523 lines
233 KiB
Plaintext
523 lines
233 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-09-03T15:44:35.182316Z",
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"start_time": "2025-09-03T15:44:35.179598Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import talib as ta # Make sure TA-Lib is installed: pip install TA-Lib\n",
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"import statsmodels.api as sm\n",
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"\n",
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"import warnings\n",
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"\n",
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"# 忽略所有警告\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"# --- 0. Configure your file path ---\n",
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"# Please replace 'your_futures_data.csv' with the actual path to your CSV file\n",
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"file_path = '/mnt/d/PyProject/NewQuant/data/data/KQ_m@SHFE_rb/KQ_m@SHFE_rb_min15.csv'\n"
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],
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"outputs": [],
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"execution_count": 52
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-03T15:44:35.226997Z",
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"start_time": "2025-09-03T15:44:35.194022Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"# --- 1. Data Loading and Preprocessing ---\n",
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"def load_and_preprocess_data(file_path):\n",
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" \"\"\"\n",
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" Loads historical futures data and performs basic preprocessing.\n",
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" Assumes data contains 'datetime', 'open', 'high', 'low', 'close', 'volume' columns.\n",
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" \"\"\"\n",
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" try:\n",
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" df = pd.read_csv(file_path, parse_dates=['datetime'], index_col='datetime')\n",
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" # Ensure data is sorted by time\n",
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" df = df.sort_index()\n",
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" # Check and handle missing values\n",
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" initial_rows = len(df)\n",
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" df.dropna(inplace=True)\n",
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" if len(df) < initial_rows:\n",
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" print(f\"Warning: Missing values found in data, deleted {initial_rows - len(df)} rows.\")\n",
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"\n",
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" # Check if necessary columns exist\n",
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" required_columns = ['open', 'high', 'low', 'close', 'volume']\n",
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" if not all(col in df.columns for col in required_columns):\n",
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" raise ValueError(f\"CSV file is missing required columns. Please ensure it contains: {required_columns}\")\n",
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"\n",
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" print(f\"Successfully loaded {len(df)} rows of data.\")\n",
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" print(\"First 5 rows of data:\")\n",
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" print(df.head())\n",
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" return df\n",
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" except FileNotFoundError:\n",
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" print(f\"Error: File '{file_path}' not found. Please check the path.\")\n",
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" return None\n",
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" except Exception as e:\n",
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" print(f\"Error during data loading or preprocessing: {e}\")\n",
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" return None\n",
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"\n",
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"\n",
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"df_raw = load_and_preprocess_data(file_path)"
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],
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"id": "548c68daa68af8c1",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Successfully loaded 25470 rows of data.\n",
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"First 5 rows of data:\n",
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" open high low close volume open_oi \\\n",
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"datetime \n",
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"2020-12-31 14:45:00 4352.0 4400.0 4345.0 4388.0 213731.0 1221661.0 \n",
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"2021-01-04 09:00:00 4356.0 4368.0 4309.0 4336.0 338332.0 1217327.0 \n",
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"2021-01-04 09:15:00 4336.0 4342.0 4307.0 4318.0 144479.0 1197881.0 \n",
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"2021-01-04 09:30:00 4318.0 4329.0 4312.0 4317.0 85679.0 1194567.0 \n",
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"2021-01-04 09:45:00 4317.0 4338.0 4316.0 4338.0 66461.0 1194592.0 \n",
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"\n",
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" close_oi underlying_symbol \n",
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"datetime \n",
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"2020-12-31 14:45:00 1217327.0 SHFE.rb2105 \n",
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"2021-01-04 09:00:00 1197881.0 SHFE.rb2105 \n",
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"2021-01-04 09:15:00 1194567.0 SHFE.rb2105 \n",
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"2021-01-04 09:30:00 1194592.0 SHFE.rb2105 \n",
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"2021-01-04 09:45:00 1198035.0 SHFE.rb2105 \n"
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]
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}
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],
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"execution_count": 53
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-03T15:44:35.563309Z",
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"start_time": "2025-09-03T15:44:35.232102Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"\n",
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"def calculate_atr(df, period=14):\n",
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" \"\"\"\n",
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" Calculates the Average True Range (ATR).\n",
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"\n",
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" Args:\n",
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" df (pd.DataFrame): DataFrame with 'high', 'low', and 'close' columns.\n",
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" period (int): The lookback period for the ATR calculation.\n",
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"\n",
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" Returns:\n",
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" pd.Series: A Series containing the ATR values.\n",
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" \"\"\"\n",
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" df['high_low'] = df['high'] - df['low']\n",
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" df['high_close_prev'] = np.abs(df['high'] - df['close'].shift(1))\n",
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" df['low_close_prev'] = np.abs(df['low'] - df['close'].shift(1))\n",
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"\n",
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" df['true_range'] = df[['high_low', 'high_close_prev', 'low_close_prev']].max(axis=1)\n",
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" df['atr'] = df['true_range'].rolling(period).mean()\n",
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"\n",
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" return df['atr']\n",
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"\n",
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"\n",
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"def analyze_atr_data(df):\n",
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" \"\"\"\n",
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" Loads data from a CSV file and performs a statistical analysis of its ATR.\n",
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"\n",
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" Args:\n",
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" file_path (str): The path to the CSV file.\n",
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" \"\"\"\n",
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" try:\n",
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" # Step 1: Read the data\n",
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" print(\"Data loaded successfully. First 5 rows:\")\n",
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" print(df.head())\n",
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" print(\"-\" * 50)\n",
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"\n",
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" # Step 2: Calculate ATR\n",
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" df['atr'] = calculate_atr(df)\n",
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" df.dropna(inplace=True)\n",
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" print(\"ATR calculation complete. First 5 ATR values:\")\n",
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" print(df['atr'].head())\n",
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" print(\"-\" * 50)\n",
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"\n",
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" # Step 3: Descriptive Statistical Analysis\n",
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" print(\"Descriptive Statistics for ATR:\")\n",
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" print(df['atr'].describe())\n",
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"\n",
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" skewness = df['atr'].skew()\n",
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" kurtosis = df['atr'].kurt()\n",
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" print(f\"\\nSkewness: {skewness:.4f}\")\n",
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" print(f\"Kurtosis: {kurtosis:.4f}\")\n",
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" print(\"-\" * 50)\n",
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"\n",
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" # Step 4: Visualization\n",
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" sns.set_style(\"whitegrid\")\n",
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"\n",
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" # Plot price vs. ATR\n",
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" fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), sharex=True)\n",
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"\n",
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" ax1.plot(df.index, df['close'], label='Close Price', color='blue')\n",
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" ax1.set_title('Price Movement', fontsize=16)\n",
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" ax1.set_ylabel('Price')\n",
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" ax1.legend()\n",
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"\n",
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" ax2.plot(df.index, df['atr'], label=f'ATR(14)', color='red')\n",
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" ax2.set_title('Average True Range (ATR)', fontsize=16)\n",
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" ax2.set_xlabel('Date')\n",
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" ax2.set_ylabel('ATR Value')\n",
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" ax2.legend()\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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"\n",
