keras_g3_conv_errors.ana.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "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 os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "\n",
    "from event_display import  table_display, table_display_compare, get_bin_centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pics_prefix = os.path.join(\"plots\", \"v1_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time open 0.38165950775146484\n",
      "Time split 0.43625712394714355\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11, 1)\n",
      "(50000, 11, 11, 1)\n",
      "Inputs shape new = (50000, 11, 11, 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",
    "input_data, answers, values = build_true_answers_train_set(data_file, 50000, norm_func=norm_func, rnd_shift=((-2,2), (-2,2)))\n",
    "print(np.shape(answers))\n",
    "print(np.shape(input_data))\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",
    "print(np.shape(answers))\n",
    "print(np.shape(input_data))\n",
    "\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": 49,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1563 [==============================] - 4s 3ms/step\n",
      "[      0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0.       0.       0.       0.       0.       0.       0.       0.\n",
      "       0. 6242019.]\n"
     ]
    }
   ],
   "source": [
    "# Load NN model\n",
    "from keras.models import load_model\n",
    "model = load_model(os.path.join('trained_models', 'g3_conv_errors.hd5'))\n",
    "\n",
    "# Run ML on data\n",
    "ml_events = model.predict(input_data)\n",
    "# reshape events\n",
    "print(ml_events[6])\n",
    "#ml_events = np.reshape(ml_events, (len(ml_events), 11, 11, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        6.100e+02, 5.670e+02, 5.950e+02, 5.510e+02, 5.700e+02, 5.670e+02,\n",
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       "        5.140e+02, 5.440e+02, 5.390e+02, 5.620e+02, 5.950e+02, 5.300e+02,\n",
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       "        0.000e+00, 2.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([   29896.326,   171165.27 ,   312434.22 ,   453703.16 ,\n",
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       "        11331412.   , 11472681.   , 11613950.   , 11755219.   ,\n",
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       "        13591715.   , 13732984.   , 13874253.   , 14015522.   ,\n",
       "        14156791.   ], dtype=float32),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 50,
     "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": [
    "vals = []\n",
    "for event, value in enumerate(ml_events):\n",
    "    vals.append(ml_events[event][len(ml_events[event])-1])\n",
    "plt.hist(vals,bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "ml_events1 = []\n",
    "value = []\n",
    "for i in range(50000):\n",
    "    for j in range(len(ml_events[i])-1):\n",
    "        value.append(ml_events[i][j])\n",
    "    ml_events1.append(value)\n",
    "    value = []\n",
    "print(ml_events1[20])\n",
    "ml_events1 = np.reshape(ml_events1, (len(ml_events), 11, 11, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max true 7613.295605203398 max eval 0.0\n",
      "sum true 7613.295605203398 sum eval 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# exponentiate data\n",
    "event_num = 6\n",
    "true_event = np.exp(answers[event_num]*11)\n",
    "ml_event = np.exp(ml_events1[event_num]*11)\n",
    "\n",
    "#print(ml_event)\n",
    "#print(\"true event\")\n",
    "#print(true_event)\n",
    "\n",
    "# clear low data for display\n",
    "ml_event[ml_event<2] = 0\n",
    "true_event[true_event<1.5] = 0\n",
    "\n",
    "table_display_compare(true_event, ml_event, cal_size=11)\n",
    "print(f\"max true {np.max(true_event)} max eval {np.max(ml_event)}\")\n",
    "print(f\"sum true {np.sum(true_event)} sum eval {np.sum(ml_event)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def data_to_energies(data):\n",
    "    # We took log(hit) to make hits linear. /11 as a scale. Now reverting it    \n",
    "    denorm_data = np.exp(data*11)\n",
    "\n",
    "    # Get rid of array form of cells\n",
    "    # For ML you have arrays like \n",
    "    # [num_pictures][x_pixels][y_pixels][colors]  where colors=3 for RGB,  colors=1 for BW\n",
    "    # We use colors=1 (we just have an energy deposit in a cell) and we have num_pictures=num_events, [xy]_pixels=[xy]_cells\n",
    "    # No we want to remove this useless [1]\n",
    "    # (len(data), 12, 12, 1) => (len(data), 12, 12)\n",
    "    reshaped = np.reshape(denorm_data, (len(data), 11, 11))\n",
    "\n",
    "    # Sum along (x_pixels, y_pixels) axis => basicly summ everything in one event\n",
    "    sums = np.sum(reshaped, axis=(1,2))\n",
    "    return sums\n",
    "\n",
    "sr_e = data_to_energies(input_data)             # SR = simple reco = energy from original data, summed together\n",
    "ml_e = data_to_energies(ml_events1)              # Energy from ML (sum everything)\n",
    "true_e = 1000*np.reshape(values[:,0], len(input_data))    # True energy, 1000 to convert from GeV to\n",
    "\n",
    "# create pandas table... for fun\n",
    "#pd = pd.DataFrame({\"sr_e\": sr_e, \"ml_e\": ml_e, \"true_e\":true_e})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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(sr_e, bins=10)\n",
