TimingPlot_reco.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89b7cea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import awkward as ak\n",
    "import numpy as np\n",
    "import uproot as uproot\n",
    "import matplotlib.pyplot as plt\n",
    "import mplhep as hep\n",
    "import vector as vec\n",
    "\n",
    "from utils import *\n",
    "from utilsPCA import *\n",
    "from plotting import *\n",
    "from efficiency import *\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rc('xtick', labelsize=16) \n",
    "mpl.rc('ytick', labelsize=16) \n",
    "mpl.rc('axes', labelsize=16, titlesize=20)\n",
    "\n",
    "#plt.style.use(hep.style.CMS)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46f352b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = 29.9792458 #cm/ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a6f0f272",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['new_ntuples_15719812_0.root', 'new_ntuples_15719812_10.root', 'new_ntuples_15719812_11.root', 'new_ntuples_15719812_12.root', 'new_ntuples_15719812_13.root', 'new_ntuples_15719812_14.root', 'new_ntuples_15719812_17.root', 'new_ntuples_15719812_18.root', 'new_ntuples_15719812_2.root', 'new_ntuples_15719812_22.root', 'new_ntuples_15719812_23.root', 'new_ntuples_15719812_26.root', 'new_ntuples_15719812_27.root', 'new_ntuples_15719812_28.root', 'new_ntuples_15719812_29.root', 'new_ntuples_15719812_3.root', 'new_ntuples_15719812_30.root', 'new_ntuples_15719812_32.root', 'new_ntuples_15719812_34.root', 'new_ntuples_15719812_35.root', 'new_ntuples_15719812_36.root', 'new_ntuples_15719812_37.root', 'new_ntuples_15719812_38.root', 'new_ntuples_15719812_39.root', 'new_ntuples_15719812_4.root', 'new_ntuples_15719812_40.root', 'new_ntuples_15719812_41.root', 'new_ntuples_15719812_42.root', 'new_ntuples_15719812_43.root', 'new_ntuples_15719812_45.root', 'new_ntuples_15719812_48.root', 'new_ntuples_15719812_49.root', 'new_ntuples_15719812_5.root', 'new_ntuples_15719812_6.root', 'new_ntuples_15719812_7.root', 'new_ntuples_15719812_8.root', 'new_ntuples_15719812_9.root']\n"
     ]
    }
   ],
   "source": [
    "path = '/eos/user/w/wredjeb/HGCAL/TICLv4Linking/Timing/NtuplesTiming/'\n",
    "import os\n",
    "files = os.listdir(path)\n",
    "print([f for f in files if ('sys' not in f and '15719812' in f)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b25794c",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_simtrackstersSC = [path+f+\":ticlNtuplizer/simtrackstersSC\" for f in files if ('sys' not in f and '15719812' in f)]\n",
    "file_simtrackstersCP = [path+f+\":ticlNtuplizer/simtrackstersCP\" for f in files if ('sys' not in f and '15719812' in f)]\n",
    "file_ETLclusters     = [path+f+\":ticlNtuplizer/ETL\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "file_tracksters      = [path+f+\":ticlNtuplizer/trackstersMerged\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "file_clusters        = [path+f+\":ticlNtuplizer/clusters\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "file_associations    = [path+f+\":ticlNtuplizer/associations\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "file_candidates      = [path+f+\":ticlNtuplizer/candidates\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "file_tracks          = [path+f+\":ticlNtuplizer/tracks\" for f in files if ('sys' not in f and '15719812' in f)] \n",
    "\n",
    "TICLtracks        = uproot.concatenate(file_candidates, [\"track_in_candidate\"])[\"track_in_candidate\"]\n",
    "TICLtracksters    = uproot.concatenate(file_candidates, [\"tracksters_in_candidate\"])[\"tracksters_in_candidate\"]\n",
    "events            = uproot.concatenate(file_tracksters, [\"event\"])[\"event\"] - 1\n",
    "simToReco_en      = uproot.concatenate(file_associations, [\"Mergetstracksters_simToReco_SC_sharedE\"])[\"Mergetstracksters_simToReco_SC_sharedE\"]\n",
    "recoToSim_en      = uproot.concatenate(file_associations, [\"Mergetstracksters_recoToSim_SC_sharedE\"])[\"Mergetstracksters_recoToSim_SC_sharedE\"]\n",
    "recoToSim_index   = uproot.concatenate(file_associations, [\"Mergetstracksters_recoToSim_SC\"])[\"Mergetstracksters_recoToSim_SC\"]\n",
    "\n",
    "ETL_time    = uproot.concatenate(file_ETLclusters, [\"mtd_clusters_time\"])[\"mtd_clusters_time\"]\n",
    "ETL_timeErr = uproot.concatenate(file_ETLclusters, [\"mtd_clusters_timeErr\"])[\"mtd_clusters_timeErr\"]\n",
    "ETL_vx      = uproot.concatenate(file_ETLclusters, [\"mtd_clusters_x\"])[\"mtd_clusters_x\"]\n",
