6.dataset_pairwise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c07744a",
   "metadata": {},
   "source": [
    "# Datset: Pairwise approach\n",
    "\n",
    "Get tracksters from a certain neighbourhood.\n",
    "\n",
    "Neighbourhood:\n",
    "- get links from ticlNtuplizer/graph\n",
    "    - figure out how these links are formed\n",
    "\n",
    "Graph:\n",
    "- linked_inners\n",
    "    - nodes linked to the given tracksters within its cone\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d07322fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import awkward as ak\n",
    "\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "from reco.datasetPU import get_event_pairs\n",
    "from reco.data import get_bary_data, get_event_data\n",
    "\n",
    "# plotting config\n",
    "import matplotlib.pyplot as plt\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",
    "ds_name = \"MultiParticle\"\n",
    "file_name = \"new_ntuples_15163313_1.root\"\n",
    "\n",
    "ds_name = \"CloseByTwoPion\"\n",
    "file_name = \"new_ntuples_15101852_0.root\"\n",
    "\n",
    "data_root = \"data\"\n",
    "raw_dir = f\"/Users/ecuba/data/{ds_name}\"\n",
    "file_path = f\"{raw_dir}/{file_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": 3,
   "id": "da5bafec",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_data, trackster_data, simtrackster_data, assoc_data = get_event_data(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f37196a",
   "metadata": {},
   "source": [
    "## Candidate pairs\n",
    "\n",
    "Compile a list of candidate pairs based on the CLUE3D graph and node distance.\n",
    "\n",
    "Reject all nodes with above a distance threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d94324c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63 144\n"
     ]
    }
   ],
   "source": [
    "eid = 4\n",
    "dX, dY, _pair_index = get_event_pairs(\n",
    "    cluster_data,\n",
    "    trackster_data,\n",
    "    assoc_data,\n",
    "    eid,\n",
    "    10,\n",
    "    pileup=False,\n",
    "    bigT_e_th=50,\n",
    ")\n",
    "\n",
    "\n",
    "pair_index = np.array(_pair_index)\n",
    "pos_index = np.array(dY) >= 0.5\n",
    "neg_index = np.array(dY) < 0.5\n",
    "\n",
    "# for i, p in  enumerate(_pair_index):\n",
    "#     print(p, dY[i])\n",
    "\n",
    "print(sum(pos_index), sum(neg_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c430369b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 482
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive = pair_index[pos_index]\n",
    "negative = pair_index[neg_index]\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "all_tx = list(set(ak.flatten(positive).tolist() + ak.flatten(negative).tolist()))\n",
    "\n",
    "bary = get_bary_data(trackster_data, eid)\n",
    "ax1.scatter(bary[:,2][all_tx], bary[:,0][all_tx], bary[:,1][all_tx], s=trackster_data[\"raw_energy\"][eid][all_tx])\n",
    "\n",
    "pos_edges = []\n",
    "neg_edges = []\n",
    "\n",
    "def plot_edge(ax, edge, color, label=None):\n",
    "    a, b = edge\n",
    "    ca = bary[a]\n",
    "    cb = bary[b]\n",
    "    x = (ca[0], cb[0])\n",
    "    y = (ca[1], cb[1])\n",
    "    z = (ca[2], cb[2])\n",
    "    ax.plot(z, x, y, c=color, linewidth=1, alpha=0.8, label=label) \n",
    "\n",
    "\n",
    "plot_edge(ax1, positive[0], \"#009E73\", \"positive\")\n",
    "if len(negative):\n",
    "    plot_edge(ax1, negative[0], \"#E69F00\", \"negative\")\n",
    "    [plot_edge(ax1, edge, \"#E69F00\") for edge in negative[1:]]\n",
    "\n",
    "[plot_edge(ax1, edge, \"#009E73\") for edge in positive[1:]]\n",
    "\n",
    "ax1.set_xlabel(\"z (cm)\")\n",
    "ax1.set_ylabel(\"x (cm)\")\n",
    "ax1.set_zlabel(\"y (cm)\")\n",
    "ax1.view_init(elev=30, azim=-110)\n",
    "# ax1.set_xlim(360, 460)\n",
    "# ax1.set_ylim(-5, 20)\n",
    "# ax1.set_zlim(-90, -50)\n",
    "\n",
    "ax1.zaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "fig.savefig('export/multiparticle-pairwise-edges.pdf', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3247338",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01960ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from reco.datasetPU import TracksterPairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "219c077d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_917.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_956.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_995.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_983.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15163313_4.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_921.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_96.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_976.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_999.root\n",
      "Processing: /Users/ecuba/data/MultiParticle/new_ntuples_15256267_960.root\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([512830])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# would need to implement some sharding here - roughly 36 files for 1M samples\n",
    "ds = TracksterPairs(\n",
    "    ds_name,\n",
    "    data_root,\n",
    "    raw_dir,\n",
    "    N_FILES=10,\n",
    "    radius=15,\n",
    "    bigT_e_th=30,\n",
    ")\n",
    "ds.y.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "79ad0cff",
   "metadata": {},
   "source": [
    "## Dataset balance\n",
    "\n",
    "- CloseByTwoPion, 10cm radius, 0.7 threshold on 1000 events: 88%\n",
    "- MultiParticle, 10cm radius, 0.7 threshold on 1000 events: 51% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd1787d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5182163715362549"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(sum(ds.y > 0.7) / len(ds.y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4142e49c",
   "metadata": {},
   "source": [
    "## Dummy evaluation\n",
    "\n",
    "Let's talk baselines on this dataset. How good is a random guess?\n",
    "How good is connecting to all nodes in a neighbourhood?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6261c52b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from reco.evaluation import pairwise_model_evaluation\n",
    "from reco.dummy import DummyPleaser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f6eb4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.851 R: 0.066 F: 0.585\n",
      "\ttarget_to_sim:\tP: 0.851 R: 0.073 F: 0.605\n",
      "\treco_to_sim:\tP: 0.864 R: 0.093 F: 0.654\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.782 R: 0.067 F: 0.555\n",
      "\ttarget_to_sim:\tP: 0.817 R: 0.186 F: 0.723\n",
      "\treco_to_sim:\tP: 0.808 R: 0.191 F: 0.719\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.142 F: 0.764\n",
      "\ttarget_to_sim:\tP: 0.929 R: 0.239 F: 0.836\n",
