Exploring Weather Trends.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple SQL quaries were used in order to extract three tables from the database:\n",
    "\n",
    "select * from city_list\n",
    "\n",
    "select * from global_data\n",
    "\n",
    "select * from city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JPierson\\Desktop\\PythonFiles\\Project1\n"
     ]
    }
   ],
   "source": [
    "cd C:\\Users\\JPierson\\Desktop\\PythonFiles\\Project1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_data = pd.read_csv(\"city_data.csv\")\n",
    "global_data = pd.read_csv(\"global_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>avg_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1849</td>\n",
       "      <td>Abidjan</td>\n",
       "      <td>Côte D'Ivoire</td>\n",
       "      <td>25.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1850</td>\n",
       "      <td>Abidjan</td>\n",
       "      <td>Côte D'Ivoire</td>\n",
       "      <td>25.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1851</td>\n",
       "      <td>Abidjan</td>\n",
       "      <td>Côte D'Ivoire</td>\n",
       "      <td>25.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1852</td>\n",
       "      <td>Abidjan</td>\n",
       "      <td>Côte D'Ivoire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1853</td>\n",
       "      <td>Abidjan</td>\n",
       "      <td>Côte D'Ivoire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     city        country  avg_temp\n",
       "0  1849  Abidjan  Côte D'Ivoire     25.58\n",
       "1  1850  Abidjan  Côte D'Ivoire     25.52\n",
       "2  1851  Abidjan  Côte D'Ivoire     25.67\n",
       "3  1852  Abidjan  Côte D'Ivoire       NaN\n",
       "4  1853  Abidjan  Côte D'Ivoire       NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>avg_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1750</td>\n",
       "      <td>8.72</td>\n",
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       "      <th>1</th>\n",
       "      <td>1751</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1752</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1753</td>\n",
       "      <td>8.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1754</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   year  avg_temp\n",
       "0  1750      8.72\n",
       "1  1751      7.98\n",
       "2  1752      5.78\n",
       "3  1753      8.39\n",
       "4  1754      8.47"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Considering the large number of cities in city_data and the fact that the names of the countries are not crucial for the purposes of our research it's a good idea to expose the data in city_data in the form of panel data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_data_exposed = city_data.pivot_table(values='avg_temp',\n",
    "                                index='year',\n",
    "                                columns=['city',])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    206.000000\n",
       "mean      11.099806\n",
       "std        0.923263\n",
       "min        5.500000\n",
       "25%       10.580000\n",
       "50%       11.065000\n",
       "75%       11.617500\n",
       "max       14.120000\n",
       "Name: Baku, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_exposed.Baku.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>Abidjan</th>\n",
       "      <th>Abu Dhabi</th>\n",
       "      <th>Abuja</th>\n",
       "      <th>Accra</th>\n",
       "      <th>Adana</th>\n",
       "      <th>Adelaide</th>\n",
       "      <th>Agra</th>\n",
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       "      <th>Albuquerque</th>\n",
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       "      <th>Xian</th>\n",
       "      <th>Xuzhou</th>\n",
       "      <th>Yamoussoukro</th>\n",
       "      <th>Yerevan</th>\n",
       "      <th>Zagreb</th>\n",
       "      <th>Zapopan</th>\n",
       "    </tr>\n",
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       "      <th>year</th>\n",
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       "      <th>1743</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.34</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.88</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.06</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 329 columns</p>\n",
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      "text/plain": [
       "city  Abidjan  Abu Dhabi  Abuja  Accra  Adana  Adelaide  Agra  Ahmadabad  \\\n",
       "year                                                                       \n",
       "1743      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "1744      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "1745      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "1746      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "1747      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "\n",
       "city  Albuquerque  Alexandria   ...     Wichita  Windhoek  Wuhan  Wuxi  Xian  \\\n",
       "year                            ...                                            \n",
       "1743          NaN        5.34   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1744          NaN       13.88   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1745          NaN        4.00   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1746          NaN         NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1747          NaN         NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "\n",
       "city  Xuzhou  Yamoussoukro  Yerevan  Zagreb  Zapopan  \n",
       "year                                                  \n",
       "1743     NaN           NaN      NaN    5.49      NaN  \n",
       "1744     NaN           NaN      NaN   11.06      NaN  \n",
       "1745     NaN           NaN      NaN    2.30      NaN  \n",
       "1746     NaN           NaN      NaN     NaN      NaN  \n",
       "1747     NaN           NaN      NaN     NaN      NaN  \n",
       "\n",
       "[5 rows x 329 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_exposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_data_exposed.insert(loc=0, column= \"Year\", value=city_data_exposed.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1746</th>\n",
       "      <td>1746</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>1747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 330 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city  Year  Abidjan  Abu Dhabi  Abuja  Accra  Adana  Adelaide  Agra  \\\n",
       "year                                                                  \n",
       "1743  1743      NaN        NaN    NaN    NaN    NaN       NaN   NaN   \n",
       "1744  1744      NaN        NaN    NaN    NaN    NaN       NaN   NaN   \n",
       "1745  1745      NaN        NaN    NaN    NaN    NaN       NaN   NaN   \n",
       "1746  1746      NaN        NaN    NaN    NaN    NaN       NaN   NaN   \n",
       "1747  1747      NaN        NaN    NaN    NaN    NaN       NaN   NaN   \n",
       "\n",
       "city  Ahmadabad  Albuquerque   ...     Wichita  Windhoek  Wuhan  Wuxi  Xian  \\\n",
       "year                           ...                                            \n",
       "1743        NaN          NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1744        NaN          NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1745        NaN          NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1746        NaN          NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "1747        NaN          NaN   ...         NaN       NaN    NaN   NaN   NaN   \n",
       "\n",
       "city  Xuzhou  Yamoussoukro  Yerevan  Zagreb  Zapopan  \n",
       "year                                                  \n",
       "1743     NaN           NaN      NaN    5.49      NaN  \n",
       "1744     NaN           NaN      NaN   11.06      NaN  \n",
       "1745     NaN           NaN      NaN    2.30      NaN  \n",
       "1746     NaN           NaN      NaN     NaN      NaN  \n",
       "1747     NaN           NaN      NaN     NaN      NaN  \n",
       "\n",
       "[5 rows x 330 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_exposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_data_exposed.to_csv('city_data_exposed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As one of the goals of our research is to compare average temperatures of the individual cities with the global average temperatures it's useful to combine global data with the city data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271, 330)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_exposed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_data.rename(columns={'year':'Year'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = pd.merge(city_data_exposed, global_data, on=[\"Year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Abidjan</th>\n",
       "      <th>Abu Dhabi</th>\n",
       "      <th>Abuja</th>\n",
       "      <th>Accra</th>\n",
       "      <th>Adana</th>\n",
       "      <th>Adelaide</th>\n",
       "      <th>Agra</th>\n",
       "      <th>Ahmadabad</th>\n",
       "      <th>Albuquerque</th>\n",
       "      <th>...</th>\n",
       "      <th>Windhoek</th>\n",
       "      <th>Wuhan</th>\n",
       "      <th>Wuxi</th>\n",
       "      <th>Xian</th>\n",
       "      <th>Xuzhou</th>\n",
       "      <th>Yamoussoukro</th>\n",
       "      <th>Yerevan</th>\n",
       "      <th>Zagreb</th>\n",
       "      <th>Zapopan</th>\n",
       "      <th>avg_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 331 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Abidjan  Abu Dhabi  Abuja  Accra  Adana  Adelaide  Agra  Ahmadabad  \\\n",
       "0  1750      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "1  1751      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "2  1752      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "3  1753      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "4  1754      NaN        NaN    NaN    NaN    NaN       NaN   NaN        NaN   \n",
       "\n",
       "   Albuquerque    ...     Windhoek  Wuhan  Wuxi  Xian  Xuzhou  Yamoussoukro  \\\n",
       "0          NaN    ...          NaN    NaN   NaN   NaN     NaN           NaN   \n",
       "1          NaN    ...          NaN    NaN   NaN   NaN     NaN           NaN   \n",
       "2          NaN    ...          NaN    NaN   NaN   NaN     NaN           NaN   \n",
       "3          NaN    ...          NaN    NaN   NaN   NaN     NaN           NaN   \n",
       "4          NaN    ...          NaN    NaN   NaN   NaN     NaN           NaN   \n",
       "\n",
       "   Yerevan  Zagreb  Zapopan  avg_temp  \n",
       "0      NaN   10.19      NaN      8.72  \n",
       "1      NaN   10.53      NaN      7.98  \n",
       "2      NaN    4.97      NaN      5.78  \n",
       "3      NaN    9.44      NaN      8.39  \n",
       "4      NaN    9.24      NaN      8.47  \n",
       "\n",
       "[5 rows x 331 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now it's time for some cleaning work. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                0\n",
       "Abidjan           113\n",
       "Abu Dhabi         105\n",
       "Abuja             116\n",
       "Accra             113\n",
       "Adana              41\n",
       "Adelaide           91\n",
       "Agra               58\n",
       "Ahmadabad          58\n",
       "Albuquerque        70\n",
       "Alexandria          1\n",
       "Algiers             3\n",
       "Allahabad          54\n",
       "Almaty             80\n",
       "Amritsar           88\n",
       "Amsterdam           0\n",
       "Ankara             18\n",
       "Anshan             80\n",
       "Antananarivo      114\n",
       "Arlington           1\n",
       "Asmara            124\n",
       "Astana             62\n",
       "Athens              3\n",
       "Atlanta             1\n",
       "Austin             70\n",
       "Baghdad            69\n",
       "Baku               58\n",
       "Baltimore           1\n",
       "Bamako            106\n",
       "Bandung           105\n",
       "                 ... \n",
       "Toronto             0\n",
       "Tripoli             3\n",
       "Tucson             85\n",
       "Tulsa              36\n",
       "Tunis               1\n",
       "Ufa                21\n",
       "Ulaanbaatar        70\n",
       "Vadodara           53\n",
       "Valencia            0\n",
       "Varanasi           54\n",
       "Victoria           81\n",
       "Vienna              0\n",
       "Vientiane          74\n",
       "Vilnius             0\n",
       "Virginia Beach      1\n",
       "Volgograd           2\n",
       "Warsaw              0\n",
       "Washington          1\n",
       "Wellington        103\n",
       "Wichita            49\n",
       "Windhoek          107\n",
       "Wuhan              91\n",
       "Wuxi               91\n",
       "Xian               85\n",
       "Xuzhou             91\n",
       "Yamoussoukro      107\n",
       "Yerevan            48\n",
       "Zagreb              0\n",
       "Zapopan            92\n",
       "avg_temp            0\n",
       "Length: 331, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which columns have missing values with info()\n",
    "\n",
    "combined_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It can be clearly seen that most of the missing data in the columns occur in the earlier periods and that's quite logical taking into account lack of statistics in previous centuries. Therefore we drop all the data (mostly missing) before the year 1808. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"max_columns\", 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Abidjan</th>\n",
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       "      <th>Ahmadabad</th>\n",
       "      <th>Albuquerque</th>\n",
       "      <th>Alexandria</th>\n",
       "      <th>Algiers</th>\n",
       "      <th>Allahabad</th>\n",
       "      <th>Almaty</th>\n",
       "      <th>Amritsar</th>\n",
       "      <th>Amsterdam</th>\n",
       "      <th>Ankara</th>\n",
       "      <th>Anshan</th>\n",
       "      <th>Antananarivo</th>\n",
       "      <th>Arlington</th>\n",
       "      <th>Asmara</th>\n",
       "      <th>Astana</th>\n",
       "      <th>Athens</th>\n",
       "      <th>Atlanta</th>\n",
       "      <th>Austin</th>\n",
       "      <th>Baghdad</th>\n",
       "      <th>Baku</th>\n",
       "      <th>Baltimore</th>\n",
       "      <th>Bamako</th>\n",
       "      <th>Bandung</th>\n",
       "      <th>Bangalore</th>\n",
       "      <th>Bangkok</th>\n",
       "      <th>Bangui</th>\n",
       "      <th>Barcelona</th>\n",
       "      <th>Barquisimeto</th>\n",
       "      <th>Barranquilla</th>\n",
       "      <th>Beirut</th>\n",
       "      <th>Belfast</th>\n",
       "      <th>Belgrade</th>\n",
       "      <th>Belo Horizonte</th>\n",
       "      <th>Benghazi</th>\n",
       "      <th>Berlin</th>\n",
       "      <th>Bern</th>\n",
       "      <th>Bhopal</th>\n",
       "      <th>Birmingham</th>\n",
       "      <th>Bissau</th>\n",
       "      <th>Boston</th>\n",
       "      <th>Bratislava</th>\n",
       "      <th>Brazzaville</th>\n",
       "      <th>Brisbane</th>\n",
       "      <th>Brussels</th>\n",
       "      <th>Bucharest</th>\n",
       "      <th>Budapest</th>\n",
       "      <th>Bujumbura</th>\n",
       "      <th>Bursa</th>\n",
       "      <th>Cairo</th>\n",
       "      <th>Cali</th>\n",
       "      <th>Campinas</th>\n",
       "      <th>Canberra</th>\n",
       "      <th>Caracas</th>\n",
       "      <th>Cardiff</th>\n",
       "      <th>Casablanca</th>\n",
       "      <th>Changchun</th>\n",
       "      <th>Changzhou</th>\n",
       "      <th>Charlotte</th>\n",
       "      <th>Chelyabinsk</th>\n",
       "      <th>Chengdu</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Chisinau</th>\n",
       "      <th>Colombo</th>\n",
       "      <th>Colorado Springs</th>\n",
       "      <th>Columbus</th>\n",
       "      <th>Conakry</th>\n",
       "      <th>Copenhagen</th>\n",
       "      <th>Cordoba</th>\n",
       "      <th>Curitiba</th>\n",
       "      <th>Dakar</th>\n",
       "      <th>Dalian</th>\n",
       "      <th>Dallas</th>\n",
       "      <th>Damascus</th>\n",
       "      <th>Dar Es Salaam</th>\n",
       "      <th>Datong</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Denver</th>\n",
       "      <th>Detroit</th>\n",
       "      <th>Dhaka</th>\n",
       "      <th>Doha</th>\n",
       "      <th>Douala</th>\n",
       "      <th>Dublin</th>\n",
       "      <th>Durban</th>\n",
       "      <th>Dushanbe</th>\n",
       "      <th>Ecatepec</th>\n",
       "      <th>Edinburgh</th>\n",
       "      <th>El Paso</th>\n",
       "      <th>Faisalabad</th>\n",
       "      <th>Fort Worth</th>\n",
       "      <th>Fortaleza</th>\n",
       "      <th>Foshan</th>\n",
       "      <th>Freetown</th>\n",
       "      <th>Fresno</th>\n",
       "      <th>Fuzhou</th>\n",
       "      <th>Gaborone</th>\n",
       "      <th>Georgetown</th>\n",
       "      <th>Guadalajara</th>\n",
       "      <th>Guangzhou</th>\n",
       "      <th>Guarulhos</th>\n",
       "      <th>Guatemala City</th>\n",
       "      <th>Guayaquil</th>\n",
       "      <th>Guiyang</th>\n",
       "      <th>Gujranwala</th>\n",
       "      <th>Hamburg</th>\n",
       "      <th>Handan</th>\n",
       "      <th>Hangzhou</th>\n",
       "      <th>Hanoi</th>\n",
       "      <th>Haora</th>\n",
       "      <th>Harare</th>\n",
       "      <th>Harbin</th>\n",
       "      <th>Hefei</th>\n",
       "      <th>Helsinki</th>\n",
       "      <th>Hiroshima</th>\n",
       "      <th>Ho Chi Minh City</th>\n",
       "      <th>Houston</th>\n",
       "      <th>Hyderabad</th>\n",
       "      <th>Ibadan</th>\n",
       "      <th>Indianapolis</th>\n",
       "      <th>Indore</th>\n",
       "      <th>Irbil</th>\n",
       "      <th>Islamabad</th>\n",
       "      <th>Istanbul</th>\n",
       "      <th>Izmir</th>\n",
       "      <th>Jacksonville</th>\n",
       "      <th>Jaipur</th>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Jilin</th>\n",
       "      <th>Jinan</th>\n",
       "      <th>Johannesburg</th>\n",
       "      <th>Juba</th>\n",
       "      <th>Kabul</th>\n",
       "      <th>Kaduna</th>\n",
       "      <th>Kampala</th>\n",
       "      <th>Kano</th>\n",
       "      <th>Kanpur</th>\n",
       "      <th>Kansas City</th>\n",
       "      <th>Karachi</th>\n",
       "      <th>Kathmandu</th>\n",
       "      <th>Kawasaki</th>\n",
       "      <th>Kazan</th>\n",
       "      <th>Khartoum</th>\n",
       "      <th>Khulna</th>\n",
       "      <th>Kiev</th>\n",
       "      <th>Kigali</th>\n",
       "      <th>Kingston</th>\n",
       "      <th>Kinshasa</th>\n",
       "      <th>Kitakyushu</th>\n",
       "      <th>Kobe</th>\n",
       "      <th>Kuala Lumpur</th>\n",
       "      <th>Kunming</th>\n",
       "      <th>La Paz</th>\n",
       "      <th>Lagos</th>\n",
       "      <th>Lahore</th>\n",
       "      <th>Lanzhou</th>\n",
       "      <th>Las Vegas</th>\n",
       "      <th>Libreville</th>\n",
       "      <th>Lilongwe</th>\n",
       "      <th>Lima</th>\n",
       "      <th>Lisbon</th>\n",
       "      <th>Ljubljana</th>\n",
       "      <th>London</th>\n",
       "      <th>Long Beach</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Louisville</th>\n",
       "      <th>Luanda</th>\n",
       "      <th>Lubumbashi</th>\n",
       "      <th>Ludhiana</th>\n",
       "      <th>Luoyang</th>\n",
       "      <th>Lusaka</th>\n",
       "      <th>Madrid</th>\n",
       "      <th>Maiduguri</th>\n",
       "      <th>Malabo</th>\n",
       "      <th>Managua</th>\n",
       "      <th>Manama</th>\n",
       "      <th>Manaus</th>\n",
       "      <th>Manila</th>\n",
       "      <th>Maputo</th>\n",
       "      <th>Maracaibo</th>\n",
       "      <th>Maseru</th>\n",
       "      <th>Mashhad</th>\n",
       "      <th>Mecca</th>\n",
       "      <th>Medan</th>\n",
       "      <th>Melbourne</th>\n",
       "      <th>Memphis</th>\n",
       "      <th>Mesa</th>\n",
       "      <th>Mexicali</th>\n",
       "      <th>Miami</th>\n",
       "      <th>Milan</th>\n",
       "      <th>Milwaukee</th>\n",
       "      <th>Minneapolis</th>\n",
       "      <th>Minsk</th>\n",
       "      <th>Mogadishu</th>\n",
       "      <th>Monrovia</th>\n",
       "      <th>Monterrey</th>\n",
       "      <th>Montevideo</th>\n",
       "      <th>Montreal</th>\n",
       "      <th>Moscow</th>\n",
       "      <th>Multan</th>\n",
       "      <th>Munich</th>\n",
       "      <th>Nagoya</th>\n",
       "      <th>Nagpur</th>\n",
       "      <th>Nairobi</th>\n",
       "      <th>Nanchang</th>\n",
       "      <th>Nanjing</th>\n",
       "      <th>Nanning</th>\n",
       "      <th>Nashville</th>\n",
       "      <th>Nassau</th>\n",
       "      <th>New Delhi</th>\n",
       "      <th>New Orleans</th>\n",
       "      <th>New York</th>\n",
       "      <th>Niamey</th>\n",
       "      <th>Nouakchott</th>\n",
       "      <th>Novosibirsk</th>\n",
       "      <th>Oakland</th>\n",
       "      <th>Oklahoma City</th>\n",
       "      <th>Omaha</th>\n",
       "      <th>Omsk</th>\n",
       "      <th>Oslo</th>\n",
       "      <th>Ottawa</th>\n",
       "      <th>Ouagadougou</th>\n",
       "      <th>Palembang</th>\n",
       "      <th>Paramaribo</th>\n",
       "      <th>Paris</th>\n",
       "      <th>Patna</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Perth</th>\n",
       "      <th>Peshawar</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Phoenix</th>\n",
       "      <th>Podgorica</th>\n",
       "      <th>Port Au Prince</th>\n",
       "      <th>Port Harcourt</th>\n",
       "      <th>Port Louis</th>\n",
       "      <th>Port Moresby</th>\n",
       "      <th>Portland</th>\n",
       "      <th>Porto Alegre</th>\n",
       "      <th>Prague</th>\n",
       "      <th>Pretoria</th>\n",
       "      <th>Pristina</th>\n",
       "      <th>Puebla</th>\n",
       "      <th>Pune</th>\n",
       "      <th>Qingdao</th>\n",
       "      <th>Qiqihar</th>\n",
       "      <th>Quito</th>\n",
       "      <th>Rabat</th>\n",
       "      <th>Rajkot</th>\n",
       "      <th>Raleigh</th>\n",
       "      <th>Ranchi</th>\n",
       "      <th>Rawalpindi</th>\n",
       "      <th>Recife</th>\n",
       "      <th>Riga</th>\n",
       "      <th>Rio De Janeiro</th>\n",
       "      <th>Riyadh</th>\n",
       "      <th>Rome</th>\n",
       "      <th>Rosario</th>\n",
       "      <th>Sacramento</th>\n",
       "      <th>Salvador</th>\n",
       "      <th>Samara</th>\n",
       "      <th>San Antonio</th>\n",
       "      <th>San Diego</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>San Jose</th>\n",
       "      <th>San Salvador</th>\n",
       "      <th>Santa Cruz</th>\n",
       "      <th>Santiago</th>\n",
       "      <th>Santo Domingo</th>\n",
       "      <th>Sarajevo</th>\n",
       "      <th>Seattle</th>\n",
       "      <th>Semarang</th>\n",
       "      <th>Seoul</th>\n",
       "      <th>Shanghai</th>\n",
       "      <th>Shenyang</th>\n",
       "      <th>Shenzhen</th>\n",
       "      <th>Shiraz</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Skopje</th>\n",
       "      <th>Sofia</th>\n",
       "      <th>Soweto</th>\n",
       "      <th>Stockholm</th>\n",
       "      <th>Surabaya</th>\n",
       "      <th>Surat</th>\n",
       "      <th>Suzhou</th>\n",
       "      <th>Sydney</th>\n",
       "      <th>Tabriz</th>\n",
       "      <th>Taiyuan</th>\n",
       "      <th>Tallinn</th>\n",
       "      <th>Tangshan</th>\n",
       "      <th>Tashkent</th>\n",
       "      <th>Tbilisi</th>\n",
       "      <th>Tegucigalpa</th>\n",
       "      <th>Tianjin</th>\n",
       "      <th>Tijuana</th>\n",
       "      <th>Tirana</th>\n",
       "      <th>Tokyo</th>\n",
       "      <th>Toronto</th>\n",
       "      <th>Tripoli</th>\n",
       "      <th>Tucson</th>\n",
       "      <th>Tulsa</th>\n",
       "      <th>Tunis</th>\n",
       "      <th>Ufa</th>\n",
       "      <th>Ulaanbaatar</th>\n",
       "      <th>Vadodara</th>\n",
       "      <th>Valencia</th>\n",
       "      <th>Varanasi</th>\n",
       "      <th>Victoria</th>\n",
       "      <th>Vienna</th>\n",
       "      <th>Vientiane</th>\n",
       "      <th>Vilnius</th>\n",
       "      <th>Virginia Beach</th>\n",
       "      <th>Volgograd</th>\n",