    "ETL_vy      = uproot.concatenate(file_ETLclusters, [\"mtd_clusters_y\"])[\"mtd_clusters_y\"]\n",
    "ETL_vz      = uproot.concatenate(file_ETLclusters, [\"mtd_clusters_z\"])[\"mtd_clusters_z\"]\n",
    "ETL_t0      = uproot.concatenate(file_ETLclusters, [\"track_t0\"])[\"track_t0\"]\n",
    "ETL_tmtd    = uproot.concatenate(file_ETLclusters, [\"track_tmtd\"])[\"track_tmtd\"]\n",
    "ETL_beta    = uproot.concatenate(file_ETLclusters, [\"track_mtdbeta\"])[\"track_mtdbeta\"]\n",
    "\n",
    "track_hgcal_idx  = uproot.concatenate(file_tracks, [\"track_id\"])[\"track_id\"]\n",
    "track_hgcal_x    = uproot.concatenate(file_tracks, [\"track_hgcal_x\"])[\"track_hgcal_x\"]\n",
    "track_hgcal_y    = uproot.concatenate(file_tracks, [\"track_hgcal_y\"])[\"track_hgcal_y\"]\n",
    "track_hgcal_z    = uproot.concatenate(file_tracks, [\"track_hgcal_z\"])[\"track_hgcal_z\"]\n",
    "track_pt         = uproot.concatenate(file_tracks, [\"track_pt\"])[\"track_pt\"]\n",
    "track_hgcal_px   = uproot.concatenate(file_tracks, [\"track_hgcal_px\"])[\"track_hgcal_px\"]\n",
    "track_hgcal_py   = uproot.concatenate(file_tracks, [\"track_hgcal_py\"])[\"track_hgcal_py\"]\n",
    "track_hgcal_pz   = uproot.concatenate(file_tracks, [\"track_hgcal_pz\"])[\"track_hgcal_pz\"]\n",
    "track_hgcal_eta  = uproot.concatenate(file_tracks, [\"track_hgcal_eta\"])[\"track_hgcal_eta\"]\n",
    "\n",
    "SC_trIdx    = uproot.concatenate(file_simtrackstersSC, [\"trackIdx\"])[\"trackIdx\"]     \n",
    "\n",
    "reco_vx     = uproot.concatenate(file_tracksters, [\"vertices_x\"])[\"vertices_x\"]\n",
    "reco_vy     = uproot.concatenate(file_tracksters, [\"vertices_y\"])[\"vertices_y\"]\n",
    "reco_vz     = uproot.concatenate(file_tracksters, [\"vertices_z\"])[\"vertices_z\"]\n",
    "reco_ve     = uproot.concatenate(file_tracksters, [\"vertices_energy\"])[\"vertices_energy\"]\n",
    "reco_vi     = uproot.concatenate(file_tracksters, [\"vertices_indexes\"])[\"vertices_indexes\"]\n",
    "reco_vt     = uproot.concatenate(file_tracksters, [\"vertices_time\"])[\"vertices_time\"]\n",
    "reco_vtErr  = uproot.concatenate(file_tracksters, [\"vertices_timeErr\"])[\"vertices_timeErr\"]\n",
    "reco_vm     = uproot.concatenate(file_tracksters, [\"vertices_multiplicity\"])[\"vertices_multiplicity\"]\n",
    "reco_bx     = uproot.concatenate(file_tracksters, [\"barycenter_x\"])[\"barycenter_x\"]\n",
    "reco_by     = uproot.concatenate(file_tracksters, [\"barycenter_y\"])[\"barycenter_y\"]\n",
    "reco_bz     = uproot.concatenate(file_tracksters, [\"barycenter_z\"])[\"barycenter_z\"]\n",
    "reco_be     = uproot.concatenate(file_tracksters, [\"raw_energy\"])[\"raw_energy\"]\n",
    "reco_trIdx  = uproot.concatenate(file_tracksters, [\"raw_energy\"])[\"raw_energy\"]\n",
    "    \n",
    "reco_CALO_time     = uproot.concatenate(file_tracksters, [\"time\"])[\"time\"]\n",
    "reco_CALO_err      = uproot.concatenate(file_tracksters, [\"timeError\"])[\"timeError\"]\n",
    "\n",
    "NTracksters = [len(x) for x in reco_be]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d79c2cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/_connect/_numpy.py:195: RuntimeWarning: invalid value encountered in true_divide\n",
      "  result = getattr(ufunc, method)(\n",
      "/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/_connect/_numpy.py:195: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  result = getattr(ufunc, method)(\n"
     ]
    }
   ],
   "source": [
    "vx     = reco_vx    \n",
    "vy     = reco_vy    \n",
    "vz     = reco_vz    \n",
    "ve     = reco_ve    \n",
    "vi     = reco_vi    \n",
    "vt     = reco_vt\n",
    "vtErr  = reco_vtErr \n",
    "vm     = reco_vm    \n",
    "bx     = reco_bx    \n",
    "by     = reco_by    \n",
    "bz     = reco_bz    \n",
    "be     = reco_be    \n",
    "\n",
    "new_bt, new_btErr = barTime(bx, by, bz, vx, vy, vz, ve, vt)\n",
    "\n",
    "CALO_time     = new_bt     \n",
    "CALO_err      = new_btErr "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97142db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3700 events in the file\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(recoToSim_index)} events in the file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c34ebcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_link_with_etl(ev, nodes, etl_z, TOLL=0.03):\n",
    "    if (len(nodes)==1):\n",
