SI_Paper_Engine_Degradation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Script overview**:\n",
    "\n",
    "This script computes engine degradation curves over aircraft lifetime. It does so using data from Mr. Urs Glarner, Sr. Field Representative at the Customer Service of Pratt & Whitney/ IAE International Aero Engines.\n",
    "\n",
    "The average engine degradation of all aircraft types present in the global flight schedules from OAG in 2018 is calculated using the average age for each aircraft type from the aircraft fleet data from Planespotters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and preprocess data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load engine degradation data from preprocessed sheet, compiled based on input by Urs Glarner:**\n",
    "efficiency losses in percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "raw = pd.read_excel('datasets/aircraft_engine_degradation_manual.xlsx',sheet_name='overhaul_and_engine_wash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medium_haul</th>\n",
       "      <th>long_haul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  medium_haul long_haul\n",
       "0        2600      5200"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight_hours_per_year = pd.DataFrame(raw.loc[4,['Unnamed: 9', 'Unnamed: 10']].copy()).T.reset_index(drop=True)\n",
    "flight_hours_per_year = flight_hours_per_year.rename(columns={'Unnamed: 9': 'medium_haul', 'Unnamed: 10': 'long_haul'})\n",
    "flight_hours_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>efficiency_loss</th>\n",
       "      <th>years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.07523</td>\n",
       "      <td>0.0384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.15046</td>\n",
       "      <td>0.0769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.22569</td>\n",
       "      <td>0.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.30092</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  efficiency_loss      years\n",
       "0               0          0\n",
       "1        -0.07523  0.0384615\n",
       "2        -0.15046  0.0769231\n",
       "3        -0.22569   0.115385\n",
       "4        -0.30092   0.153846"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medium_haul = raw.loc[39:,['Unnamed: 3', 'Unnamed: 7']].copy()\n",
    "medium_haul = medium_haul.rename(columns={'Unnamed: 3': 'flight_hours', 'Unnamed: 7': 'efficiency_loss'}).reset_index(drop=True)\n",
    "medium_haul['years'] = medium_haul['flight_hours']/flight_hours_per_year.loc[0,'medium_haul']\n",
    "medium_haul = medium_haul.drop(columns='flight_hours')\n",
    "medium_haul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>efficiency_loss</th>\n",
       "      <th>years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.0869733</td>\n",
       "      <td>0.0384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.173947</td>\n",
       "      <td>0.0769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.26092</td>\n",
       "      <td>0.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.347893</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  efficiency_loss      years\n",
       "0               0          0\n",
       "1      -0.0869733  0.0384615\n",
       "2       -0.173947  0.0769231\n",
       "3        -0.26092   0.115385\n",
       "4       -0.347893   0.153846"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_haul = raw.loc[39:,['Unnamed: 11', 'Unnamed: 15']].copy()\n",
    "long_haul = long_haul.rename(columns={'Unnamed: 11': 'flight_hours', 'Unnamed: 15': 'efficiency_loss'}).reset_index(drop=True)\n",
    "long_haul['years'] = long_haul['flight_hours']/flight_hours_per_year.loc[0,'long_haul']\n",
    "long_haul = long_haul.drop(columns='flight_hours')\n",
    "long_haul.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "def func(x, a):\n",
    "    return -a*np.log(x+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## medium haul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.28188286])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt_medium, pcov = curve_fit(func, np.array(medium_haul['years'],dtype=np.float), np.array(medium_haul['efficiency_loss'],dtype=np.float))\n",
    "popt_medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAFWCAYAAAA7XqpNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28bfda0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(medium_haul['years'], medium_haul['efficiency_loss'], label='Raw input data', color='#264653')\n",
    "plt.plot(np.array(medium_haul['years'],dtype=np.float), func(np.array(medium_haul['years'],dtype=np.float), popt_medium), 'r-', label='Fit: $\\delta$(t) = -%5.3f log(t+1)' % popt_medium, color='#f4974b')\n",
    "plt.title('Medium haul aircraft', fontsize=14)\n",
    "plt.xlabel('Years', fontsize=14)\n",
    "plt.ylabel('Engine degradation [%]', fontsize=14)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.savefig('gfx/Engine_degrad_medium.png', dpi=500)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## long haul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.33837184])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt_long, pcov = curve_fit(func, np.array(long_haul['years'],dtype=np.float), np.array(long_haul['efficiency_loss'],dtype=np.float))\n",
    "popt_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAFWCAYAAAA7XqpNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsnXl8U2W6x79v0ibdKaW0QKE0TVkS\n1KuCIq6Iu7iMy3UdBZ0Zl3HjqqPOOI6Mw+C4jIrCdVBUdNwYt5lRB3RGwBUvoqBC2Vpa1rJv3dIl\nee8fJ0mTtM3Spsk55f1+PufTJjnL7+TknPOc530WIaVEoVAoFAqFQqFvTMkWoFAoFAqFQqGIjDLa\nFAqFQqFQKAyAMtoUCoVCoVAoDIAy2hQKhUKhUCgMgDLaFAqFQqFQKAyAMtoUCoVCoVAoDIAy2hQK\nRa9CCDFeCCGFEPkJ2NZcIcQH3VzHVCHEynhp6sL2M4QQbwshDni/t5JkaVEoFOFRRptCoegS8TBY\nFAA8DpySxO1fD5wMnAgMBDYLIaqFEHcnUZNCoeiAlGQLUCgUikMZKWUdUNfZ50IIi5SyOdb1CiFS\npZQtUcxaBqyWUv4YsGysm1MoFAlAedoUCkWPIIQoFkK8J4So9U7vCiEGB3w+VQixUghxhRCi0jvP\n3wOHNYUQKUKIJ4UQ+7zTk0KIZ4UQi6OQ8F9CiP8TQjQIIZYJIY4OWG8/IcQbQogtQohGIcQqIcR1\nIfoXCyFmhrwXk3dRCGEWQrwghKjybme9EOIeIYQpYJ6g4VHfNoQQ9wohtgBbvO9bhBDThRAbhRBN\nQogNQojbvZ/5hoTPFUIsFUI0A2cJIexCiH8IIbYLIeqFEN8JIc4L3EfgDuBk7/KLve8NBR7zvqfa\n5igUOkEZbQqFIu4IzVXzd6AQmACcCgwC/i6C3TglwOXARcCZwFHAHwM+vxuYDPwcOA7tmnVVlDIe\nBu4Djgb2AK8FbDsN+A44DxgFzABmCyFOi2E3o8EEbAUuAxzA/cBvgOvCLYQ2XHoEcDbg0/QycC1w\np3ddPwP2hyz3CPBbYCTwf0AWMB84A/gv4B3gXSHESO/8FwMvAUvQhkYv9k5bgIe87w2MbZcVCkVP\noYZHFQpFT3A6mpFgl1JWAwghrgIq0IyQ/3jnSwEmSykPeOd5jmCD5g7gESnlO97PpwBnRanhASnl\nIu9yDwFfAEXAFinlVuCxgHmfE0JMAK4EPoltVzvHOzz5u4C3qr0evyuBF8Is6gKul1I2efUPA64A\nzpFSLvDOs6GD5aZKKT8OeL0L+D7g9R+FEOcDlwLTpJR7hRANQLOUcrtvJiGEG6gNfE+hUCQf5WlT\nKBQ9gQPY5jPYAKSUG4BtgDNgvo0+g83LNqAAQAjRBxgALA1YhwS+iVLDDyHrJWDdZiHE/UKIH4QQ\ne4QQdWgepuIo1x01QoibvMOzu7zb+Z8otrPSZ7B5OQrwAIsiLLcsZNuZQohHhRDl3uHlOmBMFNtX\nKBQ6RHnaFApFTyCAzmKhAt8PDZSXtH+Y7GpMVeC6fevwrftu4C40T96PaIkA0/EadV48aPsRSGos\nAoQQlwNPebf3FXAQuAVtODgc9aGrinKTocs9jjbEejewHmgAXgEsUa5PoVDoCOVpUygUPUE5UBRY\n80sIUYoW11YezQq8HrjtwLEB6xDAMXHQdyLwvpTyr1LKFUAlMDxknl20j+f6ry5s5/+klDOllN9J\nKSsAexf0fod2vT61C9t/RUr5jpTyB7RYtWi23wyYY9yWQqHoYZTRplAoukOOEOLIkKkELWbte7Tg\n/9FCiDHAa2jGx8IY1j8DuEcIcZEQYgTwZzRDqrsZjeuA04QQJ3qD8mcCtpB5FgLnCCEuEEKMEEI8\nAQzpwnaOFkKcI4QYJoR4gC7UZJNSrgf+BswRQlwihLAJIU4SQlwTxfYvEkIcLYQ4HHgVLQkjEtXA\nSUKIIpGAIsUKhSI6lNGmUCi6w0nA8pDpcW/s2U/QvFWL0WKxtgM/8X4WLY8Df0XLcPza+957aIH6\n3WEaWqzcfOAztGHF10LmeTFg+hJtCPW9GLczG83Yeh0tFq8EzfDsCtd61/M0sAaYC/SJsMydwE7g\nc7R9/dr7fyR+h2agVqIdQ4VCoQNEbNdPhUKhSC5CiO+AL6WUtyVbi0KhUCQSlYigUCh0ixBiKFqJ\nj0/Rrlc3oMWV3ZBMXQqFQpEMlNGmUCj0jAdtWPAxtHCOcrRaZcvCLqVQKBS9EDU8qlAoFAqFQmEA\nVCKCQqFQKBQKhQFQRptCoVAoFAqFAeiVMW35+fmypKQk2TIUCoVCoVAoIvLtt9/ullL2jzRfrzTa\nSkpKWLZMxSkrFAqFQqHQP0KIjdHMp4ZHFQqFQqFQKAyAMtoUCoVCoVAoDIAy2hQKhUKhUCgMgDLa\nFAqFQqFQKAyAMtoUCoVCoVAoDECvzB5VKBSKQxWPx8OWLVuor69PthSFQgGkpqZSUFBATk5Ot9el\ne6NNCPFL4FfAQGAVMEVK+XlyVSkUCoU+2b17N0IIRowYgcmkBlMUimQipaSxsZGtW7cCdNtw0/UZ\nLYS4HJgBTAeOAr4C5gshipMqTKFQKHTK/v37KSwsVAabQqEDhBBkZGRQVFTEzp07u70+vZ/VdwJz\npZTPSylXSylvA2qAm5OsS6FQKHSJ2+0mNTU12TIUCkUA6enptLS0dHs9ujXahBAWYDTwcchHHwPH\nJ15RG2uWLebrt59OpgSFQqHoFCFEsiUoFIoA4nVO6tZoA/IBM7Aj5P0dwIDQmYUQNwghlgkhlu3a\ntatHhe1e9g9G7PiYloYDPbodhUKhUCgUCh96Ntp8yJDXooP3kFI+J6UcI6Uc079/xJ6r3aJ1yFgs\nZtix/KMe3Y5CoVAoEsfixYsRQrB79+5kS4mJW2+9lfHjxydbhiIB6Nlo2w24ae9VK6C99y2hDB51\nPFvrBe5KlcSqUCgU3WXy5MkIIRBCkJKSQnFxMTfffDP79u1LqI7jjz+empoa+vXrl9DtgvYdnHfe\neQnZVnV1NUIIli1blpDtKeKHbo02KWUz8C1wRshHZ6BlkSYN25AiFm5PpW9DNbJRDZEqFApFdzn9\n9NOpqamhurqaOXPm8P777/PLX/4yoRosFgsDBgxQMYEK3aJbo83LE8BkIcTPhRAOIcQMYBDwl2SK\nMpvNrBfFmJG0Vi1JphSFQqHoFVitVgYMGMDgwYM588wzufzyy/n44+A8tCeeeIIjjjiCzMxMioqK\n+PnPf87+/fv9nw8YMIB58+b5X59wwglkZ2fT2toKwPr16xFC+GtmhRI6PDp37lyysrL45JNPOOyw\nw8jMzOTUU0+lqqrKv8zUqVM57LDDmDNnDsXFxaSnp/OTn/wkaIi1Iy+abznf/y+//DIffvih3+O4\nePHiDjW63W7uvvtu+vbtS9++fZkyZQputztongULFnDSSSfRt29f8vLyOOuss1i9erX/c5vNBsAx\nxxyDEMI/tPrNN99w5plnkp+fT05ODieeeCJLlqh7nJ7QtdEmpZwHTAF+C6wATgTOlVJuTKowIGuw\ng031JlorPku2FIVCoehVbNiwgQULFrQrXWIymXjqqadYtWoVr7/+OkuXLuW2227zf37KKaewaNEi\nABoaGli2bBlWq9U/DLh48WLKysooKiqKWktTUxMPP/wwL774IkuWLGH//v3cdNNNQfNUV1fz6quv\n8o9//IP//Oc/rF+/nuuvvz7qbdx9991cdtllfm9jTU0Nxx/fcZGEP//5zzz//PPMnj2bJUuW4Ha7\nee2114Lmqa+vZ8qUKSxdupTFixfTp08fzj//fJqbmwFYunQpoBl3NTU1vPvuuwDU1tZyzTXX8Pnn\nn7N06VKOPPJIzj33XMPF+PVmdN8RQUr5v8D/JltHKM6yUv71kYkbM3/EU7sTU3ZBsiUpFApFO/74\n7Eus2VAVecY4MrLUxv03XxfTMgsWLCArKwu3243L5QI0z1ogU6ZM8f9fUlLCo48+yoUXXsjLL7+M\nyWRi/PjxPPXUUwB8+eWXlJaWcuyxx7Jo0SKOO+44Fi9eHHPAfmtrK7NmzWLEiBGAZmBdd911eDwe\nfwHjxsZGXnnlFYqLtbrvs2fP5qSTTmL9+vUMGzYs4jaysrJIT0/3exvD8dRTT3HPPfdw2WWXATBj\nxgw++ig4Ke6SSy4Jev3SSy+Rk5PD0qVLOfHEE/El6/Xr1y9oexMmTAha7plnnuGdd95hwYIF/PSn\nP424H4qeR9eeNj3jsNt4f5MJgaR13SfJlqNQKBSG5uSTT2bFihV+79m5557L7bffHjTPwoULOeOM\nMxg8eDDZ2dlcfPHFNDc3s337dgDGjx/PunXr2LZtG4sXL+bUU09l/Pjx/qHGTz/9NGajzWq1+g02\ngEGDBtHS0hI0LFtUVOQ32ADGjh2LyWQKGpKMBwcOHKCmpoZx48b53zOZTIwdOzZovsrKSq666irs\ndjs5OTkUFhbi8XjYtGlT2PXv3LmTG2+8keHDh9OnTx+ys7PZuXNnxOUUiUP3nja9MsJWzE5XCltN\nfRm89hNSj74cIZQNrFAo9EWsHq9kkZGRQVlZGQBPP/00p556Kn/4wx+YOnUqABs3bmTixIn84he/\n4KGHHqJfv3589913XHnllf5hP4fDQWFhIYsXL2bx4sVMmTKFY445httuu43y8nK2bt0as9GWkhJ8\nm/QlKXg8nqjXYTKZkDK4UlU8quN3xvnnn09RURGzZ8+mqKiIlJQUnE6n/3vqjEmTJrFjxw6efPJJ\nSkpKsFqtnHbaaRGXUyQOZWV0EavFgr14MIt25yAPbsdTsyrZkhQKhaLX8OCDD/LII4+wbds2AJYt\nW0ZzczNPPvkk48aNY/jw4f7PAjnllFP48MMPWbZsGaeccgolJSXk5+fz6KOPxhzPFi1bt25l8+bN\n/tdLly7F4/HgcDgA6N+/PzU1NUHLrFixIui1xWJpl1AQSp8+fRg4cCBff/21/z0ppT9GDWDPnj2s\nXr2a3/zmN5x++uk4HA5qa2v9yRi+bQHttvfFF19w2223MXHiREaNGkV2dnY73Yrkooy2buCwl/C3\nVbVgyaB1zb+TLUehUCh6DePHj2fUqFFMmzYNgGHDhuHxeHjqqaeoqqrijTfe8MevhS43b948hg0b\nRkGBFmt8yimn8Oqrr/ZYAdr09HQmTZrEihUrWLJkCTfddBMTJ070x7NNmDCB5cuX8+KLL1JRUcGj\njz7Kl19+GbSOkpISVq5cydq1a9m9e3ennrg77riDRx99lLfffpu1a9cyZcqUIMOqb9++5Ofn8/zz\nz1NRUcGnn37KTTfdFOQxLCgoID09nY8++ogdO3Zw4IBWumr48OG8+uqrlJeX880333DFFVf4DTyF\nPlBGWzdwltnYsns/rUPG0Vr5BdJVm2xJCoVC0Wu48847eeGFF9i4cSNHHHEEM2bM4IknnsDpdDJn\nzhwef/zxdsuceuqpuN3uIAOto/fiSUlJCVdccQXnn38+EyZMoLS0lJdeesn/+VlnncWDDz7I/fff\nz+jRo6murm5Xg+4Xv/gFDoeDMWPG0L9//3ZGnY+77rqL6667jp///OeMHTsWj8fD1Vdf7f/cZDIx\nb948fvjhBw477DBuueUW/vCHP2C1Wv3zpKSk8PTTTzNnzhwGDRrEhRdeCMCLL75IXV0do0eP5oor\nruD666+npKQkjt+UoruI0HH23sCYMWNkIio9/9/3K7n2nqn89f6fMap8FqnHXYflyEsiL6hQKBQ9\nxOrVq/3DcoqeZ+rUqbz99tusXLky2VIUOifcuSmE+FZKOSbSOpSnrRs47FqBwuXbGjENPIzWVf9C\nesLHJCgUCoVCoVB0BWW0dYOcrEyGDCykvKKK1MPOQ9buwL1J9XJTKBQKhUIRf5TR1k2cdhurK6sw\n28YhMvNpWflBsiUpFAqFIkFMnTpVDY0qEoYy2rqJw25j47bt1Dc2keI8B8+W5Xj2bY68oEKhUCgU\nCkUMKKOtmzjLtLi2NRuqSXWeBeZUWn74R5JVKRQKhUKh6G0oo62b+JIRyiurEOm5pIw4nda1/8FT\nvyfJyhQKhUKhUPQmlNHWTQr69SW/by6rK6oBSD3yEpAeWpW3TaFQKBQKRRxRRlsccNhLWFWxAQBT\nzgDM9pNoKZ+PbKpLsjKFQqFQKBS9BWW0xQGn3Ublpi00N2ttRyxH/Te0NNKy8v0kK1MoFAqFQtFb\nUEZbHHCU2Wh1u1m3cRMApn4lmIceQ8uP7yObG5KsTqFQKBQKRW9AGW1xwOlNRlhdUeV/L/XoK8B1\nkJYf/5ksWQqFQqFQKHoRymiLA0MGFpKVkUF5ZZvRZi4cgbnkOFq+f1c1klcoFIpuMnnyZM4777yE\nbW/fvn0UFhZSWVkZdr5LL72UJ554IkGqjE1PHsNoj1e8SfTxV0ZbHDCZTDjsJUGeNgDLMT+F5kZa\nVryTJGUKhUJhDCZPnowQot20YsUKAGbMmMGrr74KwPjx47n11lu7vK2dO3dyzTXXMHDgQKxWKwMG\nDGDq1KlB80yfPp1zzz0Xu90edpsPPvgg06ZN48CBA13W01U+++wzLrjgAoqKihBCMHfu3IjLzJo1\niyOOOIKcnBxycnIYN24cH374YczzuN1uHnjgAWw2G2lpadhsNn7729/S2toaz12MmmiPVyxE8/0m\n+vgroy1OOOw21mzYiNvd1jDe1K8E87BTaFn5Pp76vUlUp1AoFPrn9NNPp6amJmg67LDDAOjTpw+5\nublx2c7NN99MTU0NH3/8MRs2bOCjjz5i3Lhx/s8bGhqYM2cOP/vZzyKu6/DDD6e0tNRvUCaSuro6\nDjvsMGbMmEF6enpUywwePJhHHnmE7777jmXLljFhwgR+8pOf8MMPP8Q0zyOPPMKsWbN4+umnWbNm\nDTNmzGDWrFk8/PDDcd/PSMRyvEKZPHlyO4PdRzTfb8KPv5Sy102jR4+WiebdjxfJ4WdeIis2bgl6\n371/q6ybfYF0fToz4ZoUCsWhR3l5ebIldIlJkybJiRMnRvx80qRJEgiaqqqqYtrWeeedJ8vKyuSz\nzz4r169f3+7zt956S+bl5UmPx+Pfdrht/v73v5cnnHBCTBriTWZmpnzppZe6tGzfvn3lX/7yl5jm\nmThxorz22muD5rn22mujOoY+XC6XvOOOO2RBQYG0Wq1y7Nix8vPPPw9apq6uTl5zzTUyMzNTFhQU\nyOnTp/t/Bz5iPV6hmh588MGw+y5l+O832uMf7twElsko7BvlaYsTDnsJAKsrg4dITX0GkeI8h9bV\nH+HZW514YQqFQtGLmDFjBuPGjeO6667ze+OGDBkCwNy5cxFCUF1d3enybrebM844gzfeeIOJEycy\nd+5cTjvtNPbsaeti8/nnnzN69GiEEBG3CXDssceydOlSGhsbO93u9OnTycrKCjt9/vnn3fx2YsPt\ndvPmm29SV1fH8ccfH9M8J554IosWLWLNmjUAlJeXs3DhQs4999yot3/PPfcwb948XnzxRZYvX87h\nhx/O2WefTU1NjX+eu+66i08//ZT33nuPhQsX8v3337f7nmI9XvEmmuMfL1J6fAuHCPbiwVhSUymv\nrOK8U08M+swy5ipa1y2i6asXSJv4kP+HpVAoFD1N05fP4dm9IaHbNOWXYj3hhpiXW7BgAVlZWf7X\nJ510EvPnzw+ap0+fPlgsFjIyMhgwYEC7z0aMGEFqamqn27jzzjsZO3YsY8aMAWDatGkcdthhPP74\n4/6hvY0bNzJw4MCotgkwaNAgWlpa2LZtmz+mKpSbbrqJyy67LOz+FxUVhf08Xvz444+MGzcOl8tF\nVlYW7733HocffnhM89x7773U1tbidDoxm820trZy//3388tf/jIqDfX19Tz77LPMmTOHiRMnAvCX\nv/yFhQsXMmvWLKZNm0ZdXR0vvvgir7zyCmeccQYAL7zwAoMHDw5aV6zHK95Ec/zjhTLa4kRqSgrD\nSoa0S0YAEGk5WMZcRfNXz+Pe9A0pQ49NgkKFQqHQNyeffDLPPfec/3W0cVo+LrroIi666KJOP//x\nxx954YUXePTRR4Pez83NZdu2bf7XjY2NFBYWRr1dn85wnpa8vDzy8vKiXmcgr732GjfeeKP/9fz5\n8znppJO6tC6AESNGsGLFCvbv388777zDpEmTWLx4sT9+MJp55s2bxyuvvMLrr7/OqFGjWLFiBXfc\ncQc2my2q2LLKykpaWlo44YQT/O+ZzWbGjRtHeXl50DzHHtt2z8zMzAzSCbEdr+nTpzN9+nT/66am\nJoQQPP744/73Yv1+ozn+8UIZbXHEabfx76+WIqVECME9jz5Nv9w+3HvDJFJGTaSlfD7NX83BPPgo\nhLnzJ0GFQqGIF13xeCWLjIwMysrKemz97777LsOGDcNqtfrfa2lpYeXKlZx//vn+9/Lz89m3b1/U\n6927V0s069+/f6fzhBoLHdGZsXDBBRcwduxY/+vueuQsFov/ex4zZgzffPMNTz75JC+88ELU8/zq\nV7/i7rvv5oorrgC0gPyNGzfy8MMPR2W0aWFcdDjy5Hsv3DyBxHK8Qj2e9957L0VFRdx+++3+92L9\nfqM5/vFCGW1xxFFm460Fn7B91x4GFuTz47pK3G43994wCWFOwTLuZzTN/z0t37+H5ejwbnKFQqFQ\ndIzFYgnK1I+WPXv2UF9fH/Te3Llzqaur48orr/S/d9RRR7Ur7xBumytXrmTQoEFhvT3dGR7Nzs4m\nOzs77LLdwePx0NTUFNM8DQ0NmM3moHnMZjMejyeqbZaVlWGxWPjiiy8oLS0FtPi5JUuWcNVVV/nn\nSU1NZenSpdhsNv92V65cGTQMGcvxCvV4Zmdnk5eX162HhWiOf7xQRlsc8XVGKK+sYmBBPq6mJrbt\n3E1dfQNZmRmkDD2GVtvxtHz7Jin2kzD1GRhhjQqFQqEIpaSkhKVLl1JdXU1WVhZ5eXmYTCbee+89\nfv3rX/PJJ590aAAdd9xxPPPMMzz22GNceOGFLFy4kLvuuotHHnmE4uJi/3xnnXUW9957L3v27KFf\nv35htwlaIPzZZ58dVnN3hkc7o66ujoqKCkAzqjZt2sSKFSvIy8vz78/MmTOZOXOmP2HgvvvuY+LE\niQwZMoTa2lpef/11Fi9eHFSHLZp5zj//fP70pz9hs9kYNWoUy5cv54knnuDaa6+NSntmZiY333wz\n9913H/n5+dhsNp588kl27Njhj4vLysri+uuv59577yU/P5+BAwcybdo0PB5PkPct1uMVz+8Xojv+\ncSOaFFOjTcko+SGllPWNjXLEWZfKZ/46T0op5bGXTpbDz7xEfvPDKv887tpdsm7OpbLhn/f705MV\nCoUiXvT2kh9SSrl27Vp53HHHyfT09KByDi+99FLY8g4ej0c+/PDD0mazyaysLDl27Fj59ttvdzjv\ncccdJ2fObCvV1Nk2GxsbZU5OjlyyZEnsO91NFi1a1K60BRBUDuPBBx+U2q1eY9KkSbK4uFhaLBbZ\nv39/edppp8kFCxYErTeaeQ4ePCjvuOMOWVxcLNPS0qTNZpO//vWvZWNjY6d6w5X8sFgsHZb8qK2t\nlT/96U9lRkaGLCgokA8//LCcMGGCvOmmm4Lmi/Z4daSps5If0Xy/sRz/eJT8SLqB1akwuAFYBOz3\nflEl0S6bLKNNSinP/tnt8qbfPSyllPKI86+Uw8+8RL783gdB8zT/+L6se3aibFm7MBkSFQpFL8ao\nRpvemD9/vhw+fLhsbW0NO9/MmTPlGWeckSBVCpfLJQsLC+Xjjz8e9H60xyvexHL8e3udtgzgY2Bq\nknXEhLPMxurKajweD66mZgBWV1QHzZPiPAdTwQiavnoe6TqYBJUKhUKhCMfZZ5/NLbfcwpYtW8LO\nl5qayjPPPJMgVYcey5cv5/XXX6eiooLly5czadIkamtrufzyy4Pmi/Z4xZtEH3/dxrRJKZ8CEEKM\nSbaWWHDabXyw6Au2724r1FgeUnBXmMxYT7mVxnem0PTFbNJO/1WiZSoUCoUiAoEZhZ1xww3Gyc41\nKk888QRr164lJSWFI488ks8++6xdrTaI7njFm0Qff90abUbFWaYlI3y3ai0A2ZkZVGzcTHNzCxZL\nW5kPUz8bqUdfTsuy12m1jSPFfmKH61MoFAqF4lDlqKOOYtmyZcmWoRv0PDwaE0KIG4QQy4QQy3bt\n2pU0HQ67z2jTMnWOdAyn1e1m/cbN7eZNPeoyTP2H0fTZLNVQXqFQKBQKRVgSarQJIaYJIWSEaXxX\n1i2lfE5KOUZKOSYRBe46Izcnm0EF+XzrNdpGj3IA7YdIAYQ5Betpd4G7mebFM/yFBBUKhUKhUChC\nSbSn7SnAEWFammBNccdht7GuehMAw23FZGakU17Rce8/U+5gLMdNxr35W1rL53c4j0KhUCgUCkWn\nMW1CiIu7sL75UspOm29JKXcDu7uwXkPhLLPxyZJvAMhIT8NRWtJhT1IfKaMm4q5eqrW4GuDA1M/W\n4xpXrF6HJTUFZ1lpj29LoVAkFillxNY/CoUiccRrJC1cIsLbMa5LAsOAjl1KMSKEGAAMAIZ733IK\nIXKBTVJKXQeA+eLaANKtVhxlNt6e/wlut7td2w8AIUxYJ9xJ49u34/r4T6Rf8iTCktGjGv80ey4A\nbz4VvheeQqEwFmazmZaWFiwWS7KlKBQKL42NjaSmdr/neKTh0QFSSlM0E9DQbTXB3AQsB17zvv7Q\n+/qCOG8n7jgDjbY0K067jcamJqq3bu90GZHRF+vp9yAP1tD02awej29rcbtZs2Fjl/r3KRQK/ZKb\nm8uOHTui7gGpUCh6DiklDQ0NbN26lYKCgm6vL5yn7WWg06HODngViFulWCnlVAxWWNfHgP79yM3J\nZv/BWtKtVr8Rt7qyCntxxw2BAcyDDid1zNW0fPNXWgcdRqrznB7TKD3Sa0jWYC9uX+9GoVAYk/z8\nfLZs2cLatWuTLUWhUKAV4C0sLCQnJ6fb6+rUaJNSXhfLiqSUN3dbTS9BCIHTbuOr5T+QZrUysCCf\n1NQUyiurOO/U8PXYUo/+bzw1K2n+YjamfqWYC0f0iEaJ5skrr6gyrNH25ocfc/xRR1A8aECypSgU\nusFkMgU1s1YoFL2HmLNHhRCIfIYoAAAgAElEQVQpQohRQogjhBDWnhDVG/AV2c1Is5KaksLwocVh\nkxF8CGHCetrdiMx+NH30Rzz1