notes/5 pandas基础.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas\n",
    "\n",
    "摘自 《利用Python进行数据分析》 第五章：Pandas入门\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据结构\n",
    "### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "c   -5\n",
       "a    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1=  Series([4, 7, -5, 3], index=['d', 'b', 'c', 'a'])\n",
    "obj1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1['d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d      54.598150\n",
       "b    1096.633158\n",
       "c       0.006738\n",
       "a      20.085537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(obj1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4\n",
       "b    7\n",
       "a    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj1[obj1>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in\n",
    "'b' in obj1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio     35000\n",
       "Texas    71000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可通过字典创建series\n",
    "sdata = {'Ohio':35000, 'Texas':71000}\n",
    "obj3 =  Series(sdata)\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以这样创建：\n",
    "states = ['California', 'Ohio', 'Texas']\n",
    "obj4 = Series(sdata, index=states)\n",
    "obj4\n",
    "# 可以看到会根据索引和字典key进行匹配。没有配对上的即为NaN空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以检测空值 —— 缺失数据\n",
    "pd.isnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    False\n",
       "Ohio           True\n",
       "Texas          True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.notnull(obj4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California     True\n",
       "Ohio          False\n",
       "Texas         False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可通过obj对象自身的空值检测\n",
    "obj4.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio     35000\n",
       "Texas    71000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series在算术运算中，会自动根据索引来对齐（匹配）数据\n",
    "obj3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Texas         71000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California         NaN\n",
       "Ohio           70000.0\n",
       "Texas         142000.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj3 + obj4\n",
    "# numpy因为没有索引，就只是根据位置序号来相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "California        NaN\n",
       "Ohio          35000.0\n",
       "Texas         71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name属性\n",
    "obj4.name = 'population'\n",
    "obj4.index.name = 'state'\n",
    "obj4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ca        NaN\n",
       "Oh    35000.0\n",
       "Te    71000.0\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以就地修改Series的索引\n",
    "obj4.index = ['Ca', 'Oh', 'Te']\n",
    "obj4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop</th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.7</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.6</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.4</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pop   state  year\n",
       "0  1.5    Ohio  2000\n",
       "1  1.7    Ohio  2001\n",
       "2  3.6    Ohio  2002\n",
       "3  2.4  Nevada  2001"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过字典创建\n",
    "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada'],\n",
    "       'year': [2000, 2001, 2002, 2001],\n",
    "       'pop': [1.5, 1.7, 3.6, 2.4]}\n",
    "frame = DataFrame(data)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop debt\n",
       "one    2000    Ohio  1.5  NaN\n",
       "two    2001    Ohio  1.7  NaN\n",
       "three  2002    Ohio  3.6  NaN\n",
       "four   2001  Nevada  2.4  NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以指定列的顺序\n",
    "frame2 = DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n",
    "         index= ['one', 'two', 'three', 'four'])\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从dataframe中获取某一列。 注意，具有与df相同的列名和索引\n",
    "frame2['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      2000\n",
       "two      2001\n",
       "three    2002\n",
       "four     2001\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    Ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: three, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取行\n",
    "frame2.ix['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     2002\n",
       "state    Ohio\n",
       "pop       3.6\n",
       "debt      NaN\n",
       "Name: three, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.loc['three']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5     0\n",
       "two    2001    Ohio  1.7     1\n",
       "three  2002    Ohio  3.6     2\n",
       "four   2001  Nevada  2.4     3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为列赋值\n",
    "frame2['debt'] = np.arange(4)\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.0\n",
       "three  2002    Ohio  3.6  -1.5\n",
       "four   2001  Nevada  2.4   1.7"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果赋值的是有索引的series，则可以按照索引配对进行赋值，没配上对的则都是NaN\n",
    "val = Series([-1, -1.5, 1.7], index=['two', 'three', 'four'])\n",
    "frame2['debt'] = val\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "      <th>east</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt  east\n",
       "one    2000    Ohio  1.5   NaN     1\n",
       "two    2001    Ohio  1.7  -1.0     1\n",
       "three  2002    Ohio  3.6  -1.5     1\n",
       "four   2001  Nevada  2.4   1.7     1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 为不存在的列赋值，创建新列\n",
    "frame2['east'] = 1\n",
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# del 删除列\n",
    "del frame2['east']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>pop</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>2000</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2001</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2002</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>2001</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year   state  pop  debt\n",
       "one    2000    Ohio  1.5   NaN\n",
       "two    2001    Ohio  1.7  -1.0\n",
       "three  2002    Ohio  3.6  -1.5\n",
       "four   2001  Nevada  2.4   1.7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'pop', 'debt'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2000, 'Ohio', 1.5, nan],\n",
       "       [2001, 'Ohio', 1.7, -1.0],\n",
       "       [2002, 'Ohio', 3.6, -1.5],\n",
       "       [2001, 'Nevada', 2.4, 1.7]], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以二维ndarray的方式返回df中的值\n",
    "frame2.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 索引对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two', 'three', 'four'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame2.index\n",
    "# 索引是一种单独的Index类型的对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-01f623a43e3e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mframe2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'd'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   1618\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1619\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1620\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Index does not support mutable operations\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1621\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1622\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "# 索引不支持直接赋值修改\n",
    "frame2.index[1] = 'd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 可以手动创建index对象\n",
    "index = pd.Index(np.arange(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    2.2\n",
       "2    3.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建se/df时可以指定此index对象。可以多次指定用此index对象，从而实现安全共享index。\n",
    "obj5 = Series([1.5, 2.2, 3.3], index=index)\n",
    "obj5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "index提供了一些修改index的方法，如append、diff、union、delete等，具体见书"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 基本功能\n",
    "### 重新索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj6 = Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])\n",
    "obj6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据传入的索引，进行索引重排\n",
    "obj7 = obj6.reindex(['a', 'b', 'c', 'd', 'e'])\n",
    "obj7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    4.5\n",
       "b    7.2\n",
       "a   -5.3\n",
       "c    3.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj6 #6本身未变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -5.3\n",
       "b    7.2\n",
       "c    3.6\n",
       "d    4.5\n",
       "e    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可指定重排时缺失值的默认填充值\n",
    "obj7 = obj6.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)\n",
    "obj7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "2    purple\n",
       "4    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重排索引时的插值处理\n",
    "obj8 = Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])\n",
    "obj8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      blue\n",
       "1      blue\n",
       "2    purple\n",
       "3    purple\n",
       "4    yellow\n",
       "5    yellow\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj8.reindex(range(6), method='ffill') # ffill表示向前填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于DataFrame，可以修改行列索引\n",
    "frame3 = DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'], \n",
    "                   columns=['Ohio', 'Texas', 'California'])\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a   0.0    1.0         2.0\n",
       "b   NaN    NaN         NaN\n",
       "c   3.0    4.0         5.0\n",
       "d   6.0    7.0         8.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只传一个参数的话，默认是对行重新索引\n",
    "frame4 = frame3.reindex(['a', 'b', 'c', 'd'])\n",
    "frame4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Texas</th>\n",
       "      <th>California</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ohio  Texas  California\n",
       "a     0      1           2\n",
       "c     3      4           5\n",
       "d     6      7           8"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Texas</th>\n",
       "      <th>Ohio</th>\n",
       "      <th>Utah</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Texas  Ohio  Utah\n",
       "a      1     0   NaN\n",
       "c      4     3   NaN\n",
       "d      7     6   NaN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['Texas', 'Ohio', 'Utah']\n",
    "# 使用columns指定对列的重新索引\n",
    "frame5 = frame3.reindex(columns=states)\n",
    "frame5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### drop丢弃指定轴上的项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于series\n",
    "obj9 = Series(np.arange(5), index=['a', 'b', 'c', 'd', 'e'])\n",
    "obj9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "e    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj10 = obj9.drop(['d', 'c'])\n",
    "obj10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      one  two  three  four\n",
       "Ohio    0    1      2     3\n",
       "Utah    8    9     10    11\n",
       "NY     12   13     14    15"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对于DataFrame，可以指定轴\n",
    "data3 = DataFrame(np.arange(16).reshape((4, 4)),\n",
    "                 index=['Ohio', 'Colorado', 'Utah', 'NY'],\n",
    "                 columns=['one', 'two', 'three', 'four'])\n",
    "# 默认删行\n",
    "data3.drop(['Colorado'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['two'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-7865f6a4b1e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'two'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[0;32m   2048\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2049\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2051\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2052\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   3573\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3574\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[1;32m-> 3575\u001b[1;33m                                  labels[mask])\n\u001b[0m\u001b[0;32m   3576\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3577\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: labels ['two'] not contained in axis"
     ]
    }
   ],
   "source": [
    "data3.drop('two')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  three  four\n",
       "Ohio        0      2     3\n",
       "Colorado    4      6     7\n",
       "Utah        8     10    11\n",
       "NY         12     14    15"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删列则需要显式指定axis=1\n",
    "data3.drop('two', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 索引、选取和过滤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series\n",
    "obj11 = Series(np.arange(4), index=['a', 'd', 'c', 'b'])\n",
    "obj11['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj11[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj11[['a','b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "d    1\n",
       "dtype: int32"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj11[obj11<2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c    2\n",
       "b    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj11['c':'b'] #注意，这里的切片包含末端。python中的切片不包含末端下标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "d    1\n",
       "c    5\n",
       "b    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj11['c':'b'] = 5\n",
    "obj11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "one         0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11\n",
       "NewYork    12   13     14    15"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data frame\n",
    "data4 = DataFrame(np.arange(16).reshape((4,4)),\n",
    "                 index=['one', 'Colorado', 'Utah', 'NewYork'],\n",
    "                 columns=['one', 'two', 'three', 'four'])\n",
    "data4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data frame的选取操作感觉比较凌乱。\n",
    "\n",
    "下面先说一下采用 **data4[内容]** 这种方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one          0\n",
       "Colorado     4\n",
       "Utah         8\n",
       "NewYork     12\n",
       "Name: one, dtype: int32"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 【1】单个标签的方式  是对列进行选择\n",
    "data4['one'] #虽然重名， 但这里可见是one是对列的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "one         0    1      2     3\n",
       "Colorado    4    5      6     7"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 【2】含有：的切片方式，是对行进行选择\n",
    "data4[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'three'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   3434\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3435\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_searchsorted_monotonic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3436\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_searchsorted_monotonic\u001b[1;34m(self, label, side)\u001b[0m\n\u001b[0;32m   3393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3394\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'index must be monotonic increasing or decreasing'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: index must be monotonic increasing or decreasing",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-e7e7de61decc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'one'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'three'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# 这是不可以的，[：] 是对行的选择\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2048\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2049\u001b[0m         \u001b[1;31m# see if we can slice the rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_to_index_sliceable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2051\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2052\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36mconvert_to_index_sliceable\u001b[1;34m(obj, key)\u001b[0m\n\u001b[0;32m   1894\u001b[0m     \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1895\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1896\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'getitem'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1898\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m   1355\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1356\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1357\u001b[1;33m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1358\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1359\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mis_index_slice\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   3299\u001b[0m         \"\"\"\n\u001b[0;32m   3300\u001b[0m         start_slice, end_slice = self.slice_locs(start, end, step=step,\n\u001b[1;32m-> 3301\u001b[1;33m                                                  kind=kind)\n\u001b[0m\u001b[0;32m   3302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3303\u001b[0m         \u001b[1;31m# return a slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   3493\u001b[0m         \u001b[0mend_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3494\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3495\u001b[1;33m             \u001b[0mend_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'right'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3496\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3497\u001b[0m             \u001b[0mend_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   3436\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3437\u001b[0m                 \u001b[1;31m# raise the original KeyError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3438\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3440\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   3430\u001b[0m         \u001b[1;31m# we need to look up the label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3431\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3432\u001b[1;33m             \u001b[0mslc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc_only_exact_matches\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3433\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3434\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_get_loc_only_exact_matches\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3399\u001b[0m         \u001b[0mget_slice_bound\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3400\u001b[0m         \"\"\"\n\u001b[1;32m-> 3401\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3403\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2393\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2394\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2395\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2396\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2397\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5239)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5085)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20405)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'three'"
     ]
    }
   ],
   "source": [
    "data4['one':'three'] # 这是不可以的，[：] 是对行的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "one         0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "Utah        8    9     10    11"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4['one':'Utah'] # 果然是对行的选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2392\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2393\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2394\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5239)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5085)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20405)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-ca791fe17309>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#传入一个下标，既不可以行也不可以列\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2060\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2061\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2062\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2063\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2064\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2067\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2068\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2069\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2070\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2071\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1532\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1533\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1534\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1535\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3589\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3590\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3591\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2393\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2394\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2395\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2396\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2397\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5239)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas\\_libs\\index.c:5085)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20405)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas\\_libs\\hashtable.c:20359)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "# 【3】传入一个下标数字，既不可以行也不可以列\n",
    "data4[2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         one  two  three  four\n",
       "Utah       8    9     10    11\n",
       "NewYork   12   13     14    15"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 【4】如果传入一维布尔数组，则是选择行\n",
    "data4[data4['one']>5] # 选择出one字段大于5的行 = ="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           one   two  three  four\n",
       "one        NaN   NaN    NaN   NaN\n",
       "Colorado   NaN   NaN    6.0   7.0\n",
       "Utah       8.0   9.0   10.0  11.0\n",
       "NewYork   12.0  13.0   14.0  15.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 【5】如果传入同样大小的二维数组，则是行列同时选。类似numpy\n",
    "data4[data4>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过 **ix/loc/iloc** 可以同时对行和列进行选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "two       9\n",
       "three    10\n",
       "Name: Utah, dtype: int32"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要选择行，可以通过ix方法，可以同时传入对列的选择哟\n",
    "data4.ix['Utah', ['two', 'three']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       8\n",
       "two       9\n",
       "three    10\n",
       "four     11\n",
       "Name: Utah, dtype: int32"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.loc['Utah']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "two       9\n",
       "three    10\n",
       "Name: Utah, dtype: int32"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可同时传入对列的筛选。\n",
    "data4.loc['Utah', ['two','three']]"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 算术运算和数据对齐\n",
    "\n",
    "### 对齐和缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.0\n",
       "b    4.0\n",
       "c    7.0\n",
       "d    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 算术运算，自动根据索引进行对齐\n",
    "# Series\n",
    "s1 = Series([1,2,3], index=['a','b','c'])\n",
    "s2 = Series([1,2,3,4], index=['a','b','d','c'])\n",
    "s1 + s2\n",
    "# 注意，索引d处的值为nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.0\n",
       "b    4.0\n",
       "c    7.0\n",
       "d    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame，要求行列索引都要对齐，有任何对不齐的都会变成NaN\n",
    "\n",
    "# 如果希望指定缺失填充值，可以用fill_value\n",
    "s1.add(s2, fill_value=0)\n",
    "# 注意，不是最终结果变为0，而是在计算前将其中缺失一方的值记为0，加上另一方的值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add加法，sub减法，div除法，mul乘法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame和Series之间的运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认是按照列索引进行匹配，然后对各行都执行相同操作\n",
    "# 先看numpy中的例子\n",
    "arr10 = np.arange(12).reshape((3,4))\n",
    "arr10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr10[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [4, 4, 4, 4],\n",
       "       [8, 8, 8, 8]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr10 - arr10[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b   d   e\n",
       "Utah    0   1   2\n",
       "Ohio    3   4   5\n",
       "Texas   6   7   8\n",
       "Oregon  9  10  11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在pandas中的df和series也是这样\n",
    "frame10 = DataFrame(np.arange(12).reshape((4,3)), columns=list('bde'),\n",
    "                   index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "series10 = frame10.iloc[0]\n",
    "frame10\n",
    "# 如果bde这种列的索引，在两者之间不能匹配上，则结果中会都变成NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0\n",
       "d    1\n",
       "e    2\n",
       "Name: Utah, dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b  d  e\n",
       "Utah    0  0  0\n",
       "Ohio    3  3  3\n",
       "Texas   6  6  6\n",
       "Oregon  9  9  9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame10 - series10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah       1\n",
       "Ohio       4\n",
       "Texas      7\n",
       "Oregon    10\n",
       "Name: d, dtype: int32"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果希望是匹配行，并且在列上广播，则需要调用方法\n",
    "series11 = frame10['d']\n",
    "series11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b  d  e\n",
       "Utah   -1  0  1\n",
       "Ohio   -1  0  1\n",
       "Texas  -1  0  1\n",
       "Oregon -1  0  1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame10.sub(series11, axis=0)\n",
    "# axis说明是在0轴上进行匹配"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### apply 和 applymap方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.543726</td>\n",
       "      <td>0.586586</td>\n",
       "      <td>-0.654696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1.039431</td>\n",
       "      <td>0.144162</td>\n",
       "      <td>0.870128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.216221</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>0.601167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1.356383</td>\n",
       "      <td>1.118972</td>\n",
       "      <td>-0.708455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah   -0.543726  0.586586 -0.654696\n",
       "Ohio   -1.039431  0.144162  0.870128\n",
       "Texas  -0.216221 -0.000563  0.601167\n",
       "Oregon  1.356383  1.118972 -0.708455"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy中的一些元素级数组方法，也可以用于pandas对象，比如abs\n",
    "frame11 = DataFrame(np.random.randn(4,3), columns=list('bde'),\n",
    "                   index=['Utah', 'Ohio', 'Texas', 'Oregon'])\n",
    "frame11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>0.543726</td>\n",
       "      <td>0.586586</td>\n",
       "      <td>0.654696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.039431</td>\n",
       "      <td>0.144162</td>\n",
       "      <td>0.870128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>0.216221</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.601167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1.356383</td>\n",
       "      <td>1.118972</td>\n",
       "      <td>0.708455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah    0.543726  0.586586  0.654696\n",
       "Ohio    1.039431  0.144162  0.870128\n",
       "Texas   0.216221  0.000563  0.601167\n",
       "Oregon  1.356383  1.118972  0.708455"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs(frame11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# apply可以将函数应用到各行/列所形成的一维数组上，即，以行/列为单位处理\n",
    "# 先定义一个函数为最大值减最小值\n",
    "f11 = lambda x: x.max() - x.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    2.395813\n",
       "d    1.119535\n",
       "e    1.578583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认apply是对行的操作，即axis=0，对每一列，计算各行的极差\n",
    "frame11.apply(f11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      1.241282\n",
       "Ohio      1.909559\n",
       "Texas     0.817388\n",
       "Oregon    2.064837\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以指定在axis=1上，即，对每一行，计算各列的极差\n",
    "frame11.apply(f11, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.543726</td>\n",
       "      <td>0.586586</td>\n",
       "      <td>-0.654696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1.039431</td>\n",
       "      <td>0.144162</td>\n",
       "      <td>0.870128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.216221</td>\n",
       "      <td>-0.000563</td>\n",
       "      <td>0.601167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1.356383</td>\n",
       "      <td>1.118972</td>\n",
       "      <td>-0.708455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               b         d         e\n",
       "Utah   -0.543726  0.586586 -0.654696\n",
       "Ohio   -1.039431  0.144162  0.870128\n",
       "Texas  -0.216221 -0.000563  0.601167\n",
       "Oregon  1.356383  1.118972 -0.708455"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# applymap是元素级的函数\n",
    "format11 = lambda x: '%.2f' % x\n",
    "frame11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>-1.04</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            b      d      e\n",
       "Utah    -0.54   0.59  -0.65\n",
       "Ohio    -1.04   0.14   0.87\n",
       "Texas   -0.22  -0.00   0.60\n",
       "Oregon   1.36   1.12  -0.71"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame11.applymap(format11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utah      -0.65\n",
       "Ohio       0.87\n",
       "Texas      0.60\n",
       "Oregon    -0.71\n",
       "Name: e, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 上面之所以叫applymap，因为Series有一个应用于元素级函数的map方法了\n",
    "frame11['e'].map(format11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 排序和排名\n",
    "#### 对索引的排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    0\n",
       "a    1\n",
       "c    2\n",
       "b    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对索引进行排序\n",
    "# series\n",
    "obj12 = Series(range(4), index=['d','a','c','b'])\n",
    "obj12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    3\n",
       "c    2\n",
       "d    0\n",
       "dtype: int32"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj12.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "d    0\n",
       "a    1\n",
       "c    2\n",
       "b    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj12 # obj12本身没有变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       d  a  b  c\n",
       "one    4  5  6  7\n",
       "three  0  1  2  3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data frame\n",
    "frame12 = DataFrame(np.arange(8).reshape((2,4)), index=['three', 'one'],\n",
    "                   columns=['d','a','b','c'])\n",
    "frame12.sort_index() # 默认对0轴索引排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b  c  d\n",
       "three  1  2  3  0\n",
       "one    5  6  7  4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame12.sort_index(axis=1) # 可显式指定排1轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d</th>\n",
       "      <th>c</th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       d  c  b  a\n",
       "three  0  3  2  1\n",
       "one    4  7  6  5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 默认升序，也可降序\n",
    "frame12.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对值的排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -3\n",
       "3    2\n",
       "0    4\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series\n",
    "obj13 = Series([4, 7, -3, 2])\n",
    "# 【注意】在2015.10的0.17之后版本pandas中对排序做了修改\n",
    "# 书中的order方法已经取消了,\n",
    "# 对值排序采用的sort_index(by='b)的蜜汁命名方法也取消了\n",
    "\n",
    "# 对值的排序统一采用sort_values方法\n",
    "obj13.sort_values() # 默认不改变原数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    7\n",
       "2   -3\n",
       "3    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj13.sort_values(inplace=True) # 加inplace就会就地排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2   -3\n",
       "3    2\n",
       "0    4\n",
       "1    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  0  4\n",
       "1  1  7\n",
       "2  0 -3\n",
       "3  1  2"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame13 = DataFrame({'b':[4,7,-3,2], 'a':[0,1,0,1]})\n",
    "frame13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "2  0 -3\n",
       "3  1  2\n",
       "0  0  4\n",
       "1  1  7"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame13.sort_values(by='b') #df的按值排序必须指定by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "2  0 -3\n",
       "0  0  4\n",
       "3  1  2\n",
       "1  1  7"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame13.sort_values(by=['a','b']) #先按a再按b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### rank 排名\n",
    "也像是一种排序，但不同的是，它不是原数据的顺序改变，而是会新增一列，叫做rank，通过指定的方法，为数据增加一个rank值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "obj14 =  Series([7,-5,7,4,2,0,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.5\n",
       "1    1.0\n",
       "2    6.5\n",
       "3    4.5\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    4.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj14.rank()\n",
    "# 默认是按照数值从大到小分配rank的\n",
    "# 值相同的会被当作同一“组”，默认按average方法对组内成员分配rank\n",
    "# 比如对于7，出现两次，排名为7和6，取平均为6.5，对于两个4同理\n",
    "# 数值越大，其rank值越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.0\n",
       "1    1.0\n",
       "2    7.0\n",
       "3    5.0\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果不想取平均，可指定max或min作为组的rank\n",
    "obj14.rank(method='max')\n",
    "# 比如这个，对于两个7组成的这一组，就都取他们的最大rank即为7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.0\n",
       "1    1.0\n",
       "2    7.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    2.0\n",
       "6    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以指定按出现顺序先后作为rank\n",
    "obj14.rank(method='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.5\n",
       "1    7.0\n",
       "2    1.5\n",
       "3    3.5\n",
       "4    5.0\n",
       "5    6.0\n",
       "6    3.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可按照降序来分配rank\n",
    "obj14.rank(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 索引出现了重复？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "b    2\n",
       "b    3\n",
       "c    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引出现了重复\n",
    "obj15 = Series(range(5), index=['a','a','b','b','c'])\n",
    "obj15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引的 is_unique 属性表示它是否包含重复索引\n",
    "obj15.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "a    1\n",
       "dtype: int32"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选数据时，如果有重复索引，重复的都会被选出\n",
    "obj15['a'] # 索引是重复的，返回一个Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj15['c'] # 没有重复的，返回单个值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 汇总和计算描述统计\n",
    "### 一些描述统计方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.10</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame16 = DataFrame([[1.4, np.nan], [7.1, -4.5],\n",
    "                   [np.nan, np.nan], [0.75, -1.3]],\n",
    "                   index=['a','b','c','d'],\n",
    "                   columns=['one','two'])\n",
    "frame16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    9.25\n",
       "two   -5.80\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum，默认会将nan忽略，但不影响相加\n",
    "frame16.sum() #默认在0轴上的操作，即对每个列，把各个行相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.40\n",
       "b    2.60\n",
       "c    0.00\n",
       "d   -0.55\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定轴\n",
    "frame16.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      NaN\n",
       "b    1.300\n",
       "c      NaN\n",
       "d   -0.275\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean均值\n",
    "# 如果想让nan的影响更坏一些，即只要出现nan则无法相加，可指定skipna\n",
    "frame16.mean(axis=1, skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one    b\n",
       "two    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# idxmin、idxmax返回最大/最小值所在的索引id\n",
    "frame16.idxmax() #默认仍然对轴0操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# argmin argmax则是返回索引下标 (似乎dataframe没有这个方法？？)\n",
    "se = Series([2,4,3], index=['b','c','a'])\n",
    "se.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>7.10</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  7.10 -4.5\n",
       "c   NaN  NaN\n",
       "d  0.75 -1.3"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>8.50</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>9.25</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    one  two\n",
       "a  1.40  NaN\n",
       "b  8.50 -4.5\n",
       "c   NaN  NaN\n",
       "d  9.25 -5.8"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cumsum  每一行的值等于这行及其之前所有值的累加\n",
    "frame16.cumsum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.083333</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.493685</td>\n",
       "      <td>2.262742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>-4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.075000</td>\n",
       "      <td>-3.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>-2.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.250000</td>\n",
       "      <td>-2.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>-1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two\n",
       "count  3.000000  2.000000\n",
       "mean   3.083333 -2.900000\n",
       "std    3.493685  2.262742\n",
       "min    0.750000 -4.500000\n",
       "25%    1.075000 -3.700000\n",
       "50%    1.400000 -2.900000\n",
       "75%    4.250000 -2.100000\n",
       "max    7.100000 -1.300000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe\n",
    "frame16.describe()"
   ]
  },
  {
   "attachments": {
    "image.png": {
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keZ0TDV835JIVp3uSNgVdkexIkOmTQP31X//11ifvtW4/6OQUl5wbd/bWpGNTcuQf9siTdeOv+K+Q6GBTWfWUmEvsJDeSC/4q2ZVISl4k2k79JJ0So1uqwEDSWcLOb0mS14olzRJBCY3ETCJlXgKqRZfc8NfY6aa+UtxwkbBZC4mmRJuMU1evOytwsY5eq6ZHYqv1irQTUvHbG0rJIrwkl06JJb9krK+3D+CLT7VGEsQekOTXpuyMf/CIzSvd//AP/7DtD8m6tWhvWTc+t+5nqLpRcnvSAwF72kMJepUSfP6UiMfvxF+ccDVfXPzprQZrJpHtO9YwhbtTbwn5G97whg0X36m2H+0FNvsldrhJkFf93jYwzz5eDw0k9vz34Orf/u3ftvWEvTj4S8aDBjG4hq0ZPz0ggSefPGAq7mK5UfCGYRAYBAaBQWAQGAQGgUFgELgIEDiaIEt8nXi5yXazrLhhLgGQEDlVlVy5KcdTYoa3BNO8108lAfjc9JvzPVkJle9KskWvJMiPS0l+vHbqNI68OeXGbtT551RTEklGUuAUTVLAZsWcKhGULEosyEn8SgokLwqfzeGVAEVPlzZ90dLR+Ka0YoSRxDWf+I7GtmTGj2P5jrKEy+u9fOsEUeKDV8wwvvLKK7eEh174Ot31/Vkxw1/CKzFmT8FHlyTK+uJj34knLJzQO8lc1yJsrbEkXoJvTfnoVFQiJhmXuJFny5xE+hie4UUuO1q89pIHApJOP+DlpNXDBD5I8vibTHrO0yZDB5y8wuyBSQ8Y6LCufLDeSjJ4+WouLMyp9he8fa/c/pHMitsPdjnB93DJOqB75dp3up0cS1StIUw9CCDjO/8eAHlzoD0HZ3O+JuBapcvDB/veOvoVdzyuBf6gOaXuu9tO/PlOn/X1EMR/n+WV7xX/82A4PIPAIDAIDAKDwCAwCAwCg8DFgMDRBFly7Bd8JWNulN1YlwRIQpR+bVdi4OZboiAhwCeZKHl2c+47sb2uKgnB59Sv72RKRCRkXud2kobHiZnkwM27m3oyFUnhmkiil5iyXwKBzjdzWgmMvnl9vuevefRk9CXN5tkTd/Mb08k/9Kjm6KiuPDelTx5m7NIrwZEASYglwJIrJ9pO90uQnBZLbiSf5D1okExLsGAsDgkXeSexsHTqaF0lQz1UgI0E1NqKWZJoHfjjYYYTXIk7GevLVkVSJ7mydhJCJ6UwUa2Vk0m/WG3d+etEHm889KRPS6Y1ZVOCDQ/Jur3i1WNvC4hvXQMyxunKvwu1eFWy4uAf/GCBlq6SYGujtD54xBVdTAo5cdj79FpD15QE1kMg+946+LVqp+0eLoiNfWskWadb0mvdPMzAQz/d5L2i3YMoPyZm7dix9hJue4Rf+K23RNoDEP5aX2uvWm84861S3I2nHQQGgUFgEBgEBoFBYBAYBC52BI4myG78JVRu1PUlMooba4mDm3Y36hIyybSkzf/Z6qZeMmVedWqGx823m3q6FEmN70pKHJxiupl3U+8Hkrza60SNPifTbu6dkkmenZDRo5W4OW3zOqnERSLSDb0WX4kEXxpvDpz8g89cJ6XJrXr0+bBPCKmg00kt3/FIWCRCP26hV4zwVP1XQ31H1cMCic+LXvSiLYFySsi2hAtm/EWTREuWxAYbCZEHDsZ09FqwNWYPhmxJxCSv/Qq2WMhJ1nyXVnxOPyXoJbHk+cAG/dbdnrBv+CPRlNB6O0BSRk4hVxvmG+Hk9e1o1sgJZ9/NhoU9xR4eetKVTHrO05JRw8g+911kJ63isqfpd00oHjjAiw+wssftYX4q5hR0/+2UmPH50Tc4OIVvrTxMsL/tHckz7CSsbEmM7QP2+eLk3a9+08+W16J9P91aeU3bgxRJMF99VeF3f/d3tz55pYck1sPDBQ8s8PWmhfhdm8ZhenPw3IzNP4PAIDAIDAKDwCAwCAwCg8D/hwgcTZAlnxJYiVjJrZMop7tu9J00/eqv/uqWJHmFVNL0/Oc/fwvfzbhkys29G3I34L476abbTT3eX/mVX9kSqU44JZ+91uk1YKeEXimVBEpGvFL7yle+ckvGJWBu2iXHkooSZHbXm3l9N/nalb6ukXnxSSpKauI1J8Hjs0Qn/1d5MhJK/jpB98qvE710rLzn6bMJMwm3E1iJIBto1oQv4vfgwIOFklaJreRTsuQhhO+WSpDR4UIvjK2DZE1c/u9nvOLnL55e5WXPd3B7HVdiCiOJt8qXXsUlW8JGr6TcAw4Jm7U2T594JJ3WnE1xhDnbYabPr8ZwE7c4YGy9vaotcVzXhK7k0rfquBD+8dvXvnPvgQR/nebSATtY47MfjcXEpph9Z7jvB6ORUfFaK5jDkrwE1AMG+11c5iTMfH/Na15zGqeHCJJn1wo9cJU8p58umEiSXQdw1WYb9ubCT8tndLjZ0x5yOZlX6ONPdSPOP4PAIDAIDAKDwCAwCAwCg8AlhsDRBNlNtGTBKZcbcjfWfvBHUuMkT3Ls/9J1sy9RcXMtOfN9UDxOurwyKqnwWig+N/xu3t2Y94NQ5KqSDt/JlDjQKVmrkJGUSRRKStz8Gyv03pQiGSkh4C972n0RNz5YONXb25EceT1W4id22PQd3T3vXvexsdicXkqUVKeNTvX89z2SKie4TgnpViUz4eekU0WTODXHTrFKjGBGVjz8F7vWq7dO8D0AcbLPptjwOy32irT1/LM/+7Ptx8/Iw8VegSebEnuvd3toImnkD33WFp8fTZNIsmlOvMcKfa1R83wWhxgk/ip5dLz62ptT6FUk4M973vO2pBUedKoeDsBdYu6hkVjZ54s19xAIZhU+Vex7uJD39oVrysMFPLD1XWp6Jb8ebLgGzHszwYMXbwbAzA/alZR3LRWvtYYxutL6o+NZ/dG3p/C2V8RoPczFW1sc0w4Cg8AgMAgMAoPAIDAIDAKXAgKXSw66Ga5f60bbaZ9fUpYsSVrd4DvZlTQ5xZUMe13Xzb0f1nLqKbnzSrTvxdKh0Kk6icbjtMvpmeRX4YObfFXfTXt+sdOPMeF1GkdOYqGwEe9G2P3D7r6wIyFSJX6qRJGu+NmRzOGVCEko1iL5kEiJiR7yya585+2L2au0EmPJlKRFEuY1XT/QBGevRGud5ErqYSm5crpvnSQ+sJfU8Zc/sMl3Y5WsKunymrz/P1qCDGe/mi0paw3IGkuavZ7rx528Cu/Bh5NshS6vVUsE4eG/MMJr/0j67Jc//uM/3l4zFpNybM3yb9/ih4fE1Am1twrsC3ugJND8WXq3iQv8Q6/1dhLr/5eGv/UUozl7nz0Y2Lf2hYcE9gt84J4f+Q4Ta+O72x5u0A9vr1rbb64lOj1csnb0SMAl206P6Xad4PNKND8UdsyV1LIHZ2tur670MG4PsAF/Y/EpaCqamv/6UwaBQWAQGAQGgUFgEBgEBoFLCYEtQS5gN95urt0gSwAka5IxCZEEzCmg123dsLtBd9qr7wec/H+wXut10iXhklB57doNPl43227gvSrqFNKpm9MxiV83517L9Wq2m3wJQTfqkiuJm7HCTzJ81SrHbubZLWklGw8Zp2gSHSejEhexSj57LVUy4vTT6Z8Y2KdvLcZi4IcTbqemP07hH7+cQns1V5IEd3jwWZVgdbqO30k9v3vNGy+5EqWSJX7hp0PiK3kzR9br7H5FmYxX6712i9d8SbI5DzyuuuqqzSYbEnG+4nPyLQnslNQr35JDiSM5J6WSeWVdE+PWmc3WWL+y9vuurX1m7eLng9o6rzLpOaslI5l38i3hhr1E316UENt/+l67tsa+ky057hTWGtjXElt7at3vEl+4eJDCjq8QwEpC68fr6PZwxTVjz7nG7Dc/qCaRba+71sjTbU2io6lk7Q2n0vGER3HDRO3BiHUwxle5KbglM+0gMAgMAoPAIDAIDAKDwCBwsSBw+XpD7EbbzbdkQDLjVNir1W7WJZBO0yQAEgM31W7sJbgSpb/5m7/ZEjM39ZIjr6o6Ue60CmB0S0adOksovKJLHz0SBq/nSryc4pGVzPGPvdXPwOfvMXrzJQjaPR+dkl6vezuRlMhI9lW8kn5+SjokRHjXRIINiYjTVqd+ElsPBfZ28uU8Lf0SIT+eJHEVv5Pdkl3xssmXvhdsbdCd0vKTjMRHQlYCxKcqP/TNWQ9JLF56PdCQIEvaJGySMDz002u9JIjW1g9KocMHXdJnj5D1/eVOoMlL2nzf1Q9MiUtCyWZY1dKXn7VoK50dD2LotXfwtS75mux5MMcDC/vOmw9is/+sP1tO7+0JD0B8J1mS7KEIvJyUW3fXyite8YrT/1pLkqvkNyxgAENvPkiu9X2fWgz2Prz5TxaeikQWTTz2q/VQ28/oSutEp+tN1UfXhod+11K6yKdnU7aM9/Tmpx0EBoFBYBAYBAaBQWAQGAQuVgRucCTqhliyIFn0Cq0TNTfokoSSAYmBm+tu3p3i+WEq/OQkbhJkMhJqRaKguDl3QithkIBIviUDdEj0fEdVwiYJo6eb+bNu1M+ib8ZObvQlEyUL6Mb8QZNoSkadyHo9WOIicZPMODX3Gi/fJI383Nujg0wPDSSyePZ8+XNjLX0eFkiSJEyw7rVaOq0FGxI230n2sMGJJDk/LuWhguL7q348LNz3dls/iZlkSqLmQYHTYIkgGwr5Kvuws35OPiW8XjtvjSSK9oF5a1cSSI5/eO0LbyZIqtHyD4+q8A3d/qpvbF78HsbYi06leyABJ4XMWtK50o716bMHJL++PsAODFU+iM1+tkd7cMAWjD2UgGOvnsNS7DDUWqvf+Z3f2Xj4w1dVTCXD9pwEnS04WRM61WKHGX+8heHtCmuhFCM+NLphS9ZcezJec6vMMTziPWtu6IPAIDAIDAKDwCAwCAwCg8DFisDpK9bdOEsOJKkSZTf3EievnHrd2A28m2s36ZIKN+wSR9+jlGiSkwA5gXZSKHFU3LiTk3hKOH/zN39z+xVlJ294Jaq9zuwk1GutWjf73czfnAWQ2JQ4SGhKKko6JCOSX6ehXjP2S9leVSbHH8mqZM93RMNn9YNO/B4kSAz9uJIk8ccpfGQrX2FtTSo9ZHBqLXmGMQzhap3g5QQUH/z2hc9eU7a+5K2f5FrMErrWai/X2Dxb7LIlAbMvJG1OX+mRZLNjvUug+aJvXt981XqImR6JnZglhNG1MOA3vPluX9o3SrIrTvl7nlbSa735479Mas9p+Uq/XyiHs7jZx+9rBRJqfsDEQxW/Ou3BkNhVr2ybp0vlo2vHabu3KKwBffafPaSumCXnNNsbFhL5l770pRvG+/WNl34Pd+ixLn3P/zxYDM8gMAgMAoPAIDAIDAKDwCBwKSNwmiAHgptup4OSLN+FdIMtaZEsSiScdDktdgMukXFqKSl84QtfuCXIkk3zbvw7aaTLqZgbeAmZ15IlWl7h9p1V+iUGTumcMtInWeuGP99uaithk3SV9NCn1PLBK8G+VyvxcSIoEZGcOQn0A1RXXHHFaTK4ty+x8cNW/lsgCZLYnMLuE5e93FljfknGSuDXhFIc9EpIJXLG7IcRDMkp6ObpKlZ08x5qSOQ8yBAr/CVtbO35ybIHp3yix1iiuxYPG6yZQg+d+OhNfk1g+Wg/+Y67uPCg2Vd+wAqt2Oi017zyLkH0/XUJsgS0OLVO1D2kSbZ48p0+fObtR62xPe102OvtTqYl4vlsH9NjD73gBS/YfGLHd5S9AWH/++6x9uUvf/lGc/3Yz+To97CoB0oSYzGKQ8ItDvvGybh9KJkmBzc+6iv9QrhX7l1rrin+i42NsPX6ue+t28Nw8ovzXUv04FNhUYybgflnEBgEBoFBYBAYBAaBQWAQGAQON/gOcgmRk2M33W6g0SRVbqolVG7QvY7shl/yLFmTLHkt2qukEkVJjwTCzbokU+LbK7n0SR58T5WMG3/JmmTAyZuk3IkXvhKbm7tOL37xi7c46JNw0KeUdOhLMpyAS5SdFvMHjdNgseYAACAASURBVB9O/7SSRDJ82he8fa+0hOPm+g1zWMNW8ujVW7ZLcNhhz8mjNZCo4cMv4ZJcWQ/f8ZUwk6WT71oJmYcAXmW3BpI/Yw8/JFMeErBFDg7kJG+SLUmjcTjAAp8kFU3M4q/wYz0FRudDeGklpn/xF3+xrY35EmT7Cfb2BL32nj0iIW2v2U+tJ78VD2f8ujfd+cM38tr2M5ysORv4nAbT700B8cCAbXg52aWfTpjSxc6rX/3qLR77SrJKnx83s/fte9/Nxw+3q6++erPh+rFesGWXHV9LsA76Hhy5BszBt7V0TVlzsjBU8agKPjrtA7Hwm74wSp+W/3CGhfVDow8+UwaBQWAQGAQGgUFgEBgEBoFLHYH/liADRJKgdJMu0ZEwlLBI1CQTkgKJgJtxvBJhN/i+l+m0z0mZ01U34X23Fp8q8aLPqZ2bdjfsnTb+pG7W12SDzhKFddH5Iunih0RH8oDGFwlESR/avkiavHorNok0LNBubpHoSIRgJ8nynVj6YMUfSbxkzfe3/Sga3PoVa/81lNNJawV3a4K/mMUlifZfDEmsr7zyyi0Zd4LsRNkJJt2+F2t96ZGgS/IkfU5B+cImXPQ9IJFYW8e1ZBNm+EpMrYEqMXVCL6Hzw1dr0qfvtXcn8fwoGYSN02H7TnViTZd5+9Dr+uLzQ1sKfnbxtL/aY058JcnWXOG/V/q9LcBn6yk26+AtB8kn/BV4wALGYpC8kvdauR/v8ovVCrswtCZejZYc81VMHho5ke6XsyW+1sMDI7bZaO95pVzcEm18XqOng/5w1uKhnxx8XYtwtJ7m44VB2OBFh4+4j+3xLZj5ZxAYBAaBQWAQGAQGgUFgELhEELhBglzMbpTXm2rJlldInUhJBCQPXkWVgJpz0624GZcsSD4kABIrp4TdiK834PqSjhKPbK/tyr/Sz9uXRKwlfbXNnceXeNdW8ufUWXLloQI82NzrX2Uu1JfgSEr9F0YSR4ms03x4wpAd33N24ijhYkeiKGGS5JQgOx0m169as2ne6bTqO8f+r180ybYTUUmfBNO6iyFfJFNOV510Wmd9sVo3e0ISbs2LuX2Dtx/R4r/5qr3jO+vi8Tq1hJA98+LkvyTYgwt0hU1Js4S8uNLnlXwPAPiIv4Sv9UfTr/JLIspXvF5ztrclznjYsq4S3L4nzBYb+MXGF983JkeGvr464BoJR9eEBF5iiwdekmoPY/70T/90W0vXiv83Gi5ss0UHOeslwbYO5CXx8LH38Cnsuw57vd9/qeWrCvRZp3CKjx78sBYLfPibvk3p/DMIDAKDwCAwCAwCg8AgMAhcgghcdp07411BctOsummWGPR6p2SsE81uqLsBTxU5SY+beydbEh1JkmThYitiXRMyY8nLzSlw7gTZyS5dHkb475ckMzCFp9NLiWXrxJZ5CZBESlIkEZOASl75g07OSbGE0CkjusTYabVTUlVSKB62fe+XHL0lZGjG1tyJuSRZkl7JJ776Xi8/e10dDzk85lUxs4VmLlsSOHRjSRw++tiyn8RoLllxqPzlP3/N06sqxgpMJJna4tTSWYLqQYWHEPAOa/7ykZwkU3LcK/jkOzFGc42g8YcuPtFNzlrx6a/+6q82GxJhiS+9fCRjLfz4m5jJs2tNPTiQUOenePA7YfYwCjbW1rUGgwp5hY8Scwk6m/YXP9mNJ5lpB4FBYBAYBAaBQWAQGAQGgUsNgaMJMhBKkvY3ziUc3Uxrq2Si75OS6BcbwOKUYKxlj9k6d6F+uiShToklPpImSaG5cNaPN33m2OVLfFprkj+SI7olaas+fMbsFYtWQqiVoCp4FDSVbxJB8msxl91k8PCjgmedQ29c35htcs2teyxd6zw+urOnNVaipT+aVuyqxLN5usho02uOPX41twmc0LOFv7Ly6Vec+OOTrEtss6Gtb808HFAk1jCngw/pwqu/YqO/JsjZ1Eq6PQywr7TpW3mmPwgMAoPAIDAIDAKDwCAwCFyKCJyZILvprnTT70a6m3JzK0836OuN+8qzl033xdDCpyTlx42TnvTpq53e6iutQesSDR1PbfTktPX3SW86V5549/roNYeuWvO1JHeMttox3zje9DZe2xLAZIz3cfCFjlXPipP5eFb7+PGtsaCtJb34SpDzxRxZPqW3Ofz6ajr18SZHX35mZ+XPj/SsY30yqx18Z5XVLrliOYt/6IPAIDAIDAKDwCAwCAwCg8ClgsCZCfIKgJvoynrjvac3vhBPei6mtrjFtMZ+c2Okj5693sbZaJydZGrzZ88XXdtcOleavrLXd0I+Ktvcvt3b2Y/jR199ia5NJtox3tXX+PbthfSvc3t76Vntxh9tlVnnyBo3f6G57Jyn3etJJnrjfZsf6DfGu5ed8SAwCAwCg8AgMAgMAoPAIHCxInCuBPliDX7iGgQGgUFgEBgEBoFBYBAYBAaBQWAQGARC4IbvxkaddhAYBAaBQWAQGAQGgUFgEBgEBoFBYBC4xBCYBPkSW/AJdxAYBAaBQWAQGAQGgUFgEBgEBoFB4DgCkyAfx2Wog8AgMAgMAoPAIDAIDAKDwCAwCAwClxgCkyBfYgs+4Q4Cg8AgMAgMAoPAIDAIDAKDwCAwCBxHYBLk47gMdRAYBAaBQWAQGAQGgUFgEBgEBoFB4BJDYBLkS2zBJ9xBYBAYBAaBQWAQGAQGgUFgEBgEBoHjCEyCfByXoQ4Cg8AgMAgMAoPAIDAIDAKDwCAwCFxiCEyCfIkt+IQ7CAwCg8AgMAgMAoPAIDAIDAKDwCBwHIFJkI/jMtRBYBAYBAaBQWAQGAQGgUFgEBgEBoFLDIFJkC+xBZ9wB4FBYBAYBAaBQWAQGAQGgUFgEBgEjiMwCfJxXIY6CAwCg8AgMAgMAoPAIDAIDAKDwCBwiSFw+bF4r7vuuhuQL7vsshuMb6kBu9m+1a1utfXPazs5vq0y0dHSv85HP09Mq1z8dKaj/jG++Nc231baeWVXGf1juvY8N1X3qvOmyN5cufw9r/xZfCs9nbXniYN8fKuuaOmqXXnQzuKL/1ibjrNkm092z7efP4sv+ln8zWv3Nta5tb/XdV65Vcf0B4FBYBAYBAaBQWAQGAQGgf8LCFy+v7nlFFr0bnZrb8zp5I7xrTr2fI2vvfba04RTX6K8yh3Ti4ZXiVdLZ3R66u91ZHtP34/TvdKT1f7oRz863PrWt16nT/25AfFkkOw6l4117kI0snhX/lXfsX76ko1nTz+mc+VJbm2P+ULmmK5Vbu0f493bPWYnHevcXg7PMdoxWbS9L3vZ1VY6bszGypeN9uax/X5jNs6aP8uPC/Gvvu1jXefqn6XrPLLpmHYQGAQGgUFgEBgEBoFBYBD4v4LA6QmyG921dIOrVffzK+/aP+8N8zG+bGWb3vPaTaY22eRrs7H6vMqs9H0/HSs9mjbdkp29zv2YjmO0VR8eY0lT/a1z5J+zdO3p6U/FfrzS97LNXahdZehe64XkmiO/6oi+tvl8jO+8tFXf2k+3dvUl+spb/5jNC/EfkzumIz7tfr4xO1W06Kvssf55+Y7J7mk/SV173TMeBAaBQWAQGAQGgUFgEBgE/icRuOzaa6/dMuP9DX03vbXN117IyWM86VnlVr79vLk9bZU91o+/9gc/+MGWPBivNdnsxx9de4yGnky8To0r2TBOvhZtL5vc2sYfb230lXftm483+jGZPQ/eaPiPyaRv5V1p+376tGo6a/f86/gsH9KJ9yw9K33lX/XrX2huz7vqPCa7n0/+PDbIqvHWpqP2mI1jvMf4borP2as9S1/zx3Q3d0yWz/lt/hhP8tMOAoPAIDAIDAKDwCAwCAwC/xsInJ4gr8adgHYj6/RS7eY2upvbtU/e+Kyb3nhXvnjZ6xVT8+jGnZxG0yrpSt4Yv3H+pmPl0ZfQ5uc6l95otddbvPC/6dPSf/nl18NqXKVP/8YKHrxV/HQai02r7HVlZ9Uf70o71o+PDjimO3uNs4t/pe11HpvLxp53Pyar7m2sOve69mMxqIq5/fyqK/vZjL99st9PyeJLJh0Xai8kZ449Pof5qmvvv7l9fPmTnVU+WjzH9K389Y/50lwt3elHW3XXjyc+rTmVjf+rJX+L4yfhZ7H/JHT9uDpuSV9+Urp/Unp+XKz+f5E/L14X4jNXOWvvx7Ofv5BeOs+Sy95Z7V5uPz4mF4+5vZ/xrzzR4jVXv7m1TfZCPPHfmK749m1yN8XWXsexcXqPzd1StP8Nm7dULKN3EBgELn4EbpAg+wBz6urmuw9k36l1E4vmJv6HP/zh9kfjNre5zcbrjwMe/HjWPxZ9HxfNXPNaSWRydCrf+c53Nj3ZJNcNNFr8fFSao4/9eND18dFB//e///3DHe5why2GEk48+sVCD5pyu9vdbmvppUNNLmzYSbd41GuuuWazEw+54qUwupYsnQpdKj9ve9vbnuKY3XQYkysJN1bMo1fEIS524sl2/OhrZRsPXVp40ZNe/sEUNmh4zMefXjHhVdKvj3eNd2M4+YeuMCi+9NCBlk4i+YlGb2Nz+I1XW9k2p/BZDUd9c43ZI48m3mKsRYMFO/uCRk6Nv1j4AWd29FU83/3udzdd9IqJDnPpwqPSqRRb49vf/vYbRitdn5106OczmsIGGpv5rCWrzR88xumKXyx40YvHuD56ftPRnsy38MZXJZvOzcmT68O8km1rxKcKerHEyx77yWnDUp+8ikY+XnP5EIbxa9HMx88eeVU/euP8IeP6Nq+KtRjwVNHNJ4cnXbXm+QGHSrqSN1bjSbbYjKPlD5vklXwo3vzRwpZe/fxLhv6VxgZdKnoFvTEZhe34rWf4pw8NT3brN04fHfZn4+SLnS20avaN+Ul+1WkeLXt4KvXJmsebXjLKSqOXTLrSg558to1bP3z5IDYlO/r4wmubPPknO3SqfGn9sCRDF3zNk3H9s1MM+Ycnufw1xkuXmi/afCQnbteAQl8tPfXxsKnq80fLZ8U4X+j63ve+d8q/MZz4Uhx49PlFj8KnMExG22dGPoRdvsQbBuh48p8dusMZf77q02teScZ8lT59fMm1/ubCMn46xAUDfXQ8eMM+OXMK3/Hmp/l9MYeHjhUztsRYoRMNP7pK32qTPXT7KVzNo+Uzffmjxafq4+VHdorR+jWfP+u4eM3pN8aTvVUu2mqbfxV0OtDyzRyacbzJG7NlXrv205k8HnFq17rqyAYs4l9jQYtHm2484V2/OW0yybfG6UbXX8fkWr/oaGshp2S7OfzR8OijZacWPxvxr/J8Vs2pe95odGUjHvaUePZ6miOnpEOfrHF217mN+UQvv4sLb3zJ482+uXWcnn17zE+0ZLOzl4teyx4/Vh/3/uXbqv+YXjSy6a6Nlm/p13ad6FeyF824eFeeaKudFdOVt/5ZLVvp06eTLsX4vyXIbtQxqQphjD5862tdQFqB6rtgVeMMdSPoQxGti5quNfnqD/A3v/nNUwe78MgqnPaHqz+wbCsFZL6L2hzb9PWH8dvf/va2GfKhGPnFF7zmshefeTGxLYFfFxZNLKo+H/Ao/FfQ6Lrzne98SqfT/DXXXLP9gc4G/+F/t7vdbeMlh0Y/frHwGw994Y8GQ3T8xY8HHY2NMONTfyDzJR1afFWJFx34yH3jG9843PWud918x3vHO95xiw8/Hj6rcGJDwae2fubUZPhHJgyLDz742EWzNuFKbz6RR1ezRS4a3uLIH7b5h67PPjvJ042uwB2veTT1Lne5y+YTfgUtf2rN0astFrpaJ/r4bt6+wds+0cdr34obDxvx2xNoKt/sGTbw0MsHdJiGi33OjnlzZMVPjl4FDXbso1tfftgDzePBTw99YcWWihePvjn22RELH9DovtOd7rTpSZ7v+c8/pVhaz2yTgUF+G+vzhw3+02Wd2K+gNUeGXjrxwHmNE59K3+pbNLz6q4/5w/9iYadY8Iu7azqM8i9eftFPt8JH60AnGf6ab1/nR/bxtMZk8YaRlryWfjJ4w6DWPL3J4cHbWtir1pFuJV/5SIcxTMlrxVxMfKqYVxVy8C5Ge8YeJEcG3Wd167rayCY++syRJ4PGL76LyVjFo5rrusSj314VMx4+6JNjix4FnbyKz5qYV/Crivj5ZawlDxdyCho5NFibx2vd4Uw3Hv6p3/rWt051oyt0mSOv6JOjk7785HPXSTrNFRM/yLLvmuIDvvaAWBTY0o+uzye81ow+c3wP6zDATx86G2GBlp9ac/TwBy/dinE0sYmHLbUY8eUvmjhce62X+PhDD98VfOHPF/3G+PHmD9nsWXN48hFdy3+VbhiIpRKNXHab07LNFvmucTrZM6fSYR6dj7B3TYqZLF59a4EXH98VGIgnO3zTR8NXocO1QA4PvdZAq+IlY44N9sSPR5+ceQX+fEEP15WXrvDUFxv5bPDD+oUBm3QaR8PPRmvDrlj5tvqMX6GDjNj1Ff6SKb6NeELHxy+84rA2/IsfPUzJs7Pun/ygM5/xpTf8jVcaOeOwa03Yzmc8YlH18ZqnX+EbP9ktVnN4Wwd8eOwj2OLDXyz8C6/8wYNubE5hV40XTnyOD080PGysdqLhFwcb/EwvOp+tRfbYL9b4tOj4yLAhPvrMoWnJ4WMjfebIFZcWLx1K/uRDrbls2ovZElN46yts6ONX6FeVY7R854N5Lbt00MVWGGQDD5348Kyx0oeWH3jElW5+oBnjyW40vPlvLj+yQaZ4zEW39nQ0py0WPPpkW3uyeNCSM+ZXvuSjVnXd0UVGoUMhh65VYCCOsPDZdWo7I6sg5xQKC9qNBiGKbMwuKPOMCUQNXPLNoZG1eIIhqy8IMo3xJ9/FVJDkFBetAIxVMsZ8ZSP9ePujhI6PPbb0G5PVJ5f/gYdGBg+c0kd3C4a3WMwDGU2rkGPThlDMkdWyp89GNLz6qnkF9q1TMdCXrmyw300PeViGVzrFit5mYCM5fboqZNmLbk6CTIf1gYv1CTtyfMqvsG7c+ocFH+jyIcIHdoz9ocfDvrEKAzHg09IlDrb5haZYa320+tmnXzzo5vmnhDmdxWW+yp90k1fZICf+dKHxiR7FmE1rom1v8Lm9JTb+8LGbeX28bgY+97nPbQ8ksgMXsnAojvYHGtvo4lDylR3z9NKlHy7R+Mjn9rtERIztNXbNKfQVd/7jNU8vXeGAH916KnziH6zyodacPlts0GGsj2asFguMyLBHH73mxYTGdy0elQ5+tnc2hxacyOOjQxuOcFH5IQ76VTzFTAYWCjsqGTihk9Umbw4PPQp5POgK2607O+jhpF3XkE7y9lo85LOPRofCRj4ZFwteFV7Z4h/+YnGtwg6Pvpb8Xj95ckp9fLDPvjZ5PNZSHPrtOWP4ihUdHn0+8Ekc9LBvHP5iRXdtlLiwZd6cGPTpVPmGl3727P105y95suSyhyec88ccupIf+mzQQb/5/Fnl9K2jOfzZEz+fjennE2zcyPIfrtr2O5/YMKZPpY9+fPTlH9qq07oqaHjpUrLDtj5f6NSno5tqvrMNJ7HiMS4uLd/pN08fHi0aXXRbD7z0K12z+BS85IqFDXLF4yFKYzzWlKw+H+kuhmKmT+EfXjHga9+mW6uag08x8yF/0PJdLHSFg5Y/dPABH3sqObzo7GrTzzcy+OFtHg7W2d9meLLPfzbwrRjmjzas6SaHZv/jN6fS4XrTsiFWvObIKV23Wr6xz+evf/3rGw9s6W3N+YxXwcuulg1xG+cfHpiYg2EYofOjfWPMNr/YY8t+UfiPxt9iSqeY6GCPPjrQ+IOXP/xV0BX84sObP+wp8KHLvEqHdaKPP+LOHt3m+UVPawZ/42zpw0/bXsPLDlzo4LuaXTr4p2WvPj/w0YWXDtjANjoe9uCm5XdrrU+fMVnYkGdHWWPHmw1t+vjMH0Uc5tC09PIj7GGExoaCL15+ksGrKsVlzBfyfIObuTDXj1+fTnFZn/zhR9cXmpJt7bof6OUPW3jp0sIgOf6Ihay+ggcWaBV9dY2peTb4xR5ZvtOJly18YVuLF10seNBbJ37y2ZgOY0WfHP1oxviKL1xgYK55vtgzWjrbU/nIZ76o1iSdbLJFL150vhYv/WTaG/SobIina4c/6Gg+//neOuWDuPTZU9lwj0CHOdcr+5t/LYqWAwQywAgnA9PYHGfRtfgVPPHhUcxzkF4GtWgBgy8Zc5xLFhCCQK/QYX6t5tngR+ABWR8fHWyw2YLTlw7zLj7y5tXiLCZjfu/jpaM5voUFPnRjNT5teujmOxzRjc1Vsp2v8eVDuJBV48uH9PFDTKu++nTka3jRtfptHkbshz8aHQod6TMmi0YPu+T4YI3Q8JpHU11MYWdeNc8f8isNn0IOD1m+KPjyG001Lvb4yKHRlS/k8bLpQ1xfwYuvDxZjdpQ+IOjCb5wPaAqbbNBBNwwa49VXFTgpK81FSzfefDFuT5NpHibssROPPrqCZj5ffChkm78qveFEtsQiefxhoM+vdOqzxQ4e9NVv+pJFT6e+mr6wM9YvFvzJ6YeleWX1Pbn0FlM66Iad1lytftjyNR62jFX28MdnDNPVt82hE8zxFRMevOT10Y3pV/Tpg2P62cSHpq/kBzpd2U9nvmjFRGeyxujZoi9b+FTzVfNofGQPXd/+6WYPTz7WD9NioYPtfcmeOT4p4a2fv8nRq4hHX8sGmXTQGQ9a2NUWG3l9JT5xkMeLphY3nIyLpX7yxgpf6Lbns6VFo5c8nfjpxJ+NfNJW8fn7ZZysPlmFbjxVc/uCVkz61q/PB59daD5PFXrohC0beIuZr+TwkGkdjM3lE52KawhNfHjx4OULeXzo4kuXOfa10fDmE1/Q6aHbOBvJsUcnHj4oxa/NBjoZ8myo61qQz190/uNPzhwZ8nDil354mRcje12n5unKn3wmq6LzmQ5zdJDRNx8WaCpa9oyLbZUnq1bE0Tz+Yo6PTTx9BpLL33wwVvCxa//wjWy8bKDBAA996zxaWNCb7rAkj6akuzjYxddnGZvx0Jsse9H1FTYraCo9PZQhTwYf+2waa4vbOL34u57QioVeMumPXkyr7fwnT9feNhlzYWu+2JtjKx+16I3Fyx6alp7k9mO62VLTmay1dG2JWctXeuBNzrqo+mr+0oVHoZO+fFx9skeM48GXH8k0J340VSG714/GHzoUMoox3/jUvsxO9rNjLJZ1LH5jeHQvGg+9dNLHN/LZy2eyaHyL15g/eNDxGIcbW/rJskd/a2lOX0meLnT+Fnd+xZtvZPCjo7V+6MbsqnStOvDzg3/mk8dDn/tqGPCjtocysENTydNDTjVW6DNPt4Knai4+PM3zsXjQG+ujw7xrlny0bGmv3ymbyuv/4RQntIqABJizFDEgKB9OnARijtXiNxc/4yoZYElK8ahsrAth7OY8R+PxJMAFaWwu39D009fioXsqwId4PB3gVzr5JN5iqF03LH46WxwxFmcxuVkUE1ur/jU2Ns01nw70Cn52VltoVXT81mTl4bdiDh0//drmzGcze8ZqfqVzL5fdfNdaA7holewUZzLaqjmYpcc4/nxujCf/9v4a08M+u+lHI69ko72iTR9+shV0cnxIVoseLRv0wJ+89dZX8NERHsURRvllns/5R5bf6O0fc66VdOHR5wMeutjFl/76q938Kg5z9GjpWnnJF2P2+Rk28dKJlk9aPmTDmLwxfeyta45mvrWitwpD1Xyx4+vmi268aHDgn4oXHtnijwpDlS/4ipm8+eLV9xmBV8FPFxp7xeYDFa/Ph3TxVU0Onay2PUFGn772jrG+VsFf3PqNteFTm81ij86uOXS0SjpaE/40ryWTPWP9xmRUdHLocIK/cdhkK73ZohvexZs+Y3NK+tFWu/Xzj24xZoOsufyCZ/phoNCh0I2Gn474zOkreNONv7l4yYk9f+wHNo1XXv7Ys8VIL9vp2Zh3vptjO/v5RDesFfrsPS1fVLrz1ZgdPil0mEenF1+VnnALK39j8bJpvkLHWtJHbrWNB28xGPMBT/GQ7fphhw/hh488mr448KcnvvzFS5c1oR/d2P2DE1X86eFTvmkVLRl88VrTbtroMt86tw5oYYCH7Xymt3jZTKZ90mcX22jpMs6v5MNspWcvfuP2Gh/Y5K+17FQkPfSyxwcFnQ/kycZHNz3m1GIlTzes6DGOlyyaFo1fePWtTzS68Cls5AfaqjM6Pejhu8rCNr3s8kds9Krp24yc/FOM5vXjNS2ulUafgoffKpv2l9bnORlvA3RvyZ9s6JPVqgqdK01fbPTox4OP/8myjacxvvbPJrQ8KF7l4EEu3NKRD9k2z292ouFdx2wnZ66STuNkmteSUelV6KyuccbLDzV7Ymgv0ZFMtuhS2tfpZtM6oYeblnz+rbhEo4ts/hgnjx6++mr6+NbaN0cuffs+XjL2OH5+o6VvtRke0Vafomnp0fJdmz/NkVtjZitdZIuZnGqsNsaLr5L+/M9+rdjY7jpujdDEyz5/VDLZSv/aFoNW0eJfP6P4Q1f+ri2Z9gaZdKDlL50qHTdIkBExJmSsZIBCwRSUcRUvRwuSnD4a/k996lOHd7/73YfPfvazh/vd736HZz3rWYf73Oc+myNkOVdhjx1tc/qKlk4f/HiAXinAfPjKV75yeM973rO9puq4/aqrrtrkk9nHlw36s0Wn6iKLps0vLXvasEsm/fStFw55NPbEgE/fB4BS7Pr5RLc/YvAOf3zFXL9x7aZw+YCnT+VPcfLfWC0WmxgfrLTFp2VL4Qc+etDC3RhfMsb8iUe76jSnoink+EI3neJWrAE+GOQnmeySq6DjVaLX8ofv4ZjttdWPny4ysMj37PMzPnMKvWsJI3rUcKYzWraN000ue2G48q0+Zhsff8zBq7cj+ENvmGaHHN7k0I3Dp5jJ0avmE535H02Lhy01XXyhMzvobi58HlhffuInb+z6/uQnP3n44he/uL1i0HRNCQAAIABJREFU/vjHP36bJ+8B26c//enDJz7xicPd7373w33ve9/DPe95z+2G0Dx8s0WPzwF+2jdaxQ2bypYYxYfPTXW84eKzQx+Pgt8NEl/ZCytzxV+cdJHVqp6a3ute99pk0q9NV/7Tpa+aj9eYPRhXzNGt5ItY8aLDVaxsoJMlo7UuPYyEGzzoUPAr6OTQ977QgY43H/Drk2kf6KNV24fk49HyhQ3FnJLN9BvnSz5q0fFoV5zMrXztRT6pSn6xSV5bHNbbvHF7AE++oZlfbZChmx72+JMevIoY0PCYz2ZrgJYNfZUPyWlVuGUbf7xo+mEdL/6SHDz6fMoP/Sp94sBnXuFD8RvjNZ8ubXT22dWGNfvhSLb1Cnctevboy0Z/9+0Teoo9H9llD+bJoLV22Vr9ydfwd43iX2/I85e8qmitH11ht+pC4wPfwmGPHR3Fh9e4uNNViy8M6RN7Pu/9Cxeye32rHvbYVfhGzpgMW/mDln/Zhg9dMIC5vkJOpQNv9tKpVekzv5Zso2WbPFt0rv7hNZfd+uTa6/TbC+TQzCnFgoZHK/504EFHg21y+FQlGXwqnnwyn4w58RrrJ4eHfuMwad0ai5kMv8ijx0M+ffj2he5smqNjxbd+mGor5KrZMEfGetPlcwo2+njyjS9k8YrNXLbQFS1+c+TTvXVO5sk0Fx5oVXPsh5+xmM1nnz42jNOFX6x40VS0VSZ/ouVr9PzJXzrCkX39PtuzHV27yuujqXjDBJ0eLRpc2cGnxaviMZ9O/XTxL3r85irNpdO4/Y7WOBljfRWf65I9uo/5BmMyFTi7Hrvfo4eslryCR7/YzSnRmmdb4Ud68g89ufTnZ/RN+GR/wDY6PrbZucFVVSAUmlQVzJyw4J5Kfu1rX9uCppAMPkr1q+a6UNB8l/INb3jD4a1vfevhMY95zJYcu8C6OWNTBUJyAcYHOoCtfvSjHz186EMf2gJ69KMffXpzbI4M4Ojyx/Tqq68+vPOd7zw89rGPPbz4xS8+vYDw8VFs2WEjP8QrJjxodCuB3Jx5MRj3/Vx98mHhDy7s2KrQqXQzZMxvci1Ovmjp7AIhR7eCXklncZirhh1/FWtW7Gj4tGHfjUh2wqg9QF92tOlq3/ThkG48YS5OfTIuFjb106FV8ifs8hldpTMe/OKnF11VtOk2j18JN+P8Cl/86PEY6+cXnflMVk03enLo+vGQ12+f6qPBQ2z2gpp+dMmZlj/oWtdgsWpXHVtwJ9esOdhV8JFHM6fSbc/xhe+KPj627e18zi9zFXP8aT+kAw/d4jeXn/jRXBMS3De+8Y2He9zjHgcJ8MMf/vDTzxifF67bt7/97YcHPehB2zX+gAc8YPPlq1/96uHNb37zNvfIRz7y8MxnPnN76MZm9tmB6ec///nDW97yltPrCgZ4tPb4/e9//8MjHvGI7em/B3jvf//7N3/N89sP0qkwkpjz/cEPfvCWmMMHn8peeuGkFrPWPP88FOiPg72PrtChkGucvuKyFmT4oMCXnLG5fDE2d83JaXg2rLu+ipdevN5+MSdGfXLNwxDd6Yg1I5Od9PIlH/XhFg2vOPgDB4Ucne138/yAC1lzxiq/0o3PON/Q6VTpJxeNHljiZwfd/Lo29KOLMfz4Rx+6+fSwae20FbrIorNTIZuPazzo6+cd/Ssm2Usf/eTx0MeGMZvmskFORVPwr7X4zONT4GWcTH285viqGq8xWzt0JVljPO2r+mS7ZvCyXbzWQsGrwqVY8Yk1Gj420LVq/pLFi6bgoafCJ7x8WdviwJ+/ZMjCvz2IZl4c4W8sDrL2Ft0KH9Dp8JmosIOn2FoXvuAna05L/6o7fcWQjWLEGw8d+OinOz/EUSzofLG+dKwYuOZV11g66SdLF5rCTzR85BX6lGyHqfVDaz+Jjw/m00OXvoo3PY3xZ6e+OTpad77hUdmqTxe+/NVno3l6rHW2WiM4xLv6B49ktcZ8UPGv88XBt9U/9tmhlw5jceHhO1wraGGWjmzGx64YfH5VyOAzF1/Y5Td9irE94W8hXnL8M7/GQ55evMUaDznzWn6IieyqKxtkVLrykw9k00fOmhjnE5oSDxkYNs4eOXPo4WqOT3Q1p2/eHD/Mh1dy6Ok3T7dCBkbN46GveMir/p61t/DER4f5dKHTr23OWKGXX/Swiyf9+dSa4I93pfFLwZ9eY1jQqdBrbJ4ONZq2a3hjPsHA9U1mjYWfKvn0WifjPg+Mo7HPZniusZHBB0c85vhRyVd0OClkVD6bJ6+FAT6+NYdfH785fUWfDH//62rcpv4LqDYSZoUwWqWFK9AuquZrOaQI0o0mw1/4whe2hPkhD3nI6WYjzxYbBaFV0XNaMO94xzsOr33ta7fgn/a0p20315Juv+TbgtsgZCXz7Lm5ZpsddHyqDxc3hcUXHQ9fipNcusWTT/yxaOYBKk6FLRWfkqwxW2RaSLbwotOTbPKN8SvaZNK3rs1K019rNrRtKrpUfAof/JHwYdd6ZNu8WMjnn372882GNI++zhcL2/TgV7OdXjR9fHTEAze87Tv6rHVyxvQmk57m+a8vXnGSxRtG7JlX8p9Nf0DoQmObnD4aef2Kfhg1Fx+e7cI7+eCLjz8KP/iVb+bVbJl388VH/eIgy4YqBpUOPGgKn/XztThb+3xJxjhs8Oqna1N4sldWnWSzmwx79pOxfYHfdSkhfd3rXrfRXIeSVSfBxQVzJ8VOku9973sfrrzyyi0x/cxnPrMlve973/u26xoeKjvhoU/eg7SXvexl23Xu2nSti8saei3OgzPjJzzhCYcPf/jD20M8/vVZooWb5PBLX/rSpvNxj3vc4alPfep2rRejVg079ou3tWVHwaPiUdKBT0UXD57m4UbenP1jLVQFDW8+JLPSxYSnlq6uLXrscS2aufyjAx2Nb2zg0Sr04cm3jXhCx5Mu9HDoGqZHXOlhx7rQVyzk62cTPx302Vfr5268dCnatb/O6/PffrU3+BNm7KrmwgAmeOhDU4vfXPa0fFPjMa9Go0chr+RjPjVvjlz+6ONJJv1acxU8xuj6YW6e7vR0rbKfTDrwqObY11bpFYtijs58oKcxHmucLvz4XFP5QF6hu3lz+2K+v0lk6EGjf+8L+/YSX9YKbzJoWuP2YH4bo+dz/OaLVd88H/RVfPxgF97R0NV8JpPvaNZASU/+xBddi1+lj4140FpXfpnHv+KYv2TElx59JZ+N8fAjm+bTld7GeNlLP3kVjU9KNtDSG41cssXDRnLwrJBX8OFZfSmesGCbjehwzi80/a5veuhG85lCv2Ks7zMGPnjylwwbtXjpzad8pActffYIu3BY46Z79Qs//fhXefT8y1a60cnQQ7f54uJf8eQn3dHjLd78pwdfsaY7H9D10cVEX9ceOnnYKcWCl1/mYaHPvjGe5LT0odFT0TenKvSp6xqns5iNVbrwssdfff6udvCxgabfHFvhgE5HtuPHk9/68UXHjze80fmNptBPhp9ausxlx3jFI1urHbzJ0SnO9IWHlg/Z1ZIhq0TXp9s4Gh6y+Nd5NtRo+MjgLV7x4Vntw58ufppXlXThVbT0RW+9/T0hy0a8+OInmw/6+cS/4hVj43hvkCAjKhgzxlF9CtEVSjgkIKembhzd8OJDN08XGTeWaG5G3Zx6vdofOXJOicz7QMJPjk7Ba8k97GEP2/Saa5HMeaL78Y9//PDlL3/58JGPfGTTTSeg+UzWzXU0N8tOk9qI9PGPbYk7eoUvbChhok+nmo/idWOv8M2HAJrKB+MS5lWPPtsKXRYxe+h9uJDHq9KptDHiDxf29dk1l1x2NuHlH/OKlpySHF3odIWTObrSZ75+emqTN9bnOz3pIkdfNTnt3gaakm/sJpePWhiq2UBb/UBHi84nf/SM1zjqs6GfTOPVP34ln1zzja/3/vp/V1p8e73oxUfKGrBRSYf9utq2L8yhiVtpXD/Zxvt25acnXejw4xdaPpOPho4vHegVNON1n8PfdcfXj33sY9tpscRTIlzC49p3WvumN71pe/PEAzVz+CWzbEqqvWadXW2FTfw+Z+CIt4dyvnrhV1adRvsMgh9efSfDTqadXLMhdp8zPivo8llCN9/bY8Yq+31+kXMtG7OPF4+1YwvdNZ7PtWwqxtkx9lnDdj6Zox+u5rT0oVXjDZP8M+ZPvuCjj094Wit0yXp2xJGf2vr48Sh40gUDOlW2lHUvkcer6uNhM1y1qkJ/uuhQzCVrbI3y3R4zxqui803LDqyitx7ZjYdOesjxCx85fHxhWx9Nnxw62ooPO8mlGz96MRmz0WcTH7MbLvxBI9ff1mzR6++TmOnCRxc6X+CHNx5204Ufj/j6m4U/XPKDf/jsCXrJadHxxIeWr/U3won/qwwf+UWHuOjMn2hkiwGNfTL0FJd+ld940kWvOf7lM3ljevMdJrBW1v1hjEdhX8lnMukwJx40RZ8edDS2+YLGHzRjVeG3wncyfMHDNp7VdvbpIceH9hi+bJHlh7GSTWMVL3vtN3rU4jCfjz5njNlW8Omziz+M2UQzj1+fj1r7L5/Q0o0PnU94wgs9PfoVdu1VNtHJ0ec+M/4wopc+vPHnfzHQgT9e/GLiHxq+eMnGnz/sx1ObjXjQ2cdbRaug5SO7jfllXPzZjm5OQVdXnfFq8ank6I5Pq660fGpOu+qOP7t0pjdZLb610KFoq+t8/FpYrHbSv9LSoV3n00O3uKyneW37TF9pTVwD5Frv8EBL39onu9KN+QFjOhU28Rjbn8b6+LQqO9HsOXPZ3pSc6NHPx3289KqV/Iyv+T5T0POdv/GZV5rvnsM4nnSHE999DvOZHa2/Q2Rdj8WW3nTjY5sePGInb4zODn/MoWvD0HwxlYf53KCTjFaloz67eMJASwe7eJR0am+QIOe8NgBSHjgFZmNxyumO1yDf+973bor7AKQcD9DIAKubTIG9613v2r5/yDF/6Nkhi79gH/jABx5e8pKXbDe2AUbXU57ylC0hB5bXNOlzUuSG1+kSHqUxEHzX0UmSxDrg2fR6p9PlFoYP6PwPVLqSQWO3OXz0Ky2yfnwwwI/HPN+MxamQz190i08GjQ7F2CbTwpEv+LT40okXjU661HSbo884nOlLxxqv+NHzhyzf8djwYjFnnC08CjlVfPjyL9t40NjGQ94fZ2N28aHxEV862Dff2pDBRwdf0p/faNaUHN3wSnf6+YKu4CUL32zhczGxkW08fKMfX/uFDYVf7Kh4+I9Grvj8UTcmE0926IiXfj6QU4wVMq4RMuzzXRz4smWd6CmG/EFTYUsPGfLGePhUXMbZ1yrFLi7ydKEpZOkRH//Mqwqa6xG+9Epqn/zkJ2+fAz4XfI5IWp/+9KdvePNBMvukJz1po5Oh0/UtOfbATfIs6eUD3RJbfuYjXyoS7yuuuGJ7HRs/Xz3UozccyMFD4s63Zz/72ZteOiTlPYyDu4d8+MmzQyfZxvxHSze/0MJzpfMFnR66FbrQ2+fwVvC1Xub17c/sml9LvvGlObr1o/FFLPmnpQ+fUt+YPvPskoFDe8FcJf3hYmyejvRqyeZ/c/jylb7k+KFoxY6ffr4odOE1LyZzSrr2+vHGb641IZM8eoUeddUThnii6+cDGhvxxaOtrHGxqyrkFDbTgVdfRTemKzxW/fp0+Wzo84WMdYs/fdmFZf6Qd83hbW/oq/Qo2Tc+ZhsvX5sjk3xxpNsc2n7d0IslPfnLvj462XTQWT8Z15JivG+j5ZMxvWGRHTQ89PPT2L7Dn+/a1iZ8mmfXfKV+dtCzG8bwUsIYbzatbXiSU9dY8OYPehWd/8b6autAH/rqW7bze7W12mxeS0cx4FGyiW6v5U9yxop5Nd/1xar6vBA/efPFqOW7dTbfHshXOtDwrf6wx276/R1hQ3Wt8JlOY3LsotGX/+HH/upzdoovW+TCGY0+PqPRterGaxytv635sOpGU48V8uZgwA57aOLRZpv/PtfdQ7BdPHSGHTpd4iO38phTsmfMZ7L0ksuWPvv5ZAxzvOS0aHSxoUQ3h94Y37q+bJinT9ta7P/GmrO+8hA68bGVXjRj+tHSszmz/H2iR1n9QFPTB3M6FLS1Fmt2jPe85ujgA71iw5MeesXdmkRPDzlzrb15c2zlJxq+4kyW7rBIpjbfG2cHPV+KC89a6W0cLtq1NE+fvrbC72K2ts3j4Udx4TdXSWdjvGiwyc5pgpwgwK85+X8CU9CHMEWMoXPIB4abzF5NNM/BLjiAkHWKy2AA+T6bU2e2LIYL0Xy22XchOVEyp+DDw77Xqd1wex1Tcu7ml00nyRJvgRakG2e++gEe30V0YkQHXcpDH/rQ7cJwU6yQdZHhISdWffqNXegVPokJXYunD9GwMycG/sZvjn/G4iSnoOHvItfP12hki48MHWgK3sbiwxcPuoIXznSnyxz/s4/POpKPj/38RKOfTDjhpUdFo4t+Y7zp0Y9mnk784YjWWB+eChm8+NJhbH8pZPCYR6vfmIyi5Ss6GWM1P6yvNckPPPQZ46NXIV/fXOuZ3Wjo1r5xOBrTHU72suskOj428hmvMR9gWWHPWvmA9/DHPB39qqZYWstwMa9o2YcHHmP94nQdssWuOXR+KPqKOXXdn3zCzx55Mvxz7VlPvG5C0CSh/WHydgm/XbNen/bDfhJn+tH9fgF9H/jAB7aWDV+3cKrrIddznvOczX9+5a9WZUvC/cQnPnHDCd5idW2KU/E6NVp4+DEt1wr7fBcX/uLiC934tbAUq4pGVgkzcnSFK334Whdz7cNskM8GfeazZU5cxntZtNaOHfOKfm17hQ/WZfWTHSXd2ubxK/Qr0dnAp+hnMztrvObEaI5tbdjaHxVz4k4vunmy8KYTBvj4ZY4/+M3p85P+rrX8wVM/39Jv3FrQYcyPfGnt0enJhjF+a7zqRstHvOJHU8JzG5z8HchOtrVk6E8vGtnGYtfHp01vPsaX7/jxGGuNVfHASu3pP1trnHQpdLPXfkHLV2tUHOjJ5xcaPfjZpQcPncbtbTzZ83kGW3xrSRaNvipb6fcZR7eC35guthRz/BVL+vInHdmNjs8c//LbHB1wVcyxUS1+/PkEa3zigxteeunHR9fqFz/DBK85evMrW1ryaoXN1QY5+tmroqnk8ZqnwzxbaPazAkN+8kfFly/5hYds/uWTcbzJmtPH3xw77PlboZj3eaHALn38NCanhh9Z+hqbwwdvxVjJP3zV9NAtbjwKH9Aq+FdfikMbP12rPB1KOBSHli74k4Ejmj46u2jkVn1hTG+6zBvzT2lNzNtH6GhaNC26fv6tetONxo/VPt9gZE7lv/3Gb31VIYcXT7bpYdd1X4zkimP9/LRudOClA5/CdxUt39iMRr85Y2WNjx2+ZM88HetYv/izqbW/4w2P5Nkjx388+FU0c2yS0acbD//qa/c28ykfyJNT2CGfjfSVuxinMz4yirWDkXlroKzx6OeLOf3sto7pblxs1oFPxlp8eMS9+hTG7OMtVvb42x7SN6/QkU662j9k8SeTr2SzTU7Jr3g22SY2jpOLp6c9KQaaSpDSALI53dT6Hp+bWkZ7vQWPeSe3kljyXll81KMetQXjorEQnDInQH2Bqey4ocVj3gJKAPyIDpuSYjfEdLLJHr/7qX3B+e6x7zkCgIzTKR+q7AQ6P9TAEkNgFz9etDZRmKGpyW6AnnwA8Hul4xNX2LHDL76kzziZ+MMGDzzZcDHiU8kXD53WyJjvxuscGnlFv5JPxvgVfOTZFDc6PgXdPFr07BaT+NOBRzxs4ouOh85ujOlVlfQYi1PxoUgHf1SyzcfPFhvWVMkvOvBUyNKFD51PaHxSzNFlzdiNP3m42I/tCbx00CVWfa1aacwnfb7Tizf+7NKn0IcvPfHa82j4zEs44egaQG+v6Kdbnz4xpZ8vqrizgx8PXnx81KKjKfxUxY9mrnXiRzGiV+jnn/1LP35++0xwPT/jGc/Y5iS8r3rVq7aT4+z6fPHwiw6y+PX9FgE9fhXfj3WZYyPf4dCa0sWvvnbh2jLGSw6mYjLv6xs+a/hqj/DJw8D815JR+CFOOMFYS44u9ulnB399YzJk+Y9XMa/QY46uxnh620Ys5mvTRyfbHiho2VfQ8bBFpz5ZPtGbb/itKfvFYg4G+PhHnmxY4FsxxlMcbNNHls3k6TTODr4e8JgLG2vENpsrFuiqeNgWa/Nk08tHxRx79dOHN35z5OjUwldLf3S+ZAsGZJX66UMThyJ2dP7iU9jX5xN82GQPX3jlY3x49WGITz89/FPR6Qrnxub4UaHL5xofV3104k23/cAGndmHQbHxmd5sa5PH7w0t6xqdHN1KMcCFD6p59mDODh3o8XvYGI5s48XDB3zGbPHbOqG1juSKh23VnNjST55/7Tl0Y/rJKtlBJxse7Bpr48PLPzT2aunLL61i3cXNB3riZ0dtrGVTPPQozdGlr5JJN7sqf9iIjgedHtcKuyofVHzaeLTZY0PJP/6gkY9H7Ar99MSDL/36zdOFrqYnXdnhAxmxZE/bXtGmg//0FAe6Uhz6raf1je7aIEcvGTphjmYsjuzhUezd5NOpXfcNvjAnp48nXX3W0BVNa4yffn6QU4zpR1OSES9MzeureMy3x/Dr04VXX3Hd2Ivo2aSnOPHq81ulO7vFwxa6gpcM7Oghk2485uKFLT74Kub4kz1+4Yk/3fTSqZprnfosReMTOiyLlQ0ybCjR0ehU+WDMb/bwtCbtDf6GGVt4yESngy6FHvIKHvzFi8Y/PHTX6iv46OIHHdHN5ROdjVfcyIofTWWHbXLsKNriTjdeOteaDjwqPfwStz7esHPPgt8cXnZbn2yQaW3pUZqjJ3vbxMmeIsPfbNG/ypIxLmb66MpXc3jI2xv2AJ7ktpZBnRyKgSKCxgzUNw5Qhrzi6NVFJ7EMOoltocw79XHCTM4vTr/0pS/dbt768Ru8HOsGnx1BR8MHUB/evkP8l3/5l9v3A50i++9d2AUMfgmzm2zyaF77dmPthh3fc5/73C0WNsWkOln2R5fv/BU3ef425hMZC+jDyLw+G+boCZdwo0/JFl5+5Jv5ZOjCh4dNfW19OmHQB6H5/NXnQwuNz9i8yp6q0IMvGfNKfuAr/uITWzrQipdv6GTR6mdfssGOeXP5pFXZoUPf2qnrH6V8hIm4+cqX4khWPPlCJj/oz7Z++OM3ZlefXq1C1l4wr9Kn2pv2Hxre/Ncng06Plk01Gp/j1+erPSlWhRxa+ouFDN3wRCuWVSZ7xSJG/PxyY+oDkZx1U4oTTclH43Sg4VPp45+59YM5Xi18WvN08ltZMUET/wc/+MHtdNjDK/rpcA16sGWefba8YUKvX7b2EAw9f7X0iev1r3/99uozHXzhd/HAjQ166fSGydve9rYtyYyfj8VoDcg7LfYL2u1L8/xxms1fYzrV4kSrn2/hiK7PHz7iDTO0dNGtrvzFHV1sZCrN04/HvH5Y4G0uvfFpycdj3pg/fESnB3b2qzn6a/GLlX4Vv3k1+8WKlv78wV81R4bt1S/za2G7kl38VfPp11b4YUwfXnbSrW2f6NORHvLZ0RZHtHTkV/RVBx70aLV02wfpjG5Mhp/tE7zGMDKP15je5ozNibV5c2jZ35hPsMajsEXGOsOhGMKIXlXJR/3sr7Y2pt3fFD7jKSZyYaJvD6n8LD5jc/moX9zHaP1tyj4e2NEjtuTpUJVsmauSyx99NVkydMIHNvCiY42LbLFlI9/SRZ8+PasvZI212dFXtGyR0ZIvPvPt7/9H2d3z3FZVbRw/BZ+AxOI0J0Q0JgJR40vEKJCAjYkNVH44WnoqSUiMgqBIICEQCgssKP0CpzS/6f7D9azsY/KMZJ4553i5xjXGXGvtve+3Yw2jeqqRrsFWT/jDhCcHG/7VUAy7GD7Xeui9t+Hj+jHEi8XRm0+v4fpliOdryFcPxVQnHdx8YPGFzZZ9dWLzp0/oxHm+i1MvbJys+eYTZ3tnDJOooy9Ke12o9+UJV60wq4O9/lZDHHEgfNhINnv9wEE+PYNDpxb5+e6of/y6Pq3liQMfMeaGvHGzxt05kXyKF2uNc4NP/Nnybb2zeIKTEX64xeonfLXGjT8dm7PrPVP4cNnt4RK4RvnKn589/HzElkOPxeGQjn05nSSTh5/ed0b84RCc2YzuAfq4xDlM+nuCE876EH95DHqzHGwwDGcaHnsipz1bPRBbDD8+dLDDsb764MUXjrl8MMpBZ/BZXTF8Ydvn0ywuDmHqgaHf1SW3vYEvKbb8R3k7N/4NGMvlKQEkcAQUSoAbmuPGjzQwItYbJ78r7A058QFZTIfnTWl4MPzYolx+B9EF4+LxcPUjj+yKkk+8vQ8sLiYPCd/V8QbXB1+/c+wvWD/33HP/57+MwoGvN9y+i9KHXzx9F7mLy17+8uFUE/G3b6TXm3T5n6LnAqAn6yun3sFpdOj8DHp9rVf2sOjYk3RmPPWPfc9EbBzY+JkNeUk+rdubWx/H2z/ps1XP1Wf39SJd+zDMONHHr3k5F59OzWLrofiw+bLZl6f49s35wKUzepjpp318zK7DdHHZvOUOqx6JdT3T87cWb3QesAkdDvYNPgYc8dZ8XCt4GnSu5fgW616CSfjL7bpP6NhhsImnc3+4j+CyVWc8xPCvFnysxeJhHxezvRddHzL9WLR7megJvTcfXvDM9vw9E/y1aPc5XBK+td9H9izwYVusmrufqzmOfD766KPzY9v4+MmWOIbrO11E3f7AXzXK6bvHPiTrQz2vL+yw+Fvrkdnw1dO4dP724ZQvDvVXD4yeu/BhOc/OKtz26lcbvTjx1nrAh9SPuJg7J7n1zxcIykPnetFfftWMD5u9mDjAsw4z3p7H/HHi47mMizUfs37hyy+9fXp+cXB98rEPU82EDoY4fdYXs5pw1Ucc7emJPNbsxurO5nat7ptsWoyJAAAgAElEQVQy+eMrRl656GHJT1edvthbH+n49BpobeDUa5bYapAHtrk6vG5alw9PdcPWq2q0pjP4G/xc79bVml6cAZcfHkaCA3sSbntz9Vx14tRcLfrm3Og6P/efMw3DjIe52sTFQSx/PkS97tfeO9DDNsTBUAM8dfFXq17XDzb4Ytn4iRFbXnqDL7FOxPGTDyabfTFyJenh12szPT9zej4NOnjlwkMudpI+jPjRG3FJb4ZhrE/7tcGPk7lccba/JzDkEaPvztq5lEMcvs4zXDjlftIsLhsOO+5xybd5c1x5iw+/nsGvjvBh4WzeXuZHH0552+dzzc0vad350lt3fzfD7BrRS3Hw05nt+akjHHq+arA2b1x+J+DyDz+CgwFX7vS5x8V+1/zkKycbDu63MNLRV2O28M3szdXRHIY8rjF6PF2Di8Xu2qwW9Yglnl307rVqxJPds4hv1y9dr1Ved/gbPROqmw4mPmrz3LC/Cjy5+IjBMx1MQ264eMCzpo+7WAMv8YY9HGt5zeKJPXtc2Ug+ZqO4xYLptb/+xAu2wVcd1uLjAH/zw+dLxwcOScdOiq+P7AYRY6QrFqaBh7j2Ys4HZKABcOAo2IXQBUoXsYC92NNpNLsLoEJcKMiIVzhfv1P497///ZDwRpiPHH6ksd//87uEvmPkxQ1RuSqKjx+t9OHYd4C8YfUmgU7umuLCxmtrwcOFY07w4pu/fHKJ7QLmQ2oae/2xJnzwFCOnWb1mNeoJPVyDiDXE1Vs5rN0c/MVZ82vGHwZfdjZz2Pw6N7440OFoz1ds+OLFyltv4sQmju/WD796+BJzPnDYxdPB3vw4scXHWdvDwM+MM172+cnT3rq+ia0GczjZ8e2MxPOHb93AEy++bGFa0/HjA39FLlI/e9CKp5MXRj4wPDDMxcCu/2qt53HgR+SSvzh7wx5HmNUlVj3s8OSw5lccXQJDDDGzmflWtzV9cfQGPwO3fOF0FrC799Tpi2P27l1fQPNG3RezqsMs1nXhp0T8MS61yCvePYULP8+eeHRdxZHdun76zj0fGHRxjCdfdcjrJ05++tOfHp7Oyx8DhIfnVYprrh9mOmdaH+2t5cye/8azby+zyX3tOTwY+BGxcpqNFX7ps3V/wbEm8jXsXVfOjOih2HibSXjWYgmunUMx9OnyY4OPwwo+jbgXwzeO5ngsL7jVx6d70bpam8u78XSLay9vuGHnB7e1uTzmcLPrAR28aqG7nrtc7GzEno9aPG9cy93jve6GGYfixCblxM3wbHA/qUkuvsbyLj4dDGuxbGZYBEZYfNjrg7iEXxzMYgz3qTrzhUHu7bPJYehLAgPWDv5xqwZ+CVu5r9xxrD/uBdctHRwxi21NX672OJL48mnEgQ2eHPW3GDMsPBL+MOjJFY+OzcA3rjDgZ7e/1m/f2cKt3tYwy0/X2eHO5v2QufdDcsHz3qs36uxi8YKFhxGeub1rv5y4scllLbe1EY+e9/bVSec6ka8zNYtjKxd/rwk4hW+POw4GXENMvbG3ZhcnD97WsIq1rl62eLuu4o2nWK+VXjflgaduscRc7+DAtzfjkdjnLz57HMMRk65Ys9z5tIdT7rD5XPPK1WBrxEMse7zt2RL+8hPrsJrzsyfN5YFrlC87X3yJ/ldPPOone7Ewi6GPjzXceOZvdqb1Ln9+bInYK/bVZl98HLaWbPzo46Meto3fXNYGTmLCzp/+6q83/Lqf9a8ei2ODFXa45YHJ3yDs+RzF/LM4cMNwr7El5cSN2K+ET6/3Dfq4w34qoAA4GIzA98KIzNqti1WoC4Cugnuouqn94R0PFf4eihoJ303vAeCD7ssvv3z+4I54+cLxnekf/vCHD954440Hf/nLX87vB3rDK9aPQ/p9QU2C6aHiO9q+C+RDNE7+Cu0777xzMPkZpA/nvlPlxzm7UM24w8LXw4nQ40Rnzc4vTPX0wOdDj2Nrs6FX4o36CZ9/uHzCFcOPzijGzCYmX5zw0D9c6POpBrYw+W99/OHS613nIJbeGcLnh4u1vu9eDFs3i1hren5mWKQXKXt6vLLHgc2AsX5yV4eYjZNHX9LLVe/Y2sPoOuNrTeDqi/O1zicMumTtOOejf85geagXJh0/gw5GdVobfOiIdTjF8+n+Sac2HPQqfPHhWLOrm86sbthETH3MZq8W+cjyE1dvuo421/r7r5v6gOxN05tvvnnePImHI5+5a8Rzo+uNHgc6dt8lsidschpbc9fLd77znQc///nPz99LUPsXX3xxvvPMbsDrTZDnEN/f//735/mAr+9U+6KcHxGvb3LpSz3BAzaJS31VHx17tRZb/sWrj+oKz/mK3fy+MCi/WDGw9JWoic5cT/jC6PzWjg/svgBRDbD4FRO2fsklDn5SrvLDk5O+s5XHXqw8sN0D9nzUQTpXdnj04tqHR6+3fOQKl90XY5ejPQx+RKw4gheBX6640pVPvGGPN5Gjml2j+RaXPx82uAYectHxqQf0ekMPwxl0fcnnNbFa09tvDzaHeFgw6fv9YPnEySEWFh7WfHErhi8c9eUjFh4/GAY/w/0pJ+FDx+6aMMOVz0xg2sMUZ4ih52/veRoWjgSndPz0xk9CiKPvnohXPOSq99XtuvZMjZOcbAR2vJvpYdQHOfGRo1rMdGykfssRp/Dam/l3X/BVr31nEi8c1Vi/zHKwe26U297zFC6saqOHr1d0rTvTauGHp3hDDtJrQ7xgEL7F2vOvdnt+eJTfWkwcxOLS3loOUn/MxcTtuu/aCEc+uvLyh0OHg5lOLte6mU5+/J2Bnhow42DmI7bBn0+1s+tdOe07C/EELvv2iq3X+F4LxBH7a17+1ckWT3r5q11t9jDiAZOuPuBS78S3x8/ZV2M5YfIRLw97+Hzo8zWzp+dH5CNwjOqrDnP15QcHdj7W4VnDqWd8w1ZrGOVVA19+7PixxaNYPp5n7qtysYnjW5wztSZw+GSntw7bXv4w+MchPz4wev3hi0uczaS8fLteraszfzoCr3V+cvLHicSrHGx05gY/a7HloNszy07Hh291Vh/OrjF7ZxgXsXT2eInLhrfzMPBi5198cfydmXucLx9CD+98QD6auSCBGBE1CzQkIyf49mDrwcxm1AAk+s4QghqACGxk6Mx+/NEbPX+1GjEvUHJZ92bNoRkvvfTS+SDtQ7F4/w2M33P2obkC5ffi779l6ccv/Tjm22+/fZqBY4ehOd4AewPtd6QdhtqqX62ajYt65LS3xo0frPjWA3N+fMSlC1NcPvCs4fBvT6dunMTjp99488FLTFjW/LJVJx0O7L1pgSVevnDkInzEbn0w48CfxBNutdDrDY7VxE4Xhn29xiuezs45uk7yhScfPDF89aMhPp/48hHPp9ze9CSw+KqXn2HfFxS6hsXmo5bqhmstTn6jvle3fT7y4mSwx8ue9J1J+cKR7x4uPvzkhlU+/MXADN/MDqcPNPLRi4cjjlQfvRyuATZ8+Lvu4MDnW0z51Eov3rrazfKQhw8fngHXeOutt44tPPgGjH//+98PPv/8829w5OlcXEvubz7VUc2wDDiuF3ZvmP06hj/k5Y2zD7sw1GZUF670ngli4Jj9Hrprgy/d1mYtt1lN7OrtXE7ht3/wgaG/7MWFVw18CO6kHPTW5eCvJ3SEXg6cPVMJjHgt/+LCtMdJ/QR2+ZeXHPZ6Vg329PEUrwdGNcOyd47ub/4GvXg97hnOZ/3DktO6e0Bd9mqo3/Zw07vu7dmr3z6saqyHxfGh05fWMIrD0ah/9UC97h1z/RDPr9j2Zjo2A56aWpfDvRcHfOHujDNftXbWZoNv/l0PYtVSr+QTS28tF7s67MXb64Xes3eNdNby05v1IhwzgWHd9aU/8rsW4OqDWHN9OoG365ovXFK9uMA1E3xh+cI3wacexINevL0RlmtPjX5STR/kqpdw+ckFsxGfztA5wVEH4d8oBnYiHm6+8hvpws8Oqz7BwytO6tbbK/7GVtNy0gN7edVbT/jCkqceHaK3f7I7Rz7FwYp/feODHzx2voZa5IQlBo61mc06/mL1VZy1GZY1DDOxTuhg8JWfWBsk3vaw6z2+5TAb7HGMX7WrUyw9H7zkE+PaDMueDSex8uJL7+zsxRvuR/vOWIxYfq6x8Omt+cojdn9VCT4dbvESw1ccncEHBlsx8cVBvJj8+amtvOq3hsnPvjUca7pEPjrY9cJebfGpn3GPIz/SWdMb4tjkK2b7JwY3wh53OGKJeAOeGs3VbS2uvOKJfNblzL9e5bP3JxscOYo7YLd/wrOtLvkJfMOeHwwc9DFdz0T3kXgz37had67lh8lOytWZ8TfoDdzl4y8efiOO8rLDLVZvy9O5x786zQQuG3+69LDKm19185dDbbi7F/iKhYMjGz81xIeNbPzpRM051tth0CEBFIi5AkvUYZTAXnJEJUHCH9ZywftjXv7/Yn/xmiDMl92PXfvusovHjzN2EfEpp9mQwwdpNnl9sPVm9tGjR+cNPG4V7zvM3iR60Pixbb+DLIaoL/EdI28oq0ujDD44bn+s1desTpwIbDbSXNP3gMTa6ys/azq5ejjYqzcbbnzNbM3s9Aa84uTlQ1fvisNPnIE/fRh8i+18yy2OvfPhByM8OOVfXuxhwoo7rHLjQewXU08M9vKFxT+7NTyx8M38xNGXKx8zew8Raz2AZyZw6GHRXzHtq7eYrq/yVU/Xxr5Ywo8He3h7FuwGX7VUW2dDFy9x1VxfzXtdXPmWHy841ezDoIeLPHxgOHdrefhaZ4dLmtnCg0kvTh/vSXH4e6AZ/nK0P/DnVyp8ccN9Sqxhe1YQ97W/bUBXHjMORP56amaTh+zeGeinN7m+sCa3tTfMvsDmixhbl3h7Ax4sIp+hZ/oDk82aXh89o+QTR7eye+uGM4JF4g1THj6ds/o6H/4GWzrxxcjPXs7qMBP6OPKrx+nh5Ls29sYBmmeAvb46P3XwK5cZXrjN9Hz1utrVSfhUYznpstF15psr32b+nS8OzieczpkPmxGWdSJPIz+z+PTrWy2wuh74u0/CZROLi2HN5oNl15c9vTMoztzZWldL+b3WyBMeLq5L12f5PAOsG7h0HfC3hl0d+ZnpXLN4itMDgoe1WH7yuR6IPU7i6MSpsXsHP4O9ZwmO1SmeuFf7gGGPS2doxpmv5xybvpnhOAc2OentOwOz2OLFsIvjG44aqw9Xe6Ne8SNi48UmJmx2aznxTG825MYz7HplhkNP9ozZ5NMz9jBw1New+VnDIfiGI64+82FzhvpFT8eH3tmLY6eHG7bc1vJa8zeL5WttFIe3PUlnDaN6rdXCTscfJ9hsBhx7ve8a4mddDWKM8KzZCFx70mxdrmO42djTt7av1jjGiZ7gT2fIaxZfvrDo4eFphtearTxmfWGDbfDnQ+Dln02MntiLC9uef/bFgCkPGz/CbogJuz1dIz8YXWN09jtwSbLDIGx08ouxNztrM7+uTf729J1DdVZzuapdDP+E3eB/5SJP/myG+45vQ/5yWMdHHJ9mWPzKzVZutvDMYRZfHD1d/rCtw2ZTpzns8pvFN1vvOIZ572Nffms5+Jvl8DxwJnzUZLDXQzE4sPd8X7/FD5eOT9zFstFZV6dro5hyeq3Ah89y/e/dCHkkQCprgIrqIgFA78Z1A1mzdRFEUEIfgNn8l02/+c1vzn+1xI+P2Ztff7DHjy4i2QNLbj7l3VlRfrzSd4/kfv75588fCivGG2i/M+gNtg/J3kD7/1Z/9KMffXOjxFW8vH7P0dphyIuHPGFqcByWl17oD12x3tzXFxitYRA6uYi5eBh8dshJX8/trV1gcNgIvReaxSruONz6ae1c+OMFS5yztFYDXP3pBaEXTlz5PL599dK+IcYg4ZjlIeYd4pZ7Fycf9XvI4mJNxzd/vgZ+cEiczdn1Qo321WUNh00sPZ7WavYFFTHFmvvOs57HhQ9/cQYMXLuu5Ni89QLnei2OD5sv0MiDj9j62ZnylZMtvvb0RBxu8osVhy/+ZnG+q+INi+sdhpj841197HCIuqzx9kaNwKMnbOLFWsuX1BNx6tsY/MunDrF4+yDsJ0P8/rFr7b333jvn8r3vfe/k8WsTvgvsfvYHvNTjC2Q44YGnPPVKHro4Vrt8+kPPv/55c+1Hqp2NN+m4+261D8lhmsXDdq2qTQ3w2GDRqye/7PY4EL1yDnQ4FssOI97tq4ENHr0PmvrEBqdrih2egSfpOuGnLjbr9jDiTKceOOIIHEMOelJ8NZWLfQe/+qwu2Paw1IMPDH6Ly0Zg4ZuPmY1/efjlw6Yv+4ZNfXzlNcjGlxuGeIM/vVi14W7Ph+DPloSHXznins+9GV6j3Pbw9B+WPSySDz1dHPgn1jhnE5N/1z7f8sAJqzzs1uq++rY/hvlHXjnFWcPXD72yd9bOhni9pCPmHbgadDAMe2IO7yjm9ZRNbv73pBz1hf+TfMWziSHFLq54gw1mexzs6WGEs1h0neWTMJ1hZ2cWX33il1e5FuueThyexXY90Bvw6dLzi8Pm54cfHXvPmO5PdnojHSw56OBfhR63OMZlz4tPXMXzIfQ4xNveM4DAxAHXzoi+/sDnb/AjrvuuM3rrq8TP6wVc/TD4hieGjVyvkXrHN97lYIMfx54F5ez6ahaHu94k8i4Pz3g6957ewK5G685EjDx6wL/c9upzD6uZT8/ZfOgMGPUcLnx7+XCEhXs+9sXgz59cz1r+xefj9VQsX/gw6WCkt1ZLPYk7Ox6GeFwNfmL4mdXMR/5wzddc6qg+sXpejeLFGNbVgQPRS3nZ8o0LDDr4fMRam+3lNPMjfOnSh4s/G1yxuPDBQezqDtD/+Odai30Sf3kb2cxyrn82OrENfHCt79W7sdbF8XMtpjPXh+ZymdVsyJF8u0pz+2phTUJO87uhJEFM8gpzmJtQjHgfNnxAJj6A+hHHZ5999tgQh+NHHWFZe6PXRSBnX3keaqdYb2D96KX/49gb1z/84Q/nj+rAcQD+Qq0Pxzj57rIfnfZG2neR6fwonw8kXYRmgrPRBdRazXsRLR9rN6LccNw8fK07mHqlxg6pHGybl4/hoPZiCqtZDrXk0xofPuyGdXmtk2pjwyHhE5ZzZOvBaR8W/2LN6cUWX11him/wz5f9WgdduGL4qqc8/HvAbr5ymVvzJfxwarbOTw5rc7UVbw/DXn5rftUQPjuBb613XVvOsxzZ6XAg5rUvznG4nWs9iRsM0ouSdWevP/Us/uzykzDM4usnX2vYXUPW14dw9cNa7p4VctDBsr7mTHfVu+d9QPZfx+Hk+fG3v/3t3K/+aJac7nnPDb/P7Atf7mU8vdj3nCpv5wDLF9R84cwXzXzAdq/qu2Ft4MPXM8QLP133tS/KwTXgEn2xrlf1BE5nahajH/XJPj0c/nTmRnp+dN2L9HzLfcVhl0dP8g1j+ZUHFlFH62xmOnGLkS8O+dIZ9ivZzeyuI7XAtMeVyJOvXMTsPEi+9vyufPLJZo8fXCOu8rm+87eGbcDsPmAXlx++RnjLVeyKfX70+ZrztZaLH1y8Fn/rw13fFsf52ovRT/vqlTPMcNgMenH8s9EROfChT+cewNm+c6tHOLPxX6Fjk6881er83FNxrQ7x5YbHx8xPLEw6HIh73fAFsjDC7IuA4sXFMXxz14FYOHLQhY+nOHWbt9d0/OUTYx9Xa0MOEie9JfRiSf3kI54PPLnoymldHVuDtVziPDvLRReOXOWpD2Z1E/Z86Njgioe791s6eaoThjg2OPFWj/PiZ10t/Ax6vPgUdwjd+mKNhzj+coipfnniEU6ccLYuTv56QAcDnrH41kQPrPOLB8wnydUGY4e4uNPHj2795N3a4mTmpw41WJPymun4rX35lqsY+0SMITeBY/Chs3Y9xJUPW3XQ2+t1srHxDdfcuYRFR8LIh048fqRay8e/mvmJM5czO3/Xmnhrfg24rht2cXzUa8+Hf3kXOxt7PvWSjdAT8aSccPKhD5euHtCLZ8snXX5mPmLSlTOdM3S9LwY+Br7i5FgcNvp7ck+fzmzAMuQuB8yusfLjaM03DDmtO9ew6OIJR2xfGLEOq2eX84PRvveI3Uf8vdcz80uPz90PyBHggEjACGtwIAhXoHUXltl3efzYtDeaPvj6gOzHrCXn60IkPux64yqnryqzwzRXCA7sCpDfjz/+61//OsOH3or3oFWoN9D+Sxcf0H0o9wd3+Lz77rsn/mc/+9l5E46X7/yIk0sOfvLU+HS48pM/Ww0Vi3M4+Ygl6rVWh0Fg0JmNfLwZwUEPO1h+MM1ymvuqnbURthmWkU4vYcXXvgdBvQ7DXixfZyOvNb21WLj21aF+enb6emhtwIbJj9gTOHiZ64fY8K3F24sn1U8HJ2x6GGHhw0ZfbL5wsrnm2MNTg+tdHG7y28ePTu9cNwaOzr062eHVDzeuPVy+5aXrHqDPp1zlYxNbD+QJ6zRk/n89185V1CEWDk7wcYBDx2Zdn/I1N9itceJnwCD0zdWWHU+x4tRKj4MYa2/owrb3odcf2/vVr351/lCf+LB9+HWv11sY/s6A54oP1HLz0X9x9sXL4Q/2+TsFX3755bkOfTfYh2SYcfAddjx98PYBve/qu0b4Es81An+HL7rFjd0aR9jW+aqT7Lniac8nmz6Jpe+a6kUgfb4ww6ODJz48/e/eCxcnQ2z9EstO5Ohah6O3hE6uhF/X8RW7/HLIxa4n8vkAQwfLczguZsLHgF/P4ImXDxY7oTdwKyc9H7WLgWHN3j1AB19Ogy0esN3XnrPl6P4y48A/7PKqBwa9Ad+AJz+OcAkMejOdWPaNjw+beqpDvOsBP/XAwQE3+codFh0MYpaTDqd8xJdDPMx8rdnCxUMc++LQE36kGpcTjj0X+bDJbVSvuecqTDZ+5YPvmsZf/dbshK9YucXiblRnOboO+Os7384BPr1ZTmKtB8WVq9qO081PjDzlYoONJ15h12N7OPblVluCH7uBJ9z2cuEkrvNkE2PQ2bPDr5/i5IXFr9z8+JNy8KF3r4qrr/XLs5zwKw6eIUec2dLDcx3Y48iHxDPO9urLni9cQs8eL/7WZnzKL096NmIPjw+d8+EnDi49nT0/Eq9wxVmTYvnQ1T8+8HbOB3bXqn6Ia4+fsxFnDV+thniY7qfuWxz4GeWGJ560ZkunLhyKsa8n/A32+hcOXkRueHzozGLqX7biwqwX/PgQscXZWxt8+eBYfLzt2Q06frgmYkgY8SvGTIq1hlENxcfDNQ8jseZD71z0w54eTnnp5WCrT9bsnStMe7ldd2Lsi6W3xoVdPInbxlvzxyMOri8c5De2Tr7xLc5ePnnowjeLZwufDrY9PmZxrmW+cYAZ3/KJo/O+oFzdd+HKzw+eOvjb1x96e7KcxfEJl0/c+IVvdg50/M/+/Hv5p+BIFCAIQCSs6fgjFxF23/n58MMPz4cIH0i96fRGU6MQrUle4L0xViy7N5outHKYa6IccvlxbB+qPaz9H8z71WJvcv3Ytu8w+xFN32Hy45p+jPvPf/7zeUMhpzy+s+xDTh+StGFrta4mNhe/mnHFw6iRdAa7+tgIe7WpnV49Bh58q4s/e7XDyo8enw7ZxVP/y2MWC09ew5qY1eJMk3oLGxY7nRlnH5D1CW+Ci7Phy08cXHWIYSf09vWGDp4HOR0bHvzFs8kB1yDx52MdB7PcdPoh1/ra53uAbljWsBpiqh+fasGFpOOHE3s8xVm71lx7aslHjXztxfZCVr1wq9naecYFpv4Sa/b1OYZ5eMthqAm+GLnFyGsP255YG3xa86mfuDTE8LEvD18ivh6ULxxc9F8sqb78wjV3TTpHX8zyl6X9pXn7H/zgB6cmPwniHnWvhw3f2O+YyIUnXH5mOZ2He8Xw4Ta9s6PDAY7vKntGiHnhhRfOX7D2Adl3jf0Osr+Cz8dPrRD1wlKfawBnmPb06qfjh4+9tZw4WdPhLMZ5kfokVo/ZzGEW47zZ6mF68Qb88OzZCRw2s4GbXPn4IKxPdEScPJ0pv67fnofFNu85dBaw6od4/J25GD4GiZOcdHIY5cTd2nOJTzH1SDzOYuXgK0cjzuz1TEzXFHtczOLK4zqEScLDxzqdGRdy7SE//obc/FzX9uXAo7zl2DOoznz4e/3Sc9cWnPjUS1w8V5wtHV5w5O2s6A16us7Kuvxw5CN0OIiRr/hys9PxERNGfvZ82fWcFKuG6uCPg30x5vDYYOBd3HKvRveo6y2szd26Wl0z5atG/PDs+qgP9vyXp5ziYJD4mqtfLmu++BpxE0uPV34HaJ79bPDF8SfwrOURjxdZHr23igc7f3F6aM0/e3nYcXGtFWMPj7/eiskWt3rBt5riyJfevjMrPxtdeHwMfOSBr/c424uzlqM934Zrn90eTrFhnkQPHpw6YMktBxHXNcy//PysiTVsuHTW1ctuH1cYxYhL+CQwjHLIb69fBDc2esNzKZ9sixUfnNirA0Y8rdPjat/oNc4+bnziQmcPK47VYy+us6aXp/6Kbc9mz5/QZ6OPK1tcwjaL7+z4hh2XaudrwCa4u1/0Mb50nqldh+XnDy8MezHOnq81e+dQ3dVFv3zEGPASvnzqJ/vm59u1Vh618PP+Inyz4T7Fwwwnn/LSEZjV1VyP4NDxNVxvbPpUPB86Nmv9zHdrs2YvDq4Yc5jl0wMjPs5JHXREXctr97A60zC6BuQ27OE5azkNcfy//TJL7C8zgIhEMBe2LizgkijSGwn/57E3k8j6HeG++8PeocAJg5/vCPWdCjlrVP50/Xi1D+AwffB288rpja4fofRdIrg+GPuvoXyIdkH4HcaPP/74wT/+8Y/zpldcb7zlImYDH6P6NRGGHtC7kGqqmY4N1w4ejjijWovvEMqX3guQA1Wr3PL0QleOblqxfDpUGHE/Cae/zomNjxhSbmu8u0Di0iyvHEPepp0AACAASURBVITOIOrCj826XrGJgdfNI0Y+vtVWb8Jsz48Us/bWZv586wsdbHPCni9/fIvja5TLzB/n1mHxkyeBQejS8+0eWF58YbIvH2s2+s6xuT7FTy46vsZyjBM8/vxcR75wQde50smHb7iwSDz4ExhymHEyyh1HMx9Y4aWTI//Fjh9c4nlA53mB7z//+c/zBTD/H7ovnPlCl+vf3xFwjboHzWLkcJ7yuP/pDD7lFKt+H6j8iLbfW6bzxTu+4n0I9kU3P5XCz0+6eBa5D/HzjHCueoP39sheLpzqmx4Q+IQ/jvVNjNHebCyuvIa4fNn50ZF6nz2bWW51w7DHkdAb8WEzYJRr89KJ7V5WZ/7xcQ7Wm6scJ+ntH3zDhgGbXzp5Vi8n6Z7iJw+uRgInvzDh8JXToDfwh0Nnjbs8rok4F8eP1Lt4et0gYupb3O3LB5vEoWtVHoKPa0x+fMvPzzo/2DDszfiJ9XpHx063vat+ftaEjwFfjHrkZw/XPeg5kc7M16g2GPYJfbr4iDPkcm/We376Yk/4iIFBysUPv7CL0XMClw8c3Pe6ZFdHdYpl52dUi9ww5Ge3lk9eNjrCzgYnHzrCp/OzF7dz/ZcXrpkPDtbscpqNYusvX6P62fPDxWCPs5pxW378wwsfnrj87PXMnLCT5YarHKT8uxZTPdnL5exai+Env/s7DHNczXwIXP2CiQ8cvafnZ4ZlbeQjXhwde7XYEzjFWMMXYy6GH91ihmOOEx+c6DprOgO/MJv1ca8H+cTyXX86UhyexvrZqy88vgYfshjFVhM7He4NXKzpy+tZUS3Flqe84oqphuLTx8VMF0drPamu9uqyTsJz3RQrL7EP3xrPRr72SXE9V2CLrz5r+Qlf9idJHPVCfLXAiFPY4cMzlhP8rYMNB89lAt9Z4PP49sX24mHR80lgOctiy1095noTP77ZYYnPp3i++bjecUxntuerD/p7xaYTR6939vWOTs6tD458cPnio16xdPbiDGtYZna6fOB6ZtvzKRcs/tWnB99edad9//2HkwFAAR0gcgk7cPYKNXuz6Q/c+MvUvsvrw6m/Ou0NJ19FemFHUHxrH4z9JVpC182IbL7wfQD2JtpXLP0Fa3/8K55fffXV+X1F34nyhtrvO/cVet8F8qObvhP09ddfnzfIfjTT/7uMB2y5iL2aNY7A7xD4qL2D0Wz+hH9YamXrAgqzw7JnCxdm/dUjL1bqhgGXn9xwzXziBstYP/W4uOjLWZ/68NQZm41qkNOoJ3HDrw8ibN2k9HLjZcZt+wcXBptB1KYWIhd//AxrGLjzq9di2c31Sk6jWHqDrjrqQ7nLqS/LQT2+ACM+vtbV7IEcvmsUvjfLdH0F3fUGVzz+8Pm0hsGHXm3V63flnXm564k+wys37mLrrzrhmEkPUXUkuBh8YMlRv+QRAw9Henb59A12+ubwmuFaE3hii2MLU//tzfIRfr7z6i9G+0Dsw6nBh869rH9+VcIzwlnIQTxT1KNHvtPs2eCv5NP5oGvGWS6xvivtGeA7wXg4B37w5HKm3//+97/5qRTXQ2/uq6M68dMn2M7DPhsdTDGGPd9iunf588N/4631DHd2dcCxF5s9PDjVyd+ot+Ls+ZI4Fsu+18PyTS/OmapTvHrw6Fzp4BnW5TgJ5x/Y4cOCAQsOUaechN89HP4GESdncWb7Yq35hGNP7OOSL33Y/Iqh5xMOHxzZzfEVs3HF04nZPIfE5U0rXRjVt/Pa5KxmfawWOY1yxQf3Bpt4tqS45UlXfH7/a64XeC3ONSau8ebr2qWXTzyu9s248Ou6ji9sMWzhiCHm4tjgyWmEKy8ftq4d+yRsexik2Pz4dA00F5dv9YnJBsu+QZ+EnQ0/A4f1szbYEjH5Z1ezWAMnzxu95LvcyheWGVY52cWrM1974rWr8xHDTuqbPJ71YrObO4PwxFiz1T86HPjCy9c6LGs+5c0nPK9l2eAa/FuzwVKDa0GufS57/uVfDfErF/3Ww7/rzRpHPuXmG8au+fIj4tm8FuGAo5HP1mQtTp547HUtFxudfnQdwI2XfLD1IYxy1WP7cPjACXuvK893udj4meNn7XoI297oDOCT1dPB51N+3MNOL45dLvmN5cWGs7nhPUbXZzyqU1/FwyPyWBt8Noc9fz7W96ScbOW45wuHL9na+Bp06tCDq4gTb+S/PnR8qoMfWf945sMuzr41Dvq1cdbh8c83HuHZb/xxvP2TbzMurc32YtMJi0Nr9ngU3xzHjSnuRuFMdz8gL0jFuAiQCQSwCxMJPtZ+99f/T/zBBx+c7+L6APraa6+d79y6APm6AX2IlsNN6k2xN6E+wHqz6qaqcA8Fw0UgnwvYd3/9CKA3vL6D7DtN7H7H0F+79WPUvrvsO8f03vj60UkfYAxc0/sQ70M0bjUdL0M+czVu0+jdMPWkmwcG3fVg8ksP0ygnTtVMJzexTu8ilJcvfX5dnGHA1WN6ec10+cG+4rMtdj5y0K/wVS9e1o18xVrjY+AQnrkewLQvnv6aa+PyTydOnYY4exi4uYa6ZujSi8UHr3LzEy+O4A7LTIrXQ+v0MOJLj8dyo2Ov9zCzs5V/MWHnYy2H69/ML1/78OIoTkzc1eN+SeLdG5nwzDjiCoPwNegTuHy7tui7vvjilE5cmHTFmbse8PAc8KHUc0B8vxLhi17+doHvHrvXn3nmmQe/+MUvzgfi/pI0LLGGD9ieO++///754tfvfve7cw30RQJffPBh+umnnz4zTr/85S/P/8esR757/cknnxwOcvGHry7cCKzWuLLpl36k7+z0qutSvXoBrx5Z52u9Ql+vxXWefOTM32x0bvXbnj4OYgx8iOuiGPvFExNXc2elR57PeoDfxsUXpkG6Rqr3KG9feGSjh++FXY56Cau63MOufc9s/RVHtzn4dhYw8LeHE5bc+VQfXDj1CA4ecuBF6PjAoYfPxxeTrAm7fvKFFR/5xBj1m78+8qOrZs8fcdUmhk5vqgkna3EJXnxgssc13/b88LYvp3zW4q3F8AvTzP/esweW+nq+tocnpmFv8Nv+24uRIx8c2seTTU/os1vrQb2ON56+6EUfDj+9jA8MPOwJfGv9q69i6ezxNPiJM2DEORw6/vb1uVxxZ1OLfhO4fOjD4ZvI1d4abrXyyQbDutxs/OJUD/SFVMPZzH2hT6RrMLsZlpqJXGHZdw7bk7iZ6eNmL79B4gmDT/qwtt50m4+/vuilNfxy0osx489uvz72bI3qy++QvP2z+FRiniTLMV/+cNVpkLjSk+Y4iuEbH3rYzqq5uvisX/zCOgnmuuELm12f9Sl8s552D7gmEr7xl6MzXB92/Ig8+cGjN9I3r2/3XNyrFW7PXja49SJfOOU7BOaDsn1+ZrlXYNHFnw+xpzc2fvXZ5e7ao+va5BteMzupJ+mP8vaP/obNDt/zyvOuOuldB3IQeph04sPtHsePnbCJi5+9waezhWOfsMdJHD941s6YqBuPeNnzY+cnvtx09vGzrzYx4cZBXM/ScvMXJx+7vVnM5hLX6yUftmLiHL/q/bbyNLeLIpKK3wJqAALZvNn1I4p+tNmHY3+cy4fd3/72tw9effXV8x0ZcY8fPz4fhn0w7UXts88+OzjeHHsTpoiKilJ7b6jFKtJ3pX1HyE2jUfL6gOzHJIk32H/961/Ph197DcDXmzzcHAi+fLxx9qZYkzsIvjVTrEbKo4YO1Vof8DNIXDVaD2HIBdfeIGZ6fSi+A6eXP7Heg7OOE5t4ffCFBjZixrPvbMaruuRnJ/KtxANGnOOoXnHFVL+9GsWKsRYfRvjh2FejmERs+bto6zc9XLn4EXhy4GE4p3zqTb7NYqz5x69+lpsPezlgiim3vWuVTkwc4NgXbx0vPgYMgy18OK4V/Is1yxFH9bBv/8URenna87Gud7Cy1XcxYbOrqdx8rQ1+8WwtDrY9jgkOaoureD2hY4uXD7Z+4kPN4n2xy49AG37yxI9A9xzxF+h9cHWv4Ehgutfdw657H3A9S+Tw38n5CRJvSD0jXn/99bP20ywEjucNEeNXQXwRzd8k8PvHfjIFXzz8SoYvoOHqi3Dqca/hUf/0Il7yG0St9UacNT+9FatHe+2IEcsXd8/VhE4eAkdc52hu0GeTQxzMfOWnYzPyhcvHM01/rLOJ54uTeHs2s1q6VuJnn8DZ3sht6K8zjI8YzyrXAxxD//jxJ+nDo4sDH7niml4tzsuenfCFZQ8/jvR4rJ/46tF3fWA3woFpLwdcPl5nutbp2Ho22Fe3/HopD729fNb09dle/+WhIzDKER+xePKnM/Ax8CXVKV6/xdDFo33Xq1meZOPUJVYeaxj1CYfs1tUFK3542dePOLOziS82fp4dsMK2Nvjna6023PjBlYPdoOdD33WtPnt2nNwLzoOfWDpxxNxQb+tsxchtTWAsZ2v48TpOt/cGYvByzfBTHynGXF56vHFY/uLdU/RyGAS2eHN9VBuh7/qyh4ejnhA9wUmcGXbXpVjCJs4IHyZfs3Phu4OvPR8xZhKXet/ZseV/HOdZgI9nJww1mtWOr3g6ohY9lIO+uuDikK+9HsSBzV6cEV48msvPtx7GBQaxN1bYDHq5E+v6ZGbHX704mKtBTHWEoR58w7ZvHYdym8XxKY5PMfnZ15fOW276+Kodt+oRy9fravcyf6Nrg4+9upxd/OIg3pCDjz5sTja+BiwY4bFd+cAx+Ktn84oVs9e7NeFfnV1HYsvddVJ+16U1HyIWL3pzeGzXvTrYwxIbVnF8woyDPR5qkDd++NKx75odzvI7m9u9zTcMsQaRj77nrj7C4c9HPQaxrxYzftUibn3CTee1ki9hy+51Xv563v1QH9lcJ0S8EX8x4elJfbn7AbkgxQl0MSgaIDL0Hi4SeyPgxyD/+Mc/njesvnvruzX+OJYPyA8fPjw3AkwEfaXXjzj7ro2/Ru07Qm4UH3i9MVWkBsEu9xL2nWP+3ux6E6z5ivZmFkc/NikPrj5Q4+kDsT+4o7H+6i0ucfj000/Pm2JvpH1Ar+YO9XRtPmCLZcNNjvz0pcPvQPh0gfAl9uw4Vxc/672g7OOSTW7rxNqArTYfFKwNseb6rid0ZjH45rd9Xh2O4uMsr9jqKz/e+kz0g941I5aYW/MN04yHGNK1Jb7aO8ti4p+PvUHSmctnLg+f3cvXTaQHuOkjHQw6smcVVzh6lVRXHOzx4q+WOMhZb6z5dN3wrRa5xRDxbOLgiiPVyb5+8ODwtWY38FULm9h84LGzxY9drF6Iiw89H7b07WHgKW/ctm4c2/Ptj0G5l30w9jcD/Ji0v0Dv1zTc6z4cv/jii+eelwdHnOTxF6/91Ir7vu8se4bwwcFzyE+x+PDtBcizoRr9VWr3/p/+9KfzIdsXyfy6hb+X4ItleLqm/cE/H4z9CLfnlecIXNdJZ2GGK6b69IbeXq8M/MXpe776wFZf8/eMw1mt9Vf/yklHxO050Dfo2Q0515+Njm82XOw3ToycuPCzJni6Hvnjby8PWTy6OMMtVr/yp3OeYcBLt/F0rkM44vGtX9VhT2Cw48K/PrO5XjauPV9DLDwxXo/MeNBXMxwYeLPzb/A1YHWv86mv1oRdb7xJhAO/esOonvbFrS9ce2LGo7VZrJxh0fHRyziXv17xiZ9r0FgO+YmHbfDnA0tduPCz5+fMwrRnt+cTDl8YdD1z+GQXQ1+svJ1JOZ0nnzhXmxg9gF1PYKcLW+1dn3SGvXuXrxH/7GLSx5etNXs84smfqAFenNTT84WOlKf8dOFsz+KlRrmb+drLZQ2XbxzgwyFxpuOzvK27bmCI7x5hk08cH3Zn0bmXjy1u/NXLVxx+YqovnHBx52/ILacYsfZwSJzpSXaz3HzFhUXfsxlPgy5+/DyTxeL0+PK7n3TFmIkc/Ima6OEZnqdxpbcuPm7yE3ojqRfs+sHfcL0TdeW/WPyrOcx8YaYLH3Zn1/nACMesz2xw5No66OwJXxIfufC1h2HWKznl7/ys6eIGw7p8Yozs5qvgwAdWvq5ZZ+Zc6XDxHA4/fz7yFwe/50q9iI/YeoA/gc9OT1w3csOjq//Z+Viz6z2pVvquUXa85Ak/DLzCEM/OH872mg4/OkOfxJpbi2XDBSaM+PDBwRy2HtubxcJPFxcx8nSfW6sLhvVec+LhwWrUl+ZqhVFeWGF2fnCcnT3hK186a1jyxOW8Y6E0KoADZw00k/Zs9ImvUHpz6jssPpD6o1n+WyVvav34syIkQ8YDRrP9sSxvbsV6Y+q7Qz/+8Y/PG89Ilw+v4r3Z9aHb7yr70WhvVtn5emNr7cctvbHGw0NIM3zwFSuXhsvrxze9QfaGWk7Nqwdhwm2oA3d47PUBnn7Y08MyG4Qt//ra7GCs5f7mQG7x8pL0MNK5aeMcFps1//LJjXec+NDJF+eT5HYTsRtxb87HLA6eWS4ib7Lx2cONK31rXPSVbjnJXR188+HXyEfu+IQnxqgG56Zv6c3EzKYmPOsXvTziXbe+EETHp5ndWm76/OWxhpu+vTweDGKKg4F3fMKx51MN5mzm1vTymOHkxy6fGSfXW/xPsts/cRSHCwmbLXv6dPbOpXtcjvbVBJPoBYGrJveoD8a+a+uLVv5bJR9g+ykQX9T6yU9+8uCVV145XzzzouJ+pvcj2N/97ncPjg+6MH2oheNHp32ATfCAqw8emLjj6MO5Z5BnF39/ad+w7iw8X3D049u++ORZwe551ZvCfO3rndz08hBztq4JL8Z89E7fiPPRG2LG2dBDI+5sahYvFifPIbr6yzc7//KzW7OZ4bPDt8bP8xFnOjbD9Q+TXZy8MOjihQOpN9bxhiE3XCMfGPRmZ+sNhJj4idMje/eNORsefIk1XzZ4RD3wnDtZzjjAVQ/e4oqBBcPovI7xdpZw86lPsHAxxNnHO/zuvzjq274GiIXd66ScfNQVBp907oc454MvTnLwM+pHNeBu6CeOhA8uhE2eepI/W/Xh2d4sVp9xgoUv32LjEn8x5XEuOIsrhj9+dDjKp0Z2vl2HMJLw2OXHBzY9vMQ6Hzgw5eEnRzhyNuIHQ3x+nWV6tnLBCVO8PIQOrtxw1Lcc2MLIjh/hV+1sSZjiwmIrR3h08TIn8HGUe+3ti4ND115cvSsOr3S40IuJC7t9gx1u9Yipr9UCj1QfPcG5+5sue/5xapbDEEdnLSaxxiv89mu3Lo5vUs1iYHf/2LOlj4sccYBH9Ch9sziynI7idpbs2cKGh5t9tmJwkSde8Y5D+cx0hnXDHqahj+KT6sxOj4d88ck3vHzbV0MzzLjpqZw9W/mQZlhw2pdLHB0bLFw8X4g13V5z4ci15xhHM8GNnX9renHZ1U56FuFhbMziwVHftQYYdLD5w4V5T3BSU7l6neEPXzxdz2t+dDjBNxfbvvzmeqV3Blwx5aQLq/tTDEw+uNcjOnvPbGsSl/bmBjsMPmqJq9k+O5/48U1vnc2Mn1FNcFo/8QOyi8cbJc4AjZpA501nb1h999UbXG82/a6gD6u+c6xocRqhOGvijawPyf4Yjzed9mK8MYCpKG+gSQdq7Q2UD9/etPqQ7DvIcPn40UjfKdJkb768icZfoXj0Aq6BavPm99GjR+dDM/5qEsuPP9x0eHfBesNlLQdsDeZHqrU+qYNdTnH2fOzlIg4Gnh7RxxemIYbOugsEPo4GoedD4LenE89PPnWz6S0MnPjIywcHA4b6rMWYcTTYyiWGDja9dTZ55bCXk1jXAzbDPl88qrNcYZcXFzj2zkqMAUOvjXys+amTDo/6yiZmseR0DdPzFWsOT73Fi4MhxqxPeNjHXc3iw+NnOHM4MPSAuJ98CKsefuJwh1Eea7H2RHz88A3btS9ejwy2+MGW254sR1j0cPh1nbpmrN2jxfKFaxC8xNrDTFrDw1sffbHKd4x98HKvGur34ZO/54cPyO5N96pY+eh98Q0f9RE51cjmj2zxx6Ee8ysvX3X4QO3DuVz8fbCWG041w/CFNjx8oPWFNs8sfvpQbmdg4GjIS2Bbm2GpW+/ihYt1/vbi+cZfzfn3zIFT7vLkH15zdtit8SG4yGdvrYZ6Co9UEz2/Yo/x9g8d/2LFlG/94mSudnZ78XCysZevnNnM92T92GHon9pIc/Hh14M44VLdZv6NeIqlK2d+MIi9c+KPA73+2C9GHJ6kK0d57Im9GNLc+up7nOYfGPiIMzo390h6vbJ2za2IvXKSr/sLnrhymEmc4FobuxZDh8vG89E/tnocH7qrlBcPdvt07msYcYRN+FnT87Xma8+fvTOkU2vYYYhjc+bLkx89UZe1wSccezhqpyOdSTHFy0PEWDforOtdzyP7+IuxxpGvmgxrer54WNMRPKs9OxwSB/7et/ElcnuOErnEebbDDvcYb//w8frJDlMPykknHjYbvXz6A4uuOHDW+dqLxYdeXP74woIjvzUOMF3zve9RB7vnvdi4lB9uGGbXDQwx9mL2WuJvv3z4bJ/F4cAP93LCjL9a7A32+qVmNnGEPVmeOIqDR29NZ4SNYz50jfzijXtxclmLg2vELVx7tRn89BhG+eJrZhcHh489sacnZjZ8CB8cVtgNvvIYy3l9d80/P/iGvTmszmj99qzyLd4cF7ngdF4w/j/C/16MHsgT/hVTzhV7IyxzPtvPdM2LYV2fO3dnrTb9Zotvc9j2nUs6ePVMPsN+Y+u9fosX29m2lpcs7mLQN+irzfqpHDNkjJwCyR4osA7ZG3tvNL3h9OPVvrPjgyy7B50HjWbBhwXf7//5413W4r3Z9V0QPt7Y05cjfjA0wRtUuWB3CHx9OPZBu8PwAdpDkA0eHGtv/PmYvfk1G32HUK0aHOea3s2sD3DsiZo6eL6bS07CHo69+PiIZ8Pp8e3DUHY+7GYDtsFff8tlLoYtf+uw90Iq7pC7PdDEwIBL7KstzjDo+JWnOaxmOWAZ8ah3bNaJXIQvvd7S8TNIGObtSXXzbzg7fmwG6Vqx5pe+fbr8cAmbTu2uueXEHhf5YMQz3ub85LL2YcuLAn92ucJhN9hIfWKHHV724zRvVuLBTwze5YFlHyZ9Q9xKHHCDJY6OXzb+9MXKF0+45fEM6P7Ryx5mfqrDF9a8GfAc8Cbk17/+9flpEvc4u2cDfDFifbD2gdb9Lgc9bBw9V6zDb82vawt3vp5RPmj769hwfaEOLv7s/Ok9Rzyr1ImnHPjUS2v+cIm1fPawrPn0JsAbRbFsJCxrvvRy08OIr2sPRrXKY9DjVo1w7Kujvnm2EP6GHGGXy8yvnsbd7GzgJnzCYCedNxzc7OtHOez11bMZDz5h4cyPzuAHW8242vPFsd6wV4cZJzlwhSUmnnFfXsXqbTF0RJwhh5jqhGtUY33hS6rZ3hpnc3g4GXRi6dvL0RnQ40LHl48e2dO7Hu1JnJfX8oz7cb7V5kzpDdhq7LoRm21n8XjlJ67Bxrd6YDhjsx4QZ8lHPeak+uyze10WS6qPX+eXLoxm+cUm/Al/gz0+nk3Ong62WX557ZM4ie+86dRu1o/4iKNLwsxerni1776UO55s9be89ntNyWfAY8NF/ji03jn/xRFvb3ZO1bS81dSZhMHfiAfOhI6Ut57m3xzP43y7f+pJGNmacZKv+8e+nl3xxJQ77u35wjGHyb9a6OJgTczF8UuKuYeVTqxRfrHZuqfUTkfkLkex9GHAqSZ+YtnC3Jji6Ej+YXQtdPb0cKorHmLTd9b5ti+vmgx2s7jy7ZoOfratQZw+8KfPl746xLIb6cy7r47FYIdH5Ig3fbyveK65erH+4ZvrQ5j27k3PdjHLnU2+4qz5mO8JHAKjPFe/+kUPd58d6rV3zj0v6OQkYsVUW1jxkbMBBw++8WcrxjVVPfSt+dvLb64WGKRc1uz0jeNweX2GU0w8NhdddvH2V2Gnj7v16XTBHDQq4h7YiBuCDGKvMfwMft5wepPpRXcvuA6hZmsYH98Z6obs8ODDIrjA5hMna769oPETcwq5fbdMvr5z3Idzftb8YMD2htyab/Xxs8YVTjXLL76+iC+Grb5YG97ch1tM9Zvjwde6utXuBqLjt3Y4dPJa64F1ghNphtFaz+zFyBUuHPoVFxqd+vl5E2Y29EB8uGZ6OPRi4yAXvnpcL71pKV/nrE9wDDrXhhiY4cLKh60+qIseJp3RDYdPOmu4eGTH21pOfVSb2YckuOXgF6c4FNcXYHDCsTh85KQ3iysfmzOGwdbo+mI3xODDjku52fQFRj2i01v1iaH3wdIH8erg0/WFCzw6IoZOLj7iy+P86cOSg/ChF4eLNZ261EKydw4wDTF8cPDTIPnrs1+d6Lxh8MVPXrH4qc05yavH+k58kc3vCouRk39YdAZdvffFNr8GwjcO5cDdkAcndvFy+zsKbHgRa7Wwm+t5PUrH7pqBIYYfGz7yWqsFbtcSXTjlgUHf9ZUve7VVv70hN9G/8onvC4P8DXXKR+Rodh3IY8gDB+fi4iiGjuQHM1+zvM4OJ+v48Gc34MHSC/HWcho4i61P/PswBYONP33Y7eNJbxA6vkR9+YjpuksHm86gM/CoD9Zx4EvisXt6e2cZdn6w1UkfLt/y0fHlU31484lv9ajBwMuAkU3v3ddi4YTBD444/OKIs9z0zg+O+wumM4KBA72hD3wJzNawxcESa71nfwJu1w89P7gwyiMOXnprfkSMERe6/MxEfBjtYegBwbEe4FdcM19cnAHBjeiBL4LR08HTZ1h6Emdc9bU+wOXLx4xbZ8vXYKM3rw2mHNVvpiN808fZHjcx8cGPvp6ozfBM1ksSJ34GzvDFwjLs6eFak9Y4q00snQETlzDFFCdWTNeA/a7Fy0nEwCL6yha/9GzpwpHX2jMZlus6/mHETw46MdbO2PlZV1M8mj2XYIeBi3yGfOUIE0dr4pkLnw+BYS0vfDzThRs/+7DF2sM1xMmtJBhi1gAAIABJREFUt8XT25Nw2YhcYfGp52z0DX64lcc+offczkYPJ15ydibWYsuLh7xi6TYGzuaJM396Mfh1T/F3fayNnV954VuvPt507AnOzomIcc90rq4l5y+GiONrHwfnixu+9AZuXSNm++qyLz9fezY+9GHALQY2bnzZ64kY9zcu6fi1Fi/WSMRXS7z4ldeMB5t81UUXP/6dPR8irmeNHtqL1UN6nMLVr7DNy5dPIg9fkl/P9M6XPz/6cHFn99nkKRtiRkoyjgIRzHkLZzMkLbHf/VWYG4GEI14zkKgxYmDDQISeP51ZDB2B5wVHAda49dDgC4O+h7S9+HjDSKeGLgC58BAHjxTHv4ubHxGXvT5Vuz3BOTy9sGar3vZw9MQcj3QOhT9hU1s9ot8Y+XClLw978fk2x1HfCL/y42NtEDGG3siv7+Z6VVz+5mzqLCbOsHDF0whfrjA6zx4q1cUXP2IWT8I8m9veOnvnrk/40HdtwzFg0LPnb991yMdZ4sgOS4y9s6FLxOgB3vRyiVWPGHbDfnPFqT6wVad8ywW2PbEuFy7VE1dcyiM/sb+KetmN1rBghg9TLQkOesZeT6xJnPnzg+mFobV6DQ8+uOywiJx4d6+ww+MTPht9+ePeHo46+bPlG092eDiosx7BJfwNfHHBCbYhhsBmz58ePj1sEq45Pux6wVdMOGL4EHovWr2I4sKuFj71xt66nPixd271hc41F6f6pDbP7ergJ8YzCEZ7fvTrh48aCH584bZfm1h2MeWuB/TF9kVL/uFbOyN44eCRj7X6E3EGO94ke9e0fHT1VQ/hi2Ojx9M5wLevH/TFlSc8vmL48LcnuMgXf/dF+bJVH47ii5UL/p65vSFX12/+8nTfbSy7IR98Pn1xBFYiD0zCr7xqFS9nnM30+ei3mHJYE3YiTz1Wr6FPW0MxfMNVEx+4XpeJ2gh/94ncsA1+rmFftBeHs7iwFxd2vVcfP3s+2eq1eMNeDoN0nnHyRQc8+MLkD5OdnoiNBx8DDt9i+NbvZpzE8jHTy8NXjuLZt65y5dM1AkNsMzx78WZxbAYdSdceZmt+9QevasU7PLnLKY6NWNPbd+2aYcLSV+dZvfSea+UWa9gbasHFgBNP+K67+gPTno/3mkmxcFwXnlly1ku5CJ318qIz5AofJznqn7WY9NZiyhsnGHTZylkO+2q3zpedPj95rOU1x7dncf4w3E9qFrPY1iRMs3wwt696q252wqc66eQm4uTqXPnwJeXgE2c5krDbN8PobM3wzK07T34wwqYnctCb8RJX3ebq5cNOymE2CLzrF8N7L8gmvmcm/XLs+oxLmPY4LO96xsfaUFOci6nH8sRdHtdj/mz2sPjTV1/9sl8O8WYvJ3x7Njg4wGSnj8vi9AyPnxjr7hF7fDzvxcWtXlcHfIN/95Q5nmpmr05rePbLU8ypfgEV0YMaAYUoFkAChJ7wTzo0eMANvhENwz6hy08cDJjW8rdG3kVlb4gJL52ZtC9+OcDQKDp2zbKWTxxd63DiVEPlbeDBbsCBxxaWufryC8/MVz6D0JX/KG4XY3Y5YJa/mY4t7O1DNjzgdCGGZS53OduLLV+85BDTwIGwy6G/Xmi8oBVLH1d+fIiZD53rrot/OYkrni+hc1bis9Fbw8Kxs4Tbi3J52WA40zi4pvmFg4Pz9EJBquVsbv/I5ZqSF5YPfXDjJdZ3IQkfeg9DPka8+o4oPHnxUwN/Ix0O9tW4XPirgS9cvnTlEmOEUa/ElCfsrmM1wRCnzsVqXV/Y+cGzNoha1Ox6kNMQQxdm9YmXU9/44GF2zRL+OJYbdn1lwzXuemD44LV9EEvkggVDDjjEmk91wwgbZ/z4iufDnw8s63LBvdanznCrQa3W8baGR0/Hv5ze9Ncj2Hz82LncRvzrC1949gRvWNXczM/a2bl3zWrBV+9h4xF/+2LNctQ/nOHVo+bqxtt90ptRvvCKx5POwMMbuJ4L8YgLbnzwgrv9p4eBH3/71vYr9sVWo5oacAiOYdjDxEls/WK3FmtuH+f6weZ66tphN2C25tOIbzb1Wus3H7zNJN7W8WMn8rO7DuSHQarbzE7KnY6+npYLnnUx4qzh0offXM/sjXorzl4uNe01kb6a7WHrrfz25Y9LudkadNUvn1zF84kDXIM9nTUfGGHrH/7G6jdGHFszDD0wiNjFpVvf+pNPZ2DPhr9e4QvTbL9yrXn5yKXfPWPhx60csMpbrnR8YJBmPvGGzcd1boZjFI9vfPAMh56/AcvZh0knFxx+4tjoYC0+X7oVfuWlF9sQG06x8ONWbfz5GtZXyZ4Nh7jRwRGbXrwcchqtm4vVh40rP87xhW+IMcPIj4/c3Uv6QJaPa2Fjrbsmei3iEya7IU+5YMpFesaFW37xrg8c8CHqoG+EDUstSZyqh56unPWgs4yXfLDt8cDJB1m68K1hieUTdnH0V5FXj8zq5VtNsAgs+eNpbi2usTo48SknOw6GOsqXHU7Smm/5slUvn/DY2vOX276RPR9x8YO3fny3FjZnXS57dlLs+vMji7kxuxZn1C+x9mZ+9HLY50PvPIzuhzDozp1RYMQFaXoXFH17B8yfjzdnZnZgvViwR7bD6yJDDpG+SyHWRRWpYrv57NnFLY/ymuFtI/gadHgRb7DE878KPX64ihPDz6Aj1abB/L1pY8sXR+s+BIgtn4s3/uWGQ9RHygfH8BASZ62O3sjgad8QL7a+4SA2TPF4xAVnvZV/sejbw4Nv7mGYDm69rSZY7M1yiS9HsfLKY7Cb8b43YONO4JFqhmespNs6YZS7WJjp5MUBT3bCFh96w74Y+HRm+MWYy7F5nZszyU+cPpfDnK14uQhb3GCGCwPvqyzXesv3KvAbclrHh295YWSvT+sXbnztrcXgal7d+hXLJz9zNcSBnxr0kK66ra8ivj7Vt3Ka78VcMe7txS5OeZwjgRt2vs341DuzIa61OGuYG6NmozPPVp540hMz3z1vuPxhJ/Z6szYx7fmFU87lVr7w7ONkXW/oYG6t8ibF5OcZ7lmTj7h40xl862d8y8nGR8xiF7tzuHFpLoc9HwMP3GAa9oa8uBBxntXp9dNrY320L5Z/Z6SGcNirBZ5npbzlC5tPtcCPM70hb9zNcHsNkbuc7qeexzDEyoFbOelIr0XWq7PmL89V2IzeuMkBF59qNcffmsDKFzYMe73AXc1GOa3VoW6i1urtTPjSiVeLXOLg82HHjd0aXhzys48fHeGPF2x2o2s4H7h8SM8wOYlc9Y9fPbOGVf3i9lzlNdRhJmJJODjhII5OTfb5bz3s9OXrvOn9YcJwxeCMj3V8xXY94RQeX6N4fgYe/LYP9vBwYIePB7GH44vI8vCDQ8KkC1d+/u31n335iCuX3PKl85qtb3u2fZGVT6/pegwDrjWc5czGP/7VaK8OIi89v3pKj+8Kn5WwqrPYcPgWc8Vqb+ZjxJNuRzjwydqKNeMRnlmN7fOLYxhhy01XDlh6Z5+Ob3t4/PkZCTv9Ve7pYJC1pWvOVr6r/5PybJxr0V4/CI7W9aca5NAHw5pftVnT2RcXRvHyrN+VW7HiYOW/MfQGH9e+e4DdtZrNHlYYrvfOTxy7eg1n2P1aD8TxWRGPDxFTLhysxYqJc/jmYsUROjFxao8HH3zDtVcLXP5EDWI8m4xvPiADXYf2Aq0BVRgAg67Bh93eeveIVCC92MRejm0uWw0TJ54fKW/r9jD4mhth5yOmA8OTnzxxouOb/+akaw/H2sCtRmevD2KsjR6gYkk2OYkZNwfIn10MgYurHslF2Bv55Gc2YDZbF+8CKYfZXu76wTe7mM4mPLb65oW3+tP3ImFWQxcnvnE1x6lcfA17Nrj4y28Wn2+c6HYdd/5x5FPf+Nqzqxf/6x5vej56I6b+4xc3PnHlW218+fD1Ymq99foiCj7ydj3Cqf64mePNN1x4YdIb4VdzL/Lp+YhRCzHbw98HBj0ucrPXHw8L683Lt7rhw4o7v84SJw/b8vLVY3ox5Syvnli35wMvTvwN/YDBj92wx1UOPrDEGXQGv7jgSGDwL44PndxdC3Fmqza9wyOcxYYlVu3ywyPliJOzojNgEXi4w8OLTz8K13UAOx6wcDXwDEcsjPbWBi64VzccvTP2msWJLi78xXcmXTt0MOph9corxqBj5+eM2ouFIxfb1kIv//akHOLEmLOziSflMhOcWx/F7R8Y18EEJyxzNeAopwEPbvHp6cjyq3fx7Uxg0IXBrzWM8tCReOkLW/5w4pgfu+tGrHxi4synfhfHT0zx5s7aOi78Sf71yd5I5JI3vTg597oRm91sL+ZJmPRwytNajIGvmSxG50Rfvu1ZvtWYH/xyhLm+a1tO9Px2LMf0YuS0ZzcnMOjyWfxd6yffuIQtFna+8XcddAb5sMWZPwx+2bum4RF6wlcfCRs/OHRymNMfp7mGYXgWEPkWu321hGMPn5R7eW6t1g1xCR2e8uEQHh97drb26nDdXrHYG2HwKY7OKE5++NXCZo+/GPri5bPvumXHiZ7OvpzWMAzieU3o4cERx5+ueHUZcawndHIZJGy4XneKYeNTHfmbxfArHw7xwcXAjR//rZ0+jDiL3cHHiEM2/uFtLHx6r8dyE/UaWy9MWNea6Iop71Hc/mFXC5s+WROzM5a7Uf/50hHr9s301UUXb+ts27dw4nqc5j6Ri7/aDNz4li8scXEtf5jm4rOVRwys+DUvFl0YG2dNH4dwwmgfVntzePHp7OxX6MnaV2cdhl6fV/GKEuji32AH7TDp+FmbBRvWCZs3V3y7UOx7IxeWGQmNaIZlHcGKF09cYIZYg/DnJ698NVY+OpjE7E0CiUv4vjIortiaDTuxNnCBW1648JJuAnp+9aH84cihb27K8vJXlzoMuHys4YqNe2dRnvCa6bPBh2NkDz+O8Pnnox57/vqGF64Ej/ya+fKrL/Tl1TPxdPLBzBYf8f2+mHU5OnuxdMWZ8+kBrCfwxJeLT3rzxvERq6ds2e35GaQz0jN86PmqpR+nlpdUJ5/qNNPTyWmEw1YsTDa+/2HvTpsuW4qyj28EAQVkFpR5FFQEQRkP8yiDGigqvjMMv4dfwPdGSITBCyMMRFFGmUVmlFEBFRyQQUURVBQQlSd+6zn/tlzu3X33cA592qyI1bVWVVZm1lVVuTKr1r1bMibqyCnpj7L4dA979NGqd49nNOX6py7a+invqp21gb4EA3yk+GlTWXjL8VdHF8/aGhvltVUnxUO5euVwCJPa6GOy0aKRd61zVJsufMheExnkSvh61td4eXYPA3zUVdZ4ypW56qt7/ZXilZzWnbroq9PGJbiVhxV+5KOTN87JJGMt8yypXy8YVE4PWHEUVj3ck92YJ0OZpDz9yMHTRa/WXfLl2pGTDfCsHN/mA77K6xs+9RVt40Z2mDjVSldt6YAWD3lpxSXZ9IlW2SobT3Vd2rtXnk7rGOqHtacsHMjWRrlcWnXUh8q11069e5c6/SCPfAnN+rdsytS50NV/7c1J5WsZeuNWP+gQ7+b1Oh8afzR0cXInDyt8Gpd0JkO9tvqgXELrnj5yz3jBrHu8+sRRuVQ/8JTooq5nvJT5Mqz55Vm9nLywRuvej/jVj/jQATbhjhY/SXmy6wc6l5T+5NE3HnJ1rujyM6qjB1nVo1WnzOW+1JytbG1Hrr5KK0/9czUPa0uO9sape8/uXTCGlTHHFw8ylKmXK1eG1oUXDMh3oZGvGK/t0dIHv/Sqz561k+ORXp7RKKu/Pauz6UwXyfh5TiY6+pKLd7LwQSO5jx8a5asuaMhG40Kj3/FSZ5zI0a6knl7RaUdWfcNDm3innzKXhEYbfK0TtPyl+oPOXCVHe8/G3cYDLLSVlKt3rWOJL7p0SF7y9UubdICNZzLRale9PNySS5Z7MhpPOUz0QxtJGZ+qcUvGuuGNRr9bE9qFrVwb9ejo5Z6O6pTRTb/IUOdZ3UpH17Uf6OtT9Giio4P7cKkv5JIZLRlkScmHob7QFe/6ji6Z6R5O8u7xwhcf9ORJeCovxd9z/OjFfiYrenrDHK/0VLfnTwdtJbzQaKO8OZM+zS902jRnutc2HeGtHJ90UOfe1XjQB51nFwzCLx08R0+G9aNM0j7d0fCHtFMOAzrf7BtKdgkjHVFVA4tPuURJClNOWlnURvsUQNN9+dbw+n+SoY6cQKSwDpGT/IBMdnzwoJfc4NRh9cqBok361U5O3prQkCNfU/zjIc9Bqswg0VlKZ/xbpPFDH876TT+Yar+m6NDQnw7uG4uV9lLv8XPRs34kTy4prz45ytTDunFTR0+0dE3f2tc2nnI84rXWn7pHn17kuuJDXokOp3SmIyMAd7JdylySPBnxW+VWdiw/NXe0b06gaQ2FTRicRe6x8ccbjzXtealrTFa6U/dr+/qVDHXhDef6VnltV3q4Jl+beJ5PfvTx1yY+tSMjXugzqOqT7z6ZaFzasBdShtR9ujcflOHTtTU48Q+a/bw7RrrvA5pebOzKl770pU0neq16xCsde177WZl8T0cGO6P/2R3YhkNtw7FnOZo9HR6uNbHd6IwD/dUfk7G26V4bOhsbV3qsGFS39lmbta/utdXOeOgznZTtMW28Vn7K8NC+tZrjQL5ytt7fUqOVok8ufs3b9JFL6pKLlzbq0Htf0JUDIxdgqUNPdnpqc76U/PSJHs/0IRtPY0ausUrH8mTgl+zK0Byj0w+0eNM7HOOhjCwY0qfxQUcPdOFTPf21kaP3p1ptYiWvPqafZ+O3pnSQa0f/9CPbOMOD7urwcB3ra30vX+Vc6D49Vjp9rt+V0811IRnxS09t8JJq3/zpORkwgjMe6vTX/Avv6GBj/cDNGOTvaOMKU3L2Sf0+7em0V5YOYb9vp4/q0Boremkr6BIg55Qr14c1JUM/9BlGeLnfJ+VrSu5apv2+Lbp9f+lqzsIaX/XRae9SV6CIL5zldEZbW+X46YM+uiTz1/vDDzC2ttDmq4Zt4+rZRYZERqk6Zehd9KAnen0IH+Xx0D6d17L6XBt0q2x9c4Wb+eXHBI2lS7m+1PfwS1+5NjDBxzxgH1YZ0R4r0z/lEnnmFTzTZ22rv2uq3Vqmnf6ro7N7/8uNVFnt0IYPvJXrgzbw1idlx+TSU1s07rVbxzG+6RZPOumfBDdy/PeaX/jCFzZ+MFCmPZ5y8s21bK96epmz6ei5OUOGdrX1jC//Rq68MUu/+o+GPv1eibGkeyk5ZPvfRcxxOLJbdKSPSxu06UcHdPoCL/+dp/VCbvQbhscCZI32g7CWBbx8T5fi+7w2+/L98ypHhyjZJIv2lNzKgQF0OX7Ku+Kx5mftgzYrrXu6reXHytTv5aPrUoePfB38jfH1Mo+1r/5yc7wl+hy7j7/6NfXcROs5mvrU877ec/KiuZhc22N47Xmscld5lcv3ujSuK6+17Vq+v4/fWq5t+tLZZY6GHdr0Wduduo/fWm+97HkcozvWt5VP92fBFn+pPssrK68+XSqvTfJO5afoK6/d2ve9rJVGv9SHff1ccdnz1n7tW/xO5Wfp2zEZdNGW3WPgzRHXlUyrbvVZmfmzpubnWpZ+a1lYrmXNRXVda/357tHTJ1m1l5fUS+Xu1/ro1rJ4KtPvfd3Ka98+Wjq5l0scKg5C9crw8Ry2lclXusqjr87L2/iTAUe551J0tav8LHl9TH9tKjPee733PKPdl6dT5enes/wUbzjh69IuumRVFw/1Lti4jIEEN2nFais4gnvl8Y6ncvrQw6W+ulXP2q85uotN9fFC7U7xPtY+nfHULpp49Fx9stXXZ331nHPsuQRncwWtew4l2vijI0P9Pq001RnDfaoP5KbLniZeaJsL7unD4TYPtG097dvTT319xm+vizI0+7QvI7f5F+2xtmSx69rvedQH2ApazOt0k3evHRoyXZ5r61kdDDj+cnXauo9PujWOyo+lZKgjRztl0StzSSvt+qy8pP0+VSbvQuOe7vCCBV2VJSe+5fHVprE3D2BZm2jk+rBvqy/KycGjueN5TRfDT7vGQDv67Pux4rnKoo826rtf9Yi3daqezu7htfJBtz7jqW+uaHu2wSQghXcYuF/xj1Zf9M2FD/oVVzLV0S391ZuLZMiVp3N9046OAl1BsTkgNYejCzd6CNjDFj8XfVx4ueiojYsMuTlCBlnK6gO9jp4gxywl9nn1mEnle7qLfaZsIAIV303JI3LosKZjOqQnuugb5LUtuft0jN9Kpx7PdNbePf7RxaN8L8Pzvi49V1pl6FbaY3Rrm0u9T8Yp/sfKV93W+rV8rw+6lbb687WJZt82ndUf41k7OdraN9d6XunWebeWr7LW8vX+GL/apV957dJLHm115tOe/ph+x+iO8dvzT86F8r0O6I+VneKz1+Wsbfd0F9J/pT8ms/r47J9X/aurbM+v8n2+b7ev7/kUnfHNBqZnbcr3bS+Vbs8n/sf6ej7a2snRaS+vzTF+a5v1fm0Xr7Xe/an+rnTJ3tPv2650a/uVDo01tpax942TdmjidWyNrryjrwzfZGgbH/kqs/t9eXz2efR0d0n01r5LWY6E+7XN1uAi/8E3PvGqrPI9S3Ro1hzN2m5tA3f9oXdtTvFe23W/8l11TIfoytcxib66lVdlZ80vhtcqp3ble3kr7VoX/b5euTKXexd89XtN1cvVm0ul6nre5+r36VgZmnSI3vOxVHu5i76tSW0q27eNPr49r3Tq9v1f67vXtrVV2bG2aNItOjladekK03W+pZtcWuu0rT6e2uO31jVO8djzqe0+jz5e6pV5dp1KtVvrlXVVHp89L88rJtHv870cz/Cp/anx27eLb+XakR+v6uVootuXr8/1LXr5MX3IKYVDeeXyY2XZQXXp3Fifaps+At36p4weglDBZeXHMEhfNPEii9z1WZlniX7VsdmC3my3Pqw6R6scTfL2/Y+fwHfPL73TMf1WnbQnQ3tYSNGTdd4AuY5trY78szE4zwKpSXzKK9/n6gMgwFYZa/2+rWeAoNdB98fkrfyO8ajsWFt16bDqmTwyDeaptvEu1y4+ytK/+mN5vNd2x+gupgzPfb8qOx+fdKhtE6vn87VVd0wGnvE91V47Kbqe17La7mVoU5mxMs/iUxvjoC+ufVpl7esu9pkcifxVh/VePbq93D1Nss9KF/358n3/411+qu2p+lM6n+JzQ5WnX/rIlVV+Prm1OR/NWevOyuuYbtru218M3Vl1vFS69Dum0+XwvFDb/Rj2HFbl+FR3IZ7aRLu30V7qrePz8UlufM5Hu6/Tpvb7ugs9a5f++KRrzoiydFr7krzokxO/nuUrj7Xc/Uqf/MqSUZv1Ge36nJxo5XRz7W11/Yl2zyde2qrr2rer/ZrHq7y6s7SNdp+f4rXnuaeLz6nyfXv0yrLr+3rPeFUe3/LknaJrPNA3v2pTHu+ez5cfk7unX2lW3uu9NvV5bY+GU0xXfJoPK82ptnsavPYyj7VFQ06pNvSLx34+o42udmtZGKw07uNZmxWDaGsbzYXy2qG72LbaaL/2P3nxKo/2lBx8XGuf4lWOF5qVZ3WncvRXOiX/GG9l6Vh9eutb/ql7uB1bV+EZH/onc+3Lvgx9bde65Fe28o1fZeXK3dNTXlrr17L6JpdO0aVLbeXpVVlyYbXyqz766CqXK5OOydnKT31ivbU6wz8JPx9pSpyPRh26+HXfc/XytSye6JOjfr2P5lR+it9Z6JOT/AboVNt9ebLP2j55tdvzu5TneGob37XsQjyjXdt2f6G2l1OfjOSv+sd3rVvLlOcQ4nOpvOJ5uXnyj/E51odjdFe6bK/TN0uPK92vPT/9/Gb0bY/vXq+eT+m2b3+5dMm7Evmq2ym9LkbOyu9Uuysh5xTvU+X0Oib3VPkpPhcqT8ZZcLgQr1P1q87JSe7aprq17BjdWu8+mtqXK+9+pXO/L98/rzKqS85ad+x+LzcafM7HIznRn482mvPlZ+W3pzsfT3XH9DrV5+j3MvbPyVz5JEdeuXbH2kYbn1P52vZ8bS5Et+qzl6VOgJCu0e7pVhn7up5P6bhvu6c79pw+8d7zqPxUW/Xq+KL1rzYrr7X9Wh7tsbw2K/2xsmNtlUW71q9l8ZUrd+lHNGs5HtGv/CrTJvrKVrpj98k5VnepZadkk5W8lWYta/zWfqy0dIq++319eh8rX9tGV57Mno/l2h/ju9Lu62tTjna971mubRjE81L47XnG60L50RPkCzWa+kFgEBgEBoFBYBAYBAaBQWAQGAQGgUHgWkPgf39Deq31cPozCAwCg8AgMAgMAoPAIDAIDAKDwCAwCJwBgQmQzwDSkAwCg8AgMAgMAoPAIDAIDAKDwCAwCFz7CEyAfO2P8fRwEBgEBoFBYBAYBAaBQWAQGAQGgUHgDAhMgHwGkIZkEBgEBoFBYBAYBAaBQWAQGAQGgUHg2kdgAuRrf4ynh4PAIDAIDAKDwCAwCAwCg8AgMAgMAmdAYALkM4A0JIPAIDAIDAKDwCAwCAwCg8AgMAgMAtc+AhMgX/tjPD0cBAaBQWAQGAQGgUFgEBgEBoFBYBA4AwITIJ8BpCEZBAaBQeBiEfjGN75xsU1uVPobQ78bQ8aNCtoIGwQGgUFgEBgEBoFrHoFbXPM9nA4OAoPATQYBAdWxoOpmN7vZwXVTSv/5n/95+JZv+ZZN7/pVP+T/9V//tfX15je/+bl75dpcSkpG7fEPM/eV442WfmSv5ReS29ikP/pkqHMfjdx1i1vcYuvfpcg5pU8ykp8Op+infBAYBAaBQWAQGAQGgbMiMAHyWZEaukFgELjBESioOiWowOhigq1TvG7I8vqRvvICVroXSK50+yD2YvVbeQl+16Ax2fH07BIga7fSRnMsR7um+Grf/cpv1WktX3ns7/cy9vU9R1dOh6t9XqT75IPAIDAIDAKDwCBw9SIwAfLVOzaj2SA62ixDAAAgAElEQVTwfwoBgU7BztpxwZ7rW7/1W7diNIKhswZ1K68b834frBXAyesDfQSWX/3qV7dcHwWtl5rwwrvT4TCiS+UFsgXHygvaLyQXv69//eubvk6GXbVNFh5f+9rXDv/xH/+x9YWcte4sMi5Es9bT30VG+Vo/94PAIDAIDAKDwCAwCFwMAhMgXwxaQzsIDAI3GAKCun/5l385/NM//dPhtre97eF2t7vdFuh97nOfO3z5y18+3Ote99rKKXAxAdcNpvAJxoI0QahLcCj4/dKXvnT4t3/7t8Pd7na3w7d/+7efC+Zi8Vd/9Vdbv7/zO7/z8MAHPrDii8r/8R//8fAP//APh1vf+taHu9/97ltbukjp8rd/+7fn8L3nPe+54XsswD0lGJ+///u/P3ziE584fMd3fMfh/ve//+Hbvu3bDv/+7/++NSGbzE9/+tOHv/u7vzvc4Q53OHzv937vVpecU7wr1z69K9vn0TQP4t3znn6eB4FBYBAYBAaBQWAQOCsCEyCfFamhGwQGgRsUga985Stb4PX+97//8P3f//2H7/u+79tOIt/1rncdBMnPec5zDg9+8IMv6YRVQHWh4OksNGcFIFnyz3/+8wd9+sxnPnN41rOedbjf/e63nbySJ7CzMfDe97738Jd/+ZeHRz3qUZcUIOP1J3/yJ4d3v/vdG/9nPvOZm4z0FdiSRY8///M/P9z3vvfdgvX96e4eA8/0++d//ucNdzz++q//+vDqV7/68F3f9V2HO9/5ztsGgMAcLwGz/EMf+tDhAx/4wOGhD33o4UEPetD/0CWdjuU2E2ySyMla06obnfQJvuQ5yaZLXxms7eZ+EBgEBoFBYBAYBAaBi0FgAuQTaHHGXGvaO2xr3dwPAoPAaQQENAKZkrVVgFOZU0jB5Jve9KbDXe5yly1AFjQLHF1PeMITNh74aCsVNFmbZMjXdVt9sgpc0cUjPmiVuyT1670yz+hqi180W6PldDs9nA6/853v3E5Ur7vuuu0TZe3U09n1yU9+cgugnR7HO37xIad2+zqfPf/Zn/3Z4T3vec8W+NYGnXvtfPb8kY98ZJN1m9vcZsMyOvXk7sdJmdPv1772tduJ8fd8z/dsJ9Af+9jHNnqBrLERDDtRdgLuFFl//vRP/3QLjtMVL/KMnzzZ4Uc2XrD67Gc/u/Ghl3p16D273OMXT2U///M/v/Xd/aRBYBAYBAaBQWAQGAQuFYEJkE8glwOXEzdO1wmgpngQuAAC1pAAR6DTetJEcLMGzdaY5y984Qsbx1vd6lbngiDtq6+t5/jJ8avMc1cBWWrWJnrl/l42/aqvvXr3eBeQ7WnWfkSPVuAqUBQkO+EUpH74wx/e+Kl3SZ/61Kc2Wp8vO01eE91uectbHh7ykIdsn5jTQ6KD9p6dsLucpPqkWZ7+0Qs66eGTaCfxaOiz9qVxWvnbuNDOCTh6n4jTx+fTTpOdSqu3gUHWF7/4xYPPvY0fneGqvL56LqWjZ/V/8zd/swXbgnJfENAHtmvbeMmdbMPO9ZM/+ZNbgK580iAwCAwCg8AgMAgMApeKwATI50Fudd7G6ToPUFM1CFwAgYJPgY4gy98ZC4YEawU8/s7YCXJBmnKXz2Z9duvvXgVm1qWgCU9BntNm1xp4CbAEm2T5u9873vGO59omT+4EVDAn0Lr3ve997tSy7qChD73p4O9qnWqTd6c73WmjV7cGyNkN+ggWBY/qfT5NlmDO3yPjK/h1/cVf/MXh9re//XZy6hR4TXT099j+Zhhe8EiGtuR/9KMf3f7+2N9p3+c+99nqBed0cAlyffYM3x/8wR/cPsOGNz700E8X3nJJneRE+HGPe9zh5S9/+Sbjrne966YH7D/+8Y9vGxp0++Ef/uGDk2m6wAodfcJn5RvvVY57utLBePkMXB+iVQdHz/rt3qfdrmjKN8Xnn0FgEBgEBoFBYBAYBC4BgQmQT4DG0eJUcsQ4Zv34TE7eiWZTPAgMAjsErJkCG1WCQ5/g+pRWECVZawWr6q2/AiJBsYDayaugV531aG3KH/7wh2+f9woeBWMCPvzf/va3b/QCwsc//vHbD1fFEw2d8OsE9Ed/9EcP3/3d373xrAtkuejks2Kffwu+f+iHfmgLGgVxeB5Lyn327Iex9FMbQam/3dVXwZ8AXtAsd8Ls75MF9GtC69RWX1dZ+i84FHA7dSbnHve4x/Y3xvon4eUHuwSsaOgOS6fa+En1UVDqb4bhUrJx4LNnY6geH2X42OAQJNPJGPlhLn3wGbeNAVii07fGxvhog06Or/by7s0FXxHATj/gJKGnmzKXZ7oI+tHEI90nHwQGgUFgEBgEBoFB4FIQuEEDZI4Xp0VyL/W8PVzgn0tpcz6Wqz7no1PHkfP5IAeMY8hxla60ThfSY+oHgWsBAevempIERAIdl9NC5dZVQR3agibl6j0LEn3Wq15wJ3CMT3T442Ptvu1tb9uCRaezAjSnoE5+1ySQ++M//uMt+PaJsIAyG4VnlyDWCewrX/nK7QTZKbJTU5fg8FgSwL7vfe/bPk1+5CMfeXjAAx6wtRUkkyvoFgzCQP/8/S47I5imQxsAPlV2Mqv/6Era+2EuOgmS0cPjNa95zcaXDBsDT3va07aTaQE+WTYF3vCGN2yfa8NK0CvA9ElzPyBGDtydmNtoQCMYh6v2TvPZRr827iT9X//1X7c+4Q8nAa4xesc73rHhg1/jy57SSZ+Uw7jU2OLp9NkY6xcMXIJiz/R1T44gnJ6NVbwmHwQGgUFgEBgEBoFB4FIQuCIBco6JPOeSc5Vjm7OlngMnuZfQ16776lea+KrDb5/IWtuvMmvLseJUSfHOQVt1wIvD9+Y3v3n7uz6fDjopQsPJw08/0NWfvT7zPAgMAv8TgdahYFUgJhckWVcCHkGYwOuNb3zj1rD1Zt0KXJ3ACuIkQangUhI0C56k1qbASkDn73IFsz6fdqIpQM5WoHW6KZBEJ+BUtyYy6CEA/aM/+qMtEBPI+rRYsOizaTZltQPo8RVI+tzYSSu++uozap9cOw13MlvAip4Of/iHf7gFnfgJ+vwN72Mf+9hNf/qGIR3p5vTUia0A3K9Fs2doBOF/8Ad/sAXbePz+7//+pucznvGMDQc0MKQrHfVHsLsP9ulsEwA2sPMr2cZIn8j4gR/4gY2fU2r6CKad/OqnQF8fJCfWxtvpucSOkt29nE6NjTHzN9N4KteWbtl1dOYFHdST5xm98UAnhcf2MP8MAoPAIDAIDAKDwCBwBgQuK0Dm4HSRxenh6PmET4CpjrPixKDTghwgtGicQnBynI74FVQ5Ryg6fDk+HCYXh5IzJF8TJ1fqpIHTRAZdtOdgudCRqw6tv+1z0kFeTlUnEv4W0GeD9OKAxoN++Nd395MGgUHgNALWSsGNdSfI7KS09cMWCMTYDGWtR20FwYLcfuVZnXJ0tV+f8bCuBXXk+ZVlv8AsMMcLrSQQzZawCezAygcN+yGoFbAJsgXqAkX/XZIA1ebZPrFVPt0WJAvW8ZborZ9si4DY5hv7+OxnP3uzkfSGAb0EtWyjk2efPq8pPfHTxknxk5/85I0/fZ2s+qRaLrD3OTu9n//8528YaJfNI98JuX6zv+kKV+MkqNYHgbjTYfLYRO1d8IStDY6XvOQl2+fTT33qU7cfC4MDe+o0XzD9mMc8ZvsM3NjQgSz19CSPffau0G/YScrxRl9yr58CYuNnXhgL+sBOsF8wjrY5UvvJB4FBYBAYBAaBQWAQOIXAZQXImHKqOCqcPicinD6/duqZY8ph8TeATn44W54F0U5fnCrIOT8cJg4zB9aFlqMjcaCccDjp4AhxKgXJa3JaIvDVlix6ubQT5HICObc+MxQ094mekw/0ZHPMOHTk+OxSuxzMAniBMhmC5XG81hGY+0HgNALWYkGKezbA6aIAR5CkzPoVhFqbaF3WGDsgcBKkCnzQFiB5FqDJS7VjU5w8+z+VBXc+dxZsPvGJT9xI44O/K/1UqpNshrEX7BqdH/GIR2z254Mf/OB2UuoTZ//375r0yQmrAJdN8fe2BYT44imoczLrU2O2hQ2lg5wcdAJsNvRYqo/a4I0HDDyzsQJa94Jt92zfk570pC04Zbu0L5jUtk2CVRYd0MltBjgd1reHPexhGx76wa46XVfu3oZjGAnqydaWjn45G/bk4Us/9ha27DQ55oDgG590gklzJP3qt/bGB07keJ9op78CZDzrR20nHwQGgUFgEBgEBoFB4HwIXFaAzPGQBLAC1Le85S2bg1PwydHhvHAQ/T2ZIFSdT/R8vuyHdDg+HDsnBk550Dz60Y8+PP3pT9/+tg1/PDiLr3jFKw5+SMd/Y7IPkDmjHK2nPOUpWz2+Egf2rW996+aUOoHy2SUHMQddH5y8OCXhPCt3EsHZEuTnwDmF4dhxuvwQTk7eJmT+GQQGgZMIWGOCMUGR5N7asqZsQBUkC6b80BRbgEYSCDuxdJooyBVAFdih84vNNrjYkDUQ0s4lcLK2BdjWNDshUOtvjQVf5NIhe0Zu93S24SfwEhA6AXUSagNPuU2+5zznOVuwtyl8OGw2g776SDf60gMv9wI4l3qbcIJB8qunEzp6rX2Kfzl6gaKcXWWrJO3hJKenDUXBKhvMlqIXzErhpOxYYtttGPrs3dgIjgW7bL3NSpcAGR27+KIXvejcxiJbqt82QNlc7wMbBnBUJ+kfHfB0T29BvXp0kr6x5zApaQM35dVra7OETG3jfapv8Zp8EBgEBoFBYBAYBAaBFYHLCpAx4swIdF/3utdtga+/yfO3cIJiDgzHySkt54gjI/jkbPn8T3l/X+cTP58tcpq1EYiq4whpp94JkgCX47dPnFU/xiN4ljhTHCQOob+Jy3n0i7ecas6nzwU5zJw9ZRw8jhz9tef84iFwFnhzavuvYMhQN87XfiTmeRC4MALWjnUsEHIJdOStW/WS9WXdtc4KlNCxC3goi7420bMfgkNffljngmybeTbFbLJpKzjdB8i1J0OAKDhk03wNI9D0FYkNOTZJoCswS7aAHq0fBWPjBMHZCnoLaunFBgmk2aY1GCabXjYAwmRjfuQftOSzfWRK2rDL+AqEbQhI9LXR6HRV0MzW0SNMN6LlH9jqt8/BfR3EtrOfNjUEoE7O2Wj2ks2GCxx8fQNnwTm7yv7awFAHG4lcSWDsJN1Y6AubDC/8BNMl+OgPHNHK8YAlTL0XjDP777N6fcPPNWkQGAQGgUFgEBgEBoGLQeCyA2ROFweKY8NBcfLrlIFDxLHlrHFmOFROEjhPTo85P5y0Zz3rWZtjx/nhUAmgOaR+/ZQzho4TlFOZA73vpHJOLoeW47Q6fnTw7BNDDp3TI84f55WDK8B3kSdI5vDRRSDPSfXsFEp/9p8j5vju9ZnnQWAQ+G8EClRaLzbMfPps3RfguncaKZCVCnCsXUEdu+IHp6x1ddawTTiBV3zL1XUqK6DyKTPbYmPuta997RaU+bvZ7Mk+SMQfLzr5QS0BJ/k2yNg2waLNPPraSBOAS+yUE0x05NrYwytd8JToJ3gVcAsSC9BX2+WXptHTbZ/wFGxqaxNQQMr2SeydSyDLZtFXwsenzGwdPftdBbLpQ5aLDuUCTZuVcPYr3/iy9z6PZyN98cNm4s02SuTZsPSlEBoy2Vq05Er0kjrt7USb7dcvv2INQ4kONgDgrI9tnMaLLvqvD2y7gLsEJ9ekQWAQGAQGgUFgEBgEzorAZQXIHA8Oor/H42RxQjmRnJSSwJjjwukqQPap4wte8IItoM5p4jBxOp3ucIT8misHjwPIEVLf6cMxhydniWOnHq3cpY6c5z73uZtTWp0TEJ8ICtr7JNIpCJlo5DmYHLja1bfJB4FB4MIItA4Fo5JnQY/1Z216lmxGKbfuCtbkgiIBmh/DEtRK7Il26kueBWpystxnO3zqfN11122fSgvwfBUiCMte4FGwHD9ybaCxceyYII9c5YI3NsGn1/jZXCuwZCcEiXTWJ3q4kiVnEwWbPgtmO/EMi/pU4Bpu6SXXR3IEmGwU+yYwpAPegmY6JxM9uww/f4v9qle9aqP1w10rjXt6hKOgF18nuOy3TQE22ifr+saW492mAEx9gi5oZcfhBD8bjE640aZ//Ql3sslxIi7phzK6OFG2wQEnuEjqXOjZb3h6hsGkQWAQGAQGgUFgEBgELhWByw6QfdrmkzhOmhNkJ0P7lFPEsXGiwsnh0Dn5yZlBwzF2WqHcyTTHh1OqLh54c4L2KScSPxcabco5cGuQq5yDqYw+HLMcMrzxUL9eqw57+fM8CAwCxxFY1+t633qqzLN1LEArVWaNqlvtxdp+vdfWc1dBshNrQbJPft/0pjdtPyYVv+SV04lN8LsIgkGynWL6qgRfPyYmYFTns2UnzYJi/NTL8XBP/jE5NgAFnGyT4I6tkQpO8WaTjiW86SQXiNtc9PVO2OHtKxzyJbng1Z+KCGzf9a53bRj4/Fpddi5ZymBOB59Xu2eTBb0CVcG2Zzh4Fvza9CSjH0UTPPv9B5sAvgBwon6WlC2GKT28N+js2b3gXz/13eV0v3lzDOezyByaQWAQGAQGgUFgEBgEQuCyAmRMOgXhsAhwjzkonBhJniODllPGAZJyCivjDKFXXzs5ObXZGl7/D76SutqgX9vSba1zL7CPJodz5VMbtJMGgUHg4hFY1461JqAR8AiErC9laJQLuNgCqXI0aAViTmytU/TK0VrDgsRjm3P4JN9mmxNTQalTX795YENOEJr9qHfakOnzaraI3bERSG961YYOAm4nzf4UI/ukfforc+2ToNJpqwBZ4Icm3nLB5vlsajo7KXby7ko+fk5/2dPwUgcDPzRGf39frI/u1aGVonevf/jC1maBwDsc5bD0Q4h9Dk1vmPoyBy6CdGMjQO5T7z0O+2d4oBWEu6dfJ+J089m2vrk3/ukvVzZpEBgEBoFBYBAYBAaBy0HgsgNkzhNnxUmD/9qEc8RhyynNSfTMCePwdDLhZIGDo1yO1qmMco4hx1eqLTpOdLTKOUSCdM6R9lK5urVt9BvR9XR4cQK70GivrQvfTq/UuSYNAoPA2RFoPVlj1pTPb/13a/I+A8bNOhO0CUjXpL1PaAVovljBgy1QLsATMPlxpv4WWdtkWt+tcTbFj2s985nP3E5Fnfz6kwo2B72rRBdfu9CTfcPfZ9npS76g0N80OynFS8BIhqSvEnvhPruiLDl4CTzl5KFTl75sKxsYL3V7+6NMe8Etui7lbG0bgNq50Ark8XYCLHjVl7VdeqNlz/1WBBvbb0c4IXZara9O1Z0MwwKtpJ3gFm9/f4zeb0kUgG9E1+Ow7w+d6ePrIZshZHgfNC/oYd5oBydJ8AzHfq9C2TGskjv5IDAIDAKDwCAwCAwC50PgsgNkJwMcx9/7vd/b/jbND7Z0csFJ4cRwaDlHHCenIpwbJy4cNH+HyClC63SCU8RBVu4UQTsOE4fO5XM/zpMTEk4dh4mDyokSzLrI1EaS55ip86wdeTlRnldHLZ3V08XfvynzrP1Kq+2kQWAQOD8CbIC1IhfU2lAT7ApqrCvry5pWbk1L69rSRsAqYEJrTcs73ZVbl9qs6xqv1q46f1Ih4BP4+jEwPOlUm+4FXYI7gXn/7ZzT3mwJWeyX5NTbSalfre7PONI9fvURPTsk2VAUdApio6Ore2Xqs4/K6jfZLjz1T75e6Gwk7vHAQxK4+rRaUAvPeONBN3bavb4KftldNladyzi1kelU39+G63sbFHSqni7GBo7K9IsNJdNFxzUJrP14GP0LeNl2z/B27x2jHT4uOtno0B/vkpW3cdjLWOXN/SAwCAwCg8AgMAgMAnsELjtA9t94cDj9qJa/6/PZoNMQjg4HyyeJnBhOmYDXj+UIqAXCfrGUA4iW4/O2t71t+zVsdE996lM3XhwqAbf/uoPD5G/gnNZw4pRzmsjlqHHqOEPlOVByThO96MRp8izn/CnLqQogz2TTy4/McKQl/SBbWmVtBfPPIDAIHEWggFHep8XsgIBGEkhZa35h2X+9Fr06QZzTST+EpQ3a6gVr7IWNt8qtXWs+3us6Veak1/9dbLPNCbANMPalwJD9ELz3t8VOj11+rIqtkNCwR+zZO9/5zu1XlJ1yCtTIpgu5dEErV6aOzuq0h0U81avLTpFDp/Sqf5sC138KXbDKLsIArQCVfaWP4FU7iQ5wI5ttSy4egkzlUnp375mdZOeND7xe/vKXb7raaHj2s5997vSYDHbd5+sCYp+0wxJGeDziEY/YbCid4NI46jM6/AqkBcVo1NUHz/pHjj5oT3e5IN6GLRtNZ1ijr19b5+afQWAQGAQGgUFgEBgELoDAZQXInA+OpR+I8eMvfs1asCqg5ExyiJzECKI5kpwXn9s973nP206c/f0aJ025k6O3v/3tm9Plb+T8l0wcSEmOBycVDRmcPwGyz+2cRnMMOUScoWMOkTpOVQkNp0q7HK19nZMKsp0kveIVr9gccbrRBT/tj8mKz+SDwCDwPz8ztt7ZDIGh9biuVwGy9djfEguK1GvjR/2sf6e4BT3q8IhPeZhbn9a2cveStsrYIf9tkb8rZksKwPAUMDq99ev2AmpBZqej2rsk9k2wzU6wEQI0ny+zS/ixESVBHhtno81/f8Qu+jScPSlQ1cZJOfn09d88CRZ9xmyTgG5OSWHkPj1sQpKvjL4Cxn48Cy/9R6veJYUtWl/uCDq1jW+85dlogTCdbWTaWBCIqrM5KbClI16/8zu/s9lkNt//bOBXs9X7wS76+J8O4Jm9pp9+w6S/84Yt3umuDo3Lpmj9oB8aF1wE7/riXnv1rugbj8kHgUFgEBgEBoFBYBA4hcBlBciYcoqc6rzwhS/cnEWnMhxFJxg5iBwVP9rCMfUJIkeLwyKw9QMv6DlseAm0OY39lyUcH3w4qU590HEIOa+coE4l/G2hz/jwz6niGHHE1HGwOZ3KJLln9f7rE04u+dWT6bSDo+xExAkI51BfO0kap+vUtJryQeB/ItC6smbYA/lahloQWUDbGrbefaKrznoVxJXwiE5Zz+4Fqdar9lKy5PiwG/4/XTaIPXEKrZxuZPkzDm197mu9sw2leKH19YtA0GafAFhAmwz0eAhCyWAT/X/xAkrlgkV2TJ8ldGTL2R991U5wKfDzbGMBvb7GnzzBKlumLnz13wZftFuD6/9mVyBLvsATf3LxxsuljRy+dICTzUiBN/3YQZsV6vHyXz85AbY56pTZZoa/U4YNPfBT/vrXv36TaQOUvjCnA/7wowMbDm/9wL85wRZLPvteN1GU4QPT/q6dPSd30iAwCAwCg8AgMAgMAheLwGUHyBwTzopgkkPC0elEgoPFERUcC1A5SZwdn1X/yI/8yOZsOVFxKiDY7cdenChrh76L4yRwRuOUgdPMieKgCoA5phw0dAXm5Pssk34cZjLwUy6RITh+0YtetH0imIOoPRpOHn05eeSpXwNtNPhNGgQGgdMIWCetudZzZeyH1LoU5AjqBEfRyK3daPe8WoPygksbajbbBFiCR23dS92j8YNd1jSe7FfBGLvyxCc+8fCQhzxkC5AFp8nfmFyvs9Pwxz/+8duppaCxTbZo8WVPPNPNMxsm8K5P6Y8GrUv/fdKMBh6Scgl9beirH77i8bfS4VaAzzbWjmyXE3Ofj3/mM5/ZglOBJx76ih89wspprKD4da973RbYk+sUG27+Wz+8bQ7g52RZWzYXdk7pYc/2+n+Q2dv+RMYpsn5pLzD2a+FOhr0L4IRPATIM2Hf0ygXB3hvmCj3phBYvffIeICtbHVYbePPPIDAIDAKDwCAwCAwCF0DgsgJkjgeHi1