Projektpraktikum/notebooks/trackinglosses_energy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "import mplhep\n",
    "\n",
    "mplhep.style.use([\"LHCbTex2\"])\n",
    "plt.rcParams[\"savefig.dpi\"] = 600\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31836 6689\n",
      "38525\n"
     ]
    }
   ],
   "source": [
    "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "file = uproot.open(\n",
    "    \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_upstream.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[\n",
    "    (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 9056\n",
    "lost = allcolumns[\n",
    "    (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
    "]  # B: 1466\n",
    "\n",
    "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
    "\n",
    "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
    "print(ak.num(electrons, axis=0))\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{all_endvtx_types_length: 7,\n",
       " all_endvtx_types: [101, 101, 101, 101, 101, 101, 0],\n",
       " all_endvtx_x_length: 7,\n",
       " all_endvtx_x: [-6.97, -52.9, -52.9, ..., -1.71e+03, -2.14e+03, -3.57e+03],\n",
       " all_endvtx_y_length: 7,\n",
       " all_endvtx_y: [-0.89, -6.75, -6.75, -7.08, -66.1, -72.6, -39.5],\n",
       " all_endvtx_z_length: 7,\n",
       " all_endvtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03, 1.26e+04],\n",
       " brem_photons_pe_length: 6,\n",
       " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
       " brem_photons_px_length: 6,\n",
       " brem_photons_px: [-161, -49.7, -156, -114, -1.18e+03, -101],\n",
       " brem_photons_py_length: 6,\n",
       " brem_photons_py: [-18.9, -6.92, -21.6, -16.8, -20.9, -0.26],\n",
       " brem_photons_pz_length: 6,\n",
       " brem_photons_pz: [2.61e+03, 810, 2.54e+03, 1.86e+03, 2.89e+03, 219],\n",
       " brem_vtx_x_length: 6,\n",
       " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
       " brem_vtx_y_length: 6,\n",
       " ...}\n",
       "---------------------------------------------------------------------------\n",
       "type: {\n",
       "    all_endvtx_types_length: int32,\n",
       "    all_endvtx_types: var * float32,\n",
       "    all_endvtx_x_length: int32,\n",
       "    all_endvtx_x: var * float32,\n",
       "    all_endvtx_y_length: int32,\n",
       "    all_endvtx_y: var * float32,\n",
       "    all_endvtx_z_length: int32,\n",
       "    all_endvtx_z: var * float32,\n",
       "    brem_photons_pe_length: int32,\n",
       "    brem_photons_pe: var * float32,\n",
       "    brem_photons_px_length: int32,\n",
       "    brem_photons_px: var * float32,\n",
       "    brem_photons_py_length: int32,\n",
       "    brem_photons_py: var * float32,\n",
       "    brem_photons_pz_length: int32,\n",
       "    brem_photons_pz: var * float32,\n",
       "    brem_vtx_x_length: int32,\n",
       "    brem_vtx_x: var * float32,\n",
       "    brem_vtx_y_length: int32,\n",
       "    brem_vtx_y: var * float32,\n",
       "    brem_vtx_z_length: int32,\n",
       "    brem_vtx_z: var * float32,\n",
       "    endvtx_type: int32,\n",
       "    endvtx_x: float64,\n",
       "    endvtx_y: float64,\n",
       "    endvtx_z: float64,\n",
       "    energy: float64,\n",
       "    eta: float64,\n",
       "    event_count: int32,\n",
       "    fromB: bool,\n",
       "    fromD: bool,\n",
       "    fromDecay: bool,\n",
       "    fromHadInt: bool,\n",
       "    fromPV: bool,\n",
       "    fromPairProd: bool,\n",
       "    fromSignal: bool,\n",
       "    fromStrange: bool,\n",
       "    ideal_state_5000_qop: float64,\n",
       "    ideal_state_5000_tx: float64,\n",
       "    ideal_state_5000_ty: float64,\n",
       "    ideal_state_5000_x: float64,\n",
       "    ideal_state_5000_y: float64,\n",
       "    ideal_state_5000_z: float64,\n",
       "    ideal_state_770_qop: float64,\n",
       "    ideal_state_770_tx: float64,\n",
       "    ideal_state_770_ty: float64,\n",
       "    ideal_state_770_x: float64,\n",
       "    ideal_state_770_y: float64,\n",
       "    ideal_state_770_z: float64,\n",
       "    ideal_state_9410_qop: float64,\n",
       "    ideal_state_9410_tx: float64,\n",
       "    ideal_state_9410_ty: float64,\n",
       "    ideal_state_9410_x: float64,\n",