    "        #print('Small')\n",
    "        return -1\n",
    "    # projection\n",
    "    T = (etl_z - nodes[0][2])/(nodes[-1][2] - nodes[0][2])\n",
    "    projection = nodes[0][:3] + T*(nodes[-1][:3] - nodes[0][:3])\n",
    "\n",
    "    #track_idx = trackster_to_track(eid, i, TICLtracksters, TICLtracks)\n",
    "\n",
    "    v2pca = C #* ETL_beta[ev][track_idx] if track_idx > 0 else C\n",
    "    dproj2pca = np.linalg.norm(nodes[0][:3] - projection) # from the projection to the beginning of the PCA\n",
    "    tproj2pca = dproj2pca / v2pca\n",
    "    time2proj = nodes[0][3] - tproj2pca\n",
    "\n",
    "    # space compatibility !!\n",
    "    #print(\"ETL  \", ev, etl_z, time2proj, ETL_tmtd[ev][0], abs(time2proj - ETL_tmtd[ev][0])<TOLL)\n",
    "    deltaT = 999\n",
    "    trackIdx = -1\n",
    "    for ii in range(len(ETL_tmtd[ev])): #loop su tracce\n",
    "        T_hgcal = (track_hgcal_z[ev][ii] - nodes[0][2])/(nodes[-1][2] - nodes[0][2])\n",
    "        projection_hgcal = nodes[0][:3] + T_hgcal*(nodes[-1][:3] - nodes[0][:3])\n",
    "        #print(\"ETL  \", track_hgcal_x[ev][ii], projection_hgcal[0], track_hgcal_y[ev][ii], projection_hgcal[1], track_hgcal_z[ev][ii], projection_hgcal[2])\n",
    "        if ETL_tmtd[ev][ii] > 0 and abs(time2proj - ETL_tmtd[ev][ii]) < deltaT and track_hgcal_z[ev][ii]*nodes[0][2]>0 \\\n",
    "        and (abs(projection_hgcal[0] - track_hgcal_x[ev][ii]) < 1) and (abs(projection_hgcal[1] - track_hgcal_y[ev][ii]) < 1) :\n",
    "            #print(\"ETL  \", ETL_tmtd[ev][ii] , abs(time2proj - ETL_tmtd[ev][ii]) , deltaT )\n",
    "            # track has ETL time     # compatibility                                # track and trackster in the same endcap TODO: find a better way\n",
    "            deltaT = abs(time2proj - ETL_tmtd[ev][ii]) \n",
    "            trackIdx = ii \n",
    "    if deltaT<TOLL: \n",
    "        #print('new link with ETL')\n",
    "        return trackIdx\n",
    "    return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "acb0cf47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7995\n",
      "7557\n"
     ]
    }
   ],
   "source": [
    "# number of tracks in all the events\n",
    "print(ak.count(ak.flatten(track_hgcal_idx)))\n",
    "\n",
    "# number of tracks linked to a simTrackster in the event\n",
    "print(ak.count(SC_trIdx[SC_trIdx>-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b558df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n",
      "7 [0, 23, 38] [0, 1, 2, 3]\n",
      "18 [0] [0, 1]\n",
      "28 [0, 9, 10, 11] [0, 1]\n",
      "46 [0, 11, 32] [0, 1, 2, 3]\n",
      "47 [0] [0, 1]\n",
      "50 [0, 21] [0, 1, 2, 3]\n",
      "51 [0, 20, 49] [0, 1, 2, 3, 4]\n",
      "83 [0] [0, 1]\n",
      "92 [0, 26, 28] [0, 1]\n",
      "97 [25] [0, 1]\n"
     ]
    }
   ],
   "source": [
    "# difference between the two numbers calculated above: when not all the tracks in the event are associated to a simtrackster\n",
    "for ev in events: \n",
    "    lista = []\n",
    "    for j, trk in enumerate(SC_trIdx[ev]):\n",
    "        if trk >=0:\n",
    "            lista.append(j)\n",
    "    if len(lista) != len(track_hgcal_idx[ev]):\n",
    "        print(ev, lista, track_hgcal_idx[ev])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "76d499c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1924"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of tracks associated to a recoTrackster\n",
    "link = 0\n",
    "for ev in events: \n",
    "    for j, trk in enumerate(TICLtracks[ev]):\n",
    "        if trk < 4000000:\n",
    "            link += 1 if len(TICLtracksters[ev][j])>0 else 0    # number of tracks linked\n",
    "            #link += len(TICLtracksters[ev][j])                   # number of tracksters linked\n",
    "#             if len(TICLtracksters[ev][j])>0:\n",
    "#                  print(ev, trk, TICLtracksters[ev][j])\n",
    "link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b1634924",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of tracks associated to a timing\n",
    "numeratorPt   = []\n",
    "denominatorPt = []\n",
    "for ev in events: \n",
    "    for j, trk in enumerate(track_hgcal_idx[ev]):\n",
    "        denominatorPt.append(track_pt[ev][j])\n",
    "        if ETL_tmtd[ev][j] > 0:\n",
    "            numeratorPt.append(track_pt[ev][j])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df5d692",
   "metadata": {},
