7.model_trackster_graph_net.classification.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6c07744a",
   "metadata": {},
   "source": [
    "# Graph net for node classification\n",
    "\n",
    "Question: does including the neighborhood provide a benefit over the pairwise classification?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3962d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import sys\n",
    "\n",
    "import torch.nn as nn\n",
    "from torch.optim import Adam, SGD\n",
    "from torch_cluster import knn_graph\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "from torch.utils.data import random_split\n",
    "from torch_geometric.loader import DataLoader\n",
    "import torch_geometric.transforms as T\n",
    "\n",
    "from reco.model import DynamicEdgeConvBlock\n",
    "\n",
    "from reco.training import precision_recall_curve, roc_auc\n",
    "from reco.loss import GraphClassificationLoss, GraphClassificationLossSingleClass\n",
    "from reco.datasetPU import TracksterGraph\n",
    "\n",
    "import torch_geometric.nn as gnn\n",
    "\n",
    "\n",
    "# TODO: for PU, I trained on 10GeV and evaluated on 5Gev\n",
    "# Retrain when larger dataset is ready (100E)\n",
    "ds_name = \"CloseByTwoPion\"\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}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02f59304-76e4-4132-a379-978a46a042fd",
   "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": 24,
   "id": "012b3572",
   "metadata": {},
   "outputs": [],
   "source": [
    "def knn_transform(data):\n",
    "    # pos coordinates are on position 3:6\n",
    "    data.edge_index = knn_graph(data.pos, k=4, loop=False)\n",
    "    return data\n",
    "\n",
    "def prepend_pos(data):\n",
    "    # GravNet: add S to the front\n",
    "    data.x = torch.hstack((data.pos, data.x))\n",
    "    return data\n",
    "\n",
    "transforms = T.Compose([prepend_pos])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba68cd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = TracksterGraph(\n",
    "    ds_name,\n",
    "    data_root,\n",
    "    raw_dir,\n",
    "    N_FILES=10,\n",
    "    pileup=False,\n",
    "    radius=100,\n",
    "    bigT_e_th=30,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "604298f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train graphs: 11165, Test graphs: 1240, total: 12405\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 = random_split(ds, [train_set_size, test_set_size])\n",
    "print(f\"Train graphs: {len(train_set)}, Test graphs: {len(test_set)}, total: {ds_size}\")\n",
    "\n",
    "train_dl = DataLoader(train_set, batch_size=8, shuffle=True)\n",
    "test_dl = DataLoader(test_set, batch_size=8, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f4a0b99-bef3-4c82-a0ac-d88e1521382b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TracksterGraphNet(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim=2, dropout=0.2):\n",
    "        super(TracksterGraphNet, self).__init__()\n",
    "\n",
    "        self.norm = gnn.LayerNorm(input_dim)\n",
    "        \n",
    "        hdim1 = 64\n",
    "        hdim2 = 64\n",
    "\n",
    "        hdim_fc = 256\n",
    "\n",
    "        self.edgeconv1 = DynamicEdgeConvBlock(input_dim, hdim1, k=7)\n",
    "        self.edgeconv2 = DynamicEdgeConvBlock(hdim1, hdim2, k=7)\n",
    "        # self.edgeconv3 = DynamicEdgeConvBlock(hdim2, hdim3)\n",
    "        \n",
    "        # Edge features from node embeddings for classification        \n",
    "        self.nodenetwork = nn.Sequential(\n",
    "            nn.Linear(hdim2, hdim_fc),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hdim_fc, output_dim),\n",
    "            # nn.Sigmoid(),\n",
    "            nn.Softmax(dim=1),\n",
    "        )\n",
    "\n",
    "    def forward(self, X, batch=None):\n",
    "        H0 = X #self.norm(X, batch)\n",
    "        H1 = self.edgeconv1(H0, batch)\n",
    "        H2 = self.edgeconv2(H1, batch)\n",
    "        # H3 = self.edgeconv3(H2, batch)\n",
    "        return self.nodenetwork(H2).squeeze(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2539cf65",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_graph_classification(model, device, optimizer, loss_func, train_dl):\n",
    "    train_loss = 0.\n",
    "    model.train()\n",
    "\n",
    "    for data in train_dl:\n",
    "        data = data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        seg_pred = model(data.x, data.batch)\n",
    "        loss = loss_func(seg_pred, data)\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "        train_loss += loss.item()\n",
    "\n",
    "    return train_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "259ee8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def test_graph_classification(model, device, loss_func, test_dl):\n",
    "    test_loss = 0.\n",
    "    model.eval()\n",
    "    for data in test_dl:\n",
    "        data = data.to(device)\n",
    "        seg_pred = model(data.x, data.batch)\n",
    "        loss = loss_func(seg_pred, data)\n",
    "        test_loss += loss.item()\n",
    "    return test_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b85638de",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TracksterGraphNet(input_dim=ds.data.x.shape[1], output_dim=2)\n",
    "epochs = 101\n",
    "model_path = f\"models/TracksterGraphNet.ParticleNetLight.ns.{epochs}e-{ds_name}.r{ds.RADIUS}.e{ds.bigT_e_th}.f{ds.N_FILES}.pt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d3f1e82-74c5-4629-9b20-f05d7662aaf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0:\t train loss: 964.989\t validation loss: 57.003\n",
      "Epoch 1:\t train loss: 482.405\t validation loss: 48.424\n",
      "Epoch 2:\t train loss: 448.176\t validation loss: 117.634\n",
      "Epoch 3:\t train loss: 413.669\t validation loss: 42.030\n",
      "Epoch 4:\t train loss: 391.110\t validation loss: 64.758\n",
