7.model_mlp_pairwise.PU.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c07744a",
   "metadata": {},
   "source": [
    "# Pairwise MLP with PU\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",
    "- cone around the candidate trackster\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3247338",
   "metadata": {},
   "source": [
    "## MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "81ffdcce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "from torch.utils.data import random_split, DataLoader\n",
    "from reco.loss import QualityFocalLoss\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('font',**{'family':'sans-serif','sans-serif':['DejaVu Sans'],'size': 12})\n",
    "rc('mathtext',**{'default':'regular'})\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from reco.datasetPU import TracksterPairs\n",
    "from reco.training import precision_recall_curve, roc_auc\n",
    "\n",
    "ds_name = \"CloseByPion200P\"\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": 6,
   "id": "b2dc29a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\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": 7,
   "id": "219c077d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([178271, 63])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = TracksterPairs(\n",
    "    ds_name,\n",
    "    data_root,\n",
    "    raw_dir,\n",
    "    N_FILES=100,\n",
    "    radius=15,\n",
    "    pileup=True,\n",
    "    bigT_e_th=5,\n",
    ")\n",
    "\n",
    "ds[0][0]\n",
    "ds.x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75c3ffff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([178271, 63])\n",
      "Positive: 66461\n",
      "Negative: 111810\n"
     ]
    }
   ],
   "source": [
    "print(ds.x.shape)\n",
    "\n",
    "print(\"Positive:\", int((ds.y > 0.7).type(torch.int).sum()))\n",
    "print(\"Negative:\", int((ds.y < 0.7).type(torch.int).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84179ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset balance: 41.94% / 58.06%\n"
     ]
    }
   ],
   "source": [
    "balance =  float(sum(ds.y > 0.5) / len(ds.y))\n",
    "print(f\"dataset balance: {balance*100:.2f}% / {(1-balance)*100:.2f}%\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "972263a2-610a-4ca9-8805-b2c6830aa83b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train samples: 160444, Test samples: 17827\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 samples: {len(train_set)}, Test samples: {len(test_set)}\")\n",
    "\n",
    "train_dl = DataLoader(train_set, batch_size=32, shuffle=True)\n",
    "test_dl = DataLoader(test_set, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d3700ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a9369f5-eacd-4e6a-a162-d8d8a6829f45",
   "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",
    ")\n",
    "model = model.to(device)\n",
    "epochs = 101\n",
    "model_path = f\"models/PairWiseMLP.PU.{hdim1}.{hdim2}.{epochs}e-{ds_name}.r{ds.RADIUS}.e{ds.bigT_e_th}.f{ds.N_FILES}.pt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1bdabc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, loss: 17461.7148, train auc: 0.9209, val auc: 0.9158\n",
      "Epoch: 10, loss: 14273.4092, train auc: 0.9252, val auc: 0.9214\n",
      "Epoch: 20, loss: 14357.7910, train auc: 0.9233, val auc: 0.9193\n",
      "Epoch: 30, loss: 14186.7607, train auc: 0.9264, val auc: 0.9213\n",
      "Epoch: 40, loss: 14160.2305, train auc: 0.9280, val auc: 0.9231\n",
      "Epoch: 50, loss: 14018.8691, train auc: 0.9309, val auc: 0.9260\n",
      "Epoch: 60, loss: 13884.3154, train auc: 0.9315, val auc: 0.9249\n",
      "Epoch: 70, loss: 13665.8945, train auc: 0.9337, val auc: 0.9270\n",
      "Epoch: 80, loss: 13509.5967, train auc: 0.9350, val auc: 0.9273\n",
      "Epoch: 90, loss: 13476.8457, train auc: 0.9360, val auc: 0.9272\n",
      "Epoch: 100, loss: 13352.7422, train auc: 0.9361, val auc: 0.9270\n"
     ]
    }
   ],
   "source": [
    "#%%script echo skipping\n",
    "\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 % 10 == 0:\n",
    "        print(f'Epoch: {epoch}, loss: {loss:.4f}, train auc: {train_auc:.4f}, val auc: {test_auc:.4f}')\n",
    "\n",
    "torch.save(model.state_dict(), model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "93072e39",
   "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": 16,
   "id": "b57db4c6-0436-44f9-ab2b-19109c7aced7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.927037585404937"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc(model, device, test_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "92dc57f8",
   "metadata": {},
   "outputs": [
    {
     "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: 2476, TN: 5247, FP: 329, FN: 948\n",
      "TH: 0.37 precision: 0.883 recall: 0.723 fbeta: 0.845 b_acc: 0.832\n"
     ]
    }
   ],
   "source": [
    "precision_recall_curve(model, device, test_dl, focus_metric=\"fbeta\", beta=0.5, step=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3eebed-4260-4441-b30e-42b21e7a9428",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "- hdim: (256, 128), roc_auc = 0.9956\n",
    "- hdim: (512, 512), roc_auc = 0.9605 (overfit?) (test auc going down)\n",
    "- hdim: (256, 256), alpha=1-balance, roc_auc = 0.9635 (overfit?) (test auc going down)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c40163",
   "metadata": {},
   "source": [
    "# ONNX export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "72ba4209-1905-4104-991d-9e436e5c709e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.onnx\n",
    "\n",
