Projektpraktikum/trackinglosses_energy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "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": 6,
   "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": 7,
   "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": 7,
     "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": 8,
   "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": 9,
   "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": 10,
   "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": 10,
     "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": 11,
   "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": 12,
   "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": 13,
   "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": 13,
     "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": 14,
   "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": 15,
   "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": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_emissions[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  41978\n",
      "lost:  8523\n",
      "50501\n",
      "VELO energy emission, eff:  0.19057048375279698\n",
      "RICH1+UT energy emission, eff:  0.1271657986970555\n",
      "Neither, eff:  0.513494782281539\n",
      "total efficiency:  0.8312310647313914\n",
      "efficiency:  0.8312310647313914\n",
      "\n",
      "found in velo/(found + lost in velo)\n",
      "VELO energy emission, eff:  0.8275150472914875\n",
      "RICH1+UT energy emission, eff:  0.7901082677165354\n",
      "eff von e die nicht strahlen:  0.8435090915005041\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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy_emissions = energy_emissions[energy_emissions.energy >= 5e3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = (\n",
    "    ak.to_numpy(\n",
    "        energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "            \"velo\"\n",
    "        ]\n",
    "    )\n",
    "    - ak.to_numpy(\n",
    "        energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "            \"rich\"\n",
    "        ]\n",
    "    )\n",
    ") / ak.to_numpy(\n",
    "    energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "        \"energy\"\n",
    "    ]\n",
    ")\n",
    "diff_lost = (\n",
    "    ak.to_numpy(\n",
    "        energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "            \"velo\"\n",
    "        ]\n",
    "    )\n",
    "    - ak.to_numpy(\n",
    "        energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "            \"rich\"\n",
    "        ]\n",
    "    )\n",
    ") / ak.to_numpy(\n",
    "    energy_emissions[(energy_emissions.lost) & (energy_emissions.quality == 1)][\n",
    "        \"energy\"\n",
    "    ]\n",
    ")\n",
    "\n",
    "xlim = 1\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 ratio\")\n",
    "plt.xlabel(r\"$(E_{VELO} - E_{RICH1+UT})/E_0$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": 20,
     "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
}