      "Epoch 5:\t train loss: 375.889\t validation loss: 38.686\n",
      "Epoch 6:\t train loss: 357.655\t validation loss: 37.124\n",
      "Epoch 7:\t train loss: 349.129\t validation loss: 35.820\n",
      "Epoch 8:\t train loss: 335.896\t validation loss: 36.291\n",
      "Epoch 9:\t train loss: 321.361\t validation loss: 36.530\n",
      "Epoch 10:\t train loss: 319.734\t validation loss: 35.473\n",
      "Epoch 11:\t train loss: 313.079\t validation loss: 33.876\n",
      "Epoch 12:\t train loss: 308.889\t validation loss: 53.213\n",
      "Epoch 13:\t train loss: 299.912\t validation loss: 35.437\n",
      "Epoch 14:\t train loss: 303.433\t validation loss: 35.867\n",
      "Epoch 15:\t train loss: 301.593\t validation loss: 32.993\n",
      "Epoch 16:\t train loss: 292.475\t validation loss: 31.381\n",
      "Epoch 17:\t train loss: 290.146\t validation loss: 34.076\n",
      "Epoch 18:\t train loss: 285.697\t validation loss: 31.757\n",
      "Epoch 19:\t train loss: 287.909\t validation loss: 30.496\n",
      "Epoch 20:\t train loss: 279.827\t validation loss: 35.895\n",
      "Epoch 21:\t train loss: 280.506\t validation loss: 31.626\n",
      "Epoch 22:\t train loss: 275.286\t validation loss: 37.383\n",
      "Epoch 23:\t train loss: 276.885\t validation loss: 31.679\n",
      "Epoch 24:\t train loss: 273.699\t validation loss: 28.976\n",
      "Epoch 25:\t train loss: 266.629\t validation loss: 31.564\n",
      "Epoch 26:\t train loss: 267.190\t validation loss: 29.678\n",
      "Epoch 27:\t train loss: 263.065\t validation loss: 35.832\n",
      "Epoch 28:\t train loss: 261.236\t validation loss: 30.947\n",
      "Epoch 29:\t train loss: 256.608\t validation loss: 29.060\n",
      "Epoch 30:\t train loss: 262.386\t validation loss: 30.244\n",
      "Epoch 31:\t train loss: 258.893\t validation loss: 29.250\n",
      "Epoch 32:\t train loss: 255.365\t validation loss: 29.247\n",
      "Epoch 33:\t train loss: 253.813\t validation loss: 29.285\n",
      "Epoch 34:\t train loss: 253.390\t validation loss: 32.451\n",
      "Epoch 35:\t train loss: 246.410\t validation loss: 28.883\n",
      "Epoch 36:\t train loss: 246.216\t validation loss: 28.894\n",
      "Epoch 37:\t train loss: 245.581\t validation loss: 28.615\n",
      "Epoch 38:\t train loss: 239.127\t validation loss: 29.870\n",
      "Epoch 39:\t train loss: 241.606\t validation loss: 28.402\n",
      "Epoch 40:\t train loss: 238.894\t validation loss: 28.138\n",
      "Epoch 41:\t train loss: 238.004\t validation loss: 30.691\n",
      "Epoch 42:\t train loss: 238.339\t validation loss: 29.200\n",
      "Epoch 43:\t train loss: 235.941\t validation loss: 29.643\n",
      "Epoch 44:\t train loss: 234.845\t validation loss: 28.067\n",
      "Epoch 45:\t train loss: 231.840\t validation loss: 28.614\n",
      "Epoch 46:\t train loss: 235.208\t validation loss: 29.661\n",
      "Epoch 47:\t train loss: 230.932\t validation loss: 28.394\n",
      "Epoch 48:\t train loss: 225.320\t validation loss: 28.262\n",
      "Epoch 49:\t train loss: 227.669\t validation loss: 27.631\n",
      "Epoch 50:\t train loss: 226.824\t validation loss: 29.695\n",
      "Epoch 51:\t train loss: 220.769\t validation loss: 29.527\n",
      "Epoch 52:\t train loss: 221.974\t validation loss: 28.003\n",
      "Epoch 53:\t train loss: 220.653\t validation loss: 28.164\n",
      "Epoch 54:\t train loss: 220.887\t validation loss: 28.339\n",
      "Epoch 55:\t train loss: 217.053\t validation loss: 27.684\n",
      "Epoch 56:\t train loss: 213.336\t validation loss: 28.337\n",
      "Epoch 57:\t train loss: 216.045\t validation loss: 27.751\n",
      "Epoch 58:\t train loss: 213.528\t validation loss: 27.633\n",
      "Epoch 59:\t train loss: 215.000\t validation loss: 27.812\n",
      "Epoch 60:\t train loss: 209.728\t validation loss: 27.994\n",
      "Epoch 61:\t train loss: 206.261\t validation loss: 28.062\n",
      "Epoch 62:\t train loss: 204.611\t validation loss: 27.720\n",
      "Epoch 63:\t train loss: 209.053\t validation loss: 26.890\n",
      "Epoch 64:\t train loss: 203.933\t validation loss: 27.301\n",
      "Epoch 65:\t train loss: 202.543\t validation loss: 27.598\n",
      "Epoch 66:\t train loss: 206.091\t validation loss: 27.080\n",
      "Epoch 67:\t train loss: 202.393\t validation loss: 27.287\n",
      "Epoch 68:\t train loss: 200.392\t validation loss: 28.066\n",
      "Epoch 69:\t train loss: 201.483\t validation loss: 27.889\n",
      "Epoch 70:\t train loss: 197.701\t validation loss: 27.214\n",
      "Epoch 71:\t train loss: 197.887\t validation loss: 27.808\n",
      "Epoch 72:\t train loss: 196.603\t validation loss: 28.078\n",
      "Epoch 73:\t train loss: 195.726\t validation loss: 27.238\n",
      "Epoch 74:\t train loss: 194.472\t validation loss: 28.531\n",
      "Epoch 75:\t train loss: 195.466\t validation loss: 27.368\n",
      "Epoch 76:\t train loss: 193.593\t validation loss: 27.288\n",
      "Epoch 77:\t train loss: 191.814\t validation loss: 26.825\n",
      "Epoch 78:\t train loss: 192.737\t validation loss: 27.107\n",
      "Epoch 79:\t train loss: 191.691\t validation loss: 27.239\n",
      "Epoch 80:\t train loss: 189.465\t validation loss: 27.541\n",
      "Epoch 81:\t train loss: 187.982\t validation loss: 27.005\n",
      "Epoch 82:\t train loss: 188.145\t validation loss: 26.880\n",
      "Epoch 83:\t train loss: 187.043\t validation loss: 27.665\n",
      "Epoch 84:\t train loss: 187.880\t validation loss: 27.057\n",
      "Epoch 85:\t train loss: 186.656\t validation loss: 26.962\n",
      "Epoch 86:\t train loss: 186.256\t validation loss: 26.952\n",
      "Epoch 87:\t train loss: 185.069\t validation loss: 27.194\n",
      "Epoch 88:\t train loss: 186.639\t validation loss: 26.921\n",
      "Epoch 89:\t train loss: 185.079\t validation loss: 26.671\n",
      "Epoch 90:\t train loss: 184.298\t validation loss: 26.994\n",
      "Epoch 91:\t train loss: 185.070\t validation loss: 27.197\n",
