Projektpraktikum/B_rework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\n",
    "#selektiere nur elektronen von B->K*ee und nur solche mit einem momentum von ueber 5 GeV \n",
    "allcolumns = file.arrays()\n",
    "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 9056\n",
    "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal) & (allcolumns.p > 5e3)] #B: 1466\n",
    "\n",
    "ak.num(found, axis=0)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8606728758791105"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def t_eff(found, lost, axis = 0):\n",
    "    sel = ak.num(found, axis=axis)\n",
    "    des = ak.num(lost, axis=axis)\n",
    "    return sel/(sel + des)\n",
    "\n",
    "t_eff(found, lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample size:  32\n",
      "eff (cutoff =  0 ) =  0.96875\n",
      "sample size:  32\n",
      "eff (cutoff =  50 ) =  0.96875\n",
      "sample size:  32\n",
      "eff (cutoff =  100 ) =  0.96875\n",
      "sample size:  43\n",
      "eff (cutoff =  150 ) =  0.9767441860465116\n",
      "sample size:  65\n",
      "eff (cutoff =  200 ) =  0.9692307692307692\n",
      "sample size:  97\n",
      "eff (cutoff =  250 ) =  0.9587628865979382\n",
      "sample size:  129\n",
      "eff (cutoff =  300 ) =  0.9457364341085271\n",
      "sample size:  150\n",
      "eff (cutoff =  350 ) =  0.9533333333333334\n",
      "sample size:  169\n",
      "eff (cutoff =  400 ) =  0.9408284023668639\n",
      "sample size:  197\n",
      "eff (cutoff =  450 ) =  0.9390862944162437\n",
      "sample size:  227\n",
      "eff (cutoff =  500 ) =  0.920704845814978\n",
      "sample size:  257\n",
      "eff (cutoff =  550 ) =  0.9260700389105059\n",
      "sample size:  297\n",
      "eff (cutoff =  600 ) =  0.9326599326599326\n",
      "sample size:  334\n",
      "eff (cutoff =  650 ) =  0.9281437125748503\n",
      "sample size:  366\n",
      "eff (cutoff =  700 ) =  0.9289617486338798\n",
      "sample size:  400\n",
      "eff (cutoff =  750 ) =  0.925\n",
      "sample size:  436\n",
      "eff (cutoff =  800 ) =  0.9151376146788991\n",
      "sample size:  468\n",
      "eff (cutoff =  850 ) =  0.9102564102564102\n",
      "sample size:  500\n",
      "eff (cutoff =  900 ) =  0.912\n",
      "sample size:  533\n",
      "eff (cutoff =  950 ) =  0.9136960600375235\n",
      "sample size:  562\n",
      "eff (cutoff =  1000 ) =  0.9163701067615658\n",
      "\n",
      "sample size:  150\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9533333333333334"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
    "#von energie der photonen abmachen\n",
    "#scan ab welcher energie der photonen die effizienz abfällt\n",
    "\n",
    "#abhängigkeit vom ort der emission untersuchen   <- noch nicht gemacht\n",
    "\n",
    "\n",
    "\n",
    "#idea: we make an event cut st all events that contain a photon of energy > cutoff_energy are not included\n",
    "\"\"\"\n",
    "ph_e = found[\"brem_photons_pe\"]\n",
    "event_cut = ak.all(ph_e<cutoff_energy,axis=1)\n",
    "ph_e = ph_e[event_cut]\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "for cutoff_energy in range(0,1050,50):\n",
    "\tnobrem_f = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tnobrem_l = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\tprint(\"sample size: \",ak.num(nobrem_f,axis=0)+ak.num(nobrem_l,axis=0))\n",
    "\tprint(\"eff (cutoff = \",str(cutoff_energy),\") = \",str(t_eff(nobrem_f,nobrem_l)))\n",
    "\n",
    "\"\"\"\n",
    "we see that a cutoff energy of 350MeV is ideal because the efficiency drops significantly for higher values\n",
    "\"\"\"\n",
    "cutoff_energy = 350.0 #MeV\n",
    "\n",
    "\"\"\"\n",
    "better statistics: cutoff=350MeV - sample size: 150 events and efficiency=0.9533\n",
    "\"\"\"\n",
    "nobrem_found = found[ak.all(found[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "nobrem_lost = lost[ak.all(lost[\"brem_photons_pe\"]<cutoff_energy,axis=1)]\n",
    "\n",
    "print(\"\\nsample size: \",ak.num(nobrem_found,axis=0)+ak.num(nobrem_lost,axis=0))\n",
    "t_eff(nobrem_found, nobrem_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weird_div(n,d):\n",
    "    return n/d if d else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8593328191284226"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#wie viel energie relativ zur anfangsenergie verlieren die elektronen durch bremstrahlung und hat das einen einfluss darauf ob wir sie finden oder nicht?\n",
    "#if any photon of an electron has an energy higher the cutoff then it is included\n",
    "cutoff_energy=350\n",
    "\n",
    "brem_found = found[ak.any(found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
    "energy_found = ak.to_numpy(brem_found[\"energy\"])\n",
    "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "energyloss_found = eph_found/energy_found\n",
    "\n",
    "brem_lost = lost[ak.any(lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n",
    "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n",
    "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n",
    "energyloss_lost = eph_lost/energy_lost\n",
    "\n",
    "t_eff(brem_found,brem_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.6551043170507098\n",
      "mean energyloss relative to initial energy (lost):  0.8273131179948844\n"
     ]
    }
   ],
   "source": [
    "mean_energyloss_found = ak.mean(energyloss_found)\n",
    "mean_energyloss_lost = ak.mean(energyloss_lost)\n",
    "print(\"mean energyloss relative to initial energy (found): \", mean_energyloss_found)\n",
    "print(\"mean energyloss relative to initial energy (lost): \", mean_energyloss_lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#in abhängigkeit von der energie der elektronen\n",
    "plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
    "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
    "plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
    "plt.yticks(np.arange(0,10,1), minor=True)\n",
    "plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
    "plt.ylabel(\"counts (normed)\")\n",
    "plt.title(r'$E_{ph}/E_0$')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#energyloss in abh von der energie der elektronen\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
    "\n",
    "a0=ax0.hist2d(energyloss_found, energy_found, bins=200, cmap=plt.cm.jet, cmin=1)\n",
    "ax0.set_xlabel(\"energyloss\")\n",
    "ax0.set_ylabel(r\"$E_0$\")\n",
    "ax0.set_title(\"found energyloss wrt electron energy\")\n",
    "plt.colorbar(a0[3],ax=ax0)\n",
    "\n",
    "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=200, cmap=plt.cm.jet, cmin=1) \n",
    "ax1.set_xlabel(\"energyloss\")\n",
    "ax1.set_ylabel(r\"$E_0$\")\n",
    "ax1.set_title(\"lost energyloss wrt electron energy\")\n",
    "plt.colorbar(a1[3],ax=ax1)\n",
    "\n",
    "\"\"\"\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env1",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}