      "\treco_to_sim:\tP: 0.916 R: 0.232 F: 0.823\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m result \u001b[39m=\u001b[39m pairwise_model_evaluation(\n\u001b[1;32m      2\u001b[0m     cluster_data,\n\u001b[1;32m      3\u001b[0m     trackster_data,\n\u001b[1;32m      4\u001b[0m     simtrackster_data,\n\u001b[1;32m      5\u001b[0m     assoc_data,\n\u001b[1;32m      6\u001b[0m     DummyPleaser(),\n\u001b[1;32m      7\u001b[0m     decision_th\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m,\n\u001b[1;32m      8\u001b[0m     radius\u001b[39m=\u001b[39;49m\u001b[39m6\u001b[39;49m,\n\u001b[1;32m      9\u001b[0m     max_events\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[1;32m     10\u001b[0m     reco_to_target\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     11\u001b[0m     bigT_e_th\u001b[39m=\u001b[39;49m\u001b[39m30\u001b[39;49m,\n\u001b[1;32m     12\u001b[0m )\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:277\u001b[0m, in \u001b[0;36mpairwise_model_evaluation\u001b[0;34m(cluster_data, trackster_data, simtrackster_data, assoc_data, model, decision_th, radius, max_events, reco_to_target, bigT_e_th)\u001b[0m\n\u001b[1;32m    274\u001b[0m nhits \u001b[39m=\u001b[39m cluster_data[\u001b[39m\"\u001b[39m\u001b[39mcluster_number_of_hits\u001b[39m\u001b[39m\"\u001b[39m][eid]\n\u001b[1;32m    276\u001b[0m results[\u001b[39m\"\u001b[39m\u001b[39mclue3d_to_sim\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, ci, si, ce, se, cm, sm))\n\u001b[0;32m--> 277\u001b[0m results[\u001b[39m\"\u001b[39m\u001b[39mtarget_to_sim\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, target_i, si, target_e, se, target_m, sm))\n\u001b[1;32m    279\u001b[0m \u001b[39mif\u001b[39;00m reco_to_target:\n\u001b[1;32m    280\u001b[0m     results[\u001b[39m\"\u001b[39m\u001b[39mreco_to_target\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, ri, target_i, re, target_e, rm, target_m))\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:135\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(nhits, all_t_indexes, all_st_indexes, t_energy, st_energy, all_v_multi, all_sv_multi, f_min, beta, min_hits)\u001b[0m\n\u001b[1;32m    132\u001b[0m ste_map \u001b[39m=\u001b[39m get_energy_map(st_indexes, st_energy, sv_multi)\n\u001b[1;32m    134\u001b[0m precision \u001b[39m=\u001b[39m bcubed(t_vertices, t_indexes, i2rt, i2st, te_map)\n\u001b[0;32m--> 135\u001b[0m recall \u001b[39m=\u001b[39m bcubed(st_vertices, st_indexes, i2st, i2rt, ste_map)\n\u001b[1;32m    137\u001b[0m \u001b[39mreturn\u001b[39;00m precision, recall, f_score(precision, recall, beta\u001b[39m=\u001b[39mbeta)\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:106\u001b[0m, in \u001b[0;36mbcubed\u001b[0;34m(vertices, t_vertices, i2a, i2b, e_map)\u001b[0m\n\u001b[1;32m    103\u001b[0m         te_map \u001b[39m=\u001b[39m e_map[\u001b[39mint\u001b[39m(i_trackster)]\n\u001b[1;32m    105\u001b[0m         \u001b[39m# normalize by the number of tracksters and add to the outer P sum\u001b[39;00m\n\u001b[0;32m--> 106\u001b[0m         P \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m get_pairwise_scores(i, V, i2b, te_map) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(i_tracksters)\n\u001b[1;32m    108\u001b[0m \u001b[39m# normalize the result\u001b[39;00m\n\u001b[1;32m    109\u001b[0m \u001b[39mreturn\u001b[39;00m P \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(vertices)\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:62\u001b[0m, in \u001b[0;36mget_pairwise_scores\u001b[0;34m(i, V, i2t, te_map)\u001b[0m\n\u001b[1;32m     59\u001b[0m i_trackster_score \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m     61\u001b[0m \u001b[39m# for all vertices of the trackster\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m V:\n\u001b[1;32m     63\u001b[0m     \u001b[39m# get the pair energy\u001b[39;00m\n\u001b[1;32m     64\u001b[0m     e_pair \u001b[39m=\u001b[39m (te_map[i] \u001b[39m*\u001b[39m te_map[j])\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m\n\u001b[1;32m     65\u001b[0m     e_pairs \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m e_pair\n",
      "File \u001b[0;32m~/devel/pattern-reco/ve/lib/python3.9/site-packages/awkward/highlevel.py:575\u001b[0m, in \u001b[0;36mArray.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    545\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__iter__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    546\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    547\u001b[0m \u001b[39m    Iterates over this Array in Python.\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[39m    See also #ak.to_list.\u001b[39;00m\n\u001b[1;32m    574\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 575\u001b[0m     \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayout:\n\u001b[1;32m    576\u001b[0m         \u001b[39myield\u001b[39;00m ak\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39mwrap(x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_behavior)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "result = pairwise_model_evaluation(\n",
    "    cluster_data,\n",
    "    trackster_data,\n",
    "    simtrackster_data,\n",
    "    assoc_data,\n",
    "    DummyPleaser(),\n",
    "    decision_th=0.5,\n",
    "    radius=6,\n",
    "    max_events=10,\n",
    "    reco_to_target=False,\n",
    "    bigT_e_th=30,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "703fcf57",
   "metadata": {},
   "source": [
    "## Generating figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da2d70df",
   "metadata": {},
   "outputs": [],
   "source": [
    "radius_list = list(range(2, 32, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6215e2b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radius: 2\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.851 R: 0.066 F: 0.585\n",
      "\ttarget_to_sim:\tP: 0.851 R: 0.066 F: 0.585\n",
      "\treco_to_sim:\tP: 0.851 R: 0.066 F: 0.585\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.784 R: 0.067 F: 0.555\n",
      "\ttarget_to_sim:\tP: 0.784 R: 0.070 F: 0.563\n",
      "\treco_to_sim:\tP: 0.784 R: 0.070 F: 0.563\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.142 F: 0.765\n",
      "\ttarget_to_sim:\tP: 0.928 R: 0.142 F: 0.765\n",
      "\treco_to_sim:\tP: 0.928 R: 0.142 F: 0.765\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.824 R: 0.100 F: 0.645\n",
      "\ttarget_to_sim:\tP: 0.824 R: 0.102 F: 0.648\n",
      "\treco_to_sim:\tP: 0.824 R: 0.102 F: 0.648\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.765 R: 0.059 F: 0.524\n",
      "\ttarget_to_sim:\tP: 0.765 R: 0.059 F: 0.524\n",
      "\treco_to_sim:\tP: 0.765 R: 0.059 F: 0.524\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.880 R: 0.095 F: 0.667\n",
      "\ttarget_to_sim:\tP: 0.880 R: 0.095 F: 0.667\n",
      "\treco_to_sim:\tP: 0.880 R: 0.095 F: 0.667\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.836 R: 0.140 F: 0.702\n",
      "\ttarget_to_sim:\tP: 0.828 R: 0.147 F: 0.703\n",