      "Epoch 92:\t train loss: 185.073\t validation loss: 27.494\n",
      "Epoch 93:\t train loss: 183.315\t validation loss: 27.148\n",
      "Epoch 94:\t train loss: 184.124\t validation loss: 26.910\n",
      "Epoch 95:\t train loss: 182.945\t validation loss: 27.334\n",
      "Epoch 96:\t train loss: 183.903\t validation loss: 26.823\n",
      "Epoch 97:\t train loss: 183.404\t validation loss: 27.149\n",
      "Epoch 98:\t train loss: 183.293\t validation loss: 26.672\n",
      "Epoch 99:\t train loss: 181.477\t validation loss: 26.789\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "models/TracksterGraphNet.ParticleNetLight.ns.101e-CloseByTwoPion.r100.e30.f50.pt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 100:\t train loss: 183.500\t validation loss: 26.736\n"
     ]
    }
   ],
   "source": [
    "# %%script echo skipping\n",
    "# alpha - percentage of negative edges\n",
    "loss_func = GraphClassificationLoss()\n",
    "\n",
    "model = model.to(device)\n",
    "optimizer = Adam(model.parameters(), lr=1e-3)\n",
    "scheduler = CosineAnnealingLR(optimizer, epochs, eta_min=1e-5)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "\n",
    "    train_loss = train_graph_classification(\n",
    "        model,\n",
    "        device,\n",
    "        optimizer,\n",
    "        loss_func,\n",
    "        train_dl\n",
    "    )\n",
    "    \n",
    "    scheduler.step()\n",
    "\n",
    "    if epoch % 1 == 0:\n",
    "        test_loss = test_graph_classification(model, device, loss_func, test_dl)\n",
    "        print(\n",
    "            f\"Epoch {epoch}:\\t train loss: {train_loss:.3f}\\t validation loss: {test_loss:.3f}\",\n",
    "            file=sys.stderr\n",
    "        )\n",
    "        \n",
    "torch.save(model.state_dict(), model_path)\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b9cbaa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipping\n"
     ]
    }
   ],
   "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": "2eb0507a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9628474468125499\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP: 18305, TN: 15414, FP: 965, FN: 3232\n",
      "TH: 0.67 precision: 0.950 recall: 0.850 fbeta: 0.928 b_acc: 0.896\n"
     ]
    }
   ],
   "source": [
    "#%%script echo skipping - only evaluating foreground\n",
    "print(roc_auc(model, device, test_dl))\n",
    "precision_recall_curve(model, device, test_dl, focus_metric=\"fbeta\", beta=0.5, step=3, truth_threshold=0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "13d25421",
   "metadata": {},
   "source": [
    "## Scoreboard\n",
    "- 2 layers, 200 files, 0.944"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb8ce4e2",
   "metadata": {},
   "source": [
    "## Eval"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8417fca7",
   "metadata": {},
   "source": [
    "Overfit on the training set - need more data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb3f12d6",
   "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",
    "from reco.dummy import DummyPleaser\n",
    "\n",
    "file_name = f\"{raw_dir}/new_ntuples_15101852_0.root\"\n",
    "cluster_data, trackster_data, simtrackster_data, assoc_data = get_event_data(file_name, pileup=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b5ffc46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Radius threshold: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.169 F: 0.503\n",
      "\treco_to_sim:\tP: 0.997 R: 0.169 F: 0.503\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.197 F: 0.551\n",
      "\treco_to_sim:\tP: 1.000 R: 0.197 F: 0.551\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.228 F: 0.596\n",
      "\treco_to_sim:\tP: 1.000 R: 0.228 F: 0.596\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.396 F: 0.766\n",
      "\treco_to_sim:\tP: 0.999 R: 0.396 F: 0.766\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.347 F: 0.725\n",
      "\treco_to_sim:\tP: 0.996 R: 0.347 F: 0.725\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.483 F: 0.824\n",
      "\treco_to_sim:\tP: 1.000 R: 0.483 F: 0.824\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.216 F: 0.579\n",
      "\treco_to_sim:\tP: 0.995 R: 0.216 F: 0.579\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.164 F: 0.492\n",
      "\treco_to_sim:\tP: 0.947 R: 0.192 F: 0.530\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.292 F: 0.673\n",
      "\treco_to_sim:\tP: 0.998 R: 0.292 F: 0.673\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.102 F: 0.362\n",
      "\treco_to_sim:\tP: 1.000 R: 0.102 F: 0.362\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.342 F: 0.722\n",
      "\treco_to_sim:\tP: 1.000 R: 0.342 F: 0.722\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.326 F: 0.707\n",
      "\treco_to_sim:\tP: 0.999 R: 0.326 F: 0.707\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.250 F: 0.622\n",
      "\treco_to_sim:\tP: 0.992 R: 0.250 F: 0.622\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.171 F: 0.507\n",
      "\treco_to_sim:\tP: 1.000 R: 0.171 F: 0.507\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.107 F: 0.374\n",
      "\treco_to_sim:\tP: 0.993 R: 0.107 F: 0.374\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.373 F: 0.748\n",
      "\treco_to_sim:\tP: 0.999 R: 0.373 F: 0.748\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.323 F: 0.705\n",
      "\treco_to_sim:\tP: 0.999 R: 0.323 F: 0.705\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.281 F: 0.658\n",
      "\treco_to_sim:\tP: 0.992 R: 0.281 F: 0.658\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.244 F: 0.617\n",
      "\treco_to_sim:\tP: 1.000 R: 0.244 F: 0.617\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.159 F: 0.483\n",
      "\treco_to_sim:\tP: 0.988 R: 0.159 F: 0.483\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.997 R: 0.258 F: 0.611\n",
      "mean reco_to_sim:\tP: 0.995 R: 0.260 F: 0.613\n",
