intro_matplotlib_line_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in /Users/manthan/anaconda3/lib/python3.7/site-packages (1.1.0)\n",
      "\u001b[33mWARNING: You are using pip version 20.0.2; however, version 20.1.1 is available.\n",
      "You should consider upgrading via the '/Users/manthan/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data read into a pandas dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx', sheet_name='Canada by Citizenship', skiprows=range(20), skipfooter=2)\n",
    "print('Data read into a pandas dataframe!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...   2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...   2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...   1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...   3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...      0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...      0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
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       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>920</td>\n",
       "      <td>South-Eastern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1191</td>\n",
       "      <td>...</td>\n",
       "      <td>1816</td>\n",
       "      <td>1852</td>\n",
       "      <td>3153</td>\n",
       "      <td>2574</td>\n",
       "      <td>1784</td>\n",
       "      <td>2171</td>\n",
       "      <td>1942</td>\n",
       "      <td>1723</td>\n",
       "      <td>1731</td>\n",
       "      <td>2112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>922</td>\n",
       "      <td>Western Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>124</td>\n",
       "      <td>161</td>\n",
       "      <td>140</td>\n",
       "      <td>122</td>\n",
       "      <td>133</td>\n",
       "      <td>128</td>\n",
       "      <td>211</td>\n",
       "      <td>160</td>\n",
       "      <td>174</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>91</td>\n",
       "      <td>77</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>60</td>\n",
       "      <td>102</td>\n",
       "      <td>69</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>910</td>\n",
       "      <td>Eastern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>615</td>\n",
       "      <td>454</td>\n",
       "      <td>663</td>\n",
       "      <td>611</td>\n",
       "      <td>508</td>\n",
       "      <td>494</td>\n",
       "      <td>434</td>\n",
       "      <td>437</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Type    Coverage          OdName  AREA AreaName  REG  \\\n",
       "190  Immigrants  Foreigners        Viet Nam   935     Asia  920   \n",
       "191  Immigrants  Foreigners  Western Sahara   903   Africa  912   \n",
       "192  Immigrants  Foreigners           Yemen   935     Asia  922   \n",
       "193  Immigrants  Foreigners          Zambia   903   Africa  910   \n",
       "194  Immigrants  Foreigners        Zimbabwe   903   Africa  910   \n",
       "\n",
       "                RegName  DEV             DevName  1980  ...   2004  2005  \\\n",
       "190  South-Eastern Asia  902  Developing regions  1191  ...   1816  1852   \n",
       "191     Northern Africa  902  Developing regions     0  ...      0     0   \n",
       "192        Western Asia  902  Developing regions     1  ...    124   161   \n",
       "193      Eastern Africa  902  Developing regions    11  ...     56    91   \n",
       "194      Eastern Africa  902  Developing regions    72  ...   1450   615   \n",
       "\n",
       "     2006  2007  2008  2009  2010  2011  2012  2013  \n",
       "190  3153  2574  1784  2171  1942  1723  1731  2112  \n",
       "191     1     0     0     0     0     0     0     0  \n",
       "192   140   122   133   128   211   160   174   217  \n",
       "193    77    71    64    60   102    69    46    59  \n",
       "194   454   663   611   508   494   434   437   407  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Type', 'Coverage', 'OdName', 'AREA', 'AreaName', 'REG', 'RegName',\n",
       "       'DEV', 'DevName', 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,\n",
       "       2010, 2011, 2012, 2013], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "        91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "       104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "       117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "       130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "       143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
       "       156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
       "       169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.base.Index'>\n",
      "<class 'pandas.core.indexes.range.RangeIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df_can.columns))\n",
    "print(type(df_can.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "# to get index and columns as lists:\n",
    "df_can.columns.tolist()\n",
    "df_can.index.tolist()\n",
    "\n",
    "print(type(df_can.columns.tolist()))\n",
    "print(type(df_can.index.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 43)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of dataframe(rows,columns)\n",
    "df_can.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OdName</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        OdName AreaName          RegName             DevName  1980  1981  \\\n",
       "0  Afghanistan     Asia    Southern Asia  Developing regions    16    39   \n",
       "1      Albania   Europe  Southern Europe   Developed regions     1     0   \n",
       "\n",
       "   1982  1983  1984  1985  ...   2004  2005  2006  2007  2008  2009  2010  \\\n",
       "0    39    47    71   340  ...   2978  3436  3009  2652  2111  1746  1758   \n",
       "1     0     0     0     0  ...   1450  1223   856   702   560   716   561   \n",
       "\n",
       "   2011  2012  2013  \n",
       "0  2203  2635  2004  \n",
       "1   539   620   603  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning the dataframe\n",
    "df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
    "df_can.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([  'Country', 'Continent',    'Region',   'DevName',        1980,\n",
       "              1981,        1982,        1983,        1984,        1985,\n",
       "              1986,        1987,        1988,        1989,        1990,\n",
       "              1991,        1992,        1993,        1994,        1995,\n",
       "              1996,        1997,        1998,        1999,        2000,\n",
       "              2001,        2002,        2003,        2004,        2005,\n",
       "              2006,        2007,        2008,        2009,        2010,\n",
       "              2011,        2012,        2013],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# renaming columns:\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)\n",
    "df_can.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding 'Total' column that sums up total immigration by country \n",
    "# through entire period.\n",
    "df_can['Total'] = df_can.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check how many null objects we have:\n",
    "df_can.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    .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>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>508.394872</td>\n",
       "      <td>566.989744</td>\n",
       "      <td>534.723077</td>\n",
       "      <td>387.435897</td>\n",
       "      <td>376.497436</td>\n",
       "      <td>358.861538</td>\n",
       "      <td>441.271795</td>\n",
       "      <td>691.133333</td>\n",
       "      <td>714.389744</td>\n",
       "      <td>843.241026</td>\n",
       "      <td>...</td>\n",
       "      <td>1320.292308</td>\n",
       "      <td>1266.958974</td>\n",
       "      <td>1191.820513</td>\n",
       "      <td>1246.394872</td>\n",
       "      <td>1275.733333</td>\n",
       "      <td>1420.287179</td>\n",
       "      <td>1262.533333</td>\n",
       "      <td>1313.958974</td>\n",
       "      <td>1320.702564</td>\n",
       "      <td>32867.451282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1949.588546</td>\n",
       "      <td>2152.643752</td>\n",
       "      <td>1866.997511</td>\n",
       "      <td>1204.333597</td>\n",
       "      <td>1198.246371</td>\n",
       "      <td>1079.309600</td>\n",
       "      <td>1225.576630</td>\n",
       "      <td>2109.205607</td>\n",
       "      <td>2443.606788</td>\n",
       "      <td>2555.048874</td>\n",
       "      <td>...</td>\n",
       "      <td>4425.957828</td>\n",
       "      <td>3926.717747</td>\n",
       "      <td>3443.542409</td>\n",
       "      <td>3694.573544</td>\n",
       "      <td>3829.630424</td>\n",
       "      <td>4462.946328</td>\n",
       "      <td>4030.084313</td>\n",
       "      <td>4247.555161</td>\n",
       "      <td>4237.951988</td>\n",
       "      <td>91785.498686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>5018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>251.500000</td>\n",
       "      <td>295.500000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>508.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>832.000000</td>\n",
       "      <td>842.000000</td>\n",
       "      <td>899.000000</td>\n",
       "      <td>934.500000</td>\n",
       "      <td>888.000000</td>\n",
       "      <td>932.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>22239.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22045.000000</td>\n",
       "      <td>24796.000000</td>\n",
       "      <td>20620.000000</td>\n",
       "      <td>10015.000000</td>\n",
       "      <td>10170.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9470.000000</td>\n",
       "      <td>21337.000000</td>\n",
       "      <td>27359.000000</td>\n",
       "      <td>23795.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42584.000000</td>\n",
       "      <td>33848.000000</td>\n",
       "      <td>28742.000000</td>\n",
       "      <td>30037.000000</td>\n",
       "      <td>29622.000000</td>\n",
       "      <td>38617.000000</td>\n",
       "      <td>36765.000000</td>\n",
       "      <td>34315.000000</td>\n",
       "      <td>34129.000000</td>\n",
       "      <td>691904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1980          1981          1982          1983          1984  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean     508.394872    566.989744    534.723077    387.435897    376.497436   \n",
       "std     1949.588546   2152.643752   1866.997511   1204.333597   1198.246371   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%       13.000000     10.000000     11.000000     12.000000     13.000000   \n",
       "75%      251.500000    295.500000    275.000000    173.000000    181.000000   \n",
       "max    22045.000000  24796.000000  20620.000000  10015.000000  10170.000000   \n",
       "\n",
       "              1985         1986          1987          1988          1989  \\\n",
       "count   195.000000   195.000000    195.000000    195.000000    195.000000   \n",
       "mean    358.861538   441.271795    691.133333    714.389744    843.241026   \n",
       "std    1079.309600  1225.576630   2109.205607   2443.606788   2555.048874   \n",
       "min       0.000000     0.000000      0.000000      0.000000      0.000000   \n",
       "25%       0.000000     0.500000      0.500000      1.000000      1.000000   \n",
       "50%      17.000000    18.000000     26.000000     34.000000     44.000000   \n",
       "75%     197.000000   254.000000    434.000000    409.000000    508.500000   \n",
       "max    9564.000000  9470.000000  21337.000000  27359.000000  23795.000000   \n",
       "\n",
       "           ...                2005          2006          2007          2008  \\\n",
       "count      ...          195.000000    195.000000    195.000000    195.000000   \n",
       "mean       ...         1320.292308   1266.958974   1191.820513   1246.394872   \n",
       "std        ...         4425.957828   3926.717747   3443.542409   3694.573544   \n",
       "min        ...            0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           28.500000     25.000000     31.000000     31.000000   \n",
       "50%        ...          210.000000    218.000000    198.000000    205.000000   \n",
       "75%        ...          832.000000    842.000000    899.000000    934.500000   \n",
       "max        ...        42584.000000  33848.000000  28742.000000  30037.000000   \n",
       "\n",
       "               2009          2010          2011          2012          2013  \\\n",
       "count    195.000000    195.000000    195.000000    195.000000    195.000000   \n",
       "mean    1275.733333   1420.287179   1262.533333   1313.958974   1320.702564   \n",
       "std     3829.630424   4462.946328   4030.084313   4247.555161   4237.951988   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       36.000000     40.500000     37.500000     42.500000     45.000000   \n",
       "50%      214.000000    211.000000    179.000000    233.000000    213.000000   \n",
       "75%      888.000000    932.000000    772.000000    783.000000    796.000000   \n",
       "max    29622.000000  38617.000000  36765.000000  34315.000000  34129.000000   \n",
       "\n",
       "               Total  \n",
       "count     195.000000  \n",
       "mean    32867.451282  \n",
       "std     91785.498686  \n",
       "min         1.000000  \n",
       "25%       952.000000  \n",
       "50%      5018.000000  \n",
       "75%     22239.500000  \n",
       "max    691904.000000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view summary of each in our dataframe:\n",
    "df_can.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PANDAS INTERMEDIATE: INDEXING AND SELECTION (SLICING)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can.Country   # returns a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering on the list of countries ('OdName') and \n",
    "# the data for years: 1980 - 1985.\n",
    "\n",
    "df_can[['Country', 1980, 1981, 1982, 1983, 1984, 1985]]   ## returns a dataframe\n",
    "# notice that 'Country' is string, and the years are integers. \n",
    "# for the sake of consistency, we will convert all column names to string later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SELECT ROW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_can = df_can.set_index('Country')\n",
    "# to reset the index, we can use df_can.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
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       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Country                                                                        \n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "Algeria        Africa  Northern Africa  Developing regions    80    67    71   \n",
       "\n",
       "             1983  1984  1985  1986  ...    2005  2006  2007  2008  2009  \\\n",
       "Country                              ...                                   \n",
       "Afghanistan    47    71   340   496  ...    3436  3009  2652  2111  1746   \n",
       "Albania         0     0     0     1  ...    1223   856   702   560   716   \n",
       "Algeria        69    63    44    69  ...    3626  4807  3623  4005  5393   \n",
       "\n",
       "             2010  2011  2012  2013  Total  \n",
       "Country                                     \n",
       "Afghanistan  1758  2203  2635  2004  58639  \n",
       "Albania       561   539   620   603  15699  \n",
       "Algeria      4752  4325  3774  4331  69439  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the name of the index\n",
    "df_can.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>67</td>\n",
       "      <td>71</td>\n",
       "      <td>69</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "      <td>69439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "Algeria        Africa  Northern Africa  Developing regions    80    67    71   \n",
       "\n",
       "             1983  1984  1985  1986  ...    2005  2006  2007  2008  2009  \\\n",
       "Afghanistan    47    71   340   496  ...    3436  3009  2652  2111  1746   \n",
       "Albania         0     0     0     1  ...    1223   856   702   560   716   \n",
       "Algeria        69    63    44    69  ...    3626  4807  3623  4005  5393   \n",
       "\n",
       "             2010  2011  2012  2013  Total  \n",
       "Afghanistan  1758  2203  2635  2004  58639  \n",
       "Albania       561   539   620   603  15699  \n",
       "Algeria      4752  4325  3774  4331  69439  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's view the number of immigrants from Japan (row 87) for \n",
    "# the following scenarios:\n",
    "# 1. The full row data (all columns)\n",
    "# 2. For year 2013\n",
    "# 3. For years 1980 to 1985"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. the full row data (all columns)\n",
    "print(df_can.loc['Japan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "982\n"
     ]
    }
   ],
   "source": [
    "# 2. for year 2013 \n",
    "print(df_can.loc['Japan',2013])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1980    701\n",
      "1981    756\n",
      "1982    598\n",
      "1983    309\n",
      "1984    246\n",
      "1985    198\n",
      "Name: Japan, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 3. for uears 1980 to 1985\n",
    "print(df_can.loc['Japan',[1980, 1981, 1982, 1983, 1984, 1985]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To avoid ambuigity, let's convert the column names \n",
    "# into strings: '1980' to '2013'.\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "[print(type(x),x)for x in df_can.columns.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare a variable that will allow us to easily call upon \n",
    "# the full range of years:\n",
    "# useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter based on criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To filter the dataframe based on a condition, \n",
    "# we simply pass the condition as a boolean vector.\n",
    "\n",
    "# For example, Let's filter the dataframe to show the data \n",
    "# on Asian countries (AreaName = Asia)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Create the condition boolean series\n",
    "condition = df_can['Continent'] == 'Asia'\n",
    "print(condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. pass this condition into the dataframe\n",
    "df_can[condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
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       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
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       "      <th>2009</th>\n",
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       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
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       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>92</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>4171</td>\n",
       "      <td>4014</td>\n",
       "      <td>2897</td>\n",
       "      <td>2939</td>\n",
       "      <td>2104</td>\n",
       "      <td>4721</td>\n",
       "      <td>2694</td>\n",
       "      <td>2640</td>\n",
       "      <td>3789</td>\n",
       "      <td>65568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>865</td>\n",
       "      <td>1464</td>\n",
       "      <td>1879</td>\n",
       "      <td>1075</td>\n",
       "      <td>487</td>\n",
       "      <td>5876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>...</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "      <td>691904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran (Islamic Republic of)</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1172</td>\n",
       "      <td>1429</td>\n",
       "      <td>1822</td>\n",
       "      <td>1592</td>\n",
       "      <td>1977</td>\n",
       "      <td>1648</td>\n",
       "      <td>1794</td>\n",
       "      <td>...</td>\n",
       "      <td>5837</td>\n",
       "      <td>7480</td>\n",
       "      <td>6974</td>\n",
       "      <td>6475</td>\n",
       "      <td>6580</td>\n",
       "      <td>7477</td>\n",
       "      <td>7479</td>\n",
       "      <td>7534</td>\n",
       "      <td>11291</td>\n",
       "      <td>175923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maldives</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>607</td>\n",
       "      <td>540</td>\n",
       "      <td>511</td>\n",
       "      <td>581</td>\n",
       "      <td>561</td>\n",
       "      <td>1392</td>\n",
       "      <td>1129</td>\n",
       "      <td>1185</td>\n",
       "      <td>1308</td>\n",
       "      <td>10222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pakistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>978</td>\n",
       "      <td>972</td>\n",
       "      <td>1201</td>\n",
       "      <td>900</td>\n",
       "      <td>668</td>\n",
       "      <td>514</td>\n",
       "      <td>691</td>\n",
       "      <td>...</td>\n",
       "      <td>14314</td>\n",
       "      <td>13127</td>\n",
       "      <td>10124</td>\n",
       "      <td>8994</td>\n",
       "      <td>7217</td>\n",
       "      <td>6811</td>\n",
       "      <td>7468</td>\n",
       "      <td>11227</td>\n",
       "      <td>12603</td>\n",
       "      <td>241600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>185</td>\n",
       "      <td>371</td>\n",
       "      <td>290</td>\n",
       "      <td>197</td>\n",
       "      <td>1086</td>\n",
       "      <td>845</td>\n",
       "      <td>1838</td>\n",
       "      <td>...</td>\n",
       "      <td>4930</td>\n",
       "      <td>4714</td>\n",
       "      <td>4123</td>\n",
       "      <td>4756</td>\n",
       "      <td>4547</td>\n",
       "      <td>4422</td>\n",
       "      <td>3309</td>\n",
       "      <td>3338</td>\n",
       "      <td>2394</td>\n",
       "      <td>148358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Continent         Region             DevName  1980  \\\n",
       "Afghanistan                     Asia  Southern Asia  Developing regions    16   \n",
       "Bangladesh                      Asia  Southern Asia  Developing regions    83   \n",
       "Bhutan                          Asia  Southern Asia  Developing regions     0   \n",
       "India                           Asia  Southern Asia  Developing regions  8880   \n",
       "Iran (Islamic Republic of)      Asia  Southern Asia  Developing regions  1172   \n",
       "Maldives                        Asia  Southern Asia  Developing regions     0   \n",
       "Nepal                           Asia  Southern Asia  Developing regions     1   \n",
       "Pakistan                        Asia  Southern Asia  Developing regions   978   \n",
       "Sri Lanka                       Asia  Southern Asia  Developing regions   185   \n",
       "\n",
       "                            1981  1982  1983  1984  1985  1986   ...     2005  \\\n",
       "Afghanistan                   39    39    47    71   340   496   ...     3436   \n",
       "Bangladesh                    84    86    81    98    92   486   ...     4171   \n",
       "Bhutan                         0     0     0     1     0     0   ...        5   \n",
       "India                       8670  8147  7338  5704  4211  7150   ...    36210   \n",
       "Iran (Islamic Republic of)  1429  1822  1592  1977  1648  1794   ...     5837   \n",
       "Maldives                       0     0     1     0     0     0   ...        0   \n",
       "Nepal                          1     6     1     2     4    13   ...      607   \n",
       "Pakistan                     972  1201   900   668   514   691   ...    14314   \n",
       "Sri Lanka                    371   290   197  1086   845  1838   ...     4930   \n",
       "\n",
       "                             2006   2007   2008   2009   2010   2011   2012  \\\n",
       "Afghanistan                  3009   2652   2111   1746   1758   2203   2635   \n",
       "Bangladesh                   4014   2897   2939   2104   4721   2694   2640   \n",
       "Bhutan                         10      7     36    865   1464   1879   1075   \n",
       "India                       33848  28742  28261  29456  34235  27509  30933   \n",
       "Iran (Islamic Republic of)   7480   6974   6475   6580   7477   7479   7534   \n",
       "Maldives                        0      2      1      7      4      3      1   \n",
       "Nepal                         540    511    581    561   1392   1129   1185   \n",
       "Pakistan                    13127  10124   8994   7217   6811   7468  11227   \n",
       "Sri Lanka                    4714   4123   4756   4547   4422   3309   3338   \n",
       "\n",
       "                             2013   Total  \n",
       "Afghanistan                  2004   58639  \n",
       "Bangladesh                   3789   65568  \n",
       "Bhutan                        487    5876  \n",
       "India                       33087  691904  \n",
       "Iran (Islamic Republic of)  11291  175923  \n",
       "Maldives                        1      30  \n",
       "Nepal                        1308   10222  \n",
       "Pakistan                    12603  241600  \n",
       "Sri Lanka                    2394  148358  \n",
       "\n",
       "[9 rows x 38 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can pass mutliple criteria in the same line. \n",
    "# let's filter for AreaNAme = Asia and RegName = Southern Asia\n",
    "\n",
    "df_can[(df_can['Continent']=='Asia') & (df_can['Region']=='Southern Asia')]\n",
    "\n",