eyIu0xV8w68dlSIxAm63mweffo5X/v5hsqUoFAqFQpEQYjLahBDj\ngGq0Ru6fApuEEGf0gC7Dc/UFZ/O7W39OVqaWUOAos1FeWRVVrJpI70Pa2Q8gmxto+uiPyNbmuOvz\neDQd0RiSesT3LZYbVL9CoVAoFLESq6ftaeA6KWUBkAf8Hng27qp6AYMK+nP1+Wf7XzvtJew/WMv2\nXdF5zkz9SrCedieeneto/mxm3BMTAodHjVjU16d59YZqFXCtUCgUikOCsEabEOIzIURgUFUm8D2A\n1O6a3wPdj6w7BPCVAYllODLFdjypY66idd1CWpa/FV9BXjvtQF0d23Yar3Sez2hraHSxqWZHktV0\njQaXi4dmzuFAbV2ypSgUCoXCAETytD0GfCCEeEAIkQLMAH4QQswTQryLVobjzz0tsjcwonQoQghW\nxxhDljr6CszDxtOy9BVa1i2Mmx4pJYX5eQAxa9IFAc5Bow6Rlq/fwGvvL2DR16oZskKhUCgiE9Zo\nk1K+DxwF9Ae+A34ETge+QItpO01K+UhPi+wNZKanU1I0kFXrY6s9LIQJ6/g7MBUdQfPiGbg3L4+L\nHo+UjCwtQQhhSKNHBlhthjQ6CYjLM6h+hUKhUCSWiDFtUso6KeXtwA3A/wK/BOZKKWdIKb/taYG9\niVFlpayurI55OWFOJe3M+xG5Q3B9PB337u43nZBSkm61Yhs8yJBGQ2AcXmd9XfWOPy7PgN+/QqFQ\nKBJPRKNNCJEnhBgNrAbGANuA5UKIC3taXG/DUWajZtdu9h2sjXlZYc0k7dypCEsmTf+aiudg590V\nokFKickkcJbZDJlBKkOGRw2dTFFZbUj9AD+sreDXj89SnTUUCoUiAURKRLgK2IIWu7YROFdKOQ04\nF7hDCPGOt0eoIgr8nRG6aCSZsvJJmzgV6W7G9f79eOq6nkDgayjttNvYvnsPew/ErZlFQvANjw4q\nyGfvgYPs3KPrdrQd4rPTausb2LJ9Z3LFdJGvV/zIu/9eRNWWbcmWolAoFL2eSJ62h4HrpZQDgNOA\nPwBIKddJKScA84GvelZi78FRFnsGaSimvBLSJj6EdB3E9f79yIZ9XVqPlBKB8BcBNpq3zeeZcpaV\nAsZMRgiMyzOi/kCMOMSuUCgURiOS0ZYF+BrYVQIZgR9KKecAx/WArl5J35xsBvbP77aBZC4YTtq5\nU5H1u2n84AGkK3YvmZSA6FopEj3g81I57N5kCoPpB4LGeA2pn4AhXoMbnQqFQmEEIhltLwMfCiFe\nB5YCfw2dQUppzHGdJOGwl8TlBm0eOErrmnBgK64Pf4dsiq3Wl0RiMpnIzclmUEG+8YLhvcZCZno6\nQwcNMJ5+QuPyjJlM4cOoRifAK3//kOl/eSnZMhQKhSIikUp+3AncCCwHbpVSPpQQVb0YZ5mNqi3b\naHC5ur0u8+AjsZ75Gzx7qnH98zfIxgNRL+vxSIT3f4fdZrjhOd/QohA+/dXJFdQFfF6q4oHGNDoh\n0NNm3GSKr1es5K0Fn6jOGgqFQvdEU/LjfSnlY1LKjxMhqLfjsNuQUrJ2w8a4rC9l6DFYz3kAz/4t\nNP7z13iijnHTEhFAMySrt9ZQ39gYF02JoM0+0OLytu7YabjOAj7D01lmY9fe/eza27X4xGTiOw5G\n7awBmuHZ0Ohi47buZWQrFApFT9Op0SaEuEwIYYl2RUKIi4QQGZHnPLRxxiEZIZSUIaO1GLfanbj+\ncV9UWaVSSkxCO/zxNiQTgc+rI4Twx+V1pQZeMvEZPKOGeZMpDOht601FjlVcnkKh0DvhPG1vAH1i\nWNfLgCr/EYGB/fPJzc7yD0dKKXlu3nts6uZTvrnoCC2rtGEvrn/ci2f/1rDzezwS3/iovxSJgW66\nbUZbmyFsJP3Qtg8jS0sAYxoNgcaz0YbY/Xj3wYhGs48bH5jO3HffT7YMhULRw6SE+UwArwohmqJc\nV1oc9PR6fJ4h3w36QG0df37xNfbX1nHPz6/p1rrNA52knT8d178epPHvvyLtnAcxF47ocF4ZMDw6\noH8/cnOyWWWgm25g9FS/3D4U5ucZ7qbr81LlZGUyZGCh4fRDm7fQqJ01oHd0pvhxXSX1jS4mX3x+\nsqUoFIoeJJyn7WW07gd7opxeA4xVoTVJOIeVsm7jJlpaW3E1NQPx87KYC4aR/pPHEKnpuN7/Da2b\nOm5GLiV+o81XZNdIN61ADw9o3sLyGPu6Jh2vwSOEpn+1AZMpCIjLM2oGrL8HrEE7a4B2PqyurFbJ\nFApFL6dTT5uU8rpECjmUcNpttLS0UrlxCxZLKqANzfi6FHQXU24RaRc9TtO/ptI0/yHk+DtIHXFa\n0DxaTFvbthxlNl5570OaW1qwpKZ2W0OP4zPavGO8DruNT79ZTqOrifQ0azKVRY0M2AeH3cZHX3xN\nbX092ZmZSVYWPb59GFVWygeLvmDv/gPk5cYSVZF8fPvg66xRmN8vyYpiRwJ1DVpnjeJBxoxScbvd\nmM3mZMtQKHRNxOxRRfwJLGjratJGn/cfrGX7rj1x24Ypoy9pFzyMadARNC96kuZlbwR5EUINRKfd\nRktrK5WbtsRNQ0/i2xPfLjjLbHg8HtZVGy+ZAiFwDtN+E2sMlAwCAUWOeyDBJlFIKTGbtEuhUePy\npMHj8g7W1TPm4kl89s3yZEtRKHSNMtqSQEnRANKtVsorq2j0Do9C/C+4wpJB2rkPkjJ8Ai3LXqPp\nk8eQrZqRGDoM5M9qNchNK9DggQBD2CD6IdjwdBpQP7QdB4c3mcJo+kEzPG2DBxm3swb4rWcjJrOA\n9tDa4HLx1Xc/JFuKQqFrlNGWBMxmMyNLh7K6ogqXqy3PoydiyoQ5Fcup/0Pq2Mm4Kz7XSoLU79GG\nR01th7+kaCAZaWmGiWuTIcOjRYX96ZOVZaiyH4Fxef3z+tI/L9dwcWG+hJbcnGyKCvsb5vcTjCQz\nI52hRQMNaXRCm8fTqEan0T2FCkWiUEZbknCU2Vi9odrfGcFsMvXYDUMIgeWoS7GedT+efZtxvfM/\n2LOaCQyfM5lMjCgdapibVoijDSEEI+0lhtEP7Q1Ph91muJtWYEKLpr86uYK6iJaMU2JQo7MtE9m4\n+jVWVxo7GWTP/ui70igUXUEZbUnCabdR39DI2qpNAAkxOFJsx5F+0WNgMvP06P2MEpvbaVq9wRgZ\naG1trILj8tZVb6LV7U6WrC4hAurlVW7cQlNzc/gF9IQMboe20WCdNcAb34mmf+uOXew/WJtsSTEj\npSQ1JcXAnTW08/lgXT1bd+xKspqu8eV333PyVTewZbtqx63oOaI22oQQQ4QQVwkhpggh7gycelJg\nb8UXuL28fA0AR48aSc2u3ezr4RuGqZ+N9EuepPxAKueYv6Pp05nI1ma/poZGF5tqdvSohrgg2xtt\njjIbTc3NbNgcvrCwXggtW+Ios+H2eFhXvSmZsmIiMKHFWaZ11jBkMoUQbUWaN1QnVU9XkBKGlxQD\nxhxiDPSuGdVbuGf/QVrdbr5fsy7ZUhS9mKiMNiHE1UAF8DwwBbgtYLq1x9T1YoYPLSbFbGb5au0E\nHz1qJJCYQGKRnsvd32XzjRxO6+oFuP7+KzwHtxsqGN4/PBrwnpH0Q/vhUX9nCoPoB21YK7BWHhhL\nP7QZnkbVD5rneaS9BIDy9QbUHzAiapTzNxTf+WykIuUK4xGtp+0h4M9AjpSyREppC5hKe1Bfr8Vi\nScVePJj6Bm0o6Wiv0Zaop+QWN3zJ4VjPfgDPwe00vjMFu2knKWZz0JNuc0uLLmNMQr1UALYhg0iz\nWgzzpO7/Wr27MHhAAVkZGYaKC5OyrR1aYX4effvkGM7TI9GGR/MM2lkDtOOQnZlB8cABhvn9B2N8\nT5vRM3gB6uob2LzdACMthzDRGm2FwBwpZUKChYQQeUKIZ4QQa4QQjUKIzUKIZ4UQxqt6GQbfcIzJ\nZKIgry8D++cn7ITXsv4gpWQs6Zc8hSmrP+5/T+OB46ysragEoNXt5pSrb+TtjxYmRFMsyMB2Al5S\nzGZG2IYaJgNTEmwMm0wmHAYLhtfiwQI7axhLPwQnUzgDWswZCukt0lxWYlijE8BqsRhSPwR01jBw\nMsVz897jstt/bYi45kOVaI22fwFje1JICIOAIuAe4HDgp8DJaE3sew0+oy3NavH2JE3cBTcwFsnU\nZyBpFz1OivMczi/Yy819v8O9bzP1DY3sPXCQJcv1VzspNHvUh8NuY3VltSEumh15Cx12G2s3bMRt\nkGSKwOFR0PSvr95Mc0tL8kTFSOC54LDb2LBlG42uaFsu6wNtHzSjc3PNDmrr65MtKSZ8p+sIWzE7\ndu9lrwGzMH3nc7wLpSeS2oYG9h44qJIpdEy0Rtu/gUeEENOEEJcLIS4OnOItSkq5Ukp5sZTyn1LK\nCinlp8CvgNOFEDnx3l6y8MXQZKSlaa/LbFRt2eYvA9KTSE9wGyuRYsV68i0syT2bAmsrjW/fQeua\njwCpyxiT0HgwH84yG7X1DYa46LQZngEZsGU2GpuaqN5akyRVseEzFnyMKis1VGcNjbZ9GDWs1HCd\nNcDrtRXCn+BkpHqF0OZ1HjXMDhg/mcKI+qHtmrTKIKMVhyLRGm2zgcHAb9C8XW8HTG/1jLR25ABN\nQEOCttfjjPRWkU+zWgDtKV9KydoEZN/5LvKh5I6awOWfWKlNLyLt25d49NgW9u3cprsyDh2V/ICO\nOyPs2K3Tp94ODE+jdaYgYHgUAtpZGUU/vsNg3M4a0DbEa7RkHB8+g8fpS6YwmH4IDncwWoiAD9kL\n4vIqN23p1XF5URltUkpTmKnHO/wKIXKBPwDPSylbO5nnBiHEMiHEsl27jFHnJyszg6GDBgR52iAx\nT2mBcTyBjCwtYZdL8C4nsW/YhZwy0MPfTnOx9ZsPe1xTTHTgpQJteMVsMvm/w/KKDZx89Y26TMNv\nMzzb3isdUoQlNdU4cXkhv6OhgwaQkW6czhoQPDw6qCCfPllZhjMafLXmfJ01jPT9Q5uxkJOdRVFh\ngeH0Q5uXKiMtzXC/Hx+9oTPF/U8+y++emp1sGT1GQovreodXZYRpfMgymcD7wFa0GLcOkVI+J6Uc\nI6Uc079//57dkThyziknMOZwBwAD++eTm52VkKccKYOHR31kZWZQUjSQ1Ruq2THgJK5ZbGGPS1BU\nPhfXJ48jXfooPNo2PBqM1WLBXjzYf9HfsXsvAN+uXJNIeVHR0fBoakoKw23Fhrloah7bttcmk4mR\npSWGKnsQOMQrhHGD+YM7UxhMv7+Ej8BZVmKoDGo/3hN6pAGTcUIx2vB6IK6mJlaurzREXHNXiKW4\n7kQhxGdCiN1CiF1CiE+FEOfGuL2nAEeEaWnANrOA+d6X50kpez7YK8H8z+QrefDWXwDeG0aCLriB\n3oVQHHYb5RXVuFxNrDtg4prFFj5rGYa78nMa591Ma9XXPa4vEqEN4wNxBHSXaGzSCgfr8SbWdlEJ\nicuz21hdYZxkitDfkdNuY41BOmuAN5kicIjabmNd1SZaWjt06usSXw9YAGdZqeE6awR6nR12G9Vb\ntlHXoK+QjEj4ztdRZTa27ez5Quk9gW8fdu/bz849xuusAZrtfLCuni079B/X3BWiLa77c+A9oBK4\nF7gPqALeE0JcH+3GpJS7pZRrIkwN3m1mAwsAM3CulLIuxn0zJI4yrRVTT98wfMMpHeEss7F1x052\n7NG8VNk5fXh+TSppFz+JyOhL00fTNK9bY/IyvPzmTgdGm7PMxq69+9i9bz+N3qQOPcZodDQ8CtpN\n60BdHdt27gagweWiYuPm0MV1gZTtk0EcZSU0NLrYuG17klTFRqjh6Siz0dzSYpjOGgCBDk+n3Zid\nNSA4Lm+NwTpT+K5JvmSKNTp8UIxEb+hM0Rvi8sIRraftXuBOKeV1UsoXvNNk4G40Ay6ueA22j4G+\nwGQgUwgxwDtZ4r09PeG022hp6fnsu0ieNoDl5WsBONo5gnUbN+HJLSbt4idIHXMV7srPaXjzJlrW\nfJwUj1DbRb79Z4HB5K4mrXSDHss4dDQ8Cm2xjb6L5t/+9W8uuuUe6ur1l4Mjke2OgeE6C4RkwBpO\nP+3biYGxgvkDz4XQ379R8F2THIZOpoDMjHTAmPqh7WFYj6Mr8SBao60YzesVynxgaPzk+BkNHAc4\ngXVATcB0fA9sTzc4y7QGEz19wkhAmDo22nwXzW9XtfVFbWlppXLjFoQ5FcuYq0i/dAamvkNoXvw0\nrn/eh2dvYp/qOyv5AW1G2+rKKv/wqB7LOHRUpw1ghG0oJpPJ/xvYu/8gzS0t+vQ8dGD8lw0dQmpK\nimEumqG15myDtc4aRtEPvs4U2j4MHlBAdmaGoW66gZmXBf3yyOuTYyj90HY+5/Xpw4D8fob6/fiQ\nUpKVkW7gzhr4XZ5GeuiKhWiNtk3AGR28fyYQ9zuhlHKxlFJ0Mi2O9/b0REnRANKt1h4/4T0eT4cG\nD0C/3D4U9Mvze/uOdo4Agp9cTHklpF34Jyyn3I5n7yYa376d5v97BdmSmLDDzrxUADlZmQwZWMiq\nig1+Txvo8Mmxk2SK9DQrtsGD/N+3z/DUY3B/R05WS2oqZUMHG+air91s246C2dtZwyj6wWd4av/7\nCnUbSn/AA4zP22Yk/RBc8NtRZjNkML+vw4mzzIDJLF7aMmCrkyukh4jWaHscmCGEeF4IcZ0QYrIQ\nYg7wpPczRZwwm82MLB2amMbxnQyPQlu9JNCyoTLS0tqdxEKYSHWcScYVfyGl7BRalv+Nxr/dQmvV\nkgQMmXY+PAptwfwNriYsqan0ycrS3UU0XFyew17i/w344/J0eBENbGMViJbM0tbOZ+PWGt3GWIUW\nCAZjddaA9sfBYbextspAnTVCHsIcdhsVG7cYq7NGQO1Ipz1xhdLjimwbot5cs4ODdcbqrAFtRtvO\nPXvZY8DOGpGItk7bbOBytOzOx9Gax48ELpNSPtdz8g5NHGU2Vvdg9l24eLBADdo8gnSrlRFhDEmR\n3gfrhP8h7fzpkGKl6aM/4vrgt3j2Vsdbup9ww6OgXfQ31Wxn1959ZKRZGRmQUaoXOhseBc3o3L57\nD3sPHKTR6y3Um37oOKYNtCH2vQcOstObzPLI868w5Y9PJFhddHRUs9BInTWgfYyq027D1dRM1ZZt\nSVQVAyFeZ6fdRktrq24TcDokMJmizIbH40lIofR44jufA0NMjIYEcnOyAX1eM7tL1CU/pJTvSSlP\nlFL2804nSin/0ZPiDlWcdhv1DY1srumZqs4+Y1CIzg+/Lxg7Pc3qf3KMZEiai44g/b+fwXLCjXh2\nVdD41u00ff4s0nUwvjtAwLBcJ5anLy5vRfk60tPStDIO1Zto1ZHnoa1sSfvP/MHYFVW4vMOjlZu2\n0NysL8+DDByXCyC0s0BtfT0bNm/VXWcNCC6X4cOIwfyBu+BIYKHueBDa4cQ5zBvbaxD9EOAtJOD3\nbyD90Gb8GzUZBLR9cBrY6IxEQovrKqKjpy+4EeydIA3pVqv/dX1DI5siGJLCZCb18PPJuPI5Upzn\n0Fo+n4Y3bqBl5ftIT/wMps7KZfj1e0/aTTXbSbNacJTZaGpu1lUZB/8+hEmmCIzLa3W7WbdRX0OM\nnWUhjywtQQjh/w03uJoS1qItVjoqfzO8xNtZwwCdKTry2Po6axglGDt0eLR4YCGZGemsrqhOnqgY\n8SdTCOHvrGGU79+HL74zv28u/fP6GuqhxYeUkpysTIoKCwypPxKdGm1CiINCiHzv/7Xe1x1OiZN7\naDB8aDEpZnOPnfDhhuV8DC4sICcrkzSv0RZrT0OR3gfrSTeTfunTmPJLaf5idlzj3SLtQ0G/vuT3\nzQW0tjJ67MkYLpkiNyebosL+rK6sosHVRFGh1uVDbzeBzur9ZWWkM3RQWwaay6XfIV5fHE8gVosF\n+9DBQQ9Oi75exsatNYlWF5GOijQbrrNGiNfZZDIx0jbUMPqhfTKFETtrBDrOnUZNpsA3RG2sZJxo\nCedpuw2oDfg/3KSIIxZLKvbiwT3nafNeXDpqY+XD5yLP8tbsGTZ0iGZIxqjJ1K+EtPP+iPWs+wG0\neLe//wp3zcouqteIFNMGbfWS0qwWbEMGYbVY9HUSR4gt1LpjVONqamJ4STGZGem6vAlE6qwBOu9M\ngezwIDjtwRmM9z42k2de/VsipUVFZzGqRuqs4SOoM0WZwTprhEQ7+EIyDNVZI8Bz7rDbqNy0JSgD\n3xB4E4ucZaVUb63RZX3L7tCp0SalfFlK2eT9f673dYdT4uQeOvTkU040njaA+2++nt/ffgOgGZJl\nQ4d0yVMihCDFNo70y2ZhOflWZO1OXP+4D9f8h/Ds7eJwWUB6fWcExuWlmM2MsBXrytMTTTLFxq01\n7Nl/gIz0NBylJfr0tIWJK9y6YycHausMkAHbHofdxq69+9m1V2vnU9/YqLvvHzrPQg7trKFnOrom\nOew2w3XWgIC4vLLShBRKjyeBWchtnTUMlAxCQFyeQTtrRCLaNlYbhBD9Ong/Vwih/6APA+Kw23qs\n/1u0RtvwkmKO8tZogzbPQ1ef3IXJTKrzbNKvfI7UY6/FXbOKxrduo2nRU3gOxpZ0ERq43BH+uLw0\nb1xeN/XHm3AlP0AzeqSU7Ni9lzSrFUeZjTUb9FXGoaNyGT7agpmr/ckU66s3666MQ0fZoxCQjFBZ\nRUtrK61ut047a3Rs/I8a5gsJ0P8luqM428BkHGMQarS1/f4NgwweHgV9PmiFw9daz2jJONESbSJC\nCVoP0FCswOC4qVH4CT1hpJRUxymepqs2i6OshD37D7Bzb/cMSZGahuXoy8i46nlSjriQ1opPaXzz\nBpo+fSZq4y0aw9P3pOWPy9NZGYdIpVd8+kFLCHHabTQ2NVG9VT+eh47LA2v49K9aX0ljUxMlRQNp\nadWf56Ezb6FveH11RZXfUNN3Z43g94eXBHfW8Hg8zHz1LfYe0F8YckeGp714sLE6a4R4/0uKBmqF\n0g1jdAZnUhuxswb4wh2gIK8v/XL7GCqZJRrCGm1CiIuFEBd7X070vfZO/w38Hq1xvCLOOEpLgLbA\n7W9+LOes62+Ly1OzR2oxIiZTbMnD8e7JKNJysI77GelXPk+K42xa135C45s30vTpTDy14Q2raAzP\nIQMLyc3OIjc7C0hci7BokaFX+RAK8/Po2ycH0OLy9JjGHm54NC+3D4X5eaxYvQ4pJUePGgno5/v3\n0VmtuexMrbNGeWWVv1YewKr1+tLfFioQvBPpaVZKBw/y/142bN7KM3+dx/sLP0+0wsh08BBmSU1l\nWMkQQ3gKob3haTabtfqWOjpfIxHYDs2InTWg7Zqk6TduZ4fOiHTXfts7SeCFgNdvA68CpwJ39aTA\nQ5WszIyg/m+79+4HYPnqdd1ed5jyYGEZ6TMk43wSmLLytUzTq54nZeSZtK79D41v3EDTZ7Pw1O3q\ncJlohkdNJhMvPzKVG6/QnjtG2LxlHHRyErcdh473QYvLKAEgPS0N+9DBpKamsEpHN7Fww6OgGfrf\nla8FtN9PRlqa/m4CYR4AfMH8roAhUb3pD3cuOALaETV490Fv+qHzMkS+ZBy9hDSEo6OHMF9nDSMl\nUwQeAl9nDT3Vt4xEYLiDs8xGxcbNuqtv2R3CGm1SSpOU0oTWe7TA99o7WaWUI6SUHyRG6qFHYP+3\nhiZvIHc8vBRRxrSFkpWZQUnRwB6LMTFl9cd68i9Jv/I5UkaeTuuaf9P4+i+0YdP9wfXVoo3LG2kv\noX9eX8BbxqFYfz0xw9bL8yVTWC1aGYeSYn3F+MhIGbxabCZARnoaI0qH6s/T1kkrLgjurOFDL0a/\nj3AGjdNuY8fuvezdf8CfBai37x869zo77Tb2HTjIjt17k6AqNjqMy7OXUNegn5CMSLTrrFFWqnXW\n2GyQzhoAtCUWOew2Wt1u1hups0YEom1jZZNS6j8FqZcR2P/N5YpfyYRovFSdkQh3sym7AOvJt3qN\ntzNoXbeQxjdvwvXxn3DvqgC67i106KidVTTHwRfbmJbWVi9PX8kUnQ+PQpt+aIvL68kWbV0hUgYs\n4PcWDhs6hHVV+irjEK7eX2Awti8uT5+dNTpOpggNJt+6YxcPzZyju2QW6CQD1mDB8KHns9Ng+iH4\nfDai/khEHdQkhMgTQlwlhLhPCPG7wKknBR7KBPZ/88XUxKPuj8fTdaPNWWZjy/adCWkkrBlvt5B+\n9YukHnUJ7i3f4XpnCq4PHiBt7zqIYDB0hLPMxq69+/zen6QShbfwiBHDMJlM5OdqhYIdZTb219ZR\ns0sfz1CRhkcdgckUaVoGbEOjK2JnjUQioVPr36f/u1VrADh61EiaW1r02VmjI6MtIDa2UdedNbS/\nobswsnSo1lnD+6D15bcreO39Bfy4rjLBCiPTkeHp66yhK+94OEKGR0uHFGn1LY2in+Dh0bbOGsbR\nH4loS34cB6xHaxb/B+B64H7gbuDSHlN3iBOYQep7So5H3Z9ohxY7IhmNhE0ZfbGMnUzG1S+ROnYy\nnj1VFH3/v7x8SjN9D6yOqT1WaE/MZBKNt3DIwEI+mP0kpx1/DBB7Z4qeJpyXCqCosD99srREEJ+n\nDaB8vc7i8jo5CgX9+tI/L5flXk+bL5lCTzeBNmOhPYGdNQLj8vT0/UPnhmdmeroWkuG93viKNOvp\n+w8lcBd8nTX0FIcajtDzOcVsZniJcTprQPD5bMTOGpGI1tP2GPAaUAS4gAlAMbAMeKRnpCkC+78F\nZq91t+5PZ0G/0ZDM2j3CmonlqEtJv/oFdg67lFwLlG14k8Y3b6Llx38imyNXvk6G0dkZgb0Kw2Ev\nLiLFrFXcGeH1POhBP4Qv+QHaTXhkQGeKYUOH6K6MQyTD02HXvJug/f7TrBad6df+hu1MUVntT0Qw\nmUyU6612WJgHSa3QuF4CkT4AACAASURBVNdo8xZp1tP376Ozh2Gn3TjtoDoKd/B9/3oJyYiEDHEX\nOstsrNVZfcvuEK3RdgQwU2pHzQ1YpZQ7gHuBqT2kTUFbZwRXUxM5WZlxqfsjvfFEJhFbyQ+Afrl9\nKOiXx6okPumKFCv7B47j4v9YqCy9HJGeS/OXz9Hw6mSavnoez8HO65jlZGUyeIA+Ggl3xeOZkZaG\nbfAgXeiHyMOjENyZQuusoa9kEEl0nTVA8/yMsOmsjEOE35Gvs8beAwe016X6K+MQzuvssNvYumMX\n+w/WtnnadKY/kHZxeSGF0j/+4msenj03Ccoi4+0XH4TDXsLBunq27ug4k19vhBbLdpaV0uAyTmeN\nSER7124O+H8HMNT7fx0wKK6KFEH4+r/tP1hLVkZ6XOr+dMfTBlpGVLKHJ6SUuKXgQN7hpF/0GGkX\n/Rlz8TG0rvyAxjduwPXRdNw1qzp8OgztKZksuppMoacn90heKoBjjnCSYjaT39cbl2e3UV6hoyf3\nSJ62wGSKNKu/jINe9LcNLXb8ua+zxvJyrVzQ0aNG6M7zEK5mob8+4YZqv6dNn501Oj6hRw3T6kP6\nrjkLv17GK3//l+46a0DHoQJG64zQLgNWZyEl3SVao+074Bjv/4uBaUKIScDTwA89oEvhxdf/7Ye1\nFVorozjU/YlU1DUSjjIbGzZvTWoj4VDD01w4grTTf0X6VS+QeuQluLf9iOsf9+J6Zwotqz9Ctrj8\nyzrKbGzctj3pjYS7GlvoKLNRs2s3+w7W9oSs2IhQ8gNgwnFj+PyN5/2lV5xlNvYeOMjOPfoo4xAu\npg066Eyhu84avv863odR3pvu8tVrSU1N4bBhdm9njfh0WIkHkWrNgRbH5muH1tLaSoXOyjh0Nkwd\nWii90dWEx+NhbZW+OmtAxw9hoZ019E9wL2FffUs9Dql3hWiNtvsBX6GW3wK7gGeAvsANPaBL4cX3\nlLNl+05vIHf36/5EalQeUZOvkXBV8jLQOtsHU1Y+lrGTyPjpS1hOvgXpaaX502do+Oskmr6YjWff\nZh01Eu6a0RbvzhTdobNuAoEIIcjzdnYAfSWDQMdDQoEMHlBAVkYGAFZLqg71h/8dFfTLI69PDvUN\njaR7e9iCfvRDeK9zXp8cBuT383emSE1JAfQX19aZ4RlaKF3X9fJo/ywf2llD74QOj6ampDB8qM7q\nW3aDaOu0LZNSLvL+v0tKeY6UMkdKOUZK+WPPSjy08fV/g7aSCdC9C5bHe4U0mbruaQvUsO9gLb9+\nfBa19T1fBiSUzm5UIjWNVOc5pP/3TNIufARz8Rhay+fTOO9mjtryJmcUuVldUZFgtcF01eHp+/71\nkJEWzfBoKCNLS7QyDjq5CUSqNWcymXDYS0izWjCZTDrsrBF+eFQI4X/4S0+zUjqkCEtqqm70Q2TD\n01Fm8/aAdTF00AAy0tN011MyXBavo6zEEJ0pOgsVCOysATDt2Rd5bt57iVQWNR1dk3z69RLS0B1i\nj0RXJBRf/zfQGp8PLykmxWzu3lNDN0p+AAwuLCAnK9P/pLiifC3v/nsRX3z7fdc1xUqEG5UPIQTm\ngaNIO/1XZFwzl9Sxk0l17eGRY1s4s2YuzUv/GnWT+njTVY9n35xsBhXk6+KiLyNF8XdAVkY6QwcN\n0IV+iM7wPHH0f2EbrIXv6q2zRjS3obbOGlbN82DTl+chUqFpp93Ghi3b2Hegloz0NEaWlujK6ITw\nWbxOu1Yovba+vs3TpjP90HmogK+zxp79WjLL598s12cPWzruEOK0l7D/YC3bd+1JgqL40qnRJoSo\nEkJsiGZKpOBDEf8FN83iv2F054TvagC8D18j3rY0fO+TYwJvAm1hPNHvhUjP1UqGXPk8s3eNYG1t\nKi3f/Y3G139G4/v307r+U2Rr4uL0uhNb6AvmTzbaRT52NP3V8ZbTJUL7LXbEjVdczHuzHvO/1lVn\njSgewgIzeH2vdVXGIcKp4Cyz4fF4WLV+A+lpaTjtNtborLNGuHAHf1xeZbU/Lk9vnTWg80LToUPq\njU1NVG7aktS45s7QniPbZ/CCPg3lWAnnaZsJzPJOLwP9gEq0RvGvev/vB8ztWYkK3wU3zeq94JZ1\nL/vRI7ULXVc9bT5Na6s20ep2+2vIJfKE6E5cnjCZoehobv4UUi6bTeoxVyMPbqfpk8doeOVamj5/\nFveuih6/oXW3Xl711hrqGxvjqilWIg0tdoazzMbWHTs54K1/llwi74MQol17n1179wX1JE0aUZwL\nvpuu7xriqz2np84a0Pk++G66jU1NpFstOMpKaGjUVxmHcMZzYAZjg8uFJTVVd501oHOvsz+O1heX\n52rW4pqr9ZUMAh3vg97qW3aHTo02KeWffRNgAx6RUp4hpfyddzoD+BMwPFFiD1V8F9yMtDTttd3G\nrr1tdX9ixe/g6WJMm09TU7PWSNj3tJXIMhSR4ngi4SjzNhLe04hl9JV877ydl+qO1mLf1nystct6\n+3Zafnwf6ToYR+VtdLczhZSStRuSnIEmu64f9PHk25W4PGeZVsZBF/q9f8Ptgi8OLD3gwQ/0Ewwf\nyes8qCCf3Gyts0ZaQGcNXQ3xhnnG65+nddZYXallwB4+3A7o5/v30dnwaJ/sLIoK2+pbNjRp2fh6\nNYJCf0a+zho+/S2trZz9s9v56POvk6Cue0Qb03Yx8LcO3n8LuCB+chQdYS8eTGZGuv+i1d26Od3N\nHoWAJ8fKKn9gbWAByZ4mUhX4SIRe9JcsX8kz/y5n12HXknHtX7Gc9EswmWn+cjYNr1yL6+PptFYt\nQbrjVxuqO8ch9Mk3WXR1eNRZpp+broyibEkovjhTfeiPPMxuMpk4+6RxHOnQnrFH2PRVxiGS4ekL\nyQDt4bXMoJ01fBmw/s4aOvn+fWiZlx1/5izTijK3ut20tGjDunrTD2jJFB3F5QWMUNXWN1C1ZRuL\nl36baHXdJiXK+eqB8UBout14ILnFrg4BUsxm/vbUdH+dq8C6P6cce3TM64sU9BsNtiGD/I2EMzPS\n/O+XV2ygoN/oLq83Wrq7D0O8jYT9GV1NbXF5Q048jtRR55I66lzcuzfQuvY/tFZ8hnvDV2DNIsV+\nIinDTsU0wIHoQleJULqyCwP69yM3Jzvp7Yi6OjzaL7cPhfl5urjpymiC2kLIzsxkyMBC/egnsuH5\n8F23+P9PT7NqnTV0oB+i8zo7ymwsWfEjaWkWLKl67KwR/lxw2m18+e33SCAzI4OROuxMES5UwGG3\n8e8vl/qTESD5D40dEVryw4fDbuPDxV+y72BtwOiQ/vRHIto7zpPArP9n773D4zrL9P/POyNNUZes\narUZzVjWjNPsJKSSQkLIhrKUhbAbILAssMDSWZYFvvTyY3dhWZaF0EMooWQTkk2vTnAckrilWHKR\nLMmSLEuWZVtWL/P+/jhlRvI0zSk6Drqv61xTdM6Z52jmvO/zPuW+hRA3CSHeqW43oXC1/ad15q1C\nQ7i5kVI10raU92e5kDGV8sOA05bndrM+2KRH2twu5adk2ySQpr0+G7hcLkXOp1Or0Uhel+eubMF7\nyXspePsv8F73JSV9uu8xpu/8F6Z+/Q/MPn0LsWO51XUYSY8KIZRi8hVXpjAW7Vxp+8FAXV4o6Aja\niZypYxygbKIhG8dzaW2v05Q1Mvn+kbDCbxmLxZS6PBOI0q1Aug5egF2qskaBz8febmcpa0Bq7sjE\n7MrUtNIM0tnb7zhljUzIlqft34C3A2cC31a3M4EbpZSWCMYLIX4shOgSQkwJIY4IIe4UQkSs+KzT\nEYm8P8uFUUUE3Qa1A21qeobiokKFxsGmSUAfpw1cQyQcZI8q5zM1k74DVrjc5DWdi++qT1Jw46/w\nvuITuMobmNt1G1O/ez9Tt32EuefuIDaefWG30TR1JBxkX2+8A+2/f/k7vvaDn+d0rlyRS5RKQ0Sl\ncVhpOZ9cHc9IKMjBwcMrwk+YiFyjzhvCLRweOcroCWtqNnNBWg1YvbY3Xpc3emKMoRFnKGukLWpD\n+X9r8JlElG420pU7aP//7bs7ADi7bR3TM7N09x9KccTKQKbI8Sbyi06pNXlOVNbIhKxzO1LK30sp\nL5FSVqjbJVLKZHVuZmEb8E4gArwKZWp4WAiRb+FnnjZI5P1ZLuL+jjGnLRoOMjY+QdfBfgpUTUa7\nIm1GolQaoqGgKudzWHccskk3inw/ea1X4nv1l/G//Rd4Ln4PCBezT/2UqV+9k6k/foq5F/6P2ER6\nTqC445m7/XNz83T19gPw9HO7ueuRx22NPGSSgEoHjcZhX8/KNlMYrcvbs+LNILndCxFH1RVmvoZA\nfR1XXnAu552hrN2d1MwCmWvaEonSC0wiSjcb6XgXNWWNHbv3ArBpQxvgLPshdY1qRWkJdVWVihxa\nwkLRafZngmPJdaWUP5RS/klK2SOl3IEin7UWaMlw6F8EEnl/lgstHG/QZ9PDzc/v3a+sHMNB+g8P\nMzZufeRBjy4YOEdiMb9W4zB8dJSRY8ezPoeroJz8s/4a/5v+E/9bbyL//LchZyeZffKHTP3ynUzd\n+WnmXryb2OSpDRpG6/K0SUtTRpiambGdxiEZJ1K2cI4cVG7pUafYnyvvol4b64BJK5uos9vt5qYv\n/ysXbTwLSFDWWPHfj4JM98IionSfQpTudrkc4TRrSOd4asoaWlnOhnUtel2zk5BOWi8SCqiRtln9\nPSeUOCwHWTltQgiPEOJLQoh9QohpIcRC4ma1kUKIQuBdwEGgx+rPOx0QNTBhaIO8y2ARfasq5zM9\nM6toGtrY0Wi0exQWCwlPTs/g9XiA3CdhV1kDnnPfSsFbvof/+h+Qf97fIafHmN1yE1O3vIOpu/6V\nud33IlUHzmi0MFBfS4HPdwrJsa2TmMysPZoK9TVVlBYV2UoVkwy5pker15RTWV624sXMuf6OykqK\nqa+pWnH7Ibeoc1GBn+b6OkfYD9mVCiQqU3g9HkLNxojSzYYkfdQ5ElKokkD5/7cGmtjtNKctjeMZ\nDQfp7j+klwQUFxY46v+fDbKdtb8C3Ah8C4gB/4xCunsU+IA1poEQ4gNCiHFgHPgr4CopZdICGCHE\ne4UQ24QQ244cOWKVSY6BwvtTnuOAZYzjTIPP6yXU1AAo3WhGqUiWAzPSo4lCwtMzM5y1PgyYY7+r\nvBHPeX9LwfXfx/+W/yH/3LciJ48x+6fvM/nLG5m689NEZtqp8+eWmgMl8rC+pVl30laiIyoXjjMN\nQgjaligL/M2HPs0tf7zHLPOygpF0shOUEbSI7WmtrJGVGNepiIac04GZTalAKmUKpyDTAkazH1CU\nKcIOU9YAhTsyDUmzlJJdHUqKd2N0PXu6ehzXTJEO2TptbwH+UUr5Q2ABuFNK+WHgC8Ars/0wIcRX\nhRAyw3ZFwiG/BjYClwP7gD8IIQqSnVtK+SNVwP68qqqqbE06rREN51ZDZgZPm4ZEXdTK8jKqKspt\nmQSkSY6