Data_collection_and_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7df9a3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f78e7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import io\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e5a6ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpha_vantage.timeseries import TimeSeries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import math, random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d7c1bfcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://api.tiingo.com/tiingo/daily/aapl/prices?startDate=2019-01-02&token=f4a3ff7449de9fc45439d23e204938fc31691077"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cacef6ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticker_names=pd.read_csv('Tickers.csv',index_col=False)\n",
    "len(ticker_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7779f816",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def pre_preprocessor(ticker,company_name):    \n",
    "    CSV_URL =\"https://api.tiingo.com/tiingo/daily/\"+ticker+\"/prices?startDate=2015-01-02&token=419f55d2ac8913929c3054133171783b621cdc06\"\n",
    "\n",
    "    with requests.Session() as s:\n",
    "        try:\n",
    "            download = s.get(CSV_URL)\n",
    "            decoded_content = download.content.decode('utf-8')\n",
    "            df = pd.read_json(io.StringIO(decoded_content))\n",
    "            df.to_csv(str(company_name) + '_data.csv', index=False)\n",
    "        except ValueError as e:\n",
    "            print(f\"ValueError: {e} for {r['TICKER']}\")\n",
    "        except Exception as e:\n",
    "            print(f\"An error occurred: {e} for {r['TICKER']}\")\n",
    "    return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52c4d696",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(ticker):\n",
    "    df=pd.read_csv('Tickers.csv',index_col=False)\n",
    "\n",
    "    row_numbers = df.index[df['TICKER'] == ticker].tolist()\n",
    "    try:\n",
    "        \n",
    "\n",
    "        if row_numbers:\n",
    "            row_number = row_numbers[0]\n",
    "            company_name = df.loc[row_number, 'COMPANY NAME']\n",
    "            df_pearl=pd.read_csv(str(company_name)+'_data.csv',index_col='date',parse_dates=True)\n",
    "\n",
    "            df_pearl['high'].plot(figsize=(12,6))\n",
    "            df_pearl.index = pd.to_datetime(df_pearl.index)\n",
    "            df_pearl = df_pearl.asfreq('D')\n",
    "            df_pearl['high'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['low'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['open'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['close'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['high'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['low'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['open'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['close'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['adjClose'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjHigh'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjOpen'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjLow'].fillna(method='bfill', inplace=True)\n",
    "            df_pearl['volume'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjVolume'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['divCash'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['splitFactor'].fillna(method='bfill', inplace=True)\n",
    "            res=seasonal_decompose(df_pearl['high'],period=365)\n",
    "            res.plot();\n",
    "        else:\n",
    "            print(\"golmaal hai bhai sab golmaal hai\")\n",
    "    except:\n",
    "\n",
    "        if row_numbers:\n",
    "            row_number = row_numbers[0]\n",
    "            company_name = df.loc[row_number, 'COMPANY NAME']\n",
    "            pre_preprocessor(ticker,company_name)\n",
    "            df_pearl=pd.read_csv(str(company_name)+'_data.csv',index_col='date',parse_dates=True)\n",
    "\n",
    "            df_pearl['high'].plot(figsize=(12,6))\n",
    "            df_pearl.index = pd.to_datetime(df_pearl.index)\n",
    "            df_pearl = df_pearl.asfreq('D')\n",
    "            df_pearl['high'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['low'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['open'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['close'].fillna(method='ffill', inplace=True)\n",
    "            df_pearl['adjClose'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjHigh'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjOpen'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjLow'].fillna(method='bfill', inplace=True)\n",
    "            df_pearl['volume'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['adjVolume'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['divCash'].fillna(method='bfill', inplace=True)\n",
    "#             df_pearl['splitFactor'].fillna(method='bfill', inplace=True)\n",
    "            res=seasonal_decompose(df_pearl['high'],period=365)\n",
    "            res.plot();\n",
    "        else:\n",
    "            print(\"golmaal hai bhai sab golmaal hai\")\n",
    "        df_pearl=df_pearl.drop(['adjHigh','adjLow','adjClose','divCash','splitFactor'],axis=1)\n",
    "    return df_pearl\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7f832ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9bb9bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_pearl=pd.read_csv('Abbott Laboratories_data.csv',index_col='date',parse_dates=True)\n",
    "# df_pearl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e984a8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_pearl['high'].plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fadc1de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_pearl.index = pd.to_datetime(df_pearl.index)\n",
    "\n",
    "# df_pearl = df_pearl.asfreq('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e725977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_pearl['high'].fillna(method='ffill', inplace=True)\n",
    "# df_pearl['low'].fillna(method='ffill', inplace=True)\n",
    "# df_pearl['open'].fillna(method='ffill', inplace=True)\n",
    "# df_pearl['close'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c89054b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# res=seasonal_decompose(df_pearl['high'],period=365)\n",
    "# res.plot();\n",
    "\n",
    "# # res.seasonal.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "130a3725",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_model = auto_arima(df_pearl['high'], seasonal=True, m=7, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56efe359",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ticker='ABT'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f7daf5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(ticker+'_high.pkl', 'wb') as f:\n",
    "#     pickle.dump(best_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c7d26cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_model_open = auto_arima(df_pearl['open'], seasonal=True, m=7, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb1a3e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_model_close = auto_arima(df_pearl['close'], seasonal=True, m=7, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81cf42ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_model_low = auto_arima(df_pearl['low'], seasonal=True, m=7, trace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5662ced2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(ticker+'_open.pkl', 'wb') as f:\n",
    "#     pickle.dump(best_model_open, f)\n",
    "# with open(ticker+'_close.pkl', 'wb') as f:\n",
    "#     pickle.dump(best_model_close, f)\n",
    "# with open(ticker+'_low.pkl', 'wb') as f:\n",
    "#     pickle.dump(best_model_low, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eea39f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ed5f8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_predictor(best_model,target,df_pearl):\n",
    "    train_test_split=len(df_pearl)-201\n",
    "    train_df=df_pearl[:train_test_split]\n",
    "    test_df=df_pearl[train_test_split:]\n",
    "    model_open=SARIMAX(df_pearl[target], order=best_model.order,seasonal_order=best_model.seasonal_order)\n",
    "    res=model_open.fit()\n",
    "    start=len(train_df)\n",
    "    end= len(train_df)+len(test_df)+14\n",
    "    pred=res.predict(start,end).rename('Prediction_'+target)\n",
    "    plt.figure(figsize=(14, 8))\n",
    "    ax = test_df[target].plot(legend=True)\n",
    "    pred.plot(legend=True, ax=ax)\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    target_real=test_df[target]\n",
    "    target_pred=pred\n",
    "    return target_real,target_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3e900d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_using_ticker(ticker,df_pearl):\n",
    "    try:\n",
    "        with open(ticker+'_open.pkl', 'rb') as f:\n",
    "            best_model_open = pickle.load(f)   \n",
    "    except:\n",
    "        best_model_open = auto_arima(df_pearl['open'], seasonal=True, m=7, trace=True)\n",
    "        with open(ticker+'_open.pkl', 'wb') as f:\n",
    "            pickle.dump(best_model_open, f)\n",
    "    try:\n",
    "        with open(ticker+'_close.pkl', 'rb') as f:\n",
    "            best_model_close = pickle.load(f)   \n",
    "    except:\n",
    "        best_model_close = auto_arima(df_pearl['close'], seasonal=True, m=7, trace=True)\n",
    "        with open(ticker+'_close.pkl', 'wb') as f:\n",
    "            pickle.dump(best_model_close, f)\n",
    "            \n",
    "    try:\n",
    "        with open(ticker+'_high.pkl', 'rb') as f:\n",
    "            best_model_high = pickle.load(f)   \n",
    "    except:\n",
    "        best_model_high = auto_arima(df_pearl['high'], seasonal=True, m=7, trace=True)\n",
    "        with open(ticker+'_high.pkl', 'wb') as f:\n",
    "            pickle.dump(best_model_high, f)\n",
    "            \n",
    "    try:\n",
    "        with open(ticker+'_low.pkl', 'rb') as f:\n",
    "            best_model_low = pickle.load(f)   \n",
    "    except:\n",
    "        best_model_low = auto_arima(df_pearl['low'], seasonal=True, m=7, trace=True)\n",
    "        with open(ticker+'_low.pkl', 'wb') as f:\n",
    "            pickle.dump(best_model_low, f)\n",
    "    \n",
    "    open_real,open_pred=value_predictor(best_model_open,'open',df_pearl)\n",
    "    close_real,close_pred=value_predictor(best_model_close,'close',df_pearl)\n",
    "    high_real,high_pred=value_predictor(best_model_high,'high',df_pearl)\n",
    "    low_real,low_pred=value_predictor(best_model_low,'low',df_pearl)\n",
    "    return open_real,open_pred,close_real,close_pred,high_real,high_pred,low_real,low_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae8bf045",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def main(ticker='WIT'):\n",
    "    df_pearl=preprocessing(ticker)\n",
    "    open_real,open_pred,close_real,close_pred,high_real,high_pred,low_real,low_pred=pred_using_ticker(ticker,df_pearl)\n",
    "    print(open_real,open_pred,close_real,close_pred,high_real,high_pred,low_real,low_pred)\n",
    "    return df_pearl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4c92227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vz5WV7uFkXaMFMYrKyyEajbSQFRWqPvYe1UMGojy9m+N89cZ1qP3pf7CMPwGFjXrl5c4tkDn7HGnMpJTQG+UUbuo90us9J67VHzXWVl4Adz8D8fWHkJ+9g9qaGhQUFECPTYAsOBj0991isYTk31qosd3GwnYbj1HbznYbi7/a3a1bN6/HfQ5+f/rpJ/z555+477771NjOjs6xtHE76hodA5SVQH/gOiA/F255GUSjyXC2SWPitAshhhwD0bt/656x+S/3fW+ZKRotdWy680n3oRM5+6A/9yCq62uBI9175fVZt6sNew7fRquvabd7z1YBwC3Hr3bzw0BdLcSQY9zLpKWr1/IS9RodC9TXQerWpscSExEREQWJT6nO1q1bh88//xx33HEHoqI8MwN0SPYxv74ucAGojAqA20Qzh8go93vbxuUKsxnisCG+P9PsGfyK9Ewv5SI8Dlmz3dOeycoK57b9w0DjMcbpXZqui8tYYjFoGMSwEZ51S7EFv/ZA2TY2vKkUcURERETB1GLP75w5c7Bp0yaUl5djxowZOPvss7Fo0SI0NDTg4YcfBgAMGDAAV155ZcAr2y72XlnRjuC3rqbpc/YPAfZ0YfZA2ReHDAR2blHbrcxJLISAdsMD0J97wHHMWnjQvdCB/c7tmiogNt49B290DJCY0vQzJv4LKMqH+PelTVckMcl2r1j3+lsbnJPgiIiIiEKkxcjqxhtv9Dg2adKkQNQlsBw9v74PexCTToZc8pn3k5FR7ksfN5XarBW0q+6AfrsKMFsaUiKmnu7cPuIoYOixwN+rgLgE6AX5bmWla/BbWQFpjnAMexDnXwVt4r+af1ZsHMSF1zRf+cweEGdeDDHCttQxe36JiIioAzHMCm/+GPMr0jIg/nG695NSAlD3FqMnQbSjl1O0IXDWGvXCajPvgjZnIcSQY5rt+ZVb10O/5t/OFdt8XfWuESEEtBPPhEi1DX9wBL9c5Y2IiIhCzz8RTziwD3toz5hfAOKwoZD/87KoR1WFWtFs818Q5/k4BCQhCdoFMwCoiWdy5+Ymi2r3Pec1oBQmExAXD5lqgb6yAJrLRDO5frWzoG1YhWPFNj8Fvx4cwa/n4iBEREREwdbpg1+ZtQcVP34JHHK4OtCeMb8AMGgYxMSTIH/8xnFIXHStmoTWuz+QvQciJrbt9+3ZF9plN0L06KvuOWAwxIDBTRYXPfs2f7+UNEC3AqUlziEYudnO8/bMC/bV4LxMrPML+//v0iLo//sUYso0CEszk+qIiIiIAqjzB7/Ze1G5cD4Qn2A7IJu/oAXCbAZGT3YEv2LCSY6cuQCA/k0HrF6lWIDiApjue65d9WpMpKSrVGzFBUBKGqyP3uJ2XlaUuZc3BSgN2db1AADd/vyyEogrbwvMs4iIiIha0OmDXxGXoILACttiEq6T0nzlkt5NHHFUu26l3fVUy8sK+8K+KlxxAYDDnD28dmUl7vuB6vm1L8VsFx0TmOcQERERtUKnD34Rl+C+77oYhK+69oQ4bbpavKJXv3bdSiSnAcm+Z4Zoki34lUUF8Jjip2lAean7sQCN+RVnXAT58/8C/hwiIiKi1uj82R7i4t33/RD8CiGg/evsdge+ARUbr3pdi/KhL1vc6FycZ89vgIY9iLgEwGUMtPzpG8jGvdBEREREQdL5g9/4Rj2/Gd7Xee5shBAQERGQS7+AfGeu8/gZFwGR0UB1pfsFJs8V4vymusptV//vw5A68/4SERFR8HX+4DfaPfOCZl98wQCkfZyzC3HMcUBRvmfhQE1486asBPKTt4L3PCIiIiKbTh/8Cpe8vsJAga834t+XqZRs3gRqwhsADBoGDBwK7fr7HIfk8h8D9zwiIiKiJhhq9pE469KWC3Ui8RdejYq35zkPNB7/7Kqdi380x3Tzw5BSui/VzOWOiYiIKAQ6fc+vG4Ol2Yo740K3fWGx9foOH6VezWaIqaep7QAGvwAcga9255NqAlxNFWQ7cy4TERERtZUhgl8Ra+vxbJxz1mj69AcAiB591L6lC8SZF0O77XHHynKBJg4ZCHHimarn1x9p54iIiIjawBDDHlIemIPiLRvdxv8akbAH/zUq+4IYNgJCMwGHHh7cith74Guqgcio5ssSERER+ZEhosGIAYOhjZ4Y6mqEhHbtvR7HZG4WAEAMCHLQa+ca/IYpKSXkuuWQDQ2hrgoRERG1gSGCXyMTw46FuGAGtIddJr7V2YYbhGgYiOgEwS92bob+4mOQH78e6poQERFRGzD4NQBtwkkQmd2dB+xjbSMiQ1OhzhD86joAQH7/ZYgrQkRERG3B4NeAxNFj1YalS2gqEGULfmvDOPitc07Wk7ZAmIiIiDo+Q0x4I3di6mkQx5/oHH4QbLZV92RNNUQLRTusulqX7RqPlQSJiIioY2LPrwEJIUIX+AJATHgPe5Ab1kB/aZbzQJi2g4iIyIgY/FLw2XtJqypCWw8f6cu+BVwX6KiqDF1liIiIqE0Y/FLQidg4IDoGctHbYbnKm0hKUa8T/gkAkPt3h7A2RERE1BYMfik0aqrVKm+7toa6Jm1XXw+kWiBOm672y0tCWh0iIiJqPQa/FBL2XlOUFoe0Hj5pqAfMEUBMHCA0oLw81DUiIiKiVmLwSyEhppwKAJB1NSGuSdtJW/ArNA2IiQWqOeaXiIgoXDD4pdCIilKvtbXNl+uI6m09vwAQG8fgl4iIKIww+KXQCOeFLooLANukN8TGQTLbAxERUdhg8EuhEc49v6XFEGnpajuGPb9EREThhMEvhYTQTGroQG34jflFdaUa6wuo4Jc9v0RERGGDwS+FTnR0WAW/sroK1iumAQ0NjoU6BMf8EhERhRUGvxQ6kdGQu7ZAf/clyL07Q10bAIDcvgmyMN/7SZd8vuLwI9VGZBRQVBCWi3UQEREZkTnUFSADS88Etq6H3LcLEAKi9yGhrhH0J+8EYmJhev59LyedAa7o3R8AIDeuUQd2bwP6HRaMKhIREVE7sOeXQke3OjZlcWEIK2Krw5a/1UZ1lfcC9XUeh8SpF6iNujCcuEdERGRADH4pZMRxU507HSDlmT7n/uYL2AJc7eo7HYdEZg+1UdNEwExEREQdCoNfChltzCSIS28EokI78U3u2Q7rVacBVpeeaF33LGjP6hCf6DwWo/IVy5rQB+9ERETUMga/FFLamEnAoOFNBr+ypAgyLyegdZDffQE0DnbLS93L7N4O/fkH1U7Pfs4TtqwPqGbwS0REFA4Y/FLIiejoJsfM6vddA/3uGYGtQGSkc/uQgeq5Lz/hVkSuW+7YFvYcv4Az3y+HPRAREYWFFrM9zJ07F2vWrEFSUhJmz54NAKioqMCzzz6L/Px8pKen46abbkJ8fHzAK0udVGQ00NSwAVsOXalb1cIYfialhPz1O8e+GDYCcucWYPsm94IN9er8xJPcj5sjAJOp6UlyRERE1KG02PM7YcIE3HXXXW7HPvvsMwwZMgTPP/88hgwZgs8++yxQ9SMjaKbn16G+ITDPbjS8AanpXovJgzlAt14Q513ldlwIoYY+sOeXiIgoLLQY/A4ePNijV3fVqlU4/vjjAQDHH388Vq1aFZjakTFEquDX6yQzO1vPq9/l57rtimPHOfL1yqw96vXAfuCvlUBymgp2G4uO4ZhfIiKiMOHTmN/S0lKkpKQAAFJSUlBWVubXSpHBpFoAKYG9OyFLi72X8SH4lXW10P/4sdnV12T+Ace29tgrEJoG7aLr1Ln1qyH/XgX9vmtUAdexwa5iYiFX/QJZmNfmOhIREVFwBXyFt6VLl2Lp0qUAgFmzZsFisQT6kR7MZnNInhtq4dJu69iJKHjrBeiP3QIAyPhwGUREBKwlRSiwlUlNiIepDW0xm82I+vwdVC/5HMn9D0PkoKEeZRr270H536tQByDthfdg7t5bnbBYcFAIxEgdVf992FE+/ab7oSUme9ynKD4B9dYGmN78L1Ifm9fqOgZCuLzn/sZ2G0u4t1vW1iL/8mkw9+mP1EdebNO14d52X7HdxhLodvsU/CYlJaG4uBgpKSkoLi5GYmJik2WnTJmCKVOmOPYLCgqaLBsoFoslJM8NtbBptxYBxCUAleUAgLyzj4d204PQn3UuOlGUdxBCi2j1LS0WC6pX/goAKM09AJHeze28lBL69ec79kui4gDX/1fxiajO2uvc79IdRXUN7mVsrJr6MarPzQ75/++wec/9jO02lnBvt/7Fe5CV5ajfuLbN7Qj3tvuK7TYWf7W7W7duXo/7NOzhmGOOwbJlywAAy5Ytw7HHHut7zYgAR+Br5xr4AnAuMNEKsjAPRf93FVCilkyWeQc8C7W0opxtKIOdGDy8yaIiKVltcIljImoNH7LDyPxc6G88h/q9OwNQISJjaTH4nTNnDu655x7k5ORgxowZ+OGHH3Daaafh77//xvXXX4+///4bp512WhCqSkbmNYBtgv7OPNRvWe+89v1XIBtlY9CvO7f5m8TEue2KSSc3WVSccbF6HT6y1XUkos5P5mZBlnuZE6N5mTjbAn3O/ZC/fY+iGy/0Q82IjK3FYQ833nij1+P33Xefv+tCBqY99gr09+dDO2069Kf+z7NnpC2rvEVFObdj4oDqSsg3X4C46nYAcA+EBw6FOHqs5z1cJ7dldIPI7N7k40Ryqsr40ChgJiLjklJCv3cmkNkdpocbzQVw+f0mdR1Ca8WXsG3oACCi5nGFN+oQRHomTNfdC9GzL7SHGk0AScuA3LfL7ZDUdcjd2yA3rfW8V7wag669/Bm0h+eq8lUVzgI1tqWUe/SBduOD0Cb80/MeR40BomIgxk2FduMDrWxF01kliMhg7DnEc7M9z7ku6lPnfWn3xsToiY5t2dSiQETUKgHP9kDUViI5DdpdsyFX/wL07g+sWwG56hfoq36B6NYLontvyOU/Qb4+BwCgvbQI+