ExploratoryAnalysis_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"./data/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of characters dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adanel</td>\n",
       "      <td>Men</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belemir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>TA 2978</td>\n",
       "      <td>February 26 ,3019</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dark (book) Light brown (movie)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boromir</td>\n",
       "      <td>Men</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>March ,3019</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lagduf</td>\n",
       "      <td>Orcs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>TA 280</td>\n",
       "      <td>TA 515</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tarcil</td>\n",
       "      <td>Men</td>\n",
       "      <td>Arnor</td>\n",
       "      <td>Unnamed wife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fire-drake of Gondolin</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     birth              death  gender                             hair height  \\\n",
       "0      NaN                NaN  Female                              NaN    NaN   \n",
       "1  TA 2978  February 26 ,3019    Male  Dark (book) Light brown (movie)    NaN   \n",
       "2      NaN        March ,3019    Male                              NaN    NaN   \n",
       "3   TA 280             TA 515    Male                              NaN    NaN   \n",
       "4      NaN                NaN    Male                              NaN    NaN   \n",
       "\n",
       "                     name    race  realm        spouse  \n",
       "0                  Adanel     Men    NaN       Belemir  \n",
       "1                 Boromir     Men    NaN           NaN  \n",
       "2                  Lagduf    Orcs    NaN           NaN  \n",
       "3                  Tarcil     Men  Arnor  Unnamed wife  \n",
       "4  Fire-drake of Gondolin  Dragon    NaN           NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load characters data\n",
    "chars = pd.read_csv(data_path + 'lotr_characters.csv')\n",
    "chars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adanel</td>\n",
       "      <td>Men</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belemir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>TA 2978</td>\n",
       "      <td>February 26 ,3019</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dark (book) Light brown (movie)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boromir</td>\n",
       "      <td>Men</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>March ,3019</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lagduf</td>\n",
       "      <td>Orc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>TA 280</td>\n",
       "      <td>TA 515</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tarcil</td>\n",
       "      <td>Men</td>\n",
       "      <td>Arnor</td>\n",
       "      <td>Unnamed wife</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fire-drake of Gondolin</td>\n",
       "      <td>Dragon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     birth              death  gender                             hair height  \\\n",
       "0      NaN                NaN  Female                              NaN    NaN   \n",
       "1  TA 2978  February 26 ,3019    Male  Dark (book) Light brown (movie)    NaN   \n",
       "2      NaN        March ,3019    Male                              NaN    NaN   \n",
       "3   TA 280             TA 515    Male                              NaN    NaN   \n",
       "4      NaN                NaN    Male                              NaN    NaN   \n",
       "\n",
       "                     name    race  realm        spouse  \n",
       "0                  Adanel     Men    NaN       Belemir  \n",
       "1                 Boromir     Men    NaN           NaN  \n",
       "2                  Lagduf     Orc    NaN           NaN  \n",
       "3                  Tarcil     Men  Arnor  Unnamed wife  \n",
       "4  Fire-drake of Gondolin  Dragon    NaN           NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#map the races\n",
    "race_mappings = {\n",
    "    \"Dragons\" : \"Dragon\",\n",
    "    \"Dwarven\" : \"Dwarf\",\n",
    "    \"Dwarves\" : \"Dwarf\",\n",
    "    \"Eagles\" : \"Eagle\",\n",
    "    \"Elves\" : \"Elf\",\n",
    "    \"Hobbits\" : \"Hobbit\",\n",
    "    \"Orcs\" : \"Orc\",\n",
    "    \"Balrogs\" : \"Balrog\"\n",
    "}\n",
    "chars['race'].replace(race_mappings, regex = True, inplace = True)\n",
    "chars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>race</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Men</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Elf</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Dwarf</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Ainur</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      race  Size\n",
       "23     Men   388\n",
       "19  Hobbit   142\n",
       "8      Elf   106\n",
       "6    Dwarf    44\n",
       "0    Ainur    24"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get some stats about races with grouping\n",
    "race_counts = chars.groupby('race').size().reset_index(name='Size')\n",
    "top_races = race_counts.sort_values('Size', ascending = False).head(5)\n",
    "top_races"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>gender</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  Size\n",
       "1     Male   633\n",
       "2  Unknown   143\n",
       "0   Female   135"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#map the gender and get some stats\n",
    "gender_mappings = {\n",
    "    \"Males\" : \"Male\",\n",
    "    \"male\" : \"Male\",\n",
    "    \"Most likely male\" : \"Male\"\n",
    "}\n",
    "chars['gender'].replace(gender_mappings, inplace = True)\n",
    "chars.loc[chars.gender.isna(), 'gender'] = 'Unknown'\n",
    "gender_counts = chars.groupby('gender').size().reset_index(name='Size')\n",
    "gender_counts.sort_values('Size', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>race</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Ainur</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Dwarf</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Elf</td>\n",
       "      <td>79</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>101</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Men</td>\n",
       "      <td>335</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     race  Male  Female\n",
       "0   Ainur    14      10\n",
       "1   Dwarf    43       1\n",
       "2     Elf    79      24\n",
       "3  Hobbit   101      41\n",
       "4     Men   335      51"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get some stats about races with grouping\n",
    "male_race_counts = chars[(chars.gender == 'Male') & (chars.race.isin(top_races.race.values))].groupby('race').size().reset_index(name='Size')\n",
    "male_race_counts.sort_values('Size', ascending = False)\n",
    "#get some stats about races with grouping\n",
    "female_race_counts = chars[(chars.gender == 'Female') & (chars.race.isin(top_races.race.values))].groupby('race').size().reset_index(name='Size')\n",
    "female_race_counts.sort_values('Size', ascending = False)\n",
    "\n",
    "all_genders = female_race_counts\n",
    "all_genders['Male'] = male_race_counts.Size\n",
    "all_genders['Female'] = female_race_counts.Size\n",
    "all_genders.drop('Size', axis = 1, inplace=True)\n",
    "all_genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABeMAAAMWCAYAAABybp05AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZgcVb34//eHyUb2BMJ2gYQAQtgUCJsiixiuKJuiLC4QlUX0yqIgi/oDhIuAggjoV+GqIYIiiOyiECGKskg0KIEohECEQEgg68xkz/n9UdWZptPd0z2Zngn6fj1PPdVVZ6lTW0/yqdOnIqWEJEmSJEmSJElqnPW6uwGSJEmSJEmSJP27MxgvSZIkSZIkSVKDGYyXJEmSJEmSJKnBDMZLkiRJkiRJktRgBuMlSZIkSZIkSWowg/GSJEmSJEmSJDWYwXhJkqQKIuLCiEgRMa6729IdImKPiLgnIt6IiFX5sbiwgdtL+TSiUdtQ9/M8q5qIeCm/Pg7o7rasCzwekiT9ezEYL0mSOiwixhUF1iZFRFTJe9N/cmD77SYitgUmAocCQ4A3gNeB5m5s1n+MiBicPwy6sLvbIkmSJKlz9OjuBkiSpH8buwMfBn7V3Q1RpzgZ6As8AhyeUprfze35TzMYuCD/fGE3tkOSJElSJ7FnvCRJ6kzfiAj/ffHvYcd8fquBeEmSJElae/5nWZIkdYbfA61kAdyPd3Nb1DnWz+cOSyNJkiRJncBgvCRJ6gyzgOvyzxdGRF1D4bX3QseIGFHIUyZtYp42NiIGRsQVEfFCRCyOiOkR8Y2I6FOU/6CI+G3+UtKWiPhDRLy3hjauFxFnRsTf8nJvRsTdEbFnDeU+FREPRsSciFgWEa9GxC8iYq8KZVa/ODYv/z8R8eeImJ+vf1d77S3Z/mcj4vcRMTcilkTEixFxfURsUyb/S/lxPiBf9ZOi8/NSrdvN64qIOCYi7ouIWRGxNCJm5sf8zIjYoErZLSPihoh4JS/3YkR8OyIGVsg/NCJOiIjbI+IfEbEoP0/PRsRVEbFZhXJvubYiYu+I+GVEvBYRKyPi6qK8O0XE1yPikYj4V96uN/Nr8MSIaGrnePSLiLMi4tGiczE9v44+ERE983wTgReLyqWS6cIK+3FtRPwzIlrz/f9LRJwTEf0qtGf1fRcRoyLixoh4OSKWR8SdRfk2iohvRcSU/JguyfM9Gtn9NbzafrdzTHaKiFvy62NJfu6+HhG9yxy7hXl7D61SX+TXSoqIk+tox+qXZEbEf0XE9/NzszQinirKt2lEnJpf08/nx3phREyOiIsiYnA72+nQPRER++bHqXA/vBkREyLiuIjK7+qo0o5eEfGhyO6xv0X2fbgkImZExM0RsXuNx2poZPfXi0X7ckNEbNrO9j8REY9HRHN+LzwUER+qdz/K1NsUEWdExN8j+xswJyLujYj35Ont/a0ZFhHfjIin87a15Nf9/0bE0HXxeERE/4g4PyKejIgF+Xl8PiKuiYgtKpQp/rs5OCIuz++91ojwl1CSJDVSSsnJycnJycnJqUMTMA5IwC3AUGBBvnxSmbw35WnjyqSlfBpRYTsjCnnKpE3M084Epuafm4FlRfXenef9PLAKWFnU1gQsBd5Tpu4L8/Qbgdvzz8uB+UVlVwDHVGj3AODBoryrSra7EvifdrZ7Z9F25uWf31Xj+ekL/LZoe8tK2r4YOKKkzJNkD1cKx29BvjwLeLKOa2NQmX2fl+9zYd3YCtfBEcCb+eeF+TEvpD0J9CyzvW8X5Sm0e0XR8mxgl2rXFnB00bbm58fg6qK8b5Sc9/kl27wP6FHheOxAFmAv5F1edD7fcv2TvXdhTtH6WSXTWSV1fyQ/l4X8rWTXdGH578DGVe67TwEtRcd7MXBnnmc48GrJfs/Nz2dh3efq/N4olPs42b1aOF/FbX4M6F9S7vo87fYqdR+U52kBBtbRppfycicXHfuWvH1PFeX7Zck5K72mpwGbd9Y9kZe7vGSbC0vK/BxYr85zcGhJnS0l19By4FPtHKtPFn1uAZYUlX8RGFKh/HVF+Vbmx6BwPZ1WVOcBde5TT+DXJfswr+jzUUVpa/ytAfal7Xsn5ddja9Hyv4Dt1qXjAYwqSi/sZ3PR8lzK/22bmKefDbyQf16SX1vz6znuTk5OTk5OTvVN3d4AJycnJycnp7fvRFEwPl++sCho0bskb6OD8fOBfwD75ut7ASfSFlz9Ollw9VJgcJ5nOPBonv7nMnVfWFT3CrKA//p52tbAA7QFP7cuU/6OPP1vwAeLyg4GzsuDPStLgyVF212UB0hOBfrmaRtRY5AR+EFRkOWUwjkB3gE8TFvQ6B1VjuvYDl4b9xYdm9OKjnkvYCfgItZ8EFAc4PwdsFO+vjfwGdqCW58vs70zgW8Cu5IHcYEmshcL/yYvNwWIStdWfrx/SVtQvEfxNUkWJD8R2JI86A70IwvCvZbXcXaZtg0luycSMJ3sYUOvPG0AWRDwxxQFcalyzZfUvQfZdb0CuCxvW+T7vhfweF7Pb6vcd4vy81043kF+PeftSsDzwHvJg775OdkJuBg4ss5ro7Dd+cCfgZ2Lro2xtAVAry8pt2e+fhmwYYW6C98z4+ts00tFx+LvwLuL0rYp+vxN4KtkD1f65Ot6Avvn+5KA+zrxnjidtodJpxaV6QN8jLYHJefVub8H5Of2fcAGReu3BL6T17kY2LLKsZoHTAb2KbpfDqctAH5FmbKfKDr/3yran43JHj4uo+3B0AF17tNFtD0wOp2279vhwD289eHXiJKyw4vSbwC2I/sVeZANv3Z/nvYM0LQuHA+yhzsv5ml3kH33Fb6XRgDj87RZhXqLyk6k7Xr/F/AB2u7tbeo57k5OTk5OTk71Td3eACcnJycnJ6e378SawfiBtPUsPL0kb6OD8cvLBRGAHxXV/+My6cNp64W4ZUnahUVlv1qmbB+yBwAJ+L+StPfT1iNyaIX9+kqe594q2z25g+dmOG29Z08pk96XrBdv2cAlaxGMJ3vwkPLj+oE6yhX2eQolD3Py9Gvz9IfqbE9vsiBaAvavdG0Bf6TOHsZF9by3cL7LpF2Rp80B/qvG+ipe8yX5/pjnO7NC+hBgZp5ndIXj/QJ54LJM+WfzPGV//dHBY1XY7uvl7g2ygHzKr9/hJWl/o8z3S542iLZA/gF1tukl2gKqa/yKoMY6hpIFzROwVUla3fcE2UO7RWTfbXtWyLN3Xudc8gc8nXSOCt+bF1Q5VrMoCuQXpX85T59esj7IHupU+jsQvPWXAzWfQ6A/bT3Czy+T3hN4qqjuESXphb9P361Qf6+i8h9dF44HcEm+/k5KHjIW5bkvz1P6a5qJtD3Y2qmzrhsnJycnJyen9ifHjJckSZ0mpbSQLPAIcH5UGKu6QW5LKU0rs35C0edvliamlGaQBaUh651aTitwdenKlNIS4Mp88aiSsZtPyOfjUkpzK9T7s3x+YJQfb/xNst6rHfERsp6ds4D/K01MKbXSdq4+UmH7HXV8Pv9tSuk3HSh/VUppaZn1hXHMK52nsvK6HswX31Ml65UppVX11F20jUfIenqPiDXHp/9UPv92SmlmR+ovJyK2JtufxWS/gijXrnlkvXoBxlSo6rqU0uIKaQvzedUxrzvoBxXujfHAK2TX74dL0grX8qfLlDuW7MXDL5C9VLojxqeUXu9IwXxfHs0X9ylJ7sg9cRRZkPmPKaU/V9jm42S/thhC9iuQznJPPq92v1yfUnqzzPrCfbpVyd+AdwGF91SU+y5OZL9c6oj/JvuVyhLgmjJ1LweuKlcwItYn+5UBlfKklJaR/WoGKt9HXX08Cn9jvpPnLefn+bxSm+9PKU2psg1JktTJ6nq5miRJUg2uJRsyZGOyoRjWCDI0yNMV1s/O50toC7qXeh3YliygVc6klFJLhbRC0G8wsBVZYAzg3fn8zIg4tULZgr7ABkVtLd7uinbKVrJbPn8kpbSyQp6H8nk/smEZnu3gtkrtnc9/3cHyT1ZYXwhklz1PEbE98D/AfmQ9y/uT9S4tVvZFrrnH2mtYRHyUbFia3YBhZL+OKLUZ2fAh5C+K3CRf39HjUUnhGusFvFjlPZ7983nZlzlSfb9/TTbczeURsS1ZQPLxKsH7ekwstzKltCoiHgGOo+06LriJ7CHSOyNit5TSX4vSPpPPf1IlONmeWq6BPYHPkR3/zcnun1Kl11lH7onC+d0rImZVyVd4segW1ND+gvyFpF8ADiG7/weRDW9UrNr90t59Ctn3YuG7s3AuZ6eU/lmh7KNkw8zU+//UXfP5Uyml5gp5HqmwfjTZPQTwRJX7aP18Xuk+6rLjkb+YdfN88baIqPQQsbBfHbn3JUlSAxiMlyRJnSql1BoRlwLfBc6OiO+nlBZ0waZfq7C+EIh+vUqArpCnZ4X0ar2Zi9OG0RaML/QkHpRP7elbZt2cGspVMiyfV2v7K2Xyd4aN8/m/Olh+UYX1S/L5Gv+GjYhjyXpUF85h4WW5hR72/cmCptV+rVHxeEdED+BW3tpTeynZS10L188wst7cxdvYuOhzR49HJYVrrKlkO5WUu8ag+nV2OVmP68PJXoD8eWBFRDxJNk71DSml+bU1dw213FdvuS5TSvMi4ldkL3/9NPBXgIjYgWxM+VVkY213VNV7LiLOInsYUIjYFl64uSxfHkT2gKb0OuvIPVE4v+vTFgiuptL5XUN+vB7irdfNItpe4tqL7KFXtful7H2aUlpSFNAu/k5t9zsppbQ0It6g7QFWrTbM55X+DkD+gKyM4l99rM191JXHo7jNtXx3d+TelyRJDeAwNZIkqRF+CLxMFsz5cje3pdEqdaMs/DvriJRS1DC9VKaOSj3a69G7SlpHew+vUyJiGNlLF3sCvyDr6donpTQkpbRJSmkTspdSQuXzRZVfEACcRBaIbyV7OeQWKaU+KaVhRdsoBPuKt1Fxe52gcI1NrvEaG1uhnor7nVJamlI6gmzYlStoeyFsYfm5iHhn5+3SatWO2w35/OMRUbi+C73iH0gpvVKmTK0qHouI2JHs4UQA15G92LN3Smlo0TVQGMqkM8574fx+p8bzO66Oun9CFnj+K9nLOweklAamlDbO96MwbEsjr99KOrLNWspU+r4rHOd5NR7nAzrQvrVRbt+K/x8/qIY2j6hQd2f8jZEkSXUwGC9JkjpdPkb3xfniGRGxYbX8tAUEyg35AbX1LG+kakM1FPdQLO5lWBh3eofOb05NCm0ZXiVP8dAFndlDsrDv1bbdmQ4h6/n+LPDxlNJf8jGii9XS47WaQnDy4pTSNaUB33zM/XLXefHwIp19PArHedu8537DpJQeTymdk1Lah+wh23FkvbyHUeadBDWq5b5a47pMKU0kG3JqKHB4vu+fzJM7+o6FWhxF9v+n36aUvphSerbMA5xK11lH7omGfIdExJZkvyJYCRyeUvptmaFd1vZ+KadwLiue94joRTZkV0frrvZug0rbLRznIRFRb4/8tbE2x6P4vQbd9TdGkiR1gMF