n",
    "    correct = 0\n",
    "    for batch, labels in data:\n",
    "        model.eval()\n",
    "        batch = batch.to(device)\n",
    "        labels = labels.to(device)\n",
    "        z = model(batch).reshape(-1)\n",
    "        prediction = (z > 0.5).type(torch.int)\n",
    "        total += len(prediction) \n",
    "        correct += sum(prediction == (labels > 0.5).type(torch.int))\n",
    "    return (correct / total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c1bdabc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdim1 = 256\n",
    "hdim2 = 128\n",
    "\n",
    "model = nn.Sequential(\n",
    "    nn.LayerNorm(ds.x.shape[1]),      # normalization as a part of the network\n",
    "    nn.Linear(ds.x.shape[1], hdim1),\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(hdim1, hdim2),\n",
    "    nn.Sigmoid(),\n",
    "    nn.Linear(hdim2, 1),\n",
    "    nn.Dropout(p=0.2),\n",
    ").to(device)\n",
    "\n",
    "epochs = 100\n",
    "model_path = f\"models/PairWiseMLP.QFL.{hdim1}.{hdim2}.{epochs}e-{ds_name}.{ds.RADIUS}r.{ds.bigT_e_th}eth.{ds.N_FILES}f.pt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%script echo skipping\n",
    "model.load_state_dict(torch.load(\n",
    "    model_path,\n",
    "    map_location=device\n",
    "))\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de2aae2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipping\n"
     ]
    }
   ],
   "source": [
    "%%script echo skipping\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)\n",
    "scheduler = CosineAnnealingLR(optimizer, epochs, eta_min=1e-5)\n",
    "loss_obj = QualityFocalLoss(gamma=2)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    loss = train(model, optimizer, train_dl, loss_obj)\n",
    "    train_auc = roc_auc(model, device, train_dl)\n",
    "    test_auc = roc_auc(model, device, test_dl)\n",
    "    scheduler.step()\n",
    "    if epoch % 5 == 0:\n",
    "        print(f'Epoch: {epoch}, loss: {loss:.4f}, train auc: {train_auc:.4f}, test auc: {test_auc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "849a1318",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP: 1633, TN: 16321, FP: 615, FN: 1431\n",
      "TH: 0.25 precision: 0.726 recall: 0.533 fbeta: 0.677 b_acc: 0.748\n"
     ]
    }
   ],
   "source": [
    "#%%script echo skipping\n",
    "precision_recall_curve(model, device, test_dl, focus_metric=\"fbeta\", beta=0.5, step=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63d01ce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%script echo skipping\n",
    "torch.save(model.state_dict(), model_path)\n",
    "del ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "951d1251",
   "metadata": {},
   "source": [
    "# Evaluation\n",
    "\n",
    "Give the model a list of candidate edges to evaluate.\n",
    "Reconstruct the event and evaluate against ground truth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d877b173",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from reco.data import get_event_data\n",
    "from reco.evaluation import model_evaluation\n",
    "\n",
    "from reco.dummy import DummyPleaser\n",
    "\n",
    "file_name = f\"{raw_dir}/new_ntuples_15163313_1.root\"\n",
    "cluster_data, trackster_data, simtrackster_data, assoc_data = get_event_data(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5212be61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Radius threshold: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.151 F: 0.469\n",
      "\treco_to_sim:\tP: 0.987 R: 0.162 F: 0.488\n",
      "\t|S| = 10 |T| = 60 |R| = 54\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.953 R: 0.148 F: 0.456\n",
      "\treco_to_sim:\tP: 0.944 R: 0.156 F: 0.469\n",
      "\t|S| = 10 |T| = 77 |R| = 63\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.201 F: 0.555\n",
      "\treco_to_sim:\tP: 0.980 R: 0.210 F: 0.565\n",
      "\t|S| = 10 |T| = 29 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.973 R: 0.236 F: 0.599\n",
      "\treco_to_sim:\tP: 0.929 R: 0.272 F: 0.626\n",
      "\t|S| = 10 |T| = 76 |R| = 65\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.144 F: 0.450\n",
      "\treco_to_sim:\tP: 0.885 R: 0.153 F: 0.453\n",
      "\t|S| = 10 |T| = 56 |R| = 38\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.978 R: 0.159 F: 0.482\n",
      "\treco_to_sim:\tP: 0.978 R: 0.159 F: 0.482\n",
      "\t|S| = 10 |T| = 56 |R| = 56\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.242 F: 0.608\n",
      "\treco_to_sim:\tP: 0.961 R: 0.266 F: 0.631\n",
      "\t|S| = 10 |T| = 47 |R| = 43\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.191 F: 0.539\n",
      "\treco_to_sim:\tP: 0.985 R: 0.203 F: 0.557\n",
      "\t|S| = 10 |T| = 32 |R| = 29\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.153 F: 0.471\n",
      "\treco_to_sim:\tP: 0.958 R: 0.184 F: 0.520\n",
      "\t|S| = 10 |T| = 49 |R| = 37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.578\n",
      "\treco_to_sim:\tP: 0.980 R: 0.233 F: 0.597\n",
      "\t|S| = 10 |T| = 72 |R| = 65\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.225 F: 0.585\n",
      "\treco_to_sim:\tP: 0.964 R: 0.235 F: 0.595\n",
      "\t|S| = 10 |T| = 69 |R| = 58\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.237 F: 0.600\n",
      "\treco_to_sim:\tP: 0.921 R: 0.254 F: 0.603\n",
      "\t|S| = 10 |T| = 93 |R| = 86\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.951 R: 0.236 F: 0.592\n",
      "\treco_to_sim:\tP: 0.892 R: 0.249 F: 0.588\n",
      "\t|S| = 10 |T| = 56 |R| = 43\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.087 F: 0.321\n",
      "\treco_to_sim:\tP: 0.951 R: 0.103 F: 0.359\n",
      "\t|S| = 10 |T| = 103 |R| = 83\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.164 F: 0.487\n",
      "\treco_to_sim:\tP: 0.923 R: 0.190 F: 0.521\n",
      "\t|S| = 10 |T| = 79 |R| = 61\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.288 F: 0.650\n",
      "\treco_to_sim:\tP: 0.869 R: 0.323 F: 0.650\n",
      "\t|S| = 10 |T| = 85 |R| = 74\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.188 F: 0.528\n",
      "\treco_to_sim:\tP: 0.959 R: 0.192 F: 0.533\n",
      "\t|S| = 10 |T| = 62 |R| = 52\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.126 F: 0.415\n",
      "\treco_to_sim:\tP: 0.959 R: 0.129 F: 0.420\n",
      "\t|S| = 10 |T| = 100 |R| = 89\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.139 F: 0.443\n",
      "\treco_to_sim:\tP: 0.968 R: 0.152 F: 0.466\n",
      "\t|S| = 10 |T| = 104 |R| = 89\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.171 F: 0.500\n",
      "\treco_to_sim:\tP: 0.894 R: 0.214 F: 0.547\n",
      "\t|S| = 10 |T| = 55 |R| = 42\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.972 R: 0.185 F: 0.516\n",
      "mean reco_to_sim:\tP: 0.944 R: 0.202 F: 0.533\n",
      "mean n_tracksters:\tP: 10.000 R: 68.000 F: 57.500\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.151 F: 0.469\n",
      "\treco_to_sim:\tP: 0.990 R: 0.151 F: 0.469\n",
      "\t|S| = 10 |T| = 60 |R| = 64\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.953 R: 0.148 F: 0.456\n",
      "\treco_to_sim:\tP: 0.950 R: 0.139 F: 0.438\n",
      "\t|S| = 10 |T| = 77 |R| = 82\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.201 F: 0.555\n",
      "\treco_to_sim:\tP: 0.990 R: 0.197 F: 0.548\n",
      "\t|S| = 10 |T| = 29 |R| = 31\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.973 R: 0.236 F: 0.599\n",
      "\treco_to_sim:\tP: 0.972 R: 0.245 F: 0.610\n",
      "\t|S| = 10 |T| = 76 |R| = 75\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.144 F: 0.450\n",
      "\treco_to_sim:\tP: 0.959 R: 0.121 F: 0.401\n",
      "\t|S| = 10 |T| = 56 |R| = 60\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.978 R: 0.159 F: 0.482\n",
      "\treco_to_sim:\tP: 0.978 R: 0.153 F: 0.471\n",
      "\t|S| = 10 |T| = 56 |R| = 62\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.242 F: 0.608\n",
      "\treco_to_sim:\tP: 0.982 R: 0.234 F: 0.599\n",
      "\t|S| = 10 |T| = 47 |R| = 49\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.191 F: 0.539\n",
      "\treco_to_sim:\tP: 0.989 R: 0.183 F: 0.526\n",
      "\t|S| = 10 |T| = 32 |R| = 34\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.153 F: 0.471\n",
      "\treco_to_sim:\tP: 0.983 R: 0.142 F: 0.450\n",
      "\t|S| = 10 |T| = 49 |R| = 50\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.578\n",
      "\treco_to_sim:\tP: 0.983 R: 0.223 F: 0.584\n",
      "\t|S| = 10 |T| = 72 |R| = 71\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.225 F: 0.585\n",
      "\treco_to_sim:\tP: 0.970 R: 0.226 F: 0.586\n",
      "\t|S| = 10 |T| = 69 |R| = 72\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.237 F: 0.600\n",
      "\treco_to_sim:\tP: 0.972 R: 0.218 F: 0.574\n",
      "\t|S| = 10 |T| = 93 |R| = 103\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.951 R: 0.236 F: 0.592\n",
      "\treco_to_sim:\tP: 0.951 R: 0.191 F: 0.529\n",
      "\t|S| = 10 |T| = 56 |R| = 58\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.087 F: 0.321\n",
      "\treco_to_sim:\tP: 0.969 R: 0.092 F: 0.334\n",
      "\t|S| = 10 |T| = 103 |R| = 102\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.164 F: 0.487\n",
      "\treco_to_sim:\tP: 0.957 R: 0.159 F: 0.477\n",
      "\t|S| = 10 |T| = 79 |R| = 81\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.288 F: 0.650\n",
      "\treco_to_sim:\tP: 0.949 R: 0.273 F: 0.634\n",
      "\t|S| = 10 |T| = 85 |R| = 96\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.188 F: 0.528\n",
      "\treco_to_sim:\tP: 0.965 R: 0.186 F: 0.526\n",
      "\t|S| = 10 |T| = 62 |R| = 63\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.126 F: 0.415\n",
      "\treco_to_sim:\tP: 0.973 R: 0.117 F: 0.394\n",
      "\t|S| = 10 |T| = 100 |R| = 116\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.139 F: 0.443\n",
      "\treco_to_sim:\tP: 0.977 R: 0.138 F: 0.440\n",
      "\t|S| = 10 |T| = 104 |R| = 110\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.171 F: 0.500\n",
      "\treco_to_sim:\tP: 0.963 R: 0.176 F: 0.509\n",
      "\t|S| = 10 |T| = 55 |R| = 62\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.972 R: 0.185 F: 0.516\n",
      "mean reco_to_sim:\tP: 0.971 R: 0.178 F: 0.505\n",
      "mean n_tracksters:\tP: 10.000 R: 68.000 F: 72.050\n",
      " --- Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.168 F: 0.500\n",
      "\treco_to_sim:\tP: 0.944 R: 0.189 F: 0.525\n",
      "\t|S| = 10 |T| = 34 |R| = 17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.200 F: 0.543\n",
      "\treco_to_sim:\tP: 0.497 R: 0.375 F: 0.467\n",
      "\t|S| = 10 |T| = 33 |R| = 11\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.261 F: 0.635\n",
      "\treco_to_sim:\tP: 0.809 R: 0.310 F: 0.612\n",
      "\t|S| = 10 |T| = 14 |R| = 5\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.294 F: 0.665\n",
      "\treco_to_sim:\tP: 0.472 R: 0.400 F: 0.455\n",
      "\t|S| = 10 |T| = 28 |R| = 12\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.201 F: 0.545\n",
      "\treco_to_sim:\tP: 0.543 R: 0.332 F: 0.482\n",
      "\t|S| = 10 |T| = 33 |R| = 7\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.187 F: 0.529\n",
      "\treco_to_sim:\tP: 0.952 R: 0.212 F: 0.560\n",
      "\t|S| = 10 |T| = 30 |R| = 25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "\treco_to_sim:\tP: 0.790 R: 0.331 F: 0.619\n",
      "\t|S| = 10 |T| = 19 |R| = 12\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.211 F: 0.569\n",
      "\treco_to_sim:\tP: 0.804 R: 0.250 F: 0.557\n",
      "\t|S| = 10 |T| = 17 |R| = 8\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "\treco_to_sim:\tP: 0.853 R: 0.316 F: 0.637\n",
      "\t|S| = 10 |T| = 15 |R| = 6\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.263 F: 0.635\n",
      "\treco_to_sim:\tP: 0.948 R: 0.311 F: 0.672\n",
      "\t|S| = 10 |T| = 32 |R| = 22\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.314 F: 0.683\n",
      "\treco_to_sim:\tP: 0.858 R: 0.350 F: 0.665\n",
      "\t|S| = 10 |T| = 26 |R| = 16\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.330 F: 0.690\n",
      "\treco_to_sim:\tP: 0.804 R: 0.377 F: 0.655\n",
      "\t|S| = 10 |T| = 44 |R| = 28\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.947 R: 0.278 F: 0.639\n",
      "\treco_to_sim:\tP: 0.402 R: 0.435 F: 0.408\n",
      "\t|S| = 10 |T| = 26 |R| = 5\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.963 R: 0.125 F: 0.411\n",
      "\treco_to_sim:\tP: 0.695 R: 0.206 F: 0.471\n",
      "\t|S| = 10 |T| = 57 |R| = 23\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.953 R: 0.230 F: 0.585\n",
      "\treco_to_sim:\tP: 0.845 R: 0.295 F: 0.616\n",
      "\t|S| = 10 |T| = 33 |R| = 16\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.351 F: 0.706\n",
      "\treco_to_sim:\tP: 0.726 R: 0.426 F: 0.637\n",
      "\t|S| = 10 |T| = 37 |R| = 21\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.217 F: 0.572\n",
      "\treco_to_sim:\tP: 0.770 R: 0.271 F: 0.563\n",
      "\t|S| = 10 |T| = 39 |R| = 23\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.170 F: 0.500\n",
      "\treco_to_sim:\tP: 0.873 R: 0.210 F: 0.535\n",
      "\t|S| = 10 |T| = 49 |R| = 29\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.177 F: 0.512\n",
      "\treco_to_sim:\tP: 0.665 R: 0.248 F: 0.498\n",
      "\t|S| = 10 |T| = 59 |R| = 30\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.959 R: 0.228 F: 0.584\n",
      "\treco_to_sim:\tP: 0.687 R: 0.357 F: 0.580\n",
      "\t|S| = 10 |T| = 26 |R| = 8\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.968 R: 0.238 F: 0.590\n",
      "mean reco_to_sim:\tP: 0.747 R: 0.310 F: 0.561\n",
      "mean n_tracksters:\tP: 10.000 R: 32.550 F: 16.200\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.168 F: 0.500\n",
      "\treco_to_sim:\tP: 0.990 R: 0.153 F: 0.473\n",
      "\t|S| = 10 |T| = 34 |R| = 57\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.200 F: 0.543\n",
      "\treco_to_sim:\tP: 0.948 R: 0.148 F: 0.456\n",
      "\t|S| = 10 |T| = 33 |R| = 66\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.261 F: 0.635\n",
      "\treco_to_sim:\tP: 0.990 R: 0.215 F: 0.575\n",
      "\t|S| = 10 |T| = 14 |R| = 26\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.294 F: 0.665\n",
