Projektpraktikum/trackinglosses_energy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "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": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10923 2390\n",
      "13313\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": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#electrons[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "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",
    "\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",
    "    # [:,\"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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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(\"lost\").boolean(brem[itr, \"lost\"])\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": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13313\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>{lost: False,\n",
       " energy: 3.63e+03,\n",
       " brem_photons_pe: [],\n",
       " brem_vtx_x: [],\n",
       " brem_vtx_z: [],\n",
       " photon_length: 0}\n",
       "-----------------------------------\n",
       "type: {\n",
       "    lost: bool,\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 {lost: False, energy: 3.63e+03, ...} type='{lost: bool, energy: flo...'>"
      ]
     },
     "execution_count": 178,
     "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": 179,
   "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": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAHOCAYAAABttoiYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAvUUlEQVR4nO3dz28baWLm8UdJthUjgrtMz2GzQIBx8ZQ5GUX1JVjAA5hM/wEm5UMGyKHXLAx0a2BYoyCA5RNBIlcBKQoBZhd9kUjf9jJhOWhjgQALizU+JUCyLAeYwyww3XS1ocRRZwLl4FS1+Eui+LNK/H4AoU1W1Vuv31aTT7/vW++7cX5+fi4AAICU+q1VVwAAAGAWhBkAAJBqhBkAAJBqhBkAAJBqhBkAAJBqhBkAAJBqhBkAAJBqhBkAAJBqhBkAN14QBKuuAoAFIswAuPFKpZLCMFx1NQAsCGEGwELU63XduXNHGxsb2tjYUKFQiH+y2Wz8/qL5vi/TNGUYRiLrB2B2v7PqCgC4mSqVirrdrhqNhiqVimq1Wt/xIAhUKBQWXg/XdWXbdmLrB2B29MwAWJiTkxNJGhkKTNNUPp9feB08zxt7nyTUD8DsNtg1G8CiRMM04z5mwjAcGv6Zp1arpXa7Ldd1Rx5fdf0AzAc9MwAWwvM8SRrq3Wi1WvGfFx0Ujo6ORg4xScmoH4D5IMwAWIhmsympfwgnDEMdHR0t5f5hGCoIAlmWlcj6AZgfwgyAhYh6Po6OjpTL5ZTNZnXnzh198sknS7n/8fGxHj9+nNj6AZgfnmYCMHdRr4hhGOp0OvF7Dx8+nHpSbRiGOj4+VqfTUa1Wu3IIyHVdvXjxYm71s21b2WxWX3/9tT755BMVi8Wp/h4A5o8wA2Dujo+PJfXPRzEMQ/l8fuywz1UMw1Amk9Hx8fHYCb2RIAiUyWTGBp7r1q9UKsk0TVUqFUlSLpeLzwewegwzAZi7drstafiR5729vZnKffXqlXZ2dq48b9zaMtPULwgCtVqtvvIeP348tC4NgNUhzACYu2g+ymDwuNhTEp1z3XInWciu1WpdOgx0nfr5vi/pw7ozEcuy5HkeWyQACUGYATBXQRAoDMORWwhEGo3G0OaP0fYCrVZL9Xpd2WxWjuPEx8MwlO/7yufzarVaymazI3tfLlskb5r6vXr1aui8TCYjSer1emPvA2B5CDMA5ipap+ViT0YkDEPZti3btod6RSzLins6KpWKXNdVvV6P3/M8T6Zp6uTkRKZpynGceAXfi64aYrpu/cIwjMPLIHbjBpKBCcAA5qZer8e9KZ7nKZfLKZPJqNfrxU8QSVKxWBzq7Wi327Isa+zwULvdlmmaymQysixL1WpV29vbQ+f5vj92kvE09ctms/GE4UGjAhGA5SPMAJibSqUSP/FzXZ7n9a0LM7jbdTR8FAUVz/Pihe8ijUbj0l6ZaepnmubQ3JhoeIkwAyQDezMBSISNjQ11u904IGSzWbmuq3w+rzAMdefOnfh4EATKZrNDeyrlcjm9ePFirtsQRPe6WLd6va6jo6N4jRoAq8WcGQAr53meDMPoCwvFYjGeyOt5nizLio9HvTS+78e9JletLTMt0zRVLBb79mw6Ojri0WwgQRhmArBy7XZbmUxGnucpCIK+BeqkD08UXRyCCsNQvV5PvV4vHna6auLvLJrNpmzbVr1e19dffy3btlkwD0gQhpkArFwul5Nt2yqXy1OXUSqVhubQAFgPhBkAKzc4XwYAroM5MwBWKhoailbaBYDrWouema+++ko///nP9f3vf1+3bt1adXUAAMAE3r9/r3/6p3/Sp59+qu9973tjz1uLCcA///nP9aMf/WjV1QAAAFP44osv9Cd/8idjj69FmPn+978v6UNj/OEf/uHcyj09PdWDBw/08uVLbW1tza3cyKNHj/T8+fPUlLuosmnn5ZW9yLZOY3vQzsspm3ZOd9mLbOe///u/149+9KP4e3yctQgz0dDSn//5n48dZtrd3dXu7u61yn337p0k6f79+7p9+/ZslRzh1q1bY5dlT2K5iyqbdl5e2Yts6zS2B+28nLJp53SXPWs7Hxwc6ODgYOSx9+/fS9KVU0QWFmZ+/OMfa3t7W5999tmibnFtz58/X9gvCAAAuL7LOhN831cul7uyjIU8zfTmzRu5rjv1Hi0AAACTWkjPzL179+S6LmtGAACAhVvYMNOTJ08WVTQAAEBsYYvmffrpp3r9+vWiigcAAJA0Q8/MxU3fBoVhKM/zdHx8rPv37097i7V33aerVl3uosteFNp5OdLYHrTzcsqmnW9G2as09QrAv/VbV3fqZLNZ/eM//uM0xc9VNBu60+nM9Wmmd+/e6eOPP9Y333yzkEeG8QHtvDy09XLQzstBOy/HItt50u/vqYeZisWiut2u3r59O/RzcnKicrmciCADAAButqnDjG3bunfvnj7++OOhH8uylMvl9Gd/9mfzrCsAAMCQqcPMw4cPLz1umqZc1522eAAAgIlMPQH4sieVgiCQ4zjTFr0wjx49mut2BgAAYDaTbGdwlanDjGVZ2tjYGHv8/Pxc9Xp92uIXgu0MAABIlnlsZzB1mDEMQzs7OzIMY+jY3bt3ZVnWlUNRAAAAs5o6zBweHurRo0fzrAsAAMC1TT0BmCAjbW5u6unTp9rc3Fx1VW402nl5aOvloJ2Xg3ZejiS089SL5l3lxz/+sba3t/XZZ58tovhrWdSieZK0vz/ZewAA4HoWvmjeZd68eSPXdVWpVBZRPAAAQGwhu2bfu3dPruvKNM1FFA8AABBbSJiRpJ2dHX388ceLKh4AAEDSgoaZJOnFixfa29tbVPEAAACSZuyZ+Zu/+Ru1222FYdj3fq/Xk+/76vV6qlars9wCAADgUjOtM2Pb9qXnlMvlaYsHAACYyNRhxnVdtdttbW9v69WrV/rFL36hn/zkJ5KkMAz105/+VH/5l385t4rOA3szAQCQLCvdmymfz8fbFeTzeR0eHsbHDMNQLpfT3t5eooaZ2JsJAIBkmcfeTFNPAP7mm2/6Xu/s7Ogv/uIv+t5rtVrTFg8AADCRqXtmTNPUb//2b+vOnTs6OTnRo0ePtL29rXa7LcMw1Gq1Rm5CCQAAME9Th5mf/OQn+uqrr/SLX/xCmUxGknR8fKxCoaA3b95Ikmq12nxqCQC4uf52f9U1uNof7a+6BrjETI9mD4YV0zTV7Xb15s0bZTKZmRfNC4IgHqoql8txT4/v+6pWqzJNU2EYqlAoqFgsznQvAACQTgvbzmAWQRDIcRyFYTi0LUIQBEObTmWzWfV6PR4FBwAklu/7Ojo6kud5kqROp7PiGt0cC1sBeFrRzOVMJqN2uz20v5Nt28rn831PJTmOc+WaNwAArJJlWXr8+LF831/ofYIgGFrM9qZLVJgJw1APHz6UaZpyXXfkcc/zVCgU+t7f3t6WJDUajaXUEwCAaSxjeZBSqaRer7fw+yRJosJMNLQ0buLwycmJJA311kS/HO12e7EVBAAgwUql0sJ7fpJoYbtmTyPqWWm323IcR0EQaHt7O543EwSBJI195Ds6Ps7p6anevXs3df02Nze1ubk59fUAAIwThqEcx5FhGHEgcRxH+Xx+5DnRaIXjOCqXy2q1WvF1tm3LMAzt7e2tdLHYs7MznZ2dTX396enpROclJsxE/wIsy5Jt26rVagqCQIVCQdlsVm/fvlW325Wk+FHwQVeNET548GCmOj59+lT7+/szlQEAwCDf9/Xw4UO9ePEiDh+NRkOFQkG1Wk2VSkWS9OTJE5mmGY9gNBqN+LuvWCzq1atXqtfrQw/PrEq1WtWzZ88Wfp/EDDNFvSq2bcf/Ai7OnalWq8pms5I0dizwqn9xL1++1DfffDP1z97e3rz+ugAAxJ48eaLt7e2+XpRyuSzLsuKRCknxk1AXz0myvb29mb53X758OdF9EtMzM27oKOpei3pppPE9MFeFma2tLd2+fXvqOgIAMG9BEMj3/bj35SLbtmXbtlzXVa1Wk2maqtfrunv3bnz+qOuSYtbpGVtbWxOdl5iemeiJpGgoaVAmk4nPGZwbE72
      "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": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  4943 , lost:  1757\n",
      "0.3554521545620069\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>[-26.5,\n",
       " 19.5,\n",
       " 34.7,\n",
       " 36.4,\n",
       " 532,\n",
       " 95.5,\n",
       " 3.25,\n",
       " 4.11,\n",
       " -3.04,\n",
       " -112,\n",
       " ...,\n",
       " 3.61,\n",
       " 8.6,\n",
       " 54.6,\n",
       " 26.1,\n",
       " 26.2,\n",
       " 23.6,\n",
       " 31.5,\n",
       " -67.7,\n",
       " 25]\n",
       "--------------------\n",
       "type: 4943 * float64</pre>"
      ],
      "text/plain": [
       "<Array [-26.5, 19.5, 34.7, 36.4, ..., 31.5, -67.7, 25] type='4943 * float64'>"
      ]
     },
     "execution_count": 181,
     "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": 182,
   "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": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13313\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": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>{lost: False,\n",
       " energy: 9.36e+03,\n",
       " velo_length: 3,\n",
       " velo: 6.51e+03,\n",
       " rich_length: 0,\n",
       " rich: 0,\n",
       " neither_length: 0,\n",
       " downstream: 0,\n",
       " photon_length: 3,\n",
       " quality: 1}\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: 9.36e+03, ...} type='{lost: bool, energy: flo...'>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_emissions[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found:  10923\n",
      "lost:  2390\n",
      "13313\n",
      "VELO energy emission, eff:  0.1835048448884549\n",
      "RICH1+UT energy emission, eff:  0.1139487718771126\n",