PF6eMMcZ5yctRxbgpOOTcSekFztE4vODicSxdHR1v8u+eg+pzR6QLHixOFD6eJM4QHuehd2rr8GI1PoslGL1VHLw6kgFwdXWuLjoMluOY44q/eSRNd8Zg0CAwCZ0OgNbdS78usUTbEn2A4ART8sAfWr3JrXRm67MN+HVq/6gV8bAW6ToejVS/hKbBki9gTATI7I7EbNsQEwGzAahdqHw/BnrZ0dSqKViKPzaA/3dka9RIe9T+96Oqq3t/vRqOMjaJzurB7eL74xS/eNigLhmHkdDjbSId0xo/90l+BMWx8aq4PNgTTHb0AVEqeMYFrP/DF5qujB1oXHoJ/uuBNHhrl7K32gvfGRH/1yaYpfujxUU6H8sr1DQ5o3IcXGcr1y5jhNWkQGAQGgUFgEBgEBoFLQeBm38jDuJTW02YQGAQGgSuEgE0on9v2C8YFxE48BXyC0ILVAqcrJPqKshG4uQR4vqbxZyeCQxt7VyrpvyCxH6wSBAsQC4TPJwfGPvf2ObRNQBuEgkqB7prwF3D6esbfQcO+MVjpbEz6bNxlMwAdfQS1a4oXepsXdEUDK3Wu2pSv7dd7/T/26tIO306UL8Rn5Tn3g8AgMAgMAoPAIDAIQGAC5JkHg8AgcFUgIOARJAnMnAgW7HmWKiswqv6qUH5RYg3eBH/0p6sA9kqlVUY8z4oHjG1G0K2vYWDrWhMZaJIlP0ZXH9VL9EC3D07jB48VC+Voa7/qcDH3tcer62LaD+0gMAgMAoPAIDAIDAIQuKxPrAfCQWAQGASuFAKCmgKrNcAp8FMmCCoQulJyrzSfVU/3gtD6cKVkhcOKkzLPF0p0WU94T7ULZ/QugfAp/ivNqtteF+0LxPEr1Y9kVn6+/JQup8rPx2vqBoFBYBAYBAaBQWAQCIEJkENi8kFgEPimI7APlHqmWIGcYOxqTwVpBYvpfqX0DpeLCSiTXdv1ufs139Mlq77taT1XV77SrPXuT41jcvZtz/p8pbE+q9yhGwQGgUFgEBgEBoFrA4H5xPraGMfpxSBwTSBQcFR+LNA6VnY1dr4+lJ8KCC9Hd7xvLDw6Qd7Lq3/Ku9enPZ2y6o/VrfXRncLmfGX6UMsAACAASURBVO3XuvX+FK8pHwQGgUFgEBgEBoFBYEVgAuQVjbkfBAaBQWAQGAQGgUFgEBgEBoFBYBD4P4vA1f+t4v/ZoZmODwKDwCAwCAwCg8AgMAgMAoPAIDAI3JgITIB8Y6I9sgaBQWAQGAQGgUFgEBgEBoFBYBAYBK5aBCZAvmqHZhQbBAaBQWAQGAQGgUFgEBgEBoFBYBC4MRGYAPnGRHtkDQKDwCAwCAwCg8AgMAgMAoPAIDAIXLUITIB81Q7NKDYIDAKDwCAwCAwCg8AgMAgMAoPAIHBjIjAB8o2J9sgaBAaBQWAQGAQGgUFgEBgEBoFBYBC4ahGYAPmqHZpRbBAYBAaBQWAQGAQGgUFgEBgEBoFB4MZEYALkGxPtkTUIDAKDwCAwCAwCg8AgMAgMAoPAIHDVIjAB8lU7NKPYIDAIDAKDwCAwCAwCg8AgMAgMAoPAjYnABMg3JtojaxAYBAaBQWAQGAQGgUFgEBgEBoFB4KpFYALkq3ZoRrFBYBAYBAaBQWAQGAQGgUFgEBgEBoEbE4EJkG9MtEfWIDAInETgG9/4xsm6KtC4/uu//quiGzw/i143uBI3cQGN29qNxrG68+Eczdq++/O1i2byQWCPwDqn9nPIMxuzlu/vPXftee+f17bVHSurbs1P0Z0qX9t2j7ZrX+b5YnjVfs3jXb7WXe79+XTby9s/n1X2hWTs58Ke76n2p8r37ed5EBgErj4EbnH1qTQaDQKDwP8VBDgeN7vZzbbucia63/c/R0PehfYU/b795Twn+8aQdTl6Xk1tV8war1U/WOZ07sfxGM7x+JZv+f97utFUjndlq5y5/7+HQDbF3GhOlIeGuuiUNY/Mr+pq35yLHi91+7SXUX28PUeTDLwrW+mjPdVWfTrs9ak8Hiute6k2aLuvn+or///UF/53lRn/Y61gGMbaJHNtv8c5XaJd+arb81mfj+lSmxV3Zado6bPyDK+9Hiu/6urLMd2jmXwQGASuTgQmQL46x2W0GgSueQQ4Hf/xH/+xOR85Ere61a3+l8MICHScDHTub3GLG9Z00Y2s9CL7mAN0zQ/SJXYwh1MOt3//93/fxvnmN7/5Oad4z7o2yvdYq3P953/+51bXM7ouZft2exnzfG0j0JrNVpgPx4KT/VzxLNXes/vWPT49o4te+Xq/oqt8vZq71kA81J+as7VFy+ZJaLu2gut1oRv+8pXfLW95y3M6rOXxRh/f+ldd/Pf5sfpjGO/bea6tfC9bvTL9UB9Oytf7U3z1TzspOe7DSx5/MrxrVtq9zK3y+n/ijab78PJ8LKn/6le/ur2rvK/QdR2jn7JBYBC4uhC4Yb3Mq6uvo80gMAhcRQjkbHz2s589/MM//MPmsHzv937v/wp+c3bkf/u3f3v4whe+cLj3ve99uMMd7rA5HDdEl772ta8dvvSlLx2+/OUvH+5617sebnOb2xx1tG8I2dcKT+Mlce7/5E/+ZHNav/M7v/NwpzvdaXMcb33rWx848JxGzqSLI7x3OJsn8pxS80XQ/W3f9m3bPNCO01tAc61gOP24eASad+bKqYAEjavAqzZy7f75n//58C//8i+Hb//2b9/W/8on2rVsP2fTGq15yY586lOfOpjz3/Vd37XZk7V99GtOD+3R/d3f/d22jm5729sebn/725+b51//+te3JujYUXpbX3e+8523vil3SfHCrzLl5AjkBIzf+q3fem79JR/Nqf5tjJd6beJ5Kmje64S+snRke+EGf7b3fCkc42FM3ZfiKcfzX//1Xw/sO7teW7TpXjt5/dYX9kZqczYZ0azt3H/lK185/MVf/MU2Ft5VBeSn6Pft53kQGAS+uQjc/Bd/8Rd/8ZurwkgfBAaB/2sIcC5cHLy3ve1thw9+8IOb8/KgBz3ofwVJOSIcmPe9732Hd77znQeBVk7gDYHdP/7jPx4+8pGPHP74j/94c6Q4phN8nR1pjmjjxvn+jd/4jcOnP/3pzdkVFP/+7//+4d/+7d+2sfYMW/Q5rKsTadx7RoPfm9/85sMnPvGJrY15wLGXan92TYfyWkLAvBOYWL8CGnNFQHTqMgeldW2bYx/96EcPH/7whzf79N3f/d3nbBI6bQSjf/RHf7Tdm7/N4T2W5i6d/uZv/ubwute9btPr7ne/++E7vuM7Np7RN789u2/taP/FL37x8KY3venwl3/5l1twpj094i0XUL72ta89/Omf/unhdre73eFud7vbphN+5KOJd/zJUAert7/97Yd/+qd/2taRTSc0NrbSY68f+V3qCh43Idf/o3xth5ex+au/+qtNX5gVNNIPrZw+b3zjGzd7YUMhO7/ySk798mxDQ0BqbASx+K8bINrro82697///dtmBf5tCqjfywgj/fvMZz6zbVR4Z8HoQqfCxvxlL3vZpodNDbLwh9ukQWAQuPoRuFFOkBlFLyrGgfFmIPaG6OqH6rSGDLr+MYB2bycNAoPA+RGw/l1//ud/vgXHdvQf8YhHbA5STlkccoLkn/vc5zbH9LGPfew5py+6NUfLwbMuc5LUk8n+kCFX59rbIw71Jz/5yc1RfuQjH7nJwrO26Pd67nmQh+ZY+arrlb6/oWWepd+wQseJ5ITD0knQQx/60C1Y4aByVL/ne77n8AM/8AOHBzzgAds47HnvseGocoBf9apXbY4nbJ3IOaEzNuTJpXDgzJ4KYNC4GlftGq/ax2utc3+pCd9jKbnH6i5UFs949KxdZRficS3UC14FkjZPbGpJAifzw/u5eRAmcDIH73jHO25Bj3pzyByzOSb98A//8Lm5ZF6zVeyQjT0nnOwDHk4kBXx4rvi7N2//7M/+bGuLB/ku91L6uEfvWc53slEn+FWmf+azRA9riv0SGKKhj34r0xe0bJnyu9zlLtuVXPytTfjoK37f933fd/jBH/zBbQPS1zpwk1pT6amtS5Irv8997rP5P2hd0W5E19Ppzwc+8IEtQL7//e9/ePjDH36uDTqbAX/wB3+wBcgPfvCDD/e9733PYRSffR6GNuBsQghgn/vc527jnd7pqu7v//7vDx/60Ie2r5Ge/OQnH8jhl0r1a9UdD9j89V//9TavjPEP/dAPbV8xwR9tGKy62bR4y1vestkn9i3eK83cDwKDwNWLwBUPkDNEGQ1d9zJhwBntRz3qUeeMurrVEF2NMGXUGGG7j5KXTruBnhlbu4v3vOc9D0996lOv+j5djTiPTtc+Aq0lPXXPMXOSKHjiXHE27Lpz7NgKjp4LrfXHUZFz6jp5WFFDV+LQ+AyX3XFCgB9bk+PGUePQOolu0662csE1x9Ln3MlO/+Skl3ryOF/aSZxsdmKVvfK/EvfpQ6ZL39YA8YawrfWZ/vobpmSvCSZojZXLM1rjwFn//u///s3x9+UAnZ3SGXcJL21L9UMfjYfg2rgaO5+efuxjHzvHv3khICHThiVHXHDQKTO+eJKBp7Hz7D49o6HTesK06qS9a9/3aPY52uS6l3qWH3veCk/8s+pqLPQZvnirk+iWjHUuJu8E65tssQ0xc0SA6x0NB4Hpxz/+8S2IdfpqjoSJuemEko9ivQrIzBlzgY1Q37yBqwsdPmgFlu94xzu2gE/QdK973WvDLtpyMo2R8TD/z4K/vgj68DfPrROn2gI8fZP6TFgfBWRo6GTjke4Cd0G1E2V/vuJT39YXHcxv/eWXscXvete7Nj/H+rTOyELjai7B1IW//kkwhAtbilb5sT7CEp0Tep+cC8Cf8pSnbO3oSe8//MM/3PS1ccanynZvgnb/kOFiq22MaCvIp4N26zqgk3eMgNin6G9961u3ZzorR4tGrq1LUoafcTMnzCVY26T1xZPTYTpEn4rwQW9M9Dv8qp98EBgErm4EbpAAmbFgVFwSo+eTGUaGs8JAXu3GIsOqL+4Z1N/7vd/b9PbSYWQziHY8XV4yjL02kwaBQeC/EcgecDwlzpBPFN/97ndvO/lsAodMLnFanDa6sifWG0eDs8mWrOsMf+u0Mo6JIMqnuNrlFOPNUXV6aTPruuuu25wj5dkr93h5FnTI02GVoVxwxVlyuuBLEg6fck4TB1rgz5lVxuZdqYRfOn7+85/fNgNgxxH2+aaEJjyupNxkk2s8jQkncE2ce3TKbSxyYI2BTRH3HEtzQCADNwEA592471PYw9bJ4O/+7u9utJxaOhQM0wMmnHqnX8bdGAtmJPqUGwu0dDF25gR9jCfHtrkmeHDBdN0U7f2Fp+usODdmmyLX/5Ne9fMs/NDgRU/3MBbkKUsGHeGv3jjd4x732PqijetaTOaEdSeY0X/9NhfMCXPB6ag5Ftbqbc789m//9va1yTOf+cxtbsENRniEVeNiHmvzhCc8YZsTNsgFTU6hfc2AvvlQm8bBHM0ewB9dtI2HNuaj8fTnJE6QnbTS2zoRMHvmS/FLBJo2Fp2GClSVsW/WFTq2yAYRbJJVrn/m9qMf/ehNPB/HeqCvfmnD37EZhVY7+q3zzDPbA/O17/WnXFv1+LHPgn1BKl/K3PTptXeAPj7jGc/YAnp9NkZk0+lYwpcdgANagStbAkNylKFJdzzpAFeBOtkFufULJvqjb5Jxu9/97rfxpAvd2TjvoYc85CFbvtet/tJbm6493TwPAoPA1YnAcYtzGbpmUDJGWPlsxu6eF0OOUwbrMkTdoE0ZSolR44A5IfZ3dJ5/9Ed/dHsJ56R7oTC0XkKTBoFB4H8jwC50Fbz+zu/8zuZwcFY4doInAQqHpoDFOizwyeHgZFmHnkvxLueU4CkQQ8v2CHzwJufXf/3Xt0/kHvawh21lZJAp4Ys2/tq48KEPB5kctkzA5u/3OJNSwRonjOP3Mz/zM5tzGa+N6Ar8Qz5d5Jy1/i776U9/+naCQoR6cul9JRO+fT7IMfVsTNYEy+TDxAkXpxOuHFO5gJQzj5dx94m9U6PwjZ8+wp+dtaHiXcKhF4w4QROY6CN7LFDgaBsXmwUCGXLV4+OCiZyOnGp2vS8FlNNNf+htHAVc5olLsBwv+sXT/YXGGC1MGo/w0S6d4ldZGBzLo1UnCPR3qvqtXMKDLLnkveWUEx778doIroF/BGoCHpd+21SxPrybBU7m2BoomlfojL+A0jyEmbFXJ6kP68bOHLX5ZX4IpMxjstG6atez9vjJL4Q9Gv6GNW2+m3/Pf/7zt7nuhFQd22OtOHywHnzC+8IXvvBcEF2AK6jzg4b6TOfmg9z8c6E1rx/3uMdttpZ9pLd1IDD2ifkDH/jAc31yQ8dy92jNK7xOJbLMU/afPOvTBpkviOCvb74osZ5d/Ea44gtrcsI22eRKNkPhIlg1x42lDVgbR2y7dW5M8dB348x2wc640MG44G88H/OYx2x91p9kep/YYDGXYGlzLfnRHOs7mS7pfHTH2k7ZIDAIfPMQOG3NrqBOjEPGeDUWV1DEFWfFkNE5fRnCAuJekurRcbQYXPkYwCs+FMPwGkDAWpE4I5wSf5vFGXrWs561nXLY8bfGWncCWg5UTkvrarUjKyzqV6ePk8NpxJdjxLnh7JDPSXvpS1+68XbyI6Di3KKLjxMnzqeco8r58rm3oKyvR3z+6IRaoE9fARSHObvBHmQ/Vl2v1D1dYcYZdGLEGdz/vWT9uVIy46OPbB5nVx+POcZo4E0HthMenehw8Dt1Mm6wPzW22hkjn3/6nPqJT3ziNp54clZzWjnTfTbpax4BEWfZ6ZDUWGS/mwvmImdcwOET2QJ088S4c64F+Jx5gbnPPs0FSd8uJuljY4a3eemEGxaS+rOmZMvNAevJ5oP5af5L0ehz8/Gs/G+KdMa4frMdxtCmibEWLFsnsEYHG4GYOYTWuMMtrIzTsbTKMH6CKfMXX3zWREbzrnytP3VvzqXrj//4j29zWOBKDh19Rm0zRP+sJRtFTpzJsxbNT4Ele8QG0q25kP5tUMGGrdWG/dDv/tQFJnjASbv6Q+/4ua+u/qx1ldH9DW94wxb0wonedLS20FvjyvBy4MC+0JEdFyAfS2j9KY0fJ2NbnvOc52ybAU7ZXfpiA1YAbv7LrTt23eaZU3i2Gw+BND3gQba09sM9HG06sCl4wX0Nolcd0evnfk6sNHM/CAwCVy8CVyRAzjjKGSEvefcS48PgMHwSg7Uana3wesdZnbYMCuenFC/P2iYnWWR4qanL+UGLLll4M3qe8WfUVlo6olemHr0ydHThFP3ET/zE1p4DkhNFjjaudMOHLDleDO8qa9UtHfWbPBc+2pBdvTaSZ6l+bQ/zzyBwFSNgzprf1oB15RM6zhDHi5PiNMG9hM665tQ59UDfumJDOEFyjhM666u1QE7rTBk6QSxHU7DEybKm7Pxz1sgQVAqSOYfK8MNDsEVP9U4XOKecYfUcJLLw9ava+P30T//05iijkax//Pp0b9Wxe3T79ey5flSHbm3TMz1dTsn9aQdZTnzQrhc++/Z4HEvJRL/eo/VsfNSR9bSnPe0cZuwVbIwXOnplQ9Xl3AsmfJoqqRe0yOHfyYz26UseZ13g65ReMPmiF71oG1d/1iIocAItuOUkv/e9793s9ZOe9KTtRIwDXsITPykd6WkemX9O3x//+Mdvuqh3wsSZFozg7UeQzAlOuBM1gUp6JiPeazlekjKXOSzQf/3rX7+d9vus1zusNtGtPPf3aOgeVr2/vKd8tQAn9ejQwNhJJJyVl1bdKpPHdy1b72u3lu3vk12uvnv5lUzpo2/WeMkml2BT/wVi1jKszUdr1KmpsadPeGnrfn2OHz5dzSX8wpW9ML7mPJ5o2YECNHWCseYOvclBL/jiV+Dbp83mr7+p9XWAeWgjRBk++mbctWEPnZSThbd5isY6hUf90Q8yXeyWQNSGpU0mwSK5aM39/KsVW3U949U4rmVrebjJ6SZohRUb2tcf9HMJwtGw25J1bcz0zZxOVjzJRO/kmS3mp+HJXvghMLZFcKxvnY6zDzbEjMuP/MiPbNjqJ97Zd/fWD52O9YudgzFsJfriZx5ItVGOF/7GQj+Mc8k9XpMGgUHg6kTgv98kl6AfQ+Bi0BkIht/OXJ+eWPwcYIaKIUeX8aitlxRDqG1OL0PD0HGcvTQYZUl7xoYMl5eRMnTRM1wlOtGFs4vWKYGEf5/4eCHgyZFm0Lws1feC4LQ5NWLUGfDa1299TD998QLrwhcv7RhfPBhdqf7DBT50c+HBqHLeyZYzpOFW2zGsG4zzz00EAWvEZS47rfCDMdaGOc+xM+fVW4/WK2eeA2LeswGtfQGLNk5POER4aMdJtFasNc/oOU/sik+gyUXLPuCBb58fkpeM1nOnMRwbQZNTDO3ZB2vYunWi6ASHXCeLOZiGRL1Ebqk1zy5w0K17Ouo73hzf+qNMH8jPYVTPnrnwpYtcUIQ3POgiecZDrj1ZLjwbB+3hrc/RsployEDPHukvvdgvMtAqI1c/YSUAwIu+ytTjRb7PWgWUbKox9fmrBD+OPJ42L/Rv1d09XWDs82ptBbF4mUNsuh8W+s3f/M3tc0qfWdLJWLhgkZ2kc9cm5PofUoNf2NHLvNNXtDYbzBFl7p1+ca45z+ZZdPoJJxesjS++8DDfYCHh6R0HB46/zRV42XCVzEFtjA8MCxbwJGOlIVuiu0sbOJJnbcGhRC4crZH6a/6RUR/IoL8xwAdP/aAvPTxL2psHcjJL+BsPsvBUp6051PyhG9760rx2H1bqtMMjefjjTTf85MYULbzkntFoV/LMTgic9M3f4poP8TXvBIbmEh2SGx9l8HGtCU82Rb/IgxE8zAn44mnzLwzwQaMNP8ecF9DRPd5k04394Gt4Zm/iiT//xDrgt/gBLX1qrtCDbHnjaFOAXTOerQF9R7NexsynyOakzSufKPOlagMPKXy0XdP6jH/4JmulVUY//fSFhz5qHy2Z7ptv3gv6nI1ceblHa04Ihtl0NsFmJxneD36/gu02xuYYWnXmjHK2BIY2Hown7PuU25ylmzHSjr8JC+VyZc2d7Bh7Sif1Ljqgo5sNMTyMU8kaNZ7JqnzyQWAQuDoQuKwAmRFgEBgdTpL/9oCTwjCrs/AZQ84NA+SlwOi41DN86t7znvdsDkMvTQbM38049bF7yvBIXjReFH7wi4ORo+jFy+Ax8GTQiQwvM4bJC8BLCz25jCXj6QTEDiPdOD4MGbn4acOY4cvwetHZ8cfbaQ1jWvLyYEi9dH0GaFdWkAwXdYJjDptPMDlavQi04Vg6GXF5ieKvnxwwL0KnLWTVJ/2aNAjclBAwZ3Ow2ASfsZrPnEiOOqeSk6HMJ34cSXRO7dRZr5L1yJmxTtkaa1ud9cSp43S52A/yrGMbdJwgp42eOUBslDXJAWUH6MBRtDbphJ9gjGz2CY2gg34uvOVsgpyuLu0k/aVrdK1ZtNY8G8a+4J+jzUHmxAuW6Cah5yBytNhGJyFsC+eXTYCVPrAhaNhJbTld2krawdant/pNPn30jzOMJyea/tlk/MhxQmUs9IUNdAojiFzHAy+Bnh/X4fDpjyBGYt9hr1/smZMd74ZwYZMFuPRlhyV1Lnzrgz6RbfzwIr+Agt0k3/uH/oJxAe3qrBqX+MkleeOmnbH07iiwoUPPAhS0xuK3fuu3Noecc2wM8PFega254h3AKYaZ4Np4moP4SvosaDO3YWHcnKrB2rtH/8hTh59L8AJLY2e8jRv5ZNQf+uqni96wgp9LmT6iddGPPLrSMb3I8b7TL+8gmHrPeo/CBw/zxnroPZl8+hkHz+aB/mpLDn3QC4isMX1Tbh3QZeWp7/pSIrfA0nrxjrS+rXc8yTJ/myvaNZ4CLG3g/2M/9mPn3qN4ej+zJXjROeySGw95vNGxPeY63eEPM2vCZ/9si3pr2xxFrz3e+gBH99YifKzNxoeO+gEfba0X60JCYzz4E9YWfNCaD/ibm42PMnytw9YfmXTpwtO9NeLrE1hY769+9au3uczvUo8nXfBf29Z+U+76f9RfKKHBTx9hZZ0qW/HFw7NyAaW50/Oev3L9Dw/Pxgb+5jDfUf/wgIt1x3Y6ePAVSPaKXTJXvQeMZTYAP+3U8znhDwu4wvgXfuEXNtnWsCDdmqNPY6GdMbfW5eZ284LOflRNG+WTBoFB4OpD4LICZAaEMeA4+P/n/C2eBc/Yc2AY9X6lFg1HR5uSFzTnwP9pyWhynBkfLxmf1Mk5WIycFxmj71cWyWJgGFhy8FFHnkQncnyWxJH2omf0OBdyDo3Ly6Y+eEHQlfEkS8K/l5AXuc9z6Cfo3xt2vLyM6cZIeumTRTbn3AuVXpxaMiQG1+aAwFtf9Z8j6sXg5c6JIM+n3elE7qRB4KaGgHnbxTHrRJazbK6b/+a6vxmVWzvW9eoAck4Ee9aHNWStcDjYAg4l25MMebbD/4n5a7/2a5vt4JhxlAQYBYbWKFuBTj3Z8YWzteeZLvhax3TpqxU6sWMcZk4Xh4xTiq6knX7p0yte8YrNocIPD7YFD3r59VYbcHST2A22UGDFHvg7UwGs/3uVfYKBz0bZHfdsHB3YNfy1YTPZGTrDhCPIYWfPJEElZ5x+9IAFemOjT/rCiYMNfbNFcNcvunESjamxZTMFMpzRggCYkNsmILmCSv1TRw6d8ZNgR38XZ7fAwTjAi0yBBudef13sL5l09z4QdJsT+NMVr2Sku5wsl3t9RbMm84FtFqSz1RxeepureJKpXH9gKHkfwBeNX0/mrONrngvM0KIRYDlRNH+NQxgaN+NOFjzhAztjaFPDXLXhKsEM7/Aqr059uKKDTRsLAmT8yfFONMZ00T/vThjDBi7GEkZO6Z73vOedw1e9ueITdGOMB9wFKsrJNC/NWXgIYmBgLZCJt3JBbAHN1rHDYRtfOGoDOzroizkIK4GpcWk8tdN/89uXI8rxJtscVqe/fBFYNq/CKj7l6RHG1o91Vr3Ait9h08JmiDWpP+r120UO/wcecLDBwD/w/keH3rM1mBxytfWs3kWOsXPpS/PWPMLHs7551k9tyE5XvNZkLcGQD0U2O8G2WF/JhzM+a9v1fuWn/FRddNYH3aLTR89S7et3Nnilj49cv+DgoMPaMZeMh3nizxbgbLzItM4Kcm0A2PDU3trWd/PUFyICZPMFX/qgcaULbKxN18/+7M9ua8Vc8j4hi65d1qzLuypfNdtpnNhKMtDDeNIgMAhcXQhcVoDMcNgZ4+AKjp3CcO68sHL8OFt20rwM1qQth0Zgyki94AUv2Awa46jMf+XB6XSK6uXBSfLSw4+R4kj6RIZx9zInD532DCKnxX/dwPllKOnlZcBZyvHzskLP+DFUHG91DKQ+oPfCVufl5AVUTn/P2ruqY+g4RHa33XMcX/nKV24OMceDsWRQ0dObYyvn7Dz72c/eXqCcPsZcwP6a17xm29Xv5EY7+kwaBG5qCHAESjlnnjk21ol1ax1wIsxzjp4TrZ7ZEOWcZGsMLdshcQTd41uyRjkt1jJHn5MtCKWHvxlmO/DjFPbZox8N48xY0+jUyz1bd609cjjEdGBjnL6wDRwvwRTbsjpZeLAv7JevVdgqX5Wg13/2jgPNcWLL6EwmZ5jD54RCQMGGCPwK/tCwhwJbuuJFR+WcQjbEKS1bxF5y7ukh6PC1C34uumoLH7bcWHA6C+zUcehguk94668xkdMDH3oJhCT2m05sm3E1Xmy08WFn4VFC58IHHrAz5oIMureZ4L3ClvrKBo5OMDmuNiQFfP4uWX8FQQI284g8DrG5Ek7kuMjcp8Zb+/TUBxfd4EE//HKCvWPU+wE3QW51cmMrgHQyDIeca7iZNxxq/NSZYwJ/NDZMzAPjKXA1Xjn5dKa7+Qcn79V0o586Opnv+OMNQ2vC+OBvDtj4JUef8ZLb7A4v2AuOBcLw0EZfJPKMj3pjAAt8BY42A5TRn24CffoIzvkM3vc2jWBAx3WDx1iSp405T6a5iZ81C2dzC3bGV9/IgZFgyRcl5oB7NOaZeenSPzLNa/kjCgAAIABJREFURf3VhxLc1LvW5L1OnjHShnz2A725Zb0YF5g3n9Ca637sjz/BFzEPyCM7OeXopVW2+ancWjOG+pq+K716upS0U7byqo5++BhjOsHG+OuHjQ5t6kNtLifHC+6w4DN2ok5Hsugd7mjNFfavzcK9bLTmoTGGgU0z89oGkq8J9ct8UC74NSfMOX6ksdDeO4U+5qGNRn+uYb4LeM1R42O8vRfQs8F0s248060xtybVNR42M/1CvvngNxHoA2/15rB7/CcNAoPA1YnAZa1ORo2TwvFkXOwAC1y9yLzcvUA4BRw1xlDKeMgFhgwgx0pQyiBlyL14GDGfUQqSveQYM84oA+5F6gWsvJccmdoz7l6qXsyMl/8igcNJF/W9NBgoiaFTTic8X/ziF58LzLVR7yWT7vVDeWUZUj/8oC8MN570k//SL/3S5gAz3j6jgw0nlUPB8YKbOolBZsQZbvWcPY4BB5UBJnfSIHBTQcCc7bJe2AMBgnVozZrjnGs0nF7r0r3NJM5yTp9yFx5sAIeuNW2Nda24KGNX2BhyybI22SgODd6cVY684MNGFL7k048s9yX8cjgFBz6TY5c4THTHnyMkOGMHBGfoBZiceoEA3Z0ACuzYMYlOL33pSzcaQYFTDnJb6+6184u2eLMROY7ptNK6p5OTQokNpKu+0ZddEvDoM1kcf06bMjaWDKdzNijw4tjSFd6lcCGf004f/LP72sEPjgImeHNEOZnGRK4f7F+64yWlC8wEffTyrtGGbYQF226DAm/4wZozbKNBgF4gyXaaN+ynd4nTRM9k6gP9XPtEF+WNg76z83TjeAtQ6MHp1m/jrF/o4SWo4yTTB5YCUPMMrfebcYa7jVF1+pCNx9MYkUeGZP7CwsaGAAOWMEdDJr3IFAx0Mkp/euJvPprfknJzUlvBgE9O1blgA1O4oTNusBIIOmW0WePdbP62EWz8rSOBqD74Uso7zb0A2GY1vW1A+++mzLnmoI2EX/3VX934CibokJ2woSToMWbrppa5bVPK1wLmgPXk3c2OODm2yaNf5hZ/wOaJ9UpncwmdfgmGjDP8XOv9BtTuH/aqeQM7cyJ7oLw6uOHVM1rjoxyPtU0i0Eq1qVyuHf3U4WuN4SevDRq4KTfm6OVk1Q5tfWzeKDOPzE3+WG2Vu8fzUhMd1sRXMx7sEv+MDuwRTMw7F331wThpb16tKZ76IeFh3rO9LhuB5jte1gg/05xAzw/lr3r/GDtBOAzNX/PIvDCHybSWJesPr2yZOdMYkY0PnfDv2hpe/ycu+tOmhnaSNu57jn7yQWAQuHoQuKwAmVFgUDr9sWvnJeTFw1AwKl70XlScBGUMrpxR5IhxevBhLBkSRt6Lm4OhnJH0zJAwKl7Idojx42hweLwYvczRMGKMopcuZ4ITxdlDS24Gyb1Lqh0jZpfZy5gRpA8aPNH0QlEeH3Vo6MYx5oSki3JOgDI6cu7g1cuN88TZk/QXBnRmkL1E9J8jJlAWVK8O8dZo/hkEbgIItM6sFY6Pdc9hNfc5RoIaTgw6m0atKSeYAih2RFqd056VXShZR9amgDsbYw2yNRIZHGeOP7uSDdOOTumPVln2wnq0qSVwZOc48tYq508frWUnM4IV61mwZ837PFXgwAYKOvDnjLlggi7ZZNITBuySwEJf2Bt9YR9gikc2KT0FJnhx9thNzm99Jksgos90pZ8yNpCd8yyIY3vZQjxgTWb8jVN6sl/6rs9kwII8/NDpL7uIJ730By29BGMrxvjDhj309Q1+3hv6qb+e2X3jJrGrEpreOWQ6IVWmL9rRhbz0pnsXnspLyqW13pypPB5oCmD1VwBIFjzgiK93mPldIO9dpI3xEhR7N7SRAge8YYUGH+8z/TA3tEGDp7ljzHovaWe84IZWuXbK0alzr70L/gJSway5BR+XPpINS+OgX/rk3hxwWbNwRYsXvZqTghMbK9aE+YJeQG+u4X/ddddtX1+QYV4ZV39mJWjRL4neZNjooLuNMm3pQR68rGfzScBjA8I8pZP54N5ntsYbX+VO8+nrHczvMP/xlugIL/igWce6PqILu8qaN2u5OpdUDku8XVLjsNLEE6/K06d2cKYnXN3jG3360x1W6tyjVYe+lCzPeKs3R80d9s/aqk/p4rn+JDN+x3K00cvJt0mXP+cZfmSa74LnNnyUm0/GuDWb/vHt2RphU2w4WSvmn/E2b/WNfWan4cB31Fc05JkLfDTvGrmvFtMr/trV9+rwoCNd1Ne/FQc05lVYrXrj03P1a9u5HwQGgW8+ApcdIHMIvKS86O3Weum14BkI5Yxfu9AZei9bL3EvL86ynT8G0Quwi1OFF0PEoHAknKBwmjh+5NpJFoB7QXIcvRQYYEE7mV6CXqYSXhm9nhsCxoruaMlBh75r/6xcYhxdXkgcHwZ9TTDQf7p5ATHaZMHBi5xxt4tp1x8PLzTyYaN/XhgcCOUSudqSOWkQuKkgYN6a9+Yth0WwkKNkbjsdM6/ZEDn6fVDGDkjq3aNxnS9Vbz1xnpLvtNeaZJ+s2354yEkEJ53jjLZ1n4xkKldfIENXTjeH3lcvHDF/PqE/+LN3eFv/+mojwElFfNg8691aZ1Nb4xwscthGdo59onN6wDRHrTK5AIO9EahKAhS2pn6R4WIv0RoDQYWgBhbaOYE1RoJR/dBHuuAv0VFSpm/+LAR//LJfbBkdteF0C6J98k0eXMgjN574oWc32WPvAHRoJHafHM5s86c2eGoDUxh7t9hQsEGqn/TtywE0JfLUyaV08dylT/DyTCfvFjpqZ+zoZextNhhrwafx5aiz4Y1pvJNtfOniUoe/vPHzfvMFEYxhSo7AGJ35DFP3El5w8rUU518b+rkEGcYi+cbJPfybo60pvMi3DrybvEvNTXibF+rcw1siP/zcF9TAXKKTNU8mLMiEIfxcdFPvdK8xJcNcgZ97QVT9oTddvT/lnayTrY826p0G+vTWunHR1el3f/JFJ4F6Y0hPfahPZErrPAm7xopsF73wUU+HLu3do68+nvGSH7uvbbiiSS4dyZVX5pksdGTxJfKnGgc8JXTl6cru8MPMX2vI+pLIX3XYCi/hH2vEGjA2dDTOzSEbJ8afbfMJurmhX3Qytu7NA3ZA23Smhntz1JcEgmRrwLpzbx1aH+bBC1/4wm0e+eTZXBYgOzxho+FjDtosIsN4yskNK3KlFXf10aBzvyZ8wl49Hmjk8Vvp534QGASuLgQuK0C2yBlPRoNBZpgZhQyAe8nLBp3n6nsReWE6teVwcPwy/ox7pzOCXO3Q+FRSndMBL267x5xdBtgnXQwdfTgonFe0jGY6HTNMGTfGzMtBPzJo0TNsDHYvi4YRnXLy62ft1cWHc0Avl74o5yyT6aXOoaGniw5e4J0UMeT6Xh/oMGkQuKkhYM6b306DOETmszLznbNrHdnoMr/VWTPWkmT9Ne9bB9peKGnDFvgklHMvQOKE+TtgAae157Le/J0ZR4xzSIZ1undoki0vWcNsGAfP6QsZHLZf+ZVf2YI0ctg1DqH+uOcsStpmL+hFtnUvkaGOHnL20b1L0v/0Sc/asTNkkAlXwYeLvZWU0ddJH/tqXNhKf1f9Uz/1U5v+ghLBKMfZFyz+fEQwZVzCnlynj9qvNkowjRa29KWnwMgJva972Mqf+7mf2zZKtF2TMaMLPPxAIb4ufcHHD66ZQzAPi/oET4leMPAOECCjpzPdyVPvuTmVfPzrW9jKjZmAEab65sKH3ff+8Sc9Nkjga/zI5WSbc70byJSSS7ayVY46z95vcLLZ4tkmBd74Wiv0Ma/TH405Yl350SLYxBuN97NLGczMJVjhaf1JeKSbzQUbGU7ltCMXnRyG3l3wrQ/a00l79MaPLM/kudJHfeMmpwde1p55SQ+8nO4p10+bPfI1oTW23vmNu3GBu2R88Ccv7F7ykpds+GljTpPlMh9tIJHh2ZiF7SpTnX5I7unXmHnWRln9i07deoVNZauMxmCVb/7CxxowN+Bj/eqbccdHMGkTxZrT3zbryUpnvPW1NuTS15wyj8mBITo0aNWvSZ1E5lmScTJ+5pMglI2hJ/tirfBvzDeBLTuMxtixVw5ObJRIfrOBTqX0sBlJf5hr795cNScE28pgBR+4GdtsvvEWSONlXtMN7s2ndXzQdLFH5MEVTzgZizVpiyYe6zigOyt+K8+5HwQGgRsPgf+2Npco04uQEeIcMHIcxE44GBPGx4vObjejwkgoZ4DQcc58dsOpxCtjzEBKnuOXAfSDB045GFSnHP7rDT9yY1eQfIbKi5zhcrJBtmeGj/y9YcqAVS6n45o892KhR/XKvAAYXX11Mc7qo2GEGWdJHzOaMPCsL/5+m9HOyNJd3+GgLCcGT+0nDQI3JQTW9WDOu6xFa8faZEM4f9aAMg6UNYVOam1ykJRZE5WdwsH6EdDYRPNpM94+peYEKnM5YeBQ46eOjeAosVlnSemgf9Yz59Jateb1QyAhl6IVvDjlk1vf2US5Pmcfsh/n0wNP9HCJT7I86wsnlDy82VIJbziyV33aiA/Hmj0WzDq9hJFPxjmqHE0BGJtNrosMn+gKVNwrg7tTThjCn+PtE0b404ctNN5sPrz2iW54GRft8DMu+qhMnb8z5FjnMGvDFqtDKxB3MpWjLF/t5h7b+rPXpf54jwhMBClOpGBKFife58E2EfwNJ+z6O2NfBekr/SW64ieRX7+2guv/UWbO+FVsPAU5PhVuE8JGD2wFAWjXfrjHH7Yu9/rce6/6vR7qu9Q5YbPB43TNPP7Jn/zJ7R1l88Y73qfP5MM7nuFuPlsHK9b1j7747fVWb42Yn3imi3u01mibHPGSk9G70dzSX5d5rU776Iy/OU1P810Q5isWSRsBM/+k9bFVHPknvLWhJ73T09wQrEn4kYnmYhMZ1g2/BW99gT1eDgVsWli35ob1ZMy0MSZ+CwZWgn9rCx8JH3TWL9ou/WB3/X24eWdTyqUdbCW0l5rojD+9zSfrF+6+tnCa789NHHr4tNlmkL75kzS6SuYLP49/aYzz7+gtyWHDdrMVxlAf0bonL9/POwVu5ocyCS54wNjcMH89xx+N/lemP57xQG9ThZ2Tw33SIDAIXDsIXFaAzFAw0owWB4oj5CXEMDGujJGXqc/CGCeOqaSdlwdD5gXIAApuGS2GSr3EGGW43DNSXkiMp5cmOcrttPs0yAuF0eIccNo4GJwajpjTaEZN6gWNFk/yXMpdeO5TtGRnvNGhZ1SVean4tMfLlyw8K4MDB9NLS5/QOxXwQuV8ebk6cakuPeXK6ElWhlo+aRC4qSBgLbSG1rlrDZnf7IW1zSFy2uCTYLv+PsnjcLcm3XNiWxP7/pMjtTnGLnHE2Aq8+lVsP+rjM2dOGfvAFuVIxTN9ey5PBufKvfUv6Zdn61RddkE5nTly7IL6nP61LR7a6Ss+LjqwFXKp8nK8XepXfbUhj4MIK1gKDOAcHRqytHcveaYTW0RHdpnd+uVf/uXtU1+BmuAQHZslqOFYd/rmmR0WZAkO2X242gzgwCunA8deH/AxVtq7yKJLdXR1Lzf22fD6ECZyZV36sNrL6NTjp89yqXvYa4NGopsyDrB3GydeX3y+CwN1+mMTQZ+ccNm89W6jp7JkGYM1KcffnCfTPT3w9C7z3vLsE3FztX7jI+CAs/pSctJdufpo5GR0Fdh51rZLu9afoETgYTOid3Pjh59xo69xkfBqXepTfUwHZcZYG2Xpql3P0eJj/snhadPBJ9Hq8ZUaS3y980uVR0tPp/+Cfu9avgBc2YTkeWZ7jCv+ZJpD8Yi3utq4h2O2xoYMGeY6e4JHqTY9Xyi3VtgmmyHG3piT5eJXsSf4t46USfoOM8n6pBOfwz1bYO7aBKMP3I2F+W1Dhu8mOFZfYI3PWXRvTFba5rW++Axanc01epu/5gL94eWH6viS+qedXF9sgplzvmShox8b5EeyEfXB3GCvtGvs3cMMLnJybGYpN+50cN8cXvWnG/7JqE5eG7qzc+aVYJ/cYwFybbcBmX8GgUHgJoXAZQXIesrgCfQ4m17qXjBeNF4QjKwyn5kxeBlPRsPLh2Fk6P1tkJcAA8OIo0PP8UKjjDFn5AXTeHspM1LK0DPqHBJJvc8lBZ/oBdD4ZVi1096Lw4tfezp5WaBzn674uWcA5WgYSQkdenWMqZev0wQG1g4mnTkb+scQezF7CeGj/5wPDjx8OF/KvOhhoa02+LbzvQmdfwaBmyAC5nyOm3XTGjO/m+scGJta1oTP75yCWDMFGuisNZe1cSzhi47jIsh24ubeKYXgzpq3fgUyTsMkTpR29MNbe/q6PO+TeuteIMNGWbN0ZFOsfWve6asytku5e+tYME4up0oAyylMhrZkct7ICK8VO+X1sXv6udevcjzJsmEnUIWrzQF2iZ1B53JPHlkwZbM5lMrUsavZXzixfxJd/W0sG2vc4KCdcrlxhYsgRz9tEDqJNhY2VDm9ntUJ3vHBz4+aKdNeagz0WaInZ5euxiA6dWhccKCrC51+rnw8w0dfXHh4FswLFsjAR3tOsNNo80g/Oy0XyCdHf/UVXtrSS9s2P2Gorj6QZU4oEwB5b5gHMKentt6hYU0/99515hw9leGHXt5VP+nhXiKv8nBqrihfL/XG0jyAnfez9xH+5OqTwJnujYHxgKl+0zPe2rinCx3SY9W1e/1pHOiAv89d+RPmipNRJ4TmYuNDlov8eNe/cvMRZuaaXzL2tZY5Zm3gU3KqqT/40Jl89/rVfEnX2ig39sYQf/PX2nd6rK20jsGqYzyO5fjS2zjAnO2ArfHnKygTFCpHCwP0dHbplz5oT2c6mE/KzS0YNuZ40VsACxf+Fjp8ydQ/cs0H+odBffEsyc1pV7jiQZ6xY88Fu/jTkfzWgPHrBN8Y6Jc14dNqwax3gHs+pvHjs1lT+lj/5K07+tKb3SPLPGJjjA2d2EAytHE1j+IBD3K1sa5L0eoTbG2asQnkmU/HUjKaB8dopmwQGASuTgQuK0C26L3YBbY+O2ME/VIkA8MIM1IMCWPEKDGIEuPq8rd5DJ4THv/fLydKO0aFoWTUfYKT8eFY+n/qBMBe2uq9MCTyBcD4eqnbEfZZD8fQri7jKJinA53cc5Lx0o9ezgwo+b0A8FbP6OeYMcwl9+o4TOSSSfdoOYSMuz74/M5Op8Rw2831UvJ5Ex05Z4w/Pngw3E59vFT0d9IgcFNFwBpyWVtS99aZxOngpL3sZS/b5jqHlX1gO9Bal9ZaARj6eG0Mln/QObWwOcZJ4lhzsjjb1rkkWHVq0WlC+uBvA43Ty3nM2SMrGrqwIU4OBaACG0EdW8KRJJcdyCG0nrN3ZDoNsWnGueK8sUmcO4EAO2ljwJonjz5sgfZ0Twd9UBYG+txVmX7D1J+h+BtZferTTzz1oZNsziB7ynY7uaIHW6SNPsKJzWSfJfr6MsgJFTz0HQ2MjRlMOLHsln76+0OBleDYZgV7Z/OUnTTWbCQ9nWCxpb0r1n7qH7nksfvkhMn/Y+++njZrqrqPX0gQREWiKDlnCaISJOckuTCU4e/wyHNPPba0LIXCAC8okjNKzhkkGsggGQF567NrvkM/+7nvZ2aY4Mwzq6v29N7dq1f4dVqr977ugYtLf7Ffv6PVBk4u9VIYqkNLV3iwHW9lnvUl++1LAmX90u+w2WUMytlkvAkE2GRtt+7jB2N7JGwkvNmmzNou4ORoG3Nw0O/Gi3pyjSU/D4CTPcwzuQ562BRPfCX2h0X2Znv06LKzdlvjE/8ICupnOBvL+NLVJ7CCFzZqG4Z46wt05aseytigXfpo7/IMR/2ZjjDy9YIDCYGtPRKNOQVLcgUmcuMcPXnsctk7YUp/89RFN/vtehDPZO1gjr51xbyrLqw8px+9jS/jwxwztvUd3nv+8QmPjfFV/IOut+fGj2f6mJfGhHHTf8sFV/rxtegm51Pxf7SDN1zhxEbYoaO/Mc5nMxf1ty/fjKvGEDprkjGnPX5kxVdeHyrvMIyc+tU8sE7A1ksL60R40E0/wVEb8x4f65A1R5871PRlhrZ/8zd/s/U5LOhGJ+35hcYmWdYaFxsk61Hz0zyChX0FbvQ3bvAhE19znj8KY18cwYstYUIeGpf1yrrPt7M2HJXIcE0aBAaBSw+BswqQmWvT4uT5nZJFzZsTC5WNQ53FQyAo+OMwdUpu0bFw+YMiFjsOEgfABtBmil6dS7KYWWDR5ShZUH165TM0DoakvYWx3/X25oLDayG2SObI4W0B4zCrl9NtTerxtBGTa1FsA8CLjWzl1Mi9HaKjDYjOTnu9vbHQa5s9NiMOs83JBs7xsGHh6WIrx3UW2bU35v5SRmAdy+YQ54RDY+xzeNVzijlGDrzMH3SunCdOY2UrFuol84uT3+fBnEXzPV5kuHeohpajlgMoyGkd4/iam+olMuOPB2fUesWJ5EChU46XtcvhnjXOXFZOB58IcsKtSb4a8XYyvZQLAPqpBXl4ClQ5b9bOHLVNkRPOvcADjQuNpC0+HEuy/X/HHDqy0oeeMODYW4usV9Zpwbv21kpl7POZLRytR+rwYRu9OJGCZ+sVHayB+kLQ2NtX66FDCuu9dZpuf/d3f7dhAAfrKqcT79bk1Ua6qpPrf79nZKsymLis0bUluzVYmUuCRfee2eEwQIADnwIpNGzQ/7Cxdlv/9Q9c8CHPei7o8ntjhwVkFgjqu95CNTa0097Y5IA7LOGQ2zMc2AoyXPYL4wt+5geM6Wlc6y840BG/MCMXDvR2qY8m2+XoJbz01Tqm8NOXHH7j31z4i7/4i5Ny8LXfCzzR0kNyT0c82Voinzw4q4OZMSUpX3Wni9S+aSwZL4IX/eL/EPcmP3zRwgQeeLMDP/QCJoFdfWI+CiqtK/jS11US+BgD5jNe/dX35spKTwa7tTEWXQIq88NYMCboKK0ytDudBDN2GXt4mxf6whoJI3YYK+pLxoqxx3/Qf77qS3d9JjUe3NOFv2Mdgq1DK+stvemsn/gzeHrJAEvl7IazPq5v8bd2sd/ajUfjgi+oP60VdDIO0NfXDl/UwZxOaPmO/o9r457vqL+sU/DW1/mQ7BB4O3wzT7SFm0sfaKff8fH22Rzz1R496E4PGMGX/6kP0er7dCEDHXqJndZDctlCL7+ZptdRCVa1xQeGPR9FP2WDwCBw8SBw1gEyUyzUNnsbp9NTp/cWSBumMguCxdjiYPGSW4DUczgsxBwaJ4EWeouvhQ1tDiY5FiGBuIUuZ5qzgcapoIWzxczi5USU02aRtaHbOJUr46R5m0IXixiHRKDrFPOoRCfOLb1rp62F3pthCyad6dMhAV0s/DYeDpF68rWT3HMU0eR02KTVs4VzRc/V4ThKtykbBC5FBJoHHCpz2Hx3qMWBMUc5aaUcFk6Q+31SVrn5XMBmDnKUckrQuLc+FeigcdHH2uVNA4fQutYbK3XaotNe295cmbMcK+uLNU1AZd5aE835ZFsrvCF9+tOfvs17zqd1A09znLNmHbMGZbs1T3BqvYMRunTJFmtLb2esNSuu1j52WLOsr5xs8uhijaEPnfGyHtHbGs6xRUcndvZ/16LFX7mA15qp/+DBzjCChbUcjj5xRUtHfcsGfc3Z9McVBWHKOOmCQ/drwjO+yulAV5dyWK2OqDI60icsVn7da0cevRwM6AttYMMmF9zgC3tjMt1go72+0j/acPYFNNrBUV+wnT7te/Sht4NYb6PdCwjghwc75LBQBhsOPFneDOIj+MNXEBAG7v0hNgcN+rUUFj3L8XVoS77xbf+p79CzCS+6ervtEMS9OUVv9nmbCZPkw0WABE9jAj2M5HiiNYbMO2NHeXX4CSzhgE5SBws2083hkn3VPKOPOuOavJWf9cE4f+ELX7gFvPDQf/Z38xG22bpiQkeYOhwydr39N2bZTBZ9JHTskuMlILR3m2eCL31OhxI6id21q+6qcrTmoLWI3b5gYAs8/G8d1jR6CRpLZLEtXZW7V+bquZxOjUU84YkeH+uW/hI4khEG2qAxHuX0dK0HeNlMDj4OJfSxsUsPvASj5pPAFs/GLD5sNRa0622vevOMzHTsHm/jgCx9Yc6yBQ/9SIaf7Ri7Alp9JNGTj8WfFEBbx/UdTPCwjtN3tcdcoRM9f+u3fmvjhwc89ol++7Ty2tfN8yAwCFxcCFzjR2cxYzV1WeAkC6UFxqXc4tIGqs6C4bnFtbbaW4S70KHR3uYnjz8a/OWSjRltPLW1AONN5so7PdOrjbUFXo7PKnMTcmIxLShPJj7k2Ljok30WT8/q6IInuyU0rurk7MGDfit25GhnI9Zm0iBwdUPAmOeYFkhwAM0/c8b8aq6gE3xwYDnnghqBHNqSuSSZx13at340h+LZs3bo0XGovEHgXHHiBJAcbA4auuY5Oc1bc1edHM/mLMctxzIdydFOMFa71iU2m+vasQuttYTD7r7AMP3JpLMACjbWCw4c+cq1cdFZvYB0DcjR4ck2PNHSyWEmes94CqgETa17bKEzGeXsXBMbOZwCvD5PFmyssujik2SYs0Ww4cCQTniX1Lnohd6b5z/4gz/Y+id807/c4YO3UN7uCPb0ZYldJdgKRJJBrn5gqzL9ASfPeHdpXxu26gN2whg9Zx1uDgngL6DJdjzqE7L1MTvQcLZhqj6edNRGEEAffYOvwxOy0MPS/uResExfiT3arkkfCzRdxotAxZiT2ISeHexx0U85Wnbhqa2Ah02eYUpfeihDhw+7tMXHnGKXQIXeK34Opsg0zuBQQkPf2pOBp4vO5MCMvfRQb3143vOet/FxIOMQQFAEO4lee1zoLxj1Rt8BBFsFiAIqeEp0QSfHA9beOEsCMno3btTXBj28/vRP/3RbS377t39701v5Xg9tyDCmvDX2ZYL1yLxgi4MNwVsY4OHS977EYHtfq+GNTkohC+b+AAAgAElEQVSf7eGELcaQANgYLIiNng7Gmb7XVrk8+/GWepbrD9eKgbHLFu3hT1f3Prv25loef/xgisYBUz81MY+UmZdySRuX8eTQT44/+crxwd8XB9YhY85XIPYXYwwNXtaIDqHoRXfrFAwF255L6I1DawvMHIoY6/Ryab8mgfkf//EfH/7wD/9w42ccHdXfa5u5HwQGgYsHgbMKkJlh0XC14MjX8kxdy1tca4fGgoamS5lFd01rnbYSXungORpt3UfXfbSetY3uODlrebTxXOsqS590WeUpW59XXZWv+qgrKZ80CFwdEeA8mfscHHOAM9S8yN7mHTqONGdQsMCJbW6s86p2ykrNp8p6Vq+MDIlDxzGkjyCVAyTP+UkXbfCorfp4rDJXOdHL0cYrmnh6drUmolPHCZPcS2iS33Nt5VL1ePVcHey60LmSKU8/crVBK48nm2u3MV/WX+0L7t0Leo/CUV/CWxJgoElOPOV4GCuCZJ/B55xmy0rrHk/Ot+Cpn75Ek/6eV5vx6qKDMZAuyQkTz/Gpjo4uCTbhUxu8XJIy/CV88KhN9PHz7J7zn16eBQTa4YMGHym58Uy/rfKEbEFntOmVHsrdr/I9oysQCJvsUZ8O8Utu5fHTx+pctUPjCgPltaePOjKNAfzTRwCjTfzYJRD3Nl8g5C2g4LWAPJnydM9e7frSzLwX0Dg8qC2ZLkl79oQDHeJH12iS5+BA8E0fhzYFi+pda8JX8imvwyO5Qzr6OBCAHxnxdg8XgSBab9vRxjudk6GcDOXaudgr1cZ99lYmj5f7lYYO7K8c//opvluDEzIchjjMME+zd623tuu3xgq58d7zo79UH8TP4YVLvYNO2Ntf0El0djDbep9t+tzYaS9iB1r1YR1mdFqvjfGJfxw++D/bfQ3iYIM9UuNkpZ37QWAQuPgQOOsAeTVpXcRabKq3iFS2v0dj4ZFabNxHv1Wc+Ef9WrfyUr7W7+tOsNj4RlfZml9VHbqj9Kr9KrN79CvP/XM8o19p1e2fkzX5IHCpI2DeG9+Nffas491cceVU5Kjk5GQ/mlLtr6osGm2S4b5yufLy6vZlta+dZym6E49btvKKRp5t2pTQhk3lydjz3j/jsdKqj6by5BxFu9LUrlyd+9p1X33lrefZli3xjr72lW+Mj/gnfug5170pOoJ0K0InWBJA0WHPX/1VyUafTPe1r01yPVcnP45vdCuNsuN0i385XcJSO6nn9FQW/9p5dq0pHfd16Yg2GbVbn8ldn1eaVZb7PZ3nlSZZ0e7r4r3m0e55VS5A0vcC23WdOIp+5atdwRUbXXim0+nYkj3y2rnH11tHOgnA6ruVJl2SI/gWWOtfAWMHZNHJ0Xahd5ERLf7x27dTl/x49LzSdr/yii7e8srQN1735Suv7uPhOR77divNSudeXfTpKHfYaa2ANfzivcrVL8qrw6d+j07dKn99rl20ay7wdpjnwEKf6xN81jG50s/9IDAIXFwInNMA+eIybbQZBAaBQWAQGAQGgUFgEBgELiwCDgm8aRYQuwqyOxy5sNqMtEFgEDhTBK78lwXOlMPQDwKDwCAwCAwCg8AgMAgMAoPAhoC3y70tFhxf1dvmgWwQGAQuPgTmx60XX5+MRoPAIDAIDAKDwCAwCAwClzAC6+faguTeIl/CJo3qg8Blg8C8Qb5sunoMHQQGgUFgEBgEBoFBYBA43wj0xrj8fMsb/oPAIHBuEZgA+dziOdwGgUFgEBgEBoFBYBAYBC5zBCY4vswHwJh/SSMwn1hf0t03yg8Cg8AgMAgMAoPAIDAIDAKDwCAwCJwrBCZAPldIDp9BYBAYBAaBQWAQGAQGgUFgEBgEBoFLGoEJkC/p7hvlB4FBYBAYBAaBQWAQGAQGgUFgEBgEzhUCEyCfKySHzyAwCAwCg8AgMAgMAoPAIDAIDAKDwCWNwATIl3T3jfKDwCAwCAwCg8AgMAgMAoPAIDAIDALnCoEJkM8VksNnEBgEBoFBYBAYBAaBQWAQGAQGgUHgkkZgAuRLuvtG+UFgEBgEBoFBYBAYBAaBQWAQGAQGgXOFwATI5wrJ4TMIDAKDwCAwCAwCg8AgMAgMAoPAIHBJIzAB8iXdfaP8IDAIDAKDwCAwCAwCg8AgMAgMAoPAuUJgAuRzheTwGQQGgUFgEBgEBoFBYBAYBAaBQWAQuKQRmAD5ku6+UX4QGAQGgUFgEBgEBoFBYBAYBAaBQeBcITAB8rlCcvgMAoPAIDAIDAKDwCAwCAwCg8AgMAhc0ghc62LW/kc/+tHhGte4xsWs4ug2CAwCp4FAc1kuNa/3z1fF6n/+53+26mte85qHn/qpnzrJ46raqCPjf//3fzcycruqkytD973vfe9wrWtd60CG55V2Y3DMP9Gu1cokPKTjaJK90m4NTvEPfsmobbLYe7q6axuf2l+V6JXW/em0OY7fnld0eK51lZ8qX9usfX467Va8flK7tKvtakPyTwer2sul2uyf1UUb/8kHgUFgEBgEBoFB4OwRuKgC5ByAzOLgcFQnDQKDwKWNQHPZHHd5/ta3vnX46Z/+6cO1r33tLeBl4RqkZHFtPvaxjx2+//3vH25+85sfbnazm50MHKI7Kq/tF77whcM3vvGNTZ72ZK4BzA9/+MPDF7/4xcNnPvOZww1veMPDL//yL2+0BeJH6ZU8MrSX0+8HP/jBlXRTJ63rGQzwvd71rncy0Fnr439Unkx17r/yla9s+c/93M8dfuZnfmbTBy/8Tyfh4UL/9a9/fesfel33ute9QnN2qv/qV7+66Q0rfQin00nJib5nWHz3u9/ddCDTIUX6ZIPnUmU9y9WHqfw73/nOVkaWK1nJXtuiVw6zFbej5KztkluZQ5xvf/vbmx76Ai9yky8/Fc/0lBtLeNJPv+In4aFef+h7+Q1ucIONJl3kaErda3sqHWoz+SAwCAwCg8AgcDkicNEFyG3iaz6b+eU4NMfmqwsC5vJ6cfoFWB/60IcON77xjQ+/+Iu/ePj5n//5LSgSGK1JO86/AOF1r3vd4b//+78PD3nIQ7YAeaU76r62gtZ3vOMdh4985CObrCc+8YknA4kCFsH6m970psPb3va2w93udrfDQx/60MMv/dIvnQy48BecCAaPSmQJsP/rv/5rC5DQWbfwp7/LffahhwOed7nLXbZAVL3y01nv0EUrf+c737kFhHe/+90Pd77znTe98TvdFD9Yvec979mC1Tvc4Q6H29/+9if1QeMN+8c//vGtL+54xzseHvSgBx1ucpObXCEQI/MoG/AW7MnhAJPKBJUCPf18m9vc5nDTm95048kGZUelo2TQUdKf//Zv/7ZhTFaBKl4djqw8tdMfgkyyBaNnmvBwCPPJT35yG9/3vve9t8MWeqo7St+9DHRd6oz3T33qU9uYuuc977npV5DMFngas/D71V/91cNtb3vbK7CM1172/vkKjeZhEBgEBoFBYBC4zBG4ojd6kYBx3KZ+kag3agwCg8AZIrDOaUGWN7Uve9nLtmDl137t1w6CCW92Of0FAKsIDr1A4Wtf+9rhfve732kFG9oLwj772c8eXv/6128B+b3uda/Dgx/84C2AEhAJnq5znetsfF/60pcePv/5z29B+5e//OUtUKGreoGIoEnwtg8u2Ca9733v2+TQ8Ra3uMXGmy140IMsl/aeHRKo//3f//3DrW9962OD7xWH7vEoAHbvAIDOP/uzP3u4053udLKObnt947Hm0Qjw/umf/mkLXB//+Mdv9iaHzgLPj370o4dXvOIV2xtktukz15rYVbvKBXuf+9zntjfQ6skSFMPLW2nYS0996lO3wFJ7+gui45eso4JcbdmBxkHF85///O3QQL85sMCLDb2hzub0I/+ud73r4ZGPfOR2MHC62K1y2fL2t799C84d/PzCL/zChgNe7NnLTHa5enRsdv+f//mfh1e+8pXbWMGLHfRHY/wKjPU9/Bxm3OpWt4rVyTyZdJg0CAwCg8AgMAgMAqdG4KIKkNvA29A5AKfjVJzazKEYBAaB/0sEmtN0MKcFLQIIbz4FTQKahz/84QdvLfeBiWeXIFWA6W3z6SSBkqBMMCd4FbwIsl/wghdsgYb661//+ltQKVh7//vfv+nlU26Bh2BEYCYgEbAIQAS+grNSunlmwyc+8YnNvnvc4x5bW3b7VJksuuMnWdu84fz0pz99eMYznrHVrxjF/7gcrUvAB0+BpgBZwCqd6bqJl0+c2fCWt7xlC7S+9KUvbTqyUUIjqGejA45vfvObW1CrX+ixJp/A+/x6TQLQt771rdsbaNjCRH9oD1d9AWv4wp8+MMNbGZvgJhkDDi6UleoLfAWMxpZDF4GlN8Pq8dIf+LJHUu5zbP0Pv/vc5z4nA2R10SVnn6tHJ2kPQ2/Z8Sypj+Z0+KHBC87vete7Nnxgr0/oz262/Md//Md2AAQXY9gYXxM6WGtTWjGrbPJBYBAYBAaBQWAQ+DEC5yRAbvM/auPNKSDyVI5BtBwjvxl0cUo5Whyk6o/jVX0Oy6nk/RiGs7tLblz2ctXvy6I913m6HCVP3VofzYXU71zbO/wufgSMM2uDXKBpLpvXAkOf5772ta/d3roJenwiWhDJMmNTIOASMAhsBNeCoJVujwJ6AaO3a//yL/+y/abYp8zWFm/krCk+EfZZtKCjN3ACOzTeAgpIvG0WYNGZbHLTa80rF4h4i9tvpNnc/AoD7eAgIFSH98qrebkVnsAgHuXx9SzRTSAkaAyb6o7jp37FEAY+e2f/r/zKr2y66ZuCUrRoPvjBD24BrODtX//1X08eAKz2/cZv/MaG2yqbjvRzwYj9H/7whzd9fUVw3/ved+PFDm+p9d2NbnSjbexoi79gWvD8gAc8YHu7bSyUVkySa3wJvvGRjAv8V7uVC2br3/is9oQ72j2unvHVDg9Bvoud8Vrzo3goK+FHtsMdATDdvNU2Vh0yqE8HffDv//7vmzyHQPZMsiQ0MPaTARiwWV31yZt8EBgEBoFBYBAYBK6IwFkHyG3WnLI23vLq5OvG7LnUvXpOgWeOgN8bOjl/2tOetjlOnFR10ee8kKvMc05qfNIjWecrT278yS+lbzrKK6Nf99GvbSvb0xzV7jha5dHTM11XBxH/C4VVek5++SBgfDXGGn+CQsGLt8aCHI6+JNCINoQ8Kzc3BB0FMp734zZZgmPrh2BYMPaIRzxiC5LJec1rXrO1+/Vf//XtzaugQ8D6pCc96XC7291uCwL9HvnNb37zFogJpAUa+CSbbmSV088lgCPbW1ZzrPlMz+ahMsG+YEYbssNlb0/81auTN3c9uw+faNKrZzxWvurVucJQEEwfQam3rT5Fx5st6rR3eRYkw8KBhk+u2YwPbOT4a5MeG0iHw4a/34/7raw+x9+bVuv9LW95y+0zczK8mRY4v/jFLz785m/+5sYTLd7ekHrDbew4hFgD5OzEg3yBvk+UjRlv2LVnMz3R0M+lHH8BqWdjM2zSPVrP7rVPnmf0ylyCY3+gC5/akem+9uglcvdJHVpj1YWX3x/7mQA72GbcSL5A0Af4O9CBHXskPBwEGdPok7W3bSOefwaBQWAQGAQGgUHgJAJnFSC34cetZ/nqgKzP0cpt1DkKOS/a2eS9QfDHYryJ8LswTlsJP+3Qym3+nJHVMagezflOexlk7+XTkRMj5dxkc/ik/15f9Ws66llZ7enj2bXq5j6a+EXX8+SDwPlAoDEpbxyaD4Icb2n9cSnl5rFkXJYrX4PIreLEP9HtywRG3qgJvh796EdvbxvNO/PSHwbzGawAShKACKKU4+deANZfy/b7YAFhuuNhHnkuefb22EGee4FJwRuetRXcqCdb0K2cXmiOsgX/6uQCVLhpKxCT0kOeXmjxhZvydImfMle8rbmC+j5LZrtP4GGizltMvGHjk3gBvt+Nw6XAi15wk/wV8FIy9K16ctHCgl7KPUto4Uuee38sDX9tJJ+l+400mhI6qbKe8bZv1I/4VCdHL2eXJOBf9VC3YoSmvlLepczlmU0Sm7SHv9xVe3RS/JO/lvlc2gGPPvG7dzb49NvhBOwdSkhy/QFvb/3rf2/J2c8mmLNLHZtdq8yN0fwzCAwCg8AgMAgMAicROKsA2Uabk5ADsDoDOQCkRSvfJzyUu2zcNnVBsXufXHI846+t+5WPZw7K6kj1vJd1rp/Ta5/nMLFh1S/5bFZeu+yqfs1zqsNJG/drWuuUr/Ww8ty1lzvO0ork3J9rBBp3xnHj0vxojggqBZSSsVqQ4dlYVSagdK9NAYLgo2QMm/8FQQIUn5UK8gRZ/oK1N8L+wrA6n+n667/aCQAFGu9973u3nBxvU9HEs3mcTuygF33UuQTaAkbBjcCcHDSuaAR+eLLHmz9rXfMRHb7q94kcOvbHsbzh9deqtUWvXTLSUTAteFLuDaS8hN5VAO0zXcGxN7r9VW3YScp9Vs0ePOnNDn/8TJBOB3wEZPRik0A6u8iFcTgrJ1s/eiOsHF74pRPsBXl+y41XmGQjOgmvcJNLxg+87Bv4OAyhb8GvunRDjxe+2rMx/aJR7h5N/ZZM7dcgGN5sgZMvExxkaMsGcuKzKXo4bAdEZGZPuYMAn/nj4XNy7fWL8evtugMg+JCTTmRrz35/gd3Bjq8fyGyMxD/5kw8Cg8AgMAgMAoPAlRE4qwDZxuviLHAKOUicCI4cx4TjwEnp91ho1XGwbOIcAw4EGk6LzdvFWfb5IwfQbxXXAFl7zg7ngMODj2ftOQF06M0KeRfCIciBogPb6AsH9nMC5fTgPNPbvTJ6y9HSme77FMbwxCucYBfe2q884UIOPLSX6EgW/NStjuCFwGhv1zxfXggYf5Jx7De/Pgc17oxFDr7fiHrTJUhyGatS49a4FwAYt3736guT5hI664iAwIGa8S849vbQXJQLhn2Oag7d//73Pym7eWMdMQfpYl6SZU0zh8wvCa3LnKKXt3nmu3p6CfTJ9xlsAWLt0OAluVfvzSD98ISJXHDpvxnaJ/bj+4//+I/bmuiT5H3SHu/WPcGVT8VhKWDa821tYK/DAQcI1gf4xYeN6vxxLYeWHV4mH54w8Me20PqUHd7aS2HlwMEbUZ8MK2O/MeAtP/mvfvWrty+GYGSdFKizmS6uFTsyWwvjr9/Ib12TC9bVay/BpzysPKMxloyLfgNPN+XGVe09e6Prj3n5HDud6Gn8wA7mAlgHDn4mxL7GFpn6Aj/44P2oRz1qezsMD8m4Mkf0Gz54k+tCY5wInOnBPjwEweoEy2QIrh1u6Ct/0VyZS6pftof5ZxAYBAaBQWAQGASOROCsAmQcbehO6DlQnNI2cY4Qh4Xjy+nwOyhOi01fHadIgCcA9ulYwZ6NnIOAxsVZ4rhyKjgXHGPOAWdam07s8SOb0+lzzT7lO9Lqc1jIcSl5c8DJ5IzSjTPjpJ8zR38Y+Cu97GGDv5abU6eNP6aSg48nLLTlZHHuOW3w5szBxX93ox3eHCny0cIZH1hInunC6eTAeStTX9Efz5y9bJl8EDhXCOTgc84FncY9J958NaeNW5+H+j91BVLqzWfJ2BT8aGuumzveovnjRa0Z6ASnnhvz5ofgoSQQFTCYCw7gzC1l5BfAuKcTfc0HNOmOj3tldNFGEGROeQOKXoCCps+stVHWfDVP3bvMV3MbPR74sVUQug9kyaSbgwMYwKo37umVnfhVxhZrhrfn3j4KxpThlx2wffe7372tW9YGgS9b6CKlJ5utT9Zr9+rZI7e2wECQzCZl+KcHnfAhSzCtTpm1nD10Uq7P0kuwSQ/9TV5t8Fx5pyPdrX1wkuijf/HEvzXS3qJcH+CTnWT4AgCdANgfK1PmzbvPl7VBSy+64o82vZTjbT136XfjGg9J/7qnH7tcknbpACNv1L2xd6ADS2MWX23JQg9Hsv38CB/t4InGPSzMH9c6HsJtEzz/DAKDwCAwCAwCg8CxCJxVgGxDtvELZJ/73OduQZnfrNnQBYacBMlptmCuNzmcizZ6Di0Hxl8w5bzaxLV7wxvesP31WQG29hwdG77fJfvDO3hxRDkjUp/qCRi9aXnYwx62/X4Oz/OdwoHNz3ve87Y3WXSDAwcVRhxBtjoMYBMHDD392cU+GDznOc/Zgvv0xsObCBhzmHKsBBpw9EdsBMMcI7z8xV74kfPEJz5xw0CdTyS9pRGAcMAdIMCd7pMGgfOJgDndODN+BT397leQJIAzJgW4/X/A5oxUcGXse3Ms6DVPjN/WC3QCmA6LsiWZeBRICOYEUuoESYINAYpAhAxyzUfz1fM+qMBLmTbamt8OCPH9wAc+sB2ACSLNOTT+CjS9HIzRUVsyBErq6WW+oqeTdXGftKGTQBp25ntBtDo6aYtfASIegl1yYWzNDDfBFXrtrCPe7LIDX/1CRol+aK0t7ISZMnKVk2ftVQ8vOuBbQueirzX5gQ984EYDA+0cmFqHHB5ay6ybeOKlD8jQvuSZ/umgjkz0+Fn7rasudvT7cToJHNnqrao+MX60xVPOdvf1ATkwtP+Eg6+a/B64/kLvnj3aCa6NBYcF2rK5v2jeeKWzNnRKD/faa+uPcUnGEbyl9NSWrNZ/dXRzSfYEl7GGpyus1K9lW4P5ZxAYBAaBQWAQGASuhMBZBci4cRA4bxwDb1R8psaJuNWtbrU5K5w6Qa26nES/qbLhe3PhTeob3/jG7a1vGz1HgTPjLZNP2TgTOQYcIOWefVrmzQh5HDxyvGXxV2o5Nf5aaoHmlSw/RwUcDrrQnd6cUW+3vAmiF/2kl7/85Ztzpp7jxMH3GTnblAlgObHa+S9PBNElhwE+p+ttCFtf+tKXboEzh5IDxiHihHLeOJ3eZLv3Vg6W3vDDTdkaXNB90iBwvhEwzswVY9mc8AWDtcPbOAEyp98Y9rbOOEYrlatXjqYvLdaxa/4JtORrO+2TY65582od6g2iOau+N3XWJcEWeQ6TBEmrHLw9W1escYIwQY21x6ev5qMvWMjCW4BMb19t0J1+5JEjuUcrOFNvfu4TWkEPnMxdQZW1oMRGOsVbro3DBLpYgwVd1hky9IGkDfvwtFbSw1oRn+rZCI/+yyFt0agXzHZQAJMVL/UuCb5slBz6+UrA2q69yz5gTNg/6O2whA3waQxsjU/8o6yLTGugtsaTt6+CeXzsEQ5tJTo7JISfILmAOD7sJNOlznhC21jBgx0uKfvQw4cd5Bo/eOkzuXEiuWePRCa88Wg8ZC/Z9kj4ODxZadmgnTHn03cy2U9XvBwSOFggc+3H+oHs9X5TZv4ZBAaBQWAQGAQGgSsgcNYBco4Sp8fGLij0R3E4hRwETqPgz2eRAsOnP/3pm3PCKeDACPQEb7/7u7+7ORD45RBwOjgDnqXKOSCcOp8a+v8hvSXgpHF+/UZPsC745hzS6UIk9nA86EZnDoo3IpxxenNo/+zP/mxzct0/85nP3N7yaudNAefon//5n7eg13MBMocWH04aJwhvb0dgCjeOpk/tYMnxhb1DBG9KXvKSl2yY+mTPG2uYPeYxj9n6IYcqbMdpuhCjZGQIfIxp491lzhYgmPucfTT7ZJxqJ8gQoDQ/9nQ9G8/r2LaWWCMEUAIPc6X1hUzlgg1lAlt5Act+bnims8CL/oIogYkA0fy37rUO0Me8o7fgiU7sdEkOFwVB5q014yi76CMgMtcdlAkGWyfpQk+5MjqEp9xbYW+OrRV+16qtNVGdBEtvddkrOPZHwPDAzxWG7PQFjCAWXulvPWI7m6076GsrL2mDVh/A4UUvetEmU1+zT3sHm3Tw9Qt9pJVHvMhYL7roT7aQQQ97AGwdJqp3ecZPv5HpAAQfurHZuIje4aWDHOuktmFMh7BLn3JjiP74OOS0TjushBn8wgV999lJD3KMFX91HS5s8FWCtV99WHjWj/ba2rFB8ibfQYbn2tQuuRvh/DMIDAKDwCAwCAwCxyJwVgFyG6/c5s7x8F9/eMNjE+cMubzN5bhwADkenA5OhrelHACfU3MAOTkclVMlzgrHDy9vZnJg3HMq+musnNQLnWAhWOXI+v0ap42+/njNC17wgs158ZbIp3ccfk4MLPzG2Ftmb3s4WhJe8OCAc469HYeTNwScII4mh47jRwY+gmtBMGff2yt1Ps+mi3KYJZfs5Gw3888gcB4RMJ5z2o3V7s1fz1Jl51KN5oZcIGbumGOerUv0sl7RQ8AieBL0FbykCzpXesrp3WVdE9gIQM0x60924iVw86ZUACgQEhhb/wRRfoKy8k+mnC7a+U2rtoKjoxLs2CSX5HSxHjtk8/OLflNrrc1uaylaWBQsqlv59HMRdlmTCsjYZQ3qE2s8arfXUTnsrUsO+J7whCds/aE9+yVYOFzFR6IHm/YJL3VS93SCt35UR0/rLwzw0Mf6KN2tn8ZBf+wKvvYPa6c36vFaZZCVvMp7Zpt+0rcOfB1Q6l/7n//ve99W+9U+etHVwS9cHSqzhd50KeFDxwL+FQdjbsXL/do2HpMPAoPAIDAIDAKDwPEInFWAbKOWbNA28hxEeU4DZ47T6K0pZ8WblJwC5crQclxXh1QZJ8nm3gafI0AWfpyJ6tA7pRekcwoEkpyIfcITH1c6xndPezrP8UCLDyeH/d4E0VGiI904l5wdAXR12rMDvTo6K2NDjroDBG/iOZJkwInDirZDCG3gQr7PCjnCPqv01ohcb0M4oTBP5xwp7SYNAucbAePOJZWbG82L5Dcue25+GtvuC56qL29cx3stt+6YY+YO/oKZfluavIJAgZU56mCKrPiVp49cmct9c58sZdqS5z5ZPov1VYmgVfDk2Xz1G1xrQrzprp3kgMvvph0wmsfWi2Sq18ZFnjWBPZXJyX7IQx6yHcD1B7sKkLXXB/Fjuzal+FiLBMHWNutF6y5Z/R6W7Nb3dE8/uglI3/KWt2yXQJ9OPvvGQ1AIE2+R4e7tLhl4xg4zHxAAACAASURBVANPl3Uy/umannL8XPoDpnSifzSefWKtv9kEU18hWWf1O3z1ifYSPWrrOdnpJcfHZ/beGvsawCfScPFTF2/v9bk38OGmjYQvfvvyMFaffDa5R2+/9LbavXJ59wJnn/qj3fM9IXayQWAQGAQGgUFgELgKBM4qQLZ5l3IgKrNZ25w5Ym3cntvwK+O4uArSlLvQ5eS0yStTh5YjJ1cWT84bp4ZT1VvV9CvHszbJSa9oziTHo4Qv+WTQzX31ZCRPXc9yDpsgQbsOCdB6c/SOd7zj8Jd/+ZcbDQfSW3fOD0e3/w5ndf7o4NmbETneZHjmVHWAoAytRK7nSYPA+USg8UaGe2Nc4OTeZewbs8a9dcOcQGNsusxvdAVNq67o4qfcmEbnjZpygY8ARfIFhi8yHvzgB2/zQRne3ugKWP0URIDkzar2gh+8zFt0LjzTm27qrDkCb3OTHQ6vzLneJJu31gRvT9ntaxG2/tZv/dYWvOOBr0R/9946C6Tx9RWKA0fy1jnrOV3KlcULjmz3+25/PMq6YR1Bu6b4rLYVdAuy/cZXoMf+NXnzqu8EbL3txCP55MDBWiaI1LePfexjt99bW5+0EZR6c/y4xz1uw1r/wMpBp0M9963r2tBr1TN9yIKj+t7Q63ttfaVDD4ErG9AJ2h0k+iTal0cw8Ek6fdSHCf7hVd94poN+9pMettmX9BOdBckONf28xafrfl5kDIRf+pOxJuXKwmatJ5NuAnyB+KoTvVxslPCZNAgMAoPAIDAIDAJnjsA5C5BX0TbmNn+OTSlnw6busplzrFzrZq6Oc8Jxii6nJL7xwNt9vD2jJVf5PnlrgAcnJUdlT3Mmz8ko13bVsWc5py3Hjo7auNgqV7fy4Rz7gzMcLL9Z9pmetx1sFfByoMiKl3tYcrw51e69NfYH0ryp8btkTijnTZscr/X+TGwf2kHgTBFYx7fx2iWgFNB4WyoYFKQIVEvareO18jXHyxxCJ2jppwjml0DU1yoOiASIaM0Dn8NaB6w1gihyBJRkm1+CP2tGQZwA1RtO7UtkmkMSft4GC4pdAqL+2JJ2giZvUc1p7bzV9rMHuoQNXuEiUBTUehMaHXvWpN16qYsXPvCgh0+J8RJAVb/ycZ9d1acHbPqNLlzW5ADB4YC0tuuZPTAUjFqXYSBAx8cz/AWW7vvbFWykp3VMgIsGH30H1zBKHlnKXMrwxsshB+zIFxizx/jy7I283wf7w13GnfHiEMDbbJh1bYYd8Q9expnA29tn99Zbhyv0d6jgU234eIvspzXenCs3nuiKbrVhFaOuerlEJzi52jfpER7GlIRn5dpMGgQGgUFgEBgEBoHTR+DH0evptzljSpu1TbrNHgObd9fq2KDt4gTsaXveKpZ/apMDwaFStk/+kjNHySfPnODeFOzpTveZvFUOW45Ke1s9ayvXhmOTw5OzIwjmfKnj6HG8OFccVQ4/uhzLdPBpos+qOZbeMjzrWc/a/jo2R5sT500SJy35dHU/aRD4v0DA2DN2HQYJIgSPAlRjV/mZjE30rTHNHZ8mm1cCIrn5YQ0QQAnABMCCFUGUQMZcsz4IoswtgSGe7gXHgieBTvNNsKYdedYrurvwIUu9uScYEyD7LbDPiAXIAjjPguoCIH3A5i66WKN8AuzvC2Rf8k+nz6wz7CWL7t4kH5fCuzw59PCbWHauuuIDR2+P2bxfD/FJvi9g2CBQh1H9BbfWwuTCHX5+Nx1f/WScwBu9a03WQ4E6HbVxINIBCH7KBLHGhEMMfSmA9Rm0INkfvbI+WmOlbF9l0I8cyaGOceLtsENIfwPDVwnZRj/jmD7ehL/4xS/e9PNHFQXm6o+SUVl5+4Fn98Yyud5ad1ignH10smcYW2G56j/3g8AgMAgMAoPAIHBqBC5YgMypyrlLLRs+J6G6HIY2enmbfLQc1RyH+MjRaa9ecByvPQ2enFNBJmfIW1lOVMH4Sn8693RJn5yndF7bR7faWj16enHiODie0edwa+Ptizcc7PO2QzDBGUSDtjY+Y/TWmf3+H2S/85M4lj7t5Mz1e8fapn/6TD4IXCgEOPs+QzW2jVHjWKAhuCkpO1VqDJsrkmBKQCvoMacEEuQIiAV73mIKkATG2gggtXGwJEAqGMXLXELj0+D+ZoJywZa5KKj3NwLo749MtfZ0kCVgsiaZ44JMiVzJvPZlBz37S8dbxQm59FBOdl9+hEc2R39cjt5cF5gKtAXc+4RGkOWSPLvorIy+DugEdvskQGZ7b+a1Szftyda+P8TljXABXbTou9QpZ6+DBQcbMHUptwbuk3JBI/z1hz+WxU489RkeBfH6WX9rY1zg59m9oNxYNAboqf2atJGsvQJRAbw11998sNbqLylb2G08k/93f/d32+Fl48/XQHv+2iajPUGZ9sagMvV0s3/ZMzyHp2c4SMpgNmkQGAQGgUFgEBgEzgyBsw6Q27i9lei03qZv03ZxIjkqnNScRBs9Go6Jjd5nhgVr2tjUOUeuaHOQODKcjpykVRbTtXF6jm7vfHjmwHBq/V5MkMmheMpTnrJ9gnhm0P2YmiPiwt8nnJzZnJmcOnKUc7x87sjOUk4kHOABC+3Y4bNKgS3HXYIzx5uTxUa02ucAsglv7bzNgL03Fv54zKte9art7QlenOXesu9xSq/JB4HziYBxWnBlDAtk/K7Sf89mLpgDUs4/+sbq3vlXp0xCY66ZS34zaw3yxYV5ZB4Ijv0eVIBsfmhLvuBZoKv8yU9+8rbGmMfJbQ0iQ1kBlWDMW0JzyhtaPK1RglEBlHmMj0DKVxzWAJ/yOhwQMP/93//9ZrP/AYC+bGeDy2EWPskME/KtKb0tZaOy1qGtwRJs4UUvmMQjmnJrCH7WBZ9iC3r9NMPbUW2UH7Wu0sN6LPc21hpoL4AF2/HVnuzskq/9mg7V97bdX963plvn8PH2Xz85NFjtwMthg7WN3jBuP8pu+DiMgKm23sCyz57gTa9+FGDrV1/s6A96rDbbx/B3yKq9/vNm/FGPetR2yNGaShZb9Lv13k8GyPR/3TvAFIQLqAXJ6slQr51kPPY2HA/9oW/oZyzDRDsypNqhZSMd/dTG76od0BhD2oRZ7bbG888gMAgMAoPAIDAIXAGBswqQbcqcFg6dP67CGeotiA24ep/0coR90ozeJi5xmDgm6jjHtbHxa8Pp4aBwvvCS4yH440ja8LVB55JyrPDgGOwTZ4kD7JTdGwB/oMX/pYz+J0nZyKHhlPsDLZxjOCjLIeFYkQuj3ijAQXuXck4WHNIFrj6L9NtMunIMYcUBReuPAcGBPPb7jM/9gx70oC0ohgX5+AiWYcWh8yZNP0jKXJMGgQuNgLFpzvg9qoDGPBQcm9fmRuNSbrw2V9wb7/uxu9KzRcAmEDFPzB1vONE86UlP2oIasgU0+Jqf5qt7iXzrR2tV2KhvXmvrLbX1yCGUe/O+dYl8/7Ub3gIqc1fQZo1gs4BHAOOyDrFJezQSXQWGex3UNd8FpAJtwaF1YQ3m6BpGcni3HmXPmsOVjvQXLHp2oECWtVqQBke81iSgFMwJwgSordP93hYfZelSW2VS/Ulfl3JBp7XQeqYf4EAP693v/M7vbBjRs4S3dVPAaY/Q1vpH7zBxKCpApasgUv9oY+/ydlxA6Y+0+SSe/fjjswa9cIa54NghJX0e//jHb29z1wMAeoUTe8jxh8CUOSQR6HtbbR33Zt1hAjo26m+YOPSgqzYOda3d+iAa9ujP5CSTXXgY+3Qlu7EOR+moMbVVzD+DwCAwCAwCg8AgcDjrANkGzSF56lOfujkS69sP+HIaOL42fHU2Zhs8R8jG7TdgTus5yG3egkWfPvtszWk/B0kbn8Z5S8PxseGjK+HHkfGpIqdGGzT7RL63sJwSJ/GcieTuaU/nOacODvh6M5WM+Krj/HhjwFlmd06O9uph8OxnP3try0FWro1PDP2VW58Hcl7x5kRzhtkQDsrh4v8W5RC6hwc59PCWBCacMu1z5ti4OlinY/PQDALnAgHjvreuxrMx6gBLeck8kIxjdeartQTNOm739+jNF28IBUXmgiDE+uDNIHnmV4kcbaxRAiP3eK58V1r35pm1z3zCy/ySm4vaC4DN6w7PfJ7sDbk1zRrADjYJLAXQ5vo6L5N3lA74OxATbJGDFz7WILasbbovj++aq4MRfegPB8GpdQTmEtusJXvsybauCZTp77mgDN/kluNVOXoXmcpa99jiwCE81KGxtjtgdL/2n3q2kyvZK1x0pTO87Bf6S7/pM7YZD/jpc2spux2oWCfJ0rYxyC5Bpy8R2OsAwGFke9Q6bumTnXJ8YGvswRlvgTg5AuHGGx7JQ5f99IIvHTug0T/hRgYeaNjmHi882AsrfOO9KTf/DAKDwCAwCAwCg8CRCFzjR2exY65vXGzIbco2bYljZYOWcxhs2HIio0XnvjZpGW/lbeycDE5KbXMO4pdTkgOBvrKVr3tvUX06iB8HiXP7kyTtS/QgW+KQrA4TndHSx4UuB0+dpL2UzittdqtnN9vCIfoVhxWb6vHQF/Ta67fquikx/wwC5xkB41UQaxwbjwIaydgtNaaNXW9jBRPexHnT3Lhead0rR2+OC3a87RM8CcAFqYKo5gd6MswLB2be5gqkvYkUWOznBVrBERnq0kHePO4e3ete97rNNkGLNYYOtUOXbIEX++km8DlVoq834i972cs2ftrQ2ZtpcsjAmwzX6STroT/wxw6fBAvK4qF993t+6Lvgik5ODwGeutXmVRd/EwHu/eGqeOPhss5ZK9dy910rr9be6shVFhb9zMQBpANZl4OSdEQvwPRHwOiETr/VXr2g9v3vf//WR95yC7jVH7XXrLq5197ljT9dvBUWKHdgsvIwfgXixi97Hv3oR2+6ZSN+6Mku4W3MrSks9GX8tVnn2Eo/94PAIDAIDAKDwCBwOJxVgGxD5sRINl2bt7KcT5v8cRsxujbv2sfLs3v1crRSvFa6lTZ6ZfGWr4mO2nfhSfc93drmqu7xSy6e8clxqQ5dctB5zh72uXJg4qlttsc3vemUjPRDkzxl0a50yvBcdVFG9qRB4EIiYBwae43tZDeOPTeGzQkBnMBQ0LJ+PRIdGsm80k6w4O2qIFzg6S1hc2Evgy7aC14E6wWZe93wRYdeXfyUl2qDhmyHhC4JvfbJl9OXXXi4b12I31E5HmxzCMAu/AXYdMcTr1XGUTzWMvT4+YSajgLHDgjwkdC49qlyubbRl+/L1/beggtK9U/y4ocOPzhK+FWn3LUm+010q+xofK4ML29yyWq9RevSB3L410fd08E9jIwR7R1K0EdSt9cnueVo6ZhN7NZubZve5AvUfcYteaOujh6rzOjRKNcOjXJyqq+NXNms9/XK5IPAIDAIDAKDwJUROOsAec+yDdgm3KbcJo22+jbxyjgJ6moTXc/oVj57udXlQOBX2UpbvbIcmqPo1jZXdZ9+5WjTfeWbvZXt6bVT51ppV7r0UJaMyrIl+jXfO0qcqH0ZvCYNAhcKAePTOG/crnKbI8rQRdu98gLB2qkzrqV1LJPRfFploU9OubI1rfSVR1Ou3L1rP6fWNtG3zq3tlKUjXdKn9kflbC0ghEXt09nzqs9RPNYy+mlTu5Vf+mRDz/v2YbCn83ycXWwgqz47iveqC5kr/5VeeXWrbrXBR4JLdNqHGV16bo1Upx36+gmd++pqs+qyl7/qgA6/ZKhTVrlnMvCPp3ttei6Pr1yKZq3P7sqy5USTyQaBQWAQGAQGgUFgh8BZBcg22uOSzbj6Nma0beDV1T6afXn1p8r37Xvet1v5H0ezb3M6zyvf6Ff+1VfWc7Tl6qtb76s/Lj8dvtrGe8+n9vvyeR4EzhcCxuLpjLujxuxR7aJb6yq7KhuiX/VZ749qexzflZd2nlfa/fOepvZHyVzL8EzHlWftq1vbnOo+PVd+2qw81+eV31Ft13bdr23c125fftRzPGrT80pb3Vp2qvv4HNVWXeXr/Z5nPPbl++d47cv3z+iuiudadxTPM6nfy57nQWAQGAQGgUHgckfgrALkyx28sX8QGAQGgUFgEBgEBoFBYBAYBAaBQeDqg8AVf8R19bFrLBkEBoFBYBAYBAaBQWAQGAQGgUFgEBgEzgiBCZDPCK4hHgQGgUFgEBgEBoFBYBAYBAaBQWAQuLoiMAHy1bVnx65BYBAYBAaBQWAQGAQGgUFgEBgEBoEzQmAC5DOCa4gHgUFgEBgEBoFBYBAYBAaBQWAQGASurghMgHx17dmxaxAYBAaBQWAQGAQGgUFgEBgEBoFB4IwQmAD5jOAa4kFgEBgEBoFBYBAYBAaBQWAQGAQGgasrAhMgX117duwaBAaBQWAQGAQGgUFgEBgEBoFBYBA4IwQmQD4juIZ4EBgEBoFBYBAYBAaBQWAQGAQGgUHg6orABMhX154duwaBQWAQGAQGgUFgEBgEBoFBYBAYBM4IgQmQzwiuIR4EBoFBYBAYBAaBQWAQGAQGgUFgELi6IjAB8tW1Z8euQWAQGAQGgUFgEBgEBoFBYBAYBAaBM0JgAuQzgmuIB4FBYBAYBAaBQWAQGAQGgUFgEBgErq4ITIB8de3ZsWsQuBoh8MMf/vDwox/96Iwtqo286ygmezo0lR1Fvy9D+7//+78nZbi/qnQmvPFBv8o4jnc06tPnONpTlcdrtcV9z+qvjoldXRe7fWsfna/+aBwl62LH5OqqX/jXz+Xnyl78us4Vz4uNT/adC+xaC88Fr4sNp1WfMKvsOHuVn84+Hb/j+CTnQuR0qB8vhLyRcWkhcK1LS93RdhAYBC5HBGy80jWucY2T16lwaCNG133t17bVRVedcvSnk2yydLzmNa95LDl+pe5Phz/aNUj5qZ/6qYNr33alU6eN/Kp0Sp+jcvy6koWnFP+j9DiK18Vexk42ZW/P2X2x6k/P+uR89AX+jeuwIWfShUdAP7vqZ/3R/bnQpv7F61zyPRe6nQse2SeXftJ1UduVlzXi6ohXdhpz7GXnuh6u92jRrfu0sn0bfNBVt+Zb4QX8J13k6bG36QKqM6IuQgQmQL4IO2VUGgQGgR8j0IbaRlbNqTaz4+rxOapu5b/eJ++4HK2LnvhyvI7ir71ytMfVX5UMzsep2qlPxnG8Trd8lZVtlXnmFIZT5afL+2KjWzHLlvKLTdf0CXv9IJ0Pfev388U/WyY/fQTqd7nrfPR7Ms51v698s/h86B/vfX4uZeF1Lvntdb0YnvWXNaD1/riDAHT17bpmHEUfZuX/l3aufUj/i0Gn/0s8RvYVEZgA+Yp4zNMgMAhcRAi0QVPpO9/5zqbZta51rcNP//RPn9Zm1sb9gx/84OCyAWq7f3tgU5e++93vbnXXvva1T4v/1ujEP5wBQSyZdFw3W/wLcCuXd7/yOeoeT+1d173udbfgdE+XrfRgK1p27m3dtzvu+fvf//7BhR88JPqSw57w3Nt6HL+LuTynbsWZXVd12PF/bQ9dpcYQG37Svj7Klvq48USe+3Mp4yi5U3ZlBGCvn+EvNV6vTPmTlzSO4pDM8srPJqf3Pl2o8cQ+8tlzLlJ88N1jdy74Xyw8ss/Y6/44e6s/1Zip/jg+F8L2xmJzqv68ELJHxqWBwDX/5E/+5E8uDVVHy0FgELgcEbBxfe973zu8973vPfzbv/3bFvjd9KY3PaVTol0b8ac//enDJz7xicM3vvGNw41vfOMrOfk2S3XveMc7Dt/+9rcPP/dzP3e4znWuc0oZ9YeNXsD4la985fD1r399ayeQLeGprqDSppzDEc1xORscDrDhk5/85Bbg/+zP/uyVdMtefL70pS8d/vM//3Ozny0/Sfqv//qvw6c+9anNnhvd6EYnnXM2fPOb3zx85jOf2fpC8FwA/ZPIuRjadHjhgOTLX/7y4fOf//x2yMGuC+XAnwkOxuv//M//bAcYOaXa5+ydCa/jaI0nuOjrr33ta5ssc8LBwaQLi4C+KFkLv/Wtb219bWyeyz5fx5Ix1rP8XKSCkpXXudR/5bu/J9t4duhnDTubNQse1gq8pKvrnDDW7F3w6lC6PlzHxDo+rUv2K2X78Rn+cul098CN+Bz/0/jGVj/qT/14ocbjOTZn2J0HBGanOwWo68RfSS0O6tZFYq2f+0FgEPgxAs0Vuc1IECLg/YVf+IXDbW5zm4MAzOZkPq1zqmf5W9/61i3we8hDHnK4xz3u8WPmy91+8ybPZvy2t73t8MEPfnCTdYc73GFrkTw0HIH3ve99h5e+9KWHe9/73odb3OIWh+td73ondYnvURs63dQLSt/4xjduAeX97ne/w33ve9+tPf6CzXe/+92Hn/mZnzn86q/+6mYvvQTlsBDwC6j3DgVFtRd0/8u//Mvh3//93ze9bnazm2020AefdVO30f/rv/7rFsCyBe1aj9+KsWdpLfP8uc99bsONTre97W033dDqvy9+8YuH5z//+YcHPOABm52wOiqhd8GG0wQnusCeIyWtct0L/jug0FaZdnJtu69dMgRynLmcL+XZ7X6Vpfz617/+Jku5enzhbFy+/e1vPzz72c/eaFZHOj7afPWrXz18/OMf3xyr2iuHV2OLLpxLl3LjTH/c6la32g5q0k+7q0rhhoYs44ae9L3b3e52uPWtb33S1qviUx1+LvoZU//xH/+xjck73vGO28FQdDD+whe+sI3rm9zkJocHPehB2zitvpxOLvRyae0rsoybHFFjAX7wcGBlvsE5PFY+yai/e97LNMeMMzzxgXc2yiU8Vj7K64v1QCsZ+zzbVv3e//73bzJvfvObH375l3/5SDl7PuszHcLOOMEPTr/0S7+06ZZMbYxxh2TWs1//9V8/3P72t9/mirrwiPdqZ2Vy5Q7b9Lk5cOc733nr03SgjzVEv3/sYx/b+sb4Mi9Le9572caVOSClP77WlA996EOHG9zgBptc8pXv+e2f8Vnp8GyspFN5uux5KP/v//7vzSZj5eEPf/imBz7qopfHwxplrTPP9Ydxapygec973rONYX1uDhq/tZW7jkrskIzT1gVtk1m77PVMR7kyl+f4OLRa9Xd/HDZH6bMv017C31iDGbv1VXLTKblyl3HrcNFB8+1ud7vDne50p5PjJv0dVLuMKXtxe4f26R1fehhL2iqT7Jf2arytpcZZNHB07/DWgbS+iQaPNZGFp0tfmHfWAuucw4BSOvU8+eWDwATIx/R1E6fc5GqCd6/OJjxpEBgEjkfAPLFp5bDaRDkdr33tazcH4V73utfhPve5zxa8CozWjUxbGzWnhEPNQbQJHpf285Vcm7GAkbPH8bMRKscHX5c3Mi960YsOH/jAB7Yg9hd/8Re3QEHgniMjmLept6GnA/04OgKWN7zhDVuxgNJbt970epv7pje9adus73rXu242KuN4ffaznz089rGP3XgLoPeJrpyCd73rXSedkGTavOHJSYAvhw7ff/qnf9qCKBs950YdWrpzdOThDFM8em594yB7g8y23iC6hyEH8+Uvf/kW3MAEhhL8Jdhy3MjUxuEAvciCp2AimWwRMOkDut3lLnfZHE4OqTr6lNe/q9OiDEacfo6R4BG9Mvf1c/qgJ0cw2GEJHfGkgz555StfeXjGM55xEpPNqMU+PGDwj//4j1sbY0MfKE+e5/DSL54FBwKSRz/60Ycb3vCGG/9wT8Y+xzP7w4LT+rrXvW7TgS23vOUtt2YrLns+PcePbux985vfvI1NB0/4tKeR1dgzVwVjv/Irv7KN4XjJ4ycnX66dcWL8sF1fO7zQH+axZEyjFVysXyioo4M6Se6iD/5yzzCRK2OL+ffOd75zky0AMyaVky1f2+NLH2P8cY973HZo1RuyTegx/5BXIl/SD+bHgx/84C2I2usa/XE5PvHCxzqExyMe8Yht7sZPbv58+MMfPjzvec/bnH9zT7kk7/K8joXKyVH+lre8ZZuTggwHlGuQhda4gOXLXvaywyMf+citf9a1qX7IJm30uRzW7q0xlTeWBFwvfOELt7lnDWiuoJPCQfk+4alekG3/0MY4wXuf9Lk13IVXNPBlu8NGB5gF/XixKZ5ysjqYfNWrXnV41KMetR3oxs9agxc76NHhkrb7sZZ+6tih3rrvkIKu7Xu9raULOfjSw+VeP5lP6rQz9+9+97tvderxP5ukfZfDJofK+tMYKSVDTgdjRU5H+vvK6//9v/93eNjDHrbZ9fM///ObvcYP283TV7/61YenPvWp23pj3iUTLu7xaozhrc/1lTmN/5//+Z8f/uiP/mgbY2QKysmxRllj9K99/0lPetK2zsKmMbDaQY5yF3td9h/7N/lS9bWb/PJBYALkY/raxJEsDhYkz20QJnCTbb0/htUUDwKDwAkEzBeblbdG3m5yMASvnGUOujev+2SjcrWZrae7K+1+Lpq3+HLuOeraO1X+h3/4hy0go4NNlyNnExbECWI4LpxUGz8nygZsYxfYcIZs0uv815ajLYClJ6eTg+6UW/Bv47aO4OfCX0AoGLfZk6l8r3+21YZjzAmziQviWqM4zfQSZOQAfuQjH9l0EJQKgOgoMGWDAL2Algw0gj319Nc/cs4RJ5B+3pZwxmBagCxIYws6OtHHxQ76CMw5fsoKjBxSuH7zN3/zpONCf3IcXAiW8IcXjOgBezrh4xlfuT4gS1KnnwRf+hwP+rGdk6xP5NHDii1shpnceCALVuqMDbbvU30fDuq9edDn9JaM0cYpfsrp7CsIWHO88cmmeO5l7Z/p76In+2DkGe9s27c56hk92+BlbnAw6c9hXx1Wuhuj6PShcb6Xg4+9Ufv6hL3acFTNOXbClBwHGeaVNzUOCYwR/YtOP2QPXvEjw/hDu8oPN/zpTld64Fvfoakv4sku2DmwoAu5Z5LokB7rmIMrXdLrdHiuOsGGDcYqDM0LQRGe+lxg6IKVeWPe6Zdkys1tOFnX8F1oLgAAIABJREFUGhdkxMO4/ehHP7qNc1jBRl0JrblkfXJv/SrY9SwlD3+pPjOujRFrljZsgLV2aAXI6s0t9/TXr/Fko36zztV/8SdDP1k/rLXGE93TaWNyIqCxpjmwEKQ5vKq/8GcbzOhg3NWPyWFb9DA2Z9lDFptqYx21zgrEBNDsE6AJXlee6VVOX5d550sAMhx04AsTfWs+CdLwsp65jAO4NFbNJXOIzDDcY5HMM8kbJw5IjG26FeSmO36whKP1vz3SnLK+GcPWafOQ3vrSG2VjwZ7BRnixy5iRPKO1NkswJI8+guq+NCHTXFdOP/Lthw7v6IonGer0Pb5H4bL2Ef0cNDj0lcwf2B7VbiOYfy4LBK68+18WZp/ayCanCe/0yoT2tsGG0gIpnwl0aiyHYhCwSUnmi3ljEyxI4kRzBDlk3iSjWefVem8jywFZy0PYpqnc5m2DFHT53IvDKWjjsHmWc55s7BwuDhCHxCbJ8bLpclYEbL3pM/fXwDKZHBxvh60T2vuEmh7eQtqgOTUcK04MO32GTQYdBW7WFc4E5+Aom6w9dOR4cBI4bJ6zk4PmbZPgl+PgQIC9LvpyQpVrA3t2SnTUL5xJAXBOCxoXJ4dMjiGZnLYcXrQwgxG9OVDsceHr8ANuMNNn97znPbe3BZw7BwfeSBSoc/Q4Tt6+e2vsmb69zbcGs0NbujzrWc/aeDem2OIehnRETz9OtL4hxyf5nCf644XOZ+2CY+NPfzzwgQ/cMGV742yVQU79I7dHCDbYK3mmIxmuytnmGU+6RbPy2xgc8w9Zyc2poxc92WI+pM8xLK5QHC+FxiOHUl/B3jimHzn1pYMZQRnM9BF5azLGjXuHQXSiGxq64YuX8Z+O+oWzLDA1Powd7RxmkIMOdnjob/qgwd8cwh/P1Q7t4ZzuOeXRqg+72mpv/tdPK7/VvlPd61tYSfGQd3+q9tHSHTbsaK3yhYJ748zlUMR8d29+mIOSNmyVO6wx3pvn6rNZbl00x9Dph7U/6cIe80dQoo/MK/3AxvBM5zUnx1hy0Cj4w5dN5pZ2nq01ghflxpNxoa/ohb9LH9PNfNknfAQvHSDQa036snXJusoWvKV0TRcBcvas9dFra+2Eg7XB1xXJQwNfh7qvf/3rt/UTj9/5nd/ZxjxZaOT7VDk94WCOwFnSNy7z4v73v/+mnz2seWEdpZeg0tz18wjP5JztOKaXSx84xLAWwNAexW784VVCZzzaK+XqjUtrrnHgsEf/1k5bdPRmu3UFvmwh1xy3JvvZjrEh1Wd4aoe/hK9LYOyQiy7WLzJghQ6u+K06b42Xf+ofuf4VFNt7yNfn1rBJly8CV16BLl8srmB5E8dGYgHkVD/xiU88/MZv/MY2iUzoq5p4V2A2D4PAZY6ATbLNTs5h5eTYFDnYHKo2U3VrWjdu5eYdfns6dWjxF/j0KRdnw9sEQRkHjfPnDYrACb1Nm2P5zGc+c3MIbN70EszIf+3Xfm3LbZo5SOnH+RMY+2TMBivAF6jazMlyCo8HXemNt5zTwfEUtNnYOY/K0z/+8oJbQadNmz340ZkzxdHgCHBqBKxs92kZ/pxJ9tKHI8IpdEmcColT4CCAs7b2EYzx4sAKTvQP/djMMVPuN14+SRO4q3NJysMKT4cEHDm2eGvCMYc/Z1j/46ctOQJob2Y4yB1YcJA4Q3Kfo8MgWeSRIZCAA1zQcr7w82kkHZXTnVy2wBudoIQs+CqHrWc8j0rrWEaLztjQzwUFbFdGR3LZjmf88XChP51EhivZeHHejFF2nGnCixPJucWD420c4EWGRHd42QPZk31sXlN6KXMvodGv5h057JbTWZDN8da3nskz9pSbH8atsUUmPuYqe8k3blYd3dMzuWQrqy181aUjWdGjUV4fVr4ZcAb/1A4f9/o6uafDhk4u/eCLGv1gDTHGzVl84aMvOP8CTNhq422a8aU++dqb29lMh57p5qCsuWHeag/fxqhDAzKMjac85SmbHvFA69I/rlIYGPN+80k3fU5ustG4zHl9aY2wPhWEVO+ZPvvEPn0GG2OEjD0d3RzqGUvq96n+xss4kZStiR7qBHkOIayxAliYW38as2SbN/DSD+TWT3ioh8GaslEZHWBh3bIm0cO6Z72GobULvf3Juo5X6zR5DjXpiI++k+OhzU+Samcc+qpLbm/ST80zmJLRZS9xUGD9Rq//JeNfHczopp3xIthnn/UYD20cjJHt6zEH52u/KWeXAx9+uPkAM/ootzbBpYNm5fY6Y85+Tx/88CFvTdmgLn7eQvs7JP4GhfbWqX27lcfcX70RuPIqdAb2GlhrWieP8uobYHJlK1008YkWjUEbbfXooylfZbnXTmphRoePti0ia5t0OIqfBdemxGkzeThR6LRJl9rvecZP3v2m2Hn+Z9UnUck/ru6ocphJ8NR+pen+OL7KaxMtPu7r09qmozpXdGt9vKKV7/n2XLv12b3y6o7iE8/j6NY2c39mCMBfv9e35qa5aNPkCPhMjAN4XP9oy/mw2eLVtdeiscpxEPwIGv2xJZt8zp3A3KmzjdrmaiPnzGgrgJM4Yk6gbfCcBI6ktNrQ22dvir3V4SyRgQenii0CZ/wLlmy43pxzdDiJbBJIC0Q5fQI87dgncTysPRyz5zznOdsf5slB41yoJwMfTq8AgwNJD7pztOjJyXK4JyhRLoWV4JWDQaZ2dFWnb/QTnR7/+MefdPbI4MhxovyhG3+4CY328dQOVsqaT+o4TnKHCjDl7Ogj+mkDa33nGVZ+q0t/zqdyecFHOMWfTeShEfR6g/G0pz1te6MPK06PMUEvDinnnL2w5ZgZjxI90jue2SZvDKhzD3uYGDMS3PB10Q0Nnq50Viah2Tv56NLFnKC7lGPoHob6idzsl2srVdb9VngCHzaggwUn270xYFx4IwZ7cs01jjg6Y9U89XUE3uTTm830Uq9fSmSoN+/Yqo3xyS7P6pVJ7iXzwHgVjOgP7elmrOhTekVbO89o6COHFUyUswGPaMK0tmGkftXJs6sUXe0ql+f8u0enXXn3K339Hs/k0hnOgtd+e2w9cG9tkOgfjaCELwIvY4jt5BnTAmTzsZQsh5CCEYGvwMLBgzGkD+FmDJhz8HaI5UDDHDLP8YUlTM1XbzetM8ok8tHLydZ/2khhJKeLeYjGmm+NRbeOC/cCdIGjsZX++ti9gwOXpGxN9At3ufo1j5c2yiVl7ruUsUNQ6usSPOgCP7i1/jaejFt9YK4IzNWzVT3b2ADbDqDw0+9y/a7PX/Oa12xYkmFfIt/PfOgGC+PfuuL37s1NdMrIcukL9GeT6GXv8qkxf1eQSIZEX3qR4V5uDbd3wEXwqj+NJXtetsMFT3uVMWe9NHaMIfYbX+itLQ6M4QU7/NXDxJi0D/iqSXt7gcMkc8Mebg23ltnbHWqopzts8THG6c4+soy/1iv1Xf1URt/7aso8Y+ukyxOBswqQDTbJ4JI8d9/io7zJ5F75vt3W+ERdg9FglpqQ7vHWPhlrHs94qdM22fFTXjttWhTRudIvPUwkjqXkFMvkXdsp10aqHH/3q6w2ko3wPP+TTcQcpUv6hkPP5astqQqPbELnXlK+ylCmvnL3XdHVp57RqXev3L2Ev7pSbSuLZ/U9oyutOq78oo1XdHsZK694Tn7mCMDRhmcThzVnwmWDao6aV5w1mx9HGJ12+ko7dDZe7Wx+nIP6Bw3+HBOXZxuizc6zt7pOrjlm2nHmbcQ2V/JssOgEfZL2nB56CBo4CPgrbxy5t+nj+c///M9bPb28tTbX6Sq3uSvjyBpvHApBQ+uIoEGAzRH1x4I4fnTH3+XtMB7KrT8Fh3ih45BwQNT3tpW+r3jFKzYb1LMTfo95zGM2TOLBJontHAw6cCjIwYPTA2MOEFr3Lk4LZ4czzRGGIRkcomjQx38TcmJOw48j5c26XJ9rSza8OC8cSXxzZLQnA62+Y2u8yZPgYZzoA594cpIEEJwwfWjcoIFn2MKr/iRbvWe05EWnzD1ZcCxpQx/jpMMJZfolPDzjx6mV2MA2/Y9fesWzMrlLMMIWyTjWn3hK+ojO6CQOpIMaz4IrtNXRv8QG/e0TSniYC/qO3r6CYItDHrq56EwWfI2J7373uxse+oCjK3gwh+oTcuBnftDJXKCH/ubowoLTTzZb2KCvtYGNwwsHJWHtsENbtPWJNtmkf8g2LvSHZE7Ut9kBc6m27LG/K68MT+1cdKa//qUb+XRSVzIXHKCjE8SwKxo8V750ZKfLvSt7rDcOuoxf5c1tb8ytD/jAuADQM73oxA6XMnM355/+yiQ8BSd/+7d/uwUOvcETcOhP65gvM9gmCHco5yAPX3OdPnjBQTDHBsGDsZENbLfO6mNzTp322nRZy+CFHxmwtx659BM6Oug/h1vWmMZwuONpPBinxuWa1MHQ2HOvrf5gv/ae48ee7svRoNWX1pHWc3KUtU6FK3r4sdWlH/EiWx27zENrGkz0l3q2KjffrBXmFblo9LN5/1d/9VcbDZxd6aU9m+wj9hWyVltWPM7kXj+2thsXT3jCE7b9Co/wIaf+Vu7eODSXtc1GdtJX0hcCe31rflp7jBV+tb6CjT0m/xpmJW28ETZW9Lc+MIbwtO9a57SHBwzdG6/0hGsHHHSTrHPWO2sFHetHdWzUz/rEAboA3MEwm7IlvSa/PBA4qwC5SWOCWtAMQhuFwWrgGWwGl0WwhaE2BrMyNCaBSWbBMIGUmXh4WQjlNgY8OXPoLDDkejbY8UWD1oKlTE4v5WSZeMq1IdMkUy9Z8NXRlewmjkXP4qS9iWVhV89GGz+b0aAnGz+6eMbThDS5tMn28zm0LMpksY8udHRPH7hafOnLjiY9W1wWqxwddrnorw+1xVvSJy59ABd46jf9UZln9C4Lor6ALQz0NawkGCknV/vGEL3pqU6u7+gYhmyqr3Ma2EOX9EULCzqRiQc5dKaXejy01xYm7Jx0fhCAsX7lQHGSPOtz/eDkl2On3OmtPjNm9ZM2+kUfuvcGRFtXY0y/29j67Rp++to44XRrh6/cybi57iIjHYwv46W52trgWaKni8zojClBr0u5eWF8oePc0qG5gb/AwwZOD7pxFJxUN57jTR4Z7KanecH50lYiw9wxN4xf8tmFHnYwssGbdzDxqas33HTJjo3RCbu0oYvAWg5bziB8yJBrRyeOiqAFb3Y4WFDP6RYsxV++JjSSNtZehxXuYQZrdkjsJRMm8MUHLfysxfp6z9uzdYaj7y2OccRe7YwbvMiHDxp4WdskbdkPe/f1rbruycaDgybhhW9fF9BJn7jU0ZssOSyV4d2bJPw4fvgbn+zVVkJHF0lf9BUC2/UfumjohJdkjHgbA1tv3h0QxDN+6GBuLjikgJNxau0LH/0pKLf2Wks54uThxXEn0xyGLftWOZsiJ3TBB73+lfSPcSnHH37sx0MgVB+R035Arvrwi7+cvQWU6unrrSQ+6sIo241XCa2kX8wNuTFNF2PCpa1nTjZnmb+i3DixXkhoYGBe2kc48WjI0Sf4ssW9i00CAbamA93wEGB4u0v3xoz22vmbAua6Nc1za5u1QFt4WlfoYw0QNMRfmUQO3mx66EMfus1TfS4Z0/oFBuYGXuz4vd/7vW1s0psdeOhLQQc9whUP8uBgTNHJT0HIQydXZswJ0vGmozkDT3KbP8amMSkwtWbpzxJe7NG39PW3HMyJbEWHxriSsk8bdrskuLrCZis88Q8a/Wd9s37Sm98HA19QGC/mNZnJjW9lxgwdPRuTbBPIkimhN0dhSn84qkNfP7FDP+h38hun2mQPXmyEFznpdcKUM87I178OSCRrW/tFzPaYacMe+rKBvsajxG5rG54wZYexK8B1+WTaeLC+2DuMi/oFDmTh4U2wcusJTMx5/PSNOu35DsaZn0K2DvAHjAX4eLtsb/J1Cp33dvSsDj8xhgNE8qzN+n/S5YfAWQXILQwmgAXNAmhDtIGYyAaW02AnQ052WmRtJgafQc8ZskA7+baoWoTQmgDKbDrqtTF40duQDXyLqQXUpxktOhZOk8/vGejiJMxiZNMxef3fgSaTOrwt6hwzC5g3Ci6TowlDBicFX4uRBUNOP+VOGU1EE4jTQJ5FHx3Hg0NkMyD/fCb201lOPrss8hYTi7VyOlqg2GjB9awNeosIx5KucOK82bRhDV/4wAk/fc15thCyixOFzsbA0bUo+t2mOv3GwdAnsDAGYGlzp5NFy+IIK3rqc7I7DdTGZbFGLxlf6jlZ9DEWlJGn7/R9hxkWbuOT82Hh9MYjhw8//YWHxdlmxXGGSf1/PvvscuIdpsaHTdGGZTwZH+aS02T/nYi5agzY7KwP+s840z/6Cb2xZA5yCGykaIxZ642+NcaMbTRt3NYObfE3p40X48YYQEM/8shFSyfzwphSviZtXNYiG662+MaDfG3wKWgnmzwOpCCUbuxnm7beDpoLOTn442G8WqsEPi9+8Yu3MQwHY1kb8w4d22EgsaE1yjMngrNpbuxtyS5ytWevz+voxZn3h8zYwBZYS/TmnHBe6Gndt96YZ95Cta7Euxw+eNDBnLYWWyc5yJL1R1/kKFmT8NfOmkCuoJcuytaEDh+fhlrX2WGdsYbpR3jpF3a6OFt0sE6QKeFRcu+irxwNfTh1+hJPY1nfwd7axS660VO9iyw83Ev0hhO9rKGejSHrn/Ek4UOmNmQa77DSp/UfnvoZjfva4eFtTePOXkcGfpJxr6+8BTSu8NOPcnTpx0bjEd7kKicHDupc5ikM2LxP6Mwhl3sXefaCnumfXmuePXue+2d7CD/CuKaPeSHBnAy2KsdPGV2yhTz1xqp93N5t3zLm+SDZpC265hS8PZesJWSZ23Aljxz88YArehde2qvLXuPGODVuG0t00jbZ9jpz09qmT8x59hhnxjm5LvjGP/32OXo2tj+qNzeMG31pDuFtPfb1hfvGHX3oiMc+sbOxqD/8oSVjht3GcAci5OBtjzY2yeND8SHSyf5gvWNfOJFHhmd6mj/mIn+FfiUy6cxnMafoqizM3XfVZs3RGVf6w5pjTEj6A/78JzYZa/xUc59e0cCIr2E88SXY2bqDB/5oyDAX2EJHZfjIm1vWYHPTemGMStmSPDl7eymwYrE1OIN/8CbTWNJP1uGj+hpLdqCXyDRmjT+2CyrtD74mMG/gpa/NK/46X8688QfojKc/+IM/OHkAEG9YSWyrj80TWOoDfhub8THflMONbHudMUYm/nSS8DEutEN/XNLOxZ81BoxL9k26/BA4fpScBhYms4lisXCqaCE0oE0qG4aNXTDlN4AGpoVTG5tC9BYYi4nF1YImWDLgOV3+rz+LiAmorYSfBdRkNGh9NpiTalJ6G8MBNYHoY/Oy0FqI1NPJBDP4LTocNAuCSWQC+uM2HHXtLFjaOJXinLLL5LZ4mPgWaf+nn2d2kKOdRYYNBYo+FcoJOQ1Yz5hEH6wLloWCA8jR1R82V/1hM7KoWTz8wTEBIWxhDwO/eYGNhdEpojKbpz6Bv37C04k2W1uU9YXFEN7616bAeYcf3PWJdhZHC5PxYmEjV27htBnDWh/ADi+5QN7v4p785CdvutBfG5+C+r9eLaTKLMJstJlwPP2lW23V4SMg42j4oyP6UIITZ9Jm5TehNkD6S3TXdtK5QaA+Mt44efrEHDJPjVXjDg2nglOuP/Wt/jDWjBvjTnuHbgWGxj0a5XjUpr6TR2O9wDd6tM0bOR7GllSbrI+Pcpe25pVAxPg3jqwDxo11Ai9j0Rg0vuXWHGUFovQxr/o9lvWvjTv92WS8Kje+8WotM1/oos66Y9xb36yd1qYcTmuPdSAb2BT/7JOzSTkedIWzINLh2B5XdirjJOofa4N2BRLqjkr408Vcw19/m5cOQempHj7sMi/ZJXF8rBfWIrzVrzbUJ/rD4VzYGltwsvYYV/Az761B1ja8krvyIzMZyl1o9a21XnAIb231H93QcMjgoP8dTFgj8Wk86yt0xqBLHRvlJfWe7ZPWbHPFm0FjBe611Y5O0dOLffYq+yT5xg9eaCTrKuffZ7veZKqDQ/MCHRlk6lPtG5PkslMd3TyT79onfQgX456M9kS4e2ZLNuHvGbarrnue+2e64EuWQxHYG0/6mh30NDfJtBcbO9KqM3kuc0r5ahda/WdvMJfoF/a1M09gbXxqj7a5EeZotWMnPdRXl+9hHTQ+2WF+NE7QGstsNbfWPtdneKuTG3N03PcH2eTJtTFOomWje/K0tdaY89ZXfkBttXfRJz02MJd/6K0f63t6wZ4PZ782zhygtaboH3NI8AznDgDox4dQRiZ76FFu/prTbOH7oV+T9Qi9IKe5rS2MJBi69gk/84P/SF99RQf+prYwYpOg6QUveMH2kxj9HU5oyBbwW3foRv7a32SyCU7WKrao11ZOb36p3NzzJYg1Vp2ELiw8u1dHzmojGT9Jam0kU3/u5aVDvNmL1gEeTPlZxrR5CTe6wUEQ7GDG/FQHS/sUGXjAvkR3F9lsQgMTuFvT7Bt//dd/vf39C/raJ8x96405hIYcfoZcf1iH2s/Ilo7CSJmYQN+TbTzQb9LlicBZBcgGkwlqANtAbEwmtclq0FpIBZfKTKIGnQXZwmngGsAmjreaBjAn08bs9wVOmGziHB6bG1kmidNvGwmeAm+JLiYZviamyapOO/poJ0jzuZpFz4RRZ5Mmj1NBV7oJ7tK1TdaCyVmxQJo4+JuUytHQ00LADk62hUKwZwOFjYXORne+ksUkHOgHR7rACP42FDqxUWBJJ4s05xeuFi1BsYXdwsJ+n2LByAZgoXCo8dznPnfbSPUJ/hYbzrhNjqMAP9gqbwHSJ96c4W8htZDh32bUqSN9XL44gKETYmNEv1r4jA+JfTZh4w19GxT+2rzkJS/ZxhQMjC8bKFqfj3mrLhCBiY3bGwj17MEzHOXpvwmdf84JAvrEnNPHxp03BTZKGyn8jQtjc5/MO4dRcs6TNxRHJTwlY0QfrhcZzV15z+5tgtYJl/v1wrNxgdazXHIv6DWGOX/mm3munnNubbGJsw2ttccftTL+yDfGrJ/sNndKypNpLhnjxnrriPmGBs9yTrrgx3wrOLMWmvMwJY9ucrLwpKe1AY1PFulMP+ul9Vt764G1DjYSp1YgIaggi+PrdF6ZgzJ6cv7YtCZ6ks0uPKwJnBG8yaOTemXyght9Ya1obYknPnhKcg6xPoA3zNBbC6x78PDXrI0/euIHC4cO8JfCOz3l8JGjwd+aSDdY2Q8kaxNZ9gR4udhuzTEerF/q8IindtmK7+rkR4M/PeHOEV9p0rWAKB0FGeaWfrAe2wO1b7zK9Rcaf2BNMEAP80rC15gqUOTs1lY5W4wLesklsvcJtsZcdfZCewz++sdarU+6sgNOyVt5hslaJpjzO1lzy75FVxjbJ9hDVoc2+t26QX92kOfyjJZMfPRF8sk0R4xH9/i5JDldjVH48gFcsHXwjHe2rzqTrW11ximZ+kngaC+lExp6uIwtfgRb9IuknT0SH/NXPX2keG8PS5/iqw3fzJzCgxzPeOgv49v+rG/5S/waY4pddMdb29WG5JjzjXM02psL9lwBjn2XDp7pYTzoM/jyQxqn1sZnPOMZ25pibGYT+RKcJTjTj25r0p/rnE5Xuncf37Ud3axFdNYn/Bj28PEkfaEdvfmRaKyN9A8bsq3DaKwBMG484UG+ZG0w9tHB3/xIJ31MrvbKGsvus6FxBiuywy7+m5Az/EdbcxMG7DKGpT3Pxpdyl7lOV3sbTNjmMFt/6yP4mKdwME/tNWz2dRK9HZBahxzuGYPmNdnsssfAxv8zjb8XKZIDaT4AfMUC1vH8eX4eHdHL7VF0cIXhxuSIf+hI9v9n775+bFmuAg4PIuecc84552yyrQtYsjA8wAPiz+EN3owED1zAvtgkm5xzzjnnnDEIAfpa8ztebu89s2fmzGXmTpXUp7urVq28Vq2q3uccudaFPr9Y7WFq4EYb5AJfMPnyWvByKAuS4OesChSO3VdFjmqu4BJMX/EVX7EtDvr1WTwlIDC+dPqiKxEYkzg4rQATQN4LWPACxoJiY+QrokVHUEjEgt/mELwi3VdJeMPpVFBhATf+NbDmw+l5XgUxvn2VfeKJJ7YAx6dN/5NPPrkVgTb0EoREchsNPc0df5KnE2AJw7t+i4rkJdmSU4EKRgGXTPRHRxY6stjww2WxUXizCd181Vd91VaIgtPI5uszHdsswENfGpld7AKXr+nw0hkeLKSSowJZoYkum+BTwfLiF794+7rkhJ0tySJxSaYWR7Q0ePBDzhe+8IVbIobPwguPfw2UbX35QY/9X/ayl2382rQrnsDxBTjpZLXHqwFxVPPcwiOOvbfIg8lvPbO58eItfw3XvOcPzc+O/LE45+P8i0/ybf0Ktp6N8afpB8VWfeHFV7mB//FJcvBDGxzj8MpZCi2HAc95znO2AkQRqjjQp1hSGIiRcE8Z8Ogdzxqd4ckdr+jYmCpuxYfixIEW3Ipfv+JoQyc32kSIDzjkaXB4ceglHmwsFYoOMGwSxI156IhFxTr7oaE4wh9c3uUZttI/m3HNPPHuXcxFH24yyg1wePazawUjfZJJ0RMe4zXP5MYjGLjYER94oycw+thQjgFjY6GPzcJnnJ69d4EF4yAAffyxKz2QBW145EC+RGfxTT42AoMX8vMZuOQxz8lOHvTTUYcd8Yuf5HcH694z/5GPraFshjfrALpoyIc2QezMPvwCn+EAgzYb8CU/V5yNHvmteXjLD9NdsOHzTscOQxTHbIg3c9nWRttG3QGAZh6c+wY/WScd/s5/wNMlWa0/1ijy0je/YTd+gQ/vaPALsrKZ2ERfMc93irHJw6Sr3zu6Nn70gQ+4HNBaU9UP+HXNNt/hsCFwqBNfeIaXDfAZLf6GPznGPHjI6NkhHBkjeW6EAAAgAElEQVT4ornm6a95Dhc61nL2pQNN3PNbh8R0ar2mQ2u6Ooa/yBf64UdXS5bo4c9mWr++8h1a1ld8qIVsIOECDy/ZkhtPbIUP8ZUcyevOxjZE7OSAAi70XPhjB2NsIh48Nw4fux+ysX52c1iBvhiW6/gPusbxiV9+TD6653vpGF90LLbAmJcMm9LO/9BnTG4QG34tqRbOn+kMD2okLb7Jx1b48A9I2cQb6wr2nMyVbnTE7/BPVtch/sFp9Gwz7UODDxlkV9uRW60ld9CNXweBw7d+9pfPxD38/NFaQ5d0zwZ0r8ml8hia8prY6sOaWBU/LnDsAs4/RGeNl+PkVjoUN3wBPVc+vBHZ/ZGu+Qg8dL7aw9TAjTbIBWVBJFkJBIHiUpBxMH3uE54jSwI2PYoEAc8hBYlEL6gkZ5s4SYCja3CUVNGQROt3b9EwV1BUpAl8RSheJR5B7L0koBhQPAgmfOMvfvElCXuvNSZ4JAEn8mSRTM3FowVAISHA0b+tFi/hJyOe9StE2EXSsxAq1CTlijZz8Atek3QUC4pkC5XGdvQiiSniHQZI5mxmrsTjAMTip1iItrF8g53Yg54sot7pzjzFuMQHp6sxfCjQ0VVITTktpHyCfAofiyb54HTZFCj8NLDwW6TR6mfVFibFo4MbcuGV/5gf3xuC9cdj0QD7zeY9fXumcy04dz5Un/fGts4Df8zx4NnTMz9QgIh/ceBdPx74Er+TD8SwBZofG4sH5PKNSOPZHH6vAIiWd8WznGK+frjgduerYkpxoRhVTIgn114G8OJXDuHX8pP4DacxTU5DT7Hg1F3cwMe3NX02J3KTPnTwpiARG/rEP9xwyK/4FHvFA1nprk2I3G6+/NgYXXo+1LIDG8gD5s6C1zw6UvTIq37ZQ065Q05nMy09T13pt37gCb/0YtPUGuTrHPxkJqucYD795ntw6OvaiI0+cGjzDc/0ZK3iO+xDXzYr5qMrP7nkSDLTraLbXId25Cn3Rgt/8Fo34JCLwemH91BrjHzyJl/gfw6n5bj0Zdy628aB3ejJfDDpBx3P+vYN7+TV6M7cfYtPRTQebI75Il74FX+nC/1wWSsP6WLihbMLTXorRsWrMbZ32INvsW5tAItPv7rgD/r5HTnol1/gbfrXpNsz/OnDsxj3lZ6/WhPp0iGyNU0tAOe+mTebWLFZwJ/Yzq+Cw6NnOiKj+PSu3xzNOld90rxJwzNbwp1NxXD9zaEn43iiHzZhO81cfpWvNGcbHDUEOGPgbIAcwokL73KG+IRbvUVnYtxBNTq+CIoJNRudzganC49ig+/AaR6/oQu2cUdPDLIB+tUx4Qum9+5kB4sH+uY7cnQ2iAc2sFnDt1qV34gpcGIWX2pL+c2c2fbveOZH/FBO8rEJbbjokixg8KaPrfkwP6eHeIvGHn/9p9zpFj72l7PQTub9fLCtA36Fhy/+wq5iPn+Vx+UavNrYyo/yILvQj+YZTTaDk7zxAg8f4fvg+ZB4ZXO8ghVnLjEAXjyhZ62zHsoHPoiwbfox91CDT0Ofn7i7VnuYGrjRBjknkkgUen5mKAFyTs4lUbgLGMGm5aCCX6KUDC1YxuGDCw7zJB5OzembB4dkJBgkSYnDvAI5BxdMgk4zDg4dd8Ek4OAtCCQ9vCgqBGp44rlgnLQKMvyYjyfzwHhHD34JUN9ttXRDh3iSZCQiiURSUsDhCx9O79gFDN7iNznxLKlIYuFlP0WNJOhUzoEFHaYTc5uDruRVyy50rSClF7ZH2yKo8GUT9uAPbJZNJDrw5GLrcHmXwJ3g2uQq4NmMXPi02HjPD+HHq8MWPuoUUzFCdgudhR9dNgIXnWRY99vTAFuyQ6346v0m9+zYnd/lm/kf3xYX/FNMeC4mzMsX42Mfx2D4jNgXM06xNT6oj+9p8YCen4TxTbnG5tC8Fvw9fnPxw7flRgWhIsF8usMz3FpxZSPsEEhh4r+OUijCYa6iQb9iTENPLJdr8eTnw/gRj/jHn69L5AGP5/Qkpn01UNjhAx2xJB/sm3H40HcQ5muB+O4g1dc3Msoz8j6+HbqRyybRO/rpco+fLmy4HAywG/3QhQNCje7xRzY/Iyc3XGDRYMfLGvpwuMwhP1noBj3jimZ3uU3e0hwM2rQqoBVsWl9/tpfdH/I0O9OlA2T4Tm10Kk9bR/2Cy5cmsoYj/ZXrwuudXFprOjvPBgc58cfHvdNffh6sfvomA7uCVQjzJWNysZhjH/ygTVY6PbXBkyxsaj2BHx1j+tjCWqDfesLX+KZCnh+KB+sI/bgua+hpfI1+ra/wOmCiqxe84AXbobjYsTk4pZE5/eFbS67o4VuN5a6PvvAA3lrGHt6z36QLPrxkFq/yFP0bE/PyBXsVC3Tk33dBT95QF9ioHGvomouOXCXm6NM8+OmY74Oz3tI/HdGZXEY2fqWWkmvEFRn3DX44xY48kT7MjQc4fZVUC6gD5RItXeKTrvYNPT7EnmDJkd4mLB06BPFLPIddNl/5tc0rPZLNdVnj82iixY5447d4IQ9cntlK48tqLPkGbDJdRueUcbLiRf5wMID2oTZpluvxwi5qMbUZfdC/+IeHrYzxdfojL58go/Ukm8pbZEMje7KtefxHPlO/Wb/4FZ3lE/3cXj72M24fV6xrdGZ96mMZvHQ65UhOuFxgjPPhYjOYdX84Gjh9NTqiEwWAgs/PWi1GihgOKggEhQTC0XI8aDgnhxeMAsx4zmqO4LFh4tCSxZxrPoedSUQfGP05s3s4u9cn2Uy8zcUPvsgkKGuCv4IUrBbP7uZEN1pogJWIGwvfbdzRKvAt3IoSp9kKEwt1p3Vo45dM4DU8e3YZcyVfeCU5cySvFlbwxsGSsXn6w+lO1+a50kXzjLk0fV30L2nyA3O8h9PiK/k5wZV0+3IGhzE+xce0ePQuSVq0yCJpSn5OevkZ3Ghrnld7ejTArvwq3U973dQO4WRvhZcTbgu2L702Ub6kylNo8jE+4VBJn1ygiFMsTN8I59QO/zVH0WnhBg+XBdyYd5fCTSGIHlhFoI2ThV184hHcvvFnY/KiXKugEBf68CPuFCOKdcWsQyP+7iudAyA8meO/LZGbbU5s3sxFj+ye8euLrRjys0oyySXiVlELpy8X+BE74oluxZTCRks/h+SY4+aQmX7NoSd519cIulNgKdLpCi66Eqe9l2vSVbLgCQyfcskP1hT42BMuc+FzN6/14BjP0Yj/eHDviyV/Qc9hpLUCXhsKa2KHdoo19qNXdmB7NOM9OvrY0SZVbnOhVU4L7tAdHLnYq38Xgl/YdLAbWvCwm2c8m6OFX0zyCbaxEZkNrDWFjejNnEN6A4f/vrL5lY5DDhs5c9hf/Mm//Jr+oo+vU1uweKB3thbf3l2KdXexYuMsBvDGt6yLDlTYgZz8/JRmvsJcEc6//COS9MvONsr+viTb8znXRQ3/+KuxnTblMq5fzIl7enKxk8aGeNc3cW2Duz/4AN8T/541d/lIDKILDzr04v/C9fNffkxWm83pM6FH1zwxLLf6a0x8hE7kE3nEM7uridiKbcQCWnIznZqn+fcC+MRs9I6OfKk2SH48G+uOB7WPzbOcO3UJH/4PbZCNgXWRRQxkj8kHfYlJthXv/E284ccGWVzQk/mXNTD0jp54Y2PP6Boj1+QDLJj4vAz/Vcbpll7lRmuIHHGogcMXHvm5vwJpXWVn64x47lDWZp6+rS1g5GD6YXv5AR4xi6YYZFvw0TBms85e1k4HWvQMh3fj/Io/m08v8qy/565GRBeP1kAxcoreyB0NdqTz1R6mBm60QRbIkqZTHYWN/6bJ3xmWWCURi6F/LZMTa5w+BxUYJSF9JRNBV+KW4MAVLHCYA16/qz4wPYMpMcIL3jjcwYGtPxrRERzGNH2eBX19zYNbghd4cAe/PZzzav6xRBNd8OGc/IXnsru5LnJrTuXp3Qmdv4ftp0sKETqxcfb3sNlEIko/4SDHlCU9w403ic3CJ6E11z2dgUmGng+9wxdOfOANDg28MXxIhO7G8IJPRY3CSxL3hcxPosgnifrJm69OzYPHHDj5Kt4tMBY3ideppLkKBHAaOvG2daw/bk0D2RwBPmDRZBNFnCJWK07y0YuYCQYui3VFknebJ3d2twFhc0VJ+YaP2CBbaBW8fAQ+BRC/51MK7Vn08hP+r+BTLMUvP0Pbwg2mJhfA7yuOIsLCrWi8yN/A2LBWfOATHzaMfB5fxvAuNhSceJKz8EE+p+4Khn4xYQ48Gj4V+3I4WRU8in4bITGDt2ygAHSRwT8YZsMn5hS49CKWkyX8aNBjGysFLBv7UsP+vqiyB179lRSFsTFFufwKH3jFkY24gqUYTa/wk8nXVuuPOXC7s43+Zz/72a801xjdW7cq1OkMnmM5Gz1yyUnm0z9e0MKDPrrnZ3RBDn3+wUn9Cnf5hn+hvW/mWTd90QPvJ8l40+BPp+7w7pt+cPxOocgfrM/kn34Ot4Z3lzmu+hXKNgH8dNIhE/vRk8NvrTnm1/gj37De2Aj7OW2bIOslPvlpvk9/4ZYD+AK+9KEVn97xkx7Q08fG2YKfgcEXvOZa79hVzPcLA8W4mPA/G8z8T46JP7nSrY1c64wYJp/1kJ18rf/qr/7qLZbI6UujWJ0tfFMOuL3zK+N41tz5Ij/i12jVzDFuE0A+Oaq5xrrARzOc9YFBE+/8g86a553d2BIP3umYvoMJ75QFrE0Lm+iXK9owlWvEuBjgA3yVnmx4+JSDHfkHPXTidXs4/7Uae8EdL5417/ilczK54rH5cO77GnOnjxr9iskJT0fwsgVfkqfIygae2aqDuvgKX3im/vTByYf4DJp48FyLZ3Bik5yezW2sZ+/XaXgVj+wh5sR568vEmQx4tH7K++xprjn8UY7Tp9bSx65wWtMdJmjxLUbZi97qd8eP+fihXwdPaPNJh13m+0WgTTjd0QvdgUffv4fhcFANPOvV6E6ZNsLnPMk9/Fe+cIjrvtrD1MArMsE15ZfwFDgCyc/WOL/kJRgsVJK2VvB6FtwufYKAU3cXbJKLoHEKJOGUOMyFU4GrcJOIzavBA6fAzfkLBv2dBB2jH6zEE159MxF5d2lgJAFXSdWYfjTQM4ZP9GczrtDEs0RooZAM0Lpqg9sVXcVkenNgQX8SOlslewUcWvGWPBam+vBHNidp8OBZsvMuOWrkYxNJBY70nC7g4B/pYfILVn+FTPykB7B45k+e4XQSqVimNyfcikj68zXHvEkLP+bBbxNABwoji5tCVMEqaTtEIDd4/K52+xrID9zb4FnQ+JjNDH9jb/YvBptziLvG2M8iZ3PgEIVd8yHFt68Z4eVXxvJR72Kjd0Ub2uDlJr7G771r7ujyfXeLqTsc/NOVD8oF8qN/qM4BAN+HW3zJZ3KbRV7+nE0+RJsP1+BUePBpz/xfIYo3dz8xVGiSFz56BaPItuiTzzxN3MgZihD/YJCvN+JbE5v4JpNxm3AHAb4G2kiLQ3QUJWKIPOSil3RDv/KRHGGjzjbmwAc/25Nfc9qPT0WWDZbCGYzDPg3fZKhoicY2eP7vDXjWTz76z3768FLBSy668a+pguUn1it3uiazOftWHzi6dOigL5vjlV74n1xDV1//9V9/9jVf8zUbP/TGj+B34a9n/kfH8pR5wU2Y+Equ5pLBRUfyvU2JXwvQAf7imzzmas2dz/RCFjLQM/+EV6MXdrMx4H/FzjY4/jDfhsEm299FJQcccMeL9+QyVb9awr+Ay180ePiK2PA8+TXeuzEy+yrs4MY7XfI5dqBTX7EdNIl/xbNDGbKIRcU9vYUTb+F2j2cym+cAwzrjcFa8kcP6bV0RQw4lwNCdjSA9hiM6G7FzuT0bN0bH6TVY/sAe5QDjcHsnj6934sS7C/9dyREN4/C60Ey+7vFpPp3KB2DJiE7P6Sgc5tOBr4T+FWrjcgV4OcxBnzxEFn7pH3aVexzo2yD5Kxc2Npq5Uw44opc87vrQb8zcdMfv5VNwWjjozXWohc8cePgRO5vrQqvGp+VNOYMcNmqeHUaRNb0EHx/xrT986MiLZPYMRqzU6B4/6Igr/irPJlP84h/sdZvc6yBLbmM761rrcDjTN1o18lozzHHIKe75AbvL7XIiPxXHYkg8kJUMcqirdTX+yWTds66Ya5x/+Ul7B7jWZ7jkyg6i6A3P4K0jfBid2bJl+m8MTbQcFPMf6xpdrPYwNXA4S1xBFwW05GxBEgAlC8lQgHFYDjmbd86olUgKCIHl9Ftxa5HRz1HdOa7iTzISzCXDcMAnwDh1zh8d/Z57n+PxV4KcY54lVPfmouO5Ii85uoOFy4XHfaM3izc5BK8EY6FWsM3Es5936H3yWlJnB8kEj2gpQisWJGDyJGt3eOjINXFKFJKm4t5/3eBLE/3qM1cR7kuBDSc9oueejcM1+8gRPThcWvo15rkrHcLhORrGvZNXcpaIyTftAlaS5o98UcFigVZwKPScQtI/n1vt6dHAtCtbsYXDCsXw/HtI4PKfyzjLZ9z5CbsrblqM5SiLsyJPUWvxFQfRqGjKH8MBjwYXXwteH1rRVRgpwuERExZmfqnBBc6i7cuvMe8anA4D8WVTs98gwycmw8G/8eSu2JR3zVEk4A1uMftN3/RNW6ySX7zamMmt0U4OhYXYtrlQfCgu4IcXjPlkt9EQX3DZ9MhXNrA2sr7EyTdsh1c8k69YtZEH53DLlwKHUmDYAT65yVc5m1905RJ8fNEXfdEmN99wMOcf2cOL3EOP5qYXum6TgC44OpX75Aabc3IpRNGGQ0GO73IeebP3ZpzxhzG8KaAc4iiGrW98F11+bIOHlnebPDpVOCrUbQTQgx+sXIQPeDV3OmMfh81yFD3pc5FzD9u8weYGy4fp1oaQbGjBYX50eu5OZ1q+JlbkTesTfODA4J1OPesjT/aOhrUZ/61r3tkHnuRBy/O882d+Bj/cfJwe+SDd402/efRhs2ANVXPAn63dXWzFB8jv2QYWL3yA/RzIwDd1ix99XfELHx+1uaYXuvUlq8KcbDYu+unI5sm6KBbZHRw5wKHn8jxbNOMBbXPYgm+Rlw74V0W/L9pkpKvkCE/6RQe8d/oSN72ThT7MTVb0PZuHb2Pwa/pc4Y5Xd34AXmyJdTHLP/TxZbGANp24xIrc51mN56oGQrPmOb+ML33xkbx8ki8Yw4vLvJq53vUfa2DC59578YEm/DaB8hWZ2EdOAG9TKHd51rrDU+sZLvx4pwM5jZ/LXd5ryU6XxXJj8LvSRf3XucMtP6uF+Jv1Yr8ewRv/0aBPffhTn+NFbMhldCP/8V25kmzgk5Ef0tneJmQit/wMr9gqbm2M5QrrBF47gMMP/+ZX5uCdXfgiHsr18b2/s6tNtZgQs3y2ONvDrvdnvgaOZ4kTZOfACj4LMceT6DiTILORVRBI7lqLqSAq0RRk8Hh2lzwVif6BCAuawsoCYKEVdBYCzq8JqOY2H8xM/sZrxkpG+ptb8sFXPM05ZBBc8MLRPLIKQpfA0uLDM1g8HgowYxIRPdm4oa0o8kUUnau0eLZokkVSkKAt6GxCd5KTzaBFGz/4al56YTeypqPkAWsx8KVE0oRTMcLucLFPRaL3qcepK2PpB03PaMWPPs2caBt3GavfxoZMkp5DlAoDPCiu0GcTd3bBq6Qt8dnAkIOO/BzuG77hGzb9V7RLwvhKNxsj649b04DFUzGpWJVLfHFzYsw+2aB7PtP7nql8jd3Fq8W9opT/W0zd2dgGz0EJmubxEwu2n3FZcH1VnHkELe987xB9PizmLPSNg7egFw/4F6Ntnr0bUxD5L2/oAr+HWrK5y3/0ZRHn73hXiCjUksMmQNzCSV4/S3PqXiGCR7Aamgo97+bhR5wrQBRpcrtC15c9+rOJdtGxzQC92Tj4b2HKFWiKW/wWu+D7uiKfwI22WKU/l+KWfOaxgU0WHaVD8pLb3692IKCghl8O6CsFmeHEl5wHp7umjwzyh1zXfHbRjMkXcGrp3TP90Lu/viJnK8TQIbvc6YKHbhVWfJgO0aYLP1eG2xdI9nK4QE8d1KABB/9jJ8Uy3eNhNnzQB9r7MbTzv3kQ0zo88cxnuKwX8MrnbMFGHfjAiRba/IOc1mL+HTzfQQcsOWw+5VW48oFJ0zOcyWJe64wNln52BUOPyZXMbGkN8FeG+KvDCnLYwIKhc77AJ+nZpVjWxzZ4ZW8HN/RsPeOT0ekOJ7uxM9vzP8W/HCLmyBesOzzG0Hfg96IXvWj7Gb859EE34OgSn82duiE3e5CJrvmTQyxFO3noxnxz+SE58MUe5sk9bbDAaWDZzvqttsKfJj7o0eHBoWYe+7mjS2948I7/YiU70hX8Dgc6JJJngyMPnZovDthaXIt79uFz+uVTcmRvdzTwbS48Ls8aOdmWzvWbG2y8gtPHZqc2NMnE3/FtLl74u79z7p2d1FZs6x08XdMTXvTxMePmwoNfY+lNHx1lF++HmvWS77uDIZt5ruRMJ4fmX9SHX3p3mMrf+YU1OV86NpdM0WYDMkzdG5PrHaCKg3yYjeRg9p4Nvtn4j3wvx8unvlLTL1pwwkcX3sU/GF+l5Vh1oX940jgfb22a+HsWR60/1kw2y2+DWfeHo4EbbZAFtoTv73BJzhYpiyVn5Vgc3xiHmwuPwLGwSLQFRk4oMCxeFhjPTv0tjgoTOAWvhUKRKej0aZKN5KOw8GVDMVbAGveMrkU72gU12gJVoWIhwmv8kAWP+gWWRKcZlyAVWoJXf0GNF/TwIEgVKvDM5t3CIEE6SPAVk0wSE16u2qYsilc696VX0UpespPLpoGt8E6G5uHVwp786BsDQx4y2LjgV6Hsi4/FjP7pJZtYoMnuMje6YCRI78Y0d7LiDz/pNtre6Z5d6NhcPLGvZKkQsKGxINIn+YxZnCtEJEX+4+8lw6PgVmRointySKg22mSAd7Xb10A+YJGTB/gGv3V4UaEaF/xIM6d5jXXnFy7NXS4Qm/xWsWnBVOAoLNDwd9f5QflHP9/mZ+bZMPLN6ZNw7+mjpY/viRPviiUn3H7Wye+N4weMHOnvz/NPcqHpsuD3NSKZ9nf4NcWkfOHAR5zLMfiUI+VfBwE2YeLcZkuRwNfJtc8t+KV/fFYEignFreIIn/SmwHC3ybM5LR7pkD7JYJPvwMnhpp9LmitmXXjolwHi3Xzyo9+BAfkcZPEFhxf93WAxLG7pD/82KXjFs/VA7mI/Miv+5SCwdE42OvAOznz8WJvwlz3JpjhinzYPxhrPFnhGG4/8tNxEHr6kSKY3NBx+8gH+QB/yp1xvQ+aXOBWf+uVdOYwd6ccaCAf9ZHfP9IxPmyK2biz+8AvOneyu3rsHO+9yqV8ByJXyqXc5mY7xla3QYw86oHMFK3+mV7nT+hpNspjn2usx2njSjCte+ShfZR+01Bhsx9f5WjK4G7dJsAkzjz3wrAbIV8gBB/7YQh6gQ/WFr/viTizRJR8tBuHGkzud8EvrBN9yyOOn1eKOjcAlH774nXU22f0Kjk+RAQ3+yF+ONTkRn3yRX7I1O8BN5+7olrPYQh9d8H+y8G0bG36VLM2XM9iIXTXxZ66WHNvL+R/0BQc65PBMtn0Dh1e5zz+Gxv/FrVhmO34jF7vDwT/wR5fsJy4cxIkhMugnR/K6p2s6gl+O00cXNkf0xZ7yPh/iB+bVwOI92es/dgfPnnxMfuELah79cJBFE5PlHndwYMDiixzyn/qEDhxKkMGdrOwhl7AznOQxv1Ys8WMwcj0a7EEefi+/8YvrNvTC5WMNP0HDHd+HGjvSL57d2+zjw5h+F/8igxiX79Dpiq65mv6aMXjY08EJGHmTjsVv73yOrfmdWo8u/FUHMeCdb8kFdO5QQ/6edKKnpmVLuPkumGmH4Nb9YWjglXdt15BZorf4C1BfezmX4LB4WEgkXw5t4eFsgkhiUURJFC0k0wklAQutv4cIB6eVZBQgHFfAWuwsVnOhUSwJbImxU6/wCh4b0uc+97lbsFlMCxA8SdAWTT/VsFHCg7kCk3zeBajA0g+fpPG85z1vW5DJGy1q9AyPfyCAbsDPZhxOX8sVnckoGVynwVfStwBLuPRgoSUnvp3O66NzRYTFyTyw+H/iiSe2xELH+rVsBoZNbCzMVUTTv8WDLiVtxR+bKvCaa55EI5FZJOkhvfMT9qMj/fhD17i7PrrHO3tmE3b098wsPnwLPB3rZz8LEbzZGI9swV7G8ETP5CAPPUji4G6ywGxCrz9O0oCYYzf24ht8xsLJZvkeRD0Hz04KtX3bw3mXi/i/r33ii2/yfxs8MSBe8kXwihI+VeHDH/YNH9FqDA55QoFYMaB4Q9Pf3xMb8PJBGwobWIWceXChhx9feN33DUyXMf4rFuAVq/wXTiflCi4bMjqCS24RfzZcWjSTO3kUD3SlwMC3wlORoahxIKaIk/9shsVR/BiXb4obGz845IqZE/dymS8Gu7MNXObYRLjKT3iVA6wFeCK/Qof86BqnD0WoHMAGik05gV9ZN+jf3cWu3uFBH7w1xd8bpUNFoUIOP7OhA58NnDnWG3kme9AlW+pjBzkSHTnHekcH8NKNrzN0TC64NLx07xlNF12BczjCn+VaGyi4yRS8ezaFq/Wk8cbcxV39Clg+o/CmR/qjW3ohU3jNc8nN2cOmiA7Mj94myLlM+YYxdMwni3dj8JNRLiYLHaPXmC9O8j+7pQ/jbM5fFMLszjdc9A8n/OLNGsG/bXLxybccvljzzfcPqNm8imHrcfoWzx2gigH+1C8X0MMfWLykn97xyt70hKbDB4dMdKQPj8Hiky3QI79YkB+8izvron61FL8nmwtcl3G+xXfLQ+TLXukaXTmWTvmmxubyB3g6nQ0+McFX0ffBQg5lI/KTu4Y3G1x1IH2rxeQLsHyEz/JdPOEXT3SBrlgip3GxIRfPuIgOuT2TEd82P7GSlckAACAASURBVJ7pD7xncctOak6619Ayj77YABwa2TAZ3PFXMw6HeXzX3Xv8FEP6+I4mL9EFGLisQ+bhofdqZWN0K4/DJXeDC2988BUXPPDjhV87yNTkAf5Gr1r8bS8n/pHcdKw2crAhx8g51g+48YA3jQ4dZvCd4tlBglrdWsK27vwGDvZy+BYdutXIm33oUXyKY+Ng6VIcq/nFIz58SLLR5Z905vDCmmvjzIfVEnxPzLrgN/+lL33pxo8cLseV49GhU/6Hb7mt9cfYdfS5Cbf+uNcauNEGmQO7JLcKmBxdkHF274LHs5aj9YWYg86FwnNNAueogj+8kppgk+TQ5MQaPiQOX6HAw+tCz8XpLbKSO9hJF00JwOIRr/g1z6IpUSgyBV44LYASuuQOH3h4BJN3TfFvIdUPft/IZJ5E7lmA7heo/Zxj78lpnN4UrRZCOCU09sAD/vGVffCGJvn9vNt7MsKpJY93yUbykcSyiX7Fh8TIBnODba4inU3iIbxwS4Q2yOimw8bxhQ7d48ml4cFmg3xoJgsbw0H28KGvCHr+85+/zTcOv3F3RYWCn93Q078S4qbmW/2D7tlGbLEBG4rnfG0S52dsItb48CEY8HB2eZcrKsz4Wb7EH9gabI1vsT1f47+e0Zkwwc47OAutnwNX2KPFpyzcNpVyCL+zoMsxCgW8wY2GMXPRhWvfyO4CK5blRJsseoFPkaWAV5xocqIYd5FVQbHXGdp4r8EFD97Am4sXha4iyV9HkFvhw68WDu9yoQPNYoidJv7oTH0aTzbw5PIPmMmH8kiw3eGmV4ec5pEdr8blPEUxHZM1+3p2oQWuu+fw6kPfmqKRHS/yyWzg4cVf9ggf/bnYXv5hJ/4qR1kDHA6CVXg6NIHD1x9ysn05F73ynGc005FnBaFiUTHIB/gre8MdfDx5n/2Nu8OJXzySU/7np/h2GTO3+ebMNnkCQ8f4EMf4bK67d82d/irkvXeZTyfe4Zh06UhuMDb76clm3gZ5Pwc9ffyVPfkoGfmpd/PMt4640yk7oEM2dMSoeHBIoB8dMcCGxUBy7e9wwMv+z3nOczZbWSP5K92SBYw7fas7PMuF+DSPL1m/0WoOPUXLfP3urmk3OKyTGhiNzDatYgSdNsjWUM/g6boGp/i3YaUf+OlELrAZxAueNbCexRF/F4t0BaeDdDp0QODgyObcfPiyp3lihA3wh2fxrcELf8/e6UR+Mq93+jKHnPDzZ3jQ6A6WLeVaGzx49GloRKe7uXyWPdQxYMVLc7aJ53Ph08wxDge6bGsO3vQlM//EM58Uy3yjtscPTzKoY+mKf6Gp3x3urmiE7yp3tPHGR6xh1gWb3ORGT0PTQQ7fSF7wDp/Y3ZzqT7JXx9vkaskIHx1Ve8sDk5ZDARtfBzVk5j98RC1vzMbX4aZfMLE9+tZd/oEGOP8gHN+2YXcYxo/RdYHxbrMvFuhXjsi/43Njev3xoDTwav8rou5IK8G0MDkhm0lQMHBu//iMYlDB+5Vf+ZWPFqybJIWnWwXULiHgWRIRmILV4vu45ChZlgQEetdV5WUbyc/XJYupSwJhK4nwqaee2n7SLbl9+Zd/+baYXJXGVeCTzSKbTBUPV8GzYP9/NcA3syG/P+T7YNjbT+de/vKXb5skRdhlC5cNj9NtPmtxFFttRhQAtXyJLzt8U0C1EbyMBt4VEPKSzYa4gE+f4tLhoSJOIwca8ph54DR+qyBQrCkA9jowzwXOHHPlDs/kEJu+3CjmzbdRUZDAiba+y5ocZPNl02aeAsc8/YpaRYY+xeLUHbzZh2zma202LqNLBgU0G/kZnJN9/E+eyatli97pY+oqPZUPvGvgmrt17P4AJ4cpwMhgzVFc22TsZd1N3V7JwB5aBwpsyefoLDvgO37R4ct4K5cewl1fsvEzax86dKxQ5mPsgg/4Tmnw4ccXE3zwXXiu2+g3/03Xe174ko2SIlUBi95ltrkqP+nJPLpmU0U8G7CnDUkxkS3IT59sLW7c6VJcOWBTh9CVgw++MTdVx/gzHy/umi/BYsNcfLQRxQMY+OUrhT6fEQN41egILi3dbi8H/ghO/CRndYb1my749pQT7XyxOVDjS+z3c+n4ckiVz6VDONAWy+xsswdeg5vfyt/sIJfI32QkDxzhgYPe3dnBdUjm9MU2+KTXLjTEVPPoD7w7O9igyZU28ebkg/Cgm03YXnzg+8u+7Mu2zRfY/GOv/ujNfnThTUYw+uQb/dHU3/z6Jx7PjdffXO907UIHXPoM9tR7tNlAs/lVmzogsPkkPx2B41e+fvMR/mZMXzrEQ7q1HpKbbfb60+8y193mFEyy8Cl/TcE7n7E282G20/iAtcNBrkMMhyP8E0/w4aG9gxjk4+pU9/TEZ/m5d76JRvS7b8TWHw9KA3dqg6xo0CxWfsrj7zZJ5BYKTsqJnfIoEASBE11fbgTKsUR6l60peCWamsQmmB9Xm7jDuU+y9V92lySd1PkHUSQ5hYKiTL9NiFNmSclPCZ0wS2C31Uri4fdOrq761/3ua2Dvo4f8E4zFU17wrPiZi9sxKS3UFkaLvcWyRRwO+WQ2fcEbA3vKwmheizv48FYkTHkmXc8ubfrtfI4/cPDtx/S5NEVoxYC4lEfi4ZScApaOXWSPbziNwWkMrj0+4+DM8awdgtsGdn+AZx/8K+BtHtBKj8DD6Tm+9s+hTafee97rLdh5x7/1RSO/udaVyceEn89g6QYdz3yO/NakdGUMjekjk7+J79AzHUxdwSUGZoFv3in8gkMbPsWnZzp3XbdNu8BxSOd4pps2UK3Z6ei6tOc8sqATD8U/WvRVS/fpyxx93vHjOX2rS8hj3TN2SLbwdje/Czxck69wRAuscRsnNsXrjEN4zbmswaNFu3dy4aEYpo/g9MMNJn1sg+dfCR2csRmebHz5SbYLDp1kgS88+sllvo0U2eRvOMCDS6fxE+/hCa/+dBBMcQePMf09p2/v+jW5hk+Y5yA0uSfeZHJgYSPtUm+SHxx+DrVwzDE86HfFg2f94dG/nxtsuMLRuzv9NDf8we3xzXnHnuGr8Xn+p76jM7Jnd7wH6xkPZKHn2eLBnb7NCa754MPhWT9a7smCFz7IdzrgmnT4lrhxt4k3n3/CAXf5HB6ywMsHa97121eoW/mFOVo8uK/28DRwpzbIJTuLtr907+8GcXynT5y9BcpPUvzdVD/ZUFBx3n1w3gdTkklyKRmSo6R5U/7hLMm0QETnOrrKJi984Qs3m1SQS0AWHF8gfP3xD7U4bWwBvqkch+ans+QBsxLZIU3d7b7sx0+1/PLQYmTx41PFR/a+TMJwT7hDc+OlO/hDcBOPZ/ijYS4ZzNNX3IUnOO9go+Wur7meZ4tGske3+RPeM/j6wjnxHXqe/Ji7f4dHfnZ3zVaBE01jp9KFM/nDz86TBvwa/JPGfDYez90P8bohOvAHncWHYTQr5g6Av0rXtK3BePAcn8Ec0k0wr4J44Mqu4cZfz+adKq855EMTzubpfxwN3kPywJ8OogVu+vVN6UdDwTvXIHSMxZvn3tGML8+KY+/GZ/Oe7Q7JN2E9wxEeMsYDPPrjBc76zAt34xNvY7NvPhdD+tBg5+IpfvJrNKPrnh9MGsG4a8EnT/IZMz+Y9NR488MdXu/h8hyPPceT/mSJxsS1ET7XXbTqg6O+7sb0w6vBFVww8eIdjzVw0a7v2N1cF/g5Z9rJXGPBHcMVTOPhnnx7dl2n0a9mPv4mz9HiS8a8R2fCmX9MjmP99NwYXOHYHs79eE9r8mouHPmRdy3b6tdXvzs6xuPdM5xiPx7Q1Bfe+Fn3h6OBO7VBznEtbr4m+Frs71o5PeKofpbkJxZ+4mNj7KRI48g59X0y3QzYnmcivoks8M0EAFc0SjZXwc8mTlL9BNXPtNjEJllCYQc/i/ETND/7g/9xyXGMx2Qh4321/zHZHlJ/fkrmY3YE4/TX4lycn+LD5uUnU6ctivUFo7/nxqLX+/4+aXi+iC/jEx/fbb7+5J8w6Ml94FqoPZurHaIXnavERnyEz7sWLjz1vA2MP4LVdRWa4JMNXc8aOeNjD6M/ens9gTWWXqdON8QX/DHnTLzz+YLprzIUH+aHIxpkmP2vMvlIh/k1+NNReOE8Je+aS9fgPbuHK/xXvU/ewgXvbGh16TfeNeFu8gw/XshHF96P0YqXPZ/4D8/kD15je/hD/DY/+nu7NA5X+po6nDwfwn+sL7tOGdBGB/5DMuDFlaxTvvifMBOH52Caj7dJ3/uE2cvbPPd4zHbxUr81YPIyccMbjujV1/vUG9iJq/dwNsfdWDJ5T4aJ79gz3rU5J5zRiG9317EWvPE9XDgPjR3DN/vDDa/n8LknA7v0POWZeIwfGwtu8h4td/PMb1yfFk/hba3w3nwwXeboD0/zm9d742CL0SlfeSS4+F/3h6GBO7VBTuWclZM6qXJ51icYOLFNmeectnvz79udbLXHKQu8jwtfNrFRljRc4UeDTbILWR4X3fRy7B4Px8ZX/93XABtqF/nMhLmrNr+Mr/14MmWhY/IHN8cP9YWn+55e/bd5vyrN5Jg8TTn1T5j92JzX81Xh57xT8Ad/2X2vi/i6KY09nt7xcyruOecq847JPPFdxkOwl8Edo3VZf/j3cIfogT3Ub+5+bP++x79/n3wcotF4Y71PPI3Nvouew9E87z2bt38P17H+xufcSaPnCbenN8c8N97c3oM7xMsedsLsx+A51Bf+/X3immPhqC8+j8EHt7+Hp/mHxo1dFe8ez22+Txnm8yGa15Wjed3hnrQu6o+PYzpuPHy9z3tzg7nrNpm8r+fb0cCd3CDfjqgL69LA0sDSwNLA0sDSwNLA0sDSwNLA0sDSwNLAcQ1c7y8rHMe3RpYGlgaWBpYGlgaWBpYGlgaWBpYGlgaWBpYG7qUG1gb5XpptMb00sDSwNLA0sDSwNLA0sDSwNLA0sDSwNPC4NfCK/wz0cWNe+JYGlgaWBpYGHqQG/D0u/36Ef6vAP6zj347o73g9SIUsoZcGlgaWBpYGlgaWBu6NBtYX5HtjqsXo0sDSwNLA/dCADbLNsf8Crn9k8X5wvrhcGlgaWBpYGlgaWBp46BpYG+SH7gFL/qWBpYGlgcesAV+LfTXu6zH0/eugj5nUQrc0sDSwNLA0sDSwNLA08Fg1sDbIj1WdC9nSwNLA0sDSgM2wy3/H51ptaWBpYGlgaWBpYGlgaeC+aGD9HeT7YqnF59LA0sDSwD3RgJ9X+z/TV1saWBpYGlgaWBpYGlgauG8aWBvk+2axxe/SwNLA0sA90IAvx/3DXN3vAduLxaWBpYGlgaWBpYGlgQeugVf73/UXwx64CyzxlwaWBpYGHq8G5rKyNsePV7cL29LA0sDSwNLA0sDSwO1qYH1Bvl39LuxLA0sDSwMPTgNrU/zgTL4EXhpYGlgaWBpYGnjGaGD96ynPGFMuQZYGlgaWBpYGlgaWBpYGlgaWBpYGlgaWBm6igbVBvon21tylgaWBpYGlgaWBpYGlgaWBpYGlgaWBpYFnjAbWT6yfMaZcgiwN3F0N/M///M/23/303//0E9zul/2d1f/+7//e5oMHe5X/Osi/qBx+812H5scbLQZ3qkbn3OagEV19wRyi3Rx09y0cxuhRO8Rf+I31jNYhnHsax97hCd+EqT+7HqMRnLsW3P59j9v7hGnehNs/B6//MvgJO+HrNz/ZJp3G9XkOzn1Pc/8OX3ATzx5u0ts/N88czxPnVfDAO+deRid6aF7kv3s8+/epU7iSZ4/3VFmav6fjfeK4CK65E76+Y/fwmUMm756vq5uJz/N8j0b8ZbdkrP8Yr8f6Jx18z3c446H5k858bvyUe7xP/NfFdQq9YKKbTFPe6HdvTvfmumvHbGy+tWaOH8MZ7nVfGlgauLsaWBvku2ubxdnSwDNCA4qSWWR4VzhUPDROWH2v/uqv/kpyG3f90z/901aAvNZrvdbZG77hGz6a/0rAuxd0zfVfDrngf53XeZ1XKmJMAef6t3/7tw3mtV/7tc9cpzYbeC1Z0MGngik5o/Gar/maGxxYzbhnd9cssMIJj/n/+q//eva6r/u6mwzh3ZCcywCP+fgx5zVe4zXO0LtOMx8eOOGBN57dp2zHbIbn5Gru5Lu+PX/6G0snc94e3nvw7hfB7nFPWGPxHM79eLSjlx4mnOdpR7Dg0lM8gJvzwn3oHm/GzAknOtlm0jyEo75w7fmc457ThftNWzQnHn1aOmzsFJ1MfIfgwx3O/d14ejR2TBdzXvow18We+sIFh3d2OMTTxOXZvO50IM7CPWHDGWz0vJ9CZ+IKB5w1tOsPX3y451eetVPl24DHH3s5DEVvgD3Wx+SANJmTozseDslknG6M5aNg5zx4vesPfzDF+2MVaCFbGlgaeFo0sDbIT4uaF5GlgYetgQosBURFxz/8wz882uxVnBzazIFXaPz6r//62T/+4z+eveM7vuPZB33QB12q0OiY+xd/8RfbZdP73u/93lshqojRwNk8/83f/M3Zb/7mb5698Ru/8dm7v/u7n7xBNp9crj//8z/fNrFv+qZvevb2b//22wYTfRvvv/3bv93e8Y+P5riDoYP0tBfuP//zP8/+7M/+7Oz3f//3z97zPd9z08F+A19RBt9f//Vfn9Hvm73Zm2187PGd8v7P//zPZ3/1V3919vKXv/zsfd7nfbaNNlld+n7v937vjJxv8RZvcfb6r//6r4Iy+fBlo+2enaec8M3mPdvAYdPQ+4Q79Ax+zj8Eo9AFl77p3jsa5Pq7v/u7TXfs9EZv9EaPNrVwTT7I9B//8R9n//Vf//UqNJPTHPx06SePQ5pDvn6I39mHPlxosg/6b/Imb3L2Bm/wBhPspGe8wMU34cGXgyf6QIdOXGjxA/Bv/uZvvsGcROAIEP3PC438Bz+nNjymD/PYQxzvcYDB+2xo4kG/g6xg9nBzjudwgw+Hgzv9r/d6r/coro1r3fd49u/mw+f6l3/5l21YfLuMweOOZ4dk7PGWb/mWm3/i/6qNvdmdrfl4NNBxJZtYcCDHv9q8X5XWhGcj+uL7HXJepvM5/zrPyZRc4oaO5S1xeFHLHmTH88wV8IkNuOiTPMUhfa62NLA0cL818Ng2yCXY66jjJnOvQ++ZPGfp8pls3fspm0JiNkWHzdtP//RPb8WZjchbvdVbbQXmHpY/g9f/sz/7s2d/+qd/evbxH//xJ2+QzbfpQesXfuEXtgLfpg7OCj4FmsLz53/+58++93u/d9tAKxoVp+iC1RRIbUqLM3cXHDbAP/qjP7ptTj/8wz/87G3e5m22MeMK2h//8R/fitInnnhiK25tehWL5r3t277ttjFXgB1qirCf+ImfOPu5n/u5sy/4gi84e+u3futHvAQfLzYJ4P7oj/5o0xM+yDF1O5+bv787VPipn/qpzVbv+q7vuukrffz93//92Ute8pIN/0d8xEc82uBMHGArftmNjulQf3Ia9453d82YDZMNh/5Tm42D4pf80XWfuD03zsYOQ/QFY/4v/dIvnf3kT/7k2fOe97ytgLZB2Dc+wXa/9Vu/tR3a8At8w6Oh6wLn0m/cpsSG9p3f+Z3P3uEd3uHRWLLv6ezf0xVZf+VXfmXznQ/+4A/eDnSSeT/n2DtcCvw//uM/PvuDP/iDrcCHS5EPV7T45/d///dvuviYj/mYCzfI6RFNcsPP7p7d6V4f/l02FvQiXsx1nSoHWC2aYswlruAU3xo5uuYcmzXXO73TO2154ZCdNwQX/EEePiDHsClc8Y/mqS1YeH75l39541de5CPZAi76+t3f/d2zb//2bz971rOedfYBH/AB2wb/VDrB/cmf/MmGRwx86Id+6KNcEh9sZHMs54j993qv99rszobpNVyn3vmAmJGLxYBDzmI8uhfhynZ4o/djc/Tj08FBhx/hFX/sRYef/umfvo3TL9z5Edie//3f//3sF3/xF7dDAnpwQNQYOuQRhw5G+bCD1Vp4wTWnsXVfGlgauPsauPEGWeBXAEgEM5lfJL45XRapQz97vGj+bY/hTSOTRk6y3eU27YBPvMf/RXwnKzuQseuiOWtsaeAqGuCHxRA/sxFRqNhovdu7vdvZh33Yh20bU4XtPs7yYV9FFW2Kn0MFx77PPL5tc2bza+OjgFUcKWjkHLgUxjZKNuC//du/vRWLv/M7v7NtDBVYCm48+WKjCE4W9/kMr42pAu4DP/ADzxRX5TWFmU3IX/7lX559xmd8xlYY4ktxheanfMqnbHPagKdbMpHBl3MbfHwrovHtnsz484y2wwcbdRsQPNvg0CteFbd4Ihd488gHXw2cZp6vxDbK+NcPnv18bbfhV2D7ou1L9b6BtSHGE/3Hu3eFsYYXePXhAV82Bu/3fu+3+UV8wQUu3nqPJh3RL/2Tn5z0U26D1+UdDl97FOg2GHQTnA0Iuyjiv/ALv3DTUTTc07e7zZgDFRtMBxY29frxPL82we1Ch639soAPuJ/a4K15JptfVNjoOFwRQ+kmuMvu8LArPGz5Lu/yLhueNvt4thEh58/8zM9sMtpAtzmJp2kXfWyj8Sv88V329d6BkAMfduJLYvL93//9t7gzN33BATec4Z22Sj4w5ohZfo9f8ZyPNRdutgknW+Pt8z7v884c8gQf3kN3c2eDzwZKXqIXvgtmDzfnzGe8Be8uL373d3/31vfJn/zJ21dO9iCfJqbF5Hd913edve/7vu+2IeN34Qj3jA809u3Xfu3XtkMgNodHHNbA0w19fs/3fM/ZJ33SJ5293du93XZwgo947plN9Ln0ueglPwgvW8slP/iDP7jpSS4VJ/QGBxnCsc+DcMCrwcHG4PdNnzinR4cLLrg1uOnvN37jNzZ/d9AK5+QfnD6wmjXn677u67a44Cc29sGDs35ZN+jT5ngvN5nTQzY6xPdGbP2xNLA0cKc08Ng2yBKBpNLiepmUYC1Okp1kZoGS2O5K8ihJlty893yZbP8f4/Tpmny3MFzEjzmSuoLFwqHQtRgqfO+KLS7if43dDw3wM00M2bzwLz/bVXgrMGye5AHF2IwzPsiP+bV5vm4pfve+GX5wLk2fDcAP/dAPnf3hH/7h5uc2fU8++eS2QVLs8H2Fsa+s/F8OUrjb+CjcFO9g0PPTbF+655cmNOS+NqVokwtOX93Ig048wW9DpaBCz2ZMkWjDg04teHcbC4WYAwXz5cxf/dVf3Xiy8QCDT7FrA+KrD73SlY0pmfFJv4phXzqSy1ybNj/dlo/xQs+aQtyY+b4WK8TZBk18G7dph9tGfDabNgWjAlXDNzvindy+xoBJfwpem1LrAH1aR9JLawpe8YZWG+tJ0zwwLvr0k3R6AOsL2Hu8x3ts8/EBlr7J0kbNTy6NoWGMj+LhUOMPNhH8ShGt2DdfMxceusILGd31gWVPd/3w7H25OXDNMc/maO58m03RmzGzAVzyh/kuBx1sLP4+6qM+ajvAgRc+eOmR3+GX3/MF8PGSXHwte0Yab/Tv4IjM9CqmHGI4QOKvcJqnOTzS+Are4EYHL+78QOzXr89V41t4Y1sbbvTSpWcXe2cXPkc2h3PkPKWhl+7A8w94XNbQyRO4yd8x/HjU6Js9HFjIR+SgEzSiyQ7yh7sDODFrXnThoif2aNPb3O7g/VUSuYKu4Tc/PvACt9xhQ2kcnJafhau7Mc9iCc/yVbIHQx6HWHIfvL6UizH8oJFd4JKnjO3p8R/+xK/gi8bG3Dl/8hQd+kLMx+gi3ObwS/qbByLG00H88llx4dBFTcR30gdYvMAjF4KjIzD6jYt3fsUObKKv/vhd96WBpYG7q4EbbZBLJJKihUaik9gqNC4SW6KwoP3wD//wlmDMK1FfNO/pGlMkOP2W8CTZFn9837XGDhY3vFr4LG4KeQXqZfwal9D9HPMbv/Ebt8LC1w2FyGpLA49LA9MPFar80xcSheD3fd/3bUWfLzEf93EftxWyEz4eFDHmyhP7Frw4cCmEFGkKpR/7sR/b/q6sTZICxwZS0fLRH/3RW86yOVQM4skXFfEkn/ni7Nk8NF1wRwsPxhXGvsAp3Pxc8RM/8RO3WHzpS1+65Q1fQzXzxKaf7yqc8ChGbRbQ6AskWHQ0+MmhsJSTbLYVZObD526jKmZtOuUs+OnVRhLP4H3hUlT6KmqjD69Lg58OFPn0Yg7ccoJ5aNjUVrB6BytvGDfXfTa4FZVsplC08SUnW9hU4sHPdeEklw2/r2K+RtMnOHLhhV40ha33z/7sz36EO5r4ZTt5C19g4eBT9MKvfC0G5+sx/dJlcOb4olQxi29Frfts5IIjX1QUm4NHshiHy7hndNxdteZ7b0zffJ/9xrrANL9YoJM9nxuyC/6AA29srMi3ZvNdOsO/xj+tKWLBxg0dvmVjwabe4aADBwTiR7+mHw0bYHpRG9AT37Huf8mXfMkWf23kzPuRH/mRzR7mmJ8O8cGuDp7Y0Bh4+NMTWDT0sW8HNjY5Gpz49R5evMBDj1fRX7pDu7nu5Jt8bYQv+SNcwOi6gy8+i2f1ETr4M+6AQk7z7guvsewPF9v5ii2+3LODO1g6sjGWE9Q0chM65mpgwNp4O/zyywQHS2yogQNTm+/65Vf5QOzShQafC22bZ/ENv8MMjX35EHgwdOnXFW1s4UUnHvmfmow+6tsQnf+hjx7xDkbTBw+90Rd64Tee/dMTeHmRjv2s+iM/8iM3vfmijkfNnS87iJPX5Rq5jQ3I4JLb+krvr/ZM3W1I1h9LA0sDd1YDN9ogk0qSkCCeeuqp7VmyVXiUHI9JLgEpTiRSC7DTeEm4RHVs3tPRL4kp+HxFcnfCrNi7q63kT58ve9nLtoJHQrdAWrQvaxaRvrZI6uautjRwWxrgr/KDQlbR61kOEPtizwVm3+SaCpj9mPfm8mGHdookMaygshlTxCswFcno2bBp/F+h42fONqyaNBYKQgAAIABJREFUYkYRp9C24VUk2TDBFR13hZEvOr5S+5kteeQKhaDxNhSKQHRtRhR/4BROYMuX5czw04Fiy+bYBtUhIp7lSTLSF/rf9m3ftuEis6KTPDZ74G0syOyrHRltxG1k4Dbf3ddkssnBisf4cGgAF737GoNP8DYWZHYggB8bK7LPpoC16cpefV1NVney06u8Jf+gY2NPDrjdfQlEj5xkU3Da7KKH1xq+yJocbQJsqHzFVyyTT9Erv7Gj+TaIClvzyaKvjSHe9y3b0F3+quBnb/AuOjOmgU8H7mTFJx0bc0e75jm80XI/1KJn3LxjcMfm2syIA7q1oRKPZKdjuNnD5TDEPZ75JFp0hVf9rpq5+sn5CZ/wCdvhCHjz2BUddmZ/GxVjYsPBFf2ID+sWG5ljw+VdbcEfZjPXRX4XeHKJffyxuX6bIbT4Et70iQvvextM/Kc+R/9U+ODwjh9655v+zj/9+OUFfuULuMU7Xm1sxYp48k4GY9EnP1npczbj7AzeJg4eeYCtHKrhQeywGZ1VlzmwYhNxosGvmQvWPDjg19gIb3DgXyMjGDK6wJhrg95cNggXebxn2w3J+UbbmE29XK5Fd8Kw/w/8wA9s9p84PDfHvOigWwuGv5PZV2652oEF3/UBQnyTg385OJJrNHqFszHPdKfJsdaUPb/b4PpjaWBp4E5q4MYbZAlFMlZMSnaSh/spLXgLsoRUctrPrf9xJBe4LsJj3CXBKyglQEVzPOzn79/3vD9d7/iweEnWNvWKNjqd7RCv+sBZVNjBIrpfXCcOz3s8+/c9/FXfb4LPXO0iG1+VnwV/OxpQBCl8FWEKZv6r6DhkO7miAutYfpm25/+KQHnJ11UFFf9WsCjMFNAKUkW4DSe6Cjqx49kXQRtYGz2bOPwppPCMPh7FiuLdT/B8afBF3Bi6cMPhi5yCV3EkN6Jtw+2rjE0imjaCClc0FJdopAM5yE8SjZPDhk8hS1ZxS77iHm1y29ST07tiFC4bOBtAGyF0tfRls1lhjEc6wYPcRxY4HBLqT27jZLHhxhMcxuB0p0MXHhSKNc/xKVfBQwbykQfv9EoOOD/1Uz/1ES/gFan0isa+wd2mgl1s7Hxt9pNyOG2E6ZKsil6+h6ZcT7YKdPokBz73DV2XMZdnc+mUn3jnp3B5Do873mz++EM2hsPzbOE1nz3kZjj5EJ3W4MQ/+OiAjxa8njW4as1z8OJrJNx9JXT4wB5owgU33wbD1vwALvKRhy3I46L/ZA6GXvDonQ3Au8ix1xGftbljL7EHP7q+2tnEGcc7XJrnLrjIKzZcDofD7443NG0q04s7vo0fa+FH09W7uzZ1T845Hp/ggvc8+3umb3p2EGYD6PDApkyjNw1+urMRE3sOptzx38Wn5+FUvoS+nwHLR3RJD/TpgMRm2Lsc6QBC3NuoizcwNol8hQ7Fihwp14l7vg83XcqvLnqGK5nx4GJLcppDDvECNr9JF2RBgy/IzfXD59lcOQlO87Vo0RH7m4un5N+Adn8YRwtOV/iBiQEHcnTjrx7gFU35WX2Ejrm+sstVfFWu0uAJrxiS1+WcxraH9cfSwNLAndfAKyqXa7IqsUgaij6JQVEj8dT0ubT5PN8lMWPursbg7tm4pKMFY7y+aDRHAtO81we2/m3wfFx/893BW9gtQBYnX2LIZKwrvJL+5MFz+ILBbzDRvY27hcfXEQV3f98uecjtOd7Qxxce3SVyTXFIpsabv3Wc/xRpymMc7mzuPTpwa96bo8/7bNkkHRm3aJqjzX59xtPtfAZrfPIDLpk9Nz+c5uhf7enRAHvlY2zA1xQ5Nkbs4N2V7eNKUaI4U8hViDU27/wGXoWiItm7ol9R6SecilC0FFCKQ4UNvGj72st38KOQUyiKfThcYPBfswm26fKVWkEFn9hDm4yKS19a/bTZFwTzFa9i00ZJA68YtWFXdPZ1E6yYxK/iVHEmH8m1+MMHPeEfPfnKBliBa65/6MfPM33poS8F52d91mdtX6bIkn7Bxi9efDXBn4IOfmMOMGzOFJ7g9bOHTYxNE9riX/7RwMx84H3SM5e8GjvQEznbaNkc6FOU2tCbT2Z0FMf90qjY3xCdbyDIapPh5/G+Gn/Ih3zIhodP4J8u2YsObETwqR9O9/JCvonvZEkG8vMTFzxs7asenwkWzxoewZkLfzYzTg/oeO4C36WPj9nEst/HfuzHPvpyHjy84DX4bH7cfYFks3jGV3DufMvf/XfgwBfZG382ZYp+ftsBBp1qNgLiiAz4Jrt/tIsfwI8n/fDTifno1F9Mmc/XHIqgmR7hYHP+ZvNVs1EBr8GNRi2Z4IBLnKBrDrriwx1NsO749jz5gy9dTfz4l3OMZWs44PTeuDpBDuC3xuIzfXiHA49iCowL38balDqQ8tcR+CNd0Ik4Lkeho08ctDGLnhjxSxn5DUw8uLvY1JdV4/6BOrkonvkBW/IDm0J5TfzYgNOhBqfmr5OIUeP40MLv3xGQQ8zDs376IqfLmI063fmFSrkMHDnQoDOyPfvZz95yERgtncFnnK7pSUuP/I6v0PXUb/izbe/h3JCMmkg8y+d8lJ7wa9PPfnIVn5iHA/IYPWVv9Pmi/KXffA291ZYGlgbuhwZutEGWZCQ0CUTx5l0BJxl4lsiMSxYSiqRmsdXfFxWJTVID77LgSKwWeQXRTKDGwbYISUCSFBwWO7ASGHoKC7j0SeKKLw1t8y3W8BnDS8k/syUTXG36wWvmWqgkRfjJ5EKP7HQwk2Xzwv247/Djn4zsQNcWjpIy/ZBD84x3tjCPDvVZtCX3Ejh4uiKXPvLQrWaecbZACxx6eGA3fWjTbQWEOzxzITEPH8Y0PODHXDyhY467xi/gNqaf7tm/RQ8MWDyAwbMr2fGHZ5dWQe8ZvtVuTwPZkP18FVWA6GMLNnLKriBUPPn6J575B5uxDRsrevmuL7YKyvwb1/zGpsAGUX/xq9C2SexfizbPT271w5mfu+MnX1FMKh75JHz5GBjP/MudDykGFYzyiBg0B99tPPSRQZ+vmr7mwgkXOv5aRF949blqcNGLnKVg8xM/c9Elozs9yZU2gwpcuU+h7e922/Q7KPAVxLj4Ax8/7mQWWzY/No+KZfjQE0tkS37w8h++NfnBhgoOuYeuk4G8mjHz6MxlnN3hJp8xPsCGYhqP5pajjJMfj3IKmfXtG3gbbwchePevzuKPzswt9vlPuQFNz+iRsQuP+E4Wd7zzSz5ozOUARe7DE97xlTx8vVwDLxps0AGNn8/Cy775TDKhBZ5f+Pk+X0QLLDrhiz/z6Ijs4qd/mRkOLVk840kcwMsf2dc4vvmWDTm/MZfv2AAboyubD/6DdzFMd+yOnxpYcL5WsgM85KMPmy/x718y5qPm4cehjEOA+UU0nszHlwvuaNERnPgxJofYXJY78EM/8ICxzpABPpd4lTMcXsHJf9z5mIYWOfmUgwey8iMX2t7BO5DBu2c0WjPZCR2weOAj8pODmWgYE082XTbC8WAe+vi26SQnP0PDL9rMZ0O+SH9sYa5fRqhXwJnvSo/o4NOXThs3/IlB9hDzcDisK7a/9Eu/9NEvLfADHr1v+ZZv2XjGO/zpGW6xZj47sjkYvKDNL/2MXh1B92SAlz7h8cxefJ3M/poIWePfuGfjvmrzLzlQoye0XJ5tbOnFVYxEA3y8x/+G5NzmdM3mLrzhX+NjdN764zAJL3zKGJ7Jxg50wR/9GmLSmM/RXPelgaWBu6mBG22QC3YLs68kkpGkJ5EbK2E5NbSIWGQkWElH8Si5KOZKaubo8zM4xbAvIi44jUnmTjYlcwlYwSAJ67e4SFISr2SMHjrGLYAWJnMkvRZpi5k+J6cSsaIQHQkVT+jAhU5j5sBt4XfSDY5cFlKJ1aKJZ4ugQghPcN5mCz9e+trgdFex2qJi0ZC0LVIWMAuIeeSiX4WY+RK8hdCzhd6CxmZOT20q6NM8drKos60DAYWJAsAiSzcWMkWPDQ/c9Mg/9FmcLSpg2ZOd6By/dGqRp7d0Z8FBh094btHDC1zm5HN8AX1yWqCM8wnNGNspChURilX6SX+3aaOHjHsWKGygMLap4mP8UEzxHwURP2FrRRs78wE24pf813w+xb/yD7plfz6FlpYfew4HOyt2bMDqK9bhc+EJjALfXSxUIG2Iz4so8xRn+b0NOH+r6EPLXDDigyxgfMFUhIodtOQNcslPcqI5mjE8yj3iTzHoq7ANEh3xfXP8Q2P4Ro+eNO825fIRPDY4Nkx4gVPL57ONvGWOu/9jlcxo+UktubzDCx4/YprtXOzJXtaBz/3cz914mzrr2XwXGmLcr47oBj7+QD55imzudAOeXOiBw4v58T3lwIuvW74ek10O6KA0/m2IwCnkrQv0Cz96yedOb+4aWuxn48V25oPni/IgnPGkzyUX1od29N3xzA/kYbB+NaDY5sPRS67JK73gFT/GXenBvSZn4wlO+gqnu1hjLxtU+oEDn+VU/kFO8lsTHaygCS79GGOv8nHyRd8dDHvxbePiUUxZf1v32VfDu006nU45zPOOdjQ8uzR5fK4fbA0Hv0Uf33jo2TrDx4t942SiY/6rWReS2Th94RevZIDDRV/GXfweL57FGjnp1IVX9NFhXz7nXSMbftUZ/o0AfFTrgE9WMrA9e5qvGec7+vHGl9gDv+lqAzz/oz48savcwUfxwB98zbW24gVuX6LlDPnIHPM1+PFIh9khG5mHH/j91QbzyMef2jirS9RbDin9+gRN8SonO4yhI/WUAxr44jtZ9NE3P+r/gsaLfnrFE7mq6awJ+uIxnYI/1ODgUzbg7I4vc5KXLehbfuEr7BGffIL85KVHcHxitaWBpYH7qYEbbZAlHZfi5UUvetGWNBQ+NlMSijFfJb7jO75jS4ISusVLIpGULSTuYFtEJGDJ3r8Aa6NsUbLJkSgVzk4vbaz8ozH648HXEvgUXGBb5N0lTBtHPytqwyfRWVArVv2cx0k4WAnPIuikUJKzSFhQNP348i+u2gRL8ApWCwHevSuEP+dzPmf7aacNOB5bYG7LTeiPfhWZkjP9KJ7RluCdfCqyfSHCo0Tuont8G7fwzAWETBY2C4aNLXiLGv1YjNlIoe/vRjoUsCCynb8zZhOjuGYT+okG/fupoC9dbA+/hieLCZ3iyaa3xSWdmos2HtkBXj8T/PzP//zNdjbC6PAP/8WOYohMFmN+pCDEs2KGfvgDXGzjvtrtaGDq1rMCS3GhOYThSw6T/MRYwcGPxC3/4wuaeeJKYWJT2hfgcLOv2HevL7vyGXjEAn90gGLMJW5c5ZFgFcwKJP21YOIHLXw72FFQ8UkHTppYxKt8J3YUfmiLD36LDnx4tVG0SVI85vPRhE+sgINPXIgZxXQFGH2ZJw/bkOAdP3Qsn3VQBgeZ0UW/5pmu5Tn8ijf5kS3kc5usdJTOzIGP/PIC3uRn9vQObt/wiAa92azji3xswyfELjrpUA6hq/IKnXaoNflHJ13CZf1RGONDjqcTtGyGXexkXTIOD57CEc/pyN2FLzLKHXyT7tkS7s/8zM/ceDeunx7gZk+2az4d0ws9ZAe8yFHG6FNzdxmTl9mDf9CLufHkPpt855DJl2GbDPku2wfHrg6IrW02MvCzh4YmnPEGFm90aowO8hW2ABds+Luzr7xrowKnNb+NBxnohkxiFt/grV1sjdahNvs9481aR+/WEpcNjbqAL+GbLehP/HV4bq4rO4jzvvrilZzGyYY/m0V+hzdyGNfAwi3e2B1+/9Af27v2toKPnPmdd76knmAvOY0M/BHueECLvtGm/3TuWZ+L/7g08/ZNX7YDFx5w+CQbXxX/8oYDFnjRMpcu8QQ2vUXHHRwYPirnyBl042IjH1DoiC7lFLTEktxvXVdX2CCbB7+cKYa15E0neGcDtJ773Odu9PDQOHgw7AFm8tnzXj/e4RTPakn+ID72cQk3++BR7iNrekdfLMnD4PjgaksDSwP3VwM32iAntoXcibiEIKlKHpKNBcxi7ZKAK2yNW7hs2CR+i3iLQqesko9TRKemxiQe8Da0ikQnziU/idkip4D2FcYJsL93qDnplpx9OXTqKAFbtC0EFiU/b4QXPptZ9CQ6sihCFRDkK/lKvIo2xQeaijGbZ3PJYiOevBK0BbbF5aLknC6vc8ebS1KucFOM0zN+8UV2fNnQZge6ZiM8W6TILNm74FK0Wszo0EZUIUMm+OhUoW4Ro7OKLGN4MIfcFj4FC/uzgSKITi3C7I4XRYOClb0VbmzkJJ7dNXd6tKG1sCow8E0mf6fKIgVHxQf/UIzxB/KzrT4yoAOOn5Ax3d2Wba5jz2fiHPrlV2zHtmygT0GrYDfGh4zzO+8amHybzcSe4knByn41z/yEzzXHmDzUuzE+zL/4o37+AcbdOxzebQ7yv2i4g9HQMyd68gFfl7fgxqf8Ig6Dc+ereJcvzNUnTvGEN3jR2N/1iRe5Sz6CX7yKN/zSkZzKx20YxJdNiALUV1V05UX6hVvcimWt/G2uYtZhlyJPPxnkOYUjHsjWmOJXISkm5UE8KXrxhFfyzGY+mcnbYQG8YhlOPNE5vjwbs2FA02YU3+KYzuBy1TzTvcMGOQVtOpGHbLT5Fr3L+/ptavgZ3cEfTvjC6w5Wiy86BCuX4dsGx6YbLmufdVAffdvw8HM0XOGFjyxw8n8bA/Bg6MYYuvIkfeLP4S0axrT42l7O/4CPX9mE2HTJjw4c6XPShk8s0YcvydkEGvjpig3Znh7zcfyRXWzSKft7PsQLGnzQxU/5Jl/hU/CQy+EFfm3GogPXIXxTzp7FjJ8L48G6waesE/NnrWizC3+kF/pNz3g0Rt7kNp8ek5VdxEz+kR7d0c0+nsUd+ejcvGCTxx19+kxO8HD3xd9XUXPBNc84OniytuLVMxvhTa4iu35XdNPTxIOemOITxSfcYpetHGrLr/QidvSJbbVWDQ38RcfdRXd8Hv9kdFDt67C8wv5w0LUDSrUDGuIQrNgt7uHxSxQHdGwBdzJ4zof4sLVEXMyWbvEYn82fcPtnOhXT9Kpm0dRL5K2lX3ByEvzZ0DP5xAZfoIfVlgaWBu6vBm60QZasNMmnhVMCcUkaEogNjOT9aZ/2adtXVQlNIrHxkkQkZ0lwFm6Sk1N5RacvgZpCwtc/C54iqJ9eo41eCVDS9EVT0aJf8SZRt2F74oknti+rkrHkrJj55m/+5q2gUDAoKuGy4LkkSxdZu/RLkBK8/8/RwmsBxpvNop8r2ZxZLBRUFuJ0tQlzC3/EG17pFY/6JH2Flp9w2YhYdHxxVcwrQhUtFkELmWfw5tIdvhVmimYbW1+fLZYKI4cK9OTE3AEB+2kt/hZtG1O2Qgs+RYB5Fkubc/pR4FhIFMjwogPmi7/4ix8tMGwPVnGMJ7j4F9v6Wk02RTH7oc/H/PciCjyFIh/iYxZqi6kv2HDBQ9585xbMslAODdAz+4g9xZKc4RCKH+pn2678OduwleKDb/Idtte3b3A2pztc8Mo7YkPcs7t+feHhs+DgEDPiGb2JxxzNvXlwySNiAF98zRx3OI3DZcPnEkPyhH6bT/lJgUoP6Gt4gN8FjzF99FYBHRw8aItjeZWePPN9soo3eOVa+cHGwibCZlHDm6IZrM2aL3sOq+QEB2fiz2YLDy48K3LFlw0WnugVH3DAK9/bzM+GfzmTrnzhlm+yCd6Ma2KUHuHDA96tIeIaf/r5S7YwxzO9yDXpHj66LRfiK78jExyKWeMuePHjMpfe8w/4yV7ekH/w5o53uMlGN3TRQSt9WCvYkRxo0Dfc8qFcxHbTx9B22WzLi+zVLyvyOfD4b146YHs51waEjeBok25uOqJf9B2O4g8e/JtPVjaQv22YJk184V8f/PR2qMGR7ugDrAtOMSDXO8S0SYZLfs8OaMyWPqasntlSLvCs1nBoARY9/AfPbuxDX/KNOznB8UG88i1xKW6M40l/cuAnfPo0tiQLH0BDsxZZM8OhD0/NcYcbLs9yoXgTK2xj/Wtu9MDj3xdN9Uk46Ysf4YHf8ZXobEDnf9QHH73jUX7TD4d4dJBmY2qjSgdi29+p5ZvWaJt+z9E21wVn8rAtedgdXjkFHu823eQTj2zF1/i1Wo/u1E/kpitw1nO2hVuLBnpsJq+yMRsm3wZ4/gfbmCPm8B1MOgUGBs3GwNPzs571rE0XDtNd8WAOWPKIKYc7eMALPsChJcfu503e1vPSwNLA/dDAjTbIiSgxSNQWCglIEpEwJEKbXAuPBUBxIYGAseArZmxaFCnmS1aaJGXTaa6fbr/kJS/ZFjSFiA2ZokJyQgcul2Sn6LGIWKBKigpGdI1ZAIyhba7Ll2anthYZC0X9Ejy87iU/vOFTvyIBn+ZL/iV3C6+FwoYczhJ1urqNezynBzyjq+mjX5tcfRYeRY/TY/MUSmAs0hYvC5R3Y+505md7Cj0bzSeffHIrqBT0imCLZ18DzY0mfSjCHWTAkR4UZfSi0PUTcIUJfvFDjw5U+vuZ/AoPdG7htvBYdJ2iK+r4AzvgTQENVrMoomtjwLb8xxxzLX4OLSy+GhpkXe32NUDXU9/sxX78RtHBzsa1bDLv/MSlhWt7Ofdz+MA3B0w+yR/5mELMIVq+oh9tcF18VS5T3IUrPGAnneRR8Ml34RUfinHxBQe+FXbucIgBGwZ5wl1ec6ijOEyG7vQir8hP6KCBR8/4926eQ0H5D69+4WGDLA/aMBnHkw2vIhsc3s2FX04EQz/6FNLygThTTMNDFnwYw4u8Iu7Evxgnl2LR/VCDl/zyKb7NYxNz6NudjjTFs+s7v/M7Nz5thvAsj2cL+LKPOfrp3EaBX4ElA37Fvw0UGelNjrYRgANtutDw5dn8nqPhrq/GBnKSTR6ZvdMx+cCiI0fhCf/4gZe9ySn/yUfm4kMzj47IIg/yEweQNhHxARaPePE858Ilj/Jx9rfJ6YsqOHY05tLog37QhD988BtLR2TDkyvd8Jt0tCEbf8BjDjz0blNGV/xcvpaf6cfGTD863qOPF7TiCa4aGP3zah7e+CT9os021SU2kTYudMkWcNKvtdEXS3UCeeBNt9HUp0XHM/4chrurK+B0uOvv39JzNjMXL+jFs/mebSjFALx4AbOXlU3FCv9Xz0QbzmwBB7j43IDGH+nM2shu+ANP5/n/nCvO8aY+4wdk4VdkdcFXSzY8uIyTQU6BBw1x4aOBAxmykMOhCL31QQEf1ma+gDa8WjqDE272lXvEtr9yR54aWJtW9Pi4jb36M9mm/eBLL+azuTpDTQde3LpPPtD3Lk86BHMX72KIPvMffl0ui7d1XxpYGrhfGnjFan8Nvks6EkYLSslTUrQgWfwUIza0kjLYkpSkK4EqjPS7NAlP4rHQ+JJhcZV8FAr6FE4lxeagix/4ouNdUad4syFyb2HAA1hJDi7J0qXFo/mucBuzwJAVPy1uJUV9cErQ8eUOx202smgl6PjXh7ZEb1Ehp0Vr8mcOndiwJpe+GlwKaguXL9BswV42xw4qLEJogKOnrmwIt3E40aUzfexiIYxH8HRLFvjNcXm3+NjsKiwdmlTQuSsQ0Mx26JiDDv4spC94wQvOvvVbv3U7BHAow7fCP22bzOt+OxqYfsVGLvZTnPOfxtmGXTT9tezM1mAOtXzG2LSxfKSJTwUfup5n3uKXeFLweOaT4YsfPE7a4I3BA2+xhW/5L56bD7fNq+KJT4vN5Ilfc3tODvzyfT8lVdDboNp0mauwJB8a4lyc+DcSxJO/a28zQscKVMWoXKgQ1MyRh+VWcecQyb8ZAY9+sA61/BdRil98+Wsu/lFEzSGhjX3Fc3GoP5mzobme4VEcwykH0JONhV8IkUVxiz88fe3Xfu0m2/Of//ztAACObOB5NrawOfa1Gw480JVnG1WysytdKczlII1u2A6+rmkTMPqzCzqad/OsK4p+jQ7pDE6Hf+ShyzY37G2Thie5eC8L3GjZLPEPzV8tme0QL/rii193QOgAhn7xpIFJDjK2dnnmL5px/PG3NoDkAUN3xmy++bAYKW63yeMP9pd//fIID37hg68Xv/jF2yEt/fNJ+omveAsNuTTj80pefY3rs0lVa4gXtnbhk06sVfyBzWya+DdbkEXciqfowTmfNyLjD/rAtwMI64n1ES82RmIDH+xfC1f89m6czHRlLD17BuNuzKGCL+78Qst28gnf5vfwHGrhMCb22KG/hpCN2IeONPbkzw6/5RkHHOzMh6yr2uS/Z3f4xAA/51v0ygfgwL/DNrmC/vFiTL6iR796wKt/iwKdQ/IYp1s5CZ/7r+bmsKu/VsLX5Bd2iEfzXRpea/WDI6sxuIqPOV9MOwyVx+mJLdiBrHzNwSo5+dlqSwNLA/dXAzfaICe25NEiKbFINu42xxZUCcfiW8IDD8a7C2wJCM7wdepuvkRlQZWEPAdXsrOwhCf80Yi+e3Qak/Th8x7vYFzx21h9ZDXHggImnPGER2P6zX06Gjro4W3yrV+xwA768ZWt4ts73RjvSubmsIUrOhaGFrH6kjN82dy4Cy66ic8KsvTWfLA1PmRj/tRTT22nz3xAYemQxELs8MUiGQ/JhBbbdihiM22uxRV/4PZzornut68B+nfxS1exy1cVG8UsToK9iKvsOWOYfV0KGH7PZxVuiifFmIMfBRQ/NI+vKZjBKqz5GZ/R4OFT+Uy8hB8OhTmcnvkb/1QwyU3xZ9NjMygObAL9ckOxp3i3oUNDA19TaIk1tHyBgVtxKQboTsGmIPOTXxsOspFToWkjCL93RZ14sDlVoGpwuuAUZ3TjZ7m+oij8K0DJhmdwbfrox5dZGyaxVl6BV36k92yX/OSjX0WsO73Tkcsc8tGfOGWb+INfnzZ1s3Wc/wE3GzvswxMb0h2+5S76hhtf+sCjSSfg8I+fmdsn/mRwL4fQP1rm8F0bGPaXefeiAAAgAElEQVTW0NTPNnRvY+KLJvqKdu8TZ7TIbGMKVn5zqHdKg4tM7n5N4KDAxsMGEC36uKihaz4dkI+O6DwbuJPReOumZ/KQczb9+PdvfNho+2LIpzT6gRuPfNivFNiAbdDXzI/u1nHgD3K6wLrjTczxT37Kx/QZ8ywO2AtfxT5f7xn9yxqe8M+mdAuXX7v5So+WTaUDJPFMd+TSzIvfSaM+dzCH5KZfuYlcLrDkoKt8TSx5h2PfsincnuU0fMkh4PFdXIDR5F85wl9Xc7hsgy6HmIv+vtWHHznDv3fCFjbYcoT53tmbzmyYHTDQkblsw2b+mzK+4ZcGZNo3fuZn2figEz4dbbDkwYODfLLMnLTHFfzsT0f64E136TV94d0XbDKB0dAlm6ucMHGv56WBpYH7pYFXXtWuyLtkUeKQiCQK75KM5KJPcpcsJBIJRIIrCUvyruBKTnBIlooEBYfCxjwJyd9dU1xIoubFAzpayTIeSnLu+qIN1lw44DbuPRzeXcEZq685FpF9A4M3Y54nPbD4pAf4LADR2OO5zjtcZJny6LOI4MfGQyFK5xbE5MWPIgVvXeib65LwFfaKAgurxcG7nwD287/45QPx4Nn8Gl1UpKHdwjJ12xx9LrQU/f6uo6LD1yoFvkLY4mtTYOEGqzXPou8kF59w/h979/Vs2VGdAfyIDCJnkbMAIQRIIKIACRBCiULYODy4/Ef4nSe/+83lJ79QpuSyDZgoLKIIAgkBIiNyFElkRJTr1+gbWlvnzj137szcOzNfV+279+6w1uqvu1ev1d37XMY8WiZrfVEbkS3tM8sZeXs/sgjAXN/T57QD48+Ogh0RxhkHLX1kE0nQy8V40j+0Ofre7VwwlBlr9AsHRhxDUX+Rn9HLiONQiDdG5bfDou+K0/fSX+gCeYwpTpCg/xtrDEVl0qfT1/RFuxvueJFDkI+sys+B42zHBx7oZfwai8q4yM4RMlYYnRwr+cST2c5Wxqvxo/8L6kEncEwdv+YkM/h9VqFtjC3YqKfL+NFGeBuLnDC0OUDaLHVEW12UEYeW9jUX4AdT5egl45TsdD3nQv2NdfpGe3HGyGHxgmHsHc3gipdx752hDU/85Akvx2fJK4+8aKAtnx1auIqDB5mkkX0Zwif9DD261Lv6wgru7vSMemgL9dOO5i/tY2FE26M3h2AOB3TJZv4TlnmX72QgMzksLnD88NVGnOzMN8rJuwzwSj1gYFGG7MmrnDzkUUftlrglLf1fuhNgZPFZj/6uDdN2cIAzevjBEmba2tgR1CdzU+SYeS3j1F0/0++1vUtbwFWf10fhoR3ks8MOY/05tGZcEzfzpAvgak5yAs140s4w8dspdkTtLupTxi19oc7COnr4uaTp+8mTO9lhhZdFBfF4uaNrTMLbeJI3427mJy+MyeSeC9/wTLvI69I2+NHH6CsrD/qRd8ZFnDRtaHwL9K5xpY3hry05r9pEfWDjrm/STy5zOv0jLgHt8FSGTiOLeiSQWR6ykt34mWnIl7qlzPI+85FXSH2l4alPGrvaPXOUNLzU3Tyg/6l7QxEoAscuAre1xHZYj1mZULIUKKVCiXg3wVIaDCpKw/cgjJcoWRMNQ1UeSo0iyiRp1dkxQROBb+goQke1HMPjlDGUTBpoufDFM8qebBQZelF04qLsxElzJZ/qp07ihaSLn3llggntGTo4KJd6Kpt8JmUOpzvlSpGTeeY309r0GX0XudAT1FUczE1S8OYw4s9YU295KHNHz0xa3lOO3CZcO1ImNW1lRRkNho7vkxgXjOPUD00ypP5pG/Kgp61NXKlzcFUuZaVp98iGn6Ofjos6Nm2BRB4GtEkoNOTH17tJncHP8I9x7PtmDoR3q9Bkljeyb4p18x0+BLSZNmBY5Agxp1Zb0x/pJzhq24O1VdLk01ftUlhk06/1QzqGE6Y/6Tf6PSMeD/3cmDRG9EP9zZjRjwR9mqHPsSVXeLnjpzwj2+6l/s0wj3MuD/7yoWFnl+FJf+GNXxxRcXGYg7I4fZYhKaTPus9OJCMdLcd5jVn1VydOAUdAGt3JUSFLAqfV+Gdo2wkjHwMwxjQdpSz8GK/aieF/8cUXjzoyai0swIXhqr7wEPBRd3i6sruNDvxhpX3w1iZkQ8ecYez67Qq6Xrs5Xk5/aAPyzH0DD3iQWZ5gDm9xMFQn+VzSBfEMXWXk02+ke98qqBss9F19hP5TFo70FiwtNMT5Qp9D5fc0tIcfSVQvdYgcZHehS7fCg1NrzoSRfPiRDabyZuykLHnl805+zq0+I59LmguNhDzDMmmRg5NKXwryiRfUX1tqr/AbCdOfyGo8OH4MAxiJj/5XFgbqE/m0O73tnnrATruk3thIc5FLWbjrp9qbbtcWsLZDbIyj59L/2SL0ggvdyy67bMyHoRfa5HMJ6cdo6u9kNPb+9m//dmCcOhl7/r2VUyJ2Q+kNC07qrq7hEZrhlXf58JqDOqoPfsYCmWAvSNNX6Kn0J3FLub1rY3LCSxvKh9ec1/McZ3xoP/hycGMjyDPLmXqQwWkT+hsf2MNMu+jPsLdAwcm1aEImixV0l7oZo2QjB5pziJwwDZaRQVrKkFW8fC5yeEfPXdlg5x7Zg1v4upNFnT1LT5pn9TJv4SsfbLWLeHHaMvnX1WOO63MRKAL7D4FdOchzdSgdCiJKinIwWTAETfhWxClOR/4oqawUWmmUl0EZpU+pco6tPjuy5B/WC/KadDjKDDGTLlqULCUoeM+dLC7KDO3kkS5eXheFGiNamvzq4y5QsvIL8skvzfMyhCe6lCOeoScvAyiTBCOGAQQnRuFuwsw39Qo9kw8ecOXUMhK0A+OOYS6OgcugV6cZJ0aF41JWpxnpfgWc0cHYYyhzOjnbJjx8gxO8gxM5Eu85k4Y4z/CJ/HB3pb3QgJkJKMcltTfDhhxOFZB5pi8vw84qvvKXXnrpWGThMJicTdYwMflHnrn8iOyfo4KAPsOg8H2afkAv0BkxgmYhtNHcx+Y0z8pnzHlHg27Rb/Qn/YoxafzpAxwI40659EF9i6EtXr8WbzwoixYdNwf89Df0jSs/vCQf5088R0JZ+cjOCOQEGnvi1MndAhS5GPeOF86BTIxT5eUljwtuxoF08nJupaOND72LLmyNIeOAzpQmyKt+9JDdRvVltMJGGvlhKL93OgAtdaWX1Zch7JtlvyZPJ3snjzwC2njQL6mjhcFgT2+oGweYHrJzlMUSPDkv6pNj8fJxFizMRa7BaLUaY10ZF570AlrmFu1u/FsAsYtFRnV2gsDChnoqI53O8K7+4uAuoIsmZ4QupTu1Mz2oPfCBuTRlOfLqD1dl6SQYCfpI5NQ/8Akv/U8/4lDYEZcPfWGpp8gZ+dKX5BenLGdN/3elLD7ypF+KRyflB6Opf0ROdyGyupNLXdES8EVH/SzMvPrVrx5yaEN9UMAr+dEQYI6O/qyttE/kgjM+8rhLI4tnNPUJc5c+Z0x41y7mBuPPHR+LM455G3/6moUMzxakyCvIh69LwMflHf8sBrFFLBIZc1nQUndtZ4FJH7MIxI5R1okUY1Qe9PAJXXzkSXnYhL+0tBOaWbALlsqorwtN70LqgUeCNHRh7NI3YalPuyKPPOGPjvEqDb74wB2tyOldUEbbym9MGRfGPbntIOuPMMRXf+AMc/otJlhwoPvofnYiOvpD6Eb21Cf1lC4u8WTR9t7N73RL0tzJnL4eucXnkj5jBxf5yOKOL8zoCeMcHsqKR1d+/U28OofWqMitf4LtHNfnIlAE9h8Cu3aQKYAoFwPfu8uEY6fParnvR60CU452bygYxopVdmVmBRSHzb87oGD8GBSjjuJxfMmkxkmmXCliypRyQjuyuEfRRbbcxUe5RlFRfpFbPiFyoZv0TMxpRhOGcstAVvHKKR/e8qHBWJHuF1pN2vllZfGHEoJ7ZEVDHcW7w4nBxdm1Wsu5ZYyb2BkjjF55XQnkY/Bxjk1yZLN7bLdEu5io/aq1iyGEPnqhoU1TdzRhQBbxaOeSpkxwh4++I584kxyDHW2ya3fGtQlKn2IQJS9a2sQRtyuvvHLksVPD4VCOjH7R2o+oWJjwvqx36t/7kUdA++pjDBr9SbtnB0af4oxmrEaa9C9l8yzNe+L0B/1GG1sIQZeRpv/LQ4fQK8adsZHy5DAWOH6cSUbeTJcBxOCa+Xomo4vDpp/qw/qhe+qQPJw7O3Pio4uMBcc1GfOMx2UIj/CN0c+4pE/FqyMDHE9jggHKWDdOxHEMMg68z4HcjvyqKzpk5QgwAGHJqfNuYZL8FteMK3ntjsGV/H58iZ7geOIdWupHF6ibNtG2OV5JZjhYuPJjevSShS98OHc5Fo0WOha9Lr/88mGEkjk79uSkH8wfnAc6mHOsrfUtdeJkw0zb4AsjDrI0tOFITvjBYtYr8JJHP2LQc7A4ZbAke9pSunzRU3jTR+LsxGkP9TOHmdfo0tlxgpExkGOk+ptAHjzcI4t6pF+lbyTdOxm0rX4LP3HkkMdFdnfx7vq/OQtu2s/8bcEkQR4Btvjqexwt/Vkf0fbkJ6f+jT/dLW/kU375jC6sycNh5XjqI/KJJ492m+uPjrbiqHJCI4v6aRvltI/2ENSHTmBz6CPkIrN+ZuGCvOYVdUhb4o8eWvSUBSLznfpy5NgjdETGU9oCHWnKk8Wcgw9nGl/toc5o4xU8lA9vdYNt5LfIYly5lNUG+Lr0MflgASv9Fx18QhsG+JHDOJFP/dUbThZ29MO08QBtmpuDgz5nfMmXdpWGj3fxeNBRvj83R8PDWBNiS6Wu5KSj2YPGNmzpCHSMDfKhnXrkWZu4UkcyoI23dlKeLlZHvISlzMonSBPCx7M4V3SP/N7JbFxqC/0l9dcWnl30GIzJhP9ShvDtvQgUgf2LwG0tpR3KGWVg8mFoUOieBUqEYrGaSpnbDabAGVIUiYnUjqQJkZOmHGVnkrb6Rrn5xWqr8JQcXibOCy64YByNMiE4KsxBloYW5W1SjmJzp/BMLiY0ky6+4uY8VuopOkZJFCAZGV/epVF0mXRMchxFip8CnwPaFLt6mXAENChXaepJscKGYWUCZcQwGJU5lDDXhUwmCTLAJTLDUfsIWbUle5S9lX6OMKOIIUFexhzjjnywd5E7Drd2MtkyTrIzZSKEm9VgdZ2xxgvGDEAYJk28AC9p+ot2MqloO7tFjtkz2PUffYPsZGQkM9BNWN6VJbM6kkH/i1FCfpOnycvCBINFO8OPLA1HFwG4G7P6l7bnEDlCb5zqg2kT+QR3fdo9cZE4efUZ/SCGE2OOnnBx8ty1u/5kzOk3aApxKslDj3iPYSMdD301vOa46C/pxh/HjHEoXiAPXvSbUxfqqO9FrzAo9VO7LOtC6i7Nzgxnj1HLCKNPyMnxYsgbl/jTt3SKsc9gR598aMnvnvrAgGxoaxOLSGjAAh26yvijR42p7IpK58Qy6JV54xvfOBxB49KcEGy9k4MuVcZdwD/GPhoMZXH0id1k+chJR5NZv1B3jgo9QBfRSdrZuCcnDNAQ8KeP1D26y3yiHKy1gcBpgps+AkcBv7mtxaFHL9JPdgXJQOfGodUmaVO4occBseiiz6FNX9oxg7W+mN1FMuOpn7iTLfzd4RCnzy4VPQazBOmCu0ufC030hNyNkTnos1lgoE/hab4g6zLgySnTbvqEBQP1z+5feOQ+l49sifMOU3LCCB0LCcafQE55YMmRyriWJl7/JwPMzBPGLKdV/5TXsXxtIE4dzXH6t0Ue7WZBzEUvWAzQ79ASyA8HTpux7Eg2W0W/pqvoDzzmoJ3EmUedstBP2D6ceDzIKI2+m/EJDuqr/8AdxngrR4+wefQ9ebWB/gE3GKDlri7yGGP46PfyC3DWZvq5/GQzj3Ko0Q7Wc32U0Q+Ug0XGBt5oqK87HtK1k3EIc5e+DWs6KjYb+jBCQ7s4JWLhwthVRt3JRM9kATP9P3UhjzkeRuLILo4OgwG9wQYy5hPkc6mT/O7e0RZyT/75TiY62mcf+gC6aKT90YIlDIKNduD0i9eftTnMG4pAETg2ENiVgxzlyEm1U0w5MMgoiKRRDHZrGCMmdIqPcjE5mcSUMQkwNCgbk5MVRBMlY5mCjxIyQfrujRMlv8kQH5OEeBMLRexdiAwmtEsuuWQoqBhc0il2tDndFJeyyrgYYhS0ycREgyZl6s5Yc+ybco+hl+ZWd8YgZajujDH0lA1PZeChfpRtJh/5DjWoC0XMCGOomLwzgaFLVrs95DLJaQfx3uFqIlLGxOo5dbXwoL6MF/HBVhs4TpmyJnD85LFbpX7SyJSgLDraDU3YZ4JSltPAqNCO8sivPygjmJxiLOhz6AvqwxlGA/76FQNGuzHKI7N3fZFhKo9JrWHvEND/9BOOkPGu33BmZqMm0jFG5Hetazf9KH0p7c1gpHMYxYwV74wmfOgYfUufEUJXP8Zfv6UTxB8s4ImG/oSvd4YgQ4+zxNjHhz5RN2NMvHEfnnGy5DUuliF1U2+8yIVmdAy9pW5OTdClxja9Kl496QN6WZ3QQE9I3chNBxlHToxwMDlJ5HIx8BminAc6BN3oeGXVSxuibWeUgWpsqmv4GNuCcuqAd+plvIuje5SRFw/6Ux481Bc22sfFoSUbHYKWuimnn7jQUka6NPi70Esbe0dbfoa9z4AY92jDSz5yJXjGEwYcWzpKHjzSZ8iiL8PPIpz8FlOVoUs956g6x45jYH6ST9lc6Xveg5O7/mzHkw7jJBs/eCeP/JFZ3PyuHqHvWbq8Lv2FE4Bm6qlPWCSZg/LaWR+h6wV9nfzezTORZy7nWTmOHb4wT8BPGScctBndrv6CvPQ/3S1NXjII2pUdYS7W3tpCubS5O7zUycKCBRxt5vSDi6zoWnSVT3/wnjmbjPo9/WFsoa+chVZtljaKnOqnP5GZbGh5d+lbLk46+dPeoyK3/sFPWeOBzPQI+yM2hj6qvDzaRlBH5eBqHGoL+TyrjzGUNiMTe8pCgLGknuKiG6UF98hFFn0ADnSMOdjYpkfxRjv09SF93uKPwA4yTuTluEozvsifsUcfaHttqy2NYZ9iqKeFDJjObe5dIBcH2e68upMBXVhIM+/DH/2UUc6zfNpkDsovg3xoWSCwQKIOnOTgLU7bkA892MlDdnUSr2+pm0UCY18faigCReDYQOCwOMgMArsIFAejjVIS3CkOk7g8lBalJ57SoJwpIIqVwhRv0qLAZ1pRfhQQRyhKP8qGImJcUn6ZtCi3KC7GDgMWfQotadIpeU6lZ2lRlGQjh4kITfVQTqB4yYk/RTgHPBhC6qyceqGJfgI6yjGayIwW3A41BB8ymrzJHMUd3mRheDAkKGp58FYHk7WyMJI/ZbUZhxVGJlN1CwbeMwmpGxruaJgUGeae4ZiAh3jOdSZIMuDpyqQuDn30xKOjfRklJiB01ANPefUJeMqrngx5bYCGSzxank3a8quLKzwiY+9HDwHtoj/o/zGQ0iazFNqYnohu0f7rAnr6p7vAcGL8WVTRL+ze6PvaP7ojdJTDhzzGLh76xnZBHv2Qca+fKmf8oEEnMow4DuI8G1Pkod8iq74sXf9nTK4L6cP46dvKcFKML7jkR4PQZGTSkeqKprzkQiMXHsEpz+GhLchOFkYxQ5CuMnY4sNJTFl3jCqbGtHrAkbGdMYn+jDcZXcEXFuYFZegUbQDTGP+RGX26g5PDaIWldpVOjjhS6OINm6TpVzMGqbO7QG551EV/TH1ST3k8qwdc8UBfXdOO0skGfxipg7kCLQ6//OK0h7pySPFL++CBnhBswl+8OPW2kMGxJie8tJN88zWILP5IRycyG1P4u5NLnyGv9PCNHDMp7TX3X7hqK/OLOzzWhcST1zO+ygqwp9+9exZSH/XU1nkPHXn1fZ/KeHaRV7o6CMqRy7s2saOpPYxJssIeXW1h/OpjaIhTxli1kCFYWDO3cRD1A3kS8IxciZdHX9C38LOwoV+mLyoLxwT89B99mrNonEsnj/laORc+dBv81EG9vbuCg3qnXwQPbWxuNE6MMbzkp0fwICcM5iDNzq7vqfEjDxz0E7yF1Jts+MIX5uTXdmTnGDs5Y2FQm0nHU3DXDuSRn70mDi3yCbCBa8rglTErj2ftqz/DmnzaSZnQCB154Ut+6Wkv6akLfp6Tx3glG30aOvpKypBLu8ELvZTX3oK6RB+NiP4pAkVg3yNw0i3RCIcgaorOd4qCgkgcsp5nReQ9l3hlkmcWQ1xC8lN2iU9cyidv7rMc4R9F611AY35OWfnQnfOEnzhlkp4yuWdSkydl5ryJiwFPCVPs6jbnC73t7qEnn2cB79Qr9znfUrbgkvJoZGJJuRmTJY/kEa8O3sND3EzXu5D0yCcOD3ln3snrHrqehcgk3oX3TG/OPz8r612Y84+I/jniCMzYM7yEjG3tkTaRz3jSzo5y2h1leDGmkyfChmbeGSuMWzsqjC9OK6MxfORDQzn0OWl294xFxlWMm9Bbd1eWE2mXhfPIaDKucyyR4cdwSr/Ej8GoTsqKZ+y5R57t6hU5yOyS366tXR781ZMxF2McbbzkW9IOLXXnDNghIQ8DVvnEcfDQjJGHDpqR2V072tVxZ4yTZZkn/Nwjk3ayI2r32W5gjNc5b55n+dVdEOd5xlA8+kv+s9yhKY821Fey+8MgzoJH8i1p5t09ctmpVBfy6GsMY4Y7TNNW8mr/eZEybRSa8oQm+WBqntDPHEO3swgnfU5bxVhXZllnNAXxuYKdT3yMKXRm5zi8by16m9tcjySEZ97Xlb/55pvHzqgj8pws/TQOyCw3Gt7nuDzj4zkh76lP8mUOtmtpXGgDjpNFgIxHNJQzXvV/eiGOLzpo2Fm2K+uZc6xfL2UOT7KkD4Z25NS/9At5yaBvoKldlRFvx9gJDrrKeNOu+k1oyRP6eClLZvFouadtxCev8vLCgS5EF/+MZbT0AfnFKSuINx60l34n3bjgZOt7HE085RPwNpa1M5zILs27RR278GhYNLBYyREmF55zmGkmHh3xgrv6kk37okkWY0A7u6duyY+PctE19JzPTpIvddAXwp9Tb3zQAxZW0JYGH/n1A3V2CeKDv/TkMc7lDbbkbSgCRWD/I7ArB3n/V29/SxilTEqKt6EIFIHbImCMMIYYFYwbBghDgyG7XZCXYZTyDBjXurGGT/JLZ9xtasgo61LO5XkOW/FLnnXpSdvqHnnVjfEFG8/kjiEWY02dtwuhx0AU4gTAz+Udne0wmeu+Sb3kJzcHhcFrQYH82/HZrj47TYcVOchDbvxdm9ZBPmVhxSBWNvXYikaw2io9dQhd+KMNK7JyCtImaORKuYPd1Rddjps7p9F1JAOZOUvambNhASY4Hy6+6uLCCzbqp0/rv/gFa3e8BfkTkp53eJNZvPKCcmjvJOBBDnflyaMNMkbRl85Jlg4f7bGUZ+aJVtLz7C4kPvnhEV24bixvVU68U1scaHUml/Jb6cfQwdczvu5wtHikTvpt2kI6ekt5I/fB7ugaC8rO40CZmV7kgGtkwTcOb3hoC20gn/J0qoUotC0MqHNoy7PTMMu007LNXwSKwNFHoA7y0ce8HItAEdgQAQZNjHnPjAxXjJiDkZE/5VNO/vk55ZN35pG8ybOf7qkXbGARjFK35X072dETcld+fk/8JriPghv+idzuAr4M0vDfkMyus6V+7njnfVNDWH7XXI/DhVXoRq5gBqdgFgA2kRc9zlJCsI4DkPjDfSc3B0TAM/ikHoeL39wGcdCCXfAJ/+14Kh+8lUm50Nmu/JweucShI2iL3Gc+MDkUHoPY4g8e85V65L7IfptXMnFu3cmjj6C1iXzKwC/BMxqu8A6t5NnpHQ8h9LYqn/rL75KfYz4HeSKjeM/GSWQWp9z8PpfvcxEoAscXAn9eEju+6tTaFIEicJwhwHhhmAieNwmMGSEG2SZlZtrht0m5vcqjjpHZc+o8x20i20wn+WcajMXQTvrhuIdm2ggffBN/OHhsQmOuq/x531SWyJs+4z1xm/DfJE9kwSPyhUfeN6Ejj3LKBPdNy+0mH55xwo8Ub3RnLNIe4uL8xEFK2lZ1UobMcz7vrkMJyoXmXD7yZqFgNzxmunlGP1dkkLZJPWCl/vMlbpOA/jrsUr/ItAmtg+XZpB7yBGcybVUm+dxd2iTtcjAZmlYEisDxh0Ad5OOvTVujInBcIcBQiVETI2cnFVR+Dst3aYmLQZf3udx+e46MuQebvB+KvFvRQDPYHArdrcqgG57ypJ23yn8k42fcdlpXdUg90JlpHQ6Z0Q42wWzmEd6b8kJLmdDIfdPyh5IPj+Aa3keCL5rBY6Yf/DZ1eOayM705fic4oDHTSVn0DpVmaGx3n3nshBfM0mZ4KBsct+M5p888l7LAZE6fy233PON5MDrS5rxznWYe5MgCgOfkcz8Y/ZlGn4tAETg+EOgR6+OjHVuLInBcIhCjZlm5TQ2q3ZZf8t1P76lbsFi+71RW5dfRQF98+OyU7nb5wxf9I8VjExl2wzt1wOdw1yO0t6IrPWEndZjLRe7QORL38CNjno8U39APHt5dW2G4VX1DJ7vOoZf7VuXWxS9lCO1gML8nbh2dncahu5R3XdymdCPnkuayvHyzsznnn5+X5XbyPtNXbiu6ZJnlPli+ZVuHrvJblduJzM1bBIrAsYFAHeRjo50qZREoAkWgCBSBIlAEikARKAJFoAgcYQR2/lN8R1igki8CRaAIFIEiUASKQBEoAkWgCBSBIrAXCNRB3gvUy7MIFIEiUASKQBEoAkWgCBSBIlAE9h0CdZD3XZNUoCJQBIpAESgCRaAIFIEiUASKQBHYCwTqIO8F6uVZBIpAESgCRaAIFIEiUASKQBEoAvsOgTrI+65JKlARKAJFoAgUgSJQBIpAESgCRaAI7AUCdZD3AvXyLAJFoAgUgSJQBIpAESgCRQQO/nEAACAASURBVKAIFIF9h0Ad5H3XJBWoCBSBIlAEikARKAJFoAgUgSJQBPYCgTrIe4F6eRaBIlAEikARKAJFoAgUgSJQBIrAvkOgDvK+a5IKVASKQBEoAkWgCBSBIlAEikARKAJ7gUAd5L1AvTyLQBEoAkWgCBSBIlAEikARKAJFYN8hUAd53zVJBSoCRaAIFIEiUASKQBEoAkWgCBSBvUCgDvJeoF6eReAEQ+CWW265XY03jVNwmXf5fjvit0bM+Tzn2ir/buJD2/1Pf/rTAVKJPxAxPSQt9ylp48fdlF3HBL2GnSGwHWbbpe+M297m3o912Y8y7W0rlXsRKAJFoAjsBoE77aZwy65HIJP1SSedtD5DY4vACYaAMbEcDxknc9r8PEPE4YzTeYc73GHQWtKb8+d55vHHP/5xOMh3vOMdR3l0ki6/53U85jzybcU3ZUPnTnf6s3pNeeXwTJjrFLrLPMm77o5ueEkP7dylbSXrkl5kX4fJTG+uS2hsyiP55/tML8+5o+siW2SYy276PNdNGe/ohk/qvEk9lJnLRr7Eew+dxLmnDomL7Mmb993eZz54ufT3Qw3KC+TMc/Dbrk3kT7llPUNrGb9TOVNfYxstMoXm4eKxU5mavwgUgSJQBI59BOogH4E2zMSMdCbrI8CmJIvAMYGA8TBfhDYuxDFw3Rm2cVS8L8eNuB//+MerP/zhD6uTTz55dZ/73GfbuiuToNzvfve7weuud73r7ejLx8j+0Y9+tLrzne+8ute97rXi4EaO3ENveVcPIffULzLkHQ8h7/KTC6+Z38i04R+00FBHGMIHH7S9b+IgkTOyus/1CB335Ek6EcmNz6GE0HMnM1nDX73QDq/teMxyelYWJugI4jz//ve/HzyCTejLk7xb1SXyhpZ84sIHz2UeNOXHF09X+LhvV6+tZFnG4+vCy/Wb3/xm9IsHPOABy6wbvaOV/hqZU7fwQmh+DmH1cs1peUYrz/Jv0j9Dd76HBhn1fwH+rvBOfu8NRaAIFIEiUAQ2ReCOr3/961+/aebm2wyBGE/zpDw/b0aluYrA8YFAjFWGsbHBsP31r3+9+sY3vnE751DedQ4DI/hDH/rQ6rOf/eww2h/+8IevzbdEDD8G+A033LD61Kc+tfre9743nGtOcPiQh4Pz05/+dPV///d/q5tuuml13/ved3X3u999yBqnDe2UmfnEUP/lL385+HCy73nPew5HW76f/OQnqx/+8Ierm2++eTiv8gvqJK96qTd+d7vb3Q44TzOPrZ7DGy7qqB4PfOADD+C8FZ4Ho/fb3/520CK3ukcmWKb91OVzn/vcwAqW97jHPbYiedB49PDjzMHWhfb3v//90VacO3UUry4HC2jJ4w4Hfez6668fzxZFOE54felLXxr1w0c7xaFSdjse+Gs3MoXXjTfeuPr0pz+9+tWvfjVwCJ/IwzFWn09+8pOjT8AzfSr9aRO+B6t70sITv29961tDpoc85CFJ3tFdHdFLXZeFxbuE3Jd5kpZ0jqxx/7Of/WzgB4tgsK7sdnHoag9t/e1vf3vU1+LZEs/l+3Z0m14EikARKAInNgLdQT4C7Z/JmHGRwCBqKAInIgKM2FzGBoOW88UxNEae+tSnrp7whCcMp3TdOIlxfd11162++c1vDufi2c9+9paGezCO8cxZuvrqqwc/ji+nixN5l7vcZRjSeDLS5fnf//3fIc9DH/rQ4TgpywEks3zKL2VUJxdHiYPNQXrd6143HF55Ge4cWDxe/vKXr+5973sPmhxq8W94wxtGfrTtXEd/pB7r7uoW54VsH//4xwc2z3rWs1ZPfvKTDzgdm9BCP/nQ4ly9853vHHK+8IUvXN3//vcfIpAfX5hw7P/rv/5r9fjHP371ohe9aGC6Ts7t4n7xi1+svva1rw3snvnMZw58Octw+eIXv7jSDptiog7kc+eUWjC4/PLLV2efffbqBS94wXD0OWj60Ec+8pFRP7Lju9WpgqX8aHPGfv7znw8+2vIrX/nK6k1vetPqKU95ypBVu3LCyf6whz1sOPwcdX3r7/7u71b3u9/9Bj+00XMdrqD+MOWwW+g59dRTD5k0ufRf/QxuxiyacWjxcgm5h5nFHmMMPnO6BYr3vve9o/8bw7A41BDs3PHXj/RNizWwn+ffQ+XRckWgCBSBInBiIlAH+Qi2O0PJxch40IMedDvD+giyLukisO8QiEGdO2eMIc+xdHz6aU972uoRj3jEMMCXToMxZCzZ9ctu4DIPunNcDGfOCofr61//+nDkrr322mFEhw4j3rHkt7/97cOZZbTbbf7qV786xqxdLrJyYMko/8wHX8a4HUQ80OIgJHAo7CBzpDjbL3nJS4bT+YMf/GDIxaFRJju1KTff8ViGyAAbO3LooUUWTvqhBA4GR4NDx7kib3B0VxcOImwsKJxyyikDo8iXvHhHvqUcczt95zvfGQsXnNZHPepRY0cXD7v5dqg5uRZQyLFdmHnDQRvqX0984hNH+0m3KMJpxUMaR5dDZVEB/suQeolPffRDZbX3GWecMXa/7YrS8ZxIO7cccIsLnHtOoXSnF7QNOqEV+nlf8t/JO1oZU1/+8peH88lJ3SqkHdzzLG9kcU+8caf/WrSI0ww7fUGenJhIWViceeaZI55zLV4bWrhAw/h40pOedEC08DkQcavTHXpzfJ6VcZEHPZfxZ3yfe+65YwxoZ1dDESgCRaAIFIGdIHBoVtROOByjeddN2OuqwiBhMGWniaEbw4LR95nPfGYYCS972ctuYzSvo9W4InC8IpAxYVwxWO3iPve5zx1G7Be+8IXVBz/4weGsXHrppSM9hrG8nvPOyI4jidYcOB8x3sUbmxyWd7zjHWPXUFlOzdve9rbBgzPOYQpNTh/D3g6mXS7Gv50ocRxODtzsxEUmvKIvOAD4ZkfS84Mf/ODh+NMF2XVFjzGv7k9/+tNXj3zkI4cc0SNzvdBWtznE6Effc7AIVu6Ji2xL52ymFx6cOQ4MmhxJsictfCxmqIu6ktuCgjwueSLTjE94kUE+7aS8xQsXvDhZynjWPziudsYdp+f8oJ36oeN9XUDXDrcjzXa/OfFoK6OtH/vYx44dZXqbE37FFVeMPNLIRb6E1Ct18Y6OUwEcRu2ItjqLtxBy1VVXjR1X9UCPI2nxghywwgcdNINV6ha+m95nWfUdCwNOMcDuGc94xtjFXUdLufAmt7JkJUdkCW3v6qk+xl7yqz/HHy8nF9RLPkHdlOccG1fmRfWf+cqHFvq5z7KKC620+5yOlkt5sj3ucY8bOGv3xzzmMatHP/rRnXNnwPpcBIpAESgCGyNQB/kgUGWCliUT8WwoiWOQMO4ZRiZkuwaMboHx6+gdQ1M8I6+hCJxoCMS4zXhi0HIq7AxyYBjdduQsKHFKGbvyCCnjWVwcWsa98TeHjE1xDH5Okp28973vfWO36pxzzjng/Ep7znOeMwx5u8t2Mh21Zeij+4lPfGLsbNqBc4zYPU4bPvKQzT2ycDDiZMX54Gx79h0oA96RXw4DRxRfjpPjv+T17PtJDvkc8EFjDuGpnItMsIER2uSa8fAsHxk5MXNa6OID/89//vNDZ5GXzsLLpZw77DjRHB7OK+cHbWno5grd3Mk0X3ZUtTue9GMWIpSHud16CxXaxK49R0x5cqRfpJ/MPNTfMXH4XnDBBYOWPhX59DdtzUHWPuprIUWflE/AJ3WesRJnV1h/sMBht5isZIIBbFwWenw2QD59y648h1V/10fmEEzE4aVuy3rN+ednZRPUW99S71e+8pVj53yrOSc8LSZwYpXFU3510U/Sju7i1dMYkN+JBW0nn3jtB9fMfZ4tBshngUpfsaur7jDWlsEhsqRN1Sf1mnefyTUHcgUnd/OsRS+nH6688srVX/3VX3XOnQHrcxEoAkWgCGyMwG1nnI2LnRgZTdIMIkaEnSfGKwMghoM0uwMcZLsgdsR87xYjgaHESXY8jfHUUARORARi7Bo3wnznFD3/+c8fO7XGyTy+5E3ZjMU4YozppIUmI1kc+hweR4D/+7//e+yC+tb0tNNOG0d3OUYf+MAHhgPDWDeGGeIvfvGLRxzDnUPgmC8n53nPe95Y/EJ/NuItjnHyXHjaVfTMOfjoRz96wMngSOVHuuS75pprhmycGTQcI/7ud787ynHcyDEHu3ScuDnQPepKpuyEqjNnlaPHAZEHv+ACO/EcGot5c/BtKTk5FxYHHB12TJfzqrxyvrHF00IGeejDj33sY8MBlAd9+HBUHGu2+7wMyssjPxw4mBYPfI+atlfGzj0H+d3vfvfYkcUfzdmpWtJWVyF614KDNte+H/7wh4cexl8/w19bkYUzbRGF86o/cPoE9MhAFrLJq4z8dtH1GZhxAsXBHX3xsMDf5wM5Km4hRDkBj+SFo7J4iBPcU58RseYPeYTc1VO/1neddODEr6OBtr5BFo67/qX9OaAcWe3srh/DSZCWRSJzobZDxyKX7+rtynOYtb1LnaRbHPDbAfqoca6O6btki3zu8gvSLVzAL7v0+ut8JFu+ubx3tDnIdo+Ne3MxOfSdhiJQBIpAESgCO0GgDvIWaJmsXYxOzi8j4hWveMVYQWcsJJ3BxsAzoTMghEz0W5BudBE4oRCIETxX2hhh2LtzpHzf693uHKPcsytlPWe3y12IIxO6yeudYxej/7LLLhs8LFZxaJR3OSZsZ5JBTgYOLkeWoe1ubDOu8cE/gQMkjXNLN+TXie2q5ttPR4MFzijHhX7gvNppU04+NDjEgnROlZ28ZSCL7yrnEPyU8a0teuokLycuzixMIr96qi+Mlw6yMo4Gc3jlQZ8jHOcJDpwNsnOi4aHNOFicQ23G8eTw4i3/0kEmi4uc+FlEgIlvVfOjSpEVNo5vn3feeQdk4rBxxPCWb25v2JBZu3NILY4IZNQPtI0FD+WCsXaky/G2QKH9pWt/dxdHMbu+8DMfqL9+xOmDB/zVO31EnTiL+OFlMYFTyVnMwgkMBPw58b6BtjsrBKfxssUfdSVP5iL0OOMcZCcjOKnSlhghJy/n01FsclpQIHvaxmKSy4IEZ3sO6gMri8L6CV5k1/b6KOy1p91yfUC/gKdn7Udu/GFPfnzFpc7Ggb5hXMEt/dGO+NJBnuXKszmY3OqkXczNHOaGIlAEikARKAI7QWDXDrLJTTDBeTbxmQBNfiZHK/Im6kyG4rwLJsfsOpg858kcLWkuhkom++RJeiZa9EzELjykJy/e4ZN075E15SJXyspjl8cRMYYEQ0ZeRoO88jESrIzbaTAZM+yCxajkNg4z2eCFl6CuMFMHIbKMl/4pAscoAhmL+jknhsNg/OvnxoDxbxdNvJ2tpMdRUY6jI54TyCGMTgCJZ44JZ0YZzhwj3Zg866yzxrFTzoNyDG07w4x9Do/xRw7OnvGrPB6cHjS8C3EiYtTLa/xzsMjPKeMokIOzI+30008f3xjjgY50u5mcZs7e3/zN3wza8EGX/MuAvt2wZUCLA8sx4qjBIAsDHHPOIbrRh+iTEybLoDx8Oc92BJVFDw5ZJLSbRx/iydF3YkadXPgoz3lCi2zrgrbWvr77dbdz7BtsWJJPiM4jw0tf+tLhMHFwo6cd646enXkoR0aLExwseZSxq0incr7cyUBe+fH0nBMD8sCIoyUfrNAIjtrH4gKHjyPHCYO/uYRDDH8LBGRU3qIM/MwP8NR/4ccZR8OcAi/l57rP9drqmfwC2chtocKihl/K1v+C41xeHDkththlVW8Orh1nbeZosmPt8hlD4tEXyKh99V/45sSFesERT9irI5z1fUH/0Bbu6MJFfpf3BHwsOnCM3/Oe94w6wQ/mHN5NQvq3/o+OkxDaK3XYhEbzFIEiUASKQBHYlYNscstlsmP0MRYYBCZbhoUjlAwdEziDlNHI6DBhyc8AY1Qsf6nUJIoOeow2dEzmjD3GpmNtDCx35clhIs1ulAnahEwuE7a86DGW8GYkmHRNyOQjkwkej0zaDA6TrJ0mxyDtFMnPeHIxIEz8yuDDMCbfHNBi3C+NAe8wigEKH/LCjHFFJrSVF99QBI51BPRlRjbHk4FtHOvvHAS7PAxqO1t2jYw9OiDj2LMxqIwjm8azsoxz49kYRIPDZRxythnJdiHpBeU4cMYdh0C64D3BGMQHPfk5zOgb0xnX0oxLd+OfM0R3qJdfwTZW0c5RVWni0FV/F2ePHqTTyIyWS0B7Geg0dVJWPndy0x0cFnX3vac8dCV9YwcRbQ4D2ZVTxjPHbBnoIuXI7SiwOijD4XLRga7oY6dp6GxBPhd5tJ064OUSnwBT7UuPcpDJx3nRxtpGCM7wUl59ONB2O+2+ag+7m5kPohvl1352MTlVApzkp08tMph/XOJglrJk9GxnWJvDzCKKfLBKe8tjjtDm8IaZC1b6sIC+OP0OLXOcPnDJJZeMuUn9HBvXv8lkh9xdfxBmvEbEQf6gpY4uPDPvqi+Zkha68NQGdrL9yylOum+lLYiYF+GnH1sQ1o4WcNSDTGhpX0er9XPzoP6iP5hHlZMPDYvJcFPvxOENR2Xg6oJn2jv1Jp+2JL9xqs8F04NAcSAJPWUtSPgWm33QUASKQBEoAkVgpwjsykHGzMRp0jMRWVn2/ZqVdROmSdDkb6LlkIrzgysMVpOllXS7OibZf/qnfzpgJKBpomRIM6SU9x2gSVyaCdkqt9V7EzLjgAwMVkabnQ0r8/KabDOxWzW388GhdkSOcYkPg2guR250yfaud71rGObe/eAW2Rnedo3RUieGGyPSarUV9wT8XWTLczCLIYEHHBgQJnZGg10ru14MyBgQodl7EThWEch4ZMhzkI1Nxq/nHNWkF1ycJsHd2DAOODXGkjEXWsaqEOMfTWni5WeoK4smI5wjY6waa3gb/9LxIRf6HE06hp5QZr7wCk96w0XvGMPouTiBjHP6AG08ORRkJLuxT0YLdxwlssnHGfS8DHhYDFCGLO7ocHQcafVjWnGQ1ZnepFPpKfF0Ssqh7XkZxOENL2XIohy5PeOn3o6v0oEWM8QrlwuWHL3wg6Xg3TNcOFh2B+XVVnakOcepm3ZAD454agt1IZv2UdbdzjL9SAYBPQsocFeec8XhJbM6oa8d7PLDiwNsEVK8C/7awpxikYDjmr4zGNyKG/nIYu6weKoO5htzn/mJw68/ay99LAs0/mWWNIETr16wzK8/kyGB/NsFeeCqTwnpV+qrPwbHuX6e9Wn9gwxkw19b6mMwNKdxcOWBhaPNgjSnL3yrDVe4+z/m8sAuC7oWLoKNfOZVZcmKhz5PRhiKUzZBXeACK4tCHH3OuPk79Uzere7qiLYFIrvZWTzfKn/ji0ARKAJFoAisQ2DXDjKinGOT0X/8x3+MnR2TpYnSJM5osWOSnQ47r4wlBoSJz24CZ9UEL2RCN6k6LseBNMnluBaaVuXRY1jhwxAhgwk834YxRky2gvLS3vKWt4yJ3+Rucs5llRovhit6JmYTMsebHGQis4tBlEAW+RipjArGaSZ8aS5l5XFPID/jwb98YeQqBxfywIpxogxD1dVQBI51BNL/jXvGrwUsY4XzQ3e46/92nTgcxqJ0xnScXmOYE8Po5iAZX4xhd4EBbixl3CU+92BIFnH4GdOe0UlZMuKLv7yRPeWX9OgJO270GX1hd9sut8Uyx1A5z3Y1jXu6yAKiOA6bfzmFHh3me2ROE97LIM8sC31HV9A7jtTSQXSGBUl6zzeiHCB6JfSWci95oE9+zinnEDbaIRhxXDie8lnwpH85InAXhz5M4QhDgUyC3UoOlcVE9b7wwguHDufsWoxwyRPH23wAK7RhibfTAXSjRUuO1vnnnz/6gvqR5f3vf/9wnh2f18c4sNLIpQ9pH2X1H3MUZz64ups76GOY6Q/aX3xCaFicVRf1cPTdfMHRz8KmX07Xvv61n3kI3zjQaGo7GJtrEmY+idvunjKwJ5t20m6c49RbmitzkHYjm4UPOGk/7ZX2s+hgQVp9zN36Npp4kd0OuhMGcYjNw+qjz9m11Y+1kb4uv/7gIo87OV2RJ/KlLspbwBCvHq7Q2A4P6ejIr9/oPy64iGsoAkWgCBSBIrApAre3xDYteWs+hoyjZAw9K+eOv1mFNsmarKzo+67JxCwwPDIpupsoTWCC9wSTKEOT4WOS8y7Iz8G1o8xgYgQqZ5J2NIyRxIBxBNBEmwlWeUYVR5jhYgeYccAgtEttZZyzbkUdTUaDejAQGBOM0de+9rXDgGVAufA18bpHvtkwSV3c5XHBiwz/8z//M7Dxry/snjFsGU0WBNTDTnx2GGY6fS4CxyIC+j59wHFihOvvdIHdXOM1xrR0afILMZw9M56NfeOTw6SM9Iwtz2im3GyES6ML7K5xntBRTh5jlkEdPWQsG6eM/pn/ILz4owwHiI4jn91VO5AcSU6qnUpOZvSIO+fBRVa6BS+XNPKsC5FVnekJ+ojjiB89mAVDvB1J/vd///fhNHPe7GxuF9CnHzk3dmk5QOrOmecswYjO4/RbpLAgYEeYnqXrtRvZ6FM4xCFCQ51gZJ6gL+m8LBzAmu6m78wfjvva+RbofTIFW46ZuLe+9a0DN89p49wdf9Y3BDLh74rTJM7uqcUI/SjOfdqR3OqobZSbA1nhoby2csqHTLBAR791agDedLxn8xdH0wLGq171qiEvx1pf5DzDbDch9VN/FznUdZY94wMv/c4igHTtZpFglsF7FmXJLn/a1nykbbWxcYSGsavt1MWuu7zmbW2T0wTqF/nc9XV9GJ5kE9zRg3/kFQ8nl/hNAhouGOARPpuUbZ4iUASKQBEoAkFgs1knudfcTfYMAEYDw4Dxw2AzkQqMBpMsw8vEGsPDJJbJUnwmUPGCSTLpJjmTqgnVZM54Y4DGoGWAmpizI2AnghFAHvHooKcsA5JDykFmAIeP3QbGj12ql7zkJWOCZSiZ+DnLylohZyTME7byMW4ZTd5Th1GRW/+Id8GCcWuXgYGCJoMQD3Iyuuw2MCZzTDNG/0yvz0XgWEEgfd+4cBmT6dOeM14YwcaZS1BuDt6lGSvKzfmSN7SUk5748DRGOeX4hx6+rugTZTmB8iaEzkzfeLUDaneYg2oH0djmeHEcfZKBpgUBuoiec9l9RNv49w0oOgx6DijdIuCHl/vMmw7ksNK3dl3tonKC1Q8du310CsecXqbvODx03cEC/mSlpzl8OaZLv3OEYYkH+eTl2DuuHN1Ih5GTQ0v3x/lJG3Hk/boxnJ0eQN8ihTqiD29l6Ffyy0evO1mQ9oYPOvQ//OBKHjTU0cKnZ/npcXLCxCVeOU7rG9/4xrGLig+HUpr6ZCdUW4lTLvLDThwM1FW57KbDS7w6wR9/eJOXDOrJ2dTv1MkcQJbwOVi7HCwt/SJ50lfg6Irs4skuwA1/aTBLv/Hukk/dEs/RTVuqh3FjvpMXXXd50PUur7qbi+Gg3miGv3vaZJbLs5B83oO/+zw2R8aD/EEjdQm9g2RvUhEoAkWgCBSB2yGwKwfZ5MOw4FgyLvKrlgwDE5TLZMmQ4ChzWgXlMiGaiE3IgomQQZEQA8fkmMkSzcTblcDbJMxQtQPBoJPfu8k6E2QmY8YrwyrH26TbcWAsOHqGXvKSi/yMNU66e4wH5WKEyI9n5AxP93milg9OHHgGIQNZHRiyDA/5ya2uVuHJw+CMERhcei8CxxoCGRPGgODdeGFsG8/6uPEl6P/SXfIbQ4LxNuuBEbn4M9OXFFqJZ+Q7WsqJwxtP/KMr5Gfsc2TixIeOe8azZ3mMXY6io+GOBxu/aNN5F1xwwdB7HCd8Uh+7i+LoIKdcEsiBfmRJfjKRXzxn0uKaBTQLePlWFo1g4xjsOeecM34MyqmYHGmPDOgKoY82xx1vNOlxjqj644cvPZS605XqalHRYh5HiMOnPeWDK5ro5+Koy6OOnCfpykmns8lGThf58ZIOY3mii+HGwcbL3IGeoP/ESVcX+KAZOdDTLo5XK0O30sHaDW70PkfPqaL5BEPKBy98LJrChC43p5l36HL1iGOOlr5GXjzQ0U9gAyM4S1e3Qw1ougT1c6k7HvBRf3F4uAvkliaoCyzQgIFLXuWCHXqC8vJxri0GRW4LGOoDCwtF+ox82lrfRl/boadMykWuvOOhXPK4exdSp/GyzR9l1EMdyeJK3bcp2uQiUASKQBEoAgcQ2JWDjApDgJNssmUEcSgz+bmbkBlEJskEk5jL5MnIMYnFqJLH5GiSUzYTnnf0XBxiP/bC+ONg4o2GSRodgWGVCT+TLVoMP6v86IRXDFgT/bzirZx87mQxUaPhPRf5BWkxPJZ5klc8vBgZ5GPEOc7IUIKFCx9GNr7kly/0BqP+KQLHGAL6/xz0Z0H/tvBk/GYhKmM+Y2a+ZywqG5rz2IiOMA7DQz7P4WV8c2rwMd7wlsc445AZm9LoFY5yxnzkDz93efx+AcfAyRmLb/SQ8urjGC8aLrK7pOGReE5V4mPMe+fMpQ7Bip51bNuOpUVIx5HtHssfPNzVyaci9IvfOnAkGb18LxxMlBPCR/3pQs41B0c6R9KCniPCyrnUzQKA3es3vOENYzebPBYeYA9r+ULfHX96Dq/wkwce9DYsOZ7yyZ/y6MlDNpd49XMtA12MtxAHz3PoKYMHZ1YfsBPvSDaanvGyaKEuZIz84UMOjrTvvuMYwkYb+oTIrr5TP/oSHGFoR9xOMYeYrkfDAoNF2swdob+TezAMnvqyOVYd9C3zoHq5pAkpI25uI/WUJs4zGb17nh1bNOHmcyR5yW9xwEkG/eSd73znyK+d4KqO8qGX+TEyoe0SxM13z9KUPZQAA/Os8aTNU/9DodUyRaAIFIEicGIisGsH2aQZJ85kOk98mbxNdJmcwezZpCi/ycuExlBO/Jw3k7lJVkDLEUXfDZuYHSW0o2DXwfFGBrDJGo3QQyPvmTDFZYKOEUAGV80slAAAIABJREFUdREvPfyUjfGaeHHLfGRTH2lC8ooPP/WI8WJngfHEYVcmBpNdFIaiujFGQ2cQ7Z8icAwiMPdhz/q7u7FgTHh2GSscJka2sWqMJa9xmrEaCJRJCI28p5x4u4KOQKPndIbvJo0v48/YZsBLsyPKseFIORLMmSJLxnXGsjt9IXA8fYuKhoAf3hwVP0rFsU1+us7iHodM+n/+53+OMmjRA/QYx8KnFtF5aMEELb/1AAPfGTsuTC60EyKfOvidBrukfgSNTK95zWtGveDqPcGzuOiaWUeiD585yA8Tn6rYTcSDQ0JOsiWER+7iPc/v8OB02VXnVMFgzhNMybBOjplX6q48WeDu7hLUBb52v7UBubUBvWvuUCdtSfcKsxx5hxEaFlOVN9f4rYr0I+0Kb7/FgS8aaPqk51/+5V9GObv+drt3E1JHNPAhu4Vo/cgc6K4dBf0azu7iONLGHcdefGhJ9wwT5S1ooJl0betdXWEtvwUDC7rmMpc20oYWGZSTz6I03DxHnoON45Fpwl+7uTYNxk2O5mfsblq2+YpAESgCRaAIQOAv1swh4sG4MOkykLLrgpTJMwaC1fUYUJmQTZ7KmihNyHZVMoEq79kknuekKe+HuBxlY/Bedtll4yhXaNnRYbRlsh8EtvlDlhh2kW8uYnKWTp4YW9KVW3elrLwmazQ9kykGCiOFoXbxxRcfOIKHh/zhZ3JnrOHRUASONwSMB4Z4DHIGO6fDrqdFL46isWDsuDIuthoPiQ/d7BaL59DYHXTE1q4fx4vhbmfPWCRHeNAldmY51YJdQYa/fMpIE+wE2iXmTDtOyjkyhpWnr/DgPJCDDshCIlmke1dfY55uiRPBASNLdCC96t/p+NV7eZ3U4fz5t3UwITt9aMGQrGRUZ/Szk+l4L9k4bnZ6OdBxoNRFmjriqayLDEmTLogX8OUsOY7NeSc7erDfSXCEm3MMI7venCsBbxfc0MQ/MkUu+SLP/KwOMNGf5vqgQ6fSu3aB8eZIwYKDrH9w/tRNuWWAj34JP+W1L+feUXo76trVjyz6jtvvWHASBdhYaFGeDHS6K7It+WzyDoPIiGYWV/DAP58NSUtez/oDh10d9Svz8kxHnczF+iOa5FQOzrDj2DspIcib4Ne8/fp8+kDaCw2/OG8skc1Yl+ZC90gE/d5YzPf3cx85EvxKswgUgSJQBI4/BHbtIMfINBEyMhzrY+AxnkzMJuH8iI0JdTYKTLguxh4Dz8Rt4jTBmeTRYugIMYoYiPlOmDHD8GC4omuyd8WQWjZXaMzx4uRnMMSY8I6Pd5N6DF7GFPrht26CT/3QdXlHK+9W5RliaKojeoyQGBbkENB2dXKfW6vPxwsC6dccIOPDrq5PJuzscjrsRnFeBWNHyBhVVlxojMTJqUKPDkHH97rGID1C/3DE6BpOgIU7Y9oYl58snBw6iPNk/HH+lMWbXuMIMbylcTY4IujSf+hkzHrOLq70xJPVUVs7mHSXX8ZXD3noBOXoF3Hk9sNOju66yOhYMxnVze4luuTzL4roErpW/dFRnowcF7oGFspyki0uOqky4xgnHkbBHE3x0U9oCuTDGxboeNaG8uIdPZa2yT10lSeLNufMcLrsIKt7Al4cNbRcyig/y5y87tJdgrJwIFdCnrWhvkVOdU1fwX/GZMkHbWX0D7Q5e5xffclChzZxmcc40nS9wFG3KKOMOPm1a2SIXJFz03vKkVP7mFfUTV/QFuZHaUKws6jDyZXXQrL5GfZwJ59FFvOrOloIcg8O2kA+GAj6rPe0jf4rr/7o8m5u0/88o0WO3NEI7UFwzZ+0Z+qxzJL41E+bqDu8/Wq4em7HY0mz70WgCBSBIlAEduUgm3gYgYxEOzMxSE1WWWW2G5RvhTnNDJdMZt6tsjOqHJtmgJqUGS3KcJBNePLn0mQmaBOwyZwhywhkoPgeisFrog+PeQLF22Q+GxbJK794d/kiJwdeHeWzO00+8jIMGDuZfGMkeHfJ7y5eSLwyDCN07JwwlNWHUeUuX5xmiw8Mi4YicLwiYJwY437wiZFv/Dr6zJDPeFD3jB/jUpllyPiVzzhmhHNa6RdjyJjFx53u4JxyJuz+4iPeQh1HlFHt21FjVOAIoG/s0lnRE8otHQj5IgPdgQ+ZBWn0gbpyHOg7dUVPnaQpG92BL2ee0yvPRRddNJxb+tHOdOSiL7y7OD9+nVndlYEj/UhXksOdnqavE4ItfDgWdDadp270K92qbVK3uRyZ4RBMYCvOtS6QAdZk910z/aeMY8qcU+Wky6deHDi88dguqIeQO/nxsRCCHhmloWW32rvLvEEGmMkHx8ih72hHMgjShcx76Nmd9104bDnb5kNl4IWWecPRffrcTioH2WKFttEO+gJZg6/7dmGZR3l93Y6+f/+ljZ1q0F/lTfvgle+wzT92wuGur1uMcFLBYoq+yckX1FGb6B+u4OvZApIFAGX0VfW38GF+1Pf1PbSUSf1gq13Q3CoEZ/US8j7nRzPxaVtjw+KDchY8YB5557J9LgJFoAgUgSJwMAR25SCbnBgOJuJXvvKVqze/+c1jB8jKtB0g6QwsBooJ05WJ3aTFyFDW5Oy7LRMro0E5xpHJLYZJKmHi8/2dVW6GiYnRRGwXgiGnrDx4CfgwiKz0M4DEL403ZeRDy+WdnC6TO2OZrIw5BgGjxg6M45Um+eQNP/cYvPjiia7g3cR9ySWXjP8hqQ6MUkaKujI6GByMXN81MmYaisDxiIBxw6DlMHAu6AHf/eb/mxunxqKxYzwxvukFzozxqvwc8q6csW4HjaNCHyhDZ9BFjHY8cvQTffTQ939d6STf71q0Ck185JvHsjIJ5BTwVoa8+BrT0nKhId5FL8kvzpXgWX55OOv0HUeHU0cPcbLiXODlmf6jdx0f9oNhyqITHhYsYWHXGc4wEOb6wZbT5Ftn5TmHdLK4hNRZOZd3zk7w046Cei0DTDhhdCin1I+HaX8yO6KbxUDpFivQ4uypO70Z3uoV/brk4V2ai3za3LyAJkzR0CZ0rICHY+nqIC9+5FB/ZcxHFlL0CXxTZ31LH7Fo4rtwZaUpy0Hj/FsIVU9H4eGe/xnNKZV++eWXry688MJRf84tzNLu6+q1jMMvwbO+oU9bWMbP3MHJjdzyks+uv1MEvn/X1vqF+cbc85a3vGWMR7u+vnEXyKWdyMwJTpz8nH/zl/YyttIv9Tf1h7fy4nPB2nj0njYdRG/9Iw4Oyhlv2mJdPtnFu9Rfm1sYcJLN3KzN5n4z8+hzESgCRaAIFIGDIbArBzkTNCfSxGwiM4maTBklJkCGRL4lZJSYrDOhWc1mHDlmyEE2yZp0YxQyBDnJJjp3/Eya/t8l2gwbxoiJEV0Tuh0UhiKHkzzKMGLs1FhdJwuDQJAWeXxXxkjjvAr4SCcLYyHGtbrZ1YhRqC4MKBM6GgnKMkQY4jHSxZEJJv7/KaPIij0jzbEw6TFo0RdiKIRu70XgeEFA32bAckSNdb+ezLmjT+IsZRzG4THePG8VjCHBOGJcc4Ys0nEg7XJxeoxzOsR4NTbRdNEpxqc4454OoR+2Cnipg7KeOX+e0RAiS2iEj7qRT3zSkjd50JWP40J3oRljX72ku5SDkUuQRvbIFR5o0bXqLA/dOwd1d9GVFhyVQzOLF5xnAb/Iqh3oX3nobs4gPQk/PJaBbvdvseyeWmxUhpNp7siJI2U4qXby6VrtRv9y6skTrINTZFEuce7pI5FNH4jOzqKFfGkzGKNPfmU908vmHf2Rsxcs5THHqLvFA1jbNQ5unEjzmYujqp6wd+SXztfX8fa9sgUIlx88y6KytLleSxyX78mrjclpN9684qSCxV2LRXNQJ/2fY6sOftNDXvXgXJtHfUPNwU6dObTSsijNGYeR/mKxWHurG2zx0176ofLqAy/87KibP+Vd10fIKb9AHgs5eKjbMiSfeBhoY+0BTwvQxq9xFnyW5fteBIpAESgCRWArBA6Lg2zy4gxaITe5cvYYBSYwEzZDh3FqhVmcic+kFWfRD1XZJWGQSDN5KsfQyiow40EZV46wcT5NhiZaE7Jy3jnn3pWX3wSLPgOTc2rSFU8Wl2fOM+eYMZRAFrTx+/u///shHyOPgcHRTlnGBCMBf3ExKtBjLMToRlc6vMhh1x1tx0otHjDMYhBIN8E3FIHjEQHjQHAagzHLsLbrw3kwHmNYy5NxZjwbOym7HS6Meka5463ueHCEOCOMf2Mbr4ToHmMQ/00CWYxbctmB9pzxP5eXzyWNPsCLQ7oukCly0VnqLf9c78g300Urji7nZS6DHhnxlrYMsIAJveMod7Chy8XDj1MkRDZ1tYPI6aXjOT90HoyTd+bDQeUo2UGl7zhpTslkATX4SKPD5bcDauGEA032YJD6z/TVN9i5a28LARy1OKXKq7976hG+8qdd0DeHcbDMCfLAVllOLyefM8bBpsP1Kc4omhYKOL/udr8tAqhDnHz4RA6LAfS/eUvAN3LNddvkWTnl8+viFgc4jfCdg7a1UPLXf/3XYzHC3GxeU39zuAUJ7ajfoQcT85gFAGMKBjCVX/80XlzpX9rJs0t5/QQPmFhcQE+fMl+uq2vqoZyFBX1pXnxOXeRzpc0spqizuV65zP/reIRG70WgCBSBIlAE1iGwKwfZJJ9g0mQMmJQYWSYtE5NJlKGQiTbGScrFAFHWJI2mOJOrvFnZn42jGHN2nvARxMnvSBxjKJO0NDJw4Dm2aHsXIr87R9ykTU7vubwzAvLrnfihjYY0QVmTufgEaeSL0yx/JvTkYVTk+F6MZfKhE/nVqaEIHI8I6NvGKufB2LCQJS5jzz3BuDOGpctnvC3HU/LmLq9x587gN4ZdjHvjTHnBHS93Dpt0jlXSQ2/dXR5j3Z3uM+bzmQi+CcmHj3FPR+KzDOGpbJ5T1l356J2UFQ8bAe/UJXKJlwfNOS7l3ZW3sy4P+egfdCzScbg4HHFoIody9LQAZ7vdHBn35Q61PDBXb4uR8qOJr3jtmTaxUJIFUTqbY8lBw1dI/fI+Im/9I47snCptjk7mA3VL2ZQJDXfYSHcJ0cnmluAhD1nQp/ddHGT1EK+fys/BN6fBQ33UQf0E/US8O8ef46nd0BaWMo7IDf5EbnwtVFuwcGJiGdSVjPCBjYVrjq82US+Yq0vwUidzoPZS35RH1zNslPWc4NmVuiivjhaRzOl4e5/LzGWVc3HWLT7rk+uC8nAjv/aCs/4395d1PNbRalwRKAJFoAgUgSBw0i2ZVROzg/uyqIk0waSUiclR6H/9138dx7j+8R//cfxrIxNm0pVZGifi0Bc/58MjfMVLn/kqJ15cyiZ/7qHnnrj5nvTQcg+95JvTPAtJS/nQF59n+TznXdpcbk77M9U/Y5Pn3ovA8YBA+r3+zrD1ztD2LmRM5Nn4822ho76cNobwnH8dJujaUbJrGQOd4xbDX5nw46Dh4RQLYz9O4jq6c9xcD7utjtcqz6infxKSz7sjphwojj6HYxnI4SLnMohf6gjv2ZXl8GRHdi4745nyc7rFSReZOW7BZc6T+kgLPTuDdo+9qw8nmvOV3cO5vB3hfEfOWZQ/jqd6eRY4O/IKsIwe9x651tUBDUFbwtf3tU73hA88g9/IONFLfVI37+GVvLlzKLW1/mV3mUOahQL0s1OuHpxAl7qlPeVJPv0T3i6YhO9WvCPDuruyZELb7jfa6Cx3kJWVR14hZSJj8I4M8npG35X00InM3j0L8uTZnSzGlvFrDBrDxuRMaxS89Y8yGZOeYZf+kXziyYaG+tqdlscijHvkzz3lei8CRaAIFIEisB0Cu3KQl8RNWMtgcvKrqP/2b/82dpL/4R/+YXx/uzR81pULvUxw3hMn/xx/sPfQVjZl5vyeZ9rz5J6ymYjzvt09fGZ552fprvB1T9yybN6349n0InCsIJB+r28bWwIjOOPAfQ7eOR7u8rmM001CDPzkxXMd/Tl9J2MutPDhZCprB2sOySNOvsi0zuhPntQvsqAROomT13M+0aBXOaiJd08Zd3nnsiPjlGfOP9NImdBKGicmz3OedXxSds43Ct/Kf9a7c975OflDI+/uyeeZ02f31I4ouskvT55T1vtcNrTEz3mTZ86/7nkdD+2tz4Z2aKWNEz8yTHNb3je5o+lK39I2cFj2xfBKvtBOXeY6Jy5l3JOee+qSPOJTLmmJC6/t7qlLeMx8UzZ5YOiou7oaT7tdaAj93otAESgCReDEReCwOshbweibrSuuuGL8wuR55503jk0xFnY6aW5Fv/FFoAgUgSJQBIpAESgCRaAIFIEiUAR2i8BRcZAdq3PM0fEqRwB94xTneF4h3m1lWr4IFIEiUASKQBEoAkWgCBSBIlAEisChInBUHGRHvXI80hGo7h4fanO1XBEoAkWgCBSBIlAEikARKAJFoAgcKQSOioM8f4eUZxXq7vGRatbSLQJFoAgUgSJQBIpAESgCRaAIFIGdIrDZr9zslOoif45TJzqO8ewsJ633IlAEikARKAJFoAgUgSJQBIpAESgCe4HAUdlB3ouKlWcRKAJFoAgUgSJQBIpAESgCRaAIFIGdIHBUdpB3IlDzFoEiUASKQBEoAkWgCBSBIlAEikAR2AsE6iDvBerlWQSKQBEoAkWgCBSBIlAEikARKAL7DoE6yPuuSSpQESgCRaAIFIEiUASKQBEoAkWgCOwFAnWQ9wL18iwCRaAIFIEiUASKQBEoAkWgCBSBfYdAHeR91yQVqAgUgSJQBIpAESgCRaAIFIEiUAT2AoE6yHuBenkWgSJQBIpAESgCRaAIFIEiUASKwL5DoA7yvmuSClQEikARKAJFoAgUgSJQBIpAESgCe4FAHeS9QL08i0ARKAJFoAgUgSJQBIpAESgCRWDfIVAHed81SQUqAkWgCBSBIlAEikARKAJFoAgUgb1AoA7yXqBenkWgCBSBIlAEikARKAJFoAgUgSKw7xCog7zvmqQCFYEiUASKQBEoAkWgCBSBIlAEisBeIHCnvWBankWgCBQBCNxyyy2rP/7xj+N+0kknre54xzveDhjpN9100+oOd7jD6m53u9u45nxoCMqvC0n/3e9+N3jd+c53Xt3pTne6XX75fvvb3w5Z0L/LXe6yjty2ceGXjORKnOff//73q5tvvnnU5+53v/u4J+9W95SXPj+jt6QfGnO+lEv+5Om9CBSBIlAEikARKAJF4LYI1EG+LR59KwJF4Cgi8Kc//Wn14x//ePXzn/98OIoPe9jDhmPKkRM4eZzWq6++esQ/5jGPWT360Y8eeeVRPo4gBzrlUgVp8ri+853vrH72s5+tHv7wh6/ud7/7DWc8DmPofPWrX11xpB/0oAeNfKFzsHv4y/OHP/xh9Zvf/GbIfK973WvFGcdDHhcZv/Wtb62+973vre55z3uunvCEJ6zucY97HCC/lF9C6uA5dcHH5Z3D7Tr55JNX9773vQ9gIE0ITQsN+K/DaWTsnyJQBIpAESgCRaAIFIFVHeR2giJQBPYEgThwX/ziF1fXXXfdcIBf85rXrO5///vfZieZI/i+971vZbeVw8lB5uzF8Yzw6M07y+I5l/JyWq+99trV1772tdUrXvGK4UjGUUwe9/e85z1jd/d5z3veirMe5zI8lndlXPLhj89XvvKV1de//vXVs571rNWDH/zgUa/w4Ox/8IMfXN1www2rxz/+8auHPOQhY0d85jM/48f5/fWvfz0cYu9o/PKXv1z94he/GPxuvPHG8f7kJz95deaZZw4RlzRErosbmfunCBSBIlAEikARKAJF4AACdZAPQNGHIlAEjhYCHEaB08ZBvuaaa8aOLWeWQxjn2Z0zaGeX48wBFTjNoRE6645Eoy+fnWOOKwd5naOYPNdff/1wnpeO9mC65s9Mi6x2n+1UX3XVVasf/ehHq1e96lXDCVZUGsf4ox/96HCK73Of+xzYYZae+sw0xdtt5tyT/773ve9wltGGi51weDkyfsoppwzcOP6hFZHRJJ97rqT1XgSKQBEoAkWgCBSBIvAXBOog/wWLPhWBInCUEIjD5mg155fz9shHPnL1hS98YRy35igL4jm3nESO32c/+9nhaErjcHJkOYeORJ911lljt3ZZBXmUtwv7wAc+cPXQhz500Eo+suD3pS99adB0jPuxj33scDKlbRc4oy5573rXuw6HGI8PfehDq8c97nEHvpv+xje+sXrzm9885H3BC16wOvvssw/sZOOv/Dp+6skhtpDwiEc8YmDyuc99bmXn+KKLLlo98YlPHIsH5P7Vr3416urYejCEGwwsLnDKH/WoRw0MtqtX04tAESgCRaAIFIEicCIicNgc5BiI884FY2/5HpCTP+/uyRsj8WB55nJ5Trm8h573ZVry9F4EisCRR2A5lr1zfjml3/zmN8dOqCPCjib7RjfOnd1RTjSHz5HnH/7wh+NdWY4jh9Sxa8eVn/GMZ9xmR1Ye5f0g1mc+85nhaHPC7T6jSSc4tu2HvzjPH/7wh0deO9GOMX/3u98dDjfnkpMp3vfCsy5Rj1xQJDdn3fFqu8Ucet8akxH9K6+8cnXppZeOupDvpz/96cq3yujnh8PQm4Nj2Oecc86KA2wXXT5y2HXnaOc7ZvHf//73V1dcccWQXR51I79Lfew4v/SlL62DPAPc5yJQBIpAESgCRaAITAjs2kGOccjYY+QJMfBiSOZduriUkTd5PKMhJB9jk2EnhMY6PtJSRl550A29mcf8PAj3TxEoAkccgYzf3DHkhDo6zDk97bTTVqeeeurY4bR7y4k1/qVxmD/ykY+MdN/YclSlc345l5xkjuPyiDUH+gc/+ME4Vs1BtruKpmPUHFbpdnjtytph5nCSSZlPfOITIy/adIu7I8xPetKThoM6A0anqBd6nGK00OGMovXJT35yON6hT4b8UBeZzzjjjOFU03XoBKPcObl2fTnKcaIf8IAHjGc7wi555LcY8PnPf37sTFsMsGOOLrzsQpPtJz/5ySx+n4tAESgCRaAIFIEiUAQmBHblIMeAc8+FduLDJw5r4ue8s8MqXl73GItz2aQpk2um5Tn8GaFznsgy80tc70WgCBw5BNaNS+PTEWHf43JcOXmcPneOnJ1kTp0dXrqAA+jZxVlV3i4wp9XOsl1azuMcHCm2Q/32t799OI6caI7rxz/+8UHTN73nn3/+cKzFcRzt/vq2Vzk80LRrjSfn3G7tHOgT9aObOKccecef/ZCYMupg51YaZ9hxaHLLY7cXDz/kRbbQWuKFRmTB2zt65LSAEMdfec9+vMtuvB11v9gtxPG3UGBhoaEIFIEiUASKQBEoAkVgPQK3tSjX5zloLGOOYeYS8j4XWqZLm+OS107NHORhGDKQQ99zQuK8M/qkhQY55l3ldXKFTu9FoAgcOQTmceqZM+losR1VR5Dt5op3cSrtur773e8ezp6dU44gJ9i3yPIb18Y7Z8+PYHFqOc7LMc6JtGPKEb744ovHMWzl0ONIvv/97x/fP3OuPZ9++unjaDR6eIQep5eTzIGe6wIxeXL3THZO61Oe8pQD/74pC3vy5ZlsHFmOMoc3dOQJj9BzBB1/utBCgZ1q3yPDkGPv2b93ggFnXLp39eB8C/iJWy4ijMT+KQJFoAgUgSJQBIpAETiAwK4dZMYcQ45R5tdbYyAyMB1/ZOAyzBh2jDTpdo78iA0Dbv4foI4dMkIZenZgcmTSjpKLcev4JSPwpptuGu8Mar/sip7n/KCPXRrBsURpZJmN0AMI9KEIFIEjjoCxR1e4OKecune84x2DL13B+RM4kMawH7SiG+yA0h350awslmUXllOLnnLozAFNOsfurN1UecnBIbV7+7a3vW3oIz8MRu+89rWvXT3nOc8ZR6PRib7AizMeR37mEWfWXTpn27fCvg2m9+Z6k1GQl74kt3rGuU9ed3VxwcJuunzKw4Kz7Ci3HXJp8uGFt/z0ZPjkjmYwWuI0MvdPESgCRaAIFIEiUASKwEBgVw4yg4vhZUfmy1/+8vhfpZxfBhpDljF37rnnjm/sOK8MTbtG/teoH4p5/vOffxsH+WMf+9j49s83ga973esO7Bz71yz+dyjafqyGcc1IZPzi4zs79Dz7RVzf4MVQRMvRSD+a45vAGKFt/yJQBI4eApxC+oJjaEf06quvHk4fp9VYNXZdcZQ5m5w+i2gWyjh/cRxJTZ/QL/kmF23l54AWfhxtNKQn36wHHO32f48dn8ZXGrru8pM9DutWzqU8gnTPeOdZvDhX+JLHlXh3QXoucXSWHyBzT16/jm2Xmmx0n0UEi4ToWWT0/bS00HH3Th4YbFWHIUD/FIEiUASKQBEoAkXgBEdgVw4yg40BanfjrW996/gxGju/+e7NEUDHCGNk+g7OLrNfc/XjOM985jNvA7+jkI4MZkcoRqPdHUcRP/WpT42dHoYxg5kxy2nmdGfHmOEo3q4MOgxxu9bi7OosjejbCNCXIlAEjggCxjJHzeKZ8Wy8+nEqP3rlR6ziTHIs6QnpdMd11113wFmNg0ifSLNAplzKoh+doRLe0bPTSgdwIpUVZ4FNugW0F73oRSOPnWU7spxyV5zJONbLo9DhgY6Atvpx+L/97W8P3RiHe2S49VeuUw9yhbb0yJ50cZx3ukxdxcMOH3qOPHCgCzn3nGgLB/LRy/KFZjAS52ooAkWgCBSBIlAEikARWI/Arh1kxiyj8qqrrhoGp51al8DI9Q0cA46hxjBj6PmXLQw8huoc7EQzABl9gjKMT0anNMeq0WTQcrAZiL5J/PSnPz2+IWRM4v3iF7948LTz/KY3vWmkO2bt+CSDNUbjzLvPRaAIHFkEjGW7nJzSF77whWMH2Fj3q9LGsiCPZ3rFzq8f7eLcKZfFLXrDIlnyu68b08opY1HOd8R2V5VFn3NK15x88snjMxD66L3vfe9YiHvqU5+6eu5znzsmKV/wAAAgAElEQVSc0yXdOMOD+a1/xOFFv6Ht+LYfH+OQc1jxlMeudJxT+fP/nzmzCfjNPLNLnk9RLBKiT14Y0X9oco59TuJTEuXRxDe0Qpcc6+oQ/r0XgSJQBIpAESgCReBER2DXDjIjjLHG6WXMckQ5rww7hhsDNUYasBlnjEhpS0NNHHpJSzlOLUOTwfnsZz97OMCMQY6z7wv9wI5fnfXDOAxbxyWV8W9OfDvIgV/3YzgneuO3/kXgaCJgPHN0jcv8eJTjwsYxXRAnjiPJcfXd8dlnnz1ENP5nB5m+ifNJn6T8XB9OKFqcRQtrHGILdE6W0DNxTPFVnv7iRIe2zzakcUTJKHhfF/BSN6dn7FajgSddhp/gyHj0ITr4+/GxxEUfLnl4l4YOJ54sjlY7oYOfb6jVh5x2m1M+93XyNq4IFIEiUASKQBEoAkVgPQK7cpAZbRxRxxEZs3Zs7ZwwFjnJjLcYtWGvzOwEJ96dMRnnOPGMPM62i0HMQc6/RMGboeiHwBxp9OM4LvzRsasiHQ0/8rWUJTx6LwJF4MgjYFwa/5xWY9IzZ48+8Dw7dOI4mha4BGnGr3wcYs5hTqGIWxfE0wMW05wsoRs4yHatnUjxSQcdIh/n1g95WUyjwyy6Weyz6MaRjZyzjDNPfNCin+g9dSUnneg4N+f4aU972uAjTbCLrRwdFZ2VtJk2npxju+A+Z/FpCt3mdx8uvPDCcULG7zJYJHRkXX51cs/zTK/PRaAIFIEiUASKQBEoAlsjsGsHmdPKGWaovetd7xo/oMWwZYw60uzbOAYg45HByNBktDEMXXNIPOM4xp30xCvPOUaLkS3EoJRm98QRQ4a0eHTyAz2MXPFoNRSBInD0EciYjkNr/IuLXvCeccshdHEC5XdlgUs+aXZMObaudeNaPvrGpxf+/7D/DRxe/v2TfyWVQJ+4fL7BgeZwckj9pgK+dM5W+oNs+NjJpQ+f/vSnD93DAbd450fJpHOSTz311LHYR16/xs8hJ4vTMevoo20n2lFqv7qNnl11R60dHSefOMfW6Ub8g6H6z8E7nbiMn/P0uQgUgSJQBIpAESgCJzoCu3aQGXWOSzqOyMhj0No18YvUdjTEu+wyM04ZZ4w491wagcHIsGMQimesehbPuXXFiE1edxejD210OcRkQkvw7pLH1VAEisDeIGCsJhijQhzPjHvjlsNrsYte4QwqJ7/Ls7x+COsDH/jAyCteXPRFeHgX6AX0nEIRRw84As1ppVfom8jjyPNZZ5015CJL0mbdE/q50z2Ob/u9AzvOfp3f7vCVV165uuiii8YiYX4nwW42hxY9u9h+cVpdyOfXqh0tj9zoo+1bZc46x/38888fzj49Sz6/70DvumDpJE0WDNTPjjl6OVruXj2Yluu9CBSBIlAEikARKAK3R2BXDnIMXgao44j+BZMjfhxjv1TtXzPZ5fGjN9lFZsjZ5RUYcAxQgRHHWGW8MfxcCYzExCcO7xjLaMyGbNLQyA4T2njMxmdo9V4EisDRQyB6wz2Op7FqbLpz9Diz0o19C1zyZVwb69EPnpULzbkWoe8eHSE973SKK+Xlwccur11nuovDKo8ycaJnHp7pMQ6rXWcOMHrK+t0D/66Ok2/H2w6zfBYLOcQu3z37Dtt32fSo0zjqFhnRsdPsh7kcE3cM3PFq6fKRiY6jf707zg0/zrdyZHFxjNHIse5lHfpeBIpAESgCRaAIFIEi8GcEduUgM7yEGGucYMcIOcF2O/ygDIPQs8AglIchaAfHd4RxfH0j7KghQ3I2ELcySgfBbf6QD32GbwzhbYo0uQgUgaOIQBxP49yzy7jlGDqafO211w69QaQ4hJ7zq/YcPmNbUE75BM/KSOdo0wPi3IXoGXG50OC00mOeOZvJS0ZllZsDncXJtZtrB1p5Dil9R9c5/szx9jsNp5xyynBq6T+OrR8V9EmKxUSLAhYD0JCWwOn2w4O+YeYIkyl1cxfImgsmvt2md9UjQZw6kKWhCBSBIlAEikARKAJFYD0Cu7KUGGR2Tzi3N9xww3B+GXkMPrsVjDcGIaOPUSnNsUlGW77xYwgy2nxjZ8djNkCVx+NQQwxGND3HMD5Uei1XBIrA4UPAmDTGjX+XZ85sHFqOJqdSvLE7O3e+y+XocXzj/NIxoUlK5QR6xQ9vWYBL8C/jXPK4Um7p/Mo/80j53DmjfvnaUWn/Z923ztnxJj8ZOcni7Q7jxaGW3yLAaaedtrrssstW//zP/zx2mB2zpjPJQxZl7Wgrm99XWKfPUldpdLKyLvqVDDBFa8Ywdei9CBSBIlAEikARKAJF4C8I7MpBZmzl12D9gAyD0PE/DrOdYzshL3vZy8axQ8YaB9lRQj/edc011wwj0S4yI843fHZKHIV2rNE9/z9ZOU513v8i/p93gXJU0c7K7ATHwCQXAzFG5Fy+z0WgCOwNApzAjFfOou9nfW/raDLHk6PoOLJxHAfWGDaWleOI3njjjeP/KPtFa7pn3n3NeKePfPbhW98E+fFAJ7pBfnw4xOSJo8nB5tRyNJMnckTX0U1+XZrMArro538thy9eZPH9NPk5yBYBfFtM59Fl2SFWBk/6ED04pE7hoYwg3oWm76D9krZvqbN7TU/7QS+LBH7Mq6EIFIEiUASKQBEoAkVgPQK7cpBjlNmZcKzasUG7MoxLhq0fqzn33HOH4crAY/gxYP1wDaeV8cjoc5155pnDUfYDX2jJH6MQrdNPP33Ec5ZnI5Fz7ZeyGbaMU2mecznSrWxDESgC+wsBY5WucDkO7Fef/bK0RTOOHufvuuuuG3qBs5pAL3A+Lahx/PwAFkfVLqtvh+kZIfrDqRXOa3ZwOcA55WJX1vvs/NJjnHX6CQ06CS/HmwX5LQ5ydsmhHn6I0O4vuaN7lHUyhlMavSS/BQDfCpOJI8spfvnLXz7oWghcOsJ4iUvwHiea4y6g7+L8nnfeecOp5nh7F8+Bd5zbzjt92lAEikARKAJFoAgUgSKwHoFdOcgMQQ4rw5OTzOj07R2DjKFnx4LTyqCTl5HHgDzjjDOG4cYoZvhKZygyUnPskUEnv3LSONryM4DlT2BcOtrox2sYsN7xF5R1tJEz7pkznbSU770IFIG9Q4CTydmjF4xtu590ivEaR5AeoCfEJWRs0xl0D31hMSy6Rj5jHV0LaBbJ3MWhxQlH1w6re/Jzajm0fjXaj2rZvcXDMWf/ug7fON5okZnuWfJXjoPqO2r/yomzbyFAfZX3w4UWBfODhRYOI8M6HTXHeabn8EQTPYFs4jjewqwL6T56lI5Un4YiUASKQBEoAkWgCBSB9QicdMtsda7Ps2Vsirq7GGt2VxhwDFWGWIzPEJFPHEMx5ZPfu/ICo3nOJ40hyNBzj8GY/NLxmw1kcbmUkeZK2cjUexEoAkcfATrg+uuvH7vEFtKcOHH0mcM76w1j17UMxnbGsvx0BqfQszS7pZxTdw5pjhZHZ3CS8eLMJuDj9xR8I2znmOPKifbr/J7pGHnwwBsf9PAWpImzUMjBtgtNL3KQXfSXBTv1Rc9pmTmgmTrN8ctnn6j40S3/OYDTqx7KLXEKPTIJ7mR3NRSBIlAEikARKAJFoAjcHoFdOcjIMbhm4yssxDGAOaQxxsTFuIwhF2PNu0sZIc5u3kNXPIM0RmSMU/nEhVfyu0e+rdLnvH0uAkXg6CBgzHIkHY/m4C2P/maMr5NmHtNzunjlXOjbLc64z+KYeHokeZWP/qE/yMR55qzbIc4ucvjMOgYNZSNr3uWVL/zFuzjI4rfSV5E9vLa6+546J2qiK+Vd8kdPnEBOAX9YNBSBIlAEikARKAJFoAjcHoFdOcgx+txjIM4sxMeYjJGW9BiV0mPExYCTJ/QS590lf+LkC+/kD5/5XZxL2cgTOXovAkXg6CNgPFrc+n/27rNLt6JMA/CLYMKAoJizgAkFA6KCopgJpnEtXTNr5kfML/AHzKf5Ml/nyzizdMwwKhgRc0BMYMSAiIpZFEzMumrOfabYvn1On9OH032671pr9967wlNP3ZWeu6r224haFrxCYKNN+rJ7+nPC3Jf+3o0NGVPE8S5t7sLEy10c4bMsz3RCYuk0y4seuUcPaSJLXt6lDRGedZifyZ6dsFn2HDY/008+yPHSzbosw7xvNo91aetXBIpAESgCRaAIFIHdjsCWCfLBAIqxF6NtGT/h/DeKM6cRf128yEnY8j0y4p/33otAETj6COinrpmo0mLun+nLh6KdNLOMpF36i7OUP6dbhkVO7geLm/xyT7rcN/Kf5Sbuunv0W1eOdfFnv83mMafpcxEoAkWgCBSBIlAE9goCWyLIewWklrMIFIEiUASKQBEoAkWgCBSBIlAEdj8Cdz7ft/vL2xIWgSJQBIpAESgCRaAIFIEiUASKQBFYi0AJ8lpY6lkEikARKAJFoAgUgSJQBIpAESgCew2BEuS9VuMtbxEoAkWgCBSBIlAEikARKAJFoAisRaAEeS0s9SwCRaAIFIEiUASKQBEoAkWgCBSBvYZACfJeq/GWtwgUgSJQBIpAESgCRaAIFIEiUATWIlCCvBaWehaBIlAEikARKAJFoAgUgSJQBIrAXkOgBHmv1XjLWwSKQBEoAkWgCBSBIlAEikARKAJrEShBXgtLPYtAESgCRaAIFIEiUASKQBEoAkVgryFQgrzXarzlLQJFoAgUgSJQBIpAESgCRaAIFIG1CJQgr4WlnkWgCBSBIlAEikARKAJFoAgUgSKw1xAoQd5rNd7yFoEiUASKQBEoAkWgCBSBIlAEisBaBEqQ18JSzyJQBIpAESgCRaAIFIEiUASKQBHYawicsNcK3PIWgSJwbCJwxx13rFzHHXfcuA6lFNLNjoylSxxhyetud9u7a4jBwz2Yx28dfks8+14EikARKAJFoAgUgWMRgRLkY7HWqnMR2GMIIGZ//etfB3FFWg+FoEmbC2whezOEwv/yl7+MMPKT314nyDDngkNwDIbe8zzj2eciUASKQBEoAkWgCByrCJQgH6s1V72LwC5AAMGa3Trii6TFP/c5zcGel2mW79LPhDg6rYt3sLx2a3gwcc+lrDAqTru11luuIlAEikARKAJ7E4ES5L1Z7y11Edh2BBCt7FBGmeXucMiYe1z8DoWYiTvLiCz3yDv++OP3P4ufXdM57l573gjj+G+E6V7DqeUtAkWgCBSBIlAEdg8CJci7py5bkiJwTCEQYjorzS/kK/5//vOfVz/+8Y8Heb3//e+/OumkkwaxRmg36xyfJod86e5+97vfKWny/P3vfz+OWt/jHvdYnXjiiXeKs9defv3rX6/+8Ic/rB7wgAes7nWve416Sf1YPPjjH/+4+t3vfre6973vveex2mtto+UtAkWgCBSBIrCbEShB3s2127IVgR2KQMix+y9+8YvVTTfdNAjsk570pNV97nOf/bu3wm+//fbV1VdfvUJen/a0p62e9axnjVJlhzekLSR3LnKOZ7t/61vfGkT7UY961Opxj3vcIMlJk53sr33ta6uf//znq4c//OGrs846a78es8z5WTok8fvf//6I650+0Q0RR8wRdP6elc8zP9ef/vSnQUIf+tCHru573/vO4vc/J/4tt9wy8BDvlFNO2b+YIJweFhLocuaZZw6Z97znPffrsl/YvgdpOBh4RnhdWRj48pe/vPrhD3+4ev7zn7969KMfvUKYlU84ufJTLw960INWT33qU0d+yzz6XgSKQBEoAkWgCBSBYw2BEuRjrcaqbxHYJQggZi7E9eMf//ggZ8ir3co4hAxp+9znPjeIITKGVCKgSJ1LHG65K5wwO8a33Xbb6lOf+tTqi1/84uqSSy4ZBJiMHKtGUjmE7yc/+cnq3HPPHWR8eB7gW1uEF6GmP3khwuR6tuNN3m9/+9ux0yrOgx/84KEz4q9siP/pp58+8kSeQ9qTd8pBR6T1l7/85Yh/8sknj/KLJ87NN9+8+shHPjKuf/7nfx4k9oQTTvgbeSHE0sTBZybXSPBXv/rV1bXXXjsWE+zaf/7znx/5POEJT1g98pGPHOW+4oorVqeddtrqYQ972CDIZLpSt5HfexEoAkWgCBSBIlAEjhUESpCPlZqqnkVgFyEQEohE3nDDDeNylNeuJNKYcGTrN7/5zQh/4AMfOIjlr371q3EPCUVSHYk+9dRTVwhhXMiaO5L6gx/8YMhHUMVHVuPEIffrX//6IHoPechDBtHjz7nTaU6TtPwQSnrQKeQQAabPd77znUE+7Y7bieWH1LvIvd/97jf0mfOJ7NzFE58sZJt+HD/5uduF/8Y3vjEWEhx7zoKBMBfduJTFnZMeQf7ud7+7+tCHPjTCn/KUp4x8kHHE/Prrrx8LDHaukWVYq7cbb7xxhTDzJy96woTcuiJQBIpAESgCRaAIHGsI/L81eaxpXn2LQBE4phFApkLMEOXHPvaxq6985SuDyCLJcZ4d9RXHrqYd11tvvXUQQM8I3CMe8YjVy1/+8kFuQ2JDVOWD/CGW4iF0886qcHkgx3aDEVlx5EdGSJ9n6SKffkinndznPe9543tdYYipNPRyFPmzn/3siEeunekQZHki0dKQYYFAunUuZfnZz362/8g20ouUyw8eyLFyPvOZzxw719IIJ9OdLsoUHZMPOfyUBUZ2wy0icPyUw860I9aOpiPn/ORlx9tx9HwXLo1864pAESgCRaAIFIEicKwiUIJ8rNZc9S4CxzACSBuyhhDbibQj+YxnPGMcE0YcXZw4dpARPLu+yByihqAhlwi2sMSfIZEHfz809aUvfWmQP0eD+SHC0tn5tPtLzsc+9rERV56IKNIpT/GQPnERWWQyzrP0jhg7/uy7YCScHzn0lQbxfMxjHjOORiOddPvRj360+va3vz0I8+Mf//hBNjcil/EnDyFGsslwCYOh49DkWWj48Ic/vH9HHU4woJO749y+5aYHR08XksvfrjDMxVV2OPhOXDokXzx1gJDbEXcsPosC5MEk+gan3otAESgCRaAIFIEicKwgsGWCPBtpjCLvs4uhlHgxLtfFSxzppYu8WUb8koc08XOfn9fJk27OO3Fmoy5+G+mRvHsvAkXg0BBI30P07P76hhUhQ77OOOOM8W2rO4KGyIrjR6euuuqq/cQOCbUbiiQjfUgs0uaosv4fJy3i6LtjP76FKCJ2ZCF0P/3pTwcp9wNTjid/+tOfHmODZ2TZ7mh2ZxFSetkpnl3GG7r4ThrpfPaznz3kZkyZCa1y80fI7Vi///3vH7vVyh/9YZRykIukIrDKk/T8lIdDZK+55pqVH/BCVqV1FBuGsJHO8XH+8nzFK14xfsQr5ZCf+BYRxCUbFtddd904Gk628tPZUWuylNMzwmx337t0GTt9e22n2Q+PxaVMee+9CBSBIlAEikARKAI7EYEtE2QGm4uBxACKAaywMfT4i5P3GFEBJOn4i8eRx3lfGlbicZET+QxP1xweeeIIS7rck6e78OjCIJ3TkJPwIaR/ikAR2DQC6cf6FYfcOaKLlNmt1d/t0GbHFvny/Svn++EcD+YnLWIs3ROf+MSxe+v75IxBI9FqNXY+EeN3v/vd48iwo8DkIMDiOkqM+CFzn/nMZwbBe/KTn7z/CLHvoY0BCCBiTffzzz8/4sfduCEOfRyRRsalIxOhFuZSfgQyz0i/3XO6IN30Sph7xqr8wrc7uXQRbkFAHPpbSHD0HAbIL9IqP8QcnvSGG7zohzgvnfAcY4c3sm9xgeyPfvSjg8TTEYG2oEGm8tgZ9yNrypNdd6cB7Jb7FXBHtekbNz/Hr/ciUASKQBEoAkWgCOwkBLZEkBmHLsaSXQxGGYPMnRFrx4GRxrByZReCv4uR6hdr/aBMDKfcySWHXEbavDvBGI28xHcXJ0cvPUvPiBRXPvKP4Rm9GXz8XOJHBnlJpwzi1xWBInDoCKQv6++e9VG/uIx4IYj6nDCXPoqsfeITnxjky7fA4vu3RQi03UtkVVy7vOQ50mzcyVgQDcnVv/Xr8847bxDH6IAkSu+Hu8j1C812fl/60peOXU+yXNIiz3ZRM1ZEfu7i0fucc84ZO66Ivx+7spsbgkoXF32VwQ6teHaO/dsqY1ryFCfOUXBEGgl11BlexlsXDI2z5Lk7ov70pz997Hwjy/KmF3l0d/w6vxCecVA+nv0StfjyQI4R3uiMBNu198Nd8KOLhY2zzz57/HsnvyxOT2mQbDo4Mu5oOSd/ZasrAkWgCBSBIlAEisCxgMCWCTLDh1HlezSGq2/SGGvf+973xjd2dlIYijGYGFIMNbsTjDWGmV2bHC+cQUOMHf1jxDICGcr8GJUuR/gYdRxD1i4LA80Oh3zEZfzm2z+G3Gyo2ZFx5FDeSLo8pOfvnXzlcUyQoVlXBIrA4SGAJCF1nH5qJ/PKK68cC2hZoBLm2eKZ48/GDruQIc76LjmImz76jne8Yz85JNtC1uzEdxkDjBdkeZc+u8ju+jbC/IY3vGGQVTvBnLicf3/0zW9+805+42Xfn+RjjLMbbOyxO/3CF75wjHsZt8QzTn3hC18YJNTY9cpXvnKMj8vxRTk5xNkYZKyEi8UDd98Z2xHP8XH5Gmfpaky76KKLRjqLfPKFIRne5ZuyycMzWcIdq4a9hQnObrJx0JhqZxhRDtG/4IILBmm2m22cRpyNoYi6nX15Rb485jxHQP8UgSJQBIpAESgCRWAHIrAl1seIczF47QYxzGJY2d3gj6TazbC7woBDjnMcj6Fl94eh9YIXvGAYsgxkRqRdCz96YyeGkcaAZACTy8BkEDrumPyQciTdL8Yy5jh52zlh8Ppl14svvngY3eTIm9x3vvOdI1/fGzK6+TG2pWOgM/YYuslnB9ZhVSoCOx4B/Vq/0u8sjvmfxBbW7Er6sSrOWIJEuacPGgeQRCdJQrCE8XePy1iUOJEXWSHhyUO4uPo4Mof80YWfsSQk0jty6fI8p5/zJl+afM+MeNv59eNd0ZMM49f73ve+QXKf+9znjl1YY1v0j0x5GQcdUUZKhdMLdgjyc57znEH4Ld4h5nZtPSOpSLO8LrzwwrEwYLGPowc957LwlxfibefYj3vB2pgsHdL84he/eCwc2jW2oEknWFkIEG58VQbjLTJNX4sSnDIIk0ddESgCRaAIFIEiUASOBQS2RJAVkOHG0LWDYNeEYegbOGTYrg2DzvFFO7N+QZbBhGzaFXHE0M4M48yOiAtRJc+3g295y1vGd238GbF2KsjxL0c+8IEPDHwZa4wxejD+7AaTz1BjECLkDFWkXP6Ieo5MM7I/+clPjp1pedhpphd59JbObgqDkqHLmK6hdyw06+q4kxDQZ/TNkED9yY6lS19D+MRxiYOgWayyG4nwIZ7GDenc9WtkzK6vOxfStyy3uE62GAdC1OSjXwvzb4sQQAtixh2Lcoim74fplvzIpwf9ZuedPE4ZkUVHj+mFPHLyNeZEF/Ed5zYWIaGzjJFg+iNP6aUVTx759AOpt3hnsTC7teJb5ENmLUwas4xrnJ1sCxQuLnjD2i9gO3JuB/p1r3vdwMXJHeVw7Fzaq6++eixoGivhaQcfUZaPsRT5t0s/40Tf5BOcRub9UwSKQBEoAkWgCBSBHYrAlghyjF5GJCOXkYmwMtrsvDIKGaIuhifDzJHCSy+9dBBhRtnll18+iChC6gg0Q4+Bh2z79u+SSy4ZBpqdC0Yqg40hi9gi3nZSEHLp7N4w3jh5i4vwMt4QarsjDDokmu7kkcWAtoPtB24YzGQxFOlLR7vSL3rRi4bO0tUVgSJwaAggR/oO8oR8Gie8O3WSX4sWx4WMiWdRyskP/TTkVq7Gm/RpMiJ7STS9h5Ra7Mq3y8YGp1sQQ89IpD7vsxAkkW4csscZ28iiA0dm9DTmCacjPw7pNRaJZ3yx8yuOcdDCHXKsbMpoFzZjinLRx465O3khxeIIl8adLi66i8PxtzDpeLWxj1NO+lh0oAds6SUund3hYLHBgoCdY8RdvfBDwuGhjhBhesFKnhZDLTwqi0VOCxpZWAhWwST3oVT/FIEiUASKQBEoAkVgByOwJYLM6IkRx+hivNnp9b80EU3GFwMLSWVwMfwcKxTG0GK0Ic7IsN0Hxps0DMn82qpdX4YZ+cLtyth5kpdfTWXUypMejDiGWXaDGHjykJZBjex658SP0emOgDOIHQ8U5o5MO7Zt15rxRxd61xWBInBoCITE6WtOgrj4OXmCqPGP08f0b0TMXZ+e+510yJ5+6uLc+ec9ftKSg0jq0/KKLMTRu0scO8d+WIpO8vX5h0U740dku7s4Y4qFPTut0itDymFxDSm22GexzZjml6bpg7zyNz5FP/L40zHHmMkwHpIZjJQvz9Ikv+hnjDVOOV4NI+TX0WnPwTz6k+Wig7LCRfnFlUa5LD6SAQP5Gn/hBhfE2Pjo3TF5+iLIdIjsAVT/FIEiUASKQBEoAkXgGEJgSwQ5hpa7i2FkB9eOA6OLUeXO6HP0ENFFlmPU2bFwMQzt7jCwOCSWUSm9f/finXzGIgPO8T4kmBGGuHLCyWCwSmNnw64GUi2+PLyTuXSMW0Sejhy5DEG60h15zy7SMm3fi0AR2BwC+iiHaOlj+qL77MQRjqTpw75V9swvzrOxAsl0j4zcEy935NiOrsvYI57FNb9vMOtk99UOqm+E/ViVUyove9nLhp7iJa47OXaHLZ5Z5DOGGOvoJkwcY4ZFO2MUkklfC4Xi0CFyjJuc+MZPxDNlcc+z+MZAZTaeueSVMiXcmGcsM+759W1jqWPWM3EVlyMPUTfGwduCo3JZtKQz8uv3JcSXr0VEF0wtdvoURrwsXiLOWYAYGfRPESgCRaAIFIEiUASOMQS2RJBTVgYa4xCpZJi5M6j4cwxCRpPLzgwnPP7uDLWkiTHGOGM0Ir5zGv6MXYTbzgcD0lFC38jZefbMMHWR7RgjA5bhJ5+lExYDN+F04Edfz4x590EROwIAACAASURBVLoiUAQOD4GZ7M3P+tXct0KcfQqBLFog049DFN2RQ+OJuC6OPzmJx08ccuwO21nlxAmZTb82Vhm3nH4xTviG14/9IeeI4XJ8kofFPcQz45+7K3qENNo5No7ZjfW7DImTsSZjonws1Bm36Jh40Vl8cexGI9rC4eK/ANDFoiESjuQi7sbNkG7jJRyNnca1yHQPXmST4Zg5fGBhN5u/f7sFS+OtfOnoUxoLCY6vG/f9K66M/XSlW10RKAJFoAgUgSJQBI41BLZMkBlXDCbGE+fZFUPVnRHKyIphKjxGmXtkMMo8M6wYYIxT/yPUUWfypWeAMY7JE8c3fdIw0nxnzED0ozW+Gc7RbP4xUmOU0pU8cpJv8nbnGIt0EE7nuiJQBA4PAX0q/YoEfc/Fb35OGCLnSHD6t/5nXEifdRpE/+fSp0OU575KvnT84i++Z30/4xK5/Mg05iDUFtrmcc04kHjydZolu9Nz2VKGlAu5RFQRTkef57ie8y6+Z3rQ0fMtt9wydruRXWW2K+we3XMqx/fAftDMDx+Ka5xE3hFcO+PK5X8vI9IWDGBCvruy+i0Hn67YDbfjbSedrk7RGFOvuOKK8UvZCHIWDe122zFGkunseDV56iHlcE8dec778OyfIlAEikARKAJFoAjsQAS2RJBj7DCOYhhlR2ZpDMWwZLjF8RPfRYZLOgYcw4zRZxfGMecYyjFSI4+xJg3D0a6JtAxRuyr8Gbnictk58SyMEUp+dI+/MBeCjIzLUxx+dUWgCBwZBNKH06+8h7zKwU6pndz0eX2Q0ycd63XnjBuccUQccsgky66pb4WRPsQxeTlWnN1h6eQhTHq7oY4jk5/xSjiynPTJV95Lf/m74sgUz1iF+JMRHWd54tM5Y6I87Qb7t3a+8aWP8QqR9a+dkF+74sivBUK/saBcfpPBj5vZfUfglYFcJF1+Kav8vNPJZzHGO/IRf2Wyk24XHfEWz7+SyicyZBmf1YNyKZ+j3Mg00i6PlNE9dbMsbzDqvQgUgSJQBIpAESgCOwWBLRFkhk+MoBip8UsBGUTC4p9ndxdDKoajd/EZa37plcHnyLRdCv52PxibDFsGn3fGnWdyXJ4ZxIw7Rpx/I2VXRRrG4mygec5F35TFMzmMOndx3OuKQBE4MgjM/S59DTkL8RWOqCGCxgdxjA+c3VPxENmMB8idcO/Szg45DskT7uKHcCZ+0rgbR/hbUJOv8USeSyIsj6Sb88vzMsz7Or/EjzxxjD2cMYkOSC4s7PQaH/ObCcgxfBDX7IYjyIizNMkPtr67dp/1yLOyWYh0DNyv+SO6dr6Nm9lJN97C2NiKHPuvAHB0wkeYHzfz3TYd7Uobm+NSd3nvvQgUgSJQBIpAESgCOxWBI0KQGZN2XOzeMipjlCm0Z37CGXLrwhhXjNIYawjx85///EFu/eDWZZddNn4t1c4EWb4pZvz55k08+fvfxy5Gmm/oGHbi2Xnhl3znO33IdHdxDNLc6SQfTrqEDY/+KQJFYMsIIHUIoMWs/FAU8oWUOmKdH/yTUfqgfoh86ffZyfRsAcwRYGMEh5Tp18YCP96HVCOeyCBCbbd1XgRLYejDH5m0A21nFJkmV94ZQxL/SN7JpnfGGsTXGEQX46IyGktD1hPPcWekVNqMp8FWWYyFdqPhhFSvczDNfxjw42h+4AtRlr/8fKYirXHRv74TBxn3Y12cH/O68sorB2b+zR9Z+eVs5borcVtXnvoVgSJQBIpAESgCReBwENgSQU6GDDJHmhlrjkIzpuIYRYw33/XZ1WCwMuIYoZ4ZtHYc3ENGGWqOD/793//96qqrrhq/rur4tB0QBhdjFbGVH1kuOx+MagacY4G+pZM3o/K8884b8hjKs3GI0CPY4tmRmQ04Mh1RdFxbOrtZjMu6IlAEjhwC2fVEXn0m4X+fI73GAsTOCRD9MkQwOadPultEQ2KNGfq7cUUaY4yxwvhkfPEtLpn6tl1R41GOUrtz8vGNr+PdSKUxxwIb8mnMORRHlvwiF8k9mKO3CwmV3gKBKzKE5Xk87PtjXFNO4fJUdrvmvmG24+tfNiG4Z5999ijLnNaznWBxLCRYqFB++VqEdKTbogWy7Je/1Q9M4OlXvsVRNjq/973vHTvL6sPvQBj3Mx9E92XefS8CRaAIFIEiUASKwE5CYEsEmcHjQh6RUCTVD8MwlGZjyLdriDNCylhi+LkYpYg1YxiJZuQxXhNmF4IRa+eDccbwI4MhaCeFARd5DGM7GQxZ8RnG8iSXfAaxbxDpytHPkcLXvva1453es870YHDHUCdXueqKQBE4Mgjob/qvo9L6s74WgqvfJlxuCJ9xgeNvHLj00ktHn8xiG7+5j3rW388555zV+eefv/8XpMlAfPV//Xpe+EL0kEHfPiOBCDj9/Aq03dzNuIwjyL/0ykU3+ShDxrh1soRL71KuuMjMe7DIO5lc5Mvb4oJfu7ZwaAECtv5FkzF16cQR3wKFfOnsM5f8YBnCbIfYZy/GUce94W/Mt2jJnXvuuaOMdpGVOzv0WbBY5tn3IlAEikARKAJFoAjsRAS2xPhitCG6yGTIaIy1FNhOQwzRpHFnHPNnxDq+OBNr4eQgwYxL/x6F4SYNg0vcOX6MYbvPyC4yjXAz9MhhHPITTrZLuB+z4cTjN7t858fojJw5vM9FoAhsDQF9zK6mxSzfsiKh+vLSzYQw/Ve/5m9c4GccSj/2jnQjhHZAjQnp+9IZdyzqGUukifzIQmb5O0Vi/DL+SCPecpxY6updvOiGTGZBL/msS8Nvlr3R87q04tLdxdEfsTd+2vmWP6wtSFoYWDplNUbCH16OR7uTASMYWLA0/iLfdo0dr4Y5POVvnEaqEWZx5C1tXREoAkWgCBSBIlAEjiUEjrvjYBbbNpcGqY2Lqoy/2Xicw8XJtVG8xO+9CBSB7UFAH9W37TI63ougIXAI3pIgp99vpGnCETV9Po58x4F9Rzzv4JI/k0npXNHJbqoTJ3ZdpUMunVpB9ow74h7MRRYdfPJBDwQUWSVDuPus78FkHixcmYIF2XbCHa+WB3wtCFokUI5lGWCkHuwOp7wWLaUNNvBw7FweFjSEL/GggzzdkWn5cmSI66orAkWgCBSBIlAEisBORmDHE2Q7EVwMOgYnA3dpaMXgdM91JI3PnVyJ1a0IHGsIpI+GqLrr47mW5RF/dklvHPDsvkzLn9yMGUmTccE7l7FE3MTJc3ZA4y9t4s/6LJ/Fly+Xb4+lDTnnT050WaY/nPfoKC0sjJ38PAcfunhf5qu8LvFcnrm8k0V3d2HIsTv5yW88TP+XOmkjZ74nbu9FoAgUgSJQBIpAEdhpCPztWcYdpiEja+kYZUt/7zHWxM/7Mt5SVt+LQBE4+gjol+mb83OI29yXaYfUzU544kTOHJ7nkMPkkTTC8yzM83xPfvFL3Mg92D3yxMvOM1mcsOh1MDlbCQ8ZD5FNmdaVJX7iiJ+4yT+6q5/UUeKmTIkzp+VHnnvCI7P3IlAEikARKAJFoAjsRAR2PEGOMQY8Rpz3dYaWsPjnvhMBr05FoAjcGQGEynUofXgzfXwZJ/L5hzR65s95nsebxBFGP+GuzTjxZqKYPA5H1mbyE0ceKWPSRG+72cLm8iVO7svypazSrTu1EyItnjjeuVkO/1wJS369F4EiUASKQBEoAkVgJyKw449YL0FjbMVwW4b1vQgUgWMLgbk/z893RSlm+Z6XbjmuLOMsw5fpN/seuUdKXvKNXO9L2cL4Jc668KRbxkna5JP70j/pIsd9nV/S914EikARKAJFoAgUgZ2IwDFHkHciiNWpCBSBIlAEikARKAJFoAgUgSJQBI59BO78Yd+xX56WoAgUgSJQBIpAESgCRaAIFIEiUASKwGEhUIJ8WLA1UREoAkWgCBSBIlAEikARKAJFoAjsNgRKkHdbjbY8RaAIFIEiUASKQBEoAkWgCBSBInBYCJQgHxZsTVQEikARKAJFoAgUgSJQBIpAESgCuw2BEuTdVqMtTxEoAkWgCBSBIlAEikARKAJFoAgcFgIlyIcFWxMVgSJQBIpAESgCRaAIFIEiUASKwG5DoAR5t9Voy1MEikARKAJFoAgUgSJQBIpAESgCh4VACfJhwdZERaAIFIEiUASKQBEoAkWgCBSBIrDbEChB3m012vIUgSJQBIpAESgCRaAIFIEiUASKwGEhUIJ8WLA1UREoAkWgCBSBIlAEikARKAJFoAjsNgRKkHdbjbY8RaAIFIEiUASKQBEoAkWgCBSBInBYCJxwWKmaqAgUgSJwhBC44447Vscdd9wBpYnDHSzeAYWsVityIivytiqTnMgka87D+yxf2DLOrPMy/hw2P5Pz17/+dXX88cePu7DkE//E956w3BN2oPu6MsVPOnkfjgsG0YtOKc/d7vZ/a7bR8y9/+cth50M3ecwu8me/6JP7HHakn5dlTjmPdD47QZ6ywnvGdX6OjvHLPf4Hu4svj7RD75EB19R9MM59I7lJn/A5vrA4/nNY8lnXtpJm3Z3MYETe4cpZJ7t+RaAIFIEisDUESpC3hl9TF4EisAUElkap93WGJuNxNkrn5wNlTx6SRaY0yS/Py7TxF49bp8syTWS6i5/3xIss755Daj2vK8c6v8jKHR7JJ3LcuRjdeec3P29G/pwmmMSAH5nsy0fYocqLLu55Tn7Lep7rLvludCcrukRu7klDXuLEz52fuMHVe4jXHG8rz5FPdtpJ8t6K3J2YVlnnKzrGb64Dflzuc1jSrbtHlvuybSaMrMglYyPZiT/nk3TuSZf2mfFE/MRbti1pkm6ONz9Hb/EiZ85v1qfPRaAIFIEicPQQKEE+elg3pyJQBBYIMBBdjEvXPe5xj2FUzoal8J/85Ccj5X3uc5+VC3mZ4yzE3un1T3/60+rud7/76s9//vPq9ttvH+nufe97DxmM0dkg/e1vfzv0EP+e97znQQnynF4+v/nNb0Y+973vfVcnnnjikC0Opxzk/+pXvxpluP/97z/yuJOym3xJ2cn0/Lvf/W4/fjCUpzDucIkeuXP55AFD2KgD8meisBnVZ3nwUuf0ozMnz1tvvXU8z/ilvAfKI3GShzt9YU5PdSJO4pElDh3+8Ic/jLo44YQThp/4h1q2A+kmLPUhz9///vdDrwc84AGj7PLajQ62HMxTxrRZfREW+qK7K26uo/gt75GpHZGp7uRBDtl//OMfV/e6173uNKYkH2nnPPK8DCdXG8qlnWr/4icuvYTTgzNuCF+2n5RbmGfY3HbbbUO/9NnoT050GkL7pwgUgSJQBI4qAse/+c1vfvNRzbGZFYEiUAT2kROGIuJ14403rn784x+v7ne/++03aAMSw/NDH/rQ6oc//OEwTh/0oAcN43EzBiQjlvEq7ve+973VN7/5zWE8I0sMXY4O4jFOr7322tX3v//9YbzKh9/BnLTcz3/+89UXvvCFkR75Pemkk/aTIvkjRV/96ldXV1999SCFp5566jDghS2vg+WZ8BjdX//610f5vJ988snD8Eb66BbiII/o6nkzbo4Hu+985zujHMFmSQIOJlP+0eFHP/rRqHNEAVby8vyNb3xj+CPICM6hLIZIjxhpM3RDjq+55pqxcEFnWFiggBO54v/iF78Y9S5MnsgKXXIdrEyHEq7sdNPer7vuutVDHvKQTS3EHEoeOyGucqZfIasu/RDGHNz155/97Gejz6cvRve53cVveSffYop+DVMyXJ4tqOlr2gICHgJKRtrfnEf0FU4u/dw5/ei73/3u6gc/+MFYGCKPXP5pI9qQ8FtuuWW0Zflt1DekkZ+2CQP5GI/Ed63TbyjSP0WgCBSBInDUENhTO8gmIpNPJrV5gjxqiDejIlAEBgL6IoMQUfrEJz4xiApyZwfGxYnDGP3kJz853hmST37yk/eHJY47WUunjzOakdPPfe5zqy996Uur5z73uatHPOIRgwzFWJWOYY28MnLPOeec1WmnnbbfoF/KXb7TE7G78sorh8H7wAc+cHXKKacMoooU0IHcj3zkI6uvfe1rI+9HP/rR+41w8uiCEDLAD+ZmvY1rX/nKVwYxf/rTn7567GMfO54RdjrIJwY4uRkDD5ZHwsV3IQkWEGD3xCc+cZQpcTZzJyMOAcliwhlnnLF62MMeNrBGopQFgbRwoh7Ua4hV0q+7k29HTrkRskc+8pGrX/7ylwNz8k8//fRB0j772c+uHv7whw+ckGTE5gMf+MDqwgsvHOQG/vCFq7w9HwkXmRaCtJWbb755lG/G5Ujks1NkKK/rpz/96Wj72qB2w6kn9YwgO43wuMc9bvgfChbGBXX3tre9baXdaysWOLIA8T//8z+rRz3qUavzzz9/9ZSnPGWQ5HVjhIxn28D7r3/969FWtAUk3NhhnLLwpY+mTOTSH0n/2Mc+NspgIcb4pW3NDhbKJy8X/S06GSvIcJog5Re3rggUgSJQBLYPgV1PkE04mZQYXCYmxqoJzJ1bTkbibDSRbl9VNecisPsQ0M+QhS9+8YvDgFVC/ZTTbxEd7wzhGKv8GJ/powxicRng65x4DN4bbrhhkBKG6EzApUHYGMJ29YwH2QEil1uOEXM+xgs6Itef//znh2wk0u6Q3Uok1U6h49cf/vCHh+GNHNpBUqaUAwlkcD/nOc+Zxa99jl7S0p3Bjng94QlPGFggsxYd7FIjfk972tNGGaTbqCzKwS3jJC9lRBLoSQZ/aWZ59Inf7J9CSENfu28f//jHB27IKsdfOJKDOL3vfe8bWD7jGc8YBGKWJ25wkyb6wNiO8U033bS69NJLh0z1wNEf8X77298+MIGNehbOX7siJ/qPRIf5h4xZP++cNmaRQVtEFuf6T1bLtMoXWYkzl5nfjI2w5CddwuIXrJYyyYncdc8Ji7zosu4ujkue+va3vvWtQYItOkUOLGCvrVrEoY+wjVzSJdyilxMN73//+wfJPPvss0eexgZ9zvxuMURbMzYg4cEjZQjW7tFZ2yJXn7KwIq1270LsjUX6uoWYhz70oYMwK4d6tTiVckR29M09ZdTOtTnYaIcW7rLLHv2SpvciUASKQBE4ugjseoJsknKZiExiJmsGGUPU5MaZ9BirJiVHPK0Q1xWBInDXIqC/2eG7/vrrxw7v4x//+EFgETsElqGqXzIaER5kgsGrL+un6dfi2bWxg2SHJ4Y/Q9QlHqMWeUJU7UCTL/8Y3Ug2ok6fpz71qWP3mByyudlg5U+nGMLSfPrTnx47w3aD6IBoI8EMbLtDdsuUhVFs/GHc2022i4Qo8ScPaVvnYlQvw/hLhxSQIS8ENkeg6aH8whjvy12tyIt8d/GX5RUP4ZCXMM/iuhLXPXIid3kXrv6QIsRAXSD19Ao5eNaznjV2HK+44orVu971rlFnyM2c56yj55TL3XFXJBkGsI3Ojt2+4x3vGOXLUX4LGI6nq7cHP/jBo43RMTKTT8qpjCnvsmx5T/rEI0M78m7HUDtDiM4666z97XCjtLOs6CKuZ07ZuOiX5+V7/EfkffG1+WDD33Py805f73F5Trniv+4urvpUF/qBvoccc3Q3x+rv6kRdWQTRbzZy5C3zdyJD++bvtID0ZMNWm7rkkktW73nPe0Yc/eL1r3/96F/zaYSUMfJhQAZSnYWWJz3pSUMtOsvTzr8yabvam75sQQee2i49+JNJVvCa9ReOeDvVYOyQl0UCbVZYXREoAkWgCGwvArueIGfiY5T5tvA//uM/Vueee+6YKBnLDBfE2REqkxmj5cwzz9xveGxv9TT3IrB7EdA3kWO7NQxYZIYB+u1vf3sUmsFplwmJZIAij4gyI9ZRbH2XccohUPot4hMn3K4hAmtn1w4rckqmRTGGNCNV3sYHRyTFpZd0xosQYUY1kulbWUTNWOFCABzbdpyTfo56kukYNaP3+c9//ti9/sxnPjPKxkhHmkOqESZHjHOcW9jSZQyb/WN004FBTSc6eoaPHWN3R77haXeLbIsMSTvL8wwXGLknjjvZsBQGF3WB8MtLePL0rM4ca4UT/9kpB2ehANZ0R5LUpR1cThyYW7ykr3yQaeTEJY049FHX68qDZEiv3MiKunbRHzl905veNPCmn11C9fS85z1v6MGPfGWRh/vsYOpa+s9xhIkzO350sPgDw2c/+9mDlPNfJwtBSzulD73ow4mftguTONjIQ9vSZ8SzEKR9u5Z6a/PyUB/qTZzsvEo7YyGOvqaNWUjYjCMftjDWt82tkUkXda8/f/SjHx0L1yGX6/BIfjAgQxtxrFm7ls6JDWW2q5v0j3nMY0b/sxCFhFpAkxZ2MI3TRlLnuWt78FAPFlHIdmmnyD4MXvSiF402g+TrE+rCGASjyM897YFu0U/++n7K4XTJ6173ulFn0a33IlAEikAR2B4EdjVBNjllgjKxMr5NyPm+yETF3wRuZ4oBwODKyvA8kW1P9TTXIrA7EdAvGdCIqz5pB4lxyR9hYrgzSBn8wq+66qpBap75zGcO8oOEMnZdCJwdQEenY4hCjXGLXMkD0dK/EYEvf/nLI29GdnYw7Qo5XYJkMIgZ3gxkafgxehn0jkEaF+RLR7ugdKPnC1/4wrFLSy4SRB6j2bP0DHFEmIGObCPr+a7RThLCl6Pfc63TWVlmp5ywMn6RLQ685OWiH6JqN5yjn3FPfGOc8NmRhUAi+spPV3qK5x4sQ76UmRMmf/rIX3ne8IY3DFyVcXZwEyekCIm2qGCRIcdMUx6ETH2qZ6QEpnShp/rwDjO7bsoTcogAG+cRPXXnh5qQF/ohItlZpyei46gzfeBOjzj5yIOO2uM6Ip646+7z3OFZuZxi0K60I5fyzPHI8c5fXdjZ1i69u+jj0qYQwvkoPn/1a0cVltomWfCzWICcwkR9pa6U2wKNu76oviysOGkwfw8LC4sNMIPfZggyfdQD8ktfRFB7J0tZ1BdcLWxZYPINONniwXrplEVaOHo2ZyurRaxXvepVo13NP8olnnylUS79Q1x9Q/o4/QLBVl7tOO0T7tFD+4ERfJXJuGC84a/PanNIMlz1EwvunpXTnQ7qwDjl3RWnfvRTuiPIL37xi0e+c5zE7b0IFIEiUASOHgJ3OUE2OcwT0tEr2v/tRsgvk6vJkD4mZ1fCTFJWjBkVJr9OTkezlprXXkSAwcrIdVTRro8+yIDmGJY5kuwd0WHsMjAZ3vqpOAgOgmSniAEqzjzWiGeH2jeKjHGkBPFDPhi7jFLEADmyw8vP0UxxGMJIGhmMX3kxru0IGzvkYzxBGMlAnBF85MSOuDBGevKQRj6MevkYY8hlvNONgc7YnvVPu0DcGPezi/ENR2noK54FAXmGnLrDR3y6yQu5gtXSIcYWCkMA6ERPxEEe3tUT4oA48nMJVy/q8VOf+tQw8pV9nRPHAgUc7MDJK0SVTgicMZo8eKkPeGkD/F0cvRA18egAX/ojfMicd3ioC9gJkxZJ5IfwcEir+kNq3NO+xNXefIqDQGk7mRfkdzAnDgyk8Ux/u6n0yA532tFSlvhIl8UKuquzYEwm7POjVEkLI+T7rW9962i36ptTHuWQv5NT5jkytO0PfvCDIx/h2glMLBhcdNFFoy2rGzion/RTC1QHc9LQRztUH76Bt4OsPoXRTdmVSVtUFj+0JQ96IM3Rf5kXPJFY/doYoc6MG8iq9q1c+pFy01//EEebVe+utE04k2VBQBo46OPkWlRTBuHakXJIR2eLOsphwcV3z+pK+xEGU3f9X1tWHuV+yUteMj4VWJZLeehv/HKqQv/Vz+hSVwSKQBEoAtuHwJYJsokilwknRkSKZKKaJ4WEJ4148UsaYTEeEha/WVbiiys8sujBj0s+7nSJnKQh3yTteyUTKQOKHxmzSzr+uUyYnsVPPtJ4T/6J651/XRHYqwjoI/qDvsDoRpQYuoxaRiVDUp9C9hyLRE4REwZqvlGUjvFpVxGpPe+88wbhQJTJnh3jWFry7DLZLU4cfna3EALGsaPIDPNXvOIVw5AVz1jDQPYL2sgDQs8JUxZGtx0vpJGejGFyGNiInV/PRfrEM66QlwsGSIsyClP+jRySZIdtduRKg6Aj2sgB4x4BZdgjQsYbGLiMfUgpY9xx4nW7gOKRgcw4jkoGp7xwYPCLgyDY6eKUQ7ixE1HwA1ghIBkT3ekiDgJg9xlR5Acz4y9cyNEGyMxYrYze7cxlLkF8xA3Rj3xpfS+KgKp39aadwEh6RNMuv09rtDk7/PR5wQteMOpL+bQHJApB0V7ULbkZu+W7GZey051T1z7jyUKQRRgyxVvKlAb5opswCy9IsfJzMPPNLWyk57RPPxylTyFj2p8w/cuPnXHaOTmwthuMzMFK34IlQuu7b7K1EzirdzLVrTYjbJ3O5MdfG9Fm9WF5km+XdMYwz8hgSKbFFTK0baQVDonHn1Nm7d2us/bMiaetwkU9qztY6XfCtAl39Zh2RC5ZHEy8w1obyO8UkGHRgV7akXjGH33dohNstGMYwzTHq8nRtuRnfNDu0ieWdS1/dRBclcspE+2jrggUgSJQBLYPgY2tsk3oZNJyMciyMmsSZQg5osQwMSGYbE0AjAOGi4mfwWBSclTJhOMurotME5pJxUTuYvh5NwlmojaxmJjEFyZtdnzsDMiDHBN9Jk3vLmnEnydgz5EnjglQvsrizqiSRjlMovQ2CUdnhoEJVN4mfhMwbBgvZJkwGZeMEffkvQmoG6UIHNMI6G+59CWGoJ1dTh/S7/QjDkljkDKuGZgcg1H/4sjRJ5FcshDm+EeGd8/6HAPV0VGGurTyklYf1T/1V/327/7u78YOcfpm+jUjmMHPeOb0W3Loh0wiI/zoQXfjnjz4Z5yTlh+j2/hhjETG5CuMvOg8HqY/8KHT7KSBBxkIp7GJ/OSH7BsryXXJ14WMIyDrnDJIYzfLGOVKemGIgLGUDATGmMsfTsoub3kY9/gbQ4OhUn67TwAAIABJREFUuMZ9ixrIFhlkqx840VddwFp9wZZTJnIsANgZ5uxiSieMfOGepUPs1a3dSDohuXb/jP/mDTjxox8Z5qV//Md/HGWmN1KtHOajV7/61QMDuqQc7pt1wU4adYXQIT/KTp9Z98iUhoODcO32jW984yhb4rjTPzJgqw377l3ZfOfuDhc4yBfJtXAT8mW+1scsDljsgI06QKbVk/6lHZivEV34Oq4vDh3pFl2jl3eYayfSkPOyl71s/4KTtkFnWHjm6Ahfu8z/9m//NnZRyUHy1Y12HpyU0+WUg7LqO/oGWcYJ/Vn++qC61JbpDSt3ednpVXZ9QN+VRn4pC120LycH9HmLYuwMLotS2o32z98Ypj8j+a985SuHPuZ6uJPlW3M4qnM6LB3dlEG4Mc6xem20rggUgSJQBLYXgS0R5KieSdSEa+Iw2SCFjj0xBE0kjqkhyiYnE6hJzWouQ8RRuwsuuGBMaCZDk6BJwmRh9RZBJieTmAmFIWQnJCvT0jGWGQMmUHeTsQnWhGbCNHGanMXl3E1Q9LzssssGiWUskCkvOlhVZ9QxuuiQSZrBalL3HZiJ0OQXo/e//uu/xoo2Y5KxbFKWr8nbZCkNA8CkKP+6IrDbEdCf9DfGo/5gNw3pQQD0b/1TuHj6kr6rb/BjgHLicPqgPs1g1b/Th5LHiLSPRIjnEsaJm2f5SM8Q1x99pyk//Zhsz7nE45e8+MdFJp2lNQ7Z5TIueJdP4hgHyFEGRIxjuEf3WW7k+9bW2Ll0jooblyzWIQPyV1YOrsiMsVLeyUMc+S+dfMVzpcx0Sjp+dEZKEFn+LmWb0xrTIyM4e6enH6hC1JAx5HvGOO0CQTW2O3ZLNhnuyLHdefVg3HePnuRzsEVqHI/VvuDrhAEZyIcdPnOPMVn+cHMph7EYdvBRTv7ahXQp4xKzA73TjZO3i0wYKJe6WobnPXcEWbmQfpd6DB7kJp67RWHzrbp57WtfOz47gK1yOVkBg7e85S1jMUWbUU7xzcHqwvwMz2Agb7LMl+Y/RBeRthMrbfImP7qkbZiz1TMCS548PJv3OXopF3/1lStzI738oJY6Us+Ivp1bedKHHKRUO7OLS09huczl2hi8LIjpg6kDdwtKjq7TzVwMW2mFccqk3bAf7MjTB1lGlPlrF/KwAMCf/hYdEHTy6Moe0o7YNkg4f+9ppyOjfX/kJ3/6im/cMGbUFYEiUASKwPYisCWCnInHgG7iRWatZNtZNZE49sQIdkzJkbGQ2UxMwky+JgkTCaPFRG3Ct8tgUmdUkMfgYyCbnOyYMEKl871UdpfsSjk2Rq4J0KSFpJJBHn+yo7eJiTPxWl1npOV/KSaOiZ+ju9VmZWUEOELFsJePSZzRY9JnXMCBrgwsGJjIOcaZydvkDx8GQAzaEaF/isAuRCB9SdH0We/6JbLAuLQQpe/wj8HI8LSLxUBn5AqP0yeRNGNBjMkYyInjHj/GtHEG0Ta+yIMRayxAEBAIBjp//VPfNObo0zFq3V0ZM5b5eE+c3JN/dEma2Z+fch/IIRXLcQI+xlTjWjBkhCufstk1Nf4oC2I1u3VloANcyYU1LFz8xU84vI2T3hMn4YmTNPIUBx50Ml4qh3pHPOgrLidPZAdBVk/wn3f3tAUyLMAiE9Iln+Rv7DU3IMjcy1/+8lG/xl1jsMVPC5vmKHLkDyeECYawo4P2EgKfRRny1uE2Mtrgj/jBELbaGN1nkrlOprjKKi2s03a1A31B36GXeDAwn+gLcPYtvDjkes/CgDZgARvRy/Fg8mEwt4/oqz7M6Ugi4maXX72EuNOFDvKgg75kDjX/6pvqD1lU58rNjzzxEW15SqO90RUhpZfdW+MBWfKSZzAjx3fc5n72gLTIeDCkhzAEGx7aP6yio3h0QELhIoyfMrs8hxwj4eIqNzk+xzDXW8zXPizY6AuO7GtD8rXwB6t8iqEe5AMruuWKvpoNP07bQ9azmL5Bk6p3ESgCRaAIHCUEtkSQ6WiAN1ExLEwMdjvsxDCCDPomSgYJQ85k71tfP3Rj8mS8MU6s9vtG0AQkjQmfH2PHN4F2apFdE4tJ1cQl3ORoQjURmRC980dKX/Oa14wdFEYugiw/q8cmy3mCUgbGgMnO5ZkzYZpAkV8r7Iwa78oqPwabnWWr4yZQ+WQCNFkyAKwwKyuDQJ5Itv/LiGArm131peE7Mu+fIrDLENDvuCwcMTQZvIgAP/0uxqK7/sPY1O8ZmbMTV5/mT26M+uSRuPocWcYLBq5xyPgiPXJBhv5nHPJu0cu3vsYPO8qOlC5lRnbu8sh4QgZHH2OdhTd34YgIp8zyN44wtA/VBSPkwW6aMe0f/uEfBlkmFwFk5FukM94wukOY5EU3ZYrOy/zJQFCMwYiJuC5jo7FXeeit3sRVLuGehcfNuPAzPloERUyRDkd5pVGe1Lfxn37qy1ySd7h6F2Y+UYdLR4ZyIz/akznIN+B++IiOCK+5SV0jbbBRJqTRwqq2QL5yqRt6zsRxmd/B3lNPcPBMtnLQLXKXGJEpbtqGtqKO3/nOd465Rz0qQ3Yr4c6pKwQULsqZtka+ZwvS8lVGcflZfHA3VymzviRcHSKldED8LCqbm+mMqNNJOfTNLBrTOaettA/kFf76lnkZQbRIjvypczv58pOH+HRCrLULC9TanSPK9FKGOGWAgQUt8umDwAdr8bQz6ZFkdWsBTBqO3vqLsjiRAQPyg2PKYU6Xlj5OM4iv/pzIyGK4MHi7a08Is3dzfnbLyafLrJ/nuUx5Vjb2hTZMz7oiUASKQBHYXgS2TJCpb/Iw2ZkgfHNjAjN58megOY5kYjJ5MYwZSiYdk6XJyCTHeGWQmjBMqnYBTN5IpMnQpG0yNemYRE240jCmGAzkW4Wmh0nNboGJWT6MBnnbjWIEmBDlY7KaJy9lyYTl2QSvHCZTchmldCaXPJOZidQ9jmyGB0OMHsqbY1yMAkcATabItfLUFYHdjsDcp/RHC04hP3YM0x/hwFD07i6OXaUQPP4uBqS+JNyYw6Uvz3kFV3kmPrmIERn6qXTGKXH0WfIZ5+QgygxsLnknL/c5r8ihj7yMU/p9SKB8Xfq8dIiGuPJejkEjw4nMznmSJz1yYDGQUW3MRVqNl/lV73/5l38ZYwzCjLDQP3LkmffklTDjnAU8Y5+yu4tr7DNWIwOIvx02eisTfcg0FisfGcE3YercbltwIleaOO/q2mIikiYfZITzbk5QXwgiGeqL7DjPcPdLzfK2aCmeeYRO5g9jtks8d5jBR3ngKR9kTZmM+WSSMeeT/A52TzppXcrqopv7Mjzy+HNw0H5gb86CLUzoq4zmN/Oe+PR3eU59JE84WRzW1pRPPDhqJ/wRYPOmd+1eu9R25YnQqjf9wmdL/MiDG3l26M3N8NQOzd/Ib+ZM5UAY6WIBHXFEGOkvb057dhKC3mQqn9189Zf2nDaoDp26gp/02an1zsmHrtr7v//7v48TW0gt/TjYm3/JoaN5XFrp4ugnjfKwU7QT/YzuFlzka1ygH3l0lqe+AUvlZJdIn4V19QK3dU7ewtUzDLRV73VFoAgUgSKwvQhsmSAb4A3+Jl2TCyOHYcVPmEmeIWtSMrlYmTcRmARMIiYvE42JJROd1XAGC4cIm9g5hhdnQhWHzKwym5Q8y8sEaWKTD5mZeK32Wlmml4ujR4wWcT2brPh7JxP5djFQGBB2KRwRJINu9JplmtCt2puErbRHpsnfZEovMlLeoUj/FIFdjsBs+GV8cHelz+lH+qu+ob8xTvUXV+KKw9BH2tK3+HkWLy75GRP8xoHFNvkgyNL6v6PSiSdP4wOj3ykP446FNTvJjFaGfIjfrKu85JvxwjiYsY6BLG3GFTIy3pFhnJB/rsiY9U8Z+GW8QJaQGeOgb0ORG/mSb9xDVHx/acyyk6z8xiLpyZPf0iVv+sLG+OQZnnQ1Drqyw5UdM3UijrzpRQ91Y5xEQOI8I50cYuNdPcTJAzZOHlk8tPBIZuLL1/yhrFwwGy/7xnEEDnmTP3KFVCMuMHF0nq7SycsiqzHaZVyWp7mKDHhJI97hOvnMWMNemdOWyOW3rAvvMLRra27RtpQH9k5IWbww99HPAoJwF6wiT74uTpnNyy5586cHEuq3MNSZb7vNU+ZQBDfkVBvQvszFSLQ87eDT5+Mf//gg0tJJAzsLINEh5aeDPM3v2kROAAh3aTsufQveLrqqZ0Q16d31F/ZD/OCkfPJ0cRbTxNHe1b0TZmwS8umgXSDt5mGODhwdyXB3kav/OCnGZvCMAGuDLuHaM9mw1B+Fu5yWS9uKfslnZLb4Iz/1px7ZRFtpdwvRfS0CRaAIFIHDROD/rcnDEJBBPxOdgd2E4N3lmWN8msxMcDE8TAomRv7ukSV+DFgTpWNeDB0uE6HJhPFpImeMcpmATdQmMC6TXSY8Bti6ySfhwjLR8jM5MjAZJnYW6C8/upJF70xs4qfc8k65hiL7dEneJsK6IrDXEEgfTz/RZ/Sh9AvvnHFDHzEOuPSxGMPipp+Kk/5KZgzc5EMWmeLbFTNmcOLJF1FI/tKIi6AhIHbMfO/IwGZc55I2+iUffvKgJ5nGOOQGKTMWhRjxM4YgJcYR/gxs41z0j8zc6ZRLPohiiJKdrJe+9KXDqJ71Iw/B8sODvr+lr18TnneEBxCLP+IZPxEIu9J0lydHd+QMyVI+u4AZy9WN/O3OIZiMfA4muad8ysJ590y+K+92Ti2KGHsd2zXO2smkG0KCvKqzpSMLrsrrLq0FFhgrt/nBgogwchAY+CHHyuooNj34qXOO/qmHZX4He095Ul5zINlwVIf8tV/YzU46+aoD+oinXUmHNPoBSMTP5wBvetObBi5ku6KvdFxkeReGPGp32iCC6Pc7HC+HE7zVvfak/cNPHLvU//qv/zpwR4AtvMDSjqoLqc7JL3m44uTrUl5zuMULcza9go+40sBCvapn78Lj8jzL5uddfBgF57Rhp7ecGFO2jCH5bli5tAlulk0HZbc4Rl9kWnptGrF3t5BiAUneF1988VhEI097gqV/kyUfCzDaWfRMWZZ34XS3eCBvbUT98ItuyzR9LwJFoAgUgbsegb+1NA4jTwN5BvN1z5nITF4ucTIBmBRNCCZ4jr+JilHAOPINLyNSOo4hRp44jF7HoLgY0NKZbKJH9DJRmyjJX+cSL2He/VCIb+WsHvsW2s40o9EKuqN7jLHkK508ovucT2TnnrInr96LwF5BIH1AeT3ryyGp/PQNfoiN3SmE1fiQOMI4Brz+n3Ehsmb54okvjn5KBqefkolIGnciQ1wkAWESxjhG9siMjMgnw7PxiC7CkRjps8uFdPncZDZ6kQQEB/Gwy0UPsrIA592V8ZC+MDGuGL/sVDHS5W13jxxYBZfgZ1xkcPu8RXzjol10RjuilPgDkH15wBPxZdw7yoxMkUc/d+Oe3UQLDTCCafITB/kh245aSH/k5y4+Fxzdo0tkwDy7pp4RDvIQxMgVdykH7nCz+2xxwyKHX3W2wykdOXb3tCl6qjPjOdLn1AAClO9M6RkdR0aH+CfllEz5YKlOkC75qI+NnHzV6ewsRvCzm2nhxaKBtsQfRi6YaIv8go/2qR1Y2LC4oI7pAyc70IidcG2LP5ze/e53j/ZmoUBbRISdrnBpy7DTdu0qI5LaJjfjNZffaSu70fqfOkp/EyeXtPJKH51lBYeln3fXnJd3WNgdV//6cAgtAisPZYDD7MiQFnbavtMjcJbeOOS3U/RtCyzaF3zJkE69aDfi+Q0DMuDjnSP3QI4M7d0ihTYufV0RKAJFoAhsLwJHhCAb4F0bOZPJfJmIxTdxeDZhRoa7CcfkYtJg0DkaiSyTITzpGWgmE3JM9CYsRqpVbQYcOcJM4AwHRgWDIXltpG/CGVMuBgGCjKgzLEyadrbJjuFO1lxGeZJTVwSKwMYIpP+7c/oMI1Zf1p8cLdXP+BknEo/Rq6+Lk3Tizc67NDG6yebnzp9M/Tl+0YUBzciWR/QglxxxXeLq48gO4sUZrxANpAvpEJa8jFWc3TRk2CKe8cmFdJJnLDPuyd+ulLSujF+Iih81NJYhAMgsQqMsuegGE/r7PhSJ8gNlLnj5hhMBQJaUPU46+Rrn5C19dCaPfLpmzKWnNNGRHOOkstNplp085rt0HBkuTj7ShljA026lBQWyEXB6iCd90kWOuUK56GjxEqYWEOzGwsGRdHUOOzvHHD2Vlz+Z6tjzLHtEPIw/8qcrB0+E87//+78HyVIXmbuWooNN/OlCLyTb5Zlc8oVpo8qNdCuj+lUucrRHBFW7swuqfqSVjiy4iqeNaZN+E0DbRfjgxs+lXsSHrcsiScrnvs5FLmJqYUD7QjxTh0lDb35wV7atOHlqpxZBEFrtxxgCazvD6jonKZb5KAcs2Q7aHiylo7fFKHVowQkO8EZm5QdLcl1sA/1Ous2WRVuwgGC80P/UCZl1RaAIFIEisH0IbG022mfUmOBcJph5sjR5uISZgN1N1CZC8eJnwjZJiGuiZCAxbByJYxTaAWD8CDNxuDIxkeNZuAmKMWVCZgia2OTFoHT8z4o3Q0m+MVxA73l+F86wsDpuorQabOXbBCnMZE8vcpeEm/FATzrNMuc8OvltX4NvzjsHAX0k/SV9In1bH7LrhUzqc4xNfi4uu178pTG2xCCd+50weTDy9Wnp3fXr9Enyky/Z4huDGMv8jTHzXRx+5PieNv8iLjLlhZQkTsqQfMXjxyiGgV0pOhirclrGWMYJV1bj2uWXXz7GHT/8x2C3KJgy0I/znrEHQUAGyXXc2i8F08u/AkIC8o0nPV3Ik4VIsshxJ4vzDGP3YM5fOL/IQBw47+ucuJy69Syf1F3yMtbSXV3ZVbOAYDxHalM+aRPfswsplG/aAVJn9xy2iJIdQfME4mcsJ0tdITX0UXbxLH7a7Y0cslMeafK8rnzxk4ZzpyddYP7e9753tBlzSwhWMEwa6VK2WZ45Ulugg7ZJJow8I6zK6WSTMmrDnPL5LlbbUzfikp08lSX5mhv9dwUEEEaIpHkTDvIkXzr1Ra508iFDWPpm9JfGXGnXW/52rOUvPjnu5k82gWftNJin3Ju5k5U83emEiDvxpU7pKh/16li0diS/2QUDOuh7dFdOmFgoc4qMTPM+3J1sixxx1YsLuWUXaLN23KUhO2WWZ/LyLK0xxOI9f6c36FxXBIpAESgC24vAlgky9U0kJm+TkEHelQnY3WXyNEmYrEyCmTAy2bpz/Bl+fjESoXWkkF9+VMWziYhDgE1C8vNsQmRsWin2rzEcnZMvPwamic5ETU8TevTMZEhP8jnPjDTGAmMW6WYwWOX1XTKjWJnJjLEnL0Za5JLBke85d+F1RWCvI6BPuPQjlzFA39S/GZmMTQtlDNLE1XdcwpECfU6/cknLOPY89zF9lJFuHJAHg1wcRqnFN37GJHmk/xsfXOTxj4tcd3EZswidccU4QC7jWJ4IgW9qEU9hxj56M8KRGOHih4CQp0x2wDI+IbRIj1/6Z5wjvAgychtdg407eVz8kKKcpvnP//zPIcPuFiJOb/FSlpSNH1y5yBPGTxjdEpa4Cct7ZI2Iiz9kwov+FgvUS+SLingYe8VTZvVKZ/GNxZww77ASJr28OYsbxn4OKfKZjHEb+VUf8oSLujCO25WHO0zpZRdVncFa/aZtkCcP14HKNzLeR4ToKL47vO0uan/0siOuHAl31xbteLorm7bCaT+ODNsZ56/dmmuk1z9ygko70a5c6sl3475ZVw5zJWy5uQyeg4Xjw3Yx7ULLO7LVkTDtVttGGIUhvME+bZZ8dWYRAPZ2YunrSn+iN6ff64fKpx6VabMOpun/9E8bkgdnUYU873RH1hFW+YTgk0EXclwcPKTzbTW9LNI4gm9RxSILbLSPxNffLcQgur79h413+PmsgW0SjEYG+/6k7i1C6Od2vbUR9VtXBIpAESgC24vAlgiyQd9lImDQIJAGd5MOZ6LhTKImJpOz+Al3N8lmt8aEIw3jxQotQ8GPkfjRFpMQGSZhBrQJygTIqGHAkOF7PwaOX9i0+ywNJ404frjDxJijYiNw39FJ8kxYMbZMqo52m7xMiu9617v2G1UmY/nJW3zlUC76Kz8clFWY8pArjmfhjDHvrroisFcR0Gf0DX0WeUGGLEQxSBm0+pFd1uyoMLr1ZY7hyohlWFpE06+NG0hADGTyOQY9eYiJK3IQJXKkTb8XX7h4do/I8MkGHRMn45r87QLacRKXHLuRyJg+7v+3M5AztsjLmOY7RcdtL7300v1jx1B0H6kyjmTMoLtxDDFEjP2IkjyVMWMOfaTJeJJ7ZDLskZN/+qd/Got7xq6MQfKR3hW8ck/6hCuvMOQt8d3jos8y/RyeZ/Uob+OrMsLbuEw2/ZUP5j5xUTaYqucQRlibO9Q30iuNfMklB4mhG9wQNOOuBRftDGmUXtuxcILI2VlEUByXRyiN9+LCzQkidZgypwybudPJJS19tQnzk3zkh3Bywl3mFgsidFPWzCHaKuKuPBaLtavIhQ8dETq/SO20QHbFETWEzdwoXeo9upOhXcrP7jE87R4nnnndAot6sDCsb6gL/cn8a6GFg7/6VH90Fa4c5mGkL78lIk6wR44Rb8RfGzVnwggOm3Gpc/nJizPPp43AkjOewEA5kfGQV/mQob04MZBTG/zUt0UWetHPj29pk2RrYxYqlF1/Vp8WPBxhf/WrXz0W9vnJB75ObFjA0H/UlXzJcIeB9gm3V77ylaO+5V9XBIpAESgC24vAlgiyAd5lErXja4KwWpqJS9FMECYfLpOEZ+lMNtL4hVUTs/f4W123GisNY4Kxwgg1oZi0TGbyjZFg8vLjMJzJTRqGFwMDsZaPCZiRJC9Gorw4xgDyTBaDxeTl2Sq8f/tiAjURmvQZ4SZORjuDniz5cXQglxFLf7qTY1JkGMCFIcNYMZGbMOuKwF5FQP/TN/RnzwxF38oaB/gxGhFm/Uo/Qpj4pd/qq/oV492iWciMvqWviSdd+rfd6DiEQP/Tn8nkxJXGThNdGNbCEFT9PUdiI0MedKOX+Pn0gtEvrm+EjTnIGbnIBYcEKB+ZZMxOvOjtTne7jsY7RMW4Ah+65qIjHGN8k5cwz/KQH3wsVkpPXsYk9+S51CWyYtArL9zEl9+cT+LOMuZnaRJHHSEw6g1+xmp4eJaHi17ysBApLTItP/7iGXfVH904MpE38RAi4zP5iKi5Qbi2oq6EGdOl9a+8/F9hdWX8hhdihxTRUTrzRuqKDofqpJEeibXTaNGFLhaByOeUEYlDgoXBJHWj7Qgz9zheb1FAfHLdzTXaG6LmSLWyccqv/ZhjzYHaIj2k4aRHWOkEu9e85jWD+MFfHO3GfCV/cfRPmJnn6WJuFS/ysgBhUdsJMPMdbOlAlrTKhUAiq/SzQIFEIpyHQpDlqe1bXNGflUN54CnMs3YCQ9iZz5UhxFla8ZQBfurCu7gWGLQReEqjbWTeR8j1cQsKysAukNYigIWZyPEr2nAQn02gzykfvejkLg/6aOPkR/fUzaik/ikCRaAIFIGjjsARIciMLivaJgITTQwnpTEJMFBMACZnE0T8Y7SZOBgm3hPGiEM+kWAr3yYRE6CLIcWf8Uh+0kjPCDaZmexNvPIUj3xkmVGF8JrEOLqaIE1gZEV/Exjji9FhkreTZbLlxziQF73c6WJiNbnJ2y9e8hc3upEtL/rBS1y61RWBvYqAPuFKX9bP9EP9k0GuD+oz2XlhuIYMwUxf1ifFDembF530MX3N+OPzC30POXAhsPqqvI0L8uL0YwaxX0Fm4OvHxht9XfoYtvTm6GOcsSvMUNenjVuIkPLQJ3GlVQb5kpv8xsP0J7goF/2RC8a9dPPYmiQpPzzIVYaU03PGJqTHbp4wz3SPbuIsXdKKQ3dlgReyKC050XWZ9kDvyqCOzRfqAV4WP9WDsghXd8kXZikPPVzyVwbjtTsnPqKUE0f0RFrUvzFeOgsM+W0Lc4LxHTkzB5Fj0cU7HRAgedNpHT4HKuO6MPrRSVtUPu0mO+fBEgbajjuSFjIHe7ohYOZS+KXM7tqJNspZHCY39aXM+R53TpMywdK8rJ3BSjuiKwcTabVriw7ILblIeuKmnZCD3Os3FiHoyS7Qb7QbZVQHnIVusjjh/oWUeRUum3V01O+VXV+Tlr785CM/9gLHP+3Iu3AXGbDUFjhlQPK1IWOANsp+cOLDiTKyLW74jMtxdmVXTgvsWQQgT7lhaoEPyZY+echHOu3KYg47wSZCMBIW/IdS/VMEikARKAJHHYHj7jAaH6YzwSQ5g8KKbCZ6Ig3yJpzZ8cvkFf/lhBCZZHHik8PfXV7Jh7x5MklaaWJsiZ842W2JbHd+4ouTuJEjf3kyxLlMwtLxj9wRuO9PZAlLOD/O3SW9STvhc/o+F4G9gIB+YCfGZxSMcjso+p2+oV+kD6afZAxImHjikJN0DHnv4jCILWwhGjFAI1OcjA/6tPiRa1eIkS8dUspADqGOAR8djR3K8La3vW0YwAx1cRn7SE7yU5/yQwz8WJbdXLuA8tzIKS+XvMiKnknDj1wYIidIlCOy0hjLhM/pMmalrMFwKZd8cYMlGXYmkSQkD4lMGjJcB3NkcQiIo8Dqxu4kLMifZUQ2f+ky5uednMgTxklDP8dfkS87ksgL4qENiY84OvqqztQR8qeOkz8Zym1nUxuwOAJT5CY6uR+OkwfZ2iUijxxpAwgS0kmuOOoTscupKXkJRwS1qbkNpj5h59lCg7JrZ3Chv0WTeTE2eMFDnvJSXtiEaNLDlbJGH3HkT250ES/527n2ex/aorLZtU7ZUj46Io3iSAdbfYa8tIHoeCCc/Ru1t7/97avXv/71YwFJOemWuiYDJtFP/mkz/LjoJK5n8WHn6D2Sj+wi8HAJwYWVvuDIuYUVCxraGkzyznm1AAAgAElEQVTIpQM52rf2aMENuTaOqAf5qGOLH45vq399NrvLMBDHVVcEikARKALbg8CWCPJS5Uy4BnYTRe6Jl/CEzRNA4mbiykTpPZOaScz70m+WM4fJd5YjXnSITsJNZst4whNfGnITh79nfp7dZ5c8hM1ylvHISJw5fZ+LwF5AQH9AMBmk+gIDOf1pNpDjt8REP0s/JCv9Kf1PfP6MUUbrHJfMXEu5dLIg5s5Ajtz0X/c5rbiMaadSGOnRPePVLF86p1sYw8q7zokTpywcv5Q3YXM8RjsSg4ww1Dl6xyVtdOcv/fyeuLkLC5biMuiRGwSEUS8s6ee8kn55F5dDEB1LVe9IKuIQXYQnnmd5cOQnD+FzHGldiU++dDBeOv5wMubDyo6gZ+kjXxrv87zAT55znKXsg73PetMBnnTQxrRPLuXYSBYZyhB9o6N3Yd7T/jaSkXzEj/OsbEu/yE143ud6WcrTH+ihLxzIkRG5kUd+rgOlFYZ8IuMIqnakXUZe7mRx3l3ze/xTp3OYRQbjhj6qfqSdnX6A+OprFozmvh554sMBHlmgSB78LTZYUCAj/Uka6RNvzrPPRaAIFIEicPQQOKIE+WBqZ4LKZLPZSWAZP+/yWydjXTg/cRM2p0sYefNzypM0eZ/Txm++b0aG+AeTM8vscxHYjQjoK+lfc3+Ynzcq99zPNnqWdpY/Px9I7hzvYLqIy5hmJM/G8UbpGMfC5rgb6TL7y2cjmSEb4kfuHPdAaec8ls9zujmPkLA5fJl2o3dpLD6QhxhG1hxfnNnNZYl/4sxh8XMPDonvzj9xpHPN78u4S9nz+xx3s8/yImPGkp6HIjcyUp457Ry2WZ02ijfLmp+Tr/ucd+SI61qHf+IsZYgft05mwua7RQZEFYkNORYesnogOQfLTx/lyFhXDvUnjrDUX2TO+fJzzX7kSp+LvvrBuvRDif4pAkWgCBSBo47AUSXIR710zbAIFIEiUASKQBHYdQiEYIbEhmCuI7Q7pfAhzPQJcab/kkDvFH2rRxEoAkVgryLw/2fw9ioCLXcRKAJFoAgUgSJwTCEQYkzpmWzu1EKEHLtzIcYlxzu1xqpXESgCexmBzf9k5F5GqWUvAkWgCBSBIlAEdhQCyOZO3jGewQpBjl8Ict57LwJFoAgUgZ2DQI9Y75y6qCZFoAgUgSJQBIrAJhCYd2I3EX3bo0TfKNKd4yDRexEoAkVg5yFQgrzz6qQaFYEiUASKQBEoAkWgCBSBIlAEisA2INBvkLcB9GZZBIpAESgCRaAIFIEiUASKQBEoAjsPgRLknVcn1agIFIEiUASKQBEoAkWgCBSBIlAEtgGBEuRtAL1ZFoEiUASKQBEoAkWgCBSBIlAEisDOQ6AEeefVSTUqAkWgCBSBIlAEikARKAJFoAgUgW1AoAR5G0BvlkWgCBSBIlAEikARKAJFoAgUgSKw8xAoQd55dVKNikARKAJFoAgUgSJQBIpAESgCRWAbEChB3gbQm2URKAJFoAgUgSJQBIpAESgCRaAI7DwESpB3Xp1UoyJQBIpAESgCRaAIFIEiUASKQBHYBgRKkLcB9GZZBIpAESgCRaAIFIEiUASKQBEoAjsPgRLknVcn1agIFIEiUASKQBEoAkWgCBSBIlAEtgGBEuRtAL1ZFoEiUASKQBEoAkWgCBSBIlAEisDOQ6AEeefVSTUqAkXgCCPw17/+dfXnP/959Ze//GW/5DvuuGPlOpA7WPhdlfZAcu/KsHXlXeK0Ls5dpdPRyEvbqNsbCCzb8kal3my720y8zcTZSI/6F4EiUASKwPYgcML2ZLv9uWaivNvdukaw/bVRDfYiAjMx0R+9H3/88QOK4447buXarDtQfyb3D3/4w+p3v/vdSn9/4AMfOMQm/4wBuSdPMpFqOkWX3BMn9+gfvb0j4yeccMJIG/3m+HlOmo1kJ577nM9SX+WZyySuOJuVG9nKS/ekI5MclzjCko+4Mz6zrnkWN7JSBnd+s/8ynnzU293vfvdxJX9pPa9z9HORK71LXPXACeMi609/+tPq97///erEE0/cX47o5J748RuJ9/1JmNd14eviRqa0dAi2c1zPwnN5F3ddHtJHD+HibcZJNztp18kXJ3okn410meV5Tn1uJHcZf35PXsl7o7LN+CWNuKl3fp7dtSP9Wfi6NitOdE36dXiKlyv5k7d0wlwcOetkLdP0vQgUgSJQBHYGAse/+c1vfvPOUOXIa2ESi8tElXv83TMpzn59LgJF4K5DIAamHP74xz+ufvjDH66++93vDiP2Hve4xzAmxdFf3dNHvcfPnSE7u8SLX/L5zne+s/r0pz+9uuGGG1aPfOQjV/LgECSXeNwsnx/5/BjW3DpDWBzkW/zopEy33XbbfuNcHPlETt75iSsd3XONzPb9iW7CxEcaxWfwz47sX/3qV6sf/OAHQxfh64jAnCbPs+50+/73vz/yuec97znqIriK95vf/Gb1rW99a3Xdddet7ne/+63ufe97768f8sSln4uTxkX3W2+9dRDSe93rXvv9hXHBwLtnZfnkJz857upLmsjeiKQlL9h/73vfW33ta18bsqWVZtYL7uJ86EMfGrKRZJgl/zmufF2w4ZTFM5nRX3jSzn7keJ/Te4+syB6CJxLvXRz17ULyk190c//JT36y+vWvfz3a9LJNROZ8j46Rcfvtt69c0qYMiZ+4v/jFL1a//OUvR1uF09JJNzvpknYu/xwnzxul1Z5vueWWUWd0U/bZkR83P6dc5GqrxpYf/ehHo53O/XfOd05PJtyFL/OMbLppP5y0iTvLgal2GFzFmfOM7r0XgSJQBIrAzkNgV+8gm8wyIWWSdo+h0Qlr5zXIarQ3END3YnwyJL/85S+P601vetPq/ve//zA6Gcc33njjIJ/3uc99RnyGqz7M6ceI6amnnrp6zGMeM3YBZ/QYqzFYf/zjHw+yxUB+yUteMqKR881vfnMY0PKUR8YLEfKMfND1oQ996Oq0007br7c4ZPz2t79dfelLXxoERRp+M/lhJCNUDGVEDekUzsgWV/iDHvSg1aMe9ajVKaecsn+3M2VJOcT99re/PTB52MMetnra056WKONOz6985Surq6++evWKV7xi9cQnPnHkd6dIB3mhF6ze9773rR7+8IevXvCCF6we/OAH78dc8p///OerT3ziE6vrr79+hJ188sl3woS+dHUPhtIhx4i1hZDHPvaxo7wPeMADBh7iuZJWeqTsPe95z+r0008f5Cb5iLPOJU9YI0Xvfe97R34vfelLh1z1q/4RG/V98803D3J8+eWXD5zUz0knnTR0JwsW973vfUe7QND5udQX+T/72c+GXvzI1UboJn9xOIsH5PIjw11cZXVP2WacgoM7zCzqWPQ499xzRzuRjzzlhQBeddVV4/mCCy74mz6wDid+0pMPA/WhLZ5zzjmjvEkjHAbq4VOf+tTQhb45gSGMLq6NHB1dweZAcSNDvvRByC1eaCtnnXXWnXRLXLI55YmLnzv8LPboN9/4xjdWT3jCE1aPfvSjV9qSfJZOGv703CjcYoU+QqY+q39ox9LARDpjl7HF6YRnPvOZ+8OX+fW9CBSBIlAEdiYCu5ogm+wyWTKKGBkMF8YlQzRGzbqJcGdWV7UqArsLgRi2yAZDPURYKfVXBjLid8YZZwyyoT8znhmiCPI111wzwhAbaZcuY4A+Lo0+jxxxxoQvfOELq89+9rODbCLAMeAzboj39a9/fcRlpD/+8Y/fH0cY/RF8RCNp3PkjRu7XXnvtIFN2rpWDP12EicuIVybEg6G9dHQXD7n66le/Osj4s571rL8hyIjARz/60aHvy1/+8iFGHsp8MCcPl7LA4zOf+czq6U9/+igvYx8udHAhuRYELF54Vg8hkMLlCWNlsdvoPeQB6YGn3WeLDU95ylMGARJX/lx0gYk2gKhzZCRMPok/Avft5MGVXgidhQLjPfwtYtjtRniRFuO/UwXKydlJtthC3yxiwPvJT37yKBsMk7d289Of/nQQpJRbnQYj8uEibws35hu4iktvccmSD8LGb+lgxtHbQoR6FRdOc15wVFaE73nPe95SzNp3+bnoC5vPfe5zQ+6ZZ5454kdH+Wib+shll1020niXjnPXZhFECwGzI0P9iQ+H5Je0iWuxaNnmk1a/1/+V60lPetLfEGSYak833XTTaOOpI+lTVxYQMraQ9bjHPW513nnnjbanPuLg7aIPAr3UM/H4K5f6108sSD33uc9dPfWpTx1tigztyAKSMP1AOzc+1RWBIlAEisCxg8BRJciZuDYDz6HEXScv6WNomCivvPLKcRzNjoLVcsZQ4q2TUb8iUASOPAL6HMfQ5BidiA3igTQknKGJnDFaGcgMaWHiIi/i69P6sWcuafV7fnZ7OHfvDGcEhgzPDGxGPCKDvIZMRo47kuZCVpaGs3fE/Oyzz94fTl/kgpMPEiNPRvKzn/3skS+59OHvjkTNO6R0ixM3l3FMGuXnJ547EoJ4Ijt2o/nBDslAYMWDCX/6IovRkR8nXPyPfexjox7oi3zYCUOAlMUFC3mRiTxZHCCLHDLEsdOIcGXRQlzP/IUjZo5Pyw9RSd3CM/q4qzeyxUnYsg6Ck/Zkd/vzn//8II1IsLaBMCPD8kBk6Asv9UmuOnGcG6HhtAWkBsYIVXCTr2dYWChBfsiRBznaDp2VyQ4z3S26aBtkqQfp1bd4yLq2sq48MIKZMtHTEeHgIixYIMcI4EMe8pCBK+JGj+isPOQoe9qBd5d2rx7pazGELuoa8aS3NHZJP/KRj4x5U7mF0wXO2qtdbQRxSZDlH/naofeZwCoXHezAmotnp5ziK6fdaxjPGAUHemqLV1xxxcg/WArXZuQvj/R9uFtEEm7RwVijDsShjzzhqOxkxfGXli7S0ocffBDhOGWhs8WWD37wgyMusq2/wkz59TtXsEja3otAESgCRWBnIbBlgmzCcHExIDybdGYnjklFnDle4oov3D1xTSJxcx7x2+hOjvjRwbNJyjFOhlJWypPXRnLqXwSKwJFBIP1R32REIgzIHkPWHQkIwREew5XhjYDYRUR4OOHkMJCNETG2+bk4RnHInfRIKvLA8EWgpEFK5Us3ZNBOGHneuYwPDFp6IEEZU0aEfXHIZAj7xlm4I6HeoxdDWnrGN+IVHemHvNHVcWgEUnrlix508e7ynDIbQxnt8JOPcQ3BQxjIQT6FyYtM5Ehaxr1dW6QGEViH2Re/+MXVG97whrEoIU10Io/jR2c7msoVHYVnfJcmOCYcTnbR4SFvR5uVU9nixM1Ft+QvHtlcZCde0iIw6gBplO6Nb3zjIEEWDez2IYJ09s0uoqf+L7zwwkHatY13vvOdo43IC6mVPz0RKX7ydefsnMIf2VbX2mZIFbKdcHlYeFFG+iNKSBo5yHdIa8rgHrzkx4mT9MFA/0FS1TkiaH5Tb8qt/tWzOKn/iy66aOgUmfLQZuij3TuRIO773//+kU75ld2uqziOb6s7ceBn5907DOi3dORrIwi4Rajooa0qiyPKymJ3+BnPeMadkqf8ZKe80imP9xkL44RPLPRf6VywpZO60dbUj0sfE66POK0iDDmf0yH93IwTPegLG3GVxbihPSmjUx36snYiX+2QfLKQZeWnu/4BV0eu6S1ucEl+dwKiL0WgCBSBIrBtCPztzHYIqpgs4jJxZMDnzy8DP3/viWeS4zIBipdw/sL5Jb0wjpykGR6LP4nHW1pxk2cmtUy0kb0Q0dciUATuAgT0XRejkuHoCCwj0U4x0sqofOtb3zp25hjN+ivj1DeYvollBOvPZOi7DE7EBvFkFPNP/0ca5IEsIbaeEQpGLOMeoUAKkBnG7Yc//OHxg07kkpWxQ3y7pBx/8teNG/L2g1B2DhnBjHZlo6/4kZexyBjkm0gkEcm0g8dglweXsiAGMBDfRW8XzCwsRLYjuMpop90CoPGTDHd5wkAaRJE83y8HK/FcdvocWWfQI2/IvLjwlRcs3GHGwBdOb2SQHnRHEl3qypVxXJnkJ44FBxh5lg5Biy658+fgR4aLiz99M7anjPC3o6fun/Oc54zdYu3Dzp62pf6loTvdLLq46GvX2LO6sIDgOL28kt/IfF/+6gQBRTB9Iw2T888/f/8Ci7jauHgpDz/PCDyMEWekzS70unzETdoQqbSD9Bk7u8qqfpQJcZWvZ0RZ2ZFbcRDRnFCgC92cFIAH4ojgaz++Z7Y7jsQjt/rKi170otUll1wyCDEM9Qc6qH9xEfKlUya4akfw59SZfN3J1x7tfuszS0d/pFR5tEd9FObaDrmcNk028s/PYhPSakFM26K7vPRF8SxUZIGCHvBwokG7sXCiPqTNQo88Ug9wcgkjW95OakivXixUIMraslMKsKGTPsPJj5Oei1z6wYrMuiJQBIpAEdg5CGyJICtGJnGDvOcM+N65dfekmdNnAhmJFsQ5fokvfeQmLDJzN5klXuLG0HKnZ10RKAJHD4H0R0SPIZudNDsuDGCEmJ++m91NfgxfBmgM/PRf/Vra9OXIVyIGvPgMeOEMbgSQwWqHl1zEjqHL8GaMI35k0g0RMCY5fksWEkKnjCVBzTvjVhw7akgFsi8PJFC4K+NRdLSL5Zgxg92PhsWQJlfcjKN08T2rRQJl8CytPBngjHMEwEKAd0RGvuLKN/nRD1ZwlMa7POQlHgzkgTTBlyzkTT50Y/xzyBYcyaWbuoElrFyIAfKNgHLykJf4ccnT971x0iZedBYP+cq4zT/YiM/RnRMmLwQlBBeBddRVGm0OJkgTvZFIZbbrynnPrp92wUX2eJn+8KcrsgNLixzaGnKt3HThT2/PLvkjsBYyYIiQhYjN+SiDBRB4I4awtpDijlzrJ+oFcXzXu941CKiFHgQw2GjT5Dg1oV3PfURecFBvThnACmZIqnYrrR1ORFEcYcg/TMikE8y0D2VAEpUzxC8w0QWmFlCUWz2SATdtSj1IQxeLX0unfultF9hxaLLI0K+UN+0L5nQVn15OBqQNqBP9ixwY0hWusLMggsjS08mynFBRfvWVOvFMZzIvvvjikc8cLp665eRjvHnVq1416ogsZRWuD3mm75zHLGuJQd+LQBEoAkVg+xDYEkE2uJscTQCMpP9l776bdSvK9PE/COacsxhRTKgYUb4SBhQw1ThWMTP/zSvxncxMTdWUlqOIKIKIoqAiICLmnHPOGX716R/XsVk8O529z2Gffa6uWrvX6nDfd1+9Vq/76u5nbbEXgBcpJ8AMsZeCF7oXmpdqZoQ5Aep76XlxCV5E84spqyTK0kOWFw1Hlaw5kCV4UarHCWKPF6A67FGfHEfKR98sq+dFoAjsHQKeNWOC4Nyza7Xmta997XAWrahx5D27HHZE00oowuaZRbo46Va5PM/Kcaw905xizzD5DmOImENNBseXjGyxNRb4gBUZHFXEMiTAqh75SCSyYBzxQR9jmjEMSd5ovKDX7zGRbDZdeeWVq8c//vFjbFMn9bQf+UZ2EDKrWuedd94gWPBhOxsEdYx9Jg1sEzZucfiRJbYjGEiMMZRe20Wt2MFFW9gd3WRmzGSDuuIEcrUZwQ+5k8ce+nykiUyr3OQit4iJPGOtmC1WUxEn5dQV2GClEJ7a4JoO7wQkxnkmSOCojLrkaX9Ikj7XDodz/arv9DGZVs69G9gFH5MJ+h2pMomAoMHAoQ3qsxcO3hX0SHMdO8mdgzz61bd66HsWb3/728d95B6x8qwM/WS4z8SIJbIOBwTPiq72L+VrtzYjcu4PpMuz4f3qOckEkj5Gat/85jeP9sE7uxYik27EULvYI51t2uz+0+fuUb8zds/78JoJIQRZoJM+/aDvIg+5hbl7Dgknw3M7h9ignkkX9lnVR0zluWdgKB0my0AX+fqFDCTbPetAbGFNTg795h40ZmijZ9FEDZzYmC312mMygc1ZMSYjstmhviA9MflshRN73QOCsmwkl72ebbr0rbFGPvvhZRyTpo8d8siJniGwf4pAESgCRWBfILArgmxgN8B72XFEvBw4aAZ/27W8wDk8XkScF86MbV+2NHnJeLF4Ufk9HKeYPAeHwktcOasA5HOiBC9GzjKnh2MopA7Hgnx1OAbqcAwQcGXoI9uLydFQBIrA0UFgfrY9k8YCz6+xguPPiUR6OPOcW/lWSj2niClC5tkX1PFsc6KleaYdcTbVMe5wepEh58g155SjyzFX33XKIE8IINs44+Qqyw5kWzl6jXGcX3nalLHEuXEGwVAG2TB+aQu7Ul4btddBpt92Gh+dz4EuMulGPtivrt+EkictZIxDzvm2VZaTbgusMRKBSzvIZoODLkfGQGOt1exsJU++ssFavxjDHfrNWApXNgpW+Yz3CElsZ2eC94FxWT69CDGig9QrDy/tsIWWbP0Dx/QFQmdMF/SROsraih+CDH/tpdc7g20mZPWz94t7wbm+lkeGI7iQK6RPxWnfyLgjT5o65LBfu5AwZB9JdX+wQTscbHEv2MbPDvcIopg+mHU4R/DzftN+NiKz+pNsWAa/bBt2jXiTnwAfeNDD3vS3lXN9xX7PnXsRuXQfvOENbxgEUj/BClGn3zPj3vJMymOjCQh9wgaTUXN/06WeNARb+127jz23iL+JFP3nPtLfyuaecq+z0YSNe84kEnu0CQYCefAKfuLcu2zN5ABb+SUw1UfshydS6xmiEz6RM7cjWMqTbrLChIJrbTTupH9NgtAj370EM+Wyw0LfsJ+c6BLnPLoaF4EiUASKwN2PwJ4QZC8jvyf0EuSoeTl4WSCsXkZWP7yM8vLg+HghKuMF7AXiozBmaL2sOBVeRBwKRNoLzsuIk0aXPL87sn3LC9ELx0uffo4UZ8+LnyPnpSp4+XvhevGSlZdSXuJ3f1fUgiJwMBHIsxZnkDPqORc4z1Z9PNOeTU4tosR5Nw4YQzihxgGOqHNyxGSIHcrGyZ0d3BADacoZP5A5Yw/HWzBWIDnyjDXOEQZ1jU/GEfWNG0iWlTDEI20gI2MK4mNc4oAbj7LNWhk2Ijx+52nMQ56RJeOlNkWGOG1ABuhHQpA9BEU7kGHk2moYG23t5IAbI5El2379dpQtSIk68BWMmWynx/iHhBgzjcnsMYbKZ4ODo2/8dOiPmXAhOw51tUFe+jmY0QM37fQ+gDcyLg2xQirZrjwCkf50byiD+JpEyBZ3OuiEizz16BQb76M/dnnf6A9t0r/aTEeC8vpG/3sHCWzeKCgvaA9ybkeCd4t7Fwk744wzhnw64KLP9DlSaJuuvsx7iZ4c0SfPfaOu54I+spFxE8p5l9Lt2v3smdG/b3rTm8Z9O2MCpzwbdMAAMYUpXYiuiWW26w/3Nlnwcy/lY2f0wNLkjP+z7X689tprD+06IEtbgg+89Y+2+L20nRXuU7a5D9yXsNDH+gee0tir7eqylZ2eFfmC+sEs+ujURodgrHBfs8nEgXYhxybaPN+eQxMNnh9tJWeWO4TccR/oR1g4lGdT2ui+MTkikO9e0B7y2MseZbTLOV3qKiNEzrjonyJQBIpAEdg3COyKIHsJeHkgpxxOLwovTSsDnE8vBc7vZZddNl6AXnReLlY3OCIcSI6SD53YDqVOZt85Zcp7ocYJMjNrm5p/7eIlbmWJgyBwaG3lssLCLr/r40BwprwIEXgv4TgOXkzK5eiLat/ckzXkgCPgmRPEPqrECff8cYw5sp5hDiSHFJnh0NrWiVQiMRxvhMoYwQHmmEae51sdgUOaa+OSuratXn755WNl1tgicFqNH/Spg5gpb/wwriF0gvw478Y9sgW2O+Qb35BX9dUjT1mH9tIlWLlCtE0EsHc5/qgn0GEMU1cZ4x4SaWUZsTQ2hnDBhX3G13e+852jDnuQCzrYRF4cdzId8oy1nH/2mNDk1LNZWTK0zSSFsZR+ugTpyAzMxME79qZdJg7YzgbkJL9BVd57AjGnL211jvTZ2m3lVJtMvpKnjnwYsc91QvLp914Su0fcKykvZqtAn/YjbNIdgnrLkDaJc042wuh94z4mL/Z4x8knOxMiiKn20klXZCVmvyP3hXL6WNDfcPTe8770jGiX+827zS4uBBBO0tV16D82OejxjoWne9GkB9LLVj95IBumAjv02dlnnz1i5/pbfup7nkwASE8bUpc+9+mZZ5459LivLr300qFLXnZnILMINH3uVX4AbLQvhFJ75M9h1ufcPYvoe2bJNLkAR/eb+4VOcrVXGrvVUV598sVzyH3vXhRcR444GJMLF8+G85TznLgn4GwSSh6b9Jk8MpbtmvX3vAgUgSJQBO4eBHZFkPNCMeh7ERrwrRL71xkcHi8LL24rHHGIrBS//vWvH46cFQGOqZViZdT1EibHdkfbvbx0yPci48B5Ab7rXe8aM+ocEmleMup7AVvhsB3r4osvHo6IPC9CL1gEOw7q3QN3tRaB4xOBOKAcSudiz7IVKIQByUMk/vmf/3n1P//zP8OhVcZziyA4OL0ccuMDcsmBNs4gAFbn4gTHyfWsq8MZtnolNh44EA3jirIcVqtciDad7BBbUfN7TOU4vOSHlOhFJAsJ5vxKj356jW1kI+Vx2I11SBsCgOxoGyc+9tJpvENyjafsFOQ7jLfazuEWC2xDbuJkIx1Wlq0kI6PGQ+2mm3zl1HGkjkkJ46h8OGpLQsrQhzwhX3BKOru0VxySDAfXCcoa1/WToG36nEzltBMWCdLYglzCQwwT75NlIDsynMcu5ZzDim4YCdpGr76T71rMNjrgQl7kOBdik7Z6FyVdOefuH+8vcnL/0C2PXPcRjJFTusmgSx9puzayK3qH0ulP7gV1lE87pCPo7kPvP/eiez73JhzZETvZQ0b6wm9qPVPu12x3J4t97Eld5elVVxDLcyi3DPTkeXBP+s01u/JBLveRCSj2K8tuW73Jkq7P2J57SZklNmkT3fL5DCa9gsFyRgkAACAASURBVL8+8dya1PFxNLK00f2nHyLPhL57etbhXFDf88s2mMOVXexkuwUAK/rSYcgPgU0IsvuXHv0MUzK03SE9xxK/XheBIlAEisDdi8Bd32w7tMdLwgvBC8PLgPMXB9BLxsoE59dLygvEDLGXkTquOQ1eEpxlLxl18lJkCoLt5eng6Jn95WR6iXsR5gXLAVaWPjPWnLm8uK02caS9QJVpKAJF4Ogi4BlP8Hx7jn0gi+OI7HJY7UDhOPrwkLHBlk5Oud8qmnTzbHvmPfscbnI4q4iZ8SUfF/LcIzK2vSINnnnbLTmoVm5MvBkfXBtL6EYykBTjCYfX5Bs5xgxOLT3agCiknFVBq9tkc7o5xcY1dpGrPnlkIajIiLFPQIytyJJl3CQfFmyxYopUGQvjqAc75XIknw7BNf1k+Jq1MRXO+amKrb9wYR9ioyxZyKM6xl/jLLsd8gXlQwxhwN7Y5Vo5GKUce2KTfDpc0ykon7LiOUSu8qmrvnNpyRfnmOsrtwzaFdLHdm2bCYzy7HA/SI++yKJHPjkmW91P+jc2pb40QXnEyeQEUi8WTD74rbV6MBAjX+5r971Jk9QfJ3f8US66lud0wd4kR+4ZW+QRTH0pX5vVE8TS2OTeNUHlPvR+NKninoFz+tM9Lh82efey3T0NE7huFNKH8DbZZNeEL2d7V/v4XdrrmfLOtsKKYCK5nm92O4LVUo92OPQX7D3T7vHo1VZ9ZpyxRZ0dfnLAH3AuuCeVg52xxfNIn0C2c1iyKeMAfWk74u25tXKvfcpGNjuck+/5cW+5jm8iv6EIFIEiUAT2JwK7Jsia5QVgsHdYjeFweRF4kXjBcTKleRGaTZanLKfSC8VLyAvbSycvPLPNHE9bsLPy4gXmZe2F5MXnpR39cVo41vldE/0CJ9sKFH15+Y2M/ikCReCoIOC5zjjhWbdq5dnmWDqSZoyw8sNxRXA9u8gbYum55txyoj3v6iDRxoJsl9YYYwuHWX3OOJLCKeXMc4QRbs6ufDKkc/g5sMYrY4w0YxA7pdFhDGOr8UswXiEQJu6QEfWNL9pqbAohZIuxSFnEWBvVzfhoXHJNhrHNeCnID26unbODs52YTm2Jo++aDHYgNEiHthln2UGPoC9yaI9zQT0ych396pk4YLv+cC1Pebr1B70Z28lK3SH4jj/kSmeLsikvzaHvci6mJ7rmdOJif3QlP7aL9bd+M/5rF33akLJifSVdOVhKYwf8Y6809fW195K+UsaRoF/UCVb6SN+S73AfkSNfPdi5x9Tx3mKjdGXm4Fp98t2vrtUV2K2+a3Xd71Y7TQRpj3yHoJ72aId7w++iPUvquUdMpEh3ryC07PfONXFFvkko12R4drUDwYfnMiiTttALV3Id9GuH+0W73D8IvsNWZhNJJs3ghbDG/qWOtIl8k2QIv+dWu9mmHt8huzSsTNMnTXvIh5367NFe7Z71OdcvJva1SdAPJhLYaawwniDHxq3UTfthINCjLdpqTFNOmcgchfqnCBSBIlAE9g0CuybIBngvG3FeSnHWvBzy4vYSyosyLwYvqLykvLi8RATOh5f82972tvHS4jzYEsnZ8SI0GxwHMUh62ZLv5aOc87x8YqMXVF5Y8umLLZHTuAgUgb1HwHM2P98IlfHCbxmtoHne8zwitJ7POJHIlPoZB5A+v7E1ZiBrVoUcxpo898YgK1Q+0MPpRYaREc+/cYAuZIdjLN33C1yTx8kmh/Nv9YluTrZVPtsxM4YYZ6SJ6SZT3jy2QNI2a+OVstprzEtQHnlQl+Nt/DKJSIYQWfIRC7YhKRx046Cx0oqziUllERyrl0gNci8gHggTPfSx1aFd4gTXDrqkK0tnyKxJBjt+kBx9p5zyyhl/2b5OnnyBfWmPts760j59I12IDambdNepOwpOf8ihQyDLVm5YZZLVPYHQydMn0h1IjqAv3TuCfLY76IOf9uu/2X5l6YQV/fqHTORJ39jZECKo7IwbHe5rpC7tE5MlwFkZ/Z5VW8+FvggRpMtEkefCz4ik2+4tWPGGYzARk6m/3D905XmTh0Syx/3kuWCHZ0fbnXs+6HefaRO8fAckfUweOdFHl0CWZx4mngNk2z0KT20xkeC/WbifHdpLVu4n8lzPQRosBbL9Blu/mMRiG92ZINK/MPGMwyT3hnL0kU2WsUJIG7TLfa390pRXH852p+hrz4P6VuBT1zWc2J++ZJf7L+Pasj2jcv8UgSJQBIrAvkBg1wQ5zoOXy/zil55rLxUvedfSUyexF1mcIedeQD6kY4b73//938dv6rLt0IvJR3yU8+LJS0ZMvhe/F56XWhwd+r3IEpSdX4BJb1wEisCRQ8DzKXg2M+Hl5xjGAc+z59TBgRQ8o0lXhjPO0eXAGwN8qMj3BjjWnOyZCNBhVTnOvrGCU6xMxgoEgT4kinNr3DDOINtsoF+wNdbKnHoZs6QjWhzqONWj8B1/1NVesZUmJAYxsHWUjBxzHeepp93KcPgdiDxygfQiwMg7m5VBqAUk/tZbbx2OO93Ih5Ut7bEKhpBEr3hd0EZHxmO4kQVPExmINrzYOR/KIGmpH6zocJ5YHTKtpgcfZMgkgnQ7BJARgY0zmYjskbn4Iy/jPDmCvvThKe8TZAY+rmGh75A2eJoEQZ6sEtLvXvIeMTHAFvcNO9ijr91XadNshrbpN/eLPoKhCRW/CdfWtGWus+48GOlzfYygIqTuec8AG020sAf5d29a1faORcb1Nxv1Nxn0zoFdSLQPhrEPGUbc7NJA9sTel9pgMgA2niOBbeRpj7aKlU/arEdZ+fojH+RExv3EwVZr5JVc/0sansYCgVzX6pKRY5btPOl0e3bdU9qFiKrr69yeE/a98Y1vHG30LRRk2rPhXibDPQArWMImcslgR2zRr8YMRNzKO2zh7Nlwb5GDgLtn6NRX6np+3W/6Dc4bPXvL9vW6CBSBIlAE7j4E7vzm3KEdeZF4oXkJ5Fq8nZDyXhheMmKyOIJeqK79psh2Ky8bDgvHJS/m6BFznG3PMsvOUfMikk6GOl5+Xmheeg1FoAgcPQTynEcjgsVR5tBzHD2XHNOsrHIqhdSLk+r5RwaRPqSGE8rRFSsrpG7GlJCDPPfSnRtjkArjDtJjfLAS5t/XIO+RY6yxtZhDHDuGoh3+UZfuhLltSZtj+eogSOy04oYAsBNhY7e2wcPqGwed84+MIyTIng8SaVu2+cI75HHWtdk5m4MFokkWEgXDHGyFEaKrD+c6s+zIUR6ZhS0Coy8QD4QWec19MNfd6pxMGBj7kUk2uKfoJE/smu0IjHzpcEyee9E95t1DjvcGAmRrf1b104aN7JEf3dG/VZ1Zlv6BB/2IHMxtHbbtHmnV996FdLgnTCKbNNYuv+N1eLZMDsPEe9n1bIM8pPiCCy4YmGh3JiW0X9+oJ8hDpD1ndJLpEGaZ0twP0nK4JgeefutsEsRv430VG+5Isq/YI8aIsnHBPUuWvnFvwIOc6ByK7/hDD4wd+opM/WXizHOCyHpGEHx9yH74uU8dVsORaW03CSEoMwf2m9xyf+oPfcEu/WFnRiZP2OcZ0Cb65LmX3ePaoM88O0v5s66eF4EiUASKwP5BYM8IshdUXmTrXmbrmqycF0YcCWVce4F7QTrM8nuxks9ZtBXSdbYUkuEwo44ge0lxGszWcgzY5MXGweUgbte2dfY2rQgUgZ0jEEfas+15FOf5Jm2Zpvz8nDrn0Hq2OfCILCfbc23lx6qR5904QnYCOdlFIl0+XRxeH9ex2uN3g5xqpIRNnGVxbBBzvNXn7JJxOIGc2Ja2iaXPwXXSxNqNACFMSITJATHCAgdjnPZb3eKEc/itICIEJg7mtpC1E4JMPxu1G1GAfXbixPFPbAXPOUIS++d25TxtVs5kJoLELuM8YiOkTOpsJ1bHBAKZDv3MDnJhJw0+dOlr+hFB94LYvSB2v+R9pKxzZMiEK3n6cNk+umO3WJm8w9SXDxvp6+rP7WMDcuc3vt53gkkO/W4Hgi3QVrjdk9rBRiv78n1oC1HWNsH7Tr95V8ZmsYNd2gQnE1QmFcgyyeAaMc5zSZ4jMobwaZs7wog4egbdc9quve4V2F9xxRWjTUij9zS9Jr6RUvrUlaaedgkwc6+K2ZF7K7rF7IGnPGXF0tyniCpS7pzvkBVf19picsG/loKtuukv9ekTyIafcefKK68ctnjWfGDMyru+UFYddpqcohfh107PaTCU71kls6EIFIEiUAT2PwJ7QpA10wvGbKoX7xy8ILyQ5HHWErwwvCzyUnQuTX0vdA4ep8YLxwvOy8W13yALZAp5mXlhWWnmqNlmyBniQHohcjQccSSjawjonyJQBI44AnE8PeOC6wRpnNvZmZTmmoOK+NiOacskx52jfeGFF64uueSS8awrl3+jgyCElGRsMH5kcixOuw8SWdExzljpETj6SLcQ+9STrp6xarsh7Ux5Y45DWOalzBynLOKgbValECHjqDYaD5F5NpFn1VsaZ59jbvyTHpKB9FiNNAbLWwZlYxfdiBrShGAij/BACKzyWnlTNn3m3PZSE5f6Snl9oK9mUpA6+pR8q3LGdAQUqUBWtG8O9LIn/THnzefyEUcrkNpKFz22CdPBHjjazuu+cOjTm266aZAnK+4wRtCUJU++leNsKd/MBvoEZdhLBh1JT3/ONq87V48+239N3HhnuYa5foOpPiRXX7gHYMZOZQV6vff0HSwywaMO+/Sb96R8/eXdmG3cSDc9ZJOjn5QV5/0dHMjzfJjAecc73jF2X5hwQpJhT+YHPvCBgb/2IMXuTXKQaSvT7g96ckS2+9Zz6/6GnfxlUJYN2qONeRbY6h6UBi9lXPMZPE/ax2b3hgkSbaDDoayDbAeZnjfPHr/EM8B27SCfXcoL2kKHWB2HMNvPViHtTDwS+6cIFIEiUAT2DQK7IshaYYD3wrHF2YvWyzzk1YvBy8eLhfMhT/BSUU9+Xj7ZWifNS8gKkZe1lywH1UvH4aM7rjkEHAOy1KHDb728rGytMpvLaZPvhe0FbQZeHY5xXlT7pidqSBE44Ah45hGAhIwDYs8jp9gRRxdBMSFmDEBcOdZWRxEhB+feKpFVSGXOP//8sbJjPIqjy1G3s8QqFUdfyHZlK0HGBSSCbQi4scQ4FZJk/GKbsQ0JzXiTNqyL6VZOIDexc+nGx9g3Mqc/0oOT2Lho3JOuXQIb5aWsOCSKrZHPZhhKM5Yig3HyJ5XjVBnEgiw4I09WMWGH+CJixl3YGT8FZQVtQkDgiHToE3rgZbxGJBLgqd8E5yYxrbYhr9oa3LXP4V5waPNmQVkEEUb63Goom0L4/Bsv5NhKq6C97FVOHROsyiBU0e1epDeH9I3CnKc8vJBUbYy8jerO6frOuw1hJwd2ZMEl/Z3yngHvNuUEuvSVclahvf/0P1xhKOgrbfZRPD9TQBDhRS9ZJinsTkAA9SXZ+lye+1BbpNFBlvomr9xbsHUPKaPtnl2rr3YGIMeeNe9f+WL3Br8B/hkX1EOM3SN+WqCf6NK2+T4ajbnjT54pbSCHnSbL6dNufaqu+9o5+03OsE15/ZwQfcFbHfexsUU/pH1whiPb1NemYG9iCf7sSUgb2OP5cQRLcUMRKAJFoAjsLwT+MYIfhl0GdgO/lydCa2Y1s9rEyXd4QXoJeikixOoIrr3E3vKWt4zZcrPkypPHefUy83LmmHnBmVHnyFhx8PJBeMnygvRCI8uLBxnnUHvxq+cF50Xs5aYMOV52sV/cUASKwJFFII78rCXPr2eQ8xhnXYzQcjo961ZwONSccOOM59cYoQ5CZosxx9RYIJDnnBNrK6UtlpxdK5XGKB8KQkKMJVaTBPnqOWIrGbFJPlKVlaFRaYs/HG7151VsssnNODiLoFtIrJ2OdSFlEs82k4+0mCjMaqEJAL87NRYuA1s4+giKc7izWUy+j4tpt7SZUJCDvAhW4/UVGYgpwqosW2KjcsZk4zjipg+tOioLqyU25M11h6IN/ngH0KuvkSv3BJKmv+mx80AfsoceuHrHSGOn+s4T6N5OWGcfHbDS9/BM32wlL3Yhycuw1OM69nq3aeu73vWuUQ1Bc+19CGttzf3GLrsJ3B/y3Q85PFsmW8jO5MFll102JkpMesBIoFe/02Hyib2eUXiqSx9ZftNvUgvp1u/BQRnPHiLOLoc0diPvDs+ldqknb6NApntOWUGbEGSBnWxe1ieTje4Tv5HWVv0926IP1eWXkGkywAfR+CQC/8V9475V1/jjniMPxvF1yCSLjTDkDxnH1G8oAkWgCBSB/YnA9jyADWz30jHwZwbe7LNzTmuCcw6KF6Rgdlcddb28kFkrxl5EeRGqYyvUWWedNWaSvWykeeEoJ0+Q5uWYlxp5XtJm1tXJy5VcZemNc62seg1FoAgcfQTiNHqeOZdWEq12zYEDbXURkbJ11oRYVrHUNx4guZxOq4WefU6nsUW+mHxjUibJ8uEqRJmDTHeceZNn0k3QRYYxhONLL+ffGBLZs63zubqpz3mmHwGkJ3lz+b06j2w6BeSM7Q5jIJJoJQ/Wy2AcTX1YIUnGbJOJxs4QSGWWQZ50OkJW2ABH+M51jLvGYBMUxmljfyZG1dFfQnAmQ51ZxlJ/rpWx8mjl029ftQFRsjrt3kFy0nfsZJt+QSDVpXMvAllsph8mdGqX+EgFsk3emBygS/v0o23M7mttFdjmPvZO9gV472zn+sI9Hhthof/PPffcQQhh6hnLO9e97B2LWHo+lcsKPBl00Ov9ro6D7o2CPAe9JsQReO3whXr20bcuqKMc+Z4zerU95Z2v0yvdvXLVVVcN3NhqvJGe52fWJ1272JfD2ODedwjuY/bCEd4mEGY8XatDftq7zrZZb8+LQBEoAkXg7kHghNszgh+Gfi+KubrB38sqL2h5yc9LR540L4Y5n/q8NJyTnTrJy0tqNjV1okesrrKJU0Y9Lyk2xGFQxrUyDUWgCBwdBDynIV1WVjjgVl8QWM+mfCs20jiciFLIb55rZTy3HHVkEBlRJuOEcnRw7D33HGhOLPme+TiqthPTj3AjCsiNumQrh7j7mBC5Vkk545sFdpFNhvNbbrllbNFGBq2mRT4Z5O9VoItOh2ALqRU++CKCCJuVPkQx4190q6OcFTzffUCsEAb2Re48jqZe4jkv5eEFq/Sna+X0CUzhIE+6I/dDzvWbDzwhenCz2rhZoJf9ts+KkT/9qc/dF7FDOXboI9vQrQpaxSZfud0GOOpz2/6tWiOO2iTQu1dBO2BGpnMTIXZL6DP9re0OBFb/siHtZiNym2cGNsGHfeR5rtz3sPSMkJH+VMb73nV0mbgmP23NfZi06J/br4wjzwE97lkEGck1meOe9WyybxnUZasVZ7a6Z+00kSbQ7VgGbbI13M8z2G9FVz/BCqbzM8pueowhxiP5bEk74eDckf7Qnllv8JRuPNMe+ctySzt7XQSKQBEoAncPArsiyByMvIhivuu8LJx7scwvCuXWXafcLC+yIjvxXGaWtUx3nXznOdgnPeVzHfmNi0AROLIIePbyzIuz4wOZkef55Gw6OJEc0vlZdq6ePOWNRXE253LSI0u+I/XEDmU4v3TMeiCgPAc4TnAI+GbosCftU85KFQLP4UdOyUww9uxVSHsij830Sufwp/2zc5+y7IU1HGzJ5sBr64xlzlNnjtVfBnql0+dcmGU4X9ZLmnS25zfE7Mmq4FJPrunQZsRPXfdS2jyP8dEZHcg60kL+XvSH+wmGkUt2wl7IjywxXQLctN3qq6DvMgER/OnWZjjBRZz+kD7blnLuCToQ6uhSN/1Edkizc/U2ksmuWYdrNsQedelwD+o/evSJWD3xHFJemmeMjcqbTCNTYEuOua7xBlYmFWBl8sg9Roc2axP5kSGNDZHrXH5i5XKubGyOTvX0jyCPTdJgts6+1GtcBIpAESgCdw8CuyLIeVnEdAP9/FKRvu5auTnkZZF0dXIujgx1cj2XmWVtdh45kZ2yy+ukNy4CReDIIZDnMRryTM9xynhGl89pyqk/n7tWVppjdmwjY85P+cgRJ6Sc67lu8jeKozt1XCckzfV8nvzDjaMz9ZeyM15Lh8kcYp94Wc/1uvTUT935OnXkIQTRnTJLHUlPPMtc2poyy3iu45wOesXr9KX8RvlL+Tu5Jnud7nV27ETusmzasEx3Lc8x4zenOWePWJhtS9qcnrrr8IqsIWj6E5mRl+sUiUw2pkzyEkf2RnVTLrFykeV8WU+56J3rpF70JU+sL2ccI2OWnXMkf1k2MsQpR0/KJU1+QxEoAkWgCNz9COyKIN/95teCIlAEikARKAJFoAgUgSJQBIpAESgCe4PAnZcR9kZmpRSBIlAEikARKAJFoAgUgSJQBIpAETjmEChBPua6rAYXgSJQBIpAESgCRaAIFIEiUASKwJFAoAT5SKBamUWgCBSBIlAEikARKAJFoAgUgSJwzCFQgnzMdVkNLgJFoAgUgSJQBIpAESgCRaAIFIEjgUAJ8pFAtTKLQBEoAkWgCBSBIlAEikARKAJF4JhDoAT5mOuyGlwEikARKAJFoAgUgSJQBIpAESgCRwKBEuQjgWplFoEiUASKQBEoAkWgCBSBIlAEisAxh0AJ8jHXZTW4CBSBIlAEikARKAJFoAgUgSJQBI4EAiXIRwLVyiwCRaAIFIEiUASKQBEoAkWgCBSBYw6BEuRjrstqcBEoAkWgCBSBIlAEikARKAJFoAgcCQRKkI8EqpVZBIpAESgCRaAIFIEiUASKQBEoAsccAiXIx1yX1eAiUASKQBEoAkWgCBSBIlAEikAROBIIlCAfCVQrswgUgR0jcPvtt++4Tiqoezj1D6dOdM7xTuQcrq2zvqN9fizafLQxqr4iUASKQBEoAkXgYCBw0sFoRltRBIrAsYbATCpDwO5xj3usTjjhhC2bctttt41yyqau+MQTT9xWXXrIUIcM14K0hKTleqN41j/bPp+rq5wQHbPejWSnnrpb2RP5qSPeqs5meiOH3OBN3rJdW8lofhEoAkWgCBSBIlAEjiUESpCPpd6qrUXggCCwJHOuc2xFwFIOFLMc9VxvVj91E//9738/VD5p5JKBFG5FMNWZw3w925H0pY657lbn7ImcjYhq8ud4tmMrHXM+GSHzIchkHa68WXbPi0ARKAJFoAgUgSKwXxHoFuv92jO1qwgcYASQrJA4zXSeYzvNDklb1kn6VjJSL+VznXqutxNSX1nn8zHXTzkxcrsRwZ3r5DwylzYmf10cfevytpsWfeKd2Ltd+S1XBIpAESgCRaAIFIH9iEBXkPdjr9SmInDAEUC6sir517/+dfXHP/5xXN///vffkowhf+r/+te/Xv3lL39Z3ete91rN9TYjhzPZRPr+9re/Dd1//vOfVw984AOHLLKVk79VSDtSJ21Sf97uLf1Pf/rT6je/+c0QSdd973vfba3Gku1gDzna7Pze9773ncxLObp///vfjzr3uc99RpvuVHAHF2QJdJLJZlg3FIEiUASKQBEoAkXgoCJw4lvf+ta3HtTGtV1FoAjsDwRCIGdrkD1E75e//OXqG9/4xur73//+6qEPfeggfogZ8opQfvvb3x5E8qSTTjpEWuV95jOfWX31q19d2SatHqK8HVIbG+j47W9/u/ra1762uvXWW1ePfOQjVwhlSOF2ZaU8ov+DH/xg9cMf/nDlHAlOYOMvfvGL1bXXXjvK3O9+91s9+MEP3pa9sAvxRuTZ+7Of/Wy0F2GVn+AcmYUNPGH2kIc8JNk7ime5v/rVr4bMn//858Pme97znneaACA4OGxXCfm/+93vBi76AfYwJ0deJgO++93vDnIuTx83FIEiUASKQBEoAkXgSCLQFeQjiW5lF4EiMBBAdrKi6hwBQnjEyN4NN9yw+ulPf7p6xjOeMYiRSn/4wx9WX/nKV1bvfe97Vy9+8YtXL3rRi1aPetSjhhyrsTfeeOOoK++pT33qjpAOmUPQPv/5z6+uueaa1Qtf+MJBtkPSCGTfHFIvaSFzyrEXAf7Wt761esELXjAId9qMMCOXV1111SCs2vGkJz3pUFsjT7zUQbYjq90f/ehHB7E866yzVi972cuGzakDW2TziiuuGLLPOeecoWcz+clbtjXXYqT7S1/60urHP/7x6olPfOLq5S9/+eppT3vaKkQ5+iNLnPrp5znPOVu/973vrT73uc+N9rAVLlbG1bWrwITDe97zntWjH/3o1Ute8pLVKaecMsTQt07nUkevi0ARKAJFoAgUgSKwUwS23kO4U4ktXwSKQBGYEEB2kBkxUmQ11ZFg6y6iZMXzAQ94wCHijFgpJ++d73znIMRIJrImRpDJffjDHz5WFkPIInddPNvAFsTVCvY3v/nNYZ/8yJGf6zleyk2bEPybb7559cUvfnG00xZwBBxxRvbokWdVXBsciP58IMGzrtiinbBQ97Of/ewgjlaP6Y6d7NKen/zkJ2NFnHyrskt5kTm3Y10ZOnNYDTd5Qf5HPvKR1f/93/8NG1yvC5EX22Y7tUM7k4cEf/jDH15dfvnlQyY80tZPfOITo5+tyls9py911+ltWhEoAkWgCBSBIlAEdotAV5B3i2DrF4EisCkCIVkKIU6uxQiSLcBiwWrk/PtW22mtVp5//vmrt7/97atPf/rTq8c+9rGj7E033TSI5/Of//zVqaeeeqet0aPAHX/omcNM3BBwuhEuIXbkPHYnnuXkPPLI+vrXvz4IrC3NiP773//+QwSTDluFEXvE2ZZuRBZxnsOznvWs1Utf+tI5achIgq3oSKLV26c85SmH8AymCPSXv/zlQTBPO+201dOf/vRU3TImYw5pG1wQ5DPOOGOs3l999dWr73znOyvbrq3sqqf9grLqIbjirKBLVwbJVQ8OyDtcbI+XZ9WbPBMK7guTFldeeeU4f9CDHjSwgjF9ZrM4nQAAIABJREFUtsNL65brucd6XgSKQBEoAkWgCOwFAiXIe4FiZRSBIrAlAghTSJjVTSvHSDFC5FpslRW5ko5IWV1EmhFOv0W2FRvRRJzII8Nvcv2m9zGPeczqEY94xCGyhnxZwbXlWFkhBA4Bk2Zlkk76EVb20S2W70DeHCHwbElIGXL97lc9272VtdXaaqhAPl1idehClBHaOfhd8jIgxLBBKrWfLQirugi263zwyxbo66+/ftihjdqOlM4BsVzqQXgdc2Bn8EJ09Qt5MA4R10d0s0FZZRzIL7L75Cc/eVyn3/UXkmuyQxmHNug7v9G2Os4Oslzbfn/yyScPfV/4wheGeWQ973nPGyvaJchzj/W8CBSBIlAEikAR2AsE9pQgc6jiWDJuPo+xynCmxBuFOFPL/Fl+yiRtWbbXRaAI7C8EkB7PbbZUWw1FbBFchBHZQpwQOuQKIfS7V+TQc67+LbfcMkgi4oQkkoHgWrW1miwOaUK8rEL6kNe8Fdj443eu7EDAlaHDdl7kD7lFLh1INpudP+xhDxuEj83SciCEtoHbXm2797Of/eyxcotUaoOAqPutrZVjq8QIHqJKvoNMZPHxj3/8XTpNXRiR7zfZsNDmT33qU2MsRbr95hlxRy5hRJZ2wYztAgxc0211eQ5w9NtmQbsEerQNjsi99rOTHDbbLs6urBIr75BnhddvupFbOjPmOycz26XJkve4xz1u9J18OEmzSv6qV71q9If+Uke6gw5lG4pAESgCRaAIFIEisNcI7Iogc4YS4hzFuVqmu06ZnC/LcHzW1Z/Ly+cYpRyZcZoir3ERKAL7C4H52Uf4kDerobbXIsEImmfa1mmED4mzcowoI1S2VpOBFFqZRfAQUfXVs4qZVdS0HPmy0oncIXnK5UC46LQdmTw2+RjYM5/5zPGhKOQ7ZJD+fDjKSmfaQpZgVfW6664bZJG9yCGi7iNcP/rRj0Z5MpA65DsEL0TTSjK5VlvVi9y0I1uyP/CBDwwcTAAg9AhqyLN6yLkPjklDLuGIuNPHZqvLJiG0a0mQfRwLnhmD2aAOPfrKturXvOY1g9yrLw/xFhuPxepI01b4wUFAoOUL+tQKOxlkCxm/xVaN/RYd3j4EBi/tINuh7WSQTUZDESgCRaAIFIEiUAT2GoFdEWSOkCPOEQeGMySWlnSxcgIniKOYMs6zisD5SX35KcMBU1/MYSJPPWnKWy2Jrr0GqPKKQBHYGwTyjCI2flvsGXaOxMlDqhBj51ZSrVh6vgXjhufdyqmt1lYn/9//+3+DLGXCzDiQ8uogzFYwjSvGGOOJoDwiZ9XaSjQSa1yx0owgO5Bb9TLuKG+FFhFln4NNVjUROquviCwdIY4IoK3gyiHyiKvfDSPtVoKlq0PvW97ylrGKyo7YOYxdrQa5txJN13nnnXfIBtu3YffBD35wkF8E2DZkq8mvf/3rh72xU/s//vGPj+3NSPIyIOdIMt05lFEWDjB7znOeM1Z09dMcgpH2ZHzWD+pJIy9jvj6iB0ZWihFiNiqvjP6AE31wTd8po4662kcP2Q1FoAgUgSJQBIpAEdhrBHZFkBnDcfObQE6fD8JYQeGYcmg4TpwaKzW2MnKObC+UzzFUz5ZATi9HiPPDcSaDE6YuOVZ/OEucQf/mI1snOYh0ctyWTuVeA1V5RaAI7A6BPKN+/2q10rOM9Fk1lof0GAM804iz5x8JMo4YB5A844JxBFm1YprfA8t3zKRJOUTXmCKPDjF5SJgVWVuqXSOCVjatXCLn/n0S0jbLQ9qUJcfBHqvPyLFVVrIzZilrfDL2sQExZrt0ZcTaro66yLryGwXjo3HRv7TKh8oQcFuc/+u//msQXyut7LvwwgvH5EFWvbWDLmOwFWY6l8GYrPwctJGd+oKM9Ie0ObgOLjAPeQ1O9DkXxK4zhvvyd2TDE2m29V2aSQSrxdLVY19k0pnz2ZaeF4EiUASKQBEoAkVgtwjsiiBzYhBdDu4nP/nJsQXP/7LkJHFgOEKcPtsP/S7uCU94wnA+OWPIrnpWTzhEnB2OkBUfX3F1cCjJkO+3e/4NiC+pWmXgGHKeOG2c7YYiUAT2LwKeY+OFWAi5MUFmDLCKa6y49NJLV8YQk17IL+KKhHrOjQMIpzEH0bMqa6wxnthym+3PZC8Doku3PETSeGUlOgTah60uuOCCsYXbKqx/aYSQzoEM9SPLpKBxCUFGrtmqjYIybGafCYH8fheRRHatlhrvEPWsusozbkZGdAcr5YWQ0diiPbAzEYDcP/e5zz00/ioTzNXLeWQnVi56lZnLOac7/76KvXOQxwbt8pthuAUj5WKntjnkwcREiHeCenSzT/8a902ewBR+gnzlsqV+tm+2pedFoAgUgSJQBIpAEdgtArsiyJRzTjkvnFwrDZwzqzJxsqyM+PAOR5jzZhWAI8RBtF2RI5xVgg996EODTHM0leWkCsrmf4wix2Soy7m1isQBaygCRWD/IuAZzZjAylybOLM12JiAaHn2r7rqqkG4ECjPu/+765m36mhiLB/vIsPYg5D5QJcPPWUsiC6xtKQjcvSZbDN22dFCJ9JnddYXlhFnq5dWs9kVexG7yJEmj3zj3ete97ohN+nKss3YZQxELl1LR5zVRRaNa7YZRw95y0CnfDttTBaYKFAXXuoLsHr1q189VooRd23wO2RtoFd5JNr5kuCqT0d0i5WXxn4r1WKTGAgrGXOQb3VajNiaDNDGWS6Z8vWd9kZfyLGy2qiPEWR9I0+IPjaZUDApIc+EiPdAyo3C/VMEikARKAJFoAgUgV0isGuCzBlCVJFj/6LDyjDHjENrW5/VGB/h8cVW2wyRYU4iAqyuwGGLc2zbtdUhjiqnj3PEueLYco44rlZ6zjzzzPH/TzmADUWgCOxvBDzDOViK7FgJNmYgXbYNGxvOPffc1WWXXTYm1VxbGTa+KGOMQG4RLGOMFUZjh0kyB/khdmKHsUaQh4Ah3D70hWhfdNFFgyAj6cYZK7B+28wmaSbvrHSGoJGB2AlieS95yUvGrheTee94xzuGvthAN/JmrPO7ZnUQR4dz9lgxFWtHbB0Kpj/Rr+3XXnvtWFlXhz3GWMQTqbQaC5sPf/jDY6IRlraKGyOVNYbS49gssI1O7UCqkVp94be/Pu61JKR02iXkK+CC+su2SNPf/hWWCdNMlNIjDzH2MxqHCQ8TASYE2O3Q3shF1r1v7CYyIQvPpb7N2te8IlAEikARKAJFoAhshsCuCTLHiQNrm7Pf9Pkq7KmnnjrSOGScUQ6UfOmcM44Ox5fTwwFDfOMkMtYKiBUJjpGycZLkcaislPhAT7bbSU/ZzRrbvCJQBI4+AvPz61xA6kycZWcJwmMMMPGFJCmH+CB98pwjbFZMjQ3GE185lmdMQbjUCSmOHjHyJN3Xpk2+XXHFFYNcWzG2Aul3r+Qjfq94xSvGV69NxPkJyCtf+cqhgwxjUQ5toNskHrJOvvoZr1KObSYCTQ5aFSUnh/ayzwp0VmbVWwblQ5LVoSvlkMXIkUaOsdWXu315Wrtgqr5yYvLWhchMTE9WfY21PqAGsyVBRtytWNu2njZGRvTENm2dt05rv3eAiQLE2cfCXvSiF40ybJXPDu0Uuxa0waSreMZjqTf6GxeBIlAEikARKAJFYLsI7Jogc0g4gFZefCWVA8qRsmqB6FoxsNKCHHN2E5Bn27JtPbR1z7VVYw4wx4hjlkBHnCMrTZzSkGMOk/w6RkGrcRHYfwjMz6lnGyHy9WUEDmEyBjj87vi1r33tWFE0riB8VmAFK8ghywgxsmWCLiQq5Cm6kCfnDiuUiLDfC5uw+4//+I9BbK3AZtJOOZN9VkqtNL/vfe8bZNO2YSu0xpjI3gzhjEVi26GtVmtXiGlsIsN4Z+xDAKVvFNhoZdW3GfIbX3XsuIGFoD49p59++vjatZVkB4xsQUdslUGUtxOUhZuVXMQVITU2L4Nyxmft019p/7KcMRv5ZYv2COTDR9+Tf9ZZZw3bTSiE/MZeOtwD2pGYHOnuI+XU2Uj/0p5eF4EiUASKQBEoAkVgHQK7IsgcIwenzIoO59IXWRFdv+0TWyHiENmCyLkVODI333zz2C6oPAfOkd+ecVqVITshuvzOcHYIOUMcJHEdo6DVuAjsLwQ8vyFXCLIttH5+YZssgmz7bciNccQqscP4kRVLZMq1lWCTa1ZKrVgKnn0kyTZp45HzjAeIpPK2JyOUZ5999iBqCBmyrFxIlrHFeBWC7N80ybdFmtx5TNoI4YxVIXjGMzaH6EUGudptBw3Cx4Z1QXmy2Is4GgNdI9Yw1VZ1HdLZ6X8Wwxn5NElp/A2O0b9O15wGN+TYCjHSnUmCuYxzeslkC/y0y7V4DvIR6LQTJuzzG3N9I2iLn+nQqz7MQobVQ4i9a8ghb9Yh3/WcNuvveREoAkWgCBSBIlAEtoPArggyBZwRzgrH1O/BOJScX46P3/JxDjlnVo85NJwuTqmP5NiWZ3WGw2p1gSPGMbVNm/M3k+Q4icpxkjiCwjpHbDsNb5kiUASOHgKeV8+qgATZVeLZ91OJpIfcGCesKPodsN+/InzIj624tj5njFDPWKAemcqZaEOaXRs/1DEeXXPNNYMkI3m2RCNnxhh6jDvOkWck3LnfNPuiPhJvfCOHvUiq63UkjI1zW+hHSq2A22HDPiF1lTVBiBxqM4zkSZ9jco2b0hFQ+iPHpKOy0e3cgUT7n8lWm2OTsVhexs4hZPqTcso4t00b3gi8XTt0pYxqzpVlm7Zqw0zC5c1BGWVh7B3gHaGP7SJiE1v1h1V1tgowU49uZYz/+sXuAvoStH+jdqVM4yJQBIpAESgCRaAIbAeBXRHkOEAcFQ6v3+/5HZqVYx9tEXNkzPj74qjyHBkrBMgz5weptqUxK0HSlONERb6GOOcAzXHS53g7jW6ZIlAEjh4CeWajEUn1ASwk1OqxlWJjSAiX59xvg/2cAiE0JiCtyJVJNWm2VyNUIWTqII5ZwVTW6qyt3L6NgCQj08Ypq8l+56wOMoYE2wrtN7vGKcG1FWry/GwEofM1aluE2UWvdoUwikMUQ1YRO0QPwWSL/AR15UtX3irpLEu5kL7IZlN0SFMHLs6VJc+5drE7P0VRBsEUCzOxjD2R4ZptVp+tPBuntTf/dks5h0CnstrogK+JCWnrAhxgCHuY60vljf/52rZ7wy4B/UWPSQntMvGhf90LmRSZ9SijXXPaOhuaVgSKQBEoAkWgCBSBrRDYNUGOs8TRsa3a9kgrA1Zs/PsUvymbVx84VdL9PpkTnH/TwcHhGHFYs92QszM7PMrYesdJnNO3amTzi0ARuHsRmJ9X5A0RRXAF1yFWyJ3DOGJssHKLjHrmjR3Z8os4+UhXJtbIUU8dRMkYY3XS74iNK742bXsykmjVVuywGo24GceMPWSoj4RZqbTzxRZgv1+2mq0dSJqVTOXUk6aecYud0uZ0tiB8WflNTxjPrJgil6kb0pky5CgnH2lFqOmmUz34sJV+aQ5BDBsryWSoK5aWSYXoEMuj2yHYxXPjjTcOff6FFTkhpsoon4NsB53s2ChYkfala8Rbe6ys+16F71OYjICdACdtI8sOA+lsdp/M7WRH9KXtaf9GNjS9CBSBIlAEikARKAJbIbArgkx4nCTOotVizg5n0NY8Ti8Hx+oAx0VZ5Th4HC6Oqa9cS+NgcUStKnAIlZ0dNtfS4xBt1bDmF4EisL8RCJnxTBsDEkvPdwmMASnnHLE2zlh9RrBCkJVxGCfECBgyjdRa9fWbXOWRVTGyhYQhqPRK9ztbpB0RlE+PscuqppVnBFqIHroSpCWPnVZ7jWnaZbXbR660aQ4IujFNXbaYNESA6c6WYrIy5ll9tcpt7JSGHGsjXcGPrNhHV8ZR59mVo+y6wBY4IMf+Jz3sYOJ34pnAiEz40k+XHQCwMXHALmnrAhmIrm3qJjiQYxMhrtnEVphoOwwEfSA9cp3niA76gpG8jfSnfOMiUASKQBEoAkWgCGyGwK4J8uyccGz81s4KDfLLmbWqzDGM08JJsjLj93E33HDD6tZbbx2/AeQQcdBsubRiwGni9HB4OKwcYr8dtBq9XInZrIHNKwJFYH8j4BlHBB0hP8aLkJ3EGWuQqRDCmewp55ocY46fdiBkvoGAVCLTSBzSm/HGqqbt07YS+7kH8qwOOcYqMtW3Mm1MQ5YdMyGDrnLGJXX8phnR9ZtmpJc9iJ94DsY5RFNQ1n8BEFu19rXqkOcQdiSaveTQj6AipvkAGHySR6bydNBtIsDkY1ZiZzucs9NKe770jXT7l07+l7It1mQ7BHqRYlvP6TOOI+/Gframb5Y69IEJU8TY+E4HOepmrCdPG7ULNvolkyDkRbZ2Ic8mLugUYt9Sb6+LQBEoAkWgCBSBIrATBPaEIEch58UKMvKLBCPInFFOYxwsDhBn0/8y5iRZaebIWWGxmsAJ5fDZcqcexynO7pve9KZBkpcrMdHfuAgUgWMHAaRSQARzID4hPPKNFyknDzlzGDOEkCJlUk4dBNiYYgwyXoRYJZZPHsJIhnN5rhGyEGCypCOJzqXHvuhXn/30yLfyGvJqHJTvewyIauwUG9/UQ8zZ47e5Vnmlp93qukbKbRE/55xzxgcR2QAD33NAqo2pS3uQYmQTYUd+2YV4w2QZlPH9CAcbfE/C/4CmM+1li6Ad5PqXWcpadbbybhs7W9PGlI8ubcy/5kKKbYFXXzqM9YE6yLEJBtfXX3/9aHtkkK8sfEy+qhu80mcp27gIFIEiUASKQBEoAoeDwK4JMqVxhDgoHCDOFacvKzYcmgRlXOffsfj4C4eLg+fgFLmOI0S2lWnEm2Pn3CpQQxEoAsc+AoiOYPUVCQzJlebZN15IQySNC8YOz784485cVpo8B/KUybRZ7lA4/UEAlbNiqRwZDufkCMajjYI2qGtSjyyr0AijLdYZy1I/EwFibZOurpg+Y6ZxLu0Qw8bqKwKKFMqnh17E3bl08gRynCOaCK9JSOOqHTi2TLNvXdBWO3VOO+208eEsY7Txdg7soc9YTY72GcPJFpNBv0PZObjOhIT2sp+d6shL7B1i8lQ+/cpGltghTV3ndCm7Tuesv+dFoAgUgSJQBIpAEdgOAht7fdupvSjDWeE8WTV2rAvKCJxXjp5jq8AZ8rtlR0MRKAIHCwGE1wQYYhTSk3FCjPwICJQVWYRSPBMn+alDxjIkD4lSL+WRTztXfPDLdt2QtNRfJyt5c6weAmv8QxZtk47dypGjjLQc0mNXZCkT+5KGLNouDp9sDw9O0l71qlcN4imNbLFDe7QNKYUxu2wXl74M5GZiUznENGR2Lgs/Y7f2+V03fSY2jM1iQZuW7Uq6GEYmE6xmq6/Nc2D7VkEZJJ0tDUWgCBSBIlAEikAR2EsETridx9NQBIpAETjKCCBHgq3CtulajbQqiTCFYBmelHM491EqP8Ow0ok4bodMzc0iI0OeVVy6yfQ7ZN83QE7pdyi3lXxl2MZeW5jVsxqNXC6DsmmXOHYsy6VM0uf2kx99bIv+2Jk8dbXNFmYTCkip1WkxGSkfHZEjhkG2Oys7h5STL8+1sJTnetkOZVNfHfnLMrOuzc7ndkbuunZtJqN5RaAIFIEiUASKQBFYh0AJ8jpUmlYEisARRwDJEcQhWkuSE/KjXLYnI1XKOXZKsOiK3siO7qzczsRtK/nqsku5yAs5jNwZyMibyy/zl2STnNisXvQp5zx2z/pSPu1VT/kcsSO61Q2+yL1rZdaVi8zYqWwOtsiPnsgXKyMvZWPTUsfyepaRc7aSE7mzrO3Uj5zGRaAIFIEiUASKQBFYIrCnW6yXwntdBIpAEdgIAUQGYRJvRWqSjxQjYchR0jaSvy5dnZnYRT+5wixzPl8nK2kpR24IYNLmMsu05G0njs3kOycrutQP+XQ+Txw4n6/V28gO6cqGwJK1rmzKhajPZZzH1mW70mfycz7rWpbf7Jqe6I2sOW2zus0rAkWgCBSBIlAEisBmCHQFeTN0mlcEisARRQC5WYYQn6SHACGEwkywUmYn8awz59EpXqZtJjtlUybXkZd08ZyWcnN+zudySUs813PuCCFN3rr669IiU5y6c5rzZb25XM5TxnX6ZrO6Kb+Z3qUd27me5W6nfMsUgSJQBIpAESgCRWAdAiXI61BpWhEoAkWgCBSBIlAEikARKAJFoAgcdwhs/bnQ4w6SNrgIFIEiUASKQBEoAkWgCBSBIlAEjkcESpCPx15vm4tAESgCRaAIFIEiUASKQBEoAkXgLgiUIN8FkiYUgSJQBIpAESgCRaAIFIEiUASKwPGIQAny8djrbXMRKAJFoAgUgSJQBIpAESgCRaAI3AWBEuS7QNKEIlAEikARKAJFoAgUgSJQBIpAETgeEShBPh57vW0uAkWgCBSBIlAEikARKAJFoAgUgbsgUIJ8F0iaUASKQBEoAkWgCBSBIlAEikARKALHIwIlyMdjr7fNRaAIFIEiUASKQBEoAkWgCBSBInAXBEqQ7wJJE4pAESgCRaAIFIEiUASKQBEoAkXgeESgBPl47PW2uQgUgSJQBIpAESgCRaAIFIEiUATugkAJ8l0gaUIRKAJFoAgUgSJQBIpAESgCRaAIHI8IlCAfj73eNheBYxyB22+/fXXbbbetxLsJ6kdG4shbXid9O/Fu6q6TvxN5yu6kPH3Kw3PG9HDkrLN9J2mxY7Z/ec7GnYS5HfP5TmS0bBEoAkWgCBSBInD8IHDS8dPUtrQIFIH9iMBMgE444YRB1ua02CwvQf7f//731YknnpikEc9l7pQxXagbPSGEJ5100pAn/R73+P/nDVNuqnqX09gpzrlCzmPbbNNcRrk57y7C70iYZcfulF1epz3aoJ787eoI8VQ+9TfSk/TE29GRsst4xiT2r7NBvWCRti1lLa+VI5M8R67TN0s5u2nHUnevi0ARKAJFoAgUgWMTgRLkY7PfanUROOYRCDkRI7viEJeQtTRyJmwhMcr/5S9/GWQOwU166qyL1XEIYnro/utf/zrkRAZ9OV8nZ04jh4zZZnXJiB7Xyv35z38eadq5XZtVSH0xPWkDOTkXx4aUYUMwHYo3+ENudESOOOmRG1s2ELPj5Nn2tC065OkXOM1BuZ2ElNcG/SREr7S0USw9eem/nehq2SJQBIpAESgCReDYR+DOnsex3562oAgUgWMAASQEOUFC/va3v61+85vfDPL40Ic+dHWve93rUAvk/eEPf1jd7373W93znvc8lI7M/O53vxv17nvf+64e9rCHDSIYMnSo4BYnCNOvfvWr1S9+8YvVE57whNW9733vQyRpO7JmUuUcYWczUhdiF9KF7P3whz8c7X74wx++etCDHrSFdf/IhlWIm3az+z73uc+wNzgqHXvgyQ64PeABD/iHoG2ckQFzMTz0UXRIWxdiW/I2Kpf8OQ4+6vz2t78d7dSn+ls7tAGWIazinM9yNjqHAx3kOyfXeSY1TCD88Y9/HDLpkZfyzhuKQBEoAkWgCBSB4wuBE9/61re+9fhqcltbBIrAfkLgl7/85erTn/706jOf+czqcY973CBE7EM2f/KTn6xuuummQVoQJeQZeUFubr755tXHP/7xQawf+9jHDuKzHeKkvnJiuun94Ac/uCLj/ve//50I+nYJEvKr7Ne//vXVV77yldXvf//71aMe9ahDZIuun/3sZ6tLLrlk9aUvfWn1kIc8ZPXoRz96W0RPXQeSChP2fuELXxikjgxBe2IrEvihD31olJP++Mc/fsvuJl8QI8ef/exnB1lFrpFkujcLsTExW2LPduopo7z74Bvf+MawA5G99tprV7/+9a9HnyDNkTm3dyv53/ve9wb22kBGSLBr9sL0k5/85OoHP/jBuHafIc10xK7NdDSvCBSBIlAEikAROFgIHNUVZM5IHBznCUnL9TJOWTHHmOPi3MGRUX8rGUuZvS4CReDoI5Bn2fPqHEmxInrrrbcOUnb22WePdJYhej/96U8HCf72t7+9evWrX716+tOfPgibrco33HDD6qtf/epYIY3c7bYouq0cImTXXHPN6swzzxwkWd52CRh9yhuXEEs2Ia/Pf/7zV8997nMPjU3KWA39yEc+MiYAnve85x1qZ2xWRshY5jpjW/CC1fXXX7/6/ve/v3rFK16xetGLXnSn8vBkx1VXXTXwC4GODvGsZz6PbnZeffXVqyc+8YkDDyvd9JusQPy1dQ4hkyGcWdE32TAH/WkigU4kVJxDOXkmPKQh5g984AMHlvT96Ec/Wr3whS9cPeUpTxn3jDJCsHIeWcHMtbomJPSxVfuXvvSlY6dA+td99K1vfWv1nve8Z0xouAce85jHHHqvDCX9UwSKQBEoAkWgCBxXCBwVghxnJjGE5/MgPjs7SVOO4yVwdmyF/PGPfzxWAWyr5PRkRSB1GheBIrC/EMgznOceQfE8u0awrOIhX0hVCI7VYuTMSqxV02wpthXaqiBybMX5qU996qFVTnLnEH3SMr4klmaVUh1EGWnLtTx157Kz3OW5csalL3/5yyMm03nGJrK+853vjBVrBNJqMvtn+QgiUqhNWSmPHerDEEm1Ss3Ok08+eciXl4MMYyRdL37xi8fqsTyBrshxDXcyY0PyEFmkUV+wX7q++uY3vzmI+Z/+9KchL3/Yqr10O9j/nOc8Z9RPGbGVYHaJTzvttJGvHvn632QIMisPSYXTs5/97DHpcOONN442P+lJTzpkd+qS7Vxb0obcbwiw9phgcQ/ZTv/GN75x4CzdRMNHP/rR0V52I+ZwkRfiH3zmtvS8CBSBIlAEikAROLgIHBWCHMcsjgZny3mu46TleiO4OV8c48suu2wQ4zPOOGP89nCj8k0vAkXg7kcgBCskJhZ5nqXNY4Fr5ESM3Mh7xjOeMVZNr7vuukGqjB8f+9jHBrmxgopE2yqNXEYeHfTNHNIvAAAgAElEQVTSQZ5zssQJzhEzZcTkRqcyyiNJCa4RRkcCGQ56kTvE18QdgsfemWD6/bGV0Ac/+MGrL37xi4OskZlgdRjZR9LIEGKvMZKdSDUC/MxnPnOs8EqLfuVNMtjibRWYDJgg/5HFTkfapS4byM8hDx5IOJIKFxjKT/kh8I4/8h3qwE+s7DKwyU6Bz33uc2OV2yo7G2Hkt8ef+MQnDk1+soGsJz/5yaOftQmZNQlhIkO72WbSRFltim0IuIkENisnXx2Yf+ADHxi4+a07O5F+/SRoLztMDrCfbQ5bzBuKQBEoAkWgCBSB4weBf3h6R7jNnJc4MBwTDhWHhHPEGXG+LsiLE8nhscrwqU99ajg5Vhc4RmQpk3Lr5DStCBSBuw8Bz2bGAM+0bcCeZcTl5z//+SAmxgVEV7AqKR+BQTqtWiKYfitqFdCqn7QQQiuEz3rWs1ZWGENgkSNlHcYO40RsEAvyQqa++93vjjEE6cpYFVnKSvfbYbtW5qA9SKjfz4ptn7YV2EpvyBXy69Bu5BlJJht5g03GMdexM5i5Vpd9VkGlI77aDgNtQ+RgCStbsNM+2NIpwJc9VuQf8YhHjDL0kkeG/JRzDj+H9pFny/UjH/nIUWb5R75y6pmwgNMyILRs1Mf/+7//O8ra8gwLv/+1zRn5189+Aw0LeLLB/SDPSrKVbWTbZAKc1Y9+OsmCgbZrF7vcS8rA5/LLLx8yvXOkKUeGewimWRHXj6effvqhPly2p9dFoAgUgSJQBIrAwUTgqBJkjgqHxWoAJ8nHYzhdcdLWQcyBEcQcRasKZvk5tnH81tVrWhEoAvsDAQQM2cmzjJBYRfQBJoTJ8+xDXFYH7Q5BBhFdBAZ5teqKmCFYzh0IktVFZf2+FClDPG2/TjA+WHFFtpwjWiFMIYTGoa997WuDQL373e8eH85CPpFX9iJLCYil7b9WH41ZCUhh9LCRDWx5+ctfPsYpcrKqyQb5yB052mhMQ9Do0o6shBvvYAefW265ZXzgC0kmzxj6tre9bZA3Y6Hfbp9yyimDRFuJZZ8VU8TQOfyRQ+E1r3nN6oILLhhyyKcf9vLJRkTphLNt4toED20yZs+TBuSpk5DzGZ/kIc1W/GHAdhMK+pl+NtsVYNs4Uozkar/2OvyWml4kFkFmI6xgqW0h8dojn63uraQj0i95yUtGu2zzdv9pyytf+crx22b9oG6wEqd/Yn/jIlAEikARKAJF4PhAYE8JMueIkxEnybng2sFZ4dheccUV47do55577nBkrMzIU14scHqEyIsMjm2cW45WykbXSOifIlAE9h0CSEeeY6QIEXKNDCF5ghjh9IwjOUipr0urK82Xqz//+c+Pbdc+qGRF1FiBYFoVVS7BmOAa8TSuIEFkOBfbSuuDWggTW3xACwH2+1l62RbiRCY5GZeiQ4xMqkueD3Mhbuzym1YfnbLKS6eJAWRTjOiFkCKifi9MNyJIB9vZyH7k3mq0D4mdd955Y3WWbXBi94c//OGBkfHQFma6/PwEURTIIA+pROR96Ez9BHKs1MOVTn3gQEyt2BufHfAmk3yBTOfiBLoie46lI7gILRnqsBc2iLI2sANhtjOIjdoGW1ugbSl/wQteMOqxRR5ZzjORQCY9Jl2t8muDNiedPe47QVsRYNv3lZUX3MnUDwj2cjIg7WxcBIpAESgCRaAIHFwEdkWQl46S1RjOH+dF4FxwQmz/E+RxSDmTttFxTKwYcG45O3FkOTwcI6saYtecFs4PZ9E1Z4bzKubcOGZHbSjsnyJQBPYNAnlGPfe+wIwEeX5NmFkpRpCQo6c97WljzLCSKh+p8ewbb4wtCKVxw/ZcZTz3xhrjhzjjACKG7IYQZpwQI4L//d//PVZYyTQ2sQsxQi6tNtLpSGCLcYh8tjiMT1ZZEWGETF7GJO1B7hAt9iLwF1100dCDHCpPr/GQ3VY5TQqwT6CbPIexjn1vfvObx1gpnwxjpNVXK8VWqa2Gv+xlL1tdfPHFY1xNfbYYY7WVrDmwVxr8BDJtNSbfWOswXuc31sqzTXkEl73BSV4wyKo+mbFDvveBydGsUNs2TgcSbDJEf5loMJlqEgE2JhsQZHbCXZq+cB39bMg5nX6zjAjTnTznJllgBX9tIofM4AJnq91+Cy7IS0g7ct24CBSBIlAEikAROHgI7AlB5txwZPw22NY/ThgHRuCgctg4FpwRH0mxJVIZzgvn0Va3173udcNBIYuzrGx+b6guEo1sI+GcMg4PHfJmB+bgdVFbVASOfQQ8pwmIlWfZs+95RlaMH0gcsoQkIURWW5E0YwXi41AGuZVudVBAbJCcrExGl3QE2tghqG+sMMZYtTbOIK2InN+2vupVrxoybdtWD0EK8Uv92EEOEokcW9kNOc6YpJxgQg8xtTrMRulkZtyykmxMNKaxS3oOZFKQ7lA/K+rKSCMLFvDTTuTTtmH4aVfshyHiRybb1U8g10qqbewIP5JNNvzIsaprZdp4LUgP5uwnC1bSyKcTDiY6yJSm3dEpX99aFbe1Xn9anSZLW5B95Nl9gazDUBqSzlbX3hn6m57IjnzXZIpD+p2TDWfto99khH6W575iF1vTDhhEZt4xyjiSHgwbF4EiUASKQBEoAgcHgV0R5DgJHFWrIO9973uHQ8Kp4rxkBcAKEMeDQ6ksR4UDwxlzzUlxzQnhCFmNsapkBYCzzJnmKPlNHUeQXLqj/+B0R1tSBA42AvMza0xAVB3GAuQFUVXGWGAlEYH2W2UExrZlRA2RQl6MGSG4SJzf/CJMCeQgMwmIkGAcsqUXQT711FPH2OQ3vrYvX3LJJWMCz3bpfAQw9WM7Oc6NQyYETeZZ8URC2RgCJUbm6LGNGylbBnnqsTu2Rk9iWGR8NJbCSZ401/L9Hjv2mpQ0rpJnbBU7lIeb8nPQD4i1MRsJ1R9sEtuSjZTna9LqZuuxc/nqWnmni12xG5nNefS5ZgPs1IWlj2GxXRoSbtVdnokKW7+N/e4L9wBblTv//PPHb4f1f3SIlWE7jJBzNrDTO4c87w86bcOmV/+rowwyTYZ+co2gO5cOP3nRlfY0LgJFoAgUgSJQBA4eAv/wJg+jbZwGzgYn78orrxyrP//6r/86VjCsKHBErWpkJcYWOlvrfDSHo+Krs//0T/80CDAnhLPHAbaiwlE666yzxjZKDhqnyOrS+973vuHoHYa5rVIEisA+QgBhu+GGG8akmFU7BM1Xg02EZeXPVldExTjjQGZyIFqItXREFHlxPpMY58oZqwTkCenyMTCkz7ZdBAzpc41cIuC2LVtVnQm3+uQhTw5kGDG38mwsIyckKzYgZsY1Y6F4DmRLY5/yOeYy0UmOyUN42YGjHhzEiB87bFtHDG3bvvTSS8eEgY9iIbBkkyFGMpdBOgwRU6SfvRlzjdXGbavgxnPthrP8d7zjHWNl2qq1iUxtIR8+yGnOlU/f0EUGrAUfLIO/9qhjpdvEKPmIvrJkW/F17n6wOr7sG7qtSvsZD6y8dwTpDu3zbwLVN/ECR+10sDPvM+f0CCZp9C3dIckjo3+KQBEoAkWgCBSBA4vArggyVDg9nDIOCQfHrD2HJx+biWPKQfEbO47Qf/7nfw5HlePBueSQcJqsGHMAOWnykG2yOM5kq2/rne2McbgObM+0YUXgACLguRUQO0TUv+NBZKzoyfu3f/u3Qe6QZ2TWOOALzQi0sUQ6IopcWfFNOiJtDHEkRFfGCuOU7bUm4BBrK85IMAIYMuV3za5N4iFaSGHGMHIjHwGkE6E38WelERFV1lgn0Gu8s9XYb5qRZ2Nd8pxbpf3gBz94iHxF/ii0+AMPBM/459yYyA5tgUvIuWs/TxFLYyNdysALsQw29JHBZrharVdGe6yK+zK2beRW1K24SkcoEUjtVc/4nK3USDiCO5NXOtIHmgQjRNvqLzKvX8hFYN0HJkbhrp1k0Y+Av+ENbxjl3Dt0snMZyEKAvY+ca5u2wyofHWOrXU7S6TDBwCZl2cp2hzTEGG7S0w5xQxEoAkWgCBSBInBwEdgVQeZQcHzM5tuqZkY+/6eSM+W3dxwhjoZDWU5JjjgiHA5pyC8ZnKwXvvCFh8ixuhw9DhXH2BZsuh1CHZaDe4O2ZQcDgRAysefbVl7E0ArhOeecM65NjCEuF1544SAtJtkythgfPO++eGyV0a6SrDx6/o0l8p076InOjD0IoPHJ+OG7CLb1IrkZj9RHZqUj7n4yYiUTEVSGXDF59AmInbFJQIYFpJFu8sQIW34/GxKrDHkIIgI22zuETH8y/sECaUXw1Ef6rISyIQE2VsIRfSvxfvqCTCLx9AgzPsHVaq2VY9+ReP7znz8mCIy1tjJbnQ4Z9e+RjNEmFkxeaA8Z6iP7CHx+A5062qYNCWxHsq+++urRz1a+vSeCg3JpM0zpQMiDuTbK1445yNd/VpuRbfXSByZjTBio60NnvhoOR8H9KMz3T+rpe7YFs6XOUbF/ikARKAJFoAgUgQOFwK4IMmfBwdF49atfPWboOSK2L+ZrqpwtqwCcpTgXHCpOEieEo5PDTD7HiaNDJsfPuXICh4djnDSOV2yI7APVO21METggCHg+sxLnGTc+IEhW6JAaJMUqpWfbtlZjgTSruYJ09dWVbgVVGXJDlowHfgebscZ1AgLnZxsIHtLlo1AIuKB+xhIrySb7jGN+l4woI5sm59ggROe4mOojX2wjy2HcQmJt6VbHYQwTZ+xDnpckWf4ySEMSEVMTj+TDA+nXnoyh2o4gWl03+WD11TWSG1KtnkBGAnLrt9TsRVj9ZposukwisBfmdvjYxmwclgYTcuSZ4Hz/+98/7GGrMuye9ThHXvWDFXp2ZQJCHe0xUQInhNa5dlj9ZR99ZHo/ODIpkXbof2lso0t79CVS70B2TSDQqb8EegV1yKbDu0f72eF9tWzHqNA/RaAIFIEiUASKwIFEYNcEGSpWNszIc1it0HB0rURYNbGFDXnmCAscDc4cJ43DlmuxNE4NByVOifQE55wZ8ex0zWVStnERKAL7CwEExLNv6yvSY7y44IILxheJkS7PPIKJxCDGVjSRLkQFYbG1FkmyfRZx8jthhEg9wZhgay5ipg59An1WU5Fd30uweoxoKoOsIVGIt9gYZNUYSabfNw/I91tectlvvMmYQ2fOozNjk3R2I3JIv5Vc9RPYZ4xEQEM0Z3kpJ82hrvYqL7CVzfTOdiF1Vn9NAlhBtqoLa+OtOiGC6pBLBlyRaZMVJjXf/e53j3JsNmmgLVb8je3GYMQ5/Qkf5YzxdPgytQkFdoZ8spcM/YsEX3755aPNdhrRyQZ2I+of+9jHBvZs1v8wsnMACVef3bbH27Y+E+S0K/eDvlXfjgE4IMhsNDljwiT9RkZwF2sLm7SRrJRLnH5pXASKQBEoAkWgCBxMBHZFkEHCaeCgcQJ9yIUjyHn1vyY5SlZIbIWcV2DU40g5OCSCmDPCoXKOWM/5KcPZTZ1RsX+KQBE4JhAIiUNIEJA3vvGNYzsuEumZDgERGzeQZquvSCECidwhV1YWkT4rvMiMcUMeGbZoWzE1JrlGkpTzu2ME0G4Wq5aIMh2IMZnIN/JHTlYQkWi/5fXBK2VtHUaaZtIH+IxHsTHtkIf00Ydg0528jH/ah7zNRGw7nUnO8ogtcIax/+ds8hJeMGYfPQnqsx3x1D51fBOCneyOfPJMWCCucNIW34OAXYJ+sOXa9nhE2thvhR6G5AjaDGt5DpMjPpLm3YAEw9XKLoz9Gyc26Q8E3fsFjkiwNsTG6E8sn430WKX2HvI+ksY+BBnxtmqvTcqbDBHYiYBri3vOvRTbI79xESgCRaAIFIEicPAR2BVBzox9nAhODOeU88vpsU2Pk8KZ5ZxwdDhknC/BqkFWQDhCnDmrR7bEcWrykRYOEV1m/a0CcHobikAROHYQ8PwiaGLkwwonAuLcc21cMI4gSWJEyZhhHEHujA/GAWQOuTn55JPHb4WRGUE+Amb8UA7RQYJ9+R5hs+qsjo9AGY9s2bXF1oQbIo7s+YK+dPWRQmOZr+zbFUMGQmVVFqFCJpWZQ+ppI3vExiqrosi58S8YsE8ZRM7viF0L2g6L4JFzeeTPIXlJdy2QQbfx2HZpsk0YCCHJKZs0v7tGHvWLPsgkgPEZufRvsbTDb5ltOU+fyE/fwN7WbmO3VVqTC1bjYcWmrOaSZZuzVeCQUPnkINRk2AkAF9vwYYdIX3TRRUNW2pBJEG3RF/rfJEdWw71H3C/eRyZpEe/YTab2wMZ9Rj8c2UC+1e/gKy9B2nyd9MZFoAgUgSJQBIrAwUFgVwSZs8ARMyOPDHOqOJ2cJg4nR4LjogznJc4FR8zvzzgoHCmOC+fRigNnhsOKWF9zzTXDUeIEW02wTZITa0WErITIzXXjIlAE9g8CeVbFOTdJZhVQQExCOpAeYwZiJR9hEYwfynn2jRu2AfuAlHFDMMY4XCuHmPoqvm3VxiKrqUigPORICFkVC8YuYxj97LClOlu2bdNFvNiEZIdAZuzJGJc4dsvPCi0yRpc0+cbJ7JRxrY3qwyKxsg5BfeOm7ekhhAg2OcorJz3BtXY4kFzltN91Al3GV+1kgzyTBsEE3sZ2YzEyi3DbJaReMHBOrsOEhq9Q+z/2JjPZipSyxVifrdIXX3zxGOuTp+36jC3shJn2JkiTr33BUOw+cpDPfhMcdgzoe+8VW7iRepMFsZdM5dkHFyvikastDrK1R7kcruGsrDINRaAIFIEiUASKwMFEYFcEmaPAKeGkcaA4NBxIDpbf71m14UxxdjmfHA0x5xbRRYStzPhitd+Tqeu3zFZ+OLaXXHLJ2HpnZcJqghUBwYpEnCTXcWDqtBzMm7StOrYRmJ9LY0YCYhPCIc1znBDi43omf559RAeRsspnBVpQVzm6xMgMsozMIn++/mwCTp7tu8o5rF4br4xHxh55dKtPF8Jn27HVZCvXSKIxLHroDZEiB9FD7hA1E4fyjV/GQPaqF3uNnXTZOWN8Q0TZnLaFiJGvHjJ93XXXDXkhucZeerRLiF3OpdHvYJv6xk34xQ4x+XQqxyZkVAhpRZK1wSqurdUh0cZ79pMXG8VWoW2ZNjFBZgLZMLCSb7zX7vl+INc7w7uD7Ww2GaKMdBMbsHeN2CLX+hcRVp5uOHufIMX6yiSHvmNnbJlxcc6umTyn7WkfjIJTZKRNjYtAESgCRaAIFIGDh8CuCDJnIQ4DZ4YDwwHjYDlsl+MIcYg4KAKnxkdyzN47rIhIM9PPYeJcccw4O+T52BenyGHbpW15dGTrJf1xYA5e97RFReBgIOAZXRdCApOX8STXiVMfoXEISUsZdSMP4UGe/A9lJBpJCglynvpWOPNFY+TK1l/jUWSTiWgZmxBfYw8yKZ8dKUceoiUNOTTJh7gil7YW+4J2Vrtjr7L0I3u2ffstMmJtO7Bxjh3aI7DDQTf72WGcpMPYKAQX5+rZdYOkko9I24GjHjxmu52TPQfX0tlsHEeQ4QCrrOIrb9wnE7aRaaLTSjO82Jo+sTuIHCTZ+C3Iiy7tsPJri7aJC+2zrZ08q8dW8KND2/SJwC4yvDNMcLBRedjCU19kQkF5xNdqdr4ebnKEXcGaDrL0p3uCPO2jI0fsGAb0TxEoAkWgCBSBInCgENgVQY6DaJbetjofUeGYSOdYmdmXZntbHCGOyimnnLL6l3/5l0Nb3OZVG87Q6aefPmb+EWSrHpwSTorVC05vHCKy5TUUgSJw7CGQ8QMRyoG8zGRLq5CRkD+xsuL52Q9xCQoIDtKTFWYyQoCUUT4kBxEyoSdNGenJUxYBRPQQO0HeUjeb2B47TQga63zYC/ELqR4C7vhDrnFNGeOlOuxAOmMfudLYZTy1VVzsmi5EEYnMF7bVi21WYm1pRhKNo36fazVc/e0GfYHMmjgQ2Iao+sp1VnzZrRyb6Nau+YNa0WXctoqfstKDpVh9/UWXSQJpuS9gqE/T9+yQxrZgQTecHUix9puYUC8TtMqQYUUaJnSYmEC25bkXpCnv2gSv3Qd2Ewh0yW8oAkWgCBSBIlAEDi4CuyLIcYg4on7HZRWYMyfEaeJkKJfgnGNj26OP3XDylOVsRV5WkjlJ8jk0nC6Oj2sOJWeUo0L+HEdP4yJQBPYvAp7bjA2eX2OIbxMgKNI3CsYGRGte8VOWjIwzGQ+kS3OdEL3SchhXyEy5OVbfmOOIvMhSLrY6RwqNVbb3+mmJFVTpsSFll/WNZcY/OpQxPoZEkoeckfemN71p7LDJVm1y5b/5zW8ehJKM6BDTb+XY1m15PjDmX+7Z0bNRYC9CqYxJAfLnIN8Yj5QrZ1u6Fea0UVm65UkT1BFcO+QlXVmHMsixtlt99jMcaak7Kkzbxl2nXuRJi84Q+0yiJj1yTNrqJ/K9W+h17+kLQX15sI/82LO0KTIbF4EiUASKQBEoAgcDgRNu52UcZpirxrnjUHEgOBVxJMTKOjgqHJE4LEmby8QcMpMuLTLVoSd1k556jYtAEdjfCHh2HZ5v22mt9NmK7KvRCGtIiVakrNiqoH+NZNeJrw8jMgmRJ55D0qUZUxzGH4ftwrYe+7CTrycjymSqs5SjPnsdCSkj9sVs45LVXFtzQy7lqZMxb64rz6G9OVcWaQsGCKmdOQivHTbyBOW0xTZqNkunI/b5ZkNWj23XRrKtWCN9kTHb4pwNSKLfOlvVRiJNSkSmfKRbf9GtnVak5wmLtCM2LnXMspQlx0q4f8nk3M9s5lVuZVInspRLevJyrQzsrTLDLvmpK0595+rBQ5w+C/ZwM4EbvNQjb53MWX7Pi0ARKAJFoAgUgWMXgV0R5LnZnAthdlKSz5lIvvM4GXN+6iZto3iWFV11VjZCq+lFYP8ikDEBIUNU/VYWAURI5mc65cTIoMOKnxXHEJnNWpn6yjhHgshXN7ptt7WqiJwjmSFQ6+Svs41sdpE/756Z7ZrrxZbkJ2+2dU6T7liS7MhJWdc5RxBhakISwZsnHlIm+iMnMcKq7Qii1ec5wCw/pUn+TMwjQ7zUow1JS5tgTSad0pDtbGneSJZy2wnRtSw713fO/qSJUy9x6s95SWtcBIpAESgCRaAIHCwEDhHkOAfL5i3TOQzrnIZlvXXXy3rKLOVLW1dunbx1dbdTTr11ju+6urtJ2659c3vnOtJdJ20uF7uSljLSk+Y89cVxAiN3WTbXys5lkj7Lm9OW566XITKX6du95kRnYoVtaWPiyKFnXZjLpcxs03yuvuukzXUje11a6iReRxqW9ZQVlunr0hA75SNXndSPXTuNUz/6I1M6vAXPSvJTXh47hPl8JNyBX/pLWmye01J2u3F0x5aN6inniH2blZM3y1Nvvt6o7k7SY4u2OxdiW/QtdabcUs92yy3ruVaX3Fn2Onlz/mzrOpk7TSN7qXOdjNiQWJ3US6xe8mcZSUu827qRPetN2hzTtyyzLm2us5NzstKm1JvblrTdxmRGT+Jlu+iQt0x3j89hL+1bZ8u6tFn/8nxp7zI/8pbp8/U8hi3lpX7aneu5vvON6i3Lrbte1l1XZrtp6+xLWvSIkzbLTf6clh0Y8nLM456y0jP+zXW3q2O75WbZPS8CRaAI7BSBLX+DbDBbNyBRJF3+fg8G47Rhjpe2r2tPyi/Lzm3eTpm5/Gbnm+nZqF70b5Q/p5MfHZvVW+a5Tj3xnJ/0Wc98nrKJtyo/1825OjNRk06eI/aItyM7ZRKTNZ+vu44dG8WxYylnWT75wSK6kr4sP18vJ3Wicy5zuOezfueOPDfLPDpmW+bz6I+Mua68pKfc4cRLmUsZW+VvVX6n9cmb+3MpP9dp+yx/Pk+5ncTr6m/Hlo10kLfOeV2WP1wd5K+zeSl/O2XUWVduXdosfzPb1V3W36z8LHdd3Tl/t+fk73XfLNvKxrm9zjdq17q6y7FglrUX7Sdjnd7tyD7cehvJ3kreXrZ9Ixv2W3owScy+7dyz+60dtacIFIEicGgF+XiCIi/946nNbWsRKAJFoAgUgSJQBIpAESgCRaAIbI7APz4vvXm5A5U7z24eqIa1MUWgCBSBIlAEikARKAJFoAgUgSJw2AgclwT5sNFqxSJQBIpAESgCRaAIFIEiUASKQBE4sAiUIB/Yrm3DikARKAJFoAgUgSJQBIpAESgCRWAnCJQg7wStli0CRaAIFIEiUASKQBEoAkWgCBSBA4tACfKB7do2rAgUgSJQBIpAESgCRaAIFIEiUAR2gkAJ8k7QatkiUASKQBEoAkWgCBSBIlAEikAROLAIlCAf2K5tw4rA8YOAf922n/7v6H6zZyd3wn6z/WjaM99D8/lO8DuWyh7tNh5tfcdSX9TWIlAEikAR2D8InLR/TKklRaAIHI8IcJrzr9duu+22QxBImx1q1ymnkLzkJ5Z/j3tsf95PPXWidyd1Dxk62ZL6kXfiiSceKpa0Qwl3nCzbtcyfr+d2kreubvSwRfm0UdmtgrrRwfboUC/p0RnZ0bMd+ev0x155ZJA74xi9M5br5OxFGlvSHnrTptizFzr2WsbSZvK3Y6/2qZt+3m69w7GfHlgm3o59h6OndYpAESgCRaAI7AUCJch7gWJlFIEicFgIcNJDgHKe65CT+Zozn2vOdhzupO3E8VYn9cgRdlJ/bjA5kSE9chP/f+zdZ5Mmt3U24JbEnHPOOeckZjGIySRFUaYplWV/tMu/Qn/BHx2qZFep5KIpURQpUsxJzDnnnHPOQXrrwrv3Gmw9Mzu7O7uzQx5U9XQ3wjkHN7qBcwPoZ5LXfY7Uz3km5C/lyEpZcbke6xD/1VdfLUwf50v+8ZlMwTn4us81OY7k6+s9Ux2TdKYuY9nka5ekj8vO1j35jtQn98tD95LWIfYGM/czDalfykTWkr4DU+mFZ3TI019PVabiC4FCoBAoBAqBuUSgCPJcol+6C4FvOQIhVJzm3nF2HWIkj+vcO200vrMAACAASURBVAvOX3755bDyyis3wiZfiELkTgVv9OUs36LKTCUr8dFNpusQDXaGACctep2TFjmLOqfsouyVL5jFlkXJJjOHvAh2yrqOreSmjtGxKNlTpZOvHYXYHMyiQx7XyzKQr37q+dlnnzXsVllllRa3rHUvbb3YDLvgNRN58jqC+UzKLG4edn3++ecNy9VWW22Zt+Hi2lf5C4FCoBAoBAqBSQgUQZ6ESsUVAoXAckEgzvknn3zSHPWVVlppcHDcnb/44osWz5jEu0agkJgPP/xwWHvttRemIVLyzSTQzYF/++23h1VXXXVYa621FhLAmZQf50HyPv3000bYES0He0IgpX388cftfo011liYT11nEsiBBz3qvvrqqw8I3Li+6gXP5JE+U4KZ9ogO5WCT+jirh8M1zISQrZnUY5yHzmCATL3xxhvDe++9N2yyySbDuuuu2/SwJxMh4/KzfQ+7F154obXTjjvuuJB4zrae2ZLnuXjrrbfau6A9Nt5444WTGovSoaz6egc8T+uvv37De1HlZpruOXnzzTeb/J133nkokjxT5CpfIVAIFAKFwFwi8L1f/OIXv5hLA0p3IVAIfLsRQOQeeOCBRoyQuZAuBIzjjjDJgwgjbAgVx/u5554b7rjjjhYnDVkUZkrWkE0E+4orrmjyN9hgg+bAT2qNnsRJH9+ThQjcfffdbcUMEXAIIcjvvvvucM899wz3339/I37rrLPO18htSOIk/Yn76KOPhmeeeWa4/fbbm3z1RhwT2PXBBx8MTzzxRMuD9MATNuSP7U65nKUjo3C/5ZZbGqFH5h0Jr7zySqsDUrbRRhv9FXFdlB46HEJfZ3GI8V133TXccMMNjbitueaazX75tL2Q8s59+Za4FH/I01aPPPJIe7bo23LLLRthnE09qUNMHcv2bGtD5/5AZB0mddiWd8EzAa/bbruttd3mm2/eJjUif7oz+Q8++ODwpz/9qb0DyLVnRgjOngfPN739Ib4/5PfO9oH8l156qbWp94vsTNj0+eq6ECgECoFCoBBYkRCY2VLLLFrMAREM7nEcZ1F8iSoECoEVHIH0AYgBp5oTfeONNzYShshtttlmC53zZ599drjvvvsaSTrllFMaSVYesX3ssceGq6++ekCitt5664WEQTpHPQ4+OOgaExHpiAjdhx9++LDHHntMRI48hz4rNisbmc6IA+J4wQUXDLvuuuvAVgRYOQGRQP4QZHbvtttuAyITQtHLir3O0ZO6kIEcI0MIB6yyKkcGfSHqt95667DddtsNW2yxRbNBWuRFX0sY/VGX1157bbjwwguHAw44YFhvvfWaLmURJSRNm2g3ZGffffedODlBB8z6wAar6PCQpr3JFe9A9qQj+PC0ovz973+/tXEmQNgnL/kwhqHrxQ3BNGXV7f3332+TLjDdfvvtJ8pVLkTRdQI5kZW4Pj1xzrGf7XkGgtWTTz7ZbCDL6n30sQ8eO+2007DPPvu0yQkTR6+//vpw3XXXDY8//njDVRpcFhXIVR5B9lwir+JiW9rDZIkDJkkn23UfrD57D/vg+VDO++q5Pfjgg5v9yvblJ2HXy6nrQqAQKAQKgUJgeSKwXAmyAdegaDB0zkA8diqWJwClqxAoBJYfAr1TnGuOP+ce2eOwp3+QjsAiY0iTLbeHHnpoI2Wvvvrq8NRTT7X4MUnSrzgEMiJv3M+4R3Teeeedlg8ZSbkeETISH1mRG9KMLL744ovDzTff3AgBQmHFWECAEEIrrognQqPOyC6dCWQjgQhFSBO9vU4ykBmEg24HWxy5Jt8qM7KHRFthTp7oypnscUCA1cVK9+67795s1y7qwG5Eip2I/rXXXttIuDg2B5fUIffRoT5ZoZWm7ZzJ1RbOcKOLfoRZ+2QlGeGSJ3YgXFaxQ56jZyZnegVndnnW7EhAzpHykL20QbBim1VRz6B2F5KW81RxdCmvrp5nkxfaKLbQ9dBDDw2//vWvh0033bRhK01b0mnC6Gc/+9mw7bbbtokLzwFCrb09G2x6+eWXF06KNOOmmCBigzp7j+BLn50C7IOzdLs37rzzzuHee+8dttlmm/aMSWen560PJoaCWeI9F1bh99prr+Hiiy9uekwM2dVAhrrlOe6xS/k6FwKFQCFQCBQCc4HAciXInCwHR4oDVQPiXDR56SwE5haB8XuvP0AU9Qkcc+kOzrOV1v32269tIT3//PPb6hNCYdXr+eefH4444ojmfIfcxtkO4UhNIy/30h300e+QBynoQ2xBMqWFLPXkQJzVzocffrg5/kcddVQjdlZw6VAWoUNwQo6RSyRaoIM8eZEU370ijiEQ4qUjJsgjEnTkkUe2Fc5sR5cnAQlHfJASkw6wkZ489E0V6ESKTFggnoccckhbtUfC2E4WrGzFpRuZM4Eh3qGe0g866KB2HuuBodXOK6+8sqVbpRXHXnggnFasydpwww3bBMhVV13VSBydCGXIm3T3/fb6sb5F3cMCLvT67vh3v/tde8asvCOL0dXLMYZpP1vQMwnSp+c6z07O4uFLlzofdthhTYfnOUG8VWKEH4Ynnnhim5DwbPkUAPm1Ym+1Ftba2pZ0MsSRbZu/9yZjbN6J6MhZPZR/+umnm0w4mowQYOJem2jfyy+/fDj99NMbwVUHQRv1wfM5KSDJnqM//vGPbfIBtla5yZnuWZwkq+IKgUKgECgECoHlgcByJcgcrEcffbQ5jJxejlANkMujmUtHIbBiIMDx5hg7cs2pF8Rx+jnu0hAFaUgjcsRR58wjo4gZZ14aQoeEIgic+hASDjt5DvLGpJYexMwqJaKDDCAVk4KyCBM9vRxyHYgL2zj/bEWgrMypE5JFF4KM1CvPfv1fyFJIOlJrhU09BLLVh43qaQXRavoOO+zQ0tksnV1WAa00IrPyHnjggQ0j+eXJyp8fvkJalBHfB1ixkwyrx1b/rFJef/31bVu3VUJBfRyCtNhpFROG8qlfj5W87tUZobP6e8IJJ7T2gR/iDLOTTjqpbXfPaiZy+Ktf/aqRfYQxW5/h6oef5FuSkLaDgfY36YIwnnHGGcNWW221UGQmTxIBN/iLz7MbWc59SHzi6GK3clkF9oykHXJGkD1HJim0lTa0dR7RVGfPh/HUO2GFF3k1YeDeIc/ee++9cAs6OyI7tmSngPb1XPrUwOq5iQ9xSKx30L2JFjq8YwJZcOiD528c6NXm2gixR95ttfZ8qFdf93HZui8ECoFCoBAoBOYKgeVKkDleZsE5UBwQjloGSAPupEEcMFPFJ805g/+kvH1cfz0JdOkJkZn7OhcChcDSI4CEISKccKQN0UWsrEghv/oFAWlBJJE9eTnrVrKQCwROQGr8WBfCYNLNyhnH27sbsod8IZhIR95peslEMqzI2s7LwefIy8OpT1/hWj+1//77N6IQQpa+gj36NnWSJ1tIkUtp5MlrxVEdEBnfPJPDRrKRDXYjReyI7JRFEm1NVl/56UNQXSNcyBZyjRyZhISZbdKXXnpp62/JY4uzFW4ELwSn16Vcvv/98Y9/3AgWfG0Hh5+tx/QJVjvhqDw7TTbYYs42aeoW4t8KLNhuLk4d4WCbsXwwFqecsSEYStNGbNXGiJoj+cWRtySBDBiyXb1ha+Veu5ELW/bIJ08CW6xcmwCQLgSD5Mk52ObemTzljIPavC+rLuTDGs6uvQfaUj6TB0gqwuyZtXoMR5MhsFTeD3bZzsz+Pffcs5FscoToot+75pn0HGpH8tjjGYZ7nl12sNX2abpTJ/GRl3o1Jd0fuOVAuNlr8shqPZKc8j2+XfG6LAQKgUKgECgE5gSBWSPIBmPB4J8BtK+RAdDqhlUWgy3nVFAuA6hyBvhxnHtyhX5AFR9dZMTxcHafNOfYlfJkjeOkpYz0yGyK608hUAgsNQLeL844MsLp54hbGdMvcMiRJP0Ep50zjlAizrYdu0cWrDIiJ+Lk856Syenv32/9A+Itv3L6HcRDQIzo9T1vzkgHcoYksINMQRnX9CGw6SfSVyCUSCmZyBU7rSSzUZp4daDfgYTJo/9BMtiJ5CC46iKkHunHrLwhFgJioUzIPYzYjrRl2y872J1VcdggJcgrcpwV0r4PpdPEg0kJ7aK+2UbLbjKt+rKZXbFNXv02e6xEOo8xaoYv6HORNW2tzW21jv0h5lZEPRt0s8+quTaykukbYTZqI+Qv/wYq8hfnzP48M+TbVp7ty7BL+8rXB+3muYOJkLqKDybi3Qt9eXnphFd09+mu4StdUH+TIyZ54G/bteCbYL88rX1PO+20NkGC1CO3sP3973/fJpM8F+pkwgHu9NPhHYM3Uuy7fmWRV+OyH6uj3/PkvRFCmK06x17vBF3ywgJmSWuFFvwRpx4mcayI+17as7zLLrtMiXFfvq4LgUKgECgECoHljcCsEGSDrgFwUUGeOIUGTQOrgbcP8iRf4t33A28ckl4vublP3r5cX4Zc9znLxxZnh0BGHJwWUX8KgUJgqRDI++ld41w7vP9IrNVXhBJZEufdQ+T8MJBy7jnntmfKjyj88Ic/bI45x58s5Tnqef/FI1AcfWcOfAgfx56eEFgEybZfTrsDiWWL/kAZNpCfVTPx4siwaorAiqM/BAdZtOJNDgJspZwsxAihZjOiIt7qHGLC5nFQn0wmWCnM7htyBURTOmLp32UhQ0cffXQj6ch5+jbYIVvqHvuVZw984WoF2ko1Aog0qYtAl1VWhIo9wRgGDvdWmZFj8sgn09EHedP+2jztQD797hEossiUP/HInrpqR+1pxRSGUxGzXu9U13mu1I092t7kCBunC+wSgoP6CuKT5pw6tMRROt3J06dHjmeLTSYFtKNdB2wzQXDJJZe0iQPPg23q4gVtj/AizvJ4vuBnazaC6vmj16SDiRDPm2fF2QQKnXYYyOMwucRGMjxf9KR+6qa9PCMmbUwKKTMOqaN3yrtoRwK5eUaCUbAcl6/7QqAQKAQKgUJgeSMwKwTZQOfIjLhKGDQNlv2gF8dJ3gyyvSPC2ZFH2aQr7z46MthmII588co6hOiW7lCe/Ngmf8pEJidEXjalfCtQfwqBQmCpEfCeCQjNcccd196/bMv175oQPz+6ZZUSQZPPu5h32vtpOzQn3TZbZMZ7mj7E2fsbPZx+efxbHHHykiWPlTH3VrT0Dc70c+DPPPPMZqd8fSAv8snTnyCcVsNtVw3Bl8eB5FmxFc9eK57qFJudkQWkjyx2CNGberiPrb4LRqQRcfV12D6OSPkxK3nhd/zxxzeSr75k60NNMFhhJkuIfGfYImJIk8mC4EIeu5B4aX7EKn17ZEhPGyCa6qucfM7jQL88Vsz9UBVc6bct2Ir+Oeec08gvu8hA5uj3L6eQP7Yp4/tzhG+SjrHOSffks1s7aT82eAZgO11QzqHewTJn8QnSx7ZJ1ybBS7qjL6e8stoL5p4PxNezbPJCG3jufGOcSaLYQo6Jnh/84AftubjpppvaZ02eEd8Qe77lRaAFzwk5nh82eVbUJXaJk1+7+kZZQKLlS108L+eee27DTruMQ2SJZ5t3wHthUsR16i5fhUKgECgECoFCYEVAYFYIsgGTk8HptEXLvdlig6GZ/qzAxFkz4Ca4tjLAATLbbcC0apSBU7rBXZrDjLVBmFxb7my1c598HB3xVmw42eINvJwNTgBZVmvkSZrB3gy5dOU4YPRXKAQKgdlDwHsYIoGEcIy9c1ZTxSOaHHgrg4ggcoRA61P0HfoVfYx3mIOPLHv3lc2WZdaSS1ccb+muOfviOeaIqdVSK9FWf5EP/ZWVLf2M/oXsyHEW0me4ZsMf/vCHZiMCzF59SfLo9/SDyKAVNn1LbIk8fZ+VWnmVV1Ye6clDF9vFsymr1NLhok90tgUXiUR40ucql7Kpf6tI9wcxhL1fGYaN/hfubIlu/SWS5jtSExTBRh4HfNNns0+92Ze2iDpx7NBv264OF/cwpyPEi0x4SCPDvf6cbY5gQe5YR3Qt6qwc+Z4vz5Vrzx/spgvq4GATu+FHVg5pgvRJQT7kXv2Sd5xP/aQ7YKV9EWW7Eqzie24d4pFmk0Dyks0mBPTYY49tEzMmGLRX9Mljd0bGTrrYKg952l4e98ZNuMDc5JV3xL186o04//d//3crI34cUj9nbeu5gK/xluzUM/nG5eu+ECgECoFCoBCYCwSWmiAbSA3Gt912W9uuaBB0cGZ8++SbKI6bfAZh5/6ac2fA902Vgd0P7XCQOVscYk6oNE40Ek62QdXg7hs58hFeAzaH+Te/+U1zcvyYjB/soUuwfcz/7LQV8dRTT20/amJANzCT63suqxe2m3E8yFe2Bu65eCxL5zcRAe+T9zSOunfft5VWybzr+gfbR72L3nN9AecfaeW0y+M9tmLlG8asmCKYViJDUmHnvdVPCOkDXNPBOVce2bA9lXwEgxxkmT36Ls48GbHbNbkO9uk3kCsrofou8qTJ73CNcCI2Vm6RePHqAQNnW5LVycrlpECGoK+Cl/7QEYKqLkg24mI10OSefOqUbcjyqjeb4e+aXId79bDSqC1s2ZVPGySwk93k6VPZDovgG5lpV2c2IEKxP7KcyVIWkUaK3SNbxgyyjCX6X3KEbNsmTzyCPklur2Mm15GhfvSTbzIjeqeToSzcTCxod+0MV2GMS/RI046es2OOOaZNEEyliwx19WvUSKj3wBZnz6Vvjr0bdMLKs/fzn/984Zhlkthq/I9+9KM2YZJv27OyD2Pk1DtFv/HV2OlZkmY8hof2zuSUaxMvnjO2qSv9nhn3yqXeU+HmufIswoB+ZRxs6DGaqnzFFwKFQCFQCBQCywuBJSbInJoc/j3HNddc0xyr/HCKwZeTZABMMIAaCJUzwHJM/DAKR9e3gL5h4kTKI92A7bs5PzjCebGCYRWGQyi/bWYGaWniDbS+wRPP8UKgDchsQLD9OxIrRxwF2xQ5KgKHWx3k4YAI9Neg3aCoP4XArCLg3RI4/ggxAsRpzr9I4nRfdtllbWKNU+299o4r595ZX+LdFkympW/JO5uzdNfpc/QdCLB+wGohwkcex9+qGrJqu7dJOjqRFLLlIYMsB3v1I0gpYq7PUR8kT5BXObY7i8+quTgHu/VT0hESZ7Kdle+DvCb3ECJkxr2AoCHMVvisxpJnK6wfXGKff4uUlWv9oD5XXQQ6YmcmGXyrCgP/w1mQ14q9vli7qKcgDgb0IYrysSk4sccKZuxshRboNC6or7rK79oZGUQc2S2QLz67CqzyB19llzb0MmCedunjp9IhT/BjI2wTlzFPurheXvJ4jpPuLJCTwB71V/d8U2xSR3ltagUakUWGvT9CdCG12snkj3EOuRXIo4tu7WniRhnt59m1m8M75f3wnljh98yyS1ntor2VEef9cHh2lEs9mrIFfybFeQbJJzvPPJkVCoFCoBAoBAqBFQWBJSLIBj2HQdLZagOH0+qrfw1iQObEWYHJqokBkLNkIDUoGiSVsUWRM2cFhbNpC1/SrYL4lVNO9N///d+3HynhGHAIrcj8x3/8R1v54KD6ARNnjq2VYGU4XnRyGDgT9NlKx4FkJ+eX/ZwFRJ2DpDwnIE7BitJQZUchMN8R0Ad4x7xb3n/vqJUx7xyn3YSW95/zbFXL2f+8zTfEVsq8z/oFZAoBIE9/oa9xTUeOMV6Iiz4A+SPbShwSwBYyyNQ//Nd//VfTiYzZnSIt5CIyEQM7YxyCXTRCT+CUsdqpz7KjRZr+hrzYrS6ISQiWNPYL8vb5TQbqq2CQPIgNwilOEK9MSC5SQ7d+eBwi2+SAb1nZhOyYMBTo01ZwR479eJZ+lExkTX6k3Ko7vbDS5yJtJh58C61dYiuZ5OmPyVZXn9Z4BtRBOyinX5auTvIo42AvW9ioj468Xn6LXMw/2oke+kPWxU0X2MIOE8ImVtgXW/tybBMfG5UTYCg+9+JyLa/DM8EOz7+JW7ga9/yIlucPPmyAp2dLe6SM8nBM3aJfuqCc8dI9OcZrdYCr58G5bzvvJz15bsnQZsh1ttzDL5NWTcmCP+pFf/CBMb0IshDb+jJ1XQgUAoVAIVAIzCUCS0SQxwYb4DhgWeE18NmOZXCWlgHS4CrOQGywtzps5ZmDccopp7SZ8gywHCez4A6O1pFHHtkcTYOsbYR+5Mc2Mt9XcTJtsTTQ207GeTNwc3Y5CWbYrTjTyylAhjnnHAC2Sed42VrNWY+jkUF9XN+6LwQKgSVDwPvmPeSQmxhDvEyM2cHBybeKjGj5BhmxjJOvjPfS+ynOJJx0/QkyJY1sh+txyKqZzyzoRGysrimv33C20oqg0223CccfUUBm9AX9MZY/6R4JsJUUcUD46RHYpw7ureIhuUjJpBC8yIILsmt7LTz0hSYnbb2lJ/WXjsz8+te/Hi644IJmv2+T1dGhXPo2ZZCtTASQK8ALAaLXhKS+FOb6YX2sFX7ltB1dCJc+2c4h/+vePT3jQC6CpJ/X3tpCfuOBPpg+bRy7YCS/CQi2n3XWWa1NtIs86rGkQXn1pYM9Jk20hXGBrulC7DPBkiDOMQ69jblOPvrFJT5lpXuu4QVL7UMX3OHtWZWmfMZMZclRNm3nPu0gL3y1mzb0/Msr/d///d/bO+mXrq1Q570zlpoUMR77Zp8tedfgZTLFmS1kTxdgyi42qYv8dAeL6cpWWiFQCBQChUAhsDwR+GtPcgm0I8aIqu/YOJUcTAM6py9OIbEGQoM05xgxNvDK4xc3kVMDt2AANSD7tkteDoFV4X7WPQ4Ax40DZ7DlKFgF4jxZlWETQswJ5XzZomdF6vzzz2+/2GrFij0INh22FyLf9DvY63BdoRAoBJYOgbxHHGq/mOz9RHJNgJks8/46TLZxwk1yeS/TbyjnfdeneP9DmjjnynG2ka7xaqm+QR9hN4otyvoaK6ZIgH5BHyAoj3j4dvM///M/204Tumz71aeEAKR/WBQaiDH5yCubstKmn7NFma3O7B+Hvv+RFt0mH8liszyu2UZGSJ17JJleP6CE2OgHhfRprtMe6eOcEw8LZAjhRV61BXvJZHP6X/GInLZR1xAgtkR+E7rgT8rBgHx9ut0/Vqc9A1b4rZj6Fpo+ck1o2kqOvBkvtFvajI5Jenqd013DlR3kujbm2BWQ1c2pyvaY0R/s4DZdkC95+3KT6iCfejrg6cjzSo/nCz6efSFyXauLcn0cHcnrmryke6/IRniNyyZUvIPawDkTWto6dTRxoR29Mw7yJoXUQ5r30L2xlw2T6j1JRsUVAoVAIVAIFALLE4GlJsgGON+acaTMNtsybUXDAMvxNRByaOTLjLgVW98Km63nNBqAOaIGToOsgd3AzxFz5khzADOrLR/nQBznhgyDNh2+fTNYG9CtPtuahlxLM2tuC5/VId8bI+GcAnaTYfXIKkkcAGUqFAKFwOwg4L0N0fNeW0UyoWWCLQTXO+f9917auqsPcHjflbcTxLut//D+y6v/0G/oC0yCIcDkpJ+wLRlJ9CvNCJk+yXtOrn6LDMSNTmUQJH2FMlZo9U36M/KnIgEQUtaRa3mRVSTWd6QpH3v1M4iYb4vT16R8j7g4NsqPkCgfXSknf66d9YFIpj5WfZSDlyPynFNmrM+9eiOnSCtiTK+VcIQZYWWPdtE/q6u2Jd/Ehb46bRYd6qAtnBEvK7XaA5m3c0D57PzxTCD1ZJMDu5NOOqn138qynf5JOPR1me46dmkjEzXGAPqMGZ6PpE+SIU09EtxPl7/Pp9yi8qobDL0nnk2TvJ57tgpkeF7hCSfyYJEQe4JP4oNb8sOcHG3q2vvg/yfTb4VZ8BxkIsMzIM0haHftYxdYyDcdkc/O2IVQI+Du5c9zTE7KNKH1pxAoBAqBQqAQmGMEFpsgZ7BjdwZJjpjAqbEa6/ANHAeUY8YJFgykHCiDp4GeE8eB44haceaEkmlQNVi75rTYcpeV4QzQ5OTfmfT/X5QzyiGk37dtZqzNiIvjiLERSZbGkaOfc8BBk4fDGueCza4rFAKFwOwg4J32jpmMQlQ59/qIONfR4v3XDyAHJtbcC/oQZI2znQPR0h+Qq4zDe6usLbz6FyvH+i5EywSdFWyBLKvUiDcyrBynHwmzm0RehMFkmh0m+o9M1DUBC/5Eb+x0L6Q/Izd1kuZaGkKiTpkgTP8qf39NFrscCdLJ6HUkDZ5s9Yv9yuhv6aRLGMumT4CjoAxy5ptXpFVfLcCKLNjLk7agj+wcqX/0ONOPbMuDkJKjzWBtBddKMjy0FWLu2qcw+nBbu6VnWz15jtjdjFuCP7CjX9/veTSpasLXmCNtHNKO6pK6iZN3prYEm8ieVI5sOPleXv3hgCAbw4yJ2smqrzHWRJNxMXKDDZtyTUeu6Y1O75YJYs830goHk8cXXXRRq5P3hWzvgnFem5Gr7R1wcMjjng305Do20UmHumhzWJOTdGViU3CpcyFQCBQChUAhMFcILDZBZmg/mBnUzLZzXvxgi5lu3xb+6le/ar9OzaExGGbwU5azxeFBeuXjBHBSrP5ymA2aBlGOANlWc84999w2SBtUM7BmIM5gTYcytuhxijlZfpHTCo1vlOkjl6MbJ8yMNifBv4vi+JItxN65apjSWwh80xDwTnGkHZlUU0eOvjjvswPZ4pBbCUbA8p4jvBx0pNVKsW9evbshyMpl+zG5SK++xQQZQudHuUyG6Q+c6SRTmgm1EF99ADlsNImmr/jf//3fZosfIrTVV5/TB30WW8hy7VBf5MHnHkiIMuKckR9ndoQ0KCOor8AO+R2CMvBwKCsf+X255HXW17FVurLJJ34suylYsDWXXAQMbgiyX1JGwmw/RqKQNHWFn34/K8b62JC6yCOLXge7rTwL+nltKZ4tDm1KrgnWyy+/vMl37wcczzjjjEZa1VsIRj0+LWEx/pARHPJr0XYZIKKIojFJunxwdq19Pa/uBfojY5Itwbw3i7xMRCirgO4tggAAIABJREFUPTIBkXzKIb+eD/jCwbiGvHs2TR5YPRbg5tkX2OWggz0JroO1ONfykeO7fBM0vm83cWW12LfrJi18LiWf9OxeSJ1Tb7rpS+j1JE5e7eoZMHHjU6fY5OxZqlAIFAKFQCFQCKwoCCzxqJRB0JnTYJDj9Jh5N2D7VylWC8x8Z8BWaQMhZ4CT5TtAzq7/Xez7M+VOPvnklsfgTJ78nFcrF5xmA61gUCaX3j5wcjkSdNx8883tR00M3uxCtjkjVqpd26ZpexlHjTPOCahQCBQCc4uAdx7pdSR4zznXyIt3G4ExcSakT+j7Gf0SMmabtr7i9NNPb7/+61tXfQ4iJ1gd1ffYwWJHigk28vQJyCACTd/vfve71pfZdjrpfxaH9OgLlUEmBPLUg/yQO/b0BAlhV8aKbchC8jYhC/4g2ib+4EO+OiLe6qPP60NPWMhUX7aRi2CRMSmQKS9ckGN2mjwkg53q4d5Kq2DSkTz9qolQ5a1A6ldTR3HqhmD7/AYZha3VSfWWjgBqX32wdj3vvPOaDcYAO360LZvgyXZt1NdxUl0WFUev4KyNjQHqjMixEYlLO9DFBm1gmznyKo0dOWDk6AMMlOuDcvCg17NAl9/h6AM56mkyV15YwdeEB6xgbFyEvedWXmXyHPayci0t9WGTFV2TIDfccEObbDImG4ONjbaaa2+TBNpIPehNXeljP5kO1/ST7xAnrzhpZBhvtZ/2hbe0CoVAIVAIFAKFwIqIwGQvaRpLDXwG1wzGBm8k2KDIETQYcoQMpkiuATx5M2gaXDkGglUgq7gckwsvvLCR4HwLzPmy9ZlT5dc7rehwjJFgTpwZdDqQXwNvBmkOh3wGZXI5Y5xaTi/d0syMczI4ZRwDeWLTNNWvpEKgEFgOCMS5jip9iD7GO4qMZTVLnxJHO/2MMt5z77S8+gjflSIZ+hTER1+hH0O6kF99QibOlI98+fw7KbtbyEEQQ35jmzN72eaMXAj6F32UvsnBlthLN3sQSfL0Zz770G9aYUuf1uuwmmiiELFRRlmTkGTqz5wFsh1sJxe5QewQKn0iAjTdZCA50hFTq8K+n0bEyRSvHfq6sEXd2e6gTx5YJGTrNNsRUSvS7Dd+IO7GAOMIzNgqD3KmHDx/+ctfNiKonDEFdvRqN7b0uqJzurM6OlKOLGON8cgkijHE7id6PFeeJ/Vij3QTNcFCOjnOjj4oox3SNslr3BFHrvqPCXLya4OMW/Jqfxj5wTnlrOB7LhNiR+5zpld5crUj7JFjYy5MEXHPuPImon/605+2NmG/XVh0eQbgpD55FyNPnGdA25AT++n3zCmrjRFw43twV6bPG3vrXAgUAoVAIVAIzCUCi02QGZvBzUBre6AfvTLghiBzxAx6HM4MhspweDgcrjkXzgZT2xY5Tn5My69bc044K5wD2yLl882gGW1El7PJseJI0cGBE0envGanOcJm3G2HM8NuJYJOeRBsjp/vE9lkVYgzpGyFQqAQWP4IhNB5P0MyvI/uBXE5OOeuOeXC+L1NGX2AFTdOfYgOsqFcf3DS9Q3SkA3l6XCWD4HQTyBi8sa+pnzBH/aHICIe+h2EVrDaaPVMWfkcZJCp/3SPiPgGWjk/bkiXPk1gh7L6KATDVlj2Ii35hIQsOJAVu5F/BNrZdnOfnCCUyLfyk+pBBl10+2QGhgi7VdXgrpw+1Rmu7EXcECB9qrFAH05OcGaryQITFQ6y9PfsU+es6htD1E892SANITWmOJssVT9p2urss89uYwK8FiewXV2dUy9bi/2PYWOLOrCJnjxfzsYR25y1qfopPz56O9R/HNI+iVfnBGnkIbPsoj+TzHRqh0svvbThYmyFvXzR49rzzrY+kJugfp5Hz5vnzxhrBwIM6XZ2b6xNm7LDf6mIffSwkV7PrueOnZ4LYzo58oqHpVV3k0vGWs+fdCH5Ijc21rkQKAQKgUKgEJhLBBabIBvQMrgZ1JBNAypnwgAuSLfqYlYayeVYGEizSqNcBkllDZrKclDdG1A5VBxC3ypzhKwEc8I4WRw+Mjh5nAEDdYJ4cQbqM888s32/5X91khXbleNYkCXONQcz9YqsOhcChcCyR8A7i1zGyadRXN7XWJB7fYQ+hWPOUe/fW9fKCibKBPdkp3wIT4hp5MmXtOTN2aorXVMFhERf6IxE6d+sVJOnL8vEIDvoJVe/hcDr/8QjIfo45JJNCdLE6R+RIv0quWQgI0gp3fo18erh0KcilH6IUJ+pDvrTRdVF/6nPRRhji3q5j2w2089m9iCwyLpVYPHINZtcswkJtCoMB5Of6mnMYJ808XRlGzEM6YQdkm5yVH9tNVl9kTxh3P7BbCZntjkihyztoT50mIBFEuEhn3Srq3muUj4yptLZp8NvHIKpeM803IxXxiTPCPwEz0HIqEkGk7zsI1+e5POceC7cR3f0kiG49xyQYVJAvdQvZejWtp4p7ebaM+nZjS6yycuhDT33sYOebNe3imwim11kxi55+utmXP0pBAqBQqAQKATmGIH/Y5YzNKQfzAzeVldsS+NQ2H4mGHg5hxwJzoVgEDU4nnjiie2aMyTOQC2frWIcIY6TwZgeDpLVC3nJ41BJN0gbrDlxBmQz14Iy5Bmg8x2ylWnbD2OHfK7J812ivJwDZfu6NYH1pxAoBJY5Ahxzk2fec0523mOK+3dSPu983u2xYcmbc9Ld9067ewey4ax/0M/oz9z3wT29iwqIipVPfZgJN32Xss5LEkI69GcOn4EgG4gTmSHZCB3C2YeQGERLX4fY6nv1eX7xf1E20aEOsEZmBHEIjjOckNpgqt1cZ5JUmp099AdTfaxxQmAfTNltqzIybTs8UozcwS2Hfp58B0yQfmONlU16lImNTfhi/KFDyNk1e30jS69JG9gLqWu7WYo/va5ejLolWGk94ogj2juRSQZ2aEtpfqfD++KZFTeWqY1OOeWU1lZjMkoPmTA3dromM4EsBxwEz45nz7G4IXaxEaYmP7SvuiSNzP56cXVU/kKgECgECoFCYFkg8J2/xANYCukcHofB12AX5zAiqehXe9zHIXDN+cggGXP6e9fix2lxWpKWMjknfy8/NkV/zvJEXvLUuRAoBJY9AoiI729NrJksM0EmjN9HfYwJMluX5TU5tzTvLXm+BbUqiWwhDIjRkgQrZGQhA470hSEaiytT30XGuN/UtzkiX/3T30WHtPSJVlrlQZRC9KWl3LgsTEx0OuCrjDzkwB7u6pf46CRT/ZF4dbYCyY7oST5ndUqAW+Qljr6xXeSnnLTIHeeLjCU50xFc1UMwlvV4LYncRZWJXrrhqg3S7rDs6yjNfX+M5QcrGAnJ6zpyXdMn0Km+8o31tQxL8YcOOh3xC+ihs0IhUAgUAoVAIbCiIjArBNkgaFB2ZDDur1VeHgN28iU9ac4G56RHZuKdyU46RyGDrLiky9OH2NPHuSbfISRPHIpx3rovBAqBZYOAd9e7jCT3pEdf0L+Pee/jbEv3/nt3+3wztZI87z959JPBgU+fMlM5yRdZ6Util/OShL5/Ymvkxm5ntgaDXof6KJ8ySVPGoa7SlR1jJ31cXh7xKRMZfd2STlf0yq9sn086zBPoijx5kz9xyecsLkd0jGX3+ZfkOvL7stHVx832ddqL3OAfLPo6ShMvxK4+Xbw6kNeH5FU++VNX8hI/6Zno5SzuNTscdJHtLLiuUAgUAoVAIVAIrKgILPYW60kVyYCbNPcZfJM2vpdXXJyApDsL4nNNRgb8yMsAK4+4HPIlT3T09034gj8p41a5OB59nrouBAqBZYuA99DKbRxp93n3ozlx4q04uhdyTr7FOSurH8mq2dLKygQfOTkWx54+r/L6o3G/lD4KDrnuy7lOWefcB0/n6WyLXLJDxpJfnOusNkY+HWO5uW8GdH8iP1FkinMIKZe4XkfKSAsusSlpS3uO/l7OpLg+fbauoyd1St3FJ4iTnndFfPIlj3Nw7eOSt9cT2d4DcibJGstYnPvYG505L46MylsIFAKFQCFQCCxvBGZlBZnRGWinqkAcGgNm8mawdHZkUF+cQToy6FWOnnH58f0ke6N/KvsrvhAoBGYfAe9dQn/tnR2/t9KtPiKiCeM8iZ/Judcn/4oiK7azzzGdXZPSUi9pKZ+49MPRMS6ffNKnytsT58jpy0WvtOnk9+nKO8b5+/vo6PNK7/PEniU9x4boIjtxSypzJuWib6q8fXrGyeSdqv59meR1nq4+U8nqyy/u9SQ7loWexbWr8hcChUAhUAgUAlMhMGsEeSoFFV8IFAKFQCFQCBQChUAhUAgUAoVAIVAIzAcE6h//zodWKhsLgUKgECgECoFCoBAoBAqBQqAQKASWOQJFkJc5xKWgECgECoFCoBAoBAqBQqAQKAQKgUJgPiBQBHk+tFLZWAgUAoVAIVAIFAKFQCFQCBQChUAhsMwRKIK8zCEuBYVAIVAIFAKFQCFQCBQChUAhUAgUAvMBgSLI86GVysZCoBAoBAqBQqAQKAQKgUKgECgECoFljkAR5GUOcSkoBAqBQqAQKAQKgUKgECgECoFCoBCYDwgUQZ4PrVQ2FgKFQCFQCBQChUAhUAgUAoVAIVAILHMEiiAvc4hLQSFQCBQChUAhUAgUAoVAIVAIFAKFwHxAoAjyfGilsrEQKAQKgUKgECgECoFCoBAoBAqBQmCZI1AEeZlDXAoKgUKgECgECoFCoBAoBAqBQqAQKATmAwJFkOdDK5WNhUAhUAgUAoVAIVAIFAKFQCFQCBQCyxyBIsjLHOJSUAgUAoVAIVAIFAKFQCFQCBQChUAhMB8QWGk+GFk2FgKFwDcbgb/85S9fq+B3vvOddi8+11/L0N2k7KLydUUWynaxuOXGcvr7mdgrf/Itru0p1+tcFtexi+yp8Eke6bnubZmqnDzJ3+dJXGSM0/r75FkW5+nsmG190TXTuskvb8rFnj5uprKUnSQnMmfrPLZ5ceybLRtKTiFQCBQChUAhsDgIFEFeHLQqbyFQCMw6AhzoP//5zwuddQ6047vf/W6Lk+46IU59fx473eP7lM2519nL7q+Td6ZndXB873vfa0Um2UBvdMsnv5Ay0+lK2eSZJD9p8gry9NdJn+6cekR+MCHHdWx2n/o6yx990tQpMnp9KScusl1Hr3RhpZX+b3gSF/ktccGfSfL79MW9puerr7762nPX27G48uRPfVI2NosPlv2z0NezLys+9ykXGWz88ssvG0Zk9bhG7/isrLoKrslXbiZlx7KmumcnHX2domeqMhVfCBQChUAhUAjMNQL/54HMtSWlvxAoBL51CMTBzxkAHHT3Qs4cbfHO4jjZrpGCEJjk7Z3xRQGqzGeffdbkkbOk5CBEwJl+obejtzd5Up/kX5St0tnrQDqmsjfpydPbsSgdyqYuIe3iElw74C5fj1dfj75MyjqnfB+X+Jw//fTT1iZrrrnmsMoqq7QyU2Elvtc7lru49+oFt/fee6/VcYMNNmh17OsZOyO7r+sY6z4t+cflc0+vQJdjjBXZkQd71+Jcf/7558Mnn3zSJiVWX331r7VLEzrhT+Q7ewfoXHXVVReWja4JRSdGTWoHdn344YfDF198May//voLJ03GeE4UWJGFQCFQCBQChcAcIVAEeY6AL7WFQCHw/xHg4CMknOg11lijHVLEIyxx3tdee+1WgOOeMq+//vqw4YYbDuutt95Cxz7Ebjp8Q2TIeeyxxwayN9tss4Vke7qyk9JCMj744IOWTN5qq63WbArZoevll18e3njjjWHTTTdt+mLHJJmT4pAohOO1114bNt5442HdddcdVl555a9lhdlbb701vPrqqy3PRhtt1IjP1zJNcaPsRx991MgWTMey1ZMNzzzzTGuDbbfdtuXJJEXqM4ksURnSBZM+j3j3ngEYPfvss8Pmm28+bLHFFq1tyIffOEReL2ucZ6b3ZNHx8ccfD08++WS7hoH42DeVrNghvb92PyaDSWezZ9U9vc799SRZ8pE3xk9bv/DCCw2rHXbY4a/abZLdwcwz+/TTTw/rrLPOsPXWW7d3IHZMKjdV3Ngm+bSn51177rvvvu09NelRoRAoBAqBQqAQWJER+N4vfvGLX6zIBpZthUAh8M1FgJOOAN94443D448/3pxzxC8kl+P/6KOPNicb0UOUOO9Wyx5++OHh0ksvbffKWP2Spmyc/6mQkw/RQw5++ctftjOCjHAuaUBKr7766kYeESuy2IE40Gc17corrxx++9vfNvKMjCALk4jFJBuQIxMJjzzyyHDJJZe0iQSrclYM+yDP7bffPlx44YUtzyabbNLyLAoTMuAB73vuuWeAN9nBnH7YImJkI5EhVJnIcKYnhJnMXi8ckCZtLi+Z7nPA8O677x6uuOKKhc8DoizI3x9ZcZ1JezcBM/hD5v33398Io8mB7bbbrpFNdRjXQ10cSXNOXH+mtr9X5wTxSXdWF+1nAuKll15qz71JIBMir7zySsP+/fffb+1qAoYsWF5++eXDb37zm1Z2xx13bEQ0OqY7W63X1p4n2JrwMEnFLrLhIQ+bTBxMd8jvHeyDOGVvvvnmhtNaa601ODzzFQqBQqAQKAQKgRUVgVlZQc4gn0r2jkTi5uIcu1YUe+YCg9JZCKzoCCBHDzzwQFu5RHRDNJzffffd4a677hqQAs73nnvu2QiLVdTnnntueOqpp4Z99tmnVTFEtH/f0wfI0MfnGgFAPqxCI7AJfblx2eTpz8q++OKLjWjssccew8477zzYnovwZPUTubCahvDQi+SzQ54QBmfELKSvt0NeMqx4P/HEE8MhhxyyEKveFpghWFbtkB3lEJXoSF7xkZ9ruCLIt956a1vxQ8DZo7w6ImPXXnvt8OCDD7bVaXpMbMijnnDcaqutFhJkcvtADoL95ptvNt3s0/6xkX56xNMh3lbrYKQOqYezFWY6kcWlCXCg10qq541M5Fid2NDXQ9t5HpF55fptydKE5HcW58xe50wMqGN2GohPW3iuEV4r6VZ1lWObtofPXnvtNZx22mmtrHjE+bbbbmsTJyY0lNtmm20W2tDb02NEn/Y00eSZMVnDptghL1u11x133NEmS1IH9o4Dcn3ggQd+LdpzYbJI2n333deeE3VS79R5kqyvCambQqAQKAQKgUJgOSMwKwSZo2cg5cRkW+FcDXoG9wzwue5XM5YzvqWuECgEJiDQv6MIhG29IXFxnOXhXHOmEcLLLrusrWpa2URGOe5Wo7ISSw0HnhwyIif30oT0Tc7iHBz5EEF5UsY1O+SJPHGRlXzsZxOyePzxxw/vvPNOIwTy5TtaK2nID1LjGjFBSPSb0YngIDchRunDYgdiRoZVVTiQHQIWTJFw5A12ZOmT5dFHsye2qw/7HZFDBiKGxIfEKOtAzqwaX3fddY0gmgAwsUEXnWxG2tlGTnBulVvwR7wVYquWAiIqBBM4steqpX7bDgKr7uwJ2QphhtvRRx/dnoElJcjsccAAtsi/s4mYLbfccmG7NyMX/EHotYFVV2XVW1Dft99+u9mJNLuHtWeBzfBkM1IKL5MoBx98cDsnr7NnA8mVd7fddmsEnFwTF94DW5Vhw2Z5EU+TPHDXdnYPsN+zFbv6NqcjdTZ2m4iAH/uykp+JAfI80969gw46qMnUDnlmyRHI12bjIB4+iLNnxeSOHQ3qRYY6sCfHuHzdFwKFQCFQCBQCc4HArBBkTowBnXOXFR4Do0FvLoJBOwO3Adj1XNkyF/UvnYXAiopA3ktkLe+ld5SzzKFH1NwjZPoQBHj33XdvRAmp+v73v9+IHrKAFOy6665tJRPpIAMxiVzlyXIIiU9fID72hBCwS5CmvPSUCyloGUZkHEm0+qiMFW0Eyuq2gByRZ9XUiisiYfusMuqMnChHN1LrQCqi2xke6ogoPv/888Nxxx3XvmNOOXViH9n0yrfLLrs0Ei0+xEd+GItzLbDNQQccpCNBVo/lEY80IvR//OMf20qlVXJbebNiqW3oIzdklU3BuilaQCKNF4i2OlpZpNMKphVvtiNT2hwRVN7YYlu5gIDbCq+d6eonGKJjpme4qrcAWxMctgIj3ezSbvKMAzyQXp8FIKWwEOS1kquc1Wf1Uy+rr+rjgC1Seuedd7bt7CYw4CaoT2ySz7N90kknNeLqeSfLtv3999+/7U4wqWCSyIQFO8iHoS3ijhBpbZjnG550ODwr3iGY+27Zc+U5gYk6mIDxHFihlufss89uExrBPnLSznZ/jIM0zwM8t99++0bw77333oaPtqNLHnWvUAgUAoVAIVAIrCgIzApB5uyYebcaYBDkGMxViINBP8egBt65aonSWwh8HYH+3eTsIwrirExaCeOUi0eIOOrukTJpiBhCgFggmVbNrKohaRxuBEDfYzWZAy9wvhE7RJQOzj5nXHCWbsUSSUAOrJTRT5a0EABnpAgBQBo5/H1dpCHEbKXfD3AhUPKRQ55AlzqzD9EhI+QgMkM+xMdG/RjCiwzZ/qu/PeywwxoZshIpH7KB5MAOHlYarYAiSrEVAZeXjdLodC/AEmGCsT4TQWM7vfLBiG51VD+rgEiZLc7IPjL3gx/8oJFb+dlMrxAdrslGvBAmx7nnntvywot92goJPuWUUxZ+w62+6gm/U089tRE/ODlgS9eSBHZpO+W1e541z5H69Xb38uX3HJjIQKZPP/30hTYg2drWBIYVYquzntfDDz+82c5