   "source": [
    "### new, uses only MergeTracksters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97e207be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_628/3220559836.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;31m#tmp[int(sim_to_track)]=1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0meff\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m             \u001b[0mbase_numeratorPt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_pt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mev\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_hgcal_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mev\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mreco_to_track\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0mwrong\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mwhere\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/highlevel.py\u001b[0m in \u001b[0;36m__array_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1432\u001b[0m         \u001b[0mSee\u001b[0m \u001b[0malso\u001b[0m \u001b[0;31m#__array_ufunc__.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1433\u001b[0m         \"\"\"\n\u001b[0;32m-> 1434\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1436\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/_connect/_numpy.py\u001b[0m in \u001b[0;36marray_function\u001b[0;34m(func, types, args, kwargs)\u001b[0m\n\u001b[1;32m     41\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/operations/structure.py\u001b[0m in \u001b[0;36mwhere\u001b[0;34m(condition, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1799\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1800\u001b[0m             akcondition = ak.layout.NumpyArray(\n\u001b[0;32m-> 1801\u001b[0;31m                 \u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0makcondition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1802\u001b[0m             )\n\u001b[1;32m   1803\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnplike\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0makcondition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/operations/convert.py\u001b[0m in \u001b[0;36mto_numpy\u001b[0;34m(array, allow_missing)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m     elif (\n\u001b[0;32m--> 225\u001b[0;31m         \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"datetime64\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m         \u001b[0;32mor\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moperations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"timedelta64\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     ):\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/operations/describe.py\u001b[0m in \u001b[0;36mtype\u001b[0;34m(array)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mContent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPartitionedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mak\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypestrs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/awkward/_util.py\u001b[0m in \u001b[0;36mtypestrs\u001b[0;34m(behavior)\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__typestr__\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypestr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m         ):\n\u001b[1;32m    338\u001b[0m             \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtypestr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# how many of the tracks above are correctly linked\n",
    "eff = 0\n",
    "wrong = 0\n",
    "fake = 0\n",
    "base_numeratorPt   = []\n",
    "base_denominatorPt = []\n",
    "for ev in events: \n",
    "    for r, tks in enumerate(TICLtracksters[ev]):\n",
    "        if len(tks) == 0:\n",
    "            continue # empty trackster -> only track in candidate\n",
    "        ass = recoToSim_en[ev][r]\n",
    "        s_max = np.argmax(ass)\n",
    "        s = recoToSim_index[ev][r][s_max]\n",
    "        sim_to_track  = SC_trIdx[ev][s]\n",
    "        \n",
    "        reco_to_track = TICLtracks[ev][r]\n",
    "        if reco_to_track > 4000000:    \n",
    "            continue\n",
    "        base_denominatorPt.append(track_pt[ev][np.where(track_hgcal_idx[ev]==reco_to_track)[0][0]])\n",
    "        # now reco_to_track >= 0\n",
    "        if sim_to_track < 0:\n",
    "            #print(f'recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "            fake += 1\n",
