beta-neutral-pair-trading-auto.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "0e43aa20",
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   "source": [
    "# Pairs-Trading (Beta Neutral) Strategy Analysis\n",
    "\n",
    "The beta neutral trading strategy is a market-neutral approach designed to eliminate systematic market risk by constructing a portfolio that has a net beta of zero. This strategy aims to generate alpha (excess returns above the market) by taking both long and short positions in securities such that the overall portfolio is insulated from broad market movements.\n",
    "\n",
    "Key Concepts:\n",
    "- **Beta**: A measure of a security's volatility in relation to the overall market. A beta of 1 indicates that the security moves with the market, a beta greater than 1 indicates higher volatility than the market, and a beta less than 1 indicates lower volatility.\n",
    "- **Market Neutral**: A strategy that seeks to minimize exposure to market risk, focusing on generating returns from the individual performance of securities rather than market movements.\n",
    "\n",
    "Here's how it works:\n",
    "\n",
    "- **Identifying a Pair / Basket of Tokens**: Identify a set of tokens with varying betas.\n",
    "- **Calculating Beta**: Use historical price data to calculate the beta of each token relative to a benchmark token (e.g., BTC and ETH). This can be done using regression analysis where the token's returns are regressed against the benchmark's returns.\n",
    "- **Constructing the Portfolio**: \n",
    "    - **Long Positions**: Select tokens with positive alpha (expected to outperform their beta-implied returns) and go long on these.\n",
    "    - **Short Positions**: Select tokens with negative alpha (expected to underperform their beta-implied returns) and short these.\n",
    "    - Ensure the weighted average beta of the long positions is equal to the weighted average beta of the short positions, resulting in a net beta of zero.\n",
    "- **Position Sizing**: Calculate the dollar amount for each position to ensure that the portfolio remains beta neutral. This involves balancing the dollar value of long and short positions based on their respective betas.\n",
    "- **Monitoring and Rebalancing**: Regularly monitor the portfolio to ensure it remains beta neutral. Adjust positions as necessary to maintain the desired beta neutrality, especially if there are significant changes in the market or individual tokens.\n",
    "\n",
    "In this notebook, the pair trading negative / low correlation strategy is demonstrated using cryptocurrency price data sourced from the Binance, OKX, and Bybit APIs.\n",
    "\n",
    "Special Note:\n",
    "There are **two notebooks** for Pairs-Trading (Beta Neutral) Strategy Analysis. **This notebook uses CVXPY package to optimise portfolio weights given a set of data. The other notebook allow users to manually set the portfolio weights and plot the returns of portfolio compared to market.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6485699-1817-44c9-9c53-08bfb86511e9",
   "metadata": {},
   "source": [
    "## Credits\n",
    "\n",
    "This notebook references [quant_playground](https://github.com/rukawa917/quant_playground) repository by [rukawa917](https://github.com/rukawa917) and [mkt_neutral.ipynb](https://gist.github.com/FilippoGuerrieri26/8086c42db442835234dd61ea6c07decb) by [FilippoGuerrieri26](https://gist.github.com/FilippoGuerrieri26). I want to extend my sincere gratitude to them for creating this valuable resource.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab73ff6-fa1f-46cc-900b-8268a0c14305",
   "metadata": {},
   "source": [
    "## Prepare your Environment\n",
    "\n",
    "Ensure that the correct kernel is selected for this notebook. If you are following the instruction in *README.md*, the kernel is 'crypto-trading-analysis'. To check, click on 'Kernel' at the top bar, select 'Change Kernel...' and select the correct kernel. For convenience, ensure that 'Always start the preferred kernel' is ticked. Click 'Select' to confirm the setting.\n",
    "\n",
    "Install the environment's dependencies using the command below. After installation, restart the kernel to use the updated packages. To restart, click on 'Kernel' at the top bar and select 'Restart Kernel' and click on 'Restart'. Please skip this step if you have already done it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8d92236-023d-4e09-94c8-a65fcecd9c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90ad883-ce63-4504-8af6-52998aedb87d",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1f89e4a",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "papermill": {
     "duration": 29.034008,
     "end_time": "2023-11-01T02:04:18.534845",
     "exception": false,
     "start_time": "2023-11-01T02:03:49.500837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gordon.oh/miniconda3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "/Users/gordon.oh/miniconda3/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import os\n",
    "import sys\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from itertools import combinations\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from utils import calculate_profit, plot_strategy\n",
    "from data_manager import load_ts_df, process_data, sanitize_data\n",
    "from typing import List\n",
    "import cvxpy as cvx\n",
    "import warnings\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf0de13",
   "metadata": {},
   "source": [
    "## Process Price Dataframe\n",
    "\n",
    "- Before proceeding, ensure that the price data has been downloaded using ***'data_manager.py'***.\n",
    "- Enter the ***cex*** (Centralized Exchange) and ***interval*** values used for data download to load the relevant *.pkl* files and retrieve the dataframe.\n",
    "- You can specify a batch of pairs to load using the ***selected_pairs*** variable. The key represents the pair and its value represents the direction it must take (1: Long, -1: Short, 0: Does not matter). Unlike other notebooks, no pairs will be selected if the dictionary is empty.\n",
    "- Note that some pairs might be new and may lack sufficient data within the downloaded timeframe. Such pairs will be removed based on the ***nan_remove_threshold*** setting, which defines the maximum percentage of NaN values allowed relative to the total data points. For example, with a ***nan_remove_threshold*** of 0.1, if a pair has 100 data points and 15 are NaN, the pair will be excluded.\n",
    "- From the remaining pairs, you can filter the top N rolling or mean volume pairs using the ***top_n_volume_pairs*** and ***volume_filter_mode*** parameter. \n",
    "- This part of the code will also ensure that all timeseries columns have the same number of data points.\n",
    "- The earliest and latest dates for all pairs will be recorded. These dates can then be used to determine the timeframe for slicing the data in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13057dad-759b-4606-b673-6b699765f2c1",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ffa57f5-0ef6-49ca-847f-b439c1c63bdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### INPUTS #####\n",
    "cex = 'binance'\n",
    "interval = '1d'\n",
    "nan_remove_threshold = 0.0\n",
    "\n",
    "# Select only the pairs below to analyse. The key represents the pair and its value represents the direction it must take. \n",
    "# 1: Long, -1: Short, 0: Does not matter\n",
    "# Unlike other notebooks, no pairs will be selected if the dictionary is empty. \n",
    "# selected_pairs = {'SOLUSDT':1, 'OMNIUSDT':-1, 'XAIUSDT':-1, 'STRKUSDT':-1, 'TNSRUSDT':-1, 'PORTALUSDT':-1, \n",
    "#                   'WUSDT':-1, 'ALTUSDT':-1, 'BTCUSDT':0}\n",
    "selected_pairs = {'1000PEPEUSDT': 0, '1000BONKUSDT': 0, 'SOLUSDT':0}\n",
    "\n",
    "# Select the benchmark token.\n",
    "benchmark_token = 'BTCUSDT'\n",
    "\n",
    "# Select only the top N mean volume pairs from the selected pairs to analyse.\n",
    "top_n_volume_pairs = 100\n",
    "\n",
    "# Select volume filter mode. Options: ['rolling', 'mean'].\n",
    "volume_filter_mode = 'rolling'\n",
    "\n",
    "# The train_test_split parameter is beneficial when separate datasets are needed for non-rolling and rolling portfolio weights \n",
    "# optimization. If False, all data will be used as train and test. If True and train_percentage is 0.5, the dataset is divided \n",
    "# into two halves. The first half is used to compute the non-rolling portfolio weights for the dates within this segment. In \n",
    "# contrast, the second half is employed for rolling portfolio weights optimization, utilizing the specified rolling_window parameter.\n",
    "train_test_split = False\n",
    "\n",
    "# Only applies if train_test_split = True\n",
    "train_percentage = 0.5 # only from 0 < train_percentage <= 1\n",
    "\n",
    "# Rolling Window\n",
    "rolling_window = 7\n",
    "\n",
    "# Minimum Long and Minimum Short. If min_long == 0.1, the weight for any pair selected for a long position will be optimized to at \n",
    "# least 0.1 after normalization, though this is not guaranteed. Similarly, if min_short == 0.1, the weight for any pair selected \n",
    "# for a short position will aim to be at least 0.1 after normalization, but this may not always be achievable.\n",
    "min_long = 0.1\n",
    "min_short = 0.1\n",
    "\n",
    "# Rolling optimisation unsolvable count threshold before exit. If total number of datetime is 1000 and unsolvable_threshold == 0.05, \n",
    "# then the max_unsolvable_count = 1000 * 0.05 = 50. If there are 50 consecutive unsolvable optimisation, the rolling optimisation\n",
    "# will stop.\n",
    "unsolvable_threshold = 0.05\n",
    "##################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "975a7b02-2eec-4c14-840c-79baa72d984e",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_pairs[benchmark_token] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa468df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mode: Pairs-Trading (Beta Neutral) Strategy\n",
      "CEX: Binance\n",
      "Interval: 1d\n",
      "NaN Remove Threshold: 0.0\n",
      "Selected pairs to analyse: ['1000PEPEUSDT', '1000BONKUSDT', 'SOLUSDT', 'BTCUSDT']\n",
      "Top N Volume Pairs: 100\n",
      "Volume Filter Mode: Rolling\n",
      "Train-Test Split: False\n",
      "Rolling Window: 7\n",
      "Minimum Long Size (percentage of portfolio): 0.1\n",
      "Minimum Short Size (percentage of portfolio): 0.1\n",
      "Rolling Optimisation Unsolvable Threshold (percentage of available data): 0.05\n",
      "\n",
      "Columns that contains NaN values:\n",
      "           Pair  NaN Count     Remark\n",
      "0  1000BONKUSDT         95  To Remove\n",
      "\n",
      "Removed 1 pairs as they contain too many NaN values.\n",
      "Successfully loaded candlestick dataframe for all available pairs.\n",
      "\n",
      "Earliest time series start date: 2023-08-19\n",
      "Latest time series end date: 2024-08-17\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMode: Pairs-Trading (Beta Neutral) Strategy\")\n",
    "print(\"CEX: {}\".format(str(cex).capitalize()))\n",
    "print(\"Interval: {}\".format(interval))\n",
    "print(\"NaN Remove Threshold: {}\".format(nan_remove_threshold))\n",
    "print(\"Selected pairs to analyse: {}\".format(list(selected_pairs.keys())))\n",
    "print(\"Top N Volume Pairs: {}\".format(top_n_volume_pairs))\n",
    "print(\"Volume Filter Mode: {}\".format(str(volume_filter_mode).capitalize()))\n",
    "print(\"Train-Test Split: {}\".format(str(train_test_split)))\n",
    "if train_test_split:\n",
    "    print(\"Train Percentage: {}\".format(train_percentage))\n",
    "print(\"Rolling Window: {}\".format(rolling_window))\n",
    "print(\"Minimum Long Size (percentage of portfolio): {}\".format(min_long))\n",
    "print(\"Minimum Short Size (percentage of portfolio): {}\".format(abs(min_short)))\n",
    "print(\"Rolling Optimisation Unsolvable Threshold (percentage of available data): {}\".format(unsolvable_threshold))\n",
    "\n",
    "merged_df = process_data('beta_neutral', cex, interval, nan_remove_threshold, selected_pairs,\n",
    "                 top_n_volume_pairs, volume_filter_mode)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66614d32-8464-4265-b709-8a2d7a681acf",
   "metadata": {},
   "source": [
    "## Sanitize the dataframe\n",
    "\n",
    "- Slice the dataframe according to the specified ***start_date*** and ***end_date***. Choose ***start_date*** and ***end_date*** within the timeframe shown by the output of the previous cell.\n",
    "- Interpolate any missing values in the dataframe.\n",
    "- If the interpolation fails, just backfill with the latest valid value.\n",
    "- Verify that all is as expected with an `assert` and check the shapes of 2 random pairs, which should have the same dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1705815e-af19-428e-b1b2-2f1ffd123c1b",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57b0551a-033f-4b9a-8ac0-d82ceef0861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### INPUTS #####\n",
    "start_date = '2024-07-01' # refer to the previous output to set\n",
    "end_date = '2024-08-17' # refer to the previous output to set\n",
    "##################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1c499c5-4313-40e1-bb52-ae3462912b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "BTCUSDT's Data Shape: (48, 1)\n",
      "SOLUSDT's Data Shape: (48, 1)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "data_sanitized, sorted_available_pairs = sanitize_data(merged_df, start_date, end_date)\n",
    "\n",
    "if data_sanitized:\n",
    "    print(\"-Data Check-\")\n",
    "    keys = list(data_sanitized.keys())\n",
    "    count = 0\n",
    "\n",
    "    for key in keys:\n",
    "        print(\"{}'s Data Shape: {}\".format(key, data_sanitized[key].shape))\n",
    "        count+=1\n",
    "\n",
    "        if count == 2:\n",
    "            break\n",
