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nagishin/backtest_hourly.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "backtest_hourly.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/nagishin/e79ec81cdef8a9b1dafed7eb65383fc1/backtest_hourly.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IT8KDp1hQmYv"
},
"source": [
"---\n",
"# 【時刻アノマリーバックテスト】\n",
"<b>タイプ</b>\n",
"* 特定時刻 定期トレード\n",
"\n",
"<b>ロジック</b><br>\n",
"　日本時間 AM9:01において\n",
"* 直前の5分間の値動きがプラスであれば売りエントリー\n",
"* 直前の5分間の値動きがマイナスであれば買いエントリー\n",
"* エントリーした場合、28分後に決済<br>\n",
"本家ロジックでは毎時1分の検証・考察をされていますが、取引手数料をカバーする値幅が取れないため、特性が顕著なAM9:00に限定してバックテストを実施します。\n",
"\n",
"<b>対象データ</b>\n",
"* tickデータ ： BitMEX 1m OHLCV\n",
"\n",
"<b>対象期間</b>\n",
"* 2020-01-01 ～ 2020-09-29\n",
"\n",
"<b>[参考]</b><br>\n",
"[Hohetoさん著 ビットコイン価格における時刻アノマリーの存在](https://note.com/hht/n/nc0caf98477db)\n",
"<br>\n",
"<br>\n",
"<b>[注意]</b><br>\n",
"本バックテストは上記noteにて公開されているロジックを参考にテストコードを作成していますが、動作を忠実に再現しているものではないことをご了承ください。\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y_9-OuvGNvWW"
},
"source": [
"# 1. 環境構築\n",
"Google Colaboratoryでテストを実行するために必要なセットアップです。\n",
"* backtest_assistant.py (バックテストツール)、chart_creator.py (チャート作成ツール) をアップロード\n",
"* 必要パッケージのインストール"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9qvSZ0v3zeks"
},
"source": [
"## 1-1. バックテストに必要なファイルをアップロード\n",
"以下のツールに同梱されているpythonスクリプトファイルをアップロードします。<br>\n",
"(本notebookは以下のファイルがないと動作しません。)\n",
"* [バックテスト補助ツール「BacktestAssistant」](https://note.com/nagi7692/n/n0c874a0cf2b2) -> backtest_assistant.py\n",
"* [時系列データ可視化ツール「ChartCreator」](https://note.com/nagi7692/n/n401a95653ad0) -> chart_creator.py"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mMMxBUEPzbEr"
},
"source": [
"from google.colab import files\n",
"files.upload()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2avmQKiAw5Ps"
},
"source": [
"## 1-2. 必要パッケージインストール\n",
"上記ツールを使用するために必要なパッケージをインストールします。<br>\n",
"また、テストデータの取得や加工に便利な以下のツールもインストールします。\n",
"* [テストデータ取得＆加工ツール【DataUtility】](https://gist.github.com/nagishin/1677ffa401476e9e98191a04012ac189)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RzalryYnwxue"
},
"source": [
"# 共通\n",
"!pip install numpy\n",
"!pip install pandas\n",
"!pip install datetime\n",
"!pip install pytz\n",
"# ChartCreator\n",
"!pip install matplotlib\n",
"!pip install mpl_finance\n",
"!pip install plotly\n",
"!pip install webcolors\n",
"!pip install beautifulsoup4\n",
"!pip install lxml\n",
"# DataUtility\n",
"!pip install git+https://github.com/nagishin/DataUtility.git"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2NS_ZXf9N244"
},
"source": [
"# 2. バックテスト準備\n",
"テストを実施するための前準備を行います。\n",
"* テスト期間の設定\n",
"* テストデータの取得・加工"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "STbU1X9uYkFy"
},
"source": [
"## 2-1. バックテスト期間の設定\n",
"2020年の年初から現在までをテスト期間として設定します。<br>\n",
"ここで設定した期間を基にテストに必要なデータ取得を行います。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LT5IYePqzY_7"
},
"source": [
"# coding: utf-8\n",
"from datetime import datetime\n",
"import numpy as np\n",
"import pandas as pd\n",
"import DataUtility as du\n",
"from backtest_assistant import BacktestAssistant\n",
"\n",
"#テスト期間\n",
"TEST_START = '2020/01/01 09:00:00+0900'\n",
"TEST_END = '2020/09/29 09:00:00+0900'\n",
"\n",
"# テスト期間の日付文字列をUnix Timeに変換\n",
"start_date = datetime.strptime(TEST_START, '%Y/%m/%d %H:%M:%S%z') # テスト開始時刻 (datetime)\n",
"end_date = datetime.strptime(TEST_END, '%Y/%m/%d %H:%M:%S%z') # テスト終了時刻 (datetime)\n",
"start_ut = int(start_date.timestamp()) # テスト開始時刻 (UnixTime)\n",
"end_ut = int(end_date.timestamp()) # テスト終了時刻 (UnixTime)"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ClYxRtnRZsX_"
},
"source": [
"## 2-2. バックテストデータの取得・加工\n",
"ロジック判定および注文は毎時処理ですが、途中の決済注文、含み損益の可視化、ファンディング手数料計算の観点からバックテストに設定するtickデータは1m OHLCVとします。<br>\n",
"(tickデータに沿って含み損益や証拠金維持率、ロスカット判定、ファンディング手数料etc.が執行されます。)<br>\n",
"\n",
"* 1m OHLCVを取得 (tickデータ df_ohlcv_1m)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oMtkc78OMjnI"
},
"source": [
"### BitMEX OHLCV 1分足取得"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rWmpYDCj10IJ",
"outputId": "4f960fa6-ac75-4260-c984-fa632d1d9f53",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 436
}
},
"source": [
"# DataUtilityを使用してBitMEX OHLCV(1m)を取得\n",
"df_ohlcv_1m = du.Tool.get_ohlcv_from_bitmex(\n",
" start_ut = start_ut, # 取得開始(UnixTime)\n",
" end_ut = end_ut, # 取得終了(UnixTime)\n",
" period = 1, # 時間足期間(分)\n",
" csv_path = './ohlcv_1m.csv', # 取得結果保存先(CSVパス)\n",
" request_interval = 0.5) # RESTリクエスト待機時間(秒)\n",
"display(df_ohlcv_1m) # 確認表示"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"ohlcv from request.\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"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>unixtime</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1577836800</td>\n",
" <td>7163.0</td>\n",
" <td>7163.0</td>\n",
" <td>7156.0</td>\n",
" <td>7156.0</td>\n",
" <td>2237930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1577836860</td>\n",
" <td>7156.0</td>\n",
" <td>7156.5</td>\n",
" <td>7156.0</td>\n",
" <td>7156.5</td>\n",
" <td>1477103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1577836920</td>\n",
" <td>7156.5</td>\n",
" <td>7156.5</td>\n",
" <td>7154.0</td>\n",
" <td>7154.0</td>\n",
" <td>1205792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1577836980</td>\n",
" <td>7154.0</td>\n",
" <td>7157.0</td>\n",
" <td>7154.0</td>\n",
" <td>7156.5</td>\n",
" <td>1273786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1577837040</td>\n",
" <td>7156.5</td>\n",
" <td>7157.0</td>\n",
" <td>7153.5</td>\n",
" <td>7153.5</td>\n",
" <td>1054617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391676</th>\n",
" <td>1601337360</td>\n",
" <td>10698.5</td>\n",
" <td>10698.5</td>\n",
" <td>10695.0</td>\n",
" <td>10695.5</td>\n",
" <td>559076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391677</th>\n",
" <td>1601337420</td>\n",
" <td>10695.5</td>\n",
" <td>10695.5</td>\n",
" <td>10690.0</td>\n",
" <td>10690.0</td>\n",
" <td>1026840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391678</th>\n",
" <td>1601337480</td>\n",
" <td>10690.0</td>\n",
" <td>10690.5</td>\n",
" <td>10685.0</td>\n",
" <td>10685.5</td>\n",
" <td>1901241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391679</th>\n",
" <td>1601337540</td>\n",
" <td>10685.5</td>\n",
" <td>10691.0</td>\n",
" <td>10683.5</td>\n",
" <td>10688.0</td>\n",
" <td>1656884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391680</th>\n",
" <td>1601337600</td>\n",
" <td>10688.0</td>\n",
" <td>10688.5</td>\n",
" <td>10666.0</td>\n",
" <td>10673.0</td>\n",
" <td>8236698</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>391681 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unixtime open high low close volume\n",
"0 1577836800 7163.0 7163.0 7156.0 7156.0 2237930\n",
"1 1577836860 7156.0 7156.5 7156.0 7156.5 1477103\n",
"2 1577836920 7156.5 7156.5 7154.0 7154.0 1205792\n",
"3 1577836980 7154.0 7157.0 7154.0 7156.5 1273786\n",
"4 1577837040 7156.5 7157.0 7153.5 7153.5 1054617\n",
"... ... ... ... ... ... ...\n",
"391676 1601337360 10698.5 10698.5 10695.0 10695.5 559076\n",
"391677 1601337420 10695.5 10695.5 10690.0 10690.0 1026840\n",
"391678 1601337480 10690.0 10690.5 10685.0 10685.5 1901241\n",
"391679 1601337540 10685.5 10691.0 10683.5 10688.0 1656884\n",
"391680 1601337600 10688.0 10688.5 10666.0 10673.0 8236698\n",
"\n",
"[391681 rows x 6 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1kna4sNd3AcU"
},
"source": [
"# 1分足DataFrameからテストに使用する列をndarrayに変換\n",
"np_ut = df_ohlcv_1m['unixtime'].values\n",
"np_open = df_ohlcv_1m['open'].values"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "20KNI_OxfZSG"
},
"source": [
"# 3. バックテスト実行"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kQqcfZ3R3dAm",
"outputId": "9e8e6dbd-1b2e-4192-fbd7-36771eeae548",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"start_balance = 1.0 # テスト開始時の残高(BTC)\n",
"leverage = 0 # 取引時のレバレッジ(0:クロスマージン, 1～:分離マージン)\n",
"lot = 10000 # 取引ロットサイズ($)\n",
"\n",
"# BacktestAssistant生成\n",
"test = BacktestAssistant(\n",
" balance = start_balance,\n",
" trade_leverage = leverage,\n",
" tick_mode = 'ltp', # tick_mode='ltp'のため、\n",
" take_cost = 0.5) # 成行注文時の取引コスト(スプレッド)を$0.5に設定\n",
"\n",
"test.set_output_option(disp_progress = True) # 出力オプション設定 (進捗率表示ON)\n",
"test.preset_ltp_ticks_from_ohlcv_df(df_ohlcv_1m) # tickデータ設定 (1分足OHLCVのopen, high, low, close価格をtickデータに展開)\n",
"\n",
"# バックテスト初期処理 (テスト開始時刻を設定)\n",
"test.initialize(start_time = np_ut[0])\n",
"\n",
"# テストループ (1時間毎)--------------------------------------------------------\n",
"for i, unixtime in enumerate(np_ut):\n",
" if test.time() < unixtime:\n",
" test.sleep_to_time(unixtime) # 指定時刻(unixtime)までtickを進める\n",
" if i < 5:\n",
" continue\n",
"\n",
" now_utc = test.nowutc() # 現在時刻(datetime UTC)取得\n",
" now_price = test.get_price()['ltp'] # 現在価格取得\n",
"\n",
" if now_utc.hour != 0: # UTC　0時でない場合は読み飛ばし\n",
" continue\n",
"\n",
" # エントリー判定\n",
" if now_utc.minute == 1: # 時刻 1分\n",
" pre_5_min = np_open[i - 5] # 5分前の価格を取得\n",
" if now_price < pre_5_min: # 値動きがマイナス\n",
" test.market_order(side='Buy', size=lot) # 買いエントリー\n",
" else: # 値動きがプラス\n",
" test.market_order(side='Sell', size=lot) # 売りエントリー\n",
" # イグジット判定\n",
" elif now_utc.minute == 28: # 時刻 28分\n",
" pos = test.get_position() # 現在のポジションを取得し、クローズ\n",
" if pos['size'] > 0:\n",
" test.market_order(side='Sell', size=abs(pos['size']))\n",
" elif pos['size'] < 0:\n",
" test.market_order(side='Buy', size=abs(pos['size']))\n",
"#-------------------------------------------------------------------------------\n",
"\n",
"pos = test.get_position() # テスト終了時にポジションがあればクローズ\n",
"if pos['size'] < 0:\n",
" test.market_order('Buy', abs(pos['size']))\n",
"elif pos['size'] > 0:\n",
" test.market_order('Sell', abs(pos['size']))\n",
"\n",
"# バックテスト終了処理\n",
"results = test.terminate()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Now initializing...\n",
"Completed initialize process.\n",
"[Start] time:1577836800.0 ltp:7163.0 balance:1.0 [Fee] taker:0.075 maker:-0.025 fr:0\n",
"preset ticks period: 1577836800.0 - 1601337645.0 (918502)\n",
"0% complete. (1 / 918,502)\n",
"10% complete. (91,851 / 918,502)\n",
"20% complete. (183,701 / 918,502)\n",
"30% complete. (275,551 / 918,502)\n",
"40% complete. (367,401 / 918,502)\n",
"50% complete. (459,252 / 918,502)\n",
"60% complete. (551,102 / 918,502)\n",
"70% complete. (642,952 / 918,502)\n",
"80% complete. (734,802 / 918,502)\n",
"90% complete. (826,652 / 918,502)\n",
"Now terminating...\n",
"Save order_history 542 records.(./result/orders.csv)\n",
"Save executions 542 records.(./result/executions.csv)\n",
"Save position_history 18297 records.(./result/positions.csv)\n",
"Save balance_history 18297 records.(./result/balances.csv)\n",
"\n",
"[Result]\n",
" [Time ] 2020/01/01 09:00:00 - 2020/09/29 09:00:00\n",
" [Balance ] 1.0 -> 1.0293 (+2.93%)\n",
" [Orders ] Total:542 (Open:0 Executed:542 Canceled:0)\n",
" [Position] Max Long:10,000.0 Max Short:-10,000.0 Min Margin:3804.09%\n",
"\n",
"[Execution statistics] PF:2.03\n",
" [Total ] Count:542(Size:5,420,000) PnL:+0.4882 Avr:+0.0009 DD:-0.0729(-6.00%) Price Range:+3,738.5\n",
" [Profit ] Count:164(60.74%) Sum:+0.9609 Avr:+0.0059 Max:+0.1304 MaxLen:10(+0.059)\n",
" [Loss ] Count:106(39.26%) Sum:-0.4728 Avr:-0.0045 Max:-0.0356 MaxLen:5(-0.0158)\n",
" [Fee ] Trade:0.4589 Funding:0.0\n",
"\n",
"[Fiat balance]\n",
" [Start ] 7,163.0 (Balance:1.0 Price:7163.0)\n",
" [End ] 11,182.8 (Balance:1.0293 Price:10864.5)\n",
" [Result] 4,019.8 (+56.12%)\n",
"\n",
"Completed terminate process.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bSfpX3JyBWv4"
},
"source": [
"## 4. バックテスト結果\n",
"バックテスト終了処理で各履歴CSVが出力されます。\n",
"* 注文履歴 (orders.csv)<br>\n",
"注文の発行、キャンセル、約定など注文データに変更があると記録されます。\n",
"\n",
"* 約定履歴 (executions.csv)<br>\n",
"注文の約定、ファンディング手数料発生など資産変動に関わる履歴が記録されます。\n",
"\n",
"* 建玉履歴 (positions.csv)<br>\n",
"注文の約定、未実現損益の変動など建玉変動に関わる履歴が記録されます。\n",
"\n",
"* 残高履歴 (balances.csv)<br>\n",
"残高に変更があると記録されます。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JG1XVyWfE9CX"
},
"source": [
"### 4-1. 注文履歴 (orders.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1NraYu2v_8eV",
"outputId": "4b11e22c-a18e-4211-de06-8e937f5f7bff",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_orders = pd.read_csv('./result/orders.csv')\n",
"display(df_orders)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>id</th>\n",
" <th>status</th>\n",
" <th>type</th>\n",
" <th>side</th>\n",
" <th>size</th>\n",
" <th>price</th>\n",
" <th>trigger_price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1577923260</td>\n",
" <td>1</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>7173.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1577924880</td>\n",
" <td>2</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>7168.5</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>1578009660</td>\n",
" <td>3</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>6942.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1578011280</td>\n",
" <td>4</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>6932.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1578096060</td>\n",
" <td>5</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>7334.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>1601080080</td>\n",
" <td>538</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10668.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>538</th>\n",
" <td>1601164860</td>\n",
" <td>539</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10729.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>539</th>\n",
" <td>1601166480</td>\n",
" <td>540</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>10746.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>1601251260</td>\n",
" <td>541</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>10786.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>1601252880</td>\n",
" <td>542</td>\n",
" <td>Filled</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10865.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>542 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id status type side size price trigger_price\n",
"0 1577923260 1 Filled Market Buy 10000 7173.5 0.0\n",
"1 1577924880 2 Filled Market Sell 10000 7168.5 0.0\n",
"2 1578009660 3 Filled Market Buy 10000 6942.5 0.0\n",
"3 1578011280 4 Filled Market Sell 10000 6932.5 0.0\n",
"4 1578096060 5 Filled Market Sell 10000 7334.5 0.0\n",
".. ... ... ... ... ... ... ... ...\n",
"537 1601080080 538 Filled Market Buy 10000 10668.0 0.0\n",
"538 1601164860 539 Filled Market Buy 10000 10729.0 0.0\n",
"539 1601166480 540 Filled Market Sell 10000 10746.5 0.0\n",
"540 1601251260 541 Filled Market Sell 10000 10786.0 0.0\n",
"541 1601252880 542 Filled Market Buy 10000 10865.0 0.0\n",
"\n",
"[542 rows x 8 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_m9FwRKRQlSQ"
},
"source": [
"### 4-2. 約定履歴 (executions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uZ2L11cqANvr",
"outputId": "912c14a9-766e-48eb-ca4c-2dfa51f23eb1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_executions = pd.read_csv('./result/executions.csv')\n",
"display(df_executions)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>id</th>\n",
" <th>type</th>\n",
" <th>side</th>\n",
" <th>size</th>\n",
" <th>price</th>\n",
" <th>cost</th>\n",
" <th>fee</th>\n",
" <th>pnl</th>\n",
" <th>price_range</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1577923260</td>\n",
" <td>1</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>7173.5</td>\n",
" <td>1.394020</td>\n",
" <td>0.001046</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1577924880</td>\n",
" <td>2</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>7168.5</td>\n",
" <td>-1.394992</td>\n",
" <td>0.001046</td>\n",
" <td>-0.000972</td>\n",
" <td>-5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1578009660</td>\n",
" <td>3</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>6942.5</td>\n",
" <td>1.440403</td>\n",
" <td>0.001080</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1578011280</td>\n",
" <td>4</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>6932.5</td>\n",
" <td>-1.442481</td>\n",
" <td>0.001082</td>\n",
" <td>-0.002078</td>\n",
" <td>-10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1578096060</td>\n",
" <td>5</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>7334.5</td>\n",
" <td>-1.363419</td>\n",
" <td>0.001023</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>1601080080</td>\n",
" <td>538</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10668.0</td>\n",
" <td>0.937383</td>\n",
" <td>0.000703</td>\n",
" <td>0.002498</td>\n",
" <td>28.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>538</th>\n",
" <td>1601164860</td>\n",
" <td>539</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10729.0</td>\n",
" <td>0.932053</td>\n",
" <td>0.000699</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>539</th>\n",
" <td>1601166480</td>\n",
" <td>540</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>10746.5</td>\n",
" <td>-0.930536</td>\n",
" <td>0.000698</td>\n",
" <td>0.001518</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>1601251260</td>\n",
" <td>541</td>\n",
" <td>Market</td>\n",
" <td>Sell</td>\n",
" <td>10000</td>\n",
" <td>10786.0</td>\n",
" <td>-0.927128</td>\n",
" <td>0.000695</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>1601252880</td>\n",
" <td>542</td>\n",
" <td>Market</td>\n",
" <td>Buy</td>\n",
" <td>10000</td>\n",
" <td>10865.0</td>\n",
" <td>0.920387</td>\n",
" <td>0.000690</td>\n",
" <td>-0.006741</td>\n",
" <td>-79.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>542 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp id type side ... cost fee pnl price_range\n",
"0 1577923260 1 Market Buy ... 1.394020 0.001046 0.000000 0.0\n",
"1 1577924880 2 Market Sell ... -1.394992 0.001046 -0.000972 -5.0\n",
"2 1578009660 3 Market Buy ... 1.440403 0.001080 0.000000 0.0\n",
"3 1578011280 4 Market Sell ... -1.442481 0.001082 -0.002078 -10.0\n",
"4 1578096060 5 Market Sell ... -1.363419 0.001023 0.000000 0.0\n",
".. ... ... ... ... ... ... ... ... ...\n",
"537 1601080080 538 Market Buy ... 0.937383 0.000703 0.002498 28.5\n",
"538 1601164860 539 Market Buy ... 0.932053 0.000699 0.000000 0.0\n",
"539 1601166480 540 Market Sell ... -0.930536 0.000698 0.001518 17.5\n",
"540 1601251260 541 Market Sell ... -0.927128 0.000695 0.000000 0.0\n",
"541 1601252880 542 Market Buy ... 0.920387 0.000690 -0.006741 -79.0\n",
"\n",
"[542 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mf66IwavQtT3"
},
"source": [
"### 4-3. 建玉履歴 (positions.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DD6Ecjf3Apwr",
"outputId": "85cc7545-9009-44e1-e663-382e97616fc8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_positions = pd.read_csv('./result/positions.csv')\n",
"display(df_positions)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>timestamp</th>\n",
" <th>size</th>\n",
" <th>avr_entry</th>\n",
" <th>pos_cost</th>\n",
" <th>order_margin</th>\n",
" <th>pos_margin</th>\n",
" <th>margin_rate</th>\n",
" <th>liq_price</th>\n",
" <th>unrealised_pnl</th>\n",
" <th>leverage</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.577837e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.577923e+09</td>\n",
" <td>10000.0</td>\n",
" <td>7173.5</td>\n",
" <td>1.394020</td>\n",
" <td>0.0</td>\n",
" <td>0.013940</td>\n",
" <td>71.72303</td>\n",
" <td>4192.42</td>\n",
" <td>-0.000097</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.577923e+09</td>\n",
" <td>10000.0</td>\n",
" <td>7173.5</td>\n",
" <td>1.394020</td>\n",
" <td>0.0</td>\n",
" <td>0.013940</td>\n",
" <td>71.63607</td>\n",
" <td>4194.26</td>\n",
" <td>-0.000194</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.577923e+09</td>\n",
" <td>10000.0</td>\n",
" <td>7173.5</td>\n",
" <td>1.394020</td>\n",
" <td>0.0</td>\n",
" <td>0.013940</td>\n",
" <td>71.64804</td>\n",
" <td>4194.26</td>\n",
" <td>-0.000097</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.577923e+09</td>\n",
" <td>10000.0</td>\n",
" <td>7173.5</td>\n",
" <td>1.394020</td>\n",
" <td>0.0</td>\n",
" <td>0.013940</td>\n",
" <td>71.62411</td>\n",
" <td>4194.26</td>\n",
" <td>-0.000292</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18292</th>\n",
" <td>1.601253e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>10786.0</td>\n",
" <td>-0.927128</td>\n",
" <td>0.0</td>\n",
" <td>0.009271</td>\n",
" <td>111.90197</td>\n",
" <td>0.00</td>\n",
" <td>-0.006614</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18293</th>\n",
" <td>1.601253e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>10786.0</td>\n",
" <td>-0.927128</td>\n",
" <td>0.0</td>\n",
" <td>0.009271</td>\n",
" <td>111.90306</td>\n",
" <td>0.00</td>\n",
" <td>-0.006699</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18294</th>\n",
" <td>1.601253e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>10786.0</td>\n",
" <td>-0.927128</td>\n",
" <td>0.0</td>\n",
" <td>0.009271</td>\n",
" <td>111.90197</td>\n",
" <td>0.00</td>\n",
" <td>-0.006614</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18295</th>\n",
" <td>1.601253e+09</td>\n",
" <td>-10000.0</td>\n",
" <td>10786.0</td>\n",
" <td>-0.927128</td>\n",
" <td>0.0</td>\n",
" <td>0.009271</td>\n",
" <td>111.90306</td>\n",
" <td>0.00</td>\n",
" <td>-0.006699</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18296</th>\n",
" <td>1.601253e+09</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>18297 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp size avr_entry ... liq_price unrealised_pnl leverage\n",
"0 1.577837e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"1 1.577923e+09 10000.0 7173.5 ... 4192.42 -0.000097 0\n",
"2 1.577923e+09 10000.0 7173.5 ... 4194.26 -0.000194 0\n",
"3 1.577923e+09 10000.0 7173.5 ... 4194.26 -0.000097 0\n",
"4 1.577923e+09 10000.0 7173.5 ... 4194.26 -0.000292 0\n",
"... ... ... ... ... ... ... ...\n",
"18292 1.601253e+09 -10000.0 10786.0 ... 0.00 -0.006614 0\n",
"18293 1.601253e+09 -10000.0 10786.0 ... 0.00 -0.006699 0\n",
"18294 1.601253e+09 -10000.0 10786.0 ... 0.00 -0.006614 0\n",
"18295 1.601253e+09 -10000.0 10786.0 ... 0.00 -0.006699 0\n",
"18296 1.601253e+09 0.0 0.0 ... 0.00 0.000000 0\n",
"\n",
"[18297 rows x 10 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BuE6IZSsQxeW"
},
"source": [
"### 4-4. 残高履歴 (balances.csv)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Olp3YKuyBCsN",
"outputId": "7a3f1578-987a-4708-c02e-bcc1bb226eb6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df_balances = pd.read_csv('./result/balances.csv')\n",
"display(df_balances)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"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>timestamp</th>\n",
" <th>balance</th>\n",
" <th>unrealised_pnl</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.577837e+09</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7163.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.577923e+09</td>\n",
" <td>0.998954</td>\n",
" <td>-0.000097</td>\n",
" <td>7173.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.577923e+09</td>\n",
" <td>0.998954</td>\n",
" <td>-0.000194</td>\n",
" <td>7172.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.577923e+09</td>\n",
" <td>0.998954</td>\n",
" <td>-0.000097</td>\n",
" <td>7173.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.577923e+09</td>\n",
" <td>0.998954</td>\n",
" <td>-0.000292</td>\n",
" <td>7172.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18292</th>\n",
" <td>1.601253e+09</td>\n",