    "# note: When using 'and' and 'or' operators, pandas requires we use '&' and '|' instead of 'and' and 'or'\n",
    "# don't forget to enclose the two conditions in parentheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions:  (195, 38)\n",
      "Index(['Continent', 'Region', 'DevName', '1980', '1981', '1982', '1983',\n",
      "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
      "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
      "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010',\n",
      "       '2011', '2012', '2013', 'Total'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>...</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>71</td>\n",
       "      <td>340</td>\n",
       "      <td>496</td>\n",
       "      <td>...</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "      <td>58639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "      <td>15699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Continent           Region             DevName  1980  1981  1982  \\\n",
       "Afghanistan      Asia    Southern Asia  Developing regions    16    39    39   \n",
       "Albania        Europe  Southern Europe   Developed regions     1     0     0   \n",
       "\n",
       "             1983  1984  1985  1986  ...    2005  2006  2007  2008  2009  \\\n",
       "Afghanistan    47    71   340   496  ...    3436  3009  2652  2111  1746   \n",
       "Albania         0     0     0     1  ...    1223   856   702   560   716   \n",
       "\n",
       "             2010  2011  2012  2013  Total  \n",
       "Afghanistan  1758  2203  2635  2004  58639  \n",
       "Albania       561   539   620   603  15699  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reviewing the changes we have made to our dataframe\n",
    "print('data dimensions: ', df_can.shape)\n",
    "print(df_can.columns)\n",
    "df_can.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VISUALIZING DATA USING MATPLOTLIB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are using the inline backend\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlin version:  2.2.3\n"
     ]
    }
   ],
   "source": [
    "print('Matplotlin version: ', mpl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)\n",
    "mpl.style.use(['ggplot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLOTTING IN PANDAS\n",
    "# LINE PLOTS (SERIES/DATAFRAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980    1666\n",
       "1981    3692\n",
       "1982    3498\n",
       "1983    2860\n",
       "1984    1418\n",
       "Name: Haiti, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haiti = df_can.loc['Haiti', years]  # passing in years 1980 - 2013 to exclude the 'total' column\n",
    "haiti.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121ad78d0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.index = haiti.index.map(int) # let's change the index values of Haiti to type integer for plotting\n",
    "haiti.plot(kind='line')\n",
    "\n",
    "plt.title('Immigration from Haiti')\n",
    "plt.ylabel('Number of immigrations')\n",
    "plt.xlabel('years')\n",
    "\n",
    "plt.show()  # need this line to show the updates made to the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "haiti.plot(kind='line')\n",
    "\n",
    "plt.title('Immigration from Haiti')\n",
    "plt.ylabel('Number of Immigrations')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "# annote the 2010 Earthquake\n",
    "# syntax: plt.text(x,y,label)\n",
    "plt.text(2000, 6000, '2010 Earthquake')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>8880</td>\n",
       "      <td>8670</td>\n",
       "      <td>8147</td>\n",
       "      <td>7338</td>\n",
       "      <td>5704</td>\n",
       "      <td>4211</td>\n",
       "      <td>7150</td>\n",
       "      <td>10189</td>\n",
       "      <td>11522</td>\n",
       "      <td>10343</td>\n",
       "      <td>...</td>\n",
       "      <td>28235</td>\n",
       "      <td>36210</td>\n",
       "      <td>33848</td>\n",
       "      <td>28742</td>\n",
       "      <td>28261</td>\n",
       "      <td>29456</td>\n",
       "      <td>34235</td>\n",
       "      <td>27509</td>\n",
       "      <td>30933</td>\n",
       "      <td>33087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>5123</td>\n",
       "      <td>6682</td>\n",
       "      <td>3308</td>\n",
       "      <td>1863</td>\n",
       "      <td>1527</td>\n",
       "      <td>1816</td>\n",
       "      <td>1960</td>\n",
       "      <td>2643</td>\n",
       "      <td>2758</td>\n",
       "      <td>4323</td>\n",
       "      <td>...</td>\n",
       "      <td>36619</td>\n",
       "      <td>42584</td>\n",
       "      <td>33518</td>\n",
       "      <td>27642</td>\n",
       "      <td>30037</td>\n",
       "      <td>29622</td>\n",
       "      <td>30391</td>\n",
       "      <td>28502</td>\n",
       "      <td>33024</td>\n",
       "      <td>34129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1980  1981  1982  1983  1984  1985  1986   1987   1988   1989  ...    \\\n",
       "India  8880  8670  8147  7338  5704  4211  7150  10189  11522  10343  ...     \n",
       "China  5123  6682  3308  1863  1527  1816  1960   2643   2758   4323  ...     \n",
       "\n",
       "        2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  \n",
       "India  28235  36210  33848  28742  28261  29456  34235  27509  30933  33087  \n",
       "China  36619  42584  33518  27642  30037  29622  30391  28502  33024  34129  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare the number of immigrants from India and China from 1980 to 2013.\n",
    "df_CI = df_can.loc[['India', 'China'], years]\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1222b6c18>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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96lcVL4tUH6XPkCHvzdditO5MlXSfjU0OU48N93u7/qbsPuyww/j2t7/Nzp07qa2t5fnnn+foo48uaVlEeqOeg0gF9Tdl9+TJk7n66qu54IILcvmWHEdtOik//ZXJkDeQFn659Ddl94QJE5g/fz7z588H4Fvf+hZjxowZtOOX6qGeg0gF9Tdld/42H374Ib/61a8477zzBufgpaqo5yBSJqVK2b1gwQJ27dqF4zjcdtttRSe5RcpBKbtlSFLKbpF9o2ElEREpouAgIiJFFBxERKSIgoOIiBRRcBARkSIKDiIiUkTBQaRM+pOyO5FIcM011zB79mzmzJnDyy+/nNvmzTffZPbs2Xz605/mH//xH9Hsc6kEBQeRMulPyu7//M//BODFF1/k8ccf55ZbbsHzPABuvPFG/u3f/o3f/e53rFu3juXLl1e2IFKVFBxEyqQ/KbvfffddZs6cCcDIkSNpbGzkD3/4A1u2bKGtrY1PfepTGGO48MIL+e///u/KFkSqktJnyJC3YsUKtm7dWtJ9jho1irlz5/Z7u75Sdh9xxBE888wznHvuuWzYsIE333yTDRs2YFlWQaK9sWPHsnnz5pKVQ6Qv6jmIVFBfKbsvvfRSxo4dyymnnMLXv/51PvWpT+E4Tq/nF4wxlT5sqULqOciQN5AWfrn0lbLbcRy++c1v5tY744wzmDhxIsOGDWPTpk255Rs3bqSlpaWyBy1VST0HkQrqK2V3PB6nvb0dgBdeeAHHcfjEJz5BS0sL4XCY1157Dd/3eeyxxzj99NMH7fileqjnIFIm/UnZvX37di666CIsy2L06NG5WUwA3/3ud7nmmmvo6OjgpJNO4qSTThqU8kh1UcpuGZKUsltk32hYSUREiig4iIhIEQUHEREpouAgIiJFFBxERKSIgoOIiBRRcBApk1Kl7P72t7/N0UcfzYQJEypdBKliCg4iZVKqlN3z5s3j2WefrezBS9VTcBApk1Kk7AY49thjlU9JKk7pM2TIC23+OXbXhpLu060ZR8fos/u9XX9Tdv/VX/1VSY9bZG+p5yBSQf1N2S0yWPTXJ0PeQFr45dLflN0ig0U9B5EK6m/KbpHBop6DSJmUKmX3rbfeypNPPklHRwdTp05l/vz53HjjjYNSJqkeStktQ5JSdovsGw0riYhIEQUHEREpouAgIiJFFBxERKSIgoOIiBRRcBARkSIKDiJlUoqU3fF4nEsvvZTp06czc+bMgquoRcpJwUGkTEqVsvurX/0qr7zyCsuWLeP3v/89y5Ytq2xBpCopOIiUSSlSdtfV1XHCCScAEAwGOeqoo9i4cWMFSyHVSukzZMh77cP/YGfHn0u6z6bQBI494Av93m5fUna3trby7LPPcsUVV5SsHCJ9Uc9BpIIGmrI7lUqxcOFCrrjiCt0uVCpCPQcZ8gbSwi+XgabsvuGGG5g4cSILFy6s7AFL1VLPQaSCBpKy+zvf+Q7RaJR/+Zd/GZyDlqqkrKwyJO0PWVnzU3aPHDmy15TdS5YswRjD+vXrC1J233nnnYwfP56NGzdy9NFHM2nSpFx5vvzlL3PZZZf1+p3KyiqlouAgQ9L+EBwGg4KDlIqGlUREpIiCg4iIFFFwEBGRIgoOIiJSRMFBRESKKDiIiEgRBQeRMilFym6Aiy66iDlz5jBz5kz+7u/+Dtd1K14WqT4KDiJlUqqU3T/4wQ944YUXWLlyJTt27OAXv/hFZQsiVUnBQaRMSpGyGyASiQDp5HvJZBJjTKWKIFVMifdkyKtf/wFOvKOk+0zVhWg/cHy/txtoyu4LL7yQN954g5NOOomzzjqrpGUR6Y16DiIVNNCU3Y899hhr1qyhq6uL3/zmN4N1+FJF1HOQIW8gLfxyGWjKboDa2lpOPfVUnnnmGebMmVOxY5bqpJ6DSAX1N2V3LBZjy5YtQPqcw7Jly5g0adLgHLxUFfUcRMokP2X31KlTe03ZfckllwCwffv2gpTd2VlM8Xicyy+/nK6uLjzP44QTTsgFFJFyUspuGZKUsltk32hYSUREiig4iIhIEQUHEREpouAgIiJFFBxERKSIgoOIiBRRcBApk1Kl7M66/PLLC/YlUk4KDiJlUqqU3QBLly6lvr6+cgcvVU/BQaRMSpWyOxaLcf/993PddddV8Oil2il9hgx5yZ/cj7d+XUn3aR04kcD8r/R7u4Gk7L799tu58sorCYVCJS2DyJ6o5yBSQf1N2b1mzRref/99zjzzzEE+cqk26jnIkDeQFn659Ddl9yuvvMKbb77JtGnTSKVSbN++nXPOOYenn356UI5fqoeCg0gFbdu2jZEjR/aastv3ferr6wtSdn/iE5/gS1/6EgDr16/nsssuU2CQilBwECmTUqTsFhksStktQ5JSdovsG52QFhGRIgoOIiJSRMFBRESKKDiIiEgRBQcRESmi4CAiIkUUHETKpFQpu8855xyOP/545s6dy9y5c9m2bVvFyyLVR8FBpExKmbL7vvvuY8WKFaxYsYKRI0dWrhBStRQcRMqkVCm7RQaD0mfIkPfA7zaybmdnSfc5samWhX89tt/bDSRlN6SHqCzL4jOf+QzXX389xpiSlkekJ/UcRCqovym7IT2k9OKLL/LLX/6SV199lccee2wwiyBVQj0HGfIG0sIvl/6m7AYYM2YMAOFwmM997nO88cYbXHTRRRU+cqk26jmIVFB2plFvKbvb29sBClJ2p1IpduzYAUAymeS5557jsMMOG5yDl6qinoNImZQiZXdXVxcXXXQRyWQSz/OYNWsWl19++aCVSaqHUnbLkKSU3SL7RsNKIiJSRMFBRESKKDiIiEgRBQcRESmi4CAiIkUUHEREpIiCg0iZlCpldyKR4IYbbuC4445j+vTp/PKXv6x4WaT6KDiIlEmpUnbfcccdNDc38+qrr/LSSy8xffr0yhZEqpKCg0iZlCpl96OPPso111wDgGVZjBgxolJFkCqm9Bky5L35WozWnamS7rOxyWHqseF+b9fflN2HHHIIALfddhuvvPIKEyZM4Dvf+Q6jRo0qaXlEelLPQaSC+puyO5VKsXHjRj796U+zbNkyjj32WP7pn/5pcAshVUE9BxnyBtLCL5f+puxuamqirq6OM888E4DPfvaz/Nd//VflD1yqjnoOIhXU35TdxhjmzZuXm730m9/8hsmTJw/OwUtVUVZWGZL2h6ys+Sm7R44c2WvK7iVLlmCMYf369QUpu++8807Gjx8PwAcffMBVV11Fa2srzc3N3HXXXRxwwAG9fqeyskqpKDjIkLQ/BIfBoOAgpaJhJRERKaLgICIiRRQcRESkiIKDiIgUUXAQEZEiCg4iIlJEwUGkTEqRsjsWizF37tzc47DDDmPJkiWDUh6pLgoOImVSipTd4XCYFStW5B4HHHBALpWGSDkpOIiUSalSdmetW7eO7du3c9xxx1Xg6KXaKfGeDHkrVqxg69atJd3nqFGjmDt3br+362/K7r/6q7/Kbfvkk09y9tlnY4wpWTlE+qKeg0gF9Tdld76nn36a8847bzAOW6qQeg4y5A2khV8u/U3ZnbV27VpSqRRTp06t7AFL1VLPQaSC+puyO+upp57i3HPPrfwBS9VSz0GkTPJTdk+dOrXXlN2XXHIJANu3by9I2Z2dxZT185//nEcffbTiZZDqpZTdMiQpZbfIvtGwkoiIFFFwEBGRIgoOIiJSRMFBRESKKDiIiEgRBQcRESmi4CBSJqVI2Q3pnEqzZ89m9uzZXHTRRezYsaPiZZHqo+AgUialSNmdSqVYsmQJTz75JC+++CKHH344P/zhDyteFqk+Cg4iZVKKlN2+7+P7PvF4HN/3aWtrY/To0ZUtiFQlpc+QIS+0+efYXRtKuk+3Zhwdo8/u93YDSdn93e9+l9mzZ1NXV8fEiRO5/fbbS1oWkd6o5yBSQf1N2Z1MJnnkkUdYtmwZa9as4fDDD+euu+4a5FJINVDPQYa8gbTwy6W/KbvXrl0LwMEHHwzA2Wefzd13313ho5ZqpJ6DSAX1N2X3mDFjeOedd9i+fTuQPmE9adKkwTl4qSrqOYiUSSlSdo8ePZq/+7u/4+yzz8ZxHMaPH6+eg1SEUnbLkKSU3SL7RsNKIiJSRMFBRESKKDiIiEgRBQcRESmi4CAiIkUUHEREpIiCg0iZlCpl99NPP83s2bOZOXMmt956a8XLIdVJwUGkTEqRsnvnzp3ceuutPPHEE/zmN79h27ZtrFy5suJlkeqj4CBSJqVI2f2Xv/yFiRMn0tzcDMCsWbNy24iUk9JnyJD32of/wc6OP5d0n02hCRx7wBf6vV1/U3bPnDmT9957j/Xr1zN27Fj+53/+h2QyWdKyiPRGPQeRCupvyu5hw4bx3e9+l7/927/lrLPOYvz48di2PcilkGqgnoMMeQNp4ZdLf1N2A5x66qmceuqpAPzoRz9ScJCKUM9BpIL6m7I7f5vdu3fz8MMPc9lllw3CkUu1Uc9BpExKkbIbYMmSJbz99tsA3HDDDRxyyCGVL4xUHaXsliFJKbtF9o2GlUREpIiCg4iIFFFwEBGRIh/rE9IbN24c0HbNzc25G7ZXi2orc0dHB4FAgFQqNdiHUjGO49DR0THgfxcfR9X2dw37VuaxY8fu9brqOYiISBEFBxERKaLgIFImGzZs4Pzzz2f27NnMnTuXH/zgBwDs2rWLiy++mBkzZnDxxReze/duAHzf5+tf/zozZszg5JNPZs2aNbl9PfbYY8yYMYMZM2bw2GOPDUp5pLooOIiUieM43HLLLbz44ov88pe/5JFHHuHdd9/l3nvv5YQTTuDll1/mhBNOyF3wtnz5ct5//31eeuklbr/9dm666SYgHUzuuOMOli5dyq9+9SvuuOOOXEARKRcFB5EyaWlp4cgjjwQgHA4zadIkNm/ezLPPPssFF1wAwAUXXMAzzzwDwLPPPsv555+PMYZp06bR2trKli1bePHFF5k5cybDhw9n2LBhzJw5kxdeeGGwiiVV4mM9W0lkbzR8uJFAR2dJ95kM1RI9YO9nfnzwwQesXbuWY445hu3bt9PS0gKkA8iOHTsA2Lx5c8FskjFjxrB58+Y+l4uUk3oOImXW3t7OFVdcwa233kokEulzvd4y2Rhjel0uVcyrzP081HOQIa8/LfxSSyaTXHHFFZx77rmcccYZQHqe+pYtW2hpaWHLli2MGDECSPcI8q9R2LRpEy0tLYwZM4ZXXnmlYPn06dMrWxApPy+BnYpiuW1Yec8Fy1JtmPX1cODfl/1wFBxEysT3fW644QYOPfRQFi5cmFs+b948Hn/8cRYtWsTjjz+eu1fDvHnzeOSRRzj77LNZvXo1DQ0NtLS0MHv2bG677bbcSeiVK1fmTlbLx4CXxHLbelTyUWw3XdlbqTYsN4rlFQ99+sbBsxtwnQZSNWPw6iZTO6wyiRUVHETKZNWqVTzxxBNMmTKFU045BYCvfe1rXHXVVXzlK1/h0UcfZdy4cTzwwAMAnHTSSSxfvpwZM2YQCoX43ve+B8Dw4cO59tprOfPMMwG47rrrGD58+OAUSrr5qUzlHsXOq/QtN4qdausOBF5H8abYeE6m0g+OwnMOwXMacoHAsyN4TgO+VQvGFGxb29wMFbgq/GOdslvpM/ZetZU5Ho/T0NBQdekzotEodXV1g30oFVOWv2vf7W7Np7It/mhumZ3KVvrx4k2x8ZwInh1JV/JOBM9uSFf8TgTXTj/7Vl1Rpb+3KpU+Qz0HEakOvovlxnJj9+lhncLxfNuNYrntxZtidVf6gSYStQdlKvzuVr6bq/SHxjwfBQcR+XjzvUyln23tZ4d1elT+bjuGwoESH4NnhzMV/TBSteMLhnU8pwHXjuDb9UOm0t9bCg4isn/yPYzb3mM8PzPMk6n8zV9ijExG+6j063Mt+1TNuMx4fiQ3tp/uCYSrrtLfWwoOIlJZvofx4t1j9wUzePKXtWHwijb37Prc2D0NBxNPBTIt/EyF7zRkKn17EAo3dCg4iEhp+D7Gi+eN57flZu90n9jNVvpu0eaeVZcbu08FW3IncwtP7IbBdFdbzc3NtFfRRItKUnAQkT3zfYzXWTiskx3Pz0zZtDMt/94r/VBueCcRbM6bvZMez8+19K2ochNdAAAgAElEQVTAIBRO+rLXwcHzPL72ta/R1NTE1772NbZu3cqdd95JLBbj4IMP5uqrr8ZxHJLJJPfccw/r1q0jEolw7bXXMmrUKACeeuopli9fjmVZfOlLX+Loo48G4A9/+AMPP/wwnudx0kkncc4555SntCIVtGHDBhYvXsy2bduwLIv58+fzN3/zN+zatYsrr7ySDz74gPHjx3P//fczbNgwfN/nG9/4BsuXLycUCnHHHXfkEvfNnz+f1atX86lPfYof/ehHpTlA38d4XT2maUYLhnWygcD4xVOCPas2N3afDB2UGdbpnsGTPbGrSv/jaa+Dw3//938zbtw4OjrSF3T8+Mc/5swzz2TGjBk8+OCDLF++nHnz5rF8+XLq6+v5/ve/z8svv8xPfvITrrvuOj788ENeeeUVvve977Fr1y6++c1vctdddwHwwx/+kCVLljBixAhuuukmjj32WA444IDylFikQrIpu4888khisRinnXYas2bN4rHHHuOEE05g0aJF3HPPPdx7773cfPPNBSm7V69ezU033cTSpUsB+MpXvkJHRwc//vGP9+q7jdeV17LPzuDprvSzgcD4xXl6PFOTG7tPZmfv5M/gyVT8WMGS/rxk/7JXwWHHjh2sXr2a8847j6VLl+L7Pm+//TaLFy8GYM6cOTz++OPMmzeP1157LZeO+LjjjuOhhx7C931WrVrF9OnTCQQCjBo1itGjR/Pee+8BMHr06FyWyunTp7Nq1SoFB/nYa2lpyf1d90zZ/bOf/QxIp+w+//zzufnmm/tM2d3S0sLMmTO78yt5yfSJWt8F38X4HuBC0iMYe5/mTUux/ETR8fgmkGnNN5CqGUeifkr3Sdy8k7m+VVOpH5Hsx/YqODzyyCNcdtlluV5DW1sbdXV12HZ6NkBTUxM7d+4EYOfOnblEYrZtU1dXR1tbGzt37mTSpEm5feZvk10/+/pPf/pTr8fx/PPP8/zzzwNw22230dzc3K/CZjmOM+BtP66qrcxbtmwB0uVO/dcDeOvXlXT/1oETcS5d+NErZqxfv563336bT33qU2zfvp1x49L5ccaNG8eOHTtwbIstWzYzfmwLDl3guYwdPYptG/7EuCYbfBc7uQPjdeIktvTYuwHLBuNgAmFMyyy8YCMEhkGwEYLD0q/tWixjhlQq5mr7u4bKlfkjg8Prr79OY2MjEydO5O233/7IHfY37XBf6/fm5JNP5uSTT869H+gl5NWWSgKqr8xdXV3U1NSQSqXwPK/kaa89z9u71By+T3ssyoIFX+LWb/wjdQEXfB+vYyvGd8H30o/29ZDqgMQO6NyW2TYFXhLf9/Bx8E0NGAc3MByw8Y2dma5pwBgcx6ErWce2uind35/MPGjPPIaWavu7hv0ofcY777zDa6+9xhtvvEEikaCjo4NHHnmEeDyO67rYts3OnTtpamoC0i3/HTt2MGLECFzXJR6PEw6Hc8uz8rfJX75jxw4lFZOSsi6+ovQ79X3wUkBmWMd30zN1/ML3yUSCv73ies4/6yTOOvlYSO1mZPNwtm7eQEtLC5u37aK5eQSe08DoseP5YGucacEWMBYbt+xk5PjDcYPpCR2eE8Y3TvpqXZEy+8ge5qWXXsr999/Pvffey7XXXssnP/lJrrnmGo444gheffVVAF544QWOPfZYAKZNm5a7heGrr77KEUccgTGGY489lldeeYVkMsnWrVvZtGkThx56KIcccgibNm1i69atpFIpXnnlldy+RCrO9zMt9gTG7cCkYlipVqzkLuzEduyuLThdG3G6NuAkNuMktmEnd2CndmOlYhivC/DSLXxTy7X/eDuHTjqMK75yDangKFI1Yzhl3un89Ocv4gZH8X+efp55p56O5zRwyqln8LMnf4FvHF5f/Ydcym6RwTDg6xzmz5/PnXfeyU9/+lMOPvhgTjzxRABOPPFE7rnnHq6++mrC4TDXXnstAOPHj+f444/n+uuvx7IsvvzlL2NZ6di0YMECvvWtb+F5HnPnzmX8+PElKJpIHt+HzElckz2Rm/+e9BBP+nUvm2eHcIyNZ4JgbHxsMFbe8I5VkGnz97//PT978hdMmTKFk087C8ik7F60qF8puwHOPfdc3nvvPeLxONOmTePf//3fmTNnTpl+WCJK2V01hmyZe82/E2W3fwB1jaPwvVReAOhlc2Nncut0j+F/VKW/v1LK7uqw35xzEBkURfl3uq/ILQwEsV7z77Q1nA1eE2DhWwF8Y+NjpSv/vIDwcaj0RQaDgoNUVn7+nR43USlcFtvL/DsNvdxYJUxXR4KaugbcKrrZj0gpKThIaRTk3+mRTz//xip9Jl3Lz79zSFE+/eydtPKTru1Z8UVgIrL3FBxkz0qaf2eC8u+IfEwoOFSxfc+/k6n0C/LvZId3lH9H5ONMwWEo8hJ5lXwmlXJ7ioa2LQXLlH9HRPqi4PBx4iULcufnTuL2vLGK11W0qW8CBDKt+VTNGLy6T+RO7BZU+qZGM3hKpFQpu9euXctNN91ELBbDtm2uvvpqzj777MEungxxCg77Az+Vq9ztgpuo9Dix63UWb4qdqeQbMrN3JvU6g2fEqAMK0pRI+ZUqZXcoFOKuu+5i4sSJbN68mdNPP505c+bQ2Ng42EWUIUzBoZz8FFYqlncSt3vKZkEg8DqKN8XOjd2nAiPxQvkzeCK5E7u+Fdq7lr56AxVXqpTdhxxySG6fo0ePzuUpU3CQclJwGAjfxXJjxTdFzz+x67ZhucVZMH2s7hO2gREkaicUz97JVfpDKbny4PnBa1t4f1dxr2tfHDy8lr85du/zHn3wwQesXbuWY445hu3bt+eCRktLS65Ht3nz5oIrWMeMGcPmzZsL8iu98cYbJJNJJkyYUJqCyMeC7/t0dnYSjUbZvXs3w4YNK/t3Kjjk810stz1vmma08MRu7rkdQ2HWER+TadlH8ALDSdUemDuxmx3Pd+1IOqOmKv2q0t7ezhVXXMGtt95KJBLpc72PSl+/ZcsWrrnmGu68885cXjIZOlKpFG1tbbS2thKNRmltbaV1926iu3fT2tZGMnNBZ40dYOFVV5b9eKorOPg+wfg74HrUtW7MS82QfY71UemHu4d4asbh2Q09rsiNpG+Qrkp/v9SfFn6pJZNJrrjiCs4991zOOOMMIJ0bJ3uHty1btuRudjVmzJiCfGGbNm3K9Rra2tr4/Oc/z4033si0adMqXxDZZ77vE49GiW7eSOv2bUR37qS1rY3WeJxoV4KYW1j3GN9QY2qwnQZCgUOJ1IZx7Ai1OPi+3+d9b0qluoID0Lj5vzB+kjDg2eHM1beR9Ayegko/e7/ccCb5mkj/+L7PDTfcwKGHHsrChd13jZs3bx6PP/44ixYt4vHHH+fUU0/NLX/kkUc4++yzWb16dS5ldyKR4Mtf/jLnn38+Z5111mAVR3rhd3VCWyu0RSHWSrJ1F9Gdu4hGW4nG47R2dhFNeUR9Q6sdJGUV1iVB3yJoQtiBEQyrGYHjNBCw00EgZAxhp5P6YJJwnU9dg0O4qZbxnziQqNd7IslSqq7gYAy7DvgKw5oPYHs00Y9UDCL9t2rVKp544gmmTJnCKaecAmRSdl91Vb9Sdv/yl7/kd7/7Hbt27eKxxx4D4I477uCTn/zk4BRsiPJ9H7o6INqarvBjUfy8ip+2Vry2KPFYjGhXF9FkilYrQDQYyj3aA7V5ezTYfi21gRoCTphGpxGcJhy7AccO49hhgo5NfdhQ3xggHLGoD9vp54hFINj7SESwaThUIBOtUnZXiWorczwep6GhYe9u5TlEKGV3Id/3oaM9Xbm3pSt3v6274k+/z1b8mXVSSZLGKqjwo8EQ0dp6WmvqaXOCpHoMH4ecIDXBMIFAA7bVgO83YJkIATuMZdVi24b6iEV9xCYctrpfRyyCNaZ/w0O+T/OwYWxvbR3Qz0spu0VkyPE9D+Kx7oo81krc9/A2beiu/GPRvGGeKLjFjQMfaK9vJNrYRGv9MKKNY2kbcTCtxibqQdwtTAHvOAHq6xqoqYkwyg5j+WF8tx78dOvfMjbGQF19YcVfH06/DtXtZQDwfaxUCjuZxE4ksZNJrGQy8z6zPJmEYBCmTC7RT7VvCg4iMih8z4VYW8GwjZ9X8dMW7W7pt7VCext4hRV3W/ZFqA7CDRBphBGjSB50KNHaMNFALa2WQ9SDaNKltbOTtvY4rts9Zm+Mob4mTKguQlMgwig7gvHr8VL1+G4Yy9TkKvdQyFAfsTMVv0U4YlMfsairt7CsPQSAbMWfqfSzAcDKe22nUpgeAzm+MbgBBzcQIFEXwg02EBreVIof/0dScBCRkvBdNzNcszuvYu9R8eeGcHZDeyxz+9Ze1IXTFX2kAUaNxRxyGESGpd9HGvHDEdrtGvxIAx/u2EW0vT099TMzDbQj2gHRGBADIBgMEgk3EI6MpGlEBMeEwQ/jJetIJeow2UknLgQdQ7gh3epPB4D0uYC6sIXj9BIAfL+7kk9mA0Ai9zr7Wc8t0xV/ADcYIBGuT78OBPCCge7XTvENqWqamytyzkHBQUR65aeSBUM4flt3xd/reH081vuOjIH6SHdlP2Y8ZvIRmffph8m2+iONUB/BOA5dXV0Fc/5zzxv/QjQaxcvrRRhjiEQihMMNjB0zgWAggp0JAKlEPV1xBzCQAj8FfoB0q785bxgoEwQCwbzK2POwUynsRCdWW7JgyCf7sJKpoorfMyZXySfC9bh5FX42AHj2/n0nQgUHkSrhJ7r6HsKJ5o/XZ07YdsR735FldQ/hhBsw4w9OV/rhTEUfaSho5VMfxljF08E9z6Otra37gq+/bCAa/WMuEHR2Fl7VXltbS0NDA01NzYwbczABJ0KodgRdHQGSXbXE28FzwWuHTsCyIRy2aGiyqT8ofQ4gOwwUrDEY30+37pNJ7GRnupW/tTAAWKleKn7LyrX4U7W13ZV+0MENBHEDAXz743Hf8T1RcBD5GPJ9Pz0k47rpGtF18fDxNn6A94dXi8frY1Ho6iOFiO10V/aRBkxzS67iz1X2mYqfhkYI1WP28grtzs5Oott3FAz5ZF+3tbUVXBVuWRaRSITGxkaam0dRWxMh4ESwCOOn6ujsCNAec0m1QzyTmSZqpU8EhyMWo0bnDQPVG+qdFE4ylRniyVT2bUnsnZnhn15msnmWlWvlJ0O1eJkg0B0AAvjWx7/i3xtVFxxeer6NQKATTIpAwOAEDIGg+cjX6R7g0P+DkNLpT8ruxsZGfM/NpOxeQShUyx3f/jZHTvkEH37wAVdcez2u65JKpvjShZ/j8rM/C37hyVkX4C/r8Jct7R7CCTdiRo/LVO69VPaRhnRlP8C/bdd1i1M+5L1OJArvGRIKhWhoaGD06NFMOnQyNTWR9IwfwrjJEPGYT6zNo2OHT346ylCdoT5iOOCgIOF6QyTk01DjMnZYDZ27dxee5N2axHZ7uRWtbWcqeYdkXV3uRG/+GL9vl+eCV9/38fHxfRcfF8938X0v/YyH56fwfS/zmYfvZ9bJf096m7g1nDoOKstx5qu66xxee6UdfId4e4JU0ieZ9Ekm/D7Pi2UZCwKBwsDhZN4Her4PGpwAhcsdg9nTbIYy03UOleX7Pls2bWLrls0cOWUKsWgrp59zHj+8+y4ee/ophkUiLPri57nnhw/T2trKzV9dyLKXf8vDTzzJf/7b7ax++/9yy133sPT/u49EysW3LGpqQ7R3dXHShRfz9H/+iNFjxoBtp8dPbBsnGCQaa6Mu3FCyhkw24VtflX8sFitq/Tc2NtLQ0EBjYyORSAO1NREcKwJ+HV0dDu0xj7a2JJ0dLuCBccF4BGs96uqhPuRSX5uiPpikPpCg3u7CdhOYVBJSCfCS6V5S3n8pC1zH4NqGlJ19BtcyuDa4FnjGS1fAmYrXI/3az3udraxzrwvW8/DyKvbuSj5TgZOt8FOZCj8/AJTuiuZQYDifnXz3gLbVdQ578GHTjXgkoQkgM//YNxg/CG4tuCGMFwK3FuOGMu9r8d1aEpkHbi3EasGtyWxTA95e3BnN6gKnC2MnMHYXOOnn9PsExsk87GR6uZPMvbecBMby0+OfxmAwkHlkK4LsMoPpsQ6Etofo7OrqsQ4YssMD2f2YzBZW0Xel32eWk7f/3DoUfXf3On0dX/cxZL+/cJ3ejyX9tu9jqfFGUZOyMlMWiytKk/f/3j7pdYnnpVvrngeulxngzrz33B7LXJp9aG5uwNu2gTrg0AMPYOOf3+O5Zcv5Pw/cg+d7fO6cs7job67kpq/dyLOrXuNzF5yP3zKaY8aOJXr7v7ExWEvLgS2QqV46du7EA9xIPalw9mpcP10ZmQRxt5Wd0Xcyrcyerc7uCi5Xifkerpeks6uTrq4OOhMdJBJdJJLpRzLVhe+7mV+LjzE+dsjCiVhEJtgMdywsm9wwi+/7eL5L0nPZ5nts9V387M/FeFDjQa0LI4tbYx1AwaVdPtDzZoVW5tEbL/Movqttj9+nhTE2lrEw2JnrFPJfd39mjJVeRnqZZRwcqyb33hgbQ3q5MRZW/jbGxsp8V/p1/v66t8lfr+d22XXzj2lE06j0SZUyq7rgcPDwWdTUOMQ7Okj/o/KB9Pitn0m65xe87yTdwfXzWkh+j3V8PB/8VBAv5eCnAvhuAC8VzLzOe3aD+KnMcyKM5zbhu0FI1dD3X32GSYHdBXZXOrjYnen3TubZyjzbHZnPOvEzr61AAo8OfLzCcgN+ZngiuyxbzvS65C3L/oS8TE+rcD/7kyOHfx67Jt1rWLe2jvbW0g4X1De6TPxkHydsgZ6/yw0fbmLNe3/ikFmfZNuSXdROaqKVJLWNEbbt2kFrbRsfbttAw7h6Ws128GBky3DWfbiG0PAkmzdu4atX3MgHf9nA9f/wVeqaXNoSm4q+dVv8f1mz60d7XY6iHrMBatIPC0NNZmG67u8OzPgGD+jyTWY8K++zbKi2DHZmqYWNbezuZoaX1wAw2TF8K3Muw6R7Q5n3JvMZGKzseqS3CwZrSCZTWJnPjTGZaakmvcxY6aaFMViZ6i5dIRc2XvpudOWv0UvDx3Q3cUyP7cgcRVHDKHMM6R6Kj/E9PGMwPuQaQgX7KTwWpyNJozlsr3/HA1V1wWFqy0X75RCL7/u4KUgm/YLhrmTCJZF0SSZSJFMWyWSQROY5lQqRTLmkulxSbgrPS3e2s910THe3PZV5bzsetu1hOz6W42Fl3hvbw8o8jO1hWd3PWC7G8jDGwze9db+zrdAe3fP8MdM+u+CF+yll97u0sv+su99ZOAU9oOwnuVeZCrW9Pc71i77BTV+/gWGNzQAErFDengwBqw7jWzimloBVn/nEJmCHCNphDhwfZukzT7Bly1au+tvrOfPMM2ke2dzjCKHGNNHoHUmiK0kimSDZlaArmcQr6EEZAo5DsCZIbbCGYDBIMBggEAzgOAF838r8HXqZZ5dUysfzfDI1GAYfx/YIWB4By808ezgm8zcD+JbBM+n5/LnXlsE33csx3Q0PyDQ3cg2R/AaMD3i4fmEDxk10kHJT7G0DpveGUHfDsHhZfmOpcJvC7SontHXgw0r9UXXBYXNsDTFTx+7oroJxw2z3u+BkUaYC29PJoezy3Oui8cueJ54KK8juSjR/jDKVqzAL/vgcyv8b84HsML1vdT+wwU93cfGz3en8bnO6u1zwsILYlkPAsrAtG9u2sazMZ5kudrbrXNj9tjC+wXQlMIkEVlcC09mF6ezC6ujEdHZiOjow8ThWe/rZdHZiuT7GT8fFwKlJIp6HAY4eF4fxdno6pWWBlXmdGa9Pv3ZyY/elnHiQTCb5yqIvcP55F3L+2ZcBMHLkKOK7rVzK7ubmkURqRnPAuAns3t5JpCadpnvrlh0cfMDhhIOj0sM1nsf4lmFMnnQYr/32bU455RRc18V13dyc/90b4d2Xa3GccG7sf1xjY8F5gLq6CF0dFu1tLvHWJO1tLu1bPWLtPtFkftl9IgGXxkCSxmCKYcEUDcEUjYEkoToLagrn7qdn9aSnc3oBpyIzevaXhp7v5wehfQlUffXYs+tA0/DhJPfUaS2RqgsOL62/E9fvOZC5N0zeWKGdN9ZYOEZZ/Fn62TGB3BhlumK0e4xROnnjnt3jnIVjnnkVKhbGOHn7zB+jLB73HD58BK2tUaxejzG9DN/guzaplCGVsEilSPdiEn5hj6Zn7ybvveemRxn6bv/7mRZmigAJHLeLgBvHSbQT6GrD6WzFie8k0NmKk4oTSMZxUh0EUu0EUnEcP4FVX583zfJAGJs3DTMzvz7e1ELtyJGkvPLnve+zpP1M2X3KKafw8MMPc9ppp/H6669TX19PTU0Nb7/9NpFIhJqaGqLRKKtWreLCCy8kmUxi2zY1NTXYtk0gEMC2baZMmUJNoIbOthQdu5O0R1O0Rz22bfJZ1wHxROEdCuucFI2BFAfXpWgMpojU+YTrDHVhC1OTrfTrcgGg3XFo18y99Jicn8L4KSw/mX7tJTF+CpN972fee4Xv8ZMYr8d7P5Vbtsdtt9Sz7cB/KHvxqm620o74/zJs2DCirbFeK97CE0DdJ6LMx/xGPqVsYaVTG3d2z6Fvi+JnLqxyozFSsQ6SHV0kO5Iku1xSCY8UQZJOiJRTn34O1JF06kgF6kkGw+n3doiUqfnIFqdtUzztuMf7xqYkTc0N+J6bGYcm90gPFZe/cvv973/Pueeey5QpU3Lfd+ONN3LUUUdx1VVXsXHjRkaPHs2///u/Ew6HcV2X2267jd/+9rfU1tbyT//0Txx11FH87ne/41//9V+xLAvf9/niF7/I5ZddhvF9fDcTkD0f34Otm6P871qfWMLGzxviqrVdGgIpGmo9IiGfcNgQDluEIjZ2qHsuv+dUpsVfKs3NzWzftnWPlanxk+Cliivt3iri3LrZSntP6+7bTDgfG98K4BsHjINvMq8zy3LvTQDf6n4fijSzvebTA/rO/sxWqrrgAPtPV7SSPjq1cbwg703u6tnMFbWFeXKikOyj9xUIZubTp1vwJm9+PeEGTKSxYA4+obqCitr3fVLJvHMviR69lUTfvZbsZ54Hk4+EltHhPn8eJhMg8oOGMemTqIVBpK91e69APc/LDfXkD/mkUplzQj3+udm2XfywrPQD0gHA89PXumUmSbkeuL7B9QuPwQC7tkWJfuASrjOEIxZ1DTahxgCBcLD86Rp8t+9W8R4r4r1YN1fh563rJTGkMP6+nacqrIidXIXtm0BBpe1bhe97Vtq5z6zibYsrfIeB3jlyX+ovTWWtcr7nZfLYd+e9iePibdrYI699prKPRaGv6wFqaruvnm0cjhl3UPoq2Uhj+gKrvIqfSCPU1O5Tq9wYQyBIYX6bfnJdn3h7Ow0NQZIpNz0e7GVGAXKPzLUtmfeel1320de8dB8rZMeF0+eIvMw4cuFJVsuycJyadIVvGxzLwrEsbGMw+LkA4HngJsH1fbp8D9dLB4D8HgCAnT5tQsAGyzJYdvoRCDoEamqYMLkG4+UNVfgxjJvCSiYHXhHvYRgk9zo3Vt5/PiZTmXZXsJi8CtWqwbfCRZV2qL6BWGeyR6XdRwXfW6VtPl49pUpScPgY8D03ncGyt5Z89o5V0d25G5gQi+5lauORmIMO6W7JR4blVfaZMfzgXly/sZ+xbYPtWNiOlTm5179//OmWfrbl7+G5Pq7n4WWGbgqnFprc1EhjApmvyk5GzMzsMekAgJee8ZPAJ2lS6cBAegZQ5iqB9LrGx7Eg4KRfW8bP9FbS65vMjKH86GZSPqR8atr+zKhNSwf8s/OxerSKCytUzw4VVtp9tIq7h0s+Yt3segysV1Pb3Ey8ykYBKqXqhpV+umY7wZoQ8Xh8j5PQ8n8se14v7/Xe7s/308MyXV2Q7MJPJNLvE134yUT6faILssuTSbpnPuR/n4FAID2UE6wh0+TGDwZzr02wBj8QpDbSQKfrpWfr9HH8hfv2+1xv4D8Pf+/W28Nnfp/rFa45e1yQA0cNw/f8vEqY3GvjexjfxcLLvPYweN2f090z6B5SMt3LDbnXufWzlTvdgWGgcuHCz9u7X7gsU6qC97u2b2HXhj/R5TsksejyHRLYdPk2Xdh05j0nsEn6Dgk//Zz0bdz86zN63luAPj/a6/X63uPA/sZsx+lxFfxe/o3t7THu4W+sFD+PguP9iOPKaqqv4TsnH9D3F+6BhpX24On/u5OE6+V+EQUz1IsaLqaXV32s63fP/878i4XMkEHu81xLj+51ge7LjTJXvNoG6gzUZy+Y6R7sNoUD34XHlXdQxgO6Mg/A2t2eHm7qpTC9z9IvLmSf6/VY0Nt6Fh5By6XGShG0XILGJWBcaqzMs53KLUt/nsp9FrTSnwWNS8DKW24K95XdLmBc2mtPJmQfhLH3vZJODxulf1seZCrpTMXsdVfe+N3PucrbBy8z4JJ9djPPfmaZn6n4/ez35XoemYff4336wOi+7Cq7zLAz7vP/bxiF4/e8/gJsoD7zAPDwSRkf1+Q/g2sVLnPz17HILfe6r/Uq+r33ubzgvdnrv7G+OhXBYIBkovB3bPo4kJ772FM/xfR5/GYv19vb/VH4e9qLf5tNDfVUQtUFh59eNPkjT+j4yUTBeL2fn7M+O16ffzvCjvbed2QsCGfz2DdmTsb2TG3cPYRDONJrauNSSM/q2Aa43Sfzep7c83ufpYH/Uev2HLvuZd0Sj0f7OLi+RdKFRMoikYTOLkM0YYh3QjBg0Vhn8LEywTh79ayFZQyWsbAx2MbKTENOrwPpi7R8DB4WLulxf9czuZPBPUbsMCY79p8e0kpfSpF5rmA+LcdxCNUmOPzwuvRFlS5FJ/ALpiPnnezP/yx74r+X3HXF3xnIyzkWNEX5x3q+zl8vndBy334+mlxSPlUVHHzfh7Wv05F/39lYXl77bGXf1dH7Dmy7MLvlQYcW3rCkZ9vNyN4AACAASURBVLbLunDvqY3z5kd3V6hdmER7UQXb69zpj5xL3cu661KM9JL7NNzhYzLjz93j0fljyr2OR/c1/tzzhGPP8Whs4l0pom1xdkdjRKNtBUnfeiZ8s60Aw+rqaQqFaKqt5aCxIwiYMI5Jz/zJ1tF+psflm+4g0JWp/D0/Pz1S8Ylp205n5w0EM4EgExD2NHtpsBhjcBx6v3PZXvK84iBSdN1LwWfQEfdpy1xZnUx295L7Ytn0OR35o5NbmqLZX1I6VRUcjDG4999ONJEZa3ECMKwRM6wBM7wB68BRmEg91NdjwiGs+hCEajG1NZjaAMaxMLi9tqjxoxhvR3clvTuF2bWH1vc+8LH3eCLQs+qKZmmE6hqIdybzKuK8KXo91u2r0saUtleTSqVy6Z5bW3d33+mrtZXWaJRksjCDWrimhqbaEIfU1TNi+AiaamszwSBEOBiEvNTLW8L11NbX4/o+LoaEb3KtfjflZ3Lj5QeA7MyidIUfDFjZi6nTy6z+B4D+pOweNmwYvu9nUnYvJxQKcccdd3DkkUfm9tfW1sacOXM47bTT+Na3vrUvP/q9YlmGmhpDzQDnJOSnhOkZUPJ7MD17Nx3t3cHF+4jeizGtOE62N0L/ejCZZ2sfey9DVVUFB4BR1x2NZTKt6oLreJPArsyjh056zYLom+LZHPnT5TyrtvuzvZ1a11ul3WPbgcyPrm1upr3C3W/f94nH44WpnnfvJppp/bfFC3MAOJZFU22I5tpaJo9soSlUS1NtuvJvjISxs3fd6nHzlc5AgKjlEG/3ibW5tLd51NpdDI8bUm56mmh+EzY77JPuAZjM1NCBBYA9cRyHW265hSOPPJJYLMZpp53GrFmzeOyxxzjhhBNYtGgR99xzD/feey8333wzy5cv5/333+ell15i9erV3HTTTSxd2j3z6F//9V857rjjSnZ85WZMOnW9EzCE6ga2D9fNG+7qZRgsEAgRbW0v6MF0tHtEE929mY+S7b30dlFlr697vLed/a/nWApVFxy6Go8iFAoR70z1Xmn3cQFMcaVtD6iSHmqSySTRaDRd4e/aRdvu3elA0NZGayxGssfAdUOwhqbaWiZFGmgaOYqm2hDDwhEaGiPU1YfxgsGiAOAFHHYZg+el/+HHYh7tuz3a21xibR7tbR10xAuHF6ZMBYan/9HnzgfkhoEq8w+5paWFlpZ0nqRwOMykSZPYvHkzzz77LD/72c8AuOCCCzj//PO5+eabefbZZzn//PMxxjBt2jRaW1vZsmULLS0tvPXWW2zbto05c+bw1ltvVeT49wfpoTxDTW3vnzc3j2D79r6HloouqtyLXkwy4RNv9wouqtwTY8gFiuLeCXt1M7FKnpvaWx8ZHBKJBLfccgupVArXdTnuuOO48MIL2bp1K3feeSexWIyDDz6Yq6++GsdxSCaT3HPPPaxbt45IJMK1117LqFGjAHjqqadYvnw5lmXxpS99iaOPPhqAP/zhDzz88MN4nsdJJ53EOeecU7YCx0aeNSit6I8r3/dpj8Vo27WLtp270i3/aCu729rYHWsn1uPWk0HLpilUy6jaEJ8YPZbh4XoawxEaGxpoGNaI6dH6z6ZrSJBO3e/7Pp0dfrri3+7R3paiPdZFrM0jHvMKzgM4AagP2zQ1O9RH8m4RGbZJpjpoaAiSSqVYuXIl27ZtK+nPZeTIkcyaNWuv1//ggw9Yu3YtxxxzDNu3b88FjZaWFnbs2AHA5s2bC6Yajhkzhs2bNzNy5Ej++Z//mbvuuouXXnqppOUY6kp1UWXR+ZWPOA/THvNy7/em92I7ez73kv+6qyNOTeij97mvPjI4BAIBbrnlFmpra0mlUnzjG9/g6KOPZunSpZx55pnMmDGDBx98kOXLlzNv3jyWL19OfX093//+93n55Zf5yU9+wnXXXceHH/4/9t483JKqvvf+rLVq2PMZe+4GRLpBEARpB0ARBBmMCSpocEiuMaAyBIx5jYJelJjBPEZNvGKIU+I1iXlk0hvzvGIQ8L6ixkaEBIwBlUAPp6cz7HnXuN4/qnbt2ufsM3Wf09302d9+qqtq1araVfvUXt/1m3fwgx/8gE996lNMTk7ysY99jL/6q78C4Etf+hIf/vCHGRkZ4aabbmLr1q1s3Hhgfrx9LBJa4zca1CYmqE5OJfr+crXKZL3GVLOJn5o6CWAg1v1vGR5hsFBgoFRkoDRAcXCQTLEz+w+N7sCmRvKRGtfR0ey/5saz/1gSqIVdemapIF+QlAYU6zaaM4vEzyIFeIenAFxP1Ot1rr76am699VaKxeKs/XoZV4UQfOUrX+HVr341GzZsWM7b7GMWzCe9zAcdanw/LbX0SGg5TYJxWpp6tWN7SVeEzeYcLvz12d+jpcK85CCEIJOJvpV2rhghBE888QQ33ngjAOeddx533HEHF110EQ8//DBvetObAHj5y1/Ol7/8ZbTWbNu2jbPPPhvTNFm9ejVr167lF7/4BQBr165NZlJnn30227Zt65PDUkBrlOchWg6VySnGd+6kPFWmXK1QrtWYrNeZbDaoTTP82koxnMuxqlBk87p10ay/NEBpaIjc4CAym5k1T4+T2o5mUD71ahgRQC2It4Ou2ZQQkCtEs/7RNSb5ooylAEUmOzsBLBSLmeEvNTzP4+qrr+YNb3gDr33ta4HIFbGtLtqzZw8jIyNAJCmkAzvHxsZYs2YNP/nJT/i3f/s3vvKVr1Cv1/E8j3w+z80333xYnqmPhUFrDUEAvofheRi+R9b34hwpHuh4wQPpgfLQoQehC6YPwgPh4oRlmn6ZptOi5XmELQtY/r/9gmwOYRjygQ98gN27d3PxxRezZs0acrkcKi7GPTw8zMTEBAATExPJy66UIpfLUa1WmZiYYPPmzck10+e0+7e3n3rqqZ73cd9993HfffcB8PGPf5zR0dGe/eaDYRgHfO4RgzCEloNwoqVVqTA5Ps7k5CST5TKT1SoT9ToTrSaTrVZcJCWCAAZzOYYKRU5as5qhwSGGRkYYGh1laM1qssVibxfcHvD9kGrZozzlUZnyqJTdaD3l0Wx22xsKRYPSgMXa9RalQZPSgMnAoEmhZC65znXPnj1A9Lc+XNBa8973vpctW7Zw3XXXJe2XXHIJd911FzfccAN33XUXl