nJiQ8NT1D9ZpyGmqrTbffVdGM5/wb8F//A/xv+R75m96CnB7jgtlnuefaGWZu/xiz224l\ndrRn2YNfNBSkQ5XzWYlIm8Q439/e7l69maKjq5vHn9lpknXZwYjjGQ0F6TrYz+zsynWgGekrioQc\noqyRa11eKMjA0BGOj520wqxlIZtSgcvO38irXn4hG9Yp5LoaUbojlDXIgmsunOC0eb1EQgHGxicY\nGHJOoESmif5rgQWtLu/cDW1MTjtLWSMTsnXaaoB29fk4UKY+vx+4Zhmf9x2UxoJ02zPazlLKE1LK\n/VLKJ4C/QVFfeNMyPu8ljUgoSFdvPzOzs5l3TkBMGyANKCJo0G4Cv8+jv7Yl3aXX8RhsplCFhAeG\nj+hM61atfIUQuCoCeM5/GwXXf5/b/H/Nd17MQ+R5mdv2G6b+8E9M3foeZp76KQuDu5GxzKu/SDjI\n5NQ0BweH4hqwditTGPgKIuGgLuczNz/P/MKC7St3Q3V5qrLGvt6D5hq1DBgiaQ4rkYeVbqbItdxB\nj+4f6DHZouUjnbOgoaKslO9+7pOUlxQDCcoUjom2pb8GTVkD7M+uZAtJ8u5RgLoqRVljb7fye9ea\nKZxkfyZk67QdRGkCAIVg91Xq84uArJdoUsoRKeWeDFsqsl6hbt5sP++ljmgoqOq/LW/CMCO1qEGr\n0UjkTrJDSNhM2hKAubl5ZeUYDtJ76DDjE9ZzRp8QJdyyPw//G/4d/ztuwXPZP+EqrWf+hf9j+s5/\nYfKXNzKz+bvM9zyNnJtOeg69tnH/AaZmZijw+xg8MsIxmyIPRqJUABsSOhg1p/Po8RMM2xh5yGay\nTYVkckqT08m/K6tgpKHFMXJQOTqejrEfUjLxp4OTlDVA+xpSX4PGbwmK09YacJayBqRP8WrKGtr8\ncUZrSFHWcJD9mZCt03YHcJX6/L+ALwkhulHE4n9itlFCiLAQ4l+EEOcKIZqEEBejSGbNAHeb/Xmn\nK3KVAjI3PapxJymrr7gjaS33jVHtUQ1toYB+g/t9cU3DPTas3BOjSa6CcvKj1+J79ZcoeOdv8F79\nz7jXnsl815+Yuf8rTN78d0zf8wWlE3VsSD9uXXMjeW43OzsUwstz2hSZIjv58oz8joINaxU5H1Xe\nR4Otk5gB2pKlyhpDR0d52ZveyVM7nzfTwvQw0JRTU1lBeWnJikd6ci13qHCUssbyv4PiwkKa6mod\nYT9ktwjT5h2f14Pf56WlYa2jIlWZKHy0FG+e202Bz8e6QKOj7M+ErGSspJT/mvD8NiFEH3AJsE9K\naYUTNYMikfUJlFTsEPAEcJGU8vRJPlsMjfdnuVJGZjk8ACVFhfzNtVdx8SalDT8uRH1AL+y3AmZx\nzRX4fAQb1nKgbwCfdzF30nlnRg1amR6pmPiFp4C88OXkhS9HLswRO/Qi8wefZaH3WRa23ATchChv\nIq/5fNxN59Pa3MCO9j0AbNqwnq07n2d35wH9O7EWuUepQG2mCDYrtCtLuJOuuMB6OTQwpuqwVFlj\naGSUufl5/vzcbp2awmrEF2HLh6KssfLF/Ea6wZ2krJHLlxAJBxxDO5FN1Pn6666hrKRYX6hHwkGe\neX63DdZlh2zkxCDeDBIJBXn0z9sMZw3sQsZImxAiXwjxOyFESHtPSvm0lPLbFjlsSCn7pJR/JaWs\nllJ6pJSNUsobpJR7rPi80xUa70+7ytOVLbRB3uUyh6bvax97P9e+/CIgwZG0eBA1M1qo38ReD9UV\n5awpK7VlEM3GWRDufNyNG/Fe8l78f/sj/G+9Cc9F70YUlDH3/B+ZvuvT/PisLm4o3cN1jQs0VhSy\ntrrStknYjIFOk/NJTCvaOQmn43XKBtFwS1xZQ70Guzt4gZxXYZFQkP09fSsq5xOPOufWAesMZY3c\n5dCcoKwB2TUWrQs08k9ve4t+rdFQkKGRUUYTNElXFJk6YPVIYbyD95iTlDUyIOOsLaWcQ2k2cFBP\n7yo0RNTuu+W0LFtZ463VDFg9aZkZLdSiaz6fV7fflnTFMpn4hRC4yhrIP/sN+F/7dQpu/A3eV36a\noYIQ51Uu8NXz5ri674f8z/knOGtyOwuD7Vk1MxiBkSiVhmg4yMmJSfarcjIlRYW2potSyd5ki2hY\nU9YYZFol7bS7gxcMaMCGg8zNz9N1UFHWWFhY4NCwvd2ARrrBNWUNrbh8pZBrqYARonSzkYvj6TRl\nh0zp0UB9HX6vF79Wh+0w+zMh21DL7cAbrTRkFbkhGgou0n97eOszfPRr3057jFEm/ow2hYPs7T6o\nkzBaCTOuIbq0Li8cpLO3z3IaB6NRKuEtJC90KVOb3s0193l5+2YPAzUvx+/z8sa1x5m+81NM3vy3\nTD/wNeba7yM2Zn5lQa4SUInQ6iK1NvyN0fX0Hx7mxMlxg2fODkbr8rRi8vbObj1aeGT02LKUNYzA\n6AJmqbLDA1ue5pp3fYihkfQybGbCUHpU1fRc6WLyXCO2RojSTUcOTTm6soYT7CfzuOp2u2lradbT\no20tzY5S1siE5XSPfk4Vbf9/QoiPJ25WGriK9Fi6Svjzrhe474mtaXl/YjFrnbZIKMjM7CzdfdYJ\nCZuZHt0YXc/rr76c89UaNo3122oaByNUE4loCwWRCHYfc3G0+VoOnPlBrrrHy0Db28lruZTYkU5m\nn/gfpn7zD0ze+l5m/vQDpSN11qQOWYPXsF5V1tixW63Li64H7Is8GE2Phpoa8OTn097VrUfawL5J\nzOi9EKivW6SsMXxUqct7YV+XaTZmgpGO9rXVlaqyxsp3wOZiv0KUvvLKGpDbmOQkZQ1QI88ZruGT\n734bn3j3DQAU+p2lrJEJWTUioAi3HwPOUrdESCB9aGcVliHU1KDrv73uFZfpmmrtXd1cXlGe/CCt\nps3CSJtmw7pAoyWfEa/jMX4uv8/LN//5Q/rrRBqKM9aFUh1mGEY5zjQUFfgJ1NfRMzCI3+cl2LCW\nsTnBs2MltL7ueqSUyOP9LPTvZKFvB/N7H2Z+9z3gysNV04a7YSPuhrNxVa1DuNzLu4YcdTsT4fV4\nCDU16NQ1idxJF55zhqFzZwOjEc/8vDylA62zm8baGv39jq5uLjt/oxkmpoVRSTeXy7VImUKj62nv\n7Obqi19mio0ZYbCZIhIOrHh6y0jE1raSjAxQxtXc6gqX2xBnFbJppljaZBYNBXhuz34LrTIPWUXa\npJTBNNuqgPsKIs/tpjXQpN/wWgdeukJuq9OjLY31lgsJm6E9mgpLaRysQjayN9kiUdOwtmoNZSXF\n+iAqhMBV3kj+ma/Dd90XKXjXb/G99mvkn/V6mJ1k7tlfMn3HJ5n8+VuZvvdLzD13BwsjB5AylsU1\nmOJ3EgkFdEe8obbaXhoHg+lRiMsRaY0IFaUlNqZbjNd3JlXWsLOuUH00okyxr/ugrqwxevyEXjJi\nF4zy/eVClG42cr2GSChI78Ag45Mrq6wBuY2rmrKGXfyWRmBO++AqVhTRcJCOTkUCSaupSTfgGmjU\nygpLHUkrYKXjuZTGwSqYlR6FhI4otZkiHQ2CcOfjrj8bz4XvxP/m71Jw46/xvvLT5K27ktjYILNP\n/ZTp2z7M5C/exvSDX2fuxXuIHe9PqlJgVpu8Zj8QV6awMb1ohhza8ZPjHFAdhY3R9bZ28ILBurxw\ngMkpRc5H48uztYPXIOF3orIGwHd/+Xve8akv2qyskXtDSyScG1G62ci1sWiDrqzRY75ROWC517BB\nrYvc44BoZyZklR4VQvwsxZ8kMI2ikvA7KaW9S5tVAEqU4nf3PsSh4RG9piYdZUUspkRQXMI6nz0a\nDnLfE1st474xs6YtGSLhILfd9wgLCwu43ctLGWYLM5wFDX999eWcnJikqU5Jz0XCQW754z3Mzc+T\nn5f+Nhf+UvJCl5IXuhSA2PgIsUMvsDDwnLId2KrsV1CBq/5s3PVn4V57JqK4BkxIj0K8mBygwOcl\nEgryxLM7mZ6Z0VvzrYKRyVZDVG+m2IMnP58zWkM88tSznJyYoLiw0AwzUyK+CDPQAZugLKCNIYdH\njjJ6YoyK0hKjJmaE0XKHRPvXB5s5dmKM4aOjDI2MUlu1xiQr08NIJ7XmNHR0dnNmq3X8lpmQa7lD\nopzVeWdEzDYra+TalKPXhnd228avmCuynbWrULpHXw+E1e316nvrgU8Be4UQ51hh5CrSI/GG0dIz\n6Xh/TBjjM8JqIWGrF9DRkEbjYCGXs4np0Zo1FXzi72/QufeioSBzc/N09fYv+1yuokryWq/Ee+VH\n8d/wM/x/+2NFYmvtGSz072R2838x9Zt/YOrXf8+7qru5rPQIsWN9hqIaWgcmKDVuUS3y0G195MEM\n2pL1agfagb4B/F5vfOVug6anEboMDeHmRkXOp6t7Rfjy9GvI8X5IVNYA9Gih3dQxuX4FuRKlm41c\nr6F6TYXNJQHJkWvEtqK0hNrKNY6oK8yEbJ22J4H7gAYp5WVSysuABuBe4EGgGbgH+JYlVq4iLRL1\n36ZmZnX+mVTdd1ZHqWCxMoIlMJGnLRn0lbuVKV4TnIVU0GkcDNovhMBVWqdIbF39KQre8Uv8b/ke\nnkvfj6umjTb/SW6o6mPqd+9X0qkPfJ255+9kYaRrWRxxxYWFNNbV4PfGufLMsD8bmEFboilrAPh8\nnlNoNKyEGVrCnvx8ws0NijLFzAzVayoA+5weozWqicoaQLwuz+YUb672a0TpiePNC/s6OXjIZgGg\nHMckIYRSprPizSAGSJrDQUdw5WVCtk7bR4AvJ4q5q8+/BnxMSjkLfBNYjbStADT9t3Z1wD2rbR2Q\nesAyUzA+FdYHm3G7XJYN+lY3U4SaG8jPz7O+Ls+iryBQX4vf6zXdfiEErooA+We8Gt8r/4VPHtjA\n5w+egeeKD5PXdB6xkS5mt/6Y6ds+onDE3ftFZnfexsLhDuRCet67aChIgV/hyquvqaK0qMgep8es\nFK8uj+Ojek05leX20DiYtQiLhILKwm96htrKCluVNcxYhCn2K80UWges3U6Ekd9RJBRk74E4Ufqn\n//17fO0HqSqTrIGhurxQkM7e/pVV1lAfc+XL63aAskYmZEv5UQTUAR1L3q9V/wYwtozzrcJkRMJB\nnn2+nZiUNNbVcKCvP+WApQ/yLuucNp/XS0tjvWXplXgmzppryM/Lo7W5yeIOWOu07jQCSeubKQQj\nCz7y266BtmsAiI0fITa4m4XBF1kY3M3CwW3MAeR5cVW34q6NKlQjNW0IX7F+rg++7c16ZEEIQZtd\nmpgmRTwj4SB3b96C3+tRXifQaFgKPUpl7DTRcJDbH3yM3kOHWVtdpTtxdsAMxzMaDvLbex6k//Bw\nvLbX7vSowQ5eTVkj1NTAyclJdluVqUgBI1HnaEhR1ujs7VtUo2onjAQkEpU1zom0mm2aacjWyboD\n+KkQ4lPAsyjDxMuAf0NRS0B9vc90C1eRFaKhIP/36J/Ic7vxez1peX/sSI+CMon9edeLlpzbTBmr\nVIiEgzy89RkLNiLpRAAAIABJREFUmyms/Q4i4SB3PvIEsVjMNJ3ZpUjWXu8qqsK17gry1l2h7DN1\nnIXBdhYGdxMbfJG5nX8AlU5ElDfirongqm1jXU2E1sD5+nmioSC/ufsB5hcWyLOoGSR+DcahRdoS\nNQ2f2vkCs7NzeDz5JnxCcpgVddZSun2DQ4Qa64mGFSHtiakpCv1+w3amg1HKD1hcEqDV5Q0MHeH4\n2EnKSorTHWoKcuU405BYDB9qamBqeoax8QmOjB6jKhXnpskwUrKRSPS+4k5bjlxzoPz/ney0ZTuS\n/yPwAPAroAs4oD6/H/iAuk8H8B6zDVxFdtBumPmFBfw+X1reH4P60lkjGgoyfHSUo6qQ8Nj4hGkr\nX6vTo6AQLh4fO8ngkRFLzm+UiT8ToqEgE5NT9A0OWfchWTi0wl9GXsvFeC95D/6/+S8K/v73+F77\ndfJf9g5cxbXMd29ldvN3lbq4m29g+t4vMbvj91y81o1YmNFpHKy7BHOccu0eLPDFNQ1tUdYwqdyh\nrSWgn8PnVTp4pZTstaOZwoRFmKasoXXANtRWA9BhEw2FUQqflsZ6XVkDVrCZIsdraF5bS4Hft4i5\n4LYHHrFNzg0wFHV2jLJGBmRLrjsppfxHoALYCGwCKqSU75dSTqj77JJS7rLO1FWkg7bKB/B5PWl5\nfzTSVCsdHjhV0/CWP97DWz7yGb3exAisJNfVYLmmoYXpUYjbv9vKFC/LHyBFvg93/Vl4Nr0F33Vf\noOCdv8F//Q+UurjgRcRODjH3zC2cO/BbnnjNDMWPfYmZLTcxv38zsROD1nBvmfA9lJcUU19TTVFB\nAbCYhsJKmPXvKCrw07y2FlAaKzbYqOkpTVhJej0eQs0NtHd1MzUzE1fWOE34/vLz8mgNKiUZ8wsL\nzM0pRMG28uUZWEi6XC7aWuLKFMfHTvLZb/+AX991v4kWpoeRxbxTlDUyYbk5E7+67dGctVU4A6XF\nRdTXKCtLv0pOCslveG18tErGSoNG46CtXI6fHGd2bs4UGod4GNw6aDQOVq28rE6PrmtuJM/ttrgD\n1rjjKYRLUWxouwbvFR+m4PrvU/DOW8m79vP8ssvDiZkY83seZuaR/2Dq1vcw+YsbmL7nC8w++2vm\nD25DTp0wdg2YF3X+zmc+xkdu/FvARmUNE6PO2kLL5/NQU1lBeWmJTR28yqN5yhQzrK2uslVZwwyF\nk6jeDBKnXbHViTC4AIiGguxRlTW0FLXddYVgTFljb3evrqzhRGRLrlsM/Ax4E8rXug44IIS4CTgs\npfyiZRauImtEwwEGhobx+7w01Cor/mS8P2asarOB5khqK/VEeRytwzVn2NABq9E4WBVpMJNcNxk8\nnnzCzY2WRkqM1vGkgvAV4w28jMfnWnm6y8st3/w8sdFeYsP7iA3vZWF4Hwvbd6BLOJXU4qpej7u6\nFVd1K67KFkRedqS8ZsqJJf6ubVPWMPFe0EixfV5NWcOeZhAzuOZAcTr/+PDjAPi9HluVNcxoaImE\ngvz+vocXlQSkI0o3G0bvhUg4wORdirKGRuJub3rX2PGRsMpvebCftpaAKTaZjWwbEb4JrEVJi25J\neP9uFNqPL5pr1ipyQSQU5KEnn8Hv9SoTRooBN2aDw6MhGo7bMG0id5IZhcvZIBoKsn33Hv31nq4e\nJFKPSBiBGUz8mRANBXn82R3WNlNYStKsOBEIF+7KFtyVLRC9Vvns2UliRzpZGN5H7Mg+pWO1U5mw\ncblxVQQUB0515kR5AyKJCoiVXbz2KGsoj6Y0UyytywsF+cUd9zA7N4cn37pmCrMWYYlyaH6fj0go\nyOPP7mRqega/z3plDTM6eAF27N4LKKTB3f2HbFHWAOP3QmKGp6leSbUPjYwyevwEFWWlptiYDkaj\nzroyRVcPbS0B9nT18OPf/5EPveN6AvV1ptlpBNmmR18HfFStWUv0ZTuAVcF4h0DvXksYcBN5fzRo\nK3Or06OaDT0Dg4xPTMY1DU1YeRmVvckWkXCQwSMjupDwN350M5/59vdNObeV5LoaIuEAR4+fYHj0\nmCXnN4vjLBU2rGthbHyC/qHhU/4mPAVKbdzGv8F3zWcoePvN+N/+C7yv+hz5Z78RvEXMdz7O7Obv\nMPX7DzD5s+uZuvPTzGz9CfP7HlNVHLT6TovsD7foNA6WwcxIWyiI2+WitFhhcoqGFRqHroPLV9ZY\nDsyqUU1U1vB7PTqNw74ee5opDCtrBBWi9O27FXYtrS7PDmUNUCd3A5eQqKyRyHdmZ4oacv8dBerr\nFH5LNbDQPzTM3Zu3MDE5ZZqNRpGt01YOHE3yfjGQPe35KizFBeecwfXXvZLzz4wCiohvsgnDjs5L\nDZojuedAj34T7+0+yPyCsZ+NXbQlS2sDxyen2Nd9kNlZ4wSSdohZW10Mb2WUCuI1Vtna7ypcQ17w\nQjwX3Ij/tV+j4F2/VZocrvwYea2vgIU55nffy8yj31K6VX92PT95+QwXzG6LO3LLUHLIbH8AsLaY\n38z7uaKslN/919d506teAZzaTGQV4jJWxqApa0A80gZ2NlMYuwKNKF2LtG3asB6wT9nBaHrUk5/P\nukCjXleowT77lcdcr8DtdrO+Ja6sofH9Wa1/vBxkmx59FiXa9h31tTbbvA/YarZRq8gNBT4fX/7I\n+/TXS3l/dNhE+QEQXad2oKkdXQAzs7N09x1iXaAx5/Pa5Xgmcg9dvOkspqZnmF9YYH9vHxvWGQsy\nm8UPlg5aXUZ7VzdXXHCu+R8gLW4GUWkc2ru6uebSC5d9vBAuRHkjrvJGWH8VAHJhHnm8j4UjnSwc\n6cR9+F7Wz+1j5lGVOzzPh6uyBVdlCFdVGHdVGFHWgHAtP70ZamrQaRxe+4qXL/v4bGBAuScpEgXL\nA/V1FPh8lte1mVlnGw0F6Rscwu/z6soadsgTmdXQEgkrnJsATXW1tilrgDmLsEhI4ffTnDYrm7lO\ngQlR54jKeRqLxfQ5q8Di1PpykG2k7TPAV4QQP0Zx9D4uhHgUeDvwOauMW4Ux6Lw/S1i1tQJRq6NU\nANUV5awpK6Wjs4fpmRl9FWw0XG4X11x5STF1VZV6pGfaZO4kq53OosICmtfWLopU3fXoE6al66xO\nj3o9HkJNDaZGCoU7D9eaIPltryT/4vfyrie8/Lro7xRN1Ss/Rl7bK0FK5vc8yOxj/8nU7z/I5M/e\nwtQdn2Rmy03MdTzIwpFO5PypHIhLkZ+Xp0QeLG8GseZ+drlcrG9ptj7SZiKFj94B6/XoNA52KAuY\nFXVeTN/kJRKyx34wx/GMhoIcOzFG7yFljFkXaEzaEGcFzFjMR0MBxicn6T88rHfx+hzktGUVaZNS\nbhVCXAx8EoVc9ypgB3CRlPIFC+1bhQFovD9LHQx9YW6hjJUGTfxb00WNhoMMHz1GR2c3f33VZbmf\n2Kb0KKhyREuEqM2ahO1IUUdCQV7c36W//sy3vs/rr76cr37s/YbPLa3uRED5/z9llbKG9kRrXKgI\nxCNysQXk8QEWRjqJHVG2+T0Pw7xKx6BF8dYEca9pUaJza4II/+KC62goyENPPm1hM4i1UedoKMgd\nD2+2XFkDzAkWainFyvIyQLH/V3fdz9z8PPl51iktmtWFHElopijwKfRNW3c8z8zsLF6Px/D508KM\nSJtq//YX1bq8aBu/u/che5Q1TFjMJ2ZXtPSoJk3nBGT9C1adsxsttGUVFiAaCvLAn/68aMKwQzB+\nkQ3hID+77S6KiwopKiigNXCqI7lc2HkN0XCQx57ezsTU1CLaEqOwmvJDQzQc5P4/PcXY+AQ+r4e5\n+XnTIoXS4vQoKPbf+cgTjBw7rk/EZiFdlEq43IiKJlwVTdD6CnX/GPLEILGj3co2coDYoRdZ2L85\nflxBherAKU7cec1l3Ha/oqyxtrrKVPshsZPa9FMDyiT26/+7n4ODQxZ20Jl3P19w9hk88LP/1m2N\nhIPMzs1xoG+A9cFmw+dPCZPWL5EEqgmf10t0XYtOlJ6YurYCZjiebSq/5c52RdVy04Y2fnvPg+w5\n0Mu5amOFVZAm1Aq0BuLKGi6XCyGE9c7yMpDSaRNCNGV7EimltTotq8gZGu/PoeER6muUCcOuIv5E\nG+YXFjh2YkxZOapcUEYiD1ZPVImIhluQUrLnQK9e46B15RqhcbCK42wp9GL+rm79+b6eg6ZEHqxu\nRIDFxfCXnb/R1HMvVz5JCBeirB5XWT2ELo2fZ+pE3JE7eoCFkW4W+ndCbIFXApe8Fhbu/xwzgbP0\niJyrohmRbzzyYPX9vCGhNtYqp81shZNEOxObcax02swqFSgrKaa+poqBoSP4fYuJ0q132jA8JBX6\n/TTX19HTfwiIRz47Orttc9qMfA+Jyhqhxgb8KmehU5BuxO4he35k69ScV2EIGu9PR1f3qU6bTb/D\nRO4kn9dLqKmB3937EANDR3R9wOXCLoJgiA/6u9qVjq6WxnoO9A3Qe+gwLY31OZ9XqR+xJ1IIym9A\nm8zm5ubp6u2nLYEiIRdYXdMGi53Oy87fyOzcHP9586289/o3UG5UCNwkZ0H4S3E3nIO74Zz4qRfm\niB07yPTgXu685YdcsW6e4s4noP2++HHFNbjWBNTUbLPiyJXWI9zLd6at+h40GoeOrm6uu/xiSz7D\nyjEp2LAWn9dDe1c3r3/lFeZ/gAozSwUioaDutJWXFKckSjcbZt3P0VCAnv5D5LndNNRU26esoT6a\nUZe3ZftzrK2uspzfb7lINzKcn/C8Ffg34CbgKfW9i1C6R//FGtNWYQY03p/2zm6uvvhlQCJPmzX1\nKUvRpMr5TExOKeH+BCfCqNNmR7SwtmoNZSXF7GjX2vDbONA3QEdXtzGnzab0aGV5GVUV5bR3dnPF\nBefp77d3dRt22mzgB6akqJCG2riyxp6uHn522100r63lra++xtC5rexCFu583JUhCitD/O+xB9g+\nUMf3v/gvyJPDxI4eUBQeRnuJjfaw0PssqJxxuPJwlTcgdEdOeRRFVcnttHgR5snPJ9zcYIOyhjX3\ns9vtZn2w2YYOWPPi5hecvYEdu/fg83jSEqWbDbMi55FQkHsf34rfF1fWWNoQZwXMolHSlDX6Bg87\niu4D0jhtUsrt2nMhxLeBj0kpb0vY5VEhxF7gI8Ct1pm4CiPw+7yKFFPCDW82RUAmuFwu2oLNbN+9\nhwKfl9aA4kju7jzAKy+5IKdz2hhoUwedIDtUZYQz1rVw58OP097ZzauvuDTD0alhpnxSJkTDQTq6\nenRVClCctjdypaHz2nUNmqYkwKSZyho2cOWBYv+2FzsQQiBKanCV1EDworgdC3MKR1yCIxcb3L2o\nVg5PAa7yZlxrmhOcuUA8HWIxX97jz1iorKE9sUqZIhTkns1bLE3nm3nuG157LW+85hV640ckFOQP\n9z1sqbIGmOd4agtzzeE5XZU1du3ZT13lGsNmmYlsY/AvA55P8v7zgAXkT6swE5FQgG0vdOivYzoL\nvH15+mhYkYPyeb06gaSRDkw7CYJBKWbeulO5BUqKCnUCSUOwQRFBQzQcZMu2XRwbGwNU7iSTnB5b\nOmDDQR588mnGJybjtCum2K88Wi6HFg5y9+YtjJ4Yo6K05JS/K1G5FqhczP0nZybiTpzq0M13bYH2\n+/V91nlK+J+LZ1nbczdzngFc5Y24ypsQPoOp4yX23/7gYwyPHqNmTYVp59VgdclGNBzkt/c8SP/h\nYZ12yGyYWSrgdrspKojXO0YTiNIXcW6aDnNpS/yq07Yh3MLc/DydvX1Ew9aJKMXjEUa55gIATExO\n4ff5jBllMrJ12nqADwAfXfL+B4BeMw1aCqH8gu4DXgW8eUm0bxVZIBoKcvdj8QlDm6jsSo9CvC5J\nIymMrmvhz0ZoHOzugE3gTtKY1h/98zaDzRT2pEdBsX8hFuO5PfsBpUOq40CPYRoHo7I32WKRsobq\ntO3rUZQ18ow0g9jk/OslAZ3dXHLu2XT29vHz2+/mix96T9pmEOEtxF0XxV0XjdssJXJylNhoD/Jo\nL8e6dlE6tJOK4WeYPfxk/Fh/GaK8SXfiXBrJsL9s2derN4PsP2Cx02ZdpA0UR98qp81KoulEZQ0r\nnTazFmEVZaXUVFbo9WCJNBqWOm0mOf/FhYU01dVycPAwfp9zOkche3LdjwHvE0LsEULcrG4dKDVt\nH7fOPAA+wapUliFoqgR6ZMXmRgSAcyLrcblcVFWUKzaFggwfHeXo8RM5nc8m6VEdiQLxfq9HJ5A8\nfCSZult2sCtKBfFBM7Eub2Jyir7BIUPntSu9mIw7aWZ2lgN9A4bOayY/WDroToMand2y/Tluu/8R\n9nUvv/FeCKHIdTWeS/45b6Q/9BbettlLxwVfxn/Dz/Be90U8F/097ubzYWGG+f2PMbvlB0z/32eY\nvOXtTN78d0z98VPMPP495p6/k/m+HcTGR9J+l20tAYQQlheTW5VqT1TWsApW3s+JyhpWQkmPmnMN\nl5+/iXXNiupN89paCvw+Ojp7TDl3KpjZoBYJBwBnSVhB9uS69wsh1qFE1tpQxrjbgZuklH1WGSeE\nOA+lZu5cwNjs8heMSIKU0SXnnk3M5igVQKipnod+/j29gzVx5fvy885Jd2hSSMy7ObNBoL4Wv9fL\n1MzMomaK9q5u6qorczqnXZQfAA011ZQUFbKzPa5peOvdD9De1U2zARoHu2raEpU1zlgf0t9v7+ym\nNZA1O9EpsCs9WlZSzNrqSj2lnsj3Z1gOTYsWuty4iqtxFVdDU7zhREqJnDiKPNZH7HgfsWMHiR3r\nY/7AkzBzMn6ifL8ekdOkv1yl9YiSWooK/IqyhkVOg9XpUV1Zw0qnzcJOak1Zw+pifmliN8VXPvqP\n+nOXy0VbS8AWpxPMcTw1jtPTqXt0EaSU/ShyVrZACFGM0uDwPinlsJN4Uk43aLw/2oBlBgFhLkjs\nFNXC/R1dOTptNjuebrebtpZmdnbsw+/z0lhXo2vqXXXR+ZlPkARmaRVmAyEEbS0Bnnl+NwBnrV9H\nnttNR2c3f3VZ7jQOdtGWCCGIhhVljXCzkh5yu1x0dHXz+qsvz/3ENv6OIqGgXoen1eWZoqyRIVoo\nhEAUVUJRJe7GjQmHSZg+QWxUceJixw4SO97HQt925N6H4ydw5SFK6/jqOVO0D+9ibs9DuMoacJU1\nmFY3Z8f9HAkF2LrTOgEfq9dg0VCQB7dYp6wB1jqe0VCQ2x96zFplDcxz/rXovv90jLStEG4C7pdS\n3pvNzkKI9wLvBWhqyn3l/VJFJBRktzpBaD9slw0yVqlQWlxEfU11zsXkdqdHQbmJd3bsw+/1Uuj3\nE6ivM1QMb2d6FJS6Ks1pKy0uItTUYHzlaxNtCSiD/k933sXY+ASgfB9GmxHMHOQzIRpW6iAnpqb0\nujxzlDXUJ8u8CCEE+Mtw15fhrj9r8TmnTxI73k/seD/y+ACx4/3Un+hg3doxZjf/l77fpPRQVBfG\nVVaPUB05V1k9orh2WVxzZkZIUkFT1jgyekwv0zATVkedo+EW/nD/I4uI0s2GtLA5KhIOMHnXNL2H\nDhNsWGvJZ5i5CNObKU7TRgRTIIT4KvDZDLtdCTQCZwPnZdhXh5TyR8CPAM477zx7Cm1OI0RCQR55\n6lkmpqZsSwllQjScO/eQ3ZE2gIvOOZM7HtxMRZnS/RcNB/V0Yy6wk/IDktTlhYM88exOY80UNtfl\nzS8s8PzeTjz5+WwItxhX1ohzTZhmZypEQkGklOw90KvX5e3p6jFJWcPce0H4inHXRnDXRvT3Dmzf\nxXs/+xV+9YUPcdbaAu647dfMHu3lDXUw3/ss7HkofgKXG1Fci6u8QUmxqs6cq6zhFG1WSCx3MO0S\nToFWAN/e1c3lVjltFt4LyYjSzYYyJlmDRGUHy5w2FWZ8D1UV5Vx67jmc3bbOBIvMg92Rtu8Av8qw\nz0HgnUAUGF/yz/+dEOIpKWXu5Fh/oYiGg7oUk4yplB8rbFMkFOShJ59hfHJqUXt7drDfaXvlJRew\n9ffnUKCuvBTupyc5NnYyN2Z+G4hpE6EN+pqWXjQc5I6HNhuicTCzcDkTdGWKPUq0MxoO8rt7H6J/\naJjG2tw6Am3VsE1QdpicVkTnFRqHw4SajChr2NNMEQ0FWZCC5w6dZNNFV/DQ8Yd5fPsA5777A7SF\nAsiZcWJqVE4e79efLxzcDrH5+Im8RbhK1yJK1+IqqcNVupY188MU51vr9OglGZ3dXH7+JtPPb3W5\nQzKidPNh3XegKWu0d3Vz3RWXWPIZZmdgfvr1z5l0JvNgq9MmpRwBRjLtJ4T4LPAfS95+AfgkcKcF\npr3kkbjKKVQdpBWPtCXQOJx3RiTD3othJ7muBiGE7rBBgv1d3Vy08axUh6WEHRJQiWhprMfr8eBW\nRZATfxM5O21W8hwsQWOCskZNZcUi+407baaZmRKaskZ7Vw/TMzPk5+cxNzdPR1e3MafNpsi5RuPQ\nnqSZoi0UQHiLcNesx12zfrF9sQVFBUJLtZ4YIHbikEoe/DggeQ3wmtfA7C/fzrzm0Kmb8rwO4S0y\nZH9xYSGNdTXWFcNbHGlLRpRuNqxMj2rKGpY2g6xABsZuLMtpE0JUAiFgl5RyJtP+uUJKOQAs6uVX\nv4Q+KaX1WhgvQdRUVuj6b5qDJFawpg0Wh/uX77TZJ2OVCjoNRWeOTpuJWoXZIM/tpjXQxMDQMKDQ\nOIAy6V5xQW4c2XamR10uF5GWANte7MDv89GaQONwzaUX5nTOeDmYTc0UoSAdnd2Ul5bQ2tzE/t4+\n2ru6ec2VxpQ1tPNbDc1+WOy0pVPWEC43orQOV2kdNC9u2pHzs8ixw9x91x94/tk/8ck3X4w8OUhs\n8EUW9j+2+ES+klMdudJ65bWnYBn29+ivP/nN/6KtJcA/vPmvszo+Hewod1hKlG42rKbwiYSCPPb0\ndguVNVZgNW8zsnLa1E7OnwJ/gzLOrQMOCCFuAg5LKb9omYWrMAWa/ltHVzebNigr4ZV0eACq11RQ\nUVqSUzG5nYLxqVBRWkJt5ZqcV75mahVmiysuOJcdu5VBv6iwQKFxMNRMYe+qNhJW5KD8Xg8+r5eW\nxnqD9tvr/EfCQW654x6i4SBFhQWKssZp1EwRCSl1kNMzM3Fliv25r6NFngdR0cTBvEZ+2ZnHZy77\noN5ZKOdnkGOHlajciUPIE4eInRhkYeB55L5HF5/IX6anWkVpLa6SOkSJ8oivRP+NRkJBHtjyZ05O\nTFBcWMjTz+2mp3/QHKfN8BkyYylRutmwoy7v9gcfY2hklNoq8+WhHDAtWI5sI23fBOqBTcCWhPfv\nBr4GfNFcs06FlPIl/DXYA03/bW5OqS9Z6RCyRuOQS7g8x4Y50xFRNT1zgd3dowD/9LY3L3odCQV5\ncX+XgTNaV7icDFpKVNc0DAdNUtYwbFpW2BAOMjc/z54DvVx0zhk01dXw0NZnjP0WbOi81BANK8oa\n+7oP6h2wpihrJIkWijwvokLRWT1l/7lpxaEbO0TsuObQHWJhYBdy3xLC63w/rpJaREkt1/hc9Afn\n6dv+IG1nX8DczJQpyhr6NVj8FSxV1jAbVlP4JDaDWOO0rXwGxmpk67S9DniDlHKXECJxQdEBWKdJ\nsQpTEVUnjM7efgBcK+3xoDgNN99+97KFhJ1SuxANBdn89HYmp6cX1btlA7tr2pIhGg5y/5+eYmx8\ngpKiwmUfbzttyVI5tFCQux55gqPHT7Cm7NSuxEywQ2w9EVoH78zsrCKHFg7yh/sf4fCRowZJmrHl\nGhKVHaamZ/B5PUxOTXNwcIiAIZJm5THb35LI9yHWBHCtCZx6rvkZpYZubFB17A4jxwaJHR+g5sQg\nnzlnHtp/ylT7T3nwajg8KTjxv5+iqCagR+eUx9pl1dHZcS8k/v81p83Mz5UWU/i0tTQryhqd3bzi\nwqzJIbKG3ZrUK4FsnbZyIJleTzGrElOnDbRVzm41suKEH3Y0FMxJSNgpKyqtK3fvgV42RtdnPiAR\nK5AeXYpIQkfjBWefsezj7U6PhpobyM/Pi0fajCpr2CTDpSFQX0eBz8fk9LTSAZswCRt12uz4Gupr\nqigtKqK9s5upmRnOWr+OZ57fTXtnt0GnzbzvQeR5dUWHUz8nxutufDfXnhPk3ddeyI9+9D80FEgu\nrhjD1/M0TB1ffIC3KMGJq0GU1CnOXHEtoqgS4XInnNv68SiZssYr3vF+vvqx9+dM8r0IFtflFfr9\nNNfXWdeM4BA6KyuRrdP2LEq07Tvqa+0Oex+w1WyjVmENdP23Az3AyqcWIa6LurszNyHhlb6GRDmr\n5TptK5EeXYrEZpDcnDZ7ueby8/J43Ssu0zUNTVPWsOkaXC4X61ua2dm+F7/Py3o18mBUWQPsa6Zo\nU2tjp6ZnOHN9mB2799DR1c11lxtR1rDnXhDCRU1jiAf2HeMd77uEmzp+BMA7my/hX9/3TuTc1OLo\n3NiQ8jjSxUL3U4upS4QLUViJKKnBVVzDtSV9DLhh4dCLiOJqROGaRU6dWUgkSh89McboiTH+tG2X\nKU6bHYuwaCjAc3v2W3Juu7SEVxLZOm2fAR4QQmxQj/m4+vxlwGVWGbcKc6Hpv+3YvUd5Y6U9HqBJ\npXFYbjG2U9KjdVWVlBUX5VRM7oT0aGV5GVUV5bkrU6xAuPDrH/+A/lxT1tidYzH8ShQuR0MKKbPP\n56XA51NoHE6jZopoKMiv7roPKSWlRYWEmxuNK1PYGLGNhoI8tfMFTpyc0N/T7Bf5fsSaIK41wVOO\nk7EFRcN1bJDYySHk2BCxk8PIk0Ms9O/k2tKjuMpg+q5PKwe43IucOlFcjSjWntcgCspzcuoWKWuo\nfH9mRa6UMcmUU6VEJBTk3se3cnzsJGW58FumgRMa1KxGtoLxW4UQF6PwpHUBVwE7gIuklNaJua3C\ndERDQd1pc0JNm8vloi3YvOwOTKekR4UQiqZkLs0UFmsVZouoATkou9OjybAhx2YWWJkamLimoQdQ\n7snt2kIc6HQGAAAgAElEQVQqB9iZHgXF/rl5JeLk9ykp3sef3WGwmcK+hpZEZQ1AWTR2dWe0X7jc\niOJqKK4mmav1nk9/Ed/CSb7z4bcpTt3JYfVxiIWD25GTo4sPcOUhiqpUR0516EpUh664WnHqxKnN\nHdFwi06U7slXpvC9B3oNK2uAPTREenT/QA8XnXMmQM41tUthZ9R5pbAcwfgXgBsttGUVNiASDujP\nnfLDjoaD/O8Dj+mDzle//1OEcPHZ978r5TErpHmfFJFwkF/eeS9z8/Pk5y1Hb9He1GIqRMNBtmzb\nxfTMjF4rli2ckOKNhIM8+OTTjE9MUlSYHV+XjhWI2C7VNIyEg9y9eUvuyhoq7LqGDeF4FMrv9RIJ\nB7j9occMKmvY9zvS/v872hVHeWOklS3bn6P/8DCNdbmRNAPMScHJOS/uxo1JnTo5P4McP6JH6eJO\n3TDzvc+cWk/nzlecuqJqXMXKoyiq4sxiL42FMfbu72RdSCkpUZQ1Bgk1NeRsP9hTl5dIin3ROWey\nY/ce3v7PX+DuH/2nYXkruxcwK4HlkuuuBaqBRe6/lHKHmUatwjpoNww4x2mLhIJMTt9H76HDtDTW\ns6N9LyPHjqd32hzUJRQNBZmbm6frYL9OWJsNnODwgCpPpNI4nLVsnT17KT+SYZGyxpnRZR1rtuxN\nNmgNNvGqSy/kZaqtiZPYxZtyJGnGvqhzsGEtPq+H6ZlZfF6vPtEaU9aw717WlDW0jMPGaBtbtj9H\ne2e3IacNSOstiDyvqsGa3LGSc9OKUzd2GKmmXWMnh5DjR1g4uA05eQyAAuDOa4DDNzE7UsQvLp9h\ncFIw+9RPmTuxUY3eVeMqqgJv8bL+r3akR5cqaxwaHmF+YYEdu/eY57St+KhkHbIl192Iohnaxqnj\nm4SkC4tVOBCa/tvc/LwjHAZYXAzf0ljP9MwsQyOjjB4/QUUKGgen1LRBov09y3PacMaKMJLQTLFc\np80J6dFE+5fvtNn/O8rPy+O7/++T+utE+404bXbNU263m/XBZp7bsx+/z2uasoZd9rtcrkVlIme3\nrdOVNV718tyUNcB4jarI96XsegWQC3PI8SPI8SP85Oc/wbdwkkvX1zNx6AVaSyVrj+1g9slnFx+U\n50UUVSnp16KqRQ6dKKpSmiXccaoluxaS0VC8JMPMujy7JN1WEtlG2n4E9AHvAQ5hD/nzKixAXP+t\nxzE/7FBTgyIk3NnNq6+4NM603tXNpecm7whciQhJKgTq6/B7vbR3dvOGV14BZOkMOCTS1lBTTUlR\nYc7KFCt9DdUV5awpK10kT5QtnCB7U67ROBioKwSb6/JCQcVp83p1ZY32ztyVEewuFYiEgjz7QjsA\nZSVFhJqMa2Ja7XcKdz6idC2UrmWs9mX89x33UHDBK/jck3spLSoiGg7w8y9+lNhJxbGTJ4eJqU6e\nHD/CwpEumD6x9KyIwgrdoXt/6xQNsov57qcUZ69wDfhLk9bWGUEkFOTxZ3cyNT2jkzQbbWZRsJoe\n1RAFNkop91lpzCrsQSSksPg75Xftyc9X5HzUQXNyOn4Tp3LacFB61O12s76lWbdfSslVN36Qd7/5\nddzw2mtTHueUmjYhBG0tgdyUKWzmOUsGw80grPwgn6v9sDKlAlp02aeRHIdb9ML+XGBnehTi9oNa\nlxcKsHWnsZ46W+vy1GaQF/cpnJsbo63s6tgHvlLc/jKoTh4xV+rqRtTaumHdoYuNDxM70smbm2fw\nsJOZB3bGD3LlKRG5okpEYSUu9THx9XIduw3rWojFYuzt7mV6ZhYwWVnDAeOqVcjWaXsBqAVWnbaX\nAC4992we/fM2/L7lFZ1biUhIaWOXUjKtOm3ZOBFOcNpAsf+uR58gFosxP7/AwNAwT25/Lr3TBs4I\nFaJMArfe/eCy5XzsnmxTIRoO8rPb7mJ2dg6PJwdlDQeQND/65225KWuswDVcffHL2NW+T+fJi4aD\n3PfEVk6cHKe0OHsVAQ1WM/EvhWY3KJJo0XCQOx95gpFjx6ksL8vxrPY5bZElzRTnnhFh8zM7ODQ8\nQn1NVcrjlLq6eiirT1rTdPHr/pZ3v+YKPvD6VyAnRpRtfITYuPI8NrSHhQNHF/PVwamOXeEaNXq3\nBlFYpbyf4NglkmJPqulRM5U1VnwVZiGWw9P2b0KIz6E4cHOJf5RSjiY9ahWOxHWXX8K1L7/IcHu4\nmYiGgvzvA49y+MjRrMLlTqppA6Wj7ta7H6BvcEjnHsrkdDohtaghEgoyMztLd98h1gWS19Qkg1Ou\nIRJSaBz29/axYd0ylDUcErGNhHJX1liJYOeaslK+8ckP6q/jyho9XHjO8kmarWbiX4pQUwOe/Hxm\n5+bw+7yLnIjLzt+Y0zltYMvQoSlr7O/pA2DThjZAGXPSOW2ZEJOCKVcB7up1QIponYzB1AliE0eV\nqN3EEeT4UWKagze8l4XxkdSOXeEa1hRW8qmNgoKeR6nPLyFaFmN4StC+v8uY07aaHtXxsPr4IIvr\n2QSrjQinHYQQjnLYIF6MvbN9LwBFBQX0DAwyPjlFUYH/lP0dkJVbhMRi8k3qpHtoeCQtjYNT0qOw\nWNlBc9pGT4xRUVqS/kCbIySpkGj/cpw2p8jeJMpZLV8ObeUnqsRmolycNrsbWvLz8lgXaGT3/gOL\nnLaOLiNOmzS99isVEpU18vPy2BBuweVy0d7ZzdUXvyzn88os+PKEcEFBOe6CcqgKpzwP0yeUCJ0a\npZMTI/rr2JH9vKlpmnzXbgCuu1I5Lrbnm0z2/UR17iqUxwLl0aU6fKJwDeT7k/5enLaYtwLZOm1X\nWmrFKv7ioQkJa+H+cyKtbNm+S6FxOCNyyv5OifBoaG1uIs/tpqOzW58AAPZ0dXPRxuQdgXauzDOh\npbEer8dDR2c3f33VZfQMDHLtuz/Mr/79S2k7Mu3gdcoGhpU1rDBqGaitWkNZSXHOyhqwshPVmrJS\nqtdUGKvLs9n8aCjI7v0H8Hk8uHwuGmqrDStT2M33t7N9Lz6vB79PoV7J9f+vwSyVFiEE+MuU+roU\njt1///Bm7n7gPl534QZGDx2godhNXZGbV7dGiE2OEjs+gBx4HmYnTj04z6c7cKKwQnHoCirwjcc4\nqyKGb/YEcmFuUWfsSwXZKiI8brUhq/jLhiYkvGO3EmnbtGE9W7bvoqOrO7nT5gAJqER4PPmEmhpo\n7+rmr9QaDVDSLamcNjtrYDIhz+2mNdDEbrUD8PCRo0gpefaFjvROm0Ouwaiyxkp7z0KIRTQIy0E8\n6rzS0cKAgQ5Y+39H73jDq1kXaNIL36Oh3JU1wH4KH11ZQyNpDgXY9kKHsZPaGPGMrGvhZ7cv8Mje\nEfLc5dAY5ldPb+cNn/roIhvk3LQiHzY5ipw4qqRlJ0b192KHO1iYUOrsKoGbLwf2f4/J/d8DX2lC\nhG5x5E6L3uErccQYli1SOm1CiE3ALillTH2eEqvkuqswA9FQgPv/9GdAqdmoKC1JOYnZSOuUNaLh\nIH/atosptZECMtflOWmw0IrJpZQ67Up2dXl2WJcZS5U1soGTZG8i4SC3/PGenJQ1YMX9TiLq7z83\nZQ37I7atgSZaA03662i4JXdlDewvd9CUKRLl0O5+bEt2ZQ0pkE161CxoJQHd/Yc4szVMNBzk9gcf\nY/joKDWVa/T9RL5Pb55IBSUdO0bX3uf52re/zUff/CoidSWLnLuFI/tPVZ0ApdZOd+Qq4s8LytUI\nXjmipA6R5zH9f5AL0o0M21A6RofV56mUEldr2lZhCqLhFu59fCugrB6j6TQlHebwgFKMfcdDmzk4\nOARAWXFR2siP0xzPaDjI7+59iP6h4XgzSEanjZX3FlQsVdaYnZtjYOhIWpZ1pzg8kKCs0dtPW0J3\nYyY4IT0KxpQ17GDizwStLi8XZQ3A9jFJI0rXIm16XWFnN5ece3ZO57RzIZmorLG0GSTRacsGSjq2\nlNmierYOublhzSbOPu/U2j65MIecPLYocicnRtXo3VFio73I/p0wO7noON/r/x137akZn5VAOqct\nCBxJeL6KVViKRIktv9dDJBTk5tvvZnZuDk/+4toEp0WpID5oxuVx1rP5mR0paRzsEGdeDhLllLRo\nYd/gECcnJiguTC7mLHFOM8WGsNKA0N6pKGvc+cgTfOm/f8wTv/5hZmUNB1xDJKEZYVlOm0OaKYwo\nazhhEZb4/8/FabN7/aIRpcfTo3H7c3XaANsuIpmyhhCC9q5urrzwvJzOmalBTbjzEcXVUFyd/jxz\nU0qUbvIYcvJoSpWKlUDKVhcpZa9URzT1ecrNPnNX8VJGJIHw0uf1KpGH+Xk6e/tO2ddJHGcaIqqc\nj+a0bdrQptM4JINT6sE0tAabdDkfLdIGCo1DKjgpPRpqbiA/P0+PDo6MHmdufp7daVPUyqMTvodA\nfa2irJFjXd5KX4ExZY2Vd5yr15RTWV6WczPCSiwkP//Bf+ATf38DAGWassbpVJenOpo+r4eiAj/N\na2sN2m9O1Fnk+3GV1eNeewZ54csR3uVzD1qFrPqThRCXpdheLoQ4XwiRm0rwKlaRgIrSEmrVsLjf\n5120cl8KJ9FlaCgqLKCprpb9qpN5rtpAkWoSdlp61Of10tJYT0dnt05wDBnkZRygPaohPy+P1uYm\nvRg+u7o856RH3W43bS3Nyy/mdwjNga5MkbMcmgVGLRORUMCQ02b3Hb1pQ9uiRq1c//8aVqouryAh\nWtiegxydhr8Eyo9sSWU2A4+p2+aE15uBp4FhIcTtQojkOZRVrCJL6B1RXi/Na2sp8PuSako6MT0K\nEAkH9OctjfWUFRelnISdeA2RcJD2rh6mVGmZyvKytE6P05xnxf5upJSL5NBSwUnpUVDs1+R8AAaH\nR5JGmhPhpGaKaCjIvp6DzC8sLOs4hfLDGfZ3HexndnYu885L4IRqh2g4SM/AIBNTUzkdb/eYpI33\nWuNKNBxkYGiYEyfHczqfkzSprUK2TturgQ7gbUBY3d4G7AbepG7nAP+fBTau4i8IWl2Vz+dVaBxa\nAskjbaz8AJkMWl0VQIFaXJs63eU8py0aCjJ8dJSBw8N48vPZsK4lvdPjgALyRERDAY6PneTwkaNZ\nRdqcJnsTDQWZmJyiT21m+dbPf837Pv+NtMdIB81UkbCirHGgb2BZx9lNrpsKkbCirLGv9+Cyj3VC\nuUOiskausPMSWgNN5OfnUax26yYqa+SE1Uibjq8CH5FS3iqlPKButwIfBz4npbwT+DDwGqsMXcVf\nBq6/7mo+8fc3UF1RDijh8z1dPSwsWbk7LcKjQRt0hBB48vOJhJXIw9z8/Cn7OmFlvhSJmoZ+VZOx\n62C/7gAthZO6R2FxMbZWl6cpaySD02Rvolozheponjg5Tv/hYY6NnUx5jJOihdGEDsDlQGaqILcJ\nic04y4UTIue6/TnUha1EatHr8fDzb3yed7z+OmCxskYucEontZXI1mmLAsmWTgPq3yAuKr+KVeSM\nmso1vPf6N+g3nULjME3vocOLd3TAAJkM0YT0rkaYOjc3T9fB/lP2XYkamEzQhLR7Bgbx+7xxGoee\n5Ck6J0xUiVivKmt0dHUv4svbc6An6f5Oq4FZ19yoK2tAQl1ehmgnOOMago1rFWWNHJopVt56aFSV\nNXJSFnBAjWpt1RrKS0tozyFSFXec7b2K88+MUqUu0jVlDY3ke7lYoUuwFdk6be3AZ4UQOmOi+vwz\n6t8AGoHDSY5dxSpyRqqVlwP9HUCpAauqKMfvi9doQPJwv9McHoDS4iLqa5R2eF9CM0iqSVih/HAO\nCv1+AvV1tHd2Mz0zQ11VJZDGfgdFqUBR1gg3N+pOw9S0UluYie8PnBEtzHO7WR9sWhRp+/OuF+kZ\nGMx4rBPuBZfLRaQlN2UHJ6RHlYVigPYcnB6ncBZuCAcNKWuAc+5nK5Ct0/YB4FXAgBBisxDiMZQo\n26uA96v7tADfN9/EVfwlI9TUQH5e3inpFicMkKmwIRykwK90QwXq6xQahySDkMMyizriHV3ezDQO\nDqlFSoRGyjw5PUPT2tq0yho4yOHRoMlZSSmZUiXR0k5iDpuoIqEgew706BPop//je3zrp79Ke4yT\nFjDRcAt7DvSeUpKRCU7hXYyEguzv6WN2bnnNFE5paImEghzoP7QoUp4tnBY5twJZOW1SyqdRCHY/\nC+wAdqFE2YJSymfUfW6RUv67VYau4i8Tnvx81gUak0TanFnTBvCJd7+NL3/4fYBKINnSnHTl69Rr\nSOzoEkLQ1hJIG6ly2gAZCQU5NDzC4SMjFPi8RMOpmymclFrUEAkHOHr8BMOjx/Qu3vSRNmddQzQc\nZGx8gv6hYQDGJyfTcuWBcyg/QLF/amaGnoHlJY6cQuETDSv8lslKMtLBKVGqaDhILBZjX8/ymykc\n1ldkCbKNtCGlnJBS/lBK+XEp5ceklD+SUk5YadwqVgEqd09Xz6JiZYcsapOiNdDExZviIvGR0GIa\nBx0OdHggXsysaxqGg+ztTh55cEotUiI0+w8Njyh1eeEgnb19SWkcnObwwOJieK2mrbv/EJNq1G0p\nnDZRLS3mn56ZzUjj4Ax6YAVaXWdOdXkO+BIiBptBVvoScrUfnON4WomsnTYhRKMQ4u+EEB8VQnw8\ncbPCMDUNK5dsv7Xis1bhbERDQY6dGGNoZFR/z8np0aWIhgKLaBw0ODU9qnPlJRBeTs/M0t1/6JR9\nlWtw1kUsVdaIhBQah/3JlDWc0bS4CG2qsobWAdtUV5teWcNJRW0sVtaYm5/XO6czK2s4w/5QUwOe\n/Hw9uiml5Obb/4+jx0+kPc4pY1Kgvo4Cn2/5HZgOUQepr6mitKgoN9oPBy7CzEa2igg3AJ3Aj4GP\nAh9K2P7JMuvg50BdwvY+Cz9rFQ5FUmUEh6YWkyGdsoMTB5fqinKqKsopUfVGo2nsd0odTyIWKWuo\ntCWQwn4HpkeLCgsUOR9VA3bThvVAOmUNZ0UXNGWN9oRIIWTiy3NOejQ/L08pyVAjPYeGR/jGD3/B\nbfc/kv5Ah9wKLpdLLclYZqTNIfeCEIK2HJUpnBZ1tgLZRtq+DHwLKJFSBqSUwYStJdPBBjAppTyc\nsKVf6qziJYk2lcYh8SZ2oK+QEq0BJfKwtJjciZQfoAyaP/rKv/Khd7wFUJQdvB5P0mJ4J022idDr\n8nxemlQah1T2w8pPVEsRCQXZ2bEPKaWurJGyLs9ZgTZAcfTbO7v17ldIX5en0GU45wKiobiyhtYM\nksmJcFKNajRVSUYaOIUrDwwoazjxZjAZ2TptNcBPpJTL+w8ax1uFECNCiN1CiP8QQhTb/PmrcAAK\n/X6a6+sWrdSdGOFJBa/HQ6i54ZRJy6kODygddGurqwCFxqE10JRy0nXKRJWIxA5Yl8tFW7A5RaRQ\neXTaFUTDQY6MHgNUHd5QahoEp0RIEqHZ3384XhKQiWvOSfZHwkFdWWMqKw1bZ6V4I+Egk1PTHFxS\nkpEOcX9n5a8hd2UNZ0WdrUC2Ttu9wAVWGpIEvwFuAK4EvoIilXV7qp2FEO8VQmwTQmw7cuSITSau\nwi5sUGkcNDhtkM+EDeGWU2o0nCLdkw2i4SAdnT1JV+NOvAatmDlR03BvMhoHB0faNPi9XqLrWtjX\nm1xZw4nXEFfW2AsodWIH+gbSKGs4awETTVTWUKkneg8dZnxiMuUxEhzj/S9Vpjh6/ARfv+nnzMzO\npjzGSc5/7soayqMDLsEyZOu0PQR8UwjxVSHE9UKINyZu2X6YevzS5oKl2xUAanfqA1LKF6SUvwWu\nB64WQmxKdm51//OklOdVVVVla9IqThNEQkEGho7ocj5Oaa/PFpFQkJFjxxk+ekx/73RyPCOhACfG\nxxkYii+InNJtlgxntoZwuVxUlJYAqZU1nDRRJSK6pJlCV9boTaasoTw66Qo0p237ix0AnLuhTVHW\n6E6u6emkKBXElTXaOw/okTZIrawBzkqP6soa6kJ3647n+cUd97DthY6Ux8SjVCsPTVljuSTBTr2f\nzUS2TtsPgQYUbrZbgdsStj8s4/O+A0QybM+kOHYbsACsW8bnreIlAm3ltSeho+t0ujGTKTs4kS4j\nFVLZD84cIGsq1/DH7/87r7niUiCDsgY4Y6ZKgKasAfH0KKRvpnCS91xSVEhDbTU7O/YBsGlDG5Cm\nmQJw0pdQ4PMRbFir1uXFnbZMfHlOuRd0ZY0lcmjZKWus/DVoyhrL74B17phkFrIl13Wl2dzZfpiU\nckRKuSfDlir+fCbgBjLroaziJQe9A7Pz9HTaIhqNQ2K4/zRKj7YGmnG5XEuaQRzq8ahYH2zG48kH\n0ihrOLgGRqvL83u9BOprFWWNVB28OO+3pFH1gPL/T6es4bT0KKjF/F09TKtOm8vloqOzJ+X+TqPw\nUexXmymyqctz2O8oqpaULKdBwolRZ7ORNU+bnRBChIQQnxdCnCeECAghrgN+C+wEnlxh81axAtBo\nHBInLYeMLVmhqLCAprraU+ryTpeL8Pu8hBrrl9iv4HS4BE9+Pq2BU1fuTnV4YHEHrNvtpq2lOUUH\nrPLotCtI5Msr8HlPO2WNaDjI4JERBo8cBUjbjAM4jix7kbKG6nimqxGLpxZtMS8jouEgJycm6T+s\nKGts2b6Lb/zw5rTH/MV3jwohtgohyhJef0MIUZHwulIIkbxIwRhmgauAB4C9wHeBB4GrV6CDdRUO\nQSQc1Iv5ndSeni0i4cCiQf90So+C8v9faj840+FJhkg4yG5V01ODkx3P886IIISgWk2TRsLJaRyc\n+j1EE5spfD5VWSMFjYPDKD8gnlLf0b4HgE0b1qdU1gBn1bSBEqkCaN8fr8vrGRhkYmoq6f7x28IZ\n17BUGeGRp57l5tvvzqCs4cx7wUxkirRdCHgSXn8QKEt47QbqzTZKStknpbxcSrlGSumVUoallB+R\nUo5mPnoVL1VEQ0G6VSFhp3KcpUM0FKRvcIixcUX97XTqHgXF/qGRUUZVZngnpxaTIRoK6DQOGpx8\nDZeeew6P/+omGutqAOX/n0xZA4dOVImRNr/XQzTcwszsLN19yZQ1nJce1ZyGnWoH7KZoW0plDVCd\nHgddQ1tLQGmmSOiAzUZZwym/o/UJyhoA0yrnX3plDeXRGVdgDZabHn0p/y/+//buPEjSur7j+Ps7\n9+x9zO6y7O7M9HTv7HbDIgoCAiaAQUFIBY8iakKCSWl5YCkVyytagXjEUlCMRxATA4qGskyMUAki\nxDLRYFQQA1lglz1m75N72Xv3lz+e5+np6e05erZ3nuc7+3lVdbHT3cx8n1//5nm+8zuer2RcUkh4\n1foNmSyfNJpi1WL4LE4JjeSYxfDOZiJqLubP2IWq2oK4sgMMX1kjqzNC8+fMZu6smUA80jbSZooM\n/i7MmjGdU+d38eJLe2ltbWHFsgIw0maKbB3DtCmdUWWNOGlraY6Wn480RQ3Z6UftbW3kuxeX4x3L\nurysJZ4nQibXtInUUjlcnsWFy6MpF9JOkrYMji6MJCmkXd4MktERnuEkt3Hwui6vv6c7uo2Dk80U\nZlaeYmxvax3xNg4ha6v4Y8k5p7N9sLLGcLehyGLiWczneHzNAPsPHGBB11xmzZjO48OMVA3+LmTn\nGIoV5aySHbArR7gNiLdz0niMlrQFBj/LyudEJtyp87viQsLrM7d+ZCzmzZnNvDmzBhcDZ3Adz0hm\nTp/GogXzh4wUgp8T5NTOTnoXLay9A9bBMbS1tZLvXszKYZK2LB7D+S8/gyULF9DU1FS+jUOtxfBZ\nXd+ZJJ0d7VFljWJf7/CVKTJ478hSIceWHTvZ8fQzTOmI7vc3fPzZS/6Tyhq7n32OvfEU70iVNbyN\n/o/HaEmbAXea2d1mdjfQAXyj4utvnfAIRWLJX+6Pr13v6nYZlYr5oZspvB1DqdBb/kvd4V6QqLJD\nrc0UGbpQjSSqrOFnM8W1b7ySH/3D35a/LuZzPLnu2Ns4ZG1qMZGMtE3piCprFAs5nqxVWQMgg8eQ\nxP/ok2voaG+nWMiNobLGREY4ssrZlWSkbV28rnkkXn6fx2O0pO0OYCvwdPy4E9hU8fVWlLjJBCoW\ncqxev5FDRw5n6uQyVqV8jrUbN7P/QLyZwtkxFPM5NmzZxp69+1xORRTzObbu3F2urJHwcgiVt3FI\nZHnEMxlhS5QKOV7Y8xKbd+wc+saM/hGWLGkol0PL59h34AADW7Yf894s/hGWjBTu3b+fzo5RKmsk\n/8jQMQwmbdEO2NbWFo4ePcrqgZE3U2TpGBptxKQthPD2sTwmKliRUj7HwUOHWLtxc+ZOkGNRLOSi\ncj4Dm6LNFM6ytlI+RwhhyGiJp8/h2Moa0fNejqG8LrLGFK+HvlQrfsju9Ogp86J1YJ3t0U0Uqtel\nVgoZXO4wd9ZM5s+N7tI1amWNDA6dJ5U1kh2wK/rjzSCjTfFm62NoKG1EEFeSvxyfGtiEu2Eqoukt\nYHBdnrOzS2U5KI/b64+prOFstHB5UlmjxkXXwyH0V93GIZHV6VEz49Lzz+G0pXkA8j2LaW1tGT7p\nyd4hUIo3EHW0tw1W1qi5GSSbyX+0Di/aTFHoXhyvax6o+d4sbqZoNCVt4krvooV0trdz6LDP6dHF\np8xn+tQpbnfAzp87hzkzZ0RJj8ORtjkzZ7BwXlfFbUuSC5UP06ZOiW7jUGukzcHn0NHeTt+SRTVG\n2rIb/6eufzefeO+fA9Da0kJ/T3ftyhQZ3aOX/KEypaMjqqxRsSOzUlZnFkuFPjZu285zL+6hs6N9\n2Pghu4lnIylpE1eam5tZ1tcDZPckPxIzo5iPyvlk8xQ/smQzSBS/n2ShUtT+A0BlwXg/x1DMD1OZ\nwsmFKqqsMTDkuayOUtWSVAY5ZjNFRhPPWuvyalbWyOjvczK6f+jQYTrbo3V5qwdqV9bQ9KhIBiXr\nMrxcpKoV8zlWrY92oGXtBDkWxXyONRs2c/BQtAPN2yGUCjnWbdrC3v37XY1SJUqFHJu37xxSWQNw\nkxROyTMAABH0SURBVPSU8jl2Pv0MT8eVNSB7JaBGUquyBmRzIwIMjrQl6/KKhRx79+1nY3VljYz+\nLiTne6C8A/bAwYOs27Rl2P8na8fQSEraxJ1kjYbX38tSPsf+AwfZumOXmwtVpVI+x6HDh+N1heAm\nW4gV480Uq9ZtGBxdSDmmehSrFsNndYRkONU1JQFXFU6GX8yfzc0UixfM53fPeQWvOG05MDjyVj3F\nmNU1qvPnzqZrdlQ9M9kBC7U3I3gbdR4PJW3iTvKXo5eTfLUk/iNHj2bvDDkGpaVxIep4MbO3z6FU\nUQ7K2+5RGLoZBHytaYPByhpDdmA6Wt9Zq7IGZHd61My47ZMf49ILzgVgac+SqLLGMfFntx8lfaaz\no71cWaP2Dt6MZp4NpKRN3OnvjXaguTnLV+lbsoi21lYgmyfI0STlfFaWk7aUA6rTwnldzJo+jSfW\nrM/0hWo4XbNnMW/O7MEdsM6uUzOnT4tu41C1mcLLZzC1s5Pc4lNrjFT5OIa2tlYKPUuOjT/D5QSS\n0bXOjvaKyhrDlEPD1+9zvZS0iTvtbW0Uepa4uUhVa21poT/XDfg8uTQ1NbE811M+6Xs7hmgzSLyY\nvDy1mHJQdTqtkKu46PpLPEv53DE1YD1NaZXyuZpJmxdJ+w+prJHdnK1iXV5cmSKuLFOrsgb4+l2o\nl5I2cem1F57L6f35tMMYt1J5M4VPpUKO9Zu3Ar4utoliIdqBdijZTOHsGIrxZoqktA/4ulAVCzkG\n4soa4Gv3KETxb9s1tLKGq3V5I1XWyOAH8aozV3DxeWdzxrLo5rqnLe3jxZf2snn70MoaWT6GRlHS\nJi5d98dXc9OH3592GONW3gHr5CRfLVnMD9n8y3w0STmf8g40ZwdRyseVNdZvdDnFW65MsW4A8DO1\nmBiuMoWXY6gZf4ZHqWbNmM6tN36EBV1zgdqbWcBnPeR6KWkTSUHJ+WaKJH7AXcIDg/GvfMrnurxi\njc0UnhSP2Uzha3Sksv3LHG2mqFlZw9F6sGXDVNbIYtH7RlPSJpKCZbkempqaXF2oKuW7F9Pa0gL4\nutgmksoaXtflLV4wnxnTplZU1vAV//w5s5k7ayaPPzW4Ls/TIcyeMZ2F87qqRtr8/C6MWFkjraDq\n0N7WRr578bE7YDM8WtgoStpEUtDZ0c4bX3sxZ68oph3KuLS1trK0dwng86/a5uZmlvf1sHHbdsDP\nxTZhZizv6y1XpvB2karcDAK+phYTxXzv0MoUzj6HUqGPlTWSNi+/0MUa5awGR519HMN4KGkTScmn\nr383b3rdJWmHMW6Vdyr3qFgxxevkOjVEqZBj1fqNHDly1OUlqlTIsWbDJg4ePJTZe5yNJNmMs3f/\nfiBJPFMOqg6lQo4tO3by/It7AH/F1kuFHLueeZbdzz5Xfs7zOtuxUtImIuNScr6ZopSvTNr8HUMx\nP1jOx2v8h48c4akNm+IyVr5UVtaAeJTH0ecwWFljAPCX8NSurKHpURGRmiZLZQrwNz0KFZUd1qxz\n+RkMqUxB8JMtxEpVmxECfuqnwgiVNZwcQ3U5N9DNdUVEhrViaZ4rL76Qs+Kaht7093TT0twcfeHw\nHN+3ZBHtbW1s3bnbZfxJZY2oMoW/C21lZQ3wNz06d9ZM5s+dU7GuMHrey+cwY9rUmpU1wOWvw5gp\naRORcWlra+Xmj3yAQs+StEMZl7a2VvLdiwE/F6pKLc3N9PfGlTUcXqbKlTXiO/N7OwIzo1ToG9yM\n4DDxLOV7K3aQ+poehRqVKRzes7BeStpE5KQ1We6X5zn+Ves2cPToUZfHUK6scfhwnHj6OobKyhre\npkchin/jtu28+NJLgL/NFOOhpE1ETlrlyhSOLlSVBpO2lAMZp2I+x979+9mwdZvLC21SWWPths3x\nLT/Sjqg+QytrxE86OojByhrJZpBkjjetiE48JW0ictK64qILuOaq19O7eGHaoYxLyXnSeVqhD4Bd\nzzznKVcoK+9gTCpTODuIYvVmEHwdQjn+NT43U4yHkjYROWl1zZ7Fx9/9Z+XqDt70x+V8PI5SAeR7\nFtPamrS9v2PoXXRKVFljrc/KFNWVNcDX1GJSWeOJih284OsY6pXppM3MzjGz+81sj5m9aGYPmllX\n2nGJiGRBR3s7fUsWecx3AGhtaaG/J95M4fBCm1TWeGJNXJki7YDqlFSmeGLtepejVNFmkFzFSFvy\nfIpBnWCZTdrM7Fzgx8BPgfOAs4CbgEMphiUikinFQo4my+ypfFRF7+vyCjmeWDdAOOpvpA2iKfZV\n6zdy+MgRwN/nUMrnWLtxMwcPHhrcPeoo8axXlucEvgh8NYTw6YrnVqcVjIhIFr3z6jdwwcvPSDuM\ncSvlewG/F9pSPsd377kP8DlaWCwMVtYAf8dQLESVNVZv2OhyirdemfzzzMzmA68CtpnZz81sh5n9\nzMxek3ZsIiJZsrR3CVddelHaYYxb0Xk5tKGVNfxJNrM8/lQ0xejtc0jij6aoI84OoS6ZTNqAvvi/\nNwLfBC4DfgbcZ2Yvq/U/mNk7zewhM3to165dExSmiIgcj2V9PZiZ2wttZWUNbwkPQG7JqbS3tbFy\nzbq0QxmXJXFljccr1uW57UxjMKFJm5l9yszCKI+LKuL6egjhmyGER0IIHwN+Bbyr1vcOIdwWQjg7\nhHD2vHnzJuiIRETkeEzt7CS3+FRanO7grays4TFZaGluZlmuu7wD01vi2dTURLGvNy6HpjVtjXYL\ncOco79kILIj//XjVa08A3Y0OSkRE0vPZD76XttbWtMMYt9MKfaxav8FjzgZEU9SPrloD+EvaIJqi\n/v69/8GVR44CLnPnMZvQpC2EsBvYPdr7zGwA2Aosq3qpH3is8ZGJiEhaXra8P+0Qjkux0Av3+x3h\nKU2CdXn7DhxgYMtWwGfiOVaZHI8OIQQz+zxwo5k9CjwCXE1064/rUg1ORESkQsn5ZookfvB5DEn8\nK9f4nOKtRyaTNoAQwi1m1gbcDMwFVgKXhxD+N93IREREBi3v66WluZmO9ra0QxmXpLLGkaNHXSY8\nSWWNJ9cOAD5HC8cqs0kbQAjhc8Dn0o5DRERkONOmTuE7N3+S3OJT0w5lXDra28l3L2b1wEaXGU9S\nWaO8A9Zh4jlWWb3lh4iIiBtnFvuZOX1a2mGMW9H5TY6H3C9PSZuIiIhMVqWC93V5veV/+zyCsVHS\nJiIicpI7sxjdrGH61CkpRzI+pUJf+d9eE8+xyPSaNhERETnxziz2c/etN9Pf6/NWqElljRDCpE7a\nNNImIiIiLMv1uE14pnR0lDeCOD2EMVHSJiIiIu6V75c3iVe1KWkTERER9155RompUzpdl0Qbjda0\niYiIiHtXX/57vPbC82hrm7xJm0baRERExL2mpibmzJyRdhgnlJI2EREREQeUtImIiIg4oKRNRERE\nxAElbSIiIiIOKGkTERERcUBJm4iIiIgDStpEREREHFDSJiIiIuKAkjYRERERB5S0iYiIiDhgIYS0\nY2g4M9sFbDjBP6YL2H2Cf8bJRm3aWGrPxlObNpbas/HUpo01Ue3ZE0KYN9qbJmXSNhHM7KEQwtlp\nxzGZqE0bS+3ZeGrTxlJ7Np7atLGy1p6aHhURERFxQEmbiIiIiANK2sbvtrQDmITUpo2l9mw8tWlj\nqT0bT23aWJlqT61pExEREXFAI20iIiIiDihpExEREXFASds4mNl7zGy9me03s4fN7NVpx+SRmd1g\nZqHqsT3tuDwxs98xs7vNbEvcftdWvW5xO281s31m9lMzOy2lcF0YQ5veXqPf/k9K4WaemX3UzH5t\nZi+Y2S4zu8fMTq96j/rpGI2xPdVH62Bm7zWzR+M2fcHMfmFmV1S8npn+qaStTmb2h8CXgM8ALwce\nBO41s+5UA/NrFbCw4rEi3XDcmQb8H/B+YF+N1z8E/AXwPuCVwE7gfjObPmER+jNamwI8wNB++/qJ\nCc2li4CvAecDlwCHgQfMbE7Fe9RPx+4iRm9PUB+tx2bgw8ArgLOBnwD/amZnxK9npn9qI0KdzOyX\nwKMhhHdUPPcU8P0QwkfTi8wfM7sBeHMI4fTR3iujM7M9wHUhhNvjrw3YCnwlhPDp+LlOohPOB0MI\nX08rVi+q2zR+7nagK4RwZVpxeWZm04DngatCCPeonx6f6vaMn7sd9dHjYmbPAB8l2j2amf6pkbY6\nmFkbcBbw46qXfkz0V4/Ury+ehlpvZneZWV/aAU0iOeAUKvprCGEf8F+ovx6vC81sp5mtNrNvmNn8\ntANyZDrRtefZ+Gv10+NT3Z4J9dFxMLNmM3sL0Yj7g2Ssfyppq08X0AzsqHp+B9GHKvX5JXAtcDnw\nDqI2fNDM5qYZ1CSS9En118b6EfAnwGuIpkzOAX5iZu2pRuXHl4DfAr+Iv1Y/PT7V7Qnqo3UzsxXx\nyPoB4FbgDSGEx8hY/2yZ6B84SVTPKVuN52QUIYR7K7+OF8quA/4U+EIqQU1O6q8NFEK4q+LLx8zs\nYWADcAXwL+lE5YOZfQG4ELgwhHCk6mX10zoN157qo+OyCjgTmAW8CbjDzC6qeD0T/VMjbfXZDRzh\n2Ox6Psdm4VKnEMIeYCWwNO1YJolkJ6766wkUQthKtJBZ/XYEZvZF4K3AJSGEdRUvqZ+OwwjteQz1\n0dGFEA6GENaEEB6K16f/FriejPVPJW11CCEcBB4GLq166VKiuW85DmbWASwHtqUdyySxnuiEU+6v\ncRu/GvXXhjGzLmAR6rfDMrMvAW8jSjCerHpZ/bROo7Rnrferj9avCWgnY/1T06P1+wLwbTP7FfDf\nwLuAU4nmwKUOZnYTcA+wkeivlk8AU4E70ozLk3jnWCH+sgnoNrMzgWdCCBvN7BbgL83sSWA18HFg\nD/DdVAJ2YKQ2jR83AP9MdAHsBf6GaCfZDyY6Vg/M7KvANcBVwLNmloxY7Akh7AkhBPXTsRutPeP+\newPqo2NmZp8F/g3YRLSx421Et1a5InP9M4SgR50P4D3AANGCxYeB30k7Jo8P4C6irdQHgS1EJ5lS\n2nF5ehCdWEKNx+3x60Z0At8G7Af+Ezg97biz/BipTYFO4D6iC+BBonVCtwNL0o47q49h2jIAN1S8\nR/20Qe2pPjquNr09bqcDcbs9ALyu4vXM9E/dp01ERETEAa1pExEREXFASZuIiIiIA0raRERERBxQ\n0iYiIiLigJI2EREREQeUtImIiIg4oKRNRERExAElbSIyaZnZnWb2WzNrq3r+NWZ2yMzOTys2EZF6\nKWkTkcnsOmAu8FfJE2Y2A/gm8PkQwgmpHVidJIqINIKSNhGZtEIIzwFvBz5kZufET38ReJaoLA1m\ndrqZ3WtmL5rZTjP7jpktSL6HmZ1rZveb2W4ze97MflbxvTCzFjMLZvYuM/uhmb0E/LWZtZnZV8xs\nm5kdMLNNZvbpCTt4EZl0lLSJyKQWQngA+DvgW2b2ZuCPgGtCCAfNbBFRHcFHgFcClwKzgB+YmcXf\nYjpwB/Bq4DzgMeBeM5td9aNuBH4IrABuBa4Hfh+4GugH3gI8daKOU0QmP9UeFZFJz8w6iRKzpcBH\nQgifj5//DHBWCOF1Fe/tAnbFz/+mxvcyoqLS7wsh3GVmLcAh4JYQwvUV7/saUCAqPK0TrYgcN420\nicikF0LYB9wEHABurnjpLOBiM9uTPICB+LU8gJktMLPbzGy1mT0PvEi0Tq676sc8VPX1PwJnA6vM\n7MtmdrmZ6ZwrIuPWknYAIiIT5DBwNIRwtOK5JuAe4MM13r89/u+dRFOmHwA2ECV+PwWqNxu8VPlF\nCOHXZtYLXAZcEn+fh8zsMo28ich4KGkTkZPZb4A/AAZCCIeHec+FwDtDCP8OYGYLgVPG8s1DCC8A\n3wO+Z2bfBn4O5IB1xxu4iJx8NFQvIiezLwNdwD+Z2Tlm1mdml5rZ38fr4ABWA9eYWTHeNXoX0Wjb\niMzsg2b2FjNbbmZLgbcCzwNbT9CxiMgkp6RNRE5aIYTNwAVAM3AfsBL4CrCXaHMBwLVE06OPAN8F\nvg5sGsO330M07fpQ/DgduCyEsL9xRyAiJxPtHhURERFxQCNtIiIiIg4oaRMRERFxQEmbiIiIiANK\n2kREREQcUNImIiIi4oCSNhEREREHlLSJiIiIOKCkTURERMQBJW0iIiIiDvw/FLFLFx5BWgAAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a28c09bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(long_haul['years'], long_haul['efficiency_loss'], label='Raw input data', color='#264653')\n",