       "    ideal_state_9410_y: float64,\n",
       "    ideal_state_9410_z: float64,\n",
       "    isElectron: bool,\n",
       "    isKaon: bool,\n",
       "    isMuon: bool,\n",
       "    isPion: bool,\n",
       "    isProton: bool,\n",
       "    lost: bool,\n",
       "    lost_in_track_fit: bool,\n",
       "    match_chi2: float32,\n",
       "    match_dSlope: float32,\n",
       "    match_dSlopeY: float32,\n",
       "    match_distX: float32,\n",
       "    match_distY: float32,\n",
       "    match_fraction: float32,\n",
       "    match_teta2: float32,\n",
       "    match_yCorr: float32,\n",
       "    match_yCorr_def: float32,\n",
       "    match_zMag: float32,\n",
       "    match_zMag_def: float32,\n",
       "    mcp_idx: int32,\n",
       "    mother_id: int32,\n",
       "    mother_key: int32,\n",
       "    originvtx_type: int32,\n",
       "    originvtx_x: float64,\n",
       "    originvtx_y: float64,\n",
       "    originvtx_z: float64,\n",
       "    p: float64,\n",
       "    p_end_scifi: float64,\n",
       "    p_end_ut: float64,\n",
       "    p_end_velo: float64,\n",
       "    p_upstream: float64,\n",
       "    phi: float64,\n",
       "    pid: int32,\n",
       "    pt: float64,\n",
       "    px: float64,\n",
       "    py: float64,\n",
       "    pz: float64,\n",
       "    rad_length_frac: float64,\n",
       "    scifi_hit_pos_x_length: int32,\n",
       "    scifi_hit_pos_x: var * float32,\n",
       "    scifi_hit_pos_y_length: int32,\n",
       "    scifi_hit_pos_y: var * float32,\n",
       "    scifi_hit_pos_z_length: int32,\n",
       "    scifi_hit_pos_z: var * float32,\n",
       "    track_p: float64,\n",
       "    track_pt: float64,\n",
       "    tx: float64,\n",
       "    ty: float64,\n",
       "    ut_hit_pos_x_length: int32,\n",
       "    ut_hit_pos_x: var * float32,\n",
       "    ut_hit_pos_y_length: int32,\n",
       "    ut_hit_pos_y: var * float32,\n",
       "    ut_hit_pos_z_length: int32,\n",
       "    ut_hit_pos_z: var * float32,\n",
       "    velo_hit_pos_x_length: int32,\n",
       "    velo_hit_pos_x: var * float32,\n",
       "    velo_hit_pos_y_length: int32,\n",
       "    velo_hit_pos_y: var * float32,\n",
       "    velo_hit_pos_z_length: int32,\n",
       "    velo_hit_pos_z: var * float32,\n",
       "    velo_track_idx: int32,\n",
       "    velo_track_tx: float64,\n",
       "    velo_track_ty: float64,\n",
       "    velo_track_x: float64,\n",
       "    velo_track_y: float64,\n",
       "    velo_track_z: float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {all_endvtx_types_length: 7, ...} type='{all_endvtx_types_length: i...'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{lost: True,\n",
       " rad_length_frac: 0.129,\n",
       " energy: 1.17e+04,\n",
       " photon_length: 6,\n",
       " brem_photons_pe: [2.62e+03, 812, 2.54e+03, 1.86e+03, 3.12e+03, 241],\n",
       " brem_vtx_x: [-6.97, -52.9, -52.9, -55.2, -1.71e+03, -2.14e+03],\n",
       " brem_vtx_z: [112, 859, 859, 895, 8.7e+03, 9.68e+03]}\n",
       "---------------------------------------------------------------------\n",
       "type: {\n",
       "    lost: bool,\n",
       "    rad_length_frac: float64,\n",
       "    energy: float64,\n",
       "    photon_length: int64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {lost: True, rad_length_frac: ..., ...} type='{lost: bool, rad_leng...'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lost_e = electrons[\"lost\"]\n",
    "e = electrons[\"energy\"]\n",
    "brem_pe = electrons[\"brem_photons_pe\"]\n",
    "brem_z = electrons[\"brem_vtx_z\"]\n",
    "brem_x = electrons[\"brem_vtx_x\"]\n",
    "length = electrons[\"brem_vtx_z_length\"]\n",
    "rad_length = electrons[\"rad_length_frac\"]\n",
    "\n",
    "brem = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(electrons, axis=0)):\n",
    "    brem.begin_record()\n",
    "    brem.field(\"lost\").boolean(lost_e[itr])\n",
    "    brem.field(\"rad_length_frac\").append(rad_length[itr])\n",
    "    # [:,\"energy\"] energy\n",
    "    brem.field(\"energy\").append(e[itr])\n",
    "    # [:,\"photon_length\"] number of vertices\n",
    "    brem.field(\"photon_length\").integer(length[itr])\n",
    "    # [:,\"brem_photons_pe\",:] photon energy\n",
    "    brem.field(\"brem_photons_pe\").append(brem_pe[itr])\n",