4SZLUKD8he5HiAODcdvIWhrnYvEL6Hp3VqA7aIyIq/cx//3w+H3ixaH1hLN6jGtaq6gpjae9Vpe3vy+ctQKUxizvi8Xz+wU6ss5rCdfP3ci9gzV+s+77S9R3cxuQK6e+hzMOk/BcPhYB8Pcdj9X6UvBi4WOEa6w8cXEfdayWl1JJSugU4OV+1ewdf1rx/uZX5/r43X/xruTzAj/L5Z4APkQWP3wTu6kA7alX1GsiPwd7l0ujYPVE4v/tHREcC1JUU9mNOlRcKv78Tt1dQOJcbR8Q7KuR5Nx0bSrVwTt4VEf0r5HlvhfWTyMZlh+zF112lw8cjpfQibQH5rmyzJElaSwbjJUlSQ+QvHr0wX/w81XvBFl6+ekRpQj4MxRmd2rj69SUbmuQt8rZ9KV/8ZcmY9OPy+eiIOL5a5RFR6cWxa+NXZAHdDWgLmhZvsy9wdiFvO0O01Gt8Pj84Ij7QifVWUngnwU4VAtcnAVt30jZ2Lk3Ie2ZfUqXsT/P5lyPiv2rc3sKiz4PLZUgp/YO2IO/l1QLiEbF+0ZAuNct75lZSeIlr0PaiyHqcGhHl9u2TZL/aWEV2HZczjiyAejBtD/tuSiktq5C/M1S8BnJfJXv4WE5H7onbyB6U9QG+VS1jnd8hhf3YOCI2KlPXzmRj8ne2p2h7ifY5ZbYbtP/gtpIHaDtWXyhTdw+yF4uvIaW0CLg9X/xaRFT8VUBE9KgS7K/X2h6Pcfn88xExqlKmyHT3r8skSVLOYLwkSWqkn5ENHbI+cGCVfLfm85Mi4tOFoGE+RvOvqR7I7woLgIsj4vSIWB8gIkaS9cIdRfZi0cuKC6SUfkNbIPHHEXFRRKweQiEihkTEERFxF3BVZzc4pTQDuD5fvCwiTi46ru8A7gO2IRsDvVoguSPuz6cAbo+ILxaCrhHRKyJ2jogrI+LITtreBLLxoHcCrina1sCIOBv4Hlmv6bXxYD7/en7emvJtbA/cQzbsR0uFspeTvaRxQ+CRiDi8EOSOiP4RcUBE3BIRq38Zkr8UtTAG/aertOuLZC+S3Smv+/2FIWsiYr2I2DEivkb2K5VqQ3hUMiUiLo2IPYraHBGxJ3BtnufJlNK8DtTdB/hNROyU19szIk4AfpCn/yilVPaFpymlWcC9ZP+fKfRGb+QQNdB2DXwoIs4v/OIkIoZFxLeA86h8ndV9T6SU3szrBPh0RNxaOFZ5uT4RsW9EfA/4Ux37MZXs5c0B/CIitsnr6xkRH8n3s3TYmrWWP6y8MF/8TERcXnQMNiY7f+8j+06qt+5FtL0X4pL8+Ba+q7ckG8t/qypVnAvMJbtHHo2IDxc/vIqIbSLiDLJjN7re9lVo89oej8vIXhjeD/h9RJxQ/KAgIraIiJOAv/DWF09LkqTulFJycnJycnJycurQRNYzLwG3VMlzVJ6nMI0rk6cnbS+GTMBysgB4IgtuHVFIK1N2Yp42tsL2D8jTX6rSxrJ1kAVKEnAjWWA9AcuAeUVtXQEcW6HefsAdJfs/v2jfCtNPKmx3jWNV5/npS9ZjNFVo+xKyF8zWfEzq2PbgojoS2fjUc/N5Kld30foRFeocUeU6uKrkmM7Nz00CfkP2wGGNY1qtzpJ8Q8l6sRYfy8J5XAGMBV7Klw8oU35nspcaVzoXa+w3cFFRWnNe/0vAGSX5Dsmvq0LepcAb+TaK6x9ez/HO8xTXu4Lsfiyudw6wS53XRqHsx8keYBTui6VFaY8B/dup50NF+Set5b1S8dyV5Lu9aJur8utsVb78I9q+Ey/sjHsiL/e1om2k/JiVlnuxzv39cEn5hUXHfwbZrxPKfm/WcqyqXVtkL78tvqaKj+FptZ6LMvX2An5bVPeyvO7C5w8XpW1apvweZA/Niv8OvUH2PVl8H+2/rhwPsgeqz5ZcU2+SBfCL23xCSbnCdbjG9ebk5OTk5OTU2Mme8ZIkqdF+ReVxnwFI2cs2x5ANxfASWSCihSywtTvwt4a2sH2J7AWeXyLrGdmLLJB6L/DulI2fvWahbGztDwOHkh2HmWS/EuhFFtj9GfBRsmF8Or/RKbWSBWpPBB4hC9D0JQu2/R+wc0qpIWNsp6xn9/uAE8h6rs8lG9v8NeD3ZEMP3d2J2/sS2XA8k8mCij3IhoE4gyxou6Jy6Zrqn0vWA/v/kfUqhmyYljvJgnPj2in/NLAjWWB1Ul62D1nP1jvJXoj6Skmxb5ANX/F3sl7Mw/PpLUO7pJTuB95B9sDhr2TBw8FkAdZHgf8PGJWyX0vU6wjgm2Q9r18lO4fL8jZdBuyYUvp7B+olb9teZL+MKQSC/5m394C05ktFS/2Gtl7Dje4VX3AMWS/qqWTB2iA7NieklD5brWBH74mU0iXAO8l+6fJ8vs1+ebn7gVPJjmPNUkp35G15EFhE9kB0BvBtYFfWvBY7TUrpf8iC/U+Qnfcg2/9DU0rXrEW9y8ju9S8DU8j+jqwk++XKfsDDRdnnlyn/JLA92T33KNlxGUx2r04i+4XLHiml33e0jRXa3eHjkVKaRna+Pk+2f3OBgWTfd38n+/XK/rQNlSVJkrpZpJS6uw2SJEmSVJeIeA/wR7KHD5vmwW6prIg4iOwhyIyU0ohubo4kSfoPZc94SZIkSW9Hn8vntxmIVw0KL6x+sGouSZKkBjIYL0mSJOltJSL+m2xoH4DvdmdbtG6IiKaI+GVEfCAiBhWt3zEifgn8N9nQQh0eCkeSJGltOUyNJEmSpLeFiHiJ7L0LG+WrfppSOr77WqR1RUT0IAu2Fywke3dE33x5FXBqSun6rm6bJElSgcF4SZIkSW8LEZHIXvQ6E/gF8PWU0uLubZXWBRERZEMX/TewM9kDm57ALOAPwNUppaovE5ckSWo0g/GSJEmSJEmSJDVYj+5uQFfbcMMN04gRI7q7GZIkSZIkSZKkfzN/+ctf3kgpDSuX1uFgfET0B6YCm+erPp1SGlchby/gDODjwDbAirzsT4AbUjvd8yPi/cDpwF7AQLKfpd4LXJpSer2edo8YMYJJkybVU0SSJEmSJEmSpHZFxIxKaeutRb2X0BaIr7bxgcCjwOXAO4Ege+nS3sAPgbvzl+1UKv9V4EHgUGADYCkwEjgNeDoidlqLfZAkSZIkSZIkqeE6FIyPiN2A/wGeqCH7DcDuwFzgMKA/2RvtxwJLyILsF1XYzgfJgv4AVwKDU0qDgJ2Ap4BhwF0R0bsj+yFJkiRJkiRJUleoOxgfEeuR9WgHOLWdvLsCR+eLn04p3ZsyK1NKNwLn5mlnRsRGZaq4NJ/fmVI6K6W0CCCl9AxZYL+ZrJf8yfXuhyRJkiRJkiRJXaUjPeO/CIwG/l9KaXI7eT+ez/+ZUrq7TPr1wAKyYWs+UpwQETuSDWsDcEVpwZTSK8DP88VP1NZ0SZIkSZIkSZK6Xl3B+Ij4L+Bi4HXgazUUOTCfP1AuMaW0GHgkX3xfhbILqDwczm/z+Z75C2UlSZIkSZIkSVrn1Nsz/lpgAHBWSmlBtYwREcD2+eIzVbI+m893KFlfWJ6aUlrVTtnibUmSJEmSJEmStE6pORgfEYcBHwYmppRuqqHIQKBf/vnVKvkKaZuWrN+0JL1a2XLlJUmSJEmSJElaJ9QUjI+IfsB1wHLgCzXW3a/o8+Iq+VrzeekwM4XytZQtV361iDg5IiZFxKQ5c+ZUqU6SJEmSJEmSpM5Xa8/4bwBbAt9JKT3bXuZcFH1OdbXqreWrla2p3pTS9Sml0Sml0cOGDetAUyRJkiRJkiRJ6rh2g/ER8S7gdOBlsqB8rZqLPvetkq+Q1lyyvrkkvZzi3vel5SVJkiRJkiRJWif0qCHPd4Em4Ktk72WtNBxM7zxtVUqpFVgItJAFzDerUn8h7bWS9a+WpFcrW658p0kpsWjRIhYuXEhraysrV65s1KakDmtqaqJv374MHDiQAQMGkL1DWZIkSZIkSdK6oJZg/PB8Pr6dfD/IpxnAiJRSioipwGhgxyrldsjnpcPfFJZHRcR6KaVVVcomYGo77euQlBKzZ8+mpaWFoUOHsskmm9DU1GSgU+uUlBIrV66kubmZN954g8WLF7PRRht5nUqSJEmSJEnriFrHjO+oh/P5mHKJEdEHeG+++LsKZQcBe1So/+B8/kRKqaWjjaxm0aJFtLS0MHz4cAYPHkyPHj0McGqdExH06NGDwYMHM3z4cFpaWli0aFF3N0uSJEmSJElSrt1gfEppREopKk1FWT+drxtRtO7n+Xz7iDi0TPUnkQXbFwN3lGz3WeBv+eLZpQUjYjPguHzx5vb2o6MWLlzI0KFDaWpqatQmpE7V1NTE0KFDWbhwYXc3RZIkSZIkSVKuoT3jU0qTgVvzxXER8UGAiGiKiOOBy/O076SUZpep4vx8flREXBERA/LyOwD3AAOA6cANjdqH1tZW+vevNEy+tG7q378/ra2t3d0MSZIkSZIkSblGD1MDWe/3vwAbAPdFRAvZi11vBNYH7gUuKFcwpfRr4Ov54tnAvIhYADwD7Aa8ARyRUlraqMavXLnSXvF622lqavJFw5IkSZIkSdI6pOHB+JTSQuDdwLlkw84kYCnwOHAKcHhKaUWV8peQjTl/HzAP6E3WG/4aYKeU0pSG7gA4RrzedrxmJUmSJEmSpHVLj7WtoGTc+Ep5lpENSXN5e3krlJ8ATOhIWUmSJEmSJEmSultXDFMjSZIkSZIkSdJ/NIPxkiRJkiRJkiQ1mMF4qcGmTZtGRNCjx1qPCiVJkiRJkiTpbcpgvBpq7NixRES709VXX93dTZUkSZIkSZKkhrGrbmc58MDubsHaefjhhlbfs2dPhg4dWjG9X79+Dd2+JEmSJEmSJHUng/HqEu9+97uZOHFidzdDkiRJkiRJkrqFw9RIkiRJkiRJktRgBuO1zvrDH/7AMcccw+abb07v3r3ZYIMNGDNmDL/4xS/K5p8wYQIRwTbbbAPA/fffz0EHHcSQIUMYMmQIBx98ME888cTq/PPnz+e8885j2223pU+fPmy55Zacd955LFmypGz9M2fO5Hvf+x6HHHII22yzDX379mXgwIHstttuXHTRRSxYsKDD+zp79mzOPfdcdt55Z/r370+/fv3Yeeed+drXvsa8efM6XK8kSZIkSZKkdYPD1Gidk1Li7LPP5sorr1y9bsCAAcybN48JEyYwYcIE7rnnHsaPH89665V/nnTNNddwxhlnEBH079+fhQsX8uCDD/LHP/6Rhx56iJEjR3LggQfy7LPP0q9fP1atWsXLL7/MZZddxtSpU7nzzjvXqPMLX/gCd9111+rlwYMHs3DhQiZPnszkyZO5+eabmThxIptttlld+/uHP/yBI444gvnz5wPQq1cvmpqamDJlClOmTOGmm27iwQcfZNttt62rXkmSJEmSJEnrDnvGa51z1VVXceWVV7LRRhvxgx/8gHnz5rFw4UJaWlq45ZZb2GSTTbj55pvfEqwvNmvWLM466yy+9rWvMXfuXBYsWMD06dPZa6+9WLx4MWeeeSYnnXQSKSX++Mc/0tzcTHNzMz/84Q9pamrirrvu4oEHHlij3u22247//d//ZerUqSxevJh58+axZO5tlXkAACAASURBVMkSHnroIXbffXeef/55Tj311Lr2dfr06Rx22GHMnz+fU045heeee47FixfT0tLC008/zcEHH8yMGTM46qijWLlyZYeOpyRJkiRJkqTuFyml7m5Dlxo9enSaNGlSzfmnTp3KqFGj2s944IFr0ap1wMMPN6TasWPHcuONN9KzZ0+GDh1aNs8hhxzCT37yEwDmzp3LFltswYoVK3j00UfZfffd18j/pz/9iX333ZcNNtiAWbNm0aNH9gOPCRMmMGbMGABOPPFEbrjhhreUmz59OltvvTWQ9T7/xz/+wVZbbfWWPCeccALjx4/npJNO4vrrr695P9944w2222475s2bx4wZM9hiiy1Wp02bNo1tt92WpqYmVqxY8ZZyxx57LL/4xS/40pe+VPbhwtKlSxk9ejRTpkzhjjvu4Mgjj6y5TTVfu5IkSZIkSZI6RUT8JaU0ulyaPePVJZYvX87rr79edioeE/22226jtbWV/fbbr2wgHuA973kPw4cP580332Ty5Mll85x33nlrrBs5ciQjR44EsiB4aSAe4KCDDgJgypQpde3fhhtuyD777ENKiccee6ymMs3Nzdx+++0AnHnmmWXz9O7dm6OOOgqABx98sK42SZIkSZIkSVp3OGa8usT+++/PxIkT28336KOPrp5vsskmFfPNnTsXgJdffpk99tjjLWn9+vVbHXQvtdFGGzF9+nR22mmnsukbb7wxQMWXpj7++OP88Ic/5NFHH2XmzJm0tLSskefVV1+t2O5iTz75JCtWrCAiGD267MMyABYvXgxk+ypJkiRJkiTp7clgvNYpr732GgCtra20tra2m79cnmpB/KamJgA23XTTqunLly9fI+2yyy7j/PPPpzC0U48ePRgyZAi9evUCYMGCBSxZsqRsgL6cwr6mlHj99dfbzV/L8ZAkSZIkSZK0bjIYr3XKqlWrADj77LO54oorurk1bf72t7+tDsSffvrpnHLKKbzjHe9YHbwHOO6447jllluo9T0MhX0dNmwYs2fPbki7JUmSJEmSanHarU93dxP0b+Cao3fu7ias0wzGa51SGCbm2Wef7eaWvNXtt99OSokPfehDXH311WXz1NK7vVhhX+fMmcOcOXMYNmzYWrdTkiRJkiRJ0rrJF7hqnbLPPvsA8PDDD1cct707vPLKKwDsuuuuZdMXLVrEn//857rq3HPPPVf3rL/jjjvWroGSJEmSJEmS1mkG47VOOeaYY+jbty+tra2cc845VfN2ZbB+0KBBADz9dPmfbF188cU1jxVfXOeRRx4JwDe+8Q3mzJlTMe+KFStobm6uq35JkiRJkiRJ6w6D8VqnDBs2jEsuuQSAG264geOOO45nnnlmdfqSJUt45JFHOPXUU9lvv/26rF1jxowB4K677uLyyy9n8eLFAMyePZsvf/nLfOtb32KDDTaou94rrriCIUOGMHPmTPbZZx/uuusuli5dujr9+eef56qrrmL77bfnqaee6pydkSRJkiRJktTlHDNe65wzzzyThQsXctFFF3HLLbdwyy230LdvX3r37s2CBQtWv/h0m2226bI2ffCDH+Twww/n7rvv5txzz+W8885j8ODBzJ8/n5QSJ598Mi0tLdx888111Tty5Ejuv/9+jjzySF544QWOPPJIevTowaBBg2hubn5LYD4iOnu3JEmSJEmSJHURg/Gd5eGHu7sF/1YuuOACjjzySK677jomTpzIzJkzaWlpYdNNN2WXXXbhiCOO4CMf+UiXtumXv/wl3/72txk/fjzTp08HYN999+Xkk0/mk5/8JJ/85Cc7VO9ee+3FP//5T77//e9z9913M3XqVObPn8+AAQPYeeedGTNmDB/96EfZbbfdOnN3JEmSJEmSJHWhSCl1dxu61OjRo9OkSZNqzj916lRGjRrVwBZJjeG1K0mSJEmSanXareXfkyfV45qjd+7uJnS7iPhLSml0uTTHjJckSZIkSZIkqcEMxkuSJEmSJEmS1GAG4yVJkiRJkiRJajCD8ZIkSZIkSZIkNZjBeEmSJEmSJEmSGsxgvCRJkiRJkiRJDWYwXpIkSZIkSZKkBjMYL0mSJEmSJElSgxmMlyRJkiRJkiSpwQzGS5IkSZIkSZLUYAbjJUmSJEmSJElqMIPxkiRJkiRJkiQ1mMF4SZIkSZIkSZIazGC8JEmSJEmSJEkNZjBekiRJkiRJkqQGMxgvSZIkSZIkSVKDGYzXf4SJEycSEYwYMaK7myJJkiRJkiTpP5DBeDXU2LFjiQgigp49ezJ79uyq+e+8887V+SOCcePGdU1DJUmSJEmSJKmBenR3A/5dnHbr093dhLVyzdE7N3wbK1as4Gc/+xlnnHFGxTzjx49veDskSZIkSZIkqavZM15dYssttwSqB9vnzp3LfffdR//+/Rk6dGhXNU2SJEmSJEmSGs5gvLrEPvvsw9Zbb83kyZN55plnyua55ZZbWLZsGUcddRTrr79+F7dQkiRJkiRJkhrHYLy6zKc+9Smgcu/4wvrjjz++Yh3Tp0/nyiuv5KCDDmKrrbaiT58+DB48mL333psrr7ySxYsXd7h9L730El/84hfZbrvt6Nu3LwMGDGD33Xfn8ssvp6WlpcP1SpIkSZIkSZLBeHWZQjD+5ptvZtWqVW9Je+6553jiiSfYYostOOCAAyrWcfTRR3PWWWfx0EMPMWPGDNZff30WLlzIE088wVlnncV+++3HokWL6m7br371K0aNGsV1113Hc889B8CyZcv461//yrnnnss+++zD66+/Xne9kiRJkiRJkgQG49WFRo4cyXve8x5mzpzJQw899Ja0Qq/4T3ziE6y3XuXLctddd+Xqq69m2rRpLFmyhHnz5rF48WLuvvtu3vGOdzBp0iTOPffcutr15JNPcuyxx7J8+XLOOeccZsyYQUtLC62trTz++OPstddePP3001V77EuSJEmSJElSNQbj1aUKAe3ioWpSStx0001vSa/khhtu4PTTT2frrbemV69eAPTu3ZvDDjuM+++/nx49ejBu3DhaW1trbtOZZ57J8uXL+da3vsVll13GlltuSUTQ1NTEXnvtxf33389mm23GAw88wKRJk+rdZUmSJEmSJEkyGK+udfTRR9OnTx9+9atfrR6H/fe//z0zZsxg9OjRjBo1qsN1jxw5kh133JHW1laeeuqpmsq88MIL/OlPf2L99dfnc5/7XNk8Q4YM4ZBDDgHgwQcf7HD7JEmSJEmSJP3n6tHdDdB/lsGDB3PYYYdx2223cfvtt3P88cfX9OLWYg8++CA//vGP+fOf/8xrr71W9qWtr776ak11Pfroo0A2PvxWW21VMV9zczMAL7/8ck31SpIkSZIkSVIxg/Hqcscffzy33XYbP/3pT/nYxz7G7bffTs+ePTnuuOPaLXvaaadx7bXXrl7u2bMnQ4cOpWfPngDMnTuX5cuXr+51357XXnsNgJUrV9b0gtZ6hr+RJEmSJEmSpAKHqVGX+8AHPsBGG23EQw89xHXXXcfChQs55JBD2HDDDauWu//++7n22mtpamriwgsvZNq0aSxdupQ333yTWbNmMWvWLPbaay8gG4e+FqtWrQKyF8OmlNqdxo0bt1b7LkmSJEmSJOk/k8F4dbkePXpw7LHHsmrVKr761a8C8KlPfardcrfddhsAJ554IhdccAFbb701EfGWPLX0bi+28cYbA/D888+zYsWKuspKkiRJkiRJUq0MxqtbFMaHX758OUOGDOGwww5rt8wrr7wCZL3Yy5kxYwbTpk2rqx377LMPkI0J/8ADD9RVVpIkSZIkSZJq5Zjx6ha77747F154IYsWLWKXXXahd+/e7ZYZNGgQAE8//XTZ9PPPP7/m4WkKtt9+e/bee28ef/xxzjnnHPbff3/69etXNu/ixYtZb731amqrJEmSJEmSJBWzZ7y6zQUXXMC3v/3t1b3k2zNmzBgAfvjDH/LjH/+YZcuWAfCvf/2LE044gZ///OcMGTKk7nZce+219O7dmylTpvDe976XCRMmrB6yZtWqVTzzzDNccsklbL311qtf+CpJkiRJkiRJ9TAYr7eNsWPHsvfee7NixQo++9nP0rdvX4YMGcLw4cMZP348F110Ebvsskvd9Y4ePZo77riDQYMGMXnyZMaMGUO/fv3YcMMN6dOnDzvttBNf//rXee2119YYo16SJEmSJEmSauEwNZ3kmqN37u4m/Nvr1asXEyZM4JJLLuHWW2/l5ZdfpkePHowZM4bTTjuNQw89lN/97ncdqvuQQw7hueee49prr+XXv/4106ZNY/78+QwePJjtttuOD3zgA3zsYx9j+PDhnbxXkiRJkiRJkv4TRL1jbL/djR49Ok2aNKnm/FOnTmXUqFENbJHUGF67kiRJkiSpVqfdWv4dfVI97LAMEfGXlNLocmkOUyNJkiRJkiRJUoMZjJckSZIkSZIkqcEMxkuSJEmSJEmS1GAG4yVJkiRJkiRJajCD8ZIkSZIkSZIkNZjBeEmSJEmSJEmSGsxgvCRJkiRJkiRJDWYwXpIkSZIkSZKkBjMYX4OUUnc3QaqL16wkSZIkSZK0bjEY346mpiZWrlzZ3c2Q6rJy5Uqampq6uxmSJEmSJEmScgbj29G3b1+am5u7uxlSXZqbm+nbt293N0OSJEmSJElSzmB8OwYOHMjcuXPtHa+3jZUrVzJ37lwGDhzY3U2RJEmSJEmSlKs5GB8RoyPi4oj4TURMi4gFEbE0ImZGxF0RcWSFciMiItUwjW5n+++PiHsiYnZELImIFyLiuxGxcb07XY8BAwbQr18/ZsyYwfz581mxYoXjcWudk1JixYoVzJ8/nxkzZtCvXz8GDBjQ3c2SJEmSJEmSlOtRR94TgVOKlpuBVcBmwOHA4RFxO3BcSml5hTper1J/pTJExFeBS/LFVfm2RwKnAcdFxPtSSlNq2os6RQQbbbQRixYtYuHChcyePdte8lonNTU10bdvXzbccEMGDBhARHR3kyRJkiRJkiTl6gnGPwb8A/gD8FxKqRkgIrYAvgicDRwFnAtcXK6ClNIm9TYwIj5IWyD+SuCilNKiiNgRuAl4F3BXROyQUlpab/01toGBAwc67IckSZIkSZIkqUNqHqYmpXRjSunqlNJfC4H4fP3LKaWvkAXGAcZ2chsvzed3ppTOSiktyrf7DHAYbb3kT+7k7UqSJEmSJEmS1Ck68wWuT+bzzTqrwrz3+zvzxStK01NKrwA/zxc/0VnblSRJkiRJkiSpM3VmMP7d+fzFTqzzwHy+AHiiQp7f5vM9I6J/J25bkiRJkiRJkqROsVbB+IjoHxG7RMT3gGPy1ddVyf9YRCyMiMUR8WJE3BQR+1bZxA75fGpKaVWFPM8Wqge2r2sHJEmSJEmSJEnqAvW8wBWAiNgceLlM0hLg0pTS96sU35uslzvAiHz6RER8FzgzpZRK8m+az1+tUmdx2qYVc0mSJEmSJEmS1E060jN+JfB6Pi3L160Avkn5XvFLgO8D+wEDUkqDgb7A7sA9eZ7TgfPKlO2XzxdXaU9r0eeyw9RExMkRMSkiJs2ZM6dKVZIkSZIkSZIkdb66g/EppddSSpuklDYB1ge2A8YDFwFP5S9dLc4/K6X0hZTSIyml5nxdSin9NaV0OHBbnvX8iBhcsrkoVFOtSTW0+fqU0uiU0uhhw4a1v5OSJEmSJEmSJHWitRozPqW0KqX0XErps8BVwJbATRFRT73n5PN+wEElac35vG+V8v2KPjdXzCVJkiRJkiRJUjdZq2B8iWvz+buAXWstlFJ6ESiMHTOyJLkwHvxmVaooTnut1u1KkiRJkiRJktRVOjMYP7Po89Z1lq00HM2z+XxUld72OxSVnVrndiVJkiRJkiRJarjODMZvVfS55uFiImIrYMN88aWS5Ifz+SBgjwpVHJzPn0gptdS6XUmSJEmSJEmSukpNwfiIaIqIaCfb2fl8BfBYUdn2yl2azxcDDxUnpJSeBf5WUn9xuzYDjssXb25nO5IkSZIkSZIkdYtae8ZvAUyKiM9ExOaFlRGxXkS8KyJuBk7MV1+bUppXVHZiRJwXETtFRFNeLiJi14i4Azg2z3d5SmlumW2fn8+PiogrImJAXscOwD3AAGA6cEON+yJJkiRJkiRJUpfqUUfe3YAfAUTEErKhaAYAvYvyjAO+UlJuOFnv90uB5RGxEOgLrF+U5zrgG+U2mlL6dUR8HbiYrHf8lyKiBRiYZ3kDOCKltLSOfZEkSZIkSZIkqcvUGox/FTgGOAjYE9gU2ABYArxANizNT1JKfypT9mxgTF5uE2AosAz4J/An4PqU0hPVNp5SuiQiHgfOAPairTf8vcClKaXXa9wPSZIkSZIkSZK6XE3B+JTSMuDWfKpLSuk24LZ6y5WpZwIwYW3rkSRJkiRJkiSpq9U6ZrwkSZIkSZIkSeogg/GSJEmSJEmSJDWYwXhJkiRJkiRJkhrMYLwkSZIkSZIkSQ1mMF6SJEmSJEmSpAYzGC9JkiRJkiRJUoMZjJckSZIkSZIkqcEMxkuSJEmSJEmS1GAG4yVJkiRJkiRJajCD8ZIkSZIkSZIkNZjBeEmSJEmSJEmSGsxgvCRJkiRJkiRJDWYwXpIkSZIkSZKkBjMYL0mSJEmSJElSgxmMlyRJkiRJkiSpwQzGS5IkSZIkSZLUYAbjJUmSJEmSJElqMIPxkiRJkiRJkiQ1mMF4SZIkSZIkSZIazGC8JEmSJEmSJEkNZjBekiRJkiRJkqQGMxgvSZIkSZIkSVKDGYyXJEmSJEmSJKnBDMZLkiRJkiRJktRgBuMlSZIkSZIkSWowg/GSJEmSJEmSJDWYwXhJkiRJkiRJkhrMYLwkSZIkSZIkSQ1mMF6SJEmSJEmSpAYzGC9JkiRJkiRJUoMZjJckSZIkSZIkqcEMxkuSJEmSJEmS1GAG4yVJkiRJkiRJajCD8ZIkSZIkSZIkNZjBeEmSJEmSJEmSGsxgvCRJkiRJkiRJDWYwXpIkSZIkSZKkBjMYL0mSJEmSJElSgxmMlyRJkiRJkiSpwQzGS5IkSZIkSZLUYAbjJUmSJEmSJElqMIPxkiRJkiRJkiQ1mMF4SZIkSZIkSZIazGC8JEmSJEmSJEkNZjBekiRJkiRJkqQGMxgvSZIkSZIkSVKDGYyXJEmSJEmSJKnBDMZLkiRJkiRJktRgBuMlSZIkSZIkSWowg/GSJEmSJEmSJDWYwXhJkiRJkiRJkhrMYLwkSZIkSZIkSQ1mMF6SJEmSJEmSpAYzGC9JkiRJkiRJUoMZjJckSZIkSZIkqcEMxkuSJEmSJEmS1GAG4yVJkiRJkiRJajCD8ZIkSZIkSZIkNZjBeEmSJEmSJEmSGsxgvCRJkiRJkiRJDWYwXpIkSZIkSZKkBjMYL0mSJEmSJElSgxmMlyRJkiRJkiSpwQzGS5IkSZIkSZLUYAbjJUmSJEmSJElqMIPxkiRJkiRJkiQ1mMF4SZIkSZIkSZIazGC8JEmSJEmSJEkNZjBekiRJkiRJkqQGMxgvSZIkSZIkSVKD1RyMj4jREXFxRPwmIqZFxIKIWBoRMyPirog4sp3yvSLiKxHxVEQ0R8T8iHgsIk6OiKhh+++PiHsiYnZELImIFyLiuxGxca37IEmSJEmSJElSd+hRR94TgVOKlpuBVcBmwOHA4RFxO3BcSml5ccGIGAg8BOyer2oF1gf2zqfDIuLDKaUV5TYcEV8FLskXV+XbHgmcBhwXEe9LKU2pY18kSZIkSZIkSeoy9QxT8xhwJllAfUBKaUBKaX1gS+BbeZ6jgHPLlL0hLzcXOAzoD/QFxgJLgEOBi8ptNCI+SFsg/kpgcEppELAT8BQwDLgrInrXsS+SJEmSJEmSJHWZmoPxKaUbU0pXp5T+mlJqLlr/ckrpK8BN+aqxxeUiYlfg6Hzx0ymle1NmZUrpRtqC92dGxEZlNn1pPr8zpXRWSmlRvt1nyAL7hV7yJ9e6L5IkSZIkSZIkdaXOfIHrk/l8s5L1H8/n/0wp3V2m3PXAArJhaz5SnBAROwLvzBevKC2YUnoF+Hm++IkOtFmSJEmSJEmSpIbrzGD8u/P5iyXrD8znD5QrlFJaDDySL76vQtkFwBMVtvvbfL5nRPSvramSJEmSJEmSJHWdtQrGR0T/iNglIr4HHJOvvq4oPYDt88VnqlT1bD7foWR9YXlqSmlVO2WLtyVJkiRJkiRJ0jqjR70FImJz4OUySUuAS1NK3y9aNxDol39+tUq1hbRNS9ZvWpJerWy58pIkSZIkSZIkdbuO9IxfCbyeT8vydSuAb1LUKz7Xr+jz4ip1tubz0mFmCuVrKVuuPAARcXJETIqISXPmzKlSlSRJkiRJkiRJna/uYHxK6bWU0iYppU3IXrq6HTAeuAh4Kn/pakEUF+1A+wrlq5Vtt96U0vUppdEppdHDhg3rQDMkSZIkSZIkSeq4tRozPqW0KqX0XErps8BVwJbATRFRqLe5KHvfKlUV0ppL1jeXpJdT3Pu+tLwkSZIkSZIkSd1urYLxJa7N5+8Cds0/LwRa8s+bVSlbSHutZP2rJenVypYrL0mSJEmSJElSt+vMYPzMos9bA6SUEjA1X7fjGiXa7JDPny1ZX1geVdTbvlLZ4m1JkiRJkiRJkrTO6Mxg/FZFn4uHi3k4n48pVygi+gDvzRd/V5JcKDsI2KPCdg/O50+klFoq5JEkSZIkSZIkqdvUFIyPiKaIiHaynZ3PVwCPFa3/eT7fPiIOLVPuJLJg+2LgjuKElNKzwN9K6i9u12bAcfnize20T5IkSZIkSZKkblFrz/gtgEkR8ZmI2LywMiLWi4h3RcTNwIn56mtTSvMKeVJKk4Fb88VxEfHBvGxTRBwPXJ6nfSelNLvMts/P50dFxBURMSAvvwNwDzAAmA7cUOO+SJIkSZIkSZLUpXrUkXc34EcAEbGEbCiaAUDvojzjgK+UKXsS2TjyuwP3RUQr0FRU9l7ggnIbTSn9OiK+DlxM1jv+SxHRAgzMs7wBHJFSWlrHvkiSJEmSJEmS1GVq7Rn/KnAMcD3wFLAAGAwsJ3vJ6o+AfVNKn04prSgtnFJaCLwbOJds2JkELAUeB04BDi9Xrqj8JWRjzt8HzCML4k8HrgF2SilNqXE/JEmSJEmSJEnqcjX1jE8pLSMbaubW9vK2U8fltA1LU2/5CcCEjm5f0v/P3p1H6XnddYL//kql0lZaLdmWHctRYseO4+xOyALZSNIhIeE0IUC6oQmbYZo5YWno0PTQrJ1DmKFnWM4cOskA4TSkO0Awk62TCUkgAQcwjh2viXfZlrVYslTaVaq680e9kmtXlaSnZEWfzznvue9dn/v+paOvru4DAAAAAJwtcz0ZDwAAAAAAnCJhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRszmF8VW2qqp+qqo9V1ZaqOlJV+6rq1qr6jaraOMO8p1dVm8PnupM8//W9Z++oqsNVdV9V/XZVXTTfHw0AAAAAAAupfy6DquqyJA8mqXHNQ0lWJHle73N9Vb29tfb5WZbaPkvf8CzP/49Jfr1XHU2yP8kzkrw7yTur6nWttdtP9jsAAAAAAOBsmOvJ+EW98hNJ3pFkXWttdZLlSd6c5IEka5PcUFUXz7RIa+3iWT63Tjenqt6cJ4P430qypvfsa5PckmRDkr+qqiVz/C0AAAAAALCg5hrGP5Hkha21b2+t/Xlr7Ykkaa0dba19KmOB/OEkq5L82Bne43t75Q2ttZ9tre3rPfuOJG/Nk6fkrz/DzwUAAAAAgDNiTmF8a23vTCfXe/13J/lyr/riM7GxJKmq5yR5fq/6m9M895EkH+5V//WZei4AAAAAAJxJc36B6xzs6pWLZh01P6/tlXuT/MMMYz7dK19aVYNn8NkAAAAAAHBGnJEwvqr6k7yyV53xRapVdWNVDVXVoap6oKr+W1V98yxLX9Mr72qtjc4w5s7jyye5el4bBwAAAACABXCmTsb/RJKLk4wm+eNZxr2sNyZJnp6xq2W+WFX/V1XVNOM39sqts6w5vm/jjKMAAAAAAOAsOe0wvqqelydfsvp7vRerjnc4yf+d5FVJVrbW1iRZnrG75T/WG/OTSf7DNMuv6JWHZtnCwXHfp72mpqqur6qbquqmnTt3zrIUAAAAAACceacVxlfVxiQ3ZCxc/+ck75k8prW2rbX2bvciZwAAIABJREFUE621L7bW9vfaWmvt5tba25L8WW/oL1TVmsmPOL7MLNuYre/4Ht7fWruutXbdhg0bTjYcAAAAAADOqFMO46tqXZLPJNmc5J4kb2mtHT6FpY4H+CuSfOukvv29cvks81eM+75/xlEAAAAAAHCWnFIYX1Wrk3w6ybVJtiR5fWtt+6ms1Vp7IMnxu2OeMan7+H3wl8yyxPi+x05lDwAAAAAA0KV5h/FVtSLJJ5Ncl2RbxoL4Lae5j5muo7mzVz67qmba6zXj5t51mvsAAAAAAIAzbl5hfFUty9hLV1+RZFfGgvh7TmcDVbU5yfpe9cFJ3Z/vlauTvGSGJd7YK/+htXbgdPYCAAAAAABdmHMYX1UDST6a5LVJ9iR5Y2vtjjnMq5MMeW+vPJTkc+M7Wmt3Jrm1V/25ada+JMk7e9U/OdleAAAAAADgbJhTGF9Vi5L8aZI3JdmX5NtaazfP8RlfqKr/UFXX9tZJjXlhVf1lku/tjXtfa233NPN/oVe+vap+s6pW9ta4JmOn9FcmuT/JB+a4HwAAAAAAWFD9cxz3yiRv731fnOSGWQ68P9xaG3+lzOUZO/3+3iTDVTWUZHmSZePG/F6SX51usdbaJ6vqF5P8WsZOx/9MVR1Isqo35PEk39FaOzLH3wIAAAAAAAtqrmH8+BP0S3ufmRyeVP+5JG9I8tIkFydZl+Rokq8l+bsk72+t/cNsD2+t/XpVfTnJTyX5pjx5Gv7jSd7bWts+x98BAAAAAAALbk5hfGvtC0lOdvf7THP/LMmfncrcSet8NslnT3cdAAAAAABYaHN+gSsAAAAAAHBqhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHSs/2xvAAAAAABgoV26ZmmuuXhlNq1blgtXLsmKJYuyYqA/x0ZbDhw5lseGjuSux/blyw8+kaPHRqfMf+Flq3P5umXZtG551izrz4qB/gz09+XIsdE8cfBoHn7iUG7esjd3b98/7fOv2LAi737tM+a015HRlp/+89tP6/dy9gnjAQAAAIDzzmuuXJ9v2rx2SvuivsqS/oGsWzGQ52xcmTc8e0Pe/6WH8vAThyaM+8GXb5p23eUDi7J8YFkuXbMsL9u8Lrc9OpQ/+vKWDI+0Tn4H5w7X1AAAAAAAzGD1ssX54VdsSl+d2vznXroqb7rmwjO7Kc5JTsYDAAAAAOedg8MjuXnLnty9fX927T+avYePZWBRZdO6ZXnTNRdlzfLFJ8auWzGQZ65fkXt2HjjRtm3ocO58bF/uf/xg9h4azsGjI1m5tD8veNrqvOrKC9JXT6b3L960Jh+7bfus+7nh1sdyy8N7p+1zpv4bgzAeAAAAADjv/OUtj03b/siew3l8/9H8r6+ZeJ/74NKJUep7/+c9U+bu3H809z9+MCsGFuUlT3/yCpzBJSePYfcfOZbdB4fnsnXOUcJ4AAAAAIBxqqbeSbPrwNFTnr97DnPf+OwL8x3P25jlA4tyeHgk2/cdyR2P7cvf3bc7B4+OzPnZPHUJ4wEAAACA89ZAf18GBxalauzlq5vWLc+/ePbEO94f2n0wW3Yfmnb+yqX9WdxX6V9UWbV0cZ536aq8aNPqCWO+dN/uk+7jwpVLTnxfsaQ/z1jSn2esX5HXXLk+f3jjltw77ooczk3CeAAAAADgvPWCp63K9730shn779mxPx/68sMz9r/rZZflygsHp+07emw0f/21nfnbe3ed8v5WLu3Pj77y8vwfn703O/fP/XQ+Tz3CeAAAAACAady9bV8+estjGTp8bN5zR0ZbPn3XjnzxnpmD+INHR/LVR/fm7m37s23oSFpaNq5amjdec2EuWb30xLhlA4vylmsvyh/N8o8CPPUJ4wEAAAAApnH1xSvznjcO5i++snVOV82Mt6iv8tbnXpyXb16X3//iA9mxb+Kp9kf2HMp/+thdOTrSJrQ/tvdIbts6lH//hitz0aonr6659pJV6e+rHBudOJ5zhzAeAAAAADhv/eODe/KPD+5JX41dCXP5uuV50zUX5mlrlyUZC9W/60WX5P7HD2br3sNT5v/uFx5IkgwsqqxetjjPf9rqvOmaCzPQ35ckWT84kO//psvyW5+9b8K8w8OjM+5peKTlb+99PO940aUn2gb6+7J+cCDbho6c9m/m7Og72xsAAAAAADjbRluy99CxfPXRofzuF+7PkeGRE319VXnZ5rWzzj860rJz/9F89u6d+Ytbtk7ou3zd8jxtzdIZZk5v14HhKW3LFi+a1xo8tQjjAQAAAADGOTQ8OuVlqRsGB+Y8/6Fdh6a0rZ/H/CS5YMXU8YfG/QMB5x5hPAAAAABwXlm3fHGWD8x8ynzNssW5cOWSCW3j73a/dM3S1CzrX33x4JS2yXfD/+DLN2Xj6iVTxiXJ4kWVV195wYS2w8Mj2bHPFTXnMnfGAwAAAADnlSsuXJF3vOjS3L51KHdt25fH9h7JoeGRrBhYlMsvWJ7XPmv9iTvfj7vv8QMnvn/nCzZm7fKB3PLI3ty/80AeP3A0oy1Zs6w/z7t0dV75zHUT5h4bGc2Duw5OaLvqosE8/2mr8rVt+3PLI3vz8BOHMjLasnH10rzxmgun/GPAVx7eG+9uPbcJ4wEAAACA886S/r68eNOavHjTmpOOfXz/kXz5gScmtK0fHMjrr96QXL3hpPO/cM+uHDw69YqZvqo8e+PKPHvjylnn7zk4nE/cvv2kz+GpTRgPAAAAADCDB3cdzB/euCVHj43Oe+7IaMvnv/54PvbVbVP6jhwbmfWqnOO27jmcP7hxS4YOH5v383lqEcYDAAAAAOeV2x4dyh/euCVXbliRy9Yty8ol/Rlc0p++So4cG83uA8PZ8sShfPXRvblr2/4p8//s5q256qLBXLFhRTasXJLBJYuyYqA/I6MtB4+OZPu+I7lv54Hc9NCePH7g6DQ7SH7tk1/PNRtX5qqLBnPZ2mVZv2IgSxf3ZbQl+48cy8NPHMqtjwzl5of3uJ7mG4QwHgAAAAA4rxwaHs1XHt6brzy895Tmbxs6km1DR/I39+w65T0cG2356qND+eqjQ6e8BueWvpMPAQAAAAAATocwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI7NOYyvqk1V9VNV9bGq2lJVR6pqX1XdWlW/UVUbTzJ/oKr+fVXdUlX7q2pPVd1YVddXVc3h+a/vPXtHVR2uqvuq6rer6qK5/gYAAAAAADgb+ucyqKouS/JgkvGh+VCSFUme1/tcX1Vvb619fpr5q5J8LsmLe00HkyxL8rLe561V9S9ba8dmeP5/TPLrvepokv1JnpHk3UneWVWva63dPpffAgAAAAAAC22uJ+MX9cpPJHlHknWttdVJlid5c5IHkqxNckNVXTzN/A9kLIjfneStSQZ7c9+V5HCSb0/yK9M9uKrenCeD+N9Ksqb37GuT3JJkQ5K/qqolc/wtAAAAAACwoOYaxj+R5IWttW9vrf15a+2JJGmtHW2tfSpjgfzhJKuS/Nj4iVX1wiTf3av+YGvt423MSGvtQ0l+vtf301V14TTPfm+vvKG19rOttX29Z9+RsWD/+Cn56+f4WwAAAAAAYEHNKYxvre1trd06S//dSb7cq754Uve/6pVfa639v9NMf3+SvRm7tuY7x3dU1XOSPL9X/c1pnvtIkg/3qv96tt8AAAAAAABny5xf4DoHu3rlokntr+2Vn5luUmvtUJIv9qqvm2Hu3iT/MMNzP90rX1pVg3PbKgAAAAAALJwzEsZXVX+SV/aqt49rryRX96p3zLLEnb3ymkntx+t3tdZGTzJ3/LMAAAAAAOAp40ydjP+JJBcnGU3yx+PaVyVZ0fu+dZb5x/s2TmrfOKl/trnTzQcAAAAAgLPutMP4qnpennzJ6u/1Xqx63Ipx3w/NsszBXjn5mpnj8+cyd7r5AAAAAABw1p1WGF9VG5PckGR5kn9O8p7JQ8Z9b6fyiDnMPem6VXV9Vd1UVTft3LnzFLYBAAAAAACn7pTD+Kpal7GXsm5Ock+St7TWDk8atn/c9+WzLHe8b/+k9v2T+qcz/vT95PlJktba+1tr17XWrtuwYcMsSwEAAAAAwJl3SmF8Va1O8ukk1ybZkuT1rbXt0wwdSnKg9/2SWZY83vfYpPatk/pnmzvdfAAAAAAAOOvmHcZX1Yokn0xyXZJtGQvit0w3trXWktzVqz5nlmWv6ZV3Tmo/Xn92Vc201+Nzxz8LAAAAAACeMuYVxlfVsiQfS/KKJLsyFsTfc5Jpn++Vb5hhzaVJvqVX/esZ5q5O8pIZ1n9jr/yH1tqBGcYAAAAAAMBZM+cwvqoGknw0yWuT7EnyxtbaHXOY+uFeeXVVffs0/T+asbD9UJK/HN/RWrszya296s9Ns6dLkryzV/2TOewFAAAAAAAW3JzC+KpalORPk7wpyb4k39Zau3kuc1trX0nykV71j6rqzcfXrKp/k+R9vb7/s7W2Y5olfqFXvr2qfrOqVvbmX5OxU/ork9yf5ANz2Q8AAAAAACy0/jmOe2WSt/e+L05yQ1XNNPbh1trkK2V+NMkzk7w4ySeq6mCSRUmW9Po/nuSXplustfbJqvrFJL+WsdPxP1NVB5Ks6g15PMl3tNaOzPG3AAAAAADAgprrNTXjxy1NctEsnw2TJ7fWhjJ2z/zPZ+zamZbkSJIvJ/mxJG9rrR2b6eGttV/P2J3zn0jyRMZC/PuT/E6Sa1trt8/xdwAAAAAAwIKb08n41toXksx4FH6OaxzN2JU07zvZ2BnmfzbJZ09nDwAAAAAAcDbM+QWuAAAAAADAqRHGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMeE8QAAAAAA0DFhPAAAAAAAdEwYDwAAAAAAHRPGAwAAAABAx4TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdE8YDAAAAAEDHhPEAAAAAANAxYTwAAAAAAHRMGA8AAAAAAB0TxgMAAAAAQMfmHMZX1cqqeltV/VpVfaqqHq+q1vtcfZK5bQ6f7zrJGtdV1X+vqq1VdbiqtlTVB6vqirn+BgAAAAAAOBv65zH2W5P85Wk+7/EkIzP0HZ5pUlX9QJIPZmy/LclQksuS/HCS762qt7XWPneaewMAAAAAgE7MJ4xPkh1JbkryT0keTfL+ec5/SWvtwflMqKrnJflAxvb6J0l+urW2s6ou77W/IclfVNWzWms757kfAAAAAADo3HzC+I+11m44Xqmqp5/x3UzvV5Msztg/AvxAa20kSVprD1XVdya5M2On5H8+yb9boD0BAAAAAMCczfnO+OMh+EKqqjVJ3tyr/pfJe2it7U/y+73qO6uqFnJ/AAAAAAAwF3MO48+Sb87Yqfgk+cwMYz7dKzcmeXbnOwIAAAAAgHla6DD+I1X1RFUdqapHquovquots4y/pldua63tmmHMndOMBwAAAACAp4yFDuNfkmRRkuEklyb5ziQfr6qPVNXANOM39sqtMy3YWjuUZM+k8QAAAAAA8JSxUGH8h5K8Kcna1tqq1tpgxq6U+cNe/zuS/N4081b0ykMnWf9grxycrrOqrq+qm6rqpp07d85v5wAAAAAAcJoWJIxvrb2rtfbp1tqecW13t9Z+KMn/3mv6kaq6etLU4y9kbaf5/Pe31q5rrV23YcOG01kKAAAAAADm7anwAtdfydjJ90oy+f74/b1y+UnWON6/f9ZRAAAAAABwFpz1ML61diDJ7b3qMyZ1H78r/pKZ5lfVsiRretXHzuzuAAAAAADg9J31ML5nputo7uyVF1fVBTPMvWaa8QAAAAAA8JRx1sP4qlqR5Dm96oOTur+UZLj3/fUzLPHGXvlYkrvO6OYAAAAAAOAM6DyMr6o6yZBfTLIsY6fiPzm+o7W2d1zbz1TVhP32gvwf71X/tLV2Wi96BQAAAACALswrjK+q9cc/SdaO61ozvm9SaP6RqvrPVXVdVQ2MW+uqqvpAkvf0mj7UWpvumplfytjp+Jcm+aPes1NVm5J8NMmmJHuSvG8+vwUAAAAAABZK/zzH75yh/cZJ9c158sqZDUm+K8kvJBmpqr1JliRZMW78n+fJE+4TtNZuraofTfLBJN+f5PuqaijJ6t6QA0ne3lqbaW8AAAAAAHBWLcSd8e9N8rtJ/inJjoyF8H1JHkjy4ST/orX2jtbakZkWaK19KMnLk3wkyfaMXWvzcJI/SPKC1trnOv0FAAAAAABwGuZ1Mr61drL736eb85kkn5nvvGnWuSnJ95zuOgAAAAAAsNAW4mQ8AAAAAACc14TxAAAAAADQMWE8AAAAAAB0TBgPAAAAAAAdm9cLXAEAAIAz790fue1sb4Fz3O9893PP9hYAOAkn4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY3MO46tqZVW9rap+rao+VVWPV1Xrfa6ew/yqquur6saq2lNV+6rqK1X1c1U1MIf511XVf6+qrVV1uKq2VNUHq+qKuf4GAAAAAAA4G/rnMfZbk/zlqTykqhYnuSHJm3tNR5OMJHlB7/OOqnpda23/DPN/IMkHe/ttSYaSXJbkh5N8b1W9rbX2uVPZGwAAAAAAdG2+19TsSPLJJL+S5Pp5zPv1jAXxh5O8K8nyJCuSvDXJ7iQvSfJfp5tYVc9L8oGMBfF/kuSi1tqaJE9P8v/11vmLqtowz98CAAAAAAALYj4n4z/WWrvheKWqnj6XSVV1cZKf7FXf01r70Ljuj1fVD2Xs1Pw7q+p9rbWvTlriV5MsTnJTkh9orY0kSWvtoar6ziR3ZuyU/M8n+Xfz+D0AAJB3f+S2s70FznG/893PPdtbAADgHDDnk/HHQ/BT8PYkS5LsTfL+adb9qyRfT1JJ/tX4vqpakyevtvkvk/fQu9bm93vVd1ZVneIeAQAAAACgM/O9puZUvLZX/m1r7fAMYz7TK183qf2bM3YqfvyYyT7dKzcmefYp7RAAAAAAADo0n2tqTtU1vfKOWcbc2SufXVXVWmuT5m5rre06ydzj4++cYRwAAADwFLB4UeWKDSty+brluXzdsly+bnkGl06MKH7543dn98HhWddZv2Ig33LlBbn6osGsXb44VZW9h4Zzz44D+bv7duWRPTOdCUwuXbM0my9YnssvGNvDhSuXpG/cf7j/1B3b86k7dpzeDwWAcRYijN/YK7fOMuZ432Dvs2+uc1trh6pqT5I148YDAAAAT1GXr1ue/+VVm09rjZdvXpu3v/CSDPRP/E//F65ckgtXLsnLN6/Np+/aMWOg/iOvvDwXrBg4rT0AwHwsxDU1K3rloVnGHBz3fXCec8fPH5yus6qur6qbquqmnTt3nmQpAAAA4KnsBU9ble+57tIpQfx4fX2Vb3vORXnts9Yv4M4AYGYLcTL+uHbyIVMc//9hpzL3yQe39v70Xh573XXXndZaAAAAwOnbc3A4D+4+mC27D2bPwWP5Ny+7bE7zlvT35btedMmEK2W+tn1/Pn7bthwbbfnWqzbkusvXnOh7y7UX5ZZH9uaJSVfeHDk2mnt3HshDuw7mod0H8/qrN2TTuuVn5scBwDQWIow/kLErZGb7E2183/5pvp/sT8Pj/ftnHQUAAACcdffuPJD/9PG7T9TXLV8857kv3rQ6q5Y+Of7Q0ZH8P3//UA4PjyZJ/ts/PpzL1y3LhpVLkiQD/X15xTPW5RO3b5+wzm98+p4J9W+54oJ5/w4AmI+FuKbm+H3vl8wy5njf/kwM1E86t6qWZSzsT5LHTmWDAAAAwLnheZeunlD/2vb9J4L4JBltyW1bhyaMef7TVi3I3gBgNgsRxt/ZK58zy5hreuVdrbXx18gcn3txVc30T9TXjPt+5wxjAAAAgG8Al61dNqH+2NDhKWO2DR2ZUL9wcMms98sDwEJYiD+JPt8rv6Wqls4w5g298q8ntX8pyfFL3V4/w9w39srHktx1SjsEAAAAnvIWL6qsXDrxxt39R45NGbfv8MS2vr7K2mVzvwoHALqwEGH8R5McydhVMj8yubOq3prkqoy9pPXD4/taa3uTfLJX/Zmq6ps0d0WSH+9V/3TSqXoAAADgG8iyxYumtA2PTI0ChkdGp7QtW+xkPABn17z+JKqq9cc/SdaO61ozvm98aN5a25bkt3vV36yq76+qRb313pzkD3t9H26tfXWax/5Sxk7HvzTJH/WenaralLGgf1OSPUneN5/fAgAAAAAAC6X/5EMm2DlD+42T6puTPDiu/r8luTbJm5P8cZIPVNVIkuW9/n/KkyfcJ2it3VpVP5rkg0m+P8n3VdVQkuNvbDmQ5O2ttZn2BgAAAHwDODQ8MqVt8aKp5wynazs0PPW0PAAspAX5P1qtteEkb81Y4P7ljF1b05LckuQ9Sb65tbZvlvkfSvLyJB9Jsj3JsiQPJ/mDJC9orX2u0x8AAAAAnHXDI23KffArl0y9umbyvfKjoy1PHBqeMg4AFtK8Tsa31upUH9RaG03yX3ufU5l/U5LvOdXnAwAAAOe+h584lGs2rjxRv3j10iljNq6a2LZj/5EcPeZkPABnl7eXAAAAAOeMrz66d0L96osGJ7ycta+S5166asKYWx8ZWpC9AcBs5ntnPAAAAMBp6atkzbLFJ+prli+eMmZy255Dwxltyc1b9ubN116UVUvH+pcuXpQfesXl+fht23JstOX1V2/I+sGBE/OOHhvNjffvnrL+yqX9Wdz35AUAk++ZX7Z4UdaN28Oh4RH3zgNwWoTxAAAAwIJas2xxfvnbr551zE+97pkT6r/88buz++BwDh8bzZ/fvDXvevmm9NVYmH7VRYO56qIrpl3nE7dvz+6DU++Lf9fLLsuVFw7O+PzXPGt9XvOs9Sfqn7pjez51x45Z9wwAs3FNDQAAAHBOueWRofyPf340wyMzn1QfHW351B3b8/mvP76AOwOAmTkZDwAAAJxzbrz/idyz/UBedeUFufriwaxZtjh9Vdl7aDj37DyQL927K4/sOXy2twkAJwjjAQAAgAW1++Bw3v2R2057nccPHM1Hb3nslOb+7hceOO3nA8B8uKYGAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAhRfy2AAAgAElEQVQAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI4J4wEAAAAAoGPCeAAAAAAA6JgwHgAAAAAAOiaMBwAAAACAjgnjAQAAAACgY8J4AAAAAADomDAeAAAAAAA6JowHAAAAAICOCeMBAAAAAKBjwngAAAAAAOiYMB4AAAAAADomjAcAAAAAgI71n+0NAAAAC+OlT1+T73vpZfOac8+O/fndLzwwoW1Jf19evnltrr1kVTauXppli/ty+Nhodu47mtsfG8oX79mVw8dGz+TWAQDgnCeMBwAA5uzp65blXS/flHUrBia0Dy7qy+CS/mxevzyvvuKCfPDvHsqDuw+dpV0CAMBTj2tqAACAGe0/MnLi+/rBgfz4qzZPCeInW7Vscf7tqzdnw+Ds4wAA4HziZDwAAJwnbnlkKPfuuHvG/gtXLsm/ffXmCW1/d9+uE9/fcu1FWT6w6ET94NGRfPSWrdmy+1DWLl+c73j+xlyyemmSZOniRfmeF1+a3/ubiVfcAADA+UoYDwAA54mjx0aze5a73F931YYJ9a17D+frOw4kSaqSay9ZNaH/M3ftyD8+uCdJsm3oSA4ceSQ/+4YrTvQ/66LBXLJ6abbuPXymfgIAAJyzXFMDAABk6eK+vPTpaya0/c09j5/4PrikP0v6J/714bFJIftjQ1ND9+deumpKGwAAnI+E8QAAQF62eW2WLn7yCpoDR47lpof2nKgfG5l6on7y3fHrlk+9I/6ytUvP4C4BAODcJYwHAIDzXCV51RUXTGj7+/t3Z3iknagfGh7Njn1HJox5w9Ub8sz1y7N4UeXClQN5x4sumbL26mWLO9kzAACca9wZDwAA57nnXLIy6weXnKiPjLZ88d5dU8Z94euP57tffOmJ+roVA/nJ1z1z1rWXjTttDwAA5zMn4wEA4Dz36ivXT6jf8sje7Dl0bMq4L923Ozfev3vWtUZH24T68DTX2wAAwPlIGA8AAOexi1ctyVUXDU5o+5t7pp6KP+7DNz2aP/j7h3LvzgMT7pHfc3A4n7pje/7+gYlh/cGjI2d2wwAAcI5yTQ0AAJzHJp+Kf2jXwTy46+Csc255ZCi3PDKURX2VFQOLcmy0nQjd3/2azRPGbt17+MxuGAAAzlHCeAAAOE8tH1iU6y5fM6FttlPxk42MtgwdfvI6m3XLF2fz+hUTxty388DpbRIAAL5BuKYGAADOU694xtos6X/yrwR7Dw3n5of3zDpncMn0L2Rd1Ff5nusuzaK+OtE2dGg4t2/dd2Y2CwAA5zgn4wEA4DxUlXzzMy+Y0PbFe3dl0vtXp/iFNz0rd23bl9seHcq2oSMZbcmla5bmW69an03rlk8Y+z/v3JFjJ1sQAADOE8J4AAA4Dz3/0lVZt2LgRH