Projektpraktikum/trackinglosses_endVelo_momEff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
    "from methods.fit_linear_regression_model import fit_linear_regression_model\n",
    "import sklearn\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41978 8523\n",
      "92337\n"
     ]
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"tracking_losses_ntuple_B_EndVeloP.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",
    "notelectrons = allcolumns[\n",
    "    (~allcolumns.isElectron) & (allcolumns.fromB) & (~allcolumns.lost)\n",
    "]\n",
    "\n",
    "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
    "print(ak.num(notelectrons, axis=0))\n",
    "# ak.count(found, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
    "eta_found = ak.to_numpy(found[\"eta\"])\n",
    "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
    "eta_lost = ak.to_numpy(lost[\"eta\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "endVeloP_found = ak.to_numpy(found[\"p_end_velo\"])\n",
    "trueP_found = ak.to_numpy(found[\"p\"])\n",
    "\n",
    "endVeloP_lost = ak.to_numpy(lost[\"p_end_velo\"])\n",
    "trueP_lost = ak.to_numpy(lost[\"p\"])\n",
    "\n",
    "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"p_end_velo\"])\n",
    "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n",
    "\n",
    "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n",
    "\n",
    "nbins = 100\n",
    "vmax = 200\n",
    "\n",
    "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
    "\n",
    "a0 = ax0.hist2d(\n",
    "    trueP_found,\n",
    "    endVeloP_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax,\n",
    "    range=[[0, 20000], [0, 20000]],\n",
    ")\n",
    "ax0.set_xlabel(f\"True $P$\")\n",
    "ax0.set_ylabel(f\"EndVelo $P$\")\n",
    "ax0.set_title(f\"found P\")\n",
    "\n",
    "a1 = ax1.hist2d(\n",
    "    # trueP_notelectrons,\n",
    "    # endVeloP_notelectrons,\n",
    "    trueP_lost,\n",
    "    endVeloP_lost,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax * stretch_factor,\n",
    "    range=[[0, 20000], [0, 20000]],\n",
    ")\n",
    "ax1.set_xlabel(f\"True $P$\")\n",
    "ax1.set_ylabel(f\"EndVelo $P$\")\n",
    "ax1.set_title(f\"lost P\")\n",
    "\n",
    "plt.suptitle(\"Momentum at Creation and EndVelo\")\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a0 = plt.hist2d(\n",
    "    trueP_found,\n",
    "    endVeloP_found,\n",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=200,\n",
    "    range=[[0, 20000], [0, 20000]],\n",
    ")\n",
    "plt.xlabel(f\"True $P$ [MeV]\")\n",
    "plt.ylabel(f\"EndVelo $P$ [MeV]\")\n",
    "plt.title(f\"found P\")\n",
    "plt.colorbar(a0[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tuner",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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