Rogachev_alignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "Каждому из вас даю одинаковое задание:\n",
    "\n",
    "    Даю каждому свой файл данных с колонками: время, показаний 3-х акселерометров и трех датчиков угловой скорости (ДУС). Размерности указаны.\n",
    "    Ориентация осей либо продольная ось (X), вертикальная ось (Y), правое крыло (Z), либо правое крыло (s1), продольная ось (s2), вертикальная (s3. Это легко уточняется по колонке, где показание акселерометра равно примерно 9.81….\n",
    "    Требуется определить три угла: тангаж, крен, курс (в градусах), географическую широту места (градус).\n",
    "     Требуется построить графики накапливающихся средних значений показаний акселерометров, ДУС, а также соответствующих среднеквадратичных отклонений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подключим библиотеки, определим необходимые константы, считаем файл:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # для удобной работы со структурой данных\n",
    "import numpy as np  # для быстрой работы с данными\n",
    "import matplotlib  # для графиков\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from math import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_earth = 6371000\n",
    "a = 6378137.0\n",
    "b = 6356752.0\n",
    "e2 = 6.6943799901413 * (10 ** -3)\n",
    "dt = 0.0025  # посмотрел в файле\n",
    "#phi_init\n",
    "#lambda_init\n",
    "h_init = 100\n",
    "u = 2 * math.pi / 86164.090530833\n",
    "#global R_E \n",
    "#R_E = a / ((1 - e2 * (sin(phi) ** 2)) ** 0.5)\n",
    "#global R_N\n",
    "#R_N = a * (1 - e2) / ((1 - e2 * (sin(phi) ** 2)) ** 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNFparser(filename):\n",
    "    '''\n",
    "    Тут мы расписываем парсер файла данных - при необходимости, строим дополнительные колонки и \n",
    "    делаем прочие манипуляции\n",
    "    '''\n",
    "    CNFnames = ['Time','AccZ','AccX','AccY','OmZ','OmX','OmY','H']\n",
    "    CNF = pd.read_csv(filename, sep = '\\s+' ,header = None, names = CNFnames, skiprows = 1)\n",
    "    # добавим колонку с некоторым инвариантом, удобным для поиска интервала выставки:\n",
    "    CNF['Invariant'] = CNF.AccX*CNF.OmX +  CNF.AccY*CNF.OmY +  CNF.AccZ*CNF.OmZ \n",
    "    return CNF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "CNF = CNFparser('./INS_3_2.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на файл данных: определим последовательность осей, посмотрим на статистические характеристики"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>OmZ</th>\n",
       "      <th>OmX</th>\n",
       "      <th>OmY</th>\n",
       "      <th>H</th>\n",
       "      <th>Invariant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1909.348041</td>\n",
       "      <td>0.179717</td>\n",
       "      <td>-0.173474</td>\n",
       "      <td>9.867469</td>\n",
       "      <td>0.015186</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>-0.000367</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.001030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1909.350541</td>\n",
       "      <td>0.184395</td>\n",
       "      <td>-0.116951</td>\n",
       "      <td>9.804570</td>\n",
       "      <td>-0.011850</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1909.353041</td>\n",
       "      <td>0.177705</td>\n",
       "      <td>-0.036077</td>\n",
       "      <td>9.760693</td>\n",
       "      <td>-0.002734</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>0.000496</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.004373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1909.355541</td>\n",
       "      <td>0.175688</td>\n",
       "      <td>-0.181746</td>\n",
       "      <td>9.877270</td>\n",
       "      <td>0.013422</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>-0.000457</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1909.358042</td>\n",
       "      <td>0.185866</td>\n",
       "      <td>-0.095217</td>\n",
       "      <td>9.799156</td>\n",
       "      <td>-0.012572</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000404</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.001595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Time      AccZ      AccX      AccY       OmZ       OmX       OmY  \\\n",
       "0  1909.348041  0.179717 -0.173474  9.867469  0.015186  0.000801 -0.000367   \n",
       "1  1909.350541  0.184395 -0.116951  9.804570 -0.011850  0.000058  0.000412   \n",
       "2  1909.353041  0.177705 -0.036077  9.760693 -0.002734 -0.000388  0.000496   \n",
       "3  1909.355541  0.175688 -0.181746  9.877270  0.013422  0.000420 -0.000457   \n",
       "4  1909.358042  0.185866 -0.095217  9.799156 -0.012572  0.000293  0.000404   \n",
       "\n",
       "       H  Invariant  \n",
       "0  100.0  -0.001030  \n",
       "1  100.0   0.001843  \n",
       "2  100.0   0.004373  \n",
       "3  100.0  -0.002237  \n",
       "4  100.0   0.001595  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>AccZ</th>\n",
       "      <th>AccX</th>\n",
       "      <th>AccY</th>\n",
       "      <th>OmZ</th>\n",
       "      <th>OmX</th>\n",
       "      <th>OmY</th>\n",
       "      <th>H</th>\n",
       "      <th>Invariant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.000000</td>\n",
       "      <td>676251.0</td>\n",
       "      <td>676251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2754.674061</td>\n",
       "      <td>0.180927</td>\n",
       "      <td>-0.108482</td>\n",
       "      <td>9.812357</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>488.050287</td>\n",
       "      <td>0.004446</td>\n",
       "      <td>0.071474</td>\n",
       "      <td>0.055071</td>\n",
       "      <td>0.012124</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1909.348041</td>\n",
       "      <td>0.156578</td>\n",
       "      <td>-0.279866</td>\n",
       "      <td>9.676982</td>\n",
       "      <td>-0.025576</td>\n",
       "      <td>-0.001963</td>\n",
       "      <td>-0.002049</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.023161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2332.011051</td>\n",
       "      <td>0.177940</td>\n",
       "      <td>-0.175552</td>\n",
       "      <td>9.760486</td>\n",
       "      <td>-0.011611</td>\n",
       "      <td>-0.000279</td>\n",
       "      <td>-0.000213</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-0.002650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2754.674060</td>\n",
       "      <td>0.180932</td>\n",
       "      <td>-0.108247</td>\n",
       "      <td>9.812340</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.000570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3177.337070</td>\n",
       "      <td>0.183913</td>\n",
       "      <td>-0.041506</td>\n",
       "      <td>9.864075</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.003792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3600.000080</td>\n",
       "      <td>0.208240</td>\n",
       "      <td>0.067274</td>\n",
       "      <td>9.949390</td>\n",
       "      <td>0.025093</td>\n",
       "      <td>0.001930</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.023477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time           AccZ           AccX           AccY  \\\n",
       "count  676251.000000  676251.000000  676251.000000  676251.000000   \n",
       "mean     2754.674061       0.180927      -0.108482       9.812357   \n",
       "std       488.050287       0.004446       0.071474       0.055071   \n",
       "min      1909.348041       0.156578      -0.279866       9.676982   \n",
       "25%      2332.011051       0.177940      -0.175552       9.760486   \n",
       "50%      2754.674060       0.180932      -0.108247       9.812340   \n",
       "75%      3177.337070       0.183913      -0.041506       9.864075   \n",
       "max      3600.000080       0.208240       0.067274       9.949390   \n",
       "\n",
       "                 OmZ            OmX            OmY         H      Invariant  \n",
       "count  676251.000000  676251.000000  676251.000000  676251.0  676251.000000  \n",
       "mean       -0.000038      -0.000013       0.000061     100.0       0.000579  \n",
       "std         0.012124       0.000415       0.000440       0.0       0.004907  \n",
       "min        -0.025576      -0.001963      -0.002049     100.0      -0.023161  \n",
       "25%        -0.011611      -0.000279      -0.000213     100.0      -0.002650  \n",
       "50%        -0.000046      -0.000010       0.000056     100.0       0.000570  \n",
       "75%         0.011541       0.000252       0.000338     100.0       0.003792  \n",
       "max         0.025093       0.001930       0.002131     100.0       0.023477  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9041cb5ba8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CNF.AccZ.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С самими данными всё нормально - пропусков нет, выглядят чисто. С точки зрения выставки ситуация хуже - видно удары.\n",
    "\n",
    "Построим графики накапливающихся средних значений показаний акселерометров, ДУС, а также соответствующих среднеквадратичных отклонений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "CNF_det = CNF.loc[:,['Time','AccX','AccY','AccZ','OmX','OmY','OmZ']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.1734738  9.8674688  0.1797165 ... -0.         0.        -0.       ]\n",
      " [ 0.         0.         0.        ...  0.         0.         0.       ]\n",
      " [ 0.         0.         0.        ...  0.         0.         0.       ]\n",
      " ...\n",
      " [ 0.         0.         0.        ...  0.         0.         0.       ]\n",
      " [ 0.         0.         0.        ...  0.         0.         0.       ]\n",
      " [ 0.         0.         0.        ...  0.         0.         0.       ]]\n",
      "100000\n",
      "200000\n",
      "300000\n",
      "400000\n",
      "500000\n",
      "600000\n"
     ]
    }
   ],
   "source": [
    "det = np.zeros((676252, 14))\n",
    "mu = CNF.loc[0,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values\n",
    "sigma = mu*0\n",
    "det[0] = np.append(mu, sigma).reshape((1,14))\n",
    "sigma_list = list\n",
    "mu_list = list\n",
    "print(det)\n",
    "x_prev = CNF.loc[0,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values\n",
    "i = 0\n",
    "for x in CNF.loc[1:676251,['AccX','AccY','AccZ','OmX','OmY','OmZ', 'Invariant']].values:\n",
    "    mu_prev = mu\n",
    "    sigma_prev = sigma\n",
    "    i = i+1\n",
    "    mu = (i*mu_prev + x)/(i+1)\n",
    "    if i==1:\n",
    "        sigma = ((x_prev - x)**2) / 2\n",
    "    else:\n",
    "        sigma = (1-1/i)*sigma_prev + (((x-mu)/i)**2)*(i+1)\n",
    "    if i%100000 == 0:\n",
    "        print(i)\n",
    "    det[i] = np.append(mu, sigma).reshape((1,14))\n",
    "    #mu_list.append(mu)\n",