       "      <th>Warsaw</th>\n",
       "      <th>Washington</th>\n",
       "      <th>Wellington</th>\n",
       "      <th>Wichita</th>\n",
       "      <th>Windhoek</th>\n",
       "      <th>Wuhan</th>\n",
       "      <th>Wuxi</th>\n",
       "      <th>Xian</th>\n",
       "      <th>Xuzhou</th>\n",
       "      <th>Yamoussoukro</th>\n",
       "      <th>Yerevan</th>\n",
       "      <th>Zagreb</th>\n",
       "      <th>Zapopan</th>\n",
       "      <th>avg_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.095</td>\n",
       "      <td>15.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.64</td>\n",
       "      <td>9.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.48</td>\n",
       "      <td>13.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.42</td>\n",
       "      <td>11.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.62</td>\n",
       "      <td>8.06</td>\n",
       "      <td>9.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.89</td>\n",
       "      <td>8.66</td>\n",
       "      <td>6.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.57</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.42</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.41</td>\n",
       "      <td>19.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.45</td>\n",
       "      <td>16.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.04</td>\n",
       "      <td>7.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.40</td>\n",
       "      <td>16.27</td>\n",
       "      <td>19.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.58</td>\n",
       "      <td>9.21</td>\n",
       "      <td>8.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.81</td>\n",
       "      <td>6.36</td>\n",
       "      <td>9.04</td>\n",
       "      <td>3.79</td>\n",
       "      <td>4.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.22</td>\n",
       "      <td>23.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.57</td>\n",
       "      <td>3.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.92</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.90</td>\n",
       "      <td>18.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.63</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.14</td>\n",
       "      <td>14.82</td>\n",
       "      <td>5.47</td>\n",
       "      <td>6.95</td>\n",
       "      <td>11.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.75</td>\n",
       "      <td>9.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1810</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.335</td>\n",
       "      <td>15.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.47</td>\n",
       "      <td>9.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.76</td>\n",
       "      <td>13.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.47</td>\n",
       "      <td>11.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.70</td>\n",
       "      <td>7.89</td>\n",
       "      <td>9.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.06</td>\n",
       "      <td>8.46</td>\n",
       "      <td>6.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.24</td>\n",
       "      <td>9.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.31</td>\n",
       "      <td>10.32</td>\n",
       "      <td>9.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.70</td>\n",
       "      <td>19.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.43</td>\n",
       "      <td>15.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.72</td>\n",
       "      <td>16.54</td>\n",
       "      <td>20.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.21</td>\n",
       "      <td>9.63</td>\n",
       "      <td>8.170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.85</td>\n",
       "      <td>6.59</td>\n",
       "      <td>9.22</td>\n",
       "      <td>3.86</td>\n",
       "      <td>4.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.33</td>\n",
       "      <td>23.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.59</td>\n",
       "      <td>3.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.32</td>\n",
       "      <td>8.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.24</td>\n",
       "      <td>18.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.76</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.46</td>\n",
       "      <td>15.16</td>\n",
       "      <td>5.60</td>\n",
       "      <td>6.91</td>\n",
       "      <td>11.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.83</td>\n",
       "      <td>9.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.420</td>\n",
       "      <td>15.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.07</td>\n",
       "      <td>9.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.99</td>\n",
       "      <td>13.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.58</td>\n",
       "      <td>11.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.73</td>\n",
       "      <td>8.70</td>\n",
       "      <td>10.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.27</td>\n",
       "      <td>9.93</td>\n",
       "      <td>7.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.43</td>\n",
       "      <td>10.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.78</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.40</td>\n",
       "      <td>15.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.37</td>\n",
       "      <td>8.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.95</td>\n",
       "      <td>16.73</td>\n",
       "      <td>20.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.48</td>\n",
       "      <td>10.38</td>\n",
       "      <td>8.795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.04</td>\n",
       "      <td>7.59</td>\n",
       "      <td>9.37</td>\n",
       "      <td>4.04</td>\n",
       "      <td>5.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.30</td>\n",
       "      <td>3.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.48</td>\n",
       "      <td>23.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.80</td>\n",
       "      <td>8.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.38</td>\n",
       "      <td>18.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.53</td>\n",
       "      <td>18.17</td>\n",
       "      <td>0.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.88</td>\n",
       "      <td>15.28</td>\n",
       "      <td>6.05</td>\n",
       "      <td>8.38</td>\n",
       "      <td>11.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>10.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.630</td>\n",
       "      <td>15.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>16.27</td>\n",
       "      <td>12.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.71</td>\n",
       "      <td>10.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.63</td>\n",
       "      <td>7.66</td>\n",
       "      <td>9.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "      <td>7.35</td>\n",
       "      <td>5.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.48</td>\n",
       "      <td>8.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.95</td>\n",
       "      <td>9.68</td>\n",
       "      <td>8.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.30</td>\n",
       "      <td>19.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.11</td>\n",
       "      <td>16.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.45</td>\n",
       "      <td>0.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.34</td>\n",
       "      <td>7.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.10</td>\n",
       "      <td>19.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.52</td>\n",
       "      <td>8.51</td>\n",
       "      <td>7.355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.53</td>\n",
       "      <td>5.42</td>\n",
       "      <td>8.34</td>\n",
       "      <td>3.17</td>\n",
       "      <td>3.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.39</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.55</td>\n",
       "      <td>7.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.97</td>\n",
       "      <td>18.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.54</td>\n",
       "      <td>17.35</td>\n",
       "      <td>0.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.74</td>\n",
       "      <td>14.06</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5.89</td>\n",
       "      <td>10.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.02</td>\n",
       "      <td>8.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.60</td>\n",
       "      <td>25.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.450</td>\n",
       "      <td>15.63</td>\n",
       "      <td>24.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.83</td>\n",
       "      <td>9.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>16.55</td>\n",
       "      <td>13.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.38</td>\n",
       "      <td>11.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.13</td>\n",
       "      <td>8.38</td>\n",
       "      <td>9.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.07</td>\n",
       "      <td>8.66</td>\n",
       "      <td>6.08</td>\n",
       "      <td>24.66</td>\n",
       "      <td>12.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.61</td>\n",
       "      <td>9.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.49</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.63</td>\n",
       "      <td>20.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.66</td>\n",
       "      <td>16.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.46</td>\n",
       "      <td>0.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.67</td>\n",
       "      <td>7.94</td>\n",
       "      <td>25.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.07</td>\n",
       "      <td>24.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.78</td>\n",
       "      <td>24.30</td>\n",
       "      <td>18.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.62</td>\n",
       "      <td>16.41</td>\n",
       "      <td>20.50</td>\n",
       "      <td>24.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.14</td>\n",
       "      <td>11.39</td>\n",
       "      <td>25.40</td>\n",
       "      <td>14.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.90</td>\n",
       "      <td>6.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.72</td>\n",
       "      <td>8.87</td>\n",
       "      <td>8.270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.50</td>\n",
       "      <td>6.27</td>\n",
       "      <td>9.67</td>\n",
       "      <td>4.70</td>\n",
       "      <td>4.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.82</td>\n",
       "      <td>3.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.81</td>\n",
       "      <td>23.08</td>\n",
       "      <td>24.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.66</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>2.46</td>\n",
       "      <td>3.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.77</td>\n",
       "      <td>24.55</td>\n",
       "      <td>0.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.50</td>\n",
       "      <td>25.46</td>\n",
       "      <td>13.87</td>\n",
       "      <td>23.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.82</td>\n",
       "      <td>7.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.31</td>\n",
       "      <td>18.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.39</td>\n",
       "      <td>17.59</td>\n",
       "      <td>1.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.73</td>\n",
       "      <td>15.60</td>\n",
       "      <td>24.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.08</td>\n",
       "      <td>15.07</td>\n",
       "      <td>6.57</td>\n",
       "      <td>7.05</td>\n",
       "      <td>11.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.56</td>\n",
       "      <td>8.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.71</td>\n",
       "      <td>25.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.315</td>\n",
       "      <td>15.18</td>\n",
       "      <td>24.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.76</td>\n",
       "      <td>9.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.30</td>\n",
       "      <td>16.51</td>\n",
       "      <td>13.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.53</td>\n",
       "      <td>11.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.25</td>\n",
       "      <td>7.49</td>\n",
       "      <td>9.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.87</td>\n",
       "      <td>7.46</td>\n",
       "      <td>5.66</td>\n",
       "      <td>23.84</td>\n",
       "      <td>11.945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.36</td>\n",
       "      <td>8.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.65</td>\n",
       "      <td>10.06</td>\n",
       "      <td>8.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.77</td>\n",
       "      <td>20.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.52</td>\n",
       "      <td>16.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.22</td>\n",
       "      <td>0.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.55</td>\n",
       "      <td>8.00</td>\n",
       "      <td>25.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.92</td>\n",
       "      <td>24.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.60</td>\n",
       "      <td>23.50</td>\n",
       "      <td>18.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.81</td>\n",
       "      <td>16.45</td>\n",
       "      <td>20.24</td>\n",
       "      <td>23.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.32</td>\n",
       "      <td>11.24</td>\n",
       "      <td>24.86</td>\n",
       "      <td>13.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.51</td>\n",
       "      <td>6.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.45</td>\n",
       "      <td>8.45</td>\n",
       "      <td>7.585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.24</td>\n",
       "      <td>5.57</td>\n",
       "      <td>9.55</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.61</td>\n",
       "      <td>24.06</td>\n",
       "      <td>23.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.16</td>\n",
       "      <td>9.53</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.22</td>\n",
       "      <td>23.80</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.28</td>\n",
       "      <td>24.99</td>\n",
       "      <td>13.63</td>\n",
       "      <td>22.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.67</td>\n",
       "      <td>7.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.16</td>\n",
       "      <td>18.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.29</td>\n",
       "      <td>17.11</td>\n",
       "      <td>0.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.13</td>\n",
       "      <td>15.16</td>\n",
       "      <td>23.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18</td>\n",
       "      <td>14.81</td>\n",
       "      <td>6.59</td>\n",
       "      <td>6.17</td>\n",
       "      <td>11.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.79</td>\n",
       "      <td>8.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.13</td>\n",
       "      <td>25.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.045</td>\n",
       "      <td>15.82</td>\n",
       "      <td>24.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.75</td>\n",
       "      <td>9.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.13</td>\n",
       "      <td>16.56</td>\n",
       "      <td>13.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.95</td>\n",
       "      <td>8.17</td>\n",
       "      <td>9.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.13</td>\n",
       "      <td>8.38</td>\n",
       "      <td>6.37</td>\n",
       "      <td>24.19</td>\n",
       "      <td>12.215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.13</td>\n",
       "      <td>9.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.57</td>\n",
       "      <td>9.92</td>\n",
       "      <td>9.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.56</td>\n",
       "      <td>20.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.42</td>\n",
       "      <td>16.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.86</td>\n",
       "      <td>7.78</td>\n",
       "      <td>24.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.29</td>\n",
       "      <td>24.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.07</td>\n",
       "      <td>23.84</td>\n",
       "      <td>19.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.54</td>\n",
       "      <td>16.39</td>\n",
       "      <td>19.96</td>\n",
       "      <td>24.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.67</td>\n",
       "      <td>10.66</td>\n",
       "      <td>24.92</td>\n",
       "      <td>13.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.43</td>\n",
       "      <td>5.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.82</td>\n",
       "      <td>8.86</td>\n",
       "      <td>7.820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.96</td>\n",
       "      <td>6.45</td>\n",
       "      <td>8.86</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.22</td>\n",
       "      <td>2.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.25</td>\n",
       "      <td>8.89</td>\n",
       "      <td>-14.35</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.06</td>\n",
       "      <td>24.08</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.56</td>\n",
       "      <td>24.99</td>\n",
       "      <td>13.26</td>\n",
       "      <td>23.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.78</td>\n",
       "      <td>7.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.52</td>\n",
       "      <td>18.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.99</td>\n",
       "      <td>17.82</td>\n",
       "      <td>0.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.26</td>\n",
       "      <td>15.84</td>\n",
       "      <td>24.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.37</td>\n",
       "      <td>14.51</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.34</td>\n",
       "      <td>10.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.18</td>\n",
       "      <td>8.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.77</td>\n",
       "      <td>25.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.615</td>\n",
       "      <td>15.16</td>\n",
       "      <td>24.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.03</td>\n",
       "      <td>7.78</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.63</td>\n",
       "      <td>16.14</td>\n",
       "      <td>12.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.78</td>\n",
       "      <td>10.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30</td>\n",
       "      <td>25.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.54</td>\n",
       "      <td>7.18</td>\n",
       "      <td>8.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.57</td>\n",
       "      <td>7.75</td>\n",
       "      <td>5.15</td>\n",
       "      <td>23.81</td>\n",
       "      <td>11.570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.83</td>\n",
       "      <td>8.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.47</td>\n",
       "      <td>9.80</td>\n",
       "      <td>8.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.26</td>\n",
       "      <td>20.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.57</td>\n",
       "      <td>16.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.46</td>\n",
       "      <td>0.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.89</td>\n",
       "      <td>24.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.76</td>\n",
       "      <td>24.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.75</td>\n",
       "      <td>7.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.57</td>\n",
       "      <td>23.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.75</td>\n",
       "      <td>12.32</td>\n",
       "      <td>16.00</td>\n",
       "      <td>19.55</td>\n",
       "      <td>23.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.33</td>\n",
       "      <td>10.09</td>\n",
       "      <td>24.46</td>\n",
       "      <td>13.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.19</td>\n",
       "      <td>6.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.33</td>\n",
       "      <td>8.19</td>\n",
       "      <td>7.070</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.54</td>\n",
       "      <td>5.20</td>\n",
       "      <td>8.27</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.81</td>\n",
       "      <td>3.23</td>\n",
       "      <td>26.13</td>\n",
       "      <td>2.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.69</td>\n",
       "      <td>24.73</td>\n",
       "      <td>23.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.28</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.89</td>\n",
       "      <td>23.81</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.20</td>\n",
       "      <td>24.54</td>\n",
       "      <td>12.87</td>\n",
       "      <td>22.86</td>\n",
       "      <td>22.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.45</td>\n",
       "      <td>7.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>18.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.97</td>\n",
       "      <td>17.15</td>\n",
       "      <td>0.41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.83</td>\n",
       "      <td>15.13</td>\n",
       "      <td>23.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.17</td>\n",
       "      <td>23.52</td>\n",
       "      <td>4.61</td>\n",
       "      <td>14.16</td>\n",
       "      <td>6.12</td>\n",
       "      <td>6.57</td>\n",
       "      <td>10.32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.91</td>\n",
       "      <td>25.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.665</td>\n",
       "      <td>16.07</td>\n",
       "      <td>24.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.18</td>\n",
       "      <td>8.94</td>\n",
       "      <td>9.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "      <td>16.57</td>\n",
       "      <td>12.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.76</td>\n",
       "      <td>10.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.60</td>\n",
       "      <td>25.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.75</td>\n",
       "      <td>7.99</td>\n",
       "      <td>9.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.88</td>\n",
       "      <td>8.92</td>\n",
       "      <td>6.44</td>\n",
       "      <td>23.98</td>\n",
       "      <td>12.080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.57</td>\n",
       "      <td>9.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.59</td>\n",
       "      <td>10.37</td>\n",
       "      <td>9.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.65</td>\n",
       "      <td>21.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.46</td>\n",
       "      <td>17.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.50</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.44</td>\n",
       "      <td>24.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.92</td>\n",
       "      <td>24.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.91</td>\n",
       "      <td>8.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.72</td>\n",
       "      <td>23.62</td>\n",
       "      <td>21.38</td>\n",
       "      <td>20.91</td>\n",
       "      <td>12.74</td>\n",
       "      <td>16.38</td>\n",
       "      <td>19.63</td>\n",
       "      <td>24.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.45</td>\n",
       "      <td>10.39</td>\n",
       "      <td>24.69</td>\n",
       "      <td>13.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.21</td>\n",
       "      <td>6.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.44</td>\n",
       "      <td>9.24</td>\n",
       "      <td>7.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.66</td>\n",
       "      <td>6.51</td>\n",
       "      <td>8.54</td>\n",
       "      <td>3.51</td>\n",
       "      <td>5.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3.17</td>\n",
       "      <td>24.14</td>\n",