14ZDR/f//uWWaMWdxXecnla/OSy9fOOu7mLXvypftOvh4AAJwvhPEAAHAeetWkF7f+85Y92X9k5LTXHRlt+Zt7Hs9ffXXbaa8FAADfSITxAABwnrl0zdJcsWHii1a/8PW5vbj1wzc9mqsvHsymdcuzckl/li3uy5Fjo3ni4HC+tmN/brz/iezYd6SLbQMAwDlNGA8AAOeZR/cczrs/ctspzb354b25+eG9Z3hHAADwja/vbG8AAAAAAAC+0QnjAQAAAACgY8J4AAAAAADomDAeAACA/7+9Ow+TqyoTP/59u7N3SEL2ABIgLEkA2UQFgbC4gIKACxBZhBlHB2dA/I0j4oLoMAPo6GhwG3x8FBRBGDdABFQWkYhAZBMICBK2SFY6Iekk3UnO7497i67eqjvprqqu7u/nee5z7z33nFtvoRxuvX3uOZIkSSozk/GSJEmSJEmSJJWZyXhJkiRJkiRJksrMZLwkSZIkSZIkSWVmMl6SJEmSJEmSpDIzGS9JkiRJkiRJUpmZjJckSZIkSZIkqcxMxkuSJEmSJEmSVGYm4yVJkiRJkiRJKjOT8ZIkSZIkSZIklZnJeEmSJEmSJEmSysxkvCRJkiRJkiRJZVaRZHxEnBkRqZttTYn2EREfjog/RkRjRLwaEQ9GxL9HxLBKfAdJkiRJkiRJkrbWkAp/XguwsotrazsrjIihwC+Ad+ZFzcAmYN98e39EHJlS6jKZL0mSJEmSJElSNVV6mpr5KaWpXWwzumhzMVkifj1wJjAKaACOI0vsHwj8bwVilyRJkiRJkiRpq/TrOeMjYirwsfz0/JTSlSmlTSlzE/AP+bW5EfH66kQpSZIkSZIkSVJp/ToZD7wXGA6sAq5ofzGl9EvgKSCAD1Q2NEmSJEmSJEmSeqa/J+OPyPe/Tymt76LObfn+yArEI0mSJEmSJEnSFqv0Aq57RsRjwC7ARuA54DfAvJTSs53Un53vHytxz8fz/ayIiJRS6rNoJUmSJEmSpEFg3rfPrXYIGghOuqPaEfRrlR4ZPxGYBTQBI4A9gfOAxyKis2lmpuX7xSXuWbg2Ot8kSZIkSZIkSepXKpWMXwx8HtgLGJFSmkCWOH8X2cj2kcBVEXFYu3YN+X5diXs3FR13moyPiA9HxAMR8cCyZcu2Jn5JkiRJkiRJkrZaRZLxKaXbUkpfTCk9llJqzss2pJRuBg4GngbqgUu7ukUvP/+KlNIbUkpvmDRpUm9uJUmSJEmSJEnSFqv6Aq4ppVXAf+Wnb46I4mz52nw/qsQtiq+t6cvYJEmSJEmSJEnqC1VPxuf+lO8D2KmovDAf/HYl2haurcFkvCRJkiRJkiSpH+ovyfgoOi6ekubxfL9nibaz8/0TKaVeTWcjSZIkSZIkSVI59Jdk/BuLjp8rOr4j3x8aESO6aPu2fP+7Po9KkiRJkiRJkqQ+UPZkfEREN9fHAJ/KT+9LKS0ruvwzYAMwDvhQJ22PA/YgG01/TZ8ELEmSJEmSJElSH6vEyPjpEXFvRPxjROxYKIyIYRFxNHAPsDuwGbiguGFK6WXg6/nplyLi9Iioz9u/E/h+fu2alNIj5f4ikiRJkiRJkiRtjSEV+pw35RsRsR5YC4wBhubXm4B/Tind3knbzwJ7Ae8ErgK+GxGbgFH59fuBfy5f6JIkSZIkSZIk9U4lRsYvAc4FrgOeJEu8j833DwCXAbNTSj/srHFKqQU4jizhfi/ZtDUJeAg4HzgkpfRqmb+DJEmSJEmSJElbrewj41NK64DL821r77EZ+N98kyRJkiRJkiSpplRqmhpJkjo497pHqx2Caty8k/audgiSJEmSJPVIJaapkSRJkiRJkiRpUDMZL0mSJEmSJElSmZmMlyRJkiRJkiSpzEzGS5IkSZIkSZJUZi7gKkmSJElSlc379rnVDkG17qQ7qh2BJKkbjoyXJEmSJEmSJKnMTMZLkiRJkiRJklRmJuMlSZIkSZIkSSozk/GSJEmSJEmSJJWZyXhJkiRJkiRJksrMZLwkSZIkSZIkSWVmMl6SJEmSJEmSpDIzGS9JkiRJkiRJUpmZjJckSZIkSZIkqcyGVDsASZIkSZIkSaq4D34QzjyzZ3UXLIBPfKJt2eTJsM8+MHMmzJoFM2bAsGGt1x96CD7+8T4LV7XPZLwkSZIkSZIkbamzzoKjj652FKohTlMjSZIkSZIkSVKZOTJekiRJkiRJks45B5Yt6/xac3PHso0b4a9/hYULs23PPeGd7yxvjKppJuMlSZIkSZIkadkyWLKk5/W/8pW255Mm9W08GnBMxkuSVEU7TRjFeUfsQl1dtCm/6KaFrGxqaVM2a+poZkxqYPr4UWw7aigNw+oZMbSelk2baVzXwkuN63n4xVU8/OJqUiW/hCRJkiQNBP/5nzBlCgwfDqtXZ6Pe77wTfvtb2LSp2tFpADAZL0lSlQytD049cIcOifiunHTA9kxoGNahvL6unqlD65k6ZgQH7DiOZ5c3ccUfFrG22YdFSZIkSeqxGTNajydMyLY3vxlOPBE++1lYvrx6sWlAcAFXSZKq5Ni9pzJlzPA+v+/OE0fxvv236/P7SpIkSdKgtMcecMklMMRxzeod/x8kSVIV7DJxFHN2nQBAy6bNDK3v/u/jK9c28/jfX+XppWtpXNfCmg0baRhWz6xp2/C2mZMYUnSPfbYfw5C6YONmJ6yRJEmSpC79/e9w113w8MOweDGMGpUtxHr66TB2bGu9XXfNFme94YbqxaqaZzJekqQKaz89zc1/WcLx+0zrtt3ldz7boWwZsGjlOiKCo2dPfq18SH0dI4fW8+qGjX0WtyRJUlnNng3z5kF9fdvyU07puKDiuHGw774wcybMmgW77QYjR7Zef/llmDu3/DFLqm033ABXXQWp3SCmhQvhgQfgiitgWNFUoXPmmIxXr5iMlySpwo5//TQmbZNNT7Pg+UYefGFVj5LxpbSfdX7Dxs0m4iVJUu0YNgzOP79jIr4rxx8PZ55Z1pAkDQKvvNL1teeeg/nz4fDDW8t22aXsIWlgMxkvSVIF7TapgUN2HQ/AqnUtXP/nxYwYsmVLuDQMq2f4kDrq64LRw4cwc+pojtpjYps69zyzos9iliRJKrsPfQh23LHaUUhSWy+/3Pa8oaE6cWjAMBkvSVKFDBtSxwcO3IG6yMax/2TBSzQ1b9riZPwJ+0zjTTtv2+m1TZsTf/zbSm545OVOr0uSJPU7e+0F73lPdtzc3HZKiK5s3gyLFmVTSTzxBEyeDKeeWtYwJQ1C09q9wbxmTXXi0ICxZb/+JUnSVjthn6lMGJ39uPzTs6/wl8Wv9vln3P30Cm5+bAmu2ypJkmrC8OHwqU+1Tk/z/e/3rN0PfwhnnQWXXZbN3/zCC+WLUdLAtNNOcN55bRdpbX/9oIPalv31r+WOSgOcI+MlSaqAPaaM5pAZEwB4pamZnz20uCyfc/juE3nDjuP47j3P8eyKprJ8hiRJUp/5yEdg++2z49tvhzvuyMokqdzq67P1J44+Out/7r0Xnn8+eztn773h9NM7vqnzm9+0PW9ogNGjW8+LjyFrP2VK63lzc+l56jXgmYyXJKkCTthn6mvH19z/EutaNm/1va6+/0Wuvv9FhtQFY0YOYc9p23DM7CmMHpH9Z330iCGcddCOfPHmJ9noEHlJktRf7btvlggDWLECvv51GDmyujFJGnyGD4djjsm2UhYsgN/9rm3Z+95XejHp2bPh2mtbzx96CD7+8a0OVbXPZLwkSRUwYmj9a8cfnbNzt/UvOnYmkE1nc/X9L3ZaZ+PmxMq1Ldz99EqWvtrMvxTdd9yoocyetg2PvLS6l5FLkiSVwYgR8MlPQl0+e+5XvwqrV5uMl1Q5zc2wcSMM6UF69Pe/h0svheRgJ/WOyXhJkgaA51Z2nJJm4ugeLH4mSZJUDWef3bow4i23wPz51Y1H0uDzwgvw3vfCIYfAfvvBLrtki0GPHAnr18Py5fD443DrrfDww9WOVgOEyXhJkmrAtLHDWbJ6Q5cLs86cMrpDWcumrZ8KR5IkqWwOOADe/e7seOlS+MY3qhuPpMFr9Wq4+eZs2xpXXpltUg+ZjJckqQK+fvsz1EV0em3cqKGcd+SMNmVfu/0ZGpta2LAxS6gfufskZk4dzUMvruLppWtZtqaZlk2bGTNiCLOnbcNhu03scN9nlrmAqyRJ6ofOPrv1+L//G9aurV4skiRVkMn4Qerc6x6tdggVN7Q+2HVSA9PHj2L6+JFMHz/qtcUOCy66aSErm1p6fM+3zpzIu18/rU3ZirXNfOFXT/ZJzP3ZvJP2rnYIUk1pXLdxy+o3tXToj8aOHMqc3SYyp5PEe3t/fr6RxavWb9FnSoPVvG+fW+0QVOtOuqPaEUi1paGh9fhLX+q+fmHxw1tugcsuK09MkiRVgMl4DRrTx4/i7MO6XzSxp6aOGc4xe07ps/tJUl/YnBL3L2rkJwteqnYokiRJkiSpiMl4aStEwKkH7sDQ+rpqhyJpkPj1Y0t4dkUTu01uYOqY4YwePoSGYfVsTrC+ZRNL1zTz7PK1LHh+lSPiJUmSJEnqh0zGa1BpbGph0comnl/ZRGPTRs548+u26j5vmzmJ6RNGAdkCiSblJfXGyqaWbqcPW9nUwvy/rWT+31ZWKCpJkqQyOeccqK/v/NqkSXD55R3rL1sG69a1lo0YAWPHtp4XH0N2/ylFbzJv2gTLl/cubkmSeslkvAaNp5et5cKbFr52Pn7U0K26z7Sxwzl69mQgG416x1PLna5GkiTVlgkTYL/9YP/9YdddYfLkbA7nlhZ45RV46in4zW9g/vye3/OUU+AjH2lb9vLLMHdu38YuqfZtaVJ82TJYsqRt2Zw58KlPdd1m0qTWuebB/kiS1C+YjJe2QF3AaW98HUPykfC/fORlWjZtrnJUkiRJW2DMGPi//+v82pAhMHIkbLcdHH443H8/XHghrO9m+qvp0+Gss/o8VEmSJGkgcW4NaQu8Y/ZkXrftSAAWvvwq9zzjdBGSJGkAO/BA+PSnS9epq4Pzz4dhwyoTkyRJklSjHBkv9dD240bwtpmTAGhq3sSP73+pyhFJkiT1QksL3H03/OEP8Oyz2fzLBx8MJ5/cNrF+6KGw885Znc7MnQuzZmXHzc0m5SX1zpIlcMQR3de79dZskySphpiMl3qgvi447Y07vDY9zc8eWkzjupYqRyVJkrQVUoLbboPvfrfjvM0LF0JjY7ZYYrG99+48Gb/zznDGGdlxUxNcfz188IPliVuSJEmqcU5TI/XA0bMns/24bHqavyxezX2LGqsckSRJ0lZ69VW45JKuF1D83e86lo0a1bGsvj5bPLEwEv4734G//73v4pQkSZIGGJPxUjdGD6/nrfn0NGs3bOTaB5yeRpIkDTJLl3YsO+002H337Pj+++HGGysbkyRJklRjnKZG6saw+jrq6wKAhuFDuPjds0rWn9AwjHkn7Q3Aj+57wVH0kiSptrz97W3P162DP/2pbdmMGXDqqdnxmjXw5S9XJjZJkiSphjkyXpIkSVJmjz3grLPalv3kJ7B2bev5kCFwwQUwdGh2/o1vwLJllYtRkiRJqlEm4yVJkiTBXntlI9xHjmwtu/tuuOqqtvXOOCMbGQ8wfz7cemvlYpQkSZJqmNPUaNCoCxg3cuhr5+NGDe1Qp31Z47oWGte1cNFNC7u8776vG8sJ+0xrbdPUwtdufwaANc2behu2JElS+b3lLfC5z8Hw4a1l99wDX/wipNRaNnYszJ2bHa9aBV/5SmXjlCRJkmqYyXgNGuNGDuWiY2eWrHPekTPanF9000JWNrWwsqmlyzZrNmxsc74ppZL1JUmS+pVjj4XzzoP6+tay226Dyy6DzZvb1h0xIpumBrLE/E9/WvreU6fCHXdkx5de6ih6SZIkDWom4yVJkqTB6owzOp8j/jvfqU48kiRJ0gBmMl6SJEkabCLgYx+D449vLdu8Ga64IkvGS5IkSepzJuM1aKxsauHc6x7t8/vet6iR+xY19vl9JUmSyqK+Hi68EA47rG35lVfCnXfClCkd26xbB6tXw7JlcMopXd97zhw4++zW82XL4JxzsuNVq3oduiRJklTLTMZLkiRJg8nEiR0T8ZBNV9N+ypqCW25pnUN+yZKu790+4b5pU+n6kiRJ0iBSV+0AJEmSJEmSJEka6EzGS5IkSZIkSZJUZk5To0FjaH2w66QGpo8fxfTxI5k+fhSjR7T9V+Cimxaysqml5H0mNgzj0N0mMHPKaLYdNZSIYNW6Fv66dC33PLOCFxvXl/NrSJIk9c6SJXDEEeW59623ZpskSZKkDkzGa9CYPn4UZx+2c6/ucdDO2/Le/bZj2JC2L5VM3mY4k7cZzkE7b8utTyzl148t7dXnSJIkSZIkSRpYnKZG6qF9dxjDyW/YvkMivlhdXXDMnlM4YveJFYxMkiRJkiRJUn/nyPhBat63z612CJW3zz6w12fgiSeybfly+Mxn2lS56EcXZa9utzdyJPzoRxDRWrZgAXzve9DcDHPnwlFHvXbpxFnjOfHSj3d+r4HipDuqHYEkSZIkSZJUM0zGa/B4+GE46aTW8ylTet72qKNg/PjW8zVr4POfh7Vrs/P/+i+YORO23z47Hz4cjj02S9ZLkiRJkiRJGvRMxks9ccghbc8XLGhNxANs3gz33NM22X/ooSbjJXXQV4tJS5IkSZKk2mIyXuqJ3Xdve75oUcc67ct22AFGjID168sVlaQa1BeLSUuSJEmSpNrjAq5Sd4YPh223bVvW2Nix3iuvtD2vr4fJk8sXlyRJkiRJkqSa4ch4qTsNDR3LNmzoWNbc3LO2kga9xqYWFq1s4vmVTTQ2beSMN7+u2iFJkiRJkqQyMxkvbY2IakcgqUY9vWwtF9608LXz8aOGVjEaSZIkSZJUKU5TI3WneKHWgmHDelbWWVtJkiRJkiRJg47JeKk7GzZ0nA++/RzyAOPHtz3ftAmWLi1fXJIkSZIkSZJqhsl4qSeeeqrt+U47dazTvuzFF2H9+nJFJEmSJEmSJKmG1FQyPiKmRsTXI+KZiFgfEUsi4saIOKrasWmA+8Mf2p4fcEDbxVnr6+Etb2lb5+67yx+XJEmSJEmSpJpQM8n4iHg98BfgXGAXYAMwETgW+E1EfKqK4akW1NXBlCmt26RJHetMmtS2Tl3+r8jtt8PKla31Ghrgi1+EWbNgxgy44ALYbrvW6xs2wE03lff7SJIkSZIkSaoZQ6odQE9ExEjgBmAC8CBwekrpsYgYA1wI/BtwSUT8OaV0WxVDVX82aRJce23pOpdf3vb8lFNgyRJoaoJ58+DCC1sT9PvvD9/6Vuf3+d73snaSJEmSJEmSRO2MjP8IMB1YAxyXUnoMIKW0OqX0CeAXeb1LqhSfBoO77oKvfhWam7uus2kTXHklXH995eKSJEmSJEmS1O/VxMh44NR8/+OU0kudXP8ycAKwf0TMTCktrFxoGlR+9St48EE48UQ48MBstH1dHaxYkZX/8pfw9NPVjlKSJEmSJElSP9Pvk/ERsQ1wQH56axfV7gVWAWOBIwGT8epoyRI44oje32fxYvjmN3t/H0mSJEmSJEmDRr9PxgOzgMiPH+usQkppc0Q8CbwRmF2pwCRJvTPv2+dWO4TKq6tru4D0pElwbNv1Ki669ZuwbFlrwbJlsHlzhQKsMSfdUe0IJEmSJEnqkVpIxk8rOl5col7h2rQSdSRJqq7eLCYtSZIkSZJqVi0k4xuKjteVqNeU70e3vxARHwY+nJ+uyUfRS92ZCCyvdhD9VkT3dSR1sPvuuw97EvbekjZ7PPjgo0899VSJ1aMHMfsiqRJ8JuqOfZFUCfZF3bEvkirBvqg79kUA07u6UAvJ+F7/L5hSugK4og9i0SASEQ+klN5Q7TgkDTg7Ac9uSYMnn3zy3cCicgQjSd3xmUhSf2BfJKk/sC9Sb9VVO4AeWFN0PLJEvVGd1Jckqb9ZRPaH5m63iFiQHy+qRqCSJEmSJKnv1EIyvnie+O1K1Ctc+3sZY5EkSZIkSZIkaYvVQjJ+IZDy4z07qxARdcAe+enjlQhKg4JTG0mqNvshSf2BfZGk/sC+SFJ/YF+kXomUUve1qiwi7gMOBL6TUjq7k+sHAfPz05kpJRdolSRJkiRJkiT1G7UwMh7gx/n+1IiY1sn1T+T7BSbiJUmSJEmSJEn9Ta0k4/8XeA7YBrgpImYDRMQ2EfEl4D15vU9XKT71UxFxZkSkiLiz2rFIUq2IiN0i4tqIeDkiNuX96A+qHZek6oqIH+T9wUVdXLfvkNQn8v4jRcROW9H2zrztmVvR1t+PkqSyqolkfEppHXA8sALYH3gsIlYBjcC/k80pf0FK6bbqRalKiYgTih7O/N9cUlkUJZ2Kt5aIWBERT0fELyLi0xGxc7Vj7UsRMR64GzgZmAK8AiwBVlUzLkl9p4v+ravtvB7e075DGkCK+ok7+7JuLcsT9RdFxL7VjkVSae2edVoiYnI39U9o9/xzZoVC1SA0pNoB9FRK6eGI2Au4ADgW2J4sOX8f8D8ppd9VMz5V1AeLjo+KiB1SSi92UXcV8CTwfPnDkjRAtQAr8+MAxgDjgRlkfyi+OCJ+Cnw0pbSsOiH2qblkibSngMNTSn+vcjySyqe4f+vK2h7ey75D0kBQ6vfjmcAcYBHwUOVCktRLQ4APAF8rUeeMCsUi1U4yHiCl9DLwsXzTIBQRE4B3AU3AL8g61NOASzurn1L6OfDzigUoaSCan1I6vLggIsYBbyb7UfZ+4H3AQRHxppTSSxWPsG/tme9vNJkmDXgd+rdesO+QVPP8/SgNOM8DO5Il2ztNxudv970LWAM0kw28ksqmJqapkYp8ABgK/JJsLQFoO1JeksoupdSYUrolpXQK2YPberI3tn5a3cj6xMh8v6aqUUiqNfYdkiSpv/kj8AywX0Ts2UWdU4BhZL/l1lUqMA1eJuNVawqJ96vJ5iV9HpgZEW/srHKpBXgiYlF+7fCIGB8RX42IZyNiQ0S8FBHfjYhpXdz3tbZdBdrVokPFi59FxPCI+ExEPBIRr+bl43ryD0JS/5BSugX4RH76pog4DiAiTs//nb6vfZuImBQRm/PrN3dyfWZ+bX1EDC8qHx0R74+IqyPiLxHRGBHr8jnsr4iI3bqKs7hPiohZEXFlRLyQz6H4i8JiZ2Sj/QE+XzxvYi/+EUkawOw7JHUnIo6IiJ9Ftrhzc77/eUQc2cP2e0Xr4tDrI2JhRHyu+BmpRNttI+J/IuJvedsX82emrn7ndfj9WCgjm6IG4Pvt5pZe1JPvIalqfpjvu5qKplB+VXc3iojjIuKXRf3Z0oi4MSLe0UX9Nn1K3v6O/Hfcmoi4NyLmbukXUm0zGa+akf8V8wCytQJuSykl4Jr8cm9Gx+8A/Bn4ODCZbEHg7YAPAfMjYtte3LuUEcDvgYuBmcCmMn2OpL0ys78AAAtnSURBVPL7LtlChZC9wQNwV77fPyK2aVf/MLL55wEOiYj6dtcLP/b+lFLaUFR+JnBd/hl7kv13vI5s/vp/Ah6MiLd2E+uhwANkD51jgY15+cr8O6zPz9fm54VNkjpj3yGpSxFxMXA7cCLZb621+f4E4HcRcUk3tzgYuJdsceiRZM9PewBfBO6MiNEl2k4A7gfOA6aSPfNsT/bM9HBEzOrh11hH1p+15OeradvPDYQ1g6SBrJCMPzUi2uRBI2J34E3AC8CdXd0gIoZGxI+AG4B3k62Tsw6YRLau5S0R8aVSQUTE5/L2h+VFDfln/zgiztvC76QaZjJetaSQcL8upVR4ELo6358SEcO28r6XA68AB6eUGoDRZIsyNgI7kS0aXA7/AuxO9krU6JTSuPzzerpQmqR+IqXUTPZDE7JkNyml54HngHrgLe2aFB7AXgW2Afbr4vpd7cpXkPVZBwPjUkpjyP6wN4usP2wge5hrKBHut8h+mO6dtx8F/FtK6T0ppanAT/J6/51SmlrYStxP0iBm3yGpKxFxCvCZ/PQbwOSU0rZkyavL8/JPRcRpJW7zLeBx4PUppbFkz01nkSXB3gx8tUTbz+X1jyP7vTUaOBx4No/h+ogY2t33SCn9JO/P5udFHyvu51JKB3Z3D0nVk1L6G3AP2R/j2r+RUxgVf3VKaXOJ23wJOJVsAecPANsU9UkfIfsj3b+XGOW+D/B5sn5pQp7/mQr8X379ksjmrtcgYDJeNSEfNVp4SPtxoTyl9CjwKNkCG8dt5e03AG9NKf0xv+fGlNINZCPWIVuYsRxGAyfnD3fN+Wc/V/SHBkm15dF8v33RD7tCMn1Ou7qF8290c71NMj6ldE1K6dyU0h9TSqvyspRSWgicDvyW7MdlqX5rKXBMSukvRe2fKf3VJA1QB+evWZfaxlQ7SElV1W0/QTZqvY2ICOA/8tNrU0rnpJSWA6SUVqSUzqX1LeeL249WLbIBODr/3UdKqTml9APgo/n1f4yI6V20HQO8L6V0UyHJllK6CziGbJHGPTuLXdKAVJiC5rWpavJ+6rR21zvIpwI9l2zA5lH5b7I1ACmlNSmlK8jeuIHWP0C2Nw74fErp4pRSY952CdlvuGVkA6yO3ZovptpjMl614u3ANLJRpve0u1YYHb+1U9VckVJa0Un5L/L9zt2MMt1aj6SUbivDfSVVxytFx4VRDb/P968l2/Opr/YGngCu7+T6DLJRGy1kCw71SD5116/y0/Yj8Yt9I6XkwkSSAIaSvWZdavP3gjS49aSfGNFJu32BXfPjizu5DvCFfD8d6HQNMOA7KaWVnZRfBbxI1ked2EXbu1NKd7cvTCk9Seto1HINvJLUv1xHNqXee4ryO3PI+p8HUkpPlGh7Bllf84t8lH1nfkb2x8M9u1iTYj3wtfaFKaX1wK356V7dfgsNCD5cq1YUEu3X5AmnYteQzfN+TERM2op7399F+UtFx+VYVLXHSTZJNafQTxVGtr+h6KHvULL//t4FPEw2wuLQohFhhcT8/SmlpvY3jogdIuKyiFiQL/yzqWihxP/Jq21XIjb7HkkFd6WUoputsdpBSqqqbvsJ4MpO2u2f75ellB7r7MZ5UvyldvXbu7OLtpuBQqJ9i9rmXlvbp0QdSQNE/jxzI9m0nu/Ni3u6cOvB+f59Jd4QepHsj5cAr+vkHo+nlLqakrjQD5ZrvUL1Mybj1e9FxFiyOdyhaIqagnxe5ruBIbQunLglXu2sMP8LZUG3cwluBRf6kQaW4oenVwBSSk8Di8n6kIPya4Vk+535D8k/kP3B7/XtrrefL56ImEM2ov6TZD8ex5L1YYUFxFbnVUu9zWPfI0mSyq0wSOqlkrWyBFZx/fZKtS9cK0dbSQNPIel+ekSMJEvKt9A6ZVZXCiPdR9OztwlHdXKPTvNOuULuqRx5J/VDJuNVC06m9dXHRwojQIs3Whc73NqpaqphU7UDkNSn9s73L7Zb+6H9VDXtk+3t55Uv7AvtAMjnof8R2UPgb8n6vZEppXFFCyX+v0L1EnHa90iSpEoZXsZ7l3reKWdbSbXpFrL1s44E/pVsXYlfF9azKKGQO/1YD94ojJTSneX7ChoITMarFmxJgn2/iNi7+2q9tjHfdzY/YmE0v6RBIiKGAUflp+3nJn0t2Z4vhLgv8GRK6eVOrk8nm7dwEx3XxzgI2AFYCRyfUrq73Rs8kI3IkCRJqrbCm3g7dlNvh3b12ys19V5htGo52koaYFJKG4FryXKh/5kX/7AHTZfk+9nliEuDj8l49WsRsSut83PtSzYNRFfbjXm9SoyOL8yfukMX1w+sQAyS+o9/Aibnx1e3u1YY4f4m4G1APW2noPkzsIZspPvhhbKUUvtXGQv9zVOdzSWfe+uWhS1JklQWf873DRHR6eKsEbE72aL1xfXbm9NZYUQE2To8W9y23bWu2nZmc+Hjt6CNpP6lMFXNULKpRW8sUbegsObWcfnbylKvmIxXf1dIrD+cUno4pdTY1QZcn9c9NSLqyxzXo/n++PYX8gfD88v8+ZL6iYh4B/Dl/PSPKaVfFV9PKT1ONupqGK19w51F1wuj4CcA/5IXd5gvHliV73eLiA5v5UTE24Ejtu5bSJIk9amHgKfz4093UeeifL8IuK+LOmdHxLhOyk8jWyRxM/CzLtrOiYiD2xdGxG7A+/LT69tfL6GwNk9n8UiqASmlBWR9z1eA81JKG3rQ7EqyvmY74IJSFSPCRVjVLZPx6rfypPbp+WlXD1jFbiRbfGMq8I5yxZW7Lt+/KyLOj4gGgIjYiWzxjzeU+fMlVVFEjI2Id0TENcDNwEjgBVp/2LVXmLqm8NZM+2T7Xd1chyxh30SWtL8qIqblsYyMiH8Afgqs2NLvIkmS1NdSSgn4bH56fERcHhETACJiQkTMA+bm1z+bL2rfmRHALRGxV952aER8EPhOfv17KaXnu2i7GvhZRLwz/21JRBwK/JpsLvvHaP1d1xOP5fv3OC2pVLtSSl9IKX0ipXRV97UhpfQE8LX89AsR8c2I2KVwPSJGR8TbIuKHbNkf+DRImYxXf3Y42dzJkCWZSspHx9+en5Z1qpqU0q/J/kAQwKXA6oh4BXiWbLT8yeX8fEkVdXBEvFy0rSWbquoW4BSyfuA6YP+U0uIu7lGcXP9rJ/WKr28G/tD+BnkfVxiJ8X5gcUQ0kv3Q/B7Z6LMvbNlXkzTIte/fOtu+Xu0gJdWmlNJPaJ2X+V+BpRGxkmwBxXPy8ktTSu2n+Cv2UWBv4NH8uWcN8ANgFHAvrYvXd+Y/8vq/AtZGxKtk0wfOIHtr8aSUUssWfKUfAs3AIcDyiHgpIhZFRIfnNkkDzieBb+fHHwWeiYhCHmg1cBvZGzvlnqVBA4DJePVnhYT6Uymlx0rWbFVI2h/fxeuMfWku8BngSbIFXVvyz39TSum2Mn+2pMoZSrYw6hRgIrAB+BtwA1kfMCOldHJKaXmJe9zVxXHB/WSj3gEeyRPvHaSU5gHvoXWU/BBgIfB5svU12s8zL0mlFPdvXW2O/pS01VJKnyVb5P6XwHJgNNmbfDcAb00plZzyAZhPtu7OdWTPYIns99eFwOEppTUl2q4ge+vwa2QLMA4DFgPfBfbNpxLcku+ykGz9n1vIpg+cSjZ4rKt1xCQNECmlTSmlj5L9Me5HwHNkfcpI4Hng52Q5rBOqFqRqRmRvj0mSJEmSJEmSpHJxZLwkSZIkSZIkSWVmMl6SJEmSJEmSpDIzGS9JkiRJkiRJUpmZjJckSZIkSZIkqcxMxkuSJEmSJEmSVGYm4yVJkiRJkiRJKjOT8ZIkSZIkSZIklZnJeEmSJEmSJEmSysxkvCRJkiRJkiRJZWYyXpIkSZIkSZKkMjMZL0mSJEmSJElSmf1/gttWAfZIqT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('font', size=24)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=24)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=24)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=24)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=24)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=24)    # legend fontsize\n",