    "    #sigma_list.append(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRUwJv4iIiIhIEVPCLyIiIiJSxJTwi4iIiIgUMSX8IiIiIiJFTAm/iIiIiEgRU8IvIiIiIlLElPCLiIiIiBQxJfwiIiIiIkVMCb+IiIiISBFTwi8iIiIiUsSU8IuIiIiIFDEl/CIiIiIiRawk3wEUuw4dOnj37t2zuo5ly5bRsmXLrK6jJhRPZoons3U5nnfeeWeeu3fMycqkwdB+JP8UT2aKJ7OC2I+4ux5ZfOy8886eba+++mrW11ETiiczxZPZuhwP8LYXwPeWHoX10H4k/xRPZoons0LYj6hLj4iIiIhIEWuwCb+ZtTez8WY2Lfxtl6beWDNbaGYvJpX3MLPJYfknzaxpKN/EzF41s/fM7EMzGxRb5lIzm25mn5nZgdl9hSIikqwG3/0nhDrTzOyEWPnOZvZR+C6/zcwsU7sWuS3U/9DMdoq1lW7/8ljYT0wxswfMrElVbYmIZFODTfiBocAEd+8JTAjTqdwEHJei/Abg1rD8AuCUUH4Z8JS7/ww4GrgTwMy2DdPbAQOAO82scT29FgB+WrmGoc98yKIfV9VnsyIixaTK734zaw9cCewG7ApcGfthcBdwOtAzPAZU0e7AWN3Tw/IJ6fYvjwFbA9sDLYBTq9FWvSj97DtG/ndmfTcrIg1cQ074hwAPhecPAYemquTuE4Al8bJwRGcf4OkUyzvQJjxfH/g6tr4n3H2Fu88EphPtSOrNY5O/4Im3ZnPbK9Pqs1kRkWJSne/+A4Hx7j7f3RcA44EBZtYZaOPuE0Nf14djy6drdwjwcOgeOwloG9pJuX8J5aNj/WnfBLpW1VZ9OfHBt7jqhan12aSIFIGGPEpPJ3efC+Duc81swxosuwGw0N1Xh+k5QJfw/CrgJTM7F2gJ7BfKuwCTYm3El6nAzE4nOnpDp06dKC0trVZQ02ZGR/bnzJ5Nael31XslwNKlS6u9jlxQPJkpnswUj1ShOt/9XYDZsenE93WX8Dy5PFO76dqaW1WgoSvPccD5NW2rtvuRhJrWL7TPueLJTPFkpngqK+iE38xeBjZKMWtYXZtOUebh7zHASHe/xcx2Bx4xs15VLFOx0P0e4B6APn36eP/+/asV1Lur/gefTaN9p870779DtZaB6Iu9uuvIBcWTmeLJTPFIPXz3p/u+rvb3eDXaqo47gdfd/Y2atlXb/QhjRwHU+DNbaJ9zxZOZ4slM8VRW0Am/u++Xbp6ZfWtmncORmM5A9Q+JwzyiU6kl4Sh/V9Z23TmF0KfT3SeaWXOgA9GRmG6xNuLL1IvbJkRdef7x5myuP6z6Cb+ISDGph+/+OUD/2HRXoDSUd00qT3yPp2u3Vt/9ZnYl0BE4IymurO5HRERSach9+J8HEiMvnAA8V90FQ7/KV4HDUyz/JbAvgJltAzQHvg/rO9rMmplZD6KLrt6s42tIa9zH37BydVm2mhcRaaiq890/DjjAzNqFi3UPAMaFLjtLzKxvuJbr+Njy6dp9Hjg+jLDTF1iU6PqTjpmdSnQdwTHuHv8ir3FbIiL1oSEn/COA/c1sGrB/mMbM+pjZfYlKZvYG8E9gXzObExtO8w/AhWY2nahP//2h/CLgNDP7APgHcGK4wOpj4ClgKjAWONvd12TrxZ3xyDtsedmYbDUvItJQVfnd7+7zgWuAt8Lj6lAG8FvgPqKBFz4HxmRqFxgNzAj17wXOSgSSYf9yN9AJmGhm75vZFVW1JSKSTQXdpScTd/+BcCQ+qfxt1g6BhrvvkWb5GaQYZcfdpwK/SLPMtcC1tQxZRETqqAbf/Q8AD6Sp16sG7TpwdppY0u1fUu5bM7UlIpJNDfkIv4iIiIiIVEEJf4FbviprvYZEREREZB2ghL/AbX352HyHICIiIiINmBJ+EREREZEipoS/AYiu8xIRERERqTkl/A3AqQ+9ne8QRERERKSBUsLfAEz4tCY3ERYRERERWUsJv4iIiIhIEVPCX6BmjRjMhftvWT79n2nz8hiNiIiIiDRUSvgL2Hn79ix/fuz9k/MYiYiIiIg0VEr4C1CnNs3yHYKIiDRgq9aU5TsEESkgSvgLUEmjtW9LvFvPs+/NyUc4IiK1YmbXmVnb2HQ7Mxuez5jWFY9N+iLfIYhIAVHCX4DM1j6Pd+u54MkP8hCNiEitDXT3hYkJd18ADMpjPOuMn1bpCL+IrKWEvwAN6b1x2nndh47KYSQiInXS2MzK+yiaWQtAfRZFRHJMCX8B2qxDqwrTb/5x3zxFIiJSJ48CE8zsFDM7GRgPPJTnmERE1jkl+Q5AKot36QHYsE3zCtMz5y2jR4eWOYxIRKTm3P1GM/sI2Bcw4Bp3H5fnsNYJyfsREVm3ZfUIv5ltZmY7h+d7ZHNdxWS7jdevVDZrxODy53vfXJrDaEREasbMxpnZBWa2tbuPcfeL3f0iJfu5457vCESkkGS7S889wFFmdjxwXJbX1eC1W68JAB1bV93Fdc6CH7MdjohIbZ0ALACuMrN3zewuMxtiZq2qWlDqxxvTvs93CCJSQLKd8H/i7r8H2gF9s7yuBq8sHJFplOZUbPwof78bXs1BRCIiNefu37j7SHc/GugDPAzsDIwzs5fN7Pf5jbD4/bhyTb5DEJECku2EfzSAu/+V6Gi/ZHDDr3Zgs44tad28Sdo6bddbO891zlZECpy7l7n7RHe/wt1/ARwN6KYiWfb+7IVVVxKRdUZWE353HwNgZn2JjvAQplub2W7ZXHdDNKDXRrxyUX8apzvED7x/xQHlz3tcOjoXYYmI1IqZHZb8ACYDh5rZ7vmOT0RkXZGrUXruAnaKTS9LUSa1sFq3TxeRwnUv8HxS2frufmQ+ghERWVflahx+81j/E3cvQ0OC1tr0aweWP99i2Jg8RiIiktGX7n5S/AHMrkuDZtbezMab2bTwt12aeieEOtPM7IRY+c5m9pGZTTez28yiASzTtWuR20L9D81sp1hbY81soZm9mLTux8zsMzObYmYPmFmTUN7fzBaZ2fvhcUVdtoWISHXlKuGfYWbnmVmT8DgfmJGjdRedksYV37YfV6kvv4gUpI3M7DIzO8vMDjGzlkBdv7CGAhPcvScwIUxXYGbtgSuB3YBdgStjPwzuAk4HeobHgCraHRire3pYPuEmUo9A9xiwNbA90AI4NTbvDXfvHR5X1+B1i4jUWq4S/jOBnwNfEV2stRvRF6fUUnzEnrMmaIhOESlINwMrgY2AI4ApwJZ1bHMIa+/W+xBwaIo6BwLj3X2+uy8gusPvADPrDLQJFxE70bVlieXTtTsEeNgjk4C2oR3cfQKwJHnl7j461HfgTaBr3V6yiEjdZLVbjZkdA7zk7t8Rjcwg9WjEYdsz9F8fAfD590vZvKOGuBaRwuHut8Snzawt8JKZvQJc7e6ltWi2k7vPDe3PNbMNU9TpQsWuQ3NCWRcqjhCUKM/Ubrq25lYVaOjKcxxwfqx4dzP7APgauNjdP06z7OmEA2OdOnWitLS0qtVVUpNlli5dWqt1ZIviyUzxZKZ4Kst2P/pNgX+GL70JwBjgzXh/fqm9o3fdpDzh3/eW1yoc9RcRKTTuvpCoi01GZvYy0VmBZMOquapUQ515hvLatFUddwKvu/sbYfpdYFN3X2pmg4B/E3UVqrwC93sIw1n36dPH+/fvX701jh1V/vTJOa2569idq7VYaWkp1V5HDiiezBRPZoqnsmwPyznC3fcBBgEfACcD75rZ42Z2vJl1yub61wVPnL72fmbdh46irEy/pUSkMJhZRzO72cxGm9kriUdVy7n7fu7eK8XjOeDbRJea8Pe7FE3MAbrFprsSHVGfQ8XuNYlyMrSbrq2qXvuVQEfgwtjrWuzuS8Pz0UATM+tQVVu1NWbKN9lqWkQamJz04Xf3Je7+rLuf4e4/A4YTfRE+XMWiUoW+m21QYXqzP2psfhEpGI8BnwA9gD8Bs4C36tjm80Bi1J0TgOdS1BkHHGBm7cLFugcA40KXnSVm1jeMznN8bPl07T4PHB9G6+kLLEp0/UnHzE4luo7gmDAqXaJ8o9ioQLsS7YN/qMFrFxGplZwk/Ga2Z/wBdADecvcDc7H+YjdyQMsK07999J08RSIiUsEG7n4/sMrdX3P3k4G+VS1UhRHA/mY2Ddg/TGNmfczsPgB3nw9cQ/Tj4i2i6wXmh+V/C9wHTAc+J+pqmrZdojvGzwj17wXOSgRiZm8A/wT2NbM5ZpbYp90NdAImJg2/eTgwJfThvw04Wl1cRSQXcjUWfinwNvANa/tDOvB6jtZf9GZeP6j8zrtjpnzDW7Pms0v39nmOSkTWcavC37lmNpioK0ydRqxx9x+AfVOUv01s+Et3fwB4IE29XjVo14Gz08SyR5rylPtWd78DuCPVvELm7jz/wdds2ak1HVo1o2PrZvkOSURqKFcJ/2CikQqaEJ3ifcHd1+Ro3esEM+PTawaw9eVjATji7om6iFdE8m24ma0PXATcDrQBLshvSFIToz6cy9mPv5t2vvYzIg1Drvrwj3H3XxMNMTYImFjXNmtwt8V0d0LsYWaTw/JPmlnTUL6Jmb1qZu+FuyoOCuX7m9k74Q6N75jZPnV9DfWteZPGTP7j2gNU3YeOylBbRCS73P1Fd1/k7lPcfW9339ndn893XFI9Ez//IWOyD7BGA0WINAi56sPfxszOJDq6/xNwWj00W+XdFoN0d0K8Abg1LL8AOCWUXwY8FS4uPppoWDWAecDB7r490QVdj9TDa6h3ndo0p2nJ2rdVSb+I5IuZbWZmL5jZPDP7zsyeM7PN8h2XVO1Xd/0fx9w7qcp621wxNgfRiEhd5apLzzdEw3K+ACwnusBpX3f/cx3aHAL0D88fIrpO4A/Jldx9gpn1j5eFURL2AX4dW/4qolumO9FpZ4D1CcOvuft7sSY+BpqbWTN3X1GH15AV/xs+sEKiv/fNpbx6cf/8BSQi66rHgb8BvwzTRwP/ILrbuhSwd75YUK16K1eX4e6EwYeyZvp3S9nvz69VKs9ll6Iflq5gdZmzusxp0aQxzZs0Yr2m2Uujvlm0nL7XT0hfIdxzIVvb4IelK+h/cylLlq+usu4vt2hCtoeZn7voJ5avKuOvL/+Pf78fjYx797E7MaBX56yts6zMufRfH/Hk27PT1um8fnP+b+g+9f4/4O7MnLeMzerppqq5SvhvJEqkm4RHfajO3RbT2QBY6O6JT3H8botXEd0J8p+ThpAAACAASURBVFygJbBfiuV/BbxXiMl+wqwRg8uT/pnzltF96Cj1tRSRXDN3j58NfdTMzslbNFIt3y5ennbedhu34eOvF1co63Hp6KztX1asXsNWl6U/i5DtfduRf5/ImzPDAE9jX05ZZ+b1g+o12Vu5uowtLxtTdcUgsa/fpP16jPjV9vx889rf2mFNmTN5xg/8+r7JNVru2emreDZNj4J+W3TgP9PnsUfPDmzYujntWzZh+ndLAfjVzl3ZsWtburVfr8Iyy1etKb8mMZMzH63c5eyQHTdml1ZlzJ7/I01LGvHt4uVMmvEDR/aJbqfRvElj7v/PTDq1ac5Pq9Zw+b+n1Oi1Jpu7aHn5oCkZja1dj4srD96Wk37Ro1bLxlkhjwhWxd0WH3L3trG6C9w9XT/+/kS3MD8oTHcEJrr7FmG6GzDa3bc3swuJtsstZrY7cD/QKzGWspltRzQu8wHu/nma9cVvib7zE088UYtXX31Lly6lVavKvwBXlTmnvfRjhbLkITxzGU++KJ7MFE9muYxn7733fsfd++RkZTlgZiOAhcATRAd9jgKaER31TwyfKVXo06ePv/3229Wqm9yNs7rJcPxOoGOnzK2USF0zZDt6dGhFv55RMunuFZKcQ3bcmNuO+Vm11lUdr7z6KieP+7HqisCO3dry3Nm/qLd1A7zzxXx+dVfdLjccfmgv7nhlOt9k+AHV0By2Uxd+t++WdGvfAoDtrhzHjyvXvTFYWjZtzLIcve6pVx9YozNJZpZyP5KrI/y14u6pjq4DYGbfmlnncHQ/3d0W05kHtDWzknCUP37nxFOAAWH9E82sOdF9A74zs67As8Dx6ZL9sFztboleS5lu2Tyjv1e4GdeJY5dl/Uh/IdxCOk7xZKZ4Miu0eBqYo8LfM5LKTyb6AaD+/AUoOdn//LpBNG5U8Qh28hHt5z/4misO3pYOrepnyM50yf7n1w1i5eoyXvzway55+kMAPpi9sMIPnZcv3IsOrZqyfosm1TryvnpNGUtXrKbtek1ZvaaMLYalPrr+t1/vxFYbtebTbxazbec27HNL5S5GcZfV8chx725t+XeaHzKJ76XqHgmvjZEn7UL/rTJ3nph69YDyeNpt3pshf/tvvccxePvOXHHwtnRq0zzl/Ofe/4rzn3i/3te7Tec2jD6vX43O3nw4ZyG3TZjOy598W2XdC/ffkoN33JgVq9fQpnkTvlm8nHe/WMCn3yxhiw1bcfoem9GoUf2dOSrohL8KibsijiD93RZTcnc3s1eJboLyRNLyXxKNxTzSzLYBmgPfm1lbYBRwqbvX/yc6Sxo1sgpj9EP2T4GKiAC4e93PQ0udTPlqEb26rF/r5TPtK+JdRwH6DH+ZDVs3481haY/VVUuqwSY+vOoA2jSPegS3aNqYI/p0Y+WaMoY9WzmpTtXXvy5mXj+I1157jf47RH3Ft9gwOuM3a8Rg3p+9kENrkeR2aduCNi2acNXB27J8dRknPPBmhfkfXHkA67eougd08yaNK7xH7s7EGT+wYevmLF6+ivbrNaV18xI+/noxm3VsyWX/nsLPN9+ANWXw8803oKSx0b5lU1o1K6F187r1uN6xW9ta5xbuzso1ZTRp1Igyj66TaN6kcbWWHdK7C0N6d6lQlq8DNTt0bct9J1Q+SVudeDZu24KdNknZUaVeNOSEfwTwlJmdQpSkHwHR3RaBM9391DD9BrA10MrM5gCnuPs4ogt8nzCz4cB7RF13IBov+l4zu4DoCNSJ4QfCOcAWwOVmdnmoe4C71+TMQl6YWaUvZiX9IiLF7+uFP1VK+Kd/t4TrRn/Ktb/sxaQZP/CLzTtwz4craNV9PjtvWreE47slK1Im7Mfvvil79uzImY++w+oy56z+m9OyWQl7bdmRpStW07tbW5o3aUxyN+NMie9vdtuUPbboyJ43vVqnmNP56KoDqkyCe6dJcteUOavWlNG0caNqHaWtr/2xmaXsw7/nlh0BGHnSrvWynvpmZjQriRL8Rhgl1cv1pQbykvCb2VnAD8AzsQtna6QGd1tMdyfEGUClT767TwUqnUNz9+HA8NrEWihSJf3xoyYiIlJc7nl9BgdsF10Kt3zVGu54ZTp3vDodgN2vf6VC3f+7u2Kf9T8O2rrK9pP3K+k8PPELHp74Rfn0naVRr9ibxn2WdpkXz+1X5VHuTTZYj1kjBrNsxWp+WrWGv7z8Px6d9GWV8SS7/KBtuebFqQCcs/cWnLdvzwpDXNdU40ZG40bKWqVw5OsIvwH9gN8Ah+QphnVS8pfzDle9xO8HbMVZ/bfIY1QiIpINb3+xgJWry2ha0qjGfb1P26N6l1hUN+mvqZp0RWrZrISWzUoYfuj2DD90+wrzVq8pA6CkceYE/pR+6oEmxSsnN95K5u5/c/dz3V3Jfh7MGjGYf5zWt3z6xrGf6QZdIpJ1ZtbZzOrnqk6ptrJajsZXk4sVZ40YzF+O6l2r9aTy9/3Xq7pSNZU0blRlsi9S7HJyhD/0q98D2JjoTrtTgJc1JFv+7L75Bky7diA9Y6MRdB86igdP2oW9q7gqX0Sklh4BNjezZ9z94nwHs64oc2fYsx9lfT2H/qwLh/5s7cWTX/7wIy2aNubbxct5aeq3nLpHD1o1LeHu1z/n1U+/45FTdiu/MHPZitUc+rf/csH+W/LzzTfg/Tf/L+vxiqxLsprwm9mJwHnATOAd4DOiUW/6AX8wsynA5e5e8w53UmdNGjeqdCr2pAffokOrprx92f55jExEipG77xfudL5tvmNZl3w4ZxGPTa7ZbvbWo3as83o32SA6St+xdbMK3XPO6r9FpW6kLZuVMP7Cveq8ThFJLdtH+FsCv3D3n1LNNLPeQE+iUXYkT2aNGMzCH1fS++rxAMxbulKj+IhIvTCzdqw9uzsr3MTw4/xGtW5JXIxaE4O33zgLkYhIvmS1U1voq58u2W/q7u+7+4RsxiDV03a9ppUSfPXrF5HaMLP1zeyPZvYRMAn4O/AU8IWZ/dPM9s5vhOuWj79eXKP6R+/SrU4j1IhI4cnJf7SZlZpZ99j0rsBbuVi31IySfhGpB08Ds4E93H0rd+/n7n3cvRvRPVSGhHuoSB5dPWS7SmWjzuvHiF/tkIdoRCSbcvUT/npgrJmdZWbXAncDJ+Vo3VJDs0YM5s9Hru2/2X3oKB6eOCtv8YhIw+Lu+7v7I+6+MMW8d9z9d+5+f6plJXeO3717+fN79o/Gs99u49rflVdECldOEv5wZ9szgb8CJwOD3P3dXKxbauewnbry77PX3n/siuc+1tF+EamR5KP4ZtbYzK7MVzyy1vRrBwLRAZ5ZIwbTtHH1h+AUkYYnV116LgduB/YErgJKzUxXhBa43t3a8vGfDqxQ1n3oKBb9tCpPEYlIA7OvmY0O4+/3IurP3zrfQa3r3vj93hqXXmQdk6v/+A7Aru4+0d3/DhwI/C5H65Y6aNmshFkjBnPaHmvvQLjjn17S0X4RqZK7/xp4CPgIGA38TuPv51+39vV3UysRaRhy1aXn/PhoPe7+hbuXD/RuZrfnIg6pvWGDt2XGdYMqlCnpF5FMzKwncD7wDDALOM7M6pRtmll7MxtvZtPC33Zp6p0Q6kwzsxNi5Tub2UdmNt3Mbgv3BUjbrkVuC/U/NLOdYm2NNbOFZvZi0rrvN7MPQv2nzaxVKG9mZk+GtibHB7MQEcmmQjmn94uqq0i+NWpkKUfxOfHBN/MUkYgUuBeIbq54BrAXMI26j9A2FJjg7j2BCWG6AjNrD1wJ7AbsClwZ+2FwF3A60T1gegIDqmh3YKzu6WH5hJuA41LEeIG77+juOxDdZ+acUH4KsMDdtwBuBW6o2UsXEamdQkn4pQFJHsWn9LPv6T50FF8vTHnLBRFZd+2auNeKR24BDk3MNLPa3NJ7CFE3IcLfQ1PUORAY7+7z3X0BMB4YYGadgTahe6kDD8eWT9fuEODhEP8koG1oh/DaliSv3N0Xh9dnQAvAU6zjaaJrHHJ6tWzymVoRWTdk+067UqQO26krh+3UtUK3np+PeAWA+w9Q/1ARWZv4JpVNi03eQJSM10Qnd58b2pprZhumqNOF6D4ACXNCWZfwPLk8U7vp2pqbKUgzexAYBEwFLkpuy91Xm9kiYANgXorlTyc6o0CnTp0oLS3NtLpqe/3111KWL126tN7WUR8UT2aKJzPFU1mhJPwaD6yBmjViMCtXl7HlZWPKy0556Ud4aRTTrh1IE40EISLppfzuN7OXgY1SzBpWh3Y9Q3lt2srI3U8ys8ZEI9QdBTxYk7bc/R7gHoA+ffp4//79q1plZGzma6vStVNaWpp2Xj4onswUT2aKp7JCycb+mu8ApPaaljRi1ojB3Hd8nwrlPYeNofvQUfy4cnWeIhORApcu2d3P3XuleDwHfJvoUhP+fpeiiTlAt9h0V+DrUN41RTkZ2k3XVtUvzn0N8CTwq+S2zKwEWB+YX522RETqIqsJv5k9aGYPmNmtmeq5+8hsxiG5sd+2nZg1YjCHbN6kQvm2V4yj+9BRnP7w20TdZkVEau15IDHqzgnAcynqjAMOMLN24WLdA4BxocvOEjPrG/rOHx9bPl27zwPHh9F6+gKLEl1/Ugn1tkg8Bw4GPk2xjsOBVzyHX4qXDd4mV6sSkQKT7S49I8PflVlejxSQw3o25bbTDuCD2QsZ8rf/lpe/NPVbelw6unx66tUHsl7TQulVJiJ5MKsWy4wAngp38f0SOALAzPoAZ7r7qe4+38yuYe2IQFe7e+JI+m+J9k0tgDHhkbZdovsHDAKmAz8CJyUCMbM3gK2BVmY2h2gUnvHAQ2bWhqgLzwdhnQD3A4+Y2XSiI/tH1+L119qvd9skl6sTkQKS7WzrCnff18xuACZmeV1SYHbs1pZZIwaz8MeV9L668nV5214xrvz53cfuzIBeqbrsikhDY2Z7hqcrw8g2Kbn7YTVt291/APZNUf42cGps+gHggTT1etWgXQfOThPLHmnCTDnUtLsvZ+0PiZzTARaRdVe2//s7m9lewCFm9gRJFyy5+7tZXr8UgLbrNa0wfv/9/5nJNS9OrVDnzEffKX8+/oI96dmpdc7iE5F6lzgKvhBIm/CLiEhuZP0IP9HNS7oCt1Ax4XdgnyyvXwrQKf16cEq/HgB89s0SDvzL6xXm73/r2unkG32JSOFz95OqriUiIrmS1YTf3Z8Gnjazy939mmyuSxqmrTZqXZ7UXz/6E/7++owK8xPj/L9y0V5s1rFVzuMTkdozsx2IurAkutBMAW5391Qj64iISJZke5Se7gDpkv0wmkHXVPNk3XPpoG2YNWIws0YMZvsu61eYt88tr9F96KgKN/oSkYJ3B7CI6GLVe4jO8j6b14jWIY+ftlv583+d9fM8RiIi+ZbtLj03mVkjouHN3gG+B5oDWwB7E10gdSUV73wowgvn9gPgx5WrK1zcC2uP+r92SX823aBlzmMTkepx9z3j02Y2iegmVJIDbZo3UbdIEQGy36XnCDPbFvgNcDLQmWhYs0+Ihjq7NoxaIJLSek1LyndYFz71Pv9696vyeXvdVFr+/LPhA2hW0jjX4YlINZjZpcCRwAakGfFG6t/WG2nwAxGJZH2MLnefSvVvhy6S1p+P7M2fj+zNytVlbHnZmArztrpsLACn7dGDYYO3zUd4IpLefcBYYC/gIuCF/IazbihpnNVeuyLSgGQ14Tez37v7jeH5Ee7+z9i869