Projektpraktikum/trackinglosses_energy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\t\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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41978 8523\n",
      "50501\n"
     ]
    }
   ],
   "source": [
    "# file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "file = uproot.open(\n",
    "    \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
    ")\n",
    "\n",
    "# selektiere nur elektronen von B->K*ee\n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
    "                   (allcolumns.fromB)]  # B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
    "                  (allcolumns.fromB)]  # 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": 67,
   "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_770_p: 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_fraction: float32,\n",
       "    mc_chi2: float32,\n",
       "    mc_dSlope: float32,\n",
       "    mc_dSlopeY: float32,\n",
       "    mc_distX: float32,\n",
       "    mc_distY: float32,\n",
       "    mc_teta2: float32,\n",
       "    mc_zMag: 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",
       "    phi: float64,\n",
       "    pid: int32,\n",
       "    pt: float64,\n",
       "    px: float64,\n",
       "    py: float64,\n",
       "    pz: float64,\n",
       "    quality: int32,\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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 68,
     "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": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_cut = 0\n",
    "photon_cut_ratio = 0.25\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] > 3000\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] > 3000\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] > 3000\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": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20636\n",
      "50501\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: [],\n",
       " brem_vtx_x: [],\n",
       " brem_vtx_z: [],\n",
       " photon_length: 0}\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": 70,
     "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": 71,
   "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": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50501\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],\n",
       " brem_vtx_x: [-3.61],\n",
       " brem_vtx_z: [35.6],\n",
       " photon_length: 1}\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": 73,
     "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 + length_lost)\n",
    "ntuple[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_found = ak.to_numpy(\n",
    "    ak.sum(ntuple[~ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
    ") / ak.to_numpy(ntuple[~ntuple.lost][\"energy\"])\n",
    "Z_lost = ak.to_numpy(\n",
    "    ak.sum(ntuple[ntuple.lost][\"brem_photons_pe\"], axis=-1, keepdims=False)\n",
    ") / ak.to_numpy(ntuple[ntuple.lost][\"energy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHOCAYAAABttoiYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwWUlEQVR4nO3dz28baX7n8Y+S3VGMCO4yPZcssMB08TQ5GUX1JVjAA5hEH3cBkfIhA+QwMImBbkaiinKxfAlRRK4CUhQGmF30RSL9B0xYDtpYYIFFixXfAiTLcoA5zAIzTVcb2nXUSYZ70Fa1+Eui+LNKfL8AYSxW1cOnqz3kp5/nqe+z0ev1egIAAEip31l1BwAAAGZBmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAEAAKlGmAFw5wVBsOouAFggwgyAO69UKikMw1V3A8CCEGYALEStVtODBw+0sbGhjY0NFQqF+CebzcavL5rv+zJNU4ZhJLJ/AGb371bdAQB30/7+vjqdjur1uvb39+U4Tt/xIAhUKBQW3g/XdVWpVBLbPwCzY2QGwMKcnZ1J0shQYJqm8vn8wvvged7Y90lC/wDMboNdswEsSjRNM+5jJgzDoemfeWo2m2q1WnJdd+TxVfcPwHwwMgNgITzPk6Sh0Y1msxn/edFB4eTkZOQUk5SM/gGYD8IMgIVoNBqS+qdwwjDUycnJUt4/DEMFQSDLshLZPwDzQ5gBsBDRyMfJyYlyuZyy2awePHigzz77bCnvf3p6qqdPnya2fwDmh6eZAMxdNCpiGIba7Xb82pMnT6ZeVBuGoU5PT9Vut+U4zo1TQK7r6vXr13PrX6VSUTab1ddff63