" <td>1.036687</td>\n",
" <td>-0.006614</td>\n",
" <td>10863.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18293</th>\n",
" <td>1.601253e+09</td>\n",
" <td>1.036687</td>\n",
" <td>-0.006699</td>\n",
" <td>10864.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18294</th>\n",
" <td>1.601253e+09</td>\n",
" <td>1.036687</td>\n",
" <td>-0.006614</td>\n",
" <td>10863.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18295</th>\n",
" <td>1.601253e+09</td>\n",
" <td>1.036687</td>\n",
" <td>-0.006699</td>\n",
" <td>10864.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18296</th>\n",
" <td>1.601253e+09</td>\n",
" <td>1.029255</td>\n",
" <td>0.000000</td>\n",
" <td>10864.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>18297 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp balance unrealised_pnl price\n",
"0 1.577837e+09 1.000000 0.000000 7163.0\n",
"1 1.577923e+09 0.998954 -0.000097 7173.0\n",
"2 1.577923e+09 0.998954 -0.000194 7172.5\n",
"3 1.577923e+09 0.998954 -0.000097 7173.0\n",
"4 1.577923e+09 0.998954 -0.000292 7172.0\n",
"... ... ... ... ...\n",
"18292 1.601253e+09 1.036687 -0.006614 10863.5\n",
"18293 1.601253e+09 1.036687 -0.006699 10864.5\n",
"18294 1.601253e+09 1.036687 -0.006614 10863.5\n",
"18295 1.601253e+09 1.036687 -0.006699 10864.5\n",
"18296 1.601253e+09 1.029255 0.000000 10864.5\n",
"\n",
"[18297 rows x 4 columns]"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9QfrSGDB-ykd"
},
"source": [
"## 5. バックテスト結果をチャート化\n",
"バックテスト終了処理のresultsにはチャート作成向けデータが出力されます。<br>\n",
"このデータからチャート作成ツール「ChartCreator」を使用してテスト結果を可視化します。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MCDYfTdM4BTd"
},
"source": [
"from chart_creator import ChartCreator as cc\n",
"\n",
"dict_data = results['data']\n",
"dict_statistics = results['statistics']\n",
"dict_chart = results['chart']\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# レイアウトカスタマイズ\n",
"#-------------------------------------------------------------------------------\n",
"cc.initialize()\n",
"cc.settings['title'] = 'Result chart'\n",
"\n",
"# X軸設定\n",
"cc.set_xaxis(start=0, end=0, is_unixtime=True)\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# メインチャート(ax:0)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=0, label=\"USD\", grid=True)\n",
"\n",
"# DataUtilityを使用してOHLCVをダウンサンプリング(1m→1d)\n",
"df_ohlcv_1d = du.Tool.downsample_ohlcv(df_ohlcv_1m, period='1D')\n",
"# ローソクバー設定(OHLCV)\n",
"cc.set_ohlcv_df(df_ohlcv_1d)\n",
"# 買い注文ポイント設定\n",
"cc.set_marker(dict_chart['buy']['timestamp'], dict_chart['buy']['price'], ax=0, color='blue', size=30.0, mark='^', name='Buy')\n",
"# 売り注文ポイント設定\n",
"cc.set_marker(dict_chart['sell']['timestamp'], dict_chart['sell']['price'], ax=0, color='red', size=30.0, mark='v', name='Sell')\n",
"# 清算注文ポイント設定\n",
"if len(dict_chart['liquid']['timestamp']) > 0:\n",
" cc.set_marker(dict_chart['liquid']['timestamp'], dict_chart['liquid']['price'], ax=0, color='magenta', size=30.0, mark='D', name='Liquidation')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# ポジション推移サブチャート(ax:1)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=1, label=\"Position\", grid=True)\n",
"\n",
"# ポジション設定\n",
"cc.set_band(dict_chart['position']['unixtime'], dict_chart['position']['size'], dict_chart['position']['zero'], ax=1, up_color='blue', down_color='red',\n",
" alpha=0.5, edge_width=1.0, edge_color='dimgray', line_mode='hv', name='Position')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# 損益推移サブチャート(ax:2)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=2, label='PnL', grid=True)\n",
"\n",
"# 損益設定\n",
"cc.set_line(dict_chart['pnl']['unixtime'], dict_chart['pnl']['unrealised_pnl'], ax=2, color='green', width=0.8, name='Unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], dict_chart['pnl']['pnl'], dict_chart['pnl']['zero'], ax=2, up_color='skyblue', down_color='red',\n",
" alpha=0.2, edge_width=1.0, edge_color='dimgray', name='PL')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# フィアット損益推移サブチャート(ax:3)\n",
"#-------------------------------------------------------------------------------\n",
"cc.add_subchart(ax=3, label='Fiat PnL', grid=True)\n",
"\n",
"# フィアット損益設定\n",
"fiat_blc = dict_chart['pnl']['balance'] * dict_chart['pnl']['price']\n",
"fiat_start = fiat_blc[0]\n",
"fiat_blc = fiat_blc - fiat_start\n",
"fiat_unrel_blc = dict_chart['pnl']['unrealised_balance'] * dict_chart['pnl']['price']\n",
"fiat_unrel_blc = fiat_unrel_blc - fiat_start\n",
"cc.set_line(dict_chart['pnl']['unixtime'], fiat_unrel_blc, ax=3, color='green', width=0.8, name='Include unrealised')\n",
"cc.set_band(dict_chart['pnl']['unixtime'], fiat_blc, dict_chart['pnl']['zero'],\n",
" ax=3, up_color='orange', down_color='red',\n",
" alpha=0.2, edge_width=1.0, edge_color='dimgray', name='Balance')\n",
"\n",
"#-------------------------------------------------------------------------------\n",
"# チャート生成\n",
"#-------------------------------------------------------------------------------\n",
"file_path = './result_chart'\n",
"cc.create_chart(file_path + \".png\", \"png\")\n",
"cc.create_chart(file_path + \".html\", \"html\")"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ka8uIr9vR6jr"
},
"source": [
"### チャート画像表示 (matplotlib)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "m2DZLkdB4KZc",
"outputId": "5d738e1d-4051-48ef-dd13-f442d1eb319e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 649
}
},
"source": [
"from IPython.display import Image, display_png\n",
"\n",
"# チャート画像表示\n",
"file_name = file_path + '.png'\n",
"display_png(Image(file_name))"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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VLFiwgO7du/PQQw8xceJEtm3bxvLlyxu0HrPZzKWXXsqePXvU59xuN3v27CElJaWpN7tNkGSHEEIIIYQQQgjRQG63m4ULF9KxY0fGjRuHyWTioosu4rHHHmPz5s2sW7dOfW9tbS1HjhxRp3k/ffo0R44c4cyZM+p7brzxRr766is2b97M8ePH+eCDD7DZbOrsLKJhZDYWIYQQQgghhBCiEXbv3s1ll12GxWLRPH/kyBESExPp0KED4JlBZf78+QGfv+aaa/if//kf9fGGDRv45z//SXl5OZdccgm//vWvpbKjkSTZIYQQQgghhBBCiKgi3ViEEEIIIYQQQggRVSTZIYQQQgghhBBCiKgiyQ4hhBBCCCGEEEJEFXNLb0Bb4HQ6KSwspFu3bhiNkl8SQgghhBBCCNG83G43J0+eJCMjA7O57d36t70Wt4DCwkKGDRvW0pshhBBCCCGEEKKN2bJlC0OHDm3pzdCdJDt00K1bNwC+/vprLrroohbemuhmt9tZu3Ytt912G1artaU3J+pJvPUl8daPxFpfEm99Sbz1I7HWl8RbPxJrfTU23sePH+c//uM/1PvRtkamntXBDz/8QM+ePTlw4AAXX3xxS2+OEEIIIYQQQogod/ToUVJSUjhy5AiXXHJJS2+O7mQACRFVHA4Hn3zyCQ6Ho6U3pU2QeOtL4q0fibW+JN76knjrR2KtL4m3fiTW+pJ4N44kO4QQQgghhBBCCBFVpBuLDqQbixBCCCGEEEIIPUk3FiGiiN1u5/3338dut7f0prQJEm99Sbz1I7HWl8RbXxJv/Uis9SXx1o/EWl8S78aRZIeIKkajkeTkZIxGObT1IPHWl8RbPxJrfUm89SXx1o/EWl8Sb/1IrPUl8W4c6caiA+nGIoQQQgghhBBCT9KNRYgoYrfbefPNN6XESycSb31JvPUjsdaXxFtfEm/9SKz1JfHWj8RaXxLvxpFkh4gqRqOR3r17S4mXTiTe+pJ460dirS+Jt74k3vqRWOurtcd79utPY7FaQ/4zZ2a29CaqWnusWxuJd+NINxYdSDcWIYQQQgghRDiz384md9zzGIK8pgDKqFG4PvpI780SrZh0YxEiithsNl555RVsNltLb0qbIPHWl8RbPxJrfUm89SXx1o/EWl+tPd5K716aREfZtTerywbANWOG7tsUSmuPdWsj8W4cSXaIqGIymUhPT8dkMrX0prQJEm99Sbz1I7HWl8RbXxJv/Uis9dXq452YhHvUKJwGs+Zpp8GMe/RoGDy4hTYsUKuPdSsj8W4c6caiA+nGIoQQQgghhAhl3KaF1LrsdPvSycuzswBPZUf7/H8CsPPdrfT7deQkO0TrIN1YhIgiNpuNefPmSYmXTiTe+pJ460dirS+Jt74k3vqRWOurtcc71mTlH1/+DysYhQNPdYcDMysYw/RlkTM4KbT+WLc2Eu/GkWSHiCpms5kRI0ZgNpvrfrO4YBJvfUm89SOx1pfEW18Sb/1IrPXV2uNtrzaz98ue5JKHBScAFpzkkMvKlUaKi1t4A3209li3NhLvxmm13ViWLVtGSUkJZ86cYerUqfTs2ROHw8H//u//cuLECSwWC0lJSdx777107doVgIqKCt566y1OnTqF2Wzm3nvvJTU19YJeqw/pxiKEEEIIIYQIZdymhfy4P5nPJ/wGMPAxY7jx2irW5SdyF58ACiNGKPzjH64W3lLRmkg3llYqIyODJ598ko4dO2qeHz58ODk5OUyfPp1Bgwbx7rvvqq+tWLGC3r17k5eXxwMPPMCbb76Jy+W6oNdEZLHZbDz33HNS4qUTibe+JN76kVjrS+KtL4m3fiTWza8ye4663Nrj7bSb4Nx8LAPYBcAvWYGCAQUj6zeYsFitWKxWzJkt262ltce6tZF4N06rTXakpqbSoUMHzXMWi4WBAwdiMHhOEikpKZw+fVp9vaCggOuuuw6A3r170759e/bt23dBr4nIYjabGTNmjJR46UTirS+Jt34k1vqSeOtL4q0fibW+mjve5sxMZs9/RE04+P+rTwJi+3a48koz27d71uf97JKbJvGzmk3nEhsG+rGPUOX3CqD06dOkbWuoumLt27bGxkqcJ+eSxmm1yY76WL9+PYMGDQKgsrISl8tF+/bt1dc7derEmTNnGv1aKA6Hg5qaGvVfbW0tAHa7XX3d4XCozzmdzoBlm82mWfZWkvgu19bWapbdbrdmWVEUamtrURQFt9utbofvssvl0ix7s4W+y06nU7PsbYfvcqS0yWQy0a9fP3VboqFNkbyfTCYTKSkpaoIxGtoUyfvJ5XKRmpqKyWSKmjZF6n5SFIU+ffpgMpmipk2RvJ8cDgdXXHEFBoMhatoUyfsJ4IorrsDhcERNmyJ1P7ndbq644gpcLlfUtCmS95P3OtD7/U3dpnAJBgVwp6TU2abcXBP79xvIyYGyTn3CJjQMIV4zAK4ZM3A6nRQUOLjySjNFRS5d95PBYKB///44HI6g+ynnidvDts2dkhJVx57/clO3yWQy0cfn+GtIm9qyqE12/P3vf+fUqVOMGTNG9+/+/PPPefLJJ9V/M2fOBGDt2rUArFmzhjVr1gCwfPlyvvjiCwDeffddNm3aBMAbb7zB1q1bAVi0aBElJSUAzJ07l7179wLw7LPPcvjwYQCys7M5efIkAFOmTKG8vBybzcaUKVOw2WyUl5czZcoUAE6ePEl2djYAhw8f5tlnnwVg7969zJ07F4CSkhIWLVoEwNatW3njjTcA2LRpk9o16IsvvmD58uUR1aba2lpmzJjBM888EzVtiuT9VFtby5QpU9T2RUObInk/LVmyhOnTp1NbWxs1bYrU/fT1118zbdo0amtro6ZNkb6fsrOzOXz4cFS1KVL3044dO8jOzuaZZ56JmjZF6n5asWIF2dnZLFu2LGra1NL7qSIlBYvVSmxcHEnt2mHcuIHYuDgSk5KwWK2c7dmThQsXhm2T44orSEhMxGK1Ep+QQHxCAharlYTEROLi4zFnZgZtkyvLMyVsMAbgyPjxAFSkpBATG4vFaiWpXTtiYmOxxsSQ1K4dq1abyJ7zO1avsZK8cVXYhIbdEqvOylJ27c2ebcdMtSUOy9ChxMXHc/U1Cezbb2TosDi1Tfb+/Zt9P/34449kZ2eH3E9HTOawbdt1992t7thryf+famtrmTp1Ktu2bWtQmyoqKkLshbah1Q5Q6pWVlcWECRPo2bOn+twXX3zBtm3beOyxx4iPj1eff/zxx8nLy1OrNF544QVGjx5N//79G/1aMA6HQ83GgWdgmNTUVPbs2UNKSoqawbNYLNjtdoxGI2azWbPszU57l81ms/prrne5trYWi8WiLlutVoxGo7psMBiw2WzExMSgKAp2u53Y2Fjcbre67HK5cDgc6rLT6SQmJkaz7HQ6cblc6rLb7cZqtWqWI6VNFouFgwcP0r17dxISEqKiTZG8nwD27dtH3759sVgsUdGmSN5PNTU1/PDDD/Tp00fN8rf2NkXqfrLZbBw6dIi+ffuqv+609jZF8n6qqanhxx9/5JJLLsHlckVFmyJ5PxkMBo4ePUrXrl2JjY2NijZF6n5yuVwcP36c7t27Y7FYoqJNLb2fjHfdhfnTT9Ub6bJrb6Z9/j8BT8VAxY03onz8MfHx8SHbZPzlLzGuWRP0ZjxcRQVAzl9+T+7kxRhcTvW7FZMZ562/wPXhh5hMJrjzTs02BqxjznjyJi8O+bzbYMDRoxclfUcx9MuXAtoZjgK4Ro7EvnRps+4nk8nEkSNH6NatG/Hx8QH7aVbhMmY+9xHu1WsxK+dj5TSYMY66HfsHH7S6Y68l/38yGAx899139O7dG6vVWu82nTp1ir59+7bZAUqjLtmxbt06tmzZwsSJEzWJDvD8KtqxY0dGjhzJwYMHefXVV5k9ezYmk6nRr9WHzMYihBBCCCFEEygqYvaG19RkgX8SwLF1KwweHPAxc2Ymhh071Me+CQf/dYRKeCjA4y8+xYKn/xzwOc33FhVhGTYsZBN8vzvU8o53trL/r/9Ayd/CaFZSUc9kR0NiENC+gQNxFhTU6zvqWl/OnPE89OJqLj7lqTLwjdXOd7fS79eB2yeanszG0kq99957TJs2jdLSUhYuXEhOTg5nz55l+fLl1NTUMH/+fJ599lnmzDk/QvOYMWP4/vvvycnJ4e233+Y3v/mNmrBo7GsistTW1jJ58mS1H51oXhJvfUm89SOx1pfEW18Sb/1IrJtBejp06ozToB2o0Wkw4xg5ksnvvhs03kqf0ONjqO8xmXGPGBG2+0WyUqr9DPAxYyjG5+Y9PR33qFEoJm0XlLDffe6/Dsx8zBgefd0zgGceuQE3bDlzxmse+3ZxOTtidNBEB4SPQUMHPa2trWV7dXXYmO5yD2YFo9SuOA7MrGAM05fJ4KQNJeeSxmm1w7mOHTs26PPeflLBtGvXjscee6xJXxORxWq18sQTT2C1Wlt6U9oEibe+JN76kVjrS+KtL4m3fiTWTW/cpoU4E9thVpya582Kkx335vDEdV2DxtuVlYVl1aqw6za4nDhffJG8kuXMGP9nTfeLkJ8BnjHlsPzGIVgqtqvP58wZT94qZ8jPBVsPgAUnz5hy2FdggIFQTDqfMJobqcKNAWOY9IIFJzVbd9IhxPHm2abQ3++cMaPe22u1WumycCGG224L+Z7/Ov2PgO3LIZeSlUaKi12hcjIiCDmXNE6rrewQIhij0chFF12E0SiHth4k3vqSeOtHYq0vibe+JN76kVg3jzOl3VnBaE3FwCeMJuvDzKDxHrdpIeMq/o9Jr07EibY62z91YBk6FE7/FJBMCWUdN1DgSqewInzlSLg5MRyYOUZ3wFMlUuBKp6LCwCUvPY3d7mDEzy0oJnPQRIe3Pd6KkG+qBtRZwQLaahPFbMY9OnRFSDBGo5HON99crwoW7/Z8zBhKGAwozJolVfINIeeSxpFoiahSW1vLxIkTpcRLJxJvfUm89SOx1pfEW18Sb/1IrJvemf1dqC6LIZdcLHgSEhac5JLHypVG/vu/5wSNd+/Erpzu2AMzLs3z4QYj9VIAt987HZhwYuJxFgCQR07YdRmBfzHCJ0Fz/mbfgpM/8jLVxJPDTPVbvQkBV3JHStv35BO/BM86RqjtseAkj9w6tyMYg9MZdqaZYGpra/mfZc9QM3kyBlf4xJABqCHWp20Gvv++oVvZtsm5pHFabTcWIYKxWq3k5eVJiZdOJN76knjrR2KtL4m3viTe+pFYN72SJdfQe/QWTfcO34qBysqnCBXuk2e6sILR3M6ngCdhcIrOJNTxnQbA4FcvYcFFIZnsIA2AYjJYlzSaEdWrMbnO13F4lz5mDDPJoYgM9fP/YoT62sf8iuuwq+vzJAQ8S+3fXsi3d80hj4e5k5XnPu9kEvP5kuvU7yom3TOex+Tz21lXNxwHZrZ1H8mQEFUdoQYhtQD95own4f7hmuf9B3dVDAYcl/XHfGkP1l+6kPZvLwy5LSI0vc4lL730EuXl5RiNRmJiYrj77rs1s556ZWVlYTab1e255ZZbuOqqq5p12xpDkh0iqhgMBmJjYzEYJFusB4m3viTe+pFY60virS+Jt34k1k2v4lh7dTmbWVxDlqZi4NAhMwaDK+BzJ/Z2oLqslFxyGeOTMPgjL7OQt/k3N/CfbFKrRUCbqDCgMNInSbKakRhJZP0ts/jZmj953lg0HdOwlZrvNXK+qmEHaXzCaM/0sqNHc112Njfcey/ObR9gT3Mwq1Bhnd0RtN01NQaKyVA/703w/JurGNLAGPpXlXQ8sRNLmJvouqbj9eX/PoOiUDv8RqgO3i5RP3qdS8aPH6/OaFpUVMSSJUuYPn160PeOGzcuaCIkkkg3FhFVbDYbU6ZMwWaztfSmtAkSb31JvPUjsdaXxFtfEm/9SKyb3h2L36H3kJM8sG4e6+9ZiOXS7nz5p1WcumcC5eUVXH/9H4PGe+Pr3goMT0UInK+o+IareZK5mkQHaBMVeX7dZvLIBWDOzpEAzCpcBunpfN7xZk1Xk48Zw0X5q9WKjWxmcdCaiis7G9LScO7cCWlp1OWqqxTsdgcjt2WjpKZyx7YZ2O0OMm/p0OBuKxZcnCVZjcFOrtTUrY0aTMIAACAASURBVGjG80CbwKhrZpljdNe0/+vYEcSv+QjL4e/pvPQ1LFZrwD9zpszOUpcLPZfYbDZqamrUfw5H8OSTN9EBUFNT0+oTtVLZIaJKTEwML7zwAjExMS29KW2CxFtfEm/9SKz1JfHWl8RbPxLrpuHblWIJ52YVmbwY8Nx8J/xrJaU3jg4b79KjiXQ7t5zNLH7BYp+KEKjsO5hPvhsdUL2REDdUTVT4Jkne5gF60Y0xR6eDFQxzxmO5+n5+7vOd3qTI1q0On7E/+wM7g7ZzRsbddQfDmyA5x9WhMzhdKAYDBuV8ysKBCYvP+CROTORNXqy2K4lUbKSSw0xMuCia00WNqS8DkPPaU8yYMD/koK0OzGzkOgYQz2O8xEfcrbb/4dr5rOv6YMibzoZOedtWXei55LnnntN0gbntttsYOXJk0Pf+7W9/Y+/evQD84Q9/CLnOt956C4BevXoxZswYkpKSGrVtzUmSHSKqKIpCbW0tVqu11WciWwOJt74k3vqRWOtL4q0vibd+JNZNQ+nTB3bsCFrBoADu5I4AmIcMwRpkfIklAExiatyj9F53kv+97lHgzzz7OICDymyFAbsUnjmYw52u811cnjHlsCDhU+zeriXbsyn6+i3u2DYD96hfUqOmTwK3CTxJkV3mQcyerfDhh4FdaxoicebkkK+54uI1iQ7P9nvGBBkCrGMEN7FBbZdnINNPmZS6mvWZczk8eSF/W98ZJ2bMBElolJaGnZ3GgpMnmUdJviej49vVZqdhIHnkMJtFQT/b0Clv26oLPZdMnTqVHj16qI/N5tBpgN/85jcAbN68mRUrVgRNeEyaNImOHTvicrlYtWoVS5YsCZsYaSnSjUVEFbvdTk5ODna7vaU3pU2QeOtL4q0fibW+JN76knjrR2LdNFxZWeTOGR/0NQN4xoQAXL16eQboDEIBnFZL0NcqK2HVKiPbXNoxMQpc6Vy3ejrFxefemJaGMmwYpKVRPWBIyO4jBqD2XPcXp9PAypXG8+toYu3fXogpbQCbu48O6D4zkZc4kX+Ix3lJU5VSwmCGspVP9/Wn89LXyBxipYP7p+CJjiB8Z6fxftfkd65UX89mFnvOVY24FBMLD9+JIzYRt8Fz6+ntCqOYGj7lbVt1oeeSmJgY4uLi1H8WS/D/F3xdc8017N27l8rKyoDXOnb0JBhNJhM33ngj+/fvb9R2NTdJdoioEhsby4IFC4iNjW3pTWkTJN76knjrR2KtL4m3viTe+pFYB5pVuAyA7dvhyivNbN9ejw+lp0Onzih+v0YrBgPuuARc3S7yPJGTo3ndd3wJA3C2R5egqz90CLz1GL436ue+RZ0C1perQ2fccQk40b7mvfnP51qfmVWCr6Op7P/lZH5/InBMkTe39qePfScF9v6asT5O3TOB+DjFb36Z+jMARhTyJi9WK2CeesqMN4Y7SKM/ezXtP0AKRsWtXY+r4VPetlV6nEuqq6spLS1VHxcVFZGQkEBCgna+IpvNRnV1tfr43//+d8QOVCrdWERUcbvdnDx5km7dumE0Si6vuUm89SXx1o/EWl8Sb31JvPUjsQ4tL8/E/v0GZs40qV08ZhUuCxi3Ytymc1OVdorD4NRWHhgUBXdysvrrrXvQINzFHVHM5oD3Aly8+/tz41JMAkAZOBBnQQE1NZ7JZeH8jbrPt6hTwIJ2XI1KSzId0HZP8SYa2mvG5dCuo6nNnm1ip3kwnzjPV28EdJ/xG+vDnZxc58CmTkyYfdrnBtrn/1OdneZOVqoVMJwItyYDpx3t+ITAMVEuZTBS11E3Pc4lNTU1LF68GIfDgcFgIDExkYcffljtNvPOO+8waNAgevTowWuvvYbb7Ulede7cmQcffLBZtulCyVlXRBW73c68efOkXFQnEm99Sbz1I7HWl8RbXxJv/Uisgysq8nQbAUJ28TBnZmKxWlly0ySW3DSJlL1HNK8rwNexI1As5wdstNvt/GCxBE10+PMdGNM700mofwUFgetr//ZCDllTWYm2+8gKxpAyOo0R1yt1rqMpeGPpdBrIZhZjJ2+sV/cZxaVwqkt/ddu9vLUX6xihSXSA58bxIJeSw8ygFTCjRrlDxjCxg4VnTDma6pNnTDnMnt18FS/RRI9zSadOnZgyZQpZWVlMnz6diRMnaio27r//fgYNGkTnzp2ZNm0aWVlZZGVl8fvf/55OnTo123ZdCEl2iKgSGxvLnDlzpFxUJxJvfUm89SOx1pfEW18Sb/1IrLXMmZkYNm5g6DArCgZKr/0vFAxcNdQzBalh4wZ1OlJDkIFGfRmAh2vnYzx2lA7PZ9F56WsktWvHpT+erNe2GADXuYExww3+GUpREfz0k4Ecvylpc8hl5UojQYY5aBaeZEHo7iPBus+0f3shpiv68cyV74acbtc7zof/OCApHGIHaUG+y8D33wevFfHGyjsmCqBWhDTneCbRRM4ljSPJDhFVXC4XBw4cwOW6sBGvRf1IvPUl8daPxFpfEm99Sbz1I7HWCjfFaEPHj1jHDew0DKRSia/zs/7jajTFwJjeioRi0gMG/wSF7xbc1+h1N4QnwRB6qNRQCYjKSliwMTNoQmMz19Lp+gFqJYbv2ByjR3uqN2pqatmzZy81NbV1Vq/4Vm9kM4tq4uscE6U18lYjhfpnzsxs9LrlXNI4kuwQUcXhcPDmm2/icDhaelPaBIm3viTe+pFY60virS+Jt34k1lrhBqP0vyUPNauKAxNPTX6fx1mASzGx13VZ2LEnvuGqgO4YTTEwpm8SIfAGPnSSoakVFDgb3AUHzg/KmneuMsWb0MgjF4CNG41hKzEacmz7xmIHaXzD1fWqCGltcp8YGTLx5tttqjHkXNI4kuwQUSU2NpaZM2dKiZdOJN76knjrR2KtL4m3viTe+pFYaxWRzk90DhgnoiEsuBjOV+rNciWJ5CfchNNv0EYHJpyYGM8bQasXii9wWMyCAifXnxuXo8Den7jrh1Fg79/sY3Q0Fe+grMErU6Cu2Wkacmz7xurUPRMYfukhTt0zodXEyle4WYSU3r3CTkfs7TbVGHIuaRxJdoio4nK52L17t5R46UTirS+Jt34k1vqSeOtL4q0fibXW7NkmDtA7YJyIuni7oXgTFSV+iYo/W6dhdmunNbXgopBMdpCmVi94npeBMUE7KKvvtLR2uwPPTKPa2Wn8KzEaemw3ZlyUSDH79afVriiZQ6zs228kc0hgNxXDtm1h12MZOrTRXVrkXNI4kuwQUcXpdLJixQqc9RiFW1w4ibe+JN76kVjrS+KtL4m3fiTWWl9uP00lSZpKi3CcmDSzgviPG3HqngkM77qHR4d+FbR6o5JEIHBcjaYaGLM138Brtt07LW2aJ6ExYu3TdXaNaVPHdmycpntK2bU31+tjod7XmC4t4eIdrtqkrZNkh4gqMTExTJ06lZiYmLrfLC6YxFtfEm/9SKz1JfHWl8RbPxJrrTsWv0PvISfZ9uplmuoOZ4hbEjMuJjE/ZKLCO6vI/NjpQac1tVrBtztGcw6MOSPj7iZbV2vQlo7tcN1T6pv48NWYLi3h4p2XZ2L/fgMzZ7btaqVgJNkhoorL5aKwsFBKvHQi8daXxFs/Emt9Sbz1JfHWj8Q6uMOXXYzt4l4AnO3eDzPuoO/zdlkJl6iorIRVq4IPpmm3g293jD9PXhCVA2O2hGg+tv1nVamre4o/B4FJB29SpLEzAfnH23cbV64yoWBgxUpTk8z8Ek0k2SGiitPpZMOGDW2jpC4CSLz1JfHWj8RaXxJvfUm89SOxDq164FAUk5np7lmsYJSmC8o/uUGT3AiXqPDOKgKB1Rt35k9l1Ch3g2cqEXWL5mNb6dMn5GxA9WHBxb8YEfS1umYCCjV9bWxcHMOuvprYuDhPAmbHDs3nfCtMLnTml2giyQ4RVWJiYnjiiSfaREldJJB460virR+Jtb4k3vqSeOtHYh1a4ur3qEgfzis/3k0ueZouKE8yz296Uhg4UAmaqPDOKgJSvaGnaD626zMtsRtwnzvufJe948VM5CWqiWcdNzRoJiClT5+Q09fW14XO/BJNJNkhoorT6SQ/Pz8qs8yRSOKtL4m3fiTW+pJ460virR+JdXjeqgzvAKJ5kxdrZlvxrcoIVYXhO6tIdXUNGzd+SXV1jVRvNLNoO7Y1FRXDhgV9j2/3FCNgPJeW8C7nTV6MBSd55LKDNPYkD2OK6S8NmgnIlZUVcnyQ+lSbODCzufuYBneTiVaS7BBRxeVyUVRUFJX9ByORxFtfEm/9SKz1JfHWl8RbPxLr8HyrMrKZxR5S1S4omyaPbXBVhsRbP9EW6/pUVPh2T/mYMZoxYnyXPck6hdJSQ9CxZMLNBDRL2Yt71CgUU/jZikIlPiw4mXAi94JnGooWkuwQUSUmJoaHH344KkvqIpHEW18Sb/1IrPUl8daXxFs/EuvwfKsyCuz96WPfSYG9f6OrMiTe+om2WOdOvDVkRQV4pkHe3H0MK0bMV8eFyWYWJ/IPqcu+yTp81lbfmYDGbVrIt2U/kH17KgaX59hvyGwv3m4yJQxq0pmGWjNJdoio4nQ6Wb9+fdSU1EU6ibe+JN76kVjrS+KtL4m3fiTW+pJ466epYh1qQE7dZxVJTMI9ahTOIDOpgGca5Akncvnsh0H0yF/JDtLYQRr92atZZuBA7HYHAwcq3Jk/DfCMJeM7Bk0xg1m5yhTQ1iU3TeKKov38fvZyPmG0OtZHMIo6Rsj57fV2ofnk2ufYt/EYZQ882lTRabUk2SGiitvt5uDBg7jdwacwE01L4q0vibd+JNb6knjrS+KtH4m1viTe+mmqWIfrPqLXrCLeioqJg4ZhJni3nI8Zwy7zIAYMUELO7uNbjVRQ4FSfO3XPBEZYvkLBgIKBQWwPuz27q/sx05itjvUBqEkYB2bWMQLDuahZcFHSaQQA7tGjWXfPIoZ33cOXv5h1oWGJCpLsEFHFarXy29/+FqvV2tKb0iZIvPUl8daPxFpfEm99Sbz1I7HWl8RbP00V63DdR5p6VpFQVSTeiopH577DCp+KCu80yGMnbySHmTidhrDjbYSjmC31nmXlqdNzKHJnqmN9rGMEZlzqAKiTmM9PdAY8SZj/Pv0S7th4XNnZtH97Ie0/XOz579sLG76hUUaSHSKqOJ1OPvvsszZRvliZPUddbqkSwLYU70gg8daPxFpfEm99Sbz1I7EOL3Hm5CZdn8RbP00W63PdR7wDcnrHqFBMZtyjR6uzijTmWnf7dvgucYj6HsOOHZrX/cfD2FV9ObnkerqDnEssPMk8tauKR/DxNuriTk5WkzqhBhd1YuKsuSMlDALOj/XxOC8FDIB6gBR1jJAdDOQ3SZ9AWlrQ9bZlkuwQUcXtdlNaWhqV5Yv+J3njxg2ak3dLlABGc7wjkcRbPxJrfUm89SXx1o/Euvn5Jkwk3vppyli7srLUATm9DC4nrqws9XF9u7v4Xi9nDrHS3x6+y4ivO1lJERnqY99pkH22rMGzBAEolhhsF/fCaQy8/fYmXcy4OGjojXdw0x2kqeOD+A+A+tnkh3ySMI3bprZAkh0iqlitVu67776oKV/0PWEbduwImQlWQLcSQF/RFu9IJ/HWj8RaX2013rMKl7XI97bVeLcEibW+JN76acpYF5GuGZDTO6tIsU+iYc+9WZprXd+qDN9r3bJOfUJeL0PoqgpfClBBgs/sKTBwoNLoWYK8qgcMwRwiOeTAzCeMwZ4Qw4xv3uWBdfMYOPB8esd3MNSm3KZoJ8kOEVUcDgeffPIJDoejpTelSdRnzm8InehwGrQlgE0t2uId6STe+pFY66stxtucmYnBp0JPzxkI2mK8W4rEWl8Sb/00Rax/+db7/F++izseOs5sZqgDclpw8owph843nu+CcsXYYUHX4d/dJZcczev1SW74MwCxWzdc0DTIwXiTOqFmUskll+rSWHat6wloBzmtqqpm6dJlVFVVS4KjASTZIUQEq2vOby//E7laDqc42X13VrCPRM5UX0II0QaF616o1wwEQgiht+S/f4Tl8Pd0Xvoaq3/3INfZN3GiqBfbuEp9z8eMocCVTmGF9ke/YIkLg8uJYedO9fp1/sYhDdoe/x8VnUZj8/xQWFvLiztHMtOYrZlJpZIE9S3FpHMD6/lo2vUsuWmSXI83AUl2iKhisVi48847sVgsLb0p9RYu6WDYtg0lKSnknN/hODCzgjFMXxb8JOlfNeJbDljfC+3WGO/WTOKtH4m1vtpivH37ovtrzu6H0Dbj3VIk1vqSeOunsbF2JSaFHXsD4JesQMFA0ZwuYX/0c2LiY8aQNflX9aqEPj996/nrav/1m93usOfnxmj/9kJqevbjs6MZFLkzNTOpfMOweo1FIsd240iyQ0QVu93O+++/j91ub+lNqbe6uqqUXHRxyDm//T/nXw6XQ27IKbJcWVnkhijtq++FdmuMd2sm8daPxFpfbTLe6enQqTNOg1nzdHN3P4Q2Gu8WIrHWl8RbP42NdfWAIWHHmavv9KzgGdDzl6zAXPpTnUkR7/St4Kmo+BcjADhG97BjhTSVQ4fA27ovJ49VBxt9krn1GndPju3GkWSHiCpGo5Hk5GSMQUY6jlSurKywJ+gZifP95vwOnY32PXmfH0E6xBRZ5y60vVN9efn3fQynNca7NZN460dira+2GO9xmxayL7EdZsXT57o+3Q+bSluMd0uRWHvoNRivxFs/jY21q0NnHLGJ6nWtlwMzZ7v3q1f37YYy42IS8zXTt07kJaqJ5zFeChgrZPbshldU1+XhA9PwnWXFO9hoMemconPweIw4fz0ux3bjSLREVDGbzdx6662Yzea63xwp0tNxjxql/rrnveB1YqIyqT2rC37uN+d3+Gy09+R9fgTp4NNRjdu0kAOd4uqc6iucVhnvVkzirR+JdXjbt8OVV5rZXv8Z/cJqq/E+U9rdL5lt5hNGh+x+2FTaarxbgsRaXxJv/VxIrA+QoiYYvCw4yba+EDQR4gbc6qCeZjb7jO9RH94fALOZxdjJG8lhJjtI4xuuZjm/5hNGkzd5sTpWSKiq6AvhO9io779T90zgrKVr0Hjk+Qy2Ksd247TaZMeyZcvIysrikUce4ciRI+rzP/74Iy+++CK5ubk8//zzHDt2rFlfE5HFbrfz5ptvtpoSr3GbFvLfG+YycdAw9dc9LzMuvnelAArF50ZvhvDZ6NnMoFeqBcul3Vl/z8I6R2u2mzrUOdVXOMaMDBnkVEet7fhuzSTWgXzHF8ocYmXffiOZQ5rm//e2GO8z+7tQXRajJrPBOxp/XrNcaPuy2+08snZBm4p3S2mLx3ZLknjrp7Gx3n72Eo7Wdg56/bnw8Bj2O3sH3PgbAaM6qKeTCSwOWg3hTYrkTV6MAzP/5AY1uQGB07femT8NgGxmqd1KPEJURTeDA08u5KijmxoP77Z/zBgWbMhQ/xbIsd04rTbZkZGRwZNPPknHjh01z7/33nsMHz6c3NxcbrnlFpYsWdKsr4nIYjQa6d27d6sp8Tr7fSdO776I1Z+NYwWj/H7dG8PZ2vZ4S958T8T+2WjwJEEKyeTTff2pcpwfvCjUAKhLbprE5btLGMDOgIRJvcv3UlLqNaiSaBqt7fhuzSTWgcKNL3Sh/7+3xXiXLLkGICCZHbb7YRMxGo3ExsW2qXi3lLZ4bPvznWZ5yU2TSNm2S51poql/GJF466exsX5x50gA8vwSvXnkAgonnF2CJkL8z5MHg1SH+CdFnmQe/dkLAwcGVFQMHHj+L5p/EiRUVXRz8F5zh4qH92+BXsf2Sy+9xOzZs3n22Wf5y1/+oikq8NVaigFa7ZkgNTWVDh06aJ6rqKjg8OHDDBvmmYc5IyOD0tJSfvzxx2Z5TUQes9nMDTfcENElXr7Jh9W/e5Dhtk0c3HYRY1jl9+teLm63kd+9/SkPrJtHgb0/few7KbD3Z9Qot7o+35I8gGIGkXx8D52XvuaZ0WXHjrDbs5MBAQmT+v6q6M7OrtegSqJptIbjO1pIrAOFmwr7Qv9/b4vxrjjWXl3OZla9uh82ldhhw+hYXExcfLxU5TWztnhs+9NzmmWJt34aG+sjHdKBUIlez3kv2I1/NjOxGeO5Y9sM7HYHsZ0TWOmXFFnBGDZ396zTPXo0/7ZfGbLCOVS3krqqopva998buDN/Wsh4eP8W6HVsjx8/nqysLKZNm8ZNN90U8kf+1lIM0GqTHcGcPXuWdu3aYTJ5MmAGg4EOHTpw9uzZZnktFIfDQU1NjfqvtrYWQC07cjgcOBwO9Tmn0xmwbLPZNMsulytguba2VrPsdrs1y4qiUFtbi6IouN1udTt8l10ul2bZZrMFLDudTs2ytx2+y5HSJpvNxqJFiygrK4vcNtVRDQG+Jzn4x/whoKDZT999p6ild/7Z6O+oe05yX7nkBbnQ9mSS62qT7YormPzXiYHjjRjMOEaORBk0qM0ce3q0qaKigr/+9a/YbLaoaVOk7qeqqio11tHSpgvZT+O+fJlvnWXkD87EgVkzVbWXZehQ9WbZmJ7eoDaVlZXxyiuvaP5mRvuxd8fr79A78wQPrJvH+ntexnJpN7780ypO3fMQFRWVFBQ4L6hN4boZ+ifB/aced/bqFTHHXkvvpwttU2VlJa+88gqVlZVR06aG7ifHNM/1SjAGoGbKlCZrk/c6sKKiolnbFI37qaFtqqmpUa+5G9Km/Pwqrr9eoaamlp9vngpx8dy2ZRoVFZVqtUWwG/8dDKI4bhikpfHvfzv56ScDOX5JkRxy+d2JWdRemkrt1KmtYj99800NdruDysoqbt0yHSU1lV9s/hPV1Z7n8/Or1HX99a9/pbq6usFt8r7f9/7Ue3z4i4+PV5dramowGAIT762pGCCqkh2R4vPPP+fJJ59U/82c6bmBXLt2LQBr1qxhzZo1ACxfvpwvvvgCgHfffZdNmzYB8MYbb7B161YAFi1aRElJCQBz585l7969ADz77LMcPnwYgOzsbE6ePAnAlClTKC8vx2azMWXKFGw2G+Xl5UyZMgWAkydPkp2dDcDhw4d59tlnAdi7dy9z584FoKSkhEWLFgGwdetW3njjDQA2bdrEu+++C8AXX3zB8uXLI6pNJpOJnj178pe//CWi2mTvn47FaiWpXTtMq1eH/XUUzs8vfgPrObjtIpbcPImkdu0wZ2ayd+9e7r77Gex2B998s4UXXpiD3e5g48YvOXrnb7jpt5eEHcnae1HrwMwpupz7A+LpFuNfvlfXfnrkm9c50rFL4HgjipNptl5t6tjTo03Lli0jPj4ek8kUNW2K1P1UWFhIeXk5JpMpatp0IfvJbndgxkgeOQFlw/4U4FuHo0Ftsl1+Oadt22nXvj1J7dphsVqJjYtTl5VBg4K26U9fvam2yXnllSQkJmKxWomLj1eX4xMSiE9IwJyZGVH7yeV2URvkAtnlcoXdT9u3Q9++LhYt2hSwn3zbdNBorDOxHowBmBMfHzHHXkvvpwtt0+eff056ejorV66MmjY1dD8VGwyeaZbRds3yTrOc9eGHTdYmk8lEp06deP3115u1TdG4nxrapqqqKgYOHEh2dnaj27Rw40aUYcPYUlPDG2+8QUGBk8GDz/Dqq68xcls2LmsM5tlXYbc7WLp0Gb17nQLg0Uc9/w2eFBnIPenfsnDjxla3n/5x/DjOnTt5p7g4YD+ZTCZOnz7N7t27G9Qmb+Lvueee09yffv7554Tyt7/9jWnTprF69WoefPDBgNcbUwzQUgyKojRkOuOIk5WVxYQJE+jZsycVFRXk5OTw4osvYjKZUBSFqVOnMmnSJOLi4pr8ta5duwbdJofDoWZKAY4ePUpqaip79uwhJSVFzaRZLBbsdjtGoxGz2axZttlsmEwmddlsNmMymTTLtbW1WCwWddlqtWI0GtVlg8GAzWYjJiYGRVGw2+3ExsbidrvVZZfLhcPhUJedTicxMTGaZafTicvlUpfdbjdWq1WzLG0KbFP8Nddg3Lkz6DGSM2c8eZMXa5YdmHHHJVCTPpTk/H8GvE8BlFGjsC9dGrJNVb+diCmlF2zZQtK/PsWMS7MO8CQ72p9b/4MfvoDS0cLc1zwnTlNKL1wHDpHw5oJ67aeHvn6FH/e35/cTVnE7n1J97Qji8zewhtt5Y+SHLF+uRPx+isZjT9okbWrqNo378mXsNRaW3vFHPmYMI66tpkP+FyH/Nld99RWmzMx6t8l4113Mvv4izbnKSwHcd9xBzXvvBbRpyqalrHpoLMuWuRic80uMa9YETfZ6z581770XMftp3JcLPVtmMAScg2MXz9Xsp/hrrgnbJdE9YACuwkKMGRmYQvzd8ef7t8H7d8FpMGMcdTtVb78dMcdeS+8nadOFtWncly8DBpyna3nvnmkBx97Od7dy6R2Xt6o2ReN+0rtNjmcWEJfzJE6nkzm7VjI17ZdqmypmPE/MjElB2/Tc9uVkZ95LRoaR2e1e4M78aQxkOx9xF79iufqj3cCBCvn5VbKfams5deoUffv2Zf/+/fTo0UP9G2A2m7FYzo/xF8zmzZvZtm0bf/jDHzTPHz58mDfeeIPc3Fz1uRdeeIExY8Zw+eWX1+tvkF6iqrIjKSmJnj17smXLFsDzy1xycjJdu3ZtltdCsVgsxMXFqf9iY2MBsFqt6uveg8tqtap9r3yXY2JiNMvezJnvcmxsrGbZO2CNd9lgMBAbG4vBYPAMRnZuO3yXTSaTZjkmJiZg2Ww2a5a97fBdjpQ22Ww2XnrpJbXkqiXbRN++DepOYsHJvPY5dfaJD9cmMOA6cJhKSwfMuALW4Z/Z7H1gH0tumkTnpa9jOXyApP/1XJT4t8mcmUliUhKxcXFqhUpsXBxLbprE1WX5XOk3yGkueaxZY6GkpO0ce3q0SVEUXn75ZfWPcDS0KVL3k8vlYtGiRerFRDS06UL2EwYDpccSAYU8bYjzQwAAIABJREFUcvGvGfBWjCkmzy+11qFDG9Smusrc3dnZnlhmZvLcy38kMSnJcy7atpH935nIHGLFtGYNuSHOs97zZyTtJwyeuPo8UJf991PuEyPDV2n07ev5pN/fHV/Buh75MytOdt+dFRHHXuywYVisVhISE9WxRRKTktTlhGuvbRX/P7ndbubNm4eiKBFz7OlxLvfuvyU3P8mSmyeReuh79TjLm7wYBVjBGKYvy2zSNnmvA72i8e9TpLTJ4XAwf/58DAZDg9uUOHNyyDYZDMaQbTIYPN9fWOjm+usVz9gaPmPa+Y63EW37ydtFy/uDekPa5H2/7/1pXYkOgGuuuYa9e/dSWVmpeb5Dhw6Ul5erXWcUReHs2bMB42lGglab7HjvvfeYNm0apaWlLFy4kJwczzzEY8eO5f/+7//Izc3lH//4Bw888ID6meZ4TUQWs9nMiBEjzl+gt6Bwg/l5KaCZYmrqiSeoMQfOL+7AzNkRo2Fw+Clh27+9kPZvL+SQNTVg0KZ1jAi7PQrgTu4Y/LUwszAA9GO/5nEx6SgYuGqoDHrXlCLp+I52EutATrsJMFBMOqfoHPQ9BpcTV1ZWg9dtzMzE2T4ZxaQd/8ebPPGe+xoykGGodegt3IxYKdt2MXv8i3V+htM/ac7fvokLA2BctQqL1Ure8K51/t0JxYlJvfmMBM05+4+e2uq5pK7rBgOQQ26TT7PcVuPdEpoq1jMy7m6iLYpuehzb1dXVlJaWqo+LiopISEggISFB877GFAO0lFZ7Jhg7dmzQ57t168bTTz+t22sisphMJjIyMlrku82ZmZoyY0MdlRxw/nc83ymm9hj6cz0