example_event_display_ecce_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'/home/romanov/eic/eic_ai_pid'"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import uproot\n",
    "import matplotlib\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 awkward as ak\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "matplotlib.rc(\"figure\", dpi=200)\n",
    "\n",
    "from event_display import gdml_read_ecal_info, build_calorimeter_section, mpl_plot_mask, mpl_plot_hits, mpl_plot_event_array\n",
    "\n",
    "import os \n",
    "dir_path = os.path.dirname(os.path.realpath(\"__file__\"))\n",
    "dir_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "module_size_x    : 20.5\n",
      "module_size_y    : 20.5\n",
      "module_size_z    : 200.0\n",
      "total_modules    : 2816\n",
      "num_modules_x    : 61\n",
      "num_modules_y    : 61\n",
      "min_x            : -615.0\n",
      "max_x            : 615.0\n",
      "min_y            : -615.0\n",
      "max_y            : 615.0\n",
      "min_z            : 0.0\n",
      "max_z            : 0.0\n",
      "border_left      : -625.25\n",
      "border_right     : 625.25\n",
      "border_top       : 625.25\n",
      "border_bottom    : -625.25\n",
      "unit             : mm\n"
     ]
    }
   ],
   "source": [
    "# Load geometry file\n",
    "ecal_info = gdml_read_ecal_info(f\"{dir_path}/data/ecce.gdml\")\n",
    "\n",
    "# ecal_info is of EcalGeoInfo class, which is a helper holding information\n",
    "# about all needed ecal geometries.\n",
    "# Print what information it holds:\n",
    "ecal_info.print()\n",
    "\n",
    "# Data file name\n",
    "#data_file_name = f\"{dir_path}/data/test_gun.edm4hep.root\"\n",
    "data_file_name = f\"{dir_path}/data/disk_gun_10k.edm4hep.root\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(<matplotlib.colors.LogNorm at 0x7ff127ce4880>,\n <matplotlib.colors.ListedColormap at 0x7ff126c9a130>,\n <AxesSubplot:>)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x1200 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Open root file and get \"events\" tree from it\n",
    "tree = uproot.open(data_file_name)[\"events\"]\n",
    "\n",
    "# Number of events to process:\n",
    "entry_start = 0                  # (!) Try changing those\n",
    "entry_stop = 1                   # <==\n",
    "\n",
    "# Read energies, x and y positions\n",
    "energies = tree['EcalEndcapNHits/EcalEndcapNHits.energy'].array(entry_start=entry_start, entry_stop=entry_stop)\n",
    "hits_x = tree['EcalEndcapNHits/EcalEndcapNHits.position.x'].array(entry_start=entry_start, entry_stop=entry_stop)\n",
    "hits_y = tree['EcalEndcapNHits/EcalEndcapNHits.position.y'].array(entry_start=entry_start, entry_stop=entry_stop)\n",
    "\n",
    "# Flatten arrays for simplicity\n",
    "energies = ak.flatten(energies).to_numpy()\n",
    "hits_x = ak.flatten(hits_x).to_numpy()\n",
    "hits_y = ak.flatten(hits_y).to_numpy()\n",
    "\n",
    "# At this point we have `hits_x` and `hits_y` a cell centers and corresponding `energies` arrays.\n",
    "# We can draw how such data looks\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "mpl_plot_hits(energies, hits_x, hits_y, ax, ecal_info.module_size_x, ecal_info.module_size_y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(<matplotlib.colors.LogNorm at 0x7ff0b789d1f0>,\n <matplotlib.colors.ListedColormap at 0x7ff126c9a130>,\n <AxesSubplot:>)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_data = ecal_info.arrays_to_event(energies, hits_x, hits_y)\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "mpl_plot_event_array(event_data, ax)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61 61\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create module mask:\n",
    "# a numpy array of size (num_modules_x, num_modules_y) that represents ecal modules layout\n",
    "# Array values: 0 - no module here, 1 or 2 - module is here, 2 - modules on the edge of ecal\n",
    "modules_mask = ecal_info.get_modules_mask()\n",
    "\n",
    "# We can plot this mask with matplotlib, which the most self-describing:\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "mpl_plot_mask(modules_mask, ax)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61 61\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can set the radius of edge modules:\n",
    "modules_mask = ecal_info.get_modules_mask(edge_width=1)\n",
    "\n",
    "# We can plot this mask with matplotlib, which the most self-describing:\n",
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "mpl_plot_mask(modules_mask, ax)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61 61\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x1600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot this mask with matplotlib, which the most self-describing:\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ecal_info.mpl_plot_event(event_data, ax)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.45 s, sys: 7.91 ms, total: 2.46 s\n",
      "Wall time: 2.45 s\n"
     ]
    }
   ],
   "source": [
    "%time events = ecal_info.read_events_from_file(data_file_name, 0, 1000)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('ai')",
   "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": "3.9.12"
  },
  "metadata": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  },
  "vscode": {
   "interpreter": {
    "hash": "f5c7603ba5c706ac0ece86f855ab939dae7cd06332146575f448356a26e0dec1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}