PPvtMxWJxqn8OAPNHmAEwd6enp5L616MYhqF8Pj922ucmhmEok8no9PR07ILeSBAEymQyYwPPbftXKpVkmqb29/clSblcLj4fwOoxzQRg7lqtlqThR54PDg5maverr77S7u7ujeeNqy0zTf+CIFCz2exr7+nTp0N1aQCsDmEGwNxF61EGg8fVkZLonNu2O0khu2azee000G365/u+pMu6MxHLsuR5HlskAAlBmAEwV0EQKAzDkVsIROr1+tDmj9H2As1mU7VaTdlsVrZtx8fDMJTv+8rn82o2m8pmsyNHX64rkjdN/7766quh8zKZjCSp2+2OfR8Ay0OYATBXUZ2WqyMZkTAMValUVKlUhkZFLMuKRzr29/fluq5qtVr8mud5Mk1TZ2dnMk1Ttm3HFXyvummK6bb9C8MwDi+D2I0bSAYWAAOYm1qtFo+meJ6nXC6nTCajbrcbP0EkScVicWi0o9VqybKssdNDrVZLpmkqk8nIsixVq1Vtb28Pnef7/thFxtP0L5vNxguGB40KRACWjzADYG729/fjJ35uy/O8vrowg7tdR9NHUVDxPC8ufBep1+vXjspM0z/TNIfWxkTTS4QZIBnYmwlAImxsbKjT6cQBIZvNynVd5fN5hWGoBw8exMeDIFA2mx3aUymXy+n169dz3YYgeq+rfavVajo5OYlr1ABYLdbMAFg5z/NkGEZfWCgWi/FCXs/zZFlWfDwapfF9Px41uam2zLRM01SxWOzbs+nk5IRHs4EEYZoJwMq1Wi1lMhl5nqcgCPoK1EmXTxRdnYIKw1Ddblfdbjeedrpp4e8sGo2GKpWKarWavv76a1UqFQrmAQnCNBOAlcvlcqpUKiqXy1O3USqVhtbQAFgPhBkAKze4XgYAboM1MwBWKpoaiirtAsBtrcXIzG9+8xv94he/0A9+8APdu3dv1d0BAAAT+Pjxo/7pn/5Jn3/+ub7//e+PPS9RC4ALhcLY/VparVa84M73fVWr1bj+Q6FQuHYfll/84hf68Y9/vJA+AwCAxfriiy/0x3/8x2OPJybMBEGgIAjkOE7fo5WdTke1Wi0OMkEQKJfLqd1ux08xZLNZdbvdsYsHf/CDH0i6vBk//OEP59bn8/NzPX78WG/evNHW1tbc2o3s7Ozo1atXqWl3UW1zn5fX9iLvdRrvB/d5OW1zn9Pd9iLv89///d/rxz/+cfw9PlYvIVzX7b1//37odcdxevl8Pv49n8/3/R5de90/Srvd7knqtdvtufW31+v1vvnmm56k3jfffDPXdiM//OEPU9XuotrmPi+v7UXe6zTeD+7zctrmPqe77UXe50m/vxe2APinP/2pfvazn018frlcHlns6uTkRKVSSdJlbQnP81QoFPrOifZnqdfr03cYAACk0kKmmd69eyfXdXV6eqqf/OQnU7cThqF83493r412yB18fDOabmq1WtfWqTg/P9eHDx+m7s/m5qY2Nzenvh4AgHVycXGhi4uLqa8/Pz+f6LyFhJlPP/1UruvOXDPi9PRUlmXFIzbRjrbjypVHx8d5/PjxTP158eKFDg8PZ2oDAIB1Ua1W9fLly4W/z8IWAD979mzmNhqNRl8J806nI0nKZDIjzx/c2XbQmzdv9OjRo6n7w6gMAACTOzg40PPnz6e+/u3btxMNRCwszHz++edyHGfq8BCtj3FdN34tm81Kkrrd7shrbhoJ2tra0v3796fqDwAAuJ1Zl2dM+nTU1GHm6ojJoCiInJ6eTh1mPM+TaZp9ASX687gRmLtWCn1vby9V7S667UXhPi9HGu8H93k5bXOf70bbqzR1BeDf+Z2bH4TKZrP6x3/8x2maV6lUkmmachwnfi0MQz148ECO4/TtqBsEgbLZrFzXHbkA2Pf9odo08/Dhwwd98skn+uabbxjxWSDu8/Jwr5eD+7wc3OflWOR9nvT7e+pHs4vFojqdjt6/fz/0c3Z2pnK5PHWQkaRmszk0+mMYhizLUqvV6ns9qhocPfUEAADWx9RhplKp6NNPP9Unn3wy9GNZlnK5nP7iL/5iqrabzWYcXAYdHx/L87y+J5ccxxmqHAwAANbD1Gtmnjx5cu1x0zT153/+5/rLv/zLW7d9cnIydpTFsiy1223Zti3TNBUEgWzbvra+DAAAuLumDjNv374deywKGNNqNBrXHrcs68ZzAADAepg6zFiWpY2NjbHHe72earXatM0DAABMZOowYxiGdnd3R65TefjwoSzLunEqCgAAYFZTh5nj42Pt7OzMsy8Lt7Ozo3v37o08tre3d2efvwcAIKmOjo50dHQ08tjHjx8namPqMJO2ICNJr169mmudmc3NTb148YJtDhaM+7w83Ovl4D4vB/d5OWa9z9cNJkR1Zm4yddG8m/z0pz/V9vb2TLtmz8uiiuZJ0qh9J9mLEgCA2S28aN513r17J9d1+6r0AgAALMJCNpr89NNP5brundsrCQAAJM/Cds3e3d3VJ598sqjmAQAAJC1omkmSXr9+rYODg0U1DwAAIGnGkZm//du/VavVUhiGfa93u135vq9ut6tqtTrLWwAAAFxrpjozlUrl2nPYLwkAACza1NNMruuq1Wrp/fv3+pu/+Rs5jqPf/va3+u1vf6tut6tyuay//uu/nmdfAQAAhkw9MpPP5+PtCvL5vI6Pj+NjhmEol8vp4OAgUdNMVAAGACBZVloB+Jtvvun7fXd3V3/1V3+lP/3TP41fazabiQoz864ADAAAZjOPCsBThxnTNPW7v/u7evDggc7OzrSzs6Pt7W21Wi0ZhqFmszlyE0oAAIB5mjrM/Nmf/Zl+85vf6O/+7u+UyWQkSaenpyoUCnr37p0kyXGc+fQSAHB3/Y/DVffgZn90uOoe4BozPZo9GFZM01Sn09G7d++UyWQomgcAABZuYdsZAACA7/i+r5OTE3meJ0lqt9sr7tHdsbAKwAAA4DuWZenp06fyfX+h7xMEwVAx27uOMAMAwJIs44naUqmkbre78PdJEsIMAAB3RKlUWvjITxItbNdsAAAwuTAMZdu2DMOIA4lt28rn8yPPCcNQnufJtm2Vy2U1m834ukqlIsMwdHBwsBb11QgzAACsmO/7evLkiV6/fh2Hj3q9rkKhIMdxtL+/L0l69uyZTNOMnyau1+vx+phisaivvvpKtVpNruvKNM2V/LOswlpNM+3s7OgP//APR/6MK6UMAMCiPXv
      "text/plain": [
       "<Figure size 640x480 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.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": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  16049 , lost:  4587\n",
      "0.2858122001370802\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[-3.61,\n",
       " -33.8,\n",
       " 65.2,\n",
       " -26.6,\n",
       " 31.6,\n",
       " -52.1,\n",
       " -44.7,\n",
       " -103,\n",
       " -10.2,\n",
       " -47.1,\n",
       " ...,\n",
       " -25.5,\n",
       " -90.3,\n",
       " 55.2,\n",
       " 152,\n",
       " -144,\n",
       " 330,\n",
       " -13.1,\n",