      "mean n_tracksters:\tP: 2.000 R: 1.050 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.169 F: 0.503\n",
      "\treco_to_sim:\tP: 0.997 R: 0.169 F: 0.503\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.197 F: 0.551\n",
      "\treco_to_sim:\tP: 1.000 R: 0.197 F: 0.551\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.228 F: 0.596\n",
      "\treco_to_sim:\tP: 1.000 R: 0.228 F: 0.596\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.396 F: 0.766\n",
      "\treco_to_sim:\tP: 0.999 R: 0.396 F: 0.766\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.347 F: 0.725\n",
      "\treco_to_sim:\tP: 0.996 R: 0.347 F: 0.725\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.483 F: 0.824\n",
      "\treco_to_sim:\tP: 1.000 R: 0.483 F: 0.824\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.216 F: 0.579\n",
      "\treco_to_sim:\tP: 0.995 R: 0.216 F: 0.579\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.164 F: 0.492\n",
      "\treco_to_sim:\tP: 0.987 R: 0.164 F: 0.492\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.292 F: 0.673\n",
      "\treco_to_sim:\tP: 0.998 R: 0.292 F: 0.673\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.102 F: 0.362\n",
      "\treco_to_sim:\tP: 1.000 R: 0.102 F: 0.362\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.342 F: 0.722\n",
      "\treco_to_sim:\tP: 1.000 R: 0.342 F: 0.722\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.326 F: 0.707\n",
      "\treco_to_sim:\tP: 0.999 R: 0.326 F: 0.707\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.250 F: 0.622\n",
      "\treco_to_sim:\tP: 0.992 R: 0.250 F: 0.622\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.171 F: 0.507\n",
      "\treco_to_sim:\tP: 1.000 R: 0.171 F: 0.507\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.107 F: 0.374\n",
      "\treco_to_sim:\tP: 0.993 R: 0.107 F: 0.374\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.373 F: 0.748\n",
      "\treco_to_sim:\tP: 0.999 R: 0.203 F: 0.560\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.323 F: 0.705\n",
      "\treco_to_sim:\tP: 0.999 R: 0.323 F: 0.705\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.281 F: 0.658\n",
      "\treco_to_sim:\tP: 0.992 R: 0.281 F: 0.658\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.244 F: 0.617\n",
      "\treco_to_sim:\tP: 1.000 R: 0.244 F: 0.617\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.159 F: 0.483\n",
      "\treco_to_sim:\tP: 0.988 R: 0.159 F: 0.483\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.997 R: 0.258 F: 0.611\n",
      "mean reco_to_sim:\tP: 0.997 R: 0.250 F: 0.601\n",
      "mean n_tracksters:\tP: 2.000 R: 1.050 F: 1.100\n",
      " --- Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.231 F: 0.597\n",
      "\treco_to_sim:\tP: 0.986 R: 0.231 F: 0.597\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.255 F: 0.631\n",
      "\treco_to_sim:\tP: 1.000 R: 0.255 F: 0.631\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.363 F: 0.741\n",
      "\treco_to_sim:\tP: 1.000 R: 0.363 F: 0.741\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.472 F: 0.813\n",
      "\treco_to_sim:\tP: 0.982 R: 0.473 F: 0.808\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.409 F: 0.774\n",
      "\treco_to_sim:\tP: 0.996 R: 0.409 F: 0.774\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.586 F: 0.876\n",
      "\treco_to_sim:\tP: 0.999 R: 0.586 F: 0.876\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.643 F: 0.898\n",
      "\treco_to_sim:\tP: 0.996 R: 0.643 F: 0.898\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.923 R: 0.269 F: 0.621\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.379 F: 0.751\n",
      "\treco_to_sim:\tP: 0.994 R: 0.379 F: 0.751\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.138 F: 0.445\n",
      "\treco_to_sim:\tP: 0.999 R: 0.138 F: 0.445\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\treco_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.340 F: 0.720\n",
      "\treco_to_sim:\tP: 0.999 R: 0.340 F: 0.720\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.492 F: 0.816\n",
      "\treco_to_sim:\tP: 0.740 R: 0.512 F: 0.679\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\treco_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.240 F: 0.602\n",
      "\treco_to_sim:\tP: 0.826 R: 0.267 F: 0.582\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.426 F: 0.788\n",
      "\treco_to_sim:\tP: 0.999 R: 0.426 F: 0.788\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.369 F: 0.745\n",
      "\treco_to_sim:\tP: 0.999 R: 0.369 F: 0.745\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.496 F: 0.826\n",
      "\treco_to_sim:\tP: 0.981 R: 0.499 F: 0.822\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.263 F: 0.641\n",
      "\treco_to_sim:\tP: 1.000 R: 0.263 F: 0.641\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.266 F: 0.640\n",
      "\treco_to_sim:\tP: 0.981 R: 0.269 F: 0.641\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.993 R: 0.360 F: 0.710\n",
      "mean reco_to_sim:\tP: 0.970 R: 0.365 F: 0.704\n",
      "mean n_tracksters:\tP: 2.000 R: 1.300 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.231 F: 0.597\n",
      "\treco_to_sim:\tP: 0.986 R: 0.231 F: 0.597\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.255 F: 0.631\n",
      "\treco_to_sim:\tP: 1.000 R: 0.255 F: 0.631\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.363 F: 0.741\n",
      "\treco_to_sim:\tP: 1.000 R: 0.363 F: 0.741\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.472 F: 0.813\n",
      "\treco_to_sim:\tP: 0.995 R: 0.471 F: 0.814\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.409 F: 0.774\n",
      "\treco_to_sim:\tP: 0.996 R: 0.409 F: 0.774\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.586 F: 0.876\n",
      "\treco_to_sim:\tP: 0.999 R: 0.565 F: 0.866\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.643 F: 0.898\n",
      "\treco_to_sim:\tP: 0.996 R: 0.643 F: 0.898\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.379 F: 0.751\n",