    "    # [:,\"brem_vtx_z\",:] brem vtx z\n",
    "    brem.field(\"brem_vtx_x\").append(brem_x[itr])\n",
    "    brem.field(\"brem_vtx_z\").append(brem_z[itr])\n",
    "    brem.end_record()\n",
    "\n",
    "brem = ak.Array(brem)\n",
    "brem[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_cut = 0\n",
    "photon_cut_ratio = 0.1\n",
    "\n",
    "cut_brem = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem, axis=0)):\n",
    "    cut_brem.begin_record()\n",
    "    cut_brem.field(\"event_id\").integer(itr)\n",
    "    cut_brem.field(\"lost\").boolean(brem[itr, \"lost\"])\n",
    "    cut_brem.field(\"rad_length_frac\").real(brem[itr, \"rad_length_frac\"])\n",
    "    cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n",
    "\n",
    "    ph_length = brem[itr, \"photon_length\"]\n",
    "\n",
    "    tmp_energy = brem[itr, \"energy\"]\n",
    "\n",
    "    cut_brem.field(\"brem_photons_pe\")\n",
    "    cut_brem.begin_list()\n",
    "    for jentry in range(brem[itr, \"photon_length\"]):\n",
    "        if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n",
    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "                or brem[itr, \"brem_photons_pe\",\n",
    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
    "            ph_length -= 1\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem.real(brem[itr, \"brem_photons_pe\", jentry])\n",
    "            tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
    "    cut_brem.end_list()\n",
    "\n",
    "    tmp_energy = brem[itr, \"energy\"]\n",
    "\n",
    "    cut_brem.field(\"brem_vtx_x\")\n",
    "    cut_brem.begin_list()\n",
    "    for jentry in range(brem[itr, \"photon_length\"]):\n",
    "        if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n",
    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "                or brem[itr, \"brem_photons_pe\",\n",
    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem.real(brem[itr, \"brem_vtx_x\", jentry])\n",
    "            tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
    "    cut_brem.end_list()\n",
    "\n",
    "    tmp_energy = brem[itr, \"energy\"]\n",
    "\n",
    "    cut_brem.field(\"brem_vtx_z\")\n",
    "    cut_brem.begin_list()\n",
    "    for jentry in range(brem[itr, \"photon_length\"]):\n",
    "        if (brem[itr, \"brem_vtx_z\", jentry] > 9410\n",
    "                or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "                or brem[itr, \"brem_photons_pe\",\n",
    "                        jentry] < photon_cut_ratio * tmp_energy):\n",
    "            continue\n",
    "        else:\n",
    "            cut_brem.real(brem[itr, \"brem_vtx_z\", jentry])\n",
    "            tmp_energy -= brem[itr, \"brem_photons_pe\", jentry]\n",
    "    cut_brem.end_list()\n",
    "\n",
    "    cut_brem.field(\"photon_length\").integer(ph_length)\n",
    "\n",
    "    cut_brem.end_record()\n",
    "\n",
    "ntuple = ak.Array(cut_brem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48551\n",
      "38525\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>{event_id: 0,\n",
       " lost: True,\n",
       " rad_length_frac: 0.129,\n",
       " energy: 1.17e+04,\n",
       " brem_photons_pe: [2.62e+03, 2.54e+03, 1.86e+03, 3.12e+03],\n",
       " brem_vtx_x: [-6.97, -52.9, -55.2, -1.71e+03],\n",
       " brem_vtx_z: [112, 859, 895, 8.7e+03],\n",
       " photon_length: 4}\n",
       "-----------------------------------------------------------\n",
       "type: {\n",
       "    event_id: int64,\n",
       "    lost: bool,\n",
       "    rad_length_frac: float64,\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64,\n",
       "    photon_length: int64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {event_id: 0, lost: True, ...} type='{event_id: int64, lost: bool, ...'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ak.sum(ak.num(ntuple[\"brem_photons_pe\"], axis=1)))\n",
    "print(ak.num(ntuple, axis=0))\n",
    "ntuple[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nulltuple = ntuple[:7000]\n",
    "# onetuple = ntuple[7000:14000]\n",
    "# twotuple = ntuple[14000:21000]\n",
    "# threetuple = ntuple[21000:28000]\n",
    "# fourtuple = ntuple[28000:35000]\n",
    "# fivetuple = ntuple[35000:42000]\n",
    "# sixtuple = ntuple[42000:49000]\n",
    "# seventuple = ntuple[49000:]\n",
