keras_g3_conv_make_errors.ana.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 220,
   "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": 221,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pics_prefix = os.path.join(\"plots\", \"v1_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time open 0.33496642112731934\n",
      "Time split 0.3615715503692627\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11)\n",
      "(50000, 11, 11, 1)\n",
      "(50000, 11, 11, 1)\n",
      "Inputs shape new = (50000, 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",
    "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": 223,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1563 [==============================] - 10s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "# Load NN model\n",
    "from keras.models import load_model\n",
    "model = load_model(os.path.join('trained_models', 'g3_cae_true-table_200000.hd5'))\n",
    "\n",
    "# Run ML on data\n",
    "ml_events = model.predict(input_data)\n",
    "# reshape events\n",
    "ml_events = np.reshape(ml_events, (len(ml_events), 12, 12, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max true 2311.0000000000005 max eval 2771.01806640625\n",
      "sum true 2311.0000000000005 sum eval 2771.01806640625\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 = 4000\n",
    "true_event = np.exp(answers[event_num]*11)\n",
    "ml_event = np.exp(ml_events[event_num]*11)\n",
    "input_event = input_data[event_num]\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=12)\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": 233,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2789.8235 4567.125   596.338  ... 4627.984   557.8096 4044.4927]\n"
     ]
    }
   ],
   "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), 12, 12))\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_events)              # 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",
    "print(ml_e)\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": 226,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[188550.03607895   8285.550625    77527.72691371 ... 348198.99336511\n",
      " 741993.47235722 453457.69579583]\n"
     ]
    }
   ],
   "source": [
    "sqerr = (true_e-ml_e)**2\n",
    "np.save(\"g3_cae_errors.npy\", sqerr)\n",
    "print(sqerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000\n"
     ]
    }
   ],
   "source": [
    "ml_event = []\n",
    "true_event = []\n",
    "input_event = []\n",
    "sqerr2 = []\n",
    "\n",
    "for i in range(len(ml_events)):\n",
    "    max_true = np.max(np.exp(answers[i]*11))\n",
    "    max_eval = np.max(np.exp(ml_events[i]*11))\n",
    "    sum_true = np.sum(np.exp(answers[i]*11))\n",
    "    sum_eval = np.sum(np.exp(ml_events[i]*11))\n",
    "    #if sum_eval == 121:\n",
    "    #if sqerr[i] > 500000:\n",
    "    ml_event.append(ml_events[i])\n",
    "    true_event.append(answers[i])\n",
    "    input_event.append(input_data[i])\n",
    "    sqerr2.append(sqerr[i])\n",
    "print(len(true_event))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exponentiate data\n",
    "'''event_num = 14\n",
    "true = np.exp(true_event[event_num]*11)\n",
    "ml = np.exp(ml_event[event_num]*11)\n",
    "input = np.exp(input_event[event_num]*11)'''\n",
    "\n",
    "#print(ml_event)\n",
    "#print(\"true event\")\n",
    "#print(true_event)\n",
    "\n",
    "# clear low data for display\n",
    "\n",
    "data = []\n",
    "error_data = []\n",
    "true_e = []\n",
    "ml_e = []\n",
    "input_e = []\n",
    "for i in range(50000):\n",
    "    true = np.exp(true_event[i]*11)\n",
    "    ml = np.exp(ml_event[i]*11)\n",
    "    input = np.exp(input_event[i]*11)\n",
    "\n",
    "    ml[ml<2] = 0\n",
    "    true[true<1.5] = 0\n",
    "    input[input<1.5] = 0\n",
    "    max_true = np.max(true)\n",
    "    max_eval = np.max(ml)\n",
    "    sum_true = np.sum(true)\n",
    "    sum_eval = np.sum(ml)\n",
    "    true_e.append(true)\n",
    "    ml_e.append(ml)\n",
    "    input_e.append(input)\n",
    "    data.append([round(max_true), round(sum_true), round(max_eval), round(sum_eval)])\n",
    "    #if max_eval == 0:# & len(error_data) < 100:\n",
    "    if sqerr2[i] > 1000000:# & len(error_data) < 100:\n",