      "\treco_to_sim:\tP: 0.828 R: 0.147 F: 0.703\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.883 R: 0.132 F: 0.724\n",
      "\ttarget_to_sim:\tP: 0.883 R: 0.132 F: 0.724\n",
      "\treco_to_sim:\tP: 0.883 R: 0.132 F: 0.724\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.808 R: 0.086 F: 0.610\n",
      "\ttarget_to_sim:\tP: 0.808 R: 0.086 F: 0.610\n",
      "\treco_to_sim:\tP: 0.808 R: 0.086 F: 0.610\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.868 R: 0.143 F: 0.726\n",
      "\ttarget_to_sim:\tP: 0.868 R: 0.148 F: 0.730\n",
      "\treco_to_sim:\tP: 0.868 R: 0.148 F: 0.730\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.843 R: 0.103 F: 0.650\n",
      "mean target_to_sim:\tP: 0.842 R: 0.105 F: 0.652\n",
      "mean reco_to_sim:\tP: 0.842 R: 0.105 F: 0.652\n",
      "radius: 4\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.851 R: 0.066 F: 0.585\n",
      "\ttarget_to_sim:\tP: 0.851 R: 0.067 F: 0.588\n",
      "\treco_to_sim:\tP: 0.851 R: 0.073 F: 0.603\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.784 R: 0.067 F: 0.555\n",
      "\ttarget_to_sim:\tP: 0.792 R: 0.104 F: 0.631\n",
      "\treco_to_sim:\tP: 0.776 R: 0.110 F: 0.630\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.928 R: 0.142 F: 0.765\n",
      "\ttarget_to_sim:\tP: 0.928 R: 0.161 F: 0.784\n",
      "\treco_to_sim:\tP: 0.923 R: 0.166 F: 0.785\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39mfor\u001b[39;00m radius \u001b[39min\u001b[39;00m radius_list:\n\u001b[1;32m      9\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mradius:\u001b[39m\u001b[39m\"\u001b[39m, radius)\n\u001b[0;32m---> 10\u001b[0m     result \u001b[39m=\u001b[39m pairwise_model_evaluation(\n\u001b[1;32m     11\u001b[0m         cluster_data,\n\u001b[1;32m     12\u001b[0m         trackster_data,\n\u001b[1;32m     13\u001b[0m         simtrackster_data,\n\u001b[1;32m     14\u001b[0m         assoc_data,\n\u001b[1;32m     15\u001b[0m         DummyPleaser(),\n\u001b[1;32m     16\u001b[0m         decision_th\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m,\n\u001b[1;32m     17\u001b[0m         radius\u001b[39m=\u001b[39;49mradius,\n\u001b[1;32m     18\u001b[0m         max_events\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m,\n\u001b[1;32m     19\u001b[0m         reco_to_target\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     20\u001b[0m         bigT_e_th\u001b[39m=\u001b[39;49m\u001b[39m40\u001b[39;49m,\n\u001b[1;32m     21\u001b[0m     )\n\u001b[1;32m     22\u001b[0m     clue3D \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(result[\u001b[39m\"\u001b[39m\u001b[39mclue3d_to_sim\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m     23\u001b[0m     clue3D_precision\u001b[39m.\u001b[39mappend(np\u001b[39m.\u001b[39mmean(clue3D[:,\u001b[39m0\u001b[39m]))\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:277\u001b[0m, in \u001b[0;36mpairwise_model_evaluation\u001b[0;34m(cluster_data, trackster_data, simtrackster_data, assoc_data, model, decision_th, radius, max_events, reco_to_target, bigT_e_th)\u001b[0m\n\u001b[1;32m    274\u001b[0m nhits \u001b[39m=\u001b[39m cluster_data[\u001b[39m\"\u001b[39m\u001b[39mcluster_number_of_hits\u001b[39m\u001b[39m\"\u001b[39m][eid]\n\u001b[1;32m    276\u001b[0m results[\u001b[39m\"\u001b[39m\u001b[39mclue3d_to_sim\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, ci, si, ce, se, cm, sm))\n\u001b[0;32m--> 277\u001b[0m results[\u001b[39m\"\u001b[39m\u001b[39mtarget_to_sim\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, target_i, si, target_e, se, target_m, sm))\n\u001b[1;32m    279\u001b[0m \u001b[39mif\u001b[39;00m reco_to_target:\n\u001b[1;32m    280\u001b[0m     results[\u001b[39m\"\u001b[39m\u001b[39mreco_to_target\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mappend(evaluate(nhits, ri, target_i, re, target_e, rm, target_m))\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:135\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(nhits, all_t_indexes, all_st_indexes, t_energy, st_energy, all_v_multi, all_sv_multi, f_min, beta, min_hits)\u001b[0m\n\u001b[1;32m    132\u001b[0m ste_map \u001b[39m=\u001b[39m get_energy_map(st_indexes, st_energy, sv_multi)\n\u001b[1;32m    134\u001b[0m precision \u001b[39m=\u001b[39m bcubed(t_vertices, t_indexes, i2rt, i2st, te_map)\n\u001b[0;32m--> 135\u001b[0m recall \u001b[39m=\u001b[39m bcubed(st_vertices, st_indexes, i2st, i2rt, ste_map)\n\u001b[1;32m    137\u001b[0m \u001b[39mreturn\u001b[39;00m precision, recall, f_score(precision, recall, beta\u001b[39m=\u001b[39mbeta)\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:106\u001b[0m, in \u001b[0;36mbcubed\u001b[0;34m(vertices, t_vertices, i2a, i2b, e_map)\u001b[0m\n\u001b[1;32m    103\u001b[0m         te_map \u001b[39m=\u001b[39m e_map[\u001b[39mint\u001b[39m(i_trackster)]\n\u001b[1;32m    105\u001b[0m         \u001b[39m# normalize by the number of tracksters and add to the outer P sum\u001b[39;00m\n\u001b[0;32m--> 106\u001b[0m         P \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m get_pairwise_scores(i, V, i2b, te_map) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(i_tracksters)\n\u001b[1;32m    108\u001b[0m \u001b[39m# normalize the result\u001b[39;00m\n\u001b[1;32m    109\u001b[0m \u001b[39mreturn\u001b[39;00m P \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(vertices)\n",
      "File \u001b[0;32m~/devel/pattern-reco/reco/evaluation.py:62\u001b[0m, in \u001b[0;36mget_pairwise_scores\u001b[0;34m(i, V, i2t, te_map)\u001b[0m\n\u001b[1;32m     59\u001b[0m i_trackster_score \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m     61\u001b[0m \u001b[39m# for all vertices of the trackster\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m V:\n\u001b[1;32m     63\u001b[0m     \u001b[39m# get the pair energy\u001b[39;00m\n\u001b[1;32m     64\u001b[0m     e_pair \u001b[39m=\u001b[39m (te_map[i] \u001b[39m*\u001b[39m te_map[j])\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39m2\u001b[39m\n\u001b[1;32m     65\u001b[0m     e_pairs \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m e_pair\n",
      "File \u001b[0;32m~/devel/pattern-reco/ve/lib/python3.9/site-packages/awkward/highlevel.py:575\u001b[0m, in \u001b[0;36mArray.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    545\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__iter__\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    546\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    547\u001b[0m \u001b[39m    Iterates over this Array in Python.\u001b[39;00m\n\u001b[1;32m    548\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[39m    See also #ak.to_list.\u001b[39;00m\n\u001b[1;32m    574\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 575\u001b[0m     \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlayout:\n\u001b[1;32m    576\u001b[0m         \u001b[39myield\u001b[39;00m ak\u001b[39m.\u001b[39m_util\u001b[39m.\u001b[39mwrap(x, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_behavior)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "clue3D_precision = []\n",