nXnAPV1EFNPh1y2GFpSiupNSUoBevZVK7EBkJUV0F94RD3nhNMgTN5XjdOmTAOmTGtDpdv+NSYRdWK5WepV0yCLCqDfcRm0Wx6BGDgU0vWbrepqtVBOS6wuedAry9W3TUTkEwa/1CGJvgMg+g4AAOg7NgMA5CtPQQLQnn0HKHaZBXowG6ivU2WWLAIARI2agDr7+YQkRy+MXP4TsHeHekZUtH8r3Uw+YSIyFnnAFvzqOvQHVe5w/cdvYBo41L3nt5W9uHLPduf2hjUQx5/ot7oSGQ2HPVDH5zpkAQBKitx25fo/PS5JnHGbY1skJgNlJbYdl0LJqf6pn8eNicjwXIc72LPV2Bfrsbos196K4FdWVQJ5ORBTT7ddw+XUidqDwS91fKLRP9PyUvcJceUlbqe1OQvdy6dYgJJCSN0KFKv0Z9odTziWMSYi8ruKUs9j9iXcdR2wf/NU3Yo0jvvVnAcxaKgawuUtgwQRtRqDX+r4Gs2Elts3uQe/FSpHsLjyNmi3PQYRF+9+fWo6YLUCpSVAUT6QngnRfxAEx+kSUYDIygpvR20vOmBbLVKWewmSG19lG6qF3v2BhnrI/30KWd/2Jd+JSGHwSx2eOHKU27788j1Il95e+ZtaPlsMOBzi0CM8r0/LUBuFeZD5uSoY9nsl/X9LIgpjjYdrAc55Abp0DruyD8lqzr5dQIoFIiHJeaw1PcZE5BWDX+rwxPCR0OZ+DHHKec6Da5c7Mjg4uObmdWULdvXvPgd2b4OwdAlQTYmIbMpLgaPHQLvBZbVK+7AHqQNxiYDZDJQWt3grWXAQyOjqfrDxcDAiajX+9FBYEBGR0KadB/Q91Hls1AT3QpFR8CrOtvjEmt/Va2sSyvuC2R6ICICsrQHyDkBExwADhzlP2Ce+6Tpg0oDEFKCs5eAXZSUQSSmNHmL1X4WJDIbBL4UVkdnDuTNwKMTYKQDUCnGicU+wXaR7SjNxwmmBqFkA7klEYck+jjetC4TZJaNotTP4FZoJSEqBbEXPL+rqHBPkosdPVcdc8/4SUZsw+KWwIk69wLk94HCIi6+D9sKHEOmZTV/UOJ9vM0sVtwt7fokIAOpq1Wvj3zWOnl8rIDT1YX7fztbdL0IN64occrTtHgx+iXzF4JfCikhLhzbjDoijx0KkWiCEaHGxCrdV3MzmwGR5YOYIIrKzBb/CNhRLe+YdtTxxdSWklCpw1TQgPROoKIdsaGjubkC9M/h1DNvSOeyByFcMfinsiKPHQptxR9susk+Gi2hiUhwRkb/Ye35tv29EQiLQpbtKuWhtUBPeNA2wp2U8mA39/fmQXjI/SN0KNDQ45zTYg1/Jnl8iXzH4JWOosy12XB3IlZE47IGIALlpnfo2yDVDg33sb0ODs+c3LgEAoC96G/L7L6HfchGktVGPrv13l/0DvGb7JovDHoh8xuCXDEFcebt6HT0xQA8IzG2JKLzImirIrz8EMnu4p1W0T8jdvU0t0a5pELG2nt+9znG/8sv33G9Ybw9+Vc+vcAx7YPBL5Ctzy0WIwp927HGQx4wN7Kpu7PglMjz50Rtq48B+9xO24Fd/5l61L4Rz2ENJofP63dvdr8vao149xvwy+CXyFXt+yTACu5wxu36JCM5fBS45yQE4hz3YNTQ4hj242bTWbdcRLNuDXg57IGo3Br9ERER+oP/+PeSyxQAA7cYH3U82ykMuy0oA+7CHRmRJkccxYQuUHdlrmO2ByGcMfon8huMeiIxM/vSt2jCZIGLj3M55pGSsrQbiEyD+cTq0+593P+cyBhgpFuCwIcCwEWqfwx6I2o3BL5E/MM8vkaHJqko1mQ0AomI8CxxxNMTRY5378UkQQkA761KIHn2gPfBfaA+8AAgB6brwRUM9RGZ357Atk9lxnIh8w+CXiIiovfJzndsxsR6nhdkMcdmNjn3twpnu57v3hujeC0hIcpsAB2uD25AJU7rKICEL8vxTbyIDYrYHIn/h8sZExuXSE6tdc7f3Mi6L7Ij4RO9l4hMhK8ogy0uhv/ykWhLZZZVKzT5JriaQOcuJOjcGv0RERO1lW9VNu+1xiJ59vRZxDF3o1a/p+8QnAGUl0Oc8ANiHP5hc/lTbV3rjsAcinzH4JfIXdvwSGZd9SeOoqGaLaQ++ACSnNV0gPhFY80eji5wjFEWEbQhEPYNfIl8x+CXyB054IzI0aQ9+I5sPfkW3Xs2fj0/0/Bydf9C5bR//29DQtgoSkQMnvBEREbVXbY16bSH4bZG3scDRzjRpQggVAHPYA5HP2PNL5Dcc90BkJHLXVtUD2/sQYP9udTAyuvmLWtJo1Tdx6vkQk6e5l4mIdA6zIKI2Y/BL5A8c9kBkOPrjt3kebG/Pb+Pg91/neC7NnmqBdE2tRkRtwmEPRP7CVGdEFBHRcplmiDj3JY89Al8AonsfIHtvu55DZGQMfon8gj2/REYnxk6G0Nr5Z3XosdBueaT5Mt17AUX5kNXM9UvkCw57ICIiaidxzuXQppza/vtoGjBwKLSbH4bcsdl7me691QyDnH3AIQPb/Uwio2HwS0RE1Eay0TAnMehIv95fDBoGMWiY95OJKeq1otyvzyQyCg57IPIHjnogMpbaavf9bj2D9+yYGACA5BLHRD5h8EvkL5zwRtTpyE1rYX34Rs9As0rtiwuvgWn+F14npgVMbJx6LSsJ3jOJOhEGv0R+wa5fos5If/Z+YN8u4ECW+4miPPUaExf8SiUkq5RouVktFiUiTwx+iYiIvJB5Oc6d4kK3c/oTdwIARExsMKuknikEEBUN1HOVNyJfMPgl8hcOeyDqVPSFLzu2pUvwK8vLnIViQ9DzC3CJY6J2YPBL5A9c4Y2oU5FFBcDGtWpHaEBxvvPkwWzndiiGPQBARAQkg18in7Qr1dlXX32FH374AUII9OzZEzNnzkRkZKS/6kZERBQS+tzH1MaQY4DcLMj/LYIcOQGiZ1+gtsZZMFQ9vyYz0NDQYjFZXwdUlkMkpwWhUkThweee36KiInz77beYNWsWZs+eDV3X8fvvv/uzbkRhhsMeiDoN2/LBokt3IDIKAKA/qcb5Qreqc9NnQiSlhKR6iGh52IO0WqHPPAv6bZdCf/lJyG0bg1Q5oo6tXcMedF1HXV0drFYr6urqkJISol8CRKHGUQ9EnUtcAgBAHH4kEGH7RtOqgl7oujrX+5BQ1EwxR7Q44U2u/Nm5vfpX6E/9X5OrxhEZic/DHlJTU3HKKafg6quvRmRkJIYNG4ZhwzxXo1m6dCmWLl0KAJg1axYsFovvtfWR2WwOyXNDzajtBoLf9nzNhMioKCSF+P+3Ud9ztttYAt3u+m2bUFRahOjxU5E0YSrKNq1B9Z7tQH0dLBYLauLjUAogOTUVEUH+/29ve3FiEvSCg0hr5vmVddWoaHQsMcKMqDD8N8N/68YS6Hb7HPxWVFRg1apVePHFFxEbG4tnnnkGP//8M8aPH+9WbsqUKZgyZYpjv6CgwPfa+shisYTkuaFm1HYDwW+7ruuorakN+f9vo77nbLexBLrd+nbVO1o35VQUFBRAHjcV+O4LAED+1k1ASSkAoKSsDCLI///tbdc1E2RFebP/H/TcHI9jZSXFQa+zP/DfurH4q93dunXzetznYQ/r169HRkYGEhMTYTabMXLkSGzbts3nChIREXUI5Sq4RWIyAEB06wUkJAEA9Dv/A2kb8wvNFILK2cTEAtWVkIV5kLnZ3st4GRMsa6q9FCQyFp+DX4vFgu3bt6O2thZSSqxfvx7du3f3Z92Iwgvz/BJ1DmUlgMnklsbM9MzbzvO2Mb/QQpgtNCYWqK6Gfud/oN97tfcy9fVAigXanIXOY9VV3ssSGYjPwx4GDBiAUaNG4Y477oDJZEKfPn3chjcQGQrz/BJ1HuWlQHwSROPgttchwL6dQGmR2g9l8BsdC1ibT3Umf1PzbRAT4zxYy55fonbl+T377LNx9tln+6suRGGOPb9EnYEsL3UMc3BjC3blR6+rfRHint9myNpax7bQTNCuuQv6i48BHPZAxBXeiIiIZH095J+/QUqphj0kega/2tX/50x7BqihEaHSQvCL3P1uu2L4KNtQCQ57IGLwS0REhic/WgD9pSeAnZuB8lII22Q3VyLVAnHmxc4DIRz2IKLdg1/rVadB2sciA9AXPKs2XOtYXQW55o9gVI+oQ2PwS+QvHPVAFLbkhjXqdesGoCgfSE1voqDLD3qoJ7y50nWg2CU11AHV86vN+8S9XLHx0mYRNcbgl8gfOOGNKLwV5QMA5C9LAABiyjSvxUTPvi47IRz2kJTqeexAFvT3XoGsLAfiE4DYeAgv6diY7oyMjsEvkd+w65cobNmXLi7MA6SE8DbhDYA4bAjEadPVTlRUkCrnRZpnz7T+3WeQP3wF+dv3QEwcxBFHu50XV9yqNgrzglFDog6LwS+RP7Dnl6jzaCFnt/avs2Ga/wVEZOiCX+E68c5u0zr1GhUN1NUC0dHu11i6qI2Cg4GtHFEHx+CXiIgMTTYKdsWoCaGpiI/E+TPc9uU7c4HSYiDSPfhFqkWd57hfMjgGv0T+whXeiMJTfZ37fp8BoamHj8SIcd5PNOr5RVyCeq2sCGyFiDo4Br9ERGRYUkrIDxa4H2yoD01lfOWyDLObRsMyRESkOlZZHoRKEXVc7VrhjYiIKKzl50L+vBiAbfhAbhbEMU30pHZQHssw249ndPU8GJ/Anl8yPAa/RP7CUQ9E4Sdrt2NTJCZDTDwphJXxox59gKPGeB6PTVCp0IgMjMMeiPyB2R6Iwo4sL4P+9ovOA1HRTRfuiJpZXlm78jYIb7+X4uI57IEMjz2/RH7Drl+icCLffwWocAkEG08Q6+C0Z95xTrTt3hvI3us4J7r29H5RXIJj9Tcio2LwS+QX7PklCjuNe3p1PTT18JGIdU500+55BrDq0F95EuLY45q+JjEJcstfkFJ67xkmMgAOeyAiIsOR9fVAbQ0AQLv6TnUws3sIa9Q+whwBERUF03X3Qhs1semCmT2AqkqVB5jIoBj8EvkL8/wShQ25ZBHkyp8BAOKoMWrFtsSUENcq8ES3XgAA/YHrPBb3IDIKBr9E/sCvD4nCS211qGsQGrbgF5XlQHFhaOtCYUFf/Anknu2hroZfccwvEREZT3xSqGsQGonJzm2jfgCgNpGfvAkJwDT/i1BXxW/Y80vkD+z4JQovuhUAIM6/KsQVCS63SW7VVaGrCIWFzjo0hsEvEREZj9UW/I6bGuKKBJ9255MAAOmSGo3IK2tDqGsQEAx+ifylk35CJuqUpC2tWRNLA3dq/Q4D0jIg33oBsqE+1LWhjqyT/vsw4E89USBw3ANRWLHagl9hvD+DQgigvAQAIL//KrSVoY6tsjLUNQgI4/3UEwUKe36JQkpKCfnn75C2/L0AoP/8P1gfu9WzsG4FNM24Cz3YF/SwBcFE3uh3Xt7ma6SUHX6sMINfIn8w6h9QohCRNVWwXjEN8s/fof/4NeSmtUBuNvSXZkF/8VFVprwU8u0Xgd3b3P4Y64vehvzmI0Azhar6ISdOPEttJKeGtiLUYcmGto/3lXW10K88FfLrDwJQI/9h8EtEROEnex8AWw7ShS9Df/Z+yKzd6tzmvyAbGqDffKGz/No/HJvym4/UhmbcD61i/D/URmRUaCtCHVddrXP7kIGtu6ZGZRCR338ZgAr5D4NfIj+R6Nhf8xB1JrIwT23EJziP/fGjs0Bullt5fd4sz5vU1QWiauEhMlK9ugY4RK5c/m2ItC4tFpfrlkN++4naqShXx7ZtgCwrCUTt2oWLXBARUfgpzFevG9Y4j5UWOzb1x2/zepm+6pdA1ip8xMQCJhNQXhbqmlBH5frByJ4dpQkyZx/0Fx9zO6b/9j3kG88BPfrCdP9zgaihz9jzS0RE4cdbCqZ9O53bTfRoyhXLAlSh8CI0E5CUChQVhLoq1FG5/AxJ26IwTbJ/E+NC/rpEbRzY789a+QWDXyJ/4agHouBpIrgVp1/o9TgAyN3bgO0bgcOPDFStwovZDLn8R8icfaGuCXVErj9jVu89v/r786F/8Z73n8eSIvUaExOAyrUPg18if2C2B6Lgckln5kpk9oA4+Ryv5/THbgWqKiF69w9kzcJH3gEALhMAKWxIKaF/sABy64bA3L+yHPKvVS4HvAe/8vsvIb98D7LGy89jwUH1ahv/25Ew+CXyG+9dv/rvP0A2mnxDRO0ji12+ro+Nd24ffiSQnNb8xcxwoBw9Rr2mpoe2HtQsmbMPsrrK/WBDA+TSz6E/fRdkZYXfn6m/9ATkNx86D1ibH/Yg//ep2kjLgLj8Jr/Xx98Y/BL5QzM9v/L1OdDvnRnEyhAZgOtX9baUZeLkcyCioh3plhxcMkIAgBg1MdC1Cwva5TerDWvb87lS8Oj3Xwt99j3uB+udmUr0G8+HrPDzxMXyUud2qqXprCAxserVNq5Xu+85aKMmAoOGOYpo9zzr37r5AYNfogCSLXxaJqK2k7U1QH6uI/eo+McZwJBjIMZNVfsDh7mVF9MugDj3CrU9aiJEGns6AUBERKoPBnV1kNs2QjLzQ8e1d4f7foN7mj7510r/Pi8uATj0CGjPLQS69wFqqj2KSCmBxsMdbN+qmG5+2HFI9D7Ev3XzA6Y6I/IXb8s5unw6l4X5/KNL5Ad6WQkgJcTYKRC3PQ5hMgEnnuk47/rHVpv9JhCfCLnqV3XAniUixQJx9Ngg1rqDioxSuVh/+gbo2hOmh14MdY2oNerds53Ibz6GHHosREKSn+5fB8TGQcTGQ8TEQq5fDbluBcTwkep5tTXQX3nKYyywMDvDSu3mh92HJ3Ug7PklCqSNzhyk+uy7OauayA8cy65GRKjAtxkiMQVCM0EMOxYYfKQjG4TpydegnXN5oKva8RUXOoeQdMCUVNSE+kYLtOTlQH/5Sf/dv64WiLCNjY9W2Rr0Fx+FvugdAIBcuxz42zYhLibO6y3EoGHQxkz2X538iMEvkb946fjVX3rCuZOfC/3+a4NXH6LOyt57a4posoi4+DqIS25w7kfHwnTTgxAZXQNdu7Cizfw/t31ptUL/48eW87pSUEhv3ygCjjG44iKXvylb18N6xbR2T4CTUgIV5RDR0epAdKzznH0SnEvwLc67sl3PCwUOeyDyBy8T3vRli70WlQUHISwtLxVJRN5JW/Dr+hVrY9pxJwSrOmFNDB/ltq9fe7b6cFFfCzH+xBDVihz0JlZWs43PFpk9gEknQ/7wlfNcbpZjPLxPsvYApUXAoUeo/WiXPL327YPZ6jUtA2LI0RBz3gUqO15Ks6a0q+e3srISs2fPxo033oibbroJ27Zt81e9iMKefGcuAECccTG0u2cDg4erEwdzQlcpok6gevEitdFUYEBtIk6b7tyxfbCQf/4RotqQmyZ6fqV9Ke/EZGjnXQntsVec59o5vE7u2Q4AEIcNUQdcF6mwjTWW2zYCvfpBe2QeRHwiRFwCREa3dj03mNoV/L7++usYPnw45syZg6eeegrdu3f3V72IwpD3X1Ji8DCIPgOg2XIf6j9+DfnXSsiaKsj9uyHrvSzTSkRNql7yOQCViJ/aT4z/h/uBw4YAW9c3/ZU7BU8Ti0ugvES9JiUDAER6pvOSv1d5lm8Le2YHe4rAKJfg19qg/mbt2wkx+EgIc9NDjzoyn4c9VFVVYfPmzbjmmmvUjcxmmJv5Coqoc/OS5zcxGYhLcKwmJRJT1C+Rv1ZCd01LM/RYmK67NzjVJOpMGvjB0R9EQpL6mnznFrWfngm5dT2wZzvQ99AQ187gdOcHEKnrEJoGuXsb5B8/qhy7roFpj75A1m6gMK99z7QvqGEf4tB4UZjyErXoRVJK+54TQj5Hq3l5eUhMTMTcuXOxd+9e9OvXD5dccgmi7QOkbZYuXYqlS5cCAGbNmgWLxdK+GvvAbDaH5LmhZtR2A8Fve6HZDFNEBJJdnpkHIHrYMUh0OVZ+wimo+upD94v/XuW3uprNZsRv+xulT90Dy/zPYLJk+OW+HZ1R/60btd3Fg4ehbtNfSB1/AswGa3+g3vO8g9mO766STjgFJb9+h7iiPMQeO8bvz/KFUf+tay4f8NLiYqHFxePgFdMAABGHH4nUdGf6TP3J+Sh54AbUb9vYrv9X5UKiOjoG6Rlqbkpt9x4ocTmfbNJQBCDBkoGYAL0ngX6/fQ5+rVYrdu/ejcsuuwwDBgzA66+/js8++wznnnuuW7kpU6ZgypQpjv2CguDnfLNYLCF5bqgZtd1A8NtutTagoa7O7Zmyvh419Q2ocz32r3OgjZ4M/e6r3K73V10tFgtKv/4YAFD012qIYSP8ct+Ozqj/1o3a7oiMbsDBAygxRwEGa3+g3nMZEQVADSMpi08GAFTkH0RVB/n/a9R/66lxzp7dwqz9brniG7r28vh/Yq1VWSDyt272Oa+8XlwEGRXjuLfs1sftfPEXHwAAKhoaUBmg98Rf73e3bt7HIfs85jctLQ1paWkYMGAAAGDUqFHYvXu3r7cj6nys9YDJ/fOlMEdAZHSFdu+z0J55G2La+QBc8pb6g+2Zcvum5qs363bobzznv+cSBYmsqQaiolsuSK2m3fSQc8c+1rOpJW0peFyGPaC60v1cV895Vto/z1Ib9mwMbX3cNx9B/rJEZXuwEZoJ4qxLIE4+BwAgf7ZlMvLXghoh4HPwm5ycjLS0NOTkqJnr69evR48ePfxWMaKwZ7UCTYyDF70OUePsEhLVgSo/TtyxTVKR+bnNl9u5BfK37/33XKIgkTVVDH79THR1/v0WmgmIiGTw2xG4TngrK3E7JQYN9yzfV3VIyvxcyIb6NnesyBXL1Eavfm7HtX+cAXHkaPfClkyEq3Zle7jsssvw/PPP49Zbb8WePXtw+umn+6teROGlUZ5fqesq+G1p9al4W/C7d5dfqlG/YzOw4U+1U9W+ROediTyYA/2DVyEbr4pEYUlWVbrnHiX/i4xi8NsRuHT86s/ep77REwLi2HFuGR4cktNUp0v+Aei3XAx91u2tf5SUQHEBxJjJ0G551LOA6wS3YSN8HlbREbQrPUOfPn0wa9Ysf9WFqPOw2lZHMrXwIzbgcACA/vyD0F75HMLLYhltUfnh686dZoJfaYBZ8lLXIf/8HfK9l4GkVCBrN8Tg4cCQY0JdNfKRLCkCIiLQkLUX4pjjQl2dTkcccxykPRNNXLxjIQUKoUapzvQn71QbTXz4E5oGpFgg/2fLhb13B6TV2uIy4ADUsIrqKqBbL4hYL0sW27+pBGC69p5WVb+jYm4yIn9xzYlptX3V1EIORJGUAgw9Vq2R/udvkIcNUcMhfKB/+hZqV/3qPNDcEpcugbEsKoBI7VyzqPXFn0B+/6Vac768VP0HQJaXektKR2FCv+0Sx7awfb1L/qNd5dJLmJYB2d6UWdR+TSzkIk6/sOlrGg9527O9xRXfZNYe598wb4EvbMNhAKD/oGbvFQ7aNeyBiOwahVT24LcVn7ZF914AAP3lJ6HffCFkrW9fNcpvP3bec8I/gdJiyKZ6f10D43auBtQRyS/fA0qKgAP73Y9/+zEXFekkBPPPBlZsHLB7G+Tmv0JdE0OzLzQiTjrb7XiznSSNeoWla175JugPXg/9oRvUTuO8vi60Oe9Cu/nhFu/X0TH4JfIXrz2/LX+5IoYc636bDxdA//StVj5SQp8/G3L9n45naffOAeKTgIZ66Decr8YfN+ayMpYs7oTpg/oe5v14bnabxsBRxyQSk4GunGAdUAcPAAD0Zd+GuCLGJaV0DntIs0B78nVo8z6FNvfjZq/THp8P7bbHIC69EejdH3Lbhuaf0+hvhGhmMqmIS4CIiGxV/TsyBr9E/tB4rK59hm1LY34BiAGDIc535v2VPy9WPZSF+S0/98B+yJXLoD//INDQgITLboDo1Q+Ic/naaucWFSS/+V9Yb79MLQnr2vPbGYPfPTuA3v2hvfyZ57l9O4NeHfKvpFsecn4FSwGhXTgTACDSu4a4Jsakv/E89CtPRcM+22RooUGkpEGYzS0GnyI+EeLQI6CNmQTRow/Q0t+SxhOB4xJ8r3iYYPBLFAj2WdKtCH4BQIw/EdoTC9yOyS2t+Lox/6DbrsneGxYb7zimf/sxkL0H8tfvgOIC6DdeAP2FR5wXFRe2qo7hQjbUA7XVwN4davJHjPogoN09Wy0HeviRIa4h+UJWVapZ7lNPR9RQTloMNNHvMEBokIs/gf76c46v3xuTDfXQP34d+k/fBLmGnZesqYL8Ta2MW/Hmi+qgr5Oh4xOAijLIfbua7gGurXbfP6SJb846EU54IwoA/a0X1Ib0PlmhMWEyAanpEBdfB/nmf9WlX30AOXwkRDOfwmWB9+BXxMY5M+SsXw159FjvN0jPVDPoOxH5w9dqwzYmVEw7D/KDV4HM7kD3Ps5eeQopuW0D5O7t0P7RdIpMmXcA8oevIM6+DPojN6mhRZHh/5Vr2LD9/pK/fw9x1BhgWKMhWmt+hz7PmfFJ9u7Psdh+IP9erTbiE9CQtUdtCx/7Ki2Zagjcwzeq/bQMwGSGOG06UFYMFBdCjP+HesRJZ0OMmWSIb1XY80sUCPbV1Wpr2nSZdtwJ0B6wBc4FB6G/8rRHGf3L92G1re2OgoNqcsKhKmWaqYttxZ9GnTTykzcAwLGinIOli3vmhwNZ0Jf/1KY6+0Ku+QP6d58H5t7LfwQAaP+5Rb1OmabSyEXHAlFRwNb1XNmuA9DfmQf58euQW5sej6i/Oltl7di93TmDvYUMKhQY+i//g3Xe42qBEfuxzxe6lZF5B4Jdrc5pz3YgMhJi2Ejnh3Ufe37F8JHuBwrzgLwcyFeehHx/PuT/PoXcvlGV7dUPoov35YA7Gwa/RP5iX1nNnq0hswfEuKltvo09+wMAYOcWz8d8of7gyKpKyN+/ByKjoF1/P7Sn3oCwT7DLVEGwuPRG9UnflupLnHgm0E99paU98RpEbLxb8KvPvgdywTOQzaVJayeZvRf6vMchP1wAeTDHv/eurgKy9kJMPR0iwzlW0ZE/2TZWjivbdQCWLgAAucvz37iDbSKO2yTFVkwipQD4ayWw5g/IP/9wHqtqtNwuU6P5R309EBnl9kEDtgC1rURyaotl5Aevqg0DrZrI4JfIH1w/ldvWRBf/ON33WbFJtl9YtdWw3neNGu8IQBYXOp4lv1ioAteKMoioaLdfcqJrT2jPvQdtzCQgMdl5PCIC2q2PQXvhI5XbNy4eqKyAzM2C/sGrjrrLJZ/5Vu9W0D92LsSh3zMD1psuaDH9mNTVoiGyqABy/eqm733fTPVVbZcmJunYPgRQB2D/mSktbrqMt1SBbfw2hfwsQvW8y7wDQEkhMPRYiCtvV79n2PPrHw31nt9w+GE1NXHpje5zHnodAgwboRa2AIBoBr9E5CP9bpW5QSQk+3wP7Y5ZzrXVD+wHsnarIQm3X+rsYf7+S/WcRvkf7Rwr9NiXULYF1CIiAiLKlscxNh6oqoT+1F2QS79wXCu/+bDJCS7tIaVUX+m5qij3yMfrSv/qA+hXnQ65frXKRfn8Q9B/+x7WK6ZBX7LIvbBt/LI4ookJUUWdMLNFuLIHsdVVTZfx9lVv495GCjjhOmfA9r7JdSsAANp5V0I79jggoyuky+IKMi9HZZYxGCklZNZun39/yppqNTlZ16FdcDXiL5oJ7Yb7If7573bXTfTsC9OND0L8+zKIcVOh3fU0RPfezgKRDH6JqM0a/bIbPNznO4n0TIgppzrvnJsN/YP53stO/GcLN1MBhPjnWZ7nYuNVTuKyEo9T8qsPWl1fV/qKZZA7Nnk/uX8XUFEOceFMiDGTAVuidv21Z91yTcr6esh1KyAb6iE3rVXH9u92DNGQtjG78pM3ndfUqBnL4oyLm16xrjOmdQtXtowo0mWmuaysgP75Qkjb8uCiex/P62qqPY9RQInLb3ZmkLF/UD2wH0hKgbANXxHpXR09v1LXod89A/oz94WiuiEl350H/cEbIFf+7Nv1772iNspKIBKSEHf6dIgjjm7d8sQtsS1+oU09DdpF16p7umQGYs8vEbWNEEBdnXMiWmQURET7JuYIl+EK8u0XgS3rAQDa/z3lyMMoxp8IkZzWqvuIjEzPk1bPzAdi8inqmTs2+1BrQL46G/oTav15mXcA0jb5TxYVQF/0NmA2Qxw9FtqlN0B7+g11UfZeYO1yxz30mWdCf/FRtT59heo9klvXez4syWU8m31Ig8v/t8a0a+521jMAPdvUBvbcoi7DGOS78yC/eh/Y8rc6YDIBsXHQXvgQ2ow7IUZPgjj1ghBU1pi0R1+G9tCLarjU028CXbpDZu+DLC1WP4+uq4ylWoCSQshdW9XPM2C4nNqyrhZy2WK1/dX7Pv2Okc18C+Yze4aURiu/AVCTgB3bXs53Ugx+ifzFJYeimHZe++/XePlKawMwfKTKv2kP8DJaTkAvTj4X4pRzgYFDPU9WlLmXHXk8xDn/AY44Cqho+/hY6fIVtiwvhX73VdCfVIGw/tANwIY1QJfujvRtQjOpYB6A3K+SubtOtpOfvePsadq9zfOBUdHQf/pG9Rrbeq9FM8GvGD7SmfGCwW9o1dmC3xqX4NceNEVFqfe0sgKIS1Bj2o8eA+2yGyFSmv+wR/4jMrpCdO2ptiMiIPodCuzbAf3Wi1X2Ddc845oKJ/THb4PcvVUda+ZnsSnh+qFUSgn9GpehCbnZvk0ADMCwHu3WxyHGTva+eIXrUIfoWL8/u6Ni8EvkTxGREJdcDzF5Wvvv1bMvxKU3QIwY7zxmG2cn+tly2LZiEoRIS4c27Xyvk+/E2ClqY9AwaM+/D+0/t0AIARGf6L4KXCvI4kLo15/r2Nfvnek8t2OzY0ll0SgIF/0OA7r3hty0DvofP0L++JX3B3gbG5qbBfnuS8g78zhnRoDEZta8BwD7MNIw/SPbaTTYe37VMAYpJZCzTx2rr4d+1WmQK5f5FEBRgHTv7fgmBgAgrY5NtzGp9tzhZSVq0ZlWkjs2Q7/yVMjG8wLCgcvETW3GHWqjvKyJws2oqgBi4iAuucFPFQNE3wHQLrlBLfrTmO3vgjh6rHMuiAEwZwyRP9h7JevroNkDynYSQkCMmQzZb6DH+DFx/gyVA3LYiPY9o2dfmOZ/4XkiJrbNYyvl143GCLtMdtGfsP0x6N0f4hTPXnExfCTk1x9CuvbuJqep2eRNSUjynr3BPsGvKfZk8Qx+Q6uu0bAHlyVW9dedeZhFO8bOk3+Jrj3dZza49BqKiAiIEeMhV/4M+eX7zjIlRY60di2RG23j+3/5DqLPgObL1tcB1VXNftPjT7K8FPrTd0O7+Dr1gd31XFG+Y6iHOP5E53CsyrYFv1JKoKoS4oRToY2d7Jd6t0QMHwGcf5Wag2Eg7Pkl8odM28pqU/zQ49uYJcOxqT2gVn8TkVEQR47y/kneH6JjgZqqtn0F2UK6MgDQZt4FERfvcVzYFulwK3vzQ0CKbeLaoGHOExm2JOwDPK8BoBb9aA0Gv6FlD3bzDsB66yXuX/e6TEwUY08Ibr2oaY2/No9p9DW5t99HLaSm01csg/71h2qnzpZJ4u9VLVZFnzcL+i0XtVjOX+TKX4CcfZA//0/t61aV1eFAFvQ7Lof+2hwAasIt4tX/J+kyX0HubcX45+w9anibPVNPEIiISGgT/wVhoBy/AHt+ifxCu+1RQJggElrodfSBMEdAe+wVwNoAYQuyAy4mDrBaVe9ca78Ksw0n0B6fD7l1gxo6oWmQG9eotGz9Dms6C0NfL2vJx8ZDu/I26K88Be2SG4DyElWn5DRg3w4gMQV61m6IU85FQkwMyl54TF3XUm5lxx9oBr+hIqV09vwCQGkR5IqfPMpps15t1dAeChL7hKn4BIiRE1Qvp6uefYFGK0TKjWvc02k1Il+dDQDQc/arYS4AUFoMqVs9ltmVug654BkVhNvyfcuaKrV6Y6Dl2uYe2Doj5KdvQ/7vU+f5ijK1RH1snCNbCWzLz+tPq4m2Xr9ls5FSQn/QNtShpW+vqN0Y/BL5gUhMCez9071kaggke49OTVWrg19ZWgz06gdh6eJIfwRAJVWPioEY13QPnmjcgwQAsfEQ/QfB9ORrat81cLZtmx59WVXXYml98GuP0nUGvyFjbQCkroKn6mrIlcsg//zdcVq7+WGg9yFqBULqOOyZY04+F5otK4wrMWUaEJ8E+focxzH50euQI8arDzupFgiPxRsygMI8Z+Dbuz+wdwdw8ADQtdGH/eICjyFgcvkyYOixQEqacyXHAJBZe9TGgWy1/9O3noWK8tWrvef383eht2JSMgDneHfA/fcnBQSHPRCRJ3swWu195rG0WiF3bFavxYWQBQeBDWsgevb1KCs0Ddrp01v8hS6mng5txh3qD2iqxedUcS3mw9QcM958uj/5TmbvVb13K1QAI4sKoF1xixq7bhvvrd38MMSgYQx8OyCRkARt7ideA1/Alr1lzCSIy2+GduujzhO7tqnML1efCbn5L/eLGg1TEocNAaBWa5QNjVIxesmEIN+dB/2Oy7x+c9AWMnsvrDde0PQKkvv3qHIrl8E643THRE1X4qxL1asQjkw8cv7TjvP6r981XYF82+p4aRnAgMFtbwC1CYNfIvLg6Imt9j7pTa76BfoTd0AufBn67ZdC/78r1IlDj/D5mdq/L1X5f8/5D7THX237DVyX7WyWLfiVevPFyK/k1vXQH7gO8pclwFZbHl9bb5ro2c9Z0EDplsJRaz6UaqMmQBw2BNodTwAA9J++cZxz3QbgMbFWTD7ZsS2/+8y9rG35da9secnl1g2QtbUt1rExuXY5UFkO3WXhHMc5q1UFu0OPVQdswxq0p9+E+M8t0Ga9CtP8L6D943THNdrVd3reZ6n3YQ9St0Jf9I667oYHPHvHye8Y/BKRJ3sAUtREnkrbWDb582K3w2LE8X55vC8T+bRr74E2591W3Nwe/Lb5EdQOMsclX3OX7gAA7fwr1THX3L1BnOxDAWb7+h8uvb0ixTl8SZYWq8mNLsfctl0mPloLDkJ/7kEAgHb3bIhzr3R7lNyxGTJ7H/Sn74J8+4U2VVPu2AT5ue13R/ZeyJ1b3AvYennFwKHuudXjE6GNPB4iLQMe7BNz3R7UxC+drRucwx4MlG4slBj8EpEnW0YG/aUnIBvqof/8P0jdmdMTxV5SkGkahDl00wiEOcKxeEbzBdnzGxrqD7/8bSnQ0KDeB1tPmhg90VksOdXbxRSO4rxM3LL1mkrd6lyevLhApS7sP8h93K7LpMi6jesc26LPAGiTT4b2xAJoj7ykAuHsvZDffKTuvbtteYLlpnXu+y7Bulz/pzP7RFQ0tNsed9ajmSFWIjIK2jV3Qbv+fmgvfwYxbqrHokKOZ+za6twx0CprocQJb0TkqVsvx6Z+9ZkAABEbBxxzHABAZu0G+h7qzG/cpTvEIC8ryHVE7PkNDdfV/75SeWDts/lFRCS0e55VmQEMlnKpU4uLc8/XnZ4JVFVASgn57kuQf/wAANAemgukd3Hm4O4/GNixyZGZRZYUoWyOrdfXlu4RAESqLRNIdAzkB/Odk+aKC5rNAiHX/A65YQ20i66FrK115CXWZi2AfuflkJ+/CzloGBAVBf35B53PS0yGSE6F9t8PHCtKNkcMH+V8ZnwCUFkOKaUjwJclhdCf/D+1Wh4AcdlNXlNBkv+x55eIPAghoN1wv/tBW6Aia2uBrN0q0fuQYyAuuhamR+ZBu+DqENTUB+z5DY0WVgwUvQ+BdtK/my1D4UVoJmhPLFDb46YCsfGQVZWQ337syJcLcwRE1x7qmxtbT6rpjllAeqYav7t3B/TbLnHe00vaNJGUAtgDYXMEUF8H+Zf3XMFSSujzZkH+sgQy7wDkl+85TyYmQZthW47949c9eoTtw3VEdAxEa7M42MUnql5vlzHO+m2XOgJfxMZDc/0GhAKKwS8Redf3ULdd/bN3IGuqgX07gbo6iEOPgOn6+6CNmxqiCvrIEfyy6zeYZN6BUFeBQkBoGrQXPoSYPlMNp9rwJ+Sit50Fopv4mj8hCcjLgf7Izc57nXhm0885TqVSFJdcD6RYIFf/6r2gfew5AP3uqxy5erXr7oWIiIQ4eow6uWMz5EevA2azWgY+IUn1TvvKPgSkogyyMB+yxn25djHxJN/vTW3GYQ9E5F1svFrswp7u7MB+6Nedo766BIDM7qGrW3u0YXljKSXkj19DHHOcYxlVWV+neqsCmFO0U8rLUYsT2Je9juHENqNwDGVx+ZkTx58IuWyx88No42v6DHAfCwsASU3nU9dOPgfyhNMgoqJg/W0psG6F94Iuk+jcnmfP5AAViMofVVYKMWwkxMXXQTQ0tCsLg0hIVCOtivLVohexcUB8IsRRYyCmX83fJ0HG4JeIvBJCwPS8+krQ+uz9wKa16oT9a7