      "\treco_to_sim:\tP: 0.994 R: 0.379 F: 0.751\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.138 F: 0.445\n",
      "\treco_to_sim:\tP: 0.999 R: 0.138 F: 0.445\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\treco_to_sim:\tP: 1.000 R: 0.374 F: 0.749\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.340 F: 0.720\n",
      "\treco_to_sim:\tP: 0.999 R: 0.340 F: 0.720\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.492 F: 0.816\n",
      "\treco_to_sim:\tP: 0.973 R: 0.488 F: 0.812\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\treco_to_sim:\tP: 1.000 R: 0.184 F: 0.530\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.240 F: 0.602\n",
      "\treco_to_sim:\tP: 0.945 R: 0.212 F: 0.559\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.426 F: 0.788\n",
      "\treco_to_sim:\tP: 0.999 R: 0.426 F: 0.788\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.369 F: 0.745\n",
      "\treco_to_sim:\tP: 0.999 R: 0.369 F: 0.745\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.496 F: 0.826\n",
      "\treco_to_sim:\tP: 0.981 R: 0.493 F: 0.819\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.263 F: 0.641\n",
      "\treco_to_sim:\tP: 1.000 R: 0.263 F: 0.641\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.266 F: 0.640\n",
      "\treco_to_sim:\tP: 0.981 R: 0.269 F: 0.641\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.993 R: 0.360 F: 0.710\n",
      "mean reco_to_sim:\tP: 0.991 R: 0.355 F: 0.705\n",
      "mean n_tracksters:\tP: 2.000 R: 1.300 F: 1.400\n",
      " --- Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.978 R: 0.349 F: 0.719\n",
      "\treco_to_sim:\tP: 0.653 R: 0.358 F: 0.560\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.451 F: 0.804\n",
      "\treco_to_sim:\tP: 1.000 R: 0.451 F: 0.804\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.394 F: 0.764\n",
      "\treco_to_sim:\tP: 1.000 R: 0.394 F: 0.764\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.754 F: 0.933\n",
      "\treco_to_sim:\tP: 0.637 R: 0.764 F: 0.659\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.429 F: 0.787\n",
      "\treco_to_sim:\tP: 0.990 R: 0.429 F: 0.785\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.607 F: 0.881\n",
      "\treco_to_sim:\tP: 0.947 R: 0.607 F: 0.852\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.703 F: 0.918\n",
      "\treco_to_sim:\tP: 0.871 R: 0.712 F: 0.834\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.923 R: 0.269 F: 0.621\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.698 F: 0.918\n",
      "\treco_to_sim:\tP: 0.590 R: 0.701 F: 0.609\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.156 F: 0.479\n",
      "\treco_to_sim:\tP: 0.999 R: 0.156 F: 0.479\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\treco_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.443 F: 0.798\n",
      "\treco_to_sim:\tP: 0.999 R: 0.443 F: 0.798\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.737 R: 0.639 F: 0.715\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\treco_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.355 F: 0.719\n",
      "\treco_to_sim:\tP: 0.806 R: 0.392 F: 0.666\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.498 F: 0.827\n",
      "\treco_to_sim:\tP: 0.990 R: 0.498 F: 0.827\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.525 R: 0.657 F: 0.547\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.758 F: 0.926\n",
      "\treco_to_sim:\tP: 0.684 R: 0.776 F: 0.700\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.293 F: 0.673\n",
      "\treco_to_sim:\tP: 0.997 R: 0.293 F: 0.673\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.479 F: 0.815\n",
      "\treco_to_sim:\tP: 0.791 R: 0.490 F: 0.705\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.990 R: 0.473 F: 0.781\n",
      "mean reco_to_sim:\tP: 0.857 R: 0.482 F: 0.696\n",
      "mean n_tracksters:\tP: 2.000 R: 1.600 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.978 R: 0.349 F: 0.719\n",
      "\treco_to_sim:\tP: 0.873 R: 0.284 F: 0.617\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.451 F: 0.804\n",
      "\treco_to_sim:\tP: 1.000 R: 0.451 F: 0.804\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.394 F: 0.764\n",
      "\treco_to_sim:\tP: 1.000 R: 0.368 F: 0.744\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.754 F: 0.933\n",
      "\treco_to_sim:\tP: 0.985 R: 0.747 F: 0.926\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.429 F: 0.787\n",
      "\treco_to_sim:\tP: 0.990 R: 0.427 F: 0.783\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.607 F: 0.881\n",
      "\treco_to_sim:\tP: 0.993 R: 0.607 F: 0.881\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.703 F: 0.918\n",
      "\treco_to_sim:\tP: 0.993 R: 0.703 F: 0.918\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.698 F: 0.918\n",
      "\treco_to_sim:\tP: 0.996 R: 0.698 F: 0.918\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.156 F: 0.479\n",
      "\treco_to_sim:\tP: 0.999 R: 0.156 F: 0.479\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.416 F: 0.781\n",
      "\treco_to_sim:\tP: 1.000 R: 0.374 F: 0.749\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.443 F: 0.798\n",
      "\treco_to_sim:\tP: 0.999 R: 0.443 F: 0.798\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.967 R: 0.607 F: 0.865\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.189 F: 0.538\n",
      "\treco_to_sim:\tP: 1.000 R: 0.184 F: 0.530\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.355 F: 0.719\n",
      "\treco_to_sim:\tP: 0.956 R: 0.328 F: 0.692\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.990 R: 0.498 F: 0.827\n",
      "\treco_to_sim:\tP: 0.990 R: 0.488 F: 0.821\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.758 F: 0.926\n",
      "\treco_to_sim:\tP: 0.966 R: 0.743 F: 0.911\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.293 F: 0.673\n",
      "\treco_to_sim:\tP: 0.997 R: 0.293 F: 0.673\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.479 F: 0.815\n",