rNO/zvJ5D4CVxoKIi+OknA7nkMpqV6ucnMZ95PM6QMNti3bebzvt2w9LXNK/lzBlP3qq6v1s59y9kGXkruTiNVC15fLc1EutAPa48zQPr5jH3tW+h0kB1fCfyqwfxn3j6FTsws5qRXMpgGppWeOjrV6BbEgaXdjwQg8uJ0yd54srKgg2v+X88LP916E3p0wd27AiZhDjVoxO9GvgZLzfamhB/3u4Cis/7wRBQmQN4uj2SS8lKI8XFrpbKDalyJ97KzFXB//AYAMOqVRjP/TqqDByIs6BAx62rv2g+l/hf9zTEOm7QTLP80UeB1aiNEc3xjjQtEeu2nBjRI941NTUsXrwYh8OBwWAgMTGRhx9+WK2Yf+eddxg0aBCDBg1i7NixLFmyhM8//5zY2NiILQZotZUdQgRjs9l47rnn1IHe9KT06VNnVxVfDswcozugnWLqrCMp6PziCzZk1OvXD+983cEGbZrISzgxBQwS5jYYUUymsL/C1Ee4i26ZivbCteTx3dZIrENr//ZCTGkDuO/mH5li+oumC9svWdG4ii5FodKVEPTcV+ybOklPh06d1QoQ9eP+qzv336DraAahqjfqU23x8YM/D3jOlZVVrwoNI4TsuuPLuy4jBE10gO/fIc/NZ4tLTMI9apQ621cokZ5Ij+ZzSbjqjZw541G6d8dp1N5qODBRQQKPs+DcM007zXI0xzvSNFesE2dObtL3RQs9ju1OnToxZcoUsrKymD59OhMnTqRnz57q6/fffz+DBg0CzhcD5Obm8qc//YmLL7642bbrQkiyQ0QVs9nMmDFj6lXiFe7itDFdLupTuu3wSTJYcPJHXmYPqVz+vmfO8FGjPL+7BZtfvL4XoL4XDf5Tyu4gjW9NAwPG8zAqbtwdOtW7/DlY328nJtxxCdhTr8B9rk9lpJSRR4uGHN/iwkisw6ushFWrjGxzZahJ22DqfyNqoOqnDsw0ZmvOfbOZoSZw1XX27hVYARKwNtR1BEvANHW3urpK9s92TFKTzOoU3ZioSk7iSF/PBaL5+A90eD6LzktfwzJsWNDkuZvwM6n4vs99Lgr+iXX/hNI/ucFv6vGG33w29d9Tr113Z6mzfYUacyTSE+nRdC7x38/GVavCXjcYTpzAfG7aSy8LLmK3bggYX6HJtjGK4h3pJNb6kng3jiQ7RFQxmUxcccUV6iA94TR5f+AQvzh6Exx5kxdjwcW/GAF4Ljo/5lf0Zw/T3k8HPImKO/OnqVUZeZMXa6o+6nMBWlDg5PaNT6uDNsVdP0xzUXFpeoegv55WWDpoEhUNqVIBTwl0pSWZqpQrMCrai5vG9uMXWg05vsWFkViH9+jhqXhvux/jpZDvq++N6JnvulBTFkuRO1NTkVZIpqZP/7hNC9leURNwDjtG94DHEDox0NTVAHVVYiy775GAJLMZFwmlFecGiX4Ng8NeZyLDSOjqOf/3eSs4vIl1b0LDP5n+JPP4hqvpebEz4OazvkmMpv57Om7TQr4t+4HbXunACkap+9b7HWrCyBD5ifRoOpf47+f6XifoWWkVTfGOdBJrfUm8G0eSHSKq1NbWkp2dTW1tbZ3vDXdxGu4CvTJ7TsBz4zYtZNymhRzoFBfwi6N/gmMiLzF28sagv6IVFJy/2By5LRslNZU7ts1o0l8/Dh2CZ0w5Ab+eHi5Npnb4jQGJCl8Ov+4vTkzqAKsrGMMhayr7Z39Qdym6aJSGHN/iwkisw/OcszznreX8mlN0DhhYWTHX/0a05K1r1GX/ijT/qraak8nkoT2HPc58zeM/8jI1xOrWrW6Wshf3qFEBA6x6TfjrC3WuI9R4R15OTGplBpyv3PD9PJw/5/omzD/mV3zD1ewgLWgXR4CDlV0AbYIj3HgMCmDYsaPOX/h9B1BtSKWH0RbPka8uI5c8dd/6f4d39phI5n8uaa4qGD3UldQLV33j+cHHyTOmnIBqraYk5279SKz1JfFuHEl2iKhisVj47W9/q06nFO6iYvaG11CSkgL7fvt1udi+Ha680sz27eG/u3diV+ym4FUT3d7/i5q4CDVFVoC0NJw7d0Ja2gXFxHcwJ+/gpdtcGZoL4UIyOV0Tz7LdgzTb7883cbOOEeovlRac5JDLTz8ZePppE7OZobnxaO6Lm7bC//gWzUdiHZ5vYvbUPRPocFGM+v+894bH4Kx/RVfF8fbq8g7S1BtzgGIGkz58gjqDydVlX1PE+RvCMtrxIfdobuA/5lds5lrN+ay5utV5qxCyb08NSHY3hAFwXJoSMMaBlxkXz5hy2HBDHm5rbMDYG/4DXmcziz2k+iSNzstmFi5rHLdvnc6peyYw/NJDfPmLWUDdXXJ8vy/Ur/zhprmtb6WHd6pj3+SMfwVPJM0eE4r/uaS1zTKjuY4K073Km3zzb5t/N6oCV3qTz8DiS87d+pFY60vi3TiS7BBRxWQykZKSopZ41XXRpmRmqhen6gW6y4lx5Ur1j3vmECv79hvJHHKuj+rGDUGnEJx59f1cvruEAewMuNGf/kF6kyQuLpRvwiHYhfCMg7/TVH0Aaj9zb+JmIi9xIv8Qj/NS0F8HN240UsAQykhS17HNlcGKlaZW8+tVpPI/vkXzkVg3jOJSsF3cC+XciO2KwYDt4t71TigMn/YZlhgnI19fot58/3/2zj0+ivLe/++9JiAIKFa8IAQPipBACILQgqJoPa0YYmtBOdWeFs7xWHu8YAXRkBuIClXQUs+vVkWxKAW5qcVa5Va0UQMhQEBB5CZQBJRLAtn7/P7YzGR29ppN9tlLnvfrta/M7szOPM9nJrPPfJ/v5btHn+DYuHvILcyJ+N2NjTWmgj1CoEdh/4D7GSQmrC7bYudo+xsajSvRrxmjV4bPZMaXlU19n4FBOQ5U1AfF59cO4DZWBpUXNya8riWXPuzUjEZ6asll25q5mEOcn1jKpoPfuBFPWslQnh6hflOvOfVPzSOwhOkcqdzP/Twf8PuaiNKlrYHeQJDdrh1XXHkl2e3axeQFk2r5R2IxfunDpox90+cnC+et1ZrIe7c4pNZikXrHhzR2SDIKh8PB5MmTNRevaC6XD4y4ieWMIXzghp9Is1RGttMvwGsi0bMYzUGf86OWPPqwK2AgfORIk9cHBHtvlFPWOID2fy/0zKF/wLORq9Nq9iodMF7fksSRjlqHCrETQafX52G56grMy5dgUhofeBSFs/3CFbr2o38gfOe/fsFw5wbe+a9f+BN0HtijJes0hyk/qjKJZ4FgjxCA+yvHCwurW/+nvEavttAlNNVkoA0Ee2WYFR/k5oYMAwxOIKqwfr2Z0sbcG2p4gD4vh5/I9bE+/vhI6OtbrYLSaLSJ1WNDRV/mVjXqhPueQrAngJ7+bEPBxDb64ySbpfwshJE9RarH6IjmvVE699chvUqVjh2xDR4c0RAkOvwl2jjKGF6let+oIa5N+cn0443WrcCiJx3v3emK1FosUu/4kMYOSUZht9t56KGHsNvt/g/y8yl9+dGgOGqP2cy35q68894EKswlQf8IzU3OqaeM8hBGgNQYjOmTlxpfXbs2DTvVGVLVe8OYKDU3V8HlcrPRcQXt9n/IRscVuFxuptVNRR1cP8yzaTV7lQ4EXd+ShCG19jN98+KYtutQMRny83Fe0gMAV++r8HY5P+J3orrzRzmmYrFywnIBW8kPu81Wx5VCwupcZ63s+kd3qhmkPfQZseFh0A/Pw3HeRSENMPV0YMYMS1BOJTWBqP5B0Rjeoc/L0auwX8h7/HXXKdqyw+GkutqD3W6n89/ewnZgj9/IZLdjWr8O89tvByVUjZVoZW71hg+jOSaSUeQr/AbyfuwA4CesQMGEgpmVb1uEGwEiES0nGC5XSK9SpaAg7v8JNX9Kq/ffMI4yYsWrhU2F8r7xV5ODqqrA67E1K7DokfducUitxSL1jg9p7JBkFGazmYsuugiz2RwxaajV5+N833H2bbqIzb6m2UfjQCJiCIzhvRsLx7ig0X3Y6DWRuFmM1qKhoWnYqZ8hVQ03f/638qBBil5vaIrjPzbuHtY++ld87c7RqruoyDK08WPUW5I4pNZ+zwtTY9jeglGTyNm0gwWjJkV8iDqbOxjFaqNhxKio+48ULhHJJ0Hbxuuhc36PgAeokboH+kGDFOrqTAEGCKO3Xaw5mSJxYs/5HNl5HkZj8WquDzBofNKtiA7vvslBc48gg8YTllK++qqpnG+oEEH1YTE3tymVaSjvunC/NR0qJmvL+uvb26FjxAdsf/uDk1OH3i58mdtQx4h1YsEEjK4qxuVy0+OS6FVr9O1qrhdhqyQQbTQQeEyhE9aGw7x+fYv+J4zE2v9ofXbVnQqZj0Y/EaKOe5LtfSPv3eKQWotF6h0fUi1JRuFwOHjggQc0F69QSUMjDeqMA4mI9eMJHMDY8HIBxxpnmvwvX25ewmcxWourr1ZCPjSoCVX/suOqoO8Y9Tbi69xZq+6in70SWYY2nTPfG4mmt6T1kFpHfkgK9xDl7Xwenou74/tet+gH6NCRE9cVBiVEdmPFnd0BV++rgpJ1GiuO1NMhYL0+IfP+/U3f0Ofz2EJ/FExcPTg4J1Os9whjCM51vnUomLWQCx9mHubZAIPGPUfKWLwYRrzzeFCS6GpvPidPmgLaq6/atWHyndrDoj45rDHh9cjrlJh+a/TX99l+gyJ7IRA+ObV+u1DhNPoyt/Ga+z1mc4CB3NjeSIaEcF6EkX4XTLW1LQrBVCdavuxwLlal9X73jYahWAwoseRHiVZ1B8Ducge8V4APuT5iAtzAdeImfOS9WxxSa7FIveNDGjskGYXdbqe8vDzAxevk4Q5UmEuiDrhMwBpGBiV8U9/r44/VddE8P9I5L4X+oSEcofRW8e49QJ3TnvQytOmW+T4SkfSWtC5SayIaJVsaiqZWMLnz5ISgBKI2POz19eBMzlVByTqNJSz37Q9/jHDeal8ReE+I9BAZ7h4R7b7itWez3TogYIZ7h7U/v/2tFb1BI/hhsam98XgHxnLfhsDr29ulK7525wR5axi9NB7g+YDwRsXwe6huF67Mrf731Kid/vfViNXnC7gWw7U33PkIlQMjWkndsjAeJ/rrPpzBZMGoSSwYNYlLvj7CCt3vX6g+GvOFxWPQSAYmYIrlWaaM+SKgslw4I5zICR957xaH1FosUu/4kMYOSUZhMpnIzs7G1FgR4MiuLpw9lUWNryDkgEvFZ7ZyjK5MYk7ATNyDzNXe6+OP1XXRPD8yPS+FUW+VTq/Po9Pr88K6a4ssQ7vzjsix0+l0jsLpLWl9pNZAfj6c3xXFakikaG2dUDSzsz3vbykMaRA95Poeq+4OTtZpLGF5/LgpbPJn1Vvt2Lh7+O7RJxh+2X6OjbuH0Z89HvZh1ki4e0S0nAy3rn8Uj8cU4KHh8Zg4ciTQAGNMEq3mQwr1as2HReP1XW/rHOStoXppqO3XG4zKKcOk+z0MrrbRhOpVo8/lYNTOmN/DWAXMaCAP1V51n/EmQzXuR8W4P9V4Es0bor3vLEWsDDmGUDEDXsxBhptIpVxDrWtuEtlQxBpSVD75JVZzfUolX9cj793ikFqLReodH9LYIckonE4nU6ZMwel0Av7s+CqhBlwqZp+HfeRos1Cqa/ESxgXMzOmXlzCOY3QFgmdrPKb0y0uhj+eOFaPeempq4PhxU1D8uYgBkn7G7arxQwLWqQPBdMwdEklvSeuS6VqHq9xinK3m2+OYPIbSrZ7WCUU7ebgDoGihDqrHhprQ8Le/tQYZS40P1frwDiP6e1rHl3/flIRzyJCQ2xsfKiPdxyfUfcRDLzwUMgTnH+cXsZX+QCiDhkJhoS9sAlFRs9/667vT6/PYb+8dVMp2BUV4x/xEm6H/7LOmUIYt5HO88fcvdLWNJsNNr8J+XFy5IiCXg37SQTVoLGcMnSo/1MJk9B48egN5uPb69xnekG7MedGSROTxcJhuIQ171QwKMtxEKuUaaZ1KLJVwIvU/3HfcWKjjHB7kucZPUiP5up5Mv3enElJrsUi940MaOyRCEFUSMSsri6effpqsrCwATh5qiueuJY+LK1dqAy69C+4yiripciYQ7Fqsj/VWs42r6/aSwz4uC5qtsSoePh8rLi9FsjDqrWfGDAu3VU4FAjX0k9gBUtlDoyO6qYP43CGtQSS9Ja1LpmltNGKYGxOPGvNSRArPAP8D1CfdipplJAx3//e4LIApqKqImpBTXwpbNUDHW8LS1/m8qLP6xr1Eu4/vp1fIEJxHG0pD7K3pKKmQrFp/fauGaWMp21LKAgzT+ns6+H//mn4rA404t6x/RDPc+PvblFA1XMUO9bdXDZOB0Abymhp/3hO1veo+/J6YgUYD40N7uAf8RHtDdKr8kP/l90GGvScspbjsHYIMN6FKuUZf1/SbGq4STnMMPKEMRza8DOfjlE6+nmn37lRGai0WqXd8SGOHJCHEOrBubRRFweFwoCj+H/l73lhFz0HfcNfqORQW+lDn7owuuOWUagM1Y6wpubma665xlu4MHchhvzZbo9aVX0ERjy9On8SX8WLUW49+gKt3f/aT2AGS0rNHxBAjDxbhuUNag0h6S1qXTNM61tw1kSqkgP8B6p4jZa3imXVx32+Z9ulC7lo9h1s2TsPTqxejNxaHLIVtLOWtf7COKSHn8Bui3hOMD5WR7uPf7b6A4/XnhZyprzybH1RmU0TJzeagv75Vr4nQRqcmw7T+ng5QTwfd72H4e7oxoertuTtDVuzQ/75GKt/ub29w6V29J2Y8RMobAk2hNeEMI+E+95isHP3BLSzj9qA2V3vzuWX9Y0GGm1ClXPWeT6GNRk1JZI2VcEKhTvgE5iNrMnCo+zOObXLG5KXc9awn0+7dqYzUWixS7/iQxg5JVGKtZqHfLlryr0QlhXS5XLj69CErO5sFoyZRcc3PtXKJK9+2oGBmC/2DXHC3kh9xoHbL+kd0Jf+CKTcMVIwzYpmKy+WitLQUl8sVtE4/wD027h4tXj7eAVKs1+GEDfP4wnuKygEFYQd6VrzMMJcycmTLSk6KJpLektYlE7Sevnmxthwtx4RascG0aRPeczoGVUFREymqiTZbI++O66yVP9x+Kyf2nI/ziiu4/+abcV5xBRCcXLSlpby9F14UMqmlihVvQI6mUPdxfZnarQuGAsH3fn8ITuq59hvRX996LSNV0dDf041lfptzT1f3M3pTCWV/Ws+tm6YF/b5GOudGTxG912Cosr+R8OiGweG8ISB0BZpQhPKGsCoe/mPXfxHJeGcMoY1WyjXcugd4XvM+1V+b/r425UFZzUgqJv8pqN+qgaNT5Ycsoygoj1k6jG0y4d6dLkitxSL1jg9p7JBEJeYZQUPoQDgSmRQyOzubi4YPj9je3MIcXC4351/dE6V3b22gFctAzTjYm3/Bo4SaYRJdVz5ZZGdn89xzz5GdnR33PvQPEJFoTlWVbIudpyxPBLmYqyyjiM2+fM6cMfHww+lzjlpDb0lsZJrW05Vd+AoL8ZiiPwBaztQFVUExAw6ytUSbrfHAc/JwB777+ly2vPr9IL1HrnokrGdEPMZS794DIZNaAmFzNG1lAFvoz9WD/QZVfZnaAx/35nrWUsNAbR+nOLfx3p96rv1G9HpXV3u4zlBqXEgVjbw8PNu3Q57foGH8fQ13zo2eIrXnNHkNql6E+rK/QAQjly+kN4TqyfAh1wdUoAldnS26N8SnjkIiGe8gdHUeNe/J6E0lAeMVvWFInwS3ad+5AeMSYx6UScwNykem/h88wPNaO9JxbJNp9+5URmotFql3fEhjhyQqsc4I8u3xqMm/Ep0U0ufz8fivb4yp+sa04b8KGGjFg3GGKVl15ZOFz+fjX//6Fz6fsYBeZPReGvoHiEhhTtGuQ/W8nthzPge3duXt6n8PcjFXB676weS6dak9S6UnXr0lzSddtNZ7b+ixFhRg0oUPmtavw/z221iV2B9c1ccpLTyDYQGJNpv7wGMtKGDG3F9r5TmvOfVPFEwc+Lg3NrsdpX//hOitVocKldRyDdfzpSl0jiaAP866JcDIGqnSR8eRBSnr2m8kXa7vWFCr7rhcbgoLfdxWOTXkg34oInlD2PDwMHMCKtAYK6v4jQeB4SOhvCHq6kx89pkrsjEnkqEpgmHI+D2/ISQwR4oxD8pWBgSMWfTLRmNMuo1tMunaTnWk1mKReseHNHZIopOfj6+wMKj8YCiUbt0izhwmOimky+XiwHcncN9yS9BMTiIqpEQacKTDgLeluFwu5syZ02yXulPn94qYKM1UWxts+MjPp/TlR7XrMFw5wHf+6xeMcP8DBRO3NZb9U2ezHmYOPsxsoz8KJu2lztwmMp9MaxCv3pLmkw5aGw0a+txI0UIJ9X/DPcCrjzNqeMaGyeMD1oZ74LH+6yBdnir2Vz7RtStam75wuRKmtzEJJ/j79RBzuELZxfyqPhwbdw+bL7iJ9pzV7hFdOR7WyKrHBHhnz05I2xOB8fqOpxpXqqBve6gQF6NXhmr4DvSG2EkteWGT5eo9ONUQD2P4iGokC+cNUV4uxkCgH5eQmxs2D4reqFFLHrfn7gwYv6Tr2CYd7t2ZgtRaLFLv+JDGDklUpm9e7J9Vbyw/GClruenIkagzh+pDaSyv5j543lf1Eu6uWUwaOCxoJqetVEgRSXZ2NrNmzWq2S10ZpQHvI82WquEpEzbMY+/57YLKYEZDfaBTB50NZId9yEtkPpnWIF69Jc0nHbSOdK1GCik0Gf6GQi3lCYEPffrEm+EeeBSrLaaQRmObLNP/kjC9Y03CGUvVFiPpWMY6Ha7veDBOQGRfN4Rehf2CShg/zJygUJJwISPT6qYSyoPT6A2hT8Idyhti//7oE0atTbQJmVRONBovmXptpyJSa7FIveNDGjsksZGfz4nrCrWZkWgzgk3uz9HdnCMOynUz/LEYP058dT7f7riId96bwIo2WiFFJF6vl7179+L1Rk/cZv3XQW2md+76QTHtPyi/S8eObPpBbsj460iz08pll2kD15Ode8YUDpOKNEdvSctIB60jecmZgNPmLngMSRr1Rgx99REjNjz8L7/XZr/9xBa64uvcOaIhxWv4/1Xvzw/OvzJhehuTcAb2q8lLJVrVllCkYxnr1ri+pw0c24otSgz19fD22+agEsYRjXcRQkb0nhLGcI/bKh/TDCZ6w0JDg4OdO3dRVeVMigZtjXS4d2cKUmuxSL3jQxo7JBGZsGEeX5w6yIQN8/jtrvHazEi0wWCT+3NTLGu0bSGy10i4WXd9/od3/vsXDHdtYN+miyhqDGHwtyM9soinG263m/nz5+N2u6NuG+tsr3oNhJstffX6OwK8dsIlngP/Q9SJkWPw7N4NeXnU1MDg954MWZJP3TaVZ2ebo7ekZaSF1vn5cH5XFEtog8W5vhNYDUl6VSPGWdoHlK2EwGoNyyhiGbfHVQVFsWXh6n1V2P9Ni8HrTr0/f/hhR6qrEzOIizUJp1q1RTEF9lOfkDIwOaU1LctYp8X13Qrs3w+RqqA0N+9MrElU9bQVrVMFqbc4pNZikXrHhzR2SKKSbbGTffhKXvlXU6Z6/Yyg8QFWnTnUZ/aOVAouUu4GPeFm3aNV6YD0ySKebmRnZ1NRURGTS523XftmzZiavB7MK1cGJDTM2bSDXz+7gBUUAtHLAfpzDTSFzPhd2ZWgknzqtmVKKalMc/SWtIx00FoL7fLG5n7uw8QKivhnt5/yKdcElK3U/y/pS6gWFvricnXfdtXtUUt1lk9+CR+mgPvz00+3i2n/8RBrXgpf586YlMBfFX15zsDklB6esJS2SilekaTD9d0a3Lv3MVqzhHE8tBWtUwWptzik1mKReseHNHZIYmL9n/IwWXxawi/9jKBxqKDOHBpjWfWl4CJ5cIQiUkx0tCod+7gsbbKIpxter5fPP/88qktdp9fnYcnrxyfdgj0q9KgzwZE8QK46u4si3gagO4dClgNUQ5eWUcRz6wZq3jxq8jo1bt84O7vm2/yY+p0sYtVb0nLSRWuXpUtITyUj5ZNfwoxCKWUcORIY0qFP4hjo5h/n/dLhYNqm21hBYdRQRnOj8dGPia++av7hWhPv3gMoXoV/Zo8zNQqfAAAgAElEQVQMSGhpTEipX6725qed12C6XN8tJR5PjNamrWidKki9xSG1FovUOz6ksUMSFddZK7v+0R3Fa9YMF/oZQb2XhzoonFo1RnMTPjbuHoZ/bycXX6I0Dn5De4SEyvOhGkUixkQb8omoqGEJl7h2p00W8XTD4/GwYsUKPDEkDa2vh/85EuxRoaKfWY7l8UoBttM3ZDlACJydVr159APfkT+0BGx7a9XjKX9tNEdvSctIF61PHu5AhbkkZDiKilqkTu9BMf+CRwOSOFa7+gQlZoznfnlg8jyqTl3Je4cGUkY5tjDeHer93jdmDBtdfamrq6e8vIJPPjnbrOO1JmqJ2obuV/BrR2D50HLKeHtQuVZxozVCIpJJulzfmYDUWixSb3FIrcUi9Y4PaeyQROXk4Q6Emms3enmEc+lVB49Ng9/QHiHGPB9GA0qkmOhySkOGJZST2mEJ6U5WVhZTp04lKysr6rb798MWBoQsAaifWYbIiRNVOld+SBnlEcsBRpqd9nbpiq/dOQA4L+mZ0rk6VJqjt6RlpIPW3+2+gLOnsqjxFYQMR1ExE6OHmyExYzyUl1twOkFfqjMUauJgb0kJkFp6/+bAVLZbBwTcS3ZY+7N0V38urlwZMjllunkNppLemY7UWixSb3FIrcUi9Y4PaeyQRMXjshBqrj2Ul0c4l141SZh+8Gv0CNGXOZxE4KzaT1jB1YPDl6jtdMv/cYpzgwwk+hAGSevj9XrZvHlzTC51DQ3+EBK9J4ZaAlAt2RcqTCoUisVKZbcittIfCF0OcN3Isqiz077OXfB2OZ+zuVc3v/NJoDl6S1pGOmi9dcFQbdkYjqKvRLWMInLYz/yqxHq41dT4K1/4aSrVaczZ5MbKJ92KtMTBkDp6q33weEwBVVs8HhN1daaA6h3pXLozVfRuC0itxSL1FofUWiwi9Ha73fy///f/KCsr44knnuD555/n6NGjIbctLi6mrKyMmTNnMnPmTDZu3JiwdrUEaeyQROXivt8y7dOF3LV6Di6Xm1vWP4LL5aaw0MdtlVOB6FnO1QdddVvjA62+QkApFWwhXzN+xMomCoLcjtPNtTjd8Hg8rFu3LiaXupGr/NdNlasfvkJ/glHfmDH4cvtr2xgNaOG8O0xeD787p5RQiefU5bXH+4f8rh7FZuf0PQ/h7XxeDL1NPs3RW9Iy0kHrusOdtGX1f6eWvCCDoqh74YwZFqxWvxeg1arw40s2s3bcPJznXRiQs8mGh3uOlAUYolNFbzWJMYRKaJlYDWNNoNoapIrebQGptVik3uKQWotFlN7Dhw+ntLSUxx9/nP79+7Nw4cKw206YMIHHHnuMxx57jKuvTs2JQ2nsyAD0pVdDvawFBQk5rprsEaJnOb/6akWb/epV2C+kR8gybufiyhXUkgvA/TwfcLxoVVseYm7rJdiTxERWVhYPPfRQs13qvKWl0K493pKSgJKQqjGtsNDHE5bSkHkItLwwi/olPfGcaOLVW9J80kHrW1/6Mz0HfaMZos/xR2UFeNDFci9sjYdsvUcEgMdjYtWhgez77TwOmntoYSHq/XmHtX9AuGOq6K3/XQsmc35PUkXvtoDUWixSb3FIrcXSUr2dTicNDQ3aK1QJW5vNRm5uLqbG8us5OTl8++23LWp3spHGjgzAWHpVX+lEaVyfCOLNcr5nj4nbKh8Dmrw89LHk6kBzKT/jGF2DZvfDVerYQj63sRKAosu3ZfRDb6rg8XiorKxsvpU5L4/ie34XMjeA+tC0yTswKA9BOpd6bA3i1lvSbNJRa71RuTWSjTYHvVeHitWq8MgjFo4fN2lhIaoHoMdjCgh3TBW9U6F6hwhSRe+2gNRaLFJvcUitxdJSvZ988kkefvhh7fX+++9H/c7atWvp3z+8p/Rrr73GjBkzeP3116mrq4urXYlGGjsygGilV73TpolsTlT0g8lqVx9mPvi7gGopLpebY+Pu4di4e8g6Nysod4Pa13DlaxXAd9VVie2EBPDHD9bU1LQ4flA/s6x3IzfmIYD0LfXYGrSW3pLopL3WrZBsNFaMXh0qHo+JdeuahhmRwkLSXu80Q+otDqm1WKTe4pBai6Wlek+dOpVnnnlGe918880Rt//b3/7GsWPHKCoqCrl+0qRJFBcXM3XqVDp06MCCBQvialeikcaOTCA/H19hIYrV4AFhteIbM0arMhEt3CXUa8GoSeRs2sGvx09tUROb6yatlgDcb+/NSkP1Dn9i0/Cz+iZAKZVVWESQlZXFvffe26oujMbwqN9Nfk5LXprOpR5bg0ToLQlNOmotMueDHr2BMphwn4M+LCQd9U5npN7ikFqLReotDqm1WFqqd1ZWFu3atdNeNpst7LYffPABNTU13Hfffdjt9pDbnHeeP9+dxWLhhhtuYPfu3XG1K9FIY0eG4C0uxmRwazJ5PHiLi7X3xnAXPQqRh6TdvvqaBaMmJTQHiJGaGjh+3ESpIdneg8zFRmirphsrewZci6dfPyFtbOt4PB7Wrl3bqi6Mes+f3NymqzLdSz22BonQWxIaqXXsRMtzcc45RA0LkXqLReotDqm1WKTe4pBai0WU3qtXr2bjxo3cf//9tG/fPuQ2TqeTs2fPau83btxI9+7dE9queJHGjkwhP58T1xUGeECcGNnk1QHB4S76MJBIQ1U9icwBYkTNyWBMtreEcdp7IzY8THVMwefzCWljW8fn87Fv376E6d1W4udjJdF6S5qQWseO8f9Un3DY5XJz9dWRTOl+pN5ikXqLQ2otFqm3OKTWYhGh94kTJ1i6dCkNDQ3MnTuXmTNnMmvWLG39n//8Z7Zu3UpdXR1z585lxowZzJgxgy+//JJf/OIXCWtXSzApihJ9FCJpEQcPHqR79+7s3buXSy65pNX3P33zYqYNHMsjN21j7vpBnBp2I50qP2Ti4GrWn8jnL3/xaGHblttvx/fOKqyKR9suGqWzJlI++SXtvbuqKsCIkigKCqzU1vpNMLls499nvcTfJk+kljxy2cbfhxWzo7KOa9mADQ9urLzDaH7Kcqqq3CKaKEljTt31G6z/Okjnf6wKu42Sm4unulpgqySS2JmwYZ62/PKI3ySxJYHUl8wKCKkxvpdIJBKJRCKGQ4cOkZOTw9dff82ll16a7OYIJ2M9O2pra3nyySeZOXMm06dP55NPPgGgrq6OefPmUVpayvTp0/nyyy+178S7LhWoqYHn1hcEeEC8XDWQ3btNVFRYmLBhHhM2zOPn/zEUqxI8G24s6xoq+afHbA7IAZJoIiUy7VXYj4srV/AwzwaEuJRTBiiUl2fspZ1SeDwe3nvvvbR1YVSstqRUMoqXdNc7nZBai0XqLRaptzik1mKReotDai0WqXd8ZOQToaIovPrqq9x111089thj/PrXv+aNN97A4XCwYsUKevbsSXl5OXfddRfz58/XstrGuy6ZTNgwjy9OHeSnD+3HYvLSjx0A/IQVKJhQMLFipYUFoyaxYNQkLvn6CCsYEzHBZzisPl9ADpBkosaIG0NctjIAMLF3b1Kb12bw+XycPHkybV0YfZ07hw3fOj3sRsxvvx1TIl9ReWzSXe90QmotFqm3WKTe4pBai0XqLQ6ptVik3vGRkcYOAJPJRENDAwANDQ2cc845WK1WqqurGTFiBAA9e/akU6dOmpdGvOuSjdnZnq8//je8ioXt9I2YhPSsrz1llEVI8BloBPE0vndjZRlFbCE1YkP0Xh+jN5Wg9O7NrZumaZ9t3ixvBCKw2+3ceeedYTM1pzqKLQtX76u06zyufSDOAyTd9U4npNatRywhLFJvsUi9xSG1FovUWxxSa7FIveMjI40dJpOJCRMm8OKLL1JcXMyzzz7LL37xCxwOB16vl06dOmnbnn/++Xz33XfU19fHtS4UbrebhoYG7eVwOABwuVzaerfbrX2muiPpl51OZ8Cy6kWiX3Y4HJz46nyO7DwPtZZKOWURk5DuJSfAG8KIDS9rGAnAakZixUv55Jew4eEJSykVFWatHx6PJyF90i+r1kt1WVEUHA4HiqLg8/n82ubl4dq2DUfv3rjdbpYuXUpdXR3gr0ntdDqDlj0eT8ByyvUJApa9Xm/Acqr0ye12s2TJEu246dYnHA62nH8d1jDGv1gwAQ1Tpgjp05kzZ1i6dClut7vNX3uJ7lNDQwNvvfUWbrc7Zfvkt7QpKXeeOlRMbnaf6urqWL58OU6ns81feyL65HA4WL58OXV1dRnTp1Q9T2fPnmX58uWcPXs2Y/qUyudJHQfW19dnTJ9S9Tw5nU6WLVtGXV1dxvQplc+T2+3mrbfe0o7bnD61ZTLS2OH1ennvvff47//+b2bMmMH999/Pq6++Kuxkv//++zz88MPaq6KiAoBVq/yJEN99913effddAJYuXcoHH3wAwMKFC9mwYQMAr7zyClVVVQC88MILbN26FYBnn32WXbt2ATBz5kxqXh2Gz9dUS2UL+Ryjq1aVxchgqlAwcRsrAajjnIAKLsso4gGe50jlfh7k+YAQkWpvPu+8Y+Gpp94D/DWYly5d2up9OnDgAAAlJSV88803AEyZMoXTp0/jdDqZMmUKTqeT06dPM6XxIfObb76hpKTE36e6OmbPng3Arl27ePbZZwHYunUrL7zwAgBVVVW88sorAGzYsIGFCxemdJ8OHDjAzJkzU7JPlZWVHD16NO361On1eRw6J4sp7Z5iBU2VjIyeUep7Y14bAI/Jytmbb6Z4yRIhfVq0aBGHDh2K6zxl4rWXyD5t2rSJ2tralO6TT/HR0DjoSffzVNwYInn06NE2f+2J6NPu3bsBmD17dsb0KVXP03vv+cdMy5cvz5g+pfp5On78OC+++GJG9SlVz5PX66W4uDij+pTK52nLli3a2CTWPqkTwG2VjKzGsn//fubPn09ZWZn22VNPPcWYMWP44x//SHl5ueal8fTTTzNmzBj69OnDgw8+GNc6I+pMoMqhQ4fo3bs3O3fuJCcnR7Pg2Ww2XC4XZrMZq9UasOx0OrFYLNqy1WrFYrHgdDppP3Qo5u3btf0bq6X8a9itXFT5DkBQxRX9tgqwjpFczzrt8wHUNOa98JPLNt7ip9zOUmrJAxRGj/aybJmCx+PB5/Nht9tb3Cd12eFwYLPZtGW73Y7ZbNaWTSYTTqeTrKwsFEXB5XKRnZ2Nz+fTllWPA3XZ4/GQlZUVsOzxePB6vdqy2g/Zp7bVp6oqNz/4QXsGUEMNAwP+XyL97+jX1f65ih6FV6ZMnzLxPLW1Pp3z/e9jbhzMRMN4/1dyc3FWVaVcnzLxPMk+yT7JPsk+yT7JPqV6n44dO8bll18uq7FkEl26dOHUqVP861//AvwzRcePH+fCCy+koKBAs5Tt27ePkydP0rt3b4C41xmx2Wy0a9dOe2VnZwNoMVY2mw2bzaZ9ZrVag5azsrICli0Wi7bM5ZdHzMtxhnNYzpiw3h0qJuBB5rKcMZRPfkmX4LOJWvLow65GQ4f/W/v2+dtitVpbrU/qcnZ2dsCy2WwOWDaZTGRnZ2MymTCbzZq26rLL5WLx4sXa9ywWi18zw7LVag1YVvuRin1S265fTpU+uVwuli9frhn30q1PTz+dDSgBoV2H6Rbg7aR/7wN8jV5UbqysoIjiJQXC+gSwZMkSXC5Xm7/2Et0nn8/HsmXLcLlcwvtEr15h7/GRUPPHpON5MpvNvPnmm3g8njZ/7Ynok9fr5c0338RsNmdMn1L1PCmKwptvvqmty4Q+pfJ5UseBJpMpY/qUqufJ4/GwaNEirW+Z0KdUPk8ul4tly5Zp4SrN6VNbJiN7f+655zJ+/HhefvllnnjiCV588UXGjRvHeeedR1FREXv27KG0tJTXX3+d//zP/9QujnjXicZbXBy2goQJ2EcO5ZRpJVmBoASMnsaQla0MoITp7KQ3pVQ0rlUoLPShJvs0vqqrU7fkkdlspnPnzm3+H1sU6a63WtUHoITpnKU99/N8QDnjB5mrvTcD5sbHUBseSilj5UozW7aIaW+6651OJFPrSPf4SJgA77Rprd0cIchrWyxSb3FIrcUi9RaH1FosUu/4yMgwllTj4MGDdO/enb1793LJJZe0yj43XfYzBh35q/+Bq9GN2Y2VTd1GM+jAYgB8Ob1xXNYb7xdfcd4Jfy1WvcuzMWRFT26uktJGDYmktampgUMPzOa2ysdYRhE3DDvD6soO/JTlLKOImlkXMGDycUwoAev8xkGFt95KfilqSeZguf12fO+swqp4gkJV9KjrPCYr5sJb8Dbmj5FIJBKJRCI5dOgQOTk5MoxFkj7U1EDFpB8HeG6Af6b5niNl2iyz+e1lKO3aUzZgOSt1YS2q+33OmLy09N6IhMvlYv78+VqWY0liySS9Z8xo8n5SvTxUbyf9e+M6MDV6iCSeTNI71UmU1tM3L45pux1ji7Eqoe/D+ipbKlbFw+dji1vUtmQir22xSL3FIbUWi9RbHFJrsUi940MaO9KQnz60n3o6BOTlcGthKf2ZPr3xoS0vj7q+Q/j9ugGU6sJakuF+Lwqz2UzPnj2li5cgMklvvcGiljw+5RotV43+vbrc/RKPcONgJumd6ojWesafHsFmt2uvJYdfDFgfyQVTNWA/vrggsY1MIPLaFovUWxxSa7FIvcUhtRaL1Ds+pFppSN1hf0UYNS9H+eSX/H8pwzjL/JsDUzEmYGxKRKo0GUYyBKvVyvXXXx+Q0FGSODJJ7+pqD9ddp2gGjGl1UyNuv6/+AkEtayKT9E51Eq21p29BgHGDM2cibm8C1jBSZ+BuundnggFbXttikXqLQ2otFqm3OKTWYpF6x4c0dqQht770Z3oO+ob+q9fiKywEwDdmDBtdfYNmmY0JGMdPXp8U93tROJ1O/u///g+n05nsprQJMlnv6mpPQGjXyEZDyLFx9zD8sv3849+nC29TJuudaiRCa2tBAab167DZ7bTbHVtpWZXVXM8k5ug89LysYaShklb6GrDltS0Wqbc4pNZikXqLQ2otlkzR+7vvvuOFF15g5syZAHz99desXr06YceTxo40x1taitK7N96SkpDr9Q9s1a4+9HJtp9rVJ+1zc4TDYrGQn5+ftEo5bY1M07tDxeRkNyEimaZ3KLZtg759rWzbltx2JEJrpVevgPelsyZG/Y4bC7+d/CYP8pzmodep8kOWUcQDPG+opJW+Buy2cG2nElJvcUitxSL1FofUWiyZovebb77JoEGDUGukXHzxxVRWVibseNLYkcJYCwLdnNXXglGTyNm0gxkTZ0NeHp7t2yEvL9nNTQmsVivDhg2TLl6CaEt6Txs4NtlNaBN6l5db2L3bREVFcn/ME6G1t7j5CURteMmuWqcZqUdvKkHp3ZtbN03LKAN2W7i2Uwmptzik1mKReotDai2WTNG7rq6Oa665BpPJPzljsVgSmodEGjtSCKNxw1Qb6OZsnAW8bO+/tG2tBembmK41cTqdzJkzJ+1dvNIFqbdYMlVv/b1v5dsWFEysWGkJaew1vhJ172ttrSdsmMeEuo843aGjlncjHJ7GfBxq4ukt+hLhGWrgztRrO1WReotDai0Wqbc4pNZiyRS9zWaz5tUBcPbs2cQeL6F7lzQLpVeviBn3w36PYPfotorVamXkyJFpb/VMF6TeYslUvY33Pn1pVYXIlUhMtbUJMX4YtQ7nadec4/bs8D320yuobLiKGyurGYkVL+BPPPqEpTSgLHKmkqnXdqoi9RaH1FosUm9xSK3Fkil6FxQU8MYbb+BwOPj44495/vnn+f73v5+w40ljRwrhLS6mTOe9EUs8N/jTj3qnTUtQq9ILi8XCwIED0z6eLV2QeoslU/X2FhcTLtOECQLui5FoTcOvUetIxuhYj3tkVxeO159nKBseWFVlEnNZzhgt8Wi1Nz+tq6zESqZe26mK1FscUmuxSL3FIbUWS6bofeONN3LFFVdw2WWX8fnnnzNq1ChGjhyZsONJY0cqkZ8P53dFscRusfOYrPjGjIEBA6Jv3AZwOp08+eSTae/ilS5IvcWSsXrn5/PQ3EejhndAoNeHkdY0/Bq1jmaQieW46//kDz1Ry4ZD6KoqJUw3JB5N3yorsZKx13aKIvUWh9RaLFJvcUitxZJJeg8ePJiJEycyceJEBg8enNBjpbcfTJpiLSgIysehMWsiJm+wi7OH0ANdq+Jh+9hirmjNBqYxVquVoqKitHfxShek3mLJVL1vm/8XfK6csOEdsaBYrSi33NJqht8grfPz8RUWYlq1CpMnuJ023Y+1kpuLp7rav5/G+/0CACZROnoi5fwHAOWTX+IU5/IAz/MWP9WMG7Xk0Yddur2b2LOnVbqVsmTqtZ2qSL3FIbUWi9RbHFJrsWSK3qdOnWLNmjUcPXoUn8+nfX7vvfcm5HhJ9+yor6+nvr4+2c0QSrTcHIfppnNxborhLp/8EuA3fJRPfgk3VlZQxOOLZXJSFYvFwlVXXZX2Ll7pgtRbLOmg9/TNi5v9na0LhlJPh4Dwjkh4Gn+69F4eJo8nrmonevR5ObLbtaP/gAFkt2unfVZ+Uw/N0BHOw8QY0hIt/GUjgzTjRi1NSUdzcxWtwko6V1mJlXS4tjMJqbc4pNZikXqLQ2otlkzR+8UXX8TlcjFgwAAGDRqkvRJF0owda9as4dFHH2XKlClMmTKFRx99lDVr1iSrOUIpe+BHYd2hAR4yzdG5ODfFcANByetKKWsT8dyx4nA4KCkpweFwJLspbQKpt1gyVe+6w52AwPAOiODRho81jMTXeCd1Y+WTbkUt9upQevWKnCupoSHkxwFGFwJDWqKFv1xbNSvAqNFWjBtGMvXaTlWk3uKQWotF6i0OqbVYMkVvp9PJuHHjGDp0KEOGDNFeiSIpxo5PP/2UDRs2cPfddzN79mxmz57N3XffzUcffcRnn32WjCYJY8KGeXzhPcX7592IG2vAINlrNnOMC1is3BGQoE4fw/0gz2uGD3VdW4jnjhWbzcYvf/lLbDZbspvSJpB6iyVT9b71pT/Tc9A39F+9FuclPQD4rkuOZtg1sowiJjEHc6PPhA0P9xwpCzD6xuNh0lLPEMUSnENpurILX2EhHkMN+VDbtmUy9dpOVaTe4pBai0XqLQ6ptVgyRe/LLruMo0ePCjteUowdH3/8MRMmTKBv3760b9+e9u3b07dvX371q1/x0UcfJaNJQnGftTPlu9lB8ekWn4999AQISlBXS67m5hycvM7Enj2RfEXaDhaLhZycnLR38UoXpN5iaQt6m99ehtKuPWUDlrMyoGqJlQ+5nrO0p5QKtpDPMboCquG3f8uNvoYk0ZGSoYbC5A0MpZmwYR5fnDpIyS29seriUkNt29ZpC9d2KiH1FofUWixSb3FIrcWSKXqPGjWK3/3ud8yePZu5c+dqr0SRFGPH6dOnufTSS4M+v/TSSzl9+nQSWiSOl0f8Bt8rD7DdOoDljAmK5R5MFQomttGfK/mShY2J7MCkxXBXu/rQy7WdalefNuvyHA6Hw8HkyZPT3sUrXZB6iyXd9Y7J2yIvj7q+Q/j9ugGUNoa1lE9+CRseHmYOn3KNlttiLzk6w2/LjL4TNsxjwoZ57D2/Xcgk0XrcWPBgYTXXBxhjllHEFgI9NbItdo62v8FQbjb0tm2ZdL+20w2ptzik1mKReotDai2WTNH71VdfZcSIEdx8882MGjVKeyWKpBg7srKy4lqXCdTUwNtvm/F4TJRTFjF3hwLkFuZIg0YzsNvtPPTQQ9jt9mQ3pU0g9RZLa+u9bRv07Wtl27ZW2V1I6ktmAY1VSdav05J96l8LRk3ikh1N5Ub27wdQ2EJ+iLA9KCz0cWzcPQy2beFKvmQb/f1G4lqzts9wxwr1shb4kzz37PA9XJYuBsNE8AyKDS+bKeBhng3Ir/QTVnD14MB+5WzawcP3PEyFuSRg2ycspcyYkd6zM62JvJeIReotDqm1WKTe4pBaiyWT9L711lvp378/eXl52itRJKV2TV1dHWvXrg27LpPxD24VwMQW8jlBZzqF2dYEeHSJ7iTRMZvNXHTRRcluRptB6i2W1ta7vNzC7t0mKiosLFkSOj9Ga6GvUBIKe4ODBaMmAZMYDmzhLwxgKyVMpy87tLC92yofIzdXgX6gWG2Nd9OWYaqtbTw2PPK7+6kwl3CbbyXgN2ysYSQj8IdYurHyDqP5aeVMAJYzhhs4E7EdV579gs00ZRpfRhHV3nyqV8KWLV6ZtgN5LxGN1FscUmuxSL3FIbUWiwi93W43L7/8MkeOHMFms9GxY0fuuOMOvve97wVte/ToUV577TXOnDlDdnY2d999NxdffHHUY/Tq1Yuvv/6a7t27J6ILQSTFs6NPnz58/fXXIV99+vRJRpOE4XezbhoSf0lv6jgnqOqAxyST18WDw+HggQceSHsXr3RB6i2WePVWvSsAPH2byquufNuCgokVKy1BXg6twbZt8NlnJrZta17yT71XW6SwPV/nzgEGhoiVVGLYTgHO+tpT4ysI8CiZxJyAcJpyyrTvlDCdBrJjaocC/Hbym7p8SzK5tIq8l4hF6i0OqbVYpN7ikFqLRZTew4cPp7S0lMcff5z+/fuzcOHCkNu98cYbDB8+nLKyMn74wx+yYMGCmPb/1VdfMWvWLCoqKnjyySe1V6JIimfH3XffnYzDpgT6UJRTd/0GHA6+GHo3gz/5v4DtrIqH7WOLuUJ0A9Mcu91OeXl5Rrh4pQNS78QzffNipg0cC8Sn97Zt0G3DRro0fidSDm+F6B4YRqwFBZhqa7X3plkTsV3zcwAK8Cf57DTocf9KnRHg1LAb6VT5Ych9xurVptiycPW+CuvunZgVX8Rtwx1P/7kJfx4Q8Bsx+lb+lFKWUkseyxnDbawMCKcBqCWPTxiGgoXR/DUo8bSxX8P5WMs54s8zErWbbQJ5LxGL1FscUmuxSL3FIbUWS0v1djqdNDQ0aOJfrsQAACAASURBVO+tVmtQZRebzUZubq72Picnhw8/DB471dXVceDAAf73f/8XgIEDB7J48WKOHj0a0gtEz9ixY+Nqf7wkLYwlKytLO1lfffUV1dXVXHDBBVx77bWYzUlxOBFOp9fnATDjdgu/4jA/5q+A3036r4zm1cUFLPlZYl3LMw2TyUR2djYmk6xOIwKpt1hi1dtaUEDZ3UMpn/ySZnAIh/Fhv7mhc0qvXlBb2+JQEm1/FivK6Fti82pzODiTcxVdvvy8xcf1YOH99v9OPR0AvxGjD7u09cZwGoDcXIW1/f4HAHufS7HND2/o8GDBPGY0G5f0Bdwtbm+mIe8lYpF6i0NqLRaptzik1mJpqd5PPvlkgKHkxz/+MaNHj474nbVr19K/f/+gz0+cOMG5556rVYYxmUx06dKFEydORDV2XHGF2Kn8pFgVXnzxRU6ePAn4433mzZuHx+Nh8+bNLF++PBlNShpqwlK16gD4k9eVUsbKlWa2bElyA9MMp9PJlClTcDqdyW5Km0DqLZZY9W6udwb4jayfdCtqduict7g4bkOHJ8RPUKwlWTu9Pg/LVVewe8aigISiPsDX2KJQyUXDYcXLpsdupuegb7hr9RxOn67j3nt/zenTddGrYDkcbDp0YUA7Qu3/87Gy1Gw45L1ELFJvcUitxSL1FofUWiwt1Xvq1Kk888wz2uvmm2+OuP3f/vY3jh07RlFRUVzHC8ecOXMCSs5mZOnZs2fPalafTZs2MWDAAO68807uu+8+duzYkYwmJQ01YaladaB88ks6N2kZz91csrKyePrppzO+qk+qIPUWS6x677wj8kN1qJwSNjzcc6Ss+QbW/Hx8hYUo1sCHfGNZ7VBY8bGGkS0qyTpjhoUnLKWasdgMmBuPriYXDVdZRc2V5MbKcsbwxgdjtHWxaq0aXeZmPx7QDnX/5ZNfwo2VFRTx+OLWy4eSach7iVik3uKQWotF6i0OqbVYWqp3VlYW7dq1017GEBY9H3zwATU1Ndx3330hw2a6dOnC6dOn8Xr9EQiKonDixAm6dOkStR033nijVm72uuuuo3379vTs2TOuPsVCUowdVt2geM+ePVpSUrvdrrnDtBX0CUtLmM5OeuvcpE2N6yWxoigKDocDRYnlUUvSUqTeYomkt7WgKfHoVeOHNGu/qpFhK/3jMrB6i4sxeQJDOGK5c+mTf0LzS7LW1/s94zZ5BwYkFNUbjgP37zd+AKxmJFa82nHLKGfn+ss4uLUrJ/ac36xrO1Q7jPuX3nqRkfcSsUi9xSG1FovUWxxSa7GI0nv16tVs3LiR+++/n/bt24fcpmPHjnTv3p3PPvsMgM2bN9O5c+eoISxAQLnZgQMHMmHCBPYkMIFZ0owdBw8e5NSpU3z11VcBsTtud9uKZa6u9jS5REdyk5bEhMvlorS0FJfLleymtAmk3mKJpLfSq1dM3hRN3gxNBoWmCiPxGVinK7v4pNuYAA+Nw3QLE1pi5UOu5yztKaUiyKut2psfs1HgNwemovqQlDCd8ZPXU0pFgOFY3T/4DSEP8DzjJ6/nQZ4P+Fz1pvO4LWx59fvNurZDtSPc/qW3XmjkvUQsUm9xSK3FIvUWh9RaLCL0PnHiBEuXLqWhoYG5c+cyc+ZMZs1qquj35z//ma1btwIwfvx4PvroI8rKyvj73//OXXfdFdcxfT4fp06dapX2hyIpCUrHjBnDc889h8Ph4LrrruO8884DYPv27Vx44YXJaJIkQ8jOzua5555LdjPaDFJvsUTSu+yBH1Hx9tthv+vBwnpGMIp1QJOXwyDgn9kj2Xg6cuLMGX96hPL7Qh/bNGsi5x3ZHuCh8SBzWcwdgGpVV7R1UyzPMt2+ilr8Gb+Dk3/6jQJvvRU5QbPeM86YUNSYXHQoxZRSEbBdCdMZyW/5CStQGvdTykTKP/4POBdeAJQNG/BUV8fVjuB+yeor4ZD3ErFIvcUhtRaL1FscUmuxiNC7S5cuvPDCC2HX//znP9eWL7zwQh555JFmH+OPf/yjtqwoCocOHaJfv37N3k+sJMXYccUVV/D000/jcDgC3GP+7d/+jcsvvzwZTZJkCD6fj2+++YYLL7ywzVT1SSZSb7FE1LtDR05cV0iH9atClj+14mUScymjFPB7G5RSwd8p5mnrb3gr2sGz26EQPjxlB/3YTj9t30sYx3Le5AbOsIwiTI3GDtV745b1+dp3jYaKWI0CsXu+9aG+ZAjV6/ugN+icumsefGMP2y8F8OXktKAdfYDt+E0lbctrsbnIe4lYpN7ikFqLReotDqm1WDJF7wG6RPhms5mbb76ZnBjGWvGSFKUOHjzI4cOH+e677zh48CCHDh3i9OnTZGVlkZ2dnYwmSTIEl8vFnDlzpEudIKTeYommdzmlIQ0d0BRKUcJ0HKb23LppGmvHzcN2WTdeviWqqQOlZ4+IeThKKaeE6Vp4Cvg9G45U7tdCS9Qwk8Y9Uljo00L2jC8RIXydXp9Hh1klYftlAhyPPprwdkjkvUQ0Um9xSK3FIvUWh9RaLJmi99ChQ7XXkCFDEmrogCR5dujdV1Tq6+u54IILmDhxYkzJTSSSUGRnZwfElkkSi9RbLJH0rq+H59YXcB1jGM1fAX9+jPWMoB/tufLNabh+6gb6MH3zEMjLa97BO3TEV1iIadUqTB4PpbMmapVGjnFBY04KqJ3c5GJZS27Y0JKUCenIz+eTbmMYdOSvAYYiN1Y2dRvNoMGDk9i4toO8l4hF6i0OqbVYpN7ikFqLJd31fuutyBNrt99+e0KOmxTPjunTpwe95syZww033MCSJUuS0SRJhuD1etm7d69WCkmSWKTeiWH65sUhPw+n94QN86jd7QAUyikLyJ3xMHP4lGt47M38EHtsYts26NvXyrZt4bcJVXXFhocLOIaCSXttoX/jWhO5uUpSvTdUOlRMDvl5TQ38z5GyII8YtRTv5s0+Ec1r88h7iVik3uKQWotF6i0OqbVY0l1vfdnbUK9EkVIBP0OHDuX06dPJboYkjXG73cyfP7/NVfVJFlLvxLJtG3z2mUkzQLjdbupLZml615c0Wfh9LhtgCqo+spUB3Fb5WECVlWkDxwLQ+W9vYTuwh65/eZGCQXa+3G2mYJBdK2Grf5nWr8P6q1/x/nk3EunxXwFyC3PSpqLUjBkWtjCA5firyajeKmop3oqKlPqZzFjkvUQsUm9xSK3FIvUWh9RaLOmu9y233BLxlShSbhTn88lZNEn8ZGdnU1FRIXO/CELq3fpYCwowrV+HzW6nYJCdaxrWagaIjueey+UH9tPx3HOx2e2Y16/DWlAAwMV9v2Xapwu5a/UcRm8qgXb+vByRjA7eDh0DytWeGnZjxLZ9YrEz5bvZEX84TIB32rTmdzxJqJVUjB4xaine/fuTEu3Z5pD3ErFIvcUhtRaL1FscUmuxZIreDoeDRYsWUVZWRllZGX/5y19wOBwJO15SjB0NDQ1Br+PHj7NixQouueSSZDRJkiF4vV4+//zztHXxSjek3q2P0qtXs7Y31dayYNQkcjbt4Nfjp/o/zMuj+J7fRc3LcbbfoIhJR42UmcrYbh2geY74DN9WPSK2MCDU11OS6moPLpebKlc/fIWFAPjGjOHThivZsmUrVVXOJLewbSDvJWKReotDai0Wqbc4pNZiyRS9Fy1ahM/nY8KECUycOBGfz8eiRYsSdrykGDt++9vfBr1+97vfcerUKX72s58lo0mSDMHj8bBixQo8ntR2nc8UpN6tj7e4OO7vnrikecmdO7y7CHfnrrgNuapVbw/V08ODhfqOnfh7za14PCZKmM4vJ/8dc4BfiOoRUcr06Za4+5BMvKWlKL174y0pkde2YKTeYpF6i0NqLRaptzik1mLJFL0PHTrE+PHj6d69O5deeil33nknhw4dStjxkuKf+4c//CEZh5W0AbKyspg6dWqym9FmkHongPx8PDV+A4Q+aaYCUb0w1vzXT5p9uH305GpDck7jcax42ePN0VpRSx457GcZRQGVX95hNFvJx7dHIS3Jy8OzfTsAWSCvbYHIe4lYpN7ikFqLReotDqm1WDJFb5/Ph8Ph0MJxHA5HQtNYpFzODomkJXi9XjZv3pz2Ll7pgtS7dZmwYR4TNsxju+eioOogkQwdHiyc6dyRb67o0azj1dTA4Pee1JJzgt9ocZhuAe+XU8QJR6egVhjzXNxa9XhaJCWNBXlti0XqLRaptzik1mKReotDai2WdNe7pqYGn8/H0KFDmT17NqtWrWLVqlXMnj2bYcOGJey4GZt5ze12s2zZMnbs2IHNZuOSSy7hl7/8JUePHuW1117jzJkzZGdnc/fdd3PxxRcDxL1Okjp4PB7WrVtH3759sVjS05U+nZB6tz7Zh69kX/1JljMmwGviGF25gOMhv2PFy7Eu3WieqcNfiUQtV3sbKwG/0eJB5rKYO7T3ZZTRybedW158neX/eYduD/3w3d6U54IB6ZOrIxry2haL1FssUm9xSK3FIvUWh9RaLOmu96pVq1i0aBFDhgzhuuuu49ixYwD85Cc/oV+/fgk7rklRlDT1N47MW2+9hdfrZezYsZhMJk6dOkWnTp2YO3cu11xzDcOGDaO6upq///3vPProowBxr4vGwYMH6d69O3v37pUJWCWSNOfUXb/Rlju9Pq9V9z1hwzw+Lb+dC0dv4PTkvtQwkNJZEymf/BJjWcRi7uDUsBvpVPkhCnC6cRnQtlNRcnPxVFdHPF5BgZXaWr+3xjKKuGHYGVZXduCnLA96P2LWG+xbOYSv1hpMKtu2Yb3jDjyLFkVNiCqRSCQSiUQiEcehQ4fIycnh66+/5tJLL01qW/bv309lZSUbN27koosu4vvf/z6DBg3Cbrcn7JgZGcbidDr55z//SWFhISaTfyDfqVMn6urqOHDgAEOGDAFg4MCBnDx5kqNHj8a9TpJaeDweKisr0z55T7og9W59Th7qAMAW8rWqJ8soYgnjtPerGRkxrEUhtqouaiUSl8vN6E0lNJjbU0oFAP3YAcBPWIGCietZy4GPe2Oz+8vgqiVvtTwXGWbokNe2WKTeYpF6i0NqLRaptzik1mLJBL179OjBHXfcwZNPPsm1115LVVUVU6dOZeHChQk7ZkYaO44dO0b79u15//33eeqpp3jmmWf44osvOHHiBOeee67m+mMymejSpQsnTpyIe10o3G53QFldtXawy+XS1rvdbu0z9aLVLzudzoBlNT5Lv+xwOAKW1eQu6rKiKDgcDhRF0ZLBAAHLXq83YNnpdAYtezyegGW1H/rlVOmTGs925syZjOlTKp8nr9fLpk2btH1mQp+inadGUwJqzZLW7BOKwj1vrKJnwRHu+vBZRm8qwZfVTjNAlDCds7TnQZ5nDSMJhwnwTpvWvGuvXz9sPxhEtasPx8b9Nz3+zUI4tz8F8OXkpPR5aum153Q6qa6uxuv1ZkyfUvk8nTlzhpqaGtxud8b0KZXPk8vloqamhjNnzmRMn1L1PDkcDmpqanA4HBnTp1Q+T+o4sKGhIWP6lKrnye12a2PuTOlTKp8nr9dLdXW19t3m9CnVsNlsFBQUMGLECC644AI2bdqUsGNlpLHD5/Px3Xff0a1bNx599FHGjh3Lyy+/LCyhy/vvv8/DDz+svSoq/A8qq1atAuDdd9/l3XffBWDp0qV88MEHACxcuJANGzYA8Morr1BVVQXACy+8wNatWwF49tln2bVrFwAzZ87kwIEDAJSUlPDNN98AMGXKFE6fPo3T6WTKlCk4nU5Onz7NlClTAPjmm28oKSkB4MCBA8ycOROAXbt28eyzzwKwdetWXnjhBQCqqqp45ZVXANiwYYNmffvggw9YunRpSvUpKyuLH/3oRzzzzDMZ06dUPk9ZWVkcOHCAkydPZkyfop0nAEVRtIFUpD7d997zzeqTq/EHz+324HZ7IC+Pals+teQCUEsen3INteTxAM/jwYKHwLhNr9nMqeuvhwEDmn3tTas7ovVvd84VYb1HTMCOsWNT+jy19NrbunUrZrOZrKysjOlTKp+nkpIS7r33Xk6ePJkxfUrl87R//37uvfdennnmmYzpU6qepw8++IB7772Xd955J2P6lMrnKSsri2HDhvHSSy9lTJ9S9Tw5nU5+9atfUVJSkjF9SuXzlJWVhcvlYufOnc3qU11dHanE4cOHeeutt3jsscdYvXo1I0aM0LRMBBmZs6O+vp4pU6bw+9//HrPZb8956qmnuOmmm1i4cCGzZ8/GYrGgKApTp05l0qRJtGvXjtLS0mav+973vhd0fLfbHeBidOjQIXr37s3OnTvJycnRLHg2mw2Xy4XZbMZqtQYsO51OLBaLtmy1WrFYLAHLDocDm82mLdvtdsxms7ZsMplwOp1kZWWhKAoul4vs7Gx8Pp+27PV6cbvd2rLH4yErKytgWbUmqss+nw+73R6wnCp9slqtrF+/nmuuuYYOHTpkRJ9S+TwpisKaNWsYOXIkdrs9I/oU7Tw5/+th7X+70+t/iNinGTVLKB10R8x9uuef/0fPjheyr87/I/Xytf9L4Z8W87f7/kM75vJhM7mt8jEAPhj2GDdWPgkE5uxwfvop5oED4772Tt11H6Yel2LeWE3HNe9ixavt32OyYi68BdeiRSl9nlp67TkcDjZs2MD1118PkBF9SuXzdPbsWaqqqvjBD36Az+fLiD6l8nkC+Oc//8ngwYNp165dRvQpVc+Tx+Phk08+YejQodhstozoUyqfJ4vFwvr16xk6dCjnnHNORvQpVc+T2Wzmo48+YsiQIZxzzjkZ0adUPk8Aa9eu5dprryUrKyvmPh07dozLL7886Tk71q9fT2VlJSdPnmTIkCEMGzaMiy66KOHHzchqLB06dODKK69kx44d5Obmcvz4cb799lsuv/xyunfvzmeffcawYcPYvHkznTt31gwW8a4zYrPZsNls2nu1jrCafEW/Tp+QRb+clZUVdVndbyzLJpNJWzabzdqyxWLR/oHCLVutVqxWq7asol9OlT65XC4OHDjAiBEjMqZPkfqR7D65XC4OHTqUUX2Ktuw0+Ds8seUtpg0cG7IfqrE15j415hjS/gKDr4a3XU0ujgd+c5j6D87QMGESAMd296Hzsd2Av2pLVf4ABg8c2Kw++Q9p0n1uQtl/iHpbZ7oQ6BFnVTxsH1vMFSl+nox9au61ZzabOXjwoDYgyYQ+pfJ5stvt7Nu3jx/84AcZ06dUPk8ul0vTWz1uuvcpUtuT2SdFUTSt1XXp3ieVVDxPxnFgJvTJuJwqfXK5XOzfv5/hw4djMpkyok/G5VTqk8vl4uDBg1o+ylj7pI5Fk01tbS0333wz/fv31zQSQUYaOwDGjx/P66+/zooVKzCZTIwfP57OnTszfvx4FixYwPvvv092djZ33XVXwHfiWSdJHex2O7/85S+T3Yw2g9RbLHa7nW7dLsJut9PQ+Fk5pczjTsBfJracUt5t4XHUKjNf/HQW1Yzhx7oSuH9lNK8uLmDJz9KzznusyGtbLFJvsUi9xSG1FovUWxxSa7Gku9733XdfUo6bscaOrl278tBDDwV9fuGFF/LII4+E/E686ySpg8fj4YMPPuCmm24KsJxKEoPUWywej4cNw/K4qTFMrr5e4Q/H7mAUiwB/1Za/19zKli1uBgxo2bFqamDEO48zgBrGsFILkSmljK0rzWzZ4m3xMVIZeW2LReotFqm3OKTWYpF6i0NqLRapd3ykhl+LRNJK+Hw+Tp48mZKZhzMRqbdY9Hp3en0eB809sFoVrUpLKRVYrQozZrTcPdC/DyWoBO5WBgAK06eLc0FMBvLaFovUWyxSb3FIrcUi9RaH1FosUu/4kMYOSUZht9u58847A2LdJIlD6g2mjVXY7PaQL9P6dVgLClrtWHq9Vc8Lj8cUUKXF4zGxcqWZLVtadqw9e0zQmJ+khOnspLdWAhdMjeszF3lti0XqLRaptzik1mKReotDai0WqXd8SGOHJKNwu90sX75cy24sSSxSbyC7HfqSVqeG3RiwWunVq9UOpddb9bwITcs9L6qrPbhcblwuN9WuPvRybafa1afps2pP9J2kMfLaFovUWyxSb3FIrcUi9RaH1FosUu/4kMYOiUQiaQFKzx5E8nHwTpsWdR/WggJsdjsLRk2i4pqfk7NpBwtGTcJmt1Nxzc9DeofoPS8ANkwer1ub+Z4XEolEIpFIJBJJJKSxQ5JR2Gw2brvttoBSTpLE0db1nrBhHl94T7HpB7n4TIG3U8Viha5dCZfFc/rmxU3b9uoVwUejyTtEr7fe8yLUK9M9LxJNW7+2RSP1FovUWxxSa7FIvcUhtRaL1Ds+pLFDklG4XC7efPNNXC5XspvSJpB6Q7bFzqf/cydmJTBhlMnrQenRM+R3rAUFmNav03J7mN9+O6x3iIkm7xCptzik1mKReotF6i0OqbVYpN7ikFqLReodH7JujSSjMJvNdO7cGbNZ2vFEIPX2U9NYsWQ0fwXAjZV3GE09HbRttm2DceOs/OUvHvJjzOOhWKwoo2/RvEOk3uKQWotF6i0Wqbc4pNZikXqLQ2otFlF6L168mK1bt/Ldd98xdepUunfvHnK74uJirFarljD1hz/8IVdffXVC2xYP0tghySisVis/+tGPkt2MNoPU28/6P+WxiWncxkoAbHh4wlLKjTWvYRv1cwAKgC8BBjV+afhE7fulsyZSPvmloP2avB48xcXae6m3OKTWYpF6i0XqLQ6ptVik3uKQWotFlN4DBw7kpptu4plnnom67YQJE8IaQ1IFaYqTZBQul4v58+dLFy9BSL3BddbKrn90p5pBLGcMAMsootqbT703O2wuDgDFELziA3yNn7mxsowittCU80PqLQ6ptVik3mKReotDai0Wqbc4pNZiaaneTqeThoYG7RWuqkvv3r3p0qVLS5qaUkjPDklGYTab6dmzp3SpE4TUG04e7oA/jaiJfuwAevATVqBgopSJAeaMU8NupFPlh9p7k8EU4lfR/5nqHdJ9hoUlS7z+9VJvYUitxSL1FovUWxxSa7FIvcUhtRZLS/V+8skntZATgB//+MeMHj26RW167bXXAOjRowdFRUV07NixRftLBNLY8f/Ze/f4KMqz//+z5w1CIBA5igYEQVROokiVglrx1T60ok+1LRWsPr/Wtv7Ux0OFaCAhoDYKeKx4wCAIKNR4RBQoAokICk9AxCrgiZAASchpc9rj7PcPOuPsZnd2dnbmntnZ6/168XLc2dxzXZ/rmmtm77nvewhTYbfbceWVV+ptRsZAegNBvw38K2C/xCj0RXvCvwnAjgZbb7yN6/BfUet88B0g/OiQyneAzz8PYcwY0pslpDVbSG+2kN7sIK3ZQnqzg7RmS6p65+fnY+DAgRHtpcK9996L3r17IxQK4d1338WqVatwxx13pNSmFlBXHGEqfD4fli1bBp/Pp7cpGQHpDQwc1YB5n67B0V//AVffelbct6qIcSCIPqFGzMA7cCAofLYARZiPhehANxSi+D/fDmPhQhsA0pslpDVbSG+2kN7sIK3ZQnqzg7RmS6p6u1wuZGVlCf9SfYVt7969AQA2mw1XXXUVvvnmm5Ta0woa2UGYCpvNhrFjx8Jms+ltSkZAekcS6jcAOFqNIGywIyR8zgEJO0GOoz8O/Gd9joMPPCXaY8F3353eIr3ZQVqzhfRmC+nNDtKaLaQ3O0hrthhJb5/Ph1AohG7dugEA9u7da9iFSqmzgzAVdrsdkyZN0tuMjIH0jiT0fRU6Hb2QI+roAOQNobsTzwjbF14YRmVlsMt3SG92kNZsIb3ZQnqzg7RmC+nNDtKaLaz0Xrt2LQ4ePAiPx4Nnn30WbrcbCxYsAACsXr0ao0ePxsCBA/Hiiy+C4zgAQG5uLm655RbNbVMCTWMhTIXP58MTTzxBQ+oYQXr/SMFfxqDnq8/iqHM43sF1CPynL5l/qwr/phYuaoxH2G4HcnPxuv86+P0B+P2BmB0dAOnNEtKaLaQ3W0hvdpDWbCG92UFas4WV3jNnzsQjjzyCZ599FiUlJUJHBwDcfPPNGD16NHJzc/Hggw+ioKAABQUF+POf/4w+ffpoapdSqLODMBV2ux1Tp05NedEdQh6kdyT79wOnTllQiKKYa3GEbA5Yo97AYgkGET4nT1b7pDc7SGu2kN5sIb3ZQVqzhfRmB2nNFtJbGaQWYSpsNhvGjRuntxkZA+kdyaJFNtwK4HOMxVu4DgseWI43MeM/a3GEcdA+BnZ3Jy7xfgzg9KiP/+s/HejeXVb7pDc7SGu2kN5sIb3ZQVqzhfRmB2nNFtJbGTSygzAVPp8Pjz76KA2pYwTpHcl331lw/a4HAQDzsRCHMFz0VhULOq098FfvExGjPm4/WYS2Nnntk97sIK3ZQnqzhfRmB2nNFtKbHaQ1W0hvZdDIDsJU2O12zJgxg4Z4MYL0jiRyrY2RAL5EJQAgAAD49L/D+NI+Bm8FT6/f8SZm4N/20Rjy9EXA5BASQXqzg7RmC+nNFtKbHaQ1W0hvdpDWbCG9lUFqEabCZrPh/PPP19uMjIH0lg+/nkcwaMF8LMQbD/w3fo0yBIMWvPOOBZ9/HsKYMdJtkN7sIK3ZQnqzhfRmB2nNFtKbHaQ1W0hvZdA0FsJUeL1ezJ8/H16vV29TMgLSWz6LFv34XvSDuAgjcRgHcdF/Pglj4cLE700nvdlBWrOF9GYL6c0O0potpDc7SGu2kN7KoM4OwlQ4HA7ceuutcDgcepuSEZDe8vnuO4vEXkuC/achvdlBWrOF9GYL6c0O0potpDc7SGu2kN7KoGkshKmw2WwYMmSI3mZkDKS3fCorg2ibH4Z/R0BxG6Q3O0hrtpDebCG92UFas4X0ZgdpzRbSWxk0soMwFV6vFw888AAN8WJEJurd68M34Kj6DrnrXsSqq+/FkP/7N4on3oxVV98Lh9MJ+/jxprXfxwAAIABJREFUmh07E/XWC9KaLaQ3W0hvdpDWbCG92UFas4X0VgZ1dhCmwul04p577oHT6dTblIwgE/UOde+BcJx9YQDhoUM1O3Ym6q0XpDVbSG+2kN7sIK3ZQnqzg7RmC+mtDJrGQpgKq9WKAQMG6G1GxpCJendccDGsrZ6Y+ywAgvPmaXbsTNRbL0hrtpDebCG92UFas4X0ZgdpzRbSWxk0soMwFV6vF3fffTcN8WJEJuodyskFl3UGOEtk+QxareCuuw4J3x+bApmot16Q1mwhvdlCerODtGYL6c0O0potpLcyLOFwON6IbEIlqqurMXjwYHz//fcYNGiQ3uaYGo7j4PF4kJ2dDauV+vK0JhP1bpn1/8MS8KHPmysBAIWP/X9Y8MByAEBgzx7Jzo62+Y+he/EDio+diXrrBWnNFtKbLaQ3O0hrtpDe7CCt2aJU75qaGgwZMgTHjh3DWWedpaGFxoQykzAVFosFbrcbFkvi13gSqZOperehO97CdQj8ZyZgAHa8iRn4HNqN6gAyV289IK3ZQnqzhfRmB2nNFtKbHaQ1W0hvZVBnB2EqfD4f5syZA5/Pp7cpGUGm6l3V3AuLMA8OBAEADgRRbJ2PRYtsmh43U/XWA9KaLaQ3W0hvdpDWbCG92UFas4X0VgZNY2EATWNhRzgchs/ng8vlop5PBmSi3h9P/ztaWm24fteDeBMzsP+xMzHmgVP4b7wFANizJxB3Jkuq01gyUW+9IK3ZQnqzhfRmB2nNFtKbHaQ1W5TqTdNYCMJEhMNheL1eUB8eGzJR78e/nC5sz8dCdKAbClH8n0/CWLgw/uiOVDo6gMzUWy9Ia7aQ3mwhvdlBWrOF9GYHac0W0lsZ1NlBmAq/34/CwkL4/X69TckIMlHvYzljhe2DuAifYiIO4qL/fGLBd99p93QjE/XWC9KaLaQ3W0hvdpDWbCG92UFas4X0VgZNY2EATWMhCHMhno6ycN96zBt3k84WEQRBEARBEEQkNI2FIEwEx3E4ceIEOI7T25SMIFP1TnU6ilIyVW89IK3ZQnqzhfRmB2nNFtKbHaQ1W0hvZZi6s2PXrl3461//iv379wMAWltb8eyzz6KwsBALFy7EkSNHhO8q3UcYC7/fjyeeeIKGeDGC9GYL6c0O0potpDdbSG92kNZsIb3ZQVqzhfRWhmmnsTQ0NKC0tBQAcM0112Ds2LF49dVXkZOTg+nTp+OHH37Aiy++iIULF8JmsyneJweaxkIQ5oWmsRAEQRAEQRBGhKaxmBCO47B69Wr85je/gd1uFz6vrKzE5MmTAQB5eXno2bOnMEpD6T7CWIRCIXz//fcIhUJ6m5IRkN5sIb3ZQVqzhfRmC+nNDtKaLaQ3O0hrtpDeyjBlZ8fWrVtx7rnn4uyzzxY+a2trQygUQs+ePYXP+vTpg8bGRsX74hEIBNDZ2Sn883q9ACAMOwoEAggEAsJnwWCwy7bP54vY5hNbvO31eiO2+Tlc/Lb4FUUcxwl2iLdDoVDEts/n67IdDAYjtnk/xNtG8SkQCKC0tBRtbW2m8cnIceL15o9rBp+SjRP/OQuf2tvbUVpaikAgkPG5p7VPnZ2dgtZm8cnIcWptbcWKFSvg8/lM45OR4+T1erFixQrhHscMPhk1Th0dHVixYgU6OjpM45OR48Tfl7S3t5vGJ6PGyefzobS0FK2trabxychx4nObP24yPmUypuvsOH78OPbv34+f//znutmwadMm3HfffcK/4uJiAMDGjRsBABs2bMCGDRsAAGVlZdiyZQsAYM2aNaioqAAAlJaWYs+ePQCA5557DgcOHAAALF26FIcPHwYAPPLII6iqqgIAzJ8/H7W1tQCAOXPmwOPxwOfzYc6cOfD5fPB4PJgzZw4AoLa2FvPnzwcAVFVV4ZFHHgEAHD58GEuXLgUAHDhwAM899xwAYM+ePcKUoIqKCqxZswYAsGXLFpSVlRnKJ7fbjdtuuw1LliwxjU9GjpPb7YbP50NLS4tpfEo2Tv6An5lPZWVluOqqq+B2uzM+97T26YsvvsDAgQPhdrtN45OR41RYWIji4mK0tLSYxicjx6mqqgrFxcVYsmSJaXwyapz+9a9/obi4GBs2bDCNT0aOk9vtxg033ICXX37ZND4ZNU5+vx8PPfQQCgsLTeOTkePkdrvRu3dvwQ+5PrW2tiKTMd2aHeXl5di4caMwfcXj8cDtdmP69Ol48803sWDBAmGURklJCa677jqMHDkS//u//6toXyz4J4E8NTU1GD58OA4dOoQhQ4YIPXgOhwN+vx9WqxV2uz1i2+fzwWazCdt2ux02my1i2+v1wuFwCNtOpxNWq1XYtlgs8Pl8cLlcCIfD8Pv9cLvd4DhO2OafzvPbwWAQLpcrYjsYDCIUCgnbHMfB6XRGbBvFJ4fDga+//hp5eXk444wzTOGTkeMEAAcPHsSoUaPgcDhM4VOycSquXIcHRl3HxKfOzk58++23OP/88xEMBjM697T2yefz4dChQ7jggguEpzvp7pOR49TZ2YmqqioMGzYMoVDIFD4ZOU4WiwXffvstzj77bLjdblP4ZNQ4hUKnh57n5eXB4XCYwicjx8lut+Orr77C0KFD0a1bN1P4ZNQ42Ww2HDlyBOeccw66detmCp+MHCeLxYIvv/wSI0eOhNPplO1TfX09zj333Ixds8N0nR3RPPHEE7jyyisxduxYrFq1Cr179xYWGn3hhRewaNEi2Gw2xfvkQAuUssPn82Hp0qW499574XK59DbH9JDebBcoJb3ZQVqzhfRmC+nNDtKaLaQ3O0hrtijVO9kFStevX48DBw6gsbER+fn5GDx4cMzv1dXVYeXKlWhvb4fb7cbs2bMxcOBA2XaxIqM6OzweD1555RU0NDTAbrfjpptuwogRIwBA8T45UGcHQZgXehsLQRAEQRAEYUT4zo5vvvkmojPCbrcLo7TFHDlyBLm5uViyZAluv/32uJ0dTz75JCZOnIhJkyahsrISmzdvxty5czXzQyn2xF9Jb+655x5hOzs7G3fddVfM7yndRxiLUCiEAwcOYPTo0bJH3hDKIb3ZQnqzg7RmC+nNFtKbHaQ1W0hvdpDWbElV70cffRROp1P4/1/84heYPn16l+8NHz48YVutra2oqqrCnXfeCQAYN24c1q9fj7q6OvTt2zdp27TEdAuUEplNMBjE9u3bI9ZMIbQj0/R2Op1d/rEk0/TWE9KaLaQ3W0hvdpDWbCG92UFasyVVvfPz87FkyRLh37XXXqvYlqamJmRnZwudLhaLBTk5OWhqalLcplaYfhqLEaBpLARhDpxOJzyvrhf+P3vWTZj36WqaxkIQBEEQBEEYjmTX7OApKCiIO42lqqoKpaWlKCoqEj4rKSnBjBkzklrqgQU0soMwFcFgELt27aJeZkaQ3mwhvdlBWrOF9GYL6c0O0potpDc7SGu2GEnvnJwceDwehEIhAEA4HEZTUxNycnJ0tqwr1NlBmIpQKIT9+/cLJx+hLaQ3W0hvdpDWbCG92UJ6s4O0ZgvpzQ7Smi1G0rtHjx4YPHgwPvvsMwDAvn370KtXL8Ot1wHQNBYm0DQWgjAHNI2FIAiCIAiCSBeSncaydu1aHDx4EB6PB2eccQbcbjcWLFgAAFi9ejVGjx6N0aNHo7a2FqtWrRJePTtr1ixD/s6lzg4GUGcHO4LBICoqKjB58mTY7aZ/2ZDuZJresTo7/H4/s+Nnmt56QlqzhfRmC+nNDtKaLaQ3O0hrtijVW+maHWaBprEQpoLjOPzwww/gOE5vUzIC0pstpDc7SGu2kN5sIb3ZQVqzhfRmB2nNFtJbGTSygwE0soMgzIHeIzsIgiAIgiAIQi40soMgTEQwGMQHH3xgiJWKMwHSmy2kNztIa7aQ3mwhvdlBWrOF9GYHac0W0lsZ1NlBmAqO49Dc3ExDvBhBerOF9GYHac0W0pstpDc7SGu2kN7sIK3ZQnorg6axMICmsRCEOYg1jUUMTWkhCIIgCIIgjAJNYyEIExEIBPDWW28hEAjobUpGQHoDaw9tx9pD25kci/RmB2nNFtKbLaQ3O0hrtpDe7CCt2UJ6K4M6OwiCIAiCIAiCIAiCMBU0jYUBNI2FIMxBrGks/KiOmSOm0jQWgiAIgiAIwjDQNBaCMBF+vx+vvfYa/ehkBOnNFtKbHaQ1W0hvtpDe7CCt2UJ6s4O0ZgvprQzq7CBMhdVqRa9evWC1UmqzgPRmC+nNDtKaLaQ3W0hvdpDWbCG92UFas4X0VgZNY2EATWMhCHNA01gIgiAIgiCIdIGmsRCEifD7/VixYgX96GQE6c0W0psdpDVbSG+2kN7sIK3ZQnqzg7RmC+mtDOrsIEyF1WpFXl4eDfFiBOnNFtKbHaQ1W0hvtpDe7CCt2UJ6s4O0ZgvprQyaxsIAmsZCEOaAprEQBEEQBEEQ6QJNYyEIE+Hz+bBs2TL4fD69TckISG+2kN7sIK3ZQnqzhfRmB2nNFtKbHaQ1W0hvZVBnB2EqbDYbxo4dC5vNprcpGQHpzRbSmx2kNVtIb7aQ3uwgrdlCerODtGYL6a0MmsbCAJrGQhDmgKaxEARBEARBEOkCTWMhCBPh8/nwxBNP0BAvRpDebCG92UFas4X0ZgvpzQ7Smi2kNztIa7aQ3sqgzg7CVNjtdkydOhV2u11vUzIC0pstpDc7SGu2kN5sIb3ZQVqzhfRmB2nNFtJbGTSNhQE0jYUgzAFNYyEIgiAIgiDSBZrGQhAmwufz4dFHH6UhXowgvdlCerODtGYL6c0W0psdpDVbSG92kNZsIb2VQZ0dhKmw2+2YMWMGDfFiBOnNFtKbHaQ1W0hvtpDe7CCt2UJ6s4O0ZgvprQxSizAVNpsN559/vt5mZAykN1tIb3akm9ZOpzPi/9NtSlW66Z3ukN7sIK3ZQnqzg7RmC+mtDBrZQZgKr9eL+fPnw+v16m1KRkB6s4X0Zkc6au15dX3EmjLpRDrqnc6Q3uwgrdlCerODtGYL6a0MGtlBxCX6SSFg/KeFDocDt956KxwOh96mZASkN1tIb3aQ1mwhvdlCerODtGYL6c0O0potpLcyqLODkIR/0wRw+m0TRsdms2HIkCF6m5ExkN5sIb3ZQVqzhfRmC+nNDtKaLaQ3O0hrtrDSu66uDitXrkR7ezvcbjdmz56NgQMHdvleQUEB7Ha78HB82rRpmDBhgub2JQtNYyFMhdfrxQMPPEBDvBhBerOF9GYHaa0Mp9MZ8U8upDdbSG92kNZsIb3ZQVqzhZXea9euxRVXXIGioiJMmzYNq1ativvd//mf/8GDDz6IBx980JAdHQB1dhAmw+l04p577knqJptQDunNFtKbHUbXWmmnAguUrB1idL3NBunNDtKaLaQ3O0hrtqSqt8/nQ2dnp/AvEAh0+U5rayuqqqpw6aWXAgDGjRuH5uZm1NXVpWS7ntA0FsJUWK1WDBgwQG8zMgbSmy2kNzvSQet0mGYovimTWvMpHfQ2E6Q3O0jrH2GxFhzpzQ7Smi2p6v3oo49GnIO/+MUvMH369IjvNDU1ITs7GzabDQBgsViQk5ODpqYm9O3bt0ubK1euBACcc845mDFjBnr06KHYPq2gkR0mIvpJXyb2tHq9Xtx99900pI4RZtE7Xc4ds+idDpDW6iB3lAfpzRbSmx2kdSRrD20X/mkB6c0O0potqeqdn5+PJUuWCP+uvfbalOy59957UVBQgPz8fHTv3l1yuouemHJkRyAQwMsvv4yTJ0/C4XCgR48e+O1vf4u+ffuitbUVK1euRH19Pex2O377299i+PDhAKB4n5EQ31Rmz7pJR0v0wel0YsGCBYb9sWo25OidLm/1SYdzh/KbHaQ1W0hvtpDe7CCt2UJ6s4O0ZkuqertcLmRlZUl+JycnBx6PB6FQCDabDeFwGE1NTcjJyeny3d69ewM4vXDqVVddhaKiIkV2aY1pR3ZcccUVKCwsxEMPPYTRo0djzZo1AIC3334beXl5WLBgAWbNmoUVK1YgFAqltI8wDhaLBW63GxaLJS2e1CvBSKMQxHpLwT/dTXYePxGJXL2J1CGt2UJ6s4X0ZgdpzRbSmx2kNVtY6N2jRw8MHjwYn332GQBg37596NWrV5cpLD6fDx0dHcL/7927F4MHD9bMrlQwZWeHw+HAhRdeKCTDkCFD0NDQAACorKzE5MmTAQB5eXno2bMnjhw5ktK+aAKBQMQCMPxwI/5pdiAQEBaF8fv9CAaDXbZ9Pl/ENt+xIt72er0R2zzBYBDhcFj4PBwOg+M44Tvi7VAoFLHt8/mEbR6O4wRbgsGg4Id4WyufOI6L2A6Hw5I++Xw+zJkzBx6PBwCw+quPsOrLf3XxLxgMRmwb2afoOAFA44rX4Hl1PZpXrouIO2ufeL07Ozvj+sTbAED4/2ifonNPjzgFg0Fw3I/2xYrTaR9O+yH2id/W2qfW1lbMmTMHPp9Pl9wzQpxY+dTe3i5obVSfxLnH28ajZpxidbDG8ymWLeLteD61tLQItSTTc4+FTx0dHcK10iw+GTVObW1tmDNnDtra2kzjk9I48TbxbfKo6RN/X9La2srEJzPGSa5PnZ2dmDNnDlpaWkzjk5HjxOc238mQjE/JMHPmTHz88ccoKirC5s2bMWvWLGHf6tWrceDAAbS2tuLJJ5/EokWLsGjRIhw5cgS33HJLUsdhhSk7O6LZtm0bRo8ejba2NoRCIfTs2VPY16dPHzQ2NireF4tNmzbhvvvuE/4VFxcDADZu3AgA2LBhAzZs2AAAKCsrw5YtWwAAa9asQUVFBQCgtLQUe/bsAQA899xzOHDgAABg6dKlOHz4MADgkUceQVVVFQBg/vz5wvH/teVfwsnF36x7PB7MmTMHAFBbWyt8v6qqCo888ggA4PDhw1i6dCkACMcDgJqaGuzbvw8AUFFRIYyS2bJlC8rKyjT1qba2VvDD4/EIJ3o8n1wuF/76179iyZIlAICGhgbs2r1b8Om5554DAOzZswelpaVp4VN0nABg965dAIC6/7Sll08ulwsulwvNzc1xfQJO5yQAtLe1C/ZK5Z4ecaqoqICnpUWwL1acACAUOn3B4X1qa2vDtm3bmPj0xhtv4L/+67/gcrl0yT0jxImVTwcOHMDw4cPhcrkM65M493jtedSO09/WPYGHN60Q5tnH8ymWLWIb4/lUWFiIkpISNDc3Z3zusfDp6NGjKCkpwZIlS0zjk1HjtGXLFpSUlAjbZvBJaZwACA8fxTVCTZ9cLhd+//vfY/ny5Ux8MmOc5Prk8/mwYMECFBYWmsYnI8fJ5XLh7LPPFvyQ6xPf8SeXfv364W9/+xuKioowd+5cDBo0SNh38803Y/To0cjNzcWDDz6IgoICFBQU4M9//jP69OmT1HFYYQmLH7WakA8//BBffPEF7r77bvj9fjz44IN4+umnhf3Lly/HqFGjMHr0aEX7fvKTn3Q5ZiAQiOixrqmpwfDhw3Ho0CEMGTJE6MFzOBzw+/2wWq2w2+0R2z6fDzabTdi22+2w2WwR216vFw6HQ9jOzs6G59X1CAaDsNls6Dn7N/B4PHC5XAiHw/D7/XC73eA4TtgOhUIIBALCdjAYhMvlQigUQlZWFtYe2g6O48BxHGZf8DN0dHSA4zjhyR6/rZVPTqcTVqtV2LZYLPD5fHF9cjqdaGpqQlZWFnr16oXVX30k2N7Z2Sn4FwwGEQqFhG0j+xQdJ6fTicYVr8Fut4Hjwuh1y2+EHmClPkVPheno6JDlk8ViQV1dHXJzc2G322P65HK50LhiLex2O8LhMHrOPm2vVO6xjlNWVhYaV6yF1WqD1WpB9qyb4PV6u8TJ5XKhZdV6AGGEQiH0vnUm1ny9DaFQSMgxLX3yer1oa2tD7969EQgEmOee3nFi6ZPf70dzczNyc3OFGmgkn7Kzs4Xcs9vtmDliqnCeZc+6Ca2trarFqVu3bnj131thsVhgtVoxc8TUuDWiR48ewnWo960zI7b9fn9cn7xeL/x+P7p3745gMJjRucfCJ6vVivb2djidTrhcLlP4ZNQ4cRyHzs5OuN1u2O12U/ikNE5OpxOrv/oIVqsVwWAQsy/4WURdUMMnh8OBpqYmdOvWDVlZWRmde1r7ZLfbhWtNVlaWKXwycpysVitOnTqFnJwcOBwO2T7V19fj3HPPxbFjx3DWWWfF+8lsWkw9smPLli3Yv38/7rjjDjidTnTv3h1WqxUtoqe3DQ0N6N27t+J9sXA4HMjKyhL+ud1uAD8u1OhwOOBwOITP7HZ7l22XyxWxzb8CSLztdruRlZUFp9OJ7Oxs4fh2u12YwsPP7bJarYId4m2bzRax7XK5hG0e/mTj2+b9EG+r6ZN422q1RmyL56vF8snv96O4uFj4O7HtYv/sdnvEtpF9io7TaTtt//mORfSZcp+AyLcmyPXJ7/fj0UcfFTr3Yvkkbk88z1Aq9/SIk91uj9AzVpxO+3DaD7FP4hzT0icAWLhwIfx+vy65Z4Q4sfKJ4zg8/PDD8Pv9hvVJnHu8bTxqx4m/0Yo+VrRPsWwRb8fzyWq1orCwUOjokBsnM+YeC59CoRAKCwthtVpN45NR4xQOh1FYWCjsM4NPSuPE2yTWQm2f+PtA8f1Hpuae1j4Fg0EUFRUJvpnBJyPHye/34+GHHxamqyTjUyZjWu+3bt2KvXv34q677kK3bt2Ez8ePHy8MC/rhhx/Q3NwsvFVF6T69kfMKL6fTGAtaao3b7cZTTz0V0TEQC7X1iG5PjfbTIWZy9c50lMQyVh6R3uwgrdlCerOF9GYHac0W0psdpDVbSG9lmPLVs01NTSgrK0Nubi6efPJJAKd70R544AHMmDEDr7zyCgoLC2G32/GHP/xB6AlTui9d4DtEZo6Yqnrb0T/g+IV3Yv2w4/dpAcdxqK2tRb9+/RJ+V209xB1OM0dMTbr9WFppFTO1Ok/Eemd6z3Ei+FEzybzWNjqnSG92kNZsIb3ZQnqzg7RmC+nNDtKaLaS3MkzZ2ZGTkyMsDhNNdnY27rrrLlX3ZRLiH8nRnRbxfpiLXzeazA89Jfj9fjzxxBPCorDphpZaRXdwRP+QVoJYbyU9zaw7wxId2+ikqjchH9KaHYnORVY1IZOg/GYHaa0t0fXD4/GQ3oxQktt63velO1RLlGHKzg5CO5Q8nWaJ2+3GY489prcZhkXt+KmhN8vOsHjHjnVcqY49vaD8ZgdpzZbozlejX2vSHcpvdpDW2iOuF6Q3O5Rqred9XzpDua0MGgNDmIpQKITvv/8+4p3uZsGIa3co1duIvkQjXrDVKJg5v40Gac0WjuPQ1NQEjuP0NiUjoPxmB2ktjdprk5He7CCt2UJ6K4M6OwyCWotaZjpZWVkYMWIEsrKy9DZFdeQsRMuaQCCAFStWIBAIJJW/RvQlHRDrTWgLaS2N2tcqjuOwf/9+6uxgBOU3O0hradR+sEB6s4O0ZgvprQyaxmIg1FhDwSzEW/BUDvHWDkn1plyLeYZG7dSSO4XD7XZHrI9CQ89TI1E+ROttNFI5b/WG5hFHkigX1T7X7XY7rrzySlXaIhJj9FpiJkhrtpDe7CCt2UJ6K4NGdhCGRWlvf319fcyng3x7qTxBiG5DjSebao5yUGt0kFybQqEQvvrqq5hD6lK1IVNHOklpL6W3UUjnUTu87bz9Rtdaa1jGkuO4uLWbUB++ltCoUu1Jh7ptJljpTecO5TZrSG9lUGeHycnEAvz1oUPMbpiT7TxhcWGM1Rmj1bGCwSDefvttBINBSTuUouTHlhYaG+U8iqd3pt9waUW83E5njJorHMdpVruN6rMY1jbytQRQ50EAER+p6yShPiz1VvvcSYdaJYZymy2ktzJoGovJ0XJaActCnMzw+MlXXKHZcdVoh+V0Ja2P5XK5kJ+fr3q7iUiUD/GmMilF7faUTveQ0lttGwmomtusp/hITUVT+7qgRp202+2q1u5o0mGKHUsb+VpSWFio+bEyHb2uk2YlUb1Jd73T6VouV+tEMTPi2++MSLrntl5QZ0eaocWPb0BZcWH56qhkfrSfOHEC/fr1S6p9KT3UuAHV+uKl1xOAUCiEAwcOYPTo0cyPbfQfL1Ixic5nufET622z2VI10RAYea2Mffv2JdQ6mVrK8iZW7rH0uhZEH5fjONTW1iZVu9N5jRi94WuJUkh7+ZixbuuN1D2hlnqnw2gLliSjtdQ9m/h6RR0f8aFaogzq7EhD1Hhab+Z3XP9w9AeceeaZSf1Nuuuh15OAYDCI7du3Y9SoUZofKx1uMqJtlBsXud+T+5YhKa3UvHlQKyZKaprcH1up/CjjczuVmzijE137tDzPpM4PjuMU1e54N8lGR29b+dqdCun0BFpPxNdJ+oGiPVrrzTLvtfzhL3VtlHvd1EJrOddTIz8k0RKqJcqgzg6COVr32k66bJLqbRJdEcdxzpw5mh8v1U4+Fj8utO40E98ESB1LfDOWqk2Jbiqk4qL0XJf7d3I7GZR2Rtxzzz1Jfd8MaHkjL5Urdrs95dod3X6ycWd9A63nG9hcLhfuueceJrU70+G1JhIT70d2MtdvJXobdaSSuIZpYaNUjZRTP/XM7XR/SKkEqiXKoM4OgjlaPwU9duwYBg0apEnbmYjUBXb1Vx+hpqYGgwYNws3nX8XatKRJ94sjx3E4XnNck7al4qz0R5nSc90oIyV27dqFSy65BHY7XSq1huM4oZbEwwwdlkYhGAxiz549epuREfBaUy1JjNS1Rm5HbDy9E3UWqHnd0bJjAjBObZLKbb1HrxkJtTrSqZYog5QiNEFukUv1ghDrOCdrT2LAgAFJtUPER+rmg+M4w+ttpgsux4Vx8uRJzdo3SieDGCWjQ9SK+f79+zF+/Hi6qWCA3Fqi52iIeBjlqXAydoRCIezfv19rk1QnHYev81pnai1hfQ2W0tuI6yalip7nRKLcpqluP6JGZ1Wm1xKlkFLernTtAAAgAElEQVQEAPWnliS7VoGc78pt45IJlyhqR4yZfiBrid1uV0VvMUq0l7MYqBkutna7DRMumaBKW6xzXOkPQqVrMqgR97/85S+K/1YN0vGHnVKkaome9TjWtTGWPVrVmWRzQK4dLpcLf/nLX3D33XenYp4uGPEJtxS81pkMy+uwXL3NdJ+n1znBMrfNFC+lUC1RBnV26IjS0Q9akG4/CKU0+f6H73HO2eek1H666aEXHMfhaNXRlPUWo2R0Qbrd/CqF4zhUHa1KqQ3xuSOlm9p1x4gLKyfycdu2bZg8eXJST1BSHdUWbxFPQLlu6XCTKFVL9Hz1c7zONjlxUauzSurtTXLai2VHR0cHKioqkrbFiGi1DpiaQ88rKiqSriWE+g8/xGgxSiwdaq2asM7tTLg3l7o+US1RBimlM3J/2Om1+jNr5B5bSrfm5macPfhsVe0iYhMOh0lvhvB6p4LcWiJ1jqXzDV207VJ+/vDDD7j88ssV/ziWM/+c3xfrh7Ra9d6IUz+iYVlLtF6DRqpTRI1zR40O4W7duqVshxg5b4BKpvMgmXNOy0Uc1Xitcltbm1BLpL4HmHfkllKU1MJX/70VB744gMd/cw/zByFy7DXKtLdolNQmjuPiXifTjWTs1zpm8fJIrDchH+rsMDB6FQ495+2rcexxY8epZQ6RAJvNRnozxGazYey4sXqbocqPcb3qWzI/dG+//XbcfvvtEX+n1StO5Wqa7jeU8TBTLUn1DQdqoXR6n5KREnLfhJPMj08ldUbqVcosf1RG14tbb70VQPzOVsCYoxLVeD0pa4xYS5S+lp4lSl8Bz+c2wH70pZy/SSYvYz2sSPTgIlWS8TFab0Ie1NlhYIxYDNOBI98cwblDz9XbDNMiLswcx+Hb774lvRnBcRy++/Y7vc1QBbV/9GnRCVA5/xEMPXcoet3y24jP9fyBYsRFZNVAz1pixA4kNWySuofg9U7279KNWOeL1IgKLXIhGAxiy5YtuOaaawCkn75S9sbbp+c5JZXbclHbfpZTRqPbVCO343Uaah3nZDoZkh0dqYUeStuQmx8dHR1CLZF601AqdpkR6uwgTIfX60U4HNbbDNMivnkMh8OkN0N4vYmuaPHkhXKbHXrWEiP++NR6pE+ytUStHwZS+6RuzlmuIaQkH6TsS2bKkBo/UuP9nRo/fuTGEtDvvFLjOim2XevObaXXLrkjt5JdayhRe9Hklz2DUeePwqxRV8tuTwvkdryJzwOlsZWaOicXJVOKOY5Dc3MzOI6L+z3+u8SPUGcHQ4YMGaK3CRnBRRdepLcJGYPNZiO9GWKz2XDhRRfqbUbGQFqzg2qJMpT+eJGrt9ynj1JIjUZSYw0huSTzQ10JWk7dkZomJLUujNojwaTyzSg/tjKllrA8d8REnytG11rrNaui8z5WR5ma2v/ud78Ttgl5UGcHY9Jhobh056uvv8J5w8/T24yMIBQK4fCRw6Q3I0IhDkcOH9bbjIzh66++xvDzKLdZQLWELbzeiTDT4uhSviT7oyQZW/m6zaqWqOFLOg+Bl5vbhDKif9yn2z13OncSGGX0VLpBnR0EQRAEQRCErhhlZIAUakwJUHIsrdvT4q1BBGFE0rmD4NV/bxUeCmg5baiurg4rV65Ee3s73G43Zs+ejYEDByr+nt5QZwdhOs4feb7eJmQMNpuN9GaIzWbFyPNH6m1GxkBas4NqCVtIb2UoGZautG5rPQVFzZEuRoJymy2kNTtY5fbatWtxxRVXYNKkSaisrMSqVaswd+5cxd/TG+rsYEAwGBS2T1RVR+z7/sSJuPvE/x9vO/r/xe1JtR/9vXj7pOyVakOJTVLtJ6Pbjn9tw7nnnpvQDiX7kvFFjfjJ1VRJ/OTuk/pe9fdV+PbbbwW942mVjPZK7FAjF9U+d6T2Kc2Vb2qq8d233yY8llJ75Z5/SvJZjVxUev4ptWNz+Q4MlVFL5Oaskn1an8Ny8yiZNpTkkVQtUaqpEjvUOIdZ1mC5ekT7xesda5+SWiJXe62vw2pcT9S4fxHv4+u2nFqidh4preNKzj81aokadVFJbkvZoTRnlZ47cvNer3vC6GOJ77mVnsOp1tnofSxzVm2fpfZFXyel2vj666+F7ZMnTwIA2tvb0dnZKXxut9vhcDgi/q61tRVVVVW48847AQDjxo3D+vXrUVdXh759+yb9PSNgCafpUvPr16/HgQMH0NjYiPz8fAwePBiA9JAaLfbJYcOGDfjlL3+psgIEQRAEQRAEQRAEIc0NN9yAfv36Cf//i1/8AtOnT4/4TlVVFUpLS1FUVCR8VlJSghkzZmDEiBFJf88I2PU2QCnjxo3DNddcgyVLlkR8LjWkRot9cpg0aRIA4PDhw+jRo4ca7hMEQRAEQRAEQRBEXFpbW3HeeefhqaeeQp8+fYTP7fa07QZIirT1cvjw4V0+kxpSk5WVpfo+ucN0+CFCPXr0QHZ2dsq+E/Hxer1YuHAh5s2bB7fbrbc5pof0ZgvpzQ7Smi2kN1tIb3aQ1mwhvdlBWrMlVb2zs7ORlZUl+Z2cnBx4PB6EQiHYbDaEw2E0NTUhJydH0feMgFVvA9SkqakJ2dnZsNlsAACLxYKcnBw0NTVpsi8egUAAnZ2dEf/4z/n/BgIBhMNhVFdXC2t61NXVob29HQDg8/mEz30+H0KhUJdtr9cbsc1xHBobG9HS0gKO4+Dz+XDixAmEw2FwHIeqqiph+9SpU2hra0MoFEJHRwdqa2sRCoXg8/kAQNj2er2or68XPq+pqRG2g8FgF5/47draWgQCAfj9frS1taG5uTmmTx6PB01NTQiFQgiFQqiuru7ik3g7HA7D6/XixIkT4DgOXq8XLS0taGtrg9frhdPpxB133CH8XSyfeNurqqqE7fr6enR2dsb0qa6uDl6vF8FgEO3t7WhoaEAwGEQ4HEZVVZXgU21tLTo6OhLGqbW1Fa2trV184mPj9XqF7574zxy8UCgkfB4KhdDQ0CDET+yTeJt/dZzYp+rqauHzQCAg2O73+yNiw8fP5/Ohvb0dTU1Ngk8nT55EZ2enYNPs2bNht9sRCoVw7NixmD41NzejublZ8O/kyZNdfOLzo7a2VvDj5MmTwjnh8Xhw6tSphLkXz6fo3OPjJ3U+8dtie8VxCofDOH78eIQf0T7x27w20XHy+Xw4efKkLJ8A4C9/+QucTicaGhrQ0tICAOjs7MTx48dj5l5HRweampokz6dwOIxAIIBjx44BQEQexvKJ18Pn86GlpQUejydm7vG5Eh2/6Dg1NDSgs7MzZpzq6+vR0dEh1Kr6+nohfkePHpX0ifejrq4OwWCwi098/HjbT506BY/HAwCwWq24/fbb4XQ6455PfI07efKk4FNjYyPa2tq6xDY6NlI1or6+Hn6/P6ZPx48fFzRNFCd+u6WlBc3NzQlrRCgUwvHjx2PmXktLCxoaGgTba2pqhO3GxkYhflLnU0dHB+rq6mLGieM43HnnnbDb7ZI+tbe3C9cT8TkUzyfej5qaGnAch0AggObmZrS2tsasEY2NjcINnDjXAUTUe3GuJKrl4u2amhohfs3NzWhsbOwSs2AwKMQhlk8nT56E3+9HIBCAx+NBS0uL7LrHb9tsNtx5553gOC4iTn6/H/X19bJ8Onr0aMLci44Tfz2JFyd+m78vSeRTMBjEyZMnBf/4eyA59xHx4sTXo1g+1dTUCJ83NTUJ8ZPKPZ/Ph1tuuQUWi0UyTtH3QInOJ367qakJ7e3tMX2qra1FW1sbgNMPBOvq6gSf+HugUCiEuro64R4o+nyqq6uDz+dL+ZqbjE9yz6dQKITOzs6Ia1JbWxv++Mc/CscT38OK49TU1ITm5mbhe3xNU8sn8f2ylE/ie1jeJ7nnE78tdQ/Ln8/BYBAdHR04depU3HtY/n6rra1NyBWpONlsNvzud78TfJOKU1VVlVBvGhsb0draGtOnxsZGdHR0JKwRasTJ4/GgsbFRce4lE6fW1lbU19cLn1dXVyftk9PpxO233y78JpV7D8tvy6FHjx4YPHgwPvvsMwDAvn370KtXry4P+OV+zwjYisSTbdKQjz76CBMmTEDPnj3R0tKCffv2YerUqcL+nTt3YuTIkXA4HKrvy83NjWnTBx98gH/84x/YvHkzNm/ejC1btmDfvn0YPnw4xo4