6_Regression_Ebaricenter_NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Prueba Baricentro energía**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys in optree: ['eventID', 'runID', 'subrunID', 'nuvX', 'nuvY', 'nuvZ', 'nuvT', 'nuvE', 'stepX', 'stepY', 'stepZ', 'stepT', 'dE', 'energydep', 'energydepX', 'energydepY', 'energydepZ', 'E', 'StartPx', 'StartPy', 'StartPz', 'EndPx', 'EndPy', 'EndPz', 'process', 'trackID', 'motherID', 'PDGcode', 'InTimeCosmics', 'InTimeCosmicsTime', 'dEtpc', 'dEpromx', 'dEpromy', 'dEpromz', 'dEspreadx', 'dEspready', 'dEspreadz', 'dElowedges', 'dEmaxedges', 'nopflash', 'flash_id', 'flash_time', 'flash_total_pe', 'flash_pe_v', 'flash_tpc', 'flash_y', 'flash_yerr', 'flash_z', 'flash_zerr', 'flash_x', 'flash_xerr', 'flash_ophit_time', 'flash_ophit_risetime', 'flash_ophit_starttime', 'flash_ophit_amp', 'flash_ophit_area', 'flash_ophit_width', 'flash_ophit_pe', 'flash_ophit_ch']\n"
     ]
    }
   ],
   "source": [
    "file_path = '/data/vidales/opana_tree_combined_v2609.root'\n",
    "file = uproot.open(file_path)\n",
    "optree = file['opanatree']['OpAnaTree'] # Tree con número de fotoelectrones\n",
    "print(\"Keys in optree:\", optree.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nopflash = optree['nopflash'].array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nopflash: [2, 2, 3, 2, 3, 2, 4, 2, 4, 3, 4, 4, 4, ..., 4, 4, 2, 2, 2, 2, 2, 2, 3, 4, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"nopflash:\", nopflash)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energydep: 15300\n"
     ]
    }
   ],
   "source": [
    "energydep = optree['energydep'].array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energydep: [[[], [], [], [], [], [], [], [], [], ..., [], [], [], [], [], [], [], []], ...]\n"
     ]
    }
   ],
   "source": [
    "print(\"energydep:\", energydep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All values in energydep are zero.\n"
     ]
    }
   ],
   "source": [
    "# Check if any value in energydep is different from zero\n",
    "any_non_zero = ak.any(energydep != 0)\n",
    "\n",
    "# Output the result\n",
    "if any_non_zero:\n",
    "    print(\"There are non-zero values in energydep.\")\n",
    "else:\n",
    "    print(\"All values in energydep are zero.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Plot histogram of filtered_nopflash\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(nopflash, bins=4, edgecolor='black', range=(0.5, 4.5), alpha=0.5)\n",
    "plt.hist(nopflash_filtered, bins=4, edgecolor='black', range=(0.5, 4.5), alpha=0.5)\n",
    "plt.title('Histogram of nopflash')\n",
    "plt.xlabel('# flashes')\n",
    "plt.ylabel('# events')\n",
    "plt.legend(['original', 'filtered to 1 neutrino per event'])\n",
    "plt.xticks(range(1, 5))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}