      "Neither, eff:  0.5230226094794562\n",
      "total efficiency:  0.8204762262450237\n",
      "efficiency:  0.8204762262450237\n",
      "\n",
      "found in velo/(found + lost in velo)\n",
      "VELO energy emission, eff:  0.8151484818151484\n",
      "RICH1+UT energy emission, eff:  0.737481769567331\n",
      "eff von e die nicht strahlen:  0.8430802760624773\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": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10923\n",
      "2390\n",
      "13313\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": 187,
   "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": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHJCAYAAABpOFaGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAx5ElEQVR4nO3dT2wj2WHv+5/SPS1P0vCU1LPwrF66GBhBNndQVHth4KUHafIagReBMaS8ywxgDAuGNsEAYaGzme5NiCKyyEaASSFAv+xGpHFxF0GQkDbu+AJZ3BErnc3Dw8NjdXYJcD1UedB2W+0/9RY9VUNKJEVR/FPS+X4AwSMW69RhFa369anzZyOO41gAAAAG+p11VwAAAGBdCEIAAMBYBCEAAGAsghAAADAWQQgAABiLIAQAAIxFEAIAAMYiCAEAAGMRhIArJAxDNZtNRVG07qqsTRRFajabCsNw3VXBggVBoGazue5qwDAEIeCKqNfryuVycl1Xg8Fg3dWZSRAE8jxP5XJZuVxO9Xr9UuU1m03dvXtXrusaHQaXIQxDbW1trSWIhGGocrmsfD6vRqOx8uPDbAQh4IqoVqsqlUrrrsbMgiDQgwcP5Pu+Wq2WSqWS+v3+pcqsVCqqVCoLquGXwjA0PlhFUaQoisZeo0nnZ1HnzbZttVqtS5cDzIMgBFwh29vb667CzGq12kh9fd9fyL/279y5c+kyTiuXy1emlW1ZHMdRHMfyff/Mtknnh/OG64AgBGApgiBYdxVmUi6Xr0xd12HS+eG84bogCAETJJ1y8/m82u22ut2u8vm8NjY2VC6X0/e1221tbW1pY2MjvTF0u12Vy+WR9yblFYvFtLNvsVjU1taWisVi+ogh6Qu0tbUlz/Mm1s11XW1tbWlra0uu6555TxAEKpfLKhaLyuVyZ8pqt9sqFovqdrtqNpsTy5l0bM/zVCwW0zISzWZT5XJZYRimfT/K5fLIe6ad66Q++XxeW1tbKpfLYx+/DAaD9H2nr8msdW232+k1c133zM39vP1n/Y6cLst1XeVyuQv1x5l2PRfx3Wq32+m1Ou/8nHfezvvuDZ+P5Oey/ceAucUAxur3+3GpVIolxYVCIa5Wq3Gv14srlUosKfZ9P31v8lqv1xvZX1JcKpXS35P3DZfX6XTS1yqVStzpdEaOPVzm8P6VSiVuNBqx4zjpa4lerzfye6vViiXFlUol/d227fS1arUaO44TO44z9Zz0er3YsqyROjUajTPnI47j2Lbt2Lbtmc518rmGz02r1UrPwXA5vu+n7/N9f+I1mbWu1Wo1lhT3+/0Lf9aLfEdKpVJcrVZHyjp9ziY573pe9rvV7/dHzuuwSedn2nmbVtfkeJZlxZ1OJ30tOf5530Fg0QhCwBTJH/HTN6zTN4zkpjB80zw+Ph4JQnH86iZx+qYQx3EaZoZvKsl7xwWuVqs1sn8SapIbi+M4I3WJ4zi2LCuWFB8fH8dx/OWNZ/jmfB7Hcc7cKCfV/yJBaLg+jUZj5PVCoTDymSe97/Q1mbWuk27os+4/63fEsqwz53rWIDTL9bzsd2tcneP44kFolrqWSqWx55YghHXg0RgwA8uyzrx2mU6ip8uzbVvSaGfo5LVxo3gcxxn5PXmk1el0FIahgiBQrVZLH3UMP+44OjoaqcO9e/dmqnNS7uljDx9/EZ2hk899uuxOpzPy+s7OzsjvlmWlcwtdtq7z7H/ed8S2bdXr9ZFHQNVqdWIdTtflvOs5qR4X/W5dxix1DcMwfSwLZMHNdVcAwOUVCgVJX96IJM08HHncDXycaR1jk1CyjEkOkzBykbIvW9dlfNZWq6V8Pi/P89RoNNRqtcYGrUl1uQrDy2epa9LH6nTgBdaFFiHgGkjCjG3bI60iyzCu43Jy/GUM70/KnOfGedm6LvKz2ratZ8+eqVAoKAxD5fP5mTpLL/t6LtIsdU22MeweWUEQAq6B5IZ97969NDC02+2x7502emuapPVi3P7J8XO53FxlT5PcMPP5/Mz7XLauy/isYRjKsix1Op20xWSWUXrLup7LMEtdk/f0er2V1QuYhiAELEAyyd/wv4ST/17FjMXJDaZUKqWPyTzPO/OI5zLLJ9i2Lcdx0mHxw46OjmRZ1sisz4PBYCH/6m+32yNlf/bZZ2n5i6qrNHqd5tn/PMMTFZZKpbSP0XktPcu6nhc16Xs8/PosdU0eLU5aM8/0Gb6xegQhYIpZb+RJC4Lneek8OMmNrtvtph1DJ5WX/PEf3p789/BryWOZ04Er6XOSvCfphJvP51Uul1Wv11UsFtXv99ObVXLMi9x4Wq2WLMsaacmIoki+7+vg4GDm/kbTDHdCjqJIjUZDBwcH5+4XRdHIuZq1rknLTqPRSDvyXmT/Wb8jh4eHI9ctiiLZtn3uI79Zr+dlv1vj3idNPj/jXp+lrqff0+12FYZhOtdQGIbMKYTVWvewNSCrer1eOvTYtu240+nEx8fHI3PeDA8/9n0/tixrZJi0bdvpnC7J/CqSYsuy0uHgydw0+mKofa/XG5nrxbKsdKj48fFxXK1W40KhkM4NU6lU0mHJw3zfT4fV27Y9Mtx8eB6h09vOc3x8nA5/To4/PFx6eB4dfTGce3i+mEmGh/M7jhOXSqW4VCqN7NtqtdKh2Mkw7dPXZHiI+nl1TTiOE1uWdWbo+SyfddbvSKFQSL8P1Wo1LpVKY6/btPMz6Zpd9rvV6/XS15I6D9dt0vmZ9Pq0uiYajUb6Hsdx4n6/n56f00PygWXaiOM4XlHmAoCJ6vW6PM9Tp9NJWzkAYNl4NAYAAIxFEAIAAMYiCAFYuyiK0pmjr8LEgQCuD/oIAVi7caOEZll+AgAuiyAEAACMxaMxAABgLGMXXf3pT3+qf/qnf9Lv//7v6/XXX193dQAAwAxevHihf//3f9e3vvUtvfnmm5cuz9gg9A//8A96//33110NAAAwhydPnui99967dDnGBqG33npLknRwcJAuj7AM7777rn74wx9e2fJXcYznz5/r/v37+uSTT3T79u2lHec6nCuuhTnH4Fpk6xiruB7X4Tyt4hhBEOiDDz5I7+OXtbQg9P3vf187Ozv63ve+t6xDXMpXvvIVSdLXv/71pQah119//UqXv4pjfP7555Kkt99+W1/96leXdpzrcK64FuYcg2uRrWOs4npch/O0imM8f/5c0pf38ctaSmfpZ8+eqdFoMPwVAABk2lJahO7evatGo3HuqsoAAADrtLRHYx988MGyigYAAFiIpc0j9K1vfUtPnz5dVvEAAACXNneL0He/+92J26IoUrfb1eHhod5+++15D7ESf/7nf67f/d3fHbttb29Pe3t7K64RAABm29/f1/7+/thtv/jFLxZ6rLmD0CwLI7ZaLf31X//1vIdYib//+7/XH//xHy+t/GUHqVUEtesSBq/DueJamHeMZbsu54lrcb2OMa0h4ic/+Ynu379/6WMk5l5rbHd3V77va3t7+8y2fr+vZrOpH/zgB5eu4LIkJ/KTTz5ZahDC+T7//HO98cYb+tnPfrbUYcI4H9ciO7gW2cL1yI5F37/n7iPkuq7u3r2rN95448yP4zjK5/P6q7/6q0tXEAAAYFnmDkIPHjyYut22bTUajXmLBwAAWLq5+whNGxEWhqE8z5u3aAAAgJWYOwg5jqONjY2J2+M4Vr1en7d4AACApZs7CFmWpd3dXVmWdWbbnTt35DjOuY/PAAAA1mnuIHRwcKB33313kXUBAABYqbk7SxOCAADAVbe0tca+//3va2dnR9/73veWdYhLuXXr1sj/Xln/8mjdNbi0zZe/1kcffaTNzc11V8V4m5ubXIuM4FpkC9cjOxZ9/557QsVpnj17plwup62tLX322WeLLn4hgiBQPp9Xr9eT4zjrrs78rkEQkiR989G6awAAuAIWff9eSovQ3bt31Wg0ZNv2MooHAABYiKU9Gtvd3dUbb7yxrOIX5t1339Xrr78+dhuLrgIAsHrTFl198eLFQo+1tCD0ox/9SJ9++qlqtdqyDrEQP/zhD6/2ozEAAK6ZaQ0RyaOxRblUEPrxj3+sTqejKIpGXh8
      "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": 189,
   "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": 189,
     "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
}