Python_WCsyntax.ipynb

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   "source": [
    "#  **Coding Presentation (Stata v R v Python)**\n",
    "## Python Syntax\n",
    "## Fors Marsh Group\n",
    "## July 8, 2018\n",
    "> Author: Michael Siebel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament  neutral\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly    False\n",
       "1  1873-03-08   England  Scotland           4           2   Friendly    False\n",
       "2  1874-03-07  Scotland   England           2           1   Friendly    False\n",
       "3  1875-03-06   England  Scotland           2           2   Friendly    False\n",
       "4  1876-03-04  Scotland   England           3           0   Friendly    False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Load data set\n",
    "#Congrats: You already set your working directory by openning this file\n",
    "\n",
    "#Load pandas\n",
    "import pandas as pd\n",
    "\n",
    "#Import CSV & drop variables\n",
    "df = pd.read_csv('results.csv', sep=',').drop(['city', 'country'], axis=1)\n",
    "\n",
    "#Check first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          object\n",
       "home_team     object\n",
       "away_team     object\n",
       "home_score     int64\n",
       "away_score     int64\n",
       "tournament    object\n",
       "neutral         bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Load data set\n",
    "#Check variables\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "home_team     0\n",
       "away_team     0\n",
       "home_score    0\n",
       "away_score    0\n",
       "tournament    0\n",
       "neutral       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Load data set\n",
    "#Check missings (NaN's)\n",
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>1908-04-05</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Germany</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>1908-06-07</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1909-04-04</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>1909-04-04</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1910-04-03</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    home_team    away_team  home_score  away_score tournament  \\\n",
       "209  1908-04-05  Switzerland      Germany           5           3   Friendly   \n",
       "216  1908-06-07      Austria      Germany           3           2   Friendly   \n",
       "234  1909-04-04      Germany  Switzerland           1           0   Friendly   \n",
       "235  1909-04-04      Hungary      Germany           3           3   Friendly   \n",
       "253  1910-04-03  Switzerland      Germany           2           3   Friendly   \n",
       "\n",
       "     neutral  \n",
       "209    False  \n",
       "216    False  \n",
       "234    False  \n",
       "235    False  \n",
       "253    False  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Load data set\n",
    "#Subset data to only include Germany matches \n",
    "df_ger = df[(df['home_team'] == 'Germany') | (df['away_team'] == 'Germany')]\n",
    "\n",
    "df_ger.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
       "      <th>match</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  \n",
       "38817        Friendly    False      1  \n",
       "38883        Friendly    False      1  \n",
       "38987        Friendly    False      1  \n",
       "39030        Friendly    False      1  \n",
       "39059  FIFA World Cup     True      1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Load data set\n",
    "#Simple recode (total column to be counted)\n",
    "df_ger['match'] = 1\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
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       "      <th>goals</th>\n",
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       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  \n",
       "38817        Friendly    False      1    1.0  \n",
       "38883        Friendly    False      1    0.0  \n",
       "38987        Friendly    False      1    1.0  \n",
       "39030        Friendly    False      1    2.0  \n",
       "39059  FIFA World Cup     True      1    0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Conditional recode (Define Goals Scored)\n",
    "df_ger['goals'] = df_ger.loc[df_ger['home_team'] == 'Germany','home_score']\n",
    "df_ger['goals'] = df_ger['goals'].fillna(df_ger.loc[df_ger['away_team'] == 'Germany','away_score'])\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
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       "      <td>2018-06-02</td>\n",
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       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  \n",
       "38817        Friendly    False      1    1.0       1.0  \n",
       "38883        Friendly    False      1    0.0       1.0  \n",
       "38987        Friendly    False      1    1.0       2.0  \n",
       "39030        Friendly    False      1    2.0       1.0  \n",
       "39059  FIFA World Cup     True      1    0.0       1.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Conditional recode (Define Goals Conceded)\n",
    "df_ger['conceded'] = df_ger.loc[df_ger['home_team'] == 'Germany','away_score']\n",
    "df_ger['conceded'] = df_ger['conceded'].fillna(df_ger.loc[df_ger['away_team'] == 'Germany','home_score'])\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
       "      <th>match</th>\n",
       "      <th>goals</th>\n",
       "      <th>conceded</th>\n",
       "      <th>goaldiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  goaldiff  \n",
       "38817        Friendly    False      1    1.0       1.0       0.0  \n",
       "38883        Friendly    False      1    0.0       1.0      -1.0  \n",
       "38987        Friendly    False      1    1.0       2.0      -1.0  \n",
       "39030        Friendly    False      1    2.0       1.0       1.0  \n",
       "39059  FIFA World Cup     True      1    0.0       1.0      -1.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Simple recode (Define Goal Differential)\n",
    "df_ger['goaldiff'] = df_ger['goals'] - df_ger['conceded']\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  goaldiff  friendly  \n",
       "38817        Friendly    False      1    1.0       1.0       0.0      True  \n",
       "38883        Friendly    False      1    0.0       1.0      -1.0      True  \n",
       "38987        Friendly    False      1    1.0       2.0      -1.0      True  \n",
       "39030        Friendly    False      1    2.0       1.0       1.0      True  \n",
       "39059  FIFA World Cup     True      1    0.0       1.0      -1.0     False  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Dummy recode (Define Friendly v Competitive match)\n",
    "df_ger['friendly'] = df_ger['tournament'] == 'Friendly'\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
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       "      <th>home</th>\n",
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       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>away</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  goaldiff  friendly  \\\n",
       "38817        Friendly    False      1    1.0       1.0       0.0      True   \n",
       "38883        Friendly    False      1    0.0       1.0      -1.0      True   \n",
       "38987        Friendly    False      1    1.0       2.0      -1.0      True   \n",
       "39030        Friendly    False      1    2.0       1.0       1.0      True   \n",
       "39059  FIFA World Cup     True      1    0.0       1.0      -1.0     False   \n",
       "\n",
       "          home  \n",
       "38817     home  \n",
       "38883     home  \n",
       "38987     away  \n",
       "39030     home  \n",
       "39059  neutral  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Categorical recode (home vs away vs neutral matches)\n",
    "df_ger.loc[df_ger['home_team'] == 'Germany','home'] = 'home'\n",
    "df_ger.loc[df_ger['away_team'] == 'Germany','home'] = 'away'\n",
    "df_ger.loc[df_ger['neutral'] == True,'home'] = 'neutral'\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
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       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
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       "      <td>Friendly</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>away</td>\n",
       "      <td>Austria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  goaldiff  friendly  \\\n",
       "38817        Friendly    False      1    1.0       1.0       0.0      True   \n",
       "38883        Friendly    False      1    0.0       1.0      -1.0      True   \n",
       "38987        Friendly    False      1    1.0       2.0      -1.0      True   \n",
       "39030        Friendly    False      1    2.0       1.0       1.0      True   \n",
       "39059  FIFA World Cup     True      1    0.0       1.0      -1.0     False   \n",
       "\n",
       "          home      opponent  \n",
       "38817     home         Spain  \n",
       "38883     home        Brazil  \n",
       "38987     away       Austria  \n",
       "39030     home  Saudi Arabia  \n",
       "39059  neutral        Mexico  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Conditional recode (opponent)\n",
    "df_ger['opponent'] = df_ger['home_team'].loc[(df_ger['home_team'] != 'Germany')] \n",
    "df_ger['opponent'] = df_ger['opponent'].fillna(df_ger['away_team'].loc[(df_ger['away_team'] != 'Germany')])\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>neutral</th>\n",
       "      <th>match</th>\n",
       "      <th>goals</th>\n",
       "      <th>conceded</th>\n",
       "      <th>goaldiff</th>\n",
       "      <th>friendly</th>\n",
       "      <th>home</th>\n",
       "      <th>opponent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>away</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team     away_team  home_score  away_score  \\\n",
       "38817  2018-03-23   Germany         Spain           1           1   \n",