      "\treco_to_sim:\tP: 0.970 R: 0.301 F: 0.671\n",
      "\t|S| = 10 |T| = 28 |R| = 43\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.201 F: 0.545\n",
      "\treco_to_sim:\tP: 0.958 R: 0.127 F: 0.414\n",
      "\t|S| = 10 |T| = 33 |R| = 49\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.187 F: 0.529\n",
      "\treco_to_sim:\tP: 0.977 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 30 |R| = 55\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "\treco_to_sim:\tP: 0.981 R: 0.279 F: 0.652\n",
      "\t|S| = 10 |T| = 19 |R| = 27\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.211 F: 0.569\n",
      "\treco_to_sim:\tP: 0.989 R: 0.193 F: 0.542\n",
      "\t|S| = 10 |T| = 17 |R| = 27\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 15 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.263 F: 0.635\n",
      "\treco_to_sim:\tP: 0.982 R: 0.258 F: 0.629\n",
      "\t|S| = 10 |T| = 32 |R| = 46\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.314 F: 0.683\n",
      "\treco_to_sim:\tP: 0.970 R: 0.280 F: 0.650\n",
      "\t|S| = 10 |T| = 26 |R| = 58\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.330 F: 0.690\n",
      "\treco_to_sim:\tP: 0.969 R: 0.231 F: 0.592\n",
      "\t|S| = 10 |T| = 44 |R| = 90\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.947 R: 0.278 F: 0.639\n",
      "\treco_to_sim:\tP: 0.947 R: 0.222 F: 0.573\n",
      "\t|S| = 10 |T| = 26 |R| = 47\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.963 R: 0.125 F: 0.411\n",
      "\treco_to_sim:\tP: 0.968 R: 0.112 F: 0.383\n",
      "\t|S| = 10 |T| = 57 |R| = 79\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.953 R: 0.230 F: 0.585\n",
      "\treco_to_sim:\tP: 0.953 R: 0.186 F: 0.522\n",
      "\t|S| = 10 |T| = 33 |R| = 53\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.351 F: 0.706\n",
      "\treco_to_sim:\tP: 0.945 R: 0.304 F: 0.664\n",
      "\t|S| = 10 |T| = 37 |R| = 73\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.217 F: 0.572\n",
      "\treco_to_sim:\tP: 0.964 R: 0.212 F: 0.564\n",
      "\t|S| = 10 |T| = 39 |R| = 49\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.170 F: 0.500\n",
      "\treco_to_sim:\tP: 0.971 R: 0.135 F: 0.433\n",
      "\t|S| = 10 |T| = 49 |R| = 94\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.177 F: 0.512\n",
      "\treco_to_sim:\tP: 0.974 R: 0.149 F: 0.463\n",
      "\t|S| = 10 |T| = 59 |R| = 89\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.959 R: 0.228 F: 0.584\n",
      "\treco_to_sim:\tP: 0.963 R: 0.183 F: 0.519\n",
      "\t|S| = 10 |T| = 26 |R| = 55\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.968 R: 0.238 F: 0.590\n",
      "mean reco_to_sim:\tP: 0.970 R: 0.202 F: 0.539\n",
      "mean n_tracksters:\tP: 10.000 R: 32.550 F: 56.150\n",
      " --- Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.174 F: 0.510\n",
      "\treco_to_sim:\tP: 0.769 R: 0.239 F: 0.533\n",
      "\t|S| = 10 |T| = 28 |R| = 8\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.235 F: 0.589\n",
      "\treco_to_sim:\tP: 0.398 R: 0.390 F: 0.397\n",
      "\t|S| = 10 |T| = 23 |R| = 6\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.262 F: 0.636\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.304 F: 0.674\n",
      "\treco_to_sim:\tP: 0.336 R: 0.422 F: 0.351\n",
      "\t|S| = 10 |T| = 17 |R| = 6\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.204 F: 0.551\n",
      "\treco_to_sim:\tP: 0.514 R: 0.354 F: 0.471\n",
      "\t|S| = 10 |T| = 27 |R| = 4\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.223 F: 0.581\n",
      "\treco_to_sim:\tP: 0.475 R: 0.232 F: 0.393\n",
      "\t|S| = 10 |T| = 19 |R| = 8\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "\treco_to_sim:\tP: 0.402 R: 0.425 F: 0.406\n",
      "\t|S| = 10 |T| = 17 |R| = 7\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.554 R: 0.245 F: 0.442\n",
      "\t|S| = 10 |T| = 13 |R| = 5\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.270 F: 0.642\n",
      "\treco_to_sim:\tP: 0.630 R: 0.330 F: 0.533\n",
      "\t|S| = 10 |T| = 22 |R| = 9\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.347 F: 0.709\n",
      "\treco_to_sim:\tP: 0.613 R: 0.414 F: 0.559\n",
      "\t|S| = 10 |T| = 34 |R| = 13\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.290 F: 0.652\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 15 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.144 F: 0.451\n",
      "\treco_to_sim:\tP: 0.467 R: 0.261 F: 0.403\n",
      "\t|S| = 10 |T| = 37 |R| = 6\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.237 F: 0.592\n",
      "\treco_to_sim:\tP: 0.351 R: 0.388 F: 0.358\n",
      "\t|S| = 10 |T| = 20 |R| = 5\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.355 F: 0.709\n",
      "\treco_to_sim:\tP: 0.700 R: 0.416 F: 0.616\n",
      "\t|S| = 10 |T| = 30 |R| = 15\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.253 F: 0.616\n",
      "\treco_to_sim:\tP: 0.413 R: 0.360 F: 0.401\n",
      "\t|S| = 10 |T| = 27 |R| = 12\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.195 F: 0.537\n",
      "\treco_to_sim:\tP: 0.395 R: 0.289 F: 0.368\n",
      "\t|S| = 10 |T| = 32 |R| = 16\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.191 F: 0.532\n",
      "\treco_to_sim:\tP: 0.468 R: 0.305 F: 0.423\n",
      "\t|S| = 10 |T| = 42 |R| = 11\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.237 F: 0.595\n",
      "\treco_to_sim:\tP: 0.435 R: 0.389 F: 0.425\n",
      "\t|S| = 10 |T| = 14 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.965 R: 0.251 F: 0.607\n",
      "mean reco_to_sim:\tP: 0.478 R: 0.354 F: 0.435\n",
      "mean n_tracksters:\tP: 10.000 R: 22.650 F: 6.850\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.174 F: 0.510\n",
      "\treco_to_sim:\tP: 0.990 R: 0.156 F: 0.479\n",
      "\t|S| = 10 |T| = 28 |R| = 56\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.235 F: 0.589\n",
      "\treco_to_sim:\tP: 0.946 R: 0.155 F: 0.468\n",
      "\t|S| = 10 |T| = 23 |R| = 64\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.262 F: 0.636\n",
      "\treco_to_sim:\tP: 0.990 R: 0.216 F: 0.577\n",
      "\t|S| = 10 |T| = 11 |R| = 25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.304 F: 0.674\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 17 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.204 F: 0.551\n",
      "\treco_to_sim:\tP: 0.956 R: 0.128 F: 0.416\n",
      "\t|S| = 10 |T| = 27 |R| = 46\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.223 F: 0.581\n",
      "\treco_to_sim:\tP: 0.977 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 19 |R| = 55\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "\treco_to_sim:\tP: 0.981 R: 0.279 F: 0.652\n",
      "\t|S| = 10 |T| = 17 |R| = 27\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.989 R: 0.193 F: 0.542\n",
      "\t|S| = 10 |T| = 13 |R| = 27\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.270 F: 0.642\n",
      "\treco_to_sim:\tP: 0.981 R: 0.261 F: 0.632\n",
      "\t|S| = 10 |T| = 22 |R| = 44\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 13 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.347 F: 0.709\n",
      "\treco_to_sim:\tP: 0.966 R: 0.240 F: 0.602\n",
      "\t|S| = 10 |T| = 34 |R| = 86\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.290 F: 0.652\n",
      "\treco_to_sim:\tP: 0.947 R: 0.253 F: 0.611\n",
      "\t|S| = 10 |T| = 15 |R| = 44\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.144 F: 0.451\n",
      "\treco_to_sim:\tP: 0.945 R: 0.113 F: 0.383\n",
      "\t|S| = 10 |T| = 37 |R| = 76\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.237 F: 0.592\n",
      "\treco_to_sim:\tP: 0.935 R: 0.194 F: 0.530\n",
      "\t|S| = 10 |T| = 20 |R| = 51\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.355 F: 0.709\n",
      "\treco_to_sim:\tP: 0.945 R: 0.339 F: 0.696\n",
      "\t|S| = 10 |T| = 30 |R| = 72\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.253 F: 0.616\n",
      "\treco_to_sim:\tP: 0.959 R: 0.232 F: 0.590\n",
      "\t|S| = 10 |T| = 27 |R| = 46\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.195 F: 0.537\n",
      "\treco_to_sim:\tP: 0.970 R: 0.150 F: 0.463\n",
      "\t|S| = 10 |T| = 32 |R| = 89\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.191 F: 0.532\n",
      "\treco_to_sim:\tP: 0.973 R: 0.150 F: 0.464\n",
      "\t|S| = 10 |T| = 42 |R| = 87\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.237 F: 0.595\n",
      "\treco_to_sim:\tP: 0.963 R: 0.197 F: 0.541\n",
      "\t|S| = 10 |T| = 14 |R| = 53\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.965 R: 0.251 F: 0.607\n",
      "mean reco_to_sim:\tP: 0.967 R: 0.208 F: 0.548\n",
      "mean n_tracksters:\tP: 10.000 R: 22.650 F: 54.400\n",
      " --- Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.175 F: 0.511\n",
      "\treco_to_sim:\tP: 0.657 R: 0.243 F: 0.490\n",
      "\t|S| = 10 |T| = 25 |R| = 6\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.247 F: 0.604\n",
      "\treco_to_sim:\tP: 0.362 R: 0.410 F: 0.371\n",
      "\t|S| = 10 |T| = 18 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.673\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.304 F: 0.674\n",
      "\treco_to_sim:\tP: 0.335 R: 0.426 F: 0.349\n",
      "\t|S| = 10 |T| = 16 |R| = 5\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.549\n",
      "\treco_to_sim:\tP: 0.512 R: 0.355 F: 0.471\n",
      "\t|S| = 10 |T| = 25 |R| = 2\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.224 F: 0.581\n",
      "\treco_to_sim:\tP: 0.467 R: 0.240 F: 0.393\n",
      "\t|S| = 10 |T| = 17 |R| = 5\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "\treco_to_sim:\tP: 0.346 R: 0.429 F: 0.360\n",
      "\t|S| = 10 |T| = 17 |R| = 5\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.454 R: 0.266 F: 0.398\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.291 F: 0.663\n",
      "\treco_to_sim:\tP: 0.505 R: 0.345 F: 0.462\n",
      "\t|S| = 10 |T| = 18 |R| = 6\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.347 F: 0.708\n",
      "\treco_to_sim:\tP: 0.456 R: 0.436 F: 0.452\n",
      "\t|S| = 10 |T| = 27 |R| = 8\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.931 R: 0.295 F: 0.651\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.146 F: 0.454\n",
      "\treco_to_sim:\tP: 0.464 R: 0.261 F: 0.402\n",
      "\t|S| = 10 |T| = 30 |R| = 3\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.350 R: 0.389 F: 0.357\n",
      "\t|S| = 10 |T| = 18 |R| = 4\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.384 F: 0.731\n",
      "\treco_to_sim:\tP: 0.290 R: 0.550 F: 0.320\n",
      "\t|S| = 10 |T| = 24 |R| = 6\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.959 R: 0.257 F: 0.620\n",
      "\treco_to_sim:\tP: 0.407 R: 0.364 F: 0.397\n",
      "\t|S| = 10 |T| = 24 |R| = 7\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.181 F: 0.514\n",
      "\treco_to_sim:\tP: 0.421 R: 0.296 F: 0.388\n",
      "\t|S| = 10 |T| = 23 |R| = 7\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.193 F: 0.535\n",
      "\treco_to_sim:\tP: 0.352 R: 0.329 F: 0.347\n",
      "\t|S| = 10 |T| = 34 |R| = 6\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.434 R: 0.389 F: 0.424\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.257 F: 0.613\n",
      "mean reco_to_sim:\tP: 0.423 R: 0.367 F: 0.400\n",
      "mean n_tracksters:\tP: 10.000 R: 19.350 F: 3.900\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.175 F: 0.511\n",
      "\treco_to_sim:\tP: 0.990 R: 0.156 F: 0.479\n",
      "\t|S| = 10 |T| = 25 |R| = 56\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.247 F: 0.604\n",
      "\treco_to_sim:\tP: 0.946 R: 0.166 F: 0.488\n",
      "\t|S| = 10 |T| = 18 |R| = 62\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.673\n",
      "\treco_to_sim:\tP: 0.989 R: 0.258 F: 0.631\n",
      "\t|S| = 10 |T| = 10 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.304 F: 0.674\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 16 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.549\n",
      "\treco_to_sim:\tP: 0.954 R: 0.157 F: 0.473\n",
      "\t|S| = 10 |T| = 25 |R| = 45\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.224 F: 0.581\n",
      "\treco_to_sim:\tP: 0.977 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 17 |R| = 55\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "\treco_to_sim:\tP: 0.981 R: 0.279 F: 0.652\n",
      "\t|S| = 10 |T| = 17 |R| = 27\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.978 R: 0.193 F: 0.539\n",
      "\t|S| = 10 |T| = 13 |R| = 26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.291 F: 0.663\n",
      "\treco_to_sim:\tP: 0.978 R: 0.270 F: 0.641\n",
      "\t|S| = 10 |T| = 18 |R| = 42\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 13 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.347 F: 0.708\n",
      "\treco_to_sim:\tP: 0.963 R: 0.238 F: 0.599\n",
      "\t|S| = 10 |T| = 27 |R| = 86\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.931 R: 0.295 F: 0.651\n",
      "\treco_to_sim:\tP: 0.946 R: 0.253 F: 0.612\n",
      "\t|S| = 10 |T| = 12 |R| = 42\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.146 F: 0.454\n",
      "\treco_to_sim:\tP: 0.937 R: 0.128 F: 0.414\n",
      "\t|S| = 10 |T| = 30 |R| = 74\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.933 R: 0.211 F: 0.554\n",
      "\t|S| = 10 |T| = 18 |R| = 50\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.384 F: 0.731\n",
      "\treco_to_sim:\tP: 0.945 R: 0.359 F: 0.713\n",
      "\t|S| = 10 |T| = 24 |R| = 71\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.959 R: 0.257 F: 0.620\n",
      "\treco_to_sim:\tP: 0.959 R: 0.233 F: 0.591\n",
      "\t|S| = 10 |T| = 24 |R| = 45\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.181 F: 0.514\n",
      "\treco_to_sim:\tP: 0.970 R: 0.150 F: 0.464\n",
      "\t|S| = 10 |T| = 23 |R| = 88\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.193 F: 0.535\n",
      "\treco_to_sim:\tP: 0.973 R: 0.150 F: 0.464\n",
      "\t|S| = 10 |T| = 34 |R| = 87\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.962 R: 0.209 F: 0.560\n",
      "\t|S| = 10 |T| = 11 |R| = 50\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.257 F: 0.613\n",