156KYZhcOmll/LlL3+Zyy+/nJ/85CdRLYYNG7j99tuTc//pn/6Jxx57jFtuuaXnZ9q2/dx/txeB6b9lHQ/G2nPRngfxWvtenIEg3m63ew54Xndfz02d5ybHF9zXdZPtBSfqmgMWYEjIKdCGJCjlGRkZWXbb2YJ+OVJKPvGJT1Cv1/mLv/gLdu7cOWvf2UTj2fyRZ+vfCxdeeCEXXnhhsn+ggSBHfOBMGKI8D+V60drzEI5DrVKlXKkwVasyVa8z0Wox0Woy0WzRmKbYzJgmg4UCo6tWcXypxNr1G5AZm9LgIMViMSH26Wh4Ho2YtNsIgpB6PUxm/W39f60adBmCNWBnBLmiZGC1ZH3RIleIbADZfFxrOHpAtHYIcJhowEQduvyJdLouV+dAus/09CZdqRI0qKDFkGESpFIrzPszXWC6hXlOTfDwj3/MHXfcwYknncR5550PwB/84Qf4navfzQ3XXcPf/8M/sH79ev7X526n2nR42Tmv5P+99zu85KUvJZvN8vFP/AWVptN1zabr43oBlUanvf3ZSin2TdT46e5OnE4SoJ9O/pAKP+j+fvXM7zu13bmm7vo7JDWiAx/tBxAGyZogQIep9iBakmNBu08YzbLbfeKa3Lp9LOzu124ToSYMfHQYRm06yqXVeT6RhBbrrigfgU61tztp4liY6AtFy6gglJYWqBxIiZYKbUqwo+JQWsikiJRWkaNKe530l50+oQAfF1e7eLRwwxaubuLhRbE3EoQ0MYwCplHAMopYRgFL5RFCMVIqcGVsp1osli19Rj6f5+STT+app56i0WgQBAFKKSYmJhgeHgaimf/4+DgjIyMEQUCj0aBQKCTtbaTPSbePj48zNDS0mNtaFD74nWcI2I7v+6kXPF4n/3X/mKbnaEn7xi+oz4zrtzOCdtJp6Di9Rqij5GwhUV3qMP4hhskPMkc7AYIQAmEJsNupNNo/hCgXkNag2264ewVaO8AeNHuSB9XpgeIgBscE9Xg5zHjXiwY40T+8znhrT3wR3/v3X8xobwAfv71T57kO1CtRCvR3vf8W3vX+jlSwq9KdGv2siy/jrIsvY1e1d8r0/9rT4POPlQ/+5pcUiyxhKIhyfSxPsoBFoz1VnZ4Co7MvevfRQBDV3KBdu12H8bYB5OLcXO3IfRkXAJNx4a/Oh0f0FZH+SMHjyhccATaHSqWCUop8Po/ruvzHf/wHl112Gaeccgo/+tGPOOecc3jwwQfZunUrAGeeeSYPPvggW7Zs4Uc/+hGnnHIKQgi2bt3KZz7zGV73utcxOTnJ2NgYJ5xwAlprxsbG2Lt3L8PDw/zgBz/ghhtuWLYHHswYSMPAddt5i2bmcEnnfhGpP06vPiI1MxFaI3WICENkqLvXsf7R933cMMAJfJwgwA18WkGAGwT4sSW2fW1DKSzTxLLbdX4tbNvCtmwsy0wkrPZL1LmnbulLANlclmajSRhoXDf2tnA1rtfZ1qnUS0KCZQksS2LaAtuW0b4dRQnL1BfT87tIf2fpPqI7l8z077ez392nV54dkfr7TL/G8/KwppSJaif3EERnNvWWVgXRFCDKXaRTM4n0WqdYttNHzHasZ76taZ/RlYdLd7XPpUzITfyKj//kjlSfTn+RYv8oSaCI3GQME6EUGAphGKCMzlrFbXGf9nZ0zACzs47OM+N9E2GkrhPl2Yi2zXa72Wk3DGQ6V9i095nUu57uMxpPOrvfAzHHO5P6f44+i1XZtPwyU63tlFvbKTs7mGptp+LsTFWdFBSs1QzYmxjMbGTA3sRAZhMFa3VUhXEROFSaj3mzsj7zzDPcdtttSaGSs846iyuuuII9e/bMcGU1TRPXdfnsZz/L008/TaFQ4L3vfW9ibL777rt54IEHkFLyjne8gzPOOAOARx55hK985SuEYcj555/PG9/4xgXd/CEr9qOjAT6t5lGuh/S690UQ0PD9WNXTZKLVYtxxGHdaTDablJvdmWCllEld3/a6vV0qlbAPMDTVc8MZRuBWU1KecroNwRLyeRkbgFXsCRRtL4Uh+EDRMeS5kduR73WWdpZa3+9qi/TInfbGcVsorlkbkUN3IYdpA/osy/TjSwUhO6NSe7udgG62Zb7jcR9lGFTH95ErT8RJj6xo0I4i0TpthhkN6suUx+tQ4lCriP2wRbm1k7KznanWDspORAhOkCTFx1YlBjObGLA3MpCJSGDAXo8hD9DdaRoOVbGfFZeyOzs5RbFYpFKtgoh0kEJrRBAggwDpt9c+yvMRvo/y/WjmD/hhSNlpJQP/ftdhvNViotlkqlHHmeb5k81muwb99DqfzyMXaPidDt/Xid6/Xu3YA+q1ENfp/pPm8pLBYRvLDsgXVUwAkmxOJoZgHQa9B2Lf626P23SPAXq2vvh+3L+7jWRAb7enzj3I17J55bvJP3/L4gbZBR2XHZ1C1xL/Hae3JUQQnb+chGsYBpVKhVzuAMORn4NYLnIIdUDN3R0RQGt7RALODmruPtpKVyUsBhIpYCOD8TpjDCz5/aTRrwS3HNCawWe2I4DhA7yEISUj2Rwj2RybU+1tm0DboCWkBNk9mOh4gND1FrrejI1okfFN+17KEBcQhj7a9wlcD8/x8R0f342WwHEJXB8d+GTDgGzgs1r7KOEjCVD4KAJE6CO1D4GPQuM7rWQA1r4XXb89UM+XxGahELJ7ttqeqbbVEaYZ1Z7IFcCI1Q3JLDfVb9ost90mknYj7t+7rwg0xuAgwUJSi/axYqG1pulPUo6lgKlYLVRxdhHqaKInEBSsdQxljuO4gVdGRJDZRN5cxcHUlm9XHmxrZcIwnLHd65jneZimuVRfwaxYWeQgBH7gEwQ+U06LqtOk5jg0Wg1azSZuq4nXbCICH6VDDDQqDLGVxFYKW0ksKbCEwBSg0MgwTGa7uj3zDfxothxEM2kdxLPnwI89OjwWlA95Gma10UmFiPXEQsXqgkRfHOuKlYGycohsqp9SIDvb6b4zrqmMKCutMrv1yIaJMBTasJL2iAhJZtBtj5F0RtS21JbeR7S9S1J9phNr+rq9roNAB9AKQ0QQIIKgc94s78SC2vpYFvQaHOcbLNNtlUqFycnJBZ3v+S0a3iQNd4qmN0XLK9PyqviBB1qAFiiRwZLDmPIYLJHHFFmUyKA1TGrNeBgShjsJw+2LGtCDIOjZ/0BQKBR45zvfucR/iZlYUeQQhiE7/uZWjNjymiEpr7M4SAmmRWCYBO1ZsWmCEc9+TQOy2dSM1iAUZlR1K1S4oYEbGLQ8heMbBMIglAaBjPpaeQs7b2EXLOxihmzJIjtgY+aszjVV2zCootpdOi4spDuG0PY+WjM4MMDU1GTkJhq36fictmotMXj2vI6OvC/ax0n1SbV19Umdg46JtMd10veZvk7agLpYOKPDCNM8iBd8WnJzMctOvKnTOzO4pbt/r6fSs63j76Crve3xNq2vLyW1cpmx/34mGnx0SBD/nYMw8pQJEo+4xc1WZzt2sNc5vFrtaAQQEqSQSKkIpMSXEiklQjhI6SFlDSEEMm6PjnX2lVKzHpu+P/3YYvsLIbriwpYTK4ocpJQ8e+aryOVyCMsmWyySKRSx8wWEmVaBmLOoRuY25LlOSL0WG4LbtoDYLhD46fuIIoLbUcADBZnYAuzM4vXS0weJnn0GSnheb/fHw4l5ResgiGZY8Vq3B5fY110H7bYgXmt0GJAFhqSMDNK0x9OO+27HlVd33cuMbZ0ahlPbXdeIG9Lfv9aa3bt3c/OHPxx70wguv/xy3vbWtzJVLvOBD3yAXbt2sX79ev78z/+cUqmE1ppPfOITfP/73yeTyXDrrbfyghe8AICtW7dywgknAFFGgb/8y7/s+X3u2LWLhx56aFF/g6j4Ua8lLowke+1H9iojbhNSIKXqOiZEe1CL2qI+cdu0fZEMftP2pUSqdv9ou30MKbHzgn3l7TT8fdSD/dS83dT9fQTCIxQhUgqK9mpKmfUMZNYzmI1VQvYoSqnD5nRxMDhivJWOZBwyb6UUfF8nA3+t1iGBWjXsLlcoIJeTnWRw7cRwhcgQLFIRwQciWs8lavfaz+fzVCqVJZntLcWs8WBF6/lwzjnnLCoFS5fr7wFuT2/bt28f+/bt45RTTqFer3P55Zdz2223cc899zA4OMi73vUuPv/5z1OpVHj/+9/Pgw8+yN9/9at88Utf4rFHH+WP//iPueuuuxDAqS96EY+nsrF2uTHHaykVlclJjHodKQQqdg2Nqt5F9RkUICGqgBdvC8Gs0lu3FNhxrZ1NmuySOpdICjwYdKsuZ6otE3UnqT6zqC3nUokm6k5mUYn2VKO21Z09zpntPoVgeGSE/eUDi2XpG6TnwL//+7+TyWSo1WqzDlZBEOK6Aa4TrT03xHMDPC86FkX4aiBESB0XhAmRQiOkBhHFYk62QsL9R7poHeFARdylEK3nE7UP5FpKKYaGhvB9f0ED+XJgYGAgme2XSiVOPPFEqtUqDzzwAHfeeSeFQoG3ve1tXHHFFXz0ox/le9/7Hr955ZXk83nOPuccqrUalWo1cQW3s3On4jQMA8v3yK1Z3fO4Bg5rPsLpakumkY7WhKFL3dlL3d1L3dlDw91Hw92PFzSQ8T9LZikYo+TNVQwX1mP4ObLGEIYwDlj9OfN+2mrQ3mra2cjwUMgh2rLg5BOX/XNWHDl8//vfx/dn/kRE7KbYKdkuY0+EWExW8aBjSAxDogwVrVV7QDIXNPAd7KB3IIOrlJKRkRGmpqZmPf9oQ6PRSPIqFfb9M4YztqTX9+111Fb9+oL7H0zK7jVr1uA4DpdeeilKKa6//nouueSSJX2eQ4LUDFrrkLq3Pw4c25HEDdTc3ei43rgUJiV7PQOljQxlTktcRrPGUPLOtrUADt1JHw8b4one/Da8mYQ1s23afrwUSqVD8igrihzCUPOik9+M05TUKwGh7pCBaYpE9ZNOC50vSizruV/Up1Qq4bpHns1hJeBgU3YD/PjHP2bt2rU888wzvPnNb+akk07iuOOOW65bXlK0/DLl1g6mnE4Ecbm1Y1r08CoG7E1sGngpg/ZGBjIbKVhrFx09fNgR/710asK11HqB3OgoHAKbw4oiBykFQmcYGbVZu95PCsPkD9AQ3MdzA4uZ4S81liJlN0RGaIBjjz2Ws846i8cff/yIIwc/dCg7OyMCSKWRcIJK0sdWRQYymzh+6PxUGokNSxY93MfSYUWRA8DZ5xeO/KysfRwV0FrzB3/wB5xwwgm8+93vTtovuugi7rjjDq6//nruuOMOLr744qT97/7u77jssst45JFHKJVKrFmzhqmpKbLZLLZtMzExwbZt27j22msP12PF0cN7utRBZWc7NXcv7XmyEhYlewPri6cnaSQGM5uWPXq4j6XDiiOHPvo4VNi2bRt33XUXL3jBC3jNa14DwAc/+EGuu+463vOe9/C1r32NDRs28Dd/8zcAXHDBBdx///2cc845ZLNZPvWpTwHw1FNP8cEPfpB26vvrr7+eLVu2LPv9a61p+VMddVCsGpoZPbyWwcwxHDdwTpxHaBN5axWyX2P9OY2+K+sKwUp75kajQalU6ul8cLTiYHIreUEztgVsZ8rZkaiF3KCW9MkYg0n+oDYJlOz1GNJaysdYFFbaew393Ep99NHHMiDUPhVnLCGCNgnUvc5gY8gMA/ZGNpZewoC9MckwahvLX0OgjyMHfXLoo4+jEFprGt54ShqI1EJVdxehbtcNURTttYxkT+D4ofOTzKI5c7TvnNFHnxz66OO5jlAHBKGHG/rsb/yKp/Z8k3JrB17YSPrkzBEG7I2sK74okQaK1nqU7A8BffRG/83oo4/nCKLEeR5B6BJolyD0CLRLqDt2lbq3H4Hg2IGzUoVmNmCp/GG88z6ei+iTQx99HGGIMqr6MQG4KULw6IRUCZSwMGQGJSyUtLDNLHa4hhese/XhvP0+jhL0yaGPPg4jIpWQO4MI2ikkIEojoYSFqXIJEShhzrALKGn0bQV9LBn6jsh99LFM2LlzJ1dccQWvetWrOP/88/n8F27H8avs2vcr3vTmN3DW2S/jTb/5Rnbse5KGN47j1/mTW/+C115wJVe87nd55r8mGMwcx2BmE5X9Pr/729dx0QW/xoWvvogdO3Yc7sfr4yhHX3Loo48lRKQS8gi0h0+d9990PSee8nyq1Qq/+fp3csbLNvPNu7/Ny85+CX977dV88a+/wle/cA8f/tCHuf/+B9n57F5+8NAPeeSRR/ifH/4I3/rWtwC48cYbueGGGzj33HOp1+scaO3xPvpYKPpvWB99HACi1Os+XtCg6ZWpufsot3Yy2XomLkS/h+KwyUmnPB9DWowOrmfz5hOpTcD3vvsjfustv0vBWsVbrvwt/vU730VKg+985ztcccUVCCE488wzKZfL7NmzhyeffBLf9zn33HMByOfzZOdJ391HHweLvuTQx1GPR8b+nqnWMwd1Da1DNDqyBWhNwVrDiaOdtNlSGChhYqriNLtANP/avn07P3viP9l65ksXnbJ7bGyMUqnEVVddxbPPPssrX/lKbr75ZpR6jmUs7eM5hT459NFHCpqo1jLxOir+2Z1hRiBR0iBnjiREMFdq6YNN2e37Pj/+8Y+599572bBhA9dccw1f//rXectb3nKgj9lHH/NixZHD975dwTCbSBVgWgLLEl3raFtGazvaV6rvAfJcxovXvb1rP7ILBCkPIXcWV1EzlgAslIw8hqRYnEfQUqTs9jyPF77whRx77LEAXHzxxTzyyCN9cuhjWbGiyEFrTWlIIVDUqj6tRojrajxXM1f6QamYRiIyWttiFoKJjitj+UpQ9rEwtKOHu4nAQxMkfSKVUNpV1OxSCR0olipl9+joKFNTU4yPjzMyMsJDDz3Ei170ooO6tz76mA8rihyEEJzxsvyMrIZaawKfmCg6hOE60dpzdafNDanXAqbi42E4x+dJpkkk0ySTWQjGMPuFhxaLIPSouLuiGgOt7ZTk83Hl2iS1NES5hJQ0sVS+SyJYrmpjS5WyWynFLbfcwm/+5m+itebUU0/lrW9967Lccx99tNFP2X2QCPw0cUTk0kUmMwgmOj5nJmkBppkiDLu3ZDKdYExTIGVvUjlaUhu3aw93Cs1EWUWrzu5EGpBCcfrw1WwYORGBkRCBFOqoJt2DSdn9XMXR8l4vBv2U3c8RKEOQNQTZRf4ew0DjeR0S6RBJ2N0Wt9eqYdI2FwyTRDJJE8rA4Di+34rJRs6wsxyJdhXHryalJstOuxD9DvywlfTJm6MMZDaxoXhmUnayaK+l1XQpZVdWPYc++lhK9MnhMEEqga0E9iJL5+owIpVuVVeaYLoll3Ij2n/2V5Nz2lWUIiWJyDmM9bHkkhjrD96u4oculbj2cKfQzHZafjnpY6kCg/Ymnjf4yrjucJRQzlSz+fu7s7T30UcfC0GfHJ5jEDJSM1k2LCbP5sjICLt3748kk562lI5NxXM19UrIZNw2l11FSma1qZj2dFuKxpP7aOid1PwdSa2BmrsndhkFJUxK9gbWFk5lwN4UF5qJag8fzSqhPvo40rDiyGHv3r0EQUClUkEphVIKwzCS7aN1ABIiskmYpiK3CFbRWhMEpMgknMNYr2k2QqamfNyggm/sIsxEi7bHCDNjIGMDsRZIbxTD20AhfDEZNpATG8iba7BCAysQmJ5AeIKWIwisMCIaUyBmsav00UcfS4cVRw533nnnnHpoKWVCFLMtaTJZzLHFHj8S8ucIITAMMBK7Srdnjxe0qDg7E3WQ42yn0dqB2LLbXAAAIABJREFUE1STPpYsUVAbycrzyej1WMEGDG8tfmDhpQz6dVcz6fr43tx2AtNMqblmGOsjySVXCMhmQkKtEYJ46ZNKH30sFCuOHC699FJyuRyTk5MEQdBz8X1/zmOO48x6PAgCwrn0MIuAEGLJiKdUKtFqtQ74WghN3dvDVKtjE5hq7aDu7U3u15A2JXsD64svTuoOD2Q2kTFKi3ruMGVX8Zz5vcEatdiI7+kkhm3LqaBUt90hTRJCRK7Gs+3Ldl/ZJ5U+ViZWHDk873nPW3b3t0gVMz/RLISI5jvX8zxardas5y6eqDTS9jAKDYx8E6PQjLdbCKnj5wPdyqGbRXA2I50S0huEsEhDGbiGwYTSKLULpfYcnHRlKHJ2+rg5q/pPa43f9gDzmpRKFr7vR/fbXsIo4DHUEAbROTqc35tbyhSJpEije7/TJgXsGtvFjTfeyL59+5BS8ra3vY2rrrqKyclJrrnmGrZv386mTZu4/fbbGRwcRGvNLbfcwv333082m+XTn/40p556Kg899BAf/ehHk3v55S9/yec+9zkuueSS2W+4jz4OEisuzqGwZx+54SEmfA8/k4l+0Ucx2kQ1ODiY2Fvai+PVqLi7qHo7qfljNILdNPVeAjquooYuYIYjGP4Qhj+EdAcRboEwEAdEgEuFtvpvNqI59dRTWb9+PVrrhEiigbv3dgQRSx4iWmnQ7TYdten2dkw2MPv7s2/fHvbv38spLzyNeqPGFZf/Gp/73Be45547GRwc5JprruP222+jXClz880f4oEHvsvf/d3f8tWvfpWf/vSnfOQjnZTdbUxOTvKKV7yChx9+eEZm1n6cw8pAP85hOaA1hT17kWO7WQ1oIfAzNl4mg5fN4mUzeNkM2jh6vhYhBEJCzR9jr/vvsYdQFEDW8MaTfqbMMpDdxBr77EQdNGBvxDYKS3YvUZrrcEmlp7lIKAzDRHLSWieJ7dLbS4NYgkDEXCEQwNBIhqGRY3C9MqYpOO55x/Hss0/znX/9Nl/64leoVqtcdNGl/O5V/4Nrr7mRb/3zv3DJJb/O5ESD5x13EpOTUzz15H+zevWaRCK5++5v8spXvgq0QavlIwVIGRnplXrOzvP6OAJx9IyCC4EQ7D71ZFZlc9TGxjCaTcxmC7taIzc5lXQLTDMhijZpBJZ1xEsZWmsa3n6m2gFjre1MOdtnRA8XrfWM5rYwmMQLbCRnjiy7bj1tQ1luNBoNSqUoCK60Yxdms9Wjl+7e0rO0z+yAY9vsWzXSk3B6te3YsYOf//w/Oe1FpzAxMc7oqgE0LiOjJSYmxgnCBrv37GLVqkH8oA7AqlWj7Nj5NKWBjoTwf/7PPbz97W+nUp3o+dw7duzkBz/4EVJIpFTRohRKtlV2sbRlKExDYZoGhhmt53PEOBCHir695iCgNUK7iLCFCFqIsIUMm6CKwIZl//iVRQ4AQqALeZpDgzA0mDRLz8NstjCbLYxWTBqVaqI0CKWMpIyUhOFnMujDlFO/HT1cbu2IyWD7jOjhnDnKoL2RDcUXs3HVyUh3gKK1DiVX3p+9N0T3lpilvbsJiFQ4+fzCfILr9Trvf//7+djHPsaxxx6LEIJVq1Ylx6WUrF69GsuyGBwcZHR0FADTNBkYGGB4eBitNXv37uVXv/oVF198CYZhpGwobRISFPJDrF9zEr4f4PspycoPCLwARwdoHLQO4pTkAVoHQBi1EcQpy5cGS+VQMduxiYkJ6vX6vNeezU61rNA+Imghw2hgjwb3FiJsztLeQgTNVLuDYObfQu8vwrE3L/vt90eJGKFp4pgmTimVbz8MMVtOImGYzRbZySny47GqAghsa4ZaKjTNJZMygtCl7OyK00dsT8ig5XckHUsVGLA3ctzgKxm0NzIQp5FIRw+vRN1sG5WNC9ezLjUWmrJbCJEU92lLVrt372bDhg2YpgnAt7/9bS699FJKpd41IQzDwDAFJ570vBnHtI7yeS0kCNJ1QtxWgOP6uI5PGLaJI16ntoUIkCqMFhmthQwRIkTIAEQIBAjChHg0AWHYIS7XdQ+pnWphRCTJGJAxQzKmJmOE2CrAUiG2EWDJAEv6mNLDFD6G9DDxULgYuCgcJHPfu0agpY2WGUKZidbGAIFaE+9nu45pmSFUGQZH1kN9yb6WWTEvOezfv5/bbruNqakphBBceOGFvPa1r6VWq/HpT3+affv2sWrVKn7/93+fQqGA1pq//du/5ac//Sm2bXPttddy/PHHA/Dggw9y9913A/DGN76R8847D4Bf/epX3HbbbbiuyxlnnMHv/M7vHBniqJR4uSxeLkuz3aY1KpYyukijXElOC5XqqKUy2VjKsCOXl1mgdRiVmoxJoK0aqrm7k+hhKUxK9nrW5l8YEUBmE4P2JjLG4JHxffXRhaVK2d3GN77xDW666aYDupcoCJJlD4JM5wYL5ghXUQIylsDMzJKeJd43LDBN3SEfGRLqMCGPYrHI+Pj4NHLxCX0nmZHL0EFqB6UdFE48gHsYoo6BFw/yfrJuk8B8Pyk3AMeTNH1BzZO0fEErWWeitS9pee11p48bKnxtoFQvkgKlPJQKMQwHpWpdx4eHJw9JyvZ5yUEpxW/91m9x/PHH02w2+eAHP8hpp53Ggw8+yKmnnsrrX/96vvGNb/CNb3yDt7/97fz0pz9l9+7dfOYzn+Gpp57ii1/8In/6p39KrVbjzjvv5OMf/zgQpS7eunUrhUKBL3zhC7z73e9m8+bN/Nmf/RmPPvooZ5xxxrI//AFBCALLimwQAx3/fREEMWG0MGO1VG7/BLKtfwb8TEQYFdtjnxpnXO9jytuZqIcC3fbLFxSs1QzYG9k08LJYGthEwVqzbOml+1h6LFXKbojKjI6NjXHWWWcd0meYLwhyLgTBTALplRLfdTVOS1OrhLhuSOCFWMpJLa1obUT7GdMhYznkDRfbcjlGNrGkgykdDNFCGQ7SWMisvT0bz3a2ZYZAZmi0Z+yqe/YeCAtfW3jaJAjp6TQhggArCFBBgO37FA7Qld3zPJrN5oz2XC53ZJDD0NAQQ0NDAGSzWTZs2MDExATbtm1LfK9f9apX8dGPfpS3v/3tPPzww5x77rkIIdiyZQv1ep3JyUmeeOIJTjvtNAqFyPvltNNO49FHH+WUU06h2WyyZcsWAM4991y2bdt25JLDLNBK4RbyuIXOtMwPWtSqT1Ot/Tfl1rNMeTuZqO2hWWskfbI6x7Bay3rrZZSymyjljyNffB6GWmRGvj6OOLz0pS9l586dPY99/etfn9EmhOBP//RPe/bftGkTP/nJT5b0/pYFKSOqClpkaCKMFkK2kFZrhnG1rVuXsR5ehC2knj9poh9aeNrGCzM4vkXVydLyBnEDG9fPROtpS4AdDfYqizItzGkVH7tS4qtOmzK6RQjFYihy6XGoVMSLsjns3buXp59+mhNOOIFyuZyQxtDQEJVKpFaZmJhIDGoQJXybmJhgYmIiKYcIMDw83LO93b8X7rvvPu677z4APv7xj3d9zmJgGMYBn9sLoQ4oN3cxXv9vxmtPR+v6f1Np7abt4WJIm+H8sRyXfwUjmU2MiNWM+sPkGxJRqyHqDURNwz4fLX+JLuTRxQK6UEjWHISL7VI/85GOPXv2ANFzryTYtn1wf+fQh6ABfjNaB82ubeE347b4WHrbb0DQ6mlETUMLBSoHRhZUFqwcGCPRtpEjVNlkG5Xt7mvkQGWQQmEDecNI0uFoHUkmjhPitIJk7bb3WyGOE61dJ6BZD5maCHBawZzJJZWK0tzbGYWdWlvT9u2MwspIbFthZ1SUB2wZ1L2H6re84F9Oq9Xik5/8JO94xzvmDLKZrUh6LwghFuVvfuGFF3LhhRcm+wfKngfKvFprmv7kjEIzFWdXUnFMIChY6xjMbOKY4lmxq+gmCtaqGWUna0CtBLAawhCj5WC2WphtW8buvahgLOnvW1bHUyqbxctkCKyFGb9XmkHacRxs215R9RwMpXCadSbGnurygJFBM/GK6faMac/enc5MXs/9ffUyomqZR1sjhJnZjag6NrCGMgPCWLjDRhgvSUG/ZrxEmPW9lmBno6Ub7Xm/2XmmlF1ltpoqnbVPc8Lt2FXm0F4J0StjcTsn2MyaK4nkMk9yySMqCM73fT75yU/yyle+kpe97GUADAwMMDk5ydDQEJOTk5RKkf59ZGSk68bHx8cZGhpieHiYn/3sZ0n7xMQEJ598MiMjI4yPj3f1Hx4eXvADLBe8oJnkDyqnXEXdoOMmkDWGGMhsZE3+lNg4vJGSvR4lrcV/oJT4uSx+LkuTSCJDa6Tnd8giJo5MudLlYtuOx/ATI3hmTuN3H0cgotDraNHRiCi0BsJIVdMeJeM2ocPuvvG2XftvRnf/y+wfI8zugVtm0eZQZ1tm0NKeoYfXyTEryhVyFGGp7SrTa6qk7SrVSojnhvje3NedlTgswchomVXrDuqRF4R5yUFrze23386GDRt43etel7Rv3bqV733ve7z+9a/ne9/7Hi95yUuS9m9/+9ucc845PPXUU+RyOYaGhjj99NP52te+Rq1WA+Cxxx7jrW99K4VCgWw2y5NPPsnmzZv5v//3/x7SnDFB6FN1x5LaAu2CMw2vQ3CGzDBgb2Rj6aVx4FjkKrqU0cM9IQShZeJYJs4047fRcjqk0WyRm5hEhh0X23bktx+72FLs7f7YxxIhNZB3Dd7pwV2H3fvxgC6S8xYgRQuJJk7khARhoIVAI5HKwLc3UMldPuvsHbGy1GzLDaUEKivIzFZzahaE4ezuxGl343ZbklzS1ezMT3DB65b/9zxvbqWf//zn3HLLLRxzzDGJeugtb3kLmzdv5tOf/jT79+9ndHSU973vfYkr65e+9CUee+wxLMvi2muv5fnPfz4A999/P/fccw8QubKef/75QCeRmOu6nH766bzzne9ckK5usbmVQh2wu/bveGqCXRP/Rbm1nao7Rqgj2VCgKNnrkqjhwSR6ePTIdxXVGuW6MVk0E88pw+tMUQLDiFVSmSQ2w8/YR3zk94EgHSE9L5KBefpA3h68OzP06TP6biKYDwItJKQG92i/PdDLKJdT3Da9L4g5/1b93EpHP7TWDAyMUKn0tsvOh8WolVZU4j2tQ+7++bvwQ4ecORJXGtuYlJ08GqOHhe9jNlsMKoW7fzyKzWg58QAX5Zfy4shvPxWboY3nkMtsVyRqpEOvelkKpWGCwO8a3DsDezSgL3zWHg3MOh7Eo+R8ne0ZM3okOpndp9fLhz45rAwcUTaHowVCSM59fAurrBFCVYDSAKI4CMUBKBlQctH20VUNThsGbrFAODrKVC6WfbWeoZbKVCqoicnkPD/OL+WnIr+XJb+UDlMG0bbRtIkMWgswosapBnoYUVuFXwNbd7TH7Vl5e0AXCj1tQO/aFqm+COabtffCzp07lyRlN8Af//Ef893vfpcwDDn33HP5oz/6o6PqPe3jyMOKIgetNb/XvISwCXm/SX5Xk7w/ScHbFe37TQqhS96AvCnJ2waFnEU+l6VQyJIfKJIbKCFKMaEUBxCHKbfSQUEI/Fi9lI78lrGUYTabUTBfs0VmWn6pjloqi5ex8TMKgdsZxIP0gN6cpb09w28hQredIHtWHIgR1fGL2LlR/CDkQAb2pYBhGHzkIx/h1FNPpVarcckll3Duuefy9a9/nVe84hVcf/31fPazn+W2227jQx/6EPfffz9PP/003//+93nkkUe46aab+Na3vsW2bdvYtm1b4sb9+te/nh/+8IecffbZh/yZ+lg5WFHkAPDGF68nkBb7pqrUGw71lse44/OMr6kHgobuMdi7wES0SB2Q9yfI+zvJe03yeBQIyCtNwRTkLUU+Y1LI2RQKOfLFPPmBAoWhQfLFPKY6gjw9dNAJQordHbVs4WdbBHYTtxQN6sqtofwGMohm9HbDwW66gBfnzpnjI3pEogbm8LTBfmYk6kEbURsNkEbkt3+YsGbNmiT9RaFQYPPmzezevZt7772XO++8E4A3velNXHHFFXzoQx/i3nvv5YorrkAIwZlnnkm5XGbPnj0IIXAcB9eNgsN83+9K3NdHH8uBFUUOQgje/MLROXV2QahpeiE1N6Aer2tOQL3WpFarU6+3qDUVdSdDzTWpBzAeSGoY1KWFj4IW0ZLYjOq0M2VlApe8dsnjU5AheUNEUkrGoJCxKBQy5As5CsUChYxJ3pLkLUXBUmSMVFBNVzrf5swMj9OjUPeFDDmV7gjVBUSihsKKBmuVwbeyaDkcDdjaQoQG0ldIX6I8kJ5EaAswCUSWIFvEyxSj/FTZDL49d36p5UL4T19Ab396Sa8pNj0PeeXVC+6/fft2Hn/8cc444wz279+fkMaaNWsSV+7du3d36YTbifi2bt3K2WefzYtf/GK01rzjHe9g8+bNS/o8ffQxHSuKHBYCJQUFW1Gwp0sQ89dB1lrjBppa06E2WaFerlCr1qlVm9SbDrWWR933qfmaVqjRKqp57CiJ50saHjQqHoW6S3O/S9NwaSqPpnJxDJeCcikZLnnlkVceUsytjgmRBCKLVhm0XUALk8Asoe05fNlVevZuwyJyOQk/SGIx2mqp/PgEYn/H+O3bdledjKOtuFIv1Ot1rr76am699VaKc7gUzxZA+vTTT/PUU0/x8MMPA3DllVfyox/9iJe//OXLds999HF0/yqXC3MYUXNhi6Gwhci1kJkWYtUsEarzRaJq8AOJ60scN8ro2KhL9niKWlCkEmQohxkmwiyTOs+4zlMOM9QDi2pgUfdNHK1IFyLIqsiWUrAN8nYkjaQlk7wpk7aCBXnLp2CFFCyFpeZPBaCNmfml0BrDcbqy2M5bXCmTIbCXzvi9mBn+UmOhKbshkhTSHnhjY2OsWbOGu+++mxe/+MVJ/YhXv/rVPPLII31y6GNZsfLIQWsIHKRfSRlLp6UQ6DKiNuPEYB0jqgyd+T9mTiNqO+K01+y9faw7EtUgkl2KvgfVClSnoFJGV6agWkZXduBUa1TrDep1h7rjU3N96sKibmSpG1lqZpaGkaVmZKnbBXabeWpmlrq0ac2j1zckkT3FVBQSQmmTSZtQYrIxI7Jp98tZdlSvu0dxJaPVSozg3cWVRJzF9sgornQgWKqU3evXr+cf//Ef8X0frTU//OEPueqqqw7XY/WxQrDiyGH0Vx9F/tJlrrRVh82IugAIw4ShkWihu0BZLl6S59AamnWolBmQUN7xTEQmlTJUn0ZXp2CiDJUp/GqVhutTM3LUjUxEJu1tu0A9N0AtM0DdzlM3c1RVht3SoqYN6qEgnF4qbRpypkzIIp+SUqK2LAWrQL4oGBABA6HHYOAx5LkMNacY1KniSkl+qWziORUsYXGlpcRSpex+3etex0MPPcQFF1yAEILzzjuPiy666LA9Vx8rAysqCA4gN/FdcvkS9VbQqb7UdodsJwgTR3696MViIYEz2vegVoGYQHS1HEso6f2ITKhOQTsbJtBSNnUjE0klhRHqxRFq+UHq2QHqmSJ1K09dZagpmzomda2oB4KaF+IEc7+CphQUTEFRQVFqSlJTEmG0rSBvCvIZk0zWIpu3yRQyFHOK0eFBwsBfMfEA/SC4lYF+ENwyoTF8AbnRUZor7IVaCIRhwuBItDCjbHIXIqmkkZBFvjpFrlJmVbyvq1NQ/SXsiQmlXu19IcPAKw1TH1hNozhCvTAckUqmFEksRpaakYnUY1pR8zV73ZCnnYB67FEWyRUB6Yyd73rRACcG0esthUAKUEIgZe99Jdrt8baM+sgVQix99DEdK44c+lgaCCEgl4+WNdFsZE4ySaSScmwjmUrsJla1jFmZYrC8E3Y8EUkms+VEykWR7VFU+yC6OEirMEQtP0Q9O0BTZWkKk9GCYMSWhGFIoDWBJqpiHICHINQQaD1/0gzRIYmEQGRMLKKzbpOJSvWXYvZ09X30caSjTw59HBIcsFQyzfCe7FfLsPNZRPU/yNarZIF0WFjzyneTFyeCMqLYCqkQUkaLiNJkCBWl0AilIlCKQEgCRGrRhGFEIqGGUGsCrXH9qD1cgEZWCoFKSSu9Saa3ZNOXWvo4nOiTQx9HHBYvlfgdW0m1HKm0RtchB4cJPQ+CAIIA7XvowI8T7XVDAlJKLKEgJhGkAqXAMNDKANOIYjKkkQTztUmjLYm0SSPQ3et2uxvqZH8+uUX0kE6UmKYWS7WbWtDyAvACsobsSy19HBT65NDHcx7CMGBwOFqIE2A3GqhSKSKOFLSOM7TGhEEQQNjZ1oEfrwPw3OhYzw8VIBVSKaQyIhJpk8mMxZhRBRBSpNImjDShhCnCidv9sE0us0ktLv81VuPzj+1ECnp4hXXHtiTH7O4Yl7ylMOaoRNbHykCfHPpYURBCRFHfUqUrRc4KrTUi8MH3EX60JiaQSByIJRLXiYhkFlWTbhNHikCEUihpoKaTiZx/1q+nk4nW0XN5Dv/jDIu6G1J3gygNjBulgdnX8OP9AH/ulFhkDNEdHGlPD5KcFt+S9JmW5qWP5yz65NBHH3NACAGGCYbZrQTSOspvFYbIUCN0iAjDmDBCdBiwY9cubrjpZvbu348Ugrde/gauuvLNTO7fz7U3f5jtY2NsWruWv/7YRxksFaOU3X/5v7j/h/9GNpPh0x/9n5x6yskgDf7krz7D/f/f90HAjdddx2/8xm9EUotUSTJHwzCgZHHC2rldWZM0LyniSNZeQC3VVo/JZG/Ni8kmpDkPs6hYaknHtRSS4MiOBNM5niIbU6L6UssRgT459NHHgUDExX+knEEaIoyIwiyUuPXDH+ZFJ59CrVblwje8gQvOv5B/uuduzj3v1Vx/3bV85nOf43P3/DM3/+H7o5Tde/by/W//C488+ig3/fkn+NbffYnvPvBdHn/8P7j3S7fjeh5XXHcj55+8hWKcTqMtlfjKIBzbSfjEw1AahOIgIuXZRXEQcnmEENiGwDYkIwcQEhGEmrrXLZm0iaMtmdTcMCGaiFzchIDmCWsha8iuKPz8NCmlkIrCX++YBE0nObaQNC99LAx9cuijj6WEEGil0Eoxumkjo5s24mmNnbE5YfNmdu7fx7fvu497/uEfUJ7HWy+7jNe//e185A//kPseeJA3X/EmVGmIreddQOUjf8ReafHUZJmXn/dqjOO3YAQBLzjtNB78zyf59Ysv7rabhCE06+if/CCKK+nlqqsMKJZisoiLXaUIpGu/OIgwZ+relBSUbEVpRnLK+aG1puXriDicTubjXmqwuhdScwJ21zxqbou6G9Dypz/Rjq49Q4puVddsarCkTyrNS19q6UKfHPo46vHFh/fw9GRrSa/5vKEMV21ds7DOQrB9xw4ef+IJTn/pS9k3Ps7IMcfgac3Ipk3sHx9HS8nY7t1sXL0aI67bsH7NGvY98wwv3LyZT37mM7z7qqtoOg4//Lcfs+WkFyCKA10fYxgGsjiIOvv8yKBem5mDa0ZOrt07Iy8vL/rMGWSSzcfE0SaPiDQoDaSKXsWEkivMO2sXQpA1BVlTMppbgNFnGvxQx6quiEBUtsCufZMJmdS7VGUBFSdgrOYmEkw4j9SSN2XHYD+LOqwrj1jKLmMbR1CtliVAnxz66GOZ0TNld1stFW8Htk0Yr33bjmpdx33OP/scHnv0US677DJGhofZevrpWETJC7WQaDmz0p1QCgaGooUFxJU4zSRAkepUKgdXKmhxbAf6ySfmkEpUUiExUWmlCKR7fwBhWov+Lg0pGMgYDGSi/dHRIfbnZ/Eo6/GcTT/sUoO1pZXparA2yeyqdIhlQWlephNLl1dYRx02nWBypjzi4lr65NDHUY8Fz/CXAYtO2T02FqmlgF27d7PqmGPwsxmuf9/7+L33vhcRaq694fc4/thjUZ6XfI5GgOth1BtkW86iiisJISCTi5bV66K2OfrrIID6/Dm49J75pJJcR+pIVFyxVJJIJPHxbD6KPTkICCHImYqcqViVX7zU4gWxOmyanaWnOswNmGoF7Ky4ybG5qEUAuR7qsBnSiinZVFccn5/jYkuEPjn00ccyYalSdgdBQLlcZnh4mCd+9jN+9uSTvOI1r8FTKjF+i1Aj0JiOw9D+qARhd3GlKJOtn80QHmRxJaEUlIaihYVIJa2uhI06JZFQiaWUPbvQv/jPSBXWSyqRCorFmERKUCwhiiXqq1dH3mTFIqJQQBSL0bZhEOdXBnTsYhwm293tOu4bdu1P72trTYGQNUIjLA3WAq4Rb2utaXpQ8wVVD+qeoOYJqp6g7gmqnqTmC2qejBZHsrMmqfuKqidxww4xjtg7+PIVpxzU33Ah6JNDH0c+evxgkx++Dun1Q45cSv1UDele87bpbdG+6Nrt3Wch1/nxjx+OUnafdCKvec0FANz0/vdx/XvewXuufS//9LV/ZMP6dXz+r/8S6Vd5zatewgP3fZtzzj6LbDbDp//iT5B+Gbfp8MY3XA5AsVDgs5/6EyxdA7/zWVpGM2MvW6E++kuUbyM9G+llyJQz5CY7M+VQuoRGk9BoEBp1tGqgVRPEtIEu/d3OOqiGcd/Z/j6pQRkNRY0o9hpUi0ARHWrCZkDY8AnrfrwOCBrt7XHCqb2Eu3yCelRVsReEJZF5A5lTyLyByhnIXGdfxvsqrxAZdUg8nEpAIsMKwAJtiXinrWaUyXa6vRUqar5FzbfwjEFg+clhxaXsln6F4aFBJibG6X6Bu2cYM9pn/Dg6L7yYdfCaOROZ+SNavs9KXyNjW7RazXmuMcdgu9z3m76H6Z91ABgr/Br24HEHdO6Rg14DlpjW3PmGBIJW+VnWNB6M2pMBRoK2kEER4RcRQREZ5BFBHhFfTBPEZNFAGw1Co0mgmqA0nUFKolMDVtI+/bPibd2j36wD4AFeI2da1PbsQ9fqhLUG1Broag1da6Br9Wip1tC1GtQb9AxSlBLSUkehGEsnxURKoTiAKJU6thLH0d67AAAgAElEQVQhOt9F/N30vN+u55AznuNA0E/ZvUy4/8n3ARoTgSkEJgIDiSUEBiJul9GaqM0SMjlmCIFJdFwdJgOSnvGC9foRye4XuKWwwuiH3vUjnPMaAp1+oYUAjGgIl3N81rQfh17gZ802+Cz4flODhqfXYtsjBGE7YGv632qWgXf6vmh/53P0mes6YhGf1bW7+HfLMAzcoMT46pcu7IQwxGw5SfnWdv1v2eoEuflJcaWOWupIKq5UGB3Fzc0+ULbfDgAdBlCrdnJwtT24kpxcsZpr3zORDSX2GpuBTHYOQ/u02JJc4aBtJYcLK44cspljCGVAy2vR0B6+9vC0hx+6+Npj/iTOHUgUhjRRwsKUFoawMKSNIeN1al8JG1PaKJmJ22yMeLu7LYsSNkLOPks7kB/m6Ogo4yuohkXYaIBZnJFbqY8UpMTLZfFy2bgKBqA1yvMw4tKtUQnXFtlyJTktVBIvkyrfms3iZRZm/D6cEFLFBu6oXO18vyLttBLiiGqUlGfu7xtD//I/I9JJVSxMIGXHg2uaK/CM/eIAwrKX5dkPBCuOHF52/C2zimVRvhoPP3RSSwtfR+sgdPBChyB9rGsdHWuGLYKg2nWdUHs97mY2iBRZ2CjRIRKji0g6JKOmHxPdBOT6WUIdInskgOujjwRCEFgWgWXhDJQ6zUGQqvcdEUduYgIZBw5owM/YnfKtcf3v0HzuDjHCzsCqtdHC3GSSSCVpI3t1mitwZQq9dyxqc6K4mxlT0Uy2K6JdpAISKUX7fvN5kC0syzOn8dz9yy0DhBAoYaGkhU1xSa8d6iAhkm6SiYlFO/hBm4icrr5pEmr55c6xuO+C8GS0UsKcSSQJmfQgmWlSTruPOY20pDD6aQuOYmil8PJ5vHzKh1JrlONGEkZMHHatTm5yKukSGEaXSipxsT3K3pUuqWTDsYuTSlLkkezPIZVMlAaRn/zfy/5MfXI4RJBCYakcllra+r5ahwTa7ZZ0emxnsgbl6vg0aagj7TSCyZREFPXRzJO6MwWB7CKViEDaZNNNQN3SznTy6d5WwuqZ7rqPIwBCEGRsgoxNOv5c+H6XhBGRxv7YOQG0EHiZtkoqk0gbWi0+HcdzFYuWSuq1OI5kilI2wyxFd5cUfXJ4jkMIiSEyGDIzZ7/FejhEKjY/IZhA95B2uoiosx+kCMgNajS88a6+i1Ox0UO1Nr9KbUCdgOUJwlAjiFJgi9ioHW33i+EsF7Rh4BYLuMWU6iMMMRynizQy5QpqYjLp4lsmXqYjYXjZLIF15Bi/DxeETEWebzgWe3SU6iGwH/bJoY+eiFRsJkqay6JiC0K3N8nobvLplmamq9gqCXG1j7e1uKcO/TaGPZ9zgUQgErIAgRDtNjkLmXSfI2Ivq+5zouM7d+7kxhtvZN++fUgpedvb3sZVV13F5OQk11xzDdu3b2fTpk3cfvvtDA4O8otf/ILf//3f5/HHH+cDH/gA73nPe5I7feCBB7jlllsIw5C3vOUtXH/99Uv6N1l2SImfzeJnu43f0vMxWx3Dt9FskalUkpl0KOUMCcPLZI544/fRgD459HHIIYVCqiymyi7pdbXWsYqthdP0GcgN4vkeoNGEaB2tidcajdZh7KHWOR7qELTffc6i4i0iKaURTPC+D76bU154Mo16g8t/47fZ+vJTuPuuf+alZ53Bl796G3/zuS/zV5/5FB+4+f8hWzC55dab+dd7v0sQRo4RAkkYhNz8oZv52j9+jfXr1/Pa176Wiy66iC1btizp93fIIQShZeJYJk4pbfwOO8bvmDiyE1PkwzjyG5LIb1mrY4chXjvye4VLGUuJFUcOn//851FKYZomtm1jWRa2bS94UerQRFP2sXgIIRIvrVA1MJQNemn02G2S0HFwXppYdBz0lyYTrTXr1+ZZt3YjWofkCwWef8JxjO3exXf/9QH+9h8+S8uv8trXv4p3vu0Gfu/97yA7AM8fWMO932nhBIKKsxOARx95nI2b1lBaE1ILdnHRa1/FN//l67zrmHd0STjSk4w3djFeebzLvpM4EExzLEir56Q4coYCrSRePoeXT9nntEa5bkrCaGLVGxhTZUbiLoGh8FMutl42i585+ozfhwpHzhtxCKC15oUvfCFCCMrlMo7j4DgOtVot2Q6CuTM8SinnJI822WQymZ7EYxxkXps+Fo/HH2lQmVpY5s6FojSoeOGLF+5csH37dn7+s1/wipddxMT4h9ly7JkADB1zLJMTFQbtYyKiIcRSRSyVpWCtQeuQ8n6HDRuOIWsMoQnZsH4jjz36H0hh0iElnzDQNL1JfjX1YJeKbSGQQkX2m562nVlIRtgYKpNym57uiJBBCXNpJlNxxtrAtmkNdlKVjw4MUNm5s4s08vvHpxm/7TgWo0Ma2lg5xu8DxYoaqYQQ7Bs6ifWjAxyLw7qCxcC0vCq+7+O6bkIWC1mq1SqO49BqtQjDeUooKjWDSBa6WJbVJ5fnIHqm7I7Rfvek7PxdlTQwpIWlIrfRaHC2yJpRojvbKGIZOYp2d7ZZwzAww2FOXHderGLzUq7T0+03KXuPdnq7Vyd2nb0p206LUC+caAUi5ZWWSZwLZniyTSMkJTMziWiaN5wUEkwTt1DALaSM31pjtJzExdZotrArVXJp47dpdhm+vWyGwLL6UkYKK2qkCULNVx/dhxfuTdoyhmRd0WRtwWJd0WRd0WJtwWRdscCqgcFFV4byfX8GecxHNpVKJdleDLnMJrX0WjKZDL7vr0hyWcwMf6mxmJTds2HdunVdecTGxsZYs2buNOSRis3CkIuvmTAfgtBPOQH09lyb4dU2LX7HDRsE/mR3AKmeJV3FLFDCwjQySKweJJOSaAo2qmST0zlKfo6SZ5P3QnItj0KlmjJ+Ry62ftvwnc3iZzJotTKN3ytqpFBS8LU3b8GzCvzns3sYq7nsrnqMVV22lx227azhp0pFGVKwtmDGZGGxNiERi9V5E1PNJA7DMDAMg3x+8QnXtdYEQTAnkaSJptVq0Wq1ulRk85GLYRgLklpmO65WkC/6wWKxKbtnw+mnn87TTz/Ns88+y9q1a/nmN7/Jbbfdtty3PyuUNFAYiWSzVAh12C3d6LRrdG8iMm1BtT4V9YkJqOHVZ3i8zZYWRxkGo3r0/2/vTYMkO8s739/7nj0za83K6m51t9gkIYlBg2R5A4+MA3FDGDOWAAnDyIHFEmiEB4yGYcD4DmIY2/pC4EsYPA5sE3PvTHCDkAndmbhhGAtdIYw9gbggrkHySIAlWt21ZGXWlstZ3/d+OCczT27VVdVdveY/4nSeLU+e01l5fudZ3udhgQoVvUilvUiltUiRtIyFRrMlGmyYW2waTbYsn20rIjbFkEst734b5ZpLx+xcPJbJZVeVFcbn/CdKU2/HLG2HLDdSaCxtRyw30lc/7t14pYCFgsXhKYsjpRQcR6ZsjpQsDk/ZuOehZaDWus9yyYPEsixqtdpI2Pi+353fLVz2YrXkt58ruLRaLaanp4nPY22lb3/729xxxx1cd9113ZvCRz/6UW688UbuvfdeTp48ydGjR/nTP/1T5ubmWF1d5Q1veAONRgMpJYVCgccee4ypqSm+/vWv84lPfAKlFG9729v44Ac/OPR5pmmytbVFoXD+LKVzrd2M3+m42JIhC2dMRYLEx4o1xdBkKnKYjj1mkxJTqgdDH5+qWM2mFaqiSo01lNjp99Mri2OMgMqwa220JVQpXwH+/tLL91KVdQKHXUprzaaf9qPNA2N5O2SpEbEd9Pth51yja22k8LC77qupfTRmP1Pt9kc0yi22W/dYEASc7s/Jsqx9Wy22be8aLhcCHM61JnA4WIkk6Y7FyJcM6Qa/Ad+RtG1N005oWCFbpo8vciVyxo7h6beKdnKxedYc//yaz+7rGiYluw9AQghmPZNZz+S6yvD2Rpiw3IVGz/L4/lKLR9tbffuWbNmNcXRfp1J31dw5ajwySkIILMvCsixKpb0X9tJaE0XRrkEShiGtVov19fU9w8V13R1BUi6XsW0brTVCCKTMBrNdRGb9RBeWtGEQloqEpf76UmYQZMBIoTHT9pnf1oALuCSWmQa9cyVDTldfSmtFrMJuEkAeHNPT0+yhss2+dVo4fP7zn+e73/0uMzMzfPrTnwag0Wjwmc98hmq1SqVS4UMf+hClUgmtNV/84hf53ve+h+M43Hfffbz0pS8F4LHHHuMrX/kKAG9+85t57WtfC8BPfvITPve5zxGGITfeeCP33HPPgf6Av/XIBm4hQBoxridxXInrChxX4ngC15UYJns+h5JtcFXZ4KrycBmLIFasZLDouqsaEc/WfL71021yYQ4cQ3C4GxTvvR6ZslgoWHsOkJ9LCSGwbRvb3l8QNA+X3VotzWaTer3e3d6By2te85qR3+EgKDrzu103gctEfRKC2E2r0PpzvdUyjofKnjv54LcQvWypXMmQTn0pISSW4WLhAjN9H7kwf26spdPC4bWvfS233XZbXwDs4Ycf5pWvfCW33347Dz/8MA8//DB333033/ve91heXuazn/0szz77LH/2Z3/GH/zBH9BoNHjooYd48MEHgdTvevPNN1MqlfjCF77A+973Pq6++mr+8A//kCeffJIbb7zxQC5Wa431zJM07RkCZ47AmkKLYTeFYdAHC8cVOF4OIq7A9SS2I5C7uFk7puTKWYcrZ4drtcdKU20OxzdObYd891STqC9ADovFfmh0LI9DJQvrIs+qyMNlMOVzN8rDJYoiZmZmiOMYpdJBaZ3X/HySJH3rdnOOO4FkHFAmcLm8pMbVl/KD/pHfG1sUk3x9Kbu/T8Z5bK50Wjhcf/31rK6u9q174okneOCBBwD45V/+ZR544AHuvvtuvvOd73DLLbcghOCaa66h2Wyyvr7OD3/4Q2644Yauq+KGG27gySef5BWveAXtdrtbBuCWW27hiSeeODA4oDU3v3wbr3WK1smfomtVos0GQSgJnFkCeyZ7nSUoVQgKZbbtGdaMIhGjn4ZtR+B6/dDYizViSpFZBsPHVzoXIN+OulbHciPk6WqbVtS7mQlgoWD2xTY6r4enrKFjX4rKw6XVau05AN6BRB4eo4ByruGSXzeBy0UsKYkLHnHBo01mZmiNjKL+Kra+j7uZqy810FxJ2E7a7vSA/xb2FXPY3Nxkbi69uLm5Oba2Up96vV5nYWGhu1+5XKZer1Ov1/tyuefn50eu7+w/To888giPPPIIAA8++GDfZ+1ab7sH0zSZygUqdRCQrK2QrK2gqssk1RWS6rMk1W+my7VVEiUI7Wl8e5bAmSGcOkQ4e5SwWCEI5wiaJeq4+LGB0qNSXAVewcArmngFg0IhffU6r8V0nesZfdbIInDtiMtIA+QxL2y0Obnpc3LD5+Rmmxc2fZ442WK9vdm3/1zhOY7NuBydcTk663F0xuVY9jrjXnq9GFZWVgDO6biOUWDZ6/zplAfG4LyUkq2tLWq1Gq7r4rounud1523bRl5iBetM09zffeAClgJUHCMaTUSjgdhuYDUa2PV1hFLo5VUWbnnNhQmHcRoVTBx30xFCnDb4OKhbb72VW2+9tbu8X7/byAwHpwBHX5JOA5JKIbfWsWpVCvUq1FahXkXXvg//WIVaFdpNIO3vHFlFAm+eoHwlwdxRgqlDBOY8QTRDsFWktmlzKpJEYypXj7NGHDdzc+WskcOW4PCC5GcWCkAvS6UVpQHyzliO9Ujy3No23/npOl/7h2pf1nfRkn1jOPIWx7xnIi9CcARBgOM45y1bSQiBYRgHYrnstO3kyZN861vfGvsZe0k97gT9O8u2feHl6Z/LbKXzItdJp0q521xpvlBgrVbb1+EOPFtpZmaG9fV15ubmWF9fT6PnpE/++S+qVqsxNzfH/Pw8Tz31VHd9vV7n+uuvp1wuU8tdZK1WY35+fj+ndKASUsJsGWbLiJeNeo4H3WpCPQWFrFdxOgCpfx9+UoXNemoK5pTMVggrLyaYO0Ywc4SgsEDgzhIYJQLh4Yea7a2EwNedRlB9Ol1sZN41uaJiYx8TLC5Wut9NmKQB8h480jjHP677/I8T2yS507QNwaHSYJzD6g4EvJAD5Odbey3Z/eMf/3hsye7777+fRx55hIWFBR599NGRn2cYBoVCgauuumrX6cf5AZTRuKeVTB233X7GuTiOg2WdpTpLl6uy5kp6fg4uhID0KN1888184xvf4Pbbb+cb3/gGP/uzP9td/9WvfpXXvOY1PPvssxQKBebm5njVq17Fl770JRqNBgDf//73ecc73kGpVMLzPJ555hmuvvpqHn/8cW677bazd3XnUKJQhEIRjr14ZFcnHUewXsssjhQcZq2KUa/iPf/tFCzRQG6z40G5gp6vEJePEsweJSgtplaJNU0gXAIffD+FyNqqJgpHW2Ou18C2NY7Xs0bKrsUVBRt3PgWM40qE1NTaMUvb/dlVy9sRTy41CXPkkCINkB/OBv/lx3UcKlk452Eg4IUk0zT5xCc+wStf+UoajQa33XYbt9xyC1/+8pf5pV/6JX77t3+bP/7jP+Zzn/scH//4x5mdneVTn/oUX/3qV4eOddddd3HPPfeMHPzWUcda2U8wH0AptSNMRm3b2NjYE1zOZHT+BC7nVqeFwx/90R/x1FNPsb29zb333stdd93F7bffzmc+8xkeffRRFhYWuP/++wG48cYb+e53v8sHPvABbNvmvvvuA6BUKvGWt7yFj33sYwC89a1v7Qan3/Oe9/D5z3+eMAx51atedXDB6EzJZ/89666LMkzwCuAV09dCEbwiorOuUOxtt50z/qMUptVtCzgSHlqn/WNr1T6A6FoVUa9iPfcMVmObvtEHUsLcApQriPkKzC+SHF4knDpMUFxIA+uJQeAr0A6bGy0Cf/fWSMW1OeY6OBWJe1xgu4IAxUaSsBpEaXpu5rZ6vNamGfYfsOyZvTEcuVHkh0sWRfvSL8Nx6NChbg2kUqnE1VdfzfLyMl/72td46KGHALjzzjt561vfysc//nEWFhZYWFjg61//+tCxfuEXfoETJ04c6PlKKbvxif1IKbWnAZNhGO4LLvmpA8LdwGYCl73pshsh/am/eAQShRn52HGAFfrYKsRSMbaKsVTUnbeTbJ4EyzSwbTMbhGVhORa242B7LpbXeS1gFnqgIQcaYZ15ATTtt2F9DWqr6MyF1QPJWrptMKhZmob5Cs6RY4SlaSgvIsoV9FyFaLqSurB8TeBrfF8RtDWBr/D99DXwx1sjtiNSF1ZmjUhL4JOwrRXrUUQ1jDjZDjm5HbI+MIJ82jH6albl4THjnPlAwPwI6ccff5xqtXpGxxtUpVLhlltu2fX+J06c4M1vfjOPPvooP/dzP8fTTz/d3Xb99df3uV0//elPUywW+9xKnWO8853vHOtWuthHSCdJMhYq49YnSUKz2SQMw33BZS/uMdO8MBI3ziTOMhkhvYOCK68h0pJWEBImmijRhIkiShRhso+BhwpoZhNgqCSDyxaWqveAoxMcFJbQWAJsAywpsE2BbRhYpoFlG9gD8LFcB9t1sDwXx7awzDL2FRWs4wJbCmxTYkuBZQhM0sA5tZ7lQa2KrleJT/0UvboEgd8NRpuAadsU5yswX0GUF2F+AeYXEVdk83MLKGGkAGmrFCAZOPx2DyCD1oiLyXFMjuNhGGDPCIQFsdS0UWyrhHocs7oc8tTzLZpa0Uah2alSrk25cPEFyHcq2T1RT4Zh4Hkenrf7DoH5G2WnaOXprJb89maz2Z0/XfKClHJfZfYvNLjsVpcdHP71jVdQWZyn2dzAtgViIKCaKJ1BQxGqDjw6ANE9oKjechAnREFEGAREQUgYSqJQEEYGUWwQxlYGH02gYFtDpAWhlkSJJEwMotgkDE20yPvpFdDOpt3JRGELsOQitnEIqyiwpyWe6yDRTCmfir/OXGuduXad2dY60806U2t1is/9BK+VpiV3AKIRRFOzxLMV9GwZa34RY65CcX4BsbCIUV7EKhWxDYEpII5IrZC2GmuNaF9ghJJZLF6KB3kPk6mJpKYdKLZaCSdPRfxP5dNG0dIJkdSUipLFXMmRDjgqYyrl7uUJ/2zrbJTsnmh36gTk92s5jauIvBNsGo1Gd/tu4HK6jLCdQHOuu1BednD42/+ngUoa3WXLFti2wHayyU5HPltOb33JSV1KdlFg2bsbFb1XaZWgWy3iVpOo2SJstYiaLYK2T+T7hH5A5HfgExFGEWGUEIVxCp5YpcCRJpE0CaVJKC0iaXXXRdKibtqsGDahMUtkVQjnTKJ5gwiDUBhYScRCsEHF38he16kEG1S211moPsNC8G2sgWYvDdPjhDNL1Z2l5s6xXphjozDPVnGO7VIZvzCdWkZG9n9bEDhS4GLgaoGtJZYSWEpiJGAlAjs2mMHkmHIYCtK0IW5r2isJp4j4sQ5okeCjMGxBoSC59VqHF0sXQ2gsI7WszrXFcbZKdk90bnSmcNlNo7DB7fvpQjk/P89b3vKWfZ3jXnRZxRy01lRXYly7RG1tizBUhIFOpzB7DRRhqFE7fE+WlULDGgGVUcsHBZSh64tj8FvQakK7lY69aLfQrSYlCY21at+6znxnX91qEicqg4qZQSWFS2hk64QBtoshBYYASyfYSYgb+XhhCy9o4MRB33nF0mDLm2WjMEfdm6fmzVF156g6s6w4s6xYMzQx+3pp5OUg8DAoCEkBiYekIIz0FYmXzbs5q+uaV8Khw2n4XpF2fe50etaQAkeAkCClQIp0TFE6CjnbLEDSvyyE6M3TW+58vfknu7NZsvu+++7j7/7u77oDTT/84Q/z9re/ve//6WKPOexHl9I4h912oSwWi/z8z//8vj5jUrL7NNrNH1Qcp4HYMFAD8OgBpLMcZZDZCfymBbYje1aKLdLlHSBzNoGy2x+RjqIcNJpd0OjOuhxURq2j3YJkHwPPTAvtuOhCiaQ0QzQ1SzhTJpipEMwdwp+aJ3I8QtMmMh1CYRIp3Rc3CiNNEmqSCH7miMEVlRmU0qBB6PRmLhEdLgxfOz2IKJ2bz4CSXx6nPCxSgAzApbOcrZOjYNPdNrx/Hk5yYH/LsiZwuAw0CUgfkH784x+ztbVFFEXd0gKjSgqYpshKXuw+Vz+JOxDpwSMKcuuy+cDXbG8mhKHe8T5qWgxZIH2AGQEZY4TPfS8SlgXWLEzP9q/f5fu11ul4jRHWS3e+uQ0b9XRqbEGrAX4LEQaIVgNZXcIiP9571IkKsJ0sDbmUZmUVSmkqcqFIq/KLTEkLJTRII0377UxCopBECqJYkyhNkmiUBq3SazUEWEh2/PZl7z9GixQuWnRAklopinTso0ajM+BolVvW2fvOyjNawI9WtvgvT53EMgS2IbCl7M531xn5db2EhqFtcnDf3LLsLdvnyW030cHqsoKD1pq17z3JyTimGYU0w4hmHJMYEifLkuj4HL3ccn69ZY0vYmeYAm+vQEk61scoK6UHmTQjSBEFETvFvUwTrBEAmZ2rEydBbr3sWixnCpS8ROembTsw2xvtvpdPUEqlabpLJ2DlFKwtp6m7G3XY2kiBEgYQ+Om0kdXjMow0oK8VVK5AOcNVcDuSgCMEjpQpPEQPHlpKYmkSCYNIGMQYxFqgtETp1PqQgExSd1RnWQxdafaUL0V6eCN74u8si3SblL0yOTqDhdI6g0YPHr35DDK5Za01QkpaUxavuXIqZ1WprnUVxJrtIEmXR2w/UzyZMgcRmYEnB6XushwFqX6Q5ffvA5PsX3aCmDBRWHJSlPBs67KCA1rz61ddM/JpMFSKVhzTjCK2A5/tWp1mGLEZRZyKQhpRRDOKCLRCmSZWVtQsD5NBkIyzSvIyDIFXEHgF6E/bGa8k6cBk2MXVsVI625vbijBU/OOz4wsaGiZ9FkgeILadBefzVootMMyD+yFKKaFyKJ3GSAd+Orajtoqur0JtDeq58R+jbhQdEGTWw9A+WkGUIFSCpRXWiEJ4GkEiJZHoBfkDwyQSJok0AdGDB+kPzFACqVKIpPAYfRseB450Xnbh0ol9DMo0TRzlccPxvWc/aa2JFURK5Vx1/fAIVX/W3lAG30CG3+B721HCpj+wPbf//vSj7lw/OAZgZPZbSH3gGmchDYAov93ps7Akptx7D5gLXZdXzEFrRJJQnppiq7qGjONsSrrzRjYv4hgjjsc+8UZdmIRsBwGNjiUSRalVEkU0oohYCrRlYTjOWJB0Xg9yBOfcXJmlU9UMICoXLxkBmQww8Q5jigyDzJUlR7q4BiFj2amb7lyp2WgwXSoS+z7EMSRR+tqZkmh4wKCQqellmikxTROkmXNHiRQgasSkFaiEREGEIEKmkzAyiJioXMBcIrC0wtQKE43ZsT5EBi5k3/59p0mPbSnn0viUaRpsbm5hSG9P/UYuBGmth2JIXYgMQakHHtsrsL7V6AdRB1RjLKRRYBqXDLFbCRhrIQ1ZQEOuvn6LaUcwScHhShk3bu7rPCcxh3ESAm2aUCwStncxdkBrhFJ9EDFy81YcMx/HLMQxIooxkgQ5hrWJVjSjDCbNNs2NTZphxFoOJu0kITENtGkhXWdH95brunuq+GkYaT0l14PdWihK5eHRD5XB5XYznY+i8T8yaYyzUEalEMvM5bW/JzIhJcK0EO7o92qt05v6EDiiNKAejgqsi5SKppUDiNUHFNMwMAFv8HNUgsoGW8adG5aGWAuaWtL/GKKROsZSMVZmxZhaY6BT60MItDDRwiARBlpm7jQUa6sJz/x9o/Ph2AQ4IsAxYlwrwbFJCzUWTJySjTPl4Mx6mEXvvJbzFqJzY9zb+85WQFrpFBQdK6cDk3EW0lAyxIj3DlpQm5HqjpEa3L4Xw2m+cJIv3vGyM77m0+nygsNeJQTaMEgMg2QH/3XfW5IMJsmwVWLGCXNxzHwUdWFijIGJ0ppWHNMIA5pbDchaaxsAABvwSURBVBprdZpRxGoOJpEQJIaBskyk4+CNcW95nrevgKeUIq30uodSO31AGYijRANQ2Wym1sq48hyQB8pwevBQcD7bx9jFX7UQ2Y3eMIDR361WKgVEHGevOYgEfrpu4P9VS6Pf8siBRJoWjmPgjoCd0po4e9KNuq8mgVI0Bu4cArAEWEJhkmDrMHWDoVkIX6C09v8SJBaBdgiER2AUCaxpGllDK701+B8UYSQNnGgLJ27gqBaO8HFkhGPEOJbCdcApSOyChSx0apBlyQDdGmQXXknv3UoKgWMKnPN0R0zUbq0cxUxWBfugNYHDWZY2JIlhk4zpHDckpboAMUa4uWbimNkcTMwxN3mtNe04Tt1b9Q2a4SqNKGIpg8m3k4RYShLDQNsW0nFwciAZhMlerJK8zggonQyvccH5QLO5obr7jD8HePkNkBwJ+lwwaWOcnN9epmmgsjPmYeDGJqQEacOYulhaa0iSYXBklsipnz7PBz/5H6jW60ghecev/xrvedud1Bst7vtfP8GJpSWOHz3Kn/zRZ5gtL/D8c89z/0c+MlSy++TJk3zggx9kdbWKkIK33PV27vrNe4gSSUtptO59V8+YFf7r8VvSfhy5EusvduGQDLGCNtF2k2A7xG/G+O2EIIAgFATCJDBdGsxQEx6RkStjEQNbwKbCDrdxwk2csIoTPJvOB5s48RYOGVSsBNMxEZ1iln31xgqIPFS8AhQK4JXSbLnLUIYUGFLgmnA6y/5cpe9eXjGHTBd1brTWQ7ESIzcvogjCCBlHGEnqjhj3LNfOZ23l4iTNKCTQmlhKlGWiLQsxAJM8SBznzKvW7vm/QaUurMFAfAcm03Mh8+UpkiTL6lF68CF/SGkgOBs3kANKN0CcA8pOgeGOVlZWWFle4pXXXUdjY4M33H4Hf/7Zz/DlrzzM7FSJ3/7Nf8Eff/E/sbm9zcfvex9r6+u8sLzC1775LWZmZ7n3t94JpslKfYPV9TqvvOGf0vAD3vDGN/IXf/EXXHPNNWmr0uypM0Hy3Mo6f/VcO+1Hvh3SjPrjKuWCyZFSr1Jut2rulEXB6r8pJYnur6m1HeA3IoJWnK4LIIgkQWKiR6R5SBXhxhlI/HWcdi0DyUYKk2zejraQnaJcprVzteSBdTOHj7AZJX3vEeew+9/50GScwwHJ2/gWJLM4rQRleGjpoQ0PJV209LJg4AUsIVCWhdrtE5bWyCRhvjTF1lq1zyqRUYwbRXhhSCWOMZMEUzM2tz+IYxpNn+bGdhcma1FEK44IRZrFo0wzdS84NvaY4PvZaN0pZMeVNHp7q6WZnrb76t1000Gz8Qxa98Y2dNJBO/NJ0gHKzkTZCShzsxXm5yokUlAoL3LVNS9nuenz37/5Nzz00EOIQ4e4873v48477+Tj//5TLCwcYuElV/H173wvPVAUQrvFIRMOVeZh6QQl4OpjV7D0g+9z9UwJTBPDMDHMNOnhJQX48C8eRhgGWmu2Q5U1c8r15mhEfOdkgw1/uFLuYP/xTtXcw2ULIcZbUFE2fqe/ppZD4Bfw/UUavqLWVmO6H2psEaWxEdXCTZo40RZ2uInj13E213BOvoCzvYLZWu8+7GhgfdThbBu8UtdKYaT1klVLzkOn6ybzEPLSLyl/Ol1ecNCa0tpfIdYSZsbsoqSDlh6qC43ca9+824VL5xVxAf5BCZHesEtFQn+XQfgsbtJ1cyU9q8QKQubCkHIcYyiFpTTGmKfnKElobjVp1Da6MFmNInyV9OIlGUyk62C6Hl6h3ypxXfeMrZJS9b9hBktndAzd/SdVYB5hfeaNuwbKyZMn+Pu//wEve8kNVKtreO4Cja2EqWLaoc9XDsJwkBYop4AqFFGHr0xho5Ouy+rET5/nB8/+mBtf9ar0A0OfztD8BNA/fgb15S+knQvLFUrzFa4qL3LVfAVRrsCLK1A+hHBc2pFiOevHkYfH09UWjz8X9yXceqbs68cxWCk3TSCAqZmdfwND1ki3wm8KksCfZS1br11gwL0ujbRrpmMpHCNm2gURbmInLZykgRtu4QTr2P46stVAd0rD1Fd7gzLDXlOtseh3vBwwduj14hUQI9bheqlb8iLW5QUHoPrST7Aw67FePYlUbYRqI5P+1/y8Ga0h/Ha6r965XrwS9miQ5CyTLlAGtiMukK9CCLRpkJgGyZhAbZ+6GV05iyRLCdZhAEHIVBgyEyeYSmFpjTnmZh8rRWt9i+ZqjUYUsh1FrEQRAamLK5YSbVlgW0jXxXBdCsVin5vrbFgloyS6/6QyTcYOduyzUDQ0thvc/+F/yb/7dw9QXkgfSwyjlxULpA2ZMkWhJjQV25u9J3shTFrtkPd84H4+9rufxDz0UnzRiZlohIoxUagY1P/yFuRG1uvjR0/Dd/4GkqT/Rlicwi5XuHJ+kReVOyXbK/CyRSgvEBWmWW3FXXAsNVI31XPrAd9+YZs4562yZKeV7IC7qmSzWLIwc+m0hiEoFAWF4s43zvHWSK/Cb8M3qdcgDEZYNA7Y0/39RrqtdG2NI0IcWjhJCzNoIPxOOZgmtHL1xjplYbY305L3nX3yFumoCxAC3EKf9ZLGWoatly5gBuHjnPmD0ZnoArkjnSMJAcICe5bEidm5BuII6bgHkKQ9Bi5+uj5pY0R1zM6+Otz50MIaslKU0Q+UPrjkIIM8j0G8voyu3QXhe+nBPaCIKEL7AToMccOQQpLCxNZgjfmBqEjRqta7Y0s2snTgEE3hqpchsxti2/sVKMp0IFk2pcFpeSA/vk69I0hLdt/3/vfx5jffwa//+hsBqFQWaDTXuiW7FxYWmJkzujDppPkWipJOJmwYhvzOh97Hm950B6+/9Q1EkUb35eYbgMFKWOGZ+E0wBXZZYL1CYFuaotqgGNTxghpuaw27uYbVWMNcOoV8+vuIoN13kzMsmyNzCxwpV7ipvAjzlbTj4JEKyXyFNWeGlbbutpDtdAT8/5ZbBAOtZCtFqxfnyLurdmglK0TPbbiTNZKWP6/uud9IKg/wkEa5vwf7XNoyt9NOt/OaHzeio3AAJI1cmZhe2Rhaza71wmYdvfxCb1uuGNtIwEjZA0wOKluHj8Jb7xn7f3K2dHnB4UwlTJQ5BeyjYYuOEYk/DBTV7q3PrZPxBmbYAUuw86GFmQNK6vLqh4wHehG7FQ9ZLQhr9GjiA5SWksS2SXbbHC+f0ZWkUNFBgPJ9dBBiRhFzSUIlg4kjJSddD3sw40opVJKgtCbJJqV7dZEGIs3pWIkcUPY6DmAvJbvzQJFSYJgS25Hd4/zbj36El7/8av7VB+7tOz6d+kwapJBEscErbrSHsr22gjnWmCXkJSgXcIFy90CYcQvPrzEV1yhGNYpBDdev4dbWsJ9/ArO1ke5KakAtCMHC9Bz/ZL6CWOjBg6OLbEyXWTJnWYmNPnh86/kttgdayc575uiOgCWbkrM7N+1ZtUa2Emo79GLv736Y9WNwyzglibMgu9tMc+dkBa116t7KoNJf4LIDllbXUukCplYlDnbf3+VMNMlWuhikk9QiGQDKMGRGWDPK3/nQGDvGUcbGXAwPLexzDpZdSWtajQbT09MkcTYWQalsQFp6JxXobqXWTlXTUVIalFY9kGQVWjW5UqtZfuwgTJ544omzUrL76aefHnmc173udX3nupuS3VqnxR7HlasfOUYl0BBHuH4dz6/h+Wu4fg2vvYYX1Cj4KUik6h80qGyPeKaCns26Cy4s0p5fpDpVYdWeZknZLDfibryj3u5//5QtR/YfPzJlM+umjW8O6rc8PjbSs0Y660f1YpcG/daIe3prZLc6V9lKEzhc6tIKoQLKMy4ba6eGQTICKHkXmdihHJtGjnR/deGxU2Bf2geaGZbvIX1adX4CWiMy345Wil4qE+n/g+6UyR4PE63JQKJ7xfFylokWWYG4DCQi5+Y6UxfXQfVz0DotR58vszI4oDH0E/TWJmK9irFdxdpaw22nAHH9Gp5fwx4o+aCEQViYJyxViKfLtGcOU509TLVYZs2bpiod1kLNaitirR2R96K5puBwyebKcomyrfvgsVCwMM5R2ZDdWCN77cWeh4rjDlsjk1TWA9JK8ylCa55G28eUNoawMaTdnRcXeirrXiVkGvB2F4jHlJIYK60QKtwRKENwierIzKoRO3Tk1og+S2WnYL0a2K6lc3bB0rkpi/Eo7KxPYAgmWqnU/9/NiQWhBSYa0a2ZNHDArCZTasz0YKLIgSQrlYHsWSV5mJwrCSGyCiGnc91MAce6S/meKI1AE223UGtV9NoqYr2K3KxibK1hba9ROPFD5p79G44OfAOBPU3bLbPtVliePsrK9CGqxTI1s0TNN/ifJ7epBSFx7m2GgMWixRVTNoenbY7k3FaHihaWcfb+dnYbG4HdWSOjYyOpOtbI7HzAz7x6dxUbzkSXneXw0FPvJtkhOCyF1QcNQ9iY0sGQFoZw+rdJB7M7b+fmc+u78HH6QCTPcXbSObeWtEbosA8oO7q/BoL5Yod0gRQszo7ur7p8CaXZw+kgOJEV1M4K2p0XV1gufSmFiRpwceluzdZO97mRhyEHk07J7swyEVKyub6O2m5gFDysYhG7WMQ+D4MU9yMdx6jaGtFKlWR1BVVLK+yKjSrGxhrmdhUZ9/92Y8Oh6ZZZnjrOC6VjLBUWWXZnWbNK1E2LTSGIBoAzaxksOBaLhRQWR6YsrphxOD5nM1UwzmoJ+/1olDWSh4jrulx/4/7S5idupR1UbT1DqeRS36iS6JBYhSQqJNYBSXc+fU10SKICEhV1t8f59dn7d8iWHiuBzEHDwsjgYcoOVJyRls1oKA3Aq7tveuyD9M0eiLQGHZ0GJHnoDMRjdMxS6Y04sy8e8wESLdLbMEKmo3tz4Mgv5/c7p2AZBEknbaljmdAPk44XJayvc3StV55dZWVV2kmMrxShhkgKEkOizDQtWDhODyalEsYFOsJYp3nBUF+FWhVdX8VrNWi98FPUWhVRX0U0t/reo4SgNn2MF2ZezAulKzjlLrBsz1A1C6wbFu2B79NDMiMMZk2TectkwbVY9EwOFSxmCiaO26vnla9IfC6BMok57EIXQswhHWUbZ8DowCPIQSQFSayjDDRhDkrBjiDKwyvRAUrvOfkWAEPYWIaLFNYYEPVDahBEhrAy62knENnIC2UQoIpotZpZQDpK3T2o7ClddZeFzpw4g8s7SmSWyGiA9MAyvN+BgkVrDMNgo1bDbDRJfB+CAKIII04wlMLWGkcIXGngmebYzm09mGjCbIxJYsh0VH5nwKJXwCwWMAsFxD7rcJ0NDf6WdRDAejWDRxVqq+l4j1o67oP1tb4U0qbhsjR7jFPzL+JU6QqW3DLL1jRV6bExUOPIRjCNwbQwmcJgRhhMCZNpDKZMiePIkT1RrFGdG8+gJ8ok5nCRSAiR3lSxwCge6GcpneRA0gNRv2UTkIwAkeVImq2tbHvv/UHSyKygKN2WvWc/ksLAEM6QpdMFSt61llk2wxZRtl8XWvYQiKQwd3aTSCstmCdt9ED66WmfhLSGMQDpB0tnvxih9wOWFCh5gOjc+kHQ0Ik/jD10GpcwHAdvbm7sbkE2bShF4vuEjSZJu41qt9FhiAijtMCjUlgaClLgCUlBmhhaQBCl02ajd8wkoZ0kBFoRArEUJIaJtkywnRQmhRQm0nHS7+SAQCkcBw4fg8PHRvcJVwlsrGfAWKVUX+Pq+ipX1aqw+qMUJlklgUCarLrzLJUOsTJ/JUtTh1l2yywZJZ7DQeneJ1hKMKdNZiOD6YZJCYNiIikmBiWMkSC+0HuiTCyHy0R7uWatdQ8wO4Iob/EMWjr59wy643og0jsErcdJIAZcZz0rqAOiY95rWZy7Eq00QshOw86BeZHNy9z88D671hBYessis1z6l7P9utbLzlc9GDvpB4vAMCy2t+pM235f6vHZSDlWSULYahE2msStFjoI0EGAjNLy9KbS2IArJJ5hULBMrDH1iSKl8JMEX2siIDayasGWiXCcdPR7wcMsFNCmhTbGw+Qgfsu61Ui7CWYAoZ63RKqwWScWkjVnlmWvzFJhgeWZo6yUDrHkzLFilAhzFcoMAQueRcWzKDupu2rWMJkRJkUt0RF9fVNO1xNlesbmn71+fxlpE8thojOSEAIzu+EetJSO+1xnebj04j9BfyxonJtOB0SqTTveILS3CeNmWsqC7Ka8L6WgSCEhsnmZmxcj9klhc+rUMv/mQx+luraGFJK3v+M3ePd73sXG+ibvf/+/4oUTL3D8+DH+5E/+I3Ozs/zoR8/wofv/NT/4wQ/5t//mfv7l+94NWhH4PnfcdTdhEBAnCb/2htfxkQ/di1BxDjJADHb7Oeaq/3ffFaRjWdyhrK9eMN8dm3qshQNCIA0Dd2oKd2rnAaAKaAIbYYjfbPYsE99HhBEiTi0TK4OJIyVTyqSIwCaBqAWNVt8xE63wE0WgNaEgrRZsGmjLZnN2Ga01ZrGIcGyUaaIM44xgKAqltADf8ZeMtj6iCHt9jStqqxypr3Fjx3VVfxaeq6Lqa6xLl2Wv3J2WSodYKS7yhD1LK/e7EmjKrsHhaYcjZZvDUzaHixYLrsmcZWIqOdQTxSt4I87q7GtiOVwmutyueXCcQ/pnnoJCZy6gdD4FR29+1D6n21/1vbfjXqqurlFdrXH9P3k5zUaLt93+bv63P/kD/q+v/BXTM9O85967+bP/+J/Z2trm/o/cR722wdKpFR79748zPTPNu977mylwtKDV8imVisRRzNvvvIff+8RHuemmV9GzesCQks2NZbT+CTYKUyVYJGlHOR1h6RAzCTG0PzCWZaeUY9k/OFJ66M64lrOUcqyUwvd9/GaTqNlCtduowIcwQkRp6XlLZ5aJlBRMk5Jl444JnCutCVQKk0gIYqMDEwvpuEjPxfBctGWjzKz441l0c2mloLGZWhm1Xl9znVkijc0mS9rJwLHAsjfPkrfASqHChtXvmp4xNYcLBodnPY7MuByZsrn2WIVDZrCvDLSJ5TDRRAPqPdVzoNVz85CYOX4lVx1P4TFla66++uVs1RIe+/rf8aX/83+nYC3wG3e9g3f8xjv53d/9GFcsTnNk8Th/89i3kcJACiOFjVB4BYtYBbTDNlEUEqkm7Xi4YPWK/4/8/fr/scMZZjGyXHq1KSwMYWB2JgSmkJg6vUFYaEwVYCUtLJ1g6gRbR5g6wgIsRPae7DWbhEjBMgyXYaA4hsf0lIeeLaHkwo7l85tRxFq7TbvZxJUGG9VVso5FaStflWQwEbhSUrQspi2bYqygHcDG5tAx00yuPEzMXiaX5yFdh8QwU8vENNJR8eP+h6WE6bl0esk1Q9bHLDDjt7i2tpZzXa1C7Ye0VtdZbkYsRxbL7lwXHj9cXeBxZxYtBLP6Of7T3f90h+/47GgCh4kueX136T+z4T9/Vo85676Im47cPbS+H0K99SdOnOCpHz7Nz938i9TW6hw/mvYAvvLoNPXaOgVrvruvbRRxzCJTzuHuuiRJuO2223juuef4rd/6LV77i7/W5zLTWmMYEhEVWZj5eM7dFpzeTZe9RirAV37qtstn2+03QQGZwUamsEFg0gOOBRiIFC4DYDGF6GbRSeliSA/D8DCMAlIWmTZKzLolZmaO0CiaaHN6ZPl8rTXbvs9qu02r2SRqtVBtP60YHEYYcdy1TBwh8AyDomVRsmyKoYVstoGNoWsLVRp8T0vPZzBx7K5loi2ra5Uo0xxKjBBuAY5eCUev7INHCbgKeFmSwEatm7JL7QRR/Xssr7fw7RIwgcNEE130ajabvPe97+WTn/wkU6fx2Y+TYRj89V//NZubm7z73e/mH/7hH7j22mu7AXUEmIaJY04xVzh0Vs9fa5UlKAynVw8lIOSTGLoxorAvPTtSIe0utIJcCneYwW4POpUyeBAsKZBMDGFmGXBWChvLwXAcDOkiDQ9kAS0LxGYRbZRo47EaS+JAEfoBqh0jAg1BgowUMkpSd50GVwgKlkXRsihaNkXfwpTNkacZ6zQtOBJpWrDO0oKF6yBsB2X1rBJlmqllUl6E8iKCVwBpp/MXce5cxBM4THTJa9QT/rlSFEW8973v5Y477uBXf/VXgU6Z6ZVuye5yuXyao/Q0MzPDq1/9ah577DGuvfbagzrtPgkhMYWDKQ+2ZEM6ZijpZsUNw6eXph2rNipuYjuaRqOGUm0S5ZMkfi9DTkeEKqKl2iRJg5iEWCti1O7L9QvSCrZZT3QDI4VOBhspLKS20NpEJyZG4uAoFye2sRIbJ7GxlYWjLTztUDRcCtKhYDgUTWdsRleidS4tOI2X4NhpNlcYgX3wZfoncJhoogPSXkp276RarYZpmszMzNBut/nmN7/Jfffdd9Cnf86VjhkyMTBhl2GhfT1F6xgdt9DJFirZRsVbJEkDlTRQSYukMykfpdrEKkB1XG1ERFqTaEWk28S0iLUmRhMLTWzCNpp1C2JSEI0/D7Cw8CjgaY8ChWw+N6l0KkQehZaHLWy2l56Hm35+b9e8D03gMNFEB6QnnniCv/zLv+S6667j9a9/PZCW2n7/+9/Pvffey5e+9KVuyW5gqGT3F77wBR577DFWVlb4nd/5HZRSKKV405ve1D3eRPuQMBHWNMKaHtsvfaz2WD6fpIVOWiSqhVI+sVZEaBI0EboHFgIiQiKxTSxMGkjWgUgLAqUItSJSCSoRuGaJf8bBw2GSynqZ6HK75j2V7L5EdFAluy9kXVR/11n5/LF9WHZZPl9bM1Rf9NF9ncIklXWiiSaa6EJTVj5fGx5qryGDXPn8+dlSOtLwgHWJNS+YaKKJJroEJWQ6yt2aA+/IOfnICRwmuiR1EXtLz0iX63VPdPY1gcNEl6SklERRdL5P45wqiiLkDiN3J5poL7pgYg5PPvkkX/ziF1FK8brXvY7bb7/9fJ/SRBexXNdFSkmj0bgouqCdqbTWlEolXNc936cy0SWiCwIOSin+/M//nN/7vd+jXC7zsY99jJtvvpljx46d/s0TTTRCQgjK5fJl5WYpl8sXT+bORBe8Lggb9Ec/+hGHDx/m0KFDmKbJq1/9ap544onzfVoTTTTRRJetLgjLoV6v95UQKJfLPPvss0P7PfLIIzzyyCMAPPjggywsLOzr80zT3Pd7L1ZNrvnS1+V2vTC55gP9nAP/hF1olOk/yk986623cuutt3aX92tCX1QDZ86SJtd86etyu16YXPNeddENgiuXy9Rqte5yrVZjboc+uB3t5ULP5nsvVk2u+dLX5Xa9MLnmg9IFEXN42ctextLSEqurq8RxzN/+7d9y8803H9jnffSj+xt6fjFrcs2Xvi6364XJNR+kLgjLwTAM3vWud/H7v//7KKX4lV/5FY4fP36+T2uiiSaa6LLVBQEHgJtuuombbrrpfJ/GRBNNNNFEgPHAAw88cL5P4nzopS996fk+hXOuyTVf+rrcrhcm13xQuqhLdk800UQTTXQwuiAC0hNNNNFEE11YmsBhookmmmiiIU3gMNFEE0000ZAmcJhoookmmmhIEzhMNNFEE000pP8fQRQ0RYXYwhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_CI.plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>India</th>\n",
       "      <th>China</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>8880</td>\n",
       "      <td>5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>8670</td>\n",
       "      <td>6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>8147</td>\n",
       "      <td>3308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>7338</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>5704</td>\n",
       "      <td>1527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      India  China\n",
       "1980   8880   5123\n",
       "1981   8670   6682\n",
       "1982   8147   3308\n",
       "1983   7338   1863\n",
       "1984   5704   1527"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_CI = df_CI.transpose()\n",
    "df_CI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_CI.index = df_CI.index.map(int) # let's change the index values of df_CI to type integer for plotting\n",
    "df_CI.plot(kind='line')\n",
    "\n",
    "plt.title('Immigrants from China and India')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      India  China  United Kingdom of Great Britain and Northern Ireland  \\\n",
      "1980   8880   5123                                              22045      \n",
      "1981   8670   6682                                              24796      \n",
      "1982   8147   3308                                              20620      \n",
      "1983   7338   1863                                              10015      \n",
      "1984   5704   1527                                              10170      \n",
      "1985   4211   1816                                               9564      \n",
      "1986   7150   1960                                               9470      \n",
      "1987  10189   2643                                              21337      \n",
      "1988  11522   2758                                              27359      \n",
      "1989  10343   4323                                              23795      \n",
      "1990  12041   8076                                              31668      \n",
      "1991  13734  14255                                              23380      \n",
      "1992  13673  10846                                              34123      \n",
      "1993  21496   9817                                              33720      \n",
      "1994  18620  13128                                              39231      \n",
      "1995  18489  14398                                              30145      \n",
      "1996  23859  19415                                              29322      \n",
      "1997  22268  20475                                              22965      \n",
      "1998  17241  21049                                              10367      \n",
      "1999  18974  30069                                               7045      \n",
      "2000  28572  35529                                               8840      \n",
      "2001  31223  36434                                              11728      \n",
      "2002  31889  31961                                               8046      \n",
      "2003  27155  36439                                               6797      \n",
      "2004  28235  36619                                               7533      \n",
      "2005  36210  42584                                               7258      \n",
      "2006  33848  33518                                               7140      \n",
      "2007  28742  27642                                               8216      \n",
      "2008  28261  30037                                               8979      \n",
      "2009  29456  29622                                               8876      \n",
      "2010  34235  30391                                               8724      \n",
      "2011  27509  28502                                               6204      \n",
      "2012  30933  33024                                               6195      \n",
      "2013  33087  34129                                               5827      \n",
      "\n",
      "      Philippines  Pakistan  \n",
      "1980         6051       978  \n",
      "1981         5921       972  \n",
      "1982         5249      1201  \n",
      "1983         4562       900  \n",
      "1984         3801       668  \n",
      "1985         3150       514  \n",
      "1986         4166       691  \n",
      "1987         7360      1072  \n",
      "1988         8639      1334  \n",
      "1989        11865      2261  \n",
      "1990        12509      2470  \n",
      "1991        12718      3079  \n",
      "1992        13670      4071  \n",
      "1993        20479      4777  \n",
      "1994        19532      4666  \n",
      "1995        15864      4994  \n",
      "1996        13692      9125  \n",
      "1997        11549     13073  \n",
      "1998         8735      9068  \n",
      "1999         9734      9979  \n",
      "2000        10763     15400  \n",
      "2001        13836     16708  \n",
      "2002        11707     15110  \n",
      "2003        12758     13205  \n",
      "2004        14004     13399  \n",
      "2005        18139     14314  \n",
      "2006        18400     13127  \n",
      "2007        19837     10124  \n",
      "2008        24887      8994  \n",
      "2009        28573      7217  \n",
      "2010        38617      6811  \n",
      "2011        36765      7468  \n",
      "2012        34315     11227  \n",
      "2013        29544     12603  \n"
     ]
    },
    {
     "data": {
      "image/png": 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9xebNmwGYM2cOy5Yt4/jx48TFxfHdd99hZ2dHs2bN7jhWIUTtIomVEEJUA//5z38YNGgQjzzyCMHBwaSlpfHEE0+U2PVIrVazfPly4uLiaNOmDU888QTTpk2jXr16VttFRERw4MAB/Pz8rMZHFeTt7c2mTZu4ePEinTp1Ijw8nMDAQFauXFlu53mn1Go1GzZsYODAgTz33HO0bNmS8PBwNm3aVOL4qII0Gg1r167Fzs6Obt26MXjwYEaOHFliq1ODBg3YuHEjp0+fpkOHDgwdOpSOHTvyyy+/lPXUivTzzz/zwAMPMHz4cO6//36OHz/Oxo0bC7XOffjhh8ydO5e2bdsSGRnJsmXLbvsQ3JycHF588UXatm1L586dWbFiBd988w2vvfbabeNp2bIlhw4donfv3rzyyiu0adOG7t2788MPPzBnzhzLmLr58+czatQoJk6cSFBQEL/99hurVq2ySj4//fRTGjRoQM+ePRkyZAgjRoygRYsWd3yNXnvtNdRqNQEBAXh6enL06NFS7+vs7MyuXbvw9/dnzJgxtGjRglGjRhEbG0uDBg2Amzc63nnnHTp16kT79u35448/iIyMrPAHaQshaj6Vcjcds4UQQlS43r174+rqWqWJjaheDh06RPv27Tl69KjVNONCCCGqnkxeIYQQ1cDRo0c5ePAgnTt3xmAwsGTJEqKioli/fn1VhyaEEEKIUpDESgghqgGVSsXnn3/O888/j9lsplWrVqxevZr+/ftXdWhCCCGEKAXpCiiEEEIIIYQQZSSTVwghhBBCCCFEGUliJYQQQgghhBBlJImVEEIIIYQQQpRRrZ+8ouBT4KuSh4dHsQ/tFNWP1FfNIXVVs0h91RxSVzWL1FfNIvVVvZT2OXbSYiWEEEIIIYQQZSSJlRBCCCGEEEKUkSRWQgghhBBCCFFGtX6M1a0URUGv12M2m1GpVJVWbnJyMrm5uZVWnigbqa+aozbUlaIoqNVqdDpdpX5uCSGEEOJ/JLG6hV6vx8bGBq22ci+NVqtFo9FUapni7kl91Ry1pa6MRiN6vR57e/uqDkUIIYSolaQr4C3MZnOlJ1VCCFFWWq0Ws9lc1WEIIYQQtZYkVreQbjRCiJpKPr+EEEKIqiOJVTXUvHnzO9o+OjqacePGAbBp0yYWLlxYEWEJIYQQQgghiiF93u4xYWFhhIWFVXUYQgghhBBC1CqSWFVj0dHRzJs3D1dXV06ePEmbNm345JNPUKlUREVFMXv2bNzc3AgKCrLss3TpUo4cOcI777zDpk2bWLBgAQaDAVdXVxYuXIinp2cVnpEQQgghhBD3JkmsbsP8y9coCfHlekxVQz/U/5pQ6u1jYmLYtm0bPj4+DB48mH379tGmTRtmzJjBsmXL8PPz45lnnily3+DgYCIjI1GpVPz000989tlnzJ49u7xORQghhBBCCPEPSayquXbt2uHr6wtAQEAACQkJODg40KhRI5o2bQrA8OHD+eGHHwrtm5SUxOTJk0lJScFgMNCoUaNKjV0IIYQQQojaQhKr27iTlqWKYmtra/lZo9FgNBqB0s3+9cYbbzBx4kTCwsIs3QqFEEIIIYQQ5U9mBayB/P39uXDhAufOnQNgzZo1RW6XmZmJj48PAMuXL6+s8IQQQgghhKh1JLGqgXQ6HR988AHjxo1jyJAhNGjQoMjtpk+fzqRJkxg6dChubm6VHKUQQgghhBC1h0pRFKWqg6hKiYmJVr9nZ2fj4OBQ6XFotVpLNz9R/Ul91Ry1qa6q6vOrPHl4eHD16tWqDkOUgtRVzSL1VbNIfVUv+fMdlERarIQQQggh7mGKolDL76MLUSkksRJCCCGEuId9tT+ZKSuOVnUYQtzzZFZAIYQQQoh7VJbBxJa4DAwmhXS9Fy46+eonREWRFishhBBCiHvUzvPXMZhudgOMTc6u4miEuLdJYiWEEEIIcY/aHJdOQ2db7G00HJXESogKJYmVEEIIIcQ96Hx6LqdT9YT5u9DWt64kVkJUMEmsqqGUlBQmT55Mly5d6NmzJ2PHjuWHH35g3LhxRW7/0ksvcerUqUqOUgghhBDV2da4dLRq6NGkLvc3cOZipoG0nNrx+AkhqoKMYKxmFEVh/PjxjBw5ks8//xyAmJgYNm/eXOw+c+fOrazwhBBCCFED5JkUtsdn0qm+E846Lfc3cATgaHI23ZvUreLohLg3SYtVNbNr1y5sbGysWqcCAwMJCQkhOzubCRMm0L17d5599lnLMylGjBjB4cOHAWjevDnvv/8+oaGhhIeHc+XKFQA2bdpEeHg4YWFhjB492rJcCCGEEPee/ZdukJFrIrSZMwDNvergYKMmRroDClFhpMXqNhbtTyY+TV+ux/Rz1fF0R+9i1588eZKgoKAi18XExLBt2zZ8fHwYPHgw+/btIzg42Gqb7Oxs7r//fmbOnMnbb7/Njz/+yLRp0wgODiYyMhKVSsVPP/3EZ599xuzZs8v13IQQQghRPWyJS8fdXkv7ejdbqrRqFa097WWclRAVSBKrGqRdu3b4+voCEBAQQEJCQqHEytbWlr59+wIQFBTEjh07AEhKSmLy5MmkpKRgMBho1KhR5QYvhBBCiEqRmp3HwaQshrV2R6NWWZYH+TiwP/EKqdl5uDvYVGGEQtybJLG6jdu1LFWUFi1asG7duiLX2draWn7WaDQYjYUHoGq1WlQqVaFt3njjDSZOnEhYWBjR0dHMmzevAqIXQgghRFWLOpuJWcHSDTBfkLcjcIWY5Gx6+DkXvbMQ4q7JGKtqplu3bhgMBn788UfLskOHDrF79+4yHTczMxMfHx8Ali9fXqZjCSGEEKJ6UhSFLWfTCfSyp56TrdW6Ji52ONqqpTugEBVEEqtqRqVSsWjRIv7880+6dOlCr169iIiIwNu7bK1n06dPZ9KkSQwdOhQ3N7dyilYIIYQQ1cmxKzkkXc+jTzOXQus0ahUBXg6SWAlRQVRK/tRytVRiYqLV79nZ2Tg4OFR6HFqttsiufaJ6kvqqOWpTXVXV51d58vDw4OrVq1UdhigFqavqaf5fSfx14TrfDvdHp/3f/fP8+lp74hrfHEhh0ZBmeDrKOKvqSt5f1Uv+HAclkRYrIYQQQoh7QHaeiV3nM3mwiZNVUlVQoNfNmy8y7boQ5U8SKyGEEEKIe8DO89fJNSmEFtENMF8TVzucbNXEpEhiJUR5k8RKCCGEEOIesCUug4bOtrRw1xW7jVqlIsBbxlkJUREksRJCCCGEqOESMnI5eTWH0GbOlseuFCfQy4HkG3mk3MirpOiEqB0ksRJCCCGEqOG2xGWgUUHPUjyfKsj7n3FW0h1QiHIliZUQQgghRA1mNCtExWfQqUEdXHTaErdv5GJHXTsNR5OzKiE6IWoPSayqmYSEBHr37m21LCIigi+++OK2+x0+fJg33ngDgOjoaPbt23fHZYeEhHDt2rXbLj9y5AgPPPAAMTExbNq0iYULF95xOUWJjo5m3Lhx5XKs0jhz5gx9+/YlLCyMc+fOWa3Lyspi5syZdOnShbCwMPr162f1wOay+vrrr8nJySly3YgRI3jwwQfp27cvPXr04Icffij2OC+99BKnTp0CYMGCBaUqe+zYsWRkZNx50HepuNduREQEzZo1s5pKtnnz5nd8/ILnXdR7p6IU9165nREjRnD48OEqK18Ice86cOkGGXoToU2Ln7SiILXqn+dZXc6mlj91R4hyVfJtjXJkNpuZOXMmbm5uzJw5k5SUFD7++GNu3LiBn58fzz33HFqtlry8PBYuXMjZs2dxcnJi2rRpeHl5AbB69Wq2bduGWq3mySefpF27dgAcOnSI//73v5jNZvr06cOQIUMq89SqXNu2bWnbti0Af/31F46OjnTq1Klcyzh27BgTJ07k888/JzAwkMDAQMLCwsq1jMry+++/89BDD/HSSy8VWvfSSy/RqFEjdu7ciVqtJjU1lV9++aXQdiaTCY1Gc8dlL1q0iOHDh2Nvb1/k+oULF9K2bVvS0tLo2rUro0aNwtbWtlDZc+fOtfz+ySef8Pzzz5dY9pIlS+443ori5ubGl19+yaxZs+54X0VRUBSl1OddGkajEa327j8S82NSq+V+lRCicm2Oy8DVXsv9vo6l3ifI24G/Eq6TkpWHdx3bkncQQpSoUhOr9evXU79+fcvd+h9++IGBAwfStWtXvvrqK7Zt20ZYWBjbtm3D0dGRTz75hF27dvHjjz/ywgsvcPHiRaKjo5k3bx5paWnMmTOH+fPnA/DNN9/w+uuv4+7uzquvvkrHjh1p0KBBZZ5epRgxYgTt27cnOjqajIwMIiIiCAkJITo6mi+++IJ33nmHJUuWoNFoWLlyJW+//Tb+/v7MnDmTS5cuAfDWW2/RqVMnrl27xpQpU0hNTaVdu3a3vWt1+vRppk2bxoIFC2jfvj0AS5cu5ciRI7zzzjtMmzYNJycnDh8+zJUrV5g1axbh4eGYzWZmzZrF7t27adiwIYqiMHr0aMLDw4mKimL27Nm4ubkRFBRkKSstLY3p06dz4cIFdDodH3zwAa1btyYiIoILFy6QkpLC2bNnmT17NgcPHiQqKgofHx++/fZbbGysH3YYExPDzJkz0ev1NG7cmIiICA4cOMCiRYvQaDTs3r2bFStWWLY/d+4chw4d4tNPP7V8QXZ3d2fKlCkAltefj48PMTExbN++nZUrV7J48WIMBgPt27fnvffeQ6PRMHPmTA4fPoxer2fgwIG89NJLfPPNNyQnJzNy5EhcXV2tyr5VdnY29vb2luStefPmTJw4kT/++IM333yTDz74gDfeeIN169ah1+vp27cvLVu2ZOHChTz11FMkJiaSm5vL+PHjeeyxx4CbLR0bNmwgKyuLxx57jODgYPbv34+Pjw+LFy8ulOxt2rSJBQsWYDAYcHV1ZeHChXh6ehIREcGlS5e4cOECly5d4umnn2b8+PEAzJ8/nxUrVuDr64u7uztt2rQp8vz+9a9/sWzZMv7973/j6upqte7LL79k6dKlAIwZM4YJEyaQkJDAY489RpcuXThw4AABAQFW5/3KK69gMpmYMWOG1Tk5OTlx7tw5Zs2aRWpqKvb29nz44Yf4+/szbdo0XFxciImJISgoiDp16hR7XkW5NabFixcTFxfH3LlzMRgMNG7cmI8++ghHR+svO0W9NvLrZ+TIkWzevBmj0ciXX36Jv7//Hb1XhRC1y7UcIwcSbzD0Pjc06ttPWlFQkM/NcVZHk7MlsRKinFTardXU1FQOHjxInz59gJt3d2NjY3nggQcA6Nmzp6X72v79++nZsyeApduZoijs27ePLl26YGNjg5eXFz4+Ppw5c4YzZ87g4+ODt7c3Wq2WLl263FVXuFvFHMwmetv1cv0Xc7DsA0WNRiPr1q3jrbfeYt68eVbrGjZsyNixY5kwYQKbN28mJCSEN998kwkTJrB+/Xq+/vpry5e4jz76iODgYDZt2kRYWJgl8SrKU089xdtvv01wcHCx2yQnJ7NmzRq+++473nvvPeBmMn3x4kW2bt3K3LlzOXDgAAB6vZ4ZM2bw7bffsnr1alJSUizHiYiIIDAwkC1btjBz5kymTp1qWXf+/Hm+//57Fi9ezHPPPUeXLl3YunUrOp2OrVu3Fopp2rRpzJo1iy1bttCqVSvmzZtHnz59LNfo1sTm1KlTtG7d+ratDocOHeLVV19l+/btnD59mrVr17JmzRo2b96MRqNh1apVALzyyits2LCBLVu2sHv3bo4dO8b48ePx9vZm+fLlxSZVzz77LKGhoXTv3p1p06ZZEqvs7GxatmzJb7/9ZlUPr732Gjqdjs2bN1u6ZkZERPD777+zfv16Fi9eXGS3sfj4eB5//HGioqKoW7cu69evL7RNcHAwkZGRbNq0icGDB/PZZ59Z1p05c4Yff/yRdevWMW/ePPLy8jhy5Ahr165l06ZNLFq06LZd3xwdHfnXv/7FN998Y7X8yJEjLFu2jN9++43IyEh++uknYmJiAIiLi2PEiBFs2rSJjz76qNB5F3dOL7/8MnPmzOH333/njTfe4NVXX7WUd/bsWZYuXcrs2bOLPa/bKRiTg4MD8+fPZ+nSpWzcuJG2bdvy1VdfFdqnqNdGPjc3NzYjRePWAAAgAElEQVRu3MjYsWMt3Sjv5L0qhKhdtp/NwKxAn9s8u6ooDeva4qzTcPSyTGAhRHmptBarb7/9lscee8zSWnX9+nUcHBwsXxrd3NwsX/6uXbuGu7s7ABqNBgcHB65fv861a9esxmEU3Cd/+/yfT58+XSnnVd5KmiIVYMCAAQC0adOGixcvlrj9jh07LGNxAG7cuMGNGzfYvXs3ixYtAiA0NBQXl+I/lLt168bPP/9Mz549i+3+1q9fP9RqNS1atODKlSsA7N27l/DwcNRqNV5eXnTp0gW4+eW1UaNGNG3aFIDhw4dbxhPt3buXr7/+2lJuWloamZmZAPTq1QsbGxvuu+8+zGYzvXr1AqBVq1YkJCRYxZOZmUlGRgadO3cGYOTIkUyaNKnE61XQ/Pnz+e233yw3BgDatWtH48aNMRqN7Ny5k6NHj1rqRK/X4+HhAUBkZCQ//vgjJpOJ5ORkTp8+TevWrUssM78rYGpqKg8//DC9evWiQYMGaDQaBg4cWKq4Fy9ezIYNGwBITEwkPj4eNzc3q20aNmxIYGAgcPO1dOv1A0hKSmLy5MmkpKRgMBho1KiRZV2fPn2ws7PDzs4ODw8Prly5wp49e+jXr5+l5atv3763jfOpp54iLCzMql727t1Lv379cHC4eTe1f//+7Nmzh7CwMBo0aECHDh2KPV5R55SVlcWBAwesyjAYDJafw8PDrV7TRZ2Xr69vsWUWjOnAgQOcOnWKwYMHA5CXl1dkvLd7bfTv398Sf34d3sl7VQhReyiKwua4DFp72lO/7p21OqlUKgK9HDiacnOcVWm+fwghbq9SEqsDBw7g7OxM06ZNiY2NLXH7orq5qFSqYru/FLd9UbZs2cKWLVsAeP/99y1fgvMlJydbxlm0C65bYqzlSavV4unpSUZGhtVYj4yMDJo0aYJWq0WlUmFvb49Wq8XW1haTyYRWq0Wj0aBSqdBqtajVatRqteUYiqKwfv36Qt288rcvWJZGoyk0zkSlUvH+++/z8ssvM2vWLMvYHo1GYylHrVZb4sovMz/egrGoVCpLGQWXq9VqSzy3xqFSqbCxsSlUhlartXT902q1ljILXs+Cxyz4+63XKN99993HsWPHLOunT5/O9OnT8fPzs1zn/G5d+ccbNWoUr7/+utVxzp8/z5dffsnGjRtxcXHh+eefJy8vz7JPUdf51uvj7e1NmzZtOHz4ME2aNLF82S9q2/x4AHbt2sXOnTtZv349Dg4ODB061DJ+KH8fjUaDnZ2dZR8bGxsMBkOhmN58800mTZpEv3792LVrF3Pnzi2yvvMTE7VabRVTcdc5f7m7uzvDhg2zjP0q6jWTv23+TZZbj1UwhqLOyWw2U7duXaKiogpdb7VajZOTk1VZRZ1XUe+J/OtYMCa1Wk2PHj348ssvi63bS5cu3fa1kX88W1tbzGaz1eu2pPdqfjJYk2m12hp/DrWF1FXVO5KYSeJ1A0+ENC+xLoqqr87N8th1IY5cmzo0cCl63K+oGvL+qpkqJbE6efIk+/fv5++//8ZgMJCTk8O3335Ldna2ZQKAa9euWe6ou7u7k5qairu7OyaTiezsbOrUqWNZnq/gPgWXp6amFhqzkS80NJTQ0FDL7wVnJQPIzc29qwkJykqr1WI0GrGzs8PLy4uoqCgefPBB0tLS2LZtG0899RRGoxFFUTCZTBiNRkwmE4qiFPrZ3t6ezMxMjEYjAN27d2fRokVMnjwZuDnuKDAwkJCQEJYvX860adPYtm0b6enplmMXlD8of+HChTz66KO89957zJgxA5PJhNlsxmg0YjabC+1rNBrp2LEjy5cvZ/jw4aSmphIdHc3gwYNp0qQJ58+f58yZMzRp0oRVq1ZZ4s+P64UXXiA6OhpXV1fs7e0xm82W8gqWARS5zsHBgbp167Jr1y5CQkJYunQpISEhlnhv3R5utni0adOGd955h5dffhmNRoNery90nfPL7tKlC08++SRPP/00Hh4epKWlkZWVRUZGBvb29jg4OJCUlMTWrVstZTs6OpKRkYGzc+FnjRSs35ycHI4ePcrkyZMtcRaMt+C2NjY25OTkYGNjQ3p6OnXr1sXW1pYTJ05w4MABy3b5+5hMphKvH9xM6r28vDAajfzyyy+W61DU9iaTieDgYF544QUmT56MyWSydGm79bgF958wYQIDBgywxJh/jH//+9+WmwILFiwoFDNgdd7FnZOTkxMNGzZk9erVDBo0CEVROHbsGAEBAYVet8WdV1HviaKuY7t27Zg5cyanT5/Gz8+PnJwcEhMTadasmWWf9PT0Yl8bxb2/S/tezc3NLfSZVtN4eHjU+HOoLaSuqt7Kg0notGrauKlKrIui6svP0QzAnycuEeYvLeHViby/qpfb9VwpqFISq0ceeYRHHnkEgNjYWCIjI3n++eeZN28eu3fvpmvXrmzfvp2OHTsC0KFDB7Zv306LFi3YvXs3AQEBqFQqOnbsyIIFCwgPDyctLY2kpCT8/f1RFIWkpCRSUlJwc3MjOjq63GYKqwrz58/ntdde4//+7/8AePHFF2nSpEmp9+/bty+TJk1i48aNvP3228yZM4fXXnuN0NBQy5e0//znP7zwwgtMmTKFhx56iAceeID69evf9rh2dnYsXryY4cOH4+npWeysdgUNHDiQnTt30rt3b5o2bUr79u2pW7euZVKKcePG4ebmRnBwMCdOnLCc74svvkhoaCg6nY6PP/641Od+q48//tgyeUWjRo0KjUkryty5c5kzZw5du3bFxcUFnU5X7Mx1LVq04OWXX2bMmDGWFrN33nmHDh06EBgYSK9evWjUqJHVDI2PPvoojz32GF5eXkWOs3r22WfR6XQYDAZGjRpV7OQPBT366KOEhoYSFBREREQES5YsITQ0lKZNm3L//feXuH9xpk+fzqRJk/Dx8eH+++8vsrtgQUFBQQwaNMjSbS8kJKTEMtzc3OjXr5+l+2dQUBAjR460dHscM2YMgYGBRZZd8LxfeeWVYstYuHAhr776KvPnz8doNDJ48GACAgJKjO1Oubu789FHHzFlyhRLd8OXX36ZZs2aWbYJCAgo9rVRnDt9rwoh7n05eWZ2ns+kW+O62Nvc3ZD5+nVtcdVpOJqcLYmVEOVApVTy9FL5idXMmTNJTk4uNN16fvedhQsXEh8fT506dZg2bRre3t4ArFq1iqioKNRqNU888YRlhrqDBw/y3XffWcbdDBs2rFTxJCYmWv2enZ1tGdtRmfJbrO5FWVlZODo6cu3aNcLDw1mzZo1l+vya6l6ur3tNbaqrqvr8Kk9yl7bmkLqqWlvi0vlk92XeD2vEfZ4lv++Lq6+InYkcTcnmv0ObyTirakTeX9VLaVusKj2xqm4ksap4I0aMICMjg7y8PCZPnszo0aOrOqQyu5fr615Tm+pKEitRmaSuqtbMTee5nmtiYbhfqRKi4upr4+l0Ptt7mU8H+dGgrl0Re4qqIO+v6qVadQUUtdvtntUkhBBCiDtzMTOX41dyeLy9Z5lbmYK8b96MiUnOlsRKiDKqtOdYCSGEuDfk6s2cPZUrDyoWoopsjctArYJefoUnQbpT9ZxscLfXcjRZnmclRFlJYiWEEOKOnD6mJ/bvHNJSTVUdihC1jtGssO1sBh3r18HVvuwdj1QqFYHeDsQkZ8vNEiHKSBIrIYQQpWbMU0g4d3PGw9SU2jF2TYjq5GDiDdL1JkKblb21Kl+QtwPpehMXMw0lbyyEKJYkVkIIIUrt0gUDxjzQ2sBVSayEqHRb4jJw0Wno4Fun3I6ZP85KugMKUTaSWFVDDRs2pG/fvvTu3ZuJEyeSk5NDQkICvXv3LnL7Dz/8kD///BO4OQPf4cOHARg7diwZGRl3FcP333/P8uXL7+4EhBD3JEVROHc6l7ouaho2sSXtqhGzSboOCVFZ0nOM7L90g15+zmjV5Tc1uncdGzwcZJyVEGUlswJWQzqdjs2bNwM3Hxb7/fffM2DAgGK3nzFjRpHLlyxZctcxjBs37q73FULcm9KumsjMMNOmoz22diriTxtIv2bCzVP+lAhRGaLiMzAplGs3QLg5zirI24EDiVkoiiLPsxLiLkmLVTUXHBzMuXPnADCZTMyYMYNevXoxZswYcnJyAJg2bRq//fZboX1DQkK4du0aCQkJdO/enalTpxIaGsqECRMs+4aEhPDOO+8wcOBABg4cSHx8PAARERF88cUXwM1WsPxtunXrxp49eyzxzJkzhwEDBhAaGmpJ5JKTkxk2bJil1S1/eyFEzXbuTC5aG6jf2Bb3f5Ip6Q4oROVQFIUtcRm08rCngXP5T4se5O1AZq6JCxkyzqoqmc1mtm7dypEjR6o6FHEX5Dbjbfz5559cuXKlXI/p6elJ9+7dS7Wt0WgkKiqKnj17AhAfH8+nn37Khx9+yKRJk1i/fj3Dhw8v1bHi4uKIiIigU6dOvPjii3z33Xc888wzANSpU4d169axfPlyZs+ezffff19kLOvWrWPr1q3MmzePpUuX8vPPP+Pk5MT69evJzc1lyJAh9OjRg/Xr19OjRw+mTp2KyWSyJHFCiJorV28m8WIeTZrZotWqQKuiroua1CuSWAlRGU5e1XMx08CzIT4VcvxAyzirLBq7yPOsqkpqaiqxsbHExsbSvn17unbtilot7SA1hdRUNaTX6+nbty/9+/enfv36jBkzBrg59iowMBCANm3akJCQUOpj+vr60qlTJwCGDRvG3r17LeuGDBli+f/AgQNF7p/fFbFNmzZcvHgRgD/++IMVK1bQt29fwsPDSUtLIz4+nnbt2rFs2TIiIiI4fvw4deqU3wBbIUTVuHDWgGKGJv7/+8Ll7qnl2lUjJhlnJUSF2xKXjk6romtjpwo5vncdW7wcbYiRcVZVKjExEYCgoCD+/vtvfv31V/R6fRVHJUpLWqxuo7QtS+Wt4Birguzs/veFRqPR3NEb7db+0gV/L+7ngmxtbS3lGo3/u0P99ttvW1rUClq5ciVbt25l6tSpPPPMM4wcObLUsQohqhfFrHAuLhcPby116mosyz28bSzjrNxlnJUQFUZvNLPj/HW6NqqLg42m5B3uUpC3A3svXsesKKhlnFWVuHz5Mo6OjowYMQIvLy+ioqL45ZdfGDRoEO7u7lUdniiBtFjVEpcuXWL//v0A/Prrr5bWK4C1a9da/u/QoUOpj9mjRw++//578vLygJvdDbOzs7l48SIeHh48+uij/Otf/+Lo0aPleCZCiMqWnGREn63QxN/Warmb580vePI8KyEqVvSF6+iN5nKftOJWgd4OXDeYOZ+eW6HliOIlJiZSr149VCoVAQEBDB8+HJPJxLJly4iLi6vq8EQJ5BZjLdG8eXOWL1/OzJkz8fPz4/HHH7esMxgMhIeHYzab+fTTT0t9zEceeYSEhAT69euHoii4ubmxePFioqOj+eKLL9BqtTg6OjJ//vyKOCUhRCU5dyYXnb0Kb18bq+W2tmrqumi4mmKkRUAVBSdELbD5TDq+Tjbc52lfoeXkP88qJjkbP1ddhZYlCrtx4wbXr1+nXbt2lmX16tVj9OjRrFu3jnXr1hEcHExISIjM3FhNqRRFqdWd4/P7subLzs7GwcGh0uPQarVWXezKU0JCAo8//jjbtm0rtC4kJIQNGzbg5uZWIWXfqyqyvkT5qk11VRGfXzeum4haf52WgTpaBBT+ohX7dw7nzuTSb5gzGk3Z/9B7eHhw9erVMh9HVDypq8pxKdPAvyPPMradJyMC7r4rWGnra9KvcTR2seO1Hg3uuixxd06fPs2GDRsYNWoUgYGBVvWVP6HZ8ePHadq0KX379rUaIiIqlq+vb6m2k66AQgghinX+jAGVCho1tS1yvbuXFrMZ0lJNlRyZELXDtrMZqFXQu2nFdgPMF+jtQExKNiZzrb7vXiWSkpLQarV4enoWWqfVagkNDaV79+7Ex8ezbNky0tPTqyBKcTuSWNUCDRs2LLK1CmDPnj3SWiWEKJLRqJAQb6BeAxt09kX/uXD31IAKUlPyKjk6Ie59JrPC1rMZdPB1xM2+ckZvBHk7kCXjrKpEUlISXl5eaDRFT1CiUqlo164dQ4YMIScnh6VLl1qedSqqB0mshBBCFCnxgoG8PMVqivVb2diqcf5nnJUQonz9nZRFWo6RPs1cKq3MIMvzrGTa9cpkNBq5cuUK9erVK3Hbhg0bMnr0aJycnIiMjOTAgQPU8pE91YYkVkIIIQpRFIX40wacnNWW2f+K4+6lJT3VhMkof9iFKE9b4tJx1mnoVL/yngfp7mCDr5MNR5OzKq1MAcnJyZjN5lKP5XF2dmbkyJE0a9aMXbt2sXHjRssszaLqSGIlhBCikPRUE5npJpr425U4+5SHZZyVtFoJUV4y9Eb2XrxBLz9ntOrKnQEuyNuR2JQcGWdViZKSkgDw8fEp9T42Njb079+fzp07c+rUKVasWMH169crKkRRCpJYCSGEKOTcmVy0WmjQuOhJKwpy89SCCukOKEQ52h6fiUmBPpU0aUVBgd4OZOeZOZumr/Sya6vExERcXV2xt7+zKfVVKhWdOnVi0KBBZGRk8Msvv3Dp0qUKilKURBKraqhhw4b07duX3r17M3HiRHJycm67ffPmzQstu3z5MhMmTCh2n4yMDL799tuyhiqEuAfl5ppJTMijQRNbtDYl3ym3sVHh4qqRBwULUU4URWFzXDot3HU0cqn8KbUDZZxVpVIUhcuXL5dqfFVx/Pz8GD16NDqdjtWrV3PkyBEZd1UFJLGqhnQ6HZs3b2bbtm3Y2try/fff3/ExfHx8+Prrr4tdn5mZeVfHFULc+xLOGjCbue2kFbdy99KSds2EUcZZCVFmp1P1JGQY6OtfeZNWFORmr6VBXVtiJLGqFOnp6ej1+jIlVgCurq6MGjWKRo0asX37drZt21ZrnuNYXUhiVc0FBwdbptJ86qmn6NevH7169eKHH34otO21a9cYNGgQW7ZsISEhgd69ewNw8uRJBg4cSN++fQkNDeXs2bO8++67nD9/nr59+zJnzhyysrIYNWoUDz30EH369GHjxo3AzYcL9+jRgxkzZtCrVy/GjBlTYguaEKLmUswK5+IMuHtpcXK+/aQVBbl7aVHMkHZV/ogLUVZb4jKw1ajo1tipymII8nbgmIyzqhSJiYkAZU6sAOzs7AgPD6dTp07ExsayatUqsrJkIpLKUjkPRaih6lyJRJubVK7HNNrV44bnoNJt+89Ttnv27AlAREQErq6u5OTkMHDgQAYMGGB5BtWVK1d48sknefnll+nevTsJCQmW4yxZsoTx48czbNgwDAYDJpOJ1157jZMnT7J582ZLWd988w1OTk6WBC0sLAyA+Ph4Pv30Uz788EMmTZrE+vXrGT58eDleFSFEdZFy2UhOlpnWbXV3tJ+7hxaVClKvGPH0samg6IS49+Uazfx5LpOujZxwsCn9zY3yFujtwIbT6Zy5pqelx52N+xF3JikpCZ1Oh6ura7kcT61W07lzZzw8PNi8eTO//PILAwcOvKOJMcTdkcSqGtLr9fTt2xeAkJAQxowZA8DixYvZsGEDcPPuRnx8PG5ubhiNRkaPHs0777xD586dCx2vQ4cOLFiwgKSkJPr370/Tpk0LbaMoCu+//z579uxBpVJx+fJlrly5Atwc8xUYGAhAmzZtrJI2IcS95dyZXOx0Knzq31lypLVR4eyq4WqyEYIqKDghaoHoC9fJMZrpW4nPripK/jirmORsSawqWFJSEj4+PiXOwHqnmjdvjouLC+vWrWPlypX07t2b++67r1zLENYksbqN0rYslbf8MVYFRUdHs2PHDiIjI7G3t2fEiBHk5t58KrpGoyEoKIjt27cXmVgNHTqU9u3bs3XrVh599FE+/PBDGjdubLXNqlWrSE1NZcOGDdjY2BASEmI5vp3d/8ZZaDQa9HqZJUiIe1HWDRMpSUZaBNihvovpnT28tcSdyMVoVNBqK3d6aCHuFVvOZlDPyYbWXlWbzLjotDRytuVocjbDA9yrNJZ7mV6vJy0tjVatWlXI8T09PRk9ejQbNmxg8+bNKIpC69atK6QsIYlVjXH9+nWcnZ2xt7fnzJkzHDx40LJOpVIxb948Jk2axMKFC3n22Wet9j1//jyNGzdm/PjxnD9/nuPHj9O6dWtu3LhhdXwPDw9sbGzYtWsXFy9erLRzE0JUD+fPGFCpoFHTu5uFzN1Ty5njuVy7asRLugMKgaIoZOWZuZ5rIjPXRKbeREaukcxc0/+W/bP85jIj1w1mHmvrUe6tF3cj0NuBbWczMJqVSn+WVm2R//yq8hhfVRx7e3sefvhhIiMj2bp1K7a2tvj7+1dYebWZJFY1RM+ePVmyZAmhoaE0bdqU+++/32q9RqPhs88+44knnqBOnTr06dPHsm7t2rWsWrUKrVaLl5cXL7zwAq6urnTq1InevXvTq1cvpkyZwuOPP07//v0JCAiQN5wQtYzJqHAh3oBPfRvsHe5uXiO3/HFWKZJYibtzJSsPG7UKF/vq//UkM9fEwcQb1gmSJVH6X/JkKmbuB61ahbOdhro6DU52Gpo62lHXzgFXey0DW5bPWJuyCvJ2YP2pdM6k6mnlKd0BK0JSUhJqtRpvb+8KLUer1RIeHs7q1av5/fffefjhh2nUqFGFllkbqZRaPsl9/kws+bKzs3FwcKj0OLRarUyJWYNIfdUctamuyvL5lRBv4NDebDr3dMTD++6Top1brgPQLfTuZjPz8PDg6tWrd12+qDzlVVd5JoW9F6+z6Uw6hy5n4+tkw8LwpmiqcQuJWVF4ccM54tNudplXq8DJ9maCVNdOg7NOQ107reV3yz9d/s9adFpVpbZK3U19ZeqNjF15hsfaejAy0KOCIqvdVq5caRkrX1BFfRbq9XpWrVpFeno6Q4cOrdCWsnuJr69vqbar/reEhBBCVLhzZ3Kp46TG3atsfxbcvf4ZZ5WnlOrhwqL2upRpYNOZdKLOZpCRa8LTQUsvv7pExWcSFZ9BaBVP3nA7f124TnxaLs908qZb47o42qpRV4Oue8VRjHkoeYY73q+uTktjFzuOJmczMrACAqvlTCYTycnJlgnCKoNOp2Pw4MGsWLGCtWvXMnz4cDw8JGkuL/IcKyGEqOXSU42kXzPRxN+uzHfQPby0KApck+dZiSIYTGa2x2fw2ubz/DvyLJEnrtHay57ZvRrw5eBmTO1cD383HUuPXiWvuD50VcxkVvjpyFUaOtsS5u+Ck52mWidVAMpPX3J14jCUpDsfPx3k7cDxKznVtj5qsqtXr2I0Git9GnRHR0eGDh2KVqtlzZo1pKenV2r59zJJrIQQopY7d8aARgsNmtiW+ViuHlpU6pvjrITIdz49l6/3J/PkqjN8FJ1EaraRse08+WaoPzO7N+B+3zpo1De7xj3SxoOULCNb4qrnl70d5zO5mGlgTJBHte6umE8xmVAO7MKcfg1zxOsoKYkl71RAkLcDBpPC6dScCoqw9qqMiSuKU7duXYYOHYrZbGb16tVcv3690mO4F0lidYtaPuRMCFGD3c3nlyHXzKUEAw0a22JjW/YviVqtChc3DVclsar19EYzW+LSeXnjOZ5fF8/vp9NpX8+ROX0a8vnDTRkR4I5rEZNU3O/rSEsPe5bHpGIwmasg8uKZzApLj16liYsdnRvd3TjCSnf2JGRn4Tj6KTDlYY54AyU1pdS7B3g5oAKOJmdXXIy1VFJSEk5OTjg5Vc1ryc3NjSFDhqDX61mzZg05OZI8l5UkVrdQq9W1ZqC7EOLeYTQaUavv/CM9Id6A2QRN/O9uivWieHhpyUgzkZcnN6pqo7hrej7fe5knVp7hk92XyTKYeep+L/47tBkvdatPGx/H23adU6lUPNbWg9QcIxtPV69Wq6j4DBKv5/FIG49q3/0vnxJzANRqHAaNRv3C/4E+G/PcWSjXSjcxgpOdhiaudsRIYlWuFEUhMTGxyieP8PLyYtCgQWRmZvLrr79anmEq7o5MXnELnU6HXq8nNze3UmfrsbOzkxdzDSL1VXPUhrpSFAW1Wo1Op7vj/c7FGXDz0FDXRVNu8bh7aTl9LJdrV4x4+8q067VBdp6JP+Iz2RyXTty1XGw1Kro2ciLM34X7PO3v+O9pGx9HAr0dWBGbSpi/C3baqr8PnGdSWHo0FX83HcEN6lR1OKWmHN0PzVqhdnRC1agZ6mlvYZ73BuaI11HPeBeVi1uJxwjyduD30+nkmczYaKq+Lu4FN27cICsrq8oTK4AGDRowYMAA1q1bR2RkJEOGDEGrlRThbshVu4VKpcLevvKf1SBTDNcsUl81h9RV8a5cNpJ9w0yrwPJ9xISruxa1GlIlsbqnKYrCyas5bDqTzo5zmeSaFPxc7ZjY0ZsefnWpY1u2ZP3RNh68uvkC606lMay1ezlFffe2nk0nJSuPZzp5V4uH95aGkp4KCfGoho2zLFP5tUA9dTbmj/8f5nlv3EyunJxve5xAbwfWnkjj5FU9gd6V/0iae1FVjq8qip+fH3379mXjxo2sX7+egQMHotGU3w232kJuOwghRC117kwutnYq6jUo3+RHq1Xh4q7harJ0q75XpWbn8eTPh3h543l2ns+kh19d5vZrzEf9mzCwpWuZkyqA1l4OtKvnyKpj18jOM5VD1Hcvz2RmWUwqLT103O/rWKWx3Akl5iAAqsAOVstV/q1RP/cGXE3GPO9NlKzbT1wQ4OWAWoV0ByxHiYmJ2NjYVKupzlu2bEmvXr04d+4cW7ZskXkH7oIkVkIIUQtlZ5lITjTSuJktak3533338NKSkW4izyB/mO9Fv51M4+zVLP4d7MN/h/kzJaQezd3vvMtfSR5t48H1XBO/nUwr1+PeqU1nMkjNNvJIG88a01oF/4yvcnGDBk0KrVO1DEI9ZRZcTsD80WyU7Kxij1PHVoOfq46jya6TciwAACAASURBVMVvI+5MUlIS3t7edzU2tiIFBQXRpUsXTp48yfbt2yW5ukPVqzaFEEJUivNxBlBBo6blN2lFQe6eWpDnWd2Tco1mNp9Jp1tTdx5q7oKDTcV1F2rhYU+n+o6sOX6NG4aqabXKNZpZHptKgJc9bX1qTjc4xWiEY4dQBXYoNhlUBbRH/cyrcDEe84K3UPTFt0gFeTtw8qq+2s3UWBMZDAauXr1abboB3qpjx4506NCBo0eP8tdff1V1ODWKJFZCCFHLmEwKF84a8PG1wcGxYv4MuHrcHGcl067fe3aez+S6wczwtpXzpfCRNp5kGcysPXGtUsq71e+n00nLMfJoDWut4uwJyMlGFdThtpup2nZCPWEGxJ/C/MnbKMVM9hPk7UCeWeHEFZmSu6ySk5NRFKXaJlYAXbp0ITAwkP3793PgwIGqDqfGkMRKCCFqmaSEPAy5Ck38y/5A4OJoNCpcPbTyoOB7jKIorDuVTkNnW+5vcPsJD8pLUzcdnRvWYe3xNDJzK7fVKifPzMrYVNr6OBBQwyZtUI4eAI0G7mtX4raqDl1QPfUCnD6G+dO3UfIMhbZp7WWPWiXPsyoP1W3iiqKoVCp69uxJ8+bN2bVrFzExMVUdUo0giZUQQtQy587k4uikxsO7YieGdff853lWBuk6dK84laon7pqe/s1dK7X1ZkwbT/RGM2uOpVZamQDrTqWRkWvi0baelVpueVBiDoB/a1T2pUsI1SE9UD3xHBw/jPnz91GMeVbrHWw0NHPTyQQW5SApKQl3d3fs7CqmK3Z5UavVhIWF0bhxY7Zt28apU6eqOqRqTxIrIYSoRTLSjKSlmmjSzLbCvxh7eN1M3FKvVO2Mbv+fvfsOj+q6Fr//3WeK6kgjjToCAQJRBQZEM8V027jh7jQ7iePcFJN64+SmOXGSa187TnHs++aXXMd2irtj3HCh2aYYWzQh0SRACCGNetdI085+/xiEKRIIaarYn+e5z3UkzTlLSDpz1ll7r6X4z9rSZmKMGotHJwT1vDnWKObnWHjzUDMt3cGpgjrcXtbsb2RGVhzjUoI/hmUwZFMDnDiGmDz9ol6nXb4U8YVvQPEO9L884tundZr89FhKG7twetTDkoGSUmK328O6WnU6g8HAypUrycrK4r333uPYsWOhDimsqcRKURTlEnLssAvNANmjArcMsIfVZkAzoJYDDhGt3R62VLSzeHRCQBtW9OWOKSm4dcm/9wWnavX6wWbaXTqfnRKB1ap9J9us5xdc9Gu1hVch7rgHdm9H/u33SP3TByP56bF4dDig9lkNWFNTEy6XK2ISKwCTycR1112HzWZj7dq1VFVVhTqksKUSK0VRlEuE26VzosJFdo4Zsznwl3+DQZBsM6oGFkPEusOteHTJyrykkJw/OyGKK0Ym8HZZC40O94VfMAgdTi+vH2hidnY8Y2zRAT1XIMjiHZCUAlkjBvR6bel1iFu+iCzcjHz6MaTuq1CNT41R86wGKRL2V/UmKiqKG264AYvFwhtvvEFdXV2oQwpLKrFSFEW5RFSWu9C9kJMb+GpVD1uakbYWLy6nWjoUyby65J2yZqakxzI8MXT7Qm7PT8GrS14JcNVqzYEmOt06n50SPsNb+0t63HCgCJHfd5v1/tCuvAlxw2eRH21C/vN/kbpOrMnAWFu0amAxCHa7nZiYGBITg9P8xZ9iY2NZtWoVUVFRvPbaazQ3h3a+XDhSiZWiKMolQErJscMukmwGrMmBbVpxOtupfVaqahXJCqs6qHd4Qlat6pFpMbM0N5F3D7dS3xmYqlVbt4c3DjUzb4SFkUmRV63i8AHo7kJMPn+b9f7Qrr0DsfI25Ob3kM//FSkl+elxlDV20eVWD0sGorq6mszMzMhq3X8ai8XCjTfeCMCrr75Ke3t7iCMKLyqxUhRFuQQ01Hro7NAZOSa41QZrstpnNRSsLW3GFmtkVnZ8qEPhtsm+KtJLJYGpWv17fxNOj85nIrBaBSe7ARqMMGGKX44nVn0OsWIVctNbyJefYlJaDF4JBxvUPquL5XA4aG1tjbhlgGezWq2sWrUKl8vFq6++isOhKpg9VGKlKIpyCTh22IU5SpA53BTU8xoMgmQ1zyqinWh1UlTj4KqxVgxa6J+yp8aZWDEmkfVHWqhpP3fe0mA0d3l4q7SZhSMTQrrkcTBk8U4YOxER7Z+5W0IIxC1fQixeiXxvDeM/fh2jBsU1nX45/qWkP/urpJTo77yCx34iWGENSGpqKtdffz3t7e1s2bIl1OGEDZVYKYqihIHuLp3Sfd2cOOaitdmL1yv9duwuh05NtZsRo80YDMG/MbalGWlr1XGqfVYRaW1ZC0YNVuRaQx3KKbdMsmHQBC+UNPj1uK/sb8SjS+7Ij9BqVWM9VB9H5A9+GeDphBCIO76KWLCCqLXPM0Z0qn1WA2C329E0jbS0tL6/aG8h8pVn6Hz5meAFNkBZWVnk5+dz6NAhWltb/XJMXUq2HW/jsY/sSOm/98FgCd5Ce0VRFKVPFUeclO5znvrfQkCcRSMh0YDFaiAh0UBCokZMnHbRa/MrjjhBBrdpxelS0owcAprqPWRmhyYGZWC63DqbjrYyb0QC1pjwuWWwxZq4aqyVNw81c/MkG9kJg68uNTrcvFPawuJRiWQlRObvqSzZCeCX/VVnE5oGn/8GuN1MPrqdf+csweH2hqT1fqSy2+2kpaVhNPb+tySlRH/rRQCcH3+IuPXLCGNwVxlcrGnTprF371527drF4sWLB3wcry7ZXNHGy/saqWx1kWUx0drtDavrTn+oipWiKEoYaG70YknQWHSVhelzYxkzIYq4eI3mJi+Hirsp3NLJhrfaefvfrWxZ305RoYPyUicNde7zdtzTvZKKIy7Ss4zExoXmBsiaZMBg8O3zUiLL++WtONx6yJtW9ObmSTZMmuCFvf7Za/VSSSO6lNyeb/PL8UJBluwEWxpkDg/I8YWmIb74LfLTYtER7Fv3QUDOMxR5PB7q6urOv7/q4F4oL4XLZiM722HfnuAFOEAWi4UJEyawb98+Ojsvfnmo26vz3uEWvvHGUX6/zY4mBN+fl8Xj146OuKQKVMVKURQl5KSUtDR5ycw2YUk0YEk8MwHyuCVtrV7aW720tfj+v/2Em+NHP91fEhUtSLD6XpuQaMCSqGFJNFBzwo3LKYPetOJ0mkGQnGpUnQEjjJSStaXNjE6KYlxK+HXHs0YbuWZcEq/ub+KWyTZyrAP/Ha/rcLPuSAvLcq2kx0dotcp9ss36nEUB7TgnDAbGf/5zGF8uo6SolILhiX5fejgU1dfX4/V6ycrK6vNr9LUvQWIy2t3fRf7XPcjCDxFTZwYxyoGZMWMG+/fvZ9euXSxYsKBfr3F6fAnVqweaaHR4GGuL5ssz0pg5LB4tQjsmgkqsFEVRQs7RoeN2SazJvVeUjCZfA4jklE8v2VJKnN3yVKLV1uqlrUWnsc6J3lPAEmDQIDZOIzUjtJd7W6qRg8XdOLt1oqLVYolIsK+ui+OtLlbPyQjb1tA3TrTxdmkLz+1t4EcLhw34OC+WNACCWydHbrWKw/vB2Y3ILwj4qaKjzeSlxlHSNQ597UsYVGJ1QT2NKzIyMnr9vDxyEA7u9S3/i47FPGcR3ZvXI11OhDm8G6lYrVby8vIoKSmhoKCAmJiYPr/W4fbydmkLrx1sorXby6S0GFbPyeSyjNiwvc5cDJVYKYqihFhzoxeAJFv/L8lCCKJjBNExGmmZn67B13VJZ4d+qrrV0aYzLMcU8jeslNPmWWUNj8yKwKXmrdJm4s0aC3ISQh1KnxKiDFw3PokXSxo52tTN6OSLr6zZ211sONrK1XlJpMaF936W85HFO8BohPH+abN+IfmZcbzUkEHnzqNYyksRo/KCct5IZbfbSUhIIC4urtfP62tfgjgLYuGVAETPW0r3+jegZCdMvzyYoQ5IQUEBhw4doqioiDlz5pzz+XanlzcPNfHmoWY6XDrTMuO4dbKNSWn+6V4ZLtRjQ0VRlBBrafJgMEB8wuAvyZomsCQYyBpuZnx+DAXz4sKiYURisgGDUc2zihSNDjfbK9tZlmslyhjetwo3TEgmzqzx7N6BdQh8obgBoya4ZVIEV6sAWbIL8iYjooKzbDM//eQ+q7QJyPfWBOWckUpKid1u73MZoKwsh72FiGXXIaJ91R5z/nSwJCILI6OVuc1mIzc3l6KiIpzOTxsxtXR5eGZ3HV9Zc4TnixuZlBbLb6/K4RdLhg+5pApUYqUoihJyzY1eEpMNaGEwIyhQNM23nLFBJVYR4d3DLUgJV48NnxbrfYk3G1g1PpnCqg5KL3Jo7YlWJx8ca2NlXhLJEbhRvodsqAV7ZVD3Oo1PicESZeDDCSuQO7f5YlB61dbWhsPh6LNxhXz7ZYiOQSy+9tTHhMGImHE5cm8h0tkdrFAHZebMmTidToqLi6nvdPOXHbXc89oR1hxoYtaweP64ciQ/viKbsba+lwpGOpVYKYqihJDX69sndTHLACNVSpqRjjYdZ7eaZxXO3F7Ju2UtTM+KI8MS+mpnf1w7PglLlOGiq1bPFzdgNghumpgcoMiCI5Bt1vtiMmgsGpXAJ9JGa1Q8csMbQTt3pKmurgZ6Hwwsa6qQO7YgFq1ExMWf8TlRsABcTuTewqDEOVhpaWmkDxvOtk928o3XSnnn5LDtJ64dzffnZzEyKfya4PibSqwURVFCqL3Fi67TZ+OKocTWs89KVa3C2keV7bR0e7kmDFus9yXWZOCmicnstndyoK5/g2srWpxsqWjn2nHJJEZH9oMNWbwTUjMgfeANPAZiea4Vj4QPp9+E3LwO6egI6vkjRU1NDWazmeTkcxN4+c4rYDQhll9/7gvHToDEZGTh5iBEOTjHW538bms1b3dkgMfJovhG/nx9LqvnZA5oLpz0ROb7hEqsFEVRQqi56eIbV0SqxCQDRiNqOWCYe7u0mYx4E9Oyet9kH66uyUvCGm3gX/2sWj23t55oo8YNEyK8WuV2+brJTZ4e9CY1OVZfK/711olIZxdy87qgnj9S2O12MjIy0LQzb7tlYz1y+ybEghWIhHMfZAjNgCiYB8U7kV39e2AQbOXN3Tz04QlWv1nOxyfauSI/l9T0TGIaDmOLGViaoW9+D/2/v++b5RVhVGKlKIoSQi2NHqKifR3+hjpNOznPSiVWYau8uZv99V1cnWeNuFkyUUaNWybZKK51sLfm/INKjzZ181FlB9dPSCIhKsKrxaX7wOUMSpv13izPtVLpgNLJi5Eb3ojYSkOgOJ1OGhoael8G+O6/ARArbuzz9aJgPnjcyD0fByzGgarvdHPfuxXsrXFw22Qbf101hi9NT2Pu7Jl0dHRw8ODBiz6mvu415N8fh8QkMEbGUuTTqcRKURQlhJqbvFhthpC3Qw+WlDQjHe063V1qn1U4WlvajNkgWDo6/JtW9ObKsVZsMUae3duAlLLPr3t2bwNxZo3rx0d2tQpO7q8ymiAvPyTnn5+TQLRRY/3YpdDcgNy5NSRxhKuamhrg3P1Vsq0ZuWUdYs5ihC217wOMHgfJKWG5HPClkkZ0Kfn9ypF8bmrqqYcUOTk5pKWlsWPHDnS9f9d6KSX6688iX3wSZlyO9s2fIKLCe35Xb1RipSiKEiIul05nu05S8tBfBtjDdto8KyW8dDi9fFDexsKRCVgitIpjNmjcOtnGgfoudtt7r1qVNnRRWNXBqgnJxJsj8/s8nSzeCePzQ3YTGmPSmJ9jYWt7FF1Zo5DvrTlvUnupsdvtCCHOGQws170OHg/i6lvO+3qhab4mFvv3IDvDZw9bbYeLDUdbWJ5rJT3+zMqSEIKCggJaW1spKyu74LGklMgXn0S+8Txi3lK0e36AMEbmTDmVWCmKooRIy8n9VVZb5N/c9Vei1YDRBA21KrEKNxuOtuL0yohqWtGbZblW0uL6rlo9u7cBS5SBa8dF9vcJIOvsUFsV1G6AvVkxxkq3R7J19q1w/AiUloQ0nnBit9ux2WyYzZ8mH7KzA/n+WkTBPER677OtTidmzgevB7n7o0CGelFeKmkEBLdM7n3+W25uLsnJyezYseO8ibbUvchn/oRc/zpi6XWIO1cjDJH7nqgSK0VRlBBpaTyZWCVdOhUroQlsqUZVsQozupS8XdbM+JQYRidHdktkk0Fw2+QUyhq7+aTqzCf8B+oc7LZ3ctPEZGJNkXvz1uNUm/Ugzq/qTZ4tmhGJZtaLYWBJRFcDgwHQdZ2amppzlwFuehO6uxArz1+tOiVnDKRmhM2w4Jp2FxuOtnLlmERSYnuvLPVUrRobGzl69GivXyM9buRffovcuh5x3R2I27+C0CI7NYns6BVFUSJYS5OH+AQNk/nS2F/Vw5ZmpFPtswore+yd2NvdrMyLzL1VZ1s8OpGMeBPP7W1AP+1p+b/2NmCNNkR8Va6HLNkFaZmItAtXPQJJCMHyMVZKm5wcX3Aj7C1E1pwIaUzhoLGxEbfbfUZiJbu7kOvfgKmzENmj+nUcIYSvicXBImR7a6DC7bcXSxoxCMHNk3qvVvXIy8sjISGh16qVdDrRn/gNcudWxG13o13/2SGx11glVoqiKCEgpaS50XtJ7a/qYUv1fc+q7Xr4WFvaTGK0gctHWEIdil8YNcEd+SmUNzv5qNLXsnlvTSfFtQ5umWQjyhj5tz/S5fS1WQ9RN8CzLRqZgFGD9ekFYDT59hBd4ux2OwBZWZ8mvvLDd6CzHe0Ce6vOJmYuAF1H7tzm1xgvVnWbi03lrVyVZ8XWR7Wqh6ZpFBQUUFtbS2Vl5amPS0cn+h/vh327EXfei7b8hkCHHTSRf2VRFEWJQF0OHZdTXlL7q3okWg2YTEK1XQ8TtR0udlR1cuUYKybD0LktWDgygewEM8/tbcCrS57d24AtxsiVY4dGVY7SEnC7EJOnhzoSABKijczOtvBBVTeeuUuQH20Mi+pKKNntduLi4rBYfA8spNuFfO81GD8FkTv+4g6WPRIyhiF3hHY54AslDRg1wc0Tz1+t6jF+/Hji4uIoLCwEQLa3oT/6Uzh6CHHPD9AWrAhkuEE3dK6giqIoEaS5Z39V8qWXWAlNkJxqUIlVmHi7tAUhGDoJx0mGk1WrylYXT3xcw4H6Lm6dbMM8RJJHWbwTzGbImxzqUE5ZMcZKu0vnkykrwe1Cvv92qEMKKbvdTmZm5qklbnLrBmhtQlt560UfSwjhq1qVliBbmvwdar+caHPy4bE2VuYlkRTTv9UWRqOR6dOnU1VVRfWhg+iP/BfYK9G++RO0mfMDHHHwDY2ri6IoSoRpafSiGSDBeuklVuCbZ9XZodPlUPusQsnp0Vl/pIXZ2ZY+N6FHsnk5FnKsUWw42kpanJFluUMneZQlO2HcFIQ5fGb9TMmIJS3OyLpGA+QXIDe9hXS7Qh1WSHR0dNDW1nZqf5X0eJDvvAKj8mD8lAEdU8xcAFKGbDngC8WNmDTBjRMvbv7b5MmTiY4yU/jay9DcgPbtX4TNElZ/U4mVoihKCDQ3eUi0GtC0yN+sOxCn5lmpqlVIbaloo92lD5mmFWcTLhefTWwB4LY8CybD0Ph7k7XVUGcPeTfAs2lCsDTXSlGNg/qFN0B7K3L7+6EOKyTOHgwsCzdDYx3aNbcNuEmDyBwOw3KQO4I/LPh4q5PNx9q4ZlwS1uiL2xtsrKtmau0xKqITaPzKDxDjwqfK6m8qsVIURQkyXZe0NntJsl16jSt6JFgNmMxCNbAIISklb5W2MDzRTH56bKjD8RvZUIu+6S28jz2A/t3PMfOZ+/nDJ4+y5OB7oQ7Nb061WQ/x/KreLB2diADWkwXDRyHXvYbUL73KdHV1NQaDgdTUVKSuI99+2bdPasrMQR1XzFwAhw8gm+r9E2g/vVDcQJRR48YJF1etkuVl6I/8mPz2esxGIzuq6gIUYXhQiZWiKEOWs1vn2JHwmVTfo73Vi+69tAYDn02Ik/OsVGIVMqWN3Rxp6mZlXlJEtzmWHjfy4F70l57C+/Nvov/XPchn/x/UnEAsvBLtO79kxPjRsOlNZGf4XQ8GQhbvgIxhiNSMUIdyjtQ4E9Oz4th4tBV9+SqwV8K+3aEOK+jsdjvp6ekYDAbYvR3slYirbxn035o4uS8pmE0sKlqcbK1o59pxSSRcRLVKHir2NaqIiSXmvt8w5bLLOHz4ME1NodkjFgyX7uNSRVGGvLL93ZSXtbHkGgtx8eGTxPQ0rki6BBtXnM6WZqSmyo2jUyc2Tj3nC7a1h5qJMWosGpUQ6lAummxpQpbs9DVw2L8burvAYIS8SYiFKxCTCxAZw059vZZgRd/1EXLTm4hr7whh5IMnnU44VIJYdHWoQ+nT8lwrD22uYs+waUy32tDXrcEQZssWA8nj8VBfX8+0adOQUqKvfQnSshAF8wZ9bJGWBSNyfcOCV9zoh2gv7PniBqKNGqsuolol9xai//l/ICUd7bsPIJJsXBaXwJ49e9ixYwcrVgytboA9VGKlKMqQVWf3VUPqazzEjQmfJKalyYs5ShBziScTKafts4odZQ5xNJeWlm4PW463s2JMIrGm8Pnb6IvUvVBe5kum9u6A40d8n7DaEDMXIKYUwPipiOiYXl8vho+CqbOQ699ALrseER3BSx8P7QWPO+z2V52uYFg8iVEG1h/rYMaSa5H/fgZ5/ChixOhQhxYUtbW16Lru21+1bxccP4K4azVC88/fmpi1APny08j6moBXLcubu9l2vJ3bJtuwRPUvfv2TD5F/+z1kj/I1qrD4Ht7ExsYyefJkioqKmD17NomJiYEMPSQu7Xd1RVGGrI52L50dvnX99TXhtdysudGDNdkQ0cuv/MGSqGEyq3lWobD+cCseXbIyLynUofRJdrajf/wB+pO/Q//+XegP3Yd86yUwmxE3fgHt539Ee/hvaHfei7hsTp9JVQ9t5a3Q2Y784J0gfQeB4WuzHgVjw7cBgMkgWDw6kcITHbTOWgZR0ch1r4U6rKDpGQycmZmJ/tZLkJyCmLPIb8cXBcFbDvjc3gbiTBo3jO9ftUr/8B3k/z0KuePRvv/rU0lVj2nTpiGEYNeuXYEIN+RUxUpRlCGpp1qVNTyGOnsXui7DogOf2y3paNMZNkJVaIQQ2NKMNNS5Qx3KJcWrS94ua2ZKeizDE8OnVTeAbKxHfvy+bw/RkUMgdYi3+Jo05BcgJk1DxFkGdGwxehxMvAz53hrk4mvCqk15f0kpff82E6YiTOHdHn95biJrDjSxqdbDqvnLke+vRd50JyKpf4NlI5ndbicpKYnoyqPoh/cj7vgqwui/n5ewpcHocb5Og1ff4rfjnu1IUzcfn+jgM/kpxPejWqW/+yry5adg8gy0r/0IEXXu35jFYmHChAns27ePWbNmERcXF4jQQ0ZVrBRFGZLq7G7iLRrjJiXi8Xy6rynUWpt8Cd+lOBi4NylpRrocEkdnePx8LgWFVR00ODxhV62SjXXoD/4n8tV/gMuFuOZWtB89jPbo39Hu/h7arIUDTqp6aCtvg7YW5JZ1foo6yGqqoLEOMXl6qCO5oOzEKCamxrDucCssuRZ0idz4ZqjDCjgp5anBwPraF8GSiFiw3O/nETPnQ2U5suaE34/d47m9DcSZNa4bf/5rhZQSfc0/kS8/hSiYj/bNH/eaVPWYMWMGUsohWbVSiZWiKEOOxy1prPOQlmkiKzsGIaC+JjyqIj0JnkqsfGypvoUTDbVqOWCwrC1txhZrZFZ2fKhDOUU6OtEfewBcLrSf/xHDz36PdsPnELnj/bYvBYC8STBmIvKdfyM94XFNuBjh3Ga9N8vHWKlud3GARJg+B/nhO8jurlCHFVAtLS10d3eTYdJg327E8hsCUh0VM+aDEL4mFgFQ1thFYVUHq8YnE2fu+29Q6jry+b8i33oRsWAF4p7vX7A6Z7VaycvLo6SkhK6uofX7oBIrRVGGnIY6D7oO6VlGzFEGrDZD2OyzamnyEhevYY5Sl1/w7bMyRwka68Pj5zPUnWh1UlTj4KqxVgxhsDQWQHo86H9+CGqr0L7+I1+jiQARQqBdcxs0NyA/2hSw8wSKLN4BmcMRKemhDqVfLh9hIcaose5wC9ryVeDoRG7dEOqwAqpnf1X6vk8gNg6xaGVAziOSbDBmgm85YAA8t7cBi1nj2vNUq6SUyL8/jtz4pi+B/MI3+/0gpKCgALfbTVFRkb9CDgvqnV1RlCGnttqN0QjJKb5qSFqGiZYmL05naIdUSil9jSsu4flVZ+uZZ9VQ50FKGepwhry1ZS0YNcGKMdZQhwKcvDH75//CgSLEF+5FTJga+JNOmgY5Y5Bvv4z0Rs4SVNndBWX7wrob4NmijRoLRyaw9Xg7juFjIXc8csPrvi6PQ5TdbifKZCJp9zbEkmsRMYHrQClmLgR7JbKqwq/HPdTQxc7qTlZNtJ23a6jctgG5dT1i5W2IW798UQ2ZbDYbubm5FBUV4XQ6/RF2WFCJlaIoQ4qUkjq7m5QME5rBd5FPzQiP5WbdXRJntyQpWfUNOl1KmpFuh8TRGdrEd6hzuL1sOtrKvBEWrBcx5DOQ5NqXfDdm19yGNm9pUM4phEC79jaor0EWfhiUc/rFwb3g8UTMMsAey8ck4vJKPjzW5qta1dfA7o9DHVbA2O12MnQnwhyFWHJdQM8lZswFofm9avXc3gYSogxcc559mLKpAfnC//lmx93w2QF1uZ05cyZOp5Pi4uLBhBtWVGKlKMqQ0t6q090lSc/89MbRmmTAZBYhXw7Y3HiycYWqWJ3Bdto8KyVwPihvw+HWw6Zphf7xB8g1/0TMugJxw+eCe/Ips2BYDnLty0g9MhJ6WbITomJgzMRQh3JRxiRHMyopinVHWmHabEjNQF+3JtRhBUR3dzdNTU1kVJcjrrjqnFbj/iYSkmB8PrJwi98q/gfqHOy2d3LjxGRiTL2nCVJK8dariQAAIABJREFU9H88Dl4v2l3fQmgDSyfS0tIYMWIEu3fvxu2OvD2PvVGJlaIoQ0qt3XdxTsv8dPOs0ASp6Ubqa9whXW7W0uRF0yDBqhKr08UnaERFCxpUYhUwUkrWljaTmxzFuJToUIeDLNuPfPqPMHYi4ovfCvpMN6FpiJW3gr0Sdm8P6rkHwtdmfSdMmBL2bdbPJoRgWW4iR5q6KW9xI5ZeD0cOIo8cDHVofldTUwNAZnc7YsWqoJxTFMyHumo4ftQvx3u2uIHEaMN5H8DILeugZBfi5rsQaZmDOt/MmTPp6upi3759gzpOuFCJlaIoQ0pdtZsEq4HomDMvb6kZRrq7JO2toXs63dLoIcFqwGAIj6YB4aJnn1Wj2mcVMCV1Do63uliZlxTywdSythr9id+ALR3tGz8OWaIgCuZBWhb6Wy+E/++dvRKa6iNqf9XprhiZiEkTrDvSgpi3FGLjhmTVqvroEYSUpE+dgbAObF5XbYeL9UdaqGnr7tfXi+lzwWDwy3LAfbUO9tY4uHmijWhjH9WqpnrkS3+Dcfl+acwxbNgwsrKy2LVrF94I2vPYl6Assna5XNx///14PB68Xi9z5szhtttuo66ujj/84Q90dHQwatQoVq9ejdFoxO128/jjj3P06FEsFgvf+c53SEtLA+DVV19l48aNaJrGl770JS677DIA9uzZw1NPPYWu6yxdupRVq4LzpEBRlPDhcuk0NXoZO+Hc1rapGSagi/pad0gqRlKXtDR7GTFKDQbujS3NSHWlm/bWobEcJNysLW0h3qyxICewS5MuRLa3oT/2SxAC7Vs/R8SHLh6hGRArb/VVzop3wJSZIYvlQmRxZLVZP5slysDcERY+ONbGF6elYVp4FfLdV5H1NYjUjFCH5zf20gOkdrdjvvorF/W6+k43W4+3saWinbJGX0L1j6IGfjAvi8np529+IeITYMJU5I4tyJvvGtSDk2eLG0iKNnDV2N6b20gp0f/+OOg62l2rB7wE8GwFBQW8/vrrHDx4kEmTJvnlmKESlIqVyWTi/vvv55FHHuHhhx9mz549lJaW8s9//pNrrrmGxx57jLi4ODZu3AjAxo0biYuL409/+hPXXHMN//rXvwA4ceIE27Zt43e/+x0/+clPePLJJ9F1HV3XefLJJ/nxj3/M73//e7Zu3cqJE4EbmKYoSniqr/GAPHMZYI+YWI34BC1k+6za23S8HrCqxhW9Sjm5z8peNbRmmoSDRoeb7ZXtLMu1EtXHU+hgkG4X+hO/hqYGtG/+ZNBLiPxBzL4CbGnob4Z31UqW7IRhOYjk1FCHMmDLcxPpdOl8VNmOWHItaAK54Y1Qh+U3ntYWarvdZFji+5Us1ne6ee1AE/e9e4yvrDnCU7vq0SXceVkqv142nIRoEz/fcJx3y1oueCwxcwE01kF56YDj31vTSUmtg5sn2fq8Tsgt63yzuW6+y68JcU5ODmlpaezYsQM9QvY89iUoV1ghBNHRvjXdXq8Xr9eLEIJ9+/YxZ84cABYtWkRhYSEAO3bsYNGiRQDMmTOHkpISpJQUFhZy+eWXYzKZSEtLIyMjg8OHD3P48GEyMjJIT0/HaDRy+eWXnzqWoiiXjrpqNyazIKmP4bupGSYa6z14PcG/gVKNK84vzqIRHSv4ZEsDu7Z3Umd3o+vhe6MbSd4pa0FKuLqPp9DBIHUd+dQf4chBtLu/ixgzIWSxnE4YjYirbvbdkB7cG+pweiW7HVC2HzF5eqhDGZTJ6bFkxJtYd6QVkWRDzFyI3LIO2dkRlPN3ury4vIG7aa9/Zw0ezUDWjNl9fk2jw80bB5v44bsVfGXNEf62qw63V/KFy1L58/Wj+d3VI7l5ko389Dj+cvtUpmbE8b+f1PCXwhq857keisvmgNE44GHBUkqe29tAcoyRK/uqVjXWI1980rcE8IqrB3SevgghKCgooLW1lbKyMr8eO9iC9uhU13V++MMfUlNTw5VXXkl6ejqxsbEYDL6bjOTkZJqamgBoamrCZvOtTTUYDMTGxtLe3k5TUxNjx449dczTX9Pz9T3/Hek/GEVRLo6UkroaD2mZRkQfg0/TMoyUlzpprPf0WtUKpJYmLyazIC5ebW3tjRCCOQvjsVcKjpa1U1XhxhwlGDbCRPZIM4lJhpDvDYpUWyramJoRS4YldMtQ5Zp/Igs3I266y7fZPoyIeUuRb72A/taLGIIxR+tiHdgLXg8ivyDUkQyKdrKJxT+LGqhuc5G5YhVy+ybkh+8irr45oOf++EQ7D2+uQpeQZTEzKimKkdZoRiZFMTIpCluMcVDXF+noxF5SBKmjyJw4+YzPNTrcfFTZztaKdvbX+yryo5Ki+PzUFOaNSCArofe/S0uUkZ8uyuaZ3XW8drCZyjYX980fhiXq3IdzIjYOJk33LQe89UsXvUSvqMbB/vouvlqQjtlw7mt9SwD/BFL6dQng6XJzc0lOTmbHjh3k5eVF7PU+aImVpmk88sgjdHZ28tvf/paqqqo+v7a3crwQos8yfV9f35v169ezfv16AB566CFSUlL6E37AGY3GsIlFuTD18wo/9TXduJyt5OYlk5JiOfXx039W1kSdHVvL6Wg1MTE/uD+/9tbjpGXEkJoauUt5Ai0lBcaOMzJ7gZsTFZ0cKW2n4kgn5WUuEq0mRo+zkJtnwZIQWV3RQqm+w0l1u5ubL8v2+zWrv9dBx7rXaX/7ZWKWX4/l818Nyxumzhs/T8dTj5FQX415wpRQh3OGtrISumNiSZm9AGEc+G1bOLxv3VJg4dm9DWy1u/j6vJk0TynA8/5b2O74csCamHxc0cwjW6oZmxrP7JwkDjd0crihk80V7ae+JiHaSG5KHGNSYsm1xTEmNY7RtliijP1bYdD5ylpqTDEkxMUyatQoGjtdvH+4gY1lDRRVtSGBXFss98wdweKxKeQkXXhosNFoJD0tlfuuTGXS8Foe2XiYH62r5H+un8jI5HNf37VkJW2//wWJDdWYJ17W738fKSUvbawiLd7MZ2bnYu5lGaDjvTW079+D5T/+k9gJk3s5in8sXryYV155hYaGBiZMCI+q9sUK+mL/uLg4Jk6cSFlZGQ6HA6/Xi8FgoKmpieTkZMBXcWpsbMRms+H1enE4HMTHx5/6eI/TX3P6xxsbG0lK6r1N5LJly1i2bNmp/93Q0BCIb/OipaSkhE0syoWpn1f4KT3YBQJi4rpoaPh0ivvZP6ukFAPHy9sYPT54sXk8kuYmF6kZQv3eXEBKSgrNzY3EJcCUAhMTpiRQXemmqsLF7o+b2P1xE0kpBrJzzGQNN2GOUhXA8/mgvBWA0fHS7797/bkOyv270f/8MEyahvOmL+I67b06nMgZ8+Glp2l+9q8Yvn1/qMM5RUqJvmMbTJhKY8uF99qcTzi8bwlgRlY8b5XYuWlsHNqileiPPUD9u2vQ5iz2+/n21Tr4xaZKshPM/HRBJvFRBhgbB/iWBla0ODnW4uRYs5NjLd28UdJG98ml4pqATIuZkVZfVWukNYpRSdGkxJ5Z3ZJOJ/prz1GdMwPNksZ/PL+LfbUOJDAi0cwdU1KYN8LC8MSTTZW8DhoaHBeM/fSf1+w0A79aOpwHP6zinuf38J/zspgxLP6Mr5ejx4PJTMv6N9HSsvv9b7SruoMSeztfm5lOW0vTOZ+XjXXof/sTjJ9C5/T5OAL4O5SZmUlCQgIbN24kJSUlrB7CZGVl9evrgpJYtbW1YTAYiIuLw+VyUVxczA033MCkSZPYvn078+bN4/3336egwFfmnjFjBu+//z55eXls376dSZMmnVp/+dhjj3HttdfS3NyM3W5nzJgxSCmx2+3U1dWRnJzMtm3b+Na3vhWMb01RlDBRW+0hKdlwwRvttAwj+4u66XLoxMQG56a8tckLUjWuGAiTWSMnN4qc3CgcnTpVx12cOOaieGcXJbu7SMs0kp1jJj3LpNrY96K41kG8WWNk0rmdMgNNVlWg//l/IHM42n/8cFDVlkATUdGI5TcgX/0HsuIwImdMqEPyqaqA5gbEdXeEOhK/WT4mkcKqDnZUdzBr0nTIHI58bw1y9iK/3kiXNnTxq/dPkBZn4hdLhvuSqtPEmQ1MTItlYtqn1R9dSmo73BxrdlLe0s2xZidHmrrZerz9tNdpvmTLGsXIpGg8B/by0aibSZXHOdQWDTEebs+3MW9EAiOs/vu7m5Aay6NXjeQ3H5zg1x+c4K5pqdwwPvnUv5mIjoX8AuTObcg77kFoF662SSl5dm8DqbFGluWeu7dKSon+zJ8AArYE8HSaplFQUMDGjRuprKxkxIgRAT1fIATlKtfc3MwTTzyBrutIKZk7dy4zZswgOzubP/zhDzz//POMGjWKJUuWALBkyRIef/xxVq9eTXx8PN/5zncAGD58OHPnzuV73/semqZx9913o538IX/5y1/mN7/5Dbqus3jxYoYPHx6Mb01RlDDQ3aXT2uxlfP6FB5+mZpigqJv6GjcjRgfnZrO56WTjij6aaij9ExunMXZCNGPGR9HW4uVEha+SVVvlwGiCrGwzw0aasKUObr/EUFJc62BSWixakP89ZEujr616VLSvrXrMhZc+hZpYfA3y3X/79lp948ehDgc42Q2QyG2z3puCrHiSog2sO9zK7GyLL6H9++O+5iF+2uN2rLmbX26qJDHawANLh2ON7t/triYEmRYzmRYzc0d8uqTc4T5Z3Wr2VbjKm51sONpGt6cFSGN8dAd0wVcXT2TK6GEBu/6kxpl4aEUOf9hm56ld9VS0uPjGrHRMJ/dFabMWoO/aBodK+vVvubO6k7LGbr45OwNTLw+m5AfvwIEixOe/gUhJ9/v305vx48fz8ccfU1hYqBKrvuTk5PDwww+f8/H09HQefPDBcz5uNpv53ve+1+uxbrrpJm666aZzPj59+nSmT4/sjjmKogxMnd03+ygt88KXNEuiRnSMoL7GE7TEqqXRS2ycRlS0WrbmD0IIEpOMJCYZmTglmoY6DycqXFRVujhe7iI6VpA9wsywHHNIZpaFi7oON7Udbq4b1/vS+ECR3V3of/o1dHag3fdgxLQIFzGxiCXXId98HllVgRiWE+qQfPOrskchkgY2bDYcGTTBktGJvHqgiUaHm+Q5i5Cv/gN93Wt+aR5yotXJzzdUEmXUeGDpcGyxg9+7FWsyMCE1lgmpnz4g8Lqc1L72Ku4t6ylfeCX7a43kj8oK+EOdaKPGfQuyeKG4geeLG6lqc/FfC4eRFGOEyQUQFY3csQVxgX/LnmpVeryJJaMTz/18Qy3y5adhwlTEwisD9N2cy2g0Mn36dHbs2IHD4SA2NvwfypxOvcsrihLx6uweomNEv26ihRCkppuor/Ugg9TOu7nJo9qsB4jQBKkZJqbNjmPFDYlMnxtLQqKBI4ecfPBuOx+820Z5mTOsZxQFSnFtJwD5Fxgw6k9S96L/36NQWY721R8gRuQG7dz+IJZeC1ExyLUvhzoUpKMTjhxA5A+9h8bLcq3oEjYdbUOYzIjF10DxDqS9clDHrWl38bMNlQgBv1o6gvR4/3fClB4P+ofvwk+/Tvp7z5I9YQw1bi8ZGRmnVlEFmiYEn5mSyn3zsyhv7ub77xzjaFM3IioKMXUWctc2pOf8Mxs/qergSFM3t022YTyrk67Udd8SQHFyCWCQK975+fl88YtfjLikClRipShKhNN1SX2tm7RMU78v/qkZRtwuSUuzN8DR+ZYpdjukWgYYBEajYNgIM7MXxrP8+gQmT49BCEHJrq6QDYYOpeJaBwlRBr/u87gQ+cKTUPQJ4jP3IKbMDNp5/UXEJyAWXYUs3IysrQ5tMAeKwOtFTI7sNuu9yUowMzk9lnVHWtClRCy6Gkxm5LrXBnzM+k43P9tQidur88DSEQzro435QEldR//4A/T7v4n8xxOQZEP73q/wfvl71NfXk5kZ/IHX83IS+J8VOQjgh+9VsLWiDTFzPnS0n3cuW8/cqox4E4tH9VKt+vAdOLgXceuXELa0AH4HvTMajZgC1CUy0FRipShKRGtq8OBx928ZYI+UDN/XBuNmu6XJl7wl2cJ34/5QFBWtMWpsFPOWxmMyCU5UuEIdUlBJKSmudTA5PXj7q/T1ryM3volYfgPa4muCcs5AEMtX+Yatvh3aqpUs2QkxcZAbxBamQbQ8N5GaDjcltQ6EJRExdzHyo03ItovvftjS5eHnGyrpcHn5xZIR5PjxYYKUErnnY/QHvo38v0fBHIV278/QfvQwYsJUamtrkVKGJLECGJ0czaNXjWRUUjQPb6nmOTkKPSYOWbi5z9dsP9FBebOT2/NTMJxdraqv8S0BnHgZYkHwlgAOFSqxUhQlotVVexAapKb3/+lWVJRGYpKB+hp3ACPzaW70IAQkXsJ7fULJYBBkDjdRc8KNx33pLAes6XDT4PAEbRmg3LMd+eKTMG0O4pYvBeWcgSISkxALVviG1zbWhSQGKSWyZCdi4mUIw9C8dswdbiHOpLHuiG8kgFh2A3jcyPfXXtRx2pxefr6xkkaHm58vymaM7cJNjPpLHihCf/AH6E/8BtxuxFd/gPazPyCmzjy1QqKmpgaAjIwMv533YlljjPxm2XCWjE7khf3NPFrwH3QV7US6z32P009Wq7IsZq4YmXDG5z5dAijQ7gz+EsChQCVWiqJEtFq7G1uqEaPp4t4A0jKNNDd6cbsCe7Pd0uQlwWrAYFRvUKGSPdKM1ws1VYFPpMNFca1vTk4wEitZXob+19/CyLFod38/4C2Zg0FceSMgkO/8OzQBnDgGLU2QP3S6AZ4tyqhxxagEPjreTofTi8jMhikzkZvWIl3OCx8A3yyqX26spLrNxU8WZTMhzT+/7/LIQby//Qn6734GrU2IO+9Fe+AJtJkLzvn9rq6uJjk5meho/yV0A2EyaHxrTgZfmp7Kx4Z0fjL+Lur37Dnn6z6qbKeixcnt+bZzq1UfvA2HihG33Y2wRUbTmXAT+Vc/RVEuWY5OLx1t+kUtA+yRmm5CSmioC9zNtpSSliaP2l8VYskpBmJiL63lgMW1DpKiDWT7eZ/J2WRDLfrjvwKLFe3enyCigj8vKxBEciri8iXILeuQvQxNDTRZvMMXx6Sh17jidMtzrbh1yQfH2gDQVqyCjjbk9k0XfG23R+dX75+gvLmbHy0cxtSMuEHHIyvL8f7pV+gP3QfVxxF33IP26/+HtmBFr5VDKSU1NTUhWwZ4NiEEqybY+MnCLGpjbPznITMH6j4dRqxLyfN7G8hOMLMg56xqVc8SwEnTEPOXBznyoUMlVoqiRKy6at8eqfSsi9/kmpRiwGgM7D6rjnYdjxuSVEfAkBJCMCzHTH2th+4uPdThBNzp+6sCuZRH72xHf+wBcLt9s6oSgtvWPdDE1beA7kWuWxP0c8uSnTBiNMKaHPRzB9Po5Ghyk6N473CLr3Nn3mQYkYt88W94/3A/+mv/QhYVItuaz3idy6vz3x+c4FBDF9+fl0XBsPhBxSFrqtD/8gj6A9+Gw/sRN34B7cG/oi29DnGeJgpNTU04nc6wSax6FAxP5CGxi9juDn66/jjrj/j2rW2taOd4q4s7ztpbJXUd/enHwGBAu/NetQRwEAa0m9rlcqFpGsYwnqKuKMrQV2t3ExuvERd/8c+INE1gSzdSV+NBShmQN5KWxpODgVXjipDLHmnm8AEnVcdd5I4L7ZKdQKtqd9Hc5SE/ffBP8PsiPR5aH34A6qrRvv0LRFbkDfK8EJGagZi1EPn+28irbkFYEi78Ij+Qjg44chBx1S1BOV+oLc+18ufCWg43dTPWFoP2le8h172GLC9FvvUSUp58GJKcCqPG4h2Zx/94xlPUqvHtuZnMyxn4z0U21vvmlm3bACYzYuVtiCtXIWL7l6jZ7XYAsrKyBhxDoIwomM5Df3qQ3135U/60vYaKFie7qjsZkWhmXo7ljK+Vm9ZCaQnirtURM3cuXPXr3f7vf/87l19+OWPGjGHXrl08+uijCCH4zne+Q0HB0GsDqihK+PN6JA11HnJGmwecFKVlmKit6qKzQyfe4v+qUnOjF6MJ4i1qcUCoWRIMJCYZqKpwD/nEqrgmCPurSnbi2rsD8YVvXnAQaSQTV9+C/PgD5PrXETd+Pjgn3b8HdH1Izq/qzYKRCfxtVx3rDrcy1haDyByOuPNeAKSzGyqOII+VwbEyPOVl/L5rFDvSNL5a+ipXlFahjxoLI/MQo8bCsByE8cIrGGRbM3Lty749RYBYci3i6lsQCdZ+x63rOvv27SMuLo7ExHNblofcuHwsMWZ+VvM2TxXcxesHfVW/+xZkndEpVNbZkf9+BibPQMxbFqpoh4x+JVZbtmzh9ttvB+Dll19m9erVxMbG8swzz6jESlGUkGio96B7IS1z4LMuUk9rux6IxKqlyYs12aiWVYSJ7BwT+/Z0097qxZI4dJdnFtc6sMUYybQEbg6MLNvne8I/d0nAzhEORNYImD4XuelN5EVUMgZDFu+E2HgYNS7g5woH8WYD80ZY+PBYG1+ekUa08dMHUSIqGvImIfImoUvJ/7fdzrajbXwx08nVlpHIY25kUSFs3YAEMJp8SyhH5cHIMYiReZCWearhhOzsQL77b+SGN8DjRsxbhrj29gFVaYqLi6mtrWXFihVheY0XBgNixuVo2zZwzxdXMzopisON3cwd/mm1ytcF8DEwGNG+8M2w/D4iTb8SK6fTSVRUFO3t7dTW1jJnzhwAGhoaAhqcoihKX+qq3RgMYEsb+DK7uHgDsfEa9TVuRo3176Z7r0fS1uIld/zQ2Mw/FGSNMLOvqJsTFS4mTIkJdTgBIaWkpM7BtIy4gN4kybL9mMZOQI/QIZ4XQ1t5G/rObciNbyGuvT2g55K6jty3CzFp2pBts96b5blWNpW3se14O0tG9zKwVkr+UljLxqNtfGZKCjfmpwBTT32OxjpkeRkcK/UtIdz8Hmx4w5dsxcbByLGI1AzkJ5uh24GYuQBx/WcR6QNbwtfR0cFHH33E8OHDGTcufBNgMXOBbynr3kKWzVrIstwzPy83vQWl+xBf/BYiOSU0QQ4x/bojycrKYvPmzdTU1DBlyhQA2traMJsD221IURSlN1JKau0eUtKNGAyDu3lMyzBSecyF7pVogzzW6VpbvEipBgOHk+gYjdR0I1UVLsbnRw/Jp7OVrS5au73kZwRuGaB0dsPxI5hXfY7ugJ0lfIgRo31twDe8jlx2PSLa/0m51HXYvR397ZehtRmmzvL7OcLZxLQYsiwm1h1uOSexklLy9O563i5r4aaJydw+2XbG54UQkJKOSEmHmfN9r/F6wV6JLC+FY2XIY2XI0hKYNB1t1ecQ2aMGFe/mzZvxer0sXrw4vK8jYyaCNRlZuAVmLTzjU7Ku2rcEML8AcfnSEAU49PTrHf/uu+/m6aefxmg08rWvfQ2AoqKiU0mWoihKMHW063R16ozxQzUoNcPEscMumho9pKT57+n7qcYVqtV6WMnOMbP7YwdN9d5BVTvDVVDmVx09BF4vpomXXRKJFYC28lb0h+5DfvgOYsWNfjuu9Lh9e7jeeQVqqnzL1u68F3HWTfBQJ4Rgea6VZ/bUc6LVSXbip9f2F4obWXOgiZV5Vu68LLVfiYwwGCB7JCJ7JCxYAeC3JkXHjh2jrKyM2bNnY7X2f09WKAhNQ8yYh/zgHWSXAxHjuy5IXUd/6jEwmNQSQD/r17tKSkoKv/71r8/42IIFC8jPzw9IUIqiKOdTV+2bPTWY/VU9bGlGhPDts/JnYtXc5CU6VhAdoxpXhJOMbBOGnXCiwjVEE6tO0uKMpMcHbkWJLNsHQsM0Ph8cXQE7TzgRueNhwlTke2uQi1YizIN7qCOd3cjN7yLfew2aG2D4KMRX70PMmIvQLs2HMYtHJ/LPonrWHWnlS9PTAHh1fyPPFTewZHQi9xSkDyoB8Efy4Ha7ef/990lKSmLGjMgY3ixmLkBueAO552PE3MUAyI1v+NrKf+nbiCTbBY6gXIx+veN/+9vf7vXj3/3ud/0ajKIoSn/U2j1YEjVi4waftJhMgqQUA3V2/86zamn0kpQ89G7cI53RKMgYZqK60oXXK0Mdjl/pUlJS62ByANusA8jSfTB8JFpsYM8TbrRrboPWZuTW9QM+huxsR3/jefQf3Y184UlIzUD79v1oP/sD2sz5l2xSBZAUY2RmdjybyltxeyVrS5t5enc983Ms3Ds744xOdqFSWFhIW1sbixcvjpyRQ6PHQXIqsnAzALK2GvnqP3xLAId485lQ6NdvhZTnvvk4HA40TT2JVRQluNxuSVO9h9xx/msKkZZh4mBxN85unajowV/XnN06jk6dnDFqH2o4ys4xU1Xhps7uJjN76PyMKlqctLv0gC4DlB43lB9CLLgyYOcIW3mTYcwE5DuvIBes6Fdb7x6yuRG5bg3yw3fB2Q1TZ6FdfYuvEqacsjzXyvbKDh7bbufDY23Myo7nu5dnnTHMNlQaGxvZtWsXEyZMIDs7O9Th9JsQAlEw37dHsL0N/ek/gtGEdqdaAhgI502svv71rwO+gcA9/92jo6ODefPmBS4yRVGUXtTXuJHSP8sAe6RmGDlY7FsOmD1y8DfaLU1eAFWxClMp6UaiogUnjg2txCoo+6sqjoDLhRg7KXDnCFNCCLRrbkP/4y+RH21CnNy7cz6ypgr53qvIbRtB6ohZCxFX3uTb+6OcY1pmHLYYIx8ea+OyjFh+MD8LYxgkVVJKNm3ahNlsZv78+aEO56KJWQuQ772K/sSvfcOnv/xdhFUtAQyE877rr169GiklDz74IKtXrz7jc1arNSwnTSuKMrTV2T0YTZCU4r8lM4lJBsxRgvoat58SKw8ISFSNK8KSpgmGjTBTftiJy6ljjhoaqy+Kax1kxJtIjQvg/KrD+33/MXZCwM4R1iZNh5wxvqrV5Uv7bIkuK44g335GQxKmAAAgAElEQVQZuWsbGE2IBSsQK1YhUjOCHHBkMWiCz1+Wym57J9+cnYHZEB5/m/v376e6upqlS5cSExOBoxpG5EJqBhw5CFNnIeYsCnVEQ9Z5E6uJEycC8OSTTxIVpWaxKIoSWlJK6uxu0jJMaH58iimEIDXdSH2txy+do5obvSQkaBiNoX/SqvRuWI6Jo6VOqivdjBwT+e9vXl2yr9bB5SMsF/7iQZBl+yF9GCIhKaDnCVenqlb/+9/IHVsQs6849TkpJZSWoK99GfbvhphYxFU3I5Zdd8n+ew3EktGJvc6yChWHw8HWrVvJyso6dV8caYQQiHnLkBveQPv8N9QSwADq1zoVg8HA+vXrOXbsGN3dZzZXvffeewMSmKIo4aniiJOjpU7mLYkP+pP+1mYvzm7p12WAPVIzTFQdd9PWopOYNPBKk5SSliYvmdlDf3BqJEtMMhCfoFFV4RoSiVV5s5NOd4D3V+k6lO1HTJ8bsHNEhKmzIGsE8q0XkTMX+D62t9A3g+roIbAkIm66E3HF1YhLrMHHULRlyxZcLlf4z6y6ALHyVsSKGxGXwFDvUOpXYvX4449TUVHBjBkzSEwMn6cIiqIEl5SSo4ecdLTr7C/q5rJZAdzL0Yuezn1pmf7fu5Sa4TtmfY17UIlVZ4eO2yXV/KowJ4QgO8fMweJuHB1eYuMj++dVXNsJQH5GAG/k7ZXg6ICxkfnU3l+EpiFW3or8v0eRLz6JPFAE1cd9Q2o/9zXE5UsH3Y5dCQ+VlZUcPHiQgoICbLbI3pMkhACVVAVcv+5OioqKePzxx4mLU09eFOVS1tLopaNdx5KgUVnuYliOidT04F2o6+xurMkGv3TuO1t0jIYlUaO+xsOYQWwfaWk82bjCphpXhLthJxOrExVu8iZFemLlYFiCmeSYwP3eybJ9AJdk44qziZnzka8/h9zwBgzLQXzl+4iC+X3uuVIij8fjYdOmTSQkJDBr1qxQh6NEiH7dnaSkpOB2uwMdi6IoYe54uQuDAeYujicuXmNvYRceT3BmATmdOs2N3oAsA+yRlmGiqcEzqO+ppcmDwQiWhPDYdK30LTZOIznVwIkKV69jRSKFV5fsr+sKbDdAgLL9YE2GlPTAnicCCM2Adu9P0b77S7T7H0ObfYVKqoaYnTt30tLSElkzq5SQ69c7/8KFC3nkkUfYsmULJSUlZ/yfoiiXBo/n/2fvvsPjrM7E73/PM0UjzUia0ah3y5Is23LFBTAuGMewhCWGkBBSdiF92d2wJJtk80vZkA1JNvuSEAJhC2VTliSQ0EISDMQVY8C2XGTLtiRbsiSr9zJ9nvP+MVjYuI0tPZqRdD7X5QszGs255dHMPPcp9y1pbQqQU2AhwaYxf2kSnhGd2oO+i3/zOOh6ZxtgVq5xH3AZ2WZ0HXo6L79ZcF9PGKfLhIiDEsHKxeUXWRkZ0hnoC8c6lMtW3+vDGzL4fJWUyLoaRNncSX3OZDyJnHzEnEXq32MK6u/vZ/fu3ZSVlVFUVBTrcJRJJKorlJdffhmAX//612fcLoTg4YcfHv+oFEWJO+0tQUIhKJgRKUeenmmmsMTKsVo/uYUWnAb3bOpsC5JgE2M6/3QxaRlmNFPknFVW7qWvjIXDksH+MDPK1fmKySKnwMLBKi8tjQHDf4eNcqp/VaWRK1Y9ndDXPe3PVylT36meVSaTiVWrVsU6HGWSiepT5JFHHjE6DkVR4lxzQ4Aku4Y74923jTkLbHS0Btm/y8PK9yWPawn00+m6pLM9RHauxdDZYZNJ4M4w09l+eStWg/1hdB1VuGISsVo1snIjFSHnLJSG/Q4bqbrDQ2GqFafNwPNVtafOV6nESpnajh49SnNzM2vWrFG1BZRLpg4BKIpyUZ7hMN2dIQpmWM9IbCxWjXlXJDLYr3PsqN+w8ft7wgQDkkwDtwGekpltZmRIxzOiX/L39veqwhWTUV6RhYBf0tVx+VtAYyUYlhzu9Bh/vqq+BpLskKu2RSlTl8/nY/v27WRlZVFZWRnrcJRJKKpPf4/HwzPPPENNTQ1DQ0NnHPJ99NFHDQtOUZT40NwYACC/2HrW13LyreTkB6k96CMn34IjefxXazraggjBhFQgzMixwD4fXe1BimZe2pa+vp4QCTaBLXHyrXpMZ5k5FixWwcnGAFkGFkcxQn2PF39YMi/L2Jl1WXcIZs5GaGo+Vpm63njjDXw+Hxs2bEBTv+vKZYjqt+axxx6joaGB2267jeHhYT75yU+Snp7O+9//fqPjUxQlxqSUNDcESM8yk2Q/91tG5eJETCbBgV0eQ6qrdbYFSUs3YbEan7A4kjVsiYKuy9gO2N8TxuU2q8Psk4zJJMgtsNB2MkgoOLmqA1Z3eBDAXCMLVwz2Q/tJVWZdmdLa2to4ePAgCxcuJCMjI9bhKJNUVInVgQMH+NKXvsTSpUvRNI2lS5dy7733sn37dqPjUxQlxro7Q3g9ksIZZ69WnWJL1Jiz0EZPV5im44FxHd/r0Rns18m8jGISl0MIQWa2ha6OILoe/UV2wK8zMqzjdKvzVZNRXpEVPQxtJydXa5HqDg/FrgRSEgz8vauvAUCUq8RKmZrC4TCbNm3C4XCwfPnyWIejTGJRJVZSSpKSIrNhNpuNkZERnE4n7e3thganKErsNTcEMFsgO+/CiU3BDCvuTDM1+734vJd+Pul8OtsiF7oTuUUrI8dMKPjumalojJ6vUoUrJqW0dBOJdo2TJ8Z3YsBIwbDOkW6vsdUAAVlXA1YrFM00dBxFiZV9+/bR09PDmjVrsFrPP4moKBcTVWJVVFRETU1kxqqiooLHH3+cxx57jJycHEODUxQltoIBSVtLkLxCKybzhbe3CSFYsCQRXYfqPd5x2xLY2RYiMUngmMCGu+mZZhCRsuvROpVYpU7Skt3TnRCC/CILXR2hcZ0YMNLRbh+BsDS8cIWsq4EZsxDmyXX+TFGiMTg4yFtvvUVJSQklJSWxDkeZ5KK6Uvnc5z43ut/0k5/8JFarlZGREf7hH/7B0OAURYmtk00B9DAX3AZ4OnuyiVlzbbSfDNLWMvYtVeGwpKsjSGaOsWXW38uaoOF0mS7pnFVfTwhHiobFos5XTVZ5RVaQkd/7yaC6YwRNwNxMA89X+TzQdFyVWVemJCklW7ZsQQjB6tWrYx2OMgVcNLHSdZ0tW7bgdrsBSElJ4fOf/zz33nsv+fn5hgeoKErsNDcESE7RSL2E7W0lsxJIcZo4WOUlEBjbzH9vV4hwiMtq1jtWmTlm+nrDUf0MUkr6e8OqzPokl5xiItVloqVxcpyzqu7wMMNlw2E1cPvpsaMgdZVYKVPSsWPHaGxs5MorryQ5OTnW4ShTwEUTK03T2LhxIyaTOjegKNPJ0ECY/t4wBSXWS1ot0jTBwmWJBPySw/t8Y4qhoy2EpoE7c+ITloxsC0jojqK3kXdEJ+CXqjHwFJBfbGWwP8zQQPTn62LBH9I52u2bgG2Ah0DToKTC0HEUZaL5/X62bt1Keno6CxYsiHU4yhQR1VbA1atX8+qrrxodi6IocaS5IYAQkF906Qd5U11mZs5KoKkhQHfH5c/+d7YFcWeaMV/kfJcRnGkmzBai2g7YN9oYWCVWk11eoQUhoCXOi1gc6fYS0ifofFVBCcKWaOg4ijLR3nzzTUZGRli7dq3qWaWMm6imgevr63n55Zd58cUXcbvdZ8xe33fffYYFpyhKbOi6pOVEgMxcMwm2y/vAKZ9ro60lyP7dXlZff+nJ0chQmJEhnRlll9akd7xomiA9y0JnexAp5QVX7fp7wmgmSE5VidVkl2DTyMg203IiQMU8W9z2JKtu96AJmJNpXMIjg0E4fhSx5kbDxlCUWOjs7OTAgQPMnz+f7OzsWIejTCFRJVbXXXcd1113ndGxKIoSJzrbQvh9ksIZl5/UmMyC+UsS2bllhNpDPuYsuLQLwI62yEpRZk7szi1lZptpbwkyPKSTnHL+pKmvJ0Sqy4SmxedFuHJp8oqsdL7poacrHKkQGYeqOzyUptlIshiYzJ+og1BQna9SphRd19m0aRNJSUlcddVVsQ5HmWKi+sRYs2aNwWEoihJPmhsCWBPEmJOa9CwLhSVWjh31k1tgwXkJpcg724I4kjXsjtitAmVkR+LtagueN7HSdclAf5jimbFZWVPGX3aeBZMZTp4IxGVi5Q3q1PV42TA7zdBxZF2kzQoqsVKmkAMHDtDZ2ckNN9xAQoJ631bGV1SfGJs2bTrn7RaLBbfbTVlZGRaL6m+hKFOB36fT0RpkRnnCuKzAzFlgo6M1yP5dXla+zxHVY4ZCkp7OEMWlsf3QS7KbsCdrdHWEKJl17vsM9ofRw+BU56umDLNZkJNvobU5QOXiREym+FqJPNzlISxhXrbd0HFkXQ1k5yOSUw0dR1EmyvDwMDt37qSoqIiysrJYh6NMQVElVtu2baO2tpbU1FTcbjc9PT0MDAwwc+ZMOjs7AfjKV77CzJmqK7uiTHYtJwJIGX3vqouxWDXmXZHI7h0ejh/1UzrbdtHv6e4IoeuQlRv71YLMbDMnjgcIh+U5L7D7VeGKKSm/yEpLY5CO1iC5BePzWhgv1R0ezBrMzjDwfJUehvrDiCUrDBtDUSba1q1b0XWdNWvWxO35SWVyi+qqJT8/n2XLlnHjje8eYH355Zc5efIk3/nOd3j22Wd54oknuP/++w0LVFEU40kpaW4I4EwzjWshhpx8K9n5QY4e8pGdb8GRfOHH7mwLYjZDWnrsE6uMbAsNdQF6u0KREuzv0d8TxpogSExSVaWmkvRMMwk2QcuJQFwmVmXuRGxmA3/nTjaBdwTK5xo3hqJMoIaGBo4dO8ZVV11FaqpahVWMEdW78o4dO7jhhhvOuG39+vW8/vrrCCG4+eabaWlpMSRARVEmzkBfmKEBnYJxWq063bzFiWgaHNjlQUp53vtJKeloDZKebUGLgy1Y7kwzQjt/2fW+3hAut0nNfk4xQhPkFVrpbAsR8I+t0fV48gTDHOudoP5VgChTiZUy+YVCIbZs2UJaWhqLFy+OdTjKFBZVYpWamsqePXvOuK2qqoqUlBQAgsEgZnPsZ5aVdw0Phnn9tSF6uy7eg0dRTmk6HkAzQV7h+CdWtkSNOQsS6ekK03T8/D2ChgZ0fF5JVgyrAZ7ObBakpZvpaj+7H1cwKBke1C+pKIcyeeQXW5A6tDZffi+28VbT6UWXGJ5YUVcDaekId6ax4yjKBDhy5AhDQ0OsWrUKk0lt21aME9XVwF133cWPfvQjCgsLR89YNTU18cUvfhGAurq6s1a0lNgJ+HXe3j7CyLBOdZWHVeuT1Wy6clHhsKS1KUhOngWL1Zjfl8ISKyebgtTs95KVa8GWePbcTmdb5CI2Myd+CuJkZps5fMCHz6ufEXN/b2TiQhWumJpSnCaSUzRaTgRiXkjllMj5KsGsdAPPV0mJrKtBzJpn2BiKMlGklOzdu5eMjAwKCgpiHY4yxUW1YrVgwQJ++tOf8r73vY/i4mLWrVvHww8/zIIFC0a//qEPfcjQQJXo6Lpkz04PXo9OSXkCg/06LY3xM9uqxK/2k0GCQUlBiXHnSYSI9LbSdaje4z3nfTragqQ4TedMumJltOz6e7YD9vdEClc401RiNRUJIcgrttLXHWZkOBzrcACo7hihIt1GgpHnq7raYaBXlVlXpoSGhgb6+vpYvHixmmRWDBf1O3NKSgqrVq1iw4YNrF69muTkZCPjUi7TwSov3R0h5i9JYs5CG6kuE0cOegmHzn+mRVEgsg0wMUkY3rfHkWxi1lwb7SeDtDafuSUwENDp6w7HRTXA06U4TSTYxFnbAft6Q9iTNazW+EkClfF1alvsyROxn6Aa9oc53utnXtYElFlHna9SpoaqqiqSk5MpLS2NdSjKNHDeq5f777+fr3/96wB861vfOm+Wf9999xkTmXLJGur8nDgWYGZFwmjxgTkLbOzcMkJDXXRlrpXpyTOi090RonxuwoTM6JXMSuBkU5CDVV7Ss8yjiUlXewgp42sbIERWLtKzzO/EJxFCIKWkvydMRlZ8JYHK+Eqya7gzTLScCFA2Z2JeH+dzqNODZCLOVx0EezLk5Bs7jqIYrL29ndbWVlauXKnOVikT4rxXBKtXrx79+9q1ayckGOXydbUHObTXS1aumdnz3k2g0rMsZOaYqTvso7DEijVBzawrZ2tpjKwcFRRPTFlpTRMsWJrI668Nc3ifjwXLIheKnW1BLFaBKw631mVmWzh5IshAXxhnmhmfV+L3SZxulVhNdfnFVvbv8jLQG47p813d4cFqEpSnGztJJutqoHQ2QlOfF8rktnfvXqxWK3PnqtVXZWKc9xPimmuuGf37mjVrJiIW5TIND4XZ84YHR4rG4ivtCO3MGdU5CxLZsnGI2ho/lYuMO/CsTE6nele5M80kOSYuoXGmmSmZlcCxI37yiiy4M810toXIzDGf9TscD04/Z+VMM9PXEzlvFY9JoDK+cvItVO/x0nIiEPPEqiIjEYvJuIRHDvRBZxtilSpIpUxuAwMD1NfXs3jxYqzW+OpFp0xdUX9CHD58mIaGBnw+3xm333rrreMelBK9QCBSAVBosGylHbPl7AvS5FQThTOsNNb7mVFmxT6BF89K/OvpCuMZ0SmvnPitouVzbbS1BNm/28uCpYkE/DLutgGekmDTSHGa6GwPUjbHRn9vGE2LnL9SpjaLVSMr18LJpiBzFkq0GCT+g74Qjf1+PrYg3diBRvtXqcIVyuS2b98+hBCjhdYUZSJElVg98cQT7Ny5k4qKijOyflVdJbZ0XbLnDQ+eEZ2r1jhIsp//Am9WpY2TJwIcOeDjiquNPfisTC7NDX7M5sis/EQzmwULliSyc8sIVTs9ICKlzeNVZraZY0f9hIKSvp4QKU5TXDQxVi5MhkKIMfZazC+20tYSpKs9RFbuxL9WDnZ6AOPPV8m6GrAmQOFMQ8dRFCP5fD5qamqYNWsWDocj1uEo00hUnzTbt2/ngQceIC0tzeh4lEtwaG+kAuDCZYm4My78VNoSNUpmJVBX46ekJ4RLnQtRgFBQ0tYcJK/IitkcmwQhPctC4QwrTQ0BXG5TXJ8DzMg2U3/ET1dHkIHeMIUGlqZXxofc/zb6oz9A3HAr4qaPXHaClZltxmIVnDwRiEliVd3hwWYWlLmN3c4t6w5ByawxJ6KKEkvV1dUEg0EWLVoU61CUaSaqK5j09HQslvjcnjNdNdb7aawPMHNWAgUzomtcWVphw5ogqNnvRUpVfl2B1uYA4TCjVSRjZfZCG45kLeZxXIwr3YzJDMdr/YTDkXNiSnzTX34WTBryj0+j/+AryLaWy3oczSTILbDQdjJIKDjx75/VHR5mZyRhNnAbovSMQEuj2gaoTGqhUIj9+/dTWFhIerrBW2cV5T2iSqw+//nP81//9V/s3LmTmpqaM/4oE6+rI1KmOivXzOz50Z+LMVsEsypt9HaF6WgNXfwblCmvqSGAI1nD5Y7tOSGrVePaG1MomhndJEGsmEwCd4aZ3q5Is9hY/7spFyabjkF9DWLDJ9A+/y/Q04H+b/+E/peXkLp+yY+XX2xFD0Nby8T2tOr3hmgeCBhfZv3YEZBS9a9SJrWjR4/i8XhYvHhxrENRpqGopluPHz/O3r17OXz48FmVVR599FFDAlPObbQCYLLGonNUALyYwhIrx2v9HD7gJTPHHJND2Ep8GB4M09cdZvZ8mzoveQkysy10toWwWAVJjvjdtqiA3PQSWBMQK65DJDnQZlag/+Jh5G/+G3ngbbQ770G43FE/nsttIsmu0XIiMKGrq9UdE3W+6hCYTFBSYeg4imIUKSVVVVWkp6dTUFAQ63CUaSiqxOrXv/41X/3qV5k/f77R8SgXMFoBUEQqAFrOUQHwYjRNMHu+jd07PDQ3BOJ+hUAxTnNjACEis/BK9DJyzLAXnGkmlZDGMTk0iHxrG+KadYikyOF14UxD+8dvIrdtRD79OPq3/xHx8b9DW7oyqscUQpBXZKGuxo/Pq2NLnJjEurrDQ6JZY2baBPSvKpyJSFCfC8rkdOLECfr6+li/fr16f1ZiIqpPhYSEBObMUXuuY+n0CoBLVtjH1G8oO8+CK93E0YO+mJwVUGJP1yUtjQEyc8wTdnE4VdgdGtn5FvKLVEIaz+Trr0AoiLj2/WfcLoRAW30D2rd+Alm5yP/+D/T/eQA5MhzV456aiDh5IjDuMZ9PdYeHuZmJmIw8XxUMQGOt2gaoTGpVVVU4HA7KyspiHYoyTUV1RXX77bfzv//7v/T396Pr+hl/lIlxqgLg/CsuXgHwYoQQzFmQiN8nOV7rH6cIlcmkqz2EzyvjvlhEPBJCsHSFXa30xTEZDiO3/AlmL0DkFp7zPiIrF+2r/474wEeRu7ej3/cF5OH9F31sR7IJZ5qJlhMTc86qxxOkdSjAvGyDz1c11EIopApXKJNWZ2cnLS0tLFy4EJNJnX9VYiOqK/RT56heffXVs77229/+dnwjUs5yqgJgyawECkvGZ4tGWrqZnHwL9Ud8FJZY1arFNNPcEMCaIMiK02a8ijIm+96C3m60Oz57wbsJkwlx00eQlVegP/4j9B99E3HdXyNu/RuE9fzvtflFVg7u9TLYHza8QfS756uM7T8o694pRlU629BxFMUoVVVVWK1W5s5Vq65K7ESVWD388MNGx6GcR/c7FQAzc8zMuYQKgNGomG+j/WSQ2kM+5i8xeDZUiRt+v057a5Di0gTV3FaZkvRNL4E7E+Yvjer+orgM7RsPIp/9OfIvf0DW7EP71BcRRedukptbaOHQPi8nTwRIcRrbV6q6w4PdqlHsNPbck6w7BLmFCEeKoeMoihEGBwepq6tj0aJFJKgzgkoMRZVYZWRkGB2Hcg7DQ2F2v1MBcPFVl14B8GIcySaKZlo5cSzAjPIEklPU0vl0cPJEEKlDodoGqExBsqUBag8ibrsLoUX/niYSEhB3fBY5fyn6//4E/fv/jPjrOxA3fBDxnm1FCTYNV7qJni7j21Yc7PBQmZlk7PkqPQzHjiCWrzZsDEUx0r59+xBCsGDBgliHokxzUSVWHo+HP/3pTzQ2NuLz+c742je+8Q1DApvugu9UAARYepkVAKNRPtdGS2OAwwe8LLvGYcgYSnxpbgiQ6jIZvoVJUWJBbvojWK2Ia9Zd1veLuYvQvv1T5K8eRT7/K2T1brRP3ovIzDnjfqlOE00NAaSUhlUf6xoJ0j4c5KZZLkMef1RzI/i8oApXKJOQ3+/n0KFDlJWVkZycHOtwlGkuqsTqRz/6Ebqus2zZsrP6WCnjT9cle3ZGKgBetdqBfQwVAC8mwaZROtvGkWofPV2hMRfGUOLbQF+Iwf4wlYuN3b6kKLEgR4aQb21BLF+DsF/+BZawJ8NnvwwLlyP/7z/Rv3MP4sOfQqx8t4RzitNEOASeYR17sjHv0RPavwpU4QplUjp48CDBYFA1BFbiQlRX0XV1dTz++OOYzeqieyLU7PPS1R5iwdJE3JnG/5vPKE+gsd5PzT4v16xzqN4PU1hzQwBNg7xCVbRCmXrk669CIIBYe9OYH0sIgVi+Glk2B/1/H0L+8hHk/rfR/vYfECkuUlIjydTgQNjAxGqE5AQThYafr6oBdyYiTW37VyaXcDjMvn37KCgoUMdWlLgQVSm4iooKTp48aXQsCnDimJ+GugAl5eNXAfBizGbBrEob/b1h2lompoSwMvHCYUnLiSDZeRasCaoKpDK1SD2M3PwnKK9E5BeP2+OKtAy0f7oPcfunoWYf+re/gNz3JsmpJhAw2B8et7FOJ6Wkuj1yvkozcLJLSgl1h9RqlTIp1dbWMjIyolarlLgR1XLI3Xffzfe//31KS0txOp1nfO22224zJLDpqLsjSPWedyoALhjfCoAXU1Bs5Xitn8MHfGTnWlS1uCmoozVIMKB6VylT1IFd0NOJ9qFPjvtDC01DrLsZOWdhpCz7I99DrFiHI+NvGDAoseoYDtLlCXHLHIMrtna0wtAAqMRKmWSklFRVVeF2uyksPHe/OkWZaFFNW//617+mp6eHgYEB2traRv+0t7cbHd+0MdgfYPcbHuzJGouvHP8KgBcjtEjTYM+wTuOxwISOrUyM5oYAtkRBRpba0qtMPfqmP0JaOixcbtgYIrcQ7Wv/gbjxw8g3NpHcXcdQv27IWKPnqwxuDPzu+apKQ8dRlPHW1NRET08PixcvVkcYlLgR1RXWG2+8wU9+8hNcLoMrE01TwYDOtlfaAFi20o7FGps3iIxsM+lZZmoP+SgotsYsDmX8eT06ne0hymYnTHjSrihGk61NcHh/pLGvydhql8JsQdzycXSLmeT9+2izlREMynGv3Frd4cFpM1GQYvAKc10NJKdCdp6x4yjKOKuqqsJut1NeXh7rUBRlVFQrVllZWZgM/rCazva+7WFwIMiSFXZDKwBejBCC2fNtBAOS+iO+i3+DMmm0NAZARrZ8KspUIze9BGYL4pr1EzamWHMjyb7Iro2hcd4OKKWkusNDZVaS4TPxsu4QlM5WM/7KpNLV1UVzczMLFy5U16dKXIlqxWrlypX88Ic/5IYbbjjrjFVlpdo+MFalFTZmzbaT6vbHOhScaWbyiiwcr/VTXJpAYpIqcjDZSSlpbgiQlmEyrHqZosSK9Awjd25GLF+FSE6ZsHGFI4XUOcUADLQOkpaRNm6P3ToUpNcbMr7Mel8PdHeMSxVFRZlIVVVVWCwWdQ2qxJ2oEquNGzcCkbNWpxNC8PDDD49/VNNMWrqZ9PRkurtjn1gBVMxLpK05yNFqHwuXG3xwWjFcX3eYkWGdsjmqd5Uy9cgdf4GAPybJQeJ178P8ygiDRzthwfglVtUdkebw87Ls4/aY56L6VymT0dDQEHV1dcyfP5+EhImpnqwo0YoqsXrkkUeMjkOJI0l2jRllCRw76mdGeRu5sdsAACAASURBVAKpLrXKMZk1NQQwmSEnX20DVKYWqevIzX+MbGUrnDnh42uZ2aSIGgYHJNLnQdjGZyKqusNDWqKZ3GSD+83V1UBCIhSUGDuOooyj/fv3I6Vk4cKFsQ5FUc6i9nkp51Q6JwGLVXD4gDfWoShjEApJWpsD5BZYMY/z4XpFibmDe6CrHbH2r2MWQkqek6GkXPStr4zL40kpOdjhYd5Ena+aOcvwgh+KMl78fj/V1dWUlZWRkjJxW38VJVoXXLH61re+ddE39vvuu29cA1Lig9WqUTYngZp9Prrag2RkGzxzqhiirTlIOITqXTWN6f/9H5BfjHbjh2IdyrjTN70ETjdi0ZUxiyG10E1jh5eRHW+QfN1NCPPY2hk0Dwbo94WNP181MgytTYglKwwdR1HG06FDhwgGg6ohsBK3LvgJsHbt2omKQ4lDxaUJNNQFqNnvY1WWWVWNmoSaGvzYHRpp6WpGejqSPZ3IXdth13ZkVi7iiqlzES3bW+DQXsQHPjbmZGYsUlIjr62hkAPH7tcRV64Z0+NVt7/Tv8rgxIr6wyAlomyuseMoyjgJh8Ps3buX/Px8MjMzYx2OopzTBT+N1qxZM0FhKPHIZBLMnmej6k0PLSeCqlT3JBMI6PR2hSmfa1NJ8TQlD+yO/CU7D/3Jn6BlFyDyCmMb1DiRm/4IZjNi1fUxjSP5ncRqMKeS7I3PIZevHtPrrbrDQ0aSmSyHsbsEZN0hMJlhhuoBpEwOdXV1jIyMcN1118U6FEU5L3XGSrmg3EILqS4TR6q9hMMy1uEol2CwL9JbR61WTV+yejdkZKN96btgS0T/2feQnuFYhzVm0utBvrEJsWQlIsV58W8wkMkscCRrDBUugpYGOLz/sh9Ll5KDnR7mZU/A+ar6GiguRVhVVTUl/kkpqaqqIi0tjaKioliHoyjnpRIr5YKEEMxZYMPnkTTUxkc5eCU6A+8kVimqquO0JP1+OHIAMW8JwulG+/xXoacD/bEfIXU91uGNiXxjE/i9cdN/KcVpYkhLg1QX+sbnLvn7g2HJ4S4P/7e/myF/2Pgy6wE/NNYjSlWZdWVyaG5upru7m0WLFqkdGEpci93GdGXSSM+ykJljpu6wj8ISK9YElY9PBgN9YWxJggT1fE1PRw9AMICYtwQAUToH8ZHPIP/vP5F/+A3iAx+NcYCXZ7TEesksxIyyWIcDRBKr1uYgoWs/gPn5/0W2NCDyZ5z3/iFdUt/j42CHh+pOD4c7Pfjf2RFQkZ7I0jyHsQE31EI4pM5XKZNGVVUVSUlJzJo1K9ahKMoFnfeK6+tf//ro35955pkJCUaJX3MWJBIKQW2NWrWaLAb6wqQ61WrVdCWrd4M1AWZVjt4mVv8VYsV1yJd+g9z3ZgyjG4OavdBxMm5WqyCSWAEMz38fJNiQG58/4+shXXK028vvDvbwr5ua+dgztXz1lRP8cn8Xfd4Q60qd/MvKPH75wVL+/foikhOMfd3KukMgBJTONnQcRRkP3d3dNDU1sWDBAswxLFSjKNE4729oa2srgUAAq9XKSy+9xIc+NPVK9SrRS041UTjDSmO9nxllVuwOdcEez0IhyfCQTm6hKpM/HUkpkdV7YPYChOXdojNCCPjY3yFbTqA//mO0//cAIic/hpFeOn3THyHVhbji6liHMupUYjXks+JauZ7glj/TsOZ2DvoSONjh4XCXB18osiJVlJrAdSWpzMuyMzczkRTbxF8oytpDkFeEsBu8MqYo42Dv3r1YLBbmzZsX61AU5aLO+46+dOlS7rnnHjIzMwkEAvzrv/7rOe+n+lhNH7MqbZw8EeBotY/FVxl7BkAZm8H+yPmqVJea3ZuWWpuhpxNx421nfUlYrGh3fw39u19E/9n9keQq0eDS3uNEdrbCwT2Im25HmONn0sCSAJoZ9jWM8HPntdRcuRTfm4MAFKZaWVuSyrysJOZmJpEag0TqdDIchuNHEVerdipK/BseHubo0aPMmzcPm80W63AU5aLO+w5/9913c+TIETo7O6mvr+faa6+dyLiUOGRL1CiYYaWpIYCuSzRNHSCNV6cKV6SqwhXTkqzeBYCoXEJvby8mk4nU1NTRr4u0DLTPfRX9R99Af+JBtL/7F4QW/2fx5OY/gaYhVt0Q61BoGvCzu2WY6g4PNV1erpNOtF7odgiulW3MrX2Lyn+6F5crJdahnqnpOPh9oM5XKZPA/v37kVKycOHCWIeiKFG54NRZRUUFFRUVhEIh1dNKASAtw0xjfYDB/jDONLUaEq8G+8JYEwS2RJX8TkeyejfkzyCc4uSZxx/H7/dTVFTEvHnzKC4uRtM0xKxKxIc/hfzN/yD/9AzipttjHfYFSZ8XueM1xBXXIJxpsYnhnXLoz9X0sqd1BICCVCvXzkih2Gsl2AWfev8MaJLoO36GePs1uP7WmMR6PrLuEACiTFUEVOKb3++nurqa0tLSMyaGFCWeRXVlvHbtWg4ePMi2bdvo6+vD5XKxatUqKisrL/7NRA4ePvLII/T39yOEYN26ddx4440MDw/z4x//mK6uLjIyMrj33ntxOBxIKXnyySfZu3cvCQkJ3H333ZSUlACwZcsWnn32WQBuvfXW0YTv+PHjPPLIIwQCARYtWsRdd92lSnIawOWOrID096jEKp7194VJcZrUa2AakiPDUH8YccMHaTxxAr/fz6xZs2hpaeGll14iOTmZyspK5s6dS+Lam6CxHvniU8jCEsT8pbEO/7zkzs3g9SDWvn/Cxw7rkrdahni2ppe6Hh+pCSY+tiCd98104kqMvA82Hfezv92LZ1jHXjQTKuYjX/sD8rq/jqtti7KuBjKyEU53rENRlAuqqakhEAiwePHiWIeiKFGLau/HX/7yFx588EGcTifLli3D5XLxk5/8hNdeey2qQUwmE5/4xCf48Y9/zP3338/GjRtpaWnh+eefZ968eTz00EPMmzeP55+PVFLau3cv7e3tPPTQQ3z2s5/lscceAyJ7bX/3u9/xve99j+9973v87ne/Y3g40uzyf/7nf/jc5z7HQw89RHt7O/v27bucfw/lIhKTNKwJgr7eUKxDUc5DD0uGBsNqG+A0JWv2gq4j5i2hvr4em83GunXruPPOO7nxxhtxOp3s3LmTJ554go0bN9K27hZkwYxIf6uO1liHf05SykiJ9aJSKJm4csuBsM7Gun7+4aXj/Pv2Vob8YT6/NIv/2TCTD1emjyZVACmpkdfb4EBkG652/S3Q34N8e/uExXsxUkqor1H9q5S4Fw6H2bdvH7m5uWRlZcU6HEWJWlRLDi+++CLf+MY3KC4uHr3t6quv5oEHHmDdunUX/X6Xy4XL5QIgMTGRvLw8ent72bVrF9/+9rcBWL16Nd/+9rf5+Mc/zu7du1m1ahVCCMrLyxkZGaGvr49Dhw4xf/58HI5IJaP58+ezb98+5s6di9frpby8HIBVq1axa9cuFi1adCn/FkoUhBC43Cb6esKxDkU5j6HBMFJX56umrQO7wZFMqLCE469uoby8HJMp8rtQWlpKaWkpfX19VFdXU1NTQ21tLe6iRVQG91H+s+9h+9oPEbY4K2ZxeD+0NSPu+qcJWYUdDoR5ubafPxztpd8XZmaaja9ck8GVBcmYznO21JFqAhEpHJOTD8xdDHlFyFeeQ151bXysHre3wPAgqG2ASpyrr69naGiI1atXxzoURbkkUSVWQ0ND5OefWZI3Nzd3dLXoUnR2dtLQ0EBpaSkDAwOjCZfL5WJwMFJFqbe3l/T09NHvcbvd9Pb20tvbi9v97vaFtLS0c95+6v6KMVxuMx2tPgIBHas1/g+8TzeqcMX0JfUw8uAexNzFNDW3EAwGKSs7u4nuqe3cV111FbW1tRw4cICt6TN4Ixxi1mOPMv8jf3vGe3Cs6ZteguRUxNKVho7T4wny4pE+Xq7rxxfSWZRj59Y5aczLSrpoYmQ2C+wOjcF+HYhMQon1G5BP/gQO7YXK2G9nevd8lSpcocQvKSVVVVW4XC5mzDh/o21FiUdRJVYVFRX84he/4GMf+xgJCQn4fD6eeuqp0RWiaPl8Ph544AHuvPNOkpLOPyMqpTzrtvN9qAkhznn/83nttddGtzD+4Ac/iJuLB7PZHDexXExghocj1a3IkJ303OlZdj2en6/6mi7MFh9FxZnxMUseY/H8XI23wNGD9A0PkrziWpqamkhKSmLBggWjK1bnkpOTw6pVq2hpaWHn73/L4e4+Dj71FEVFRSxbtozZs2dPaFPO9z5f4Y5Wug/swv7Bv8GRk2PImA09Hp7a08IrR7uQUrK2PIOPXpFHecal9XnKyArR3ekbjV/+1a10v/B/mDe/hGvNeiNCvyQDTccIpLpInzt/XN4bptNrayqYLM/X8ePH6erq4uabbyYjIyPW4cTMZHm+lDNF9Wn5mc98hgcffJA777wTh8PB8PAw5eXl3HPPPVEPFAqFeOCBB1i5ciXLly8HIDU1dbQYRl9fHykpkbK0breb7u7u0e/t6enB5XKRlpZGTU3N6O29vb3MmTMHt9tNT0/PGfdPSzt31ah169adsX3x9HFiKT09PW5iuRhhjiSyJxr6SEjyxjia2Ijn56u9dZiUVO2M18R0Fs/P1XjTt78GQqMvdwZHtvyGWbNm0dfXF9X3JiYmcu3H/pbl//3/UXPiOIdsNp555hmSkpKYO3culZWVJCcnG/wTnP186c/+CoTAu2wNvnF+Hg93evh9TS+7Tg5jNQmuL3PygQoXWQ4r4KO723dJj2dLDDE8GKKtrQuLJZK4yGvfT+D3P6er6i1E4cxxjf9ShQ/uhZmzx+29YTq9tqaCeH++pJRUV1fzxhtvYLfbyc/Pj+t4jRbvz9d0k5ubG9X9okqsXC4X9913Hz09PaOJ0Olb7y5GSsl//ud/kpeXx0033TR6+5IlS9i6dSsbNmxg69atLF26dPT2l19+mRUrVlBXV0dSUhIul4uFCxfy61//enQL4v79+/noRz+Kw+EgMTGR2tpaysrK2LZtGzfcEPs+J1OVxSJwpGj09agCFvFG6pLBgTCFM6yxDkWJAVm9G2bOoqmr57zbAC9ECEHSnf/I4h98hUWHttDyyS9T3dTCrl272L17NzNmzGDevHkUFhZOyGqo9PuQr7+KWHw1wjU+Vex0Kdl1cphnD/VypNtLcoKJO+alc2O5k5QxNu9NcUZWBof6w6RlRB5LrLoB+cenkRufR3zmS2OO/3LJnq5I0+h1N8csBkU5n+7ubjZt2kR7ezsFBQVce+21E7pSrijj5ZJ+a91u9yUlVKccPXqUbdu2UVhYyJe//GUA7rjjDjZs2MCPf/xjNm3aRHp6Ol/84hcBWLRoEVVVVXzhC1/AarVy9913A+BwOPjgBz/I1772NQBuu+220UIWn/70p/nZz35GIBBg4cKFqnCFwVxuM+0ng0gp1XazODI8rBMOqfNV05Hs74Gm44hbPkFdXd1ooaBLJRJsaHf/P/TvfpGC55+k6F9+yJA/wMGDBzl06BDHjx8nNTWVhQsXMn/++GwpOx/51hbwjCDW3nTR+15MMCzZ2jjAczW9tAwGyLRb+OySLNbNTCXBPD5nRU8lVoMDpyVWSXbEyvXIv/wBeesnEO7McRnrUqnzVUo8CgaDvP322+zduxer1cr69euZNWuWuq5QJq0JmQ6oqKjg6aefPufXvvWtb511mxCCT3/60+e8/9q1a1m7du1Zt8+cOZMHHnhgbIEqUXO5TTQ3BCI9W5LVRXy8GHyncEWKU830TTeyeg8AoTkLafjTq8yePRtNu7yEQWRko33mn9Efug/585+S/Jl/5uqrr2bZsmUcO3YsUuxi61asViuzZ88ezx9jlJQSuemPUDADSi9/jLAu+cPRXl443EevN8QMVwJfWpHLisLzV/i7XLZEgcUiGOw/s2qqWHczctNLyNf+gLj9U+M6ZtTqasCWCAXFsRlfUd7jxIkTbN68mcHBQebMmcOKFStITEyMdViKMiaqpJtyWU41B1Zl1+PLQH8YTYPkVPXSnm7kgd3gSqcxIAmFQpe8DfC9ROVixC2fQO7ajnw10mPQbDYza9YsbrvtNnJycti+fTter0HnLGsPwskTiLU3jWn2+g9He3myqov8VCvfXlvAj/+qmFXFKeOeVEFkUjDFqZ2dWKVlIJZcg9z+CtJz6dV0x4OsOwSlsxGamghTYsvj8bBx40ZeeOEFTCYTt956K+vWrVNJlTIlXPTqS9d1Dh48SCikztMo70pO1TCZUees4sxAX5jkVBOaAReNSvySwSAc3jfaFDgpKSnqg7YXIm74IFxxNfJ3P0ce3v/u7UKwdu1aAoEAO3bsGPM456JvegkcyYhlqy77MYJhyYuH+5iXlcS/XVfIohy74VuMUpwmBgfCZ1WrFdffCn4vcuvLho5/LnJ4MNIHTDUGVmJISsnBgwf55S9/SV1dHcuXL+eOO+44q52PokxmF02sNE3jhz/8oTpEqJxB0wROl4n+XrViFS+klAz0hdX5qumo7iD4fQTnLqKxsZHS0tLL3gZ4OiEE2p33QE4++n//ENnTOfo1t9vN4sWLqampoaWlZcxjnU72dMLetxAr1yOsCZf9ONtPDNLjDXHL7HNXiTVCitNEOASeYf2M20XBDJizEPmXlyKJ8ESqj1TTVeerlFjp7e3l97///eiZ+o9+9KMsX75cXVsqU05Un7yzZ8+mtrbW6FiUScbpNjPQHyYcjr6PmGIcr0cSDEiVWE1D8sBuMFtoTEgel22ApxO2RLS7/x+EdfSffQ8Z8I9+benSpaSkpLB58+Zx3dUgt/w5MvbqGy//MaTk+cO9FKUmsHgC++2lpL5bwOK9tPW3wEAv8u2tExYPgKyrAbMZZozf74WiRCMUCvHmm2/y1FNP0dvby7p167j11lvP2xJHUSa7qKYKMjIy+P73v8+SJUtwu91nbKW4/fbbDQtOiW8utwmpRwomuNLVrFOsDfRFLmxTnSqxmm5k9W6omEd94wnsdjs549xIV2Tlon36i+gPfxf5y0fgk/cihMBisXDttdfywgsvsGfPntEehWMh/X7k9ldg0XKE+/Kbg+5tG+FEv597rsqZ0ApjjlQTCBjsD5Pz3h1OcxZC/gzkxueQV183MSXrD+5B7vgLlFQgLKoNgzJxmpub2bx5M/39/VRUVHDNNdeQlJQU67AUxVBRrVgFAgGWLl2KEILe3l56enpG/yjTl8t9qoCFOmcVDwb7wyAgWSVW04rsaIXONoJzFo/rNsD3EvOXIm6+A/nmFuSml0ZvLyoqory8nF27dkXdjPhCfNtfhZEhtDGWWH+uppe0RDMri1LGHNOlMJsFdofGYL9+1teEEIjrN0BbMxzcY2gcMhxGf+5X6A99B5xpaJ/4e0PHU5RTvF4vr776Ks899xxSSjZs2MD69etVUqVMC1EtM5zqI6Uop7MlatgSBX3qnFVcGOgL40jWMJtV4YrpRFbvAqDRmUU43Diu2wDfS9z4YeSJY8inH0fmz0DMqgRg5cqVNDY2snnzZm655ZbLXomRUuL50zOQVwTllZcd57FeHwc6PPztwgwspol/PaQ6z3/+VCxZiXz2l+gbn8M0b4kh48v+XvTHHoCj1YgV6xB3fA6RcPln1RQlGlJKjhw5wvbt20cn5JcuXarOUSnTStS/7S0tLbz55psMDAzwqU99itbWVoLBIEVFRUbGp8Q5l9tMvyq5HhcG+sK4M9UH2HQjD+yGnALqO7oM2QZ4OqFpaJ+8F/17X0L/6XcgKxcS7dhsiVxtdrClpYXD//c4FekuSLSDLRGRZAdbEiQmRW5LTIzcfq6y33U1hBrqEJ/4+zFtk3u+ppdEs8b1Zc4x/LSXL8VporU5SDAosVjO/DmE2YxY99fIZ55ENtYhisc3EZaH90eSKp8Hcec9aCuuG9fHV5Rz6evrY/PmzbS0tJCTk8PatWtxu92xDktRJlxUV2E7d+7kscceY/ny5ezYsYNPfepTeL1ennrqKb75zW8aHaMSx1xuE20tQfw+nQSb6p0UK36fjs+rCldMN9LngdpDBK+9icbGRubPn2/4uR2RmIT2j99C/ulp5NAgeEegp4s53kaOpM3g9c4ARa//GVs4UvnuvKVtEhLfSbZO+9PTiXAkI5avuez4OoeDvN40yM0VaditsXk9pLyzHXeoP0xaxtkfs2Ll9ciXfot85XnEZ788LmNKXY88Jy/+BrJy0b74b4g8NfGpGCsUClFVVcWuXbswmUysXbuWuXPnTui5RkWJJ1ElVk8//TTf/OY3KS4uZufOnUBkX31jY6ORsSmTgNP9bqPg7DyVWMXKQF9k1VAVrphmavZDOERDZgF6x2FDtwGeTmTmIO6856zb13Z385vf/Iadt3yW65YvBa8nknj5POD1ID0j4PNGbvO+c5vXE/m6ZwQQ2D/8Sbxj2Lb24pFeBHDTLNfl/4BjdCqxGhw4T2KVmIRYdT3ylReQt3wCkZE9pvHk0AD6Yz+Cmr2IZasRn7gbYVPNVpXxoes6IyMjDA8PMzQ0xPDw8Ojfu7q6GBwcpKysjFWrVmG3T1wFTkWJR1ElVgMDA2dt+RNCqBkJhVSXCSGgvzdEdp4l1uFMWwP97yRWasVqWpHVuyHRTv2QF4fDQXb22C7Qxyo9PZ1FixaxZ88eKubOPavxZzSfGPb0dLzd3Zc1/rA/zKvH+llZnEKGPXbvR7ZEgcUiIgVlzkNcdzPytReRf/kD4iOfueyxZF0N+n//BwwPRhKqlderz2YlalJKvF7vGUnTe/87PDx8VsNri8WCw+HA5XKxZs0aiouLY/MDKEqciSqxKikpYdu2baxevXr0th07dlBaWmpYYMrkYDYLklNN9KlzVjE10Bcmya5hsapVw+lCSoms3kNg9kJONDWxYMGCuLigXrZsGXV1dWzevJk77rhjQg+u/7muD19ITmhD4HMRQpDi1C6cWLnciGWrkNtfQf71RxD25EsaQ+o68pXnkM/9EtKz0L72Q0ThzLGGrkxRHo+H3bt309bWNpo0nUqcwuEzf081TSM5ORmHw0FeXh4Oh2P0/5OTk0lOTsZqtcbF+42ixJuoPvHuuusuvvvd77Jp0yb8fj/3338/ra2tfOMb3zA6PmUScLlNnGwKIKVUb7QxMtgXJkWtVk0vTcdhoJeGvFL0xpMTtg3wYiwWC2vWrOHFF1+kqqqKZcuWTci4gbDOS0f7WJhjp9hlm5AxLyTFaaKp4cLvi2L9Lcidm5Fb/ox4/4ejfmw5MoT+xINwYBdccTXa3/xjpEiIopyDlJIXXniBrq4uhBDY7XYcDgeZmZnMnDnzrMQpMTFRfZYrymWKKrHKy8vjwQcfZM+ePVxxxRW43W6uuOIKbLbYf3gpsedymzhxDIYHdZJT1cX9RAsGJCPDOvkzVPPP6URW7wIhqAtBcnIyWVlZsQ5pVHFxMWVlZezatYvy8nKcTuOr821pGKTfF475atUpKU4T4RB4hnXsyed+XxT5xTB3EXLTS8j1G6Jq4CuPH0X/rx/CQB/iI59FrH2/ughWLujIkSN0dXXxgQ98gIKCAkP63CmKEhH1qyshIYGKigrmzJnD7NmzVVKljHKqRsExdWq7kSpcMb3IA7vxFZfT3NpGWVlZ3F1cr1q1CpPJxObNm886nzHedCl54XAvM1wJLMiOjyakKanvFrC4EO36W2GwH/nmlgveT0qJ/tqL6D/8GgiB9tUfoF13U9w970p8CYVC7Ny5k8zMTBYtWqSSKkUxWFQrVt3d3Tz00EPU1dVht9sZGRmhtLSUL3zhC2RkZBgdoxLnHMkaZgv094YpLIl1NNOPKlwx/cihAWiso2HNBvTuobjZBng6u93OihUr2Lx5M0ePHqWiosKwsXafHKZlMMAXr86Jm0TDkWoCEZn4yMm/wB0r5kNhCfKV55Er1iHOceErPSPoP38IqnbCgmVod91zyWeylOlp7969DA8Ps379epVUKcoEiOpV9sgjj1BSUsKTTz7JY489xpNPPsnMmTN55JFHjI5PmQSEEDjTzGrFKkYG+kIk2AS2RPWhOV3I6j0gJfWajZSUFDIzM2Md0jlVVlaSnZ3Ntm3b8Hq9ho3zXE0vGUlmVhSlGDbGpTKbBXaHxmC/fsH7CSEQ62+B9hao3n3W1+WJY+jfvRf2vYX40F1of/91lVQpUTlVsKKkpOSsCp2Kohgjqiux48eP8/GPf3x0+5/NZuPjH/84x48fNzQ4ZfJwuU0MDuiEQsZu+VHONtAXVqtV0031bnxONy3dPXG5DfAUIQRr167F7/ezY8cOQ8Y42u2lpsvLzbPTMGvx9e+Q6jRdsDLgKeKKFZCWgb7x2dHbpJToW/6M/oOvQDCI9uXvoa2/JW6fayX+vP3224RCIVasWBHrUBRl2ogqsSorK6O+vv6M244dO0Z5ebkhQSmTj8ttBgkDvars+kQKhyTDg7pKrKYRGQohD+2loWwhuq7H5TbA06Wnp7N48WJqamo4efLkuD/+czW92C0a62amjvtjj1Wy04RnRCcYvPCEkzCbEetuhroa5PGjSJ8H+dgDyP97FGZVon3rQUTpnAmKWpkK+vr6qK6uprKyEpcrds2yFWW6Oe8Zq9/+9rejf8/KyuL73/8+ixcvxu1209PTw969e7nmmmsmJEgl/jnTIhf2fT0h3JkT17dmuhsaCCNlpAKZMk0cOwLeEeptqaQmiElxzvVUb6tNmzaNa2+rtqEAbzYPceucNJIs8fcaOFVQZqg/TFrGhX9msfJ9yD/8Bv33P4fBPuhoQ2z4OOKvbjvnuStFuZAdO3ZgNptZvnx5rENRlGnlvO/WPT09o3+CwSDLly/HYrEwODiIxWJh2bJlBAKBiYxViWMJNo0ku0afWrGaUKcKVzjVitW0Iat34bUm0jwwFNfbAE93qrdVX18fVVVV4/a4LxzuxaQJOaq9eAAAIABJREFUbqqIjxLr73VqwuNilQEBhC0JseYGqD0IXg/al/4N7f0fVkmVcslOnjzJ8ePHWbJkCUlJ8VElU1Gmi/NOod19990TGYcyBbjcJnq6VAGLiTTQF8ZiESTa1cXXdCEP7Ob4zHlIKeN+G+Dpxru31YAvxF+OD7BmRgppifG5Sm5LFFgsIqpzVgDihg+C1YZYuR6RqrZvKZdOSsnrr7+Ow+Fg0aJFsQ5HUaadqK/G/H4/J06c4OjRo2f8UZRTnG4zPq/E67lwFSxl/Az0hUlxmSbFqoUydrK7A9qaOZaSidPpJD09PdYhXZLx7G3159p+AmHJhjhpCHwuQghSnFr0iVWSA+2m21VSpVy22tpaOjo6uOqqq8Zty62iKNGL6lW3detWnnjiCcxmM1brmZ3hH330UUMCUyYfl/vdc1aJSdaL3FsZK12XDA6EKZ6ZEOtQlAkiq/fgMVlo8fpYMnfepEuo7XY7V199NVu2bBlTbyt/SOePtX0szbNTkBrfv/8pThNNDQGklJPu+VIml1AoxBtvvEFGRoahfeMURTm/qBKrX/3qV3zpS19i/vz5RsejTGIpThOaFmkUnFsQ62imvuFBHT2sGgNPJ7J6N8fzSpGSSbUN8HSVlZUcPnyY7du3U1xcPNrG41JsOj7AoD/MhtluAyIcXylOE+EQeEZ07A71WlWMs3//foaGhli3bp1K4hUlRqLaCmg2m5kzR5V6VS7MZBKkOE2qUfAEOVW4QiVW04P0++HIAerTC3C5XLjd8Z9UnIumaaxduxafz3dZva3CuuT5w72UuW3MzUw0IMLxlZL6TgGLKLcDTmemQAeprb8gte0XaKHBWIczqXi9Xnbt2kVxcTEFBWpmU1FiJarE6vbbb+cXv/gFg4PqjU65MJfbxEBvGF1XjYKNNtAXRjOBI1kVrpgWjh5gREJrUJ801QDPJyMjg0WLFnHo0KFL7m31VssQ7cNBbpmdNin+DRypJhAqsboQER7B0fUiaU0PYfE1YPXUk9b0IAnD1bEObdLYtWsXwWBQNQNWlBiLaitgbm4uTz/9NBs3bjzra6f3u1IUp9tMQ12AoYEwqS51cNZIg30hUlJNCC3+Ly6VsZPVuzmelotk8m4DPN3y5ctHe1t99KMfxWS6+MqrlJLnanrJdli4siB5AqIcO7NZYHdoDParoj5nkWESB97E3vsaQg/gTV3GSNo6tLCHlI6nSW1/Cm/yIobTb0aaLn3L6HTR39/PgQMHmDt37qRdyVaUqSKqK9+f/vSnrFq1iquvvvqs4hWKcrp3C1ioxMpIUkoG+sPkFarX43QgpUQe2E193lzS0tKmxMWTxWLh2muv5cUXX6SqqoqlS5de9HsOd3mp7fHx2SVZmCbRhEKq00S/6vH3Limxeo7i6P4T5mAXgcRShtJvIpyQBUDYZKcv//PYezeT1LcZq7eBwawPEUwsiXHg8WnHjh2YTCbVDFhR4kBUV77Dw8Pcfvvtk2LbhRJbSXYNa4JQFxEG84zohILqfNW00drEyOAArXlmlk+B1apTiouLKS0t5e2336asrOyiva2eO9xLcoKJdTNTJyjC8ZHsNNHaHCQYlFgs0/tz1OTvwNH9RxK8dYQs6fTn/C2BpFnw3usLYWLEvQ6/vZyUjqdxnnwMj3MlI+73gVCTdqe0trZy7NgxrrzySux2e6zDUZRpL6rDGWvWrGHbtm1Gx6JMAUIInGmqgIXRBvpU4YrpRB7YzbHUTABKS0tjHM34WrVqFZqmXbS3VcuAn7dbhrmx3EmCeXKdK0x1Rl6nQ9P4nFXkHNULpDU/hMXfzFD6TfQW3kPAXnF2UnWakK2Q3oIv4EtZir1/G67mn2Hyt09g5PHrVDNgu92umgErSpyIatqnvr6el19+mWefffasGcX77rvPkMCUycvlNtPZ5iMYkFis03t21igDfWGEgORUlVhNB7J6F/WZRbjd7imxDfB0DoeDq6++mq1bt1JbW0tGRsY57/f84V6sJsGN5ZOveW7KO4nV4ECYtIxpttpynnNU0nQJqyualaHMW/DbZ5PS+XvSmh9m2H09XucKEJMryR5P9fX1tLf//+y9d3wdd5X3/565vRd1We699967HSfuCRCysGGBQIBkYfe3D+wDS9l9dtkCIYGwyw94YFlKTGxHLnGLS2zHluMm23KvcZOscnV7vzPz/CFb2LhJ1pXulTTv10svSffOfOfcMjPf8z3nfM4t5syZg06ny7Q5KioqNNGxmj17NrNnz25tW1Q6CHfqrHz1KfIK1Yt9a+D3StjsIhqN6rh2dJRwiNDVy1T1n8KEDpQGeDdDhw7l7Nmz7Nmz54Er795oil1XAszu5cBpbH+OidEkoNMJnUsZ8M/qqOKmvoRyFzXWUT0JCcsAPN1exV7zDjbPJgyRswTyn0XWPTqFtCOSSqXYt28fubm5ajNgFZUsokl3qBkzZrSyGSodCaf7toBFvaQ6Vq2Aoij4vRIFRep72xlQTh3lkq0hitPR0gDvcKe31VtvvUVpaSmzZ8++RyVw4zkvkqywdKA7g1Y+OYIgYHeKncaxanId1ROgaKz4C1/AGDyCtXYD7uuvE8xbQtw2Ig2Wtx8qKioIBAIsWbIEUey8UTsVlWyjSY7Vzp07H/rcrFmz0maMSsdApxex2kR8ap1VqxCPKSTiCna1vqpzUHGYi+5icnJycLvbp2PRFPLy8pg6dSp79uwhFovx1FNPodVqiSZltlzwMr6rlWJ7+1XBtDs1XLuSQFGUDisEJUhhLPXbMfkPooh6grlPE3VMACHN1ypBIGYfQ8LUq0GWvXoVsfAZgnlLUTTZ3zS6pcRiMQ4ePEi3bt3o3r17ps1RUVG5iyY5Vnv37r3nf5/Px61btxgwYIDqWKk8EGeOhpqqVIeeRGQKVbii86DIEoEzFdzqNpKJ/fpl2pxWZ8SIETidTtavX8+6det45pln2H45RCghs2xg+64tszs1SKkGRU+LtYOdu0rqdh3Vjtt1VOMJu2c3r47qCZB1bnxdPo/ZuxtL/XZ00Y8aZNnNHTOye4dDhw6RSCSYMmVKpk1RUVH5M5rkWH3729++77GdO3dy8+bNtBuk0jFw5Wi58VGSaFjG3NEmERmm0bFyqu9rh+fKBS5pGyanHaEpcFMYM2YM8Xicbdu2sfadd3hfO4yBeWYG5LXvSIT9ttBMwCd1HMdKUdCHz9yuo6ojbr5dR6V/8jqqZiOIRNwzSZj7Ya9ehavyl0QckwjlLACx46VL+/1+jh8/zsCBA8nNzc20OSoqKn/GEyfmzpgx45Epgiqdm7sbBaukF79XwmIV0XbyfjidAeXEYS46C8jLcT+2x1NHon///jz11FPU1tbS9VYZi3q2b6cKwOrQgECHqbMSpBjC2ddxVv0GEPAVfRp/0Ytt61TdRcrYhfquXybimIjZvx/3jZ+gjVdmxJbWZP/+/YiiyIQJEzJtioqKygNokmMly/I9P7FYjO3bt6vN6FQeis2hQdSg9rNqBfzelJoG2Enwnyyn2uygb//Op/rVq1cvqgrHYZGjXCnbQjAYzLRJLUKrFbBYRQI+OdOmpAWT/wCC/xTB3EVN6kfVJoh6QnmL8RW9iCBFcV3/KWbvblA6xnteVVXFhQsXGDVqFFarNdPmqKioPIAmpQJ+4hOfuO8xt9vNSy+9lHaDVDoGotjQKNhX3zFWZ7OFRFwmGlHo0Ud1rDo6itfDpUgcHB1XDfBRVFRHOBW3M3bcHKrLd7F69WqWLVvWriN3DmcHuSYqEqbAART7AKLO7KvzSVj6Ud/tVWy167B6tqAPnyVQ8Byyrv31QLvDnWbAZrOZUaNGZdocFRWVh9Akx+onP/nJPf8bDAbsdnurGKTScXC5tVy5EEeSFLXfUpq4k0akRqw6PkrFYS46Csh3Otq1M/GkvHO6HodRw6IxvfH1dFFaWtroXLXXJsk2p4bK60mSSQVdO07l1YfPokn5kQs/CVkaDFI0FgIFnyBhHoC1bj3ua68TKHiWhHVwpk17Ii5fvkxVVRWzZs1Cr2+/6pgqKh2dJqUC5uXl3fOjOlUqTcGZo0GWO05NQTZwR7hClVrv+PhPHKXG7KDPoPY5EWwJV31xjlaFebqfC71GJD8/nxUrViAIAmvWrKGmpibTJj4RdwRngu38mmj2lyFpneAalmlTHo0gELOPor7rq0j6PJy3fou5/n1QlExb1iwkSeKDDz7A7XYzaNCgTJujoqLyCB4Zsfrud7/7yJ0FQeAf/uEf0mqQSsfBldPw9fJ5pMa/VVqG3ythNAkYDGpDyI6MkkxysboG8np2GjXAuyk948GgEVjQ70+pWzk5OaxYsYJ33nmHtWvXsnjxYoqLizNoZfOx3VEG9Eu489rnNVGTqEYfvUQoZz7mdPenaiVknQtvl89jr1mDtX4r2kQ1gfzl7UY18OTJk/j9fhYvXqw2A1ZRyXIeeWWfOnXqAx+vr69n8+bNxOPxVjFKpWNgNAkYTQLe+hQ9MWTanA6B3yupaYCdgQsnuWRxk2+z4HA4Mm1Nm1IbirPnowDz+zixG+79rjudTlauXElpaSmlpaU8/fTTdOvWLUOWNh+TWUCnE9p1FN/kP4AiaInax2DOtDHNQdQRKPgYKX0B1vptaJIe/EV/gay1ZdqyRxKPx/nwww/p2rWr2gxYRaUd8EjH6s+b/waDQd555x127NjBpEmTWLlyZasap9K+EQQBp1urSq6niVRKIRSUKe7WPlZZVZ4cX/lBaswOJg8emmlT2pw/HqtEVmDJQPcDn7fZbKxYsYLS0lLWr1/PwoUL6d27dxtb+WQIgoDdKbZbx0qQYhgDR4lZh6Fo2qEqnSAQcc8kpc/HUb0K14038Rd9ipQheyOfhw8fJhaLMWXKFIRMqy6qqKg8libFlCORCG+99RavvPIKfr+ff/3Xf+Wll15qtwXEKm2HK0dDJCQTj2dphXM74k/CFe0zhUil6Vz46BoAfQd0Lpn1SFJiXcUtJna1UWB9eIG+2Wxm+fLl5OXlsWnTJs6ePduGVrYMu1NDwC+htLM6HwBj8AiikiDqmJhpU1pEwjoYb8kXAHDd+C8MoZMZtujBBAIBjh07xsCBA8nLy8u0OSoqKk3gkY5VIpHgnXfe4Stf+Qo3b97ke9/7Hl/5ylcoLCxsK/tU2jnOu+qsVFrGHeEKNRWwY6PcuslFrYkCU+dTX916wUc4IbFs0IOjVXdjNBpZtmwZxcXFbNu2jYqKijawsOXYnRqkFETC7WyxSZEx+Q+QNHQlZSzJtDUtJmUoxlvyJVL6Qhy3foe5fmfWiVqUlZUBqM2AVVTaEY9c+v7Sl76ELMssXryY3r174/f78fv992wzZMiQVjVQpX3jdGlAAF99ioJiNYWtJQS8Ejp9Q92aSsfFe6SMOpOdKQMHZtqUNiOckFhzysP6s15GlTjom2Nq0n56vZ4lS5awadMmdu3aRTKZzPoeP/Y7AhY+CYu1/SyS6KMX0Sbr8Bc8l2lT0oasteHt8jnstWux1r+HNlFDIH9FVohaVFdXc+7cOcaMGYPN1sI6MEVBG78OAQ8o7sw3clZR6cA80rG60yth27ZtD3xeEIT7elypqNyNVidgt4tqnVUa8N0WrlDz7DOLoij8cF8Vw4vMzOmd/v5SF85fAI2FvsNHpn3sbCMlK2y94OOtijoCcYkZPe18dXZ/iAaaPIZWq2XRokVs3bqVDz74gEQiwfjx47P2PLE6GhabAj6JonYU+DH5DiBrrMStHazuT9QRyH+OlL4Ai2cbrkZRi8xFi+80AzaZTIwePfqJxxGkKMZgOabAIbSJWwA4jd0Ju+eSNLePukQVlfbGIx2rN998s63sUOnAOHO0VF1PoihK1k52sh1ZUggGJHr1U9UVM80FT4w9VwMcuBFkQJ6JEnv6PhMlFuFiUqHQKLZ8lTqLURSFgzdC/Lq8lspggiEFZj4zKp/ebiO5Fj110eaNp9FoWLBgATt37uTgwYMkEgmmTp2aldcbrVbAYhUJ+NpPKqCYrEcfOUvENQOEDljjKQhEXDNI6fKxV6/Cdf22qIWxS0bMuXLlCjdv3mTGjBkYDM28vigK2tg1TIGDGEMVCEqSpKELgbxlWK0WNNfW46r8BQlTb0I580gZ24+qpopKe6ADXiFVsg1XjoZrlxOEgzJWe/tJfckmggEJRVbrq7KBnZf96DUCeo3AG2VV/Mvc7mjE9EzgvYcP4DFamdpOVO6ehAueKL86WsOpmigldj3fnF7CmC6WFjtBoigye/ZsdDodx44dI5lMMnPmzKzs++NwavDVt58ovsn/ISAQdYzPtCmtSsI6CJ/uCziqfoPr5s8IFDzb5hG6aDTKvn37cLlcDB7c9Obgf4pOHUSbqEYW9MRsI4naxzU6iNbcXDxCP0yBD7F438d94z+JmwcQzpmb1cqIKirtCdWxUml17jQH9nok1bF6QlThiuwgKcnsvRpgQomNMV0s/HB/FevO1rN8UHoUUi+calAn6zOufauuPYjqUILfHqtjz9UADqOGL4wtYF4fZ9qcUmhIT582bRo6nY7Dhw+TTCaZO3cuGk12nTc2p4bK60mSSQWdLvuiavcgJzAFDhG3DELWdvyeailDEfUlL+O49Tsct35PyD2HiGtWq9clpVIpjh071vi9feaZZx7/vX1odGo5cdswFPEB0S5RR9Q5hZh9LCbffsy+Pbiv/5iYdShh9xwkfX7rvEAVlU6C6liptDpWm4hWC15Piq49Hy6hrPJw/F4JjRYs1uxbfe9MHLoZIpSQmdnLzsgiC/uvB/n98TrGdLHSzdGylEBFlrkYjFBkMGJzpr92K1OEbgtTbDjrRRDg2cE5LB/sxqxrHWdHEAQmTZqETqejrKyMZDLJwoUL0Wqz53bncDa89qBfwp2bPXY9CGPoOKIcJepsfWdfURTef/994vF4Y/QxEyhaG74un8VWsxZr/Xa0iWoC+StBTP/9S5Zlzp49y4EDBwiFQvTo0YPJkyc/sp3N/dEpA1HbKGL2sU1OX1REAxH3TKKOCZh9ezH59mEInSRmG0nYPRtZ93hlThUVlfvJ7iu6SodAEAWcOdp2lfqSbfi9Eg6nKlyRaXZeDuDWw5Cf/2/k+ho+b3Dw6uDP8/rbZfzL9TVotFrQ6UCjBa2u4W+tFkGra/hfe/vxxuf+9Hidz49Hb2ZqSVGmX2ZaSEoKWy54WXXSQyguMbOXnU8OzyPX3DaT5bFjx6LX69m9ezcbNmxg0aJFjYJMmcZ2lzJgVjtWioLJV0ZKX0DS2LPVD1dRUdEomx8MBlm8eHHza4zShaAlmP8skr4Ai2crrmT9bVGL9ETtFEXh6tWr7Nu3D4/HQ0FBAfPmzaOk5CGKJvdEp04gKCmShpJHR6eaYofGRDhnHhHnJCzePZj8ZRiDx4jaxxJxz+wUUUoVlXSSxVd0lY6E063h0tk4qZSCVqs6B81BkRUCfoluarQvo3ijSY7eDPLMjT1oQgGE8TNwSSleks7zH6YRlHabwYrwKUglG36iYQgmIZVCSSUheftxKdX4twJUmZ2czCnhkj0fLQp9J03J9EttEYqicOB6iP8+VkNVMMmwAjMvjsqnl9vY5rYMHz4cnU7Hjh07KC0tZfHixRiNbW/Hn2MyC+h0QmPT72xFF7uKLlFFIG9Zq6fC1dbWsnfvXrp3786gQYPYunUra9asYenSpZjN5lY99kMRBCKu6aT0edhv3S1q0TI5x+rqavbt28eNGzdwOBwsWLCAvn37PnDhrCE6dfS2st+d6NRoYo5xaa2LUjRWQrlPEXFOxly/C1PgEKbgEaKOCYRd01E01rQdS0WlI6M6ViptgitHi6LE8XslcvLUr11zCIVkpJRaX5VJlHCQPb/fhGQezkxrFPHl1xFsDXLMU4GyvTdZdWMw45Yvoofr8RP3RCLB2bNnqThxAk99PXq9jmG9ezN0yGCsBe03YnWurkGY4kxtlK4OPd+aUcLo4pYLU7SEQYMGodfr2bJlC2vXrmXJkiVYLJaM2QMN6Yp2p5j1jpXJX4YsGonZRrTqcRKJBJs3b8ZoNDJ37lzMZjMGg4GNGzfy9ttvs2zZsow2y05YBuEt+SLOqv9uELXIf5a4bVizx/H5fJSVlXHhwgWMRiPTp09nyJAh99dSKQq62FWMjbVTt6NT+cuJW588OtUUZK2DUP5SIq5pWOp3YvLtw+g/SNQ5mYhzKoqmaT3mVFQ6K+oMV6VNcOU03Dh8npTqWDWTwG3hCrtTfd8ygXLxDPLP/51dvT5JH22U7i+/ep+j8NLYAk5WR3jjQBX/Nr8H2ocIMng8HioqKjhz5gzJZJK8vDxmz55Nv379MlZPkg6qQwl+c6yWD64GcRo1vDyukDm9HWkVpmgJffr0YfHixWzcuJHVq1dnfKIOYHdquHYlkbVtKMRUAEPoJFHHxFapLbqDoijs2rULv9/P8uXLG6NT3bp1Y9myZaxfv563336bpUuXPrLuqLWRDIXUl3wJx63f4qj+A+FENWH3bBAeX/caiUQ4dOgQFRUVGLQwbfxghg/siUGTQAwfQ5RCiFIY4fZvTdKDNulptehUU5B1boIFK287WNuxeHdh8pcRcU4j6pzUqs6dikp7Rp2pqbQJBqOIySLiVeusmo3fJyGKYHOowhVtiSLLKFvWoKz7HVeLBnLF2oXPjyh44CTYYdTyhXEF/OveStac8vCxobmNz0mSxOXLl6moqODGjRuIokjfvn0ZNmwYhYWFWTmpbiqhuMTbpzxsPOdFFOC5ITksG9R6whQt4c8n6suWLcPtzlyBvt2pQUpBJCxjsWbf+2UMHERAJuqY0KrHOXXqFOfOnWPChAl06XKv8EJRURErVqygtLSUNWvWsGTJEgoKClrVnkehaK23RS1KsXh3oknUEMxfjqAkEKUwYiqEKIUQpDCiFIZkgJC3Cm20njkuiaXzQCdKwE2o+bOxBR2yxoKssSLp8om4prd6dKopSPp8AoXPE4lXYfG8h7V+G2b/PsKuGUTt40FsvwtCKiqtgepYqbQZLrcGryeVaTPaHX6vhM2hQcyS1f/OgBLwIv/yNTh9DGHMFN4f8Um0l4NM7f7wpr2TutmZ1j3Eqoo6xnaxkq9PcerUKU6ePEk4HMZmszFp0iQGDRqUuZqRNCHJCu+e97Kqoo5wQmZWLwefHJ5LThsJUzwpRUVFLF++nNLSUlavXs3SpUvJz8+MvLT9LgGLrHOslBQm/0Hi5n5I+tzHb/+E1NXVsXv3brp27cqYMWMeuE1ubi7PPvss77zzDmvXruXpp5+ma9eurWbTYxG0BPNXkNIXYPVsxnjl5AM3kxWRcEJAigmgsSI6i0maXMS1VmSN9bYT1eBIKRoLiqBv9Tq2lpAyFOEv/hTa2DWsnm3Y6t7F7N1L2D2LmH1skyJ3KiqdAdWxUmkznDkNvVtiURmjSb0INwVFUfB7JYpKsnvC2pFQTh9D/uUPIRpB+IuXkSfPY3fpJUYXW7EbH33J/OyYfC5fu8ZbpRuwR6qRZZnu3bsza9YsunfvnpXNap+E3xyrpfRMPcMLG4QpejahrixbyMvLa5yor1mzhsWLF98XKWkKJu9eZK2N+BPWH1kdGhAaHKuilmkhpB1D6BQaKUjQsbzVjnGnrspgMDB//vxHnhsOh4OVK1eybt061q1bx8KFC+mdySbagkDUNZWUoQu62EeNDpIsWrhWVc++gxVU1fooLCxiypQpFBcXEwfimbM4baSM3fB1+Sy6yCWs9duw15aiTdQQynsm06apqGQFqmOl0mb8qVFwiqISVeGuKUQjCsmEogpXtAGKJKGs/wPK5rehoAviV7+HUNKDozdD+GISs3o9XHY4Ho83iFFUVNC/vp6koMNQ0o9nZ47D2YF6UgGcrI6w7kw98/s4+eK4B6dGZjtOp5OVK1dSWlpKaWkpixYtokePHk3eXxu9is2zCUXQ4jH2QNY1/zPWagUsVpGAT272vq2NyV9GSucmYe7XasfYvXs3Xq+XZcuWNSmCa7VaWbFiBevXr2fTpk3Mnj2bQYMGtZp9TSFp7kXS3AuAW7du8cEHH1BZWYnT6eSppxbRu3fvdnl+NIWkuTde0xew1m3A7N9P0tSDuHVops1SUck4qmOl0mY4XBoEEXz12bdCm634vQ2pk3caiqq0Dkp9LfLPfwAXTyNMno3wiZcQDA1RmJ2X/dgMGkYX3y837PF4OHHiBGfPniWZTJKfn8+cOXPYWm9j07Uws2QjHcmtiiQlXi+rpNCm4zOj89v1pNFms7FixQrWrVvHxo0bmTdvHv36NcGRUGRsdRuQNDZEOYrVs4VA4cefyAaHU5N1/f208Ur0sasEcxe1WnrXmTNnOHPmDOPGjWtWWp/RaGTp0qW8++67bN++nXg8zsiRI1vFxqbi9XrZv38/ly5dwmw2M2PGDAYPHny/0l9HRBAI5T6FLnYDW/UaUvqiVk0dVVFpD6iOlUqbodEI2B0avJ7smkhkMwGfBALYVMeq1VCOH0T+1euQSiH81dcQJ8xofC4Ulzh4I8Tc3jZikRDeaJRIJEIwGOTcuXNUVlai0Wjo168fw4YNayys7xaXOF5zhdf3V/GDhd3RaTpGCuDPD9dQF0nx/XndMWrb/2sym80sX76cDRs2sGXLFhKJBEOGDHnkPsbgUXTxm/gLnkObqMXi3UUkOpGUqXuzj29zNqRHJ5MKOl12OKkmfxmKoCNmG90q43s8Hnbt2kWXLl0YN25cs/fX6/U888wzbN26lb179xKPxxk/fnybO/ler5ejR49y5swZNBoN48ePZ+TIkVnThLrNELT4C5/Hff0N7Ld+h7fkZVXQQqVTozpWKm2KK0fD9Y8SKLKCoIoxPBa/V8JqE9Wmyq1AKh4jsuY3RMp2EynqRmzm00S0BiJ79hCJRIhEItT4gkwMR4jXJvnVgXvnYEm1AAAgAElEQVT3t9vtTJkyhYEDB2Iy3dvbxWrQ8OXxhfzj+zd4q8LDX4zIa8NX1jocuB5k52U/zw7OoX9ux+llYzAYWLJkCZs3b2bnzp3E43FGj36wUyHIMSyerSSN3YhbR5BQEhgDR7DVbcRb8sVmR3juRKKDfgl3buZvx4IUwRg8Rsw2qlX6FSWTSTZv3oxOp3tsXdWj0Gq1LFy4kJ07d3Lw4EFisRjTp09vE+eqqqqKI0eOcPnyZTQaDYMHD2bcuHEZ742WSWSdk0DBx3BW/Rpb7XqCBSsybZJKO0eQIugjF4jbhmfalGaT+Su5SqfClaPlo4sJggEZuxqFeSx+r0ROvnqaPgmSJFFVVcWZM2eora0lGo0SDocbnKZwiFg80bBh3/ENvw8dAUCn02E2mzGbzfgxE7c5mT+wsPGxOz92u/2RE7kxXazM7uVg7WkPE7pa6ZvTfp0RXzTFmx/eopfLcI+UfEdBp9OxaNEitm3bxr59+4jFYkyaNOm+z9dcvwuNFMJf9CkQBBTBQDhnPvaatxscEvuoZh3XdpcyYDY4VsbAYQQlRaSVJNb37NlDfX09S5YswWq9P7W2OYiiyOzZszEajRw9epR4PM6cOXNaJQVPURSuXLnCkSNHqKqqwmAwMHbsWIYPH97uFT7TRcLSn7BrBhbv+yRNPYjZWyfiqdLBUWSMgSNYPVsQ5DgeU09kbWZ7DjaXzF/JVToVztuNgr2elOpYPYZ4TCYWVYUrmkMgEODq1atcvXqV69evk0wmgXudJaciUVx9FZOcwjJ2CpYBQ+5xmO406q0MJPjthst8emQe4wc9WWPSz4zO59itMD/aX8VrT/VA3w5TAhVF4Scf3iKalPnq5GJ0mo4ZPdVoNMyfPx+DwcCRI0dIJBLMmDGj0bnSJOow+/YRtY0mZfxTXVDMNgKTvwyLZysx65BmNdM1mQV0OqEh5TfTKDJm/wESxp5IhqK0D3/u3DlOnTrFmDFj6N69+WmTD0IQBCZPnozBYKCsrIx4PM5TTz2FVpueqU0qleLcuXMcPXoUr9eLzWZj2rRpDBo0qPOl/DWBsHsOutg1bLXrSBq6IBkKM22SSjtCG7uJrXYduvh1EsYeBPMWtzunClTHSqWNsVhFdHoBn0eiewbVctsDfm/DZEsVrng4qVSKGzducO3aNa5evYrX6wUahAkGDBhAt27dGDZsGOFwGCUeR1n1c5S926BXf8TP/38IOQ/vYbTrih9RgOk9nvzCbtU3pAR+d9cN/nCijk+PzEzPpJaw/ZKfQzdDfGZUPt0cmW1W2tqIosjMmTPvca7uREGsde+iCFrCOfPv3UkQCeY+jfvmf2Hx7iacM7fJxxMEAZtTzArHSh85hyblJZS7MO1je71edu7cSXFxMRMmpDcaJggCY8eOxWg0smvXLkpLS3nmmWcwGJ78uxqLxaioqOD48eNEIhFyc3OZP38+ffv27TAtE1oFQUOg4OO4rv8Yx63f4u36ZRSx/bRiUMkMghTB4tmGKXAQWWMhkP8sMdvIrO7r9ihUx0qlTREEAVeOBm+92ij4cfhvT7bsasSqEUVR8Hq9jVGpmzdvIkkSGo2GkpIShg4dSvfu3XE6nY2RBpPJROjCGeSf/RtUXkNYsAJhyScRHrGqLSsKuy77GV5oaXHT21HFVub1cVB6pp7xJTYG5LWflMBbwQS/OFLD0AIzzwxwZdqcNuHuKMj+/fuJx+Msm9EXQ+QsoZyFyNr7m0SnTN2JWYdh9u0hah/bLPl1h1PDtSsJFEXJqMqiyVeGpLETt6RXwjyVSrF58+bGiGBrOSZDhw7FYDCwbds21q5dy5IlS5qdphcMBjl27BgnT54kmUzStWtX5s2bR9euXdu1AmZbImttBAo/jvPmL7DVrCVQ8Il2O0FWaWUUGWPwCNa6rQhyhKhjImH3XBRN+3bGVcdKpc1xurXUVMVIJRW0WaKElY34vRJmi4he37lXSOPxODdu3Gh0poLBIAAul6vRkerSpcsD038URSG6fSPyz/8DDCbEV7+DMOTxdTAnqyPURlJ8Kk0RphdH5VNeGeb1sip+9FQPDO1AUU+SFV4vq0IU4NWJRYidbHI0ZswY9Ho9e3bvQrxWTtLqJuKc9NDtQzkLMYRPY/VsJlD4iSYfx+7UIKUgEpaxWDOziKJJ1GKIXiDkngtCem3Yu3cvdXV1LF68GJvtfqc0nfTr1w+9Xs+mTZtYvXo1y5Yta9Ix6+rqOHr0KOfPn0dRFPr168eoUaPIy2v/ojOZIGnqRThnPlbPFpLGHkQfcd6odE4elPbXGinImUB1rFTaHNftOitffYrcAlWW9WEEvFKnjFYpikJdXV2jI1VVVYUsy+h0Orp27dpYo2G3PzpFT4lFUH77nwQ+3A0DhiH+1dcQnO4m2bDrih+zTmR8ScsK7O9g1mn48oQivr3zOr87XstnRhekZdzWpPRMPadro/z1xCLyLJ3zPB02bBg9jRdxibdYf6aI4QVJTKYH3zZlnZOIcxoW704i0UlNll+33yVgkSnHyuQ/gIKGqH1sWsc9f/48FRUVjBo1qlkNmFtCjx49WLp0KevXr+ftt99m6dKluN33n/eKonDjxg2OHj3K1atX0el0DBs2jBEjRjz22qLyeCLOqeiiH2Gt20TSWELK2C3TJqlkAYIUxVK/DZP/Q5QOkPb3IFTHSqXNcbrvCFhIqmP1EJIJhXBIpqRn5ymQvnr1KufPn+fq1atEIhEAcnNzGTlyJN27d6eoqKjJil/KtUvIP/t3qL2F5ROfIzrjKQSxaftGkzL7rwWZ2t2e1sjSiCILC/s6WX/Wy4SuNgblZ6+a2BVvjN+fqGViVxszenbeSaYgheiprcAnl3DkcoILdQ1RkIcp2oVd0zEGDjdLft3q0IDQ4FhlonG6IMcxBo8Qtw5FeUCa45Pi8/nYsWMHhYWFTJw4MW3jNoXi4mJWrFhBaWkpq1evZunSpeTnN0SfZVnm4sWLHD16lJqaGkwmExMnTmTo0KEYje07BSmrEEQCBc/ivv5jHLf+QH3XL6NoOq8kfadHkTEGj2Kt23JX2t+cVmnrkGlUx0qlzdEbRCw2Ua2zegR3itk7i3DF9evXWbduHQaDgW7dutG9e3e6devWbElmRVFQ3t+E8sdfgtWO+Lf/hHXSDGJ1dU0eo+x6kFhKYVYvR3NfxmP59Mh8jlSGeeNAFT96qmdWNtlNSjKv7a/Cqtfw8riCTl1bYvW8hyAnkLo9y5IlCTZs2NA4UXc6H1BHJeqbLb+u1QpYrCIBn9wKr+DxGIPliHKciDN9zs+duipRFFmwYEGzJNAVRUFRQGxhn8O8vDyeffZZ3nnnHdasWcPChQvx+/2Ul5cTCARwOp3MmjWLAQMGpE1FUOVeFI0Zf+HzuG78DHv127fbFGTfNU+ldbk37a87obwlpDpI2t+DUL/hKhnB5dbg80goipJpU7KSO8IVnUFqPRaLsW3bNlwuF5/5zGdYuHAhgwYNar5TFQkh/9f3UX7/MxgwHPEfXkfoN6TZ9uy64qfQqmNgK4hMmHQir0wspCqY5H+O1aZ9/HTwu+N1XPXF+cqEIuzGzjvh1MYrMQYOEXVMRNLnU1JSwvLly0kkEqxevZq6hzjrMdsIkoYSLJ6tCHK8ScdyODWZUQZUFEz+MpKGLqQMXR+/fRPZt28ftbW1zJkzp1lpdZKksG9HiP27Qihyy+8NTqeTlStXYrVaWb9+Pbt378ZsNrNo0SJeeOEFhgwZojpVrUzK2JVQ3iIMkXOYvbszbY5KGyJIUay163DdeBNNyksgfyW+Lp/v0E4VqI6VSoZw5miJxxSiEdWxehB+bwqDUcBoyr5TVJEklFgkPWMpCjt27CAajTJ//vzGHlLNHufyOeTv/TUcP4iw8kXEr3wLwdb8iFNtOEnFrQgzezlaLVIztMDCov4uNp7zcrI6Pe9jujhVHaH0TD3z+zgZ0yU99WXtEkXBWrsBRWMm7J7d+HBBQQErVqxAEATWrFnDrVu37t/3tvy6Rgo0eSJpc2qIhGWSyba9Huqil9Emaog6JqatxuHixYscP36cESNG0Lt303tqKIpCxZEoXo+Et07iysVEWuyx2WysXLmSMWPGsHLlSp577jl69+6tyqa3IVH7BGLWYVjq30MXuZRpc1RaG0XGGDhMzrUfYPJ/SNQxAU+3rzU0je4EEcuO/wpVspJGAQuPmg74IPxeKSujVcrpcuTvvoL86vPIv34Dpa66ReOdOXOGS5cuMXHixMYaiGbZI8vIW99B/revAyD+3fcR5y9DeMJJ0/tX/CjAzFauK/rUiDwKrTreOFBFNJmZFLA/J5KU+FFZFQVWHS+Oan/9ttKJIVSBPvYRIfe8+2oAcnJyWLlyJQaDgfXr1xMIBO7bv0F+fThm317EpPexx7uT8hv0t23UyuQvQxbNxKzD0jKe3+9n+/btFBQUMHny5Gbte/VSgutXEvQdZCCvUMu5iijRSHrODZPJxKRJkyguLk7LeCrNRBAI5i9H0uVir34LMXX/OaPSMdDGK3Hd/Bn2mjWkdLl4u36ZUN7iDllL9TBUx0olI9idGkRNg4CFyr1IKYVQQM4qx0qpqUJ68/8gv/ZtSCURJs9B+XA38je/iPy7/0Lxepo9ps/nY/fu3ZSUlDBq1ONrUe6zKRhA/sk/oaz+FQwfh/itHyH06t/scRrHUxR2Xg4wON9EgbV1RUOMWpFXJhZRE0ry3+U1rXqspvLLIzXURZL89aQiTLpOfGuQE1g9m0gaionZxzxwE4fDwZIlS5BlmY0bN5JMJu/bJpSzABCwerY89pC2u5QB2wox6cMQPt2gBCi2XERIkiS2bGl4rc2tq6qvS3GyPEp+kZb+g40MHW1CVuBkebTFdqlkB4powF/4PKIcx37rLVDUe39HoiHtbz2u6z9Bk/TclfbX+RYzOvHdUyWTiKKAw6nBq0as7iPol1CUBucz0yixKPLa3yB/+0tw5jjC8k8hfvdNxE99GfH//AxhyhyUvVuR//7zyKt+iRLwNWlcSZLYunUroigyd+7cZqfdKedPIn/vFThzDOH5lxC/8HUES8tS1857YlQGE60iWvEgBuc3NN3dfMHHiVvhNjnmw/jwepDtl/wsH5TDwLzsVStsCyzePWhSfkK5Tz8ybcXlcrFgwQI8Hg/vvffeffWiDfLrUzGGTqCLfvTIY5rMAjqd0KaOlSnwIQBRx/i0jLd//36qq6uZM2cODkfTz6FYVObwvjAms8jICWYEUcBi1dBvkJFbN5JUV97vtKq0TyRDIcG8pehjV7B43su0OSppwhA8djvt78DttL+/6TRpfw+ic75qlazAlaPF75OQ01Ck3JHIBuEKRVGQD+xC/tYXUTavRhgzBfGf/hNx4UqE23VQgjsX8YWXEf/xPxHGT0PZsaHBwVr7G5Rw8JHjHzp0iOrqambNmtWspqGKLCFvXIX8H98EvRHxG/+OOHNRWuqhdl72o9cITOrWuk1M7+aF4XkU2/T8+EAVkWRmVnB9sRRvfniLni4DHx+amxEbsgUx6cPs203MOoykqedjt+/RoweTJk3i4sWLHD58+L7nw67pSBo71rqNoDw8rU0QBGxOse0cKzmJyX+IhGUgss7V4uEuX75MeXk5w4YNo0+fPk03Q1I4vC9MKqkwdrLlnmbovfsbsNpFKo5ESKXUe0RHIWYfRdQ+FotvN/rwmUybo9JCtNGrOKpXIWndnTLt70GojpVKxnDlaJCltk1/aQ/4vRJaHZgtmTk9lY8uIP/r/0L55WvgcCN+/d9uN9fNeeD2Ql4h4l++ivi9nyAMH4eyZQ3yNz6HvP4PKNH7xRkqKys5dOgQAwcOpG/fvk23y+9F/tF3UNb9DmHsVMRv/RChW9OL4x9FQpLZezXAxK42zLq2c2gNWpFXJxZRF0nx66NtrxKoKAo//fAW4aTMVycVo9N0Xml1AKtnEyAQylnY5H1GjRpF//79KSsr4/Lly/c+KeoJ5S5AF7+JMVj+yHEcTg0Bf9sopRpDFYhymIij5RLrwWCQ9957j7y8PKZMmdKsfU8daxCrGD7OfF+EXtQIDBtjJhpROH8q1mI7VbKHYO4zJPVF2KvfblINokqWokjYakuRtA58Xf6qU6b9PQjVsVLJGM6cBplbn1pndQ8NwhXaNu8fpAS8yL9+A/mf/xZqqhA+/RXEv/8PhN4DmrS/UFiC+Lm/Rfz2GzBwOMqGPzQ4WJvXoMQbJkbxeJxt27Zhs9mYPn160227LZrBpTMIn/4Kwme/hmBMX8raoRshwgm5zdIA72ZAnoklA9xsveijvKptUwJ3XPbz4Y0QfzEil+5OQ5seO9vQRa9gDFUQdk1D1j2gR9VDEASB2bNnk5+fz9atW/F47q03jFuHN0l+3e7UIKUgEm59MROTv4yULo+kqWULE3fqqmRZZuHChc2SLr9+Jc5HFxP06m+gS7cH1zTm5Gnp1lPP5XNx/F71PtFhEHUEij4JyDhu/R4UtSSgPWLyl6FL3CKY+wyK2LnvH3ejOlYqGcNkFjAYBbXO6i5kWSHgl9q0MbCSSiJvewf5m19EObALYe4SxH/6L8Qpc59IXU/o0h3NF7+B+M0fQq/+KGv/u8HB2r6O93ftJBgMMn/+fPT6xwtEKJKE/M7/IP/oO2BzIP79DxvsSrPTufOynxyTlqEFmakven54LiV2Pa+XVfH+FT+pNkiPrQ4l+MXhGobkm1g8wN3qx8tqFBlr7QYkrZOIc1qzd9dqtSxatAidTsfGjRuJxe6KsAgiwbxn0EjBR8qv29tIwEIbu4YufiMtEus7duygqqqK2bNnP7hh8kPw1ac4cThKbr6WgcOMj9x24HAjOr1AxZGI2vewAyHpcgjkP4sufgNr3buZNkelmYgpPxbPe8TN/UlYBmXanKxCdaxUMoYgCDhzNHjr1ZXIO4QCMrLUdvVVSsUR5O++gvL2r6D3QMTv/Bjx2c8gmC0tHlvo3gfNK/+A+L/+Fbp059yWjZw7f4Gx+S4K8x5fy6PU1yL/x9+jbHobYcpcxL//AUKXbi2268/xRVMcrQozo6cdjZiZVDi9RuRvpxRj0Ym8tr+Kz5deYu0pD6FE65wbkqzwo/1VALw6sRixjaOj2YYxcBhdoqohBVB8MkVIm83GU089RTAYbIzi3CFl7PZY+XWrQwNC6ztWZn8ZsmAgZm++Eucdqqur2bx5Mx988AFDhgyhX79+Td43HpM5tC+MwSgwaqIZ8THnnN4gMmiECa9H4uql9PS2UskOEtbBRJxTMPsPYAieyLQ5Ks3AWrcJAZlg3uK09cDrKKgtx1UyisutpfpmjERCvqdwubPSVsIVSnUl8qpfQMVhyC9uaKg7bGyrHEvoM5Dw5/6O3b/9HwrjEUZvfxu5/H2EZz6OMGEmwgNkmZXjB5F/9TqkUgif/RvE8U1PG2wuuz8KICswMwNpgHfT02Xkx0/35GhlmHVn6vnvY7WsOulhbm8HzwxwpVUCft3Zek7XRnl1YhH51pZLbbdnBCmK1bONhLEncevQFo1VXFzMzJkz2bFjB/v27WPq1KmNz4VyFmAIn8bq2UKg8BP37avVClisIgFf66UCCqkghmAFUce4ZqfuyLLMlStXKC8vp7KyEr1ez5QpUxg2rOk9sGRZ4WhZhERMYfJsKwZj0675Jd113Lii5cyJKIVddFnZOF3lyQjlLEAbu46tZg0pQxGSPi/TJqk8Bl3kAsbQCULuOci6Tp7t8ABUx0olo/ypUbBEfpF6s/R7JUQNWGyt814o0QjKu6tQtm8ArQ5h5V8izH4GQdt6k2tZltm2bRuKIDLvxc+hvTELufR3KL9+A2XTaoTFn0AYOxVBFFFSSZQ1v0HZvg669UL8/N8hFLRuQeyuK3765hjp6sh8jrgoCIzpYmVMFyuX62OsO1PPpvNe3j3vZWJXG0sGuumf2zLFpY+8MX53vI4JXa2t3gi5PWCp34EgRwjlPZ2WldfBgwdTW1tLeXk5ubm5DBw4ELgjvz4Ni3cH0ehEkqYe9+3rcGrwtWIE3xQ4jIBE1DGhyfskk0nOnDnDsWPH8Pl82Gw2pk6dyuDBgykuLqaurq7JY505EaOuJsWIcSac7qZPPwRBYOhoE7u3Bjl9LMqoiS2PqKtkCYKGQOEncF/7MY5bv6O+5OUnjhqrtAFKClvtOlK6nCdKm+4MqI6VSka5c3P1eiTyizr3yjlAwJvC7tA8Nj2muSiyjHJgF8ra34DfizBpNsLyTyE4Wi61/DiOHDlCZWUlc+fObajDcI5GHDwKjn/Y4GD94gcN6X7zlqK8vxk+uoAw62mElS82Sru3Fle8Ma5443x+TEGrHudJ6OU28tXJxfzFyDzePedl6wUf+64FGZhnYslAN+O6WJudupiUZF7bX4VVL/LyuMI2F0jJNjSJGkz+MmL2sWlVtJo6dSr19fXs3LkTl8tFYWEhAGHXNIyBQ1jrNuItefm+Pi82p4bK60mSSQWdLs2fjSJh8h8gYeqDpM9/7ObhcJgTJ05QUVFBLBajoKCABQsW0KdPH8QnqL28eS3B5XNxevTR07Vn8xcxrHYNfQYaOH8qTknPJPmF6v2ioyBrHQQKn8NR+WtstaUE859V08uyFLN3D9qkB1/xi2lpLN4RaRPH6qc//SlHjx7F4XDwgx/8AIBQKMRrr71GbW0teXl5fPWrX8VqtaIoCr/61a8oLy/HYDDw8ssv06tXLwDef/991q5dC8Dy5cuZMWMG0NBD48033ySRSDBy5EhefPHFTj9haC9odQI2h4ivXhWwUBQFv096qELWE497+RzyWz+HK+ehZz/EL/1vhJ5Nr4loCbdu3eLDDz+kX79+DBjwJ3VBQRBgxATEYeNQjuxDWf97lF+/AWYL4he/jjBqUpvYt/OyH60IU3tkb+Qm16zj0yPzeXZIDjsu+Vl/1sv399yk0Kpj8QA3s3s7MGqbNtH9/Yk6PvLF+eb0EhzGTr6upihYazeiiHpCOXPTOrRGo2HhwoWsWrWKd999l49//ONYLJZG+XVH9R8xBssbmmjehdPdEMG/+VGCHn3TG0E1hM+gkQIEnUseuZ3H46G8vJyzZ88iyzK9evVi1KhRFBUVPfF9NeCTOH4wgitXw+ARTx5x7TPQyM1rSSoOR5mxQItGq97nOwoJcz8irllYvDtIGnsSc7ROarrKkyMm67F4dxGzDiVhbps5RHukTe6sM2bMYMGCBbz55puNj5WWljJ06FCWLl1KaWkppaWlvPDCC5SXl3Pr1i3eeOMNLly4wC9+8Qv++Z//mVAoxOrVq/n+978PwNe//nXGjBmD1Wrl5z//OS+99BJ9+/blX/7lXzh27BgjR45si5emkgZcbi1VN5MoivJEN25ZVkgmFZKJP/0kbv/WaCAnX4vFmrlmu00lEpZJJdNXX6XIEspv/xNl7zZwuBBe/GuECTOeSOnvSUgkEmzduhWLxcLMmTMf+NkKoogwdirKqElw4hB0642Q0zY59ilZYfdHAcZ2sWI3ZP/3w6zT8MwAN0/1c3HgepDSM/X8/4er+cOJWub3dbGovwu36eGX9NM1Ed45Xc/c3g7Glljb0PLsRB85iyF6gWDu0yia9L8fJpOJp59+mj/+8Y+8++67LF++HK1W2yC/7ivD4tlK3DrknlqnvAItuQVaTh+PkleoxWJL3/fS5C9D0jpJmO9vn6AoCtevX6e8vJyrV6+i1WoZPHgwI0eObJba34NIJGQOfRBGqxMYM8mC2IJeaRqNwLDRJsreD3PhTIwBQzt3I9KORtg9C13sKra69aSMXdS+SNmEomCrXY+CSCh3UaatyWraxLEaNGgQNTU19zx26NAhvvOd7wAwffp0vvOd7/DCCy9w+PBhpk2bhiAI9OvXj3A4jNfr5dSpUwwbNgyrteEGOGzYMI4dO8bgwYOJRqONqkTTpk3j0KFDqmPVjnDmaLh2JYHfK6HTC/c7SA9xmpIJmWRCIdWEYJfZIpJ7e9KSm69tctF0W3KnT0vaHKsNq1D2bkOYsxhhyfNp7fvUFPbs2YPf72fFihUYDI9efRc0GhjZ9LqPdFBeGcYfkzIuWtFcNKLA5O52JnWzcbY2SunZetac8lB6pp5pPewsGeCih+teCetIUuJHZVXkW3V8ZvTj08A6PEoKa927pHT5zao3ai65ubnMmzePTZs28f777zN79mwEQSSY9zTuG/+J2bubcM68xu0FQWDEODO7twQp/zDCpFnWtKQFa+K30EcvE8pZcE/6oSRJnD9/nvLycurq6jCbzUycOJEhQ4ZgMrXcaVFkhfIDEaJRmUkzrWkRncgt0FHSXcfFs3G6dNdjs2f/oohKExFE/AUfw339xzgr/y9h53SijvFqzVUWoA+fxhA5RzB3EbK2fd0z25qM5YL4/X5crob6DpfLRSAQAKC+vp7c3D9JMefk5FBfX099fT05OTmNj7vd7gc+fmd7lfaD63aj4L3vhR66jSiCTi+g0wvo9QIms4DdqUWnF9HrBXQ6ofH5O9vo9AKJhEJddYq66hSV1xNcu9wg12t3asgt0JJXoMWdp0WbBSklfq+EIIDN0fKJgnK6HOXdVQgTZyF+7LNpsK55XLx4kdOnTzNmzBi6dOnS5sdvCjuv+HEYNIwubp/RG0EQGJhvZmC+mapggvVn69lxyc/Oy35GFFlYOtDNiEIzgiDwf4/UUBNK8s9zu2HWqRNRs29fQ51A0YsgtO770adPH8aNG8fBgwfJzc1lxIgRt+XXR2D27SVqH4us+1Oto8ksMnS0iaMHIlw6G6fvoEf3eXocmkQdVs9mFEFL1D4GgFgsxsmTJzl+/DjhcBi3282cOXPo169fs5r8Po5zp2LUVKUYOtqEOzd94w4aYaK6KsWJwxEmzbSqqf8dCEVrxVf8Ira6jdg8m7D49hBxTiXqGK82oXxGtg4AACAASURBVM0UcgJb3QaS+sKG/ncqjyTrkuwf1ADwYRdNQRCa3TBw+/btbN++HYDvf//79zhxmUSr1WaNLW1NTo5CcoaRVFJBbxAxGDUNv+/6W9vEGpIH0bOhRA9ZVvDUxqm8HqHyRpSPLkS5fC6OKEJ+oZGiEjPFXU3k5hsfu0rcGp9XNFyJ062noKBlqXBSfS2eX76GpqQHOa/8bwRj26bLBAIBdu3aRXFxMU899VRaJ2pPwoM+q0AsyaGb51g6tIjC/PYv75ubC0N7FvOVWJJ3TtxizfFKvrPzOr1zzEzo4eK9S35eGF3CtEHp7wOWblr9WpjwI1zeheIajr1729Ty3elv9cEHH9CzZ0969+4Ntk/A8W+RE9yJ0u+le7bPyVGor6vm/KkQ/QbmkZPXzAmlnIT6Ywg1exACZ1EQUUqeQdDZOFBWxtGjR0kmk/Tu3ZtJkybRp0+fJ3JOHvVZXb0c4sJpH30H2Bg9Pj/tzs+4yQb27arFW2ug36DsrZHMJtrPPCMXugxBDlxAuLkRq2czFv9elKJ5UDgTNC1bbGgvZMvnJVxbg5DyIwz+Arm27BN6yjYyNuNxOBx4vV5cLhderxe7veHCmJOTc498q8fjweVy4Xa7OX36dOPj9fX1DBo0iJycHDwezz3bu90P19WfM2cOc+bMafy/OVKxrUlubm7W2JIJch5wrkoKRKINP+lC0ECXHtClh4FUSo+3LkVtdYq66iTlB+spPwhaHeTkackr0JFboMVqF++bFKT781IUhdrqKAVFuhaNq0gS8g+/CbEoyt/8E55QGELhtNn52OMrCqWlpSSTSWbPno3P52uzYz+MB31Wm897SUoKk4r0He68W9TLxLzuPdnzUYB1Z7387shNejgNLO1rbhevtbWvhbbq1RjlFPW2OUht+H5Mnz6d6upqVq1axcc+9jEcDgcWx1Qs9TvwXR99n/x6/yEiVTcEdm29ydS5NjRNqE3SJGoxBg5hChxFlMNIWidR91yuRrtycMd5Ll9+HUEQ6N+/PyNHjmyctN19D20OD/usggGJD94L4nBp6DtEfOLxH4UrT8Gdq+Hgvlos9lhWpndnG+1vnuGCvL9Aa72KxbsTw/W1yDc3E3FOIeqYhNLBHaxs+Lw0iWrclduI2sYQjDsh3p6+P+mluLhpNX8Zc6zGjBnD7t27Wbp0Kbt372bs2LGNj2/ZsoXJkydz4cIFzGYzLpeLESNG8Ic//IFQqCFd7Pjx4zz//PNYrVZMJhPnz5+nb9++7NmzhwULFmTqZam0I7RagbxCHXm3ZXsTcZm6moa0wdrqFNWVDR6dwSjcLipvcLRM5vTfwOMxhURcwd7C+ipl/e/h/KkGoYrito9OlJeXc/36dWbNmtWY6puN7Lrip7vTQE9Xx0wt0WlEZvd2MquXgzO1UQptenQadeKpjV3HFDxC2DkNSd+2K8F6vZ6nn36aVatWsWHDBp577jlwTcMYPPxA+XW9QWT4ODMH94Q5VxFj0MPU9OQkhvApTIFD6KOXURCJWwYSto7hdCUce+841dUnMRgMjB49+p5a5dYgmVQ4/EEYUSMwZrKlSQ7hkyAIAsPGmBt6Wx2PMnK82tuqo5IydcdvehFt7DqW+p1Y69/D7NtLxDmZqGMyikYVMWkVFAVbzToU0UAoV51XN5U2cax+9KMfcfr0aYLBIF/4whd47rnnWLp0Ka+99ho7d+4kNzeXr33tawCMHDmSo0eP8sorr6DX63n55ZcBsFqtrFixgm984xsArFy5svHm8NnPfpaf/vSnJBIJRowYoQpXqDwReoNIcVc9xV0bCmUjYanRyaq5leLG1STQ0LzX4YgjyUm0WgGNRkCjBc3df2uE2//z0G20GgFR0zBBSIdwhXLySEM/qMlzECfNavkb0kxqa2vZv38/vXr1YvDgwW1+/KZyIxDnXF2MvxyZ1+FrMwRBYFB+24qWZC2KjK12A5LGRsTd9ucHgNPpZOHChaxbt45t27axaNEiQjnzHyq/XlCko3tvPZfOxSko1pGT/6dbdkMProMYg0cR5SiS1k0oZz5e3WBOnPmIEyf2EQ6HcblczJgxgwEDBqDXt64IgKIoHPswQjgkM2G6BbOldZ15m0ND7wEGLp6J07Vnktx8ta9ORyZl7Iq/+NNoYzexeHdird+B2fcBUcdkIs7JKBr1WpdODKFj6GNXCOQtQ9GoCxdNRVCaW6TUwaisrMy0CUB2hHxVHo6iKAT9MrXVSTw1KRRZSyyWREopSJKClAJJUpDl5o+tuT1XklKwcLkD7RM0BlXq65D/8VVwuBG/8R8Ij1HhSzfJZJK33nqLRCLB888/nxZFsXTx5+fW/xyrZe1pD79c1ueR8uQqmaG1roWGYDmO6j8SyF95nwPT1hw7dow9e/bw/9i77/i2rvuO+59zcbFBENxDpEhq7z0tWbIkj3glTuKkSdysZu90PGmaPmmTV9unbdqmGW3irDppRuNmNMNJbEexLVt7UluyBqnNvbFx73n+AEVRlmRL5gBA/t6vF17YF4e8IHi/OOf8zrJly1ixfDkF57+Okeqio+bPr5mgn0pqNj3di9aw9i4PgcRhvN07ccUa0TiIB2YRDS6jKRKifv9+jh07hmVZTJw4kQULFlBTUzNiXyC8dF+dOBLj2MEYsxZ4mDx9dIZppVKaTU/2ogxYe8/NDZkcr8bacYYZv4iv41k84UPYykU0dBuR0OoxEwIyub+UFaXo7BexzAI6qz54zWLm41HWDwUUIpcopQiGHARDDiZPv/EHnm1fCVnp0JX+xz84fFmp9OXUoMuWpfHnGa8uVKVS2N/6F0gmMT7wl6MeqgC2bNlCZ2cnDz30UFaFqpeytea5hm4WVvglVI0jyo4TaHuSpLuKWF7mRzTMnz+f1tbWgUqBM6oul19/jnDRPVc91nQqli/pJXpqG6VnDuM0YqScRfQV3UsksJCG823Ub6vn3LlzmKbJzJkzmT9//lXVckdDy6Ukxw7GqJzoZNK00fsMMk3F3MVedjwf5tSxONNmj+15N+KKlLuSnopHCMeb8Hc+g69zE96urUTzVxAJ3Y42c7PiazbwdzyNssL0Vr5bQtUtkiMLIYaRYSgMFzgZvW9N9S9+ACePot7756iKqlF73csaGho4cOAACxYsYOLE7K46d7A5QlskxbsWylpO44mv8zkcVg/dFY9kxUGCUop169bR0dHB73//e0JvehO+wIL0sKbgsnT5dTuBp+8gnp5dlMbOYJc4aOicBuXLcZfVcvTYMerrf053dzeBQIDbbruN2bNnZ+SLjXCfxd7tEYL5BvOX+kZ9iG1phZPKaicnjqSDXWAYF1YW2c9yl9NT/jYc8Wb8nc/i63oBX/c2ovnLiYTWYJt5mW5iTjFjF/B27yCav1IWaX4VJFgJkcP0/l3op36OWnMPxvK1o/76kUiEjRs3UlxczG23jU7p6qF45nQ3fqfB8mr5JnO8MJId6cCSt5CUJ3uCv2maPPDAA/z4xz/miSee4G0P38uE8GHyWn+B5SzC07sPw46RcpbQW3QfEf9Cth3rpvnwUcKxZ0kkE5SXl7Ny5UomT56Mw5GZMJFKpYtVoGHJan/G1gScvdBLS1OSg3uirFjrH/PzJ8W1LHcZPeVvwZFYj6/zObxdW/B2bycaXEakYC22KWX5X5G2yWv9BbYjQLjwrky3JidJsBIiR+n2VuzHvgRVdagMLAKstWbjxo0kEgne8IY3ZHy9qlcSSVpsO9vLHXX5uKRC3rgRaH8SUNcMscsGfr+f+++/n5/97Gc88fQWHllzO3ldz6CVSdw/h2j+MhLuGi5eukT91k2cbjyN1lBUUMf6OxdTUVGR0fZrrTmwK0JPt83yNX78gcz1FHm8BjPnejm4N8qFM0mqake2UIfIXparlN6yNxMp6A9Y3dvx9uyky7WYNtc6cKW/AHCYqr+4FKhXWLtyvPD07MIZP0932R+N+XL2IyW7j4TGCX3iCLHTCo0B+QUQDKGc8k9B3NjAvCorhfHBv0S5Rn9e1YEDB2hsbGTt2rWjPp/j1dh2tpe4pVk3Sb61HC/M6Bk8fQcJF2zANvMz3ZzrKi8vZ/369fz+97/nqcNzuWvxIyS8k0hqFydOnGDfvh/T1taGx+Nh8eLFBH3TaDhukoplvgLakf3dXDibZPpcD6UVma/IVzPZxbnGBIfro5RWmLjc8gXKeJYyi2jgdXT2rKQ09QKTC3bh7D3CH049RFvk6iFuxuUKvqbCdHAldJlXbk/fxlWBzHSmLztd/Sdn+tzIwaCmUn0E2p8i4Z1EPDA/083JWRKssoD9+1/QvW/71Td6/RAMQX4IlRcaCFwEQ6hgAeSHrlw3M/8PTYwu/X//DaeOod7/KVTZ6I+Bbm9vZ/PmzdTU1DBv3rxRf/1X49mGHirynMwozt7iGmIYaU2g/Xfp8uoFt2e6NS9r5syZtLW1sW/fPgJ5q0kmD3LgwAGi0SiFhYWsX7+e6dOn43Q60bamu6OPQ/siFJWaI17S/EbOno5zYHcX5ROcTJ2ZHevBKUMxb4mXF37fx9EDMeYvzXz4FKPLtjUdrSkunkty6XySRFzjcPjpqXwQy7mcScb/8uDMH3HeuJcme3G6kFQKrJROF5pKaVLWleuJSH+xqYH7b64dDhOcLoXLeTl0GVcFr8En10tuy1QoC7Q/ibIT9Ja8DmQo7asmwSoLGI98iNBb30vX2TPonk7o6UqfujvRvV3o841wpB6iYQCuqY/vC1zp6eoPW+QXQH4BauIkqJyIMmQy71ih63egn/4F6o77MJauHvXXT6VSPPXUU7hcLu68886cmMvQ0pfkYHOEt80rzon2iqFzhw/jip2hp+QN15Qwz0arVq2ira2NzZs3A1BbW8uCBQuorq6+6j2rDMXCZT6ee6qX+p0RVt4xuvOJLEtzaG+Us6cTVFR5mb/MlVV/U/kFJnXT3Jw+Hqe61kVhiRzmjHXXD1NQVumkotpJaYWzf+5fHd3WRwk2/y8TI09QkneB3pKHwLj5EUJap6v9WpdDWH/oSiYGnZKaREKTTNgD18N91sD9lvXyr+EwweVSLL3NTf4oDQZxRhvSi6cX3IHlkuJOQyGfOFlA5RfgLC5GFZS+bC05nUwMBC56uq4KYbq7//zMyfRtsWj6OQBeH9RNR02egZoyEyZNQ3nkm7xcpNua0/OqJk5Gvfk9o/76TU1NvPDCC7S1tfHggw/i9+fGeiHPNXQDcEedDAMcF3QKf/vvSLnKMr5m1c0yDIP77ruPw4cPU1dXR0FBwQ0f6ws4mLPQy/5dURpOJEatvHkkbLN7S5juTospM92suqOSjo72UXntWzF9toeL5xIc2BNhzd15OTksS7y8mw9TV9MOP90V78Tf8Qy+zmcw45foKX8Ey1V8U6+rVHo44FCKtNjW4PB1dSC7fLm9NcULf2hmySo/5RNGeFSStshr/SWWGSJcsG5kX2sckGCVQ5TTBUWl6RO8fAiLx6GzDd34Ipw6hj55FP3Ej9Fap8sNV9WgJs+EKTNRk2dAUWlWfesorqVTSexv/gtojfGBT6GcozcEtKenh61bt/Liiy/i9Xq56667qKurG7XXHwqtNc82dDOnzEdZQOYujgfe7h2YyQ66Kt6VFeXVb5bb7WbRokU39djqOhdNF5IcPRClpNwkLziyoxJam5Ps3RbBtjRLVvmoqHJlbWAxnYq5i3zs2hzm9PE4U2bKJPyx4NWGqWsog3DRnSQ91QSbH6fg/H/QU/pmEoFZI/9DAIZD4XYo3C/ztkwlNbs2x9izLczKOwIUFo/c4bq3aytmopmuirffUu+duD4JVmOUcruhfAKqfAKsSH8DoaMROH0cfepoOmhtexae+226VytUCJNnoCbPTPdqVdfJ3K0so3/6XWh4EeODn0aVjk41sHg8zu7du6mvrwdg6dKlLF68GJcrdz58D13q5WJvkodnZ3+BDTF0yori7/gDCe8UEr5pmW7OiFFKMX+pj+ee7GXf9gir7wyMSNDRWnPyWJxjB2ME8gyWrgoQGOEQNxzKJzgpn+Dk+OEYldVOfBmsWChevWELU9eR8E+no/pj5Df9kFDT9wmH1hIuugtU5t8rplNx1wOV/Op/z7DzhTCr1gfIyx/+dhmpbvwdG4n7ZpLwj06wHOskWI0jyuuD2QtRsxcCoG0Lzp9BnzoGJ4+mA9eeremg5XRB3dT08MHJs2DydFRAhlFlit67Ff2HX6M2PIhaPPLrRVmWxaFDh9ixYwexWIwZM2awcuVK8vJyb6HF3x1twe1QrJyYe20Xt87X+SzKjtFXfN+Yn4Dt9hjMW+Jl95YIJ47EmD5neAuzJJOa+h0Rmi4kqax2Mn+pD9OZO7/TOYu8PPu7JAf3Rll2u6xtlSu0nR4KNxJh6qVsZwGdEz5AXtuv8Xdt6i81/ha0mfm1Dj1eByvu8LN5Yx/bN/WxakPesBerCbQ+gULTW/LAsG53PJNgNY4pwwETJ6ULXKy7DwDd1X5l6OCpY+inf4G2fpZ+QnlVetjgjHmoBctkntYo0a1N2N/9KtRORb3xXSP7WlrT0NDAli1b6OzsZMKECdx+++2UlubmZNZ4yuYPL7aycmIePmfmv4UUI8tIduLr2kosbxEpd2bXeBotFVUuqmqTnDgSp7TCSUHR8Pxb7+222LUlTKTPZtYCD5OmuXMumHh9BjPmeDhcH+PS+SSV1bnT0z4eWZbmXEOCU8fiRML2iIWpaxhOekvfQNIzkbzWX1J47qt0VzySFQuK+/wOVqwNsOWZ3v5wFcA9TMsIuMLH8YQP0Vd4N7azcFi2KSRYiZdQoSJYvAq1eBXQP1frzIkrQat+B2zZiHa5UPOXo5avTfeCybDBEaGTSexvfAEUGO//f0Z0XlVLSwubN2/m/PnzhEIh7r//fiZNmpRzB1ODbTvXS1/CYv2k7FzDSAyvQPtT/fMn7sp0U0bVnIU+2lp62LcjXaxhqAegF88lqN8ZweFQrLgjQHFp7h4q1E51c64xyaG9UUKFmStPL24smdCcORXn9Itx4jFNqNDBjHk+yipHMExdRyy4hJS7kvxLP6Dg/DfpK76PaP7KjPd8B0MOlq0OsH1THzufD7NyXWDovxc7SaDtV6ScJVm/HEWuyd1PSzEqlNsN0+agps0BQNt2ep7Wjk3o3ZvRu14Afx5q8SrU8jUwZRbKkH9cw0X/5Dtw5iTGhz+DKikfkdfo6+tj27ZtHD16FI/Hw9q1a5kzZw4OR2738MRSNj/c38qkIh9zy6R3dawzY+fw9O0nXLAuaxcDHilOV7oE+7bnwhw7EGXOolf3frdtzdEDMU4fj1NQ5GDxbX68vtz+PDcMxfylXrY808ezv+2hbpqbqTPdOF25/XONBfGYzekX4zSejJNKQkm5yZSZbopKzIx9oZdyV9JR/TGCzY+T1/ZrnLGz9JS+IeNFHYpKTRat9LF7a4TdW8Isu90/pDmVvq5NmMkOOivfA0qiwHCS36a4Jcow0pUEp8xE/9F74Wg9evsm9PZn0c8/CYXFqKVrUCvWwoTanO7tyDR712b0s79F3fk61MIVw779RCLB3r172bt3L7Zts2jRIpYuXYrbnf1r/tyMnxxqpyWc4nP3zsRQyUw3R4wkrQm0/RbbESBSsDbTrcmI4jIndVNdNJxIUFbppKT81nq34zGbPVvDtLda1E5xMXuBF8MxNj6/Q4Um6+4NcvxQlFPH4pw9nWDqLDe1U9w4xsjPmEvCfRanjsU515DAtqGi2smUGW5ChdlxSKodXror3oGvcxP+jt9TmGiiu/wRLFdJRttVUeVi3mLNgd1R6ndGWLjc96qOsRyJNvydm4gF5pP0TRmBlo5v2fEuFjlJmSbMXYKauwQdj6UXrt2xCb3xl+infp5emHj5WtSyNajiskw3N6folovo//4q1E1DvfEdw7pt27Y5cuQI27dvJxKJMHXqVG677Tby88fOt/znuuP84mg76yflM39CPm1tbZlukhhBrvARXLFGekoeyonFgEfKzHleWptS1O+MsPY1ebhuslemsy3F7q1hEgnNgmU+quvG3lwkn99g4XI/k6alOHogxpH6GA0nEsyY42FCjVO+BBwFPV0WJ4/GuHAuiaGgqtbF5BluAnlZODpCGUQK15H0VJHf9GMKzv0nvWUPEw/MyWizaia7icc1xw/GcLtjzFrgubX3rtYE2n6FxpEu8COGnQQrMSyU25Oeb7V8Lbq3B71nM3rH8+j/+z76/76fLuW+/A7UklWovLFzAD8SdDKB/eg/g+FIr1c1jPPXzpw5w+bNm2lvb6e8vJz777+fioqxNclfa803djXjMQ3etTCz3zCKUaAtAu2/I+UsJRZckunWZJTDVCxc4WPzxj4O7Y2yaMXLL+CttebMqQSH9kXxeg1Wb/CTXzC2DwvyC0xWrA3Q2pTkyP4Y+3ZEOHXcwawFHkrKZK7wSGhvTXHyaIyWSykcJkye5mbSdDceb/YPx0z6pg6UZM9v+iHh0O2Ei+7JaEn2qTPdxKPpYZRuj7qlddrc4cO4IyfoLX4A25RKzyNhbH+CioxQeUHUHffBHfeh25rRu15I92T96FH0j78Jsxele7EWLEd5hrc88FigH/82nGvA+OhnUUXDU42vvb2dzZs3c+bMGYLBIK95zWuYOnXqmPyWdlNjDwebI3xoWRn5HvmIG+vSiwG301XxzqxYfybTQoUm02Z7OH4oRvmExA0r4VkpzYE9Ec43JimtMFm4wnfTPVxjQUm5kzVlJhfOJDl2MMr258KUlJvMmu8lGMrM+ygRt2m+mKKtOYnpVHj9Bj6fgddv4PUZuD0qZz6ztda0XEpx4miMzjYLl1sxfa6H2imunHuf2c4QnVUfIND6BP6uF3DGztNT/lZsMzNLeCilmLPISyKenhPp9iiq6165p17ZcQKtvybpqiCaP/zTC0SaHHWIEaWKy1D3Pgz3Pow+35gOWDufR3/ni2iXOx2ull2uLChvR3vHJvSmJ1H3vB41f+mQtxeJRNi+fTuHDx/G6XSyevVq5s2bhzlGf9d9CYvH9rYwtcjD3VNCmW6OGGHKivUvBjyZhG96ppuTNabMdNN8McmB3VEKi81regbCfRa7t0To6bKYNtvDtNm5V0p9OCilqKp1UVHtpPFEnBNH42x6qpeqWifT53hHpYJgLGrTdCG9XlN7SwqtweVW2LYm9ZKpoYaRLiF/OWj5/AZen7pym9fI+Lw429ZcPJvk5LEYvd02Xl86BFTXuUa1wt+wUyZ9pQ+R8kwkr/UXFJz7Kj3lbyPprc1Mc5RiwXIfiUSY/buiuNwGZZXX9rgqO4Yzcgp35Diu8HEcVg/dFY/Il1AjaGweXYmspKpqUVW16Ne/Pb1W1o7n0Lu3oHc+D4E81OseQa29d1z+gwfQTefR3/9aetjkQ28f8vZ6e3v5n//5HxKJBPPmzWPZsmV4vWO7h/BH+1vpiVv8zbpqjHH6PhpPfJ3PoezouFgM+FYYRvqg6/mne9m/K3LV4rjNl5Ls2x4BDctu91/3YGy8cTgUk2d4qJ7k4uTROA0vxrl4NjliFQQjYYtL59NhqrPNAsAfMJg8w01FlZP8AgdKKZIJTTRiE43YRMLp82j/eculJPGYvmbbHq+6OnwNuhwIWNiWHpHwZaU0ZxsSnDoeJxq2yQsaLFzuo3Kic0jV67JNLLiIpLuC/KYfErrwLfqK7yWavyojnz8Oh2LpKj9bn+1j99YwK+8IUFjkwJFoxh15EVfkOM5oIwob23CT8E6lN29hVqzPNZZJsBKjThkGTJ2FmjoL/Zb3weF67I2/RP/wUfShvRjv/Ni4m4elE/H0elVOE+P9nxpy751t2zz11FNYlsVb3vIWiouLh6ml2etke4zfneji3mkFTC68+THnIjcZyU583VuI5S0g5a7MdHOyTl7Qwax5Xg7ti3L2dIKJk1ycOBLn+KEYwZDBklV+/AH51nowl8tg1nwvtVPcw15BsLfHoqk/THV3psNUMGQwfY6HiiongaBxzZeKTpfC6XLccGiiZWliEZvIoMAVDWsiEZuujnR40/ZVrQDSPV8OU2E6FaZJ/7m6cn75toHr/ZedvORxCltrzpxMcPrFOIm4pqDIwZyFfsoqM1cyfaRZ7go6qz5KsOUn5LX9Bnf4GAlvHSlXOSl3BbYZAjU6wx1Np2LFapOGXccxTp2ioKcBp90NQMpVTiR0Own/NJKeGumlGiUSrERGKdMJ85dizF2MfubX6J99D/vzH8d49ydRsxdmunmjRv/oG3C+EePjf4sqHHoI2r17NxcvXuSuu+4aF6HKsjWP7moi6HbwyLyx//MKCLQ/DUC48O4MtyR71U510XQxyeH6KJfOJ2ltSlFV42TuEl9uD8saYcNRQVBrTU/XlZ6pvp50wikocjBrvofyKueQg63DofDnOfDfoKqe1pp4TA/0djkdPrq6+7CSmmRSk0qlT1YyvUhvNGynb0tqUqlba0tphcmUGR4KSxxjNlANph0eussfwdu1GV/3DgLRUwP32cqF5Soj5S7vD1vpc+0YpvUUtcaRbMEVfhF3f69U1QSLhOXiUlctvup1UDBj3K3nly0kWImsoAwDdefr0NPnYX/rX7G/9LeoO1+LesM7Uc6xPVTFfuFp9JaNqPvejJq7eMjbu3TpEjt27GD69OnMmDFjGFqY/Z4+2cWJ9hh/dlsFfpd8KzfWmbHzePrqCRfcge2UuXQ3opRiwTIfzz3ZQ1tLirmLvNRMcY2LA9/hcMMKgvM9110nTGtNZ1t/mLqQJBq2QUFxiUntFDflE5yjuuCyUgqPVw3MsSsuDtHWdnOJSWuNlWJQ0LoSuAZft20oq0wPXxx3lEG0YA3RgjUoO44j0YwZb8JMNGEmmnH3HcJr7xp4uOUIXhW2LFc5KVfJzS3QaydwRU/hCh/HHTmO5xOT6QAAIABJREFUI9UFQMpVRiS0ioRvGm2xKrYejuJpMli1IYBLjvAzQn7tIquo6jqM//eL6J8+ht74K/SxAxjv+wtU5dgcE6wbT6B/9CjMWoB63VuHvL14PM5TTz1FXl4ed9xxx7g4gOqKpfj+/lbmlflYUyvlY8e8gcWA/eN2MeBb4fUZrFqfrl6WqWp3ue6aCoKb0hUEZ87zkpdv0N6S4tL5JE0X0vOeDAOKy0ymzXJTNsGJ251bVfAgHcpMZ3qoGWN7au6w0IablGfi1fOXtMawegfCliPRhBlvwhc5hSI9HFRjYLlKSLnKSLkqSLnLSLnK08MJo014u7bjDh/HGW1AYWErF0nfFMIF60j4pl31xVK+D5atdrB9Ux87ng+zcl1AeqYzQIKVyDrK5Ua97YPo2Yuxv/cV7L//M9Sb/gR1x9gqbKF7e7C//k8QLMB471+gjKEf9Dz33HP09vby8MMP43aPj4VSv7evhXjK5gNLy8bU+0NcnytyDFesgd6S16INmUt3MyRQDd31Kgg+/3QvTqcimdQ4HFBa4aSiyklphROnSz6Lxj2lsM0gCTNIwj/tyu3awpFsu9K7FW/CGTuLp+/AlYcoE6VT5AEpZynR0G3EfdPSVQhfpoerqNRk0Uofu7dG2LM1zNLV/jFVPCQXSLAaoy71Jvjl0Q5eN7OQirzrr2OS7dT8pRh/+xXs7345vQbWoT3pwhbB3B/6o20L+9v/Bj2dGJ/6Z1Te0Htajh07xvHjx1m+fPmYW/T3Rg43R3jmdA8Pzy6iKn98BMlxTVsE2n5HyllCNLgs060R49DgCoKnjsWJRzVlE0xKy504pHdA3AzlwHKVYbnKiDP/ys1WrL9nqxkz0YKnsI4OewK2s+CWNl9R5WLeYs2B3VHqd0ZYuNwnXzqOIglWY4zWmidPdPHY3hbiluZkR4x/vrsGR45+Y6HyCzA+9jfoZ3+D/ul3rxS2mLMo000bEv3rH8ORfai3fxhVN3XI2+vu7ubZZ5+loqKCpUuHvv5VLkj1F6wo9Zu8eU5RppsjRoG3ZxdmspWuirdLhSuRUS6Xwcx5MkZODB/t8JD01g6sjeUpLsZua3tV26qZ7CYe0xw/FMPtiTF7gbxXR0vuDfwVN9QaTvK5Z87x6K5mZpZ4ee/iUk60x/jZkfZMN21IlGFgbHgQ46//DQJB7C9/Dvvxb6OTiUw37VXR+3ehn3gctWoD6vZ7hry9y6XVlVLcc889GMb4+LP+1bEOznYneN+SMtzm+PiZxzNlx/B3bCThqSPhm5np5gghRFZLLxPg4vTxOCePxTLdnHFDeqzGAK01zzb08O3dzaRszQeXlvGaqSGUUhxri/L4wTaWVAaYlONr+6iqWoy//jf0T797pbDFe/8CNSF3Clvo1ibs//oiTJyEetsHh6V7fufOnTQ1NfGa17yGYHB8FG9oDSf58YE2llUFWFaVl+nmiFHg69yEYYXpq5DFgIUQ4pUopZiz0Esirjm6P4bbbVBdl5tTQ3KJBKsc1xVN8bWdTew438esEi8fX1lx1ZyqDywt51BzhC9vu8S/vqYW5wisuD6a0oUtPoCeuxj7sS9j/8OfoR5+F2rd/Vk/hljH49hf+0dAYXzw0yjX0OcEXbhwgV27djFz5kymTZv2yk8YI76zpxkNvG9xWaabIkaBkezC17WZWGABKU9VppsjhBA5QRmKBct9JBJh9u+KoBTk5RuAuur7qYHLatDZSy6rl9xx+TlKgcutpEhGPwlWOWzr2R6+vrOZaNLm3YtKeHB64TVzqYJuBx9ZXs4/bLrA4wfb+OMFJRlq7fBSc5dgfO4r2I99Bf0/30Qf2ovxro9nbWELrTX6h1+HC40YH/ssqqR8yNuMx+M8/fTTBINB1q4dP2Wnd1/oY9u5Pt6+oITSwNhe40yk+TvSiwH3FcliwEIIcSscDsXSVX62PtvHvh2REXkNZYA/YJAXdBAIGgSCDvKCBv48x7gr+S7BKgts376dtrY2lFK43W5cLtc1p8G3p5TJDw518fzZCJOLvHxyZQUTQzfu/VhWlceGSfn87Eg7S6sCTC8eG5MYVbAA4+N/g37mN+ifPtZf2OITqDlDX2R3uOnnn0Jvewb14FtQc5cMfXta88wzzxAOh3n44YdxucZH9348ZfOt3c1UBV28bkZhppsjRoEZu4Cnt55I6PZbro4lhBAivR7ZbesCtLWk0FoD0H+WpkG/9LIeuHrVnYOfp3X6FI3Y9PZY9HRbNF1IXvUYr98gb1DYCvSHL5drbM6NlmCVJZLJJOFwmEQiQTweJ5lMvuzjncAGFK4+J39ocl83hLndbioqKqiuruY9i0vZ3xTmy9su8e/31o6Zyf5KKdSGB9Az5mJ/61+xv/x51IYHUW98J8qZHWFDN7yI/vE3Yc5i1ANvGZZtHj16lBMnTrBy5UrKy4fe+5Urfnq4naa+JH+3oTrnh7WKm6A1gfbfog0vkYI7Mt0aIYTIWaZTUT5h5Ed5WJYm0pcOWn09l88t2lpS2NaVx7k96pqwlRd04PaorJ/a8XIkWGWBFStWUFxcTNugsppaaxKJxMCpJxLlN0da2H+hh1KPZm21jzzTHrg/Ho+TSCSIRqN0d3cTj8eJx+Ps2bMHh8NBVVUVbyiu5L8bHPxgfyvvGWNzU9SEmnRhi599D/2HX6cLW7zvL1ATajLaLt3bjf3oP0F+IcZ7/ww1DBX7urq62LRpExMmTGDx4uzrnRspF3oS/PxIB2trg8wr92e6OWIUuCLHcUVP01v8INoxNnrahRBiLHM4FHn5DvLyr14SQ9uaSMQeFLZs+noszp9JkBrUl2A6IZDnIC/oYM4iL6Yzt0KWBKssdXlYoNvtThef2NNFa9jH6xdW8dZ5xbgcr3yAnkqluHjxIo2NjTQ2NtJ15gyrgJbtfn7RNokls6dSUVGBwzE21oNRThfqLe9Dz16E/diXsP9+UGGLDJQg17aF/a1/hZ5ujE9/AeUfevU6y7J48sknMQyDu+++e9yUVtda841dTbgdincvKs10c8RoGFgMuIhoviwGLIQQuUwZCn/AgT/goKzySs+Z1pp4TNPXY9HbH7Z6e2zaW1M4cjCl5GCTx494yub7+1v59bFOKvKc/OPdE5lZ4rvp55umycSJE5k4cSJr1qyhq6uLF0+eZuPeozQeP8TZ4wdxuVzU1NRQW1tLTU0NPt/Nbz9bqbmLMT73VezvfgX942+hd2/BeOfHUOUTRrUd+pc/gqP7Ue/8GKpm8rBsc/v27bS0tHDfffeRlzd+yoxvOdvL/qYI719SRoFXPrbGA0/PbsxkC13lfwxK9rkQQoxFSik8XoXHa1A8BgZTyX+rLPViW5QvbbvEhZ4E908L8Y6FpXiGOC8qFAqxbMkiAhNn8NmnT7O+OMYUs4vGxkZOnDgBQFlZGXV1ddTW1lJSUpKz41xVMITxsc+itz6D/t9vY3/+46jXvg1190OoUeih0/Xb0b/9Cer2uzFW3zUs2zx37hx79uxh9uzZTJkyZVi2mQsiSYtv72lhcqGb10zNzqqPYngpO06gYyMJTy0J/6xMN0cIIYS4KRKsskzS0jx+sI2fHWmn0Gvy+fXVLKgY3vkks0p9PDCrlF8c7eC29TPZsGEDra2tNDQ00NjYyPbt29m+fTt+v5/a2lpqa2uprq7OucpzSinUqg3o2Quxf/Qo+uffQ+/p772qrhux19XNF7H/60tQMwX11vcPyzaj0ShPP/00oVCINWvWDMs2c8WPDrTRFU3xmTUTrllOQIxNvs7nMaw++ireLosBCyGEyBkSrLJIY2eML227RENnnA2T8nnP4lL8rpHpXXlkfjG7L/Tx1e2X+Mr9dZSWllJaWsry5cuJRCID87JOnDjB4cOHMQyDCRMmDPRmhUK503OgQoUYH/or2LMF+0ffSC8qfO/DqPvejHIOb4UcHY9hf/0fwXBgfOjTw1KZ8HJp9Wg0yoMPPohzmNuczU53xPjN8U7umRpi2hhZJkC8gkQnvq4XiAXmkfJMzHRrhBBCiJsmwSoLWLbmv3ed4zvbzxBwOfjM2gksrxrZ+TMuh8Enb6vgU0+d4Tt7mvnEysqB+3w+H7NmzWLWrFlYlnVVAYznn3+e559/noKCAurq6qirq6OioiLriygopWDJaowZ89CPfwf9xOPoPVvTiwpPmj4sr6G1Rv/ga3DxLMYnPocqGp4iC4cPH+bUqVOsXr2a0tLxU7jB1ppHdzWT53Lw9vljY2Fr8crUuV+CtukruifTTRFCCCFuiQSrLPCFzRfYfq6PVRPz+ODSMoKe0dktU4u8PDy7iP891M6K6rzrhjmHw0F1dTXV1dXcfvvtdHWl52Q1NDRQX1/P3r178Xg81NTUUFdXR01NDW73jRcrzjQVCKLe86foZbdjf/9r2P/0KdSG16Ie+mPUENutn/stevtzqNc9gpq9cFja29HRwfPPP091dTULFw7PNnPFH051c7wtyidWVhBwj43KleLlmfFL0LqVaGg1tlMWgBZCCJFbJFhlgXumhLh39gQWFI3+XII3zylm14U+vrajiZnF3lcMdaFQiAULFrBgwQLi8Thnz54dmJt1/PhxDMOgsrJyoDcrW4cMqrlLMD7/H+iffRe98Zfo/Tsw3vFR1Ix5r2p7+tQx9OPfgXlLUfe9aVjamEqleOqppzBNk7vuuitnC4m8Gj2xFN/b18KsEi/r6oKZbo4YAcqK4Ui240i29Z/acUUbweElXLAu080TQgghbpnSWutMNyKTLl68mOkmAFyzQPBoauyM8edPNrK8Ko9P3f7qSpLbtk1TUxMNDQ00NDTQ0dEBkBNDBvXxg9jf+yq0NqHW3IN647tQvpcvGDJ4f+meLuy/+1NwOjH++osof2BY2rV582b27t3LAw88wKRJk4Zlm7niq9sv8ezpbv79vjpqQkPrSczk39Z4p+wYjkT7SwJUB2ayDcMKX/VYyxHEchVjTryfNqvyBlsU2UT+tnKL7K/cIvsru1RW3tz/JemxEtQWeHjL3GJ+sL+NFxp7uL321nsILvdUVVZWsmrVKrq7uwdCVrYPGVTT52L87VfRv/oR+ve/RB/YjfH2D6PmLX3F52rLwv7mv0C4t38R4OEJVWfPnmXv3r3MnTt33IWqo60RNp7q5vUzC4ccqsYzI9WDK3wUd/g4yo6hDRfacKdP6vJl18B1+/J9hgutBt1nuF52HSllx3Ek2vrDU/tA75OZbMew+q56rOUIYjmLiPtnYTmLsJxFpJzFWM5CMNKFXooLikEOJoQQQuQgCVYCgDfMKmLH+T6+sauJOWW+IS/Cmp+fn1NDBpXbjXrTu9FLVmF/9yvYX/071PK1qD96HyrvxkFT/9/34fhB1Ls/iZo4PAEoEonw9NNPU1hYyOrVq4dlm7nCsjWP7mym2GfyR3OLM92c3KI1jkQL7vAR3OEjOOPnAbDMAiwzhGH1opLtKDuOshMonUBxcwMWNI5rghmAkerEccPwNAPLWdwfntIh6nJ4EkIIIcYiCVYCAIeh+OTKCv70d438545L/PXaqmGb0+N2u5k6dSpTp069ZsjgCy+8wAsvvEBBQQFTpkxhypQpFBcXZ2w+kaqbhvHZf0f/9ifp05F61Fs/gFqy6po26b1b0U/9HLX2NRi3rR+W19da84c//IFYLMZDDz00rkqrA/zmxU4au+J8es0EvM7sGzaadbSFM3YWd/gIrvARzGR6CG7SXUVf4d3E/TOxXGXXXwtK26BTGHY8HbZ04krouu71QZftBAqbhH9Gf89TsYQnIYQQ454EKzGgKt/N2xeU8J09LTxzupsNk4e/F+lGQwZPnz7N7t272bVrF6FQiClTpjB16tSMhCxlOlGvfRt60W3Y3/0K+ptfQO9cgfHIB1ChIgBSF85gP/ZlqJuG+qP3DdtrHzx4kIaGBtasWUNx8fjqsWmPJPnh/jYWV/pZUTU8QyrHJDuBK3Kiv2fqGIYdQeMg4ZtMJLSGhH8Gtpn/yttRBigXtuECRnZ5ByGEEGI8kGAlrvLA9AK2n+vl23tamFfup8Q/sj0mg4cMRiIRTp8+zYkTJ9izZw+7d+8mPz+fqVOnMmXKFEpKSkY1ZKmqWoy/+pd01cBf/gj7bz6KevOfoJaspusLnwHTifHBvxy2RYbb29t54YUXqKmpYf78+cOyzVxh2Zpv7m7G1pr3LykbVxUQb4aR6u2fL3UEV/QUSqewDQ8J3wzigVkkfNPQhsxHE0IIITJJqgJKVcBrNPUm+MRvG5he7OXz66szcpAbjUY5deoUJ06c4Pz582ityc/PH+jJGu2QpZsuYP/3V+HEEcjLTxer+OTnUTOHJwClUikef/xxIpEIjzzyCD6fb1i2mwvOd8f50rZLnGiP8c4FJbxhdtGwbj+b/rZumtY4kq3pXqm+I5jx8yg0lhki7p9F3D+LpLcW1Nhb3ysn99c4Jfsqt8j+yi2yv7KLVAUUr1p5not3Lyrl6zubefJEF/dOKxj1Nni9XubMmcOcOXMGQtbJkyfZu3cve/bsGfWQpconYPzF/4d+/kn0/32fwDs+THQYQpVlWQM/V3t7O6997WvHTaiyteaJ4518v74Vt2nwqdWVrKoZx2tWaRtn7Cyu/uITZrIdgKR7AuHCDST8s0i5yq8/X0oIIYQQGSfBSlzXPVNCbDvXx2N7W1hQ4aciL3MT0l8pZAWDwYHiGCMZspRhoO64D732XvwlJUSH8E1SLBbj0KFD7N+/n3A4TEFBAXfffTe1tbXD1+As1tyX4CvbLnGoJcrSCQE+srx8yJUoc5Wy43h69uDt3oaZbEvPl/JOIhpaRdw/6+bmSwkhhBAi48bnkYx4RUopPrainI8/0cBXtl3i7++ciMPI/DflLw1Zl+dk7du376qQNWXKFEpLS0ckZA1lm11dXdTX13PkyBFSqRTV1dVs2LCBmpqacTGvSGvNxlPdfHtPCwr42IpyNkzKHxc/+0sZyQ58XVvx9O7GsOMk3VX0lL6JuH8W2uHJdPOEEEIIcYskWIkbKvY5ee+SMr687RK/Pt7BQzOHd+7LUHm9XmbPns3s2bOJxWIDPVmDQ9bkyZOpra2loqIC08zM211rzYULF9i3bx8NDQ0YhsH06dNZuHDhuKr81xFN8Z/bL7H7Ypi5ZT4+vqKC0sD4KieP1jijp/F1b8UVPgoo4oE5REKrSHkmZrp1QgghhBgCCVbiZa2rC7L9XC8/qG9jcWWA6vzsrDzm8XiuClmXe7L279/Pvn37cDqdVFVVUVtbS01NDcHgyM/lsSxroDettbUVj8fDsmXLmDt3Ln6/f8RfP5u80NjDN3Y1Ebc0711cyv3TCzDGUy+VncTTW4+veytmognb8BMpuINo/nIZ6ieEEEKMERKsxMtSSvHhZeV89DcNfGnrJb5wT01WDAl8OR6Ph1mzZjFr1iwSiQTnz5/nzJkznDlzhoaGBgAKCgqoqamhtraWysrKYe3NisViHDx4kAMHDgzMn1q/fj0zZszIWK9ZpvTELb6xq4nNZ3qZWuThk7dVUBXMznA+EoxUN97u7Xi7d2LYEZKucnpK30gsMB+McdZbJ4QQQoxx4+soT7wqIa/Jh5aW8YXNF/nZ4XbePDd3hq+5XC4mTZrEpEmT0FrT2dk5ELIOHjxIfX09pmlSVVU1ELTy819dD0JnZyf19fUcPXp0XM6feqndF/r4j+2X6E1YPDK/mDfOKsr6UD4stMaMncXXvRV33yFAk/DPJJK/iqS3Tqr6CSGEEGOUBCtxU1bVBLn9XC+PH2pjyYQAkwpzb3K9UorCwkIKCwtZuHAhyWRyoDersbGRxsZGNm3aRCgUoqamhpqaGqqqql62l0lrzfnz59m3bx+NjY0YhsGMGTNYsGDBuJo/NVgkafFfe1r4/aluakJu/mZddU6+X26ZTuHuO4ivayvO+Hlsw0M0dBuR/JXYzsJMt04IIYQQI0yClbhp719azqHmCP/4/AU+sbKCOWW5vd6S0+mkrq6Ouro6tNZ0d3fT2NjImTNnBkqhOxyOq3qzQqEQkF7Q9+jRo+zbt4+2tja8Xi/Lli1j3rx542Ydqus51Bzhy9su0RZJ8sZZhbx1XjFOh5HpZo0olerD27MDb/cOHFYvKWcxvcWvJRZchDbGz7BHIYQQYrxTWmud6UZk0sWLFzPdBCB3Vtg+3hbli1su0tSX5M7J+bxrYSl5bkemmzXsUqnUVXOzurq6AMjPz6eyspLz58/T29s70Ps1ffr0cTd/arB4yuYH+1v59bFOyvOcfGJlBTNLsiNgjtTflhm/iLdrK57eehQWcd80ovm3kfBNBTW2w+RIypXPQiH7KtfI/sotsr+yS2Vl5U09bvweCYpXZXqxl6/cX8fjB9v4v6Md7LrQx3sWlbKmNjim5hGZpkltbe3Agr1dXV0DIevUqVNMnDiR9evXM3HixDH1c78aJ9qjfGnrJc73JLhvWoh3LizFY47BYKEtnLFzuCIncUVexBk/h61cRPOXEs1fieUqzXQLhRBCCJFBEqzELXObBu9YmA5T/7GjiS9uvcSzDT18aFkZZQFXpps3IkKhEKFQiPnz5wPyTRJAytY8frCNnx5up8Br8vn11SyoGENl5LXGkWjGFT2JK3ISZ7QBQyfQKFLuCfQW3UcsuATt8Ga6pUIIIYTIAhKsxKtWW+Dhn++u4ckTXfx3fSsffaKBt84r5nUzCsdH9bdx7ExXnC9tvcjpzjjr6oK8d0kZAVfuDwk1Ut39PVIncUZP4bB6AUg5i4kFF5HwTiHpnSRhSgghhBDXkGAlhsRhKO6fXsDy6gDf3NXM9/a18nxjDx9ZXs7UIjn4zFYpWxNN2sRSNtGkTfTy+eDLN7gvlrQ52RHD7zT4qzUTWFGdl+kf51VTVgxn9PRAr5SZbAXAdgRIeCcT9k0h4Z2C7QxluKVCCCGEyHYSrMSwKPY5+czaKrad6+Wbu5r51FNnuG9aAY/ML8bnzP2ejFzTEU2x+0If+y6F6YymrglISfvmatYYCrxOA69p4DGN9GWnwYZJ+bxtfjEhT459hOgUztjZgV4pM34ehUYrJwlvHdH8ZSS8U7BcZbLelBBCCCFuSY4dFYlst7I6j3llPn6wv5XfHO9k27lePri0jGVVudurkQtsrTndEWf3hT52XujjVEcMgBKfSUWei2DAiXdQMPKaBp7+c+/gc+egAGUauBwqt4tz2EkIn8XbuSfdKxVtQOkkGoOUp4pIwToSvikkPdWg5ONQCCGEEK+eHEmIYed3OfjA0nLuqMvnP7c38Q+bLrCyOo/3LSmlyOfMdPPGjFjKZn9TmN0X+th1Id0zpUhXbnz7/BKWVgWYmO/K7WB0PXYSw+rrP/VipG5w2erDsOMA5AEpZynR4JL+IDUJ7RgHixYLIYQQYtRIsBIjZnqxly/eV8svjnbw+ME29j8R5h0LSrhnaghjrB3sj5LWcLI/SPVxoClC0tZ4TYNFlX6WTAiwuNJPfq4Nz+unrDCOZEc6EL00IKV6r4QpO3bd59uGB9uRh+0IkHJXDlz2FVbTkSrBNvNH+ScSQgghxHiSm0dgImeYhuLh2UWsmpjH13c28eiuZp5r6OHDy8upCbkz3bysZ9makx0xdp1Ph6nGrnQPTHnAyWumhVg6IcCsEh9OR44GVW3jipzE07MTd/goCvuqu68OSxXYjkD6uhnov3zl+o2G8vmKi7HHeWl8IYQQQow8CVZiVFTkufj8+mqea+jhO3tb+NPfNvCGWUW8eW4RLscYXEx2CCJJi/pLYXZdCLPnQh/dcQtDwawSL+9aWMLSCQEmBHN7iJ9K9eLt3YO3exeOVAe2w08ktJqkt/ZKWHL4wZCho0IIIYTIDRKsxKhRSrFuUj6LK/08tq+FnxxuZ/PZHj68rJx55WNoYdmbkLI1kYRFOGnTl7AIJ2zOdcfZdaGPwy0RUjb4XQaLKwIsrQqwsMJPnjvHqytqjTN6Gm/PTtx9h1FYJLyT6Cu6h3hglhSPEEIIIUROkyMZMeqCHpNPrKzkjrp8vr6zic/+4RzrJwW5vSZIZZ6LEr8z6xcYtmxNb9wi3B+Owv3hqC9hEU6mL1++7cr1K/fHUtcvd14VdPHA9EKWTQgwo8Sb9b+Hm6GsMJ6evXh7dmEmW7ENL9H8FUTzl2G5SjPdPCGEEEKIYSHBSmTM/HI/X76vjp8caufnR9p55nQPkJ6XVR5wUhl0UZnXfwo6qcxzUeg1R2UIXCRp0dKXpDmcvOq8JZw+hRP2yz7fUOBzGvhdDvxOg4DLQWXQid/lwX/5dpeB39l/7nJQ7DMpC7hG/GcbFVrjjJ3B07MTT99BlE6R8NTQU/AmYoG5MsRPCCGEEGOOBCuRUW7T4I8XlPDamYWc645zqTfBhZ4EF3sTXOpJsu9i+KrFbD2moqI/bFXkuZgwEL6c5LkdNx26okmb1nCS5kFhqbkvkb7cl6T3JcHJYypK/U7KAk5mlnipKAiiUrGBgBQYFJD8rvRaUOOx8qGyYnh69+Lt2YmZaMZWbqLBJUSDy7DcFZlunhBCCCHEiJFgJbJC0O1gdqmP2aW+q263taY9kuJCTyIdunoTXOpJ0NAZY/u5XqxBI+oCLiMdtvJcVPQHLp/TuE6AStITt656HZfjSnCaVuSlNOCkzO8cOH9paCsuLqZNKs0NMGPn8fbswNO7H6WTJN0T6Cl5A/G8eWhDqj8KIYQQYuyTYCWymqEUJX4nJX4nCyquLnCRsjUtfUku9qZ7uC7293QdbomwqbGHwbOYnEZ6O6UBJyurPZQGnANBqszvJN9z871dIk3Zcdy9+/H27MAZv4hWTmJ5C4gGl5HyVGW6eUIIIYQQo0qClchZpqHS87CC185LiqdsmvqSRJM2JX6TAq85LofmDRttYaR6caS6MFLdOKMNeHrrMXSclKuc3pLXEgssRDs8mW7i648PAAARr0lEQVSpEEIIIURGSLASN0+nMFJ9gI1t5oPK3vLfbtOQBYhvltYoK4wj1Y2R6hoIT45UV/9t3RipHtSgPkCtTGKBuUSDy0l5JoKEViGEEEKMcxKsxEBgMqyedK9E/7mR6sGw0ucOqxfDCl95Cga2mY/lLMRyFmCZhVcuOwvRhl8OtrOEsmL9gal7IDBdud6FI9WD0qmrnqOViWXmY5shEt7J2Gao/3o+ljOEZRaAMUYqGAohhBBCDAMJVllA2TFI9qIGgovqPzHo+uWzK7fr6z0GrgQarTGs8C0FpivbNrAdAWwzD9tZQMozEcsMYjuCoBSOZMfAyRU+hsPqu+r5tnJhO/vDllnQH7quXJdy28NMawyrB0eiBTPRjJloGbhs2LGrH4qBbeZhmSFS7iri/lA6MJn95858CcZCCCGEELdIglUWyGv+GcbpQ5SMwmu9XGCyzTxsM4jtyMN2+EEZN79hO5EOWqnOQaGrE0eyHVfkBEonr3q45cjDchZiD+7tMvPTbXAE0YZHDuyvpz9AmYnm/uB0/QBlG35SrlJigfnp3/PlHidnCNsRyOphnEIIIYQQuUiCVRaIBZfgKplDuC8MV9WyG3RZX327ut5j0IOuakBhO3zpsPRqA9PNMlxY7nIsd/m192mNsvr6g9flwJUOX85oI+7U/qvm7wBo5cTqD1mX224N/BxXQuCYLeV9nQB1+fJ1A1TeAixnKSlXGSlXKdoMZLDxQgghhBDjjwSrLJDwT4fiYqJjdV0kpdBmHikzjxQ1196vUziSXf1DFgcPW0xfNuMX+ucBJa95qq3c/cExHcKsQZfTISyA7fD294BlUS+NTmFYEZQVSQ/XtCMYqe6rhvFdFaAcflKusnSAcpWScpaRcpeiHRKghBBCCCGygQQrkXnKxHIVY1F848dojdLxgbBlWD04Uj2D5ov14oydxW1dW4jhMlu50Q4vtuF9ybkPbXj7A5gX7fCkb7v8GMPz8r182uoPSWEMK4JhD7pshVF2f3i6fN2KYOj49dv40gB1uQdKApQQQgghRFaTYCVyg1Jo5cFyebBcpTd+nNYoO9YfuHowrD4MK4qyo/3nEQwrhrKjmMk2VCyCYUdvGMYgPfBSG+5B4cuDuqQpinent/eS4hCD2cqFdvjTQzIdfixXMbZx5bp2+K5cN/PQDv8NtyWEEEIIIbKXBCsxtiiFdnixHF4sym7+eXYyHbAGAtjg80j63I6irGi6iqPpJ6mC2P2hSTv82Ibv6usOHyj5ExNCCCGEGA/G1FFffX09jz32GLZts2HDBh566KFMN0nkCsOJbTiBINZNPLy4uJiesTonTgghhBBC3LIRKA+XGbZt853vfIfPfOYz/Pu//ztbtmzh/PnzmW6WEEIIIYQQYhwYM8Hq5MmTlJeXU1ZWhmma3HbbbezatSvTzRJCCCGEEEKMA2MmWHV0dFBUVDRwvaioiI6Ojgy2SAghhBBCCDFejJk5Vlrra25TSl1z28aNG9m4cSMA//RP/0Rx8cuU+B5FpmlmTVvEK5P9lTtkX+UW2V+5Q/ZVbpH9lVtkf+WmMROsioqKaG9vH7je3t5OQUHBNY+78847ufPOOweut2VJAYLi4uKsaYt4ZbK/cofsq9wi+yt3yL7KLbK/covsr+xSWVl5U48bM0MBJ0+ezKVLl2hpaSGVSrF161aWLFmS6WYJIYQQQgghxoEx02PlcDj4kz/5E/7hH/4B27ZZt24d1dXVmW6WEEIIIYQQYhwYM8EKYNGiRSxatCjTzRBCCCGEEEKMM2NmKKAQQgghhBBCZIoEKyGEEEIIIYQYIglWQgghhBBCCDFEEqyEEEIIIYQQYogkWAkhhBBCCCHEEEmwEkIIIYQQQoghkmAlhBBCCCGEEEMkwUoIIYQQQgghhkiClRBCCCGEEEIMkQQrIYQQQgghhBgipbXWmW6EEEIIIYQQQuQy6bHKEp/+9Kcz3QRxC2R/5Q7ZV7lF9lfukH2VW2R/5RbZX7lJgpUQQgghhBBCDJEEKyGEEEIIIYQYIsfnPve5z2W6ESJt0qRJmW6CuAWyv3KH7KvcIvsrd8i+yi2yv3KL7K/cI8UrhBBCCCGEEGKIZCigEEIIIYQQQgyRmekGjGVf+9rX2Lt3L/n5+fzbv/0bAI2NjXzrW98iFotRUlLCxz/+cXw+H6lUikcffZSGhgZs22bNmjW8/vWvB6C+vp7HHnsM27bZsGEDDz30UCZ/rDFpuPbVRz7yETweD4bx/7d3vzFV1v8fx5/ncCLAg3QOMEyUXIouCkYbZCtTaHmnqBsuK6sVGiujZNOZ4GLaLDZrEa4NFm7MNZx2w0F1o9ZmGeTKAkGYOpA/tkl/IP6DcATO+XxvMM/v1/f7tX3POJzDDq/HLXfBufy8fe3D9Xlf14dLK2FhYRw+fDiYZYUsX/M6evQoXV1dWK1WcnNzuffeewHo7u6mvLycqakp7r//frZv347FYglmaSHHX1m98847DA0NER4eDkBxcTExMTFBqytU9ff3U15ezvDwMBaLhccee4zHH3+c8fFxysrK+Ouvv4iPj2f37t3Y7XaMMRw7dozm5mZuv/128vPzvduXvv/+e2pqagDYsmULWVlZQaws9Pgzq2effZakpCQA4uLiKCwsDGZpIcnXvH777TcqKiq4evUqzz33HE899ZT3XFoXLmBG5s2lS5dMV1eX2bNnj/dYUVGRuXTpkjHGmG+//dacPHnSGGPMDz/8YMrKyowxxrhcLpOfn296e3uN2+02b775pvnzzz/N9PS02bt3r7l27Vrgiwlx/sjKGGPy8/PNyMhIgEe/+PiS19dff23Ky8uNMcYMDw+bffv2Gbfb7f1Me3u78Xg8pqSkxDQ1NQW4ktDnr6wOHjxoOjs7Azz6xWdwcNB0dXUZY4yZmJgwBQUF5tq1a6a6utrU1tYaY4ypra011dXVxhhjzp8/b0pKSozH4zHt7e1m//79xhhjxsbGzBtvvGHGxsb+9mfxH39lZYwxL774YuALWGR8zWt4eNh0dHSYEydOmC+++MJ7Hq0LFzZtBZxHKSkp2O32vx37/fffueeeewBIS0vj559/9n7N5XLhdruZmprCZrMRFRVFZ2cny5YtIyEhAZvNxkMPPURDQ0NA61gM/JGVBI4vefX09HDfffcBEBMTw5IlS+ju7mZoaIjJyUnWrl2LxWJh48aNmlvzwB9ZSeA4HA7vU4zIyEgSExMZHBykoaGBTZs2AbBp0ybvXGlsbGTjxo1YLBbWrl3L9evXGRoa4sKFC6SlpWG327Hb7aSlpXHhwoWg1RWK/JWVBIavecXExLBmzRrCwsL+dh6tCxc2NVYBtnLlShobGwE4d+4cAwMDADz44INERETw6quvkp+fz5NPPondbmdwcJDY2Fjv52NjYxkcHAzK2BcbX7O6qaSkhMLCQk6fPh2UcS9Wt8pr1apVNDY24na76evro7u7m/7+fs2tIPI1q5sqKip46623OHXqFEbvXZp3fX19XL16lTVr1jAyMoLD4QBmF4ijo6MADA4OEhcX5/3MzXn07/PL6XRqfs2juWQFMD09TVFREW+//Ta//PJL4AtYZP6XvG5F166FTb9jFWCvv/46x44d49SpU2RkZGCzzUbQ2dmJ1WqlsrKS69evc+DAAVJTU//r4kG/AxIYvmaVkJDAu+++i9PpZGRkhPfee4/ly5eTkpIS5EoWh1vllZ2dTU9PD0VFRcTHx7Nu3TrCwsK0MA8iX7MCKCgowOl0Mjk5SWlpKfX19d67vOJ/LpeL0tJScnNz//GJvC/XKF275oc/sqqoqMDpdNLb28uhQ4dISkpi2bJl8zbmxex/zetWtC5c2NRYBVhiYiLFxcXA7HaYpqYmAM6ePUt6ejo2m42YmBjWrVtHV1cXcXFx3ru5AAMDA947GzK/fM0qISEBp9MJzD7Cz8zMpLOzU41VgNwqr7CwMHJzc73fV1xczJ133smSJUv+Y27dzE/ml69ZAd5sIiMj2bBhA52dnWqs5snMzAylpaU88sgjrF+/Hpj9mTY0NITD4WBoaIilS5cCs3fL//9TxZvXKKfTyeXLl73HBwcH9bNwHvgjK/i/+ZWQkEBKSgq//vqrGqt54EtetxIbG6t14QKmrYABNjIyAoDH46GmpobNmzcDs2/huXjxIsYYXC4XHR0dJCYmsnr1av744w/6+vqYmZnhxx9/JCMjI5glLBq+ZuVyuZicnARm70i1trZ637Ik8+9Wed24cQOXywVAa2srYWFhrFixAofDQWRkJFeuXMEYQ319veZWgPialdvt9m6PmZmZ4fz586xcuTI4gw9xxhg++eQTEhMTycnJ8R7PyMigrq4OgLq6OjIzM73H6+vrMcZw5coVoqKicDgcpKen09LSwvj4OOPj47S0tJCenh6UmkKVv7IaHx9nenoagNHRUdrb21mxYkXgCwpxvuZ1K1oXLmz6D4Ln0ZEjR7h8+TJjY2PExMTwzDPP4HK5+OabbwB44IEHeP7557FYLLhcLioqKujp6cEYQ3Z2tvfVmk1NTXz66ad4PB6ys7PZsmVLMMsKSf7Iqre3lw8//BAAt9vNhg0blNU88SWvvr4+SkpKsFqtOJ1Odu7cSXx8PABdXV1UVFQwNTVFeno6O3bs0JYKP/NHVi6Xi4MHD+J2u/F4PKSmpvLyyy9jtereoL+1tbVx4MABkpKSvHNh27ZtJCcnU1ZWRn9/P3FxcezZs8f7Cu+qqipaWloIDw8nPz+f1atXA/Ddd99RW1sLzL5uPTs7O2h1hSJ/ZdXe3s7Ro0exWq14PB6eeOIJHn300SBXF3p8zWt4eJiioiImJyexWCxERETw0UcfERUVpXXhAqbGSkREREREZI50u09ERERERGSO1FiJiIiIiIjMkRorERERERGROVJjJSIiIiIiMkdqrEREREREROZIjZWIiIiIiMgcqbESEZGQ8PHHH1NRUfG3Y5cvX2bHjh0MDQ0FaVQiIrJYqLESEZGQsH37dpqbm2ltbQVgamqKyspKXnrpJRwOh9/+Ho/H47dziYhI6LAFewAiIiL+EB0dzY4dO6isrKS0tJSamhoSEhLIysrC4/Hw+eefc+bMGSYmJkhNTSUvLw+73Y7H46GsrIy2tjamp6dZtWoVeXl5rFixAph9EhYVFUVvby9tbW0UFRXhcrk4fvw4AwMDREVFkZOTQ05OTpD/BUREJJgsxhgT7EGIiIj4S2lpKTMzM7S3t/PBBx8QFxfHl19+SUNDA7t378Zut1NVVcXMzAy7du3C4/FQX1/P+vXrCQsLo7q6mo6ODg4fPgzMNlbNzc3s37+fNWvW4Ha72blzJ/v27WPdunWMj4/T19fH3XffHeTKRUQkmLQVUEREQsorr7zCxYsXefrpp4mLiwPg9OnTbNu2DafTSXh4OFu3buWnn37C4/FgtVrJysoiMjLS+7Xu7m5cLpf3nJmZmaxduxar1cptt92GzWajp6eHyclJ7Ha7mioREdFWQBERCS133HEHS5cu9W7lA+jv7+f999/HYrF4j1ksFkZHR1m6dCknTpzg3LlzjI2Neb9nbGyMiIgIAG+DdtPevXupqanh+PHj3HXXXbzwwgskJycHoDoREVmo1FiJiEjIi42NpaCg4L82P2fOnKG5uZkDBw4QHx/P2NgYeXl5/NNO+eTkZAoLC5mZmeGrr77iyJEjlJeXz2cJIiKywGkroIiIhLzNmzdz8uRJ+vv7ARgZGaGxsRGAyclJbDYb0dHR3Lhxg88+++wfzzU1NcXZs2eZmJjAZrMRGRmJ1arLqYjIYqcnViIiEvJuvrHv0KFDDA8PExMTw8MPP0xGRgbZ2dm0trby2muvER0dzdatWzl9+vQ/nq+uro6qqio8Hg/Lly9n165dgShDREQWML0VUEREREREZI60d0FERERERGSO1FiJiIiIiIjMkRorERERERGROVJjJSIiIiIiMkdqrEREREREROZIjZWIiIiIiMgcqbESERERERGZIzVWIiIiIiIic6TGSkREREREZI7+BdzezB+8BaW2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the trend of top 5 countries that contributed the\n",
    "# most to immigration to Canada.\n",
    "\n",
    "df_can.sort_values(by='Total', ascending=False, axis=0, inplace=True)\n",
    "\n",
    "# get the top 5 entries\n",
    "df_top5 = df_can.head(5)\n",
    "\n",
    "# transpose the dataframe\n",
    "df_top5 = df_top5[years].transpose() \n",
    "\n",
    "print(df_top5)\n",
    "\n",
    "df_top5.index = df_top5.index.map(int) # let's change the index values of df_top5 to type integer for plotting\n",
    "df_top5.plot(kind='line', figsize=(14, 8)) # pass a tuple (x, y) size\n",
    "\n",
    "plt.title('Immigration Trend of Top 5 Countries')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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