di/fffx/ffPMNOjo68NJLL6GtrQ2jRo1CYWEhKisrMWXKFJSWloLjOJx11llYtmwZsrKy0L9/fzzxxBM488wzkZubi0cffRR5eXno1asXCgsLceGFF6KkpATl5eWYOHEiVq5ciY0bN2LKlCmorq7Gs88+izPOOAMulwuPPfYYtm/fjpEjR+Kpp55CeXk5+vbti9dffx2XX345vvjiC7z55pvYu3cvNm7ciOrqagwaNAhLly7F4cOHcdlll2HLli3Yv38/LrjgAsGnESNGoKysDP/85z9RX1+PgwcP4v3338e//vUvVFdXd/HpmWeeQUVFBYYMGYLdu3dj3bp1GD58OHr37i341L17d8ydOxcTJ06ExWJBQUEBdu3ahdzcXDz99NMoLy/Hp59+ih07duBnP/sZGhoa8Pzzz2PKlCk4cuQIVq1aFeHTxIkT8c477+CNN97AsGHDcPDgQZSWlmL//v3w+XwRPh06dAivv/46vvrqK7S3t+OVV15BeXk5srKy0NTUhOXLlwMAhg0bhqKiIuzbtw8//elPJeO0detW7NixAx999FGET1OmTEF7ezuKioowbdo0PPfcc/jwww8xbdo0VFVVYdmyZYJPTz31FLZv344BAwYIPu3ZswebNm3CxRdfjIqKCuzcuRNjxowR4pSbm4ulS5eiqqoKEyZMQFlZGY4fP45hw4ZhzZo18Hg8yMvLQ2lpKd577z3U1tZi165d2LZtG7Zs2YIvv/wSAFBaWordu3fj3HPPxeLFi1FZWYmxY8eioqICr7/+OsaPHw+bzRbh06JFi1BeXo4xY8agpKQEH3/8MdxuN1577bWIONXX12PdunX49ttvMXHiREHTKVOmoKioCOXl5Zg2bZpk7sXzKTr3Tp48iZdeegndu3fH2WefHfN84nPvk08+AcdxePnllyPilJOTg+XLl6OiogI/+9nPusSJz71PP/0UK1asgNvtRn19fUSc3nnnHWzatAlerxdffvmlpE9r164Fx3EYMmQI5s+fL+hRUlKCbdu2YdKkSfjHP/4RkXt79+7Fpk2bsHPnzrjn05QpU7Bx40b885//xJQpU9DQ0IDHH38cV199dRefli9fjo8++gjBYBBbt27Fu+++i08++QRHjx7tkntFRUU4ePAgrrjiioj4RceprKwMlZWV+OabbyLi5HK5sGzZMnz66ac466yzhPN95MiROHbsGF566SWceeaZGDBgQEyf+Djt3LkTbW1tWLduXYRP3bp1w/Lly/H5559j8uTJmD9/Pnbt2oWrr74ae/fuxc6dOzFhwoSY5xMfp/LycmzYsAH19fXC+k579uyB3W7Hhx9+iI0bNyInJyepWr5jxw4cP34c69evj/BpxIgReOqpp7Bt2zZMmzYNtbW1ceMkrnvLli3D9u3bkZOTI1kjvvvuO6xbtw4+nw8jR46MiJM4fs888ww2b96MvLw8rF+/Hhs2bMDevXtRWVkpeX1avHixcH2KjtOCBQtwww03oK6uTtInXnO+RiSqexdccAHeeustvPHGG3A4HKisrMRrr72GHTt2oL6+vkuNeOGFF7B7926ceeaZePfdd/H+++9jyJAhsFgsWLJkiaDN/PnzsWPHDkybNk0y98RxuuCCC7B06VLs3r0bV111FQoKClBRUYFp06ZFXJ/27NmDl19+WbBP7NPHH3+Md955B8ePH8fx48exatUqfPzxx8J1KFHd43Ovb9++yMvLw9///veIONXW1qKsrCzh9emTTz7BypUrMWzYMLS2tsbNvejr09tvvy1oH+t84uteSUkJPvroI0ybNk3Sp5qaGqxfvx6ff/45+vXrh6eeego7d+7EeeedF7OWy4nT9u3b8c0332Dbtm0RPg0bNgxLlizB/v37MXnyZMybN0+In1SNKCsrw+7du9HW1iZ5fTpw4ABef/11nHnmmRg4cGDC+z3ep4qKCnz66afYvHlzF5/48/aaa67BwoULhZzdunUr1q5di6FDh6KmpgbLli3D/v37kZWVFXE+VVRU4K233sK3336LY8eOpXTNlXMPm0yc+BrB38Pm5uZi3bp12LRpE6qqqvD555+jsrIS77//Pqqrq7vUiHnz5gk17fnnn8eHH36ISZMm4b333lPFp+LiYpw4cQLjx4+X9Km8vBy7du1CeXl5wloe736vtLQUlZWV8Pv9Ebl3+PBhvPbaa/j3v/+N9vZ2rFq1SriH9Xg8ePHFF8FxHIYPH46ioiLhN8iCBQsEbaTi1K1bN6xfv144V+LF6auvvsILL7wAp9OJQCCAp556Cjt27Ih5D/v222/jwIEDqKqqkqwRauTe448/joqKCowZM0ZR7iUTJ/H59/rrr+Ott95CXl4edu/eLdunwYMHY9WqVejWrRtOnTqFF154AVlZWcjLy5OM07hx4/Dss88iPz8fLpcr4W/roUOH4r333sOWLVtw9OhRzJo1S5iZsHr1agBAv379JL9nJNJ2gVKegoIC3H777Rg8eDBaW1tRWFiIxx9/XBhSk5+fj3vvvRdZWVmq74vXexX9JNbj8aB///6oqqpCnz59hB68jz76CFu2bMFtt92GUaNG4f777wcALF68WHjyYrfb4fP5YLfbYbPZIra9Xi8cDoew7XQ68cADDwAAHnvsMcydOxccx+Hxxx/HsWPH8PTTT2PSpEm4/vrrhe+VlJRg3bp1qKysxH333YfevXvD5XIhFAohGAzioYceAgA8/PDDOHbsmNCJ8Mtf/lLoGXY4HIJP/HZ+fj569uyJOXPm4MEHHxTaiPZpzpw5gh3Lly/HkSNHsHDhQmRlZQk+Wa1WYdtisWD37t0oKyvD9ddfj4svvhgFBQUAgEWLFgk5MW/ePPTs2VPwQ+yTy+XCjh078N5772HmzJkYPXq0sAbL3//+9wifOjs7UVxcjPPOOw+33XZbxPe2b9+ODz/8EH/4wx9w4YUXdolfvDiJ7eV98vl8cLlcCIfD8Pv9cLvdmDt3LoLBIBYvXoxQKIRAIAC3241QKBShG+8T/2SI3+Y4TngqzffgL1u2DD/96U/xq1/9CoFAABaLBXa7HX6/H1arVYjNQw89hB49emDu3LkROXDkyBG88sor+NnPfoZrrrlGsKO4uBhr1qzBoUOHUFxcjKysrAifxHn52Wef4Y033sB1112HSy65RPApGAzihRdeQFVVFR5++GG4XC7cf//9GDRoEO65554IfaVyL55P0bm3fft2fPDBB7jllltw0UUXxTyfrFarcHG76KKL8Jvf/CYiTlu3bsW2bduEczg6Tnxsjh8/jqVLl+LSSy/FDTfcEBGnt956C59++inuuusuDBgwQNInj8eD4uJiLFq0SMijxYsXY9WqVThw4ADy8/PRvXv3iNyLzrfo84mP08svv4yvv/4axcXFcLvdQh5G+1RZWYl169Zh+vTpmDRpUkR+ROfe/fffj4EDB+Lee++NiF90nOLViHA4jIKCAgwdOhS33357RN6Xl5fj/fffx6xZszBmzJiYPoXDYTQ0NKCkpAQXXHABfve730X4tG3bNmzdujVmDW5ra0NRUREWLVoEu93e5XziY/POO+9g9+7duOOOOzB48GDMnTsXFosFjz76aES9SKaWFxQUICsrCw899FCET0ePHsVLL72EqVOnYvr06eA4Lm6cxHWP1+3vf/+7ZI146aWX8N1336G4uBjdunWLiJNYm507d+Ktt97CjTfeiLFjx0bkgNzrU3ScPB4PFi1ahOLiYlit1rg+xco3KZ8cDgdOnDiBJUuWYPz48bjxxhuRn58PAHjkkUe61Ag+ZiUlJcjPzwfHcSgpKcGJEyfw5JNP4rLLLsOvf/3rCD3i5R4fGz5O1dXVeP7554UaLG5DHLNgMIi5c+eiZ8+egg28TwcPHsTq1atx7bXXYurUqYIvfI4lqnt87gWDQRQWFmLevHno3r27ECe+AyHR9am8vBzvvvsuZs6ciTFjxsTNvejrEx+/Rx55JOb5xNe9wsJCtLe3Y/HixZI+ffjhh6ioqMAf//hHDBs2LKJGRNdyuXEqKCgQbBD7VF1djWXLlmHy5Mm47rrrulyT4tUIPkaFhYXIysqKG6cVK1bg0KFDWLBgAc4444yE93u8T+IaH+1TvGvoxx9/jLfffhu//e1vMW7cuLg1orOzEwsWLMDw4cNx2223pXTNlXMPm0yc+NiUlZXhs88+w913342+ffsKOTZ//nwUFxdLXp94PV599VV8/vnnyM/PR3Z2tio+3X///ejTpw/y8/MlfRLHL1Etj3e/F+8eNhAIYP78+Rg6dCj+9Kc/RXyvoqIC77//Pm6++WaMHTs2Qo+FCxeipaVFqG/x4rR582aUl5fj97//PcaOHRs3TidPnsTixYsxYcIE3HjjjZL3sA899BAsFgsefvhhyRqhRu7xdjz22GOKci+ZOIn13bVrF8rKynDjjTdi/Pjxsn0KBoMoKCjAggULsHfvXuF3zPjx4yXj5PV6MWjQILS0tBiyM0Jr0nbNjliIh9RMmjSpy5AaLfbFwuFwRLzKR3xyiv/Lww9HEiPueYu3LZ6vFT13y2r9cYaSxWKJ+H/xts1mE45vtVqF9sWfRx+X/1y8sI3YJ347HA7D6XQm9EPcpti+eP7x7Vssli7f4TgOBQUFwkKwYj+ifeUR+xHtE99pxRefWN8TtxXLv3h+xNqO9okn2o9Yn9vtdsGueLbyx+D944kVp3A43CVmvK/8BSOWLxaLJcIP/njAaa1ixS8638T2iP8+lk+xci+eT9Hb4vbjxSZWLKPjxB9Lbr6J48Tvs9lsMWuEeLt79+4oKCiI8I3/W6BrzJLNPb4N/sdmLJ9428X1QqyTnAWv5MaJr50Wi6VL3vNIxc9iscTUIzrfomsyAHTr1k3QOl69cDgcEfHj94XD4ZjnYLK1PHqb91Ucg3hxilUv5NaIeNeq6P2xakQiP3i7xZ9bLBZkZ2ejoKAAbrc7QtNYPomPJccnsb1yco8/XqztWPUoUS0XxynaNvExoq+tsfJIHHuxL/zncuqey+WCw+EQrpXieCQ6n2LVdanci3V9EtsZr5aLkfKJt52/yRfbFM+PeD6J4xQOh+PWi1jX/ES5BwBnnHGG8LdSucd/R637iFg5K673UjVCbGei65Pc3EvVp3h1T3xNslqt6N69e5fjxrs+8ZrLrRFyfBIjJ05yanky93tiP5K9hxXnjFRs+Ha6devW5d4vOk5A13yL55P494Na93tScZL63SEn9+TWPbHfvJ/J+GS1WoWHIXJs57f5KUGZStp2dqxduxYHDx6Ex+PBs88+C7fbjQULFmDmzJlYtWoVNm3aBLfbjVmzZgl/o8W+VIh1AdIKqWPJtYOlvVIk8kV8w6akDbOipc/JtK1GLrJE63MnWe3k5LeWGKVuqdFGutUSrY+ld17pndtSaJ2LrElVbzPnYjxStclIPhnJllSJ54uZfFQKq/OUtGaDuG6T5vJJ286OmTNnxvy8X79++Nvf/sZsH2EsfD4fCgoKhOGABBv4BZgIbRHnN6EtVEvYQnqzhfRmj8/nI60ZwS/ST2gLvyAmPwqT0Ba6B1SGqd7Gkq6wXDZF6lhy7TDKMi+x7HC5XMK8VaVtEKdJRhu5estt34hx0frckdNGsvmtJWaqW2rUErXR8/zQ4/zTW2+5qKGNEeqbWnobwRfAOHZIYfTcTnfEORA9zVIvjJKXWl1PYk1rJ7QjVt02So4ZGersMDHpNsRJDXvFr4tSAz2nXKjdvpbDsMPhcEbmG+tjxctvsw2xl4PW9sqtJek2LUsNtPBL7dotxqhTNZSQDrVEDYw4vTbVY6VSS/TCiDbJIRwOJ1VL0tXPVGB57cpEfaVQqoeW10kzQ50dBiDdklbrESCp6OH3+7Fo0SLVF+NJt1EIUmjxZFKst9p6mOkpdqrHSpTfLJ86m2EkmBTJ1hKj+KkGesRWq9otxigxYjUSTAoWtSSd0UJ7vWqJmUb0yoV/g4aSv1PbDjOT7DQWs+uRLFrflxCnoXFHhKlwu91YvHix3mZkHMnMQ87EJ+FqYYT8zpQYGUHrTIL0ZgvpzR5ar4Md8d5sR6hLrLdAEdohrtuZci+mBjSyQ0eMMvzSiMNFpZCyg+M4nDx5EhzHKW6DSJ5EeqczWnfOJNOG3PzW2g5W6Pk2Fi21Vkq6TZ1LBiPqLcZsU8VS1dtIvqQLRsrtdLvvkyKejUbSWy9YxY+0ZoPRr5NGhTo7MgyzT8fw+/145plnMnLoudokkytqDxc1Yly0tldOG8nmt1Z2sEYP7VloLUU6nB9qT2PRU2+5GEX7VEmkd7pNbzCKHVIYPbelSAd9xTYqncZiVrS6ntDbWFJDyTSW6LqdDuem3tA0Fh1Jt6d06WCv2+3Gww8/rII1p6EFSuW1wb/3O51It5FVQPz8Tjft1UDrJ5Nya0mi2KbTjYieT3vVrt1ijLJAqZFGhxi9lhhx5EGqx0o0rSKdfDESsXxJdhqLmfSQixr3JXKnsWSivlJofV9CREIjOwhTEQqF8MMPPwi9zQQbSG82UH6zg7RmC+nNFtKbPaQ1O8LhMOnNAL4zn6ZVsIHqtjKos8MApNOTP8DYb2MJBAJ49dVXVV8ZOh2GlMtFC3vFeqv9qs5MeRuLHDsS5TcLX/gY6T3MPdknI8naoVUtSQf08CVZvZVglBgZ4boTT2+tz6t0QQvt9aolZrp/kSLaFyW1JJPexqLGaAv+R3cwGJT1fSPrkQ6I6zaNlpEPTWPJEMz0Bgwpe91uN+bNm5dSG0bBiFCkY6IAACAASURBVDZKTWMxK0Y6d+TmtxKMmG9ao0YtYYlRYqTVNBaj6S3GKNqrRap6G0UPo9ghh3S8TqaTvtGko95GQW7cHQ4HAHobS7KkMo3FyNdJo0IjOzIMlj38ejyRD4VCOHToEA3xioGaT+yi9ynRW++nTWqOUGD1tIJFfqcyMkdJe6m2oVX7crXWqjPMCPmWCDXt0DK3kz3XpUinkWBSJNJb7xwz4giTVH/4p/N9id75oIRQKJTWnTVqolUt4aevpHNu60my2seq2+l4brKGOjt0JBMXuNS6/WAwiHfffVf2kLpEGOmpvpEJBoOkhwRq5VG8/Cbt46NUG7VrSTqgZx4ZUW8jXkPVssmIepudRFrTAqXKiOdLMrltJj1Ywv/QTuWhACEfqtvKoGkshKlwuVz429/+prcZGYfL5dLbhIyA8psdpDVbSG+2kN7soeskW0hv7bHZbAB+nM5CaAvVbWXQyA4DkG5DkIy8YGQoFMLnn38ue0hdOk1hUAst7E1mSJ3UkHI9FiiVixGmYyTKb6MMsWeBltOyAO1qiVz0rDl6xDZZvZPBaE8V9T53gPh6G3H6iB5ocf5lYi1hidbTa5VgZH3VqItyR3ZEf59QhrhuG+26ZmSosyNDMNN0DCl7g8EgysvLVRsuqqc2RoyL1HBRI9qrBkY6d+TmtxLMGj8p1KglLDFKjLSww4h6izGK9mqRqt5G0cModsjBSNNYMgGj1pJ0QG4u0podykhleq2Rr5NGhaaxZBhmX6DU5XLhzjvvZHKsdEPLJ+Hi4aLpsghgOi5amCi/WfhixgVKYyG3lhipM4w1amqvVe0Wk07neqJjpWoHi1qSCkYcYZLq+Sx3WoXe2sfCiDYlwuVywWKxJGV7OvopB61qid1++mckTWNRRrLax6rbZs1ZNaGRHYSpCAaD+PTTT6nXkzGkNxsov9lBWrOF9GYL6c0e0potpLf20MgOtlDdVgZ1duhIOrzdRMv2tGhf7rxvI62KbwbMfKHTOleSaV+tefaZhFLttVxDQinpUIOVkgl6G+m6k6reVHOSx8y5rSfxfElGbzPpwRJ+VAHf6REP0lcdjHidTAc0ncby/fff49SpUxFBueyyy7Q8ZFpilGGxLNvQqn2Xy4U//elPTI6lZhss0cLeZFY9N8oCpVovcKlFG8nmt5bonfdax0+PWiK3PSPXYKVomdtq3mib5ZoRT28jTh/RAy3OP73eDpKpC5Qa4W0sRtZXjbrIT2Ph/5sII+uRDojrtppTsc2OZiM7XnvtNZSWlqKyshJffPEFvvjiCxw8eFCrwxExEBcyM80rT7So4I4dO0wxxMuIccnEBUql0GOB0lj5baQnxmbBTLVEbbRaoNRoesu9hrJELTuMqLcSjBIXOdACpWxJJrdJ+0iSXaCURnYkRyoLlJqhbrNGs5EdX3/9NebPn0+L1qQRZliglOM4HD16FJMmTdL8WHL2GYlke4GT8Vl8oVNjgUtxG2Z8iq2URPlNC5Sqh9xakuxIJTOhpvZa1W4x6XSuA9rmPYtakgpGHGGS6pPURD8IeYw4SkzvfFACx3GGzCM90KqWyJ3GosaxzEiyerC4TpoRzTo7evbsKXtYE0GohdPpxOzZs/U2I+NwOp16m5ARUH6zg7RmC+nNFtKbPXSdZAvprT02mw2A/GksRGpQ3VaGZtNY8vLy8NJLL+H//u//cODAAeEf8SMsn/qlwzB3NdoPBoPYtGkTk+GiZn9qmwxmHlJnpAVK5eY38SNKtTei1ulQg5ViRL3FmO2akareRvIlXTBSbpspflLTawlt4Ucm0IKZbDD6ddKoaNYVd/ToUQBAeXm58JnFYsHo0aO1OmTaQguUKms/1vfC4TBaWlpMPTRTKVre3CjRUWrYP8shyXIxwgKlyea3luhdt7SOn95aZ9oCpVrqTQuUxrYhlt407D8+qeZRJmmlB9H6GkFvI9gAaDfNkm/XKH6aHXHdpgVK5aNZZ8c999yjVdOETOQWMqP08Kthr8PhwE033aSWSYrtSIf2lRDPJofDYUh7tYa1z/Hy22xPnY0Ay1qSbmiRK0bU24jnhFo2GVFvJRgxRvFItIZdOvmSDiSzZiBpH4lcPfhpLPx/U20vU1Cqh1nqNms0m8YCAJWVlVizZg3WrFmDffv2aXkoQgWMskBpKnYEAgG8++67CAQCituIhRkWKOXRYhSCWO9UFrhk+epZVu2rSaL8NusCpXq0L7eW0AKl6qBV7RaTTuc6oO11h0UtSQWj1Rk1jiU3t2mBUnUIBAKGziOjkIrP/MKkcqdVZKK+UiSrh7hum/3+Qk006+zYuHEjNm3ahAEDBmDAgAHYtGkTPvjgA60ORxAEQRAEQRAEQRAEAUDDaSyVlZV44IEHhNWQL7/8cjz++OP4+c9/rtUh0w6jLFCaaB/f82iUXsRE01h+9atfpdSGWdHS53R8xbQa06ZYtgHIz28lpNs5obX2atQStTU189Q5o+e2UdpQi3h6G2X6q5G04knVJiNdJ42or1KkptcS8VEjB+htLGzR8jppZjSdxiJ+7ZPL5dLyUGmNUYZfpttwxlh2BAIBrF+/XrfhomYiGW2UDD1Pt6lBRligNNn8VoIR64Ae2rPQWopMW6BUb73lYpYFShPpbcQ6IIVR7JDC6Lmd7khNr9ULo+SlVtcT/i0s9HYQNsSq20bJMSOjWVfcOeecg1deeQWXX345AOCTTz7BOeeco9XhiBhk4lNFi8WCnj17MnlqkQ56qE08mywWiyHtNQpqaRMvv0l79WFZS9INLTQxot5GvIZqXUvSjXSyP5GtRhntaxaS8TET9EgGtUd4kb6RKNXDLHWbNZp1dtx0003YuHEjysrKAAAjR46kKSxxSLdeOa2f+KSih91ux7XXXqv6sdJtFIIUWjyZFA9hpAVKlR1Ljh2J8pvlK3vN/uRXq1qSDugR22T1VoJRYmSE645aehtFU5Yo9VnuUH9aoFQZse5L9F6gNB10k0L8itNYWK2nJwgkehuLuD3iR1K5L6EOD/loNo3F5XLh+uuvx9y5czF37lzMmDGDprIQmuP3+7Fq1Sr4/X69TckoSG82UH6zg7RmC+nNFtKbPaQ1W0hv7aFpLGyhuq0M1Ud27NmzB5dccgm2bdsWc/+VV16p9iHTFqMMWZS7QKlRSNTLfM455wi9zUraMCta+pxIbyPCenFRNdqQm99a22EEtNZejVpixGkQerYvhdFzO93Oj0TE09sow9KNqHeqNqXjdTIdiBcX0lsaNc4xXmMjnq9mRMvrpJlRvbOjtrYWAHDs2LEu+9L9ZKirq8PKlSvR3t4Ot9uN2bNnY+DAgXqblRRmn45ht9sxZcoUHawxH8nkipKVuNMtF41gL4v8zkTtjVhLjJBviVB7Gks65LZRtE+VRHqnm5/pYG86v7EiHfSNJp31Vhul1xO501jox7cylExjia7b6Xhuskb17Jw+fToA4IYbbsDs2bMj/l1//fVqH44pa9euxRVXXIGioiJMmzYNq1at0tskIgqfz4cXX3wRPp9Pb1MyCtKbDZTf7CCt2UJ6s4X0Zg9pzRbSW3v46StGePNNJkB1WxmadXs+88wzyM/PT/hZutDa2oqqqirceeedAIBx48Zh/fr1qKurQ9++fWW309zcLCSpx+MBADQ1NQkjYoAfR8dwHCf0loq3w+EwwuFwzH3iNvg5XbW1tWhqagIAtLW1dTlWa2srAKChoQFut7vLcfnvNTQ0CFrU1tYKvYl8r2+0HT6fL6Ff4n3t7e0AgPr6enR2dsb1ubGxUdAvuv1gMIi8vDycOnUKdrs9bhvNzc1CPKRs5At5R0dHl+8pjV+i7/FvNhHHL9k2xN/j/5+PH58DieIXDAa7+MXnEZ8DPKdOnYqIn8/ni2sv30ZLSwtqa2sjfO7s7BTa4Nf46ezslKWv+I0wcs6dlpYWAD/GL14bfBza29sF3aLbaGxslGyDP8diac/va2xsRPfu3ePGDzh9QzFkyBCcOnUqQo+2tjYhDtF5L6Wb2A6+jfr6egSDwbi6RcdPqn0A8Hq9SdUB8T6+/sTKgehzWEn8pM7hRLUkOn4NDQ3Izs6OaIOv9Ym0j6VHOBzu8nfJ1GAlNaejowPA6Rzo6OiI2waf93JyQK4dgUAgrt5KfBHv4+tKrDou1b7ca2gin+PFL5Ed4msov4+3I9b1T8qO6PsGcS2xWq0xc8DpdMq+hsqtwYns5dvwer1xvyf3HE6kBxD//IvVRvQ1VO6xeBLVEqlrqNz7vlj75NTPeG3wazLwbUjZoeQ6nEobvG7R11Cx3vyP8ETneqJrqNI84q9/SuOXag4kcw0V7xOff3w7sezgdePrUbz48bnNX4elfE5Gj3jay42f3GNJ7VNS+6KvoXLbEN+XRMfP6/WiZ8+e6NWrF4hILGGVx78Eg0EEAgEsXboU9913n5CMnZ2dePrpp1FUVKTm4ZhRVVWF0tLSCPtLSkowY8YMjBgxIuK7gUAgYrEej8eD/v37o6qqCs8//7xws0IQBEEQBEEQBEEQqbJ48WJ4vV44nU5YrVZ4vV54vV4MGjQILS0tEQ9lMgVbkcq9Dx988AH+8Y9/oLW1FZs3bxb+7dq1C5MnT8bw4cPVPBwzWlpasG/fPkydOlX4bOfOnRg5ciRyc3MjvstrwPu+ZcsW7Nu3D8OHD8dPfvITtLS0oGfPnpgwYQJaWlrQr18/XHzxxWhpaUH//v1x8cUX4/vvvwcAXH311Th16hT8fj+6desGh8Mh9FQ7nU7k5OSgo6MDVqsVl156KQYNGoSamhpcfvnlOOecc1BVVYUpU6bgrLPOwrFjx3DllVeiX79+OHHiBCZOnIj+/fvj1KlTGDNmDPr16wePxyO0n52djcsuuwzA6ddK/eQnP4Hf74fb7cakSZMibDx+/DhCoRAuvfRScByHHj164OKLL0ZHRwf69++P4cOHo7OzE3369IHH44HdbseoUaMwdOhQNDQ04MILL8T555+P6upqjB8/Hueff35E+/z2lClTcPToUYwePRrDhg3DsWPHcNlll+HMM89EXV0dHA4HQqEQnE4npkyZErONq6++Gi6XCz6fD1dccYXwyqyLL7444onV4MGD0bNnTyFnc3NzceGFF6KtrQ2DBg3C2LFjcerUKQwdOhRjxoxBXV0dzj33XIwZMybiWDU1NQiFQsjKygLHcRg7dizy8vJQXV2N/v37Cz3jvG/83/Xp0wcnT57EVVddhTPOOAN1dXXo0aMHRo0ahZaWFkyYMAG9evVCY2MjOI7D4MGDYbfbhaeYZ555pvCUbsCAAZg4cSLa29vRu3dvXHLJJRE2VldXg+M4TJgwAV6vF3379sXYsWPR1NSEoUOHYsiQIWhubsYFF1yAgQMHoqGhAePGjcNZZ52FmpoaTJgwASNGjMAPP/w/9s47Pqoq/f/v6ZNGSCEkBELoJRA6AYP0YiAgiGABxM5asCGCgOCCZX+yX3cXdcUurAXbV0VcRMUV1waIKKKiIj0JBJKQPn1+f+R7rzPJJEzCzJ07yXn74uXNlHPP83me+5wz555z7hGys7Pp3LmzbEt2djbHjx9n6NChtG/fnoKCAvr3709qaipFRUXynaPIyEhGjBiBRqPB5XIxYsQIeUQ7OzsbqFkXKl1D1dXVpKenExMTI9/dS0lJke8gJCYmkpKSIg8u9uzZU54R0adPH/r06UN+fj59+/YlIyPDZ6yMHTuWw4cP07dvX3r06MGxY8e8rvWxY8diMpkoLi6WP+sr3qKioqisrGTEiBGYzWa5Hl27dkWn09GzZ09MJhMRERFyPHTo0EG2Kz09ne7du5OXl4dWq2XkyJGcPXuW1NRUBg4cSFFREe3atWPQoEGcOnXKK0f06dOHtLQ0Tp48SY8ePeRzS36RYi81NZXjx48zbtw4kpKSyMvLA2Dw4MHk5+cD0L9/f8rKyujTpw+JiYlYLBYGDBiA2WyWNXE4HCQmJjJ8+HAsFgvR0dFkZWVhs9mIiooiKyuL4uJirFYrHTt2xGg0kpiYSN++famoqMBqteJ2u4mJiaF79+506NCB0tJSMjIy6Nq1KwUFBQwcOJCePXty9OhRhg4dSs+ePevkCE8bBw0aRPv27Tl58qQ8AC/lCK1Wi9VqZeTIkeh0Oux2OxdeeKFXeVJeiIqKIjU1tU6O6NKlCxqNhsTERLp3747VaqVdu3b07duX4uJiOnfuTJcuXcjLyyMyMpKRI0d6xcfRo0dxu92y/3r37k3v3r1lOyRGjRpFdHQ0p0+frhNjp0+fxmazkZWVRZs2bTh58qQc69XV1fTv35+YmBiqq6ux2+3ExMQQGxvrlSOGDBlCVVWVzxxRXl5OQkICQ4YMobS0lMTERAYPHkxRURFpaWn06tWLoqIi3G43LpcLo9FIv379KCgoACArK4tOnTpx7NgxsrOzMRgMlJSU0KlTJzIyMuRY9Iw3KbadTic6nY6IiAiysrIwGo04HA6Sk5MpLS31qannscPhwGw2M3ToUKxWK3FxcfTu3Rur1Yper8dqtRIVFUWXLl3o3LkzZWVldO/enV69enHq1Cn69Okj17F///6ybzIzM+nTp498rn79+nndtfREq9UyduxYDAYD5eXljB49GrPZTGVlJaNGjaKwsBC73c7QoUOJiYnBarUyfPhwDAaDPJsxPT2diIgIunfvjl6vJzY2lt69e2Oz2WjTpg12u12eIVO7H+GrTTIajeh0OjkHa7VahgwZIvcRaucIz/YpKSmJgoICxowZQ0JCgleOkGIvIyMDi8WCzWZDq9USFxfHoEGD5OsvKSmJs2fP0qpVK0aMGOFVx+rqamJiYsjKyqK8vJyqqioiIiJo1aqV3Ma1atWKbt26kZqaSllZGRkZGaSlpXH69GkGDBgg1ykrK4uOHTty/PhxoqOjGT58eJ0cATVtRHFxMYMHD6ZNmzaUlJR4tU8jR44EkNsn6Y73iBEjOHnyJA6Hg8GDB2M0Gr1yhDRjLyEhQW4LTCYTnTp1kn3bs2dP+vbty+nTp+nRowd9+/at139SvkhNTaW8vJyBAwfSsWNHTpw4gdlslm+2jRs3jsjISIqKihg7diytWrWiuLiYMWPGEBsbS0lJCaNGjZLrlJWVRWRkpNxGdOnSBb1eT69evTAajURGRnq1T4mJiZSUlBAfH88FF1xQbxsq9WFNJhMmk8mrfUpISKCyslLuw0rtTu3Ya9OmDfn5+URHRzNgwACv9ql2HzYzM5OSkhIiIiIYPnw4UNOH7du3L3l5eRgMBsaMGYPdbsdsNjNs2DA5RwwbNgyLxSLb6dmPio+PJzEx0asPW1hYKMd9VVUVVquVjIwMMjMzOXr0KIMGDfLqz3peR23btqWystKrDyv5z2g0Mnr06HpjoLq6moqKCjp27EibNm2AP/qw0qyB9u3bN7kPe+rUKTp27ChrLeULaVaHxLBhwygqKsLhcNTJEZ71TUhIoLCwkFGjRtG6dWuvPmxJSQkul4vU1FQMBgN9+/bFZDKh1+u92qeMjAzy8vJ8aiPVcdCgQVitVq8cIc2wjIyMpGPHjhQXF6PVahk0aBAdO3YkPz+foUOHyv2PAQMG0KNHD585Iisri4KCAtxuNwkJCTidTq8cYTQa5fbVsx8xbtw44I8+rDQzZvjw4TgcDq8+bFVVFW63m8TERNq1a+eVI6Rrs3fv3mRmZpKfny/3Z6VzTZw4kVWrVtGnTx+io6NZunQpAwYM4PHHH+fee+9tkU9GDfgylilTpjBlyhReffVVrrjiikAXHzLi4uIoKyuTO11ut5uSkhLi4uLqfHbSpElyYEPNzI7nnnuO3NxcEhISyMzMBMBgMDBhwgQ0Gg16vZ5x48ah1WrR6/Xs3buX4uJicnJyyMrK4qGHHqJjx47MnTuX5cuXA7By5Uo+/fRTPv74Y+bNm0dGRgZarZZp06ZhNBrRaDRcdNFFmEwm3G43OTk58jKVqVOnYjabcTqdTJ8+XT52OBxy+UuWLMFkMuFwOHA6nZhMJiZMmCB3ZLdv3w5ATk4OZ8+eZc+ePYwaNYr4+HjZpkmTJsk2Wa1Wjhw5wjPPPMMFF1zAlClT0Ol0TJ06Fb1ej06nY9y4cRgMBnQ6Hdu3byc2NpacnBz5XLm5uUyYMMGnTTNnzuTFF1/k4MGDLF68mNatW/PJJ5+QlJTkVUZOTo6XTdKUfakD/dhjj5GXl8eCBQvQ6XTyAJNk0+TJk2Wbxo0bh06nQ6/XM3bsWNmO7777jqKiInJychg2bBgPPvggaWlpzJs3Tx5tzc3N5bPPPuPkyZPMmzePzMxMduzYIdfR5XLJvjlz5gz79++nS5cuXHHFFTgcDkwmE06nk/fee4/PP/+cKVOm0KFDB9l/d955JytXrsThcHDbbbdhMBgYP368HHueepSWlvLNN98watQoZsyYIds0ceJE2Sbph4FOp8NisWAwGADo1KkT/fv3x2AwMG7cODn2duzYQUxMDBdffHG9sTdjxgxWrFgBwPLly2WbJPvq81NiYiKvvfYa/fv3JysrS14ed9ttt/Hkk09y7NgxbrvtNrRaLStWrKB9+/ZcffXVLF26FIB58+bJ8SbZ5KnHvn37OH36NJMnT2bs2LGyTZMmTZLr+8ADD8g2SYOAnmXs3r2bsrIycnJy6tgk1eP666/3ij232y3bcvPNN7Nq1SpsNht/+tOfOHPmDLt27SI9PZ3JkyczceJEn7E3duxYli9fjlarZeXKlfL1dPHFF1NQUMCBAwfIzs7m4osv5osvvpCvK7fbzeTJk2Wbdu7cSVRUFLNmzeKbb74BqBN7tf30r3/9iwMHDvCnP/2J1q1bM2HCBNxud53YS0pK4tVXX5X9J11POTk5PPPMMxw5coRFixYRERFRJ/Yuuugi2SbJN1qt1itHHDhwgFOnTjF16lSv2Lv44otl/61cudIr79W2ac+ePZw9e5bJkydTVlbGgw8+SKdOnZg7d67sv5tvvlm2yTNHSD/upGtIp9NRXFzMzp076dmzZ518VFlZyddff82IESOYMWOGbNOECRO84k3Ke77iraSkhG+//ZbRo0cTHx/Pd999R0REBPPmzfPy0y+//MILL7zAkCFDGD9+PMuWLZOvHYPBwMSJE71yRFxcHDk5OV7t09ixY+XY87yGJk+ezHPPPcehQ4dYunQpERERfPvttyQnJzNz5ky0Wi0XXXQRRqOR3bt3c+jQIQYNGsTQoUPlWJw5cyZ5eXkUFBRw6aWXkpuby+rVq0lLS+P666/38tPOnTs5evQo06ZNY/DgwV56eLahnrE3ceJELz9t376d7du3M2vWLHr27Cm3VZJNnjlwzJgxcuyNGTNG9lN1dTVffvklI0eOZNasWV4+W7lyJS6Xi9WrV8vxJv3I8ow3zxwsdaJr54s//elP8vUk/WD0jL2XX36ZH374gSuvvJL4+HifbWhOTg7Jycm8/PLLZGRkMGPGDFauXAnA6tWrZZsuueQSDAYDX3zxBUlJSeTm5tZpnzxzuWeOOH36NHl5ecyZMwebzcb9999Phw4duPHGG71y+a5duzhy5Ag5OTkMGTLEq47jx4+X/XTBBRewZs0a0tLSuOqqq7z6KL7ap2nTpmEwGDhw4AAVFRXMnDmTs2fP8uWXX9KlSxcvWy6++GI0Gg2ff/45Y8eOZe7cuV5+uv/+++V+UUPtU1lZGbt375Z/mC9duhS9Xs/NN9+M0+nkl19+oUePHhw/fpynnnqKrKwsJk+eLPv26quvRq/XM378eK82KSYmpo7/UlJSeOmll+jbty833XST3D7l5uayceNGfv31V1asWEHr1q292qf6Yq+2TU888QTHjx/nmmuuQavV1ts+7dq1i0OHDjF+/HiGDh1abxuanZ0t++/qq6/26sN+/vnnfPDBB1xxxRX069ePnTt3yn754YcfKCsrIzc3l7Nnz/LNN9/QuXNnZs6cKX9u1qxZdfqwer2effv20bNnTyIiImQ/lZSUsGvXLjIzM8nJyam3fRo/fjz33nsvWq2WO++8U86RixYt4sSJEzz55JNkZWUxbdo0lixZIsf9pk2b2Lt3L7m5ubRp08YrR3i2Tx07dmTjxo1kZmYyatQorz7sSy+9xIEDB7j77rvlazg+Pr5ODHi2ocOHD/fqR6xfv54jR45w4403YjAYmtSH9TyWrr8OHTpwzTXXeOU3nU5H165d6datG5GRkV45QmpDpT5QbT9Jsbd161Y+/fRTcnJy6NSpk1eOWL16NRaLhdtuu42SkhJ27txJRkZGg23oJZdcwvLly9FoNCxZskSOt+XLl1NQUMDjjz9OVlYW06dPR6fTMWXKFIxGI6+99hp79uxh/PjxJCUlsWPHDiIiIrz6SjNnzqRXr168+OKL9OvXj7Fjx3r1YV955RV+/PFHbr31VhISErza0Nq/NTxzuWcf9plnnuHQoUPceuutGI1Gli1bRnJyslcfdu7cuRiNRi8/SXoA3HfffRiNRtlP0rKklkrQ9uxoTgMdADExMXTo0IFdu3YxfPhw9u7dS+vWrX3u1yElFwnPTonn/2sfS4FZG8/XPUfkzGazvL5LSqjS656fgZo1bdKxVquVj3U6nTyzwfPY81x6vV7e1bq+3a09d2uuzyaTySTXUbo4fdnUEJ521GcTQEREhM+6SdRnk16vlz8vdWrPZZOvY89z1mef57HBYKhTT0+bPNf81ecznU7ndS5pFN6z/p521D7XuWyqz44hQ4b4fL0+O3zFnmf5nq/X5yfP+vpzPUmNvISv2PPE0xf1+ay+2PNFbZsk6juWbJH85/leYmJivdrVPq5dX8kunU7npaG0VrS+ePNlR31+kupeu96evvEsv6Hr6Vw5wpfunr6rL/Y8v9tQDvT1Wn02+ZMjatfPs57nss9X3quP+nzm6SfpR0zt+teXI/zJe/X5T6PR+GyfDWkMUwAAIABJREFUJGpfz55x6vn52jmidn6rj4b8JJ3bYDDI2vh7PUl46n0u/50r3qDh9qkhm3zlC19In09JSSEyMrLe+nquiW+ofaodb57+k/TTaDRefYpz1bc+nzWmfZLK95XrPO3wjNPafpLKOFf75FlmbT8ZDAb69evnVRfpx2bt7/hz19VTj/riTSrHn/5ebZskWxrq+3nWwVcM19cHqq8PazAYvPx0Lp95ntuXHQMGDJA/U7t98tUW+5vL67t2PY8bynUajUYuv6E+bH39SV8aNJQjfOX1xubyc+W0/v3716lfbfyJPc980VC81d4Do/ZrDdkkfc7zvL7yRX3xptVq5br48pnnzNHaNCaXS8ee+cXzO1L59eWL2n6S9p9qqQT8aSyPPvooUDMCevfdd8v/pL/DmSuvvJLPP/+c+++/nw8//JB58+Ypev5weHRvoOvY2PKkzZjUeGE3ZIu/dgaijEBitVpZu3ZtvTtDqyVm1VKPQLBv3z6/duIOZayoRe/zqYfUaQn0rufB9kuoc7Cv7zXGZk+9/S1DLYSb/z777LOw2tU/2PoGS3tf7WSw23w1Xi9K1fdc/RJ/CaX2avSfLwKldWPwV3u1XDvBaEPDKW+rgYDP7LjuuusA5GlfzYm2bduyePFiRc7luSauqc/Hbol43pFoKoHQNNB+UWsM6PV6pk2bpqrn2avFf8GKgY4dOzZZ72DHSnPMR9KyhvOlOWoTDPR6vd9aBSIvBvtaV6PfpTr17t07KLlbzTY39r1AoMZ2siECff01Vfum+qU+vcPhWlfjtdMQDcW2knHkL82lDxQuuUQtBFyt2NhY+f+SM86cOUNBQQEZGRmBPp1A4BNf0yoFgUen09V5GpEguMTGxor4VhChtbIIvZWjTZs2Qm8FEO2ksgi9lUNoHRpE3m4cAV/GIiE9+qaiooL/+Z//Ydu2bWzatClYp2sRqHnKYmNHM4NVX2kZS1Omi9YmUHUM5Uh9sP1isVhYs2ZNyDY/CsR0RjXjy3979+5VTO9QLncJ9VRgSXtPrdUcR75iJdhL587ne/XlpqbEdqhjJRAEsp3w15bt27f7pbfSS5lqE+5tqK92MhC5qbHvNZZg54im4M+5ztUvUcu1Hm5LmfyN7UCWrySBbEMD0f9s6HstfcPRxhK0wQ6Xy4XZbGb//v0MGzaMu+++m0OHDgXrdIIwIdidFs/NrsIBtUxZbGo9DAYD8+bNU1xvtegWCrp16xY28V0f4TQV2HOj2PNBLUsd1D6NN9xjO5Q0VvuBAweGVG+1x2KgzuXZTrbktut8aIxuoeqXKIla2hMltVaLzWrIW805toNB0AY7pOcO//bbb/IUJ1876ArUSahnQzS1PM9d4INVp6bSHDco1el0pKen1zulTi3aNydiYmL8msKo5rv64YCvXBLIchv7XiDKDxWNsbm+3K223OeLYN/NC3R58fHx551LlCRcNyg9VzvpTxmBfC9UKNUmnY/e/p6rOfbnzoWvOgVK6/Oth6/3mtMGsw21k4JzE7TRh+7du7N69Wp+//13unXrRlVVlRjsaARig9KmIQ2ync8UL7XdWT5XeaGsr8ViYfny5YpPqVOrHoEuw1d5u3fvbrLeargjES5IS+Ka23RRtfvIYrGIDUqDjFSnrVu3BiW+1WxzY98LRPmhaiebiho3KG2Mj+rTOxyu9XC7dhqKbSXzuL+EQwz4Q7jkErUQtO1cZ8+ezYkTJ0hMTESn0+FyuZgzZ06wTicQAH/MHvL1jGtB4DEajSxcuFDorSB9+vQReiuAdCfFaDSKx7wpiIht5RgxYoTQWwFEO6ksQm/lEFqHBqF34wjaYIdGo8HpdLJjxw4AevbsSXp6erBO1yJQ85Q3tWxQKhGIKV6B2kDMH22CpUew/aLVaklOTm7UdwLJua6Jxm4EpjZ8+S8yMrLZzJJTc06T8NRajdNbJZrLBqWB1ttfQu3bUGwsGRMTE9Rc0tI3KJWOfbWTYoPS4HGufkkoN96GwPbLQt2GBqIP2FA/rbHtWiDq4e+5QtmGNpc+oFIETa3PP/+cZ555hvLycioqKnj22Wf54osvgnU6QZgQ7E5LuE09V+OUxcZgsVi4++67w0ZvJQmWb7/++mvVLmPxl3CYCuz5NJbmMvVVCc7XlkDprSRqqW9jp41v2bIlpLm7pSyr82wn1VIntRCMvBjMfola8rha6qFkH1AtNgcbf2wRfe7GEbSZHZ9++ilLly4lJiYGgEmTJvGPf/yD7OzsYJ1SEEDCYXO1hj7vOcUrXG1pbBmhuDNpNBpZsWJFvVPq1HbHpzkwcOBAv6YwqvWORLhhNBrD6lG/atS+MbHYlFwSbjYH43v1ldFQeePHjz/vXBKM7wWzvFC0oedqJxtb3vm+FyqUapPOR29/zxUOsRhoAh3bwaClbFCqFr3DhaDOg5EGOmofCxpHSxnNDCRqaiias480Gg1ms1lVegcCNfpMqpNOp2t2eqsRSWOhdXCo7xpTWm81XutKYTAYwiK+w91HSraTLXF2QW3q0zvc40iNNNc+oNoRejeOoA12JCUl8e6771JcXExxcTHvvfceSUlJwTpds0Mk5aYhLWM5nw0F1ai9Wqf9W61WVqxYofgGjmrtZCnB7t27VbthplqWoASCQOQSNaIWfeujtt6NXc/tz3vNlcbavHXr1qDEt5JPwvGXULahoWonm0q4PQGjNuGmtydqbEObGttqzMHNZUlqOMZ2KAnKMpb8/HwGDx7Mnj17ePjhh9FoNPTs2ZMrrrgiGKdrMah5yluwNsJsyoaZAGazuVHfCzTB2ugoEPUIJCaTiQceeACTyRTU89RHIJ63rmZ8+W/o0KHnrbda9FBzTpPObzKZ5GUsapzeKhFOm6s1hBpziRJlBCtXN1SnnJwcxfQOZSyeD+fjF88cUrudDHasKBnPoc7VtTlXvyTUS1Ca0walgegDig1KG/+9ULWT4UrABzt27NjB5s2badu2LSdPnmTevHkMGDAg0KcRhClKbUimxhFlX4RLPevD7XZjsVgwGo2q6/CEmmD51uFw4Ha7m6S3WuItHDYm9MwlSt0Nag7X0PlqFSi9lSRc6yvlkuaKWmzzbCfVUie1EIy75MHsl6jhrr7S52oIJfuAog31/kxzsFUpAr6M5bPPPmP58uXcc889LFq0iE8++STQpxAI6kVKEjabLcQ1aRnYbDYeeOABobeCfPvtt0JvBRFaK4vQWzk++ugjobcCiHZSWYTeyiG0Dg1C78YR8JkdOp2O+Ph4AFJTU8W6ogAQipFktey+3tTzey5jCXWdlHrufSCWbTT2e2azmb/+9a8BK6+xBGsKp1rumngi1Wn48OF+LdMK5bT8UF9zgcAzl1RXV3u9pkZ83dkK9RTnhs5V3524pixBVOO1rpansTTEtGnTFMsl51N+KNrQQJZ/rnayseU1dR+bQJzb33OFcklAfXoHa7l1U1FLfvaXQMe2P+UHqzx/ryu1LCEKRDvZkgn4zA673U5eXh4nTpzgxIkTdf4W+Icaf2wFAqXskjYXbApq1F6tG5S6XC5Onjx5Xno3BbVMJQ0FVVVViuvtL4HYmFAt/pPq4XK5VFOnQKB2W2rHttig1H8aa3N5eXlQconYoNSbULWTTSXcNygNN709UWMb2tTYVmMObi4blIZjbIeSoAx2rF+/nqeeeoqnnnoKh8Ph9beg6TR2hF9J1LJBqZQA7HZ7o74XaEKxQWkoYsBms/HYY4+pfkpdY7VRSyPtqx4//PCDX3qHw10jNdcxWEviQn2XXKnymprjlc4lgWxDw22GyWeffaaY3qHWNxTlS+X5aifDzRZfBHKGSSDre65+SSD6n2KD0hoC0QcM5AyhQBAObaja+9xqI+DLWB544IFAFykQ+E0gnsailrtNSpbRVMxmMw8++GDIzq9mguWXYcOGNTm+w2Fj0EAQyHqYzWaqqqrOuxyxuZp/mM1m1cSRv4RrfXNzc4MyHdpTj1Bqo5YZJp7tZLjFSiBo6p3wpmoVzH6JWmfZhoqGtFZyhom/bWg46OtPHcUylsYR8JkdgsATDhenWpBmdjidzhDX5A+as/+cTidHjhxRld7NFSmOysvLhd4KEi5ah1ueqa++4aJ3c6C4uDgs9A632K5NoNtJNUyjVzP16S20CTzNqQ8YTvHRHPRWEjHYIfBJuG5MKCUrz2Usoa5TIAnlc719Ybfb+de//lXvsqFwm24YDvzyyy+KLdNSeglDIM4daMI5l6hF38acqymxrUa/hMMGpbt37/ZL72DXSa2xGKjyz9VONrY8f95TI0rF9vnorSTB3rBVieUYgdQ61BuUBrKMYNui9thWGwFfxiIIDOE0wtgYgm1XIJaxqBG1Lj8wm83cd999Aa7NuWnJd7YGDx4c9vEdTv4zm81UVlaGuhoBQ625RKJ2bIsNSv2nsTZPmjQp6LlELT4KZdyHqp1sKmq8dhpTp3DTuymoZYPSlqB1bdSwlCnc+4BKI2Z2hAH+dPZCPcKvlg1KPZ+gEEpC8di1UMSA0+nkl19+qXdKXajjUqI5bFAqHZ89e9avKYyhjBW1+D0QBHq6aLhpH4gZCo2JRaWn57bkDUoLCwvDajp0uM0wkcrz1U6G251lXzR2IFKp+tbXL2nstR7KDUr9zZ+h5lx9QH8I1galarl2gtGGhlPeVgNisEPQrJASo8PhOO8ylECNjxlrDA6Hg82bN5+X3sEkHDbHayyHDx9ust5qv6sfKAJ5dyVQsd1SNig9XxwOh2riyF/Ctb779+8PSu5Wix7+bpQa7Pp6tpNq0UZJlN6gNJj9EjXc1VcTDWktNigNHmrtc6sVsYxFEBDUkkCkRGcymUJckz9QizbBwGQysXjx4lBXo0UxcOBAVcV3c8dkMlFRURHqapyTcMsz9dW3pcZ2KPw3bty4sNA73GK7NoFuJ9V680MtqL1f0px8pHatG0M4+SUc8raaEDM7wohwm3YVjPLPhZSsmjJdtDmh1OZ4TqeT77//PmRT6pqrbxuy68yZM4rpHW5LLoKBkrlEjRu0BePcDdGUJXGhXoISyPKUtCUvL++8l8T5S6iXXCiFL1tC3U4GG7VtMHsuvdUSb+G2fDnYsR1ueTyUbVJzzSXBQgx2qJRwGmFsDErZFS5TvNTi5/OZLvrZZ58prndLvrOVn58fNvFdH+EwFdhzGUtzWv6jdltqx7bSU+DDmcbafPDgwaDnklD6SC3XlWc7GQ5xqcY6NqZOoeqXKIlaNihVi9ZqudaV6r+EWu9wQyxjCQPCqUOnlrtjod6puKVsUGoymVi4cGG974fbHRQJtV1X8Eed+vXr59cUxnC8q68mPJfElZeXB7zcYKGWHOxvGbXfU3p6biCv9XC7Mzl69Oiwmg4dbjNMPHNI7XYy3DZZ9EUgN5YMZH3r65cEciP9cJtpFizO1Qf0B7FBaePLCKe8rQbEzA5Bs0SMeiqDw+Fg586dQm8FOXnypNBbQYTWyiL0Vo4jR44IvRVAtJPKIvRWDqF1aBB6Nw4x2KFS1HhnuSHUUl/pkbPBegxWqMpT6+yeUK1F9leP5vg0lvPZs6M5TRsPNr72/wlEecFCjdo3JW85nc6we0qVWpZeNfZc/u7Z0dTy1VReKGMl0O2kGqbRB6q8YNgSzH5JIOqrlrzlLw3VqSGtW2IeUMpmsWdH4xDLWMKIcJt2FYrytdqa8Tuz2dyiH+eo1OZ4JpOJG2+8sUnnCgSh3hA3WDRU3759+yo2hTFc80Ag8VzGEm56BHvpXDCoL7bDbdp4KGPb33NnZ2ef95I4fwn1kgul8GWLr3ayudscynOdq1+iljwebsuX/Y3