    "            continue\n",
    "        elif (sim_to_track == reco_to_track):\n",
    "            #print(f'correct match between SIM {sim_to_track} and RECO {reco_to_track}')\n",
    "            #tmp[int(sim_to_track)]=1\n",
    "            eff+=1\n",
    "            base_numeratorPt.append(track_pt[ev][np.where(track_hgcal_idx[ev]==reco_to_track)[0][0]])\n",
    "        else:\n",
    "            wrong+=1\n",
    "    \n",
    "    #eff+=np.sum(tmp)\n",
    "print(eff, wrong, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "076c9af6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# new link only: use this to create an array track-trackster, then calculate eff on the array\n",
    "eff = 0\n",
    "wrong = 0\n",
    "fake = 0\n",
    "new_numeratorPt   = []\n",
    "new_denominatorPt = []\n",
    "for ev in events: \n",
    "    tmp=np.zeros(len(track_hgcal_z[ev]))\n",
    "    linked_tracks = []\n",
    "    for r, tks in enumerate(TICLtracksters[ev]):\n",
    "#         print(f' -- tk {r} --')\n",
    "        if len(tks) == 0:\n",
    "            continue # empty trackster -> only track in candidate, could be break (?)\n",
    "        ass = recoToSim_en[ev][r]\n",
    "        s_max = np.argmax(ass)\n",
    "        s = recoToSim_index[ev][r][s_max]\n",
    "        sim_to_track  = SC_trIdx[ev][s]\n",
    "\n",
    "#         reco_to_track = TICLtracks[ev][r]\n",
    "#         if reco_to_track > 4000000:    \n",
    "#             reco_to_track = -1\n",
    "\n",
    "        nodes = nodes_pca(vx, vy, vz, ve, vt, vi, vm,\n",
    "                          #vx[vt>-99], vy[vt>-99], vz[vt>-99], ve[vt>-99], vt[vt>-99], vi[vt>-99], vm,\n",
    "                      bx, by, bz, be, CALO_time, ev=ev,\n",
    "                      tr_id=r, verbosity=False)\n",
    "        nodes = np.array(sorted(nodes,key=lambda x: abs(x[2]))) \n",
    "        #print(r, nodes)\n",
    "        reco_to_track = find_link_with_etl(ev, nodes, np.sign(vz[ev][r][0])*ak.min(abs(ETL_vz[ev])), TOLL=0.03)\n",
    "        if reco_to_track < 0:    \n",
    "            continue\n",
    "        # now reco_to_track >= 0\n",
    "        new_denominatorPt.append(track_pt[ev][np.where(track_hgcal_idx[ev]==reco_to_track)[0][0]])\n",
    "        if sim_to_track < 0:\n",
    "            #print(f'recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "            fake += 1\n",
    "            continue\n",
    "        elif (sim_to_track == reco_to_track):\n",
    "            #print(f'correct match between SIM {sim_to_track} and RECO {reco_to_track}')\n",
    "            #tmp[int(sim_to_track)]=1\n",
    "            eff+=1\n",
    "            if reco_to_track not in linked_tracks:\n",
    "                new_numeratorPt.append(track_pt[ev][np.where(track_hgcal_idx[ev]==reco_to_track)[0][0]])\n",
    "                linked_tracks.append(reco_to_track)\n",
    "            else:\n",
    "                print(\"track already linked\") #TODO link the best one, not the first one\n",
    "        else:\n",
    "            wrong+=1\n",
    "\n",
    "#     print(tmp, ETL_tmtd[ev])\n",
    "    eff+=np.sum(tmp)\n",
    "print(eff, wrong, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a294b9ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# both\n",
    "eff = 0\n",
    "wrong = 0\n",
    "fake = 0\n",
    "for ev in events: \n",
    "    tmp=np.zeros(len(track_hgcal_z[ev]))\n",
    "    for r, tks in enumerate(TICLtracksters[ev]):\n",
    "#         print(f' -- tk {r} --')\n",
    "        if len(tks) == 0:\n",
    "            continue # empty trackster -> only track in candidate, could be break (?)\n",
    "        ass = recoToSim_en[ev][r]\n",
    "        s_max = np.argmax(ass)\n",
    "        s = recoToSim_index[ev][r][s_max]\n",
    "        sim_to_track  = SC_trIdx[ev][s]\n",
    "        \n",
    "        reco_to_track = TICLtracks[ev][r]\n",
    "        if reco_to_track > 4000000:    \n",
    "            reco_to_track = -1\n",
    "            \n",
    "        nodes = nodes_pca(vx, vy, vz, ve, vt, vi, vm,\n",
    "                          #vx[vt>-99], vy[vt>-99], vz[vt>-99], ve[vt>-99], vt[vt>-99], vi[vt>-99], vm,\n",
    "                      bx, by, bz, be, CALO_time, ev=ev,\n",
    "                      tr_id=r, verbosity=False)\n",
    "        nodes = np.array(sorted(nodes,key=lambda x: abs(x[2]))) \n",
    "        #print(r, nodes)\n",