       "      <td>4.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.64</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.06</td>\n",
       "      <td>23.87</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.32</td>\n",
       "      <td>24.77</td>\n",
       "      <td>12.86</td>\n",
       "      <td>22.92</td>\n",
       "      <td>20.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.12</td>\n",
       "      <td>7.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.12</td>\n",
       "      <td>18.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.72</td>\n",
       "      <td>17.85</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.05</td>\n",
       "      <td>16.27</td>\n",
       "      <td>24.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>5.33</td>\n",
       "      <td>14.07</td>\n",
       "      <td>6.08</td>\n",
       "      <td>7.42</td>\n",
       "      <td>10.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.12</td>\n",
       "      <td>9.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1818</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.32</td>\n",
       "      <td>25.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.290</td>\n",
       "      <td>16.43</td>\n",
       "      <td>24.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.84</td>\n",
       "      <td>9.47</td>\n",
       "      <td>10.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.57</td>\n",
       "      <td>17.10</td>\n",
       "      <td>13.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.94</td>\n",
       "      <td>26.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.54</td>\n",
       "      <td>8.84</td>\n",
       "      <td>10.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.56</td>\n",
       "      <td>9.00</td>\n",
       "      <td>6.91</td>\n",
       "      <td>24.33</td>\n",
       "      <td>12.895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.91</td>\n",
       "      <td>10.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.26</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.21</td>\n",
       "      <td>11.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.38</td>\n",
       "      <td>17.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.08</td>\n",
       "      <td>1.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.42</td>\n",
       "      <td>8.74</td>\n",
       "      <td>25.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.65</td>\n",
       "      <td>24.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.65</td>\n",
       "      <td>8.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.43</td>\n",
       "      <td>24.04</td>\n",
       "      <td>5.07</td>\n",
       "      <td>21.65</td>\n",
       "      <td>13.26</td>\n",
       "      <td>16.95</td>\n",
       "      <td>20.29</td>\n",
       "      <td>24.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.80</td>\n",
       "      <td>11.33</td>\n",
       "      <td>25.43</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.26</td>\n",
       "      <td>7.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.71</td>\n",
       "      <td>9.50</td>\n",
       "      <td>8.390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.40</td>\n",
       "      <td>6.95</td>\n",
       "      <td>9.42</td>\n",
       "      <td>4.71</td>\n",
       "      <td>5.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.11</td>\n",
       "      <td>24.83</td>\n",
       "      <td>4.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.58</td>\n",
       "      <td>26.89</td>\n",
       "      <td>24.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.67</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.76</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.46</td>\n",
       "      <td>25.39</td>\n",
       "      <td>13.38</td>\n",
       "      <td>22.99</td>\n",
       "      <td>21.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.50</td>\n",
       "      <td>8.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.84</td>\n",
       "      <td>19.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.76</td>\n",
       "      <td>18.30</td>\n",
       "      <td>1.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.54</td>\n",
       "      <td>16.55</td>\n",
       "      <td>24.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.27</td>\n",
       "      <td>23.45</td>\n",
       "      <td>5.55</td>\n",
       "      <td>14.50</td>\n",
       "      <td>7.30</td>\n",
       "      <td>7.40</td>\n",
       "      <td>10.68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.14</td>\n",
       "      <td>9.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Abidjan  Abu Dhabi  Abuja  Accra  Adana  Adelaide   Agra  Ahmadabad  \\\n",
       "59  1809      NaN        NaN    NaN    NaN  17.38       NaN    NaN        NaN   \n",
       "60  1810      NaN        NaN    NaN    NaN  17.49       NaN    NaN        NaN   \n",
       "61  1811      NaN        NaN    NaN    NaN  17.51       NaN    NaN        NaN   \n",
       "62  1812      NaN        NaN    NaN    NaN  17.41       NaN    NaN        NaN   \n",
       "63  1813      NaN        NaN    NaN    NaN  17.88       NaN  24.60      25.92   \n",
       "64  1814      NaN        NaN    NaN    NaN  18.07       NaN  23.71      25.26   \n",
       "65  1815      NaN        NaN    NaN    NaN  17.70       NaN  24.13      25.45   \n",
       "66  1816      NaN        NaN    NaN    NaN  17.33       NaN  23.77      25.01   \n",
       "67  1817      NaN        NaN    NaN    NaN  17.57       NaN  23.91      25.23   \n",
       "68  1818      NaN        NaN    NaN    NaN  18.34       NaN  24.32      25.78   \n",
       "\n",
       "    Albuquerque  Alexandria  Algiers  Allahabad  Almaty  Amritsar  Amsterdam  \\\n",
       "59          NaN      15.095    15.55        NaN     NaN       NaN       8.64   \n",
       "60          NaN      15.335    15.49        NaN     NaN       NaN       8.47   \n",
       "61          NaN      15.420    15.91        NaN     NaN       NaN      10.07   \n",
       "62          NaN      14.630    15.31        NaN     NaN       NaN       8.00   \n",
       "63          NaN      15.450    15.63      24.90     NaN       NaN       8.83   \n",
       "64          NaN      15.315    15.18      24.03     NaN       NaN       7.76   \n",
       "65          NaN      15.045    15.82      24.43     NaN       NaN       8.75   \n",
       "66          NaN      14.615    15.16      24.10     NaN     24.03       7.78   \n",
       "67          NaN      14.665    16.07      24.22     NaN     22.18       8.94   \n",
       "68          NaN      15.290    16.43      24.46     NaN     22.84       9.47   \n",
       "\n",
       "    Ankara  Anshan  Antananarivo  Arlington  Asmara  Astana  Athens  Atlanta  \\\n",
       "59    9.13     NaN           NaN      11.16     NaN     NaN   16.48    13.41   \n",
       "60    9.35     NaN           NaN      11.55     NaN     NaN   16.76    13.57   \n",
       "61    9.45     NaN           NaN      11.65     NaN     NaN   16.99    13.71   \n",
       "62    9.12     NaN           NaN      10.24     NaN    0.65   16.27    12.90   \n",
       "63    9.49     NaN           NaN      11.35     NaN    0.41   16.55    13.92   \n",
       "64    9.75     NaN           NaN      11.13     NaN    1.30   16.51    13.69   \n",
       "65    9.37     NaN           NaN      10.70     NaN  -12.13   16.56    13.32   \n",
       "66    9.10     NaN           NaN      10.32     NaN    5.63   16.14    12.89   \n",
       "67    9.43     NaN           NaN      10.25     NaN    0.50   16.57    12.97   \n",
       "68   10.08     NaN           NaN      10.68     NaN    2.57   17.10    13.60   \n",
       "\n",
       "    Austin  Baghdad   Baku  Baltimore  Bamako  Bandung  Bangalore  Bangkok  \\\n",
       "59     NaN      NaN   9.42      11.16     NaN      NaN        NaN      NaN   \n",
       "60     NaN      NaN   9.47      11.55     NaN      NaN        NaN      NaN   \n",
       "61     NaN      NaN   9.58      11.65     NaN      NaN        NaN      NaN   \n",
       "62     NaN      NaN   9.71      10.24     NaN      NaN        NaN      NaN   \n",
       "63     NaN      NaN  10.38      11.35     NaN      NaN      24.23      NaN   \n",
       "64     NaN      NaN  10.53      11.13     NaN      NaN      23.91      NaN   \n",
       "65     NaN      NaN   9.94      10.70     NaN      NaN      23.79      NaN   \n",
       "66     NaN      NaN  12.78      10.32     NaN      NaN      23.30    25.96   \n",
       "67     NaN      NaN   9.76      10.25     NaN      NaN      23.60    25.83   \n",
       "68     NaN      NaN  11.44      10.68     NaN      NaN      23.94    26.48   \n",
       "\n",
       "    Bangui  Barcelona  Barquisimeto  Barranquilla  Beirut  Belfast  Belgrade  \\\n",
       "59     NaN      15.41           NaN           NaN   17.62     8.06      9.56   \n",
       "60     NaN      15.40           NaN           NaN   17.70     7.89      9.95   \n",
       "61     NaN      16.02           NaN           NaN   17.73     8.70     10.59   \n",
       "62     NaN      15.00           NaN           NaN   17.63     7.66      9.09   \n",
       "63     NaN      15.39           NaN           NaN   18.13     8.38      9.40   \n",
       "64     NaN      14.86           NaN           NaN   18.25     7.49      9.15   \n",
       "65     NaN      15.64           NaN           NaN   17.95     8.17      9.33   \n",
       "66     NaN      14.81           NaN           NaN   17.54     7.18      8.98   \n",
       "67     NaN      15.96           NaN           NaN   17.75     7.99      9.76   \n",
       "68     NaN      16.29           NaN           NaN   18.54     8.84     10.10   \n",
       "\n",
       "    Belo Horizonte  Benghazi  Berlin  Bern  Bhopal  Birmingham  Bissau  \\\n",
       "59             NaN     17.89    8.66  6.51     NaN      12.210     NaN   \n",
       "60             NaN     18.06    8.46  6.57     NaN      12.235     NaN   \n",
       "61             NaN     18.27    9.93  7.77     NaN      12.805     NaN   \n",
       "62             NaN     17.74    7.35  5.48     NaN      11.855     NaN   \n",
       "63             NaN     18.07    8.66  6.08   24.66      12.625     NaN   \n",
       "64             NaN     17.87    7.46  5.66   23.84      11.945     NaN   \n",
       "65             NaN     18.13    8.38  6.37   24.19      12.215     NaN   \n",
       "66             NaN     17.57    7.75  5.15   23.81      11.570     NaN   \n",
       "67             NaN     17.88    8.92  6.44   23.98      12.080     NaN   \n",
       "68             NaN     18.56    9.00  6.91   24.33      12.895     NaN   \n",
       "\n",
       "    Boston  Bratislava  Brazzaville  Brisbane  Brussels  Bucharest  Budapest  \\\n",
       "59    6.57        9.59          NaN       NaN      9.42       9.94      9.37   \n",
       "60    7.24        9.84          NaN       NaN      9.31      10.32      9.63   \n",
       "61    7.43       10.83          NaN       NaN     10.78      10.82     10.60   \n",
       "62    5.48        8.86          NaN       NaN      8.95       9.68      8.72   \n",
       "63    6.61        9.33          NaN       NaN      9.49      10.00      9.23   \n",
       "64    6.36        8.90          NaN       NaN      8.65      10.06      8.84   \n",
       "65    6.13        9.24          NaN       NaN      9.57       9.92      9.05   \n",
       "66    5.83        8.90          NaN       NaN      8.47       9.80      8.86   \n",
       "67    5.57        9.82          NaN       NaN      9.59      10.37      9.68   \n",
       "68    5.91       10.16          NaN       NaN     10.26      10.74     10.01   \n",
       "\n",
       "    Bujumbura  Bursa  Cairo  Cali  Campinas  Canberra  Caracas  Cardiff  \\\n",
       "59        NaN  10.41  19.87   NaN       NaN       NaN      NaN     8.45   \n",
       "60        NaN  10.70  19.93   NaN       NaN       NaN      NaN     8.43   \n",
       "61        NaN  10.89  20.00   NaN       NaN       NaN      NaN     9.40   \n",
       "62        NaN  10.30  19.93   NaN       NaN       NaN      NaN     8.11   \n",
       "63        NaN  10.63  20.51   NaN       NaN       NaN      NaN     8.66   \n",
       "64        NaN  10.77  20.43   NaN       NaN       NaN      NaN     7.52   \n",
       "65        NaN  10.56  20.30   NaN       NaN       NaN      NaN     8.42   \n",
       "66        NaN  10.26  20.51   NaN       NaN       NaN      NaN     7.57   \n",
       "67        NaN  10.65  21.88   NaN       NaN       NaN      NaN     8.46   \n",
       "68        NaN  11.21  11.60   NaN       NaN       NaN      NaN     9.38   \n",
       "\n",
       "    Casablanca  Changchun  Changzhou  Charlotte  Chelyabinsk  Chengdu  \\\n",
       "59       16.04        NaN        NaN      15.01          NaN      NaN   \n",
       "60       15.74        NaN        NaN      15.19          NaN      NaN   \n",
       "61       15.89        NaN        NaN      15.34          NaN      NaN   \n",
       "62       16.04        NaN        NaN      14.45         0.59      NaN   \n",
       "63       16.37        NaN        NaN      15.46         0.34      NaN   \n",
       "64       16.16        NaN        NaN      15.22         0.96      NaN   \n",
       "65       16.35        NaN        NaN      14.87         0.76      NaN   \n",
       "66       16.00        NaN        NaN      14.46         0.48      NaN   \n",
       "67       17.03        NaN        NaN      14.50         0.03      NaN   \n",
       "68       17.24        NaN        NaN      15.08         1.60      NaN   \n",
       "\n",
       "    Chicago  Chisinau  Colombo  Colorado Springs  Columbus  Conakry  \\\n",
       "59     9.04      7.69      NaN               NaN     13.01      NaN   \n",
       "60     9.22      8.03      NaN               NaN     13.18      NaN   \n",
       "61     9.37      8.70      NaN               NaN     13.33      NaN   \n",
       "62     8.34      7.46      NaN               NaN     12.44      NaN   \n",
       "63     9.67      7.94    25.31               NaN     13.52      NaN   \n",
       "64     9.55      8.00    25.14               NaN     13.31      NaN   \n",
       "65     8.86      7.78    24.86               NaN     12.88      NaN   \n",
       "66     8.27      7.89    24.37               NaN     12.43      NaN   \n",
       "67     8.54      8.44    24.66               NaN     12.53      NaN   \n",
       "68     9.42      8.74    25.14               NaN     13.19      NaN   \n",
       "\n",
       "    Copenhagen  Cordoba  Curitiba  Dakar  Dalian  Dallas  Damascus  \\\n",
       "59        7.39      NaN       NaN    NaN     NaN     NaN     17.04   \n",
       "60        7.31      NaN       NaN    NaN     NaN     NaN     17.09   \n",
       "61        8.77      NaN       NaN    NaN     NaN     NaN     17.12   \n",
       "62        6.44      NaN       NaN    NaN     NaN     NaN     17.07   \n",
       "63        7.85      NaN       NaN    NaN     NaN     NaN     17.62   \n",
       "64        6.43      NaN       NaN    NaN     NaN     NaN     17.68   \n",
       "65        7.51      NaN       NaN    NaN     NaN     NaN     17.42   \n",
       "66        6.73      NaN       NaN    NaN     NaN     NaN     16.97   \n",
       "67        7.82      NaN       NaN    NaN     NaN     NaN     17.15   \n",
       "68        8.20      NaN       NaN    NaN     NaN     NaN     17.99   \n",
       "\n",
       "    Dar Es Salaam  Datong  Delhi  Denver  Detroit  Dhaka  Doha  Douala  \\\n",
       "59            NaN     NaN    NaN     NaN     7.60    NaN   NaN     NaN   \n",
       "60            NaN     NaN    NaN     NaN     7.89    NaN   NaN     NaN   \n",
       "61            NaN     NaN    NaN     NaN     8.01    NaN   NaN     NaN   \n",
       "62            NaN     NaN    NaN     NaN     6.77    NaN   NaN     NaN   \n",
       "63            NaN     NaN  24.56     NaN     8.07  24.90   NaN     NaN   \n",
       "64            NaN     NaN  23.73     NaN     7.92  24.51   NaN     NaN   \n",
       "65            NaN     NaN  24.09     NaN     7.29  24.43   NaN     NaN   \n",
       "66            NaN     NaN  23.70     NaN     6.76  24.19   NaN     NaN   \n",
       "67            NaN     NaN  23.86     NaN     6.92  24.21   NaN     NaN   \n",
       "68            NaN     NaN  24.37     NaN     7.65  24.26   NaN     NaN   \n",
       "\n",
       "    Dublin  Durban  Dushanbe  Ecatepec  Edinburgh  El Paso  Faisalabad  \\\n",
       "59    8.02     NaN       NaN       NaN       7.02      NaN         NaN   \n",
       "60    7.90     NaN       NaN       NaN       6.87      NaN         NaN   \n",
       "61    8.64     NaN       NaN       NaN       7.82      NaN         NaN   \n",
       "62    7.72     NaN       NaN       NaN       6.60      NaN         NaN   \n",
       "63    8.33     NaN       NaN       NaN       7.35      NaN         NaN   \n",
       "64    7.45     NaN       NaN       NaN       6.33      NaN         NaN   \n",
       "65    8.09     NaN       NaN       NaN       7.15      NaN         NaN   \n",
       "66    7.20     NaN       NaN       NaN       6.18      NaN       24.69   \n",
       "67    8.01     NaN       NaN       NaN       7.03      NaN       22.74   \n",
       "68    8.88     NaN       NaN       NaN       7.82      NaN       23.43   \n",
       "\n",
       "    Fort Worth  Fortaleza  Foshan  Freetown  Fresno  Fuzhou  Gaborone  \\\n",
       "59         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "60         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "61         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "62         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "63         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "64         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "65         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "66         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "67         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "68         NaN        NaN     NaN       NaN     NaN     NaN       NaN   \n",
       "\n",
       "    Georgetown  Guadalajara  Guangzhou  Guarulhos  Guatemala City  Guayaquil  \\\n",
       "59         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "60         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "61         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "62         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "63         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "64         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "65         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "66         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "67         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "68         NaN          NaN        NaN        NaN             NaN        NaN   \n",
       "\n",
       "    Guiyang  Gujranwala  Hamburg  Handan  Hangzhou  Hanoi  Haora  Harare  \\\n",
       "59      NaN         NaN     7.91     NaN       NaN    NaN    NaN     NaN   \n",
       "60      NaN         NaN     7.76     NaN       NaN    NaN    NaN     NaN   \n",
       "61      NaN         NaN     9.25     NaN       NaN    NaN    NaN     NaN   \n",
       "62      NaN         NaN     6.91     NaN       NaN    NaN    NaN     NaN   \n",
       "63      NaN         NaN     8.16     NaN       NaN    NaN  25.45     NaN   \n",
       "64      NaN         NaN     6.88     NaN       NaN    NaN  24.95     NaN   \n",
       "65      NaN         NaN     7.90     NaN       NaN    NaN  24.98     NaN   \n",
       "66      NaN       22.75     7.06     NaN       NaN    NaN  24.77     NaN   \n",
       "67      NaN       20.91     8.22     NaN       NaN    NaN  24.77     NaN   \n",
       "68      NaN       21.65     8.59     NaN       NaN    NaN  24.74     NaN   \n",
       "\n",
       "    Harbin  Hefei  Helsinki  Hiroshima  Ho Chi Minh City  Houston  Hyderabad  \\\n",
       "59     NaN    NaN      2.24        NaN               NaN      NaN        NaN   \n",
       "60     NaN    NaN      2.55        NaN               NaN      NaN        NaN   \n",
       "61     NaN    NaN      4.19        NaN               NaN      NaN        NaN   \n",
       "62     NaN    NaN      2.64        NaN               NaN      NaN        NaN   \n",
       "63     NaN    NaN      4.15        NaN               NaN      NaN     26.120   \n",
       "64     NaN    NaN      2.98        NaN               NaN      NaN     25.495   \n",
       "65     NaN    NaN      3.84        NaN               NaN      NaN     25.660   \n",
       "66     NaN    NaN      3.51        NaN               NaN      NaN     25.220   \n",
       "67     NaN    NaN      3.75        NaN               NaN      NaN     25.445   \n",
       "68     NaN    NaN      4.62        NaN               NaN      NaN     25.900   \n",
       "\n",
       "    Ibadan  Indianapolis  Indore  Irbil  Islamabad  Istanbul  Izmir  \\\n",
       "59     NaN         10.26     NaN  17.49        NaN     12.40  16.27   \n",
       "60     NaN         10.46     NaN  17.50        NaN     12.72  16.54   \n",
       "61     NaN         10.59     NaN  17.56        NaN     12.95  16.73   \n",
       "62     NaN          9.60     NaN  17.64        NaN     12.30  16.10   \n",
       "63     NaN         10.78   24.30  18.28        NaN     12.62  16.41   \n",
       "64     NaN         10.60   23.50  18.46        NaN     12.81  16.45   \n",
       "65     NaN         10.07   23.84  19.18        NaN     12.54  16.39   \n",
       "66     NaN          9.57   23.44    NaN      22.75     12.32  16.00   \n",
       "67     NaN          9.72   23.62  21.38      20.91     12.74  16.38   \n",
       "68     NaN         10.43   24.04   5.07      21.65     13.26  16.95   \n",
       "\n",
       "    Jacksonville  Jaipur  Jakarta  Jilin  Jinan  Johannesburg  Juba  Kabul  \\\n",
       "59         19.91     NaN      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "60         20.01     NaN      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "61         20.18     NaN      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "62         19.53     NaN      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "63         20.50   24.79      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "64         20.24   23.95      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "65         19.96   24.32      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "66         19.55   23.91      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "67         19.63   24.09      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "68         20.29   24.62      NaN    NaN    NaN           NaN   NaN    NaN   \n",
       "\n",
       "    Kaduna  Kampala  Kano  Kanpur  Kansas City  Karachi  Kathmandu  Kawasaki  \\\n",
       "59     NaN      NaN   NaN     NaN        10.61      NaN        NaN       NaN   \n",
       "60     NaN      NaN   NaN     NaN        10.65      NaN        NaN       NaN   \n",
       "61     NaN      NaN   NaN     NaN        10.83      NaN        NaN       NaN   \n",
       "62     NaN      NaN   NaN     NaN        10.19      NaN        NaN       NaN   \n",
       "63     NaN      NaN   NaN   24.14        11.39    25.40      14.04       NaN   \n",
       "64     NaN      NaN   NaN   23.32        11.24    24.86      13.52       NaN   \n",
       "65     NaN      NaN   NaN   23.67        10.66    24.92      13.56       NaN   \n",
       "66     NaN      NaN   NaN   23.33        10.09    24.46      13.25       NaN   \n",
       "67     NaN      NaN   NaN   23.45        10.39    24.69      13.34       NaN   \n",
       "68     NaN      NaN   NaN   23.80        11.33    25.43      13.67       NaN   \n",
       "\n",
       "    Kazan  Khartoum  Khulna  Kiev  Kigali  Kingston  Kinshasa  Kitakyushu  \\\n",
       "59   1.16       NaN     NaN  5.63     NaN      5.60       NaN         NaN   \n",
       "60   1.20       NaN     NaN  5.93     NaN      6.08       NaN         NaN   \n",
       "61   1.92       NaN     NaN  6.85     NaN      6.22       NaN         NaN   \n",
       "62   1.94       NaN     NaN  5.51     NaN      4.57       NaN         NaN   \n",
       "63   2.76       NaN   24.90  6.31     NaN      5.84       NaN         NaN   \n",
       "64   2.22       NaN   24.51  6.10     NaN      5.65       NaN         NaN   \n",
       "65   2.07       NaN   24.43  5.72     NaN      5.14       NaN         NaN   \n",
       "66   2.12       NaN   24.19  6.24     NaN      4.71       NaN         NaN   \n",
       "67   1.54       NaN   24.21  6.69     NaN      4.68       NaN         NaN   \n",
       "68   3.44       NaN   24.26  7.07     NaN      5.22       NaN         NaN   \n",