    "plt.plot(np.array(long_haul['years'],dtype=np.float), func(np.array(long_haul['years'],dtype=np.float), popt_long), 'r-', label='Fit: $\\delta$(t) = -%5.3f log(t+1)' % popt_long, color='#f4974b')\n",
    "plt.title('Long haul aircraft', fontsize=14)\n",
    "plt.xlabel('Years', fontsize=14)\n",
    "plt.ylabel('Engine degradation [%]', fontsize=14)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.legend(fontsize=14)\n",
    "plt.savefig('gfx/Engine_degrad_long.png', dpi=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average engine degradation factor for fleet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load 2018 OAG data to distinguish between medium and long haul"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# Connect to DB\n",
    "%load_ext ipython_pg.LAV\n",
    "%pg_connect dbname='mobility' host='lav-fileserver.ethz.ch' user='heldm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter password:········\n",
      "password for heldm@lav-fileserver.ethz.ch:5433:········\n",
      "SUCCESS: connected to lav-fileserver.ethz.ch\n",
      "  PostGIS integration enabled"
     ]
    }
   ],
   "source": [
    "# Connect to DB\n",
    "%load_ext ipython_pg.LAV\n",
    "%run pg_password.ipynb\n",
    "%pg_connect dbname='mobility' host='lav-fileserver.ethz.ch' user='heldm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCES: matched 191618 rows\n",
      " results stored as 'oag18'\n"
     ]
    }
   ],
   "source": [
    "%%pg_pd oag18\n",
    "SELECT * FROM \"OAG\".\"2018_schedule_ps_conform\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oag18 = oag18.rename(columns={'frequency_2018': 'frequency', 'fkm_2018': 'fkm'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wavg(dta):\n",
    "    \"\"\" Takes data with at least two columns (gc_distance and frequency) and returns the weighted average.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        return (dta.gc_distance * dta.frequency).sum() / dta.frequency.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return dta.gc_distance.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_code_iata</th>\n",
       "      <th>gc_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CN2</td>\n",
       "      <td>45.932968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DHP</td>\n",
       "      <td>55.571411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PA1</td>\n",
       "      <td>57.694043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BNI</td>\n",
       "      <td>61.411922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CNT</td>\n",
       "      <td>98.665807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ac_code_iata  gc_distance\n",
       "0          CN2    45.932968\n",
       "1          DHP    55.571411\n",
       "2          PA1    57.694043\n",
       "3          BNI    61.411922\n",
       "4          CNT    98.665807"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oag18_trip_len = pd.DataFrame(oag18.groupby('ac_code_iata').apply(wavg))\n",
    "oag18_trip_len = oag18_trip_len.rename(columns={0:'gc_distance'}).sort_values(by='gc_distance').reset_index()\n",
    "oag18_trip_len.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2990cbbe0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2990cb6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oag18_trip_len.gc_distance.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We separate the data set into medium and long haul flights at an average gc_distance flewn in 2018 of 4000km.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CN2', 'DHP', 'PA1', 'BNI', 'CNT', 'DHL', 'PAG', 'PA2', 'CNA',\n",
       "       'DHT', 'D28', 'CNF', 'DH7', 'YN2', 'CNJ', 'DHC', 'EMB', 'J41',\n",
       "       'J32', 'BE1', 'SWM', 'DH2', 'DH1', 'SF3', 'MA6', 'J31', 'AT7',\n",
       "       'EM2', 'AT4', 'DH3', 'PL2', 'ATP', 'BEC', 'F50', 'DHD', 'D38',\n",
       "       'DH4', '142', 'BE2', 'AR1', 'YK4', 'CR1', 'A38', '141', 'FRJ',\n",
       "       'S20', '143', 'F70', 'A40', 'AR8', 'CRK', 'CV5', 'BE4', 'M81',\n",
       "       'ER4', 'CS1', 'ER3', 'F28', 'CR7', '318', 'CR2', 'M87', 'CR9',\n",
       "       'E95', 'E70', 'AN4', '722', '717', 'AN6', 'EP3', 'C27', 'M82',\n",
       "       'M83', '100', 'M88', 'GJ5', '735', '290', 'DC9', 'E75', 'CS3',\n",
       "       'A58', 'GRJ', '736', '733', 'TU3', 'SU9', 'E7W', '73G', '734',\n",
       "       '319', 'M90', 'YK2', 'A81', '320', 'A32', '32N', 'CJ8', '73L',\n",
       "       '32Q', '738', '321', 'AB4', 'T20', 'L4T', '739', '7M9', '313',\n",
       "       '7M8', 'AB6', '753', '762', 'TU5', '752', '333', '763', '781'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_medium_haul = np.array(oag18_trip_len.loc[oag18_trip_len[oag18_trip_len.gc_distance < 4000].index, 'ac_code_iata'])\n",
    "ac_medium_haul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['764', '351', '332', '772', '773', '788', '359', '77W', '744',\n",
       "       '789', 'IL9', '343', '77L', '346', '388', '342', '74H', '339',\n",
       "       '345'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_long_haul = np.array(oag18_trip_len.loc[oag18_trip_len[oag18_trip_len.gc_distance >= 4000].index, 'ac_code_iata'])\n",
    "ac_long_haul"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Planespotters data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUCCES: matched 28411 rows\n",
      " results stored as 'ps'\n"
     ]
    }
   ],
   "source": [
    "%%pg_pd ps\n",
    "SELECT * FROM \"Planespotters\".\"pax_fleet_2018_12_31\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Change Planspotters ac name to IATA code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ps = ps.rename(columns={'ac_type':'ac_name_ps'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_code_iata</th>\n",
       "      <th>ac_code_icao</th>\n",
       "      <th>ac_name_oag</th>\n",
       "      <th>ac_name_ps</th>\n",
       "      <th>widebody</th>\n",
       "      <th>narrowbody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CS1</td>\n",
       "      <td>BCS1</td>\n",
       "      <td>Airbus A220-100</td>\n",
       "      <td>Airbus A220-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CS3</td>\n",
       "      <td>BCS3</td>\n",
       "      <td>Airbus A220-300</td>\n",
       "      <td>Airbus A220-300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AB6</td>\n",
       "      <td>A306</td>\n",
       "      <td>Airbus A300-600 Passenger</td>\n",
       "      <td>Airbus A300-600</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB4</td>\n",
       "      <td>A30B</td>\n",
       "      <td>Airbus A300B2/B4 Passenger</td>\n",
       "      <td>Airbus A300B4</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>313</td>\n",
       "      <td>A310</td>\n",
       "      <td>Airbus A310-300 Passenger</td>\n",
       "      <td>Airbus A310-300</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>318</td>\n",
       "      <td>A318</td>\n",
       "      <td>Airbus A318</td>\n",
       "      <td>Airbus A318-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>319</td>\n",
       "      <td>A319</td>\n",
       "      <td>Airbus A319</td>\n",
       "      <td>Airbus A319-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>320</td>\n",
       "      <td>A320</td>\n",
       "      <td>Airbus A320 (with and without Sharklets)</td>\n",
       "      <td>Airbus A320-200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32N</td>\n",
       "      <td>A20N</td>\n",
       "      <td>Airbus A320neo</td>\n",
       "      <td>Airbus A320neo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>321</td>\n",
       "      <td>A321</td>\n",
       "      <td>Airbus A321 (with and without Sharklets)</td>\n",
       "      <td>Airbus A321-200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32Q</td>\n",
       "      <td>A21N</td>\n",
       "      <td>Airbus A321neo</td>\n",
       "      <td>Airbus A321neo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>332</td>\n",
       "      <td>A332</td>\n",
       "      <td>Airbus A330-200</td>\n",
       "      <td>Airbus A330-200</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>333</td>\n",
       "      <td>A333</td>\n",
       "      <td>Airbus A330-300</td>\n",
       "      <td>Airbus A330-300</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>339</td>\n",
       "      <td>A339</td>\n",
       "      <td>Airbus A330-900neo</td>\n",
       "      <td>Airbus A330-900</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>342</td>\n",
       "      <td>A342</td>\n",
       "      <td>Airbus A340-200</td>\n",
       "      <td>Airbus A340-200</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>343</td>\n",
       "      <td>A343</td>\n",
       "      <td>Airbus A340-300</td>\n",
       "      <td>Airbus A340-300</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>345</td>\n",
       "      <td>A345</td>\n",
       "      <td>Airbus A340-500</td>\n",
       "      <td>Airbus A340-500</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>346</td>\n",
       "      <td>A346</td>\n",
       "      <td>Airbus A340-600</td>\n",
       "      <td>Airbus A340-600</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>351</td>\n",
       "      <td>A35K</td>\n",
       "      <td>Airbus A350-1000</td>\n",
       "      <td>Airbus A350-1000</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>359</td>\n",
       "      <td>A359</td>\n",
       "      <td>Airbus A350-900</td>\n",
       "      <td>Airbus A350-900</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>388</td>\n",
       "      <td>A388</td>\n",
       "      <td>Airbus A380-800 Passenger</td>\n",
       "      <td>Airbus A380-800</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A40</td>\n",
       "      <td>A140</td>\n",
       "      <td>Antonov AN-140</td>\n",
       "      <td>Antonov An-140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A81</td>\n",
       "      <td>A148</td>\n",
       "      <td>Antonov An148-100</td>\n",
       "      <td>Antonov An-148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A58</td>\n",
       "      <td>A158</td>\n",
       "      <td>Antonov An-158</td>\n",
       "      <td>Antonov An-158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AT4</td>\n",
       "      <td>AT43</td>\n",
       "      <td>ATR 42-300 /320</td>\n",
       "      <td>ATR 42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AT7</td>\n",
       "      <td>AT72</td>\n",
       "      <td>ATR 72</td>\n",
       "      <td>ATR 72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AR1</td>\n",
       "      <td>RJ1H</td>\n",
       "      <td>Avro RJ100</td>\n",
       "      <td>British Aerospace Avro RJ100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AR8</td>\n",
       "      <td>RJ85</td>\n",
       "      <td>Avro RJ85</td>\n",
       "      <td>British Aerospace Avro RJ85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>141</td>\n",
       "      <td>B461</td>\n",
       "      <td>BAe 146-100 Passenger</td>\n",
       "      <td>British Aerospace 146-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>142</td>\n",
       "      <td>B462</td>\n",
       "      <td>BAe 146-200 Passenger</td>\n",
       "      <td>British Aerospace 146-200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>CR1</td>\n",
       "      <td>CRJ1</td>\n",
       "      <td>Canadair Regional Jet 100</td>\n",
       "      <td>Bombardier CRJ-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>CR2</td>\n",
       "      <td>CRJ2</td>\n",
       "      <td>Canadair Regional Jet 200</td>\n",
       "      <td>Bombardier CRJ-200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>CR7</td>\n",
       "      <td>CRJ7</td>\n",
       "      <td>Canadair Regional Jet CRJ-700/705</td>\n",
       "      <td>Bombardier CRJ-700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>CR9</td>\n",
       "      <td>CRJ9</td>\n",
       "      <td>Canadair Regional Jet 900</td>\n",
       "      <td>Bombardier CRJ-900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>C27</td>\n",
       "      <td>AJ27</td>\n",
       "      <td>Comac ARJ21-700</td>\n",
       "      <td>COMAC ARJ21-700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>DHT</td>\n",
       "      <td>DHC6</td>\n",
       "      <td>DHvilld-Bombardier DHC6 T/Otter / BN BN-2a Mk....</td>\n",
       "      <td>De Havilland Canada DHC-6 Twin Otter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>DH4</td>\n",
       "      <td>DH8D</td>\n",
       "      <td>DHvilld-Bombardier DHC8 Dsh 8-400/8Q</td>\n",
       "      <td>De Havilland Canada DHC-8-400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>DH1</td>\n",
       "      <td>DH8A</td>\n",
       "      <td>DHvilld-Bombardier DHC8-100 Dsh 8/8Q</td>\n",
       "      <td>De Havilland Canada DHC-8-100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DH2</td>\n",
       "      <td>DH8B</td>\n",
       "      <td>DHvilld-Bombardier DHC8-200 Dsh 8/8Q</td>\n",
       "      <td>De Havilland Canada DHC-8-200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>DH3</td>\n",
       "      <td>DH8C</td>\n",
       "      <td>DHvilld-Bombardier DHC8-300 Dsh 8/8Q</td>\n",
       "      <td>De Havilland Canada DHC-8-300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>EM2</td>\n",
       "      <td>E120</td>\n",
       "      <td>Embraer 120 Brasilia</td>\n",
       "      <td>Embraer EMB-120 Brasilia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>E70</td>\n",
       "      <td>E170</td>\n",
       "      <td>Embraer 170</td>\n",
       "      <td>Embraer ERJ-170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>E75</td>\n",
       "      <td>E75S</td>\n",
       "      <td>Embraer 175</td>\n",
       "      <td>Embraer ERJ-175 Base</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>E7W</td>\n",
       "      <td>E75L</td>\n",
       "      <td>Embraer 175 (Enhanced Winglets)</td>\n",
       "      <td>Embraer ERJ-175 EnhancedWinglets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>290</td>\n",
       "      <td>E190</td>\n",
       "      <td>Embraer 190 (E2)</td>\n",
       "      <td>Embraer ERJ-190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>E95</td>\n",
       "      <td>E195</td>\n",
       "      <td>Embraer 195</td>\n",
       "      <td>Embraer ERJ-195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>ER3</td>\n",
       "      <td>E135</td>\n",
       "      <td>Embraer RJ135/145</td>\n",
       "      <td>Embraer ERJ-135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>ER4</td>\n",
       "      <td>E145</td>\n",
       "      <td>Embraer RJ145</td>\n",
       "      <td>Embraer ERJ-145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>D38</td>\n",
       "      <td>D328</td>\n",
       "      <td>Fairchild Dornier 328-100</td>\n",
       "      <td>Dornier Do-328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>FRJ</td>\n",
       "      <td>J328</td>\n",
       "      <td>Fairchild Dornier 328jet</td>\n",
       "      <td>Dornier Do-328JET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>100</td>\n",
       "      <td>F100</td>\n",
       "      <td>Fokker 100</td>\n",
       "      <td>Fokker F100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>F50</td>\n",
       "      <td>F50</td>\n",
       "      <td>Fokker 50</td>\n",
       "      <td>Fokker F50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>F70</td>\n",
       "      <td>F70</td>\n",
       "      <td>Fokker 70</td>\n",
       "      <td>Fokker F70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>IL9</td>\n",