      "mean reco_to_sim:\tP: 0.965 R: 0.216 F: 0.559\n",
      "mean n_tracksters:\tP: 10.000 R: 19.350 F: 53.450\n",
      " --- Radius threshold: 25 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.191 F: 0.537\n",
      "\treco_to_sim:\tP: 0.413 R: 0.275 F: 0.376\n",
      "\t|S| = 10 |T| = 20 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.248 F: 0.605\n",
      "\treco_to_sim:\tP: 0.362 R: 0.410 F: 0.371\n",
      "\t|S| = 10 |T| = 16 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "\treco_to_sim:\tP: 0.300 R: 0.452 F: 0.321\n",
      "\t|S| = 10 |T| = 14 |R| = 3\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.511 R: 0.356 F: 0.470\n",
      "\t|S| = 10 |T| = 23 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.227 F: 0.586\n",
      "\treco_to_sim:\tP: 0.429 R: 0.269 F: 0.383\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.347 F: 0.718\n",
      "\treco_to_sim:\tP: 0.346 R: 0.429 F: 0.360\n",
      "\t|S| = 10 |T| = 15 |R| = 5\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.454 R: 0.266 F: 0.398\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.504 R: 0.345 F: 0.462\n",
      "\t|S| = 10 |T| = 15 |R| = 4\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.356 F: 0.715\n",
      "\treco_to_sim:\tP: 0.370 R: 0.466 F: 0.386\n",
      "\t|S| = 10 |T| = 24 |R| = 5\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.147 F: 0.455\n",
      "\treco_to_sim:\tP: 0.459 R: 0.266 F: 0.401\n",
      "\t|S| = 10 |T| = 28 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.350 R: 0.389 F: 0.357\n",
      "\t|S| = 10 |T| = 16 |R| = 3\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.402 F: 0.743\n",
      "\treco_to_sim:\tP: 0.288 R: 0.553 F: 0.319\n",
      "\t|S| = 10 |T| = 22 |R| = 3\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.261 F: 0.624\n",
      "\treco_to_sim:\tP: 0.405 R: 0.365 F: 0.396\n",
      "\t|S| = 10 |T| = 20 |R| = 5\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.181 F: 0.514\n",
      "\treco_to_sim:\tP: 0.428 R: 0.296 F: 0.393\n",
      "\t|S| = 10 |T| = 21 |R| = 5\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.194 F: 0.536\n",
      "\treco_to_sim:\tP: 0.351 R: 0.330 F: 0.347\n",
      "\t|S| = 10 |T| = 28 |R| = 3\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.434 R: 0.389 F: 0.424\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.262 F: 0.617\n",
      "mean reco_to_sim:\tP: 0.403 R: 0.374 F: 0.389\n",
      "mean n_tracksters:\tP: 10.000 R: 17.250 F: 2.500\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.191 F: 0.537\n",
      "\treco_to_sim:\tP: 0.980 R: 0.160 F: 0.484\n",
      "\t|S| = 10 |T| = 20 |R| = 55\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.248 F: 0.605\n",
      "\treco_to_sim:\tP: 0.946 R: 0.174 F: 0.502\n",
      "\t|S| = 10 |T| = 16 |R| = 61\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.989 R: 0.258 F: 0.631\n",
      "\t|S| = 10 |T| = 10 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 14 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.954 R: 0.157 F: 0.473\n",
      "\t|S| = 10 |T| = 23 |R| = 45\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.227 F: 0.586\n",
      "\treco_to_sim:\tP: 0.977 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 13 |R| = 55\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.347 F: 0.718\n",
      "\treco_to_sim:\tP: 0.981 R: 0.306 F: 0.681\n",
      "\t|S| = 10 |T| = 15 |R| = 26\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "\treco_to_sim:\tP: 0.978 R: 0.193 F: 0.539\n",
      "\t|S| = 10 |T| = 13 |R| = 26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.978 R: 0.270 F: 0.642\n",
      "\t|S| = 10 |T| = 15 |R| = 41\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 13 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.356 F: 0.715\n",
      "\treco_to_sim:\tP: 0.963 R: 0.242 F: 0.603\n",
      "\t|S| = 10 |T| = 24 |R| = 85\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.946 R: 0.253 F: 0.612\n",
      "\t|S| = 10 |T| = 11 |R| = 42\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.147 F: 0.455\n",
      "\treco_to_sim:\tP: 0.936 R: 0.129 F: 0.416\n",
      "\t|S| = 10 |T| = 28 |R| = 73\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.933 R: 0.211 F: 0.554\n",
      "\t|S| = 10 |T| = 16 |R| = 49\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.402 F: 0.743\n",
      "\treco_to_sim:\tP: 0.944 R: 0.374 F: 0.724\n",
      "\t|S| = 10 |T| = 22 |R| = 70\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.261 F: 0.624\n",
      "\treco_to_sim:\tP: 0.958 R: 0.235 F: 0.593\n",
      "\t|S| = 10 |T| = 20 |R| = 44\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.181 F: 0.514\n",
      "\treco_to_sim:\tP: 0.966 R: 0.151 F: 0.464\n",
      "\t|S| = 10 |T| = 21 |R| = 85\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.194 F: 0.536\n",
      "\treco_to_sim:\tP: 0.973 R: 0.149 F: 0.462\n",
      "\t|S| = 10 |T| = 28 |R| = 86\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.962 R: 0.209 F: 0.560\n",
      "\t|S| = 10 |T| = 11 |R| = 50\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.262 F: 0.617\n",
      "mean reco_to_sim:\tP: 0.964 R: 0.219 F: 0.563\n",
      "mean n_tracksters:\tP: 10.000 R: 17.250 F: 52.800\n",
      " --- Radius threshold: 30 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.219 F: 0.579\n",
      "\treco_to_sim:\tP: 0.413 R: 0.275 F: 0.376\n",
      "\t|S| = 10 |T| = 18 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.606\n",
      "\treco_to_sim:\tP: 0.362 R: 0.410 F: 0.371\n",
      "\t|S| = 10 |T| = 14 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "\treco_to_sim:\tP: 0.300 R: 0.452 F: 0.321\n",
      "\t|S| = 10 |T| = 14 |R| = 3\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.511 R: 0.356 F: 0.470\n",
      "\t|S| = 10 |T| = 23 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.429 R: 0.269 F: 0.383\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "\treco_to_sim:\tP: 0.345 R: 0.430 F: 0.359\n",
      "\t|S| = 10 |T| = 14 |R| = 4\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.454 R: 0.266 F: 0.398\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.313 R: 0.382 F: 0.325\n",
      "\t|S| = 10 |T| = 14 |R| = 2\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.954 R: 0.357 F: 0.715\n",
      "\treco_to_sim:\tP: 0.370 R: 0.467 F: 0.386\n",
      "\t|S| = 10 |T| = 23 |R| = 4\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.158 F: 0.474\n",
      "\treco_to_sim:\tP: 0.459 R: 0.266 F: 0.401\n",
      "\t|S| = 10 |T| = 24 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.350 R: 0.390 F: 0.357\n",
      "\t|S| = 10 |T| = 16 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.942 R: 0.406 F: 0.745\n",
      "\treco_to_sim:\tP: 0.288 R: 0.553 F: 0.319\n",
      "\t|S| = 10 |T| = 19 |R| = 3\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.625\n",
      "\treco_to_sim:\tP: 0.404 R: 0.366 F: 0.396\n",
      "\t|S| = 10 |T| = 19 |R| = 4\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.181 F: 0.515\n",
      "\treco_to_sim:\tP: 0.428 R: 0.297 F: 0.393\n",
      "\t|S| = 10 |T| = 19 |R| = 3\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.196 F: 0.540\n",
      "\treco_to_sim:\tP: 0.352 R: 0.330 F: 0.347\n",
      "\t|S| = 10 |T| = 25 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.434 R: 0.389 F: 0.424\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.265 F: 0.621\n",
      "mean reco_to_sim:\tP: 0.393 R: 0.376 F: 0.382\n",
      "mean n_tracksters:\tP: 10.000 R: 16.050 F: 2.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.219 F: 0.579\n",
      "\treco_to_sim:\tP: 0.978 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 18 |R| = 54\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.606\n",
      "\treco_to_sim:\tP: 0.946 R: 0.175 F: 0.503\n",
      "\t|S| = 10 |T| = 14 |R| = 60\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.989 R: 0.258 F: 0.631\n",
      "\t|S| = 10 |T| = 10 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 14 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.954 R: 0.157 F: 0.473\n",
      "\t|S| = 10 |T| = 23 |R| = 45\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.977 R: 0.172 F: 0.504\n",
      "\t|S| = 10 |T| = 11 |R| = 54\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "\treco_to_sim:\tP: 0.981 R: 0.307 F: 0.682\n",
      "\t|S| = 10 |T| = 14 |R| = 25\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.978 R: 0.193 F: 0.539\n",
      "\t|S| = 10 |T| = 12 |R| = 26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.978 R: 0.270 F: 0.642\n",
      "\t|S| = 10 |T| = 14 |R| = 41\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.965 R: 0.329 F: 0.696\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 12 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.954 R: 0.357 F: 0.715\n",
      "\treco_to_sim:\tP: 0.963 R: 0.242 F: 0.603\n",
      "\t|S| = 10 |T| = 23 |R| = 85\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.946 R: 0.257 F: 0.616\n",
      "\t|S| = 10 |T| = 11 |R| = 41\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.158 F: 0.474\n",
      "\treco_to_sim:\tP: 0.936 R: 0.134 F: 0.425\n",
      "\t|S| = 10 |T| = 24 |R| = 71\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.256 F: 0.614\n",
      "\treco_to_sim:\tP: 0.932 R: 0.210 F: 0.553\n",
      "\t|S| = 10 |T| = 16 |R| = 49\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.942 R: 0.406 F: 0.745\n",
      "\treco_to_sim:\tP: 0.944 R: 0.374 F: 0.724\n",
      "\t|S| = 10 |T| = 19 |R| = 69\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.625\n",
      "\treco_to_sim:\tP: 0.958 R: 0.236 F: 0.594\n",
      "\t|S| = 10 |T| = 19 |R| = 43\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.181 F: 0.515\n",
      "\treco_to_sim:\tP: 0.965 R: 0.152 F: 0.466\n",
      "\t|S| = 10 |T| = 19 |R| = 82\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.196 F: 0.540\n",
      "\treco_to_sim:\tP: 0.973 R: 0.149 F: 0.462\n",
      "\t|S| = 10 |T| = 25 |R| = 86\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.962 R: 0.209 F: 0.560\n",
      "\t|S| = 10 |T| = 11 |R| = 50\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.962 R: 0.265 F: 0.621\n",
      "mean reco_to_sim:\tP: 0.964 R: 0.220 F: 0.565\n",
      "mean n_tracksters:\tP: 10.000 R: 16.050 F: 52.200\n",
      " --- Radius threshold: 35 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.220 F: 0.580\n",
      "\treco_to_sim:\tP: 0.413 R: 0.275 F: 0.376\n",
      "\t|S| = 10 |T| = 17 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.607\n",
      "\treco_to_sim:\tP: 0.362 R: 0.410 F: 0.371\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.306 F: 0.676\n",
      "\treco_to_sim:\tP: 0.299 R: 0.455 F: 0.321\n",
      "\t|S| = 10 |T| = 13 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.511 R: 0.356 F: 0.470\n",
      "\t|S| = 10 |T| = 22 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.429 R: 0.269 F: 0.383\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.351 F: 0.721\n",
      "\treco_to_sim:\tP: 0.343 R: 0.432 F: 0.358\n",
      "\t|S| = 10 |T| = 12 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.454 R: 0.266 F: 0.398\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.313 R: 0.382 F: 0.325\n",
      "\t|S| = 10 |T| = 14 |R| = 2\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.330 F: 0.696\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.357 F: 0.715\n",
      "\treco_to_sim:\tP: 0.367 R: 0.467 F: 0.384\n",
      "\t|S| = 10 |T| = 22 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.158 F: 0.475\n",
      "\treco_to_sim:\tP: 0.459 R: 0.266 F: 0.401\n",
      "\t|S| = 10 |T| = 22 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.941 R: 0.258 F: 0.615\n",
      "\treco_to_sim:\tP: 0.350 R: 0.390 F: 0.357\n",
      "\t|S| = 10 |T| = 13 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.942 R: 0.406 F: 0.745\n",
      "\treco_to_sim:\tP: 0.288 R: 0.553 F: 0.319\n",
      "\t|S| = 10 |T| = 19 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.625\n",
      "\treco_to_sim:\tP: 0.404 R: 0.366 F: 0.396\n",
      "\t|S| = 10 |T| = 19 |R| = 3\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.182 F: 0.510\n",
      "\treco_to_sim:\tP: 0.427 R: 0.297 F: 0.393\n",
      "\t|S| = 10 |T| = 14 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.204 F: 0.553\n",
      "\treco_to_sim:\tP: 0.352 R: 0.330 F: 0.347\n",
      "\t|S| = 10 |T| = 24 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.434 R: 0.389 F: 0.424\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.960 R: 0.266 F: 0.622\n",
      "mean reco_to_sim:\tP: 0.393 R: 0.376 F: 0.382\n",
      "mean n_tracksters:\tP: 10.000 R: 14.950 F: 1.400\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.220 F: 0.580\n",
      "\treco_to_sim:\tP: 0.978 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 17 |R| = 54\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.607\n",
      "\treco_to_sim:\tP: 0.946 R: 0.175 F: 0.503\n",
      "\t|S| = 10 |T| = 12 |R| = 60\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.989 R: 0.258 F: 0.631\n",
      "\t|S| = 10 |T| = 10 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.306 F: 0.676\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 13 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "\treco_to_sim:\tP: 0.954 R: 0.157 F: 0.473\n",
      "\t|S| = 10 |T| = 22 |R| = 45\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.971 R: 0.169 F: 0.498\n",
      "\t|S| = 10 |T| = 10 |R| = 53\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.351 F: 0.721\n",
      "\treco_to_sim:\tP: 0.981 R: 0.310 F: 0.685\n",
      "\t|S| = 10 |T| = 12 |R| = 23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.978 R: 0.193 F: 0.539\n",
      "\t|S| = 10 |T| = 11 |R| = 26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.978 R: 0.270 F: 0.642\n",
      "\t|S| = 10 |T| = 14 |R| = 41\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.330 F: 0.696\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 11 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.357 F: 0.715\n",
      "\treco_to_sim:\tP: 0.963 R: 0.242 F: 0.603\n",
      "\t|S| = 10 |T| = 22 |R| = 84\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.946 R: 0.257 F: 0.616\n",