      "\treco_to_sim:\tP: 0.986 R: 0.478 F: 0.814\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.990 R: 0.473 F: 0.781\n",
      "mean reco_to_sim:\tP: 0.983 R: 0.463 F: 0.770\n",
      "mean n_tracksters:\tP: 2.000 R: 1.600 F: 1.800\n",
      " --- Radius threshold: 30 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.438 F: 0.784\n",
      "\treco_to_sim:\tP: 0.669 R: 0.450 F: 0.610\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.530 F: 0.849\n",
      "\treco_to_sim:\tP: 0.979 R: 0.530 F: 0.837\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.394 F: 0.764\n",
      "\treco_to_sim:\tP: 0.947 R: 0.394 F: 0.739\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.631 R: 0.792 F: 0.658\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.709 F: 0.922\n",
      "\treco_to_sim:\tP: 0.554 R: 0.712 F: 0.580\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.832 F: 0.957\n",
      "\treco_to_sim:\tP: 0.590 R: 0.836 F: 0.627\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.708 F: 0.919\n",
      "\treco_to_sim:\tP: 0.867 R: 0.716 F: 0.832\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.923 R: 0.269 F: 0.621\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.755 F: 0.936\n",
      "\treco_to_sim:\tP: 0.578 R: 0.760 F: 0.607\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.317 F: 0.699\n",
      "\treco_to_sim:\tP: 1.000 R: 0.317 F: 0.699\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.478 F: 0.821\n",
      "\treco_to_sim:\tP: 0.834 R: 0.478 F: 0.726\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.714 F: 0.925\n",
      "\treco_to_sim:\tP: 0.624 R: 0.715 F: 0.641\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.737 R: 0.639 F: 0.715\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\treco_to_sim:\tP: 0.875 R: 0.566 F: 0.789\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.534 F: 0.833\n",
      "\treco_to_sim:\tP: 0.778 R: 0.584 F: 0.730\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.621 F: 0.886\n",
      "\treco_to_sim:\tP: 0.646 R: 0.624 F: 0.641\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.525 R: 0.657 F: 0.547\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.682 R: 0.781 F: 0.700\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.659 F: 0.905\n",
      "\treco_to_sim:\tP: 0.998 R: 0.659 F: 0.905\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.489 F: 0.821\n",
      "\treco_to_sim:\tP: 0.791 R: 0.500 F: 0.708\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.991 R: 0.589 F: 0.855\n",
      "mean reco_to_sim:\tP: 0.761 R: 0.599 F: 0.696\n",
      "mean n_tracksters:\tP: 2.000 R: 1.900 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.976 R: 0.438 F: 0.784\n",
      "\treco_to_sim:\tP: 0.882 R: 0.363 F: 0.686\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.530 F: 0.849\n",
      "\treco_to_sim:\tP: 1.000 R: 0.530 F: 0.849\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.394 F: 0.764\n",
      "\treco_to_sim:\tP: 0.999 R: 0.394 F: 0.764\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.992 R: 0.784 F: 0.942\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.709 F: 0.922\n",
      "\treco_to_sim:\tP: 0.988 R: 0.701 F: 0.913\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.832 F: 0.957\n",
      "\treco_to_sim:\tP: 0.995 R: 0.832 F: 0.957\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.708 F: 0.919\n",
      "\treco_to_sim:\tP: 0.987 R: 0.703 F: 0.913\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.755 F: 0.936\n",
      "\treco_to_sim:\tP: 0.993 R: 0.748 F: 0.932\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.317 F: 0.699\n",
      "\treco_to_sim:\tP: 1.000 R: 0.309 F: 0.691\n",
      "\t|S| = 2 |T| = 1 |R| = 2\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.478 F: 0.821\n",
      "\treco_to_sim:\tP: 1.000 R: 0.478 F: 0.821\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.714 F: 0.925\n",
      "\treco_to_sim:\tP: 0.999 R: 0.714 F: 0.925\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.967 R: 0.607 F: 0.865\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\treco_to_sim:\tP: 0.989 R: 0.558 F: 0.857\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.534 F: 0.833\n",
      "\treco_to_sim:\tP: 0.957 R: 0.503 F: 0.811\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.621 F: 0.886\n",
      "\treco_to_sim:\tP: 0.973 R: 0.598 F: 0.865\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.966 R: 0.748 F: 0.913\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.659 F: 0.905\n",
      "\treco_to_sim:\tP: 0.998 R: 0.659 F: 0.905\n",
      "\t|S| = 2 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.988 R: 0.489 F: 0.821\n",
      "\treco_to_sim:\tP: 0.987 R: 0.488 F: 0.819\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.991 R: 0.589 F: 0.855\n",
      "mean reco_to_sim:\tP: 0.982 R: 0.579 F: 0.845\n",
      "mean n_tracksters:\tP: 2.000 R: 1.900 F: 1.950\n",
      " --- Radius threshold: 50 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.659 F: 0.893\n",
      "\treco_to_sim:\tP: 0.583 R: 0.677 F: 0.600\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.649 F: 0.902\n",
      "\treco_to_sim:\tP: 0.676 R: 0.649 F: 0.671\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.808 F: 0.954\n",
      "\treco_to_sim:\tP: 0.515 R: 0.809 F: 0.555\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.631 R: 0.792 F: 0.658\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.838 F: 0.960\n",
      "\treco_to_sim:\tP: 0.544 R: 0.842 F: 0.586\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\treco_to_sim:\tP: 0.588 R: 0.844 F: 0.626\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.718 F: 0.922\n",
      "\treco_to_sim:\tP: 0.856 R: 0.729 F: 0.827\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.923 R: 0.269 F: 0.621\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.755 F: 0.936\n",
      "\treco_to_sim:\tP: 0.578 R: 0.760 F: 0.607\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.414 F: 0.779\n",