    "ax2.hist(ml_e, bins=100)\n",
    "ax3.hist2d(sr_e, ml_e/sr_e, bins=(15, 100))\n",
    "plt.savefig(pics_prefix + \"e_2dhist.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sqerr = (true_e-ml_e)**2\n",
    "#np.save(\"g3_conv_errors.npy\", sqerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3677., 1992., 1590., 1300., 1235., 1094., 1018.,  960.,  867.,\n",
       "         845.,  819.,  747.,  760.,  700.,  707.,  654.,  661.,  586.,\n",
       "         585.,  568.,  573.,  539.,  540.,  506.,  489.,  501.,  528.,\n",
       "         474.,  459.,  461.,  477.,  453.,  476.,  479.,  432.,  433.,\n",
       "         477.,  379.,  426.,  412.,  428.,  384.,  390.,  429.,  421.,\n",
       "         376.,  372.,  378.,  423.,  356.,  351.,  355.,  354.,  357.,\n",
       "         358.,  344.,  342.,  342.,  376.,  329.,  363.,  324.,  293.,\n",
       "         321.,  295.,  356.,  340.,  304.,  317.,  329.,  291.,  311.,\n",
       "         307.,  313.,  297.,  294.,  276.,  310.,  306.,  286.,  300.,\n",
       "         305.,  238.,  294.,  289.,  268.,  280.,  284.,  274.,  265.,\n",
       "         266.,  247.,  256.,  283.,  267.,  252.,  272.,  232.,  264.,\n",
       "         287.]),\n",
       " array([   32041.,   321057.,   610073.,   899089.,  1188105.,  1477121.,\n",
       "         1766137.,  2055153.,  2344169.,  2633185.,  2922201.,  3211217.,\n",
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       "         6968425.,  7257441.,  7546457.,  7835473.,  8124489.,  8413505.,\n",
       "         8702521.,  8991537.,  9280553.,  9569569.,  9858585., 10147601.,\n",
       "        10436617., 10725633., 11014649., 11303665., 11592681., 11881697.,\n",
       "        12170713., 12459729., 12748745., 13037761., 13326777., 13615793.,\n",
       "        13904809., 14193825., 14482841., 14771857., 15060873., 15349889.,\n",
       "        15638905., 15927921., 16216937., 16505953., 16794969., 17083985.,\n",
       "        17373001., 17662017., 17951033., 18240049., 18529065., 18818081.,\n",
       "        19107097., 19396113., 19685129., 19974145., 20263161., 20552177.,\n",
       "        20841193., 21130209., 21419225., 21708241., 21997257., 22286273.,\n",
       "        22575289., 22864305., 23153321., 23442337., 23731353., 24020369.,\n",
       "        24309385., 24598401., 24887417., 25176433., 25465449., 25754465.,\n",
       "        26043481., 26332497., 26621513., 26910529., 27199545., 27488561.,\n",
       "        27777577., 28066593., 28355609., 28644625., 28933641.]),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,6))\n",
    "ax1.hist(sqerr, bins=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 19, 19)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build numpy 2d histogram\n",
    "err_bins = np.arange(0.4, 1.4, 0.05)\n",
    "e_bins = np.arange(0, 6000, 500)\n",
    "err_bin_centers = get_bin_centers(err_bins)\n",
    "e_bin_centers = get_bin_centers(e_bins)\n",
    "de = ml_e/sr_e\n",
    "dee_hist, de_bin_edges, e_bin_edges = np.histogram2d(sr_e, de, bins=[e_bins, err_bins])\n",
    "e_bins_num, dee_bins_num = dee_hist.shape\n",
    "e_bins_num, dee_bins_num, len(err_bin_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total subplots: 11 plot rows=3 columns=4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-58-a694d72342d5>:16: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))\n",
      "<ipython-input-58-a694d72342d5>:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n",
      "<ipython-input-58-a694d72342d5>:19: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mean = sum(x * y) / sum(y)\n",
      "/home/name/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py:807: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_columns = 4\n",
    "plot_rows = int(e_bins_num / plot_columns) + 1\n",
    "(int(e_bins_num/plot_columns) + 1)*plot_columns\n",
    "print(f\"Total subplots: {e_bins_num} plot rows={plot_rows} columns={plot_columns}\")\n",
    "fig, axes = plt.subplots(plot_rows, plot_columns, figsize=(plot_columns*4, plot_rows*4), sharey=True)\n",
    "\n",
    "from statistics import NormalDist\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# Fit fragment is taken from here\n",
    "# https://gist.github.com/cpascual/a03d0d49ddd2c87d7e84b9f4ad2df466\n",
    "def gauss(x, H, A, x0, sigma):\n",
    "    return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))\n",
    "\n",
    "def gauss_fit(x, y):\n",
    "    mean = sum(x * y) / sum(y)\n",
    "    sigma = np.sqrt(sum(y * (x - mean) ** 2) / sum(y))\n",
    "    popt, pcov = curve_fit(gauss, x, y, p0=[min(y), max(y), mean, sigma])\n",
    "    return popt\n",
    "\n",
    "# axes is a 2d array consisting of rows and colums [[...], [...], ...]\n",
    "# flatten it as we need to feel it one by one\n",
    "axes = list(itertools.chain.from_iterable(axes))\n",
    "sigmas = []\n",
    "dee_peaks = []\n",
    "for i in range(e_bins_num):\n",
    "    hist1d = dee_hist[i]\n",
    "    ax = axes[i]\n",
    "    # (!) using ax.step() for histograms\n",
    "    # But commented ax.plot() to check ax.step() centers correctness\n",
    "    # ax.plot(err_bin_centers, hist1d, \"-o\")    \n",
    "    ax.step(err_bins[1:], hist1d)\n",
    "\n",
    "    # Fit using Gaussian function\n",
    "    H, A, x0, sigma = gauss_fit(err_bin_centers, hist1d)\n",
    "    std = np.std(hist1d)    \n",