    "clue3D_recall = []\n",
    "target_precision = []\n",
    "target_recall = []\n",
    "naive_precision = []\n",
    "naive_recall = []\n",
    "\n",
    "max_events = 10\n",
    "\n",
    "for radius in radius_list:\n",
    "    print(\"radius:\", radius)\n",
    "    result = pairwise_model_evaluation(\n",
    "        cluster_data,\n",
    "        trackster_data,\n",
    "        simtrackster_data,\n",
    "        assoc_data,\n",
    "        DummyPleaser(),\n",
    "        decision_th=0.5,\n",
    "        radius=radius,\n",
    "        max_events=max_events,\n",
    "        reco_to_target=False,\n",
    "        bigT_e_th=40,\n",
    "    )\n",
    "    clue3D = np.array(result[\"clue3d_to_sim\"])\n",
    "    clue3D_precision.append(np.sum(clue3D[:,0]) / max_events)\n",
    "    clue3D_recall.append(np.sum(clue3D[:,1]) / max_events)\n",
    "\n",
    "    target = np.array(result[\"target_to_sim\"])\n",
    "    target_precision.append(np.sum(target[:,0]) / max_events)\n",
    "    target_recall.append(np.sum(target[:,1]) / max_events )\n",
    "\n",
    "    naive = np.array(result[\"reco_to_sim\"])\n",
    "    naive_precision.append(np.sum(naive[:,0]) / max_events)\n",
    "    naive_recall.append(np.sum(naive[:,1]) / max_events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d7bfe38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 453,
       "width": 723
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(radius_list[:8], (np.array(target_recall) - np.array(clue3D_recall))[:8], '--', label=\"target recall loss\", c=\"#0072B2\")\n",
    "ax.plot(radius_list[:8], (np.array(target_precision) - np.array(clue3D_precision))[:8], '--', label=\"target precision loss\", c=\"#D55E00\")\n",
    "\n",
    "ax.plot(radius_list[:8], (np.array(naive_recall) - np.array(clue3D_recall))[:8], label=\"naive recall gain\", c=\"#56B4E9\")\n",
    "ax.plot(radius_list[:8], (np.array(naive_precision) - np.array(clue3D_precision))[:8], label=\"naive precision loss\", c=\"#E69F00\")\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Neighborhood radius (cm)\")\n",
    "ax.set_ylabel(\"Loss / gain\")\n",
    "\n",
    "fig.savefig('export/multiparticle-radius-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "463ef1f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radius: 10 energy: 10\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.86 R: 0.15 F: 0.18\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.19 F: 0.22\n",
      "\treco_to_sim:\tP: 0.75 R: 0.15 F: 0.18\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.84 R: 0.23 F: 0.26\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.76 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.19 F: 0.23\n",
      "\treco_to_sim:\tP: 0.68 R: 0.19 F: 0.22\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.19 F: 0.22\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.78 R: 0.28 F: 0.32\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.24 F: 0.28\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.23 F: 0.27\n",
      "radius: 10 energy: 15\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.86 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.75 R: 0.17 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.83 R: 0.23 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.19 F: 0.23\n",
      "\treco_to_sim:\tP: 0.68 R: 0.19 F: 0.22\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.84 R: 0.27 F: 0.31\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.30 F: 0.34\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.24 F: 0.28\n",
      "radius: 10 energy: 20\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.86 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.75 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.85 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.21 F: 0.24\n",
      "\treco_to_sim:\tP: 0.67 R: 0.22 F: 0.25\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.30 F: 0.34\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.24 F: 0.28\n",
      "radius: 10 energy: 25\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.86 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.75 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.85 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.67 R: 0.22 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.35\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.25 F: 0.29\n",
      "radius: 10 energy: 30\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.84 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.75 R: 0.20 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.85 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.67 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.35\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.25 F: 0.29\n",
      "radius: 10 energy: 35\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.84 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.75 R: 0.20 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.67 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.35\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.25 F: 0.29\n",
      "radius: 10 energy: 40\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.84 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.75 R: 0.20 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.27 F: 0.31\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.67 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.85 R: 0.30 F: 0.35\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.25 F: 0.29\n",
      "radius: 10 energy: 45\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.84 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.73 R: 0.23 F: 0.27\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.67 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.75 R: 0.29 F: 0.33\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.82 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.79 R: 0.26 F: 0.30\n",
      "radius: 10 energy: 50\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.84 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.73 R: 0.23 F: 0.27\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.67 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.82 R: 0.35 F: 0.39\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.82 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.26 F: 0.30\n",
      "radius: 10 energy: 55\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.84 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.73 R: 0.23 F: 0.27\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.66 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.82 R: 0.35 F: 0.39\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.82 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.26 F: 0.30\n",