      "\treco_to_sim:\tP: 0.794 R: 0.415 F: 0.671\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.694 F: 0.919\n",
      "\treco_to_sim:\tP: 0.643 R: 0.694 F: 0.653\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\treco_to_sim:\tP: 0.607 R: 0.830 F: 0.642\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.737 R: 0.639 F: 0.715\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.680 F: 0.914\n",
      "\treco_to_sim:\tP: 0.884 R: 0.680 F: 0.834\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.553 F: 0.842\n",
      "\treco_to_sim:\tP: 0.778 R: 0.603 F: 0.735\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.744 F: 0.931\n",
      "\treco_to_sim:\tP: 0.588 R: 0.749 F: 0.615\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.525 R: 0.657 F: 0.547\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.682 R: 0.781 F: 0.700\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.761 F: 0.935\n",
      "\treco_to_sim:\tP: 0.985 R: 0.770 F: 0.932\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.494 F: 0.823\n",
      "\treco_to_sim:\tP: 0.790 R: 0.506 F: 0.710\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.991 R: 0.673 F: 0.892\n",
      "mean reco_to_sim:\tP: 0.695 R: 0.685 F: 0.675\n",
      "mean n_tracksters:\tP: 2.000 R: 2.000 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.659 F: 0.893\n",
      "\treco_to_sim:\tP: 0.888 R: 0.585 F: 0.805\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.649 F: 0.902\n",
      "\treco_to_sim:\tP: 1.000 R: 0.649 F: 0.902\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.808 F: 0.954\n",
      "\treco_to_sim:\tP: 0.999 R: 0.808 F: 0.954\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.992 R: 0.784 F: 0.942\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.838 F: 0.960\n",
      "\treco_to_sim:\tP: 0.992 R: 0.833 F: 0.955\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\treco_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.718 F: 0.922\n",
      "\treco_to_sim:\tP: 0.972 R: 0.706 F: 0.904\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.755 F: 0.936\n",
      "\treco_to_sim:\tP: 0.993 R: 0.748 F: 0.932\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.414 F: 0.779\n",
      "\treco_to_sim:\tP: 0.992 R: 0.405 F: 0.769\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.694 F: 0.919\n",
      "\treco_to_sim:\tP: 1.000 R: 0.694 F: 0.919\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\treco_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.967 R: 0.607 F: 0.865\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.680 F: 0.914\n",
      "\treco_to_sim:\tP: 0.962 R: 0.637 F: 0.873\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.553 F: 0.842\n",
      "\treco_to_sim:\tP: 0.958 R: 0.521 F: 0.820\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.744 F: 0.931\n",
      "\treco_to_sim:\tP: 0.993 R: 0.744 F: 0.931\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.966 R: 0.748 F: 0.913\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.761 F: 0.935\n",
      "\treco_to_sim:\tP: 0.985 R: 0.770 F: 0.932\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.494 F: 0.823\n",
      "\treco_to_sim:\tP: 0.986 R: 0.493 F: 0.822\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.991 R: 0.673 F: 0.892\n",
      "mean reco_to_sim:\tP: 0.980 R: 0.664 F: 0.882\n",
      "mean n_tracksters:\tP: 2.000 R: 2.000 F: 1.950\n",
      " --- Radius threshold: 100 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.686 F: 0.903\n",
      "\treco_to_sim:\tP: 0.584 R: 0.704 F: 0.605\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.659 F: 0.906\n",
      "\treco_to_sim:\tP: 0.669 R: 0.659 F: 0.667\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.814 F: 0.956\n",
      "\treco_to_sim:\tP: 0.515 R: 0.814 F: 0.556\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.631 R: 0.792 F: 0.658\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.838 F: 0.960\n",
      "\treco_to_sim:\tP: 0.544 R: 0.842 F: 0.586\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\treco_to_sim:\tP: 0.588 R: 0.844 F: 0.626\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.727 F: 0.925\n",
      "\treco_to_sim:\tP: 0.851 R: 0.738 F: 0.826\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.923 R: 0.269 F: 0.621\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.761 F: 0.938\n",
      "\treco_to_sim:\tP: 0.578 R: 0.766 F: 0.608\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.559 F: 0.862\n",
      "\treco_to_sim:\tP: 0.704 R: 0.562 F: 0.670\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.766 F: 0.942\n",
      "\treco_to_sim:\tP: 0.649 R: 0.766 F: 0.669\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\treco_to_sim:\tP: 0.607 R: 0.830 F: 0.642\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.737 R: 0.639 F: 0.715\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.757 F: 0.939\n",
      "\treco_to_sim:\tP: 0.889 R: 0.757 F: 0.859\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.598 F: 0.863\n",
      "\treco_to_sim:\tP: 0.776 R: 0.650 F: 0.747\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.758 F: 0.935\n",
      "\treco_to_sim:\tP: 0.587 R: 0.763 F: 0.615\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.525 R: 0.657 F: 0.547\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.682 R: 0.781 F: 0.700\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.801 F: 0.942\n",
      "\treco_to_sim:\tP: 0.918 R: 0.823 F: 0.897\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.494 F: 0.823\n",
      "\treco_to_sim:\tP: 0.790 R: 0.506 F: 0.710\n",
      "\t|S| = 2 |T| = 2 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.990 R: 0.696 F: 0.901\n",
      "mean reco_to_sim:\tP: 0.687 R: 0.708 F: 0.676\n",
      "mean n_tracksters:\tP: 2.000 R: 2.000 F: 1.000\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.988 R: 0.229 F: 0.594\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.686 F: 0.903\n",
