Projektpraktikum/trackinglosses_endVelo_momEff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40402 10099\n",
      "91861\n"
     ]
    }
   ],
   "source": [
    "file = uproot.open(\n",
    "    \"tracking_losses_ntuple_B_endVelo_idealstateP.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",
    "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stretch factor:  0.24996287312509283\n"
     ]
    }
   ],
   "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": 25,
   "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[\"ideal_state_770_p\"])\n",
    "trueP_found = ak.to_numpy(found[\"p\"])\n",
    "\n",
    "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"ideal_state_770_p\"])\n",
    "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n",
    "\n",
    "stretch_factor = ak.num(trueP_notelectrons, axis=0) / ak.num(trueP_found, axis=0)\n",
    "\n",
    "nbins = 100\n",
    "vmax = 50\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, 30000], [0, 30000]],\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",
    "    density=False,\n",
    "    bins=nbins,\n",
    "    cmap=plt.cm.jet,\n",
    "    cmin=1,\n",
    "    vmax=vmax * stretch_factor,\n",
    "    range=[[0, 30000], [0, 30000]],\n",
    ")\n",
    "ax1.set_xlabel(f\"True $P$\")\n",
    "ax1.set_ylabel(f\"endVelo $P$\")\n",
    "ax1.set_title(f\"notelectrons P\")\n",
    "\n",
    "plt.suptitle(\"True and idealstate_endVelo Momentum\")\n",
    "plt.colorbar(a0[3], ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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