       "\n",
       "    Kobe  Kuala Lumpur  Kunming  La Paz  Lagos  Lahore  Lanzhou  Las Vegas  \\\n",
       "59   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "60   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "61   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "62   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "63   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "64   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "65   NaN           NaN      NaN     NaN    NaN     NaN      NaN        NaN   \n",
       "66   NaN           NaN    14.15     NaN    NaN   24.69      NaN        NaN   \n",
       "67   NaN           NaN    13.13     NaN    NaN   22.74      NaN        NaN   \n",
       "68   NaN           NaN    13.81     NaN    NaN   23.43      NaN        NaN   \n",
       "\n",
       "    Libreville  Lilongwe  Lima  Lisbon  Ljubljana  London  Long Beach  \\\n",
       "59         NaN       NaN   NaN   14.58       9.21   8.010         NaN   \n",
       "60         NaN       NaN   NaN   14.21       9.63   8.170         NaN   \n",
       "61         NaN       NaN   NaN   14.48      10.38   8.795         NaN   \n",
       "62         NaN       NaN   NaN   14.52       8.51   7.355         NaN   \n",
       "63         NaN       NaN   NaN   14.72       8.87   8.270         NaN   \n",
       "64         NaN       NaN   NaN   14.45       8.45   7.585         NaN   \n",
       "65         NaN       NaN   NaN   14.82       8.86   7.820         NaN   \n",
       "66         NaN       NaN   NaN   14.33       8.19   7.070         NaN   \n",
       "67         NaN       NaN   NaN   15.44       9.24   7.600         NaN   \n",
       "68         NaN       NaN   NaN   15.71       9.50   8.390         NaN   \n",
       "\n",
       "    Los Angeles  Louisville  Luanda  Lubumbashi  Ludhiana  Luoyang  Lusaka  \\\n",
       "59          NaN       12.02     NaN         NaN       NaN      NaN     NaN   \n",
       "60          NaN       12.19     NaN         NaN       NaN      NaN     NaN   \n",
       "61          NaN       12.33     NaN         NaN       NaN      NaN     NaN   \n",
       "62          NaN       11.42     NaN         NaN       NaN      NaN     NaN   \n",
       "63          NaN       12.54     NaN         NaN       NaN      NaN     NaN   \n",
       "64          NaN       12.35     NaN         NaN       NaN      NaN     NaN   \n",
       "65          NaN       11.87     NaN         NaN       NaN      NaN     NaN   \n",
       "66          NaN       11.39     NaN         NaN     24.03      NaN     NaN   \n",
       "67          NaN       11.52     NaN         NaN     22.18      NaN     NaN   \n",
       "68          NaN       12.21     NaN         NaN     22.84      NaN     NaN   \n",
       "\n",
       "    Madrid  Maiduguri  Malabo  Managua  Manama  Manaus  Manila  Maputo  \\\n",
       "59   10.65        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "60   10.40        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "61   10.84        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "62   10.43        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "63   10.66        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "64   10.26        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "65   10.85        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "66   10.20        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "67   11.37        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "68   11.67        NaN     NaN      NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "    Maracaibo  Maseru  Mashhad  Mecca  Medan  Melbourne  Memphis  Mesa  \\\n",
       "59        NaN     NaN      NaN    NaN    NaN        NaN    14.94   NaN   \n",
       "60        NaN     NaN      NaN    NaN    NaN        NaN    15.00   NaN   \n",
       "61        NaN     NaN      NaN    NaN    NaN        NaN    15.17   NaN   \n",
       "62        NaN     NaN      NaN    NaN    NaN        NaN    14.57   NaN   \n",
       "63        NaN     NaN      NaN    NaN    NaN        NaN    15.60   NaN   \n",
       "64        NaN     NaN      NaN    NaN    NaN        NaN    15.40   NaN   \n",
       "65        NaN     NaN      NaN    NaN    NaN        NaN    15.00   NaN   \n",
       "66        NaN     NaN      NaN    NaN    NaN        NaN    14.52   NaN   \n",
       "67        NaN     NaN      NaN    NaN    NaN        NaN    14.69   NaN   \n",
       "68        NaN     NaN      NaN    NaN    NaN        NaN    15.45   NaN   \n",
       "\n",
       "    Mexicali  Miami  Milan  Milwaukee  Minneapolis  Minsk  Mogadishu  \\\n",
       "59       NaN  21.81   6.36       9.04         3.79   4.01        NaN   \n",
       "60       NaN  21.85   6.59       9.22         3.86   4.28        NaN   \n",
       "61       NaN  22.04   7.59       9.37         4.04   5.55        NaN   \n",
       "62       NaN  21.53   5.42       8.34         3.17   3.76        NaN   \n",
       "63       NaN  22.50   6.27       9.67         4.70   4.86        NaN   \n",
       "64       NaN  22.24   5.57       9.55         4.64   4.21        NaN   \n",
       "65       NaN  21.96   6.45       8.86         3.75   4.13        NaN   \n",
       "66       NaN  21.54   5.20       8.27         3.02   4.58        NaN   \n",
       "67       NaN  21.66   6.51       8.54         3.51   5.15        NaN   \n",
       "68       NaN  22.40   6.95       9.42         4.71   5.42        NaN   \n",
       "\n",
       "    Monrovia  Monterrey  Montevideo  Montreal  Moscow  Multan  Munich  Nagoya  \\\n",
       "59       NaN        NaN         NaN      3.56    1.90     NaN    4.61     NaN   \n",
       "60       NaN        NaN         NaN      4.10    2.09     NaN    4.72     NaN   \n",
       "61       NaN        NaN         NaN      4.30    3.21     NaN    5.70     NaN   \n",
       "62       NaN        NaN         NaN      2.57    2.40     NaN    3.39     NaN   \n",
       "63       NaN        NaN         NaN      3.82    3.45     NaN    3.87     NaN   \n",
       "64       NaN        NaN         NaN      3.62    2.79     NaN    3.46     NaN   \n",
       "65       NaN        NaN         NaN      3.22    2.82     NaN    4.01     NaN   \n",
       "66       NaN        NaN         NaN      2.81    3.23   26.13    2.96     NaN   \n",
       "67       NaN        NaN         NaN      2.73    3.17   24.14    4.21     NaN   \n",
       "68       NaN        NaN         NaN      3.30    4.11   24.83    4.66     NaN   \n",
       "\n",
       "    Nagpur  Nairobi  Nanchang  Nanjing  Nanning  Nashville  Nassau  New Delhi  \\\n",
       "59     NaN      NaN       NaN      NaN      NaN      13.22   23.23        NaN   \n",
       "60     NaN      NaN       NaN      NaN      NaN      13.33   23.49        NaN   \n",
       "61     NaN      NaN       NaN      NaN      NaN      13.48   23.33        NaN   \n",
       "62     NaN      NaN       NaN      NaN      NaN      12.74     NaN        NaN   \n",
       "63   25.05      NaN       NaN      NaN      NaN      13.81   23.08      24.56   \n",
       "64   24.23      NaN       NaN      NaN      NaN      13.61   24.06      23.73   \n",
       "65   24.59      NaN       NaN      NaN      NaN      13.17     NaN      24.09   \n",
       "66   24.22      NaN       NaN      NaN      NaN      12.69   24.73      23.70   \n",
       "67   24.39      NaN       NaN      NaN      NaN      12.85     NaN      23.86   \n",
       "68   24.66      NaN       NaN      NaN      NaN      13.58   26.89      24.37   \n",
       "\n",
       "    New Orleans  New York  Niamey  Nouakchott  Novosibirsk  Oakland  \\\n",
       "59          NaN      8.87     NaN         NaN          NaN      NaN   \n",
       "60          NaN      9.35     NaN         NaN          NaN      NaN   \n",
       "61          NaN      9.43     NaN         NaN          NaN      NaN   \n",
       "62          NaN      7.80     NaN         NaN          NaN      NaN   \n",
       "63          NaN      8.93     NaN         NaN          NaN      NaN   \n",
       "64          NaN      8.70     NaN         NaN          NaN      NaN   \n",
       "65          NaN      8.27     NaN         NaN          NaN      NaN   \n",
       "66          NaN      7.92     NaN         NaN          NaN      NaN   \n",
       "67          NaN      7.78     NaN         NaN          NaN      NaN   \n",
       "68          NaN      8.13     NaN         NaN          NaN      NaN   \n",
       "\n",
       "    Oklahoma City  Omaha   Omsk  Oslo  Ottawa  Ouagadougou  Palembang  \\\n",
       "59            NaN   8.81    NaN  1.57    3.05          NaN        NaN   \n",
       "60            NaN   8.83    NaN  1.59    3.53          NaN        NaN   \n",
       "61            NaN   9.01    NaN  2.99    3.70          NaN        NaN   \n",
       "62            NaN   8.39  -1.32  0.90    2.04          NaN        NaN   \n",
       "63            NaN   9.66  -1.57  2.46    3.35          NaN        NaN   \n",
       "64          10.16   9.53  -0.81  1.11    3.17          NaN        NaN   \n",
       "65          16.25   8.89 -14.35  2.21    2.67          NaN        NaN   \n",
       "66            NaN   8.28   3.71  1.36    2.21          NaN        NaN   \n",
       "67            NaN   8.64  -1.57  2.09    2.21          NaN        NaN   \n",
       "68            NaN   9.67  -0.35  2.72    2.83          NaN        NaN   \n",
       "\n",
       "    Paramaribo  Paris  Patna  Perm  Perth  Peshawar  Philadelphia  Phoenix  \\\n",
       "59         NaN   9.90    NaN -1.12    NaN       NaN         11.13      NaN   \n",
       "60         NaN   9.87    NaN -1.19    NaN       NaN         11.57      NaN   \n",
       "61         NaN  11.17    NaN -0.51    NaN       NaN         11.67      NaN   \n",
       "62         NaN   9.36    NaN -0.42    NaN       NaN         10.17      NaN   \n",
       "63         NaN   9.77  24.55  0.44    NaN       NaN         11.25      NaN   \n",
       "64         NaN   9.22  23.80 -0.26    NaN       NaN         11.01      NaN   \n",
       "65         NaN  10.06  24.08 -0.09    NaN       NaN         10.63      NaN   \n",
       "66         NaN   8.89  23.81 -0.26    NaN       NaN         10.29      NaN   \n",
       "67         NaN  10.06  23.87 -0.92    NaN       NaN         10.16      NaN   \n",
       "68         NaN  10.76  24.00  0.63    NaN       NaN         10.52      NaN   \n",
       "\n",
       "    Podgorica  Port Au Prince  Port Harcourt  Port Louis  Port Moresby  \\\n",
       "59       9.64             NaN            NaN         NaN           NaN   \n",
       "60      10.05             NaN            NaN         NaN           NaN   \n",
       "61      10.57             NaN            NaN         NaN           NaN   \n",
       "62       9.23             NaN            NaN         NaN           NaN   \n",
       "63       9.50             NaN            NaN         NaN           NaN   \n",
       "64       9.24             NaN            NaN         NaN           NaN   \n",
       "65       9.46             NaN            NaN         NaN           NaN   \n",
       "66       9.05             NaN            NaN         NaN           NaN   \n",
       "67       9.79             NaN            NaN         NaN           NaN   \n",
       "68      10.16             NaN            NaN         NaN           NaN   \n",
       "\n",
       "    Portland  Porto Alegre  Prague  Pretoria  Pristina  Puebla   Pune  \\\n",
       "59       NaN           NaN    8.05       NaN      8.92     NaN    NaN   \n",
       "60       NaN           NaN    7.86       NaN      9.32     NaN    NaN   \n",
       "61       NaN           NaN    9.28       NaN      9.80     NaN    NaN   \n",
       "62       NaN           NaN    6.79       NaN      8.55     NaN    NaN   \n",
       "63       NaN           NaN    7.77       NaN      8.82     NaN  24.03   \n",
       "64       NaN           NaN    6.77       NaN      8.67     NaN  23.49   \n",
       "65       NaN           NaN    7.69       NaN      8.78     NaN  23.58   \n",
       "66       NaN           NaN    7.18       NaN      8.45     NaN  23.13   \n",
       "67       NaN           NaN    8.08       NaN      9.12     NaN  23.37   \n",
       "68       NaN           NaN    8.34       NaN      9.50     NaN  23.80   \n",
       "\n",
       "    Qingdao  Qiqihar  Quito  Rabat  Rajkot  Raleigh  Ranchi  Rawalpindi  \\\n",
       "59      NaN      NaN    NaN  16.30     NaN    13.52     NaN         NaN   \n",
       "60      NaN      NaN    NaN  15.95     NaN    13.78     NaN         NaN   \n",
       "61      NaN      NaN    NaN  16.12     NaN    13.90     NaN         NaN   \n",
       "62      NaN      NaN    NaN  16.28     NaN    12.83     NaN         NaN   \n",
       "63      NaN      NaN    NaN  16.50   25.46    13.87   23.59         NaN   \n",
       "64      NaN      NaN    NaN  16.28   24.99    13.63   22.86         NaN   \n",
       "65      NaN      NaN    NaN  16.56   24.99    13.26   23.13         NaN   \n",
       "66      NaN      NaN    NaN  16.20   24.54    12.87   22.86       22.75   \n",
       "67      NaN      NaN    NaN  17.32   24.77    12.86   22.92       20.91   \n",
       "68      NaN      NaN    NaN  17.46   25.39    13.38   22.99       21.65   \n",
       "\n",
       "    Recife  Riga  Rio De Janeiro  Riyadh   Rome  Rosario  Sacramento  \\\n",
       "59     NaN  3.58             NaN     NaN  11.46      NaN         NaN   \n",
       "60     NaN  4.00             NaN     NaN  11.83      NaN         NaN   \n",
       "61     NaN  5.53             NaN     NaN  12.47      NaN         NaN   \n",
       "62     NaN  3.49             NaN     NaN  10.95      NaN         NaN   \n",
       "63     NaN  4.97             NaN     NaN  11.38      NaN         NaN   \n",
       "64     NaN  3.92             NaN     NaN  10.76      NaN         NaN   \n",
       "65     NaN  4.39             NaN     NaN  11.25      NaN         NaN   \n",
       "66     NaN  4.40             NaN     NaN  10.59      NaN         NaN   \n",
       "67     NaN  5.03             NaN     NaN  11.56      NaN         NaN   \n",
       "68     NaN  5.46             NaN     NaN  11.86      NaN         NaN   \n",
       "\n",
       "    Salvador  Samara  San Antonio  San Diego  San Francisco  San Jose  \\\n",
       "59       NaN    1.22          NaN        NaN            NaN       NaN   \n",
       "60       NaN    1.26          NaN        NaN            NaN       NaN   \n",
       "61       NaN    1.83          NaN        NaN            NaN       NaN   \n",
       "62       NaN    1.96          NaN        NaN            NaN       NaN   \n",
       "63       NaN    2.70          NaN        NaN            NaN       NaN   \n",
       "64       NaN    2.31          NaN        NaN            NaN       NaN   \n",
       "65       NaN    2.01          NaN        NaN            NaN       NaN   \n",
       "66       NaN    2.00          NaN        NaN            NaN       NaN   \n",
       "67       NaN    1.53          NaN        NaN            NaN       NaN   \n",
       "68       NaN    3.35          NaN        NaN            NaN       NaN   \n",
       "\n",
       "    San Salvador  Santa Cruz  Santiago  Santo Domingo  Sarajevo  Seattle  \\\n",
       "59           NaN         NaN       NaN            NaN      8.64      NaN   \n",
       "60           NaN         NaN       NaN            NaN      9.06      NaN   \n",
       "61           NaN         NaN       NaN            NaN      9.70      NaN   \n",
       "62           NaN         NaN       NaN            NaN      8.12      NaN   \n",
       "63           NaN         NaN       NaN            NaN      8.45      NaN   \n",
       "64           NaN         NaN       NaN            NaN      8.10      NaN   \n",
       "65           NaN         NaN       NaN            NaN      8.39      NaN   \n",
       "66           NaN         NaN       NaN            NaN      7.92      NaN   \n",
       "67           NaN         NaN       NaN            NaN      8.78      NaN   \n",
       "68           NaN         NaN       NaN            NaN      9.11      NaN   \n",
       "\n",
       "    Semarang  Seoul  Shanghai  Shenyang  Shenzhen  Shiraz  Singapore  Skopje  \\\n",
       "59       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.92   \n",
       "60       NaN    NaN       NaN       NaN       NaN     NaN        NaN    9.32   \n",
       "61       NaN    NaN       NaN       NaN       NaN     NaN        NaN    9.80   \n",
       "62       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.55   \n",
       "63       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.82   \n",
       "64       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.67   \n",
       "65       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.78   \n",
       "66       NaN    NaN       NaN       NaN       NaN     NaN        NaN    8.45   \n",
       "67       NaN    NaN       NaN       NaN       NaN     NaN        NaN    9.12   \n",
       "68       NaN    NaN       NaN       NaN       NaN     NaN        NaN    9.50   \n",
       "\n",
       "    Sofia  Soweto  Stockholm  Surabaya  Surat  Suzhou  Sydney  Tabriz  \\\n",
       "59   7.70     NaN       5.25       NaN    NaN     NaN     NaN    7.81   \n",
       "60   8.09     NaN       5.49       NaN    NaN     NaN     NaN    7.51   \n",
       "61   8.53     NaN       7.06       NaN    NaN     NaN     NaN    7.91   \n",
       "62   7.39     NaN       4.70       NaN    NaN     NaN     NaN    8.00   \n",
       "63   7.67     NaN       6.39       NaN  25.73     NaN     NaN    8.69   \n",
       "64   7.62     NaN       5.03       NaN  25.13     NaN     NaN    8.85   \n",
       "65   7.61     NaN       6.08       NaN  25.26     NaN     NaN    8.35   \n",
       "66   7.36     NaN       5.38       NaN  24.83     NaN     NaN   11.33   \n",
       "67   7.96     NaN       6.20       NaN  25.05     NaN     NaN    8.09   \n",
       "68   8.35     NaN       6.83       NaN  25.54     NaN     NaN   -4.65   \n",
       "\n",
       "    Taiyuan  Tallinn  Tangshan  Tashkent  Tbilisi  Tegucigalpa  Tianjin  \\\n",
       "59      NaN     3.07       NaN       NaN     6.75          NaN      NaN   \n",
       "60      NaN     3.53       NaN       NaN     6.83          NaN      NaN   \n",
       "61      NaN     5.12       NaN       NaN     6.90          NaN      NaN   \n",
       "62      NaN     3.24       NaN       NaN     7.02          NaN      NaN   \n",
       "63      NaN     4.73       NaN       NaN     7.56          NaN      NaN   \n",
       "64      NaN     3.59       NaN       NaN     7.79          NaN      NaN   \n",
       "65      NaN     4.30       NaN       NaN     7.18          NaN      NaN   \n",
       "66      NaN     4.07       NaN       NaN     6.95          NaN      NaN   \n",
       "67      NaN     4.59       NaN       NaN     7.12          NaN      NaN   \n",
       "68      NaN     5.23       NaN       NaN     8.14          NaN      NaN   \n",
       "\n",
       "    Tijuana  Tirana  Tokyo  Toronto  Tripoli  Tucson  Tulsa  Tunis   Ufa  \\\n",
       "59      NaN   14.88    NaN     4.90    18.45     NaN    NaN  17.63 -0.25   \n",
       "60      NaN   15.27    NaN     5.24    18.56     NaN    NaN  17.76 -0.27   \n",
       "61      NaN   15.72    NaN     5.38    18.87     NaN  14.53  18.17  0.28   \n",
       "62      NaN   14.51    NaN     3.97    18.30     NaN  13.54  17.35  0.46   \n",
       "63      NaN   14.75    NaN     5.31    18.59     NaN  16.39  17.59  1.24   \n",
       "64      NaN   14.48    NaN     5.16    18.22     NaN  10.29  17.11  0.76   \n",
       "65      NaN   14.76    NaN     4.52    18.73     NaN  13.99  17.82  0.60   \n",
       "66      NaN   14.31    NaN     4.00    18.14     NaN  10.97  17.15  0.41   \n",
       "67      NaN   14.99    NaN     4.12    18.63     NaN  13.72  17.85 -0.09   \n",
       "68      NaN   15.39    NaN     4.84    19.21     NaN  12.76  18.30  1.60   \n",
       "\n",
       "    Ulaanbaatar  Vadodara  Valencia  Varanasi  Victoria  Vienna  Vientiane  \\\n",
       "59          NaN       NaN     15.60       NaN       NaN    7.79        NaN   \n",
       "60          NaN       NaN     15.46       NaN       NaN    7.82        NaN   \n",
       "61          NaN       NaN     15.95       NaN       NaN    9.06        NaN   \n",
       "62          NaN       NaN     15.31       NaN       NaN    6.85        NaN   \n",
       "63          NaN     25.73     15.60     24.75       NaN    7.51        NaN   \n",
       "64          NaN     25.13     15.16     23.95       NaN    6.90        NaN   \n",
       "65          NaN     25.26     15.84     24.29       NaN    7.36        NaN   \n",
       "66          NaN     24.83     15.13     23.98       NaN    7.17      23.52   \n",
       "67          NaN     25.05     16.27     24.07       NaN    8.00      22.75   \n",
       "68          NaN     25.54     16.55     24.26       NaN    8.27      23.45   \n",
       "\n",
       "    Vilnius  Virginia Beach  Volgograd  Warsaw  Washington  Wellington  \\\n",
       "59     4.14           14.82       5.47    6.95       11.16         NaN   \n",
       "60     4.46           15.16       5.60    6.91       11.55         NaN   \n",
       "61     5.88           15.28       6.05    8.38       11.65         NaN   \n",
       "62     3.74           14.06       5.95    5.89       10.24         NaN   \n",
       "63     5.08           15.07       6.57    7.05       11.35         NaN   \n",
       "64     4.18           14.81       6.59    6.17       11.13         NaN   \n",
       "65     4.37           14.51       6.00    6.34       10.70         NaN   \n",
       "66     4.61           14.16       6.12    6.57       10.32         NaN   \n",
       "67     5.33           14.07       6.08    7.42       10.25         NaN   \n",
       "68     5.55           14.50       7.30    7.40       10.68         NaN   \n",
       "\n",
       "    Wichita  Windhoek  Wuhan  Wuxi  Xian  Xuzhou  Yamoussoukro  Yerevan  \\\n",
       "59      NaN       NaN    NaN   NaN   NaN     NaN           NaN     6.75   \n",
       "60      NaN       NaN    NaN   NaN   NaN     NaN           NaN     6.83   \n",
       "61    12.74       NaN    NaN   NaN   NaN     NaN           NaN     6.90   \n",
       "62    11.66       NaN    NaN   NaN   NaN     NaN           NaN     7.02   \n",
       "63    14.72       NaN    NaN   NaN   NaN     NaN           NaN     7.56   \n",
       "64     8.20       NaN    NaN   NaN   NaN     NaN           NaN     7.79   \n",
       "65    12.13       NaN    NaN   NaN   NaN     NaN           NaN     7.18   \n",
       "66     8.78       NaN    NaN   NaN   NaN     NaN           NaN     6.95   \n",
       "67    11.87       NaN    NaN   NaN   NaN     NaN           NaN     7.12   \n",
       "68    10.78       NaN    NaN   NaN   NaN     NaN           NaN     8.14   \n",
       "\n",
       "    Zagreb  Zapopan  avg_temp  \n",
       "59    9.21      NaN      7.08  \n",
       "60    9.63      NaN      6.92  \n",
       "61   10.38      NaN      6.86  \n",
       "62    8.51      NaN      7.05  \n",
       "63    8.87      NaN      7.74  \n",
       "64    8.45      NaN      7.59  \n",
       "65    8.86      NaN      7.24  \n",
       "66    8.19      NaN      6.94  \n",
       "67    9.24      NaN      6.98  \n",
       "68    9.50      NaN      7.83  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data_final = combined_data[combined_data[\"Year\"]>1808]\n",
    "combined_data_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data_final.to_csv('combined_data_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's have a look at the data by visualizing it before starting manipulations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib for additional customization\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Seaborn for plotting and styling\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2a00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig, axarr = plt.subplots(1, 2, figsize=(10, 10))\n",
    "ax = sns.distplot(combined_data_final[\"Baku\"], color=\"green\",ax=axarr[0])\n",
    "ax = sns.distplot(combined_data_final[\"avg_temp\"], color=\"orange\",ax=axarr[1])\n",
    "        \n",
    "plt.tight_layout(pad=8.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2a0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = ['Baku','avg_temp']\n",
    "col = ['green','orange']\n",
    "\n",
    "fig, axarr = plt.subplots(1,2, figsize=(8,8))\n",
    "for i in range(len(arr)):\n",
    "    ax = sns.boxplot(y=arr[i],hue=arr[i],data=combined_data_final, color=col[i],ax=axarr[i])\n",
    "    for patch in ax.artists:\n",
    "        r, g, b, a = patch.get_facecolor()\n",