tQJYfijL8Lc/f94LhI7GMpXGImR1hQLiNAjeEUnc3Qz3Fy9/ZBWrx3/lsULpjx46Q610fTbVLLX7xRKrTiRMnVKu3v4TD3ZVAb1Cqljyg9hkmjdmgNBCo8VpvKs11g9JwyOPn2sRRaidbSryF8q5+ff2S5pRL1NKeBKIPGIiZumrRXqnyw70PqDRisEPgk1A+Nu98ypOShLScJRh1airNcYNSl8vF0aNHvfT2RC3aNyfKy8vr1duTUN7Vb05+90frxqCWu4pK0phYbIrearE5EHfzlGxDi4uLzzuXKEm4zTCRyjtXO+lPGYF8L1Qo1Sadj97+nivcYjEQ+KpToLQ+33o09nPhPlNQSb2bA2G5jOWHH35gy5YtFBQUcOGFFzJr1iz5PZfLxRtvvMGPP/6IRqNhzJgxjB49OmjvCdSFlBCMRmOzuoOiVoxGI1dddVWoq9Gi6NWrF0ajMdTVaPZ45hKBcgi9lSMrK0vorQCinVQWobdyCK1Dg8jbjSMsZ3YkJSUxb948xo8fX+e9Xbt2cfLkSe6//37uuecePv74Y/Lz84P2XrAItx/qaqlvIKaeq2Uavb/lhbK+DoeDbdu2KT6lTi3TGRsiWPU4evRok/VWw/TLcEEtS+L8JdhLAppCU67TxuRuteQBNbYZ/pzrp59+ajC+w2H5iL+Esg0NdDup1v5AU8oLxmbjweyXNKcljf7S1NhuLm1SMMrwl4baSYH/hOVgR9u2bWnfvr28GaUne/bsITs7G61WS1RUFAMHDuSbb74J2ntKEm5T3hoi2PVVY4MRDhvMNmXZUGlpacj0Vusmi+dLQ/W1Wq2qjO+mEA5+8dQ63KYuh+MGpfXFdjgs/1FLPPtbD4vFolguCcdYbAq+ljL5aifDIZ7VeC5/OFe/JNy0V3PfP5B9QDUu51Zr3moufUClCMvBjoYoKSkhPj5e/jshIYHi4uKgvecLu91OdXW11z/pden/nsfSCJ3NZpOPo6Oj5fKk18xmM1arVX7dYrHIU5mcTqe8hstiseByuXC73XJnxuVyYbFYALyOnU6n17FUfnR0tHzscDi8jm02Wx2bzWYzUPM0lPpsslqt8gCVTqeTH51ktVrlY4vF4vVIpaioKK/znMsmSTfPMqS6eVKfTQ6HQz6nP36yWq1ex9J5Pf0nlRERESH7RrLV039S8oqIiPCyCf5Ilmaz2ctPTqfTazqb9HpMTAw2m00uy1fseSLt7KzVauu1yZefDAYD06ZNQ6fTya9LsSfp4E/sedbd0776/KTX16zAMxgMXvbY7XbZfzabTa6vyWTyGgn3ZZOnzpGRkfJrta8nz9dr2wR/xFtMTIzX+TztkKgde5It0dHRXrY4HA7Z5j59+mAwGBr0k3R+z+vJYrHIftLpdF5rPn1dT1I9PPWpHXu1/eSpfW2bPGNPygMGg6HO9SRpf67Y8/SNJ263Wy6jvtiTvtuQTdI1bDAYvK5NT/81JkdI/vM1/dRgMMi61JcjfOU9T6RrXavVeuWh+mJPr9d75fL6coSkpT95z9N/nnaYzeY67ZNkl8Fg8PKhTqeTy3A6nXIdIyIi6vhJylueHT9Jy9o5uD4/SfVwuVx+573a9knn1Gg0sm+io6OxWCxyPjhXm+vZhtbOexL1XU+STZ66SdRuQ6V6AgwfPtzrs7Vjz9N/tTvXtXO5hNPp9Ok/z/6LlPck/3n+GJBylKefpPI9YwDO7ScpB7tcLvn6q22Ly+Wqc/1J57RYLPJ1da72qbb/4I88DjB79mz5fcnO2r6tbQf49p+E0Wis0z5Jn29Mf6+2TZL/6mufoMY/nv0GCV9tqGRbZGRkvX1YKbdJWrpcLtl/brdbLjciIsIrX/iyyWAwMHPmTPlzkp+kGJD+39D1BH/0o6TPW61WuR56vb7euPd1DUnUzlW1/VTbf5LNtZGuE4PBUMdPUj3Opw/reSyVERkZWacN1el0zJo1y6sPK8WFZw5uKPY882ftHCHFgN1u9+r31UaKI6kPCzX+s1qt8nv1/QaR/OQZs7X7sBKex57+k8qv3QeCP/xX3/VU+7rybHOlOprNZq98IdlRO19I1JfLWyqqHOxYu3Ytixcv9vmvoUEGtbBt2zYWLVok/1u+fDkAW7ZsAWDr1q1s3boVgLfffpvt27cDsGnTJr744gugpoHLzs4G4I033iA7O5spU6awbt06ZsyYwW233cbatWvp1q0bl112Gc8++yyFhYUArFixgrKyMqxWKytWrMBqtVJWVsaKFSsAKCwsZM2aNQAcP36ctWvXAjU7s69bt45rr72W0aNH8/TTTwM1M1s2bNgAwBdffMGmTZsAGDJkCH369AFqEk3Pnj2JiYmp16YNGzZQVlbG7Nmz+f7779m/fz8A69at4+DBg0CN748fPw7UNCYXXXSRrOvcuXPPadOsWbNIT0/nkUceQavVMm7cODlmpk6dStu2bRu0afv27cTHxzN//ny//LRhwwb27NkDwNNPPy3b5HQ6mThxovz6hAkTyM3NZc2aNV5+GjRoEJdccgnr16/HarVyxRVXyINjnn6yWq1EREQwadIk2U8A+/fv58iRI1x++eUcO3aMDRs2cO211zJw4EA2bdrEnXfeyYABA3j77bfrxF7v3r0ZNmwYAFVVVQwYMIDY2Nh6bfLlJ7vdzqpVq+RlXZ6xB3DZZZedM/ZuuukmLr30Ui+bzhV7JSUldOrUib59+7J161YyMzNZsmQJb7/9NmlpacyfP59Nmzaxe/du5syZQ1VVFXv27GHRokW0bdvWp01RUVHMnTsXqNm4b/r06XVsWrFiBbfccgvXXHONT5umTp0qN8QTJ06kdevWPm1KS0vj9ttvrxN7b7/9Ntdddx09evRg69at3H777XTv3p3t27eTmppKamoqhw4dwm63N+iniRMncuONN3pdT2vWrCE6OprZs2ezdetWysrK5EfG+bqe9Ho9V199NcePHyc6OpoFCxbUib3afrrssssYNGgQb7zxhpdNtWPv8OHD9O7dm8zMzDrXU0ZGBvPnzz9n7Ek2SdcTwM0334zVaiU/P585c+b4jL277rqLSy+9tE7eq23T4MGDiYqKwm638/PPP9OhQwdycnLYvn07GRkZ3HbbbY3KESkpKSQkJJCcnAxAfHw8M2bMAGqWE0yYMIH4+Pg6OeKGG27g6quv9pn3rrzySjneBg0aREREBFFRURw8eJDY2FiuvfbaOn76+uuvmTVrFna7nU2bNnHLLbcwfPhwn37q2rUrqamp9drkK/aGDRvGVVddxdq1a8nPz2fu3Ln8/vvvddqnzMxMADp37kxZWRlQ06bk5+dTWFgox97jjz/OnDlz6NOnTx0/XXjhhQwePJgdO3YANXtQdO7cGYAePXrQrVu3OjbV9pPJZOKyyy7jww8/9Dvv1Y69L7/8kmnTphETE8OKFSuYO3cuc+bMYcWKFdx5551ceumlDba5ktaJiYnExMTUyXs9evRgwYIF9V5Pkk2XXnopaWlp7Nq1C4CUlBS5jW7Xrh0TJkwAaq7L7OxsTpw4wSOPPMKMGTNYvHhxndirqKjg8ssv55dffsFqtbJgwQIkaufyqKgolixZwsGDB6murmb+/Pns37+fjRs3MmfOHNLT0+vk8gsuuIDevXvz/fffAzWP1R44cGAdP23dupUBAwaQm5vLhg0bGD9+PAsXLjynn2655Rb0ej2FhYUkJSXJMSK1TzfddBNlZWV8/vnnXH755bjd7jp+uvPOO7nooovO2T4lJCTQoUMHYmNj2b59O926deOGG25g69atbNmyhc2bN/PWW29x5MgRZs+ezdGjR/niiy+49dZb6dixo8/Yi42NZcSIEUDNjy6pTXrrrbeYNGkSgwcPrtM+zZgxg0suucTv/p4vm7RaLfPnz/fZPi1atIh+/fqxdetWhg8fTqdOnTh69CgAnTp1YsyYMUDND2OpD/vqq6+SnZ3NtGnTWLduHZdccgkLFy5k7dq1dO7cmdmzZ/PCCy9QWFjIDTfcgMVioaysjGuvvRbAa2DzoosuorCwEIPBwO233+7TJrvdzosvvsj69eu9/JSamkr//v3lGG/oeurfvz833ngjmzZtYuTIkdx4441s2LCBkpISZs+ezf79+9m/fz+33nor8fHxHDx4kOnTpxMREcGZM2eAuu3TnDlzsFqtvPzyy0yfPp0ePXrU8dNll13GpEmTePTRRwEYN24cpaWlAEybNk3uwzqdTtq2bUtmZmYdP7Vq1eq8+7Ce19M///lPJkyYwMUXX8yaNWu46qqrWLhwIStWrKC4uJi3335bbp+uuOIK+fqaOXOmPEDRUOwdPnyYyy67jLy8vDo5YuHChXIfNiUlhe7du8uDA7179yYrKwuo2bh9wIABxMfHs2HDBrKzs1mwYAFPP/00kydP5sYbb2TdunU4HA5mz57NV199VSeXS9eX52CM1I8AWLBgAYWFhbz22mvMmjWLlJQU1q5dyy233MLMmTNZt24ds2bN4sILL+T5558HagaUJXxdT1LsZWZmcs899/D222/Trl07rrrqKjZt2sTXX3/N3Llzsdls7NmzhzvuuIOIiAg5Z9bXh62dy8vLy2nJaNxhPBdmy5YtVFdXe21Q+sQTT5CVlcXgwYMB+N///V/0ej3Tpk0Lynu+8BxBBSgrKyM5OZljx46RkJAgX6jSXWmNRiPfZdNqtej1eqxWKzqdTj7W6/XodDqvY4vFgsFgkI+NRqN8Z8JoNMqjpCaTCbfbjc1mk0fMpWOn04ndbpePHQ4HJpPJ69jhcOB0OuVj6a6ctLba8+56qG3S6XT8+9//Zty4cfJdzXC3Sc1+crlcvPfee0yZMgWTydQsbFKznyorK/noo4+YMmUKLperWdikVj9VV1fzwQcfkJubi0ajaRY2qdlPlZWV/Oc//2HSpEm43e5mYZOa/eR2u/nwww8ZO3YsERERzcImtfrJbrfz8ccfM378eIxGY7OwSc1+0mq1vP/++0yYMIGoqKhmYZNa/aTRaPjggw8YO3Ys0dHRzcImNfvJ7XazZcsWcnJy5Nly/thksVhITU2ltLSUVq1anfsHdjOj2Q12fPXVV+zatYuFCxdSXV3Nww/4EX93AAAgAElEQVQ/zE033URqampQ3vOHsrIyYmNjKSgoaJFBJhAIBAKBQCAQCAQCZSkrKyMlJUUMdoQTBw4cYOPGjfIa5oiICC6//HIyMzNxuVy8/vrr8mNiR48ezdixYwGC8p4/SHt+7Nmzx2sdvyDw2O12PvjgAy666CKf6/oEgUXorSxCb+UQWiuL0FtZhN7KIbRWFqG3cgitlaWpepeXlzNo0CCKi4uJi4sLYg3VSVgOdoQbu3fvZujQoaGuhkAgEAgEAoFAIBAIWhi7du1iyJAhoa6G4ojBDgWQZnYcPnxYzOwQCAQCgUAgEAgEAkHQKS8vp1OnTi12Zoc+1BVoCUiPiIqJiWmRa6WUxGKxsHLlSlavXu3zkbOCwCL0Vhaht3IIrZVF6K0sQm/lEFori9BbOYTWynK+eku/R1saYmaHAkgblJ45c0YMdgQZl8vFqVOnaNu2rdez3wXBQeitLEJv5RBaNw2Lw8JHhz5iavepjfqe0FtZhN7KIbRWFqG3cgitlaWpepeVlZGYmCg2KBUEDzHYIRAIBIKWwBO7n+DOj+7EtswW6qoIBAKBQNDiaemDHWIYTtCssFgs3H777VgsllBXpUUg9FYWobdyCK2bhsPtaNL3hN7KIvRWDqG1sgi9lUNorSxC76YhZnYogJjZoRwul4uysjJatWolptQpgNBbWYTeyiG0bhr/2PUPFn+8uNEzO4TewcXutFNpr6S1uTUg9FYSobWyCL2VQ2itLE3VW8zsEAiaERqNBrPZjEajCXVVWgRCb2UReiuH0FpZhN7B5bZtt5H0aJL8t9BbOYTWyiL0Vg6htbIIvZuGGOwQNCusVitLlizBarWGuiotAqG3sgi9lUNorSxC7+Cy//R+r7+F3sohtFYWobdyCK2VRejdNMQyFgUQy1iUw+12Y7VaMZlMYuRTAYTeyiL0Vg6hddNo6jIWoXdwuXDDhezM2yn7ReitHEJrZRF6K4fQWlmaqrdYxiIQNCPcbjcWiwUxhqcMQm9lEXorh9BaWYTeyiL0Vg6htbIIvZVDaK0sQu+mIQY7BM0Km83GqlWrsNnEYw+VQOitLEJv5RBaK4vQW1mU1HvZm8t4cduLfLv/W44dP0ZZVVnQz6kmRGwri9BbOYTWyiL0bhpiGYsCiGUsAoFAIGgJNHUZiyC4jNwwkq/zvj4vvxw5e4RjpccY2XEkAPtO7KNrfFfMZjOLP1xMl6gu3DDsBt7Z8w7vvvEuraNaY4ww4jzp9CrHprFx9XVXkxKXQkJcAlFRUedjmkAgEAgaoKUvY9GHugKhprCwkA0bNlBZWYnZbOaqq66iXbt2Xp8pKipi48aNHD9+nMTERJYtWxai2grOhcvl4tSpU7Rt21Y8BksBhN7KIvRWDqG1sgi9g4vBamBA5QBeePcF4mPjef3311kzYQ3p7dM5UX4Co85IcnQyTpeT7fu3Myh1EFFRUbzwxQvEa+LpkdKDNW+sIbUolU2mTWjQoLfWdCHduNGg4Rd+4e437wYggQS0MUasFiuuVg4uzp1DmzbJrFx/G12tXXnl2VcAcOFi2bJlpLRN8apvQUkBT7z/BFlpWSS1TiK2VSzd07o3KjZcbhdFVUW0iWoDQPyaeF67+DWGdx7Oum3r+G7vd/RM6YlGo+GX338hrV0aUVFRVGoquWHKDXRr3y0Q0ovYVhiht3IIrZVF6N00WvzMjr///e9kZWUxfPhwvv32Wz788EOWLl3q9ZnKykoKCgqorq7mvffea/Rgh5jZoRwWi4WVK1eyevVqzGZzqKvT7BF6K4vQWzmE1k2jqTM7hN7B5dZ/3IrukE4emJCwRdvIt+VTra1mTPcxFOQVoClqeOO72N6JVFSWc6TgIOMuyMVqt1BSVkRCVBsiY6IoLT9LXKt4po69vM533W431VVVlFWc5dPvPuDQf37EZrJhijLxe+XvpCSlMH/cfLZ9s43S/aV1vm832KlwVaA36RkyaAgJsQl0bNuRoX2HotFo+OrEV7SLaUfH2I785f2/8N0n35EUm4TJbMKWVzcmrZE2sLgxuUw4opw4nU5MFiNWjZXh44aTFJdERqcM0lLTmqB6DXkledz7yL08veppzGYzeeV5pMakAvC3LX/jh/0/kNEug7jYOHp06EH2wOwmn0sgcomSCK2Vpal6t/SZHS16sKO8vJxVq1axdu1adDodbrebe++9l7vuuoukpKQ6n//111958803xWCHQCAQCAQ+EMtY1MkDzz1A3i/5rFr1D6xWK+998hqnS05SXVHJgYL9RLgiiNPEY7IZARgx8yKKy89wuPA3Lsmei0aj4VRJPiltOtA+uWNA6lRtreLStWMY1XY8lqoq9AXegyx27Cy+7y+UVZzl2g0zyEmfjtFl5MCP3xPhjCDCHYHOrQPArXHjjnSjray522k32nG5XBgcBrQpBuzVNirLy+k/bBht4pP56tAOOqZ1Yf6omwEorDxJUlQyAHP+PIkuli6Y3CZ5YCgiPYLo6GhOW09z/fjr6ZDcge/zv+fA0QNc2PVCkuKS0Jg0JMQkAHCs+BgxhhhaR7dm9r9mc+rnU8zImEFsVCzff/o9Tpy4I9zoq2tmx9j0NoyOGu37jepHu+R2fPTdR8RGxtIjuQepCan0SO9BmzZtAqK9QCBoObT0wY4WvYylpKSEVq1aodPVNJYajYa4uDhKSkp8Dnb4i91ux+FwyH9XV1cDyBvK2O12AAwGAzabDa1Wi16v9zq2Wq3odDr5WK/Xo9PpvI4tFgsGg0E+NhqNaLVa+Vij0ciPKHK73dhsNsxmMy6XSz52Op3Y7Xb52OFwYDKZvI4dDkfN3Y7/O3a5XBiNRq9jtdhkMBg4cuQIycnJREVFNQub1OwngN9++40uXbpgMBiahU1q9lN1dTUnTpygc+fOOByOZmGTWv1ktVo5evQoXbp0we12NwublPCTy+UCaLRN1dXVFBYW0r59e5xOpypsKrOVsSdvD2M6jQl7P7mcNX7RaLVodTqmT5xDYWEh8fHxGI1G2X+fHf+YazdfyqLMhzEYDGg0Gmw2G0ajkfbt0uXzulwu7HZ7nbo7nTWzI4xGo9exw+nA5XTJx26XmwhTJO/e+wVulxuDwUClpRKnzYHNZuFg/gFKnCVERkVhMBrZvOhL9Lr/s2+WVj4+XX6S69ZfwpS0GVRWlfNL/o9o0DCwyzCqqyuJj03kmtm3o9VqsVqtsk0jB0/CaDTKdiRFJcvHL6/aVuMnm40Dh37gvQ82UVpcRmlBKUarkWd/e9arz/cTP8nHTq0Tt8EtL/Fx4aLd//3367e/YXTWtJtRqbFoIrVUV1cyIHM4E7KnceTEb7zy1JN8v+N7vud7AIoo4ku+BKBEV8LGRzdSVVXFvL/Po39Sf5JjkzFGGsnKzCK9TboqY0/pvKfX6zl8+DDt2rUjMjKyWdikVj/pdDqOHz9O27ZtiYyMbBY2qdlPGo2G33//nfT0dIxGo982Se1yS0Us+AkC27ZtY9GiRfK/5cuXA7B582YAtmzZwpYtWwB46623+OijjwB4+eWX+e9//wvA888/z+7duwH45z//yb59+wB49NFH+fXXXwF46KGHOHbsGAArV67k1KlTACxZsoSysjKsVitLlizBarVSVlbGkiVLADh16hQrV64E4NixYzz00ENAzcyVRx99FIB9+/bxz3/+E4Ddu3fz/PPPA/Df//6Xl19+GYCPPvqIt956S1U22e12nnvuOf7yl780G5vU7Ce73c6TTz5JXl5es7FJ7X569tlnsdvtzcomNfpp586dPPXUU9jt9mZjkxJ+On36dL025Zfnc+3ya33atGzZMl544QXy8vJUY9Pi7YuZ/Ppk2U9T/t8Ufvr9p6D46c3P3yTmf2J82jRv/TySVyfz7rvv8vrrr7Mrb5dPmywOC7c8c4tPm44eO4rb5ZbtPnz4MJvf28yLL77IyZMnAVi/fj0DWg/l+F3VrHtsHZWVldhsNtY9tg6bzUZlZSXrHlsHQHFxEevXrwfg5MmTPP/C87J9kg9+++033njjDQB++vEnNm9+F4Dv9u5l69atAOz8eifbt28HYNdXu9iz51sSE9tyNr+aVlU1sxi2bt3Kd3v3ArB587v89GOND9544w3KT1fxwfJviHa047KJC/i49cd8H/cjM8bNZ/GCB6k4paW4uAig0Ta9sOFF+mcMZe4lC+mYMpD7//w47cb3JLZzGhdemoOuZxTGzvHM/tMNpA7pTlV7F+0Hd8Edr6cstpJ2/TsR0SkOVzsD9gQDQ7NzGJl7CYvX/IWuHQdzYb8pLLt1LZWFTn768Se6duxFl4wsps+7joXLVkGHGC657gaWPriWPVF7iHPGcesdt7Lk3iW0P9WeMz+cYe8Xe/n2w2958q9PsmDZAlb+YyVPbXpK1Tki2HlP6gc++eSTzcYmtfqpqKiIF154gWXLljUbm9TsJ7vdzvr16/nuu+8aZVN5eTktGbGMJQjLWGrP7CgrKyM5OZn8/HwSExNb/MiksEnYJGwSNgmbmqdN/9z7T5Z8soSyu8rq2NT/6f6cOH2CE4tOYDabsdlsftl0ynqK1KhUue5Nsenel+8lxZRC+/j2REVH0bdbX1JapzRo06THJtHxaEdMJhM6s45DVYe45OJLuC77Or/99MpPr3Df+/ex+7rdxMfHo9frcTgcmM1mHvnsEarKqrjzwjt55/d3WLF5BZ/M/4RWka1Y8ckKJnaeyOjOo7nnpXtodeSPqcc2jY3Z18xmRO8RbNy9kcK8QkZ1HcWe03t4/rPnuW/sfbSNa8uLP73ITaNvYmDqQFY/vZqCgye5/8+PYbPZ0Ol0deJQmvlQexaENLPD7XbLszkCMbPDYDB4H/9fv0mv12O329FoNeh13sc2mw2t7o+ZHZId0vH4lwYxvfvl3Dp0cbOxSafTcepMAZ/u+QBLVSXFpUWUVJ1hydUPodcZ+WzPNvbs/ZLqqkqMRXo0aIjsEElcYhwWjYVrJ11L++T2qskRzTHvCZuETWq2yWKxkJSU1GKXsbTowQ6Av/3tbwwbNqzBDUolxJ4d6sfpdPLrr7/SvXt3eXmSIHgIvZVF6K0cQuum0dCeHcOXDWdo5VAAXBoXLpOL1UtWkxCf0KDexoeMPJv7LFdlXtXguWdumklWmyzuuPAO8ivyefajZ5nWexrJccn8de1fAbw259QkaTBGGDmrOcuTtz1Z57xZK7MYWjqU6O5xVFdW4c6zkzU5iysnXUmZtQy7005CZAJut5svT3xJdoeajSXv+fgeJnWZxLhO47hp/U0YfzZ6lZs7K5fh/Ydz6ZpL6WHp4Zeudr2d8TOmc7roJD99sge7xg7GmqesNPg97MR1ieNUwSk0di0PrKm52+10Ojl27BhpaWkivoOMklrnnzrGc//6O45qG9jAZDfi1DvpPKAzEZERDO49mCE9hwS1DqFG5G7lEForS1P1Fnt2tHCuvPJKNm7cyLZt2zCbzcybNw+Al156iczMTDIzM7HZbNx///04HA6qq6tZtmwZQ4cOZfr06SGuvaA2DoeDd955h7vuukskXgUQeiuL0Fs5hNaBx+Q2UaGtIHviBE7kH6FoXwGni06TEJ9wTr3zy/MBWPnmSr7f+T09U3oSFRmFy+zivnn31czO3OMmz5bH4s2L5e+9/GXNtGENGjqM7MZl465l4qODSLOm0Z1e5Oflk+BIoLCwkJQU78efOl1OnDi549pV2O021t63lOMnjvPN/m9Y9PIi2tvakxCbQLW2mqPlR3nqlqeIj41n78d7yd+az+bYzdgqau7OjZ91CWeKCtj/8W62vLGFLW9soQc9OGk4ycyp8zl99hRnyk8xuv8kCorzOFL4Oz1SMrA5rZw5W0j7th0Zljmqpl4GF+UlpVRWlvHloR107dKLmyfcw6mz+Rh0BjqldqeisoxVz91Ba1drigtLcFgdnDKc+sM2p5NPP/2UOXPmiPgOMkpq3a5tGvfd/aj899+fuZ+S/DP8tvcgRoeBAzsO8FbHt4iMjGTfwX10S+9GQusE3EY3XZO70i+tHxjgxf++SG6vXNq2bstxy3Ey22USHRXNDyd+4D97/8OILiNIikvizYNvsvCChRgMBlxuFxo0aDQNP80n2AQid1fYKjDrzei1etxuNw6XA4Ou4YHFlohoJ5VF6N00WvzMDiUQMzsEAoFA0BKoPbPj8NnDdIztiFajZfS9o+lg7cBfH3yRQ8d/ZdMT61lwywJ6d+/tVcb7v73PjDdmyGVMvWcqrVytSGydSGlxKRGuCGilxVZpIdoRzZ2L7iQ9LZ0FyxagNxvp1X8Ar+/dQJm9lBeufYfTZ0/yp3ev5Nmr3yQjuR89Hk+kyl7J8Turee2/L/D7+z9wz5J7SG2XSpW9CqPOiF6rZ9CKQWSVZ7HqL4/hcjp54P470du97xG5k7U4SmwYrHXvHblTtNiqrcQntuH262vWeX/12w6MVgNnSgt5Zc/zdO7cg1W5a4PgCW/GbxzML0U/cvzO6qCfS6BOvvnxSz7Y9hZ2iw231YXZasKhd+B2g8F5fvc+nVonOpcOm8aGxqzBoXegN+tZecNKYqJjWLt1LanmVDq36Uzb1m1Jb59OdFR0gCxrGtNfn45RZ+T1ma/jdrv5/OfP6RrflajIKOatmUc7ezs0Jg0ljhJK9aW8cu8rmM1mTCaTXwM6R84eISU6BZPeRH5RPu/vfJ/ubbuT0CqB5LhkEhMTFbBSIBAzO1r8zA5B88LpdLJv3z4yMzPFqKcCCL2VReitHELrpuGodpBdls3Dzz9MbEwsT+57knkj5nHHmDt8fl663+Kp964juxhUMYgnXn+C+Nh40q3p2KLtuLQaKrQV/Gr+lbfv/S//3vO/fPfG51Tban7AazQaWrWOY9aE+UwZfSnfn9xDu5QOtEvpwFe9fpPP+dG8b7A4ar4TZarZFPSBdQ9giDRgK7ZRqiulbXxbuld3l7+j1elY8ee/Y6muoqSsmOOFh0lL6Uy7pA4AnDlzipNFeZw5W8ia7Uu4fuIdXDH4mjr2Du82Sj6elK3c7NBnpm7i8NmD8t9Op5PffvuNbt26ifgOMmrRenDGBQzOuMDne06Hg7NlJRQUHaew+CQmnZnOad3JKzrGf3/fTu/WfSmvKKW0ooT05K706tKPwrP5HC48iNvqorS8hILiE0Q7ItGbDJw9cgZDuYGHH3pYPkcJJexnP1CzjG3a5dNIjE0ktU0qSYlNfwJiHVs8csmbu97kpbdfok9KH6Iiojj8y2F00TqiI6OpPltNmaaMt+PfJu9kHr99/UeOSCe9ZglYels0v2lIsiSxatWqmvI1ThYuXEiPLg0vQZv//+aT6EikS5suWPOsALL9ADfeciMZ3TMCZncoEO2ksgi9m4YY7BA0KxwOB59++im9e/cWiUABhN7KIvRWDqF102hV0YrM6kyO/XwcreMEY11jyft3Hov/vZhMMinRlQA1j0AFePypx9GYNZyxnyEiJoKnez9N67LWDK0cys9f/YzOpUOLlnFjpzLqgos4XHKQSnsFAJGGSACefvNp2rRpg7PaiVaj/b/3ohjeYaTPOqbFpsvH4/pN4c3PNtK3VT+sVRaK9AWY3CYcbjdOjZMjpiPyZ7VaLZFR0URGRZOakuZVZmJiWxIT2wIwOuui85Ux4HSK60qnuK7y306nkz179tC5c2cR30EmHLTW6fUkxLchIb6N1+spSe0Z3Mv3AEm7lA7075Xl8z23201e/lFOFudxprSQyqoKJg6bhtvl5sXXHqPicAlbXt0if/6+++6rM9PhVMUpdhfsJrdbbp3yn//ueU6UnWDlyJoZUz+f+Zleib0A79x98LeDpFvTKSkupdhWgtFtxGl3YXU46Gbphg4dn77zqVzu6Jm5WBzV/Fr4MxdnX0FKYs3GyAd+30dJWRHFJafZv303n+z6BKvdyraftnH09FHGdhuLUW/kvV3v0Tu1N62iWtGruhc2g52yqgrO6otxRDp54E9PcKLgCO/962VKykt86naw5CDd4rvVec/hcmB32okwRPjUPBQEop08WXGSrOezOHrb0QDXrvkh+iVNQyxjUQCxjEUgEAQTl9vFe7++x8U9LgbgkS8fIadrDn2T+oa4ZoKWxqsfvsrX73/N3X9+GKPJxPWbZjMmaQJR7iie+eoflOhL+HLprzhdTlb+63baGzpQVVmJ9feaAQxtspaKigoiKyJZ8uDamp3wrRYifUx5L608y6JHribV0B7soLVr6HXBQGbn1p1R4S8d/hZBbveZPDnlJa55ZyYfH/63WPohEAQYu81GdXUV3/70NV+++yF33303HTp08PrM3Hfm8vpPr/vc7Dh5dTJp1jRuyrqJM/YzPPbDYxy7v+axm6/segVNtYZ+qf1477P3OP7Dce576B8AnKoooG30H3vzuF0urDYrVVUVfFOwk4kZdQdWatf74fvvRu+q/16xS+NC49bUPBVnYGvumL3S6/3SsyU88Zc15MzMYfTQ0VjcFswGM2a9mdd+eI2lby7l1ZmvktgqkbiYOOLj49FoNFy9+Wpe2f+KTz3CmW2/b2Pqa1ObnV1qQixjEQiaEQ6Hg927dzNkyBD0ehHewUborSz16f3fY/9l1luz5M7Cik9X8OWJL3ln9juhqmrYI2I7MDx7+evy8dgRU7A6LADotDoenP+4/N7Ih3vTydqJLo7ulFnLKDQWotVq0Wg06PW+1/bHRrXm2T8HNsa/uPZnEiNr7m7fN+ovjO88OaDlqwWH08FPP/5E74ze6HUivoOJ0LouBqMRg9FIYkLN8hU3de+7SrO0JFxul/zaGNcY2lS0Ye/2vWjQMIMZLLxjIc4IJ4aqmo1Ev+brmrI9JPcc6ICaGWZmcwRmcwQT4xse6JDqfe+f/0pVZSUl5UV8/Mv7dEvrzbC0Czly5iArP72L1+Z+iNvt5nDRQTrEpdctw2DEjZutb21l61tbATidcppXlr7C2byzTDs7jVefe1X+vFPnJLpDNEdPHaWPvg+lpaVERETIj1GuzfGS4yx9cyl3XXAXbWLbUOoqJaNDRh09A0FD7WRJdQk7ju1geo+Gl+vpteKa8BfRL2kaQilBs8LpdPLdd98xcOBAkQgUQOitLPXpbXPWvSMiJu2dHyK2A08rUyyYYn2+d8+MB/jt29+55dLb+SLvP9yx7fqQPNXBc4lL57hudI6rO528OeByuvj111/o2bMniNnQQUVofW4eeeoRIqIjcGgdTBs1jZGDR2LAQJI9iaPHjxIbE8vlD19OijmFpNZJmMpNVMdYWLPsn+w68jl/fmUR0zpdiqW6ir3Hd2OLcnL/9L9SVl1KQuvAbgRqMBiJbW0ktnUc13e4XX69R/s+vDb3Q6Bm/6DOib5zR2RUFHNvuYVjpw5ztryY77d9RZuCNtxy1y043A7MmJl7061U2yv5x2sPEKOJwVnsJrM6E4CVK2tmitg0Nu69517S2nkvqXvn83dI3J/Ixv0b/3gxHob+f/buOz6KovHj+GevJ5feC6F3JdJCkY6iFLv42FusoKgoxZ/4YEMUFftjF6xYEUVAEQu9N+k1AQIE0nPJJdd29/dHyGFIQHIky3GZ9+vly71kb2/mu3PhbnZmtls3IkIj6NiqI8nxyXWSxan+nZy0YBKrV6xmd7vdhFpDmbV6Fh2adKBVXCtWrVlFsCMYxaxgx86lXIrT6cRsNtdJuQKV+FziGzGNRQNiGosgCPVpzvY5vPD5C9x0wU0kxSXx8KKHuaD1Bfx8089nu2hCA3PiNBZBEISTsZUVkf7KVXSM7ILb4cKc/++3d3WFeSi128iwZPDDfxfj8Dh4YckEnu7/CpIkkfJaEH0aX8SMa+f867H8QebB3azetISyslLybLmUqaVMTH+1Wmev2+1m6+4NFJUWcPToYfYu20JyajLNmjRjY/5G+qT2YXC7wXw5/0tWz1vNzSMewOYoYtaXn6J36ZGoOJ7T4mTknSOJCYshyBpEZHhkvdRr8ozJHF11FJfBjV7WoVcrevo8kgfDseE2Ye1j2HdoD1HFETz++OMkJiZyqOQQ0UHRWAwWFFXBo3gw6U31UsaGQkxjEYQA4vF4WLJkCX369BG9nhoQeWvrZHnvythFV3tXtq/Yzi51F1dzNWu3rT2LJT33ibbtm5qGo58Oj+xh44YNdOzUSQz114DIWzsi65MLC47g+4kLvY/Hzx5JkppISnAzSkqLsBpDaNsqlQJbHtnFh7i6z02YzGaySw6iqAoAFoOFZwZMrXLcEpsNj+w5J/Ju1qgVzRr9+wgyo9FIx/bdACgvL2PK2vFkbcri0KZDAPyy/BfmRMzF4SjHipWkxBSamFrQ4dkuQMW/af/94EFCDwTz8bsfe48b3TKaJo2b4JAcDOw6kFZJ1cuyK28XzSKaYTQY2bhvI0v+XkKHxA6EWEJ47rvn6NumLxEhEcxfOh+AlOgUSkpKMGPm6UlvARXro1QuTF1gzwcJooKj+WrJx2TO3eo9nxe9fBGt9a25uM3FbMzfyG95v3HwqYO+xhtQxOcS34ikhICiKAr79u2jV69eZ7soDYLIW1sny1tRKj4kjBo7kaBgK08/OwqdXPfzcxsS0ba1pSoqhw9nc8EFHcVQfw2IvLUjsj59U65457T2SwxtdNLfRQfF0s7YEVVRAzbvoKBgnn7mLVRVxe12sXjVb2zauRaXw0GxYiOLHRiMVUfJGAwGJt7zGgWFubidLg4e3ceCn37kSOYRcvbmoFf1LF+3nGfTn8VsMVPkKaJZTDNMJhMvPv8ioUoosk5Gr1SEuoeKW1k3pSkH1hxgH/tIpGJNlHKPE0UHhkYW7+tXdnQARFmjj9fl2F21Jr8yGdWocqnjUgB2rtpJiBLCMGlYPSR4bhKfS3wjprFoQExjEQShPr08+2UO/nGQkeMmEBEVzeMT7sYhOUjrmEZiTCJp7ZXjXb4AACAASURBVNJo2bTlvx9IEM7QjN9msGruKjGNRRAE4Rzy/KuPIeVU/0qooiIhYWwcRGR8LNuzNxMbn8CIQWPIKc5GURXaND0fVVXxuN0YTrJw6snsyN3Kgx/ezPUtb6O8rIzV+5fSr89g7hn4CB99/xqH12Zy5Q1XEhseS2xULI0SKjq6MosyKXeX0z62Pfll+dw/736+G/5dneURSMQ0FkEIIB6PhwULFjBo0CAxxEsDIm9YfWg13ZK7afJaJ8tbQamy3y7LLpo4m7B74x72e/az7M9lvP3y2yceTjgF0ba15ZE9rFq5iu49up8TQ8/PdSJv7YistSXy9s34US+Sk3+EvOIjHCo8yK6jW2kV3hZbWTEul5OrL76Z0JCqC0xbQ0NZtXIVLVIqpgwZTbVfW6Nt7Hm8d/83tIxqU+13skVBj565X8/1/qzT5Z244+I7uPr1q2lZ2pImsU0oVorJKslCuUZBpwvcUa3ic4lvRFJCQFEUhaKiIu+wfqF+NfS8d+TtoPenvSkaW0TwsaGY9emrLV8hFUrV8q6c61ppTcgaQjtE89pVn/POVy+SvzW73ssWaBp629aaqqiUlJQE9NBzfyLy1o7IWlsib98YjEaSElJISkghlTSGcPW/Pqeusq6powPg3mGPUn5xGYpLJrfgKF+99y6//fIb2/7eRkdbR0yKCYfTRVBBEN3V7jz2zGNYo60oBoX0y9NpmRJYI1rF5xLfBG73l9AgmUwmbrzxRkw+9C4LtdfQ83bIjiqPC8sLcXgcJ9n7zN017y6iu0VXy/vEzo6qP5OqLBm5M39nvZUvkDT0tu0rXxcoNRqNDB48GKPx3+/GIJw5kbd2RNbaEnlrp76zliSJYIuVkLAwmjZpSUZUJuERkZQU2/FIHjYHb2bi2NcZ/eRzGJoE4dJ7yDuaj22njWlzptVLmc4m8bnEN2JkhxBQ3G43c+bM4bLLLhP/0GlA5F1V/GvxXNXmKr699tt6e42ly5dycZOLq+Rd09JLsiIDHLvZ3PHfd3i/A8vvWE7XpK71VsZAINq2tsQq89oSeWtHZK0tkbd2tMxakiQ+GjfL+/ihX+4kpDwKAKs1lHEjnvf+7qmnHiDMXHdrU3y19Cu+nfstbeLaEBQUxMIjC5k5ZiZRIVF8+tenbNi8gdYJrYkIiSAiOoKh3Yee8nhut5trJlxD45DGhFhDyHZm0zyhOcMvHM7mws3c88c9lD5ZWuPzxOeS2hN/BQRBEHxU2clQef96gK25WwFYnrWc6X9P58PLPqzT15RVudrPTlyzo6JsFT/LK89F59Ex6plRBIcE0620G0eLj0JSnRZLEARBEARBE28OmX6K30rs376fR6c8isFsoFPbTtw4+EZUVeX2V2+nsbUxseGxWIOtDOg4gOaNm5NrzyX9s3ReHPQicRFxmIPMhAaHotPp2LR1E8llyRQXlFBSXkKqO5VnJjyDGq4iFVd8/tuSuRWjUvG1etv2bURFRbFx10ZCQkJIikxi867NlBeWExYWjlFvoLmzOU6TC5unlMjiSAqPFvLh3xWfF2+VbsXj8WAwGHDJLoy6ikVfPYqnyujhHUd20CK6hej4+Beis0MIKEajkauv/vd5hkLdEHmf3OurX+fHnT/W2NlRUF6AS3aREJJw2sdTVZW00jQK8wr55OdPsOvttG7RmqHnD615GsuxDpA9lj2UB9voaOmCraiELvYu7Ni5g2HtxO3cTkW0bW0ZDAYGDBhwtovRYIi8tSOy1pbIWzv+mnWHnmns3rUVp8NF8dFilmQuodheTKgllMgDkeTp8iiQCjHKBjb/uRkFBR06WtCCD3ce/8ymoKAYFGRZRmfU8/QTb6KqKjPmfEBu3hHKyko5LB+kfWonRl4xju2Zm/jkozfw/O3hgHIACQk7drbrtmNRLFiwoAs2UlZuJ9d0lJG3/h8dGndGVRTs9lJyirKZvegb2FLE8x88T3hYOCs3rkQKkmge15zcvbkEqUEsX7YcJNApFatRKJJCuVRObGosL9z5wtmK3W+Jzg4hoLhcLmbOnMm1114r5rRpQORdXeUoj3+O9jhRj2k92Fe8D9cTrtM+rtPppKu9K85yJ39n/Y1e1bN46WIGTR5U4zSWyg4Qp8nJitAVfPvQn5TZS3n9uYmUu8trWauGR7Rtbbndbv744w8uuugicZVKAyJv7YistSXy1o6/Zj188O0wuGJ70Zr5/DV7DluWbEGvVqyiOvSG6+me2peVuxezafsaQqRQSuw2TKqJ/hcOIb/oKGPm3U/3uF40C25JWVkpjZObAxXTaW6+/L4aX7dds1SmPP8xgPdWvJV3qLG77DjcZURbY8kvy6Xj+40ZF/ZixTF1OkJCwwgJDeOagbfwwYFXcBzIJtudTbwnHqfHSb5SiCzJlEgltOragRJ7EWv3ruDqnjdR5rSzdeU61u9YX5+xnrPOuc6Ojz76iLvvvvtsF0PwUzqdjoiIiIC+9ZQ/EXlXqOme8qe6z/y+4n0+v1bT887jluvv4a5JV9LW0ZYxY8bglJyYMXv3eSBtLAObXQpUXbxUrzegopK7NJcHlj8IFpXuPbtz2xW3+VyeQCXatrYknURoaCiS7uTvG6HuiLy1I7LWlshbO+dC1v3SLqVfWsXnIY/bTYmjhMjQirU+erTqS49Wfas9p1mTViy8YMsZva4kSVVuxWs1WbGarMDxhbwNUvWv4Y2TmjPpiXe8j1dkLaZzYnfMBvMpb6s8as2NZ1TeQHbOfYrLzMw820UQ/JjBYGDIkCFiUSqNiLxPTied3p9Xj+Lhu23feR+/vup11hxec9L9mzVvjtFgZEnYEn6K/InWAztiS7KzzrqOsIhIAB7v/SzdknsBkBrf2ftcs8VC3KWNaT8ojUadm+ORPWzausmX6gU80ba1ZdAb6NWrV7UPcEL9EHlrR2StLZG3ds61rA1Go7ej42yKDorlg8u/plFY43/dt2dKX8yGiotZp8q7TFeGQ1d/dwM8l51znR01DdcWhEoul4vp06fjcp3+9ADBdw0975oWKK10qmks//Tr3l+5+cebvY/H/TGOxxY8dtL9t27egtvtxiN5OGw6zPBLbuOSwVexOmR1jaMQnujzPLtHFXof3zNgNFdddCPpwx9GH2YUf1NPoqG3ba253W5mz56N2+0+20VpEETe2hFZa0vkrR2RtW8kSWJIyytPOQK4JqfKe/TIp/l6wtd1VcSAcs51dtS2YQhnx8J9C8krywMgvzSfWStnkZOTg9Pp5HDJ4Vofr6C8gIzCjH/dT6fT0bRpUzH0XCMi75M73b9VNS4uWsPPKoWFh1UbMqpy8g4LnaTDYrCcrJSnVcaGSLRtbUk6iaSkRL8eDh1IRN7aEVlrS+StHZG1tk6Vd4f4TiSGJJ6FUvk/vxx3NHny5Bq/KKiqSklJyVkokXA6CssLiQyqGEb/1v/eIs4dh2SSMDorFi1ayEIA3JIbl8FFUHAQB10HCbWE0qtDL8xBZhbmLeST2z4BYMj4IcTIMUSGRbKndA95xjyWTFyCyWQ66RdJf10ZOlCJvE/uVCM7WpW3ol15O554/QnKjGU0czRDVVUkSaJdWTvMO8y89dVb6IP1uEPcPHzRw97nNm7cBIPewMz//O4dlXEmozNO1VHSkIm2rS2D3kDXrmlnuxgNhshbOyJrbYm8tSOy1pbI2zd+2dkxfPjws10EoZYWb1/M9A+nE2QKQm/Rk+BKwBnmJjElhRUZiwB4+KonOZp/mPeWv8qFcX3RuXWEHw5H79Szeclm9KqecMJ5YGPFwoktHC1wBruQTAZaOVvRytmKcePGoUgKNp2NJ0Y/QZuUNlXK4XQ6mTZtGunp6ZjN5pqKKtShhp53ZUdBTZ1vp1qzo1dQL6QSiYLcQsylJgYzmM9mfUa3Dt3oVNYJi2Jh+5rtGGUjZboyuOj4czdu3EDPLgO8a3LAqUeCnMrpTrU50eQ/JzOy20giQiJ8ev6Jih3FGHQG7+Jd/qCht21f+drx5nK5mD37J6644kpx9xsNiLy1I7LWlshbOyJrbYm8feOXnR2tW7fW7LVycnL49NNPsdvtWCwWbrvtNpKSkqrtt2zZMn777TdUVaVNmzbccMMN6PV6zcrpjz75+xMcbgf3d72f8uJywuQwghJCcbgdZNn30DWtL/cMepibyu/loO2Ad6HES/tdjUlf8SZdn70Ks97CeXEXYC8tZc6ibykszqe0tBinw8HNQ++iXctUXC4X67YtJzvvIEeOHkK3WceB/QdoFt+MPGceZr2Z6OBo9Ho9HTt2bPDnRisi7+oqOz5ONY0l1BSKM9zNfx9/jcXbF/Db5zNZv2g96xetJ5xwnE0Vnrr/dS6b1IM0expOp9P73LjYOHT6qh0pPo/OkCq+nFZ+QT1gO0CT8CbVdjtSeoTs0mw6JXSirKyMwz8d5qmfnkLWyagWlT59+3DDkBt8KwMQ92ocnRI6sTJ9pc/HqGuibWtLp9fRunWbam1bqB8ib+2IrLUl8taOyFpbIm/f+GVnR6WysjKWLl1Kbm4uinL8yuWtt95aZ68xY8YMevfuTc+ePVm/fj2fffYZjz/+eJV98vLymDNnDo8//jhhYWG89957LF26lH79+tVZOc5F076exgVlFzDyq5GgAyNG7rr+EcIiIlFUxXvVOCoohqigGO/zKjs6ADondvduW0NCuH5Yeo2vZTKZ6NmxPwD20lJe3/xf5nw3hznfzUFGZn/cfn6e8DNZpVl8kPsBPQ0966HGwokMBgM9ezbcrE9coDTWHYslx8LOvTvRuXWcTh+EMdTCp7GfsvO+XA4cyeSeb/5D88iKEUtuyY1RNTJu3DhkvYwePUnJydVW4va1syPTtodWjlY8NPohSnQlFBgKmPXCrGpf8K+beR2rDq3C9YQLl8uFHj0x5ydhsBjZt2knG7ZsOKPODhWVTTn+dVeYht62tWbQG0hNTT3bxWgwRN7aEVlrS+StHZG1tkTevvHrrqEPP/yQgwcPkpSUROPGjb3/1ZWSkhIOHDhAt27dAOjUqRNFRUXk5ORU2W/Dhg106NCB8PBwJEmiT58+rF27ts7KcS5ZfWi1d8h8amgq5SYHiec3JbRxJOHtYgkJCwcqhvDX12Ky1pAQ0h95lP7XX855l6ShR0/znOaMHDeSCZMmcGjZIcrLy+vltYWqnE4nr732WpWRBw3JidNY+tr6cmHehbzz5juELg5lRM4Ixk8dzyufvsLX87+u+c4exzpMgoKstG3egX2WfcjGivdYdJsEfoz8kbYXdyb6/CQMLSxk7Myqdhxfp7FcfvWNGNKCadKjDaYoC82czSgtLfUes7Izp9x9/P1U+bNO53cnffjDSFb9aXXq/Bu95F8jKBp629aay+Xiyy+/FHe/0YjIWzsia22JvLUjstaWyNs3fj2yw2az8fDDD//7jj4qLCwkLCzMexVTkiQiIyMpLCwkLi7Ou19BQQFRUcfvyxwdHU1BQcFJj+t2u/F4PN7HlV+8XS4XS9cspbiwmOiwaMLCwryvq9fr8Xg83m23241Op/Nu6/V6dDpdlW2Xy4XBYPBuG41GJEnybleW5Z/bJpMJVVW924qi4PF4vNuyLGM0Gqtsy7KMoigYjUbu+vwu0juk069lP/QuPUGWYC7pegWyLANwIGMvHvlYPXT6qtseN5JO593W6Srq8c/tanU16NFJx7clScLj9mAwGkgISSLWEk/ksCg27VqHq9yBp8BDbFksW7duJSwsrEo9XB4XTreT0KBQypxlPPb7Y7w15C0kVUJVVQwGA7Ise7crz2Hl9rl0nv5Zj/qsE0Dz5s3Zu3cvBoMhIOpUm/N0pOQIya5ktm7dSpA5iAh9BOboYNK69OGbFZ+glHpIyE/EdqiErPVZlBeX06NDD+RyGdywd9cOcnOySXYlk7F7J6qqkuxKJrjYwt6dO7CWBKNDxwWNu9K5aXdcbhfZh7M5uC8TRZG97ydbViHJrmT27dldq/dTJ2tXOrTviMFowIKF3bmbycjIIDg4mMFfDebeTvdydZuribJHkexKZseOHRQWVdzGNj87lz2WbUgeCdkps3XrVp/PU5/iPsS743nsuccItgbTJLEJPVN7ntX3k9vtpkWLFmRmZnqn+vhT2/PX91NxXjEAe3Zux2IJOq2/5ZX1SElKICszw1vGE+uhKDIGQ9VtWZFRFeX4tqpi0Buqbh/790mv12v275OqqsieY2VXlePbflInJImUpAQy9+zCaDAGRJ389TwpikJKUgL79u5Gr9cHRJ38+TzpdHpSEuPZt2c3JpMpIOrkr+dJp9ORkhRP5p5d3n+TzvU6+fN5kpBIio/lQOZeDHpDtfq546JITk7G4XBgMpnQ6XQ4HI4qsyMaIkk9k2X869m7777L7bffTnBwcL0c/8CBA0ybNo2nn37a+7MpU6Zw1VVX0abN8YUvv/nmG8LDwxk8eDAA2dnZvP322zz//PM1HnfOnDnMmzfP+9jlcvHxxx/z3nvvsXnXZpTyht3oBEEQBEEQBEEQhLrRrl077r//fsaNG8fo0aNJTEzk4YcfZsyYMbRo0YLi4mLvhfaGxK9HdphMJl544QXat2/vvaIFdXe3lsjISGw2G7Iso9frUVWVwsJCIiMjq+wXFRVFbm6u93F+fn6VkR4nuvTSS7noouO3T7DZbHz88cdcccUVXKZcRkZWBll5WWRkZXB482HvfioqHVolcU2Hnkzf9gu7dhZgMOrQG6AIG2MvHE5cUNjx3kiXC/2xq4HuY1cGpX9uS5L3aqCqqng8HowmE+qxK4DGyiuA/9w+xdVAD/Dyzz/TolMLGiU34tO1n9IipQUThkzA7XYDeK+ISpKEwWCosu1yudDpdN5tvV6PXq+vsu10OjEYDN5to7HiilPlduXVzn9e1TSbzSiKgtvtZt2edcz+YTZBapB3HYXU1olcdX5PRv7xJgnFjbx5u3Fz24DeNA9LqPkK57HeVsMJVzVP5wrnlD2f8PHh2ezq+4Pm5+mUV23PoE5Vto/VSZZlZs2axVVXXonJbA6IOtXmPG0uy2Dyiq8ZGnchTSMS+HPrVmJSYnnk1keQZRlZljGZTLjdbl544QW6n9+Mi1ukMnnRd6iKkScenMDyg8u5e97dbLt3G6qqct6H59G7UW/eH/J+tfdTaWkpn332GXfffTeA9/20dN9S0uels2vkLp/fT/PXzGf1r6sZPXQoISYTj//0JUbViAE9OipGv7mCXOAEg2Lg9jtuJzkhmUnvTSJI1TGm75UUqXam7PmEF1s/iMFg4GDZUeKMEZiM5irnqcRZSpdVt7Gi53TCJSsPzfuAMl0ZHzz6AT8t+4ntS7YzZuhQLMeuTJzpefKl7TkdDn6aPZurr74anU7nd23PX99PP+9ZzZbtRxk9ejTBwcGn/bfcbrfz+eefk56ejiRJmM1mZFnG4/F4tyvfT//c9ng8KIri3VZVFaPRWGXbX/598qc6ybLM9OnTufXWW2t1nvy