    "        error_data.append([round(max_true), round(sum_true), round(max_eval), round(sum_eval)])\n",
    "        #table_display_compare(input, ml, cal_size=11)\n",
    "        #plt.savefig(os.path.join('analysis', 'event_{}.png'.format(i)))\n",
    "    \n",
    "#print(f\"max true {np.max(true)} max eval {np.max(ml)}\")\n",
    "#print(f\"sum true {np.sum(true)} sum eval {np.sum(ml)}\")\n",
    "\n",
    "np.save(os.path.join('analysis', 'max_sum.npy'), data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2153\n"
     ]
    }
   ],
   "source": [
    "#print(data)\n",
    "print(len(error_data))\n",
    "#print(error_data[0:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.9542e+04, 2.4700e+02, 9.3000e+01, 4.7000e+01, 2.0000e+01,\n",
       "        1.4000e+01, 1.2000e+01, 9.0000e+00, 2.0000e+00, 1.0000e+00,\n",
       "        2.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 2.0000e+00, 1.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([1.38475895e-04, 3.27286500e+06, 6.54573000e+06, 9.81859500e+06,\n",
       "        1.30914600e+07, 1.63643250e+07, 1.96371900e+07, 2.29100550e+07,\n",
       "        2.61829200e+07, 2.94557850e+07, 3.27286500e+07, 3.60015150e+07,\n",
       "        3.92743800e+07, 4.25472450e+07, 4.58201100e+07, 4.90929750e+07,\n",
       "        5.23658400e+07, 5.56387050e+07, 5.89115700e+07, 6.21844350e+07,\n",
       "        6.54573000e+07, 6.87301650e+07, 7.20030300e+07, 7.52758950e+07,\n",
       "        7.85487600e+07, 8.18216250e+07, 8.50944900e+07, 8.83673550e+07,\n",
       "        9.16402200e+07, 9.49130850e+07, 9.81859500e+07, 1.01458815e+08,\n",
       "        1.04731680e+08, 1.08004545e+08, 1.11277410e+08, 1.14550275e+08,\n",
       "        1.17823140e+08, 1.21096005e+08, 1.24368870e+08, 1.27641735e+08,\n",
       "        1.30914600e+08, 1.34187465e+08, 1.37460330e+08, 1.40733195e+08,\n",
       "        1.44006060e+08, 1.47278925e+08, 1.50551790e+08, 1.53824655e+08,\n",
       "        1.57097520e+08, 1.60370385e+08, 1.63643250e+08, 1.66916115e+08,\n",
       "        1.70188980e+08, 1.73461845e+08, 1.76734710e+08, 1.80007575e+08,\n",
       "        1.83280440e+08, 1.86553305e+08, 1.89826170e+08, 1.93099035e+08,\n",
       "        1.96371900e+08, 1.99644765e+08, 2.02917630e+08, 2.06190495e+08,\n",
       "        2.09463360e+08, 2.12736225e+08, 2.16009090e+08, 2.19281955e+08,\n",
       "        2.22554820e+08, 2.25827685e+08, 2.29100550e+08, 2.32373415e+08,\n",
       "        2.35646280e+08, 2.38919145e+08, 2.42192010e+08, 2.45464875e+08,\n",
       "        2.48737740e+08, 2.52010605e+08, 2.55283470e+08, 2.58556335e+08,\n",
       "        2.61829200e+08, 2.65102065e+08, 2.68374930e+08, 2.71647795e+08,\n",
       "        2.74920660e+08, 2.78193525e+08, 2.81466390e+08, 2.84739255e+08,\n",
       "        2.88012120e+08, 2.91284985e+08, 2.94557850e+08, 2.97830715e+08,\n",
       "        3.01103580e+08, 3.04376445e+08, 3.07649310e+08, 3.10922175e+08,\n",
       "        3.14195040e+08, 3.17467905e+08, 3.20740770e+08, 3.24013635e+08,\n",
       "        3.27286500e+08]),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 235,
     "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": [
    "plt.hist(sqerr, bins=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 19, 19)"
      ]
     },
     "execution_count": 119,
     "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": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total subplots: 11 plot rows=3 columns=4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 120,
     "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": 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, sigmas, \"--o\")\n",
    "plt.title(\"Sigma by energy\")\n",
    "plt.savefig(pics_prefix + \"errors\")"
   ]
  },
  {
   "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": [
    "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": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a=-0.0033483769674103377 b=0.309298435558072 c=1.4800722643383747e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2c0228e430>]"
      ]
     },
     "execution_count": 123,
     "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": [
    "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": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}