z9j9lcvxSnpiWNyo/6vz97IYfGbu517xszufeNmeXT068dmPP4RCRiZgOBl9z9e+B7M/sCHeEXEcm5bP/8j99F8NKkeQOyvG5ZB/QON/dK1091i2FjOHHsMroPHcXqNWU5jk5knXcmMNfM3g799z8Fbs1zTOuEkkZWdSURWWdku0uPpXmealqkThJJ/+o1ZWx1+VjWlHmF+VsMq9gN6PPrBtFYO0WRrHH3IWbWEuhJ9J0/3d2X5DmsdUIjfbeJSEy2E35P8zzVtEi9KGnciM+vG1Q+fenI8fzj05WV6m3+x9EVpv83fCBNS9TnVaSuzKy7u88CcPdlwPtJ8w3o4u4aoS1L/vVbDcMpImtlO+Hf0cwWEx3ZaRGeE6abZ3ndIgAc2L0J15+4PwBLV6ym15XjUtZLvhBYZwBEak1DMufZNp3b5DsEESkg2R6WU+MkSkFp1aykQn//RT+tYsc/vZSybvwMwIvn9qNX0s3ARCQ1DcksIlJYsj4sZ7aYWXvgSaA7MAs40t0XpKg3FugL/MfdD4qV9wCeANoD7wLHuftKM9sEeAhoCzQGhrr76NhymwBTgavc/ebsvDrJlfVbVLwxjbuz7y2vMWPesgr1Drr9P+XPN+vYkgkX7kXUK0FEUtGQzCIihaMhd1geCkxw957AhDCdyk3AcSnKbwBuDcsvAE4J5ZcBT7n7z4hGGbozablbgTFIUTIzXrm4f/nIPw+etEulOjO+X0aPS0fTfegoug8dxYP/nZmiJZF1l5n9Pvb8iKR51+U+onWPDkeISFxDTviHEB2JJ/w9NFUld58AVBgVIlwwtg/wdIrlHUh0flwf+Dq23KHADODjuocvDcHeW21YnvzPvH5Qyjp/emFqefLffegovluingqyztOQzCIiBaTBdukBOrn7XAB3n2tmG9Zg2Q2Ahe6+OkzPAbqE51cBL5nZuUBLYD+AMLTcH4D9gYszNW5mpwOnA3Tq1InS0tIahFZzS5cuzfo6aqKY4xk5oOXadlc657zyY6U6u147ocL0+Ts1o3fHxuVdgIp5+9QHxVMUNCSziEgBKeiE38xeBjZKMauu/UJT7XASw4QeA4x091vMbHfgETPrBfyJqAvQ0qr6brv7PcA9AH369PH+/fvXMdzMSktLyfY6amJdiuegA9Y+H/XhXM5+/N1Kdf767oqkEgOW8dDJu7LXlh2zEldNrEvvV20UWjwNhIZkzjNdYiQicQWd8Lv7funmmdm3ZtY5HN3vDHxXg6bnAW3NrCQc5e/K2q47pxBOObv7RDNrDnQAdgMON7MbiS7oLTOz5e5+R81fmRSjwTt0ZvAO0QXA7s6D/53F1S9OTVv/hAfeTFn+2fABNCvRAFfSoGlIZhGRAlLQCX8VngdOAEaEv89Vd0F3dzN7FTicaKSe+PJfEo0RPdLMtiHaOX3v7nskljezq4ClSvYlHTPj5H49OLlfjwrly1etYevLx2ZcdqvLKs/XsKDSkGhIZhGRwtKQE/4RwFNmdgpRkn4EgJn1Ac5091PD9BvA1kArM5sDnOLu44j64z9hZsOB94D7Q7sXAfea2QVEp55PdHedgpZ60bxJY0YOaFmpi8g3i5bT9/oJqRei4rCgCZ9cPYAWTZVXiYiISGYNNuF39x+IjsQnl78NnBqb3iO5TiifAeyaonwq8Isq1n1VDcMVyWij9ZtXuB8AwIrVazjhgTeZNGN+ymW2uaLymYC3L9uPDq2aZSVGEWk4dJ8QEYlrsAm/SLFrVtKYJ07fvULZmjLn0L/9l4++WpRymT7DX05ZftPhO3D4zl2VBEiDV4ObLp5AdF8VgOHu/lAo3xkYCbQguuvv+aGbZ8p2wzDOfwUGEd0t+ER3fze0le7GjvcDfYiuWfhfWGapmZ1IdG+Yr0LVO9z9vjpvFBGRKijhF2lAGjcyXji3X4WyH1euZtsrxmVc7pKnP+SSpz9MX2HsqPKn1/6yF0f16UZJ44Z8mw4pYombLo4ws6Fh+g/xCiF5v5Io6XbgHTN7PvwwuIto2ORJRAn/AKKbKaZrdyDQMzx2C8vvFlZ1E7AecEZSjBe4++IQy5+Bc4i6oQI86e7n1MeGEBGpLiX8Ig3cek1LKnUHcnc+nLOIIX/7b43bG/bsFIY9OyXt/DeH7cuGrTXQiuTNEKB/eP4QUEpSwg8cCIx39/kAZjYeGGBmpUAbd58Yyh8muunimAztDgEeDtdyTTKztokR4tx9gpn1J0ks2TeiMwm6DkxE8koJv0gRMjN27Na20g+BZO7Og8+9wk3vrOKnVWuq1XbyjcXiurRtwb/P/gUdW+s6Asma6tx0sQswOzaduLlil/A8uTxTu+nampspSDN7kKgb0FSiwSASfmVmexJ19bnA3WenWb5ON3Csaf1Cu8Gc4slM8WSmeCpTwi+yDjMzNmvbmE+uqXT9ewUz5y1j75tLq2zvq4U/scu1qa8jSOjUphm3HNGbPt3b0byJRhmSyurhpovpbq6Y6aaLNW0rI3c/ycwaA7cDRwEPAi8A/3D3FWZ2JtGZhH3SLF+7GziG7nk1vVlcod1gTvFkpngyUzyVKeEXkSr16NAy5dkCd2dNmTPs2Sk8+XbKA5WVfLt4BcfePzlzpdg1Bcf23YQrDtqOpiW6pmBdUQ83XZzD2u45EN1csTSUd00qT9x0MV27c4BuaZap6nWsMbMngUuAB8Pocgn3AjdUpx0RkbpSwi8itWZmlDQ2bjh8B244fIeUddyd0R99w9mPv1urdTw66UsenfRl2vkDe23E1UN6qRvRuqM6N10cB1xnZu3C9AHApe4+38yWmFlfYDJwPNER+EztPg+cY2ZPEF2suyjR9SeV0G9/c3efHp4fDHwa5nWOLXsI8EmNX72ISC0o4ReRrDIzBu/QmcE7ZL6eIKG0tJRefXbn2lGf8Ox7X1VZf8yUbxgz5ZuMdTbr0JK7jt2Z7h3Wo1mJuhE1cFXedDEk9tcAb4Vlrk5cwAv8lrXDco4Jj7TtEo3kMwiYTjQs50mJQFLd2BEYDzxkZm2IugN9ENYJcJ6ZHQKsBuYDJ9bHBhERqYoSfhEpOB1aNePWo3pz61G9U85ftaaM8/7xXpWJfsKMecs48C+v1zqeO9p/zYDtNtJQpQWgBjddfAB4IE29XjVo14Gz08SS8saOpLl5o7tfClyaZhkRkaxRwi8iDU6Txo2469idM9ZZsGwl+/75NeYvW1nn9Z3z+HtV1jl+9025dOA2tGiqMwgiIlJYlPCLSFFq17Ip716+f42Xc3cWL1/NJf/8gJemflvt5R6e+AUPT/yiQtnAXhtV+cNEREQk25Twi4jEmBnrt2jCPcf3ATIPp/b1wp+4fsynvPBB6kFb3pw5P2W5SLZcftC2TP9uab7DEJECo4RfRKSWNm7bgtuP+Rm3H/OzSvPcHdf9VSXHTunXI98hiEgBUsIvIpIFZoalumWTiIhIjmnICRERERGRIqaEX0RERESkiJmrk2lWmdn3wBdVVqybDsC8LK+jJhRPZoons3U5nk3dvWOO1iUNhPYjBUHxZKZ4Msv7fkQJfxEws7fdvU++40hQPJkpnswUj0juFdrnXPFkpngyUzyVqUuPiIiIiEgRU8IvIiIiIlLElPAXh3vyHUASxZOZ4slM8YjkXqF9zhVPZoonM8WTRH34RURERESKmI7wi4iIiIgUMSX8IiIiIiJFTAl/ATKzbmb2qpl9YmYfm9n5oby9mY03s2nhb7tQbmZ2m5lNN7MPzWynWFsnhPrTzOyEeo7nJjP7NKzzWTNrG8q7m9lPZvZ+eNwda2tnM/soxHqbmVk9xnOVmX0VW++g2DKXhnV+ZmYHxsoHhLLpZja0nrfPk7FYZpnZ+znaPs3N7E0z+yDE86dQ3sPMJofPwpNm1jSUNwvT08P87lVtt3qK57HQ7hQze8DMmoTy/ma2KLZ9roi1VR/vV7p4RprZzNh6e4fyrP5/iWRDhu8l7Ucyx6P9CNqP1CGewt2PuLseBfYAOgM7heetgf8B2wI3AkND+VDghvB8EDAGMKAvMDmUtwdmhL/twvN29RjPAUBJKL8hFk93YEqatt4Edg+xjgEG1mM8VwEXp6i/LfAB0AzoAXwONA6Pz4HNgKahzrb1FU9SnVuAK3K0fQxoFZ43ASaHz8VTwNGh/G7gt+H5WcDd4fnRwJOZtls9xjMozDPgH7F4+gMvpminvt6vdPGMBA5PUT+r/1966JGNR4bvSe1HMsdzFdqPZPqe1H4kczwjKdD9iI7wFyB3n+vu74bnS4BPgC7AEOChUO0h4NDwfAjwsEcmAW3NrDNwIDDe3ee7+wJgPDCgvuJx95fcfXWoNgnomqmdEFMbd5/o0Sf94dhrqHM8GRYZAjzh7ivcfSYwHdg1PKa7+wx3Xwk8EerWazzh6MqRRF9GadXj9nF3Xxomm4SHA/sAT4fy5M9P4nP1NLBviDnddquXeNx9dJjnRDuojJ8f6u/9Srd90snq/5dINmg/Urt4Miyi/Yj2I1XGk2GRvO9HlPAXuHBa7GdEvx47uftciL4cgA1DtS7A7Nhic0JZuvL6iifuZKJfrwk9zOw9M3vNzPaIxTkny/GcE06XPWDhVDX53z57AN+6+7RYWVa3j5k1Dqd+vyP6AvkcWBjbscbbLt8OYf4iYAPqcfskx+Puk2PzmgDHAWNji+weTpWOMbPtkuPMYjzXhs/PrWbWrIr11vvnRyQbtB+pcTzaj6D9SB3iKcj9iBL+AmZmrYBngN+5++JMVVOUeYbyeo3HzIYBq4HHQtFcYBN3/xlwIfC4mbXJQTx3AZsDvUMMtySqpllvTrYPcAwVj8pkffu4+xp37010tGNXYJsMbWd9+yTHY2a9YrPvBF539zfC9LvApu6+I3A78O8q4qyveC4FtgZ2ITq9+ocq1luvnx+RbNB+pMbxaD+SWEj7kdrEU7D7ESX8BSr8Wn0GeMzd/xWKvw2ngBKn7b4L5XOAbrHFuwJfZyivr3gIF5gcBPwmnFIjnLr7ITx/h+iowJYhnvjptnqNx92/Df+AZcC9rD1tmM/tUwIcBjyZKMvF9omtayFQStRnsG2IJ7nt8u0Q5q8PzKcet0+KeAaE9V0JdCTaYSXqLE6cKnX30UATM+uQ7XjCKXV39xXAg+Tw8yOSDdqP1Dwe7Ucq036k+vEU9H7Es3BhgB51exD94nsY+EtS+U1UvNjqxvB8MBUvBnkzlLcHZhJdzkzdRQAAIABJREFUCNIuPG9fj/EMAKYCHZPKOxIuyiG6MOarxHqBt0KMiYuJBtVjPJ1jzy8g6jcIsB0VLxqaQXThTkl43oO1F+9sV1/xxLbRaznePh2BtuF5C+ANop3pP6l4sdVZ4fnZVLzY6qlM260e4zkV+D+gRVL9jaD8poC7Al+G7VFf71e6eDrH3s+/ACNy8f+lhx7ZeGT4ntR+JHM82o94xu9J7Ucyx1Ow+5GcffnoUaMPUj+iUzofAu+HxyCi/nATgGnhb+Kf24C/Ef3C/wjoE2vrZKKLZKYDJ9VzPNOJ+p4lyhL/7L8CPg7/SO8CB8fa6gNMCbHekfiHrKd4Hgmv/0PgeSp+cQ8L6/yM2IgFYbn/hXnD6nP7hHkjgTOT6md7++wAvBfimcLaUR02I7qoaTrRl3azUN48TE8P8zerarvVUzyrQ9uJbZYoPye2fSYBP6/n9ytdPK+Ez88U4FHWjsCQ1f8vPfTIxiPD96T2I5nj0X7EtR+pQzwFux9J/PoREREREZEipD78IiIiIiJFTAm/iIiIiEgRU8Iv8v/s3XecVNX9//HXe5cmItKsoIIRk9gLauyIXfOVNGuKxpiqKfpVA98kamw/NSbGFo2xxkTRaAqJiqKydlQULGChqqhRLKgoAst+fn/cszC7OzO7wMzOsLyfj8c89s655577mdnde88995xzzczMzDowV/jNzMzMzDowV/jNzMzMzDowV/jNzMzMzDowV/grTNLWkh6T9Jykf6dHY+fLd6KkyZKel3SzpG4p/QRJ0yRFeopcY/6vS3o2vR6VtHV7fSYzMzMzqx6u8LcjSUMlXd8s+Wqypx5uCfwDOCXPdv2Bn5A9qGELsqf7HZFWPwLsA7zSbLOZwJ4RsRVwFnBVqT7HqkTSQEnzJU2SNEPShSl9qKQHJf1D0hRJV0qqSev2SxdxT0v6m6QeKX2WpJtzyr5F0qy03CWV9Xy6+JvVhti+lS7onpF0Yzk+v5mZrRifR6wauMJfeZ8FHkzLY8menpdPJ2A1SZ2A7sAbABExMSJmNc8cEY9GxPvp7XhgQCmDXsVMj4htgJ2BY3LSdwT+F9gS+AzwlXSX5ZfAPhGxHTABOClnm/Ul9ZbUh+zR3432BzqnC7q9WgtI0uZkTy8cFhFbAz9d3g9nZmZl5/OIVVSnSgdgPA8cAvwLOBTYoHmGiHg9tQi8CswH7omIe5ZhH98B7ipBrKuqz0iaBAwCLsxJfyIiZgCkFpfdgE+BzYBHJAF0AR7L2eZm4Ciyx2zfBIxM6YuB7pJq2xjTMOC2iHgHICLeW47PZWZm7cPnEasoV/jbgaTHga5AD6BP+qcH+DlwLHCJpNOA0cDCPNv3BoaTHSjmAn+T9I2I+Esb9r0XWYV/t1J8llXU9IjYRlJ3YEJOt6xoli/IDsBjI+LIAmWNBq5L+Y5h6YH6HuArwBzg9TbEpDz7NzOz6uTziFWUu/S0g4jYKd3KOw4YHRHbpNfdEfFiROwXEduTXbVPz1PEPsDMiJgTEYuAvwO7tLZfSVuRjREYHhHvlu4TrbIWkLWg9E7vd5Q0KPW5PBx4mKz71K6SNgGQ1F3SpjllLEx5HiPn4i4i6snu3pxCzq1YSf0l3ZcnlvuAwyT1Tfn6lOYjmplZGfk8YhXhCn+FSVo7/awh67N3ZZ5srwJfSP/0AvYGXmil3A3JLgy+GREvlzbqVU7jrdjngXER8WxKfww4L6XPBP4REXPIWlxulvQs2UH5c7mFRcTpEXF6bpqkw4CeEXFNs32vB9Q3DygiJgPnAA9Iegb43Yp9RDMzKyOfR6yiFOG7Oe1F0lDgmIg4Jiftp8Dx6e3fgZEREZLWB66OiINSvl+TXf3XAxOB4yJigaSfAKeSDdx5G7gzIo6TdDXZAODG2XvqI2JIuT/jqiL9Lk+OiC+WeT8nAK9GxOhy7sfMzNqXzyPWnlzhN1sO7XWgNjOzjsnnEWtPrvCbmZmZmXVg7sNvZmZmZtaBeVrOMuvXr18MHDiwrPv4+OOPWX311cu6j2XheIpzPMWtyvE89dRT70TEWu2yM1tp+DxSeY6nOMdTXFWcRyLCrzK+tt9++yi3cePGlX0fy8LxFOd4iluV4wEmRBUct/yqrpfPI5XneIpzPMVVw3nEXXrMzMzMzDowV/iryOKG4KNPF1U6DDMz6+Aigg8+WcSC+sWVDsXM2oH78FeREbc/y9+ems2Mcw+ipkaVDsfMzDqo426YwH0vvg3AxUdsQ8/wjH1mHZkr/FXk7xNfB6Ahghpc4Tczs/JorOwD/HTUpGzh7ju45ugh7P35dSoUlZmVi7v0VCG3s5iZ2Yr4+W3PMnDEHYx+5o1l2u47N0xgzkcLyhSVmVWKW/iriNv0zcysFG6Z8BoAP7l5IodsvX6TddPe/qjotjuccy8A6/TsyuP/t095AjSzduUW/irkrpRmZlYqz86eu2T55bc+Yp/fPdim7d76cAFvzJ1frrDMrB25wl9F5CZ+MzMrsUMue4S6l95m4Ig72O+itlX2G+1y3v1lisrM2pMr/FUo3IvfzMxK6Jjrnsyb/uJZBzD6hF3Zeb1app97UN48A0fcUc7QzKwduMJfReRe/GZm1o66da5lqwG9+P7W3aitESfstUnefANH3MHvxr7MzHc+bucIzawUXOGvJqm+7z78ZmbFSTpA0kuSpkkakWd9V0m3pPWPSxqYs25kSn9J0v6tlSlpUCpjaiqzS866wyRNkTRZ0k056WMkzZX0n2ZxFSyrGpy8/2e558Q9+MePdmmx7pL7prLXhXU8//oHFYjMzFZE1VX42/MgnrP+Uknzct4fI2mOpEnpdVzOuqPTgXqqpKNL+tlLWZiZWQclqRa4HDgQ2Aw4UtJmzbJ9B3g/IjYBLgLOT9tuBhwBbA4cAPxBUm0rZZ4PXBQRg4H3U9lIGgyMBHaNiM2Bn+Xs/zfAN/OEn7esSrj9hy0r9QCbrrMG227Ym6d+mX+Gni9e+jDH3fAkny5azMARdzBwxB389p6X8uad/MYHvPKu7wqYVVpVTcuZc8DdF5gNPClpdERMycm25CAu6Qiyg+fhzQ7i6wP3Sto0bVOwTElDgF55wrklIk5oFl8f4HRgCNl0+U+lst4vxec3M7M22RGYFhEzACSNAoYDueeK4cAZafk24DJJSumjImIBMFPStFQe+cqU9AIwDDgq5bkhlXsF8F3g8sZzQEQseZpVRNwnaWhu0Gn/hcpqV1PPOZDOtcXb/Pr26MplR23LCTdNbLHu3hfe5nO/GrPk/aX3T+PS+6cVLW/CL/fh57c9ywZ9unPGIZsD8LNRE/nnpDd4/tf78+H8Ray3ZjdUhhksIoK/TZjNqbc/uyRtj03XYtO1e7DjoD4M/ezadOmUfR+LG4IasVxxNDQENTUrZ/NdRDBvQT2jnniNc+58YUn69/fYmG037MVPbp7EwsUNrNGtE3f+ZHc26NO9gtHasqqqCj/teBAHpqQLjN+QHXy/3Ib49gfGRsR7qayxZC1ENy/fx83PXXrMzIrqD7yW8342sFOhPBFRL+kDoG9KH99s2/5pOV+ZfYG5EVGfJ/+mAJIeAWqBMyJiDIUVK6sJSd8DvgewzjrrUFdXV6TYphracBJ55KGms/XMmzcv7z56ANfu352Jby/m0okr9kCuIWffu2T5+kdnNVm3xel3t8h//H/v5fJJC+hUA/UNLcsb3KuGAwZ1pnMN/O6pZY/twZfn8ODLc7j64Zlt22BM+w1e/v5WXdlpvVpE/guPQr+vRh8tDB6avYhbX160wrH88cEZTcv+tJ7dLxjXMmMZv5+fbdeVtz4Jdli3ll5dVfB7adTa99PeqiGeaqvwt+dBHOAEYHREvJnnD+erkvYAXgZOjIjXCsTX4mC9vAfqaMiOaA8+9CBda9veQlANf0i5HE9xjqc4x2NtkO8A2byWWyhPofR8zd3F8kN2Dh0MDAUGAA9J2iIi5ubZplhMLRMjrgKuAhgyZEgMHTq0QJEtfbKwHu5uWYEGGL7N+uy32boM3Wq9Jul1dXUU28cw4H8Ph0vvm8pvx77c5lhWxOWTskp8vso+wNS5DUxdwYuQavXHZxfwx2eL5RCw6nSV+v3T2e/55hfbukW1fT/LH8+s8w4uSQTVVuFvt4O4pPWBQ8kO1M39G7g5IhZI+gHZbddhbYxvuQ/UNffdBQ0N7L777nTv0vZfTWsH6vbmeIpzPMU5HmuD2cAGOe8HAG8UyDNbUidgTeC9VrbNl/4O0EtSp9Qyn5t/NjA+IhaR3Vl+iewCIP8cmMXLKpl35y0suO7iI7ZdobJ/vPdgThi2CTuccy+9unfhL9/ZiXXX7AbA2x9+ysX3TWW7DXvz1e0HLNlmcUOw2WljWFDfwDe/sBE3jn9lybrDhgzgoanv8OYHn65QXK3ZcWAfbvn+Fwq2Cs9fuJjHZ75LQwTbbNCb/3fnC6y3Zjc26NOdU24rXPOuEfzh69sRAVsOWJMBvbvT0BAsXNzA2ClvsVHf7hxy2SMAXHjo1vTo2okt+vfk0envcmqRcsutf6/VeD09VO3k/Tbl+L02Wa4uTJ8uWgzAww89yLOLs/bPI3bYgIX1DWzUtztzPlpA59oatj1rbN7tfzxsE3beuC8TX5vLUTtuyJqrdaamRixa3MDpoydz0+OvLucn7DiufmgGx+2+8QqXU20V/vY8iG8LbAJMS3/k3SVNi4hNIuLdnPx/Ig32SvsY2qysurZ/vOI8LaeZWZs8CQyWNAh4nWz81lHN8owGjgYeA74G3B8RIWk0cJOk35GN9xoMPEHWoNOizLTNuFTGqFTmv9I+/gkcCVwvqR9ZF5+m/R9ytFJWyYx6Mn8lqVQthZKY8Mt9W6Sv3bMb53x5yxbptTXipbMPXPL+rC9t0eo+7r1/HKPf6sWJ+27Kemt2o2unmrwV0k8XLaZLbc0K95tfrUstQz+79pL3vzl06yXLhw7ZYJku/GtqRLeaWv5n6/WB/N/7YUO6c9iQDVqkNxepe9b8RYupe2kO73+ykP9354vMW1DPDcfuyA4De7Na51oWNwS1NSrL+IdiunWuBaBTjThp2KYt1q/dM7sYbO1vb5dN+jV537m2hnO/vCXn5vl7aotSNtREBJL46NNFHHjxQ8x+f9mfPl0j+MXBm7HbJv3400MzuO2p2W3e9thdBy3z/vKptgp/ex7EJwPrNhYqaV6azQFJ60XEm2nVIUDj6JW7gXMl9U7v9yOboaGk3IffzKyw1J3zBLJjci1wbURMlnQmMCEiRgPXADem8VzvkR37SfluJRsbVg8cHxGLAfKVmXb5c2CUpLOBialsUt79JE0BFgOnNDYYSXoI+BzQQ9Js4DsRcXeRskrmyZkr/zwSnWrEJUe2fjeiscLZUTVW4Lt36cRBW2bdsL6+00bU1dWx56ZrLcnXaRm6AduyafwdrNGtMw//fNhylVFXV8fQ3bKK+4WHbs2FOReU7aWqKvwVOIgX8hNJh6Ry3gOOSft4T9JZLL1de2bjAN5SmJ9ujY2d8hZf2jbvOC4zMwMi4k7gzmZpp+Usf0rWbTPftucA57SlzJQ+g6WTQOSmB3BSejVft3uBfectq5QWu9XIzJqpqgo/tO9BvFmeHjnLIynQch8R1wLXFitrRT04dY4r/GZmtlzqG1zhN7Omqu7BW2ZmtmqQdK6kXjnve6euLrYCFjcsndbm0RFZF4QLvrZVpcIxsyrgCn81cuOMma0aDsydwjI9wOqgCsbTIbzyzidLltfvtRqzzju4TQNEzazjcoXfzMwqpVZS18Y3klYDuhbJb23w0YL61jOZ2Sql6vrwmxv4zWyV8RfgPknXkR36jiV77omZmZWQK/xVKDzDgpmtAiLiAknPAXuTTaF8Vpq60szMSsgVfjMza1eS7gbGAHdFxF3AXRUOycysQytpH35JG0vaPi3nnYPYWuf2fTPr4I4G3gfOkPS0pCskDZfUo7UNzcxs2ZW6hf8q4GlJmwO7AQ+VuPxVgnv0mFlHFhH/Ba4HrpdUA+wEHAicKmk+cE9EXFDBEM3MOpRSz9LzQkScCvQGvlDislcZru+b2aoiIhoi4rGIOC0idiV7evrsSsdlZtaRlLqF/06AiLhY0uISl21mZh2IpK/kSf4N8JSkmRHxWHvHZGbWEZW0wp8GXyHpC8CfG9MlrQFsFhGPl3J/Zma2UvsTMLpZ2poRcVglgukIPlm4dA7+oZ9dq4KRmFk1KdcsPVcA2+W8/zhPmhXgaTnNbBXxakR8OzdB0sRKBdMRNOScPvqu7meYmVmmXBV+RU6tNSIaJHkK0DZydd/MVhHrSvol8B5Zv/378CFwhdRo6fLs9z+pXCBmVlVKPWi30QxJP5HUOb1+Cswo0746nPkLPfzBzFYJFwILgXWBQ4HngU0rGtFKTiyt8R+104YVjMTMqkm5Wt1/AFwC/JKsteY+4Htl2leH8+j0dyodgplZ2UXEb3PfS+oF3CPpfuDMiKirSGArsQ/mL1qy3KOrb6ybWabUD946UlLfiHg7Io6IiLUjYp2IOCoi3m7D9gdIeknSNEkj8qzvKumWtP5xSQNz1o1M6S9J2n8ZyrxU0ryc9ydJmiLpWUn3SdooZ91iSZPSq/lAs5L5dFFDuYo2M6taETE3InaMiGGu7C+fM0ZPXrLcqbZcN/HNbGVT6qPBRsDfJD0k6QxJO0lSq1sBkmqBy8kevrIZcKSkzZpl+w7wfkRsAlwEnJ+23Yxs7ubNgQOAP0iqba1MSUOAXs32MREYEhFbAbcBuQ9/mR8R26TXIW35XGZmlp+ktSRdKOlOSfc3vtq4bbs1EEkalMqYmsrskrPusNRINFnSTTnpR6f8UyUdnZNel/bR2Hi09rJ8Z60ZM/m/S5Y71bTp9Gtmq4CSVvgj4ryIGAYcBDwDHEv25N2bJH1L0jpFNt8RmBYRMyJiITAKGN4sz3DghrR8G7B3uqAYDoyKiAURMROYlsorWGa6GPgNcGqzzzAuIhpHOo0HBiz7N2FmZm3wV+AFYBDwa2AW8GRrG1Wggeh84KKIGAy8n8pG0mBgJLBrRGwO/Cyl9wFOJ3uC8I7A6ZJ658T29ZzGo1bvfi+vWlf4zSwpSwe/iPgI+Ed6NR5gDySbm3//Apv1B17LeT+b7GCZN09E1Ev6AOib0sc327Z/Wi5U5gnA6Ih4s8hNiO8Ad+W87yZpAlAPnBcR/8y3kaTvkcYsrLPOOtTV1RUqv6Bl2WbevHnLtY9ycTzFOZ7iHM8qpW9EXCPppxHxAPCApAfasN2SxhwASY2NOVNy8gwHzkjLtwGXNW8gAmZKamwgIl+Zkl4AhgFHpTw3pHKvAL4LXB4R7wPkVN73B8ZGxHuprLFkFxc3t+1rKY3Ota7wm1mmLBV+SXvkSX6y+QCt5pvlSWs+PVuhPIXS893BCEnrk80IMbRgMNI3gCHAnjnJG0bEG5I2Bu6X9FxETG+xg4irgKsAhgwZEkOHFtxNU2PuWLLY5m3ILg6WJX+5OZ7iHE9xjmeV0jjC9E1JBwNv0La7qu3ZQNQXmBsR9Xnybwog6RGgFjgjIsYUiK9/zvvrlD2N/nbg7NxprBuVouFo8jMT+WhmbZvyVtuFreMpzvEU53haKtcQ/jpgAvBfllbGA3iwyDazgQ1y3g8gO/jnyzNb2bz+a7J0/uZC2+ZL3xbYBJiWWve7S5qWbv0iaR/gF8CeqRUo+wARb6SfMyTVpXJaVPjNzKxNzpa0JvC/wKVAT+DENmzXbg1EreyrEzCYrPFoAPCQpC1a2ebrEfG6sifQ3w58k5wn0y/JXIKGoyMO2osundrWc7faLmwdT3GOpzjH01K5hvAfTNaPfgFwDfClNgxyfRIYnAZHdSHrY9l8JpzRQOPgp68B96eWkdHAEWmQ1iCyA/AThcqMiDsiYt2IGBgRA4FPcir72wJ/BA7J7Vspqbekrmm5H7ArTW8fm5nZMoiI/0TEBxHxfETsFRHbR0RbZkBblgYi2thAVCj9HaCXlj48Mndfs4F/RcSiNH7sJbLzT8H4IuL19PMj4CaWdicqOffhN7NGZanwR8RdEXEU2e3Ig4DH2rBNPVm/+rvJBnHdGhGTJZ0pqfFi4Rqgb+pzeRIwIm07GbiVrAI+Bjg+IhYXKrOVUH4D9CCbbSh3+s3PAxMkPQOMI+vD7wq/mdlykrSxpH9LekfS25L+lbpMtqY9G4iC7Jj/tVTW0cC/0vI/gb3SZ+lH1sVnBtk5Z7/UUNQb2A+4W1KnlA9JnYEvkj1srCxc3zezRuXqw9+TbIDTIcBUsoFNrYqIO4E7m6WdlrP8KVnf+3zbngOc05Yy8+TpkbO8T4E8jwJbFivHzMyWyU1kM+N8Ob0/gmxga/P++E2kPvmNjTm1wLWNDUTAhHSX4BrgxtRA9F4qm5SvsYGontRABJCvzLTLnwOjJJ1NNnXzNSm9sWI/BVgMnBIR76ayzmLpjENnRsR7klYnq/h3Tvu4F/jTMn9rbVRkQgozW8WUqw//f8mm5fw38CnZ9Jl7R8TvyrQ/MzNb+Sgibsx5/5dU6W5VezYQpZl7WnS9Sa3/J6VX83XXAtc2S/sY2D7vBzIzK6NyVfgvIBug1Dm9zMzMmhuXHnA1iuyccThwR5rHnsZpLc3MbMWUax7+M8pRrpmZdSiHp5/fb5Z+LNkFQFv685uZWSvK1cJvZmZWVEQMqnQMZmargnJNy2lmZmZmZlXAFX4zMzMzsw6sXSr8kn4k6fCcB5eYmZmZmVk7aK8WfgG7AX9vp/2ZmdlKRtJ6jU80NzOz0mmXFveIuLw99mNmZiu1G4HPSLo9Ik6udDBmZh1FuZ60OwTYHVgfmE/26PB7PaeymZkVEhH7KHs87GaVjsXMrCMpaZceScdIehoYCawGvAS8TdadZ6ykGyRtWMp9mpnZyktSb0mbS9pYUk1kJlc6LjOzjqTULfyrA7tGxPx8KyVtAwwGXi3xfs3MbCUhaU3geOBIoAswB+gGrCNpPPCHiBhXwRDNzDqUklb4G/vqS+qTr/tOREwq5f7MzGyldBvwZ2D3iJibu0LS9sA3JW0cEddUJDozsw6mXIN2H5c0CbgOuCsiokz7MTOzlUxE7Ftk3VPAU+0YjplZh1euaTk3Ba4CvglMk3SupE3LtK8OaeCIOzjmuicqHYaZWdlIul3SwZL8EEgzszIqy0E2DboaGxFHAscBRwNPSHpA0s7FtpV0gKSXJE2TNCLP+q6SbknrH5c0MGfdyJT+kqT9l6HMSyXNW5F9lEPdS3PKWbyZWaVdARwFTJV0nqTPVTogM7OOqCwVfkl9Jf1U0gTgZODHQD/gf4GbimxXC1wOHEg2LduRkppPz/Yd4P2I2AS4CDg/bbsZcASwOXAA8AdJta2VmaYQ7bUi+1iW78bMzDIRcW9EfB3YDphFNpvbo5K+LalzZaMzM+s4ynUb9TGgJ/CliDg4Iv4eEfURMQG4ssh2OwLTImJGRCwERgHDm+UZDtyQlm8D9k7zNg8HRkXEgoiYCUxL5RUsM1XWfwOcuoL7MDOz5SCpL3AM2d3gicDFZBcAY1vZrt3uBksalMqYmsrskrPuMElTJE2WdFNO+tEp/1RJR+ekby/pubSPS9K5xcysrMo1aPezhQbqRsT5ki6NiB/nWd0feC3n/Wxgp0J5IqJe0gdA35Q+vtm2/dNyoTJPAEZHxJvNjrnLs48lJH0P+B7AOuusQ11dXZ6P2jZt2XbevHkrtI9SczzFOZ7iHM+qQ9Lfgc+RPWH3fyLizbTqlnSHuNB2jXdu9yU7Dj8paXRETMnJtuROraQjyO7UHt7sTu36wL05Y8wKlXk+cFFEjJJ0ZSr7CkmDyZ47s2tEvC9p7RRfH+B0YAgQwFOprPfJujF9j+xccifZ3eK7lu8bNDNrm7JU+NswK8+uBdLztXQ0L6tQnkLp+e5ihKT1gUOBocsQR1viIyKuIhu0zJAhQ2Lo0Hy7yGPMHS2SNtpiBwb1W73oZnV1dbR5H+3A8RTneIpzPKuUyyLi/nwrImKIpH0jIl9L/5I7twCSGu/c5lb4hwNnpOXbgMua36kFZkrKvVPbokxJLwDDyMYaQHb39wyyivt3gctTRZ6IeDvl2R8Y2zg9taSxwAGS6oCeEfFYSv8z8CXKUOHfasCapS7SzFZi1TYzwmxgg5z3A4A3CuWR1AlYE3ivyLaF0rcFNiGbRWgW0D0d+JdnH2Wz14V15SzezKxiClX2c5xfID3f3eDmd1ub3KkFcu/U5tu2UHpfYG4qo/m+NgU2lfSIpPGSDmglvv5puVjcZmYlV64uPcvrSWCwpEHA62S3XY9qlmc02aw/jwFfA+6PiJA0GrhJ0u/IbtMOBp4ga5VvUWZ6dPu6jYVKmpcG6S7PPszMrPQK9W9vt7vBreyrE9l5YChZA9BDkrZYjn23sKJdQz/66KNl2qbauq45nuIcT3GOp6VKVfjzHsRTf/kTgLuBWuDaiJgs6UxgQkSMBq4Bbkyt8e+RVeBJ+W4lu6VbDxwfEYsB8pXZSnzLvA8zMyu5Qt1Dl+Vu8OxluFObL/0doJekTqmVPzf/bGB8RCwi6x70EtkFwGyadhcdANSl9AGtxA2seNfQNXv2ZOjQQr1nW6q2rmuOpzjHU5zjaalSFf6LC62IiDvJBjLlpp2Ws/wpWd/7fNueA5zTljLz5OmxIvsop4aGoKbGEzmYmSXteTc4JI1LZYxKZf4r7eOfwJHA9ZL6kXXxmQFMB86V1Dvl2w8YGRHvSfpI0heAx4FvAZeW8otp9Pn1epajWDNbSZW0D7+k6yRdK+miYvki4vpS7rej+0PdtNYzmZl1PLPyJaaW9sY7ty8AtzbeDZZ0SMp2DdA33ak9CRiRtp0MNN6pHUO6U1uozFTWz4GTUll9U9mkvO9KmgKMA06JiHfTYN2zyC5MngTObBzAC/wQuJpsWufplGmGnq/R3GzlAAAgAElEQVRt76EBZrZUqVv4r08/F5a43FXa7++dygnDBlc6DDOzkpC0R1pcGBHjC+WLiK8UWddud4PTzD0tnrmSZqQ7Kb2ar7sWuDZP+gRgi3xxlZbvCpvZUqWu8J8WEXtLOp/sNqqVQH1Da7OcmpmtVL6dfs6l6bNNrET8OC8zy1XqCv96kvYEDklzGDc55ETE0yXen5mZrWQi4tut57IVUeMav5nlKHkLP1k/yQHAb2la4Q+yh5eYmZkhaSuybjeNXVyeBy7NeYCVLafPrbtGpUMwsypS0kG7EXFbRBwIXBARwyJir5yXK/sr4O9Pz249k5nZyuUysgdiXUM2BaWAf1Q0og7CLfxmlqukLfySBkbErIg4q8B6Af0jwrXXZfSrfz7PV7Yb0HpGM7OVRETskfte0njg8AqF06G4vm9muUrawg/8RtLtkr4laXNJa0vaUNIwSWcBjwCfL/E+VwkfL1zMwBF3cPZ/plQ6FDOzkpI0UtJE4BnyzHhjZmYrptRdeg4FfgV8FrgceIjsASXHAS8BwyJibCn3uaq5+uGZlQ7BzKzUrgaOBX4H/G+FY+kQ3MBvZrlK/qTdiJgC/KLU5dpSdz33JgduuV6lwzAzWyGSDgTuiYg5wBxJrwDHVzisDkHu02NmOUr9pN1Tc5YPbbbu3FLua1X2w78+zYm3TKp0GGZmK+oHwJuSJqT++y8CRZ/UbmZmy67UffiPyFke2WzdASXe1yrtHxNfr3QIZmYrJCKGA4PIun3+EPhMRPyhslF1DG7fN7Ncpe7SowLL+d7bCooI37Y1s5VO44xuABHxMTCp2XrP6LaCfGows1ylbuGPAsv53lsr/vGjXYquHzTyTt6dt6CdojEzKxnP6GZm1o5KXeHfWtKHkj4CtkrLje+3bG1jSQdIeknSNEkj8qzvKumWtP5xSQNz1o1M6S9J2r+1MiVdI+kZSc9Kuk1Sj5R+kaRJ6fWypLk52yzOWTd6eb+kttp2w96t5tn+7HvLHYaZWUl5Rrfy891fM8tV0i49EVG7vNtKqiU78O8LzAaelDQ6zfrT6DvA+xGxiaQjgPOBwyVtRjZ+YHNgfeBeSZumbQqVeWJEfJj2/TvgBOC8iDgxJ6YfA9vm7H9+RGyzvJ+xXF75cDEAD7w8h+036k2PriWffMnMrKQ8o5uZWfspdQv/itgRmBYRMyJiITAKGN4sz3DghrR8G7B36us5HBgVEQsiYiYwLZVXsMycyr6A1cjf5ehI4OYSfsZl9p8f79ZqntMf/ZS6l97m6GufYIvT7+bvT8/m00WLW+RbUL+Ytz/8tBxhmpm1mWd0MzNrX9XUFNwfeC3n/Wxgp0J5IqJe0gdA35Q+vtm2/dNywTIlXQccBEyh2cNeJG1ENnvE/TnJ3SRNAOrJ7gb8M98HkfQ94HsA66yzDnV1dXk/cGuab3fmLt3YsGctx4z5uEXeY657csnySbc+w0m3PsP1B6xORPD4m4vp2gkufjrr73/S9l3Zaq3y/ernzZu33J+5HBxPcY6nuGqLp4M4ArggLY8E/paz7gDg/9o9IjOzDqyaKvz5Ohw2b3UvlKdQer47GEvKjIhvp65ElwKHA9fl5DsCuC0icpvKN4yINyRtDNwv6bmImN5iBxFXAVcBDBkyJIYOHZonjDzG3NHkbeN2F/d6nbFT3uJbh2wHwLXrvsWx109otbh8FwYAv3tqAdB0sO8xuwzkoalz+P4en+GwHTZoW7wF1NXV0ebP3A4cT3GOp7hqi6eD8IxuZmbtqJq69MwGcmuaA4A3CuWR1AlYE3ivyLatlpkq9LcAX222ryNo1p0nIt5IP2cAdTTt3182w7fpz2VHbbfk/V6fXbvk+7j+0VlMn/Mxp97+LANH3LHk9fGCen57z0tL3s+YM6/k+zazVc4Kz+jWzpM8DEplTE1ldknpx0iakzOZw3E525wv6fn0Ojwn/XpJM3O2qbpxYWbW8VRTC/+TwGBJg4DXySrcRzXLMxo4GngM+Bpwf0REmjHnpjT4dn1gMPAEWUtRizJTv/3PRMS0tPw/ZE94BEDSZ4HeaT+Nab2BTyJigaR+wK4svSXdriQx7uShXPvwTG4c/0pZ97X56Xc3eT/stw/kzTdko97c9sPi04iamSVbS/qQ7Bi9Wlomve/W2sYVmOThfOCiiBgl6cpU9hVpm1si4oRm8R0MbAdsA3QFHpB0V+PYMeCUiLitLV+UmVkpVE2FP/XJPwG4G6gFro2IyZLOBCZExGjgGuBGSdPIWvaPSNtOlnQrWV/8euD4xq44BcqsAW6Q1JPsBPMM2VMeGx1JNgg4t6Xp88AfJTWQ3Rk5r9nJpV0N6rc6Z31pC25/ejafLGw5QLe9TXjlfQaOyOmSlLonvXDmAazWZbknbzKzDmhFZnRLlkzIACCpcUKG3GPycOCMtHwbcFnzSR6Amel8smPK16JMSS8Aw1jaAHVDKrexwp/PZsADEVEP1Et6hmxswq3L/YnNzFZA1VT4ASLiTuDOZmmn5Sx/ChzafLu07hzgnDaW2UDWQl8ojjPypD1KG54l0N6e/tW+fO5XY5a8n3Xewbz23ifsfsG4Jvk614rxI/emb4+uTdLnfLSANbp14vanZ/PV7Qbw7scL2eOCcSxuKM1z0j5/Whbbn741hK0HrMnrc+cjiW026FWS8s1sldSekzz0Beamynvz/ABflbQH8DLZdM+vkTUinZ7uOncH9qLpxcg5kk4D7gNGpIuPJlZ08odlzV9tg9MdT3GOpzjH01JVVfht2XXrXMuFe67GyQ/MX5K2QZ/uzDrvYG598jX++sSr/Ov4gtc2rLVGdgHw9Z02AqB/r9WYfu5BAEQEg0Zm10qzzju4yXbH3fAk977wdpvj/O6f8w8yPmTr9Tl8hw14ZvZcfjR0Eya/8QEDenVn8hsfsMsm/dpcvpmtUtpzkodi+/o3cHPq6vkDstb/YRFxj6QdgEeBOWTdQxsvGEYC/wW6kE3u8HPgzBY7WMHJH5Z1oHm1DU53PMU5nuIcT0uu8HcA/Var4ZERw+i7epcm6YftsMEKzbgjqUVFv9HVR+/QIm3egnp6dO3E1y+5my/tshmn3PZsq/sY/cwbjH4mG0d9wZiX2hTX1d8aQvcutb4gMFt1LcskD7PbOMkDBdLfAXpJ6pRa+Zfkj4h3c/L/iayvP2ndkrvOkm4Cpqb0N1OWBWlq6JPb/KnNzJaTK/wdRP9eq1U6hCVP+P3uVl0ZOmQDDh2SnTsfnvoO37jm8ZLt57gCdwv691qNe0/ak26da5bcmVhizB1MOm1f6huCax6eyTsfLeCCr23lx8+brZzabZKHtM24VMaoVOa/ACStl1OBPwR4IaXXAr0i4l1JWwFbAffkbpPGE3wJeL7E342ZWQuu8FvZ7Ta4X4s7BRHB9DnzeOqV93nqlffp36s7F937MgAXHb41J97yzDLv5/W585eMGchnmzPHNnn/t6dmF8z70Kl7sX6v1ait8QWBWbVpz0ke0i5/DoySdDYwMZUN8BNJh6Ry3gOOSemdgYdSg8KHwDdyxgD8VdJaZBcYk4AflPbbMTNryRV+qwhJbLL2Gmyy9hocvsOGAPx0n8FL1n952wFN8kcEkogIIuDM/0zh+kdnlS2+5oOeGz1wylBenzufEbc/x0Z9u/PQ1Hd44hd7s/Yarc4kaGYl1F6TPKT0GSydySc3fSRZn/zm6Z+SzdSTb9/D8qWbmZWTK/y2UmjseiMJCc44ZHPOOGTzJnkaGoJJs+fylT88ykOn7sUGfboDSwfLLKxvYNHiBrp2qqFG4rpHZ3HWf5ZtZtU9f1O3ZPnV9z4BYMdz7sub97jdBrHumt34+k4bcf+Lb7PvZuvQpVM1PevOzMzMVgWu8FeRbTboxaTX5lY6jJVWTY3YbsPeBQcad+lU06TC/Z3dBvGd3QblzfvJwnoWLGpg27PG5l3fFlc/PBOAs+94oeXKMUufWXDJkdty8JbrufuQmZmZlYUr/FVkQO/VllT41+3pLiKV1L1LJ7p3aTkdaT4ffbqIia/OZbUutRx65WOt5m/uJzdP5Cc3T2xz/h8O/Qzf231jPlm0GAHrrdnNg4/NzMysIFf4q0innBbe875adc/4sgLW6NaZPTZdCyh+gbCgfjFn/WcK455/jdfnLf+Dza6om84VddOXe/u8cu44LIs9Nl2LB1+e0ySta6cadh+8Fj/bZzCT3/iATddZg/69V6NzTQ29unem8fnVNb6jYWZm1i5c4a8iuVXAPVMF0jqOrp1qOftLW1LX690WD+BoaAhe/O9HHHTJQ5UJbjk1r+wDLKhv4N4X3uLeF95a/oKX8wIEYOsBa/LM7A/4wsZ9uOboHVi9qw9zZma2avOZsEq5i8aqpaZGbLZ+zzZ1ISpm/sLFHHTJQ8x85+OCebYasCbPzv6AA7dYlyu+sT11dXXsvsee1NaIGXPm0W+NrvTs1pmGhuDG8a9w+ujJBcuqRs/M/gCA8TPeY/PT7y5Noct5AfLUL/ehb4+upYnBzMxsObnCX4VGHPi5SodgK6nVutQy7uShy7xd44DhjdfqsSStpkYcvctAjt5lYImia6mhIXj344XUiCUV43HjxrHXXnsB8OmixTz48hzemDuf/3fXiyyobyhbLOWw/dn3rvBFnJmZ2Ypyhb+KDOq3OgBb9l+zwpGYtY+aGrHWGk1bwHPvbnXrXMt+m68LwDG75p9RqRw+XbSYWye8xj8nvk7two84YvctOPfOF3j344VA1uXut4dtzax3PmaL/tkdkx/f/DRvfbigSTm3fn/ndovZzMysEFf4q8gJe23CkI36sOsm/SoditkqrVvnWr6180C+tfPA7DkO2w/gq9sPaJGvX7orseOgPjz+f/u0d5hmLTx06l4sWrxy3Qkzs/Jzhb+KdKqtYbfBruybmdnyaXzgoJlZLj/208zMzMysA3OF38zMzMysA3OF38zMzMysA1PE8j/x01onaQ7wSpl30w94p8z7WBaOpzjHU9yqHM9GEeGn7lkTPo9UBcdTnOMpruLnEVf4OwBJEyJiSKXjaOR4inM8xTkes/ZXbX/njqc4x1Oc42nJXXrMzMzMzDowV/jNzMzMzDowV/g7hqsqHUAzjqc4x1Oc4zFrf9X2d+54inM8xTmeZtyH38zMzMysA3MLv5mZmZlZB+YKv5mZmZlZB+YKfxWStIGkcZJekDRZ0k9Teh9JYyVNTT97p3RJukTSNEnPStoup6yjU/6pko4ucTy/kfRi2uc/JPVK6QMlzZc0Kb2uzClre0nPpVgvkaQSxnOGpNdz9ntQzjYj0z5fkrR/TvoBKW2apBEl/n5uyYlllqRJ7fT9dJP0hKRnUjy/TumDJD2e/hZukdQlpXdN76el9QNb+95KFM9fU7nPS7pWUueUPlTSBznfz2k5ZZXi91UonuslzczZ7zYpvaz/X2blUOS45PNI8Xh8HsHnkRWIp3rPIxHhV5W9gPWA7dLyGsDLwGbABcCIlD4COD8tHwTcBQj4AvB4Su8DzEg/e6fl3iWMZz+gU0o/PyeegcDzBcp6Atg5xXoXcGAJ4zkDODlP/s2AZ4CuwCBgOlCbXtOBjYEuKc9mpYqnWZ7fAqe10/cjoEda7gw8nv4ubgWOSOlXAj9Myz8CrkzLRwC3FPveShjPQWmdgJtz4hkK/CdPOaX6fRWK53rga3nyl/X/yy+/yvEqcpz0eaR4PGfg80ix46TPI8XjuZ4qPY+4hb8KRcSbEfF0Wv4IeAHoDwwHbkjZbgC+lJaHA3+OzHigl6T1gP2BsRHxXkS8D4wFDihVPBFxT0TUp2zjgQHFykkx9YyIxyL7S/9zzmdY4XiKbDIcGBURCyJiJjAN2DG9pkXEjIhYCIxKeUsaT2pdOYzsYFRQCb+fiIh56W3n9ApgGHBbSm/+99P4d3UbsHeKudD3VpJ4IuLOtC7ITlBF/34o3e+r0PdTSFn/v8zKweeR5YunyCY+j/g80mo8RTap+HnEFf4ql26LbUt29bhORLwJ2cEBWDtl6w+8lrPZ7JRWKL1U8eQ6luzqtdEgSRMlPSBp95w4Z5c5nhPS7bJrlW5VU/nvZ3fgrYiYmpNW1u9HUm269fs22QFkOjA358SaW/aS7yGt/wDoSwm/n+bxRMTjOes6A98ExuRssnO6VXqXpM2bx1nGeM5Jfz8XSerayn5L/vdjVg4+jyxzPD6P4PPICsRTlecRV/irmKQewO3AzyLiw2JZ86RFkfSSxiPpF0A98NeU9CawYURsC5wE3CSpZzvEcwXwGWCbFMNvG7MW2G+7fD/AkTRtlSn79xMRiyNiG7LWjh2Bzxcpu+zfT/N4JG2Rs/oPwIMR8VB6/zSwUURsDVwK/LOVOEsVz0jgc8AOZLdXf97Kfkv692NWDj6PLHM8Po80buTzyPLEU7XnEVf4q1S6Wr0d+GtE/D0lv5VuATXetns7pc8GNsjZfADwRpH0UsVDGmDyReDr6ZYa6dbdu2n5KbJWgU1TPLm320oaT0S8lf4BG4A/sfS2YSW/n07AV4BbGtPa4/vJ2ddcoI6sz2CvFE/zspd8D2n9msB7lPD7yRPPAWl/pwNrkZ2wGvN82HirNCLuBDpL6lfueNIt9YiIBcB1tOPfj1k5+Dyy7PH4PNKSzyNtj6eqzyNRhoEBfq3Yi+yK78/A75ul/4amg60uSMsH03QwyBMpvQ8wk2wgSO+03KeE8RwATAHWapa+FmlQDtnAmNcb9ws8mWJsHEx0UAnjWS9n+USyfoMAm9N00NAMsoE7ndLyIJYO3tm8VPHkfEcPtPP3sxbQKy2vBjxEdjL9G00HW/0oLR9P08FWtxb73koYz3HAo8BqzfKvC0seCrgj8Gr6Pkr1+yoUz3o5v8/fA+e1x/+XX36V41XkOOnzSPF4fB6JosdJn0eKx1O155F2O/j4tUx/SLuR3dJ5FpiUXgeR9Ye7D5iafjb+cwu4nOwK/zlgSE5Zx5INkpkGfLvE8Uwj63vWmNb4z/5VYHL6R3oa+J+csoYAz6dYL2v8hyxRPDemz/8sMJqmB+5fpH2+RM6MBWm7l9O6X5Ty+0nrrgd+0Cx/ub+frYCJKZ7nWTqrw8Zkg5qmkR20u6b0bun9tLR+49a+txLFU5/KbvzOGtNPyPl+xgO7lPj3VSie+9Pfz/PAX1g6A0NZ/7/88qscryLHSZ9Hisfj80j4PLIC8VTteaTx6sfMzMzMzDog9+E3MzMzM+vAXOE3MzMzM+vAXOE3MzMzM+vAXOE3MzMzM+vAXOE3MzMzM+vAXOE3MzMzM+vAXOE3MzMzM+vAXOE3K0LSQEnzJU2SNEPShSl9qKQHJf1D0hRJV0qqSev2k/SYpKcl/U1Sj5Q+S9LNOWXfImlWWu6Synpe0nON6a3E9i1Jz0p6RtKN5fj8Zma2YnwesWrgCr9Z66ZHxDbAzsAxOek7Av8LbAl8BviKpH7AL4F9ImI7YAJwUs4260vqLakP2aO/G+0PdI6ILYC9WgtI0uZkTy8cFhFbAz9d3g9nZmZl5/OIVVSnSgdgthL4jKRJwCDgwpz0JyJiBkBqcdkN+BTYDHhEEkAX4LGcbW4GjiJ7zPZNwMiUvhjoLqm2jTENA26LiHcAIuK95fhcZmbWPnwesYpyhd+sddMjYhtJ3YEJkq5P6dEsX5AdgMdGxJEFyhoNXJfyHcPSA/U9wFeAOcDrbYhJefZvZmbVyecRqyh36TFruwVkLSi90/sdJQ1KfS4PBx4GxgO7StoEQFJ3SZvmlLEw5XksLQMQEfXAfOAUcm7FSuov6b48sdwHHCapb8rXpzQf0czMysjnEasIt/Cbta7xVmxXslaXZyUNJTvYnkfW9/JB4B8R0SDpGOBmSV3T9r8EXm4sLCJOB0j9NEnLhwE9I+Ka3HRgPaC+eUARMVnSOcADkhYDE2naL9TMzKqHzyNWUYrw3RyzZZUO1CdHxBfLvJ8TgFcjYnQ592NmZu3L5xFrT27hN6tiEXFZpWMwM7OVl88jBm7hNzMzMzPr0NzCX2b9+vWLgQMHlnUfH3/8MauvvnpZ97EsHE9xjqe4VTmep5566p2IWKtddmYrDZ9HKs/xFOd4iquK80hE+FXG1/bbbx/lNm7cuLLvY1k4nuIcT3GrcjzAhKiC45Zf1fXyeaTyHE9xjqe4ajiPeFpOMzMzM7MOzBV+MzMzM7MOzBV+M7MSiQj+/Ngshl/+CANH3MELb35Y6ZBsFfPAy3O4/8W3Kh2GmVUZD9o1M1tOEcGl90/jd2Nfzrv+/hff5vPr9WznqGxVdvS1TwAw67yDKxyJmVUTV/jNzJbB3E8WUvfSHH52y6SCee49aU8+s9bqSGrHyMzMzPJzhd/MrIiI4JqHZ3LWf6YAUCNoaPb4kq036MU/f7SLK/hmZlaVXOE3M2vmnXkLOPW2Z7n/xbdTypQl6340dBOGfX5tth7Qi9oaV/DNzKz6ucJvZh1WQ0Pw4aeLWLQ4WKNbJ7p1rs2b7/W58zn2uid56a2PCpZ11093d398MzNbKa30FX5JBwAXA7XA1RFxXrP1ewC/B7YCjoiI23LWXQAcTDZb0VjgpxERkrYHrgdWA+7MSd8auBLoAcwCvh4RnobDrErMeudjhl5YV5Ky+vXowtgT9+SZJx9l6NChJSnTzMysElbqCr+kWuByYF9gNvCkpNERMSUn26vAMcDJzbbdBdiV7EIA4GFgT6AOuAL4HjCerMJ/AHAXcDVwckQ8IOlY4BTgV+X4bGbWNlc+MJ3z7nqxJGVd9+0d2Ouza5ekLDMzs2qxUlf4gR2BaRExA0DSKGA4OR1uI2JWWtfQbNsAugFdAAGdgbckrQf0jIjH0nZ/Br5EVuH/LPBg2n4scDeu8Ju1q4X1Dex1YR3vzFvAgvrm/9Zw2hc349u7DvQAWjMzs2Rlr/D3B17LeT8b2KktG0bEY5LGAW+SVfgvi4gXJA1J5eSW2T8tPw8cAvwLOBTYIF/Zkr5HdoeAddZZh7q6urZ+nuUyb968su9jWTie4hxPcfnimV8f/PDeTwpuc8qQbmzeL/XPr3+FBx54pazxmJmZrUxW9gp/via8yJPWckNpE+DzwICUNDb1959fpMxjgUsknQaMBhbmKzsirgKuAhgyZEiUu/9vXV1dVfUxdjzFOZ7iGuNZtLiBP4ybzkX35n+o1YOn7MWGfbu3WzxW3ST1AW4BBpKNsTosIt7Pk+9o4Jfp7dkRcUNKLzR2K2+5ym4hXQwcBHwCHBMRT0vahqxbaE9gMXBORNyS9nE9WdfRD9L+j4mIwg90MDMrkZW9wj+bpq3sA4A32rjtl4HxETEPQNJdwBeAG1l6EdCkzIh4Edgv5d+UbMCvmZXI3E8WMv7Nev45aiL3vfg2H31av2Td4LV7cMv3d6bP6l0qGKFVsRHAfRFxnqQR6f3PczOkyvvpwBCyhpyn0riv9yk8dqtQuQcCg9Nrp7T9TmSV/29FxFRJ66d93B0Rc1MYp+ROHmFm1h5W9gr/k8BgSYOA14EjgKPauO2rwHcl/T+yOwV7Ar+PiDclfSTpC8DjwLeASwEkrR0Rb0uqIWshurK0H8ds5XfWf6ZwzcMz+cYXNuQv419t0zZH7rgBNz+xtHde7+5z2H/zddl8/Z58edv+9OruSr61ajgwNC3fQDYBw8+b5dkfGBsR7wFIGgscIKmOwmO3CpU7HPhzRAQwXlIvSetFxJJbUhHxhqS3gbWAxgq/mVm7W6kr/BFRL+kEssGztcC1ETFZ0pnAhIgYLWkH4B9Ab+B/JP06IjYHbgOGAc+RtfSMiYh/p6J/yNJbu3elF8CRko5Py38Hriv7hzRbCbz/8UK2PWtsk7S2VvaBJpX9E7bpys8OHUan2pqSxWerhHUi4k2A1HCTb7qlfOO++qdXobFbhcotVNabjQmSdiSbGGJ6Tr5zUrfQ+4AREbEg34dZ0bFgy5q/2saqOJ7iHE9xjqellbrCDxARd5Ldfs1NOy1n+UmadtFpTF8MfL9AmROALfKkX0zWZ9NslffxgnrGTnmL0c+8kfNE2syxuw7i/w76XItKe0Qwfc7H/PuZN7jr+Td5d95C/u+gz/OV7fovmVWnrq7OlX3LS9K9wLp5Vv2irUXkSYsi6ctTVrYym/HtRuDoiGicTmok8F+yi4CryO4UnJmv8OUeCzbmDoBlHndSbWNVHE9xjqc4x9PSSl/hN7P2s2hxA4N/cVeTtPXX7Mb399iYPT+7Fjtv3LfodJiS2GTtHpy476acuO+m5Q7XOpiI2KfQOklvpS41b6bK9tt5ss1mafccyBqD6lJ63rFbpOma85RbcAyZpJ7AHcAvI2J8TvyNrf8LJF1Hs+fDmJmViyv8ZtaqK+qmc/6Ypg+32nnjvpy476YM2ag3NTWe894qbjRwNHBe+vmvPHnuBs6V1Du93w8YGRHvFRq7VaTc0cAJ6fkvOwEfpIuCLmTdSP8cEX/L3XnOhYPIxgg8X4oPbmbWGlf4zSyv2e9/wm7nj2uRfuAW6/Lbw7amexcfPqyqnAfcKuk7ZJMyHAqQnq3yg4g4LlXszyKb8AHgzMYBvBQeu5W3XLKupAcB08hm5vl2Sj8M2APoK+mYlNY4/eZfJa1F1h1oEvCD0n18M7PCfMY2syUigl//ewrXPzqrxbrbf7gL22/Uu+VGZlUgIt4F9s6TPgE4Luf9tcC1BfLlG7tVqNwAjs+T/hfgLwViHFb0Q5iZlYkr/GYdSEQw8u/PMerJ14rmu/yo7dh3s3XoXCsmvV3PMSPuKJh3xrkHucuOlVSaDvPQxrnpUxebURGxf2UjMzPrmFzhN+sAnp09l0Mue6TN+Y+/6emi6yf+al96+wFXVj79ch5ERXpybb5pNM3MrARc4TdbSUUEg0bemXfdhYduzde2XzrpSENDMOGV9/nr46/wr0n5H0b9yIhh9O+1WlliNWumQdKGEbdggtQAACAASURBVPEqgKSNaH0aTDMzW06u8JutZN6YO59dzru/Rfr6a3bj0ZEtuhoDUFMjdhzUhx0H9eHiI7Ztsq4a5ge2Vc4vgIclPZDe70F6yJSZmZWeK/xmK4mHp77DN655vEX6vSftySZr96hARGbLRlKniKiPiDGStgO+QDZjzYkR8U6FwzMz67Bc4TercheMeZE/1E1vkrZF/578+4Tdij7kyqwKjZc0GxgDjImI/1Q6oI5qcUNQ68H2ZpZUtMIvaTiwcURcJOm4iLi6kvGYVZPX3vuE3S9oOg/+ZUdtyxe3Wr9CEZmtmIgYkvrrHwj8XlJ/4GGyOe8fiIgFFQ3QzKyDqnQL/3eBP0r6DbAm4Aq/rfKOv+lp7nj2zSZplxy5LYds7Yq+rfwi4hXgSuBKSZ2B3YEDgLMlzYmIgysaoJlZB1TpCv+0iPi3pI+Bv1Y4FrOSiQg+XrgYgB5dW/83a2gIhl/+CM+9/kGT9EdHDGN9z5xjHVRELALuB+6XtD3wOUn93J/fzKy0Kl3hvwwgIu6XdFCFYzFbIQ0Nwcb/l3+azBbGFH7QFcAvD/48x+2+cQmiMqs+ki7Jk3w48Ov/z96dh0lR3fsff3/YFwXBBRFUcN9XROOKGhU1Skyi0dzrEmPUGO81udGIMVGj0R8x8SZRs6lR1Bu3EE2IOxIHTSIKKi64gUAQRVRwYV9mvr8/qnroGbp7ema6p5vh83qefqbq1KlT366Z6TpddRaSp72u8JuZlVBFK/wRMR1A0rfJusOfzrp4SkT8plKxmRVrxao6tvvhIyUp67lLD2eT9buVpCyzKjYCuKxR2vH+zDczK49K3+HP+GZE/Dqzks66+E3AH/5WtRYuW8muVzy+RvqrPz6qYDOeex76O0t6D+b+F+ew7+ANuXj4DnTp1KGcoZpVm/kRcXt2gqTvVCoYM7P2rloq/B0kKSICQFJHoEuFYzLLadnKWnb40aNrpL9x1XC6de7Y5P6b9uzAsAMHc+aBg8sRntnaYFtJTwALgDnAgyTj8ZuZWRlUS4X/MeA+Sb8jmV79XJJxms2qyqCRa7a9n/n/jvF4+GbNMwzoCKwHDAYuBHaVtDnwUUQsrWBs7YI/kcwsW7VU+C8GzgG+RfI59TgeotOqyPXjp/G/495qkDbjmmPo4IltzJotIp5vlPQHSdeSdNr9LTCp7aMyM2u/Kj3x1k0kE648ERG/Jfmgb24Zw4FfkdwtuiUiRjXafjDwS2A34OSIGJO17VrgWKADMA64ICIiHR5uNNAdeDgrfQ+S8aO7AauA8yLiuebGbGuPXM13Jv/w82y0XtcKRWTWPkXE9ysdg5lZe1XpnoK3ArsDD0saL+liSbsXu3Pa1v/XJLM27gScImmnRtlmA2cAdzXad3/gAJIvArsA+wCHpJt/C5wNbJu+hqfp1wI/jog9SEaYuLbYWG3t8517XmxQ2T9l6BbMGnWsK/tmrSRpP0mTJC2StEJSraRPm97TzMxaotLDck4EJgJXSNoQOBL4nqTdgBeARyPivgJFDCWZvGsGgKR7SIZ7ey3rGLPSbXWND09yp74LSTOizsA8Sf2BXhHxTLrfHcAXSZ5EBNAr3b838F7L3rlVsw8XLmefq59okOZ2+mYldSNwMvAnYAhwGsnNFSuRZatq6dGlWlrtmlmlVc2nQUTMB+5OX6TNaoYX3AkGAO9krc8B9i3yeM9IehKYS1LhvzEiXpc0JC0nu8wB6fJ3gMck/Zzk6cj+ucqWdDbJEwL69etHTU1NMSG12KJFi8p+jOZYm+M549HFDdZ/ckB3Bq7fgQkTJlQknrbgeAqrtnjai4iYLqljRNQCt0n6V6Vjak/eeH8he23Rp9JhmFmVqIoKv6TGE7AAEBFXNrVrrt2KPOY2wI7AwDRpXNreP9foEJkyvwV8NyL+LOkk4A/A53PEfRNwE8CQIUNi2LBhxYTUYjU1NZT7GM2xNsZzxdipjP7XrAZps0YdW7F42pLjKaza4mknlkjqAkxJ+1LNBXpWOKZ2JYq6EprZuqIqKvzASGAKMBZY2Yz95gCbZ60PpPhmNicAEyNiEYCkR4D9gDtZ/SWgcZmnAxeky3/CIwlVnSdem8dZd0xec8Ojq4fT7CC44ZS96NKpA9/MkXfSpZ9n4/XdTt+sjE4leUp6PvBdks/xL7e2UEl9gXuBQcAs4KSI+DhHvtOBH6arP8lMAlZgwIac5Spp5/cr4BhgCXBGRLwgaUvgfpLBJDoDN0TE7wodo7XvfU2u8ZvZapXutJuxGfBHkrGZNwfGRcR1Rew3iWQCl8Hp3aKTSb40FGM2cIikTpI6k3TYfT0i5gIL005lImlb+td0n/dY3bH3MGBakceyMvts2UoGjXwod2W/kbqAb9/1whqV/ZoLhzFr1LGu7JuVWUT8G6gjqUDfD4yMiOklKHokMD4itgXGp+sNpJX3y0mafw4FLpeUafuSb8CGfOUenZX3bFaPNDcX2D8d4GFfYKSkzZo4RknVub5vZlmq4g5/RHwC/FrSXcAokjvnQ4vYb5Wk80km7uoI3BoRUyVdCUyOiLGS9gEeAPoAx0n6cUTsDIwhqbS/QnIr5NGI+Fta9LdYfQfmkfQF8E3gV5I6ActI2+lb5UQEgy95uEHasO03ZvTXV//5ZJpkfLZsJb+f8Da/fvLtBvmn/vgoenatin8Fs3WCpGNJhjh+m6Rp5mBJ50TEI4X3bNIIkhtHALcDNSTzvGQ7iuSm0oI0lnHAcEk15B+wIV+5I4A70jv0EyVtIKl/euMooyvpzbUmBoUoKTfpMbNsVVHLkXQkyZ30riTDZ3672H0j4mGSx6LZaZdlLU+iYROdTHotyWRfucqcTDJUZ+P0fwB7Fxubldd/3f0if3upYQuuQu3ue3XrzEVH7cBFR+1Q7tDMrLDrgEMzd/UlbQ08ROsrvv0yle2ImCtpkxx5cg32MCB95RuwIV+5+cqam84a/BCwDXBRRLzXxKAQDbR28IcXXnyRJf/uWHT+auuc7ngKczyFOZ41VUWFH3iUZBjOucDXga9LIiKOr2xYVo1q64Ktf9Dwrv7LVxxJr26dKxSRmTXTB42a8MwAPihmR0lPAJvm2HRpkcfON9hDSwaByLtPRLwD7JY25fmLpDHNOUaLB39I+yvtvvsefG7rDYvbh+rrnO54CnM8hTmeNVVLhf/QSgdga4cbxk/junFv1a9f9cVdOHW/LSsYkZm1wFRJDwP3kVR4TwQmSfoSQETcn2/HiFhjZLQMSfMyTWrS5jO5vkTMYXXzHEieANek6fkGbMhXbpMDR6R39qcCBwH/LHCMkgp32jWzLFVR4Y+I0g1ybu3WoJEPNVj3ZFhma61uwDxWD4LwIdAXOI7kC0DeCn8TxpKMpjYq/fnXHHkeA67J6qh7JHBJRCyQtFDSfsCzJM1Mb2ii3LHA+emkj/sCn6ZfCgYC8yNiaXqcA4D/TbflO0Zpub5vZlmqosJvVsjrcz/j6F89Xb9++ue25Mcj1uhiYWZriYj4epmKHgXcJ+kbJCOxnQiQtp0/NyLOSiv2V5GM8gZwZaYDL/kHbMhZLkn/sWOA6STDcmbe147AdZIyTYV+HhGvNHGMkrr56Rnsv81G5SjazNZCrvBbVfv+mJe4b/LqPm6vXXmUp4s3s5zSGdsPz5E+GTgra/1W4NY8+XIN2JCv3CDHIBMRMQ7YLU+MOY9Rak+++WG5D2Fma5FqGYff2omI4LX5tfzwL6+waPmqVpUzaORD9ZX9jdbryqxRx7qyb2ZmZtZMVVl7knQ7yePRX0fEq5WOx4rz8CtzOe+PL6Rrs/m/ibOB5jfBmb9oOXv/5In69fvP25+9tuhTYA8zMzMzy6cqK/zAjcAWJNOvN540xarQiBv/wUtzPs257fZn/s3tz/ybV644kvWbGDrz1D88y9PTPqpfn3b10XTu6AdRZu2ZpBHA+xHxbKVjMTNrj6qywp9OljUJ+HOlY7HCVtbWse2lq/ucXTx8B3bknfrxZr9330v8+YWkWc6uVzwO5J4cK9fY+oUm0TKzdmVfYFdJnSLi6EoHY2bW3lS8wp8OX3YyyRjFmwFLgVdJZ12MiLoKhmcFPPTyXL591wv16xMuGsaWG/akpmb1xJPXnbQ71520e4MhNQeNfIgdNl2fR79zMBHB4EsaVvR/+uVd+eo+W5T/DZhZVYiIH1Q6BjOz9qyiFX5Jt5FMK/4g8FOSyUy6AdsBw4FLJY2MiKcqF6XlcuQvJvDWvEX1602NiT9r1LE8O2M+X71pIgBvvL9wjXH1AWZccwwdOnhsfbP2TNImJGPTZ9/kmewbPGZm5VHpO/zX5emU+ypwv6QuJG35rYo0rqgX2/Rm3602ZNaoY/nz83P43p9earDtyQuHMXijniWL0cyqj6RDgZEkk2y9yOqbPF8EtpY0huS68FnlojQza38qWuHPVPYlnQr8JSIWZrZJ+kJEPEgyoYlViezK/u9P3Zujdt602WV8ee+BfHnvgU1nNLP25hjgmxExu/EGSZ2ALwBH4P5bZmYlVS3Dn9wAPC1px6y0KysVjOWWXdl/5IKDWlTZN7N1V0RclKuyn25bFRF/iQhX9s3MSqxaKvwzgTOBMZIyU5a7IXcLDBr5UP2r1OVmTLnsCHbs36uk5ZvZukNSraRRyur4I+mFQvuYmVnLVUuFPyLiBeAQ4GxJPwc6VjimtU7jSn6pKv3Z5bx0+ZFs0KNLSco1s3XWVJLrz+OS+qZpvsljZlYm1VLhnwsQER8BRwEBFD816zquLqJBpfz7w7evX25tpT97/xd+dAS9uxeeOMvMrAirIuL7wM0kzTn3JvncNzOzMqiKCn9EHJu1XJe286yPTdINlYms+tXWBWc+tqR+/cH/OpDzhm3DDafsWZ/25JsftKjs7Mr+v0YeRt+evrNvZiUhgIi4DzgJuA3YqqIRmZm1Y1VR4S/CAZUOoBqd98fnG8xOO+3qo9llQG8Ajtt9s/r0r982qdllD/vZk/XLd31zXzbboHsrIjUza+CszEJETAUOBP47kybpiEoEZWbWXq0tFf68JA2X9Kak6ZJG5th+sKQXJK2S9JVG266VNFXS65Kuz3Qgk7S3pFfSMrPT75U0JX3NkjSlbd5lQx8sXMagkQ/x8Cvv16dNv/poOnds+OvMHh+/OU17np0xn1nzk6cGX9t3C/bfeqNWRmxmtlpEPN9o/bOIuCMr6adtHJKZWbu2Vlf4JXUEfg0cDewEnCJpp0bZZgNnAHc12nd/kicHu5H0F9iHpNMwwG+Bs4Ft09dwgIj4akTsERF7kIwTfX/p31VhnyxZwdCrxzdIGz28J5065v5VvvmT4fXLD778XpPlf7pkZf1suADXnLBrCyM1M2sxd+A1MyuhtaXCn+/DfygwPSJmRMQK4B5gRHaGiJgVES8DjadsD5IZHrsAXYHOwDxJ/YFeEfFMRARwB8kskKuDSe74nwTc3bq31TwRwR5Xjqtfn3LZEU3Octu10+rBjs6/60WSt5Tf7lc+Xr9c7Ay6ZmYl5g68JfDZspWVDsHMqkRFZ9pthl/lSR8AvJO1PgfYt5gCI+IZSU+SjBAk4MaIeF3SkLSc7DIHNNr9IGBeREzLVbaks0meENCvXz9qamqKCalJZzy6uH559PCeTHnuXwAsWrSo4DFGD+9Zv+/gSx5m9PCeRZXf0ribiqetOZ7CHE9h1RaPWbGmzVvI3lv2bTqjmbV7Fa3wS7qN5E7OpxHx3Xz5ImJ0viJyZS/y2NsAOwID06Rxkg4GlhZR5ikUuLsfETcBNwEMGTIkhg0bVkxIBTWe+Cp7LPyamhqaOsYbB9Syw48eBeAbjy/h7WuOyVv+a1ceRY8uLf/TKCaetuR4CnM8hVVbPOuIWZUOoD1o4oGuma1DKt2kZzRwO3BfC/efA2yetT4QaLqheuIEYGJELIqIRcAjwH5pmQOz8jUoU1In4EvAvS2Mudmym+Gcf+g2LZr4qlvnjnxl7+Rt1dYF1z3+Zv227Mr+A+ft36rKvplZPukgCgdL2q9Qvoj4UlvF1J6tqnON38wSla7wXxYRE2jURr4ZJgHbShosqQtwMjC2yH1nA4dI6iSpM0mH3dcjYi6wUNJ+aVv904C/Zu33eeCNiJizZpHlMfiS1UNvXnjU9gVyFvbzE3evX77h79MZNPKhBpX9Cw7flj236NPi8s3MmvD19PXVUhcsqa+kcZKmpT9zfphJOj3NM03S6Vnp+UZny1muEten+V+WtFeavqWk59PR3KZKOjfrGDXpqHKZ0d42KfV5yHZy1gAMZrZuq3SFv7+kQ4DjJe0paa/sV1M7R8Qq4HzgMeB14L6ImCrpSknHA0jaR9Ic4ETg95KmpruPAd4GXgFeAl6KiL+l274F3AJMT/M8knXYk2njzroZD/7Xga0uI19H3JtO3ZvvHrFdq8s3M8snIr6evvI24WyFkcD4iNgWGJ+uNyCpL3A5SV+vocDlWV8Mco7OVqDco7Pynp3uD0m/sP3T0dz2BUZKWj0xCvxHZrS3iGjZrIhmZs1U6bYbl5F8eA4ErqNhm/wADmuqgIh4GHi4UdplWcuTaNhEJ5NeC5yTp8zJJEN15tp2RlMxldLVD71Wv5yZVKu1Zo06lnuem83I+18BYMY1x9Chg0fBM7O2IWk3kpswmc/ZV4EbWlkBHgEMS5dvB2qAixvlOQoYFxEL0jjGAcMl1ZCOzpamZ0Zne6RAuSOAO9LR3CZK2kBS//QpcUZXKn9jzcysshX+iBgDjJH0o4i4qpKxVKubn55ZlnJPHroFJw/doixlm5k14UaS5pd/AGpJ5kR5gNbNqt4vU9mOiLl5msvkGtltQPrKNzpbvnLzlTVX0ubAQ8A2wEURkd237DZJtSRzufwk8oyVXKrR3ordr9pGo3I8hTmewhzPmio9Ss+gdJz8nJX9tA3lgLZsL1+tXr9yeNOZzMzWAhFxcPa6pIkU0a5f0hPApjk2XVrkofON7NaSEd/y7hMR7wC7pU15/iJpTETMI2nO866k9Ukq/KeSzPWyZkEtHe3t0Yazqhe7X7WNRuV4CnM8hTmeNVW6Sc/PJHUg6RT7PPAhyWRY2wCHAoeTtLdcJyv8Mz5cVL/cvUvHAjnNzNY+ki4hmcRwQ+DbTeWPiM8XKGtepklNOoFiruZBc1jdPAeS5p41FB6dLV+5TY4SFxHvpf3GDgLGRMS7afpCSXeR9CPIWeE3MyulirYtjIgTgR8B2wO/Bp4mqfyfBbwJHBYR4/KX0L4ddt2ESodgZlZOtwBnAv8LfK+VZY0FMqPunE7D0dUyHgOOlNQn7ax7JPBYE6Oz5St3LHBaOlrPfiTzycyVNFBSd4D0GAcAb6Yjwm2UpncGvkDSd6Gs6jw0p5lR+Tv8RMRrFP84dp30xP8cUukQzMxKRtLRwOMR8SHwoaR/U8Qd/iaMAu6T9A2SYZdPTI81BDg3Is6KiAWSriIZ0hngykwHXpLR2UYD3Uk66z5SqFySwSKOIRnNbQnJcKOQTOh4naRMU6GfR8QrknoCj6WV/Y7AE8DNrXzPTTrl5once87nyn0YM6tylW7D//2IuDZdPjEi/pS17ZqI+EHloqse22yyXqVDMDMrpXOB2yXNBlYBWwFXtKbAiJhP0gy0cfpkkqfGmfVbgVvz5FtjdLYC5QY5vqSkT6V3y5G+GNi7qfdRas/OXNB0JjNr9yo9XNjJWcuXNNq2TvdSXVlbV+kQzMzKIiJGAINJKuLfAraOiN9UNiozs/ar0hV+5VnOtb5OuerB15rOZGa2FpE0KLMcEYsjYkpEvBgRC9PtkrTGvCnWOh8uXF7pEMyswipd4Y88y7nW1yl3PPPvSodgZlZqP5P0Z0mnSdpZ0iaStpB0WNq2/p8kbeCthPa5+olKh2BmFVbpTru7S/qM5G5+93SZdL1b5cKqHjUXDqt0CGZmJRERJ0raCfgPktF5+pN0eH2dpBPs1RGxrIIhtlu/rXmbbw3butJhmFmFVHqmXQ8u34RBG/WsdAhmZiXjkdkq46ePvsE5B29Fhw7rdGtZs3VWpZv0WA6fLllZ6RDMzEpO0vezlk9stO2ato9o3bLVDx6udAhmViGu8Feh3a98vNIhmJmVg0dmq7BBIx9iweIVlQ7DzNqYK/xVTH7yambti0dmqwJ7XTWOQSMfYvmq2kqHYmZtpNKddq2AV684qtIhmJmVkkdmqyLb//DR+uVzduvK/qvq6NLJ9wHN2iNX+KvMR4tWj5fcs6t/PWbWrnhktir1+5eX8/uXH8m7vU+Pzlz1xV1YvHwVK2uDDxYu5/jd+7PNJutTVxfuDGxW5VyjrDL/ecuzlQ7BzKwsPDLb2uvjJSs5/64XG6RdP35aeQ/66EPlLb+5HE9hjqewFsbz5k+G07VT6z86/eyuyrzx/sJKh2BmZmZmVeAH979aknJ8h79KnXPwVpUOwczMzKxqbda7G8tW1bGqto7Plq2iUwexqq5hd6AOgrqAARt0591PltK3Z5dmjVS1Yc8uzF+8gv69u7F+t05s2LMr73+2jD49OjPzo8UsW1nH0pXl6QC/5xYbcN1Ju5ekrLW+wi9pOPAroCNwS0SMarT9YOCXwG7AyRExJmvbtcCxJE86xgEXRERI2hsYDXQnmf3xgoiIdJ//As4HVgEPRcT3KYPvHrFdOYo1MzNrc/17d+OYXfuz/9YbsuvA3qyX9lHr0SV3NaSmpoZhw4a1YYSFOZ7CHE9h1RDPWl3hl9QR+DVwBDAHmCRpbDqTY8Zs4Azgwkb77g8cQPJFAOAfwCFADfBb4GxgIkmFfzjwiKRDgRHAbhGxXNImpXw/dVnfSrt1dlNXMzNrGyds05kP6c3Fw3dgx/7r8+a8hey8We9Kh2VmJbJWV/iBocD0iJgBIOkekgp5fYU/Imal2+oa7Rsko0J0IRkhojMwT1J/oFdEPJPudwfwReAR4FvAqIhYnpb9QSnfzA1/n17K4szMzOpt0KMzEy46lN7dO6+xLbkDuW/9uiv7Zu3L2l7hHwC8k7U+B9g3T94GIuIZSU8Cc0kq/DdGxOuShqTlZJc5IF3eDjhI0tXAMuDCiJjUuGxJZ5M8IaBfv37U1NQU9WZ+8cTi+uVi9wFYtGhRs/KXm+MpzPEU5njMSuuB8/Znzy36VDoMM6ugtb3Cn2vg36Imb5G0DbAjMDBNGpe2919aoMxOQB9gP2Af4D5JW2Xa99dnjrgJuAlgyJAhUXS7rawhm5rT1qsa2oZlczyFOZ7CHI9Z6fz34du6sm9ma32Ffw6wedb6QOC9Ivc9AZgYEYsAJD1CUpG/k9VfAhqXOQe4P63gP5c2E9oI+LDF78DMzKwMxn/vELbeeL1Kh2FmVWBtH4d/ErCtpMGSugAnA2OL3Hc2cIikTpI6k3TYfT0i5gILJe0nScBpwF/Tff4CHAYgaTuS9v8fle7tJL6y98CmM5mZmRXgyr6ZZazVFf6IWEUyROZjwOvAfRExVdKVko4HkLSPpDnAicDvJU1Ndx8DvA28ArwEvBQRf0u3fQu4BZie5snMN34rsJWkV4F7gNMbN+cphf69PcO8mVlzSeoraZykaenPnG1ZJJ2e5pkm6fSs9L0lvSJpuqTr05s+ectV4vo0/8uS9mp0nF6S3pV0Y1PHKLWaC4eVo1gzW0ut7U16iIiHSYbOzE67LGt5Eg2b6GTSa4Fz8pQ5GdglR/oK4D9bGXKTjt99s3IfwsysPRoJjI+IUZJGpusXZ2eQ1Be4HBhC0j/r+XQ454/JMyRzgXKPBrZNX/um+2cPHHEVMKFRjPmOUVID+nQvdZFmthZbq+/wt1c9u67138PMzCphBHB7unw7yZDKjR0FjIuIBWklfxwwPHtI5vTJ7R1Z++crdwRwRyQmAhuk5ZBO4NgPeDxz4CaOUVJleWxgZmst1yyryPpdO7Fw+SpPumVm1jL90n5YRMTcPJMj5hrOeUD6yjckc75yc5YlaR5wHXAqcHijY+c7RgMtHd4546mnJtChGa2Fqm34WcdTmOMpzPGsyRX+KrKyLpkbrFtnP3gxM8tF0hPApjk2XVpsETnSokB6S8o6D3g4It5p1ES/6GO0dnjnww49tLj8qWobftbxFOZ4CnM8a3KFv4pcefwuXP3w63Tr5Dv8Zma5RMTn822TNE9S//QufH8g12zoc4BhWesDgZo0Pd+QzPnKzTc09OdIJmk8D1gP6CJpEfCrAscoiZOGDOSDhctLWaSZtQO+lVxFTtpnc166/Eg6dHDrSzOzFhgLZEbdOZ3VQypneww4UlKfdLSdI4HHmhiSOV+5Y4HT0tF69gM+jYi5EfEfEbFFRAwCLiRp5z+yiWOUxLVf2Z3RXx9ayiLNrB1whd/MzNqLUcARkqYBR6TrSBoi6RaAiFhAMnrOpPR1ZZoG+YdkzlkuySg7M9L8N5M05WlKvmOYmZWNm/SYmVm7EBHzadhJNpM+GTgra/1WknlVcuXLNSRzvnID+HYTMY0GRjd1DDOzcvIdfjMzMzOzdkxlmCjWskj6EPh3mQ+zEfBRmY/RHI6nMMdT2Locz5YRsXEbHcvWEr6OVAXHU5jjKazi1xFX+NsBSZMjYkil48hwPIU5nsIcj1nbq7a/c8dTmOMpzPGsyU16zMzMzMzaMVf4zczMzMzaMVf424ebKh1AI46nMMdTmOMxa3vV9nfueApzPIU5nkbcht/MzMzMrB3zHX4zMzMzs3bMFX4zMzMzs3bMFf4qJGlzSU9Kel3SVEkXpOl9JY2TNC392SdNl6TrJU2X9LKkvbLKOj3NP03S6SWO52eS3kiP+YCkDdL0QZKWSpqSvn6XVdbekl5JY71ekkoYzxWS3s067jFZ+1ySHvNNSUdlpQ9P06ZLGlni83NvViyzJE1po/PTTdJzkl5K4/lxmj5Y0rPp38K9krqk6V3T9enp9kFNnbcSxfPHtNxXJd0qqXOaVYnYAAAAIABJREFUPkzSp1nn57Ksskrx+8oXz2hJM7OOu0eaXtb/L7NyKPC55OtI4Xh8HcHXkVbEU73XkYjwq8peQH9gr3R5feAtYCfgWmBkmj4S+Gm6fAzwCCBgP+DZNL0vMCP92Sdd7lPCeI4EOqXpP82KZxDwap6yngM+l8b6CHB0CeO5ArgwR/6dgJeArsBg4G2gY/p6G9gK6JLm2alU8TTKcx1wWRudHwHrpcudgWfTv4v7gJPT9N8B30qXzwN+ly6fDNxb6LyVMJ5j0m0C7s6KZxjwYI5ySvX7yhfPaOArOfKX9f/LL7/K8SrwOenrSOF4rsDXkUKfk76OFI5nNFV6HfEd/ioUEXMj4oV0eSHwOjAAGAHcnma7HfhiujwCuCMSE4ENJPUHjgLGRcSCiPgYGAcML1U8EfF4RKxKs00EBhYqJ42pV0Q8E8lf+h1Z76HV8RTYZQRwT0Qsj4iZwHRgaPqaHhEzImIFcE+at6TxpHdXTiL5MMqrhOcnImJRuto5fQVwGDAmTW/895P5uxoDHJ7GnO+8lSSeiHg43RYkF6iCfz+U7veV7/zkU9b/L7Ny8HWkZfEU2MXXEV9HmoynwC4Vv464wl/l0sdie5J8e+wXEXMh+XAANkmzDQDeydptTpqWL71U8WQ7k+Tba8ZgSS9KmiDpoKw455Q5nvPTx2W3Kn1UTeXPz0HAvIiYlpVW1vMjqWP66PcDkg+Qt4FPsi6s2WXXn4d0+6fAhpTw/DSOJyKezdrWGTgVeDRrl8+lj0ofkbRz4zjLGM/V6d/PLyR1beK4Jf/7MSsHX0eaHY+vI/g60op4qvI64gp/FZO0HvBn4DsR8VmhrDnSokB6SeORdCmwCvhjmjQX2CIi9gT+B7hLUq82iOe3wNbAHmkM12Wy5jlum5wf4BQa3pUp+/mJiNqI2IPkbsdQYMcCZZf9/DSOR9IuWZt/AzwVEU+n6y8AW0bE7sANwF+aiLNU8VwC7ADsQ/J49eImjlvSvx+zcvB1pNnx+DqS2cnXkZbEU7XXEVf4q1T6bfXPwB8j4v40eV76CCjz2O6DNH0OsHnW7gOB9wqklyoe0g4mXwD+I32kRvrobn66/DzJXYHt0niyH7eVNJ6ImJf+A9YBN7P6sWElz08n4EvAvZm0tjg/Wcf6BKghaTO4QRpP47Lrz0O6vTewgBKenxzxDE+PdzmwMckFK5Pns8yj0oh4GOgsaaNyx5M+Uo+IWA7cRhv+/ZiVg68jzY/H15E1+TpSfDxVfR2JFjb+J+lg0rWl+/vVZGeQO4BfNkr/GQ07W12bLh9Lw84gz6XpfYGZJB1B+qTLfUsYz3DgNWDjRukbk3bKIekY827muMCkNMZMZ6JjShhP/6zl75K0GwTYmYadhmaQdNzplC4PZnXnnZ1LFU/WOZrQxudnY2CDdLk78DTJxfRPNOxsdV66/G0adra6r9B5K2E8ZwH/Aro3yr8p1E8KOBSYnZ6PUv2+8sXTP+v3+UtgVFv8f/nlVzleBT4nfR0pHI+vI1Hwc9LXkcLxVO11pDUfJk+kgf28HIGtyy/gQJJHOi8DU9LXMSTt4cYD09KfmX9uAb8m+Yb/CjAkq6wzSTrJTAe+XuJ4ppO0PcukZf7ZvwxMTf+RXgCOyyprCPBqGuuNmX/IEsVzZ/r+XwbG0vCD+9L0mG+SNWJBut9b6bZLS3l+0m2jgXMb5S/3+dkNeDGN51VWj+qwFUmnpukkH9pd0/Ru6fr0dPtWTZ23EsWzKi07c84y6ednnZ+JwP4l/n3li+fv6d/Pq8D/sXoEhrL+f/nlVzleBT4nfR0pHI+vI+HrSCviqdrrSObbT4ukPbB3ioipLS7EzMzMzMzKplkV/rS3+mbAUmBWJG3czMzMzMysSjVZ4ZfUm6Rt1ikk7Z0+JHl004/kMclvIuLJMsdpZmZmZmYt0KnpLIwh6UhyUCQ9ketJ2hs4VdJWEfGHcgRoZmZmZmYt16o2/GZmZmZmVt2KHodf0nhJxzRKu6n0IZmZmZmZWak0Z+KtwcDF6QQHGUNKHI+ZmZmZmZVQcyr8nwCHA/0k/S3tzGtmZmZmZlWsORV+RcSqiDiPZOrnfwCblCcss+ogaZCkpZKmSJoh6edp+jBJT0l6QNJrkn4nqUO67UhJz0h6QdKfJK2Xps+SdHdW2fdKmpUud0nLelXSK5n0JmI7TdLLkl6SdGc53r+ZmbWOryNWDZpT4f9dZiEiRgNnAI9n0tIx+s3ao7cjYg/gcyR/9xlDge8BuwJbA1+StBHwQ+DzEbEXMBn4n6x9NpPUR1Jfkqm/M44COkfELsChTQUkaWeS2QsPi4jdgQta+ubMzKzsfB2xiipmWE4AIuL3jdafJ5kOOGM8sFeJ4jKrJltLmkLSj+XnWenPRcQMgPSOy4HAMmAn4J/JRNR0AZ7J2udu4Gsk02zfBVySptcCPSR1LDKmw4AxEfERQEQsaMH7MjOztuHriFVU0RX+IqiEZZlVk7cjYg9JPYDJkkan6Y3HtA2S/4NxEXFKnrLGArel+c5g9Qf148CXSCa2e7eImJTj+GZmVp18HbGKak6Tnqb4j8bau+Ukd1AyzdeGShqctrn8Kkm/lonAAZK2AZDUQ9J2WWWsSPM8ky4DEBGrgKXARWQ9ipU0QNL4HLGMB06StGGar29p3qKZmZWRryNWEaW8w2/WXmUexXYluevysqRhJB+2o0jaXj4FPBARdZLOAO6W1DXd/4fAW5nCIuJygLSdJunySUCviPhDdjrQH1jVOKCImCrpamCCpFrgRRq2CzUzs+rh64hVVMlm2pX0YkTsWZLCzKpc+kF9YUR8oczHOR+YHRFjy3kcMzNrW76OWFtq1h3+9PHSzunq1IiYnrX58JJFZWYARMSNlY7BzMzWXr6OGDTjDr+kF0m+ILxJ0v5sH+BPEXFx+cIzMzMzM7PWaE6Ff6vM0FHpemfgtYjYtlzBmZmZmZlZ6zRnHP7MOLEHkoy3vx9JD28rYKONNopBgwZVOow2t3jxYnr27FnpMNo9n+e24fPcdnyu24bPc9vweW4bPs+rPf/88x9FxMaN01sySs/GJLPBbQI839rA2rtBgwYxefLkSofR5mpqahg2bFilw2j3fJ7bhs9z2/G5bhs+z23D57lt+DyvJunfudKLrvBLUiQeAB5I06YB15UmRDMzMzMzK7Xm3OGfLelvwGsk47keTNKB18zMzMzMqlRzZtrdk2RShm2AHYG/AyeWIygrj9nzl/DM2/MZNPIhzrlz3WtmZGZmZrYuKuoOv6Td0lnhJkbEzeUOysrj4J89Wb/82NR5FYzEzMzMzNpKsXf4z5S0LfCNcgZj5fPRouWVDsHMzMzMKqDJCr+ky9N8E4EOki4rZ0CShkt6U9J0SSNzbO8q6d50+7OSBmVtuyRNf1PSUU2VKWm0pJmSpqSvPdL03pL+JuklSVMlfT1rn9MlTUtfp5frPJTaeX98oeRlLltZyxH/O4G3P1zEy3M+4ZanZzS9UwGfLFlBsfNCmJmZmVlxmmzSExE/lnR8mveJiBhbrmAkdQR+DRwBzAEmSRobEa9lZfsG8HFEbCPpZOCnwFcl7QScDOwMbAY8IWm7dJ9CZV4UEWMahfJtkknFjpO0MfCmpD8C6wGXA0OAAJ5Py/q4pCeiDJ6buWCNtPc/XcbG63elYwe1qMwdfvQoAIdfN6E+bfBGPfnG7Un/gOv7vMd/3/0iX9itP1eN2IXX5n5Gjy4d2XOLPg3KeWveQo78xVP167NGHVvU8RcsXkGfHp35bOkqunXpwOz5S7jrudnc9s9ZnHvI1vTt2Zn/3G9LenTpxKraOmYvWMJmG3SnW+eOLXq/ZmZmZmujYkfp2TcizpN0FVC2Cj8wFJieNcnXPcAIkpGBMkYAV6TLY4AbJSlNvycilgMzJU1Py6OIMhsLYP203PWABSQjEx0FjIuIBWlZ44DhwN2tedOVst//S+ZNm/n/jiF5q62XqewD/PfdLwLw4MtzefDlufXp5xy8FZccsyMAdXXRoLIP8Ncp7zJijwEATP9gIZtt0J0eXTrxwcJl9OrWmW6dOzJo5EMF4/jdhLcBuObhNwrmu/qEXdh1QG96d+9M984d2aRXtyLfqZmZmdnaoagKf0RcKulU4NrsdElfiIgHSxjPAOCdrPU5wL758kTEKkmfAhum6RMb7TsgXS5U5tVpM6XxwMj0C8ONJF9s3gPWB74aEXWScsU3gLXcdY+/xYVHbd+sfW79x8wWH+/3T83g90/N4JELDuLoXz29xvYL7pnCBfdMaZD2zYMGc/PTLT9mPpc+8Oqaxz98W848cDCzPlrMszPn88U9BuT8IrBg8QpmfrSYvbfss8a2tVVdXbBsVS0ra4MeXTrSuWNzBvIyMzOzatSccfhvAL4n6ZSIeD1NuxIoZYU/123mxo268+XJl56rxpIp8xLgfaALcBNwMcl7OgqYAhxGMqvwOElPFxkfks4Gzgbo168fNTU1OXarHjc+OZ0hXec2nRF4YNoKenQWd7+xotXHzVXZz6cclf18fjV+Gr8aP61+vamnBBmjh7f9tN6LFi1q8d/X+4vrGPn00tIGlOU7e3Vlj02Sj5iVdcGCpcG/F9YxqFcHNu6ukj1VagtNneeI4IInl/BZ6/8t6m2xfgc+t1knDhiQnMMuHaBbp7XnnLVUa/6m1xV1ESxbBc/MXcWdrzX9R7fPph05a9eudO2o+n5SixYt9nluA/57bhs+z01rToV/Jkn7+TGSroiIP5G7Atwac4DNs9YHktxlz5VnjqROQG+SJjeF9s2ZHhGZWu5ySbcBF6brXwdGRfLJOF3STGCH9BjDGpVV0/hNRMRNJF8gGDJkSFTFdM+PFm4CM2zYML577xQeePFdzjl4K47YqR/X/306T731IQC3nDaEs+4o79j9XTt1YPmquhbv37t7Z24+bQh7brEBMz9azHb91mfGh4u4fvw0Rn15t/q2+7+f8DYfLlzOLa14SpHLGY8uBuCJ/zmErTbqSYcW9o1ojmKnE6+rC+56bjY//MuaTzTK5ZcvLAfKPzrUz76yG5NmLeC+yXMAGL7zpvz2P/di+aq6ZvfXWLaylvc+WcphWf1SEgIWlybgIs1eWMfsN1dw75sl/BaRZaP1uvCdz2/Hxut3ZeuN12PrjXtW9EtYRFBbFzw5YUL9/1JTzj54K977ZCkPvjyXQ7ffmJW1QV0E+wzqy2E7bMJrcz/j7Q8WNet//awDB9Ohg6irCxYtX8WJQzanW+cO1NYFO/bvxbMzFrD/1hsW/P9enj4le++TpZxy00TmL17BGfsPYssNezD+9Q945+MlACxctooFi8vz+8026f1aJr2/pFFqaf+mD95uYy49Zke233T9kpXZHhT7GW2t4/PcNBU7KoqkFyJiL0kbkbRZfwk4MiJ2K1kwSQX+LeBw4F1gEvC1iJialefbwK4RcW7aafdLEXGSpJ2Bu0ja7W9G0kRnW5JPtZxlSuofEXPTtvq/AJZFxEhJvwXmRcQVkvoBLwC7A3XA88BeaTgvAHtn2vTnMmTIkJg8ubKTXL04+2NO+M2/CuaZNerYJtvF53PzaUPYdUDv+j4Bt5w2hO/eM5mFK5LK1w1f25PaumD0v2Yx6pHcd8tfv3I43bs03Ta/ccwfLFzGxut1LUlF5dFX3+fc/3u+1eUUcuc3hrJpr25s26/hRTEi+GjRCl5971P69+7GDpv2Kqq8fB9yr8/9rFlPUDJ6devEXd/cjzkfL2Xzvt35j1ue5ZMlK9l3cF+ezdHx28zMqkuXjh04drf+XHTU9gTQv1c3MpfI5l4r6+qCAO58ZhZzPl7KnI+X8u4nS1myYhWfLl3JRUdtz8A+PVi6opabn57BgsUr2GPzDRjQpztf3msg1z72Jn976T2uOG4njt61P/