5Tv56nlwuF59++im33347ZrM5IOrkz+cJ4KOPPuK2224jJCQkIOrkr+dJVVWmTZvGrbfeSkhISEDUyZ/Pk6IofPjhh6SnpxMUFFStTlZrxR3tnn32We/dWqZMmYLD4Tjpd9aGwG87Ow4fPkx2djYtWrSot16o0NBQUlJSWL16NT179mTDhg1ERERUmcICFWt5TJ061dsjtmTJErp06XLS4xqNxiqdM5UN2WQyERYWRlxcHD3oAVSsG7L70G4yjmTw+7zf2bgni78P7qPM4yRUDSOyRSS2EhtRB828v/sn/tf/1NN6ssqPEh0cTrDe4mssJ1V2bI5YixYtGNZnGNcMuqbOX+NMxcfH0zK2JS1btsTlcjF6wmg27cpmw+7viCYem87GfXfex679u1j3+zpcLoUwq5WNpTuI0oXTOrSO1oRRdfS39WfSX98QZDKRpxTwVJ9bSY6Orpvj+wlFlhkycCDxkZHo9P615oIWFhdsJrU8lf0Hijm4vxQTZg67DxN9wnmu/AAWZDYSFxGBzgCyXNFee4f0Jnx5OPHx8QAMaDeA9I7pJCQkVHu9iIgIhg8fTlxcXJVFRK+MvpI97fcQGRRZ7TmnyxhU8Tfrw3W/EhcWhhEjbbq14aEbH2LaqmnM2D6DP9P/BEBRFHS6Y0s+6UGHREJUFHOzFvNl4VymR/4XgMRlg3ixzSgebXZzldeSzAol+hIUk4f4kEhkScaj85CYmEiwtWIkX3xkJCbD2fsnSgkOZsjAgcSFhzfItu0ry7F2lJiYWKtb9kZFRTF8+HASEhKqLZAr1D1Zlhk+fDgpKSki73pWmXVycrLIWgOVeTdq1EjkXc/E3xFtybLMddddR2Ji4inztlgsVbYb+hofftnZsWjRImbPnk18fDybN2/mlltuoVOnTvXyWjfddBOfffYZ8+fPx2KxeO/08sUXX5CamkpqaioxMTEMGzaMqVOnAtCqVSv69OlTJ68fGhpK57ad6dy2M4lRiSxZv4TC4kIOHNpOVtBBFty/AFVVeeiRh/AcsPDgdx9STDEt4xMZ3+M6zMd6ICt1+uNW+oek8WqHR4iyhhJsNqM7g4VCVVXFrXow6Y533tTXwqN1Qa/X065dO6Ci0+nmu25mf9Z+ikqKmLt1LnKoTI/UHjRPbs6639cxc9kaZi5bg4yMQ+fAZDJQRhk5ah5fXv5/WM3mk37hsrns9JnzICMaDyc5JJYN9u20DEvmkvhuJDgTcbpCUMJVispdBNkimTx7Fk0bR+KQHLRNTOK6FgO0jKZe6PR6mjZrdraLwdribUQbI2gWXP220fVJpWJK2oOjHiQ5Ppk2U9vQNqbtaT772B1crLFkjMrw/vT74d+f9Bn/bN8nOpOODgCb2UaGOYNGpY0ozHNiUk1kObPQ6XTc3fNu7u55t3dfb0fHMZULtbodKhcVX8R/F3xBtDWUNuVtOFRaMSrOpbgp9ZQRZQrH41G5Nv9aXps3jxCLmQg5ArveDvjP3xd/adsNxanatlD3RN7aEVlrS+StHZG1tkTevvHLzo7FixczYcIEoqKiOHToEF9//XW9dXbEx8czduzYaj+/5ZZbqjzu3bs3vXv3rpcyVOqV2oteqb2Aik4GWa24GixJEk0GNMFZ4MRms6HPNFGQ6WF85lfIkkyxrpj3rhuJ1WxmUPEg4vLjmLr/2JohQTJv/Kfm27mejnuXvoy8LxiLyYTBICFh8t6NwR85HA4mT57ME088gcVioV+HftCh4ncP8ZB3v7joOG4bdRvFhcXkFuUyZdEUYg2xdIvthnPPYVoprXj6+4ovnS7JxXW90hjYoiOfZc1jW/4+JrS5k/3FOfQv6c/2rXlsJw+AfA6xilkAyMi8/eTbAAweP5h4OZ6MLBWT28TSvQdYuPF93DoXu937WHDtSxjOwV5xl9PJtOnTSb/zTkynuIqrqAqyqmDU1d2fnDX520kNa4nJYOCB39+imaMZiSFRmEx6LCF6nulzW5291r+SIDQklDxjHor+5GvyVHYK+PoOOrF91ykLzI+Yj+uJiisAP23/ib5N+/7r0yqniwGEOsNp7WhNfqGD/KMOBsoDCSqsqO2l8x6jdcF56E0SeoNEnCcOXaQOl+qhXFdOfKOKkS0ndnbYPHZC9cGad4KcbtsW6ka9tm2hGpG3dkTW2hJ5a0dkrS2Rt2/8srNDr9d718RITk5ukLf+kyQJg3T89Iy5aox3OyMvg3Xb16E4Feavn0/UoSiKy8qwms2E60OJaBzBsIuH8ePvP1KSafM+L8dZQKwp8l+/NAyY+Sgx7liahyVQXKQQqwQR1TgKu93OXtdeXFb/HQ5lNBq58847q0wjOpkuLY9PRbp+0PXebZfHxdx1cwlWg8krymPFLyv4fs0qftmxAVeeDrDy5N/fevd/ePTDJMQlsOfoHlS3SlFJEZk5meTL+d59Zj43E6upYi7d/PXzmfvXXEKIoLS4lNTyVF5e9AOxYaFsc+zlsmY9GZDUyW+ucJ+KwWDgissv996Z5WTuXT2F3QdyuCqpH1FBIRjNEje0G1BthMDpKiqz89mclehYjSLJ9FArpoUpJjiSX0RETgTuC2WM9dyBVFOnRZw1roaf1o3atO/aOi/2vCqPr2x35ek/+VgQ5mMjwF7+78tYrVbue+Q+SrPcjJs7ncTixkhIxDSLo8RWghIs89Sop4gIj6hyKLu7YoTH+DmfYA0y82fJKu7odDH3triKbEceR1z5dAprQ23ctHECL7d9mGTL6Z+b023bwol868qrz7YtVCfy1o7IWlsib+2IrLUl8vaNX36Kc7vdHDp0yDuC4MTHjRo1OtXTA17zmOY079McgKPyUfYe2lvl90aLkW7ndWPFlhXezg5VVbn5pynE6aJpEZ6EXbJzXqMU0tsORVEU5matpFdUB0KCgmhc3gSDZKDUpWBRLBw0HeR/D/wPANNkE6OjRmtb4VrQ6/U0O8Oh5yaDiau7X+19XFBcQOa+TMrLnRTri3DoHPxn2H/ILshGURWapDRBr9eT2izV+5x+9KtyzMqODoBLO1/KpZ0vrTh2UQFPvvwkWdnFHM4qRUcIP+3dzCz+RtHLhIQbmXL57WdUH18cdeYTY4pAL526s0Cn15OUnOx9rKqqt5PmrgUvY3SaaRqWSMEhmVRXKrsz89DLhejQERO0gUuan3ztm1MpdZajQ0ezrs0wBZkoLinmkh6X0KVdF65941oiMiL4cfsKkkOiibOG0yImEUmSKPHYKZMdxJvrZu0U9YQvdpvv20xiSGK1/SRJQkZm4bpd/LFpK26PjGotq/Xr1UX7PpmejXp6R3XU1sm+3nbs15Gdu3ZiL3NjVI3sN+/nvRHvnfJYthAbW4K2cL75fPJLHaTZ09i0/TALpc1MXPcxLR0tCbUuIcRiJjEsint7XIrJYGBm5iLyS0sZmNAZt+TmnW2zuLbRFJWm2gAAIABJREFUAJqHJ/Lz4aX0i+rMfY2vPe06ndi2hfpVn21bqE7krR2RtbZE3toRWWtL5O0bv+3seO+9qh+I//n4ueee07pIfstwwrQAFdU7rPxw2WF0qp4HP/8Y9Crt3e0ByFXLkcr0/Hl0B430CewpOcTOzQX8zm4AIogguWMy4+4cV+NrJof57xcAh8PBxIkTefbZZ+tsiNeo60d5tyctmcT2vO1cN+C6Ojl2VEQU7zz/DlDRUfDpsk8pLyzHIBvYum0rZTnyaR0n11VIrKn6mg0uxU2Ru4Q488nvHgSQaT9M/z8e4OeurxJvjeT2X6cSLoeTFBKNwSjRJbklN6T2q/Y8l9PJe++9x/33388q2w7GL/qQ/yRdTERQCMbscBQU9tuKSHAnkG3M5vtXvmfvob28+dKb5DuLT6tulRbmrOPPgxu5t+lVFDsrOgr6d+lPx/Ydq+x3UfuL2JKxhaXr9gIVHYGhMQYu79CVl7Z+SZlNpmfMeYQFBZMSFsvV7XvWaoSJR/GgoNa4jk2b6JpHHOh0OmZGzeTKhCtJkBJYs2cNoTGhtak+UD/t+0xJSKCqKIpSbYrbiKtH1Pp4xiAjS8KW8McTfwBw/ZjrSchPYNaS9XSiYjqjLkxHXnEZpbkK+1rl0Do+ia9WLCPZncxWfqvYhwhmZW4ANpBOOvY8BWqx/vA/27aYxlL//LFtBzKRt3ZE1toSeWtHZK0tkbdv/LKzY9KkSWe7COcM75e0GtbRKIwqZFnYMu5qdxeFxYVsyd7Cmw+8SUp8Cj2f6Ek3ezfmLt/s3X/QNYMoKi+ioLiAK/vWPITd1yu/WjGZTIwePdp7y6W69mSfJ+vluFDxhfmO3nd4H0+0TaT4aDEjZ3wAesiTCxjb+1pSE5vy+v4ZlDlc/F+bO9hRmsmIP97guqSLCTcHMzXrc25qOohH2l7PxIx3efvAN7gGrwTgy8O/cH3CIAw6A/9Z+iQHjhZwd9MrOVyaz1WFV/HxgsUAtKHiS7tigLw8G4tzdrO/OIdwk5WVu/dgCdITHhRMbmkJzkYOCpfOZEfRftLsaezem49eKcKMmbjucUy4aQIvLnuRdTvWARBsqrjbhkupuEvR//Z/S5OgRC6Lq77o77ObptPB0opO0a14etGXXFB2AS9vnuP9vdlY/UvoyEEjUS9WcTqdFJUUMemFSZTkwYy/VtKIFgBk59k54i4jUy6mS0IrmsacenrDwbIcEi3R6HV6Llv7CFuKMzk4aG61kR2nkm/MJ61bGnd1uuu0n3Oi+m7fvthXuo/mzuY88vlnuCU3Js6sbCdOA5o8YTIpISkU24pZtHMRpmATV3S8gq37tvLBax/w9q+/IRs8xHviURNU0q9JZ8qfU1h9ZDWrHlpFxpEMvvjgC2KkU3f4nchoNHLTzTef9eGibsWDzWMn2hR+VstR3/yxbQcykbd2RNbaEnlrR2StLZG3b/yys0M4fZXreshq9UURW8a25JOgT3j0pker/e7RWx7FI3sYkjKE7/7+jqcWPsUb/d6o9/LWN51OR2Ji9WkE56LrL7qeFwtfpH1Ye+yldox7zHzx13Jg+bE9TIxfNwOAbnRj/24bCkUMYhC5f8OEv78BorhddzubsjNJConm7r8nEWuM5JLYHkhHgkmzt+DvLYfRUdFpds+IeyhyFPHa0tcYO2wsPZr1YN6qefzwww/szyrC4LYTTjgul4tSg4LsVoksj+WIbCfEGYFNZ+P9Ke9jMBjYn7+fxtEVl9If7/U4j/d6vKLU+oo/0ou27WBfdj6/Fi3jaPARLhtavbMj628H+cpeFrKXC7iAIn0R9995P0cKjuB2u2ndvHWN2UmShMViIcGSwJuvvInD4aDQVsjB/IOkxKaQFJfE5r2b+ejNj/DI/z56ZuLMbwlWglENMm08abQhjQe++AiX3kkwIad9Ts90HRZ/bN+Lwhaxw72DcZ3H8fv239lv209wcLDPx7vxvBvpkdzD+7hFZEUHVWx0LMMvHO79edOkpvwZ9ycPtH+AvMI8lu5ZyuALBtOxTUcmRk/k3bXvEhMdQ0x0DJ/zuXfUSb6rmBKPnab/cuceSacjJibG53rUVr6ruMYOjcd2vMZ7B2Z6OywDlT+27UAm8taOyFpbIm/tiKy1JfL2jejsOMdVTmPZeCQDGQVJlSrvaMljPR4jvWPNd2K5rv3xaRgx8THkGfPqvaxacDgcjB8/nilTppzzQ7zOSz6Pzx/+3Ps49tlY0lun0ymqE1sOb8EsmWmT3Ibc4lwKHYVMuGkCAB8s+4BINRKjbGTZsmUE5wTz8W8VIzbu5V5++GUTP5k2E+2OwxBr4NUJr1JWXsaKgys4v/X5APTuePzOQ0O7D2Vo96Hex7N3zuby1pcjSZI371eefgUM8OveX709zs1ia55XGBEewbagbSRKiRwpsNO5vDPFUsVirktyNzJy6Ws82+Y+Eq1RmFQTzbs0p09aH75a/xWyLNOzQ89a5ajT6QgODiY4OJjkhONTsCo7eJQaRmeoqkqeu8g7NShECcGYbCQ6Npo/d/6JSTHRp30fdh7YyYGSA0RF1m7UgK/8sX0Paz+MhJAE7rnkHvJi8pi1aNYZdepIkkTzyOb/up/VZGXzhM01/q5dTDveHPym97GKyrIte1iTkYGj3EOprpRG4TGEBQURHxbO3V0Goz9hKpPL6eTNt97ioVGj6mway8A/RvFQyg10iWlLlDWEYJMJSZJYW7yNPsvvwT54KTpJYsiSR+gflsYdTYeRYTvs++17zgJfb9blj207kIm8tSOy1pbIWzsia22JvH0jqf58H9EAYbPZCA8PJy8vj7CwsDo99s8bfmb+J/PRc3whyeQLkhmXXvN6GzXJsedw5+w7mXvj3Dot29mgKAo2m42wsDCf7/Thr0b/NpoJvScQE1y7q83FxcXkFuZyKO8QH6/6mLahbSkvK8deYqdnp55cd7Hv64/4mveyrGVc2OhCJEli1LOjMKPyymXp3L9kKuaMqvWLSYvhv7f81+cynsyOzB28+/q7jBh8MW3jk+my7BYGRXfnxbajeD/zB17c9CVzu79GgjWSZ374ga6XdOXWYbdWOcbmnM10+agL+Y/lE2o+9Rocpskm3h/6Pnd2vNPnMvt7+568dDJPL37a76a7tZ7Ymg7mDrQIasHu7N1EeaIIigjCVeoiyBXE2Msvp1FU1Q4rVVGw2+1YrVakWmT9xM7/cVF0GhfFdGNpwUY+OzSXDzpMwC3LPPrFZ95ONoDoRDMTL7mBnzKX8vviijWTFKOMwV196kxis2BigsNwGhzc1+GyareqVlWVLaV76RDasjbR1LnPNv3Oug2HeOmllzDXopPI39t2oBF5a0dkrS2Rt3ZE1tryNW+bzUZMTAzFxcV1/j30XCBGdpzjzmt6HtfHXU/mvZkczD1IdkE2vTr0qtUx4qxxAdHRAcenL5wLt22trdcuec2n54WHhxMeHk7Lpi3p17X6IqNnwte8e6Ucb6MFzgKiS6N54IuP8KhBmIGJEyficDsY9MkgPuj9QZ2WuZKkqyjz27/ORzHKtFE7sjB7OyVNysk5aueKoit4f/5fVcp5oso1O063/mfaLv29ff+n/X/YeHTj2S5GNa/c8goDmgwg1BxK30/7sjh3Mflj8lmzfQ1fvPcFm7P3YdDriAy2Yj62RoeKilsv1zrrT3b9whxlNZ91jeG5v78gKb8JD+78AIvZiA4daRel0bJZS974/A1cRyJ45MePkFTQYaR5WnOKncXYim3069SPsLAwnp35LPHOeFwHwznkLkWHjt+s6xnaKq3K6/6Su5xb1zxN5sDZWM1mFuSvpHvE+UQaz40PNv7etgONyFs7Imttiby1I7LWlsjbN6Ib7hzXPLI5BeMLiI+Np0v7LlzW+zIiw6vflaOhcDqdjB8/HqfTebaL0iDURd4T75tI2/5tad65BbEtYrE3sRMVFUVyQjLbHt9G76a9//0gPkhJTGFexDxa9WpFcvtGxHpi6W7vzpPffkveTgUVlREPjWDYTcP4Lfw3WreveX2Q2qi8U5Kv/L19t4xqybfXfnu2i1HNFa2v8I68WXDzArIezgJANsh48PDr2s288ONPjJsxg9dX/gjA6xkziF88GJerdqNUBtgGMKBoINN/X0Lb3A6EKWEERYXiQCXHkENUoyh6dOjB0uCl7LLsQg0y4kBFDpe5/7r7mXjXRF559BUuH3A5/br046VRL9F4WGPefeVdXpz8IgD78/LIyD3Cypyt7CjZB0BRYTm35N3Cf7/9lkc//5xZv2zm7kUv1VGC9c/f23agEXlrR2StLZG3dkTW2hJ5+0ZMY9FAfU5jEapS1Yq7cJjNZtHzqYFAyts6yUpjXWNe7P0i+3P2Y7VauWtIxZ1TytxlBBurL7q5KWcTXT/qSuGYQqwm6ymPb5ps4sNhH3L7Bbf7XMZAytsfbDy6kQs/upAdd+4g40gGn33xGcnxETw36Bbu3zyZaYdm47p0BZyQ9Yt7P+GwM5c324+lxGOn/8r7WdPrU3SSjlu/eI0jpiO8O+JdZm6cyVc7v2LjuIrRLrvyd9E6uqLT7N217+KQHYzuPvq0y+t2u3lk/CMY5OODMmVkkpuGUOZwYTsi02dYH0pcJaz6fRU7Q3bw1zWvVjvOtKyfuC15WLVbl5/K0uy/mbF9If1iOxIVFEaTsDhaxSYhSRJfH/6NRHM0/aK7+DyNRbRtbYm8tSOy1pbIWzsia235mreYxiIIAURVVRwOB6ZjC/8J9SuQ8nbr3OiidDXedrmmjg6A2vYVn2lGgZS3P7gg7gKWpC+hcWJjGic15uvvv8IgVV8L48SsJ+2cRqQnkokp97PVlkHPvQN4KGM6ikkmTA4jS8qiZZOWjG8ynvGM9z6vsqMDYETXEbUur9Fo5I2X3qCwqJCDuQeZuWAmeUfyOHC4GL1bB3qJy/pehsViYekfS0ksT+KZP2aQW17EQfdRBiZ0ITLIyvP7v6CNtSm9oi6o9hoexVNjJ8j4ZR/R1d6V5Vn7jtfh2Ho3by6fTUtHS763rEOWVYzUfuE00ba1JfLWjshaWyJv7YistSXy9o2YxiIEFJfLxVNPPVXroeeCb0Te2hJ51y1JkuiS2OWfPznegXXsf263u9rz+pX14ZrCa3juhx/44feN6NHT9IJmxDaLJ8OSQfM2/343GV8ZDAZiY2Lp1K4Tkx6axHuT3+Pdl9/l7df/x5uvvOldob0ssYxgSxBH8u3IBUaa2Jqzc28Oa/8+yGVFl/H7mm38tns9aw7upKC8BIDf81YR/NvxaWPf7/+LNYd3UmS3Y1ANFOuLefnll7nvwfsAeOP3uTz43Qd0KOuAhIQlOhRDqIngJkEYjdUXWT0V0ba1JfLWjshaWyJv7YistSXy9o0Y2SEEFIvFwhtvvHG2i9FgBFreZ7qmxr8e/wx74gMtb39UueisQdKT6ErkiN1GDGEE/eNKStvgZjiRufKqK5m/bT4L9ixgw+0b0Ol0OD1OjPrafdGvK/9cnf2rcV95tzt92ImtuVtxPeHC4XAw5skxFBxxMffI8Vv3enQeyiQ7Q/RDkBUFvU7H3MVbCFMOANCRjhTpizCZTCQ1SmJx6GIGxg/E4DGwLXsbuy272fHYDp/LLtq2tkTe2hFZa0vkrR2RtbZE3r4RnR1CQFEUhaNHjxIfHy9ug6WBQMs7Mqh2i/vW9m4s0UHRtS7TPwVa3v7mn2cxrDiaqwqvYurP87w/a9UmihvP64+qgt6oY1C3QXiiPLx5+E3v+TAbTn+dCq3ceN6NzNgyA6j4sPT2K2/j8Xiw2+10erUTVsXKXe3vYm/mXsKOhjN+7idYLWasqpXYtrGkdUrjsbmPUawvBkBBYWvwVr668SvaxrTFNNlEbHDsGZVRtG1tiby1I7LWlshbOyJrbYm8fSOSEgKKy+XitddeE0O8NBJIea+9ey0zh8+st+NvH7GdIS2GnNExAilvf1TucXCooIBP1i9ALTWgoHDd7dfR+4qKqR27dxbw7A8/IOcZ4diti3ul9OL2VN8XndXCuAvHsfHeqrcENhgMhIeHs/ixxfzx+B88cuMj/Pf+/2JKMeEAcorKkA0y3Tt159Iel6Imqxw1HQVAVmQA9LqK9U1S41K5pcMtZ1RG0ba1JfLWjshaWyJv7YistSXy9o24G4sGxN1YBCEwbTy6kW4fd6NobNFJFzEVzh19/68vrRytCFaC0aGjXCrng9c/AGDDwQ1YXBb2Hd3HwbyDdGrVia5tu57lEmunzTttyCzKxPWEi/yyfBJfTyTjwQwahTU620UTBEEQBOEkGvrdWMTIDiGgyLJMZmYmsiyf7aI0CA0978q+4vpe66NSQ8+7vq0MXcmfzf/kjdfeIP/CfL6K+cqbdadGnWjXvB1Deg7hnsvvaVAdHVBxl5ZK0cHRrL5rdZ12dIi2rS2Rt3ZE1toSeWtHZK0tkbdvRGeHEFDcbjfTp0+v8Q4KQt0TeWtL5F2/Oid05tYOt6LT6ZANMk6dU2R9zMQ+E0nvmO593DG+Y50eX7RtbYm8tSOy1pbIWzsia22JvH0jprFoQExjEYTAtOHIBrpP607x2GKCjEFnuzhCHXp5xctM+GsCrifE3FhBEARBEM5NYhqLIAQQWZbZvn27GOKlEZF3hTO9pezpEnlr55G0R1h1xSqRtUZE29aWyFs7Imttiby1I7LWlsjbN6KzQwgoHo+HH3/8EY/H8+87C2esoeet9ZodDT1vLSmywq8//yqy1oho29oSeWtHZK0tkbd2RNbaEnn7Rkxj0YCYxiIIgWl99np6TO9BybgSzAbz2S6OIAiCIAiCIHiJaSyCEEBkWWbDhg1iiJdGRN7aEnlrR2StLZG3tkTe2hFZa0vkrR2RtbZE3r5psJ0diqLwzTffMHHiRJ566ikWLlx40n2//fZbnnzySUaOHElWVpZ2hRRqzePxsHDhQjHESyMNPW+VY9NYNFqzo6HnrSWRtbZE3toSeWtHZK0tkbd2RNbaEnn7psFOY1m5ciWrVq1i1KhRlJeX88ILLzBy5EiSkpKq7bt7925iYmKYOnUq9913HykpKbV6LTGNRRAC07rsdfSc3pPS8aWY9KazXRxBEARBEARB8BLTWBqodevW0atXL3Q6HVarlc6dO7N27doa923VqhWRkZEal1DwhcfjYcWKFaLXUyMib22JvLUjstaWyFtbIm/tiKy1JfLWjshaWyJv3zTYzo7CwkKioqK8j6OjoykoKKiTY7vdbsrLy6v8B+Byuby/d7vd3p9VNtp/bjudzirblfOz/rntcDiqbCuKUmVbVVUcDgeqqqIoCg6HA6DKtizLVbadTme1bY/HU2W7sh7/3PaXOlXOZ7Pb7QFTJ38+T7Iss27dOu8xA6FOtTlPlQPjtKpTeXk569ev9x6vIbe9+q6T0+n0Zh0odfLn82S329m4cSNutztg6uTP58nlcrFx40bsdnvA1Mlfz5PD4WDjxo04HI6AqZM/n6fKz4GVn70DoU7+ep7cbrf3M3eg1Mmfz5Msy6xfv9773NrUqSEL2M6Ol19+mbFjx9b4X111apzM/Pnzeeyxx7z/TZgwAYDZs2cDMGfOHObMmQPAzJkzWbBgAQBffvklS5YsAWDatGmsWbMGgHfeeYdNmzYB8Oqrr7Jr1y4AJk+ezIEDBwCYOHEiR48eBWD8+PHYbDacTifjx4/H6XRis9kYP348AEePHmXixIkAHDhwgMmTJwOwa9cuXn31VQA2bdrEO++8A8CaNWuYNm0aAEuWLOHLL78EYMGCBcycOdOv6mQ2mxkyZAhTp04NmDr583kym80cOHCAoqKigKlTbc8TwDPPPKNJnb7//nvat2+P2Wxu8G2vvuu0adMmdDodZrM5YOrkz+dp4sSJjBgxgqKiooCpkz+fp/379zNixAimTp0aMHXy1/O0YMECRowYwc8//xwwdfLn82Q2m+nZsycfffRRwNTJX8+T0+kkPT2diRMnBkyd/Pk8mc1mXC4XO3furFWdSkpKaMga7Jod//vf/+jevTtdu3YF4IcffsBgMHDFFVec9DlPPvnkaa3Z4Xa7qwwxstlsJCQkcPjwYWJiYrw9eEajEZfLhU6nw2AwVNl2Op3o9XrvtsFgQK/XV9l2OBwYjUbvtslkQqfTebclScLpdGI2m1FVFZfLhcViQVEU77Ysy7jdbu+2x+PBbDZX2a7sTazcVhQFk8lUZdtf6mQwGFi0aBHdu3cnJCQkIOrkz+dJVVX+/PNP+vfvj8lkCog61eY8bSnYwoWfXEj+w/mEWkPrvU5lZWUsX76c/v37I8tyg2579V0nh8PBkiVLGDBgAEBA1Mmfz1NZWRlr1qyhV69eKIoSEHXy5/MEsHz5ctLS0ggKCgqIOvnrefJ4PKxcuZIePXpgNBoDok7+fJ70ej2LFi2iR48eWK3WgKiTv54nnU7H0qVL6datG1arNSDq5M/nCeCvv/6ib9++mM3m066Tw+EgLi6uwa7Z0WA7O1asWMHq1aurLFA6YsQIkpOTT/qc0+3sOJFYoFQ7LpeLL7/8kptvvhmTSSwYWd8aet5rD6/lwk8uxD7ejlFvrPfXa+h5a0lkrS2Rt7ZE3toRWWtL5K0dkbW2fM27oS9Q2mA7OxRF4dtvv2Xr1q1IkkT//v0ZOHAgUDEEadOmTdxyyy0AzJgxgy1btmCz2bBarVgsFu+w9dMhOjsEITCtObyGXp/0ouzxMgw6w9kujiAIgiAIgiB4NfTOjgb76Vyn03HDDTfU+LvU1FRSU1O9j2+66SatiiWcIY/Hw4IFCxg0aBAGQ4Nt3poReWtL5K0dkbW2RN7aEnlrR2StLZG3dkTW2hJ5+yZgFygVGiZFUSgqKmrwKw9rReStLZG3dkTW2hJ5a0vkrR2RtbZE3toRWWtL5O2bBjuNRUtiGosgBKYsWxYt3m6B4/8c6CTRdywIgiAIgiD4j4Y+jUV8OhcCitvtZtasWd7VjYX61dDzTglLwfWES7OOjoaet5ZE1toSeWtL5K0dkbW2RN7aEVlrS+TtG9HZIQiCIAiCIAiCIAhCQBHTWDQgprEIgiAIgiAIgiAIWmro01jEUq4akGUZgEOHDlFSUnKWSxPYXC4X8+bNY+jQoeKe3xoQeWtL5K0dkbW2RN7aEnlrR2StLZG3dkTW2vI1b5vNBhz/PtrQiJEdGlizZg3dunU728UQBEEQBEEQBEEQGpjVq1eTlpZ2touhOdHZoYHCwkKioqLYtWsXoaGhZ7s4Ac3hcPDSSy8xbtw4LBbL2S5OwBN5a0vkrR2RtbZE3toSeWtHZK0tkbd2RNba8jXvkpISWrduTUFBAZGRkfVYQv8kOjs0ULlmR3Z2doOcK6UlRVHIyckhLi4OnU6sv1vfRN7aEnlrR2StLZG3tkTe2hFZayuQ8na73RgMBiRJOttFqVEgZX0u8DVvm81GYmJig12zQ3R2aEB0dgiCIAiCIAiCcDqKbcU89+xztOnchntuuudsF0c4hzX0zg7RDScEFIfDwZgxY3A4HGe7KA2CyFtbIm/tiKy1JfLWlshbOyJrbQVC3oqiMPmdyQDsXL+TOUvmnOUS1SwQsj6XiLx9I0Z2aECM7NCOoijYbDbCwsLEkDoNiLy1JfLWjshaWyJvbYm8tSOy1lYg5P3WjLfIXJ/J0MvP4691Wyk/DIOuGcSQC4ec7aJVEQhZn0t8zVuM7BCEACJJEhaLxW/nNwYakbe2RN7aEVlrS+StLZG3dkTW2jrdvPPK8igoL/A+drlcNe6nKAqKotT4u4yMDI4cOfKvZXI6nZSWlgJQVFTEmDFj2L17d437/rnmT/av30/T1DAGXdCZ5+64GXOCyh8//MHk1yfjT9eoRdvWlsjbN2JkhwbEyA7tOBwOnnzySSZNmiRWhtaAyFtbIm/tiKy1JfLWlshbOyLr+rX76G4efPtB5j09D71ef9p53zf+PkLlUK685Up27d/F9iXbiWwVydg7x2IymQDIzslm6ktTAQhrE8bA7gMJ9gSTU5bDX8v/Qs6RAZgyZQp6vb7aayiKwk9//sSyX5cB4Na7MMqm4zvEgsvponef3ug8OlYvX41SoiDFuHjpvjvQHftS6/HIjH/xawB0CTrGpo8lNir2zMM7Q6ebdeV+5w88nzuG3qFdAQOMr39LGvrIDtHZoQHR2aEdVVVxOp2YzWbR86kBkbe2RN7aEVlrS+StLZG3dkTW9WvshLGoTpVLLruES/pfclp52+12nnrqqRp/5w5yk35rOjsyd7Dyj5XolYpODI/BjcFj9O7nspYTbrZSXqDQuGtjHrrhoSrHeeO3N9i9ZDeWcguqXkGPjsYdwpHdMllbS/+fvfMOj6Lq/vhnd7akF0JCAoTQAiSU0DuCjSKICqKIovD6KnZ/oIAUkVfAhmBBVKyAUgQFVBBpgtKkE0BqIA1IAiGB1N2ZnZnfHwuLMdSYbNr9PI+Ps9PuOd87ucyePfdcAuuYyMzOhXQrADo6Bpz2jn6mNyFVAgrcL/FkOst+387xpDRMqhkDBiLbRJLjyOHQ4UM0atGIl/u9XDQRi8iNaC3LMuOnj0dLd2bHvPnmm5jN5iueK7g2RR1LKnuww1TaBpQU48ePx2QyuaKz3bt3p3Xr1pw5c4Y5c+aQm5uLh4cHjz76KNWrVwco8jFB2UHXdWw2GxaLRbxUuAGht3sRersPobV7EXq7F6G3+xBalxwH4w+i252/2a7ftd4V7Lie3jMXzATgnnuaE1UjHHQ4k5+JhMTHi1fxzWffAOAdbuCVAffh6+WFpmkcTDjJN8v/4KHeHWlery4A7y35kfid8exttZfmkc1dbRxbc4xcKZdHHuhIhwZRBQ247/Lmbwf3ciIljf5tOyEhsW1vHMGB/oVsjqhZlRcfvovz2Tm89fWPKFlwbMcxFJNCgCOA1C2pxDaNJSZ4gO8uAAAgAElEQVQyxnWNLMt8vOxj+t3Sj9AqoRiNRiRJQlEUft7yM3/F/cWYx8YUOfhwPa3z8vN4dfp41PMardvXYvefJ/n4+4958aEXi9ReZUeMJUWjwmZ2jB8/nmHDhhEeHl5g//vvv0+7du3o0KEDu3fvZvXq1bzyyiv/6tj1EJkd7kOki7oXobd7EXq7D6G1exF6uxeht/sQWpcMqqoy4vURoKmE1fYl7ZCNMWPH4Ofld029Dx4/yJeffEntFr680PueQscvTRmpEuTFuKfvK3T8Sue//OFcFF1j+oTp2Ow2Ppz/IeePnse3lpGJjz5ULP7+k/NZefj6eCAZjWTl5vLq5wvR8yQGDx1M66jWJKUmMe3TaZhzCgcyFKOCWXPur9WqFi889EKhc26ES8/2pEmT2LBzA+t+XAeAHqBhsVhRzigoRoVB/dvTvmEj3v1hKScPZ/Pci89Rr2a9ojtfSRHTWIpGpQp2ZGdn89prrzF16lQkSULXdcaMGcOIESPw9PQs0rGQkJDr2iKCHQKBQCAQCAQCQfHw/rz3SdqTxJCHOxMZWp0x7y0kJCqEsUPHus7ZeHgjn/z8CdOGTCMsOAxN03h5yssosp33/28opivU2SgKR06e4rPZGwBnIMEA+Fc3M/LB+/D19CqWNq5HTl4+479YgJ4tUadJHU78dQLdpNKtYxSSyYCiqfy2Yz/kmqjbsAoPdO7C0q1bSTqQTY8BPejervsNtaMoCpqmYbU6p98kpybz0ZyPUM8665d4VjVgN9vRUpyZ9Y8+3IGYOs4sGLusMPL9bzD6mJj2yjSxgoubqOzBjgo7jQVgzpw5AERERHDvvfeSmZmJn5+fq4iQwWAgMDCQzMxMbDZbkY5dKdihKAoOh8P1OT8/37X/7/83m80oioLBYMBkMiHLMkajEZPJhN1uR5Ik17bJZEKSpALbNpsNs9ns2rZYLBiNxgIpTpfmdum6jizLeHh4oGmaa1tVVRRFcW07HA6sVmuBbYfDgaqqrm1N07BYLDgcDnRdd/lRFnyyWCykpqYSEBCAl5dXhfCpLPeTwWAgOTmZGjVqYDKZKoRPZbmfbDYb6enpVK9eHUVRKoRPZbWfZFkmNTWVmjVruqrxl3efynI/2Ww2srKyqFq1Kg6Ho0L4VJb7yWg0cu7cOfz8/LBarRXCp7LaT5qmkZmZSWBgoMv28u5TcfWTwWBg06ZNtG/fHk9Pzxv2aerHUzmbcJZajX2Jql4di9VKjXo+JB9KJScvhwuZFwgKCmLcd+Nom92WqW9Pxb+OP35WP4wXjDx0fwcMuo7D4cBkMrne2S9tGwDJZMJx0T/JZEK56N+lbUmSMEoSiiwTGRaKxSoh21X8qpkZPqAvvlaLa3qIbLe73pkuvavquo6iKFisVnRNc21rF/vj0raqqpgtlgLb6sW+ubSt6zo+Xp5Mfnwgr723lOT9yXgEw9iHHiDAz9flU7/2HbDLdqwmC5LJxNO9evJq6kJW/LCCiGoR1A+vjyRJnMs8x19Jf9GpSScURXE9e5t2bGLZd8uc32W8ZCRMGPOMyCY7PXo2oXfL1i4/Nu04wtJVO4mpU9flk9VqpX/P1vz0035GjRrF22+/LcaIm/DJaDRy8uRJwsLCMJvNN+zT1VYTqixU2JDaiBEjGD9+PGPGjMHHx4e5c+e6re1Vq1bx0ksvuf4bN24cAMuXLwdg5cqVrFy5EoClS5eybp0z7WvhwoVs3uys2Dxnzhx27doFwGeffcaBAwcA+PDDD4mLiwNg6tSpJCcnAzBp0iTOnDkDOLNasrKysNvtjB8/HrvdTlZWFuPHjwec9UcmTZoEQHJyMlOnTgUgLi6ODz/8EIADBw7w2WefAbBr1y5X4Gjz5s0sXOisCL1u3TqWLl1apnySZZkZM2bw7rvvVhifynI/XdL71KlTFcanstxPCxYsYMaMGciyXGF8Kqv9tH37dpfWFcWnstxPEyZMcI0lFcWnstxPhw8fdv1bWVF8Kqv9tGLFCmbMmMGSJUsqjE/F1U8rVqxg5cqVN+yTLMuMnzGeswlnARjQpjVz5zrra9wZ1QiTZuLL5V8yY8YMpn80nZbZLQEwB13gfMJ5Th0+hSYpdGrUmB07drBh/XoAtmzezJaLtm9Yv54dO3YAzvf52NhYwPkOf+jwYQCWLFnC8ePHXX4nJyfzxssPUqeKjWd73UqQny9ffPEFGRnOpW0//vgTcnNznTU0Pv4EWZbJzc3l448/ASAjI4MvvvgCgLS0NJdPycnJrj44fvw4S5YsAeDQ4cOu7xSxsbGsWrXK2Zf7YqkV4pze0DMygn17dhfwSTJKrF/7m8unX1as4IG2zdAtKtM/mc7vW3/nix++4LU3XuOnb35i+PjhPD/5ebbu3MqUWVNY9t0yVKMDz6pG8h3n0M12AGqZs+neNKaAT1F1gqgXeL6QT7X/llmwc9dOMUbchE+X3rkv9d+N+pSdnU1lpsJOY/k7Fy5cYOLEibz++utumcbyz8yOrKwsQkNDSUpKIigoqNJHJoVPwifhk/BJ+CR8Ej4Jn4RPldunz7/9nMTDieAHr414DV8f3yv6JMsy2/Zs4/sV3yPZJCRdwgC8M2ZggSyIibO/IzdFxx5ox5JpQUfHvyGMu+dBdIOBGQtXMqRPV4IC/Z0ZEVzO5oCiZ3Zc2pYv+ndp22w2YzAa3ZLZYTKbUR0OHKpG2rlsqof437BP8WfO8MlXzsCPiopHuEr7Jg3YeyiJzEQZk+6cCFC/tT/D7uxV0L8i+HT67Dne+2INne/uTK/2vcTfUwn7ZLPZqFGjRqWdxlIhgx12ux1VVfHycs6TW7duHbGxsYwYMYL33nuP9u3bX7HQaFGPXQ9Rs8N9qKpKcnIy4eHhSFLxzMUUXB2ht3sRersPobV7EXq7F6G3+xBaX52x08Yip8goRgXdqjP8qeHUqVGH02dOM/enuaQfTsfh7cCUe3nW/ROPdSWyRnXQQZIKJqgfSTzFZ99sAKB1l5r079gJi7lCz9gvNt5ZuISsvDyG3d2T8OCqrv02u8y4qYsZ0Lct7ZtFFrhGU1XS0tKoVq0axpt8tl949yv8wgKY9OykYrG/MlDUsaSy1+yokNNYsrOzef/995k8eTKTJ0/m2LFjPPbYYwAMGjSITZs2MXHiRFavXs3gwYNd1xX1mKDsoCgK33zzjStSKihZhN7uRejtPoTW7kXo7V6E3u5DaH1lFEUhPy2fsGaevDi0B2gaM9+bycsvv8z0d6aTfjgdAO8qBuq3DSCyUTCvPn8vjcJrIhmNhQIdAA0jari2+7dvLwIdN8Gogf2Y/J9HCgQ6ADysFqaNf7hQoAPA4XCwYsUvBbLZb5RqNX24cPICFfA39xJDjCVFo0JmdpQ1RGaHQCAQCAQCgUDgZMueLSyZt4THh3QhumYtsvPymDh9KZh0OnWti37exJZdx5g2/uGbum+H95+lZo1AFg+YXEKWC4qDjfv/YtmPe3nqhaeoX6t+aZtToRGZHQJBBUJVVY4cOYKqqqVtSqVA6O1ehN7uQ2jtXoTe7kXo7T6E1ldmw84N2C02omqEA+Dr5cW08Q8z7ZVH6NehI/17tb3pQAfAsB49eDa6N5rQu8TRVJXEhIQiad2uUUM0g8qv238tActKlszMzKtmpMiyzLHEY8xYMIOXX36ZNz57g6ycrGJpV4wlRUPkdwkqFA6Hg59++okXXnhBzI11A0Jv9yL0dh9Ca/ci9HYvQm/3IbQujK7rnElIo0p1KwaDoVjv/XC9nixcuBA1stVN15EQ3ByqqvLHH38wcODAm9baYjYhBWrEHY0rIeuKH0VRmP7ldM7GOVcDcngrmHKdywtrfirGrMsaOIwKJsxkHM1gwv9eRfPW8Q3xZfLTk4v8zF9rLLm0vKzRKPIY/omYxuIGxDQWgUAgEAgEV0PXdXJzc/Hx8SltUwSCEic+OZ6ZH8yk593R3BnTorTNEZQSX65aw/6dKbw95W0sFgsADs2BZJCKPQj2b9B1nR0HdzBv4TykfAkjRqpFeZCdl09eotNOcw0HFrMJ+1noeUcTukQ3wSRJnM/KZc5vv5F0wJnd0aZHGx6888Fit2/kyJHUrl2b5557rtDxyj6NRWR2CCoUqqpy4MABmjRpIn5BcQNCb/ci9HYfQmv3Utn1nj5zOikJKTzzwjPUrVW3xNur7Hq7E6F1YVb+uRKHwUG36KbFfm9NVTl+/Dj16tUTmR0lzL/VuntMCw7uOMO63evo1b4XAPdOuJe6tery4ZMfFre5N0VKSgrxKfH8secP0g85i+UafFSeeOw2osNr3fB9Avy8efHeu8nraWPS3O/ZsmYLnWI6UTOk5nWvvRTEaNS6EU2jmxLkHURcXByegZ4cOnGIuJ1xyCYZSZWQkEhISCiquxUakesiqFA4HA7++OOPIlWGFtw8Qm/3IvR2H0Jr91KZ9dY0jZSEFAC++fkbt7T5d71VVRVzwEuQyvxsX41jh49hrqphLoHVUlRVZffu3eKZdgP/Vuua1YJQTQ7Wfb+Ok6knGT9jPI1sjbActfDV8q+K2dobZ9dfu5g2bRpL5i/hdNwp1/73Xhh6U4GOv+Pl4cG4R+4HSWfq51NdNT90XScjMwNZlgtdM2fZHAAO7zzM4rmL+fSTT1m7Zi0/L/qZQ7sOkWXMonptH2LaVieqZQgPDX2oSLZVdMQ0FjcgprEIBAKBQCC4Ept2bWLZgmWYgzVs6TqvjnuVKgFVSrRNRVHYsHsDv2//HVuiDYB33323RNsUCACyc7KZOHEizTtX59Fut5W2OYJS5qXJ81zbskkmtLY3ugIZiTLN72zO4B6DXccVRUGSJA6dOMTXn34N/Ltxy+FwIEkFp8xs27+NhXMXgkHnof4daNsgslin1Gz+6xBLlu4GwG62Y1bMGC/mHgwfPZwawc7lk7fs3cIP3/5AQB2Jkf37k5GTzd6j8WReyKN7+2YEB/j/wy4jekDMFdsU01jKCfHx8dSpU6e0zRCUcRwOB7t27aJVq1aYTOXm8S63CL3di9DbfQit3Utl1vvHVT9i987jf4MfYdz73/HFsi8YNWTUVc8/kXqCuqE3N9UlJSWFkydPEn8+nu2bt0OOc7/sYcOCBwBxyXHUDy97S0Dqus6SJUu48847y+WLemV+tq/E7B9mY8BA75ZtSuT+qsPBocOHiWrUCEnoXaIUh9a+Ph5k59gIDLPy8iMP4GE1o+k6b837gdg1scSuiaVGyxpkpmeSl5RX6PpX3h3N/w0ZTmjVUMA5XkybNo3GjRsTUT+C+OR4Ms5lUD28Ojn5OSQmJGIz2EhJPo10wWlzcNNg7ul6D+nn0/lh3g/gr/DmE4/gabUWXZyr0KlxFCt/20f+BQc+fmaiGlRHztU4uD+Vt6a9xZChQ6geWJ3vFnyHMdDBuIFDkSSJGh5BhAb4c+jwYYJ8fcpUTZOyTrkZBb744gumTJlS2mYIyjiqqhIbG0vz5s3FS4UbEHq7F6G3+xBau5ei6n3u3DmCgoIK7T946CCbtm7iyf88WZxmFjuHTxxGz9DpdnsjfLw8CY305vTB0+Tm5eLt5V3g3Ni4WOYvmo+aodK+R3vuv/P+K95T0zRiD8Wy4NsF6N46sipjzja7jqt+NkxGDxpFGHlswGAwGhkxfQ5fLv6SN0e8WaL+FoV5P85j79a9bN26FT1Qo2pgMH1u60PTRsVf76EkEGOJE1VV+XzR5yTuTwQgyM+3RNrRNI1jR4/SsEEDRMWOkqU4tH5i4K1M/2IlLz3cFw+rc5wyGgyMHtSPUW8sACBhXzy6RcOCB37hEt3bxVC3anVW7dzNjr3xvPuWM7vDq7YXWaezMMkmUlNTYd3ldmK3xRZo1xKiQ6CGXVZIOXiaL/Z/4Ww7UOXNJwZjvVgwtSToc0cLFv+wg+4dm3FbjDMb41zXbKZ8/QMLPnf6rJlV/vfYwAJ1fsSzXTTKzTSWsWPH8sYbb5S2GUVCTGMRCAQCgeDG2LZrG98v/J6pU6de87zdu3czf/58Ot3Wia7tuxIQEEBcchzf/vit6xfAiFYRPPvgs2ViOb6TaSd5f/b7DH90ODXCnKnKr374KudTM/ng5aFIRiPpWVlMnrGMeq3r8dyDzyHLMvNWzuOvjX8B4DDLmBQLKir12tTj6fufxmAwcDLlJBv3bmTPpj04cGBSLn+p9qipYTvp9H/Us72pFhhQyLZ5v69n98bT/Ofp/xBdL/qK9tvtdkwm01WLbKqqyp8H/mTd7+vISsqiekx1aoXWol61ejSJaoLZbL7idddixe8rWP/zeozVZOrUDOF4YhqkO39tfeSJR2jesPlN31PgfhJOJvDR7I/QzmuERFt5uW8/zJU48CO4Meyyg6zsPIKDrv7dKScvn9emLwFAM2j41zFSPbAqkTXCqF4liLCAQOyyAw+rBU+rmX2Hk2jcIByr5fLz51BV1u6MZX9cEs/ffxce1pILdADkOWzU+eI+4v7zA74WL9d+u6ww9p1FAAwa2I5W9W8m005MY7ka5SbYMW7cuHKb2SGCHe7D4XCwefNmOnXqVKl/QXEXQm/3IvR2H0Jr93JJ7zZt2jBhwgQAWndvzcDuA694/unU00x/d/oVj8lmO42ahJCdKXMmIQ9rmIXGTZrQu0NvAvwKf9EvbjZs2EBUVBTVqlVz7dt/fD9fzvoSk2Yi35zHU/99mhD/EN5+620atA7i6Z53uc597dv55CToeNX24kLyBcyqM0hw+52N6NG6Baqq8sHPP3H6UJ5rrvff8ayp06tDCzo2iHalOl+aFz9t/MOAM/08NjaWmJgYJJMJVdN4cdpXePn78Paot1FVlWW/LWPrqq3O840qkuYMctRoWYPBvQYT6BfI/mP7+X3P7yTvSi5kh2yyY3FcTgOX/WQ8fTyJaRHDoFsHXVfHLXu38P2332Ou7uCtIY+5AlZ5NjtjZn2Lnifx9FNP06hOo+veqzS51lhy6NghrFarW1bgKSk0TSP1TCqbYzcTFhJG66jWeHh4oGkam/duZvna5ahnnAUs+93fnE6NGpeoPf98tgUlR1nRWtU04hLSaFg3rNRsKC5kxUFC8lkaXMGXa+stgh1Xo0yNAvv27bvqMUVR3GiJoLyiaRqJiYl06NChtE2pFAi93YvQ230Ird3LJb1/O/Cba9+21duoX6s+rRu1LnDu3iN7+frrr3FYFJ59qDu+Fm8Onk5kzYrDALz/8hAkyfnFeMWu7az59TB7UvawZ80epk6dWqJznTfv2szy5ctZvnw5VaOrMqjnIE6knmDZwqXgqTLgrvYsWLGJrz79CgUFg8HAY91uL3CPx3vcyQezVpOXkEdYA2/uadMBq9FMvQhn8ESSjIzs35/Y+HjmztuCYlAYcG9rWtWNJCsnn9Dg6wd0dF0nJSWFZs2aOe9pNNKtUyO2/pbI+7PfJyEuHpPNGWQJiDBRJcgfJVsn+VgWSXuSeGv3W657OYwOTJioEuZJ/zva0qBWDYwXNVYUByu37iU5M41TZzOxn1bZfXo329Zuw8vXCyVdIbhRMFm5WdiT7WCGW3rfQp3QOiyctxBToMYbjw4pkJnj5WFl0n8fYtys+Xwy62OGvzCC2tVr/6t+K0ne+ekdjmw/QnDNYFfWTMb5DGbM+5DseGfxlOBmwTw34Dm8Pb2vdasyQ25eLrMWzOL0odOFji1jGbJVxgCY7RZkr3yqRwTy9D09CPDzKXHb/vlsC0qOsqK1ZDRWiEAHgMVsumKgA8qO3uWNMpXZ8d57713z+PDhw91kSfEiMjsEAoFAILg2i9YuYvuv22l9S036d+jE6I+/QbZpvDryVapVcX7R/3Xnr6xcuBJ8Hbz2nwcI8r38b+of2w4THORLVP0aBe57IDGRr7/ZBECbnm148I4HS8T+/Uf389XnXyHpEh5VjGSft2PWLs5BD1J4/T8P4Wm1IisOpi5eSsYJmRRzCvNHv1xYi42b6BQVRY2qheuRFIW1mw6wc98JXnmm71XP0XSdkVPmOz8E2RncqxvNaxfOOMjNszPvj/Uc2XmOPr2bcUtMY6QbnCZ0PjeHFXu2czw5jYxkO5Ls1Efxy8ec5Vno/Ndf6o+3p8cV73UuO5spH/yIAQPGYCNhtcJIOpVEWI0whvUehr+f/3Xt0XWdlNQUdu3fxe2db8fL63JKuaqq/Bn7J2fPn6Vvt75FmgqVkJLAR9M+cn2Wve1Ycq2oBhXVqFK3flXy82XOJOeiGTRqN6pNREQEh48fJuNoBkY/I/Wi6hHiHYK31Zs8Yx7xJ+M5FXuKJl2acEvzW4ioGeGaWqRpGna7HU/PwloWB3EJcXzz4zdcOHUBk+b8vbRuiwCaRdQmukYtzuVkszshjuOnU7DZFPq0b0PbBg1EMUWBoMQRmR1Xo0wFOyoqItjhPhwOB+vWreP222+vcKnnO3bsoEmTJhgMBr5e9TU6Ok/3fbrAS0RmZiaffPUJt3a7lQ6tSv4X6Yqsd1lE6O0+hNbuZcbHM0g8kYh3TfjfY4MwGAykn89i0qdLMDnM1G1bl5OnTyKflAF4c9QDWCw3VwPi1a/mcz7VzrjR46gW5Aye6LrO8ePHqX9Tc6MLk3A6gfc+eA/NU2bq04/iafVAlhXGXJx//c6Yh1zZJpeYu3UNVf38uKtxu3/VdlFQHQ527NhBmzZtCqRDvzL3a5QkC1PHDXJlZ5Qkuq4X+iKcdu48K/bsQNZknure+5rX7zx+lAULdqD5yOh5RqSLX8B1dHQvnapVqvLs48/i73s58JGbm8vc7+dyPO44DtWBSb7sv91ix+FwYPWyQj6YVOcxu5edrrd1pX/X/oXs1XWdzMxMUtNTaVC3gWu8WLF5Bat/XI1B0mnTMITgmqGs334QJcOIZ5CRUY/eg5+3M7hyJvMCU+f8hJbjfEbsHvlY7FYcVgVdNmDRLtcQ0AwaRv3ys6SjY8CAw6xgUpx/Ex7hHrSOac0dre/Ax+ffZ1PIssyniz8laU8SAEENLDzRvTvBAdcPKLmbqz3bguJHaO1erq23CHZcjTIb7NA0jaysLDRNc+2rUqVk150vKUSww30oisLSpUu57777ilQMrTTJy8vjf5P/R9NWTXmk/yMFjh04fIDZX8wudI1slYmIiqBZnWZs3LWRnKQc1xzuCRMn4Odz48+bLMtYbrL