    "onnx_filepath = f\"onnx/PairWiseMLP.{hdim1}.{hdim2}.{epochs}e-{ds_name}.{ds.RADIUS}.{ds.SCORE_THRESHOLD}.{ds.N_FILES}f.onnx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d30c7df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.onnx.export(\n",
    "    model,                      # model to be exported\n",
    "    ds[0][0].reshape(1, -1),    # example input (add batch dimension)\n",
    "    onnx_filepath,\n",
    "    export_params=True,\n",
    "    opset_version=10,\n",
    "    do_constant_folding=True,\n",
    "    input_names=['features'],      # the model's input names\n",
    "    output_names=['output'],    # the model's output names\n",
    "    dynamic_axes={              # variable length axes\n",
    "        'features' : {0 : 'batch_size'},    \n",
    "        'output' : {0 : 'batch_size'}\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2c92ad02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import onnxruntime\n",
    "\n",
    "ort_session = onnxruntime.InferenceSession(onnx_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0978249f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_numpy(tensor):\n",
    "    return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fb029278",
   "metadata": {},
   "outputs": [],
   "source": [
    "ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(ds[:16][0])}\n",
    "ort_outs = ort_session.run(None, ort_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab649d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported model has been tested with ONNXRuntime, and the result looks good!\n"
     ]
    }
   ],
   "source": [
    "torch_out = model(ds[:16][0])\n",
    "np.testing.assert_allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05)\n",
    "print(\"Exported model has been tested with ONNXRuntime, and the result looks good!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f364820",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ad1b1329",
   "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_15643817_2.root\"\n",
    "cluster_data, trackster_data, simtrackster_data, assoc_data = get_event_data(file_name, pileup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a639dec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Radius threshold: 3 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.836 R: 0.298 F: 0.614\n",
      "\ttarget_to_sim:\tP: 0.818 R: 0.347 F: 0.643\n",
      "\treco_to_sim:\tP: 0.808 R: 0.347 F: 0.638\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tNo data\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.260 F: 0.627\n",
      "\treco_to_sim:\tP: 0.941 R: 0.260 F: 0.617\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tNo data\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.499 F: 0.816\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.524 F: 0.825\n",
      "\treco_to_sim:\tP: 0.957 R: 0.524 F: 0.821\n",
      "\t|S| = 1 |T| = 4 |R| = 4\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.890 R: 0.022 F: 0.100\n",
      "\ttarget_to_sim:\tP: 0.902 R: 0.023 F: 0.105\n",
      "\treco_to_sim:\tP: 0.890 R: 0.023 F: 0.105\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.867 R: 0.408 F: 0.708\n",
      "\treco_to_sim:\tP: 0.772 R: 0.430 F: 0.666\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.138 F: 0.444\n",
      "\treco_to_sim:\tP: 0.995 R: 0.138 F: 0.444\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tNo data\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.677 F: 0.903\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.686 F: 0.905\n",
      "\treco_to_sim:\tP: 0.979 R: 0.686 F: 0.902\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.744 R: 0.001 F: 0.005\n",
      "\ttarget_to_sim:\tP: 0.794 R: 0.003 F: 0.016\n",
      "\treco_to_sim:\tP: 0.757 R: 0.004 F: 0.021\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tNo data\n",
      "Event 15:\n",
      "\tNo data\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.679 F: 0.904\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.701 F: 0.910\n",
      "\treco_to_sim:\tP: 0.984 R: 0.701 F: 0.910\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tNo data\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.768 R: 0.153 F: 0.426\n",
      "\ttarget_to_sim:\tP: 0.777 R: 0.401 F: 0.654\n",
      "\treco_to_sim:\tP: 0.777 R: 0.401 F: 0.654\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.451 R: 0.147 F: 0.266\n",
      "mean target_to_sim:\tP: 0.453 R: 0.174 F: 0.292\n",
      "mean reco_to_sim:\tP: 0.443 R: 0.176 F: 0.289\n",
      "mean n_tracksters:\tP: 0.500 R: 0.950 F: 0.950\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.836 R: 0.298 F: 0.614\n",
      "\ttarget_to_sim:\tP: 0.818 R: 0.347 F: 0.643\n",
      "\treco_to_sim:\tP: 0.814 R: 0.347 F: 0.641\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tNo data\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.971 R: 0.260 F: 0.627\n",
      "\treco_to_sim:\tP: 0.971 R: 0.260 F: 0.627\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tNo data\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.499 F: 0.816\n",
      "\ttarget_to_sim:\tP: 0.964 R: 0.524 F: 0.825\n",
      "\treco_to_sim:\tP: 0.964 R: 0.524 F: 0.825\n",
      "\t|S| = 1 |T| = 4 |R| = 4\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.890 R: 0.022 F: 0.100\n",
      "\ttarget_to_sim:\tP: 0.902 R: 0.023 F: 0.105\n",
      "\treco_to_sim:\tP: 0.902 R: 0.023 F: 0.105\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.867 R: 0.408 F: 0.708\n",
      "\treco_to_sim:\tP: 0.881 R: 0.399 F: 0.710\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.138 F: 0.444\n",
      "\treco_to_sim:\tP: 0.995 R: 0.138 F: 0.444\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tNo data\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.984 R: 0.677 F: 0.903\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.686 F: 0.905\n",
