comparison_sum_e.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from geant3_parser import build_train_set\n",
    "from geant3_parser import Geant3DataFile\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_bin_centers(bins):\n",
    "    \"\"\"Calculates bin centers out of bin boundaries\"\"\"\n",
    "    assert len(bins) >= 2\n",
    "    return bins[:-1] + (bins[1:] - bins[:-1]) / 2\n",
    "\n",
    "def histo_rms(histo, bins):\n",
    "    \"\"\"Root mean squares from histogram\"\"\"\n",
    "    bin_centers = get_bin_centers(bins)\n",
    "\n",
    "    rms = np.sum(histo*(bin_centers*bin_centers))/np.sum(histo)\n",
    "    rms = math.sqrt(rms)\n",
    "    return rms\n",
    "\n",
    "# Some constants with calorimeter information\n",
    "max_cell_index = 5\n",
    "total_columns = max_cell_index * 2 + 1\n",
    "total_rows = total_columns              # square calorimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Load data model from saved HD5 file\n",
    "model = load_model('g3_dense_with_xy.hd5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time open 0.5888464450836182\n",
      "Time split 0.28359079360961914\n"
     ]
    }
   ],
   "source": [
    "# Open data file\n",
    "file_name = os.path.join('data', 'shower_geant3_new.dat')\n",
    "data_file = Geant3DataFile(file_name, skip_lines=3)\n",
    "input_data, true_e, sum_e = build_train_set(data_file, 50000, add_real_xy=True)\n",
    "\n",
    "# Run ML on data\n",
    "eval_e = model.predict(input_data)\n",
    "\n",
    "# Flatten arrays, so it is easy to build histograms\n",
    "eval_e = eval_e.flatten()\n",
    "true_e = true_e.flatten()\n",
    "sum_e = sum_e.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using tuple unpacking for multiple Axes\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,6))\n",
    "\n",
    "ax1.hist(true_e, bins=100)\n",
    "ax2.hist(sum_e/1000, bins=100)\n",
    "ax3.hist2d(true_e, sum_e/1000/true_e, bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# using tuple unpacking for multiple Axes\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
    "\n",
    "ax1.hist(true_e, bins=100)\n",
    "ax2.hist(sum_e/1000, bins=100)\n",
    "ax3.scatter(true_e, sum_e/1000/true_e)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true Energy vs reconstructed\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.hist2d(true_e, eval_e, bins=100, range=[[0, 6], [0, 6]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate square root difference vs true_e\n",
    "eval_dee = np.sqrt(np.power((true_e - eval_e), 2)) / true_e\n",
    "mask = np.isfinite(eval_dee)\n",
    "eval_dee = eval_dee[mask]\n",
    "true_e = true_e[mask]\n",
    "eval_e = eval_e[mask]\n",
    "del mask\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.hist2d(true_e, eval_dee, bins=100, range=[[0, 6], [0, 0.1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\romanov\\AppData\\Local\\Temp\\ipykernel_3836\\285248547.py:10: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  rms = np.sum(histo*(bin_centers*bin_centers))/np.sum(histo)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dE/E\n",
    "\n",
    "# Build numpy 2d histogram\n",
    "err_bins = np.arange(0, 0.1, 0.005)\n",
    "e_bins = np.arange(0, 5, 0.1)\n",
    "eval_dee_hist, _, _ = np.histogram2d(true_e, eval_dee, bins=[e_bins, err_bins])\n",
    "\n",
    "# Get bin centers and slice data by bins\n",
    "e_bin_centers = get_bin_centers(e_bins)\n",
    "eval_rmses=[histo_rms(h1d, err_bins) for h1d in eval_dee_hist]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 8)) #, dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(e_bin_centers, eval_rmses, 'bo')\n",
    "ax.set_title('50_epoch-10000_data-123_90_40_1-relu')\n",
    "ax.set_xlabel('E, GeV')\n",
    "ax.set_ylabel('dE/E')\n",
    "\n",
    "del eval_dee, eval_dee_hist, eval_e\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.7"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}