Projektpraktikum/e_tasks.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",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
    "\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)\n"
   ]
  },
  {
   "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):\n",
    "    sel = found[\"energy\"]\n",
    "    des = lost[\"energy\"]\n",
    "    return ak.count(sel,axis=None)/(ak.count(sel,axis=None)+ak.count(des,axis=None))\n",
    "\n",
    "t_eff(found, lost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96875"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finden wir die elektronen die keine bremsstrahlung gemacht haben mit hoher effizienz?\n",
    "nobrem_found = found[found[\"brem_photons_pe_length\"]==0]\n",
    "nobrem_lost = lost[lost[\"brem_photons_pe_length\"]==0]\n",
    "\n",
    "\"\"\"\n",
    "die effizienz mit der wir elektronen finden, die keine bremsstrahlung gemacht haben, ist gut mit 0.9688.\n",
    "allerdings haben wir hier nur sehr wenige teilchen (<100)\n",
    "\"\"\"\n",
    "\n",
    "t_eff(nobrem_found, nobrem_lost)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8603431839847474"
      ]
     },
     "execution_count": 5,
     "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",
    "brem_found = found[found[\"brem_photons_pe_length\"]!=0]\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",
    "\n",
    "brem_lost = lost[lost[\"brem_photons_pe_length\"]!=0]\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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean energyloss relative to initial energy (found):  0.6475128752780828\n",
      "mean energyloss relative to initial energy (lost):  0.8241268441538472\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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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(0,1,0.1), 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",
    "found: elektronen verlieren durchschnittlich 0.65 ihrer anfangsenergie durch bremsstrahlung\n",
    "lost: elektronen verlieren durchschnittlich 0.82 ihrer anfangsenergie durch bremsstrahlung\n",
    "\n",
    "-> wir können sofort erkennen, dass verlorene elektronen im schnitt mehr energie durch bremsstrahlung verlieren als gefundene, \n",
    "aber auch die rate der gefundenen elektronen steigt für raten nahe 1, wenn auch wesentlich schwächer als für verlorene elektronen.\n",
    "die meisten verlorenen elektronen verlieren >0.8 ihrer anfangsenergie.\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ist die shape der teilspur im scifi anders? (koenntest du zum beispiel durch vergleich der verteilungen der fit parameter studieren,\n",
    "#in meiner thesis findest du das fitmodell -- ist einfach ein polynom dritten grades)\n",
    "z_ref=8520 #mm\n",
    "\n",
    "def scifi_track(z, a, b, c, d):\n",
    "    return a + b*(z-z_ref) + c*(z-z_ref)**2 + d*(z-z_ref)**3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9056"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scifi_found = found[found[\"scifi_hit_pos_x_length\"]>3]\n",
    "scifi_lost = lost[lost[\"scifi_hit_pos_x_length\"]>3]\n",
    "\n",
    "scifi_x_found = scifi_found[\"scifi_hit_pos_x\"]\n",
    "scifi_z_found = scifi_found[\"scifi_hit_pos_z\"]\n",
    "\n",
    "scifi_x_lost = scifi_lost[\"scifi_hit_pos_x\"]\n",
    "scifi_z_lost = scifi_lost[\"scifi_hit_pos_z\"]\n",
    "\n",
    "ak.num(scifi_found[\"energy\"], axis=0)\n",
    "#scifi_found.snapshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scifi_fitpars_found = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_found[\"energy\"], axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]))\n",
    "    scifi_fitpars_found.begin_list()\n",
    "    scifi_fitpars_found.real(popt[0])\n",
    "    scifi_fitpars_found.real(popt[1])\n",
    "    scifi_fitpars_found.real(popt[2])\n",
    "    scifi_fitpars_found.real(popt[3])\n",
    "    scifi_fitpars_found.end_list()\n",
    "\n",
    "scifi_fitpars_lost = ak.ArrayBuilder()\n",
    "\n",
