example_root_data_manip_explained.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import uproot4\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import LogNorm, Normalize\n",
    "import awkward1 as ak\n",
    "from lxml import etree as ET\n",
    "import os\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# file_name = f\"{Path.cwd()}/data/2021-12-07_nomat_10000evt_pi-_1x_21.5y_0z_2GeV.root\"\n",
    "file_name = f\"{Path.cwd()}/data/2021-12-07_nomat_10000evt_e-_1x_21.5y_0z_2GeV.root\"\n",
    "cal_size=3\n",
    "start_event=0\n",
    "process_events=None\n",
    "\n",
    "\n",
    "root_file = uproot4.open(file_name)\n",
    "tree = root_file[\"events\"]\n",
    "#print(tree.keys())\n",
    "\n",
    "entry_start = start_event\n",
    "\n",
    "entry_stop = start_event + process_events if process_events else None\n",
    "events = tree.arrays(['EcalEndcapNHits.cellID', \n",
    "                        'EcalEndcapNHits.energyDeposit', \n",
    "                        'EcalEndcapNHits.position.x', \n",
    "                        'EcalEndcapNHits.position.y', \n",
    "                        'EcalEndcapNHits.position.z',\n",
    "                        'EcalEndcapNHits.truth.pdgID'],\n",
    "                        aliases={\"EcalEndcapNHits.cellID\": \"id\"},\n",
    "                        library=\"ak\",\n",
    "                        how=tuple,\n",
    "                        entry_start=entry_start, \n",
    "                        entry_stop=entry_stop)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Array [[3735911, 3285607, ... 3965287]] type='10000 * var * int64'>,\n",
       " <Array [[0.266, 0.000504, ... 0.00162]] type='10000 * var * float64'>,\n",
       " <Array [[10.3, 308, 267, ... 154, 133, -30.7]] type='10000 * var * float64'>,\n",
       " <Array [[215, 267, 308, ... 318, 277, 256]] type='10000 * var * float64'>,\n",
       " <Array [[-2.01e+03, -2.18e+03, ... -2.01e+03]] type='10000 * var * float64'>,\n",
       " <Array [[-211, 11, 11, 11, ... 22, 11, 11, 22]] type='10000 * var * int32'>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events\n",
    "#events[0].type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import hist\n",
    "from hist import Hist\n",
    "# Create histograms\n",
    "hist_hit_xy = Hist(hist.axis.Regular(50, -500, 500, name=\"X\", label=\"Hits x [mm]\", flow=False),\n",
    "                   hist.axis.Regular(50, -500, 500, name=\"Y\", label=\"Hits y [mm]\", flow=False))\n",
    "\n",
    "hist_hit_z = Hist(hist.axis.Regular(50, -2170, -2030, name=\"Z\", label=\"Hits z [mm]\", flow=False))\n",
    "\n",
    "hist_total_dep = Hist(hist.axis.Regular(200, 0, 3, name=\"DE\", label=\"Cal total cells E deposit [GeV]\", flow=False))\n",
    "hist_true_e = Hist(hist.axis.Regular(50, 0, 10, name=\"E\", label=\"Energy [GeV]\", flow=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events processed: 1000\n",
      "Events processed: 2000\n",
      "Events processed: 3000\n",
      "Events processed: 4000\n",
      "Events processed: 5000\n",
      "Events processed: 6000\n",
      "Events processed: 7000\n",
      "Events processed: 8000\n",
      "Events processed: 9000\n",
      "Events processed: 10000\n"
     ]
    }
   ],
   "source": [
    "# Events loop\n",
    "train_data = []     # Resulting data for each event\n",
    "inc_hits = []       # Cal incidence x,y,z for each event\n",
    "event_count = 0\n",
    "for event in zip(*events):\n",
    "    arr_id, arr_de, arr_hit_x, arr_hit_y, arr_hit_z, arr_pid = event\n",
    "\n",
    "    # Print what event is being processed\n",
    "    event_count += 1\n",
    "    if (event_count % 1000) == 0:\n",
    "        print(f\"Events processed: {event_count}\")\n",
    "    \n",
    "    # Skip event if we don't have hits or generated particles\n",
    "    if not len(arr_id) or not len(arr_de):\n",
    "        continue\n",
    "\n",
    "    # in a simple scenario (1 particle gun particle per event) 1-st hit is an incidence hit\n",
    "    hit_x, hit_y, hit_z, pid = arr_hit_x[0], arr_hit_y[0], arr_hit_z[0], arr_pid[0]\n",
    "\n",
    "    total_deposit = 0\n",
    "\n",
    "    # Form a flat data training array. Indexes are like:\n",
    "    # [0] [1] [2]\n",
    "    # [3] [4] [5]\n",
    "    # [6] [7] [8]\n",
    "    # event_train_data = cal_size*cal_size*[0]\n",
    "\n",
    "    for de in arr_de :\n",
    "        total_deposit += de\n",
    "\n",
    "        #print(cell.col, cell.row, cell.adc)\n",
    "    # train_data.append(event_train_data)\n",
    "    inc_hits.append((hit_x, hit_y, hit_z))\n",
    "\n",
    "    # Fill some histograms\n",
    "    hist_hit_xy.fill(hit_x, hit_y)\n",
    "    hist_hit_z.fill(hit_z)\n",
    "    hist_total_dep.fill(total_deposit)\n",
    "    # hist_true_e.fill(arr_true_e[0])\n",
    "\n",
    "histograms = {\n",
    "    'hit_xy': hist_hit_xy,\n",
    "    'hit_z': hist_hit_z,\n",
    "    'total_dep': hist_total_dep,\n",
    "    'true_e': hist_true_e\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ColormeshArtists(pcolormesh=<matplotlib.collections.QuadMesh object at 0x0000022B219FEEB0>, cbar=<matplotlib.colorbar.Colorbar object at 0x0000022B219C0B50>, text=[])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_hit_xy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StairsArtists(stairs=<matplotlib.patches.StepPatch object at 0x0000022B21DB3550>, errorbar=<ErrorbarContainer object of 3 artists>, legend_artist=<ErrorbarContainer object of 3 artists>)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_hit_z.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[StairsArtists(stairs=<matplotlib.patches.StepPatch object at 0x0000022B21E446A0>, errorbar=<ErrorbarContainer object of 3 artists>, legend_artist=<ErrorbarContainer object of 3 artists>)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_total_dep.plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a707b6ce8c685eb936424fcc3009d4b4b7a52543c4db09380a3fc49186ceb509"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}