root-files-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import uproot\n",
    "import matplotlib.pyplot as plt\n",
    "from sificc_lib import utils, Event, Simulation, root_files\n",
    "\n",
    "pd.options.display.max_columns = 100\n",
    "pd.options.display.max_rows = 100\n",
    "pd.options.display.float_format = '{:,.3f}'.format\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analysis of the 'highest statistics' root file\n",
      "Scatterer:\n",
      "\tPosition: (200.0, 0.0, 0.0)\n",
      "\tThickness: (13.0, 100.0, 98.8)\n",
      "\n",
      "Absorber:\n",
      "\tPosition: (400.0, 0.0, 0.0)\n",
      "\tThickness: (39.0, 100.0, 98.8)\n",
      "\n",
      "processing root file: 100%|███████████████████████████████| 875985/875985 [04:37<00:00, 3161.09it/s]\n",
      " 875,985 total entries\n",
      " 710,484 valid entries with distrbuted clusters\n",
      " 407,459 compton events\n",
      " 253,310 compton + second interaction\n",
      " 155,255 compton + second in different module\n",
      " 118,909 ideal compton events\n",
      "\t 117,641 ep\n",
      "\t   1,268 pe\n",
      "  44,758 ideal compton events with matching clusters\n",
      "     210 ideal compton events with overlapping clusters\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "histogram bars' count: [4.0245e+04 3.7731e+04 9.1420e+03 1.8510e+03 4.2500e+02 8.5000e+01\n",
      " 2.7000e+01 8.0000e+00 2.0000e+00]\n"
     ]
    }
   ],
   "source": [
    "simulation = Simulation(root_files.HPC_LLR_20)\n",
    "print(\"analysis of the 'highest statistics' root file\")\n",
    "utils.show_simulation_setup(simulation)\n",
    "print()\n",
    "utils.show_root_file_analysis(simulation, only_valid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing root file: 100%|███████████████████████████████| 875985/875985 [04:45<00:00, 3068.97it/s]\n",
      "Features normalization:\n",
      "clusters entry\n",
      "\tmean 1.7750611473108673\n",
      "\tstd 1.588047947069924\n",
      "\n",
      "clusters energy\n",
      "\tmean 1.421916339110481\n",
      "\tstd 1.902366137816775\n",
      "\n",
      "clusters energy uncertainty\n",
      "\tmean 0.03560808826620114\n",
      "\tstd 0.024890506769033635\n",
      "\n",
      "clusters position\n",
      "\tmean [ 3.05037323e+02  1.07851841e-01 -7.90035363e-01]\n",
      "\tstd [97.35787096 30.03624762 27.49783761]\n",
      "\n",
      "clusters position uncertainty\n",
      "\tmean [ 1.06228859 11.49932913  0.94706298]\n",
      "\tstd [1.00977742 4.07463448 0.74580474]\n",
      "\n",
      "Targets normalization\n",
      "real e energy\n",
      "\tmean 1.3483193304874133\n",
      "\tstd 1.8436350099160583\n",
      "\n",
      "real e position\n",
      "\tmean [ 2.02035957e+02  4.36810732e-02 -2.86436225e+00]\n",
      "\tstd [19.5244683  27.86458018 27.34153256]\n",
      "\n",
      "real p energy\n",
      "\tmean 2.229320222904948\n",
      "\tstd 1.7060496720372578\n",
      "\n",
      "real p position\n",
      "\tmean [3.94001939e+02 1.55154095e-01 1.04931962e+00]\n",
      "\tstd [22.75771642 28.32632043 28.09068483]\n"
     ]
    }
   ],
   "source": [
    "utils.calculate_normalizations(simulation, only_valid=True)"
   ]
  }
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