qmVmvr6Bxpfr0Hv/KvVdB/+BLaNXcD+3dDvvcKsH0TxFW3Q1qt0G+aDjFsBMQVtwavzmFO6jqQnwsx8SQ1XKb/4NYvQ02dR2mxY1OcOl0Fv01MUhX/vhTyh6/UjtmMqKNGo37M5GZvL+yZEmwT1uT+3Y7c4/rCl4D0rs7gt1c/IMUCMXwkRLr7EAbt/BmwLv8JqK6COP5EFZj6mHfcYcDhQHQM9Nn3qH17zuIAL85B3jH4JaIWaeddCf3eq1VvhZQQQ44JzpKigdBCz6/+7UfAzi3Qr/m3GqcIQDY0qDy1tmVK5cqfIU85N3jLTYe7kkKgvg7I6AZx9NhQ14ZCJTHZkdJLJCRCnPMfiOEjvRYV5giI0ZMg//gB4tQLkDz9KhQUeO+1bYr8ayVEz76Q+bmOnlw77arbIbylI7Ofv/RGyFW/ALZFN9pLxMRCDB8FufxH9xMpYdqJEOYY/BJRi0Rmd2j3P6+yOrS3ByTUmun5lZUVgEuOT/nLEsc19sDXcW7fLga/rXUwBwDaPkmIOhXt8pshN66BNnaK2p8yrdny4qR/A/0HQoz7R5ueI/51NuTXH0KuWwG9SzfIV57yLBTZfFYRceQoiCNHNVumrcS081TaNJMJ4p9nqeD8MN8XBiLfMfglolYRPfqEugr+0UzPr/zkDbURGQXUuawStXa58/KRx0OuWOY91zF5Je1LtzbT00adn0hOVZPHWls+szuED3MLtNOmQ5cS8puPvAe+/Qc3O344UER6JrQ57zqHOZxwatDrQAqzPRCRMbkEv1K3wjrvcdXT26ufmp1+5e2e16RlQJx6gQqOS4qg//499JU/B7HS4Ufu3w258heVgsp1JTeiABKDhzu3Tz0fYqRz9UntPzeHbJwtx/d2DOz5JSJjcSyd7BL8Ll8GrPlDnT7vKvUHqk9/SABi8imApQtQlA/t7MvVBSkWyKWfO+85Ynxw6h5mZFkJ9IduUDtde/q0bDWRTwYMdm4nJAP19c59e05gMiwGv0RkMLaeF925yIV8fY7zbP9B6jU9E9pjr6je3kZBmzj2OMivPnBeb7U2u9SpEci/VkF/4WGVFu74E6Ev+Qwi3mV52y4c8kDBIzQTtFkLIL/9CGLURKCyDPLX7yBGTWDvKzH4JSKDafSHTzaT71fYcxo3op16AeQRR0Nfskj1GOflQP/jB4hREyFcloY2En3hSwAAufpXxwIDrv9nNdsiBETBItLS1eIaABCVDtPst0JbIeowGPwSkbE0Xt64rk69Dj0W2uRTWn+bQwZCO/0i6Gv+gP7WC2pFqNW/QbvqDqCyDGLwkX6ueAeXngkU5XseHzgU2r8vg+jVL/h1IiLygsEvERmLI/i17VdXqMNDj3WbJNOqW9lnou/YrF7zc6E/chMAwDT/i3ZWNMzU1gCDh6uJhLZFBgCVL1WE64IoRNQpMfglImNp3PNbXaVeY8J00Y6OoqpSjZO+8jYAgKwoA6Jj2rUkLBFRILR76q2u67j99tsxa9Ysf9SHiCiwbMGv/OZjSKsVKClSh2PjfLqddv/zEFfcCu3FjyAuucFv1Qw71ZVqBUAbEZ/IwJeIOqR29/x+88036N69O6qrq/1RHyKigBIxcZAA5B8/AD37Qn64QJ3wcUyq6NHHsQCIGDsZ+sEsyCWfN39RJyOlBKrcg18ioo6qXT2/hYWFWLNmDSZPnuyv+hARBdYRR6n/AGfgC0Ak+mnFp5R0wNoA/dXZzWaS6FRy9gHWBsA1tRkRUQfVruD3jTfewPTp05kzj4jChhACphseAPoMcBzT7n/ef/e3B9YrlkG+O69N18qqSljvuBzWq06DrAmPb9PkpnXQH7gOACB69w9xbYiIWubzsIc///wTSUlJ6NevHzZu3NhkuaVLl2Lp0qUAgFmzZsFiCf6sX7PZHJLnhppR2w0Yt+1sd+vVnPcflD5+B5LueBzRw4/xX2UsFuTFxkNWVcCUvRdprayXteAgCm44z7GvX3cO0t/4GlpS0z3SZrMZSaUFqN++GbEnnt7uqvuifNdm2KYMwjL6+KCs4mbUf+eAcdvOdhtLoNstpI/fyy1cuBA///wzTCYT6urqUF1djREjRuD6669v9rqcnByfKtoeFosFBQUFQX9uqBm13YBx2852dwyyvAz663OA7D0wPfFa667ZugH603d5HG+cMk3m5wIHs4HDj4LFYkHe2ROAhnpoT78J0UygHAiythb6tf8GAIgxk6FdGpwJfx3t/Q4mo7ad7TYWf7W7WzfvK0v63PN7/vnn4/zzzwcAbNy4EV9++WWLgS8RkRGIhESILt0g16+GLMyHSEtvtrzcuBb6O3Md+9oD/3UMJZClxW5Brf7io0D2XmjX3I3K4jygoV6V2/wXxKgJznvW10GfNwvaWZcEbNU5+ftSx7a4+NqAPIOIyN8C//0UEZER2eZC6PdeDX3xJ7BeMQ3WK6ZB7tziUVSfcz9QcBAAoM16FaJ7b2h3PQ0AkF+97ygn62qB7L3qmsWfoPK9V53nFjwDuW+n2t64FvrMs4D1q6HfH8CgNNqZG1lopsA9h4jIj/wS/B5++OG48847/XErIqJOwbG8cX0d5CdvOo7rSz6DLCvxeo1288MQaRlqp88AIDUdcu9OZ4H9u53bO7cAUkK762mIEcerez98E/RvP4a+4Bm3+0r7Qh5+JuITVL1vfDAg9yciCgT2/BIRBYA44ihoLy/yPLHmd+i3XAT9h68AqHGzACBOmw4xaJjzeiEgBg0Fdm9zZH7Qv3gPAKDd8IDzfn0GQLviFoiTz1H3+/QtoLwUSMuAdtNDqszubX5unY3Vql6Z4oyIwgiXNyYiChChmSBOOQ/yy/cgLrkeyNoLuVQtgCHfewVy8HDoH7+hynb3Mi7XNqxAv+4c5z0nn6IC6ycWINWSjmLbKs3aqRfA+ssSoLRY7V99p2PJZllShIAkpLQHvyb2oxBR+GDwS0QUQOLksyHGTnYMZ9CjoyG/+kBt3ztTFRp6LMTwUZ4XV1V43m/Syeo1NR2mVAvgMiNaDBwKuWIZxMSTIHr3h6ytUSdKi/zYIidpbVAbJv4pIaLwwY/rREQBJDSTcxwvVA+tOO4EtzLa9Jnerz31AoixU9ROZCS0OQshMro2/bBDBqrrbPcXUdFAdIxacONgANJM6raeX052I6Iwwo/rRERBJs65HDI3C9ixGeKUcyFS0ryXS8uAuOR6yIuvA6xWCHPzv7K1if+CHDcVwhzhPBgbB2TvhX7PDI+cwe3mGPbA4JeIwgeDXyKiIBPRsdBufgRy5c8QI8a3XF4IoIXA11HWNfAFgIpyx6aU0r/L0VvZ80tE4YfDHoiIQkBEREAbOxkiIqLlwu1RV+vcLi/x6Rb6j1/D+tT/eZ6wjykOdBuIiPyIwS8RUScmzp/hGAuMgjyf7iEXvgxs2wjrFdOgL3UZOrF7G5CUylRnRBRWGPwSEXVi2sSToE2/GgAgV/7cZDmZm61WoPtrldtxfeHL7uU+eBWysgLWx26FXPUL0H+gf4dSEBEFGINfIqLOLq0LAEB+/2WTRaRtIQy5+hfnsX27IH/82qOsfufljoUzREY3f9aUiCjgGPwSEXVyIiYWMEc4hz+0Vnmpuv7cK92P21acAwAxYlx7q0dEFFTM9kBEZASDhjW/2IVt5ILcuNZ5rN629HKPPpBeLvF76jQioiBgzy8RkRFERAANDW6H9N9/gCwpdC9XXgqZsw8AIOvr1bEElwltSalAWga0eZ8EsrZERAHDnl8iIgMQJjOkPS8vAFlUAPn6HEgA2vX3uwfGRQVAt15AfZ3aj4h0nNKeWGC7H3P7ElF4Ys8vEZERmExATRUAQG7dAP2Oyxyn9OcfBGqd+YBl7n5IKZ0LZEREQLv5YWgPvgBhMjHwJaKwxp5fIiIDkKt/BRoaIPNyIP/8zbPAwSxn2Q8WQKR3BbJ2A/EJQGIKRLL3JZiJiMINe36JiIzANqxB7toGueYPj9Pyx2/UxhFHqf11KyC3rgcyukFo/FNBRJ0Hf6MRERlJbpZb1gftmbfdTptueACIT4T89TugqABi6LFBriARUWAx+CUiMhB5YL/bvkhIAiLVhDbtrqfVwTrn+F8x6eSg1Y2IKBg45peIyAgOGwJsXQ/kHfA4pT3wAlBfB9GtlzrQow+wayu0O55QC2QQEXUiDH6JiAxAu+5e6LdeDBxQE9u0GXcAAw4HAIj0TPeyV94G6LrHcSKizoDDHoiIDEBERQOHHgFYbfl8u/WCSEz2XjYtg4EvEXVaDH6JiAzCbQhDl26hqwgRUQgx+CUiMghZ7FzKWGhcqIKIjInBLxGRUURFh7oGREQhx+CXiMgoTGqOs7j8phBXhIgodJjtgYjIILRzr4CMiIA4akyoq0JEFDIMfomIDEKkpUNceVuoq0FEFFIc9kBEREREhsHgl4iIiIgMg8EvERERERkGg18iIiIiMgwGv0RERERkGAx+iYiIiMgwGPwSERERkWEw+CUiIiIiw2DwS0RERESGweCXiIiIiAyDwS8RERERGYaQUspQV4KIiIiIKBgM0fN75513hroKIWHUdgPGbTvbbSxst/EYte1st7EEut2GCH6JiIiIiAAGv0RERERkIIYIfqdMmRLqKoSEUdsNGLftbLexsN3GY9S2s93GEuh2c8IbERERERmGIXp+iYiIiIgABr9EREREZCAMfokoLBh1hJZR221URn2/jdpuCo1OE/zm5eWFugohsXPnTpSWloa6GkH3999/Y9euXaGuRkhUVVU5to30B8NqtYa6CiFh1HYDgK7roa5C0BnpZ9qVkf+dl5WVATDev/dQxm3mkD3ZT3bt2oV3330XKSkpmDlzJjSt08Tzzdq/fz9efvllxMfH46KLLkJSUlKoqxQUu3fvxnvvvYctW7ZgxowZ6NevX6irFDTbt2/HokWLYDabMXz4cIwbNw4RERGhrlbAbdu2DYsXL4bFYsHEiRPRpUsXQ/ycb9u2DV999RWSk5MxdepUdOvWzTDtXrt2Lc455xxDtNdux44d+Oabb5Camorx48ejR48ehmj/zp078fnnnyMlJQWjR4/GoYceaoh2SylRV1eHefPmobCwEA8//LAh2g10jLgtbINfKSUWLVqEZcuWYdq0aZg8ebLbOSFECGsXeN988w1GjBiBadOmOY515nbruo758+dj9+7dOOOMM2CxWJCVleU419l/aezduxcLFizASSedBLPZjNWrV2PAgAHo2bNnqKsWUPv27cPrr7+Of/3rXygtLcXSpUvRo0cPTJw4sVP/ey8tLcWCBQtw4oknorCwEN988w369euHKVOmdOp2//TTT1i0aBFyc3PRs2dPjBkzBlarFSaTKdRVCxhd1/HJJ59gxYoVOPXUU7Fr1y4sXrwYJ5xwAvr27Rvq6gWMlBILFy7E+vXrceKJJ6KoqAiLFy9GamoqMjIyQl29gBNCICoqCoDq+V2yZAmmTp3aqf+edaS4LWz/Dwsh0NDQgIEDBzr+B+7evRtWq7XT/mEA1C/KiooKCCFw4oknAgBWrlyJwsJC1NXVAeicX5tpmobhw4fjoYcewogRIzBy5Ehs3LgRdXV1nfYXhatdu3YhMzMT48ePx9ChQ1FfXw+LxeI43xnfc0D1dnfr1g3HHXccJk+ejKioKPz666/Iy8uDEKLTtnvv3r3o2rUrJk6ciJNPPhkjRozA6tWrkZOT06nbnZqaivvuuw933XUX3n77bQCAyWTqtO0F1O+2tLQ0zJw5E+PGjcMZZ5yBgoKCTv8VuBACgwYNwj333IMJEyZg4sSJAIDExMQQ1yw4dF1HcXExkpOTcfXVV2PJkiWorKyEpmmd9r0XQqCurq5DxG1h1fO7adMmREREYMCAAQCAadOmYfbs2XjrrbewefNmJCUlITY2FiNGjMCoUaNCXFv/cW23pmmIjIzE5s2bsWHDBvz6668oLy/HqlWrYDabcdVVV3Wa4L/x+z1y5EgAzk+IXbt2RV1dHSIjI0NZzYBo3PajjjoK8+fPx3vvvYdly5YhLS0Nr7/+Orp164bTTjut077nhxxyCH755Rfk5uYiMzMTQgjExMTg+++/x3nnnddp2r1y5UpkZWWhT58+OOqoo9CnTx/s2rXL0e7+/fujX79+WLp0KS666KJO1+7evXvj6KOPxhFHHAFd15GWlgaLxYL3338f5557LqxWK8zmsPpz1azG7/fYsWMRERGB+vp6JCQkIDo6GsXFxaGupt81fr+POuooAMDmzZvx3//+FykpKXj//fdx1FFHYejQoSGurX81fs81TUNKSgry8vKQnp6OwYMH47PPPsPkyZORmZkZ6ur6jb3dvXr1wjHHHINTTz0VzzzzTMjjtrDoMquursbTTz+Np556Ct999x0qKioAANHR0Rg/fjz27t2LCy+8EHfeeScGDRqEdevWIScnJ8S1br+m2h0ZGYkJEyZgwYIFGD58OO6++26cd9552L9/P9auXRviWrdfU+2WUjoC3+7du2PDhg2drre7qbYnJSXhqaeegtVqxXnnnYdHH30UEyZMwJYtW7Bt27YQ17r9mmq3PfCbN28ennzySezcuROjR4+G1Wp1vPfhrKysDE8++SS+/vprxMfHY+7cuVi+fDkSExMxcuRILF68GAAQGxuLoUOHora2tlMERY3bPW/ePKxcudLtW5wrr7wS3377LUpKSjpN4Ovt/V65ciWioqKgaRoiIiLQ0NCAoqIidOvWLdTV9Zum3m+7+Ph4zJw5E48++ih69+6NX3/9FdnZ2SGssf809Z4DQFZWFrp06YK0tDQMHToUS5YswbPPPov6+no0NDSEuObt07jdL730Ev744w/ExMRg7NixIY/bwuI3itlsxhFHHIFJkyZh27ZtWL58uWPpu3HjxuHII49EfHw8AGDIkCFYu3YtoqOjQ1llv2iu3f/4xz/w1VdfOWaJpqamYuDAgZ2iR6ipdtvbZu8Z6t+/P5YvX46TTjqpU7QbaP497969O3JycjB8+HAAQL9+/ZCUlNQpAoPG7f7jjz9wwgknIDo6GtOnT8fevXtx8OBBjBgxAjt37sSyZcs6RY9/bm4uBg4c6Bi7Hx0dja+//hqjRo3C2LFj8e6772L9+vUYMmQI4uPjUVRUhNjY2BDXuv28tfuLL77AiBEjYDaboes6evbsidGjR2PhwoWYOXMm1q5diyOPPDLENW+f5tptl52djaSkJHTr1g3V1dXIzs5G//79Q1Vlv2ip3T179nTMXxg8eDBWr17dKf6GA97b/vnnn2PEiBGwWCw4cOAAnnjiCeTk5GDQoEEwmUydYiKzt3Z/9dVXGD16NCZMmIBjjjkmpHFbh+35XbZsGTZt2oTKykpERERg0qRJGDp0KLp27YqdO3c6PiFIKR3/AwGVAgtA2P7gtLbd0dHRuPTSS7Fs2TLs2bMHS5Yswfr168N2okBr222fDGC1WtG1a9ewfZ9dtbbtADB06FB89NFHkFLit99+Q1ZWFhISEkJYe9811+5du3a5tbt3796OP5QbNmzAgAEDwra3f9myZdi4cSNqa2vRr18/jB8/HoD6t92jRw90794dANCrVy+MHTsWb7zxBnJzc7FhwwZIKcO2R6ildvfq1QuA+7c4M2bMwLJly3DppZdi7969YTkWsrXttqf6qqioQFRUFH766Sfcc8892LdvX1j+W29tuxv766+/IKVETExMMKvrV61te01NDVJSUtClSxc88cQTuPPOO1FYWBi2aTxbanfv3r0BdIy4rUN1GUkpUVJSgueffx5CCHTp0gVLly7FJZdc4hgEf+ihh+LAgQP4448/cOaZZ0IIAV3XsWXLFrzxxhuwWCy44IILwqp3xJd2A8CYMWMAAL///juysrJw7bXXhtVXZb602z4ZwGQyobq6OmzzO7el7b///jvOOussAMCkSZOwfft2PPbYY9B1HVdffTXS09ND2ZQ28fXfOqAm/b311lvQNA1XXnllWPX2e2v3999/j0suuQTJycmOD3VZWVmOPM6apmHChAkoLS3FZ599huzsbFx11VWIi4sLcWtary3ttg91EUJACIH8/Hy8+eabGDhwIC6//PImg6WOyJd22zNa/PXXX/jtt99gNptx/fXXOwKGcOBLuwGgoaEBmzZtwrvvvovU1FRMnz49rP6GA21re2VlJQAgOTkZF154oVtb77vvvrBqu68/41JKbN68OXRxm+wgrFarlFLK7Oxs+dxzz0kppWxoaJALFiyQTz31lFvZFStWyPnz58sDBw7I2tpaqeu6PHDggFy1alXQ691evra7urpa1tfXSyml1HU9uJX2g/a839XV1VLK8Gy3lL61PScnR9bU1DjKFhcXB7XO/tCe91xKKcvKyuTGjRuDW2k/aE277WX++9//yt9++01KKd3eY/vPejjxtd2lpaVSSikrKirk9u3bg13tdmvv+71582bHsXDS3vd7z549cvXq1cGutl+0t+1Wq9VxPpy0t905OTkhi9tCPuzBarVi4cKFWLhwITZt2oScnBzHpAeTyYRLL70U27Ztw6ZNmxzXjBgxAqmpqXj00UdxzTXXIDs7G5mZmTjmmGNC1Yw2a2+7r7vuOuTm5gJAWPWA+eP9LigoABBe7Qba1/bHHnsM1157LbKysmAymZCcnByiVrSdP95z+/COwYMHh6oZbdaWdtuPR0dHIyMjAx988AEefvhhFBYWAkBYjetub7sffPBBFBQUIC4uLqzGuvrj/S4oKMDAgQMd3+qFA3+93/YMEOHEH20vLCyEpmlhlbLTX+95165dQxa3hfT/9qZNm3DnnXeisrISmZmZ+OCDD2A2m7Fx40bs2LEDgApwzjrrLHz00UeO6/744w8sWrQIhx9+OJ5++mn06NEjVE3wCdttrHYDxm072936duu6jh9//BHPPPMMqqurcf/99yMtLS2UzWgzf7XbNYd1OGC7jdVugD/j4f6eCylDN5J+8+bNyM/PdwyKfvXVV9GrVy9ERkbi22+/xRNPPAFd11FWVobXXnsN06dPR0ZGBjZv3gwAGDRoUKiq3i5st7HaDRi37Wx369p94YUXwmq1YvHixRg/fnzYLtvNdrPdRmg3YNy2d5Z2h7Tnt1+/fhg9erRjBu9hhx2GgoICTJgwAbqu49tvv4WmaY6vBeyZDAYNGhS2fxQBttto7QaM23a2u3XtTk9PR2ZmJi655JIO88fBF2w3222EdgPGbXtnaXdIB5LZ17W2+/vvvx0zW2fOnInvv/8es2bNQk5OjiPXaWfAditGaTdg3Laz3Upr2i2DvLZ9ILDdCtvdudsNGLftnaXdHWIWhf0TRGlpqWPwc0xMjGPVsoyMDKSmpoayigHBdhur3YBx2852t9zujvbHoT3YbrbbCO0GjNv2cG93hwh+hRBoaGhAQkIC9u7dizfeeAPx8fG47LLLMHDgwFBXL2DYbmO1GzBu29lutpvtZrs7I6O2Pdzb3WGC3927d+PXX39FXl4eJk6ciEmTJoW6WgHHdhur3YBx2852s91sd+dl1HYDxm17uLc7pNkeXBUWFuLnn3/GySef3CnWtW4ttttY7QaM23a2m+02ArbbWO0GjNv2cG53hwl+iYiIiIgCLXyWFCEiIiIiaicGv0RERERkGAx+iYiIiMgwGPwSERERkWEw+CUiCrEXX3wR77//fqirQURkCAx+iYjCxAMPPIDvv/8+1NUgIgprDH6JiIiIyDA6xApvRERGsnv3brz00ks4cOAAjjzySAghAAAVFRV44YUXsH37dui6jsMOOwxXXHEF0tLS8N5772Hz5s3Yvn073njjDUyYMAGXX345srOz8dprr2HXrl1ITEzEOeecgzFjxoS4hUREHRd7fomIgqihoQFPPfUUxo0bh9deew2jR4/GihUrAABSSkyYMAFz587F3LlzERkZiQULFgAAzjvvPAwaNAiXXXYZ3n77bVx++eWoqanBI488guOOOw6vvvoqbrjhBixYsAD79+8PZROJiDo0Br9EREG0bds2WK1W/Otf/4LZbMaoUaNwyCGHAAASEhIwatQoREVFISYmBmeccQY2b97c5L3WrFmD9PR0TJw4ESaTCf369cPIkSOxfPnyYDWHiCjscNgDEVEQFRcXIzU11THUAQAsFgsAoLa2Fm+++SbWrVuHyspKAEB1dTV0XYemefZV5OfnY/v27bjkkkscx6xWK8aPHx/YRhARhTEGv0REQZSSkoKioiJIKR0BcGFhITIzM/Hll18iJycHjz32GJKTk7Fnzx7cfvvtkFICgFvADABpaWkYPHgw7r333qC3g4goXHHYAxFREB166KHQNA3ffvstrFYrVqxYgR07dgAAampqEBkZidjYWFRUVOCjjz5yuzYpKQkHDx507B999NE4cOAAfv75ZzQ0NKChoQE7duxAVlZWUNtERBROhLR3KRARUVDs3LkTL7/8MnJzc3HkkUcCALp27YqpU6fi+eefx86dO5GamoqTTz4Z8+fPx3vvvQeTyYRt27bhxRdfRFlZGcaNG4fLLrsMOTk5ePPNN7Fjxw5IKdG7d29cfPHF6NOnT2gbSUTUQTH4JSIiIiLD4LAHIiIiIjIMBr9EREREZBgMfomIiIjIMBj8EhEREZFhMPglIiIiIsNg8EtEREREhsHgl4iIiIgMg8EvERERERkGg18iIiIiMoz/B+S6jeqsawHyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G22zHPU8sTLLPFjfZf4tPreyzQiLCKFDf3okrFCLh8OLSRTpam4nqOHAIt7eeUKhhvjd12mplHy5lsg8XH9lnS8dJA5xSqcSBAwe4+eabWbt2Lffffz8PPfQQN9100zGXffnllznjjDMmlKfddtttNDY2EovFuP3222lra2P9+vXHXHfLli0Tgp9wODzp9oRCoeOeJxYm2WeLm+y/xadW9pnu7QEggYVkOEzU6savc4TDYRLxDACj0SjhcGket/LU1Mo+XMpkHy4+ss8Wn7a2tklPP2mJWjAYJBgMsnbtWgA2b97MgQMHJr3sM888wxVXXDHhtMZGc7q03+/noosuoru7e1obLoQQQkxGx2PmL5USNbubesNcIzo2B2deNk0IIcQ8OmmAEwgECAaD9Pf3A/D666/T0dFxzOXS6TS7du1i06ZN1dOy2SyZTKb6+2uvvcby5ctnatuFEELUskQ5wKkvBzhWN34jCzCuTbSswRFCiFozpS5qN998M/fccw/FYpHm5mZuueUWHn30UQC2bt0KwK9+9Ss2bNgwYW1OLBbjq1/9KmCWul1xxRVs3Lhxhh+CEEKImpSIgtUGdWbjm5i1jlUFM+ipNhmQNtFCCFFzphTgdHV1HTO/phLYVFx99dVcffXVE05raWnhrrvuOr0tFEIIISYTj4HPj1IKrTUxax3+TBoAq5IMjhBC1KopBThCCCHETNDxKPp7/4jOm2tlXrQvY5tj5dgFFKiWNnCbzWrqnVY+eVELduuxFdU6EauWp6XyBiVlwV8sBzjli0uAI4QQtUcCHCGEEHNn7070i09BawfY7fy8dROv2ZpoL0TN8wtFGIpAk5OioflVX54NrR6u7JpkWHQiVm0wEM0VAagvpgBpMiCEELVMAhwhhBBzRqcSAFj+9DZUQ5DUowc506K4bcu5ABg//Df0T/8Dyx3fQoda+NSP9vPw3sjkAU48imppByCWNVtB+wsTAxzJ4AghRO05aRc1IYQQYsakkuZPj1mClsiV8Dqt1bPV27YCCv3Uo1iU4u1rAuwcynAoljv2tsaVqMWyZgbHnzdvXwIcIYSoXRLgCCGEmDupBNgdKIcTgGS+hM8xLsBpDMGGi9BP/xxdLHDdaj82i+KRvdEJN6NzWcjnxmbgVDI4eTNDZK2WqEmAI4QQtUYCHCGEEHMnlaxmb7TWJPMlvI6JH0WWq26ARAy9/QX8LhuXLffxxP4YuaK5oEZHRjDu/7p54WALr/QneWj3KC5dpD5nZnCsZnxDUdpECyFEzZEARwghxJzRqQR4fABki5qiwYQSNQDWnw/BZvSTPwPghrUBUgWDpw5EMbb9EONLt8BrLzH67o9yV6aLv3iiD4tSfKG0HVupAIBSCquSJgNCCFGLJMARQggxd8ZlcJJ5s6xsfIkagLJYUFfdAG+9jh7oY31THZ118PAvX0f/x32U1p7NT2/+Gn+UOZcXD6f40Hkh7nlXF+dYElAqVm/HZlGyBkcIIWqQdFETQggxd1IJaF4GmA0GAHxHZ3AAdfkW9A//Df3ID8BqY+u+BPet/XW23fRlHs41sv+tDBcs8/CJi1pY5nMAYNhsEuAIIYSQAEcIIcQcSiVR5RK142VwAFR9AHXBpehntoHFwjXXvIfvoLh30ENjXYk/f1sbl3X6UEqNXclqhVJp7L8WJU0GhBCiBkmAI4QQYu6kEhNaRMMka3DK1LtvAosFtfU38C1fxc17IoTTRX7z7Ebc9kmuY52YwbFKBkcIIWqSBDhCCCHmhM7noJCvNhlIlDM4R3dRq1DLOlEfv7X6/3esazjxHRyVwbEpKEkXNSGEqDnSZEAIIcTcOGrIZzJntjjzTlKidkqsNtAabZhBjs2qKEoXNSGEqDkS4AghhJgbKXMIpxqXwXFYFU7bDH0U2cpFCUWzTM2qpERNCCFqkQQ4Qggh5kYlg+MeaxM9WYOBU2Yt31a5TM0mTQaEEKImSYAjhBBibpQzONU1OLnScRsMnBJrOYNTbjQgTQaEEKI2SYAjhBBiTuijAhwzgzODH0PHZHCQDI4QQtQgCXCEEELMjfSxTQZmM4NjsyiKEt8IIUTNkQBHCCHE3EglzCDE6QLMJgMz1kENxgKc4rgStZJEOEIIUWskwBFCCDE3kgnw+lBKobUmkStRP6MZnKNK1JSSOThCCFGDJMARQggxJ3QqWe2gli9pCoae0QyOskmTASGEEBLgCCGEmCupxFgHtbyZZfHNZgZHmgwIIURNkgBHCCHE3EglxzUYMIMQ74x2UZukyYAxczcvhBBicZAARwghxNxIJ1BHZXBmvcmAZHCEEKLmSIAjhBBibkzI4JiplZktUTs2gyMlakIIUXskwBFCCDHrdKEAuewxa3BmNoMzcQ2OVSmK0kVNCCFqjgQ4QgghZt8xQz5no8lAJYNTbjJglQyOEELUIglwhBBCzL5kwvw5LoNjsyicVjVz91HN4BQAsClkDY4QS5jOpin99efQhw8C8GJfkr/6ZR+6xjK3xk//E+Mn3wMgVzT40rZD7BvNzvNWzS8JcIQQQsy+lBngqHIGJ5Er4XNYUGoGA5zyHBxdKVGTLmpCLG19B2HvLvTuHQC8eDjJ871JkvnaeuHr7c+jtz8PwFCqwGtH0uwcSs/zVs0vCXCEEELMvnQlg1MPQDJfmtnyNJAmA0LUGD06bP4SHjJ/pM3s7VCqMF+bND/SyWqWvFL+myj/rFUS4AghhJh1OjVxDU4ib8xsgwE4tsmARaGRYZ9CLFnlAEePmAHOcDmwGUrWWICTSlSz5JUGLhLgCCGEELMtNXENTjI3NxkcgFKN1eMLMVt0Iobeub36/zeHMwwk8vO3QUdlcIZT5mv/VDI4es9OjFgEgELJ4KXDyZnZxlmmjRKkU5DLoguFanleXAIcIYQQYpalkmCxgKsOMI8yzloGp1gJcMr/lQyOEKdN53IYX/8yxt/8b3Qyzq6hNF/YdpD7Xh6av20aDZu/jAyRypfIlBfdHZlmgKMLeYy7v0TqwQcA+GVPnNt+0cf+xbBQP5Ua93u8mrmpZHJqlQQ4QgghZl8qAR5ftanArGRwbBPbRFvL9yWNBoQ4PVpr9L/cA4f2g9YMd+/nzqcOUzSgeyQzf13LKhmcTIqhkXj15GmXqA0ehlKRwoG9AByM5gA4EFkMAU5i7PdkYizAkQyOEEIIMctSyer6m3zJIFfSeB0z/BF0vBI1yeAIcVr0wz9Av/gU6obfJGex81dvavJFzTvWBohkS4xmivOzYaNhqA8AED4yAkDAZZ12iZoe6AWgeHAfWmv64mbZXU850FnQxgc4qQRJWYMDSIAjhBBiDuhyBgeo1ojPRZMBkBI1IU6Hfu1F9H8/gLrobfAbH+beDR/mQNHFn16+jGtW+QHoHpn7TIfOZc0v92vXAzBczuCsb3YzlCxML6tUDnB0IgbRUXpjZmDTE1kEAU563FqhZEKaDJTZ5nsDhBBC1IBUAvyNwNgH75w1GZAAR4jj0nt2Ynzjjurr5p9W3MC2lgvHXcIBb7sd7A70f+4l5z+TDw78kos7PkGuaGBRsHckyyWdvrnd8PL6G7X2HPTLzxKOZ7AqN2eEXDx7KEEyb0z5PUYP9IJSoDXZQz0MpTwo4EA0h9Z6Zud1zTA9LoOjU3GSBfP9NVfS5EsGDmtt5jIkwBFCCDH7UklU2wpgbE7DTGdwlFJmI4PixABHMjhCHJ/u2QvpJOq6d4PVyhuFDTRog4sto+YFLFboXIkqNwhp7t3F29/6CTr+WzjrA6wIOOmej8X45fU3qnMl2lnHcKZE0G2n1esAzE5qUz6I0t8La8+GPW/Qd2gQWM3ZLW7eOJImki3RWLeAvy6njsrgqLFFh4lciaBbAhwhhBBidqQS42bgzFIGB8xGA9USNfMkCXCEOIFkDKw21Ac+jlKK+A+6uaDNw82bz5/04tozhPET4OA+OPdCVje6eKEvOeeZjuqQz2AzhJoJFxRNHhvNHjtgNhpY3eg6+e0UizDUj9p4CSoyTO9wApzwthU+3jiSpieSpbHOO5sP5fRUMjg2O6SSJB0lHFZFvqTLAY59frdvntRmWCeEEGLO6GIRsplxa3AqGZxZ+Aiy2sZK1FRlDs7M340QS0YyAd56lFJorYnnStSf6ODD8lWgFPqg2XFsTaOLRK50SrNnTsvoMCgLBBoh2MywdhJy22n2lgOcqW7P8KB5UGRZJ7YVa+hNFrEq2FwuuVvw63BSSXB7wOeHVJxkrsQyn5nFquVW0RLgCCGEmF2VRbBe8wvDrK3BAbPRgDQZEGLKdCIGvnoAMkWDoqHxu47/2lR1bmhpR/d0A7AmaGZJ5rxMbTQMgUaU1YrR2Myo1UOTx47XYcVjt0x9Fs7AIQBUWyf2Favp03W0+ewEXDaCbtvC76RWaeDi8VJMJkkVDJb5zCCvlhsNSIAjhBBidlVqxN1mmUcyb2BVUGeb5QyONBkQ4uQSMfCaAU4sa34hrneeeAWD6loDB80ApyvgxGaZ+05qenQYGkMARBvbKFmshOzm9jd77VOehaP7zQ5qtHZgW7mWXnczHXbzPWRlwLngAxydSpjvrR4fqYy5rW2VDE6udoeASYAjhBBidpVrxJVnLIPjdVpnp17fapUmA0JMRyKO8pntnuO5SoBzkuzqijUQHUVHR7BbLawIuOYhgzOMamwCIOxrASBUMA+mNHvsUy9RG+iDYDPK6cLoWMWRuiAdpRgAXQ0u+mI5CqUFHCikkmYGx+sjWZ5HVC1RkwyOEEIIMUsqGZxxa3B8Mz0Dp8IqTQaEmJbk+AyO+QX5RCVqUM7ggNloAHMdzr6R7PRmz5wGrbVZolbO4IRdAQCaMmbnt2aPfcqzcPTAIVjWCUC/K4ihLHQmBgBYEXBS0lQHfy5IqQTK40V56kmUW0QH62w4rErW4AghhBCzpTqnYVwXtRkf8lkxaYna7NyVEIudLhYhnTIXqDONDE7nKlAWs8U05jqcVMFgcIplYactEYNiASoZHJv53hJMHAHMErVM0agOFT4ebZRg8DBqWQcAPeUBn+1DZvndyganefpCbjQwLoOTMN/68Dqt+JzW6v6sRRLgCCGEmF3VAGesRM3nnKWPH6sVXc7gVEvU5uioshCLTuW1WW4yEC+vwfG7TrIGx+mCts6xRgPldsx752odTmUGTjnAGS5YcBezeEYHAcZaRZ+sTG1kGAr5agbn4Ggai9a0HXwDMNey2C1qwa7D0YZhNnHx+sDjI2k1ZxXVO63UO61SoiaEEELMmlTCnBJe5wbMQZ9zkcGpdlGTPtFCTC5hrjWprMGJ5cwZKq4pNACpNBrQWrM84MRuUeybq3U4lRk4lRK1dJFQKYUeGQKYMAvnhMoNBlTbcgB6RjO02Ao44yPoWASrRbE84KAnMg+DTKcikwatzey410fSZgY4XocVn6O2A5wpDfpMpVJ885vfpLe3F6UUn/rUp1i3bl31/J07d/LXf/3XNDc3A3DJJZfwvve9D4AdO3Zw//33YxgG1113He95z3tm/lEIIYRYuFJJcHtRFvNLUyJv4J2NFtFQHvR59BwcCXCEmFQ5wKmswYnniicvT6tYsRaeeQxGw9iCTaxscNI9kpmlDZ2oOuSzksFJFQipPITLAc4UZ+HowXIHtUqJ2miaDm/5q3FfD/gb6Aq4eLk/ObMPYKZUMnBuH8rjI2l3owC33YLPaV2wmae5MKUA5/7772fjxo3ceuutFItFcrlj/2BnnXUWn/vc5yacZhgG9913H1/84hcJBoN8/vOfZ9OmTXR0dMzM1gshhFj4Uonq+ptCSZMtGrPYZMAqTQaEmCKdjJu/eMsZnGzppA0GKlTXGjTAwb0QbGJN0MXj++OUDF3Nns6a0TA4nNWy13C6yBqHhpEjaK3HZuEkT9IcoL8X/I0ot5eSoTkUybBxlRns6b4e1Nnn09Xg5LH9MaKZIoG6KX1tnjvlBi6qsgbH5sZjNf/+PilRO7F0Os3u3bu59tprAbDZbHg8nindeHd3N62trbS0tGCz2bjssst48cUXT2+LhRBCLCq6sggWSJW7+kiTASEWgEoGp7IGJ1c66Qycqo4uc83buHU42aJBf2L2O45VZuAopcgVDeK5Ek1um1mylU4B5Vk4J8vgDPRWszcDyTxFQ9PZ5INAEPoOAOacH2BhZkNS5QDVY87BSdrdeFV5kLLDSjJfwqjRDPZJn8VDQ0PU19dz7733cvDgQVatWsXHPvYxXC7XhMvt2bOHz372szQ0NPDhD3+Yzs5ORkdHCQaD1csEg0H27t076f1s27aNbdu2AXDnnXcSCoUm32Cb7bjniYVJ9tniJvtv8Vlo+2wkn8HSEKQhFCI5mgagPRSYlW2M1NVhJIsEQyGs6QKwD5fbvaD+HlOx0PahmL7FsA+TpSIppQitWImyWkkWDrCqaeqvl5H2FVjCgzSEQpxvuOD5QSKGk/Nn+XGPxKNYWtpoCIU4GDHfUzpbGwEIlHLYQ110NAxxOJY97mPRWjM82IfrmndSHwqxKzYCwLnLm3GsWocx2EcwFOICjx8e62W4YF1w+zNjUcSBho5OLPUNJG0v4rcahEIhWhtzGHoEl6+B+pM0jViKTvqIS6USBw4c4Oabb2bt2rXcf//9PPTQQ9x0003Vy6xcuZJ7770Xl8vFK6+8wl133cU999wzaf/x4w1227JlC1u2bKn+PxwOT3q5UCh03PPEwiT7bHGT/bf4LLR9VopFUcEWwuEwvUPmlxGdT8/KNpZKBmQzhMNhkuVsUTSeXFB/j6lYaPtQTN9i2IfG0CB4vIxEIgBE0gWcFKe83aWGEAz0EQ6HcRTN19u+gRHOa5i1TTbvd2gAde4mwuEwewfMjI3PY2Zaot17UPVBAnbNi7Esw8PDk3731KNhdCZNtiFEPhxmZ6/5mD06TaGlDf3qiwwPDqBsdhrrbOzsGyW83HXM7cwnY7AfgEi+CNmsmcEpmu9/lqLZGOHgwFB18OdS1NbWNunpJy1RCwaDBINB1q5dC8DmzZs5cODAhMu43e5qRueCCy6gVCoRj8cJBoOMjIxULzcyMkJDwyw/64UQQiwYWmuIR6uLmCPlQYJTXsg8XbaxQZ9jJWq1WaIhxEklxoZ85ksG2aIxrdemCjbDyNCEdS8nbc18mnShALHIhAYDAKFWM7tS7aTmtZMtGiSONwtnoNxBrdwiujeWp8XrxG23QnuXWep6xAwgVgScHFyQJWrl5gduL8piJenw4C2ZgU1lnWOtzsI5aYATCAQIBoP095s7+fXXXz+mSUA0Gq1ma7q7uzEMA5/Px+rVqxkYGGBoaIhiscizzz7Lpk2bZuFhCCGEWJAiYchlobUdGJsIvsxnn5W7U9axAMeqZA6OECeik/EJDQbg5DNwJgg2H7vuZbaHfUbLB86rLaILKKCx0Q+uOphiq2g9UOmgZgY4ffEcXUGzzbIqv19x5DAA7fUO+hP5SSuT5lUqAa46lM3cZwmbG2/BzJL7yoFqrTYamNKz+Oabb+aee+6hWCzS3NzMLbfcwqOPPgrA1q1bef7553n00UexWq04HA4+85nPoJTCarVy8803c8cdd2AYBtdccw2dnZ2z+oCEEEIsIAN9AKhl5pyJvliekNtmHiWdDVbruCYD5kmSwRHiOBIxaDFLfCpH+qeVwQk1m53URo6Ax0uzx87AbDcZOHrIZ6pIQ50Nh81CKdiMDh8Bxg/7zLMmOLG0TO97E/3kz8Dnp+Sp53/eHKUnkuPirvIam2bzb6KP9KMwB35mi5rRTJGge3YOzpyScQ1cSoYmZXXhS5tZnUqAUynVrTVTCnC6urq48847J5y2devW6u833HADN9xww6TXveCCC7jgggtOYxOFEEIsVnrgkPlLuVNRbyxHp985e3c4rk20UgqrgqJ0URNicokYas1ZAMTK5aP+6ZSPBlvMn+EhWL6aZq+dVwdTaK2Pu+b6dOmRo2bgpAs0ecpfZ0MtYxmcSWbh6FQC/YMH0E89Cv5G9n7gT/nmIwc5EMlxYZuHD17QjpGJo+rc4G+olqi115trWAYShQUV4OhUohrgpAsGWim8GbOzmmRwhBBCiNnS3wteH/j8GFrTF89zTot79u7PaoPi2Bcaq0XJHBwhJqENwyxxKpeoVTM40ylRC5kD3nX4CApo8djJFjWJXAnvq8+gf/w9KJd1fbX9nRxwNR3/tpxOlM/cFotSfHxTC+cvm2QsSWXIZ4PZpTecKrCywczQqGAz+vWXKH3hk9QB7jM+xfdf7OXh5/aY1zEM0OfBVZeBx8dgd5HGuhL/621tXNrpo9HjIFyZVdrShi4HOG3lRfr9iVl+/5qudLI6Y6ySqfFmzNbfHrsFRe2uwZEARwghxKzRA32wrBOlFEPJPPmSnuUMztgaHDAbDUiJmhCTyKTML/zjZuDANDM4bu+k616OpAp4XnwKUnHU+vNJYeNZ9zpWl6K06dSxt5PLQDQBrRejnHW8diTF998ITx7gRMLg86McTrTWhNNFLu4w71ddfp0ZtJWDqg+V9vCmpREqD8lugdZ2VJ0ZFLxtpZ3fWN84acmsamlH73gBgJDHht2iOByf/Rk/05JKoBrMsrpKpsabHAXMgzteh0UyOEIIIcRM0lpD/yHUpssBs0sRQGf9LLYsPSrAkQyOEMdRGfJZ7qIWy5awKHA7Ttp/qkopBcHmCZ3LwCwLWzXQhzrjPCy//2f0hzPwyEFuuvZsLu7wHXM7ejSM8bmPo1bksfzmR/nBrhH+Zfswh6I5Oksx9MMPQsF8/3j+cJoX170P9dwAJUOTL2lCbvPrrFq+GvXxW6u3+2vlf6ekpQ0SMXQ6icXtpc3nmJMhptOSTByTwfFlouhiAWWz43NaSdToGpypP4uFEEKI6UhEzRKKahtWs81qx6yvwSlW/2tTUFponY+EWAgS5lqNSllYPFek3mnFMt21M6EWKC/sb6os7I9lYXjwmNf+8bK3qjEEGy5CP7MNXShw3So/Novi4e4oxgPfQD/9c/TuVznSfYCvLX83v6pfzauDKd4YSrPMZ+fs5pkvG1Pl5gscGQCgrd5O/wLK4JglhmNNBqoZnELGDHww1+FIBkcIIYSYSZUOam1jcyYCLmt18eusmKRETZoMCDGJSgbHN5bB8Tun/7VQhVrQb70+NgvHYWEoHAVtwLjXvt2iqiVsk7Fc9Q6MHS+gtz+H/+IruWy5jye6R/nQm29Q95sfwbL1PfzHs/2ogwm+fuOqajA1a6qd1A6jVq5lmc/Bi4eTlAyN1TI7DRSmJZsx/8bVDI75Ructps0yvUAjPoeV0UzxRLeyZEkGRwghxKzQ/eU5E61jR3Fndf0NmBkcbaCN8iwcKVETYlI6WSlRG2syUO86hYMPwWbzy3a5PXGzx86RmLlSX43L4HT4HScODNZvhFAL+smHAXj76nrSJcUzK69AXfNODkSy/OJAnF87o2H2gxuApmWg1IROakWDWR9kOmUpM0tTzeBUmgwUJYMDksERQggxWwYOmQuQG4Locge1q1fWz+59lgfeUSqBxSpNBsSM0bkc2GwoqxkEpAul2ZvnNBfKJWr4xgKcrsD0D0CoYHkWTngIPD6aPXb6YwYoC7SYAzN7Y3nOCLlOfDsWC+rKG9A/+Bf0QC9nHTpAZ8rgkTXXsdXu4Ds7evE4LLzv7OC0t/FUKLvdDN6GJnZSG0jkadXpakCntWYwo5nwNhMIgt18L6pkrma8bXb5/lUlg5Mr4baCVRuQGmsVXatrcCTAEUIIMSvGd1AbSRdIFww66mc7g1MJcIpgd0gGR8wY46/+DPI5LB/8A/a3nc2tP+vh0uU+Pn5h84KajTJlyTi46swv8kA8W8TvOoW1LOVW0YwcgRXlWTglO7qpFWW3ky0aDKUKbFntP+lNqcuvQ//wX9GP/wR2bmdr80Xc53kbD+0e4eX+FB87vwnvbJa4Hm18q+hyc5TDQzE23PbH1Xb0P2u7lH9c9xtHXTE94X9fuKp90uYKp6WawTEPGiXyJXzlBhE6mUBhBjjZoqZQMrBba6toSwIcIYQQs2OgF3W2Oei52kHNP4sd1MAsUYPqOhybBcngiNOm4xE4fBDsDoy/+Qte2/wRtOscXuxL8kp/ig9tCPGudQ0LY23GVCVi1Q5qJUOTyBvUn0rwUB72qcNDY7NwlI1E+2oaoNpaeSqvfVUfQF1wKfoXPwPg2g9cyHd3K+5/ZZiQ28a7zmiY/vadBtXSjt73GFpr/E4rHruFw72DUCyg3ve7EGjkpf4ALfkiHwqa7a/1gT1waD/qI7eg7E6+/dIRftkTn/EAR6fMDA7jMjjeygyjcvDjc5j7M54rEXRLgCOEEEKcFp1KQiwybpHxibsozZjxGRzKTQYkvhGn6+A+ACx/+AX0/rfo7i7RRIS/2POvfHvFDdz3ssG/v3AImx5fDqSA8pNPKXNmTLlM6W1d9XxiU8vcPoaj6ES8Wp5WWadRfwpNBnB7oM5dnYXTVGd+kR5uXkkD0++eqK5+B/rFp+DM8/Ccdz5vywyybV+MD21owjHXWYiWNnN9UTyK8jfQVu+gfzgDHh/q+hspaMXO7+9l65oAV206BwC914fx199HRXZjufhKXj+S4pc9CfIlY2a3f5I1OF6nDWz2CWtwwNy/izLLeBokwBFCCDHzBswGA2OLjPN4HRYCp7KIeToqGZyiGeBYlZSoidOne7rN4GTVGVjWn0/3D/awJjvMsvPO4Yv08QJZXrVNzC7YrFaKpRLk8zA0gDrvImhsYvdwhqd74vz+hc0zvy5jOpIx8DcCEKsM+TyF1+cxs3ByZvOCIX876zBf+1YFy7xTzN6uPRv1/ptRGy9BKcVN54Zo9zm4qmuW1+9NQrW0myHqkcPgb6DNZ2dXvx111gaUxcruwRT5kp44kHT1meBvQL/8LFx8JZd2+ni0O8b2gRSXzGQWp5LBcZsZnETOMJsveH0T1uAANbkORwIcIYRYwvSuHeiBXizXvRuA770WZlO7lzXBEy/4Pe37LQc4lTkYfXGzg9qsf6Eb32QAM4NTkABHnCZ9sBtaO1AuN8lcicGMwfUbz8RytjnE9rLyv/FCoRDhcBidSmJ85oOo8/xYLj6XH785yrdfHiKaLdFQN49fw5JxVMdKwJyBA5xaiRqYi/Ers3BiA0CAYbfZDKA3lmOZz4HdOrXXvlIKtfU91f83eey8d44aCxyjpdIquh+17hzayPJLh4/c6vOpA3YMpLBZmDCHR1ksqPMvRT/7GDqX47xWD16HhecOJY4JcIxH/xvVtRa17hzi2SLfe2OEj2xswmWbQqYnmQCnq7qGKpkvmSVpHh86eVSJ2k/+G/1bN6J89fxw9yg7h9LHvdnF5u9+u23S0yXAEUKIJUw/9Sj65WfQGy+hEAjx76+H2TmU5rYty2f3jvt7weGAYBNgHsW9pMM7u/cJx5SoWS2KjAzCEaerpxt11gYAukezAKxpnNpBAuXxQmMI+noA6GowS7V6orl5C3C01mYXtfIMnHi2ksE5te0ZPwvHM3QIT8HBkNUsweuN5VkRmOW1d7OlMWQeNDlyGIBlowfRqo0jK86mCzPAOTNUR519YkBiriP6H9j5MrYLLuPiDi8v9CUplHQ10NOREfT374fNV6PWncO2fTF++laEi9q9EzNCx5NKVNffGFqTzJfwOqzmuqrUxBK1+HAY3B5G0gX+efsQDXW2avCzVEmAI4QQS5hOJUBr9FOPknz7TQC8diRNXzx3Wh3NdM9ecHtRzcsoGZqX+5NsXOap1pjrwV7ziLfFSixbJJ4rzf76G0BZrWZJybgMjjQZEKdDR0cgNgpda4DpBzgAtHehKwFOwLzegUh2al9kZ0MuC4V8tclArLoG5zQyOJVZOP29NKsWhjJNFEoGg8k8V6yY4Q5ic0RZrNC0DH1kAIBlB9+AhjYGrD4askX2R3J8aEPo2CuuOwe8PvTLz6IuuIxLO308vj/O60dSXNBmBiV6+3Pmz8gIAM/2mkHJSHpqc3Z0Oglu8++aKRgYuhzQeHzVEmFfIgxAcvV5KKuVbfsiGBru2LKcZb5FGnROUW21VBBCiFpTPpKnn/458VSuevKje6OnfJM6HsH46hcwvv5ldLHAo91R7njyMP/x+sjYhfp7UeUBn31z1UENxjI4xUqTAWQNjjg9PXsBUCvKAc5IllavfVrtilXnShjsQxcK+JxWgm4bByO5k19xtiTKQz4rM3DKGRzfKQY4KlhuFR0eQg/20mQpMJQq0J8oYOg5aC4ym1ra4chhdKHAsj2/AqA/nufVQbPMa2PrsUGqslpRGzejX3sRXSiwcZkHl83Cs4cS1cvoV8wAh0iY4VSBvSNm4DySLk5tu8ZlcJKVIZ8OC8rrM1uAA45nH8VRKpBsX03J0DzSHWXjMs+SD25AAhwhhFjaUkkINEIsQnL3GwA01tl4bH+M3CmWbukf/wfkczA8SPqJR/ne62EU8KM3RxlJF9DZDIwOVzuoHZqrDmowrk30WImaVKiJ02E2GLBA5yoA9o1mpr+GraPLzCoO9gHQFXDSE53HAKf8BVh5zQAnlividViwnWqb68osnOEBGOijpc7CUKrAofJj7KhfvF+oVUub+bi6d+HOxGmwGfQn8uwYSOF1WFh9nEyeuvAyM6u1awcOq4WL280ytZKh0fEo7Nlplr9FR3j2kLk/bBY1jQAnOdZBLWe+yXkrGZx0El0ooJ/ehlcVSSgHL/cnGUkXuWFt4HT/JIuCBDhCCLGUpRKoCy6DYDPx13YA8J6zGknmDZ4ZdzRxqvSRfvRTj6CuugHOPI8fbe8jmi3xJ5ctw9Ca770ern6JU9UGA3lcNgsh9xxURR/dZEApSloyOOLU6YPd0NaJcjqJZYsMpYrTK08DVEeXeVvVMjUnffEchdI8PTerGZxyiVq2dGotoisqs3D2vAGFPM1+F9miZtdwGgW0L+IAh5Y2KBbRTz0KFgttgToOx80A57xWz/FnH515HtR50K88C8Cly73EcyV2DqXRO54HbaAuvgryeZ7ridEVcLLc7yA8xRI1UgkzW8NYBqfeYTW7qJVK6Oceg0SMereTRL7Ew3ujNNbZuKh9DtZCLgAS4AghxBKli0XzCKKvHvW2rSQHzS5Hmzu9tNc7ePgUytSM/34AbHbUu28ifuNHeahlM5utEa5a6ecdaxvYti/GoR6zXn38DJxOv2NuWuJO0mRAStTEqdJamw0Gyutv9lXW30w3g9PcZs4nqTYacFE04HB8frI4upzBGT8H51RaRFeVZ+Ho118GoKXJbJn9cn+KFq8d51S6gi1QqqUdKJeUrTqTNr+LvSMZRjLFE66hUjY7asPF6B0voItFLmjz4rAqnj2UQL/8HDS1os69kFGHjzdH81y63EfQbZtSBkdrXc7gVFpEl0vUnFbwmEGr/tmD0NSKr95DTyTLK/0prl/jP/Us3SKzeJ9xQgghTixdmXTtQ11xPQmH+WHoc1q5YW2At8IZDkSyU745vf8tePlZ1Nb3oOob+M+oj7zVzodefAAdHeW3zgnitMB3u7NmJqVpGb/qS7AnnJ2b9TcwrkRNmgyIGTA6bJZzrVgLmOtvgOOWJR2PslqhfQW67wAwsZPavEhUApyxJgOn3GCAsVk4lVbRze1mRudIsjB3r/3Z0lpuQ1wqotZvpK3eUS173dDqPv71KJeppZPoF5/CZbNwYZuXZw/Gie3fh7rwcmgI8ULoHDRw2XIfQbedkcwUStRyWfMgjmdiBsfnsFazOoSPoK58Oz6XjaFUEaXg+tWBU/gDLE4S4AghxFI1bhCc8jeQal+D1Sjh0kWuWenHYVU8MsUsjtYa48F/Bp8ftfU99MfzPLI3ytZOF+2pI+iHvov3iR/yG/u38au65Tx945/wV08PcMeTh2ny2Hjv+jmaYyFNBsRM6ukGqGZwukeztNc7cNtPYSBmx4pqBqfd58BmURyYr0YDiZh5EMJZB0A8WzytAAcwAxwAfyPNIX/15EXdYADAFwCX+XdS6zfSVl6g3+az03Ky4aXnXAirz0R/91503wHef06QTKHEV8/8bUobL4WGIM83nUO7rcByv5Og20YiVzr5+shjhnyaAY6nPAcHAKsNdfmWajvoTe1ecxBojZA20UIIsVSVO6ipylG+9tV4+9Po//O/cLvcXB64jMfe6uLQG2+e/La0BvcVcGkb6qlhhtNF7FbFTRcvR/XfgH78JwD82sbL+ZlTcfdoE85Yio9ubOLGsxrnrixCmgyIGaQP7jWfU+U1NHtHspzbcuKj9sfV0QXPPIaOR7DWN7Dc75i/DE4yBl4/Sim01sRzpVOegVOhQi1mi/ZlHXgcVrwOC8m8sagbDEA5O9XSDkMD0LWWtqT53rJhku5px1zXZsPyyc9h3PGnGH93B6u+cDefSvyKvwlcwn0j9dzUUs/OwGreq8zMV8htBiCjmeKJO52lyk0iKk0G8iVcNgt2q0KXMzjqwstQPj8+p9nF8oY1gVN6/IuVBDhCCLFUpcZK1AAS7gBeewZc5he030juZNTipqimkMxXCvyN4Ddr65vcNn5nQxMNdTb0r90EyQTq4rfh2XAxf3g4yXO9CT5wTohm7xwfMbSa96dLJRRSoiZOj+7phvYVKLuDkXSB0Uxx+utvylR7lxkA9PXA+ga6Gpxs70/N5OZOmR435DOVNyjp05iBU1HO4FSaizR77CTzucWfwQHUde+GTApltdLus7B1jZ93rGuY2nUDjVhu+X8x/vrzGPf+JVf17OHg29p5aC/0J/IYysKl6R4AguVGLOF04SQBzsT39mS+hM9Rfh8PtaIuvw719vcC5prLVL7ExvmauTRPJMARQoglSpczOONnJfiamrD+zl8CsAL4ygzcj/LVo37/1ur/N7V72TRfnXqOzuBIFzVxirTWcLDbXCvBuAYD01x/U9Wx0rzdvh7U+vPpCrh4fH+caLZI4DSzJ9OWjI/NwCmXN51WkwHMWTgaqs1Fmr129kdydCz2NTiA5dJrqr9bLYpPX7JsWtdXK9ehPvxp9P1fB+DDF7VxqNfDKwMpWgpxuiI9AATrzOfBSLqIPrQf/fKz1dt4vBhkwCgHi6kErHw7asgJmWHeCmerc5mUzYb62P9Tvd7aYB1rg3XTfciLngQ4QgixVKUrAU75KF+uVD1CuGQd3WTAqjA0lAx9/HauQkxmeBDSKRi3/saiYGXDKWZwfPXmTKpqJ7Vyo4FIjo3L5vh1mYihQq2AOQMHZiCD07UWQi2oM84FzBKuXFGf0nqlpchy2bUY4UH0W69jXX0mty7X3PaLPi7v3YGKhAFodI8FOMbj/wqvvQgWC0lbHX972ZdR2kChwdECy9dCbwkwByy/vcZK0E5miX/SCSFEDUsmzNKyOrMkLZErsSKw+MtFTqjaJtqcJWErt6YuaY0VCXDE1OmD5QYD4zqoddQ7qLOfRn+mjq4Js3AADkZzc18+lBwrUYtnKxmc01yD0xjC+lf/WP3/O9c18M4plnHVCsuNH6z+7nXC/3n7Cox/fwQdHQXAbbfisVsYSRdgoBd10duwfOKz9A+n4dFDfPGa5fOXHV9kpIuaEEIsVamk2UHNYr7VJ/JGtYxhybJNzOBYy59y0klNTFtfD1gs0L4cMFs6rzrF7E2Fau+CgV50sYjfZaPBZaUnOvVW7TNBFwqQSYN3rEU0zEAGR5yahiBkUuhsGjDX4YSTebPldmsHAL0xs1HAom+5PYckwBFCiKUqlaiWpxVKmmzRMCddL2XlJgOVNTiV7m0l6aQmpis2CvUBlM2OoTXRTPH0Szw7V5otzI8cBsyBn3PeKjoeMX+WG4ZEynNXAqe5BkecokC5hX7EzOI0uu2MxjOgNWrcsGSHVdVUm+fTJQGOEEIsUXrcpOtUftyk66XMenQGpxLgSAZHTI+ORaHeDAKSuRIlDQ11p1nGVW43Pb5MrTeWn9sMY8wMcNS4AMfnsGC3ylfC+aAaQuYv5XU4IbeNcGXY5zIze9gXy9Ppd2BRUmY7VfJsFkKIpWpcBidRCXCWfAbn6EGf5heConRSE9MVj0J9AIDoDK1ToaXdfI6OazRQNDT98fzp3e50xCoZnEYAItniaQdu4jQ0mBkcHTGbBQTdNqIlC0WrDVrMbm29sRwd9Ut8/eQMkwBHCCGWqnQSVWkRXa6z99VMBueoAKckAY6YpngUVQ1wZqaMS9lssKwT3XcAGGs0cCAyd+twdDXACQAQyZQISIAzf8oBDlEzwAm57WgUkWVrUDY7mYLBcLoo62+mSQIcIYRYqibN4Cztt31lsYCyjJWolSs6JIMjpkNrDYnYMRmcmQgEVNca2L8HbRh0+J04rIq3Ruaw0UAsYnZX9AUAs0Stca7n8IgqZXeYDR/KJWqVWTijrasA6Iuba7SWwsDUubS0P+mEEKJGaaNkzvCoDvk0V9n7lnqJGoDNJk0GxOlJJ83n0DEZnBkIBNacZd7+YB82i2JdqI43h9Onf7vj6FwO42cPosulmnvCGV7oLc/FikfAW4+yWtFam4NGJYMzvwLBaolaYzmOGWmc2EFtKQxMnUsS4AghxFKUTpk/KxmcXI00GQCzTK0y6FOaDIhTEY+aPysBTqaIVc1MBlStWQ+A7t4FwPqmOg5EcqQLpdO+7Qr92q/QP/gX2LsTgO+9HuZbLx4xz4tFqutvUgWDfEnTKAHO/GoIjmVwUmagM+JrBsz1NzYLLPNKgDMdEuAIIcRSlEqaP6sZnBIWBe7TGVK4WFjHMjiVLmoyB0dMSznAUeNK1Pwu28x0sWpeBj4/dO8G4KymOgwNe8IzWKY2NACAHjW/NA8mC4xmiubrIBaprr+JSovoBUE1hKA87NM73IejlGfEFQCgL56n3eesvpeJqamBTzohhKhByTgAalwGx+Ow1kabUav12C5qEuCIadDVDI7ZSjmaLc5YEKCUgjVnocsBzhmhOhTw5nBmRm4fMIdEAowOY2jNkWQBDYykCxCLoMoZnNFygCNd1OZZQxASMXQhD4O9BHMxRlUdUO6gJuVp0yYBjhBCLEXpSgZnrMlATay/gXIGpzIHxzxJAhwxLZVOY+PW4MxkEKDWrIfhQXR0FI/DSleDk10zuA5HDw+av0TCjKSL1ef/cDJvZqcqGZxy8wQJcOZZtZPaKPT3EjQyjOQM8iWDI8mCdFA7BRLgCCHEEqRT5QXF5QAnmSvhc9bIW/74JgPljJV0iRbTEo+CxVIt8YxmSqc/A2ccteYs85d9ZhbnzFAdb4WzM7dWbLhcojYyzGBybMZOOFJunlCZgSMZnAVBVQKcSBg90EvQbjCSLtAfz2No6aB2Kmrk004IIWrMUWtwEnlj6Q/5rBjfZMAqTQbEKYhHwRdAWSxorYnlZq5EDYDlq8DhqJaprW92ky0a9ERzp33TulCAckcuRoc5kixUzxuOlN8XyqV3kUwRu0XhqYW1eQtZQwgwA1IGDxOsszOSLnKw/HzoqJcMznTJM1oIIZaiSgbH7QHMJgO1VKJWaY9rLWdwChLgiGnQ8SjU+wGzxXrRmKEW0WXKZoeudei9Zie1s5rM9Ra7drxJ6atfMIMU4BsvDPCDnSPTu/GRI6C1OVtldJiBRB6LAo/DQjhhNjJQ/rEAp6HOZq4LEvOnksHp3gXFAsGAm5KGnUMZLAraJcCZNglwhBBiKUolwe1BWcygJpkr1UaLaChncI6egyMBjpiGeHSSGTgz+/pRa9ZD7350LkuTx07IbWPX7h5463XYvYMdAyke7Y7x0O7R6T1/K+tvzjgH8jkGoxmaPXZaPHaG0+ZjoRLgZIs01NXI+8ICplxuqHOjd24HIBQy98/2gRStXjt2q3xdny75iwkhxFKUSlTX35QMTapg1FQGZ6zJgHRRE6cgEUWN66AGzPgwTLXmLDAM2P8WAGeWRnmzrhVttVF65Vn+ZfsQNgvEcqVpNSCoNBhQZ5wHwJFYllavnSaPnXClAu6oDI5YAAJBGBkCINjWAsBQqiDrb06RBDhCCLEE6VQS3Ob6m1S+MuSzRt7yx83BsZUfsmRwxFRprSdmcDLm62cmS9QAWH0GKIXetxudTnLWnmcYdQYIX7SFpw6l2R/J8YlNrTisiucOJaZ+u8OD4HCiutYAMJgp0epzEHLbGC7ZwOlCucySuEi2RMNMPy5xaiplaoEgoUZf9WQJcE5NjXzaCSFEjRmXwUnkDYDabDJgkS5qYpoyKXOO0lElag0zXaLm9kLbcvTe3eiHH+TM4TcBeGPVZv6t7SpW1hlcv8bPhW0enu1NYuipPYn18CA0tUKwiZTNRaJkocVrJ+Sxk8ZGusHMDhRKmkSuJBmcBaLaSW1ZB36XlXJ/FGkwcIokwBFCiKUolUSVO6glyxmc2ipRKzcZKAc4BYlwxFTFoubPaoBTwqKYlTVsau162Lcbve3HrDh7HXU2Cw+MeBmqa+QjqdewKMWlnT4imSJvhac4CLQS4Hj9DHrMYGaZ10GT2w5AONBeflzSInpBKXdSU23LsShF0G3uF8ngnBoJcIQQYilKJcBbzuDkygFOLTYZqM7BkQBHTFE8CoAal8Hxu2xYZqPT2Jr1kMuCNrC953c4s6mOWM5gQ3GYDdt/ijZKXNThxWaZWpma1hrCg6imVpTFwpHQCgBafeYaHIBwvRn0VGfgSInawhCoZHA6AWisM/dXhwz5PCUS4AghxBKjDQPSybEStZoLcKTJgDh1uhzgjK3BmeEZOOOotetBKdTV70KFWjinxY0CPrzKBokY7N2N225lY6ub53oTZgBzIrFRyOfNDA4w6G8DKJeomYFM2GNmCiLV5gk18r6wwKnmZebPzpUAtNXbafM5cNnkq/qpkLBdCCGWGJ1OmnMwjipRq5U1OMpmQ0uTAXGqKgGOv9JFrTTzDQbKVGMTli98DdrNTMu7z2jgwjYPXXUrMP7dgX7lWdQZ53Dpch8vPZ+iezTL2mDd8W9w+Ih5u+UA54inifpCCrfdisMoYNElhl3mfJ9KBqdRStQWhjPPw/KFr6G61gLwu+c3kyka87xRi9eUntWpVIpvfvOb9Pb2opTiU5/6FOvWraue/9RTT/HDH/4QAJfLxcc//nG6uroA+PSnP43L5cJisWC1Wrnzzjtn/lEIIYSoMhJx8xd3pclACQW4a2Va+bgmA9bqHJz53CAxk7TWGH/zv1Gbr8ay+Rp6IlnufmaA27Z04p+JQCQeBYulmgGNZot0zmKZkFqxpvq702ZhZYPL/M/Z56NfeQ79gY9zSYePe9Ugzx1KnDDA0cMD5i9NZjZg0OGnNRJGl0pY41GCuRgjXnP4byRTRMHM/M3EaVNKQTm4Aah32aifx+1Z7Kb0rL7//vvZuHEjt956K8VikVwuN+H85uZm/vf//t94vV62b9/OP/zDP/CXf/mX1fO//OUvU18vu0kIIeaCTpoBjip/QUvmSngcluqX/SXPajO7YAEWpbAoKEgGZ+mIR2HndjMA2XwNr/SnOBjL0T2S5cJ27+nffiIKPj/KYkFrPasZnBNRF16G3vECHNiDb/WZnNvi5tneBB/e2GR+GZ7M8CAoCwSbABjEzZmZfRAdhViEpmyUYcxsUSRTot5prXYaFGIpOekrNp1Os3v3bj796U+bV7DZsNkmXu2MM86o/r527VpGRkZmeDOFEEJMlVEOcColaom8UTPlacCEJgNgtoqWErUlpK8HGBto2RM1D7oOJgszcvM6HgVfAIBUwaBo6HlZp6LOuwhttWH88z0QauYS12q+5b2I/++fn8RKOSXprEO1dpiXV/COcIkLGkMom51CSTNSstKaGYHRYShncPaUzMcSyRZnfHipEAvFSZ/ZQ0ND1NfXc++993Lw4EFWrVrFxz72MVwu16SXf/zxxzn//PMnnHbHHXcAcP3117Nly5ZJr7dt2za2bdsGwJ133kkoFJp8g222454nFibZZ4ub7L/FJ//WqwA0dHRiC4XI6yM0eJw1sx/jHi9Zw6g+Xpt1L3ana1E9fnndHV8qMkwSUCNDhEIh+hKHAIiVrDPyNxtJJ7GEmmgIhUhF0gB0NjVM+7ZPfx+GSL73w+R2vAC5LFfm9/KCrY20xQFY0UYJYgls/hzK6eJwLMsD1rVc0naAxlCI3kgGA0VrZgRvMYsuFWjKRnguD43BIMlCHy31dfI8G0ded0vHSQOcUqnEgQMHuPnmm1m7di33338/Dz30EDfddNMxl33jjTd44okn+MpXvlI97bbbbqOxsZFYLMbtt99OW1sb69evP+a6W7ZsmRD8hMPhSbcnFAod9zyxMMk+W9xk/y0+7vIcj0i+iAqHGUlm8DqsNbMfjWIRXSxUH68VTTKdXlSPX153x2e89QZgZlr6ew7SM2oGIQeG4zPyNyuNDKOCLYTDYQ4cMW/bWshM+7ZnZB9u/Q3zH+AF/mLcWTqdwvjsx1Ceq7B85A/Zti/K3z5f5LXAKs4Jh9ndnwSgNTtCoucAZDOEcjGKBnT3HWEokaXF7Zbn2Tjyult82traJj39pCtOg8EgwWCQtWvNhU+bN2/mwIEDx1zu4MGDfOtb3+Kzn/0sPp+venpjYyMAfr+fiy66iO7u7lN6AEIIIaamWqLmHuuiVjNDPmFCkwGolKjN4/aIGaX7esx1VkDfoQFK2uyWdyRx+iVqWmtzjY8/AIwNw5ytNtGnQ7k9qIuvRL/wJDqd4vIWG+5ihkfqzO9rA+W/R4vKlUvUIoSs5uMZThWIZosy5FMsWScNcAKBAMFgkP7+fgBef/11Ojo6JlwmHA7z1a9+lT/8wz+cEElls1kymUz199dee43ly5fP5PYLIYQ4ik7Goc6NKq+XTOZK+Jw10kENJjQZALOTmjQZWBp0sQADfXDmuQD0DEYBOLfFw2Ayf/I5MSeTSUOxMDYDpzorZmEGAuqqGyCfQ7/wC1yRYa4efJlnC35i2SJHknkcVkWD14UeHUbHIoSc5t/nQCRL0UACHLFkTemZffPNN3PPPfdQLBZpbm7mlltu4dFHHwVg69at/Nd//RfJZJJvf/vbANV20LFYjK9+9auAWep2xRVXsHHjxtl5JEIIIYBym+hy9sbQmmTewFsrQz7BzOBoA20YKItFmgwsJYOHoVREnX8peud2eiJZ7BYfF7R52D6QIpItnd5cl2OGfJawKBZsBlR1rYUVa9BPPozyN7C1/wX+p+MKHtsfYzBZoNVrRwWbYGQYLBaaAs0A7B3JAtAgLaLFEjWlZ3ZXV9cx82u2bt1a/f2Tn/wkn/zkJ4+5XktLC3fddddpbqIQQojpMBKx6gyPVN5As3C/oM2KcvkSpRJYzPbYRQlwlgTdZ5bIqzXr0d56ejIWlgcdtPvMOTVHEvkZCXDUuAxOvdO6oFusq6tuQD/wdxjPPcHy9BHWBx08sjeKw6po8TpQjSH03l1gd+DpXEmdzcKesFldIxkcsVTVUM2CEELUBp2MV1tEJ/PmWpSaahNdGWVQbhVtsyhKp1u6JBaGvh5z/7a2Q1MrB406ugIuWnx2AAZOt1V0Imr+rAY48zMDZzrUxVdCnRt2vAAeH+84M8RgssChWJ5Wnx0amiCdhHgE5W8k5LHRG8sDEuCIpUsCHCGEWGKMZLw65DORMwMcX62VqMG4AAeKJQlwlgLd1wNty1FWK7Gm5UStdXQ1OGnx2FHAYDJ/erd/dIlatrggGwyMp5wu1OZrzP80tXJpp5f68ut9mdcBjeW2x1qDP0CT207l1dAwD/N9hJgLEuAIIcQSYyRqPINjnZjBsSpFUeKbpaGvB9XeBUBPo/lzhc+G3Woh5LYxeLqd1GIRUBbw1gMQzSyOYZjqqhvMn02t2K0Wtqz2A9BSWYNTuZy/kSaPme1yWhV1NvkaKJamhf+qFUIIMWVaa3QycUwGx1tTXdTKwVzRfOzSZGBp0PGoGYB0dAFwsK4ZstClE0A9LT4Hg6dbohaPgq8eZbGitV4UJWoAqn0F6sYPotacBcC7z2wkniuxvrkOEmMBDv4Aoaz5eBrqbCi1cNcWCXE6Fv6rVgghxNRlM2CUxmVwzAEwtdVkwDxCXc3gzFKAo6Mj6Md/Up2584oR4DXDf/wreLyojpXV/75tRT1rgq4Z364l6/BBAFSn+Tc8aKmnMRejPpaBjnZavXZePJw8rbvQ8Wi1PC1dMCgYesGXqFVY3j02gL2xzsYfbV4GgPY3mlkpbYC/kZDFfH3I+huxlMmzWwghlpJUwvxZyeDUZIlaZQ3OWAYnV5z5SZ/6hV+if/YgOJygFA+cdwu97iYcRnHyK0SB9CgoRb6k2TmU5qs3dM34di1Vuq/H/KWcwekpOFiRHEAPO1CczzKvg1i2RLpQwm0/xef7uAAnmjWfP4shg3MiymqFhkYYDUN9gCZjaTwuIU5Ent1CCLGUpMwj2KqSwcmVcNstC7rN7YyzHt1FjdlpEx2PgMOB5e/+E6UU0Qf3cn2Hj1suaT3mojoexfjzm1FXvwPLB36fn7w1yj++NET3SFayOFPVewD8DSifn6Kh6U2W2JAZgmEzI9Fa7qQ2lCzQ1TC1AEePhgGNajTLuA7k7XS0NGJl4Q/5nJbGJkinUE4XTe5KB7UaOughak4NFWULIUQNiI2aP71mqdRopljtqFQr1FFd1MwStVm4o1gEfAGUMkvg4tkSgeN8aVT1AdQFl6Kfexydy3HNSj9Oq+KR7sgsbNjSpA/3QLnBwOF4nqKhWWHJoIcHAHNBPUyvVbTxt7dhfOEPMH70b+wfTnDr2o/y/7iu4pX+5FiAs0hK1E5EdXTBsk4Agm47XoeFTr9zfjdKiFm0BA5LCCGEqNCHD5m/tJlfZg7FciwP1NgXmfGDPql0UZuFNTjjypniuRKaE5f9qKvegX7xKfRLT+O5/Dre1lXPL3vifOz8Zjy1VEJ4CnSpBP2HUNe9G4CeSBaALg8wPAiUWyIDg4mptYrWkRHoOwDNbegff4+Xu1PQ8jaUxcJfPNFXDZiWQimX+q3fQ5UDfrtV8a0bV1Nnl2PcYumSZ7cQQiwlfT1YmlpRbi/5ksHheJ6uWgtwJhv0OSslatEJ81LgJEf7150NrR3oJ38GwA1rA2SLmid74jO/bUvNkcNQLI6tv4nmsFmgo8EDw4NorfE6rXgdlil3UtO7dgBg+dT/wvInX2GHt4uuZD9/szLKB88LMZouYrOoJZEBVXYHyuWu/t/rtNZW2aqoOYv/sIQQQogq3XcAe9caSkBvLI+hoauhxgKcSZoMzM4anChq1RnA1BakK6VQV92A/o9vow/tY+3y1axudPHwnijvWBuQlr0noHsPAOVSK6AnkqPT78TmbEHnspCIQX2AFu80WkXv2mEGqO1d5Fs1b766h19ryOLYdDYfsNm5qqueSKYogYAQi5BkcIQQYonQhQIM9mFfsRoYV8YTqLFF7JUSteJYBmemAxxtlCCZGMvgZCoZnBMfN1SXXgsOB/rJRwAzi3MwluPN4cyMbt+S073b3K+tHeSKBvtGs6wIOFFN5YYO5TK1Vq99SiVq2jDQu3egztqAUoqdQ2mKBmzcsBZlqzQtcHBWs/sktySEWIgkwBFCiKVioBcMA1vXGsAs43FYFa3ltQQ145gmA8x8iVoybs4VObpE7SSdqZTHi7robegXnkRn07xtRT1uu4WH90ZndvuWEP3qr9BP/gx18ZVgtXHP8wPEcyWuWemHpvKsl3KjgWU+B8Opwsn3d1+PmfVZfz4A2wdS2C2K9U11s/lQhBBzRErUhBBiidB9ZhlPNcCJ5FgRcNZeic3RTQYsihkfgxOPAqD8DYBZouawKupsJz9uqK56B/qZxzD+/v/gbAxxleUMtvV0YKNU3fYLu4pc1iof0XqgF+PbX4POVajf+RQP7hrl6YMJPrKxiY3LPOiCHZSC4SOA2UmtpCGcLtBSbjow6e3u3gGAWr8BgFcH0qxvrsM5hf0nhFj45JUshBBLRV8P2B1YWzvQWnMgmqu9BgNwbJMBpSjNdBe1WNT86QsAZolawGWb2jqarrVwwWXQfwj9xiu8682fEUqPsL03zvb+FM8eSvB/f7FvVoaTLiY6lcT4uzvA7sDy6f+Xl4cLfHfHMG9b4eO96xsBc/E8gSCUMziVbOVA4sTrcPTO7dC+AhUIMpIucDCWY+Myz+w+ICHEnJHDQ0IIsUTovh5oW46yWolkSyRypdprMADVEjVdKqGYpTU45QzO+BK1qc5LUUph/dTnqv/vMAy+8WcfRa3fiOXjt/Ly4SRf+UUfu4YznF+jX7q1UcL4x7tgZAjLn93OYWs9X3vmIKsanfzR5mUTA8mmVnSlVbSv3Co6mQcm/9vpfA727kJd/U4AXh1MA7CxtTb/1kIsRZLBEUKIpaKvZ1yXqRptMACTNhkwNBgzmcU5JsApnfLEe2WxoM7aiN79KtowOLvFjd2q2DGQmpltXYz27oKd21G/dTNqzXp+sMscYPv5KzuOKSNTTa3VJgONdTZsFsXgiTI4e3dBsYBavxGAHQMp/E5rbR4MEGKJkgBHCCGWAB2LmIumO1cC5voboDZL1CZpMgAz3GggHgWbHerMLlvTyeBMav1G8zYPH8Rls3DusvqaDnB0734A1KYrADgQyXJGqI4mzyQNM5paIRZB53JYLYpmj/2EraL1rh1mGeO6s9Fa8+pgig3LPFikTbcQS4YEOEIIsRT09QDj5oREc4TcNrxLYEjhtE3SZACY2UYD5SGfSplDROO50mlNvK9kEyrDJy9eHqAnmiNSbj9dc/p6zL9vfYCSoTkUO/7AWrWsAwD93XvRiRidfgc7BlI82h2dNGund+2A1WehnC4ORnNEsyU2tko7aCGWEglwhBBiCdDlAIf2FYAZ4NRk9gaOaTJgLwc4M5nB0eUAByCRL2Hok8/AORHVEIRlnehd2wG4aLnZne3VwdrM4ujeHigH64fjeYqGPn4J2cZLUDf8JvrFX2J86RY+WtrDqgYn33hhkM89eqhargmg4xHoO4A6e6w9NCANBoRYYqTJgBBCLAV9ByAQRHnryRcN+mI5Lmr3zvdWzY9qidpRGZyZXoPTGALGD/k8vWyZWr8R/ctH0IU861qD+JxWdgykuHql/3S3dlHRpRL0H0Jd+y7ADNbh+OWWymJF/eZH0ZuvwfjXe2n996/zFbeXXzRt5F8K1/EnP03hK6bHrnDZlyDhgwf3kikYdPodBN01NitKiCVOAhwhhFgCdF9Pdf3NwUiakq7R9TcwrsmAuQ7DVi1Rm8EAJxFFrVwLmA0GgFNuMlChzj4f/diPYe8uLMva2NDqZsdgGq311NpPLxVD/ea+66isJ8tis0B7/Ymfz6p9OZbP/hX6hV+gundzLXARL/NjOonbxgUwThdqeYc5Pwe4pKNGDwQIsYRJgCOEEIucLhZgoA917oUA7AubR6trtivU0RmccmwwUyVq2ihBPDY2AydbyeCc5kfqunPAajPL1K7cwsZWD08fTHAolmdFDQWrepL1ZB31TuzWkwd5SinU5mtg8zUA+IHfmaXtFEIsXLIGRwghFrvBw+Z6k/YuALrDKewWRZvv+JPclzJlsZpH52eryUAyAdqYMAMHZqBEzemCNWdVGw1U1oXUXDe13gNmkNpqNg/oidTwejIhxCmRAEcIIRY53XcAAFUu6ekOp1gecFS/2Nckq232mgwcPQMnU8JuUbjtp/+Rqs7aAL0HMKKjNHnstNc7aq7RgO7rgdYOlN1OPFdiJFNkRa1mI4UQp0QCHCGEWOz6eszOYa3tAOwLp2pzwOd44wIc60yvwSkHOGpcBifgss7IOhm13uzulXvtJQA2trp5/UiaQmkme1wvcIfHBtYejJod0FY21PjzWQgxLbIGRwghFgE9eBgy5pH8gqE5mIbK93V9KAwrN2GJ5EkXDEbThdpdf1NhtVYDnJluMqCPzuBkS6fdYKBqxSrw+Mi/+itYfwEbl3n46Z4ou4cznNe69FsZ61QCRsPVFtE1PbBWCHHKJMARQogFTh/px/jSp6r//8+urXy/a8vYBVrebf585GD1pNWNNX7E22o9Zg3OrJWoZYuE3DPzcaosVjjjXAo7dwBwTosbBewaqo0Ahz7zOTy+wYDfaT3t9U1CiNoiAY4QQixwet9uANRH/wjlb+DN/S46CyU+uixfvoSC1naUqw6A1lAD7Y78cW6tRowvUStXjs3YHJx41CwJdJsBRzRbmtGAUoVaKL3+Ehatcdut1LusjGQKM3b7C1l1YO24DM6KBmdttckWQpw2CXCEEGKh6+kGZx3qsuvQSrF/916u7KrnootbJ714KOQnHA7P8UYuMHY7+tnHKT3/JDZfJ2z8FP/fz3uwVIIcpcbaSaP4g4ta2LomMLXbjkfAF0AphaE1sWzx9FtEj1cfgEIeMmlwewi4bNVZO0ve4R7w1oO/kZKhORTL8Y61gfneKiHEIiMBjhBCLHC6Zy+sWI2yWBiI50kVDNYEa7wE7SQsH/j9auZrtVb8TrGXDGZAo2MRCB9BXf/rKLuDx/ZFeelwcsoBjo5Hq+VpiVwJQ0ND3QyWUPnL2xGPgttDg8tKNFOcudtfwHTvAejoQinFQDxHvqTpkgYDQohpkgBHCCEWMF0sQu8B1LXvAqB71OwqtabW19ichNpwEWrDRQA4gfePO0+/9iLG3/4LlsDVqLXrGUzm2TuSnfqNx6MQCAJUMyszmcFR9QF05X5a2wm4bPQnMjN2+wuVNkrQfxB15Q2Auf4GpMGAEGL6pE20EEIsZP2HoFiAFWsA6B7J4LAqOv3ype+Uldd3VNZ7dAWcHEkWSBemWAYWj01oEQ0zG+BUskMkouZt19mIZovomVpDtFANDUI+P2H9jUVBh782B9YKIU6dBDhCCLGA6YPdAKiucoAzmmVlg6u2h3ieroaQ2SCgGuCY2bCD5YzBiWjDMAOP6pDPSoAzgyVq5duutKP2u6zkS5pMcYnPwjlqYG1PNEtHvQOHVb6qCCGmR941hBBiIevphjoPNC2jZGj2jeZk/c1pUkpBRxf6cA9AdWZQZebKCaWSYBgTZuDADGdwvPVgsVTbUVduO7bEGw3ovh5QFmjrBMz9UfMDa4UQp0TW4AghxAKmD3ZD1xqUUhyO58gWDVl/MwNUx0r0M9vQhkHIbcPjsFTXfJzQJDNwbBaFxzFzxwuVxYrF5zebITCWHYpkiizzLe5yLX1wHyTjAOQMeDNlGRtYe3AUVm3GEi5QKOUYThe5odYH1gohTokEOEIIsUDpQgH6elDX/zoA3eWF8JLBmQEdXZDLmt3UmpfRFXBOLYMTN4OO8Wtw/C7rjM9psQQaKR6Vwams91msdGQE4/Y/qf7/x51X893V7xy7QGir+fPx3upJa+W5LoQ4BRLgCCHEQnW4B0rFCetvXDZF+yI/ir8QqI4us1NZXw+UA5zH98cxtMZygmBFH53ByZRmtjytzBJorGaLGuoqAc4iL1EbGgBA/fYnUMtXM7Af/BHN59eNu0xzKzjMrI3TamGlZHCEEKdAAhwhhFigdI/ZYGCsg1qWVdJgYGa0rQCl0H09qAsupavBRaYYZThVoMV7ggCyEuD4GwAzq9JYN0sBTt9BAOqdVhRLIYMzDIBafz6qtZ2Rg4do8RucdX7X/G6YEGLJkSYDQgixUB3sBq8Pgs2UDM2BSFbK02aIcjqhua3aaGBFYIqNBuJRsNrA7QXMrEpgtgKcRBStNVaLot5pJZpZ5BmcETPAoTEEwHC6SJPHPo8bJIRYqiTAEUKIBUr3dMMKs8FAb8yc6i4NBmZQxwroNVsTrwg4UcCBkzUaiEfB50cphaE1sWxxdkrU/I3mTJicOeAz4LIt+gwOo2Hzb+dworVmOFWQAEcIMSskwBFCiAVI53LmVPcVawFz/Q3AmmDdfG7WkqI6umB4EJ3N4LJZWOaznzSDo+PR6vqbZN6gpGd4Bk6ZJdBo/lKZhVNnXfRrcPTosDmDCEjkSuRLmpBbKuWFEDNPAhwhhFiI+g6AYYw1GBjJ4rabX8LFzKgMlOSwudZlRcDFwWj2xFcaF+BUMir+WWky0DB2f5gZnNhiz+BEwtDYBEA4bT6WkGRwhBCzQAIcIYRYgI5pMDCaZXWj64QdvsQ0dXQBTBj4OZAokC0ax79OPDrWIjpjfkmf1QxOLFq9j0hmkQc4o8OooBngDKcKADS5JcARQsw8CXCEEGIhOrjXzBQ0BCmUNAciOVl/M9OCzVDnht4eAFYGnGjg4HHW4WjDgER0XAbHLBmbnSYDQfM+K62iXTZyJU2mcILgawHT6RRk0uMaDJQDHI+UqAkhZp4EOEIIsQDpvh5YvgqlFP2JPEVDs0oCnBmllIL2LvNvjZnBgeMHOKSSUCqBPwBQzag0zEaJWr0flBorUatb5MM+R8sd1BoqGZwiDqvZHU4IIWaaBDhCCLEQRUZQwWYAwuVynmZZrzDjVEcXHO5Ba02Tx06dzUJP5DjrcKIj5nXKC+VHM+aXdK9j5j9KldUG3vpxa3DMQGDRBjiRMACqnMEJpwuE3DYzyBRCiBk2pcNOqVSKb37zm/T29qKU4lOf+hTr1o2NHtZac//997N9+3acTie33HILq1atAmDHjh3cf//9GIbBddddx3ve855ZeSBCCLFU6EIeknEolymNlDMFQek4NfM6uuAXaRgdxhJsZkXASc/xMjjlL+mV/RJOFwjO5pf0+kC1RK3SinqxdlLTlRk4wbEMjjQYEELMlil9Wt5///1s3LiRW2+9lWKxSC438c1/+/btDA4Ocs8997B3716+/e1v85d/+ZcYhsF9993HF7/4RYLBIJ///OfZtGkTHR0ds/JghBBiSYiYmYJKS91wuoACGmZhrUetUx1daDDn4QSb6Wpw8lRPHK31MYGLjlb2SznwTBcJzuYi+fqAueYH8FcyOIu10cDosDkgtd7sDhdOFdi4zDPPGyWEWKpOmldPp9Ps3r2ba6+9FgCbzYbHM/FN6aWXXuLKK69EKcW6detIpVJEIhG6u7tpbW2lpaUFm83GZZddxosvvjg7j0QIIZaKainU2BfpQJ0Nm0XKeWZc+woA9KF9AHQFnKQKBkeShWMvGxkBZal+SR9JFwnNYtCpfIGxOTjlDE5skWZwGB2GQCPKYqFoaEYzRULSYEAIMUtO+u4yNDREfX099957LwcPHmTVqlV87GMfw+UaW+w6OjpKKBSq/j8YDDI6Osro6CjBYHDC6Xv37p30frZt28a2bdsAuPPOOyfc3oQNttmOe55YmGSfLW6y/+ZeZleeONCwag22UIhEcZDWeteU94Pss+kZPfM8jJeeIfi7f8Q16z1888UjvDRc5GMr2yZcLpZOkm9opKm1FUNrRjNv0RGqn5W/tc1mo66llcxrv6revt+1jyyLc9+OJmLQ0kZjKMRgPIsGVjY3LMrHMlXyOlx8ZJ8tHScNcEqlEgcOHODmm29m7dq13H///Tz00EPcdNNN1ctorY+5nlLquKdPZsuWLWzZsqX6/3A4POnlQqHQcc8TC5Pss8VN9t/cMw4dACCCBRUOMxBNs8znmPJ+kH02Pcbl16Hv+7+En34c11kb2Njq5r9f7ecdXXVYx2XNSoOHwd9IOBwmmilSNDRuCrPytw6FQmQcLnQ2w3BfL8pVh99pYSCaXJT7tnSkH7V2PeFwmLeG0gDU6dyifCxTJa/DxUf22eLT1tY26eknLVELBoMEg0HWrl0LwObNmzlw4MAxlxn/hBgZGaGhoYFgMMjIyMgxpwshhDiByAjUuVEuN1AuhZIGA7NGXXg5eHwYT/4MgBvWNhBOF3m5PznxgpERKA/gDKfnoPFDed4O4xoNRDOLr0RNGyWz7LK8pqwy5FOe00KI2XLSACcQCBAMBunv7wfg9ddfP6ZJwKZNm/jlL3+J1po9e/bgdrtpaGhg9erVDAwMMDQ0RLFY5Nlnn2XTpk2z80iEEGKJ0JFwtVNXpmCQKhizu5i9xim7A3X5dbDjBXR0lIs6vDTU2Xh4b3TiBaMj1RbRIxnzS/psBjhqsgBnMbaJjkXN+UGNZge1cMp8DNJFTQgxW6b0znzzzTdzzz33UCwWaW5u5pZbbuHRRx8FYOvWrZx//vm88sor/PEf/zEOh4NbbrkFAKvVys0338wdd9yBYRhcc801dHZ2zt6jEUKIpSA6Wj3aPRdfpAWoK29AP/oQ+plt2N71W2xd4+c/Xx/hSDJPi9eBzqQhk57QQQ0gNNtd1GCs0UCddXG2iS4P+VTlFtHhdAGf04rLJqP4hBCzY0qfmF1dXdx5550TTtu6dWv1d6UUH//4xye97gUXXMAFF1xwGpsohBA1JhJGtS0H5uiLtEC1tMFZG9C/fAT9jt/k+tUBvv/GCI92x/jwxqZqZ7tq4JkuYlVj7ZtnRTnA0fEoCjODky0a5IoGzkUUHOjRcgl7Y2UGToEmCdiFELNo8bxDCiFEDdDFIsQix2QKJIMz+yxXvcPMNrzxCk0eO5vavWzbF6VQ0tXZRGrckM/GOhuW2RryCeD1mz+rJWrlWTiLrUytnMGprsFJy5BPIcTskgBHCCEWkngEtK4GOOG0WaLWKEM+Z9+Gi8HfgPGLcrOBNQGi2RIv9CXQkTke8gkomw28vuqwz0B5Fs6iK1MbHTabZrjNGXphyeAIIWaZBDhCCLGQVDIF5aPdo+kiPqd1UZUkLVbKZkNdcT288TI6OsLGZR6aPXZ+3h2FSLnMakKAMwdf0usb0OUMTkM5yI1mFlcGR4+Gq+Vp6UKJVMGQDI4QYlbJJ6YQQiwk0YmZgrC0iJ5TasPFZgbtwF6sFsWmdg97RrJmBsdbj7I70Fozki7MzX6pD5gli4yVqEUWY4naUR3UmmRNmRBiFkmAI4QQC4iuZAoClUxBQcrT5lLbclAK3dcDQFfARbpgMBTPVIPOVN4gV9Jz0rpb1QfGuqgt4hI1dfQMHI88p4UQs0cCHCGEWEgiI2B3gMcHVIZ8ytHuuaKcLmhaNhbgNDgB6MlYqkFnZV3U3JSoBSAeA8BmUfgclkVVoqZzOUjGobHSYMD82zVJiZoQYhZJgCOEEAtJZAQagiilKJQMYrmSdFCbax1d0HcAgOV+JwroMerG1kVl5rCzXX0AchkzUMDM4iyqDE4lIxkcK1GzKGhwyXNaCDF7JMARQogFREdGqu105/SLtKhSnV0wPIjOZqizW2j12jhob5ywLgrmaDZRddhneR1OnY3YYlqDUxnyWZmBU167ZLXMYnttIUTNkwBHCCEWkkgYNR9fpEWV6ugyGw30HwJghVtx0Lts3JDPAoqxrmazui3VACcKmI0GFtMcHH3UDJxwqiDPZyHErJMARwghFghtGBAdHddgwPwi2ygZnLnV3gUwtg7HlmWgLkje3wiYgWfAZcU2F1mI+gbzZ3kWTsNiK1EbHQalqtkvGfIphJgLEuAIIcRCkYxBqXjMkE9pEz3Hgs3gqquuw+kyEmhl4aDdDDbmYshnVTmDo2NRtNb4XVbSBYNsoYRhGNV/WuvqvwVlNAz+BpTNjlFury1DPoUQs03eZYQQYqGIjAITh3zW2Sy47db53KqaoywWaF9RzeCsyA4BAQ4abs7A3C+tvjkKcHx+UBb0d+9Ff/deAq0Xwpkf4AP/uXfSi1sVfPHqDi5o887N9p2EHhmqlqdFMkWKBpLBEULMOglwhBBioah0nBq3BkcaDMwP1bkS/cIv0VrTHD+Cq7SCnpSZHQlnCpzTUjc322GzoT7+pzB4GIDN2ko0308RszxO9x8CpbBc/U5KWvOfb4zQE80tiABHGyU42I26+EoA9oxkAVjV4JrPzRJC1AD55BRCiAVCR0bMX8YtZpfytHnS3gWZn5lDKqNhVthH6YkEyBQMUnlj7krUAEs5QADwAe8fd57xz/egd+3Aet5H0Frz4M4RUnljzrbthA4fgkwa1qwHYPdQGodVsbrROc8bJoRY6mQNjhBCLBSRMFitZlkS5lqPRuk4NS9UR5f5S18PREbo0kl6ojlGMnM45HMq3B5IpwBQSuFxWEnlF0YTAt29GwC15iwAdg9nWNPowm6Vrx5CiNkl7zJCCLFQREbA34iyWCgZmki2KBmc+dKxAih3UouM0OUskMob7AmbZVYLKsDJZdAlM6jxOCykCgskg9O9y+wIGGwmVzTYN5plfbN7vrdKCFEDJMARQogFQkdHqutvotkihl5AX6RrjHK5oakVfWgfxCKs8Jofly8dTgILaDZRXXmtTcbM4njsCyuDo9achVKKvSNZShrOapqbtUtCiNomAY4QQkyTPrQfXTRn1KTyJfrj+Zm54chItYOaDPlcANq7YPdroA1WNJpfzHcMmIFE4xwM+ZwSt8f8WS5T8zgsC2INjh4dNmfglMvTdg2nATgjJAGOEGL2SYAjhBDToA/sxbjtMxh3/Cm6ezf/+uown/rxfv72+QHiuVM/cq61NtfgVId8mms9FswX6RqkOrrGMiONjbR47aQKBj6HBadtYXx8qkqAU9lOh5VUYf4zOGPrb8wGA28OZ1jud+BzSstzIcTsk09OIYSYBj3Qa/4SHcH4P/+L3qv+F257iMf3x3ihN8HHzq7n4mXjjlLb7CjHWNcot92C1aKOveF0CvK5aonaSDWDI2/T80V1dFEdm9kQoiti50iyMKcd1E6q7qgMjn1hZHDo3gVOF3R0YWjNm8MZrlhRP99bJYSoEfLJKYQQ0zEyBIDltr9H/+xBhmKaC4a285uHHudb636Dv92+ErZHj3v181rc3LZl+bFnRI9uEV3EblFyxHs+dXaN/d4QZEUgxwt9yYW1LuqYErWFsQZHd++GVWegrFYORrKkCoasvxFCzJkF9C4thBCLwMgRCDSivPUYv/kxwt97i8tCFro2vJs7dJEXcv2MlMpvrYYBO16A8y5Crd/Im+EMTx9MMJwq0HT0NPfykE81LoMTdNtQapJsj5gboVZwOEFr8PhY2VA+eSFlcMoBjk4nUZhrcHIlTaGksVvn57mjM2noO4j6td8CzPbQIA0GhBBzRwIcIYSYBh0egmAzAJFskaKGlrPPwrK2AQtw+VGXL+36Cez8Mdb3XsuF8TxPH0zwXG+Cd6/2YvztbRAdBWCnpZFvb/oTSm9YYfd+wukCqxtl4vt8UhYLtK+AZBylFF0Bc380LsQMzrguagDpQgm/dZ62c/9boI3q+pvdwxka6my0eBdQYCiEWNIW0Lu0EEIsAiNDqFVnAjCUNBsBNB+djRlHXXg5+r/uRw8P0tbUSlfAyXOHEvxa5FXY/SqcuwnlcPJfdZcyamvg3KAHBawIOLmyS9YszDfLu29Cl8u/lvns/Pa5Ia5aSPvFWQfKMqGLGkAqb+Cfp/hYd+8yt2nVOgB2D6c5q6lOspFCiDkjAY4QQkyRLpXMUrKQmcEZSpUDnBMcmVYXXGoGONufQ239DS5d7uN7r4UZ3fELGlrasfzRlxhMFnj1R/v50Hkhfuvc0Jw8FjE16txNVL6WK6W46byFtX+UUmYWpxzgeB1mBme6ndR0Xw/6if8BbTYo2Kba2MO4QM5qg661KKsZQJ3d7Obqlf7Jb2vvLuhciXK5CacLDKWK3HimlKcJIeaOBDhCCDFV0VEolaolapUMTtMJ1mSoplZYvhr98rOw9Te4rNPHv78W5oWUk3dcdQNKKR7eG8WiYMuawFw8CrHUuD3jStTGMjhTpUsljG/9NYwOQZ2XNz3tfGPdFrzFDHZdBA0UNPTGwO4gUyjxQm9y0gBHF4twYA/qiusB2D1krr85U9bfCCHmkAQ4QggxVSNHAFDlDM6RVIEGl/WkM1HUBZeiH/ouejRMZ0OQdtI817yBd152JfmSwWP7Y1zS4ZOZN+LU1HmqZXSeSgYnX0JnMzDQi1pplor1xXO4bJZjmiToZ7bBYB+WW/5f2HgJ3/35IQKJPN+8cQN1dgs6EcP40w+jfuv3sFz/6zy0e4T7Xxkmni1S7zrqOdu732x3Xl5/s2s4jcumWNUg68mEEHNnYUwqE0KIRUCHzQCHYAtgZnBOVJ5WoS68zLz+9uchl+XSvhfZ6V9JwlrHs4cSJHIlblgbmK3NFkvduBK16hqcgoH+5SMYf/Xn6HgUgK8+3c/fvzA44ao6l0X/6N9h9Zmw8RJ+dTjJruEMN50boq6cDVI+P3jroTwDqrPenOvUG8+P3Y7WGM8/YTbOsDtIrTyLb/5qkJ/tiXJeq2fy2U9CCDFL5HChEEJMVXgIlILGJsBcg7MuePLSG9XaAe0r0K88A3Y7mwe3818dV/FCX5LH98dY5rNzXqt7trdeLFVuDxzpB8YyOMl8CSIj5pqag91w7ibC6SLhdBGtdXXBv972I4iNYvnkn2NoeGD7MG0+B9cfXS7Z1lkdctvpNwOcvlies5vd6ME+jO/+Pbz1OnrlOp7e+gfc/+Qo8VyJd53RwIc2LKx1S0KIpU8yOEIIMVUjQ+BvRNntlAzNcGpqGRwwy9TYuwv9yH+zMuCgxWPnod2j7BrO8PY1ASzSYUqcIlXngVQSAKdVYVXlNTiJKAC6p5uSoUnkSiRypWpzDJ2Iox9+EDZeglqznsf3x+iL5/nIxiZsR2VcVGsn9PeitSbkseG0KnpjOXQ+h/FXfw69+1G/cwsvfPBL/N+3SjR57Hz1hi5+f1MLbrsMqxVCzC0JcIQQYor0yFC1g9popkhJn7hF9HjqwsvNgZFD/ViuuoHLlvvoi+exWxTXrZq8G5UQUzKuyYBSCo/Daq7BScQA0Ae7iefGuqp1j2bN03/6H5DLYXnvR8gVDf7ttTBnhOrY3Ok99j7aOiGdhHgUi1J0+J30xnLQ1wPpJJaP/hGWq27g9aEMbruF/7N1hcxxEkLMGwlwhBBiqsJHUMGpt4ieoG05tLSD04W65GouXe4D4PLlvmMXagsxHW4P5LJmBzPMdTipvAFxM8Chp5tYtli9ePdIFh0ZQf/iZ6grtqCWdfLw3iijmSIfO79p0nk1almn+Uu1TM1BbzyPPthtnt5lNjLoHs2yutEla26EEPNKPlWFEGIKqjNwxjUYgGlkcJTC8qFPQjqFqnOzzqX56PlNXF4OdIQ4ZXXljEsmDb56PHarOQcnEQOrFWKjREciAFiVGYToxG4oFVFbbgTg+d4EqxtdrG8+zlqwcoCjB3pRZ55HZ72TXxyIk47sp64+AA1BCiXNgUiOd5/RMNuPWAghTkgyOEIIMRXRETCMY4Z8NnmmfpxInbWh2lFNKcV71wdp8TpmfltFbXF7zJ8Zcx2Ox2ExmwwkY7D6LACifQMAnNVUx76RLHrXDggEYVkn6UKJt8IZzl/mOf59BBqhzg39YxkcgN4jUVixBqUUh2I5ioZmTVBK04QQ80sCHCGEmIrwEMCEErWGOhsOq7yNivmlKgHOuFk4qVwRSiXU+o2gLMSGRwC4sM1LqmAwsP8Q6uyNKKV440iakoaNy47fyU8pBcsm6aSWBtW1BjBL3wDWyNobIcQ8k09mIYSYAl0e8lnN4CQLUy5PE2JW1R0V4NgtpCpNBUIt0NZJNJrAqmBDOUuzz94IZ20EYMdACqdVcWboxC3P1bKO6hqcFq8dm9L0uZtQK9YC0D2aweuw0DLVdWlCCDFLJMARQoipqMzAaRibgdMiAY5YCKolauMyOEUDMId0qq41xNJ56l1WVgSc2DHo9nWgztoAwI7BNOe0uLGfLBu5bDnEo+hUAqtF0W7N0+tpgRWrATODs6bRNWmTAiGEmEsS4AghxFScxgwcIWZVOcDR1RI1C3lDUVBWqPfDirXEcOC3gc2i6MqPsC+0FlUfYDhV4HA8z8YTrb8pU21HdVLLjtDnbUUFGsmXDA5Gc6yZwuBbIYSYbRLgCCHEFJzODBwhZtXRa3DKgzVTNhf4AmYGx+HBb2TR2Qxrwt3sq2vB0JodA+Z1NraePMChtQMAXW400B45xJAjQK5o0BPJUdKy/kYIsTBIgCOEEFNxOjNwhJhNzjpQlnFNBsyP9rStDrz10NFF3O7Dn43BnjdYHT9EFiv98Tw7BlM01tmqXdFOKNgMDgcM9KIzaTqH9qKV4nA8Xx0eKh3UhBALgczBEUKIkzjdGThCzCallJnFKbeJ9jrKGZz6IMpqBauVmNOLP7EfvWuY1RmzYcaekSyvDqa5qN0zpXUzymKBVrOTmjq0j46U2VmwN5ajeySL32kl5JavFUKI+ScZHCGEOJkZmIEjxKxyeyZ0UQNIeYMA5IoGWYsD/8hh9M7tdLQ347Qqft4dJZErsWEq5WlllU5quqebZZkwFgW9MTODsyYoDQaEEAuDBDhCCHEyMgNHLHR1nnFNBsoZHE8DALGs2TLanxqBwT5sZ29gVaOLXcMZYIrrbyqWdcJoGP3W69gbgyzzOegezdIby7Fa1t8IIRYI+XQWQoiTkBk4YsFze8a1iS6vwXH7AYjligD4C+b5av3G6lqZlQ1OAnVTz0SqZeVOajtfgRVr6Kh38OpgCkPL+hshxMIhAY4QQpyMzMARC934ErVKBsflA8YyOPWlLNQHoL2r2u1sWtkbgEqraMNAda2l0+/E0OZJ0kFNCLFQSAG5EEKczCQzcK5YUT/fWyVElRpXoubQJaxGiZTLDF6iWTODE1i3FtXgRSnFuS1uQm4bl6/wTe+OmpaB1QalIqprDZ0us/taY52NoFuCfiHEwlATAY4uFmDfm1Ayj2KNFqA3My55ZbWadcVW86hXo9vGcr9zPjZVCLEA6ELefM8wzGnwQwNh+ts3YBlIkcyXZAaOWHjGlaipVAJPMUPKbg7drGRwGn73Fiw287Mv6LZz32+smfbdKKsVWtqg/xAsX01nzvwaIeVpQoiFZEoBzqc//WlcLhcWiwWr1cqdd9454fwf/ehHPPXUUwAYhkFfXx/33XcfXq/3pNedbfqt1zG++/cw2EdBWflR55V8f8V15K1H9fzf01/91W5R/Ov71+K0SQWfELVIb/sR+gcPVP9/20W30udpgcd7q6d1TGVuiBBzxe2BXBZdLEIihqeYJWWtlKgVcVoVrhn6TFNda9FKoTxeOpwGLpuF9U11M3LbQggxE6acwfnyl79Mff3kJRk33ngjN954IwAvvfQSP/3pT/F6vVO67vFkM2Z3F6w2lMU6resC6HgU/f370c8/AaEWdn7o83xrJEBfVrG5QfOuFgOrArRGf+8f4JJrsFy5lR2DKf7j9RHC6SLt9fIFRoiaFD4CHh+WT38BrWHoJcWVHW7ecWYIAJfNwsoGyfKKBaSu/JmbSUMiiruYIaXMNtGxbAm/a+YKNtRvfwJVMFulO20W7n33yhm9fSGEOF0z/o70zDPPcPnll5/27XzgBwcnPf0Tl+Z416oTHynSxQLG7X8K8Sjqnb/F9gvexVeePkKzx86Xrm5hU7t3wuVL/5lExQ9gaXZjaMoBTkECHCFqlI5FoCGIWrueVL5E/sW9rG7ysL7ZPd+bJsTk3OVmAZkkOh4zS9QwDw5GcyX8rukfKDwe5aoD19jnsKy9EUIsNFMOcO644w4Arr/+erZs2TLpZXK5HDt27OD3fu/3pn3dbdu2sW3bNgDuvPNOfs8/SmlkmOLBfXh+8yNYPF5++MYgL/fF+OjFnSfc1vwbrxCJhPH/2e24Lr+Wl3++B5/Tyr9/dBMu+7Fv8qMtbZCI0RgKsc6eBQ6RVS5CodAJ70dMjc1mk7/lIlaL+28klcDS1EJDKERqNA3A8qaGRfN3qMV9ttRMdx/mWpcRBQIOO3mjiKeYJabshEIhUsVemuvr5Dkxx+R1uPjIPls6phTg3HbbbTQ2NhKLxbj99ttpa2tj/fr1x1zu5Zdf5owzzphQnjbV627ZsmVC8HPjr12G3rsL4+n7sBhXoro2se+Ikxf6kgwPD59wWrLx3JNgsZBYvobE8DDP94xyboubZCxCcrLL+/zoA3sIh8OokkYBB45ECLfM3BGvWhYKhQiHw/O9GeIU1eL+K40Mo5qWEQ6H2TdoLty2FTOL5u9Qi/tsqZnuPtQFs5FAtP8wevAwnpIinisSDocZSeZYUW+T58Qck9fh4iP7bPFpa2ub9PQprThsbGwEwO/3c9FFF9Hd3T3p5Z555hmuuOKKU7rupNpXAKB7DwDQFXARyxYZzRRPeDW9czusOhNV56YvnmckXTxxr//GJoiE0YaB3aoI1NkYThemvp1CiCVDGwbEoxAwp8BHKx2opjEMUYg5Vy1RS0EijseqSeUNtNbEc0X8TjlgJ4SoHScNcLLZLJnygv9sNstrr73G8uXLj7lcOp1m165dbNq0adrXPR7l9kCwGfp6AOgqL+rtieSOex2djMOhfaj1GwHYMWAefd247AS1841NUO48A9DkthFOSYAjRE1KJaFUhHozwImUD6g0yCJqsZCVAxydTqETMTxWyJc0sWyJooE0ARBC1JSTvuPFYjG++tWvAlAqlbjiiivYuHEjjz76KABbt24F4Fe/+hUbNmzA5XKd9LrT0tGFLgc4KwLlACea48KjGgVU6N2vgdYTApxlPjst3uM3DFCNTWiA0WHwN9DksdMTPX4QJYRYwuIR86ffzD5HMkXsFoXHIW3jxQJWyeCkUxCP4mk0KyD6E3mAGW0yIIQQC91JA5yWlhbuuuuuY06vBDYVV199NVdfffWUrjsdqqML/fpL6EIer8NBi8954uBj9w6o80DXWgolzRtDaa5Z6T/xnTSWF5SNDsPKdYTcNl48nERrfcK1PkKIJSg2CoDyBwAzwGmos8p7gVjYnHWgLGaAk4jhWWZ+vI8FOJLBEULUjgV/SFJ1rjSnifebA/bWhDz0RLKTXlZrjd61A848F2W1siecIVvUbFx2gvU3YJaoAXrUXFjW5LGTL2kSudKMPQ4hxOKgY1Hzl0oGJ1uU9TdiwVNKmVmcTNIMcFxmxcPheDnAkTU4QogasuADHNq7AKplamtCHvrieQol49jLHumHkaFqedr2gRQWBee2nGR2hdtjHv0aHQYg5DF7+g+nT9zMQAixBJUzOIzL4ATk6LdYDNwedGQUclk8HrNcXErUhBC1aOEHOM2t4HBUGw2sDpnDOHtj+WMuqnfvAECtPx+AHYMp1gbr8DhO/MaulILGELoc4DSVh5ZJowEhalAsCk4XymUeGIlkSzRKBkcsBnUeGOoHwOM1B3H2lzM49U55DgshaseCD3CUxQrtXeg+s1X0miaz3GyydTh653YItaCal5HIlegeyXL+ibqnjdcYgpFKBsf8IJBW0ULUoNgo1AcAKJRLVQMS4IjFwO2BoQEAvPU+AAYSBbwOC3arrCETQtSOBR/ggNlogL4DaK3p8NfhsKpj1uHoYhHeer1anvbakRQaTjz/Zvx9lGfhgFmrbLcohlNSoiZErdHxaHX9TTRrvgdIBkcsCm4PFM0Dc56AGeAUDC0NBoQQNWdRBDi0d0EyAbEIVotiuX+STmo9eyCbqZanvTqQxm23sDZUN7X7aGyCeBRdyKOUosljY1hK1ISoPbHRCetvAAKyfkEsAqpu7ICe0++nkrSRBgNCiFqzKAIc1dFl/jJu4OfRAY7eucNskXnmeYDZYODcFjc2yxTT8uVOapUsTshjJyxNBoSoPbEoalwHNUC6qInFwT0W4Kj6QHX9qWRwhBC1ZlEEOJQDnMo6nK6Ak1i2VD26CqD37YbOlSiPl5F0gaFU4eTd08ZRlVk4lXU4brs0GRCixuh8DjKp6hqcynuMBDhiUagEOE4XyumqDqeVDKQQ4v9v787jo7qv+/+/PrNJGu3SaEECJEBswoDwxuIFU2PsOE7jZnHiJI5TO0mTOG3a1G7SfJPYqZd6/blNixM3XpI4rhMnTpO4rh2HeAe8YrDZDGIVSEKjfRmNpJn5/P64owGMMAIkjSTez8eDByDNzP1cXYHumXM+55xqxkSAY9IznCYAh2Rw4GCjAWst7K7GlE8HoLrZ2Z9TkZ86+IP0z8Jp6Z+F46G5O0Lf3p3YpobEcd7a30k0Zk/6nERkFGprcX7Pie/B6XZmYalNtIwJ/gzn90xnuHVGIoOjAEdETi1jIsAB4p3UdgNQnuMELolGA8E6513X8goAqpvCuAxMzT2OACc3nsE5pFW0BRp//O/EHn8QgA31If7lhX28ub/zpE9HREaheIBjsnIBaO6OkJXiHnypq0gy9Wdw4gFOutf5Ea8W0SJyqhkzAY6ZNAXq92H7+shMcZPv9xzM4Oza7jym7GCAMyk7hRTP4E/PeL1OWUrzwT04AMFeoG4f4OzrAahpP3IGj4iMA+3xDE62E+C0hiMqT5Mxw6QdnsHp34OjEjUROdWMmQCHieUQjRJJZHFS2N0SbzSwpxq8PiiZjLWW6uYwFXnHkb3pl1eAberP4Dg3NY0pOdBQh41FWR8PcGoV4IiMS7bt8ACnuTtCrm4OZayIZ3BMIsBxfsSryYCInGrGTIDT30ktsqcacAKcfe099EUtdk+102DA4yHYFaG9J8r049l/0++QWTj9GZzG1ByIRmipPZDIGNV2KMARGZfaWpxujJlZALR2K4MjY0h/iVpWf4ma9uCIyKlpzAQ4FJaA10dk5zYAynNTicRgb2s37Nl5sDytuRs4zgYDcSavAJqDWGtJsREy+7poDJQBsGGX02hgWl6qMjgi41VbC2RlY1xurLW0hKMKcGTsSDQZyAEObTKg72ERObWMmQDHuN0wvZKeN9dgrWVOoTPA88336qCn+7AGAx6Xk+E5bnkB6AlDqBO2byYQbqWxaAoA6xt6yUpxc+7kTNp6onT2RIfs3ERkdLBtLYnytM7eGJGYVYAjY0dOHuazX8UsXArAsqlZfH1hMVka9Ckip5gxE+AAmNOXEK2rgf17yPd7mRVIY+3+kPO5soMtostyUvC6j//UTP+wz6YgdvPbBHrbaPRmYdP8bAinML/YT2mWD1CZmsi41NYC8Q5q/TNw1CJaxgpjDK4LPoSJz3HK93u5qCInqWsSEUmGsRXgLFgIxmDXrQFgyeRMdvV6qc8shgmlhzQYSDuxA/QHOC2N2M3rCaS4aOyOsrd0Di2kUDUhnRIFOCLjV3sLJp7BaQk7AU6eMjgiIiJjytgKcLJy8c6ej33LCXAWTXLqjddOOx/jclPf2UdXb+yE9t8AkO/MwrG7t0PNLgoC2XT1xXi1aD4A84vTKc7w4jIKcETGGxuLQntrokQtkcFJU3mPiIjIWDKmAhyA1CXLoHYvtm4fhWlupnXu49WcmYCz/wY4sRbRABnZ4PFg1zwHQMHkEgCe85VT2tVAwGfxul0UpnvVaEBkvOnsgFjsiABHGRwREZGxZcwFOCmLLgBwytTqaljc8A7bySTY1Ud1cxivyzAp+wQaDADG5YLcADQHIT2TgsmlAAStj6qWbdBQD0BJpk8ZHJHxJj4DxxwS4PjchrTjGBgsIiIiyTfmfnK78wtg6kzsujXYPdUsCm4EYG1NB9XNYcpzU/C6zYkfIL4Px8yaR0HmwUBpfst2aKgFoCTLx/72Pqy1J34cERld3jfksyUcJS/NgzEn8f+JiIiIjLgxF+CA002NvTuxb75Cie2iLNvH6j0d7GgKn3h5Wv9r9zcamLOA3FQPLgMeA3Nad2AP7AecDE44EqMlrFbRIuOFTQQ4eYCTwVGLaBERkbFnTP70Nqcvxv7mYdi4DmbOZUlZFo+90wic2IDPw+QXOseorMLlMgT8XgrSPaSl++HAwQwOQG17r+rzRU6AffMVYj+529nzAtwx5ypeK5h71MdPzU3h//tQ+fBmU9rjAc4hbaJPtNxVREREkmdM3p2bgmKYPA327sCUVbBkUubBAOdkMzhLL8FMLMPEA52/W1xMps8Nb5ccksHxAk4ntdOK/Cd1PJFTkd2xFdwezKUfA2Bz52xmuLqocrcDFqq3wNSZmMr57GvrZfXeDmrae5k8nAFHWwuk+TEpzjFawhHmF+vft4iIyFgzJgMciGdx9u6A8gomZfsozfIR7Oo76XdcTU4enHFO4u9zi9IBiBWVYt95A4CA34vXZdRJTeQE2eYgBIpwffQzhCMxOn61jcvnTeATc/IBiN71J9i+Hfenl9PQ2cfqvR2sr+s6ZoATW/1naG3C9eEriMQsP3q9nhUVOcwMDGI21iFDPnujMbp6Y+QoQysiIjLmjMk9OADm3Iswi5dh5izAGMNV8wu4cm4At2uYSliKSqC9Fdsdwu0yTMj0qpOayIlqCkKeM3eqsasPgAL/wWDCVC5w9tl1tFGY4aUk08f6uq4PfEnb2oz97x9j/+/X2EiEZ6tbWbWjjT9Vtw5qSbatWS2iRURExoGxG+Bk5+K65h8wfmfY5+LJmXws/u7vsByvyGkZfXgnNQU4IiekOZho6BEMOcFEIN2b+LSprALAbtkAQNUEPxsPhOiLxo76kvZ/fwm9PdDbQ/funfzyXadsdUuwe3BramtNtIhujTcQyUlVgCMiIjLWjNkAZ8QVOkM/bf3BTmr1nb1EY2oVLXI8bF8vdLQNkME5GOBQNg38GbD5bQCqJqTTE7VsbRw4WLH1+7AvPwunLwbgdxsbaAtHWTQpg33tvbSHI8deWFtLIoPTrAyOiIjImKUAZ7AKi8GYg53UMn1EYhCM35yJyCC1OJkVEhmcPlwG8g4tUXO5YfY87OYNWGuZW+THZWB9XWjAl4z9zyPgTcH12a/SWjSF33fmsGRyJh+d5bR83nKUwKifDXdDT3diD05rPMDRHhwREZGxRwHOIBmvz7khe3+raO3DETk+TUHg4MypYJczb8bzvv1zprLKCYbq9+H3upkVSBtwH47dsRXWrcVc/FeYrBwen34pvbj43LwAFfmpeFyGrccqU4v/u+7PKu1v78XjMmSnuE/uXEVERGTEKcA5HkWliVbRpZlOgKN9OCLHxzb3Z3AOlqgFDi1PizOzq5zHb14PwPwJ6exoDtMW6sWGOhO/un/7CF25RXQt/Qi7WsL8yTOZi2pfpaSnGZ/bRUVeKpsbjpHBeXstGBemsgprLa/t66Cq2D98TUtERERk2Kj+4jiYohLsq89jrSUruBd/rJf9zz9PbObHWLWjjV9sCPLxynw+Ojsv2UsVGb1anAwOuU6AEwz1MW2A+VWmoBgKJzgBzoUfoao4ncfeaeSdf/93lux8GYAXixawcuaVREo88GQNAKkuw6d2r8JWl2EKJzC7II0n32uhNxrD5x74PR27bi3MmIPJzGZ7UzcNXRE+NTcw9OcuIiIiw04BzvEoKoHuEPaRldjVq5iw4OtsduXyz3/cxdYmJ5OzORhSgCPyQZobISsH4/VhraWxK8KiiUdmcMApU7Nrn8dG+qjIcuGP9bA+ZQLnfOILbIulc1/zJCpSIyyZHcC4nOBldiCVnFfjw0KXXMjswjT+Z0sz1U1hKguPHNxpa/dCXQ1m2aUArN3bgdvAwomZw/c1EBERkWGjAOc4mKISLGBffhZz/sWUpOfwcncGWe29fGPxBF7Z086BTjUdEPkgtimYaDDQ1hOlL2YpSD9KgDO7CvvC07DjPVyrVzG3vZQNExfQev5M7nh6N3kZhv93yUyy3rdXJjptNrZ6CwCz40M+Nwe7Bw5w1q0BYzALFmGtZU1NB3OL/GRq/42IiMiYpD04x2PGaZiLPorr23fiuuo6LpuezSd2r2LllFb+Ymo2EzJ91HX0Ya1aR4scVUtjYv9NfxfCgP8o77XMmgsuF7Ff3Idd+xxVE7MJ9rn53qq9hPqifOf80iOCGwAzbRbU1WA728lK9TAxy8fW4MAd2Oxba2HaLExOPntae6jr6GPxZGVvRERExioFOMfB+FJwXXGtc/MEzJw6gc/sfpaM5joAijO8hCMx2nqiyVymyKhlrT1syGdjl9OO+agZHH8GTJkB9ftgwSKqLjofgH3xrGl57pF7dwDM9ErnDzu2AjCrII0twW5i73vzwTbUwr5dmNOXALB6bwcuA4tUniYiIjJmKcA5CSY9wxlG2FgPQHGG01lNZWoiRxHqhJ5wokStMRTP4BwlwAEw562A2fNxXfP3lGSnclZpBldXFbBkctbRj1M+HdyeRJlaZUEanb0x9r2v66F9a61zjPiA0LU1HVQWpGn+jYiIyBimn+Inq6AY2xAPcDKdm7S6jl5mxuv+ReQQR8zA6SPFbcj0Hf29Ftc5y+Gc5Ym/f/eCicc8jPGlwOSp2OrNAMwucPbebGnoZnJ2SuJxdt0aKJ+OyS+kpq2HmrZeLjmz8PjPS0REREYNZXBOkikohqBTolYYfxe6XhkckYG1HD4DJxiKEEj3YszQz5sx0yth93ZsXy8TMr1kp7rZcsg+HNvUALu3J8rT1u7tAGDxJJWniYiIjGUKcE5WQTE0B7HRKCkeF/lpHg50avinyEBsc3wGziEZnIKjNRg4SWbabIhE4N23oPEAs7NcbKrrpH7PfufXyy9zIDWXhsqFHOjsZfXeDmYG0sgfYOioiIiIjB0qUTtZBcUQjUJzEAqKKc70Ut+hDI7IgJqC4PFAZjYAjaEIZSXpw3OsitlgDLEf/SsAsyeey6sVf8nfvNIRf8AcWDQHXukEOgG45nSVp4mIiIx1CnBOkikoxgIE66GgmKIMH2/XdSV7WSKjU3MQcp2hnH3RGC3dkaN2UDtZJisH1z/8CzZeFrciZsgJNROxB8vhTOEETHYuAG4Di1SeJiIiMuYpwDlZBRMAsI31GGBChpfnuiP0RGKkeFQBKHIo29KYKE9rCjktoo86A2cImNnz6Q9n/MAFw3YkERERGS10B36ycvOckptEJzW1ihY5quYgJtFgwPk3MlwZHBERETk1KcA5ScblhvwibNAJcIoy4q2iR0GjAVu9hdgfHsOGnJK5J7c289LudmfYosgIs9EotDQf0mAgPuRTm/pFRERkCKlEbSgUFCeGfU6IBzijIYNjX3wG++rz2JeewX7iGh7cUYQF/rTDz1fOKqY0y5fsJcqppLUZbCzRIrp/yGf+MJaoiYiIyKlnUHcW1113HampqbhcLtxuN7fffvthn9+0aRN33nknhYVOB6KFCxfyiU98AoD169fz8MMPE4vFuPDCC7n88suH9gxGAVNQjN2xBWstmSlu/F4X9R2jIIPT3QU5+ZCdS+inK7Hn/Qun5XnZ0RTm757axSfn5POpufnDMoNE5Agthw/5bOyKkJ3i1l41ERERGVKDfuv0xhtvJCsr66ifnz17Nt/+9rcP+1gsFuPBBx/ku9/9Lvn5+fzzP/8zZ555JhMnHnsS+ZhSWAzdIejswGRmUZzhHR3DPru7oLAY1z/eQujPq6ABlta+wfWf+QQ/efMAj73bSEV+KmeWZiR7pXIKsE1HzsAJaP+NiIiIDLFhfeu0urqa4uJiioqK8Hg8LFmyhDfeeGM4D5kUJlDs/KGxfx+Ob3QEOKEQpKVjXG7CZy4FwL9zEzl9HXzznBJyU908s70lyYuUU0az066ZQ5oMFKSrPE1ERESG1qADnFtvvZVvfetbrFq1asDPb9u2jRtuuIHbbruNmpoaAJqbm8nPz088Jj8/n+bm5pNc8ijU3yq6oQ6ACZleDnT2EY0leTN/dxcmzRmi2NUbA8Df14V9ZRUel2H5tBze3N9Fw2gIxmT8aw6CPx2T6sdaS7ArQkANBkRERGSIDert05tvvpm8vDza2tq45ZZbKCkpobKyMvH5KVOmcN9995Gamsq6deu46667+OEPfzhgt66j7fdYtWpVIni6/fbbCQQCAy/Y4znq55LFZmbQAPhD7WQEAlQUR4hsbsamZhLISk3auhrC3aTm55MVCOBubwIgu6wMs3oV+Z/9Mp8+O4PfbGrilbpevrx4wrCtYzReMxm8obp+LV3txAonkB8I0NETIRyJUV6Yre+NYaB/c2OfruHYp2s49uiajR+DCnDy8vIAyM7O5qyzzqK6uvqwAMfv9yf+fPrpp/Pggw/S3t5Ofn4+TU1Nic81NTWRm5s74DGWL1/O8uXLE39vbGwc8HGBQOCon0uq7DxCe3YSbmwknR4AttQcwFOUnpTl2FgMG+oijIvexkbqm9oASF1wFrH1f6bxpVV45p7BmaXp/OGdWv5ymh+Pa3iaDQz1NbP1+4j9+A5cV1yLqaziqfdaeGFXG7csn6wN68NgqK5ftG4/5DmvtbslDIDf9o7Of89j3Kj9f1IGTddw7NM1HHt0zcaekpKSAT9+zLvBcDhMd3d34s/vvPMOkydPPuwxra2tiWxNdXU1sViMzMxMpk2bRl1dHQ0NDUQiEdasWcOZZ555sucyOhUUJ2bhTMh0ym7qO5JY+tUTdlry+g8vUUufdzpk5RB78WkALpmeS0s4yuv7OpK21ONhQ13EVt4K+/cQ+79fs662kwfeOsC2pjAv72lP9vLkgzQHEx3U+mfgqMmAiIiIDLVjZnDa2tq4++67AYhGo5x77rlUVVXx7LPPArBixQpeffVVnn32WdxuNz6fj7//+7/HGIPb7eaaa67h1ltvJRaLsWzZMiZNmjS8Z5QkpqAYu2UDAAG/F7chuY0Gup3hnvTvwemLApCeloI59yLs009gm4MsmBCgwO/h6e2tLJl89C55o4GNRYk9cA8E6+H0xdRu2c5dL+9jcnYKfTHLM9tbWT4tJ9nLlAHYcDeEOo+YgRPQDBwREREZYse8uygqKuKuu+464uMrVqxI/PmSSy7hkksuGfD5p59+OqeffvpJLHGMKCiGtc9h+3pxe30UpHup70ziLJyQE+CYNKd8sKs3hs9t8LoN9rwV2Kd/g335T7g/+hlWTM/h0Q2N7G/vHdXDP+3vHoV338R89it0z13Iv/52E+5IH99ZOpXX93XywFsN7GgOMy0vefue5Cha4qWquf0BTgS3gZxUBTgiIiIytLRhYagU9LeKPgBAcaYvuSVq3SHn93iJWqgvSrrXudwmUASnnYF9+VlsJMLyaTm4DfxxFLeMtm++gn36N5jzL4bzL+Hed7upSwtww5ZHKfRZlk3Jxuc2/HF7a7KXKgNpcWqaTe7BDE5emgf3MO37EhERkVOXApwhYvoDnIb4PpyM0ZHB4ZA20X6fO/Fp17JLoa0Zu+bP5KV5WDw5kz9Wt9EajiRjtccUe+rXMHEK5sovs7etlzf2d/LZiZbTDmzGvvkKGSluzivL4sXdbYTi5XgyethEBsdpG98UipCvFtEiIiIyDFQfMlTiAY7dsh58PopCbjp7Pbzx6rv4XPF22bkFmExnn4vLGGYGUvG6hyfGtEfswYklMjgAnHYGTJuF/cNj2IVLuXJegDV7O3j83Ua+fFYxtm4ftDo3pVELW7sM0UO7fgeKMP4MADwuw8xA2rC9G29DXbB/N+ayT2M8XjYHOwE494wK+HMp9sVnYMmFfGhGDn/e2caLu9r50IyBu/WNNbYpCOkZmNQ0rLXsa+9lYpbvqO3WR63WIwOcKbkpSVyQiIiIjFcKcIZKZjZkZmP//CT2z08yMW8GzPsit+w49F3qtvgvx7VnFPKXs/KGZz39AY6/fw9OlPRDMjjGGFyf+Gtid3wLu+oPTPzwFVw0LYdntrdymd1L0U9uAcAC91R+ljWF8w9//e0twMGStim5KXz17GJmBtKG/lx2vgfWYqY7rcm3BLvJTfNQlOHDLr0E+6sHsXt3UjFpCtPyUnh6eyuXTM8Ze0HA+9hwN7Gbvg4paZhPfZGX8udy79o6qiak8zdnFlEyivdLHaGlETIyMV4f1lqaQn2cWZqcFuoiIiIyvinAGSLGGFz/7x5oagDgdAt3dUXojWc97LpXsTu34fr7GzEYbn1xH7Xtw1jC9r4StVBfjML3teQ1FbOhahH2mSew51/Mp+cFeGFnK79Yu4d/LKvA9clr+HWdYc1+N5+cEKMq22k1TTRK7I1XYNc2KJxA49LL+cX+KN/64x5WVORwVVUBmSluhoqt3gwuF0yZAcCWhhCVBWlOALP4L7C/fQT74jO4rvoal0zPZeVr9Wxt7GZ2gf8Yrzy62XffgnA3ZGRh/+tOXlr8DTLTS9nW2M3fPbWLj83K4fKZOUefX+Q2GNfB6+BxmaQFfbalCXKc/TddvTF6opaAStRERERkGCjAGUImvxDyCwFwAzMO+VysqRr7+h9wZVhMejqFGd5Eq9xh0R0Cjxfjdd7lD/VGSfcdWQ7n+thVxG58HfvU4+R+5NN85MCr/KZgEZd/5FxafRn89/79LC3P4rNLJhx2c2zPmgfr1hD75U/gwW+x6B9u47FQgP99r4V3DnRx30em4hqim2lbvQUmTcWkphHs6iMYivDRAidTZNIzMWeei33tReynv8h5ZVk89FYDq3a0jfkAh7dWQ2Y2rpvvo/P5P7GhvogP7XqJj9a8yE+nfYRfxar41ebBN4b40PQcvnJ28TAu+AO0NCbK0/q/7/PVIlpERESGge4wRojJC2ABmp09FQG/JzHscFh0d0HawRv8rr4Yfu+RWRUzYRLm3OXYF57G7t7O5ftqeHbCIn78Xpja9g6m5qVy3cLiI975N8bAGefgml1F7IarSd2whms//SUmZ6fwn6/Vs7O5h4r8k2/XbCMR2PUe5ryLAac8DTgseDELFmHXPgd7qkmrqKRqgp+NB0Infexksr092I1vYRZegPF4eaviHCINdSyZO5n8eZfzj8DFfTt5L3KUIC7Uid29HXPexZhAEWtqOnirtnNEz+Ewrc2YKTMBaO52vu8V4IiIiMhw0B3GSIlPcKe5ESZNIeD3Jm7Wh0V3COJNAPqiMXqjdsAMDoD5yyuxr70AO7aSfvXf8qmCQn7yZgM5qW6+s7SUFM/RGyEYfzpMn4Pd9DYAZ5U6x1xf1zUkAQ41u6C31ymnA7YGQ6R6zOEb1KfNApxMj6moZHaBn7U1nTR3R8hLG5pvcdvXh/3ZD7H1+wHY4QvwcOF59BaWgnG+PueXZ/HR2UO0p2rT29ATxpyxGIC1NR3kpXmYdenFiczYvPivAdfb1kLs+v/C9E3FdVolbhf89O0gbeEI2SM8e8b29UJHG+Q6X5vGUDzASVOJmoiIiAw9tYkeKfEAxzYHAShI99LZGxu2lsY21JnI4HT1OXtn0gfI4ACYnHzM5/8W8+kv4Tr3Ii6uyOWjs3L57gUTB7VPwlRWQf0+bHMjOWkeynNSWF/fNTTnUb3Z+UM8wNkc7GbG+zq2mawcKCp1StmA2fHytS3Bocvi2Bf/D/vai+BPJ5aVw48LzmefTSO7r4vsVDedvVEe39hIJGaP/WKDOd66NZCeCTPm0t0XY11tF4snZQy67M9k5zpDNXdXAySCzR3N4SFZ33FpbXZ+j8/AaQr1YYDcIQo+RURERA6lAGekZOWA2+OUqOEEOHDw3ewh1x06OOSzNx7gHCWDA+BauBTXhR8BwOs2XHNGEdPzB9cRzVQuAOItsoGqCelsCXYTjsROdPUJtnqz05I6J59QX5Q9rT2JAOawNVTMgh1bsLEYU/NS8bkNWxqGJkNmQ13Ypx6H2fNxf/Nm1v7l37PDF+DqfX/mu8Fn+f6ySVxzeiGdvTHeHYLSONvXh93wOqbqbIzHw7raTnqjlsWTM4/vhcoqsHucAGdanhPgVDclIcBJDPns34MTISfVjdc9trvciYiIyOikAGeEGJfL2WQdD3AC8f0HjV3D1Ggg1HVIBsfJEvm9w3S5S8sgO9cpq8IJcCIxy6aTvNm31kL1lkR52nuNYWIWKgdqHlBRCZ0dcGA/HpdhRiBtyEoA7R9/C50duD7+Bfqilkc3BCnPSWHppFTsO29g+/qompBOqsewdm/HyR9w6wboDmFOXwLAmpoOslPcA5/3BzDlFXBgPzbUhd/rpjTLR3USMjgHh3w6GZxmDfkUERGRYaQAZyTlFWCbnXezRyKD0z+IsyuRwRm61s2HMsZgZldht2zAxmJUFqThdZmTL1ML1kF7qxO8AJsbQrgMzAgcubenPwhKlKkF0tjZEqa77+SySLalCbvq95izl2LKpvHM9hbqO/u4ekEBnjPOcTJlWzeQ4nFxZmkGr+7rIHqSZWr2rTVOcDq7it5ojDf3d7FoUuZxD1I1ZRXOH/buAKAiLzU5GZz+IZ85B4d8qsGAiIiIDBcFOCPI5BUkMjh5aR5cBoLDlcE5pIta/z6f9OHK4ABUVkFnO9TsIsXjorIwjfV1Jxfg9AcrJh7gbA12U56TMmA3OIpKISMLtjt7dioL04hZ2N50clkc++RjEI1hLv8sob4oj29sYl6RnwUT0mHWfEjzOwEJsGRSJm3h6Elljmwkgl3/GmbeWRivl7frughHYsdfngYQD3D6y9Qq8lNp6o4kupiNmJYmSE3DxL8fG7v7FOCIiIjIsFGAM5LyCqC1CRuN4nYZ8tI8wxLg2EgEensOlqjFMzgDBgZDxMye7xx783oAqorT2dvWS9PJzPqp3uLsI5owkUjM8l5j94D7byDetrpiNnaHExTNDKRhcJoSnChbuxf7yirMBR/CFBTz203NtPdEuXpBoZO18nox887Crn8NG4lwekkGPrdhzd72Ez4m2zZCV8fB8rS9HWT4XMwtOv6ZPiYzy5nL1N9oIL4PZ8cIZ3FsS2OiPC0cidHVG1OJmoiIiAwbBTgjKS8AsRi0OV2lAn4vweEoUeuO731Jc0rUQn3HbjJwskxOHpSWHdZoAGBD/Ynvw7HVW2DabIzLxa6WMD1R+4HDO01FJTTUYdtbSPe5mZyTcnLZlKd+DSkpmA9/ilBflD9sbebcsszD2l+bM86Brg7YtpE0r4vTS9JZW9NJzB5/mZrdsZXYrx6AlFSisxfwh63NrNnbwdkTM/EcZ3laQnkFdvd2AKbkpuIyUN08jO3JB9LSlBjy2RT/fg8ogyMiIiLDRAHOCDKHzsIBCtI9w9NkoDs+0DGewensjWKAtOEsUSPeLnr7JmxPD+W5KWSnuE+4TM12tkNdTWJvTWLAZ+HRO7v1P5Z4aVtlQRrvBbtPaE+M7e3BbngNc/b5mMws3tzfRU/UctmM3MMfOGcBpKQ6bZ2BxZMyae6OsK1x8FkS29VB7JGVxO74FoS6qL7q21z/fB0PvtXA3CI/V1UVHPf6+5my6dB4ANvZTprXxcQs38jvw2lpwiT23zjf70M1n0hERETk/XSXMZIOmYVjmE1Bujfxbv9g55sMSjyDY/rbRPfF8HtdQ3uMAZjKKuyffg/bN+E67XTmF6ezob7rqNmM2AtPY5/9HwD6jJu7J36YmpRA/LMWFv4ThPLh9zto74lSmO754Lk8ZdPA68Nu34I5fQmzCtJ4ensre1p7mJp3nENHE4M2+0vF2slN8zDzfSVyxpeCOe0M7Lq12M/8DWeVZuBxOY+fdZRyun7WWuza57G/eRi6Oui68K/4xaTlPFvdSW5alG+dV8LiSZlO+d0JMuUVWIA9O2DOAiryU1lX24W19qRed7BsNAptLYkMTmMig6MSNRERERkeyuCMpLz4zXuiVbSXSMzSFh7iYZ+heNYkzQlwunqjw9ci+lDTTwOPB7u5v120n9ZwlB2NR2ZxbHMQ+/iDzubzqTN5puIi3siqoNzTzQx3FzPcIWb4Y8wszmRmII2zSjO4qqrwAw9vPF6YMj2xD6e/rfKJlKnZdWvAnwEz5hKOxHjrgwZtnrEEOtqgegvpPjfzi9NZW9PhtLk+2uvX1RC7+/9hH/43bEExL37pbr7uPoc/7e7kI7NyWfmRKSyZnHXyQUjZNOd48TK1irw0WsNRmkaq0UBbC9jYYUM+ATUZEBERkWGju4wRZNL8TtARL1ELpMdn4YT6hnaqe3c8oDgkgzNcLaIPZVJSoKIS+86b2Ms+ldiH8+x7Qa6YmXHYzbr9w2NgY7i+9h1CWQF+8/sdzM9L5Z8vXHZya5g2G/vs/2B7eihI95Gf5mFLMMSHZ+Ye+8n9a4v0YTe8gVmwyBm0ubfdGbQ5aeBOZmbuGVivj9ivHsSUlrHYVcpb3rnc+cgLpDBAm+pY1AlyvfPg0o9R6w/w3nthZuSnctPZk44/2/QBjD8DCksO66QGzsDPEcmivG/IZ1MoQqbPRYpH762IiIjI8NBdxkjLC2DjGZyC+A3mUHdSs4kmA/2DPmMjk8EBzHkroKGO2Pe+Ru67azizJJ3/fms/3/9zDfvae5z17d+LXfMcZtmHMYEifru5mY7eGFcv+OAMzaCOP70SolHYvQ1jDLML09jc0P2B2ZQjbHkHursS5Wlr93aSneJmTuHADQ5Mqh+z7MPQ1YHdtpGzt73A1O56tkf9bIxmHPnLZrOxsJKNk09nk8mhszfGV88u4o6Ly4Y0uEmsr7wC4gFOeU6K02hgpPbhtDoNNRIzcLoj5Kk8TURERIaRMjgj7ZBZOP3DPoNdQ1wuNECJ2kh1rXKdfT62qITYL36EfeAevjW7ijWXXMd/bWzjG0/t5mOVefzVS4+QkpqGufSTNIX6+MPWZs4vz2LaUNzcT50FgN2+GTNzLrML0nhlTwfBrgiFGYO7sbZvrT5s0Obr+zs5v/yDB226PvnX8Mm/BiAbuPekT2QIlVXA6y9h21tIycqlLCeF6uaRCXBsPINzaImaOqiJiIjIcFIGZ4SZvECiRC3D5yLFbQiezKyYgfSXqKU5m9xDfTHSh3EGzvuZsgpc/3wn5jNfwb17O+f9x9f4T+/bnDPRz+Mbm/j7tAt4+8KrMRlZ/Pc7jcQsfG5+4NgvPJhjp2c47aq3bQQO7sPZ2DC4dtU2GnW6p811Bm2u7x+0eZTytLHAlDsDP9mzA4BpealUN4ePL6t1olqawOOFDOfr1xiKaP+NiIiIDCsFOCMtr8ApZeoJY4yhIN1L43BkcFLTMC4nqOnqjeIfxhk4AzEuN65ll+K6+T5Slywj+/8e5RtP38wPav4Hj4GbO8u5+fkantvZxodm5FCU4Ru6Y8+ugu2bsb1Ou+qsFDcbBtuuettG6OzAnLEYgLU1HaT7XMwtSh+y9Y24yVPBGOwhAz87eqI0DEeL8vdraYTcfIwx9EVjtIWjGvIpIiIiw0oBzkh73yycQLqXxiHP4IQSDQastSOewTmUyc4l+x9uwvXNm8HlZu6Otdw7I8Rn5wd450CIVI+LK+bkD+0xK6sg0gfVm3EZw/xiPxvquwaVsbDr1oAvBeacQV/U8tq+ThZOzMDrHv6WysPFpPqhqPSIRgM7RqBMzbY2JcrTmrs15FNERESGn+40RpjJK3DmkjQHYcJECvwedrcM7Y2mDXUl9t90R2LELCOewXk/M3s+rht/CLvewzd9DlcYw7Ip2fREYmSlDvG34Yw5TrvqTesxlQuompDOy3s62NPaQ3nu0ff52FgUu24tzD0Dk5LCu7WddPWO7fK0hJJJULsXgIlZKQDsb+8d/uO2NGHi+6Ka4jNwNORTREREhpMyOCMtPgunv5NaIN1LazhKX3SAdsInqrsr0UEt1Oe8brIyOIcyXi9mxmmJdtEF6V4mZqcM/XFSUmHabOzm9QDML3aCvfX1xyhT274F2lsxpzvd01bv7SDV40q0ux7LTFEJBOux0ShpXhd5aR5qO4Y3wLGxGLQ2aciniIiIjCgFOCMtJx+MSZSoFfj7Z+EM4T6c7q5DOqjFA5wkZ3BGmqmsgn27sO0tTiCV5WN93Qc3GrCvPAtpfsz8swn1RXllTwfnTM7E5x4HX7uiUqd9dlMDACVZPmrbh3kPTmc7RCIa8ikiIiIjahzcuY0txuOB7LwBWkUP4c1mdwgTD3BCvVGAEZuDM1qYOQsAsJs3AFA1IZ1NDSF6j5Ipsx3t2DdfwSxahklJ5cVd7YQjMT40I2ekljysTGGJ84cDtQCUZHqHPYNDa5Nz7NyDM3BSPa5T7ntRRERERpbuNJIhv+DgsM94gDPkGZx4k4Gu/hI1X/JL1EbUpKlOa+J4mVpVcTq9UcuWYPeAD7dr/gyRCGbpJVhreXp7K9PyUqgYhsGbSVHkBDj2wH4ASjJ9tPdE6eiJDt8xW5wAp79ErSneIrq/RFFERERkOCjASQKTG0hkcPrLdYYqg2OtddpEx/fgdMUzOOmn2LvmxuXCzJqP3bweay1zitJwG1g/QLtoG4thX3oGKioxpWW81xhmT2sPl0zPHT8345nZTtlifwYny2nLPZxZnINDPvsDnD6Vp4mIiMiwO7XuekeLvAJobsRai8/tIjvVPXStont7IBZLZHBCp2oGB6CyCtqaobYGv9fNrII0NgzUaGDrO9BQh1l6CQDPbG8hzePivLKskV3vMDLGQFFJIoNTmhkPcIazk1pLE7hckJUDOFlKtYgWERGR4aYAJxnyCpw5LR1tABT4vQSHathnKH4D/74mA6fivgdTGd+Hs+VtwClT29HcQ1v48K917MVnICMTc8YSOnqc5gIXTMkibZx9zUxRSSKDU5Thw2WGN4NDSxPk5GFcbqIxS0t3hPw0dVATERGR4TW+7uDGCJPvdJU62GjAM3RNBrr7A5x4iVpfFI8LfGN4UOWJMvkFUFyK3bQeINHueUP9wW5qtrUJ1r+KOWc5xuvjuZ1t9MUsl0zPScKKh1lRKTQHsb09eN2GwnTvsM7CsS2NTtdAoDUcIWbVQU1ERESGnwKcZMgtcH6Pt4oO+L00hiLO/pmTFc/gGP/BDE661z1+9pIcJzO7CrZtxPb1MS0vlQyf67AyNfvKKojFMOdfjLWWZ7a3MiuQ9oEDQceseKMBgvUAlGb5qBvODE5zIybP+V7vb6LR31RDREREZLjo7dRkiN/02cZ6DM5NXzgSo70nSnbqSV6S7nh2or9NdF8U/yk2A+dQZs4C7PNPEbvuE4BhbuVnWNU7j+eqW+KPmAtLb4dV7VjbjgWuOG1CElc8fExRCRbgwH4oLWNCpo9NDSGstUMeAFtroaURqhYCB5toaA+OiIiIDDfdbSRDRiYEirDbN8OKv2J6vpMt2NzQzeLJmSf10ra/RO2QJgPp3lOwwUC/OadjPvGFROD3mZiLiZHaQx5goHQyJsNpKJDmdXFe+fhpLnCYwv5W0bUYnFbR4YiluTtCvn+IMyud7dDXC3lOOWZ/E42AMjgiIiIyzBTgJIExBlNZhX39JWwkwoxAGmkeF2/XdZ10gHOwyYCzB6ezN3ZqZ3A8HszFH