    "            \n",
    "else:\n",
    "    print(\"No data found.\")\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a468d4-82a4-4a1d-9128-ecc1b2c09159",
   "metadata": {},
   "source": [
    "## Select Pairs for Detailed Analysis\n",
    "\n",
    "- Please select any pair combination from the output below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fde203b-8256-47b1-aea0-bd19c1ff9152",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selectable pairs:\n",
      "- 1000PEPEUSDT\n",
      "- BTCUSDT\n",
      "- SOLUSDT\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSelectable pairs:\")\n",
    "\n",
    "for pair in sorted_available_pairs:\n",
    "    print(\"- {}\".format(pair))\n",
    "\n",
    "sorted_available_pairs = {pair:selected_pairs[pair] for pair in sorted_available_pairs if pair in selected_pairs}\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87a62a5",
   "metadata": {},
   "source": [
    "## Compute Percentage Returns\n",
    "\n",
    "To compute percentage returns with a time series of close data, you first need to calculate the daily returns by finding the percentage change between consecutive prices. This can be done using the formula below:\n",
    "$$Percentage \\space Return = \\frac{Price_{t} - Price_{t-1}}{Price_{t-1}} \\times 100$$\n",
    "1. ${Price_{t}}$ is the calculated price on day t.\n",
    "2. ${Price_{t-1}}$ is the calculated price on the previous candlestick.\n",
    "\n",
    "In a pandas DataFrame, this can be efficiently computed using the pct_change() method on the price column. The resulting series represents the daily percentage returns, reflecting the day-to-day performance of the asset. Summarizing or compounding these daily returns over longer periods can provide insights into the overall performance of the asset over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e53c419-c425-46da-8e69-c9087ef98586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Percentage Returns' Data Shape (should be 1 row lesser than printed above): (47, 3)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "returns = pd.DataFrame()\n",
    "\n",
    "for pair, df in data_sanitized.items():\n",
    "    tmp_return_df = df['Close'].pct_change().dropna().to_frame(name=pair)\n",
    "    returns = pd.merge(returns, tmp_return_df, how='outer', left_index=True, right_index=True)\n",
    "returns = returns.T.sort_index().T\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Percentage Returns' Data Shape (should be 1 row lesser than printed above): {}\".format(returns.shape))\n",
    "print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349e6d60-0f0a-4fea-ae2c-169712cf7307",
   "metadata": {},
   "source": [
    "## Split Train and Test Dataset\n",
    "\n",
    "This is for non-rolling (Train) and rolling (Test) portfolio weights optimisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f2ebbce-f593-42c2-8401-c2b050a0c1de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Train Returns' Data Shape: (47, 3)\n",
      "Test Returns' Data Shape: (47, 3)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "if train_test_split:\n",
    "    total_length = len(returns)\n",
    "    train_length = int(total_length * train_percentage)\n",
    "    test_length = total_length - train_length\n",
    "\n",
    "    if train_length != 0 and test_length != 0:\n",
    "        train_return_df = returns.reset_index().loc[:total_length-test_length].iloc[:-1].set_index('Open Time')\n",
    "        test_return_df = returns.reset_index().loc[total_length-test_length:].set_index('Open Time')\n",
    "        full_return_df = pd.concat([train_return_df, test_return_df])\n",
    "\n",
    "        print(\"-Data Check-\")\n",
    "        print(\"Train Returns' Data Shape: {}\".format(train_return_df.shape))\n",
    "        print(\"Test Returns' Data Shape: {}\".format(test_return_df.shape))\n",
    "\n",
    "    else:\n",
    "        print(\"Train and test length must be more than 0.\")\n",
    "        \n",
    "else:\n",
    "    train_return_df = returns.reset_index().set_index('Open Time')\n",
    "    test_return_df = train_return_df.copy()\n",
    "    full_return_df = train_return_df.copy()\n",
    "\n",
    "    print(\"-Data Check-\")\n",
    "    print(\"Train Returns' Data Shape: {}\".format(train_return_df.shape))\n",
    "    print(\"Test Returns' Data Shape: {}\".format(test_return_df.shape))\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "824af7af-dcf9-4baa-a9c5-0f3a0f828bd9",
   "metadata": {},
   "source": [
    "## Market Neutral Optimization\n",
    "\n",
    "In order to be able to compute market neutral weights, we need the VCV matrix of the constituents and the betas to market.\n",
    "Recall that portfolio beta is equal to the sum of the (weighted) beta of the constituents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd4abaa-25d4-43be-b67c-738fe6603497",
   "metadata": {},
   "source": [
    "## Full Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a16ece79-b2bb-4755-a64f-49e06cb859c0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
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       "      <th>1000PEPEUSDT</th>\n",
       "      <td>0.002773</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.001540</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1000PEPEUSDT   BTCUSDT   SOLUSDT\n",
       "1000PEPEUSDT      0.002773  0.001026  0.001540\n",
       "BTCUSDT           0.001026  0.000522  0.000690\n",
       "SOLUSDT           0.001540  0.000690  0.001305"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sigma = train_return_df.cov()\n",
    "Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ec059a-a3e9-45d8-aa61-5071fcb8da46",
   "metadata": {},
   "source": [
    "## Full Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a0141d3-8821-4b83-b297-88cdb393686f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852853</td>\n",
       "      <td>0.809591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.852853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.809591</td>\n",
       "      <td>0.836726</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1000PEPEUSDT   BTCUSDT   SOLUSDT\n",
       "1000PEPEUSDT      1.000000  0.852853  0.809591\n",
       "BTCUSDT           0.852853  1.000000  0.836726\n",
       "SOLUSDT           0.809591  0.836726  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev = np.sqrt(np.diag(Sigma))\n",
    "std_dev_outer = np.outer(std_dev, std_dev)\n",
    "correlation_matrix = Sigma / std_dev_outer\n",
    "np.fill_diagonal(correlation_matrix.values, 1)\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57a9d85",
   "metadata": {},
   "source": [
    "## Calculate Betas given the Datetime Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b84588b9-faad-4b0d-a6a0-f0e4a7e14c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Betas:\n",
      "1000PEPEUSDT    1.966649\n",
      "SOLUSDT         1.323599\n",
      "Name: BTCUSDT, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "betas = Sigma[benchmark_token] / Sigma.loc[benchmark_token, benchmark_token]\n",
    "betas_without_benchmark = betas.drop(benchmark_token)\n",
    "betas_without_benchmark = betas_without_benchmark.sort_values(ascending=False)\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Betas:\")\n",
    "print(betas_without_benchmark)\n",
    "\n",
    "betas_without_benchmark = betas_without_benchmark.sort_index()\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be775818-37b0-4687-896c-97ae1411ced7",
   "metadata": {},
   "source": [
    "## Covariance Matrix without Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c2a8423c-a2b8-4c14-8912-cb69837dee98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <td>0.002773</td>\n",
       "      <td>0.001540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.001540</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1000PEPEUSDT   SOLUSDT\n",
       "1000PEPEUSDT      0.002773  0.001540\n",
       "SOLUSDT           0.001540  0.001305"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_return_df_without_benchmark = train_return_df.drop(benchmark_token, axis=1)  # since we are using BTC as the benchmark\n",
    "final_Sigma = train_return_df_without_benchmark.cov()\n",
    "final_Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11379dbb-6786-4ad9-a9cf-ff285e171c91",
   "metadata": {},
   "source": [
    "## CVXPY Optimization Function\n",
    "\n",
    "We are going to setup a minimization problem.\n",
    "We want to minimize the portofolio variance like in a simple MV framework, but subject to an additional constrain:\n",
    "The sum of the constituents (weighted) betas must add up to 0, in order to achieve market neutrality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3240d250-65de-499a-9cbe-ed5239f10a2d",
   "metadata": {},
   "source": [
    "## Optimising Portfolio Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31056231-516b-4d8e-ba5c-12f3082b6b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dynamic_scaling(ratio, base_factor = 1):\n",
    "    return ratio * base_factor\n",
    "    \n",
    "def optimize_beta_neutral_portfolio(cov_matrix, mkt_betas, sorted_available_pairs, min_long=0.1, min_short=-0.1, tolerance=1e-4) -> pd.DataFrame:\n",
    "    index_direction_dict = {mkt_betas.index.get_loc(pair):sorted_available_pairs[pair] for pair in mkt_betas.index if pair in sorted_available_pairs}\n",
    "    index_min_long_short_dict = {}\n",
    "    for pair in mkt_betas.index:\n",
    "        if pair in sorted_available_pairs:\n",
    "            if sorted_available_pairs[pair] == 1:\n",
    "                index_min_long_short_dict[mkt_betas.index.get_loc(pair)] = abs(min_long)\n",
    "            elif sorted_available_pairs[pair] == -1:\n",
    "                index_min_long_short_dict[mkt_betas.index.get_loc(pair)] = -1 * abs(min_short)\n",
    "\n",
    "    try_count = 1\n",
    "    finish_flag = False\n",
    "\n",
    "    while try_count <= 10 and finish_flag == False:\n",
    "\n",
    "        finish_flag = True\n",
    "\n",
    "        n = cov_matrix.shape[1]\n",
    "        weights = cvx.Variable(n)\n",
    "        pf_var = cvx.quad_form(weights, cov_matrix.values)\n",
    "        objective = cvx.Minimize(pf_var)\n",
    "\n",
    "        constraints = [\n",
    "            # weights.T @ mkt_betas == 0,\n",
    "            # cvx.sum(weights) == 1,\n",
    "            cvx.abs(weights.T @ mkt_betas) <= tolerance,  # Allow a small deviation from zero\n",
    "            cvx.abs(cvx.sum(weights) - 1) <= tolerance  # Allow a small deviation from 1\n",
    "        ]\n",
    "        \n",
    "        for i, d in index_direction_dict.items():\n",
    "            if d == 1:\n",
    "                constraints.append(weights[i] >= index_min_long_short_dict[i])\n",
    "            elif d == -1:\n",
    "                constraints.append(weights[i] <= index_min_long_short_dict[i])\n",
    "    \n",
    "        prob = cvx.Problem(objective, constraints)\n",
    "        if not prob.is_dcp():\n",
    "            raise Exception(\"Problem is not DCP\")\n",
    "    \n",
    "        prob.solve()\n",
    "        if prob.status != cvx.OPTIMAL:\n",
    "            raise Exception(\"Optimization problem is not solvable\")\n",
    "    \n",
    "        weights = weights.value\n",
    "    \n",
    "        sum_weights = np.sum(np.abs(weights))\n",
    "        weights = weights / sum_weights\n",
    "\n",
    "        # print(index_direction_dict)\n",
    "        # print(index_min_long_short_dict)\n",
    "        # print(weights)\n",
    "    \n",
    "        for i, ls in index_min_long_short_dict.items():\n",
    "            if index_min_long_short_dict[i] >= 0:\n",
    "                if min_long > weights[i]:\n",
    "                    ratio = min_long / weights[i]\n",
    "                    scale_factor = dynamic_scaling(ratio)\n",
    "                    # print(min_long, weights[i], scale_factor)\n",
    "                    index_min_long_short_dict[i] *= scale_factor\n",
    "                    finish_flag = False\n",
    "            else:\n",
    "                if abs(min_short) > abs(weights[i]):\n",
    "                    ratio = abs(min_short) / abs(weights[i])\n",
    "                    scale_factor = dynamic_scaling(ratio)\n",
    "                    # print(-1 * abs(min_short), weights[i], scale_factor)\n",
    "                    index_min_long_short_dict[i] *= scale_factor\n",
    "                    finish_flag = False\n",
    "\n",
    "        # print(\"\\n\")\n",
    "        \n",
    "        try_count += 1\n",
    "\n",
    "    w = pd.Series(weights, index=cov_matrix.columns)\n",
    "\n",
    "    return w\n",
    "\n",
    "w = optimize_beta_neutral_portfolio(final_Sigma, betas_without_benchmark, sorted_available_pairs, min_long, min_short)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c15440-dceb-40ce-8084-d9723b555df2",
   "metadata": {},
   "source": [