      "radius: 10 energy: 60\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.84 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.72 R: 0.24 F: 0.28\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.66 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.82 R: 0.35 F: 0.39\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.81 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.82 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.26 F: 0.30\n",
      "radius: 10 energy: 65\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.84 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.72 R: 0.24 F: 0.28\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.66 R: 0.23 F: 0.26\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.82 R: 0.35 F: 0.39\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.81 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.31 F: 0.36\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.26 F: 0.30\n",
      "radius: 10 energy: 70\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.84 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.72 R: 0.24 F: 0.28\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.75 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.82 R: 0.35 F: 0.39\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.81 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.27 F: 0.31\n",
      "radius: 10 energy: 75\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.84 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.72 R: 0.24 F: 0.28\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.73 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.81 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.27 F: 0.31\n",
      "radius: 10 energy: 80\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.85 R: 0.14 F: 0.17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.72 R: 0.24 F: 0.28\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.24 F: 0.28\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.73 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.81 R: 0.26 F: 0.30\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.87 R: 0.24 F: 0.28\n",
      "mean reco_to_sim:\tP: 0.80 R: 0.27 F: 0.31\n",
      "radius: 10 energy: 85\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.85 R: 0.14 F: 0.17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.76 R: 0.30 F: 0.34\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.73 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.91 R: 0.21 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.28 F: 0.33\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.81 R: 0.28 F: 0.32\n",
      "radius: 10 energy: 90\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.85 R: 0.14 F: 0.17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.76 R: 0.30 F: 0.34\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.73 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.19 F: 0.22\n",
      "\treco_to_sim:\tP: 0.91 R: 0.20 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.28 F: 0.33\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.28\n",
      "mean reco_to_sim:\tP: 0.81 R: 0.28 F: 0.32\n",
      "radius: 10 energy: 95\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.85 R: 0.14 F: 0.17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.76 R: 0.30 F: 0.34\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.73 R: 0.33 F: 0.37\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.19 F: 0.22\n",
      "\treco_to_sim:\tP: 0.91 R: 0.20 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.28 F: 0.33\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.28\n",
      "mean reco_to_sim:\tP: 0.81 R: 0.28 F: 0.32\n",
      "radius: 10 energy: 100\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.85 R: 0.14 F: 0.17\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.75 R: 0.30 F: 0.34\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.93 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.84 R: 0.28 F: 0.32\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.75 R: 0.40 F: 0.44\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.70 R: 0.27 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.19 F: 0.22\n",
      "\treco_to_sim:\tP: 0.91 R: 0.20 F: 0.24\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.80 R: 0.37 F: 0.41\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.83 R: 0.28 F: 0.33\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.80 R: 0.33 F: 0.37\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.90 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.24 F: 0.28\n",
      "mean reco_to_sim:\tP: 0.81 R: 0.29 F: 0.33\n",
      "radius: 20 energy: 10\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.80 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.19 F: 0.23\n",
      "\treco_to_sim:\tP: 0.54 R: 0.15 F: 0.17\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.73 R: 0.20 F: 0.23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.51 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.21 F: 0.24\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.73 R: 0.18 F: 0.21\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.70 R: 0.19 F: 0.23\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.53 R: 0.19 F: 0.22\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.63 R: 0.18 F: 0.21\n",
      "radius: 20 energy: 15\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.79 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.53 R: 0.16 F: 0.19\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.72 R: 0.21 F: 0.25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.21 F: 0.24\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.71 R: 0.19 F: 0.23\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.68 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.22 F: 0.25\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.62 R: 0.20 F: 0.23\n",
      "radius: 20 energy: 20\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.79 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.52 R: 0.18 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.69 R: 0.22 F: 0.25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.48 R: 0.19 F: 0.21\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.71 R: 0.19 F: 0.23\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.22 F: 0.25\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.61 R: 0.20 F: 0.23\n",
      "radius: 20 energy: 25\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.12 F: 0.14\n",
      "\treco_to_sim:\tP: 0.78 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.51 R: 0.18 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.69 R: 0.22 F: 0.25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.48 R: 0.19 F: 0.22\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.71 R: 0.19 F: 0.23\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.61 R: 0.20 F: 0.23\n",