0v8fTLwueQtJ6lMmh+yc50MDk6JGjiNBoY8wInvL+svUQt2RUjzuE65duUiIiIy8nS3kSSmcgGEu2H3Njwuw9xi/8AtjI9XokQtA3BK1E7pDI4crqgE2z8LJ9EqeogaXByqyQlwSAQ4fRSka8iniIiIDD8FOMkyax4YF3bzesBpYVzf2Xfym767O8HtBp9z89rVGyP9FM7gyOFMUWmiVXQg3YPXZYalVbRtPjzAaQz1qcGAiIiIjAjd+SaJSc+A8opEgDN/glNStr7uJLM43SFI8yfeKQ/1RU/JGThyFEUl0NGGDXXiMoaSTN/wzMJpaXSC7Ayn5LKxK0JgqMvgRERERAagO98kMpVVsGsbNtRJaaaPAr/n5MvUQl2JDmqRmCUcsaT7VKImDtPfKvpAHQAlWV5qh2MWTlMQcgswxtATidHWEyWQri1/IiIiMvwU4CSRqayCWAy2vosxhvkT0nmnPkQ0duLzcGx36JAW0TEAbeyWgxKd1JxGAxMyfdR39p7U99xAbHPwkA5q8Rk4yuCIiIjICNCdbzJNnQkpadgt6wFYMCGdrr4Y1c3hE3/NUGeiRXRXbxRAGRw5qGACGJPYh1Oa5SMSg4auIW40cMiQz8QMHGVwREREZAQowEki4/HCzNOwm94GYF5xOgZ4+2T24cT34MDBDI724Eg/4/VCfmGiVfTBTmpDV6ZmI33Q3nJYgwFQBkdERERGhu58k8xUVkGwHhusJyvFzdS8VDacZIBjjsjg6DLLIQ5tFX3ILJwh09IE1h4sUeuKYIB8vzI4IiIiMvx055tkpnIBwGFlau81dhPqi57YCx7SZKArsQdHJWpyUH+raGst2Slu0r0u9p9ABsdaS+yhf8Nu2QBATVsPt724j3Cw0TlOf4laqI+cNA9et/67ERERkeGnO45kKy6F3AB203oA5hf7iVp490DouF/KRqPQ032wRC2ewVGJmhymqMT5PmlvxRhDSZbvxOYvdbRh1z6HXfMcAG/u7+S1fZ1srWtzPn/IHpyAsjciIiIyQgZ113HdddeRmpqKy+XC7XZz++23H/b5l19+md///vcApKam8sUvfpHy8vJBPfdUZ4zBVFZh316L7WhjdkEmKW7D6j0dnFWaget4Jr+H40GR/30ZHDUZkEOYolIsOPtwsnMpyfSxJXj8ATXBegDsnmoA6judvTbVrb3Mg8O6qJXlpJz8wkVEREQGYdBvq954441kZWUN+LnCwkJuuukmMjIyePvtt/mv//ovbrvttkE9V8AsXoZ99QVi3/0q7o9fzQVT5vHH6jbqO3v56tnFTMlNHdwLheJ7d/rbRPeqyYAMoKAYABusx8w4jeJMLy/tjtAXtXjdgw+obdCZpUP9Pmw4RH08C7Sj2w0ZWRhfCtZagl19nFmSPuSnISIiIjKQIakbmTlzZuLP06dPp6mpaShe9pRhZs7F9f1/I/boj7GPrOTL02Yxa8VX+enOPr759G4um5nL56sK8boNNtSJffctZxM3sKvHw97e+GXs7MAWLsDVmwu72tjW1E2qx4XbdRxZIBn/cp3MCs3OXpkCvxcLNHf3UZThG/zrBA84v1sLe3dS35kGQHUsPVGe1tETpTdqKUhXBzUREREZGYMOcG699VYALrroIpYvX37Uxz333HMsWLDguJ+7atUqVq1aBcDtt99OIBAYeMEez1E/N6YFAti59xN+4Rk6Hvo3lr/8My75f//Gj1bv4g8bD3DapAAfriyi4xe/IfTEzxNPu2Xxd2hOyYn/LQcqr4RaoNZ5d31qvj/pX69xe83GsGBuPr5QB9mBANO63EA9fd50AoHsIx57tOvX1t5CT5of2x0ipaGOYGgyfp+bYG864QlTKAsEaGroBGBKcZ6+B0aQ/s2NfbqGY5+u4dijazZ+DCrAufnmm8nLy6OtrY1bbrmFkpISKisrj3jcxo0bef755/mXf/mX437u8uXLDwt+GhsbB1xLIBA46ufGhblnwZnn0vfaS0Q7WvjreTk8uzXIhr2NLCx0E936LpSW4frqP9PaE6P55U4+OcXHsgnxd8h9PkxmbuLlctLcSf96jftrNgbFcvIJ19bQ19iIL9oDQHVtIxNTjhz4ebTrF923GyZNhcZ6dm/dTjRtEmeXpvPCrna2pJeQ3thI9f4OAFKi3foeGEH6Nzf26RqOfbqGY4+u2dhTUlIy4McHtTkjLy8PgOzsbM466yyqq6uPeMyePXu4//77ueGGG8jMzDyu58r7lJZDdxc0N+IyhrKcFPa0hJ3P7duDmTwNU1TCHq8TyMybWkTplEnOr9IiSrJ8iV9+tYiWgeQGoDkIHBzAGew6Mrj5QMEDmIIiKKugvqEFgCVFPoyNsSO10HlI/5BPlaiJiIjICDlmgBMOh+nu7k78+Z133mHy5MmHPaaxsZG7776br3/964dFUoN5rhzJTJri/GHfbgCm5Kawq7WHWHsrtDXDxHIA9rQ677yXq0OVHCeTVwDNjVhrSfG4yExx0xiKDPr5tqfH+V4smIApq6A+Hn9PsR2UhIJUG6fULdgVwesyZKco0BYREZGRccwStba2Nu6++24AotEo5557LlVVVTz77LMArFixgt/85jd0dnbywAMPACTaQR/tuXIMpU4QaPftwsw/i/KcFJ7ZHqNx1x7yORgA7WoJk5fmIStVM0bkOOUHoLcHujogI4sCv+f4MjiNTotoCoox6ZkceKcZj7HkdTVS0bGPjblFQHwGTroHczztzkVEREROwjHvjIuKirjrrruO+PiKFSsSf/7KV77CV77ylUE/Vz6YSfU7rXzjGZz+DM3umiD5kMjg7G7tUfZGTojJK3Bm4TQHnQAn3Ut9x3EEOPEZOKagGAqKqU/bTqHpxdXSyLSOfbzYewbN3REaQ5FECZyIiIjISNCAlNGqtBwbD3DKcuMBTlMIsnMxmdlEYpaatl7KcxXgyAnIddo497eKDvg9if0yg2GDh2RwMrI4kFFMcW8rNAeZFnI6+O1oCtPY1UdA+29ERERkBCnAGaXMpHI4UIvt7cHvdVOU4WV32JXI3tS29xKJWU2IlxOT77TBtPFGA4F0L6G+GF290cE9P1jnDJRNz8RaS31qLkVttdAcZIq7B5eBrY3dNHdHKEhXCaWIiIiMHAU4o5SZWA42BrV7ASjL9rHHleV8HGf/DajBgJygjGzweI/opDbYRgM2eAAKijDG0NETpdt4KW6pwe7dSWpeDpOyUnh9XwcWCKhETUREREaQApzRKh7I9JeplXt6qE0L0FPifHx3aw8eF0zMVoAjx8+4XJAXSJSo9bdxHnSjgWC9s08MqOt0nlPU3Qx1NZi8Aqblp7K3rfew1xYREREZCQpwRqtAMaSkJhoNlPU0EjMu9uU6Hdb2tPYwKTsFj0vdqeQE5QYOKVFzysgGE+DYWBQaD2AKJgBQ3+EEMsXdTc4D8gqoyEtNPL7ArxI1ERERGTkKcEYp43JBadnBDE6L8/sedw4Au1t6tP9GTkr/LByA3FQPLjPIErWWZohGoMBpBX2gP4OTGf9+zA1QkX8wwFGTARERERlJCnBGMTOxHPbtxlpL4f5tpMT62N0eob0nSlN3RPtv5OTkF0BrMzYSwe0yBPweGgdTohZ0uqT1Z3DqOvvITfOQWlbufDy/gPKcFNwGMn0uUj36b0ZERERGju48RrOJ5c4gxtZm3Pt2MZkudrf2sDveYGBKbuoHP1/kg+QVOI0s2poBpxnAYFpFH9oiGpwStQkZXiirSLxuisfF5JwUZW9ERERkxKk4fhQzE6dgAfveO9DaRHma5dXWHna39gDqoCYn5+Cwz0bIL6Qg3cuWYPexnxisB7cbcp1W0wc6+5g/wY+Zvxy8Pihx9on9zZlFRO3wrV9ERERkIMrgjGalZQDY114EoDyQTkdPlLdru8hOdZOTpvhUTkLe+2bh+D00hfqIxo4RlQTrIb8Q43bTE4nR1B2hOMOH8WfgWnYpxjiNL2YX+jmtyD+spyAiIiLyfgpwRjHjT4f8Qti0HoDyyc6m7vX1XcreyMmLZ2ASs3DSvUQttIY/uNGAPaRF9IH4np2iDJWiiYiIyOigAGe06x/4mZlNWWk+ADGr/Tdy8kxqGqRnHjEL55id1IL1mP4Ap8MJcCZk+oZvoSIiIiLHQQHOKGfiAz+ZWE5miicxU0QtomVI5AUOK1GDD56FY7s6IdR5sMFAZ3wGjjI4IiIiMkoowBnl+gOc/t/Lc53ARiVqMiTyChIlav0dzz5w2OcALaJTPS6yUtzDu04RERGRQVKAM9qVVYBxwZSZAMwIpJHmcTEpWyVBcvJMXiAR4KR7XaR5XB9YomaDB5w/9A/57OhlQqY30VhAREREJNnUhmuUMwXFuG79MQScG8q/mp3HX0zNxutWbCpDIK8AQl3YcAiT6qcg3UOwqw8bORjkRHr76Ot1sjr2QC3WuHDlFWFilvrOPgXbIiIiMqoowBkD+jd0A3jdLgJ+BTcyRPIKnN+bG6FkMgG/h+CO3cR+8TUAtmaVccu8awh50uJPmAdL58H/7E28xNkTM0Z40SIiIiJHpwBH5BRm8gLxYZ9BJ8Bpq6PapGIu/Ag2I5uHOyvwWy+X++oPPicnH4qcPTguY7hgSlZyFi8iIiIyAAU4IqeyeAbHNgehO0Rg+9u0l15A78ev4a26Lra/XMt3LprOwkI1ERAREZGxQbVOIqey7DxwuaC5Efvs/xBodzI1DV0RfrE+SFl2CpfMKkzyIkVEREQGTwGOyCnMuN2Qk4/dtQ377O8oKJsEwKPvBKnt6OPzCwpwu9QhTURERMYOBTgip7q8AGxeD9EIBRddDMCrNZ2cVpjGGSXpyV2biIiIyHFSgCNyijPxfTjm/IsJTCqhP1/z+QWFmm8jIiIiY46aDIic6komgz8dc9mn8bpdlGT5mJqbwsxA2rGfKyIiIjLKKMAROcWZSz6OueBSTLozz+bOi8tI0SBZERERGaMU4Iic4ozbDekHh3Vm+NQSWkRERMYuvU0rIiIiIiLjhgIcEREREREZNxTgiIiIiIjIuKEAR0RERERExg0FOCIiIiIiMm4owBERERERkXFDAY6IiIiIiIwbCnBERERERGTcUIAjIiIiIiLjhgIcEREREREZNxTgiIiIiIjIuKEAR0RERERExg0FOCIiIiIiMm4owBERERERkXFDAY6IiIiIiIwbCnBERERERGTcUIAjIiIiIiLjhrHW2mQvQkREREREZCiMygzOt7/97RP63FC5//77h/0YI3Wc0XAuQ3XNTpWv12g7xrGu31g6l9FwnJE4xkj8Pwnj5+s1Usc5nmOczDUcbecy2o8zXMc49Brq6zU6j/P+YwzX/53j5bqMxmt/tGs2KgOcZDvjjDPGzXF0LqPvGCN1HJ3L6DzOSJ3LSBhPXy+dy+g7xkgdZ7wcY6SOo3MZnccZL8cYquMowBnAmWeeOW6Oo3MZfccYqePoXEbncUbqXEbCePp66VxG3zFG6jjj5RgjdRydy+g8zng5xlAdZ1QGOMuXLz+hz8nopGs2tun6jT26ZmOfruHYp2s49uiajT1Hu2ZqMiAiIiIiIuPGqMzgiIiIiIiInAgFOCIiIiIiMm6MygDn9ddf54orrmD//v3JXoocwxVXXMF//Md/JP4ejUa59tpruf3225O4KjkRV111VbKXICfoWNfupptuYseOHSO0GhkM/Zwb+37729/yzW9+k+uvv54bbriB7du3J3tJMghNTU3ceeed/N3f/R1/+7d/y8MPP0wkEjnq45966il6enpGcIUyFEZlgPPKK68wa9YsVq9efVzPi8Viw7QiOZqUlBRqamro7e0F4J133iEvLy/JqxIRGd1O9OecjA7btm3jrbfe4o477uDuu+/me9/7Hvn5+clelhyDtZa7776bs846ix/+8If8+7//O+FwmMcee+yoz/m///s/BThjkCfZC3i/cDjMe++9x4033sidd97JFVdcwaZNm3j88cfJyMigtraW2bNn88UvfhGXy8VVV13FZZddxoYNG/j85z/PrFmzkn0Kp5yqqirWrVvHokWLWL16Neeccw5bt24FoLq6mp/+9Kf09vbi8/n42te+RklJCd///ve55pprKC8vB+B73/seX/ziFykrK0vimcimTZt48sknE4OzHnzwQaZNm8YFF1zAddddx9KlS3nrrbeIRCJ885vfpLS0NMkrln4fdO1kdDnaz7mjXb9169bx85//nMzMTKZMmUJDQ8OIDXOVgbW0tJCZmYnX6wUgKysLgJ07d/Kzn/2McDhMVlYWX/va18jNzeWmm26ivLyc6upquru7+epXv0pFRUUyT+GUtHHjRnw+H8uWLQPA5XJx9dVX8/Wvf51PfvKTPP7442zYsAFjDBdeeCHWWpqbm/nBD35AVlYWN954Y5LPQAZr1GVwXn/9daqqqigpKSEjI4OdO3cCzo3y5z//ee655x4OHDjA66+/DkBPTw+TJk3itttuU3CTJOeccw6rV6+mt7eXPXv2MH369MTnSkpK+MEPfpD4If7f//3fAPzFX/wFL7zwAgC1tbX09fUpuBkDMjMzueOOO1ixYgVPPvlkspcjMiYd7efcQHp7e/nJT37Cd77zHW6++Wba29tHcKVyNPPnz6epqYlvfOMbPPDAA2zevJlIJMJDDz3EP/7jP3LHHXewbNmywzID4XCYW265hWuvvZYf/ehHSVz9qaumpoYpU6Yc9jG/308gEOC5556joaGBO++8k7vvvpvzzjuPSy+9lLy8PG688UYFN2PMqMvgrF69mg9/+MMALFmyhNWrV3P66adTUVFBUVERQCJDsGjRIlwuF4sWLUrmkk95ZWVlBINBVq9ezYIFCw77XCgUYuXKldTX1wPOHh2AxYsX88QTT/C5z32O559/Xu8yjxELFy4EYOrUqYk3GUTk+Bzt59xAamtrKSwspLCwEIBzzz2XVatWjdhaZWCpqanccccdbNmyhU2bNnHvvffy8Y9/nJqaGm6++WbAKZvPzc1NPOfcc88FoLKyklAoRFdXF+np6UlZ/6nMGHPEx6y1bN68mRUrVuB2uwHIyMgY6aXJEBpVAU5HRwcbN26kpqYGY0xiT837b5oP5fV6cblGXSLqlHPmmWfyyCOPcNNNN9HR0ZH4+K9+9SvmzJnDDTfcQENDAz/4wQ8AZ+/OvHnzePPNN1m7dq2aEowSbrebQ0dj9fX1HfZ5j8f5L8PlciWCVRkdjnXtZHQ42s+5M888c8Drp1F1o5fL5WLOnDnMmTOHyZMn88c//pGJEydy6623Dur5A91oy/CaOHEir7322mEfC4VCNDU1Jd5El/FhVEUGr776KkuXLuW+++5j5cqV/OhHP6KwsJCtW7dSXV1NQ0MDsViMtWvXqhxtlFm2bBmf+MQnmDx58mEfD4VCiaYD/SVp/S688EIefvhhpk2bpndKRolAIMC+ffvo6+sjFArx7rvvJntJMki6dmPD0X7OAQNev9LSUhoaGmhoaABgzZo1SVu7HFRbW0tdXV3i77t376a0tJT29na2bdsGQCQSoaamJvGY/mu3detW/H4/fr9/ZBctzJ07l56eHl588UXAybL9/Oc/54ILLmDevHn86U9/Srx519nZCTjZunA4nLQ1y4kZVRmc1atXc/nllx/2sYULF/KnP/2JGTNm8Oijj7J3715mz57N2WefnZxFyoDy8/O59NJLj/j4Rz/6UVauXMlTTz3FnDlzDvvc1KlTSUtLS2z2k+SJRqN4vV4CgQCLFy/m+uuvZ8KECUfUKsvoo2s3thzt59wrr7wy4PXz+Xxce+213HbbbWRmZmpj+igRDod56KGH6Orqwu12U1xczJe//GWWL1/Oww8/TCgUIhqNcumllzJp0iTAKXn67ne/m2gyICPPGMP111/PAw88wBNPPIG1lgULFnDllVficrmoq6vj+uuvx+PxcOGFF3LJJZewfPlybrvtNnJzc7UPZwwxdgzkv9/fXUbGh/7OJPfee6/KDJNs9+7d3H///fzrv/5rspcix0nXbvwLh8OkpqZireXBBx+kuLiYyy67LNnLkuNw0003cdVVVzFt2rRkL0XklDCqMjhy6njxxRf55S9/yec//3kFN0n27LPP8vTTT/OFL3wh2UuR46Rrd2pYtWoVL774IpFIhClTpnDRRRcle0kiIqPamMjgiIiIiIiIDIbeOhcRERERkXEj6SVqjY2NrFy5ktbWVowxLF++nEsvvZTOzk7uvfdegsEgBQUF/MM//AMZGRm88847PProo0QiETweD1dddRWnnXbaYa95xx130NDQwD333JOksxIRERERkWRIeoDjdru56qqrmDp1Kt3d3Xz7299m3rx5vPDCC8ydO5fLL7+c3/3ud/zud7/jc5/7HJmZmXzrW98iLy+PvXv3cuutt3L//fcnXu+1114jNTU1iWckIiIiIiLJkvQStdzcXKZOnQpAWloapaWlNDc388Ybb7B06VIAli5dyhtvvAHAlClTEnNVJk2aRF9fX2IgWjgc5n//93/5+Mc/noQzERERERGRZEt6gHOohoYGdu3aRUVFBW1tbeTm5gJOENTe3n7E41977TWmTJmC1+sF4Je//CUf+chH8Pl8I7puEREREREZHUZNgBMOh7nnnnv4whe+MKjpvjU1NTz66KN86UtfApxZEPX19RoAKiIiIiJyChsVAU4kEuGee+7hvPPOY+HChQBkZ2fT0tICQEtLC1lZWYnHNzU1cffdd3PddddRXFwMwLZt29i1axfXXXcd3//+96mtreWmm24a8XMREREREZHkSfocHGstK1euJCMj47BhdY888giZmZmJJgOdnZ187nOfo6uri5tuuomPf/zjLFq0aMDXbGho4I477lAXNRERERGRU0zSu6i99957vPTSS0yePJkbbrgBgCuvvJLLL7+ce++9l+eee45AIMA3v/lNAJ555hnq6+t54okneOKJJwD47ne/S3Z2dtLOQURERERERoekZ3BERERERESGyqjYgyMiIiIiIjIUFOCIiIiIiMi4oQBHRERERETGDQU4IiIiIiIybijAERERERGRcUMBjoiIJN3KlSv55S9/mexliIjIOKAAR0RExoybbrqJP//5z8lehoiIjGIKcEREREREZNzwJHsBIiJy6tm1axc//vGPqaurY8GCBRhjAOjs7OQ///M/2b59O7FYjJkzZ/KlL32J/Px8HnvsMbZs2cL27dv56U9/ygUXXMC1117L/v37eeihh9i5cydZWVl86lOfYsmSJUk+QxERSRZlcEREZERFIhHuuusuzjvvPB566CEWL17Ma6+9BoC1lgsuuID77ruP++67D5/Px4MPPgjAlVdeyezZs7nmmmt45JFHuPbaawmHw9xyyy2ce+65PPDAA3zjG9/gwQcfpKamJpmnKCIiSaQAR0RERtS2bduIRqN8+MMfxuPxsGjRIqZNmwZAZmYmixYtIiUlhbS0ND72sY+xZcuWo77WunXrKCgoYNmyZbjdbqZOncrChQt59dVXR+p0RERklFGJmoiIjKiWlhby8vISZWkAgUAAgJ6eHn72s5+xfv16urq6AOju7iYWi+FyHfmeXDAYZPv27XzhC19IfCwajXL++ecP70mIiMiopQBHRERGVG5uLs3NzVhrE0FOU1MTxcXFPPnkk9TW1nLbbbeRk5PD7t27+ad/+iestQCHBUUA+fn5VFZW8r3vfW/Ez0NEREYnlaiJiMiImjFjBi6Xi6effppoNMprr71GdXU1AOFwGJ/Ph9/vp7Ozk1//+teHPTc7O5sDBw4k/n7GGWdQV1fHSy+9RCQSIRKJUF1dzb59+0b0nEREZPQwtv9tMRERkRGyY8cO7r//furr61mwYAEAEyZMYMWKFfzwhz9kx44d5OXlcdlll/GTn/yExx57DLfbzbZt21i5ciXt7e2cd955XHPNNdTW1vKzn/2M6upqrLWUlZVx9dVXU15entyTFBGRpFCAIyIiIiIi44ZK1EREREREZNxQgCMiIiIiIuOGAhwRERERERk3FOCIiIiIiMi4oQBHRERERETGDQU4IiIiIiIybijAERERERGRcUMBjoiIiIiIjBv/P+DLAjirCJcpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Somtirtha\\Anaconda3\\envs\\myenv\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2024-03-30 00:00:00+00:00    5.74\n",
      "2024-03-31 00:00:00+00:00    5.74\n",
      "2024-04-01 00:00:00+00:00    5.76\n",
      "2024-04-02 00:00:00+00:00    5.57\n",
      "2024-04-03 00:00:00+00:00    5.63\n",
      "                             ... \n",
      "2024-10-12 00:00:00+00:00    6.30\n",
      "2024-10-13 00:00:00+00:00    6.30\n",
      "2024-10-14 00:00:00+00:00    6.52\n",
      "2024-10-15 00:00:00+00:00    6.40\n",
      "2024-10-16 00:00:00+00:00    6.39\n",
      "Freq: D, Name: open, Length: 201, dtype: float64 2024-03-30 00:00:00+00:00    5.734203\n",
      "2024-03-31 00:00:00+00:00    5.746913\n",
      "2024-04-01 00:00:00+00:00    5.734435\n",
      "2024-04-02 00:00:00+00:00    5.762070\n",
      "2024-04-03 00:00:00+00:00    5.575401\n",
      "                               ...   \n",
      "2024-10-27 00:00:00+00:00    6.392862\n",
      "2024-10-28 00:00:00+00:00    6.384989\n",
      "2024-10-29 00:00:00+00:00    6.395647\n",
      "2024-10-30 00:00:00+00:00    6.386181\n",
      "2024-10-31 00:00:00+00:00    6.391296\n",
      "Freq: D, Name: Prediction_open, Length: 216, dtype: float64 date\n",
      "2024-03-30 00:00:00+00:00    5.75\n",
      "2024-03-31 00:00:00+00:00    5.75\n",
      "2024-04-01 00:00:00+00:00    5.66\n",
      "2024-04-02 00:00:00+00:00    5.61\n",
      "2024-04-03 00:00:00+00:00    5.69\n",
      "                             ... \n",
      "2024-10-12 00:00:00+00:00    6.37\n",
      "2024-10-13 00:00:00+00:00    6.37\n",
      "2024-10-14 00:00:00+00:00    6.60\n",
      "2024-10-15 00:00:00+00:00    6.38\n",
      "2024-10-16 00:00:00+00:00    6.42\n",
      "Freq: D, Name: close, Length: 201, dtype: float64 2024-03-30 00:00:00+00:00    5.75\n",
      "2024-03-31 00:00:00+00:00    5.75\n",
      "2024-04-01 00:00:00+00:00    5.75\n",
      "2024-04-02 00:00:00+00:00    5.66\n",
      "2024-04-03 00:00:00+00:00    5.61\n",
      "                             ... \n",
      "2024-10-27 00:00:00+00:00    6.42\n",
      "2024-10-28 00:00:00+00:00    6.42\n",
      "2024-10-29 00:00:00+00:00    6.42\n",
      "2024-10-30 00:00:00+00:00    6.42\n",
      "2024-10-31 00:00:00+00:00    6.42\n",
      "Freq: D, Name: Prediction_close, Length: 216, dtype: float64 date\n",
      "2024-03-30 00:00:00+00:00    5.770\n",
      "2024-03-31 00:00:00+00:00    5.770\n",
      "2024-04-01 00:00:00+00:00    5.765\n",
      "2024-04-02 00:00:00+00:00    5.685\n",
      "2024-04-03 00:00:00+00:00    5.730\n",
      "                             ...  \n",
      "2024-10-12 00:00:00+00:00    6.380\n",
      "2024-10-13 00:00:00+00:00    6.380\n",
      "2024-10-14 00:00:00+00:00    6.610\n",
      "2024-10-15 00:00:00+00:00    6.420\n",
      "2024-10-16 00:00:00+00:00    6.455\n",
      "Freq: D, Name: high, Length: 201, dtype: float64 2024-03-30 00:00:00+00:00    5.770\n",
      "2024-03-31 00:00:00+00:00    5.770\n",
      "2024-04-01 00:00:00+00:00    5.770\n",
      "2024-04-02 00:00:00+00:00    5.765\n",
      "2024-04-03 00:00:00+00:00    5.685\n",
      "                             ...  \n",
      "2024-10-27 00:00:00+00:00    6.455\n",
      "2024-10-28 00:00:00+00:00    6.455\n",
      "2024-10-29 00:00:00+00:00    6.455\n",
      "2024-10-30 00:00:00+00:00    6.455\n",
      "2024-10-31 00:00:00+00:00    6.455\n",
      "Freq: D, Name: Prediction_high, Length: 216, dtype: float64 date\n",
      "2024-03-30 00:00:00+00:00    5.730\n",
      "2024-03-31 00:00:00+00:00    5.730\n",
      "2024-04-01 00:00:00+00:00    5.640\n",
      "2024-04-02 00:00:00+00:00    5.570\n",
      "2024-04-03 00:00:00+00:00    5.630\n",
      "                             ...  \n",
      "2024-10-12 00:00:00+00:00    6.280\n",
      "2024-10-13 00:00:00+00:00    6.280\n",
      "2024-10-14 00:00:00+00:00    6.515\n",
      "2024-10-15 00:00:00+00:00    6.370\n",
      "2024-10-16 00:00:00+00:00    6.385\n",
      "Freq: D, Name: low, Length: 201, dtype: float64 2024-03-30 00:00:00+00:00    5.730000\n",
      "2024-03-31 00:00:00+00:00    5.730000\n",
      "2024-04-01 00:00:00+00:00    5.730000\n",
      "2024-04-02 00:00:00+00:00    5.643020\n",
      "2024-04-03 00:00:00+00:00    5.572349\n",
      "                               ...   \n",
      "2024-10-27 00:00:00+00:00    6.384513\n",
      "2024-10-28 00:00:00+00:00    6.384513\n",
      "2024-10-29 00:00:00+00:00    6.384513\n",
      "2024-10-30 00:00:00+00:00    6.384513\n",
      "2024-10-31 00:00:00+00:00    6.384513\n",
      "Freq: D, Name: Prediction_low, Length: 216, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pearl=main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d50a065",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_open=SARIMAX(df_pearl['open'], order=best_model_open.order,seasonal_order=best_model_open.seasonal_order)\n",
    "# res=model_open.fit()\n",
    "# start=len(train_df)\n",
    "# end= len(train_df)+len(test_df)+14\n",
    "# pred=res.predict(start,end).rename('Prediction_open')\n",
    "# ax=test_df['open'].plot(legend=True, figsize=(14,8))\n",
    "# pred.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73c23ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_close=SARIMAX(df_pearl['close'], order=best_model.order,seasonal_order=best_model.seasonal_order)\n",
    "# res=model_close.fit()\n",
    "# start=len(train_df)\n",
    "# end= len(train_df)+len(test_df)+14\n",
    "# pred=res.predict(start,end).rename('Prediction_close')\n",
    "# ax=test_df['close'].plot(legend=True, figsize=(14,8))\n",
    "# pred.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65c220c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_low=SARIMAX(df_pearl['low'], order=best_model.order,seasonal_order=best_model.seasonal_order)\n",
    "# res=model_low.fit()\n",
    "# start=len(train_df)\n",
    "# end= len(train_df)+len(test_df)+14\n",
    "# pred=res.predict(start,end).rename('Prediction_low')\n",
    "# ax=test_df['low'].plot(legend=True, figsize=(14,8))\n",
    "# pred.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ca30b02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model=SARIMAX(df_pearl['high'], order=best_model.order,seasonal_order=best_model.seasonal_order)\n",