    "plt.rc('figure', titlesize=24)  # fontsize of the figure title\n",
    "\n",
    "y_pos = np.arange(5)\n",
    "\n",
    "r = all_genders.race.values\n",
    "m = all_genders.Male.values\n",
    "f = all_genders.Female.values\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(y_pos, f, width, color='#ff4040')\n",
    "ax.bar(y_pos, m, width,bottom=f, color='#6da9d2')\n",
    "\n",
    "ax.set_title('Number of characters by race and gender')\n",
    "\n",
    "for i, v in enumerate(m):\n",
    "    if i == 0:\n",
    "        ax.text(i - .05, 14, str(v), color='white', fontweight='bold')\n",
    "    else:\n",
    "        ax.text(i - .05, f[i] + v / 2, str(v), color='white', fontweight='bold')\n",
    "        \n",
    "for i, v in enumerate(f):\n",
    "    if i >= 2:\n",
    "        ax.text(i - .05, v  / 2, str(v), color='white', fontweight='bold')\n",
    "    elif i == 0:\n",
    "        ax.text(i - .05, 2, str(v), color='white', fontweight='bold')\n",
    "    else:\n",
    "        ax.text(i - .05, -1, str(v), color='white', fontweight='bold')\n",
    "plt.xticks(y_pos, r)\n",
    "ax.legend(labels=['Female', 'Male'])\n",
    "\n",
    "plt.savefig(os.path.join('race_gender.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>hair</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>Dark</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Golden</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Black</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Brown</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      hair  size\n",
       "26    Dark    29\n",
       "45  Golden    21\n",
       "5    Black    12\n",
       "16   Brown     6\n",
       "60    None     5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#same for hair color\n",
    "hair_counts = chars.groupby('hair').size().reset_index(name='size')\n",
    "hair_counts.sort_values('size', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>realm</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>Gondor</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>Númenor</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>Rohan</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Arthedain</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Arnor</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        realm  size\n",
       "22     Gondor    33\n",
       "47    Númenor    28\n",
       "56      Rohan    20\n",
       "7   Arthedain     9\n",
       "4       Arnor     7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#and realm...\n",
    "realm_counts = chars.groupby('realm').size().reset_index(name='size')\n",
    "realm_counts.sort_values('size', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Date for birth and death are not categorical. In order to get some insight, we are categorizing them based on the era*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeline_mappings = {\n",
    "    \"Third Age\" : \"TA\",\n",
    "    \"Second Age\" : \"SA\",\n",
    "    \"First Age\" : \"FA\",\n",
    "    \"Years of the Trees\" : \"YT\",\n",
    "    \"Fourth Age\" : \"FO\"\n",
    "}\n",
    "def birth_death(d, cname):\n",
    "    d[cname].replace(timeline_mappings, regex = True, inplace = True)\n",
    "    d.loc[d[cname].str.startswith(\"Late\", na=False), cname] = d[cname].str[-2:]\n",
    "    d.loc[d[cname].str.startswith(\"Mid\", na=False), cname] = d[cname].str[-2:]\n",
    "    d.loc[d[cname].str.startswith(\"Early\", na=False), cname] = d[cname].str[-2:]\n",
    "    d.loc[d[cname].str.startswith(\"Perhaps\", na=False), cname] = d[cname].str.replace(\"Perhaps ,\", \"\")\n",
    "    d.loc[d[cname].str.contains(\"Arda\", na=False), cname] = \"BA\"\n",
    "    era = cname + '_era'\n",
    "    d[era] = d[cname].str[:2]\n",
    "    d.loc[d[era] == 'Un', era] = 'Unknown'\n",
    "    if cname == 'death':\n",
    "        d.loc[d[era] == 'Im', era] = 'Immortal'\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>birth_era</th>\n",
       "      <th>death_era</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>FA</td>\n",
       "      <td>FA</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>SA</td>\n",
       "      <td>SA</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>TA</td>\n",
       "      <td>FO</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>BA</td>\n",
       "      <td>Immortal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birth_era death_era  size\n",
       "62        TA        TA   211\n",
       "21        FA        FA    60\n",
       "39        SA        SA    41\n",
       "55        TA        FO    37\n",
       "10        BA  Immortal    21"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping data based on birth and death era to get some insight about lifespan\n",
    "chars = birth_death(chars, 'birth')\n",
    "chars = birth_death(chars, 'death')\n",
    "birth_era_counts = chars.groupby(['birth_era', 'death_era']).size().reset_index(name='size')\n",
    "birth_era_counts.sort_values('size', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "birth_counts = chars.groupby(['birth_era']).size().reset_index(name='size')\n",
    "birth_counts.sort_values('size', ascending = False).head(10)\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "d = birth_counts.set_index('birth_era').to_dict()['size']\n",
    "\n",
    "ax.set_title('Number of birth by age')\n",
    "\n",
    "ages = ['BA', 'YT', 'FA', 'SA', 'TA', 'FO']\n",
    "data = [d[age] for age in ages]\n",
    "\n",
    "for i, v in enumerate(data):\n",
    "    if i == 3 or i == 5:\n",
    "        ax.text(i - 0.04, v - 14, str(v))\n",
    "    else:\n",
    "        ax.text(i - 0.04, v + 5, str(v))\n",
    "        \n",
    "        \n",
    "ax.plot(ages, data, 'go-')\n",
    "#plt.savefig(os.path.join('birth_age.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Playing & showing some facts from data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Battle of the Pelennor Fields is at March 15, 3019. Let's see who has died in this battle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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>name</th>\n",
       "      <th>birth</th>\n",
       "      <th>death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>Fastred (Pelennor Fields)</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>Horn</td>\n",
       "      <td>Possibly late ,TA</td>\n",
       "      <td>March 15 ,TA 3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>Dúnhere</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>226</td>\n",
       "      <td>Déorwine</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>Hirluin</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>266</td>\n",
       "      <td>Herubrand</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>269</td>\n",
       "      <td>Herefara</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>277</td>\n",
       "      <td>Harding</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>291</td>\n",
       "      <td>Halbarad</td>\n",
       "      <td>TA 2916</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>314</td>\n",
       "      <td>Denethor II</td>\n",
       "      <td>TA 2930</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379</td>\n",
       "      <td>Black Serpent</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>445</td>\n",
       "      <td>Guthláf</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>452</td>\n",
       "      <td>Grimbold</td>\n",
       "      <td>TA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>462</td>\n",
       "      <td>Gothmog (Lieutenant of Morgul)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>March 15, 3019 (film)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>545</td>\n",
       "      <td>Witch-king of Angmar</td>\n",
       "      <td>SA</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>791</td>\n",
       "      <td>Théoden</td>\n",
       "      <td>TA 2948 ,Gondor</td>\n",
       "      <td>March 15 ,3019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name              birth                  death\n",
       "59        Fastred (Pelennor Fields)                 TA         March 15 ,3019\n",
       "164                            Horn  Possibly late ,TA      March 15 ,TA 3019\n",
       "222                         Dúnhere                 TA         March 15 ,3019\n",
       "226                        Déorwine                 TA         March 15 ,3019\n",
       "260                         Hirluin                 TA         March 15 ,3019\n",
       "266                       Herubrand                 TA         March 15 ,3019\n",
       "269                        Herefara                 TA         March 15 ,3019\n",
       "277                         Harding                 TA         March 15 ,3019\n",
       "291                        Halbarad            TA 2916         March 15 ,3019\n",
       "314                     Denethor II            TA 2930         March 15 ,3019\n",
       "379                   Black Serpent                 TA         March 15 ,3019\n",
       "445                         Guthláf                 TA         March 15 ,3019\n",
       "452                        Grimbold                 TA         March 15 ,3019\n",
       "462  Gothmog (Lieutenant of Morgul)                NaN  March 15, 3019 (film)\n",
       "545            Witch-king of Angmar                 SA         March 15 ,3019\n",
       "791                         Théoden    TA 2948 ,Gondor         March 15 ,3019"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars[(chars.death.str.contains('3019', na=False)) & \\\n",
    "      (chars.death.str.contains('March', na=False)) & \\\n",
    "      (chars.death.str.contains('15', na=False))][['name', 'birth', 'death']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting information about some key characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "      <th>birth_era</th>\n",
       "      <th>death_era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>667</td>\n",
       "      <td>BA</td>\n",
       "      <td>January 253019 ,Battle of the Peak immortal</td>\n",
       "      <td>Male</td>\n",
       "      <td>Grey, later white</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Maiar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BA</td>\n",
       "      <td>Ja</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    birth                                        death gender  \\\n",
       "667    BA  January 253019 ,Battle of the Peak immortal   Male   \n",
       "\n",
       "                  hair height     name   race realm spouse birth_era death_era  \n",
       "667  Grey, later white    NaN  Gandalf  Maiar   NaN    NaN        BA        Ja  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars[chars.name == 'Gandalf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "      <th>birth_era</th>\n",
       "      <th>death_era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>819</td>\n",
       "      <td>22 September ,TA 2968</td>\n",
       "      <td>Unknown (Last sighting ,September 29 ,3021,) (...</td>\n",
       "      <td>Male</td>\n",
       "      <td>Brown</td>\n",
       "      <td>1.06m (3'6\")</td>\n",
       "      <td>Frodo Baggins</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     birth                                              death  \\\n",
       "819  22 September ,TA 2968  Unknown (Last sighting ,September 29 ,3021,) (...   \n",
       "\n",
       "    gender   hair        height           name    race realm spouse birth_era  \\\n",
       "819   Male  Brown  1.06m (3'6\")  Frodo Baggins  Hobbit   NaN    NaN        22   \n",
       "\n",
       "    death_era  \n",
       "819   Unknown  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars[chars.name == 'Frodo Baggins']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "      <th>birth_era</th>\n",
       "      <th>death_era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>669</td>\n",
       "      <td>April 6 ,2980</td>\n",
       "      <td>Still alive, after going to the ,Undying Lands...</td>\n",
       "      <td>Male</td>\n",
       "      <td>Blond (movie)</td>\n",
       "      <td>1.22m (4'0\")</td>\n",
       "      <td>Samwise Gamgee</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rosie Cotton</td>\n",
       "      <td>Ap</td>\n",
       "      <td>St</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth                                              death gender  \\\n",