      "\t|S| = 10 |T| = 11 |R| = 41\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.158 F: 0.475\n",
      "\treco_to_sim:\tP: 0.936 R: 0.134 F: 0.425\n",
      "\t|S| = 10 |T| = 22 |R| = 71\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.941 R: 0.258 F: 0.615\n",
      "\treco_to_sim:\tP: 0.932 R: 0.211 F: 0.553\n",
      "\t|S| = 10 |T| = 13 |R| = 48\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.942 R: 0.406 F: 0.745\n",
      "\treco_to_sim:\tP: 0.944 R: 0.374 F: 0.724\n",
      "\t|S| = 10 |T| = 19 |R| = 69\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.625\n",
      "\treco_to_sim:\tP: 0.958 R: 0.236 F: 0.594\n",
      "\t|S| = 10 |T| = 19 |R| = 43\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.182 F: 0.510\n",
      "\treco_to_sim:\tP: 0.964 R: 0.153 F: 0.468\n",
      "\t|S| = 10 |T| = 14 |R| = 79\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.204 F: 0.553\n",
      "\treco_to_sim:\tP: 0.962 R: 0.160 F: 0.480\n",
      "\t|S| = 10 |T| = 24 |R| = 83\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.238 F: 0.596\n",
      "\treco_to_sim:\tP: 0.962 R: 0.210 F: 0.560\n",
      "\t|S| = 10 |T| = 11 |R| = 49\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.960 R: 0.266 F: 0.622\n",
      "mean reco_to_sim:\tP: 0.963 R: 0.221 F: 0.565\n",
      "mean n_tracksters:\tP: 10.000 R: 14.950 F: 51.600\n",
      " --- Radius threshold: 40 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.220 F: 0.580\n",
      "\treco_to_sim:\tP: 0.413 R: 0.275 F: 0.376\n",
      "\t|S| = 10 |T| = 17 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.607\n",
      "\treco_to_sim:\tP: 0.362 R: 0.410 F: 0.371\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.463 R: 0.357 F: 0.437\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.306 F: 0.676\n",
      "\treco_to_sim:\tP: 0.298 R: 0.456 F: 0.320\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.209 F: 0.555\n",
      "\treco_to_sim:\tP: 0.511 R: 0.356 F: 0.470\n",
      "\t|S| = 10 |T| = 19 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.429 R: 0.269 F: 0.383\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.351 F: 0.721\n",
      "\treco_to_sim:\tP: 0.342 R: 0.439 F: 0.358\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.454 R: 0.266 F: 0.398\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.482 R: 0.357 F: 0.451\n",
      "\t|S| = 10 |T| = 12 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.313 R: 0.382 F: 0.325\n",
      "\t|S| = 10 |T| = 14 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.331 F: 0.697\n",
      "\treco_to_sim:\tP: 0.340 R: 0.452 F: 0.358\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.357 F: 0.713\n",
      "\treco_to_sim:\tP: 0.367 R: 0.467 F: 0.384\n",
      "\t|S| = 10 |T| = 21 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.362 R: 0.449 F: 0.377\n",
      "\t|S| = 10 |T| = 11 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.162 F: 0.481\n",
      "\treco_to_sim:\tP: 0.459 R: 0.266 F: 0.401\n",
      "\t|S| = 10 |T| = 20 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.941 R: 0.258 F: 0.615\n",
      "\treco_to_sim:\tP: 0.350 R: 0.390 F: 0.357\n",
      "\t|S| = 10 |T| = 13 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.411 F: 0.748\n",
      "\treco_to_sim:\tP: 0.288 R: 0.553 F: 0.319\n",
      "\t|S| = 10 |T| = 18 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.626\n",
      "\treco_to_sim:\tP: 0.403 R: 0.369 F: 0.396\n",
      "\t|S| = 10 |T| = 18 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.183 F: 0.510\n",
      "\treco_to_sim:\tP: 0.428 R: 0.298 F: 0.393\n",
      "\t|S| = 10 |T| = 13 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.205 F: 0.552\n",
      "\treco_to_sim:\tP: 0.352 R: 0.330 F: 0.347\n",
      "\t|S| = 10 |T| = 21 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.254 F: 0.615\n",
      "\treco_to_sim:\tP: 0.434 R: 0.389 F: 0.424\n",
      "\t|S| = 10 |T| = 10 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.959 R: 0.267 F: 0.624\n",
      "mean reco_to_sim:\tP: 0.393 R: 0.377 F: 0.382\n",
      "mean n_tracksters:\tP: 10.000 R: 14.250 F: 1.100\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.220 F: 0.580\n",
      "\treco_to_sim:\tP: 0.978 R: 0.168 F: 0.497\n",
      "\t|S| = 10 |T| = 17 |R| = 54\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.607\n",
      "\treco_to_sim:\tP: 0.946 R: 0.175 F: 0.503\n",
      "\t|S| = 10 |T| = 12 |R| = 60\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "\treco_to_sim:\tP: 0.989 R: 0.258 F: 0.631\n",
      "\t|S| = 10 |T| = 10 |R| = 23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.306 F: 0.676\n",
      "\treco_to_sim:\tP: 0.971 R: 0.278 F: 0.648\n",
      "\t|S| = 10 |T| = 13 |R| = 44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.209 F: 0.555\n",
      "\treco_to_sim:\tP: 0.954 R: 0.157 F: 0.473\n",
      "\t|S| = 10 |T| = 19 |R| = 45\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "\treco_to_sim:\tP: 0.969 R: 0.188 F: 0.529\n",
      "\t|S| = 10 |T| = 10 |R| = 52\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.351 F: 0.721\n",
      "\treco_to_sim:\tP: 0.981 R: 0.310 F: 0.685\n",
      "\t|S| = 10 |T| = 12 |R| = 23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "\treco_to_sim:\tP: 0.978 R: 0.193 F: 0.539\n",
      "\t|S| = 10 |T| = 11 |R| = 26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "\treco_to_sim:\tP: 0.983 R: 0.178 F: 0.517\n",
      "\t|S| = 10 |T| = 12 |R| = 40\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "\treco_to_sim:\tP: 0.978 R: 0.270 F: 0.642\n",
      "\t|S| = 10 |T| = 14 |R| = 41\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.184 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.331 F: 0.697\n",
      "\treco_to_sim:\tP: 0.970 R: 0.283 F: 0.653\n",
      "\t|S| = 10 |T| = 10 |R| = 56\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.183 F: 0.521\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.357 F: 0.713\n",
      "\treco_to_sim:\tP: 0.963 R: 0.242 F: 0.603\n",
      "\t|S| = 10 |T| = 21 |R| = 84\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.958 R: 0.148 F: 0.457\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.296 F: 0.652\n",
      "\treco_to_sim:\tP: 0.946 R: 0.257 F: 0.616\n",
      "\t|S| = 10 |T| = 11 |R| = 40\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.070 F: 0.273\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.162 F: 0.481\n",
      "\treco_to_sim:\tP: 0.936 R: 0.136 F: 0.431\n",
      "\t|S| = 10 |T| = 20 |R| = 70\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.129 F: 0.419\n",
      "\ttarget_to_sim:\tP: 0.941 R: 0.258 F: 0.615\n",
      "\treco_to_sim:\tP: 0.932 R: 0.211 F: 0.553\n",
      "\t|S| = 10 |T| = 13 |R| = 48\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.236 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.411 F: 0.748\n",
      "\treco_to_sim:\tP: 0.944 R: 0.374 F: 0.724\n",
      "\t|S| = 10 |T| = 18 |R| = 69\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.972 R: 0.156 F: 0.476\n",
      "\ttarget_to_sim:\tP: 0.958 R: 0.262 F: 0.626\n",
      "\treco_to_sim:\tP: 0.958 R: 0.236 F: 0.594\n",
      "\t|S| = 10 |T| = 18 |R| = 43\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.092 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.183 F: 0.510\n",
      "\treco_to_sim:\tP: 0.964 R: 0.153 F: 0.468\n",
      "\t|S| = 10 |T| = 13 |R| = 79\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.980 R: 0.133 F: 0.431\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.205 F: 0.552\n",
      "\treco_to_sim:\tP: 0.962 R: 0.160 F: 0.480\n",
      "\t|S| = 10 |T| = 21 |R| = 83\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.136 F: 0.434\n",
      "\ttarget_to_sim:\tP: 0.955 R: 0.254 F: 0.615\n",
      "\treco_to_sim:\tP: 0.962 R: 0.210 F: 0.560\n",
      "\t|S| = 10 |T| = 10 |R| = 49\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.974 R: 0.150 F: 0.455\n",
      "mean target_to_sim:\tP: 0.959 R: 0.267 F: 0.624\n",
      "mean reco_to_sim:\tP: 0.963 R: 0.222 F: 0.567\n",
      "mean n_tracksters:\tP: 10.000 R: 14.250 F: 51.450\n"
     ]
    }
   ],
   "source": [
    "r_ranges = range(5, 45, 5)\n",
    "results = []\n",
    "\n",
    "clue3D_F = []\n",
    "target_F = []\n",
    "naive_reco_F = []\n",
    "model_reco_F = []\n",
    "\n",
    "max_events = 20\n",
    "\n",
    "for r in r_ranges:\n",
    "    print(f\" --- Radius threshold: {r} ---\")\n",
    "    result = model_evaluation(\n",
    "        cluster_data,\n",
    "        trackster_data,\n",
    "        simtrackster_data,\n",
    "        assoc_data,\n",
    "        DummyPleaser(),\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=10,\n",
    "    )\n",
    "    clue3D_F.append(np.sum(np.array(result[\"clue3d_to_sim\"])[:,2]) / max_events)\n",
    "    target_F.append(np.sum(np.array(result[\"target_to_sim\"])[:,2]) / max_events)\n",
    "    naive_reco_F.append(np.sum(np.array(result[\"reco_to_sim\"])[:,2]) / max_events)\n",
    "\n",
    "    result = model_evaluation(\n",
    "        cluster_data,\n",
    "        trackster_data,\n",
    "        simtrackster_data,\n",
    "        assoc_data,\n",
    "        model,\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=10,\n",
    "    )\n",
    "    model_reco_F.append(np.sum(np.array(result[\"reco_to_sim\"])[:,2]) / max_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a91584f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1NyIi6jgCLiQDwKJFizB37lxcfPHFGDFiBF555RVYrVbfbBe33norunTpgqVLvUu0lpeX4+DBg76vz549i7179yIoKAi9enk/dn3ooYdw9dVXo3v37sjKysJTTz0FlUqF2bNnK/MkiTo5WZZ9Y4dLd3+Osytvhuxy+jdSqaHvPsxvaragARN9gZmIiOh8BWRIvvHGG3Hu3DksXrwYOTk5GDJkCDZs2OC7mC8zMxNitfGFWVlZGDq0ahnWZcuWYdmyZRg7diy2bNkCADhz5gxmz56NgoICREVF4dJLL8XPP/+MqKioNn1uRJ2RLHngPHvQr5c4YupfEHaFdxlmbXQvyC4nVEERFT3E3qEThsSLIeqMCldPREQdkSDLcu11U+m8lJaWIiQkBCUlJTCbzUqXQ9SuuS0FKPruTdiObIf92M+Q7P6zw4Rcciu63PkBAG+ILs87Bm1Msq93mYiI6Hw0Na8FZE8yEQUOWZbhyjsO29EdEHVBMF88CwAgCCqc+/JpoOLvdEFngrHnqKpp2HqO8p1DEFXQxV6kRPlERNRJMSQTUYuSyh1wnEr3DZuwHd0BT4l3NhrDRZf6QrLKFIqIqQ9BE9kDxl5joOs6AIKKv5KIiKh94P9IRHRBPPZSqAzej6tkWcbRR5LhLjzj10ZQa6HvMRym3mP9tsfc+GKb1UlERNQcDMlE1GSy5IHz9G+wHd0B+xFvL7HsLkfyy6chCAIEQYC++zDY3eV+K9jpewyHqNUrXT4REVGTMSQTUaOKtq5CaerH3gvsHBb/nYIAd3E2NGHxAIAud/0Loj6IF9gREVFAY0gmIgDeoRLluUe8PcRHdiB2zssQdSYAgPPMflgPfAcAEPXBMPQaDUOv0RUX2I2EyhjiO0/lyndERESBjCGZqJOSyu2wn9jtNzexpyzftz9kzByY+njHEJtH3QRtXO+KC+z6QxBVSpVNRETUJhiSiToJV1EWRJ3J1+tbvPWfyPnX/X5tBI0O+h4Xw5g8BuqQWN92Y8+RMPYc2ab1EhERKYkhmagDkj1uODJ/hf3oTu9Fdkd3wJV/CnG3r0LY5X8CABh6eYOwIfkS3wp2+u5DIWp0CldPRESkPIZkog7EmfM7st+7C/bjuyCX2/x3CiJcBZm+u/oew5D8ahYvsCMiIqoDQzJRG3MVZMJdbexvTergSGgiutW7X5ZllGdn+KZh03cfivAJ9wIAVKZw2A5vAQCIxlAYe42GodcYGJPHQJ+Y4ndRHcMxERFR/RiSidqQqyATRx/pDdnlqLeNoNGj1wsZvqAsSx7YMn7yhWL70Z3wWAt97Y39rvSFZHVwJLrc8xH0CYOgjesDQRRb9wkRERF1UAzJRG3IXZbfYEAGANnlgOXg9wi77LaKLQJOvzYLkq3Y10bQ6GFIGgFDrzEw9R3nd3zIqJtatGYiIqLOiCGZqB0699njvpAsiCLMw2dBclqrVrDrNhiCWqtskURERB0YQzJROyQaQyE5rb7FPOLveFfhioiIiDoXDlgkaoe63PUvX0AmIiKitseQTERERERUA0MyURsqzz2mdAlERETUBAzJRG2kJHUtzq6crXQZRERE1AQMyURtoPin93H27zcDkgcQGn7bCRo91MGRbVQZERER1YWzWxC1ssLNbyHnQ+9iH6Fj5yHy6sf9FgOpqbEV94iIiKj1MSQTtaL8b5Yhb+1fAADhkx5AzM0ve5eDjuqhbGFERETUIA63IGol5/7zrC8gR179eFVAJiIionaPPclErUQdEgcAiLr+fxF19eMKV0NERETNwZBM1ErCxt0BQ+LF0HcfonQpRERE1EwcbkHUQmTJg7zPn4K79JxvGwMyERFRYGJIJmoBstuFsytvQf5/nkHm8qmQJY/SJREREdEF4HALogskuZw4+9aNKEv/D6DSIHL6YxBEldJlERER0QVgSCa6AJLThtOvzYJ1/0YIGh263vc5ggdPU7osIiIiukAMyUTnyWMvw+mXp8OW8SMEnQndFnwFU78rlS6LiIiIWgBDMtF5yn5vHmwZP0I0mNHtwW9hTB6jdElERETUQhiSic5T9A1L4cw+jPg/rYIhcbjS5RAREVELYkgmagbZ7YKg1gAAtFGJSFqSDkHkJDFEREQdDf93J2qi8vxTOPbEQO8sFhUYkImIiDom/g9P1ATOnCM4+b+XoTw7A7mfPgbZ41a6JCIiImpFHG5B1AjHmQPIfHEC3CU50Mb1Qfe/bIKg4luHiIioI+P/9EQNsJ9MR+ZLk+CxFECXMAjdH94EtTla6bKIiIiolTEkE9XDdnQnMpdPhWQrgT4xBd0f2gBVULjSZREREVEbYEgmqkdp6ieQbCUwXHQpui1aD5XBrHRJRERE1EYYkonqETN7OTThXRF25d0QdSalyyEiIqI2xNktiKqx/b4dstsFwDu9W8TUBxmQiYiIOiGGZKIKJTtW4+TSsTj79q2QJY/S5RAREZGCONyCCEDRlneQ/f5dgCxD0OiVLoeIiIgUxp5k6vQKNr6K7PfuBGQZYeP/jPjbV0EQVUqXRURERApiSKZO7dzXzyN39QIAQMTUhxD7hze41DQRERExJFPnde4/z+LcZ38FAETOfArRN74IQRAUroqIiIjaA45Jpk7LmHwJBI0eUbOWIPKqh5Uuh4iIiNoRhmTqtEz9rkSvFzKgieimdClERESdkiTLOFToQrFDQqheRN9wDcR28qkuQzJ1GrLHjdyPHkTYFXdB16UfADAgExERKSQ124H3D1pQ6JB828L1Im7rF4SRccrPNMUxydQpyO5ynPn7bBRueg2Zy6dCKncoXRIREVGnlZrtwIr0Ur+ADACFDgkr0kuRmq38/9MMydThSeV2nH5tFsp++QyCWovYW16DqFX+L1QiIrowkizjQEE5tp914EBBOSRZVrokagJJlvH+QUuDbT44aFH89eRwC+rQJIcFma/OgO3g9xA0eiQ88CWCBk5WuiwiIrpA7f2j+kAkyTLcEuCWKv6VZbiq3/fbLsNTY5tbAlzV28p1HCvJyHd4avUg11TgkHCo0IX+Edo2eva1MSRTh+WxlSBz+TTYj+6AqA9CwsJ1MPUZq3RZRER0gSo/qq+p8qP6RcPQroKyLMvw1AyMco1AWS1o1hVKqwJoA21rPIar3rBad9v21g9f3EiQbm0MydRh5X3yqDcgG0PR7aENMPYcqXRJAACP9TQkx7l694v6aKhMXduwIiKiwNGUj+rfPWBBfJAKkizUCJX1h9L6A2Xl9ia2raM31q1s1jtvKgFQiwLUovdfjQiohar76rruiwLUQmV7/22qivsFdg82ZTY+5jhUr+yoYIZk6rCi/+dvcBWcQvT1z0PffYjS5QAAZI8T+V+nQHLk1ttG1Mci+oaTEFS6NqyMiKj1SLKMcg/g8MhwemQ43XLV19XuOyq+rtxe2cZRbVuxQ2r0o/pip4SHfixqo2fXfAJQK2hq6gmaftuqhdL6AqimzvDqPb/K77z1tfVuVwlotQW2JFlGWl55g69jRMV0cEpiSKYOxWMvg8oQDABQGUPQ7cFvFK6oBlELlalbRU9yXb8cRKhMCYCo3Bgsos6iPc/PqoTKIOusFkzrCrI1Q2v14Ft5v/p+763tn49OBejVItRCjUDZSO9nVXj0D5++ntQabes7Z/Vz1OyN7cw/Z4D3+d/WL6jOITOV5vYLUvz7xJDcCiRJgiTV/deRKIp+7RrCts1rW37uBDJfnICICfMRMfXBRs8rCILvr+S2bGsa+hyKvp/pvSM5IVSEZVlQA4IGpqHPQZZlyDWu6q1+3rr2B3JboOp17shtgfb/PurobSt/LlOzHfjwYBlKnFXtw3QCbukbhJRYvV/b5py3tdtWD7Ll1QJpuUeGUwKclb21bgnlHsm3zyF5w2p5RXi1eQC723sel0eCW6o/yLplQIa3BgEy1A3kFo8MSHW01VQ7RqcCtCrvR/FatQidSoBeBRhVgFYteO+L3n91Ffe1KhF6tQidWkC2xYW1GdZGa3gkJRT9wjWKv+/928qAjFqzNrSH90Zbt02J0WLR0CD865AFRU7v98MlC4jQi5jbLwgpMdoGz30hNTTWvhJDcis4fPgwgoKCam0PCgpCjx49fPcPHTpU75vMaDQiKSnJdz8jIwMeT92/wQwGA3r27Om7f+TIEbhcrjrb6nQ6JCcn++4fO3YMTqezzrYajQa9e/f23T9x4gTsdnudbVUqFfr27eu7f/LkSdhstjrbCoKA/v37++5nZmbCYql/fNmAAQN8X585cwalpfX/5SmU5KLoh38g7Mq7kXWuCMXFxfW27dOnD9Rq71sgJycHhYWF9ba96KKLoNV6e3fz8vKQn59fb9tevXpBr/f+J3vu3DmcO1dz/HE8MGwXACDk4E1Q2w4AABwxt8LWdSEKiwEUH6x13h49evh+rgoLC5GdnV1vDd27d0dwsLdHvbi4GGfPnq23bUJCAkJCQgAApaWlOH36dL1tu3TpgrCwMACAxWLBqVOn6m0bFxeHiIgIAIDVasXJkyfrbRsTE4OoqCgAgN1ux/Hjx+ttGxUVhZiYGACA0+nE0aNH620bGRmJ2NhYAIDL5cLvv/9eb9vw8HDEx8cDADweDw4fPlxv29DQUHTt6h03LssyDh6s/XpVMpvN6NatatGahtryd4RXa/6O6NevH3blOLEivRTXRJRicEyNcZGFeThY8avgfH9H5ObmoqCgoN62ZeYE2KCF0yPD5ChEhKe43rZfl0Qgy6mBwyNjsMGCcaF1h0MBwCc5oTjl9NZwcZANUyOqfc/EilvFp9cf5YXgjN07pGuQyY4ZkWX11vBDWShyJCP0KgHdNXYM09X/fbAZoyGaQqBTCdC4rHAX1P+7p/rvCIvFUvt3hKfiBv/fEdZgDxLt9V/XsbXYiANOM/qGa/g7op3/jjABuMv76xySIEIV28v3ic7x48db7XdEVlZWvW2rY0imDkUX2wfdH1oPUWcC0H7Ho9VJDtArO4gCSFMu+qr04xkHHLIIp0dGuNOFiAbaPrWjEAUuFRwe4NJgO8aE1N929WErzrm8oeLykHKMDa2/ba5NQl6593eDu5HJGroEqRAUpIFeJSBJW95g2+t6maAxBkGnEiDaRTgK6w/Jt/YL9v0hXVIi4vTp+kNycpgGYWHe8F1W5sSp+v9WOG9N+QC+PXxUT82jEQX0VXC6t7oIcmOfF1CTlZaWIiQkBEVFRTCbzXW2ac8ffQRiW/uJNGS+OgOSrRj6hEHotvBraMxRTTpvW3w86racge3kp3CcWAt30b6qRioDBNEA2ZkDoaKrRBWegoirdtZ7oUR7GBbB4RYX1hZon++jjt5WkmUU2CXk2NzYk1eOb095A6oKMsQGcpRLBiojWXPaipChqmhbObRAJ8I3fECtEqFTeYcZGNSAQaxoU214gU4lQK8WvO3UIvQqAVoVoBUBfcU5a4bA9vBebsvfEb/kOPw+qgeAcJ2AOX2DMTLecN7nVbIt0H7fRx2pbWlpKcLCwlBSUlJvXgPYk9wqRFH0e0Eaatecc7Ktf1trxk84veIqyI4yGHuOQrcHv4XKFNqmNdRFchbBfurfsB9fjfKcraiceVIQ1NB1mQJD0s3QJVwDV942FG6a4jtOHdQVKpWqSTVU/8+FbQOnLdD+3kcdpa0syygpl5FtdSPb4kG2tfLmRq7NA1cd/4d6IMBTT3bpGqRCtFFVMV62MrBWBVm9qmaYrdjm+7ruINtW2sPPe2u3HRlvREqcocGLL9tTvU0VKO+5QG7b1PYMyRSwnJl7ITnKYOwzDgkLvvLNaqEE2e2A48w62I+vhvPMN4BU9VGnJvpSGJLmwNDjeoj6SN92bfwkaCJS4Cr4BQDgzPwPXIW/QhM+uM3rJwoUNpfkF4CrvvbA7q6/t04tArFGFUwaARlF7kYf508DghVd6YuaRhQEvk7UahiSKWCFT7wPquAoBA+bAVFraPPHlyUPynN+gP34ajhOfQ7ZVXWxkDp0AAxJc6BPvAnq4B51Hi8IAoKHP4+S1Psh6iLhyvsJJTvvQcS0bRCEpv9VTNTRlHtk5Ng8yLa4K/6tCsUl5Q18fA8gyiAiLkiNOJMKcSYVYk0qxJvUiDSIEAUBkizj3u8L2v38rESkvIAdk/zmm2/ipZdeQk5ODgYPHozXX38dI0aMqLPtgQMHsHjxYqSlpeHUqVN4+eWXsWDBggs6Z10qxyQ3NsaFzl/Zr9/A2GuM37CKtiTLMlwFaXAcXw37iY8h2XN8+0RTAgyJN8PQcw40YQObdV6P9QzOfdEXstuCkDHvwHjRHS1dOlG74pFknLN7/HqCcyp6hvPtUoPL44bpRMRWhGDvzRuKY4wqaFSNf6xd35LGlRYNM7erJY2JqGU1Na8FZE/y2rVrsWjRIqxcuRIjR47EK6+8gsmTJyMjIwPR0dG12ttsNiQlJeGGG27AwoULW+Sc1PaKt32ArH/+CYakEej+yHcVM1i0DXfpEdiPr4H9+Bp4SqumCBJ04TB0v8EbjKMvOe8eYJWpK4KGLkHZLw+iNO0R6LvN9BuaQRSIZFlGkbPa8AhL1fCIXJun3vHAAGBUC4gPquwNVlcLxCoY1Bf2ScvIOD0WDQPeP2jx61GunJ+VAZmIgADtSR45ciRSUlLwxhtvAPBeqZiQkID77rsPjz76aIPH9ujRAwsWLKjVk3wh56zEnuTWU7j5LeR8eC8AIHTsHYi7bSUEsWkXuZ0vjy0HjpNrYT++Gq78X6p2qAzQJ1wDQ8850MVPhqBqmfFwsuRG/tfD4S7aB0PynxB6yaoWOS9Ra7OUS8iqGA6RY/XvHXY2kIQ1Ivx6gqsPjwjWNu9ip/PBFfeIOqcO25NcXl6OtLQ0PPbYY75toihiwoQJ2LlzZ5ue0+l0+k2g3dAE9nT+8r9Zhry1fwEAhE+8HzFzXmm99eTLS+HI/MI7M0X25qq5iwUVdHEToO85x9vLq2n5iwQFUY2Q0X9HwTeXwH7kXRh7/QnamEta/HGIzofDLSPHVtETbPEfHlHmqj8IiwIQbajoBQ7yHx4RrhcVDaW86IuIGhJwITk/Px8ej8e34lalmJiYBle/aY1zLl26FEuWLDmvx6TGybKM/P88i3NfPAUAiJj+GKKv/98WD8iyxwnn2Q3eC/BOfw14qlbg0kSOhKHnHOh7/A9UhpgGztIytNFjYEi+HfYjq1Cy825EXpMOQeQFRNQ23JKMPJunztkjGrrQDfAOVajsCa7eMxxtVEHd0ATDRETtVMCF5Pbksccew6JFi3z3S0tLkZCQoGBFHUvB+hd8ATnquucQdc1fW+zcsiyhPPcnbzA++Rnk8qrV+VTm3jD0nAND4s1Qm3s2cJbWYR7+AhyZX8JdvB/Wg68haMCDbV4DdVySLKPQUTE8omKMcOUQiTy7B1IDA/CCNUJFj7D/8IhYoxp6NYMwEXUsAReSIyMjoVKpkJub67c9NzfXt/56W51Tp9NBp9Od12NS44KHz0LBxlcQedWjiJi84ILPJ8sy3IW/wn5iDezHP4JkO+PbJxriYEiaDUPSHKjDh7b6WMiGiPoImC9+ESXbb4dl71MwJP4PVCb+8UVNJ8sySisX1rD69wznWOteWKOSTiX4XSRXfXhEkJZTExJR5xFwIVmr1WL48OHYvHkzZs6cCcB7kd3mzZsxf/78dnNOunC6uN7otfTwBU/35i47AfvxNXCcWAN38UHfdkETAn2P62FIuhnamLGtfiFgcxh63QbbkVVw5e1A6a6FCLviM6VLonbI5pKq5hG2+c8eYWtgYQ2VAMQYVYgPUiHW6N8zHKYTFf0jkYiovQi4kAwAixYtwty5c3HxxRdjxIgReOWVV2C1WvHHP/4RAHDrrbeiS5cuWLp0KQDvhXkHDx70fX327Fns3bsXQUFB6NWrV5POSa1PdruQ9d48hIy6GUEDJwHAeQdkj+McHCc+gf3EGrjydlTtEHXQJ0yHPulm6LtMg6Bun1M9CYKIkFF/R/7Xw+A49W84znwLfdepSpdFCnB5ZORWjBPOsvoPjyh21t8lLACINIh+PcGVcwtHGVRQcZwwEVGDAjIk33jjjTh37hwWL16MnJwcDBkyBBs2bPBdeJeZmem3LndWVhaGDh3qu79s2TIsW7YMY8eOxZYtW5p0TmpdksuJs2/diLL0/6As/UskLzvZ7IAsuSxwZv4H9hNr4Dz7X0D2VOwRoI27Eoakm6Hvdi1EXfPOqxRN+CCY+i2A9cBylKbOhy52PwR1268sSPVrqSnEJFnGObvkHR5h8R8e0djCGiE6EXHG6rNHVC2soW3CwhpERFS3gJwnub3iPMnnR3LacPr1a2H97b8QNDp0nf9vBA+5qknHypILzrMbvcE480vIbptvnyZiOPRJc2BIvBEqY3xrld+qJFcZzn3RF5LtLIIGPYHgYc8qXRJVSM121FqMIlwv4rZ6FqOQZRnF1RfWqDZ7RK7NA3cD44QN6prjhKuGRxg1HCdMRNQcTc1rDMktiCG5+Tz2Mpx+eTpsGT9C0BqRsOArBPUf3+AxsizBlbcT9uOrYT/5CWRngW+fKrgnDElzYEicDXVon9Yuv03YT/4bxVuuB0Qtombsgzqkt9IldXqNLWt8cx8TwnUqZFvdFYtseIdIOBpZWCPG6A3AVSvNee+HtMHCGkREnUWHXUyEOg6PtQiZy6fCfiwVosGMbou+gfGi+hfPcBUd8E7ZdnwNPNZTvu2iPhr6xJtgSLoZmsgRHS5M6LtfC12XqXCe/RYlP9+L8EmbOtxzDCQOt4R3D5Q12GbNYWud2wUA0ca6Z46IMCi7sAYREfljSCbFFGx8FfZjqVCZwtHtLxthSBxeq43Hehr24x/Bfnw13EX7fNsFdRD03a+FIWkOtHFXQhA77o+yIAgwj3oD577sj/LszXCc+BiGpNlKl9UhuTwyChzehTMK7BIKHB4UOCQUOjzIt3v/LS1v2odv3YNV6BWmQZxR7VtpLoYLaxARBYyOmyyo3Yu65gl4SnIQNv5e6BMG+rZLzkI4Tn7mXRo698eqA0QNdF2mwpA0B/qE6RDURgWqVoY6OAlBgx6HZc9ilP6yCLqu0yBqQ5QuK6C4PDIKnRIK7N7gW2CvCMMOjy8QNzUAN8WMniZc0qV9zp5CRESNY0imNuUuzoHKHAVBVEFQqRF320oAgOy2wXF6HezHV8N59ltAcvmO0cZc7g3GPa6HqAtXqnTFBQ14GPZj/4Kn9HeU7XkSISNfU7qkdqNmAC6sFnwLKnqAS5oYgDUiEGlQIUIvIlzvHQYRoa+4bxCRb/PgpbT6xyNXCtXzgjoiokDGkExtxplzBKdeGI+gAZMQ98e3AUgoz97sXejj1OeQ3RZfW3XYYBiSboYhaTZXm6sgqHQIGfUmCjdOhO3wmzD2ug2aiGFKl9XqXB4ZRU4J+XX0/BZW9Ag3NwCH66uCb0Tl/YowHKRp+CK5bsFqhOtFv1ktaoqomA6OiIgCF0MytQnHmQPIfHEC3CU5cJzehJId98B55j+QHFVLgatM3aFPuhmGpDnQhPVXsNr2Sxc/AfrEm+A48TFKdt6NiGk729VKgc3lluRawbey57fAIaHAIaGkgQUzqtOI8AZfg7cHOLKi57d6GG4sADeFKAi4rV9Qg7NbzO0XxIvwiIgCHKeAa0GcAq5u9pPpOPP6eKiCiqHrooWoLfftE3QRMPT4H28wjh7DWRuawGPLxrkv+kB2lcI86u8w9blb6ZLqVBmA/YY+VLsgrrAiADflF1BlAA6vCLsR1Xp+vcMgVAhugQDcHHXNkxyhFzG3nnmSiYiofeA8yQpgSPbnsWWh9JcXYPntTaiDPL7tgtoIXcIMGJLmQNdlEgSRH0s3l/XQ6yhNvR+CNhRRsw5DZWjblSHdkowiR/Xg6w29+dV6gZsTgMP1/sE3XC9WDYtQIAA3VUutuEdERG2H8ySTIqTyEjhO/Rv242tQnv09ABnqIECWAV38JBh73QpdtxkQNUFKlxrQjL3vge3Ie3AX7kHZ7r8g9LIPW+zcNQNwob12GC5uYgBWi97e1dq9wBVBWK9CcAAvlCEKAvpHaJUug4iIWgFDMl0w2e2A4+w3cBxfDcfp9YDk9O1zlwAQeyPu1g1Qh/RQrMb26EJ6IQVRjZDRK1GwfhTsx/4PhuTboYsd2+hxbsm7NLLvIrgacwEX2JsfgMOrjfmtOSOEOYADMBERdW4MyXReZMmD8tyt3hXwTv4bsqvEt08d2s87ZVvibDjPnoYhaQRELcdoVlfXeNZwvYjbmjGeVRs1Asbed8GWsRKlO+9B2PQ9KHara4z/9Z8RoqkBWCXAv8e3+owQFQE4WCtwaAEREXVYHJPcgjr6mGRZluEu3AP78dWwn/gYki3Lt080doUhaTY8tigYL5oFXWwvBStt31KzHQ3OjLBomLlWUPZI3mnQKmd+yK/o+S21FGJaxsUwevKxzvRXfGe8v9HHrwzA4ZVDH6rPBVwxM4SZAZiIiDoojkmmFuMuPQb7iTWwH18DT8lh33ZBGwp9jxtgSLwZ2tjLUfzje8h+bx40EW8i8aldUJujFay6fZJkGe8ftDTYZuW+MhwqLEeho2qJ5CJHfT3ABjiNT2FO2X2YZF2BvfpZEEzdq8b+1nERHAMwERFR4xiSqU4eex4cJ9fCfnwNXOd+rtqh0kOfcHXFzBRTIKh0AICCja8hd/UDAICgQdOgCopUoux271Chq8FFKADA5pbx7UlHre0qAX5DHnxjgfV3wLP7U2gLfsSzxmcQfuV/Wqt8IiKiToMhmXwkVxkcmV/CcWw1nNnfAXLFtG2CCG3ceO84426zIGr9P5rIX/c35H36GAAgYupDiL7xRV6sVY8zZe4mtRsWrcWgSG21WSBEmHVivT3Arsv+jvz/DIbz9FdwZH4FfbdrWrJsIiKiTochuZOTPeVwZv0X9mOr4Tj9FeCx+/ZpIlO8wbjHjVAZY2sfK8s49/li5H/1HAAgcuZTiJr5FANyHU6VurH+hA0/nandQ1yX6UnGZk0tpgntB1P/B2Hd/wJKU++HNm48RI3pfMslIiLq9BiSOyFZluDK2w77sdWwn/oUsrPQt09lToYhaQ4MSTdDbU5u8DyFm17zBeTo/3kBkVc93Kp1BxpZlvHruXKsP2HDvnyXb7taANwNXC4bUTEdXHMFDX4SjhMfw2M9Bcu+52AevvR8yiYiIiIwJHcqrqLfvD3GJz6Cx5rp2y4aYmFIvAn6pDnQRAxvck9wyOg5KN66CmHj7kT4xPmtVXbAcXlkbMtyYN1xG85YvENWRAEYGavD9CQjCuyeBme3mNsv6LwurBM1JphHvoai72fAun8ZDD3/AE1ov/N+HkRERJ0Zp4BrQW0xBZzHehqS41y9+0V9NFSmrr77bsspOI5/BPvx1XAX7/dtFzTB0He/DoakOdDGXgFBVDXp8WVZ9gvRkssJUaM7j2fS8ZSWS9h0yo7/nrKjxOm9OM+gFnBlgh5TehgRbaz6Htc1T3KEXsTcZsyTXJ/CzdfAefpraGPGInzKDxz+QkREVA2ngOuAZI8T+V+nQHLk1ttG1Mci8urdcJz+yjszRd62aju10HW9Coakm6HvehUEtaF5j+8ux9mVt8DY70qEX3m395QMyMiyuPHNCTu2nrGjvCLzRuhFTO1hxPhuehg1Yq1jRsbpkRKrO+8V9xpiHvEazmV9V7HYy79g7PmHCz4nERFRZ8OQHEhELVSmbhU9yXVNIyZA9tiR92l3AB7fNm3sOG8w7n4dRF3YeT20VO7AmTeuh+XX9Sjb+zWCh1wNTXiX83wigU+uWFJ6/Qkb0nLLfXMYJ4WoMT3RiJFxOqjFhgOvKAjNujivqdTBPRA8eDHK0h9D2S8PQt91+nm/7kRERJ0VQ3IAEQQBwcOeReGmKfW0kH3LQ6vDh8KQdDMMiTf5Db84H5LTitOvzID14GYIGj0SHviy0wZktyQjNduJdSdsOF5SNZ3b8GgtpicZ0Tdc0y6GN5j6L4L92IdwlxxCWfrjCBn9d6VLIiIiCigMyQFGGz8JmogUuArTq+YxriRqYer/EAw9b4EmtG+LPJ7HVoLMFVfBfmQ7RH0QEhaug6nP2BY5dyCxuSRsPu3AtydsKKgYR6wRgXFdDZiWaEB8UPt6KwkqLcyj3kLhf6+ALeMfMPT6I7RRI5Qui4iIKGC0r//ZqVEN9SaHXfkf6LvW18vcfB5LIU4tmwzHid0QjaHo9tAGGHuObLHzB4JzNg++PWnD96cdsFfM2xaiFTC5hxETuxtg1tYeb9xe6OLGwdDzD7Af+z+U/HwPIq/a1eQLNImIiDo7huQAVNWbnAbIEiCooAkfBl2XyS36OKW/fArHid1QBUei+182Qd99SIuevz07WuzCuuM2pOY4IVUMOO4apMJViUZc2kUPrUr5IRVNEXzxS3Cc/hrugnTYMt6Cqe99SpdEREQUEBiSA1Ct3mTZg+Bhz7b4WNjQcXfCYy1C8NBroOvS8efblWQZabnlWHfChsOFVYt/DIzU4KpEI4ZEadvFeOPmUBliEDzseZT+/GeUpT8BfffroTLGKV0WERFRu8d5kltQW8yTXEmWZRSsGwlXwS/QRKQgYnpqiwS48vxTUAdFQNQHtUCVgcHpkbHltB3fnLAjx+Yd560SgEvi9bgqyYAe5uavfteeyJIHBd+Mhiv/F+gTZyNs7BqlSyIiIlIM50nu4ARBQPDw51GSej+Chz/fIgHZmXUYp16cAG3sRei2aD1EbfPmUQ40xQ4PNpyyY9MpOywu79+KJrWACd0NmNLDgHB9xxi/K4gqhIz6O/LXj4DjxEdwJv8JuvgJSpdFRETUrjEkBzBd/AREzzrYIudyZO7DqZcmwlOaB5UhBJK9tMOG5MwyN9Yft2FblgPuiummY4wipiUaMa6rHnp1+70Y73xpIofD2PvPsB1+AyU/34uoGfsgqLgQDBERUX0Ykgn247/g1LLJkKxF0Hcfim5/2Qh1cKTSZbUoWZaxL78c647bsS+/3Lf9ojANpicakBKra5HV7tqz4GHPwXHqM3hKf4dl/0sIHvyE0iURERG1WwzJnZzt923IXD4NkqMMhp6j0O3Bb6EyhSpdVotxeWRsz3Jg/QkbMsu8440FACNidZieZMRFYYE93rg5RG0IzCkrUPzjzbDs+18Ykm6GOjhJ6bKIiIjaJYbkTsx68Htkvnw15HIbjH3GIWHBV1AZgpUuq0WUlUv4LtOODSftKHZ6x1ToVAKuTNBjWqIR0caOMd64ufSJN0F7ZBXKszej9Of5CJuwPuBm7CAiImoLDMmdmCooAoJGB2Pvy5Fw378h6oxKl3TBcqxurD9hx9YzdjgrFiQM14uY0sOACd0MMGk63njj5hAEASGj3sS5/wyC8+y3cGR+AUP3a5Uui4iIqN3hFHAtqC2ngGspzqxD0EQlQdQE7kVcsizjcJEL64/bsDu3HJU/0D3MakxPNGJ0vA5qkb2l1ZWlPwnLvucgGrsiatYhiJrOM+UfERF1bpwCjupUvP1f0ER2h6n3ZQAAXXxfhSs6fx5JRmqOE+uO23CsxO3bPixai6sSjegfoeFQgnoEDXoc9uOr4bGcgGXv0zCnLFO6JCIionaFIbkTKfx+JXI+uAeiPhhJz+yBNqan0iWdF5tLwg+nHfj2pA3n7N7xxhoRuLyLd7xx12D+WDdGUBtgHvUGir67CtaDr8DQay40YQOVLouIiKjdYJroJAo2rEDuRw8CAEIv+yM0UYkKV9R8+XYPNpy047tMO+xu76CKYK2Ayd0NmNTdiBBd5x5v3Fz6rtOg734tHKc+R8nOexAx9UcIAr+HREREAENyhyfLMvK/eg7nPl8MAIiY/hiir//fgBqGcLzEhXXHbdiZ7YRUMeA43qTC9CQjLuuih1YVOM+lvTGPeAXOs/+FK2877EffhzH5T0qXRERE1C4wJHdgsiwj79PHULD+BQBA1HXPIeqavypcVdNIsow9eeVYd9yGg4Uu3/b+ERpMTzRiSLS2wy/+0RZUpgQEDXkaZbv/gtLdD0OfMAOiPkLpsoiIiBTHkNyBFf/0ni8gx8xegYgpCxWuqHFOj4wfz3gX/8i2eudwUwnAmHgdrko0IjGk8yz+0VZM/R6A/egHcBfvR2naowi95B2lSyIiIlIcQ3IHFjJ6Dsp2/xvBQ69B2BV3KV1Og4qdEjaetGHjKTvKXN4xFUa1gAndDJjSw4AIQ+dc/KMtCKIGIaNXouDbS2E/8k8Yk/8IbfQYpcsiIiJSFOdJbkHtYZ5k2eMGRJVvzLEsSRDE9nsx1ukyN9afsGHbWQdc3okqEGUQMS3RiCsS9DCo22/tHU3x9tthP/Iu1GGDEHl1GgSRf0MTEVHHw3mSOyHJ5cTZt26CJrI7Ym5+GYIgtMuALMsyfivwLv6x91y5b3tyqBrTk4xIidFBxcU/2px5+AtwZH4Jd9E+WA+9jqD+7X94DhERUWthSA4wroJMuMvya22Xy+3IWfsXOI7uhKDRIeyKu9rdQiFuScb2LAfWn7DjVKl38Q8BQEqsDtMTjegdzvHGShL1kTAPfwElO+bBsmcxDD1ugMrUVemyiIiIFMGQHEBcBZk4+khvyC5Hg+3ibn+3XQVkS7mETZl2/PekHUVO75gKnUrAFQl6TO1hQKyJP4bthSH5T7AdeReucztRumshwq74VOmSiIiIFMF0EkDcZfmNBmQA0MX1aYNqGpdjdeObE3ZsOWOH0ztRBcJ0Iqb0MGBCdwOCNO1vKEhnJwgiQkb/HflfD4fj1GdwnNkAfdcpSpdFRETU5hiSqcVlFLqw7oQNv+Q4UXlVaLdgNaYnGXBJvB5qjjdu1zThg2Hqez+sB19Gaep86GJ/g6A2KF0WERFRm2JIphbhkWTsynVi/XEbjhS7fduHRGlxVZIRAyM0AbXKX2cXNHQJ7CfXwlN2DJbf/obgoUuULomIiKhNMSTTBbG7Jfxw2oFvTthwzu4db6wWgcu66HFVohEJwfwRC0SiJhjmEa+geMv/wPLb32BImgN1yEVKl0VERNRmmGDovBTYPfj2pB2bM+2wub2DKoK1AiZ1N2BSdyNCdRxvHOj03a+HLn4ynFn/RcnP9yJ80kZ+GkBERJ0GQzI1y4kSF9Ydt2FnthOeigHHcSYVrko0YmxXPbQqhqiOQhAEmEe9gXNfDkB59ndwnPwEhsQblS6LiIioTTAkU6MkWcbevHKsO2HDgQKXb3u/cA2mJxkxNFoLkT2MHZLa3AtBgx6DZe/TKN21ELouUyFqlVlNkoiIqC0xJAcQdXAkBI2+wWngBI0e6uDIFnm8co+MH886sP64DVlW7xxuogCMjtNhepIRSSFc/KMzCBrwCOzH/gVP2VGU7VmMkJGvKF0SERFRqxNkWZYbb0ZN0dS1wC9EfSvuVVIHR0IT0e2CHqPEKWHjKRs2nrKjtNz742FQC5jQzYApPQyINKgu6PwUeJxZm1C4cRIgiIicvhuaiKFKl0RERHRemprX2JMcYDQR3S44BNfnTJkb35yw4cezDri8E1UgyiBiag8jrkjQw8jFPzotXfxE6HvcCMfJtSjZeQ8irtoBQeDPAxERdVwtFpL379+P9PR09O/fH8OHD2+p01Irk2UZBwq8F+PtOVfu294rVI3piUaMiNVBxcU/CIB5xAo4z34DV34q7L//E8bedypdEhERUas5766g8ePH+75es2YNbrrpJuzfvx933XUX3njjjRYpjlqPW5Lx0xkHHt1WhGdTi7HnXDkEACkxWiwZHYrnxoRhdLyeAZl8VMZ4BA99FgBQmvYoPPY8hSsiIiJqPefdk1xYWOj7+tVXX8V3332H2NhYWCwWjBkzBvPnz2+RAqllWV0Svsu0Y8NJOwod3jEVWhEYl2DAtEQD4kwcgUP1M/a5F7aj78NduBdlux9G6GXvK10SERFRqzjvRCTLMux2OyRJgiRJiI2NBQAEBQVBpeKFXe1Nns2Db07Y8P1pB5wVExyH6kRM6WHAhG4GBGs5vpQaJ4hqhIz6Owq+GQP7sQ9gSP4TdLGXK10WERFRizvvkFxcXIz+/ftDlmUIgoDs7GzExcXBYrGAE2a0H78Xeccb78pxovJVSQhWYXqiEZfE66Hh4h/UTNroUTBeNA+2399G6c/3IPKavRBETgdIREQdy3mH5JMnT9a5XRRFfPHFF+d7WmoiSZZxqNCFYoeEUL2IvuEa34Iekizjlxwn1h234fdit++YQZFaTE8yYFCklssL0wUJHr4UjlOfw118ENYDLyNo4MNKl0RERNSiWvwzdqPRiMTExJY+bS1vvvkmevToAb1ej5EjR2LXrl0Ntv/000/Rp08f6PV6DBw4EN98843f/ttuuw2CIPjdpkyZ0ppP4bylZjtw7/cFeObnYry2txTP/FyMe78vwLazdnx7woYFWwqwIr0Uvxe7oRaBcV31eOnycPx1ZCgGR+kYkOmCibpwBF/8EgDA8usSuC2nFK6IiIioZbXKQNRhw4a1xml91q5di0WLFuGpp55Ceno6Bg8ejMmTJyMvr+6r7Xfs2IHZs2fj9ttvx549ezBz5kzMnDkT+/fv92s3ZcoUZGdn+24fffRRqz6P85Ga7cCK9FLfRXeVCh0SXt9bhvcPWpBrkxCkETCrlxFvXhGBewab0S2YF+RRyzL0mgttzGWQ3TaU7lqgdDlEREQtKiBX3Bs5ciRSUlJ8U81JkoSEhATcd999ePTRR2u1v/HGG2G1WrFu3TrftlGjRmHIkCFYuXIlAG9PcnFxMb788svzrqu1V9yTZBn3fl9QKyBXJwrArX1NuCLBCL2aPcbUulxFB5D/1RBAdiNs/NfQJ0xXuiQiIqIGNTWvXVBPclFREdauXYsVK1ZgxYoV+Pjjj1FUVHQhp2xUeXk50tLSMGHCBN82URQxYcIE7Ny5s85jdu7c6dceACZPnlyr/ZYtWxAdHY3evXvjnnvuQUFBQYO1OJ1OlJaW+t1a06FCV4MBGQAkGehm1jAgU5vQhPWHqf8iAEBp6n2Q3TaFKyIiImoZ5x2SV61ahdGjRyM1NdU3DVxqairGjBmDVatWtWSNfvLz8+HxeBATE+O3PSYmBjk5OXUek5OT02j7KVOm4MMPP8TmzZvxwgsvYOvWrZg6dSo8Hk+9tSxduhQhISG+W0JCwgU8s8YVNxKQm9uOqCUEDX4SoikBHstJlO37X6XLISIiahHnPVD1xRdfRHp6Okwmk9/2Z599FsOGDcPtt99+wcW1pZtuusn39cCBAzFo0CD07NkTW7Zs8VtdsLrHHnsMixYt8t0vLS1t1aAcqm/a3zRNbUfUEkRNEEJGvIaiH2bBuv8lGJJugSa0r9JlERERXZDzTlOCIKCsrKzW9rKysladPSEyMhIqlQq5ubl+23Nzc30LmtQUGxvbrPYAkJSUhMjISBw9erTeNjqdDmaz2e/WmvqGaxDeSACOqJgOjqgt6brNgK7rdEByofTnP3OudCIiCnjnHZKXLVuGsWPH4rrrrsP999+P+++/H9deey3GjRuH5cuXt2SNfrRaLYYPH47Nmzf7tkmShM2bN2P06NF1HjN69Gi/9gCwadOmetsDwJkzZ1BQUIC4uLiWKbwFiIKA2/oFNdhmbr8g33zJRG1FEASYR74GqAwoz9kC+/HVSpdERER0QZo8u4XNZoPRaPTb5vF4sGvXLmRlZQEA4uPjMWLEiFZflnrt2rWYO3cu/vGPf2DEiBF45ZVX8Mknn+Dw4cOIiYnBrbfeii5dumDp0qUAvFPAjR07Fn/7299w1VVX4eOPP8bzzz+P9PR0DBgwABaLBUuWLMF1112H2NhYHDt2DA8//DDKysrw22+/QafTNamu1p7dolJqtgPvH7T4XcQXoRcxt18QRsbpW+1xiRpj2fc8ytL/ClEfjahZGRB1oUqXRERE5Kepea3JY5JDQkKQnZ2NyMhI3zaVStVgb2xrufHGG3Hu3DksXrwYOTk5GDJkCDZs2OC7OC8zMxOiWNVJPmbMGKxZswZPPPEEHn/8cSQnJ+PLL7/EgAEDfM9j3759+OCDD1BcXIz4+HhMmjQJzz77bJMDclsaGadHSqyu3hX3iJRi6v8QbMf+D56Swyjb81eEjHpT6ZKIiIjOS5N7kkVRRE5ODqKjowEAl112GT777LNas0Z0Zm3Vk0zUnjmzv0fhf8cDEBAxPRXayBSlSyIiIvJp9XmS9+7dC6vVer6HE1EHpYu7EvqkOQBklO68B7JU/zSKRERE7RXnCiOiFmdOWQ5BEwJXQRpsGSuVLoeIiKjZmhWS16xZg/T0dLhcrtaqh4g6AJUhBsHDnwcAlKU/Do+t7oV+iIiI2qsmj0keO3Ys9u7di7KyMmg0Grjdbtx888247LLLMHToUAwaNKhdXuTWljgmmaiKLHlQsH4UXAW7oU+ag7DL/6V0SURERE3Oa00OyZWOHDmCtLQ0pKen+27FxcVQq9Xo27cvfv311wsuPlAxJBP5K8/fjYJ1IwDICJ+8Gbq4K5UuiYiIOrlWC8l1OXHiBNLS0vDRRx/h3//+94WeLmAxJBPVVvLzfNgOvwmVuTeiZvwKQdW5P3EiIiJltfrsFpXKysqwadMmvPjii/jyyy8v9HRE1MEED30Ooj4GntIMWPYvU7ocIiKiJjnvkPzjjz9i7ty5iI2NxRNPPIGEhISWrIuIOghRFwpzinepesu+5+AuO6FwRURERI1rVkjOycnB3/72NyQnJ2PatGlwu9349NNPkZWVhSVLlrRWjUQU4PRJN0MbewXgcaA09T60wCgvIiKiVtXkkHz11VcjKSkJP/30E55++mnk5uZi9erVmDZtGtRqNQQuiUxE9RAEASGj3gJEDZxn1sOZ+aXSJRERETWoySF5/fr1uPbaa7FkyRLMmTMHJpOpNesiog5GHdoHpgF/AQCU7HoAksuicEVERET1a3JI3rFjBwwGA6688kr07t0bzzzzDI4dO9aatRFRBxM86K9QBfWAZD0Ny6/PKF0OERFRvZockkeNGoV33nkH2dnZeOSRR7Bx40ZcdNFFGDVqFF5//XXk5ua2Zp1E1AEIaiPMI18HAFgPvAxX0X6FKyIiIqrbBc2TnJGRgVWrVuH//u//kJubC0EQ4PF4WrK+gMJ5komapvD7WXBmfglN9KWImPojr2kgIqI20ybzJPfu3Rsvvvgizpw5g88//xxXXXXVhZyOiDqJkBGvQlAb4crbBvvRD5Quh4iIqJYWWXGPvNiTTNR0lv0voWz3wxB1kYiadRiiPkLpkoiIqBNosxX3iIjOh6nfAqhD+0Ny5qM0/TGlyyEiIvLDkExEihBEDUJG/x0AYP/9HZTn/axwRURERFUYkolIMdqYy2DodRsAoGTn3ZAlt7IFERERVWBIJiJFBV/8IgRtGNxFv8J26A2lyyEiIgLAkExEClPpo2Ae/jcAQNmeJ+GxnlW4IiIiIoZkImoHDBfdAU3UKMhuC0p/WaR0OURERAzJRKQ8QRARMurvgCDCcfITOM9uVLokIiLq5BiSiahd0EQMgbHPfQCAkp/vhex2KFwRERF1ZgzJRNRuBA99BqIxHp6yo7Dsf0HpcoiIqBNjSCaidkPUmmFOeRkAYNm3FO7SowpXREREnRVDMhG1K/oeN0AbPxGQnN5hF7KsdElERNQJMSQTUbsiCAJCRr0JiDqUZ22E49RnSpdERESdEEMyEbU7anMyggY+CgAo3bUAUnmpwhUREVFnw5BMRO1S0MBHoQruCcmWBcvep5Quh4iIOhmGZCJqlwS13jvsAoD10GtwFexVtiAiIupUGJKJqN3SdZkMfY8bAFlCyc9/hixLSpdERESdBEMyEbVr5pSXIaiD4Dq3E/Yjq5Quh4iIOgmGZCJq11SmLgga+gwAoHT3I/A4zilcERERdQYMyUTU7pn63gd12GDI5UUo2/2I0uUQEVEnwJBMRO2eIKoRMvrvAAD70fdQnrtN4YqIiKijY0gmooCgjR4NQ/IdAICSnfdAllwKV0RERB0ZQzIRBQzz8L9B1EXCXbwf1oOvKl0OERF1YAzJRBQwRH0Egi9+EQBg2fs0PNbTCldEREQdFUMyEQUUQ6+50ERfAtltRUnqA0qXQ0REHRRDMhEFFEEQvRfxCSo4M7+A4/R6pUsiIqIOiCGZiAKOJmwgTP0WAgBKU++D7LYpXBEREXU0DMlEFJCChjwF0dgVHssJWPY9r3Q5RETUwTAkE1FAEjVBMI/0znBh2f8i3CUZCldEREQdCUMyEQUsfbdZ0HWdBkgulPz8Z8iyrHRJRETUQTAkE1HAEgQB5pGvAyo9yrO/h+PER0qXREREHQRDMhEFNHVwEoIG/RUAULprESRnsbIFERFRh8CQTEQBL2jAX6Ay94bkyEXZnieVLoeIiDoAhmQiCniCSoeQUW8CAGwZb8GVn6ZwRUREFOgYkomoQ9DFj4c+cTYgSyjZeTdkyaN0SUREFMAYkomowzCnrICgMcNVsBu2399WuhwiIgpgDMlE1GGojLEIHva/AICytMfgsecqXBEREQUqhmQi6lCMve+BOmIYZFcJyn55SOlyiIgoQDEkE1GHIogqhIxeCUCA/fi/4MzeonRJREQUgBiSiajD0UamwNj7bgBA6c9/huwpV7giIiIKNAzJRNQhBQ/7X4j6aLhLDsF6YLnS5RARUYBhSCaiDknUhSE4ZRkAoOzXZ+EuO6FwRUREFEgYkomowzIk3QJt7DjAY0fprgeULoeIiAIIQzIRdViCIMA86i1AUMN5+ms4Mv+jdElERBQgGJKJqEPThPaFaYB3KrjS1PshuawKV0RERIGAIZmIOrzgwU9CZeoOjzUTll+fVbocIiIKAAzJRNThCWojzKNeBwBYDyyHq+iAwhUREVF7F7Ah+c0330SPHj2g1+sxcuRI7Nq1q8H2n376Kfr06QO9Xo+BAwfim2++8dsvyzIWL16MuLg4GAwGTJgwAUeOHGnNp0BEbUifcDV0CdcAsts7d7IsK10SERG1YwEZkteuXYtFixbhqaeeQnp6OgYPHozJkycjLy+vzvY7duzA7Nmzcfvtt2PPnj2YOXMmZs6cif379/vavPjii3jttdewcuVKpKamwmQyYfLkyXA4HG31tIiolZlHvgZBbUR57o+wH/s/pcshIqJ2TJADsDtl5MiRSElJwRtvvAEAkCQJCQkJuO+++/Doo4/Wan/jjTfCarVi3bp1vm2jRo3CkCFDsHLlSsiyjPj4eDz44IN46CHvBT4lJSWIiYnB+++/j5tuuqlJdZWWliIkJAQlJSUwm80t8EyJqKVZfnsBZWmPQtRHIWrWYYi6cKVLIiKiNtTUvBZwPcnl5eVIS0vDhAkTfNtEUcSECROwc+fOOo/ZuXOnX3sAmDx5sq/9iRMnkJOT49cmJCQEI0eOrPecAOB0OlFaWup3I6L2zdRvIdSh/SA5zqEs7XGlyyEionYq4EJyfn4+PB4PYmJi/LbHxMQgJyenzmNycnIabF/5b3POCQBLly5FSEiI75aQkNDs50NEbUtQab1zJwOw/f42ys+lKlwRERG1RwEXktuTxx57DCUlJb7b6dOnlS6JiJpAFzsWhp63ApBRsvMeyJJb6ZKIiKidCbiQHBkZCZVKhdzcXL/tubm5iI2NrfOY2NjYBttX/tuccwKATqeD2Wz2uxFRYAi++CUI2lC4C/fAdvgtpcshIqJ2JuBCslarxfDhw7F582bfNkmSsHnzZowePbrOY0aPHu3XHgA2bdrka5+YmIjY2Fi/NqWlpUhNTa33nEQU2FSGaAQPWwoAKNvzBDy2LIUrIiKi9iTgQjIALFq0CO+88w4++OADHDp0CPfccw+sViv++Mc/AgBuvfVWPPbYY772DzzwADZs2IDly5fj8OHDePrpp7F7927Mnz8fACAIAhYsWIDnnnsOX331FX777TfceuutiI+Px8yZM5V4ikTUBoy974QmcgRkVxlKf3lQ6XKIiKgdUStdwPm48cYbce7cOSxevBg5OTkYMmQINmzY4LvwLjMzE6JYlf/HjBmDNWvW4IknnsDjjz+O5ORkfPnllxgwYICvzcMPPwyr1Yo777wTxcXFuPTSS7Fhwwbo9fo2f35E1DYEQUTI6L8jf10KHCc+hjP5T9DFT1S6LCIiagcCcp7k9orzJBMFppLU+2E79DpU5mREXbMPgpp/HBMRdVQddp5kIqKWFjz0WYiGWHhKj8By4CWlyyEionaAIZmIOj1RGwJzyssAAMuv/wt36TGFKyIiIqUxJBMRAdAn3ght3ARAcqI0dT44Eo2IqHNjSCYigneWm5BRbwKiFs6zG+A49W+lSyIiIgUxJBMRVVCHXISggY8AAEp3LYDkKlO4IiIiUgpDMhFRNUEDH4MqOAmS7Swse59WuhwiIlIIQzIRUTWC2gDzyDcAANaDr8JVuE/hioiISAkBuZhIoPN4PHC5XEqXQQFIo9FApVIpXUaHp+86Ffru18Fx6t8o+fkeREz9CYLAPgUios6EIbkNybKMnJwcFBcXK10KBbDQ0FDExsZCEASlS+nQzCNegfPsBrjydsB+5D0YL7pd6ZKIiKgNMSS3ocqAHB0dDaPRyJBDzSLLMmw2G/Ly8gAAcXFxClfUsalMXRE0ZAnKdj+E0rSHoe82A6I+UumyiIiojTAktxGPx+MLyBEREUqXQwHKYDAAAPLy8hAdHc2hF63M1O9+2I99AHfRbyhNexShl/xT6ZKIiKiNcJBdG6kcg2w0GhWuhAJd5c8Qx7W3PkHUIGTU3wEA9iOrUJ67XeGKiIiorTAktzEOsaALxZ+htqWNuQSGZO945JKf74Es8Y8TIqLOgCGZiKgR5uF/g6ALh7voN1gPvqZ0OURE1AYYkomIGiHqI2Ee/iIAwLL3KXisZxSuiIiIWhtDMhFRExiS/whN9BjIbitKdy1QuhwiImplDMkBwlWQCfvJ9HpvroLMVnvscePGYcGCBa12/uZqbj07d+6EIAi46qqr6m0zduxYCILgd1OpVLBYLC1QMXUEgiB6L+ITVHCc+jccZ75VuiQiImpFnAIuALgKMnH0kd6QXY562wgaPXq9kAFNRLc2rKzpysvLodVqFXnsVatWYfbs2fj888+RlZWF+Ph4v/2yLGPPnj1YtmwZ5syZ49suiiKCgoLaulxqxzThg2Dq9wCsB1agZMc8iGM/gaDW12on6qOhMnVVoEIiImop7EkOAO6y/AYDMgDILgfcZfkt/ti33XYbtm7dildffdXXw3ry5Els2LABl156KUJDQxEREYHp06fj2LFjvuPGjRuH+fPnY8GCBYiMjMTkyZMBAGVlZZgzZw5MJhPi4uLw8ssv1+oZliQJS5cuRWJiIgwGAwYPHozPPvuswXrqY7FYsHbtWixYsABXXHEF3n///Vptjhw5grKyMlx++eWIjY313aKjo1vke0gdi2ngYwBESLazKPj2EuR/PbyOWwpkj1PpUomI6AIwJCtMclrrv5U3HIzP57zN9eqrr2L06NGYN28esrOzkZ2djYSEBFitVixatAi7d+/G5s2bIYoiZs2aBUmSfMd+8MEH0Gq12L59O1auXAkAWLRoEbZv346vvvoKmzZtwk8//YT09HS/x1y6dCk+/PBDrFy5EgcOHMDChQtxyy23+MJxXfXU55NPPkFsbCxGjBiBOXPm4N1334Usy35t0tLSoFarMWjQoGZ/f6jzEXURUAUnNtQCKlMCICrzyQkREbUMDrdQ2OE76/84P2jwNHRbtP68znvkwR7w1NGz3O8DuY7W9QsJCYFWq4XRaERsbKxv+3XXXefX7t1330VUVBQOHjyIAQMGAACSk5Px4osv+tqUlZXhgw8+wJo1azB+/HgAwHvvvec3/MHpdOL555/Hd999h9GjRwMAkpKSsG3bNvzjH//AmjVr6qynPqtWrfINoZg5cybuuusubN26FePGjfO1SU9Ph8fj8VsJcfDgwdi+3btwxLp16/Dggw9CkiQ88sgjuOOOO2o9To8ePWA2myGKIsLCwvDDDz80WhsFJkEQYB75Boq+m1pPCwnBw57lfNZERAGOIZnOy5EjR7B48WKkpqYiPz/f14OcmZnpC8nDhw/3O+b48eNwuVwYMWKEb1tISAh69+7tu3/06FHYbDZMnDjR79jy8nIMHTq0WTVmZGRgx44dviEWQUFBmDFjBlatWlUrJM+ePRtLlizxbTOZTAAAt9uNRYsW4YcffkBISAiGDx+OWbNm1bm0+I4dOziGuZPQdZkMdehAuIt/898hqKAJHwZt/CRlCiMiohbDkKywPm83MHuCoDrv8yYvP3nexzbF1Vdfje7du+Odd95BfHw8JEnCgAEDUF5e7mtTGTSbo3I2ifXr16NLly5++3Q6XbPOtWrVKqSkpCA5Odm3bc6cObjhhhvwxhtvICQkBIA3JC9duhS9evWqdY5du3ahf//+vlqmTp2KjRs3Yvbs2c2qhToWQRBgTnkJhZum+O+QPTD2vpO9yEREHQDHJCtM1Jnqv2lrXzV/oec9H1qtFh6Px3e/oKAAGRkZeOKJJzB+/Hj07dsXRUVFjZ4nKSkJGo0Gv/zyi29bSUkJfv/9d9/9fv36QafTITMzE7169fK7VY49rllPXdxuNz788EPcfPPNftsnTZoEo9GIjz76CIC3d7u4uLjeXuqsrCy/sN6lSxecPXu2VjtBEDB27FikpKRg9erVjXwnqCPQxk+COuJi1Pw1WrJjHgo3XwNnzo+1xr8TEVHgYE8yNapHjx5ITU3FyZMnERQUhPDwcERERODtt99GXFwcMjMz8eijjzZ6nuDgYMydOxd/+ctfEB4ejujoaDz11FMQRdHX8xYcHIyHHnoICxcuhCRJuPTSS1FSUoLt27fDbDZj7ty5ddYjiv5BZd26dcjNzcWAAQOwf/9+v32XX345Vq1ahbvvvhtpaWlQqVQXfNHetm3b0KVLF2RnZ2PChAkYOHAgLwTs4ARBgHnYc369yZrIUXDl/wzn6a/hPP01NJEpMPV/EPru10EQ+euWiCiQsCc5AKiDIyFoGu5VFjR6qIMjW+XxH3roIahUKvTr1w9RUVHIzMzExx9/jLS0NAwYMAALFy7ESy+91KRzrVixAqNHj8b06dMxYcIEXHLJJejbty/0+qrn9+yzz+LJJ5/E0qVL0bdvX0yZMgXr169HYmJivfXUtGrVKgDAxIkTMXDgQL/bF198gd27d2Pfvn1IT09Hnz59YDAY6qw3Pj7er+f47NmzteZZBuDrbY6Li8O0adNqzdhBHZM2fhI0ESkAAE1ECiKu2oGoWYdhvOguQKWHK/8XFG+9Cef+3QvWg69CcpUpXDERETWVIPPzwBZTWlqKkJAQlJSUwGw2++1zOBw4ceIEEhMT/QJhU7kKMhucB1kdHNluFxJpiNVqRZcuXbB8+XLcfvvtSpdTi9vtRt++fbFlyxbfhXs7duzwu3DParVCkiQEBwfDYrFg7NixWLlyJVJSUlqlpgv9WaKW5cz6DiWp9yNk5GvQxU/wbffY82A7/BZsh9+E5PS+dwVNCIy974ap731QmbrUd0oiImpFDeW16vj5X4DQRHQLyBBc0549e3D48GGMGDECJSUleOaZZwAAM2bMULiyuqnVaixfvhxXXHEFJEnCww8/7AvIQ4YMwd69e5Gbm4tZs2YBADweD+bNm9dqAZnaH138BETPOlhru8oQjeChTyNo4COwHfsQ1gPL4Sk9Auv+F2A9uAKGxNkw9X8QmnAOyyEiao/Yk9yCWrMnuaPYs2cP7rjjDmRkZECr1WL48OFYsWIFBg4cqHRpAYM/S4FJliU4T6+D9cAylOf+5NuujZ+EoAEPQRs3gbNiEBG1AfYkU7s0dOhQpKWlKV0GUZsTBBH6btdA3+0alJ/bBeuB5XCc+gzlWRtRmLUR6rBBMPV/EIbEmyCouFofEZHSeOEeEVEb00aNQNi4tYi69iiMfe+HoDbBXbQPJdvmIu/fibD89gIkZ7HSZRIRdWoMyUREClEHJyJk5KuIvuE0gocthWiIg2TLQlnao8j7NAGluxbCXXZS6TKJiDolhmQiIoWJujAEDXoU0defQMil70MdOgCy2wLrwVdw7vNeKNpyE8rzdytdJhFRp8KQTETUTggqHYy95iJyxj6ET9wAbdwEQPbAcXItCtaloODbcXCc/hqyLCldKhFRh8eQTETUzgiCAF2XyYiYvAmR1+yFoecfAEGN8tytKNp8Dc590Q+239+B7HYoXSoRUYfFkExE1I5pwgcj9LIPEX39CZgGPAxBY4anNAMlO+5E3mfdULb3GUiO+hcaIiKi88OQTEQUAFSmrjBf/IL3Ir+UFVCZukFynINl71PI/bQbSnbeA3fJ70qXSUTUYTAkExEFEFFrRlD/hYi67hhCL/8ImojhgMcOW8ZKnPuiDwq/n4Xy3O3gOlFERBeGIZmIKAAJohqGpJsQMf0XhE/ZAl3X6QBkODO/RMG3l6Jg/WjYT34GWfIoXSoRUUBiSCYiCmCCIEAXOxbhE75G5MyDMCTfAYg6uPJTUbzlBpz7PBnWQ69DclmULpWIKKAwJFOrGDduHBYsWKB0GUSdiia0L0IveQfRN5xC0OAnIejC4bGcQGnq/cj7tBtK0/8Kjy1b6TKJiAICQ3KAkWQZBwrKsf2sAwcKyiF1gnGHO3fuhCAIuOqqq+ptM3bsWAiC4HdTqVSwWNh7Rp2PyhCD4KHPIOaG0zCPeguq4F6Qy4tg3fc88j7rgeJtf4Kr6IDSZRIRtWtqpQugpkvNduD9gxYUOqoWEgjXi7itXxBGxukVrKx1rVq1CrNnz8bnn3+OrKwsxMfH++2XZRl79uzBsmXLMGfOHN92URQRFBTU1uUStRuC2ghTn3tgvOhOOE9/BcuB5XDlbYf96HuwH30Pui5TYer/ILRxV0IQBKXLJSJqV9iTHCBSsx1YkV7qF5ABoNAhYUV6KVKzW29RgXHjxuG+++7DggULEBYWhpiYGLzzzjuwWq344x//iODgYPTq1Qvffvttg+eYP38+5s+fj5CQEERGRuLJJ59s9Ap8i8WCtWvXYsGCBbjiiivw/vvv12pz5MgRlJWV4fLLL0dsbKzvFh0dfaFPnahDEEQV9N1nIXLaNkRM2wF99+sAQYTz7Lco3DgB+V8Pg/3YasiSS+lSiYjaDYZkhciyDIe7aTebS8J7BxoeNvD+QQtsLqlJ5zufqaE++OADREZGYteuXbjvvvtwzz334IYbbsCYMWOQnp6OSZMm4Q9/+ANsNluD51Cr1di1axdeffVVrFixAv/85z8bfNxPPvkEsbGxGDFiBObMmYN33323Vv1paWlQq9UYNGhQs58XUWejjR6NsCs+Q9Ss32HsMx+C2gh34V4U/3QL8j5LgmX/MkjlJUqXSUSkOEHmZJotprS0FCEhISgpKYHZbPbb53A4cOLECSQmJkKv18PhljH3v+cUqfODyVHQq5v+0eq4cePg8Xjw008/AQA8Hg9CQkJw7bXX4sMPPwQA5OTkIC4uDjt37sSoUaMwbtw4DBkyBK+88orvHHl5eThw4IDvY91HH30UX331FQ4ePFjvY19yySWYOHEinn76aVgsFsTExGD9+vUYN26cr81f/vIXLF++HEaj0bdt8ODB2L59OwBg3bp1ePDBByFJEh555BHccccdtR6nR48eMJvNEEURYWFh+OGHH5r8/WlrNX+WiC6E5CiANWMlbIdeh+TIBQAImmAYL5oHU98HoArqpnCFREQtq6G8Vh17kqlJqvfSqlQqREREYODAgb5tMTExAIC8vLx6zzFq1Ci/cY+jR4/GkSNH4PHUPY9rRkYGduzY4RtnHBQUhBkzZmDVqlV+7dLT0zF79mzs3bvXd/vss88AAG63G4sWLcL333+PPXv24KWXXkJBQUGdj7djxw7s3bu3XQdkopYm6iMQPPiviL7+JEIuWQV1aD/IrjJYD6xA3r+TUPTjHLgK0pUuk4iozfHCPYXoVN4e3aY4VFiOv/3S+Mefj6aEoG+4tkmP3VwajcbvviAIftsqw68k+Y+ZvhCrVq1CSkoKkpOTfdvmzJmDG264AW+88QZCQkIAeEPy0qVL0atXr1rn2LVrF/r3748uXboAAKZOnYqNGzdi9uzZLVYnUUcgqPUwJv8Jhl63wXn2v7AeWIby7O/hOL4GjuNroI29AqYBD0HXZQoEgf0rRNTx8TedQgRBgF7dtNvgKC3C9Q2/VBF6EYOjtE06n1JXsaempvrd//nnn5GcnAyVqnZqd7vd+PDDD3HzzTf7bZ80aRKMRiM++ugjAMDx48dRXFyMoUOH1vmYWVlZvoAMAF26dMHZs2drtRMEAWPHjkVKSgpWr17d7OdG1FEIggh916mImLwZkVenQZ90MyCoUJ7zA4q+uwr5/xkI2++rILtb72JhIqL2gCE5AIiCgNv6NTyV2dx+QRDb+RROmZmZWLRoETIyMvDRRx/h9ddfxwMPPFBn23Xr1iE3NxcDBgzA/v37fbeMjAxcfvnlviEXaWlpUKlUF3zR3rZt25CWloavvvoKzz//PPbt23dB5yPqCDQRwxB2+WpEX3ccpv4PQtAEw118ECU77kDeZz1Q9uv/QnLUPXyJiCjQcbhFgBgZp8eiYag1T3KEXsTcAJkn+dZbb4XdbseIESOgUqnwwAMP4M4776yzbWUInjhxYr3n27dvH9LT09GnTx8YDIY628THx/v1HJ89exYjRoyo1a6ytzkuLg7Tpk1Deno6Z8sgqqAK6gZzyjIEDX4Stt//CevBVyDZzsCy5wlYf3sehl5/hKnfQqjNPZUulYioxXB2ixbUnNktzpckyzhU6EKxQ0KoXkTfcE2770EGUGu2i7bidrvRt29fbNmyBSEhIRg+fDh27NiBiIgIXxur1QpJkhAcHAyLxYKxY8di5cqVSElJadNam4qzW5DSZMkFx4lPYDmwDO7CvRVbBei7X+tdnCR6tJLlERE1qKmzW7AnOcCIgoD+EY1fnEdearUay5cvxxVXXAFJkvDwww/7AvKQIUOwd+9e5ObmYtasWQC809vNmzev3QZkovZAEDUw9JwDfdLNKM/5Adb9y+A8+y0cp/4Nx6l/QxM9Bqb+D0KfMAOCeB5XChMRtQMMydThXXPNNbjmmmtqbd+7dy8AICkpCb/++msbV0UU+ARBgC7uSujiroSr6ACsB1fAfuxfcOXtQHHeDqiCe8LUfxGMvW6DoDY2fkIionaEwy1aUFsMtyDizxK1Zx5bDmyH34D18FuQy4sAAIIuAqbe98DYdz5UhhiFKySizo6LiRARUZtTGWMRPOw5RN9wGuaRr0MVnATZWQDLvueQ92l3FG+fB1fxIaXLJCJqFEMyERG1OFFjgqnvfETN+h2h4z6DJmoUIDlhP/JP5H/ZD4XfTYczewv4YSYRtVcMyURE1GoEUQVDj+sQedVOREzbDl23WQAEOM+sR+F/r0DBuhTYj38EWXIpXSoRkR+GZCIiahPa6DEIv/JzRF2bAWPvewCVAa6CNBT/eDPO/bsXLAdehuQqU7pMIiIADMlERNTG1OZkhIx+CzE3ZCJo6DMQ9VHwWDNR9ssi5H3SFaW7H4bHekbpMomok2NIJiIiRYj6SAQPfhLR12ciZMw7UIX0gewqhXX/S8j7LBHFP/4BroK9SpdJRJ0UQzIRESlKUOthvOgORM08gLDxX0MbOw6Q3bAf/xfyvx6Kgv9OhOPMBl7kR0RtKuBCcmFhIebMmQOz2YzQ0FDcfvvtsFgsDR7jcDhw7733IiIiAkFBQbjuuuuQm5vr10YQhFq3jz/+uDWfChERVSMIIvQJ0xEx5QdETP8F+sSbAEGF8uzvUPTdVOT/ZxBsR96H7HEqXSoRdQIBF5LnzJmDAwcOYNOmTVi3bh1+/PFH3HnnnQ0es3DhQnz99df49NNPsXXrVmRlZeHaa6+t1e69995Ddna27zZz5sxWehZERNQQbeTFCBv7EaKuOwZTv4UQ1EFwF+9HyfY/Iu+zRFj2/Q2Ss0jpMomoAwuoFfcOHTqEfv364ZdffsHFF18MANiwYQOmTZuGM2fOID4+vtYxJSUliIqKwpo1a3D99dcDAA4fPoy+ffti586dGDVqFABvT/IXX3xxQcGYK+7Vb9y4cRgyZAheeeUVpUsJeJ39Z4k6J8lZDNvvb8N66DVItrMAAEFtgiH5dpj6LYA6OFHhCokoUHTIFfd27tyJ0NBQX0AGgAkTJkAURaSmptZ5TFpaGlwuFyZMmODb1qdPH3Tr1g07d+70a3vvvfciMjISI0aMwLvvvsvxby3o888/x7PPPtsmj7Vz504IgoCrrrqqzv1jx46tNbRGpVI1OmyHiJQj6kIRNPBhRF93HCGXfQh12CDIbitsh17Duc97oWjL/6D83C6lyySiDkStdAHNkZOTg+joaL9tarUa4eHhyMnJqfcYrVaL0NBQv+0xMTF+xzzzzDO48sorYTQasXHjRvz5z3+GxWLB/fffX289TqcTTmfV2LjS0tLzeFZN47GehuQ4V+9+UR8Nlalrqz3+hQoPD2+zx1q1ahVmz56Nzz//HFlZWX6fMMiyjD179mDZsmWYM2eOb7soiggKCmqzGono/AgqLYw9/wBD0i0oz94M6/5lcGb9F46Tn8Jx8lNoYy6Dqf+D0CVcDUEIqH4gImpn2sVvkEcffbTOC+eq3w4fPtyqNTz55JO45JJLMHToUDzyyCN4+OGH8dJLLzV4zNKlSxESEuK7JSQktEptsseJ/K9TkP/18AZuKa12Mcu4ceNw//334+GHH0Z4eDhiY2Px9NNP+/Zv2LABl156KUJDQxEREYHp06fj2LFjtc6xYMECAMDbb7+N+Ph4SJLk12bGjBn405/+BACQJAlLly5FYmIiDAYDBg8ejM8++6zRWi0WC9auXYsFCxbgiiuuwPvvv++3/8iRIygrK8Pll1+O2NhY363mH19E1L4JggBd/ASET9qAyBn7YOh1GyBqUJ77E4q+n4lzX/SFNeMfkN12pUslogDVLkLygw8+iEOHDjV4S0pKQmxsLPLy8vyOdbvdKCwsRGxsbJ3njo2NRXl5OYqLi/225+bm1nsMAIwcORJnzpzx6ymu6bHHHkNJSYnvdvr06aY/6eYQtVCZuqH+l0uEypQAiNrWeXwAH3zwAUwmE1JTU/Hiiy/imWeewaZNmwAAVqsVixYtwu7du7F582aIoohZs2bVCsGVbrjhBhQUFOCHH37wbSssLMSGDRt8vbtLly7Fhx9+iJUrV+LAgQNYuHAhbrnlFmzdurXBOj/55BPExsZixIgRmDNnTq1hM2lpaVCr1Rg0aNCFfkuIqJ3QhA1E6KXvIfr6kzANfBSCNhSe0t9RuvNu5H3aDWV7nobHXvV/h8d6Gq6C9HpvXMiEiIB2MtwiKioKUVFRjbYbPXo0iouLkZaWhuHDhwMAvv/+e0iShJEjR9Z5zPDhw6HRaLB582Zcd911AICMjAxkZmZi9OjR9T7W3r17ERYWBp1OV28bnU7X4P6GyLIM2W1rcnvToL+i+IeZ9eyVYBr0V8huG5oyilpQGyEIQpMfGwAGDRqEp556CgCQnJyMN954A5s3b8bEiRN939dK7777LqKionDw4EEMGDCg1rnCwsIwdepUrFmzBuPHjwcAfPbZZ4iMjMQVV1wBp9OJ559/Ht99953vNUpKSsK2bdvwj3/8A2PHjq23zlWrVvmC9syZM3HXXXdh69atGDduHAAgPT0dHo8HERERvmMGDx6M7du3AwDWrVuHBx98EJIk4ZFHHsEdd9xR5+P06NEDZrMZoigiLCzML/ATkTJUxniYhy9F0KC/wn7kXVgPvgyP5SQsvy6BZf8LMPa8FcY+81G4cSIkR2695xH1sYi+4SQE1fn9fieijqFdhOSm6tu3L6ZMmYJ58+Zh5cqVcLlcmD9/Pm666SbfuNOzZ89i/Pjx+PDDDzFixAiEhITg9ttvx6JFixAeHg6z2Yz77rsPo0eP9s1s8fXXXyM3NxejRo2CXq/Hpk2b8Pzzz+Ohhx5qteciu23IXd1yY2DrD9C1xcyxQNCYmnX+mj2vcXFxvl79I0eOYPHixUhNTUV+fr6vBzkzM7POkAx4p/KbN28e3nrrLeh0OqxevRo33XQTRFHE0aNHYbPZMHHiRL9jysvLMXTo0HprzMjIwI4dO3xDLIKCgjBjxgysWrXKLyTPnj0bS5Ys8R1nMnm/F263G4sWLcIPP/yAkJAQDB8+HLNmzfIL1NXt2LGD45iJ2iFREwRTv/th7PNnODK/gHX/S3Dl/wLb72/D9vvbEDShAASgzm6F1v9kjogCQ0CFZABYvXo15s+fj/Hjx0MURVx33XV47bXXfPtdLhcyMjJgs1X10r788su+tk6nE5MnT8Zbb73l26/RaPDmm29i4cKFkGUZvXr1wooVKzBv3rw2fW7tmUaj8bsvCIIvDF999dXo3r073nnnHd9Y4wEDBqC8vLze81199dWQZRnr169HSkoKfvrpJ7z88ssA4JtlYv369ejSpYvfcQ313K9atQopKSlITk72bZszZw5uuOEGvPHGGwgJCUF6ejqWLl2KXr161Tp+165d6N+/v+8xp06dio0bN2L27NkNfWuIqJ0SRDUMPW6Avvv1cOVtg2X/cjhPfwXZVdzAURKChz3b7E/biKjjCbiQHB4ejjVr1tS7v0ePHrWmbtPr9XjzzTfx5ptv1nnMlClTMGXKlBatszGC2oiYOc2bckyWZRRuGAt30a+A7AEEFdRhgxE+ZWuzfqELamNzy61XQUEBMjIy8M477+Cyyy4DAGzbtq3R4/R6Pa699lqsXr0aR48eRe/evTFs2DAAQL9+/aDT6ZCZmdng0Irq3G43PvzwQzz66KN+2ydNmgSj0YiPPvoIkyZNQnFxcb290VlZWX6hvEuXLjh79mydbQVBwNixYyGKIhYsWOA3UwYRtS+CIEAbcxnCYy6DuyQDlv0rYD/yDmr3JAsQTV0heVxwl/wOVVAPCCr2KBN1VgEXkjsKQRCaPeQBAMzDn0fhpopAL3tgHv48VFrlPvIPCwtDREQE3n77bcTFxSEzM7NWUK3PnDlzMH36dBw4cAC33HKLb3twcDAeeughLFy4EJIk4dJLL0VJSQm2b98Os9mMuXPn1jrXunXrkJubiwEDBmD//v1++y6//HKsWrUKERERUKlULXLR3rZt29ClSxdkZ2djwoQJGDhwIC8GJAoA6pDeCL3kH9DGX4mSrTfV2CtDsp5G8fdXe+8KIlSm7lCZe0FtToYquBfU5l7ef4OTOGaZqINjSA4w2vhJ0ESkwFXwCzQRKdDGT1K0HlEU8fHHH+P+++/HgAED0Lt3b7z22mu+McANufLKKxEeHo6MjAzcfPPNfvueffZZREVFYenSpTh+/DhCQ0MxbNgwPP7443Wea9WqVQBQaxxzdWvWrEGfPn1gMBjq3B8fH+/Xc3z27FmMGDGizraVPc5xcXGYNm0a0tPTGZKJAoihx//Atn85XIXp3k/mIEA0xEATdQk8ZUfhKTsK2W2Fx3ICHssJlGdtqnEGASpTN2+ADu4FlTm5WoDuCUHN1TCJAl1ALUvd3rXVstTOrO9Qkno/Qka+Bl38hMYPoCZxu93o27cvtmzZ4rtwb8eOHbUu3LNarZAkCcHBwbBYLBg7dixWrlyJlJSUNqmTy1ITtQzn2f9WfTIHIHziBui6TAbgHd4m2XPhKTsKd+kRuMuOwlN6tOLfI5BdZQ2c2Ttswxuevb3Qvq+De7bokDciar6mLkvNnuQApIufgOhZB5Uuo8NRq9VYvnw5rrjiCkiShIcfftgXkIcMGYK9e/cC8M6xPWvWLACAx+PBvHnz2iwgE1HLaeiTOUEQoDLGQmWMhTbmUr/jZFmG5DjnC9BV4dl7X3aVQLKeRrn1NJBTe3pI0dilWmiu3gvdE6KGM+YQtRfsSW5BbdWTTJ0bf5aIWk5LfzInyzJkZwHc1Xqd/QJ0eVGDx4uG2Nrjn83J3gCtrb/Hi4iajj3JREREjWjpT+YEQYCgj4RWHwlt9Kha+yVnIdylR+vshZac+ZDsOSi35wC5P9U6VtRHV/Q+J/t6odVm79eiNqTFngMReTEkExERtRFRFw5t1AggqvZFwZKzCO6yY35jn30B2pHnu7nydtRx3si6Z+EwJ0PUhbXFUyPqcBiSiYiI2gFRFwat7mIg8uJa+6TyEnjKjlXrha7oiS47Csme4+2FPpcP17mfax0r6MKrjYGu3gvdC4IuggunENWDIZmIiKidE7UhECOGQRMxrNY+yWXxv3iwWi+0ZMuC7CyEy7kLrvxdtY4VtKH1XETYC6I+igGaOjWGZCIiogAmaoIgRgyBJmJIrX2SywqP5Xids3BItjOQy4vhKtgNV8HuWscKGnO18OzthfYFaEMMAzR1eAzJREREHZSoMUEMGwhN2MBa+2S3He6y4/4zcFT0QnuspyG7SuEuSIe7IL3WsYI6qEaAruqFFg1xDNDUITAkExERdUKC2gBNWH9owvrX2ie7HXBbTtQ5C4fHegqy2wJ34V64C/fWcV4jVME9676I0BgPQRAvuHaP9TQkx7l694v6aKhMXS/4cahzY0gmIiIiP4JaD01oX2hC+9baJ3uc8FhOei8irNkLbTkJ2W2Du+g3uIt+q31ilR7q4J5V8z9X641WmRKaFKBljxP5X6dAcuTW20bUxyL6hpMQVLpmPW+i6hiSiYiIqMkElQ7qkN5Qh/SutU/2lMNjPVX3XNBlJwCPA+7iA3AXH4Cz5sGiDurgpDovIlSZukEQVRXttFCZulX0JEt1VChCZUoARG0LP3PqbBiSiYiIqEUIKi3U5mSozcm19smSGx7LKd8UdpUh2l16FB7LcUBywl1yCO6SQ3UEaA1UQUm+0KyJ9C4nXjcJwcOe5bhoumAMydSocePGYciQIXjllVfaRQ3toR4iImoeQVRDbe4JtbkndF0m++2TJQ881sxaAdrbC30MkMrhKc2ApzSj8cfRRcKZ8yPcpb9D1MdANMRAVfGvoA1leKYmY0imgPP5559Do9G02ePt3LkTY8aMwbRp07B+/fo624wdOxY//vij3zZRFFFSUoKgoKC2KJOIKGAJogrq4ESogxOhi5/ot0+WPJBsZ729ztXGP7sK9kCynqp1LtmZD+tvz9f9QKIGoj7aLzj7gnS1r0VDDERdRItcZEiBiyGZAk54eHibPt6qVaswe/ZsfP7558jKykJ8fLzfflmWsWfPHixbtgxz5szxbRdFkQGZiOgCCaIKqqBuUAV1gw7jfdtlWUb+uhFwF+4BZA8AEaIpAYbkP0F25EGy50Jy5MJjz4Vkz4XsKgEkFyTbWW/obvyBIeqifKFZVT1A62uEan0UBJGRqqPhK0pN4na7MX/+fPzf//0fNBoN7rnnHjzzzDMQBAEbNmzAc889h/3790OlUmH06NF49dVX0bNnT9/xn332GZYsWYKjR4/CaDRi6NCh+M9//gOTyQRJkvDCCy/g7bffRk5ODi666CI8+eSTuP766+uspeZwi3HjxmHQoEHQ6/X45z//Ca1Wi7vvvhtPP/2075jmPkYli8WCtWvXYvPmzSgqKsL777+Pxx9/3K/NkSNHUFZWhssvvxyxsbHn9w0mIqJmEQQB5mHPoXDTlIotEkLH/KPWUI5KstsByZEHj8MbmqtCdB4kR65fqJadBYAsebc7coGiRquBqIuAaIiuFpwrgnSt3upozroRIBiSqUk++OAD3H777di1axd2796NO++8E926dcO8efNgtVqxaNEiDBo0CBaLBYsXL8asWbOwd+9eiKKI7OxszJ49Gy+++CJmzZqFsrIy/PTTT5BlGQCwdOlS/Otf/8LKlSuRnJyMH3/8EbfccguioqIwduzYJte3aNEipKamYufOnbjttttwySWXYOLEiRf0GJ988gliY2MxYsQIzJkzB08//TQee+wxvzFtaWlpUKvVGDRo0AV8h4mIqLm08ZOgifBexKeJSIE2flK9bQW13tcj3RhZckFynINkr+iJ9oVobw+1X9B2nvMGamc+JGc+gIONnl/Qhnrncq4xxKOuISCC2ticbwm1IIZkhUlSXdPXVBHFqvFQLdG2epvmSEhIwMsvvwxBENC7d2/89ttvePnllzFv3jxcd911fm3fffddREVF4eDBgxgwYACys7Phdrtx7bXXonv37gCAgQO9qz85nU48//zz+O677zB69GgAQFJSErZt24Z//OMfTQ7JgwYNwlNPPQUASE5OxhtvvIHNmzdj4sSJF/QYq1at8g2hmDlzJu666y5s3boV48aN87VJT0+Hx+NBRESEb9vgwYOxfft2rFu3Dg8++CAkScIjjzyCO+64o87H6dGjB8xmM0RRRFhYGH744YcmPW8ios5MEAQED38eJan3I3j48y12UZ4gaqAyxkNljEdjV8DIkgeSs6AqSPtCdFUPtadayIbkglxeDE95MTylvzdeizrIr4e6zmEfFV8LmmBemNiCGJIVdvBg/X9xBgUFoUePHr77hw4d8vW+1mQ0GpGUlOS7n5GRAY/HU6vdgAEDzqvOUaNG+b3xRo8ejeXLl8Pj8eD48eNYvHgxUlNTkZ+f7wvomZmZGDBgAAYPHozx48dj4MCBmDx5MiZNmoTrr78eYWFhOHr0KGw2m6/Ht1J5eTmGDh3a5Ppq9uLGxcUhLy8PAM77MTIyMrBjxw68//77ALyvx4wZM7Bq1apaIXn27NlYsmSJb5vJZILb7caiRYvwww8/ICQkBMOHD8esWbP8wnR1O3bs4BhmIqJm0sVPQPSsxntvW4sgqqAyRENliAbqWP67OlmWIZcX+cZJV++h9uuxrgjW8Dgguy3wlFngKTveeDEqfUWIjm68h1obpmigDoRVExmS6YJdffXV6N69O9555x3Ex8dDkiQMGDAA5eXlAACVSoVNmzZhx44d2LhxI15//XX89a9/RWpqKiwWCwBg/fr16NKli995dbqmj9mqOduFIAi+sH6+j7Fq1SqkpKQgOblqvs85c+bghhtuwBtvvIGQkBAA3pC8dOlS9OrVy+/4HTt2oH///r7HnDp1KjZu3IjZs2c3+XkREVHHIQgCBF04RF04UMdqhtXJsgzZVVbVG+0L0XlVFyRWC9Wy2wJ4HPBYT8FTx6wftVTO9NHQsI/K7bqIqsVcWkCgrJrIkKywfv36Nblt374Nv6Gq69279kpIFyI1NdXv/s8//4zk5GQUFxcjIyMD77zzDi677DIAwLZt22odLwgCLrnkElxyySVYvHgxunfvji+++ALz5s2DTqdDZmZmk4dWNFe/fv2a/RhutxsffvghHn30Ub/tkyZNgtFoxEcffYS7774bx48fR3FxcZ090llZWX6hvEuXLjh79mydjycIAsaOHQtRFLFgwQK/WTKIiKjzEQQBgtYMUWsG6licpSbZbavVQ+2pNstH9aAtlxef50wf0XVPmec37CMKgtjIIJUAWTWRIVlhzRkj3FptmyIzMxOLFi3CXXfdhfT0dLz++utYvnw5wsLCEBERgbfffhtxcXHIzMysFSxTU1OxefNmTJo0CdHR0UhNTcW5c+fQt29fBAcH46GHHsLChQshSRIuvfRSlJSUYPv27TCbzZg7d+4F134+j7Fu3Trk5uZiwIAB2L9/v9++yy+/HKtWrcLdd9+NtLQ0qFSqC75ob9u2bejSpQuys7MxYcIEDBw4kBcCEhFRkwlqI9TBiUBwYqNtZY+zaohHIz3UUs2ZPvBb47XoInwzedQ3D7Wx730o2XZrPWdoH6smMiRTk9x6662w2+0YMWIEVCoVHnjgAdx5550QBAEff/wx7r//fgwYMAC9e/fGa6+95jdm12w248cff8Qrr7yC0tJSdO/eHcuXL8fUqVMBAM8++yyioqKwdOlSHD9+HKGhoRg2bFitqdYuRHMfY9WqVQBQaxxzdfv27UN6ejr69OkDg8FQa398fLxfz/HZs2cxYsSIOs9V2eMcFxeHadOmIT09nSGZiIhahaDSQWVK8PbWNkKW3L6ZPvxDdLVQXbnNcQ6QPZCdBXA7C9CUmT7qKA6a8GENzlTSVgS5vivBqNlKS0sREhKCkpISmM1mv30OhwMnTpxAYmIi9Hq9QhVSW3K73ejbty+2bNniu3Bvx44dtS7cs1qtkCQJwcHBsFgsGDt2LFauXImUlJQ6z8ufJSIiao9kWfLO9OE3D3XV1Hk1h31ActV5nvCJG+qd77olNJTXqmNPMlErUavVWL58Oa644gpIkoSHH37YLyAPGTIEe/fuRW5uLmbNmgUA8Hg8mDdvXr0BmYiIqL0SBBEqfRRU+iggrOHZtGRZhuQsQuGGsXAXHwQgtateZIA9yS2KPcnUFvizREREHYXz7H+rrZrY+r3IQNN7klv26i4iIiIioiaqXDURQKOrJrY1hmQiIiIiUkTlqomqkL4tumpiS+CYZCIiIiJSjNKrJtaHPclERERERDUwJBMRERER1cCQ3MY4mQhdKP4MERERtT6G5Dai0XjXMbfZbApXQoGu8meo8meKiIiIWh4v3GsjKpUKoaGhyMvLAwAYjcZ2dQUntX+yLMNmsyEvLw+hoaFQqVRKl0RERNRhMSS3odjYWADwBWWi8xEaGur7WSIiIqLWwZDchgRBQFxcHKKjo+Fy1b1eOVFDNBoNe5CJiIjaAEOyAlQqFYMOERERUTvGC/eIiIiIiGpgSCYiIiIiqoEhmYiIiIioBo5JbkGVizyUlpYqXAkRERER1aUypzW2OBdDcgsqKysDACQkJChcCRERERE1pKysDCEhIfXuF2SucdtiJElCVlYWgoOD22ShkNLSUiQkJOD06dMwm82t/njU8vgaBj6+hoGNr1/g42sY+Nr6NZRlGWVlZYiPj4co1j/ymD3JLUgURXTt2rXNH9dsNvMXQ4Djaxj4+BoGNr5+gY+vYeBry9ewoR7kSrxwj4iIiIioBoZkIiIiIqIaGJIDmE6nw1NPPQWdTqd0KXSe+BoGPr6GgY2vX+Djaxj42utryAv3iIiIiIhqYE8yEREREVENDMlERERERDUwJBMRERER1cCQTERERERUA0NyAHr66achCILfrU+fPkqXRQ348ccfcfXVVyM+Ph6CIODLL7/02y/LMhYvXoy4uDgYDAZMmDABR44cUaZYqqWx1++2226r9Z6cMmWKMsVSnZYuXYqUlBQEBwcjOjoaM2fOREZGhl8bh8OBe++9FxEREQgKCsJ1112H3NxchSqm6pry+o0bN67W+/Duu+9WqGKq6e9//zsGDRrkWzBk9OjR+Pbbb3372+P7jyE5QPXv3x/Z2dm+27Zt25QuiRpgtVoxePBgvPnmm3Xuf/HFF/Haa69h5cqVSE1NhclkwuTJk+FwONq4UqpLY68fAEyZMsXvPfnRRx+1YYXUmK1bt+Lee+/Fzz//jE2bNsHlcmHSpEmwWq2+NgsXLsTXX3+NTz/9FFu3bkVWVhauvfZaBaumSk15/QBg3rx5fu/DF198UaGKqaauXbvib3/7G9LS0rB7925ceeWVmDFjBg4cOACgnb7/ZAo4Tz31lDx48GCly6DzBED+4osvfPclSZJjY2Pll156ybetuLhY1ul08kcffaRAhdSQmq+fLMvy3Llz5RkzZihSD52fvLw8GYC8detWWZa97zmNRiN/+umnvjaHDh2SAcg7d+5UqkyqR83XT5ZleezYsfIDDzygXFHUbGFhYfI///nPdvv+Y09ygDpy5Aji4+ORlJSEOXPmIDMzU+mS6DydOHECOTk5mDBhgm9bSEgIRo4ciZ07dypYGTXHli1bEB0djd69e+Oee+5BQUGB0iVRA0pKSgAA4eHhAIC0tDS4XC6/92GfPn3QrVs3vg/boZqvX6XVq1cjMjISAwYMwGOPPQabzaZEedQIj8eDjz/+GFarFaNHj2637z+1Yo9M523kyJF4//330bt3b2RnZ2PJkiW47LLLsH//fgQHBytdHjVTTk4OACAmJsZve0xMjG8ftW9TpkzBtddei8TERBw7dgyPP/44pk6dip07d0KlUildHtUgSRIWLFiASy65BAMGDADgfR9qtVqEhob6teX7sP2p6/UDgJtvvhndu3dHfHw89u3bh0ceeQQZGRn4/PPPFayWqvvtt98wevRoOBwOBAUF4YsvvkC/fv2wd+/edvn+Y0gOQFOnTvV9PWjQIIwcORLdu3fHJ598gttvv13Byog6p5tuusn39cCBAzFo0CD07NkTW7Zswfjx4xWsjOpy7733Yv/+/byWI0DV9/rdeeedvq8HDhyIuLg4jB8/HseOHUPPnj3bukyqQ+/evbF3716UlJTgs88+w9y5c7F161aly6oXh1t0AKGhobjoootw9OhRpUuh8xAbGwsAta7izc3N9e2jwJKUlITIyEi+J9uh+fPnY926dfjhhx/QtWtX3/bY2FiUl5ejuLjYrz3fh+1Lfa9fXUaOHAkAfB+2I1qtFv/f3r0HRVW/fwB/L7rrsixyiwRMAUERVAgvKBeNwtxyoHAYR6VJCALKuyQaoUGlec/bKGkaqBNqkwLqeCsVEkS8IIKCuLuRaGI6ipcVUZHn90fj+XGWBUVF0O/zmtkZ9vP5nM/znD179NnD5yzOzs7o168f5s6dCw8PDyxbtqzNnn9cJL8CdDodtFotbG1tWzsV9hQcHR1hY2OD/fv3C223bt1Cfn4+vL29WzEz9rQuXryIa9eu8TnZhhARJkyYgPT0dBw4cACOjo6i/n79+kEqlYrOw7KyMlRUVPB52AY87vgZUlhYCAB8HrZhdXV1uHfvXps9/3i5xUto2rRpCAoKgr29PS5duoTExES0a9cOY8aMae3UWCN0Op3oakZ5eTkKCwthaWmJrl27YsqUKZg9eza6d+8OR0dHzJo1C3Z2dggODm69pJmgqeNnaWmJb775BiEhIbCxsYFWq8X06dPh7OwMlUrVilmz+saPH4+0tDRkZmbC1NRUWOdoZmYGY2NjmJmZITIyErGxsbC0tETHjh0xceJEeHt7Y9CgQa2cPXvc8dNqtUhLS8Pw4cNhZWWFoqIiTJ06FUOGDIG7u3srZ88AID4+Hu+//z66du2K27dvIy0tDVlZWdi7d2/bPf9a7Xs12FMbNWoU2drakkwmo86dO9OoUaNIo9G0dlqsCQcPHiQADR5hYWFE9N/XwM2aNYs6depEHTp0oICAACorK2vdpJmgqeNXXV1Nw4YNI2tra5JKpWRvb09RUVF0+fLl1k6b1WPo+AGglJQUYczdu3dp3LhxZGFhQQqFgkaMGEGVlZWtlzQTPO74VVRU0JAhQ8jS0pI6dOhAzs7OFBcXRzdv3mzdxJkgIiKC7O3tSSaTkbW1NQUEBNC+ffuE/rZ4/kmIiF5kUc4YY4wxxlhbx2uSGWOMMcYY08NFMmOMMcYYY3q4SGaMMcYYY0wPF8mMMcYYY4zp4SKZMcYYY4wxPVwkM8YYY4wxpoeLZMYYY4wxxvRwkcwYYwAcHBywdOnSJx7/999/QyKRCH/61pDU1FSYm5s/c26GJCUl4c0332yRuR/H398fU6ZMeSGx9F/nrKwsSCQS3Lhxo0Xjrlu3DsOGDWvRGD/++COCgoJaNAZj7OlxkcwYe2mFh4dDIpFg3rx5ovaMjAxIJJJmzXXs2DFER0c/z/RYC/Dx8UFlZSXMzMxaLEZNTQ1mzZqFxMTEFosBABERESgoKMChQ4daNA5j7OlwkcwYe6nJ5XLMnz8fVVVVzzSPtbU1FArFc8qqZT148KC1U2iW+/fvP7e5ZDIZbGxsmv0hqDl+++03dOzYEb6+vi0WA/hvX0JDQ7F8+fIWjcMYezpcJDPGXmpDhw6FjY0N5s6d2+S4nJwcDB48GMbGxujSpQsmTZqEO3fuCP36yy3Onj0LPz8/yOVyuLm54Y8//oBEIkFGRoZo3r/++gtvv/02FAoFPDw8kJeX1yB2RkYGunfvDrlcDpVKhQsXLoj6k5OT4eTkBJlMBhcXF2zcuFHUL5FIkJycjA8++AAmJiaYM2eO0Ldx40Y4ODjAzMwMo0ePxu3bt4W+e/fuYdKkSXj99dchl8vh5+eHY8eOiebOzs6Gl5cXOnToAFtbW3z55Zeora0V+u/cuYOxY8dCqVTC1tYWixcvbvJ1Bv5/KcjatWvh6OgIuVwOANizZw/8/Pxgbm4OKysrBAYGQqvVirY9evQoPD09IZfL0b9/f5w8eVLUr7/cwtCyk6VLl8LBwUG0jZeXF0xMTGBubg5fX1+cP3++0fw3b95scBnEzz//jF69egmv1YQJE4Q+iUSC1atXIzAwEAqFAq6ursjLy4NGo4G/vz9MTEzg4+PTYH+DgoKwfft23L17t9F8GGOtg4tkxthLrV27dvj++++xYsUKXLx40eAYrVaL9957DyEhISgqKsKWLVuQk5MjKnLqe/jwIYKDg6FQKJCfn481a9YgISHB4NiEhARMmzYNhYWF6NGjB8aMGSMqMqurqzFnzhxs2LABubm5uHHjBkaPHi30p6enY/Lkyfjiiy9w+vRpxMTE4JNPPsHBgwdFcZKSkjBixAgUFxcjIiJC2K+MjAzs3LkTO3fuRHZ2tmjpyfTp07F161asX78eBQUFcHZ2hkqlwvXr1wEA//zzD4YPH44BAwbg1KlTSE5Oxrp16zB79mxhjri4OGRnZyMzMxP79u1DVlYWCgoKmjokAACNRoOtW7di27ZtwnriO3fuIDY2FsePH8f+/fthZGSEESNGoK6uDgCg0+kQGBgINzc3nDhxAklJSZg2bdpjYzWltrYWwcHBeOutt1BUVIS8vDxER0c3eSU6JycH/fv3F7UlJydj/PjxiI6ORnFxMbZv3w5nZ2fRmO+++w5jx45FYWEhevbsidDQUMTExCA+Ph7Hjx8HETV4z/Xv3x+1tbXIz89/pv1kjLUAYoyxl1RYWBh9+OGHREQ0aNAgioiIICKi9PR0qv/PW2RkJEVHR4u2PXToEBkZGdHdu3eJiMje3p6WLFlCRES7d++m9u3bU2VlpTD+999/JwCUnp5ORETl5eUEgNauXSuMOXPmDAGg0tJSIiJKSUkhAHTkyBFhTGlpKQGg/Px8IiLy8fGhqKgoUW4jR46k4cOHC88B0JQpU0RjEhMTSaFQ0K1bt4S2uLg4GjhwIBER6XQ6kkql9Msvvwj99+/fJzs7O1qwYAEREX311Vfk4uJCdXV1wpiVK1eSUqmkhw8f0u3bt0kmk9Gvv/4q9F+7do2MjY1p8uTJ1JjExESSSqV05cqVRscQEV29epUAUHFxMRERrV69mqysrIRjQkSUnJxMAOjkyZNERHTw4EECQFVVVUIsDw8P0bxLliwhe3t7IV8AlJWV1WQuj1RVVREA+vPPP0XtdnZ2lJCQ0Oh2AGjmzJnC87y8PAJA69atE9o2bdpEcrm8wbYWFhaUmpr6RPkxxl4cvpLMGHslzJ8/H+vXr0dpaWmDvlOnTiE1NRVKpVJ4qFQq1NXVoby8vMH4srIydOnSBTY2NkKbl5eXwbju7u7Cz7a2tgCAK1euCG3t27fHgAEDhOc9e/aEubm5kGdpaWmDta++vr4N9kP/yibw3xIRU1NTUfxHsbVaLR48eCCaWyqVwsvLSxTb29tbdFXV19cXOp0OFy9ehFarxf379zFw4ECh39LSEi4uLgZfi/rs7e1hbW0talOr1RgzZgy6deuGjh07CksiKioqhHzc3d2F5RkA4O3t/dhYTbG0tER4eDhUKhWCgoKwbNkyVFZWNjr+0bKH+jlcuXIFly5dQkBAQJOx6r8XOnXqBADo06ePqK2mpga3bt0SbWdsbIzq6uon3ynG2AvBRTJj7JUwZMgQqFQqxMfHN+jT6XSIiYlBYWGh8Dh16hTUajWcnJyeKa5UKhV+flRsPlo+8DyZmJg0GftR/JaI/TQM5RsUFITr16/jp59+Qn5+vrDE4Flu7DMyMgIRidr0b2xMSUlBXl4efHx8sGXLFvTo0QNHjhwxOJ+VlRUkEonoRlBjY+MnysXQe+FJ3h/Xr19v8IGCMdb6uEhmjL0y5s2bhx07djS4ea5v374oKSmBs7Nzg4dMJmswj4uLCy5cuIB///1XaNO/4e1J1dbW4vjx48LzsrIy3LhxA66urgAAV1dX5ObmirbJzc2Fm5vbU8V75NGNgPXnfvDgAY4dOybM/ejmsvpFZm5uLkxNTfHGG2/AyckJUqlUtF62qqoK586da3Y+165dQ1lZGWbOnImAgAC4uro2+EYSV1dXFBUVoaamRmhrrJh9xNraGpcvXxbtg6Hvrvb09ER8fDwOHz6M3r17Iy0tzeB8MpkMbm5uKCkpEdpMTU3h4OCA/fv3P8muNotWq0VNTQ08PT2f+9yMsWfDRTJj7JXRp08ffPTRRw2+UmvGjBk4fPgwJkyYgMLCQqjVamRmZjZ64967774LJycnhIWFoaioCLm5uZg5cyYANPurx6RSKSZOnIj8/HycOHEC4eHhGDRokLB8Iy4uDqmpqUhOToZarcYPP/yAbdu2PfMNayYmJvj8888RFxeHPXv2oKSkBFFRUaiurkZkZCQAYNy4cbhw4QImTpyIs2fPIjMzE4mJiYiNjYWRkRGUSiUiIyMRFxeHAwcO4PTp0wgPD4eRUfP/67CwsICVlRXWrFkDjUaDAwcOIDY2VjQmNDQUEokEUVFRKCkpwa5du7Bo0aIm5/X398fVq1exYMECaLVarFy5Ert37xb6y8vLER8fj7y8PJw/fx779u2DWq0WPqQYolKpkJOTI2pLSkrC4sWLsXz5cqjVahQUFGDFihXNfh30HTp0CN26dXvm32gwxp4/LpIZY6+Ub7/9tsGvs93d3ZGdnY1z585h8ODB8PT0xNdffw07OzuDc7Rr1w4ZGRnQ6XQYMGAAPv30U+HbLeqvVX0SCoUCM2bMQGhoKHx9faFUKrFlyxahPzg4GMuWLcOiRYvQq1cvrF69GikpKfD392/ejhswb948hISE4OOPP0bfvn2h0Wiwd+9eWFhYAAA6d+6MXbt24ejRo/Dw8MBnn32GyMhI4QMBACxcuBCDBw9GUFAQhg4dCj8/P/Tr16/ZuRgZGWHz5s04ceIEevfujalTp2LhwoWiMUqlEjt27EBxcTE8PT2RkJCA+fPnNzmvq6srVq1ahZUrV8LDwwNHjx4VfcBQKBQ4e/YsQkJC0KNHD0RHR2P8+PGIiYlpdM7IyEjs2rULN2/eFNrCwsKwdOlSrFq1Cr169UJgYCDUanWzXwd9mzZtQlRU1DPPwxh7/iSkv5iLMcZYA7m5ufDz84NGo+Grfv8DRo4cib59+xpc4/68nDlzBu+88w7OnTvXon9BkDH2dNq3dgKMMdYWpaenQ6lUonv37tBoNJg8eTJ8fX25QP4fsXDhQuzYsaNFY1RWVmLDhg1cIDPWRvGVZMYYM2DDhg2YPXs2Kioq8Nprr2Ho0KFYvHgxrKysWjs1xhhjLwAXyYwxxhhjjOnhG/cYY4wxxhjTw0UyY4wxxhhjerhIZowxxhhjTA8XyYwxxhhjjOnhIpkxxhhjjDE9XCQzxhhjjDGmh4tkxhhjjDHG9HCRzBhjjDHGmB4ukhljjDHGGNPzf4H3VrC7BZTTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "\n",
    "r_ranges = range(5, 45, 5)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(r_ranges[:6], (np.array(target_F) - np.array(clue3D_F))[:6], '--s', label=\"target $\\Delta F_{0.5}$\", c=\"#D55E00\")\n",
    "ax.plot(r_ranges[:6], (np.array(model_reco_F) - np.array(clue3D_F))[:6], '-o', label=\"mlp $\\Delta F_{0.5}$\", c=\"#56B4E9\")\n",
    "ax.plot(r_ranges[:6], (np.array(naive_reco_F) - np.array(clue3D_F))[:6], '-v', label=\"naive $\\Delta F_{0.5}$\", c=\"#E69F00\")\n",
    "\n",
    "ax.axhline(max(np.array(naive_reco_F) - np.array(clue3D_F)), label=\"baseline $\\Delta F_{0.5}$\", c=\"lightgray\", linestyle=\"--\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Neighborhood radius (cm)\")\n",
    "ax.set_ylabel(\"$\\Delta F_{0.5}$\")\n",
    "\n",
    "fig.savefig('export/multiparticle-radius-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "29a068ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reco 0.11263148393350464\n",
      "Naive 0.1059781299966025\n",
      "Target 0.16885371547648897\n"
     ]
    }
   ],
   "source": [
    "print(\"Reco\", max(np.array(model_reco_F) - np.array(clue3D_F)))\n",
    "print(\"Naive\", max(np.array(naive_reco_F) - np.array(clue3D_F)))\n",
    "print(\"Target\", max(np.array(target_F) - np.array(clue3D_F)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a270a5e7",
   "metadata": {},
   "source": [