      "\treco_to_sim:\tP: 0.890 R: 0.608 F: 0.815\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.283 F: 0.664\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.659 F: 0.906\n",
      "\treco_to_sim:\tP: 1.000 R: 0.659 F: 0.906\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.396 F: 0.766\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.814 F: 0.956\n",
      "\treco_to_sim:\tP: 0.999 R: 0.814 F: 0.956\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.995 R: 0.383 F: 0.754\n",
      "\ttarget_to_sim:\tP: 0.991 R: 0.782 F: 0.941\n",
      "\treco_to_sim:\tP: 0.992 R: 0.784 F: 0.942\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.250 F: 0.625\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.838 F: 0.960\n",
      "\treco_to_sim:\tP: 0.992 R: 0.833 F: 0.955\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.551 F: 0.858\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\treco_to_sim:\tP: 0.995 R: 0.839 F: 0.959\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.219 F: 0.582\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.727 F: 0.925\n",
      "\treco_to_sim:\tP: 0.972 R: 0.715 F: 0.907\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.112 F: 0.385\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\treco_to_sim:\tP: 0.968 R: 0.216 F: 0.571\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.413 F: 0.777\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.761 F: 0.938\n",
      "\treco_to_sim:\tP: 0.993 R: 0.755 F: 0.934\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.177 F: 0.518\n",
      "\ttarget_to_sim:\tP: 0.998 R: 0.559 F: 0.862\n",
      "\treco_to_sim:\tP: 0.968 R: 0.530 F: 0.831\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.340 F: 0.720\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.766 F: 0.942\n",
      "\treco_to_sim:\tP: 1.000 R: 0.766 F: 0.942\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.524 F: 0.846\n",
      "\ttarget_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\treco_to_sim:\tP: 0.999 R: 0.829 F: 0.960\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.277 F: 0.651\n",
      "\ttarget_to_sim:\tP: 0.975 R: 0.616 F: 0.873\n",
      "\treco_to_sim:\tP: 0.967 R: 0.607 F: 0.865\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.258 F: 0.635\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.757 F: 0.939\n",
      "\treco_to_sim:\tP: 0.946 R: 0.671 F: 0.875\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.155 F: 0.475\n",
      "\ttarget_to_sim:\tP: 0.970 R: 0.598 F: 0.863\n",
      "\treco_to_sim:\tP: 0.959 R: 0.566 F: 0.842\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.209 F: 0.568\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.758 F: 0.935\n",
      "\treco_to_sim:\tP: 0.993 R: 0.758 F: 0.935\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.376 F: 0.750\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\treco_to_sim:\tP: 0.997 R: 0.656 F: 0.903\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 17:\n",
      "\tclue3d_to_sim:\tP: 0.985 R: 0.268 F: 0.642\n",
      "\ttarget_to_sim:\tP: 0.981 R: 0.763 F: 0.928\n",
      "\treco_to_sim:\tP: 0.966 R: 0.748 F: 0.913\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.317 F: 0.695\n",
      "\ttarget_to_sim:\tP: 0.985 R: 0.801 F: 0.942\n",
      "\treco_to_sim:\tP: 0.969 R: 0.761 F: 0.919\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.991 R: 0.277 F: 0.654\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.494 F: 0.823\n",
      "\treco_to_sim:\tP: 0.986 R: 0.493 F: 0.822\n",
      "\t|S| = 2 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.993 R: 0.301 F: 0.658\n",
      "mean target_to_sim:\tP: 0.990 R: 0.696 F: 0.901\n",
      "mean reco_to_sim:\tP: 0.978 R: 0.680 F: 0.888\n",
      "mean n_tracksters:\tP: 2.000 R: 2.000 F: 2.000\n"
     ]
    }
   ],
   "source": [
    "r_ranges = [5, 10, 20, 30, 50, 100]\n",
    "results = []\n",
    "\n",
    "max_events = 20\n",
    "clue3D_F = []\n",
    "target_F = []\n",
    "naive_reco_F = []\n",
    "model_reco_F = []\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(output_dim=2),\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=30,\n",
    "        pileup=False,\n",
    "        graph=True,\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=30,\n",
    "        pileup=False,\n",
    "        graph=True,\n",
    "    )\n",
    "    model_reco_F.append(np.sum(np.array(result[\"reco_to_sim\"])[:,2]) / max_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c2a36a8",
   "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",
    "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=\"GNN $\\Delta F_{0.5}$\", 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\", c=\"lightgray\", linestyle=\"--\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Neighborhood radius (cm)\")\n",
    "ax.set_ylabel(\"$\\Delta F_{0.5}$\")\n",
    "# ax.set_ylim((-0.1, 0.1))\n",
    "\n",
    "fig.savefig(f'export/{ds_name}-e30-pnetl-graph-radius-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "799ffc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reco 0.2297229491640076\n",
      "Naive 0.04604952048261768\n",
      "Target 0.24366342211853498\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)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328866f1-c862-4dfb-a201-91d2baa53d9f",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "Using alpha=balance seems to converge faster (100 epochs rather than 200)\n",
    "- maybe just sticking to 0.25? (works just fine)\n",
    "\n",
    "Hyperparams\n",
    "- (64, 128, noskip), alpha=balance, roc_auc = 0.9733\n",
    "- (64, 128, 256fc, noskip, 200e), alpha=1-balance, roc_auc = 0.9771\n",
    "- (64, 128, 256fc, noskip, 200e), alpha=balance, roc_auc = 0.981\n",
    "- (64, 128, 256fc, noskip, 100e), alpha=0.25, roc_auc = 0.9796"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 ('ve': venv)",
   "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
}