       "669  April 6 ,2980  Still alive, after going to the ,Undying Lands...   Male   \n",
       "\n",
       "              hair        height            name    race realm        spouse  \\\n",
       "669  Blond (movie)  1.22m (4'0\")  Samwise Gamgee  Hobbit   NaN  Rosie Cotton   \n",
       "\n",
       "    birth_era death_era  \n",
       "669        Ap        St  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars[chars.name.str.startswith('Samwise Gamgee')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>birth</th>\n",
       "      <th>death</th>\n",
       "      <th>gender</th>\n",
       "      <th>hair</th>\n",
       "      <th>height</th>\n",
       "      <th>name</th>\n",
       "      <th>race</th>\n",
       "      <th>realm</th>\n",
       "      <th>spouse</th>\n",
       "      <th>birth_era</th>\n",
       "      <th>death_era</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>873</td>\n",
       "      <td>March 1 ,2931</td>\n",
       "      <td>FO 120</td>\n",
       "      <td>Male</td>\n",
       "      <td>Dark</td>\n",
       "      <td>198cm (6'6\")</td>\n",
       "      <td>Aragorn II Elessar</td>\n",
       "      <td>Men</td>\n",
       "      <td>Reunited Kingdom,Arnor,Gondor</td>\n",
       "      <td>Arwen</td>\n",
       "      <td>Ma</td>\n",
       "      <td>FO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             birth   death gender  hair        height                name  \\\n",
       "873  March 1 ,2931  FO 120   Male  Dark  198cm (6'6\")  Aragorn II Elessar   \n",
       "\n",
       "    race                          realm spouse birth_era death_era  \n",
       "873  Men  Reunited Kingdom,Arnor,Gondor  Arwen        Ma        FO  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars[chars.name == 'Aragorn II Elessar']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of script dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Unnamed: 0</th>\n",
       "      <th>char</th>\n",
       "      <th>dialog</th>\n",
       "      <th>movie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DEAGOL</td>\n",
       "      <td>Oh Smeagol Ive got one! , Ive got a fish Smeag...</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Pull it in! Go on, go on, go on, pull it in!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>DEAGOL</td>\n",
       "      <td>Arrghh!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Deagol!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Deagol!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     char                                             dialog  \\\n",
       "0           0   DEAGOL  Oh Smeagol Ive got one! , Ive got a fish Smeag...   \n",
       "1           1  SMEAGOL     Pull it in! Go on, go on, go on, pull it in!     \n",
       "2           2   DEAGOL                                           Arrghh!    \n",
       "3           3  SMEAGOL                                          Deagol!     \n",
       "4           4  SMEAGOL                                          Deagol!     \n",
       "\n",
       "                     movie  \n",
       "0  The Return of the King   \n",
       "1  The Return of the King   \n",
       "2  The Return of the King   \n",
       "3  The Return of the King   \n",
       "4  The Return of the King   "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "script = pd.read_csv(data_path + \"/lotr_scripts.csv\")\n",
    "script.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data seems cleaner than previous data we analyze. The only can be done to drop unnecessary index column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>char</th>\n",
       "      <th>dialog</th>\n",
       "      <th>movie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DEAGOL</td>\n",
       "      <td>Oh Smeagol Ive got one! , Ive got a fish Smeag...</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Pull it in! Go on, go on, go on, pull it in!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>DEAGOL</td>\n",
       "      <td>Arrghh!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Deagol!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SMEAGOL</td>\n",
       "      <td>Deagol!</td>\n",
       "      <td>The Return of the King</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      char                                             dialog  \\\n",
       "0   DEAGOL  Oh Smeagol Ive got one! , Ive got a fish Smeag...   \n",
       "1  SMEAGOL     Pull it in! Go on, go on, go on, pull it in!     \n",
       "2   DEAGOL                                           Arrghh!    \n",
       "3  SMEAGOL                                          Deagol!     \n",
       "4  SMEAGOL                                          Deagol!     \n",
       "\n",
       "                     movie  \n",
       "0  The Return of the King   \n",
       "1  The Return of the King   \n",
       "2  The Return of the King   \n",
       "3  The Return of the King   \n",
       "4  The Return of the King   "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "script.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "script.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we groupby data based on character name, and see how many quotes they have in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>char</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>FRODO</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>SAM</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ARAGORN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       char  size\n",
       "26    FRODO   229\n",
       "75      SAM   218\n",
       "33  GANDALF   214\n",
       "2   ARAGORN   187"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove additional blanks\n",
    "script.char = script.char.str.strip()\n",
    "# there are some dialogs where char is mentioned as character + VOICE OVER(or VOICEOVER)\n",
    "script.char = script.char.str.replace(\"VOICE\", \"\")\n",
    "script.char = script.char.str.replace(\"OVER\", \"\").str.strip()\n",
    "\n",
    "char_count = script.groupby('char').size().reset_index(name='size')\n",
    "top_chars = char_count.sort_values('size', ascending = False).head(4)\n",
    "top_chars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After getting the stats about the characters, for second step, we want to get, how many movies are there in data, and\n",
    "analyze how many quotes each movie has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>movie</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>1010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        movie  size\n",
       "2              The Two Towers  1010\n",
       "1      The Return of the King   873\n",
       "0  The Fellowship of the Ring   507"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "script.movie = script.movie.str.strip()\n",
    "movie_count = script.groupby('movie').size().reset_index(name='size')\n",
    "movie_count.sort_values('size', ascending = False).head()"
   ]
  },
  {
   "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>char</th>\n",
       "      <th>Size</th>\n",
       "      <th>The Return of the King</th>\n",
       "      <th>The Fellowship of the Ring</th>\n",
       "      <th>The Two Towers</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>ARAGORN</td>\n",
       "      <td>99</td>\n",
       "      <td>61</td>\n",
       "      <td>27</td>\n",
       "      <td>99</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>FRODO</td>\n",
       "      <td>84</td>\n",
       "      <td>74</td>\n",
       "      <td>71</td>\n",
       "      <td>84</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>47</td>\n",
       "      <td>94</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SAM</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>38</td>\n",
       "      <td>89</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      char  Size  The Return of the King  The Fellowship of the Ring  \\\n",
       "0  ARAGORN    99                      61                          27   \n",
       "1    FRODO    84                      74                          71   \n",
       "2  GANDALF    47                      94                          73   \n",
       "3      SAM    89                      91                          38   \n",
       "\n",
       "   The Two Towers  Total  \n",
       "0              99    187  \n",
       "1              84    229  \n",
       "2              47    214  \n",
       "3              89    218  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get some stats about races with grouping\n",
    "two_towers_counts = script[(script.movie == 'The Two Towers') & (script.char.isin(top_chars.char.values))].groupby('char').size().reset_index(name='Size')\n",
    "two_towers_counts.sort_values('Size', ascending = False)\n",
    "#get some stats about races with grouping\n",
    "return_of_king_counts = script[(script.movie == 'The Return of the King') & (script.char.isin(top_chars.char.values))].groupby('char').size().reset_index(name='Size')\n",
    "return_of_king_counts.sort_values('Size', ascending = False)\n",
    "\n",
    "fellowship_of_ring_counts = script[(script.movie == 'The Fellowship of the Ring') & (script.char.isin(top_chars.char.values))].groupby('char').size().reset_index(name='Size')\n",
    "return_of_king_counts.sort_values('Size', ascending = False)\n",
    "\n",
    "all_movies = two_towers_counts\n",
    "all_movies['The Return of the King'] = return_of_king_counts.Size\n",
    "all_movies['The Fellowship of the Ring'] = fellowship_of_ring_counts.Size\n",
    "all_movies['The Two Towers'] = two_towers_counts.Size\n",
    "all_movies['Total'] = two_towers_counts.Size + return_of_king_counts.Size + fellowship_of_ring_counts.Size\n",
    "\n",
    "#all_movies.drop('Size', axis = 1, inplace=True)\n",
    "all_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(4)\n",
    "\n",
    "char_name = all_movies.char\n",
    "m2 = all_movies['The Two Towers'].values\n",
    "m1 = all_movies['The Fellowship of the Ring'].values\n",
    "m3 = all_movies['The Return of the King'].values\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "width = 0.4\n",
    "\n",
    "def sumzip(*items):\n",
    "    return [sum(values) for values in zip(*items)]\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(y_pos, m1, width, color='#ff4040')\n",
    "ax.bar(y_pos, m2, width,bottom=m1, color='#6da9d2')\n",
    "ax.bar(y_pos, m3, width,bottom=sumzip(m1,m2), color='#fdb147')\n",
    "ax.set_title('Number of dialogues by main character and movie')\n",
    "\n",
    "\n",
    "for i, v in enumerate(m1):\n",
    "    ax.text(i - .05, v / 2, str(v), color='white', fontweight='bold')\n",
    "\n",
    "for i, v in enumerate(m2):\n",
    "    ax.text(i - .05, m1[i] + v / 2, str(v), color='white', fontweight='bold')\n",
    "    \n",
    "for i, v in enumerate(m3):\n",
    "    ax.text(i - .05, m1[i] + m2[i] + v / 2, str(v), color='white', fontweight='bold')\n",
    "\n",
    "plt.xticks(y_pos, char_name)\n",
    "ax.legend(labels=['The Fellowship of the Ring', 'The Two Towers', 'The Return of the King'])\n",
    "\n",
    "plt.savefig(os.path.join('character_movie_dialogue.png'), dpi=300, format='png', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For analyzing the dialogues, we first started with getting most used words, so we split each dialogue into the words, and remove the stopwords, then visualize them in horizontal bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(data_path + '/stopwords.txt', 'r') as f:\n",
    "    stopwords = f.read().split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_dialogue = script.dialog.str.strip().str.lower().str.replace(r'([^\\s\\w]|_)+', '').str.split().values\n",
    "all_words = {}\n",
    "for dialogue in all_dialogue:\n",
    "    if type(dialogue) == list:\n",
    "        for word in dialogue:\n",
    "            if word not in stopwords:\n",
    "                if word not in all_words:\n",
    "                    all_words[word] = 1\n",
    "                else:\n",
    "                    all_words[word] += 1\n",
    "cooccurences_all = {k: v for k, v in sorted(all_words.items(), key=lambda item: -item[1])}\n",
    "keys = list(cooccurences_all.keys())[:50]\n",
    "values = list(cooccurences_all.values())[:50]\n",
    "\n",
    "# plt.rc('axes', labelsize=14)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=14)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=14)    # fontsize of the tick labels\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "y_pos = np.arange(50)\n",
    "\n",
    "plt.barh(y_pos, values)\n",
    "\n",
    "ax.set_title('The most used words in dialogues')\n",
    "\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(keys)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which characters are most talkative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>char</th>\n",