      "radius: 20 energy: 30\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.77 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.51 R: 0.19 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.69 R: 0.22 F: 0.25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.46 R: 0.19 F: 0.22\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.61 R: 0.21 F: 0.24\n",
      "radius: 20 energy: 35\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.77 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.51 R: 0.19 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.46 R: 0.19 F: 0.22\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.21 F: 0.24\n",
      "radius: 20 energy: 40\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.77 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.51 R: 0.19 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.51 R: 0.17 F: 0.19\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.23\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.74 R: 0.24 F: 0.28\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.21 F: 0.24\n",
      "radius: 20 energy: 45\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.77 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.52 R: 0.19 F: 0.22\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.23\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.50 R: 0.21 F: 0.24\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.64 R: 0.23 F: 0.27\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.59 R: 0.21 F: 0.24\n",
      "radius: 20 energy: 50\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.77 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.52 R: 0.19 F: 0.22\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.23\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.62 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.64 R: 0.23 F: 0.27\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.22 F: 0.25\n",
      "radius: 20 energy: 55\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.75 R: 0.18 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.52 R: 0.19 F: 0.22\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.24\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.62 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.65 R: 0.22 F: 0.25\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.64 R: 0.23 F: 0.27\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.22 F: 0.25\n",
      "radius: 20 energy: 60\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.75 R: 0.18 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.49 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.24\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.62 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.63 R: 0.22 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.64 R: 0.23 F: 0.27\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.22 F: 0.26\n",
      "radius: 20 energy: 65\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.75 R: 0.18 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.49 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.43 R: 0.21 F: 0.24\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.62 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.63 R: 0.22 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.55 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.23 F: 0.27\n",
      "radius: 20 energy: 70\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.75 R: 0.18 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.49 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.49 R: 0.21 F: 0.24\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.62 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.63 R: 0.22 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.25 F: 0.28\n",
      "radius: 20 energy: 75\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.13\n",
      "\treco_to_sim:\tP: 0.75 R: 0.18 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.49 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.47 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.63 R: 0.22 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.25 F: 0.28\n",
      "radius: 20 energy: 80\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.75 R: 0.19 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.49 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.25 F: 0.29\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.47 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.63 R: 0.22 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.25 F: 0.28\n",
      "radius: 20 energy: 85\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.75 R: 0.19 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.55 R: 0.34 F: 0.37\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.31 F: 0.35\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.47 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.25\n",
      "\treco_to_sim:\tP: 0.70 R: 0.21 F: 0.25\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.23 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.27 F: 0.30\n",
      "radius: 20 energy: 90\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.75 R: 0.19 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.55 R: 0.34 F: 0.37\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.31 F: 0.35\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.47 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.71 R: 0.23 F: 0.27\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.23 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.27 F: 0.30\n",
      "radius: 20 energy: 95\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.75 R: 0.19 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.29 F: 0.33\n",
      "\treco_to_sim:\tP: 0.55 R: 0.34 F: 0.37\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.66 R: 0.31 F: 0.35\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.47 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.71 R: 0.23 F: 0.27\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.23 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.27 F: 0.30\n",
      "radius: 20 energy: 100\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.10 F: 0.12\n",
      "\treco_to_sim:\tP: 0.75 R: 0.19 F: 0.22\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.53 R: 0.36 F: 0.38\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.63 R: 0.34 F: 0.37\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.31 F: 0.35\n",
      "\treco_to_sim:\tP: 0.51 R: 0.31 F: 0.34\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.80 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.48 R: 0.31 F: 0.33\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.21 F: 0.25\n",
      "\treco_to_sim:\tP: 0.71 R: 0.23 F: 0.27\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.57 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.23 F: 0.26\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.57 R: 0.26 F: 0.29\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.33 F: 0.37\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.60 R: 0.28 F: 0.31\n",