    "\n",
    "# ntuple.nbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cut = \"tenCut\"\n",
    "# tree = \"Tree10\"\n",
    "# with uproot.update(\"trackinglosses_B_photon_cuts.root\") as outFile:\n",
    "#     #outFile[\"README\"] = \"The Cuts are placed on the photons. noCut: 0*E, first: 0.05*E, second: 0.1*E, etc.\"\n",
    "#     outFile.mktree(tree, {cut + \"_event_id\": ntuple[\"event_id\"].type, cut + \"_lost\": ntuple[\"lost\"].type, cut + \"_rad_length_frac\": ntuple[\"rad_length_frac\"].type, cut + \"_energy\": ntuple[\"energy\"].type, cut + \"_brem_photons_pe\": ntuple[\"brem_photons_pe\"].type, cut + \"_brem_vtx_x\": ntuple[\"brem_vtx_x\"].type, cut + \"_brem_vtx_z\": ntuple[\"brem_vtx_z\"].type, cut + \"_photon_length\": ntuple[\"photon_length\"].type} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": nulltuple[\"event_id\"], cut + \"_lost\": nulltuple[\"lost\"], cut + \"_rad_length_frac\": nulltuple[\"rad_length_frac\"], cut + \"_energy\": nulltuple[\"energy\"], cut + \"_brem_photons_pe\": nulltuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": nulltuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": nulltuple[\"brem_vtx_z\"], cut + \"_photon_length\": nulltuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": onetuple[\"event_id\"], cut + \"_lost\": onetuple[\"lost\"], cut + \"_rad_length_frac\": onetuple[\"rad_length_frac\"], cut + \"_energy\": onetuple[\"energy\"], cut + \"_brem_photons_pe\": onetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": onetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": onetuple[\"brem_vtx_z\"], cut + \"_photon_length\": onetuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": twotuple[\"event_id\"], cut + \"_lost\": twotuple[\"lost\"], cut + \"_rad_length_frac\": twotuple[\"rad_length_frac\"], cut + \"_energy\": twotuple[\"energy\"], cut + \"_brem_photons_pe\": twotuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": twotuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": twotuple[\"brem_vtx_z\"], cut + \"_photon_length\": twotuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": threetuple[\"event_id\"], cut + \"_lost\": threetuple[\"lost\"], cut + \"_rad_length_frac\": threetuple[\"rad_length_frac\"], cut + \"_energy\": threetuple[\"energy\"], cut + \"_brem_photons_pe\": threetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": threetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": threetuple[\"brem_vtx_z\"], cut + \"_photon_length\": threetuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": fourtuple[\"event_id\"], cut + \"_lost\": fourtuple[\"lost\"], cut + \"_rad_length_frac\": fourtuple[\"rad_length_frac\"], cut + \"_energy\": fourtuple[\"energy\"], cut + \"_brem_photons_pe\": fourtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fourtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fourtuple[\"brem_vtx_z\"], cut + \"_photon_length\": fourtuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": fivetuple[\"event_id\"], cut + \"_lost\": fivetuple[\"lost\"], cut + \"_rad_length_frac\": fivetuple[\"rad_length_frac\"], cut + \"_energy\": fivetuple[\"energy\"], cut + \"_brem_photons_pe\": fivetuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": fivetuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": fivetuple[\"brem_vtx_z\"], cut + \"_photon_length\": fivetuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": sixtuple[\"event_id\"], cut + \"_lost\": sixtuple[\"lost\"], cut + \"_rad_length_frac\": sixtuple[\"rad_length_frac\"], cut + \"_energy\": sixtuple[\"energy\"], cut + \"_brem_photons_pe\": sixtuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": sixtuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": sixtuple[\"brem_vtx_z\"], cut + \"_photon_length\": sixtuple[\"photon_length\"]} )\n",
    "#     outFile[tree].extend( {cut + \"_event_id\": seventuple[\"event_id\"], cut + \"_lost\": seventuple[\"lost\"], cut + \"_rad_length_frac\": seventuple[\"rad_length_frac\"], cut + \"_energy\": seventuple[\"energy\"], cut + \"_brem_photons_pe\": seventuple[\"brem_photons_pe\"], cut + \"_brem_vtx_x\": seventuple[\"brem_vtx_x\"], cut + \"_brem_vtx_z\": seventuple[\"brem_vtx_z\"], cut + \"_photon_length\": seventuple[\"photon_length\"]} )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31836\n",
      "6689\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>{event_id: 1,\n",