    "        patch.set_facecolor((r, g, b, .4))\n",
    "plt.tight_layout(pad=8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time to perform some data manipulation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas provides multiple built-in methods to calculate moving averages. The rolling method provides rolling windows over the data, allowing us to easily obtain the simple moving average. We can compute the cumulative moving average using the expanding method. The expanding window will include all rows up to the current one in the calculation. Lastly, we can calculate the exponential moving average with the ewm method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I will use the Exponential Moving average method in this research. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It is a widely used method to filter out noise and identify trends. The weight of each element decreases progressively over time, meaning the exponential moving average gives greater weight to recent data points. This is done under the idea that recent data is more relevant than old data. Compared to the simple moving average, the exponential moving average reacts faster to changes, since is more sensitive to recent movements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, we compute the exponential moving averages for my home city Baku and for global temperatures with a smoothing factor of 0.1 and 0.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exponential moving average for Baku\n",
    "# smoothing factor - 0.1\n",
    "combined_data_final['EMA_Baku_0.1'] = combined_data_final.Baku.ewm(alpha=0.1, adjust=False).mean()\n",
    "\n",
    "# smoothing factor - 0.3\n",
    "combined_data_final['EMA_Baku_0.3'] = combined_data_final.Baku.ewm(alpha=0.3, adjust=False).mean()\n",
    "\n",
    "# exponential moving average for global temperatures\n",
    "# smoothing factor - 0.1\n",
    "combined_data_final['EMA_Global_0.1'] = combined_data_final.avg_temp.ewm(alpha=0.1, adjust=False).mean()\n",
    "\n",
    "# smoothing factor - 0.3\n",
    "combined_data_final['EMA_Global_0.3'] = combined_data_final.avg_temp.ewm(alpha=0.3, adjust=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The following plots show the average air temperature for Baku and the average global temperature together with the exponential moving averages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Temperature [°C]')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba83da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# set Year column as index\n",
    "combined_data_final.set_index('Year', inplace=True)\n",
    "\n",
    "# colors for the line plot\n",
    "colors = ['green', 'red', 'orange']\n",
    "\n",
    "# line plot - the yearly average air temperature in Baku\n",
    "combined_data_final[['Baku', 'EMA_Baku_0.1', 'EMA_Baku_0.3']].plot(color=colors, linewidth=3, figsize=(12,6), alpha=0.8)\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels=['Average air temperature', 'EMA - alpha=0.1', 'EMA - alpha=0.3'], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('The yearly average air temperature in Baku', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Temperature [°C]', fontsize=16)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Temperature [°C]')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mro1VpUoV6taty8GDB8nIyMDPzw87Oztat279zP2yZ5Bzf2WdLa8VPHKysrIC4OHDh3kG4o8fP5b7mC2/J/c9fPhQTrdJSkpi8ODBJCYmMnHiRJo1a4ajoyNmZmZcunSJXbt2PbNdeZEkiaFDhxIcHMwnn3yCl5cX1apVw9zcnJiYGDZv3mywj4+PD7t372bv3r20bNmSGzduMHDgQHl2uLDXQH4K09/CvGdF0b7IyEiGDBmCWq1m2rRpNGjQgMqVK8vf5vj7+z/XcQsj+zrr0qULs2fPfuHny5aenp5nefY17ODgQFpaWpG0rSjGOWcKR27ZH+gLUlTXtCC8CCLnWhBekexlqa5cuWKwLSwsjFmzZnHo0KECj+Po6EidOnU4fPgwR44cAdCbCbaxsaFs2bIEBwfLf2Bz2rZtG4sWLeLu3bsA7Nq1i+LFi/PTTz/RpEkTOehMS0uTl/b6NzNTXbp0ISUlBX9/fy5cuED79u0LnKGrWbMmCoUiz0coBwcHFxhcu7i4ABisVAK6FS2io6MNvjq+efOmXm44QEhICAkJCdSsWROA06dPExMTQ//+/Rk8eDAuLi5yX0JCQoDCj1VgYCC3bt2iTZs2jB49mtq1a8uzh/kd083NjWLFinHo0CF5GcfsVUKg8NdAfgrT38K8Z0XRPn9/f1JTUxk1ahTvv/8+7777LiYmJnm27UWpUqUKZmZmXLt2Lc9zrVy5ksWLF8uz+kUlOTlZ7mNOly5dwsHBgQoVKhRZ2wo7znkF0qampnK7c5Ikyegl9IrqmhaEF0EE14Lwivj4+GBiYsKPP/6oNxus0WiYNm0ay5cvN2qJOdDNXkdHR7Ns2TIqVapE3bp19bZ369aN+Ph45s6dq5cPGRwczNSpU1mxYoUcRKvVatLT0/VmbrOyspg+fbr8Ryx30FkY3t7emJqaMnfuXKNSQkCXltK8eXNOnjzJ0aNH5fKMjAyD5Q3z0qVLF1QqFb/++qveH++UlBSmTp0q18kpPj6eVatW6Z0re8avR48ewNOvqnPfwHXv3j35q+q81jZ/luxgNfcx4+Pj5fPnPqZKpaJjx46cP3+eXbt2UalSJVxdXfXqFOYayE9h+lvY9+zfti+7bdm5ttlu3rzJ6tWr9dpWVExNTfWOqVar6dixI8HBwaxYsUKv7pkzZ5g9ezZbtmyRv/koSnPnztXLxV6+fDkRERF069YNExOTImtbYcc5+9uTnG3Lvtn7+PHjevcPrF+/3uj/86BormlBeBFEWoggvCKVK1dm/PjxzJw5k06dOuHh4YGdnR3Hjh0jJCSE1q1byzekFcTb25sffviByMhIvRUusg0dOpSAgADWrFnD+fPnady4MQkJCezdu5fU1FTmzJkjf43fuXNnli9fTo8ePfDy8kKj0RAQEEBoaCjFihUjLi6O+Ph4SpUq9Vz9trOzw8PDg3379lGhQgW9m6ee5euvv6Z3794MHz4cLy8vSpcuzYkTJ+TVS3KuapFbhQoVmDhxItOnT6dbt254eXlhaWnJsWPHiIiIwNvb22B1lHLlyrFgwQLOnz9PpUqVOHHiBLdu3cLb21te/aJBgwaUK1eO7du38+jRI1xcXIiKiuLgwYOo1WoUCkWhggXQXRd16tTh3Llz9OvXj/r16/Po0SP8/f3JyMjAwsIiz9lFHx8f1q5dS3BwcJ7rCRfmGshPYftbmPfs37avdevWzJs3jyVLlnD79m0qVqzInTt3OHz4sJxrX9j3oiClSpUiLCyMcePG4ebmRteuXZk4cSL//PMPs2bN4uDBg9SpU4cHDx6wf/9+VCoVM2bMeOa1+rwuXLhAjx49eO+99wgJCSEgIIBq1arx2WefyXWKom2FHefs1U5++eUXbty4wYgRI6hRowY1a9bkn3/+oV+/fjRq1Ihbt25x6tQpXF1duXTpklF9LoprWhBeBDFzLQivkK+vL7/99hsuLi7s37+fjRs3olKp+PLLL1m4cGGBq2Bks7e357333gMMZ2BBd1PS6tWrGTlyJOnp6axfv56jR49Sv359Vq9erZdCMHr0aEaOHIlSqWT9+vX4+/tTrlw5li1bxrBhwwD0ZiKfR3ZwauyHB9DNdv3xxx+0atWKkydPsnnzZipWrCjPLhe0fNqgQYNYunSpvILC1q1bsbe35/vvv8/zwTguLi4sXryYyMhI1q5dS2pqKuPGjdObdbW0tGTFihW0bduWa9eusXbtWq5fv46Pjw87duzAxcWFc+fOFZi2kpNSqWTx4sV0796du3fvsmbNGs6dO0fLli3ZsmULzZs3JywsjPDwcL39XF1d5VVqcr6f2QpzDeSnsP0tzHv2b9tXunRpVqxYQdOmTTl9+jTr168nNDSUgQMH4ufnh729PcePHy/S1JDx48dTrVo19u7dy/bt2wEoVqwYmzZtYvDgwTx48EB+/zw8PNi0aZO8ZGFRW7VqFaVKlWLjxo0EBQUxaNAg1q9frxdcFkXbCjvOHTt2pEOHDkRERLB+/Xp5GcElS5bQrVs3wsLCWLt2LSkpKaxatcrgG5dnKYprWhBeBIX0opPQBEF44bRaLa1bt6ZcuXKsX7/+VTenQPPnz2fJkiXs37+fSpUqFVhfq9USERFB2bJl5XzNbBEREXh5edG3b1++++67F9RiobDEe/ZyDBw4kL///puzZ8/qrZUtCMKrI2auBeE/YPPmzdy/f5/333//VTelQPfv3+fPP/+kSZMmRgXWoLspqmvXrnTu3Nlgjd9ly5YBvLAZQeH5iPdMEIS3lci5FoQ32BdffEFgYCChoaE4Ojri7e39qpuUrx07drB8+XLCwsJIS0vLMzc8PwqFgj59+rB8+XJ8fHxo2bIlJiYmXLhwgYsXL+Lm5qb3FEDh1RPvmSAIbysRXAvCG6x48eLcu3ePOnXqMGvWLIOv318nZcqUISoqCmtra7766qs8n5b4LOPHj8fR0ZHNmzezdetWNBoN5cuXZ+zYsfj6+j5z7Vzh1RDvmSAIbyORcy0IgiAIgiAIRUTkXAuCIAiCIAhCEfnPpIVERyfqvba2VpOUlPcjYYWnxDgZT4yV8cRYGUeMk/HEWBlHjJPxxFgZT4yVoZIlbfLd9p+duVapTF51E94IYpyMJ8bKeGKsjCPGyXhirIwjxsl4YqyMJ8aqcP6zwbUgCIIgCIIgvGwiuBYEQRAEQRCEIiKCa0EQBEEQBEEoIiK4FgRBEARBEIQiIoJrQRAEQRAEQSgiIrgWBEEQBEEQhCIigmtBEARBEARBKCIiuBYEQRAEQRCEIiKCa0EQBEEQBEEoIiK4FgRBEARBEIQiIoJrQRAEQRAEQSgiIrh+Qxw4sBc3t4b88cfaV92Ul2LPnp1069bR6PopKSns2bPzBbbo5fkv9UUQBEEQcopJTONk0EMeJae/6qa8MCK4fkP4+++jfPkK7N2761U35aXw9GzD8uXrjK6/YcNadu7c9gJb9PL8l/oiCIIgCNm0ksTakyH4Xb7LH6duv+rmvDAiuH4DJCQk8Pffpxk8eCghIcHcunXzVTfphVOrzXFwcDC6viRJL7A1L9d/qS+CIAiCkO1xSgaxSboZ66jHqWRmaV9xi14MEVy/AY4cOYipqRkeHm2oUKEie/boZq9Pnz6Jh0dzUlJS5LrXrl3F3b0pCQkJSJLEypW/07VrB9q1a8WYMSOJiAiX67q5NWTRooV06uTF559/CsDu3Tvo378n7u5N8fb2ZO7cH9BoNPI+Gzeuo2vXDrRt24off5zDyJGfyCkMBZ0vt6tXL/Pppx/j6dkcLy83xowZSXT0Q0A/LeTChXN069aR+fNn0a5dK5YtW6J3nD17drJixVKuXLmEm1tDADIyMvjpp3l06uRFx46eTJ78JXFxsQBERd3Dza0hAQFH6dmzM23atGDRogWEhAQzePAAvLzcmDhxNKmpqQBMn/4d8+bNYuTIz/DwaM6HH/bj0qWL8vmNOdfKlb/Tvn1rZsyYAsDatSvp1asL7u5N6dKlHb///mu+fenZs7PeTPaFC+dwc2uIRqPJ9/jHjh1hwID38fRszuDB/Tl9+mS+74MgCIIgFLWUDA1XIuJITs+Uy+KS9FNBktIyc+8GQGJqJlci4kjN0OS5/XUngus3wIEDe2natBkqlYoWLVrh778XjUZDw4aNsbS04NSpALnu4cP+NG78Hra2tmzZspG9e/cwefJUfvttFeXLl+fzz4eTlpYm1z906BCLF//O55+P5dKli8ybN4uhQz9lw4atjBs3iT17dnL06GEA9u/34/fflzBy5Gh+/XU5UVH3uHjxgnwsY86XLSUlmfHjv6Bhw8asWbOJ+fN/5t69SFatWp7nGERHPyQ5OZnly9fRsWNnvW2enm3o02cA1avXZPv2vQAsWfJ/XL16mVmzfuTnn5cgSVomTBitNyu8du0qZs6cz7hxk9i4cR3/+98EPv10FHPnLuTixQvs3r1drrtz51YcHd9lxYp11K/fgPHjP5cDaGPOdfHiBZYtW8OAAR+yb98eNmxYy8SJX/PHH3/h6zuElSt/5/r1q3n2xRg5jx8UdItp075hwIAPWL16Iz4+3fjqq/EEBQUafTxBEARB+DfWBISw6e8wVgeEyH8PY3MF14l5BNdarcSyY7fY9HcYm/4OexlNLXKqV92AVyXg1gMO34giQ/PyvpIwUylpXf0d3JxKG71PTEw0ly79w+TJUwFo2dKD9evXcOpUAC1auOPu7smRI4fw9GwL6Ga5hw7VzUKvX7+Gzz8fR4MGjQAYPXoCp06d5MiRg7Rv7w1Ar169qFixMgA3b97gyy8n06qVBwBlyrzDhg3rCAvT5UX99ddmevbsLZ/r66+n0L3705sOjTlfttTUVAYO9KVv3wEoFArKli2Hu7sHV69ezncs+vcfRLly5Q3K1WpzLCwsUKlUFC9egrS0NP76axNLlqzAyckFgMmTp9KxoyeXL1+kVCnd+H/wwWCqVq1G1arV+OmneXh5taNhw8YA1KvXgDt37sjnqFSpCqNHjyE+PoURI0Zz/Pgx/P334ePT3ahz9erVV257TEw0kyZ9K5+ra9eerFixlNDQ29SoUUuvL8bKefxp0ybj7e0jj3m5cj25fv0af/65kUmTvjH6mIIgCILwPDKztNx9lAzAvfgUEtIysbMwI8aI4DomKU0OwkMeJKLJ0qIyebPmgt/a4PpE0MOXGlgDZGi0nAh6WKjg2t9/H0qlkvfecwOgZs1alChREj+/3bRo4Y6nZ1smTBhNeno6ISFBPHoUh5tbK1JSUnj48AFTp/4PpfLpRZmRkaGXqlG2bDn5dxeX6qjVapYtW0JoaAghIcHcvRshB8shIUH07TtQrm9ra0vFipUAjD5ftuLFS9CxY2c2blxHUNAtwsJCCQ6+RY0atfIdizJlyho1Zvfu3SUzM5PPPhuiV65ryx054C1b9mmgrlarKV26jN7rzMwM+XXt2nXk35VKJU5OToSFhRp9rnfeeUfeVr9+Q65du8qvv/7MnTuh3LoVSGxsLFrt81+POY8fFhbG7dvBejPvGo2G6tVrPvfxBUEQBMFYKbnSOaIT0rCzMDNIC0lMNUz7iEl8WkdCIiYxnTL2Fi+moS/IWxtcN69W6pXMXDevVqpQ+/j770ej0eDt7SmXabVaTp48Tnx8PHXr1sfa2pq//z7F5cuXeO89NywtLUlMTARgypQZVK7sqHdMa2sb+Xe12kz+/cyZU0yaNJZ27bxp0qQZvr5DmTdvprzdxMQE0L/ZLvurnqysLKPOly06+iEffzyQatWcadz4PXx8unHyZACXL180qJvNzMws3205Zbdl0aLfsLa21ttmb+9AYmJCjv48lfNDQW6562q1WpRKpdHnMjNTy+W7dm17kqPdlZYtW/PZZ18watSwfM+tUCjy7F9OOY+flZVFnz4D8Pb20atjamqa7zkEQRAEoaikpOsHzQ8T0qha2taotJDcdaITU0Vw/aZwcypdqBnkVyEiIpybN68zatQYOYUA4MGD+4wf/wX+/nvp2bMPHh5enDhxnIsX/2HYsM8AsLGxwcGhGDExMbi5tQJ0Qdd3331Nly7d9Y6XbefOrbRv782ECV8DutnOyMi7uLrWA6BKFUdu3rwhp411KxEvAAAgAElEQVQkJydx9+7d5zrfsWOHsbS0Yu7chXLZn39uJHfwbqycAWi5cuUxMTHh8eN4XFyqA5CUlMS0aZMZMuRTrKysCn38oKBb8u9ZWVkEBd2iceP3nutcW7duYdCgwQwc6AtAYmIicXGx8geV3MG0SqUiOTlJfn3vXuQz21qxYiXu3YukfPkKctmyZUuwtbWjV68+hey5IAiCIBRO7uD6weNUtFqJuFxrWyel55EWkqh/n1Z04pu3HvablcTylvH334e1tQ1duvTA0bGq/PPee27Url0HP7/dAHh5tePgwQPExcXK6SMAvXv34/fff+XYsSPcvRvBvHkzOXv2DJUrV8nzfLa2dly7doXg4CBu3w5hxowpxMbGkJmpu/h79OjNX39t4siRg4SFhTJz5vekpqbIwWBhzmdra0dMTDRnz54mMvIua9eu5OjRQ2Rk5H3ncEEsLCyJjY3h3r1ILC2t6Ny5KwsWzOb8+bPcuRPG9OnfEhISTIUKFQo+WB4uX77I8uXLCA8PY+HCeaSlpeLp2fa5zmVnZ8e5c2cJDw/j5s0bfPvtJDQajZyGkrMvANWr12Tbtr+4fTuYf/45z8aNz17/+/33+3HkyEE2blzH3bsRbNv2J6tXL88zX10QBEEQilpKhv43rA8T0ohPyUCba6nZxNS8c65zik40XBThdSeC69eYv/8+2rRpj1qtNtjWtWtPAgNvcPt2MNWr16R48eK0aNFSr27fvgPp2rUH8+fP4oMP+hAaGsL8+YsoUaJknucbPPgTihcvwbBhvowe/SkqlYru3XvJq0x4ebWjb9+BzJ07k6FDP6RUqVK88045VCpVoc/n4dGGdu06MnnyJD76aCDnz59l5MgxhIffIT298P+Q3N09UCqVDBz4Po8exTFy5GgaN27Kt99O4uOPB5Gens78+T+jVpsX+tgAzZq5cf78eT78sD+3bt1kwYLF2NraAhT6XJ9/Po709DR8ffvz1VfjcHSsiru7B7duBebZlyFDhlOiRAk++mggP/44hyFDhj+zrbVq1eabb6axc+c2Bg58n40b1zNp0jc0a+b2zP0EQRAEoSjkzrl+mJhqEDRD3kvxxeSaqY5OePOCa4X0H3liRXR0ot5re3tL4uNT8qktZCvMOP3zz3nKli0n3/in0Wjo1MmLGTPmUr9+wxfZzFdq+vTvyMrKYv78eeKaMpL492ccMU7GE2NlHDFOxhNjZbzCjtXhG1Ecuh6lV9a8WmlOBD3QK7M2N2Wid235dUqGhh926q8aplIqmdzVFWWulMlXrWRJw/vJsr21OddC4R0/foQrVy4zfvwkLC2t2Lz5DywtrahZs3bBOwuCIAiC8FbInXMNcONevEFZcpoGrSTJgXPufGsAjVbLo+R0ilsbfhv8ui7T9/q1SHhtffzxMCpWrMTo0Z/x4Yd9uXMnjHnzFuWZtiIIgiAIwtspd841YHAzI+iW2kvOEYjnTgnJljs1RCtJrD0ZwpRtFwm49SDPfV4lMXMtGM3S0kp+mM3b5Ouvv3vVTRAEQRCEN0bunOvclAqFfHNjUlomNua6pWLzyssG3YohLjle341LJjDqMQDX7sa/dqu/iZlrQRAEQRAEocikFhBcl3OwlH/PudZ1zpnr8sWeLmWbe8WQW/cT5N9L2T3fQgUvkgiuBUEQBEEQhCKTnEfOdTZbc1OKWT9NJ01My5kWoguiTWJTeef6I5SPdK9zp4XkDK6dytgWSZuLkkgLEQRBEARBEIz2d0g0h2/cp26lYrStVdbg4Wc500JMTZRkZj19Grb9wzQ0x6JQlTNHU85aXo5PK+keMqNI1WC3JpBkU1McktJIa1iK6FblkSQJhUJBYmomUfEpKFI1mMak4dgp/1U7XhURXAuCIAiCIAhG0UoS+65GkqHREnDrAQ6WZjR+9+nzLDRZWjI0umBagYIKxay4/WS5ZEWqBsWam6RmarHTaHg0uIb8IJn45AyytBJWJ6NQpWWhMjdDKYH53w+QrsRyWWtGre7VCbz/GPWlGKz8IzDXwjVJTcPRTV7+QDyDCK4FQRAEQRAEo8QlpcvBM4Df5Ugql7SmlK0FAKk5VgqxNDOhtJ2FHFyrr8RikqnFRKmATC02O0JJrFEK0N3MqHycjvn56CfL6ylQmSjJ0GShSNVwZck/hG+9xUNzBdbXYwAwtzAjOSrpJfXceK8k5zouLo7Ro0fTuHFjWrduzcqVK/OtO3nyZJydnfV+nlVfEARBEARBeDHuP07Ve63Ratl0JgzNk9SPnCkhFmoVJW2f3HColbD4JxoTpQKlUpdGoopKJsEvFNDdzGh5/B48Wbu6mEtxig2ujdZOLZ8nNS6V5Bux8vEdKtlRb8Tr9xC7VxJcjxgxgjt37rBs2TJ++OEHVq5cyapVq/KsGxwczIQJEwgICJB/evfu/ZJb/Gr07NkZN7eGef4cO3YEgBEjhuLm1pBdu7Yb7B8eHoabW0OGD//IYNuaNStwc2vIgQP7X3Q3mD79O6ZOnWxU3REjhvLbb4tfWFsyMjKYNWs67du3xsenHevW5X3d5Xbp0kW8vDxeWLsEQRAE4U0QFZ9qUPYgIZV9VyIB/eDa0kxF6SfBtWl4Isq4NFQmSkxy5Ghn+t8h7MBtIs9Eor4aB4BKqaDOkHqUa1WJR0Nrkty2IlprMzKytPISfor33sF7aSdsK9q9sL4+r5eeFnL16lXOnz+Pn58fjo6OAIwfP54ffviBDz74wKD+7du3qVWrFiVLljTY9jYYMeIL2rRpb1BuY/P07liVSsWJE8fp1KmLXp2jR48Y3GSQzd9/H+XLV2D79u00auRWtI1+jS1e/BNXr17ixx8XEx39gGnTvqV06TJ4ebXLd5+QkGAmT56IqanIohIEQRD+e0IeJnL05n1qlrOnybvPjrceJDwNrh1L2sgpH3/fjqFNrbK5gmsTOV3E/Hw0ChSolAoqd65G5JHbqCISycqSODv7pG4ZvieBc/EGZShZpzSxUY9BpSStQSmS3MrjcOMR6Vcfkl67OHVbO2JqYVrUQ1EkXvrMdUREBHZ2dnJgDeDi4kJ0dDR3797VqxsdHU18fDxVqlR52c18bVhZWVO8eAmDHzMzM7mOq2t9zp07Q3q6/pONjh8/kuejyW/fDiEkJBhf3yEEBBzn0aO4F96P10Fqaio7dmxj1KixuLhUp0ULd/r1G8iWLZvy3Wfbti0MGzYYB4diL7GlgiAIgvDy+F26S2h0Irsv3pVX78jPgxxpIR3qlMfeUheP6Fb7yCAlPUfOtVqFuakJDYrZYBYUj5VahUKhwLmrM6k+jkhqEyRJQivp0j4AUCio81FdAEraPF3DOjo1g1uVrUjqVIXMSrY4lX79luDL9tKD6xIlSpCUlERS0tME9MhI3VcJjx490qsbHByMSqXip59+okWLFvj4+PDXX3+91Pa+CWrUqImlpSUXLpyVy2JiYoiICKdevQYG9Q8c2EvlylXw8GiDmZkZ+/f7/es27N69g/79e+Lu3hRvb0/mzv0BjcZwnctly5bwv/9N4IcfpuLp2Zw+fbrLKS7ZYmNjGDduFB4ezejTpztnzpySt925E8bYsaNo06YlHh7NGD78I27fDgFgz56d+abRXLhwjuDgW2RmZlCnTl35eHXq1OXmzet5thXg3Lkz/O9/U+jdu9+/HiNBEARBeN1IkkRskm5yTkIiJinvR5ADpGVmEZ+SAeiesljCRk2JHAFwXFK6QVoIQNWwFMraWWBnaUZJ19LYVbLHsowVj/s7k9agFJYuxdBYqHSz1G5lqVhT98TFYlZmvFvKcKk9E6UCxzzKXxcvPbh2dXWlTJkyfPfddyQlJfHgwQN+/vlnADIz9T8t3b59G9DNbC9dupSePXvyzTff4Of374PB/xKFQkGzZi0JCDgmlwUEHKVp02aoVIapDAcP7qdZsxaYmprSokUL/Px2/6vzX7p0kXnzZjF06Kds2LCVceMmsWfPTo4ePZxn/YCAY2RlZbFs2Vo6d+7C5MkTCQkJlrfv27cHd3cP1qzZRPXqNZg27Ru0Wi2SJDFx4hjKlCnDypXr+eWX5Wi1WhYv/gkAT882bN++N8+f2rVdiY2NwdbWFrX66eL1xYoVJzMzk/j4+Dzb+v33s2nVqvW/Gh9BEARBeF2la7RPZ43RLYmXn5yz1iVtzXU3Hlo9/Zsal5yu9wAZ5b1kTnx7lBvrr8ppqlV9nACwMTclq7QlyW0r8rifM3GjXIkdV4+SXavJNzwqFAoGuVVlUPOq1K1YDLXKBICGVUpg9uT319FLTyI1MzNj0aJFjBkzhkaNGmFtbc24ceO4dOkS1tbWenX79euHt7c39vb2gC7IvnPnDn/88QcdOnTQq2ttrUaVY6BNTJTY21uSH8W6dSiXLYUUw8T8F8bSAu1HQ5D69zequlKpYMGC2SxcOE+v3NbWloMHdYGrSmWCubkp7du3ZerU7+Q+nz59nO7dexAUFIRKZSKXX7p0iXv3IunYsT329pa0adOWffvGcv9+OC4uLs/VrRIl7Jg2bRre3p0AcHF5lz///IOoqHDs7S0xM1Oh0YC9vSXm5qbY2toxY8Z0zMzMqFu3JmfPnuLAgd00aDAJlcoET09PBgzQzRR/8slQDhzYS2ZmMjY2Nrz/fi/69OmDpaXusag9enRn6dLfnvTPktKl80/fUCol1Gq13nVRvLjuayULC5NnXi+WT772elYd4amC/v0JOmKcjCfGyjhinIwnxkrn4eNUTEyezrWmIxmMS/ZYXYl6LNetXMoWe3tLypey4fwd3QoeqVkSklKBiYkSi4MR3L/6CCu1qbyPbXlbanR0QqlSUsLekntPgvXgh4lynebV3zE4fzEHKxq5lCEzS0tCSgbFrNX53lP2Ongld2jVrFmTffv2EROjm0kMDw9HqVRStmxZvXoKhUIOrLM5OjoSEBBgcMykXF9j2NtbEh+fkm8b7FavRkpM/he9eA6JyUirV/PYu5tR1bVaCV/fIbRu7aVXrlQq5b5pNFmkpWXi4lKHhIQETp8+T8WKFbl48SLffDODK1euo9FkyfW3bt1G8eLFqVSpGvHxKbRo0RIzMzM2b/6TkSPHGLRh/34/5syZIb8eP/4r2rbV/2BTrlwVMjIk5s1bQGioLp/77t0IXF0bEB+fQkaGhqwsXRvS0jKpVs2ZlBQNKSm6T7fvvuvMrVtBxMenoNFkUbLkO3J7JUl3icbEPEattqF9+y5s3LiFmzevEx4eRmBgIHZ2dsTHpxi0Nae5cxei0UB6erredREbq3uEakaG9MzrJeXJ12DPqiM8VdC/P0FHjJPxxFgZR4yT8cRY6dyLSSIrxxMU70UnGYxL9ljdjnos17U3VxEfn4K5ArnsXkwSSqUCbUI66tP3wcpM3laueXlchzUgIUn3KHMz0DsvgEqppLLDs98XE+Dx45c4MZqPkiXzT0t56cH148ePGT58OIsWLaJEiRIAHDp0iBo1ahjMXM+cOZPQ0FCWLFkil924cUPvZsjnldazN+ZrVqJIfXlvkGRhQVrPwi0jaG/vQPnyFQqsp1arady4KSdOHCMy0hFX13pYWup/8svKyuLQIX/i4mJxd2+qV75//16GDx9lkEbi5taSGjVqya+LFTOcGT5z5hSTJo2lXTtvmjRphq/vUObNm5lvW01M9L/K0Wq1KBRPPzUrlYbZSpIkkZKSwpAhg7C1taVFC3e8vNoRHh7G2rWr8mxrTiVLluTWrUASExPJzMzE1FR3h3FcXCxmZmZ6q68IgiAIwtsi9w2M2TnVecm5xnUZO90qIMWtn6aFxCanY2Wmwiz4MUgSSoUC24q2NP3aDTtHB71j2eSx0ofzO3aYm76+6R7GeunBtZ2dHWlpacycOZORI0dy7do1Fi9ezLx5utSHuLg41Go1VlZWeHh48MEHH7B69Wrc3d05fvw427ZtK5KHyKT36kN6rz7/+jivEze3VmzevIG7dyNo2dIwT/jChbPExcUyZcoP8gosNjYWnDr1N7NnT+f06ZO4ubXU28fS0kpOwcjPzp1bad/emwkTvgZAo9EQGXkXV9d6eda/fTsYrVYrB9E3b16ndm3XAvv3zz/nefjwAatWbZA/BJw9exrpydI9BbW1WjVnVCpTrly5RP36ukXnL1++iLOzS5656YIgCILwX5eUpn9D/6N8gmutJOnlXJd+Elw75Mi5jk/OQJIkzIJ09zEpFQqqdKhqEFgDWKsN/+7WrWhY7030Sh4iM3/+fB48eICPjw8//vgjU6dOxdPTE4CePXuyfPlyABo3bsy8efPYtGkT3t7erFu3jvnz59Ow4ev3NJ4XJTk5idjYGIOflBTDlJbmzVsQEhLEqVMBNG/e0mD7gQP7qFSpMp6ebXB0rIqjY1WqVauGt7cPpUqVZu/eXc/VRltbO65du0JwcBC3b4cwY8YUYmNjDG5QzXb/fhQ//7yA8PAwVq9eTmDgDTp37lrgebI/mB09eoioqHvs3LmNLVs2kZmZ/6fsnMzNzenQwZv582dx/fpVAgKO8scfa+jZ8+mHrNjYGNLT04zruCAIgiC84ZLT9f9WP07JkB/UktOj5Awyn6RxWJmp5ODY1ESJjbluFlpCIj4+FbNQXcqlUqGgbLPyeZ43e59sFmYqqr7Gy+sVxiuZrqtcuTKrV6/Oc9uhQ4f0Xnfs2JGOHTu+jGa9ln7++Ud+/vlHg/I+fQYwYsQXemW2tnbyDHDuXPWMjAyOHTvMoEGDDY5lYmKCj083Vq1aRkLCY2xtC/e0o8GDP2HGjO8YNswXKysrmjRpRvfuvQgKCsyzvotLDZKSkvD17U/58hWZM+cnKlSoWOB5atWqg6/vEBYsmENGRgbvvvsuY8d+yYwZU7h//z5lypQp8BgjR45h7twf+Pzz4VhaWvHhh0Pw9Gwrb+/SpT1fffUtHTt2Nn4ABEEQBOENlZSuP3OtlSQSUzOxszTTK7//+GkedGk7C70bCotZqUl8kl5idjsBNE/ysqvYY10279xk61xpIbXLO6AyMXLONyMD801/YHL9Kml9B5JVu45x+70kCknK4+PJGyj6yROCsokbFYzzssdp2bIlnDv3N7/8suylnbOoiGvKeGKsjCPGyXhirIwjxsl4Yqx01p0M4WbUY72ywS2rUSXHDXv29pb8dTKEwzeiAGhWtRQdXHUz0skPkti28gJBDiZkFbfAemco6quxKBUKPIY3otbguuQlMTWT2XuuyK+HujtTofiz01ABTG4FYjl7BiZ3wgDQ1HElad7CQvW5KLxWNzQKgiAIgiAIr4fcM9eQ902Nejcz2uvyrTNTMjky5gDp4Y+x12TxuHdV3c2M6JYTLts8/wUZrMxVFLdWE5uUTjkHS8oXe/ayiCYhQZj57UG9axvkWGVEU7f+szv4CojgWhAEQRAE4S2V1+PO83qQTF43M15ffZmUhymolArIzMJu/S3Q6hIiTOzUODg949kTCgUftXQi6EECTmVs8163Oi0NswN7Ue/egUlIiP42tZqUTz4lo1MXY7r5UongWnipPvroEz766JNX3QxBEARBeOtJkqT3RMVsuWeuNVlaHuUIuEvZmBN/+xFBW28CuseRA3JgDWBWu2SBD3qxsTClfuXiBuWKxATUf/2JesdWFAkJBts1NWuRMn4S2nJ53yz5qongWhAEQRAE4S2UodHKK4DklHs5vrikdCR0gbOthRkmCgUXfvob6cmuxZ2L8/DqAxQ5lvWzqlfqudqkeBSHzajhKO/f199gakpGi5ZktGmPpn5DyOOZGK8LEVwLgiAIgiC8hZJyLMOnQCEH0I+S9Z96HZv4dInaYlZmhPoFE3s9BgClSkGzr9y4uOcq6tU3UWRkobUxw65GicI3KCMDq+/+pxdYa8uUIb1bT9LbdoBcDxt8XYngWhAEQRAE4S2U8wEypWzNeZCgy6tOSM1Eq5VQPkn3iM4RXDtYmHFt8Xn5tXOvGthWtMPeqThRvtVRX48jw9kBq1xL+RVIkrCcPxvV9Wu610oFKV+MJ6NtezB5s57a+PrOqQuCIAiCIAj/ivTkyYqaPNI/cs5c21uZYfXkwTBaSSIhx42OMQlPg2uLu0mkxeleq+3NcelXC9A9qVFbzJxUt7JklbTAIo8nMOZLo8F8+W+YHTwgF6UO/ZSMDt5vXGANYuZaEARBEAThP8v/WhTHAu9TwtqckW2qy7PRAMk5Zq6t1SocLNXyDY6PktOxfzL7nDMtRHs5Wv69QquKqMx1oWSxHI9BB91THI2hunwRi0U/YhIWKpdldOxEevdexnbxtSOCa0EQBEEQhP+oK3cfARCTlEbU41TKOTxdTzrnGtdWalPsrbTcfZQM6K8YEpMdXGdpST7/gOzwvELrynKdYtb6wbWF2TNmnFNSMDtxDDP//agunNfbpKlXn5QRX0ABK428zkRayGusZ8/OuLk1zPPn2LEjAIwYMRQ3t4bs2rXdYP/w8DDc3BoyfPhHBtvWrFmBm1tDDhzY/6K7wfTp3zF16mSj6o4YMZTfflv8wtqSkZHBrFnTad++NT4+7Vi3btUz6+/evYPevbvi4dGcAQP6c/361RfWNkEQBEEoSpIk6a1jnZxrTeuc22zMVThYPc2Tzl56TytJxCbpbnA0DUtEStUF5JalLCle/elNi8Ws9HOsrfJJC1H/tRn7Xl2wnP2DfmBtbk7qx5+QNH02mJrmue+bQsxcv+ZGjPiCNm3aG5Tb2NjKv6tUKk6cOE6nXAupHz16JN81Jv3991G+fAW2b99Oo0ZuRdvo19jixT9x9eolfvxxMdHRD5g27VtKly6Dl1c7g7pnz55m3rxZfP31t7i41GD37q2MG/c5f/65A0vLgh/RKgiCIAivUu6l9hJzrWmde+baJMfydtkz14mpmXK+tnVgPMoncUX5VpVQ5EgxyT1zbZlHWojqn/NY/PKzQXmmuwcpQz9FKlnS6L69zsTM9WvOysqa4sVLGPyYmT39hOjqWp9z586Qnq6/dM7x40eoWbO2wTFv3w4hJCQYX98hBAQc59GjuBfej9dBamoqO3ZsY9Sosbi4VKdFC3f69RvIli2b8qwfFxfHRx8NxdOzLeXKlefTTz8jIeExISHBL7nlgiAIglB4ic+Yqc792tpcJedYw9Pl+OKyl+XTaDEPipe3V3CvpHcsG3NTOaBWq0ywyB1cp6VhuWCO/FJbrjypHw3l8dpNJH/97X8msAYxc/2fUKNGTcLCQrhw4SzvvaebhY6JiSEiIpyuXXtw8eIFvfoHDuylcuUqeHi0Yd68mezf70fv3v3/VRt2797B+vWriYy8i5WVFa1be/HFF+NRqfQvsWXLlhAaGoKVlTX+/vsoWbI0n346ipYt3eU6sbExjBs3igsXzlGqVBlGjx5PkybvAXDnThgLF87n8uWLZGVpcHauzvjxX+Ho+C579uxkxowpebZv4cJfMTU1JTMzgzp16srlderUZdWqZWg0GoO2tmvXUf49PT2N9ev/wMGhGI6O7/6rsRIEQRCElyEp10x17mA759MZbcz1UzGyZ67jnqSEmIU8RqWRQA3WZa1xqKb/aHOlQkG3BhU5FRxNwyolnj618QmL1ctRRkUBIFlbkzhvIVJxw6cz/heImev/AIVCQbNmLQkIOCaXBQQcpWnTZgYBI8DBg/tp1qwFpqamtGjRAj+/3f/q/JcuXWTevFkMHfopGzZsZdy4SezZs5OjRw/nWT8g4BhZWVksW7aWzp27MHnyRL3Z4H379uDu7sGaNZuoXr0G06Z9g1arRZIkJk4cQ5kyZVi5cj2//LIcrVbL4sU/AeDp2Ybt2/fm+VO7tiuxsTHY2tqiVj/96qpYseJkZmYSHx9v0M5sZ86cwsurBb/8sphRo8ZgZfVmLGIvCIIgvN0MZ6rzD7at1Poz149TdGtdxz3JvVbfeCQHzBVaVcoz7dSlrD2+LatRu4KDXrlJ4E3UOb4lTh366X82sIa3eOY6cPN1rq+5giZVU3DlIqKyUFFjYG2ce9Uwep8FC2azcOE8vTJraxu2bt2jV9aiRSvmzJkhvz5+/CidOvlw+3aIXr2rV69w716kPFPs6dmGffvGERR0i2rVnArZIx21Ws2XX06mVSsPAMqUeYcNG9YRFnY7z/o2NrZMmPA1ZmZmVK5chVOnTrBr13Y+/3zsk76406lTVwD69RvEgQN7iY2Nwdrahs6du9CtWy8sLXV3O3fo0Ik1a1Y8aYc5arV5vu1MS0vD1FT/hgvTJzdNZGZm5LULAFWrVmP58rWcPXuSGTOm8M475ahVyzDdRhAEQRBeJ89KA8nQZMn52CZKBeamJigUCmzNTUlIy0RCIio+hbjkdJSxaZgFPkJlofsbmnOVkIIoYmKw+mEaaHVPf9TUrU9G+44F7PVme2uD61t/3nipgTWAJlXDrT9vFCq49vUdQuvWXnplSqXhFw4NGjQiKSmRmzdvULFiRa5du8y0aTMNgmt//70UL15czsVu1aoVZmZm7N27i2rVxhgcd/9+P72gffz4r2jbtoNeHReX6qjVajnlIyQkmLt3I2jQoFGefXJyctHLGXd2rk5o6NN2litXXv7d+smjTjMyMrCwsKBbt17s27eHmzevEx4eRmBgIHZ2dnm2Nae5cxdiZqY2CKIzM3X/0Zib5x+UZ+e5N2pUj3PnLrB9+xYRXAuCIAivvcS03DcwPg2uc85iW6lN5Zlox1I2XAzX3Yt1/d5jYpPSsTh8F7QSKqWCknVKYVfF3qjzK+9HYT1htJwOgpkZKaPHvdHL7BnjrQ2unXpWfyUz1049qxdqH3t7B8qXr1BgPbVaTePGTTlx4hiRkY64utaTZ3ezZWVlceiQP3Fxsbi7N9Ur379/L8OHjzJII3Fza0mNGrXk18WK6edYgS5tYtKksbRr502TJs3w9R3KvHkz822rSa6nLWm1WhSKpx8Y8s+r0HEAACAASURBVPrwIEkSKSkpDBkyCFtbW1q0cMfLqx3h4WGsXbsqz7bmVLJkSW7dCiQxMZHMzEx5xjouLhYzMzO91VeyXb16GbXaXG9Gv0qVKkREhOfbN0EQBEF4XeQMpkE/oM65zcb86d/+6u/YceV8JFkOaq5FPiI1MA7LwEdIgIlSSZ2h9Y06t/JuhC6wjn7y0BkTJckTvkJbttzzd+gN8dYG1869ahRqBvlN4ObWis2bN3D3bgQtW7Y22H7hwlni4mKZMuUHqlSpAoCNjQWnTv3N7NnTOX36JG5uLfX2sbS0KnDZuZ07t9K+vTcTJnwNgEajITLyLq6u9fKsf/t2MFqtVg6ib968Tu3argX2759/zvPw4QNWrdogfwg4e/Y0kiQZ1dZq1ZxRqUy5cuUS9es3BODy5Ys4O7vkmZv+11+bSU5OYtasBXJZYOBNqlevWWBbBUEQBOFVy51jna7JIkOjxUylNJi5liSJ+2ejCP/tPA6X7qO1VJHcoizqSzGA7v6uyp6VKeZsRK50ejrWX41/GlibmpI8eQqZ7zUvsr69zsQNja+55OQkYmNjDH5SUpIN6jZv3oKQkCBOnQqgefOWBtsPHNhHpUqV8fRsg6NjVRwdq1KtWjW8vX0oVao0e/fueq422trace3aFYKDg7h9O4QZM6YQGxsjp1zkdv9+FD//vIDw8DBWr15OYOANOnfuWuB57OzsSEtL4+jRQ0RF3WPnzm1s2bLpmfnSOZmbm9Ohgzfz58/i+vWrBAQc5Y8/1tCzZx+5TmxsDOnpuidRde/ei9OnT7Jly0YiIsJZuPAnAgNv0qtXn/xOIQiCIAivjdw51wDJT2asc64UYpGYyfEvDxHw9WES7yTo8q+TM7HaewdVlC7eUJmZUPujugbHy4v55g1PU0HUapK+n/nWBNYgguvX3s8//0iXLu0NfpYvX2pQ19bWjtq1Xala1Ql7e/18qIyMDI4dO4y3t4/BfiYmJvj4dOPEieMkJDwudBsHD/6E4sVLMGyYL6NHf4pKpaJ7914EBQXmWd/FpQZJSUn4+vbn4MEDzJnzExUqVCzwPLVq1cHXdwgLFszhgw/6smfPDsaO/ZLHjx9z//59o9o6cuQYXFxq8Pnnw5kz5wc+/HAInp5t5e1durTn4MED8vmmTp3J1q1b+OCDPpw8eZL58xdRqlRpo84lCIIgCEUhIi6ZJYcD2fVPBNonNwYaI6/gOnuFkJzbUtde58GFp39HLUwNH11u6V4Bq9IFr5alePAA8z/Wyq9Tho1A8+Tb4reFQsr+Tv0NFx2dqPfa3t6S+PiUV9SaN8fLHqdly5Zw7tzf/PLLspd2zqIirinjibEyjhgn44mxMo4YJ+O9SWO1OiCYoAcJAHSuW4HG7xb8wBWtJDFl60W0ucK8vk0d/5+9846TpC7z/7uqq3PPTE8Ou7M5Z1hYliWDoHCCYkLCiYonp4CIgXAHIqA/9RABETMqnHcqGEAUDyUjCwtsZHOYnZ2dnDrnCr8/qqe7eronbF52v+/Xa1+vqfyt6truTz31eZ6HeRP8PL12L2+29CElVKb+dDM+px0kmHbRDKZ+eA4/ffA1nCu7kNIaht/F7G+ewUWnTB3zuN5v3oX9pRcA0KZPJ/LDn0OJXKp3O7W1ZSMuO2491wKBQCAQCATvBgKxvP3xuc1dLGiuLNle3EoirRUJa8gnMg5Fru3tUWzZ6h1Vs6pZ+sVTAJhw6Wx2LK5G6YpjTPRRUzt21FrZsC4nrAES1914TArrsTj+zlggEAgEAoHgKGQgmuJ3q3bzytZCq6O12UsirfLSlrGtkKUsIeZ802s91L1R2RtFzjaHqVlYl1tv3gQ/hsdOZnoFhsdOlddZvLMscvte3A89gO8/bs7Ny5x9Luo4ihUci4jIteCwcs0113LNNdce6WEIBAKBQHDU8dymTja2B9jYHmB6XRkTqryomk5K1QrWe2NnHydNraau3D3ivkYS10NCPVIicl2zMG83mdNYgYSEgRn9Hklcu375c1y/+W+wBsmdTuL/9rnRT/YYRkSuBQKBQCAQCI4Cuiwe8N6IWbkqUkIkGxj8bX0Ho6XNRS3VQBSLNSOazJBWNQKxFGQ0lK5Yrq15zYJ85NrrVDhpqll2b3p9OZXewg7HAI5//B+u/y0U1trUqUT/339h1NUVrX+8ICLXAoFAIBAIBPtIMqPx6rYevE6FU2fU5joc7i+6YRR4q8OJrDfaIpK9DoV4WsPAYGdvmL5IcsTotVWU11e46AiYwj2aVOkNm8Ld3hlDkSSz7fnkCpzlhdHpi09o5vRZ9UyZ4CccShQsk1t24Xngvty0On8ByU98CvWEpcd8B8axEOJaIBAIBAKBYB95bUcPr2wzvc915S5m1Bd3+t0XQvF0QQJiJFFcMq+p0oOmG7RkK6QNxtIjimtrk5iGCndeXKcydGWFsrI3it1mRrVrFhRXIJEkiSqfE3m4WI5G8d39NUibDwN68ySi/+9eGNYZ+nhF2EIEAoFAIBAI9pG2/nwzt76sheNAGIwVNkTL16POi+Qyl51ytz03bW0EMxxre/OGirzojSZVeoKmuLaPIa5Hwvvgfcgd7eaEy0X0a3cLYW1BiGuBQCAQCASCfaQ/msr9ncpoo6w5PgZjqYLpIVuI1d7hc9nxOvOmg1iqdNIi5CPfAFU+R05Eq7rOnoEo6Ab2jlheXC8cn0fa/vprBeX2Yjd9FX3K2PWvjyeEuBYIBAKBQCDYB9KqRjiRjzSnVP2A9xmIDhPXJToplrkUvE5L5Do5WuQ6v8znslPmym/XHUqg9MQho2G3SbhrPePqvkg8jvuh+3OT6QveR+bc94y93XGGENcCgUAgEAgE+8DAMCGcPAiR6+H7jCYz6IZRJJILItfpUcS1VZQ77fhchWl2yt4osiRhkyVqxxm1dj/6C+S+PgCM8goS135+XNsdb4iERoFAIBAIBIJ9oH+Yx/pQ2EJ0wyCeUgtEss9lJ2051kiRa003iFuEt9epmO3NLdjbIllLiDQuv7Vt+zacT/4+N5343HUY5RVjbnc8IsS1QCAQCAQCwT7QHykUwgdqCzEMo0hcg2kNKfBcOxVScr5yx0iea+t8r1NBkiiwhTi2BnDsCGLPCu4x/daxGJ57vwW6Wc1EPeFE0uddMPaJHacIcS0QCAQCgUCwD/RHD27kOp5WSZcQ6OFEpqBaiM9lzzV8gZGrhRRssyXAUz9/gni1E+WEKgxFxvfXVgAUm0zjKU1UTPGPPDhVRb7zdmjdbU7b7cRv/PJxX8t6NIS4FggEAoFAINgHiiPXByauB6PpkvP7I0lU3RTdiizjVGRkKR+BjqZUDMMoamCTs5IYBvbn9pLRJdKDCSo29GI4bUhpc7xlTT6W3bJi5IEZBp6H7kdatSo3K/7FL6NPmLg/p3ncIBIaBQKBQCAQCMaJYRhFkesDTWgsZQkB6Azk26H7XAqSJBHeNkDlf2/F+/xedF0veexINqJtG0wiRUzhbpMlMAykoai2XebMu8/CUeYs2h5A6u3Fc/+9OJ75S25e8qqrSV9w4X6d4/GEiFwLBAKBQCAQjJNostjCcaCea2ulEKdiy0XCO4N5cV3mspMKp1h558s4OuPI7VEyzWXEUipuR6GcG4pc21sjORuJw6NANL+O9OGZ1M6qKRqL1NeH+7Ff4HjuWbBE5NPnnU/yE586oPM8XhDiWiAQCAQCgWCcDI9ag+m5LmXPGC/WyPWkai87esJAoej2ueys/9FqUqEUsiyBDq61fcRSKjVlhfsbSoK07w7nWpfP/dQStrX24NwaID21nBmnNReNw7ZjO77/vBkpECiYb6xYQfxLNwuf9TgR4logEAgEAoFgnAz3W4NZNk/TDRTbfopri4ieUuPLiWsr9t0h9jxnJhUO5TTaW0IMtIWYXFPYACaWUs0OjG0RZMWUes0nN5GRk2SmmeXzGircBdsob67Cd8/XIJl/eFDnLyB5xSfwnX82hBL7dW7HI0fEcz04OMhNN93EsmXLOOecc/jVr3414rodHR18+tOfZsmSJVx44YW8/PLLh2+gAoFAIBAIBBb6IsWRayj0XUeTGTLa+K0i1sj1lNoSnRLTGonHt+cmbba8fOt8tqVo9Wgyg9IVQ0pp2CQJd40b/+QK3HZbbp16i7i2v/g8vjtuyQlrw+cj+s3/IvrAw6jLThER633kiIjr66+/nj179vDII4/wrW99i1/96lc8+uijResZhsHnP/95/H4/v//977n00kv5whe+wN69e4/AqAUCgUAgEBzvDG8gM8SQT3prZ5D/+utG7n1m44h1qAu2y2i5knqyJNHk9yBRKGY9K7vQB83jOsoc1Pzr/Px4Xm5DSxcmNQ7G0thbI4CZyFh/YiOSJFFb7s4dZ0KVBwApFMTzwHdzNaz1ujoi9//AFNWC/eKwi+uNGzeyevVqvvvd77Jw4UKWL1/OV7/6VX72s58VrfvGG2+we/du7r77