keras_g3_cae_9cells_ench.ana.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "from event_display import  table_display_compare\n",
    "from data_tools import reduce_9cells "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pics_prefix = os.path.join(\"plots\", \"v1_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time open 0.27045631408691406\n",
      "Time split 0.25612950325012207\n",
      "Inputs shape new = (10000, 12, 12, 1)\n"
     ]
    }
   ],
   "source": [
    "from geant3_parser import build_true_answers_train_set\n",
    "from geant3_parser import Geant3DataFile\n",
    "\n",
    "def norm_func(e):\n",
    "    return np.float64(np.log(e) / 11)\n",
    "\n",
    "file_name = os.path.join('data', 'shower_geant3_new.dat')\n",
    "data_file = Geant3DataFile(file_name, skip_lines=3)\n",
    "\n",
    "events_to_read = 5000\n",
    "input_data, answers, values = build_true_answers_train_set(data_file, events_to_read*2, norm_func=norm_func, rnd_shift=((-2,2), (-2,2)))\n",
    "\n",
    "# Remove x,y\n",
    "\n",
    "# Reshape to events x 11 x 11 x 1 form\n",
    "input_data = np.reshape(input_data, (len(input_data), 11, 11, 1))  # -1 => autodetermine\n",
    "answers = np.reshape(answers, (len(answers), 11, 11, 1))  # -1 => autodetermine\n",
    "\n",
    "# Pad with 1 row and column of zeroes, so it divides by 2, events x 12 x 12 x 1 now\n",
    "input_data = np.pad(input_data, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)\n",
    "answers = np.pad(answers, ((0,0), (0,1), (0,1), (0,0)), mode='constant', constant_values=0)\n",
    "\n",
    "# printout shape\n",
    "print(f\"Inputs shape new = {np.shape(input_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 864x648 with 3 Axes>, <AxesSubplot:>, <AxesSubplot:>)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x648 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_number = 12\n",
    "left_event = np.exp(input_data[event_number]*11)\n",
    "right_event = np.exp(answers[event_number]*11)\n",
    "left_event[left_event<1.5] = 0\n",
    "right_event[right_event<2] = 0\n",
    "table_display_compare(left_event, right_event)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ench_events = np.copy(input_data)\n",
    "for event in ench_events:\n",
    "    reduce_9cells(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 864x648 with 3 Axes>, <AxesSubplot:>, <AxesSubplot:>)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x648 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_number = 11\n",
    "left_event = np.exp(input_data[event_number]*11)\n",
    "right_event = np.exp(ench_events[event_number]*11)\n",
    "left_event[left_event<1.5] = 0\n",
    "right_event[right_event<2] = 0\n",
    "table_display_compare(left_event, right_event)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a707b6ce8c685eb936424fcc3009d4b4b7a52543c4db09380a3fc49186ceb509"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('base')",
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}