    "for i in range(0,ak.num(scifi_lost[\"energy\"], axis=0)):\n",
    "    popt, pcov = curve_fit(scifi_track,ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]))\n",
    "    scifi_fitpars_lost.begin_list()\n",
    "    scifi_fitpars_lost.real(popt[0])\n",
    "    scifi_fitpars_lost.real(popt[1])\n",
    "    scifi_fitpars_lost.real(popt[2])\n",
    "    scifi_fitpars_lost.real(popt[3])\n",
    "    scifi_fitpars_lost.end_list()\n",
    "\n",
    "\n",
    "\n",
    "scifi_fitpars_lost = scifi_fitpars_lost.to_numpy()\n",
    "scifi_fitpars_found = scifi_fitpars_found.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, figsize=(15,10))\n",
    "\n",
    "ax0.hist(scifi_fitpars_found[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$a_x$ found\")\n",
    "ax0.hist(scifi_fitpars_lost[:,0], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$a_x$ lost\")\n",
    "ax0.set_xlabel(\"a\")\n",
    "ax0.set_ylabel(\"normed\")\n",
    "ax0.set_title(\"fitparameter a der scifi track\")\n",
    "ax0.legend()\n",
    "\n",
    "ax1.hist(scifi_fitpars_found[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$b_x$ found\")\n",
    "ax1.hist(scifi_fitpars_lost[:,1], bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$b_x$ lost\")\n",
    "ax1.set_xlabel(\"b\")\n",
    "ax1.set_ylabel(\"normed\")\n",
    "ax1.set_title(\"fitparameter b der scifi track\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.hist(scifi_fitpars_found[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$c_x$ found\")\n",
    "ax2.hist(scifi_fitpars_lost[:,2], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$c_x$ lost\")\n",
    "ax2.set_xlim([-3e-5,3e-5])\n",
    "ax2.set_xticks(np.arange(-3e-5,3.5e-5,1e-5),minor=False)\n",
    "ax2.set_xlabel(\"c\")\n",
    "ax2.set_ylabel(\"normed\")\n",
    "ax2.set_title(\"fitparameter c der scifi track\")\n",
    "ax2.legend()\n",
    "\n",
    "ax3.hist(scifi_fitpars_found[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=r\"$d_x$ found\")\n",
    "ax3.hist(scifi_fitpars_lost[:,3], bins=500, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=r\"$d_x$ lost\")\n",
    "ax3.set(xlim=(-5e-8,5e-8))\n",
    "ax3.text(-4e-8,3e8,\"d negligible <1e-7\")\n",
    "ax3.set_xlabel(\"d\")\n",
    "ax3.set_ylabel(\"normed\")\n",
    "ax3.set_title(\"fitparameter d der scifi track\")\n",
    "ax3.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.6785491318157854e-07"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(scifi_fitpars_found[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(15,6))\n",
    "\n",
    "for i in range(0,ak.num(scifi_found[\"energy\"], axis=0)):\n",
    "    z_coord = np.linspace(scifi_z_found[i,0],12000,300)\n",
    "    fit = scifi_track(z_coord, *scifi_fitpars_found[i])\n",
    "    ax0.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax0.errorbar(ak.to_numpy(scifi_z_found[i,:]),ak.to_numpy(scifi_x_found[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax0.legend()\n",
    "ax0.set_xlabel(\"z [mm]\")\n",
    "ax0.set_ylabel(\"x [mm]\")\n",
    "ax0.set_title(\"found tracks of scifi hits\")\n",
    "ax0.set(xlim=(7e3,12000), ylim=(-4000,4000))\n",
    "ax0.grid()\n",
    "\n",
    "for i in range(0,ak.num(scifi_lost[\"energy\"], axis=0)):\n",
    "    z_coord = np.linspace(scifi_z_lost[i,0],12000,300)\n",
    "    fit = scifi_track(z_coord, *scifi_fitpars_lost[i])\n",
    "    ax1.plot(z_coord, fit, \"-\", lw=0.5)\n",
    "    ax1.errorbar(ak.to_numpy(scifi_z_lost[i,:]),ak.to_numpy(scifi_x_lost[i,:]),fmt=\".\",ms=2)\n",
    "\n",
    "#ax1.legend()\n",
    "ax1.set_xlabel(\"z [mm]\")\n",
    "ax1.set_ylabel(\"x [mm]\")\n",
    "ax1.set_title(\"lost tracks of scifi hits\")\n",
    "ax1.set(xlim=(7e3,12000), ylim=(-4000,4000))\n",
    "ax1.grid()\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": []
  },
  {
   "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",
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   "nbconvert_exporter": "python",
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