bmbMmMFnP/tZTjjhBH7/+9+X2LNAIBAIBALBocXqg1bkvIxKZcxI9fq9AQwMEmmVrV3F9o7hBGL5MnyVXgeKTcZrbVWuG7g2DDAUrF78uaXUnT0ZvdwBgBpJ0/5qW271ZEYjnEhjbw0jYbY3rzuxAYB/WTKRBRMruXTpZCrc5vau//01UtxMnNQbm4h8/8foU6buyyURDOOwi+u9e/dSUVHBtGnTcvPmzJlDX18f7e3tBeuuX7+eefPm4fPlX5EsXbqUdevWHbbxCgQCgUAgEAComl4ghusrXLm/hyLXcUtjl1hy7Mj1QCwfCa/ymmXxKtz5Wtb2tghSLIMsSbiqXEw+byo+t53kCWbLcs0w2PV03jLSG05AWsPeEUOxSUiSRP0Jprhu8nu47JSpLJlcBYDc043zz3/KbZv47OcwqqvHcSUEo3HYxXVNTQ3RaJRoNF8PpqOjA4DAsOzUvr4+6uoKW3JWV1fT3d196AcqEAgEAoFAYGEwlsLAtE/4PQ68zrwIHurSGE/nxXV0hA6KBfu0NJAZEtc+a6vyLaY2sskSE8+cjCRLeJ0KyUU1IEvousHApn663uwEoCecxN4eBU1HscmUT6nAVVWYvDiE61ePgGqOUZs7j8xpZ4x9EQRjctirhSxevJiGhga+/vWv8/Wvf51YLMYPfvADADKZwie8RCKB3W4vmOdwOIrWA/D5nChK3qhvs8n4/Z5DcAbHFuI6jR9xrcaPuFbjQ1yn8SOu1fgQ12n87M+1agslcsmETdU+vE4lN6047fj9HtK6kZunS9KYx0hoem795voy/H4P9ZVedvZGQNNxbQ8iSRJ2u41575+N3++hUTeQK5xk5lfj2BLAZpNZ98Aqpvz6Q0TSGs7WCJIk4bTbmHLapNJj2LkT20vPk/Ob3HQj/krvQbtWxzOHXVw7HA4eeughvvSlL3HyySfj8/n4yle+wvr16wvsHwBOp7Mgwg2QTqdxuVwMJxotLI3j93sIWoqvC0ojrtP4Eddq/IhrNT7EdRo/4lqND3Gdxs/+XKvWrhBatgpImcOGpum56cFgnGAwTjiWRsu2LB8IJcY8Rnt/NLcPpyQRDMaxY6BpOvaWEMQzplCudOFo9hEMxtFSGTRNJ3LOBJytYTRNJ9oX5/mvvcAuh4HzzR4Mw0AGyudUF41B6u/Hd/sduSYxmVOWE5s6B0YYq7iviqmtLRtx2RGpcz1//nyeffZZ+vv7KS8vp62tDVmWaWpqKlivvr6erVu3Fszr7++ntrb2cA5XIBAIBAKBgAFLybwan4tQIm/pSKoaGU1H1fMl+KJjVAsxDINuS/3o+nIzeFiWTTZ0Zi0hssUSAuDJdmQ0vHZCF02h8e/tgETXqk6SwTgYpnWlYlJFLplxCNuO7Xi/dhtyf785Q4Lkp/9t3NdAMDaH3XMdCoW44oorGBgYoKamBofDwQsvvFCUuAimhWTr1q3E4/mnpdWrV7NkyZLDPWyBQCAQCATHOaF4Xkz7vQ6cSmG1EKvfGsiV2BuJcDJDIruNU7HhRWLnU9vIrOlBSqo4tgcBsEkSzWdPzm2n2ORczer0tHImXzILAF030LLCWmsu4z33X4DNYQNdx7ZpI+6f/YiyL16XF9ayRPwLX0abNmN/LodgBA575LqiooJkMsm3v/1tbrjhBjZt2sQPf/hD7rvvPsBsMON0OvF6vSxbtoympiZuvfVWbrjhBl588UXWr1/PN7/5zcM9bIFAIBAIBO8i4imV37zRgm4YfPyUaZS57WNvNAahRD4S7Xc7CFgi2amMVlApBExxPVpb9B5r1LrCzcZH1rHr6R1kNJ2qWAqydhGlykXVnMIqHh6nQiKbRDnp8nmENvXRs20AgORJdZRfOhNvjQdlzdt47v1WXlBnMXw+Yrd/HXXpyft6GQRjcESayHzve9+jp6eHSy65hAceeIC7776b8847D4CPfOQj/OIXvwDAZrPxwx/+kMHBQT70oQ/x1FNP8YMf/ICJEyceiWELBAKBQCB4l7CubZDW/ihtAzHW7Bk44P0ZhlEQua7w2HFaCimkVI3EsGYuumEUdG4cToElpMxF+ytmvWqbLOWENUDZyY1FAt1aqSRhGJzxrXPxv3864Y/NJHb+JOqqvEh9fXjvuqNIWOsTm4l8/0dCWB8ijojnesqUKTz22GMll73wwgsF05MnT+bXv/714RiWQCAQCASCY4SwxQ8dToxdb3osEhkt19Lcoci47DacdostRC22hYAZvXY7Sssta+TaH8rQEzIj4bIEEpJZ9k+SqD69OKjoc+b3GU2quJrLMM6dRKalD4C6Miee79+XaxBj+HxkTjuDzOlnkll6MtgPPJIvKM0REdcCgUAgEAgEh5KEJWI8WvR4vBRErd0OJEnCZY1cZ4oj12CK65oRCkt0BfPi2rYrmPt70rlTaa+1kXynD7XBQ/XM4sYuXou4HvJ294bz+5u96S3sb7yeX+eub6IuEjlrhwMhrgUCgUAgEBxzJNOHUFx7stU87IW2kJEi16VQNZ3+SN6zndyct67Un9RIuUNlsNI8TpmrOMpsbZEeS2cwDIOesNnt0R2PMPWPP82P7eIPCmF9GBHiWiAQCAQCwTFH8iBHroPxwmRGoMBznSyR0Agji+u+SDLX7bFKUQhuzfuiG5Y2siyWYO9gjBqfi6m1+WpqUiiI869PMyuYZlfaR3d9M7GkSjSlkkymmbt1NWf/88849AggodfWkrjmswd07oJ9Q4hrgUAgEAgExxwJSxQ5dTAi1xYPd7nHTqQ9zFt3v0J5KEb4g9NKluKDkWtdW5MZq/uS6Gq2rfp0P64qNwur3MxsqMChyMjZZEYpHML35Rux7WllRlrlqlgKQ5KQKyvx1tXw790B/ME+7IoMZWbL8/iNXwFv6c6LgkODENcCgUAgEAiOOaye68RBtoX4PQ62/M9Goi1B7ME47rd7SZ0xgURaQ0qoeF9sRyt3kFjROGLkutvit3bvieT+rl/amPvbZbGdEI/j+89bsO1pBbIVRQDJMLCHQ9gSUfzZBwC7LGN4vSSuuRb1lOUHfO6CfUOIa4FAIBAIjhO2dAbZ2RPh1Bm11JS5jvRwDilWK8jBjlxXuB1s2dDDUHU81zsDBE5tIJrK4PvbHhzbzM6KhlshNqmq5P6skWtjR5ChQntWcZ0jncZ31+3Ytm4xpyVIrDiDvjWbqenvQjcMMtnOkGm7k+AlH0a67jMYZeUHdtKC/WJUcf3888/v946H6lYLBAKBQHAs0hNKsLp1gLlNFUytHaEcxFFENJnh8VWtqLpOfyTJp86cediOvXbPAP2RFCtm1hVUuThUDK8vnVI1dMPI2Sv2h5DFc+2Ipon3xgHJ3OdgEqU9Siim4dyer/rhXtlN9LypJffXk63sIQdTaL1xFJuMzWGjZkFd0bqeHzyAsmZ1bjp+/ReJvvf9/Pwv72BTM5SlEpSlY+ixGAPVjVx2/mIhrI8go97h1113HZIkYWRbaY4XWZbZvHnzAQ1MIBAIBIKjmSfebKUnnGB1az9fuXDBiLWMx6IzEGdje4BFzVU0+N0HeZR5esJJ1Gx0s8dSsu1Qs6c/yh/f3gNARtO5aPGhbwSXVvWieamMNupnpGo6b+zqw2GTOXlaTUHTFt0wCmplp3fmBbQkAQa4Ngxg2GWwaCY5mib+z3Y4b3bBsaLJTM4u4t4TQbGZx6pdXGe2K7dgf+UlHH/7a246+YlPkb7kUtyGgYSEptgJKnaC3nKoBK9DoblKeKyPJGN+Ezz44IPMmTNn3DvcvHkzN9100wENSiAQCASCo5l4Ws0J1LSqs6s3woKJlfu8H1XTeey1ncRSKps6gnzxvfNGbJV9oAQtnuFYSkXXDWT50BzLypbOUO7v3sMk6hMlEguTY4jrtXsGefadDsBsLW79PCPJTK6yh9epMLixL7dMliQ0DJxbAgXCegjjpb1ot6rYLBF7qyWkYlcYsqaQ4ZYQqbcXz/335qYzZ59L8qqrc8f1OG0Fnm6HInPladMLSgQKDj+jiuuZM2cyZcoUJk2aNO4dJhIJZsyYccADEwgExyaBWIqN7UHmNlUc855PwbGLtbMewI7u8H6J65beSE4cDcZSpFS9MIntIBKMpQqmYymVMveh79LX0hvO/X0wSuKNh1LHGevYVsE7/GFpeDJj34bW3LTktEFcB8v+jToPRlxFjqYxIhl2Pr2D2R+ZW3QspTOG0hoBjwNJhgmnNecHpOt4v/MNpGjUnKyvJ3bjl8Hy8OV12nP3jyLL/OtpM0TU+ihAHm3h008/zaxZs0ZcnkqliubNnj2bp59++sBHJhAIjkl+t2o3f9/YwWOv7ULfR8uZQHC00BNKFkxv7w7v1/28qTNYMG1t2X2wCcQL9x0ZoUTcwSSWUumyiNZSHQwPBaWOM5a4ti4f/vBkFde+DEQ7zOoesl3Gce7k4p2dOQHtjCYADAw2/2YjajIfYR4S1+6VXdizlpDms6fgbfAhBQZxPvkHyq6/FmXDenMDWSJ26x3g8xUcZkad6fVXZJmrVkxjSk3hcsGRYVRxDRAIBLjzzjv5zW9+UzA/lUpxxhlncPvttxPNPlUJjg36I0lWtw6UfK0mEBwIqqbTEYgDZgT7cEWxBIKDTXcoXjAdTWUKSquNB103CiwTQIGv92ATjBWK62jy0Ivr3X2RgukjGbkeq2KIdXlPOFHwsGRtIOPujOX+rp5bg+e0JrDYa3SfA+cJ9TiWN6GXmc1mkoEk2x7P56L1hhLYeuM4dgRRbKYUm3P5fOwvv0jFlR/D/fD3se3Ynj+fKz6BtmBh0ZjPX9DElSum88X3zWN6vUhgPFoYVVwHg0GuuuoqnnrqKXS9MDkgmUzyvve9j7/+9a9cfvnlQmAfI6iazs9f3s6Tq/fw1Jq9R3o4gmOMyLAf80yJpCOB4N1Ad6hYSG/vDpVYc2Ra+6NFQYzQCOI6GE+zunWA2AFEmwPDbCHR5MEPoHQE4qxvG0TXTWG6q7dQXCfS2j4XSdgfStW1TmZG/76xCvK0qhc8jFgj13Jr/nOuWVCHu9JNeoY/v5+T6vB6HHi9DuKnmx5qzTDY+ttNxLqj6IZBXySFZ2U3AHabzITTm6l0xvF+99uQsXzGio3UxR/M+ayHo9hk5jRWUJHtGCk4OhhVXP/85z8nEonw1FNPceWVVxYsq6io4O677+bxxx+nr6+Pn/3sZ4d0oILDw0A0lfNv7R0QD0yCg8twcV0qo18gONrRDYPecLJo/vbucIm1R2ZzR7BoXilbiKYbPJINevzy1Z1o+r6LU1XTCQ/7/3ewI9eBWIqfvLCN37/VylNr2wDTU27FwDgs/++TIyQ0jrqNWrjc+gBlrXGttuTFde2iOpx2G7ELmslMqyC1uIbEyXW4HTa8ToXUohrUBi+6bqBndNb/eDWD0RRGXwzH1gA22SzlN/fj8/B899uQNO8rvb6e+I1fJvT4kyS+cBPYRILiu4lRxfVzzz3Htddey+TJJfxEWWbOnMk111zDc889d9AHJzj8WJ/205oQPoKDS5G4FveY4F3IYDRFJnvvOhUbUrbSw97B2Ijd+IajGwZbOkuJ62LB2xmM5yp99IQSrNrVV7TOWJSKiEfHOdbxsqc/lquosaZ1gI3tAQZjxblZh8MaUtIWoo7fFgKFvuuhyLWUUEl3mIEnSYaquTU47TJ6mYPwZTOJXjQFFBmPQzHrecsSsfObcxaTjtfaefuht6h4bBsYBoos07CsiYZNLxb6q++4m/T7LxG1qt+ljCquu7u7mTlz7CLzCxYsoKOj46ANSnDksD7ti6ii4GATSRT+mKfH+LETCI5GrDWiJ1Z5mFjlyU3v7CmMXkcSGXZ0h4vEXkcgnosk21tC+P68G6UtQqSECG7tK3yL+OKWrnGL+CGGVwqB4ofdsQjF0+zqjYyYuDncdvKHt/aUXO9gtCIfi1IJjWMlUw4X14WR6+xn1R4lm39I5cxq7B47TqU4quxxKnhdZkE2daIP9yn5Enudf9uFlLXk2O02FpxXjfuRn+aWJy+7Em32+EsgC44+Ri3F5/f76esb+wk5EAhQVnb0d6cSjI31S083DFRNzyVbCAQHivBcC44FrKKrocKN26Gwd9BMclu7ZwDFJhFLmnWrd/dFMTCoKXNx7Tmzc2X2hiwh9l0hqv/YQkbVcOwIEppeXM5vT3+huE5mNJ7f3MklJ4y/TG4wXmw32RdbSG84wU9f3E5K1ThhcjWXLp1UVI97eJRa1Uv//z4skesSD+6jJTQahlHkyR76nNOqTjwbeHK0RbBlkxdrFpmdFEvVlPY4bED++jj+ZRpsC6Im1NxbD93vZOEHa5nyozsgbX4+2rRpJP/1k+M8S8HRyqiqadmyZTzxxBNj7uQPf/gD8+bNO2iDEhw5hn/pidf2goOJsIUIjgWsVUEaKjzMbMi/ut/VG+G3b+zm6XV7aemL5GwSWLG1NQAAIABJREFU/ZEkT681k8SjyQwb2gZRumKU/6kFb7axiZTWSL5YmEiuGwZ7SuS/vN0ysE/VSYaX4YPx20JUTefxN1tztoq1ewZY3xYoWq+UgAdAN6hcP4D3721ICbWkH/pgUzJyPYq4Tqt67rMaYjCWIpXR8j54w8CzM9/wpW5JAwAupVBKVfd3sfjBb3DC9+/h9H/+hSmtW1BJc+IXTkaySag2ifjZE2haEeXsp+5FCmbtQXY78Zv/A+yHvva44NAyauT66quv5rLLLuO2227j5ptvprKy8Ik6HA7zne98h5UrV/Lzn//8kA5UcHhIDvtCyqg6iCRkwUFCJDQKjgWskev6ChcNFW78HsfI4jLLhr2DTKj0sKZ1gFh7mIondiKrOh6vjVBcwsBAXtVNPJjAk22D3htK5oIeXqdCQ4WbXT1hDE3n7xs7+MTp42vaFojtf+T62Xc6iuo+P72ujeZqD9W+fCOowWg+cm23ybkIbfmGAXzPtxNPq0hpjcQZ08d13AMhmSkW8NbIdTSZQbGc/0h+7N5IknR2O1tPHCWchjIXdq+duhPqgcLIdeVgL1f87/eoklJIEpwRMRMUbX90UnXWCib86zJeWrmVWRv/RE1/Jw6/ByQJo7yC6Ne/gTZ9bCuu4OhnVHE9f/587rnnHu68806eeeYZFi5cSFNTE7qu09XVxTvvvIOu69x2222cdtpph2vMgkPI8Cd7IX4EBxMhrgUHSiiRxutQjphdLZnRciJaliRqy1xIksTly6fx+s5eUtl7WpYkJlV7aQyk+eeTW+gYjGHYZV55bi/23WH8/QkkJPxeB85yJ5LNwBhIIKU1Nv5uM8uuXQqY5foAbN0x6ndFqU3oBNZ3I6U1Olc0op46bVzXIhgvnVg4lvVvW1eINywJlE7FRkrVSKs6T7zZymfOmoVikwuqkUhIXLR4Ik+taUMOp/H/sytnIXHsCBE/BCUAhzNaE5mOwRg/eXE7drvMZ86cSaPfM6JVpTuUQM6O3bktmLOENC2fgE1Xcf33fzMhkqTeqCfuKePy396PLxZCzt4XQ0hqBvtbb+J/4w1WZD37NllGkiS0yVOIfePb6A2NxQMQvCsZVVwDXHrppSxatIjHHnuMlStXsmHDBmw2G42NjXzsYx/jyiuvZOrUqYdjrILDQJEtRCScCQ4iw5O10pq4vwTjZ/Xufp5c00a528EXzp9b0us6GqF4GrdDwaHsvzDvtSQz1pS5csK0qdLDh0+eklsWbAnwziPrePvNTpyGgS+SRB1mg/J7zVrIK+46i67ntpP8H7PJSMuT21hyxQIcZU72DESx7whS/qcWcCrEnXbsqoEKuFZ2sf2fbcw7awpjYa3ZLCHlLBCRZIZKr7PkNhlN509v55MS5zb5OXtOAz95cRu6YdARiPPajl7OmtNQELUvd9tZOqUaTdfZfu8qHLKNqGSeu5RUCe8YhNn1Y475QBit/fmGvQEMDDTdYHNHkEa/h9QINbB7ggnzPjMMnNnSeQATzpiE5/57cTz/D+o0nU+HE2iyDZtuHkNyOYle/RlWv7CaSXt3UN/fCZCL5gPITgfp899L/LobwStalh9LjCmuAaZPn85dd911qMciOAoY3tBAeGIFBwtVyycFDSESGgX7wtutA4BZC3pnb5j5E4qT/0bi5a3dPLepE7tNZnZjBYuaK5lZX77PEfDhyYzDSUdSbPjZWnb/3y6GLLySJFHpcdIfSeZEbVWFmyknNzHvyoXULKyjPBwh9uxubP0J0vEM63+8hgXXLGHvP/dS/sddoBu5qhSKTWLov87677/FzJMnYPeM7NMdHlWuK3flKp5EU+qI4rojECeW/T/rdSp88MRJeJwK5y9o4tl3zAph77QHOGtOQ0EyY6XXgSRJTOhI0rYrDNlazkOEN/TC++eOfJEPAqOJa2u976Hzs64vS1KuIsrO3jCRZAZbfxJ5MInidaK4FCY4+3A8/4/s+uZ2Q8Jak22E77gHTj2Vf9jnYGBQEeznVn+I7pdeY2dUZ8fMxTRecDbvOenQW2QEh58xxXUkEhGVQI4jiiPXQvwIDg6lkqfE/SXYFwYsnt7B6Oj+ZiuabvDa9h7AjBxubA+wsT3AhEoPnz1ndoHwG4vCZMa8uDYMg7bnd7P+x2tIhSwWDAkmnzcV/4xK2nsi7O6JMHtpE8vPn4HizgviCq+DXac1UvZUC5pu0Pr3FnY9uwslnADdQJYk/BPKOOGGZawNRth292tISZV4T5RNv1rPks+fNOKYrQ1Qytx2yt32vLgexXdtTTycWOnF4zQlw0lTa3Liui9sRuStkfEqr5Pe9T2seeit3DxXo49QyyAAsY1jVyHTDTOybB/l4UfXDXTDKHpAUjW9IEKcO58hcW25HkP2EetvX5PfQ3vArP4ydM+5twZQbDJuu42Gkxso++n38+OY0EwquRdnKoEu2/jzBz/LFaeeiiRJeJw2YimVkL+GwYvO5h9NS9mcrW++oDbf1VFwbDGiuF63bh233HILbrebUCjEV7/6VS666KLDOTbBEaDYcy1e2wsODqVq6oo3I4LxEk+pBW/WSjUnGYnWvkjJShEdgTjdoQRNfk+JrUozPJkRQEuprPr2Sjr+WVjpo2FZE4uuWULFNDPCPmuU/Za7HaTnVJJZV4bWbSbBpdIaZLsxOuu9nH3f+XjqvAy0B1hzfjO+p3eT0XR2PLmNiWdNpmZ+bcl9W5MZ/Ro490TAYYAsjdoC3XrNXI68Bcdlt1HldTIYS6EbBj3hJIPZY0ixDJnfbuXl9f359atcTLj1FHo++zcwDDJ7wqQjKRxlpSPm0WSGH7+wjURG5aoV05laWxzgC8bT/OSFbaQ1jU+dMZOJVXlbRU4o6wblmwZJZ3SSS2pQ0c0ovsWeFs8+9FsTGuvKXQxEk4Xnvz1IlceJJElMtbVh273bXOB0Erv3fh58qZWmrlai3grSjU05v7XXac/VJI+l1ILOnnXlxW8+BMcGIz4S3nzzzXzyk5/kySef5L777uOWW24hFAqNtLrgGGF4tZC0tu9tdgWCUpRqjlEquiQQlKIvXFitwlqZYiw2WTohLphYSW1ZvsJFfB+asZhCsjBynQqnePnm5wuEtbvGzYq7zuSMb56TE9ZjUe62gywR/thMHB+dRe2iupzg06tczLx9BZ46b/6486vITKsw/w8ZsO6Hb4+476EyfFJSRfrxBqI/Wkf573eCbowaubYmBbqG+dsbLQ8kncE4gVjKTF78xWYSb3XnliluhWVfPZVJe9bjV4LIuo6uGfSs7mYkVrcOEEqkSas6b7b0l1znzZY+oqkMaVXnn9t7C5YNiWvn5kHcz+yh7O9tuN7uzS0rENclbCFOu416y1sJeTBJZUTFrsjYZJ1pr/82f42uuhrq65GdTvY2zyRQVYfHkY9b+pz5vweiqYK3L9b7UHBsMaK4jkajVFdXA1BdXU0mkyGVGv+XmeDdiUhoFBwqSkauhS1EME76LBE/GH/kWjcMtnTkA0PLptUUCKd96XQ4GE3l7lmvU0EOpnjxxmcZ2JwXgNMvnsl7H7mYCSuax71fyIprAEUmubiGs+87n+j1i4l8eAaBT81lxsya7IBj1K99g9Pe/DtVc5PomPaIwPZB4j2xkvsesmw4twSQQilssoR9Vwj3mz1EUqPYQiy/B+5h4rrB50QOm/vtyopr73N7kaMZlKwJufnsyVz4rWVM/813mPz9b7E48DZVgz2gqXS/3Tnicbd15T+vvkiy5Drbu/OdMHf2hNH0fCBoKOLs2BZAlk0PtfutHtANBqKpgg6T8ewDhLVMn8tuY0Kl+fAgB1NMeL4Dn0sBQ2dicheOlFnBRZ8wkdSHPmoey5IkaxXXfk++lu2Tq9tynvtKr/OAEmsFRzcj2kK+9rWvcc899/Dss8+ydu1arr/+eurq6g7n2ASHGd0wimp9CvEjOFiES0SuxcObYLz0DxNZoXhmXB1k2wdjRLMC0utQmFzjY2N7PpI9PMl2NLqC8dzfDRVuVn3zNSLtEXOGBEv+fSkzP7R/bavLLf7rcCJDIJYi4JRglh87BlPffhX3P55F2bgeNJ3zIgkyqs7K4DIGI5ORamvoerODptnF1pChMnzO9f0ossyQxdzzcgehk5pghE6PyUzpyHX/O7103/4qlR0hkkvr6PyYh/DaHlzbzMYyiiyz4sZFTIu9g/vWeyCZRJMk6hz9bI+mqejromdlBYaxvKjLYzyl0j6Yv84DEVMMW33xoUS6oO52StVoG4jm7COJtAq6gX1PBAkJWTKQw2kc24P0npS9jzQdZClnNRp+ridNqqL9/1rQntlNhc0GEsjd3czyvZPr/RC//kZwOHLbDD2oeSzR6uUzanmnPUBG04usJ4JjlxHF9fve9z5WrFhBS0sLt9xyixDWxwGlsquFuBYcLEq9fhb3l2C8DLeFGBgE42lqxni1vskipOc2+ZGzSWZD7IstpMuSzFgb0RjcZlYvkRWJU247nYlnjr8duRXb5k00vvISC7sMWqfMISJVs60rjJJJMXPHBi5862+UJQo7ItptMhlVp9HeTTBahZyM0/XXDfCvS4r2H4ilsfUlcHREcCdDyIkEhs1BwuUl+st3SF80t6T/2epxd2WjsW0vtPLWva8jZSO+rtW9hJMajvYokgGeZJQ55XuY/+DPCvYlyTYqHBHsUgZUSG/YTuypl/FdcibI+Qeknb3hgk6Jqq4TiKWp9uXHZ41aD7GjO5wT16mMhtIdR0ppKDLZSLWEe3Uvfe9PouyNUPZkCxISwatmoWp67vdPHkwS+ON2Xnq7h4pgEuwKYGDr7+NExwYm+8y3FIlrPot60rLc8YcquQC4Lf70Rr+HK0+dxq9XthS0g68XfutjmlGrhTz22GN89KMf3Sdh3dPTwxNPPMH1119/wIMTHF5KFd0XnljBwaKULUTcX4Lx0hsqtgcMxlKjimvDMHKVGQDmTqgAyLUbh3wptvHQaYlc2zbkK15MPGvy/glrXcf1m1/jeuwXoBtcEoyjGwbh8ip8eoZFUTMqXuFxgDMf2dZmzGSgspHkW6upV/vYHMW0PPxjA5m1G2HqtILDBOJpql5voXqgGwWNOleInrgbVzIGoX7+euJ3qPar1E+0Me2Oj2A/cT5QmNDottvY+ttNvPPIOkinsUUj+KMJ0oodZX0GRVUpiwTwGglOqS/0f+uTJhP56m08+YfXaP7VG3Qn6iGTYeCeR2j68yOkLv4A6qw56JMmsbulkym7N9PYtQddltky9yT6IskCcb2jhLje3h3mgoUTzHGnNeytYdzJOP7QAGAQdXmJtOp0vtqG//HtuEJRDFkm9UYPsStVUqqOvTVM+e92MOB24EZHSqWQ4jH82gBnVm2g1mV+HqmLP0jqsisKjm+tuW61hQBMry/n8uVT+Z/XW3KWlIYKEbk+lhlVXD/88MOceeaZ1NePv9h7d3c3Dz/8sBDX70JKRa5HagkrEOwrwnMt2F8MwyiyhcDY5fi6QolccxOnYmNaNrJpfW0fT43vO84wjHzkWjeIvdmVWzb5vH1vpCYNDOD9zjdQ1q7JzZNlCV0zKA8PFqzrUmwYHg+pj1xG6qKLMaqrCfdF+MUrO2jsamXyAytJZTxoKvRceye17z8RJAlV1+mNJDl1T4D+l2QymgI2mcVVbXQrGmtCi8DQMdIZ+nsl+ns1wtc8wrJ//AdGTU1BgntmfSc77n0eWzgM6TQVjjgT5UH2hAq7Ci6q2o7LpoIsoS46gfSZZ5N+74XgcLBnYZrkOQa+v5mNadYPTmLSzjVU/vChoavMe4MJsxRhfBIp3cHpL/wZfcMKlKsuQ5s0hYy/ko49Xcxo3U7z3p0400lCFVUEK2qITbLjnT2DREajamMbFcF+kMAhZ/AkorhSCTIPDOBLJpCy0XHPqhjx+AWkMhqVr+6mor8Hj5ZGBjxKitkVXSys3Ysim99VmVNPI3H9jTDMzuIcwXM9xKzGCi5fPpW/rm+nyutkbpMow3csM6q4NgyDm2++Gbd7/K8vEonE2CsJjkqSJSI4QvwIDhalqoWI+0swHuJprajBFeSTGjsGY/xlfTvNVV4uXDQh5+Pd3JGPWs9urMj5s63ix+q53tEd5rlNncxtquDsuYWiMZzM5Nb1tkXRI2lAwlXpou7EhnGfi9TXh+vx3+B85mlI5x8OtLnz6AumKdu+GUU1/6/oso1kVQ2Zj32Q1Ecvwygrz60/lJTZ1TgFzojR8NIu0HXaetxMfPpJIok00aSKE4OJyVq6tBORkPA4VCpvvZq3XljHjJW76emrJGmUo+k6hgEdg26cX/8ayfsfJJnRsKdTnPj2iww8E0COm8dscAc5r2kTqVQcm5GiJT4FgCp7gEm1YRLXfJb0+y7C8BdWSnHZbew5Zwm12yUaB8MQCfF85wIunrQGp00lrepkdIm1wcV0pcyg3mCmknNXrcS3yYyGpzSd68IJoqqHtaElxHQHNY4dzHO9SvnfH8O9fBlVU06gaks7phlEYmnVVl7rW4CsaziT8YIxOQMxjP/6ETX1jXjWmM16JFnmnKYtTPb15RrEGGVlpM89n8Rnri2wsQxhfWDzuUpLqzlNfmY3VhT5zAXHHqOK64svvni/drpw4cL92k5wZClVB1Z00BMcDDTdKPn6XbQ/F4yHgWjpihFD4vqv69tpH4zRPhhjyeSqXN3qlt5Ibt15WUsIgEMzcK3uRW3yErd4X/++sYPuUILOYJx5E/wFdYi7AnlRVrkjBJgCadK5U5DH0eXRtm0rzif/gOOl58H6RlCC5OX/SvITn+Ktde2s29lNZaCPuMdHwu1j+awGahZPLNqfx6FQ7rITTmboWzKZqu1JHJ0d7I1VoWp67k2RZsjsiWctKw47E685E/UDF7DWMZPgGSlcyRjvq/Oy5mv/pLy9j7QmMbi2g5r77+Wk9hiz1rxKf6CCjXHTy61IGmdN2oV82gr6qxqxv7aaedt2EEt7SCyvZ811P2HZiTNKXgOX3QYOG4Mfn0vdU3sgXUMwHOZFpZpTm1rI7N7Bm8GFdGmNaF4nUibDQLqKNYFZvMfXCkAypRJT3bw+uIy04UIC2hITaEtM4J1wmvexhboXtrFZPwEAn0cl/LmP4Ll3HfFE3orhdqVIJE2rSfiZ9cw2/sla3bTD1LgiTGnOoE09kfTsOWROWYE2b35JUT3ESVNr2NYVwue0M2+UqLQQ1scHo4rre++993CNQ3AUIGwhgkOFNZnR7VByUciMamAYhvjBEYyKtTaw3+PIWT0GoymSGa2gukQwls6Ja2tX0KEEMi2tsfHOV/Gu7cRw2IjfYK5rGEbBcXb1RgrEdeeQJSStoWwNZBPdYPL5hf7m4Sir3sD92C+wbd9WtEybOYvEv/076glLAbORjKbY6a9tyq0zu6G8aLvcOVW4CSczqE1etAovmn0KsWScTRfOZdW2flJ7DTLdEo4yGw6HHamsjMlXLQegzGUnGE+TdPt4PmNHXzwZKZShPBKgI1ZJ0z+eZXEwjqpLbI2cbD5L2BVmfnA2mW/cQcblQoql+EPTcjCMnE3iyoaaEcfrzr4x0GrdTLv2BPY8vAbDX8leo5K9Hc30pU4hU6lhABVuB6FEGruaIREvo8lTzVzHLgYHA6wMLCNiK0fx+ZDtCrFowoz2pyWe65hPld1MOjQkGeX9Swmeczobu2uo/d9NSBjEzp5AeGINzb96G1cyxt54DQpmDXHNplD97xcR+tIFo36uw2mu8nLzRQuRJCGgBeNofy44fhAJjYJDhdVv7XfbSasamm5gYLY4Vmzix0gwMlbRO7OhnLeyjUUCsTQtvZGC6hJW+4i1EsiQsHvnkXVEd5t2ESmtoa3rxfi4QUotbJm9qzfCqTPyyfxDZfic2wIo2cOVT6mgYlrpKKU0OIDnhw9hf/nFomXq/AUkr/gE6snLCry71nJ8YFYEmVzjK7l/gAa/mx09YZAlbHOrYUM/uCp5+ZkMgxHTX+72KJRlkwHrltRTNtEU6z5X/liRZAbHlHKiq8uxZ9J0JfxgmA++rfHJRKUyHHW1OJprmXHXpZDd1u9x4LbbCt56VvlKd12EwnJ+npMamH3ZPLb9bjNg1uoeKs0pIXHCp5bw/Mu7YNMAwfIqXolO4FVlKd1yDGoMkGQm1Pk48fqT+cs/tqO92YUST4GaIBJyoEsSQX8NU86eh91uIz6/no7rfGAYqBN8kNYIVtfh7+8hlJbQDQ9ph4uAv5bmK04d8RxGQ5bF95jARIhrQQ5Rik9wqLDWuC5zm5HHhJ5t3qCOXatYcGRIZTT+sbGzoOX3xCoP75nfdFg/M6u4nlDpZaM9QCKjoeo669sKk/+GmoLoemHdfrfDRtebnez441YkSUKSJAzDwL5pkJSqF5WKbO2LoutGTjB1BRNgGLjWD2DPnvvk90wrjlIaBo7/ewb3T3+IFI3m5ysK6bPPJfWBD6HNmVvyPIeL6+l1ZaNe5wZLM5zEtHJ8G8yHDs3yvW2TJbwNXiadM4WZH87X4B7uC85M8oEkEayopi8mkcDJlqmzeatlEWmfi0a/h3lXL8buzY9RkiQa/R5a+vL2G2vTlOFYG9EkMhonfXoJrkoX7a+00bHDLGuILOH+4EyWfHoJbzQ5iD20BltPHFUzyKQzmFX1JFwuhdPvPouGk5qY1uRkzQQP5b/bQcTjJu2pIKNqGLJM/eJ64tnDqk35Fuk4bGSmVRBUdSQ1QVLVSbi8qM1llNeP/EAjEIwHIa4FORIZkdAoODRYhUuZS8Fuk3PRrrSq4XWKr6Kjkbd397Oqpa9g3p6BKA0VHpZMrjps47CK62qfkyqfk46sB3qrpZsf5BMUrYmKbruNdDDJW/e+npsnSxKaYaB0x+hrCZCpLIy4plSNjkCc5mov8ZRKKJHGuWkQ+94oit+NJJt+ayty+148938XZcO6gvnp91xA4rOfw6gc/ZqVuwuF6ayGihHWNLGK64FGN5UeBT2lo+kGus9Oem4VCz4wh/PeO6voIcDnLBTyhktBbfCgdMVIN01k7c0/4u/PbMW9uxNFkvA1+Zh6UbGXutHvzolrr1PBodiK1hnCGrlOpjUkWWLWh+cy80NzePj5rQx2m/s5ZfkUAOpqvKz/yAzKn9iJGlVzbyUMp41ZXzmFhpOastepnFVTy4mfNQHl1U4wwJAk1EYPZZVu9HjpqjLp2X6UHQHiHl8uuKQvqhG2DsEBI37RBDmSJWwhooOe4GAQtoprtz37A2zOE9ajo5fecOlEwpFaUo+HWErl5a3d+D0OTp1RO6aQMb3Q+eNV+5xUefPiOtfKOuv7LSmuHQprH3qLVNDcj6vSha3agbbTbMzS9mIrvouKvdMtfRGaq710BeNIsQze5/ZiV8yo9/RLZuOpNf3a6DrO3/8O968egUz+XtcbGoh/8SuoS08e17UZHrmeNYrfGqDKm38gCBk6p33rXKJbBng1FiPgBmSJ+vmlr3GZy140LzOlHKUrhm4YdP5zL663esDcDfM+sQibvVg4N2b97WC29B4Na3MV65vStoGY2XHRbT54L5lkPoTUlrnQyx0Er5lHZbWPrp4w6AY2h41TL5qV235qbRmKLJNYXo/SFcO5zbT9pKdX4LLbSJUYN0Bmpj9bsjB7D0kStoUje8YFgvGyX+/1dF1ncHAQTWT6H1OUsoVkNKPEmgLBvhEpiFzbsVtqwoqKNEcv1s9tUnX+lfq+tAwfzqvbenh9Zy9/29BeUM1jJGIpNfcGzaHI+JxKka/Xub6fqoc2UPaHncQT5tisOSTuQJr2V/fmppfdugLnaRNy012vtBFOpHG+00/lTzZS9oed2PeE2dljNivpDCbwPt+OlFCx22x46jws+NRiwKxX7bvtK7h/9uO8sJYlUh/7OOGfPTpuYQ1mBZChWtxzm/xm85hRUGwyFZZot9RcxgnXnEh8opehGnLDo+FDlCoXl5liHls3DLpf2YuUrQEu13qYdM6UkvuZ2VCee/O0uLmy5DpDuOz5Y1rflL5peTuyqLky54+vtXSN3D0QBUUGh43ZTf7cOmB606fVlYEkEXn/FOLL60meXE9ieQMuuw2Xo1hcl7vsGF67mQya/Z3LTPLhqhTNXQQHzj5Frjds2MCDDz7I22+/TSaT4YknnuDRRx+lubmZG2644VCNUXAQaRuIsrUrxImTq4s6m5UqxafqeoHv8FhANwze3t1PWtVZPr1W+H0PA9Ya1+Uue0HDhbSIXB+1WN84TK720TYQA/atZfhwOgKx3N+tA1Gm148enS2whHidSJJUELF1re7F+/c2ABzbg8TW98Dp0wseAGyvdeT+blo+gfoTGymLx4j8rw0yGrGOCO2PbsT391ZzP4NJHNuDhJ5v541V/ezcM4hzk+kJttskTrzxFOweO8q6NXi/cRdSKF9PW5sxk/iXb0GbMXM/rg5cffoMesKJgkolo1HpNatqgJngCRCK5z+3kQS6NaFRQmJqrY8WVQdFRtdBkvKBFc97pyCN8BvgcSjc9N75hBMZastHF6ZWW8jQw080mWGTpR75smm1ub9rR7gGpSxJsxrK2d4dAoeN+Dlm6UJFllFscsFxh6ivcNPSHyW1oBqlw/TGpxbVUDVClFsg2BfGrSrefPNNrrzyStLpNNdddx1G9lXc1KlT+dGPfsSjjz56yAYpODioms6vV7bw6rYefv9Wa9HyUpFrOPbEz/buME+v3cuz73SwunXgSA/nuKAocm15oEmJyDXv7B3k5a3dI/4fPFJELQ9F9RZ/74FErq1iuadES/Ph9FvWr9Qk3vru6wz89yac6/pwr+zKCeshUhv7s2PMRl1DKYx1+cjo7I+btYy9PiepmaanWdcNIi8V7gdA7omz5entRCzdGCecPZnGZU3Ydu3Ad8dteWEtQfLjVxB56Mf7LazBrDjR6PdgG2dAw/qgEYilUDXw9s0dAAAgAElEQVSdaKrw/1spGivcuWVLp1ab/m1FJjPRh2YYDDkl9CoXvlOaSu5jCKfdNqawhkJbSCp7r69pHUDLHmxilZemSqvNxIEyrLa0225jZokHslIWmqHjOUv4wBv85v2cXFJD9KIpRC+eSmp+VUEbc4Fgfxl35Pree+/lggsu4L777kNVVb73ve8B8LnPfY5UKsVvf/tbrr766nHtKxQKcc899/DKK6/gdDr5wAc+wE033YTNVnxT33HHHTz++OMF82677TY++clPjnfogiyxVD4hpCMQL/oht5awkpBy5a1Sqlbyyf/dSo+l8oG1CoLg0GEV1z63HUeBLeToEpSHm9WtAzy52mwHHU+pXFiiYciRQNX0XOMfCYk6i3iK7WfkOmNpbgLj+/83aBHXxp920rorhKYb+EL52tbW7ytj6yCGbuSi6+43e5CzQrF2UR01883IqMepkJpXhXPzILph5ASe2uTFOakc7e0eUPVcVBhALney4sZTzNblt98KSfPhwKisJHbr7agnnrQ/l+WAqPTmI9ODsTQhS/Jemcs+okhXbDLXnz+XnlCC5iovK3f0AqY1RO9OMLRVfEUjnhEE+r4yPHKtGwZv7e7PzVs2rdDvLEsS1WXOgu/sBc2VJd82Vnqd1Ja5CvIBho6n2GQUWUbV8w/yQ3XPkSVSi2uKthEIDoRxi+tt27bxhS98ASgukH7qqafyy1/+ctwHveuuu+jr6+PXv/41g4ODfOUrX8Hv9/OZz3ymaN2dO3dy8803c8kll+Tm+XyiTM7+MDxxrDecoCHr7wNIZfLLy1xK7pVwKU9sVzBO20CMhc2VBa2E3w1YK6AcbZHCYxHDMAr8r16HUhC5Th/Hvv7eUIK/rst7gTsC8VHWPrxYBbTXpRTYCPZXXAdiqaLpVEYbMVq4tTPI6qz4kqIZMlsHsNsVbDK5UnoArul+4p0RpISKEckwuH2AREZFimVwretHznp9Z182P39ODoXMtHIMl4JugGYYqE1ewpfN5Lylk3j+7Tbsu8NI2e9Fu2LjY1csweuz4fvSbcj92UYlHg+R/7offcrU/bomB0rlsMh1MJYX18MTJIfjcShMzf4GeLOfb3paBfrKHiQZ9EoXqflVBRHnA2F4QmNLbyTXEMhtt7FwYrFnu7bMVSCuh5IdSzGrobxAXFuP53LYiCbz3/2VXgdOxUZ82O+ia5RqJwLBeBm3LaSqqordu3eXXNbS0kJl5eiJDFZefvllrr76ambNmsXy5ct5//vfzxtvvDHivhcsWEBtbW3un9s9Pi+aoJDh4toaNcpoeu6p3iZLeCyl0YaX40tmNH7+8nb+sm4vf1vffghHfGiwXodSFVIEB5eMpucqOgx5IK2vaY/XyLWq6Tz2yvaC+9EaJT3ShIdZeawP0UNRx33FGoUeoidcHL1WNZ0/rd7D/7zekoueOzcP4shaBMomlmObV41e4SQ9p5L5t5+GOsNs5mJgsHdlO/G0ivvtXlB1ZAn80/00nNyYO4bHqYBNJnbuRAy7TGp6BeHLZmK4FBY1V4LbTnpeFanFNRhL67jiuuVM8xv4br81321Rlojd/vUjJqyhMHIdiKUIxvPXuGKEZMZSeLNCVKv3YLtwKvY51YQ/OBVkqaStYn8oiFxn1IKE1kXNVSUj0rWW3KBKr5PmKm/ROkPMHFa60JpAOTwiXe524C2R1ClsIYKDwbjF9Qc+8AEeeOABnnzySUIhs66oruusWrWKhx9+mH/5l38Z90H9fj9//vOfSSQS9PT08OqrrzJ//vyi9fr6+ggGg0ydeuS+uI4l1GERQqu4topMl91W8Np+eAv0vnAyJ7iPpkjbeLGKmVK1vQUHF2ui7FDWvrVayPFaS/25TZ20D8QK5oXiGXT96IjkD09CtclSTqAYGCU7uo7FQAlxXcoa8tymLtZY8iF8Ljvz+9Wc+Jr10XlM+9IyAp9fSOLDM5k/rRp5bj6i2fF6O9HeGO5cKTmJ2ZfNL3jrOvSwkFpcg/ofJxP88HQMl4Iiy/g9DmY1mh5eRZa5asV0pm1ZTfm1n0JZtya3j8Tnv4B68in7fB0OJgWR63i6IHI9VrURK15L3evMaY04/20hWoMpZA9e5DovZpMZjdb+fJOdaXVlpTZhRn1+/rJpo9egnlztLfjtsgrq4RHpMpe94JxLbSMQ7C/jfp9/ww030NnZya233pq7uS+//HI0TePcc8/lxhtvHPdB77zzTm6++WZOPPFEdF1n+fLlJauN7Ny5E0VRePDBB3nllVeorKzkk5/8JB/60IfGfSxBnqLIdTD/o2YVmW57YSOA4dtZa1+rR4kQ2BcKItfCFnLISVmu8VCHNsdxntDYHUzw2o5ebMMidQYG4WRm1C53h4vhSahgCtKh/zPxlLrPzX8GY8WR+eHiWtcN1u7JC+sFEys5u6aC1x7aCICsyEw8cxITXQpNfg915S4qvU5c86rJyBLoBsGdAaK/3gJZS4dvSgXNZ00uOI717VyfZVzVRgpl+zYuT3XR07uLCd17KPvTTuSuzvzGEiSv+iSpDxz53yKfU8n5iZMZjS5LwGMsW4gV6/WIJtUCIew+SIJTkSVssoSmmx739sH8WCfXlI5IT6r28cnTZxBPa8yfWLrVfG7/NpnpdeVs6QwWjdtajs/ntBe9oc2tJ8S14CAw7m9GRVG49957ufbaa1m1ahWBQICysjKWLl3KggUL9umgbW1tzJs3j+uuu45oNMo999zDd77zHf7zP/+zYL2W/8/eeYfJUZ7Z/leh80x3T86jkUYRhCSQEAiRwSIbg7HX2MZce52ABYxzWmxsrxMLXnDA9t1dbF+8OGCDwXhtcrBBSAQJ5TSjyXmmc66q+0d1V1eHGc1II1Do8zx6HnV1d9XXNdVd5zvfec/b0QHA4sWLue6669iwYQO33347DoeDSy65JOe1ZWU2ZBMhlCQRryncvgSwh+I5N/OxcAJBFPB6nUwkFOM5t8tKuctmPLbaLTnn0uKPGs9l3n80QZREY/wpjWmPv3RNTR/mczUeT5muLRterxOv22Fsk63ScXdeX+4cRZJEBOCElkoi8RQ9Y7qKp4hHxnWmCILxN6qrcuH1Oqkot+NPk27RKs94nOGkUjChmIgmc/azd9BPXFGRJBGP08rH1p3AGz9/HUHQr6u2c9qoTZOs2ups/Y23poyBOW4s+wO6X3ifzxCCVt12OhUmO4Hw8MO477+f9yZdvLz2Uvqb26n2jXDOs3/kpH2b8Hr08dSaB5oZd20N6te+jn3lKo6UROS6CieDPp2odgwHjHPcWFM+7b+R3Wk13hdXVBQwHtdUlc3aNVnusOZM3ECgzuOgqW7ybpQrZ3DsM5Y0sDudT76wpcIYt6fMhjSqf55qjwOv10mZ3VJwPVZVOI+I79+RhtL9b2aYNrm+7rrr+NjHPsbZZ5/N/PmFLVCni+7ubr797W/zzDPPUF9fD4DNZuMjH/kIn/jEJ6iuzlbtvv/97+eyyy7D69V/SBcvXkxXVxcPPvhgAbkO5S03er1OfL6jz7JwODHhj6CYVNuIojLqjyIpKiNjIeM5CdBSivF43BfNOZdjvux+orHkUXeeg5GEMf5gJMHERHha7W5L19T0YT5XI+Nh43yLmobPFyGZSBrbAsH4cXdeN3eOoqQJ5EmNHrb2Thjno2coQFURL+hbjSHT301S9b+bLGBsGxoLzXicA6Z9ZtA9Esz5Dq7fOWi8ZkFNOX5fhF1/3oWm6ceuXdtU9HqRNIjPcyN3+kmlVBRVRdM0EidU4llYqb9HVXH87CfY/vh7BE2j3Rehfc9m+hvmUj/UjagqCBa5YIwAyBKJ8y4kesO/oJW74Qi6ZsssUvb3OhQ3/i+r6rS/W5qmQTo1RVFUxvxRYz/JWGLWvqOyIBSc30aPY9b2P8dj590r55BSVeZXuoz9CunPBWCXBHy+CA6rVDCWVPzou6e9FSjd/wpRU1PcygQzINc7d+7Eaj30pcqtW7ficrkMYg2wdOlSFEWhv78/h1wLgmAQ6wzmzZvH3//+90Mex/GIfM81QP9EhBa3PccXa7NIU3pizakiR2MHR/PnUTWNpKJhlY+dJjlHGqJ5fn4Aqyl2M3GcdXoNxZJGrYIgCLTXltM7nvVeHylFjbm2EP1W4TJZBWaaGKKqmpEMAXr2cDylkEip+CIJKlw2VE1jh6mhyJImL2PbRwkPhpEkEUuZhYbTmortHqdVIjHfg+vpHlRNQ1UBi0T4vGbd4hCL4frON7G8pN8/BEEwEkcaB7LF+pIooMydi1pbh1pTizJ3HsqiJShz58Es3AMPB8xFjWZM1p2xGARBwGXNpkSZC1ods5gIVcx20VY9ewlggiBwUpFOkebjZs5LWZGIwZItpITZwLS/MVdeeSU//vGPqaioYO7cuQdNtGtrawkEAgwMDNDQoFdu79u3D4Dm5tx81+9+97t0dnbys5/9zNi2Y8cO5s2bd1DHPt6R750G6J8I6+Ta3CrYKuVUh+eTH/PjVFodmo7ye6QgP50ilkxhlY/Mm+axgFiRgkbrcVzQuCe9ZA16EZfDKud4rM0E9O1EMc+12WM900YyvkjCSBgps1uoKbfTOaKnRQz6o1S4bPRPRAxS57BIzPE6efk/Nhr7aD6rFWmS4jqHVUattKNU2lEiChoakTPqEb12rIkYZV/9AvKWN43XJ884k719QeZtfhkhPa7u1oVEPn4jp1x85ow+29sNc1FjBgLCpA1kJoPTJueQ6gxmy3MNxYsjZ5NcT4Y6U7fHpnSjmpLnuoTDhWmT6y1btrBt2zbe9a53IQhCQda0IAi88sorB9zPihUrWLJkCV/60pf44he/SCwW4/bbb+fKK6+ksrKS8fFxbDYbLpeL888/n+uvv55f/epXnHvuubz44os88sgj/OIXv5jxBy1Bj7fKR994BOZUEcsraDQ3HphKuQZQVA1ZOorIdZ7aHksqTLPT8FEPTdMYDcbxuqw5WdOHE/nXFhzf5Hr3YJZcn5DO9T3iyXW6MM5MRmaqXI+bMq4rXTbqPY4ccr2k0ct2k2q9uM7Dxu+/xODGbHfEtnXtk+4/Q/zD72ih/Jl+EnPLia6uo0pLUvaVzyNv3WK8Nn7Ne4l+7Ab+8dxunj71Ihbtep3B+jl0zDuRq5e0zehzHQmoLKJcl9nlaXd5NN5jswC5BaaZ+MzZQn7UnddpnVGqycHipJYKwvEkGrA8nZVdrCB3tmIHSzi+MW1yvWbNGtasWXPoB5Rlfvazn/Htb3+b66+/HovFwsUXX8xnP/tZAK655hquuuoqbr75ZlavXs1dd93FT37yE+68805aWlq4++67WbXqre+CdSygmIUjU1meoy5aJMxCdD75SeQpvylV42j6Pcof//GUGPLCriGe2taP12nllnUn5BDsiXAct8M64xuyqmlMhONUOG2IRd6bf21FhsMM/7UDMRFDrbSTKOZvPUahqhp7TOR6Sbowz3OEkWtF1XLIc5nt0JVrM7muKrPltFMf8kfRNI1tGXKtatgf66D3lSyxPumDJ1G9tGbS/TvSv0HJeR5iy+sIBmNYEjGu/s1PkCeyjXqiH7+B+HveB+jpJ33VDbxUnY2SnanaeySgmHI9k4zrDN4KJTdfuZ7zFqjWoNt91i6sy9lW7POWcq5LmA1Mm1x/6lOfmrWD1tXVcc899xR97plnnsl5fOmll3LppZfO2rGPZxSzhYwGoiRSSq4v1ioZnc+gsENjPtlOKupRtZSWfx6Op0YymexgXyTB/pEQC+r1LN/ndw7y1LZ+asrt3HThkhkR7L9s6uWVjhFaq1x89JyFBRah/Jzrl77+AsM7R/FoChOfXFq0A+ixiu7xsJEb77ZbaPA68fujucp1OPG2W61C5nb1NosxaXIegud6PJSdNFS6rNSbyPWgP8pQIGYQcPezvYR2B41zMP9di1h106n4i7VLV1XsD/yS0379AF7Zw9/WXctAy3wqxwa5+uGf0TgxCOlGJNFP3kT83e813lpMuTwayXWx6MaZxPBlUOx82Gcp4zoDhyX3GG+FJWQyFMu5Nq+qlVDCwWLa5Pqxxx474GuuuOKKQxpMCYcXxWwhGjAciOWoiw6LlFP8mN9EJj+XuNh+j2TkK/jR40S5jiWVHPVwNBRjATq5fjXdYnokGKNvIkxr1fRueKFYkg0d+nu7x8IEosmCJV7z5MUSV5jYM44AiMEE1j0+EquOE08OsHvQb/x/YYPHII+Zxk2JlN4pNZJIFb3xv1UoVswIecr1odhCymzUuu0ICGhojIXi/Ga9Hr1qf3UY96YxhLQaO++y+ay4cWXRyYYQ8OP6zjeRX91IIqVQN9bNdQ98n+1LTmXB3jexJmKI6QlBPrGG3MlC9vMefeTaZpFw2eScCc/BWC2KkevZ9FtDoRL+9pLr3M9rkyXEo6h+qIQjF9Mm15/73OeKbhcEAYvFgt1uL5HrIxzFlGvQVaN8ch0Xsq/NX7bPt1VMtt8jEaqmGW3eMzhebCEDeTFKo0Gd7GTSGjKYCCdorZrePrf0TqCRnazEkgr5abXm86sO6qkYGaJk2zZOYkUtxwvMfuuF6VUDSCcjOW0Mp1uB+yLJt5VcB6Jmv7VO0iJDYTZ/9Vk8o0EC75pH2DFT5TrXc22RRKrKbYwGY4DevdGyz4/rqR4caWLYfHYrp9yyupBYKwqWF5/H8Z8/RRzKdmEEEDSNE7dvyL7WYiHymS+QuLhwBTTfFiCJwqx1I3yrUeGy5ZDrg1Gu3wpbiFkJd9lkqsoKLS1vFfLJ9dG0AlvCkY1pk+uXX365YFskEmHjxo3ce++93H333bM6sBJmH2Y12uOwGpFfg74oUZN/0m6RMGu7+cv2+Ur20dSlsZgF4fgh17lL6hlSMxqK5Wyfied3c/d4zuP8awNyz68ykCHX+mPrPj9Bf2FL7GMR/miCobStQRQE5uVlpHqdVhO5jhuJBm8HQkWU6y33byLc6UP2RXA900vkqsmLC/OhaVqBcg16asNoMIYQSmDbMYHrhX5cVhmHVaJycRWrP78GwWxRSiaxPfwQtkf+gDgyknOM6DXvpfPZjczt3G5sm6iooefTX+X0i9cWHVc+mSy3W46q5CMzKpzWnEhHz8HYQooo+bM92agwKepza8rf1vNts+hKdSbFpuS3LmG2MG1yXVFRmBtZUVFBU1MTiUSCb33rWzz00EOzOrgSZhdJk2LbXOnC36eTqN6JcJ4vVkYxea4L0kKOYltIMZX9+CHX+cq1TqpHAnnkukiL6mIYCcSMvOYMiiV/mM9vvE/vQmjwJVVD3DL6tnuM3wqYic+c6jLjRq4kFDbe+TLKlkEsp1aTbHPjjxTGoU0GTdPXDmZzOTuQF8OXDCfpe7EHQdAj3mzbxoiuriORUqf0qKqahigIhOIp47tnt0g4rTLxQJz5XWHGH+lE6/Bjt0g4nDYEQcBZ52LtN85BMpNfVcX1ra8bOdXG53e5iHzhKyRPP4Pf1r7B0i0vsfL15xisa+XZ867mvPbJm57lk8mj0RKSQX5R42zZQuyWadOEaWFebTmntdcwHoqzbmnjrO57phAEfaUio/jbLSW/dQmzg1n51rS0tLB79+7Z2FUJhxFm1ba1ysW2vgmAAoLU+4eddL/Yg2Whi2S7p0CNLCxoPHqU66Lk7zgpaMxXrgOxJImUwkgwl1ybFcapsClPtYbiyrV54hbry9giBMNva9s6flw08pkwTVpq3dnG2R1/3kPPc11osSTujgmC75yLb/70rDKRRIr/fG430aTCdWvbaZyl9sT5MXy9L3ajJBRAQBRBUcH1XC/h/5PEKhdf1u8dD/PAS/uwWySWt1Qa2ytS8I9/fY6Bjf1oikYFgIkY2jw2zvzmudgrcr34wgMP5BBrzeMl/s53Eb/iSrSKSkTAbpPZsuwMtiw7w3hdMavDZM8dzeQ6P45vJg1kMihOrmdXzRUFgctXtMzqPg8FTmvWq16K4SthtjBtch0KhQq2qarK8PAwP/3pT2lpOXK+LCUUh1m1rSyzcercal7PI0iiP87u/9lHStUo3zXM+I0nFai9iTylt2ir4CMUxWLfosmZeUePRiQVleE8hRpgNBQvVK6nYQtRNY03e4qQ67wMdE3TciYv4Z6g8X9BFNBUDXkwzHjHOPULqzmWkZ/zDKCpGnse3gnofl9UjfI/dTLssMHy5qL7MWNT17gxOXqtc4zGk2eJXEdzleuuJzuMx6IgoKBh6QzQ92o/FefMLbqPjR2jhOMpwvEUz+zIRuo5nu2lf6cv98UC1Cyro+XcObSc04q1PJewy5vfQPzpT8hcSfHL30n0hpsLOiY6rVKOxQ2m7i7ozLM8HM3k2qxcH0wDGZikoPEo9aBPF+bro+S5LmG2MG1yvWrVqkmXbS0WCz/4wQ9mbVAlHB6YvdEWUVcPVFHgtX1Z76J9PA6CgCiAEE1h3zJGYk3u0l2+LSSpHj3kupgtJHocKNfD/mhO4WEGY8FYgXKd6aQ3lc2geyxclITnF7smUqpxXGtMIZH2V8t2Ga2xErbpSSNdT3Ue8+TabLfJtKvuen4/4XSRpxF/qGlEfreLjsX1zLtswZT7NK86hYp01jtYmJVriz/ByJvDAAgiWJZWk9ysP979izc58ay2XF90GkVXQBIK2rYxkHQSU7m4itbz2mg+uxVHdfGJgTA8jOtbX4f071fqxKVEb7oV5MLbl9MqM0Y8b9vkhCmfTJYfhE/5SEGVaULicVpmnFcPOrk0e5Az245lmK+PY/2zlvDWYdrk+pvf/GbBtkynxtNPPx2v1zurAyth9mH2RltkEVEUuO7sBYQiCXYN6BFhroB+U83wKscrQwRXZoP3VbUwbSN1lNtCilkZjjX0+4rkAwNDgRhjoVwyomoawVhyyiYU+YWMGeRPvHJSaCayxylvdSMsKkdLk+veZ7tY/clVRUnasQIz2cyojNse3Gpsm3vZfAae70QeCJNSNTbd9xo1K+opbyov2FcGZh/9bNYOmMm176Ve4/91qxpJnd1AZNsopFSCHT761/fSdEbhyqU/Wkj2rXv9SIoGErhb3Zx/70WTe+01DeszT+L48b0IwSBIIprXS/irdxQl1lA8Wq/YtgzyM5ePZuXa47By9qJ6tg/4OXdR3YHfUASCIOC0yTkTtWNduTZfH6WCxhJmC9Mm1+3t7SxevBins1BdCAaDPPHEE6xbt25WB1fC7MKs2mba2UqiyPtOm8tjb/SweyjAXGRiZMm16I8jbh9DvVpXMosR0aMpiu94Va77TSSs0mUziN6ewUCOSpWBL5yYklzvHsjmNS+oc7NnSPdS518fZr+1bTxLLj1tXixLPMScFoRIkthohIFX+mhcc2ArxNEIVdNylP4Kp5XxnaMMpRVhURZYfv1ynmixUn7/DhiNkoql2HjnS5x397qik45ESjXiFIECO8TBIqc7o6Yx9Fy2u2Hbunn4PSLRU2pwbBhCVTX6XuwpINeqphGIZj/vZctb+MeeIVwdQSM3ueW8tkmJtTA+hvOeu3OLF0WB8Fe+hlY9+QpHMSI4FbkWRQGHRTKu06OZXAO8Y2kj7zlzPr684uWZwJVHru3HuA/ZvFpRzBZTQgkHg2mXxn7gAx9g7969RZ/bunWr0b68hCMXZoXZImb/9LIkctWqOXz+0qWUBTM/qoJx43O+PEg8ffMppvweTcp18gBRcccqzArnSS3Z5J/+SW7CU/muw/GUkSYhiyLz67J5zYnk5Mq1ZTRrP3G3ebBaZWIn6YHamgb7Hjt2i6KDsSRK2tbgssrYLBK7/7DTeL7lnDm4alx4PA5Cl7eBKKCoGmPbRtn90I6i+xzKs/rMVjOkUNzktx6KEknbVixlFhrXNOOyySSW6AWKqqYx+OoAWl4cZzieMj6vwyJx+vwabj5zATWDUeN3peW8tsKDaxqWZ57C/dHrc4i1WleH+sMfkVpxypRjL0akD9RhsMxEqD3Oo5tczwbyE1Sm8qwfC1jZVkVVmY2acntO4W0JJRwKpvzW3HrrrXR3dwN6YdLnP/95HI7Cbmr9/f00NDQcnhGWMGvIVa6LdDsTBILd2SYXoiigKBrScISBVweYd0ZLUeU63yZyJCNRZCIQSyrHdBScqmoM+bPEdllLBc/vHJzyPRNTJIYMmlpQ13nsOUup+ddHzKSmiiPZ97nneLEmo8TSCqiGTtJC/UHKGie3QcwW/rF7iO39Ps5f0kC7aXJwuGBuoOJ1WYkMh+l9oQtR0Ce5C65Zoj/ntDLe4CJyRgOpTePIEmy9fzP1qxvxtOVa7/KjFWdrBSZksnO49mRXKFrObUOySrisMql6J5rTgqpA3BdjYs84lYuynYf8pslZJhKu94Vu1JT+/atYWFlgd5E69mL/1S+w/OPFnO3xy99J9GM34G2shgMosvnpHw7LgTvurV1Yx+ObeljU4KGm3D7la48H5J/DY92HXOGyceu6EwCO2XtACW89piTX1113Hb/97W8B2LFjB83NzQV515IksWrVKq699trDN8oSZgU5nmupcNEi7o8RD+gkQLJJqKvr4eV+APb+cWeaXBcS6aPKFlJk/KqmkVRUrMfo8udIMGZMgNx2CzXldmyyVECE69wOhtJNTCamUK6HzOTa7cAqCkgjUZRKWxHPdfqxpiEMR0DUz7FnrhdrRxzVayM51402FAMNOh7fw7KPTa1OHioC0QR/29KPhsZft/Rx01tArnMsIS4b+/+2D00FJKhZVkvFfF0x86aJaPSMeixjSRiMoKZUtt6/mbV3nJOzz/xoxXhKOWAh6nRgzri27M+muzSt1S07TpsMokBinht1l06+Bzf255LraCG57n62y9jWen6b/p9oFOvzz2J7/FGknbkKvVpbS+QzXyB1yqppjz1fuZ4qhi+DlW1VnNxaiXgM+/1ngoKuhce45xpKpLqE2ceUvzyrVq1i1UDCjvkAACAASURBVKrsD9stt9xSitw7imFO9ZCLkOugKSatvNmNdFYDqTS5HntzGCWeMmL4LPsDWHpCxJZXH1W2kIRSXN2LJpVjllybSVhDhRNBEKgutxXkmy+odxvkeqpGMjnk2uOg77/fxPvn3aTqXcQ+e2rOa2PpmEMxmESIKeCUsDhlHNUOrF36NRg9pQb1L7qvt/Ov+zjx+uVIh/GGPhqMG3aK6WZ6HypyihkdVvY/kY22m3d5NhHEaPwhiZS9fwnRu18DYGB9L5GhMM46l/HaYpaeWEKZFqGcCpliRiGcRBiJgNOGKAtUL9WztzPkK9HuQU1H6g1u6OeED55k7MPcBMfjsBIdjTDypt6mHAHa5ig47rkL69NPIkQLi20Tl11B5OM3QpEan6mQ77merqWhRKyzKLUEL6GEQ8e0Pdd33nnnlMR627ZtszKgEg4fkimT57oouc4uAZe3urHWOFGq9GVSNakwum2EeEpF9Mdx/34vjr/343qi++hSricZa/wY9l2bSVimyUh1keXvhfVZBXcqz7XZFuIaijD2vG4dkwfDJEwTNMj6gKXRqFEk627zIgiCcQ0m2z3I6essEUjQ+0IXhxNmVT6RUg9Lh9EXdw3x21c6jS6Y5gYy1t6gEb9nc1tpWpv9XfWauur53BZqT64HQFN1VT8DRdWMiZAQTiKE9P3PRmZ7hhhbuoKGCl51Qg1yug16hnwl57qN3OmxnaPETdYjX44txMKeR3aRsYfXuwLUfukmbH9+NJdYyzLJc88n+IMfEfnUZ2dMrKFQuXaUiOGMYSbXNvnAtpoSSiihENOWOIaHh/nud7/Lhg0bSCaTaOmEAU3TiMVipFIpduwoXnhTwtsPRdUMtU5AKJqBGujJ+q3dLW6sskhyjhtpLIaqwcimIVIXtGLd5YP08r+lN3RUea6L2ULg2E4MMSvUDV69ZiLfW+oai9P7w9dwxCJET62bNOtaVbVsMxpNY/h3O3OWVJNd/pzXZxrISKMxY1/utHfYmmk1LAqUndlM8q/7Adh458ts/39bKG9xs+DqxdSdMrv1HL48tTqcSE2ZjDJT7BsK8MTWPkCftH3ozPk5HvbY+mxDlXnr2nNU+taqrDK9dzjAmkvmMfyG7o/v+MtelnzwJCSrxEgghqJqyL0h3L/fixBLEXjvglnpNppp027dHzAmQLUr6hC79uO68zs4AwGuVsoZrmliv6cBVECDodcGaD1fbyhj9lzHnuxi15/0YnjBN8FC/4vgyR5PbWomftkVJN5xEZo313Y4UxyMLaSEXJjJ9bEew1dCCYcL01auv/Wtb/H888+zbt06GhsbaW9v5+qrr6a+vh5FUUpNZI5wJHMyrosrEUETuS5v8WCVJZJz9KIjTdMY3jxEPKVg3ZvtriZEUySDB+7od6SgWIdGOHYTQxIpxSBLAC2VOnmrKjN1wFM1PI92MvhyH+XP9lH+h32o8VRO1nEGY6G4MZny9oTx7RjDPE9TugI5r8+cV3kkmxLhmaMzK6tp9cS+phExfV1qKox1+9nxbCfPfPkZwoOF3WEPBfl+ciN2bpbwhikDvHMkREpRGU8r10IsRWBjllwvuHxhznury+00VeiKraJqjDW7cNToj+P+uKHq9/siCLEU5Y92IsT08Ts2DBE5RHKtahq9E+l0kK4gVjlNrtsdlH31C0i7dmLt72PRrtc56++PcVrXUwijI4DGwIZ+Yz/+aAI0DceL/Yz8YZf+2QN+GoO7me/W7SGpE04kdNc9BO5/gPh73nfIxBrAYcslg1PF8JVQHGW2bGJKyRJSQgkHh2mT6/Xr1/PpT3+a22+/nWuuuQabzcYXv/hFHnnkEU4//XSee+65wzjMEg4VOcWMYvE/e7A7zxYiiyRbywB9RXd81xjh4TCW7lyyExsMc7RgMuX6WCXX+0dDRo51rdthxI6ZbSHWfX7kCV2NliQB614f3l/tZKBzomB/g/60Cq5qlD+vkykBE7vOs4Vkzqs0EjVIuLstTa5NHnfFKXPyzatx1jpB05gIxQnHU4yORXj1B68YK2WzgXw/eWQWyXUipbC9Lzv5TKkqHcNBIzfYtsOHkC7y9MzzUmUqAsxgRWs2Dmxzn492kyd775/0uMIBXwTXkz2I/qwibukJES7S4n4mGPbHSKRURF8cSyCBJApIVpGWB3+AOJhOmBEEYxWi1jaKOD6GODjI0IZ+I5LPH0lif2MU19/7kWNRxIF+moM7eEfTFiRBQ1m8hNB3/p3UshXZUP1ZQIFyXVJeZ4ymCqdhT1rafOgTnhJKOB4xbXIdjUZZtGgRoDeU2b59O6Cnhbz//e/nlVdeOTwjLGFWUKyBjBlKQskqhAKUN5VjkUQ0pwWl1ommaWiKxvBDu/RQYhNSQ0cRuTadB3Ox05FiC4klFZ7ZPsDr+8dmZX8dw9mJUHttNvrMrFw7Ngwhi9mmQqCT4de/+Cyxidxis8G0r9a2bQxpVH/O4tDTIwCE0RhxE8GLJRVQVOTRqEHIPK1uhNFRrGqW1CYUlXmXzueyX1/Fmf9zJRPXtIMgoGgafRv76Xqq89BPRhqHU7ne0e8v8PW/avpburePG2Ry7kXtRVMKljZXGBOW7rEQFee0IKYV5PGdY+z8zTZ6H92Dbau+X8PipaiMpZvSHCx6xk2qdbpFeUOyG9vOdE2NAJFPfZbnrv5n9s9ZjEcOYBcTCIEAyR37mPjLRlKKSnxogrrHtlIz3Ielv5cWtYsLG7ZiEVWUuXMJffv7B+WpPhAskmhcy1BSrg8GsiRy67oTuOUdJ3DO4vq3ezgllHBUYtrkurGxkd5evQ3u3LlzCQQCxmOn08nERKHKVcKRg+QBYvhC/UE9Ggxw1bmQbDK29A09Oafc4NOh9f0F700OH3w3sLca5iY4blNnrtgsFILNBp7fOcizOwZ4+LUuuscO3Q7RMZy1acyrKTP+b5FEvE4r8kAYuTuILIkIkkDVuxdC+vqITcR49a71OarxkD+KEE3herbPuI4WvfcElHqdKGlojO4YNV4fTSrIQ1FIqggCOOtcVP7yR3iufTdrbno/1z74A9a8/L9YB7LX1VA0SXK+l9gqPZ0imVLZfN9rBUT/YJBSVIJ5bblnk1wXawu/K93NUhqJIvfr5FWUBVovaCu6j/JokFXKBB7fKPZoiJ2+EC3ntBrPb/mvN4g/ts947LSJyCn9M0xsGjqk8Rvken8AqywijE/QPLbFeD768RtJXHYFPWvO47f/dAtblp1Bo1Mn+UIkwqbbfovl45/gnLseoHx8HFlN4bZGuaBhK7Koklp6EqHv3oVWfvjiD81q9bHeAOVwQZZEatylzO8SSjhYTPuX55JLLuF73/segiBw5ZVXsmDBAr7//e/zoQ99iJ///OfMmTPncI6zhEOEOS6vWAOZXL+1fuPLLNsn55SjbtFJQ7HleeUoItfmSYbbbjFi5Y4UW8i+oezfoX8iSmtV2RSvnhqReIqB9OcTEGiryW3asaTRy9b/2YkkCFhlkZZz5yC8ayHblSTu3+1BUVQGXumn4897aL9C9wYP+qOUPdGNEE5icTuwV9hZeM0S/rp+P6SJ4/C2EU68SH99NJHCkraKiIJAbZOE9a9/AUBKJWnbv4O2/Tuw/+NRyl5cS/ziyxD2DHLWtt04YkF2CS0kkyKJYIJN973G6V8+86DPB0AgmszpagizR66DsSR7h4IF2zO2HMerw8aqUcPpzdg8WfIibduK7cm/Ir+5GbGnm/ckUkYRpOpwIV3yXnpwoyKSUjRUTUNSFKpTY5zHBp4fPYlQmZfQ5uFDaojUMxYGTcPaFcSeSiCOjtDQqgsniUsvJ/7u9wK6IqxKMo9fdj0fb/0HXb8cRtFEfAkXL7yYYDCSIc8Cp7UNkbr6GiLrLkJpXzDJkWcPTptsZHWXbCEllFDC24Fpk+ubbrqJ8fFxnnnmGa688kq+9rWvccMNN/DEE0/gcDj44Q9/eDjHWcIhInXAjOvcYkbIKtzJljK09M3a2IsgGPYQdeToJNflJuV6tlpHHwpSisqQyVJRrKBwJugYyRK95kpnQXHSWbUexvuiWNwOREFg4TVLGHFJJNs9xE6tw/aGrkBv/unr1Cyvw1LvIvrGEOXbx43EmZW3nYbFaUFsLYcNuifXrFzHkwpy2qMvCgKNY1uN58xebU0D+fXXkF9/jRODMRakG9y0xKvY4DsV0e2m968xIv98ck7W80xRrDlOOH5o5zmDLT0TBnFvqy4jnlKNLopCJIlt6xiSRf/JXXDVIuN9lmefxvXtb+Tsy26REAQBTdMQo2Gqn3yQayxVdLWsZX+/BvE47tAEp1duoc4RRRZSlIV8JDpEAvt9eOYW98pqmsbW3glSqsby1sqcNJhIIsVoKIY0FkMOxrH5h7BJSapsIZQlJxD5l08ZlhYjUUIQGLj+Wk6es52NP9iIEAwwFPMCKoooY1taRfkjtxO1zl4ay4Ewv87NoD+K3SLRVHnw10oJJZRQwsFi2uR6fHycb3wjewNYtWoVTz31FPv27aO9vR2PxzPFu0t4u2Eu5CuacW0uZkwr1xlbiGaXkZrLYDRuKNeJxRVYd+hqtjoaPWrah+fYQuwmW8gseq5VVWNr3wR2WWJhw/S/FyPBmKFywqGT633DWXI9rzZXtR7bMcrmn72GzRS1VjG/kmQ6Ozl8bhOu3jDENJSEwnOfeQrH0mrKntLTKmRJoG3dPBrX6F37LG0eMvrvxK4xNE1XV2NxhYqMcq2kaO580TCj9X3hazz1yi4W73ydJfszOflazgSo1jZGtWUUX0hACIfou/dRFvzbwXeDzY/hg9lTrjeZLCHLWisZD8UNcm1/YxRSKrJNwDu/guqTdMsLW7bguvM7uTuSJbTGZrTBcRITPqyJGJGEgkf2saz7cdqiCUK2JNigzG5BEkVqrGMMxOuwByYY//7/o/KWC9HcbjSPB81VZpDiF3YN8fQW3c435I9x8bIm47C9Y/rKg+v1ASoCY5BSaCjzgcdN6Kt3gCX7fTFPTP2RJKs/cg4jATsdj+4kPuYjnFKJlpUx/zPnwltIrAEuPLGRuTVl1HkcpbSLEkoo4W3BtMn1tddey2233cYVV1xhbPN4PJxyyuFtVVzC7CCpmmwhB8q4btXJtUXOknBpfgWMDhre69jyaiwdfoS4ghZXiI3HcFQ5DtPoZw85thBTtvFs2kJe2z/Go2/ojVU+tHY+C+qn5y/tz2tnfcjKtYlcZ4oZw0MhXr9nA4OmODiAhdcsAcDrTBc6yiITl82h8dFu1KRK3Bdj/OlOhKhORG1VDlbcmO3eaql1knTIRjRjoCdAwiEijkQQ4gqCIOBM+nHLOoFLnbiUxJoz2RKpYstJZzA3FeLG8C6UN95g51iS8ao6ElY7C3e9QUu0j4lgDQLQ9dvXOWnOBLGPfhImSb2ZCsWV60Mn14O+qEGkJVFgaZOXvokIf989BCkVx2vDxnML370YQRAQhwaRvvA5lKT+d1ZbWoncchupJSeCzUbvgJ8H/r6Hhbs38Y4N/4snoq8ImCeIqfkLGLj2w6jffxjStcaDz+3h5L1/yA5OEtHK3URVgeUDo5yaiKGKEmGXG629hbKmBtSqKixRkQue7mRwezmSAIgiza5xwl/4Klptbc7nrXRlC2LHQvqEZcWNq5jYM07nJpVoPEn0tHqq57z1ooskCiysL4k9JZRQwtuHaZPrRCKB2334ilBKmF0Eogm6RkMsqPdgt0i5UXx5yrWmaZN4rk2vm+eB9YOomoZmk0i2lqFU2pEHwmgahPoCRwW5NhOTcnv28p9Ncr3X5Jve3DM+bXI9kNeOPL/wbibwRRJGy21ZFI1863xiLYg6sa5f3Qjof3OXTSYcT5GscXDCp1az+6evkwgmSJp8+60fXYG1LDs5sVtlAo0uLPv8esOhbSPIy6ux9OiWEEnTaIh2IKQDIuLvfm/O9TXhriT2Tx9jW98Ef1yfTQZ56YxLqR7opfXe19HiKoGkA98v/0rl+DiRL3xlxjFuxTpPRhKHTq6f3zVo/H9xgxeHVaa1qgxJFJB3TCCE9b+lq9pJ8zlzIBLB9a9fhHHdz6y53YS+9V3UxqyS3FzlQhNFdi0+hT0LT+aO5gTW3m7+981+YoKM31PFez5xNamkyvPXWpj7neewx2MMRT0kFAlNTaComt4IZGKCwXHojjTRH6snqVpwyWHK+8I0l++j1rkZVyjByMhKBDQEQaTROUHzxy8gsfq0gs9rTpvJXGeSVWLtN86l82tPE1MVImsbsu3cSyihhBKOI0ybXN9yyy3ccccdfPjDH6a9vZ3KysqC1yxevHhWB1fCwUFRNe5/YS+joRjzasr58NkL8prI5JLrYHeAVFqRtJRZsHn1QqtMFBdAqrUcd5uHoc1RYitrQRJRKmzIA3oBVLA3SM2yurfg0x0aZkO57hoNIYqCQVjzkWlLDbBnMFC002ExDPjzyPUhKNdmS0hbdRmyJJKMJI1ufwCt57ex5ANLcbfmqnx1bofh156YW84Vv3834zvH+N2Dm0h0+Um0e2g3tewmkWDuzjcYqZJgnz5ZG9k2TMWSCqOY0RELU+/Q1Vu1vp7k2rOwmqw4GdtSvykRxCqLJFIqow3NVF0q4nhiG0I4zJ5AHWc9/STJ1aeRPP8dMzovE+HZV65HgzG29Wazrc9cUMOeP+5k6I1BGvp9hDv050RBYOG7FiFJAq477kDq7NSTWWSJ8Ne+mUOsQS8arCm363YhUaBz/lJcJ65gg6h3wnXZZNwuG0o4QarCyejCVrxdI3hI8kDfecRiKURVxUISi5giqthy9h9L2BhLVNIVFZBFIfe7USFx2lcvIHHNlUU/s1m5Hg/FDVuYo8pB9J3zCKfbvs9m58sSSiihhKMF0ybXt99+OwD/9m//VuCtzfywltqfHxnoHgsxGtJvbl1jITRNy0kLyW8i0/m/e43/1yyrM/6+ZmUxJcI77ruUTQ++RsSpk26lUifhGrpyfaRgNBgjmlQKyK+qaYdc0Liz38evX+4A4Lq17QXLzylFZTyUJXCRRIr+iQjNByisUjWNgTxbSCSRIqWoRQtQD4TOkUK/9cjmIdSUfh145nk57Utri753aUuFQa43d49zxoJa1JYyBk6ugpOrEBBo8OoStDAxTtmXPsvZb26nNtXIU6kL0TQbI1uHsb1zHpaeEKKq4gr5qa/Uff3xq98DoohFyl6Tmc6ZGWsFwPKWSjZ26laIiWW12Lb6EIcG6AwpnK7uw/mjewgsOxmtunra56WY5zqWVA76PAO8uGvIKGScX+dG2TTCpvteA8ASSyJG9etBskm0X7EQx89+guWV9cb7I5/6nN5MpQhaKl2MpIlqz3gYtz1LVpsqXAiCgD2diBGfX0FoNE6510kgmjAmZ4KmIaYLmjVBwFvuQERjIhBBUhQkVf8nKgqqKJLwlnPJ796H0DK5tcJlk7FIIklFJZ5SiCYUo9V4wLTiUlKuSyihhOMR0ybX999//+EcRwmziD2DWaKrqBopVctrIpOdHCkJhf1PdBiP512Wjcoyk+tESkWURWJlFkjfqNVKXb3SNAj2FkaQvR0YDkT58VM7UTWNa05tY7mp211OHKEo4jAVO8USyrSKMs1Ray/vHSkg1yPBWEHU2+7BwAHJ9XgoXtB8BCAUTxnd0mYCM1GfU60fe3BjNku6/tTGSd97YpOXxzf1oKga/b4Iw4EoG/ZlE0CWNHpw2mSE0VHKv/BpxO4uBEGgIdVP5cQQCWcrY7vH0R7fixBO4g6MY1ejVFjDaOXlxC+6FMhdQUkqKilFzRn3yrYqg1yPuGXmt7oJaRqpaJT9oWoWiEM4f/B9wt/63rTsISlFNSLaQE/kyKxYRBKpnJWMqRCMJRn2R2mudBFNKjmFjGc0VbL5c8/kHCMYFdDQ8J4/h/IX/obtj783ntc+9CESF10y6bFaqly83qXnSPeMhfE4s+PPtEnPFO3FTqnB/uYomqahqJpxTjRAkSQQBLzLazn3A8upWFDJU8/tY8fmAeTBCPJAGNEXR7NJVH1iBe4piDWAIAhUltmMKMuxcBynTSaWVIink15kUSxF4ZVQQgnHJaZNrtesWWP8X1VVfD4fHo8HSSr9eB5p2D2YqyLHksqkTWT2P9NJIqgra656F/WrGoznzJX2oXgSVdVyIv2yyrVGsHdq5TqaSOGLJAzF83Bha6/PSNzYMxTIIddJJatOW2QRWRIN9U1DI5FSsR0gXcBMzvYNBQnGkpSbUkeG/IXtp3cP+jn/hIaC7WaYFVszgtHkjMm1qmmMh7IKbXW5HU3Tpk2unVaZRfUetvfrdoaNHaO80Z3tMri6vQZhaIjyz9+G2N8H6DzOIqaoFkeZGJLRyuay/xdbcETDOGJhqh0+BAHCN99mdOYTBQGv02r4oP+2pY9QOhbPIok0VDgNW4QmgHttM6GeIGpdA3vGRlngHsKy4RWsjz9G4vJ3HvC8+E2KqttuwWYi1+H49Mh1JJHih0/u0PO7JRGPw2pcb3OqyvD9YRfxdEtyR7WDNTesomvQj1/QuMjRg/OHdxv7Sq49C8snb4ApWpa3VmUnZT3jYUImC0tzRfY8OiwSUbeV8X9ZxnVnzOdP2/oZS9uMLl5UTywQx1pmZc1JjYZCf+UHVlB9aiOvdo4yHo4jRFNoksDa1a1MB5WuLLkeD8VpqXTleNo9TstRkSBUQgkllDDbmFH7qjfffJN77rmHV199lWQyye9//3t+9atf0dzczM0333y4xljCDOCPJHI8v6CrsqlJ2p/vfHin8f+5l8xHMCWJeBxWBHTVLRxPETYVflllEa06W8AY7AuiqVrO+zOIJlLc88R2wvEU65Y2cdaiw+fNNpNUc/Fi/uPMBMNukYyJRyylHJBcm4sMNTS29ExwxoJsksJwsLCLYN9EhFAsSZmJhOcjPynEON5B+K6D0aQxCXJYZZxWmWBPgPCgntQhO2SqTpjaSrG8tdIg1+v3jRjbq8vsLOzeSdl3vokQSMc3SiK9696J57GHWeHZwkafk8S+DmwWK7awfsxau4/ERZeQPO+CnOOcubCOP2/qKThOvUfP3m6qcBq2CGVZNcLvQHM6GQi3EVe2YZNSOO+9CzHgJ3btB6dUsM2WEK/LhihAxj0TmabveteAn2j6e5BUVMN+BXCKKrPvb9lVoFNuWU3jmmaaAdsffofjnh8bzynzFxD+wlfwHiDxpLrcjk2WiKcUwvEUkXh2gthUmZ2o2q2ybm0SBXDb8Kuq0ZJ+QVslte7CYmNREDhrUR1nLqylfyLC9n4/VlnklLaqaZ2LYkWNATO5LvmtSyihhOMU0zYZbtiwgQ984AMkEgluuukmI++4ra2N++67j1/+8peHbZAlTB/5qjVklGuT5zpNLP37fQxt1tslC5LA3Ivbc94nigIeZ5YQDvvNxWYSssuK5tKfV5MqkeFw0THtGQoYRWM7B/xFXzNbMNsK4nk+arN6n7G8mNX56DSyrgN5ZHdTXrvrYZNybW6SsmdoamXfPCkwt2wOHERiyKhJtc4QILNqXXtyPdIBJhEL6905thnQOypes+Upyr/82SyxlmXCX/sW3e//KH+55DrK5Ajn1m2kXAwhh0MImn7Oy5ssRG68peA4p86tLkr8GtMrHE0VWeV2UFXwtusrEWpVDYNuvQskGtjv/09c3/o6RCdvkW6O4atwWbONUCBn4jgVin2/ABodVoZ+lW2Q07S2mZbaKNan/obzzu/g+KmJWM+bR+jfvgeOA6friEJu4WzGcuR1WnHZst9N898qkkjhN33WA618CIJAU6WLdyxt5JzF9dP2nucXNQL4otM/bgkllFDCsYppK9d33nkn69at46677iKVSnH33fry5g033EA8Huc3v/kN119//WEbaAnTw+7BQvKaKdjKwJL2XHf8eY+xremMZuyVhTf7CpfNWOo1dw+0ySLJlKonhoSThjXEVV/Yrrt3PEsci/mKZwuReAq/6eaenwBSrJGOmVwfKDFE1TRCeeR6IO1JzhBE86rBSS1e3uzRo9Z2DwY4eU5xRVDTtBzlekGdmzd7dNJ+MMr1+AHI9VSWEBIJ5E1vYN+ymfd1DNHliyKqCvUDXTQOdtFUZsl6eSsqCH/166SWrcDWNcaby9ciqgpXvPQoV5Rv5k8dJzCRcFJuCTF42w00OAstQaIocOmyJn7x97052xu8+vlsNqmzfRMRFi2vY2LPOIgiXed+gKae3yG/uRkAywvP4d6zm8itnya18tSCY5mTQiqc1hxCPZ3EEFXVcuoZ/um0uQz6o4TDCRwP7sLfp8vg1lSEs3f8nLJP9hbsI7X0JMLf/A5aWXnBc5OhpcrF3uFcUt+YZ68yX8cjgWwzIodVxiofHutepStLnsfT59YfKRUzllBCCSVMm1zv2rWLW27Rlad8H92aNWtKBY9HAFKKmhPBlkEsmcohtZIkoiQUup7K5gnPu3xBwftAJyGZVw2ZlGubLKFpkKi0I/eG0lnXQVhVuI/+ibeGXOf7ljOFVRkkzMp1MXJ9APUyHEvmdFDMYHP3BO9Y6iCeVIyJiIDAGfNrDXK9dyiAqmqIxRr4RJOG1cAmS8ypdhnkOp/MF0MkkcJukYy4vzGTVaGqzIYSTzGcXqEAqD+10P8t9vbg+M+fYXl1A8R1cr40pVAfzO7LaZONY6SWn0z4y/+KVllljBtg08lnE7voUm5a6KDml4+jvdpN18mrqVu4iMnQXudmSaOXHf3ZOLuGtJ84Yw9RNY3xcByPyc4yvCdI6J67cdz3I2yPPqx/joF+yr74WRLnX0D0k/+CVpH13Jv9wO3bNmLb9DqjFfPpnHvCtMh193jYuKbcDisnNnk5ocHDy3e8SP/WUUgmEUeGWVu2kbLx4YL3J087nfBX7wC7/YDHMqNY5KPZEgIYSR2Qu3pzONXjyrJC5dr8Haxwlch1UxGl+wAAIABJREFUCSWUcHxi2uS6srKSzs5OzjrrrILnOjo6qKiomNWBlTBzdI2GipLXWFLNKUS0iCLjO0dJhpNIkoir3kXtivqi+zTfnM2qrFUWSakaUVMcX7HEEFXTEycySB1Gcp3vW55SuU7bQhxWs3I99diKNSABPa7ughMbDG8wQLUmIHUGqNjlI+aLoVTY6V4Toq2mULE0E5IGryOnLfuBlOstPeM8tLGLCpeVG85fjM0iGR3zAKrLbAxvHkZNfzZ3qxtXXe7qgjA6SvlnbkEYz7W4WGURSRRR0teOyyqjNjQSv+Qy4v/0/pwOiTZT8kdcE2DRInaclWRHu06Y1x4gNeKik5rYPehHUTWsskhtuX5dyZJIrdvOYHpil2xxgQBo4Ns7TjKmws2fQlm0CMd9P0II6U1rrM88jeW114h85vMk1+iRg75IHCmVZN0TD7Jk36ukVJX3RR6nt6mdsfd8AE64YkrPtnlVaGG9GyWu8NpdLzHw5A5Evx8hHGZVdQfz3Tqx1srLSS1dhjKvndTiE0itPu2guko2VxYq/ma7DOROEgf9bw259jisxsQnFNcniPtHQ8bzc6oKV7FKKKGEEo4HTJtcX3nllfzHf/wHbrebs88+G9BTQ1555RV+/OMfc+WVxZsNlPDWYdckftBoMpXruZYFhjdllcy6lQ1FCxEBvCb1aTjHFqIXAioVmTg+rWjW9UgglkP4k6nDSa7zlOs8slxcuTZ3aZxavTT7WNuqyxjyR4kmFfzpbpiZpXHrHh+2x/bzotNGeSSBmE7AeE2y0Pb5wsmpeVLQ4HXmpI/ke7zz8UbXOKqmMRaKs6Pfz4o5lTnkurLMzuDGbJFd3ao8S0g8TtnXvpxDrNWmZpKnrUGtriYZiLKrbwL3wnZcF52VowSbYTWRu0Ra3Y2aVgLsB7AmVJXZeM/qNjZ2jLJqbnWO77fR6zQI40hSwTuvAt++CTQVRreN0HBaE4l1l5BctRrHT3+M9dmnARD8Ply3f5nEpZeTXLWaqo2dnPfC4zQOdLIttZj9oWrm2HfT3LeP+T/+Np4//RfJlaeSWrkKta4etaIStaISXC4QBHYP+KkYH2JO1y5OfsrPsy+pBP0aYvqrdVJFD8sq9eLMxMWXEv3YJ9Hch96G22FqJpNBJoYv+5rs+TU3Izqc5FoUBSpcVuN629IzYXzXvU5rjrJdQgkllHA8Ydrk+uabb6a/v58vfvGLhi3k2muvRVEUzj//fG699dbDNsgSpgezH7S1qozuMV1FiifVXM+1KNJvsgnULJ88vcN8c84vCJSTIkrV1Mp1b15L78RhVK4H85TrlKqiqBpSeuJQrEtlTkHjATzX5iSESpeNGredjR16DvM/9gxT5bIhxFKU/W8XFkHfv00WyYRUDP1xN3sX1TL/ylyLhJk01dgsDPxhF7buEeLLqwkdoKDRXITZMx5mWWtFjre40mnljX/0GI9zLCGahvPu7yHt3qU/FgVC3/yerrCmUQaszLx8inHkKNfpSY155cBuPfBPzYlNFZzYVLgC1uB1QJf+/35fhPnL6/Dt0+02I28O0XCa3tlQq6wi8uXbSVx4Ea67v4cwpscHWv/yZyyPP8bF6clXMFXGpom5KDY7o34XZ0ovUyuHEXw+rE8/ifXpJ3MHYLGQcHt476APV9DPtuASXo23galgdZFngFXVHaSWLSf2f/6Z1EnLD/h5ZwJzM5nqMnvOdQvgME0Szak4h7uosNJlM8h1JpMcoL12+p7yEkoooYRjDdMm17Isc+edd/Lxj3+cV155BZ/PR3l5OStXrmTp0qWHc4wlTAPxpGLEgkmiwJJGj0Guo3mea1HVGNuRvRFO1ba8wlVcfbJZJORECsVrA0FA0yAyFEJJKjlJFH0TuQkiKUWbVrOWmcL8+fO3Z/yoxQoazYkR5kLAYjAr12V2C8tbKwxyvWvAT6XLhvOFfoRwEtllw1pupX55C8Ov9iAPhEmmVF774UYsZVbmXDDX2Jc5Bm7idzvxv9BDmS+CGFUIr6mfsnug2VfeMxbCH0kYFiCXTSa0a4zoiE4qrW5r1v4TieD8+U+wPvO08f7ojbfkEOuZwGZSpjNjyiHXB0gnmQrmbPQBX5Q1y2vZ80c9QnJkc6G3ObX6NAI/vx/nD/4dy99fAPRmShkMJupRa+pIuj2MTYR4WrmQSy3PUk1x2w/JJKn+AVzhBFsCJ9AdbTUaMVnFJKcu9tP6ntUEL/56QQvz2cKc6jKjmUwxm4h9EtuNOe3ncMCcGGK2o5TIdQkllHA8Y0Y51wALFiygsrISn89HZWVlyWt9hMBMZFxWmTJTTFcskdtEJrrPZ3hwPXM8OKomjwRzOyyGr9IMq6Q3YcEiopZbQNXQVAgPhHC3ZpfC+/OUaw29e5y5S+RswHxjNyOeypLrYrYQM1HpGAlNSfzNSQhuh4VatyOnEC+wdxzP63pWs0USOeWW1bScO4c3H99K9OdvIveFSKRUNn7/JTxzvXjn6d+dTGMQIZpi/B+9SIKAJAg4n+8j2egiHE9NmrwQN00YBv2xnPNQVWaj+5n9xuPms+cgyiLy66/i/MGdiIODxnOJy64g/s6rih5jOjB384yn9G6XEZMtxHEInfrqPdnrcyQQw3tqm+G7ntgzRjKSxJJHIjW3h/Dt38DywnNY/vEi4xNBdvdNELc52Dm0AmfSjqRqKJLEmFTNr+Z8nM985USsr21E2rMbJiYY7OhFGx/HpekNlHaFFrA/NgdcTtTyMhpPrefkL52Ho7Waqadlh45lLRXsGQoQiCY5d0lhfcRknRC9zsNrzZjM+jG3SG1BCSWUUMLxghmR64cffpif/vSndHd3G9sWLFjArbfeygUXXDDFO0s43DBbGmwWCbslS3b0KL4sOQ5uz6rWDadM3TlQFAQ8TisT4Vz6YLNIhvqrVNrRRvTnQ31Bg1znt7POIDGFEnuwKHYcyJ10JPIKGpOhBOGnu6n8r226Z/ziVsbOilNdXjzNwR/JnoNyu0z3s/spe2AbFZ1jpOqcSOMx0DQEBJpWN9F8jt7prr3Fy0vvmY/n17uIB/Rkj/1/62DFDbrhIpz2ZNvfHIWUCqKIJAooior7Tx2MXHkinvbiTV/MWd4ampFOAlBpt9L7Qva7Ouf8Nr2ZiSlzGfROgZGbbp1WC/HJYFauEymVcDxlnG+rLBZkZs9o3xaJqjLdfqCh4VNVvPO8+Pb5dN/11hEaVheJFxQEkuecR/Kc83hl5yBPbetHCCVp/b/bwUFOcouwc4KXH5lg0XvfifeDFfx9Sz/P/G030mgUORzDPhJE2hlEKdcnVS3vmMfqL5wxaa3CbEOWRP7ptLmTPm+uHTDj8NtCCvdf53FM2TCphBJKKOFYx7TJ9YMPPsgdd9zBhRdeyCc/+UkqKysZGxvjySef5Oabb+bee+/lwgsvPJxjLWEK5C/Bmz2u+e3PA9uy5Lr+lOIpIWZUFCHXVllEThMLpdKONqw/b26DPuSPFo2uOxxxfJO1Dzcru8ZxFZXgn/by5/UvkoqmcIQSxJIK7t/s4TVPORf988qi+8o0dJFGouz/7nrCO/UiQGdUIbYnGyMn2yROueVUQwGfV1vOPxwy4fOasf9hHwD9L/ey/JOnoIHedU/VsL8xipj2aouSCIqKEE6y5c71zPvxpfo2EzRNK+hCubM/m2jh2B8knG5t76xzUWsZx/F/78u+v7yc6I03k7hg3SERa9CJqiyKhiXFvGJR4bIdsg2owes0vL39vijVJ9Xh26ef85E3h4qTaxMyfmXrPr9x3dYsq6U7FkV6RVfw9z/VSfcz+7HVONm/dwy3KWFHA1KyBVkUaT69iVM/t+YtI9bTQbGVAVkUJ1W0ZwvFlOt5JdW6hBJKOM4xbfnwv//7v/nQhz7Ej370I6666irOOeccrr76au677z6uvfZa7r333mkf1O/389nPfpbVq1dz1lln8e///u8oSvFisr6+Pj7ykY+wYsUKLrnkEp5//vlpH+d4gjmj2W6RCpqjGAWNSZXA7jHjuYaVU5MSKK5+2WTJUJ+VCpvROS7Uly1q7JsoTngPRxyfmVznWBRMk46M59q2YwLfXztIRbPZ0gCoGvv/czNv/HAjSpFujf5IArk3hPf+HQRM6n95nkpXftFcyhqzBKOtugxREEjOKSchCaiqRnggRGC/n2hCQUPD0h1E9sURBAGLy0LFx5cZhNe/fZSuJzvJR1JRjfOegTlyUXkjW7TaelYTZXd+G9LnXlm0mMB//YrEhRcdMrHOwGZaLekfz3rtK2ZBPc00lQG9qLF2ebbl/NBrA0bH2Mkwkk66se7xGddt45pm5MvmkWzXV1qU9D6Gunyo6fMoiyIehxWrJCIJAo0r6lhz+9mI8uyuvBwqiq0MeF3WWa9tyEexmoyS37qEEko43jHtO8TIyIgRwZeP888/P8cqciDccccdDA0N8cADD3DnnXfyyCOPFG1Co2kaN954I16vl4ceeoirrrqKW265hZ6eniJ7Pb5hzmh2WKWcm61Zubb0hdBSOolwt7pxFOnKmA9vkaVfqyxmbSFVdjLcxpwYMhm5TqamJkIzRUpRc7pHtprydc2KfuYcSEMRxHTSQ1ljGSfduBKlXvdex1MKex/dxd/++TH6/t5tkLaUohKKJnA93YOgaEiCgCDCgqsXc+nPL8f5vsXEVtUSvqCF1msW54zPKkt6IxBZJDnPbRT89b/cm7WEvD5i2BTmvGMe1ac2El2jryooqsbOB7ei5k1K4lPFGiYUwpuyxX4Lx9YjdqcjN+x2wl++fdJYvYOF2RqSr1wfKhpzihojVC+rIy3y49s7wa7f7Zj0vaqm6cp1UsXaGcCSzppuOL0Jl9NK4D3z8X9wEZVnNKEI6QJTQUCpdTL/8gWc+uEVnP/pM7jq7ot4132XIdtnXKpy2OEoksbyVrQft0hiTi67KAi0VZfyrUsooYTjG9O+S5x++uk89thjnHnmmQXPPffcc6xcWXwpvRief/55vve977Fw4UIALr/8ctavX89HP/rRnNetX7+ezs5Ofv3rX1NWVsb8+fN56aWXeOihh7jtttumfbzjAbE8z7Utj1xnyK+lO2gIlVNF8JlRUaQoyma2hVTYDP3UnHVtjuETEAyVdbZtIcPBbLtnr9OK15ElFeY0jUxBo+RLGOfgxOuX03LeHJ6yp1D+sBfrzgmSKY3wYJiX7niRulPqOe0rZxKRQO78/+zdeXxU9bk/8M/ZZp/JZA8QIAEEZAuLuCIq2CK4oLa22qu1Ra/WWqxtrVXr1uu12uvvWlu3F97ba721t1Jb22prF1TqLsoiyE6AECCQfZJMZjvL9/fHmTlzzizJECaTBJ7368XrNcuZmXNOwuSZZ57v83RDbOoFz3PgbQIWP3kR/BP1RYmX3ngaXnivHg6Bx5za9DHnEyq8ONAeROwUP6L7e+C0iWj64BAcnxsPvjsG2+4ABIEHGMPk2jAO792Gj2e6wTaK0DSGYFMQB/95wNJlJNpH60D7rgB4lQEcB79fQ+U/f2fcF7r51kHpamH+xuCwKXNdkofg2twxpLkrAsljw6TlU7DnD3obwa3/swnFpxSjMsMagu6wDFnVIDV0g1cZeB7wjvXBW+2D+2AbwHFQxnox5gs1OHxOFQJ726EW2zFxXAmWLJg46NnffLCLvOX/GFCY4BrQS0MS/djHFLss7z2EEHIyyjm4Xrx4MR577DFce+21WLp0KSoqKhAIBLB27Vq8/fbbuPHGG/G///u/APTx6Nddd13W5/L7/Xj11VdxzjnnoLu7G++++27Geu3Nmzdj2rRp8HiSmZB58+Zh/fr1x3KMJwXzABSnJMJm+mNr6e/cGDzm4DpT5touJctCNL/daPkbbgtDCcvQJN74Kh7Qv9ZPDHnJe3DdlXyd0X6XpTzBPEgmURYiBKLGOXCP8oDjOEwY7cdnyyfAMb4V8oZ2SPG4tXnjUXz4o3dQc+cZcL7bpD+e5zBh6SQjsNaPz4UfXDwTDDBGhJtNrPBi7Y4jiE0sQlTRM8gdu9pRfDQI50dHAcbAAxilHsKYn/4XSmUF1cEoNsuzUB+ZAtgqsfP/tmLcBTVGrW9fmWvPjk5wHAcuGsHkpg+AeGwqzz8dsYsvzem8Hitz5rrZ1LUkH2Ow3XYRPoeE7ogMRdPQ2hPBzH+dg47d7Wjf1gamAR89/B4ufGZp2gTKxO+hvb4r/m0Lh9Fn6R8uzGPD1+9vR0MoAoxygwOHpXVjRkRgDejvuQ6bYBncU8jgOjGZcWIllYQQQkjOwfV9990HAFi/fn3G4HbVqlXG5f6C6wceeAB33nkn5s6dC03TcOaZZ2LlypVp27W2tqKiosJyW2lpKY6aWogRnbksxCEJ4DkODom3DkZRNIhNvYBPLwUpn1WR+jQZZQqObKKQbKfHcxDKnECP/oc92BREsNhmZNHK3XZIf21A0e529F44Nu/BtXlEuN9lswR5lm4hqqYHsYEoOJv+VbZntB6ITazw4rNDnYjMrQA7bzwm7Qqi/rXdAANat7Sg80fvQjwUBAMgiAKmfHla2n5wHAdLKBYMwr7mb7D942+Y2dGBla09YAx4Vz4LSrQWgt2Gvas2wbGhBbymwdfWiullmwAvIMSDulOF7TgSqgAaQggGOnF4zS5UL9HLTrJlroX2COwHegAbB+HQQUwcp5dRMV8RQt/9Qd5qrFOZM9fmuvp8BNcAMKrYhe4j+oLNI4EwKoucOOu+hXjjltcR6Ywg1h3Dm9/6O2Z8vQ61F000PoS09kQAVYNUHzB+Z0efWQ0A8JiC632tyZKmMyaWocLXf8nUcOKUhia4Pq2mDNsOBSAJPE6rzdzVhhBCTiY5B9fbtm3L24s2NjZi2rRpuPXWWxEMBvHQQw/hJz/5CX74wx9atguHw5Ak62Ixm80GWe57at3JyJy5TixmtEuCJbgWOqPg48M03FVuOIpzCx68jvRe13ZTzTUA8OUuoEcvCek51I2w22/c597TBeX9JoiyAtc7TZCvnDmAI8wuUbcMAG6HCJG39lxOkBUNXEgBF1PB2SWIThE2n16yMMG0CKsxFMW135wHe4kD2365BQAQ2NqafI2zR8NV4c66P1ywB47nfwH7P/4KRJJZ9dJwBFFZxdTwNuzo5MFVV6Pr0xhssSh83R0YbzuCGo/+Otr4Gig798KmyKhxNaIhMglcZyfq73gB1bNvByorjTHjgB7YJjqHODa1wqYpEA4fxVhnCzxSFMzjQfAn/wlWNnjBT7ZygHzUXAP6txK74sF1UyCE2eNL4Cx14qz7F+Kfd6wBUxmigQg2/HQd9r66C/NuPwMlU8vQ2h2B+61D4IMyRKcNdp8dpdPLAViHCCU4bSIumNZ3i8rhyGkTANPMpmy90fNtbKkbd148E6LAZfzWhhBCTjY5B9eCkJ86usbGRvz4xz/GW2+9haoqfcGW3W7HihUrcPPNN6PM9MffbrcjGAxaHh+LxeBwpPch9njsEE0ZS0Hg4fenTzI7UTGe12t2AZQWu+D3u+Bz29Fjmv4nBWXwHAdB4FFSUwy/35XzeSovcqLNPKa71IOWUMx4TanKA+GA/rPSOqOwOSSjhph7vwmCwIFTOEitYdgcUl5/Ngo4Yz8qS/RMdOI6Zz4+noPULYPjOIgCj+LxfhQX60Gy3+9Cpd+Ftp4INAAvfrQf0kQ3+GklsO3sNCpZOZ5DzZemZ9//w4ch3PFdoKFBv276AGKXBEQVDVWOFuzsVSAcOggPL8ITjcAthHBO1XaIogDtm9+E+C/X4okXPkB14x6c9Y8/QdyhQWU8OnskRP/tKVS++P8gtvcaxzl5dDF2Hu4Ei6ko/Wg/ijo6wHHA9JIjELxuqD/7Obwz8/uhJlWRx5E879B/Bj6nDeWl+Vngdkp1Md7ZrXdAaQ/FjJ+Bf8F42B9fgg8efQ+9LfER5w3dePf7b+LsuxagfXMTnBtaAY6DXRIw78a5KC7Rf+4VpR5jnxMum1+D0RW+vOxzf/L5PlXkceCoqRRrfFUR/FmGvIxEJ9t7+kDRecodnavc0bk6NjkH1wcPHsTDDz+MzZs3o6enJ+M2W7du7fd5tm7dCrfbbQTWADBjxgyoqoqmpiZLcF1ZWYmdO3daHt/W1oby8vK05w2mjK72+10IZOl9fCIK9ESgJtqsRRUEAiHwjBm3AYDUof/hVVUNot+OQCCU83lySwKaTc8V6Y0iFlGM51eKbRDil5t3t0GYXaq/zoEeKI3d4DhO77zRK6P9SBcCeewo0N4VNvaDU1TImmZc7wpGjeMLhmPgOsJgjEHTGGylDsuxjy12oTl+fe/ReL/oM8sw+WgvlPgHi8iMUjjLnBnPmbBtKzwP/BBqV7LntTq+BtHlV0A5/UxsOhjAprfW4dJX/we+rh4E1SLwShQ8p2J+8UbYBIae27+P2EXLgJ4obDYJ+8dOxv4Vd+CSNdtw+G/1AICmDw/Au/r36DhlPjRZQfWhekxqVlHaFoD2TgM6W23geB5eWxhV3iC6HnwMytiJwCD/f9AU1TjvgsBDVTV47ULe/h96Rd54/gMtPejs7DVqor3TSrH4uYux67fbsWv1dqgxfV/++eA/EewJG11fqs4cg9EX1Rr7xGKK5f9Ipc+JaRXegr135PN9yvz/nQMHFlMQCGRf9DrSnGzv6QNF5yl3dK5yR+cqXXkfPf1zDq7vuece7Nq1C1dffTX8fn//D8iioqIC3d3dOHLkCEaN0r963btXH6xRXV1t2baurg6rVq1CKBSCy6V/YtqwYQNmz5494Nc/UZnLHxzxwRGp7bn4rmSXDFdl9rKGTPxuG5CsjIAk8pYR5qwk+W1C8HAPHPFyFOfHzWlfFfce7AHymEQNWspCJEvdaTSlFR8f0IeqcBwH92jrf4zTakqxYX+7tXe0TUDL8hp4/noAclRB7/ljrK3HDh2E9MF7kD5eB/GzT4F42Q1EEaHv3WkZ0OJiLuydOBMvXH8XFv/ydwgeKgLAMLNkO/bNnQ3lmzdgzGmzjOf2OiT0xtvCiRfNB1vXDq6zEy1hH2Y/+yT8X1iBFS/9GhUth+CJl+68dvA0ADZwHDClJoLgk89AnXjK8Z7inJgXkibkqyQEAIqcklH+ElVUhGKqpaxDdIiY/tVZGH9hLd5/4G10N3RB0xi0eLkMK3HgnHvOsSxSdKf0KF9aV22Z3DiSmAfJFLmkEXschBAy0uUcXG/duhUPP/wwli1bdlwvOHv2bJx66qm4++67cddddyESieD+++/H8uXLUVJSgo6ODtjtdrjdbpx++ukYPXo07rrrLqxcuRJr167F5s2b8fDDDx/XPpyILBMa4+Ux9pRBF0J3zFhw5646tuDaPAjEJvLgOc5oxQcAWmkyuO451A1OUSG0hWGrD4BLCWDCTZm/+RiooKn0xWMXLQNFLDXXqgZbQP+Gg+MAzyhr9nxMiRvfXzYDLd1hMACr1+1HRFYRsPPouXICwHFgqgafUwLX3Azn8/8F25tr0vaH+YoQ/NHDUGdYP0EkFph1llTine/+K75avx0fHQngd/O+h7DLi5WTrUGw1yHhaKLrxjgvtNIyCMEgWiM+oDeEqc89jmB8MScHoJeVIqQVQRIAvtiHyhe/B7Vy4B+Ej5V5IWlCvhYzAvoHohK33TgnHcFoxprpZhGI3jAd/j/tQ/NHh/UbRR7266bBnlImUeF1oMRtR0dvFLPHlYzoAShO0wj0Qi1mJIQQki7n4HrcuHFZpyge0wuKIlatWoUf//jHuP766yFJEi666CLccccdAIAvfvGLuOKKK7By5UoIgoBnnnkGP/zhD3HllVdi3LhxeOqpp9Iy3ASIxHLIXMcnAAKAq/LYyjLMGUhbvP5eMgXXqluCYBOgxlTEumOIdEXh/EQfYsJxHMxtNCJNplVXx0ljTB/6Eee2i5aR4IkPHYwxyAqDMxFcA3BXpZ8Dr1OC16l/GJgxphjrG/RJjKrGIAgcBEXGqNUvwPfH3wEZFtYqs+oQuuMuaKPSJ18WuZIfMto4O7x3fh0bXttiLDp1pfy8KnxO7GnWF4keFRjsJS7EolWIHTyIQMwFnulZ+C7Oh0/dC9HS4gVKeGguF8Z9oQ62AgbWQJbgOkOP9ONhCa57oxhbav2QuPVQJ367rgEMDO6zyjC10okj7x5A+IwqnDolfTEnz3O4ZfFUtPZEMKZ4ZNcTWjPXFFwTQshQyTm4vvfee3HvvfdCEARMnz4946LCysrc+iZXVlbiZz/7Wcb73nrrLcv18ePH48UXX8x1N09KjDFLV5DEdEZHSvcGoTtmXHYfa1mI6Y+1Q2BwP3gvpn/8MebPvBCfnH4hFMbgqfaia59eb9y9/gjsW/Ux6zwHFC0aj+4/7wEARI8E019ggCIx1ehikhjJbi5PSATXqsbAwMB3RsGBy1gWkqpufIkRXAOAFI3gyt89A1+gAeZPC/L80yEvvADy/DPAStMHyCTYRAEum4hQTIHGGLpCMcvPzZWShZ1Q4cH7e/QFfPtag5h7ahmaAhGwkhK0RHyosHXh/bGLsLNxAnxhJ1xl+uN5kcekK6