       "38883  2018-03-27   Germany        Brazil           0           1   \n",
       "38987  2018-06-02   Austria       Germany           2           1   \n",
       "39030  2018-06-08   Germany  Saudi Arabia           2           1   \n",
       "39059  2018-06-17   Germany        Mexico           0           1   \n",
       "\n",
       "           tournament  neutral  match  goals  conceded  goaldiff  friendly  \\\n",
       "38817        Friendly    False      1    1.0       1.0       0.0      True   \n",
       "38883        Friendly    False      1    0.0       1.0      -1.0      True   \n",
       "38987        Friendly    False      1    1.0       2.0      -1.0      True   \n",
       "39030        Friendly    False      1    2.0       1.0       1.0      True   \n",
       "39059  FIFA World Cup     True      1    0.0       1.0      -1.0     False   \n",
       "\n",
       "          home      opponent  year  \n",
       "38817     home         Spain  2018  \n",
       "38883     home        Brazil  2018  \n",
       "38987     away       Austria  2018  \n",
       "39030     home  Saudi Arabia  2018  \n",
       "39059  neutral        Mexico  2018  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#String variables (Define year)\n",
    "df_ger['year'] = df_ger['date'].str[:4]\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>match</th>\n",
       "      <th>goals</th>\n",
       "      <th>conceded</th>\n",
       "      <th>goaldiff</th>\n",
       "      <th>friendly</th>\n",
       "      <th>home</th>\n",
       "      <th>opponent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38817</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38883</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38987</th>\n",
       "      <td>2018-06-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>away</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39030</th>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>home</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  match  goals  conceded  goaldiff  friendly     home  \\\n",
       "38817  2018-03-23      1    1.0       1.0       0.0      True     home   \n",
       "38883  2018-03-27      1    0.0       1.0      -1.0      True     home   \n",
       "38987  2018-06-02      1    1.0       2.0      -1.0      True     away   \n",
       "39030  2018-06-08      1    2.0       1.0       1.0      True     home   \n",
       "39059  2018-06-17      1    0.0       1.0      -1.0     False  neutral   \n",
       "\n",
       "           opponent  year  \n",
       "38817         Spain  2018  \n",
       "38883        Brazil  2018  \n",
       "38987       Austria  2018  \n",
       "39030  Saudi Arabia  2018  \n",
       "39059        Mexico  2018  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Recodes and Data Manpulation\n",
    "#Drop redundant variables\n",
    "df_ger = df_ger.drop(['home_score','away_score','tournament','neutral','home_team','away_team'], axis=1)\n",
    "\n",
    "df_ger.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competitive</th>\n",
       "      <th>friendly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>away</th>\n",
       "      <td>108</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <td>119</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         competitive  friendly\n",
       "home                          \n",
       "away             108       271\n",
       "home             119       285\n",
       "neutral          139         9"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Descriptive Statistics\n",
    "#Tabs\n",
    "tabHome = pd.crosstab(df_ger['home'],df_ger['friendly'])\n",
    "tabHome.columns=['competitive','friendly']\n",
    "tabHome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competitive</th>\n",
       "      <th>friendly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>away</th>\n",
       "      <td>29.51</td>\n",
       "      <td>47.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <td>32.51</td>\n",
       "      <td>50.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>37.98</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         competitive  friendly\n",
       "home                          \n",
       "away           29.51     47.96\n",
       "home           32.51     50.44\n",
       "neutral        37.98      1.59"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Descriptive Statistics\n",
    "#Unweighted proportions\n",
    "propHome = round((tabHome/tabHome.sum(0))*100, ndigits = 2)\n",
    "propHome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>conceded</th>\n",
       "      <th>goaldiff</th>\n",
       "      <th>goals</th>\n",
       "      <th>match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <th>friendly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">away</th>\n",
       "      <th>False</th>\n",
       "      <td>0.81</td>\n",
       "      <td>1.54</td>\n",
       "      <td>2.34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">home</th>\n",
       "      <th>False</th>\n",
       "      <td>0.72</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.16</td>\n",
       "      <td>1.26</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">neutral</th>\n",
       "      <th>False</th>\n",
       "      <td>1.17</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.93</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1.67</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  conceded  goaldiff  goals  match\n",
       "home    friendly                                  \n",
       "away    False         0.81      1.54   2.34      1\n",
       "        True          1.45      0.45   1.89      1\n",
       "home    False         0.72      2.17   2.89      1\n",
       "        True          1.16      1.26   2.42      1\n",
       "neutral False         1.17      0.76   1.93      1\n",
       "        True          1.67      0.22   1.89      1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Descriptive Statistics\n",
    "#Goal difference table\n",
    "goaldiffTable = round(pd.pivot_table(df_ger, index=['home','friendly'], aggfunc='mean'), ndigits = 2)\n",
    "goaldiffTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">goaldiff</th>\n",
       "      <th colspan=\"2\" halign=\"left\">match</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opponent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Korea Republic</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>1.08</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0.28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               goaldiff       match    \n",
       "                   mean   sum  mean sum\n",
       "opponent                               \n",
       "Korea Republic     0.00   0.0     1   3\n",
       "Mexico             1.08  13.0     1  12\n",
       "Sweden             0.28  10.0     1  36"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Descriptive Statistics\n",
    "#Opponent table\n",
    "threeOpponents = df_ger.loc[df_ger['opponent'].isin(['Mexico','Sweden','Korea Republic'])] \n",
    "opponentTable = round(pd.pivot_table(threeOpponents, index=['opponent'], values=['goaldiff','match'], aggfunc={'mean', 'sum'}), ndigits = 2)\n",
    "opponentTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IGKLgEBERQxQcIiJiiIJDREQMydXdcUX+K6JOd822z9d/SQFWIlL4dMQhIiKG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\nsb9tgZOT/bjUlFScHPNuHxr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c971579d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Histogram\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(df_ger['goaldiff'], bins='auto', facecolor='blue', alpha=0.5)\n",
    "plt.title('Histogram of Goal Differences')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ggplot in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (0.11.5)\n",
      "Requirement already satisfied: cycler in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (0.10.0)\n",
      "Requirement already satisfied: six in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (1.11.0)\n",
      "Requirement already satisfied: brewer2mpl in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (1.4.1)\n",
      "Requirement already satisfied: pandas in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (0.20.3)\n",
      "Requirement already satisfied: patsy>=0.4 in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (0.4.1)\n",
      "Requirement already satisfied: statsmodels in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (0.8.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (2.1.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (1.13.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from ggplot) (0.19.1)\n",
      "Requirement already satisfied: python-dateutil>=2 in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from pandas->ggplot) (2.6.1)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from pandas->ggplot) (2017.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\siebelm\\anaconda3\\lib\\site-packages (from matplotlib->ggplot) (2.2.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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cuFGWZemGG27Q4OCg4vG4i60EAABAo6jLqSYDAwN66aWXdP/99+v555+XJA0N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\nAAAAwACCNwAAAGAAwRsAAAAw4P8HoopJ9fvfJbAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c971d6b0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (122793656566)>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Someone attempted to replicate ggplot2 for python! (kindof works)\n",
    "!pip install ggplot\n",
    "from ggplot import *\n",
    "\n",
    "ggplot(df_ger, aes('goaldiff', fill='friendly')) +\\\n",
    "    geom_histogram(binwidth=.75, position=\"dodge\") +\\\n",