m9VBPzyP90wW65NH2nlGTL7D2bnjmtIlV2X/5l39pxFl7+w7+97//fbNP+3g2BPVxaBeHNpdfm6s/oi1e3Y3hVnkRfYHdnjnvgDaUL/hE3qR2SB510K7w8O6a6PK8esYmlWtK608hUAgUAoVAITBHCMwKQeZEcS7M7HMA55IgG4gdVgQM3ByJGoDn6OkqtYXACAHvpPfTaisygZQhrwgdwhGSLJ/+xI81IciIkDSEgDOPwJmQQw6sLma1kwMf4odwWVGzguecHwfSH3DckXDET3l9mJUyhAJB0I8hDuyQnyN/6KGHNnJGvhDnHtnh+KsDkshWBMBuGiTED18hKeSps/K2q4aI0iGObsFW116+dETmpptuatts1d09YqoO5LL5n//5n1tdESlxCD/CrC4CrPWLJ5xwwnDmmWc2nWRLty2XrcizEHyuuuqqtlqJ9CHGyvnG2ISoH4US/9BDDw3/9E//NBxxxBGNQGlfdQs+IUnkSoMnvUgvverOBjiKZ4cVU5gpa8VZWyJprsmFgdVXtixp/05X2lbb2/qNvHuGTMgkyNfXQRm2ycN2tqgvwss2+d1L90xrC+XZn0Mbw0Le4BR9zuLVmS7tCQ9xP/7xj9v4ajeCFWwTRojnAQcc0NqHPfSdd955jSQj6gi1MZkNdDl7DhBsK+aIsAkT7eh5OvbYY5udcGAnmeymX3rakG2xcbqJCjrlowO+JgzoQpDJSH17jHss6roQKAQKgUKgEFjeCMwKQTZoGjwNcDmriOs+jAfAPm+fL+Uir0/L9SRZ0sRzsGwJ8/0UR5STwmGpUAgUAnOPgHeUw4yweUcRYESVI40ohWAhyAiZe2WsBiPWtvkin4ge0uvd5mgjiUgJOelDvPsccY5+SIK88iBFVs3oZ4eySKqVP7axkd7kz6prZENS32dFDGFVhg75kSX9kB+7sgKqLJJOnmArrmt2INDIiNVUdqoPHQ6yBPggNfvtt9+w9957N9KiPLKB4JNNjwkENljts/WXLPVgp/Rbbrml6ROXIA0BQ7pgoB4wEY+AWelFmpD+kG2kyaQCAq6uJi/IhCPC6BqpSn2jS33YpE3h78eofMtsVZUNSDIsnekIjiZg2cYmuhBz3+8i7fIsboBtniuEEvGGoe3dnilBHvY7C6mLNvMsqqt6qlMO9+oHg+RPmvvIo9sR2U3B6I80bWFHADxsobaia0LJBAWcvCNnnHFGe37gbdLI1mpt49lTXlkrz54xWLEB9iZ1nM8555yGo3xkw4AsdXDvufOMeve8Y5HBPtips3jkV137II8DJmTadeCZ9T2yHxqTP3n6cnVdCBQChUAhUAjMJQKzQpA5DAY6Do1DEGfg4wQYQM0wx5GRFufAIDweVA3IgkG1T4s8aWRFtgGffOkcKM6UmXH3nF02RU+cFOcKhUAhsHwR8N55r5EuJIcDbjJL8D4jtFa8rNAhq7a76i8cCIn3/vrrr28OPZKMUHr38z7rLxzefXEcd7IQhfRN4hEyMgXlkaL0EYhI+h15Iw/xRNLEKUsHZ98qqNW4pJGnjkiUdGnqhLj4BWLpIY/skAehEfo+jx4h+thIjm29sBLYjNwhl/o8cqwm2oqMPMZ2fbC8Vu7o6GW7zj3bBH0w3BAjK/awR46tWiOSyD/5MEWSrL4iywibQ9tqOzL6EF3aEaFC+NVLv00XuTvttFPbOo8Isx+Js8WY/TCkF9aeodjb65jJNTuCTVavbfO2ZZ890mIrecHHtXZ3aF92wzPtBgvX2peM5HctPvfOfbmWMPqjjeGBTJJrLENiTciY6PA82rEAL3iyCR7wN5mhvMmTa665ppU78sgj20SNPCZAtLHVY8+LoC3F9Z8ReD/VUfv/6U9/WkjE6fJMwQXR9q20CaupQvDW5urtnVfedY/zVOUrvhAoBAqBQqAQWJ4IzApBzsDPcNdx3jgenA6DrNlrTpOB3L3tdYItfXFcU3GDtzzirS5wjAzInEnOmgHbigzngfMkn3uyOW9WPThs4jmOynG86ed0kdXbHL11LgQKgWWLQBxl7ysCbNUS+RG8995peWyfRYI40ZxzBBcpRRQEjrz8Cd7tvNM5S0N2kDRlEQN9k0NZxBKJQyqtzCEGrvUPVuL0HyFK0eOsPLv0K7aAIyF0KhMiRJd6yKdfshWYfHFC+iB2IEBwQKrISR75XMtLHnvUxSGOLvWGk2t2WSm0sgo/ccoJ6q/vY4sMUwfxAAAgAElEQVT+lNzokQ9OMCY7ZXKtr7biTad+WZ2QM2XI9SNTIc7IvnhEMyv60dPXR13Y6PvdtBcMrFJ6NpBB+gX2WnlUz2wZZou60L+kQb3VxTilHTxTnkk4xSaye/tzr6xxSOjzeibgF3LPxrRD8qXt3EsnXz3EC/ILnns2OUunzzb+G264oT0r2houyKY2gDuZ8qmP755h5v1yr64J2s9OBBMUnk3vghBb6JPfmT3aQJs5grt4+ZF1JN2vt08Kkcm2vKfeM3Vjn/QKhUAhUAgUAoXAioTArBBkDkEcjQzCHFlb+qxYcBqsehgMDZK+w7O10eD7d3/3d23Q7UExoNs+xqE0yCPXBlHbDG03Q7qPOuqolodzaYA/8cQTGwk2y22GnVNnMOfAcrjMlHMI4tRxRmpg7lGv60Jg2SMQEkATR55jbluwa0TFe29VWLBF2IqZSS4ONScdEbNy57tMv5ysj+BoW/VFbkIe+3c71/obwSQbPSbRrLQhGAgZvcguMpjvMFuB0R/yyEI6Yq+VMQRVWvoWfR0SgeAgNuRLU1Y+/SbCiRjOJAQj/SldAhv0tYiHFTw64KFO+jp9Y+rPFge7pgvJLw9b2e97Zm2RPll94Qevn/3sZ42guaYXvtpS6GW5V3+2s0FbIVtwUAfjg3ty1Eu88q7pNpnB/tkM5MMr9ppIZcN0QR2McyY02Ci4Z6e2Me5kIpYsOtLu7HefQ1lp7nOddtLe8LZTwjhq7LODwHPue3ht7vmzgvuP//iPDT/2mDT2I122MHsmYEuWZ4Fs+LPRhI5rbWZs1W7s912/dBMm2olMdpx11lltdTrPD5s9v7/97W+b3LxfrSLdn9SNbnLJI9sRgpw8XbG6LAQKgUKgECgE5gyBWSHIBmAOBkfDQOewkutbQQN779hwJKwyGMANroitLXMJnAUO8UUXXdRWlxFbBFngHCPAHChOJaeT82Y2nG4DMPJsqx6HwEDu3jUZ7BTkqwG5QVF/CoE5Q4BD7h3WR3D6Oex2jCBD+gfvrIkuDrttytL1GdKRP3EINmKiD1AWMRkH77p3PpN3Ju58hiHeDxxdeOGFTTbyIc6vAFuRI5MDPw5kIet0nXzyyW2rsEk/W631gyGvCBRCoN9RjxBDfRwZiIL66LvkXVSQD5ExKegaIYEfQqS8iUD6rDAir/BCatRDX6huIaSuJwXxOaTTYaLC5KL+NyuNsLelmv35IS8TF/p6K5v6bQRurEfd2Z7+WTod2sbWYFuo3SNP8HIoY4VS+2TyZJLtSxpHftpkUeSYDjbD0zOgHdmo7nCXhkx6biLTM+GZNclDvnKCcnkm3csvkKkdPePa7phjjmnE1bgHn3/4h39obWucNM56nns5xj/PiO38MLM7AqYh6/D3/Fv5zXNkHFUG7t499ovzHrCfTZ4xZ3V0CM59O7XI7o/01IsNyjvEeS+S5hyZXfG6LAQKgUKgECgE5gSBWSHIGTTVwGBqgEZwOb6cNk4k54dDKK8BVR6z1QbNcQjJzeBt8MxAalDlaJhN9wNcfoDHdj5bzTisfrCEI4BI0/WTn/ykEXArHhzoDNJjnXVfCBQCyx6BOMwcesTOllrvpE8tbMnND0shm0iH/sP3jZz1rCAjhUiYeKuQ0rI1liw6+j4p/Yc0fYeVZ4QF6QsJ1mdkW7d0n2mQrX+ht3fgXZOPANrJgjiQrf8JEYUkO6QhTIi4/ke6soKzOsIBgU78VK2AjCKm+lEEk3yENdiIVw+roOzxTbJ+z5Zr2Ari2TAOaRfx7HAEN/WCjfrq3+nWf2tD2CBV6iHIQ5dzZJCd4Fp+NiuvjzfZqV9HCNlqfAiZSzn3IZby5jrpS3pWR7JD/rI6Lz6htz9x0oOZ8Qq59INjdiIYjxBcWMNfe1ilNZHgmUJa1VEdYBT5wdt9CDh9Vn79cJYJJd8T+2aYzfIEF3LTruSQ79lmhzxk0pX210YItLzivFNIsTaw7Vpe7UMGveR5/sjznAnipNHrIGdS6OtHpqDuyqTOk8pVXCFQCBQChUAhMFcIzBpBNnAa/GzF4yxwMjm9vhkz623wzSDtPN3AmAE3A3oG3gy0zpwO27Y5I1YV4jCY8aafPQJnhHMnXVA2clpE/SkECoHlgkDeO+8zx1pfgbCa6BI4+Qix95Vjjlg6rIQKypHhfefgI6/ec4563m/XcbxboW5bL3KHeFt1s2vFjxYhRpGNHCB2+++/f/t3SfosNiHf9OqPBH2Xa7rlEeRzHaKQ/g2ZRKDyHShyIY/gbPVbOvkhMq5TvmVcUHfyTTQiMNKFbKl2rxyZVnHlsdpsdVy8X7VGLOlju7gEZcXBTkBeQvrdS9Pf0q2PRyLFxW59sLZRTp+vvWJfr4cs94gWkiyQcckllyzcnqyNtAF9sKIPmXYg0/6VFLLIlowLTdAS/Alm2k7bk2eiAn7aNrbn3KtQf0FdTAQjrvCDs/Jsd0+2H9IykeGzAPUyRpFJ/7gOiVdWmjxssQrsOUTCtavJFnlzaC9t755t2iJ1SJ6kec78wBnyHgy0RyaLTByRJz/MTcpoc+Mq+8mW5nnymwGph7YzmdIHaQ42OdPjnSDf86ye0oJnX7auC4FCoBAoBAqBuUJgVgiygc/gzNmxcuswkFoNsHJi0JQnM88GV0HcpGCg5lhkUOUo9AOogdUgjvxyFPoQJ0EZetzn6PPVdSFQCCx/BLyT3nvbpn0/iQD5cR//ksa19xxJ5rxbleR0hygoiygpzzlHTjjYynnH5eP8I4193yKeg29Lqt8+kI6UI3bkRT4Z9P7N3/zN8K//+q+NJOuL9F9Im3wCG9OHjRGkN/mkIQN0IxJsTV+kPKKhDkgIu/WPk+SS6UDi2Oz7U4EettjqbEVcWffqYJWR3H/7t39r260RIjjRJ1+OyFaObeLFuQ95oYdt+nT9shXv7AAiTzshPrbE65vZKUQ2mQlkwUMabJMm3sSFHQV2B5CFRJGL1FvRd+97XHmF2BcZ0THTMxuUVSfPhABLuCVMJVu88mzRxtoSETbmZUIHhnDzHbAJEpNCniWTM9JSj9hBZ/RJUz9pAlyVNcaSQ2cCHQ4ylXdoS2VTPnKd5TXxpD17DMmUH5k3SQRv7axu2sE76zklmwyyxCPu2Z0Vm6Y6550OGfdM0hn7pipX8YVAIVAIFAKFwPJEYFYIskHOQMlxMshydBBjg7CBPYP0TCtm5tugbMBPWdeCe2m2VBukKxQChcD8QcD769BXILmIm4kuP4yFmIiT7touFO+9/oUjzzm3eoZsIlJ33HFHIzbKcLCVs7Jo8kwQh2ggWFarffZBL+ffVu5erj5FfqTACp8f70La/UspcfozRIoN9Iwdevaxgxzp0e+eTVYWESf1SV8mLf0mAjVbgX66rJDri2EQMjaVDvXRn+q/TRqYmMi2aWVh5YA9Mm6lFVlz+DZZ/dUHBgJZyo2xUl+yYWoXACL805/+tJXz7bcfVdS359tu8vNvtLSBiQ0TAIuqz1T17OPzfLAFWUSSrfKyURuN27gvCwsBafW82QZtYsfkQMohkuR4xq0qC/CAVa6TV3ywUgZ+Dm0h+MYdcTWe5vmR3zWyKW9kOtMR/FvCNH/UNxMDCPEFF1zQ2tJ2cfUkR12QYPropU/dYef9lTcTI70q9ZM/9nhuvNuwIiM2pu592bouBAqBQqAQKATmCoFZY5hWEgyutkeaUTag+6EY1wbPOJapaAZaA6fBsQ+cAoN2Zsk5XoLBlAOW+HG5yDAox/EQN85Xg3GQqnMhMDcIWAnUN1iZ5WRzwEMoOOVWyXwXqU9BqEJIMgGn30BoTKYpL16f4pz+Rh7ft/pxIz9IZAXZd5fII0cdcSabo25VE0lQRv+DJOvTlEEYpCub3zoYo6a/oX8c1Amx1odJp0scO9XJkf5o3E9F1jg+9879tfy5pwu255xzTotDAK3C9nlSnu1sgod+GwnW11rhR5LZCCeHrb4OhF69TFj4n8vqp24InPL0iSM7NpFJPiyVlUc6YkUHoqldrPJrQwfCiiDL65MaW7iNJQKbySZjaQI5JjE8kyYUkHIELmPWuF3p9JxkPKLfs2hsyi+Sx6acY5+y2t5zncljcnod8rin30RQJng8x3YQmCBgs3dE+5ho6LGIfWx0LYztiD1wV2ft6IfQkF6Yn3/++W2SwvOO0HrP8n+U6SJPe0eua0f0Rb57h3yeCyTf++qzK4GsCoVAIVAIFAKFwIqGwKwQZM6Owyz53/7t37bB1EqAH9Iy0PvhLGlxbJw5RQZMDhGHibPAUUCMDaIGfo6AAVwwyMZpUN4gm8G5B1VcBnAOQhwZspM/A3Zfrq4LgUJg+SDg/TSRllUkhAGZ0od4r11bWfQ/crMi6T12bfWSQ28LsXROuTJ5vzn4+gfxSJyVab/Y6533L+XIQDjyK9b6HvmUVxZ5lgfhQYgRJ/9Gxy/y65f8ojDiNnbs6UMiQnToc6gb8kGGPk8+/RDSoZ7IuHQ26+tSXkuM+6vU34pt5PuGG5FnvzhngRwH8pT79IfOKS+NTVkdhAU7tA+CTLZ7dfffB+DPVv221Xj3/k0XvORjc7Z4a0OyBTrJMlFBt1VVOuV3yGcV1hZldfKt7Y033tjw81zkvx3AXXkhdXUfrFrCEvyx7dmuAf+yyEQJIp5tw32bEE1X2svOBM+MOPm0N7tiW29vbJbHM2yiQUhbkJFr5T0jyDqSDDvfXlutNbGEHNvyrA18osCeyHImi55gFblJc9Ze2hHW0u1y8O/S6LNzwq9y+5adLOOzQ1tEl7rF5uhmN1nikxYb/Np2JtFtoxef90jeCoVAIVAIFAKFwIqCwKwQZI4e589hQM3258suu6xtgzQIIs4cJnltvTYTbiDmMFkdQKCtUBhEOWYcr3w3ZyB1GJiV50w5Jg2q4uRzNhhzdmxB48ApmwF5RWmAsqMQ+LYh4N1ERhM4/3G6vdeIBkfcj3B57/OemzxD4PQfyJRt0vJ6p+VLSBkEQ0DEybK1GjlGipMH6fQjUUhOSLs0fRmChETQhVAjm0hw7Ik+Z3Y42M9Ok3/IDZKif1M2K6byk6vO7NE/wQBZIV99QkiiyxkZQlTlVZ6dVvv0cflXeQiKvI7U0ZlN6s2epDsLdFoxZ7u87CIHqUUA9dtsh1G2N7NPHeXRT8OLTYKVa+nkxx76ETqr2NJNMngGlCGDXVkR1SYmSJVhDxlWZv1YlLrry9MO9KY+Tfli/CHbIWgDK8ja2c4FeJItfTzWiGO39hb6PMYY9w5Bnly7V5fcq7djbL90+QTjomfHvTO7lLGqbtIFhsgmveREdtrSvSN6kg5vExE+U3Dkx8+0pUkhdpsQkd+1Z1KbaKPooq8P7vPsJl4c27WbCXNtZ/XYuxb85B3LSvk6FwKFQCFQCBQCc4HArBBkA6CB0UoOx5YDZMDl1Fx66aXtuzJO6rHHHtucLQTat2QGZqsEAgeNQ+SXMjmKZGbQdDZQc9wc7nOMQVOOY8uhQ8CtZHMsyTcoS6tQCBQCc4NA3uleO0e5j3eduD5eH4OYpL9JX0BW8uknciDSyIN8Vt2QoJCNlCFLX4EUWD1D3MiiSyBLGf2HwMFHPsYh/Z94xCKr1LYUC8gk4kc2mQgLOfLp9xAI3+CSw1b6EHMBFimjPJtDeOQXZzJAfOqunDz0SrNKbdUSoUJyYZLADroQYfJSP1hb4dZnWu2Ek4BcIeYIlnQrr8olkB0SFXuQc+RTfU1UkGdC1CQC0kWWyQ+r4+z177MQcvL138ggLOHvYIv2RbbY15Ot2LGoc2yTD+GGux/asnKKjLufaryAtXHOmGJV19g36Zkd26Ac/EwEGysdgviUTx7tirDSI7DXc+LTJTZ6nmx7RqK1mfxCzrlWTkh9nWFq/LVSrO4mtuly7Xm3U0IbsQmx1X433XRTmzTQtvRFbhO+4Htsz5GJhtgjj+db22tj2+i1v8mHCoVAIVAIFAKFwIqKwF97ektgqUGVM2MgNzByADgvfm2VA4QE+8Edg7B8HDE/uOLfYxg4zYZbqeBkKGv7pHwcFA4Eh8FA69rgzZmls3cEYra8BnqDMIeLbo6hbWgc5Dg85E0qHzl1LgQKgeWDgHcWmePwu57uvZTHEYI4nYVW2/QV5EWmsz5KeYfAgdfvIANx7Hu5+iV9kTAmBX0+aY6QUau9VlKVN1lHvkOgjy10IxyIhX7KveC+76PIQFiQoR/+8IdtokA5ZfStCE/qqDxya8UPCTLhiNCasES8HfpPWCeoN5JLZ9oAmUYYyaUrpFpe93QgufrVlAlR7tuHTIFOW6j1zWQjibZnI07GCfKMIcYGBM04gZgZG4wR+nMHAs0W/byJV/pjW+ozk3PaQl51NL6wjT2wgy0b+naQVx2NI8YzEzB2J9Af/Balmy5bxsn1XPWYu44+mAZXZTwb2vDKK69su6NsiTbpDFdlUg95Hb0sacnj2q4Dk9FwP+uss9qOLnIE7Wv8VUc6TbJ47nrstT/b+mDMh+luu+3WZNDnOTAp4jnUXj5bMIZXKAQKgUKgECgEVmQEZoUgc7hOO+205gwaWA2wBspso7MVksPBWTSocvYMuD//+c/b7DSHUjBwGlw5SWbw5ePkZmDnrBx++OHNIeFY0DMOHANlEGIDNidAsGpCP7vIk69CIVAIzC0CebcRDAQpjv1UVsmPXOkbhPG7rHyISp/mWt8TUiRfDv2EPofTL1+C9HFI+jiN7cgAUmGCL//bGekgFyFWVp/FvuQnnyxxzsi//FZJ2SpOXuRY/6UPtHpKlnQ/rCQOaZTeB6uNJiGRc7ZZ7bTaawcP/FKX9KMhPNErHfmkP3nZIz9dcAuBFCdNSF7XkWVc8MvU7NB+iBlSr39WX/VAnpzlDR5WGhFRacogr1aS8922dHmXJMTenMkIic8PlGkP8tUp+djrE6Gzzz67tVP/LC7KDjJgYuU718qkvrD2LJJPjzahOzi694z5HtlzZswc50H0rWo7C8oHI/jTSwdMHf4Nlbx9vuhUN7qQW+XYxy72uB4HaXmeyJDHs+k5MmHuGSSnQiFQCBQChUAhsCIj8J2/GMWWMnDErCQYGPvvtojmYNgmZybawG7ANUAa1MWZXZZHMLgqT440+ThT7sniZNIjvwFdmoG6D3EW5GGX75CVNUA74hjWIN2jVteFwNwg4N30rlth8n6bOEMGhfG7zdm2Bde2UBNpVhqVH7/L4vQDgrSkhxzkPmXPO++8ttKMLOijUqbP14R1f5KWKKu0VuUQfSQgtuu7kBO66RPvcJ0jMpxjtzIhTcqaYISP/ks/Rr+8/dH3rcogJnboWIXVX6qbiULX+loyggHdroW+bskzTmOPSUz9t22+vZxeVhO4QDZbI1v/jPBa2UbWsnWabTCMvr68MiFcnhl1RBCR2shN/pmeyVM27aUcnK3+SzOZMEk+++Cb9ox+51xPsiE49VgkTn7xtpxbtdVGJnv7dlVvkwTGPphlPCMjdVDeNn8TDQ72x07yBWOyQ5BHem87eYIzHGAd+Umbqp7aT5p8MDIOe060lYkVz3VkNSX1pxAoBAqBQqAQWMEQmBWC3A+Yrh2TBs9JcX3ZYDMunzxJz3k6efJMSk/ZOhcChcCKgQAHHFnivHP8HcL4/dUPcOptM0UOEL5JYdxfRM44PmVtHUUisoqc+JTL/XRndUCsBLL6QE6vu5fbx6eMuORJ2T5OvsSnTM6Jj1x4uUa2QlzgHEKUcs4pE93TpalvSCrcxmVz38sYX7MjpD92yzPVdeyOHPfI1tIEMugb1xkZRwphZpJjUkgdnfvy/fW4XMqM4/t7eh2ZJJE2lcxJ8oKrNs4ki/KOPn9/PxP5yRMZue9t76/lS13khWPK5Nznr+tCoBAoBAqBQmBFQWBWCPKKUpmyoxAoBAqBQqAQKAQKgUKgECgECoFCoBBYUgS+vj95SaVUuUKgECgECoFCoBAoBAqBQqAQKAQKgUJgniNQBHmeN2CZXwgUAoVAIVAIFAKFQCFQCBQChUAhMDsIFEGeHRxLSiFQCBQChUAhUAgUAoVAIVAIFAKFwDxHoAjyPG/AMr8QKAQKgUKgECgECoFCoBAoBAqBQmB2ECiCPDs4lpRCoBAoBAqBQqAQKAQKgUKgECgECoF5jkAR5HnegGV+IVAIFAKFQCFQCBQChUAhUAgUAoXA7CBQBHl2cCwphUAhUAgUAoVAIVAIFAKFQCFQCBQC8xyBIsjzvAHL/EKgECgECoFCoBAoBAqBQqAQKAQKgdlBoAjy7OBYUgqBQqAQKAQKgUKgECgECoFCoBAoBOY5AkWQ53kDlvmFQCFQCBQChUAhUAgUAoVAIVAIFAKzg0AR5NnBsaQUAoVAIVAIFAKFQCFQCBQChUAhUAjMcwSKIM/zBizzC4FCoBAoBAqBQqAQKAQKgUKgECgEZgeBIsizg2NJKQQKgUKgECgECoFCoBAoBAqBQqAQmOcIFEGe5w1Y5hcChUAhUAgUAoVAIVAIFAKFQCFQCMwOAkWQZwfHklIIFAKFQCFQCBQChUAhUAgUAoVAITDPESiCPM8bsMwvBAqBQqAQKAQKgUKgECgECoFCoBCYHQRWmh0xJaUQKAQKgSVD4C9/+cvwne98Z2Fh94nr4xdmGF3IK8wk76hou13a8pHZy+mvk96fk97HzcR+5WaSr5e7uGV621J2Op3JP86T+N6W/lr+5BmXTb4///nPg+O73/1uq/dU+ZI/50lyx3Hje2UnxUVmnQuBQqAQKAQKgULg24FAEeRvRztXLQuBFRIBhAQB+uqrr4YvvvhiWGmlldoh7nvf+14jLF9++WU7r7LKKo0khcSI/+yzz1q9pCkbApXzdJWOnLfeeqsRsDXWWGNYbbXVpisyZRpZ6sAeutmeoy/08ccfDx999NHA3jXXXPNr9sovxK5xHcTDxfnDDz8cVl555WHVVVdt9e51SGcHXWSuvvrqLe9YXl8m12kLdRGcyVCWXOfIiT0pKx6RTTtIT3Dt0MbyBJvEhwAnv7O0t99+ux3rrLPOsNFGG02s67hMbI0MZ3HqloNsNtDrSF7xyZ965r4l1J9CoBAoBAqBQqAQ+MYjUAT5G9/EVcFCYMVFIMTl5ZdfHl577bVh4403HrbeeutGXpAVJO/VV19txGb77bdvBAkxFt58883hiSeeGNZff/1hm222GdZee+2vEbnpah0iiCRdd911A3K8xx57DNttt910xaZN+/TTT4cnn3yykdOtttqqEbqQrxR85JFHhvvuu29Ql0MOOaSRV2k9GVPv/j5lxSOY77333nDzzTe3OpOzwQYbJEs7y/Pcc88N995777DJJpsM++67b8Poa5mmuPn888+Hd955ZzBpwAZH6oAssyGEWTuEQBNHrzbYfPPNv1YvZYL3M8880+Rps/XWW28hmZ5kjjIPP/zwcPvttw+77LLLcNxxx31tUmFcJnqcY1sIsXMmDT755JNWB9ipW8qRp27K9vWe1BZj3XVfCBQChUAhUAgUAt8cBIogf3PasmpSCMxLBJAsRPfxxx8f9txzz2GLLbZoxAlxQdbuvPPORgpVDqlBdhC5Z599drj22muHAw44oBFr5GymgewQoxtvvHHYbLPNhi233HKmxf8qH1lWO6+44opm27HHHtvIHMLVB/W8+uqrhxNPPHHYb7/9Fq7shoTGLvdjYiYNCUcyL7744iaD3eNghRoRl+eEE04Ydtttt3GWKe/ffffd4e677x7uuuuuRvDZEDvof//995sN6667bltt7wmySQ4TAyeddFKb5NBOIaDyIaa33XZba1MTGoi7M4zGOMVAMk0oaFvyYkvS+zP76LG6ri3UhU7E2OTLBx980FbvyTERs+GGGzaSTqY4wfWYIFupr1AIFAKFQCFQCBQC3x4EiiB/e9q6aloIrJAIICSvvPLK8NJLLzWChegIyIprq5kPPPBAW6H8yU9+0kgNovb00083Un3ggQdOSbCmqnCIFmL0+uuvD2uttVbTNVX+RcUjZuRcddVVw0477dRsRu7VrQ8In3oibQicM1sQxJBJW6Kn2uqN5D366KPDU089NRx//PGNYPfyXZMrHT6bbrppywPH1Hmcv79XFtY33HDDcPLJJ7et4NLZZ4UYwUe+DzrooLbizmYHHBFZdTNhgSiLSxvS7x5OmQyxM0AdkGR1HtunjB0EjuAxrkfKiHdk9Zwt5MOfzltuuaW1sW3aVtWRY5MsJhPkcagHeWkz17aLyxs9PVZ1XQgUAoVAIVAIFALfTASKIH8z27VqVQissAj0JCfEJt/k+qY2ASmxXffQQw9tK4LIp1VHq8yImAMB3H333f9q6y25wlTERjxChAzRSb/7lOttGMclrZdvpfKNN95odh555JFN3oMPPvi1lUkE0RZsK5yIslVaW7vFO5BQdqiPeoWwRb8zAmulfYcddhh23HHHwUqu+ORRL0TdCqqt6shqT/77fH09+mt5ENbDDz988O0vu8g1KYGgW7k3CXDEEUe0eGkO9UU6EVIyxEUfGWz90Y9+1FbqteX111/f8p5zzjkTCTA5IcjKwyhyY6/76Igd7DThYiu6bdwwfv7554czzzxzOPXUU9uKOpyRX+0hzUSFVepMWCDa9Nm+fswxx7S2iM46FwKFQCFQCBQChcA3G4EiyN/s9q3aFQIrHAKIB2KKALlGghAS987uEU7EFdlDgGyJFUI677///kbUrD4qYwstMofkID7kIkz0kIW4ue+DPHTatkwn8mm1Wkh5ZVw7kMb8KFbIWOSxE9my0omYImhWShFK9rPJ9WOPPdb0Sbd1Vzz7HO5tmbbF3EonUuiIjcpn1dxEAQIde9mBzNElD9KHkKqTVVL2yk+eesBq0tZh5ZHEF198cbjmmmtaudhJDlIPa98Fky0/uQinlWUTGnQkBCf5tIUfJvPtNdJ+xx13NPvVXR3JkSeAFekAACAASURBVE8ZZ3rl06Zwl560yHfWNoJ0+fbff/+2FZ8+cb5VP//881u7sA8u5Hhu1IMd8iH9ngVBeyLavkk/7LDDWp2UqVAIFAKFQCFQCBQC33wEiiB/89u4algIrDAIID4IjdVWxCUrhFZFES7EEdFCaG2F9c2qOIQF4UGQ/fiU7ctkIXtXXnllu7ayvM8++3ztB6mUQxitNveEkA3kWc1FBpGlW2+9tdmE7ElLQN7YY8XWN9BWJfsgjf3s2nbbbRvJtS0XGUM2kS6kkR3qjERbZQ6ZVJ4+K53IoHrLr37By6qtH6zyPba6kwsLeslHDK2QIq/y+U6Z/IsuuqilqZ9D3XyTbFUeoZ5E+pBFRJYd8GWLA1aI9wsvvNBIfP9tMzzVmR2u2U0O+Q7lc21SwA+i0W8bNDzlVU4eZROi2/04LXnEC8GLDSYYEmDbT25EPp2w0x6I8NFHH90wUg62tmmrc4VCoBAoBAqBQqAQ+HYhUAT529XeVdtCYE4RQE4QFkTOt7RWQBFEZERAZBA5+aweIi+IFxKFfCI1f/jDH9oWZN8eWw1EfJFYeaUrGzKFgNOBkCN9CcgYQm710HZk+ZRBiBA/xEkQR7ZVTtueXYeQkUEfAo8cq4/VRnqQP3Yj31aWEV/1FJBU8q0II3Ounf1Ss3q6Jjd6XCPIVs2tah588MFtmzJbkFWkFWa+580Kr1VsJJSNCClMkHOElB3S/JJ0dDTDFqzCwhOW0rPaqjy73bPPGR4CGQg7gu9aXjY7gpd47S5OOoxMNlgtp0v5yJKOGLNdvcjQPuxmm3SHQJ68gvzayaSIePUWp87O2hYG0hyxyXNmJ4IfTYtctipHr7wVCoFCoBAoBAqBQuDbg0AR5G9PW1dNC4E5RSDEBuFAhG3zRYBCapAmpCakFwGzCooUIUHyIpP+xZHV4rPPPrsRLfKQKDKRLXqQHwExtTpoNTQETjyyZZX1wgsvbMTNqiyiibDtuuuuLY4MB9kInDQy+iAeub/nnnsa0Y5eNvkGVrwVbluiEWL2OPwbJsRcfZFf+U0AIMjR64zYRRac1OWUU05pmMDClm0knAzXflwLVqeddlrbyqwMXMmyMs8WEwMI4KTAHhiSYcUbKVZHRBHBRBq1h7MfviKfzcrZgm0rvLJsDhbwg3e/rVycfCYR1MMKfyYptDMSLo8JADiqnx8P08baFy7kw1K7ILnslN8vm5PNdvWkFzG2sk6mMuyTTm+eS3iQKS2HevV1mYRZxRUChUAhUAgUAoXANwuBWSXIHA0OSGb948RwMjg8vcOU1QTOVuIDLYeJHOU4MQmcLDJ7h7mPk5czIyjPYSKLs8SxUk46O/sgr3zsjoNEN0fRvSP1UjZ6Yrc4ZR3kK0uXeA6ksu6Vo8chTVzscq8u0pyl0a9M9OTc217XhcB8QsAz7Jn2Q1RWTL0fiOkFF1zQnneriv5Fki2y3iPp3l/X3i8EVV/iQHSQMyF9DfmuBe8iko0ci0/f4F1DEpFi8hDVvOtImu+ArQDTKcgvuPcuuyeLHiQbUVQHsqzwJj/bETOHFUq25H3WN7lWJyueypOnrg4h9SBPf6C+bDM5QL8+VL2sUD/00ENt9VidrIT6ztc3tfIoT6a6wUsd3Cs7DvojPwDmO95ghCj7UTFlrK774S/6/XssW7z9mBfiTl9Ifuyngw1W8G1hh6G6INsw96NZcGI/oi/ABuGFDdKvrO+52a3d1cc12VbKPS8mW+ClLa3kK5s6ayP2s5UueqVpL7sLkHrX4tK+7Ige5wqFQCFQCBQChUAh8O1BYFYIMseEE2QFgTOTrWmIHoeIA+L7PU6PfBxCWyw5axwqDksfzPRbNbB6xIkWOCn+dQmnTTkOLDlWBzhUWUngQNLL8cm/VOGIcvroUi7OLbnKsoUjxn5pHDg2c+I5TupAHgeOg7X33nsvdKg4aZwxaVaF5Oe0sYPNVm04rezjIHPWrFqw0erJzjvv3OziMEaG/Bw+chCG2MyBq1AIzFcEQsh6wmFV1TsieA+vu+669r5ZAbXFF2HxPnjHlHctzrtqtdB7o0/Rtzi8u/qjEFFlQjRd0628d9A7j2x6r7zv3nNybbs+6qij2jurDJmCa4EMMvUdN910U+vvvNtZqU0ZefQX+hR9HPKZvif9Cp1In7MjNkZP7oNZ7tUheegRr3/z75WQVdjBFiYJdMPFJJwyZCrXBxMTCC+yqx/3La785FlZ9++dYKVvTfnjjjuubfumi3x9HfkJ2octyKt+D1HXl8PEv5KCr1VgZQX5pZGhT9TO+lNjAYyFyGcXLNWD/vyLqeCFFP/P//xPa2uTFAi5Nicfhrbrs408MmAkuCaDvRUKgUKgECgECoFC4NuFwKwQZM5rHM6sBMTR4PRlCxwHyAqLH97hWPqWjkPGWUnglFx22WXNKeKkIpBxKjmud999d9uCyRnkcCGuSDNnx0qR7/BCZq3cIOwcIPn9WxI/TsPBo4cjZmXCSgi5WTUiKySXUyuvLX7+NQlCztHi1HKspam/VZyLL7641ZUNyK+622LJyQzpZg8n3wSAdPU78cQT23ZGKzVsZxfnG0nmfPqukc0VCoH5jIB3xYF8ICSZnPL9rnfcOyMdcfV++fc6CLCttVZY038gV0iW9892bARur732ahNX+eErOhxCyA6d3m3vn/feO+uHmULA9FMm3fx6s290ES/vbeREljM72U+mHwbTB+hvvNfRnQktfZ46IGuRIY/6IGAmzQQYRFdwcu9aGn106GsF8foTWOhDYYBAIvkmGWGnD01fRZb6wCB6mqDuj3R2w8h27ayMm2BEUtPXwQw+CK7t4/pEfWXsiv3u2Qbb/AgW4k2+Qxr7rEyHjKa+zrCBG9JvvEC+o8OZvfpJ+AjSYaAP1ZeaoDA+kIXU68czkaCsvJ4ffbFATmx3ng6rVqD+FAKFQCFQCBQChcA3DoFZIciIqu8C/cIqJ44zxNnlgHAyOC1ZPfl/7N3Xs23HUQbwLVvOOQc55wy2JIxwwLJlySALBUy5KOABzAOv/B888MATVVRRgMAGTGFZVkA4CFlOOEecc845W9RvfL5zW6O1T7pHls4+PVVzZ62Znu6eb1L3zNrnMlgYKZxddAyuGtAzXEW3GN4TGIf5Yz75/1A5tW6eGLx0QOMmgePNqOPo+uM2yhmkjDy6MRAZkpxXn0gydN0Yq0tHRhfDLYYXJ5thzljTxqoXI4oRlk/7GKwxQBllHAA0jEC3MIxJhid6Tjcjl3y6ocFbOYwYbtrC+e/QCBx1BDJvjHdziqNlXpmTvgwxN6wbDtHchhr3nGDl5q/IqeKcmfvqmI/mrXoJmX/e80xmDsXMPY6gG8dXvvKVg6f1hrxXvOIVY25at+aDqfCSWh98zkwfTl/+66PooK2hwwc/7+Qq0xa6Ww9hoUxMyHtS66wbVfXUx8faYa2wTrjFtm65kbeuaaO1Q7vkqwMDa1qVE3lSNBxG6yi+6Olt/XL4ac3EC07WMmsWR1cd+MfBxIcMOsrXT/pIPqfUwaHDBOWC/s1aGz2k+hi98tNOO23wGBV2+Cc6WPMdTOiftN/hCx3g7iZZ+4Ivlp6ju3fPHRqBRqARaAQagUbgeCFwKA6y2wq3DRxVN57nnXfeMGwYRgwQkZHDoIwBIo/RtWSApE7KYrAwdATGjVuAc889dxhvbgoY1H/7t387boPPOeec8Rs8v6NjgDHkLr300nEbwUBy48OIYuwxAt0aud3wWSdDDH+GNJ3R0Tnpks7KyaFfDD4Ga2g53Mrocckll4zPFTkGV1xxxfj/OWHnJsbNMyedscloZuRyrhm4Z5111tDjeA3Pbu2mIWCumBfml8MjB2Hmu8Ms+eaIgzOfWnvn4HGM3C6qa2744sNt6Ytf/OLheKGz1ohZKypu6gnKzHV/6VlqfSCLw4nG3OeUu4X2hQuHTuQgzsE8V4ZHHDJ8PEeWdgnWEV+KcCatgdYGZdIcGHJmdwvqwMG6Ves7ZKQ/2SK96GJte9WrXrV66UtfOuTD3NpGLrrgUuXiq66DRbfPaNxY008KI9j7qoUc66nfFuNr7VKuH/QJOekPfORHJl3QCGjmkHp4BLPUnWmX3h1YWj8dXPhqCBbWfQcrvgLyx8robzzNgZwa5/J+bwQagUagEWgEGoHNRmDfDnIMF7B4Ft3Autlw+8k5ZuwytBhhDEnGBgMKrTwGjxtlhp288IoBpO6SAcVYYry5VfBbMoYsWoYXJ5Ihqh5DyCeSuflwy+Om1u2zT/bUiUz6iW5wGFH0cdvAeJLvPTrSj/6pOx62/qGDOkLaqp58enHozz777HFDJp++bq/cEsOOc+y3zWjhA0O3aco50x0agaOOQOa3drh1dLtnPhr35ipni9PEAfNJrfku3zw075XlJw/miFtJDg++5nTmauZt5MlXbl5aq9xYWzv8cSe05qgg5Rhaw/7hH/5hzGdrjbWEfHwS8BbpmBSN59Apw98n1g4R/Y0CNHTOuuVrE45ndA//pOGFL+dd/MM//MOxDuHhVvfKK68cTn8wiHPLEVRm7bGmwY+DTScBzzlYP+VzdK2bvphxm6wNDhMuuuiisXbpF7fXfhbir0Y7cLCGcZytW2Tgk+g9baGnKA/mnrOuRp/Qqj+XhWYpVU8bfc1k3XS44v+cfs1rXjP60a2x9dZtvxv5ugfhp350Trokp/MagUagEWgEGoFGYHMROJCDHDgYNowRt52ec/vCMBQYGAKjww1qNYSc3DPG5CkXQ8twYrjgnTy80DCoOKKMY4ZTDDH5nvHLp5cxrBjVDFO3NW4W8FKGh1sPRp6bWgYTp5Vh6I+K5b+bGUps/T4Nf7pH7xhR5DPA6UxP+fRJ4PgyytMOGDHy6eFTQ0ZzDGxYocvnpei8d2gENgEBY57z4vbYuHZwxGnNuDdvzFmOI0c688h64Xe1nErOmTpumM0381G5epw7cynBWmKOmefXXXfdKOfImV8CHdRHRwd/q4BDa11z02iu5m8hRBcyPWdeShOViZnj1obQWR84qsrNa2tJnLSsKdFJik4gC25i5ErpK82h3CBerYZjyJnluDp4yP+xrDzy8I5eqQdfN6++yIG/z7XJtHZxvOHuoNHaaM0i1wECOX67rW98ReQAMO0hgyzvgvaLKVcGB3QwI0+IfvL1rfc5hEaKpzXebfdrX/vacdjIOc4f8lJubOhLa7z+dwCQPQGPyi/vs8x+bwQagUagEWgEGoHNRuDADnKMHoaL21dGIIeTgcrwiREX+NCLMfLkx+ipRgkjZslAqvLwjzGNluwYocoYx3E2GVzRhWHIQJWHH6fV7ZU6fhPnN2vXXnvtMCh9OulGxGeRZKChG5194hhjGh9BysirDrI8eqTNsElb5SnHT1CWNijzLtA9+o6M/qcROOIIGPfGNyfKWHcYxWHOnDDX3Fiaixy2BPnqctLMC84rR82zuWluub30V5jNO3PU/PITDDetHDh5bqf9JtjNKH6cPA40OnK9+yyXbLfc3i+++OKxFjicq06c+kLStMG7iNY6wylzcFdp6W294LhzxumvjaHxXtcLvET1EjxztMXQS737C/icZO1w+xsnNHWXUm2Fb37rrI/cPtNdn3FAfdXiv5HylYv10Q013voQhtqivekvcmABm+R590yeyBnXZxzxfBWQeqFd0lee9uJFB3pfffXV49lhp6+arNeisUAGJ9m4gWX2kRy6GEeCgxl6BdN1sju/EWgEGoFGoBFoBDYPgX07yBUChgsDjeETIyUG0EznHT0nkGHiOUZtNULk4YlmNjbRKReVhSdeoUXjfY5oI0cZPcmJQcbIY0Tnj9swEhnRDENGlKCOwBCL7t7xZXjLlyZEhjYzzEQhuqkXvNLelNMt8tB3aAQ2BQHj3qevuf30uW7mr1Tk4Lnh45Shl2fOCRxOTlt1dJSZL3iZSwKnx99F8HWIG1Fz040zZ9jvZs0xAY1DPn/JnoPMCTTn/NErz/7IH+fKrbNPdOWnbuYm2ZnL8uic+R09OKr4yBesB5wwX64kL3W0V/DuOTzTTmV5JlfbpPLQivA9/fTTRxnM3NgL1qPwHBnlH3QOLNTnqPqdOFq4wdxn4pxN75xjzj0n3y2ygwfy5UcHaULyvMMPXw61W1yHk26o8dUOZULqBJ/wqmnK0Op7Y0ZfuSV2ewzjumaHLrzhn4MQ/Y0fh93vlzP+5KHv0Ag0Ao1AI9AINAKbj8C+HeQYFVJGDGOLocqhdIPgViafLi7Bx/BleAk5pWc04sUw8rkb4wkdYyfylnidTF6MnRjinGEGnpsPvz90S+J3aj63ZGhqp5ShpI10xoPODDCfGDLCEtAxFkVtYwzj0aEROM4IZM742kQ0T8wfcyTRPHEw5VNd8yvOiZQD42bZH/Pyx/jcBGYum2vWjsxT65HfHPs6hBPnFlh9Tp+bRvLMf/NWmltE+uDp5xecReuAPwxmvaIbJzyHXenLOt89i3TnuHIo42zRg54C58s7h11e2hqec2rNwctaQ3e8OZic++hMb2XBJPhEH3K0IeWzDPQOATir/iaDT6y11broNpZM67f1UsqR5CTTK3/7gZObvliSo83aot36Uv9oO550V1e9pbqzvtqFTmq/4Jz7441x6JXhSV7wRSso0x566HvtkaLlNGuTsRTa8dD/NAKNQCPQCDQCjcDGI3BgBxkyDAxGCcPIDQBDx3+15P8vZfwqY2wwQhig6BmF+bxNHUYYWoYI59gfV2FAMXTRM2aqEVSNm6XeYQzNdfARhZThT6/cLDCO6MuwyqeQDKcEOnL8tUc7fXpIZ++MbbdMjFQ8EsjSFnIYZyL9hOjhXYzxFj3RJC/8Om0EjjoCGffSOi8z32v7OKJo4jyaW9YP88KzdQSN+RWnT5monkM7v1NW5i/Uc7g5QG5y3RSb++a6+cyx8ke73ISSJ7gxtW6Z6/hxmtRNedXVszZZDzjhkUEenf0G2N9oMNfVR8vx9Iyv3zxb/zi81hp1YJKAHl9rjcM7+mi3P8TF4ccLb7goQy94FsihC2dam5WLoU1qDeOAc4z9JW8OMMc4/NNP6Dm0yujsIIIunv2RtXyZU9tADzrSQ5ut///6r/86cPZZvEMP63B0j45S9ZZC2qB9+l19fVjrBu/UTx2psaHvHY76bFz/4eO2HQZo1skOv04bgUagEWgEGoFGYLMQOOHN7bFdMaSQexbdsrqtcdNy2WWXDcPN79M4kIwhhqjbGJ+7MZjcOPuk2aePbhAYfoxdBhajjOHHIGOccDDjdMZ4IRMf5TGG5ZHHsJSPlvHjGW0ivdWRz5jLX2mN8cpoYxQy8M4888yhB3qOc/7Qjc8zGZluVcjh5ONFBmOKQSbkOThpV9oSvX3SKCqLzimjI3p88ZR2aASOMgLmgnmZYIzLy7yQn/liHgiZV+ZH5oh6nDlObNYKeeZc5om/I2De4uP3uGRYZ/yOOF+5KLNmWDs4ppylhMhV92Uve9lYi/DDY10wj60ffofrj2NZ0+hpfbQO0lX7s0agdxBnDXTIxjGzzohkCdqFzpcrHFxrE554OATgWOMrr64d6qKHmdtv+rjJdkOsbfiGv5RT76+KOzDwh7jwd7DgFh02eHBstUNdGPoDa/5Pep+w53feHMuXvOQlw+ms/TqEbTnr6nNm4X3BBReMQ1YOd/oObfofD/0jqGd8JHgXYZpxpV7qosMT7/CodbVNmU/+HYSEv/Xdp/QwXWpDeHTaCDQCjUAj0Ag0ApuHwL4dZBBUA8U7Z9dnbRw9f0HU54gMphgvHGNOcYxZp/XoGX1ueBhdDDAGE0eaIeZmSB1GGgOFTIYtY4ZhljwyGEO56aEDYxHvGFto8GIE5faE3owfv6lza40/WYxV9f2/yH6/x+hSpg3+oJdbKO1zi8O5Rku2/zPUMznS4KQefBiC+CdfCg8GWtqYVJlnhjB98KztGUz6n0bgiCIwrx/mp2C+1tu6+lybyqHNvMRLDA904c+RjVOUVHkcnqwd1g+yzWOxBrzI88kx+lnH0EYmR9dhmUMzTjFZ/kAWx5SDq01Zuzzj6d0BnXqca/mcYzFtk1oTrQnWIE6vPPRunznK2iE/6571zW9r3Qj7DNrhHxw43zlIpH9w4MxbC62JDgU40m51rc/+NgM+2mQdFuOUWnPhBiNOMofd58l0wTvYBCtrsN8HWxPd2lpnrXFxcKOTFF+ypPjMvNLWmh9c4WEdpQeM6SKEj3ftU64NMEm5PrMnqY9fh0agEWgEGoFGoBE4PggcyEGe4WFY+Myaw8f59Udv3BwLDDKGHuMuhot3//0Gw4eBx0DBg+PLKGNgMfi8M4AYNIwZn855D68YRVJGl98kMkI5pDGcyGQoueUgj8MagwcfDqobEY4oJ5SRp35+hxdjioEm340Kww69EKOVbvLRc8TJpDO5jFplMfLk00G+zzodCritYMQl4JO/FMtYi84p77QR2BQEjPXMCW0ydzLvaxvNAWuFdaY6eGjwmAP6OD1zGXmJ5p2IxxIfeXVuzry805cs64G5LLUWyvcVjEMz7RLxS/voKE/gIHKwrY+ce/oJaKwf1hKOpM+aOW54WGtETqz1Da2oLrlupN1mux3G15c91psl/ORx1K2lblPpT+98mo0PuW6NOdDBCjYOLrXZml3XbnqEbjRm67+g8jMc+llvrclL+MJFW+hgbQ4e4SOdeSev5sOVI6xfclASXvT2HMxSD11oq7x+bgQagUagEWgEGoHNR+CUG2IpnERbY+AxXhliIkOK0cO44RiKDDqGiKCOGw6fAOZ2lIHGWPLOeEIrL/wZj5xZRgwjToj6UvxEdcgV8FBfPdE741OeOmQxAEU6M6YYmm4UUlcaw0kb3Y7QBQ+yGOzK8VCOh3z8K1885eEnkEdfbXV7Xo1ENGQER8ZadBiV+59GYEMQMC/9BWmfAHP+zjjjjBvdJqaZ5oLfDl911VXDEfPf+LjlO8i8MA/NX/Fv/uZvhuN51llnjZ9NRN5+Ugd9bk/dwjpEs8Zk3SBLrCE6Jw2NdK5n3XR7a620DjhYg0WcbGuOmAM4PLXLOuwnKxxWaw9HmgPswC0h8qXWIX3h2fplPcp65wbaFzP4cJ7x4RAnWMfoI2QN9Jy2hC7l9FOmTm1LpbM2+9zbJ9sce+Nir+sgHPGVapOfxfgvwGBnrRW0kw4z9vI6NAKNQCPQCDQCjcDxReBQHGQGBoMnhgY4Y2QwQpIvZRCFPjTqCmiV5z3dwtBhrKkXuplvzU+9ndLoEJ50ITd81Y3BV3mjEdGjFaJveOCZ57Q3eehTFtnhgVZZ3pVHhjTPg6D/aQQ2AAFj3Px26MSh44BxvMyDOt7RieZafgbBiTNnKt1+IIlsv7vleDmcczh2kMAp5CRy6jhg0UlKZ7K0KZEMeUnzjD6x5tVn/OJU4ued/Lp+hG+cxJTho074oas8vEf3OMiVFz54OgSsB3rarQzfqgvec1AePZSnLZEbejScW7jm0HGJX+jntLbRYaMDBHlVXmSmj7QpeTO/fm8EGoFGoBFoBBqB44HAoTjIoGJgCDEuYpzkPXAmv9KmrKaVrubP/GrZume8ar3wrnmpqx3yxdQLPeMsz1Ix9DHcwlNZeIR3yqQxyDznXRoZ4Z9yaYdGYBMRyFh3g2mcc1I4W3MIXZ1zJzMv8OOscZjwIbM6fbP8nd6rbuZw9Ep+3iuP5KFJSNuUpVyeUPNSp9Jk7ZAXufIqz/CpPCO7puEvxWMpRLYydFWuPO+VJnTwFvCtOo/M8g8do0f4LPEsVcZj6nhB7z1Oe2jDL+9J1+WnvNNGoBFoBBqBRqAR2HwEDs1BjlESA2N+D5TJ9x7alNW00tX8nepUuvqMV60X3jUv9LUs9WreTJf3pOE511WeMs/hmfy8o8nzzDPvnTYCm4hAHfd1rtS27oWm0u/2HOcxdOvkpny3NPqFT96X6u1Gs65cfvhWmpofeTUvtMrm+qFPmnLvtV7Kd0pTd129lO/GO3Th4z3PO8lfKguvlB2UT+p32gg0Ao1AI9AINAKbi8ChOcibC1G3rBFoBBqBRqARaAQagUagEWgEGoFG4DggsPzt3HFoebexEWgEGoFGoBFoBBqBRqARaAQagUagESgItINcwOjHRqARaAQagUagEWgEGoFGoBFoBBqB44tAO8jHt++75Y1AI9AINAKNQCPQCDQCjUAj0Ag0AgWBdpALGP3YCDQCjUAj0Ag0Ao1AI9AINAKNQCNwfBFoB/n49n23vBFoBBqBRqARaAQagUagEWgEGoFGoCDQDnIBox8bgUagEWgEGoFGoBFoBBqBRqARaASOLwLtIB/fvu+WNwKNQCPQCDQCjUAj0Ag0Ao1AI9AIFATaQS5g9GMj0Ag0Ao1AI9AINAKNQCPQCDQCjcDxRaAd5OPb993yRqARaAQagUagEWgEGoFGoBFoBBqBgkA7yAWMfmwEGoFGoBFoBBqBRqARaAQagUagETi+CLSDfHz7vlveCDQCjUAj0Ag0Ao1AI9AINAKNQCNQEDi1PPdjI9AINAK3CAI33HDDjeSecsopK3nSncJcD+1udSq/vcio9Oueo0dkz+9zvchNOpdv6ntw0b5gta6tlTY0u9VBV+vtRl9pl3RSvhuP6DbzSv5e64c+aeUXPXbiFfqdaML7lk6j63702KldM7+daCOz1gl98vIe2k1J0760Z6mdlUa59yW68Oi0EWgEGoFNRKAd5E3s1W5TI3CEEGCAVaMsqu9mmKXeXPc2t7nNngw69X7+85+vbnvb20bknuptE5eHWYe8LxmWysQYn9gs0RX224+p94tf/GLUWaoX2duVth6WaGeam/u9tp2sYLCk20wb3ZZoUyad2+99rhO5M210Cp+Uz/WrvDzrEyG0VW7yQrtbqm76WOrduK5jFY/QKYv8vY7/nXTAt4b96l/rhld4eE9epVv3WLkgcgAAIABJREFUjDZ1k1ba8AvP0CRFm7LKK7iig5kQzJfqVpkn81x575dP2lF51DbtxA9d6qNbqoevNVEIJkt0O8npskagEWgENgGBdpA3oRe7DY3AEUagGntpRoy5nYwz9ZbKl/LCN2nl7zlhSZeU7ZRGl914KY8R7nk/8tAnMmIZsEvOUGiqvuTsR1ate5jP0SE6LukfeaH1nuekoVlKK03kBPNatlR3zlNf2Eu90CRNvfo+81/3rk7iThjRz1hA6zlO36mnHnxrD2ZVt4O0Qf0lXpVvfV4nY13+Ut11tPJhk3Ggbn2m57q6VU7aI10KeMx88p46kZX8JT7r8sJDufrek7dffqkfWfAQjLeE6Jr3ThuBRqAROC4IHHwXPS4IdTsbgUbgZkUghlk19PIszTODrhqB6onK3azVst0UjnGYNDKqcbgbj1qufhwV+d7xmm/80Pz0pz8dZZwY8vejd/RUR9vX1Q+m0XHWI/m3REpn7Uhb1umQNqYt++1jfPFYFzJ2avmM5071a736PLftZHikLp55rrLkGWfGVWgOOoYrX8/pnyW5M+1+3pdwV3+d3uRHF3RVH/kZH5Wu0kQ3tOFjDgrmILmhX9Kh8k39yjPP6MInedLUSSpvia7WWfeMh/bSU6w819Wp+cE+8tUPH89ZJzwbU4K85Fde/dwINAKNwCYj0A7yJvdut60RuJUjwBBL/NnPfrZt+FE7hlsMYO/VUJP/ox/9aNS/053uNMrwEmIA7tR8NOIPfvCDQXb7299+GMw71dmtTBvoUPWsdRjm5GnLXe961yHf8370Zbj+5Cc/GXXUU38OoZF/hzvcYa0+cz3v6Y+lsr3kBdeZFt8lR07+3H55wve///3Vd77znaH/fe973+02z7zre+pKReNk5o8+5ambesE0fbhUN3VqmnGK3jgQOWC3u93tFvuo1l16jn4//vGPB270ueMd77jYlm9+85sj/y53uctJjWFtEI0p8jMnzY2DBniEV9pUeclDE/xqmeeUa/9SXxhT0RPWSzThiRf6T3ziEyttMqbufve7b9dZqhu9lIlk1bzwluK9FNSD6V7n+hIPecaCNcS4WjcW1tWt+dYPcwsGd77znbfbj0bbtPHb3/72kHeve91rrFVL60zl2c+NQCPQCGwSAu0gH1Jv2ngTssnWPGXJD12njUAj8EsEGGNf+cpXhiF2v/vdbxhuShiEHCRGG0ONUZjw3e9+d/XpT396GKWPetSjVne7291GUQzqzLd5HiKSl/wPfehDw+A87bTTVg94wAPCfjsNXfhtF5QHZXT82te+NgxLfO55z3tuG/fh8dWvfnX1qU99ahilj3/844fzupPhqV7keibDocD73ve+Ydg/+MEPHrigiQw08PzkJz85DPLHPOYx205AeBXVb/TIwP/e97436lfZIYqu+FReZIqccQ6HA4sa8GLYaz86NGJ0Rjvzk/fZz3529cEPfnDwe97znrdn5w9fYwcOnEfOVRxeZXSITHLlJWrjfe5zn9F/VadRYc0/qYsehl//+tfHeDZmH/SgB22P5zXVd8z+8pe/PHCD7ROe8IThcKeCduiv66+/fmCkrx/xiEdst3U3/emdgBfdOU+e4YY3B+phD3vY4Fnpg1tSfGZ53vF0KKTv9Ql8M46CW+qiJVu98OWswVGf0KUGZfiKxpy2x5mvuqZO8Prv//7vwe/pT3/6WHOMDWXC3IbwkY/GHBfpmnaoR5dKG5nq0U0brG21Tmj2mn7uc58b48raoq+1lcxZ53X8QmftfNe73rW6xz3usXrc4x43UnXwEn/4wx+u3v/+96/Ie+Yznzlkpe4Sb2X70WOJR+c1Ao1AI3BrQqAd5EPqjWws2GUDrHk2kJ02mENSo9k0AkcGAfNDYGi60bn22muHsXb22WcPR0s5Q46h9q1vfWv15Cc/efXoRz96zC91OJqvec1rhoHrNpYR6gZJUDdGG6M276NwqxwPNNdcc82o95znPOcmDrJ66oeX+p7FGtBxxt7ylresPvCBD6ye//znrxjfDHplkf/Od75zdfnll4+2PPCBD1zd//73H2yUC5GTd2nWEwY455jR+k//9E/DWYIVY1lIe9B97GMfW7385S8f/BnmDg+0I7xm/QeD1WoYxg4MtIMDi57zQA+Ok3c3T8pyA64szqgbOe3WT0JtBycJNvpSux/72McOJ2Xp1k898TOf+czqjW9843AufuM3fmPguU73tEGKhoP3jne8Y/W2t71t9ZCHPORGN7kwgkeC5+BL7m//9m+vzjjjjBTvmpIXBxwWMKT3mWeeOfonTtuujAoBPRwqvOc971m9/e1vHxhwVOEukElv4+7SSy9dOeC54IILRqpMxCPPhfV4TJ+pjw4v44tTbCx99KMfHYdTDp8c+BjLFTNjwbtU/ehU5cgnx0GHeYYv/dWJ/g7A0MBPmWdjwrs+oZ/DpLPOOmvMcfUS0Brrb33rWwfPc845ZzjzbtLVr22PjsYheg5mxmn4LaWRpz589IdxlbakjjJ4oNc+8tUhz8HeE5/4xNWLXvSiA9/84mX+mJv0NqbziXh02ClVH57GlHn1yle+chwocJKtIXjRX7Tuwujd7373yiGcg4f0czDFK/0YjJLupEeXNQKNQCNwFBBoB/mQeikbg81laeM4JDHNphE40ggw0hI8iww2zowbT4Y449g8EhhtDOj3vve9qy9+8YvDMePwMYy9u0FmFHPY0GYeqhv+yZtly2fgublWn8xKU3nU5xi/8hLU/cY3vrF605veNG6Xfv3Xf30Y9oxjZWSJZLldxMMtjefoh5e2x9FHQx/1o5s62sx5+bVf+7XhUKAJfzw4sBwShw5uHDlnKa/tq3LTDvpy7tywwbU6KvqBvg4syGbw01Gglz7i4LjpY8BzIOgtBDPpRz7ykdWb3/zm1cMf/vDVC17wgtUjH/nImzi+nIvgpl85+HP/DsYL/wQP/UFXhr7DCDhEX7zoFyeKLAcAsP385z8/9I+DvITTgtiRFVrj+eMf//jKODiZANf/+7//G/3JieEscl7oqy3kOXBwo6mN2pDxFV2kHLQcpEQf/fm///u/w+GKA+8m1o21/oUP3I1Hzp93qf0t4wjvYEoGZ2sO6Ol03XXXjb7GG626IpnaiCcHUmrc4QtHXxBwzp/ylKeMMnUS6H3ve9976Gju4fWsZz1rHNLAQ4h+6ulvvLXFjby2GQtC9Bkv5R/5aa96X/jCF8YY8WUA5zH8rWPhI0/UT9rGISUXn/Cq7SjidnzEz/yGKd3Da8dKpZBMY8raaX1wSIWf94wpfayN8ow3Y8p7XUew1F5zPX0ZHIq4fmwEGoFG4Mgi0A7yIXVdNlEblk3UpiLPpih4Tjwkkc2mETiyCGSeMMoYWow0N1fmjSAvzgunmQPr5oZT5YaSU8ChNtfcqjHO1I1hja8oRNZ42fonhiUZHA5GMpliDfjHcMz8lc6B7C996UvDSTz99NOHA885w1sZ4xIfTiQHhPHpJggvutDfM0fgSU960rgFZHgmoGEUc/o4XoxjBjr63L6hxQd/Bjl+eJGNpsoKTrNRSwbHiWGcT1rR0F0fKOe0qE8+/IIvR0AfarM88oQ8a4/PORngbtLR0+uiiy4a/Vrbq66oX+Tnt7XhGVx2SvUlXYwV9YJhdJLK1z5logMCGHMiUmcnGSlLfXVgpY3GNCdM3kECPvqS0w5vDh+HljNMHmzwhrk+4YT6bNYXBuiVaZvUbaOvDWpQH67GEOfZONH3HCc36A4ujAGfBZOHt3FlTqhrLOAvFThbz372s6uIbVzpYEyh1Z9ixg5e5gjMPMOM/uSEv76kwxzwcHPuM2D1OdNu7uUbr+Skf9WlhzIpGeR5TtnMv76Tr0+MEWPKHNSm6KUNeJEnhq/xpG+sYwL6lFX+e3mmL72jO332yo9MkS6+voGDrwP0qRtx49VY0A7rk4Ms7XPA5OYalsFL+4wNX0g4JAime2lD0zQCjUAjcBQQaAf5kHrJJpWN0qbCqLGBMt5tOh0agUbgl8YhHDhGDE2pyDhLHmOfccx45wRzVjwzSjmV5pkbDc4mw4xRz9nyeS8ngrHPcIwcThIaxl3maPqCHLIZjngyFmNQy1POqMfP7SjedKkBTzfDjEjOi5tHt9xxWhicjEs6cKIZ+/RmqJJBXgxPbWXoR0+pcjqi105OAONUGQfKrZn6HAV83S4zbq0/9OYwKfesLW4F0WrPUiCPXqFXVx4HSiS34oInmuSFpzpoRQENR80to75ilMNDe9WtIXXk0SWx0qx7Rqu+FB7kWoPpHjkpo6MoXz9xztLedfzlh/9MI18MFuQnyBeiX/K9zwGtceJQyDjifIoOR8wbfUNvNDDUB9ln7D/Gm5C2mFtzQO/TZY6kMa3t8nySnoMo45gcc4+jxLEyx8SMY3ud8a8fZweZTO0LztoFZ/rTDY/ohgYPZWhg6F0dZUs4yaO/gyDjyqf75nHmLPnqi0v1Z0x2e8eHfnRLjAz5kRF53j0LnrXDe8qX5CnDS5p2hy/6pbzIRldpK3/8YG0MWR98Yq7/s4Zoj/4QrFWerREZSzBNG/Sd8TKP73Wyqx793Ag0Ao3AUUDg0B1ki/AcLN5Z1JWLWUiT1jpLPFJe6Wc6ZTVv6R2f6FNp5S/RzzTeQycVKo1nn+hdeeWVw4jw+ZdNJrSjQvkHfXgmex1tym+OtLahyk++vDyTH52TX9/rZp381Km6p668+lxpdnqu+oQOH7GGma7Kin5J57rhk/L6nufKL3lzGpp1/Gf6TXyHYcYGg5rz63ZFvt8RcgTkcYKtF26wGHKcRoFBx4j7n//5n+GIenYDwsBjUMeRZtTDmfGGP94Me06AOgy9rEc+RUZDtk80OdOcN3Qx/uK4O+zyO2h86CwKaD/84Q+P+uY6o5MjzaFNGmeUscmgx4sxHyPTjZB875wP70LGixsdzpLICacjx4TuHCZt+qM/+qPxjgY9bNwGcgzxcdOInuN88cUXr3WQydVPnCL6qM8ghgPni9PCgKaHEDz1KbrgNq+z3gVY+ApA/+oXN9HaGzzpilZMv5KR+oPJ9I+6KU8afvoHRnG+8FcWXLBC4xBCPidRG8hMW8ILLf4ZH+GVfHXgpFwdmMur+qcOGs/abjyi9y6iN35e//rXD34OFox/Y4MDCPvorJ+MK4cebuj1mX7Q5oc+9KHjMMU4xJ/uZKkr2J8ctkR/TqY2c47QoBdh4yBGmTngKwnleJmP+t7YmEPaGhzUEeWHv34hr2IdJ5pszxU/7VA39GTqS+PJbab1wHv6UTk9hdRN+7yLS4FsMtKG0HjXZ8oy3tFmzoanctF6YczTJyE0ea+p9upjmLm910eZH3BQrv14BEdjwLv+0/9zUEZH653fs1sXfud3fmfMPePKuHGQhg9Z+tsaAkefttM9fWB99Zm8oLyO3VluvzcCjUAjcFQROBQHuS72WbQDiHcLazYLi3RopPIt8jWgl1fz0WaTQ+sdLyG0lbc8NGJC+GWDrvXRhF6KlryZp3dl0ujjOe3wbPNlrMcowFu+gC6BHPnSbDKhia6hvbnT6EJO2uVZfvLShrQ/GGWz1jb03vEQ1altm3mmLkMjWFSZZKS/hiJr+nuuE9rIC55kRM/aHvXTnlo3z8oTw6vKzHNo1EPnXZpnWKR+eB+XNBjBgEPqpjO3m36TyiCLMcmgZOy5NWOwwcxnlByzf/zHfxxlv/mbvzmcUQ6fNYMRp46QfjAXOQqMdzQi4y8G5xVXXDFu4NAwrOl13nnnDSOU02GsxIAPz4ybtMcNnj9m45NqN2/awPklSxt98slp5pyqq5yDAwdRvvb57SQ6cr0nksNZ8per6eiWTj3GqXXGAYDba7/f9Nthxi/n+IUvfOFwhvGBA8NafTdIfl8sfw6Zu/DHnyMuFaxncKM/pyoHCQxyvBnOdDWXw8c7ObCgK70jF5+nPe1p493BBB2VC+rDCk45aPBMTgLe6OlKJhnq4UNPetGTc6gf9GP6Do1y9fGRL3rmaLllNybQZPyRi8b4cQhag3p0E+HEwfGsX4yF3OZru0hP5cYrJ9bYxUM7pMYrR0Z0gKAN8DIefepsTNGFnnThRMHHFwb0VWa8/MEf/MEYk+TgSy752obeeBfI5dgbS8ryBRT+6pl3sDA2tYczLijjvJOvfCmggb0DILp5l+KZsSDVf3TwjEbUP9psDEojU/9pI77aFHp185ei9ZMxh06+6Nl81W7OIKe/9iU+ZOkPqTqRQRey9Ac8jUtfsaCJ3vhrhxQecPVsrsAJL/Ty5zahU6bvr7rqqnF45C+3W+fSD9otph/hYRy59cfbb979QS18xOAipaNP9I0p7Q+NvoCBP3honji80zYytOUNb3jDwJ5esPN3DRxwaV94LPV75zUCjUAjcJQRODQH2eIuWogtmhbWLJ7Js8HIs/BmM0heBTF1bSLKBTzkV1544JU8dDbRbGToI0d+ysJPWnVOfuqljnw8ybE5KRfoZ3NBpyx5NqgLL7xwbICMAjIEqU3KxijQPZtQNmP4LGEyKtyM/6SviNC+6KVd2inUtmqLmHpJ1fWsvo0bTTUO5dvItRdfNKI2i+lzcqOHOvLFyIG559ChtWFLY0BEZ/XQC9GJXuRFj1FYHCt8yU1/o5dHhjrhJ0+fkisPT7RkJuCRfo9RkbLjlOov+MDMHGH0M8YZqj63hRkH8fzzzx/GdAxddRLQ42NMcWQ40BkTUmMU/oL+Y7g/4xnPGEad/iHbGJByFn3pEUc4qT8+RTfvZOMjkMlwFsggjzPEufebWn2cMaAtaBjgbnTdwjCQOWzK8DeO6MGJcrPD8KxtIcc7mowpuP3e7/3eNo6ca06AP4Dk02vOE7052w4UgjceZDuEYASH93go/6jLmIcbZysOE4OaQ6PcLSbn6rLLLhsYapf2GOdxlsKfXPhxgPHzDiO0ePk0VqA745u+5lL6UB1tNG84hXEkleMFQ3/dmk7WFY4pecr0M0z1m/YYG/L0U+rri2Csjmf18fW5vaBtnEK09KaPm10pPhmf5KuPxpcPsCNL28OXDuSoQxftx5/zS66oji8pjE1OSX7rTb765ot2arNx5fYPL9jAiU4cHHiqHx3JTXvNI3+MjfOjXHvVoTP+sIa9vlIHX/n55FoefvrK83xDOoDb+kc5XZ/61KcOes43+cY8J9Y4yCGHwxtjlE76zBpgTDnw4STCUYADXNHTmy4i7MiBp68TOLD6QzvIUT+33Q7nOJawToC/NlpX3JTTw3yhLx7mGtnWFLRw8e5ZH0QP/NALZMKXTr40oaM55rY7BxdpF3nGkcMNstAqM16USfHNmCNPcCimHfoDX/IiGw2e+tM6xRk2J+gcGuXk0YdTrl14kYOXNuJvnZGPJ72i92DU/zQCjUAjsEEInLDiT7JRFkwbkY2E0WgztXhazC2w/kCFTRCdTZtxYZG2cWYxjwqMMYu1DYVRZ5HGSz2bog1XPjk2R7QWdJuOOoIyMpTZCBg46sTARYMno4+u9LQxksH4s3HTj94MGOV42lzVoZMNHE8bt3Jts4l5zobvmRw60J3RhacNhwzy8MNDJAvfWyLQ3+annbld0Kcw0wYny/STp49grwxudBfwENWHv77lyKgvaKvNHAbqooEpnvrQDZxDBRs2B4YMWJOhf5UxdBL0Lxr6uNVIPX0qkCMaF4yNGGXGKZ3VMQbTb2Slrxhq9IMH48e4ox8DRN30ubYyWJWTZUzAQKCf9mlLZFX9B9Ex+wc28BP1CQMUxvqOQ6nvGP36TF/oq+CPJtgqE70bZ2L6RKpPjSl4GzsJ8s1FDgQdvBtfgmfy/RVmZcaDkJQsEZ0xy5DlIBuDxpGyjPXQ0Jmhb1wYI3QKPzysFdohhLfy8BkFWwY3WuMo7dNmRq36xhnMHDK4SZJPHj3wM/esaepGfngnVZ/TFdnGP6Na/9Cf02AOaJObKO3WVxwG7UKTGyxyBW1Ehw8dtNF8zfoPZ/lo6GUNSl35ZFkrlZtP6sdI17fGkDz9xuFBZ7xYW5VZM/ChX9Z35fqk9gVdySeL80Q/0ViAOz7KPWsz3gI84aWd9DK24EYGvThb1k3P1v20TTnnVxvU11d4Wh997i+FpT5Je9UVyUJvXKsr0o0uymAmDb3xgT6BTI6nw4WUGcvmIr3oy9E0hvAm30GP8UaOtiREtvyloI49wCe9+si6ylHUF7D0FQGM6W4MK8PL4YGDHn1kXaWL9gRvY0E/cer1mXXCOqyfHIzQMbgFo+iHR/goS1BHH8CPDuRxDO3b5oB13FiCHd3Dg976nY6wk4+XiA8ZHFO66hfjx9hCXwN5+Oh7+LNb9BU+eBg/8Kh9Kc9XCPrO78R9YWJcyBdgQG9fl8CHfuRmfKLBX9BX9mU0dMnYwMs8VS4o69AINAKNwCYjcGLHPIlWWlxtKpwPm6zFmNNgEc/C6pM+i6uF1amvT30s/j4DrA4hXjZiGxLj4Ld+67fGRmKTUc+pt82HEWcjjcNjUUfPMLS52Syc4tuEyXQC7XdTNk4bDB7ynTK7vfJu07KB2RDVk+c2yU0M3dGSybC2Wdm0beKMQxuKPPrbLK+//vph2Mhn4NAJX5900gE/hpwNi9GAhrFkU6Sn9vwqAywYAAwOGGsrZ8VmzuDQlz491V59jYZjAFftt3kKMGCo6EM3Y9oEJ8aAMkaCmxG/e7JJkwMXG7GNnEHEAIe/2zCGtDIOjP7xaZe6cBZjwLuBUqZv8GTEKIc/PBk0aJ2AMwxgDmPjyF93ZSzrbyFGqlsifOil/+RzTnzyxlFQR1DmJgYtWca6MUZv45jRzuCBn3YweI57yFwx5mCjP+Brfr32ta8d64a+MS6NNfNcf5lbxhAj0m+VjVH9Y+4bi1lL9H1CfZZnTOPhRsWYMv6MD3PSusXIZLCTR8/oGn5SPPU7p894x8ccwSvlqcuQz00XGdZEOqOni3Ep37s66wIsMk/U9y6lC54ObkTrqvFnLnivPD0b5zHaZ1nqwYIu+sV6wBEwx/UHQxzG+kQq3/9P+9znPnfbgcSbTnQNBsY8XfCCB5zMU8/WFzKtIamrbaJ+1ldkWYvVx1uZQE/16zOe5njWf/NOPbyVaZc96vd///e38+mpXIwMmOJNd+XkkmcOa7Ny9MoE7SXXnCdDuQi3rPmhUwe9+vreu6g/9RsnklNp7YzDnzZbSwVj1RjGRyRL3+JhXhiXeJIhqO9dsN84SLGWylfH/LMvC8aBMpjrF+uwfhDCL8/qZ30cBNM/dHJwAhvz3TrpWfvMM3OHQyffPPFsPMDYWgtzQRv1QaJ+sb+QTz9jNX1u7lqrU5cOntW1hvicmE4wsG4kwEH79HGwiu7qsQ8cDNmjHIgJOVCxZ9kf7Qtk0Usgm1y8PQv4wzhBefrfekAWvcwZdGkzvunn6Ien3wQbc/Zke699FJ1AjxwwOayBuX6AJx4ivlI4+rmIPtBH5ovxSQc3/8bAvKakDZ02Ao1AI7BJCByKg2whtbH5Yx3XXnvt2GBs4gwNi66NLAuyRdlC/upXv3pswDbAGLUBlkPBabVBck7xsEEwql73utcN54nBzIjIpmtzYjAzCrJZSr3beH1qxfCwaXBuKs//+q//GhsIQwYfPG0o9LTZMDYYCYx4+Wg4towJ7eaE2aBsMjZXm5vTWoaq9km1n/5k4WmDtPEINk4GJ53oCDf8vNvcpNUoCU4nm2YDxx9ONn8OPCeFTvpNP0YHbZfPGIINI4NhzJDKBmuz1scc1quvvno4/Oeee+7gow/hds0114z+YNzAmpOgXrDm7Ggv3Bmo8OQs60OyGen6ET99oh6nimGKl8BAUsZgMFYYG2TZ5PWXPmRcitr0kpe8ZNsh8s5IYUSTEyNZXW2mV4wo7eboM1bd+mgfw4/hRYZxTDeY+I0orI9r0HZ9llRfGe8wYoDpM2PfvP3P//zPMe4YY3DXx+Ygev3CcTJnjFtjwvhhsFpLyBAFsiJPH+g3DoeDPGPU59zGl3xGrzFpfFp39LuxImSujJfibNDXf4lDN3py+sgT8KSH9YfhbpyYU8YMwzMGsvbAotZTdw7q0J0Dn/bBgVw8HQ4ybBmx2meccg7lZb7gyzla4k8eHeBkbbVGmx9xYvDQDrqaa/TP/2PsOfrTLc/kKOPwcFjgqH/NMeu89UU5x0Id/Rgd0JrD1k19o09gGN70JStYcCZ/93d/d4wH4yTYPOc5zxly8Obs6A/h+c9//ugP7cVT+6TkytO3eGqDMqlyUZk0soOd1Ji03ivXB/rHmmaMZAzGKa711SVHfzl40FbtsO4laIP+E6zHwdLahqdy2Mo3L+ictZlszwI6MW239tvLjEV5nvM5rUNbfaTurC/+8KKLuBTgIJhnDo6NTfjhCxOH2dYAuGmvtttTMl7wXwr2UP/NkENJfcqxwx8O1hI4WT+E8KCLPjUP4WutdoOdgC76aqs2k6Gd+hJPc8D6rh4ac9J+yDGWx77QB6IAZwEt3Yxl7U5fRB6aHAjRg27Ws1pOD3xqVE+bHMLYu7PvZoySg49Pp62R1gd4Z7yrjwYm+Bs32Zc9m+/a4gBFVBbd1e3QCDQCjcAmInAoDrJNjVHJQbAxWYjd5tnALLA2PgaCTdbCbwO2EVuss3FVcC3GnAupRT7Bgm4TtpkzdDhejDcbF+dcvPzyy7c3tT/+4z8ez5yrV7ziFWPzZHzYxGw6oo0mzqtNi0HJ0LQRuOnUrv/4j/8Ycny65Pd/Nkk3hox4N0icLwYe/WwceNroyMpGZuNJGYOH40x/m7t6jFGyOHQwZDzQDwZSG+thBnrFOMKXQexw49/+7d/G6bM+pINNXx8xzuMo6gP66FdBu4KllFMhDx394xSQF2w4ksYHp9FhgLJ///d/HzjQheP9Z3/2ZwN3BqY/zORAgtGmDuMmhgcZDCRGCoeH7nhK7/zuAAAgAElEQVQbJ3//938/jAZOMsPrL//yL4cxYfzB3O8oX/7yl4862ocnfRkbbjb0hf7RVjz++q//ejhrjBzjRB26GBfGkVtzY9BY4oBxArSNXgynOp4Psz+PAi99L2TscX4YxwxbBjHDjTPD+NQvnGKHIb4i0R/WDxiL+pdzpo8YbOZanGNj0HwTPAvGsHGiLxjo1gTP5q0+0ceMaX3pywG/wfRsrTCW1Q9fvI0T+vjL0QLHOE6jMjFrG+P1kksuGYbx7GigMe6Nk6wP5ERvWOUZXsZU2oNe28mVGrfa4dnYzx/X4Qhy/PFi7Fq/6LcuwEU0ts1NPK1Zbuisa9ZeN1TmE+w4U1J9kPbRmQxYRX/yvKPR7hwSaIcohDb15WuXeRgs0rfRP/lS9NYmfU1vexNnQMADVg6z6AYje4e1w7s2qA9nwdiwR+GnPLqNwq1/al70srZomz1PsG55NmbxoVfqJQ1PBwbWXTe32sDZgZeorn5Rn672HeMq2OCBn71LO9100gkuYp7RiPLwM16MeeubMe9wUF9ytjzDS1+Hj75TDz/Rc5yotKOm6pGnH81nQVvw9YftrPdwd0D9J3/yJ8MxzBdgeJuX6qKJ7rU/PJPBZgjvlMunY+pJ4adPw29U2voHbQJaAW3aoA58rR+wiVzzH3YOc4w5hwFC5MGYfubUn//5n480/Afh1hoFC5g4UMqhSvhItQsvdhXZIp20ye2wPcleqS+NN3NduTXIeql91k98lNEhbTP2jCmfwmtLAhr9oE32QfVn3UPbaSPQCDQCm4LAoTjIDFwGlE2cI8DI5SRZtLOZ2mQszhbjbF42Ve9zkJeYhdi7TZIMi7fPYt0w2wT8NlZgEDAMGIRuGxjdZDLobGicWUaexT58pSL9fBpLdxsy48SGwbhRR57fa3HI6c8Jsxm6KWZwoxfCd24XPWxUjDFGFwOT4c8QUYdsTmNuPGFDp/AbzA/5HzoJNj03RTDSZrpoK92iA8zQ00fUPjrq31nH8K19mHpJ4aWPHBDYvPEi1w2APub4MFA9c47QcZT1hY2aA2p8RRaDgVHp/6BNvxsnuZVGz9jnfKnHwMTXoY5+xNONBWNTPxkz2kwv+DB4Y2gYY4wjefjK5/wyQDgQjFpfDRhvjCJGi7GK94zVIXfprZqdtmdewJVRyRBmqHFE9aEDGTc2cDXfOb3K5WcMcvbMe4cU5j6exomxgEZ/VJyVG6cCp8gNrMMba4h1yphHb6wzTK1h5rX+Mw4Y9eZ8dK+81RUiM2VS+mR8crroiK7yolc+oYUJGeExGG/9I0+b4fTiF794tIe++PpyxjhLPWsVh8Y4ddBgrFtv8K6x8s9z8NNmh4X4w0xKnnlpLtJDexjzDhDx12+MdJhlbkan8PeuzekPeAjBNu1Hl+fgOPOqPD2nHG+6isFaP5ElL8/WHQ4Jh4fjYqzBypi0ThtzZEe3yFtK0Yh4Oagj11cpdLEeWFut+8Zu2rzERxtgJ+Il4BEslGdMWbPsPeZKDfqQ86kNdUylHlr88LWGOdRz8GLc6HcOkL1JG+T7UoOzhh7v6BKZ3sWlEDnqwdhaCFcYidZW67ZxJZrP1gF9QK4xb19AYz2Hi5C+jkxyyKBjytHUGNrk5X23FH1to3WJLsa5Mv1pbRfkKTNfjC9RXW2BpfKMyyqX3vrK4YqDQvjjg38NczuVRQd7DvtEP7PHjLXsOfqSnupnHKiXKN/e5bBLH5gHykKrT+x59t+UKe/QCDQCjcCmInAoDrIN1sLO4eCgWKgtyFlALb6epUIMFSehydsNYPWz2ds0bZg2WhumDcsGLnJoLfBOQmO80ke02DMk8aGfIM8zWoYfJwk/G5ZnBrSNIye66OiMnzwbEGNkt3ZoM4PSJm7jol90Igsvm6lgI+JAatuM4SA4yX/oKoY3Z4GTyKCmlxsMxlw2VHS1P4mnMx2lu7W9qosXzBkJHBxtTju9M6JE8m3EeKPVFwxYjjzDUB/rJ/xEuOr/GAXqKcdHH+HJ+NMHgjxlcOd4w5xByhlTl+FGDgPa2FbO4IQT4wdmjIbIZ6RySnzayMDxKT1jgtPgywOyYmxUPI7TM1xFmMHUwZoxBzfGufFmnBkT+gfe6LNO6OPMXVgae1kT0HlXX1+QUYOxqj99JYGvMe4QCB8x/WhtcUjGmDXWjBtjgmGbkDbkfV0avjmAcqtDP2Mw+kqNF7rXQB9lNdDDHLDGag+8ONcO1qx70csaxdnwhcrf/d3fjUM380JdNDXOOMHTrSqDnl4OMbzT25qrn+hhnAvmAifLwZa5AlNfx5hr2hQMajviyJCNL33QRpe0w3vqJw1N5bf0nLUJRhwNmOBhXMkzd60X9i5z2V7C6dd+DiI6baWfensJ9IabeW/fcGhAlkMEY0kfxEHaCz9thUvwSr8ln55uK42BGpTrE+tZxpU8UcBHQGP++XLJmHSAqP1uk+nK2bJuodMeZfAIn8Fkl3/Q6gs6ctrIE62j9k0HKsaKNR5u5or5bs81tjjR5Mv3VZo9swb8tUc70+cpT77+87wfvcOjpsEtYzFlMMk4MY6MN4f1bA3zQT+4jVfPQak1ZQ50g4e9SFvNIWNw1jvjwZiIPnh5Rm9fNI7J05/GcPbXWab3iolnfOlr3NJXFPS9foHxXG9k9D+NQCPQCGwYAofiINukLaBuHG0QWVSDVRb5pDYTC7A6dZEP/VKKLvQcn5xihtYmwDBGZ4PKs3LyGHVSxhA+DAIbgkhf9XNjikf0wouBqD6ZgjL03hleNvDQR5+68cgjgxEgHy/18IgBoywOnw0ycsIHHf0PM+BtQ2QsMmAYJXFE9c/cpipbWTXc8JK3Ux310WmH9geDtDHv8LHZC/TQdvjoMzIZ5/pQvdRFqx765KeflamvL4WUk8EhkjI24UAWHHyW6QZRX+Cpr/AT0HjOe3gy3hwSmQMMYuXyGOLqd/jl3IE/jBmQ5imH1AGEOWL8GB/6mnOGDt7K0jccTfRuThnV+OkjwRz2qaaxIB8//WB8cwQYjpxHBxf6KQYf3sYW2Rx0n/i7+SPDvHSjw7DVj3jivVvAM7pzTjkZ6huLeCTKty7V+a1sKahvzDJiPdMfhnjCSKAbedrIGXEYoW2Rl3SJvzngFskNunkmksPQJoeebi1hJE8/cQIcOpDjU3F01hGBrBkruohC+tU7OhiEXqpc9Jz8UXGHf0KHJ3wyT2GmPcaUdhgjDsg4YcYHXR2iCL40cSAgRP54WfhHPXJg4G9kwNABDGdFv7gB5XT6msXYs0ZExwV2N8rKuJQm5JmzzcFMoEeCNtqfMi+CX2jw0F8+w3db649l0tnva9Gis3ZxlOGHXhvle4elvoJN8iO7pupxrDh/1lPj0byHkfUYfzT5nNshtfGmbRw8hz/WZnX11ewgk0WnjCnPIp3Ipad+pmudX1XHvT4HQ3yNH3qRpT1sAHKsFXTXNlhqg7HF2TeeHG6hU6+OAfrCwFhUHy1ec0j7kir3LGgffPxNBF91wM5XWNkTl3jBXn0pOmuiMWUOp73qabM1hY6hl9Y2zPz7vRFoBBqBo4zAoTjIMUAs0BZN0caZEAPDIhyjxYaQxRl9gjxBXi23ENs4ybBYzwszvvLVQxP56DzbbGwA63QjE52IRj18yLT54Z/6KfMupP2eo796yqMnnmm7ZyF8Uo88EY/UjT7yUjYqb/2DR2TU/J2eq/xKR1baFOzDf5YRXfFCKwoxlrRVHWlovJOhrv7wXPNg5p0OnoWqq3wGZ2SkLPzzPipu9QV+ieHvXaBHba82MKg4YK961avGp6UMHA6AP5iCVnRgIqTN42Xrr1kzwBlM2qftjGbOHMMjckN/HFN9LnDy3AjBkBFXfy8pTx/rh9yuwh2eDE2Gn8MUYwG2nDTleFsD8vWId3MXDwceHGQ3YoxXBx9uic1dPIwdt1o5RGEg+qLBDeNVV1016Bm3DHoGbtox9yE+c6Qnh1H0bNwZC+i01WEKZ8X4TB7+GS/yvKOlr/eUabd6db6gJUNb/DbeASbDmdOknjpVjjaEX2Q5DDBm6assB1ocHD8D8ROTOGccK84Lh4CDSU54hm9wSt9KlelnzpKDDnPGbaI09TJHQx8+c0rv4KKuZ0F92GiHuW0MkafPHQRwWB3EGAs+Rw9WbuH0tRBe42XrnypLGzhBcOEE+yJCdPBGvsMW5fDx23ZthGcdQ1VnItL+9HnWvDoO9KPDFTRCTc0DMsInadqCltPqxpvDbo3Thz6nhxF5aDhFZMJLME6NV+3KODNvMrYG0cI/8M/cU6wv6OSQhR7GDYw4hsa4dRP+buGtqfn6YoH19lhO++mOZw7StM18FrQfXfQPLkt8kxfM0l78zS2HcZ6NJzzp6QbY4ZGbYjhZc+iin/y2V6rfqi74ar/P8PGgb/aLjLPQ01t9dWpIO6xN9ix/PwXGYm27OmhFuutruuNHlvVSX0Zu+BpP+oV8dfBNO6KbdNar6tjPjUAj0AgcJQQOxUG26Fs0bQQMWBuzhTTBIptNxiLLOHX6avP1bqHOQmyxzmIsX7ToJuJDlg3XAi3KYyTbCGJ44INnXcTVcaqOVh080Yjo0UZv5fLpagP0HgNWu5TRMxu9cpFuyiLHs6AMrQ1InigvvOiS97Qt+g2irU/isjHRNZjMdJGZenNKjhg6BqkN2cm1DdoGaaOt2KGFkUgerLVR/8En7YYJHvoXrXfBMx7qwrjKl08WfNMmNJ71vzL9Sk/5+q/2E/7kyBPxE/HzLhVgTy6+QmTqY7zxReMU300YI/fCCy8cjhxaRhvDSP8IwVGqXxhEftOs/W5C8pmuPyzGKOZ00Ou4hvSL9jPGGcyCsaNPpBlfnAvGsZuajAv9pJ85vPrBb4j93ly5/tHXGSf4GJf6xGfVHGSHHGQy8hmy6ug3Br5gLHOQ6Ekfjra1zefL//zP/zw+ISbT7WKMzlFx6x/jwHjVDs/4Gyva4GbRemecZRzTkUxOilQdIXMlY8W7NoqejVEpOfRXFy6ifDgIUvOaXM9xYubxO4i3/tFeDp3bZ3KM4TgrsObIcgTd0jvg8PkwLK0d/qaAOURPQXvEtEMe/ehJZ/1jruFpvsGbQw+fhGAI1zp+Ul4xiyyY2IvI0HY3ueZecOOoKnd7evHFF4+bU2PAH0vT7/44IOeMbDLpXNtATnD2jJcxcumll442+NpAW9QXYOoWGa2DIDj6mwUOidJXkYM+sshQR3tEz4J+dwjnk2UyYKMs5WhgbB2CcfRXri6Z8uhlPNfbbvxgMMfo5VDBeHCoIKD3DIOlgI99QputgfYFc9JBAgeTI2k+i3Q2hjxzLNHSDZbVya1y0m5p2ukQx1rhSwdzj8Oo/4MPOnMADsG68pyfYYafQwIpPtYR4wp/eXAg108NjDk/q3GA5Kc2xqH+9yVRdCAXX2XmjDVJP6rjixrloa0pva1x6iSkDejgbW30RYADvjq+yEMrTyQ7vM1Zz9YibTEH8QpvmHn3MwqHbdodGnqoK6JPnejXaSPQCDQCRxGBQ3GQGYwMKRubT3AYjwzcLJRZaKUWYs6X6CbObZDNxoJvg7Txy2PMWNBt8DbYLOThmXegZ1GWWvjJERMs7oI0m0L4yMMrOqZOeGYjTX5N1at61DLPdFceWdLolrzUn8tSjo9nejPebaR0YlyInrN5ocUvm+KsT97DG62orzgsTr5t8n4byvhl9Oov/WDztnGqK7VJMjqc+usv/aYPGXLGgtN7ddSlX2QG77xHh7ST7ilLe5Kqi1/qSNEG07QvqTJxaUykbnijk2cMMnjIYugYp8rkMwoY0toqkEsfKQzcDmm7T9z8dVmf58qTcig4zeEXHY9rCjNjF+bBkMElL31mPAoZz+j0A6fPmuD2ye8kMy7Vw1cqWF8YrPrETeEFF1wwjEt95SAv8jl49MDLzSEZeDAwGaPk+Nza2sbQZ+jODrL65qjxr77U+MfH/GJMm2P40Vc0xtByEOlg3KmDj6AtAt6CMs4JpxRfPLRD3epAoVceLNDBDk3WVWUpR08XdaTkc67g5lNrY5bTJ8DdmqBP0hZzAqYwNs45NNqVvhwVt/4hgx7aqo7/CcC+YQ/gQJGV/lYFvRAMtthsJ/RNOzzjTW+fDedW0n9dpw+sVfIY+XEw4Ket0Vc+Z49OOQSEH52iC+FphzFmvTTOrMccIfshPsGVftoHG33trwz7Q2vGmnUUjviJ6tQoT9/VscVxxN+4dJARDOhFFlrttO6oC2v9rkxb8NSHvmjIGpe5gMY8DB3aBGXo3fJy8PGGS2TiuRS0J2OX0+owBL5+Z6uuvtHvdBDJgJfPqn29YR3I/1hBRtVJ2/EX7Lf63+fsnH+4cqylAjp1xaU+HUQL/8DT3Kd3frPukMjhDlw5zsYUvnTXN/rZODIm7BfK3RCTy4HV5mCnnrXHmICHdSJ9qizjQZ5xKi919UHaT3X86eAPlWq3GF5pPxlpP7ysKewKB2gcdH9cjo7kRD4a7XCoBGfzRpl+0148BXkdGoFGoBHYBAQOxUH2lymdyjt9ZFDZNBiEFk+Ls8gQsGnZ4DzbEGw4btws1oxeBgzjRmrBRWsjSMjCjn7dQoxeVB4aG0MWcJtdNgop3Wp5ZEnVt8FEHrq9hso7ukT/6FL5oZGfjW9JDiPDCT4nFoZO2xljNVSeNb8+V1w8M4gYtQw4/ef/+rXp+/TOLYdN2LsbOP0EEwYMB4Ih4K8C2/AZbAwztySceW1RF8bk0E30PLczOsEgz1Xn2q7wUR5e6aNaJ8/BPXwrr9CElzHHCWAguBlhEGkDQ4LR7faDsZB2qMdxNo4ZQLB0es+IZOzBjaHHSNdnnCv4dTiBQLDUPzUaU3OAnXVFCmt9lX5NmvEBf04APpwRxp+xwIDUnxlrbpjV5fy6bdLfAgeJ8WfdwsOYNk/IXRfwMT44reZG1jI8HCjhZ6wa/+aFd3K8cxjNb20yVhxQ1cDRMSYduqAXGKsx1MnWpgQ4KBfioHAGYUcHgQ6CeoxvRjLd3fAZ+7Di+FrP8bO2h6e1wQ0fXvTWXuPdGuD2v96QDiFb8xUPkSzrvU/sOeBSfaYdWTNSb11KJ0Gq3ziPnDD7kaC/YA9La5b1S1vh4dlhAwcfnv4PZSn9/d/lnDiOoHVO/yujGzn0V8/NsTVBPgcHHnEu6ARTfayuuc8p/Jd/+Zdx4KI+euusG+u579THV930PcfEOIG5cYJ3MICZZ3ysYyIHnn6cG2OKnNAbB6JATkLKpXmmg7pSh6fmkzL1zAf4mGtzgBXZGVM+4aanw0L7jXaYi3Hi0ZvfZJgv+lEdTq96xoh2J5Av0kXfG5tS9gUZHGQYCWkjWnKk4l4CB9IY159uh/HPATWbwrjVL+YyWrf72sSxV4eDbN7au+0N+tx4VE/QJjzpKI9+CZ61Cz/9b+zCrbYdbd71Ed2kSyHtp7c22b/sUdY3epiXaIwjPD2jMebVsb6ZY8aX+eVgS7v3iuWSTp3XCDQCjcCtDYFDcZAtkj4LsojbGGzKFk0GksVccLJuM7aRKmNAWZx9+mhDY8CobzPlaNkUbGw2Aguvxd5GnIV4XvyzsZChzHuCZ3VFizp+8my4NgEbkrJqoKjr3YZhw1ZPUC8bATnZ4Ko89eiuHprI86z9yjwLKVMfL/pnQx8EW/+gZ3jZ+OpJNqzoGH5Vj1q/Plcaz/TVJ4xUm58+8UmoU3N9CCcbs99QMVr0gXwOur5DJ2VAMZC1m/GpD9N/kemdvt61d8ZcPyzlax9c4KMvUi9Y4yk/ctJe9dL3wShl6qqjXJm+wD/jGc6cJwYavbVNu43VOs4Y0sY9nNzocLLCh0HMKWFQMMb1n7IOJxDQZ7BjfFkvPOsP+fokUQ19lr5XXvtbPSFzynxhIAtw14fojaPK25gmz5jlRAjhKzVG9CtD3vPS/CSTfNHXE5wsziyjGW8OgjlEh7RBHe01rjxzFo0VTil51UGmBz7WDzdq0YHh6qZKG9I++uNHX3Kl1lXjjw75fWzo0l5zHw3n2JiGh3UddtqlPQx9+pujdOH0mfcOF+JUOyDjVFon0NWgHeqry0FwuOpAyeFFHMtK71lbxJ0CviIM4IYf55Pu8ISPeccpZeib0xwZTgEd40xpp9tgTqVDL31DL/0Of2saLB0eKIeJcp8KWz+zFtMXLyG60YPjp74bSOsLfRyi+LpBn5KBXn2p8UE/Oltn7Zn2V+3U9/Qwb4TglHowpr9+MSatS8Y3/Cue3iOTzmTi6Tl0dPAbfHq44cw8UY7vunXNnk62LzC022EF55CTSH/tccCjTXQQtc2BDJ5SsrTB2LNPwTghdaTyjTvOOyeWA24Ma4dywbOofRxX/blboI85p536yJ6rL41vGJtf5q1+NKZghLf90ddExo7DVfPCJYJ22yODGQz1gX1IyHv0hpvxiof6HGU806bUqe/4zUG5qM3aT455TmdrCPkwNKb1N9qMFW3MWDTe0eCjDizSJ3hWPWYd+r0RaAQagaOCwKE4yBZHhqjN3QJps7PxMUgssMqz4Vo8GVT+z9hqqDDObBgMJvSMRZsj+iy4yp3o23RtTHinzLsyCzkDSVkWd53hdJthaGNKPXVtfPI5eDaFGrTH7YlAFyHyPOOp3dpsw0rgRDG4pOSpY5O1kdgwbdzK5M/8GMZwm8vUV4fD5RSawWHDdWuBvsqPHjulVS79bXoMC4YkI4ZhbdO06TMK6uatrj7yGRfMOH8MMYYQo4GONlObaIw+uqgHbxjoA3hIExgE0SH4xGDUpwwU/OFOH3Xp61bhRS960bjhNg7SNiljBqbqaVf6nszUNfYYfXQVOcgvfelLRxvdijFQyGfY0d2thvGAJ36MIYaZ23yyjFMBRsYWGka3FNYdTiCgj0R9z+AybsTaT+nP0Jjj+t+c2CmYF/pVwDvrk/fwlC+Qhx+aGvQXWvJEOiwFNOoqtz5ZT9Q1/oz51DVW5GcsaKv1UCQ/GEjxCq15Fjq/i8/6Z2z6soMceTXQwzpqLnPYjXO6WLOMXSE4eCYTHd05mA41ObEMaI4zp9E6bn5wPNTVDnI5PHgb++ZHcK365Fld80R7OIbms7Zra4JnEQYwslcsYU++QBcYWcfMUX1hTRTR0AcNPvS0vrgpk1qrOTPaSoa5TiftJVuIbt6ti+YzZ8UBgT9YBlPjjVxy0Ev1u1T7RG3XZo6ePwRofbFuJqAVI5PeeFhLrE3amP4OLZ08a6eUHLT0gofgPRiHv/xaj85oMja813ZYf2sfeFYOT+1aCvQ3DkVru0MLmNHZ4SOn35iDW26U8cEbnsaavrBW06vqHv3l2WezP5DjNhZu5AcXPL2TbVx4FncLaPE0xj3DUsBPNF/op4/wg7k9yf4oXx3rkLb47N0cqmNllp82ohPM23ydpy32GmMV9gL6GvVhAv0qnfw6N/QbXbJn0TVjODzVsW4J+k4+Orzt4cZkaKUdGoFGoBHYBAROuSGr50m0xqaAjU2Qo5QTSfk2DRsoh8JCbIFHa3G1AVj41bGZMZgs2Mo5ZhZ6dS3YAmPQZoSHU2WLdBZkdRjOnEeOkJhFXJkTXsaRPDLwlC+PoaRuDLVAQX9tEWzA2hJdlDkJpo+2MFDpYvO1meCHv02dTPlo1WFQitlY8EerTF0y6F8DGaK2u630uRbc/EEZBl02bXyiY62/27N6onY5oWYE4q8d2qAfbPzaIqQf9YlbGDdTyvRhjLJs8HDTl/DBH0+BzlVXZfoCLhkrg3DLGMEPBuSoh5932IrqJT/15OMrqEfvhHV14aBMX8CBXP1lDJChDB/PAv7k0If8aqDgFf2U0QFdhxMIGA++WvAZOkP0/PPPH+sBvBJqX/3VX/3VGI/nnXfeWFdqn4Z+L2n6GT9j0a2TW7KDBLdbjF9G/1/8xV8M/YyDOhb2yld7ROOEjnhwpNy0yedchm9ojDFt8A4r9awn/kiQT/+tzQxrjiBHBG3kRC91tAMtw9e8l8eBwcNnmOYA545DiF8NaOlBvr4Tl8Z6+lJqDqGZ6TKfgqn94WUve9n22lLlzs/0SMDXuznq2eGtmziHdxxj7bHWWqMEdKFPHVilTfTyVUB+m80p44yhUS/9Fvlzik9kOHSgj73MwbAQHNDpB3/gzy0zGW6p9XvaNPOu72jwoE/CTn0SGocbfvdrHjjw4+DhUediaINzdE5+Ta2d2gE3ax/9RXNeu7TPOLV/cp45lvjiGSy9ZzysG1No4Crin7qzLtZ0e4z/Hxzubv0drO8W0lZ0aS9ZMKabgyi2h3nDieRwGrOhT3+gFfbSF3gL9iCH0L46gBN9jQeHO/hlDg3iHf6hr+DywpcH9PMln3SpfZVVLZcfDDI28l7r9HMj0Ag0AkcZgUNxkC2eiRZhmyInS7BZMR5ikM0LqdNR9BZaC72NQx15aOWFtzSbxtIGo9zGW8uyKainHE+ysoGGp/LZeVEWPfCcdVeOvzR64iNPil5bpJEPk8gOP2n0kHonrwb1RXUZFA4QOPc2Y7TkCOrPdSufdc/qJZCj3YwJbcFP+yIDHR3pohxtDJj0YdoTutSJjKU0dcjD+9YQYBDc029Jbw36bYIOxpBDFjdpDq84XvMcyPiX+t0nx8bYjwF/UBzw86msMecQzyHZQYK5aE4yZn1unAMe7ThIoJf5ININf2sbftouGIdLYzF1pQ74HHSZuw6uOL10S328a8h4z1qIh/ntcM9azZi2lnMo4wDU+of1TK62cs59BipwDMg+SICjcWad4rCJxo72wwaOwXvmT4/sA+hgpK+tdfCEAxr1le8U0KR/9Cl9YB0HPXWDO8ztpfp8/uImtIeZ5nA04wQ+2jSPk73KTHvRw0iQB0Ntd7Aq6NccQsI6MlNnEJ3kP/pfhK2DeZhykufD6L2KCT/02mNM6Uvzy5jKmJAepB34w0pqrBgLGSvGnD5RDl/p5AQAACAASURBVKu98A+/jH/1tF3aoRFoBBqBRuDGCByqgxzWFm0xi3bSpYU4myEaG5fgudaPQVE3AuU2iPCO7PDIu9TGgC581Y2z5zmylvSrdaObVB0h8rJZhd8S7aiw1b7oXXVKXq2bOviSFRp6idqhLAYMmrQtdfeTRn98PJMXmZXPUj76dSFlS7zmsiXe6/je3Pkwjn6RtTTuUtbp/hGAb3UWOB7GQJ2PaETj0g2QPmAkGuuVbj/Sw5ORTh7D9qBOn3ES57IeBi6N973oGN0qrTwBBoJ2i7OMWpcTQjd4zeuCenNdvNHXfDLCM+sM+ZVmKHSI/5BHF/rbI7xzoJbau1ex2iXgpa+qsx0slM1BGT3oE/l59p6gbsqTN6doRPWFyOP41JC2B2Ppr2LdgRG8Z1nz2Km67vacNoZubn8wlO8Z1mlvylL3ZNJgjoeDAG2Cex3T++GPnxi9pfSufYZf3vfDG214p16wkQaXWWZol9Loqyw6HbTtS/w7rxFoBBqBTULgxI9VTqJVFukEz/tZtNXLYl2f5VU+VUalj9ykqTPzSp2kytGGb80Pr72ktV6ek6pvUxJqXn2OfHTyxWx+s/zoi8bmbnNTD314rqs781p6jy7KIstz5VnzI7PWQ5/8KiN5M23oU573WvfW8kzHquetRa+jrgdMcwu3ri0Vd7db6Yul8bSOR82v49hNIn4nYyyqHyfHM/7Sg4Z1dfHNfFxHE5mh1S60oZeu009+QujlJT9pykJ7c6RkWeccmAiRfVBZ0RmfPCetPJfy5rGRPqi89qJf5Z3npXrKyAhN2l/fq86H9Ry5s+yT4R+d7VVpK/55lobGc2Qn72Rk17qVnxv77LmV5iDP+KYtqV9lJe+g6cwbH3lz/n5kRmc89lPvoG3oeo1AI9AIHDUEDsVBnhdY78k7yAJcN89slk628wzkdXzRCFV+rTfXDV3SUbn8MxtG2ZRCnzRV6nt0TF7S0IZXfUeTesmvqU09OqHVNjF1Zhm17k7PVZfwJStBnhgjZ6bP+5L8pbzwTRoafMS8p/yWSiu2dLi16HVL4XFzyYXrXrDVH7nh3Av9XvTNeEu6lzozDV3My5PhUXnik4B33j3Xdtfn0EtDZ77CLHnS8FrSFW3qjkrlkG+JPjQ3Rxq9w3vWK/l7TdXXBnzrlwLJr3zk1RDZwa7WCe2sb60/P1ecw3OmMZ4iZx3NXOdk38nLQc/J8qr1l/SHgfFJZjBEFxyTV/kc9Bnf8PO8pM9Beas360xWZFTZ+5VRdfacMYFP+MsP3V7401U8Gb32IqdpGoFGoBE4yggcyifWRxmA1r0RaAQagUagEWgEGoFGoBFoBBqBRqARgMCJH1A1Ho1AI9AINAKNQCPQCDQCjUAj0Ag0Ao3AMUagHeRj3Pnd9EagEWgEGoFGoBFoBBqBRqARaAQagRMItIN8Aot+agQagUagEWgEGoFGoBFoBBqBRqAROMYItIN8jDu/m94INAKNQCPQCDQCjUAj0Ag0Ao1AI3ACgXaQT2DRT41AI9AINAKNQCPQCDQCjUAj0Ag0AscYgXaQj3Hnd9MbgUagEWgEGoFGoBFoBBqBRqARaAROINAO8gks+qkRaAQagUagEWgEGoFGoBFoBBqBRuAYI9AO8jHu/G56I9AINAKNQCPQCDQCjUAj0Ag0Ao3ACQTaQT6BRT81Ao1AI9AINAKNQCPQCDQCjUAj0AgcYwTaQT7Gnd9NbwQagUagEWgEGoFGoBFoBBqBRqAROIFAO8gnsOinRqARaAQagUagEWgEGoFGoBFoBBqBY4xAO8jHuPO76Y1AI9AINAKNQCPQCDQCjUAj0Ag0AicQaAf5BBb91Ag0AscMgRtuuOGYtbib2wg0Ao1AI9AINAKNQCOwEwKn7lTYZY1AI9AI/CoQ+MUvfrEthtN6m9v88uzO8ymnnLKKI5t8xPKSjyY8bnvb227z2u3h5z//+ZBV+e5WZ6k8uuBDD/qISyF6rmvXUh15kZFy73gs6R7a0OykT/jVNPWia+RLKy90NdQ2ya+6hTZpeHmvdOEnv9KGnowaKl3VDU3Vv+pGXngnRe+58phlVbn93Ag0Ao1AI9AINAKbicCpn/3sZ0fLHvrQh25mC7tVjUAjcKtGgFMSR5VDUx2WKC5PWRyW0MgTTz311MFD/l4dZLTk4hne4R+5e02rftFN3ZkfOWRKb3e7223LX3IQZ9mREb74LLUV7+jws5/9bDif6GZdZv7e1RNDi1fN91xlRk6t4zm6qR9eoQ3Pml/rV3n0Tx/p4yp7KLalc+VZscS38kaXd6mQPPWid2giI7rmvdNGoBFoBBqBRqAR2FwETn3Ywx42WhdjYXOb2i1rBBqBWyMC1h6Oyfe///3h6N7hDncYTgtdOS0//vGPxzsHSYjDlDo/+MEPVve6171Wt7/97YczNjs3O7UZz+9973urH/3oRyty73nPe+5EvrYsbaATfTm/9Jkduh/+8Ier73znOyuO3wMe8IBRvh/nK3K0Wbjzne98ExlofvKTn6zI+ulPf7q6+93vPnBdq3wp4CzSLU6j9ugDOuIrKE+I7uiUa68YJzV1Qkcnz2hgVPmGZ02/8Y1vrL7yla8M/R/1qEcttpWM6Iuv58gLL3l0pHt0Uxa65KV+6inPuEtep41AI9AINAKNQCOw2Qj0J9ab3b/dukbgVo8AB4fT+K53vWt13/ved/XEJz5xOJccGvGLX/zi6tvf/vbq/ve//3Aq43R961vfWn3wgx9cffnLX16dccYZqwc/+MHDyY3Ts9eGv/e971197WtfWzksPP300/da7SZ0nFG6fOITn1g94hGPWD3kIQ9Z3elOd7oR3ec+97nVO9/5zuG4Xnjhhau73e1u207ajQjXvGg7Z/66664bzuKTnvSkFcexBu3/+te/PuQ4dHjuc5+7etCDHrQnOfCGBT05jeTF4cXXexxQ78qk8ji/DgU4/uShFQU0ePtiif6cdv0JH2VziIxPfvKTq2uvvXYU/+mf/ukYH2TWQAYe9FUP/8j2Tq48Y4iOZOsf7VRHVF8dtOoIUmXGlbRDI9AINAKNQCPQCBwPBNpBPmL9HIMzaRtuR6wDW92bIMAR+e53v7t64xvfuHrsYx+7evSjHz1u7TgtbmM/8pGPrD70oQ+tHv7wh6/OPvvscWuq7Ktf/epwqjk9HMWEODZ53ylF++EPf3g4lHe9611vQmqekbVbQMMZffe73716wxvesHrhC184bqPn28fPf/7zQ2eOJKfNrbW6dR7H6VySy4Fzq3rFFVcMx/i00067iWra9KUvfWl1/fXXr7TpzDPPHDR7aQud4P36179+W//gSUdYO7C4973vvV0un65kcnhzWJE1inDPDhC0H95uj/XZU57ylNVd7nKXxZthPI2LT3/600N/bV/CRKF8MnwN4IDCgQtd9Yno5ph8PDjH9L/qqqvGe/3yYAjauiVXxxcFF1100XY/pbzTRqARaAQagUagEdhcBNpBPgJ9y/ATGaIMPJ9PMvYY0gxgBmwN1diu+f3cCNzSCGQsR484NpyRT33qU+NT6eQZx6Kx7iaRk+w27/GPf/xwoH16K/8Zz3jGuIlFi38cOs9C0sisqTI3ixxxYZ5L3umTGF5khdazwJnjlLoldbvNgcdbnbTpYx/72Oozn/nMimObm3F1zeXI4ryZ1xw3IXLwMfe1+6Mf/ejAwSfWKY8ePr/mrLoFdqBwxzvecawb6s8Oe+oMQVtYfeELX1i99a1vXb3gBS+4yfoCp7e85S3jpt0tMX7Bwq21cvlCMEvb0d3jHvcYjqu+hMM3v/nN1TOf+czR77NueHCkOdDKHCbM+uIdWXDgFBtHnGpOcsYIp/2BD3zgaA/MOM2XX3756n73u9+46XerXIO+EX26f/7554++iKxK18+NQCPQCDQCjUAjsHkItIN8BPqUYZvIkWBYMvwYek9/+tO3DfA24I5AZx5jFTOGcxNYx6tnTiKnMDd68jhGT3jCE4az9+pXv3r1pje9aThZHKd8rsvB4kSFL0dJeRxHkCvLzSw9hKRolXk3v6Tkqi9Gzzhn3uWjE73jz0F8xzvesTr33HPHp8AcNZ9cK1MX77e//e1Dbw4ZJ5QcZZGHH4f/kY985MiL7Mjj2HF8OXtPfvKTh4PnAEHQDvQc83wi7faYLDR4C3iRqc1zgCP80T/ucY8bBxKhU08b3/a2t60e85jHjJtiZfK1jUOqvXQILvh7Fznq1ivrllt2fPShQ484wdEnMqXq5bCg8kXrHU3y8eaguwmPM+5Tajf6bqsdPKB3gOAm/nnPe944CPB5PUzUSz+97nWvGwcc0T+6ddoINAKNQCPQCDQCm43Aqeecc85mt3ADWsdAE2Ko+YyTo+C3fk996lOHYce4C90GNLmbsOEIuGkUOSOi2zq3oxxAzguHkdPEueHIuMkz1t0Yv//97x+f0vr9MefJYRGn8D73uc+YE37Xy2GK4+ZGlfPGMTJPUhaH2i0mx4gcnwDTB18BraCc8+WT21pmzqFR160uff0mmEPn2Q1yaLQ3t9V40Bmd6J2DG52iJ9nquxnVBp8nc8I5vdpDd7LRc5rh5vbUDa12qwfP8MOL8+v30fCdAzo0+F555ZXjU2Q40ktQVx9wHh3U0QFePq32ri0ca3WE8BsvW23h/KPjGNMz/RQaKR0E9bVJnHkphz1aqaiPjBM6aDu94JU/4qZOdMMTjhxp9YT8gTj58mAYXQZB/9MINAKNQCPQCDQCG4/Aqddcc83GN/KoNzAGGgNQYPAyLDkTCSkLbfI7bQRuTQgYpxwdN5Ef//jHR8rJ8rtRThknyx+g4pC5pcynrpxDDg3n5uqrrx5NMtbV5QDhmVtAt4RCHEhz5T3vec+2o4UHRy8/VXCrK/j9qptX5XGy8cWHfL+B9ke8OJdkKxPo6rbYwRWnit6cP86VT63jnOOvPZxkt94c+vDnmHGS6e6PV3EgyRBhBi+fNrt15lhzbrWJw4wvbPxWlkP4vve9b/WBD3xgOHscWWXaIHDS6eUPXs1/3Es5WWTS42lPe9q4oaYjrDic6r75zW8eN9xup5VpPxmetdV7+OBVg3e0+dyZI+uZ3gcJ4U+ewFnXRkE/0oVeMKKbQwgRnZiADx5oRTqKaL1HTug7bQQagUagEWgEGoHNReBX9ol1DKaDQJm6SfGoz0vvB5GTOjPv5O8lVVdYZ1Dth/dMy+DD120WY4/Rx3hDN9PuRdemaQR+1QgYp8Ysh8sYdvvJEXHr6cDHmOYMcug4N76ScJvHmeGscH4uu+yyUcdfu+aIcubwcLvrGX/BXPGMFwdMRGce4cMh9wep3Brn9lrqj0flllh9fDi+8vDynkhHTqPbY38Nmw5+Q+yvccv3u2R/tVqeeRtnlmztxUfb6OYPlJHNORYyp6Xq+f2yW2nOtVtPeMAQXhz7Zz/72ePZDS/5z3rWs4Y+0ZU8tA4ELrnkkm3+Q9j0D5xgGcy801MKBw56bl3hKZ+TLw1m5KYd2uBQIk6rfG2AC0zxhhe6BM+ccn0SDLQ5fNMu9clEBwdfGKDXXocFDin0gUMY2PrdsRtluohoRQHPPHuvz9Gr00agEfh/9u6zWbPiuAP4g0A554hyTqCECCIjFhAKWMmlcrlcLrvs8qfgrf3C38AuVyHKBheyJAReoUWYJCQhCWWhnHPOWXL9pva/ag3P3Xv38uxyQ0/VuXPOhO6e/8z06Z6Z89xGoBFoBBqBnY3AShxkRkYNMTJiXDBespKvXE2PsVPrKxvjR1n00RAnPWmhp5wyc4gsSQ8/5XOfPGm55KHnuYbIkbTQSbnEylXeSY+ROfMOvVqnttG9S334uBciT/iFTseNwFZEIPOAc2QHk7PCsbELzGnleNmBlc755ehxvDJfjH87tej4nvWEE0444FCaE9IzF9xz8vItbRw6uHDWfP96++23j91iDmgcsRNPPHE4btUZM+fUJzf6QuYkx8turt1vv8BdZbWz/J//+Z+L8847b7TNbqkdYvzxVFYZ9+R1PDgOcngMZvtltuvMuYUJmXK8mlPIOfTpBUfzrLPOGuXi0KORo9Z2gPFaFoIZOe12c/A5qb4t5pzbWSc/rOxSy+cUc3Tdc6g5n8EgfSKP4y7oQwGeduPVt8BAPm0iA7nd2/3XT8INN9wwFkuCPx7613iBC7r4KI8GbPC0MGERBC7qwp6c+jcOfWiqh64Lf+2MTGnTEKb/NAKNQCPQCDQCjcCORWAlDjKjQmAMMT4YKYwRhgnDg1GVXQfPVvbtoDBQcpywIuwIovqMQLtHCYxIxlpo2j1BC08Gld0bBix5lJMvlscYI0c1ktHNrg4aDDt88Y/sjNV8jxZDncHEWIqcaHomi3ra6odipMFEOlnI70KPYZddmBhniWOQBUdtIB9ZyJD2Kq9tqac9bcRltHS81RCozgeHJrt4jgxnbhvb5qkfVDLejWdOSpwq+eaYOWKuynOZB5wdceaA2Fw0Zzja5on60sV0ELr+7Y8084yTLk95c3SeXzDVjug6jqNjzJxFeiD0yeJZG+xg2vV1BJzOq/MVvWuuuWb8YFX6q+bjQzb0Els8IINydIhddjrGr09zMu2sn3TSScNpjS5JWe2pjmF4JsbPUfILL7xw4IuPo/B2x/GwK71nz57B2/Fu34HTdRYzLETQwXbCBXVdMOZc+3/G+MOYbqMHX/WqV43YogcnX546+s29er4x1+dw1t7gI9ZP6LhgdOyxxy5OPfXUgTt6Fi04w9IdCVfGpS4c0r+wFciafGn6s0Mj0Ag0Ao1AI9AI7C4EVuYgx+C0m2LVn2HDKWQgMgyPP/74sbvCwLGqzyhkiPzVX/3VMPAq7HYTGEyMH/+mhNEl2BlgiOJl58g3eTmayRi1e8MQZfTgwUBiNDKQGZHHHXfc+OVXhm+MLLsut95669gtscujHsOPoclA43D7FV3y2/FiuMWYsmuEB+NLOlm0247Ga1/72gOGoV2q7IQw6jkGdmjQY8zmf5kyDAVGGqcBbccq3ZOFoccIhSvjjjHboRHYLgiYN7mMccG4NgcddTavzZW3ve1to1yOOnOQXOqYP3GM1TEPOMicRnqG4535WXGRZr7EaTOnzHO6gO4wf9HhJNup5CTSVZmTlVZoSKODOIl2UP34lHmeNtIxcfKzOyqNfqpB2ziy9BTa7pWb2xGdRUbl0IEFfaCOf3/F2adb6E5tjOM406r86z2a9BG57WzTuWjTceifdtppo636jL6DG4fYjz3Sa2SUJg7e7ukuTjRacEZXX/v3SxZJOa/wE5SJflPmtttuG+PkoosuOsCjyky340F2/YC/wKm3C+5YOX6+qdY2/W1hw9gTK6N+gvssksJ5o9ilfseNQCPQCDQCjUAjsL0RWImDzBBiAPq3Hf59B6ONU8sBZgwxphhuMTY4rddee+0wPOwgMHASlFHe/xNlpHKEOamMFMZM/jWIMi7GDEPHLoc8Rh2j1/E6F6NTPUZWyvs3KurhxTjNj9/YpeDgMtAYdGIOOSNNOTsnjESyaDPn1Q8GMcoF7VXPsUH5DFcLARxdtBmQDHkGrh/bcaxU+/y4DrwYhZwAvOyWwICMjF1GHYNXG+BnN4d8HRqB7YiA+WeOmG++DzX+OcicJWP+iiuuGM8Wkcxt81B5c90ilnli/NsFlm4Ry84zOrMDar6i6XJvXnJqLcJZUEMfDU4gx9C/k/I9L3qutQI+dA19Eked052Al4ucccjwIUcN8ugHbYuMNT/3MKM76B16FX914uBxjDmI8iwywun0008f+pUe3EhIv3Aq7fjSNzCw+Ei3WZSg5+zMapv/EQx7/UZHpc1pR57pPrvaZEu/aTd+2gF7O9HhH1m1V/+o499aoYNmAj4cYjSkq+8ZJhZrHa1Wl7NMPnihow0wgaW6WVjR/4L3iPeHNndoBBqBRqARaAQagd2FwEocZM4aA4RDx1FmdHIkGY+ME0YLp5ERKTB6YtQxmObAiGGgxTFOfpxHxq287AhIZ0BfddVVwyHFC/+TTz55OJcMRT/sY8fXLgYDiUyMJPLZLWBMcVjtFDP4OLIcfXQZir7b0x5GKN5CjFNtQdfxQ4akHWTGvt0ku1EMNzslvi/EE17o2bn2K+L4+Z6SceY4Jjn9i5XU006GGmdCnexGzYZ2cOq4EdiqCBjTcZ7oAY6WOWZOcljtGpu7+/btGzu8dvwsoJnj6pqvnFDz12kKc00wL+2WKpMQPp7d5+L0WfTiKJvvdhfJYs5z1MxJMnE20edALQt4kducN6fpJfM7Ifyk2d3mAArShchqzjtiLl1arlGo/OFEk53uoLNgIHDyyE+/WiBAj06iewROMt6RJ/xH5vSHAw9j+otMnMrs0HKOLdJxUi140mf0njTOJLk5ncEr/KRzWull+h4P2Con3TM+npX1nP6WnvcG