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" # Plot ATR distribution (Histogram & KDE)\n",
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" plt.figure(figsize=(10, 6))\n",
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" sns.histplot(df['atr'], kde=True, bins=50, color='purple')\n",
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" plt.title('Distribution of ATR Values', fontsize=16)\n",
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" plt.xlabel('ATR Value')\n",
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" plt.ylabel('Frequency')\n",
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" plt.show()\n",
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"\n",
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" except FileNotFoundError:\n",
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" print(f\"Error: The file was not found. Please check the path: {file_path}\")\n",
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" except Exception as e:\n",
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" print(f\"An error occurred: {e}\")\n",
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"\n",
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"\n",
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"# Call the analysis function with your file path\n",
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"# Replace 'your_data.csv' with the actual path to your file\n",
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"analyze_atr_data(df_raw)"
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],
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"id": "f07c10b87eaa78ec",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data loaded successfully. First 5 rows:\n",
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" open high low close volume open_oi \\\n",
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"datetime \n",
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"2020-12-31 14:45:00 4352.0 4400.0 4345.0 4388.0 213731.0 1221661.0 \n",
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"2021-01-04 09:00:00 4356.0 4368.0 4309.0 4336.0 338332.0 1217327.0 \n",
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"2021-01-04 09:15:00 4336.0 4342.0 4307.0 4318.0 144479.0 1197881.0 \n",
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"2021-01-04 09:30:00 4318.0 4329.0 4312.0 4317.0 85679.0 1194567.0 \n",
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"2021-01-04 09:45:00 4317.0 4338.0 4316.0 4338.0 66461.0 1194592.0 \n",
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"\n",
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" close_oi underlying_symbol \n",
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"datetime \n",
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"2020-12-31 14:45:00 1217327.0 SHFE.rb2105 \n",
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"2021-01-04 09:00:00 1197881.0 SHFE.rb2105 \n",
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"2021-01-04 09:15:00 1194567.0 SHFE.rb2105 \n",
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"2021-01-04 09:30:00 1194592.0 SHFE.rb2105 \n",
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"2021-01-04 09:45:00 1198035.0 SHFE.rb2105 \n",
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"--------------------------------------------------\n",
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"ATR calculation complete. First 5 ATR values:\n",
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"datetime\n",
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"2021-01-04 14:15:00 28.000000\n",
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"2021-01-04 14:30:00 26.000000\n",
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"2021-01-04 14:45:00 21.500000\n",
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"2021-01-04 21:00:00 21.785714\n",
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"2021-01-04 21:15:00 21.571429\n",
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"Name: atr, dtype: float64\n",
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"--------------------------------------------------\n",
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"Descriptive Statistics for ATR:\n",
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"count 25457.000000\n",
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"mean 17.103424\n",
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"std 9.116101\n",
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"min 2.214286\n",
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"25% 10.428571\n",
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"50% 14.642857\n",
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"75% 21.714286\n",
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"max 77.500000\n",
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"Name: atr, dtype: float64\n",
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"\n",
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"Skewness: 1.5228\n",
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"Kurtosis: 3.2215\n",
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"--------------------------------------------------\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1500x1000 with 2 Axes>"
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],
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"image/png": 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8CXVPdOea5nl5H7zF5+048fG5PzaMckEm9+8cAmf1NQYAAAAAgFywrCtiiRIl9MADD1h1eiA6ZGQEvq17Qi8/PaZJcPkWroFF8/oeeIvPWwK/cOG8Hb+gmznTMU9PdAAAAMA/fm8GEAMsSaIvX748x/UtWrQIUyRAhEtPD3xbfjEJj3DVRM8r557SOalYUfrnn9DGEoucB8U+dMi6OKIdN+oAAABiG3+fAogxliTR+/Tp49Fmc/qDet26deEMB4hcuemJHugvKYEkr0hw+Zab98QKlSp5tvkr52K3854HqmRJx/yePdbFAQAAAAAAwiYieqKnp6dr3bp1GjlypB599FErQgIiU256ogezPrPVCdVI7rUQznIueXkfLr44sO0i+WccLaiJnndWX2MAAAAAAMgFS5LoJUqU8Ghr06aNEhMT9corr2jatGkWRAVEoPzURPclGpOnkRTz/PlWR5Czxo0925x/fqdPSx98IG3aFL6YYtXu3VZHAAAAAAAAwsCygUW9KVeunLZu3Wp1GEDksKonOoLPOZG9YoV15372Wentt8N7/lji/LP84Qfr4oh29EQHAAAAAEQRS5Lo69ev92jbv3+/Pv74Y9WrV8+CiIAIFYqBRU+ezFssCJ5AbxbmJdHYsqU0Zox0zz2ONucbLL/84rkPNdERbnzeAAAAYlskPU0MAEFgSRK9R48estlssrtdVJs0aaKXX37ZipCAyBSKgUUDYXWCK9Z/4Qrkfc3Pe3Dvva5JdOcBMGP9ZwsAAAAgsvA3CIAYYEkSfb5bTeG4uDiVLVtWhQsXtiIcIHKFoia6ZPRwT0z0vd7qJHqsy837GgwHDoT3fIA/XGMAAAAAAFHEkiR61apVrTgtEH1CVRN98mSpd+/cx4PgCHcS3dmZM55t9AwJHD8rAAAAAAAKnLAl0ceNGxfwtrfffnsIIwGiSKh6ontLpDqLtF6ikZS4vPDC/B8jM9P/NjZbaN4HL2NSAGEXadcYAAAAAAByELYk+tixYwPazmazkUQHTKHqiZ5TKRfJ+gRXJCXNQ8HKnuhAJIj17zgAAEBBx+97AGJM2JLoP/30U7hOBcSOUA0s6i+JDt/i4vK3f0JCYD3RQ2HvXu/t/IKLcOMzBwAAAACIIvnMBgERbMsW6dw5q6PIn9z0RM9NUirBz/0zq3uiR7L4+PztX7Nm4DdHfCW982rp0uAeD8ir3Dw5AwAAAACAxSwZWFSS9u7dq/nz52vPnj1Kd0sUPv300xZFhZgxf77UqZPUooW0bJnV0eRdqHqik0TPu7z0RD9+3DFfq1bgNdHnzMn9uXKyZElwj1cQ0YM6OEiiAwAAFBz8Dg0gBliSRF+6dKkeeOABnXfeedqyZYvq1KmjXbt2yW6368JgDNoHjBljTJcvtzaO/ApVTXR/SXT4lpee6Js3O+ZLlAh/TfSqVaW0NOmNN7yv55dahBtJdAAAgNjG3xgAYowl5VxGjBihu+++W7NmzVKhQoU0atQoLViwQC1atFDXrl2tCAmITFYl0a3uie7+C1ck/QKWlyT6li2uy+FIop8545gvVUoaNSr05wQCRRIdAAAAABBFLEmib968WT169JAkJSQk6OzZsypWrJgefvhhjTF7EAP5EUlJ17w4ccIo+RGMci633+7ZxsCieZeXci7OPdHt9sDLueRHUpJj/rzzpI0b83c8IJhIogMAAAAAooglSfSkpKTsOujJycnavn179rojR45YERIQOXbtkkqWlFq3Dl1NdH+9qa3uiR7JzJ/dTz9JN9wg7dnjfx/nJLoUWBI9mKpVk669NrznBHIS7u8AAAAAAAD5YElh5MaNG+uPP/5QrVq11KFDB7366qvauHGjfvjhBzVu3NiKkBBrorkn+rRpxnTZMmMQykDlpmenv57oJNF9M5PoV1xhTDMypBkzct7HinIuzsqWlYoW9b0+mr8v4cbPKjjoiQ4AAAAAiCJhTaIfPXpUpUuX1tNPP61Tp05Jkh566CGdOnVKc+bMUY0aNTRo0KBwhgREHufkUm6SrcFMolstkhOV7uVcduzwv497T/RA3qtg3siw2SL7Z4qChyQ6AABAbOPvDwAxJqzlXNq1a6dHH31U27dvV7169SQZpV1efPFFzZo1S6NGjVLVqlXzdY6PPvpIKSkpevnll7Pb0tLS9MILL6hly5Zq2rSpHnroIR08eDBf50GEi+ae1H/84ZgPRjkXb+3+fqGJtJ+f1b+AOSf83Evh+PtZpadLTiWrJFnzeqz+GQLOSKIDAAAUHPwtAiAGhDWJPnToUB0+fFj33nuvLr/8co0aNUo7d+4M2vFXr16tSZMmKSUlxaV92LBh+vnnn/X2229r/Pjx2r9/vwYMGBC08yICRfN/0s6J83/HDkAerV8f2Hb+asQ712/ObRJ9+3br6z/b7Tl/J6L5+4LoZPV3AgAAAACAXAhrEr1Hjx76/PPP9f3336tHjx6aPn26OnfurLvuuktz5szRuXPn8nzsU6dO6YknntBLL72kUqVKZbefOHFCU6dO1aBBg9SqVSs1aNBAw4YN04oVK7Ry5cogvCogyJwTmvmtnX3NNZHXqzycnn46sO3cS7S4c074uW/r7+frXsolN/59YgeIOfntif7XX9Jrr0lnzgQnHgAAAAAAcmDJwKLnnXeeBg4cqIEDB2rJkiWaOnWqnnnmGb3wwgvq1q2bnnvuuVwf88UXX1SHDh3UunVrvf/++9nta9asUXp6ulq3bp3dVqtWLVWpUkUrV65UkyZNAj5HZgHrOWe+3mh83basrOw7RNEWv3Ps9vR05ScFbrfZZLfbPe6WZWZkeO0JavaxzrLbZbfw52bLzHSJ2S4pK8B4nPuJ2+32HPczt7XHxeV8/LQ0j22zl/3EZtu0yfW1ZGXJ7vb6Mp2OZ8qy26UbblDcSy/5jitAWVlZsmdkeJzD+fzh6BkczdeUbG4/x6h+LRbIvsZkZubrGhPfoIFxnMRE2QcODEJkiEYxcU0BEDG4pgBB5vQ3Trj+3og0XFeA6BDod9SSJLqz1q1bq3Xr1po3b54GDx6sCRMm5DqJPnv2bK1du1ZTpkzxWHfw4EElJiaqZMmSLu3lypXTgQMHcnWe1NTUXG0fK6LxdV9w9KjK/jsfbU8cXHDkSHbs9nPn8pVEP3b8uDKzslTOrf3vv//WqaQkj+2b/zs9cPCgdlr4cyu7bZsucFo+ffq01gcYT3On+aPHj2tLDvuZ29qV8+ck/sQJNfl3/tiJE9q8cmX2vv5iq/rrr6okKSshQXEZGTp69KjO7tunyk7brHQ6nmnvvn2SpCo+jxy4uBEj9Pd556mOj/WrVq6UvXDhIJwpMNF4TTGV2LxZdZ2Wo+36YjXzc3700CFtzevPzm53XKt+/93SaxUiQzRfUwBEHq4pQHCU2LQp+/fmtevW6dyJE5bGYyWuK0BssDSJvmvXLk2bNk3Tp0/X3r171bJlS91www25OsaePXv08ssv69NPP1XhECeBGjZsqHh/tZNjSGZmplJTU6PyddtKl86ez83TBpHAOXZbPu9YlypZUnan45nq1Kkj5fBzSU5OVnkLf262NWtclpOSkvL0PpYuXTqg/Wzx8Tlvd+hQ9mypsmVdtk0qVizHfeP+/WXRVru2tH69SpcuLXtysss23vavVLGi37hzo+YFF/hc17hxY6lIkaCez5tovqZkcxuUOtquL5GidKlSef/ZnTqVPZvcurWl1ypYKyauKQAiBtcUIMicOi1eWL++VKuWhcFYg+sKEB3M76o/YU+inzt3TvPmzdPUqVO1bNkyVaxYUT179tT111+vatWq5fp4f/31lw4dOqTrr78+uy0zM1PLly/XhAkT9Mknnyg9PV3Hjx936Y1+6NAhJbslsvyJj48vkBe+qHzdTnWqoy52J7Z81kS3ffutbH36eLTHx8XlOJhmXHy8/8E2Q8mtzrjNbs/T+2iLiwtoP1tWVs7bOdWpdz+mzWbLed+tW43tataU1q+XzWaTze31eds/Lj5eWrbMb+yByumnEB/m9zsqrykmt7ij9nVYLM5uz/tnzun7E1eokLXXKkSEqL6mAIg4XFOAIHH6HoX7741Iw3UFiA1hTaI///zzmjNnjs6cOaMrrrhCH330kdq0aeORUMqNSy+9VLNmzXJpe/rpp1WzZk3dd999qly5shITE7V06VJ16dJFkrRlyxbt3r2bHoSITM4Di1pl1y7pjjukhx6SLr44/OcP1s8g0GuLv0EOnZ8IcI8tp3PY7Y6BRZ17XgT6+ubODWy7QFCHLzgi4fsZC/g8AgAAAACiSFiT6H/88Yf69++v7t27q0yZMkE5ZvHixVW3bl2XtqSkJJUuXTq7vVevXnrllVdUqlQpFS9eXC+99JKaNm1KEj2WRXOiKxJi/+orYzpunDXxhPuc/pLozvHkJrYDB6STJ41Ee40aeQrNxTXXSO++K+VQmsWnnJKWkfCZQ8Hi7zsHAACA6JbXv6EAIEKFNYnu3mM8XJ555hnFxcVp4MCBOnfunNq2bashQ4ZYEgsQESL9lxgzPpstPLHmJ4lu9kT//Xdp2zbJeVyHLVuMadWqua857q2H+7ffGtOOHaWff87d8ej5i0gSrCR6pF/LAAAAAAAxwdKBRUNl/PjxLsuFCxfWkCFDSJwXdHZ74OU9rEStNEeCLS4ud8nfY8dclzMypO++k9q2lYoX938+XwJJordoYUx//11q3tyY91XKJb+Jv2nTpNw+zZPP+vpAUOUnie7+/Vm8WHrvPWnECKly5fzFBQAAAACAF3FWBwCExd13SxddJJ09a3Uk/iUE+d5WNPbUdO6J7rzsz4IFrsszZkhXXSU5DTyca+vXS9Wre8Zmcr/psWGDY95Motes6XoDJ7/vSenSudu+bVvKuSCyBOuGps0mtWsnTZwo3XtvcI4JAAAAAIAbkugoGD77TFq3zlEOI5IFO4nujbekaSSV+3BPoud2P3c//JD3WIYN8zzHiROO5WbNfMdglnNx7onuvo0vwXg/LrvMmMbFSb/9lv/jgRsOkcbb9w0AAAAAgCAjiY7Y5KtUQFwUfORbtgzu8QJNRJ86Fdzz5oeZGIuE98u9bIrdLv34o2O5Th3P9SZv5VzctwmlxETH/HvvheecQCBCUVorGsp1AQAAFBR0PgEQYyIgQ+Xw/fffq1u3blaHgViQlua9fdeunPdbu9b6ZHKpUsE9XqC/vFj9up3lpSf6ypXSf/4T/FicS7lIRmyzZzuW3WP01jO2Zs3cn/fw4dzv4y7QmxD8ggsAAAAAAOBT2JPokyZN0sCBA/Wf//xHq1atkiQtXbpUPXr00JNPPqlm7qURgNx6+WXXJKezgQN977dggVE3vUmTUEQVWV59VbrwQunIEUdbtCfRmzYNTTmHwoVdl7OyfH++JEfsZ85Iu3cb8+490QMxenTu93FHz1zEIuebPsePWxcHAAAAAkOnHQAxIAzFlx0++ugjvfPOO6pbt662bt2q+fPn6/7779cXX3yh22+/XTfddJNKBbsXLgqe557L235ffmlMN20KXix5EY5fMMwk8EsvSSNGGPPRnkQPlxUrpH37fK83YzcT+qVKSWXLet/G13KwREI5HCCUnnjC6ggAAAAAAAVAWDMsU6dO1dChQzVt2jR9/PHHOnv2rFasWKHvv/9effv2JYGO0PBVH91dRkZo44hEGzY45nNKoh88KA0YIP35Z+hjkjyT6Fb2XHA/t3sC3VdC3LmUi/PNALvdc59AP6O5ZSbR/f386BkSOH5WwUFNdAAAAABAFAlrEn3Pnj269NJLJUkXX3yxEhIS9NBDDykpKSmcYaCgCTQ5np4e2jgCFc4kXYkSjvmTJ31v17+/UV6kefPQxyRF1sCiuWXG7j6oqHsi3VlekuiXX+5/m2j8+QEAAACIfnQ+ARBjwpphOXfunAo71RdOTEyk9zlCjyS6b85J1px6oq9eHfpYnEVSORdf70fbtjlvbybR/5+9s463onj/+Oecc+lupLsbFUHsxFYMVGz9Wtj+7EJsRbFbRMBAsbvAREAUBKQUpJGSrss95/fH3Lk7OzuzOxun7n3er9d93T2bszW7+5lnPo8qqWgUIvoBB3jPkwvHjyAIgiAIgiAIgiAIIs/JqCc6AIwYMQKVKlUCABQWFuLZZ59FNTEaFsDNN9+c6WIRpRlRRB8wQD9fUVH6y5JriCKrm4ieLrsRHbkuovfuDeyxh3p+HtHP7VxMkooGaTgxsdbJheNHEFGju1/oeicIgiAIgiAIgiDSREZF9L322guLFi0q+d2zZ08sXbrUNk+MPoKJqBFF9I4d9fPlSnczk3IUFDgj7Js2BaT7yRPTSPRMH5tcEtFVHH00MGeOetrOney/bOciEkUk+v77Ax984D6PqZ1Lrlz7+crOncCjjwJHHQV0757t0uQH6bi3mzWLfp0EQRAEQRAEQRAEgQyL6KNHj87k5giCIYrNbqJiPgmJlSoBmzfbx+nK77ZfopDl5olelkV01b4fc4wloqumFxUBvMFQZeeimt8vV10FPP88MH++fp58uqZziZ07AcF6zIbqmD7yCHDbbcAtt9Axzyb775+9bRcVscawcuWyVwaCIAiCIIhcQnwvpndkgiBKARn1RD/kkEPw33//ZXKTBGHudZ4rD3aTclSsGM22TCPRs2XnwsuXK+cGABo0YHYuOoE/FgNWrAB27WI9Bpo2dc4j78+8ef7LkUgA//uf+zw7dljbGzLE/zbKIg8+yO6vr74yX2batPSVhzCn2Cou46RSQNeuLBI+V3JrEARBEARBEARBEJGSURF9+fLlSGZajCMIMRLdTYzNJaHWCz9ikVs0d656ovPtmdqRpJOffrL/vvZa73JxK5cWLZiQLiNfa9wCRuaQQ9y343XNiuutXp39r1rV/3rKEjfdxP6ffHJ2y1HaSUcvk2xex3PmAKtWAQsWZK8MBEEQBEEQBEEQRNrIAYWKINKM7B2uI1eExO3bvedRiehB7Fy++cYaziURPZfsXN55x/77hhvsv7/8Epg40fqdSlkiusrKJZUCNm1yjlOx776+iuogF45fviKfI4LQkSvPDoIgCIIgCIIgCCJtZNQTHQB++OEHVKtWzXWeQ7yiLwkiKPkgdmzY4D2Pys4lyL4tWQJs2wZUrpwZT/QnngCWLQMeeoj9njyZjXvwQaBJE+f2si0CL1niPc9HH