       "      <th>movie</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>BILBO</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>1491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>SAM</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>SAM</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>1323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        char                       movie  word_count\n",
       "52   GANDALF      The Return of the King        1872\n",
       "11     BILBO  The Fellowship of the Ring        1491\n",
       "115      SAM      The Return of the King        1440\n",
       "51   GANDALF  The Fellowship of the Ring        1426\n",
       "116      SAM              The Two Towers        1323"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_names = movie_count['movie'].values\n",
    "\n",
    "words_count = script\n",
    "words_count['word_count'] = words_count.dialog.str.split(' ').str.len()\n",
    "words_count = words_count.groupby(['char','movie']).sum().astype(int).reset_index()\n",
    "words_count.sort_values('word_count', ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look the most talkative characters for each movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>char</th>\n",
       "      <th>movie</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>BILBO</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>1491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>1426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>FRODO</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>GALADRIEL</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>BOROMIR</td>\n",
       "      <td>The Fellowship of the Ring</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         char                       movie  word_count\n",
       "11      BILBO  The Fellowship of the Ring        1491\n",
       "51    GANDALF  The Fellowship of the Ring        1426\n",
       "39      FRODO  The Fellowship of the Ring         658\n",
       "43  GALADRIEL  The Fellowship of the Ring         638\n",
       "13    BOROMIR  The Fellowship of the Ring         457"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>char</th>\n",
       "      <th>movie</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>SAM</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>FRODO</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>PIPPIN</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ARAGORN</td>\n",
       "      <td>The Return of the King</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        char                   movie  word_count\n",
       "52   GANDALF  The Return of the King        1872\n",
       "115      SAM  The Return of the King        1440\n",
       "40     FRODO  The Return of the King        1032\n",
       "105   PIPPIN  The Return of the King         848\n",
       "3    ARAGORN  The Return of the King         818"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>char</th>\n",
       "      <th>movie</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>SAM</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>1323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>GANDALF</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>GOLLUM</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>THEODEN</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ARAGORN</td>\n",
       "      <td>The Two Towers</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        char           movie  word_count\n",
       "116      SAM  The Two Towers        1323\n",
       "53   GANDALF  The Two Towers        1024\n",
       "63    GOLLUM  The Two Towers         935\n",
       "137  THEODEN  The Two Towers         930\n",
       "4    ARAGORN  The Two Towers         881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for movie_name in movie_names:\n",
    "    movie_characters = words_count.loc[words_count['movie'] == movie_name]\n",
    "    display(movie_characters.sort_values('word_count', ascending = False).head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would also be interesting to know which words main characters used most. So, in order to that, we retrieve all the dialogues the character has, and split each dialogue to words and remove stopwords.\n",
    "\n",
    "Additionally, we add an option to find that which other characters the character mention or refer most. Basically, for each word we check that this word is inside the character list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def most_used_words(char_name, movie_name, n_words, only_characters = False):\n",
    "    char_name = char_name.upper()\n",
    "    m = script[script.movie == movie_name]\n",
    "    w = m[m.char == char_name].dialog.str.strip().str.lower().str.replace(r'([^\\s\\w]|_)+', '').str.split().values\n",
    "    d = {}\n",
    "    for quote in w:\n",
    "        for word in quote:\n",
    "            if word not in stopwords and (only_characters == False or word in m.char.str.lower().values):\n",
    "                if word in d:\n",
    "                    d[word] += 1\n",
    "                else:\n",
    "                    d[word] = 1\n",
    "    cooccurences = {k: v for k, v in sorted(d.items(), key=lambda item: -item[1])}\n",
    "    return list(cooccurences.items())[:n_words]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's play with data to see what one of the main characters of each movie speak most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gollum in \"The Two Towers\"\n",
      "1. Most used words: \n",
      " [('master', 13), ('precious', 9), ('kill', 6), ('gollum', 6)]\n",
      "2. Most mentioned character names: \n",
      " [('gollum', 6), ('smeagol', 5)]\n"
     ]
    }
   ],
   "source": [
    "print('Gollum in \"The Two Towers\"')\n",
    "print(\"1. Most used words: \\n\", most_used_words('gollum', 'The Two Towers', 4))\n",
    "print(\"2. Most mentioned character names: \\n\", most_used_words('gollum', 'The Two Towers', 4, only_characters=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can discover famous words of Gollum such as 'master' and 'precious' in data  :D "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gandalf in \"The Return of the King\"\n",
      "1. Most used words: \n",
      " [('city', 10), ('frodo', 8), ('king', 7), ('peregrin', 7), ('gondor', 7)]\n",
      "2. Most mentioned character names: \n",
      " [('frodo', 8), ('saruman', 5), ('sauron', 5), ('faramir', 5), ('denethor', 4)]\n"
     ]
    }
   ],
   "source": [
    "print('Gandalf in \"The Return of the King\"')\n",
    "print(\"1. Most used words: \\n\", most_used_words('gandalf', 'The Return of the King', 5))\n",
    "print(\"2. Most mentioned character names: \\n\", most_used_words('gandalf', 'The Return of the King', 5, only_characters=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frodo in \"The Fellowship of the Ring\"\n",
      "1. Most used words: \n",
      " [('sam', 7), ('bilbo', 6), ('gandalf', 5)]\n",
      "2. Most mentioned character names: \n",
      " [('sam', 7), ('bilbo', 6), ('gandalf', 5)]\n"
     ]
    }
   ],
   "source": [
    "print('Frodo in \"The Fellowship of the Ring\"')\n",
    "print(\"1. Most used words: \\n\", most_used_words('frodo', 'The Fellowship of the Ring', 3))\n",
    "print(\"2. Most mentioned character names: \\n\", most_used_words('frodo', 'The Fellowship of the Ring', 3, only_characters=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it seems Frodo loves Sam, Bilbo, and Gandalf so much."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of timeline of events datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a seperate set for each of six ages. Each of the sets has the year and event column. First of all, we have decided to get how many years each age lasted. Because of lack of information in data, we ignore the Fourth Age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeline_data_path = data_path + 'timeline_lotr/'\n",
    "\n",
    "ages = {'YL' : 'Valian Year', 'YT': 'Year of the Trees', 'FA':'F.A.', 'SA':'S.A.', 'TA':'T.A.', 'FO':'Fo.A.'}\n",
    "ages_original = ['The Years of Lamps', 'The Years of Trees', 'The First Age', 'The Second Age', 'The Third Age', 'The Fourth Age']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Years of Lamps : \n"
     ]
    },
    {
     "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>Valian Year</th>\n",
       "      <th>Calculated Sun Years</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1–1499</td>\n",
       "      <td>1-14,363</td>\n",
       "      <td>The First War and the Marring of Arda.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>14,373</td>\n",
       "      <td>Tulkas enters Eä and Melkor flees.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1900</td>\n",
       "      <td>18,206</td>\n",
       "      <td>The completion of Arda. The Valar create the T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3400</td>\n",
       "      <td>32,579</td>\n",
       "      <td>Tulkas weds Nessa.Melkor secretly returns to A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3450</td>\n",
       "      <td>33,058</td>\n",
       "      <td>Melkor overthrows the Two Lamps forming Helcar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Valian Year Calculated Sun Years  \\\n",
       "0      1–1499             1-14,363   \n",
       "1        1500               14,373   \n",
       "2        1900               18,206   \n",
       "3        3400               32,579   \n",
       "4        3450               33,058   \n",
       "\n",
       "                                               Event  \n",
       "0             The First War and the Marring of Arda.  \n",
       "1                 Tulkas enters Eä and Melkor flees.  \n",
       "2  The completion of Arda. The Valar create the T...  \n",
       "3  Tulkas weds Nessa.Melkor secretly returns to A...  \n",
       "4  Melkor overthrows the Two Lamps forming Helcar...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Years of Trees : \n"
     ]
    },
    {
     "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>Year of the Trees</th>\n",
       "      <th>Calculated Sun Years</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Pre-1000</td>\n",
       "      <td>Pre-9582</td>\n",
       "      <td>Melkor sleeps in Utumno.  Oromë hunts througho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>????</td>\n",
       "      <td>????</td>\n",
       "      <td>About some time before the waking of the Elves...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>9582</td>\n",
       "      <td>The Valar hold council, for Yavanna and Oromë ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1050</td>\n",
       "      <td>10,061</td>\n",
       "      <td>Varda forms the Valacirca. The first 144 Elves...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>????</td>\n",
       "      <td>????</td>\n",
       "      <td>Spies of Melkor discover the Elves and afflict...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Year of the Trees Calculated Sun Years  \\\n",
       "0          Pre-1000             Pre-9582   \n",
       "1              ????                 ????   \n",
       "2              1000                 9582   \n",
       "3              1050               10,061   \n",
       "4              ????                 ????   \n",
       "\n",
       "                                               Event  \n",
       "0  Melkor sleeps in Utumno.  Oromë hunts througho...  \n",
       "1  About some time before the waking of the Elves...  \n",
       "2  The Valar hold council, for Yavanna and Oromë ...  \n",
       "3  Varda forms the Valacirca. The first 144 Elves...  \n",
       "4  Spies of Melkor discover the Elves and afflict...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The First Age : \n"
     ]
    },
    {
     "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>F.A.</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Men awake in Hildórien.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>The host of Fingolfin camps beside Lake Mithrim.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Fingon rescues Maedhros from Thangorodrim, Fin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Thingol grants permission for the Noldor to oc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>The Noldor hold a council. The Sons of Fëanor ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F.A.                                              Event\n",
       "0     1                            Men awake in Hildórien.\n",
       "1     2   The host of Fingolfin camps beside Lake Mithrim.\n",
       "2     5  Fingon rescues Maedhros from Thangorodrim, Fin...\n",
       "3     6  Thingol grants permission for the Noldor to oc...\n",
       "4     7  The Noldor hold a council. The Sons of Fëanor ..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Second Age : \n"
     ]
    },
    {
     "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>S.A.</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Foundation of the Grey Havens, and of Lindon.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>The Edain, and a few Drúedain, reach Númenor; ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>c. 40</td>\n",