    "        new_track = find_link_with_etl(ev, nodes, np.sign(vz[ev][r][0])*ak.min(abs(ETL_vz[ev])), TOLL=0.055)\n",
    "#         print(new_track, reco_to_track, r, sim_to_track)\n",
    "        if reco_to_track < 0 and new_track < 0:    \n",
    "            continue\n",
    "        elif sim_to_track < 0 and (reco_to_track >= 0 or new_track >= 0):\n",
    "            fake += 1\n",
    "#            print(f'ETL track is {new_track}, recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "            continue\n",
    "        # only sim_to_track >= 0 \n",
    "        elif (sim_to_track == reco_to_track):\n",
    "#             print(f'correct match between SIM {sim_to_track} and RECO {reco_to_track}')\n",
    "#             TP+=1\n",
    "            tmp[int(sim_to_track)]=1\n",
    "        elif (sim_to_track == new_track):\n",
    "#             print(f'found link with ETL for RECO tk {j} in ev {ev} with track {new_track}')\n",
    "#             TP+=1\n",
    "            tmp[int(sim_to_track)]=1\n",
    "        else:# sim_to_track >= 0 and (reco_to_track > 0 or new_track > 0):\n",
    "            wrong+=1\n",
    "#             print(f'ETL track is {new_track}, recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "\n",
    "#     print(tmp, ETL_tmtd[ev])\n",
    "    eff+=np.sum(tmp)\n",
    "print(eff, wrong, fake)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "954ad125",
   "metadata": {},
   "source": [
    "### old, uses also clue3d tracksters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1b0073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many of the tracks above are correctly linked\n",
    "eff = 0\n",
    "wrong = 0\n",
    "fake = 0\n",
    "for ev in events: \n",
    "#     print(f'--- EV {ev} ---')\n",
    "    #tmp=np.zeros(len(track_hgcal_z[ev]))\n",
    "    # associators loop\n",
    "    #for r, ass in enumerate(recoToSim_en[ev]):\n",
    "    for j in range(ak.max(TICLtracksters[ev])+1):\n",
    "#         print(f' -- tk {j} --')\n",
    "        # r = reco, s = sim\n",
    "        r = find_trackster_in_candidate(j, TICLtracksters[ev])\n",
    "        ass = recoToSim_en[ev][r]\n",
    "        s_max = np.argmax(ass)\n",
    "        s = recoToSim_index[ev][r][s_max]\n",
    "        sim_to_track  = SC_trIdx[ev][s]\n",
    "        reco_to_track = trackster_to_track(ev, j, TICLtracksters, TICLtracks)\n",
    "        if sim_to_track < 0 and reco_to_track < 0:    \n",
    "            continue\n",
    "        elif sim_to_track < 0 and reco_to_track >= 0:\n",
    "            #print(f'ETL track is {new_track}, recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "            fake += 1\n",
    "            continue\n",
    "        elif (sim_to_track == reco_to_track):\n",
    "            #print(f'correct match between SIM {sim_to_track} and RECO {reco_to_track}')\n",
    "            #tmp[int(sim_to_track)]=1\n",
    "            eff+=1\n",
    "        elif sim_to_track >= 0 and reco_to_track >= 0:\n",
    "            wrong+=1\n",
    "    \n",
    "    #eff+=np.sum(tmp)\n",
    "print(eff, wrong, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "361b74b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "eff = 0\n",
    "wrong = 0\n",
    "fake = 0\n",
    "for ev in events: \n",
    "#     print(f'--- EV {ev} ---')\n",
    "    tmp=np.zeros(len(track_hgcal_z[ev]))\n",
    "    # associators loop\n",
    "    #for r, ass in enumerate(recoToSim_en[ev]):\n",
    "    for j in range(ak.max(TICLtracksters[ev])+1):\n",
    "#         print(f' -- tk {j} --')\n",
    "        # r = reco, s = sim\n",
    "        r = find_trackster_in_candidate(j, TICLtracksters[ev])\n",
    "        ass = recoToSim_en[ev][r]\n",
    "        s_max = np.argmax(ass)\n",
    "        s = recoToSim_index[ev][r][s_max]\n",
    "        sim_to_track  = SC_trIdx[ev][s]\n",
    "        reco_to_track = trackster_to_track(ev, j, TICLtracksters, TICLtracks)\n",
    "        nodes = nodes_pca(vx, vy, vz, ve, vt, vi, vm,\n",
    "                          #vx[vt>-99], vy[vt>-99], vz[vt>-99], ve[vt>-99], vt[vt>-99], vi[vt>-99], vm,\n",
    "                      bx, by, bz, be, CALO_time, ev=ev,\n",
    "                      tr_id=r, verbosity=False)\n",
    "        nodes = np.array(sorted(nodes,key=lambda x: abs(x[2]))) \n",
    "        new_track = find_link_with_etl(ev, nodes, np.sign(vz[ev][r][0])*ak.min(abs(ETL_vz[ev])), TOLL=0.08)\n",
    "        if sim_to_track < 0 and reco_to_track < 0 and new_track < 0:    \n",