       "      <td>IL96</td>\n",
       "      <td>Ilyushin Il-96 Passenger</td>\n",
       "      <td>Ilyushin Il-96</td>\n",
       "      <td>x</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>S20</td>\n",
       "      <td>SB20</td>\n",
       "      <td>Saab 2000/ Ilyushin IL114</td>\n",
       "      <td>Saab 2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>SF3</td>\n",
       "      <td>SF34</td>\n",
       "      <td>Saab 340</td>\n",
       "      <td>Saab 340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>SU9</td>\n",
       "      <td>SU95</td>\n",
       "      <td>Sukhoi Superjet 100-95</td>\n",
       "      <td>Sukhoi Superjet 100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>TU5</td>\n",
       "      <td>T154</td>\n",
       "      <td>Tupolev Tu154</td>\n",
       "      <td>Tupolev Tu-154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>T20</td>\n",
       "      <td>T204</td>\n",
       "      <td>Tupolev Tu-204/Tu-214</td>\n",
       "      <td>Tupolev Tu-204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>YK2</td>\n",
       "      <td>YK42</td>\n",
       "      <td>Yakovlev Yak-42 /142</td>\n",
       "      <td>Yakovlev Yak-42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ac_code_iata ac_code_icao  \\\n",
       "0           CS1         BCS1   \n",
       "1           CS3         BCS3   \n",
       "2           AB6         A306   \n",
       "3           AB4         A30B   \n",
       "4           313         A310   \n",
       "5           318         A318   \n",
       "6           319         A319   \n",
       "7           320         A320   \n",
       "8           32N         A20N   \n",
       "9           321         A321   \n",
       "10          32Q         A21N   \n",
       "11          332         A332   \n",
       "12          333         A333   \n",
       "13          339         A339   \n",
       "14          342         A342   \n",
       "15          343         A343   \n",
       "16          345         A345   \n",
       "17          346         A346   \n",
       "18          351         A35K   \n",
       "19          359         A359   \n",
       "20          388         A388   \n",
       "21          A40         A140   \n",
       "22          A81         A148   \n",
       "23          A58         A158   \n",
       "24          AT4         AT43   \n",
       "25          AT7         AT72   \n",
       "26          AR1         RJ1H   \n",
       "27          AR8         RJ85   \n",
       "28          141         B461   \n",
       "29          142         B462   \n",
       "..          ...          ...   \n",
       "67          CR1         CRJ1   \n",
       "68          CR2         CRJ2   \n",
       "69          CR7         CRJ7   \n",
       "70          CR9         CRJ9   \n",
       "71          C27         AJ27   \n",
       "72          DHT         DHC6   \n",
       "73          DH4         DH8D   \n",
       "74          DH1         DH8A   \n",
       "75          DH2         DH8B   \n",
       "76          DH3         DH8C   \n",
       "77          EM2         E120   \n",
       "78          E70         E170   \n",
       "79          E75         E75S   \n",
       "80          E7W         E75L   \n",
       "81          290         E190   \n",
       "82          E95         E195   \n",
       "83          ER3         E135   \n",
       "84          ER4         E145   \n",
       "85          D38         D328   \n",
       "86          FRJ         J328   \n",
       "87          100         F100   \n",
       "88          F50          F50   \n",
       "89          F70          F70   \n",
       "90          IL9         IL96   \n",
       "91          S20         SB20   \n",
       "92          SF3         SF34   \n",
       "93          SU9         SU95   \n",
       "94          TU5         T154   \n",
       "95          T20         T204   \n",
       "96          YK2         YK42   \n",
       "\n",
       "                                          ac_name_oag  \\\n",
       "0                                     Airbus A220-100   \n",
       "1                                     Airbus A220-300   \n",
       "2                           Airbus A300-600 Passenger   \n",
       "3                          Airbus A300B2/B4 Passenger   \n",
       "4                           Airbus A310-300 Passenger   \n",
       "5                                         Airbus A318   \n",
       "6                                         Airbus A319   \n",
       "7            Airbus A320 (with and without Sharklets)   \n",
       "8                                      Airbus A320neo   \n",
       "9            Airbus A321 (with and without Sharklets)   \n",
       "10                                     Airbus A321neo   \n",
       "11                                    Airbus A330-200   \n",
       "12                                    Airbus A330-300   \n",
       "13                                 Airbus A330-900neo   \n",
       "14                                    Airbus A340-200   \n",
       "15                                    Airbus A340-300   \n",
       "16                                    Airbus A340-500   \n",
       "17                                    Airbus A340-600   \n",
       "18                                   Airbus A350-1000   \n",
       "19                                    Airbus A350-900   \n",
       "20                          Airbus A380-800 Passenger   \n",
       "21                                     Antonov AN-140   \n",
       "22                                  Antonov An148-100   \n",
       "23                                     Antonov An-158   \n",
       "24                                    ATR 42-300 /320   \n",
       "25                                             ATR 72   \n",
       "26                                         Avro RJ100   \n",
       "27                                          Avro RJ85   \n",
       "28                              BAe 146-100 Passenger   \n",
       "29                              BAe 146-200 Passenger   \n",
       "..                                                ...   \n",
       "67                          Canadair Regional Jet 100   \n",
       "68                          Canadair Regional Jet 200   \n",
       "69                  Canadair Regional Jet CRJ-700/705   \n",
       "70                          Canadair Regional Jet 900   \n",
       "71                                    Comac ARJ21-700   \n",
       "72  DHvilld-Bombardier DHC6 T/Otter / BN BN-2a Mk....   \n",
       "73               DHvilld-Bombardier DHC8 Dsh 8-400/8Q   \n",
       "74               DHvilld-Bombardier DHC8-100 Dsh 8/8Q   \n",
       "75               DHvilld-Bombardier DHC8-200 Dsh 8/8Q   \n",
       "76               DHvilld-Bombardier DHC8-300 Dsh 8/8Q   \n",
       "77                               Embraer 120 Brasilia   \n",
       "78                                        Embraer 170   \n",
       "79                                        Embraer 175   \n",
       "80                    Embraer 175 (Enhanced Winglets)   \n",
       "81                                   Embraer 190 (E2)   \n",
       "82                                        Embraer 195   \n",
       "83                                  Embraer RJ135/145   \n",
       "84                                      Embraer RJ145   \n",
       "85                          Fairchild Dornier 328-100   \n",
       "86                           Fairchild Dornier 328jet   \n",
       "87                                         Fokker 100   \n",
       "88                                          Fokker 50   \n",
       "89                                          Fokker 70   \n",
       "90                           Ilyushin Il-96 Passenger   \n",
       "91                          Saab 2000/ Ilyushin IL114   \n",
       "92                                           Saab 340   \n",
       "93                             Sukhoi Superjet 100-95   \n",
       "94                                      Tupolev Tu154   \n",
       "95                              Tupolev Tu-204/Tu-214   \n",
       "96                               Yakovlev Yak-42 /142   \n",
       "\n",
       "                              ac_name_ps widebody narrowbody  \n",
       "0                        Airbus A220-100      NaN          x  \n",
       "1                        Airbus A220-300      NaN          x  \n",
       "2                        Airbus A300-600        x        NaN  \n",
       "3                          Airbus A300B4        x        NaN  \n",
       "4                        Airbus A310-300        x        NaN  \n",
       "5                        Airbus A318-100      NaN          x  \n",
       "6                        Airbus A319-100      NaN          x  \n",
       "7                        Airbus A320-200      NaN          x  \n",
       "8                         Airbus A320neo      NaN          x  \n",
       "9                        Airbus A321-200      NaN          x  \n",
       "10                        Airbus A321neo      NaN          x  \n",
       "11                       Airbus A330-200        x        NaN  \n",
       "12                       Airbus A330-300        x        NaN  \n",
       "13                       Airbus A330-900        x        NaN  \n",
       "14                       Airbus A340-200        x        NaN  \n",
       "15                       Airbus A340-300        x        NaN  \n",
       "16                       Airbus A340-500        x        NaN  \n",
       "17                       Airbus A340-600        x        NaN  \n",
       "18                      Airbus A350-1000        x        NaN  \n",
       "19                       Airbus A350-900        x        NaN  \n",
       "20                       Airbus A380-800        x        NaN  \n",
       "21                        Antonov An-140      NaN          x  \n",
       "22                        Antonov An-148      NaN          x  \n",
       "23                        Antonov An-158      NaN          x  \n",
       "24                                ATR 42      NaN          x  \n",
       "25                                ATR 72      NaN          x  \n",
       "26          British Aerospace Avro RJ100      NaN          x  \n",
       "27           British Aerospace Avro RJ85      NaN          x  \n",
       "28             British Aerospace 146-100      NaN          x  \n",
       "29             British Aerospace 146-200      NaN          x  \n",
       "..                                   ...      ...        ...  \n",
       "67                    Bombardier CRJ-100      NaN          x  \n",
       "68                    Bombardier CRJ-200      NaN          x  \n",
       "69                    Bombardier CRJ-700      NaN          x  \n",
       "70                    Bombardier CRJ-900      NaN          x  \n",
       "71                       COMAC ARJ21-700      NaN          x  \n",
       "72  De Havilland Canada DHC-6 Twin Otter      NaN          x  \n",
       "73         De Havilland Canada DHC-8-400      NaN          x  \n",
       "74         De Havilland Canada DHC-8-100      NaN          x  \n",
       "75         De Havilland Canada DHC-8-200      NaN          x  \n",
       "76         De Havilland Canada DHC-8-300      NaN          x  \n",
       "77              Embraer EMB-120 Brasilia      NaN          x  \n",
       "78                       Embraer ERJ-170      NaN          x  \n",
       "79                  Embraer ERJ-175 Base      NaN          x  \n",
       "80      Embraer ERJ-175 EnhancedWinglets      NaN          x  \n",
       "81                       Embraer ERJ-190      NaN          x  \n",
       "82                       Embraer ERJ-195      NaN          x  \n",
       "83                       Embraer ERJ-135      NaN          x  \n",
       "84                       Embraer ERJ-145      NaN          x  \n",
       "85                        Dornier Do-328      NaN          x  \n",
       "86                     Dornier Do-328JET      NaN          x  \n",
       "87                           Fokker F100      NaN          x  \n",
       "88                            Fokker F50      NaN          x  \n",
       "89                            Fokker F70      NaN          x  \n",
       "90                        Ilyushin Il-96        x        NaN  \n",
       "91                             Saab 2000      NaN          x  \n",
       "92                              Saab 340      NaN          x  \n",
       "93                   Sukhoi Superjet 100      NaN          x  \n",
       "94                        Tupolev Tu-154      NaN          x  \n",
       "95                        Tupolev Tu-204      NaN          x  \n",
       "96                       Yakovlev Yak-42      NaN          x  \n",
       "\n",
       "[97 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_type_designators = pd.read_csv('datasets/aircraft_type_designators.csv',index_col=0)\n",
    "ac_type_designators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iata_to_ps_AC_lookup = ac_type_designators.set_index('ac_name_ps').to_dict()['ac_code_iata']\n",
    "ps['ac_code_iata'] = ps['ac_name_ps'].replace(iata_to_ps_AC_lookup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compute average age for each aircraft type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_code_iata</th>\n",
       "      <th>average_age_2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>26.406875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141</td>\n",
       "      <td>30.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>142</td>\n",
       "      <td>30.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143</td>\n",
       "      <td>27.989286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>290</td>\n",
       "      <td>8.260681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ac_code_iata  average_age_2018\n",
       "0          100         26.406875\n",
       "1          141         30.720000\n",
       "2          142         30.555556\n",
       "3          143         27.989286\n",
       "4          290          8.260681"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_degrad = pd.DataFrame(ps.groupby('ac_code_iata').mean().age).reset_index()\n",
    "engine_degrad = engine_degrad.rename(columns={'age':'average_age_2018'})\n",
    "engine_degrad.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply medium- and long-haul degradation factors to fleet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in engine_degrad.index:\n",
    "    if engine_degrad.ac_code_iata[i] in ac_long_haul:\n",
    "        engine_degrad.at[i,'engine_degrad'] = func(engine_degrad.average_age_2018[i], popt_long)\n",
    "    elif engine_degrad.ac_code_iata[i] in ac_medium_haul:\n",
    "        engine_degrad.at[i,'engine_degrad'] = func(engine_degrad.average_age_2018[i], popt_medium)\n",
    "    else:\n",
    "        print('Error: ', engine_degrad.ac_code_iata[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_code_iata</th>\n",
       "      <th>average_age_2018</th>\n",
       "      <th>engine_degrad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>26.406875</td>\n",
       "      <td>-4.244050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141</td>\n",
       "      <td>30.720000</td>\n",
       "      <td>-4.431402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>142</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>-4.424739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143</td>\n",
       "      <td>27.989286</td>\n",
       "      <td>-4.316005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>290</td>\n",
       "      <td>8.260681</td>\n",
       "      <td>-2.853186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>313</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>-4.326264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>318</td>\n",
       "      <td>12.394000</td>\n",
       "      <td>-3.326238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>319</td>\n",
       "      <td>13.394242</td>\n",
       "      <td>-3.418561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>320</td>\n",
       "      <td>9.715740</td>\n",
       "      <td>-3.040259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>321</td>\n",
       "      <td>7.211504</td>\n",
       "      <td>-2.699051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32N</td>\n",
       "      <td>0.953373</td>\n",
       "      <td>-0.858294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32Q</td>\n",