    "# res=model.fit()\n",
    "# start=len(train_df)\n",
    "# end= len(train_df)+len(test_df)+14\n",
    "# pred=res.predict(start,end).rename('Prediction')\n",
    "# ax=test_df['high'].plot(legend=True, figsize=(14,8))\n",
    "# pred.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e1cd0874",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "66a37471",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from statsmodels.tools.eval_measures import rmse\n",
    "#     rmse(test_df['high'],pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2dbefcd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a194d18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(len(df_pearl))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecd0695",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "78501626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred[-15:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7694892e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_df[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c02f081",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LSTM_ALGO(df):\n",
    "        #Split data into training set and test set\n",
    "        dataset_train=df.iloc[0:int(0.8*len(df)),:]\n",
    "        dataset_test=df.iloc[int(0.8*len(df)):,:]\n",
    "        ############# NOTE #################\n",
    "        #TO PREDICT STOCK PRICES OF NEXT N DAYS, STORE PREVIOUS N DAYS IN MEMORY WHILE TRAINING\n",
    "        # HERE N=7\n",
    "        ###dataset_train=pd.read_csv('Google_Stock_Price_Train.csv')\n",
    "        training_set=df.iloc[:,0:1].values# 1:2, to store as numpy array else Series obj will be stored\n",
    "        #select cols using above manner to select as float64 type, view in var explorer\n",
    "\n",
    "        #Feature Scaling\n",
    "        from sklearn.preprocessing import MinMaxScaler\n",
    "        sc=MinMaxScaler(feature_range=(0,1))#Scaled values btween 0,1\n",
    "        training_set_scaled=sc.fit_transform(training_set)\n",
    "        #In scaling, fit_transform for training, transform for test\n",
    "        \n",
    "        #Creating data stucture with 7 timesteps and 1 output. \n",
    "        #7 timesteps meaning storing trends from 7 days before current day to predict 1 next output\n",
    "        X_train=[]#memory with 7 days from day i\n",
    "        y_train=[]#day i\n",
    "        for i in range(7,len(training_set_scaled)):\n",
    "            X_train.append(training_set_scaled[i-7:i,0])\n",
    "            y_train.append(training_set_scaled[i,0])\n",
    "        #Convert list to numpy arrays\n",
    "        X_train=np.array(X_train)\n",
    "        y_train=np.array(y_train)\n",
    "        X_forecast=np.array(X_train[-1,1:])\n",
    "        X_forecast=np.append(X_forecast,y_train[-1])\n",
    "        #Reshaping: Adding 3rd dimension\n",
    "        X_train=np.reshape(X_train, (X_train.shape[0],X_train.shape[1],1))#.shape 0=row,1=col\n",
    "        X_forecast=np.reshape(X_forecast, (1,X_forecast.shape[0],1))\n",
    "        #For X_train=np.reshape(no. of rows/samples, timesteps, no. of cols/features)\n",
    "#         print(X_train,y_train)\n",
    "        #Building RNN\n",
    "        from keras.models import Sequential\n",
    "        from keras.layers import Dense\n",
    "        from keras.layers import Dropout\n",
    "        from keras.layers import LSTM\n",
    "        \n",
    "        #Initialise RNN\n",
    "        regressor=Sequential()\n",
    "        \n",
    "        #Add first LSTM layer\n",
    "        regressor.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1],1)))\n",
    "        #units=no. of neurons in layer\n",
    "        #input_shape=(timesteps,no. of cols/features)\n",
    "        #return_seq=True for sending recc memory. For last layer, retrun_seq=False since end of the line\n",
    "        regressor.add(Dropout(0.1))\n",
    "        \n",
    "        #Add 2nd LSTM layer\n",
    "        regressor.add(LSTM(units=50,return_sequences=True))\n",
    "        regressor.add(Dropout(0.1))\n",
    "        \n",
    "        #Add 3rd LSTM layer\n",
    "        regressor.add(LSTM(units=50,return_sequences=True))\n",
    "        regressor.add(Dropout(0.1))\n",
    "        \n",
    "        #Add 4th LSTM layer\n",
    "        regressor.add(LSTM(units=50))\n",
    "        regressor.add(Dropout(0.1))\n",
    "        \n",
    "        #Add o/p layer\n",
    "        regressor.add(Dense(units=1))\n",
    "        \n",
    "        #Compile\n",
    "        regressor.compile(optimizer='adam',loss='mean_squared_error')\n",
    "        \n",
    "        #Training\n",
    "        regressor.fit(X_train,y_train,epochs=15,batch_size=32 )\n",
    "        #For lstm, batch_size=power of 2\n",
    "        \n",
    "        #Testing\n",
    "        ###dataset_test=pd.read_csv('Google_Stock_Price_Test.csv')\n",
    "        real_stock_price=dataset_test.iloc[:,0:1].values\n",
    "        \n",
    "        #To predict, we need stock prices of 7 days before the test set\n",
    "        #So combine train and test set to get the entire data set\n",
    "        dataset_total=pd.concat((dataset_train['close'],dataset_test['close']),axis=0) \n",
    "        \n",
    "        \n",
    "        testing_set=dataset_total[ len(dataset_total) -len(dataset_test) -7: ].values\n",
    "        testing_set=testing_set.reshape(-1,1)\n",
    "        #-1=till last row, (-1,1)=>(80,1). otherwise only (80,0)\n",
    "        \n",
    "        #Feature scaling\n",
    "        testing_set=sc.transform(testing_set)\n",
    "        \n",
    "        #Create data structure\n",
    "        X_test=[]\n",
    "#         y_test=[]\n",
    "        for i in range(7,len(testing_set)):\n",
    "            X_test.append(testing_set[i-7:i,0])\n",
    "#             y_test.append(testing_set[i,0])\n",
    "            #Convert list to numpy arrays\n",
    "        X_test=np.array(X_test)\n",
    "#         y_test=np.array(y_test)\n",
    "        #Reshaping: Adding 3rd dimension\n",
    "        X_test=np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
    "        \n",
    "        #Testing Prediction\n",
    "        predicted_stock_price=regressor.predict(X_test)\n",
    "        \n",
    "        #Getting original prices back from scaled values\n",
    "        predicted_stock_price=sc.inverse_transform(predicted_stock_price)\n",
    "#         real_stock_price=sc.inverse_transform(y_test.reshape(-1, 1))\n",
    "        \n",
    "        \n",
    "        print(real_stock_price,predicted_stock_price)\n",
    "        fig = plt.figure(figsize=(20,16),dpi=65)\n",
    "        plt.plot(real_stock_price,label='Actual Price')  \n",
    "        plt.plot(predicted_stock_price,label='Predicted Price')\n",
    "          \n",
    "        plt.legend(loc=4)\n",
    "#         plt.show(fig)\n",
    "        plt.savefig('LSTM.png')\n",
    "        plt.close(fig)\n",
    "        \n",
    "        \n",
    "        error_lstm = math.sqrt(mean_squared_error(real_stock_price, predicted_stock_price))\n",
    "        \n",
    "        \n",
    "        #Forecasting Prediction\n",
    "        forecasted_stock_price=regressor.predict(X_forecast)\n",
    "        \n",
    "        #Getting original prices back from scaled values\n",
    "        forecasted_stock_price=sc.inverse_transform(forecasted_stock_price)\n",
    "        \n",
    "        lstm_pred=forecasted_stock_price[0,0]\n",
    "        print()\n",
    "        print(\"##############################################################################\")\n",
    "#         print(\"Tomorrow's \",quote,\" Closing Price Prediction by LSTM: \",lstm_pred)\n",
    "#         print(\"LSTM RMSE:\",error_lstm)\n",
    "        print(\"##############################################################################\")\n",
    "        return lstm_pred,error_lstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a5a861ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>adjClose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02 00:00:00+00:00</th>\n",
       "      <td>11.33</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.26</td>\n",
       "      <td>11.52</td>\n",
       "      <td>395528.0</td>\n",
       "      <td>4.032926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03 00:00:00+00:00</th>\n",
       "      <td>11.33</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.26</td>\n",
       "      <td>11.52</td>\n",
       "      <td>744879.0</td>\n",
       "      <td>3.983093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04 00:00:00+00:00</th>\n",
       "      <td>11.33</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.26</td>\n",
       "      <td>11.52</td>\n",
       "      <td>744879.0</td>\n",
       "      <td>3.983093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 00:00:00+00:00</th>\n",
       "      <td>11.19</td>\n",
       "      <td>11.29</td>\n",
       "      <td>11.12</td>\n",
       "      <td>11.29</td>\n",
       "      <td>744879.0</td>\n",
       "      <td>3.983093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06 00:00:00+00:00</th>\n",
       "      <td>11.03</td>\n",
       "      <td>11.21</td>\n",
       "      <td>10.88</td>\n",
       "      <td>11.20</td>\n",
       "      <td>364478.0</td>\n",
       "      <td>3.926141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           close   high    low   open    volume  adjClose\n",
       "date                                                                     \n",
       "2015-01-02 00:00:00+00:00  11.33  11.62  11.26  11.52  395528.0  4.032926\n",
       "2015-01-03 00:00:00+00:00  11.33  11.62  11.26  11.52  744879.0  3.983093\n",
       "2015-01-04 00:00:00+00:00  11.33  11.62  11.26  11.52  744879.0  3.983093\n",
       "2015-01-05 00:00:00+00:00  11.19  11.29  11.12  11.29  744879.0  3.983093\n",
       "2015-01-06 00:00:00+00:00  11.03  11.21  10.88  11.20  364478.0  3.926141"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pearl=df_pearl.drop(axis=1,columns=['adjHigh','adjLow','adjOpen','adjVolume','divCash','splitFactor'])\n",
    "df_pearl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b15dc579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "112/112 [==============================] - 24s 37ms/step - loss: 0.0220\n",
      "Epoch 2/15\n",
      "112/112 [==============================] - 4s 34ms/step - loss: 0.0017\n",
      "Epoch 3/15\n",
      "112/112 [==============================] - 4s 31ms/step - loss: 0.0016\n",
      "Epoch 4/15\n",
      "112/112 [==============================] - 3s 30ms/step - loss: 0.0014\n",
      "Epoch 5/15\n",
      "112/112 [==============================] - 3s 27ms/step - loss: 0.0015\n",
      "Epoch 6/15\n",
      "112/112 [==============================] - 3s 28ms/step - loss: 0.0015\n",
      "Epoch 7/15\n",
      "112/112 [==============================] - 3s 29ms/step - loss: 0.0014\n",
      "Epoch 8/15\n",
      "112/112 [==============================] - 4s 32ms/step - loss: 0.0013\n",
      "Epoch 9/15\n",
      "112/112 [==============================] - 3s 29ms/step - loss: 0.0014\n",
      "Epoch 10/15\n",
      "112/112 [==============================] - 4s 33ms/step - loss: 0.0012\n",
      "Epoch 11/15\n",
      "112/112 [==============================] - 4s 35ms/step - loss: 0.0012\n",
      "Epoch 12/15\n",
      "112/112 [==============================] - 4s 33ms/step - loss: 0.0012\n",
      "Epoch 13/15\n",
      "112/112 [==============================] - 4s 38ms/step - loss: 0.0013\n",
      "Epoch 14/15\n",
      "112/112 [==============================] - 4s 32ms/step - loss: 0.0012\n",
      "Epoch 15/15\n",
      "112/112 [==============================] - 4s 39ms/step - loss: 0.0012\n",
      "23/23 [==============================] - 6s 10ms/step\n",
      "[[4.73]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 41ms/step\n",
      "\n",
      "##############################################################################\n",
      "##############################################################################\n"
     ]
    }
   ],
   "source": [
    "a,b=LSTM_ALGO(df_pearl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "031cd310",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LIN_REG_ALGO(df):\n",
    "        #No of days to be forcasted in future\n",
    "        forecast_out = int(7)\n",
    "        #Price after n days\n",
    "        df['Close after n days'] = df['close'].shift(-forecast_out)\n",
    "        #New df with only relevant data\n",
    "        df_new=df[['close','Close after n days']]\n",
    "\n",
    "        #Structure data for train, test & forecast\n",
    "        #lables of known data, discard last 35 rows\n",
    "        y =np.array(df_new.iloc[:-forecast_out,-1])\n",
    "        y=np.reshape(y, (-1,1))\n",
    "        #all cols of known data except lables, discard last 35 rows\n",
    "        X=np.array(df_new.iloc[:-forecast_out,0:-1])\n",
    "        #Unknown, X to be forecasted\n",
    "        X_to_be_forecasted=np.array(df_new.iloc[-forecast_out:,0:-1])\n",
    "        \n",
    "        #Traning, testing to plot graphs, check accuracy\n",
    "        X_train=X[0:int(0.8*len(df)),:]\n",
    "        X_test=X[int(0.8*len(df)):,:]\n",
    "        y_train=y[0:int(0.8*len(df)),:]\n",
    "        y_test=y[int(0.8*len(df)):,:]\n",
    "        \n",
    "        # Feature Scaling===Normalization\n",
    "        from sklearn.preprocessing import StandardScaler\n",
    "        sc = StandardScaler()\n",
    "        X_train = sc.fit_transform(X_train)\n",
    "        X_test = sc.transform(X_test)\n",
    "        \n",
    "        X_to_be_forecasted=sc.transform(X_to_be_forecasted)\n",
    "        \n",
    "        #Training\n",
    "        clf = LinearRegression(n_jobs=-1)\n",
    "        clf.fit(X_train, y_train)\n",
    "        \n",
    "        #Testing\n",
    "        y_test_pred=clf.predict(X_test)\n",
    "        y_test_pred=y_test_pred*(1.04)\n",
    "        import matplotlib.pyplot as plt2\n",
    "        fig = plt2.figure(figsize=(20,16),dpi=65)\n",
    "        plt2.plot(y_test,label='Actual Price' )\n",
    "        plt2.plot(y_test_pred,label='Predicted Price')\n",
    "        \n",
    "        plt2.legend(loc=4)\n",
    "        plt2.savefig('LR.png')\n",
    "        plt2.close(fig)\n",
    "        \n",
    "        error_lr = math.sqrt(mean_squared_error(y_test, y_test_pred))\n",
    "        \n",
    "        \n",
    "        #Forecasting\n",
    "        forecast_set = clf.predict(X_to_be_forecasted)\n",
    "        forecast_set=forecast_set*(1.04)\n",
    "        mean=forecast_set.mean()\n",
    "        lr_pred=forecast_set[0,0]\n",
    "        print()\n",
    "        print(\"##############################################################################\")\n",
    "#         print(\"Tomorrow's \",quote,\" Closing Price Prediction by Linear Regression: \",lr_pred)\n",
    "#         print(\"Linear Regression RMSE:\",error_lr)\n",
    "        print(\"##############################################################################\")\n",
    "        return df, lr_pred, forecast_set, mean, error_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "34c6b72b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LinearRegression' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-1574053784ac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mLIN_REG_ALGO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_pearl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-38-f1dee071c11a>\u001b[0m in \u001b[0;36mLIN_REG_ALGO\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m         \u001b[1;31m#Training\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m         \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'LinearRegression' is not defined"
     ]
    }
   ],
   "source": [
    "LIN_REG_ALGO(df_pearl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b4ef481",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5851f929",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8021e426",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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