    "            continue\n",
    "        elif sim_to_track < 0 and (reco_to_track >= 0 or new_track >= 0):\n",
    "            fake += 1\n",
    "            #print(f'ETL track is {new_track}, recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "            continue\n",
    "        elif (sim_to_track == reco_to_track):\n",
    "            #print(f'correct match between SIM {sim_to_track} and RECO {reco_to_track}')\n",
    "#             TP+=1\n",
    "            tmp[int(sim_to_track)]=1\n",
    "        elif (sim_to_track == new_track):\n",
    "            #print(f'found link with ETL for RECO tk {j} in ev {ev} with track {new_track}')\n",
    "#             TP+=1\n",
    "            tmp[int(sim_to_track)]=1\n",
    "        elif sim_to_track >= 0 and reco_to_track < 0 and new_track < 0:\n",
    "            continue\n",
    "        elif sim_to_track >= 0 and (reco_to_track > 0 or new_track > 0):\n",
    "            wrong+=1\n",
    "            #print(f'ETL track is {new_track}, recoTrack is {reco_to_track} BUT simTrack was {sim_to_track}')\n",
    "\n",
    "#     print(tmp, ETL_tmtd[ev])\n",
    "    eff+=np.sum(tmp)\n",
    "print(eff, wrong, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "498f8bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(TP)/(TP+FP) #precision\n",
    "#(TP)/(TP+FN) #recall\n",
    "eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "f183bae9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ev 0 tk 0\n",
      "1\n",
      "track  0  ev  0  tk  0\n",
      "--- ev 0 tk 1\n",
      "1\n",
      "track  0  ev  0  tk  1\n",
      "--- ev 0 tk 2\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#for ev in events:\n",
    "ev=0\n",
    "for j in range(ak.max(TICLtracksters[ev])+1):\n",
    "    r = find_trackster_in_candidate(j, TICLtracksters[ev])\n",
    "    print(f'--- ev {ev} tk {j}')\n",
    "    print(r)\n",
    "    trackster_to_track(ev, j, TICLtracksters, TICLtracks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83859888",
   "metadata": {},
   "source": [
    "## efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9092bc69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Efficiency link track-trackster - new (toll=0.03)')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hN, edgeX = np.histogram(numeratorEnergy, bins = 10, range = (0,max(denominatorEnergy)))\n",
    "# hD, edgeX = np.histogram(denominatorEnergy, bins = 10, range = (0, max(denominatorEnergy)))\n",
    "uflow = False\n",
    "fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(16,8))\n",
    "# fig, (ax0,ax1,ax2,ax3) = plt.subplots(1, 4, figsize=(42,17))\n",
    "\n",
    "# hN, uN, cN, eN = extended_hist(np.array(numeratorEnergy)[mask], nbins = 10, range = (0,max(denominatorEnergy)), underflow = uflow, overflow = uflow)\n",
    "# hD, uD, cD, eD = extended_hist(np.array(denominatorEnergy)[maskD], nbins = 10, range = (0,max(denominatorEnergy)), underflow = uflow, overflow = uflow)\n",
    "# errs = computeErrorRatio(hN,hD,uN,uD)\n",
    "# ax0.errorbar(cD, hN/hD, yerr=errs, capsize = 4, lw = 2)\n",
    "# ax0.set_xlabel(\"Raw Energy [GeV]\")\n",
    "# ax0.set_ylabel(\"Efficiency - Simclusters\")\n",
    "# ax0.set_ylim(0,1.01)\n",
    "# # hep.cms.text('Preliminary')\n",
    "# ax0.set_title(\"Efficiency - SimClusters\")\n",
    "\n",
    "hN, uN, cN, eN = extended_hist(np.array(base_numeratorPt), nbins = 10, range = (0, 100), underflow = uflow, overflow = uflow)\n",
    "hD, uD, cD, eD = extended_hist(np.array(base_denominatorPt), nbins = 10, range = (0, 100), underflow = uflow, overflow = uflow)\n",
    "errs = computeErrorRatio(hN,hD,uN,uD)\n",
    "ax1.errorbar(cD, hN/hD, yerr=errs, capsize = 4, lw = 2)\n",
    "ax1.set_xlabel(\"$pT$ [GeV]\")\n",
    "ax1.set_ylabel(\"Efficiency\")\n",
    "ax1.set_ylim(0,1.01)\n",
    "# hep.cms.text('Preliminary')\n",
    "ax1.set_title(\"Efficiency link track-trackster - baseline\")\n",
    "\n",
    "hN, uN, cN, eN = extended_hist(np.array(new_numeratorPt), nbins = 10, range = (0, 100), underflow = uflow, overflow = uflow)\n",
    "hD, uD, cD, eD = extended_hist(np.array(new_denominatorPt), nbins = 10, range = (0, 100), underflow = uflow, overflow = uflow)\n",
    "errs = computeErrorRatio(hN,hD,uN,uD)\n",
    "ax2.errorbar(cD, hN/hD, yerr=errs, capsize = 4, lw = 2)\n",
    "ax2.set_xlabel(\"$pT$ [GeV]\")\n",
    "ax2.set_ylabel(\"Efficiency\")\n",
    "ax2.set_ylim(0,1.01)\n",