       "      <td>Dwarves of the Firebeards and Broadbeams clans...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>Birth of Vardamir Nólimon, second King of Núme...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>c. 82</td>\n",
       "      <td>The emigration of the Edain to Númenor ends.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    S.A.                                              Event\n",
       "0      1      Foundation of the Grey Havens, and of Lindon.\n",
       "1     32  The Edain, and a few Drúedain, reach Númenor; ...\n",
       "2  c. 40  Dwarves of the Firebeards and Broadbeams clans...\n",
       "3     61  Birth of Vardamir Nólimon, second King of Núme...\n",
       "4  c. 82       The emigration of the Edain to Númenor ends."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Third Age : \n"
     ]
    },
    {
     "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>T.A.</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Isildur plants a Sapling of the White Tree in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Ohtar brings the Shards of Narsil to Imladris.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>Valandil, at the age of 21, becomes the third ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>Birth of Eärendil of Gondor.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>87</td>\n",
       "      <td>Birth of Eldacar of Arnor.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   T.A.                                              Event\n",
       "0     2  Isildur plants a Sapling of the White Tree in ...\n",
       "1     3     Ohtar brings the Shards of Narsil to Imladris.\n",
       "2    10  Valandil, at the age of 21, becomes the third ...\n",
       "3    48                       Birth of Eärendil of Gondor.\n",
       "4    87                         Birth of Eldacar of Arnor."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Fourth Age : \n"
     ]
    },
    {
     "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>Fo.A.</th>\n",
       "      <th>S.R.</th>\n",
       "      <th>Events</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1422</td>\n",
       "      <td>The Fourth Age begins in the reckoning of the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1423</td>\n",
       "      <td>Death of Adelard Took.[2] Birth of Frodo Gardn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1425</td>\n",
       "      <td>Birth of Rose Gardner.[3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1427</td>\n",
       "      <td>Will Whitfoot resigns and Samwise is elected M...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1428</td>\n",
       "      <td>Death of Gaffer Gamgee.[3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Fo.A.  S.R.                                             Events\n",
       "0     1  1422  The Fourth Age begins in the reckoning of the ...\n",
       "1     2  1423  Death of Adelard Took.[2] Birth of Frodo Gardn...\n",
       "2     4  1425                          Birth of Rose Gardner.[3]\n",
       "3     6  1427  Will Whitfoot resigns and Samwise is elected M...\n",
       "4     7  1428                         Death of Gaffer Gamgee.[3]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, age in enumerate(list(ages.keys())):\n",
    "    print(ages_original[i], \": \")\n",
    "    data = pd.read_csv(timeline_data_path + age + '.csv')\n",
    "    display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "begin = []\n",
    "end = []\n",
    "curr_sum = 0\n",
    "for age in list(ages.keys())[:-1]:\n",
    "    data = pd.read_csv(timeline_data_path + age + '.csv')\n",
    "    if age == 'YL':\n",
    "        years = int(re.sub(r'([^\\s\\w]|_)+', ' ', data.iloc[-1][ages[age]]).split(' ')[1])\n",
    "    elif age == 'SA':\n",
    "        years = int(re.sub(r'([^\\s\\w]|_)+', '', data.iloc[-1][ages[age]]))\n",
    "    else:\n",
    "        years = int(data.iloc[-1][ages[age]])\n",
    "    begin.append(curr_sum)\n",
    "    end.append(curr_sum + years)\n",
    "    curr_sum += years\n",
    "\n",
    "begin = np.array(begin)\n",
    "end = np.array(end)\n",
    "\n",
    "plt.rc('xtick', labelsize=20) \n",
    "plt.rc('ytick', labelsize=20)   \n",
    "\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "\n",
    "ax.set_yticks(range(len(begin)))\n",
    "\n",
    "for i, v in enumerate(end):\n",
    "    ax.text(end[i] - (end[i] - begin[i]) / 2 - begin[i] / 32, i - .05, str(end[i]-begin[i]), color='white', fontweight='bold')\n",
    "\n",
    "ax.set_title('Number of years each age lasted')\n",
    "\n",
    "ax.set_yticklabels(labels=['The Years of Lamps', 'The Years of Trees', 'The First Age', 'The Second Age', 'The Third Age'])\n",
    "    \n",
    "ax.barh(range(len(begin)), end-begin, left=begin, height = 0.4, color = np.random.rand(5,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be also interesting to look what are the first and last events happened in each age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Years of Lamps :\n",
      "First event:  The First War and the Marring of Arda.\n",
      "Last event:  The Valar raise the Pelóri and settle in Aman. The Two Trees are grown in Valinor.\n",
      "\n",
      "The Years of Trees :\n",
      "First event:  Melkor sleeps in Utumno.  Oromë hunts throughout Middle-earth. Melkor raises the Hithaeglir to hinder his efforts.\n",
      "Last event:  Creation of Moon and Sun. The host of the Noldor finishes crossing the Helcaraxë and enters Beleriand under Fingolfin. Battle of the Lammoth and death of Argon. The Moon rises and after seven times, the Sun follows.\n",
      "\n",
      "The First Age :\n",
      "First event:  Men awake in Hildórien.\n",
      "Last event:  Morgoth thrust from Arda. Host of Valinor departs Middle-earth.\n",
      "\n",
      "The Second Age :\n",
      "First event:  Foundation of the Grey Havens, and of Lindon.\n",
      "Last event:  Deaths of Ereinion Gil-galad and Elendil; defeat of Sauron.\n",
      "\n",
      "The Third Age :\n",
      "First event:  Isildur plants a Sapling of the White Tree in Minas Anor.Death of Isildur - and his sons Elendur, Aratan and Ciryon - in the Disaster of the Gladden Fields. The One Ring is lost.\n",
      "Last event:  Death of King Araphor: Argeleb II becomes the tenth King of Arthedain.\n",
      "\n",
      "The Fourth Age :\n",
      "First event:  The Fourth Age begins in the reckoning of the Shire.[1]\n",
      "Last event:  Durin VII retakes Moria.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, age in enumerate(list(ages.keys())):\n",
    "    print(ages_original[i], \":\")\n",
    "    data = pd.read_csv(timeline_data_path + age + '.csv')\n",
    "    if 'Event' not in data.columns:\n",
    "        print('First event: ', data.iloc[0].Events)\n",
    "        print('Last event: ', data.iloc[-1].Events)\n",
    "    else:\n",
    "        print('First event: ', data.iloc[0].Event)\n",
    "        print('Last event: ', data.iloc[-1].Event)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For other analysis, we can also merge them, and store as a one file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>date</th>\n",
       "      <th>event</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1–1499</td>\n",
       "      <td>The First War and the Marring of Arda.</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>Tulkas enters Eä and Melkor flees.</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1900</td>\n",
       "      <td>The completion of Arda. The Valar create the T...</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3400</td>\n",
       "      <td>Tulkas weds Nessa.Melkor secretly returns to A...</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3450</td>\n",
       "      <td>Melkor overthrows the Two Lamps forming Helcar...</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date                                              event age\n",
       "0  1–1499             The First War and the Marring of Arda.  YL\n",
       "1    1500                 Tulkas enters Eä and Melkor flees.  YL\n",
       "2    1900  The completion of Arda. The Valar create the T...  YL\n",
       "3    3400  Tulkas weds Nessa.Melkor secretly returns to A...  YL\n",
       "4    3450  Melkor overthrows the Two Lamps forming Helcar...  YL"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, age in enumerate(list(ages.keys())):\n",
    "    data = pd.read_csv(timeline_data_path + age + '.csv')\n",
    "    tmp = pd.DataFrame()\n",
    "    tmp['date'] = data[ages[age]]\n",
    "    if 'Event' not in data.columns:\n",
    "        tmp['event'] = data['Events']\n",
    "    else:\n",
    "        tmp['event'] = data['Event']\n",
    "    tmp['age'] = age\n",
    "\n",
    "    if i == 0:\n",
    "        merged = tmp\n",
    "    else:\n",
    "        merged = merged.append(tmp)\n",
    "\n",
    "merged.head()\n",
    "#merged.to_csv(data_path + '/timeline_merged.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example for event analysis, we can search events based on the battle or war."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>date</th>\n",
       "      <th>event</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1–1499</td>\n",
       "      <td>The First War and the Marring of Arda.</td>\n",
       "      <td>YL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1090</td>\n",
       "      <td>Valar make war on Melkor in the War for Sake o...</td>\n",
       "      <td>YT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1497</td>\n",
       "      <td>First Battle of Beleriand. Death of Denethor, ...</td>\n",
       "      <td>YT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1500</td>\n",
       "      <td>Creation of Moon and Sun. The host of the Nold...</td>\n",
       "      <td>YT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>Dagor Aglareb (\"The Glorious Battle\"), Siege o...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>375</td>\n",
       "      <td>Battle of the Gelion-Ascar Stockade. Deaths of...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>402</td>\n",
       "      <td>Battle of Aglon Pass.</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>455</td>\n",
       "      <td>Morgoth breaks the Siege of Angband, Dagor Bra...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>472</td>\n",
       "      <td>Nirnaeth Arnoediad (\"Battle of Unnumbered Tear...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>495</td>\n",
       "      <td>Battle of the Crossings of the Taeglin, Battle...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>503</td>\n",
       "      <td>Births of Eärendil and Elwing. Battle of the T...</td>\n",
       "      <td>FA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>1,693</td>\n",
       "      <td>The Three Rings are hidden; War of the Elves a...</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>3,177</td>\n",
       "      <td>Death of Ar-Gimilzôr, Tar-Palantir takes the S...</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>3,434</td>\n",
       "      <td>Battle of Dagorlad; Siege of Barad-dûr; deaths...</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1447</td>\n",
       "      <td>Battle of the Crossings of Erui and the Siege ...</td>\n",
       "      <td>TA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>34</td>\n",
       "      <td>Death of Imrahil. Elphir becomes Prince of Dol...</td>\n",
       "      <td>FO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date                                              event age\n",
       "0    1–1499             The First War and the Marring of Arda.  YL\n",
       "7      1090  Valar make war on Melkor in the War for Sake o...  YT\n",
       "51     1497  First Battle of Beleriand. Death of Denethor, ...  YT\n",
       "53     1500  Creation of Moon and Sun. The host of the Nold...  YT\n",
       "9        60  Dagor Aglareb (\"The Glorious Battle\"), Siege o...  FA\n",
       "46      375  Battle of the Gelion-Ascar Stockade. Deaths of...  FA\n",
       "56      402                              Battle of Aglon Pass.  FA\n",
       "80      455  Morgoth breaks the Siege of Angband, Dagor Bra...  FA\n",
       "96      472  Nirnaeth Arnoediad (\"Battle of Unnumbered Tear...  FA\n",
       "108     495  Battle of the Crossings of the Taeglin, Battle...  FA\n",
       "116     503  Births of Eärendil and Elwing. Battle of the T...  FA\n",
       "93    1,693  The Three Rings are hidden; War of the Elves a...  SA\n",
       "137   3,177  Death of Ar-Gimilzôr, Tar-Palantir takes the S...  SA\n",
       "156   3,434  Battle of Dagorlad; Siege of Barad-dûr; deaths...  SA\n",
       "89     1447  Battle of the Crossings of Erui and the Siege ...  TA\n",
       "22       34  Death of Imrahil. Elphir becomes Prince of Dol...  FO"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[(merged.event.str.contains('Battle'))|(merged.event.str.contains('War'))]"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}