    "## Plot Portfolio Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4edcb202-0b18-48df-85ce-3f57333a5410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tAK3B08sBKsPm/vTy9eXCf1Txl5cDAADA5kroBgAAgIII3QAAAFAQoRsAAAAKUvFPL68YL72ULFq06ebr1Svpt2XvCw4AANDWhO5N4aWXkoEDk2XLNt2cNTXJ3LmtGrxLpVJuu+22HH300a02JgAAQHvm8vJNYdGiTRu4k3fm28CV9ddeey2nn356+vXrl+rq6vTt2zdjx47NzJkzCyry/5RKpdx+++2FzwMAALApWemm0ac+9amsWLEiU6ZMyS677JIFCxZk2rRpef311wubc8WKFencuXNh4wMAALQlK90kSRYvXpz77rsvl156aUaNGpWdd945Q4cOzfnnn59PfOITjf0WLVqUY445Jl27ds3uu++eO++8s8k4f/zjHzN06NBUV1dn++23z3nnnZdVq1Y1Hh85cmTOPPPMfO1rX0uvXr0yduzY9O/fP0lyzDHHpFQqNb4HAACodEI3SZJu3bqlW7duuf3227N8+fJ19ps4cWKOPfbYPPnkkznyyCNzwgkn5I033kiSvPrqqznyyCNzwAEH5Iknnsh1112XG2+8MZdcckmTMaZMmZLOnTtn5syZuf766/Pwww8nSW666ab87W9/a3wPAABQ6YRukiQdO3bM5MmTM2XKlPTs2TMHHXRQLrjggjz55JNN+p188sk5/vjjs9tuu+V73/te3nrrrTz00ENJkh//+Mepq6vLtddem0GDBuXoo4/OxIkTc+WVV2bNmjWNY+y+++657LLLMnDgwAwcODDbbbddkqRnz57p27dv43sAAIBKJ3TT6FOf+lTmzZuXO++8M0cccURmzJiR/fbbL5MnT27ss88++zT+91ZbbZXa2tosXLgwSTJnzpwMHz48pVKpsc9BBx2Ut956K6+88kpj2/7771/8hwEAANgMCN00UVNTk8MOOyzf/va388ADD+Tkk0/OhAkTGo936tSpSf9SqdRkFbs5ttpqq1apFQAAYHMndLNee+21V5YsWdKsvnvuuWdmzZqVcrnc2DZz5sx07949O+2003rP7dSpU1avXr1RtQIAAGxuhG6SJK+//npGjx6d//iP/8iTTz6Z559/Pr/+9a9z2WWX5ZOf/GSzxjjjjDPy8ssv56yzzsrTTz+dO+64IxMmTMi4ceNSVbX+/1Pr379/pk2blvnz5+fvf/97a3wkAACANmef7k2hV6+kpiZZtmzTzVlT8868zdStW7cMGzYsV111VZ577rmsXLkydXV1Oe2003LBBRc0a4wdd9wxd911V84555wMHjw422yzTU499dRceOGF73vulVdemXHjxuWGG27IjjvumBdeeKHZtQMAAGyuSuV/vBaYZmloaEiPHj1SX1+f2traJseWLVuW559/PgMGDEhNTc3/HXjppWTRok1XZK9eSb9+m26+TWCd3y0AbIDSxNL7dwKgzZUnbN5RdX258B9Z6d5U+vVrdyEYAACA9XNPNwAAABRE6AYAAICCCN0AAABQEKG7IGvWrGnrEtod3ykAAFBpPEitlXXu3DlVVVWZN29etttuu3Tu3DmlkqekboxyuZwVK1bktddeS1VVVTp37tzWJQEAADSL0N3KqqqqMmDAgPztb3/LvHnz2rqcdqVr167p169fqqpcoAEAAFQGobsAnTt3Tr9+/bJq1aqsXr26rctpFzp06JCOHTu6agAAAKgoQndBSqVSOnXqlE6dOrV1KQAAALQR1+kCAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUJB2Ebp/9KMfpX///qmpqcmwYcPy0EMPNeu8W265JaVSKUcffXSxBQIAALBFqvjQfeutt2bcuHGZMGFCHn300QwePDhjx47NwoUL13veCy+8kH/913/NIYccsokqBQAAYEtT8aH7Bz/4QU477bSccsop2WuvvXL99dena9eu+dnPfrbOc1avXp0TTjghEydOzC677PK+cyxfvjwNDQ1NXgAAAPB+Kjp0r1ixIrNnz86YMWMa26qqqjJmzJjMmjVrneddfPHF6d27d0499dRmzTNp0qT06NGj8VVXV7fRtQMAAND+VXToXrRoUVavXp0+ffo0ae/Tp0/mz5//nufcf//9ufHGG3PDDTc0e57zzz8/9fX1ja+XX355o+oGAABgy9CxrQvYlN5888184QtfyA033JBevXo1+7zq6upUV1cXWBkAAADtUUWH7l69eqVDhw5ZsGBBk/YFCxakb9++a/V/7rnn8sILL+TjH/94Y9uaNWuSJB07dszcuXOz6667Fls0AAAAW4yKvry8c+fO2X///TNt2rTGtjVr1mTatGkZPnz4Wv0HDRqUP/3pT3n88ccbX5/4xCcyatSoPP744+7VBgAAoFVV9Ep3kowbNy4nnXRShgwZkqFDh+bqq6/OkiVLcsoppyRJTjzxxOy4446ZNGlSampqsvfeezc5v2fPnkmyVjsAAABsrIoP3ccdd1xee+21jB8/PvPnz8++++6bqVOnNj5c7aWXXkpVVUUv6AMAAFChSuVyudzWRVSahoaG9OjRI/X19amtrW3rcgCADVCaWGrrEgBohvKEzTuqNjcXWgIGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCtIvQ/aMf/Sj9+/dPTU1Nhg0bloceemidfW+44YYccsgh2XrrrbP11ltnzJgx6+0PAAAALVXxofvWW2/NuHHjMmHChDz66KMZPHhwxo4dm4ULF75n/xkzZuT444/P9OnTM2vWrNTV1eXwww/Pq6++uokrBwAAoL0rlcvlclsXsTGGDRuWAw44INdee22SZM2aNamrq8tZZ52V8847733PX716dbbeeutce+21OfHEE5s1Z0NDQ3r06JH6+vrU1tZuVP0AwKZVmlhq6xIAaIbyhM07qjY3F1b0SveKFSsye/bsjBkzprGtqqoqY8aMyaxZs5o1xtKlS7Ny5cpss8026+yzfPnyNDQ0NHkBAADA+6no0L1o0aKsXr06ffr0adLep0+fzJ8/v1ljnHvuudlhhx2aBPd/NmnSpPTo0aPxVVdXt1F1AwAAsGWo6NC9sb7//e/nlltuyW233Zaampp19jv//PNTX1/f+Hr55Zc3YZUAAABUqo5tXcDG6NWrVzp06JAFCxY0aV+wYEH69u273nOvuOKKfP/7388f/vCH7LPPPuvtW11dnerq6o2uFwAAgC1LRa90d+7cOfvvv3+mTZvW2LZmzZpMmzYtw4cPX+d5l112Wb7zne9k6tSpGTJkyKYoFQAAgC1QRa90J8m4ceNy0kknZciQIRk6dGiuvvrqLFmyJKecckqS5MQTT8yOO+6YSZMmJUkuvfTSjB8/PjfffHP69+/feO93t27d0q1btzb7HAAAALQ/FR+6jzvuuLz22msZP3585s+fn3333TdTp05tfLjaSy+9lKqq/1vQv+6667JixYp8+tOfbjLOhAkTctFFF23K0gEAAGjnKn6f7rZgn24AqFz26QaoDPbpBgAAANZL6AYAAICCCN0AAABQEKEbAAAAClJI6F6+fHlWrlxZxNAAAABQMVocuu+9996MHz8+ixcvbmx7/fXX89GPfjTdunVLjx49ct5557VGjQAAAFCRWhy6r7jiitx8883p2bNnY9s3vvGN/P73v8+AAQPSs2fPXH755fnVr37VGnUCAABAxWlx6H7sscdy8MEHN75ftmxZfvWrX+Xwww/PM888k7lz56Zfv3657rrrWqVQAAAAqDQtDt2vv/56dtxxx8b3s2bNyrJly3LKKackSbp3756jjjoqc+fO3fgqAQAAoAK1OHR36dIlb775ZuP76dOnp1QqZcSIEY1t3bp1y9///veNqxAAAAAqVMeWnrjbbrtl6tSpWb58eUqlUm655Zbstdde6du3b2Ofl156Kb17926VQgEAAKDStHil+7TTTsuzzz6b3XbbLXvuuWeee+65xkvL3zV79uzstddeG10kAAAAVKIWh+5TTz0155xzTt5+++3U19fn9NNPz9e+9rXG47NmzcozzzyTQw89tDXqBAAAgIpTKpfL5SIGXrFiRd5+++1stdVW6dixxVexb5YaGhrSo0eP1NfXp7a2tq3LAQA2QGliqa1LAKAZyhMKiaqtprm5sMUr3T//+c/z5JNPrvN4586d8/LLL+fmm29u6RQAAABQ0Vocuk8++eTcfvvt6+1zxx13rHWfNwAAAGwpWhy6m2P16tWpqip0CgAAANhsFZqIH3vssWyzzTZFTgEAAACbrQ16wtno0aObvJ88eXJmzJixVr/Vq1fnlVdeyQsvvJBjjz12owoEAACASrVBofsfA3apVMoLL7yQF154Ya1+VVVV2WabbfKZz3wmV1999UaWCAAAAJVpg0L3mjVrGv+7qqoqF110UcaPH9/qRQEAAEB70OINtKdPn57+/fu3YikAAADQvrQ4dI8YMaI16wAAAIB2p8WhO0lWrFiR22+/PQ8//HAWL16c1atXr9WnVCrlxhtv3JhpAAAAoCK1OHS/+OKLOeyww/Lcc8+lXC6vs5/QDQAAwJaqxaH761//ep599tl84QtfyBe/+MXstNNO6dhxoxbOAQAAoF1pcUq+5557cuihh2bKlCmtWQ8AAAC0G1UtPXHNmjX50Ic+1Jq1AAAAQLvS4tA9bNiwzJkzpzVrAQAAgHalxaH7+9//fu65557813/9V2vWAwAAAO1Gs+/pvvjii9dqGzVqVI477riMGDEi++23X2pra9fqUyqV8u1vf3vjqgQAAIAKVCqvb7+vf1BV1bJF8VKp9J77d1eyhoaG9OjRI/X19e/5hwYAYPNVmlhq6xIAaIbyhGZF1TbT3FzY7JXu6dOnt0phAAAAsKVodugeMWJEkXUAAABAu9PiB6kBAAAA69fsle5/du+9975vn6qqqtTW1ma33XZL165dWzoVAAAAVKQWh+6RI0emVGreg0iqqqpy2GGH5fLLL88HPvCBlk4JAAAAFaXFoXv8+PF56KGHMnXq1AwcODAf/vCH06dPnyxYsCCzZs3K008/nY9+9KPZdddd8+ijj2bq1KmZNWtWHnzwweyxxx6t+RkAAABgs9Tie7oPPfTQ3HPPPfnZz36WOXPm5MYbb8z3vve93HjjjfnLX/6Sm266KdOnT8+xxx6b+++/Pz//+c9TX1+fSy65pDXrBwAAgM1Ws/fp/mcjR47Mdtttl1//+tfr7POZz3wmr732WmbMmJEkGT16dJ555pm88sorLSp2c2GfbgCoXPbpBqgM7WWf7havdM+ePTsDBw5cb5+BAwdm9uzZje/33XffvPbaay2dEgAAACpKi0N3586d8/jjj6+3z2OPPZZOnTo1vl+9enW22mqrlk4JAAAAFaXFoXvMmDH53e9+l0svvTQrV65scmzlypW5/PLLM3Xq1Bx++OGN7X/5y1/Sr1+/llcLAAAAFaTFTy+/7LLLct999+WCCy7I1VdfnSFDhqR3795ZuHBhZs+enQULFqR379659NJLkyTz58/PY489ltNPP73VigcAAIDNWYtD984775xHHnkk5557bv7rv/4rv/3tbxuPVVdX53Of+1wmTZqUnXbaKUnSt2/fLFq0aOMrBgAAgArR4qeX/6MVK1Zk7ty5aWhoSG1tbQYOHJjOnTu3Rn2bJU8vB4DK5enlAJWhvTy9vMUr3f+oc+fO+eAHP9gaQwEAAEC70eIHqQEAAADr1+yV7tGjR6dUKmXKlCnZaaedMnr06GadVyqVMm3atBYXCAAAAJWq2aF7xowZKZVKWbp0aeP75iiV3DcFAADAlqnZoXvNmjXrfQ8AAAA05Z5uAAAAKEirhe433ngjL7/8cmsNBwAAABVvo0J3fX19zj777PTp0yfbbbddBgwY0HjswQcfzJFHHpnZs2dvdJEAAABQiVocut94440MGzYs//Zv/5a6urrsueeeKZf/b/PyffbZJzNnzswvf/nLVikUAAAAKk2LQ/dFF12UZ555JrfcckseeeSRfOYzn2lyvEuXLhkxYkTuueeejS4SAAAAKlGLQ/edd96Zo446Kscee+w6+/Tv3z+vvPJKS6cAAACAitbi0P23v/0te+2113r7VFdXZ8mSJS2dAgAAACpai0P3tttu+75PK3/66aez/fbbt3QKAAAAqGgtDt0f+chHcscdd6zz8vG//OUvmTp1asaMGdPi4gAAAKCStTh0f+tb38rq1atz0EEH5Ze//GUWLVqUJJkzZ05uvPHGjB49OtXV1TnnnHNarVgAAACoJB1beuIHP/jB3HrrrfnCF76QE088MUlSLpez9957p1wup3v37vnVr36V3XffvdWKBQAAgEqyQaF7woQJOfTQQ3PggQemc+fO+cQnPpHnn38+U6ZMyYMPPpg33ngjtbW1GTZsWE455ZT06tWrqLoBAABgs1cql8vl5nauqqpKqVRKdXV1hg8fnlGjRmX06NEZOnRoOnZs8aJ5xWloaEiPHj1SX1+f2trati4HANgApYmlti4BgGYoT2h2VG0Tzc2FGxS6r7766syYMSP33Xdf/v73v78zQKmULl265OCDD87o0aMzatSo7L///qmqavHt4ps9oRsAKpfQDVAZtsjQ/a5yuZwnnngi06dPz/Tp03P//fdn8eLF7wxYKqVbt2455JBDMmrUqIwaNSr77bdfiz/I5kjoBoDKJXQDVIYtOnT/s3K5nMcffzz33HNPZsyYkfvvvz/19fUplUoplUpZtWrVxk6xWRG6AaByCd0AlaG9hO5WuRG7VCrlQx/6UD70oQ/lM5/5TO6+++5cc801eeqpp1pjeAAAAKhIGx26582b13iZ+fTp0/PCCy8kSbp165YjjjgiI0aM2NgpAAAAoCJtcOheuHBhk5D97LPPplwup0ePHjn44INzxhlnZMSIEdlvv/3a9cPUAAAA4P1sUOjea6+9Mnfu3CTJ1ltvnUMOOSSnn356RowYkX333TelknukAAAA4F0bFLqffvrpVFVV5ZhjjslXv/rVHHjggenUqVNRtQEAAEBF26Drv7/61a9m7733zm9+85uMHDkyW2+9dcaMGZNLLrkk999/f1auXFlUnQAAAFBxNmil++qrr06SvPHGG5kxY0amT5+eP/7xj5kwYUKSpKamJgceeGBGjhyZkSNHWgkHAABgi9Yq+3QvWrQo06dPz4wZMzJjxozMmTMnpVIpNTU1GT58eP7whz+0Rq2bDft0A0Dlsk83QGVoL/t0t0ro/kcrV67MnXfemYkTJ+app55KqVTK6tWrW3OKNid0A0DlEroBKkN7Cd0bvU/36tWr89BDDzWudD/wwAN5++23826W79Wr18ZOAQAAABVpg0P3mjVr8vDDDzfe0/3AAw9kyZIljSF76623zuGHH55Ro0Zl1KhR2XvvvVu9aAAAAKgEGxS6jzzyyMycOTNvvfVWY8ju0aNHjjrqqIwcOTKjRo3K4MGD7dcNAAAA2cDQPXXq1HTr1i1HHHFE40r2hz70oVRVbdDOYwAAALBF2KDQPWvWrAwZMiQdOnQoqh4AAABoNzZoiXrYsGGbZeD+0Y9+lP79+6empibDhg3LQw89tN7+v/71rzNo0KDU1NTkgx/8YO66665NVCkAAABbkoq/LvzWW2/NuHHjMmHChDz66KMZPHhwxo4dm4ULF75n/wceeCDHH398Tj311Dz22GM5+uijc/TRR+epp57axJUDAADQ3rX6Pt2b2rBhw3LAAQfk2muvTfLO09Xr6upy1lln5bzzzlur/3HHHZclS5bkf/7nfxrbDjzwwOy77765/vrrmzWnfboBoHLZpxugMrSXfboreqV7xYoVmT17dsaMGdPYVlVVlTFjxmTWrFnvec6sWbOa9E+SsWPHrrN/kixfvjwNDQ1NXgAAAPB+Nnif7s3JokWLsnr16vTp06dJe58+ffL000+/5znz589/z/7z589f5zyTJk3KxIkTN77gTc3WbQCVobIvOqs4m/vKCQDtS0WvdG8q559/furr6xtfL7/8cluXBAAAQAWo6JXuXr16pUOHDlmwYEGT9gULFqRv377veU7fvn03qH+SVFdXp7q6euMLBgAAYItS0SvdnTt3zv77759p06Y1tq1ZsybTpk3L8OHD3/Oc4cOHN+mfJHffffc6+wMAAEBLVfRKd5KMGzcuJ510UoYMGZKhQ4fm6quvzpIlS3LKKackSU488cTsuOOOmTRpUpLk7LPPzogRI3LllVfmYx/7WG655ZY88sgj+clPftKWHwMAAIB2qOJD93HHHZfXXnst48ePz/z587Pvvvtm6tSpjQ9Le+mll1JV9X8L+h/+8Idz880358ILL8wFF1yQ3XffPbfffnv23nvvtvoIAAAAtFMVv093W6iYfbo9vRygMvgpBoCKs0Xs0w0AAACbM6EbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFCQig7db7zxRk444YTU1tamZ8+eOfXUU/PWW2+tt/9ZZ52VgQMHpkuXLunXr1+++tWvpr6+fhNWDQAAwJaiokP3CSeckD//+c+5++678z//8z+599578+Uvf3md/efNm5d58+bliiuuyFNPPZXJkydn6tSpOfXUUzdh1QAAAGwpSuVyudzWRbTEnDlzstdee+Xhhx/OkCFDkiRTp07NkUcemVdeeSU77LBDs8b59a9/nc9//vNZsmRJOnbs2KxzGhoa0qNHj9TX16e2trbFn6FwpVJbVwBAc1TmTzEAbNGamwsrdqV71qxZ6dmzZ2PgTpIxY8akqqoqDz74YLPHefcLWl/gXr58eRoaGpq8AAAA4P1UbOieP39+evfu3aStY8eO2WabbTJ//vxmjbFo0aJ85zvfWe8l6UkyadKk9OjRo/FVV1fX4roBAADYcmx2ofu8885LqVRa7+vpp5/e6HkaGhrysY99LHvttVcuuuii9fY9//zzU19f3/h6+eWXN3p+AAAA2r/m3cS8CX3jG9/IySefvN4+u+yyS/r27ZuFCxc2aV+1alXeeOON9O3bd73nv/nmmzniiCPSvXv33HbbbenUqdN6+1dXV6e6urpZ9QMAAMC7NrvQvd1222W77bZ7337Dhw/P4sWLM3v27Oy///5JknvuuSdr1qzJsGHD1nleQ0NDxo4dm+rq6tx5552pqalptdoBAADgH212l5c315577pkjjjgip512Wh566KHMnDkzZ555Zj772c82Prn81VdfzaBBg/LQQw8leSdwH3744VmyZEluvPHGNDQ0ZP78+Zk/f35Wr17dlh8HAACAdmizW+neEL/85S9z5pln5tBDD01VVVU+9alP5Yc//GHj8ZUrV2bu3LlZunRpkuTRRx9tfLL5brvt1mSs559/Pv37999ktQMAAND+Vew+3W3JPt0AtCo/xQBQcdr9Pt0AAACwuRO6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIBUdut94442ccMIJqa2tTc+ePXPqqafmrbfeata55XI5H/3oR1MqlXL77bcXWygAAABbpIoO3SeccEL+/Oc/5+67787//M//5N57782Xv/zlZp179dVXp1QqFVwhAAAAW7KObV1AS82ZMydTp07Nww8/nCFDhiRJ/u3f/i1HHnlkrrjiiuywww7rPPfxxx/PlVdemUceeSTbb7/9+861fPnyLF++vPF9Q0PDxn8AAAAA2r2KXemeNWtWevbs2Ri4k2TMmDGpqqrKgw8+uM7zli5dms997nP50Y9+lL59+zZrrkmTJqVHjx6Nr7q6uo2uHwAAgPavYle658+fn969ezdp69ixY7bZZpvMnz9/ned9/etfz4c//OF88pOfbPZc559/fsaNG9f4vqGhoTKCd7nc1hUAAABs0Ta7le7zzjsvpVJpva+nn366RWPfeeedueeee3L11Vdv0HnV1dWpra1t8gIAAID3s9mtdH/jG9/IySefvN4+u+yyS/r27ZuFCxc2aV+1alXeeOONdV42fs899+S5555Lz549m7R/6lOfyiGHHJIZM2ZsROUAAADQ1GYXurfbbrtst91279tv+PDhWbx4cWbPnp39998/yTuhes2aNRk2bNh7nnPeeeflS1/6UpO2D37wg7nqqqvy8Y9/fOOLBwAAgH+w2YXu5tpzzz1zxBFH5LTTTsv111+flStX5swzz8xnP/vZxieXv/rqqzn00EPz85//PEOHDk3fvn3fcxW8X79+GTBgwKb+CAAAALRzm9093Rvil7/8ZQYNGpRDDz00Rx55ZA4++OD85Cc/aTy+cuXKzJ07N0uXLm3DKgEAANhSlcplj7jeUA0NDenRo0fq6+s9VA0AAGAL1NxcWNEr3QAAALA5E7oBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBOrZ1AZWoXC4nSRoaGtq4EgAAANrCu3nw3Xy4LkJ3C7z55ptJkrq6ujauBAAAgLb05ptvpkePHus8Xiq/XyxnLWvWrMm8efPSvXv3lEqlti4HthgNDQ2pq6vLyy+/nNra2rYuBwA2C34foW2Uy+W8+eab2WGHHVJVte47t610t0BVVVV22mmnti4Dtli1tbX+UQEA/8TvI2x661vhfpcHqQEAAEBBhG4AAAAoiNANVIzq6upMmDAh1dXVbV0KAGw2/D7C5s2D1AAAAKAgVroBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInRDO3fvvffm4x//eHbYYYeUSqXcfvvtTY6Xy+WMHz8+22+/fbp06ZIxY8bkr3/9a5M+b7zxRk444YTU1tamZ8+eOfXUU/PWW2816fPkk0/mkEMOSU1NTerq6nLZZZc1OX7RRRelVCqlVCqlY8eO6d+/f77+9a83jvPCCy80Hv/n1//7f/8vSTJ58uTGtqqqquy000455ZRTsnDhwsZ51jXGLbfc0jhGz5493/O7+ufv57bbbsuBBx6YHj16pHv37vnABz6Qr33ta43H/7GeDh06ZOutt86wYcNy8cUXp76+/n1revd10UUXrfN/PwAqx2uvvZbTTz89/fr1S3V1dfr27ZuxY8dm5syZjX0eeOCBHHnkkdl6661TU1OTD37wg/nBD36Q1atXNxnrvX6zk//7vXz88cfXOjZy5Mgmv1NPPPFEPvGJT6R3796pqalJ//79c9xxxzX+bv7zb++7v3Vf+cpXmvxbYOTIkev9HRs5cuRGfW/Q3nVs6wKAYi1ZsiSDBw/OF7/4xfzLv/zLWscvu+yy/PCHP8yUKVMyYMCAfPvb387YsWPzl7/8JTU1NUmSE044IX/7299y9913Z+XKlTnllFPy5S9/OTfffHOSpKGhIYcffnjGjBmT66+/Pn/605/yxS9+MT179syXv/zlxrk+8IEP5A9/+ENWrVqVmTNn5otf/GKWLl2af//3f2/s84c//CEf+MAHmtS47bbbNv53bW1t5s6dmzVr1uSJJ57IKaecknnz5uX3v/99Y5+bbropRxxxRJMx1hW012XatGk57rjj8t3vfjef+MQnUiqV8pe//CV33313k37v1lMul7N48eI88MADmTRpUm666abMnDkzO+ywQ/72t7819r/11lszfvz4zJ07t7GtW7duG1QbAJunT33qU1mxYkWmTJmSXXbZJQsWLMi0adPy+uuvJ3nnj7nHHntsTjnllEyfPj09e/bMH/7wh3zzm9/MrFmz8qtf/SqlUqlVannttddy6KGH5qijjsrvf//79OzZMy+88ELuvPPOLFmypEnfd397ly5dmj/96U+55pprMnjw4Pz3f/93Dj300PzmN7/JihUrkiQvv/xyhg4d2uT3unPnzq1SM7RbZWCLkaR82223Nb5fs2ZNuW/fvuXLL7+8sW3x4sXl6urq8n/+53+Wy+Vy+S9/+Us5Sfnhhx9u7PO73/2uXCqVyq+++mq5XC6Xf/zjH5e33nrr8vLlyxv7nHvuueWBAwc2vp8wYUJ58ODBTeo57bTTyn379i2Xy+Xy888/X05Sfuyxx9ZZ/0033VTu0aNHk7bvfve75aqqqvLSpUvf8zM2Z4x3/eO5Z599dnnkyJHrHGd9Yy1YsKDcq1ev8gknnLBB8wNQuf7+97+Xk5RnzJjxnsffeuut8rbbblv+l3/5l7WO3XnnneUk5VtuuaWxbV2/Z+v7vRwxYkT57LPPLpfL5fJtt91W7tixY3nlypXrrHldY61evbo8cuTI8s4771xetWpVs+cH3pvLy2EL9vzzz2f+/PkZM2ZMY1uPHj0ybNiwzJo1K0kya9as9OzZM0OGDGnsM2bMmFRVVeXBBx9s7PORj3ykyV+6x44dm7lz5+bvf//7Oufv0qVL41/OW6pLly5Zs2ZNVq1atVHj/LO+ffvmz3/+c5566qkNPrd379454YQTcuedd651uSAA7VO3bt3SrVu33H777Vm+fPlax//3f/83r7/+ev71X/91rWMf//jHs8cee+Q///M/W62evn37ZtWqVbnttttSLpc36NyqqqqcffbZefHFFzN79uxWqwm2VEI3bMHmz5+fJOnTp0+T9j59+jQemz9/fnr37t3keMeOHbPNNts06fNeY/zjHP9s9uzZufnmmzN69Ogm7R/+8Icb/+Hy7mtd/vrXv+b666/PkCFD0r1798b2448/fq0xXnrppXWO817OOuusHHDAAfngBz+Y/v3757Of/Wx+9rOfvec/pN7LoEGD8uabbzZeUghA+9axY8dMnjw5U6ZMSc+ePXPQQQflggsuyJNPPpkkeeaZZ5Ike+6553ueP2jQoMY+reHAAw/MBRdckM997nPp1atXPvrRj+byyy/PggULmnX+oEGDkrxz3zewcYRuYJP505/+lG7duqVLly4ZOnRohg8fnmuvvbZJn1tvvTWPP/54k9c/qq+vT7du3dK1a9cMHDgwffr0yS9/+csmfa666qq1xthhhx02qNatttoqv/3tb/Pss8/mwgsvTLdu3fKNb3wjQ4cOzdKlS9/3/HdXFVrr3jwANn+f+tSnMm/evNx555054ogjMmPGjOy3336ZPHlyY58NXXXeGN/97nczf/78XH/99fnABz6Q66+/PoMGDcqf/vSn9z3X7xi0HqEbtmB9+/ZNkrX+6r1gwYLGY3379m3ydPAkWbVqVd54440mfd5rjH+cI0kGDhyYxx9/PHPmzMnbb7+dO++8c60V8rq6uuy2225NXv+oe/fuefzxx/PUU09lyZIluffee7PHHnus9bn+eYyOHd95bmRtbW2WLFmSNWvWNDln8eLFSd65vP4f7brrrvnSl76Un/70p3n00Ufzl7/8Jbfeeus/f5VrmTNnTmpra5s8BA6A9q+mpiaHHXZYvv3tb+eBBx7IySefnAkTJjT+Vs2ZM+c9z5szZ85av2fvpba2Nkma7JLxrsWLF6/1O7btttvmM5/5TK644orMmTMnO+ywQ6644or3nefdOgcMGPC+fYH1E7phCzZgwID07ds306ZNa2xraGjIgw8+mOHDhydJhg8fnsWLFze5p+uee+7JmjVrMmzYsMY+9957b1auXNnY5+67787AgQOz9dZbN7Z17tw5u+22W/r379/iJ51WVVVlt912yy677JIuXbps8PkDBw7MqlWr1lpBf/TRR5Nkvf/g6d+/f7p27brWU1//2cKFC3PzzTfn6KOPTlWV/zcLsCXba6+9smTJkhx++OHZZpttcuWVV67V584778xf//rXHH/88e873jbbbJNevXqtda91Q0NDnn322fX+jnXu3Dm77rrr+/6OrVmzJj/84Q8zYMCAfOhDH3rfmoD1s2UYtHNvvfVWnn322cb3zz//fB5//PFss8026devX772ta/lkksuye677964ZdgOO+yQo48+Osk7954dccQROe2003L99ddn5cqVOfPMM/PZz3628ZLtz33uc5k4cWJOPfXUnHvuuXnqqadyzTXX5Kqrrtrgel9//fW17gPv2bNn4/ZlzbF48eK1xujevXu22mqrfOADH8jhhx+eL37xi7nyyiuzyy67ZO7cufna176W4447LjvuuGOSd/YVX7p0aY488sjsvPPOWbx4cX74wx9m5cqVOeywwxrHLZfLmT9/fuOWYbNmzcr3vve99OjRI9///vc3+PMDUJlef/31fOYzn8kXv/jF7LPPPunevXseeeSRXHbZZfnkJz+ZrbbaKv/+7/+ez372s/nyl7+cM888M7W1tZk2bVrOOeecfPrTn86xxx7bZMx3f7P/0e67755x48ble9/7Xvr06ZMDDzwwr7/+er7zne9ku+22a9we9H/+539yyy235LOf/Wz22GOPlMvl/Pd//3fuuuuu3HTTTWvVPn/+/CxdujRPPfVUrr766jz00EP57W9/mw4dOhT6vcEWoQ2fnA5sAtOnTy8nWet10kknlcvld7YN+/a3v13u06dPubq6unzooYeW586d22SM119/vXz88ceXu3XrVq6trS2fcsop5TfffLNJnyeeeKJ88MEHl6urq8s77rhj+fvf/36T4++1Zdg/encLkvd6vbt9WXO221rXGJMmTWrs8/e//7381a9+tbzrrruWu3TpUt59993L3/zmN5t8pnvuuaf8qU99qlxXV1fu3LlzuU+fPuUjjjiifN999zX2uemmmxrHL5VK5R49epSHDh1avvjii8v19fXvWZ8twwDap2XLlpXPO++88n777Vfu0aNHuWvXruWBAweWL7zwwsZtLcvlcvnee+8tjx07tlxbW1vu3Llz+QMf+ED5iiuuWGtrrnX9nt13333lVatWlX/4wx+WP/jBD5a7du1a3mmnncrHHXdc+fnnn288/7nnniufdtpp5T322KPcpUuXcs+ePcsHHHBA+aabbmrs88+/vV27di3vueee5TPOOKP817/+9T0/py3DYMOVyuVN+DQHAAAA2IK42RAAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwBosYsuuiilUikzZsxo61IAYLMkdAPAFuCFF15IqVRq8urcuXPq6uryuc99Lk8++WRblwgA7VKpXC6X27oIAKBYL7zwQgYMGJBdd901n//855Mkb731Vv7f//t/mTlzZqqrqzNt2rQcdNBBGzTuokWLsmjRovTr1y9du3YtonQAqGhCNwBsAd4N3WPHjs3UqVObHLvwwgvz3e9+NyNGjHCZOAC0MpeXA8AW7qyzzkqSPPzww5k3b14mTJiQAw88ML179051dXX69++fM844IwsXLlzr3Pe6p/vdS9lPPvnkzJkzJ8ccc0y23XbblEqlvPDCC0mSRx99NJ/+9KfTr1+/VFdXZ7vttssBBxyQ7373u5viIwPAJtOxrQsAADYPpVIp9957b6688soceuihGTZsWDp16pTHHnss1113XX7/+9/n0UcfTY8ePZo13rPPPpsDDzwwH/zgB3PyySfn9ddfT+fOnfP444/nwx/+cDp06JBPfvKT2XnnnbN48eL85S9/yU9+8pN861vfKviTAsCmI3QDwBbuxz/+cZJk6NChGT16dObPn59u3bo16fPzn/88J510Uq699tpmh+KZM2dm/PjxmThxYpP2K6+8MsuXL8/tt9+eT37yk02Ovf766xvxSQBg8yN0A8AW5Nlnn81FF12UJFmyZEkefPDB3Hfffampqcl3v/vd9O7d+z3P+8IXvpCzzjorf/jDH5oduvv27bvevl26dFmrbdttt23W2ABQKdzTDQBbkOeeey4TJ07MxIkTc8011+T555/P5z73uTz00EMZPnx4kuQ3v/lNxo4dm+222y4dO3ZMqVRKVVVVGhoaMm/evGbPNXjw4HTu3Hmt9mOPPTZVVVU55phj8sUvfjH/+Z//mVdffbXVPiMAbE6sdAPAFuS9nl7+j6688sr867/+a7bbbrscfvjh2WmnnRpXpK+++uosX7682XP16dPnPduHDRuWGTNm5Hvf+15uvvnm3HTTTUmSAw44IJdeemlGjRq1AZ8IADZvQjcAkCRZtWpVvvOd72T77bfP448/3uRS83K5nMsuu2yDxiuVSus8dsghh+R3v/td3n777Tz44IP57//+7/z4xz/Oxz72sTz11FPZZZddWvw5AGBz4vJyACBJsmjRotTX12f48OFr3dv9yCOP5O233271Obt06ZKRI0fmyiuvzAUXXJC33347d999d6vPAwBtRegGAJIkvXv3TpcuXfLoo49m6dKlje1///vfG/fybg2zZs3KsmXL1mpfsGBBkqSmpqbV5gKAtubycgAgSVJVVZUzzjgjV155ZQYPHpyPf/zjaWhoyO9+97vsvPPO2WGHHVplnksvvTTTp0/PRz7ykQwYMCA1NTV59NFHM23atOyyyy455phjWmUeANgcCN0AQKNJkyZlm222yeTJk/PjH/84ffr0yfHHH5+LLrooe++9d6vMcfrpp6dHjx558MEH88c//jHlcjn9+vXLBRdckK9//eupra1tlXkAYHNQKpfL5bYuAgAAANoj93QDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgCg0apVq/LNb34zdXV1qaqqytFHH71B5/fv3z8nn3xy4/sZM2akVCplxowZrVonAFQKoRsANnOTJ09OqVRqfNXU1GSPPfbImWeemQULFrTqXD/72c9y+eWX59Of/nSmTJmSr3/96606/sZ6+umn881vfjP77rtvunfvnu233z4f+9jH8sgjj7R1aQDwnjq2dQEAQPNcfPHFGTBgQJYtW5b7778/1113Xe6666489dRT6dq1a6vMcc8992THHXfMVVdd1SrjfeQjH8nbb7+dzp07t8p4P/3pT3PjjTfmU5/6VM4444zU19fn3//933PggQdm6tSpGTNmTKvMAwCtRegGgArx0Y9+NEOGDEmSfOlLX8q2226bH/zgB7njjjty/PHHt3jccrmcZcuWpUuXLlm4cGF69uzZShUnVVVVqampabXxjj/++Fx00UXp1q1bY9sXv/jF7LnnnrnooouEbgA2Oy4vB4AKNXr06CTJ888/n+Sd+7G/853vZNddd011dXX69++fCy64IMuXL29yXv/+/XPUUUfl97//fYYMGZIuXbrk3//931MqlTJ9+vT8+c9/bryU/d17sZcsWZJvfOMbqaurS3V1dQYOHJgrrrgi5XJ5vTWu657uX//619l///3TpUuX9OrVK5///Ofz6quvvu9n3n///ZsE7iTZdtttc8ghh2TOnDnvez4AbGpCNwBUqOeeey7JO6EzeWf1e/z48dlvv/1y1VVXZcSIEZk0aVI++9nPrnXu3Llzc/zxx+ewww7LNddck9133z2/+MUvMmjQoOy00075xS9+kV/84hfZc889Uy6X84lPfCJXXXVVjjjiiPzgBz/IwIEDc84552TcuHEbXPfkyZNz7LHHpkOHDpk0aVJOO+20/OY3v8nBBx+cxYsXt+i7mD9/fnr16tWicwGgSC4vB4AKUV9fn0WLFmXZsmWZOXNmLr744nTp0iVHHXVUnnjiiUyZMiVf+tKXcsMNNyRJzjjjjPTu3TtXXHFFpk+fnlGjRjWO9eyzz2bq1KkZO3Zskzl++tOfpkOHDvn85z/f2HbHHXfknnvuySWXXJJvfetbSZKvfOUr+cxnPpNrrrkmZ555ZnbddddmfYaVK1fm3HPPzd57751777238dLzgw8+OEcddVSuuuqqTJw4cYO+l/vuuy+zZs3KhRdeuEHnAcCmYKUbACrEmDFjst1226Wuri6f/exn061bt9x2223Zcccdc9dddyXJWivP3/jGN5Ikv/3tb5u0DxgwYK3AvS533XVXOnTokK9+9atrjV0ul/O73/2u2Z/hkUceycKFC3PGGWc0udf7Yx/7WAYNGrRWne9n4cKF+dznPpcBAwbkm9/85gadCwCbgpVuAKgQP/rRj7LHHnukY8eO6dOnTwYOHJiqqnf+fv7iiy+mqqoqu+22W5Nz+vbtm549e+bFF19s0j5gwIBmz/viiy9mhx12SPfu3Zu077nnno3HN2SsJBk4cOBaxwYNGpT777+/2WMtWbIkRx11VN58883cf//9a93rDQCbA6EbACrE0KFDG59evi6lUqlZY3Xp0qU1SmozK1asyL/8y7/kySefzO9///vsvffebV0SALwnl5cDQDuw8847Z82aNfnrX//apH3BggVZvHhxdt55540ae968eXnzzTebtD/99NONxzdkrOSdB7n9s7lz5zZrrDVr1uTEE0/MtGnTcvPNN2fEiBHNnh8ANjWhGwDagSOPPDJJcvXVVzdp/8EPfpDknXumN2bs1atX59prr23SftVVV6VUKuWjH/1os8caMmRIevfuneuvv77JVma/+93vMmfOnGbVedZZZ+XWW2/Nj3/84/zLv/xL8z8IALQBl5cDQDswePDgnHTSSfnJT36SxYsXZ8SIEXnooYcyZcqUHH300U2eXL6hPv7xj2fUqFH51re+lRdeeCGDBw/O//7v/+aOO+7I1772tWY/uTxJOnXqlEsvvTSnnHJKRowYkeOPPz4LFizINddck/79++frX//6es+/+uqr8+Mf/zjDhw9P165d8x//8R9Njh9zzDHZaqutWvQ5AaAIQjcAtBM//elPs8suu2Ty5Mm57bbb0rdv35x//vmZMGHCRo1bVVWVO++8M+PHj8+tt96am266Kf3798/ll1/e+HT0DXHyySena9eu+f73v59zzz03W221VY455phceuml6dmz53rPffzxx5Mks2bNyqxZs9Y6/vzzzwvdAGxWSuVyudzWRQAAAEB75J5uAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBD7dLfAmjVrMm/evHTv3j2lUqmtywEAAGATK5fLefPNN7PDDjukqmrd69lCdwvMmzcvdXV1bV0GAAAAbezll1/OTjvttM7jQncLdO/ePck7X25tbW0bVwMAAMCm1tDQkLq6usZ8uC5Cdwu8e0l5bW2t0A0AALAFe79bjj1IDQAAAAoidAMAAEBBhG4AAAAoiHu6AQAA2onVq1dn5cqVbV1Gu9CpU6d06NBho8cRugEAACpcuVzO/Pnzs3jx4rYupV3p2bNn+vbt+74PS1sfoRsAAKDCvRu4e/funa5du25USOSdP2IsXbo0CxcuTJJsv/32LR5L6AYAAKhgq1evbgzc2267bVuX02506dIlSbJw4cL07t27xZeae5AaAABABXv3Hu6uXbu2cSXtz7vf6cbcJy90AwAAtAMuKW99rfGdCt0AAABQEPd0AwAAtEMv1b+URUsXbbL5enXtlX49+m2y+SqF0A0AANDOvFT/UgZeOzDLVi3bZHPWdKzJ3DPnblDwPvnkk7N48eLcfvvtxRXWxlxeDgAA0M4sWrpokwbuJFm2atkmXVmvFEI3AAAAm50//vGPGTp0aKqrq7P99tvnvPPOy6pVqxqPjxw5Ml/96lfzzW9+M9tss0369u2biy66qMkYTz/9dA4++ODU1NRkr732yh/+8IeUSqVNurIudAMAALBZefXVV3PkkUfmgAMOyBNPPJHrrrsuN954Yy655JIm/aZMmZKtttoqDz74YC677LJcfPHFufvuu5O8s3/50Ucfna5du+bBBx/MT37yk3zrW9/a5J/FPd0AAABsVn784x+nrq4u1157bUqlUgYNGpR58+bl3HPPzfjx41NV9c768T777JMJEyYkSXbfffdce+21mTZtWg477LDcfffdee655zJjxoz07ds3SfLd7343hx122Cb9LFa6AQAA2KzMmTMnw4cPb7JP9kEHHZS33norr7zySmPbPvvs0+S87bffPgsXLkySzJ07N3V1dY2BO0mGDh1acOVraxeh+0c/+lH69++fmpqaDBs2LA899FCzzrvllltSKpVy9NFHF1sgAAAAra5Tp05N3pdKpaxZs6aNqnlvFR+6b7311owbNy4TJkzIo48+msGDB2fs2LGNf91YlxdeeCH/+q//mkMOOWQTVQoAAEBz7Lnnnpk1a1bK5XJj28yZM9O9e/fstNNOzRpj4MCBefnll7NgwYLGtocffrjVa30/FR+6f/CDH+S0007LKaeckr322ivXX399unbtmp/97GfrPGf16tU54YQTMnHixOyyyy6bsFoAAAD+UX19fR5//PEmry9/+ct5+eWXc9ZZZ+Xpp5/OHXfckQkTJmTcuHGN93O/n8MOOyy77rprTjrppDz55JOZOXNmLrzwwiRpctl60Sr6QWorVqzI7Nmzc/755ze2VVVVZcyYMZk1a9Y6z7v44ovTu3fvnHrqqbnvvvved57ly5dn+fLlje8bGho2rvBNpDRx0/0fEgAtV55Qfv9OALABenXtlZqONZt0r+6ajjXp1bXXBp83Y8aMfOhDH2rSduqpp+auu+7KOeeck8GDB2ebbbbJqaee2hiam6NDhw65/fbb86UvfSkHHHBAdtlll1x++eX5+Mc/npqamg2us6UqOnQvWrQoq1evTp8+fZq09+nTJ08//fR7nnP//ffnxhtvzOOPP97seSZNmpSJEyduTKkAAACbTL8e/TL3zLlZtHTRJpuzV9de6dej3wadM3ny5EyePHmdx9f3vK4ZM2as1fbP+28PGjQo999/f+P7mTNnJkl22223DapzY1R06N5Qb775Zr7whS/khhtuSK9ezf8LzPnnn59x48Y1vm9oaEhdXV0RJQIAALSKfj36bXAIbm9uu+22dOvWLbvvvnueffbZnH322TnooIOy6667brIaKjp09+rVKx06dGhyY3ySLFiwoMlj4d/13HPP5YUXXsjHP/7xxrZ3n2zXsWPHzJ079z2//Orq6lRXV7dy9QAAABTpzTffzLnnnpuXXnopvXr1ypgxY3LllVdu0hoqOnR37tw5+++/f6ZNm9a47deaNWsybdq0nHnmmWv1HzRoUP70pz81abvwwgvz5ptv5pprrrF6DQAA0I6ceOKJOfHEE9u0hooO3Ukybty4nHTSSRkyZEiGDh2aq6++OkuWLMkpp5yS5J0veccdd8ykSZNSU1OTvffeu8n5PXv2TJK12gEAAGBjVXzoPu644/Laa69l/PjxmT9/fvbdd99MnTq18eFqL730UrMfKQ8AAACtqVT+x93GaZaGhob06NEj9fX1qa2tbety1smWYQCVwZZhAGyMZcuW5fnnn8+AAQM26VZYW4L1fbfNzYWWgAEAAKAgQjcAAAAUROgGAACAglT8g9QAAAB4Dy+9lCxatOnm69Ur6devVYcslUq57bbbGreIrkRCNwAAQHvz0kvJwIHJsmWbbs6ammTu3A0K3u/uRPXb3/42CxYsyNZbb53Bgwdn/PjxOeiggwosdtMFeqEbAACgvVm0aNMG7uSd+RYt2qDQ/alPfSorVqzIlClTsssuu2TBggWZNm1aXn/99cLKXLFiRTp37lzY+P/MPd0AAABscosXL859992XSy+9NKNGjcrOO++coUOH5vzzz88nPvGJxn6LFi3KMccck65du2b33XfPnXfe2WScP/7xjxk6dGiqq6uz/fbb57zzzsuqVasaj48cOTJnnnlmvva1r6VXr14ZO3Zs+vfvnyQ55phjUiqVGt8XQegGAABgk+vWrVu6deuW22+/PcuXL19nv4kTJ+bYY4/Nk08+mSOPPDInnHBC3njjjSTJq6++miOPPDIHHHBAnnjiiVx33XW58cYbc8kllzQZY8qUKencuXNmzpyZ66+/Pg8//HCS5Kabbsrf/va3xvdFELoBAADY5Dp27JjJkydnypQp6dmzZw466KBccMEFefLJJ5v0O/nkk3P88cdnt912y/e+97289dZbeeihh5IkP/7xj1NXV5drr702gwYNytFHH52JEyfmyiuvzJo1axrH2H333XPZZZdl4MCBGThwYLbbbrskSc+ePdO3b9/G90UQugEAAGgTn/rUpzJv3rzceeedOeKIIzJjxozst99+mTx5cmOfffbZp/G/t9pqq9TW1mbhwoVJkjlz5mT48OEplUqNfQ466KC89dZbeeWVVxrb9t9//+I/zDoI3QAAALSZmpqaHHbYYfn2t7+dBx54ICeffHImTJjQeLxTp05N+pdKpSar2M2x1VZbtUqtLSF0AwAAsNnYa6+9smTJkmb13XPPPTNr1qyUy+XGtpkzZ6Z79+7Zaaed1ntup06dsnr16o2qtTmEbgAAADa5119/PaNHj85//Md/5Mknn8zzzz+fX//617nsssvyyU9+slljnHHGGXn55Zdz1lln5emnn84dd9yRCRMmZNy4camqWn/c7d+/f6ZNm5b58+fn73//e2t8pPdkn24AAID2plevpKZm0+7VXVPzzrzN1K1btwwbNixXXXVVnnvuuaxcuTJ1dXU57bTTcsEFFzRrjB133DF33XVXzjnnnAwePDjbbLNNTj311Fx44YXve+6VV16ZcePG5YYbbsiOO+6YF154odm1b4hS+R/X4WmWhoaG9OjRI/X19amtrW3rctapNLH0/p0AaHPlCX6KAWi5ZcuW5fnnn8+AAQNSU1PzfwdeeilZtGjTFdKrV9Kv36abbxNY53eb5udCK90AAADtUb9+7S4EVyL3dAMAAEBBhG4AAAAoiNANAAAABRG6AQAA2gHPyG59rfGdCt0AAAAVrFOnTkmSpUuXtnEl7c+73+m733FLeHo5AABABevQoUN69uyZhQsXJkm6du2aUsn2wRujXC5n6dKlWbhwYXr27JkOHTq0eCyhGwAAoML17ds3SRqDN62jZ8+ejd9tSwndAAAAFa5UKmX77bdP7969s3LlyrYup13o1KnTRq1wv0voBgAAaCc6dOjQKkGR1uNBagAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUJB2Ebp/9KMfpX///qmpqcmwYcPy0EMPrbPvDTfckEMOOSRbb711tt5664wZM2a9/QEAAKClKj5033rrrRk3blwmTJiQRx99NIMHD87YsWOzcOHC9+w/Y8aMHH/88Zk+fXpmzZqVurq6HH744Xn11Vc3ceUAAAC0d6VyuVxu6yI2xrBhw3LAAQfk2muvTZKsWbMmdXV1Oeuss3Leeee97/mrV6/O1ltvnWuvvTYnnnjie/ZZvnx5li9f3vi+oaEhdXV1qa+vT21tbet8kAKUJpbaugQAmqE8oaJ/igFgi9TQ0JAePXq8by6s6JXuFStWZPbs2RkzZkxjW1VVVcaMGZNZs2Y1a4ylS5dm5cqV2WabbdbZZ9KkSenRo0fjq66ubqNrBwAAoP2r6NC9aNGirF69On369GnS3qdPn8yfP79ZY5x77rnZYYcdmgT3f3b++eenvr6+8fXyyy9vVN0AAABsGTq2dQFt6fvf/35uueWWzJgxIzU1NevsV11dnerq6k1YGQAAAO1BRYfuXr16pUOHDlmwYEGT9gULFqRv377rPfeKK67I97///fzhD3/IPvvsU2SZAAAAbKEq+vLyzp07Z//998+0adMa29asWZNp06Zl+PDh6zzvsssuy3e+851MnTo1Q4YM2RSlAgAAsAWq6JXuJBk3blxOOumkDBkyJEOHDs3VV1+dJUuW5JRTTkmSnHjiidlxxx0zadKkJMmll16a8ePH5+abb07//v0b7/3u1q1bunXr1mafAwAAgPan4kP3cccdl9deey3jx4/P/Pnzs++++2bq1KmND1d76aWXUlX1fwv61113XVasWJFPf/rTTcaZMGFCLrrook1ZOgAAAO1cxe/T3Raaux9bW7NPN0BlsE83AFSeLWKfbgAAANicCd0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIK0i9D9ox/9KP37909NTU2GDRuWhx56aL39f/3rX2fQoEGpqanJBz/4wdx1112bqFIAAAC2JBUfum+99daMGzcuEyZMyKOPPprBgwdn7NixWbhw4Xv2f+CBB3L88cfn1FNPzWOPPZajjz46Rx99dJ566qlNXDkAAADtXalcLpfbuoiNMWzYsBxwwAG59tprkyRr1qxJXV1dzjrrrJx33nlr9T/uuOOyZMmS/M///E9j24EHHph99903119//XvOsXz58ixfvrzxfUNDQ+rq6lJfX5/a2tpW/kStpzSx1NYlANAM5QkV/VMMAFukhoaG9OjR431zYcdNWFOrW7FiRWbPnp3zzz+/sa2qqipjxozJrFmz3vOcWbNmZdy4cU3axo4dm9tvv32d80yaNCkTJ05slZo3Jf+IA4D3UPJHaYCKUNnrw40q+vLyRYsWZfXq1enTp0+T9j59+mT+/Pnvec78+fM3qH+SnH/++amvr298vfzyyxtfPAAAAO1eRa90byrV1dWprq5u6zIAAACoMBW90t2rV6906NAhCxYsaNK+YMGC9O3b9z3P6du37wb1BwAAgJaq6NDduXPn7L///pk2bVpj25o1azJt2rQMHz78Pc8ZPnx4k/5Jcvfdd6+zPwAAALRUxV9ePm7cuJx00kkZMmRIhg4dmquvvjpLlizJKaeckiQ58cQTs+OOO2bSpElJkrPPPjsjRozIlVdemY997GO55ZZb8sgjj+QnP/lJW34MAAAA2qGKD93HHXdcXnvttYwfPz7z58/Pvvvum6lTpzY+LO2ll15KVdX/Leh/+MMfzs0335wLL7wwF1xwQXbffffcfvvt2XvvvdvqIwAAANBOVfw+3W2hufuxAQCbIVuGAVSGzTyqNjcXVvQ93QAAALA5KyR0L1++PCtXrixiaAAAAKgYLQ7d9957b8aPH5/Fixc3tr3++uv56Ec/mm7duqVHjx4577zzWqNGAAAAqEgtDt1XXHFFbr755vTs2bOx7Rvf+EZ+//vfZ8CAAenZs2cuv/zy/OpXv2qNOgEAAKDitDh0P/bYYzn44IMb3y9btiy/+tWvcvjhh+eZZ57J3Llz069fv1x33XWtUigAAABUmhaH7tdffz077rhj4/tZs2Zl2bJljftjd+/ePUcddVTmzp278VUCAABABWpx6O7SpUvefPPNxvfTp09PqVTKiBEjGtu6deuWv//97xtXIQAAAFSoji09cbfddsvUqVOzfPnylEql3HLLLdlrr73St2/fxj4vvfRSevfu3SqFAgAAQKVp8Ur3aaedlmeffTa77bZb9txzzzz33HONl5a/a/bs2dlrr702ukgAAACoRC0O3aeeemrOOeecvP3226mvr8/pp5+er33ta43HZ82alWeeeSaHHnpoa9QJAAAAFadULpfLRQy8YsWKvP3229lqq63SsWOLr2LfLDU0NKRHjx6pr69PbW1tW5cDAGyIUqmtKwCgOYqJqq2mubmwxSvdP//5z/Pkk0+u83jnzp3z8ssv5+abb27pFAAAAFDRWhy6Tz755Nx+++3r7XPHHXesdZ83AAAAbClaHLqbY/Xq1amqKnQKAAAA2GwVmogfe+yxbLPNNkVOAQAAAJutDXrC2ejRo5u8nzx5cmbMmLFWv9WrV+eVV17JCy+8kGOPPXajCgQAAIBKtUFPL//HS8VLpVLWdWpVVVW22WabjB49Otdcc0369Omz8ZVuRjy9HAAqmKeXA1SGdvL08g1a6V6zZk3jf1dVVeWiiy7K+PHjW14lAAAAtGMt3kB7+vTp6d+/fyuWAgAAAO1Li0P3iBEjWrMOAAAAaHdaHLqTZMWKFbn99tvz8MMPZ/HixVm9evVafUqlUm688caNmQYAAAAqUotD94svvpjDDjsszz333DofqJYI3QAAAGy5Why6v/71r+fZZ5/NF77whXzxi1/MTjvtlI4dN2rhHAAAANqVFqfke+65J4ceemimTJnSmvUAAABAu1H1/l3e25o1a/KhD32oNWsBAACAdqXFoXvYsGGZM2dOa9YCAAAA7UqLQ/f3v//93HPPPfmv//qv1qwHAAAA2o1m39N98cUXr9U2atSoHHfccRkxYkT222+/1NbWrtWnVCrl29/+9sZVCQAAABWoVF7ffl//oKqqZYvipVLpPffvrmQNDQ3p0aNH6uvr3/MPDQDAZqxUausKAGiO5kXVNtPcXNjsle7p06e3SmEAAACwpWh26B4xYkSRdQAAAEC70+IHqQEAAADr1+yV7n927733vm+fqqqq1NbWZrfddkvXrl1bOhUAAABUpBaH7pEjR6bUzAeRVFVV5bDDDsvll1+eD3zgAy2dEgAAACpKi0P3+PHj89BDD2Xq1KkZOHBgPvzhD6dPnz5ZsGBBZs2alaeffjof/ehHs+uuu+bRRx/N1KlTM2vWrDz44IPZY489WvMzAAAAwGap2VuG/bP77rsvhx12WK6//vqcfPLJax2fMmVKTj/99Pzv//5vDj744PzHf/xHTjzxxHz+85/Pz3/+842tu03ZMgwAKpgtwwAqQzvZMqzFoXvkyJHZbrvt8utf/3qdfT7zmc/ktddey4wZM5Iko0ePzjPPPJNXXnmlJVNuNoRuAKhgQjdAZWgnobvFTy+fPXt2Bg4cuN4+AwcOzOzZsxvf77vvvnnttddaOiUAAABUlBaH7s6dO+fxxx9fb5/HHnssnTp1any/evXqbLXVVi2dEgAAACpKi0P3mDFj8rvf/S6XXnppVq5c2eTYypUrc/nll2fq1Kk5/PDDG9v/8pe/pF+/fi2vFgAAACpIi+/pfvHFFzN8+PAsWLAgvXv3zpAhQ9K7d+8sXLgws2fPbmz/f//v/2XnnXfO/Pnzs/fee+f000/Pd77zndb+HJuUe7oBoIK5pxugMrSTe7pbHLqTZN68eTn33HPzX//1X1m+fHlje3V1dT796U9n0qRJ2WmnnVo6/GZL6AaACiZ0A1QGofv/rFixInPnzk1DQ0Nqa2szcODAdO7ceWOH3WwJ3QBQwYRugMrQTkJ3x9aYrHPnzvngBz/YGkMBAABAu9HiB6kBAAAA69fsle7Ro0enVCplypQp2WmnnTJ69OhmnVcqlTJt2rQWFwgAAACVqtmhe8aMGSmVSlm6dGnj++YouW8KAACALVSzQ/eaNWvW+x4AAABoyj3dAAAAUJBWC91vvPFGXn755dYaDgAAACreRoXu+vr6nH322enTp0+22267DBgwoPHYgw8+mCOPPDKzZ8/e6CIBAACgErU4dL/xxhsZNmxY/u3f/i11dXXZc889U/6Hzcv32WefzJw5M7/85S9bpVAAAACoNC0O3RdddFGeeeaZ3HLLLXnkkUfymc98psnxLl26ZMSIEbnnnns2ukgAAACoRC0O3XfeeWeOOuqoHHvssevs079//7zyyistnQIAAAAqWotD99/+9rfstdde6+1TXV2dJUuWtHQKAAAAqGgtDt3bbrvt+z6t/Omnn87222/f0ikAAACgorU4dH/kIx/JHXfcsc7Lx//yl79k6tSpGTNmTIuLAwAAgErW4tD9rW99K6tXr85BBx2UX/7yl1m0aFGSZM6cObnxxhszevToVFdX55xzzmm1YgEAAKCSlMr/uM/XBrrzzjvzhS98IW+99VaSpFwup1QqpVwup3v37vnP//zPHHnkka1W7OaioaEhPXr0SH19fWpra9u6HABgQ5RKbV0BAM3R8qi6STQ3F3bckEEnTJiQQw89NAceeGA6d+6cT3ziE3n++eczZcqUPPjgg3njjTdSW1ubYcOG5ZRTTkmvXr02+oMAAABApdqgle6qqqqUSqVUV1dn+PDhGTVqVEaPHp2hQ4emY8cNyu8VzUo3AFQwK90AlaGdrHRvUOi++uqrM2PGjNx33335+9///s4ApVK6dOmSgw8+OKNHj86oUaOy//77p6qqxbeLb/aEbgCoYEI3QGXYEkP3u8rlcp544olMnz4906dPz/3335/Fixe/M2CplG7duuWQQw7JqFGjMmrUqOy3334t/iCbI6EbACqY0A1QGbbk0P3PyuVyHn/88dxzzz2ZMWNG7r///tTX16dUKqVUKmXVqlUbO8VmRegGgAomdANUBqH7vb300ku5++67c8011+Spp55KqVTK6tWrW3OKNid0A0AFE7oBKkM7Cd0b/fSzefPmNV5mPn369LzwwgtJkm7duuWII47IiBEjNnYKAAAAqEgbHLoXLlzYJGQ/++yzKZfL6dGjRw4++OCcccYZGTFiRPbbb792/TA1AAAAeD8bFLr32muvzJ07N0my9dZb55BDDsnpp5+eESNGZN99903J5VoAAADQaINC99NPP52qqqocc8wx+epXv5oDDzwwnTp1Kqo2AAAAqGgbdP33V7/61ey99975zW9+k5EjR2brrbfOmDFjcskll+T+++/PypUri6oTAAAAKk6Lnl7+xhtvZMaMGZk+fXr++Mc/5s9//nOSpKamJgceeGBGjhyZkSNHttuVcE8vB4AK5nY4gMrQTp5e3ipbhi1atCjTp0/PjBkzMmPGjMyZMyelUik1NTUZPnx4/vCHP2zsFJsVoRsAKpjQDVAZhO73tnLlytx5552ZOHGifboBgM2P0A1QGdpJ6N7ofbpXr16dhx56qHGl+4EHHsjbb7+dd7N8r17/f3v3HqRVfdh//POshAWF3RUFViIIeAE1lvEKGE1QEJDEGGOiKOlEJdiJxag1F7VeMBZNjFSlqTWZNmhmvLWd6KCpWLwVBwmgBK9I1GpjJCsRhBVQAnJ+fzg8P1cugnJkgddr5sxkz/mec77POpOzb57zPGf3T3oKAAAA2CZtdnSvWbMms2fPrn6m+/HHH8/y5curkb3rrrtm6NChOeaYY3LMMcfkc5/73BafNAAAAGwLNiu6R4wYkenTp2fZsmXVyK6vr8+Xv/zlDBo0KMccc0z69evned0AAACQzYzuKVOmpEOHDhk+fHj1neyDDz44NTWb9eQxAAAA2CFsVnTPmDEjhx12WHbaaaey5gMAAADbjc2K7v79+5c1DwAAANjuuC8cAAAASiK6AQAAoCTbdHQvXrw4o0aNSl1dXRoaGjJ69OgsW7Zso+PPPffc9OnTJ+3bt0+PHj3y3e9+N0uXLv0UZw0AAMCOYpuO7lGjRuW5557L1KlTc99992XatGk5++yzNzh+wYIFWbBgQa677ro8++yzueWWWzJlypSMHj36U5w1AAAAO4pKsfaB29uYefPm5YADDsjs2bNz2GGHJXn/kWYjRozIH//4x3Tr1m2TjvMf//Ef+eY3v5nly5enTZv1f6/cypUrs3LlyurPzc3N6d69e5YuXZq6urpP/mIAgE9PpbK1ZwDApmjlqdrc3Jz6+vqP7MJt9p3uGTNmpKGhoRrcSTJkyJDU1NRk5syZm3yctb+gDQV3klxzzTWpr6+vLt27d/9EcwcAAGDHsM1Gd1NTU7p06dJiXZs2bdKpU6c0NTVt0jHefPPNXHXVVRu9JT1JLr744ixdurS6vPbaax973gAAAOw4Wl10X3TRRalUKhtdXnjhhU98nubm5nzpS1/KAQcckHHjxm10bG1tberq6losAAAA8FE2fE/1VnLhhRfmjDPO2OiY3r17p7GxMQsXLmyxfvXq1Vm8eHEaGxs3uv/bb7+d4cOHp2PHjrn77rvzmc985pNOGwAAANbR6qK7c+fO6dy580eOGzhwYJYsWZInn3wyhx56aJLk4Ycfzpo1a9K/f/8N7tfc3Jxhw4altrY2kydPTrt27bbY3AEAAOCDWt3t5Ztq//33z/DhwzNmzJjMmjUr06dPz9ixYzNy5MjqN5e//vrr6du3b2bNmpXk/eAeOnRoli9fnn/7t39Lc3Nzmpqa0tTUlPfee29rvhwAAAC2Q63une7Ncdttt2Xs2LEZPHhwampqcvLJJ2fixInV7atWrcr8+fOzYsWKJMmcOXOq32y+zz77tDjWK6+8kp49e35qcwcAAGD7t80+p3tr2tTnsQEArZDndANsG1p5qm73z+kGAACA1k50AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDcAAACURHQDAABASUQ3AAAAlER0AwAAQElENwAAAJRkm47uxYsXZ9SoUamrq0tDQ0NGjx6dZcuWbdK+RVHk+OOPT6VSyT333FPuRAEAANghbdPRPWrUqDz33HOZOnVq7rvvvkybNi1nn332Ju17ww03pFKplDxDAAAAdmRttvYEPq558+ZlypQpmT17dg477LAkyT/90z9lxIgRue6669KtW7cN7jt37txMmDAhTzzxRPbYY49Pa8oAAADsYLbZd7pnzJiRhoaGanAnyZAhQ1JTU5OZM2ducL8VK1bk9NNPzz//8z+nsbFxk861cuXKNDc3t1gAAADgo2yz0d3U1JQuXbq0WNemTZt06tQpTU1NG9zvggsuyJFHHpkTTzxxk891zTXXpL6+vrp07979Y88bAACAHUeri+6LLroolUplo8sLL7zwsY49efLkPPzww7nhhhs2a7+LL744S5curS6vvfbaxzo/AAAAO5ZW95nuCy+8MGecccZGx/Tu3TuNjY1ZuHBhi/WrV6/O4sWLN3jb+MMPP5yXX345DQ0NLdaffPLJOfroo/Poo4+ud7/a2trU1tZu6ksAAACAJK0wujt37pzOnTt/5LiBAwdmyZIlefLJJ3PooYcmeT+q16xZk/79+693n4suuijf/va3W6w76KCDcv311+eEE0745JMHAACAD2h10b2p9t9//wwfPjxjxozJzTffnFWrVmXs2LEZOXJk9ZvLX3/99QwePDi/+tWvcsQRR6SxsXG974L36NEjvXr1+rRfAgAAANu5VveZ7s1x2223pW/fvhk8eHBGjBiRo446Kr/4xS+q21etWpX58+dnxYoVW3GWAAAA7KgqRVEUW3sS25rm5ubU19dn6dKlqaur29rTAQA2R6WytWcAwKZo5am6qV24Tb/TDQAAAK2Z6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoyTYd3YsXL86oUaNSV1eXhoaGjB49OsuWLfvI/WbMmJFjjz02u+yyS+rq6vKFL3wh77zzzqcwYwAAAHYk23R0jxo1Ks8991ymTp2a++67L9OmTcvZZ5+90X1mzJiR4cOHZ+jQoZk1a1Zmz56dsWPHpqZmm/5VAAAA0ApViqIotvYkPo558+blgAMOyOzZs3PYYYclSaZMmZIRI0bkj3/8Y7p167be/QYMGJDjjjsuV1111cc+d3Nzc+rr67N06dLU1dV97OMAAFtBpbK1ZwDApmjlqbqpXdjmU5zTFjVjxow0NDRUgztJhgwZkpqamsycOTMnnXTSOvssXLgwM2fOzKhRo3LkkUfm5ZdfTt++fTN+/PgcddRRGzzXypUrs3LlyurPzc3NW/bFAACfnlb+RxwA25dt9p7qpqamdOnSpcW6Nm3apFOnTmlqalrvPv/7v/+bJBk3blzGjBmTKVOm5JBDDsngwYPz4osvbvBc11xzTerr66tL9+7dt9wLAQAAYLvV6qL7oosuSqVS2ejywgsvfKxjr1mzJknyN3/zNznzzDNz8MEH5/rrr0+fPn3yy1/+coP7XXzxxVm6dGl1ee211z7W+QEAANixtLrbyy+88MKcccYZGx3Tu3fvNDY2ZuHChS3Wr169OosXL05jY+N699tjjz2SJAcccECL9fvvv3/+8Ic/bPB8tbW1qa2t3YTZAwAAwP/X6qK7c+fO6dy580eOGzhwYJYsWZInn3wyhx56aJLk4Ycfzpo1a9K/f//17tOzZ89069Yt8+fPb7H+97//fY4//vhPPnkAAAD4gFZ3e/mm2n///TN8+PCMGTMms2bNyvTp0zN27NiMHDmy+s3lr7/+evr27ZtZs2YlSSqVSr7//e9n4sSJ+c///M+89NJLueyyy/LCCy9k9OjRW/PlAAAAsB1qde90b47bbrstY8eOzeDBg1NTU5OTTz45EydOrG5ftWpV5s+fnxUrVlTXnX/++Xn33XdzwQUXZPHixenXr1+mTp2avffee2u8BAAAALZj2+xzurcmz+kGAADYsW1qF26zt5cDAABAaye6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCRttvYEtkVFUSRJmpubt/JMAAAA2BrW9uDaPtwQ0f0xvP3220mS7t27b+WZAAAAsDW9/fbbqa+v3+D2SvFRWc461qxZkwULFqRjx46pVCpbezqww2hubk737t3z2muvpa6ubmtPBwBaBddH2DqKosjbb7+dbt26paZmw5/c9k73x1BTU5M999xza08Ddlh1dXX+qACAD3F9hE/fxt7hXssXqQEAAEBJRDcAAACURHQD24za2tpcccUVqa2t3dpTAYBWw/URWjdfpAYAAAAl8U43AAAAlER0AwAAQElENwAAAJREdAMAAEBJRDds56ZNm5YTTjgh3bp1S6VSyT333NNie1EUufzyy7PHHnukffv2GTJkSF588cUWYxYvXpxRo0alrq4uDQ0NGT16dJYtW9ZizNNPP52jjz467dq1S/fu3XPttde22D5u3LhUKpVUKpW0adMmPXv2zAUXXFA9zquvvlrd/uHlt7/9bZLklltuqa6rqanJnnvumTPPPDMLFy6snmdDx7jzzjurx2hoaFjv7+rDv5+77747AwYMSH19fTp27JgDDzww559/fnX7B+ez0047Zdddd03//v3zox/9KEuXLv3IOa1dxo0bt8H/fgBsO/785z/nO9/5Tnr06JHa2to0NjZm2LBhmT59enXM448/nhEjRmTXXXdNu3btctBBB+Uf//Ef895777U41vqu2cn/v17OnTt3nW2DBg1qcZ166qmn8pWvfCVdunRJu3bt0rNnz5x66qnV6+aHr71rr3V/+7d/2+JvgUGDBm30OjZo0KBP9HuD7V2brT0BoFzLly9Pv379ctZZZ+VrX/vaOtuvvfbaTJw4Mbfeemt69eqVyy67LMOGDcvzzz+fdu3aJUlGjRqVP/3pT5k6dWpWrVqVM888M2effXZuv/32JElzc3OGDh2aIUOG5Oabb84zzzyTs846Kw0NDTn77LOr5zrwwAPz4IMPZvXq1Zk+fXrOOuusrFixIj//+c+rYx588MEceOCBLea42267Vf93XV1d5s+fnzVr1uSpp57KmWeemQULFuSBBx6ojpk0aVKGDx/e4hgbCu0Neeihh3Lqqadm/Pjx+cpXvpJKpZLnn38+U6dObTFu7XyKosiSJUvy+OOP55prrsmkSZMyffr0dOvWLX/605+q4++6665cfvnlmT9/fnVdhw4dNmtuALROJ598cv7yl7/k1ltvTe/evfPGG2/koYceyqJFi5K8/4+5p5xySs4888w88sgjaWhoyIMPPpgf/OAHmTFjRv793/89lUpli8zlz3/+cwYPHpwvf/nLeeCBB9LQ0JBXX301kydPzvLly1uMXXvtXbFiRZ555pnceOON6devX+69994MHjw4v/71r/OXv/wlSfLaa6/liCOOaHG9btu27RaZM2y3CmCHkaS4++67qz+vWbOmaGxsLH76059W1y1ZsqSora0t7rjjjqIoiuL5558vkhSzZ8+ujrn//vuLSqVSvP7660VRFMVNN91U7LrrrsXKlSurY374wx8Wffr0qf58xRVXFP369WsxnzFjxhSNjY1FURTFK6+8UiQpfve7321w/pMmTSrq6+tbrBs/fnxRU1NTrFixYr2vcVOOsdYH9z3vvPOKQYMGbfA4GzvWG2+8Uey+++7FqFGjNuv8AGy73nrrrSJJ8eijj653+7Jly4rddtut+NrXvrbOtsmTJxdJijvvvLO6bkPXs41dL7/4xS8W5513XlEURXH33XcXbdq0KVatWrXBOW/oWO+9914xaNCgYq+99ipWr169yecH1s/t5bADe+WVV9LU1JQhQ4ZU19XX16d///6ZMWNGkmTGjBlpaGjIYYcdVh0zZMiQ1NTUZObMmdUxX/jCF1r8S/ewYcMyf/78vPXWWxs8f/v27av/cv5xtW/fPmvWrMnq1as/0XE+rLGxMc8991yeffbZzd63S5cuGTVqVCZPnrzO7YIAbJ86dOiQDh065J577snKlSvX2f7f//3fWbRoUb73ve+ts+2EE07IfvvtlzvuuGOLzaexsTGrV6/O3XffnaIoNmvfmpqanHfeefm///u/PPnkk1tsTrCjEt2wA2tqakqSdO3atcX6rl27Vrc1NTWlS5cuLba3adMmnTp1ajFmfcf44Dk+7Mknn8ztt9+eY489tsX6I488svqHy9plQ1588cXcfPPNOeyww9KxY8fq+tNOO22dY/zhD3/Y4HHW59xzz83hhx+egw46KD179szIkSPzy1/+cr1/SK1P37598/bbb1dvKQRg+9amTZvccsstufXWW9PQ0JDPf/7zueSSS/L0008nSX7/+98nSfbff//17t+3b9/qmC1hwIABueSSS3L66adn9913z/HHH5+f/vSneeONNzZp/759+yZ5/3PfwCcjuoFPzTPPPJMOHTqkffv2OeKIIzJw4MD87Gc/azHmrrvuyty5c1ssH7R06dJ06NAhO++8c/r06ZOuXbvmtttuazHm+uuvX+cY3bp126y57rLLLvnNb36Tl156KZdeemk6dOiQCy+8MEcccURWrFjxkfuvfVdhS302D4DW7+STT86CBQsyefLkDB8+PI8++mgOOeSQ3HLLLdUxm/uu8ycxfvz4NDU15eabb86BBx6Ym2++OX379s0zzzzzkfu6jsGWI7phB9bY2Jgk6/yr9xtvvFHd1tjY2OLbwZNk9erVWbx4cYsx6zvGB8+RJH369MncuXMzb968vPPOO5k8efI675B37949++yzT4vlgzp27Ji5c+fm2WefzfLlyzNt2rTst99+67yuDx+jTZv3vzeyrq4uy5cvz5o1a1rss2TJkiTv317/QXvvvXe+/e1v51//9V8zZ86cPP/887nrrrs+/Ktcx7x581JXV9fiS+AA2P61a9cuxx13XC677LI8/vjjOeOMM3LFFVdUr1Xz5s1b737z5s1b53q2PnV1dUnS4ikZay1ZsmSd69huu+2Wb3zjG7nuuusyb968dOvWLdddd91HnmftPHv16vWRY4GNE92wA+vVq1caGxvz0EMPVdc1Nzdn5syZGThwYJJk4MCBWbJkSYvPdD388MNZs2ZN+vfvXx0zbdq0rFq1qjpm6tSp6dOnT3bdddfqurZt22afffZJz549P/Y3ndbU1GSfffZJ79690759+83ev0+fPlm9evU676DPmTMnSTb6B0/Pnj2z8847r/Otrx+2cOHC3H777fnqV7+amhr/NwuwIzvggAOyfPnyDB06NJ06dcqECRPWGTN58uS8+OKLOe200z7yeJ06dcruu+++zmetm5ub89JLL230Ota2bdvsvffeH3kdW7NmTSZOnJhevXrl4IMP/sg5ARvnkWGwnVu2bFleeuml6s+vvPJK5s6dm06dOqVHjx45//zz8w//8A/Zd999q48M69atW7761a8mef+zZ8OHD8+YMWNy8803Z9WqVRk7dmxGjhxZvWX79NNPz5VXXpnRo0fnhz/8YZ599tnceOONuf766zd7vosWLVrnc+ANDQ3Vx5dtiiVLlqxzjI4dO2aXXXbJgQcemKFDh+ass87KhAkT0rt378yfPz/nn39+Tj311Hz2s59N8v5zxVesWJERI0Zkr732ypIlSzJx4sSsWrUqxx13XPW4RVGkqamp+siwGTNm5Oqrr059fX1+/OMfb/brB2DbtGjRonzjG9/IWWedlb/6q79Kx44d88QTT+Taa6/NiSeemF122SU///nPM3LkyJx99tkZO3Zs6urq8tBDD+X73/9+vv71r+eUU05pccy11+wP2nffffN3f/d3ufrqq9O1a9cMGDAgixYtylVXXZXOnTtXHw9633335c4778zIkSOz3377pSiK3Hvvvfmv//qvTJo0aZ25NzU1ZcWKFXn22Wdzww03ZNasWfnNb36TnXbaqdTfG+wQtuI3pwOfgkceeaRIss7yrW99qyiK9x8bdtlllxVdu3Ytamtri8GDBxfz589vcYxFixYVp512WtGhQ4eirq6uOPPMM4u33367xZinnnqqOOqoo4ra2tris5/9bPHjH/+4xfb1PTLsg9Y+gmR9y9rHl23K47Y2dIxrrrmmOuatt94qvvvd7xZ777130b59+2LfffctfvCDH7R4TQ8//HBx8sknF927dy/atm1bdO3atRg+fHjx2GOPVcdMmjSpevxKpVLU19cXRxxxRPGjH/2oWLp06Xrn55FhANund999t7jooouKQw45pKivry923nnnok+fPsWll15afaxlURTFtGnTimHDhhV1dXVF27ZtiwMPPLC47rrr1nk014auZ4899lixevXqYuLEicVBBx1U7LzzzsWee+5ZnHrqqcUrr7xS3f/ll18uxowZU+y3335F+/bti4aGhuLwww8vJk2aVB3z4WvvzjvvXOy///7FOeecU7z44ovrfZ0eGQabr1IUn+K3OQAAAMAOxIcNAQAAoCSiGwAAAEoiugEAAKAkohsAAABKIroBAACgJKIbAAAASiK6AQAAoCSiGwAAAEoiugGAj23cuHGpVCp59NFHt/ZUAKBVEt0AsAN49dVXU6lUWixt27ZN9+7dc/rpp+fpp5/e2lMEgO1SpSiKYmtPAgAo16uvvppevXpl7733zje/+c0kybJly/Lb3/4206dPT21tbR566KF8/vOf36zjvvnmm3nzzTfTo0eP7LzzzmVMHQC2aaIbAHYAa6N72LBhmTJlSottl156acaPH58vfvGLbhMHgC3M7eUAsIM799xzkySzZ8/OggULcsUVV2TAgAHp0qVLamtr07Nnz5xzzjlZuHDhOvuu7zPda29lP+OMMzJv3rycdNJJ2W233VKpVPLqq68mSebMmZOvf/3r6dGjR2pra9O5c+ccfvjhGT9+/KfxkgHgU9Nma08AAGgdKpVKpk2blgkTJmTw4MHp379/PvOZz+R3v/td/uVf/iUPPPBA5syZk/r6+k063ksvvZQBAwbkoIMOyhlnnJFFixalbdu2mTt3bo488sjstNNOOfHEE7PXXntlyZIlef755/OLX/wif//3f1/yKwWAT4/oBoAd3E033ZQkOeKII3LsscemqakpHTp0aDHmV7/6Vb71rW/lZz/72SZH8fTp03P55ZfnyiuvbLF+woQJWblyZe65556ceOKJLbYtWrToE7wSAGh9RDcA7EBeeumljBs3LkmyfPnyzJw5M4899ljatWuX8ePHp0uXLuvd76//+q9z7rnn5sEHH9zk6G5sbNzo2Pbt26+zbrfddtukYwPAtsJnugFgB/Lyyy/nyiuvzJVXXpkbb7wxr7zySk4//fTMmjUrAwcOTJL8+te/zrBhw9K5c+e0adMmlUolNTU1aW5uzoIFCzb5XP369Uvbtm3XWX/KKaekpqYmJ510Us4666zccccdef3117fYawSA1sQ73QCwA1nft5d/0IQJE/K9730vnTt3ztChQ7PnnntW35G+4YYbsnLlyk0+V9euXde7vn///nn00Udz9dVX5/bbb8+kSZOSJIcffnh+8pOf5JhjjtmMVwQArZvoBgCSJKtXr85VV12VPfbYI3Pnzm1xq3lRFLn22ms363iVSmWD244++ujcf//9eeeddzJz5szce++9uemmm/KlL30pzz77bHr37v2xXwcAtCZuLwcAkiRvvvlmli5dmoEDB67z2e4nnngi77zzzhY/Z/v27TNo0KBMmDAhl1xySd55551MnTp1i58HALYW0Q0AJEm6dOmS9u3bZ86cOVmxYkV1/VtvvVV9lveWMGPGjLz77rvrrH/jjTeSJO3atdti5wKArc3t5QBAkqSmpibnnHNOJkyYkH79+uWEE05Ic3Nz7r///uy1117p1q3bFjnPT37ykzzyyCP5whe+kF69eqVdu3aZM2dOHnroofTu3TsnnXTSFjkPALQGohsAqLrmmmvSqVOn3HLLLbnpppvStWvXnHbaaRk3blw+97nPbZFzfOc730l9fX1mzpyZ//mf/0lRFOnRo0cuueSSXHDBBamrq9si5wGA1qBSFEWxtScBAAAA2yOf6QYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACiJ6AYAAICSiG4AAAAoiegGAACAkohuAAAAKInoBgAAgJKIbgAAACjJ/wP18jBbShVnUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = list(w.index)\n",
    "values = list(w.values)\n",
    "\n",
    "plt.figure(figsize=(10, 12))\n",
    "\n",
    "long_patch = mpatches.Patch(color='green', label='Long')\n",
    "short_patch = mpatches.Patch(color='red', label='Short')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "colors = ['green' if val >= 0 else 'red' for val in values]\n",
    "plt.bar(w.index, w.values, color=colors)\n",
    "plt.title('Porfolio 1')\n",
    "plt.xlabel('Pairs', fontsize=14)\n",
    "plt.ylabel('Weights', fontsize=14)\n",
    "plt.legend(handles=[long_patch, short_patch])\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "colors = ['green' if val < 0 else 'red' for val in values]\n",
    "plt.bar(w.index, -w.values, color=colors)\n",
    "plt.title('Porfolio 2')\n",
    "plt.xlabel('Pairs', fontsize=14)\n",
    "plt.ylabel('Weights', fontsize=14)\n",
    "plt.legend(handles=[long_patch, short_patch])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca6ef439-6ccd-4464-bae7-3ff0a6192aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Portfolio 1:\n",
      "               Weights Direction\n",
      "SOLUSDT       0.597727      Long\n",
      "1000PEPEUSDT -0.402273     Short\n",
      "\n",
      "\n",
      "Portfolio 2:\n",
      "               Weights Direction\n",
      "1000PEPEUSDT  0.402273      Long\n",
      "SOLUSDT      -0.597727     Short\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "weights_df_solution_one = pd.DataFrame(w, columns=['Weights'], index=w.index)\n",
    "weights_df_solution_two = weights_df_solution_one.copy()\n",
    "weights_df_solution_two['Weights'] = -weights_df_solution_two['Weights']\n",
    "\n",
    "weights_df_solution_one = weights_df_solution_one.sort_values(by='Weights', ascending=False)\n",
    "weights_df_solution_two = weights_df_solution_two.sort_values(by='Weights', ascending=False)\n",
    "\n",
    "weights_df_solution_one['Direction'] = weights_df_solution_one['Weights'].apply(lambda x: 'Long' if x > 0 else 'Short')\n",
    "weights_df_solution_two['Direction'] = weights_df_solution_two['Weights'].apply(lambda x: 'Long' if x > 0 else 'Short')\n",
    "\n",
    "print(\"Portfolio 1:\")\n",
    "print(weights_df_solution_one)\n",
    "print(\"\\n\")\n",
    "print(\"Portfolio 2:\")\n",
    "print(weights_df_solution_two)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69350688-b743-4b24-9c1a-66e7bac32c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Sum of Weights: 1.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "total_weight = weights_df_solution_one['Weights'].abs().sum()\n",
    "print(\"Sum of Weights:\", total_weight)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc76d27d-f9cc-456b-b3df-ff235ec28da0",
   "metadata": {},
   "source": [
    "## Optimising Portfolio Weights using Rolling Portfolio Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "309e9fbf-5258-4c80-b007-d25f79105a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2024-07-08</th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <td>0.002303</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.002073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.002069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2024-07-09</th>\n",
       "      <th>1000PEPEUSDT</th>\n",
       "      <td>0.003177</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.002576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.000944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1000PEPEUSDT   BTCUSDT   SOLUSDT\n",
       "Open Time                                                \n",
       "2024-07-08 1000PEPEUSDT      0.002303  0.000811  0.002073\n",
       "           BTCUSDT           0.000811  0.000300  0.000742\n",
       "           SOLUSDT           0.002073  0.000742  0.002069\n",
       "2024-07-09 1000PEPEUSDT      0.003177  0.001203  0.002576\n",
       "           BTCUSDT           0.001203  0.000471  0.000944"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rolling covariance matrix starting from the test data\n",
    "rolling_cov = full_return_df.rolling(window=rolling_window).cov().dropna()\n",
    "rolling_cov.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8034509d-2317-4bab-b94e-2bc0b5836cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rolling optimization\n",
    "dates = list(set(rolling_cov.index.get_level_values(0)))\n",
    "dates.sort()\n",
    "weights = pd.DataFrame(index=pd.Index(dates).sort_values(), columns = test_return_df.columns)\n",
    "betas = pd.DataFrame(index=pd.Index(dates).sort_values(), columns = test_return_df.columns)\n",
    "\n",
    "total_no_dates = len(dates)\n",
    "max_unsolvable_count = int(total_no_dates * unsolvable_threshold)\n",
    "unsolvable_count = 1\n",
    "\n",
    "for i, dt in enumerate(dates):\n",
    "    full_cov = rolling_cov.loc[dt]\n",
    "    final_cov = full_cov.drop(benchmark_token).drop(benchmark_token, axis=1)\n",