       "      <td>0.514050</td>\n",
       "      <td>-0.531710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>332</td>\n",
       "      <td>10.720734</td>\n",
       "      <td>-3.294214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>333</td>\n",
       "      <td>8.378738</td>\n",
       "      <td>-2.869425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>339</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-0.127560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>342</td>\n",
       "      <td>24.321429</td>\n",
       "      <td>-4.325151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>343</td>\n",
       "      <td>19.337405</td>\n",
       "      <td>-4.031794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>345</td>\n",
       "      <td>12.669231</td>\n",
       "      <td>-3.500040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>346</td>\n",
       "      <td>13.362500</td>\n",
       "      <td>-3.566253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>351</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>-0.394551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>359</td>\n",
       "      <td>1.493981</td>\n",
       "      <td>-1.223112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>388</td>\n",
       "      <td>5.729185</td>\n",
       "      <td>-2.551544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>717</td>\n",
       "      <td>16.939355</td>\n",
       "      <td>-3.700792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>722</td>\n",
       "      <td>40.857143</td>\n",
       "      <td>-4.786887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>733</td>\n",
       "      <td>25.711494</td>\n",
       "      <td>-4.211106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>734</td>\n",
       "      <td>26.258088</td>\n",
       "      <td>-4.237072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>735</td>\n",
       "      <td>24.448500</td>\n",
       "      <td>-4.149015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>736</td>\n",
       "      <td>16.467647</td>\n",
       "      <td>-3.666634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>738</td>\n",
       "      <td>7.805755</td>\n",
       "      <td>-2.788615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>739</td>\n",
       "      <td>6.074296</td>\n",
       "      <td>-2.507963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>DC9</td>\n",
       "      <td>45.570621</td>\n",
       "      <td>-4.923673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>DH1</td>\n",
       "      <td>28.601333</td>\n",
       "      <td>-4.342788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>DH2</td>\n",
       "      <td>20.662903</td>\n",
       "      <td>-3.942561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>DH3</td>\n",
       "      <td>21.684354</td>\n",
       "      <td>-4.001622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>DH4</td>\n",
       "      <td>8.962940</td>\n",
       "      <td>-2.946885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>DHT</td>\n",
       "      <td>31.570000</td>\n",
       "      <td>-4.465300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>E70</td>\n",
       "      <td>12.219255</td>\n",
       "      <td>-3.309404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>E75</td>\n",
       "      <td>7.207373</td>\n",
       "      <td>-2.698406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>E7W</td>\n",
       "      <td>1.337864</td>\n",
       "      <td>-1.088623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>E95</td>\n",
       "      <td>6.766879</td>\n",
       "      <td>-2.627691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>EM2</td>\n",
       "      <td>23.276087</td>\n",
       "      <td>-4.088555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ER3</td>\n",
       "      <td>16.607190</td>\n",
       "      <td>-3.676834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ER4</td>\n",
       "      <td>16.825631</td>\n",
       "      <td>-3.692640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>F50</td>\n",
       "      <td>27.461667</td>\n",
       "      <td>-4.292459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>F70</td>\n",
       "      <td>22.994444</td>\n",
       "      <td>-4.073596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>FRJ</td>\n",
       "      <td>17.856494</td>\n",
       "      <td>-3.764707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>IL9</td>\n",
       "      <td>22.533333</td>\n",
       "      <td>-4.227138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>J41</td>\n",
       "      <td>23.918919</td>\n",
       "      <td>-4.122058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>M81</td>\n",
       "      <td>34.130000</td>\n",
       "      <td>-4.562292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>M82</td>\n",
       "      <td>30.196296</td>\n",
       "      <td>-4.410061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>M83</td>\n",
       "      <td>26.826368</td>\n",
       "      <td>-4.263522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>M87</td>\n",
       "      <td>28.810000</td>\n",
       "      <td>-4.351792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>M88</td>\n",
       "      <td>28.233824</td>\n",
       "      <td>-4.326773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>M90</td>\n",
       "      <td>21.885135</td>\n",
       "      <td>-4.012918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>S20</td>\n",
       "      <td>23.158621</td>\n",
       "      <td>-4.082337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>SF3</td>\n",
       "      <td>25.232653</td>\n",
       "      <td>-4.187918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>SU9</td>\n",
       "      <td>3.553383</td>\n",
       "      <td>-1.943168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>T20</td>\n",
       "      <td>16.124242</td>\n",
       "      <td>-3.641182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>TU5</td>\n",
       "      <td>24.976667</td>\n",
       "      <td>-4.175347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>YK2</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>-4.115925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ac_code_iata  average_age_2018  engine_degrad\n",
       "0           100         26.406875      -4.244050\n",
       "1           141         30.720000      -4.431402\n",
       "2           142         30.555556      -4.424739\n",
       "3           143         27.989286      -4.316005\n",
       "4           290          8.260681      -2.853186\n",
       "5           313         28.222222      -4.326264\n",
       "6           318         12.394000      -3.326238\n",
       "7           319         13.394242      -3.418561\n",
       "8           320          9.715740      -3.040259\n",
       "9           321          7.211504      -2.699051\n",
       "10          32N          0.953373      -0.858294\n",
       "11          32Q          0.514050      -0.531710\n",
       "12          332         10.720734      -3.294214\n",
       "13          333          8.378738      -2.869425\n",
       "14          339          0.100000      -0.127560\n",
       "15          342         24.321429      -4.325151\n",
       "16          343         19.337405      -4.031794\n",
       "17          345         12.669231      -3.500040\n",
       "18          346         13.362500      -3.566253\n",
       "19          351          0.342857      -0.394551\n",
       "20          359          1.493981      -1.223112\n",
       "21          388          5.729185      -2.551544\n",
       "22          717         16.939355      -3.700792\n",
       "23          722         40.857143      -4.786887\n",
       "24          733         25.711494      -4.211106\n",
       "25          734         26.258088      -4.237072\n",
       "26          735         24.448500      -4.149015\n",
       "27          736         16.467647      -3.666634\n",
       "28          738          7.805755      -2.788615\n",
       "29          739          6.074296      -2.507963\n",
       "..          ...               ...            ...\n",
       "67          DC9         45.570621      -4.923673\n",
       "68          DH1         28.601333      -4.342788\n",
       "69          DH2         20.662903      -3.942561\n",
       "70          DH3         21.684354      -4.001622\n",
       "71          DH4          8.962940      -2.946885\n",
       "72          DHT         31.570000      -4.465300\n",
       "73          E70         12.219255      -3.309404\n",
       "74          E75          7.207373      -2.698406\n",
       "75          E7W          1.337864      -1.088623\n",
       "76          E95          6.766879      -2.627691\n",
       "77          EM2         23.276087      -4.088555\n",
       "78          ER3         16.607190      -3.676834\n",
       "79          ER4         16.825631      -3.692640\n",
       "80          F50         27.461667      -4.292459\n",
       "81          F70         22.994444      -4.073596\n",
       "82          FRJ         17.856494      -3.764707\n",
       "83          IL9         22.533333      -4.227138\n",
       "84          J41         23.918919      -4.122058\n",
       "85          M81         34.130000      -4.562292\n",
       "86          M82         30.196296      -4.410061\n",
       "87          M83         26.826368      -4.263522\n",
       "88          M87         28.810000      -4.351792\n",
       "89          M88         28.233824      -4.326773\n",
       "90          M90         21.885135      -4.012918\n",
       "91          S20         23.158621      -4.082337\n",
       "92          SF3         25.232653      -4.187918\n",
       "93          SU9          3.553383      -1.943168\n",
       "94          T20         16.124242      -3.641182\n",
       "95          TU5         24.976667      -4.175347\n",
       "96          YK2         23.800000      -4.115925\n",
       "\n",
       "[97 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_degrad.ac_code_iata.astype(str)\n",
    "engine_degrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine_degrad.to_csv('datasets/aircraft_engine_degradation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ac_code_iata</th>\n",
       "      <th>ac_code_icao</th>\n",
       "      <th>ac_name_oag</th>\n",
       "      <th>ac_type</th>\n",
       "      <th>average_age_2018</th>\n",
       "      <th>engine_degrad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A40</td>\n",
       "      <td>A140</td>\n",
       "      <td>Antonov AN-140</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>13.900000</td>\n",
       "      <td>-3.462829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A81</td>\n",
       "      <td>A148</td>\n",
       "      <td>Antonov An148-100</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>8.028571</td>\n",
       "      <td>-2.820648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A58</td>\n",
       "      <td>A158</td>\n",
       "      <td>Antonov An-158</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>6.128571</td>\n",
       "      <td>-2.517760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32N</td>\n",
       "      <td>A20N</td>\n",
       "      <td>Airbus A320neo</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>0.953373</td>\n",
       "      <td>-0.858294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32Q</td>\n",
       "      <td>A21N</td>\n",
       "      <td>Airbus A321neo</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>0.514050</td>\n",
       "      <td>-0.531710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AB6</td>\n",
       "      <td>A306</td>\n",
       "      <td>Airbus A300-600 Passenger</td>\n",
       "      <td>widebody</td>\n",
       "      <td>26.942105</td>\n",
       "      <td>-4.268843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AB4</td>\n",
       "      <td>A30B</td>\n",
       "      <td>Airbus A300B2/B4 Passenger</td>\n",
       "      <td>widebody</td>\n",
       "      <td>37.187500</td>\n",
       "      <td>-4.669269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>313</td>\n",
       "      <td>A310</td>\n",
       "      <td>Airbus A310-300 Passenger</td>\n",
       "      <td>widebody</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>-4.326264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>318</td>\n",
       "      <td>A318</td>\n",
       "      <td>Airbus A318</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>12.394000</td>\n",
       "      <td>-3.326238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>319</td>\n",
       "      <td>A319</td>\n",
       "      <td>Airbus A319</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>13.394242</td>\n",
       "      <td>-3.418561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>320</td>\n",
       "      <td>A320</td>\n",
       "      <td>Airbus A320 (with and without Sharklets)</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>9.715740</td>\n",
       "      <td>-3.040259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>321</td>\n",
       "      <td>A321</td>\n",
       "      <td>Airbus A321 (with and without Sharklets)</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>7.211504</td>\n",
       "      <td>-2.699051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>332</td>\n",
       "      <td>A332</td>\n",
       "      <td>Airbus A330-200</td>\n",
       "      <td>widebody</td>\n",
       "      <td>10.720734</td>\n",
       "      <td>-3.294214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>333</td>\n",
       "      <td>A333</td>\n",
       "      <td>Airbus A330-300</td>\n",
       "      <td>widebody</td>\n",
       "      <td>8.378738</td>\n",
       "      <td>-2.869425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>339</td>\n",
       "      <td>A339</td>\n",
       "      <td>Airbus A330-900neo</td>\n",
       "      <td>widebody</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-0.127560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>342</td>\n",
       "      <td>A342</td>\n",
       "      <td>Airbus A340-200</td>\n",
       "      <td>widebody</td>\n",
       "      <td>24.321429</td>\n",
       "      <td>-4.325151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>343</td>\n",
       "      <td>A343</td>\n",
       "      <td>Airbus A340-300</td>\n",
       "      <td>widebody</td>\n",
       "      <td>19.337405</td>\n",
       "      <td>-4.031794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>345</td>\n",
       "      <td>A345</td>\n",
       "      <td>Airbus A340-500</td>\n",
       "      <td>widebody</td>\n",
       "      <td>12.669231</td>\n",
       "      <td>-3.500040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>346</td>\n",
       "      <td>A346</td>\n",
       "      <td>Airbus A340-600</td>\n",
       "      <td>widebody</td>\n",
       "      <td>13.362500</td>\n",
       "      <td>-3.566253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>359</td>\n",
       "      <td>A359</td>\n",
       "      <td>Airbus A350-900</td>\n",
       "      <td>widebody</td>\n",
       "      <td>1.493981</td>\n",
       "      <td>-1.223112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>351</td>\n",
       "      <td>A35K</td>\n",
       "      <td>Airbus A350-1000</td>\n",
       "      <td>widebody</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>-0.394551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>388</td>\n",
       "      <td>A388</td>\n",
       "      <td>Airbus A380-800 Passenger</td>\n",
       "      <td>widebody</td>\n",
       "      <td>5.729185</td>\n",
       "      <td>-2.551544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>C27</td>\n",
       "      <td>AJ27</td>\n",
       "      <td>Comac ARJ21-700</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>-1.111521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AN4</td>\n",
       "      <td>AN24</td>\n",
       "      <td>Antonov AN-24/ Xian Yunshuji Y7 /Ma60</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A38</td>\n",
       "      <td>AN38</td>\n",
       "      <td>Antonov AN-38</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AT4</td>\n",
       "      <td>AT43</td>\n",
       "      <td>ATR 42-300 /320</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>19.835628</td>\n",
       "      <td>-3.892648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AT7</td>\n",
       "      <td>AT72</td>\n",