    "    ylab(\"Count\") + xlab(\"Goal Differential\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oo4/Mj0IOBoOYM3sWVpx0AoLBIG747n9AUZSafya33vZLXHftl3DDt6/HXx9+FGNjY1i+\n/DgccMD++NWvf4exsTFfn1/Mer1nnHYqWlta8bdnJ9fKvvTSK1i+/Dh88APvx6ZNm9HV1WVebORG\nQUciEVfHBMyLn0gkgo2bNmHZ4YfhqCOPwH33P1CQAQaAZ/72d3zm01cDAG68aXIXh1rdc+/9OPro\nI/G5z34KDz/yGHbu3IWDDz4IJ514Ala98K98b+WXXn4Zxx17DD5w5eXYvHkLZs7swqmnnoJg0Oxo\nEq7yPUdE+y4GwEQNKp1O4z//639xxLLDccrKFXjrKSvR2tqCVCqFrW9sw89vuQ1PPPl0QVbtuedW\n4dvf+T7e/e534vx3nwdNU7Fx42b87Gc35/upDg+P4Lvf+wEuft9FePd574Siqti9uxv//aMf44D9\nl+Dss85AS0szhofdLUSa2Pc1mUyhf6Afq154Efff/yfPRjVv2LAR1339m3jPBe/G2849C5IkY/fu\n3bbjov14frHXXluDZ//+Txx5xOF485uX4rnnV03a5uZbfoF0Oo2jjjwCK046Af39A3jq6Wfw3HOr\n8M3rr8ObDzkYW7e+4eq4N3zvB7ji8ktxwvLjMDA4iNtuvxN/fvAvk7b714svIZlMIplKTRqE4oXR\n0TF87brrceF7zsfy449DLBZFX/8Afn/XPbjv/gfy2914061IpdI45ugjseKkE9Hf348nnngKq154\nEdf/+7V48yFLsX37jjJHIqJGIxyx/Kzi1Q5EREQVybKMG3/6Izzy6OOO+v8SEe0tWANMRERVWb78\nOMRiMTz5JEcJE9H0whIIIiJy5W3nnoWDDjwQhx9+KFa98C/stJkqSES0N2MGmIiIXBFFEYce+hZs\n2LDRl8VvRER+Yw0wERERETUUZoCJiIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiI\nqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIi\nImooDICJiIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiIqKEwACYiIiKihsIAmIiI\niIgaCgNgIiIiImooDICJiIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiIqKEwACYi\nIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJ\niIiIqKEwACYiIiKihsIAmIiIiIgaCgNgIiIiImooDICJiIiIqKHI9TxYNBaHomTreUgiIiIiakCB\nQBDJxJjt9+oWAEdjcVx46UfrdTgiIiIianC/u+MntkFw3QJgK/P7uzt+wiwwEREREfkmEAjiwks/\nWjLmrDkAPvGE43HB+eehra0VO3fuwm2334nX128oub2iZKFkGQATERER0dSoaRHcrFn74QNXvh//\n/aMf4/IrPoxHH3sCn77maq/OjYiIiIjIczVlgHfv7sZVH/skMpkMZFlGLBbD2Jh9sTERERER0d6g\n5hKITCaDhQsX4IZvfwOapuH7/+8/vTgvIiIiIiJfeNIHePv2Hfi3yz6Am3/+C3z6mqvR3NzkxW6J\niIiIiDznSQCsaRo0TcMTTz6Nvr4+LF16sBe7JSIiIiLyXE0B8LJlh+GLX/hMwWOyLCOVTNV0UkRE\nREREfqkpAN68eSsOOvAAHHvM0RBFEWeecRokScKateu8Oj8iIiIiIk/VtAhueHgYP/jh/+Dyyy7B\nVR/5ADZt3oIbvvcfUBTFq/MjIiIiIvJUzV0gVq9Ziy986VovzoWIiIiIyHeeLIIjIiIiIpouGAAT\nERERUUNhAExEREREDYUBMBERERE1FAbARERERNRQGAATERERUUNhAExEREREDYUBMNVVJj0LYyOH\nTPVpEBERUQNjAEx1NdB7CnZvv2SqT4OIiIgaGANgqitdC0PXg1N9GkRERNTAGABTXel6CLoemurT\nICIiogbGAJjqygyAmQEmIiKiqcMAmOpK14Mw9oEAWMm2QdcDU30aREREVAUGwFRX+0oGePPrX8VA\n7ylTfRpERERUBQbAVFf7Sg2wpjZBU+NTfRpERERUBQbAVFe6FtonSiB0PbhPZLKJiIgaEQNgqitD\nD8IwAjCM6f3WMwPg6Z/JJiIiakTTOwqhaUfXw7n/n97ZU13fNzLZREREjYgBMNWVFfhO9+ypwRII\nIiKiaYsBMNWVFfhO5+ypYYgwjAADYCIiommKATDVla6ZAfB0Dh7Hg/jpncUmIiJqVAyAqW4MYzx4\nnM4lELpmlXFM3yCeiIiokTEAproxDBmABGB6B4/5IN6Yvq+BiIiokTEAprqZWPc7ncsHrNcxneuY\niYiIGhkDYKqbiWUP+0QGWJu+QTwREVEjYwBMdWMFjqKU3DcCYJ9fw+bXv4R0apavxyAiImpEDICp\nbqyMqSyPTPMA2P9exkq2DUP9J2N0+AjfjkFERNSoGABT3ZgBow5JTkzrGuB8GzQj4NsxkonFuf9f\n4tsxiIiIGhUDYKobXQ9BFLMQxcy0zgAbdcgAp3IBcGqMATAREZHXGABT3ZgBcAaimJ3WC8jqUQOc\nSixBNLYeqeQiGLrk23GIiIgaEQNgqhtdD0IQMxCmeQbYKuUw9BAMw59jJBNL0N71CAxDQio135+D\nEBERNSgGwFQ3uh6CKGUgCso0rwEOQpITAPypA9a0MDLp2Yg3v4ZQeBdSrAMmIiLyFANgqhvDKoGQ\npn8GWJZH8v/ttVRyIQRBQziyHdHYJiTH9vf8GERERI2MATDVja6ZAbAgZqd1AGzoQUjyKAB/6oBT\nY0sQjm6DKKqIxDeyEwQREZHHGABT3eh6MLcILuNrBwW/6VoQsjwGwJ+RzsnkEkSimwAA0dgmpBKL\nYRiC58chIiJqVAyAqW50PZxrg5bNtxKbjnQ9PJ4B1nzIACcWIxofD4B1PYpMerbnxyEiImpUDICp\nbgozwNM5AA5CkhIAdOiGt6/DMESkkgsRiW4GAMiBEQSCe5BMsA6YiIjIKwyAqW50PZRrgza9a4B1\nPQhRMl+H15nsdGouDD2EaGxz/rFofCMHYhAREXmIATDVja7l2qCJ2SlpgzY2cjB273hvzfsxJg70\n8Ph1pBJLEAzthiQn849FY5u4EI6IiMhDDICpbowpHoU8OnwY+nvOrHk/ZilHNhcAe/s6konFiMY2\nFTwWyQXAfg3dICIiajQMgKludB8zp06oaguymRkwjNre9lYphx8BcCqxBJEJ5Q+AmQHW1BYo2Rme\nHouIiKhRMQCmujEzp+kpG4WsKi0AJGQznTXtxwrkBTHjaQ2wYZgZ4OIAOBDsgywPswyCiIjIIwyA\nqW70fAnE1IxCVpVWAEA2s19N+yksgfDudSjZTmhqy6QSCEGAORCDE+GIiIg8wQCY6kbXrUVwU5kB\nBjLp2gLgwkVw3r2OVGIxJHkYgWDfpO+ZAzGYASYiIvICA2Cqm+La2Xov6lKVFkBQkc3MrGk/VgZY\n8DgDnEwsQTS2GYLN0LdobCN7AdO0kEws4oJNItrrMQCmutG18dpZQIJhyHU7tmEAqtqMSHRLzRlg\nM5A3u1kYesCjMzQzwMX1v5ZIbBOUbCeUbItnxyPymq4Fse7lHyGVXDwlxx8bWVrzIlciagz8S0F1\nM94GLZv7un5lEJoWg2EEEIuvrz0DrPnTzSKZWIJIUf2vJRTeDVFKsgyC9mqaFgMgQcm2T8GxQ1j/\n2vcxNrK07scmoumHATDVzXgbtEzu6/oFwFb9byz+OrI1ZIANXQIg5Qd6ePUaNDWKbGbWpAVwFkEw\nOBCD9nqqGjf/P7fgtJ7SyUUAJOhatO7HJqLphwEw1YVhWLWzmXwGuO4BsKAiEtsMRemAXmXpgpXx\nFcUMBMG7iXbJxGIIQhbhyI6S25gDMVgHTHsvXYsBGL/grKdkwiy70PRI3Y9NRNMPA2CqC8MIABAn\n1ACjrsMwVLUFAXkYofBuAEA201XVfqygfXyinTc1wKnkYkSiWyEIeslt2AmCnDB0Cds3fxTp1Ky6\nH1tTzeyrMgUBsPW7oWv1b7FIjad/z6kYGTpiqk+DasAAmOoinzmVMlNSA6wqLZADw5DkJCR5tOqF\ncFYALIhZiJJ3NcCpMvW/lmhsIzLpOfkgg8iOorSht/sdWP/afyCZWFTXY6va1JVAWBlgXWMGmPw3\n2HcyhgaOm+rToBowAKa6sLIyZumAAUFQ6psBzgXAABAMdVc9DKOgBELMehbEmy3QygfA4ch2CEI2\n/0FPZEfL1cC2tK3Chte+j7GRQ+p2bF21SiDqGwAbhohUchFEMc0SCKoLTYtAzbZN9WlQDRgAU12M\nlw5k8v9f3xrg1nwAHAr1IJOurhOEkQ+ArRKI2oN4XZeRTs4v2QLNIogaIrEtSLEOmMrQtCggqJi/\n5D8xY9YfsWHNtzA8eHT9jo361wCnU3Nh6CHEmtZB18J1PTY1Jl2LQFXZlnI6YwBMdWEFikKu/MHr\nIRKVKEoLZDmXAQ731JQBFgQFgqBDFLzpApFOzYNhSIjEtlbclp0gqBJdjUKSkhAEYPb82zF7/m3Y\nvO5rGOg9xfdjm10gtLoHwKnEYgRD3QgE+xgAU11oWhQKM8DTWv0mEVBDM/QwAA2CoAIwM6h1rQFW\nW/IdFkKhbowNv6Wq/VidLAB41gYtlViCUHgXJClVcdtIbCN6u99e8zFp36VpZgBsmTn7XsjyGN7Y\n+Gmoahxds/7o27F1LYpgaA8UpR2GAduphn5I5obIiGIaOksgqA50LVLXYU7kPWaAqS6swNH6QPR6\niEQlBTXA4R5kqs4AB8ez2JI3QXy5ARjForFNSCfnQ9fqd/FAU6uv5wxs33yV4+2LA2AA6Oh6BIsO\n+jZ2bv0QRocP9foU81Q1jlBkFww9VNdANJWroRelFDRmgKkOND0CTYt71gmI6o8BMNWFroUgSpn8\n10Lda4ALF8FpanNV3RSsYR4APKsBTiUWI1qh/tcSiW41n5NcWPNxaXpIJ+cjlXTezUHXohCLAmAA\naG3/J8LRN5BOzfHy9ApoWgzh8E4A9asDNozxLiqSlGYXCPKdYYj59SBT0fOavMEAmOpiYuAIeFc+\n4IRhmH+kAoEhAOYiOADIVNEL2NAmBMAe1ACbH96LKy6As4hSFuHoNtYBNxBNi+ZGDDvfvlQ5jSyP\nQFObvTq1SawSCLMOuD6dIJRsJ1S1BdF8CQQzwOQvbcK0QUVhHfB0xQCY6sIMgLP5r0UxU7caYF2P\nwDCC+UVwopSFHBioaiGcWcoxcSFfba8hm+mCpsUdB8AAEArvhpKdUdNxafrQtKiruxV2JRAWWR6F\nqjR5dWqTqGockjwKOTBct2EYqcRiSPIIAsFelkBQXYzfZajfhR55jwEw1YUZOKbzX9ezBti6RWWV\nQABmFjhbxTAMXR8v5bAW8hlGLefWCkBDIDDo+DlmEONfFo/2LroWLcg4VaJpUYhyiQA4MOxr6yar\nA0UgMFS3W8PJXAmRIIAlEFQXWu49FgztgZplADxdMQCmutD1UH7xGFDfGmDzg1iDJI/lHwuGu5HJ\nuO8FPHERnFkKIebGPFd5bmoTJHkMguA8ipblEdadNRCrBMLphZYVhNqR5FGoqo8ZYC0OSR4zA+06\nZcYmTlFkCQTVg65FAWgIhnqgMAMMAFCVOEYGp9doaAbAVBeGXrgIrp5t0MwFcCMFQWawlgywVQMs\nZXOPVf86NLUJsjzq6jlmFo8Z4EahaVHAkPOLbpxsX7IEIjACzae7B4YhQNcikKQk5MBQ3QLgiV1U\nRCnFPsDkOy33Pg8E6/c+39sN9p2MbZs/MdWn4UrNTeyOOvIIvPe9F6CjvQO7u7tx2+134vXX13tx\nbrQPsV8EV78SCKv+1xIKd2N48FjX+zImvA5ByOYeCwJIVHduuQywG7I84msWj/Yueq78QdOiBReR\npVSsAfbpvWOWHoiQ5ESuBtj/wEBTo8hmZiEaNWvoJSkNTYvUtQcxNR5Ni0CUUpADQ1wEl5NOz61r\nb38v1JQB7uqagY9/7MP4+S234coPXoU///khfOFz1yASYQ0WFdK14gC4fiUQyoQxyBYzAzzTdf3u\nxEVw1uupdwZYCrAEopFoEwJgp9vbtUEDAFn27+6BOQUOkKSxutUAJxOLIQhZhKPbAQCimAIg1VSW\nRFSJnuu0Us9a971dJjW3rq0Mpgz5AAAgAElEQVRNvVBTADyjsxOPPvYE1q59HYZh4Jm//R26YWDO\n7FlenR/tI4q7QNRzFLKqtkwKgEPhbuh61HVLqMI+wNn8Y9WfWxMktyUQ8ghUtbmmxXc0PRjG+IIb\nTXXWCk0vkwGWAqPQ1DgMw/vqNytAl+RkrgbY/8AglVyMSHQrBEEHAIiSudCWC+HIT+MZ4EFmgHPS\nqTl1HW7lhZpKIFavWYvVa9bmvz5g/yUIh0LY3d1d84nRvkXXQwUfyvWvAS7KAAd7AWjIpPeDHBhx\nvC9dD0LO9RO2FsPV8jo0NV5FDfAIYMhmoFNitT/tGww9COTGrbrJAJd6X8jyCADRfN+5eN87Oq4a\nhyBmIIpK3WqAU0VTFK0AWNPCnr8+Ist4rXt9LvT2droeQDYzE+aicMHVou6p5FkaYObMLnzm01fj\nd7+/G4kEP5SpkDUK2VLvGmBrCIZFEDUEQ325X1rnJmayBcGsA65/Bng491z+4d3XjQe9mqNhGIZh\n1sWWqwEG4EsdsKbFIElmLbwcGIKitvh+lyKZG4FskURzAAg7QZCfrAxwIDAIVWnx5Y7KdJJJzYYV\nTk6nOmBP/tX2X7IY13/ja3j0sSfwxwf+7MUuaR9jtkGbmhpgu0VwgFkHnHHZCcKYFMjX9jo0tQly\nwGUNsJyA2YCdC+H2dVYAHAj2OyqBMD98pJI1wKKUhCAovvSRnrj4TpaHAUOGpsU9P45F12Wkk/ML\nhsiYd2U0lkCQr6waYPNuoNTwf4vT6bkANAC1rYmpt5oD4MMPPxRf/coX8Nvf3oW7/nCvF+dE+6Di\nNmheTFFzyq4GGACCoe4qM8DejXSuJgMsCAZkeYwZ4AZgdYAIBvvz/11Ovg63RAAsCFYvYB8CYDWe\n72gSyP2++Xl7OJ2aD8OQEIltyT8mCIAoZfJ100R+yLdBy91ZbPRWaJnUXIQjOwDUtiam3moKgDs7\nO3DNJz+On/zsZjz2+JNenRPtg8wuEMWjkOvYBs0mAA6Fe5BxOQ7ZbjFfTTXAShNkl23QAEAKDHMa\nXAPQtAhEMQlJHnNUA1wpAAbMOmA/egGbJRDmcc1Mc9bXwCCVWIxQeBckKV3wuCSmptWHME0/eq4E\nQpSyEKVkwy+ES6fm5u/ETKcMcE2L4M495yyEQiF8/KMfxsc/+uH849/7/g+xZu26mk+O9h32o5D9\n/0XRtRB0PVw6A9x7irv96UGbUo7qPmwNw8wAu10EB4x3gqB9m5a71SrJCUclEFaWWJRSJbeRA35l\ngKP5DLAg5OqAfcwAJ4sWwFnMYRjMAJN/rN9LALlWaI2eAZ6D1o6/YbDvlMYJgG+7/U7cdvudXp0L\n7cOKM6f1qgG2PoDtaoBD4R5kMzNdrVo1tGDR66g+kDeDFcl1CQRgdoJgBnjfp2lRiHISkpR0nAEW\nxWTZ97Pk0yAVTYvnF8EB8H0cciqxBM2tz096XJTSDIDJV1YGGADkwGBDB8CGYdYAh6PbAGh1u7Pr\nhcZeukh1o+vhgtrZetUAm3Wyuu1Cs2CoG4YRgJJtd7y/4hrgWkogrMEB1WWAh133MKbpR891dBCl\nhKMuEOVaoFn8ungyM8Djx/YzM2YY5hCMiQvgLKKYhjaNPoRp+rFqgAHUberh3kpVW6CpTQhHdtTt\nzq5XGABTXdi1QTP0sO9tklSlFZI8mm+UP1EgOABBUJB1UQesFy3mq+UXXstl4dyOQgZyt7GZAd7n\nWZ0VJDnpqARCU0tPgbP4VT5jZoDH38t+9kjNZmZC12KI2gTAEksgyGcTM8CBBs8AZ1JzIQgKgqGe\nurY39QIDYKqLyYGj+d9+jyw1ewBPLn8AzG4KZis0Z50gDKNwFDJQWw2wqjZBksZsg/NKzCCGXSCm\nG8MAenaeDyXr7N/OHGucclwCUW4KnEWWR325e6CpsYIMsJkZ8+c9mkwsgRwYQCA4OOl7LIEgv2n6\neA2wWeveuAFwOjUHofAuCIIOQcw0Xh9gonIMQzDboE3MAEu1jxF2olQHCEsw3OM4A2wYMgCpYBFc\nLaUcmtpUVfYXsLJrzABPNyNDR2LnGx9EYnSpo+2tgFaSEo5rgCsHwCO+9C01jz0xA+xfCUSqaADG\nRKKYgsZBGOQTazy5daclEBiCmm3cLhCZ1FyEci3QWAJBVMS6IhQK2oeZQaTfvyylhmBYQqFu5xng\n3LkWL4KrvgbYfQ9gi+zTQibyj2EI2PXGFQDgqJ7X3M4qgUhAd1IC4SAAlgI+lUCo8dyQFpNZA+xP\nBjiVWGzbAQIAJCkNXWMATP4wjABgyPmpg7KP7/PpIJ2ek+8BzACYqIieDxwntEETzCDS7xWjpYZg\nWIIh5xlgK1vt1SAMrcoWaIC1kMn/UbPkncG+k5HJ7Idw5I38AshKrEyTqy4QDmuAvX7vFAffsuxf\nFwhzBPLk+l+AJRDkL62o1WAgOAhFaWvYv8Xp1FyEwzsBWHdEWQNMlDceOBZmTgFA1/y9WlQqlECE\nws6nwY0H8t70Aa4lAyzJIwAkx5lEmlq6LmPXtsuw35zfIxjuzi+ArPi8CSUQuh6BYZT/k6077AIB\nQ3Y0Wc4pXZdh6OGCkh45MJwLtL39mFGVZijZGbYdIACzBEJnCQT5xLq4Gu8CMQTDCHr6+zRdGIaI\nbHrWhBII1gATFcgHwEWjkCd+zy8Va4BDPchmOmHoUsV92QXytdUAx6uaAgfkghiAdcDTRF/P2dD1\nEGbMuje3CM1tCYT5Yaup5T9kNdXZIjgAnpbQWBdihX2AhwCIntcb79n9doTCuxAK77L9viSlOQqZ\nfKPlA+DxEggADbkQLpPeD4YRKCqBYAaYKM+udEAQzDrgutQAV8gAAxKy2RkV92W9DmEvqAE2Aw0N\nGjtB7PU0LYzuHe/DrHm/giRlcgvanJZAmIMwrLKGSmUQE/uTlmJmaXVP6xatwLywBtj8vfOyW0k2\n04meXRdgzoKflxz2IUppZoDJN7oWhSBkIYgaAPNvsSAoUBtwHHImNQeSPJzvs1+vAVdeYQBMvtO1\nEARBmdTuq5bg0SlVLb8ITpJHIIopZNKV64ANPWj+4ZvwwTtVNcCCYOTqgLkQbm+3Z9d5kKQkOrse\nAmAGoE5rgPVcQGtlViuVvOgOaoAFQc+dg5cZYPP1SBNGMItSBqKY8rQOeNe2yxGLr0dL+7MltxHF\nFBfBkW/Mi8zx93k9xn7vrdLpufnsL8AaYKJJdD1UkDW1iGIGuuFfAKzrAehaNH+Lyo4gAEGHdcDF\nU+CAWmuA41UHwAB7AU8HitKCnl0XYNb828czRvIYNKeL4FSz36goqhDETMXSCSddIADzveNlL2BN\njUGUkpMucr3skZoYPRADvadg7sIbIQiltxNZAkE+0ie0QLOYwzC8zQCnU3Ow840rkE7N8XS/Xsqk\n5iIc2Zn/errVAMtTfQK077MLHAH/64WsW7ylBmFYQqEeRxlgXQ8W1DEDtY1C1tQmSDYjmp3ya6Qt\nAPT1nImWtudsBw2Qcz07LkIovBNtHU/nH5PlMUc1wIYhQtfHA1pJSlZcaOM8AB71vAZ4Yg/g/HE8\nmgZnGMCOrR9GR9ejiMbt259ZzElwzACTP4ozwEBu6Eu29gs9wwBGh4/Anl3vxMjQURClNAxDwtyF\nN9e8bz+kU3PR3Loq/7UoZqfVwmxmgMl3RtEYZIsgZmD42AXC+uCV5ZGy2znOAGuhggVwQG0lEGoN\nJRCAWb7hRz9XJduGbZs+iZGhIz3fdyPJpLvQ2/02zFlwa0HZjNMaYCuIE/MBcKLih4vmoAsEkOsF\n7OHFkzkFLjHpcTMArj0wGOo/EanEYsye/4uK24qiWQPcqG2pyF96bjrjRHKN45B1LYTe7rOx9qWf\nYtO66xAI9ePgwz6GOfNvxcjg3vt3OD0pAzy9SiCYASbflc8A+xsAS/Jo/tZzKaFQD5JjB1bcn1nK\nUVwCUd0vvK4FYeihqhfBAf5lgAf7TwIgIpupvDCQStu9/d8Qb16NppYXCx53WgJhLXibmAEutwjO\n0CUYenjSh7Md2eOLp1KZZy+GYeh6ADvf+ABmzvmdozsSopQGIMLQQxCkyX93iGpht9A0EBxCJjW7\nqv3pWhCrX7wJgIAZ+z2AzpkP5rv8iFIG27d8HNlMJ4KhvlpP3VOaGoWqtOdboAH1WdjuJWaAyXe6\nHppUOgD4f7WoVFgAZwmGux0vgivOAFc7+9y6/VxbDfCwLxngwb6TAWiOOmOQveGBozHQ+1bMnn/r\npHpVSR6Droeh6+XzD+MBcCr3vGTZNmhaUX/ScmR5BJqnGeC47VhvL2qA9+x6FwxDxMzZdzva3vp5\naSyDIB+Yw2mKM8DVv89TqQVQsu1YuuyD2G/ub/PBL2B2KQqFd2Bk6KiaztkP6fQcABpC4d35x+qx\nsN1LDIDJd3alA4D/LVMqtUCzBIN9UJX2ykMGdO9KIKxBCHZBg1NywNuFTIB52z4xuhRtnU9BYQa4\nKiNDh2Pz69di7sKbEWtaP+n7Vu/nSnXAZr2vlr/rIFYogSjOGJcjB/yoAZ583FprgJVsK7p3vhdz\nFtwKUZr8N8SOmBtRq+tcCEfeM4fTFAbA5p2OKgPgxCKEIjshlbhb0dy2CsODe18AnEnNRSjcA1FU\n8o9xFDJRETNwTE963O+WKU4DYCkfkJRfYKTb1DJbQbzbekNVbYIopgv+eLgly6Oez6Af6j8Jkdgm\nNLe8yBKIKoyNHILN676OWfPuRNfse223GW9pVr4MwqrntTLIZg1wuQxw4YjWcjwvgVCjJTPAtdRG\n7tp+GSKRN9DW+YTj51h3m7gQjvyg2dYAD0GpsgtEKrkI0eiWkt9vbn0Bo8PLHA1rqqd0ai5C4Z0F\nj023GmAGwOQ7XQ+WaIPm7+0SxwFwbvV6pYDEfB2Tu0AAEgzDXTm9VsMQDIssD3teAzzQdzLaOp5E\nINSLbGYGFxK5kBg9EBvXXo+u2Xdjv7m/K7mduahNq1gHXDzVzSyBKJ0B1rUYBCHr6KLK80VwWrxg\nCpwlUEMGOJVYgP6eMzB3Ufm2Z8UEQQEEFRqHYZAPdJsaYDkwCF2LQdcDrveXSixEJFY6AG5qfgWG\nIWFsdKnrfVt0PYDe7nM8/XueTs0p6AEMsAaYaBJdD09NGzSHNcDjY2Yr3JLWw7YlEABcB/K19gAG\ncovg1CYYhovooIx0ag5SiQPQ1vkkgqFe6Hp0WrW0mUrJxGJsXPMtdM58ELPm3VF2W0EwJ6ZVGoZR\n3G/UXARXvgTCSfkD4EcfYPtjy4EhaFpTxXpnO6MjhyIa24RY0zpXzxMEQBJT0NkLmHxgVwMcyPWa\nd3u3wzDMDHCkTAZYlLJoan61pq48idGDsH3z1Y66HTmVSc8tWAAHWEktZoCJ8sq1QfO7BrhSD2DA\nnIwlSsmKGWC712EFwG5fhycZ4MAIAMmzIHWwbwVi8bUIhfcgGDRXHLMOuLJUcj42rv4O2jqfxJwF\nNzvKVsrymLMSiIIAOFG2D7DTFmjm8UdzC/HcZ6zsj11iEZxsjUN2H2yrSnPZITbliFKGATD5Qrft\nAzwCQIeSdVcGoSjt0NSWshlgAGhuXYWRGuqArcC8lizyRIYhIGOTAeYoZKIi5btA+F0C4ewDVJLG\noFbMAE/uAmEFxG5fR609gIEJwYUHt7INAxjsW4m2zicBmAGEJA+zE0QF2UwnNqy+AS3t/8S8xT92\nfKtekiq3QpsUAFfoAqGrlccgW6zSIK/KIMw+wPaL4MzjuK8D1tTmghXxbohSCto0ykTR9KHZjBsX\nBN2sq3dZ7pNKLIIkjSEQ7C27XXPbKqSSS5DNtrs+XwD5+uTEyCFVPX/S/rId0PXw5AywxBpgogJm\nFwi7ANjfsYlOa4AB85Z0pYDErg+wkM8Au/ul9yIDLEpJQFA9WcyUTi5EOjUXrZ3jE8uCuTpgKm10\n+HCIYgbzl/x3wbCLSiQH0+CKP2grDcKw609a+vjme8+rhXBmsD45AyyKKiSpusWaqtpccYhNKSyB\naGyp5HyMeRTsFbPLAAPVLYRLJRchEttS8cI5FN6JYGg3RqsciuF1BjidmgtRTCIQGCh4nDXAREXs\n2ocB/tYA67oMTWtyHACbt6SdZIDtSyDc1wA35dthVUsQUFXWwc5A38mIN7+KYHD8D1owyAC4Ek2L\nIhAYgiDorp4nyWOOaoAnftA66QLhNAAWRRWimPSkDtgwrD7AkxfBAdVPgzNLIKrNAKcZADewvu5z\n0L3jIl/2bTcKGTCHYVSTAS5X/2sRhFw7tCr7AStKG6Kx9UgnF1S80+lEJjUXocjOSYG7KGYBQ67Y\nUnRvMT3OkqY1u8AR8Pdq0cpsOVkEB+SCi4oByeRAXhAMCIIyJTXAgLWYqbZ+rmb5w8n58gdLMNQL\nJdtZ0773dboWgeiw7nYiSa48Drk4oBVzXSBKreTWbW7NliMHRqEqtfcCNi9ipZLBtxwYhlJlBliq\nMgNsjkOePrdiyVuK0u5pn2uLYQjQdfvfM3McchUZ4OhWR9s2t76A0aFlVQWXarYVTa0vQZTSSIy+\nyfXzi6XTk+t/gYklgdPjd48BMPmu3Chkv0ogrCtxpxkkJ7ekjZK1zBnXv/BeZIABaxxybRng5NiB\nyGa60Nbxt4LHzVZoXTXte1+naVFIYuW+u8VkhzXAxV0gAKnke81NBhjwbpKg9TpKDXWpthewpjTV\nVgPMDHDDUrLtjsaNu6XnWuuVGvriZhqcrstIp+ZVXABnaWp5GboeriqAVZQ2BIL9iMXXIeFBGUQm\nNbdEAJwrCdSmRxkEA2DyXbkA2K8rRVVpgSglHA+aqFRfCdgvggPMOmC3gbymxD3LANcaxAz2rUBz\n678mBRssgahM0yKOOy9MZF5wVbrjUNjVweqzW6oThJsuEAAgBUY9WQRn/d7Y9QEGqu8FbLYxrLIG\nmCUQU25kaBkSowdOybHNANj7DLB1UWU3bCYQGHR1oZdOzQMMGWGHGWBJSiPe/FpV7dBUpRVyYAjx\n5tUYG6k9AE7nSiCKVbsmZqowACbfGXoIgs0YUz9bpjhtgWapdhEcUF03C1VtghzwIACucaCBYQgY\n7D8ZbZ1PTfpeMNQHJdvpWZ/hfZHbsgOL00VwxV0ggNL9qt1ngL3pBaxpUUBQbS9ygeoywLoWhK6H\nqw6ARSmVz9ZRfSlKC7as/wI2rvkOdu+4uO7HNwwz46mqcc8H+VgXVaUXwTl/n6cSixAK74IkTZ6S\nWko17dDMn0crAoFBxJrWIDn2ppqmyulaENlMF8Lh0iUQhuFNe0W/MQAm35m1s/Udhex0CIZFqnIR\nHOA+ADZ0Cboe9SQDLNWYAU6MLoWqtKC1/dlJ3wuG9sAwAp6PW96XuOm8MJE5CKN8hkpTC0euimIK\ngF7yfaqrk0e0liPLo57USWqqOQWu1Ep2t7eGgQk1/NWWQIhpaByFXFeGAQz0noI1L/4M2cxM7Df3\nTqSTC+t+HroWhaGHAUhl+2ZXIz9u3ObzLBAYguqiD3AquQhhBwvgJmpuW4Vk4kAoWed/k3U9AkMP\n5wLg16HrASSTi10dd6JMejYA0TYDLDIDTFSoXODoZw2w0w4QgHn7ttKqfKNENwtBzLiafmMdp9Y+\nwIC1wr76AHig72Q0tz1ve+s8EOwHoLEOuIziINUpWRqtfMFVlNEVBGsaXJkSCFeL4LwZpa1psbLH\nNTPA7i6irMC82otElkDUVzYzA5vWXo9tm67GrHm/woFv/jzaOp5GNjOzbO9qP0xsRVbpb7pbuhaB\nKCZtWx7KgSGoarPjRWqp5CJEY5tdHT8c2YZAcI+rMghrOIccHIIkpRCJbUGihjKIdHoOAsE+28y1\nIOiAoE6bVmgMgMl3diOEAf9rgN1MkXJyS7p8LbPzX3itxg/3iWqpATYMEUN9J03q/mARBB2B4CCy\ne0kniOTYEiTHlkz1aRQoDlKdMktuYmXLS+wCWrNbiTclEJI86tEiuFjJBXBAdTXAZg1/EqKoVnVO\nLIGon/49b8WaF38KAyIOPvwqdM26H4KgmxlCQUU6Nb+u56Nk2yEI5ueN13XApVqgAch93oiOLyrN\nFmhbXR3fbIf2AkZctENTlTYIgpLv0x1vWlNTP+BSC+As02kcMgNg8pVhiDCMQOlBGEbAl56BbjPA\nlUbTGkbpRXBuA2BVbYIgKLa30dySAyPQqixRSCfnQVVb0dK6quQ2wWDvXjMOuWfX+di26ZNTfRoF\n3AadFjNglEpmKXU9AMMITNq3KJfPALtqg+ZVDbAaK7kAzjzOEHQ9Cl1z/qGoqU2uSpiKmSUQzAD7\nLZvpwLZNn8KcBbdi/4OvRSi8J/89UVQRjmxHqs5lEEq2HYFgP0Qp6XkrNLPm3z4ADgQHATibeqhk\nW6Aq7Y47QEzU0roKI0NHOP7cVHIL4KwSpVjzGiRGl1ZdH51OzUMosr3k90Uxwy4QRMD4iGD7PsBW\nvZD3BfOq6r4EQteiJf+omEX9ou0iOLe1zFYPYKdjc8sxM8DxqhaqpVILEQx1l+0cEAjt2Ws6QWhq\nHMnEgXtVFljXIlWVQFjZmFK3aPX8avPiDLB9AGwYQm5wRv37AJvdJ8oEwLk7MW56AatKc00lQuYg\nDGaA/da9472Ixtejc78HbP+eRaJvIJVcUNdzUpR2BIIDkOVRz1uhlav5F0UFopRwVO+eSi6CKKYQ\nDHW7PoemlpegaTEkx/Z3tL2aNRfAWeJNq6FkO6subUslF5bNXPt5Z9drDIDJV9YvQqn+uQB8uV2i\nKO4XwQEoWa9WLpB3O9LZqx7AgLVISKpYvmEnnVyAcPSNstsEQ33IZveOANjM5mjo6zl7qk8lr/oM\ncCL3/NLlDMDkfqOlSiDM2/1i2UC0mCwPQ9Oaar4DYy2CK3mcwCgAHarqIgBWq58CB+RGIbMEoiRd\nl2u++MmkZ6J/z5mYPf/2khfz4egbdV8IZ2aAB3JlbV5ngMtf8AYCQ46GYVhBpJvx6RZJTiLetMZx\nGYSitBWUAwZDfQgE9yAx6n5UtGGISCfnIVLmc2M6jUNmAEy+sm57CiVKBwD48sti9j101wYNQMky\nCCMfANdeAuHVFDgA+TZRboILSyq5oGIN2t5UAqGpcbR2PIOB3lP2ihX+hiFC16vrAmGOIk6XzFCN\nB8CFH7aSnIBuEzRbF25ua4AB1HybWNNiZe8iCIJudpxw0QmililwAEchV9K/53RsXHt9Tfvo3nEx\n4s2voqnl1ZLbRKJbkUosrOk4binZdgQCA7kuJ15ngKMla4ABqxVa5b/FqcSiqsofLNHYBqSTzmqr\nVaUVgWDheph485qq+gFn0rNgGMGyiRPWABPl5DPANvWu42MTvQ2ADUOEpja76wMsle+xamWTvFgE\nZ2aAvQmARSkJQVCqWs2fTi4seyUPmOOQ95YMsKbG0T7jcUhyEoN9K6f6dPK32KvpAwyUH4ahqVEI\nYhqCqBU+p0QJRL49k6sSiNzFU42dIMygoPwdDbedIFSlxgxwbhFco/SwTiXnu6qxVpU2pBJLoOty\nVcdLp+agf8+pmD3/jrLbRaJboaqtrtp21UpVrAywN23+JqqcAXY2DtkcgVx9AGzemXO2ONnMAA8W\nPBZrWlPVRLhUciECwb6ydzD97O/vNQbA5KtymVPBpwyw9YHuJgMsCDpEKVEyA2wV9Zd6He5qgL2Z\nAgeYq4Kr6QShaSFk0vtVnEIUCPVCybbX1DjdC4YBqFoccmAYHV1/QV/31JdBlMrSOiWVqVHUS6w2\nl6SkfQmEFgGglRxGYUcU0xAEpeaFcOb7uXzphdsAWFObqx6CAYxfcE+XWsRa6FoIr7/6QwwNLHf8\nHE2NwzAC5jSyKuzefgma21Yh1rSu7HbBUA9EMY10amFVx6mGkm2bUAJRvxpgINfzOlv+TodZRrCg\npgywuTbDWQ2vqrQiUNQRKd60BqnkQtct6tIO7hpWMxhqqjAAJl/pehiCkLWtdRIEFYDm+e0S64PW\n7SpyWRqDWjIDXK6Uo5oaYG8CYACQAiOu20yZrYmMsu1sALMEAhCRzXZUf4Ie0PUwYMiQpTF0znwI\nycTUt0QrVafrlCQloJapAbbbryQnSmaAJSnpamGlIOR6AddcAhGFXKYGGHA/DMNcBFdbCQSAhiiD\nGBo4HroWc/XvaP2dSyXc/w6lEgsx2LcCs+eVz/4CgCAYCNd5IZyijJdA+NEGrVwGWHYwDjmdmpMr\nI9ha9XkEg31mYsJB/b6SnZwBjsS2QBTTSIy+ydVxU8mFFdeN+DngymsMgMlXuh60XQAHmB/Aflwt\nqmoLRDEJ0Wb8cjnlxiHrehCCmLENMKayBhiw2lm5+0OfTi5AKLIToqiU33dgGIKQhTLFZRDWv4sk\njyEY6kNz2yr09ZwzpedkTpnSbC+KnJDLLNIp1dJMLFMCUd1ivNHaSyDUOMQKGeCAy3HItS6Cs4KU\nvaFW3G/9e04HAFfZTutOV7KKAHjX9n9Da/vfEY1vcrR9PeuAdT1gttALDkCSxnwYhFG+BtjJ+zyV\nXIRgsAeyiwWrxYKhPQAkKA4SE3Y1wIKgI9a0DmMuF8KlkgsQiZQPgFkCQZRTaniExY+WKW57AFvM\n7Jp9Rs4oMc0OcP8L73UGWK4iA5xyUP8LmBmcYKhvyluhqRMCYADonPkgBnpXTmmvV02LQJLdZV0n\nKjd8pdSADUmyf4/qWhRimYVopdQySMVSaRIcYE2dc9cFopaLRClfArFvZ4CzmRkYHT4c4egWdwGw\nGoUkjyCVcDcSNzm2P4YHjsOsCrW/E4WjW+vWCcKaehYIDEIO+JUBLlMCERyqeKcjlViEcGxrTedh\nJiaUin+XNS0MXY9MygAD7uuAdT2ATGpOxQywnxNevcYAmHxVKQD2o2VK1QGwVLpmTC8xBhkABFFx\nVcahqU2QAt60QQOqC2Kc1HJZAsHeKQ+ANbUJopjOTwZraXsekpzAYO/KqTsnLQpJrK78AcgFwCVq\nzkutNrcmyNlvX00APFVLL8IAACAASURBVAqthgywYYjQtWjFtn5uAmBdl819VvE7bBFEDYKg7PO9\ngPt7T0Ukthnx5tdKlm/Z0bQ44s2vIpVY7Gogwq5tl6Gt80lEotscP8fqBVzt4AU3FKUdEFTIgRHf\nFsFVzgC3lX2ttS6AA8zERCBYuUWltSAvYBMAx5vXIDF6kOM2iOnUXACSowCYGWAimAs0yt0i9qNl\nilJLBrhMDfDenQF2F8Q4qeWyBEO9UKZ4HLK50Go8yBIEHZ0zH0Jfz1lTdk66Fqkq62qRpETJW7Sl\nSiAkKZkrvSjaXq0yAA6M1BQkjHefqLwIzmkNsJW1q/V3RJRS+3QAbBjAwJ7T0NH1cO7OgJsMcAxN\nLa9A0+LIZmY6es7YyFKMDB2BWfPudHWekehW6Hq06sELbpgt0IYgCEauxMj7Nmjla4CHYBiBkncS\nASCdWIhobHPN5xJ0MKRIybYCgmo7qjzWtA66HnR8FyCdXIhgaDekEiWNFtYAE+WUKx0A/KsBrmaM\nqpmRKxUAB0sG8qKYhW44ew1mi7aY5zXAbjLAqhqHku10nAEOhnrr8uFVjmpTN93R9dfcYjhnE5G8\nVqknaCXlPqBLl0AkzfZeRV05Kn0wlyLVWAKR7z9c4ULAqo10kgUcX8RafQ0wYA7D0PbhEojE6FJk\nM11o73wi915ylwEOR7ZDDgw4Xgi3a9ul6Oh6GOHILlfnKQcGIcnDdSmDUHM9gAGzvl3Xo1W3erNT\nOQNcfhyyqsaQzc6sOQMM5BITFQJgVWnLXxAUk6QUIrGtGHNYBuGkbzzgflH4VGIATL6qXALh/dWi\nOQloqPKGRcwpW+VKIEos5hMzMBzOPjcDBtHjLhDDroKYdHIBBCGLUHi3o+0DwanvBayp8Um32YOh\nPrS0PY/eKVoMV23ZgaVSH2C7oHJ8YEthFrhUwFxJNXcPJrKyjuUmwQFmRxbDCDqqyVWtcheXi1iL\n7evDMPr3nI6Wtn/mbveXLqcpZhhC7o5BAtHYJkcL4bKZToyNHI6Zc+5yfZ6CUL+RyIpitkADxu8g\neJkF1ioMvhnvy24fAKeTCyGIGYRcXkTYMRMTFTLASmvBFLhi8abVjgdiOL1ryFHIRDmVF8F5XwOs\nZGbkVsm6Uy4DXH4RnOL4F9663Wx3S6pasuwuiEklFyAc3Q5B0B1tby6Cm+ISCM2+d3LnzD9jcIoW\nw+lqdVlXS7n3W6mMbn5gS1EAXFMNcE0Z4BiECbXZJY+T+xB20glCdTnGvBQzAJ4eH8RuaVoIg/0r\n0N71CIDyF+/FzIsCCZKcQCS22dEt8NHhwxAMdSMc2VnV+UaiW5GqQwbYGoMMTBxv700AbBgiDD1U\n9nfebC04WLLcJ5lYjEjkDcd/e8txkpgwewBPrv+1mAvhDnF0Z8bpuhF2gSDKKbd4DPBnxWg204Vg\n0H0ALJepySz3Otz8wpv1jXrFjJkbcsBsg+Z4MYOLBXAAEAzugaa2TGkwoapx22ljzW0vQJJHMTAF\ni+EqNcWvpFwNsF5i39b7pjhwLlUzXIlZPlNbDXClHsBALnMtqI7GxKpqE6RA7XdIxH24BGJoYDlE\nMY2W1lUAyncUKWa9dyRpDJHYZiQdBsBNLS9Xfb7h6BtI1yMDnG2HnCuBEMUUIKieLYRzOvgmEBiC\nWmIYRiq5CJEaO0BYnHTnMXsAl8kAN6+Bku2sWOKmaRFkM/s56xzEDDCRSddC+ab0dry+WtS1IFS1\nFcFQr+vnVr8IzvkvvFnLOmZbk1Uts1ZSdJzpSCUXIlyhl+NE1s/S6ehNP9iVQADji+H27Hp3flpf\n3c6pyrpbiySPwtDD0PWA7b7tAmBB1CCI6UnvU12zL5moxCqBqHaFvpMewEBu1brsrBOEWcNfW/0v\nAEj7cAnEwJ7T0T7j8fyobGtAipPRz1a3CElOIhrbBCXbBVUpHSQaRu0BcCS6FenUfN8nSirKeAZY\nEMzhRl5lgK33klih84u54NN+HHIqUXsHCIuTxITdFLiCfYR6EQztxujwsrLHSifnA4KKUIXBSQBr\ngIny9AqL4Ly+WrRuCVVfAlFuEEapNmgZAJKjDKzmcQcIAPmBAU4yHYbhfDGDRZKTEKXklLZCK+4C\nMVHX7HsAADu2fqiep1R13a3FaoTvtq2ZZDMMo9ouEJI8AkAqu2q9HCc9gC1yYAhqtr3yPpWmmoZg\nWMwuENMjE+VGJt2F0eHD0DHjkfxj5sWhaDskpZimxnNlKwpC4d0QxVTZOuBMehaUbBfiNWaADSOA\ndHp21ftwwiyBGL/lLwW8a4VmlVlVzAAH7YdhGIaAdHJhTSOQJ3KSmFCUNsjB0iUQgNlScnjwmLLb\npJILEA7vrFjqBLANGlGerocrlEB4e7WYzcyAKCWqyoZJUgK6FrUNZCtlgK1tKrHrZlArUUzlFl44\nyK4pbdDUFsct0CzBoPPZ836wMud2JCmNRQd+D/17zsRQ//EV92UYwPDgUTVno2ougbBqFG2Cz3Il\nDbYBcNU1wGagWW0dsKbGKvYAtgRCfY7uIqhqbWOQLaKY3icHYQz0nopobGPBrXSrPMhJGYSmxfJl\nK4KgIxLbUrYTxNjw4QhFtiGYy6xWQ5YTCAR7fS2DMAwRqtKS7wJhHte7YRhOJz/KgUEkxg5C7+63\nFfyve+dF0PVwTSOQJzITE4myiYlKGWAAaG57DqNDy2zvRFnSyYWOz5uL4IhyrBHCpXh9tZjNdFWV\n/QUmLpqYnEUxyox0zgfADm7Bm7fyvQ2AzYUXzjpBpJILIIpJ1z+jqe4FXKoEwhKNb8Ts+bfhjU3X\nIJspPR7UMIAdW67CprXfxEDfybWdU5V1txZRTAHQJn1AG0bpGmDAvN2t25RAVHMuZlcJrewt8HI0\nLVaxB7Al6HCgiqo05zLTtZGk9JROCvSDYZjdHzq6Hi54PN8dxEGwZ7ZhHP83i8Q2la0DrrX8IX+c\n6Fakkotq3k8pZgJAypdAAPB0HLJ1wVtp8mNTy8sQBBX9vacW/G944Fi0dT6OQA0DXooFg+VboSlK\nm+0UuMLzfQWAgLGR0mORzbuGzpImfgy38ot3DfKIbBgORiErSuXbok5lMzMQDLqv/wUmtpiKQy6a\n1KZrIQSC/bbPE1xkgLUymcxaOO0Ekc6NQHY7vjcwxeOQy5VAWLpm342R4WXYuuFzOOCQr05aaW0Y\nArZv/gQG+09Ec+vzGOxfgY6ux6o+p3JBqhOCYF50FU/w0vUwANFdCUSVGWBrYEC1vYA1NZov5agk\nGNqDkaEjKm6nqs3elUCk96t5P3uTxOghULKdaOt8suBxQdAhiklHpSxmR5Xx36VobDP27Hqn7bZW\n/e+8Jf9b24nDHInsZys0JVdeM3HRl5fjkM0xyJVr/ptbX0Rz64ueHLOSYKh0JwhdC0HXomW7QABm\nF6OmlpcwMngMmltfst0mlVyIGfv9ydE5+THcyi/MAJOvdD1UMnMKeH+1qGS7qloAB0xoMWVzG7Fc\nLbP1uGGUvoVk8XoKnEWSRxyVQJgt0La63v9UlkAYBqCWaIM2kSAYWLj/D5BOLkTPzguK9iHijY2f\nxlD/chxwyBcxa96vMDJ0JFSl+uxQrYMwAPthGPnpaiXKeMypX+PvUV0PwDACVQfjUg3T4DQt7jwD\nHOqFkq38HtI8LYHYtybB9e85HS3t/4Bs0yXDXMRb+f2sqbGCLjSR2CakU/Ns72Clkwugqq1oan6l\nthOH2QvYzxIIRWmHLA8X1Kl6OQ5Z9+D33WvlegFbrdhkBz3xm9uew/Dg0bbfU5VmqEq7yxKIQF1G\nX9eKATD5StectEHzcBFclT2AgVwWRUrY/sHU9dIjnae6BhhwPtI2lcsAuxV0WL/pB12PAIYM2aYN\nWrFAcBAL9v8hdm2/FInRgwAAhi5h64bPY3ToCBzw5i8gGtuKaHwdgsF+DA2cUP151VgCAVjBbGHQ\nolvT1UqWQCQLLtLy7ZmqHMssyyPQSlw8DQ8ejV3bLi35XPN2urPjWh/WlToVqJ4tgqtcAjHYfwJ6\nu8+t+Vh+U5QW9Ow6D4N9K9Ax42HbbZy2QjP/zcZ/lyK5jjB2fXpHRw5DJLbJNuB2KxLdikx6tm/d\nWpRsG+SiOmXJwy4QTjPA9RQIlp7SqSptADRHF5MtbauQSc9FOjV5kWIqucAc3hHudnRO44vC9/4C\nAwbA5KvKgzC8rgGeUXUGGMi1zbG5jViuD7Ag6ICgOnodftQAA7kgRi2fATZXIc93vQAOGB+7ORVX\n9dYHmNPSkZb25zFjvwewZf0XoSpN2LL+SxgbOQQHvPkLiES3AzDLD9o6nsJglXXAhiFC18M1Z4Ts\npsGZAa0OUbRvHygWlUDoVsa4ymBcLpElMwxg17bLsGf3O0t2ODH7ADv7dwmG9sAwAmWHYRiGCE1r\n8qgNWgq6Vj4DPDxwDIb6q78I8pNhiBgePBKb130Vr626A33d52DW/NvR3LbKdntZHoPqtARiQgZY\nlLIIR7YjlZxcBzw6fBiamu1vi7sVjmwHYCCdmu/J/opNHIJh8bIEotaSJz+YJRD2iQlzCtywo5ab\nwVAvwtEtGLHJAqeTCxCObHM8vMP6nJwOrdAYAJOvKrVB83LFqGHUtggOKN0L2JwEV66fsbNMtq8Z\n4Ao1wNnMDOh61FULNEsguAe6HnE8btVLqssAGADmLLgFkpTC6hdvRjKxPw588+cRLho/2tb5JEaH\nD4WSte/ZWU6+JVKVWVeLXQ2w1V+4VJ22mTUeD4A11VmD/lJKvXcSo0uRSi6EroVLTgtz2gcYQK6G\nXquwar0pf061ctIFQsl2QsmWXjQ5FXQ9gF3bLsFrL9yKLa9fC1FK4oBDvoylyz6EmbPvLRnQmNPg\n3C+CA4BofBOSY4WdIAxDxNjwoZ4sgAPMQDsU3u3bRDhFaS/oAAHkFsF59Ddrb8wAl0tMqNm2ivW/\nE7W0PWfbDs3tXUM3d0SnGgNg8lWlDLCXNcCq0gLDCCJQ5SI4oHQdnRnIl2/n5qwLhD81wE66QKST\nCyHLw2UnA5USDPUBwJQshNPUOEQx5agHpUUUFSw88LuIN7+GA9/8BYTCPZO2icQ2IRTejcH+E12f\nUz7rWqEpfiV2NcCV+gsXX6RpWhSimK56vKpUYhpc7+53oK3jaUTjGzA6fKjtc51OggPMOyWBYH/Z\nWnI1dxfDiy4Q5ijk8hlgMwD2bhGuF/r3nIa+7rdh1tzf4C1HXYKFB/wn4s2rKy5cdVwCUZQBBoBI\ndPOkDHAysQSaFkG8ebXr11BKxMeFcMU9gIFcG7QqO5wU21trgHU9YvuZpSitjup/LS1tz2Ns5C3Q\nin5n3PaNtz7vp0MnCAbA5KtKi+BEMQvDo5owJTsDgIZgiW4NTkhVlEAAuUy2Uf51GIZ3Y16LyfLo\n/2fvzqNkye76wH/j3ojcan/769evF0ktIbVaYhPSsB4Dx8YIY+CwyQMMA5JHAmyw4fh48IaXsRmY\nw2A8rBpgDBzGjBEgsGBmzggEWsAIBBJ0t5C61evbl656tWTGcm/MHzdvVi6RGRGZEZmRld/POTqS\n6lVlxVuq6he/+N7fL7UDbA/A5Z0AAZiC0nV3J47cKcu0kzOarRfw8k/712MjMY5jusDTxCAK6wAn\nZIDTJjpIedQrwO37z5JFTorPBP4ZvHTn83Hu4m9iY/MvsH9vTAEcrY90EyeZdGrdvN4GHOFPvGnO\nSso2VMohuCA4DaXWoSq0MGP3zhfg9Pn/F2cu/E6uf1/DhyPHiYYywADQXPsU2ocPD0Rd9vdej7X1\nT8z8b7xfmSuRoyChA9xdhFFEdGvWud9l8GrdxkTC15SZAZy9A7y28SSE7GB/79N7b4vjbgSCHWCi\n/MzhsUkFsJ9aOGYV+Ofg1e721oNOY3wHePLvI0snu3eYq5QMcHoHeNoDcJaXUryUJcowAm1aO2f+\nAIf7j+bubCvVApwIjhPO9Pmlu5+QAZ78qHVkCsSsG+m8/ZE5wLdvfDla60+htf5xrG99DAf3XjuS\nAzYnvWuZp0AAJgc8sQMcbsF196e6SRsmRAexrk/ILzehu3+OVYlBhMEW9vdeh53Tf5D7Y5Py5EmS\nblpaa09D6yb8zsXe2w72Xj/T9rckZc4C7l+DbJl8usy0EGV/77UTf33W1edlECKE672U+DVlZgBn\n7wA7jsbm9p8OxCDC4DSU2sjVAXaYASYyp+8Ru5MzwLK4DPAsEyAssw553Bi0SR3gMDUDbIuMUuYA\ne/egovWJ65g7U45AsyaN3ClT2hKMWTRbz6PZ+hReuv2FuT7OFp2zFmpJmfP0CMTRwLKWaWcAW657\nb+DmSWsPt69/Oc5efDccB1jfeAJaNUbW5dpryDoHGEj/N2TGBBazKMAWK+NiEKFdmOJElSmAd+9+\nLmr1m2iuPZ37Y8d97xpmNsENfj253j682s3e37HWLg7uvbaw/K/VbD2LMDhT2HIKK47NFIjhAtie\nt0hb9OJ3zuOTj/8wOu1LY9/HHIKrVgEM2GUYowfhsmyBG7a588e499Ibeh3zztGDEPKw12nOwnFi\nOM5ybINjAUylsV8AkwrHIjPAs06AAEx3bfibcxwDsW6kHOZL/33YAyplzQEGxNhRaHEs0Gk/MFMH\nOOsmr6KpDDOAZ2FiEPkKYBUVkwc0h+AG/86yRCCUavV+SKmoNdNj6uEM8Eu3vwhwFHZOf6D760do\nrX8SB0M5YBvdGM6TTlKr3ZwYo4nCzUJGbgEmAwxgbAwiCM5AyEPU6zcqkwPevfMF2Dnz/qlurNyE\n713D4jj5EBxgFmLYw45HB69CHEusbzyR/0ImqDevwnGCwmMQSq0jjmtwhyIQbm+75+QC2HZQ/QkF\ncBU7wMD4J3NhkL4FbtjW9p8iDE718uDmqWH+2FyWn4dVwAKYSnNcAE/OACN2J3YuswqCc1NvgbOS\nDpLYBRezjnOLog0IeTj1YaVJ3O56zXE5YL99H+LYm2oEmmUWGSwoApFx1NY0ds78AY4OX5k4A3Oc\nWXO3lhm7NxSBSCloTeRA9pY8zJwB9u4h1g1oVUccAzev/W2cOf/bEOI43mFywI8NXecaAJ2rKDAd\n4EmH4DYhC+oAy+7UFj1mFnAYnEGtdgde7e5C13wfX8/08Qcg2yIM829GJj6Faq49jXa3A7y/93qs\nbTwBIcc3L6bhOBqN1guFH4SzNzDDHWBHqO6GvMl/LvbGvtMZ/z2gimPQgPFfU1G4DS/HITjAfC9Y\n2/h4bxxanhXI/Yoeb1oWFsBUGvsFkNY5Ne87++OSIiIQbsJBkl4hP+GHgSP81MxTWTOAAfPn6DgB\n1JgccPvoIXi1W7keVw/zTmAEAgDqjetorX88Vxe4qB+G5oarNbAcIq2g7a3s7t6ozZwBto+Jow0c\n7j+KztGDOHvhtwfeJykHrNRaNwaS/YSRV7+JKNoeOzElCjcL+xqxHeBxEYggOAOvdhte7U4lOsCz\nxB+AbFMgejO1E7r2rbVP4ajbAd7fe33h8QfL5IAfKvQ1w+AUhDyCTDhwbQ4IT+4A2xugyR3g6o1B\nA7oRiKEbOK09KLU+0hHPYnPnw72tcJ2jh6ZqmjgFjjctEwtgKk3mDjBQyHagwD8Hb9YIREIXRXdP\niE88zOeEqV/wZc0ABsxEg0mzgPOOsklivtGeTt3kVbRpp0DkkXcaxKy5W8sUs3KgS5lWXPdWdncn\nQcx6Ot0unYiiTdy89pXYPv3+kXFS65uPj+SAh1fqZmEjSuOG96tos5AZwIDpNjpi/Da40D9tCmDv\nTiUywLPEHwBTAGvdmvg0zRbISRGI5tpTiMJT8DsXcLj/6tIK4EbzOXSKLoDDnZEJEJb0Rp+yDLM3\n9n5qB7iCBXBCB9jONs+bAQaAre0P43D/0xCGW2i3H5jq54bI0BCqgsIK4De98Q34gX/xT4p6OToB\nbEE4boVw/6/NereotYcoPFXMIbjhQ0m9TvaEQ3AyWwa4rA4wMHkSxLR38v1q9Vupm7zKUOYUCGvn\n9PvRaT+A9mG2R7NFdYPs76s/u5lWXJuFLKo3wWDWCIQjFIQ8RPvwZdi983k4d/Hdo9cp2yM5YKVa\nuUagmdc5hJBH49e3RpuFbIHrfT7h96Iiw8LgDGr1O/DqdxAsuACeNf4AoBcTmtQFVmq9O2ZudHpJ\nrX4TUh7g1rWvgONEaK1/YuprmaS59kx35FpxrxklzAC2pEzvAAfBWdQbL8LvJHeA47i42FPRzGjB\n0wM3PuZ7tJrqZrK59jS82ku4ff3NiHVjqp8bKxOBcBwHX/HmL8N3fefb4WC+nSGqNq3qcERnYkej\nqLWJ9hFQEYfghrsocYZOdpZHPmV2gIGUDnB7uixXP5OvUxMznGUoMzpi1ep3sL75OF66k60LPGvs\nwLKn8fs7VGk/aB1ncBJEEdfiuvdw/cVvQGv9k1jbSC58NrY+NrAQI+8MYMBcuzm1nhylMYfgiiuA\nxYR1yEHQ7QDX7i68Azxr/AHoi8ZM6HZO6to7jpkHfPvm38T65uO5Fs/k0Vp7CkqtI/AvFPaaYcIM\nYMssm0mJQPhnsLH10e62TG/k183PJwkpqtcB9mq3gNgd2GhpRqDdm+q8ieMAm9sfxq1rXwnXewme\nlz+T76xKAfx33vIN+OzP+ky8+zf/SxHXQydInLIGGSguAxz4ZyHE0cyHpaQ72kUxX8h64sxX88hn\n9BtnP9MBLq+TacZZbY28XWsPfvu+mSMQx5u85psDnkcEAjiOQWTpTBUVgTCHdNqD/94ybJyykyB6\n1zLjsgLX3YffuT+x+2utb34MB/vHOWCTAc6fKa/Vb46dJ20OwRVdAI8/BOfV7pgIRHiq0I5kXrPG\nHwAbjdETJ0FEKnkChNVaexpatQqf/9vPq+3C8+7g6OAVhb1mGI6OQLOku5+6DjkIznYjH2JgFrJl\nYzRVzAB7tZe6o/yOv6aicHuqjZ/W1s6HEUVbUzdNhAhSx4JWwcwF8Ht++3fwA//q3+Lmzdk6b7R4\nWntoHz5U4Os1JsYGAFNUwYlmvlsM/HOo1W8VMpcVGOyi2C1waZ3sRXeApXcPRwcvx93bXzjwn1vX\nvwKAg0bzhZk/h3ncNr8T82Z7XrmH4KztUx+A3zmPo8NHUt+3yJFIcqhDlSXT278Mo4hi3PX24Hp3\nsd0dfZZkffMJaFXvFS7jxmmlGXdqPY6F6fYX2AGWogOVsARBaxdRuINa7TZq9TuIdT01J1qWIuIP\ngJm/KuXR5AhENLoGuV9z7VMAUFr+t/d51p8amSs9izA4BXdMAey6BxPXIWtVh4o20Wg+D9e7m3gQ\nTmtzs1nFKRCOo1Gr3UHQNwvYZKLzjUDrt7H9Z3CccOq58csyBs2d9QV2d4sZWUOLt3f3jXjhU9+F\nx97wjYVsYtIZOsBAtuIxTRETIIC+A0ZDHeBJB+AAdAd/Z8kAl1fIrW88gXsvfTauPvetI7+2deqP\nJq6kzsqcmJ/f42KzwUmWeuNgebU9rG38FQ72Xoe19U9Ovq4CRyIN586zZA37F2joaPZs4sbWR82/\nkQmPvXs54HuPYW3jE1N3gL36LRzsPTbydnP4VBSaARay0zvE2u94bNZtyO60iNA/PZcbrWFFxB+s\ntG1wKlqb+Htc3/wYNrc/jFYB1zJJa+1pHB2k32hmFYbjIxDS3UcUjf9cQV98rtG8kngQrrf6vIId\nYMDEIPpvKqNgtg6wlB2cufAebG7/yVQfX8TP9HmYuQCmkyOKNhBFW4jCnbEHCvIwndMsBXB6fCBN\nEUswAHM3LYYGytsO8CRZHvmYw1zlFXKnzr4Pp86+r7TXB2yeLn3bVFGOxzbNpzDxancQhqMxkmEq\nQ0whK9c96D2ijbVErBupkQYxHIGYsQA+f+ldmd7P5oDPX3oXlGpN9TVXqyWvQ7bLOArPACd0gMPg\nDBwn7OYkY0i5jzA8jSZmy8lPo4j4gyXd0TGO/ZSa3AGuN27iFa/557NfSIrW2tO4feNvIo5RyO87\nDEbXIFuuewClxneAbXxOyCPUG1fRSTgIp1UTjhPAEWr2iy1BbWgZRhhuo5Zje1uSyw//9NQfW+SC\nqzJxDBr12GKjqCHlWtczdR0L6QAHs49As8xyguMfInGGQt5MgciSAS6/k1kmKQ9TD5QUyd6IzKsz\n53q7maZcaNWEmDF3a5k/U/P7zNppMhngNcSxgNbzG9DfnwNOe5w+jv1hPZy5jcItOE4IUeBBIzFm\nDFrQzf/aGcbmINz8ZwEXFX+wpEzvAM8jT5/GjFzbQRjO/meuVR1arU3MAKtw/J+JmQZyG44D1BtX\n4CcsxCnyhrcMwyvGo3AHbs4lGEVamQwwnRy2o1TUjEat0junQDF3i4F/FrXa7BEIYPDxMpAtypEt\nA1xuB3gepLs/scNUNBWtQ4j23DovXsYCWKkWpCgyAmELYPM1mBqBkIfQqtV3OGdeBfBxDtguwsir\nVr+JWNdHDmxG0QZc914hHUFLyvaYCISZAGF5tTtzzbZbRcYfAPO9a9IhOJVyCG5eavWbkO4+2gUc\nhLNF9NgpEN4+opQOsG2e1JtXE0ehmbGH1cv/WsMrxsNwe6oZwEVZlgwwC2DqKaMDnJadBWa/W4xj\nIOwegivC8DKMLBGItAywVjXEurH8HeCUH7BFm9cECMv19hBlikA0Z568YEn3+ImDVtkO29gxaFnf\nvyhStrG2/gns33tdd2Vz/r8br3YHgB4ZhRaFW5AFxh8A0wFOjED4Zg1y/zUtogNcZPwB6HaAJ0Ug\norW5xYkmcRwzDq2Ig3BhcAqOE479tyjlAbRqIdYy8deD4CxqtW4B3LiCMDgDNXTTVNUlGJZXvz0Q\ngYjCHbgzHIKblZArMgbN+v0/+AD+xb/8N0W9HC2AUmtwnLCwNZWmc9pJfb9Z7xZVtAGtG4UcggNG\nf4hkOQSXVsTbjKf0lr0AnvyItWjzWILRz/V2EaZ0gG1Ot7ApEPKgd1OhVKsbAxg/cs98jMl62lnA\nRRXjWaxvfQwHlzedSwAAIABJREFUe69LzZOOI0QEr3Z3JAesuh3gIgk5LgJxGl59sAM871nARccf\nAJvRnxSByD+7uSzNtadxdFhABzgwW+DG3UTI3qrv5D+X0D/Ta540GtcAjG6Eq+oaZKtWv4ko3IHW\nnlmDHG0Uco5nWlyFTEtHqRZa659A5+iBQtbdxhk6p8DsW2PMD1Ld7SzNbqQDnCHKkVbE2zE8bgW6\nL7Mw+ej5doDneTLfRiAmzYQ9zukWU3S67uFABCLLo1Y7B9gWzJNmVBdtY/NjOLj3aLcDPF0xVavd\nGpknHYVbhT8hkWMWYZgZwMcFcG0BBfBewfEHYDS+NWzayR1laK09hXYRHeDwFNwJxZ79/jHu+1bg\nn+39WxDSh1e7PTIKrajFN2WxHewwONN7gjXLGLRZrdwqZFp+KlrH+sYT0LpVyLKDrFMgZr1bDIKz\n8Gp3C9tc1P9IGshWyKc98omiDTiiAyHTbwiqzHSAW4XcIGUx79y06+0ijmu9aEGS45xukXOAjwvg\nLD9oRbfQsQVzkbnZNGubT3S/XuXUxZRXvzlaAEcbcKfYOjWJkJ3EVchhcGbglPwitsHd23s9tnY+\nXGzm2U2LQFSnA9xafwqBfz51TXGaMBi/BAOw+XjVmzIyLAgGJwiZlcjL1QGW7gGE6CDwzyIMdwDo\nwr+W8hBOkJi9rxoWwNSjVAv15ouQ7l4hB+Gyj0GbvQNs74CLIEfGoNUg5OQoh5NyxzvvTmZZzA9P\nOXa7VtHUnJZgWPaHxqRRaEXnbgczwNkmOtgO8CI6U1J2sLZu1iVP3QGu30IQDEYgzBa4Ym92hOiM\n/FuNY6c7Nms4A7wzsAK9THEMHNx7DOubf1Ho606a0hLHdhNcNb4P1RtXIUR75hzwpDXIgMkbu+5+\n4jIMk6NfG/j50Ug4CFf1DLDjHN9URsE2XHd/qjXIRREigI5nG206DyyAqUd185bN1nOFHIQzhWPW\nOcAzdID9s/AKyv8Co4sJTJY5pQPshIhjb+wP0DDchusu/9IY+8NzXgfhTM50fh1gKQ8AJ5o4CUKp\nZqacbp7P2Z8BzpLnPY5AzG8EWr/1rY91r2OGAngkArFZQge4DTUUgYjCLcSxh9pQBhiQmQ5AFsHv\nXEIUnsL65l8W+rrD37v6aV0HYrcyEQjHidFc+9TMMYgoHD8D2JJ9s7b72ckf/XnweuMqOu3hDnBx\nmx/LUqvd7nWAF3kADgAcyTFotGTsWKNm69lCDsJplb4KGSiiA1zMEgzLlYdDh+DqcDJkgM37Jv8+\nAv8Cao3rhV3jotgM87xywGrOh+AcJ30UWtacblbSPUCsG93DK61Mc3Cle4hYN7pd0/kXwJvbfwYh\nD1O/Lsap1UeXYUTRZgkZ4NEpECbqoAe6hiYvqecWgzi491rUmy/AK3hWq1mqklwAqznP1M6itfZ0\nMR3glALYdfcTO+OhfwZS7ve2AQJAPWEbXJGbH8tSq99EGJxFFG4X/u8qL45Bo6USxwJateC6B2i0\nnkOnkA7wfDLARY5AA0YnHcQZ5gDbQmDc78PvXEC9vvwFsCMUhGjPbRtcNOcIBGBHoY0vgIt+HNo7\npBOtmUhDpg6w6eKF/tmFzCfd2PooHv2Mb586v1qr3UIUnhpYHqPCzUK3wAGAEKOH4ILgDFxvd2C2\ntCMUXG8XwRwL4KK7v4D5d2FvpobZm/qqdIABOwpttkkQaREIoLsOOSECEQRnB7q/gOkAR+Gp3oQV\nAAt70pKHfapShQ7wsqxCZgFMAPoP9hyi2XoOnfYDM+fhsowPA2a/WzSHGMqMQGSbAgFg7ErnoHMB\n9RPQAQZGp2SUad4dYMCOQhv/KLyI1cP9bI5WReuZf9Da9wmCMwv7wezVpo8r2BtW23GNY6e3CKNI\nQnYQxx60dntvGz4AZ81zG1wZ+V/gOKLUX7xZKlo3E0Om7NqXobn+NPz2pZGYSlaxloii7dSRXyZn\nnxCB8M+OnB+pN64B0ANdYF3xQ3AA4HUjENGCl2AA7ADTkrHfMF33EI3Wc4hjL3ElZB55DsFNOzJF\naxdhcKrYQ3DuIbRu9W4Asvw+bB50bAf4hEQggO42uLkVwPNdhAGkRyB0wXlAE3lQUGo91xg0wD7C\nrXZnKol078HpnloHbHdSFt4Bto+2+ydBBP6Zka4fYA/Clb8Nzu+cQ+Cfx0YpBXD3Ziqh2LNrkOc5\nMSRNo/k8HEehffiyqT7ezux2p41ABKPxOSFC1Oo30ek7CFf1VchAN1YUnEUYbC+8A+x05+JPGidZ\nBSyACcDg4zHXPYBXuz1zDjjW9cyH4Ka9WzQdJFFsB1geP5IGsq5CHp8BVlELKto8EREIoDslYw7r\nkON4QREINz0DXGTRaU6pm4NwWac6mChKB0GwmAjErBzHPrI1OeAo3ARgCuMi2Tx1fwzCrEEenRle\nm9M2uIN7r0Wtfn3gEF5RejdTCTeoVZoBbAkRodl6ZuoYhFmDrFM7ntI9SDy4O+5mqN64OtAA0hVf\nhQyYryet1uB37q9EBxhA5WcBswAmAN3HY6LTy8WZg3Cz5YCzLJAAZssAB/45CNEpdHzScBclyyE4\n8+emEk+++v4FAECtcaOwa1wks21qttmdWZhxY3Kuc4ABkwGeHIEoPg9oYzd5Ok1CHiEKd5ayAwwM\nLsNQ0SYAVXiBZscXDhbA4yIQ81mGUVb+FzA3FuMmQdgOcNU0156eehJEGJwyee6UkV/jOsD9a5D7\nDR+EUxUfgwag9286DM7AXfghOHsmhgUwLQGlWnD7fviYg3APzfSaeVYhT3unaEegFTpMXh4B0L0f\nIlkOwQG2kz2aAfY7F+DVbhc2NmvRJo1aKpLt2Mg5b8/LFoEoowDOHoEwH2O+Xqv+g3kc88jWdoA3\n4Hr3Cn88b79uVd8kiGBMB9ibWwe4nPyv5bqHiSO/zEjB6t0stdafxtHBdAVwFJzKtPJXuvsjizDi\n2BwiTTpA3Whc7W2Di2MHWld/DJqQPmR31GbaocDSr6X3RLTaB+FYABMA0wEWfUPtm61n0W5P3wGO\n42yHx4DZxqAFQbETIAAzn1LKo14Blu/3MfoFH3QuonZC4g/A+AMlRbOfY/5TIHYRRXPuAMtDKJU9\nAmE/xvx39YqaLGr1Wwi7HeAo2ir8ABxgvpaHJ0GE/pnE+IE5BFduBjgMduB37i+tAwyYG8akG9So\noh3g1trTaLcfTGwepAnDnUzFnjs02Qcw31+0bsBL6gA3rqDT7QDb/PgyfJ3Zf9fugiMQDjvAtEyU\nWhvpAPvt+6b6pgQAcewCkDk2wU13pxgmnOItgnSPZwHnG+c2+gXv++dPzAQIYHRTXllUtA4hjgbG\nVc2DGYO2OXYKShlD8U2Haj3zIgzg+AeyWMAc4CL0zwI2a5CLL4ABswzDboNTUQtat+CNiUBE0dbU\n3/OyOLj3KDzvDuqNq6V9jnFTWpSqzhrkfs3WM0DsTDV6M8sMYOC4A9x/KCvwzc1O0s1QvXkVKtpC\nFJrJLEBxq8/LVKuZszCLzwCbArjqyzBYABMA84Oh/5tjo/k8AAed9uWpXs8WtFnGoDkzHIIregmG\n1d9Fyb7RLnn7jRmBdq3wa1yUpG5KGaIFjEADbPdEjjwytbLO6s31ObtFi4pyRCC677cMnakkdm5p\nHJtDcEUfgLOE8KG6Xbze5q+kCIRn3hYGO6VcB2DjD39Z6iQG+zRhmIrW5h4nykJIH43mi1MdhAuC\nM5kKYNfdB2J3YClK6J+F6+4lRtPMgWUFv3Opd/O0DFGjWv1Wd8LKfJsGwxwnBKDZAabloNQ6RF8H\nWEof9cb1qQ/C2QJ4lsIxi8A/V+gECMt2UeLY3MVmmZ05bpqFfxIjEHPIAKsFTIAAAK+7jnfcWtwy\nRiJJ96CbP5XZIxDuckcgvNpNaN3sFv6bpUQgAFO42AhEGJyGlAcDm78sk0EOSz0It19y/hewYwqT\nDsGtw61gBxgAWutP5c4Bt48ewL3dz8LG1p+lvu/xfOTjGwOzBCO5eeIIhXrjOjqd+45n5Gc4z7Jo\nXv3Wwru/gDmMOe6JaJWwACYApgM8/M2x0Xp26oNwtqDNkwHOOzMwjkvsALsHUGqtdzgve5Rj8As+\njh0E/nnUT8gECGCOGeAFzAAGzE2bEEdjD8Ll6dJmJeUBguBs93/njEAsaQFsHz0HwTlEJWyBs/oj\nEKF/JjH+AJi8sOfdLa0AjsJ1dI4eLjX/C0yKQFSzAwwAzZwb4eIYePGZd2D71IewsZV+Q2G/j/Q/\n1UlagtGv3j0Ip1XTRLGcig+1BXDqzPtw6aF3LvoyAMw2339eWAATgNEOMAA0W89N3wFWtgDOFoEA\nBOI4X/bOHGJowauV0AGW5oeI7hXA2ca5DX/Bh8FpxLGH2gmKQNg/m7ItKgIB2FFoyQVw0auQAfMD\nOvDPm/+dsaAVS34ITogQrnfXbK+KNs1j6jI+j+z0pkAEwWl49dH4g+XV7nZnyxbvYP9RSPceGq3n\nS3l9q//8Qj8zBq2iHeC1p9E+eijz9tHdO5+Pw/1Pw/0Ziz0hou4K9+PvW2FwZmLzxI5CW4YRaFat\nfhtbO3+y6MsAsBzrkFkAnwCBf2bmjSsqWkvoAKePQguDrcTPbQpH1c0CTTbtzEA7Q7SMgfL2h4g9\nATztYT6/cwGO8OEteDNPkaS7D60bA+tly7CoCAQweRRa0YswgO72wd468myPWnsRiCU9BAccL8Mw\nHeDpVytPIkSnd0M+bgawVeYs4OP8b7mdRFcmL30wa8WrWQA31z6FWDfQad+f+r5KNfDis38XF+7/\n5Vzf+4dHoQX+2bFPA4D+DnD1R6BV0TKsQ2YBfAI88ec/hcP9V8/0GuaH+lAHuPksAv987xTssDDY\nweMf+XncuvZVI79mR4dlOexxfGI0bwF8Dq53t5T5urL7Q0TnikCMfsEH/gXU6jeW4vFZVvZGqewc\nsIo2IOV8l2BYrrebmAHW2kUc1wqPHdhCX4h26lB/y86rXoZs4jh2GUYUbZa28ET2RSDMoalJHeAS\nC+C98vO/QHIEIo6rHYFw3UPU6tdwlGEhxvUXvxFC+Dh332/k/ByDyzCS1iD3azT6O8DLe5O5KLMs\nuJoXFsBLTmsXWq318oPTGp4DDJhHQHAidI4eSPyY61e+HkCM61e+Hqpvzqa5rmyjw4DJa4QnGTfE\nvAjmVP5a75qyHIJLikD4nQsnZgWy1TtQUnIOOFKLjEAkd4CPT4QXnwHO+7pSHkHI9lLfXNlRaFFU\ncge4G4FYVAdYqSaODl+OjZLzv8Dx+YV+sa4hjr3KdoABcxCufTA5B9xpX8LNq1+Dyw//ZO7GR/86\nZHN+JD0CodQ6/M4FdoCnMMuCq3lhAbzk7A/kSZurshjeBAeY3FSj8WJiDjjwz+D29Tfj4Vf+IITo\n4Na1vzV4XbkKYBuByHe3GPjnSpkBDPQfgqvDRDmi1I8Z1wE+STOAAdOlBFTpOeBFRiDGZYBVSSOR\nbKGfZ6bv2saTOH/frxZ6HfNWq98y819jt8QMcBvKRiD80xMfe3u1OwhK2AZ3eO/VENJHc+1Thb/2\nMDPCcX0gmmYL4qJXTReptfb0xINwcQy88Mw7sLnzx9jc+Uju1+/vAEfRFuK4lrgEw6rVb8FxQrQP\nX7E0GeAqYQaYSmdHtIwb2ZT5dcbkw5pjcsDXX/xGrG18HJs7H8bFy7+EG1e/Dipq9X5dqzqcDCPQ\ngOPuau4IRHC2lBFowOAhuOxRjjAxA1w7YQWw4wx2U8qiFngIzhsTgeiNRCq8AO7meUX2163V7+Di\n5f9U6HXMm1e/2Zs17rrldICl7ECrJrT2EEXb6YfgStgGt3/vMaxvPJ453jIL6R4ijr2B76e9teIV\n3ARnmUkQL0f7MPng9d7dz8XBvUdx/0M/M9Xr92eAQ/8MAD1xhrDjaNQa13B0+Ap2gKfADDCVThXQ\nAdbaQxzXErsDjdazIx1gv3MBt2/+DVy8/AtwHODU2ffB9V7Cjatf3XufOOP6YMCMH3Kc/HeLZY1A\nA45n3WrdyP77SOoAn8AIBNCNiJQdgYjWS8uFphkfgWjBccLCc+eujUAs8YG2aZg4ggSgSns8byMQ\nYbezmxaB0Ko19tzDtA7uvXYu+V+gb+RX39enUmuAE2V+KrcI65uPY33jSTz50Z/Ckx/9Mdy89pWI\nwk0ApqHy4jN/Fxcu/QrqjemaHrJvgU8QnIXr7UKIyU/2Go2r3bMIq/V1WYRlmANc7jFuKp09OR7O\n0AG2ndtxHeBb1wfjDdde+DvY2PwYNrZMns1xNO67/Et47unvwbmLvwnX288VgQCmu1sMg1Nwa+VM\nV5DuIbQ2PwizHjIafuSjVR1hePpEjUCz7GPWssRxVSMQxU+AAMzgfSGOlnam77TsExzXPSgty2wi\nEA2EwRk4TjBx41ytZrfBnYJsXink82tVw9HBq3DpwZ8r5PXS2CibitaBbodTRetw5WGpG+hmJWUH\nr3jNP4PfOYe7t74Yt679bVx59q3Y3PljCOHDEQrnL71r6td33X20Dx8BkL15Um+YfwPsAOc3y4Kr\neWEHeMkV0QG2nbxxHeAoPNW7E+8c3Y+7t74Y9z3wCwPvt336A6jXr+PG1a8F0F0fnKsAzn+3qHU9\n1yPjPOyhpCjYgSOzdYBN6P94lrHfnet6kpZgWGVvgzM3dnKhEQit1qD14GxqrZqlFanSPVy5TpPr\n7ZmitKQlGEA3AqEbvQkQk4pAIY8gRLvQg3CHB68CHI3W+icLe81JHOHDccKBr08zA7i68Yd+9cZN\nXLz8n/Caz/h2PPLoP4br3sP+7mfi/od/YqYnL/2xrTBlCUbvWppXzceyAM6NEQgq3fEhuFk6wGsA\ndOJdbr1xA47o9GIQ1174JmztfBhrG3818H6OE+PiA7+Am9e+EmGwk7sDPM3IFK0bmWem5mW74WF4\nKnMEYjgDHHTOw3X3TuQ3z7IzwIvOLLrddaLDX1dldYAB83tdtQLYcWLU6rdKW4MM2DnATYT+mdS5\nsY5T/CSIg3uPYW39ydTH7UWxGX01EIFYr/QBuCSOA6xvPoEHX/FjeN3nvAVbO3860+v1H4ILgjPw\nMswQbjRtB3i1vi6LwENwVLrjQ3CzdIDXIGXyqkfH0Wg2n0fn6EEcHT6El+58Pi4+8IuJr7O181/R\nbD2H61e+3hTAGQ/BAfnXJsaxiRiUlWmzM1bD4FTmzzGcAfb9iycy/gDYRSHlFcD2td0FzS01a3n1\nyNeVUq3Scrque7CSP2hLL4BlG1o1EAaTJ0BY5iBc8iSIOBbYvfO5mTeWxbHAS7e/ABvbH811zbMy\nh3iXswNcFnPT3i2As3aAuxGIk9jEKJtpauXb7jpvLICXnFYtCHHUXQs83T+2tBWZjdZzaB89hGsv\nfDO2T38QrTGjfBwHuO+BX8Dt62+G376UuXMK2Mcl2e8WzdpkWdoSAMeJIeVRtwDO2gEezDyZA3An\nL/4AmMK0zAywitYhxBEcoUr7HJM4jobr7o/kgLVqdsfAFe/U2d/F5nb+8U7Lrt64OvE0/qyk7ECp\nZuoSDGtSB/il21+ET/3VP8Odm3890+e+c/NLEYancPbCb+W65ln1H/gCqr0Fbl5cdx9atRBrmboG\n2fJqd+AInxngKTADTKVTqol6t8s4bQwiaQtcv2brOezdfRP27r4RFy//0sTX2tj6CNY2Po69l96U\nqzjNe2JUdxdvlBWBAEyXMwxOwcmx0EMPZYBPbge43AxwFG0svGOVNAlCReV1gM+c/7+xsTWfSQFV\ncunBny31gJi5YZFmJGGmDvAdBAkFcBw7uH7l69BoPYMrz30ronDyDaBSdVx7/ltw8f7/c2TNfNmk\nezgyBaKqW+DmxU6UiaIN8zQgQwHsODHuf+hnsDGnCR4nCTPAFadVvfIt+jRKNVHrHrKaugBO6Q40\nWs8hDE/j1NnfQ7P1wsTXchzg4mVzQC7L9jQrb15I624BXOIaWCkPuhng7As9+n8PfufiiVuCYQ13\nmIq2yAkQVtI6ZKVb7AYVTLpHpY5/szfJfvv+mTrAey99DoLORTzymv8RtfptXHvhmye+zs2rXwMh\nfZy58F+mu/AZmCc0wxGI1YvX9LPfTzrty4hjL9PNEACcvfDb8EqaNnSSMQNccS888w5cf/EbF30Z\nM9GqBdfdh3STxzZlYboD4wvg1tpT8Lw7uHD/L2d6vY2tv8TWqQ/levw/PEEhjZpTB1hFmznmAB/n\nmOO4G4E4sQVwyRngBS7BsFxvdzQCEZV3CI7KYb9HaN1MPQQHmFFowxngOAZuXPl6nDn/O/Bqe7j8\n8E/g1vU34+jw4cTXCIMd3LjydbjvgZ+f2+G3fsNPaNgBtgfZFNpHDwNQpcZuKHkuftWsdAEchtsI\nw51FX8ZMlDIdKc/bm/ognDnYM74A9mq7eOwN34RGM/vj/Je96l/j7MXsnY/pO8DZu8x59dbTTtEB\njqKt7g/cE1oAy/KnQCy6AE76mlKqyQJ4yfSPSsx+CO70wCrhg3uP4ejgEZy779cAmOkEp86+Dy8+\n846B97OuvfDfotl6FtunPzDz9U9j+AY1YgYYjmO6wO3Dh+HV7s5lK98qYwa44rRq9LKky0p3fyCP\n21yVhYqKH5GTd+B63rtFrepwRKe04fnA8Vzk7IfgfLOCNBYIOhcAqNI21S2a2+0wJf3wL4KKNuAu\naAuclRiBUK2VnNSwzEwUSwFQ8Lz0R9le7Q7iuNYbmQWY7u+ps7870EG+9ODP4ujg5Xjp9hcNfHz7\n6AHcvvFluPTQ/76wxRPDi2qU4hQIwOSA24cvO7Hfl6uEGeCK07rRGyO2rJRqQsh293HtDIfgFtwd\nyHu3qHUDsuS1nvbPJOshOJt5jrVnDtzUb57YLoP5YSp7mwiLVtkIhGpyJNKScRxASB+et5tpqojn\nmUfjNgd8dPBy3Nv9DJy/9J8H36/2Ei5e/mVcee6tvUgWAFx57tuwfeoPsb75RIG/i3xMfGswA+wu\n2RzgMkj3AO32g/AyjECj2Uyz3GreVrsAPjEd4Da8inWA88p7t6hVeUswLJuZyxOBAMwWvMA/uflf\n4PjPRqlyJkFEavEFcHIEghngZSRFO9PiAwAQ0qxLtjng61e+HtunP9RbitDv7MV3Q8g2rr/4FgDA\n/t5juLf7WbjvwZ8v7uKnMLIIgxEIAGYUWqzrmbLgNJtpllvN22oXwLrR26S2rOwPZHemDPDkOcDz\nIGTOMWi6UeoECAC90UV55gADZkWz37mA2kkugLt/NmXlgFW0DldWIQKxPRDz0IpTIJaRkO1MEyAs\nMwrtDDrtS9i983m4cOlXkl9XRLj88E/h5tWvRqd9CVeefSvOnn8PGt0VuoviygNE3Q6w1h7iuLby\nh+CA43MdWZZg0GzMwXZ2gCtLq+WPQGjVhOhlgKcdgzZ5DvA85L1b1Kpefgc49yE4835a1+B3LqBe\nP5kzgAHzg1+ITmmj0KoSgYhjbyDmwQ7wchKyk3nsFXC8De7Gla/Fxvafo7X+9Nj33dz+CDZ3PoxP\nPv6D6HQu4eLlbNNyymQiEOuIY/S+Rhfd5KgCe64gywxgmo0QQe9MTFVV98rmQOl6b5rAsjI/kNuJ\necXsr7H4x2PCybcKeR4d4LyH4I4zwCYCYeczn1SzzALW2sOTf/7j6LQvJf56NaZA7AJA7+vKdNI8\nHoJbQq57L9cTmVrtDo4OXoW7t75kbPe33/0P/QyiaAMXLv1Kd432YvUy+rrZywIv+uupCuwyjDw3\nQzQdpy8SWFUrWwDHsYN4yQ/BxbEDrZsDY9Dynso3HYLFd4CF8KFVnkNw9cyd2WnZHxiOzFgAOxEA\nBaVaCPyzqJ/QEWiWlAdTZ4AD/xzaRy/DjStfN/JrcexUYhGGkEdwnLAXLbJxKXaAl89Dj/wwzp5/\nT+b392p3sPfSm9BaewrrGbaA1Rs38OhnvBXnL/3qLJdZmF5GP1rrZoFV6Q2DZWC/p3AKRPnsz+cq\nxyBWtgC2j9u1apQ2yqls/T+QzePaWu5T+Vo3AcgKFMD5MsBqHofgehngbJ/HcQAhQvjtSwBkb0X1\nSSXdw6kzwEHnPACNu7e+GMHQ0gHz71ouvGPlOIPrkFXv640Z4GXj1XYhMt7IAoDnmbzw+fv/r8yj\nzGr126WOZczDbn1T0Xp3fffhwkayVYl09+E4Idzu0x0qj2AHuLqOpz+Iyp9UHEcNFcAAco9CO348\ntuAMsMy/CKP8CITNAGf/wekIH+32gxDyqPe47aSaJQLh++fRbD2D5trTuHn1qwZ+LeplFhf/yLZ/\nvKB9WsRDcCdfa+MT2Nr5Q2zt/NdFX8pUhAjhiA6UWjMTVTgCDQDQXHsa26c/WJkblZOs/1B4Va1u\nAdyX/V3WSRD2uoVsQ8rDgce1Wdkf6ov+BmmXSGSlVQNybh3g7AWwEAE6Rw+gXr924jsu7gwFcOCf\nR61xA+cv/WfcvvFmqKjvoFl3AYFbgVPr/eMFtWrBcYKFrLal+Vpb/yRe/up/tdSFkivNExoVcQmG\n1Vp7Fg+/8n9e9GWshP5D4VW1ugVw3/xftaQFsCleNYTodB/X5h+FpqJ1OE7YC6wvSv4McKP8DLA8\nguvu5toZL4SPztEDJ3oEmjVbBvg86vUb2D71R/C8u7h148t7v6bUusnfZlhaULb+rylOgKBlYidB\nmDw9O8A0X+b7t6r0OuTVLYB1A4DZ0lXWNquymbWsnV6ncZp1yEqtdbvHJVxgDkK2oXQzcx57Hosw\nHCfGY294S64sryNCBMH5E38ADrAZ4I30d0wQdM6jVr8Bx9E4f9+v4dbVr4LW5glAVIEZwNZwBMJm\nK4mqTrr7UGqt+z2eHWCav6qvQ17ZAtgeonKEv7Sj0MwWuOMfyN4U65DN47HFdwek6ACxmzkGMY8p\nEABy3xjYazrJW+As6e4PrFvNw+9GIADg1Ln/DzEc3L311wBUYwawNRiBaDL/S0tDyuMOcBW+x9Pq\nERXfBrchw6ZwAAAgAElEQVSyBbApoDqQor20o9BUdwmGNUsHeNHs7yNrHnseHeBp2LzwKkQg3O4P\n2Ly0qiEKT6FWNwWwECHOXXw3blz92t4ItKoUwAMRiIgRCFoeJgLBDjAtDjvAFWUPUQnZHsgDLxPT\nAT7uSE2XAW5VojtgD7SpjH8X85gCMY3jDvDJHoEGdKdAqPwFcOCfAwDU6jd7bztz4T0I/dPYu/um\nSizBsIbHoHEJBi0L+/VZlad8tHrMdCcWwJVjCygp20t9CG6wAJ4iAlGRETn20XLWPPY8ViFPwx4m\n7C/uTqppx6D5/nlIuT9wMMd1D3Hmwu/gxpWvg4o2Fr4Ew/K8XUTRBuJYQPMQHC0RKQ8RRWuMQNDC\nCBHwEFwV2UfopgO8vAWwGMoA5+8AV6M74Dgh4ERQGfPYqrId4ABe7TaECBd9KaWT7gG0bkBrN9fH\nmRFoozcI5y7+Bo4OX4F7u59dqQ4wIBCFm1BDT1yIqsyOKYwYgaAFEQ47wJV0EjrAhUQg1OLXIAPm\nsJmU7ewdYF3+HOBpCBGsRPwBOJ4dnfcgXOCf7+V/+9Xqt7Fz5vcQ+OcrVADvAQCicHvkhpOoyo4j\nEOwA02I4OcebztvKFsB2CoQQne464OWTeAgu2kQcZ/9rrdI3RyHamTLAcSwQz2kKRF5e7S4arWcX\nfRlzYWMKeXPAQcfMAE5y/tK7zGtXZAyaECGEPEQYbncjEOwA03IwUyDWKtPkoNUjRJBrwdW85Xt2\neYLYRQqOEy5xB7gFr3a809zrPa7dgFfby/QaVfrmmLUDbB+pVDECcfHyLy36EubG5LZV7hyw75/H\n2saTib/WbD2P+x78WaxvfayAKyyGiRZtcREGLRXpHiAKtxHrRmWeqNBqqfoYtNUtgLuHqIQTTL3N\natGUHpxL6rrHj2szF8BV6gBnnMhh36eSh+AcvehLmBvHiXujlvII/HMTx8RduPSrs15aoewkCM0p\nELREpHvYm3HPTXC0CEJyDFolad2AFOYQ3NJ2gIfmkgoZQMgjRFH2HHBV5gADyJzHtt/Uq9gBXjVS\nHiJS2bfBDc8AXgaut4ewmwFmB5iWRf8klap8j6fV4lS8A7y6BXA3AyyXegpEE1IMZhLNNrgcBXBF\n5gAD3Q5whjz2cQe4ehngVePm3AZnZwDXl2hMXH8EgpvgaFkcT35Q/HdLCyGEj5gd4OqxUyCE7Cxt\nAZy0mtXt/rDOIo4FtK5WBjh7B1iZ0Wm0UCYCkT0D7PvnId19SHd5Oqnma2pnZPU4UZXZxoZ0j+A4\n8YKvhlaREAGnQFTRcQf4aGkjEEq3RgqJPOuQVWQOnFWmAyyy3YyYCR4+HGcOF0UT5V2GMW4EWpW5\n3h6C4Azi2OMUCFoajqMhxBFnANPCcBVyRdlFCss6Bi2Ok1ez2rxiFnZ8VVW+QYqMNyNa15n/rQgp\nDxDlGIO2nAXwLvzORQDgIThaKtI9rEyDg1aPyQCzAK4c3e0iLmsHONY1IHYTM8BZIxA2u1mVx9Ey\nYxylqkswVpHpAOfIAE+YAVxVnrcL3Z0UwwgELRPpHlSmwUGrh6uQK8p2EZc1A2yL9uQMcNYOcAtC\ntCszuitzB1ixA1wV7hQZ4FpjuQpgsw7ZYASClonrHlSmwUGrhxGIitLKdBGFPEIce9B6uUYia93N\n7yZEILJngKszAxjodoCzTIHQjUrOAF5F+TPA55YwAmFmajuiA0eoBV8NUXZSHrIDTAtT9TFoy1X1\nFSSOj6dA2I6OVk0IUY31q1nYTulwRyrPGLQqzQAGzCrkTBEI1WAHuCKkPMi8CnkZZwADZtQboNj9\npaXTaD0P6S7PzzU6WUTFM8CrWQDrGgDRi0AApqB0veX5RqFVE47wRzpSrrsLrVrQqgYhg4mvoaK1\nSq3INGPQMmyC666xpsWT7gGijBngZZwBDJjT9K53j/lfWjqXHvz5RV8CrTCTAT7BBfArXv4yvPWt\n/z0uXriA555/Hj/xkz+D69er3eHpbRLrjkEDsHQ5YKVaIwfggOO8YhRtoSZvpbzGWqV+qIuMS0ns\nGmtaPJsBjmOkjqVbxhnAluvtwnEYfyAiyupEZ4A9z8P3fu9347d+67fxbW99Oz760Y/hO97+tqKu\nrTS2yyhEp7tMQS3dKLRxW6lMF1tnikFUaQscYDrAWjcRx5MrKRtfocUzj1dlpq+fwL+wdPEHy/P2\nKnWzSERUdaLiGeCZCuBHX/NqtI/a+OCH/hBKKfzar/8mLl++H5fuu6+o6ytFfwfYcbCUo9DGbaVy\nHA3X3c90EE6p9WplgG0eW0+OQdhFGLR49t9PloNwZgTa9bIvqRSut8t1skREOTjCByARa7noS0k0\nUwF8330XceXq1d7/j+MYN2/ewqVLFS+Aex1gk5EVsjP3AvjaC2/B1ee/eeqPV6o59lFy1nXIJgNc\nvQI47e+CHeDqsP9+suSA/SWcAGHV6jfheXcXfRlEREvD1lhV7QLPlAFuNOoIgnDgbX4QoFavbuYD\nOJ4BbPejy4zZ0yL5nYtQKvsCgWFmakVyR8rNOAlCqValRuTIblGb9ndhR9jR4gkRwhEdqGgj9X0D\n/zzWN5+Yw1UV7+LlXwLilZ0aSUSUmz2srnUNEtWLkM1UAPt+gFrNG3hbvVZDp1Pt4mR4jJaQR3Mv\ngLWuZd7YlsQUr+ML4EwRiGi9UgeS7MG21AJY1zkFokJc9yDTzdwyrkG2hAjT34mIiHqOO8DVbIrO\n1NK4cvUqLl680Pv/juPg3LlzuHr12swXVqbhRQpyARGIWNcyL6xIYg7BJRevnreXLQKhWpXKADuO\nhhDpfxdmjXW1b7JWiZTp2+C0qiMKd5ZuDTIREU3H6XWAqxmBmKkAfvzxJ7Gxvo4v+sLPh5QSX/PV\nX4nr169XvwBO6gDPeQqEjmuZF1YkfrxqFtQBrk4BDGTrxtsIC1VDlm1wfncGcG3JZgATEdF07JOz\nqs4CnqkADsMQP/hDP4K/8de/FD/7zp/AY4+9Fj/673+8qGsrjRrqAGfpOhYt1jVotQatvfR3TqBU\nc2wHOHsGuFqb4IBuNz7lZoSrkKvFLMOYXAAH/nlI916lIjdERFQex9FwnLCyHeCZF2E888yz+P5/\n+gMFXMr8DHeAF3EIzmZionALtfrt/B8/IQNsIhCTC2CtPcSxV6lNcEC2ZRhchVwtWTLAy5z/JSKi\n6TgVXoe8kseaTQfx+BCVyLiCt9hrsAXwdDEIlRqB2EIcT/j4bseuasP9hUj/uxju4NNiZckAmxnA\nLICJiFaJ2QZXzQ7wahbAqt4buQXYDnBrrtcQd6MP0+aA9YRDcK63izj2JnblVPf3W7UMcNrfhdYu\nELucAlEhWTPA7AATEa0WIYJevVM1q1kAD2eAZXvuGeAyO8Cet5v62qZgUWNnCS9KWje+t8WPEYjK\nkFkjEA0egCMiWiVVXoe8ugXwcAd4zlMg4u4/iGlnAU86BCfkERwnnFwAqzVI9wiOM9WnL01qB1jV\nu+/HArgqXJntEBw7wEREq8URPjPAVaLUaAd4/ofgPLhutmkNw+JYINaNsR1gx7GTIMYX1yqq1hY4\nS8h2r8ub5LgDzAhEVUh3cgaYM4CJiFaTiUCwA1wZIx3gDAevAKDTvoQn//zHpx5dNnANcQ21+s2p\nIhA2rjGuAwykzwJWqlpb4CyZEkfR3b8nHoKrDunuTyyAezOAGyyAiYhWiWAHuFqGN4mJjIfgOu37\n0T56GfbuvnGmzx/HAojdbgGcPwJhu9XjOsCAyQFPzgBXbwYwYKZATOrGa92A4wRwHD3Hq6JJpHsI\nrZuItUz8dTMDeG/iv1ciIjp5mAGuGK0bkH2P0GX3sXscT/7jUOEmAODurS+Z8fObu6Fa/dZUEYje\nBIcJBYWbsg7ZZICrF4FI7wDXB0bY0eLZKE2kkrvAgc8RaEREq4gZ4IoxRdRgB9i8fXIMIoy2IN17\n2Nv9bITBdIfXgOO1gN6UEQjTrVa9PdtJ0rbBmQ5w9SIQQnYmdoCV5hKMqnG7N1IqSp4EEXTOcwUy\nEdEKMhlgFsCVMZwBtgVw2ii0KNzExtZHUa9fx0t3vmiGz287wKYAnrSwIokZgTZ5gkNqBEK1KtkB\nFvJocgdY15n/rRjz9aPG5oB9/zzzv0REK4gRiIoZzgDbKMGk6QMAoKItuO4eTp17L+7c/NLpP3/3\nEF2tfgtx7OVewmFGoE3OU7op65BVtF7JDrCUnYkj6bgGuXocJ4Z0j6AmRCA4Ao2IaPUIrkKuluHH\n6PZ/q5RCNAy34Hr3cPrse9E+fDnahw9O9fntSJBa7Wb3dfPFILRqpR4oSh2Dpip6CK7bAR7XFde6\nwRnAFSQnzALmGmQiotXkcBVydfRW6fYVUY4Td6cPpHSAw0243h5q9VtY3/zY1IfhzN2QhuvtpS6s\nSLwO1UrtAHveLlS0NfZgn4oqeghOdIDYRRwnj5pjB7iazDrk0QywUnVE0TY7wEREK4gZ4AoZt0rX\nrOCd3AGOok247h4A4PS59+Lu7b+WOjkiSRx7cETQW1iRtwDW3QzwJG5vHXJyF9h0gKsXgbCzjccd\nhDP5bU6BqBrXPUjMAPudiwDANchERCuIEYgKsV3e4cfoZgXv5A5w1I1AAMD26Q9CRevY3/v0/Neg\naxAiBJAeVUiiMhXAplAf99oqquYhOPv3Mm4xyXB+m6pBysOBDHCsJW5e+1t46vEfxNrG45wBTES0\ngqq8CMNd9AXMm82iDHcR05ZhxFpCqXW4rimApWxj+/QHcefml2Bz+yM5r6EG4QQA0qc1JH58hkNw\nQoQQ8jDxtePYqXAH2I6kG9cBrrMDXEF2G1wcA3t3/xtcee7boFQL9z3wH3H6/P+z6MsjIqIFcCo8\nBWL1CuAxq3TTFjBEkVmCYTurAHDq7Hvx9Mf/ebcjm73DFesaHBH0Xm+aDHCW4nVccW2KSwHpVu8Q\nnOOEgBNBjZkEoVUDXv32nK+K0kj3EAf3HsMnH/8hHB48gvP3/RrOX/pVdn6JiFaYED4zwFUxbpWu\nEO2JY9Ci7hY42wEGgI2tj8J1D7B75/NyXkMNoq8Azh+BaPWyspN4tbs43P+0xI8Hjjd4VYnj2DhK\ncgHMRRjV5Hm7ODp4BPXGVTz6GW/FfQ/8IotfIqIVxznAFaLGZEjTDsFF0RaEaEPIoPc2x9E4dfZ3\ncedmvmkQgx3g3Ykri5PojB3ni5d/EbdvfDlu3/iygbfbw0rSrV4EAjA3I+O68cNb/Kgazlx4Dx79\nzG/Hg6/4UdTqdxZ9OUREVAFVzgCvXAE8vAXOSjsEF4WbvQNw/U6dfS8O7r0Ofudc9muIvV4HeJoM\ncJZDcACwsfUXeOiRH8Lzn/pO7N59U9/Hr8ERfu8gXtVM6gBzCkQ1SdlBvXF90ZdBREQV4nAKRHWM\nmyIgM3SA7Qi0fs3W82itPYW7t784xzXU+6ZA5M8A6wxzgK2dMx/A/Q+9E8984h/j4N5rAHRnAFdw\nCYYlJhXAqmFmBRMREVGlmTnAjbHLrRZp9QpgnVxAidQO8NbAAbh+p869F3dvfknmv2AzB9h0MV1v\nF1G0mWuecN5Dd+cu/ibOXfx1PP3kD6B99EBlJ0BYkw4kas0xaERERMvAPrEdt9xqkVavAFYNCDn6\nCF2kTYEYE4EAgJ0zvw+/cz867cvZrkHXIBzTAfa8XQACUbiR6WMBOwYtXwF73wP/Edun/xBPPfFv\n0D56sJIzgC0h29DjpkDoOg/BERERLQF73qmKB+FWrwAeU0BJMXkOcP8WuGGet2dGd6nRVbBJBg7B\ndV8zawwijvN3gAEzXeGBl/97NNc+hRtXvmF5O8BchEFERLQU6o0beO1nfVMlY5crVwBPnAIxcQza\n1tgOMGBn3WW7w+kfgyZkACGPEEXZCmBzFyVzd4ABM7XiZa/8d1hbfxK1Cs/SFaKTmAGOYy7CICIi\nWhaOo1Gr34HjVC8EvHqLMMZOgeikdIC3BmYADxPCh1bZC+D+Dq7n7SLM2AHWvRm+081YFdLHKx/7\nPsS6enkcS8g2Av/syNvNDYZgB5iIiIhmsnId4HGP0IU8yjAGLTkCAdhZd9kK4P4IBJBvFrCNBswS\nYXAcnZiDrgo5Zi217dAzA0xERESzWL0CeEIHWKlm4iSHOO5mgAsqgPvnAAO2AM7aATYF8EnugppD\ncKM3I7bDXuXinYiIiKpv9Qpg1YAc0wEGZOLOaq0biHUdcmIEIvu6P63rcPqWUJh1yNkKYKVaEKIz\nssr5JBk3k9kWxZwDTERERLNYvQJ4zCYxm6lNmj4QhZsAMPkQnMyeAY51beAavFwRiNZUB+CWiRDJ\nM5nN23RvhjIRERHRNFavAFb15AywMAVw0vzZKNoCoOG6+2NfV4hO5nV/WnsQzvQRiCqPMCvCuDFo\ndgKE4yzgooiIiOjEWLkCWI3JANuiOLkDvAXp7k+MHTg5IhDmENxgBCJrAaxUM/Ma5GU1bhWyVsnd\neyIiIqI8Vq4AHjsFQkRwnDBx+sCkLXDHH+8nHtxKvIa+OcBA/jFo045AWxayuwkujgdbvVyDTERE\nREVYvQJ4TAcYMAfhVEL2dNIWuOOP9RMP0CVfw2AB7Lq70KoFrdI/3mSAT3YBbH9/wzcUpgPMApiI\niIhms3oF8JgpEIBdhpEcgcjWAZ5+DjBgs8aTrUIGWIw5kKh1cn6biIiIKI+VKoDjWCCOaxM7wIkF\ncLSV3gHOswkuHuoAe/sAdKYYhFqBAtiOORv+uxiX3yYiIiLKY6UKYDtaa1wXUYoOVNIUiJQtcEDO\nRRhDHWDH0XDde5kOwq3GIbjkAtjkt3kIjoiIiGazWgVwt0AdN0lg3PQBFWU9BJdeAMexAGIXwgkH\n3p51HfIqHIJzHA0hOgkRCHaAiYiIaHYrVQCrtA6wPEocgxaGW3AnbIEDshfAWnu99+/nZpwEYTrA\nJzsCASTHUUx+mx1gIiIims1KFcB2qsD4DHDyITiV5RBcxk1wdlJEfwQCALyMs4BXoQMMmAOJw3EU\nswiDHWAiIiKazWoVwKoBOBGEiBJ/PakDHMcCUbSe7RBcpg6wjWFMF4FYhUNwgI2jJIxB4xQIIiIi\nmtFqFcC60ZswkCSpA6yidQAy9RBc1k1wNgIx3AHOug5Zr8AcYMCspk4cg8YOMBEREc1otQrglA6i\nkO2Rx+5RuAkAxR2C60YgREIEIvsYtJNfAMuEA4ncBEdERERFWK0COGWKgBSjRVcUbcFxAggxuegU\nspNpE5yOuxngxCkQkwtgrV0zx3hFIhDDHWClGmMneBARERFltVoFcMoc2aSiy8wAvgfHmfzaQviI\nYw+xlhPfz2yB80dez2aA43jS9ZtrW+kOMCMQRERENKOVKoBVSoZUJozeyrIFDjiONKTFILSuQTjB\nyNtdbxdx7EGptbEfq3oF8Gp0gPXwFAgegiMiIqICrFQBnJ4BHl2+kGULHHA81zdTASxGC2Cv+zkm\nxSC0anWvczU6wEmLMCYdYiQiIiLKYrUK4LQMcMJjdxuBSGNfV6fkgGPtjUyAAMziB8cJJhbASjXh\nOOHICLWTSCTksbWqcxUyERERzWy1CmDVgEyZAhHHtYEcr4lAZCmAs0Yg6okFrOPYbXDjZwFr1VyJ\n7i8w2gGOY2EOALIDTERERDNarQI4QwcYwMAotCjcyhSBsF3d4eUNw8whuNEOMAB4KZMg1IpsgQNG\nZzLrlDXWRERERFmtVgGsJhfAdtRZf+EVRZuZDsE5jolBpEUgdOyNHeXlpqxDVqq1EiPQABMJ6b+Z\nON6gxwgEERERzWalCmClJ49B63WA1XAHOD0CAQBOhmUY5hBccoY3bR2yXpE1yAAgZQdKt3r/X7ED\nTERERAVZqQJYq8lj0BzhA1CDHeCMh+CAbNvgYl2DkzAGDbAZ4MmH4FYnAjHcAe4WwMwAExER0YxW\nqwBOWaXrOIOj0LT2oHUrUwQCAIT0EU85Bg0wo9DSxqCtzCE40UEce9DaA9CNQDgRhIgWfGVERES0\n7FarAFbpc2T7R6FF4SYAZDoEB+ToAI8pgNPWIa9aBxg4PvyW5e+OiIiIKIvVKoBTOsDA4DrkKDJ5\nXOnuZ3p9IXxoNX0HOG0M2iodgrPj6o678Q0egCMiIqJCrFYBnDIFAgCkOF7BG4WbkPIg82P3LB3g\nyRGIXahoC3Gc/NeySofgbNTDduO5BpmIiIiKsloF8BQd4KwH4IA8EYhxUyDsOuTkLrBaoUUYjhMC\nTtSbyZw2w5mIiIgoq5UpgOPYyfQYfTADvAWZYQucJUSQ3gFOmQMMYGwMQq/QIgzHGfy7YAeYiIiI\nirIyBbBdUJGlA9x/CC7rATjzsekZ4EkdYCFCSHkw9iCc0s2VyQADZjHJcQa4zgwwERERFWJ1CmCV\nbY6slO3e0oWsW+CsTJvgdB1izBxgYPIkCB2tTgcYMAfh7M2IYgeYiIiICrI6BXB3kYJM6wCLNrQy\nG8jyLMEAACdLBEJ7Yw/BAZPXIasVOgQH2GUYnAJBRERExSqkAN7YWMf/9mM/gvPnzxXxcqXI2gEe\nOAQXbsHNlQH2e4X2OJPmAAPjR6GZDPPqLMIAuuuQexngOjvAREREVAh31hd45BUvxzve/jacPXum\niOspjSlM9cTiE+gevNL9UyDyRCB8xKkRiPFj0AAzCi2pA3zcwV6xDnDfFAjP213wFREREdFJMFMH\n+PLl+/F93/vd+NV3/XpR11MaO0bLcSa/X38HWE1zCC7DHODJHeDkCIS9plXqAAvR3wFmBpiIiIiK\nMVMH+Pbt2/j73/N98P0A3/33v7OoaypF1kNUZvRWA3FsD8HljEBkmAIhxkyBAEwE4mD/NXjqyR8Y\neLt93dU6BNceygCzACYiIqLZpRbAb/ycN+Af/oO/N/L29/3++/GTP/XOUi6qDFkPUZkOcAtatRDH\nXu4IRPoc4NrE69g+/UGoaANxPNqq3jn9gZUqgIVsI/DPAuhOz2AHmIiIiAqQWgD/8Yf/BN/yrW8b\nebtSqpQLKkvWQ1S2AxxF5iBa8ZvgvIkRiFrtLi5e/uXMn/MkG1mEwSkQREREVIDUAjiOY/j+8hce\nWjcgMzxCN7N8W91JDApSHmb+HGkFcBwLxLEH4YyPQNAx0XcgkREIIiIiKsrqzAHOnAE2UxaCznm4\n3j04Tpz5c6QdgtPaA4DUSRRkyP4DiTwER0RERAVZnQI4YwfRTlkI/PO5tsABdgxaPTG/C6A3Io2P\n8rMxS0m4CIOIiIiKNfMcYOsb3vItRb1UKbJ3gE0B7PsXcuV/gePObqxrcORosaZ7BTAjEFnYDnAc\nmwx32hY/IiIioixWpgOsMneAzfsEnfO5RqABx1vmxsUg4tgUwIxAZCO6h+Di2AMgmQEmIiKiQqxM\nAWw6wOmP0B1HwxEdBP6FXCPQgONogx6zDU4r2wFmAZyF3crXW2Od4e+PiIiIKM3qFMC6nrmDKLvz\nZ3MXwDLofa7Ea7AdYE6ByMTmscPuZjx2gImIiKgIq1MAq0bmDKkQHbMEI3cEwu99riSxrsERfuo6\nZjJsARyFO93/zwKYiIiIZrc6BXCOObJ2FFreDrDjaDhOOL4DrD0Ih/GHrOzc5tAWwJwCQURERAVY\nnQI4xxxZ+355p0AAdpHGmAywrsPhBIjM7N9DFOx0O+d6wVdEREREJ8HqFMDTdIBzzgEGzAG3sVMg\ntMcDcDk4joYQHYThNvO/REREVJiVKYDzbBKz2dOpOsDSh9bJGWCtayyAcxLyCFG4w/gDERERFWZl\nCuA8m8TsYbm8h+AAwBE+YpUcgYjjGmcA5yRlB2GwzQNwREREVJiVKIDzbhIT8ghCdKaaOyuEP+EQ\nHDvAeQnZRhju9A7EEREREc1qRQrgfJvEhOhMlf81Hzu5AHY4BSIXIdomAsElGERERFSQlSiAjzeJ\nZT8El3cEmjWpAI7ZAc5NyjYPwREREVGh3EVfwDzYQ2lZi6jt0x9Co/XCVJ9r0hQIzSkQuQnZBmKX\nGWAiIiIqzEoUwCpnB7jRvIJG88pUn0vIDrQa1wHmHOC8ZHciB6dAEBERUVFWIwLR6wCXX0TxEFyx\nRK8AZgeYiIiIirEaBbBqwBEdOE5c+ueaXAB77GTm1OsAMwJBREREBVmNAlg3IOdUeDqTNsHFNUYg\nchKCHWAiIiIq1moUwKo+tw6i2QTHCERRbAc46wxnIiIiojSrUQDrxtw6iEJ0EOsxm+A4Bzg3e+PC\nDjAREREVZTUKYNWYXwdYBGOnQLADnJ+QR93/ZnaaiIiIirESBbCaawfY702dGMYCOD/JDjAREREV\nbCUK4Pl2gH3oSREIFsC5sANMRERERVuNAnjuHeBJEQhOgchDCnaAiYiIqFirUQCrBuScOohC+hM2\nwXlwOAc4l+MOMAtgIiIiKsZqFMC6PvcOcJywc0PH7ADnxQwwERERFW01CuA5Z4ABiTh2R69D13kI\nLifp3sNDj/wgGs0XF30pREREdEKsRgE8xwywjTgk5YBj7XEOcE6OA5w6+/tzWWNNREREq2ElCmA1\nzw6wTC6A41ggjj12gImIiIgWbCUK4HlPgQCAeLgA1h4AcAwaERER0YKtRgE8501w5nMOFsB2NjA7\nwERERESLtRIF8Ob2R9BoXJvL53KcEIAaiUCwACYiIiKqhtFRBSfQ5Zf95Nw+l+Mkb4OLY/P/GYEg\nIiIiWqyV6ADPmxABtG4MvE13M8DsABMREREtFgvgEiRtg7ORCBORICIiIqJFYQFcArsNrl+sa3Cc\nAI6zoIsiIiIiIgAsgEshRGckA6y11xuRRkRERESLwwK4BI4IkjvAgvEHIiIiokVjAVwCIf2EQ3A1\nHoAjIiIiqgAWwCUQwkc8EoGocQQaERERUQWwAC6BEKNTIOKYHWAiIiKiKmABXIKkKRCMQBARERFV\nAwvgEowrgB2HBTARERHRorEALoEYMwVCcAoEERER0cKxAC6BkJ2ETXAeD8ERERERVQAL4BKM2wTH\nDNoXSLAAABBwSURBVDARERHR4rEALgEPwRERERFVFwvgEiRtguMcYCIiIqJqYAFcAiF9xMMRiLgG\nwSkQRERERAvHArgEQnSgEzbBCckCmIiIiGjRWACXQIhgdBOc9jgHmIiIiKgCWACXwByCawy8Tes6\n5wATERERVQAL4BII4SOOPcTx8R8vD8ERERERVQML4BII4QPAQA441l7v7URERES0OCyASyBktwDu\nywHrmKuQiYiIiKqABXAJjjvAxwVwrBiBICIiIqoCFsAlSCqANecAExEREVUCC+AS2E5v/zIMzgEm\nIiIiqgYWwCVwnBiO8AcjELrGOcBEREREFcACuCSirwCOY4E49iCYASYiIiJaOBbAJenfBhdrDwDg\ncAoEERER0cKxAC6JEJ1eB1h3C2DOASYiIiJaPBbAJTERCLMIQ8e17tsYgSAiIiJaNHeWDz51agff\n/m3/HT7tVa+E7wd47+/+Ht71a+8u6tqWmhABtG4AOJ4GwQgEERER0eLNVAC/4+1vw4svXsGP/K//\nAad2dvBPvv8f4ebNW3j/Bz5U1PUtLSH9Xga4F4HgFAgiIiKihZs6AuE4DoIgwG+8+7eglMKt27fx\nJ3/6EbzylY8UeX1Ly+mLQMTd/+YmOCIiIqLFm7oAjuMYP/y//Cj29u4BAKSUeP3rHsMLL7xY2MUt\ns8FDcGYGsOMs+KKIiIiIKD0C8cbPeQP+4T/4eyNvf9/vvx8/+VPvBGC6wd/x9rchiiL83vv+oPir\nXEJCBL3sr9Y1HoAjIiIiqojUAviPP/wn+JZvfdvI25VSAIB6vYbv+e7vwtbmJv6nf/fDCEMe9ALs\nFAh7CK7G+AMRERFRRaQWwHEcw/eT59eura3hn37/P8JLL+3iX/7rfwvfZ5FnCekjDNYBsANMRERE\nVCUzTYH4zu/4H3D79h38yI/+B8RxXNQ1nQhC9E2BiD12gImIiIgqYuoC+MEHH8BnfeanIwgC/B8/\n99O9t3/oQ3+En37nzxVyccvMRCDsKuQ6BGcAExEREVXC1AXwc889j294y7cUeS0nSn8BrHWNM4CJ\niIiIKoKrkEtiNsHZDrAHRyTnqImIiIhovlgAl8SRg3OAGYEgIiIiqgYWwCURwkeszAY4zTFoRERE\nRJXBArgkw3OAOQaNiIiIqBpYAJekPwPMOcBERERE1cECuCS2AxzHQBwzAkFERERUFSyASyJkBwAQ\nxx7HoBERERFVCAvgktjIg1b17iE4ToEgIiIiqgIWwCUR3bm/WjcQa48ZYCIiIqKKYAFckuMCuMZD\ncEREREQVwgK4JMcFcB1a13kIjoiIiKgiWACXxBEKcCJoXWcEgoiIiKhCWACXSAgfsa5DcwwaERER\nUWWwAC6RED60qnMTHBEREVGFsAAukVmGUeccYCIiIqIKYQFcov4CmHOAiYiIiKqBBXCJhPS7h+AY\ngSAiIiKqChbAJRIigFYNxLHXG4tGRERERIvFArhEQnQQResAwAgEERERUUWwAC6RED5UtNH934xA\nEBEREVUBC+ASOSJA1C2AOQeYiIiIqBpYAJdISHaAiYiIiKqGBXCJhPB7GWDOASYiIiKqBhbAJTIZ\n4E0APARHREREVBUsgEtkOsAbcJwQjhMv+nKIiIiICCyASyWED61anAFMREREVCEsgEskpCl8OQGC\niIiIqDpYAJfIdn4F879ERERElcECuES2AGYHmIiIiKg6WACX6LgDzAKYiIiIqCpYAJfIdn45A5iI\niIioOlgAl0jIDgBGIIiIiIiqhAVwiRiBICIiIqoeFsAlsoUvO8BERERE1cECuETsABMRERFVDwvg\nEgnR6f435wATERERVQUL4BL1NsFxCgQRERFRZbAALpHjhAB0LwpBRERERIvHArhEjmPyvw4jEERE\nRESVwQK4ZEL4PARHREREVCEsgEvmsAAmIiIiqhQWwCUT0uccYCIiIqIKYQFcsnrjKmr1m4u+DCIi\nIiLqchd9ASfdK179A4u+BCIiIiLqww4wEREREa0UFsBEREREtFJYABMRERHRSmEBTEREREQrhQUw\nEREREa0UFsBEREREtFJYABMRERHRSmEBTEREREQrhQUwEREREa0UFsBEREREtFJYABMRERHRSmEB\nTEREREQrhQUwEREREa0UFsBERET0/7d372FR1Xkcx99nhhmGq8p1AE0TgdS4eCczNbeMtcuueUlR\nBAQNMRRv1W7blj22u9Zma5kUipfV0nS1vOS2T2n65KVUMi8IgzdEuSSmIizI5TD7BzkrSuzO89QM\nNN/Xf/zmzDnny/N55vc9Zw4/hHAo0gALIYQQQgiHIg2wEEIIIYRwKNIACyGEEEIIh+Jk6wPqdHpb\nH1IIIYQQQjiQ/9Vv2qwBvnkiY+Om2eqQQgghhBDCgel0eupqa+8YV3oPjDHb6iRc3dypq7vzJIQQ\nQgghhPgp6XR6qv5d2exrNn0E4sdOQgghhBBCiJ9Sc3d+b5I/ghNCCCGEEA5FGmAhhBBCCOFQpAEW\nQgghhBAOxebLoP2Uogf0IyZmOC/PfxWA4OCuTE6IIzAwkPOFhWRlreLCxSIAugV3JTk5kQCjkfOF\nhSzNyKS09DsA7gkLJSEhDn8/P8oul/H+Bx9y9Ohxu9UlWh9rsjbo/vsYPWokHTq0p6iomNV/fx9T\n/ilAsuaIrMmOr48PyckJhIWGUFFZyYcbNrF3734AOnYMYkpSInfd1ZHy8uts2vwxX/7wmhCtgTVZ\nj4qKYOKEcXh7eWMy5ZO5fAVXrlwFoF/fPsSOH0v79u0oKipm5ao1nDl7zm51iab69unNuHGj8fby\npqS0tHGOM+UTFRVBfNwEvLy8yM3LY2lGJtevVzR57+OPjaBz504seec9y5i95sU2eQdYURQeezSG\nZ6anoKAA4OLiwnPzZrP/wNckT03l851f8Lvn5+HsrEen0zFnzky2bdvB5OQUjh49RmrKFAA0Gg1z\n56Sz/sONJCY9zYYNm5kzawY6nc6eJYpWwtqsBQQYSZqcwOK3lxKfOJWdu3YzKz0NkKw5GmuzAzBv\nbjpnzxYwOXkaf1v8DlOSEvD19QFgRto09u0/QGJSCm8vyWDqlMmW14SwJ2uz7ufny+z0NP6x6WOS\np6aSZ8rn2bmzAGjXrh3TUpJ5a0kGiUkp7N13gPT0Z+xZnriFn58v01OnkrViNZOTU9ix41OenZuO\nj483M9NSWZ61iuSpqVy5cpX4uAmW9+l0OmLHjyV2/Ngm+7PnvNgmG+DY8U/Rt09vtmzdbhkLCwvh\nxo0bfLLjU1RVZe/e/VRVVxF+77307NGd6qpq9u0/gKqqbP5oK506dSQoMBAPDw88PNzRaP77q6ir\nq7NHWaIVsjZrJSWlpKTO4Ny5ApycnHBzc6OysnH1E8maY7E2O6Eh3XBxdWHDxk00NDRw5sxZXnhx\nPhUVjfkJMBqBxmYDQFVVGtQG2xcmxG2szXpUZASnT5/hq68OoqoqW7Zux2j0p/NdnfDz9UGn06Eo\niiXrdbXyOdla+Pr4sHPXbnJzTZjNZvbuO0CD2czQIQ9gyj9Fzslc6urqWLd+I9HR/XFxMQDwTOrT\nBAQY+ezzXU32Z895sU0+AvHJjn9y7Vo5QwYPgp6NYxqNhtrb1hhuaGjA398Ps9lMUXGxZdxsNnPp\nUhlBQYEcPHSYXV/s4bl5s1FVFbPZzBtvviWNiQCszxpATU0NXbp05s+vzkdVVV57/U0AysvLJWsO\nxNrseHl14OLFIhLiJxId3Z+K6xV8sH4DF3/4ynjL1u0kJsSRED8RrVbLsqyVfH/liq3LEuIO1mZd\nVVVqblmeymw2Yzab8ff359DhbHJzTZbPz5qaWha8+hdbliNakHMyl5yTuZafQ7oFY3B2xs3NjeLi\nEst4RUUFNTU1GP39OVdwnpWr13DtWjmjR43E1dXFsp0958U2eQf42rXyO8by80/ToX17hgwehFar\nZeDAaIICA9HpdBgMztTedgVZU1uL3lmPoihUVVWz8PVFxMUnk/HuMqZPm0qHDh1sVY5oxazN2k0X\nLlxk4qQklmetYlZ6Gp6eHpI1B2Ntdtzd3YiMCKekpJTU6emsWbuOmWmpGI3+QOMd3/cys4iLT2bB\nnxYSO24sd3fpbOuyhLiDtVk/euw4PbrfQ2REOFqtliceH4Fe3/i4ol6vo6ysjJdeXsCkhCl89PFW\nZqWnode3/G9the35+/sxe1YaGzZuxuDsTO1ta+429lnOQPMZAew6L7bJBrg5lZWV/PWNxfw6Zjjv\nLl1MaEg3jh47QVVVFTU1tej1TZ8ncdbruXHjBgMG9KNTpyC++ebbxq9p9h2g4HwhA/r3tVMlorVr\nKWs3qaqKqqrs3vMlly9fpkeP7pI10WJ26urquXz5ez7912eoqsrRY8fJzTMRGRFOcHBXBg9+gN17\nvkRVVY4fz+HgoWwGDbrf3iUJ0ayWsl5SUso7GZnET5rA0iVvotVqKSoqpqq6ikeGPwSKQp4pn/r6\nerZu+wS1QSU8vKe9SxK36BbclVfmv8jOXbvZtn1HY7N720XKzT6rJfacF9vkIxDNcXJyoqq6mud/\n/0eg8apiyduL2LJlGy6uLjz44GDLtoqi4OfnR3FxCb2iInHSapvsS61XqVfrbXr+ou1oKWu9ekUy\n/OFfsfC1RU22r66qpmPHIMmag2spOx4eHri6ujbZXqPRoCgK3l5ezWSnnvp6yY5onVrKusFgoLi4\nhNlznwfAxcXAkyN/Q0FBIZER4XdkvUFtkKy3IlFREcxMS2XN2nXs+mIPQGM/1SvSso2npwcGg8Gy\n2taPaf6zzTbz4i/mDrBWq2H+Sy8QFhaKVqtl5G8fp7qqmvxTp8nJycXD3d3yVcyTI5+gtLSU4uIS\njp/IISSkG/dFDwCgT59ehIQEc+TIMTtXJFqrlrJ29mwBYaEhDOjfD41GwyPDH0Kr1XIyN0+yJlrM\nzrHjJ6irr2PsmFEoikJUZARhoSEczv4GU/4p3N3deHREDIqiNObovgEcPHTY3iUJ0ayWsu7p6cEr\nL/+BgAAjzs56JsSOIycnl6tXr/Ltt8eIju5P9+5hKIrCww8Nw2BwxmQ6Ze+SBODj4036jOlkvLfc\n0vwCHDqczT1hoURGhKPT6Rj31Biys49QU1PT4v7sOS8qvQfGmH/2o/xMhgwexLAHh/LS/AUA9O4V\nxaS4WNq1a8fpM2fIXLaCsrLLANx9dxemJCUQGBhAwflCMjKW8d2lSwD07dubp8aMwsfHm9LS71iz\ndh0nc/PsU5RolazJWs8e3YmfNAFfXx/OnD1H1orVlJSUApI1R2RNdgICjCQlxtO1axfKy6+z9oP1\nZGcfARrXypw4cTxBgQFcvXqNDRs38dXXh+xWlxC3sybrQ4cOZsyokbi4GDhx4iSZy1daVswZNmwo\nTzw2Ak9PDwoLL7By1RrOF16wU1XiVvGTJhDzyMN3PO+78LVF6HQ6JsXF4uXlRZ7JxNKMTMsqNjeN\nHjUSo9GvyTrA9poX23QDLIQQQgghhLV+MY9ACCGEEEII8f+QBlgIIYQQQjgUaYCFEEIIIYRDkQZY\nCCGEEEI4FGmAhRBCCCGEQ5EGWAghhBBCOBRpgIUQQgghhEORBlgIIYQQQjgUaYCFEEIIIYRD+Q+X\npM4rIi7xMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c972240198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Line chart\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "df_goaldiff_byyear = pd.pivot_table(df_ger, values='goaldiff', index=['year'], aggfunc='mean').reset_index()\n",
    "\n",
    "plt.plot(df_goaldiff_byyear['year'], df_goaldiff_byyear['goaldiff'], color='blue')\n",
    "\n",
    "plt.xticks([1908, 1938, 1968, 1998, 2018], \\\n",
    "           [1908, 1938, 1968, 1998, 2018])\n",
    "plt.suptitle('Goal Differential by Year', fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RakRERG1hMJwwNzPMy92xqAmGncxwBzTR2TXDzY5WkwHWDBMREbWMwXDCLNmC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DWyoWrxxqMbtzF+fADPH9V6PsLl/SRzGHaqe4J6lKPXDG8hpJM4vxGJ2VFl/eI8e3X25dOWSeQP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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c971f88128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Line chart\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "df_goals_byyear = pd.pivot_table(df_ger, values='goals', index=['year'], aggfunc='mean').reset_index()\n",
    "\n",
    "plt.plot(df_goals_byyear['year'], df_goals_byyear['goals'], color='green')\n",
    "\n",
    "plt.xticks([1908, 1938, 1968, 1998, 2018], \\\n",
    "           [1908, 1938, 1968, 1998, 2018])\n",
    "plt.suptitle('Goals Scored by Year', fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6XlFUbNz0JW6/47f4yQ03westxRWXX2r6DZtBEQRLRqslvXAjhD3DRERERNZK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\nERFFLIZhIiIiIopY/w9fombDuxY/vQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c971e4a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Line chart\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "df_conceded_byyear = pd.pivot_table(df_ger, values='conceded', index=['year'], aggfunc='mean').reset_index()\n",
    "\n",
    "plt.plot(df_conceded_byyear['year'], df_conceded_byyear['conceded'], color='red')\n",
    "\n",
    "plt.xticks([1908, 1938, 1968, 1998, 2018], \\\n",
    "           [1908, 1938, 1968, 1998, 2018])\n",
    "plt.suptitle('Goals Conceded by Year', fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P3MOKlas5efIUTZs2oWuXzmzb/ru3t/KOnTvp0L4dt00Yx9Gjx6hatQq9el2D2ezuaBJc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\nwAAAAAgUAjAAAAAC5f8Abs+Umt0LDhwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c97156c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Graphs\n",
    "#Line chart\n",
    "import matplotlib.ticker as ticker\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "df_conceded_byyear['conceded_neg'] = df_conceded_byyear['conceded']*-1\n",
    "\n",
    "plt.axhline(0, color='white', alpha=0.2)\n",
    "goals = plt.plot(df_goals_byyear['year'], df_goals_byyear['goals'], color='green', alpha=0.6, label = 'Goals Scored') \n",
    "conceded = plt.plot(df_conceded_byyear['year'], df_conceded_byyear['conceded_neg'], color='red', alpha=0.6, label = 'Goals Conceded')\n",
    "goaldiff = plt.plot(df_goaldiff_byyear['year'], df_goaldiff_byyear['goaldiff'], color='blue', label = 'Goal Differential')\n",
    "\n",
    "plt.xticks([1908, 1938, 1968, 1998, 2018], \\\n",
    "           [1908, 1938, 1968, 1998, 2018])\n",
    "plt.suptitle('Goal Differential by Year', fontsize=18)\n",
    "plt.title('Overlay Chart', fontsize=14)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import jtplot module in notebook\n",
    "from jupyterthemes import jtplot\n",
    "\n",
    "# choose which theme to inherit plotting style from\n",
    "# onedork | grade3 | oceans16 | chesterish | monokai | solarizedl | solarizedd\n",
    "jtplot.style(theme='chesterish')\n",
    "\n",
    "# turn on X- and Y-axis tick marks (default=False)\n",
    "# turn off the axis grid lines (default=True)\n",
    "# and set the default figure size\n",
    "jtplot.style(grid=False)\n",
    "\n",
    "# reset default matplotlib rcParams\n",
    "#jtplot.reset()\n"
   ]
  },
  {
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