+ev4T5i5bzxOsfsGP/9VlRW8eQLfvyzNvzAThl6Oa88/ESduzfi+Ur6/ho0XIGbdSTjhIrautYsqKW2rogCHp168x7nyxl8749eLJmAgcffDBLV9Ty7idL6dm1E5NmLuCdj5dwytAt6Nm1E727dy7pea5Wkp6PiCFrpDejwv9QRBybLncgGR3nexFR0ka+ko4Bfgl0BG6NiKslXQlMjoixkroBd5LM/LsAODmrQ+6lwJkkHWy/ExGP5CszTf87sDHJl4IpwLkRsUjSZsBooH+6bVRE/F+6z5nAD9Jwr46I2wq9n2qo8BdTiZ5xzTFs9YOHW1R+rlF1Cn3b3vXyx1i4fFX9+sP/fRA7bZZUcDMfLjM/Wsw2m6wHNIx/1qhjWbaytupG2mnpl6VCzth/EKP/NatB2n3nfI66CAZv1JN9r0m+YJ114GAO3HYjzrhtUrPK/94R23H2IVvRtVN5zmVEcNGYl1m/WycuHr5D/e8sIvh06UrmL17BtY++wY79e/HLJ1Y3oRqyZR+6d+nItw7Zmq/d8iwAnTqIVXXVOWTrdz6f9Pno1a38F5OIQFL9z3mfLeOXT0zj7udml/3YVn7jv3cIA9KRxGrrgojgjfcXMnvBEg7dfhO6d+lY/7tvrezP6JXpKGY1b37IY1PfZ/2unfj+8B1YsaqOE37zz6r93zOrZsWOOFhqra7wF3GAGyLiv0pSWDuytlT4W+rZHxxOvxwdWpt6vFZXF/x2wtv8575b0rtH+/nWPf71eQ1GKqomd31zX/baok/VfVkqh7q0gnLXc7Pp3rkj3/vTS2vkOf1zW7LXln244J4pbLvJelx41PYcuVO/nJWpdfVxcUQwf/EK3pq3kIkzFnB9Vt+Wlthj8w3YbWBvtujbg9M+N4jOHZNznX3O19Vz3dba4jzX1gWLV6zi48UrGLBBd2ojeOmdT3nqrQ/p27MLvbp35oQ9B+QcGjoiWFUXRQ0ckKnHzF+8gsmzPmbavIXMX7xijRsm+wzqw8A+PbjsCzuxQY/OzJq/hIv//DIn77M5J+w5oCxN2hqf5yUrVvH2B4vp2EEsW1XLpJkLeHTq++y8WS/+b2LuL+5DB/fNObS2Vbc3rhpekettW1T4X4iIvZrOuW5p7xX+fN9gfdFuKCKYNX8JR/5iAitr2+Zu2bSrj/YoOyXiv+e243PdNnye24bPc9so93murQuWrazl06Ur6dRBbNKrW/0XzQhYuHwV8xct5615C/nX2/P52r5bsH2/9SvSLypfhb85nXbNGthj8w0qHcJaQxKDN+rJtKuPKSp/XV2w1Q8eZp9BffjTufsDsHDZSm7/1yy22ng9xr/+AecdujVvvTSJm9/qQtdOHbnptL3p2aVTm3QWNjMzW1d07CB6du1Ez66rq82ZyryUDBrSu3tnttp4PYbv0r9SYRbkCn8797eXGg9y1DrP/eBwunXpyA8feJWfnViy/trWSIcOWuPpyfrdOnP+YdsCcMyuyQfKO53E/ecd0ObxmZmZ2dqjlM/7fVuxCv3koUITCjdts97d+NJeq+cW26RXN3p168z1p+xZts6eZmZmZlY6pbzD/6sSlmUlMu+z1o2D/vAFB7FBjy7870l7lCgiMzMzM2tLTVb40wmpAvg0Ir6bL19EjC5hXFZBlRpKyszMzMxKr5gmPaOB24H7yhuKtaXzhm2dM/2sAwe3cSRmZmZmVk7FVPgvi4gJwBfLHYyV1nufLM2ZPvb8A/j+8B2YNepYxp7fsMNn3/W6tEVoZmZmZtZGimnD31/SIcDxku6hUefciHihLJFZq838aPEaaY0nytptYMOhNc85OPedfzMzMzNbOxVT4b8MGAkMBK6jYYU/gMPKEJeVwF3PrjlrX65ZcZ+66FA+XbqSXQf2bouwzMzMzKwNNVnhj4gxwBhJP4qIq9ogJiuRh16ZW1S+LTbsUeZIzMzMzKxSih6HP1PZl9SnfOGYmZmZmVkptWTirfElj8LMzMzMzMqiJRV+z6hrZmZmZraWKGqmXUmnZRaBPlnrRMQd5QjMWmf+ojVn2O3To3MFIjEzMzOzSiqqwg9kz8bUFRhEUvmPUgdkpfHa3M/WSLtyxC4ViMTMzMzMKqmoCn9E/DizLGlERFxZvpCsFP45ff4aacftvlkFIjEzMzOzSippG35JXSV1kjRQUq9WxGWt9JcX3610CGZmZmZWBYpt0pPt1ALb/ghsCDwL7A4c3ZKgzMzMzMysNIq+wy/pC5I6RMSrBbK9B7wdESOBmc0NRtJwSW9Kmi5pZI7tXSXdm25/VtKgrG2XpOlvSjqqqTIljZY0U9KU9LVHmn5RVtqrkmol9U23zZL0SrptcnPfX1vq2MGDKZmZmZlZ85r0nAxMk3StpB3z5Hk6Is5Kl99pTiCSOgK/JnkqsBNwiqSdGmX7BvBxRGwD/AL4abrvTml8OwPDgd9I6lhEmRdFxB7pawpARPwskwZcAkyIiAVZ+xyabh/SnPfXlpatrOXdT5ZWOgwzMzMzqwLNmWn3P4E9gbeB2yQ9I+lsSetn5fkTgKSewLWZdEkdJPVo4hBDgekRMSMiVgD3ACMa5RkB3J4ujwEOl6Q0/Z6IWB4RM4HpaXnFlFnIKcDdzchfFWZ+tLjSIZiZmZlZlWhWp92I+Az4M0nFuT9wAvCCpP9qlHU80D1rvQfwRBPFD6DhU4E5aVrOPBGxCviUpM9Avn2bKvNqSS9L+oWkrtkHSr+gDCd5vxkBPC7peUlnN/F+zMzMzMwqruhOu5KOA84EtgbuBIZGxAdpxfh14Ias7N0iYlFmJSIWFXGHP1ej88bj/OfLky891xeaTJmXAO8DXYCbgIuB7OFGjwP+2ag5zwER8Z6kTYBxkt6IiKfWeCPJl4GzAfr160dNTU2OMMrn/mkrcqa3ZRyLFi1q8/e9LvJ5bhs+z23H57pt+Dy3DZ/ntuHz3LTmjNJzIvCLxhXciFgi6cxGeRdL2isiXgCQtDfQVKPyOcDmWesDSToB58ozR1InoDewoIl9c6ZHxNw0bbmk24ALGx3rZBo154mIzL4fSHqApMnQGhX+iLiJ5EsEQ4YMiWHDhuV8w+Xy0IcvkZyShtoyjpqamjY93rrK57lt+Dy3HZ/rtuHz3DZ8ntuGz3PTmtOG/7Rcd7PTbeMlPZOV9B3gT5KelvQ0cC9wfhOHmARsK2mwpC4kFe6xjfKMBU5Pl78C/D0iIk0/OR3FZzCwLfBcoTIl9U9/CvgiUD/6kKTewCHAX7PSemb6K6R9FI7M3qeayAP0mJmZmVmqJePw59NNUv+ImBsRkyTtAGxP0tzmjYhYWWjniFgl6XzgMaAjcGtETJV0JTA5IsYCfwDulDSd5M7+yem+UyXdB7wGrAK+HRG1ALnKTA/5R0kbp/FNAc7NCucE4PGIyO792g94IPl+QCfgroh4tCUnqtyUf240MzMzM1vHlLLCH8CtkvoANcCjwD/SzrXFFRDxMPBwo7TLspaXkTQtyrXv1cDVxZSZph9WII7RwOhGaTNIJhOrer7Db2ZmZmYZpazwExFHS+oGDCO5S/5zSbNJKv+PRsTsUh7PcnOF38zMzMwySlnhF9TfhX80fZG2qT8auFHSphExtITHtCIN2rCpQZLMzMzMrD1qVoU/HVpz23T17eyhN4FTc+2TToT1G0nvAP0lbRkR/25RtFakNW/xH7FTvwrEYWZmZmaV1pxx+B8APkcy3mMtMFjSVRFxA0BEZI9y03h0HYCDgP8AlrcqYmtSbV3dGml7bN6nApGYmZmZWaU15w7/DcBXska/2YBk6MsbcuTdETgra13AjmkHWiuzaDxdGXDsbv3bPhAzMzMzq7iiK/wR8XeAdLjN7YADgLfyZF8YEROyEyQtbGmQ1jzutGtmZmZmGS3ptHsQ8AVgK+CaPHl2zhorfw7wINCtRRFas3kcfjMzMzPLaHaFPyJuBm6W1JVkptm7c2TbjGSiq/WAwSRj528v6WDgtYj4qOUhW1M6FD1/spmZmZm1d83ptPscMIbVs9keDLyfK29EzE8XPwBmAOMlvQwcCnyUvqxsfIffzMzMzBLNucN/DvAVks64Al4BvlxoB0mbsLopz4OeeKttNG7Df8nRO1QmEDMzMzOruKIq/JL6R8SLkuZGRM67+o3yHw9cR9K05wNgS+B1YOfWBGvFaXx/f/1unSsSh5mZmZlVXrGtva+UtBHw4yLzXwXsB7wVEYOBw4F/tiA+a4HGd/iDHON0mpmZmdk6ockKv6TTgdkkY+6/I+m0Ispdmbbj7yCpQ0Q8CezRulCtWI1H6ck1Lr+ZmZmZrRuKadJTAxwITAVmAv8oYp9PJK0HPA38UdIHJB19rQ14HH4zMzMzy2jyDn9E/Jtk7P3jgQPS9aaMAJYC3wEeBd4GjmtFnNYMHVzjNzMzM7NUUZ12I+JcSbtExHlF5l8saVNgKMnkW49lDdVpbWy9ri2ZX83MzMzM2oPmTNH0O0nPSTpP0gaFMko6i6TN/5dIhvKcKOnMVsRprbBdv/UrHYKZmZmZVUjRt34j4kBJ2wFfByanE3HdFhHjcmS/CNgzc1df0obAv4BbSxCzNdNOm/WqdAhmZmZmViHNucNPRLwF/BC4GDgEuF7SG5K+1CjrHGBh1vpC4J3WBGrFcxN+MzMzM8sousIvaTdJvyCZQOsw4LiI2DFd/kWj7O8Cz0q6QtLlwERguqT/kfQ/BY4xXNKbkqZLGplje1dJ96bbn5U0KGvbJWn6m5KOaqpMSaMlzZQ0JX3tkaZflJX2qqRaSX2Lia9aNB6W08zMzMzWXc3pzXkjcDPwg4hYmkmMiPck/bBR3rfTV8Zf0595G5NL6gj8GjiC5AnBJEljI+K1rGzfAD6OiG0knQz8FPiqpJ2Ak0lm8t0MeCJtfkQTZV4UEWOy44iInwE/S2M6DvhuRCwoMr6q4Dv8ZmZmZpbRnDb8BxfYdqekP0fElyX1j4gfS9o0It5vRixDgekRMQNA0j0kw3tmV6hHAFeky2OAGyUpTb8nIpYDMyV30qtbAAATfElEQVRNT8ujiDILOQW4uxnxVYWZHy2udAhmZmZmViVKOV7jVunPKyVdAvwYOKcZ+w+gYTv/OcC++fJExCpJnwIbpukTG+07IF0uVObVki4DxgMj0y8MAEjqAQwHzm9GfJl9zwbOBujXrx81NTW5spXNjPeWNlhv6+MDLFq0qCLHXdf4PLcNn+e243PdNnye24bPc9vweW5aKSv8Iel0YDbJkJy3SjotIu4ocv9cDVGiyDz50nP1UciUeQnwPtAFuImkI/KVWfmOA/4ZEQuaEV+SGHFTWiZDhgyJYcOG5cpWNr1e/Qd89mn9elsfH5IvGZU47rrG57lt+Dy3HZ/rtuHz3DZ8ntuGz3PTmjVKTxFqgBnAVGAmMKEZ+84BNs9aHwi8ly+PpE5Ab5KJvfLtm7fMiJgbieXAbaxuApRxMqub8xQbX1XwuPtmZmZmllHKCr8i4t/AQcDxwAHperEmAdtKGiypC0mFe2yjPGOB09PlrwB/j4hI009OR/EZDGxL8pQhb5mS+qc/BXwReLX+jUi9SYYdzXQ2Lja+qtCnR+f65TMPGFzBSMzMzMys0krZpOdigIg4N10/rzk7p23yzwceAzoCt0bEVElXApMjYizwB+DOtFPuApJKN2m++0g60K4Cvh0RtQC5ykwP+UdJG5M01ZkCZOIGOAF4PCLqe7/mi68577GtdMgapqdfr64VjMTMzMzMKq3JCr+kJ0naqi+IiK/kyxcRj2ft8/fGxSRZ4rBCx4qIh4GHG6VdlrW8DDgxz75XA1cXU2aanjeWiBgNjC62rGrTqePqCv9G67nCb2ZmZrYuK+YO/7eApUBtM8rtCdQBtwIvtCAua4XsibeGDOpTwUjMzMzMrNKKacN/V9oW//8VW2hE7AucCWwHXAPsGRHP///27j3YqvI+4/j3kasXEFCCBG0AL9NgGhEJ9RINilF00pAao5hMvaRNalI7dhJtsCZondHEaGo10VrrBbVeiCiVSRWl6pHYARUVEUXhgGhQ4yUmJt6w6K9/rHfLyvbsy9ln3w7n+cys2Wu9613vftdvv7POe9Z+17trq6L1xFb+FS4zMzOzPq2aO/wD03Sb+0s6qnhnRNxW4ringfuAcWQz4FxZcy2tW/J9/K22coffzMzMrC+rpsN/MvA1YBjZ3PR5AXykwy/pPGAfsgdcvxURr/awnlYjd/fNzMzM+raKHf6IeAB4QNKyiLiqynJnAW8B+wNnp6kvIyKG1l5Vq4WH9JiZmZn1bVVPy1no7Evas9J0lBFR7x/0shp5RI+ZmZlZ31ZLx/z6utfC6kq5u/ojh3haTjMzM7O+rJYOv+8Zt7n8ByQP6TEzMzPr06oa0iPpLLIHdAWMkpT/MaxzGlQ3q1G0ugJmZmZm1jaqHcO/Prf+f8Bz9a+KmZmZmZnVW1Ud/oi4trAu6dT8trUfD+IxMzMzswKP4d8Cedi+mZmZmRXU0uGfVvdamJmZmZlZQ1Td4Zf0A0m7RMTrubRvNqZaZmZmZmZWD925w//3wF2SDs6lnVzn+piZmZmZWR11p8P/AjAd+JGk01OaR4u3IfljMTMzM7OkW2P4I+J54HPABEm3AFs3pFbWI35o18zMzMwKutPhXwYQEe9GxElABzCwEZUyMzMzM7P6qLrDHxHfKNq+NCLGF7Yl3VrPipmZmZmZWc/VMi1nKeMrZylP0nRJz0jqlDSri/2DJM1N+x+UNDa374yU/oykwyuVKWmOpGclLU/LxNy+qSntSUn359LXS3oi7VvW0/NtFI/oMTMzM7OCqn5pt0rRk4Ml9QMuBT4PbAAelrQgIp7KZftr4LcRsZukmcD5wLGSJgAzgT2BjwP/I2mPdEy5Mk+PiHlF9RgGXAZMj4jnJX2sqKoHR8RrPTlXMzMzM7Nmqecd/p6aAnRGxLqIeA+4GZhRlGcGcG1anwdMk6SUfnNEbIyIZ4HOVF41ZRb7KnBbekCZiHilDudmZmZmZtYS9bzD39ORJGOAX+W2NwB/XipPRGyS9AawQ0pfWnTsmLRersxzJc0G7gFmRcRGYA9ggKQOYAhwcURcl/IHcLekAP49Iq7o6kTSD5J9E2DUqFF0dHSUP/M6e3b9ex+uN/u9C958882WvXdf4jg3h+PcPI51czjOzeE4N4fjXFm3O/yStgeIiDeKdn2vh3Xp6h+G4mFCpfKUSu/qG4xCmWcAvyabaegKsvqfQxaTfYBpZNOOLpG0NCJWAwdExItpmM8iSU9HxOKPvEH2j8AVAJMnT46pU6d2UY3GWfnBGlizGoBmv3dBR0dHy967L3Gcm8Nxbh7Hujkc5+ZwnJvDca6s6iE9ki6StIFsuMxqSc9L+nJhf0Tc3cO6bAB2yW3vDLxYKo+k/sD2wOtlji1ZZkS8FJmNwDVkw38K77EwIt5KY/UXA3ulYwrHvgLMzx1jZmZmZtaWujOG/wlgYkSMjIhRZHfAz6tjXR4Gdpc0TtJAsodwFxTlWQCckNaPBu6NiEjpM9MsPuOA3YGHypUpaXR6FfAlYGUq93bgQEn9JW1DNgRolaRtJQ1Jx2wLHJY7pq3Iv7xlZmZmZknVQ3oi4moASTuSzYRzAPCbelUkjck/BbgL6AdcHRFPSjoHWBYRC4CrgOsldZLd2Z+Zjn1S0s+Bp4BNwN9FxPupvh8pM73lDZJGkg0HWg6cnMpaJWkhsAL4ALgyIlZKGg/MT53p/sCNEbGwXudvZmZmZtYItTy0+w2yjvYOab1uIuIO4I6itNm59XeBr5Q49lzg3GrKTOmHlKnHBcAFRWnrSEN72t22A/u1ugpmZmZm1ia6PS1nRPwwIvYCJgH/Wv8qWU+NHrZ1q6tgZmZmZm2i6jv8kuYCt7J52MxBwNsNqpeZmZmZmdVBd4b03Ez2oOw/kY17XwEc24hKWc/4kV0zMzMzK6iqwy9pUETMl3RnGkdvvcChnxzV6iqYmZmZWYtVO4b/ckmDgUsbWRkzMzMzM6uvih1+SQcBy4BfAo+kbWtjQwYPAGDU0EEtromZmZmZtVo1Q3pU9Gptbt/xI7h45kQO33OnVlfFzMzMzFqs4h3+iLgf+AxwIDA5IhY3vFbWI5KYMXEMgwd4Pn4zMzOzvq7aMfwnR8Q7wLfziZIG1r9KZmZmZmZWL1V1+CPiXUkdwIdjRCRNAR5uUL3MzMzMzKwOujMP/w+BhZIuAcYARwAnNaRWZmZmZmZWF4qI6jNLU4FFwGvA3hHx6wbVa4sh6VXguVbXowV2JGsn1liOc3M4zs3jWDeH49wcjnNzOM6bfSIiRhYnVn2HX9IPgGOAg4BPAx2SvhsR/12/Om55ugp6XyBpWURMbnU9tnSOc3M4zs3jWDeH49wcjnNzOM6VdWdIz47AlPTw7hJJC4ErAXf4zczMzMzaVLWz9BARp6bOfmH7uYj4fGFb0k/rXTkzMzMzM+uZqjv8VTigjmVZ73dFqyvQRzjOzeE4N49j3RyOc3M4zs3hOFfQrYd2yxYkPRoRk+pSmJmZmZmZ1UU97/CbmZmZmVmbqWeHX3Usy8zMzMzM6qCeHf6L61iWtRlJu0i6T9IqSU9KOjWlj5C0SNKa9Do8pUvSJZI6Ja2QNClX1gkp/xpJJ7TqnNpRmThfIOnpFMv5koal9LGS3pG0PC2X58raR9IT6TO4RJL/Kc8pE+uzJb2Qi+mRuWPOSPF8RtLhufTpKa1T0qxWnE+7KhPnubkYr5e0PKW7TddA0mBJD0l6PMX5n1P6OEkPpuvtXEkDU/qgtN2Z9o/NldVlO7eycb4hxWulpKslDUjpUyW9kWvPs3Nl+bpRQpk4z5H0bC6eE1O6+xyVRETZBbgGuBq4qFJeL1vuAowGJqX1IcBqYALwY2BWSp8FnJ/WjwTuJPvmZ1/gwZQ+AliXXoen9eGtPr92WcrE+TCgf0o/PxfnscDKEmU9BOyXPoM7gSNafX7ttJSJ9dnAaV3knwA8DgwCxgFrgX5pWQuMBwamPBNafX7tspSKc1GenwCz07rbdG1xFrBdWh8APJiuvT8HZqb0y4FvpfVvA5en9ZnA3LTeZTtv9fm1y1ImzkemfQJuysV5KvCLLsrxdaO2OM8Bju4iv/scFZZq7vDPAa5NFw3royLipYh4NK3/AVgFjAFmkLUP0uuX0voM4LrILAWGSRoNHA4siojXI+K3ZL/cPL2Jp9LWSsU5Iu6OiE0p21Jg53LlpFgPjYglkV31rmPzZ2OUbdOlzABujoiNEfEs0AlMSUtnRKyLiPeAm1Neo3Kc0136Y8g6SSW5TZeXrrVvps0BaQngEGBeSi++Rheu3fOAaemzKNXOjdJxjog70r4g+8e07DUaXzfKKtOeS3Gfo4JqOvyzI+J+fGG1JH31uzfZf9yjIuIlyP6wAx9L2cYAv8odtiGllUq3IkVxzvs62Z2MgnGSHpN0v6QDU9oYstgWOM5ldBHrU9LXwlcrDVPDbbrHSrTpA4GXI2JNLs1tugaS+qWhUa+QdWzWAr/L3SzIx+zDdpv2vwHsgNtzRcVxjogHc/sGAH8FLMwdsl8amnKnpD1TmuNcQZk4n5uuzxdJGpTSfH2uoJoO/2hJnwO+KGlvSZPyS6MraO1F0nbArcA/RMTvy2XtIi3KpFtOqThLOhPYBNyQkl4C/iQi9ga+A9woaSiOc9W6iPW/AbsCE8ni+5NC1i4Od5uuUplrx3H88d19t+kaRcT7ETGR7O7yFOCTXWVLr27PNSqOs6RP5XZfBiyOiF+m7UeBT0TEXsBPgf9K6Y5zBSXifAbwp8BnyIbpfC9ld3uuoH8VeWaTjc3emewPXz54ha8LrQ9Idy5uBW6IiNtS8suSRkfES+nrs1dS+gZgl9zhOwMvpvSpRekdjax3b1MizqSHjb4ATEtfGxMRG4GNaf0RSWuBPcjinP9KuRB/y+kq1hHxcm7/fwC/SJul2jRl0o2ybbo/cBSwTyHNbbrnIuJ3kjrIxjIPk9Q/3cXPx6zQnjekz2F74HXKt3PLycV5OrBS0lnASOBvc3l+n1u/Q9JlknbEca5aPs4RcWFK3ijpGuC0tO0+RwUV7/BHxLyIOAL4cUQcEhEH5xZ39vuINLbzKmBVRPxLbtcCoPDU+wnA7bn049OT8/sCb6QhP3cBh0kanoZKHJbSjNJxljSd7E7GFyPi7Vz6SEn90vp4YHdgXYr1HyTtm8o8ns2fjVE21qNz2f4SWJnWFwAzlc1uMo4s1g8BDwO7K5sNZSDZA5ALmnEOvUGZawfAocDTEbEhl99tugYpboXZu7Ymi+0q4D7g6JSt+BpduHYfDdybbiSUaudGyTg/LelvyMaLHxcRH+Ty75TaK5KmkPW7foOvG2WVifPolCayoeb567P7HOVE5Selx1bYL2DnSuV46d0L8Fmyb3RWAMvTciTZmM97gDXpdUSuXVxKNob0CWByrqyvkz0I1gmc1Opza6elTJw7ycYhFtIKs2t8GXiSbIaHR4G/yJU1mexiuBb4GemXtb1UjPX1qc2uIPsjMjp3zJkpns+QmyEmHbc67Tuz1efWTkupOKd9c4CTi/K7TdcW508Dj6U4r2TzrEfjyTrsncAtwKCUPjhtd6b943NlddnOvZSN86YUs0IbL6SfkmvPS4H9c2X5utH9ON+brs8rgf9k80w+7nNUWJSCUZKkW8j+I70deAR4NV0odgMOBqYBZ0XEorIFmZmZmZlZ01Xs8ANImgB8DTiAbE7lt8m+KrwDmBcR7zaykmZmZmZmVpuqOvxmZmZmZtY7VXxoV9I/5ta/UrTvvEZUyszMzMzM6qOaefhn5tbPKNrXJ3+tzMzMzMyst6imw68S611tm5mZmZlZG6mmwx8l1rvaNjMzMzOzNlLNtJzvA2+R3c3fmmyGHtL24IgY0NAampmZmZlZzar5pd1+ETE0IoZERP+0Xth2Z9/MrA+TNFbSO5KWS1on6cKUPlXSYknzJT0l6XJJW6V9h0laIulRSbdI2i6lr5d0U67suZLWp/WBqayVkp4opFeo2/GSVkh6XNL1jTh/M7PeoJohPWZmZuWsjYiJwH7Aibn0KcB3gT8DdgWOkrQj8H3g0IiYBCwDvpM75uOShksaAeyUSz8cGBARnyL70ceyJO1J9ouxh0TEXsCptZ6cmVlv17/VFTAzs15vV0nLgXHAhbn0hyJiHUC6c/9Z4F1gAvC/kgAGAktyx9wEfJVs2OiNbJ4d7n1gG0n9qqzTIWQ/DPkaQES8XsN5mZltEdzhNzOznlobERMlbQMskzQnpXc10YOARRFxXImyFgDXpHwnsrnDfzdwFPAq8EIVdVIX729m1id5SI+ZmdXLRrI78cPT9hRJ49LY/WOBB4ClwAGSdgOQtI2kPXJlvJfyLEnrAETEJuAd4HRyQ3okjZF0Txd1uQc4RtIOKd+I+pyimVnv4zv8ZmbWU4UhPYPI7t6vkDSVrNP+I7Ix/IuB+RHxgaQTgZskDUrHfx9YXSgsIs4CSOP9SevHAEMj4qp8OjAa2FRcoYh4UtK5wP1ptrnH+OPnC8zM+oyK03KamZl1V+rwnxYRX2jw+5wCPB8RCxr5PmZmvZnv8JuZWa8VET9rdR3MzNqd7/CbmZmZmW3B/NCumZmZmdkWzB1+MzMzM7MtmDv8ZmZmZmZbMHf4zczMzMy2YO7wm5mZmZltwf4fr8FjFI7PL7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x1296 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,18))\n",