7E/kYUL2X8xqai4L3J+CB4lLtOhg/u2vc5Lto8fQUTNihXAr79muxR68uEZQxAEQRAEQRAEQfgm4yL6TbyrvIZYLIY5PGEfQWSSXBE/TPIGnHEGMGOGfVzQ8m/fzkT0TESiX3UV+3/mmUD37sA++7DfK1cC335rzZcrIvrGjfppPNpcJhZzj0RX0aCBevxpp7FzrcPrnPPz9uOPQMuWwddT2vj3X+DRR5mnvNjQYYLqWJW145dNGjfWT8vWeaCkWQRBEARBEE7ovYggiFJGxkX0n376CXXq1Mn0ZomygteDOh880b1E9MmTWeTyjTdGu91M2rls3mz/PWGC/XeuJBatUkU/rUsXfUJJVSR6EOJxoHNnYPbsYMuLDRNy9HtZ5vTT2TU3ciSwenX49X3ySfh1lDWmTMl2CdJDthv+CIIgCIIgCIIgiLSQUU/0GH1cEummqCj4srkiorvZudSoAey9d7RCDV+Xm4ge9bHRrW/zZmaNsmMH+51IsP+5mJC4cmX1+EqVrEj0sCI64H5N58o1m2/88AP7v2ZNNOsrLIxmPWWJlSuzXYLooPuQIAiCIAiCIAii1JPRSPSUwYfm/Pnz0a5duwyUhiiVqARH0672uSKEmNi5qAhb/kyK6ACgsm066STg66+t3+XKsf+myWEzia4ho3JlYP16NuxmoWJK1CK6qty5cu1nirK2v2WFXLBzoWABgiAIgiAIJ/T+TRBEKSCjkegnnngiKlSo4Bi/ZcsWvPXWWzj55JNx/PHHZ7JIRGmjNESiu4nobgJN0PLPnMn+uyUWjToS/KuvgE6dnONFAR3Ivoge5JguXsz+168PeCRRNtpO1MeeRL5w93qu1BMEQRAEQRAEQRAEQWSMjIro999/P6pWrVrye+rUqbjxxhux33774ZVXXsE+++yDt956K5NFIkobXiJ6PkSiu9m5cPwIoV77xX2zMxmJPnas2XxRiugjRwLHHQf89FP4dQH6czB0KPuv89o2PZZ77MH+ZyISvayRi/ZARP5CkegEQRAEQRBOcuX7miAIIiIynlh0zZo1eO+99/DOO+9gy5YtGDBgAHbt2oWnn34abdq0yXRxiNIGRaLbGTbMOyll3brsfyYTi5oKTQXFVdTu3cBzzwG7dgFXXhlsm+efz/5/9FF2znVQcY080QnCjLlzmc99vXrZKwOJ6ARBEARBEARBEKWSjEaiX3LJJTjyyCMxb9483HLLLfjhhx9w++23Z7IIRGknTCR6ruAWie43kv6kk5xR3LNnA40bW7+bN2f/MxmJbio08Uj0zZuBSy8FrroKWLsWeP55oHt3YPnyaMsFAH/+CXTpArz9tvt+6/ahaVP2f7/9zLfJPdRVZKIBIx/ui1xh3Tpr2NSuJ9/57jvgpZeyXQpvXnyR2ShlGrp/CIIgCIIgCIIgSj0ZjUT//vvvcdZZZ+H0009HixYtMrlpoqzglVjUjVwRQqIsRyxmF9GPPpp5kRcIt37VqkBhIYvyVjF1avBkp27lMoGL6Nu3W+N27AAuuYQNt2ljnxakHPLxPvNM1tBw6qnAggX+19mwIbB0KdChg/kyZ5yhn0Z2Lull9277/eDF2Wfbl5VJpUrfMT7wQPa/c2egb9/g6ykLNjql7dwTBEEQBEEQBEEQADIcif76669j69atOOmkk3DKKadgzJgxWO8WgUkQfsl3T3SdkM3hAo2faGJR6ONe5LKHr1sU+t57u5fJFFFAixtWPVxELyxUL7tjR7gyqcqxebPZsu++qx5vch116WL/PWOGcx5+jsnOJb00bx78Otq+HfjrL/u4Cy4IX6Zc5Z9/wi2/c2ckxcg56D4kCIIgCIJwQu9IBEGUMjIqovfo0QP33HMPfvzxR5x22mn45JNPsP/++yOZTOKnn37Cli1bMlkcIp9JpVi0r4yXiP7kk8Drr6enTFFgklTUD3Ikeo0a7L/8QuMmogdh/HjncRZFdL+R6OKyX39tn+fee4G///ZfRsBbRHd78VuxQj2eL+O2jz16eBatZD1hondPPNE5TrXP4n4uWAC0b88SsZYFVqwIl2z2nnvsv8vKcQtCaRXRRSgSnSAIgiAIwgkJ6gRBlAIyKqJzKleujJNPPhlvvPEGPvzwQ5x33nl48cUX0a9fP1zCbRoIwo077gCaNQMeftg+3iSx6Jlnqsdv2xa+XGHxsk3xm1hUFtFV8x56KDBrlln5TCgsBE4+mR3ntWut8WFEdJEPPrD/vu02M1FahUpQXr062LrSRZhI9ETCOc7r2F94ITB/vpWIlch9kkngjTeARYvSu52wAnHYniO5yqpV1nDFitkrB0EQBEEQBEEQBJE2siKii7Rq1Qo33HADvvvuOzz66KPZLg6RL/DozxtusI83EdF1iIJvtjD1HleJWWLCQxGViC4un0yqI5aDIlqvbNwITJjAortFEX3jRrN1qUR01b4H7cViaivjB52wbWLPoiLTnuhR94Yo7eRCVM2oUcxXv1WrbJfEndIaiX7ppdawqs4iCIIgCIIgCIIg8p6si+icRCKBQw89FM8991y2i0LkM/kuonsJmH4jQXWR6FWq2H+HSc7pxpNPAgcfDAwcaD83OisUGZUgFaXwrVpXp07WcBgrFflcVa/O/m/a5G/5IUPY/6OOcs4TlYArridd1wKRPiZOzHYJzCitkegEQRAEQRAEQRBEqSdnRHSCiASViG4qNOaCiG4aie4H1TGZOzf67XDE4/344+z/V18FE6RVIrrfSM+mTe2/ufXLxo1qL/ju3a3hKC1NatVi/02TKfPjePfdwLffAm+/7ZznwgvZ/5NPdl+HiFdDTFkU0cM0RmQrEl08T8uXZ6cMfimtkei6+48gCIIgCKIskws9NgmCICKERHSidBEmEj0XCOOJLovFfH7RXiWbBBHRCwqc4/yK6KIoDljR5w88oJ5fjBT/+Wd/2wL0L4s1a7L/plY2nIIC4KCDgMqVndMaN2bRvePGma/PS0TPhdwA+UQ26px772XXw6efst9iA+DkyenbLnmiEwRBEARBEARBEGUUEtGJ0kW2RfQFC4Bdu4Ivb2rnohKzEglmnyLPr7JzSSc6EdlLRFdNV9mt+LVzkY8VT7apE56//trf+mX4/uu2W1gIjBnjvR5TwbJCBf28qsSiXpTFSPQKFYIvG9SPPwy33cb+X3wx+y/e43xaVPz7b3TrEiPR69SJbr3ZhqKsCIIgCIIgCIIgSj0kohOliyhEdO5d7ZePPwbatQMOOST4tsNEoicSwODBzvEqEf2YY/yVKyy1a3uL6Jdd5hynayzwg07MXrhQPb8q+l3F0KH+ysFZuVI9ntu9RIlqX1THtKx7opcvH3xZU4/7dMDPpXiPq3oshOH++53bC0ppjUQnEZ0gCIIgCMIdel8iCKIUQCI6UboImghStLCoVy/YOnhS3B9/DLY84B2J7haFXVDgfDmJxaKPzg9yjJs18y7H8887x6n2N0h0tZ/lK1Y0W0/79urxQV8QO3QItpwbqn0NK4SWRsJEomdTROeIlk1Ri+hR1h9BRXT66CIIgiAIgiAIgiCyDInoROlCJfCaCDDr1lnD1apFt22/hIlE10UdBz0mOoLsZ8OGwZZT7W9Udi46Tjwx2Ho5OjsXL/E6HSK6aSR6WSdMJLpfj/t0kM5I9CgR7VxM66Dp04EGDaxGylyERH6CIAiCIAgn9I5EEEQpg0R0onRhKtTKlhViYr6gRPGSYCqi+7E5UUWSpvOFRrVunZjvRTZEdNNIdJ0YzffTr1gd9hr8+GPnuCB2LqWd5cud48L0bsimiM7PWzoj0UXCROwDZpHo11zD7Kb4fXT22cCaNcCll5pv54wzgpUvKGXp/iEIgiAIgiAIgiijkIhOlC5MxYzVq+2/c0VE97JzcSORcPq5FxT4s2MwEX699lM1PZUKJqKnK7GoW1lM1+83Et0L+Rr0ez2pBE5Vg0BZjkRPpYAmTaJdZzYSi3KCeKK/8QZw2ml2Cys3ROG7Zk1fxXMgRqLzdb/3nr3eGzEC+OQT4Jdf2O8giZrfeEOfeyAdkIhOEARBEARBEARR6iERnShdBLVUyRURPWxi0UQCmDzZPs6PnUvVqt5l9ELlCZ9KBfNWjiKxqMzu3Xo/cyB7InpYcXvWLGv4ssuAI44ADjzQfPlUChg2zD5u2rRwZco1HntMPT7MvZvOZJlPPQU8+6x+Or9m/ESin3EGMG4cW7cJYqR90KTLHPFYpVLAa68BJ53E8lDIDZu8YUBsIPBznrIlbJOgThAEQRAEQRAEUSohEZ0IxsSJQJcu4ZJopgNTEV0WLIOK6BddBOyzDxOxckFEB+z+zrpI9DAiutd+Hn20c1wyCcyb571uGdX+hhWnJ04E/vpLP7+pSC+vl1unBPVEVyWFDcrjjwOff26eWDSVYo0vd9xhH3/++cHLkAvs2mXdU5MnA//3f9ktjx/WrQOuuII1iOiixvm5FJObmtq5rF9vNp9YJ/ntBSIjR6KvWMH+794NHHKIelti/RWmp046IeGcIAiCIAjCCb0jEQRRyiARnQjGQQcBs2cD++2X7ZLYCZpEM4iIvmkT8NJLTJz75pvwLwnJpLe/MheWVEIoF3E/+8wa5zcSvUoV73LKDB3K/Ird9j+VAr7/3v+6VaJdWBHdSyQ3FdHlxgm+XFBP9DZt/M0vIx5/t23rpq1Z4xz3xx/hypRunnySRVaL0coibdsCtWuzKOeJE/WNbLn4gi8K57r94+dS3C/T63fzZrP5ROE67HGSo/bFBr9Zs4C33rJ+830T77OlS823lUnboly8fgiCIAiCIHIJel8iCKIUkPci+uuvv45jjz0WvXr1Qq9evXDaaafhu+++K5m+c+dODB06FH369EHPnj1xxRVXYG0U1h1EbuLn4fzHH8AllzDvXJWAKJJMApMm2UWgX3+1hitUCP9isGmT9zrchKErrmD/e/SwxmVCRL/rLuC554A5c/TzBLVzCRv5CjiPmVc5dNts396+rCxCcvGSR9z7FfFkT/O99/a3fBgRvagIOO44f9vLBa68kvlfv/uuevqSJez/d9/l34u7WF7dNen3GhPrgmeeMVvmqKP8bcMNMRI9lXKeE5WwTyI6QRAEQRAEQRAEkQPkvYjesGFDXH/99Xj33Xcxfvx47LPPPrj88suxYMECAMB9992HCRMmYMSIERg9ejRWr16NIUOGZLnUeczs2UDHjtkuhR4/nujduwPPPw+cfbZ3JPrDDwP9+jH/Xo4o+NSoEdyPnWNiVaAShg46iAnYp53GfjdsaE3zm1g0jJ2LbNUg8s030SUWNaFRI2Yr89tv/pfVRfJWqWIvj2ihoVrOr52LOP2GG4AXX3Sf33RdJixaFHxbuYBJVLVXT4lcQ7xfdOeTjz/0ULN1etlFqeja1f8yOvz4x8fjrF7mli+APxE9igY4IvMsXgzce6+53RBBEARBEARBEESGKMh2AcJy8MEH235fc801eOONNzB9+nQ0bNgQ48ePxyOPPIK+ffsCYKL6UUcdhenTp6OHGLFLmDFqFDB3brZLoUcl1D70kHNcgXDpz5wJdOrkvt4nnmD/RauUWrWs4fLlwwtxfgQuUVTbvh3o0MH6LYrmmUws6rX/USUWNaFWLeDjj4OtQye+yfvnV0T3Qpz/wQf9Latbl5hwklMaxUWvay9sA1c28COi9+gBfP21+7yAM3mnCVE0MEyeDNx6q7MOkNct7/Pgwfbpy5aFL0s6yMVGmHxl332B5cuBKVOADz7IdmkIgiAIgiAIgiBKyHsRXaSoqAiff/45tm3bhp49e2LWrFkoLCxEv379SuZp3bo1GjVqFEhELwoiAuYxfH/F/Y7VrevovpCN4yLKlbbtFxbCEUc8cqRj+aING0rmSxVHPHLpKQUgKe1TPJUqmV6yvYYNS9ZRVFSEeGGhcx4/rF2LBIBUzZqIaaLSU7EYK9vu3dZ+/vKLfXs7d1rlAhAvKnKUK55MQiW1pSpWVI4XKdq922qEEMpRlEwCRUXO48/X/cILnuuWScLZXSZZUODZhabkOAGIKdahoshj/lQqZbsuYhs22OZLJRK2/UumUkiJ58Xl2PD54yWz+r9+YkVFjuVjmzc79iUViznOQ5F4PcnTIry/VXVKGHiZk0VF9mMtTU+9/TZSPXtqr4Oi3btLBF5t3SKt041I9k9xb8ll4Ne5eO6TyaTyWAAAVq3y3D8Zx3UVYN/iffsiJgnNqVQKqWTSdk6ShYXWtlIpxL/80n5PLVli3H1OPKfpJopjRDASy5cDAFLffut4DquIuk4hCKJsQ3UKQUSM8P1TVt+RqF4hiPzA9B4tFSL6vHnzMGjQIOzcuROVK1fG008/jTZt2mDOnDkoV64cqlevbpu/Tp06WOPlga1g5syZURU5rxD3u/7KlWgqTZ8+fXpGywMAvYv/p2Kxku1XWLoUXU480Wj5ROfOJcOFySSwciV4iruiRYsw49dfbdHqXQsLS6bz7ZVftgzc6GDu3Lno/NNPJfOvvPpq1P3wQ8x78UXsrlPHqEw1p09HawC7qlRBBY2IvmvXLsyaPt22bbFMAFBl3jzwuPTps2ahfbNmqFq8Pj5f282bYb8ritf/66+ooBgvMmPGDKSK/bvLrVqFbsXj/1y5ErtisZJzIxMzTWQosGbtWjSQxq1bsAD1PJbbsX07/ize15YbNqC2MG1XgwYo/++/jmX4sdlj9Wo0Uqxz+7ZtmCMc5+qtWqGtMH13KoVywu/Va9diuTB/pfnz4dbfYd26dSX7FeSeqr9iRcm9yZevN28emknz7dq503GOFyxYgA5wkkok0nJ/R1WX8mttybJlWKcoJ59e+OOPWNOgARpr1jN37lzsKI7aF69f1b7rrm+RKI5ZhaVL0aV4eMaMGUhVrOgow85duzB7+nQ0XrMG3MRp+fLlWK3Zfs0pU9Ba+L2jSxds7doVS2+8UVuOWosWoVXx8Ly5c7E9wL70VkRqxzZuxIqVK23nZOXy5SW/5yuuye1Tp8I0a8OsWbOwe+XKAKX1T71ly0rus9mzZ6MwSMQ/AcC6tpPJpK/7qKy+nxEEkR6oTiGIaKixaBHaFA/PnTsXO3btymp5sgnVKwRROigVInrLli3x/vvvY/Pmzfjiiy9w4403YsyYMZFvp2vXrkjo/JJLIUVFRZg5c6Ztv2PffOOYL6u2OLFYyfbjt94aaBXlypcHVq0q+V2wcSN6DRmC5C+/lIyLly9fMlyyv4L1SYcOdrmnyVNPAQC6vv8+Uk8+aVSOWLF/d/kGDVh3dgXlK1Vi269UyTbedg4EsbpHr17A++8jeeedSF1xhXWsNLYtFTTbFenevTvABb3vvy8Z36lzZ6B5c8/l/VCvfn3HuDoGjRIVK1cu2deYaLsDoJwgRoqUHBuNF3mlihXtx7l7dyuZK4ACKTFo/fr1UU+c38PepU49q2kg0D21ejUwYoRt+Ri3+BAor9j/dn/9pV7nEUdEen+r6pQoaNasGZq6lLNctWrYY489tNM7dOgACA1rnKD7HskxE+7x7t27O+55AKhQfE3GhHuicZMmaKTZfkyo0wCgyty5qDJ3Luq88Ya2GDGeKBdA+/bt7YmLDUl17YqY4sNhj2rV7L+F+71du3aO6PUqPqzEunTpAjSQm+DSQ+yHH0qGO3fuzHIyEKGIx+NG91G66hSCIMomVKcQRMQsXlwy2KFDB28L1VII1SsEkR/we9WLUiGily9fHs2LxbsuXbpg5syZeO211zBgwAAUFhZi06ZNtmj0devWoV49r1hWJ4lEokxWfLb9VvgpZ/OYxGIxa/sq/2eTdWzd6lg29uuv9v0S9lt1LBIan+n4zp36RJUyGzeybdesqS8r319JXLKVVZiWKCgAWrYEXnvNrAwGJOJx4KmnmGjbu7d9vG5fK1b0l1SwmLhifXED/+EY1OcJYMdQRSKRAO64Q+ufHUulXK/1mLSdeCJhPx4e10FcKFege6o474Nt+e3OuGHV/scfeEC5yljlymm5v6OuS+MFBa7HN1ZQoD3vAJCYNAno1s05PmAZI9k3IUdCQryW1q0rGR+Tp4GJj9pjoUmg7Fpe+boMsm+KBgAAiO/ebf8tlqlcOYTBtT6KmiiOEWHD9mw3oKy+nxEEkR6oTiGIiJC/ocvwfUX1CkGUDkphhjnWDXjXrl3o0qULypUrh0mTJpVMW7hwIVasWEFJRYOSiwnURo4EmjQBZswItvz69d7zBH3g+RH2uYWLFDlto2NH7/VG6bemEx6vvhq47z57klm3a6NFi2DbVzVOmCSJFMttmuBz4UJg2DD9dK/tFkhtkmESiwahZk1gzRpgyxZr3Nat4daZ7ft92zbgn3+85wt77C65JNzyUfPff8Dvv1u/xfMgRvDw/W4qmGxVrqxfbwAbs7ReA3K9Kt5jb7+dvu1GTbbvE4IgCIIgiFxEfEei9yWCIEoBeS+iDx8+HFOnTsWyZcswb948DB8+HFOmTMGxxx6LatWqYeDAgXjggQfwyy+/YNasWbjlllvQs2dPEtFLE+efz+xPhAjNyFGJuSYvAlKkpW1ZUewErMhTnYjeujXwwgvu6/WaJm5fxXvv2X8PH+6+rCjSzp7tvV2/qBoLTBoJgoiqYQXnJk3cy+BVprBCMADUrQtUEVyj99nHOY/OukVFAB/7SOnQgfWk+OMP9/m8jp3XvXrWWf7KlW5q17ZZBdkQ/bZV+33xxcDkyd7LmpLODx553eK9XWyJlTbk+jcM9FFIEARBEARBEARR6sl7EX3dunW48cYbceSRR+Lcc8/FzJkz8fLLL2PfffcFANxyyy048MADceWVV2Lw4MGoW7cunjT0qCYU5JpYEIXwaEJQEV0XMX7ZZUC1asDPP1vjuIius3N58UXL49fNF9xEZNaVXfby1djUKNfz8cfe2/VLsce3DclH2RPTaySMEDt2rDOq1qQxI92cdFK45bOd/GfpUvb/gw/c5/O6P2Mx9/MXsZd/5OjKzq9Zefp++6nnDxuJHrT+191bciOdWHf5adQ6+2x/5XnpJVaPPP+8v+UIgiAIgiAIgiCIMkvee6Lfd999rtMrVKiAO++8E3feeWeGSlTGuOii7G4/U0Kll0inE5d0Ivpzz7H/d94JHHoo0LatmZ0Lp2VL/bTu3b2X1yHuR+PG/hop3AT3nTuDl0nGpMdBEDsXL9zsXBIJW5JVAEwgfOQR/TK9ewPTplm/09EgFHade+0VTTnSjWo/RXsnr0SPuvt361Z7ZH+2+O031qtASPIJQH9+dfVOtiLRTa9DE6smFaqGNbdy8+fWJZewyP2w5FrjMkEQBEEQBEEQBBE5eR+JTmQYt+73pRkxyvjSS82X8xL5p00DbroJGDjQOxLdlCZNgDlzgJUr9fOYiD6PPKIWv7yiYlUsWuS9PVOWL/c3v6mA9/LL7tPDCmVe9i5eUf9RUbWq+by8jFu3Ascco7cY8UsqZTUa+SmHH955xxquVMn9/Ommvf8++//jj6zRQ8ivkTbEPAOcrVuBU04Buna1j9dFoss89hjQvz/z/ffDuef6j/IOQ1ARPVM9knSQiB492T6nBEEQBEEQBEEQEiSiE+HItoieDTsXHkUexhOdI/ry8ghrk0h0Lzp0ABo21E83EasaNVIfX3HZ+++3hjMlAu/Y4T1PkOtCZR0j4nW+zz3X/zZFDjiA/W/WLNx6vHDrxSCTSrHjXbUq8Mkn/n2qJ05kljLLltlGx84/n13nP/xgXg4R+fqVr71Uyi6ie507Pv277+zjK1Rg//fbj0WDF9uEpZUvvnCOi8XUljam1/m11wI//WR273D++w8YNcp8/ijI9vMkKCSiR8+mTdkuAUEQBEEQYaF3JIIgShkkohPebNvGEvulUrkXiZ4tT3T5WOheELxEdFEwX7zYOS6TyNG6/fv7i0TPlIhu8jKWDjsXr0hmL8sQGblcdeuy6OwFC3wXzRXZZqbAh4tXMukvEanMQQexZLX/+59tdHz0aDbgYcelZMYMdqzE3BbysfzzT7v1yUEHua8zlWLzH3igfXylSs750o2qTpX99jlRXduqbUZZt5uWM+g2/dRTPDkzQRAEQRAEQRAEQfiARHTCk9illzKf7V9+cU4M2v0+KjIhom/a5IyKUzUocMqVs4Z13sQcUdDkSRzD2rmYoCp71ar28fG4WhjXnfNcEtFFvGxUotquV9JGk+3WqAGUL29eJhPkJJM6QVZFMhnNPc4ThAZFPHYXXsiipK+8Uj0dsEehA6xnhlcjyB9/OMdPnAi88Ybv4oZC1fCma/gwtXPxQpXEM8oGg3R7opuyfbvdAz0TDWwEQRAEQRCE+n1pzBjg668zXxaCIIiAkIhOeBLjAtjSpbknFqS7PJs2MWFzyRLzZSpXtoaDJD7NRCS67rjJ41Uik0pwA5xRvOkibCS6n0hsETeBLxYDZs70t75YDDj1VPvvTLB5s/m8qZS5sLlzJ9ufl15yTtMJ91Hdv3IDzvjx/raj289HHgHOOCNc2fySjUh00VaKozpm6a5vo4xEVyE3akbVYJVrz0WCIAiCIIhc588/gbPOAg47LNslIQiCMIZEdCK/SXfk4u+/q8e7RaL7EdHldcTj/hI/BkUlVq1ZYyain3OOep2ZsqHJVu+H6693ny4nytQdJ079+t7rTAd+jl8yaS4QjhwJvP02cNFFzmlR9lJQlUe8z+bPZw0aBQVA27buy4nTsm1NxeE9UkRkWxlOVJHopiJ6UHLJzkWERHSCIAiCIIjssGhRtktAEAThGxLRCX/IYkFpFw904o+bJ7ooou/c6W97NWvqBccoI5X//ddsPtU2v/kmunKYICfsnD7de5l0eKJfcon7dDnKVT6PcjluvDFz0ecifu5Zt8YimfXr9dPSbfWzbZs1zKPQDznEvGEnahHdT7S/jCr5Z7otnlTlVZ331avTWw5VL4YokRuQeOJYgiAIgiAIInrcviP8ficTBEHkACSiE4QbpiK6KKCKIvqMGe7rl0WdWrX8eVYHZdky9Xj5RUfcFy9SqWD2NV6MHOl/mXSI6G7raduWJbs0nf+EE1jiVtP5s4Wfc+r2khz2mvY6NqLFEPdDP/lk8/WnUt75C/xQvXpwUd6PAM+Pi9iIEIQtW5z7rzqf994bbP3pvLZfeMF8/bI3fzoi0Ut7wzJBEARBEEQUkIhOEEQeQiI64Y+yJhC4iegioojlR3iWBcqaNc2FnXbt2P+99jLfHmeffczmEz27TTjlFP9lSTfyOYwyKnryZOCtt4BevYBGjdy3u2KFNfzAA+p5cg2V9Yupn76I7phHVZ+sW8f+L1oE/PYb297xx9u342XnEnUDkMoixYtbbgGefto5Xld2fv3cead9fL16/rb7+++s7vm//3PfZpQNDVFx0UXm95EcSS/ek2Eoa89FgiAIgiAIv6xaZf+t6n1JEASR45CIToSjtIsHbv7R4r6LkbB+ohvlaNVatcyXf+MN4PLLgVdeMd8e5/XX1ePl81m+vN6PWcX77/svSzrIlDi99976hgZZOBavkfbt2f98sHOpXTv48px0e6IPG8b+cyuXAw9kQrLp8fWKRJfvyWrVzNbrl/vv9ze/bv/8iujDhrGGwEcescapjnPQpLzZuM7XrnWO++ef9GyrtD8HCYIgCIIgwiInEN2+PTvlIAiCCAGJ6IQ/yppYIAqfInJkqzifH8FIJaKb+vT26gU89RTQpYv59jhNmjABWEa1bdOudtm+NrgwLSOfj0yVU46uUF0X2UiS+vff5vOmUs4yBolEj9KiyO2YcSuXgQOd0zZu1C+XSqkTeuqoXt1svlQqvVE2pj1lvFB5x6vWEcV5LFcu/DpkVMdh8GDnuHSJ6ARBEARBEIQT8kQnCKKUQSI64U2uW06kE1PB0M2TeN48YPZs9TRZRK9ZMzOe6IB63/baCzjjDODWW61xN96YmfKoqFHDfF5RnHPzRPcjMN58M/t/3HHmy3BGj/aeR0wYmYv3WTLpFK1372bJZWXvbjdxO2wkunhsdNtZupTZ68RiwIknOqePGKFffyoFXHut+3QRExE9FgOOOorZO4VNyPnrr/ptqPDrx66KrFfdJ3/95W+9HLGcFSsGW4fp+jmzZjnHLVoU/baB7DcgllbOOceyaiIIgiAIIndZv549tydMMF+G3p8IgshDAvbNJsokKl/hfH34Va1q5lns5uUs7rubiN6hA/u/aZNTrJJ9mFURoZmiRQsmRo0dax9/8sn+bSaiomFD83lFEV2MqA8qTi9ZAjRtClx8MYvc9yLIvZCOqNwoUYnoDRpYUd2m+xylJ/rixc5xNWsC777LhvfdF9hjD/t0L79zr3LI003tXD7/nP1/+21mvRSUM85Qj9dd236i6gFg+nTnONUxWbnS33pVVKjgL3lqlJCInl+89hr7P2pUdstBEARBEIQ711zDntuvvUbvRQRBlGooEp0om5j6fLt1M9PZuehQefSqItGzxQknqMf78ZWOGlNrG8AuSLuJnKblbNqU/W/e3Kx3gCwMmvhH52L0uUi5ck4RXWeLEiSxaBBU2z/ySMsP/eSTrfH8+L78svs6va4J3hDGqVrVfX6ZKPdfJBZTR+ZHkQA0yvs5G5HoKhYuVI8fNw7o3x9YtizY9uljMX2kq+GDIAiCIIjoUPUA1HH66ezd7Ycf0lcegiCINEEiOuGPbHg4p4M1a8zm04kufiLRxWVkVJ7omUIuTzrObd260a8TUIvkOhFdFtiCWlJ40aeP/fdzz3kv42Y7k2kGDHCOa9MGmDMn/LqjtHNR8e+/wI8/suGTTnJOnzfPfXkvEVT+MMglEV1Vl/mNRFeRqyK6n8TNItu3A6tWqaeddhrw00/AlVcGLxeRHrJdLxIEQRAE4Y1JQBnnzTfZ//ffT0tRCIIg0gmJ6IQ/ZNG3tEfg6ZIw+vFE55gIaVxEz5QvuohORDcVMVassP9u3x4YOTJcmfi25UiFXr2c84oiuuhZnSkRRvbUli1FVOSSQPTBB+rxp55qtnw6I9G9jtOECWz7ffpYPQhMywboI7d1djsmInrYBpLWrb17psRizvsOyD0RXSSsiK5qQDNpADRJKvrff76LA6D0PwezSboaoAiCIAiCiA6diE7vSARBlDLo64TwB/cWLisftrqoZTkSXXxx0L0smAhpXDRTzZtuwdVvMkKZwYPtv9u0AWrXDrdOTv/+wLHHWr979nTO07WrNWzqWR0l8r7Wr++9TC6J6OXKOe1z/Lz4BhHRo36xFq1c/OBm26TCbyT69u3+5gfM/fKXL3eOi8LOJV29jvxYNKmoUsU5jntnu6HrVeSHVAoYONAZsU4fiOkjl+pIgiAIgiDU+IlEJwgieoYMYd/C9F2SdsqIEkpEBhdaeaR0uqwxcgWVjzngLqLrMBEDctHOJaiIEbU1ghidL4voLVoAnTtbv93sXDJF797e8+SaQBTmoZvNSHTOwIHq5bwEVJ2IrtuuSsiVEY/HzTd7zy8zd673PFFFoqtslzJp5+LnPlA1YOjqaRGVt7bf+nbGDJbA9skn7ePpZTV95FodSRAEQRCEky1bsl0CgijbPP00yxH2xx/ZLkmph0R0whvxI5ZHovNIx99+y3x5MsmGDerxQTzRc01El4laRI86klU83kccYZ922GH2coqJYzMlwsjb8bvdXBCLwpyz0aP9L+O2z+I9ZWKN06sX0LKl/zIA6YlEF0XbIJHoJsRi6kh0Xk+bohKhMymim0bdA/57AXBUIrpf+5YoIvwJfwRN9koQBEEQROagdySCyA2isPUkXCERnfBHWMuPfGL7dr2PbhBPdD92Lpkg3YlFK1QILww3a2YNi+Vt0MB9OTHyORfEaYAJ/23a2C1HxOsmm0l7b7+d/ZevCT9C6uLF+mm6c+C2/pkzrWGv8w04o9D9sGOHv/lN7ILceumsXAkMHw6sX+9vuzK6SPQoyKQnekGB+fJ+RPR//2XHee3aaOxcdIjHKopEvISF2MOIIAiCIIj8hnrvEUR6oXss7ZCITpiTSpUtEX3IEP3+ypHoH33kvT4TS4tMiugyYvS2SFARukKF8C2hzz1nNl+uCOVuVKwIzJsHvP22NU60qNm8OfNl4vTowf6nS8gPcn6mT7eGTeqdoH7ogD5yW3c8TIRct/t9wADg+uuBM87wXo8b6RTRo7gWVOctrIh+/vnOcfXqqec9+mh2nAcNUkei++G334BJk9TTxGfBWWeF2w5hR+51ROQH33/P7I8IgshNCguB008Hnn8+2yUhSjuyoOe3tyRBEP7IZmBeGYFEdMIffh58n34KXHdd/j4sX3lFP00W0U2YNQtYt859Hj+2BlHSpw9wxx3RrjNsAkEAaNTIGr7gAvZ/r728lxNF2z//DF8Ov5xwgnq8LKyK9iNe10Y6EcV8kXS3ZE+cqJ/2++/W8JAhTJRxo1075zhT8V73sqGru8KK6Fxc+uIL7/W4obNziQLVuT/7bPPl//2XiduXXWY/D6p6wU+916oV0Lq1fVyTJup5p01j/7/5Bli92nvd27YBV10FTJhgH59KsRwHV12lXk48VuQJGi1lJYl5aWLZMuCAA6zGWYIgco/XXwfefBO45JJsl4Qoa0Rh+/LxxyxIIl81BoJIJxSJnnbo64Twh59I9KOPBh591F2MzleCiOhHHGEXhbONWP5fftFHcwaN8m7UCKhdO9iyKo47jgniOjFVLKcoZLkJtTreecf/MiIvvOB/GZ3/ftTceqtzHBeq0vXQ1V1Dbi+/ooi+aBETZdKF3xb7Pn2859E1TPjB695LZyT6rFnOcXXqmC//1FPMc/zZZ7090f1EogNmdjoyJtf2lCnAE08ABx/M5n/8cWDyZO/lxOdiFOedIPKZ+fOzXQKCILzI1DsnUfbweg+KQkQ/9lhm1zdqVPh1EURpQPzOIRE97ZCITnjDBab77w9m57JkSbTlyQWCdpMpS4keatY0Swjph44d1SKczF13hdtOlSrBlps9G5g6Vd8g4UamoilUyWuzFYmuY/dufWbxoOfGDb/7WbMmcOqp7vP4FYaDUFgIrFkT7Tq5KB82Sl53TFW2UX6PVSauyyeeAK6+GthnH+D9993nvf9+a5hE9OCoPqzpQyD/+PXXbJeAIAiCyBZe7+lh3y+nTLGG/SaIJ4iyANm5pB0S0QlPYjzy948/gO++y25hcoVUKniiulwRBUyFqzB+434iV8PgdkxbtDBbx+GHW8MNGwYrR6dOwJ57+luGC+4DBgTbpl9U9gg6ywQ3i5levaxhr2v633/V49u2VY+fN0+f7DMd9g47d/qbv3p173mibkBSsWoV+1++fHTr5OdV9WHip+7SNQqpGsH8Cs9BEuDq5rnxRvX4q6+2hk86yahYAEhED4OqkT5XnpeEOV7WWwRBEETpRRbR5ef4aacFX/fy5XbLzO7dg6+LIEoT4n1GInraIRGd8MfffzvHbdzovkxUH8HLlgF3323mbSvSt6/de/rtt8N7dSaTwODBwZbNZgJJkVdeARo3DmY9kmvEYnqxf9gws3Xcfz/w3nvAiBGZ9XJdtAj45x+gffvMbI97y4t07qye96GHzNbpdY/rLDF0vQpEKxcRv71aTK1Ofv7Z33orVHD63stJQsXr8cgj/a3flGXL2P8obaJ4g0fY6J6lS63hr7+2hlWe6H6eEbFYtMKq6TVuConowSERvXTwww/ZLgFBEASRLdLRY5Rz993AypXWbxILCYJBInpGIRGdCM+4cZnZziGHAHfeCZx4or/lTjsNuOEG63csZh6drCOIrQ0naALJMBHhKrp0YSLcRRelZ7tuwnbUuG3HtAyVKjFhVJc8MF1UqQI0b5657clR1L/+CjRt6n89v/3GfP43b1YLXWIk8pln+lv39Onq8c2b+2uE+ucff9s1JRYDBg2yjxs71v7bzZuuceNoysHtoerXj2Z9IuvXh1ted+xVDSd+rYyOOor9b9CA/c8loZVE9OCQiJ7/rFwJbNrkPd/YsaweFa2QCIIgiPxEfH6nU0TnPTBV2yUIgsHfnR97jOUo1PXuJgJDIjoRHi/LDpMPKhN4siq/UaM1atiF1FgsvP1BmIe2mPQyHwgqhGdSTIpCRC+LNG4M9O5t/fYraHz5JXtAq4Qu0cJlr738rffPP/3Nnw1iMeDcc9nwyy87p4t1RIcO1vDzz7PuqF507GhelnLl9NOCeqa3a+cc50fQXLRIPT4KEf2uu1hPmmnT2G+TukZnKRQU3bEgET04JKLnP7Lln+788Z58t9yS3vIQBEEQ6WfrVms4nSI6vRMQhBo5eCuZBK69Fvj00/B5CAgHJKIT4alZ0326ygImk+y7r/13LBa+C79bNxmvB3y+vQAEKW+zZu7CXjoIG42Qb+clDOPGsd4YcsJEnT+0G5s2qY+dKBRv22ZurQLkRgLeb77xnufll1luhPPPd0575RVruEYNa/iSS8y236CBeQNQLKYXb99802wdMmHFYLG7rYjKzsXvvVuxInDeeVZEv+kxzQQkogdHbEzhFkVlqV4uDZiK6ARBEETpQRTRdVaNKlTvhG7I398XXcQsOP3mNiKI0oZs57JggfXbS6sjfEMiOhEer4+kv/7KTDlUvPwyS14oR6KHtc9wE33EF4mg5FL0tOx5f+qp6vnEiGadgJYuouhdUJY45RQWKSwnQQ1y3SUS6kSPooh+yy1M8DSJwM4EI0e6T+/VCzj4YO/1xOP2fAsib73lv1xh0DXsvfaa/ffzz3uvq7AQWLs2fJlURBGJLsN7BGQS3XMvHYlvywric5U3RpAIm1+QiE4QBFH2EL995W8Jt+dApUr+tiOva8UKYMYMe+4dgijrpFLA1KnWbwrwiRz62iOC8dBDQNeubNjrIymbkejcRkF8oG/f7m8dvXo5x7lFouuSInJ0x0snTmebatWs4ZNO0ke2ig0TfiMLAODZZ/0vw4nF9L7QudQgURoZP955TS9bphTM46ZiZ7qFl+OOS//2q1YNt7yfSB5Vss1332W2OL/+ah9/8smsEcWN1q3VIrrpcXGzrIoiEl0m071e3KAX1eDkkp3L99+zXA5R2wCVdhYutP8mEZ0gCKL0I773+an3/T4jdPP7FeMJorQh27mIIjq9i0UOiehEMJo0sSLuvG7MbGYInjnTOe7pp/2tQyXCpiORSa6+AHTpYg1XqKAXpcXxfgXEJUv8WzLIUQf16pktd9pp6vH0gLHD7RRkxEYVgDWSyceufHmliB6bMMFs29k+F1FEE4f1hPRTT6nuyYEDgdGjneNNRN6lS4MnQAaAzz7TT1M1DvgRnnOlUYw80aNH7JHAz3O26oIDDgBefx24/PLsbD9fkd/3sl2XEwRBEOknaC9s8ZlhYuWo0xTkbxOCKGvIdi4koqcVEtGJYMRi1keu7oFWu7Y1vG1b+sukglt8fP+9Nc6vCKPycg7TMKCryHJFHHLDtIzHHutvvU2b+i/LIYfYf+tEdLHB44AD9JH0QaLn84Ugwp6cS8AN+ZqOx3PHukWFfB3XqmX/HYUQKtobBaFVK/N5dfflmDHqeU3u46B2Lt9/796rpjTfZwDZuYRBrKt5z6Zsv/j/8092t59vZPt8EQRBENGzbBmwzz7A2LHq6W49EE057DDveSgSnSC82bXL2xmBCAV97RHBiMW8I9FFEX3UqPSXSQXv5i9+CA8b5m8dKn/vwsLARdJSuXL064wanfjWsKF9WqY9imMxvTgnJnhU2T7cfjtw2WVAmzbpKVsu4Mcv/phj2P+rrlJPl6+BqlXV0YcmIrqu8SQqIeb//s9svv/+s/+OQkTv0cPf/KKAeOed4bcPsI+emjXtDSKmIq/K9iqVYnYNEyfql3v7bff1qu7BKIW3jh2jW5cbFIkePeI90KQJ+59tUTYfGrdzCVV+DIIgCCK/ufpqYPJkYPBg9XQxEj2onYsY8GYyvwg9q4myjnhvzJoF7NihnkZEAonoRDBUkei7dwM336xO7nHZZcC8eZkrH4cLNmLW7oYN/a2jdWvnONln2A+6iiwq4Syd6F5S5IaJTHsUu708/fabNawq1913+7f4yTf8RP9+8AGwerV5JHrfvs5r+qqrtCJ6gWgT0rmzep1RPex1L+ReL9sFBf7XKSPuw9136/fpySfZf7GhT46M98JtfwYNsp//sJHSrVsDBx1kv69EvLrUqsoa5ctdNu3DAHZ8//jD/vJKmMFF9Nq1s2/nwqEP83DI56+wELjppuyUhSAIi2w/K4n8YsMG57hvv2X1eWGhu52Lm02L32c8XbcEoUa8l6ZM0U8jIoFEdCIYqkj0F18EHnjA6o4l37CzZ6e/XEuW2MVoLoaJD3C/XsVDhjjHffGF/7J5oUuMmUvoBLgffjCbT8VJJwUvj0iNGurxYoT/zTdHs618w08kejxu7i8PsPtcvtfffFMroldasMD6EdY33ItNm4Itp4smvvhiYL/9zNYhv+h/+ql6viuvBDZvZklAOfz+MRXwRN87mbPPtp9/UzsXFVzwB4Bp04KtI12i5M03A3Xrsp4lmUD3Qjp9OtC9O3DUUZkpR2mCe6KL91/QF/9kEli/PnyZyJ7HH16R6M8+Czz4YObKQxCEmp9+ynYJiCj47jvg4YfZe2Q6UT2LDzmE1efPPutu5xImx45JOdzGE0RZZPJk+2+6PyKHvg6IYKgi0SdNcl8m3d3c27dn9hADB1rjuIguerL7tU1RfUSHEdGDVmS5EBGnK8Nbb9mnmZZ15kzgnXeiKZcoyJ5xhjUsnu/Gjdn/Pn3CbzOfiNKH2jSSWPNCXXn+fPfl3Mb7JWi3T12DTNeuwbftZm9TVGQX0fmyph8eugigNm2Yh6XoFRmVKBi0Plq0yDnOz/nW9XK57z7g33+D5VdIB6ZJdAkLHomeSKgj0VMp4KOPWGO5FwMGAHXqsF4BYciF524+4SWiq5K9EwSRWVIpYPz4bJeCiIIDDwRuuAGoXj29Qrrbe9qiRe52Lm4N2hSJThDRIFoirlpln0YieuSQiE4EQxTR+Y05bpx9HlWywXRx0EGWaMETkonbFD2P3aJfDz3UOU5V7o0b/ZcxLNn8mOc+w2eeqZ5evbr9t2lZy5ePZr/4Om69FejZE3j1VWuaKKLzc9m9e/ht5hM33sj+n3hietbv4+Fsi0SPYH1p4Yknwq9D5ROvo6DALqKr6qEgnH02uzcqVrTGRVUPi/WsH1SJff2cbzernSifMWJODxXZvkZLI/wDoKBALaK/+y5w3HFm196XX7L/L74YrkwkopuzaZO3iJ7uaEmCILwRE87lQz4mwoywjcZuuInXiUTwSHQxyM0Eevcqvfz0k9U7l/CP6Logv7vSfRM5JKITwZDtXNautXzHecS52CIGpFdEv+oqYI892LAs6AL2VnBdVO4XXwBffeUcH/VHdD5WZNOmMZFPlzn97LODrbdBg+BlApho3qiRZdNyzz3Mq1mMVhUbTfi1+dBDwPXXh/O2zycuu4y9XL/1Vvh1hfS0zqiIHjQSvVmzYMuJyC+BqgTFnFTKEtEfegjo1Ml8OxzVPcgTQImR6GHsXEQuuEA93it63k0ENyHs8qZce21mtpML7NwZjfVJWEQ7F5WI/s03ZusRbQrC9oDLBTuXVavy473BpKeO18fx5s3uYgxBlGVSKfd3CVPGjrWGvfKYEPlDlL1OZbxEdDdPdLJzIUzo35/ZRt5xR7ZLkp9EmXuA8CQHvg6IXCfVq5dzpGzn8sYb1rT27dl/WUQXk3tmCl5p3HabNU4nIHGrD5moP6JNKjJVGbMZEVepkhWNruLqq83sXAYNAj7+mAm606bpbTNMueceYNky92SxqiibGjWYh2Dv3uG2ny/EYkzgSEfC11jMn4gu2nmk+6EetRjj5x4UI8sBYOhQ/bzJJDBnDhs+7jj/5QKA555zjmvZkv33snPZZx//21u61Dnu33+BUaPcl7vsMuc41XXQqJF6ea9r2OscNWniPp3j5dfPy+yWQPSII8wT0WaTNm2Y9Ync/TPTiHYua9aw4ffft6abdOP++mvWM41jmiBZR7Yj0Z94ggUIuNUfuYLKZsdPJHphIQuCqFbNalAhCMLi5pvZs1HVo8uUZNK+vPytRuQvYq/DqHF7X5dFdD92Ln4hO5fSj2j7SZhTWGgNk2iedkhEJzxJjh7tHLl8uSXGbN/Out9w+I0rv5jpRJEo8Kos+vd3jrvwQvvvWrXUy2YjEi3fKj/dsRN56inW2HL00UzQVTXOBMFL5BBFdDfBi3AiRitxVJGdPq7XmMkHW1TXPxfiHIUIKIz5idjy0612yRL2AVKuHNC6tf9yAXahXEasw1SR6KrzHIS99/ZuLFU1xrVo4Rz33nvq5cNGoptGJpteg6rGBM6XXwIHHGC2nmyybBn7P3FiVothE9E//5wN//yzNd3k43nUKPuHRNiGw2yL6Fddxf7ng4iuQr6PTLv8Z8MyjyByHZ6UN0xPqTVrgBUrrN/UYFV6yGYkerYTixJEWYci0TNK3ovozz//PAYOHIiePXuib9++uOyyy7Bw4ULbPDt37sTQoUPRp08f9OzZE1dccQXWrl2bpRLnISqBdNUq6+NSzvDOb1T5xSwbXQZ5GVUfwv362X9zOxiZbESiq8j2x7wblSt7R6Ifc0zmyiNy+unWcLqT25Y2zjiDiZlipL/qGEYVGZJMMlHS6x4x7cEwYkToItnwE7H999/m8/KkzO3be4vEF11kvl6OeDxjMacHZatW6uVuv93fdkwSPpYv7xw3aJBzXJ066uUzZefiBT+mJvucL2TbukT0RFdh8uyU3zvCfjjk8nM3H/AjoovzZvtaJLJPURHrMfjLL9kuSe6h+14xQb4nSUQvPfj9xiksZN/Cl1/uPW+u27kQpQd67woGiegZJe/fUqdMmYIzzzwT48aNw8iRI7F7925ccMEF2CaIBPfddx8mTJiAESNGYPTo0Vi9ejWGDBmSxVLnGTprET5e7h6vi0RP5w0cZN2yzYeu0lZ9zAWNFi2tuD3w5s9n3uNBExGG5fTTmYD/v//pxUJCzwkn2KOWQkaia/nwQ2av06yZPfpURbduZusUbR1ETF/Q5KieNm3MlvPLxRez/yZe6FWr+l+/fH5mzDBbzrSe++034OCDzeaVI4MrVfIWzB56yBr2EtGjevn2uqZ59HauiOhR2KVlW7gUPdFVuJ0T3f6HtSrI9jHxw6GHsuv/r7+yXRILE8GOjxNFmnicLfvmm4BJHg2i9DFyJHDDDUDfvtkuSe7h1vPML2LPHSK/8fsu/tVXLIjjmWescboeu+K65UAMORI9G3Yuo0Zl35KOILIJiegZJY++DtS8/PLLOOmkk9C2bVt06NABDzzwAFasWIHZs2cDADZv3ozx48fjpptuQt++fdGlSxfcd999+P333zF9+vTsFj5f8BKXdWJ5LvjsuX0An3SS2TpU+69LsGlCaYhE9zp2Ylnbts2u93gsBnz0EfD889krQ74jnk9Z4IrFfN/rKS4EL10KTJkC/P47cPzxgGmdnKkeBV26ZGY7HBMR3S0Kv2ZN9XjZpuPww83KU7eu2Xz77w9MmGA2ryyim9RromDgZc/hdS2qEk+rUNXT77xjDW/YwP7ngoh++eXMC1X24fdLtgTjOXOAuXPtdi4i/BhrPp4rLlyIRJUqwJAh0X8o5NJz1wueeLVt28xtc8cO1kD90Ufq6akUq+d//539Vp1DLpKL02IxYNw41gjerl20ZSbyg1tvzXYJSidyHUkietlFbNQsKmK9PqpXVyd2FOvna66xT4vH9ZHou3b5y0/k9QzXTR8+3NnDnCDKEiSiZ5Qc6RcdHZuLkxbVKBYaZs2ahcLCQvQTKtbWrVujUaNGmD59Onr06GG87qJcEIUzCN/foqIiyJJVMpVCDEAMQLKw0NYak0qlkCwqQnz3boifn0VFRdZHcmEhsHixr8hON9msKJm0iSexCy5AbPp0JA8+mI1PJkuWLzmP8Tji3bohVuxdzMc79rViRUdrU7JfP8RVifwMUB1PW7kAxGMxyJ/utuOXZWL9+yP+7rslv4uKihCD1SpXtHNnoLLKx6Ws3XO5DD83qUTCdm2mUimkRo9WtsimKlVCbPt25/i+fRH76isWaTZyJJJDhhi36CbvvRexL79U3x8yCxc6rqlULIakUB8AQPLssxF75x3EiqNr+LrilSo56zAXvKR93b1fMr1DB9t9o5o32bCh8lgVFRUBS5YgIYjEJXVacaNyybjDD0fihRfY+k45BSldnVSzpnt5+frduvHKy8Tj9mN/+eVAKuXYp6Lata1rTjjfRR6NNrGFC12vpVT9+o5rR0UymXSWqXNn6zmyaxdQVITY4sWe126667FEcRRZ8t57kXrtNecMX32F2MyZSF1zjVMUTqVK9im5bRtSqrJu2cL+3BI4B2X1aiSKG4+K3n8fCQCpggJ7HXP22Uh+8w1iwjkR30/2eOklNvLpp5E85RTb+Qj63Cy59gAks/gcEu8VP/VP6Gtu61bmS+6Ryyb2+OOIv/gi8OKLyulFu3cj0awZG16wAPHid0eR5O+/I9WuHVBYaB33PfdETIhAp3eBskdi9eqS4Uyef7FuyTUiqZek5z3/ZgtC7JlnkGraFDj22GBlIUJjq/d371Y/7zZtYvnLGjSwjY59+aX1TF22DPEDD0SssBAYNgxFd95pm1esu1PvvIPkM89Y7w6xGGJbt1rTxWtq9Wr2XI/FEDMQ8ormz3fVBuLJpP4dbtGinLxvgdyuV3KFkvotHs/qe1fesn279ptNWzcQDkzv0VIloieTSdx3333o1asX2hVHrqxduxblypVDdSn6rE6dOlijSzqnYebMmZGVNZ/4c84c9JDGrfr3X1Rfvx5VAaz74w/UE6bt3LkTs6dPR29JWJk7dy52FLd6t7z1VtT+4gv8NXw4NhomXnOLZf5n4UJsEKNYL720uPAsMq/89u3oWjxJ7IHQcft2VJbGy9tZtGIFZFODRStXOsaZ8tfs2WgvjUtWqGArV0/A8ZKw4K+/sDXKLpwhqFq1ask+zBk9GtumT0ebpUvBY2SXjxuHNQGiGuVjT71Fcgd+bnbt3g3R5GTTli2IvfMOVPG9hdWro7xCRF/VogVEaWbH55+jsmMuNQsrV0aDLVsgZljYXbUqZiiulRpz5sDxKh6L4Y8//kDP4p9Lrr8eawYNQs+33iq55/h116hdO+zx448li3pdj81OPBH1dAkxi5d3q8fmxuPYIWxDnnf9EUdg07JlaKFZt7yMblzNRYtK6q9Fe+2FDZpyzVmzBm6x+Lo6042Zc+fanie/DxyIhq+9hsbyuhcuLFnvtr//RpXi4T/+/BNJ2YpLoP4ff6Cpy/bXV6gAjdu6jRXLl0MyKsOfCxaUPEfmL1iAbRUqoO3s2cprXyTd9Rg/Tv9t2IB/FNvqPWAAAGB+5crYLPn6F6xfj+7Fw+vfeQeLFYlfe/Xpg1hREWZ8+SV2164dYcmBuu+8A2709c/cuWgNYNuOHSXnGwAK58zBzOnT0XvUqJJx4jFtKcy7YcMGiCVcvGgR/gtw/Pkx3bx1KxZk8Tmkup+95t3ZuDFm8Xl370adTz/F5l69sEu23nOh+0EHoWDzZvzx8ccodGk8aTJ9Ohpop7L35h7Fw/9ddx2q79wJOSvCv199hRXt26PS3LngfXFikoULvQuUPfxc++kgF7/5+DHZsnUr5gc8JgVr15bU+Ryv4xvbuROt/+//sGnvvbF68GAAQKV589DpyisBANN+/TVQWYjwiPfJnDlzsFMR9d17zz0BANO//RZFxXpIuX//Rbenny6ZZ9bcuegu2KItuf9+rD/yyJKG9w5bt5Y8l4uKijBDeG9c8e+/qLtuHSoW/96yZUvJ9Vnx77/RGUBR9eooMEgYvWzUKKwdOFA7XSyHilx/VuRivZIr8Otpw+bNWJjj5zEXqbJgATpopi38+29somMaKaVKRB86dCgWLFiA119/PS3r79q1KxJlKDFhUVERZs6ciU6dOzumNWzQAPHiCO56779vm1ahfHn0/PprxzId2rcHujIJIvHFFwCA1p98guRVV4Uua4sWLQC3XgU9eqDoo4+AevVsvQ/iFSsKs7DxqU6dEBO6xbdQ+AK3aNnSMc6UtrKXHIDUhAm2csUqVnR0y2nbtq37PmaSHj1QVLs20LYt2hVfHwnBx7pJy5ZoHEFZ/fQUITJD+Sr219fqiQSgeSksV60a8O+/tnG7q1VDfalOqeTDMqFlq1aIS77gsdmz0aOxLMMCaNoUuP56qVDl0K279QnZpGlTNO7Rw9ZoVXLdjRgBvPKKc7yOt99G8u23EReT2Qr0fOMN7aKpggJ0OOYYdeLNYmrWrYuaTdUScUn9td9+iP3wg7a8PXr0YL2Aimm5ZQtSmv3q2L+/tiy69XvRdd99S4ZTBx2EHnvuyXoluKy7suBN361XL2ZdoqNqVdeEsrUMrS4aKRoBOwkNvu169wY6dkT8v/881+V6nFKpyCxDatWti5ou22pTUOA810Ji8Nq1a6OWYvlYcVRGl+3bI38GxW+7rWS4ZXHUc2UpCXm5evXQQ0oYz49pUVERdgjnoKZkd9SieXM0D1HmatWrW+dv4ULE7rgDqeuvz8qz2PR+K3fYYSXzxkaMQPzuuwEURyIZkiju1dll9WqkjjxSO1+sXj3tNADoKlhi1f3wQ6QU9XTDVatQv0cPVz9behco22Ty/PNvn1z+5qtapUrwYyLmuCnGa12x559H/OefUePnn9HokUfYyKVLjZcnMkPHDh1c7by6xmIlz6641DjaVUr+2fL229Gse3fgqKPY/MJ7V6J8eds5b9S0KWLC88V2fW7axJapX5/1bvKg6YIFaOJyPcU9gsly9VrMh3olV6hZu3bOnsecxsU2qVWrVrmjIeU4/F71otSI6HfffTcmTpyIMWPGoKHwYKhbty4KCwuxadMmWzT6unXrUM/j5V8mkUiUyYovoUjkFnc5DrFUCrGbbnKuJ5FgXqfFD1QAiO2zTyTHNBGPe/skH3OM+zr48o8/bvM8V5UvEcI7Nj51qnp94nYqVbIdJ6D4POTS9ecSKRCvXDmSspbF+y1n6dsXmDQJsQsvZMm+ionxSO0WLYB//rEtopIGdzZujEqyCO4jcVyiXDmH6MjtAhzUrw8sXAj07Fny8h674gokhOXj/L4SupmWXHfVqrEPkuLyGV2PLi/48eHDtdNibdsi4fFxED/1VEDTg6qkbELdVDKuTh2g+AOppB7m61ywQHuvJiQxU7tNU15+GQmhkSC2xx6O8qjWLX6cJSpUcK9b2rdnYty33wJnnOGYHJeTxWqIC1HPJdsuKADq1QPWrGFdJuNxb0/0/v31x+mxx5iP58SJkSStjb/2GvPFf/xxJnA89RTzS+fTAeex+/tva/pee7keW925wptvssRhl13mr8A7drDEZnz7xWJ9THrniG3ZgsS55zrLUkx14ZkqP5njsVioZ1EsFrO2dcIJrHfbO+9kxUc4EY8z7/j27V396237PHKktXyA