       " lost: False,\n",
       " rad_length_frac: 0.148,\n",
       " energy: 1.28e+04,\n",
       " brem_photons_pe: [7.42e+03, 1.88e+03],\n",
       " brem_vtx_x: [-3.61, -61.5],\n",
       " brem_vtx_z: [35.6, 8.49e+03],\n",
       " photon_length: 2}\n",
       "---------------------------------------\n",
       "type: {\n",
       "    event_id: int64,\n",
       "    lost: bool,\n",
       "    rad_length_frac: float64,\n",
       "    energy: float64,\n",
       "    brem_photons_pe: var * float64,\n",
       "    brem_vtx_x: var * float64,\n",
       "    brem_vtx_z: var * float64,\n",
       "    photon_length: int64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {event_id: 1, lost: False, ...} type='{event_id: int64, lost: bool,...'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data in cut_brem_found and cut_brem_lost\n",
    "\n",
    "length_found = ak.num(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
    "length_lost = ak.num(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=0)\n",
    "print(length_found)\n",
    "print(length_lost)\n",
    "ntuple[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_found = ak.to_numpy(\n",
    "    ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
    "           keepdims=False)) / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
    "Z_lost = ak.to_numpy(\n",
    "    ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1,\n",
    "           keepdims=False)) / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlim = 0\n",
    "\n",
    "plt.hist(\n",
    "    Z_lost,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    "    range=[xlim, 1],\n",
    ")\n",
    "plt.hist(\n",
    "    Z_found,\n",
    "    bins=100,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    "    range=[xlim, 1],\n",
    ")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.title(r\"$E_{ph}/E_0$\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  7000\n",
      "lost:  6689\n",
      "found:  7910 , lost:  12529\n",
      "1.583944374209861\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[-3.61,\n",
       " -61.5,\n",
       " 65.2,\n",
       " -5.73,\n",
       " -26.6,\n",
       " -4.26,\n",
       " -396,\n",
       " 10.7,\n",
       " 26.2,\n",
       " 19.6,\n",
       " ...,\n",
       " -38.9,\n",
       " 17.6,\n",
       " -168,\n",
       " -74.7,\n",
       " -500,\n",
       " -566,\n",
       " 4.2,\n",
       " -20.3,\n",
       " -59.9]\n",
       "--------------------\n",
       "type: 7910 * float64</pre>"
      ],
      "text/plain": [
       "<Array [-3.61, -61.5, 65.2, ..., 4.2, -20.3, -59.9] type='7910 * float64'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_found = ntuple[~ntuple.lost]\n",
    "tuple_found = tuple_found[:7000]\n",
    "tuple_lost = ntuple[ntuple.lost]\n",
    "\n",
    "print(\"found: \", ak.num(tuple_found[\"brem_vtx_x\"], axis=0))\n",
    "print(\"lost: \", ak.num(tuple_lost[\"brem_vtx_x\"], axis=0))\n",
    "\n",
    "brem_x = ak.to_numpy(ak.flatten(ntuple[\"brem_vtx_x\"]))\n",
    "brem_z = ak.to_numpy(ak.flatten(ntuple[\"brem_vtx_z\"]))\n",
    "\n",
    "brem_x_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_x\"]))\n",
    "brem_z_found = ak.to_numpy(ak.flatten(tuple_found[\"brem_vtx_z\"]))\n",
    "\n",
    "brem_x_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_x\"]))\n",
    "brem_z_lost = ak.to_numpy(ak.flatten(tuple_lost[\"brem_vtx_z\"]))\n",
    "\n",
    "n_found = len(brem_x_found)\n",
    "n_lost = len(brem_x_lost)\n",
    "print(\"found: \", n_found, \", lost: \", n_lost)\n",
    "stretch_factor = n_lost / n_found\n",
    "print(stretch_factor)\n",
    "ak.flatten(tuple_found[\"brem_vtx_x\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = 150\n",
    "nbins = 150\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
    "\n",
    "a0 = ax0.hist2d(\n",
    "    brem_z_found,\n",
    "    brem_x_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[-200, 9500], [-3200, 3200]],\n",
    ")\n",
    "ax0.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
    "ax0.set_ylim(-3200, 3200)\n",
    "ax0.set_xlim(-200, 9500)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found\")\n",
    "\n",
    "a1 = ax1.hist2d(\n",
    "    brem_z_lost,\n",
    "    brem_x_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmin=1,\n",