    "    final_beta = full_cov[benchmark_token].drop(benchmark_token) / full_cov.loc[benchmark_token, benchmark_token]\n",
    "\n",
    "    try:\n",
    "        weight_result = optimize_beta_neutral_portfolio(final_cov, final_beta, sorted_available_pairs, min_long, min_short)\n",
    "        unsolvable_count = 1\n",
    "    except:\n",
    "        if unsolvable_count >= max_unsolvable_count:\n",
    "            print(\"\\n\")\n",
    "            print(\"Rolling optimization is not solvable. Please debug the issue first before proceeding.\")\n",
    "            print(\"\\n\")\n",
    "            break\n",
    "        \n",
    "        if i != 0:\n",
    "            weight_result = weights.iloc[i - 1].drop(benchmark_token)\n",
    "        else:\n",
    "            weight_result = pd.Series(0.0, index=final_cov.columns)\n",
    "\n",
    "        unsolvable_count += 1\n",
    "        \n",
    "    weights.loc[dt, :] = weight_result\n",
    "    betas.loc[dt, :] = final_beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7cff5b8f-2ff6-4f5f-a3a9-6e3ad8140dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_without_benchmark = weights.drop(benchmark_token, axis=1)\n",
    "betas_without_benchmark = betas.drop(benchmark_token, axis=1)\n",
    "\n",
    "weights_df_solution_one = weights_without_benchmark.copy()\n",
    "weights_df_solution_two = weights_without_benchmark.copy()\n",
    "weights_df_solution_two = weights_df_solution_two * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "50a839da-9af0-4060-bf1c-e32142296f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Rolling Betas:\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-07-08     2.708598  2.476201\n",
      "2024-07-09     2.552716   2.00234\n",
      "2024-07-10     2.448048  1.898597\n",
      "2024-07-11     2.293076  1.483415\n",
      "2024-07-12     2.351118   1.99656\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-08-13     1.553635   1.34983\n",
      "2024-08-14     1.556685  1.336426\n",
      "2024-08-15     1.641443  0.995447\n",
      "2024-08-16     1.415517  1.023226\n",
      "2024-08-17      1.13448  0.797095\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Rolling Betas:\")\n",
    "print(betas_without_benchmark.head())\n",
    "print(betas_without_benchmark.tail())\n",
    "# print(betas_without_benchmark)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a1a98ba1-bf2a-4ae7-acca-3b0a526ad6a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Porfolio 1 Rolling Weights:\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-07-08    -0.477588  0.522412\n",
      "2024-07-09    -0.439584  0.560416\n",
      "2024-07-10    -0.436793  0.563207\n",
      "2024-07-11    -0.392797  0.607203\n",
      "2024-07-12    -0.459223  0.540777\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-08-13    -0.464901  0.535099\n",
      "2024-08-14    -0.461931  0.538069\n",
      "2024-08-15    -0.377499  0.622501\n",
      "2024-08-16    -0.419565  0.580435\n",
      "2024-08-17    -0.412657  0.587343\n",
      "\n",
      "\n",
      "Porfolio 2 Rolling Weights:\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-07-08     0.477588 -0.522412\n",
      "2024-07-09     0.439584 -0.560416\n",
      "2024-07-10     0.436793 -0.563207\n",
      "2024-07-11     0.392797 -0.607203\n",
      "2024-07-12     0.459223 -0.540777\n",
      "           1000PEPEUSDT   SOLUSDT\n",
      "2024-08-13     0.464901 -0.535099\n",
      "2024-08-14     0.461931 -0.538069\n",
      "2024-08-15     0.377499 -0.622501\n",
      "2024-08-16     0.419565 -0.580435\n",
      "2024-08-17     0.412657 -0.587343\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Porfolio 1 Rolling Weights:\")\n",
    "print(weights_df_solution_one.head())\n",
    "print(weights_df_solution_one.tail())\n",
    "# print(weights_df_solution_one)\n",
    "print(\"\\n\")\n",
    "print(\"Porfolio 2 Rolling Weights:\")\n",
    "print(weights_df_solution_two.head())\n",
    "print(weights_df_solution_two.tail())\n",
    "# print(weights_df_solution_two)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89e42a07-d659-488b-ba61-bd733da05cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Porfolio 1 Beta:\n",
      "2024-07-09   -0.173099\n",
      "2024-07-10   -0.012117\n",
      "2024-07-11    -0.16613\n",
      "2024-07-12    0.288806\n",
      "2024-07-13    0.134636\n",
      "dtype: object\n",
      "2024-08-13    0.132596\n",
      "2024-08-14   -0.008583\n",
      "2024-08-15   -0.222615\n",
      "2024-08-16    0.102604\n",
      "2024-08-17   -0.013326\n",
      "dtype: object\n",
      "\n",
      "\n",
      "Portfolio 2 Beta\n",
      "2024-07-09    0.173099\n",
      "2024-07-10    0.012117\n",
      "2024-07-11     0.16613\n",
      "2024-07-12   -0.288806\n",
      "2024-07-13   -0.134636\n",
      "dtype: object\n",
      "2024-08-13   -0.132596\n",
      "2024-08-14    0.008583\n",
      "2024-08-15    0.222615\n",
      "2024-08-16   -0.102604\n",
      "2024-08-17    0.013326\n",
      "dtype: object\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Porfolio 1 Beta:\")\n",
    "portfolio_one_beta = (weights_df_solution_one.shift(1) * betas_without_benchmark).dropna().sum(axis=1)\n",
    "print(portfolio_one_beta.head())\n",
    "print(portfolio_one_beta.tail())\n",
    "# print(portfolio_one_beta)\n",
    "print(\"\\n\")\n",
    "print(\"Portfolio 2 Beta\")\n",
    "portfolio_two_beta = (weights_df_solution_two.shift(1) * betas_without_benchmark).dropna().sum(axis=1)\n",
    "print(portfolio_two_beta.head())\n",
    "print(portfolio_two_beta.tail())\n",
    "# print(portfolio_two_beta)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "99377ff6-979c-4cca-9c25-b2bc6f975005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mkt Neutral Portfolio 1 Rolling Beta to Market')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_one_beta.plot(figsize=(18, 8))\n",
    "plt.axhline(portfolio_one_beta.mean(), color=\"red\")\n",
    "plt.title(\"Mkt Neutral Portfolio 1 Rolling Beta to Market\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0fb5a18-3bc3-466f-bddc-6e7b4b988c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mkt Neutral Portfolio 2 Rolling Beta to Market')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_two_beta.plot(figsize=(18, 8))\n",
    "plt.axhline(portfolio_two_beta.mean(), color=\"red\")\n",
    "plt.title(\"Mkt Neutral Portfolio 2 Rolling Beta to Market\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc627b90-8188-4167-82f9-1679022a1b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Backtest of Market Neutral Portfolio One Return')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nyZIlbNy4kWbNmmX5uWQ44YQTMtux/Nfy5cs54ogjmD59Oh988EGWWdsjR46kZMmSdOjQIfP5XrRoEU2bNqVYsWKbtSnZWf369WPevHlb7X29Iz/7nHTuuecSHx+fZdu/3w+pqaksXryYmjVrUqpUqS0+99l1+OGHk5KSQuXKlTnllFMoVqwYr776KpUqVWLs2LEAm81Mv+KKKwA2e/1Vr16djh077nQWgLlz5zJ16lR69uxJ6dKlM7fvv//+dOjQITNTdo0dO5YKFSpw6qmnZm5LSEjgkksuYdWqVXz44Ydbve3KlSsBKF68+Fb3ybhuxYoVWbbXq1ePVq1aZV5OSUmhTp06/Pbbb5nbRo4cSatWrdhrr72yvM4PP/xw0tLSmDRp0nYf3/Tp00lJSSElJYW6dety9913c8wxxzB8+PDMfUaPHk16ejonnXRSluNUqFCBWrVq5dj76d+SkpI466yztnjdySefzF577ZV5OeN5+vdzI0mS8j/bhkiSpFzpwAMPpFmzZpmXTz31VJo0aULv3r3p0qVLZnH0zTff5NZbb2Xq1KlZ+uv+uyg9c+ZM4uLiqFev3naP++GHH/LWW29xzTXXcNVVV2U776+//koQBNx4443ceOONW9xnwYIFVKpUiQEDBnDsscdSu3ZtGjRowJFHHskZZ5yRrRYeWzJnzhwA6tSpk2V7YmIi++67b+b1u6JJkybUrVuXESNGUKpUKSpUqJDZ93hLnn76ae69916mT59Oampq5vbq1atvtu+WtmXo06cP69at45tvvslso5BhxowZLF++nHLlym3xtv/t5buzWrduTbt27bjrrru44IILNrt+R372OWlLz9vatWsZOHAgw4YN46+//iIIgszrli9fvtPHGjx4MLVr16ZQoUKUL1+eOnXqZH5QMmfOHOLi4qhZs2aW21SoUIFSpUpt9vrb1s87u7b2mgfYb7/9eOedd1i9ejVFixbN9v3VqlVrsw+39ttvvyzH25KMwnRGEXtLtlbgrlKlymb77rXXXll6ts+YMYPvvvtuqx/wZOd1Xq1aNZ544gnS09OZOXMmt912GwsXLqRw4cJZjhMEAbVq1drifeyOVh2VKlXa6sKZ/31uMgrZ/35uJElS/mfxWpIk5QlxcXG0a9eOBx54gBkzZlC/fn0++ugjjjnmGFq3bs0jjzzC3nvvTUJCAsOGDWPEiBE7dZz69euzbNkynn32Wc4///xsF9oyFk678sortzqrNKO417p1a2bOnMlrr73Gu+++y5NPPsn999/Po48+Sq9evXYq957QvXt3hgwZQvHixTn55JM3K/RleO655+jZsyddu3blqquuoly5csTHxzNw4MDMXsn/9u/Zwv917LHH8uKLL3LHHXfwzDPPZDlmeno65cqV4/nnn9/ibbdW7NsZN998M23btuWxxx7L0iM4Iwdk72e/tYU309LSdjjTlp63//3vfwwbNow+ffpwyCGHULJkSWKxGKeccspOL+4Hm3+YtCXZWVQUtv3zzosyCtzfffcdXbt23eI+3333HcBmH6D9d+Z8hn9/6JCenk6HDh24+uqrt7hv7dq1t5uxaNGiHH744ZmXW7ZsyQEHHMB1113Hgw8+mHmcWCzG22+/vcVc2elrvaOv7229FrLz3EiSpPzP4rUkScozNm7cCMCqVasAeOWVVyhcuDDvvPMOSUlJmfsNGzYsy+1q1KhBeno6P/3002aLBf5X2bJlGTVqFIceeiiHHXYYH3/8MRUrVsy8fmvFmX333RcIZyf+u0i0NaVLl+ass87irLPOYtWqVbRu3Zp+/fplFq+zWwgEqFq1KhAuhJeRA2DDhg3MmjUrW3myo3v37tx0003MnTt3mwtJjho1in333ZfRo0dneRw333zzDh+za9euHHHEEfTs2ZPixYszZMiQzOtq1KjB+PHjadmy5XYLojvyfG5JmzZtaNu2LXfeeSc33XRTlut25GefMXt02bJlWYrgW5rZuzOZR40aRY8ePbj33nszt61bt26zBTBzUtWqVUlPT2fGjBmZhVyA+fPns2zZsszX5/bs7Gv+v6ZPn07ZsmWzPes64/6+++470tPTs3xAMn369CzH25JDDz2UUqVKMWLECK6//votFl2feeYZALp06ZLtTBlq1KjBqlWrcux9DGF7ldNPP53HHnuMK6+8kipVqlCjRg2CIKB69erbLYhv7Wf179f3v+XEtz8kSVLBZM9rSZKUJ6SmpvLuu++SmJiYWSCLj48nFotlmdU3e/ZsxowZk+W2Xbt2JS4ujgEDBmw2+3RLs/j22Wcfxo8fz9q1a+nQoQOLFy/OvK5IkSLA5sWZcuXKZc7MnTt37mb3uXDhwszxv+8PwhmNNWvWzNL2JKPwlp2i4+GHH05iYiIPPvhglsfz1FNPsXz5cjp37rzd+8iOGjVqMGjQIAYOHMiBBx641f0yinf/zvLFF1/w2Wef7dRxzzzzTB588EEeffRRrrnmmsztJ510Emlpadxyyy2b3Wbjxo1ZnruiRYvucgE3o/f1448/nmX7jvzsa9SoAZClT/Hq1at5+umnN7vdzmSOj4/f7DX90EMP7dTM7uw66qijABg0aFCW7ffddx9Atl9/O/J49957bxo3bszTTz+d5TY//PAD7777bmam7DrqqKOYN28eL730Uua2jRs38tBDD1GsWDHatGmz1dsWKVKEK6+8kp9//pnrr79+s+vfeusthg8fTseOHTn44IN3KBeEr/PPPvuMd955Z7Prli1blvmh3o66+uqrSU1Nzfw5HX/88cTHx9O/f//NXkNBEGT5d6to0aJbbEOzpdd3WlraZu8ZSZKk7HLmtSRJypXefvvtzFmPCxYsYMSIEcyYMYNrr72WEiVKAGFR7L777uPII4+ke/fuLFiwgMGDB1OzZs3Mr+lD2LLh+uuv55ZbbqFVq1Ycf/zxJCUl8dVXX1GxYkUGDhy42fFr1qzJu+++S9u2benYsSMTJkygRIkSJCcnU69ePV566SVq165N6dKladCgAQ0aNGDw4MEceuihNGzYkHPPPZd9992X+fPn89lnn/Hnn3/y7bffAmHrgLZt29K0aVNKly7N5MmTGTVqFL179848ftOmTQG45JJL6NixI/Hx8ZxyyilbfK5SUlLo27cv/fv358gjj+SYY47h559/5pFHHqF58+acfvrpOfNDAS699NLt7tOlSxdGjx7NcccdR+fOnZk1axaPPvoo9erVy5w1v6N69+7NihUruP766ylZsiTXXXcdbdq04fzzz2fgwIFMnTqVI444goSEBGbMmMHIkSN54IEH6NatGxA+n0OGDOHWW2+lZs2alCtXbps9u7ekTZs2tGnTZouL92X3Z3/EEUdQpUoVzjnnHK666iri4+MZOnQoKSkp/P7771nuc2cyd+nShWeffZaSJUtSr149PvvsM8aPH0+ZMmV26LHuiEaNGtGjRw8ef/xxli1bRps2bfjyyy95+umn6dq1K+3atcvW/TRt2pTx48dz3333UbFiRapXr85BBx201f3vvvtuOnXqxCGHHMI555zD2rVreeihhyhZsiT9+vXbocdw3nnn8dhjj9GzZ0+mTJlCtWrVGDVqFJ988gmDBg3a5mKMANdeey3ffPMNd955J5999hknnHACycnJfPzxxzz33HPst99+W/yAIjuuuuoqXn/9dbp06ULPnj1p2rQpq1ev5vvvv2fUqFHMnj2bsmXL7vD91qtXj6OOOoonn3ySG2+8kRo1anDrrbfSt29fZs+eTdeuXSlevDizZs3i1Vdf5bzzzuPKK68Ewp/VSy+9xOWXX07z5s0pVqwYRx99NPXr1+fggw+mb9++LFmyhNKlS/Piiy/udIFdkiSJQJIkKRcZNmxYAGQ5FS5cOGjcuHEwZMiQID09Pcv+Tz31VFCrVq0gKSkpqFu3bjBs2LDg5ptvDrb0a87QoUODJk2aBElJScFee+0VtGnTJnjvvfcyr69atWrQuXPnLLf54osvguLFiwetW7cO1qxZEwRBEHz66adB06ZNg8TExAAIbr755sz9Z86cGZx55plBhQoVgoSEhKBSpUpBly5dglGjRmXuc+uttwYHHnhgUKpUqSA5OTmoW7ducNtttwUbNmzI3Gfjxo3B//73vyAlJSWIxWJbfDz/9fDDDwd169YNEhISgvLlywcXXnhhsHTp0iz7ZDw3Cxcu3O79ZXdfILj44oszL6enpwe33357ULVq1SApKSlo0qRJ8OabbwY9evQIqlatmrnfrFmzAiC4++67N7vPiRMnBkAwcuTILNuvvvrqAAgefvjhzG2PP/540LRp0yA5OTkoXrx40LBhw+Dqq68O/v7778x95s2bF3Tu3DkoXrx4AARt2rTZocf031xA8NVXX2W5Ljs/+yAIgilTpgQHHXRQkJiYGFSpUiW47777Ml/3s2bN2m7mjH3/e/wgCIKlS5cGZ511VlC2bNmgWLFiQceOHYPp06cHVatWDXr06LHZ45g4ceI2n4dtHevfUlNTg/79+wfVq1cPEhISgsqVKwd9+/YN1q1bl2W/Lb3HMkyfPj1o3bp1kJycHACZebf2WgiCIBg/fnzQsmXLIDk5OShRokRw9NFHBz/99NMWH8O/n9s2bdps9hqYP39+5nOXmJgYNGzYMBg2bNg2H/e/paWlBcOGDQtatmwZlChRIihcuHBQv379oH///sGqVas2239rz8WWsq1cuTLo27dvULNmzSAxMTEoW7Zs0KJFi+Cee+7J8u/GlrRp0yaoX7/+Fq/74IMPNvs37JVXXgkOPfTQoGjRokHRokWDunXrBhdffHHw888/Z+6zatWqoHv37kGpUqUCIMv7eubMmcHhhx8eJCUlBeXLlw+uu+664L333tvs9ba1XNv6d+G/WSVJUv4XCwJXvJAkSZIkSZIk5S72vJYkSZIkSZIk5ToWryVJkiRJkiRJuY7Fa0mSJEmSJElSrmPxWpIkSZIkSZKU61i8liRJkiRJkiTlOhavJUmSJEmSJEm5TqGoA+SU9PR0/v77b4oXL04sFos6jiRJkiRJkiRpC4IgYOXKlVSsWJG4uK3Pr843xeu///6bypUrRx1DkiRJkiRJkpQNf/zxB/vss89Wr883xevixYsDMGvWLEqXLh1xGkmSJEnacampqbz77rscccQRJCQkRB1HkiRpt1iyZAnVq1fPrOluTb4pXme0CilevDglSpSIOI0kSZIk7bjU1FSKFClCiRIlLF5LkqR8KzU1FWC77Z9dsFGSJEmSJEmSlOtYvJYkSZIkSZIk5ToWryVJkiRJkiRJuU6+6XktSZIkSZIk5TZpaWmZ/X2lgiI+Pp5ChQptt6f19li8liRJkiRJknaDVatW8eeffxIEQdRRpD2uSJEi7L333iQmJu70fVi8liRJkiRJknJYWloaf/75J0WKFCElJWWXZ6BKeUUQBGzYsIGFCxcya9YsatWqRVzcznWvtngtSZIkSZIk5bDU1FSCICAlJYXk5OSo40h7VHJyMgkJCcyZM4cNGzZQuHDhnbofF2yUJEmSJEmSdhNnXKug2tnZ1lnuIwdySJIkSZIkSZKUoyxeS5IkSZIkSZJyHYvXkiRJkiRJknZK27Zt6dOnT9QxIvXBBx8Qi8VYtmxZ1FEyjRkzhpo1axIfH5/tn0+1atUYNGhQ5uVYLMaYMWN2S77ssngtSZIkSZIkCYCePXsSi8W44IILNrvu4osvJhaL0bNnzx26v65du2b7uHfccUeW7WPGjMnxvuGzZ88mFosxderUHL3fbalWrRqxWIxYLEbRokU54IADGDlyZI7c778LzhnOP/98unXrxh9//MEtt9yyU/c9d+5cOnXqtIsJd43Fa0mSJEmSJEmZKleuzIsvvsjatWszt61bt44RI0ZQpUqV3XbcwoULc+edd7J06dLddowdsWHDhhy9vwEDBjB37ly++eYbmjdvzsknn8ynn36a49lWrVrFggUL6NixIxUrVqR48eI7dYwKFSqQlJS0U7fNKRavJUmSJEmSpN0sCALWbNgYySkIgh3KesABB1C5cmVGjx6duW306NFUqVKFJk2abPO2b731FiVLluT555+nX79+PP3007z22muZs44/+OCDrd728MMPp0KFCgwcOHCbx/j4449p1aoVycnJVK5cmUsuuYTVq1dnXr+ldhelSpVi+PDhAFSvXh2AJk2aEIvFaNu2LfDPLPHbbruNihUrUqdOHQCeffZZmjVrRvHixalQoQLdu3dnwYIF28y4JRm3r127NoMHDyY5OZk33ngDgO+//5727duTnJxMmTJlOO+881i1alXmbbeUrW3btsyZM4fLLrssy/ObUaxu3759luf8lVdeoX79+iQlJVGtWjXuvffebeb97/O4vYy7Q6Hdeu+SJEmSJEmSWJuaRr2b3onk2D8N6EiRxB0rA5599tkMGzaM0047DYChQ4dy1llnbbP4PGLECC644AJGjBhBly5dWLVqFdOmTWPFihUMGzYMgNKlS2/19vHx8dx+++10796dSy65hH322WezfWbOnMmRRx7JrbfeytChQ1m4cCG9e/emd+/emcfYni+//JIDDzyQ8ePHU79+fRITEzOve//99ylRogTvvfde5rbU1FRuueUW6tSpw4IFC7j88svp2bMnY8eOzdbxtqRQoUIkJCSwYcMGVq9eTceOHTnkkEP46quvWLBgAb169aJ3796ZBfctZdt7771p1KgR5513Hueeey4QPr8///wzderU4ZVXXqFFixaULl2aKVOmcNJJJ9GvX7/MGd8XXXQRZcqUyVYbmOxmzGkWryVJkiRJkiRlcfrpp9O3b1/mzJkDwCeffMKLL7641eL14MGDuf7663njjTdo06YNAMWKFSM5OZn169dToUKFbB33uOOOo3Hjxtx888089dRTm10/cOBATjvttMxFCGvVqsWDDz5ImzZtGDJkCIULF97uMVJSUgAoU6bMZrmKFi3Kk08+maWgffbZZ2eO9913Xx588EGaN2/OqlWrKFasWLYe179t2LCBe++9l+XLl9O+fXtGjBjBunXreOaZZyhatCgADz/8MEcffTR33nkn5cuX32q2+Pj4zBndGcqVKweEheyM7ffddx+HHXYYN954IwC1a9fmp59+4u67785W8Tq7GXOaxWtJkiRJkiRpN0tOiOenAR0jO/aOSklJoXPnzgwfPpwgCOjcuTNly5bd4r6jRo1iwYIFfPLJJzRv3nxX43LnnXfSvn17rrzyys2u+/bbb/nuu+94/vnnM7cFQUB6ejqzZs1iv/3226VjN2zYMEtxGGDKlCn069ePb7/9lqVLl5Keng7A77//Tr169bJ939dccw033HAD69ato1ixYtxxxx107tyZyy+/nEaNGmUWhQFatmxJeno6P//8c2ZheEvZsmvatGkce+