4xD3erfyaACyvSslEogpPNFjf/zByubi50nvAmWbRCoF3HMPS2C9//6Z2WYOf/PF4vHgZZOWi8Hg/hLEmZJ5xedyjh6nsob2Gc2nlytnTV+71jZNtOgsmX/bNmt+oe62PRdR/B4tXCO26cX5Y2J16hgliY5PmMCeK7pnnC6xKC9zOq7FtWuBRx4BzjuPPX9DkMv1Sq4QKyigYxQEl/dCI52M8EXeJxZNpVK4++678dVXX2HUqFFoKkXpdenSBeXKlcOkSZNKxi1cuBArVqygVi5T/EbJyZ5n4vKTJtkT4/lsyDDeZpTLRZ1Y7JdfnOMk8dFNiMsLDIUqIo/46ivg55+BSy5RT1cIlqp7p7B+ffN7SpVE0u/92LKlldQOYPWP6HfWobjzm8eLuTFB6wuvpKKTJ+t9R8U61WT74jzr15vN5xdVzwC/2zniCNYQIkSvG70ENmign0/RKKxEdVxEcfLvv4HWrQGh+7OSPfbQT7v2WmD5cmdviTA88QT7f8wxwEMPsWPIUUWpiB+1QYX8009nYv2iRf6W++Yb+73IRVT5HC1eDCh6cCmJOnmS+FHCe6jt3q3tfePKl18Ce+5pr4/88NBDrJ7w01gh5EMIRNj3H/F8VKmirmdXrGAihdCbzUFU9TORn7zyCjB0KGBo/0j4wKTOVN1/Xs8+IvN4nUu3+lwhoqN+fbN1y4lFxXn5u5SpFdy6de7P12wkSDzvPODBB4FevTK/7bJItpLcl2YosWjk5P1VOnToUHz44YcYPnw4qlSpgjVr1mDNmjXYsWMHAKBatWoYOHAgHnjgAfzyyy+YNWsWbrnlFvTs2ZNEdFPCiuj899atdjFENW8uIO+vav/DRML89ZdznPzAUNkVRC3mpxMXSwoiT6lShUWj615ujj/eOU5xzcb8JDZRXfNBXq5E+6UOHZjlB2e//dj/qOoiscx+REmViN6qlTW89976OsDLXsqt7pDrZNPlvFi+3Ht9Xh8kn33G1iN2TzYtk050MxXRVcTj1vV38cVmonG2xL9iuzXMnWuNU3Wl9tELxPPYF0ecuSLeZ5IVXImInkhYjVthCXtf6/bZpSeWliOOAKZNA4YMCVYWbn3z/PPBlk8Hn33mPl08/lWr6u+HGTPcbZqEJJNEGWT+/GyXILcI82wOUieq7lsf9lBEjuB23aiuC/G8i8Pye3hhob5u5zYxdUyy0RTzzTf6adnQDXggpmljPhEOipgOhtu9kYt6W56T9yL6G2+8gc2bN+Oss85C//79S/4+/fTTknluueUWHHjggbjyyisxePBg1K1bF08++WQWS51nhBXROY8+qhfYvfB6WXP7+PKLvL/yy0KTJv4i6E0eBrLvfL5HogdtRf7882jLQUSPrj6oW9ds8V27zOsUVff+oB+Pv/zConQHDmT35ObN7I9Hu+s+AMJ8rJ58svm8KhFdJbapyuO3jOL8qmj/oOs1ZcYMZmNSnJRMu51YjIneLp7MWnTnM8zLeSxm2XhIXv9aTJ5xmfpgUInoojgllvXPP4HrrrPbB33wgfv6va6XjRtZgw+/ruXcKTyysaAAuOsu93UJrCtOnAog+DuGDt0++Wl8kAnTkBM1mzcD//d/wNSpLEL++uttXseu715//ul9HPyI6F27qqcB5vcbUTohASC7qO5bOie5RyrF6mRdVHgsxnp/zZhhvj6OLKJv3279VgWHcfyI6LwXs1sPKl6OTAaWUU+ozEIievRQfR05eS+iz5s3T/l30kknlcxToUIF3HnnnZgyZQqmT5+Op556yrcfeplG9aAK0tql+ggyvam9Iv6CiugjRzLfY94FHvCORHeL3JRZvBjo1897PinBSyQ2FvnIEUdEZ/FDpAc/16EqeizsdRyPB1tHnz7AFVdYy1atao9IjwqxbJUrmy+nEtEvvxy45hqbb7QSsdFKdWwGD2b/997bOY+boBd1ncPX160bszLh9dyvv7ov16kT+6iSPDxd0fV4MG3ga9XKWXfH41pPei3ffw+89pr7PFF/MDz4oHq8lGcDqZT+47dLF9bwff751rgXXghXrpEj2TPx6afZb/FDH7AazRR5DxwIH/Ax0Z88UyK6igkTWOImlWWbGMEmJNt0RU5aa1oWP/t8663M63XvvVnPkOHDATHHhM77/fPPnY3/XqxZYwkqMn/9RVFUBJGriI1lPA8I3ZO5x7hxQLt2dttFsTdazZrMDsy0J754jmUxXny/fe45/XLczkUW0VXBEfxb3k205uvOpOUHXeuZhexcgkHvUBmFrlLCm6gi0b//3nxeGe5FqiNo5Paee7Iu6FdcoZ/Hj/2EjC7hoYwsoqjsUPJJRHeLbvWCKvr8xPC8pYKK4Jx4PH+uET8iert2znHlyzMh89BDrXFekeiq6Q88AIwfb/X0EKNL3RIux2LAjTe6l9sPuvP2zjvey3bq5K87sK7e5t79ffu6L1+hAut1JBLkul27FjjnHBbpqyNqEV2R2BsAiwT/+2/guOOY//SKFXZxV/XB7NXA4Qf5w1i+HsRIdK9jvWIFi4xftQrlV62Krowyfj7mDj6YRfiJkfEcMbLOtKFYbmTww6JFZj27vLzddfesV8OQanm5UUCkqCgz9TpFFOYnJlZRZYlMfw8ICaixbBn7ny/vYWWJO+5g/8eMscZ9+601HIv5awx3i0Q3hTec1q4NjB1rje/TxzkvfxcyEQMzKbTScyOzvPpq+KANwg7V15FDIjrhTVQieph558xxnx7G/kR+EMv7q7KUAICPPrL/1n1Uqo6fnHRPFlHCiNC5QBD7BSI/0NUHpveynxdf1X0diwHTp5uvI9OIx0eO/HUjjCWV1zGtUAE46SSgVi32W6zTuFCs6mETizEBXoyE54I3j2DPVY9IXd6KVq2YmPfDD+7Lq6KhwwgXCxfqp2XK3mPrVmYx9NFH7HybWJKIDbqqxLZhXszlngViYlGvY92mDXDCCYh36WIX0dMVie5nPbJtTlERi8LnmHSlf+MN4M471WUR2bYNuPpq4LvvrHGpFLvOVWK+jNdxnjxZPd5UVDA9bslk+qOo7ryTNcSJgiCRH6h6dxDBCHIvicnL+fIkyuQujRpZw6K/+OjR/tYj1vPid6qfdyHRzuXUU63xquuHv8u6XVu8TJm0/KBrPfNcfHG2S0AQrpCITniTThHdFK9IdPEFLyzVqtl/6zLQH3OMXfg66yz1fKoHfZUq7vPku51LGFGIXlZyG925TUckuui5yInHMxuVFua+y1QyNL9lFCPRuUiqSuTE11u7tjWOW8Lw8/3qq/62rSIdOSBatdJHuNes6f0BprrO01UHZ/JjUBTz5etTdQ+LH+Oq56yf+lp8loq+2xxRRDcktmEDyotifLpEdFWd8+ij6sSa8vV8113As89av+WEqirOOAN45RX99L59WdTh7bcDjz8OHHig9zpVeF3Tuvtb914kk0si+t13s/N4663h15VOduxgtjpewSNliTA9QonwkCd6fsGf20VFzGaMo8qL4sbLL1vD4vPfK3BDZ+ciLqe6pvi7kImdSya/iTdvzty2CCIobnXyjh3UoytiSEQnvInKE13FDTcAw4Z5z+cmoteqBbRubb5NL3r1sv92+1g02VeVECiLF7KIko8vp+ecE8168nHfyxIhRXRfL77nnRdu+Wwgvvwff7zZMvffb75+1f7fe6/58oDd55g32PGETpz99rOGxXPLP4KSSfaB9thj5tvVnbsXXzRfhx/atAm+rCoaOl3XHr+nvvoK+OOP9GwDYOdRvD5NItHFXlOqe1x8PnqJ38OHW8Oqj1I/di6mhH2e8Oud+wCLXHcdcNRRzvGiiL5rF3DPPeHKwBGPyS+/sHenRx81X37LFuDNN1kPmTZtWO+UoMdZ10NPRu6xp8NLRJej8jkzZ7KIeze7JJlc91sdOpQleFXlyShL7LGHNeyVF6msken3oPbtrWGKRE8/kyaxek30M/cDf87//rtdOPMb4CQGIogiepBI9Nq12XJ167L3zm7dnPOa9PzKhp0LQeQ7p5/O9DK/DWmEFqqBCG/SHYnOPdx0JJPuETm6JGpB8bJz8bt/Ji3YsoiuirrJdfEwKgsaejEv3fiJRFflFJDF3nTj974LEjGn87A25ZBDrGGT8ooium7+hx6yhsV7kn+EpVLAzTfrE1Oq0G2rRQvzdWSKsNHhsmWXjNgbIJFgCTcPPxzo3j3cdt3wEtEXLWJWF2LZxEh0gH3ciy/hojVG9eru2xeTSroJ8mF6Mk2bZv8tbqeoiHnB79hhvj5+zapEdB2iiC5603qxYwcrn64OcWvQN0mSfM457EOqRg123t57jwnrQTAV0a+/3n36VVex/z/+qE9iCgAff6wef9hhzPud95Ax4Y03zOfNBg88kO0S5AZiT0/T662sEOZ7IMg7tsrujUgf/fqxes00EEOGP+flHoaJBNC1a7B1ij2oTQXsVMpu5xKLAcuXs3eIhg2d80dh5/K//3n3XieI0ohJ3f7bb+kvRxmBRHTCG93L2pAh6vErVkS7/SVL3H13RauBdOD24WryIisKEjrkF5J87LoaVeIVEtHzEz92Lqao7i/Vi3cU6O5lv71cxPs9HfexV53TsqX3OrwEiUcfBfbZRz1NPH8PP+y9LRFd2XOxgdBPY8/llzvH6SJnOWLjcSJhdXkG3BMwhkEW0Zcvt0+/7DIWoSxGfdavbw2PG8c+7k880RpnEs3OadXKfTp/1qr86E3hSe9UPPAAE4NOPtl8fbEY8OST/myLKlRg+7J6tbonmo6TT2blCyKiio0vurr43Xed44JG85naubjlhShXzmoo/esv4Npr/Zfj33+d41Ip4Ikn7D7xRP5BFgq5g9jARZHomUNle2YCPzdiUlGA1fc9eoQqUsl6TLa/bZv1vskTw5cvzxqaeY4eET92LroyvPiiOmkpQRD5n3MvhyARnQhGKuUtEkSFLgr91VdZ4gnxgz4qxAewn49gFW7RVRxZMFAJ77koNInQC3XZhr8ge+HnOs6kiK7jxReZP/GPP5rNLwrnUTUs+eGBB4AzzwS+/lo/j1fDnth1G1DbuYjccINZ2XTnPl3dcsM0YqRS5tdqu3asi7KIHBksPwfEBohEAvj0U+v3P/8YF9MX8bg9Clt3fGbPtobF657vg+ixKoroXs8A0fpEnJc3hPPk3FEmWk2lWLnHjgVuu42N++QT8+XffRe48kq1AO1Gly5Agwb6YAMVvFy8nJlg69Zgy5mK6F6I935UjUdffski3IP6xBO5gZx4mEgfXnW3qh6jd/70E/TdiL97yj2oYrHg70Xis8LkuxawotDLlXPmAlPlWPETie52bIL2sCKIfMYtWS+HRPTIIBGdCI7pw93PR6QKXbesc84BnnsuPQLMwQdbw/LD2O/2TCLRZdIViZhOonqhphfz/EQWEQG9vYOpOKmajyfCzBR77MEEONPuzOL9ng0RvU4dZiEhWrzIeH0AyRYe4j2pOif9+jnHqbraZjoSXYxSfestvW+yyiLITz0Uizl7LMjPCTmqUrbIEYXT885j52jiRG+x0s/zRb53dPu4Zo01/MUX7usMmjyXf9zXr+88VlF7oj/yCDB4cDTrM8WPzVEUiOfSz7Xr5cEvCx+cKOw1YjF/71P//mvmfZ7pY0+kh969neOaN898OUobfuqHwkLWaKr6JqF39fQTtEFZ9+4Zj/t7L23b1hqeOdMaNm3ol61cRFQWZPy9kTzRCSIa5G8xEtEjg2ogwhvdg8r0ARb2Yzgb3ma33w7cdRfzjnIT0bt08V5XkFZ/1T7neiR606bRrIdezEsPYfzLTzwxs9cCv5fDvmCI93s67tko1qkT0T/8EHjqKffuvqp6X2Wp4OZ3KZOuuk2MqD/1VGDPPdXzffaZc5yfSPRYzNlgJO+rvC7x2pZfcmfMAK64AjjoILsvsAq5u7Yb8rWtu7+8LNnE+9pPJLo4/eij2f94XC2iR9UdO5XS+2mni7C914IgCiNBGu51qEScnTuB6dPDr3vXLn9CSMOGzPvcTxJRIv/YtAno3NmZ3wAw8/4vC2Tqe+D//s8eVARY9Xg2ggTKGlGL6F26+DtvV1xhrU+2f3ODXyPcpk7VU7VaNec4bsc2bpz6/gcsuzkS0UsXUfVuI+zI3xdR9vQs41ANRHijEpVSKfMH2K+/htt+NkT0SpWYXU3Pns4IQnG/33mHRcS7fVCWlQrrxhuBCy+02xIE4dRT2X9V5nYiNzBpPAKAypWd42Ixsw/A994zE9EHDjQrixfvv8/uZd2LuymiiJ6rDUI6Ef3YY9X+3l52LnPnOsepznGmI9FbtWLPH11SyFWrWKLMgw5yTksmzbsEx2JOv2/RVxxwXgvih6wqav/55822PW6c2XyA/Vneti0T61Xcf7/7esSPHT+e6Cricef+x+PO3hBh+Pnn6NZlQtR5YUwQry8/iVO92LiR2aKMH2+Ne+SR6NYfRAiZONF83nnzgNGjSfDLZRYuZPaMvPHnhRf07/2zZ7MAFyL9zJoFPP64fnquvt+UJoImOOf1nXyOmjXzF9jF3xkWLgxWDh6JrspdpnrvE3s46YIeOKtX66edeaZ32YJQs2Z61kvkZw/8XENVJ8t1iFuOQcIXJKITwfDT3XrSpODbSaWsl+koP6r9IIvoYoXUvDl7+ReTeslEJaLneiR6lSrMP3rAgHDrGTGCHVM3T2ciu3zzjf23ny7Wfj68TOZ9+23z9bnRsiW77rp2DbeebNu5mGDqZ8np2NEaVtVDsoc6oH5Ry7QnOsAsAXS9ZBo00CdQTaWADz4w20YsZu/2DDgTXLpdy0E/lHft8ufVLYrobuK3aUTQ008DK1dav7duZX7UOquPjRud49audZ5/7o0eBZkQeqZNy45wLiLWNVGK6ABL0CkmY40iCp0T5N6fPNl83g4dgLPPBt54Qz19yxbg88/914npoix+4LZuzSysuOWS1/U7dGj6y1SaMa0TjzlGPZ56gmQOr3cD3bnUvXvKycVN+e03/8sAdjsXmXR+03r14DPhxReBq6+2H2OeCJuIHt5rgYgWuQ65+ebslKMUQiI64Y+ePVk3y6uvjlb40H10r1zJunYmEqwbbzaQoxHdXmpUSVDDWFqURapUYRHB9epluySEjvr17b/POUc9X6dO6vGmL88mH3u51rgkRvn89FP0649if/1aTFWvzsTOzZvV9b4qgZ/qhVj3zJC79X73nb/yRcGQIfZnjPzRePXV+mXjced5kRM2uomsQRtav/nGX/RO1F6Icr6TQYOAI45gFgAqVJH9u3Y5j53oyR6WTIjoe+4JNG6c/u24EXUk+kMPmW0rLEHeI8ePd4+M/PJL5zhZeF+9GrjpJtbjbcAAdcTt5s3Arbfqe2xEzeuvszKVVd56i/0Pa0c0bx5wyy2WgFcaifq9R3VP655Z11+vX4aIlqhFdK9puvX7FdFN7FxMruGmTfU9CdPN//7HngtizyeykEkfFIkeHpPEon568hGuUG1A+OPuu1kXv1q1on2Y6IRmHoXeurV9Hj/Rd2FRdTXX0aGDc5xKuDjgAP/lyDWxkCA4ug//Z55xjvNzHcsv+5lOKhoEUQBQdWHNBS6/nFlWnXee+TJ16jA/2jD1kG7ZPfaw/47KD9sPTz5pF9rkhobjjtMvW1DgfC7IEaX33qtfPujHAxedTEl3QqFZs9j/p59WT/frFX7BBeHKA5Qdn82oRXS395woe9io1rXfft7LtW6t9+hVNcLIdc8ppwAPPmjZLqnupVtuAe67T50jYsuWaEXEoiJmQfDkk9GtMx8YOdI5zqSR99lnnb1EOd26MUuqCy8MV7ZcJhPfA173ea6L6K++yt5x/Hh55xrpENGD5On6/Xf/ywD+7Vxkli0DrrvO+u11zcm2elEg9qCj7/D0QZHo6UGuQw47LDvlKIWQiE74Q4yYS0eLrPyA5CK6HNF64onRb1uHvK2ePf0trxIuPvzQfZk2bfxtgyAyzfDh7P/ddzNBVub664EWLYDLLnNOCxKJ3rdvOGuoTCHaArRs6ZzevXu4j/soPlwbNWIfBq+84n9ZVb2vOp+33mo2H+CMRM/FaB+vSHJ531TnWHfu5s/3X56dO5mPP9++CVEdVy9fUN2Hv6l/On/+nX22cZG0uEVU64gyMWemEEWTKBKbqu5VflyCrl9uLAPUlkk//GC2PlXegKIi9XNC3p/vv7f/3ndf5zI624p581idFVU+DkDfG7O0c/751jA/R3I+CRWXXQZcdZV6Gj+Wv/wSrmylFdN3CC+x1TRnSDaYPp0J6K++CvTrl+3S+EOsX716ZenEcp0nutsyKlIp9pcOOxdTli61hr3Kzt9xomzgEZ8dufhuWlogET09yO/jOgtLwjdUGxD+EAXhqB8mq1cz713xw1knomcS8YN6+HDWxcsPqod5lSrWsOg16ga1gBO5xLXXMmHx9tud02rWBB5+ONz6u3Wz3ztffQX06sWGw7yQpxsvT/Tdu4GjjrJ+Dxvmb/1RfRwEjUo2rfevuCL4srn4ofLmm+y/yq87kXDWz6rGVl3jqcp+wouvvmINIY0aqQVAFUGunSB2ZKrzt2sX8M8/ZsvXqsX+//WXc5qqUc6NIF3Bg0bdZRPx3EYhoqtEXd674o8/vJfv3ds57vXXneP8+JvLqOrOKVPU83q9P9Wt6xyn80nnPS3ee899nX5QbatFi+jWnw/wczR2rNn8n3ziPn3nTn2C0rKMKnl4ENJhVxcVf/9tDWfLDiQoYk8iLxtT3TPdbbybEK0KrFi2jNn5+cHEzsUU8RnhJaIHzS/jhvjsoO/w9EF2LuExSSya6z2I8ogc/FIlchpReIn6YdKgAYvwGT3aGpcLIvqpp7IIqjPPZMKhV9TfiBH236poDrFSM903engTuYYqstAEk2v5lFPsD3txmVwWF0QRXfWyMnu2vfw6P/lcRXXuwoxTkYsiet++7L+qvk6lnPum2tdXX42uPNx+4pRTzI+X3w9hgPl9q/jsM/0yqg/ZxYvNI+D4M1aViFT2mgdQFEUSMRHTSOhcQjy2USSnXL3aOY7XZyYWOapo/m7dnOOCPkN06IRVr3tEVVfroi9V1/HWreyeD5q7R9VoEXWC2FzH7zuul2D2338shxPvseODxiNGIH7MMcGsLzJBmO+BTz91jvMK9lGRy966ufgOYYrcA9N0XhHecKCa7nZNiwEeAKtPeT0Y5DkbRSS6iNc7RDq+k0lEzwwUiZ4e0tGwRAAgEZ3wi/gQTSTsL1m6yMZTT/W/nU8/ZQ9/nqizU6fsPbyqV2fdycaMMZtftnCoWtV9fsr2TZQ2orhX43H7B4D4UZTLL7JiRKHuY0WsI/zuS7ajCFQfp7IQdtBBrN6UcfO0F4XLXDy/Bx/M/qvKlko5j4tqvqAvsxs22H/v2GHZYJx6qvnx8orcVKGLNpQ/tkW2bmXn84QTrI95VVS5Dn6dyMd0v/2UAnFhgwbm6zbhxx+jXV8mEOuFTZvCr091TXEBw0TcVfUYUL0jRulZv2tXcBHdD6o6+Lnn2P+pU4GVK/2vUyWiqxqRCAvT+lSXo8GFhmPGIPb559lJcp0N5Gt6wQJlg2UJa9a4T882qveP0kiQ/BR8GVWdKDecfPqp1TOrdWv/23LzRA9CNuxcRHLx3bS0oBLRX3wx8+XIZygSPaOQiE6YcemlwDHH2KPSYjH2IrV0KbM5+fpr9bKDBvnf3tFHAy+/zCLnYjGgfXtg8GA2LRtR6X7ED7mCmjcvmnXTw5vIdXgE6V57aWdZd9RRzmv5iCOcM+ariN60qTWse+EXxSSVn7wb2Y4qUB17saFwwAD2LBAbXFu1Ag45RH2eOf37A+PGAZ9/Hl1Zo4Tvt6mIrvpAFc+dn/Mo+zd//jlLqtekiT9/w0z6Lu+/PxP6zz6bRSW75TGRhVR+f8gJkC68UCnc7GrY0LnOoBHBqVTmRfQ5c8J3ZRbrmijEV5W9iR8RXawHOSoRXbRdCMvFFzMvZBXpFtFF7+Ig51Jl57J9u3P8668D99yjXsfDD0fT2yWZBKZNy7xPe1ERcM015vOb1qFhrjGdpU+2ifodSG4U9bqOxGdS1O8kq1YBCxeGW0f9+tGUJRv4EbmCCGI8uMNERAesSPRDDvFfLr92Lm6BFrt26fNUcEyuxa1bmSWZ6bGjSPTMoHpu+rXPJZzkc6+cHIeOLGHGM88AH33kvBkrVWIf8tdey7xZVQSNVuDRIy1aAJUrM2F95kzvh2i2kUV+r8QwpknhCCLX+eMP4Oqr1b7RAJK3346NBxxgH9mokdorOh63C0Piyyvv3RIkMibdnH02cPPNwBdf6KNvqlRhSfGeftp/N9dDDw1fxjB4fUTUrm09Jz76CLjtNhbV9vXX3nXdKae4C+3ZxE1EB6xGXnl+Ef6BmkrZeyl4ffjJPRrGjWP/Tz01Oy/IfuxCli4Fnn3WPeJYfkbyhI1ywtnevc1F9CZNzMsoMm+e9zM7SmbOZO8MYSP1oo4u6tpVvw0Ti4tWrZzjVCJ6lBZHfF3t2zunedVbfo6f6nklNlz4tT1YvVovWMvJG888k+Uh+fVX+/h584AbbmDJFMPywAMsYOass8Kvyy+yHaIbYr2ZSqktiACWqDSo935Zidr76iv7782b3ecXrVyKiqI9Tnvswd7twjQslhWx0y0y+9dfnedF9ERXvXeojhsX0U89Vf1ccCubXxH9kUec4+rVY72rBg4E5O8HGZPz3qsX0L272tbICxIk0wfZuYRHVQ/LibrLyjMtA1BtQESH7uEV1FNQ5YfepQsT1HOZtm1ZlIYuAn3AAPtvUxG9rLwUEvlLx47AY4+x/AYcIUo5deed7L94LU+dqo4+qVJFH4l+9dVMoA2TlC5dJBLAffcBhx8OnHEGEyJGjnTO97//+U+SCLAPzFmzgH792O+orSy88KqHxPN0zDEs+V8+fXiMG8eux6VL7ePd9jsWY4Jv48bWOFUPg4MOYv9vu80+3usZKd4H27ZZIl4Qq7QokAU/+ZksfmjHYkwo9gOPPmraFDj3XGt8vXrKY6UU0YM+LzMdhf7uu9GsJ8oPo+eeUycnNbEOqFkT+PhjtZCdSDgFaLm3QRQcc4xznB9BLpViPWN0LF9uDXO7I7Hh2E9gxD33sDp8+HD1dN0xf/xx+29umxAFPCk4b6zLNAceaDafKAJefDE7js8/r57XT48dscEvV7830u3V/uST7tNlP/R0lGfx4uDL5ppQtHw5MH682XEyLftvvwFHHqmfrkswzesUEyutQw5hZY/FmPB87bVmZQNYwyLflkkjcefOVlJxkfLlgRo12HPFC/ldU5WbY/589p8ni/ezTvoOTx8komcGPw3VhCt59GVL5DxRi+j8Qz2bSUWDst9+QLt2bLhzZ/s0+SGvigbItRdAggjKTTcxwVfnS6oTWM8/X59YtKCACSVRJStKF+XKMXFFFAKjoHNn4MsvgaeeckYkEuE45RT2cSlHMrtFovNxouClEtJ4g9J99/kr03//WdFgn33GorGbNw9uWRI1csS4KJr//bd/X0uxTnjgAfs0xfF3iOiNGwf/2OXe/H5tloLCgwXCEsQbV8ceezgjoMVtiIEAF11kn+fzz1mvQR3Nm9t/p0OUUG1/0iT7b94IyRGfNb/+qs8FANij7HmUl9g7w8+5uP129l8n/orlEt+l5Rw9UZ7/dFuGLVjARG+dZYdbvgURsZ7gdcwll4QrG2AXb3P1HeObb/QNJ7t3Az//nF47nlmznNuMAvE61uXZykfatgVOPhl44QX19HffBa67zvm9zOvHadOYRY3YY6B3b/d6qkYN9Xg/3+TffMP+t2vH3l9M6+tUyhJFq1QBKlQwW0a1frHR0gu+/JYtLJitcmX9t4fbd7Z4HYoNpPkUEJJvhLW0I9TIGlQUiecJACSiE1Gie7iGfbnq2DHc8tlG7tYsJxJViS2qlxxqASfykVq12Iu+EHUdE31G+YcSt7p4+232clupkl5EJ9jxuvzy4LYVYXj99cxvM9t42bkA3kJW0Gv4wgvZB/N339mtXEzKZAL/UA6Knw/LY4/1t+769VkE/8EHs0h0ExG9WrXwIvp++wVb3o1zznGOi8rOzU0QUEXqu9G/v/pDi29DFKDlSF0vAdbkfdDPuVPl3+je3TlO7g3BhRlVY4lq30eMYI1X69ezaHs3ogyCENclRo7KljH5JKIfcAATE/3WBTLpKuc//1jDuRzQMn68evzNNwP77gsMGZLe7YsJ0qMS0cV3Qzd/7HyDR4V/8YV6+sCBwKOPMksq8Zrj9/WeezKbscMP97ba4SQSajuXINd0r17+l+GNPH4aosKK1DwJ6ttvM+urwkL9fVBYCPz7r3qaWJ++9ZY1nC/fIWPHAu+/H3z5VavC9QQJQhSR6J9+Crz0Uvj15CsmiUWJyCARnYgO3cNFdVOrkk7pyMdIdBHxYXzmmcC997Lh3r3ZMTv4YOcy6e6qSRDZRHxZ4oLEwoXMN5v7IQOlU0Q/6aRslyA8p5+e7RJkBtF2hX+4ul2HXh+nqZS/qCqZF1+0ujSfdlrw9YhcfDF7BqmeQ6b4uTflCGCTdX/zDasbNNvZXb26fUSjRsHqi+XLWWRxPA707eucHta3nCdNbNDAEovCrvPqq9l/NxHVb0Rn7drqdxC+DfE6X7LEPo9Xo4Au0lJEPp9uqEQXEyFG3q64T6oI32uuYZGYderYRXZV+aN8fxPLJSZ03bHD/jtKET3sNenFypXsv64XhqnI9+ef7D4yjbjW2VtIxEQRPcrjGjX16qnHc19pVQ+goNfmLbc4x4n5WaIS0WfMsIblHk5+yNXGD6+6Sc75JffEApjAacLq1c6kups2Ab/8Yra8CBfR/USi83rUrT4Rv+9jMX/Pba/30LVr3aePG8camFX1kO6+z4fvkBUrWI6eE08Mdh9s3sx6o7VoYX6thSWZDB+JvmsX64V20UVOO8ayTD5cs3kKiehEdPgR0Vu0MF9vvkeiiw/jMWMsz7fJk1lkk6rLHUWiE6UZ8eWWt5LXr8/8F8XrPJc/YIOi67ZP5B7DhlnDy5bp5+PX7F13sf9nn62eb+PGcNGBY8cyAa9Vq2CRYSq4qB9GdFixwnxeLxHh66+d47w+ruVpDRoEe15yP/Tu3dXPZZV9jioSWgcv07//MmFhw4bwkXfcF193/mrW9LcNnphSFe2oEtHfe88+TxSR6H6Qt6eK9lfBBWjVdfLcc+7Liu9nKmFWjKYNi5hIV06cefPN1rD4rAwrIOaTZcFbb5lfU9OmsT+v+kpMxJarYiwQzK89qMWLbAkApEdE/+ADa1hsJCoteN1bS5Z4X3Pr1rlbwvE8OaqG8RNOcF+3jp49neO8ejiZJBX94w9rOJXyV/eocl+ImF4/YqQ5R9fYlA/f4WEjuufMsYZ//jncukzZtCn8957YABflMzifUNUd+fQ8zzPoyBLRoXu4qCpG0xeuJk38RSXlIroHQyKh912lSHSiNGP6kpfLH7B+2biRfbzXrZvtkqSX0nTORHidXL++fp5LL2V+v6pEsgCbtmZN+LKIVi5hmTaN/c/UefMq9yGH+Fo+OXascx4TG4C//nKO87JyadDAKcDx3CcmiGXfsoUlRw77safy4+d8/z1r/PFzrXBRWnUMTK6RKCLR/ZRXPtdXXWW2HI+w49sS980rApZbjwEseo43nnFMEvaZItoByr0jRDsP8bjyfUmlmCDs997O1Ee37v139GjzdWzfbv6+/NRTzBajSxf3+TIRif7GG05fezd++81unwIw64ING9j7VCrFevOJ51p1L5rUN2LDDUd1Tx54oDU+KhFdTDzsNyF1PuB1b6mOvUzfvsCtt+qn6yxKwqAS0Xv3dl/GxM5FbgQ1rfvjcRYt7TUPJ5ViCc9V+RZ4zxgR3X2vOn8//sgCAz/6yL08mUKsA4K814n5UHiDTLqJwg998uTw6yiNmOQjIAJBIjoRHbqHn+qhpeuGKJPvUeiAZd/Cu3KboHrw5UMLOEGY4PXyzSlNgmz16t4v/UTuwrsdqz4qxLq5TRv9h/KSJdH4E0Zl5QJYVh8m99rFF4ffnvwc4/Y0AZdPNWzoXKdJt3AhR0MJPBK9f3/98vI9HEZwdEsOaIpKBObj+/Rhgq+fdwcuEO+3n5UIl8OFBdGPWxYkvfxvTep+P+WVPxALCvwtr5rX616YPt0avuEGYOhQ+/R0RdBu2GD/LUbBq0T0oUOZCH/33f62kykRXfZ158hJKwG9JcSoUeaBNjzi1EOwsdm5eInDhYX+BeRt24AzzgDOOoud08JCb7H+ggvs4j7AEh7WqsXuuTvuAFq39j7XbpHoe+/NIoNVEcbyfdKxI2tQ5kJ9FCL6kiX2cy/nj/JDFO+O48ezfBx+elp54XVv5YJ/sdjDgMPvP/E6cCurqZ2LiB87l3jcuyeGmBvjww/ZtfXZZ875VLZHfuxcDj+c+Ycfd5x7eTJF2F5JolNAkN4uQYjCD120KSpN345+UO237jnL+eMP4J130lOeUg6J6ER06B5+qhdcVau2inz3QwfYR+P27SxpDEEQSJ15Jos2k/0aHTOW0RchIneYPp1ZVvix7eCccYb9d2Gh+UeySuQFgLZtnYkTwzSw+hHRn3nGOc5vd1/549Rv0kt5X1Xe5Rde6H1MZDFpwwara/l++6mFEx713KePvjw6HnjAOe+oUd6+rSJyJCpgiTLy+dtjD6u8fkRR3lMiFnNe8yo7F/kdzaunTaVK7Djvv7/e516V2FOHLKLzxHKm6BohwhBlJLoba9daSYFVwgkX9+VIeS/81iebNwPnngt8/rn3vGKUo5/GBp2Y89NP5uswRRSr3UTn3btZfqdy5YDrrjO/hsT93rCBRcd37uwuRKumiZH899zD/ovnWrWMWyT61Kns2SKeIx0HHMD+87rFTz2mQ25Qzfb738knAxMnmvduMUGsi4uK2P27aZM1TpUMNNO8/bZ3bw3Au9eRiZ2LyOzZ5s+qggLvCFvxWfDqq2br5ehE9O3bga++st9HhrkWMkbY6ycbDTlRiOgUia7Gq/dr9+7AKacAkyZlpjylCBLRifRw8snWMPcAFzFt3SwNIjrg3RJoAkWiE6WFRIIlvhG7qqvI9scEYU7jxuy/mBi2NNC9u7ePqMorG2ANRf/8w2wEAPbRbCoQ8oS7MqedZv4sMInk4x/CJvea6gNXt+86ZKsGv1GvsjAkf8ifcw6w777ex+i77+y/f/6ZHYM2bZiwP3++Ne3WW4H27S1RUnwviceBZ59131YsxiKW5TJddJG5+HTXXUzQUa0bcH70i+fez7vD669bw6I3tLgNcVtyV3jd+RRtV7p2ZcdfzoHBCSNCJ5P+9pd7vw8bxnIOpFLqaEU/ZEpEB4BLLmH/VZHoQfF7Tw4dyhqEBgzwnnf4cGvYj/iUqej4LVsQE0UHNxF9xQrLOuPRR51JIXWIPQoWLmSNSnPnugtJqmv6iCPMtifip4HKbfv778/+82vdtJF582agQwfg2mud00Qrl7BE+e4YhcDHEQXKRx5hUd+ihVkiEb7+CJv4vWZNfV4XsZ7xEltN7FxkTOvuhg3V2z/wQPX8fht5dBZRkyaxyHMxH0WuEdbORVwmUxHKYe1c1q2zW/TJ+/3bb8BttwWv//KZ2bPN5luwIL3lKIWQiE5ER4MGLEpujz2sKAWAPRTFKOxq1VjyqnPP9V5naRHRo4BEdKKskWsRHoSeWbPYB8bxx2e7JJnjlVdYpLrOFzseB5o3t67jd94BGjUyWzePEJfhiSRNUPl+y3CRKMjH1qWX+o9akq0axOV1+yzCfcsB4MknneJakyb6Zfv3t/8WbTm4lQv3AhdtVm68kYlcKjumWEzv7cxJpdTP72TS/OP+zjuZMP700/bxfP/lj8OgIrqIHI3IrxGxzKae7n4DCVQC4QMPeC/nFR3pxuDBrMEkrB2L6fLz5oXbDmBZ7ph04f/xR+D9973X6VewVtmv6AhqD5ApEX3xYvtvNxFdPs4mEdyAveFJXMZvHRwk+turwU+HXIe0b2//bWrnMnIku+4fe8y+v5s3AxMm2OfNZhBFuhrCxOP48svs/6+/WuMSCWeyZr+45W0Rka21rrvOGtZFYov3g1tdG8TOBTC/z5s0Ub97qBqaAfckpKr3Di97JbExMNeIUkR3S2AbJWEbqqZMsf+W97t3b2ate9BB4baT68j77WXvJc4v2/cRnpCITkRHuXLAn3+yyDsx8RJgf/CuW8c80XXJ10RKgyd6EB56iP0/8cTsloMgsomX3QuRO9SsCeyzT9lq7Ovd22mtouLJJ61h0w9knXij6mat++BTfeR27mz/Lfssc0yix0ySoMncdJP9dyJhCb5eEf+A3Zd9yBDndLdEm/vua/8tJmDj4jwX2sWPOjl5pXiNh/Hfjsf9C2GyzQ9fp+ytLoroUQmQySSwdCnw3HNm84sepbqGBt3xe/FFFvH3yivWOPn8Ac4GinLl9PvrloyPc//93vN4YSLA/fcfcOyx5utculQ9nn/4ekWib93KGohOPNH7uernehk9GvjiC/P5/drtcKJKXOmF3PsC0AtRqjwEJoh1u9jo7FfwUjXWyN9e8jqzLf6JUf5i9OlXX7Hj0rq1vaF50yaWQNWtMSMd8EZVINprT7xGVJGfJj2bvDC9f8WyfP45i4zn6HzgTUV0wL+di1wmNxo1Uu/neeep55ffO0RUgR9yg44bueBjLxK28SkbjVdhRXTZykW3D7LYXtrRfRv88AM7RmIgCYnoviERnYiWggL2wdmvn328+OAVW329Is39PHxLE+ecAyxfbhdfypI4RRAA8xsliFzjyy9ZFFm3bmbzBxGbdTk0VM+BVav0837wgX3cu+/af9eowf5fcIF9vC66XmTmTO95ZOQP1kSCWXvcdx/wwgveyyvs4ZIqb9Q333SOq1ZNvc4dO6yPKx6JLn7UyZFqorA7Z45LYSVUCVDDikO69wJRRA/ao0cWg5NJZktj2iVaFBf8RKIPHszq/i++YEI6RxVU8dFH9vNRUOAUEjlyY0i6MBHRn3nGX/fpjRvV4/m+ir7Nu3c7u3A//rg1vGyZ+7b8vGvqbB9UrF8PvPGG+fwiYRPwmiKL6KtWsWvx9tud88pCzcEHAy+9FHzbUYhX8n0WlU+wXDavSF0d4rehuI6PPmL/jz3WnqfguOOAo49mUaQAu7Zvu81b5Ax7LMVcXt9/H25dIiaJReUeKrp7X4dpo5Z4n8vPUJ0FhNjzSLUvDz5oDfu1c7njDqc9mI7y5dXidZDknqL9LMdPjz/TxMaZImwkuu7e3rbNbnMXJWHtXLwi0csK8n5zuzqZ/fdnPUTEnmm69yZCC4noRHpo35518Vy9mv3WfShSIgg9jRqRcE6UbR5+mHm+iq3lRG5x6KHZLkHmOeww4PzzzecPYjHhpzu5W2TpccdZwjDgjDa54Qb2/5xz7B8i6fC2F7uLcxIJoEUL5jGq84EXUTwTi0Rx3E3cOecc++8nnmCi2a+/sneUBg2YJzpgj4Z0Ez4WLHB2i9fRooX9dxTPd13ZRKF7yZJg65YbciZOZI0TpuUWhU/dB5q8rksvtQuRomhTvrzaUkc85/3768uXKRF9yxYmkk+dao9iLSqykgj66YGweLE+aSeP8BeF8auucvZYEad7nb90WacsXx582UxZu8ki+n33sXLfcw8TgMWeRCqh5qKLgm/bTfjR2UF5oetp5Bd5W0FFKrEe4OsoKrKSisq9M3juijFj2P8XXmCC+sEHm5dBtp4Ref99ltg1CmslE7zuPZUw3KCBv20EieqV7/k77lDPJ9bHquPPe+aJdi5eIvr69ayHytCh5s+q999X11PZCLzJtQheE2svN3TLdO/O7iXRUi8q+DV76aX28abPIrnxxW2/x483L1dpQGfpsmGDvd7LtR4VeQCJ6ET66NyZ2bYA+ojzXHv45DIkqBNljYYNWddWE8sMIrMMHsz8vv105S+riD7AQZLBeaES3EVfVPGjR36O8IjlWIwlh+vRg/1WRWfpMLFm49uQt+/3xV0loovvEbrIG8Ap5H76KUtuLFq58PXzxHkqGxJR7G/YUG2xo0Jely55mR907wWyYB8Eeb+4fc7//meNExtoZEQxR+6dqKNvX3tUpChClyvn/Dg+80x7Tw+3d0rTaPiqVYFbbjGbV8UjjwCXX86S7u6zjzV+//1Zzw8xct6EFi2A//s/9TSVyMC9lkXEaFa/IrpOrPUbjZzL0YFr1gAnnQQ8/7x9vFi3TppkF3l0lhdBcTs+qmkqmxE5avnbb8OViSPX025lHTWKXWOnnOJ8NqmiZCdPZo1KNWqw+kR1fXIhWQyoMG2UadtWP+3EE4Fp05w9KrJ1raqeh3792U3vS/E4y/e8ruFBFNHdtlNYaDUYenmi16plvXecdpr7vJwtW9R1XzZ6r+eajpEuOxeeY2f//cPnDZHh7wrytWL6fijXhW7H4OSTo6+7c5VUyj0PiVejGOEKiehEZjjtNOCpp9jLihsTJ7IXWYBFoRIEQRC5x+jRLP9FphK+5TOilcLgwZnZpmkCP/n8TZ3KPmiaN/delkeNqZKEq2wedu5U+4L7QSGwpES7FZ11zoAB+g8yLqKLgvCwYcw/WNWtXWyg4IKvzirGDTeLissv916+Th398Wvd2n95ANaQIiLa1SSTTPC45x4Wgf/YYyx6UAd/lwPMI9Hl/RE/jhMJ54feCSeweshrO4C5iB6LhUtQKvYMmTaNCbQLFgA//8zGXXCB5RcftoF4zhwzQaNxY2v4zz/d5xXPwZAhLLpTda2KOQVM0Pm65wKXXcaizGWrInkfRZFOZ91SWAj89pteZDT1WBdRWWepGuFkMYnXjbL/rV927bJbdbiVlT8P3nkH+PBD+zSViM6tXAYMsFtnifNWrMiOqWmPBLfy/fOP0wJNbnxIl6CU7l4gDRuqrzvx/leVRd6uLvpd16u8Wzfg9det39yeIxZTWrBp8fMclZ/nX3+dnffRMM+KIIwd655kO8rEojqi1mf49SKL6KbH1m9Agl+LpHxBPneplHdjlzjvXXeVvUj9ENDXL5EZ4nH2Udirl/t8zZuz7sK//gpce21mypbLBE1gRhAEQeQGxx7LRJDNm909sE2FbxN0gqH8HJFfugsK1B+9qqSOqg9zNzZudH7k+o1Ev/pqZulx9dXq6boPwMMP13uXcnFTtr259lqgZUv3bfDj6RYBr8NNVLzySv00bjVz++3694Kg7wuyMNihg/33sGFA3brM9ubqq1l0PY+s5tYLHDGiXGXDokK+Ptq1Y3Y5hx6q/qD+4QdmoVG7tr7bMof3jPRi8+Zo37fq13fmGOBWhmFFnzlzzDzqxWhcr8hQseHo6adZ1J4qel5svDAhrO+tqrEuKLt2WVYn69YxwVcgya1/5AaKVq3Y/82bWUOyivPPZ9fsPfeop/P6Q45g9RvZb5LwkjcqvfIK0LOnv/WL7NgBXHON9VtVVpXHv+y1LS7Hh0U/dB0TJrBjym1dAPbs2LaN9V5xu/e5YJ5KMX/zli2966N0iegTJ7JGdd25C2OpcMUVrD40FRTFxmDTekgU3YYNY3XqXXcBM2YAp5/uTHRds6a/ffJT74plrlULOOQQf/Upt34Je66D5gcIyuDBzP5Ol5MmE4lFTfOimKKLRJfz0ejwE4kOsPfjV19l92NpoajImRMglXKvD8T7+euvWVCEn16oZRwS0YncokkTVmn27k0RjgRBEETpoEsXJpqoumZzoeOww6LbnviBLOIloutQ+WCKL+CylyUXm0Q2bAhv51K3LvN3fuwxf8u5sXEjOzemiWJFdB95kyZ5L/vrr/ppbsdl+nQW4TlkiF50EN+feOMAF7TcAhS8jsHFFzvHnXce+1DjFjgir78ODBqkbxSQyy9bL8TjrHfEl1+yeeXr9aKLmNC/dq06+aOImKTUi3QHLfD9iGI7Bxxgvj1AbffCmT9fLXaLyR7fe48J6IsX+yomvvnG3/wyzzwTbnmRVq2Y8LZxozL/wy6dyMoFszFj9OIZF3rvv999HUHrYo6JWDp3Lvt/4YXm61X5Su/caa9TVGVt1845/q677L/lKNmFC1mjTSLBItEB+7XmRiLBrMR++QW48079fNOmsWfVddfp7xVZFEyXiL5wIWt81DVy+3keys+eJ55gjZsqgV7lk96woTWs+t5WPdvEZ36LFqyBQnXs+XuOV4OdjKo+POggtU2LeKyC1KemAq0XmRTRxetSF02diUh0VXBBGPj1KQdwmN4Pcl3otQ8zZ7L3loMOMlt/PsCDQWTcjoV4P4s5kYIEhZRBSKUkcotMd4vKJygSnSAIIr9RfXDNmgU8+aR3JK0J48axRGli5KgYwSk/R7x6hwHATTdZy4nCrxjZ+PDD7COes+eezvX895/zYz1IY7nbMvyDQY6m4V70776rXq5fP/P3DzFZn+h5LeI3GZyMuI9yAvZ69VjUZiKh/8gUzzPfL/7BFDT52jHH6I+R7pycfjrwxhtqb3mZE09UXzeil774Qbhhg+XbbvJ+5Oday1QQRxTbmTXLex5RZOjTRz+f2E1ftMbh/sZjxzKbnr32Mo9Ev/deJuK++qrZ/DrhzeQaMoX7aU+f7uhBkapfH0V8W3JPDF5/66xcRHT3Jl+H3+hJ3Xrc0EXLA+rIcUAtLu7aZb9WdduWLWN+/51FkfN9lQU+Hgm6777+bD8AJnzzPA0y8naKitwbXpcutdswpdsfWNcA4mbxJaOz0lKtW2WDIyZaVtVDNWo4x8k96bzqryg8yr/+2nm9nX22fdvcxs1PfRqViB70WnnxRXdbFhWi6Ll8uboOzoSIbtqry5Swdi5+61Lu7x6GXbtYA55Jj6BMoLJ1S6Xc343Ehm2xjiAtzggS0YncQdfVmiAIgiBKA2edZf89bx6L6BoyJBo7l1NOYZG7oif2448DN9zAPGVl3Lq133QTE1zFyOXevVkUz//9HxP+OVWqsO7kHPFjj7Nhgz0CL5EILzbL8I8nHtUIMI/Hjh3Z8IknqpdzS5Aps2aNNayL+A+bNFQU4GQhT0S2CeGIYgL/INJFwJqSDj9/8WP33nv9za8SedzwEzEYRdJXE0QhK52I+65rRFmxAnjtNfUy/F7nSYTXrjUT0WfPBm67zT36XWb1arP5xozR38+mqMQgMSkvb0jgyYT5MTERYXT5Gfg65DrSTfhR3XthxZs2bdTjxcTJHFVSaBUqEfjgg4Ebb2TD4jWVSlnHUXwOmEai33ab/bfoXy8LSibCYJs2LGFlNvn0U/N5xWeE+BwyfaaKjcEq8XnYMPZffDaqnusi8jUShYgejzvP34UX2vefl8uP+CfmCwgD7+1hQOMRIxDv149dn//7H7Nl8SPoitf1oEGsMVN+VoWNjJePRzLp7EXkV/x3Y8cOqyeIfL3w9zYv/Eaie13HJlx8MbOSCpOEPErCnnexlywFbRpBIjqRO+RKa16uQpUaQRBEfiM2Fk+dahdBxWk6cTToNh98kEWOi88Rr23cfz+zbJCFpi5dgIcecu+qrfqYPewwe8LSd99N33NN/Ijq1Ml7/v79zdf9/PPWsK78bt73JjRqxETievWc3skmiAKDHBHr95iPHMlEi3R4ZdaowRp8mjY1u+bDCB5uSUdlvvgi+HZM4EK2KtdAOhBFBt35HzGCXbf83hWX4YKbaHuhsnORxQlRqDPFtAt/+/b6niUhSIlWBfx642XixyTM94rfhKOAOgrfK0EsZ+dOsx5HHFXDgiyy6sqqsisDgEcfZY0eYgPkFVdYljdBngNi0mOA2Xxx5MhrVXlV43g0uijQy77uuYJ4L37yiTWs2i9VDx/R/kF1z118MRP1xXwBfp9rfu1c5IZR3ttNvmeSSbXw7ycYL4poW5VNjgsNx4xBbMoU+/3Me/m48f77TKy/6CL7+LVrnedE3C8e4e1HNJaP9RlnsJwkIqL1h4ply1jvP/G61CEmoZXPX7Nm3ssD4UT0adPMtpFKsefN/PnsN+9ZFXWS1aConit+3pdMEpQTNkhEJ3KHTCfnyAdIOCcIgiidyPW7GE3GX9TTydix3vP4fQbddRez/RAjwQGWoHDYMHu0fZT2DBz+0SAKTDrbAk65cu4WFzL/+581rDs+uo+X3r3NtlFQwCIrly0LZvkhfozKYoHfc3ruuazreZikdzoSCWZH8vffZus3jdyeOtU5Lh4Hjj7abHm3pK9RwNcviorpRHy/Vl1PGzYAzz3HhnlknSzOPP44677OUUWiV63K7nUAWLTIKXSaMmeOd/R6FNGEqu+O5s2te0TuvaOzYvGDLMxz/O6PaRmefdZZ74jWWzKqHh5ynRHke+2ss+wR9e+/738dInIdKwppsse53AMMsEeuc7hNz4QJ1rhc/TYVxX1RJFddR6ok4GIuAFWdEIux57j4XuL3GvUbiV6zJvDZZ9bvq65i/+VzvXat2o7FT4OH+FwsKmJJZ1W2N26sXOlvfhVeQueECazHTceOzK7Pa3nxuPz9N+thVK2aeWJmeX1vvWW2nMj//sfymBxzjPe8XESvVct5HX79tdn2/Nq5iNfxihVm2/jyS3bPtG9vNn+mCSuii/Vnuu2sSgkkohNEvkCCOkEQROlBjMrOFKKIHSSRphd33gl89BETiMQkbpdeam37q6+Ydcchh0S/fW59smiRNc4r4mzPPYML+rrncseOli3Egw9a43XJn1RUqBDc7kO0ZPAS0QcNYv/FxoFMUrGiuT+tm/2QiBx5yYWZN9/0XjaRsDyzveAiT1BMfcLDIn4Un3++84P72WdZMrEuXSzhQ+4BcfXV9t+qSPRduyyf6latnJGTpnTowMopI/ZW8CNkv/CCerzKDkaVNI/blPgV0VUNDXwdsmAkC79Rcc01rBFMhG9bJTqaNGbpRBZVUloTxDqJD3sdD/kaFs+JLIaqeiw8+6xznEpMzlURfd992bNFtoBRXZuq55RY55o21GbCzuXII53j5EjjgoLwjfD8uZhKsd5QBxxgbh/CCdLTRka8l5JJZ73qloBcXp6vQ5z27LOsh8ioUWbJoE0F1E2b9B7+YkOIFzyaX9VrYdUqZnnohV/7NfEeMRXR5dw0uYbqGKRS5j0u/ASSEABKiYg+depUXHLJJejfvz/at2+Pr6WWq1Qqhccffxz9+/dHt27dcO655+If04Q4BEEQBEEQUbFgAUvAVreufp4TTjBfn6mwCDBLixkzWNfgdPsxP/qoNSx+XB96KIt2jTB5Y2qffdhHNRfwrrvOmugVOeTHykWG79eUKfbzEI8zIT+VYn70nPLlnVH6MgcdFLw8HFEAdRPRmzRhdi1ffeUenZorqCIqdVx/PRMJp02zhBkTaxzTiPvXXzeLtHPjgAOYdY9IOpKaykLgH39Yw9u3MysXgHlXyx76OvxGbUaBGAnv51ro21c9XuFHnGrSRL+e775jEfKmwqpKCNaJ6GJjW7rh0eaqxitdVLII34e997aPV0V8mzBjhnPcgQf6W4YLY8OGWQ2DbqgSeTdp4uyBETQ/wttvA0cdZbeZiZJ4nD1b5OeJabS4+FwwrXP85i7za+eiQ7SauOQS9yTXpsyezf5/9ZUlLpuIzCJRe+ifey5rfBcTAvttxHFrXPK6pwBzEb1GDfYOq8v9wNm6lb1j6HJd8GePLu+CW04Yjtxw5GV1Jd4jl1zCkp+7MWYMCxDhiOdd92zJNDoR3fQeDNprrAxTKkT0bdu2oX379rhTvMAFXnzxRYwePRp33XUXxo0bh0qVKuGCCy7ATp13G0HkIhSJThAEkf+0aQN0766exj9yhgwx9/vlCcAuucRs/m7dMtMlNYMRfMnvv2dRkNWqsRF77WVN9BJB/CQVleHJ+fbay56U0Y0uXdyny0m8vFD5k4rvCyoR/dtvgf33Z97fFSuyho1c9f4VcWt4knn4YXZNuPlBq0QFnYguW2JUrx7+vezcc522FjpBS7bC+PJL8+24RSuOGsUEjmbNgNNOs7afjQhcLyG5Vi0WEfjxxyzSXYVK1NAlAFZRrpz7eb3wQvN1yQ2VO3da500+z262CX6615s07LjdR2JiSt5QcdRR9nn4tfHxx+blckNMzhj0nuJC2h13BC9H585qUXDJEv/rOv10FpF75pnBy+OGrp6QRfRDDlEfU/G5YJovYtgwlij29dfN5g+bWJTXxWJC8mefddbR/Lnvh82b2f8wvYGiSEBdq5Y1zMXze+6xxql6Foj7L9cNYkNAKmUv4z//sIbAVIrdJ48/7ly3XysPVVCqGFBw5JGsV5GucXL8ePbfy3bPDfk86CLkObL+55XUXG4cFBtzW7dWL7NypX2+dKPrHWX6zvT999bw+ef7S3JcRikVIvoBBxyAa665BocddphjWiqVwmuvvYZLL70Uhx56KDp06ICHHnoIq1evdkSsE0ROQyI6QRBE6YZHRR1yiNpqQMUFFwALF1pexLlCJn0V43G7CCx+ZHpZLwRJ7jhxIhNpjz/e/7Ju4uS11/p/1u+1lz16kkfYcVQi+kEHsY9pk6SruYRfX3avLv+q7vvxOHDzzfZxf//t9GetVi38e5lqHfG4OiFbjx7W8PHHs0S9psjXHI/I373bSox2/fVMQObiXJhEYyZd8AF7Y94PPwD/93/26fz8iD7de++t97ZfssTes2TYMBax7EdEV4mTXbuaL89tnABnz4eTTrJsRPz0OPBTl6oirOVkofx4Xnmlc16xEWLBAmZ3IDdY8PKokpBmi0GDLF//MKhEdJPcBQsX2uthLuyJ4lSU6K4f+Xl3773eiVVNk1fXq8caeU8/XT09CjsXgNlk9expJbN26x0CMGE/KuSeQW5E8Y6TSrEeMWLjh3gcVe8LOv/qVMqe6PLOO4GffrIve+CBLDn6sGFOiy55fSZ89x1rbPvwQ2ucaMnz44/sv0kPiaANt/ya5/WaV0JSuReV3xwXP/zgPU+jRqwuVdlGpQNdJHqQe/DNN83zx5RhSoWI7sayZcuwZs0a9OvXr2RctWrV0L17d/z+++9ZLBlRAq9gVAkzyjoknBMEQZQdCgq8PwBUtGyZHhuIMHTvzj4kZH/qTCAeC7cPpJ49g3U5P+AAJjqKz2jTj0+3D0VZRDTFrRxhE4vmElEnNx02zBm5mkg4o41btXJGFUcRia6KAI3HnZG/YZFFQN648M47TPyrU8fyII+iHrn0UrP5RI/zbt2cx/OTT4DzznPPJSDaujRtar/+b7vNf+6HWAzlZOsBuWHKDbE8vCzJJPDYY/boPlNP5y++8E6yKqKyaJHPPz/HQ4c65xUtOypVskeV9u/PIhuDNDymmzVrzK87juy/nUyqxSMvEX3CBBaR2r8/S5Q8apS/cgRBV/fIz7uCAvWzSYzGNRXR/RLUzmXECJbUl0egN22qnu/nn1m9xRPC6tblB1WyUJVounWr81m+Zg0TqDdtMt/exx8DbduyXkAc8dz6iXb/4gtmX8b5/nt13Slen3KCXb8i+uWXs4Y2MaDA1OZGbrBSCe0mtoP8GPGofq9jJttsmTb6cuSGsW3b9O91l13mb90As5eRk1p7odrnWMze04GIlJCGUrnPmuIHXx2pJaZOnTpYu3atr3UVRdFtJ4/g+5v2/b7oItaltVy5aLpGlSaKisA/GYuSSTo+RF6TsTqFIEoJsmSYV/dOIsEEung8bc8utzql5Nm5e7dt+7FhwxC//XakevZEcuzY6MrWoIG1TZd1xnbv1kawFFWsGKw8qZR+f+Nx2/aSqRRS+XQdCcQqVizZl6D3gnhPFdWsCdxxB2IrViBeLMSkACR37iyZL3nllex4Ce9jAFBUvz6wbp3jHvVDUSoFFBba1pGKx5GUtgUAyV27HPuu2naqbl3EpO+b1M8/Q5TckqNGIXXTTYg/+CBiAJJDhiAlXHthmypSO3bApHkhWa8eUt9/z4S/KlWc136zZlZSTM35jp1yCuLFQllRURFinToprxHTfUqmUigvfx/6iJBM7dpVsu9FxddNbPRoxK+91j5fo0aIrVplG2e7plMpxEaMQOzGG7XHMlW1KmKSYFWUSCB24YUl17OKku+JWAxYtgyxa65B/O232bSDDkJs0CCga1dnPTFhAjtXwvdaVM1afN/jsZjRtRMFyS5dEP/8c+v3pEmIi/kCikndfz+SLj0/Yi+8wK65qVP99VoIQZFs1VFM/NBDERMSOxbt3o14UZHjmCa3bbPuk0QimuegXEfWrBnJeuMVKtjvKc7ee1u+/Pz6OfxwxASrq6IhQ5BQRFynGjdGzCCBdKpqVSSbN3fWx1dcgdj48bbjmjrySMR++w3Jb79F6rXXWO+lvn2BGjVQpKjTASB1221sHe+9Z40DkCzen5hQ76soed6nUogPG2Z076QmT7aO55Qp9kbb3bsD39NFRUWIXXVVSV1i22a9ekguWsR6Wu63HzB/PhJST7iiHTsc204lEiXHwsHWrcD33yNRPD1VfJ0U7dplu+4c69y61XGc3N4nHMv/8kvJ8ql16xCrUgWpvfZCctIk5TK+3lW++w6JM85gy5lGyM+YgdjPPzuuk2T58kgJZSmaMwexqVOBggLEi7fhRl59b0SI6X6XehE9SmbOnJntImSFsrrfuUDBunXgzrmzZ89Goc+GH4LIRahOIQgzuAvzv2ecgbXHHYcd06dnszg5i6pOaX7MMSi3di3+KipiiVw5AwagXO/eKKxfn0UQRXhM69xxBwrr1sUml3U2XbUKOnOJ6fPnB4u2TqVKrpU/NmxAUth+sw0bIJouLF22DGvz9DpKHHss2n/8MdYdfTT+DbgPorP59OJ1NCoqAo+3TW3bhvlTpoAbvUw/7TSkpk9HbMcOiO7q01euRNW//0aY7AJ/L1yIZKVKEFOnJYvL1fKww1D7q69Kxq9cuhQ8xpmXW3JpBwDsTqUgxyJvW78eYsx7/PbbsaBmTbSdPh1FlSph5n77oah4nRUWL4aHa78n2zZsgCLG3sH6tWuxuHJl9iPg+YwPHIh6u3djw4EHYuf06UCXLqh/9dXY0qMHtgnrVB0rFQsXLQp1Trdt3lyy77NnzULhv/+i2UcfQTY+if32G9YdeSTqCCIuP6+xnTvR7P77UbfYc3zDfvuhpiIadlf16qggieh/LliAit27o61LGRfMnYutQoRnjX32QZti4Wv6H3+wXjasQF676ziuqUQCsQDiC9/3Lrt2IVPZGVavWYOGwu91f/7pOE8AsCGRwEKXY9H9k09chc7trVqh0sKFAUupZv5ff2GbotdB4rrr0EMQ0efPm4c9NmxATWm+tStWlDyHpkf0PKi2cCGEviX4Y/lyJDduDL3emvvvj9affortrVrhT6+yDhuG3oKIPn36dOW9v/Koo9CIN9C5MP3TTxH/88+Sb3FOfORIx7yx4ujh+LhxWF6nDho/+yy2duyIucVe56pyxBT2QTt27CjZz0YrV2IPxxwWf/zxB5JVq6LqtGlo//PPSJYrh2SlSihwiYaPCdHmCxcutL2vVJ47FwqTMyOmT5+O3hpbwY0dOqBmsS3Un2PGoMHYsRDDW5MVKmDuzJnoLC2XTKW012fra69FTSEqfHsyicoAFv/9N/5zqfu3//knKivKrkNePiZYnfEGq9jUqbZ1qN4zTKj/8cfg/S5Mlotv3YqeBxygnDa/c2fsnju35Hk+Y8MGpDp1YteKQVmiqhdKK6VeRK9X7Ne2bt061Bc88datW4cOJhl/Bbp27YpE1F1Jc5iioiLMnDmzzO13TiF0s+rcpYu9myhB5BlUpxCEP4qWLAE2bULdDh3gI6VimcG1TilO2NgjkwUSfat1PPggIERqFb37LhInnYTkk0+ih5y80gdFixYBO3eiG092WkxM8oNu2qwZmpiUM1eZNw97AK7CggmpTp3Qgx+Hu+8GikWR+O7daCfYjHTv04cNSB7hPXr08NdtX0HrNm0c3dXjBQVs3a+8YrMx2EPwn+7hcv4KxCR8xVQREzcW06Y4oVvsoovQVUyw6sPaIdWmDWJ//eXc3pw59vmqVHGIRanKlVHrkUdQS5cg1A99+9qvB7dksh600iWKM6SyIGx27tQJaNwYMY2tRS0xiSeAHvE4UK8e4iefjNjkyUjF40gNH45qgwbZbVWKKXfOOcD999vGderRg1mSXHGFtoxtW7Sw11WVKwPXXcfK4LNuSFWvjph4Hyh6HqViMZtwp4JvN25i3xABqTZtUF86/vXkJL/F1DjpJOu4bNuG2KuvInX00UDz5gCABE9SqaGinMjQheT11yO1555IDBrEyjlgAJIjRwJLlyIhJMxu17690fOmXdu2iCsSb9YV6gm/51zL+vUlg6mCAnTbd99o7MO6d0fRAQegfIcO6OEziahu3/b47juj5bv166e2ePGgUXEOjSpz5qBHjx4oMvEEL6Zi06Yl5Y55eFp369oVqFED8ZtuYiPOPx+Jn35itkIGtC5fHqmWLS0/cR/XqkiqQgXX66iGcN46/Psv4kJDD8DeUzooenGUPA8VJCRblUrF+9CiaVM0dylLpb//tpe9V69I7gHVOpKDBvlad+yLL1zX50DTOFc0dSra9uxpS9javUcP1ovI0GYpsnohz+DfFV6UehG9SZMmqFevHiZNmoSOxQlqtmzZghkzZuB0XWIMDYlEokwKP2V1v3MCwcs0kUhE7wdKEFmA6hSCMETnBUrYyKs6pWVL28/EiSeyrthh1ysmNBSRPK7jZf1d4qefgHvvReyxx6xrRkpcJ15LJcNS1GcikXD6zftEdc3GjjySjZe2Fxe7p/PlGjcGJEuCmGGZYhMnsm7d119vvx5Untq6ddSsaTbf/vsDsmiyaVP279nzzitpPOHEFY0QiMWMvYJjQnLJkvd2jR2MfM8nhgxhdgfLlwM1ayI2bhxihx0GbNigXv722x0ieqJiRc/72/E90bEjs55o0MD/OZkzh/nOr1sHgNlVycQuucQzwV7Jdhct8rf9gMRiMcQqmMW8x7dtY/78AwYAd90FPPoocOutxo1oJrYhJdviiSGLRfRY375INGjAvOgFEps2GdXjiURCee3GhUaZyO5DYT2x2rWRCFk/2thnn0CL6fYtNn+++fIB9kNsNEokEmaJNfmynTtb5faodxLxOPNB//prIJFgYvqxxxpvK/7GG8y3+447WI4YVbJhkzLHYq7XUUxoTIkrBMrYLbcg0aED81hfsAAo7k3gtV7bOorv53gq5XpvyD1lYuXK+b8HmjYFli61jVKtI16+vL/3LeE6UZYplbI3TGkaqRI8F5GoI8XjrCzSO6iOrD+fc5wcy0IVjK1bt2LOnDmYUxz5sGzZMsyZMwcrVqxALBbD2WefjWeffRbffPMN5s2bhxtuuAH169fHoYcemuWSE4QP8jkZGEEQBEEQRLbp148JYmJSSxmVcCFGyPKourDvZap8Ac8/z/7LwhsXRkQxrTja0YYfwefMM50NdX4+nE2TkP71lz1xnt/tpIu+fZ3jVJHQl1xivk4xypFfR4sXq+eVz/3PP7Pz3qEDMGUKwH24dY0VqrKaRHKrru9DDgG6BDDyadQIOOUU93VnKLrcF7GYPYmqGzfeyBIn3n23dc9t3syiQL2SjkaFXNcYNgAgFlM34gwaBDz1FDB5cviyidvieERQ5xV+6/lu3VidJ+LH4ui55/wtd++97P9ZZ7HGdB+CfUnS1LvvBk480SEM+8ItMbp4DQ4Y4Jzerx/7/9RT9oTHfo49r2f82kkFuQf2289sPr+JuoVGWAfVqrH1iQ2NXg15Yg+mXHjmliJKhYg+a9YsnHDCCTjhhBMAAPfffz9OOOEEPPHEEwCAiy66CIMHD8Ydd9yBk08+Gdu2bcNLL72ECqYPIIIgCIIgCILIN+SPUGqQ96Z3byYoHn20+3xuH7wiEyaoRfuCAucHPxdMRVuRp54C+vRhXfRFkVZlbeDno/2GG8ItbzrvggXAm29av9u6OXaniWOOcY5TNTioBJjLLzffjhiJnUwCc+cC33yjnlclbh59NPDLL85jdNttTJhsL7jZqu5lk54EfoUdLwSrIR5BbcOPiF6pUvjyiAwfDggWCSX4EdE599wDiIlHW7cG6usyXAiEsBcqQT7X++5rvmxlwQF67Fi2Hz17suuaJ+aMmlwS0VX3hGTlEynyvj/5JPD778HWpbDisjFjBvDhh+z64JYu8+YF21ZYHnlEP231amu4iiJjhlgnyZHWmzYBt9zCnn+xGHDCCcAzzzjXwc+zaUJOkQMOMH+eA2bWfYB/4VrKcVHC7NnWtGLrLQDsXcWNKlWAf/5hjbMkokdKqRDR+/Tpg3nz5jn+HnjgAQCsK8hVV12Fn376CTNnzsSrr76KloZdGQgiZ6APX4IgCILIf6ZPB447zi7IZIqoRarSgiioFhQwceKjj9Tz8vcxURiYO5dFq/7yi3P+ihXVgunSpfrIcTHQZ//92f/One3CXxgR/bjjgE6dgi8P2MU5E3gDweGH+1suCt54A3j3Xe+GETnRXzzOjnsQmjdnVik6VGLSBx9Y/sQiw4ax6+3rr1nE6Lffqr8LVIL1BRfYf0f9PSFGcqpEHR8WQTj+eOc4VQOIKddeq7/O/YroQQmR6wLcJ1o8ZzfcYH4OO3e2C/1nnMGsaNKNoe9yRmjWzDlu7Fjz5f3eLxMm2H9feSUSppHLxfNj+3aWO0XVACQyZAj7f+qp9ga2dKJ6RngJtGvXWsMqyyydiJ5IsPrw/vute+GDD9QNm7yeueYa97Ko+P571kPNFI1FF4qK7L1x1qwBxo83F/ZFm6Fp06xhUeB/7z3zcgLsOdSokb9lgGCNEWWIUiGiEwRBEARBEERe0L07+xBUJNKKHFkAaNgw/dvMR+SP4nhcL55w8U0U4dq3Z8lseSJSkSpV7II7p0IFQEzqKSIKj7qPWZUIqyuznJj+yCPV8/kR0WvVMp8XYI1HTzzBkutmmqpVmfjcv781TnWs5Eh0fl2EEXJVTJrEhHGR0093F6Picebf/+67wEEHqcuvapSR1xl1JPqyZdbwpZealUm0rBBR7VNxz3Ibl13mXS5uVdS4sf288+34bQQKiu4ed+O334AXXlA3KnhZyPAEpGPHsn1MZ9S1SD7ZuRx8sLrXhIpMB7GVK8fO26mnes/LE4jecou/bYSxNFYl0pUbH2XEZ4Vq2zoRXUhW6wkXmrduBVatMl9OVQY36tdnib9l5s9ndZ24ng8/BE4+WV2HqRAb3Pbc03oeqepQRVJvY0zqJLFeJxyQiE4QuYxB8giCIAiCIAgjSERXo4ssExk6lEXRcQ/aI49kCdk+/dQ+3zvv2COemzRhEbEyPMGjyppBFD51IrpKcBVtYES42MLZvt18nTqvbNFv1YTmzYErrlCL/5ni2muZx/X336u77+sS+ZkKbqbss49T3Iyiu73qW0EWh6L+nhCTPlapwqxCRFQWOWefrV6Xrmw33mj/ffHF3uWaONFa5+jRzu14HW9TywYvEgnmOc855hgmksuIEdM9ewIXXWTe20Dk+++BP/9kUedAdqLCsymiy4Ky6hjGYqx3Si6is/TQcdxxzIfdD2Hq4CCNT14+5eK9GLR+EpcLEkV9wQWs4cIrifQJJ7BGWRm3ngAff+wct3Klc1vyevlxE59V557L/ssWcX5cNj7/3HseP976ZRAS0QmCIAiCIAiiLEAienDuuIMJHFwAjMeZsC4nShs4kH00z5rFumTXqmX3MeVwcdMrotstkrRZMyaqffEFEwBefJFt+7TT7NG3cnLKOXPU61MJGN9/r55X9MLOF8qXBx54gCWG27nTOV3XmCInRp0yBfjuu2jL5icpbBiijkSXxRxZGFL5OutspVTX33//OX3FdclWRZuHVq2sYVkwf/RRbxFdZQMSlA4drOGPPnKKmCNHqoV1kQkTWBLX225zn69iRbuNUDaCsLJp53LAAfbfYQX9TB8/2ebNy04qiD3PTz/5XyYMM2e6T4+iYTWsEL9uHbPQWbfOXUAuKHA26nkh15GPP84sVu66yxpXVKS3vhKfVVwsl+vZFi3My2OSF9KrMaGMQyI6QeQLFIlOEARBEIQfxHeHcuX8W3CUFUyFRT/Rwp07W+Jf1apOIZbz9NNAv37AuHH28d99xyxi3MS8v/4CNmxgPuNvvcWiw48+miXyVHlre+2H6iNedc0UFGRO9E0XRxzB/ouNAckk5rz6qvX7/ffZf3lfu3VjXvVBBOmjjrKGxXObruMpR0hGLaIfdJD9tyy+DByoXu6OO9h/sYFJ9a2TTFp5ATiiiC7un87nXCzTP/+w+0Ul3HHh/dNPo/UE9jrmRx7pLfYeeCCrI5o08bftTIlhuWLncthhwGuvWck8R49mvSXef58laf75Z3/ry/T396RJ9t/ffus+f5DksCpLFhViQ1QQxLrODTECW/RP94PqmbZkif/1zJ7NGvn+7/+YqK6CW0WZ8vnnrMcAr1Ouvpr9v/tu9n/3bia0X3+9fTne2H3iida4HTvU2yCdKKOQiE4QBEEQBEEQpZ2GDelDS8fXXzMB+oMP0reNN9+0Jy/jXfCbNWORgaecYp9///3Vnsgi5cr5i+rl6ET0mjXN/Fvvuy//r6V27ZigumiRNa5jR2zr0gVFu3cz8VE8/lu2sCjgMWOsSD4TGyCZgw+2hsVo9ubN/a/LBDkJX9Qieu/eTMiTr1+O7lq76y4mEj30kDVOZ73RsKG9F40ouo0cyc7Lr7/qbSPEfeZJ9vr2dc43ZQrzAh4wwGnTFJRYzLlf8m9Vr4ioyEZEaTZF9FgMOOssy46nbVsmTB9/PLsXVOfdDbfGyEwgJoaVee21YOs0Fbd1DXuPPmq2vKlvt9igpbMk82LTJmuY5yB5+mn/67n1VlaPPPKI3pfetA7ldf3ixawHyuuvq+dbuJD9ySxYwP6LOVXE5KMiUT+P872RPM2QiE4QBEEQBEEQpRHxw4qsXPQcdBCwYgWLFksnRx3FPpanTPHnYRoEUayVcfvgvuIK73X36FE6Eo81b84EnIULmQjrJmRXqcKSgZ55pvs6vXoriNYkLVqwhptzz1Vb/njBk7R27gyMGGGN5wJIuXLO3gUmXfn9kEgAkydbPSlk0VZnjRCLMZsTXVJBGTECW1ymcmV2Xnr31ovoTZowof3hh63jUVDAyioKeHXqWEl43XzwZXsGL+T9kkW4dIro3bunb906smnnEhSdbYqpmKjrBSEj99wIwrffssajs85yn09Xl5v2ZpDrDu7Hf801ZsuLDZRuiHVm27Zs/Ucc4S8JtWg7tmwZSzD6wgvqeXUNzwCwcaP7dnbtMhes5V5cumemrjFWNX7bNvZfbtwJKqLXqcPeR0Tq1k3/+0meQyI6QeQylFiUIAiCIIgoIBE9N2jZEthrr/Rv54orgFdftYQM0SagUyezdchRzACLqD/sMGD9eue0Cy8EXnrJnmxSlYQt12jZkomwUSB708rd72UR7bjjmCDmJuzouOEGJqrMmgVcdZU1/uefmSf+jz+y32PGsP8DBnh7LIdFFtFlj2o33nnHOY4LzhUrqpcRI1fdegYMG+a0SwD0x2P4cGYF8uefzmk336zfjkxRkfOYyCJmOu+Rgw9mVk+y13bU5IqdS1Dk6+uBB8yXPfJI9bUrNJht7tULydGjgeeeYz2fgvLCC6wO4Qkm3dDZjqh6J6iSX8tJbFX5DdzwSiiq49FHmQVK0PuiQgV2727YoI5sFxP9yshJuGX+/ddck5EbLG+91WnbBuh7i0yf7hy3ZAnLyyKL/UF1orfecr6PrFlDupMHJKITRC5DSR0IgiAIgggKRaKXXQoKgHPOsUTdffYBfvkFuPde4H//M1uHym+Xe7ur3lGrVgUuuAC46CJrnEoIKK00bGjZhXBEIaVLl+i3qRLf99oL+OEH6/ydeSY7X59+mllxZMUKJhhPnGg2P4+yFOGR1C++yBo7XnqJ/d64kYk9otAWRLTj94KcvLRRI2DwYLV3uiwuujFtmlPcF6+JggLnNRM1p54KdO2a3m2I5KOIPm2a/bdYh3nx2Wfq+1CI5I5v24bU6aezXgxuIq4bp57qr1wq0Zs3rMkcdpjzOrz4YmDUKOCkk4Dnn7dHJ8v7e955znUGsbsSCVpX7d7NkncCwJVXOqe//HLw96Hly83sXM48Uz2fnBvlrrvU9R7AGj/lYzB7NmsgEPnuO//H6txz2TuBn0ZOogQS0QkiX6AWQYIgCIIggkIiOtGnD3DLLeYWBbyb/aWXsv9PPWVNU0Wi82hobjOwxx7BPW7zFZW3LaesvcvvsQf7H1SoKSqyhKgOHdixveAC9rt6dWY7EJbzzmNWEBMmqKer7G/8+Mrv3OkuJvoRRfOFfLRzkdHZEB18MLMQkvG4t8uvWeO/DPIze/ly72WqVbOGX3nFOX3ffdUNoKtWOcdddBGLeh4/3tnwKluA6HqKyDz4IDBjhtm8ixebzQfYn2lffMEaEKpVU0fs169vJfX0y+rVZvZL1avrrb3EBoihQ1nDtoqlS83K1KeP/2fLyJGsd5r8LuDXqqqMQiI6QeQyFIlOEARBEEQUNGiQ7RIQ+Qb/wH7mGSYqifYuPOkZwKIV16yxot7LlWPzlwbfdDeGD3eOMxG6SjP9+0e3Lr9JUINEW8fjwH776T2tw4roGze6f8/5iWrPZbiIV6lSMGuifOHRR+0i6uefs/8qEVOwyUjK/uLyb1VOBDlC+aefvMv388/e86gadaZO9fbuF+nShYnrHPk6Pucc9lyQueEG8x459eo5x6nsUMaNsydg5cmxL7jAeV/zZ1rQBs2iImedoEqgWr68/vg1a2b//d57wcoibitsA+2CBcz+za9lTxmFRHSCIAiCIAiCKI2QnQsRBjFKTY5Yu+QS9v/II1m0ohwVXFDgXwTNN669lkULHn00++2VlNXUiz6feegh4L77gHnz7ONff51Zr7gl7hMFnCA9GM47jyUPlaNkw6ASuVW2MbIdDCeVUouWDz/M/NhvvTVc+XKNfLJy6diR/X/jDfPo6G3b7CLoEUew/yoRU/C+L9iwwT5NbGhIpdQJR+XrbN99vcsnRhKrRGgdgwb5r6/F+uy77+zTXn1VX17T7Rx4oP33e++xBi8VKo9wVX28ZInZtnUkk+xeF4X0KlWcOTUqVNBHokcdJBlFD6c2bVjDR1nrLRWQUv5mQxAEQRAEQRAEieiEMSecwASgI4/Uz/N//8eiHsNG0eU7TZqwpII//wzceCPz7paZMoU1Ooh2OKWVqlVZ4k3ZFuD004HNm/UR3wDQvj3zM/75Z+8EfyoSCZY8NMrEvZUqOQUyVUSw7KnNSSadYiDAyjlrlj+hM5fp2JHti1udkWt88w0wZw4TkLt1s/uFi42G999vDTdrxvy2K1YEXnvNGq8ShmvUKBlMyAmG5YYVlXgpi+hc9HdDbPTZe2/g6aed86hE3EqVmI+3H9q2Zff5Xnux3koyJoKsW/JQ+ZiKCatltm61/z72WKBVK+d83GIqqFg8aBBbVrSaKShgjYciBQX6XknpcBog8TujGBriEQSRdahyJAiCIAgiKCSiE6a8+y4TcNy80+NxoG/fzJUpl6lY0ToWF17IohDvvht44AE2bq+9ohV2SzOJRG5dV7EYawQRo0pNPJE5vXqxZILlyzvF+NJE3boskaxpvoVcYI89LFEVYJHT11/PBG4xov6mm4BDDwW2bAEaN2Z/mzfb91X1ne4WlX/44aye5RZYKhH+ySftv/fc03OXADCLmSeeAJ57jjXyNWvGROWbbtIvU1DArFYqVGCJgB980Hs7iQTw559s3+NxZuH18cesQUK3T36Qj2kiob73Egm2r7x3FABcfbW/dZvC63SxgSORYF75zzwDXHYZGzdlCvD11+p1hBHRJ09mHuicESPY/xtuYAluTzop+LoJY/KoliOIMgh5ohMEQRAEEZQtW6xh8kQnTInF8ksMyzXOOstKrkrkP/E4s13hCQBPPRV46SXnfBddZPVEmDkT+OorlkcgFisb4lZpqDMeflg9Xhaw5X2Vv9kbNmQ9eipUUAu/L73EIqsHDWK/ZVF3+XKgUSM2/PvvTNi+8EKTPWAWM9xmBgCOOQZYu9ZK+KoSkOvXB2rVAu66y2wbHLFxqaCA7TPHRKjWWZ4AThG+oECdTDgeBy6+2C6iq3p/iBY6Ytk+/hi45hp7ng8V116rTqDK13XxxZaIvn27fj1uiYY5ixezhtfVq61xH3wAtGxpn483zB14IJs3n+yU8hiycyEIgiAIgiCI0siqVdawW7dpgiAIQs/QocCECcy25bDD7NPOPpv9f+gh4MorWRRqly5MmCstiUMJd2Q7kRUrWPJQHmkuU6sWs8Rq2pT9lu1euIAOAD16sMhqN8HZizp1LLFX9uEfONC+vahwi0Tnfumi8C0ji/A1a6qFeb6dfv30ywL2pMfi9L32YkK6jBh4MHgw612kgjegiPtbrpze2mjhQvV4TmEh6z0gn6fjjnNeA2L9Uq9e6c9DkiPQUSYIgiAIgiCI0si//2a7BARBEPlPIsGiPXkCUR4V/NlnwKhRbLhmTeaXTdY9ZY/997dHq3O7j/feQ+rYYzFH9E9XsXJl+som07gxaxTijB2bnu24RaJ/+CEwfrxemAbsgvCHH+rXx+cTPOhtfPEFE6D5fQoA1apZw4mEM4cDYI86f/lllkCU4xVlX1CgXqcJvJdD5crOabJIPn9+sG0QoSARnSByGbFrGHmiEwRBEAThBzESnSAIgoiGF14A1q3Lr0SaRPooKADGjbN+Fxay/x07Ivnee9jWqZP78rVqWcNuViBRcccdzOIllWKWM+lAFnx/+80arl2bWRy59dQQtY/ff9fPx/O9PPMMszd5/XX79MMPZ1Yoov+9aHuisyESLbnkcorWMOXKOZctVw645RZ9mU1QlUvcLhBcqCdCUQqMqwiCIAiCIAiCcECR6ARBENETi1ke0wQBAM2bM0uRSpXU3tluDBzIRNcDD/S/bFDS7Z8t52Hxm5dFDCbcvVs9zwMPWD0AWrQAfv3VbN3ivnOLlMWL2Tnk1KunX75mTZbMNB5XW+XF4+Hz0IhlfO899l9u8BDz3hAZg0R0gshlKBKdIAiCIIigVKkC7NqV7VIQBEEQROkmHgd+/JEN+/1uLyiwEteWFmT/btk33otOnViE/n//6aO6b7wxWNm4Fz1gRZKLFi+NG3sL1BdfrJ/GrWWOOILZyQSha1drWEwwKkI5F7IC2bkQBEEQBEEQRGnk44+ZP+/PP2e7JARBEARRuonFKPBN5KabrGHRTsWEWAxYv54FFYrR+fXrs//DhgUvV82aLJ/BZ59Z0d01a1rTW7SwC+2mjB4N9O0LPPII+/3pp6wRQNyuivPOc44Tk72KIvrxx1vDYjJVImNQJDpBEARBEARBlEb69QOmTMl2KQiCIAiCKGtcdBFLtnvRRWrbkyAsXMgSsbZpE249cj6DWAzo0AGYOxc4/XTgjDPYtg44wHydgwezP048zoRznp9m40agfXv7MldeqRbXRa910fv8nXeAv/92rofIGCSiE0S+QK3aBEEQBEEQBEEQBEHkOq1aAdu2RbvOKlXCC+g6Jk1ivuoHH8wE8DvvjGa93B+9enXntMcfZ1Y3ixczb3xOLMaE9MJCe/R5QQEJ6FmGRHSCyGVET3SCIAiCIAiCIAiCIAgiWmrWBA49NH3rl5PGDhnC/lepArz6qnP+7dtZUlU5oSiRVUhEJ4h8gSLRCYIgCIIgCIIgCIIg8otYjHmk794N1K3rPX8i4UzQSmQdEtEJgiAIgiAIgiAIgiAIgiDShS65KJE3xLNdAIIgCIIgCIIgCIIgCIIgCILIVUhEJ4hchjzRCYIgCIIgCIIgCIIgCCKrkIhOEPkCeaITBEEQBEEQBEEQBEEQRMYhEZ0gchmKRCcIgiAIgiAIgiAIgiCIrEIiOkEQBEEQBEEQBEEQBEEQBEFoIBGdIAiCIAiCIAiCIAiCIAiCIDSQiE4Q+QJ5ohMEQRAEQRAEQRAEQRBExiERnSByGfJEJwiCIAiCIAiCIAiCIIisQiI6QeQLFIlOEARBEARBEARBEARBEBmnINsFIAjChT32ACpWBMqXB6pUyXZpCIIgCIIgCIIgCIIgCKLMQSI6QeQyBQXAf/+xKPQ4dRwhCIIgCIIgCIIgCIIgiExDIjpB5DoVK2a7BARBEARBEARBEARBEARRZqHQVoIgCIIgCIIgCIIgCIIgCILQQCI6QRAEQRAEQRAEQRAEQRAEQWggEZ0gCIIgCIIgCIIgCIIgCIIgNJCIThAEQRAEQRAEQRAEQRAEQRAaypSIPnbsWBx88MHo2rUrTjnlFPzxxx/ZLhJBEARBEARBEARBEARBEASRw5QZEf3TTz/F/fffj8svvxzvvfceOnTogAsuuADr1q3LdtEIgiAIgiAIgiAIgiAIgiCIHKXMiOgjR47EqaeeioEDB6JNmzYYOnQoKlasiPHjx2e7aARBEARBEARBEARBEARBEESOUiZE9F27dmH27Nno169fybh4PI5+/frh999/z2LJCIIgCIIgCIIgCIIgCIIgiFymINsFyAT//fcfioqKUKdOHdv4OnXqYOHChcbrKSoqirpoOQ3f37K23wRBpAeqUwiCiBKqUwiCiBKqUwiCiBqqVwgiPzC9R8uEiB4VM2fOzHYRskJZ3W+CINID1SkEQUQJ1SkEQUQJ1SkEQUQN1SsEUTooEyJ6rVq1kEgkHElE161bh7p16xqvp2vXrkgkElEXL2cpKirCzJkzy9x+EwSRHqhOIQgiSqhOIQgiSqhOIQgiaqheIYj8gN+rXpQJEb18+fLo3LkzJk2ahEMPPRQAkEwmMWnSJAwePNh4PYlEokxWfGV1vwmCSA9UpxAEESVUpxAEESVUpxAEETVUrxBE6aBMiOgAcN555+HGG29Ely5d0K1bN4waNQrbt2/HSSedlO2iEQRBEARBEARBEARBEARBEDlKmRHRjzrqKKxfvx5PPPEE1qxZg44dO+Kll17yZedCEARBEARBEARBEARBEARBlC3KjIgOAIMHD/Zl30IQBEEQBEEQBEEQBEEQBEGUbeLZLgBBEARBEARBEARBEARBEARB5CplKhI9KKlUCgDL1lqW4Ptb1vabIIj0QHUKQRBRQnUKQRBRQnUKQRBRQ/UKQeQH/B7l+q+OWMprDgK7du3CzJkzs10MgiAIgiAIgiAIgiAIgiAIImK6du2K8uXLa6eTiG5AMpnE7t27EY/HEYvFsl0cgiAIgiAIgiAIgiAIgiAIIiSpVArJZBIFBQWIx/XO5ySiEwRBEARBEARBEARBEARBEIQGSixKEARBEARBEARBEARBEARBEBpIRCcIgiAIgiAIgiAIgiAIgiAIDSSiEwRBEARBEARBEARBEARBEIQGEtEJgiAIgiAIgiAIgiAIgiAIQgOJ6ARBEARBEARBEARBEARBEAShgUR0giAIgiAIgiAIgiAIgiAIgtBAIjpBEARBEARBEARBEARBEARBaCARvRTz/PPPY+DAgejZsyf69u2Lyy67DAsXLrTNs3PnTgwdOhR9+vRBz549ccUVV2Dt2rUl0+fOnYtrr70WBxxwALp164YBAwZg1KhRtnWsXr0a1113HY444gh06NAB9957b0b2jyCIzJKpOuXLL7/Eeeedh3322Qe9evXCaaedhh9++CEj+0gQRObIVJ3y66+/YtCgQejTpw+6deuGI488Eq+++momdpEgiAyTqXpFZNq0aejUqROOP/74tO0XQRDZIVN1yuTJk9G+fXvH35o1azKynwRBmEEieilmypQpOPPMMzFu3DiMHDkSu3fvxgUXXIBt27aVzHPfffdhwoQJGDFiBEaPHo3Vq1djyJAhJdNnzZqF2rVr4+GHH8Ynn3yCSy65BI8++ijGjBlTMs+uXbtQq1YtXHrppejQoUNG95EgiMyRqTpl6tSp6NevH1544QW8++676NOnDy699FL8+eefGd1fgiDSS6bqlMqVK2Pw4MEYM2YMPv30U1x66aUYMWIE3nrrrYzuL0EQ6SdT9Qpn06ZNuPHGG9G3b9+M7B9BEJkl03XK559/jh9//LHkr06dOhnZT4IgDEkRZYZ169al2rVrl5oyZUoqlUqlNm3alOrcuXPqs88+K5nnr7/+SrVr1y71+++/a9dz1113pc466yzltMGDB6fuueeeSMtNEERukok6hXPUUUelnnzyyUjKTRBEbpLJOuXyyy9PXX/99ZGUmyCI3CXd9crVV1+deuyxx1JPPPFE6rjjjou8/ARB5BbpqlN++eWXVLt27VIbN25MW9kJgggPRaKXITZv3gwAqFGjBgDWIlpYWIh+/fqVzNO6dWs0atQI06dPd11PzZo101lUgiDygEzVKclkElu3bqV6hyBKOZmqU/7880/8/vvv2HvvvSMpN0EQuUs665Xx48dj6dKltohTgiBKN+l+VznhhBPQv39/nHfeeZg2bVqkZScIIjwF2S4AkRmSySTuu+8+9OrVC+3atQMArF27FuXKlUP16tVt89apU0frvfXbb7/hs88+w/PPP5/2MhMEkbtksk55+eWXsW3bNgwYMCC6HSAIIqfIRJ2y//77Y/369SgqKsKQIUNwyimnRL8jBEHkDOmsV/755x8MHz4cY8eORUEBfVITRFkgnXVKvXr1MHToUHTp0gW7du3C22+/jbPPPhvjxo1D586d07dTBEH4gp74ZYShQ4diwYIFeP311wOvY/78+bjssstw+eWXo3///hGWjiCIfCNTdcpHH32Ep59+Gs888wx5AhJEKSYTdcrYsWOxbds2zJgxA8OHD0fz5s1xzDHHhCk2QRA5TLrqlaKiIlx33XW44oor0LJly6iKSxBEjpPOd5VWrVqhVatWJb979eqFpUuX4tVXX8XDDz8cqtwEQUQHiehlgLvvvhsTJ07EmDFj0LBhw5LxdevWRWFhITZt2mRrOV23bh3q1atnW8dff/2Fc889F6eddhouu+yyjJWdIIjcI1N1yieffILbbrsNjz/+uK2LJEEQpYtM1SlNmzYFALRv3x5r167Fk08+SSI6QZRS0lmvbN26FbNmzcKcOXMwbNgwACxCNZVKoVOnTnj55Zcp0ShBlDKyoal07doVv/32W3Q7QRBEaMgTvRSTSqVw991346uvvsKoUaNKPh45Xbp0Qbly5TBp0qSScQsXLsSKFSvQo0ePknELFizA2WefjRNOOAHXXHNNpopPEESOkck65eOPP8bNN9+M4cOH48ADD0zH7hAEkWWy+Z6STCZRWFgYyX4QBJE7ZKJeqVq1Kj766CO8//77JX+DBv1/e/cTYmXVxwH8OzOajjoqySDhIBjVVVw40ixUFLWVCwcyEXQh2R8oMCE3etOFf1AccSExokhhggiaNloZakSLlNREWoUxUrQwwRILNGVmmLktxOH11fu+Ly/Ovaafz/Lcc89zfpvD83zvec5dnAkTJuTo0aOZMmXKgNYIVE4171V+/PHH+4J4oLrsRH+MbdiwIceOHcvOnTszfPjw/jO5GhoaMnTo0DQ0NGThwoVpa2vLqFGjMmLEiGzatClTp07tX/A7Ozvz6quv9v+5xd0x6urq8vTTT/df6+LFi0nu7My4fv16Ll68mMGDB+e5556rbNHAgKnUmvL555+nWCxmzZo1mTJlSn+fu9cAHg+VWlP279+fZ555pv816fPnz2fPnj1ZunRp5YsGBlQl1pXa2tr+85DvGjNmTIYMGXJfO/DPVql7lb1796apqSnPP/98urq6cujQoZw9ezZ79uypSt3Ag9WUSqVStSfBwCgUCg9s37JlS1555ZUkSVdXV9ra2vLFF1+ku7s7M2fOzLp16/p/8Wxvb8+OHTvuG2PcuHH5+uuv/+O1/r0P8M9WqTVl6dKl+e677+7rs2DBgrS1tT2scoAqq9Sasm/fvhw8eDCXL19OXV1dxo8fn0WLFmXx4sWprfVSJjxOKvn886/a29vz1Vdf5dNPP31IlQCPgkqtKR988EE+/vjjXL16NfX19XnhhReyfPnyTJs2bYAqA/4fQnQAAAAAACjD9hsAAAAAAChDiA4AAAAAAGUI0QEAAAAAoAwhOgAAAAAAlCFEBwAAAACAMoToAAAAAABQhhAdAAAAAADKEKIDAAAAAEAZQnQAAAAAAChDiA4AAI+hYrGYQqGQQqGQyZMnZ8aMGXnttddy+PDh9PX1/c/jdHR0pKWlZQBnCgAAj7ZB1Z4AAAAwMGbNmpUtW7akr68v165dy6lTp7J58+acPHkyu3btyqBBHgcAAOC/sRMdAAAeU0899VQaGxszduzYTJ48OW+//XZ27tyZb775JkeOHEmSfPTRR2ltbU1zc3Nmz56d9evX56+//kqSnDt3Lu+9915u3LjRv6u9vb09SdLd3Z2tW7dm1qxZaW5uzqJFi3Lu3Lmq1QoAAANFiA4AAE+Q6dOnZ+LEifnyyy+TJDU1NVm7dm2OHTuWtra2nD17Ntu2bUuSTJ06NWvWrMmIESNy+vTpnD59Oq+//nqSZOPGjfn++++zffv2fPbZZ5k3b17efPPN/PLLL9UqDQAABoQQHQAAnjDPPvtsfv311yTJsmXLMm3atDQ1NWX69Ol59913c/z48SR3drI3NDSkpqYmjY2NaWxszPDhw3PlypV0dHTk/fffT0tLS8aPH5833ngjL774Yjo6OqpZGgAAPHQOQQQAgCdMqVRKTU1NkuTbb7/N7t278/PPP+fmzZvp7e1NV1dXbt++nfr6+gd+v7OzM729vZk3b9497d3d3Rk9evRATx8AACpKiA4AAE+Yn376KU1NTbl8+XLeeuutLFmyJCtXrsyoUaNy4cKFrF27Nj09PWVD9Fu3bqWuri6ffPJJ6urq7vls2LBhlSgBAAAqRogOAABPkDNnzqSzszPLli3LDz/8kFKplGKxmNraOyc93j3K5a7Bgwent7f3nrZJkyalt7c3169fT0tLS8XmDgAA1SBEBwCAx1R3d3d+//339PX15dq1azl16lR2796duXPn5uWXX86lS5fS09OTffv25aWXXsqFCxdy4MCBe8YYN25cbt26lTNnzqRQKKS+vj4TJkxIa2trVq1alWKxmEmTJuWPP/7o7zNnzpzqFAwAAAOgplQqlao9CQAA4OEqFos5cuRIkmTQoEEZOXJkJk6cmPnz52fBggX9O8/37t2bDz/8MDdu3EhLS0taW1uzevXqnD9/PiNHjkySrFu3LidOnMiff/6Zd955JytWrEhPT0927dqVo0eP5rfffsvo0aPT3NycFStWpFAoVK1uAAB42IToAAAAAABQRm21JwAAAAAAAI8qIToAAAAAAJQhRAcAAAAAgDKE6AAAAAAAUIYQHQAAAAAAyhCiAwAAAABAGUJ0AAAAAAAoQ4gOAAAAAABlCNEBAAAAAKAMIToAAAAAAJQhRAcAAAAAgDKE6AAAAAAAUMbfOt79Mz+sRusAAAAASUVORK5CYII="
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
],
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"execution_count": 54
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-03T15:44:38.791997Z",
|
||
"start_time": "2025-09-03T15:44:35.577884Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"import talib\n",
|
||
"from statsmodels.stats.diagnostic import het_arch\n",
|
||
"from statsmodels.tsa.stattools import adfuller, acf\n",
|
||
"\n",
|
||
"\n",
|
||
"def slice_atr_stats(df_raw: pd.DataFrame,\n",
|
||
" n: int) -> pd.Series:\n",
|
||
" \"\"\"\n",
|
||
" 计算滚动长度为 n 的 ATR 序列,并返回一组统计学指标\n",
|
||
" 参数\n",
|
||
" ----\n",
|
||
" df_raw : 必须包含 high, low, close\n",
|
||
" n : 回看窗口(=ATR 周期)\n",
|
||
"\n",
|
||
" 返回\n",
|
||
" ----\n",
|
||
" pd.Series : 各统计量\n",
|
||
" \"\"\"\n",
|
||
" # 1. 计算 ATR\n",
|
||
" atr = talib.ATR(df_raw['high'].values,\n",
|
||
" df_raw['low'].values,\n",
|
||
" df_raw['close'].values,\n",
|
||
" timeperiod=n)\n",
|
||
"\n",
|
||
" atr = atr[~np.isnan(atr)]\n",
|
||
" if len(atr) < 10:\n",
|
||
" raise ValueError(f\"有效 ATR 样本数 {len(atr)} 不足\")\n",
|
||
"\n",
|
||
" # 2. 基本描述\n",
|
||
" stats_dict = {\n",
|
||
" 'n': n,\n",
|
||
" 'mean': np.mean(atr),\n",
|
||
" 'std': np.std(atr, ddof=1),\n",
|
||
" 'sem': np.std(atr, ddof=1) / np.sqrt(len(atr)), # 标准误\n",
|
||
" 'skew': pd.Series(atr).skew(),\n",
|
||
" 'kurt': pd.Series(atr).kurtosis(),\n",
|
||
" 'q05': np.percentile(atr, 5),\n",
|
||
" 'q25': np.percentile(atr, 25),\n",
|
||
" 'q50': np.percentile(atr, 50),\n",
|
||
" 'q75': np.percentile(atr, 75),\n",
|
||
" 'q95': np.percentile(atr, 95),\n",
|
||
" }\n",
|
||
"\n",
|
||
" # 3. 自相关(滞后 1)\n",
|
||
" stats_dict['acf_1'] = acf(atr, nlags=1, fft=False)[1]\n",
|
||
"\n",
|
||
" # 4. ARCH-LM 检验(是否存在波动聚集)\n",
|
||
" arch_lm, arch_pval, _, _ = het_arch(atr, maxlag=5)\n",
|
||
" stats_dict['arch_lm'] = arch_lm\n",
|
||
" stats_dict['arch_pval'] = arch_pval\n",
|
||
"\n",
|
||
" # 5. ADF 单位根检验(是否平稳)\n",
|
||
" adf_res = adfuller(atr, regression='c')\n",
|
||
" stats_dict['adf_stat'] = adf_res[0]\n",
|
||
" stats_dict['adf_pval'] = adf_res[1]\n",
|
||
"\n",
|
||
" # 6. 重尾指数(Hill 估计,尾部 5%)\n",
|
||
" tail = sorted(atr)[-int(np.ceil(0.05 * len(atr))):]\n",
|
||
" hill = len(tail) / np.sum(np.log(tail) - np.log(min(tail)))\n",
|
||
" stats_dict['hill_tail'] = hill\n",
|
||
"\n",
|
||
" return pd.Series(stats_dict)\n",
|
||
"\n",
|
||
"\n",
|
||
"# 假设 df_raw 已准备好\n",
|
||
"for n in [10, 20, 30, 50]:\n",
|
||
" try:\n",
|
||
" res = slice_atr_stats(df_raw, n)\n",
|
||
" print(res.to_frame().T.to_string(index=False))\n",
|
||
" except ValueError as e:\n",
|
||
" print(e)"
|
||
],
|
||
"id": "8b2055ca0d08510e",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" n mean std sem skew kurt q05 q25 q50 q75 q95 acf_1 arch_lm arch_pval adf_stat adf_pval hill_tail\n",
|
||
"10.0 17.10203 8.924275 0.055944 1.424996 2.548368 7.386456 10.430459 14.681663 21.737424 34.709991 0.993183 24917.586967 0.0 -4.412162 0.000282 5.503068\n",
|
||
" n mean std sem skew kurt q05 q25 q50 q75 q95 acf_1 arch_lm arch_pval adf_stat adf_pval hill_tail\n",
|
||
"20.0 17.105253 8.65925 0.054293 1.297106 1.728398 7.605461 10.433439 14.749979 21.922722 34.407901 0.998188 25301.321873 0.0 -3.649219 0.004887 6.040336\n",
|
||
" n mean std sem skew kurt q05 q25 q50 q75 q95 acf_1 arch_lm arch_pval adf_stat adf_pval hill_tail\n",
|
||
"30.0 17.108363 8.552993 0.053638 1.249836 1.45459 7.662073 10.471382 14.714624 22.060539 34.168723 0.999162 25363.812059 0.0 -3.175812 0.021428 6.161612\n",
|
||
" n mean std sem skew kurt q05 q25 q50 q75 q95 acf_1 arch_lm arch_pval adf_stat adf_pval hill_tail\n",
|
||
"50.0 17.114221 8.440261 0.052952 1.203796 1.207939 7.791217 10.519432 14.735442 22.179905 33.70525 0.999676 25380.904971 0.0 -2.645793 0.083897 6.067103\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 55
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-09-03T15:46:25.613295Z",
|
||
"start_time": "2025-09-03T15:46:23.569085Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"from scipy.stats import jarque_bera, bootstrap\n",
|
||
"\n",
|
||
"def vol_momentum_events(df: pd.DataFrame,\n",
|
||
" vol_window: int = 100,\n",
|
||
" vol_q: float = 0.50,\n",
|
||
" sign_window: int = 5):\n",
|
||
" \"\"\"\n",
|
||
" 返回事件索引列表 [(idx, direction)]\n",
|
||
" direction = 1 多头 -1 空头\n",
|
||
" \"\"\"\n",
|
||
" ret = np.log(df['close'] / df['close'].shift(1))\n",
|
||
" vol = ret.abs()\n",
|
||
"\n",
|
||
" # 滚动分位\n",
|
||
" low_vol = vol < vol.rolling(vol_window).quantile(vol_q)\n",
|
||
" # 同向漂移:最近 sign_window 根 K 线方向一致\n",
|
||
" sign_sum = np.sign(ret).rolling(sign_window).sum()\n",
|
||
" same_sign = np.abs(sign_sum) == sign_window\n",
|
||
"\n",
|
||
" mask = low_vol & same_sign\n",
|
||
" events = [(df.index[i], int(np.sign(ret.iloc[i])))\n",
|
||
" for i in np.where(mask)[0]]\n",
|
||
" return events\n",
|
||
"\n",
|
||
"\n",
|
||
"def backtest_vol_momentum(df: pd.DataFrame,\n",
|
||
" events: list,\n",
|
||
" hold_bars: int = 10):\n",
|
||
" \"\"\"\n",
|
||
" 返回收益序列\n",
|
||
" \"\"\"\n",
|
||
" rets = []\n",
|
||
" for ts, direction in events:\n",
|
||
" idx = df.index.get_loc(ts)\n",
|
||
" if idx + hold_bars >= len(df):\n",
|
||
" continue\n",
|
||
" start_price = df.at[ts, 'close']\n",
|
||
" end_price = df.iloc[idx + hold_bars]['close']\n",
|
||
" rets.append((end_price - start_price) * direction)\n",
|
||
" return np.array(rets)\n",
|
||
"\n",
|
||
"\n",
|
||
"def validate_vol_momentum(df_raw: pd.DataFrame,\n",
|
||
" vol_window: int = 100,\n",
|
||
" vol_q: float = 0.85,\n",
|
||
" sign_window: int = 2,\n",
|
||
" hold_bars: int = 10,\n",
|
||
" bootstrap_resamples: int = 9999):\n",
|
||
" events = vol_momentum_events(df_raw, vol_window, vol_q, sign_window)\n",
|
||
" if len(events) < 20:\n",
|
||
" return {'error': '事件不足 20 个'}\n",
|
||
"\n",
|
||
" rets = backtest_vol_momentum(df_raw, events, hold_bars)\n",
|
||
"\n",
|
||
" # 统计量\n",
|
||
" win_rate = np.mean(rets > 0)\n",
|
||
" mean_ret = np.mean(rets)\n",
|
||
" jb_stat, jb_p = jarque_bera(rets)\n",
|
||
"\n",
|
||
" # Bootstrap 单侧检验 mean>0\n",
|
||
" boot_res = bootstrap((rets,), np.mean, n_resamples=bootstrap_resamples,\n",
|
||
" random_state=42)\n",
|
||
" boot_p = np.mean(boot_res.bootstrap_distribution <= 0)\n",
|
||
"\n",
|
||
" return {\n",
|
||
" 'events': len(rets),\n",
|
||
" 'win_rate': win_rate,\n",
|
||
" 'mean_ret': mean_ret,\n",
|
||
" 'jb_stat': jb_stat,\n",
|
||
" 'jb_p': jb_p,\n",
|
||
" 'bootstrap_p_one_tail': boot_p\n",
|
||
" }\n",
|
||
"\n",
|
||
"\n",
|
||
"# ---------- 一键运行 ----------\n",
|
||
"report = validate_vol_momentum(df_raw)\n",
|
||
"if 'error' in report:\n",
|
||
" print(report['error'])\n",
|
||
"else:\n",
|
||
" print(f\"事件数: {report['events']}\")\n",
|
||
" print(f\"胜率: {report['win_rate']:.2%}\")\n",
|
||
" print(f\"平均收益: {report['mean_ret']:.4f}\")\n",
|
||
" print(f\"JB p-val: {report['jb_p']:.4f}\")\n",
|
||
" print(f\"Bootstrap 单侧 p: {report['bootstrap_p_one_tail']:.4f}\")\n",
|
||
" if report['bootstrap_p_one_tail'] < 0.05:\n",
|
||
" print(\"→ 收益期望显著 > 0\")\n",
|
||
" else:\n",
|
||
" print(\"→ 收益期望不显著\")"
|
||
],
|
||
"id": "5a938217f439a86b",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"事件数: 9191\n",
|
||
"胜率: 49.62%\n",
|
||
"平均收益: 0.3230\n",
|
||
"JB p-val: 0.0000\n",
|
||
"Bootstrap 单侧 p: 0.2335\n",
|
||
"→ 收益期望不显著\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 60
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|