Projektpraktikum/B_photon_eta_phi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
    "import matplotlib.ticker as tck\n",
    "from mpl_toolkits import mplot3d\n",
    "import itertools\n",
    "import awkward as ak\n",
    "from scipy.optimize import curve_fit\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10522"
      ]
     },
     "execution_count": 39,
     "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) + ak.num(lost, axis=0)\n",
    "#ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot minimal energy of photon abhängigkeit von eta und phi\n",
    "#materialpeak (beampipe)\n",
    "\n",
    "#looked at minimal photon energy ak.min(...) and sum of photon energies ak.sum(...)\n",
    "energy_found = ak.to_numpy(ak.min(found[\"brem_photons_pe\"],axis=-1))\n",
    "energy_lost = ak.to_numpy(ak.min(lost[\"brem_photons_pe\"],axis=-1))\n",
    "\n",
    "eta_found = ak.to_numpy(found[\"eta\"])\n",
    "eta_lost = ak.to_numpy(lost[\"eta\"])\n",
    "\n",
    "phi_found = ak.to_numpy(found[\"phi\"])\n",
    "phi_lost = ak.to_numpy(lost[\"phi\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "a0=ax[0].hist2d(energy_found, eta_found, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,3e3],[1,6]], vmax=20)\n",
    "ax[0].set_xlabel(\"minimal or sum of photon energy [MeV]\")\n",
    "ax[0].set_ylabel(r\"$\\eta$\")\n",
    "ax[0].set_title(\"found eta wrt photon energy\")\n",
    "\n",
    "a1=ax[1].hist2d(energy_lost, eta_lost, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,3e3],[1,6]], vmax=20)\n",
    "ax[1].set_xlabel(\"minimal or sum of photon energy [MeV]\")\n",
    "ax[1].set_ylabel(r\"$\\eta$\")\n",
    "ax[1].set_title(\"lost eta wrt photon energy\")\n",
    "\n",
    "\"\"\"\n",
    "lost: perhaps slightly more hits at larger eta but not really significant\n",
    "\"\"\"\n",
    "\n",
    "fig.colorbar(a0[3],ax=ax[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "a0=ax[0].hist2d(energy_found, phi_found/np.pi, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,2e3],[-1.1,1.1]],vmax=10)\n",
    "#ax[0].set_yticks(np.arange(-1,1.1,0.25), minor=True)\n",
    "ax[0].yaxis.set_major_formatter(tck.FormatStrFormatter('%g $\\pi$'))\n",
    "ax[0].set_xlabel(\"minimal or sum of photon energy [MeV]\")\n",
    "ax[0].set_ylabel(r\"$\\phi$\")\n",
    "ax[0].set_title(r\"found phi $\\angle(x,y)$ wrt photon energy\")\n",
    "\n",
    "a1=ax[1].hist2d(energy_lost, phi_lost/np.pi, bins=200, cmap=plt.cm.jet, cmin=1, range=[[0,2e3],[-1.1,1.1]], vmax=10)\n",
    "#ax[1].set_yticks(np.arange(-1,1.1,0.5), minor=True)\n",
    "ax[1].yaxis.set_major_formatter(tck.FormatStrFormatter('%g $\\pi$'))\n",
    "ax[1].set_xlabel(\"minimal or sum of photon energy [MeV]\")\n",
    "ax[1].set_ylabel(r\"$\\phi$\")\n",
    "ax[1].set_title(\"lost phi wrt photon energy\")\n",
    "\n",
    "\"\"\"\n",
    "Cannot really make out any patterns that might explain lost and found differences.\n",
    "See no materialpeak\n",
    "\"\"\"\n",
    "\n",
    "fig.colorbar(a0[3],ax=ax[1])\n",
    "#plt.style.use(\"ggplot\")\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",
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