yxWba1bNmSQYMGkZaWRnz8tl8j06ZNy1bGnGbxWpIkSZIkSdrNYrHYDrfuiNrZZ59N7969gXBm9dY0adKEr7/+mqFDh9KsWTNisdguHbd169Z07NiRvn37bjYreNWqVZx//vlccsklm90uYzHJWCy2WZ/v1NTUbB3738VZ+KddRseOHXn++edJSUnh999/p2PHjju8oONVV11Fz549KVasGOXLl9/h5+m/2QqCvPWOkSRJkiRJkrRHHHnkkWzYsIFYLEbHjlufNV6jRg3uvfde2rZtS3x8PA8//HDmdYmJiaSlpe3wse+44w4aN26cuWhihgMOOICffvqJmjVrbvW2KSkpzJ07N/PyjBkzWLNmTZZMQLZyTZ8+ncWLF3PHHXdQuXJlACZPnrxDjyVD2bJlt5h7v/32Y/jw4axevTqzQP3JJ58QFxe32eP/r+w+v/vttx+ffPJJlm2ffPIJtWvX3u6s613NuCvidts9S5IkSZIkScqz4uPjmTZtGj/99NN2C5y1a9dm4sSJvPLKK5n9qAGqVavGd999x88//8yiRYuyPQO6YcOGnHbaaTz44INZtl9zzTV8+umn9O7dm6lTpzJjxgxee+21zBniAO3bt+fhhx/mm2++YfLkyVxwwQUkJCRkXl+uXDmSk5MZN24c8+fPZ/ny5VvNUaVKFRITE3nooYf47bffeP3117nllluy9Riy67TTTqNw4cL06NGDH374gYkTJ/K///2PM844Y7vtOKpVq8akSZP466+/WLRo0Vb3u+KKK3j//fe55ZZb+OWXX3j66ad5+OGHt9iaJacz7gqL15IkSZIkSZK2qESJEpQoUSJb+9apU4cJEybwwgsvcMUVVwBw7rnnUqdOHZo1a0ZKSspms3+3ZcCAAZn9pTPsv//+fPjhh/zyyy+0atWKJk2acNNNN1GxYsXMfe69914qV65Mq1at6N69O1deeSVFihTJvL5QoUI8+OCDPPbYY1SsWHGzXtD/lpKSwvDhwxk5ciT16tXjjjvu4J577sn2Y8iOIkWK8M4777BkyRKaN29Ot27dOOyww7LMYN+aAQMGMHv2bGrUqJG5EOWWHHDAAbz88su8+OKLNGjQgJtuuokBAwZka7HGXc24K2LBfxvA5FErVqygZMmSLFq0iDJlykQdR5IkSZJ2WGpqKmPHjuWoo47KMkNMkpT3rFu3jlmzZlG9enUKFy4cdRxpj9vWe2Dx4sWULVuW5cuXb/PDEWdeS5IkSZIkSZJyHRdslCRJknJKejqs+BMW/wrEoHpriNv+AjiSJEmSNmfxWpIkSdpRa5bA4plhkXrxjPB80a+wZCZsXPfPfin7QdtrYb9jIM4vPUqSJEk7wuK1JEmStCUb18OS3zYVpmdsKlZvKlSvWbz128UlQOl9YdU8WDgNRvaA8g3CInbdLhCL7bnHIEmSJOVhFq8lSZJUcKWnw8q/NxWnf/3ntGgGLP8DgvSt37Z4RShbE8rUhDK1wvOyNaFkFYgvBGuXwedD4PNHYP4P8NLpUGF/aHcd1D7SIrYkSZK0HRavJUmSlP+tXZa1MJ1ZqJ4JG9du/XZJJaBMjazF6TI1oXQNSCq27WMml4J2feGg88MC9udDYN538MIpULEJtL0OanWwiC1JkiRthcVrSZIk5Q8b18PS2f8qTm9q9bFoBqxZtPXbxRWCvapD2Vr/KVTXgqIpu15cLlIa2t8AB10Inz0EXzwOf38DI06ESs3Cmdg12lvEliRJkv7D4rUkSZLytiCAj++DD+6AtA1b36/43ptafNT8pzhdpiaUqhq2+djdipaBw/vBwRfDpw/Al0/CX5PhueOh8sFhEbt6a4vYkiRJ0iYWryVJKug2rodCSVGnkHZOEMB7N8KnD4WXE4tlLUxnnmpAUvFos2YolgJH3AqH/A8+GQSTh8Ifn8Mzx0DVQ8NWI9UOjTqlJEmSFDmL15IkFRRpG2HJTJj3Pcz/MVxAbv6PsOIvaH4udLoL4uKiTillX3o6vHU5TBkWXu44EA6+MO/MXC5eHo4cCC0ugY/vDx/HnI9heOdwBna766HKwVGnlCRJkiLjX6iSJOVHa5bArI/g80fhtYvhsTYwsBIMPhBeOSdssTDj3bBwDfDVEzD2irAYKOUFaRvh1fM3Fa5jcMxDcMhFeadw/W8l9oaj7oJLpkKzcyAuAWZNgqEd4dnj4I+vok4oSZIKkJ49exKLxTJPZcqU4cgjj+S7775j+PDhWa7b0mn27NkEQcDjjz/OQQcdRLFixShVqhTNmjVj0KBBrFmzJvM4Xbt23ez4H3zwAbFYjGXLlgGQlpbGHXfcQd26dUlOTqZ06dIcdNBBPPnkk1vMnJCQQPny5enQoQNDhw4lfdPfOBn3u63TBx98sLufXu0gZ15LkpSXpaeFC9LN3zSbet4P4YzqjKL0fyUUhfL1oHx9KN8gPC36Bd64NGxdQAw635s3C4AqODauh1Fnw/Q3w8UWj38cGpwQdapdV7ISdLkPDu0DH90L3zwHMyeEp1pHQNu+UOmAqFNKkqQC4Mgjj2TYsPDbbfPmzeOGG26gS5cu/Pzzzxx55JGZ+x1//PE0aNCAAQMGZG5LSUnhjDPOYPTo0dxwww08/PDDpKSk8O233zJo0CCqVau2xaL11vTv35/HHnuMhx9+mGbNmrFixQomT57M0qVLt5g5LS2N+fPnM27cOC699FJGjRrF66+/TosWLZg7d27m/pdeeikrVqzIfJwApUuX3tGnSruZxWtJkvKKtUs3tfv4cVPrjx9gwTTYuG7L+5eq8k+BusKm872qb94apOohEJ8IYy6EyU+F2yxgK7fasBpePA1+mwjxSXDS01CnU9SpclapKnD0A3DoZTDpbpj6QvhNiRnvQu1OYU/svRtFnVKSJO2oIIDUNdEcO6HIDv1+n5SURIUKFQCoUKEC1157La1atWLVqlWZ2wESExMpUqRIlm0vv/wyzz//PGPGjOHYY4/N3F6tWjWOOeYYVqxYsUPRX3/9dS666CJOPPHEzG2NGm3+u9C/M1eqVIkDDjiAgw8+mMMOO4zhw4fTq1evLDmTk5NZv359lm3KfSxeS5KU26SnwZLfNu9NvfyPLe+fUATKbZpNXaHhpoJ1PShcMvvHbHwqEMCYiyxgK/datxxGnAy/fxZ+i+DUEbBv26hT7T57VYNjB8Ohl4dF7O9egl/eDk91u4QzsSs0iDqlJEnKrtQ1cHvFaI593d+QWHSnbrpq1Sqee+45atasSZkyZba7//PPP0+dOnWyFK4zxGIxSpbcgb9TCIvnEyZM4KKLLiIlJWWHbtu+fXsaNWrE6NGj6dWr1w7dVrmDxWtJkqK0dtm/CtQ/hG0/FkyDjWu3vH/JKpuK1A3+mVVdujrExe96lsbdw/OMAnYsBkfdYwFbucPqxfDc8TB3KiSVhNNHQeUDo061Z5SpAcc9Cq2ugA/vhO9HhS1Tpr8J9bpC22uh3H5Rp5QkSfnIm2++SbFixQBYvXo1e++9N2+++SZx2VjgfcaMGdSpUyfHstx3331069aNChUqUL9+fVq0aMGxxx5Lp07Z+/Zd3bp1+e6773Isj/Ysi9eSJO1JqWvhy8dhzmdhsXprs6kLJf+rN3XDTef1IbnU7s3XuHv4dcbXLoavngRicNTdFrAVrZXz4JljYeF0KFIWzngV9t4/6lR7XtlacMKT0OrKsIj942j4aQz89Bo0OB7aXAsptaNOKUmStiahSDgDOqpj74B27doxZMgQAJYuXcojjzxCp06d+PLLL6lateo2bxsEwU7H3JJ69erxww8/MGXKFD755BMmTZrE0UcfTc+ePbMs2ritPDH/nsmztv9xyX9kvEAqVqxILBZjzJgx29x/9OjRdOjQgZSUFEqUKMEhhxzCO++8s9l+gwcPplq1ahQuXJiDDjqIL7/8ckejSZKUu/31NTzWBt67Kfzaf0bhumRlqH1kWJA6cTj0ngzX/QXnToBjHoKDzoNqLXd/4TpDk9Pg2IeBGHz1BIy9KixoS1FYOgeGHhkWrotXhLPeLpiF638rVxdOHAYXfgr7HQME8MMr8MhBMPq8cBFXSZKU+8RiYeuOKE47WLwtWrQoNWvWpGbNmjRv3pwnn3yS1atX88QTT2z3trVr12b69Onb3a9EiRIsX758s+3Lli0jPj6eokX/aXMSFxdH8+bN6dOnD6NHj2b48OE89dRTzJo1a7vHmTZtGtWrV9/ufsqddrh4vXr1aho1asTgwYOztf+kSZPo0KEDY8eOZcqUKbRr146jjz6ab775JnOfl156icsvv5ybb76Zr7/+mkaNGtGxY0cWLFiwo/EkScp9Nm6AibfDk4fDop+hWHnoeDv0fAuumQ2X/QDdX4LDboT6x4WzK3OiDciuaHJ61gL221dbwNaet2gGDOsES2dBqapw9tvOLP638vXh5Gfh/I+gTmcI0sO+2A83D9v/LNn+H3OSJEnZEYvFiIuLY+3arbQ3/Jfu3bvzyy+/8Nprr212XRAEmQXrOnXq8OOPP7J+/fos+3z99ddUr16dhISErR6jXr16QFin3JYJEybw/fffc8IJJ2w3t3KnHW4b0qlTp2z3lAEYNGhQlsu33347r732Gm+88QZNmjQBwt415557LmeddRYAjz76KG+99RZDhw7l2muv3dGIkiTlHvN/glfPh3mbeqw1OCHsI12kdLS5sqPJ6WHB+vX/ha1OiEGnO20hoj1j3vfw7HGweiGUrQNnjoESES1wlNvtvX+4eOXf38DEgTDjHZj6PHz7YtgKqPVVsNe2v94rSZL0b+vXr2fevHlA2Dbk4YcfZtWqVRx99NHbve1JJ53Eq6++yqmnnsoNN9zAEUccQUpKCt9//z33338///vf/+jatSunnXYaAwYM4Mwzz+Tqq6+mZMmSTJo0iUGDBnHXXXdl3l+3bt1o2bIlLVq0oEKFCsyaNYu+fftSu3Zt6tatu1nmtLQ05s+fz7hx4xg4cCBdunThzDPPzPknSXvEHu95nZ6ezsqVKyldOvyjfcOGDUyZMoW+fftm7hMXF8fhhx/OZ599ttX7Wb9+fZZPZlasWAFAamoqqampuym9JEnZlJ5G3BeDifvwDmJpGwiS9yLtyLsJ6nUNr88r/1c1PIVYWhqF3roUvnyMtCAgvcNtFrC1W8X+mkz8iycTW7ecoHxDNp46EpLL5p33TVRSGsBJzxP762viJt1J3G/vwzfPEnz7AumNTiP90MuhRKWoU2o7Mv6W8W8aScr7UlNTCYKA9PR00tPTo46TbUEQMG7cOPbee28AihcvTt26dXnppZdo3br1Zo8l4zH+23PPPcfjjz/O8OHDue222yhUqBC1atXijDPOoEOHDqSnp1OiRAk+/PBD+vbtyzHHHMPy5cupWbMm99xzD+ecc07mfXbo0IGXXnqJgQMHsnz5cipUqEC7du24+eabiYuLIz09PUvmQoUKsddee7H//vszaNAgevToQSwW22LuLWVXzsn42aSmphIfn/Xbxdn9XScW7EIX9VgsxquvvkrXrl2zfZu77rqLO+64g+nTp1OuXDn+/vtvKlWqxKeffsohhxySud/VV1/Nhx9+yBdffLHF++nXrx/9+/ffbPuIESMoUmTHmtBLkpSTiq6fT5M5j1Nm9QwA5pVozNQqZ7M+oVS0wXZBlcUf0uT3pwCYmXIEP1Q6zQK2douyK3/ioN/up1D6ehYXrcXn+17OxkJFt39DbWavVTOoO2805Vb+CEBqfBE+3/cylhSrE3EySZIKhkKFClGhQgUqV65MYmJi1HGkPW7Dhg388ccfzJs3j40bN2a5bs2aNXTv3p3ly5dTokSJrd7HHp15PWLECPr3789rr71GuXLldum++vbty+WXX555ecWKFVSuXJl27dpRpkyZXY0qSdKOC9KJmzKMuAn9iaWuIUgsRlqH2yjTqDuH5flC71FsnNqQQm/1ocbCd6lWrTrpHW61gK0cFZvxLvGjBxFLX096tdaUOPFZjki0cL1rLmXj758RN/5GEuZO5dBZ95F2wlCCmh2iDqatSE1N5b333qNDhw7b7PUpScr91q1bxx9//EGxYsUoXLhw1HGkPW7dunUkJyfTunXrzd4DixcvztZ97LHi9YsvvkivXr0YOXIkhx9+eOb2smXLEh8fz/z587PsP3/+fCpUqLDV+0tKSiIpKWmz7QkJCf6SJ0na85b/Ca9dDL99EF6u1opY10coVKpKpLFyVPOzIC4O3riE+K8eC7/21fF2C9jKGT++Cq/0gvSNUOco4roNIy7BP/JyRI3WUPltGNmD2Ix3KTTyDDjuMWjYLepk2gb/rpGkvC8tLS1zocO4uLio40h7XFxcHLFYbIu/12T395w98s554YUXOOuss3jhhRfo3LlzlusSExNp2rQp77//fua29PR03n///SxtRCRJypWCAKaOgEcOCQvXhZKh011w5uuQnwrXGZr2gKMfCMefPwLvXBc+B9Ku+OY5GHV2WLhu0A1OegYsXOesxCJwyghoeGL4PL/SC758IupUkiRJ0jbt8MzrVatW8euvv2ZenjVrFlOnTqV06dJUqVKFvn378tdff/HMM88AYauQHj168MADD3DQQQdlrlSanJxMyZIlAbj88svp0aMHzZo148ADD2TQoEGsXr2as846KyceoyRJu8eqBfBGH/j5rfDyPs2h66NQtmaksXa7pj3D8zcuDQvYxKCjizhqJ33xGLx9dTg+oAd0uR/i4rd9G+2c+AQ47nEoXBK+ehLGXglrl0HrK33/SpIkKVfa4eL15MmTadeuXebljL7TPXr0YPjw4cydO5fff/898/rHH3+cjRs3cvHFF3PxxRdnbs/YH+Dkk09m4cKF3HTTTcybN4/GjRszbtw4ypcvv7OPS5Kk3eun1+DNy2DNYohLgHbXQYtLIH6PLicRnaY9wxnXb/aBzweHha8j7IGtHTTpHphwSzg+pLevoT0hLg6OugeS94JJd8PEW2Ht0vC59+vMkiTtFoHfVFQBlROv/ViQT95BK1asoGTJkixatMgFGyVJu8/apTD2Kvh+ZHi5fEM47lGo0CDaXFGZPDQs4oPFR2VfEMD7/eHj+8PLba6Fttf62tnTPnsE3ukbjhufBkc/WHA+gMvFUlNTGTt2LEcddZQ9ryUpj0tNTeXXX3+lYsWKmd0HpIJk8eLFLFiwgNq1a4drJv3nurJly7J8+XJKlCix1fvwt1NJkrJrxnvw+v9g5VyIxcGhl0Oba6BQYtTJotPs7PD8zcvgs4fDsQVsbUt6etgm5KtN/ZaPuBVa/C/aTAXVIRdBcil4rTdMfR7WLYcTnrLfuCRJOaRQoUIUKVKEhQsXkpCQ4KKNKjCCIGDNmjUsWLCAUqVKbVa43hEWryVJ2p71K+HdG2DK8PBymVrhbOt9mkUaK9dodnY4k/aty8MCdiwGHW6xgK3NpW0MPwD6dgQQC/tbN3ONk0g17h72wB55Fkx/E57vBqe+AEnFo04mSVKeF4vF2HvvvZk1axZz5syJOo60x5UqVYoKFSrs0n1YvJYkaVtmfwxjLoRlm9ZzOPgiOOwmSEiONldu0/wcIIC3roBPHwJi0GGABWz9Y+MGeOUcmPY6xOLDD4D2PynqVAKo2xlOHwUvnAqzP4Knj4bTXoGituKTJGlXJSYmUqtWLTZs2BB1FGmPSkhI2KUZ1xksXkuStCWpa+H9W+DzR4AASlaBro9A9VZRJ8u9mvcKz9+6Aj59MCxcH97fArZgwxp4+Qz4dTzEJ0K3YbBfl6hT6d+qt4Yeb8BzJ8Df38CwI+GMV6HkPlEnkyQpz4uLi6NwYdtySTvDZjuSJP3XX1Pgsdbw+WAggAPOhIs+tXCdHc17wVH3hONPHoDxN4ctRVRwrVsRtqL4dTwkFIHuL1m4zq0qHQBnj4MSlWDRLzD0SFj0a9SpJEmSVIBZvJYkKcPGDTDhVniyQ1i4KVYBuo+EYx6y/+uOOPDc/xSw+1nALqjWLIFnjoU5n0BSCTh9NNRoH3UqbUtKHTj7HShTE5b/AUM7wtxvo04lSZKkAsritSRJAPN/hCcPg0l3Q5AGDbrBRZ9B7SOiTpY3ZSlgD4L3+1vALmhWzofhneHvryG5NPR4HaoeEnUqZUepynDWOKiwP6xZBMO7wJxPo04lSZKkAsjitSSpYEtPg4/vh8fbwrzvwiLbicOh21NQpHTU6fK2fxewP77fAnZBsuz3sGfygp/CbzCcNRYqNok6lXZEsRTo+SZUaQHrV8Czx8Ev70SdSpIkSQWMxWtJUsG1eGbY03V8P0jbALU7wUWfQ/3jok6Wfxx4LnS6Oxx/fD+8P8ACdn63eCYM7QRLfgsXOj37bSi3X9SptDMKl4QzRkPtI2HjOnixO3z3ctSpJEmSVIBYvJYkFTzp6fDlE/DoofDnl2Ev3mMfgVNfgOLlo06X/xx03r8K2PfBhFssYOdX838MPxBa8SeUqRUu/ld636hTaVckJMPJz0HDkyB9I4w+F754POpUkiRJKiAKRR1AkqQ9atkf8Hpv+O2D8HL11mHhulTlSGPlewedBwTw9tXw0b1ADNrfALFY1MmUU/6cAs8dD+uWQfmGcMarYesJ5X3xCXDcY5C8F3z5GLx9FaxdCm2u9j0sSZKk3critSSpYAgCmDoCxl0b9m8tlAxH3ALNzoE4v4i0Rxx0fvhzGHcNfLSpF7YF7Pxh9scw4mTYsAoqNYPTR4WFTuUfcXHQ6c7w5/rhHfDB7bB2CXQc6L+hkiRJ2m0sXkuS8r+V8+HNPvDz2PDyPgfCcY9CmRqRxiqQDr4gPM8oYMdi0O56C9h52Yzx8NJpYU/kaq3C9jtJxaNOpd0hFoN2fcMC9rhr4ItHYd1yOOZhiPfPCkmSJOU8f8uUJOVva5bAk4fB8j8gPhHaXQctLoG4+KiTFVwHXwAE4Sz4SXcDsfDnYgE77/npNRh1DqSnQq2OcNLTYY9k5W8HXwDJpWDMRfDtC2EBu9swSCgcdTJJkiTlM37HT5KUfwVBOON6+R+wVzU47wM49DIL17nBwReG7QYAJt0FHwx0Ece8ZtqbMLJnWLiuf1y4qJ+F64Kj0SlwyvMQnxR+q+X5brBuRdSpJEmSlM9YvJYk5V/fvhDODI0rFM4KLF8/6kT6t0Mu+qeA/eGdYQFbecMfX8Er50CQDo26wwlPQaHEqFNpT6vTCc4YDYnFYfZH8PTRsHpR1KkkSZKUj1i8liTlT0tmwdirwnHbvlDpgGjzaMsOuQg63h6OP7wTJlrAzvUWz4QXTg57XNfqCMc85LcZCrJqh0LPN6BIGZg7FYYeCcv/jDqVJEmS8gmL15Kk/CdtI4w+DzasgiotwlYhyr0OuRiOuC0cf3iHBezcbPXisD3EmsWwd2PoNtSF+gQVm8BZ46DEPrB4BjzVERbNiDqVJEmS8gGL15Kk/Oeje+HPLyGpBBz/mLNC84IWvbMWsCfcag/s3CZ1LbxwCiz5DUpWge4vQ1KxqFMpt0ipDWePgzK1YMWfMLQj/D016lSSJEnK4yxeS5Lylz++CttPAHS+F0pViTaPsu/fBexJd8N7N1nAzi3S02D0ueGHQoVLwumjoHj5qFMptylVOSxg790onJ0/vAvM/jjqVJIkScrDLF5LkvKP9SvDAluQBg26wf4nRZ1IO6pFb+h0dzj+9EF4+xoL2LnBuzfCtDcgPhFOeQFS6kSdSLlV0bLQ402oeihsWAnPnQA/vx11KkmSJOVRFq8lSfnHuGth6SwoWTmcda286aDzoMsgIAZfPgZv9oH09IhDFWCfD4HPB4fjrkOgWsto8yj3K1winJ1f56hwYc8XT4NvX4o6lSRJkvIgi9eSpPzhp9fhm+eAGBz3KCSXijqRdkWzs6DrIxCLgynD4bWLw9YV2rOmvQHj+objw/tBw26RxlEekpAMJz0LjU4Nvw3z6nnw+aNRp5IkSVIeY/FakpT3rZgLb1wSjlteCtUOjTaPckbj7nD8ExCLh29HhC1h0lKjTlVw/PEVvNILCKDZ2dCyT9SJlNfEF4JjH4GDLgwvj7sGJg60FZAkSZKyzeK1JClvS0+HMRfC2qXhImHtro86kXJSw25w0tMQlwA/vAKjzoKNG6JOlf8tngkvnBy2fKjVMexDHotFnUp5UVwcHDnwn3+bP7wj7GVvKyBJkiRlg8VrSVLe9sWj8NtEKJQMxz8JhRKjTqSctt/RcMrzEJ8UtrF46XRIXRd1qvxr9WJ4vhusWQx7N4ZuQ8MZtNLOisWgzdX/LMb65WMw5gK/SSFJkqTtsngtScq75v8I4/uF4463QkrtSONoN6rdEU59IfyQYsY78OKpsGFN1Knyn9S18MIpsOQ3KFkFur8MScWiTqX84qDz/mkF9N1L8PKZsHF91KkkSZKUi1m8liTlTanrwn68aeuh9pHQ7JyoE2l3q3kYnDYSEorCzAkw4iRYvyrqVPlHelrYV/zPL6FwSTh9FBQvH3Uq5Tf7n7Tpg6jC8PNYeLF7+KGJJEmStAUWryVJedP7/WHBT1A0BY552H68BUX1VnDGaEgsDrM/gueOh3XLo06VP7x7Y9iWJT4RTnkBUupEnUj5Ve2O0P0lSCgCv44PP4jasDrqVJIkScqFLF5LkvKeX9+Hzx8Jx8c+AsVSos2jPavKwXDma+Hs4D++gGe6hgt2aud9PgQ+HxyOuw6Bai2jzaP8b9+2cPorkFgMZk2C506AdSuiTiVJkqRcxuK1JClvWbMExlwUjpv3gtpHRJtH0dinKfR4A5JLw99fw9NHhwsNasdNewPG9Q3Hh/eDht0ijaMCpGoLOGMMJJWE3z+DZ7v6QZQkSZKysHgtSco7ggDeuARWzYOytaHDLVEnUpT2bgQ93wpbx8z7HoZ3hlULok6Vt/zxVdg7ngCanQ0t+0SdSAVN5ebQ4zVI3gv+mgJPHxN+SClJkiRh8VqSlJd881w4SzQuAY5/AhKLRJ1IUStfD3qOheJ7w8JpMOwoWPF31KnyhsUz4YWTYeM6qNUROt1t73hFo2IT6PEmFCkL876D4V38IEqSJEmAxWtJUl6xeCa8fU04bn89VGwcaRzlIim14ayxULIyLJ4BwzrBst+jTpW7rV4Mz3eDNYth78bQbSjEF4o6lQqyCg3C93GxCrDgx/CbFCvmRp1KkiRJEbN4LUnK/dJSYfR5kLoaqh4KLS6JOpFym9L7hoWvvarB0tnhDOwlv0WdKndKXQsvnBI+PyWrQPeXIalY1KkkSKkTvo9LVIJFv2z6IOqPqFNJkiQpQhavJUm536S74a/J4aJexz0KcfFRJ1JuVKpK2EKkTE1Y/gcM6wyLZkSdKndJT4PR58KfX0LhknD6KChePupU0j/K1AgL2KWqwNJZMPyo8AMpSZIkFUgWryVJudvvX4TFa4Au90GpytHmUe5WslJYwE6pCyv/DmdgL5gWdarc490bw77x8YlwygvhTFcpt9mrGpz1NpSuEbYAGnYULPo16lSSJEmKgMVrSVLutW5FOEs0SIf9T4aG3aJOpLygeHno+RaUbwirF4S9c+d+F3Wq6H0+BD4fHI67DoFqLaPNI21LyX3CGdhl68CKv8IZ2AumR51KkiRJe5jFa0lS7jXuWlg2J+zLe9TdUadRXlK0LPR4HSo2CRclfPpo+GtK1KmiM+0NGNc3HB/ezw+ClDcUr7Dpg6gGsGp+WMCe933UqSRJkrQHWbyWJOVOP46Bqc9DLA6OfyzszyvtiCKl4czXYJ8DYd0yeKZr2IamoPnjK3ilFxBAs7OhZZ+oE0nZVywFerwBezcOP4ga3gX++jrqVJIkSdpDLF5LknKf5X/BG5eG40Mvg6otos2jvKtwSThjNFQ9FNavgGePg9kfR51qz1k8E144GTaug1ododPdEItFnUraMZkfRDXf9EHUsfDHl1GnkiRJ0h5g8VqSlLukp8OYC8MCRcUm0LZv1ImU1yUVh9NGwr7tIHU1PNcNZk6IOtXut3oxPN8tnK26d2PoNhTiC0WdSto5yaXgjFehSot/fRD1SdSpJEmStJtZvJYk5S6fD4ZZH0JCETj+SYhPiDqR8oPEInDqi1DrCNi4FkacAr+8E3Wq3Sd1LbxwCiz5LewZ3/1lSCoWdSpp1yQVh9NHQfU2sGEVPHcCzJwYdSpJkiTtRhavJUm5x7zv4f0B4bjj7VC2ZrR5lL8kFIaTn4e6XSBtPbx4WriQYX6Tngajz4U/vwzbppw+CoqXjzqVlDMSi0L3l6Bmh00fRJ0Mv7wbdSpJkiTtJhavJUm5Q+racFG5tA1QpzM07Rl1IuVHhRLhxOFQ/3hIT4WXe8APr0SdKme9e2NYlI9PhFNegJQ6USeSclZCMpzyfPh/Rdp6eLE7TH8r6lSSJEnaDSxeS5Jyh/duhoXToVh5OOZBF5XT7hOfACc8CY1OhSAt/NBk6gtRp8oZnw8JW+8AdB0C1VpGm0faXQolwUlPQ72umz6IOhN+GB11KkmSJOUwi9eSpOjNGA9fPhaOj30EipaNNo/yv7j48LV2wJkQbFokdMrwqFPtmmlvwLhNC5we3g8ados0jrTbxSfACU/B/idD+kZ45Rz49qWoU0mSJCkHWbyWJEVr9SJ47aJwfOD5UOvwaPOo4IiLgy4PwIHnAQG8cSl88XjUqXbOH1+FM8gJoNnZ0LJP1ImkPSO+UPgtgyZnhB9EvXo+fP1M1KkkSZKUQyxeS5KiEwTw+iWwaj6k1IUO/aNOpIImLg463QWH9A4vv30VfPpQtJl21OKZ8MLJsHEd1OoIne627Y4Klrh4OPpBaL7pA5zX/wdfPhF1KkmSJOUAi9eSpOh8/TT8/BbEJcDxT4SLcEl7WiwGR9wKra4ML797A0y6O9pM2bV6MTzfDdYshr0bQ7eh4UxUqaCJi4Oj7oGDLw4vj70SPhscbSZJkiTtMovXkqRoLPr1n/68h90Ee+8fbR4VbLEYHHYjtLs+vDzh1vAUBNHm2pbUtfDCKbDkNyhZBbq/DEnFok4lRScWg463waGXh5ffuQ4+ujfaTJIkSdolFq8lSXteWiqMPhdS10D11v+0bJCi1uZqOHxT+5pJd8N7N+XOAnZ6Wvge+vNLKFwSTh8FxctHnUqKXiwWfiDa9rrw8vsDYOLtufN9LEmSpO3ye6WSpD3vgzvg76+hcCno+mj4dW8ptzi0DxQqDOOugU8fhBV/wz7NwtdrcqmwWFx403lyKUgosud7TL97I0x7A+IT4ZQXIKXOnj2+lJvFYtD2GiiUCOP7wYd3wsb1cHg/+8FLkiTlMRavJUl71pzP4OP7wvHRg6BkpUjjSFt08AVh4evNy+CHUeFpa+IS/ilk/7ewnXF5i9dtGsfF71i2z4fA55t6+XYdAtVa7vjjkwqCQy/b9EHUtfDJoLCAfeRAC9iSJEl5iMVrSdKes245jD4PgnRo1B3qHxd1Imnrmp0d9pKe/iasWxa+ftduOl+3LBwHaZCeCmsWhaedkVQi+0XvFX/90yv+8H7QsNuuPkopfzv4wvAbCm9dDl8MgbT1cNS9fuNHkiQpj7B4LUnac8ZeDct/h1JVodOdUaeRtq/W4eFpS4IANqzeemE7Y7y161JXh/ezfkV4Wv5H9nM1Oxta9tnphyUVKM3PgUJJ8FpvmDwUNm6AYx7c8W89SJIkaY+zeC1J2jN+eAW+exFicXD8E1C4RNSJpF0Ti0FSsfBUcp8dv/3GDWHROrOwvXT7Re/1K6Dm4dDR1gfSDmlyOsQnwavnw9TnwhnYXR+FeP8ckiRJys38bU2StPst/zPsHQzQ6kqoclC0eaTcoFAiFCoLRctGnUQqGPY/EeIT4JVz4PuRYQ/sE54K34uSJEnKlWz2JknavdLT4NULwtmjlZpCm6ujTiRJKqjqd4WTnwv7YE97HV4+MyxiS5IkKVeyeC1J2r0+fQhmfwQJRcN2IfEJUSeSJBVkdTrBKS9AocLwy9vwwqmQujbqVJIkSdoCi9eSpN3n76kw4dZw3OkOKFMj0jiSJAHhQqzdX4aEIjDzfXj+RFi9OOpUkiRJ+g+L15Kk3WPDGhh9LqSnQt0u0OSMqBNJkvSPfdvA6aMhsXj4DaF768BLp8PPb0NaatTpJEmShMVrSdLu8t5NsOgXKFYBjnkIYrGoE0mSlFXVQ6