       "      <td>ATR 72</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>8.117141</td>\n",
       "      <td>-2.833161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ATP</td>\n",
       "      <td>ATP</td>\n",
       "      <td>BAe ATP</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BE1</td>\n",
       "      <td>B190</td>\n",
       "      <td>Beechcraft 1900 Airliner</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>17.460000</td>\n",
       "      <td>-3.737466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7M8</td>\n",
       "      <td>B38M</td>\n",
       "      <td>Boeing 737MAX 8 Passenger</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>0.581728</td>\n",
       "      <td>-0.587766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>GRJ</td>\n",
       "      <td>GLF4</td>\n",
       "      <td>Gulfstream Aerospace G/stream 2/3/4/5/6</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>GJ5</td>\n",
       "      <td>GLF5</td>\n",
       "      <td>Gulfstream Aerospace V (G500/G550)</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>IL9</td>\n",
       "      <td>IL96</td>\n",
       "      <td>Ilyushin Il-96 Passenger</td>\n",
       "      <td>widebody</td>\n",
       "      <td>22.533333</td>\n",
       "      <td>-4.227138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>FRJ</td>\n",
       "      <td>J328</td>\n",
       "      <td>Fairchild Dornier 328jet</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>17.856494</td>\n",
       "      <td>-3.764707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>J31</td>\n",
       "      <td>JS31</td>\n",
       "      <td>BAe Jetstream 31</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>J32</td>\n",
       "      <td>JS32</td>\n",
       "      <td>BAe Jetstream 32</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>J41</td>\n",
       "      <td>JS41</td>\n",
       "      <td>BAe Jetstream 41</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>23.918919</td>\n",
       "      <td>-4.122058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>L4T</td>\n",
       "      <td>L410</td>\n",
       "      <td>Let 410</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>M81</td>\n",
       "      <td>MD81</td>\n",
       "      <td>Boeing (douglas) MD-81</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>34.130000</td>\n",
       "      <td>-4.562292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>M82</td>\n",
       "      <td>MD82</td>\n",
       "      <td>Boeing (douglas) MD-82</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>30.196296</td>\n",
       "      <td>-4.410061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>M83</td>\n",
       "      <td>MD83</td>\n",
       "      <td>Boeing (douglas) MD-83</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>26.826368</td>\n",
       "      <td>-4.263522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>M87</td>\n",
       "      <td>MD87</td>\n",
       "      <td>Boeing (douglas) MD-87</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>28.810000</td>\n",
       "      <td>-4.351792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>M88</td>\n",
       "      <td>MD88</td>\n",
       "      <td>Boeing (douglas) MD-88</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>28.233824</td>\n",
       "      <td>-4.326773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>M90</td>\n",
       "      <td>MD90</td>\n",
       "      <td>Boeing (douglas) MD-90</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>21.885135</td>\n",
       "      <td>-4.012918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>PA1</td>\n",
       "      <td>P28B</td>\n",
       "      <td>Piper (Light Aircraft - Single Piston)</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>PA2</td>\n",
       "      <td>PA31</td>\n",
       "      <td>Piper (Light Aircraft - Twin Piston)</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>PAG</td>\n",
       "      <td>PA46</td>\n",
       "      <td>Piper (Light Aircraft)</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PL2</td>\n",
       "      <td>PC12</td>\n",
       "      <td>Pilatus PC-12</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>AR1</td>\n",
       "      <td>RJ1H</td>\n",
       "      <td>Avro RJ100</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>20.953846</td>\n",
       "      <td>-3.959662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>AR8</td>\n",
       "      <td>RJ85</td>\n",
       "      <td>Avro RJ85</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>21.070370</td>\n",
       "      <td>-3.966448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>S20</td>\n",
       "      <td>SB20</td>\n",
       "      <td>Saab 2000/ Ilyushin IL114</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>23.158621</td>\n",
       "      <td>-4.082337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>SF3</td>\n",
       "      <td>SF34</td>\n",
       "      <td>Saab 340</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>25.232653</td>\n",
       "      <td>-4.187918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>SU9</td>\n",
       "      <td>SU95</td>\n",
       "      <td>Sukhoi Superjet 100-95</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>3.553383</td>\n",
       "      <td>-1.943168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>SWM</td>\n",
       "      <td>SW4</td>\n",
       "      <td>Fairchild Sa26 /Sa226 /Sa227 Merlin /Me</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>TU3</td>\n",
       "      <td>T134</td>\n",
       "      <td>Tupolev Tu134</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>TU5</td>\n",
       "      <td>T154</td>\n",
       "      <td>Tupolev Tu154</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>24.976667</td>\n",
       "      <td>-4.175347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>T20</td>\n",
       "      <td>T204</td>\n",
       "      <td>Tupolev Tu-204/Tu-214</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>16.124242</td>\n",
       "      <td>-3.641182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>YN2</td>\n",
       "      <td>Y12</td>\n",
       "      <td>Harbin Yunshuji Y12</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>YK4</td>\n",
       "      <td>YK40</td>\n",
       "      <td>Yakovlev Yak-40</td>\n",
       "      <td>regional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.315859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>YK2</td>\n",
       "      <td>YK42</td>\n",
       "      <td>Yakovlev Yak-42 /142</td>\n",
       "      <td>narrowbody</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>-4.115925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ac_code_iata ac_code_icao                               ac_name_oag  \\\n",
       "0            A40         A140                            Antonov AN-140   \n",
       "1            A81         A148                         Antonov An148-100   \n",
       "2            A58         A158                            Antonov An-158   \n",
       "3            32N         A20N                            Airbus A320neo   \n",
       "4            32Q         A21N                            Airbus A321neo   \n",
       "5            AB6         A306                 Airbus A300-600 Passenger   \n",
       "6            AB4         A30B                Airbus A300B2/B4 Passenger   \n",
       "7            313         A310                 Airbus A310-300 Passenger   \n",
       "8            318         A318                               Airbus A318   \n",
       "9            319         A319                               Airbus A319   \n",
       "10           320         A320  Airbus A320 (with and without Sharklets)   \n",
       "11           321         A321  Airbus A321 (with and without Sharklets)   \n",
       "12           332         A332                           Airbus A330-200   \n",
       "13           333         A333                           Airbus A330-300   \n",
       "14           339         A339                        Airbus A330-900neo   \n",
       "15           342         A342                           Airbus A340-200   \n",
       "16           343         A343                           Airbus A340-300   \n",
       "17           345         A345                           Airbus A340-500   \n",
       "18           346         A346                           Airbus A340-600   \n",
       "19           359         A359                           Airbus A350-900   \n",
       "20           351         A35K                          Airbus A350-1000   \n",
       "21           388         A388                 Airbus A380-800 Passenger   \n",
       "22           C27         AJ27                           Comac ARJ21-700   \n",
       "23           AN4         AN24     Antonov AN-24/ Xian Yunshuji Y7 /Ma60   \n",
       "24           A38         AN38                             Antonov AN-38   \n",
       "25           AT4         AT43                           ATR 42-300 /320   \n",
       "26           AT7         AT72                                    ATR 72   \n",
       "27           ATP          ATP                                   BAe ATP   \n",
       "28           BE1         B190                  Beechcraft 1900 Airliner   \n",
       "29           7M8         B38M                 Boeing 737MAX 8 Passenger   \n",
       "..           ...          ...                                       ...   \n",
       "103          GRJ         GLF4   Gulfstream Aerospace G/stream 2/3/4/5/6   \n",
       "104          GJ5         GLF5        Gulfstream Aerospace V (G500/G550)   \n",
       "105          IL9         IL96                  Ilyushin Il-96 Passenger   \n",
       "106          FRJ         J328                  Fairchild Dornier 328jet   \n",
       "107          J31         JS31                          BAe Jetstream 31   \n",
       "108          J32         JS32                          BAe Jetstream 32   \n",
       "109          J41         JS41                          BAe Jetstream 41   \n",
       "110          L4T         L410                                   Let 410   \n",
       "111          M81         MD81                    Boeing (douglas) MD-81   \n",
       "112          M82         MD82                    Boeing (douglas) MD-82   \n",
       "113          M83         MD83                    Boeing (douglas) MD-83   \n",
       "114          M87         MD87                    Boeing (douglas) MD-87   \n",
       "115          M88         MD88                    Boeing (douglas) MD-88   \n",
       "116          M90         MD90                    Boeing (douglas) MD-90   \n",
       "117          PA1         P28B    Piper (Light Aircraft - Single Piston)   \n",
       "118          PA2         PA31      Piper (Light Aircraft - Twin Piston)   \n",
       "119          PAG         PA46                    Piper (Light Aircraft)   \n",
       "120          PL2         PC12                             Pilatus PC-12   \n",
       "121          AR1         RJ1H                                Avro RJ100   \n",
       "122          AR8         RJ85                                 Avro RJ85   \n",
       "123          S20         SB20                 Saab 2000/ Ilyushin IL114   \n",
       "124          SF3         SF34                                  Saab 340   \n",
       "125          SU9         SU95                    Sukhoi Superjet 100-95   \n",
       "126          SWM          SW4   Fairchild Sa26 /Sa226 /Sa227 Merlin /Me   \n",
       "127          TU3         T134                             Tupolev Tu134   \n",
       "128          TU5         T154                             Tupolev Tu154   \n",
       "129          T20         T204                     Tupolev Tu-204/Tu-214   \n",
       "130          YN2          Y12                       Harbin Yunshuji Y12   \n",
       "131          YK4         YK40                           Yakovlev Yak-40   \n",
       "132          YK2         YK42                      Yakovlev Yak-42 /142   \n",
       "\n",
       "        ac_type  average_age_2018  engine_degrad  \n",
       "0    narrowbody         13.900000      -3.462829  \n",
       "1    narrowbody          8.028571      -2.820648  \n",
       "2    narrowbody          6.128571      -2.517760  \n",
       "3    narrowbody          0.953373      -0.858294  \n",
       "4    narrowbody          0.514050      -0.531710  \n",
       "5      widebody         26.942105      -4.268843  \n",
       "6      widebody         37.187500      -4.669269  \n",
       "7      widebody         28.222222      -4.326264  \n",
       "8    narrowbody         12.394000      -3.326238  \n",
       "9    narrowbody         13.394242      -3.418561  \n",
       "10   narrowbody          9.715740      -3.040259  \n",
       "11   narrowbody          7.211504      -2.699051  \n",
       "12     widebody         10.720734      -3.294214  \n",
       "13     widebody          8.378738      -2.869425  \n",
       "14     widebody          0.100000      -0.127560  \n",
       "15     widebody         24.321429      -4.325151  \n",
       "16     widebody         19.337405      -4.031794  \n",
       "17     widebody         12.669231      -3.500040  \n",
       "18     widebody         13.362500      -3.566253  \n",
       "19     widebody          1.493981      -1.223112  \n",
       "20     widebody          0.342857      -0.394551  \n",
       "21     widebody          5.729185      -2.551544  \n",
       "22   narrowbody          1.380000      -1.111521  \n",
       "23     regional               NaN      -3.315859  \n",
       "24     regional               NaN      -3.315859  \n",
       "25   narrowbody         19.835628      -3.892648  \n",
       "26   narrowbody          8.117141      -2.833161  \n",
       "27     regional               NaN      -3.315859  \n",
       "28   narrowbody         17.460000      -3.737466  \n",
       "29   narrowbody          0.581728      -0.587766  \n",
       "..          ...               ...            ...  \n",
       "103    regional               NaN      -3.315859  \n",
       "104    regional               NaN      -3.315859  \n",
       "105    widebody         22.533333      -4.227138  \n",
       "106  narrowbody         17.856494      -3.764707  \n",
       "107    regional               NaN      -3.315859  \n",
       "108    regional               NaN      -3.315859  \n",
       "109  narrowbody         23.918919      -4.122058  \n",
       "110    regional               NaN      -3.315859  \n",
       "111  narrowbody         34.130000      -4.562292  \n",
       "112  narrowbody         30.196296      -4.410061  \n",
       "113  narrowbody         26.826368      -4.263522  \n",
       "114  narrowbody         28.810000      -4.351792  \n",
       "115  narrowbody         28.233824      -4.326773  \n",
       "116  narrowbody         21.885135      -4.012918  \n",
       "117    regional               NaN      -3.315859  \n",
       "118    regional               NaN      -3.315859  \n",
       "119    regional               NaN      -3.315859  \n",
       "120    regional               NaN      -3.315859  \n",
       "121  narrowbody         20.953846      -3.959662  \n",
       "122  narrowbody         21.070370      -3.966448  \n",
       "123  narrowbody         23.158621      -4.082337  \n",
       "124  narrowbody         25.232653      -4.187918  \n",
       "125  narrowbody          3.553383      -1.943168  \n",
       "126    regional               NaN      -3.315859  \n",
       "127    regional               NaN      -3.315859  \n",
       "128  narrowbody         24.976667      -4.175347  \n",
       "129  narrowbody         16.124242      -3.641182  \n",
       "130    regional               NaN      -3.315859  \n",
       "131    regional               NaN      -3.315859  \n",
       "132  narrowbody         23.800000      -4.115925  \n",
       "\n",
       "[133 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_degrad_full = pd.read_csv('datasets/aircraft_type_designators_full.csv',index_col=0)\n",
    "engine_degrad_full = engine_degrad_full.merge(engine_degrad,how='left',on='ac_code_iata')\n",
    "engine_degrad_full.loc[engine_degrad_full.engine_degrad.isna(),'engine_degrad'] = engine_degrad.engine_degrad.mean()\n",
    "engine_degrad_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine_degrad_full.to_csv('datasets/aircraft_engine_degradation_full.csv')"
   ]
  }
 ],
 "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": "3.6.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}