      "\treco_to_sim:\tP: 0.984 R: 0.686 F: 0.905\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.744 R: 0.001 F: 0.005\n",
      "\ttarget_to_sim:\tP: 0.794 R: 0.003 F: 0.016\n",
      "\treco_to_sim:\tP: 0.760 R: 0.004 F: 0.020\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tNo data\n",
      "Event 15:\n",
      "\tNo data\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.986 R: 0.679 F: 0.904\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.701 F: 0.910\n",
      "\treco_to_sim:\tP: 0.984 R: 0.701 F: 0.910\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tNo data\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.768 R: 0.153 F: 0.426\n",
      "\ttarget_to_sim:\tP: 0.777 R: 0.401 F: 0.654\n",
      "\treco_to_sim:\tP: 0.777 R: 0.396 F: 0.652\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.451 R: 0.147 F: 0.266\n",
      "mean target_to_sim:\tP: 0.453 R: 0.174 F: 0.292\n",
      "mean reco_to_sim:\tP: 0.452 R: 0.174 F: 0.292\n",
      "mean n_tracksters:\tP: 0.500 R: 0.950 F: 0.950\n",
      " --- Radius threshold: 5 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.822 R: 0.307 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.799 R: 0.527 F: 0.725\n",
      "\treco_to_sim:\tP: 0.640 R: 0.571 F: 0.625\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.348 F: 0.726\n",
      "\treco_to_sim:\tP: 0.996 R: 0.348 F: 0.726\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.375 F: 0.723\n",
      "\treco_to_sim:\tP: 0.902 R: 0.384 F: 0.710\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.741 R: 0.001 F: 0.007\n",
      "\ttarget_to_sim:\tP: 0.741 R: 0.001 F: 0.007\n",
      "\treco_to_sim:\tP: 0.119 R: 0.002 F: 0.008\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\treco_to_sim:\tP: 0.700 R: 0.651 F: 0.690\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.499 F: 0.816\n",
      "\ttarget_to_sim:\tP: 0.952 R: 0.616 F: 0.859\n",
      "\treco_to_sim:\tP: 0.935 R: 0.617 F: 0.848\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.720\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.381 F: 0.739\n",
      "\treco_to_sim:\tP: 0.919 R: 0.381 F: 0.717\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.866 R: 0.434 F: 0.722\n",
      "\treco_to_sim:\tP: 0.681 R: 0.473 F: 0.626\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.177 F: 0.517\n",
      "\treco_to_sim:\tP: 0.994 R: 0.177 F: 0.517\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.634 F: 0.878\n",
      "\treco_to_sim:\tP: 0.972 R: 0.634 F: 0.878\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.823 F: 0.921\n",
      "\treco_to_sim:\tP: 0.892 R: 0.838 F: 0.881\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.927 R: 0.207 F: 0.546\n",
      "\ttarget_to_sim:\tP: 0.923 R: 0.505 F: 0.792\n",
      "\treco_to_sim:\tP: 0.788 R: 0.627 F: 0.750\n",
      "\t|S| = 1 |T| = 4 |R| = 3\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.756 F: 0.937\n",
      "\treco_to_sim:\tP: 0.997 R: 0.756 F: 0.937\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.487 F: 0.822\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.617 F: 0.886\n",
      "\treco_to_sim:\tP: 0.994 R: 0.617 F: 0.886\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.733 F: 0.911\n",
      "\treco_to_sim:\tP: 0.934 R: 0.734 F: 0.886\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.898 R: 0.572 F: 0.806\n",
      "\treco_to_sim:\tP: 0.898 R: 0.572 F: 0.806\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.768 R: 0.153 F: 0.426\n",
      "\ttarget_to_sim:\tP: 0.735 R: 0.501 F: 0.672\n",
      "\treco_to_sim:\tP: 0.652 R: 0.513 F: 0.619\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.778 R: 0.333 F: 0.562\n",
      "mean target_to_sim:\tP: 0.771 R: 0.430 F: 0.626\n",
      "mean reco_to_sim:\tP: 0.701 R: 0.445 F: 0.605\n",
      "mean n_tracksters:\tP: 0.850 R: 1.550 F: 1.500\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.822 R: 0.307 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.799 R: 0.527 F: 0.725\n",
      "\treco_to_sim:\tP: 0.782 R: 0.548 F: 0.720\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.348 F: 0.726\n",
      "\treco_to_sim:\tP: 0.996 R: 0.348 F: 0.726\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.375 F: 0.723\n",
      "\treco_to_sim:\tP: 0.945 R: 0.367 F: 0.719\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.741 R: 0.001 F: 0.007\n",
      "\ttarget_to_sim:\tP: 0.741 R: 0.001 F: 0.007\n",
      "\treco_to_sim:\tP: 0.741 R: 0.001 F: 0.007\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\treco_to_sim:\tP: 0.700 R: 0.651 F: 0.690\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.499 F: 0.816\n",
      "\ttarget_to_sim:\tP: 0.952 R: 0.616 F: 0.859\n",
      "\treco_to_sim:\tP: 0.952 R: 0.616 F: 0.859\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.720\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.381 F: 0.739\n",
      "\treco_to_sim:\tP: 0.966 R: 0.393 F: 0.748\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.866 R: 0.434 F: 0.722\n",
      "\treco_to_sim:\tP: 0.879 R: 0.425 F: 0.724\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.177 F: 0.517\n",
      "\treco_to_sim:\tP: 0.994 R: 0.177 F: 0.517\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.634 F: 0.878\n",
      "\treco_to_sim:\tP: 0.972 R: 0.634 F: 0.878\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.950 R: 0.823 F: 0.921\n",
      "\treco_to_sim:\tP: 0.924 R: 0.822 F: 0.902\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.927 R: 0.207 F: 0.546\n",
      "\ttarget_to_sim:\tP: 0.923 R: 0.505 F: 0.792\n",
      "\treco_to_sim:\tP: 0.917 R: 0.308 F: 0.657\n",