    "### Energy threshold identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17c36c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Radius threshold: 10 | ETH: 0 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.170 F: 0.504\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.951 R: 0.192 F: 0.531\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.296 F: 0.667\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.204 F: 0.551\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.199 F: 0.548\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.321 F: 0.695\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.207 F: 0.563\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.982 R: 0.238 F: 0.604\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.982 R: 0.269 F: 0.642\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.975 R: 0.239 F: 0.598\n",
      " --- Radius threshold: 10 | ETH: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.168 F: 0.501\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.199 F: 0.541\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.262 F: 0.635\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.297 F: 0.667\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.204 F: 0.550\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.182 F: 0.521\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.347 F: 0.718\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.211 F: 0.569\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.245 F: 0.613\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.286 F: 0.658\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.975 R: 0.240 F: 0.597\n",
      " --- Radius threshold: 10 | ETH: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.168 F: 0.500\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.949 R: 0.200 F: 0.543\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.261 F: 0.635\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.294 F: 0.665\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.201 F: 0.545\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.187 F: 0.529\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.211 F: 0.569\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.263 F: 0.635\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.977 R: 0.234 F: 0.592\n",
      " --- Radius threshold: 10 | ETH: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.166 F: 0.496\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.200 F: 0.543\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.237 F: 0.605\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.294 F: 0.665\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.198 F: 0.542\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.186 F: 0.529\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.208 F: 0.565\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.263 F: 0.635\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.977 R: 0.231 F: 0.588\n",
      " --- Radius threshold: 10 | ETH: 40 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.165 F: 0.494\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.196 F: 0.537\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.225 F: 0.590\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.294 F: 0.665\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.165 F: 0.489\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.181 F: 0.520\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.208 F: 0.565\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.262 F: 0.635\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.977 R: 0.225 F: 0.579\n",
      " --- Radius threshold: 10 | ETH: 60 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.163 F: 0.492\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.192 F: 0.531\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.225 F: 0.590\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.290 F: 0.661\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.164 F: 0.487\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.181 F: 0.520\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.314 F: 0.687\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.989 R: 0.208 F: 0.565\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.242 F: 0.610\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.262 F: 0.634\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.977 R: 0.224 F: 0.578\n",
      " --- Radius threshold: 20 | ETH: 0 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.207 F: 0.562\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.216 F: 0.565\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.307 F: 0.677\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.550\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.206 F: 0.557\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.319 F: 0.693\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.208 F: 0.564\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.982 R: 0.238 F: 0.603\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.972 R: 0.249 F: 0.611\n",
      " --- Radius threshold: 20 | ETH: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.177 F: 0.515\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.236 F: 0.590\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.673\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.304 F: 0.674\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.550\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.199 F: 0.547\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.347 F: 0.718\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.213 F: 0.572\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.290 F: 0.663\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.973 R: 0.251 F: 0.612\n",
      " --- Radius threshold: 20 | ETH: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.175 F: 0.511\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.247 F: 0.604\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.673\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.304 F: 0.674\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.549\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.224 F: 0.581\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.214 F: 0.573\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.291 F: 0.663\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.972 R: 0.252 F: 0.613\n",
      " --- Radius threshold: 20 | ETH: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.174 F: 0.510\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.235 F: 0.589\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.273 F: 0.648\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.303 F: 0.673\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.205 F: 0.549\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.194 F: 0.539\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.212 F: 0.569\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.291 F: 0.663\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.973 R: 0.245 F: 0.605\n",
      " --- Radius threshold: 20 | ETH: 40 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.170 F: 0.503\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.945 R: 0.235 F: 0.589\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.260 F: 0.633\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.303 F: 0.673\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.201 F: 0.543\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.974 R: 0.188 F: 0.530\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.212 F: 0.569\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.291 F: 0.663\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.973 R: 0.242 F: 0.601\n",
      " --- Radius threshold: 20 | ETH: 60 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.166 F: 0.496\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.946 R: 0.231 F: 0.584\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.260 F: 0.633\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.300 F: 0.670\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.957 R: 0.198 F: 0.542\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.974 R: 0.188 F: 0.530\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.317 F: 0.691\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.212 F: 0.569\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.977 R: 0.290 F: 0.663\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.975 R: 0.241 F: 0.599\n",
      " --- Radius threshold: 30 | ETH: 0 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.224 F: 0.586\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.942 R: 0.235 F: 0.589\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.309 F: 0.678\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.947 R: 0.211 F: 0.557\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.207 F: 0.558\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.344 F: 0.715\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.221 F: 0.581\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.971 R: 0.258 F: 0.622\n",
      " --- Radius threshold: 30 | ETH: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.220 F: 0.580\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.236 F: 0.590\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.307 F: 0.677\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.207 F: 0.553\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.207 F: 0.558\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.220 F: 0.580\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.972 R: 0.258 F: 0.621\n",
      " --- Radius threshold: 30 | ETH: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.219 F: 0.579\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.606\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.296 F: 0.672\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.233 F: 0.593\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.218 F: 0.579\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.972 R: 0.261 F: 0.625\n",
      " --- Radius threshold: 30 | ETH: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.983 R: 0.219 F: 0.579\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.606\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.294 F: 0.670\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.206 F: 0.551\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.203 F: 0.552\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.215 F: 0.575\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.972 R: 0.258 F: 0.621\n",
      " --- Radius threshold: 30 | ETH: 40 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.213 F: 0.571\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.250 F: 0.606\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.260 F: 0.633\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.305 F: 0.675\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.201 F: 0.543\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.196 F: 0.543\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.215 F: 0.575\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.292 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.973 R: 0.253 F: 0.614\n",
      " --- Radius threshold: 30 | ETH: 60 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.148 F: 0.464\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.208 F: 0.564\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.956 R: 0.093 F: 0.335\n",
      "\ttarget_to_sim:\tP: 0.944 R: 0.246 F: 0.603\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.189 F: 0.536\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.260 F: 0.633\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.975 R: 0.177 F: 0.513\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.301 F: 0.671\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.095 F: 0.340\n",
      "\ttarget_to_sim:\tP: 0.948 R: 0.200 F: 0.543\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.979 R: 0.136 F: 0.437\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.196 F: 0.543\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.982 R: 0.199 F: 0.550\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.348 F: 0.719\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.990 R: 0.171 F: 0.505\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.215 F: 0.575\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.121 F: 0.405\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.247 F: 0.615\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.195 F: 0.544\n",
      "\ttarget_to_sim:\tP: 0.979 R: 0.291 F: 0.665\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.980 R: 0.152 F: 0.463\n",
      "mean target_to_sim:\tP: 0.973 R: 0.251 F: 0.613\n"
     ]
    }
   ],
   "source": [
    "eth_ranges = [0, 5, 10, 20, 40, 60]\n",
    "r_ranges = range(10, 40, 10)\n",
    "eth_results = []\n",
    "\n",
    "max_events = 10\n",
    "\n",
    "for r in r_ranges:\n",
    "    _clue3D_F = []\n",
    "    _target_F = []\n",
    "    for eth in eth_ranges:\n",
    "        print(f\" --- Radius threshold: {r} | ETH: {eth} ---\")\n",
    "        result = model_evaluation(\n",
    "            cluster_data,\n",
    "            trackster_data,\n",
    "            simtrackster_data,\n",
    "            assoc_data,\n",
    "            DummyPleaser(),\n",
    "            decision_th=0.5,\n",
    "            radius=r,\n",
    "            max_events=max_events,\n",
    "            bigT_e_th=eth,\n",
    "            reco_eval=False\n",
    "        )\n",
    "        _clue3D_F.append(np.sum(np.array(result[\"clue3d_to_sim\"])[:,2]) / max_events)\n",
    "        _target_F.append(np.sum(np.array(result[\"target_to_sim\"])[:,2]) / max_events)\n",
    "    eth_results.append(np.array(_target_F) - np.array(_clue3D_F))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5a5d28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for r, res in zip(r_ranges, eth_results):\n",
    "    ax.plot(eth_ranges, res, label=f\"{r} cm radius\")\n",
    "\n",
    "ax.legend()\n",
    "# ax.set_xlim(0, 100)\n",
    "ax.set_xlabel(\"Energy threshold (GeV)\")\n",
    "ax.set_ylabel(\"$\\Delta F_{0.5}$\")\n",
    "\n",
    "fig.savefig('export/multiparticle-energy-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fdd111f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ve",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "37c238d9819f69c2c770157eac01081978c120e64661e10d7fd52c4caf977dc9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}