b0eWyDwZZhJHg+M9eA3lM5oSNlncXOpgBe/rjBKOvplVVsHOsE+4p+Lip86e9ZgP7/ZGzJsf1/GI4mVfrw98/0TP3UUUVDvDeEEHLyyjm4FkUR4XAY3/ve97Jus2NH9hHEZPAoGjMCTJHnjeEuluBa1sD1yuAkAZzAwVl2bFm6UX4XvA4JPREZ5xzaBun9dwFFxYVv/ha8pqLxoivgHZMMrrt+swOIZ5DdE4tRev44HIgH17E8BtfmemtPfCy1OYOaGCITUzVA0cAHZXAcwPGAq6LvczCu1A2/y4ZAKAZHOIirX34S1UcOAEV6C0N18hSEb/4mlFm5rwHwu2wIxWvCm0yLQ9w2Ma02vabMY7RAbOkOwz25GPjwELSychyady3Ah6a8AAAgAElEQVQ+PLcC+57YBZ6LGLX0/knFmPWvc+CfUIxCSy1DAvIznTHb83X0JoPr+uZuvLRuv6VdJABL/XyZ98TpnJFJVZET3/78NEQVDaP9I6tHNyGEnEhyDq7vv/9+uN3utHZ5ZOiZS0LMWVtz32GhO1FrzMFV7jIGbOTKJvK4ZdFUHOnsxZx/f9Z4LgBYtPb3+NjtgGfsBcb2WnxhH7MLqP3X2eDKnHqhM2NQ2sJQYyoE2/G3d+y1lIRIxr4mplMqmgZVY5AVDUJXVG8NyHNwlrshZOjLzHV2QNy0AdLGDRA3b8J3m1rRovIQFRnOaMho2xa5fgUiX7kO4NMDyr74XTYjqD5sGg+fmrXWj0PPqB5o1z+M9FYmA6bWFqCjjQffqXcxEV0SFv77BaiYWzVkUwVT+1wLPGeU2ORLiSkTngiuFVXD7z5pgBr/nStx2zGh3Gv51gEAyr2ZM9cnkrKT4BgJIWS4yzm4PnDgAJ555hksWLBgMPeHDEA4wwAZIFkeAiDZJQPHXm+d4HVK8Nc3QtqzO+2+s17/DVovT9YZMwZA5NH9xUkonuCH56+voSzSgk6xBEwVEWzqQVHN8dcEByPWxYwAEOuOwtErI+zWr0cVFTElpVOIqd5a/GwzpPfehbhpPYT9+63HzDSEg/FFhRwHnucRuu07iF16+YD211wLe7jDlLnOEFwD+nCbRHDd7NG/dWAqQ8/BbkTXJo+9auFYVM4b2sEntpSaa7/LlvehIuaykPZ4WUhzd8T4kOWyifjauZPgdUg40hUyPsDwHIdSDwWehBBCBl/OabcpU6bQ2PFhKpoy+jzT5US9tR5YDry+1PHKy8blyPmLcbB6EgD96/fKN1cbi/wYB/RcPgFqtRvj/+cp1LzwLMb0NqK4sxWMMfQ0dg94H8wsmWuHiJ7DPfjLV/4I71NbYNvZCUBvxyerGgTTYkbPaA8QicD1+H/A893bYH/l5bTAGgBEITmJUhVEbLnh9gEH1oC1dt2cufY4Mmd4zd0r9gdC8E9KlntoO9qNy9WfmzDgfcqX1LKQfLbhSygyBey9UQVRWbWU19SWe1HstkMUeFx1eo1RIjSxwguBWtMRQggpgJwz13feeSfuvPNOBINBzJw5E253eoA2derUvO4cyY05c21ux+UwlV3wXaYuGce4mNF4jqbDkN5/x7geuuZarD6lBV974VGUtx9BGdeOqa4GHKqYi6ZZYyGPdeKSPz8P/8EtUMHBI/ZCCkVhCwXRfbBrQPuQylJzbZfQ+MZ+qDEVPAe43j+C2BQ/IrKKmKpB6EyWxnjEEHy33gS+8YD1CUUByrQZUOaeBnnOPGhjx2Ln7ia8uX4vIt4iXLJw+nHtrzm4NrcJdNsy/1esLnEbvZ0DoRick4rRuasDQLKltlruRNnU7AspCyW1W0i+FzMCega62G0zstadvTEcMQXX5lrjUo8D3/rcqdjb3I0ptMCPEEJIgeQcXF933XUAgEcffTStppMxBo7jaEHjEDEPSjHXvTrM4+CNkogBloUwBvvvX0ZifZhy2nxwEyZA3tKN15deh6+++Bg4DjhXXove68/FkwdjuOrl/8HEfVvBFTn1vtKiXt7g6upAz76OARxput6UNnytW/TuGjzHQWgJQTwaQlTRICsa+HjmWlJiqHj5v8A7mozHygsWIrr0EigzZgIu60LHGXO90Mor4PHYUXuc7dqy9R92OzL/VxR4DuNLPUZLvnBl8vUT/bwjdWVwZAnOCym1W0i+2/AllLjtRnDd0RvFkUDYuG9Uynhev8uGebW0RoQQQkjh5PwX+fnnnx/M/SDHIVMbPiAlcz3AshBh7x7Y//gKxI3rwbe0GLdHvvAl42v2w9UTsWHueRi9bx0ADq6fPoavHu0ANL1chec4dF14EYRXPwG6AV5V0fv+ZwDOG8jhWiSGqACAS+BwaEdb/Dj12+yb2xD9ggpFS5aFOHu64HXGB9nYbAit/A5iS5YmH5SC4zjUjSvJy/jXbMG1x5594d/ECq8RXLcU26BvyfS6dp5DdHpJxh7ThSalBNclnsHptWzuGNIWjOCoJbimLhmEEEKGVs7B9VlnnWVc1jQNgUAARUVFEISh/6N+srNMZ7Rlzlyby0JyHX0ufLYF3ju/AyiK5Xa1thbKvPl6dpjnoGoM/zzvcizr2gehvQ1cOGQE1gAQ+dI16Lzma/i0axXwf/oM9eDuZqC5BaisOObjNTNnrrXGILR4/Xni2xX79g6Ee6NgogAhEAXPGGzhXvgkPSALPvLYMbXSO15OmwBJ4CGrmuV2lz37/6MJprrrBjmGmWVOhFtDYGCITS6G4LEPi3pinuOMEhZgcGquAeuixj1Hu6HEf9e8Dilr7TohhBBSKMfUR2zLli244YYbMGfOHCxYsAA7d+7ED37wAzz55JODtX8kB5bg2twhhNeDnUR/ZwB6G7ocelzzR5rgefBeS2DNnE7I55yL3nseMLK8YrwVXczuROCW2/Tt4qUjDeOn4jdfvxvRm26BJArYOn8BYOf1FnkqB/HmW2F79Q8ZJx3myhxcR3YlS00Si964qIqWDw8j0hkGZA2OcC9snAwbr0CtqYUys27Arz0QHMdlzF73lbmuLHIaNdnhmALP9DKj3joypzxjf+mhUuHVM8dOm4gyz2CVhSTPX0Nbsmf6aP/InrBICCHkxJBz5vrjjz/GDTfcgNmzZ+PWW2/FT3/6UwBATU0NnnzySfh8Plx//fWDtqMnO8YYOnqjKHHb02reLX2uU8oDHKIApTtZymArdYIX+gnGgkF47r0LXLe+6JAV+dF734NQps8EROuvjChwSMS3odPPhvMH90D5dDN+w41BQ82pRiZREngwnkdnzSgU79KnyHW1yBjz5BNwvPwSeh/8d6gTT8n9hCR21bSgMbgj2dfYPs6L3j16sN2+thGqTw/IXOEgPGIYHAfEll6ctRRkMPldNrT2RCy3ZWvFB+gfFMaVebCjSR/Q411+CmxeO+rbuyDX+ODN0K97qFxx2jis39+O+ZMr01rz5UtJlqCdSkIIIYQMBzmnvB577DF8/vOfx69+9SusWLHCWEx1yy234KabbsJLL700aDtJgN990oAn/r4dv/24Ie0+S811ymAWh000ao0BwF7eT3aPMbgffSjZRUOSEPzRw1Dq5qQF1gCMNnUAoGgaYhcuQfNNt6Gh5lT99eJZ1cR2XTVVCPjLAVFAIKbvC3/0KNw/uh+IRnEsYopmlCAIGkNgZ7I1XcVXZxiBc2h3Bw7+5ycQFRmSHIVXigCiiNiFnz+m18uXogyZ676Ca8Baqx2x86j56kxE6/SFepnGjg+VCp8Ty+qqccogdufIVm6SupiREEIIGQo5/1XetWsXLr9c7++bmjk966yz0NTUlOlhJA80xvDZQT1rufVQJ5SUet1olrKQxPXEYkYAcPQz8lvYvxfSuo+M6713/ADq9BlZtxdNwXWijtjcYi6RSRd4Dhw4qKUOhB0uKDUT0Db7AjCnnm3kjzTB8eIv+9y3VL2mrLW3NaLXWzMGnxDEjB1vAGP1IIwxQImpcIX0EoJyRw/kc84F8w1Ne7bUshCe49J+bqk8pm4iPWEZMSX7z/xEJwk8fBlqqylzTQghZDjIObguKSnB/gxDNgBg3759KC4uzngfOX6KysASPfBgrbFOvZ4aaDklAUJXMrh29rOYUdy4wbgsL1gIedHn+t7etJBOUfV9NAf7iawqx3GQRA5KmR4AMY7D3qNeNF1+U3Lff/sbCPvq+3w9TWNGYGmut3Y2xSfxdXRgTNN6TPjjb7B8/0vwBrvAVAVcJIxytQWTPfWYXnwY0YuW9fk6gyk1uHbbxX5Hlvscycd0R+SUc3xyBddAemmIUxKydmIhhBBCCinnmuvly5fjiSeegM/nw8KFCwHoXUPWrVuHp59+GsuXLx+0nTzZpWaqI7Jq6YqQKbg+8MY+NPxjPzDebfR3BgBXeX/B9Xrjsjz/jH73TcqYuU7ur7nvtiTw6B3vhVbsADRAjar455sclk+ZA8+uTYDG4Hr8MfT8/FmAT//cF4opePbNnQhGFFxzVq2xqA8AxAM94MIhcO1tqKoKgOeASqEFS+1/hRx2w8t3wFEcBQcO0qgahOee1u+xDZZMwXV/vKbMdTAiW87xcCoLKZQSt92ymHGU39XvBxRCCCGkEHIOrleuXImmpibcddddxh+xa665BqqqYtGiRfj2t789aDt5spO19OA623WHTYAckrH+8XXQZA2R92Owq8n7PVV9DJCRZUhbNhtXlbnz+t03c1lI4kNAzBxci9bgGiKP7i9MxNjXGoGYhlBLCG94zsOlwlYIqgxh1044XnwBka9+Pe21th8OIBDSs/Dv7W5B3dj4tyWqBr4hAP7IEQBAlbMLkGxAbxR+qRsc12OsERAEDtHLLs8YvBeKP2VyYV+dQhK8zuQ2PRHZUnpzspWFAOmZayoJIYQQMlzkHFyLoojHHnsMN998M9atW4fOzk54vV7MmzcPM2Zkr8klxy9T5jrbdYcooGt3h6nfMwBTsOselT24FnduNxYVaqNGQasa1e++WcpC4qlka811MohNZLnVciemffd0bP/JRwADWveFsGHyVTh99//px/CrX+rHdd3XLN08WrqTHTYa24MYV6pn4cWmXrhbmgFFgVcKw11iw66Hn8V7//dnnPPB6/B1d6K9rAot5dVgs2Zh/he/3O9xDSavQwLPcdDiAb8rh8y1+ZuK7rCMqGz+AHMyZq6tH1BoMSMhhJDhos+/6k899RSuuuoqVFZWGrdNmjQJkyZNGvQdI0myyizXw6bWe4qqGeUYHPS+1t0NAeN+3vxVOcfB08eCRnO9tZJj2UTGspAs49jN2/rnVGHG1+qw9Xk9U74/PApz58yFuGkjgHiALcuIrPhXI8Bu7kpO4lM1hu2HA+A0DbNffwNyiAE8jypnAKE77oIwZgw2zz4Xm2efC07TwOKZ6gWTKzN2PSkknuPgc0pGFj6XshCnJEDkeSia/vPuMU2mHA7TGQutxE2Za0IIIcNTnymvp59+Gs3NzYXaF5JFX5lra3kAD47j0NXQZdzmmuA3glNltBu2PgI5S731nP5LQgC9z3VyPxOZ68z1wObgOqZqOOXKqeBF/fHdB3vQfsePoJw2P3k8L/0a3m/dDOnttYCiIHLgIE7Z/SlO3f4Jijta0B4I4pI/Pw/nng7jGCsWT4N89gLYpeRrMVMJSOkgDTY5Vua6a08OwTXHcZaOIW2mPtn2k7AspNTrAAf9Z24TeZR5HUO8R4QQQoiuz7/qiTpVMrRSR2Wbg2trvbX+4+w+kAyux1w5BTvnl0Jq6EZ0WoklwLXo7dXLQuKU2XNy2jdLKz4teys+ID3LLTpEFE8uRft2ffhL254u2H/0Y7gfuh/SRx8CAITdu+D+9wehgcP1nckFbAAgSzawKMPf5UUQeQA+L/z3691HstUhD5fgutSTXJDnc+bW5cKc7TYPoTkZy0IckoALp4/CJ/vbcN7UKus3NIQQQsgQOvn+Kg+hLQc78MTft+HtnUeP6XFKalmIbBr5HUtf2Na1P1kW4q/xQxnlRvisUdCK7FmDa+mzzUi031AnTgIr8ue0b5IpK6xmWtBoLgsR00tIymaUG7e1fdYC2Gzovf8hRK/4IiAl64wV0xh24/nkGFqjpfpr+4rgP38m7CV6HbYk8EZm02y4BNdnTapAmceBmjIPpo3J7Vx7TXXXiSAbODlb8QHAwqlV+N7SGTittmyod4UQQggx9Pt99DPPPJNTD2uO4/DjH/84Lzt1olqztQmBUAxvbGvCnPElOWcs0zLXsSyZa0lAJBBBNKBnNQWbgKIxXqD+iLGNeQGimaXeel7ubeqsQ2TS+1xbFzSml5CUzazArt/uAAC0ftYS31BC+JsrEbnmX2D/4yuwv/ZHyG0dCDk9aKmohiYIGN20H45ICC3RcvS6i+CqrETV6aON5+c4DnaJt5wfSeAtAepQqixy4ttLph3TY7Ltu7kEhhBCCCFDq9/gev/+/Th6tP9MK/WY7Z956ElTIJxzcK2mtOIzjzsPpwTX5pIQX00RnKZ6XpHns/6cBlJvDaTWXKeXhfRVcw0AZdPLAQ4AAwL1HZBDMiSXHkSy4hJEvn4jItevwN8+2IX3j4YAjoNdFBCLyShub4b0wkEoMg8Px6FqfjK4BvSSFHNwXeqxj+jf06zB9UmauSaEEEKGo36D68ceewyzZs0qxL6c0Bhjlgz0kUAIU0flNn47llIWYs4Mmy87JcHSKaSoxg+XLfkjzlabyx89AuFAg35FFKHMyP3nbQ6Yk634Mve5tmS549vYvHYU1frRtS8ApgEdO9pQOS+lBSDP47AqGIsW59aU4sP6FnSpRfDLh8FzHOxFdhRPLrU8zC4JQLLByLApCRkoc69rMwquCSGEkOGDvk8ukNR2ekcC4SxbpkvtFpItc22XBEu9tW98EVx2EbPHlYADhzMnlSMT2+t/Tu7nnLmAI/fOC+Yyk/5a8dkytO17a/sRHPKJxjEapSEmjDE0m3pcz68tg8jzsO3Ts/QCz6HqtNHgUkpeHCnlEqWekd1RgspCCCGEkOFvaBv+nkRk1Tr45UgglPNj+2rFl1pz3WXKXPtq9IVyX5hfg0vnjMs8JluWYf9rMriOXXxZzvsF5DKhMXNZiKJqONTRi7U7jsDmE1AZkVHstuuLGlMEowrCMb2kRm+7Zse4Ujfa93cD0PtGV81PH3iTmtFNneo30vgoc00IIYQMe32mvK644oqcFjOS/pkDTkDv9hCKpXfAyCRRbpGQbUGjXeTRbepxXVSb7EKRMbAGwL3zNriAHpCz0lLIZ56d0z4liBmy0bEsrfhSt23r0adBKmM9RmDevqMNasz6QaTFNDym3GlH+/Y2lO0KQDykt7LjeS69lATp/Z/LRnhw7cmSuc72syWEEEJI4fWZuX7kkUcKtR8nvNSOH4BeGjKxwtvvY1MD82yZa6lXhtyrT+6T3BKcZf1PreP++EckQvfo0ksA4diyoJYOIBoDYwwRObchMokpg5rXBsVvBzRAkzV07unQFzrGJUpC+O4YpP/egbWKvsiTZwADh7LJpXAUpx+rIyWjO9Jrrs1TGhNEnrd8aCGEEELI0KK/ygUiK5mC69xKQ1LLQhRNM24zB9dqU69x2VdT1G9nDP7QQXCffBK/wunB9TESeWuph6IxsHi4LvCcJfAz97lWVIagaYR3ZIwbiD8utTSkNR5cu94+DD4ox5+bR1WRE5VFDsxaPiXjvlnqvUU+pzHjw1nqlEbg5BwgQwghhAxnIzvaGEFiGTPXOQbXWvqkzIiswiPwlhIRtTkZXBfV9D+YxP6XV43L8ulngVVU5LQ/Zqnjz82LGVOHm9gE6+JHc4vBWLUHWmMIPAfs/9tenHLlVAg2/fHN3WEILSHYt3VAcuvZ5zELxsI/wY/SaeWomFuV+fhMC/1KPY4R3YYvweuQrANkTsLR54QQQshwRsF1gWTOXOfWMSQ1cw3Eg2uHhIipvjl2ODke3FxvnVEsBts//mZcjV6yPKd9SWVtxaeltOHjs24rK5qlVCZ2ShG4DR1ATEXwcA92/HorZny9DowxtHRH4Hq7CWAMoqAvXjz7gYX97pu53nukl4QkpHYMocw1IYQQMrzQX+YCyZS5buuJWhb/HctjEy34LIsbD/UYl339ZK6l9R+D69a7bWgVFVDmn97vfmSSukjRspgxJasqpWzbYyoLYS4JY7+SnFi4c/U2dO0PoDssQ90fgK0+AJ7jIAg8Zt4wJ6d9m1SZrGefnuOI8eEutdc1dQohhBBChhfKXBdI6qJEAGBgONoVxrhST5+PVTOUhSRa0xk11xpD6FC3cb9vfN8DamxvvZHct0WfA/iBfc7ymOqYA6FYWvcSM3PNtaxqlpprAHAvqEbpJ0fQvq0NTGVY//hHcJ43Fp41B/XHCzzGL6qBf2JuHWwqfE7cvmQ6orKKUf7+F3eOBKmZa+oUQgghhAwv9Je5QDJ1CwFyKw3J9NiorEJjzBg1zvfEwOJZbLvPDoe/j4EpoRCkD983rsYWXdjvPmRT5LIZixp7owq6w8mAOTWras5yh2XVMgAHAHplBfNuPwO8qNdGd+xsx44nPobQrNemSzYB079Wd0z7V+qxY3Sx64SotwbSe12nfjtACCGEkKFFwXWBmDPX5g4buSxqzFRzHZZVS+bX1aMA0ANIT3Xf7f1sH7wLxOKL4iZMgFY7od99yIbnOMtwlsOdyeNJX9CYPO5AbwypghEFRTV+TL1mhnGb+byNvWQS3FV9Z/lPdKmZ69R2g4QQQggZWlQWUiDm7PPoYhca2/XFhwPNXEdiKrpMWWJvj+lyta/P55PWvmlc1pYs6ff1+1PmsaOlWz+OJlNwnTqWO3XxY6pgVD+GqVdPR6QjjPYdbTgQDEH226FUuXDmN+cf976OdGkLGmn0OSGEEDKsUHBdIOYM7PhStxFcN3eHoWoMAp+9bEFRM7fi6zK1ZHOYgmvPmOyZa64rAGnDJ8Z19rnP53YAfSjzJktQmkyZ+NSyEKmfYSe9Eb2OXLAJmHf7GTjQFsS7b+8GoJd3FJ0gHT+OR3rNNWWuCSGEkOGE0l4FYs4++5w2Y6CJqrG0hX19PTYhnBJci51R43JfwbX0zttA/PmUadOB0aNzO4A+lHmTQa95X1MXNPY3STCRuU5oaEu2FhxfdnKXgyQ4bYLlgxi14iOEEEKGF/rLXCDmFnWSyMNjT2Yge6NKn4/N1ufaXBbCdySD677KQmxrk11C5ONYyGhW5sm8eDI1q2oeOJNJMGI9D+bguoaCawD6lEZz9poWNBJCCCHDCwXXBWLO6NoE6yju/oJrOcuERmNSn6qBtSdrtz2jMweiXGcHxM+26Fd4DrGF5+e4930r9WYu10itB+Y5zrKYM1VvVAFj+rFqGjNKZwAKrs0swTVlrgkhhJBhhf4yF4i55tom8nAdQ3CdMXMdU9Ed1oNrvitm/CCdpU6IKe3aEsRNG5LPOX0mWHFJrrvfJ5dNhNuWXr6facCJ1Ef22jzh8UhX2DhnPocEv8uWl309EYz2u4zLFb4To383IYQQcqIo+ILGV155BXfffXfG+9auXYvRKTXAK1aswPvvv2+57emnn8aFF+anpKFQzJlrKSVzHYplD64ZY5m7hcgqwnJ8AWBn1KjD7bPeeqMpuJ4zL/edz0Gp14FeU6YZyJxVlQQ+rb+1WTAiwyEJOJBSb32i9KnOhwumVcEuCSj3OlBZRME1IYQQMpwUPLhetmwZzj33XOO6pmm45ZZbUF1dnRZYA0B9fT1++tOfYv78ZBu2oqK+pw8OR5ayENEaXKcu5DPLNJ0RAHoisjFARuyIQuASPa6z1FszZslcy3NPy3nfc1HmtVvKOIDM9cBShoDbKQlGwB2MKijzptRbl1NJiJnbLuFzM45/ISohhBBC8q/gwbXD4YDDkVwA9+KLL6KpqQnPP/982rbBYBDNzc2YNWsWysvLC7mbeZdaFpJrzbViCq55joMWr0mOmhZIunpkIB5ce7Nkrvmmw+BbWgAAzOmEOmXqAI4iu0yLGjO1icvUjq+yyGkE08GIDMaYJXNN9daEEEIIGSmGtOY6GAziqaeewm233ZYxG11fXw+73Z4xoz3S9FUW0ldwbX6cUxIy9sN2dCVb8mWbziiaS0LqZgNifj9XlWdY1JitLMRM4DlLn+xgREFrT8QolXHaRMv9hBBCCCHD2ZAG16tXr4bNZsNVV12V8f76+nr4fD585zvfwYIFC/DFL34Rb7/9doH3Mj/MmWtJ4OEyLQAM9ZW5NgXXosDDkaHUQjD3uB6dObiWNq5PPmee660BZAyAM5aFpATXHrsEj8NaItPY3mtcH1/qBk/11oQQQggZIYZsQiNjDKtXr8a1114LScrc3WLv3r3o7e3FokWLcMstt2DNmjX4xje+gZdeegl1dXWWbT0eO0RTGYIg8PCbuioMNQ36PgFAeakHokMyrsc0lnVfI6bHuRwSGICIKVCHokHsiUFw2cFxQPW0Sgi2lKBW0yBs2wzEn8d53gI446+Xr/Pk9TkgiYJRtgIAFaWetADb47ZB6EgG2CU+BypLPMYxqhyH1t6YcX3q2JJh83Mcbr9Twxmdq9zQecodnavc0HnKHZ2r3NG5OjZDFlxv27YNjY2NWL58edZt7rjjDtxyyy3w+fRFelOnTsW2bdsyBtfBYNRy3e93IWAaxT2UNMYQMXUE6Q1GoMVUqPGsdCAYzbqvHZ0hYzumaRB4zrgOAEJbGBwAVdXgrnKjJxQFUp5K2L0L3s4u/Tn8fnSVVAHx18vnefI5RLTHfw4cOISCEYRTss6aoln238Zz4NTkuWgNhNAejBrXS53SsPk5DqffqeGOzlVu6Dzljs5Vbug85Y7OVe7oXKUrL8/enW3IykLeeecd1NXVobKyMus2giAYgXXChAkT0BJfmDdSWEo7eB48x8FhE8BBDzyjipqxlzVgrbkWBR5Oyfp5SOiMJNvwZSkJsXYJmWcsfsw3c2mITeQzts9LLQvxOiTLtMq2ngjaeiIA9AWco+mTMiGEEEJGkCELrjdv3mxpr5fJbbfdhgcffNBy244dO1BbWzuIe5Z/qZ1CAD1wdNmTJRO9WXpdKykLIR0pJR9CRxRCfOphtrHn1nrr/LbgMzN3DMk2OTCt5tohWhZ3tpu+gagqchrnixBCCCFkJBiyyGXPnj2YNGlS2u2tra2IRPTM5aJFi/D73/8er732GhoaGvDzn/8cGzZswFe/+tVC7+5xSe1xnZDLokbz6HOR59IWNPY3QEbYtjU58hyAPAiLGRPKTB1DMi1mBDJkru0SPI7MNfdjS9z52zlCCCGEkAIYsprrtrY2+P3+tNsXLFiARx55BFdeeSUuv/xyBINB/PznP8fRo0cxefJk/Csm4N0AACAASURBVOIXv8C4ceOGYI8HLrUNX4LbLqK1R7+crR1faubamRK0Sp1RJLrzpQ6QEXbugOee7wOyPqRGnXQKWB9lOMerwpfMXJuz0Wap4889Dgl2kYfI81A0a2nM2FIKrgkhhBAysgxZcL1ly5aMt+/atcty/dprr8W1115biF0aNJnKQgB90l5C9uDalLnO0IrP1hUDOP02c+Za2LMbnrvvABfSFyCwIj96f/DD4ziK/o0tcWNGdTH2t/bgnFMqMm6TqSyE4zh4HCICoZjlvnEUXBNCCCFkhBmy4PpkktrjOiGXQTLWrDdnqbnmu6IQe2XAI4AXObir4sGoLMP9wD3ggvqUQ+bzoec/HodWM7i16hzH4ctn1IIxlnExI5A+/jyxmNFjlyzBtdsuwu+yDd7OEkIIIYQMAlotVgCxPspCEnIJrsWUshD79g6j3rq8rhJ8/LmlDZ+Ab20FADC3G8GfPA5twsQ8HElusgXWQObMNQC4HdbPeeNKPX0+DyGEEELIcETBdQHIWctCTAsas3UL0ayBuXmhoH1bBwROf75xi5NZaemfbxmXYxdfCnXSKcex9/llDq5tIg9bfPCP125d1EiLGQkhhBAyElFwXQDHk7m21lxzRp9roSUEoTUMgecg2ASMOWesvlE0CumD95Kvff7ivBxDvpiP32vqEpKaua4uof7WhBBCCBl5KLguAGvmOpl5tgbXcubHmgNzPrmg0b61AwAg8BxGn10NyaUHqtLHH4ELhwEA2pjqYZW1BqyZe/PwGPNlnuMwppgy14QQQggZeSi4LoC+WvEl5Fpz7bAJgMZg35EMrsctrjG2sa1907gcO3/RoE1jHKjxZR5joeKcmlLjdo8pc03DYwghhBAyUlG3kAKIqapx+ViHyKimspBEn2vxUBB8t95Zw+l3oOq00fEnCUH66IPk656/KC/7n0+SwOPbn5+GUEyBz5nsBjKxwgu3TURvTMFpE8qGcA8JIYQQQgaOgusCkLO04nOZMtdhWYWqMaP7h/FYzZy55qCFZIzZ2YUgAIckYNz548HHA3bbh+8lB8bU1g56672BEgXeElgDgNMm4jsXTUcopqDYbc/ySEIIIYSQ4Y2C6wLINkSG5zi4bKLRKSQUUyyL/IBkYC4e7MH+f3yIvQ1dKNIYXD4nJIHDuEWZu4TIw2whYy7skpB1bDohhBBCyEhAwXUBZKq5Pvj2Aez8zTZ4igWE5pYBPIfeaHpwrWgMYAy+P+xDL8fDIYkAOEgCh6JaP0qn6yUUXE83pPUfG48bjiUhhBBCCCEnOgquCyA1c62pGjb+7GPEemIQeyLwNPciuHR8xkWNiqqBD8rgemVwHgcAoPTUMow6cwwmXDzJGLQivf8eoOi13erkKdBGjynAkRFCCCGEEDMKrgvAnLm2CTw6d7Uj1qMvSOQ5DvYtbYCioXf+eADetMfygSgAvfFH8eQSLPr5krTXsK19w7gcu2DklYQQQgghhJwIqN9ZAVjKQkQeLZ82G9f5+AJG+/YO7H3u07THKqoGoSMav8bBM9qbtg3X2QHx043G9dh5VBJCCCGEEDIUKLguAEtZiMCjeeNR47pjbDJY7njnIEItvZbHyhqDEM9c8xzgGeVJe37p3bcBTW/Zp8yYCVZentf9J4QQQgghuaHgugDM4895laF9e6txvea7p0Op1gNmjQEtm45aHquoGoTOZFmIZ0x65tpm6RJCWWtCCCGEkKFCwXUBmDPXvbs7oMn6dd84H/yjPIhNKgIAaBpLC67NNdcAB3dKWQjX2grxsy36FZ5DbOH5g3IMhBBCCCGkfxRcF4C55jqwtcW4XDG7Ci67BLnGBwDQGEPzxqNgLDmVUVGZNXOdUhZie2dtctu6OWDFJYNyDIQQQgghpH8UXBeAeUJjx+ZkSUjF3Cq47SKUSheYQ4TGGCKdEXQf6DK2UYIxcBG9RZ9oF+AodSafmDHY3lxjXI1dcOEgHgUhhBBCCOkPBdeDTGMMSnyEORdW0FXfoV/mgfK6SrjtIsBzkMd7ocYXJSZKQzTGwHVGjOfyjPIafa0BwPbnP0HYs1u/IgqQF5xbiEMihBBCCCFZUHA9yMxZa+ehXiBe8eGfVAKbxwaXTW81Lo/3QmMMAENLvJuIomrgEyUhKW34+Ib9cD37lHE9etmVYF7fIB8NIYQQQgjpCwXXg8xcb21v7DEuV86tAgAIPAenJCBWqwfGqga0bmmGpmqQ1WQbPo4DPKPj9daxGNyPPgTIsv6YmlqEb7ipEIdDCCGEEEL6QMH1IDN3ChEPJIPrijlVxmWPQ4JWbIfmtUFjDHJIQeeu9sxt+Hp74Xr8PyDs3as/WJLQe8/9gM1WmAMihBBCCCFZUXA9yIwe1xqD0J6sny49tcy47HVIAMdBrvEZddfNm45CVrVk5hqA/+BWFH39XyyLGMM3fxNa7YTBPxBCCCGEENIvCq4HWaIshO+OgYsXXDuKHRCdkrGNN35ZrvFCSyxq3HgUisrAx0ef+7raUb56FbjOTuNxsQsWI3rZFQU5DkIIIYQQ0j9xqHfgRJcoCxG6ouCgd/pwp/Sq9jr04DpW44Ua73Hdvr0VPc1B8MEYJEWGM9QDj6QH2qy0FKGbboF8wYV6vQghhBBCCBkWKLgeZLKiAgD4QMyIg1ODa49d/zEwjw3CWC/QGoGmMOx9QZ+86Ar1wCWEwXMMytx5CD7w74DLVbiDIIQQQgghOaHgepAlykKEQNToUe2u1INrYc9uSJ+sw/y338PUbTuxe8oc7DvzSuC1gwCA1g8Og2MMznAvPPYwACByzbUUWBNCCCGEDFMUXA+ymKqXefCBKBIFHO5RHjif+hnsf3oFAMDJKpRIBHWb30Nt+2Fs8F2JULcGFg+sOabBI4agjR0HpW7OEB0JIYQQQgjpDy1oHGSJshChKwY+nrkuat9vBNaA3us6objpAGY2vwku2AMGvSQEADxSGNHLLqcaa0IIIYSQYYyC60FmLGgMRPW4WNNQ9qcXjPuVmbMQvPMe/OPz10AVRGiMYYptH+xHG+FsOghJiQEAnA4F0QuXDMUhEEIIIYSQHFFZyCCTVQ2IqeB6ZXAOG4RAB7xcI8ABzOtF7/3/Blbkx+bwKBypGo8r/7AKVXwUk31H8VnnaKjx5wnOnQ54PH2+FiGEEEIIGVqUuR5kMUWD0KVnnwVVhq/nsFHZEV5xE5i/GBzHweMQ0TRmAn6x4l4EPrcM04oPGzXaHBjaln5uaA6AEEIIIYTkjILrQRZTNfDxKYv2jjZ4BL3rhzp5CmLLLjG2S/S6Dru8aFhxK/CLpzDx1AjsfAyuqUB00qTC7zwhhBBCCDkmVBYyyBIjzEVFgRgOwesPAxwQuu27AJ/8bJMIrgEgGJGhTjwFrf/5XWz57ABkmx2LBVrISAghhBAy3FHmepDJ8bKQRNcPrxSBfMbZUKdMtWxnDq57Ior+WFWDbLMDAESBflSEEEIIIcMdRWzHoeFv6/GP8x/E5tufBkKhjNvEVA1SewiucBAc9OA6etnladt5nebgWgYAKPEBNAAg8vSjIoQQQggZ7ihiOw7b7/0jOutl7PxdE7qvuAm2v7wGqKplm5iiwX/wKDimB8ru0T4o8+anPVdiBDqgl4UAgKIx4zZJpB8VIYQQQshwRxHbcdAqfcbldfUVsP/0P+G99SbwBxqM22VZgas5YFyXLv+cpdY6wVoWogfXsiVzTTXXhBBCCCHDHQXXx4HdcQW6Ssug8iK6Yi5s76yGsLcevltu1CcwKgpKtm0HH9NrqCVBA3f5sozP5clSc50gUc01IYQQQsiwRxHbcRhdW4LWi6aitXw0Qr5ibOqsQa9sA2QZzqd+Bv/SxTj/l88a2zuriwBfUcbnSu0WAgCqmiwLEalbCCGEEELIsFfw4PqVV17BlClTMv5rampK237nzp348pe/jLq6Olx55ZXYsmVLoXc5q1FFTkTmlUOpcKHbU4RodS0+kucZ9zPG0CO7jOuu2adkfS63QwQXHxsTiilQVI0y14QQQgghI0zBI7Zly5bhvffeM/698847mD59OpYsWYLRo0dbtg2FQrjxxhtRV1eHV155BfPmzcPNN9+MYDBY6N3OqMLnACcICH5+HBRVA7PZsc85HY3nXAXmckEBEIQHGicg5PHDPW1c1ufiOQ4uu2Bc740qUDRTzTUF14QQQgghw17BIzaHw4Hy8nLj35o1a9DU1ISHHnoobdvXX38dkiThrrvuwsSJE3HPPffA6/Xir3/9a6F3OyNR4FHmtUMZ50V0WomeaeZ4fNwyEYFX/oKPX/gT/nHWl9BcWY1ocQncozx9Pl9qaYhsKguRqBUfIYQQQsiwN6QRWzAYxFNPPYXbbrsNRUXptcibN2/G3LlzwccDS47jMHfuXGzatKnQu5pVZZETABA6fwyUeEePzj0daFzbgLaeqDH6XOR5eKpyD657IrK1zzXVXBNCCCGEDHtDGlyvXr0aNpsNV111Vcb7W1tbUVFRYbmttLQUzc3Nhdi9nFTFg2utyA7bedXG7Z/94lMcfGUXpEZ9MqMocHBVuft8rtQpjVRzTQghhBAysoj9bzI4GGNYvXo1rr32WkiSlHGbcDgMm81muc1msyEWi6Vt6/HYIYrJmmVB4OH3u9K2y7dJY4qxdudRAIC8cAz8OwMId0QQ64wi+Fo9OAaA4+Ct8mLsjCrwfQyDqShxQzjUCQBQeQ7gOAjxoLq0xG1p15cvhTpPJwI6V7mjc5UbOk+5o3OVGzpPuaNzlTs6V8dmyILrbdu2obGxEcuXL8+6jd1uTwukY7EYHA5H2rbBYNRy3e93IRDIPJI8nzwCBzWeYT7YHcY5X5mOjT/7BABDTNHAGIMyyo2zH70A3cFIn88laCz5XC3dCEVlqPEpjaFgBEq8RV8+Feo8nQjoXOWOzlVu6Dzljs5Vbug85Y7OVe7oXKUrL/dmvW/Iag3eeecd1NXVobKyMus2lZWVaG1ttdzW1taG8vLywd69nPmcEhySnjGPyCpKzhuHogl+aBqgAQifNQqhr52Kqtrifp/L40h+1tlysNMIrH0OibqFEEIIIYSMAEMWsW3evBnz58/vc5u6ujps2rQJjOlBJmMMmzZtwuzZswuxiznhOM6ouwaA5mAU5z58Aaq/Mg1d101B6PwxKCtyguP6X5DozVL2cdGsMeBzeDwhhBBCCBlaQxZc79mzB5MmTUq7vbW1FZGIXj5x0UUXIRQK4aGHHkJ9fT0eeeQRBINBLFuWeYT4UKk0B9ddYTjLXHAsHg9ljN4dpNSbXsaSSabges74UswcW5KfHSWEEEIIIYNqyILrtrY2+P3+tNsXLFiA119/HQDg8XiwatUqbNq0CVdccQU2btyI5557Dh5P3y3tCs2cuT7aFQYAtPUk66vLcwyuUxcslnrsuLiuOsvWhBBCCCFkuBmyBY3Zxpjv2rXLcn3WrFn4wx/+UIhdGrCqlMw1YA2uy7z2nJ7HJvLwu2wIhGLgOQ5XnV4LuyT0/0BCCCGEEDIsDFlwfSKp8CUz0209UcQUDW2m7iVlntwy1wDwxfk1+GR/G2aNLcaYYmp7QwghhBAyklBwnQc2UUC514HWnggYGDY2tKPDFFyXenLLXAPA+DIPxpcNr7IXQgghhBCSG+rvlien1ZYZl9/Y1gSNJdvoUWkHIYQQQsjJgYLrPDmtthQum/5FQFRRjdvLclzMSAghhBBCRj4KrvPEJgo4+5SKtNtLc1zMSAghhBBCRj4KrvPojInlsIvWEpBjWcxICCGEEEJGNgqu88ghCThjonU0e65t+AghhBBCyMhHwXWenX1KBSQheVpzHSBDCCGEEEJGPgqu88xtF3HpnLHwOSScNakCxW7KXBNCCCGEnCyoz/UgmDO+FHPGlw71bhBCCCGEkAKjzDUhhBBCCCF5QsE1IYQQQggheULBNSGEEEIIIXlCwTUhhBBCCCF5QsE1IYQQQggheULBNSGEEEIIIXlCwTUhhBBCCCF5QsE1IYQQQggheULBNSGEEEIIIXlCwTUhhBBCCCF5QsE1IYQQQggheULBNSGEEEIIIXnCMcbYUO8EIYQQQgghJwLKXBNCCCGEEJInFFwTQgghhBCSJxRcE0IIIYQQkicjJriOxWK45JJL8MEHHxi3HTlyBN/4xjcwb948LFq0CM8//7zlMYcPH8aKFSswe/ZsLF26FG+//bbl/o8++giXXnop6urqcN111+HAgQMFOZbBNJDz9OGHH+ILX/gC5syZgyVLluDll1+23L9kyRJMmTLF8m/Hjh0FOZ7BNJBzdd9996Wdi1/+8pfG/Sfi7xRw7OfqrrvuSjtPU6ZMweLFi41tTsTfq0zn6dChQ7jxxhsxd+5cLFmyBK+++qrlMSfj+xQwsHN1Mr5XDeQ80ftUbufqZHufamxsxDe+8Q3Mnz8fCxcuxKOPPopoNArg+N+HfvWrX2HhwoWYM2cO7r77boRCoYId17DERoBIJMJuvfVWNnnyZPb+++8bt3/pS19it912G9u3bx9bs2YNq6urY6+//jpjjDFN09hll13GvvOd77A9e/awVatWsVmzZrHGxkbGGGNNTU1s9uzZ7LnnnmN79uxht99+O1u2bBlTVXVIjjEfBnKe9u/fz2bOnMmeffZZ1tDQwP70pz+xGTNmsDfffJMxxlg0GmWnnnoq27BhA2tpaTH+ybI8JMeYLwM5V4wxdvXVV7P//u//tpyLUCjEGDsxf6cYG9i56u7utpyjHTt2sDlz5rBf/epXjLET8/cq03mKRqPswgsvZCtWrGA7d+5ka9asYaeddhpbs2YNY+zkfJ9ibGDn6mR8rxrIeWKM3qdyPVcn0/tUNBplS5cuZStXrmT19fVs3bp1bPHixeyRRx457vehv//972zu3LnsjTfeYFu2bGEXX3wxu++++4bycIfcsA+u9+zZwy677DJ26aWXWv7TBAIBNnnyZLZ9+3Zj229961vs/vvvZ4wx9sEHH7CZM2eynp4e4/7rr7+ePf7444wxxp544gl29dVXG/eFQiE2Z84cS/Awkgz0PD399NPsS1/6kuW57r33Xnb77bczxhjbsWMHmzZtGovFYgU6ksE30HPFGGOnn346++ijjzI+74n2O8XY8Z0rs5UrV7Kvfe1rxvUT7fcq23n629/+xurq6lhnZ6ex7apVq4z/cyfb+xRjAz9XJ9t71UDPE2P0PnUs58rsRH6f+uSTT9j06dNZMBg0bnv11VfZ2WeffdzvQ1/5yleMbROvNWPGDMtrnWyGfVnI+vXrcc4552D16tWW2x0OB5xOJ1555RXIsox9+/Zh48aNmDFjBgBg8+bNmDZtGjwej/GYefPm4dNPPzXunz9/vnGf0+nE9OnTsWnTpgIcVf4N9DwtXboU9913n+UxHMcZXxXt3bsX1dXVkCSpMAdSAAM9V62trQgEAqitrc34vCfa7xQw8HNltmnTJrzxxhu4++67jdtOtN+rbOfp4MGDqK2thd/vN26bMmUKtm7dClmWT7r3KWDg5+pke68a6Hmi96mk/s6V2Yn+PjVhwgQ899xzcLvdxm0cxyEWix3X+5Cqqvjss88s98+ePRuqqo7Y8pl8EId6B/pz9dVXZ7zdbrfjgQcewL/927/h17/+NVRVxfLly3HVVVcB0AOhiooKy2NKS0tx9OjRPu9vbm4ehKMYfAM9T6lvwG1tbfjLX/6Cb33rWwCA+vp6CIKAG2+8ETt27EBtbS2+//3vo66ubnAPaBAN9FzV19f///buNqapM4oD+L+CRRGNdor74AwpsYJYQatGNsI0mW7I4qYTYmSduvlCjMYo++BeBGd8YS4bI02YaHTRFN0+SaLGmWgiJb7FyKqo4EsVRIkRI4aqHbVw9sFwpXazILXY3v8v4UP73Od6n8PheHpLHxAZGYni4mLYbDYMGTIECxcuxJw5cwCEX04Brx6rzrZt24YZM2bAYDAoz4VbXv1fnIYOHYqmpia0tbUhIiICANDY2AiPxwOn06m6OgW8eqzUVqteNU6sU8/5i5VOp1OODfc6pdPp8O677yqP29vbYbVaYTKZelSHWlpa0Nra6jUeGRmJwYMHK/PV6I2/c/0yN2/eRHp6Ov744w8UFRXBZrMpH9pwuVw+rza1Wq3yatXlckGr1fqMu93uoFx7ML0sTp09efIEK1asQGxsrFKsHA4HWlpaMH/+fGzfvh3x8fFYsGABbt++HeRVBMfLYnXjxg0AQEJCAnbs2IG5c+ciPz8fhw8fBqCunAK6lleNjY2w2WxYtGiR1/Nqyav09HS4XC788ssvaG1thcPhUGL09OlT1qlO/MWqMzXXKn9xYp16rqs5pcY6tWXLFtTU1CAvL69Hdeiff/5RHv/XuFq98Xeu/8/p06exd+9e2Gw2REdHY9y4cXC5XNiyZQvMZjOioqLw6NEjrzlutxv9+vUD8OzO24vfeLfb7fX2UTjwF6eOV/NOpxPLli3D7du3sXfvXvTv3x8A8PPPP6O1tVV5u2j9+vWoqqpCeXm5cscoXPiL1fz585GZmankSEJCAurr67Fv3z5kZGSoJqeArufVX3/9hZEjR/rc6VFLXul0OhQXF2Pt2rXYtWsX3nrrLSxZsgSbN29GTEwM61Qn/mLVQe21yl+cWKee62pOqalOiQg2bdqEffv2obi4GKNGjepRHYqKilIe/998NQrZO9fV1dUYMWIEoqOjleeSkpLgdDrx8OFDDB8+HE1NTV5z7t+/j2HDhgGA3/Fw4S9OAPDgwQN88cUXaGhowJ49ezBy5Ejl2L59+3oVIY1GA71ej3v37gVvEUHiL1YajcbnPyC9Xq+8naqWnAK6llcAYLPZMGPGDJ/5asqrtLQ0VFZWoqKiAhUVFYiLi8OQIUMwYMAA1qkXvCxWAGtVh5fFiXXKm7+cAtRTp9rb2/Htt98q7zZ+8MEHAPznxMvGOxrs+/fvK2MejwcPHz70+VUSNQnZ5jo2NhZ1dXXKh1mAZ2/bDxgwADqdDsnJyaitrfXaa/HcuXNISUkBACQnJ6OqqkoZc7lcuHz5sjIeLvzFye12Izc3F83NzSgrK4Ner/eaP3fuXGzfvl153N7ejitXrvgcFw78xaqwsBDLli3zmlNTU6PEQi05BfiPFfDsDsmFCxe8PujSQS155XA4YDab0dbWhtjYWERERODYsWOYPHkyALBOdeIvVqxVz/iLE+vUc/5iBairThUWFuLAgQOwWCxeLyZ6Uof69OkDo9GIc+fOKeN2ux0RERFITEwMwqreUL28W0m3dN5ix+l0SlpamqxZs0YcDoecOHFCpk6dKr/++quIiHg8Hpk5c6asXLlSrl69KqWlpZKcnCwNDQ0iItLQ0CBGo1FKSkrk2rVrsnr1asnMzAz5vT5Fuhen0tJSGTNmjJw8edJrH8+OrYssFotMnjxZjh8/Lg6HQ9atWydTpkyRlpaWXltfIHUnVmfOnJGEhATZvXu31NfXi9VqlaSkJDl79qyIhHdOiXQvViLP4mEwGKSxsdHnXOGcVy/us/vee+/J1q1b5datW2K1WsVoNEp1dbWIqLtOiXQvVmquVd2JE+tU12Mlop469ffff4vBYJDS0lKvn5979+71uA4dPHhQUlJS5MiRI3LhwgX5+OOPpaCgoBdX2/tCtrkWEXE4HPLVV1+JyWSSadOmicViEY/Ho4zX1dVJTk6OjB07VmbOnCmVlZVe5zt+/Lh8+OGHMm7cODGbzVJfXx+0tbxO3YnT7NmzxWAw+Hx17Gnp8XikuLhY3n//fTEajfL5559LbW1tr6zrdehuTh06dEgyMzNl7NixkpGRIUeOHPE6X7jmlEj3Y2W328VgMMjjx499zhXOefVinM6fPy+fffaZJCcnyyeffCI2m83reLXWKZHuxUrNtaq7OcU61fVYqaVOFRYW/ufPj8FgkKdPn/a4DpWWlkpqaqqYTCZZu3atuFyuYC7vjaMREentu+dEREREROEgZH/nmoiIiIjoTcPmmoiIiIgoQNhcExEREREFCJtrIiIiIqIAYXNNRERERBQgbK6JiIiIiAKEzTURUYgqKCjA6NGjcfbs2f8c37RpE8aMGYOLFy8G+cqIiNSL+1wTEYWoR48eISMjA4MGDUJ5eTn69u2rjF28eBHZ2dlYvHgx1qxZ04tXSUSkLrxzTUQUomJiYpCfn4/r16/j999/V55va2tDfn4+4uLisGLFil68QiIi9WFzTUQUwqZPn47p06ejpKQEjY2NAICysjLU1NRg8+bN0Gq1AICmpiZ8/fXXmDRpEsaPH4/ly5fjzp07XueqqKhATk4Oxo8fD6PRiE8//RRHjx5VxouKipCdnY2NGzdi4sSJyMnJCd5CiYhCBJtrIqIQt27dOkRGRuKnn37CgwcPYLFYsHDhQqSkpAAAnjx5ArPZDLvdjoKCAvz444+4e/cuzGYznE4nAKCqqgq5ublITExESUkJioqKoNVqkZeXh+bmZuXfunTpEmpra2GxWLB06dJeWS8R0ZsssrcvgIiIemb48OHIy8vD+vXrcffuXeh0OqxatUoZ379/PxoaGnDo0CHExcUBAKZMmYJp06ahrKwMubm5cDgc+Oijj/D9998r82JjY5GVlYXq6mqkp6cDADweD7777jskJiYGdY1ERKGCzTURURiYN28eDhw4gKqqKlitVvTr108ZO3PmDPR6PUaMGAGPxwMAiI6OxoQJE3Dq1Cnk5uYiKysLWVlZePz4MW7cuIG6ujqcPHkSAOB2u5VzaTQa6PX64C6OiCiEsLkmIgoDGo0GqampqKmpwcSJE73GmpubcfXqVSQlJfnMi4+PB/Bs55GCggIcPnxYaaANBoPP8TExMYiKino9iyAiCgNsromIwtzAgQORlJSEH374wWeso1HesGEDTp8+jZ07d8JkMkGr1aK2thYHDx4M9uUSEYU0NtdEDrMYoAAAAR9JREFURGHOZDJh27ZteOeddzB48GAAgIggLy8PiYmJMBgMsNvtSE9PR2pqqjKvsrJSOZaIiLqGzTURUZjLzs6G1WrFl19+iSVLlmDQoEH4888/cfToUcyZMwcAYDQacezYMZSXl+Ptt9/GqVOnsHPnTgCAy+XqzcsnIgopbK6JiMLcwIEDUVZWhq1btyI/Px8ejwejRo3Cb7/9hrS0NADAN998A7fbjY0bN0Kj0SA+Ph4lJSXYsGED7HY7Zs2a1curICIKDfzz50REREREAcI/IkNEREREFCBsromIiIiIAoTNNRERERFRgLC5JiIiIiIKEDbXREREREQBwuaaiIiIiChA2FwTEREREQUIm2siIiIiogBhc01EREREFCD/Al2ZTyWeaI4mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2a98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# colors for the line plot\n",
    "colors = ['steelblue', 'red', 'purple']\n",
    "\n",
    "# line plot - the yearly global average air temperature \n",
    "combined_data_final[['avg_temp', 'EMA_Global_0.1', 'EMA_Global_0.3']].plot(color=colors, linewidth=3, figsize=(12,6), alpha=0.8)\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels=['Average air temperature', 'EMA - alpha=0.1', 'EMA - alpha=0.3'], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('The yearly global average air temperature', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Temperature [°C]', fontsize=16)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As shown above, a small weighting factor α results in a high degree of smoothing, while a larger value provides a quicker response to recent changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's compare average air temperature in Baku with the average global temperature with weighting factor 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Temperature [°C]')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc59bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# colors for the line plot\n",
    "colors = ['red', 'green']\n",
    "\n",
    "# line plot\n",
    "combined_data_final[['EMA_Baku_0.1', 'EMA_Global_0.1']].plot(color=colors, linewidth=3, figsize=(12,6), alpha=0.8)\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels=['EMA_Baku - alpha=0.1', 'EMA_Global - alpha=0.1'], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('Average air temperature comparison', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Temperature [°C]', fontsize=16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusions:\n",
    "\n",
    "#### Baku is hotter on average compared to the global average and the difference has been consistent over time;\n",
    "\n",
    "#### Changes in Baku's temperatures over time has been in line with the changes in the global average;\n",
    "\n",
    "#### Trend clearly shows the world is getting hotter and the trend has been consistent over the last few hundred years. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}