    "    vmax=vmax,  # * stretch_factor,\n",
    "    range=[[-200, 9500], [-3200, 3200]],\n",
    ")\n",
    "ax1.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
    "ax1.set_ylim(-3200, 3200)\n",
    "ax1.set_xlim(-200, 9500)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost\")\n",
    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "\n",
    "plt.show()\n",
    "# plt.savefig(\n",
    "#     \"/work/cetin/Projektpraktikum/thesis/brem_vtx_found_lost_hist2d.pdf\",\n",
    "#     format=\"PDF\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 150\n",
    "vmax = 150\n",
    "\n",
    "a1 = plt.hist2d(\n",
    "    brem_z_found,\n",
    "    brem_x_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[-200, 9500], [-3200, 3200]],\n",
    ")\n",
    "plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
    "plt.ylim(-3200, 3200)\n",
    "plt.xlim(-200, 9500)\n",
    "plt.xlabel(\"z [mm]\")\n",
    "plt.ylabel(\"x [mm]\")\n",
    "# plt.title(r\"$e^\\pm$ lost brem vertices\")\n",
    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
    "plt.colorbar(a1[3])\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d_found.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib\n",
    "\n",
    "# nbins = 200\n",
    "# vmax = 400\n",
    "\n",
    "a1 = plt.hist2d(\n",
    "    brem_z_lost,\n",
    "    brem_x_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmin=1,\n",
    "    vmax=vmax,  # * stretch_factor,\n",
    "    # norm=matplotlib.colors.Normalize(vmin=1, vmax=vmax * stretch_factor),\n",
    "    range=[[-200, 9500], [-3200, 3200]],\n",
    ")\n",
    "plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
    "plt.ylim(-3200, 3200)\n",
    "plt.xlim(-200, 9500)\n",
    "plt.xlabel(\"z [mm]\")\n",
    "plt.ylabel(\"x [mm]\")\n",
    "\n",
    "plt.colorbar(a1[3])\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d_lost.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = 200\n",
    "vmax = 400\n",
    "\n",
    "a1 = plt.hist2d(\n",
    "    brem_z,\n",
    "    brem_x,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[-200, 9500], [-3200, 3200]],\n",
    ")\n",
    "plt.vlines([770, 990, 2700, 7500], -3200, 3200, colors=\"red\", lw=1.5)\n",
    "plt.ylim(-3200, 3200)\n",
    "plt.xlim(-200, 9500)\n",
    "plt.xlabel(\"z [mm]\")\n",
    "plt.ylabel(\"x [mm]\")\n",
    "# plt.title(r\"$e^\\pm$ lost brem vertices\")\n",
    "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
    "\n",
    "# plt.suptitle(\"brem vtx of photons w/ $E>0.1E_0$\")\n",
    "plt.colorbar(a1[3])\n",
    "mplhep.lhcb.text(\"Simulation\", loc=0)\n",
    "# plt.show()\n",
    "plt.savefig(\n",
    "    \"/work/cetin/Projektpraktikum/thesis/brem_vtx_hist2d.pdf\",\n",
    "    format=\"PDF\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50501\n"
     ]
    }
   ],
   "source": [
    "energy_emissions = ak.ArrayBuilder()\n",
    "\n",
    "for jelec in range(ak.num(ntuple, axis=0)):\n",
    "    energy_emissions.begin_record()\n",
    "    energy_emissions.field(\"lost\").boolean(ntuple[jelec, \"lost\"])\n",
    "    energy_emissions.field(\"energy\").real(ntuple[jelec, \"energy\"])\n",
    "\n",
    "    tmp_velo = 0\n",
    "    tmp_richut = 0\n",
    "    tmp_neither = 0\n",
    "    tmp_velo_length = 0\n",
    "    tmp_richut_length = 0\n",
    "    tmp_neither_length = 0\n",
    "\n",
    "    for jphoton in range(ak.num(ntuple[jelec][\"brem_photons_pe\"], axis=0)):\n",
    "        if ntuple[jelec, \"brem_vtx_z\", jphoton] <= 770:\n",
    "            tmp_velo += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
    "            tmp_velo_length += 1\n",
    "        elif (ntuple[jelec, \"brem_vtx_z\", jphoton] > 770) and (\n",
    "            ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700\n",
    "        ):\n",
    "            tmp_richut += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
    "            tmp_richut_length += 1\n",
    "        else:\n",
    "            tmp_neither += ntuple[jelec, \"brem_photons_pe\", jphoton]\n",
    "            tmp_neither_length += 1\n",
    "\n",
    "    energy_emissions.field(\"velo_length\").integer(tmp_velo_length)\n",
    "    energy_emissions.field(\"velo\").real(tmp_velo)\n",
    "\n",
    "    energy_emissions.field(\"rich_length\").integer(tmp_richut_length)\n",
    "    energy_emissions.field(\"rich\").real(tmp_richut)\n",