    "\n",
    "    # Plot gauss\n",
    "    smooth_gauss_x = np.arange(err_bins[0], err_bins[-1], (err_bins[-1]-err_bins[0])/1000)\n",
    "    smooth_gauss_y = gauss(smooth_gauss_x, H, A, x0, sigma)\n",
    "    ax.plot(smooth_gauss_x, smooth_gauss_y, '--r', label='fit')\n",
    "\n",
    "    # titles\n",
    "    ax.set_title(f\"Eml/E for E={e_bins[i]}-{e_bins[i+1]}\\n sgm={sigma:.3f} peak={x0:.4f}\", wrap=True)  \n",
    "\n",
    "    # Add our error\n",
    "    sigmas.append(sigma)\n",
    "    dee_peaks.append(x0)\n",
    "    \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(pics_prefix + \"ml_div_true_fit\")\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(e_bin_centers, sigmas, \"--o\")\n",
    "plt.title(\"Sigma by energy\")\n",
    "plt.savefig(pics_prefix + \"errors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigma_div_e = sigmas/e_bin_centers\n",
    "\n",
    "plt.plot(e_bin_centers, sigma_div_e, \"--o\")\n",
    "plt.xlabel(\"Energy, MeV\")\n",
    "plt.savefig(pics_prefix + \"errors_div_energy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-61-077e30400007>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      2\u001B[0m     \u001B[0;32mreturn\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m/\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msqrt\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mb\u001B[0m\u001B[0;34m/\u001B[0m\u001B[0mx\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mc\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m \u001B[0mpopt\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mpcov\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcurve_fit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcalo_error\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0me_bin_centers\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msigma_div_e\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mp0\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      5\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mb\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mc\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpopt\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      6\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"a={a} b={b} c={c}\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/.local/lib/python3.8/site-packages/scipy/optimize/minpack.py\u001B[0m in \u001B[0;36mcurve_fit\u001B[0;34m(f, xdata, ydata, p0, sigma, absolute_sigma, check_finite, bounds, method, jac, **kwargs)\u001B[0m\n\u001B[1;32m    711\u001B[0m     \u001B[0;31m# NaNs can not be handled\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    712\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mcheck_finite\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 713\u001B[0;31m         \u001B[0mydata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masarray_chkfinite\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mydata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfloat\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    714\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    715\u001B[0m         \u001B[0mydata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0masarray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mydata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfloat\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/.local/lib/python3.8/site-packages/numpy/lib/function_base.py\u001B[0m in \u001B[0;36masarray_chkfinite\u001B[0;34m(a, dtype, order)\u001B[0m\n\u001B[1;32m    601\u001B[0m     \u001B[0ma\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0masarray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdtype\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0morder\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    602\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdtype\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mchar\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mtypecodes\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'AllFloat'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0misfinite\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 603\u001B[0;31m         raise ValueError(\n\u001B[0m\u001B[1;32m    604\u001B[0m             \"array must not contain infs or NaNs\")\n\u001B[1;32m    605\u001B[0m     \u001B[0;32mreturn\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mValueError\u001B[0m: array must not contain infs or NaNs"
     ]
    }
   ],
   "source": [
    "def calo_error(x, a, b, c):\n",
    "    return a/np.sqrt(x) + b/x + c\n",
    "\n",
    "popt, pcov = curve_fit(calo_error, e_bin_centers, sigma_div_e, p0=[1, 1, 0])\n",
    "a, b, c = popt\n",
    "print(f\"a={a} b={b} c={c}\")\n",
    "\n",
    "# Plot gauss\n",
    "smooth_cal_x = np.arange(e_bin_centers[0], e_bin_centers[-1], (e_bin_centers[-1]-e_bin_centers[0])/1000)\n",
    "smooth_cal_y = calo_error(smooth_cal_x, a, b, c)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(e_bin_centers, sigma_div_e, \"o\")\n",
    "ax.plot(smooth_cal_x, smooth_cal_y, '--r', label='fit')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(e_bin_centers, dee_peaks , \"--o\")\n",
    "plt.title(\"Mean MLE/E by energy\")\n",
    "plt.ylim(0.4,1.4)\n",
    "plt.savefig(pics_prefix + \"ml_div_true_peaks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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