      "\t|S| = 1 |T| = 4 |R| = 4\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.997 R: 0.756 F: 0.937\n",
      "\treco_to_sim:\tP: 0.997 R: 0.756 F: 0.937\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.487 F: 0.822\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.617 F: 0.886\n",
      "\treco_to_sim:\tP: 0.994 R: 0.617 F: 0.886\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.969 R: 0.733 F: 0.911\n",
      "\treco_to_sim:\tP: 0.934 R: 0.734 F: 0.886\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.898 R: 0.572 F: 0.806\n",
      "\treco_to_sim:\tP: 0.898 R: 0.572 F: 0.806\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.768 R: 0.153 F: 0.426\n",
      "\ttarget_to_sim:\tP: 0.735 R: 0.501 F: 0.672\n",
      "\treco_to_sim:\tP: 0.742 R: 0.465 F: 0.663\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.778 R: 0.333 F: 0.562\n",
      "mean target_to_sim:\tP: 0.771 R: 0.430 F: 0.626\n",
      "mean reco_to_sim:\tP: 0.767 R: 0.422 F: 0.616\n",
      "mean n_tracksters:\tP: 0.850 R: 1.550 F: 1.550\n",
      " --- Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.761 R: 0.659 F: 0.738\n",
      "\treco_to_sim:\tP: 0.267 R: 0.758 F: 0.307\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "\treco_to_sim:\tP: 0.975 R: 0.399 F: 0.757\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.936 R: 0.423 F: 0.754\n",
      "\treco_to_sim:\tP: 0.725 R: 0.433 F: 0.639\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "\treco_to_sim:\tP: 0.645 R: 0.824 F: 0.674\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\treco_to_sim:\tP: 0.360 R: 0.681 F: 0.398\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "\treco_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.934 R: 0.681 F: 0.870\n",
      "\treco_to_sim:\tP: 0.637 R: 0.700 F: 0.648\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.545 F: 0.837\n",
      "\treco_to_sim:\tP: 0.682 R: 0.539 F: 0.648\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "\treco_to_sim:\tP: 0.168 R: 0.572 F: 0.196\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\treco_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\treco_to_sim:\tP: 0.895 R: 0.678 F: 0.841\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.888 F: 0.932\n",
      "\treco_to_sim:\tP: 0.766 R: 0.923 F: 0.793\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.207 F: 0.547\n",
      "\ttarget_to_sim:\tP: 0.875 R: 0.634 F: 0.813\n",
      "\treco_to_sim:\tP: 0.368 R: 0.803 F: 0.412\n",
      "\t|S| = 1 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.862 F: 0.963\n",
      "\treco_to_sim:\tP: 0.980 R: 0.862 F: 0.954\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.487 F: 0.822\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.727 F: 0.926\n",
      "\treco_to_sim:\tP: 0.993 R: 0.727 F: 0.926\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.832 F: 0.929\n",
      "\treco_to_sim:\tP: 0.732 R: 0.848 F: 0.753\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.800 R: 0.680 F: 0.773\n",
      "\treco_to_sim:\tP: 0.422 R: 0.832 F: 0.468\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.779 R: 0.160 F: 0.439\n",
      "\ttarget_to_sim:\tP: 0.687 R: 0.647 F: 0.678\n",
      "\treco_to_sim:\tP: 0.289 R: 0.700 F: 0.328\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.839 R: 0.399 F: 0.650\n",
      "mean target_to_sim:\tP: 0.815 R: 0.571 F: 0.740\n",
      "mean reco_to_sim:\tP: 0.595 R: 0.609 F: 0.567\n",
      "mean n_tracksters:\tP: 0.900 R: 1.350 F: 1.300\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.761 R: 0.659 F: 0.738\n",
      "\treco_to_sim:\tP: 0.769 R: 0.423 F: 0.661\n",
      "\t|S| = 1 |T| = 3 |R| = 4\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "\treco_to_sim:\tP: 0.982 R: 0.398 F: 0.759\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.936 R: 0.423 F: 0.754\n",
      "\treco_to_sim:\tP: 0.945 R: 0.413 F: 0.751\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "\treco_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\treco_to_sim:\tP: 0.617 R: 0.651 F: 0.623\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "\treco_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.934 R: 0.681 F: 0.870\n",
      "\treco_to_sim:\tP: 0.928 R: 0.681 F: 0.865\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.545 F: 0.837\n",
      "\treco_to_sim:\tP: 0.967 R: 0.565 F: 0.846\n",
      "\t|S| = 1 |T| = 3 |R| = 3\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "\treco_to_sim:\tP: 0.879 R: 0.425 F: 0.724\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\treco_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\treco_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.888 F: 0.932\n",
      "\treco_to_sim:\tP: 0.882 R: 0.912 F: 0.888\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.207 F: 0.547\n",
      "\ttarget_to_sim:\tP: 0.875 R: 0.634 F: 0.813\n",
      "\treco_to_sim:\tP: 0.877 R: 0.669 F: 0.825\n",
      "\t|S| = 1 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tNo data\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.862 F: 0.963\n",
      "\treco_to_sim:\tP: 0.980 R: 0.862 F: 0.954\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.487 F: 0.822\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.727 F: 0.926\n",
      "\treco_to_sim:\tP: 0.993 R: 0.727 F: 0.926\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.832 F: 0.929\n",
      "\treco_to_sim:\tP: 0.913 R: 0.833 F: 0.896\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.800 R: 0.680 F: 0.773\n",
      "\treco_to_sim:\tP: 0.848 R: 0.646 F: 0.798\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.779 R: 0.160 F: 0.439\n",
      "\ttarget_to_sim:\tP: 0.687 R: 0.647 F: 0.678\n",