      "radius: 30 energy: 10\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.73 R: 0.15 F: 0.18\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.81 R: 0.20 F: 0.23\n",
      "\treco_to_sim:\tP: 0.50 R: 0.17 F: 0.19\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.26 F: 0.31\n",
      "\treco_to_sim:\tP: 0.67 R: 0.19 F: 0.23\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.31 F: 0.36\n",
      "\treco_to_sim:\tP: 0.44 R: 0.15 F: 0.17\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.46 R: 0.21 F: 0.23\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.20 F: 0.24\n",
      "\treco_to_sim:\tP: 0.61 R: 0.13 F: 0.15\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.87 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.50 R: 0.20 F: 0.23\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.25 F: 0.29\n",
      "mean reco_to_sim:\tP: 0.57 R: 0.18 F: 0.21\n",
      "radius: 30 energy: 15\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.14 F: 0.16\n",
      "\treco_to_sim:\tP: 0.71 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.49 R: 0.18 F: 0.21\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.28 F: 0.33\n",
      "\treco_to_sim:\tP: 0.63 R: 0.21 F: 0.25\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.46 R: 0.21 F: 0.23\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.14 F: 0.16\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.60 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.53 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.56 R: 0.19 F: 0.22\n",
      "radius: 30 energy: 20\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.14 F: 0.16\n",
      "\treco_to_sim:\tP: 0.71 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.48 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.59 R: 0.23 F: 0.26\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.45 R: 0.25 F: 0.27\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.14 F: 0.16\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.53 R: 0.24 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.36\n",
      "\treco_to_sim:\tP: 0.67 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.55 R: 0.20 F: 0.22\n",
      "radius: 30 energy: 25\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.12 F: 0.15\n",
      "\treco_to_sim:\tP: 0.70 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.25 F: 0.30\n",
      "\treco_to_sim:\tP: 0.47 R: 0.20 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.59 R: 0.23 F: 0.26\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.79 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.45 R: 0.25 F: 0.28\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.14 F: 0.16\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.66 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.54 R: 0.20 F: 0.22\n",
      "radius: 30 energy: 30\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.68 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.45 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.29 F: 0.34\n",
      "\treco_to_sim:\tP: 0.59 R: 0.23 F: 0.26\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.42 R: 0.27 F: 0.29\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.56 R: 0.16 F: 0.18\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.66 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.54 R: 0.20 F: 0.23\n",
      "radius: 30 energy: 35\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.68 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.45 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.24 F: 0.28\n",
      "\treco_to_sim:\tP: 0.42 R: 0.27 F: 0.29\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.66 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.53 R: 0.20 F: 0.23\n",
      "radius: 30 energy: 40\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.68 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.45 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.44 R: 0.16 F: 0.18\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.40 R: 0.29 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.66 R: 0.21 F: 0.25\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.53 R: 0.21 F: 0.23\n",
      "radius: 30 energy: 45\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.68 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.48 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.40 R: 0.29 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.47 R: 0.21 F: 0.23\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.54 R: 0.21 F: 0.24\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.21 F: 0.24\n",
      "radius: 30 energy: 50\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.85 R: 0.13 F: 0.16\n",
      "\treco_to_sim:\tP: 0.68 R: 0.16 F: 0.19\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.48 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.78 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.40 R: 0.29 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.56 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.54 R: 0.21 F: 0.24\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.53 R: 0.22 F: 0.24\n",
      "radius: 30 energy: 55\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.83 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.48 R: 0.21 F: 0.23\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.39 R: 0.30 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.56 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.20\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.54 R: 0.21 F: 0.24\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.30\n",
      "mean reco_to_sim:\tP: 0.53 R: 0.22 F: 0.25\n",
      "radius: 30 energy: 60\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.45 R: 0.22 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.39 R: 0.30 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.56 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.19 F: 0.22\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.32 F: 0.37\n",
      "\treco_to_sim:\tP: 0.54 R: 0.21 F: 0.24\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.22 F: 0.25\n",
      "radius: 30 energy: 65\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.45 R: 0.22 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.39 R: 0.30 F: 0.31\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.56 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.19 F: 0.22\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.50 R: 0.24 F: 0.26\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.23 F: 0.26\n",
      "radius: 30 energy: 70\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.45 R: 0.22 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.34 F: 0.39\n",