    "time = CNF.loc[:,['Time']].values\n",
    "del1 = 1300 # подобрал начало, чтобы перехоодный процесс в начале не мешал\n",
    "del2 = 676251\n",
    "#plt.figure();\n",
    "fig.suptitle(u\"Оценки математического ожидания на интервале выставки\")\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.3)\n",
    "ax1 = fig.add_subplot(721)\n",
    "ax4 = fig.add_subplot(722)\n",
    "ax2 = fig.add_subplot(723)\n",
    "ax5 = fig.add_subplot(724)\n",
    "ax3 = fig.add_subplot(725)\n",
    "ax6 = fig.add_subplot(726)\n",
    "ax7 = fig.add_subplot(714)\n",
    "ax1.plot(time[del1:del2,0], det[del1:del2,0])\n",
    "#ax1.axis([200, 1000, 97, 98.2])\n",
    "ax1.grid(b=True, which='major', axis='both')\n",
    "ax1.set_ylabel(u'E(f\\'_x), м/с²')\n",
    "ax1.set_xlabel(u'время, c')\n",
    "\n",
    "ax2.plot(time[del1:del2,0], det[del1:del2,1])\n",
    "#ax2.axis([1000, 1025, 98.005, 98.02])\n",
    "ax2.grid(b=True, which='major', axis='both')\n",
    "ax2.set_ylabel(u'E(f\\'_y), м/с²')\n",
    "ax2.set_xlabel(u'время, c')\n",
    "\n",
    "ax3.plot(time[del1:del2,0], det[del1:del2,2])\n",
    "#ax3.axis([200, 1000,  -90.002,-89.997])\n",
    "ax3.grid(b=True, which='major', axis='both')\n",
    "ax3.set_ylabel(u'E(f\\'_z), м/с²')\n",
    "ax3.set_xlabel(u'время, c')\n",
    "\n",
    "ax4.plot(time[del1:del2,0], det[del1:del2,3])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax4.grid(b=True, which='major', axis='both')\n",
    "ax4.set_ylabel(u'E(ω\\'_x), рад/с')\n",
    "ax4.set_xlabel(u'время, c')\n",
    "\n",
    "ax5.plot(time[del1:del2,0], det[del1:del2,4])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax5.grid(b=True, which='major', axis='both')\n",
    "ax5.set_ylabel(u'E(ω\\'_y), рад/с')\n",
    "ax5.set_xlabel(u'время, c')\n",
    "\n",
    "ax6.plot(time[del1:del2,0], det[del1:del2,5])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax6.grid(b=True, which='major', axis='both')\n",
    "ax6.set_ylabel(u'E(ω\\'_z), рад/с')\n",
    "ax6.set_xlabel(u'время, c')\n",
    "\n",
    "ax7.plot(time[del1:del2,0], det[del1:del2,6])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax7.grid(b=True, which='major', axis='both')\n",
    "ax7.set_ylabel(u'E(f\\'_x*ω_z+f\\'_y*ω_y+f\\'_z*ω_z),\\n м*рад/с³')\n",
    "ax7.set_xlabel(u'время, c')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,18))\n",
    "time = CNF.loc[:,['Time']].values\n",
    "del1 = 1300 # подобрал начало, чтобы перехоодный процесс в начале не мешал\n",
    "#del2 = 45000\n",
    "del2 = 676251\n",
    "#plt.figure();\n",
    "fig.suptitle(u\"Оценки СКО ожидания на интервале выставки\")\n",
    "fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.5, hspace=0.3)\n",
    "ax1 = fig.add_subplot(721)\n",
    "ax4 = fig.add_subplot(722)\n",
    "ax2 = fig.add_subplot(723)\n",
    "ax5 = fig.add_subplot(724)\n",
    "ax3 = fig.add_subplot(725)\n",
    "ax6 = fig.add_subplot(726)\n",
    "ax7 = fig.add_subplot(714)\n",
    "ax1.plot(time[del1:del2,0], det[del1:del2,7])\n",
    "#ax1.axis([200, 1000, 97, 98.2])\n",
    "ax1.grid(b=True, which='major', axis='both')\n",
    "ax1.set_ylabel(u'σ(f\\'_x), м/с²')\n",
    "ax1.set_xlabel(u'время, c')\n",
    "\n",
    "ax2.plot(time[del1:del2,0], det[del1:del2,8])\n",
    "#ax2.axis([1000, 1025, 98.005, 98.02])\n",
    "ax2.grid(b=True, which='major', axis='both')\n",
    "ax2.set_ylabel(u'σ(f\\'_y), м/с²')\n",
    "ax2.set_xlabel(u'время, c')\n",
    "\n",
    "ax3.plot(time[del1:del2,0], det[del1:del2,9])\n",
    "#ax3.axis([200, 1000,  -90.002,-89.997])\n",
    "ax3.grid(b=True, which='major', axis='both')\n",
    "ax3.set_ylabel(u'σ(f\\'_z), м/с²')\n",
    "ax3.set_xlabel(u'время, c')\n",
    "\n",
    "ax4.plot(time[del1:del2,0], det[del1:del2,10])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax4.grid(b=True, which='major', axis='both')\n",
    "ax4.set_ylabel(u'σ(ω\\'_x), рад/с')\n",
    "ax4.set_xlabel(u'время, c')\n",
    "\n",
    "ax5.plot(time[del1:del2,0], det[del1:del2,11])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax5.grid(b=True, which='major', axis='both')\n",
    "ax5.set_ylabel(u'σ(ω\\'_y), рад/с')\n",
    "ax5.set_xlabel(u'время, c')\n",
    "\n",
    "ax6.plot(time[del1:del2,0], det[del1:del2,12])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax6.grid(b=True, which='major', axis='both')\n",
    "ax6.set_ylabel(u'σ(ω\\'_z), рад/с')\n",
    "ax6.set_xlabel(u'время, c')\n",
    "\n",
    "ax7.plot(time[del1:del2,0], det[del1:del2,13])\n",
    "#ax4.axis([200, 1000,  -0.0025,0.0025])\n",
    "ax7.grid(b=True, which='major', axis='both')\n",
    "ax7.set_ylabel(u'σ(f\\'_x*ω_z+f\\'_y*ω_y+f\\'_z*ω_z),\\n м*рад/с³')\n",
    "ax7.set_xlabel(u'время, c')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для того, чтобы графики были читаемы, я отрезал первые 1300 измерений. Что мы видим - периодически объект выставки подвергался ударам. Для корректной выставки необходимо выбрать интервал достаточной длины, на котором не было воздействий. Для грубой начальной выставки достаточно нескольких десятков секунд, поэтому выберем интервал между ударами и произведём выставку на нём."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изучив графики, я решил осуществить выставку на интервале с 1940 до 2020 секунды (12000 - 45000 измерения), чтобы определить начальную ориентацию БИНС"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def algebraic_alignment_naive(A, phi, h, a, e2, u):\n",
    "    # выставка во втором варианте из методички, причём с известной точной широтой места проведения выставки\n",
    "    # я её имплементировал, но решил пользоваться первым вариантом - это следующая ячейка\n",
    "    L = np.zeros((3, 3))\n",
    "    init = 12000 \n",
    "    end = 45000  # получается 80 секунд\n",
    "    om_x = CNF['OmX'][:].mean()\n",
    "    om_y = CNF['OmY'][:].mean()  \n",
    "    om_z = CNF['OmZ'][:].mean()\n",
    "    f_x = CNF['AccX'][:].mean()\n",
    "    f_y = CNF['AccY'][:].mean()\n",
    "    f_z = CNF['AccZ'][:].mean()\n",
    "    \n",
    "    theta = math.atan2(f_x, math.sqrt(f_y*f_y+f_z*f_z))\n",
    "    gamm = -math.atan2(f_z, f_y)\n",
    "    \n",
    "    \n",
    "    means = np.mean(A[init:end, 1:], axis = 0).reshape(6,1)\n",
    "    theta = math.atan2(means[0], math.sqrt(means[1]**2+means[2]**2))\n",
    "    gamm = -math.atan2(means[2], means[1])\n",
    "    \n",
    "    L[:, 2:3] = means[0:3] / 9.812357#(g_phi(phi, h, a, e2))\n",
    "    L[:, 1:2] = (means[3:6] - (L[:, 2:3] * u * sin(phi))) / (u * cos(phi))\n",
    "    L[:, 0:1] = np.cross(L[:, 1:2].T, L[:, 2:3].T).T\n",
    "    L[:, 2:3] /= np.linalg.norm(L[:, 2:3], 2)\n",
    "    L[:, 1:2] /= np.linalg.norm(L[:, 1:2], 2)\n",
    "    L[:, 0:1] /= np.linalg.norm(L[:, 0:1], 2)\n",
    "    psi = math.atan2(L[0][0], L[0][1])\n",
    "    return L, theta, gamm, psi, means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initial_algebraic_alignment():\n",
    "    '''\n",
    "    Это функция начальной алгебраической выставки - в глобальные переменные записываются значения углов\n",
    "    '''\n",
    "    global g\n",
    "    global L\n",
    "    global theta, gamm, phi, psi\n",
    "    \n",
    "    init = 12000\n",
    "    end = 45000  # 15 секунд\n",
    "    om_x = CNF['OmX'][init:end].mean()\n",
    "    om_y = CNF['OmY'][init:end].mean()  \n",
    "    om_z = CNF['OmZ'][init:end].mean()\n",
    "    f_x = CNF['AccX'][init:end].mean()\n",
    "    f_y = CNF['AccY'][init:end].mean()\n",
    "    f_z = CNF['AccZ'][init:end].mean()\n",
    "    \n",
    "    theta = math.atan2(f_x, math.sqrt(f_y**2+f_z**2))\n",
    "    gamm = -math.atan2(f_z, f_y)\n",
    "    om = math.sqrt(om_x**2+om_y**2+om_z**2)\n",
    "    \n",
    "    phi = math.atan2(om_z, math.sqrt(om_x**2+om_y**2))\n",
    "    g_mean = math.sqrt(f_x**2+f_y**2+f_z**2)\n",
    "    # третий столбец по показаниям акселерометров\n",
    "    l13 = sin(theta)\n",
    "    l23 = cos(theta)*cos(gamm)\n",
    "    l33 = -cos(theta)*sin(gamm)\n",
    "    # второй и первый по показаниям ДУС\n",
    "    l12 = (om_x - u*l13*sin(phi))/(u*cos(phi))\n",
    "    l22 = (om_y - u*l23*sin(phi))/(u*cos(phi))\n",
    "    l32 = (om_z - u*l33*sin(phi))/(u*cos(phi))\n",
    "    norm_coeff = sqrt(l12**2+l22**2+l32**2)\n",
    "    l12 = l12/norm_coeff #нормировка\n",
    "    l22 = l22/norm_coeff\n",
    "    l32 = l32/norm_coeff\n",
    "    \n",
    "    #теперь нужно векторно умножить второй на третий\n",
    "    l11 = l22*l33 - l32*l23\n",
    "    l21 =-l12*l33 + l13*l32\n",
    "    l31 = l12*l23 - l22*l13\n",
    "    \n",
    "    L = np.matrix([[l11, l12, l13], [l21, l22, l23], [l31, l32, l33]])   #записали в матрицу\n",
    "\n",
    "    psi = math.atan2(l11,l12)\n",
    "\n",
    "    theta = math.asin(L.item(0,2))\n",
    "    \n",
    "    gamm = -math.atan2(L.item(2,2),L.item(1,2))\n",
    "    \n",
    "    # и по полученным углам перевычислили матрицу ориентации:\n",
    "    L = np.array([[sin(psi)*cos(theta)                      , cos(psi)*cos(theta)                                , sin(theta)],\n",
    "         [cos(psi)*sin(gamm)-sin(psi)*sin(theta)*cos(gamm), -sin(psi)*sin(gamm)-cos(psi)*sin(theta)*cos(gamm), cos(theta)*cos(gamm)],\n",
    "         [cos(psi)*cos(gamm)+sin(psi)*sin(theta)*sin(gamm), -sin(psi)*cos(gamm)+cos(psi)*sin(theta)*sin(gamm), -cos(theta)*sin(gamm)]])\n",
    "    L = L.reshape((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.94887472 -0.3154603  -0.01102566]\n",
      " [ 0.01627094  0.01399849  0.99976962]\n",
      " [-0.31523328 -0.94883552  0.01841565]] 1.0\n",
      "phi =  -31.650312892495 deg\n",
      "theta =  -0.6317363307973912 deg\n",
      "gamma =  -1.0552626747342007 deg\n",
      "psi =  108.38976156860596 deg\n"
     ]
    }
   ],
   "source": [
    "initial_algebraic_alignment()\n",
    "print(L, np.linalg.det(L))\n",
    "print('phi = ', phi*180/pi, 'deg')\n",
    "print('theta = ',theta*180/pi, 'deg')\n",
    "print('gamma = ',gamm*180/pi, 'deg')\n",
    "print('psi = ',psi*180/pi, 'deg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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