      "\treco_to_sim:\tP: 0.738 R: 0.490 F: 0.670\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.839 R: 0.399 F: 0.650\n",
      "mean target_to_sim:\tP: 0.815 R: 0.571 F: 0.740\n",
      "mean reco_to_sim:\tP: 0.813 R: 0.553 F: 0.730\n",
      "mean n_tracksters:\tP: 0.900 R: 1.350 F: 1.350\n",
      " --- Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "\treco_to_sim:\tP: 0.094 R: 0.903 F: 0.114\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "\treco_to_sim:\tP: 0.889 R: 0.414 F: 0.723\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "\treco_to_sim:\tP: 0.506 R: 0.441 F: 0.492\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "\treco_to_sim:\tP: 0.368 R: 0.824 F: 0.414\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "\treco_to_sim:\tP: 0.141 R: 0.723 F: 0.168\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\treco_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.691 F: 0.867\n",
      "\treco_to_sim:\tP: 0.431 R: 0.719 F: 0.469\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "\treco_to_sim:\tP: 0.535 R: 0.590 F: 0.545\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "\treco_to_sim:\tP: 0.056 R: 0.617 F: 0.068\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\treco_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\treco_to_sim:\tP: 0.770 R: 0.678 F: 0.750\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.888 F: 0.932\n",
      "\treco_to_sim:\tP: 0.485 R: 0.928 F: 0.536\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.207 F: 0.547\n",
      "\ttarget_to_sim:\tP: 0.871 R: 0.637 F: 0.811\n",
      "\treco_to_sim:\tP: 0.179 R: 0.848 F: 0.213\n",
      "\t|S| = 1 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.761 F: 0.940\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.834 F: 0.962\n",
      "\treco_to_sim:\tP: 1.000 R: 0.834 F: 0.962\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.902 F: 0.975\n",
      "\treco_to_sim:\tP: 0.979 R: 0.902 F: 0.963\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.489 F: 0.823\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.731 F: 0.927\n",
      "\treco_to_sim:\tP: 0.961 R: 0.737 F: 0.906\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.832 F: 0.929\n",
      "\treco_to_sim:\tP: 0.379 R: 0.855 F: 0.426\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.800 R: 0.680 F: 0.773\n",
      "\treco_to_sim:\tP: 0.096 R: 0.848 F: 0.116\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.782 R: 0.161 F: 0.441\n",
      "\ttarget_to_sim:\tP: 0.674 R: 0.707 F: 0.680\n",
      "\treco_to_sim:\tP: 0.129 R: 0.813 F: 0.155\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.889 R: 0.437 F: 0.697\n",
      "mean target_to_sim:\tP: 0.860 R: 0.629 F: 0.789\n",
      "mean reco_to_sim:\tP: 0.500 R: 0.681 F: 0.482\n",
      "mean n_tracksters:\tP: 0.950 R: 1.400 F: 1.350\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "\treco_to_sim:\tP: 0.769 R: 0.424 F: 0.661\n",
      "\t|S| = 1 |T| = 2 |R| = 4\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "\treco_to_sim:\tP: 0.982 R: 0.411 F: 0.768\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "\treco_to_sim:\tP: 0.945 R: 0.413 F: 0.751\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "\treco_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "\treco_to_sim:\tP: 0.617 R: 0.651 F: 0.623\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\treco_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.691 F: 0.867\n",
      "\treco_to_sim:\tP: 0.929 R: 0.682 F: 0.866\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "\treco_to_sim:\tP: 0.967 R: 0.590 F: 0.857\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "\treco_to_sim:\tP: 0.879 R: 0.425 F: 0.724\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\treco_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 10:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.576 F: 0.857\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\treco_to_sim:\tP: 0.972 R: 0.660 F: 0.888\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 11:\n",
      "\tclue3d_to_sim:\tP: 0.957 R: 0.679 F: 0.885\n",
      "\ttarget_to_sim:\tP: 0.943 R: 0.888 F: 0.932\n",
      "\treco_to_sim:\tP: 0.882 R: 0.912 F: 0.888\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 12:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.207 F: 0.547\n",
      "\ttarget_to_sim:\tP: 0.871 R: 0.637 F: 0.811\n",
      "\treco_to_sim:\tP: 0.877 R: 0.669 F: 0.825\n",
      "\t|S| = 1 |T| = 2 |R| = 1\n",
      "Event 13:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.761 F: 0.940\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.834 F: 0.962\n",
      "\treco_to_sim:\tP: 1.000 R: 0.834 F: 0.962\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 14:\n",
      "\tclue3d_to_sim:\tP: 0.997 R: 0.718 F: 0.925\n",
      "\ttarget_to_sim:\tP: 0.995 R: 0.902 F: 0.975\n",
      "\treco_to_sim:\tP: 0.979 R: 0.902 F: 0.963\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 15:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.489 F: 0.823\n",
      "\ttarget_to_sim:\tP: 0.993 R: 0.731 F: 0.927\n",
      "\treco_to_sim:\tP: 0.961 R: 0.737 F: 0.906\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "Event 16:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.680 F: 0.896\n",
      "\ttarget_to_sim:\tP: 0.956 R: 0.832 F: 0.929\n",
      "\treco_to_sim:\tP: 0.908 R: 0.834 F: 0.892\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 17:\n",
      "\tNo data\n",
      "Event 18:\n",
      "\tclue3d_to_sim:\tP: 0.901 R: 0.454 F: 0.753\n",
      "\ttarget_to_sim:\tP: 0.800 R: 0.680 F: 0.773\n",
      "\treco_to_sim:\tP: 0.848 R: 0.646 F: 0.798\n",
      "\t|S| = 1 |T| = 1 |R| = 1\n",
      "Event 19:\n",