      "\treco_to_sim:\tP: 0.42 R: 0.21 F: 0.23\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.56 R: 0.28 F: 0.31\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.19 F: 0.22\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.26 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.53 R: 0.24 F: 0.26\n",
      "radius: 30 energy: 75\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.66 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.45 R: 0.22 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.41 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.19 F: 0.22\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.24 F: 0.27\n",
      "radius: 30 energy: 80\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.64 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.45 R: 0.22 F: 0.24\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.57 R: 0.24 F: 0.27\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.41 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.55 R: 0.19 F: 0.22\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.24 F: 0.27\n",
      "radius: 30 energy: 85\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.64 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.50 R: 0.36 F: 0.38\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.55 R: 0.30 F: 0.33\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.41 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.23 F: 0.27\n",
      "\treco_to_sim:\tP: 0.57 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.49 R: 0.22 F: 0.24\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.52 R: 0.26 F: 0.29\n",
      "radius: 30 energy: 90\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.64 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.50 R: 0.36 F: 0.38\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.55 R: 0.30 F: 0.33\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.41 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.52 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.49 R: 0.22 F: 0.24\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.51 R: 0.26 F: 0.29\n",
      "radius: 30 energy: 95\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.64 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.30 F: 0.34\n",
      "\treco_to_sim:\tP: 0.50 R: 0.36 F: 0.38\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.25 F: 0.29\n",
      "\treco_to_sim:\tP: 0.55 R: 0.30 F: 0.33\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.41 R: 0.23 F: 0.25\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.52 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.49 R: 0.22 F: 0.24\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.51 R: 0.26 F: 0.29\n",
      "radius: 30 energy: 100\n",
      "Event 0:\n",
      "\tclue3d_to_sim:\tP: 0.85 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.11 F: 0.14\n",
      "\treco_to_sim:\tP: 0.64 R: 0.17 F: 0.20\n",
      "Event 1:\n",
      "\tclue3d_to_sim:\tP: 0.78 R: 0.07 F: 0.08\n",
      "\ttarget_to_sim:\tP: 0.82 R: 0.27 F: 0.31\n",
      "\treco_to_sim:\tP: 0.48 R: 0.37 F: 0.39\n",
      "Event 2:\n",
      "\tclue3d_to_sim:\tP: 0.93 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.91 R: 0.28 F: 0.32\n",
      "\treco_to_sim:\tP: 0.52 R: 0.33 F: 0.35\n",
      "Event 3:\n",
      "\tclue3d_to_sim:\tP: 0.82 R: 0.10 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.84 R: 0.36 F: 0.40\n",
      "\treco_to_sim:\tP: 0.45 R: 0.30 F: 0.32\n",
      "Event 4:\n",
      "\tclue3d_to_sim:\tP: 0.77 R: 0.06 F: 0.07\n",
      "\ttarget_to_sim:\tP: 0.77 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.47 R: 0.36 F: 0.37\n",
      "Event 5:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.09 F: 0.12\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.22 F: 0.26\n",
      "\treco_to_sim:\tP: 0.52 R: 0.17 F: 0.19\n",
      "Event 6:\n",
      "\tclue3d_to_sim:\tP: 0.84 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.36 F: 0.41\n",
      "\treco_to_sim:\tP: 0.51 R: 0.29 F: 0.32\n",
      "Event 7:\n",
      "\tclue3d_to_sim:\tP: 0.88 R: 0.13 F: 0.16\n",
      "\ttarget_to_sim:\tP: 0.88 R: 0.26 F: 0.30\n",
      "\treco_to_sim:\tP: 0.49 R: 0.22 F: 0.24\n",
      "Event 8:\n",
      "\tclue3d_to_sim:\tP: 0.81 R: 0.09 F: 0.10\n",
      "\ttarget_to_sim:\tP: 0.86 R: 0.27 F: 0.32\n",
      "\treco_to_sim:\tP: 0.47 R: 0.25 F: 0.27\n",
      "Event 9:\n",
      "\tclue3d_to_sim:\tP: 0.87 R: 0.14 F: 0.17\n",
      "\ttarget_to_sim:\tP: 0.89 R: 0.33 F: 0.37\n",
      "\treco_to_sim:\tP: 0.56 R: 0.30 F: 0.33\n",
      "-----\n",
      "mean clue3d_to_sim:\tP: 0.84 R: 0.10 F: 0.12\n",
      "mean target_to_sim:\tP: 0.86 R: 0.27 F: 0.31\n",
      "mean reco_to_sim:\tP: 0.51 R: 0.27 F: 0.30\n"
     ]
    }
   ],
   "source": [
    "\n",
    "results = {}\n",
    "energy_list = list(range(10, 105, 5))\n",
    "max_events = 10\n",
    "\n",
    "for radius in [10, 20, 30]:\n",
    "    clue3D_R = []\n",
    "    target_R = []\n",
    "    for eng in energy_list:\n",
    "        print(\"radius:\", radius, \"energy:\", eng)\n",
    "        result = pairwise_model_evaluation(\n",
    "            cluster_data,\n",
    "            trackster_data,\n",
    "            simtrackster_data,\n",
    "            assoc_data,\n",
    "            DummyPleaser(),\n",
    "            decision_th=0.5,\n",
    "            radius=radius,\n",
    "            max_events=max_events,\n",
    "            reco_to_target=False,\n",
    "            bigT_e_th=eng,\n",
    "        )\n",
    "        clue3D = np.array(result[\"clue3d_to_sim\"])\n",
    "        clue3D_R.append(np.sum(clue3D[:,1]) / max_events)\n",
    "\n",
    "        target = np.array(result[\"target_to_sim\"])\n",
    "        target_R.append(np.mean(target[:,1]) / max_events)\n",
    "    results[radius] = (np.array(target_R), np.array(clue3D_R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97382d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 453,
       "width": 719
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for k, v in results.items():\n",
    "    ax.plot(energy_list, v[0] - v[1], label=f\"{k} cm radius\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Energy threshold (GeV)\")\n",
    "ax.set_ylabel(\"Target recall gain\")\n",
    "\n",
    "fig.savefig('export/multiparticle-energy-plot.png', dpi=300, bbox_inches='tight', transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "206b9677",
   "metadata": {},
   "source": [
    "# Problems\n",
    "\n",
    "Sometimes - if a simtrackster is too fragmented, it might not get selected as a big trackster\n",
    "- we score 0.2 and at least 50% shared energy (or 50GeV)\n",
    "- if trackster is too fragmented, it might not pass -> then its layerclusters might get stolen\n",
    "- otherwise: multiple options might pass and then we need to resolve conflicts"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ve",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "37c238d9819f69c2c770157eac01081978c120e64661e10d7fd52c4caf977dc9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}