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": [
      "40402 10099\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_default.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": 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_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",
    "\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 = 0.1\n",
    "\n",
    "cut_brem = ak.ArrayBuilder()\n",
    "\n",
    "for itr in range(ak.num(brem, axis=0)):\n",
    "    cut_brem.begin_record()\n",
    "    cut_brem.field(\"event_id\").integer(itr)\n",
    "    cut_brem.field(\"lost\").boolean(brem[itr, \"lost\"])\n",
    "    cut_brem.field(\"rad_length_frac\").real(brem[itr, \"rad_length_frac\"])\n",
    "    cut_brem.field(\"energy\").real(brem[itr, \"energy\"])\n",
    "\n",
    "    ph_length = brem[itr, \"photon_length\"]\n",
    "    \n",
    "    tmp_energy = brem[itr, \"energy\"]\n",
    "\n",
    "    cut_brem.field(\"brem_photons_pe\")\n",
    "    cut_brem.begin_list()\n",
    "    for jentry in range(brem[itr, \"photon_length\"]):\n",
    "        if (\n",
    "            brem[itr, \"brem_vtx_z\", jentry] > 3000\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] > 3000\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] > 3000\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)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44163\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": 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": 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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.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:  32898 , lost:  11265\n",
      "0.34242203173445196\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[-3.61,\n",
       " -33.8,\n",
       " -133,\n",
       " 65.2,\n",
       " -5.73,\n",
       " -26.6,\n",
       " -4.26,\n",
       " 6.83,\n",
       " 10.7,\n",
       " 26.2,\n",
       " ...,\n",
       " -11.6,\n",
       " -13.1,\n",
       " -25.6,\n",
       " -4.27,\n",
       " -4.27,\n",
       " -103,\n",
       " 8.82,\n",
       " 12.8,\n",
       " -17.8]\n",
       "---------------------\n",
       "type: 32898 * float64</pre>"
      ],
      "text/plain": [
       "<Array [-3.61, -33.8, -133, ..., 8.82, 12.8, -17.8] type='32898 * 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.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] > 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(tmp_neither_length+tmp_richut_length+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))\n"
   ]
  },
  {
   "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:  40402\n",
      "lost:  10099\n",
      "50501\n",
      "VELO energy emission, eff:  0.2624700500980179\n",
      "RICH1+UT energy emission, eff:  0.17696679273677748\n",
      "Neither, eff:  0.3605869190709095\n",
      "total efficiency:  0.8000237619057049\n",
      "efficiency:  0.8000237619057048\n",
      "\n",
      "found in velo/(found + lost in velo)\n",
      "VELO energy emission, eff:  0.807739183424741\n",
      "RICH1+UT energy emission, eff:  0.7549417131272175\n",
      "eff von e die nicht strahlen:  0.8183166314654204\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",
    "\n",
    "\n",
    "denom = ak.num(electrons,axis=0)\n",
    "print(denom)\n",
    "\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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40402\n",
      "10099\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))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(~energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
    "diff_lost = ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"velo\"]) - ak.to_numpy(energy_emissions[(energy_emissions.lost) & (energy_emissions.quality==1)][\"rich\"])\n",
    "\n",
    "xlim = 20000\n",
    "\n",
    "plt.hist(diff_lost,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"lost\",range=[-xlim,xlim])\n",
    "plt.hist(diff_found,bins=100,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"found\", range=[-xlim,xlim])\n",
    "plt.xlim(-20000,20000)\n",
    "plt.title(\"emitted energy difference velo - rich\")\n",
    "plt.xlabel(r\"$E_{velo} - E_{rich}$\")\n",
    "plt.ylabel(\"a.u.\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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(energy_emissions[energy_emissions.quality==1][\"velo_length\"])\n",
    "number_rich = ak.to_numpy(energy_emissions[energy_emissions.quality==1][\"rich_length\"])\n",
    "\n",
    "\n",
    "plt.hist(number_velo,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"darkorange\",label=\"velo\",range=[0,10])\n",
    "plt.hist(number_rich,bins=10,density=True,alpha=0.5,histtype=\"bar\",color=\"blue\",label=\"rich\",range=[0,10])\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": 84,
   "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": 84,
     "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
}