      "\tclue3d_to_sim:\tP: 0.782 R: 0.161 F: 0.441\n",
      "\ttarget_to_sim:\tP: 0.674 R: 0.707 F: 0.680\n",
      "\treco_to_sim:\tP: 0.745 R: 0.508 F: 0.681\n",
      "\t|S| = 1 |T| = 2 |R| = 2\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.889 R: 0.437 F: 0.697\n",
      "mean target_to_sim:\tP: 0.860 R: 0.629 F: 0.789\n",
      "mean reco_to_sim:\tP: 0.862 R: 0.601 F: 0.780\n",
      "mean n_tracksters:\tP: 0.950 R: 1.400 F: 1.450\n"
     ]
    }
   ],
   "source": [
    "r_ranges = [3, 5, 10, 15]\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(),\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=5,\n",
    "        pileup=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.to(\"cpu\"),\n",
    "        decision_th=0.5,\n",
    "        radius=r,\n",
    "        max_events=max_events,\n",
    "        bigT_e_th=5,\n",
    "        pileup=True\n",
    "    )\n",
    "    model_reco_F.append(np.sum(np.array(result[\"reco_to_sim\"])[:,2]) / max_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e4065da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 459,
       "width": 734
      }
     },
     "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\", c=\"#D55E00\")\n",
    "ax.plot(r_ranges[:6], (np.array(model_reco_F) - np.array(clue3D_F))[:6], '-o', label=\"mlp\", c=\"#56B4E9\")\n",
    "ax.plot(r_ranges[:6], (np.array(naive_reco_F) - np.array(clue3D_F))[:6], '-v', label=\"naive\", 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}-e5-radius-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f8bb6d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reco 0.08252093900234114\n",
      "Naive 0.04353622488177655\n",
      "Target 0.09193837896962309\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": "code",
   "execution_count": 22,
   "id": "eb71c25e-6180-4412-8714-6f840c3bcb35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLUE: [0.2660786195101392, 0.5618487610549607, 0.6503284712982198, 0.6974920132483364]\n",
      "Target: [0.2919276870410676, 0.6258355186302973, 0.7396882199913862, 0.7894303922179595]\n",
      "Reco: [0.29198701138042893, 0.6162043906814609, 0.7303545363084473, 0.7800129522506776]\n"
     ]
    }
   ],
   "source": [
    "print(\"CLUE:\", clue3D_F)\n",
    "print(\"Target:\", target_F)\n",
    "print(\"Reco:\", model_reco_F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c3db647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Eth: 0 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.805 R: 0.309 F: 0.609\n",
      "\ttarget_to_sim:\tP: 0.763 R: 0.447 F: 0.669\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.335 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.421 F: 0.778\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.947 R: 0.222 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.794 F: 0.923\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.909 F: 0.950\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.500 F: 0.812\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.690 F: 0.866\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.354 F: 0.720\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.835 F: 0.938\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.868 R: 0.308 F: 0.637\n",
      "\ttarget_to_sim:\tP: 0.811 R: 0.444 F: 0.696\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.137 F: 0.441\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.923 R: 0.407 F: 0.696\n",
      "mean target_to_sim:\tP: 0.906 R: 0.568 F: 0.794\n",
      " --- Eth: 5 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.761 R: 0.659 F: 0.738\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.936 R: 0.423 F: 0.754\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.934 R: 0.681 F: 0.870\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.545 F: 0.837\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.927 R: 0.402 F: 0.688\n",
      "mean target_to_sim:\tP: 0.908 R: 0.549 F: 0.789\n",
      " --- Eth: 10 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.842 R: 0.306 F: 0.624\n",
      "\ttarget_to_sim:\tP: 0.787 R: 0.637 F: 0.751\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.953 R: 0.219 F: 0.570\n",
      "\ttarget_to_sim:\tP: 0.936 R: 0.423 F: 0.754\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.677 F: 0.866\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.571 F: 0.849\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.925 R: 0.434 F: 0.728\n",
      "mean target_to_sim:\tP: 0.901 R: 0.569 F: 0.797\n",
      " --- Eth: 20 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.854 R: 0.304 F: 0.627\n",
      "\ttarget_to_sim:\tP: 0.792 R: 0.408 F: 0.667\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.422 F: 0.755\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.677 F: 0.866\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.571 F: 0.849\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.930 R: 0.433 F: 0.729\n",
      "mean target_to_sim:\tP: 0.903 R: 0.543 F: 0.788\n",
      " --- Eth: 40 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.771 R: 0.430 F: 0.665\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.940 R: 0.422 F: 0.755\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.538 F: 0.853\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.677 F: 0.866\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.966 R: 0.571 F: 0.849\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.953 R: 0.412 F: 0.732\n",
      "mean target_to_sim:\tP: 0.923 R: 0.540 F: 0.800\n",
      " --- Eth: 60 | Radius threshold: 10 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.771 R: 0.430 F: 0.665\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.987 R: 0.413 F: 0.772\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.171 F: 0.499\n",