    "\n",
    "    energy_emissions.field(\"neither_length\").integer(tmp_neither_length)\n",
    "    energy_emissions.field(\"downstream\").real(tmp_neither)\n",
    "\n",
    "    energy_emissions.field(\"photon_length\").integer(\n",
    "        tmp_neither_length + tmp_richut_length + tmp_velo_length\n",
    "    )\n",
    "\n",
    "    # if (tmp_velo == 0) and (tmp_richut == 0):\n",
    "    if (\n",
    "        False  # (tmp_velo >= 0.5 * ntuple[jelec, \"energy\"])\n",
    "        or ((tmp_velo == 0) and (tmp_richut == 0))\n",
    "        or (ntuple[jelec, \"energy\"] - tmp_velo < 3000)\n",
    "    ):\n",
    "        energy_emissions.field(\"quality\").integer(0)\n",
    "    else:\n",
    "        energy_emissions.field(\"quality\").integer(1)\n",
    "\n",
    "    energy_emissions.end_record()\n",
    "\n",
    "energy_emissions = ak.Array(energy_emissions)\n",
    "\n",
    "print(ak.num(energy_emissions, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{lost: False,\n",
       " energy: 5.09e+04,\n",
       " velo_length: 0,\n",
       " velo: 0,\n",
       " rich_length: 0,\n",
       " rich: 0,\n",
       " neither_length: 0,\n",
       " downstream: 0,\n",
       " photon_length: 0,\n",
       " quality: 0}\n",
       "--------------------------\n",
       "type: {\n",
       "    lost: bool,\n",
       "    energy: float64,\n",
       "    velo_length: int64,\n",
       "    velo: float64,\n",
       "    rich_length: int64,\n",
       "    rich: float64,\n",
       "    neither_length: int64,\n",
       "    downstream: float64,\n",
       "    photon_length: int64,\n",
       "    quality: int64\n",
       "}</pre>"
      ],
      "text/plain": [
       "<Record {lost: False, energy: 5.09e+04, ...} type='{lost: bool, energy: flo...'>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_emissions[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  41978\n",
      "lost:  8523\n",
      "50501\n",
      "VELO energy emission, eff:  0.18201619769905547\n",
      "RICH1+UT energy emission, eff:  0.12653214787825984\n",
      "Neither, eff:  0.5226827191540762\n",
      "total efficiency:  0.8312310647313914\n",
      "efficiency:  0.8312310647313914\n",
      "\n",
      "found in velo/(found + lost in velo)\n",
      "VELO energy emission, eff:  0.848831840428479\n",
      "RICH1+UT energy emission, eff:  0.794479671764267\n",
      "eff von e die nicht strahlen:  0.8345505706788074\n"
     ]
    }
   ],
   "source": [
    "# efficiency berechnen als found in velo oder rich über alle elektronen\n",
    "# dann kann man zusammenrechnen mit velo, rich, und allen anderen elektronen\n",
    "# expected eff  = 81.19%\n",
    "\n",
    "electrons_found = energy_emissions[~energy_emissions.lost]\n",
    "electrons_lost = energy_emissions[energy_emissions.lost]\n",
    "\n",
    "anz_found = ak.num(electrons[~electrons.lost], axis=0)\n",
    "anz_lost = ak.num(electrons[electrons.lost], axis=0)\n",
    "print(\"found: \", anz_found)\n",
    "print(\"lost: \", anz_lost)\n",
    "\n",
    "num_velo_found = 0\n",
    "num_rich_found = 0\n",
    "num_no_up_rad_found = 0\n",
    "for itr in range(ak.num(electrons_found, axis=0)):\n",
    "    if electrons_found[itr, \"quality\"] == 1:\n",
    "        if electrons_found[itr, \"velo\"] >= electrons_found[itr, \"rich\"]:\n",
    "            num_velo_found += 1\n",
    "        else:\n",
    "            num_rich_found += 1\n",
    "    else:\n",
    "        num_no_up_rad_found += 1\n",
    "\n",
    "num_velo_lost = 0\n",
    "num_rich_lost = 0\n",
    "num_no_up_rad_lost = 0\n",
    "for itr in range(ak.num(electrons_lost, axis=0)):\n",
    "    if electrons_lost[itr, \"quality\"] == 1:\n",
    "        if electrons_lost[itr, \"velo\"] >= electrons_lost[itr, \"rich\"]:\n",
    "            num_velo_lost += 1\n",
    "        else:\n",
    "            num_rich_lost += 1\n",
    "    else:\n",
    "        num_no_up_rad_lost += 1\n",
    "\n",
    "denom = ak.num(electrons, axis=0)\n",
    "print(denom)\n",
    "\n",
    "eff_velo = num_velo_found / denom\n",
    "\n",
    "eff_rich = num_rich_found / denom\n",
    "\n",
    "eff_other = ak.num(electrons_found[electrons_found.quality == 0], axis=0) / denom\n",
    "\n",
    "print(\"VELO energy emission, eff: \", eff_velo)\n",
    "\n",
    "print(\"RICH1+UT energy emission, eff: \", eff_rich)\n",