/ctZV+ZgQb73EeeYPHjSseSEXZ7pZHz0Xd4H4Y2+PsOzQyPQCDQCjUAj0AjsLgT+ZM3diXY7amf3llHm36A4Cse4YJwwOBJimDFgGCTiZcZOyjGYGJfKSmMopY7jeOeff/4w0Bg5DGlGLcPIkT87sxxWBpYdWTIyjjitvn8jm4A2ugw1Rpsft3EUWz10GcCMUTu+LkcBGfGRDQ0G5RlnnDHk0W60GKl+MIeRhuYFF1wwMGF4agMjkxx2oO0W2ylj4Nsdtgvv11f//u//ftRjjAowYGRyzBl7ZIdvMEw8CvefRmCLIWB81vFqDNvptBhGZ9i14+iat8paFOLw2d3jDBv/9IF5aM7akbR7G8dKncwBfJTPs3vBvLRjzSnmiHMc8THXOUPm2stf/vKhS8xHDhmnGZ3QQCe8nHIRzHNOd3SVNOXJYeELD7TITV5BWZc20kfyPEdmZXIfup7JlbZJj65KeXw4+3heffXVo12wiuNLB834DIH2y0wOTjGdxpm0IMc51j9owkwfwUXa3r17Rx/Sm/AgH7nwSHsSa7t7QRscYxeSn3al3frbZXEg5cbN/j94KCsW3HPsjRHtpb+dHnrlK185FmPI6z3FOX71q189xoG6MCO3RVF62VjI987pr8q37xuBRqARaAQagUZg5yKwEgfZzg/Hk6F37rnnjm/JGFmMlRhDIGSIMICkMToYMMsCB5DBqqwQA0g9dDmyHFVGHINNOYa13R67S37gx7e9DEfGlXJ2eezM2oVlQDm2LDAAGYwMJAahcjHayMjhtVOifQwrBhYHHF3t0QaGFWOLcSidPAw6hhlM1I8hLV970JBup9hxc98sc/Lx8G9Y0GaEcpxTB10LEHbqGd3wwD/5o0H9pxHYoghkPhOPI8LpcpmfTklYLDI37Er67MC9OahedAC9YLfUnDV3zRPzILSVlyaYG+q55GdOOilCN5x99tljMYtzSh7B6RCffdiVpdcsdnGWLGihEX741GAOkoN8QniSQR1z2kkW5dRVNvdOr2hLrZc2h4469Ayc/vqv//pA29CGG1m1A0060cIBGv/8z/88FuC0C674BLfBcPqDHnxhxQGm92Bhpz8LiBYr0KNLLTxa5HAM2kWH2pXPUWjtdCXknmyCfsjCgOe0W5p77XHBVV141JDyYmW0DRYWGulyfacN2s5Z1g5t1BYXZ9hJAkfEsxiqvjFpERZvuM58qwx93wg0Ao1AI9AINAI7C4GVOMgMI7tAnEBGRYygtaCSn2+9lhkejEEGkjLuGT8J0hmtWd2Xjob07NDMhpR8hhJjhyHMQEpQNvzszqKNn3SXstnhsfvEcAxP+WQkizLopz1o+D6S0ZVvlENXfXzUYWjaoWaUMvDQ58AzhhmP6JEBL/doxBBkfHdoBLYLAsavcWwMO9XB8eLQcFTNg8x385EDrAwHRj3zWxpnRpp7zrWdUsGciA7gnHES8RLMH3PKCRNHt80rn19wAJUxj8gk4GXu2UW2eGUxCi2/jCyWv9GApksdOoDTpW2C2IU/PaBd2qB89NPMS3quyKu+drtSXp42Olbs16jtmNvhJgOecA42c1voIQ6lUywcYLod1nC2WKe/fKONH5zITG9ySP2WA12mvD7FKzKFj2c06DmXZ3pTHXKrI8z1Un+OlVNPe8hiEcKPvFnY1Md0MJraZQHSqQEY6nPts8NuQZa88KLj/+d//mecJHBCKUf4Z7793Ag0Ao1AI9AINAI7F4GVOMi+4WL0MEQYOzHe1oNtrXJx/Bg/MZSUzTMe1fhKHsNPHmMpZckgn0EnL0ZoZFNOeXkuz5Er96Eb41R66IrJokxC8rUjsshLujh1yMU4jJGONxmlq+uS5lLPpSy81evQCGxHBMwXi2l27ux2mlvGtFgwBxxZdnFgzIMsInHGcu+HnixQmRPmL2fN9/ycIXMMPQ61XU4//MWBk89ZzWcK6NNhnClp6nBaOdDqcZLtInKuOIUWt9DeSMi8dSzZjqZ2ox8doF0WAjhr5Bcy52f6aEVPzHn1OTy1wecdsOAwVt61fL0nWxxYTrbTOORxb8cVDhYmLIaiaeERfbLrF+1wiseCApzmoN14pB2e4a8f3esXvATP64WKFYfbN9r60EkbMloYgYdyLnoVf2PCyR3jwWkju+L61CKC3Wa7+j7J0W/asRFZ1pO18xuBRqARaAQagUZgeyCwEgfZ7g8jwu4s4yRHlBkmwmxcxNCrjqEy0hkvjBpGk/sa5KeMuNZRVhojZ+YXGoy4GKGpryzDSbB7QKbQDZ3IYgeJ8SYdP0a88i7PjL7KnzFtZ50Rhh8+yojVhRfaaJIrBhw+2WGJnOinXpzjGJkpk3Z23AhsRQSMU5dAZzguLHBKOImZw8pw5jgnxri5ImRO2Hk2R3wvzLExz9Q1LzmFVf9wvPyKvF1UzptdQjQ9m3fmH6fKd7acJjzQ5izHiUfjLW95y/hdA848x17espD21Tw07WzSE+Z55rL5TF/gz0FP3eidDPimKAAAIABJREFUSsO9emiY/8qqL9YGbZJfA50MI7u/7rVDOU6uusuCPO2zg462H9+yuw1jiw/aYnECTbSDmcUDn43kx7q0a1nQNpd8WKBll9rnL/pOn7jSn6GxFiZJV57za8z414C+hzautLPqfPTwdDTcfxjwaY4j4ZxptLTTpy1+1NEnMnbN/Wsr/R1ekanjRqARaAQagUagEdiZCKzEQWYYMSYYYHYXrMBXA4xhkYuBJo/BwXF0Mbo8M3IYio4DMryUza4SY01+nNK1jBXpysVg9KwuI6k6r6mfWHmGt90ERlIMPMaU7445s3a7tC08DAm0Ga3i0JLOWGN0oWlnhdPPcCebsgxydNFnoHEY4CK2c+UXcRlxdmkYjqkHY9gIyuMdvuLcjwL9pxHYYghkfHKCzAfP5rj5RgdkjnLo8lsCyhjr5r6xb4dSml9n5syoJ5gjgjz3HDuOsKO/5q9dTTR9hxpn0fxBmwxoqxMnlgPIyaKP3vrWt45fhSYzPWDntAZzGk906BKXNBe5zX/OY5w1PF3aS/8pz9FNHbF25Er7Oe7kT1DP4gK6+JAhGKpLD+X3FugcZTjBQniElhiW9BZHE5/8izrPFhe0Qxm7yZxLuNJj2sCZpK/gowz+cyAfGbRHe/NDhnCnB6XXoLxrTq9lcq9v6G/60706cKHP8x4RW5AwJmDJmXaM2qIIueDltyHo7Ztuuml8363d2iRvI3JEno4bgUagEWgEGoFGYHsisBIH2U6QXw31C9KMCrsLDCpGEuNGYIAwKhllvhVmxHAAGbDSGXGcRUcaXQwxBos0dXJJc8XIQ5vRwhhThsHLWBPjLV3M4GY4xgitxpt60v2QDrnQZ1QxPu1uMKbkM74YgZGF8cVo8ox2jF8ycQDsNjHm/SI1o9Nul3Q71Y7vWVTgEPtRIJgJDDG/wM3oZTySG19YMkb9WjgDDz+84eNeENd2jcT+0whsEQTq2MyYJZp5Y3y73Gc+u69zyr0x7xtjc5oTZH4kqJe5iRcdwinEy1FjDrI6fhfAHMdP+csuu2zwMT/PO++8wQNdvNS1AGje0m3o0gtzIA+d4zJPnaSx2GcBEB1OF91S6aIV3WEec6BTDz96MZhpG77y/cgYfuHJmYMNHSUtdaqM6tKLZEu7046U98yB5Kxyen2HjBedl36gF1OfHudcWmyg5/wyuLIWIXIcPfUii/aibzGCjrv88suHLrfYoR7HWxuiy9JG7V8rkF95F6xhpy0wVd+7Bzba7xQBvUqn+0aZrqVzBXRc2uhfFTr9o8/pamPHOypl0D2YTGvJ2umNQCPQCDQCjUAjsPURWImDbNfAj5wwQBgTDNP8OBVDhdHI+OToMfoc1fMr1BxqP4TDwEKD8cZhZoQwZMUJMX4YZ4yg5DFUEhhBDDCB8aKOWFmxeoylZYYNOn5BmjwMTt/CaQ+nmUPKwD3xxBOHcx9+jD9yMhRnmvgwshiYnGw//MJh5gijr+3ow803keigkX9zw7BmdDrqZ1eeEee7ODJqJyeBIShoZ/CIbB03AtsJAfOPnqAvzOG1xrN08y6O5dzG1EPHXPPv1cwzO8FOYqSecuZbnFpzSL55FhpoK29+26nmwGW+z3zx43TTMRbAOItOjpiz5inHVhvlW9yLk8mJ4yhyTDnznDe6g4Np55JcAjnJpb465BLoAnW1Jbow8nvmpCvjosfoWot1eKDhUl6MLv1i19j/W7eDfM455xzQWxx4AT9ywYW+4jjaZafrfDOuzf6tkmPOs4NMTno+R8o5xBYlOKreC2SBpXK5h2vaNgTYwB/ltQlunHH04MzhtZiL7549e8Z7R18IwYHMTg+QCR7XX3/9WCQxPjL2yKi8q0Mj0Ag0Ao1AI9AI7CwEVuIgM7Ycj2MsMDCt0tsdZcwxKBhSdpQZLcowXBmcDBdGm4vxZTeVI8oQ8Yuz+aEdho5g94XhYoWf0cnoSV6+nePE+jGcOI/4MWbUlc64I+9s2DCS7BJIZ8za3WZckZ8B5/9mcvq1CU9tYRySF70Yshke6jH4/PsmO8iMZW0iM4OPkepbP0Y3OuqjSW6GsfbceOONw9hkdDJAGef+nym5PKtDbjSFYBEZOm4EtgMCxq05YVy7zIOM6Vl+Zc1hsXl9sGAO0kfKmrdoRgepZ64rY/5auKMjlvGVZl7KN+eWlQk9MR7ZIcXb7qh6HEN1OaLmt3LuzWu71ILdVeWkVz7qk4FOtPOpPeSHBZ1oQY+ejV7DF4380JndUA5iFuXyb5iUU4csgsU4F31t152Owts31PQYmdShg+FBDs4m/KTBm2Ot/aE5CO//oy4973/J6z+LiHQgJ5suS5+kHfjhn+dKa637tEk++cilvgUIi7d0Lz1qt1sblBeUce+CtT7Rd/CIE70Wz05vBBqBRqARaAQagZ2DwEocZEYMw8iKOwOO8+b7LgYSw4JBxChhFDI+GKSMD0YVR1RZxglDy06ycvIZL5zlBEagH0xBl0GFb4waBo0dWt8I44W2cjGuOKu+NwsfdWtgSHGCycBIjfyeGaAMxhigMcwZdowvRvZsQOFDfgsHZGKcWQRQV1l4oYm+cjEm0YKX3XaGc/CJsa9taKPFmYBlMIhx57lDI7CdEDBmzXvj3DheKyhnzCuj7LIgz7wXEmduSMv8kEYPmMPmfHRFLete+Xot40l2C3/0Fj1lx9rCl/medoUGnpHLwkDSlc093eAef4EuoG8s8nHsBDToDTrE8W06kR6r8ts5pns4y+SzyOcXnunHqj/dkxNfdOksx6dh7DQLPmgInEty4EVG5fUJ3URHcqo9zzpRXbIpQwb0Uwf/tNV9Ajrax7FNfTwPFtBRBh2Y4AU/fDnk5MU7+rzSqnXh5aQTGfWpeglVxqR13Ag0Ao1AI9AINAI7A4Gj/hir5E60B4kYFgwpuxqcN2mMCoaUOEZFyktjdMVIZFAx0rKbpHwMPgYPYw99Rls1jtETGJjqqqOudOXE0sXS8c29XdpLLrlkHO3+13/913HcmcOqvKC+8jHKxNqGTk3zXAP6ZE0Z99LyLE4d9/ISci8dr2BJLiHl0YSZ8rWO/A6NwHZBwLy147h3795x6sIpkDrnajuMc8dkxRwxjmENmQc1zb30zJt5ftgZtbvKCXIiRNlc6mauh/ZcXxlHhp0SsdvoJArnq9Jwr17qJlY39zP9PCtj59fRbYHjTT+Ry6UcXQAzwbMLrk7n+E0HeRblXJxGPKN/1PGsTnQsvaJO0jjZaAkcVg60hbzIHl3n2b0Q3MbD/j/oyRfP+dKE0BRbbPVJivcEXC2EJr/SrfeRBUZOM5E7J4+0Sb4YnfVo1UWLlBWTNc+Vd983Ao1AI9AINAKNwPZHYCUO8naEIUYcY/vSSy8d3xr/y7/8y9iFzpE8BlAbQduxd1vmRqARaAQagUagEWgEGoFGoBFoBA4dgT/f9jz0+tu2RhxfOyUcYt/k2RHJznTyt20DW/BGoBFoBBqBRqARaAQagUagEWgEGoFDQmD5R3yHRGJ7F3Zc0L+p4iD75iwO8vZuVUvfCDQCjUAj0Ag0Ao1AI9AINAKNQCNwqAjs2iPWviFz+a7Pr1b79s4Pz3CQXfm2r36nd6jgdvlGoBFoBBqBRqARaAQagUagEWgEGoHtg8CudpB1kx9h4Shn5zg/8JIj1om3T5e2pI1AI9AINAKNQCPQCDQCjUAj0Ag0AptBYNc6yJsBq+s0Ao1AI9AINAKNQCPQCDQCjUAj0AjsXAR27Y907dwu7ZY1Ao1AI9AINAKNQCPQCDQCjUAj0AhsBoF2kDeDWtdpBBqBRqARaAQagUagEWgEGoFGoBHYcQi0g7zjurQb1Ag0Ao1AI9AINAKNQCPQCDQCjUAjsBkE2kHeDGpdpxFoBBqBRqARaAQagUagEWgEGoFGYMch0A7yjuvSblAj0Ag0Ao1AI9AINAKNQCPQCDQCjcBmEGgHeTOodZ1GoBFoBBqBRqARaAQagUagEWgEGoEdh0A7yDuuS7tBjUAj0Ag0Ao1AI9AINAKNQCPQCDQCm0GgHeTNoNZ1GoFGoBFoBBqBRqARaAQagUagEWgEdhwC7SDvuC7tBjUCjUAj0Ag0Ao1AI9AINAKNQCPQCGwGgXaQN4Na12kEGoFGoBFoBBqBRqARaAQagUagEdhxCLSDvOO6tBvUCDQCjUAj0Ag0Ao1AI9AINAKNQCOwGQS2jYP8xz/+cc32yTtY/poVO6MRaASOKAKZp3XOJm09QVKu1l2vTs1P/aRVOvU++YlT72BlUvZwxAfjm7zIWPknr6atdz/XyXOlX+8rvbXSD1YmdcS5r+Xr/Xr5tewq7vH7/e9/v2lS67VpvXyM5zZvpM5mBZ55rUcnsiTeaPll7Vqv7pw/yzo/z+Xrc5U394lruXp/MPqpe7AyldZG7yvd0J7Tkr4WzZRPfp4TJz1x0sUbDbVs6v/hD39YuDo0Ao1AI7BdEThmuwgeJXzUUUctNRq0Q15CFLXnu91t26wDRPyOG4Edh4A5yWg6+uijDxhP5mzSNLjO4QpA6ir761//enHMMccs7nnPe65ZvtZ1r/5vf/vbkeyeDOFFP0gLD/TRViZ1lYnB5z51R4HD8IcsCfgu4yctcosjr3ocu9/97ndD5tqW0FwrDj203KfNykePSk+oZchTr5SpMXqhIz3PaWPaJP7Nb34zZNDX5ElZeYcraA/6sDMOjJkHPehBa7KrWESupCVOOiLSXNoiyDvYeAouESB14VFxTL5YGf3vgh0euWo59+jnUgZdsSBGS0g8HqY/+Mx1pyIH2hza6Lk84w9vdDKnUy5xyoeu57Tf/VohPJJf6aCduuJf/epXAy+YuYRavtIgr7z0nTa4h18NylQ+ykgT0raUT7pn96mbZ/j89Kc/XdzjHvdY3Ote9xq80sehMceVpjxyRh73tb+THxrKkTdtC63ILT1BXtrm/he/+MWod9/73vcAj7l+6nbcCDQCjcBWRODoiy+++OKtKNgsUxS7dPeUbRR9LZs0+VHIFHeHRqARuOsRyJwUm8cckF/+8pdDsBheHuTXZ2mM0h/84AeL22+/fdS7//3vf8D4Wq9l6Kn/ta99bfGTn/xkFGdkCuFDlu9///uLj3zkI8NQj2NZZc79kdApeB3sih5MXHXfz372s9HW73znO4v73e9+w6hOOw+GVWgFF04iR1WIAZz6ZIMpzOIk4aFcZJl5oi8t+cEzDgfaSfvyl7+8+PGPfzxkv/vd7z7Ypm5kWHUcvL/3ve8tvvGNbwyn6cEPfvCBMRJ+KVdjeZE95WZ5tZ/z8N3vfncU0a65TOoq6xLQzXMtL91zDbD84Q9/uPjSl740xjGHqtZJWXWV5XQZJ+jPZStf9/WqcrnHY615oV4NoaP8j370o4W+NjfjUKVs2pby4mCibjBJfp5rLM/4zIV2pYOHMf6BD3xgLIrQC8EhdCMPGmh/8YtfXBgj7u9973uP+u5TPuXgG75JS1+kbaEduaSjI6QM3QSfm2++echoTuObMik3EqZxGJnkhS5Z9DsMI5cYH+1I7F4bkqaMZxfM8A1vMfrKfvOb3xxjKlhGrsSpk+eOG4FGoBHYaghsmx1kwFG8QhR+VbK5zwugAi0t+TW97xuBRuDIIWAOxqjF9ec///lwQr7yla8snvrUpy4e9ahHHTD65CtbA+OMkbh3797F8573vMUjH/nI4QDUMsvuzX91OeLvfve7B4/jjjtu8cAHPvCAPHhxKj/5yU8urrjiisVf/uVfjnJ2kuRF5yRexmfVaZEbfwsDnNUqC350ojSOhQWDpH3rW99avOtd7xoYv+51r1vc5z73GYsJ68moj9JG99/+9reHI8DQfdzjHjecben4ujjHN9xww+Kxj33s4slPfvLi4Q9/+AEZwkv5hHovzbOdOwa18NCHPnTxgAc8YND+xCc+Mfrkuc997uI5z3nOhuQPn83E2i0Yl5/61KeGE/u0pz1t4DHTC0Y1Pe8n/aFdc1uV5VhwRq+77rrRJm3Td3NAP/QiV2J0az5+NRjnHLj/+7//W5x33nmLZzzjGYssMNRyaHCEjJX3vve9A/sXvOAFYx4qhw9n3rWsLdLMKwF9DluctpmPsq7aBu3zzEG+7bbbFp/+9KcXL33pSwcuxhFaKS/OFdk8Gzvk047QFwvy8QifyGou5FRA5ELnrW9969Arp5xyyhjn6Ud0QlP/0RP0CJ7wQss84DArBwvl1E8fBg/55gkHNzSTh0+C+9qv5KOb/vu//3tx+umnjz6ycGMHOLwqvdBKmrZnd9s9fWIhUBo+2UmW55nzm5109xYL8RHQ1q7HP/7xi0c/+tFjNxsf6eGHvkUPcwlGWXBQX93IkvZ23Ag0Ao3AVkNg2zjIUcoAzIsjCjl5YnlR0nO81cBveRqB3YqAeWoHg2F87bXXLhiljGOOFsOYQVVDDDDGNEP6EY94xDC0GFvRB7V8vacXGNEf//jHF1ddddUwLhmoHEp5jDVOCuebo0Cmk046aRixysRQxItTHUe08jgc99rswvf973//cFYZ9wxwaRxmO6yc1+OPP37x/Oc/f7SFg/T5z39+LCQwUF/84hcPQ3aZIzbLDQ+XIP7617++eM973jP4nXjiiYsTTjjhgHFrl5KRbUHhrLPOGg7Wwx72sFF3rX6BdeinbWTlMGrbqaeeOhwOxrh2OC0gfvrTnz7oVv0+Elb4J2MMz8985jNDHg6AQGb5NeSZTPI5F9qdtscJSDl1tcvu8WWXXbZ49atfvXjCE54w+FS6KQdfc0T9mYZFBP25zPE1LoxlY+ZFL3rRWHwiS+SpvMjK0dOPcMbz3HPPXXC+jHuLV5ztZXNMezlP+HFoLXI98YlPrOQHLnjARzl15iBf+he+8IUx9171qlctXvaylw1dkHar79KGnOxQhyNmp9yig7LyyYSme3Ljq652SrPQY05ELrpGHYsiMLVoobwQ7MXKK2fhBrZ0wTOf+czhpMPPQhFaFnnw1DfqkUG/60vXP/7jPw6sKqb4cYKVk65tZPUszzzXDzmtId1JGGW1Kwt5wZas+LuUQSttUh5ml1566ahrLNG5yinj+upXvzrqZj5bBEj/aYMxrB3GSRZF1Et70cTHjje8LX6SkVwdGoFGoBHYDggcEQeZUqQsvVwobC8RynhZiBJPXl5KlHJeOpRvvlXyImH8ermj7wUXwyEvt9DquBFoBO56BMxLwTy2m8JYY2AycjlgnD3zew7qMfTEnEKX+R96KV+f6RO6gcFn10ceXcLpYxDTRZxljrkjtR/72MeG880I5oirzwCMoWg35FnPetYwjsPvcMTkxNtFB370ox8dOB177LHD4JRGPs4l2TiQygraIZ3OlWYnx04oBzTYSM995JfG8Gao06WwRps+/dCHPjR2GjlC0pQl0759+0Yf2E3SH7CFqSsOQujPMRp09wc/+MGxO0ZGdSIbnI0NDitH/TGPecwBvT/T2uwzXgIsjCULMDfddNOQI6cM5FWslLOLqKwQ5wMt7yjYwRpWxtaygEbGsL70Pqw84MgJu/XWW8eCjD5QB239y4F8yUtecmC3t/Igh75HN46TNFfl4Vk+p5ZTan7gSfYLLrhgkLSzb07gWwN5XXh89rOfHXK4X+YgG1Pag457YyNyeBa070lPetLi6quvHvhL8wxbfMgKWzrDooKxkDROozGuLBnMa+PK4gbnzCKRgCc+xq/++9znPjeen/KUp4z8OHoe8BTwENBNHQt69EV0gbIcRjLQZ2TDK2OZXBxczvyHP/zhIU/aP4gvFgNfY9yJDf0MF/XduyxcvO997xvyGHd2k8mr/2bHk8zk1XYYaLMgnazKGx/4WZh84QtfuHjIQx4yypBLX19yySWjnDGmT/UZmfSB+fjOd75zYCwN3bQneDndAyNyXn/99WMBzaKmEGzHQ/9pBBqBRmCLInBYHWTK0kXhMlCtRnt5MKYod4oyjjN84vRG2crzYrGyzFCyYurl5rgdY8qLg5JnDDIalKeUHb+MMg6tLYp/i9UI7BoEog/MSfcMcY6dec8R8A2gwFizM1GD8ow+Qf0YhqEpTt642W8QMujoDjsZdBCDj96hN6QzDukVO24MT/pDGQYpmRjF9A29Q4+R9UgYeLVd+DH4OVR26Z797GePJtJ5sLCjTW6BM8C51x67ZHQk/Sgfptk1D0ZivPBQNlioD2Np9CpDmENhIYPulmZHHi/YWNwQGPX6lVFNB89Y4aUfyY0mp4FDYzeOTtf30jkVFgNc2qkv7GzCP30duWtbDvW+0rKQon3GhZ1Xjhg5XTUYB3aYb7nlljEmOA9pk8UFY8Y7zq56nJPQEMNVHW3E3wVnz4JndGBqEUR/25XEAzZOOHB+jMtlIW3SF2i68FVff8iPPPgaW+ahucKhybe16lfnKLzQUD99qw+NBzLPQTk8zC1HmNHktJFJW2BZHVNyyLNbqo7y8FIOD/YDJzk04AIfO5SCNugbPJV1EkQaWV3o2Q01zjnj+tiFp3FrjuGXtqEJN31qHt14441jochYhL/yaTfa+iULfNoOa7S0BR31PQf/4EUe9PU3XsqSSTuMSw64eYK+/jEG5AvGEj7qCGi7J4fx58qYkI+/C57mqCP48Exd44Ge0C92yOWHpn4gnxgPdEMbzQRYmrvqOx0CY4tFaKZ8ynbcCDQCjcBWROCIOMgUvFVHK/OMNkZQDE0r1AwvLxcvOi+vKFAvFSuudim8QChgK7RehgxFLwovHDsQVqi9LCl3L3Wr90JeNFsR/JapEdhtCJjbMQ4ZeHHaGGQMTQZUDL8YfMFIXQaj+nSBOLoiZRKnbJwrO6B0xmmnnTbqOUrNAWFEo8lhZmhyajjI9BL9wlG0G8JhYtDTXYziIxG0DxZiF96cSAYrPQcfBrOjz3SiZ3qW4yow4oW3v/3tY7eXfrR4CLtgLF+9GLcxxi1mKqPNAvpw4jzS19kZZVjLY8BbzKTHyenCD6Y16Jf0K51NXv1gR5RsoauOMaGt+saOv3zvjThUa/V95Xewe/VDg0wWRRyThYV2GZtwn0Otl74Razsa8Pe+choCnimDjjHOMYGrPFecZW1LwEM5NDmNjs+TEWbveMc7htPH8XPVvlRfPUE6R0aZOH1iAX3p+s0OH0fG2LKQEsdUfY4+Ryd1tGWee97RdnHJuiwoj5/FFHzwUDZOb9oPc7v2kT98yJG07MpHDvYCh9luM3wsENndVAev4Oa5BuOYI20u4x+HTzkYoZ8AJw67BRELKBbV0NYX0tHQPo4oeSwYGaP6OHTE5oSgrKsGPM0XeJvbgjraTQeRVdtOPvnkobPSn+YknITQ9Iw3WbQNHZd8cW2jfiB/FnJCRzonF43kwRe9ZW1DV0iMB70J/8svv3z0PT2hnzo0Ao1AI7AdEDisDjIAKGUvH0rey4WSZCREmTuedeWVVw5jyIuFUSKP4ndkyQ/NONLk2zQGrpVISpuyZohcc801Y0WX8eSizCnwvBAiQxT3duiUlrER2KkIZF6avy5Bmnn9mte8Zhhv5veyQC8wGBmAMZ5zr7z8einLmeOEMfj27NkzyDI0OSMcMA4KY45jZheTEWunmWzu6S6GIkcaDbIeKV2CD0OYLiQPOV2eORucWIuKvt2Gg+OZdms4OHaPXNKdwHG8lR7VbkZqdGRwhpuy8hnqnhnZ+DKK1bFTyFnAmyPDqY3RL115GNHjMEMPnYqXe2nKeydwzuDOEWZQq+PCV1nHX/F1nJuDIs9YiVMS+TcTV9m8k8iDh4WFOHFV9vCAEafVu4qcnmGk7caaxRXjhrPs5EKcFPXx4ZxxoDlXFne1n5Pn/Vb7hXzGKOz1s2dOmfLGrX6FDfo1WKgwhuXZdTSGjG99pV3GEFrmhx/osrPre2iLQ3gF+4w7tOEQhy99hK50z3HYqhy5VwZGynlH++Ew/W/HNPM5cytjTh385ZNXvn43NvEiv/wEZQS4aj+czXMYCRnHdAv8jB9Yw4XNoL84pmjineCeTGRlqxijjp/D31hB38KCPrCzagdYX2ov/tpMVrTZM+TDS1oNxpJFOM63vLRfWWOIHrPwb2xyzvELbWXcC+rCCm5CcEEvl/TUlaYsvNzDSd+IgwnZ5VWZI2No1hh9z/CFF6ffOIST+SyvQyPQCDQCWx2Bw+Ygz8qUsqWIvYBqnpePdJf7vPS8bKyaOkpk1divyvomjdKl9JXzTY4dIj+q8Td/8zfDwEDDy6mV8FYfei3fbkaADjCHXZmrjKkYdAy2GpRnqHEQ6BIGKh3B0Et9Rp3nOCR0gXp2wDgGTp/827/92zBq0WBoczJjbDIM7SyjjRYHhvHMoI4BT6bwq/IdiXt8tYeRzdnlhDI6Gf12Ai0mOiLrlI4FRfnwtDAAO86Ydp9//vnDaE27lEFXkKbNjNrsjsmHJT3M+eZccFTsXuMduWDm2SKoUDEbCQU7jpkj9QznN73pTQecw5SLPBwYzrOdM7u7xgvHPEd/lbsz/ZG6MOXckgttYzG0E0c2eHBcOS3Go2Oq2qz92mw8GUvwyze9cWC85+y2cp60XX39xDGFqXdX7Y/wFJMVHZfTDWjjA6Ma0CQb/t6R2qRfOPXkRB8t49+lTzl16Bgfnu3ak1Xb8VMH9tK8g7XZnIo8ymn/siBdvcx1Y8lixP/+7/+OuQYzY4osxqmy6Lq3yCDmWL3+9a8ffPFAKyEy4GOMWygiD2fVvXSYw4AD6sSFfG0KFp7RFLtqIJu6nHtzS39Z2DFe3JsHxo85qF+ciDAGLFCQLby0W38JGXfhE75ieS59oR/s0FuU4kDrS3RgFdrKudcW45Keg2mC9qOnTA3S6b8cq5eHFhkt8qBnjhoP6rrQMbaMX/XXC8azXWT6Ck2yw6ZDI9AINAJbHYGVO8iUJiXoouwpVTGlm5ek5wTf8VDmLi8a5dFg/HrRUNj+rQGDJYYb5pshAAAgAElEQVQsJc2I9ZKlgBk0XvxecF7e4YlHXoDh13Ej0AjcdQjEAGT0xlEwR+kLRmh25cxtRmeMMPUYZXbfGGxixpu6cUiUQYNzZ7eMLsgP6cRZZGDiTWdwSmJI0inoMDxzVJixS2eRIcZ9dIvyhytU/Rge+GkrvWfh0OmZs88+e7TVzhRniQNpp9ERcfoyMnK86FD1fcv75je/eRz/5SzYlYMTni7t1xdwcjrHs0APw1+fcFo4HzCBH9xgJLZYqQ/o5dBUP7Iox5Gw8Mkwt2vqIhvHTjll1A3WeHAOOAraqf84zBZMOfPKhX7w2khc62iX3UCOIucPXQEWwSA0yUZGO4ocF+PNuFUPNvLgHyzUj4xw5PSoZyeQswZ/mOENB2UFfPC3WKTfBbEyFissjsDZVdtCBn3jHeoUgQUGGHKA0a5jGS39qr5LH+iX//iP/xjlI1P62jtZmh93cqpCUC+YjITpD56Rkxzu4XPmmWeOutoIM4E86Gkz502fc/Rf8YpXDHzTFzNPNDikfmjM3OdQ6xeB7NrEWWYnBNfKV7n0Edo14EmPwFEZMtnVxeecc84Z/ehfaul/JyvMNU61vlIXP2PWeLETHdmlC3nOvZje0Y/moM/IPNsM8CzAK1gYU/DCkzOq7QmhPbdJPlziADv1oQw6aNOv5rNTfxxbZV36h26mIyN/eC2L9bdxykE27+l8fbBMnmX1O60RaAQagbsKgZU4yBSli3KNAeVFK82KpthL2AspZdNgCjQvYUqTclaXEvZC82LwAvCy9MJBj+KmwJPv5eNImRev1VMvioRWxEGi40ZgayBAB5ivnCTzWmAMMkJ9t8mZZUhy/BhlAr1gkY1eYGipz+ilU+iQ7Mz4jlF9Dou5z4hNUFbAH136yqUcw8+9PPqG3uEgc17okxjPqV9pxtBeS9egebCQesq5yKJNYu0W3CtH13FSOD+cJDrRTqSdMw4/A5lDTP4Y5+rBQx7dybi3Uw4bi5LhqxxcPHMQGbaw1D6YMIp9C8yZ4yygBxc0gy0ngaxCZEfXvTIWN9Bw5FsaWvpUOr7S1Cc72gKZtJcRz3FntBsv+osTyvGDl/JpS+oOAmv8iVxiuOpvmKAX7NBbK3DAYM/x4WhYsODMkd/Cg3cRRxBu8HQZp3jAVX2Ov3Tyi4MdmeClLpree0IWju1mog9vY0AfoElu88P71k6fPjSv4IG2S5vybGyrm9i4gav59cpXvnIsQniv6heXnVOOqP5KCI7y5yAvlzy8tdUYNX7I433vfc4BNH+1yb22G3dnnHHGWPSBm/rhh557acpaNOLMKc+BN+aNHZfdXny0JfXIq24uslTaSVfemEDH4pRv+uGLB4cYTRiKyW7xTRuV1x8C+WCGh1Bp41nTtJltY56Yp8ZmToVkHKiTvpSv7erUeVTnQHAajPb/kW+RyfiAO5mNOeNJf5A9CyzStUewuOOq9Cvdeo8GHYO/cekyJzo0Ao1AI7DVEViZg+zl44XMUMvOkJcAA4hSpxgpfmk1eBF6EVCkXjJe6NIcDbNLwaDw/RllTIkzPLysvDgYNHk5eFkwEJRh1OalU3n1fSPQCNy1CMRQi0PgSC+9wFm2UGY3UVqMqBhl9AbDXZDPEJ2/l5THKKYHMv9jxDGG6ZgYqDGOlUvZagRyFJSRR9YYo3hIix5jkHLwOC6hPYTcXy5llU+d+V49F36MUzrM7px2pk7aoQ3aCCsGsWfBroxvSOlDThsdzPAV6FAGLafS7jJDnr7mcKiPB/rabIGTDALjmeOkf+TR4XQ1B4BjGgdHXbLrK1iRQRq6eGlbnC6OsV1s6Xa7YcfpUJbzC0c7YN4baMBPXW3F3zFhMlo88dsVAjrakoCWS931gnJCHAPvFu+hBDIsC9oEHzJ5F9m9t2Bh0UbfGJvGISztOGoTOb0LOVdwUk4d5YJXZE7sHYcmDGBMTmNOPozIyjk3X/SJsSFYTNBneMhTnswVG/fqSzcOXO4Ffel0lx1uY0B99PQReYTI7J5srjngkbEhDn+8shCBbsat8WHs22U3Ru2yW3jw3icrudInwQiexpDdVeX0izGKvrLGL4wFuEiDv0CexGSa2xH5jTlzyA+AwRs2FjjQI6e62mA8w4ec6pJZbFxzOvWjPsQn+GmHMuqgwck3pvyoqcUwfUov+oFB7cBL3dRHO3LjN4fgNKfr6/yWQL5Njwx24s1RCzjKSA8P/OXjv17Aw2KIsvpJ+zo0Ao1AI7AdEFiJg+wFZ8dn79694zs4L2RKkSL1gvdyYbDF8KKwo7QZPowLZb3Y7Ex4iTDmovi9gNDh+KKrrjQvPhfFy2gghxDa26EDWsZGYDchwBA0Pxmy9ARjkdFkUUwwhxmEDM8aGGf0AefKbhBnwPHGGIu1rPtqvOEZYxI/l2f6pJbDgyHo4pza0UNffZdA9tyLOUice/LHuYgsKasc2mJpSU8aY51h65neY5Qy7uMgK5/A4WEs20G0cIin8naYLC6+5S1vGQa6e+l4aq+FS4Y3B/nCCy8c5BjAMajxoKOzC4euS7o+oWe11Y4//u7xT1A27ZTGqeIU2j3iFNL93hFxjv2wGDqOTKtLv9spRZ/zQc+Hph1L+XZq/b9e44YDYSdZOtkE/F2HElI+dTOegrk496GbZw6qEw/eRf5l0KWXXjoWJ/wIlXYbR/I4bhx7eHu/BVcxfqGXGB/3GZvKZBzGsYavfO89708YGy/itCW4hE7oznzi/MhPWe9guBvXeEpHVz+KPaPjXgjP8bDkj3zl8TKPLdq4h0mcJjQ5uvqWY8gx84NY+htf73k01DE/BLToAxgYu//wD/8w0qVx+iwc4cUGgVmcNYVmmdPGpCfWh8aaY8IW7tGhO4JLaOFhjOpXO8bagL++01ZtwB+2+ka6Oa9N0sw/mwwccDpFX5rH5q9+8Ev8eM4BLXxhoc3okUF7atCeBHzJRAY6QCwok3rouFcuITSWyZEyNU5/iV0dGoFGoBHYLgisxEH2InVsz+6Cl5ejWQwH9xQ+A4ExlRd2BYcij6EYY81Lwa9qejF4KflWyQ/L2Fnx0qDMGV9Wm+0cMJR9o+TFxUjo0Ag0AlsXAfPdvM+uDknphmqIRfoYVQy1GGsM5SyGzeXyvFaMN4M5BqznBPcuPOkYhnsM8ZQRMxKVEytLJ3FcZ/k902FC2kHH0VOpH37wUIY+ozfDJ/XQUUZ9u470oR8cs5NqscBucfgrx4AVa4M6aLq0h+MfvpEDH845B8WlLgxcsLaQiRc59AOD3W5igvpopb344MchgA96MLLrzFmkv6XbhVPX9794uKfLtRFvz3gx/h37VUd7OQKcbO8ECyrKuVYR0q9xBpbRDH7khL3dwX//938fu7raHEeMg0RWDltOPSyjN6dpi/bbTfX+88013OHASdOn5PO+8770rxBhi9+dCXigrb/shOLnXrrACTV/DgVrZYOpucsmsBhiXHE0tdPYteurPY4w42ERxu67cakcR9WYMnY4z/rAjq1vdDmUbA6LK7AwVo0zu67GpPlO3xiDZOGsCmnXeFjjj3FpN9cOsgUmzqw5PAe08LBwokyOIWtXxqh2aL95boEo8xQubCSyq+e0gR8l43Rz/jnHZI5OqWNT+2BlzsEmIZgve4YdGS1aGTP4CFkMI4/LKb70vzoui1hsPvfrBTKQObppI3ivR7PzG4FGoBE4EgisxEF2HMoKK2Ms/y7Ci45ipbQpRco/3xvWhlGyjGNxXrxeMjGs1GWE2HGK0aG+FXovF/W8kBhKlHAr4Ipu3zcCWwcBhnK9SMaAoiekM37NafcxvtwnuDe/lVeOYa1cLaPs/IxHyqmXhTQ6Az2XOvQHA1HM6ORIchbmEDmUy1Fo5We+ecY/F9ldkYlcta3yyEcHKoOGfPSDDXntRNo1plfpXkdhlUk71VMfRtrkufJ2H/kSa4sd2ixk4qON9LsjlXagOeaOctvhD49gnjaGF2cWhp7FHFtHZjkJcEVfnwdv7UOXPncfGbVBkIaHehwOOLnQShvmeO67tZ7Vc3EU8j7CR4Bp7j2TIWnuycIxIS8ZyeterB4H1+5yflVZ3kYCebQPXnDCC/3I4hlWnGcOHOeVM1adpI3wQSdBu4wXzijHFH/5aRNHUzDWagjuNc29uvBUj8PFRrDgwfHFK3iJPTsRwPnSLk6k/uAwomOsw/ov/uIvxqIEGTh5ZDOu/KcLixDmBh4cZ7uy6GgLXIxJtLQRji7PwlptMBctQtjJtihjwd/CwRyyeONfMWmHS7s47Zxhiyjmkr4yBtgzFk/IgId5wIF3pJwdRT9xjM0dix9205XNuIrccHASA300pCs3t0d66igHOydLnELIAp80Qf+kb+xOCxx0aXbG4RlaI3ONP/rUWDIG1DWeOjQCjUAjsB0QWImDbGXfCqYXkRVWip8Sp0A5yL7HsuLrZbUsUJy5KHUXo0caBYuOe0ZCgnRK3gs3yhfP+aWQ8h03Ao3AXY9A5mdiEpnfjDxGHcPRs0uQlpA60mYDMOVr2aRVetFLFuMYg2gy2sSMZvrExSmRthafpNNvLmVdc0haZJEvLTIlTjq6QvSesklLWRjRjwxozrGdXLpQObJrI5mUT134okmPJm0wKj+mpQ5DOfzRRNvCpks+J5fzEqc0bUEXP+kM+uCMlzIui6ZiQVn0xdI4E2TkhMB+1uXaHL5oyndJD/1B+CCOTvKXxd4t2m4nG4ZkS0A/copzn3zlOXXkcc9RsZjAqVIXZpw2zp50i7vrBfXSzrRRnchirJLR2LULz4Eypjmh5NtoUNa4SEhdfBzjR1e70o94cj7lJ8gjo3hZQBMuxgYHFx4WvUNDPRc5YOQbetj5NWgL49LZGC5jwEKOunhyMJVTxuIAOnZf2QR2Xjmj0vSt/Cw8aYd0ssFS7BndXElj11iEwAN/NNTBX0g7jFv5xrCx7t+R+ddrdrnND44+uXxKxlmP3sBHP8Jae8wf+XAX8GFT5TcXMofVE7TFjryd7oMFcqpDdrpPe/UFrLNwYA4aR06R4MOxl6/v1IGB0xBkC388g0HlLx89cmm3NmrbsrK1Xt83Ao1AI7AVEFiJg+ylTwF6MVjBpQCjBClhLw5GASU7B0rUC4CBIj91KeC8CNAWklfvKXuXoE74joT+0wg0AlsOgXmOxmhjfLln8LkXM6roB88JdIpLQCv3yRejE3qhpSxngj5hsNuRYvwySuVJt9Nl14ZzYDdTPqOOoacMWvjRTZ43GpaVlVbT633oJk2cdsCF/AxWF4NdUMYujxhuAuw8B1NyS6v4KCdfulhb0eUc33LLLcPJcGSVk2EHTDpHDF2GNf0bg5nzArs4Mcq4EvCtz8ETfbLbCfQegLGyNeBV3xHkXdb3tc5G7zkrxoF/12N3UJtCOzFZhbTBMxzssDniq92wg5Gx5f9vk5czyMGyiJy+2ohc+JADzfRfMBG7yKZf7OrbCYR7ymyEhzJoh55nfel9DI/s+OszZThHxp5xlEAGfeGaQ+qZV/rPnOJ0Of6cvNSBpznoqDpn1r8hUxZffcKGMH85igJ+6HEe0fKMhmd1YUdezt5FF100eGqbdMG81g74asNsP6ApGBsugQMPL+VdQsq5DxZok8H/enYkno1kTvpdAA68dsAjgeza7KoBbW23gOTbbCF84aK9HFrON4zRlZZyVTb38swzND2fdNJJY9xm4QBt9OwgC5x27TCOoxvg5MfKKu1ReP/cyD1e9IjFNTjrF21eVi91Om4EGoFGYKsgsBIHmbKlDL1Uo7w10D1lKPYSotDnECUvjuIUe2FQqpSxutKSj4Z7jjcFrAwey+jP/Pq5EWgE7joE6hwmRZ4ZZXECGOCcCYFRzHhTjo4Rm+/0g/tcaREdED0g5sAwaukJdOxI0St2qRiVdkc4MmiiJTgKK90ujkU/xqFjhQK+jGU07kwIr/VoKJc20YOcIN8wMrg5ZXQvh4FcDH/HGcXRieiHl7i2M7yluzgn6NnxsQtqV4pzQc8y7Bn4jGtY2rmmc+MUoqV/4KJM+Fbe0tI34YkGw56TSW7/ukhaLYuu8eH9op2VhryUH5U2+YeTqb+NQbvBxg1+kbPKgx8ZyEtuP6rkyOmb3vSm8a6yc+noqp1Cx2uV9+9y4Iae8bheQF/byIE2maTB1lXfp/oUfQE+xshGA5po4eVecC/N/EDPvBELdnHJL1/QHu1zuZ+DNGXtOttd5SDVsrWO9NgQNZ1c9IHFd441OmQkk3KuyCfWhxxU39Zyysxh49Q8R0t5+Xb16ZfUzdwIDlWGtEueeYK/EP7ug0noOeFhPPttFm173eteN/4FFf2RMqG7VqyccWM+clrNRTzTX2JjhBNKT4Vu2jDTlW78GCPkhQk5tR1u5q8y8NFOfQ9vaWmrUwB4zUE+GtqqPNnMW3PEjroFF/OsQyPQCDQC2wGBlTjIeekxbClECrIGaYwuBsUqA4PNRalTzpRyh0agEdh+CNARDDbGN+fVN3V2QxhnjDRzeyPzO4YZQ9E9w9yPDdmBsbvG2aArGJqMQmUYgjGO6S67xvSKew4jg5JjwFBFw65W3f053GgzNBmeYruTsIERI9eOk10eQTvs7sLMpZ3qCNoCk6qb5XuGAR1qEQBWnDv0GbMcYztIHImU10+XX375qOu7T3TxkY4ObN0HX/USci92Me7t2JNbf3Mo5rrarq3oMswrDXzJn7Tw2UicemK0LZ5wGOxmc8jsTi4LaasxYceZc+LHoPbs2TPGiCO1dpA5yhw6+HAQXMbQRhxk7YGf8acvyCh4x3KWLIrAKenBJc/L5F4rDb4udXOZA9qQnU/yyINL7teiN6drszZwWDlXGw34aaMxqm/sZmcxi7xC+l3Z3MsjN/n1kf+L7CixOascepxCP+4pDc76VD9tNoS3GC06w3yyQ+vTMn2esUqGjQTlXGTUdt8gc67DAw35dKfdZeNQ27VPmcgUXuFrEY1MnGB6wthRr5b37CLzoQZ8wp9svo2nN/WdBZDkHyrdLt8INAKNwJFEYCUOMoPRiy8GlhchRU5Re1EwGOzKeFFWJZyGJi0KPOmJk5/njhuBRmDnIGB+cx44S5wzTgAD1w5PdjSUqU7sWjpBevIY1xwMxiDdxNjDQ5pvTTnNHBh5nHD6iiEr38WZtrsZg47+Ig9n8UgFvMnNWCUjY5ZM5HCslvOqDdpqx5dB6kivXbPIHUy0T5Be85LG+bErzQn3mxF+kIhjQrdbMAiu5MnuanAjn5A+Ik/4joz9f6Sp7+L02on2o0cMdbugdgkF+Wm7vkhfRQ55QuLxcIh/qnzaY4fLv2jyy77GByc0Bj3SymsnebzTHDPl+MHIDyvpG/L5wTTvPTjqD46IuqER7NO+PM/iS+fM2MU3bmFiEcR4JsdMM3SSjp60pM/0PacvEpOfM/ZP//RPw2HUfuMGP30qNva0OQH91JdW+VkUt/vNWfXr5Bzvml9pSM+VdLH2GI/kyLib2+hZXX3DUeQw+pdiFl38sJw5q37KaYP+lUZ2dYV6X2VwX2VLe2sa2nSXBRbjx/Fuc9Sva1ssueqqq8b4MM7tbJMh8ojnIA1+ymmHX0uXlivlyWKBKacMqqwzXfLarKDzYKq/03ZYuE/b9LX7Gub21jz3+IUeLPQFfQUH8wu9O7MQMfPr50agEWgEDhcCK3GQvSwdT7Oz4d8yeQl6+TCWGF1ejuIozsRRxHlx5TlKPeW8IJRZKyinrji01irb6Y1AI7B1EDBvGWKCRTSOMWPQN5uOp3IuEugFuoCRLKgbXZEyYmny6AKGsd1BDgYHRszhYEzSWXb9OHkxvDmZHB9HrB2RlSfQLRwEu6r02+EOaRe+wQd/CwkMfukWIu1ywgRudjO1FX4cTmUEtGABEw5WjOLoVOWUgZUdLzvS7rUTPfwZu4I6ynMAxRw4dV2hkx3l7HbJS1CG02ghwtFLv47N4TvllFNGf+sjweIEvhYlYmSTh+yVXr0Pj0OJU9+7ykIAQ94Y4ORwgr3b4EtubdcHnHXHZu22y7dDaedbu10WdmDNCUl7PKubhZfw5QApk76osqujzX6t3A4oGeDO+TIv5AvSBVjhkUUeNGHo0r45qC+PLGh4hq/FFW3yLD2yenaPrvmCV/pIn2qL8qkjT9+a1+YbB8n4XRaUNTbRdOETPPEzlkJX/eAlLTJovwULJxLMcXPXLrF5njGPbtoDdzyNYVfG9DL50i4x3rDIFRk8G9N0jPFjPOknYxtPY/2KK64YY8DCll1VcmlvFpYqb3QT5JuL0uASGYK5drjk40WWZYGz6gSC/si/4lQneKpDHthVPujB1xVH3LxQRn35aLjPM0ec7Wdu0KXwSH76YJmMndYINAKNwFZA4I5vzU1IxVDz69Vehr6Pu/LKK4eRRoFS4F7YDC4vUS8zCliIEveicslzRelSuBQ1Y68q8IhI2UrPC0G9Do1AI7B9EKAD6AU6wdzn8HEI8j9uowvSIvNdWVeMsuSJY3jJo3/sSDLMGK0uBh5nj6PLEcSLsR8DVexyHNBOGoMezxh20TmV5+G6x0t78KcfPTOSyexZ0BYGrx/T4hg4Em1HWTkBDqGhPqwZ5bAWoj+VgRVHBEYcObu7MYJH4eKY2B1FA199gSZaAlr6jVPHwUMjgRHP4XXs0v+x54RyFhjrjr2S0TtDvnIcZe8VjgZDO0536IVnng81JmuCNlgYsbNmQZeTxamDNRzTH9pmjPiu1a6eHcEspKBnvBlbHC401YURh4EDx7GGScUlMiRGX3+gAxuxoG/I5p1obEYmmKHtkwKOOWyNAXSUcc2BXMaDOBf5tQXetV5wEkvXNu0xp5wEUL++f5VBmzNv4dypBjbAWp8mGG9kN57VIzfcQxNfPMSRRXuMQQ6bcaT9TkCgwcnXN5xTbZnrGTfZbTaWOcicR3znfgkOYkEZsuhTae7VJwfZ0WYT6TP9ZDxI00d79+4dC3BwO/XUU8dGApro1HYNRkWfZb6mHcrjRXaLY74Xt3ADY2EZLfyVs2BB55l3dd5GDrE5gKd+E0cfwNm8QF/booeCTcUji0z5NXLjEVYpOwTtP41AI9AIbFEEVuIge6EyzLzMrr/++vEC97KghClsR7BdjClGkBdQlKSXPIOOos3Lk/L1ElLWS47RglYNyqDpRZ1jedJcHRqBRmD7IEBvmON+3ZVhxrBkmGUuJ47xZcHNvGdsLQspL49xx3BkFNrdYQSr64QLA3XemSJLNRrxRK/SXMZz1Wn4umIY05nay9CmGxM4S46S7tu3bxjlFhZydFR9QR2BQa/t9Ydy5rYxYvHklKJLJ2cBYRDZ/wdO5OHsMfY5Ieqipz557WTT73E4yMNI57zdfPPNw5Ehr10+7wnl1EWX86Ucx8NxVDvbFixmB3kV/VJp6Hu7p05CcT6MF++34ERG7xv/wkc6J0Hba9A/0l2CNsGew3DdddcNuvqIE1WdwEoDTScoLDBw0uENPzzhhQe8BXi5HA33i8mO23KkzSULTrV9lUfGA9ru0TBWUj5trnXcS4eLHyfjLFmg8S08XmSUL7YYYu6xBXyjrR3zezy00XByg9NHHk6lvogsyklPSLp2ksERZs4f+Tl+jrnTJ8Z62pV5gAb8ONIWYtgl6JBt2S63ttQQOtLrhRebxQIWu4UO0wZB+stf/vJx7/SBRQx6LCHtyXNi480YIW/6JmXNZYsC/t+zjQnzBJ9lIf0BK+22o8t+0ubQSz3PTvAIxpu6eMHXsXH9anEIVnMfqQtvc8eCh4UKCzzmgrxc4dVxI9AINAJbFYE/WVp3QkIKlCK0UkjxWtFkuHmB24lh/DCMKFUGU32RUtKUsJdfjCT0pHnRePEytPKiiZheGG984xvHC9WLyLOXvBcKJdyhEWgEtj4C5i3Dzi+8cn4YmeY6I6saxFpiXkujG+adi9pS+kN9gd5hpDOEfd/M6aCnGM/0UA3qoUuHzU5PLXck7qPDtIODBSOBXMkTM7I5K5w2+pDsMWqVV4ZhTe9yNtFySYuhX9sDA33CoIUVHY4evVoDPpGN8Sw/uhcNfPGsfRhZ1PNecBTZt7vkUE4993h6V+gfNL1btJ+zWOlVeVZ1TwbvIc44R9MONv7kIr8LHo7tVvzUE+TXIB3eydM2O86wtTCsfcrM9Tguefelzfgpm1290PTeU9570nuXc6kNTnXBWf0qa5VPXl14mOWoZeu9PrfD71tfPLTFcXPypT1ok+Pcc88d882YSl6l5Z7DaH7SARxc9NyvFzK/LejoE7zUhYFxDHvjbVn76ZnoEbKeeeaZw+bIgnt4q59A/iz6iOGlLn1iwQMWTjroZwFf/aOcceTfTVkA1F9rObPhpY5xr121X8iQgDaH14KLTQoLTmsFNPAkt/k7LzalHvrwC0/P9I75Sh46Wl/rWxjXoL3SLJrBFqZoSUcHzSp/rdv3jUAj0AhsJQSO+uMKtBUSXiK5vLRclKGXB4Xp3svKs8uzSznpaFCilKpneaHHiAiNgKe8l7RYnkv5uVzKd9wINAJbDwHz13xn6Jn/0Q0k9VyDnQu/SusoMaORoUlPqFMDmnRBdIgjoHYz7BIxYrMrGodOXWUFTqRdGQt3DEFlZjkqr8N1H51ILpedH7qSc8RITZs5xxYA6EJGK+cteVU2O+iMVo4ZHalNrrQ7ZdMXYuUFGMw05Qvqw1ssTYyuewZ4xVh5/WJHUV+SNaeG0Ag9NLTHmBDI671wJHR7xiNcOR74cszTLnFkTbulCdq2DFNtlY6esagdHA5OUvCRX0PkCG15yoS359xHDsdg9RncOH944KXvXJWW+nZrOabmEwfPN7Ecu1mWKlfu8bGAoL7xyPnV39qk7/GFBx4udCMH+rMsTgzYYVefA8epQm8ed+GfWFstChhTxhNZ0JjHCn4zT/OJY20RRHn18Y6s4aH/BLK4d8xY2+gsfN8AACAASURBVCzgaDc6rrQ7YzX1a6yecYBWyq01bvQrnDMP47hLd+FngUzbjSN4kV370Z/bTHZjRF3jo5aLjPK0RR/VcQDnzEnpZEcDj8iTe3F0EoyUrbRm2uHdcSPQCDQChxsB+kqgp9YLK3WQMaP8ovA9E8ZzhBG7anp9ptjl1fLo5Lk2yAui5imzrFyt0/eNQCOwdRCIsiJR9IQ5LL0aVclnLDIKGf+cK2XmOR/dklbSE4xD5RiF6oTvXJchyJFm/MXInsuE7uGMYZGAP5mk0Y9xquQzZsksXbvIXENkh5v6jGh4pHwt6z7YqReM5n5InTm/yhxZw7/WkSfdlfvEc3qtX+9Db9WxNrlgGpm0H14zfzhKCz7Ku6/lQk9a7snsuZar9/JTVrysvDT85IcneebylW7kHAT31+e8cpo4h8scppSdY7zjFMIm4wm/jC/3aUfk8Ow+z6GLVhwzYzhtmsulfI31lcu8WNYHoZE4dcmifOp4XsZXfoK2wQwtjp92S5vnXcqnrvLhr7x7vCLDsvGVumLlahnPLrSE5Ckb2uEXWVK2ps9jQtnIh74wl5GORuLIqax0z+GlT5IeespUGUaB/tMINAKNwGFGgA6KvoquPxjLlTnIlUkUZ1WCUY7KSY+gs/JdVqemVT55GSRtrXLJ77gRaAS2JgLL9MOy+VzLacnByii7LD/1kj/TnBFai8ZcbpXPs0yRYU6nA6XFUBevF9RZ7+UQPuG7jOZaZdZKRyN57tHOs3gZr2Vpy2RZVVqVJzIGrzyHl/SkpS2zvLVdoZ36c9mkJ57Lh1doinOPVsZCyklzhc4yfqkfnocSp24cqsovvMI7MuU5+eGX9DwnnsslvcapqyxZZpsiZdejVemkTo3l50Ir9KTlPrF6oVdpJL2Wy/iqaSk3102Z0E57k57y8/N69FJvLpe2hV74es59rZv68lNmWd25Tj83Ao1AI3C4EaCz8p6MDRT9tIz3ShzkZYQ7rRFoBBqBRqARaAQagUagEWgEGoFGoBG4KxGwoOgS4iAnXibXn3/4tKxEpzUCjUAj0Ag0Ao1AI9AINAKNQCPQCDQC2xABn9r5LQWf82Qnea2TMJrXDvI27OQWuRFoBBqBRqARaAQagUagEWgEGoFGYH0EOMV2kB2r9jnawXaPUfvzX3RZn36XaAQagUagEWgEGoFGoBFoBBqBRqARaAS2BQL5YcX8ECSh+xvkbdF1LWQj0Ag0Ao1AI9AINAKNQCPQCDQCjcAqEchx6oM5xZVf7yBXNPq+EWgEGoFGoBFoBBqBRqARaAQagUZgxyCwUcc4De5vkINEx41AI9AINAKNQCPQCDQCjUAj0Ag0ArsagXaQd3X3d+MbgUagEWgEGoFGoBFoBBqBRqARaASCQDvIQaLjRqARaAQagUagEWgEGoFGoBFoBBqBXY1AO8i7uvu78Y1AI9AINAKNQCPQCDQCjUAj0Ag0AkGgHeQg0XEj0Ag0Ao1AI9AINAKNQCPQCDQCjcCuRqAd5F3d/d34RqARaAQagUagEWgEGoFGoBFoBBqBINAOcpDouBFoBBqB/Qjk/+U1IHdEYNXYLKO3LO2OknRKI9AINAKNQCPQCDQCq0egHeTVY9oUG4FG4DAgwGmK45T7PK+a3R/+8IeFq8OfEAjmcFkl7qEbmvPznyTou0agEWgEGoFGoBFoBA4/Au0gH36Mm0Mj0AisCIE4TysidwcycdLiIOf5DgV3aULwXzUulV6936Uwd7MbgUagEWgEGoFG4C5E4Ji7kHezbgQagUZgQwhUx6w6UEcdddTCtYoQHpWetPq8Cj7bkUawqXi4T7gzGKXuHId2x41AI9AINAKNQCPQCBxJBNpBPpJoN69GoBHYNALZ1Q0BDtXd7rbaQzCcvt///veLo48+erCJ0xaeuzHOkWpYVDykJ+iHmpf0jcTqzQ74ZmlthF+XaQQagUagEWgEGoFG4GAItIN8MHTWycsOShtz6wDV2Y1AQSDzJkme13N0zTFlfve73y1+8IMfLO5+97svHvCAByyOOebQVNgvf/nLQUO9e93rXhFhxOT49a9/vfj+97+/uO9977u4z33us7jnPe85nLfM8cg+PyOQtBCtZd3P+Sk3xxstW+mHhjQY/fa3vx1ya8NG+YZGYrR+85vfLGCGBlpwk54Lr5/85CcjD56H2h94ocXZxseFhr61SBE+tQ31Xn59Dr2aFhpoG0PGThZA9Ld8PKWn/rhZ0qdJ77gRaAQagUagEWgEdi4CR1988cUX79zmHZ6WMaiExO6rQXZ4uDbVRmBnIGDecIg4Vz/72c8W3/nOd4bDEqdFK+f5pI7yP/zhDxfvfOc7Fz/+8Y8XD33oQxf3u9/97gCKsnVupoCd4Y9+9KOLL3/5yyP/wQ9+8IhTnjP4jW98Y3H99dcPXve+970XroSU80w+z9qBbvilTGL5uU+sfu5rXMuGR3gnVj7BPafv5z//+ZAhzimMtPFrX/va4pGPfORwCmc8Q2OOYQwHbeJMfulLX1rcfvvtwwl+yEMeMuSGPd5oWky49tprh3OpLywoHCxU+VNOGn7k/fjHPz5kf+ADH7i4xz3uMfjABS9xgjqVVm1fyqcs2mTW9z/60Y9Gn3Lm1f/0pz89xp+y97///UeV9EPqiyv9mt73jUAj0Ag0Ao1AI7DzEDi07Zed1/5Nt+hgBtqmiXbFRmCHIxCnhsPBOf7Upz61+MAHPrB4/vOfv3jGM56xeNCDHjScLbt5s1Oirjo333zz4lnPetbiuOOOW4qWchy9OIwKcXo44u9617uG4/WSl7xkOI/J44xx9j784Q8vrrrqqsVFF100HHByyMvutWdOINnw4UySCf3sQEYoabnk2dX1bCEgiwFxrtFLe/GyW+uqIdiJw5+Dx4l9/OMfPzDU5l/84heLz3/+84vPfe5zA1OOq3R01wt257/73e+OtumPL3zhC4sPfehDi8c97nGLpz71qYuvfvWrw9F85jOfuTj22GPH89vf/vbFwx/+8PG8Hv1l+doDl5/+9KeLz3zmM4svfvGLw6E94YQTFo997GMHrrXtylasZprKVoz0EZyuueaa0Q6OcBZH3v/+9w+8XvjCFy4e/ehHD1KpO9Pt50agEWgEGoFGoBHYHQi0g7yJfmacMcBjSHneiPG5CVZdpRHYUQiYMwnZ2eMk2z3kmHGUOV4cujhByufekdhvf/vbiyc96UkHHKfkKVfnZOXFiX3f+943rkc84hFj59muIueXsyrN7vG+ffuGA/ic5zxn8atf/Wo4qY5bK8fBJZuLIy9wKMkfp3Ik7v/DkYtTrK0usqIV5z16hP6I4yx+7nOfO65Kz702oas8+T7ykY8MB/acc85ZkFldDnzyvvKVryye/OQnL91pn2l7tojAaXRsmnMKIwsHnEq7r/DheNvhdQzaTvVnP/vZ0SbtWi+QP1fKao809PSDHet3vOMdo38595xvZbStLiioI23uf+kJynO8tYmsTh2kDpocZ1gZT4K66qR/3MO68gjtjhuBRqARaAQagUZgZyLQDvIm+pVRy5AUM54ZjzG6NkGuqzQCuwIBzgenhLMhNm+e97znjWPCHC9HdTlhZ5555uJpT3vaHTBJPceec5wXzeq8uJdmPopddlTthDqarS6Hx67ou9/97rEr6rjtwx72sOEgc844lF//+tcXH/vYx8Yct7PoMt85vI4acxDx4nzZ8eQ0ct5rUJ7zZQfztttuG47eYx7zmD/7dloZgbycMrGLI8dJnkPaBD/Or4sTh27aznknr3a+973vHbul9NRGAhqcfY732WefParoJ8GuNMxe8IIXDLy03cKG3WvO9LzjPfMjuwUFR8AdDU+IE6pNjoTr+0suuWScFNBfT3ziE0dRsikjuLcr/4QnPGF8S+45Iff4wZ+MH/zgBxdPecpTFk4OPOpRjxrjQnm88dBG5dWVJrjX3/KTFx4dNwKNQCPQCDQCjcDORWBLOciMkIRq5NR7+XlO2WX1kreqODzEDGHfKTL0nv3sZy9e9rKXDTYpE56znNLnMtLmcspISxx6idejUfNDu6aFTnjUZ/drlU25jhuBzSCQccVxEziEdgzPOOOM4ZS+7W1vG46mtKc//elLWXCKOLRxgOdCeLg4Usa3nVBO70033TSc2QsuuGDU/+QnPzme7bpygByt/ta3vjVkOf3004cDd8MNN4zdak4YZ5Ezy4HjgGZeOXq8Z8+ekT9/f6s8J5JzbtebnkCHQ0l+gTNGXvSyU+neAkDS08a0TT6n+5Zbbhnt04YsKMiDq13uk08+ebSbw8+hz673TDf0LSRw/h2t5iRbJCA3DLWFzLB68YtfPBxwuHLQOczkFeCOvri2J3mwsFiAbuSIQ2qn2o41OS0Q2PG94oorxoKCBQwyuLTRbrW01772tcNRzo6vPHzRFjjkjvCLzz///LFTnH5SNuMp3yQnjWOtjrY7Wp5j9YNo/2kEGoFGoBFoBBqBHY3AShzkuqofw4ShIYhrmnuX9JqnbM0L6svqyothycBJ3dBM3UONI1fo1fqRLfIwuhiHeDJ8Y5DXOvJqULeWC80Y+ykv3X14VRruUy/pqTfnzekxWlMv9CNTDNrQF4cGGVMuaaHTcSOwEQSMmzgvGVscG0drOXgcGA5J3eGrdNVRnzMjjg6QnuA+z8ar3V2OJMeMI25XlnPFAfrmN785dpc5hWTDl3NsNxQNsnCkjX1OkjL0jfrulSELh96ztojxFeRzBO2Kc/p8t6udvn9FU1A28oamOHmZd8FNOieR80g2tF760pcecFBDiyN/4oknjrbTVcr5Zpv8dR6nPJ5ocoo5x3aDYQej733ve6PNZIENR9Z3z/le2PFkZTjtaOsX99rMMU9/4cXxhUN25KVFHrvdZDjllFOGrI6uW7Qgm4VI/QFjTrqLjBzzYDlAK5iSAT3Hqy1y6EMyoZcLbwsDfugsmEvTPrvO+u8Nb3jD2KGPYx0+HTcCjUAj0Ag0Ao3AzkRgpQ7yDBEjJIYdo4MhwyCKUaZ87mOwSHOf9Bq7Zxw6nsdwYezZRdjo8cFZvvl55hWZUy4y4svoZTza8dBGeUKVPfWWxXi5Uh4vdBJqXuRSVrnKK+XF8lIv6anrOfWSF56RgWFbZUjfoeEK79kgDb2OG4GDIZDxlzFaxyaHhxPEqeEExfkNPWUddxWMQ/nZUazzTz4+LuXtAnKGHdN1dNsxYc5c9AdHyIkQFzqO4/p8whxXBi9OmCO4mSeVn7ngOXLhlbkiDX+703QU50x5DlnFQnk0YBBM0gY0pLm0WZs4io6ja7+ddo5fZFCOzJw5u8gnnXTSOC7t+2sLEfnWNnO48qPLOJTwQZvDynEMHvqFo8lp9U03B5keVP66664bGEinn+GlPznlnOG0gePOaYa33XUOuXZJt4jhe2Y/mGXRQVu1RTscxefowtAOvO+StQFmwTJYqSOQ2y9Xf+ITn1i8/vWvHzQ568EWZp71h3EnnSwWALT91ltvHXykdWgEGoFGoBFoBBqB3YPAShzkGD/VSAuEjJUYbcmXlzqMknovj0EiLYZMDBdpjBlGlO8JHQX0S6eMJLTl39kQeSOjZ/yFyMN4do9fzfe8lgwpW+kOogULZSpdBiD64ZE2plzqL4vVqUHd1JceeaSFZ9JTT5larsqhTodG4FAQyFgydowlDpC5nrFkXnHs5HECOWfqJCjLGeOEyefIcGzME+NYQIPTk11ejpfvTu1ccg79QrXvhTmsnvHCw+4xp43Dpj46nEU8OXRx2skaeWscmTnDgrah65SJ73k5dPSWdLRqQIfjR59Fl8mXnvaLXT7r8K+QOLza5Xh1vp8NTeXIjZYf7+L0cxTRe8UrXjHa6r7qA/LbSeeMqucHy/zqM2w5+dIcJ4eTPrK7TBf7NXHpHGp9Y1ebY3reeecNbNMO/FywdXGilYvjzonGA+5k0F85Fs5Zdm9BklxkkqbMWsHYgZPTA3bEYWRMZJykHn7oCXBTj2PsGDhMvF/gkjKp13Ej0Ag0Ao1AI9AI7FwE/txSuxPtZCAxMBgSLs+MQbHAKGQYyWMoCQyQep86DK/UYdQwZpW1O2NXh/Hy5je/efwrFrsJjEuGjisO3yCwiT+RIbtK2oQu2RmG7smsnItc2sngU4dhSmbH/xh8aR9R5CmrHMNeXfKGfgw45YINZwAf9GDoOXzUk66evBh/yqNBdvzgJoa9NrgExmDopt/UURcN9Vzu800kHGYDfxMwd5VdiEDGlvHDufUvg+xAGsfGIieJsyXfTq/dXWNPMI/UN/85PPLsKHJ8MnaNf7ukHCy0zAsOpJ3M1DdPzT0Olh9tMhfQFeTZ1cx8xENa5lWdy6PC9IfDd9lllw0ZMpfNV3MHH0eGOcyZf6mu7Zy9OKjhE7nwNyctCqCxd+/eIZOdY46j9Bq0z3xHB2+/DH711VePXV46yU66I9fmPuzwcdllhxuHmkzKmu94k1l5/WE3Vx84bu049Ite9KLRZ3Zq0dEHf/u3fzvqqk9+sojVRw8PzjEHFg316DxX5Anu0owJfe24uB1s/eI4PF6hHwzUh5Pvzn0nrS/oTHQSwo8suYejH2az+KosnBzLxzeypH7HjUAj0Ag0Ao1AI7BzEViZg8yYtNPA0GNYMWQZwQxExgaDzI4Nw5TDyzBjdMhjpDBM1bGDo558RhEDj4HrKLU8BoyjeIxY5RhleNih8W3bZgMjiTNohwIfstitYdAxXBmPLvIrywBNHfK47KBIZ7Q5isiQZCAyDpVluDLYGId4abt0eMFGHcYYPNRRRnnYMFyl48M4DR/fNJILvgxB9NCFn/5gkNsJ0xZ4wwkt2NpJwzc/4qOuwPiEgbrhpd3o+7VccuoXIXXI26ER2AgCxordQMdYOcLGpd1V3wf7ES3j0Vwy1o3zBGPNnDc+jWe7mBw3usB4dxnf5h/nTcDLpa65rIwFOGMbX3NPGhrKoG3eGescMYEM5l7CWmNdPXqQ42Zn1cJd6uOFvrr41eCXof27IfM8OkG+8gLe8LITzamkmxytdiQ6TimHlq4ht3aShb5BQ12LBrDzw2PqOcZMRnqVTHioY9FCv7j8GBcHXBq6dAVZ6RVtoAvo8jj8HEz8YE8fCcppu4CP9qHHMebE+tEyu9r0Cxm0g3ywo6MEdchx4YUXDn7+DzaHH71zzz139J1yeHOE6TuLCPhot3bBpGKrLrlc7i3U+OYYXbiddtpp44Kpesq4OjQCjUAj0Ag0Ao3AzkdgJQ4yo4Th5nihHSBGp90Uho6L4ce48Kuqp5566jDu4mAxijiljD87w4xShg6jS2zXgoHqWzoGj6OC+d+VnEe8GUF2ihhwDN2NGDJoKyd2oeObuhirnvFOOQY7mRhyAoOR4cQgdmyToc8QY+AxxhiJf/d3fzd2qeChnYwwv37NsFffBRvpnFzGHsNVebLJswvi2KJ8OHAMGPHy0vazzjpr/FAPx1c9RqlyePl3NgzjyMwghD3ZGajZheFUaCvZ9cV73vOe0S/oMRI5MDCwY6+O/ojhO4jvN4Bz33EjsAyBjBlz1u6c3Udz48orrxwOkXHJufLtKkfZmEwwNukH49B89+vRxmacHLE5aiybA8ZuDfLpB+OfA+b4tfLKksFl7iljLlus4ixJjx5Ac6aLB7nNT+XMEYt6FgtTD++0Za7PyaUTBLzQCh91zHXOm91TPBxfJrOFQU4pTORzmuEKE+Vc5Keb/WCXHxOkSziYaMIhx5TxsSBB/5nr9JcfTXNMGRba5MfA4MVZpQfxdQwbT3haVKPLyFHbmz7Ag3PPSae76Su6y6KGNmeXWD8rS7+5R0uwUMlxpTe9K7TFMz0NL+21oOd/KGuDBQBy0oO1H9FGM32mDmwF/OBrXJIr75O5z0bh/tMINAKNQCPQCDQCOxKBlTjIkLGrwLHivHLmGIgcVsaVHQwGyI033njAQLXzyehgqMljNHGqfYfHOItjrAwjh/Hk+7fsnDCOGEzKO2rISMuOw0Z6ipHkQp8hZleXkc4BZzjajWYUMsY4vWRgBDKqGLEMLDtRnHvy2v1iPDK2yMawdASUAcpgxUe+BQS7Noxvxhdj38ICAxd9tDgGyuMFO3mMTwYfXO3awAoPedqtHoMuxr7v6BwVhC9nQlvIwVm2e2YxwCVP+2HBOJSvHkOWrHCAB0Ocs44nueEO/xivG8G8y+xuBIzpOK7GDkfLGLfgYyHHWFXGOOW82qGswfhUhwPMuTIflE1QVzCWhfqcuS7NPWeKbjKHyWAOCBxk81R65oUxXuuPgtMf+S7tQ4P+Mm8yPxJP1caj+attkVecSwF5nGG6hG61s0s+aXH86B0OrHlJj5BFHr1s3tI3Ft/gqu3wo0dqu7KYSY9YBHNSRF1407su7YKXunQFR9eOrWcOM8zomko3bZZGbvzpK7qMfiW7NsJOG+k2/SxNX8CCflZWvnYaP8YLXSSgTS/jT6eiz8nVBxb7lEs/KqsN8MGTrkMfJnapLSbAQL/Ufkg7Om4EGoFGoBFoBBqBnY3AShxkxoeLI8mRsjPk+y0OrcBg4RT6n5aO7jF8GXqMHzsndjSs+DOYXvOa1wzjRh4DidETZ9I3gwxihq1dAcagH6ERM2bIwKDZaGAkKc/QsiOBJsMqO6ScYjRjSOGtDv5kY+hJc5TaToh7O7d+0fW//uu/xnFwO+Z4MM4YfXY8lNM+hpodFztmFhc41scff/zYdZYXozrGer73YwAzfBm76nDq8YEf2ozEffv2jR1i/6M0x1a1RT0LEoxbbYnDAjOOSr7d8+M0+tFOHsNUnr6wW05W/aY9kXGjmHe53YtAnZvGTcaO+WW8bzSYd8au+pVm6i9LCy956rrwjI4xtuWJ0ZfnOVdorxWjb05zFDmudJ4086sG9OZA/5BDCD/x/7d377qWFMkChs/xeS0MDBBXtYuExEvwEhh4GOAhISTwQCAkHF4HG2+Ovjr8oyAn196ru/dI0z2ZUnVWRcY9Lx2RWbV2PjGnrXE24WwEmnfa8Oe71hBzVVJoPdFuHZD4WXsl7RJdl/XDWuBeYTNe1lk81NZac9zaQi4eCvnJjt7mhoRSgk1OG3x4zsImsiX41lC6//LLL5d8zxJsfpDQSrptCvKNNYkd1jc/TOb/FTbC5RuFTvjTHX+68ZkNRn3N3vDqY5ub/r+yvkq8veFk7effkuOL6PxzPHA8cDxwPHA8cDzwX+WBJ0mQBYEFmgKNt99++0qABRmKYAVM4CPo8h2xQETiJVmT5AlKfI8mQFMEMcoMMAWgAjB0SkGfoLSg+Wq44x/BlCJ4ktTSQ4D3/vvvX7pJlAVd7HJqUsCUXuDkO1Wht2Q6XKfjAkonIWACRbwUJx/kSFAFZ+4FinDAnKbM4BgNOU5zbDoICOEKEgWMAk6BJRsExAo+TpbxgePKZ06FyfM6pUReYFrASxevY+JHT7IEqeSxXz/qA8Gq75cFzvVx/rwUOP8cDzynB4zdxhnS5suOTQnurm0Haz7haZ1QzGlJnTZytZkX2o17MswDG0P3FOuUOWZ+2OzDa7eBRN46VySDtwq9zH+nv+lJx9ZFyZ11ROmVcTRdJaKtW/SzlniOX21sduKqtmFoM9MbI9YYb5tINvGVnHo7iP/I9PaP9cIa5NnalI3V9HOvDY41BH9rP/19osJObwNZh9hFV/p7K8nnNR9//PH1xktrk/b6E39JMJ28es8/nvWhfmEju+Djj5/Tcf8X+f/DZuAHH3xw9Vm+xRMNvfnqlOOB44HjgeOB44Hjgf8ODzxJgsxVdvoFNU4eBEGCkhlUgAmqBCYSOKfNAi/BjwBJsiU5FsAIbArwCrDwcu8StJAlCC2AKSAK/7Hug4dnAZNEUJHgCxLJl/wVLCXbs3ZBtCBbsOgCxwtPiaSEma30w0st2HNq4VRG4CaBFnQqfIKvAn+WgnmBXIk6fcgh2yuC5Lr4xWuPTor522uDdFXQ4I2PINO9BEAdrf5gCx76iDyBq0RfYImv/iEHLNqp77k/HngeDxhDJWyNUbBdMTatLc2DHc4Ohp/1wjh379RVUoaf+aCYn8a8eWwuei7pTa8dbzA8m9/mhTUNX/JmwWfCPFsL4jFxg8FPPjnmHxifNSfpaY2VNLJJYa95ylYbEPHQFs90YbO5L2GU6FrP3dvoszb5NhkPON6C+fDDD6+3bbzxItH94YcfLpnW8dYba15yrsa/Ek424GvDT+IvWfZ2kZN3v23w5ptvXnbwow07tfWK3DY18Ki4n3La4NDOH2i1s9HnJezqsxZrsP93nIzrL77LT/gmpzqZcE45HjgeOB44HjgeOB54PT3wJAmyYEEAIkgTZBTA5TLt2iRzApZ287ULSAVe0YLBL0mePApY0At2lGBoXPeWAh68BLaCM4kgPdgSb3W841/gF04BKZ3hCN7d4ym4g89Op65eUXYaI9DzqrmEXDuYQh9XOuCHP71m8AZORq9b9swvglv0Xjcs+KcHvbJbO37pTge0fOEeD4G1YFOg7aKXk3WJuWCczHldBpx/jgee0wONSXXj0JpgfDfusWysGYdwPVdPkfEDg2tcu8wBb3dot1ElQZYI29SzCWUuSCi9feGU1fw0ziVY4HiZQ+YEvWbB00V/nzV4zdcmWfMrXDrP4vm77767Ns3QTt21tQ5EAzZ5SPBc1pHmOj7RweVH627zXxscbcmDQ2e+8MODfj9B4vrs2bPrLRl2gHvzBI61nF+8xmyj04aab3f9ZgFZ+SO9q9Od/+jTJh9ZPt2wqehEvV8y109kS6b7LAcPMDLU8SQDrPZkqsmyGUhPCb3ipNnnPfo+mskLDj/pd/7ht/hrW3Evpuef44HjgeOB44HjgeOBV94DT5IgF6gURAhGCyTULoGGJEsRQLmUAjltAphOH7QVgMSroK4AT+ACFq57AdO9BV+8yBE8C8pLwhmRSAAAFeRJREFUBMEKiLIBrKsAbcrMlnCmvl6d9kqfb+78XVLBqGCdHySjTmgK5pOXHfhoi69aUfNXbeQLPAWu4IJ6iXn+q2YjuEJ+tmgvmHYS5HRHAiG4JEOBYyNB8oCWDmCu7L8Qzz/HA8/hAWPV2DUHvREhSbLBI1FSN+axhGO+GnNraSwGd8LqLQ3fBBvb5pKEzimpzSB8yTWmtZs3NoZsYBnfkicbXX5pGr4k1GcKNopmwcf4N5fQelUY3m49mnqj85aH72wr2sFds4DTX42vdcOJrhNoawr9w0Fnzprr4K45P93Hv3sJqA0B6zA6r0J7C8XlRJxsdvlhPwk5HfhLsizx1A5GrnvrxlqSCZ5c/euHt/SD012nyU51nfZK/q01foEbHpro8MhPk697flCmP+hjbfRZDDv9yJvE3FhCM/sKHTuMS2PRD48l/2J8/jkeOB44HjgeOB44HnhtPfCvEcwLmCqYELSoBW1ewxVIggmUBB8CLKcBnp16tmsvCBO8afPtnh+HgoNXQU88qIZnSZ1AucRNG7wZPD1kCv7wBUV0dZIh0JTI0tUznPDSJ/7VZBRYwVHCZVc2CMgFf2R88sknV8AnkKa/UxN46CZfsODZGT9y3Av60CVToC/QFcCSKQAU4PM3e136xyvlAn5BKf3xguPkTD8IICXIgnc6VeIBltxsnnjhn/p44DEPGFPmsvEjOW6ueH1XIioxaaypJTTNh4d44yvZ9eaGk0/j3CUBlDhLfox5m0WN3d6ikCCaH+jpY77QUXJtzt1KkM355ggd3a+lOR2cTXiCu2aZz9HhyQe+35VYs8kP+DWP8SMbjtej0cU/3itfcOsqOmuCNdGzV5FbF/lRIitZtM7oK4mx9dJGmjXcRoL1x9q2ytzJDtZfIeBzP3Dotyr4HC8yrUdTZ3T5lt2VcPig4l6/0tkPKnYCru/pbX2s/9mPB950oYMT/nfeeedKqNEoyUnGqY8HjgeOB44HjgeOB14fDzxJgswdAgxBpYBCIiiQEGQp4F6f84qeYFeA5eRBkCEI9iuo2pyiCpScSAiuBDYCFbzx8wyOr0syLrgRyAiStN1b8HLhTZ4TGLoLOiWFeIFrF1zhL3Ck873BEdpw2SH4wpct4J3wer1awN7JbzT32jLx8BecOgH2KqG+8CefnPwIeAX8YE6e+I2sLkm1bwD9OS4/ziNZ1k/1I38JutVsqG+m/HN/PPA8HmgeSj7N5954MFfMP3POOIN3z7wIB03j0/i14WUe40eG+UaeX0WWJEmezJ3k9+qvhJgueMGBKwm+VeA6sZY4Sk7pPYs1wQWeXW0c5ouJv97D4Suvh//000+XHX652nyvZDs8G2T0Nl/vKWxzYuw7YD++Ze3gE0mwNdCv9eNv3WWjX/53b2NTwmzN4W/fC0tqyWbvWmY/8ZlNCzVaG6j91oE+sWnpW2T/N1gj4ehH+ux4r7LyK1xrXKffrevw23SBSze6GCMS/u+///46Nbce5sfwVlnn+XjgeOB44HjgeOB44NX3wP0Z5QO2CjwEFYIMrzL+9ttvVzDqlAVcwPjjjz9eHLxabBdf4CPIcErpFASN794kcZJVgakABk8BG14CIu2CJK/d4et7QcGMH32RBN5TyK24x8+vmEqOJYYCPqcmfU8nUBJUdTJScDSDvO7jq6a7YFIRWNOZr+isFgh2CiMIpQfe8a/mK7bjpa6QiU++1I4Gjl/WdvIlkP76668vPzuttwngNE3AK3hFM3X0/aSkQQLte2kJda+K2uhw2eQQkAtU6UAmPU45HngRD5SMeHPBvLcm9CfGjDHjqzLH2oTXrgbHU5GkmVc24qwn5pmkxxwn67333vvbaaeTy19//fX6ntap7O6kWHK2FjLxN186dTVPzM1ZPLvMOTq6JJrmkYS2uThpujf3rK8lx2qJqWS0jQTtbLP5Zg3jU4kdP9xT2GFTDI2/NGATwQaexNB65W+k6xM2+LFBOjtddcpLHwmytZ58r0Xjww+tW+wjQ22DztoqGbYOkmPdsVbiyTdOrfnTBqvX7a1F1jHrMX2sqZN/NpJRaY1sjSabPmxAayPDuki+Z7TWbpu2ZIORVb+35sUvOac+HjgeOB44HjgeOB54PTzw9+jtJWwSLBT8CSScPghCBBuCLIGb00l/r1eiWZDhh2wkc5I1AZckWaAiSBJwOtHQLiACw1uAJDCUWDtZ8I2Yb9YEY/AfS9boVAClFhD6Lu2jjz66gmMJ5M8//3ydtLJBsCchpKPTWcGThFzwLHDyzHYFP/fg9KSLQA8++yX1AklBGRztAnh/ikkQ6rkATq2tABcfvLPPPX0Ejmj5ywaCdhsRglffPQv6+VVg61TOCTk/OV1SyElvfhac2rDQ7lSfz/UXPH1AV68p8puirQD4Apx/jgfu9IBx1zi3IeX7VvPEjz9JTIytivEP3xg09o33kpZw1GjMgWrz0wmlk0hrhnHthNgaYi3CAz7e5jg6F3ibQJP/7p5u7MDXGzJsMf9X/cwTF9wu+vWL9+lBFzrMIon05+i8cePbXH+3dybx8NlmA84aZt21ScZOieQ9hY8kpS6+wkOi63tda4sTZWu5NcVmgNPrt95661pXbGRar6wd9FTY4bcW5kaHTQr609V3xi7rIV/pd2u5NY2f3JPlYhM/KdYea9W77757rd34z8K3Cn/Su5L/8y2/4GnThD36D40+scFALv9JzOmHLx6nHA8cDxwPHA8cDxwPvL4eeJIEWbAhqJCkCQq9nifQElwJNApqnRwLSEsMwQUvgknBsKRLsCSxK5ARDAs4CzQFxmQIyiS1TkIFS16/owOZ+M4CvhZ6gePrEhg5BXGqVPDHLgGR2um04E/x7IRLoihwUshMLjzBq3YbAOSw2ekse51M4MtfAjtBmlNzhR34K9olC/zCB/hoSw5acgSmAlhJr8J3EmEnwRJopyNOlfiObvwriFYEmTPRpZ+gVH/oC0GjRIRMfPUHHdHQJf+k88X0/HM8cKcHjBvj2JphLBnn1gjjzHhvrGPnHsyGkfEtKb017sBdaKwJTiadpqIxfvEwryTEClzyjX9z3XpgvtQ29djJ1M4Oc9xcttY5tW1OXoz+ktM9GpeTYeuO+c6+WwWudrrZsCODLWzQRi/rjDltQ4AvPdsss0F3T5G4Wv/4jB/QWWetc2SQJbGlryRWgsyXfEcvz9YGCac1hA/rh3SU6Ps7y5Jwawo8azp/6RP9rx/Y2oYeuHVz/r9CDvvwWAtZLnxsCFr39MXad/oKH0m/wg78yDYGra0S/BLk+mfls8o/z8cDxwPHA8cDxwPHA6+uB/73H6KIlyxYfP755//z7bffXgHOF198cQUafVcmqBCECDgKUsDQCUgUgatX7pzWSuYEIIIrdAKvGWiik+Q5iZC84SW4FAw5vUA/yy6YAROYCYAEkvjjK1jFmy7u6SWBpjsZ8aKnJJuOwdmiHRw9ftrSHS/6RisQw1vQ2UYCW/IRGwSq6ODA1a7gDc7H7rVFlx7kuKcj/nTzSqMTKK9Kqj/77LMrCRdUK3DSk64uz2SQTWdy9Atc8AJKz6ccDzyvB4zRNrbM4cb5HE/GWXNHcmW8SuDMi4mX7OYAOkmV5FhiZsNKUmnjCG3zCZ1xbkMIf5tW3hCx9uz4J6fa/HIKKjGU7EmorC+Tf7hrbSPKaSUaeqEhc6WlmzdQ+ErC2HqKX/ZaD7yybO63doZ3jx3e3pEAWzMkqhLU/MQ/1laJLd340mU9IJ+vXdY+PtdfEuySWG3oJLo2LOCwl6/1J19bL60ncCvkuviYfa3NcEpcV9us3QqfeWPH5oW1Xp9U6ExHNuGLht/oaAy2ptIrH0R76uOB44HjgeOB44HjgdfXA0+SIAteJMjffPPNdarw1VdfXUGTQMclEBF8CDgUgU0BYIHVhOMnSIHnHg8FzQyc0CZDuyDnyy+/vALVi+Cvf/CYdMkWnDll9X2iwDxdtHdNPu7TfeLS1XMl2mDaFc+zrWft4BUyphxt7MwvntEKGNFOenTaJMK+qZbsCz7hCAYFwL4TdErkZOjTTz+9NgjYr+ANL78LlGfBmwzFvfbkTxsmzbk/HnjIA40nY04xjhpT0cFpHhuf5kMbQrtxBx9cbZyiha+AuZTWFvdwGtO147HjfxGPf9C6FPjpr36skDVp4Uc/aZORPsmBQ2+lNjzbFGMjfvfogo8rPt17xnOtwfBNfs/Jij4d6QInXZPjecJrR+/SFq66+/DmM1j+dI+e3JUOvHa1djC6uyczHOvcKuMiPv8cDxwPHA8cDxwPHA+8dh74e/bzguYJIuywOy1w4rAGGQIOCVgBh7oAqqAFTKlttoMXnIQPNoNbcCcmfnRHYFjBbwZL4GCSafh07gQkmlnjCz/52gqgJt6qb3aolam3e7q7+GryT050yeoZL/fRTB9MuNOs33///TqtcWqCr5MX3yQ6JfF65LNnz67TG36ILzz+ILdr8hUoeg5/tftqOP8cD9zpgcaWcdRYbnxNFsYlHHO58aq9+TJx0YPHB90Ob9LEq/HcGrbi3HpOv/jcwrsFnzqm94oLx0U317Qp+mxHy5+e4d5bsgMdntbS+iWe8Vvla5/yPeOhwJ100cJfy4TBS3681d1rj9fkQ+49eJM/+vRF677nnYwp79wfDxwPHA8cDxwPHA+8Ph54kgRZMOE7YqfEvvUqaKnmrgKMgp1cGHzi1KZe8Wdb9/EQzPiOjT4PFe0CbfiSwYdkxLs6vuvzLXh46l2wFWyl3+k0Ye7nc/RqsvSD03EbFpJiGwJeM/Rqpm/yJMi+W7axMXXsvpPjntc6OcGn/HN/PHCvB+b4uXUfL+2Ny2C7Oj7V4czneT/bwV3NrR1e+LOeNBN+z30yqtHM+3gEs2as68akCS+6bOn5oTpatcv6GCy69Rl8Bwu/9tl3j+FHM3lMWPTVL4KH9hb9lLXyPs/HA8cDxwPHA8cDxwOvtwee5BVrrzw6sfSDWQIqCVjfEb7e7vvPsk7i3+aAzQr94VVr39h5FihLkv3Aju8Cfe93yvHA8cDxwPHA8cDxwPHA8cDxwPHA8cDxwP974EkSZKexczfes2Tsod350wFP74ES5OpOmWY/lECTXvvTa3I4Hg8cDxwPHA8cDxwPHA8cDxwPHA8cD7x6HniSV6yZXVL26rng9dR4lxTPTYzX0+pj1fHA8cDxwPHA8cDxwPHA8cDxwPHA8cCLe+CfCfI8WXTvx1nmj6o8JALuSn/vt2s7vpNX7TvYTAIfwnOizRY8XoUkkY50XfXd+SC7q6Op7yYfJ8bT/nv4xfcp6uRV47nrwx1sJ3/yme0rfMdvB0O30k6+T3G/k4vvKvcW3k6HlXaH8zKw+Ffjteq3Pt+SN3mEg3bSw+lacSaetvAm33CqZ1v8Ju0Km894xGfCX+b+lj6T58vIzCeP8dvJuEW7w538H7Jptj3GZ/J8mfspE5/5TIf0UGurXV1b8mvrWR2PcOOx4tZePXl0v9IE39UP8dnh/yfA6JyN1asdwemrbdI8ZAO6SbviPiSnN6jikdyVR+3pNtsfkh3epAdb/x8Ob61XurV9Pu/0CFadvZNuvQ93hXtefbniPEQ7ceF1BcebfvFY28N72Tr+8Zl9nswVJ9y1nrTaHqKbvpt4K49VRs/pBr/xs7ZNvrWpH5Oxtu/4BFOv+J4fKsXeE2dHs/KFT16yo9/RhqeNf/zeRXJrq55y4Nzit8rHc+LiV5wdXA2+lmDau4LtcLXVHu8Vbz5P3PDVEw5/+qRxNPHgRFMNNnlOud3Dnb7AexbtK794olvxV9r53D36LrD4T1nJiCac6Nb2iQf3XxJkQAmvb4r9nWHP80deckKMbtU5f7an1ITt7lcZdJDkqmeZegXf0fpTR77B7dVvyfvKC/3OWfhNXDi7zpw46bLjV1v1SpePwPUDWb2uThc2TB1We6PHH4+eV51Xu9JnrfGYOuKnVId/D794TX47XnRd9Y02eepbsInT/aovn84fDYJnjKz+jP6p6tUufHd27PB2Oqz6rr6NZrX/Fl746VUNnywXXpOfe/qu8Mlr8pnwaKIPr0239NSuv2afaTMfXPSauCs/bbUnAw3aSvb1rDZOrBdPVXYydrzp73qRwnfTLjymP+K5k7Gj5YPHdFn9S0aw6ff6Ox2qwWeJdsLc36KfeKuP8cof6LNH7blxoE7G5Df1D47OuGg88pv1Y+oNZ+fjeFRPmglDP4vnFTbb5/3aXzsZE7/7dO5ZjTbfTPhOF7izZD+4PnA9JAN+/ZPs+O1sqO9WuWQkJz1XenIUOuk//Qm24nnOfvrd4pees87mqV/rWPIn/rxHS+6kne3d114Nno5TPvuCR7vWeKBZedH1HtrHcMjD2+/WrDL8bk3ys33V79bzOt53eHjr51nQ8cvzymWna8qd9kwZ6z08F3p+neOgcYZm8q4fwYyfSRO/SQvmecpwH246ZYO6km49q/FyaZs6e36sFHtPvNbNCcN32qxNf027wG7hwUXvr1z42/V//vnnRZ/v8HHfuu1ZboDftJ+M/BSN2u/3TP3g+Isw6ZRecFe/pFt9B7f/L9CT70I39QWbMi9hyz9oyFTg4p0uyfUMj73x54d+KwpeJX7qWfBOz+DpjD4ebMS7Er90BE9P9/yQXzxPPLRd8VOTm53q6NDSQ73zXbzJZztasNVWPgL/Z4Kc8Aw2uP7444/LoWAVhCszQiYO3PUZbIcX3+qdM8Dulbvqhq/OsiDjQYcdL3g7nVfnweHctezk7vg9RofGRS493Rs86ql3OsCrwFGqu2ezqxLvSVvbrLOpWlu8q8Fqn/y0T5zw4IafrBXvIdpoqldewdd6lWFyrGVO8rXtqZ7126oLv6127PB2OhgT/65CT+OmsZbe9J19Tf49+qKHN8didq92JDvbok2H4Ok3fbiTsdOZ7FWX1S7t5t9TlZ0eO95scL1IofPOn6vvdjJ2tDu8VS++rC9r28HSoTrcp6znWIhv/UouG2ddn0ycqV/weFXP/9TxrL/yAx5gk1e0a73rr/ituP/OZzLZMgv7V/1m+0P3+Ln4BA+8+GP1SWMlfLUy8dCGl8x8XR08GdUrr/DwFBvoS3FCcld+0cdv6hWvXY1/etfeuElW8LXe0a44t57Z0rqV3wtgJ81OB/jTfrbCe8xm7ZKIx/DwXn2CxrjL5trXsTh1n/ePyYRL7uoDdvGTtp1eU8a8T171bJv38VWvJZvVyWZ3hW75fbbnm/Bae+DOEq/a8WgshLeD8UfJQ3h4xY8cPM0b1+rTaKpXfcF3fgNb4etYROtPxq62SrLoUvsbb7zxt2QwPmxDX5/Lc9gy5bqvffqdnZ5nkXCCxUONdi3w8KW3yz194+fZpYDVT/l65Tef4a99kz7BPcOrv/C3RpQge67A6wqmnjoGT+f0BV91jpe6gi6dSpDRKRNvRwsnejQuz2ztog9YPJMbb3Dy1XCn/fFX/x+8SY75A5faawAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相关系数与协方差"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "—— se和df都有的\n",
    "\n",
    "corr() 相关系数\n",
    "cov() 协方差\n",
    "\n",
    "—— df有的\n",
    "\n",
    "corrwith(another, axis=?) 传入另一个se或df，可计算axis指定的轴与传入之间的corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 唯一、计数 和 isin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['c', 'a', 'd', 'b'], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique\n",
    "obj17 = Series(['c','a','d','a','a','b','b','c','c'])\n",
    "obj17.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "c    3\n",
       "b    2\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts\n",
    "obj17.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isin 成员是否在另一个集合中\n",
    "obj17.isin(['b','c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失的数据被用 np.nan 形式表示出来（打印输出时显示为NaN），python中的None值也会被pandas当作缺失处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     ab\n",
       "1    NaN\n",
       "2     dd\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isnull\n",
    "data18 = Series(['ab', np.nan, 'dd'])\n",
    "data18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data18.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data18[0] = None\n",
    "data18.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dropna 滤除缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ab\n",
       "2    dd\n",
       "dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series\n",
    "data19 = Series(['ab', np.nan, 'dd'])\n",
    "data19.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     ab\n",
       "1    NaN\n",
       "2     dd\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data19 # data19本身没有变。。。。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1    2\n",
       "0  1.0  2  3.0\n",
       "1  4.0  5  NaN\n",
       "2  7.0  8  9.0\n",
       "3  NaN  1  3.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe默认按行来滤除，即丢掉含有NaN的行，也可指定按列丢弃\n",
    "frame20 = DataFrame([[1,2,3],[4,5,np.nan],[7,8,9],[np.nan,1,3]])\n",
    "frame20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1    2\n",
       "0  1.0  2  3.0\n",
       "2  7.0  8  9.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame20.dropna() # 丢掉含na的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1\n",
       "0  2\n",
       "1  5\n",
       "2  8\n",
       "3  1"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame20.dropna(axis=1) # 丢掉含na的列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 填充缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1    2\n",
       "0  1.0  2  3.0\n",
       "1  4.0  5  NaN\n",
       "2  7.0  8  9.0\n",
       "3  NaN  1  3.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame21 = DataFrame([[1,2,3],[4,5,np.nan],[7,8,9],[np.nan,1,3]])\n",
    "frame21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>888.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1      2\n",
       "0    1.0  2    3.0\n",
       "1    4.0  5  888.0\n",
       "2    7.0  8    9.0\n",
       "3  888.0  1    3.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fillna 将缺失值替换\n",
    "frame21.fillna(888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>888.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0  1      2\n",
       "0    1.0  2    3.0\n",
       "1    4.0  5  999.0\n",
       "2    7.0  8    9.0\n",
       "3  888.0  1    3.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 传入一个字典，可以根据列的索引，填充不同的值\n",
    "frame21.fillna({0:888, 2:999})\n",
    "# 默认返回新对象，传入inplace=True就地修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  1    2\n",
       "0  1.0  2  3.0\n",
       "1  4.0  5  3.0\n",
       "2  7.0  8  9.0\n",
       "3  7.0  1  3.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以用ffill等插值方法\n",
    "frame21.fillna(method='ffill', limit=2) #limit表示可填充的最大数量，可不指定"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 层次化索引 （MultiIndex）\n",
    "能以低维度形式处理高维度数据。\n",
    "### 索引基本使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1   -0.710860\n",
       "   2   -1.110299\n",
       "   3    2.467909\n",
       "b  1    0.035127\n",
       "   2    1.444507\n",
       "   3   -0.309462\n",
       "c  1   -0.640674\n",
       "   2   -0.186813\n",
       "d  2    0.436191\n",
       "   3    1.053822\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series上的层次化索引\n",
    "se22 = Series(np.random.randn(10),\n",
    "             index=[['a','a','a','b','b','b','c','c','d','d'],\n",
    "                   [1,2,3,1,2,3,1,2,2,3]])\n",
    "se22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3]],\n",
       "           labels=[[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 1, 2]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引类型是多层次索引\n",
    "se22.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.035127\n",
       "2    1.444507\n",
       "3   -0.309462\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选取子集\n",
    "se22['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b  1    0.035127\n",
       "   2    1.444507\n",
       "   3   -0.309462\n",
       "c  1   -0.640674\n",
       "   2   -0.186813\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se22['b':'c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -1.110299\n",
       "b    1.444507\n",
       "c   -0.186813\n",
       "d    0.436191\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 还可以在\"内层\"进行选取（选取每个大集中的索引为2者）\n",
    "se22[:, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ohio     Colorado\n",
       "    Green Red    Green\n",
       "a 1     0   1        2\n",
       "  2     3   4        5\n",
       "b 1     6   7        8\n",
       "  2     9  10       11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Frame\n",
    "df22 = DataFrame(np.arange(12).reshape((4,3)),\n",
    "                index=[['a','a','b','b'], [1,2,1,2]],\n",
    "                columns=[['Ohio','Ohio','Colorado'],\n",
    "                        ['Green','Red','Green']])\n",
    "df22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "     2        3   4        5\n",
       "b    1        6   7        8\n",
       "     2        9  10       11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以为各层指定名字name\n",
    "df22.index.names = ['key1', 'key2']\n",
    "df22.columns.names = ['state', 'color']\n",
    "df22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         0    1\n",
       "     2         3    4\n",
       "b    1         6    7\n",
       "     2         9   10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选取分组\n",
    "df22['Ohio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重排分级顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "     2        3   4        5\n",
       "b    1        6   7        8\n",
       "     2        9  10       11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以更换索引级别\n",
    "# swaplevel\n",
    "df22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key2 key1                   \n",
       "1    a        0   1        2\n",
       "2    a        3   4        5\n",
       "1    b        6   7        8\n",
       "2    b        9  10       11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df22.swaplevel('key1','key2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xindo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: sortlevel is deprecated, use sort_index(level= ...)\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state      Ohio     Colorado\n",
       "color     Green Red    Green\n",
       "key1 key2                   \n",
       "a    1        0   1        2\n",
       "     2        3   4        5\n",
       "b    1        6   7        8\n",
       "     2        9  10       11"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sortlevel\n",
    "# 根据各个级别进行排序\n",
    "df22.sortlevel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据级别汇总统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state  Ohio     Colorado\n",
       "color Green Red    Green\n",
       "key2                    \n",
       "1         6   8       10\n",
       "2        12  14       16"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df22.sum(level='key2')\n",
    "# 根据key2进行sum， 默认是操作0轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "color      Green  Red\n",
       "key1 key2            \n",
       "a    1         2    1\n",
       "     2         8    4\n",
       "b    1        14    7\n",
       "     2        20   10"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df22.sum(level='color', axis=1) #操作1轴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用df的列作为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b    c  d\n",
       "0  0  7  one  0\n",
       "1  1  6  one  1\n",
       "2  2  5  one  2\n",
       "3  3  4  two  0\n",
       "4  4  3  two  1\n",
       "5  5  2  two  2\n",
       "6  6  1  two  3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以将df中的某些列提出来，作为索引使用\n",
    "df23 = DataFrame({'a': range(7), 'b': range(7, 0, -1),\n",
    "                 'c': ['one', 'one', 'one', 'two', 'two', 'two', 'two'],\n",
    "                 'd': [0, 1, 2, 0, 1, 2, 3]})\n",
    "df23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       a  b\n",
       "c   d      \n",
       "one 0  0  7\n",
       "    1  1  6\n",
       "    2  2  5\n",
       "two 0  3  4\n",
       "    1  4  3\n",
       "    2  5  2\n",
       "    3  6  1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set_index\n",
    "# 将某些列转换成行索引，注意默认这些列就会不再作为数据\n",
    "df24 = df23.set_index(['c', 'd'])\n",
    "df24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>a</th>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a  b    c  d\n",
       "c   d              \n",
       "one 0  0  7  one  0\n",
       "    1  1  6  one  1\n",
       "    2  2  5  one  2\n",
       "two 0  3  4  two  0\n",
       "    1  4  3  two  1\n",
       "    2  5  2  two  2\n",
       "    3  6  1  two  3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以指定将这些列仍然作为数据\n",
    "df25 = df23.set_index(['c', 'd'], drop=False)\n",
    "df25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>two</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>two</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  a  b    c  d\n",
       "0      0  0  7  one  0\n",
       "1      1  1  6  one  1\n",
       "2      2  2  5  one  2\n",
       "3      3  3  4  two  0\n",
       "4      4  4  3  two  1\n",
       "5      5  5  2  two  2\n",
       "6      6  6  1  two  3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以将索引转换成列\n",
    "# 使用reset_index\n",
    "df23.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 其他\n",
    "### 整数索引\n",
    "当索引值也为整数时，取元素用这种[2]的方式，方括号中使用整数，很容易混淆是选取下标2或是选取索引值为2. pandas认为这里都是按照索引值为2而不是下标来选取。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "2    b\n",
       "1    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se26 = Series(['a','b','c'], index=[0,2,1])\n",
    "se26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se26[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果要按照下标，请使用iloc\n",
    "se26.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc表示按标签内容\n",
    "se26.loc[2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**所以**\n",
    "\n",
    "还是参见 [索引、选取和过滤](#索引、选取和过滤) \n",
    "乖乖用loc/iloc指定是按标签还是按下标，以及对于DataFrame，第一个参数指定行、第二个参数指定列，这样来清楚而有约定地来做吧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 面板数据\n",
    "pd.Panel  \n",
    "略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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