      "\ttarget_to_sim:\tP: 0.915 R: 0.346 F: 0.688\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.930 R: 0.677 F: 0.866\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.330 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.547 F: 0.854\n",
      "Event 8:\n",
      "\tNo data\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.961 R: 0.404 F: 0.718\n",
      "mean target_to_sim:\tP: 0.928 R: 0.539 F: 0.797\n",
      " --- Eth: 0 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.805 R: 0.309 F: 0.609\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.335 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.947 R: 0.222 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.794 F: 0.924\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.909 F: 0.950\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.512 F: 0.840\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.542 F: 0.855\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.960 R: 0.500 F: 0.811\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.691 F: 0.867\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.354 F: 0.720\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.835 F: 0.938\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.868 R: 0.308 F: 0.637\n",
      "\ttarget_to_sim:\tP: 0.811 R: 0.444 F: 0.696\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.996 R: 0.137 F: 0.441\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.445 F: 0.796\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.922 R: 0.407 F: 0.696\n",
      "mean target_to_sim:\tP: 0.899 R: 0.609 F: 0.805\n",
      " --- Eth: 5 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.926 R: 0.691 F: 0.867\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.974 R: 0.353 F: 0.721\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.927 R: 0.402 F: 0.688\n",
      "mean target_to_sim:\tP: 0.900 R: 0.571 F: 0.791\n",
      " --- Eth: 10 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.842 R: 0.306 F: 0.624\n",
      "\ttarget_to_sim:\tP: 0.767 R: 0.712 F: 0.755\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.953 R: 0.219 F: 0.570\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.823 F: 0.929\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.920 R: 0.687 F: 0.862\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.925 R: 0.434 F: 0.728\n",
      "mean target_to_sim:\tP: 0.894 R: 0.591 F: 0.800\n",
      " --- Eth: 20 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.854 R: 0.304 F: 0.627\n",
      "\ttarget_to_sim:\tP: 0.766 R: 0.440 F: 0.667\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.920 R: 0.687 F: 0.862\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.930 R: 0.433 F: 0.729\n",
      "mean target_to_sim:\tP: 0.896 R: 0.560 F: 0.791\n",
      " --- Eth: 40 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.746 R: 0.470 F: 0.668\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.566 F: 0.867\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.920 R: 0.687 F: 0.862\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.967 R: 0.597 F: 0.860\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.953 R: 0.412 F: 0.732\n",
      "mean target_to_sim:\tP: 0.916 R: 0.555 F: 0.803\n",
      " --- Eth: 60 | Radius threshold: 15 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.746 R: 0.470 F: 0.668\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.427 F: 0.780\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.171 F: 0.499\n",
      "\ttarget_to_sim:\tP: 0.894 R: 0.354 F: 0.685\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.822 F: 0.938\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.920 R: 0.687 F: 0.862\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.330 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.994 R: 0.573 F: 0.867\n",
      "Event 8:\n",
      "\tNo data\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.961 R: 0.404 F: 0.718\n",
      "mean target_to_sim:\tP: 0.918 R: 0.555 F: 0.800\n",
      " --- Eth: 0 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.805 R: 0.309 F: 0.609\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.335 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.947 R: 0.222 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.963 R: 0.794 F: 0.924\n",
      "\ttarget_to_sim:\tP: 0.960 R: 0.909 F: 0.950\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.512 F: 0.840\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.640 F: 0.899\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.961 R: 0.500 F: 0.811\n",
      "\ttarget_to_sim:\tP: 0.916 R: 0.696 F: 0.861\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.965 R: 0.354 F: 0.718\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.863 F: 0.940\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.868 R: 0.308 F: 0.637\n",
      "\ttarget_to_sim:\tP: 0.811 R: 0.444 F: 0.696\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.992 R: 0.140 F: 0.447\n",
      "\ttarget_to_sim:\tP: 0.992 R: 0.445 F: 0.796\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.921 R: 0.407 F: 0.697\n",
      "mean target_to_sim:\tP: 0.898 R: 0.622 F: 0.809\n",
      " --- Eth: 5 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.802 R: 0.307 F: 0.607\n",
      "\ttarget_to_sim:\tP: 0.718 R: 0.727 F: 0.720\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.221 F: 0.573\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.839 F: 0.933\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.640 F: 0.899\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.971 R: 0.500 F: 0.817\n",