    "\n",
    "print(\"Neither, eff: \", eff_other)\n",
    "\n",
    "print(\"total efficiency: \", eff_velo + eff_rich + eff_other)\n",
    "\n",
    "print(\"efficiency: \", anz_found / (anz_found + anz_lost))\n",
    "\n",
    "print(\"\\nfound in velo/(found + lost in velo)\")\n",
    "\n",
    "eff_velo = num_velo_found / (num_velo_found + num_velo_lost)\n",
    "eff_rich = num_rich_found / (num_rich_found + num_rich_lost)\n",
    "\n",
    "eff_no_rad = num_no_up_rad_found / (num_no_up_rad_found + num_no_up_rad_lost)\n",
    "\n",
    "print(\"VELO energy emission, eff: \", eff_velo)\n",
    "\n",
    "print(\"RICH1+UT energy emission, eff: \", eff_rich)\n",
    "\n",
    "print(\"eff von e die nicht strahlen: \", eff_no_rad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41978\n",
      "8523\n",
      "50501\n"
     ]
    }
   ],
   "source": [
    "print(ak.num(electrons[~electrons.lost], axis=0))\n",
    "print(ak.num(electrons[electrons.lost], axis=0))\n",
    "print(ak.num(electrons, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# energy_emissions = energy_emissions[energy_emissions.energy >= 5e3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# energyspektren angucken von velo und rich\n",
    "\n",
    "velo_found = ak.to_numpy(\n",
    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
    ")\n",
    "rich_found = ak.to_numpy(\n",
    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
    ")\n",
    "energy_found = ak.to_numpy(\n",
    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "        \"energy\"\n",
    "    ]\n",
    ")\n",
    "\n",
    "velo_lost = ak.to_numpy(\n",
    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"velo\"]\n",
    ")\n",
    "rich_lost = ak.to_numpy(\n",
    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"]\n",
    ")\n",
    "energy_lost = ak.to_numpy(\n",
    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "        \"energy\"\n",
    "    ]\n",
    ")\n",
    "\n",
    "diff_found = velo_found - rich_found  # / energy_found\n",
    "diff_lost = velo_lost - rich_lost  # / energy_lost\n",
    "\n",
    "xlim = 20000\n",
    "nbins = 80\n",
    "\n",
    "plt.hist(\n",
    "    diff_lost,\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"lost\",\n",
    "    range=[-xlim, xlim],\n",
    ")\n",
    "plt.hist(\n",
    "    diff_found,\n",
    "    bins=nbins,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"found\",\n",
    "    range=[-xlim, xlim],\n",
    ")\n",
    "# plt.xlim(-20000, 20000)\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"emitted energy difference\")\n",
    "plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})$ [MeV]\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of brem vtx with E>x*E_0\n",
    "\n",
    "number_velo = ak.to_numpy(\n",
    "    energy_emissions[energy_emissions.quality == 1][\"velo_length\"]\n",
    ")\n",
    "number_rich = ak.to_numpy(\n",
    "    energy_emissions[energy_emissions.quality == 1][\"rich_length\"]\n",
    ")\n",
    "\n",
    "plt.hist(\n",
    "    number_velo,\n",
    "    bins=10,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"darkorange\",\n",
    "    label=\"velo\",\n",
    "    range=[0, 10],\n",
    ")\n",
    "plt.hist(\n",
    "    number_rich,\n",
    "    bins=10,\n",
    "    density=True,\n",
    "    alpha=0.5,\n",
    "    histtype=\"bar\",\n",
    "    color=\"blue\",\n",
    "    label=\"rich\",\n",
    "    range=[0, 10],\n",
    ")\n",
    "plt.xlim(0, 10)\n",
    "plt.title(\"number of photons emitted\")\n",
    "plt.xlabel(\"number of photons\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\nphoton cut = x*E_0\\neffs, all photons included: x=0\\nfound in velo/(found + lost in velo)\\nVELO energy emission, eff:  0.8446167611094543\\nRICH1+UT energy emission, eff:  0.7961586121437423\\neff von e die nicht strahlen:  0.7954674220963173\\n'"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" \n",
    "photon cut = x*E_0\n",
    "effs, all photons included: x=0\n",
    "found in velo/(found + lost in velo)\n",
    "VELO energy emission, eff:  0.8446167611094543\n",
    "RICH1+UT energy emission, eff:  0.7961586121437423\n",
    "eff von e die nicht strahlen:  0.7954674220963173\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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