6dHnWuzwi9HYfQmv3kXQqiQ/f+xCA10f0w9vr8q/Su47GMX/RNgAcvjbsDoXqjbyZ0HvwTbdzITuXCR8tRgo2Me3laeTk5DDr61mkJKYQ2TySAX0GUCXg5t8zdh/YzfzZ85EtNv739ANU8b28yoSqOqv9e3kW/xKG/waHw8GG9evpduutBYJ5uq6TpeTibyn56QbFjabr3Pr+CBqH16JjcAx7NqY496NhDjNTO6g26bZ0Mo5nIOnOTAifOtC6UX1q+oQQfy6F46dTST2cD0BApETf9m1RZI3v1mxGy3DqpPqoBFcP5typcyiyglE3YHI4xwgdHcVLwZLn/LfUXE1lzMB+7Phzq0vrvHz7FZ8HVdPYEXeUpjXr4O1V8Hja+fMkpZ0lOryW65jNrrAnIY61u2OxywrePlY8LVZSE7PJle1Y8pwZMQ4vBw2bNyQyPJLWDVujqzpnz59lw44NBFYJxKE6UFSF89nnOZd5jpzUHAw5Bqo0rELtsNrk2nPZv3s/kl0itIEXT3TvQVBAyaykUhxc7dkWFD9Ca/dybb1FsONqlMlgx9atW1m8eDGSJBWIoL/zzjulaFXREcEOwbXQNI0lvy1h49qNmC++MEV3iWZo36EYDAYuZF1g4lsTkWSJ2jH+1AoOJjwwmPO5uazfv4+8i3XhNKNK3RYBdGsUwxcLNiBVMTJ11Ls3lHr7/qfvczLuJE888wQN6zYsSXcFAoHARV5eHuPeHIcmq4x+si81/rGMrKppjHpjAff2aE2XNv9+bFq1czerfz0EFP6F/BKyRebDNz68ofvtO7qPuZ/NBeDpobdRv0bFmDdeXslV8rFKZkxGE6qqcSotg1/37+SvfSmY7M4AREhjK4O73kq1gMArTr/JlvPwNFkxGQuuOrP3WDzffLcFxdOGioZHvjMro1q0B9ER4fiZvYg7k0JSajpZKTKRUcE81btXqU3hOHv+Am98uRRV1ZEUCaN+7cUqHUYF3aohGY0YbBJ2sw2T3YJJN3HeI4NXBvUnsnp1N1kvEAhuDhHsuBplMtgxYcIEnn322QKVzMszItjhPhRFYeXKlfTq1atM/RqbmJhIzZo10XWd/Px8rFYrB+MPsmL1CjITMwEwhTp44b4+fLNuPWeP2vGr58crj7/Cq9NeJe98LiMev4u6IYVfpHNybXz3y1aG9OvqSpP+ff8BfvxxL1WbVmXMY2OuaVt8cjwzP5gJgOan8e74GwuQQEG9j6YdZeyXY5k9fDaBfoE3I4/gBimrz3dFRGhd8pw9e5b3Zr9Hztkchj3SjYz4JDpeYcUKTdMwGAzF8qVR13VevliXolZzHx7q0pUQ/wAys3LYdOggG9YcA+DeR+6lc/POrutUVeWVz17h5UEvU83f+W6y8+BOvp39LZqXzJQnBuHnXT6KS4LzF8ItmzdfUe+KiK7rLF29k+6dm+LjfeUaICVFWdBa0zQOxp9k+7Gj1AqtSlhAFUIDAvHz9sQoGTFe5e9L0zSOZ5yiXpUaZWL55huhLOhdWRBau5dr6y2CHVejTD6ZPj4+FSbQIajcnDl7hi+XfMm5Y+eueV6fu5tya4yzuvIrD9zPgj82sPOPU4wdOxYNjX73tbpioAPAx9uDxwfcWmBf16ZNiDuZwsFdZ5gwZQK39bgNySgxa/EsRj0zimbhzrYURWHGFzOwe+Rzf/f2rPhpP18t/4r/9v3vTfmZl5/Hlx98SaQeycQ3JzJy+EhqhtS8qXsIBILKw4EDB5g9ezYAd/VpSqPq4WyJT7riucX5JcXOYoEAACAASURBVMtgMBDQTSY1J4MXe77k2h/o58Pd7doSEx3OtK9XsGjRIrI9spn802S6WrsiJztrhcyaPYvHH3icNfvWsHfNXvBz8NYTj+BdQgUhBcWDwWCgX482pW1GqWE0GmlSrxZN6tW66esiq4aXkFUCgUBQ8pTJzI5ff/0Vi8VCmzZtCkSuSqq6dEkjMjsqH3EJcXw681M0XUORFKxGM3WbVsHL00z8mVRsmsxDbW+jWZ3aV61iP+LjrzBkWIlqV5X/3tnjpm34+y+Y/6TL3V3oe0tfps2exqm/TvHowx1oWSeSKfMXcyY+jxdHvEidsDo31M6OgzuYO28uVrsVxcMGmhFV13jmqWeIjoi+absFAkHZJjs3m48WfsSIR0ZgtV67FsXp06cJCgrCarVyLv8cx04eY9PGTaQeTAUgKNyDsY/1d4fZN0zy2bO8+9kvmHTn+4fiZaNmeABpR2yFzp0ycgAe1purdSQQCAQCQfEiMjuuRpkMdjz77LNX3D9z5kw3W1I8iGCH+7hWUUFFUVAUpcDScuBM03zuf89h9bXy1vNvXffl/XpkZWfx+v9eB6BOSz8ev607nh43f8+DqQn8sHMT43s/XOT07Zw8G8cT0oioGYymaWz4az9b9x9BS7+sTb1WATzTy1nxPt8mM/rDeZhlC2FtwqgZXJNA30Cia0WTk5vD2u1rSdiZgIYGPqDKKpIsofsqDH+oDxEhIZw9n8WUL39AyrfQ7vZ2DOg1oEi2Cwojima6D6H11Zk4dSI5aTnoJp0h/x0CPhDgGUC4/+VfgHVdZ87PczjwxwEAXhr9En0+6UO3rG4okkyVuhZ6t2hLm8hIDAZDmSt09+2G9ezZdBqAaeMfBiDjQjZnzl8gV7ZzPieHxrXDCQ0sn4XTy5reFRmhtXsRersPobV7EQVKi0aZfDLLa1BDUPoYDAb8/f0LBQccDgdjxjhrV5hqSkRHN+bTLZ8y+7+z+XT2p3jlekEujJowkjoxdenboS9hoWHY7DaycrMIDwu/Yiq1qqlIfytipqoqk2ZMAkCSjDx3191F9iU6tDbRfWoX+XoAHy8PYqIjXJ/7depAv04d2HL4ID98vweAp3re5Tru6WFhYJ8O/LBkFyk7UkjBWc1+NasL3Ne7pgGHwcHpzHN4+Rv5cOizrpVcggP8mDRsIBPfX8K2ddtIy0rjmQHPlJv5vmWZqz3fguJHaH1lsnOyyUnLAcDgMDDn0zmuYwOGDqBd43Zk52Tzxqw3UFIUwFkI9O2pb9NF68IF6QKfvPRkoWVjDTinsJYVtR/u2o09mwpmxlXx96WKf9ldheJmKGt6V2SE1u5F6O0+hNbuRehdNMpUZkdmZibff/89aWlpRERE0K9fP7zLUcGvqyEyO0qfGd/MIDE2EQDVy46UdznTQjbbeahvB2oFB/Plr2vIStAKXe8wO2jUrhF7EvZwJv0MvVv3JjQglFUrVhFUN4hnH3yW4CrBvPH5G6QfSee+fs25Jbqp2/wrCgknz+JwaNSvXbg+zqcbltOrSRuCvQPYcewYmw4e5I4WMbSqVx+T6doV3S+haRozfl5O0v5sck25JPkmsWLciuJ2QyAQuJHp304nKTaJeUHz2PHQ1+w5mMjmdfGAc4lPY4SR7ORsPPDgzu7R3N26LTl5+Uz9fik5Sc7XjUuZEmWdaX8sZMnuzWz+vxmlbYpAIBAIBNdAZHZcjTIV7Jg5cyYhISFER0eze/duDAYDjzzySGmb9a+pjMGOoe8OxZxrpopfFbLTsvF0ePLosEdpFtmsRNuVZZmFCxcycOBAV6bBum3rWLl4JeHNvfm/PvcCcCA+ia/nbcRQVeb1IYPw+ts0k5T0TJZv2Un10AB8PbxYsWEPNlnBaDNhxJmdoBpUpL8t46aiIuH83KxjKI/ddnuJ+llWcCgKq1atokePHpiukuq/ctdO1q48AsCd999Jj/Y3X39E4ORKz7egZBBaFyY7J5tXXx9PtXre3HJLJJ3DmgPQ8athtAprSAO5AQn7LwDw/H/voHZowUDq+J++5nR2Ol89PLLQvW9kLBEUH0Jv9yG0di9Cb/chtHYv19ZbBDuuRpmaxpKRkeGq1xEVFcXbb79dyhYJ/sm6+HV0i+hWYOrG38nNy+XzpZ8TlBoEgF3KwdPhLCw7Z9YcqjSowv898n94e5VMxo7RaCQiIsI1ZSI+OZ6ff/gZU7DKi70Huc5rUqcWE16+F3+PwnaEVQ3kib53uj7f0qwxAKqqsXrfLnK0PPq36MLpc5mczb5Ag7DqfL7mV5L35ZJvyas0gQ5wpvqHhYVdM9W/V6vWHDmXRPL2fFb8uIIuMV3w8vS66vklhSzL7PprFx5WD1pEt3B7+8XBP59vQckhtC7M5z9+jkE3MKxnD4L8L78wbfnPLNf2S/vnARBWJajQ9ZP7Dr3qvW9kLBEUH0Jv9yG0di9Cb/chtHYvQu+iUaYyO958801XXYUrfS6vVJTMDkVR+L+x/weA5CthybJgrWololEEAX4BJB1PIjkuGaNmhBAbrw4aSJC3H5quYXPIfLNuPUd3ZgDgGepJ69ataRfVjsS0RDbt28SJv07gqXjiVdeLyHqRdGjcgXo16hXpj1rTNFb+vpL1K9YDMGH4vfhXgClR5ZlT6ed4Z9ZygiKDePXJV93SZly8c1UcuJhifzEzp2armjw34DlRUEsguEFycnMY979xVKvrxbiBD1z1vE++WUtcYhpvjHoQq0X8fQkEAoFAUPKIzI6rUabeRNLT05k1a9ZVPw8bNqw0zBJcxGaz4al7YgnVkIxG8rPAnm7n8KbDri+RvtXMvHB/b6oFBriuMxqMeJk9GNazF6fbZDDtk5VkZZxn4/KNbFy+EQCHpODpK6FeUMk6nUnsiVhi18QC8NSLT1E/vP4N2XjhwgU++OgDcshGy3TG8QYP6igCHSWEIsssX76cPn36YL5Oqn+NqkE0axPGX9vPsP2v7bRt3LbI7WqaxrK1y+gQ04HQkNACATFN08jMzGTBqgWc2H0CI0asYToNaofROKw2a3bEkrwrieFHh/PMkGeIiogqsh3uxm63M2fOHB577LF/vWqQ4NoIrQvy4tcv4qv58lSvntc+sYg/ON3MWCL49wi93YfQ2r0Ivd2H0Nq9CL2LRpkKdgwYUHCJypiYK0eoBKXDyZSTALRoUJcHbunEvsNJqKpG8+gIMi/ksm1PHD26xWC8RiZG9aAqruJ053NyeWfFYsICAnm2e98C1+Xk2Vi0agv7D55i5ocziWofxX/v++81U8pVVWXSJOdKKIpJpmefJtzVvE1xuC64CkajkcgGDW441X/oHXcw4uBsFn29iFZvt0KSJBITE5EkiZo1a97QPTRNY+zksTiyHGxZvQXZJGNxWDB4GjB6G1HTVed5aFSLtvJS336Y/5bB0Sa6PnuPn2DO9xv5csaXTHhtAn6+5SPSLUkSMTExSNKNFYkVFB2htRNVVdkau5XAhECAAtNXrsXNJuTd7Fgi+HcIvd2H0Nq9CL3dh9DavQi9i0aZmsZSUako01hGTRmFlqnRsWct+rfu4pY2ZVnh3aVLOXfMuYSh3WLHoBpw6A6GDB5CmyZtMBgM5OTmMHHGREh3Xvfm6AexmMtULE9wkU37D7H0x91ExESQlpmGLckGQI8BPejSogvz/5zP0C5Drzh9SVVVJsx4FftJGYBut0YSd+Y0J//KdR4PzgfFgHTeg6GPdqZJrYhC97jEzqNxLFi0jWdfeJY6teqUgKcCQdlB13UOnzjMdz9+R87pHAxWAy26tKBZrWbUqlkLPz8/VE1lX9o+moc2Z8v+LSydu7TQfa63ksqn367jWEKqmMYiEAgEAoHbENNYroZ4ExHcEL9s/gUtU8Orucx9rTq5rV2LxczYBx9g9o8b2L//FLXrBSBpJk4eyWbRnEXMM81DUiWMuhHFqNDv7hakHTqEQS+8fKyg+FFkmSVLltCvX78bTqlrGhHBUnaTGJuI7JWPBWcB21WLV7Fq8SoARvw2nIcGDOLY2WOEeIdwW8vbcKgOXv3wVRypDm7v0ZA+bS5Pg9Hv1dHhmllF/8TPx9nuruO7yk2ww26389lnn/Hkk0+KqRUlTHnTOvVsKh988gHde3WnXeN2GAwGLBYLWTlZfLnsS1L3pwKgSDJmLDhUhZ1rd7Kb3c79JgWzw1ndfbZ5NmbFuW00wWMPdebx3yaTm2NnGtdZNvZfTGO52bFEUHSE3u5DaO1ehN7uQ2jtXoTeRUMEOyoYa4+uJbJqJBFVrv6L9s2Sl5/Hr8t/xaOqgdd7DymVKsBD7ukG9/zdJjtbjhxix7FjpB9x/sr//JA7qVctlOOenpU+9dxdSJJEy5Ytb0pvfz8vLG1yeKHtAMICq7j274uP56u1a1HPmDDqGou+XuQ6tnzxcsya88tXr96N6d6iZYF7GgyGm/6OFRlWnQzPdDau30i/rv3KRVqgyWTilltuEYVV3UB50/rdt98FYMV3K1jOcq70F3H7XQ3o2byV61m32WV+2b6L0xkZOAwKKaeycJyT8A8x07djW6JqhuPpYUGSjHzS5yVOZJ26CYtuLmm0KGOJoOgIvd2H0Nq9CL3dh9DavQi9i4aYxuIG3DmNZdCYQXhpXox+bjSR4ZHFcs8pn0/h7NGzjBrWhxpVCy8nWNrk2W0YjQY8zGX/11fBjaHpOj9s2cyf6xOpWt+Kt4eVxANZOHDwwfjHiq2dHXHHWLhwOx3v6ki/2/oV230FAnficDgYOfZlLGYzzz92J2v/2suBzWfQfGU6tWmAV6DEX+ePM7LDdbIyioFZ89ZxND6VKSMfwMNqLvH2BAKBQCAQiGksV6NM/pS5YMGCG9onKEygMZAANYCZMz5i7Y61Vz1v2/5tbNyzsdB+TdP4cd2P/Pz7zyQkJvDr1l/JPJJJ41bVymSgA8DL6uEKdCiyzDdz56LIcilbVTkoKb2NBgMDOnXmjTEDGDPwfl64926mjX+4WAMdAG3qR2IMVvh93e84HI5ivXdJYLfbmTp1Kna7vbRNqfCUJ61/2fILkmbiyUG3Eh4SzNBb72Ta+Id578Wh3N+xE3dFtXdLoOPfIMZu9yL0dh9Ca/ci9HYfQmv3IvQuGmUyPzchIaHQvvj4ePcbUg7xtHsSWNtMVq7Cr9/9SuzBWF567KUC56zftZ4VC1YAEH86ngfveJCc/By+/fVb4vbFYZWdgYPf+R0Au8HO4927u9eRIiJJErfccotI8XITJa23VSr5OYn/6X07n8/+nQWrFjC49+ASb+/fYDKZ6Nu3b7mZWlGeKU9a/755A5qvQv0aYaVtSpERY7d7EXq7D6G1exF6uw+htXsReheNMvUWt3PnTnbt2kV6ejqzZs1y7bfZbOWiQFxpoCgKW7ZsoVOnTnz+/eeAsybC2EH3M2LaXFL2p2C32136LVqziD9X/YldspFvyWPv+r3sW78PANWgElTTg9YRDWlcryaZygWOnjmFn48nUjmoZwBglCQiatcubTMqDRVB76ia4VhraMSuj8WQaeCRRx4pbZOuiiRJNGzYsLTNqBSUF61PpZ3CcM5I+671S9sUJxdrOt3sDNmKMJaUJ4Te7kNo7V6E3u5DaO1ehN5Fo0wFO0JDQ2nWrBnJycnExFyed+Th4VEuXjpLg/Xr17N69Wp+WvETuqYTEmXlmd69MBqNqA3PY9gXxKgJowhvHE7K+RQMSQYCapsZP+hhJKOR5LPpTP9sJUbdyKj/9qF6tSp/u3s1WtVtUGq+FQXZbmfu3G949NHBWESArMSpKHo/3acnH8xaw969e7H72+nXtR+BfoGu4y+9PwJ7th2jUcKSacEz0JNJ4ya53U6bzcbUqVMZOXIkHh4ebm+/MlFetJ6/aj4Oo8K9bTuUtilAkRdjqTBjSXlB6O0+hNbuRejtPoTW7kXoXTTKZIHS7OxsfH19S9uMYqOkCpSqqsro0aMBcEgOHuzflo4NolzHk7JTmb9/DY4kC+fiZIwYkavm8OGwJwusqHI6J50nV7zF8gffLTbbSgtNVUlLS6NatWoYRZpXiVOR9H7h01mY033Q0S+vZBEMHl4e2BJtyFYbfoFWbKnOY+++6/6/F1VVSU5OJjw8XKQxljDlQWtVVRkxbjhB4R689vBDpW0OAKlnz7Pq930M7t/lppaCrkhjSXlA6O0+hNbuRejtPoTW7uXaeosCpVejTAY7AHbt2sXJkydRFMW17/777y9Fi+DMmTPMmTOH3NxcPDw8ePTRR6levfp1ryuJYIeu67z52ZukH0tHqZrL248Nwdvz6r8+5uTlM3P5Lwy/ty8Wi6iQLxBcjQvZeXzwzUouZNigqowjX8OU60HzbtUY3PkOPlv1K4d2nuXdt98tF8vVljdycnLYu3cvnTt3Lm1TSp2snCwmTZmErugQAhjAZDYx/OHhWEwWflr/E/u37OfxR28hulZ4aZsrEAgEAoGgVBDBjqtRJt/UFy1axLZt2/jzzz8xGAzs2bOH/Pz80jaL+fPn07lzZyZOnEj37t2ZO3duiban6zrZ2dmcOHGCX379hfT0dNc86BnzZ5BxLIOWXWrw0VNPXTPQAeDj5cnoB/pX+ECHbLfz8cyZyOVgBYWKQEXU29/XiwnP9Gfa+IeZ9tRQPhj+OFPHPcTgzncAUCMoCKNuJDc31+222Ww2xo0bh81mc3vb7uKrr75i2bJlpb4KSmlqrSgKny75lPGTxjsDHYBBk8Gm4DjpYOrbU5kyZQr7t+wHqBCBjoo4lpRlhN7uQ2jtXoTe7kNo7V6E3kWjTNXsuMTRo0cZO3Ysb775Jv379+eOO+4o8cDC9cjOziYpKYnnn38egBYtWrBo0SLOnDlDSEhIsbf32tuvkXu24Jep39b+hmbQMOrOGFWdFv4M7npbsbddnjGbzTz44IOYzRU7qFNWqCx6Gw2X48J1g0P5jaOcPHOSKN+oa1xV/FgsFp5//nkslpJfpaY0cDgcJCUlAbB43WIG9RxUIHvm5LmT7Duxj9ua3lbidTRKWuvVf6zmWPwxGjVthKIqHDh6AAMG0s+loyQ5MxqDa3vx7N29CPK//EvMzzu3YZcdVPHxwdNspVHNmiVin7upLGNJWUHo7T6E1u5F6O0+hNbuRehdNMpksMNkMmE0GjEYDKiqir+/PxcuXChVmzIzM/Hz83PN3TYYDAQGBpKZmVko2KEoCg6Hw/X5UlaKoiiMfX0s9qyCETmL2YzFbEHXdQwGAzbZhurQADCZja6XfU3T0DQDmqZjMEDaYRsTjvwA4Lr2326j665q+je0jY6zJN2l2VCGv+27+fsVlx/CJ+FTSfl0iblfzMFithSvBtfhRu+nOBxIRufYoWrOsUS6OKb+/fxL/hR3XxbVpzybc6zU0dn72172/LYHi8WMyWjC7pDRLo6L675b57TbaMDL6unypST9KM5+kh0KiuwMaMTvL7isum7QwQBWixklTeKDL9dc856/6AfK9d9TRRwjhE/CJ+GT8En4JHxyt0/NWxynX79+2Gw2LBYLRqMRm82GdvE9sLIiTZw4cWJpG/FPdu7cScuWLUlLS2Pbtm3ExcWRnZ1Np06dSs2mCxcusGfPHrp16+bat3nzZho1akTVqlULnLty5UpmzpzJ6tWrWb16NWvWrGHPnj1ERkYSXCOYsxfOYrAa8A3yJTcvF1QDulkhRDVgM8rY7ToOq4OY6BhatmhFamoaDRo0pHlMC06fTiEmJoaYmOYcPxFP8xYtaRQVzZGjx2jbrj0NGjbi4KHDdO7chYjadThy5Cjdut1KWPUaHDsWx513dicoqCoJCYncfsed+PsHcPp0Crfedjve3j6kn8uga9duWK0eZGXn0KXLLUiSCZtdplOnzmias3xjhw4dkWUFs9lKu3btycuz4e3tS5s2bcnKyiEwsAqtWrUmI/M81aqF0qJFS86cTadWrQiaxTTndEoq9etH0qRpM5KSTxId3Zjoxk3+tU+3dO3G4SNHMUqmCuNTWe6nevUjOXLkKK1bt6FRVHSF8Om6/XTrbcSdiEORz9OjaStCPDw4l5BA5+bNCTKbyTp9mo7NmhFgNJJ/5gztmzTBV9dxZGbSrnFjPBUFQ04ObaKisNhsmG02WjdqhJSbi5eq0rJBA/QLF/A3Gmlevz6OjAyCPTxoVrcu+WfOYE9Pp2PTpuSfOUOtwEAaR0SQdeoU9atVIyo8nMykJC7oOg5VJc+Rh66CjB1jbhYmbxO5uTI2exZhVf05dz4HRZG5rVEjavj5kXrsGF1atHCrT7azZwnx8cLgaSAr24ZnsCfdu3Un8XQisi6D4sz4UDUVzaKhGBRCqoegoKDbdAweDm6LbMypw4dpFx1N7aAgUo4epWPTpv/KJ6vdTtapU3Rs2hSrLN+0T9V9fWlapw65qakF+sngZSArz45dstO8cXOioqJISUmhVq1adOjQgfgT8XTu1JmoRpXk7+lvPimKg9at25CUlFxhfCqr/dSwYSOSkpIxSqYK41NZ7SdPT2/S088REhJKYJWgCuFTWe6njh07cfjIUby8fehyS9cK4VNZ7acOHToSF3ccxaFWGJ/Kcj9FRTfm8JGjREVF06xZTCGfmjRpQu3atXnttddo0qQJPj4+vPLKK7Ro0YKPPvqIMWPGYK2Eq7iUyQKlWVlZeHp6ous669atIy8vj1tvvZUqVapc/+ISIjs7m9dee42pU6ciSRK6rjNmzBhGjBhx3cyOrKwsQkNDSUpKIigoyFV01Ww2I8sySzYsYcu6LVhU56/EQVFBDH94OCaTCZPJhN1ux2QyIUlSgW2bzYbZbHZt/z2KZ7FYMBgM2O12rFYruq4jyzIeHh5omubaVlUVRVFc2w6HA6vVWmDb4XCgqqprW9M0LBYLDocDXdcxm80FfFIUBYPBgMlkQpZljEajyw9JkkrUJ4vFwvnz5/Hw8MDLy6tC+FSW+8lgMHD27FmCgoIwmUwVwqcb6acXx79ItSBPxt86EF3TUBQFi9WKdtH2S9uqqmK2WApsqxf9uLSt6zoms9m5jTOz7dL4cWnbAEgmE7LdTl5+Pv5+fjgcDoxGI5LJhCLLSJKEUZL4/cQ+ftrqrOXgVceLlKwU/M/5X3N8mzZoELquu/xwp08ORaHu2gHcl3EfPhHeTHz6f65+MhgMjJw6knqR9RjSawh+vn4F+unNb9/k3N5zVK/ryfMte7ueyUtjwb/xyaEoZGdnExAQgKZpBXzKddhQdfA1e1zVJ4PB4Oobo9GIUZJ447fFZKQqeNf2ZvR/RmO1Wiv8GHGjPtlsNmRZxsfHB4fDUSF8Ksv9ZDQ66w5ZLJYCz2F59qms9pOmaeTn5+Pp6emyvbz7VJb7yWw2k5mZibe3Nx4eHhXCp7LaTyaTiezsbKxWq+t7W3n3qSz3k9FoJD09ncDAQMxm8w37ZLPZqFGjRqUtUFomgx1llffee4/27dvToUMHdu/ezerVq3nllVeue92NrMaSkZXB0289jZ+fH5+N/uxyWpTgptB13TVACQ1Lnsqq9/DJw7FKOm/3fMy9DV/8R9disfwtFfLvh3WGL/saQ76ZMePGEBwQzMDFA9l3ZB/rnlhHteBqXJryIcsyc3+ay9HtR+nbqRldazctdnN3Zh6iiV9dPKTLvyRk5ufw9d41nDqRh1+oRI8GLZj+51K8NC9mvT7rpmtkfLb4M45uO0p4pBcvtrkXm6KQmZuL4nBQMygIyWjkQFYcjX3r3dwzegWtdV1n1YndrPnzMACyRz6e3hKOc2YG396RFqF1r3ir8/m5TFnzPVq2CY8gD14f/bpYyecfVNaxpLQQersPobV7EXq7D6G1eymq3pV9NZYyFez44Ycf6N+/P7Nmzbri8WHDhrnZooKkpaUxd+5c19KzgwcPpkaNGte9riSWnhVcGZvNxvjx45k8eXKJFzAUVF69x34wlryMLD6493G3tivb7Xz8ySc88/TTWK6Qirjy2E7Wbj/CPQ/fQ5cWXQA4l3+OxAuJtAxtWeh8TdMY+dZIlCyZt+97GC/Lv09vVDWNbSePsv74PjJOK9g98nixax/ytHw+3b4S6wVfJP65PjycNZ1lzltzbro9Xdf5ZP4nnNhz4orHNTSMGKkV6UPfuu3ZfeoEqq5Rp2oIZkkiyNOX8IDgQtd9snUFcSfOU7OWN1W8/MjLUThy7jRS/uVgjFdtL/IS8lyfb2ldjw51GuInebkCPFuSD7Jgy58YdCPR0dEMfWhohS0w+2+orGNJaSH0dh9Ca/ci9HYfQmv3UlS9RbCjDAU79u3bR7Nmzfjzzz+veLx9+/Zutqh4EMEO9yGizO6lsur9zpx3OHUomQ8f/I97G75GZoemafzfkq8xeVuYNnb6Dd8yOTWZadOmERRqZcJtA2/ABJ0n1r1HjF99wv2DqRsQRnTVcEySxMyNKzmRlAGA3WzHqhQOnjTo0ICB3Qfi6+OLruscPHGQOd/PoVXLVgzsfv32r2bT6DGj0XWddj3bEeATwKmkU5i8TWTlZnF86/FrXj/szm40CLkcuHaoKqMXLgTAZrZhUA1YNSu6QafPA33o1rqb63nPtmUz6edJWE5ZsJ10LlNrN9iRPHVMec6XET1QY+SwUYRWDS2Sf5WByjqWlBZCb/chtHYvQm/3IbR2LyKzo2iUqdVY9u/fT7NmzWjfvj2xsbHExMSUtkmCcsalud+X5twJSpbKqndwUDCpjlQ0TSvR6Qi6rqPpOtLFNi5NPzGbza4q4Jd0X3xwM2b7/7d372FRV4kfxz8zzHAVRi4ioqiVigpe0LLMFbd1bdu2TFszKEt/9fyyq5mahfIrt0Utw1rLtPKx8pKpa4qXrJRy1VLz0RQojViRiyKmICMIw9zO7w+W7zpyG5A5A9/5vJ5nn8VhLl/eno7Dme+Z8cXDjz7crMeIiojC4PjB+GnvT9iX9xPie8Y2ev2SinIYSVeGJwAAGd1JREFUzndG7nkj8lCO/chFcFcdHh8yRlnoSJicgKGxQ6HRaHDlyhXkFOTg+6zvoffS44m/PqHcl0ajQWyvWLz58pvNOuZraTQaLHp9keOFt/z3y6kVUxHXKQ633nQrosKiUG2qhtZLi1+Lf8WmTzfhvfR0DOnXFY8MHg2tRoOlP3wBAAgyBGHR3EXQarWorKqEr49vnb/vQN9ALHpgEex2O5ZvXI7TR07DR/jAJ8wH1QU1n7z1ZlIqt600wVPnEndhb3nYWi72loet5WLvlmlTz74KCgqUr3fu3OnGI6H2ymw2IyUlBWaz2d2H4hE8tXdUeBS00ML4n4+VdpWFO7Zi9mef4WBBNoqNlzD1myWYtn0Fnt/8CV787DPMWrcOQgicqjiL9Kyf4NXFC3HRcc1+nEf/8iisOiu2fp+hfFRtQ77KPQoAGD95PF555RVYA60or6jGgl1bYNPacPvvblcWOgAgICAAg/sNxjMTn3FY6JDpg8kf4Mm7n0RcdBzCQsPQtWtXdInoglGDRyF5bjJCbgpB5onzmLFxNb45lYH800Z0GdQFl42XlbHt7+ff6IKFVqvFMwnPYMS9IzB99nTMnzYfqampSE3lQoczPHUucRf2loet5WJvedhaLvZumTa1jWXBggWYM2dOna/bO25jIVKX3IJcLHtnGR75w3AM7lL/m1JeLyEEZq1bV/dyCHTo1QFX/n0FABDcVQdDmDfyMirx2FOPof9N/Vv0eB9t/Qgn9p9ApygfVFstuHzOjoF9u2Dy0D8o1yk0XsSbO7fDL9AfC5MXQqPRYPr86dBd0sHiZcZzz05Dr6heLfuB3Wz/8f3YunYrAMASYMbi/3sLOl2bOvmRiIiIqFk8fRtLm3q5yWKx4OzZszhz5ozD17X/I2qK3W5HcXHN9gJyPU/tXfveC3nG8y57jAP5NZ/4cefEOzFj1gyMumsUAGBI3BC8+sSrmPLSFEQMi8Cls1ZknjgHAIgMjWzx44lQgUptJc6eL8dFY817T2T+cg6fZe0FUPPGo6l7tgI6IHlGsnLmxtkrZ2GBBY9MebTdLnQAwMjBI5Wvp055Elqt1iPHtrt46lziLuwtD1vLxd7ysLVc7N0ybW6x4/3338cHH3wAq9WqfF37P6KmmM1mvPvuuzzFSxJP7e3n5webxoaiy6Uue4y0rMOoDjBhzC1jEBkRibvvuBvDhg3Dzz//DLPZjNhOsZg1cRYGjRqEIIsBAK5ru0TirYkYOXkk3lnwDt557R28+uqrMNxkwJHMM/jwyFd460AatFe88cikR9EhoINyu5DYEPgM98HN/W6+7p/Z3Y4EHMGvEb+i/w39PXZsuwt7y8Xe8rC1XOwtD1vLxd4t06a2sagVt7EQqc+05GnoFOqHV37fsk8QaUzOxXN4/+tvcfNdNyPhj03f/6xZs5T/j4hovU/8EELg7bVvoyijCADQ67ZeeHLCk612/21NTmkOOvp2RCf/uh9FS0RERNTecBsLkYrYbDbk5eXBZrO5+1A8gif31gXoUF5pcsl9rzm2B9X6akz4/QSHyxvqHXNPDOywIzQ0tFWPQ6PRYMYjM5Q/P3G/e95gVJbeIb2VhQ5PHtvuwN5ysbc8bC0Xe8vD1nKxd8twsYNUxWKxYM2aNbBYLO4+FI/gyb39A/1hrnb8B8dorUC1/fpOLyy5Uo7y32yIHhJd5w0yG+odFBGEDzp/AL1ef12P3ZA33ngDCxYs8KhPFfHkse0O7C0Xe8vD1nKxtzxsLRd7twy3sUjAbSxE6rN47WIUZOXjnYT/US770+Yk+Bq02Dp6vtP3Y7VbsbV4H/4a+QdUVlfj/zZtAgDM+9s8h/fGaOo+0k+n466b7mreD0FEREREqsVtLEQqYrPZkJ2dzVO8JPHk3p1DO0Nn08P6n5+9oqoKsVWx6FXcH2sz9jh9P3/buQkH9pzDzE8/VRY6ANS70NFQb51Wx4WOVubJY9sd2Fsu9paHreVib3nYWi72bhkudpCqWK1WbNu2DVar1d2H4hE8uXdUeBQ00OBSZQUA4J8nv1e+d+ynIrz67ac4eukXHCw4ibm71mDaho/x828FDvfx5a9HUWms+UcrpE8Iht89HAN+NwDPzXyu3sf05N6ysbVc7C0Xe8vD1nKxtzxsLRd7twy3sUjAbSxE6nOm6Az+8dY/8OCoW3BL196Yvvlj6OCDOTPmYOehnTj+7XHluhZvC/RmPW6P64m/9h8BAPgm9zh2HMyCJdyCj/ExKmZXuOtHISIiIiIV4jYWIhWx2WzIyMjgKV6SeHLvTqE1n9qRZzyPE78VQmfywZh7xyAkJAST7p4EX39fAEDCYwl4O+VtlHmVYd+xf2PB3n/itSOrse1gBrw6e+HtGW87vdDhyb1lY2u52Fsu9paHreVib3nYWi72bhkudpCqWK1W7Nu3j6d4SeLJvX18fGD1sqK44hI2/XwAZr0ZY24Zo3x/zstzMH36dNzc/2ZotVpsCtmE8qhyFBddgTHbCzrosGD6gjqfuNIYT+4tG1vLxd5ysbc8bC0Xe8vD1nKxd8twG4sE3MZCpE5PJz+NfOSjt7kXusRE4qXJLzV43YA3A7Di7hX4S/e/4O9/+zsAIDU1VdahEhEREZGH4TYWIhWxWq344YcfuOopiaf3tvpY0c/UDzq7Hol/Smz0uuvuW4cJfSfAEGhAfHw84uLimv94Ht5bJraWi73lYm952Fou9paHreVi75bhYgepCvezyeXpvfX+egCAtaMV3SO6N3rd+/rcB28vbwDA2LFj8fDDDzf78Ty9t0xsLRd7y8Xe8rC1XOwtD1vLxd4tw20sEnAbC5E6zVwyE5pCDYbdNQwT/zjR3YdDRERERKTgNhYiFbFardi7dy9P8ZLE03t762rO1IjtGSvl8Ty9t0xsLRd7y8Xe8rC1XOwtD1vLxd4tw8UOUhW73Y78/HzY7XZ3H4pH8PTe3cNqtq6YqkxSHs/Te8vE1nKxt1zsLQ9by8Xe8rC1XOzdMtzGIgG3sRCp08avNuJw+mE89r+PoX90f3cfDhERERGRgttYiFTEarXi66+/5ileknh675sG3YSvDV+j5w09pTyep/eWia3lYm+52FsetpaLveVha7nYu2W42EGqIoSA0WgET1iSw9N7azQa5PrmQuelk/J4nt5bJraWi73lYm952Fou9paHreVi75bhNhYJuI2FSJ2OnjuK+LXxKJtRBr2X3t2HQ0RERESk4DYWIhWxWCzYtm0bLBaLuw/FI3h6776hfXHnDXdCp5VzZoen95aJreVib7nYWx62lou95WFrudi7ZeQ8QyciUqEA7wBsmbDF3YdBRERERETX4DYWCbiNhYiIiIiIiGTy9G0sPLNDgtr1pPLycjcfifpZLBbs2LED99xzD/R6voeCq7G3XOwtD1vLxd5ysbc8bC0Xe8vD1nK1tHft75+een4Dz+yQ4MyZM4iKinL3YRAREREREZGHKSwsRLdu3dx9GNJxsUMCu92OoqIiBAYGQqPRuPtwVK2qqgpz587F/Pnz4efn5+7DUT32lou95WFrudhbLvaWh63lYm952FqulvYWQqC8vByRkZHQaj3vs0m4jUUCrVbrkStp7qDX6+Ht7Y2goCBOvBKwt1zsLQ9by8XecrG3PGwtF3vLw9ZyXU9vg8HgoqNq+zxveYeIiIiIiIiIVI2LHURERERERESkKl7z5s2b5+6DIGpNWq0Wffr0gZeXl7sPxSOwt1zsLQ9by8XecrG3PGwtF3vLw9ZysXfz8Q1KiYiIiIiIiEhVuI2FiIiIiIiIiFSFix1EREREREREpCpc7CAiIiIiIiIiVdG5+wDI81gsFqxcuRLFxcXQ6/UIDAxEQkICwsPDUV5ejlWrVuHChQvQ6XRISEhA7969ldvm5+dj+/btePbZZ3H69GmsW7cOFosFHTt2xJQpU9CxY0cAwIoVK5Cbmwuj0YjU1FT4+/s7HMPu3bshhECPHj2QlpaG6upqaDQaxMTEYNy4cdBqtThx4gTS0tKU25SXlyMoKAhJSUlyQrUSV/cuKyvDmjVrUFJSAp1Oh/DwcCQmJiIwMFC5nx9//BHZ2dmIj4/Hhg0bUF5eDq1Wi549e+LBBx+Et7c3qqursWTJElgsFgA1nwmemJiI0NBQ6c2uh6t7O9PJmfFtMpmwYsUKFBQUwG63Y/HixdJbXS8Zc0mtHTt2YOfOnUhKSkJUVJRyee3YHj9+fIM9OZc43/vpp59GZGQktNqa12ImTpyIXr16KfdTO7aHDh2K1atXo7CwEGFhYZgzZ06d4xVCYMmSJSgsLOT4bqB3ZWUlNmzYgPz8fHh5eWHAgAEYN26ccj/O9v7++++xa9cuCCEQHR2NhISEdvUGeq5uffbsWaxatUq5TWVlJUwmE1JTU5XLaueSxMREZGVlYfPmzbDb7YiMjMSjjz4KPz8/ziVwfmwfOnQI6enpylwyduxYxMbGKvfjTO+LFy9ixYoVEELAZrMhIiICDz/8cJ3nlG1ZW2o9ZsyYRns29PfQnsjo/cMPPyA9PR12ux1BQUF45JFHEBISotyPM8+5gabnf9UQRJKZzWaRlZUl7Ha7EEKIPXv2iLfeeksIIcTq1avF9u3bhRBCnD59WiQlJQmr1arcNi0tTezbt0/YbDbxyiuviF9++UUIIcSuXbvEhx9+qFzv5MmT4vLly+Kpp54SV65cqXMMqampori4WBQUFIgLFy4ox5WamioOHDhQ73G/9957Yvfu3a1QQC5X9zYajSInJ0e5zeeffy5WrVrlcAwfffSR+Pnnn8X58+dFYWGhEEIIm80mVqxYoTy+zWYTVVVVym3S09PF8uXLW7WFDK7u7UwnZ8a32WwWv/zyiygoKBAzZsxwRQqXkzGX1N5+6dKlYu7cuaKgoMDhe7Vjuzk9OZc03LuhObtW7diuqKgQOTk5IjMzU8yfP7/e66anp4u1a9dyfDfS+/333xe7du1S/lxWVuZwDM70vnDhgnj55ZdFWVmZsNvtYtmyZeJf//pXK5ZwPVlzSa3169eL9evXO1xWO5dUVVWJ2bNni3PnzinX/fzzz+u9H84l9feuqKgQL7zwgjKec3JyxIsvvuhwDM70NpvNorq6WrnNhg0bxIYNG1q9hyu1pdaN9WzOuG/LXN373Llz4qWXXlJ6Hzp0SCxdutThGJx5zi1E0/O/WnAbC0mn1+sRGxsLjUYDALjhhhtQUlICoGY1cuTIkQCAnj17wmAwICcnR7ltVlYWBg4ciIKCAmi1WkRHRwMARo4ciaysLOXV7r59+zqcWXC1y5cvo7KyEp07d0ZUVBTCwsKU4+rWrZtyLFcrKytDdnY2br311laqII+rewcFBTm88tqzZ0+HhjabDadOnUJ0dDTCw8PRrVs3ADUfn9WjRw+UlpYqf/b19QVQ84qsyWRyVRKXcnXvpjo5O771ej2io6Pb1StU15Ixl5jNZmzYsAGJiYl1Hv/qse1sT84ljfduzNVjOyAgAL169YKPj0+91y0qKkJGRgbuvPPO6/qZ3cnVvX/77Tfk5+dj9OjRyu0MBoPytbO9jx07hgEDBsBgMECj0WDkyJE4cuRI6wdxIZlj22Kx4PDhw7j99tuVy66eS06cOIGoqChEREQAAOLj4+vtybmk4d5CCAghUF1dDQCoqqpCcHCwch/O9tbr9cqr4Ha7HWazWTnm9qIttW6sp7Pjvq1zde+ioiJERkYqc3VsbCxOnDiBiooKAM4/525q/lcTbmMht9uzZw8GDhyIiooK2Gw2h//YQkNDHf7D9PX1hcFgQG5ursMpW76+vvD19YXRaFR+uWtIZmYmBg4cWOdyo9GIY8eO4amnnqrzvUOHDiEmJqbBBZT2xJW97XY79u7d69A3OzsbN954Y51Tmqurq3HgwAHcd999DpcvWbIERUVF6NChA5577rlW/dndwVW9G+rUkvGtFq5ovWXLFsTHxztcp1ZDY7sxnEucG9t2ux3R0dG49957lV+wGxrb17LZbPj0008xadIk5bRqNWjt3sXFxQgODsb69euRn5+PgIAAjB8/Xtmm5Wzv0tJSh8e4+ljaK1f+O3n8+HGEhYU5bIe7ei6pr6fRaITNZnOYaziXNN47MTERCxcuREBAAMxmM6ZNm6Zctzm9rVYrFi1ahJKSEnTt2rXd/xvqztYAGuzp7Lhvb1q7d7du3VBYWIjz58+jc+fOOHz4MIQQKC0tRYcOHZx+zt3U/K8m6nkWQO3SV199hQsXLji1R+z48eMYNGjQdT9mRkZGnfupqqrC8uXLMWbMGPTo0cPhe0IIHDx40OFVmPbKlb2FEFi/fj38/f1xxx13KJdnZGRg8ODBDte1Wq1YuXIl+vXrV+d7zz//PBYuXIihQ4fiyy+/dPrx2yJX9m6oU3PHt1q4ovXJkydRUlKC4cOH1/v9+sZ2YziXNC0lJQVJSUmYNWsWKioqsHnzZuV79Y3t+nzxxRcYPHgwunTp4tRjtgeu6G2z2ZCXl4ehQ4ciKSkJo0ePxrJly2Cz2QA431ttXP285MCBA3XmAM4lrdu7qqoKe/bswezZs5GSkoJJkybhww8/hNVqBdC83jqdDnPmzMEbb7yBiIgI7N+/36nbtUVtobWaejbFFb1r3xdv1apVeP3113HlyhX4+fkpC/vOPuduav5XEy52kNvs3r0bx48fxzPPPANvb2906NABWq0WRqNRuU5JSYmyunn1E6/g4GCHV45MJhNMJlOTp2CZTCacP3/e4Rc+k8mEpUuXYtCgQQ6nc9XKycmBxWJB//79r+vndTdX9964cSMuXbqExx9/XJl0hRA4efKkQzubzYaVK1fCYDDggQceqPdYtVotRowYgcOHD7deAMlkjO9rO7VkfKuBq1pnZ2ejsLAQycnJSE5ORllZGZYtW4bMzMx6x3ZTOJc0PbZrb+Pj44P4+HicOnVKud61Y7shOTk52Lt3L5KTk7F48WKYTCYkJyejvLy8dQJI5qreISEh6Nixo3KqdExMDGw2G0pKSprVOyQkxOExrj6W9sbV8/bFixdx+vRp3HLLLcpl184l9fU0GAwOr9RyLmm898mTJ+Hn56cseA4cOBAmkwmlpaUt6g3U/JI+fPjwdvu8pC20vtq1PZ39e2gvXDmXDBkyBLNnz8bLL7+MkSNHwmKxIDw8vFnPuRub/9WGix3kFt988w2OHDmCadOmOexxHzJkiLLKm5eXh7KyMvTu3RtGoxHV1dUIDw8HAHTv3h02mw3Z2dkAgP3792PAgAHQ6/WNPu5PP/2EmJgYZS9d7S+CMTEx+POf/1zvbQ4cOIDbbrutXZ8O7ereGzduxIULF/DEE09Ap/vv7ri8vDxEREQo7zFRO+n6+/vjoYcectj7ajQaUVlZqfz56NGj6Nq1q4uKuJYrezfWqSXju71zZetx48Zh4cKFSElJQUpKCjp27Iinn34aAwcOrDO2ncG5pPHelZWVMJvNAGq2xB09elTZb3zt2G7MzJkzlb+zmTNnwtfXFykpKe3ydH9X9u7evTt8fX1x5swZ5X6EEAgODm5W77i4OGRlZcFoNEIIgf3792Po0KGtncLlZDwvOXjwIAYNGuRw/9fOJf3790dBQQGKi4sBAPv27avTk3NJ473DwsJw5swZ5RfL3Nxc2Gw2BAcHN6t3SUmJw5z0448/tsvnJW2ldWM9nRn37YWr55La1na7HWlpaRg1ahS8vb2b9Zy7sflfbTRCCOHugyDPcunSJcydOxdhYWHKf5A6nQ6zZ8/G5cuX8cknnygfYzpx4kRER0dj3759KCsrw9ixY5X7yc3Nxbp162C1WmEwGDB58mRlhfS9997D2bNnUVZWBoPBgPDwcLzwwgtYuXIlRowYgb59+wIAvvzyS3zxxReIjIxU7jcuLk75xbCqqgpJSUlITk5u8r1A2ipX9z516hQWL16Mzp07KxNxaGgopk6dirS0NHTq1AkjRowAABw+fBiffPIJunbtqky6N954IxISEpCXl4d169bBbrcDAMLCwjBhwoR2193VvRvr1NzxnZKSgoqKCpSXl8NgMKBPnz6YMmWKpFLXT8ZccrXk5GRMnToVUVFRdcY20HhPziVN9669XKPRwG63IyoqCg888AACAgLqjG2z2Yx58+bBarWiqqoKgYGBGDZsWJ3ThUtKSrBgwYJ2+dGzMsZ3QUEB1q9fD7PZDJ1Oh/vvvx99+vRpdu/vvvsOu3btAgD07t0bDz30ULt6RVZGa7vdjuTkZEyePFl5NRVAvXNJZmYmtmzZArvdji5dumDy5MnKR3ByLnGu97fffovvvvsOXl5e0Gq1GDduHPr169es3pmZmdi2bRuAmjNwoqKiMGHCBHTo0EFirevTllo31bOxcd9eyOi9dOlSlJaWwmq1IjY2FuPHj4der2/Wc26g4flfdeR98AtRy7377rsiLy/vuu7DYrGIuXPnOnzME9WvNXoLIcRrr70mLl++3ApHpG4c3/JwbMvFsS0Xe8vDuUQu9paHreVib9fimR1EREREREREpCrtd7MfEREREREREVE9uNhBRERERERERKrCxQ4iIiIiIiIiUhUudhARERERERGRqnCxg4iIiIiIiIhUhYsdRERERERERKQqXOwgIiIiIiIiIlXhYgcRERERERERqQoXO4iIiIiIiIhIVbjYQURERERERESqwsUOIiIiIiIiIlIVLnYQERERERERkapwsYOIiIiIiIiIVIWLHURERERERESkKlzsICIiIiIiIiJV4WIHEREREREREakKFzuIiIiIiIiISFW42EFEREREREREqsLFDiIiIiIiIiJSFS52EBEREREREZGq/D/+wf62YUE+CAAAAABJRU5ErkJggg==\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tLbin79-SBOK"
},
"source": [
"### インタラクティブチャート表示 (plotly)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yqalYwNT4b5s",
"outputId": "e2a1d2ce-7616-4043-a9e6-50b5a7f3ec6a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 817
}
},
"source": [
"from IPython.display import HTML, display_html\n",
"\n",
"# HTMLチャート表示\n",
"file_name = file_path + '.html'\n",
"display_html(HTML(filename=file_name))"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script><meta charset=\"utf-8\"/></head>\n",
"<body>\n",
"<div>\n",
"<div class=\"plotly-graph-div\" id=\"fcc6685f-06bc-4ff5-a3e1-d04d11e4a203\" style=\"height:800px; width:1200px;\"></div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
" \n",
" if (document.getElementById(\"fcc6685f-06bc-4ff5-a3e1-d04d11e4a203\")) {\n",
" Plotly.newPlot(\n",
" 'fcc6685f-06bc-4ff5-a3e1-d04d11e4a203',\n",
" [{\"close\": [7179.0, 6942.0, 7335.0, 7345.0, 7350.5, 7756.5, 8155.5, 8046.0, 7810.5, 8204.0, 8023.5, 8187.0, 8104.5, 8835.5, 8816.5, 8719.0, 8901.0, 8916.5, 8700.5, 8632.5, 8727.5, 8664.5, 8392.0, 8425.5, 8326.0, 8597.5, 8900.0, 9400.0, 9294.5, 9510.0, 9339.5, 9382.0, 9325.0, 9282.0, 9166.0, 9630.0, 9763.0, 9815.0, 9908.0, 10172.5, 9862.5, 10286.0, 10363.0, 10241.0, 10369.5, 9907.0, 9934.0, 9715.0, 10195.5, 9596.5, 9607.5, 9694.0, 9675.5, 9966.0, 9660.5, 9317.0, 8780.0, 8820.0, 8717.5, 8531.5, 8532.5, 8918.5, 8757.0, 8756.5, 9070.5, 9158.5, 8900.0, 8038.5, 7928.0, 7889.5, 7939.0, 4346.0, 5571.5, 5160.5, 5338.5, 5036.5, 5327.0, 5409.5, 6185.0, 6204.0, 6192.0, 5820.5, 6486.0, 6765.0, 6685.5, 6760.5, 6363.5, 6250.0, 5869.0, 6408.0, 6425.0, 6671.5, 6795.5, 6738.0, 6875.0, 6775.5, 7338.0, 7193.0, 7362.5, 7289.5, 6868.5, 6885.0, 6905.5, 6853.0, 6868.0, 6615.5, 7106.5, 7025.0, 7256.5, 7125.5, 6833.0, 6848.5, 7133.5, 7492.0, 7503.5, 7542.0, 7702.5, 7788.0, 7752.0, 8794.0, 8627.5, 8832.0, 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