      "\ttarget_to_sim:\tP: 0.916 R: 0.696 F: 0.861\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.968 R: 0.353 F: 0.718\n",
      "\ttarget_to_sim:\tP: 0.962 R: 0.609 F: 0.862\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.881 R: 0.306 F: 0.640\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.994 R: 0.095 F: 0.343\n",
      "\ttarget_to_sim:\tP: 0.996 R: 0.371 F: 0.745\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.927 R: 0.402 F: 0.688\n",
      "mean target_to_sim:\tP: 0.899 R: 0.582 F: 0.795\n",
      " --- Eth: 10 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.842 R: 0.306 F: 0.624\n",
      "\ttarget_to_sim:\tP: 0.731 R: 0.727 F: 0.730\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.993 R: 0.335 F: 0.713\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.953 R: 0.219 F: 0.570\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.976 R: 0.793 F: 0.933\n",
      "\ttarget_to_sim:\tP: 0.961 R: 0.839 F: 0.933\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.640 F: 0.899\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.910 R: 0.691 F: 0.856\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.609 F: 0.866\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.925 R: 0.434 F: 0.728\n",
      "mean target_to_sim:\tP: 0.889 R: 0.605 F: 0.801\n",
      " --- Eth: 20 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.854 R: 0.304 F: 0.627\n",
      "\ttarget_to_sim:\tP: 0.731 R: 0.450 F: 0.650\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.838 F: 0.942\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.727 R: 0.595 F: 0.696\n",
      "\ttarget_to_sim:\tP: 0.711 R: 0.633 F: 0.694\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.640 F: 0.899\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.910 R: 0.691 F: 0.856\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.609 F: 0.866\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.930 R: 0.433 F: 0.729\n",
      "mean target_to_sim:\tP: 0.891 R: 0.574 F: 0.793\n",
      " --- Eth: 40 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.704 R: 0.482 F: 0.645\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.970 R: 0.215 F: 0.569\n",
      "\ttarget_to_sim:\tP: 0.925 R: 0.430 F: 0.752\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.838 F: 0.942\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 1.000 R: 0.511 F: 0.839\n",
      "\ttarget_to_sim:\tP: 1.000 R: 0.640 F: 0.899\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.910 R: 0.691 F: 0.856\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.973 R: 0.352 F: 0.719\n",
      "\ttarget_to_sim:\tP: 0.968 R: 0.609 F: 0.866\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.871 R: 0.299 F: 0.630\n",
      "\ttarget_to_sim:\tP: 0.816 R: 0.442 F: 0.698\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.953 R: 0.412 F: 0.732\n",
      "mean target_to_sim:\tP: 0.910 R: 0.570 F: 0.805\n",
      " --- Eth: 60 | Radius threshold: 20 ---\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.839 R: 0.298 F: 0.615\n",
      "\ttarget_to_sim:\tP: 0.704 R: 0.482 F: 0.645\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.999 R: 0.334 F: 0.715\n",
      "\ttarget_to_sim:\tP: 0.984 R: 0.430 F: 0.783\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.955 R: 0.171 F: 0.499\n",
      "\ttarget_to_sim:\tP: 0.894 R: 0.354 F: 0.685\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.989 R: 0.792 F: 0.942\n",
      "\ttarget_to_sim:\tP: 0.972 R: 0.838 F: 0.942\n",
      "Event 4:\n",
      "\tNo data\n",
      "Event 5:\n",
      "\tNo data\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.987 R: 0.497 F: 0.824\n",
      "\ttarget_to_sim:\tP: 0.910 R: 0.691 F: 0.856\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.998 R: 0.330 F: 0.711\n",
      "\ttarget_to_sim:\tP: 0.986 R: 0.587 F: 0.868\n",
      "Event 8:\n",
      "\tNo data\n",
      "Event 9:\n",
      "\tNo data\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.961 R: 0.404 F: 0.718\n",
      "mean target_to_sim:\tP: 0.908 R: 0.564 F: 0.796\n"
     ]
    }
   ],
   "source": [
    "eth_ranges = [0, 5, 10, 20, 40, 60]\n",
    "r_ranges = range(10, 25, 5)\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\" --- Eth: {eth} | 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",
    "            bigT_e_th=eth,\n",
    "            max_events=max_events,\n",
    "            pileup=True,\n",
    "            reco_eval=False\n",
    "        )\n",
    "        # ignore cases where there is nothing to do\n",
    "        c3d = np.array(result[\"clue3d_to_sim\"])[:,2]\n",
    "        tgf = np.array(result[\"target_to_sim\"])[:,2]\n",
    "        # cmp_idx = c3d != tgf\n",
    "        # c3d = c3d[cmp_idx] \n",
    "        # tgf = tgf[cmp_idx] \n",
    "        _clue3D_F.append(np.sum(c3d) / max_events)\n",
    "        _target_F.append(np.sum(tgf) / max_events)\n",
    "    eth_results.append(np.array(_target_F) - np.array(_clue3D_F))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f76538dc",
   "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",
    "# outcome: 10 cm makes sense for PU, not higher\n",
    "\n",
    "fig.savefig(f'export/{ds_name}-energy-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64add96c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11243884202745069"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(eth_results[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94ea11bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ve",
   "language": "python",
   "name": "ve"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "37c238d9819f69c2c770157eac01081978c120e64661e10d7fd52c4caf977dc9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}