    "# hep.cms.text('Preliminary')\n",
    "ax2.set_title(\"Efficiency link track-trackster - new (toll=0.03)\")\n",
    "\n",
    "\n",
    "# hN, uN, cN, eN = extended_hist(np.array(numeratorEta)[mask], nbins = 10, range = (1.5,3.0), underflow = uflow, overflow = uflow)\n",
    "# hD, uD, cD, eD = extended_hist(np.array(denominatorEta)[maskD], nbins = 10, range = (1.5,3.0), underflow = uflow, overflow = uflow)\n",
    "# errs = computeErrorRatio(hN,hD,uN,uD)\n",
    "# ax3.errorbar(cD, hN/hD, yerr=errs, capsize = 4, lw = 2)\n",
    "# ax3.set_xlabel(\"$\\eta$ [GeV]\")\n",
    "# ax3.set_ylabel(\"Efficiency - Simclusters\")\n",
    "# ax3.set_ylim(0,1.01)\n",
    "# # hep.cms.text('Preliminary')\n",
    "# ax3.set_title(\"Efficiency - SimClusters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dd1a6d",
   "metadata": {},
   "source": [
    "### plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1295cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "plt.hist(ak.flatten(reco_CALO_time[reco_CALO_time>-99]), bins=30, label='old', alpha=.7)\n",
    "plt.hist(ak.flatten(new_bt), bins=30, label='new', color='r', histtype='step')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title('Tracksters time')\n",
    "plt.xlabel('t (ns)', size=18)\n",
    "plt.ylabel('Count', size=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3c2da2",
   "metadata": {},
   "source": [
    "## Plot the times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cbcb7a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, nrows=1, figsize=(14,4))\n",
    "fig.suptitle('SimTracksters from CaloParticle', size=18, y=1.05)\n",
    "\n",
    "subplot(axs[0], CALO_time, title='HGCAL_time')\n",
    "axs[0].set_title('HGCAL_time')\n",
    "subplot(axs[1], ETL_time, title='MTD_time')\n",
    "#subplot(axs[0], CP_boundary_time, title='boundary_time')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set_xlabel('time (ns)', fontsize = 16.0)    \n",
    "    ax.set_ylabel('Counts', fontsize = 16.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a936a944",
   "metadata": {},
   "source": [
    "## Visual representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fb3d1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3d444f12fe148b592e3cc8ca5077a17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "eid = 1\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax1.set_xlabel(\"x (cm)\")\n",
    "ax1.set_ylabel(\"z (cm)\")\n",
    "ax1.set_zlabel(\"y (cm)\")\n",
    "\n",
    "ax1.set_xlim(ak.min(vx[eid]), ak.max(vx[eid]))\n",
    "ax1.set_zlim(ak.min(vy[eid]), ak.max(vy[eid]))\n",
    "ax1.set_ylim(ak.min(vz[eid]), ak.max(vz[eid]))\n",
    "\n",
    "for x, y, z, e in zip(vx[eid], vy[eid], vz[eid], ve[eid]):\n",
    "    ax1.scatter(x, z, y, s=8*e)\n",
    "\n",
    "ax1.set_title(f\"SimTrackster layer-clusters ({len(vx[eid])} Trackster)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4e8659c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "cmap = mpl.cm.get_cmap('gist_rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "febff711",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6454718ba284f66a07c503976a12c48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "eid=0\n",
    "\n",
    "C = 29.9792458 #ns/cm\n",
    "\n",
    "fig = plt.figure(figsize=(12, 12))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax1.set_xlabel(\"x (cm)\")\n",
    "ax1.set_ylabel(\"z (cm)\")\n",
    "ax1.set_zlabel(\"y (cm)\")\n",
    "\n",
    "# ax1.set_xlim(ak.min(vx[eid]), ak.max(vx[eid]))\n",
    "# ax1.set_zlim(ak.min(vy[eid]), ak.max(vy[eid]))\n",
    "# ax1.set_ylim(ak.min(vz[eid]), ak.max(vz[eid]))\n",
    "\n",
    "x = ak.flatten(vx[vt>-99.][eid])\n",
    "y = ak.flatten(vy[vt>-99.][eid])\n",
    "z = ak.flatten(vz[vt>-99.][eid])\n",
    "e = ak.flatten(ve[vt>-99.][eid])\n",
    "t = ak.flatten(vt[vt>-99.][eid])\n",
    "\n",
    "x = vx[vt>-99.][eid][tr_id]\n",
    "y = vy[vt>-99.][eid][tr_id]\n",
    "z = vz[vt>-99.][eid][tr_id]\n",
    "e = ve[vt>-99.][eid][tr_id]\n",
    "t = vt[vt>-99.][eid][tr_id]\n",
    "\n",
    "img = ax1.scatter(x, z, y, s=20*e, alpha=0.5, c=t, cmap=mpl.cm.get_cmap('gist_rainbow'))\n",
    "\n",
    "tempi = np.linspace(min(t), max(t), 4)\n",
    "\n",
    "fig.colorbar(img, location='left', ticks=tempi, format='%0.2f',fraction=0.03, pad=0.04)\n",
    "ax1.set_title(f\"RecoTrackster layer-clusters with time (ns)\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}