       " -4.27,\n",
       " -17.8]\n",
       "---------------------\n",
       "type: 16049 * float64</pre>"
      ],
      "text/plain": [
       "<Array [-3.61, -33.8, 65.2, ..., -13.1, -4.27, -17.8] type='16049 * float64'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple_found = ntuple[~ntuple.lost]\n",
    "tuple_lost = ntuple[ntuple.lost]\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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vmax = 150\n",
    "nbins = 100\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",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[-200, 3000], [-1000, 1000]],\n",
    ")\n",
    "ax0.vlines([770, 990, 2700], -1000, 1000, colors=\"red\")\n",
    "ax0.set_ylim(-1000, 1000)\n",
    "ax0.set_xlim(-200, 3000)\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(r\"$e^\\pm$ found brem vertices\")\n",
    "\n",
    "a1 = ax1.hist2d(\n",
    "    brem_z_lost,\n",
    "    brem_x_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax * stretch_factor,\n",
    "    range=[[-200, 3000], [-1000, 1000]],\n",
    ")\n",
    "ax1.vlines([770, 990, 2700], -1000, 1000, colors=\"red\")\n",
    "ax1.set_ylim(-1000, 1000)\n",
    "ax1.set_xlim(-200, 3000)\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_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(a0[3], ax=ax1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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]\n",
    "              > 770) and (ntuple[jelec, \"brem_vtx_z\", jphoton] <= 2700):\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(tmp_neither_length +\n",
    "                                                    tmp_richut_length +\n",
    "                                                    tmp_velo_length)\n",
    "\n",
    "    if (tmp_velo == 0) and (tmp_richut == 0):\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": 79,
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_emissions[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  41978\n",
      "lost:  8523\n",
      "50501\n",
      "VELO energy emission, eff:  0.15952159363180926\n",
      "RICH1+UT energy emission, eff:  0.10419595651571256\n",
      "Neither, eff:  0.5675135145838697\n",
      "total efficiency:  0.8312310647313915\n",
      "efficiency:  0.8312310647313914\n",
      "\n",
      "found in velo/(found + lost in velo)\n",
      "VELO energy emission, eff:  0.8187823965850188\n",
      "RICH1+UT energy emission, eff:  0.7830357142857143\n",
      "eff von e die nicht strahlen:  0.8443815921277473\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],\n",
    "                   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": 81,
   "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": 84,
   "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",
    "diff_found = ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (\n",
    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
    "        (~energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\n",
    "diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (\n",
    "    energy_emissions.quality == 1)][\"velo\"]) - ak.to_numpy(energy_emissions[\n",
    "        (energy_emissions.lost) & (energy_emissions.quality == 1)][\"rich\"])\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.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": 43,
   "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",
    "number_rich = ak.to_numpy(\n",
    "    energy_emissions[energy_emissions.quality == 1][\"rich_length\"])\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": 44,
   "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": 44,
     "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": {
   "display_name": "tuner",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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