{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "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[\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": 57,
   "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_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",
       "    p_end_ut: float64,\n",
       "    p_end_velo: 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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "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": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_cut = 0\n",
    "photon_cut_ratio = 0.2\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 (\n",
    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
    "        ):\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 (\n",
    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
    "        ):\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 (\n",
    "            brem[itr, \"brem_vtx_z\", jentry] > 2700\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut\n",
    "            or brem[itr, \"brem_photons_pe\", jentry] < photon_cut_ratio * tmp_energy\n",
    "        ):\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": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24758\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: [2.62e+03, 2.54e+03, 1.86e+03],\n",
       " brem_vtx_x: [-6.97, -52.9, -55.2],\n",
       " brem_vtx_z: [112, 859, 895],\n",
       " photon_length: 3}\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": 125,
     "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": 126,
   "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": 127,
   "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": 128,
   "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": 128,
     "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": 129,
   "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": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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.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": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  19848 , lost:  4910\n",
      "0.2473800886739218\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[-3.61,\n",
       " -33.8,\n",
       " -133,\n",
       " 65.2,\n",
       " -26.6,\n",
       " 31.6,\n",
       " -52.1,\n",
       " -44.7,\n",
       " -103,\n",
       " -10.2,\n",
       " ...,\n",
       " 330,\n",
       " -11.6,\n",
       " -13.1,\n",
       " -25.6,\n",
       " -4.27,\n",
       " -103,\n",
       " 8.82,\n",
       " 12.8,\n",
       " -17.8]\n",
       "---------------------\n",
       "type: 19848 * float64</pre>"
      ],
      "text/plain": [
       "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='19848 * float64'>"
      ]
     },
     "execution_count": 131,
     "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": 132,
   "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": 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],\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": 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",
    "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": 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": {
   "display_name": "tuner",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}