DHa7B3o/DD1mlvwAunwH37wTvXw/wfo04oSZJUoBWKOoAkKR/65V346olwfNwQKFI62jySJG1NpaZw/iSY9wN8+wJ89xKsXgifPRye9m4EjU+DBt2gaJmo00qSJBUozryWJOWshT/DK73C8cEXQY320eaRJCk7KjSAjrfB5dPg1Bdhv6MhLgHmfgtvX21bEUmSpAg481qSlHNWLYDnu8H65VD5YDjs5qgTSZK0Y+IToE6n8LR6MfwwCqY+Hxaxp70RnoqmwP4nQ6NTw6K3JEmSdguL15KknJG6Fl44FZb9DqX3hVNGQELhqFNJkrTzipaBg84PT/N/hKkjNm8rUmH/sK1IwxNtKyJJkpTDbBsiSdp16ekw+jz4azIk7wWnjfIPeElS/lK+/r/airwE+x0TthWZ9x2MuyZsK/LiaTB9rG1FJEmScogzryVJu278zTDtdYhPDGdcl6kRdSJJknaP+ASoc2R4WrMEvs9oKzIVpr8ZnoqUDduKNO5uWxFJkqRdYPFakrRrJg+FTx8Mx8c+AlVbRJtHkqQ9pUhpOOi88PTftiKfDw5PthWRJEnaabYNkSTtvBnj4a0rw3G762H/E6PNI0lSVGwrIkmSlOOceS1J2jnzfoCRPSFIg0bdofVVUSeSJCl6thWRJEnKMRavJUk7bsVcGHESbFgJ1VrB0Q9ALBZ1KkmSchfbikiSJO2SWBAEQdQhcsKKFSsoWbIkixYtokwZf+mTpN1m/SoY1in8GnTZ2nDOu5C8V9SpJEnKG9JS4df3w9nYP78N6ZtaiMQlQO2ObGx4MmNnpNKp8zEkJCREm1WSJGk3Wbx4MWXLlmX58uWUKFFiq/s581qSlH3pafBKr7BwXaQsdH/ZwrUkSTtiO21FCk1/kw4Je8GBdaCiLUUkSVLB5oKNkqTse+c6+OVtKFQYTn0RSlePOpEkSXlXRluR8z+ECz+FQ3oTFE0hOXUphcacB6nrok4oSZIUKYvXkqTs+fxR+OLRcHzcY1C5ebR5JEnKT8rXh463sbHXh6wvVJzYgh/h/QFRp5IkSYqUxWtJeUcQQHp61CkKpuljYdy14bjDAKjfNdI4kiTlW8XK8U2Vc8Px54PD/tiSJEkFlMVrSXnDhtXwyMFwZ1V46XSYMhyW/xl1qoLh72/glXOAAJr2hBaXRJ1IkqR8bX7JxqQ1PSe8MOZCWL042kCSJEkRccFGSXnDF4/BwunheNob4QkgpS7UPDw8VW0BhZKiy5gfLfsDRpwMqWugxmFw1L0Qi0WdSpKkfC/9sH7Ez/kYFv0Mr/8PTnne/4MlSVKBs8MzrydNmsTRRx9NxYoVicVijBkzZpv7z507l+7du1O7dm3i4uLo06fPZvsMHz6cWCyW5VS4cOEdjSYpv1q7DD55IBy3uz48VT4IYnFhQfuzh+HZrnBnNXj+JPjicVjyW4SB84l1K8LC9ar5UK4+nDgc4v3MU5KkPSIhGU54EuIT4ee3wm+dSZIkFTA7XIVYvXo1jRo14uyzz+b444/f7v7r168nJSWFG264gfvvv3+r+5UoUYKff/4583LMWQWSMnz2MKxbBin7QasrIC4e2lwNa5fCzIlhL8hfx8OqeTDjnfD0NlB6339mZVc7FBKLRv1I8o60VBjZAxb8CMUqwGkvQ+ESUaeSJKlg2Xt/OOxmePd6GNcXqraElNpRp5IkSdpjdrh43alTJzp16pTt/atVq8YDD4QzJocOHbrV/WKxGBUqVNjROJLyu1UL4bNHwnH768PCdYbkvaDB8eEpCGD+j/Dre2Ex+/fPwtnXXz4enuKTwrYiGcXslDp+9XZrggDGXgkzJ0BCEej+IpTcJ+pUkiQVTAdfFP5+89sHMLoXnDMeCiVGnUqSJGmPyDXf/161ahVVq1YlPT2dAw44gNtvv5369etvdf/169ezfv36zMsrVqwAIDU1ldTU1N2eV9KeETfpHuJTV5O+d2PSanSEbb2/y9QJTwf1hvUric3+iNhvE4ib+T6x5X/AbxPD07vXE5SoRLBve9JrHE5QvTUkFd9zDyqXi/vsIeKnDCcgRlrXxwlSGmz7eZckSTkm42+ZLH/TdHmIQk+0Jjb3W9Lev4X09jdFlE6SJClnZLd+myuK13Xq1GHo0KHsv//+LF++nHvuuYcWLVrw448/ss8+W57tN3DgQPr377/Z9okTJ1KkSJHdHVnSHlB4w2IO/+kpAD4v0oGFb7+9E/fSHqq3o9j6uZRb8R3lVnxP2VXTiV/xF7GpzxI39VnSiWdJsZosKL4/C0o0ZHly1QI7K3vvpV9y4OyHAfih0mn89ms6/Do24lSSJBU87733XpbLe1c4gwNnPUjcZw/x2cKiLC6+X0TJJEmSdt2aNWuytV8sCIJgZw8Si8V49dVX6dq1a7b2b9u2LY0bN2bQoEHb3C81NZX99tuPU089lVtuuWWL+2xp5nXlypWZO3cuZcqUye5DkJSLxb91WVhcrtKCtNNfy7mCcuoaYnM+/WdW9pKZWa4OipYjqNGe9H3bE1RvC0VK58xxc7nYX5OJf64rsY3rSGt+HulH3B51JEmSCpzU1FTee+89OnToQEJCQpbr4t/qQ9zU5wiKV2TjuZMguVQ0ISVJknbR4sWL2XvvvVm+fDklSmx9ja1cMfP6vxISEmjSpAm//vrrVvdJSkoiKSlpi7f97y95kvKgxTPh2xEAxB1+M3GJOdjbMaEk7NcpPAEsmQUz34cZ42HWJGKrFxD77kXivnsRYnFQqek/vbIrNsnadzu/WDILXj4dNq6D2p2I73QH8fnxcUqSlEds8e+aTnfC758RWzKThHFXwonDC+y3xSRJUt6W3fptrixep6Wl8f3333PUUUdFHUVSVD4YCEEa1DoCqhy8e49VujqU7gXNe8HG9fD75/Dr+HDhxwU/wp9fhacPBoaLRNZoDzU7hOfFy+/ebHvC2qUw4iRYswj2bgQnPJk/C/SSJOV1ScXghCfgqSPgpzHw7QvQuHvUqSRJknabHS5er1q1KsuM6FmzZjF16lRKly5NlSpV6Nu3L3/99RfPPPNM5j5Tp07NvO3ChQuZOnUqiYmJ1KtXD4ABAwZw8MEHU7NmTZYtW8bdd9/NnDlz6NWr1y4+PEl50vwf4ftR4bj9DXv22IWSYN824emIW2D5X+Gs7F/Hw8wPwkLvD6+EJ4B920Gry6Faq7w582njBnjpDFj0C5TYB059KfzDWJIk5U6VmkK76+D9ATD2qvBD/tL7Rp1KkiRpt9jh4vXkyZNp165d5uXLL78cgB49ejB8+HDmzp3L77//nuU2TZo0yRxPmTKFESNGULVqVWbPng3A0qVLOffcc5k3bx577bUXTZs25dNPP80sbksqYCbcBgRQ/7hwJnCUSlaCA84MT2kbwxnYv44PT3Onwm8Tw1OlZmERu3YniIuLNnN2BQG8cQnM/ggSi0P3l6DE3lGnkiRJ29OyT/gNsTmfwCvnwtnjIN7WiZIkKf/ZpQUbc5MVK1ZQsmRJFi1a5IKNUl7252R48rCw1/RFX0BK7agTbd3SOfDpQ/DNs2GvaICU/eDQy6DBCRCfKzsz/ePDu2DibRCLh9NeDnt6S5KkSKWmpjJ27FiOOuqobfeCXPYHDGkJ65dDm2vC2diSJEl5xOLFiylbtux2F2zMI9MDJRUY7w8Izxt1z92Fa4C9qkLne6DP93Do5ZBUAhZOg1fPg4eawJdPQOraqFNu2Xcvh4VrgM73WriWJCmvKVUZjr4/HE+6O1yzQ5IkKZ+xeC0p9/jtQ5j1IcQlQNtrok6TfcXKweE3w2U/wGE3QZGysOx3GHslDNofPr4f1q2IOuU/Zn8Cr10cjltcAs3OijaPJEnaOQ1OgEanQpAOo8+FdcujTiRJkpSjLF5Lyh2CACbcEo6bnQWlqkSbZ2cULgmtrgiL2EfdAyUrw+oFML4f3N8A3r8FVi+KNuOiX+Gl0yBtA+x3DBzeP9o8kiRp13S6C0pV3fTB+VVRp5EkScpRFq8l5Q6/vBMuhlgoGVpdGXWaXZOQDAeeC5d8A10fhbJ1wn6UH90TFrHHXh32qdzTVi+G57vB2qXhApPHP553FpeUJElbVrgEHP9EuF7Idy/BdyOjTiRJkpRjrFpIil56+j+zrg86H4qXjzZPTolPgManwkWfw8nPQcUDYONa+PIxeLAxjLkIFv6yZ7KkroMXT4Wls8JZ7ae+GBbZJUlS3lflIGh9dTh+6/JwFrYkSVI+YPFaUvR+HA3zfwgXPGx5adRpcl5cHOx3NJw7Ac4YA9VbQ/pGmPo8DD4QXjod/vp69x0/PR3GXAh/fBG2NjltFBRL2X3HkyRJe17rq2Cf5rB+BYw+H9LTok4kSZK0yyxeS4pW2kaYeHs4bnEJFCkdbZ7dKRaDGu2gxxvQawLU7QIEMO0NeKIdPNMVZk0K+3/npIm3hh8QxBUKZ4Cn1MnZ+5ckSdGLLxS2D0ksBr9/Gi4YLUmSlMdZvJYUrW9HwJKZUKQMHHxB1Gn2nH2awinPhy1F9j8FYvHw20R4+mh4qgNMHxvOmN5VXz8LH90bjo95KJz1LUmS8qfS1cNFowE+GAh/Tok2jyRJ0i6yeC0pOhvXwwd3huNWV0BS8WjzRKHcfnD8Y+Hijs17QXxSuHDli6fCkBbw3cvh7PSdMXMivNknHLe+Ghp3z7HYkiQpl2p0CtQ/PmxRNroXrF8VdSJJkqSdZvFaUnQmD4MVf0KJStDsnKjTRGuvqtD5XrjsBzj0MkgsDgunwehz4aED4Ksnw0UXs2vBNHj5zPAP14YnQrvrdl92SZKUe8Ri0OU+KLEPLPkNxl0bdSJJkqSdZvFaUjTWr4KPNn2ttfVVkFA42jy5RbFycHi/sIjd/kYoUhaWzYG3roBBDeHjQbBuxbbvY+V8eP6kcMGmKofAsYPDP2QlSVLBkLxX+M0uYvDNs/DTa1EnkiRJ2ikWryVF44tHYfVC2Ks6NDk96jS5T3IpaH0l9PkeOt0NJSvD6gUw/mYY1ADevwVWL9r8dhvWwAunwPLfoXQNOGUEFEra4/ElSVLEqh0afpsL4PVLYPlf0eaRJEnaCRavJe15a5fCpw+G43bXQ3xCtHlys8QicNB5YU/srkOgbG1YtzyctX5/A3j7Glj2R7hvelrYZuTvryG5NJw2EoqUjja/JEmKTtu+ULEJrFsGYy7ImcWgJUmS9iCL15L2vE8fCguw5epBgxOiTpM3xCeECy5e9AWc9Gz4h+jGteEM9gcbw5iL4K3LYfqbEJ8YzrguUyPq1JIkKUqFEuH4JyGhCMyaBJ89HHUiSZKkHWLxWtKetWoBfD4kHLe/AeL8Z2iHxMVBvWPg3IlwxqtQrVW4KOPU52HK8HCfrkOg6iGRxpQkSblE2Zpw5MBw/P4AmPtttHkkSZJ2gFUjSXvWR/dB6hqo1BTqHBV1mrwrFoMa7aHnm3DOeKjTGeIKQYcB0LBb1OkkSVJuckAPqNsF0lPhlXPDNTIkSZLygEJRB5BUgCz7AyY/FY7b3xgWYLXrKjeHU0eEfSydyS5Jkv4rFoOjH4Q/J8Oin+G9G6HzvVGnkiRJ2i6rHJL2nEl3QdqGsNXFvm2jTpP/WLiWJElbU7QMHLepddtXT8LP46LNI0mSlA1WOiTtGYt+hW+eD8eH3eSsa0mSpD2tRns4pHc4fu1iWDk/2jySJEnbYfFa0p7xwe0QpEHtI6HygVGnkSRJKpgOuwnKN4A1i+C1iyAIok4kSZK0VRavJe1+876HH14Jx+1viDaLJElSQVYoCU54EgoVhl/Hw5ePR51IkiRpqyxeS9r9JtwWntc/Hio0jDaLJElSQVduPzji1nD87o0w/6do80iSJG2FxWtJu9cfX8Evb0MsHtpdH3UaSZIkATTvBbWOgLT18EovSF0XdSJJkqTNWLyWtHtNGBCeNz4VytaMNoskSZJCsRgcOxiKpsCCH+H9/lEnkiRJ2ozFa0m7z28fwKxJEJ8Iba6JOo0kSZL+rVg5OPaRcPz5I2EPbEmSpFzE4rWk3SMI4P1Ns66bnQ2lqkSbR5IkSZurfQQ0Pzccj7kIVi+KNo8kSdK/WLyWtHv8/Db8NQUSikCrK6JOI0mSpK054hYoWwdWzYfXLwknIUiSJOUCFq8l5bz0dJiwaQX7gy4Iv5IqSZKk3CkhGU54Mmz19vNbMGV41IkkSZIAi9eSdocfR4cL/ySVhJaXRJ1GkiRJ27P3/nDYzeF4XF9Y+Eu0eSRJkrB4LSmnpaXCxNvCccv/QfJe0eaRJElS9hx8EezbFjauhdG9YOOGqBNJkqQCzuK1pJw19XlY8hsUKQsHXRh1GkmSJGVXXBx0fTScfDD3238mJEiSJEXE4rWknJO6Dj68Kxy3vhKSikWbR5IkSTumxN5wzEPh+JMHYNakaPNIkqQCzeK1pJwzeSis+AtKVIKmZ0WdRpIkSTtjv6PhgB5AAKPPhzVLok4kSZIKKIvXknLG+lXw0b3huM01kFA42jySJEnaeUcOhDI1YeXf8OZlUaeRJEkFlMVrSTnjiyGwZhGU3hcad486jSRJknZFYlE4/gmIKwQ/jYHZn0SdSJIkFUAWryXtujVL4JNNvRHbXQ/xCdHmkSRJ0q6rdAA0OSMcZ3zDTpIkaQ+yeC1p1336IKxfDuXqQ/3jo04jSZKknNLyUojFwcz34e9vok4jSZIKGIvXknbNyvnwxWPhuP0NEOc/K5IkSflG6erQoFs4/ui+aLNIkqQCxyqTpF3z0b2QugYqNYM6naJOI0mSpJx26KYFG6e9AQt/jjaLJEkqUCxeS9p5y36HyUPD8WE3QSwWbR5JkiTlvPL1oE5nIICPB0WdRpIkFSAWryXtvA/vhPRUqN4a9m0TdRpJkiTtLq2uCM+/ewmWzok2iyRJKjAsXkvaOYtmwNQR4bj9TdFmkSRJ0u61T1PYty0EafDpQ1GnkSRJBYTFa0k7Z+LtEKRDnaOgcvOo00iSJGl3y5h9/fUz4aLdkiRJu5nFa0k7bu538ONoIAbtro86jSRJkvaEaq1gn+aQth4+Hxx1GkmSVABYvJa04ybcGp43OAEqNIg2iyRJkvaMWOyf2ddfPQVrl0abR5Ik5XsWryXtmN+/gBnvQCwe2l0XdRpJkiTtSbU6Qrn6sGEVfPlE1GkkSVI+Z/FaUvYFAbw/IBw3OQ3K1Ig2jyRJkvasuDhodXk4/vwRWL8q2jySJClfs3gtKft+mwhzPob4RGhzTdRpJEmSFIV6XWGv6mHbkK+fjjqNJEnKxyxeS8qeIID3bwnHzc6BkvtEm0eSJEnRiC8Eh/YJx58+BBvXRxpHkiTlXxavJWXP9Lfg768hoeg/XxWVJElSwdToVCheEVbOhW9fiDqNJEnKpyxeS9q+9DSYcGs4PvhCKFYu2jySJEmKVqEkaPG/cPzxIEjbGGkcSZKUP1m8lrR9P7wCC6dB4ZL//JEiSZKkgq1pD0guDUtnwY+vRp1GkiTlQxavJW1bWipMvC0ct7wUkktFGkeSJEm5RGJROPiicPzxfZCeHm0eSZKU71i8lrRt3zwHS2dD0RQ46IKo00iSJCk3ObAXJBaHBT/BL+OiTiNJkvIZi9eSti51LXx4VzhudWU4u0aSJEnKkLwXND8nHH90DwRBtHkkSVK+YvFa0tZ99RSs/BtK7APNzoo6jSRJknKjQy6GQoXhrykwa1LUaSRJUj5i8VrSlqWlwqcPheO214QrykuSJEn/VawcNDkjHH90b7RZJElSvmLxWtKW/TIOVs0Le13vf0rUaSRJkpSbtbwE4grBrA/hz8lRp5EkSfmExWtJWzZ5WHje+DQolBhtFkmSJOVuparA/ieH44/uizaLJEnKNyxeS9rc0tkwc0I4btoj0iiSJEnKI1r2AWLw81sw/8eo00iSpHzA4rWkzU15Gghg33ZQet+o00iSJCkvSKkN9Y4Jxx/fH20WSZKUL1i8lpRVWip881w4bnZWtFkkSZKUtxx6eXj+wyuw5Ldos0iSpDzP4rWkrKa/BasXQLHyUOeoqNNIkiQpL6nYGGoeDkE6fPJA1GkkSVIeZ/FaUlZTNi3U2OR0iE+INoskSZLynlZXhOdTR8CKv6PNIkmS8jSL15L+sXgm/PYBEIMDXKhRkiRJO6FqC6hyCKRtgM8GR51GkiTlYRavJf3j66fD85qHwV5Vo80iSZKkvKvVleH55KGwZkm0WSRJUp5l8VpSaOMG+Ob5cNzUhRolSZK0C2oeBhX2h9Q18MWjUaeRJEl5lMVrSaHpb8CaRVB8b6h9ZNRpJEmSlJfFYv/0vv7iUVi3Ito8kiQpT7J4LSk0OWOhxjMgvlC0WSRJkpT37Xc0lKkF65aH7UMkSZJ2kMVrSbDoV5j9EcTi4IAzo04jSZKk/CAuHg69LBx/NhhS10abR5Ik5TkWryXBlE2zrmt2gFKVo80iSZKk/GP/k6BkZVi9AL55Luo0kiQpj7F4LRV0qetg6ohw3MyFGiVJkpSD4hOgxSXh+JMHIS012jySJClPsXgtFXTT3oC1S6BEpXDmtSRJkpSTDjgDiqbA8t/h+1FRp5EkSXmIxWupoMtoGXLAmS7UKEmSpJyXkAyHXByOP74P0tOjzSNJkvIMi9dSQbbwZ5jzSbhQY5Mzok4jSZKk/KrZOZBUEhb9AtPfjDqNJEnKIyxeSwXZlOHhee0joWSlSKNIkiQpHytcAg46Lxx/dA8EQbR5JElSnmDxWiqoUtf+s1BjUxdqlCRJ0m520IWQUATmfgsz3486jSRJygMsXksF1U+vwbplULIy1Dws6jSSJEnK74qWgaY9w/FH90UaRZIk5Q0Wr6WCanLGQo09IC4+2iySJEkqGA7pDXEJ4borcz6LOo0kScrlLF5LBdGCafDH5xCLhyanR51GkiRJBUXJStD41HD8sbOvJUnStu1w8XrSpEkcffTRVKxYkVgsxpgxY7a5/9y5c+nevTu1a9cmLi6OPn36bHG/kSNHUrduXQoXLkzDhg0ZO3bsjkaTlF0Zs67rdIISe0ebRZIkSQVLyz4Qi4MZ78Lc76JOI0mScrEdLl6vXr2aRo0aMXjw4Gztv379elJSUrjhhhto1KjRFvf59NNPOfXUUznnnHP45ptv6Nq1K127duWHH37Y0XiStmfDGvj2xXDczIUaJUmStIeVqQH1jw/Hzr6WJEnbsMPF606dOnHrrbdy3HHHZWv/atWq8cADD3DmmWdSsmTJLe7zwAMPcOSRR3LVVVex3377ccstt3DAAQfw8MMP72g8Sdvz46uwfjmUqgr7to86jSRJkgqiQy8Lz38cA4t+jTSKJEnKvQpFHQDgs88+4/LLL8+yrWPHjttsSbJ+/XrWr1+feXnFihUApKamkpqaultySvlB/OShxAFpjc8gPS0N0tKijiRJkqRNMv6Wyfd/05SpQ3ytjsTNeIf0j+4jrcsDUSeSJEl7UHZ/18kVxet58+ZRvnz5LNvKly/PvHnztnqbgQMH0r9//822T5w4kSJFiuR4Rik/KLH2d9r9NZl04nlvUTnW21tekiQpV3rvvfeijrDb7RU7iNa8A9++yMSNTVmbWDbqSJIkaQ9Zs2ZNtvbLFcXrndG3b98ss7VXrFhB5cqVadeuHWXKlIkwmZR7xY27OhzU7cxhx3aPNowkSZI2k5qaynvvvUeHDh1ISEiIOs5ul/7cBOLmfMxhydNI7zgw6jiSJGkPWbx4cbb2yxXF6woVKjB//vws2+bPn0+FChW2epukpCSSkpI2256QkFAgfsmTdtiG1fDDKADimp9NnO8TSZKkXKvA/F3T+kp49mPipz5LfNtroFhK1IkkSdIekN3fc3Z4wcbd4ZBDDuH999/Psu29997jkEMOiSiRlA/98AqsXwF7VYfqbaJOI0mSJMG+baHiAbBxHXz+SNRpJElSLrPDxetVq1YxdepUpk6dCsCsWbOYOnUqv//+OxC28zjzzDOz3CZj/1WrVrFw4UKmTp3KTz/9lHn9pZdeyrhx47j33nuZPn06/fr1Y/LkyfTu3XsXHpqkLCYPC8+b9oS4XPG5lSRJkgq6WAxaXRGOv3oS1i6LNI4kScpddrhtyOTJk2nXrl3m5Yy+0z169GD48OHMnTs3s5CdoUmTJpnjKVOmMGLECKpWrcrs2bMBaNGiBSNGjOCGG27guuuuo1atWowZM4YGDRrszGOS9F9zv4W/v4a4BGh8WtRpJEmSpH/UOQpS9oOF08ICdusro04kSZJyiR0uXrdt25YgCLZ6/fDhwzfbtq39M5x44omceOKJOxpHUnZkzLre72j7CEqSJCl3iYuDVpfD6HPD1iEHXwSJRaJOJUmScgF7B0j53fqV8P3IcNzsrGizSJIkSVtS/3goVRXWLIavn4k6jSRJyiUsXkv53fejYMMqKFMTqrWKOo0kSZK0ufhCcGifcPzpg7BxQ6RxJElS7mDxWsrvpvxrocZYLNIokiRJ0lY16g7FKsCKv+C7F6NOI0mScgGL11J+9tfX4WKN8YnhHwOSJElSbpVQGFr0Dscf3w/padHmkSRJkbN4LeVnGbOu6x0LRctEm0WSJEnanqZnQeFSsOQ3+GlM1GkkSVLELF5L+dW6FfD9K+G4qQs1SpIkKQ9IKgYHXxiOP7oPgiDaPJIkKVIWr6X86vuXIXU1lK0DVVtEnUaSJEnKngPPg8RiMP8HmPFu1GkkSVKELF5L+VEQwOTh4diFGiVJkpSXFCkNzc4Ox5Pucfa1JEkFmMVrKT/6awrM/x7ik6DRKVGnkSRJknbMIReHv8v++SXM+STqNJIkKSIWr6X8aPKmhRrrHxfOXJEkSZLykuIVoMnp4XjSPdFmkSRJkbF4LeU3a5fBD5sWamzmQo2SJEnKo1peArF4+G1i+M1CSZJU4Fi8lvKb716GjWshZT+ofFDUaSRJkqSds1c1aHhiOP7ovkijSJKkaFi8lvKTIIApm1qGNDvLhRolSZKUtx16WXg+/U1YMD3aLFJ+tXYZfP0sLPw56iSStBmL11J+8seXsOAnKJQM+58cdRpJkiRp15SrC3W7hOOP7482i5RfvX0NvN4bBh8IQw4N32vLfo86lSQBFq+l/CVj1nWD4yG5VKRRJEmSpBzR6orw/PuRsHR2pFGkfGftUvjx1XAci4f538P4fjCoITx1BHzxOKxaEGlESQWbxWspv/j3Lx1NXahRkiRJ+USlA6BGewjS4JMHo04j5S/fjYS09VC+AVz1K3QZBNVaATH44wt4+yq4tw480zVsLbJ2WbR5JRU4Fq+l/OLbF2HjuvCXjn2aRZ1GkiRJyjkZs6+/eQ5Wzos2i5RfBAF8/Uw4PuBMKFI6XDup55tw+U/Q8XaoeAAE6fDbxLC1yD214IXu8MMrsGFNtPklFQgWr6X8IAhg8qaWIU17ulCjJEmS8peqLaHyQeEM0c8ejjqNlD/MnRq2CYlPgoYnZr2uREU45GI4byL872todwOk1IW0DfDzWzDqbLi7JrzSC34eBxs3RPIQJOV/Fq+l/OD3z2DRz5BQBPY/Keo0kiRJUs6Kxf6Zff3FY7BgerR5pPzg62fD8/2ODmddb02ZGtDmKrjoc7jgEzj0MihVBVJXh73oXzgZ7q0Nr18CsyZBetqeyS+pQLB4LeUHGbOuG5wAhUtGm0WSJEnaHWodATU7hDM/X7vYApm0KzasCQvPAAeckb3bxGJQoQEc3g8u/Q7OeQ8OPB+KlgvXYPr6aXj6aLivHozrC39OCb8lLEm7wOK1lNetWQI/vRaOm7lQoyRJkvKpWAyOfgCSSsBfk+GzwVEnkvKuaa/D+hVQqipUa73jt4/FoPKBcNRdcMV0OPM1aHJGOJlq1Tz4/BF4sj082ATevwUWTMv5xyCpQLB4LeV1U0eEvf8q7B8upiFJkiTlVyUrhYvIAUy4FRb+Em0eKa/KWKixyRkQt4ulobh42LctHPswXDkDTnkh/FZwQhFYOgs+ugceORgeaQEf3QtLZ+9qekkFiMVrKS8LApgyPBw3O8uFGiVJkpT/NTkdahwWTuCwfYi04xbPhDmfQCwOGnfP2fsulAR1j4JuQ+GqX+GEp6B2J4hLgAU/wvsD4IFG8OTh8PmjsHJ+zh5fUr5j8VrKy2Z/DItnQGKxzVeHliRJkvKjWAyOeRASi8OfX8LnQ6JOJOUt32xaqLHm4eG3GXaXxKLQsBt0fxGu/AWOfhCqtwZi8OdXMO4auK9u2Cd7ytNh32xJ+g+L11JeNmXTQo0Nu0FS8WizSJIkSXtKyX2g423heMItsOjXaPNIeUXaxrD1JIQtQ/aUIqWhaQ/o8UbYI/vIO6BSMwjSYdYkeOMSuLsWjDgFvh8FG1bvuWyScjWL11JetXoR/PR6OG7qQo2SJEkqYA44E/ZtBxvX2T5Eyq4Z78Kq+VCkLNQ+MpoMxSvAwRfCue/DJVOh/Y1Qrj6kp8Ivb8Mr58B99cJvGksq8CxeS3nV1OfD/9wrNoGKjaNOI0mSJO1Zme1DisEfn8MXj0WdSMr9MhZqbHwqFEqMNgtA6erQ+kq46FO46HNodSWUqgrrlsFz3eDX8VEnlBQxi9dSXpSe/s9Cjc66liRJUkFVqgoccUs4fn9AuBCdpC1bOS+ceQ3Q5Mxos2xJuf3gsBvh4i+gVkfYuDZsIzLtjaiTSYqQxWspL5o9CZb8Fi5S0+CEqNNIkiRJ0Wl6VrgI3Ma18FrvcKKHpM1NHQFBGlQ+GFJqR51m6xKS4eTnoN6x4beNX+4B342MOpWkiFi8lvKiyZsWatz/JEgqFm0WSZIkKUqxGBzzMCQUhd8/ha+eiDqRlPsEAXzzbDg+YA8u1LizCiXCCUOh0alhwX30uTDl6ahTSYqAxWspr1m1AKa/GY6b2TJEkiRJYq+qcMSAcDy+X/gtRUn/mPPJP9/erdc16jTZE18Ijn0Emp0NBPDGJfD5kKhTSdrDLF5Lec03z0H6RqjUDCo0jDqNJEmSlDs0PRuqtYLUNfDa/2wfIv1bxkKNDY7PW9/ejYuDzvfBIb3Dy+OuhUn3RJtJ0h5l8VrKS9LT4etNX5Vy1rUkSZL0j7g4OOYhSCgCcz6GyU9FnUjKHdYug59eC8cH5MKFGrcnFoMjboU214aXJ9wC4/uHrVAk5XsWr6W85LeJsHQ2JJWE+sdHnUaSJEnKXUpXh8P7h+P3bg5/d5YKuu9HwsZ1UK4eVGoadZqdE4tBu77QYVN7oI/vC2dhW8CW8j2L11JeMmXTQo2NTobEItFmkSRJknKj5r2gaktIXQ2v9bZ9iJSxUGOTM8IicF7W8lI4alPbkC8eDftgp6dFm0nSbmXxWsorVs6D6WPDcVNbhkiSJElbFBcHxz4MhZJh9kf/TACRCqK534an+ETY/+So0+SMA8+FrkMgFhf28n71fEhLjTqVpN3E4rWUV3zzLARpUPkgKF8v6jSSJElS7lV6Xzi8Xzh+7yZYOifSOFJkvt4067puZyhaJtosOalxdzjhKYgrFLZFGdkTNq6POpWk3cDitZQXpKfBlE2rQzvrWpIkSdq+A8+DKi1gwyp4/X/2xlXBk7oWvn85HDc5I9osu0OD4+Hk5yE+Caa/CS+cChvWRJ1KUg6zeC3lBTMnwPLfoXApqN816jSSJElS7pfZPqQwzPoQpgyPOpG0Z017A9Yth5JVYN92UafZPeocCae9DAlFYOb78Hw3WL8y6lSScpDFaykvmJyxUOOpkJAcbRZJkiQpryhTAw67KRy/eyMs+yPaPNKe9PWmb+82OS38MCe/2rctnPEqJJWAOZ/AM8fC2qVRp5KUQ/Lxv15SPrHib/hlXDhuZssQSZIkaYccdEG4bsyGlfDGJbYPUcGw5LdwwVJi0Pi0qNPsflUOhh6vQ/Je8NcUGH40rFoYdSpJOcDitZTbfb1pocYqLSClTtRpJEmSpLwlLh6OHRy2D5k5IVwIXcrvvnkuPK/RHkpVjjbLnlKxCfQcC0XLwfzvYfhR4WQwSXmaxWspN0tP++erXs66liRJknZO2VrQ/oZw/M71sPzPaPNIu1PaRpg6IhwfkA8XatyW8vXg7HFQYh9Y9AsMPRKWzo46laRdYPFays1mvAcr/oTk0rDfMVGnkSRJkvKugy+CfZrD+hXwxqW2D1H+9et4WDkXipSBOkdFnWbPK1MDzn4b9qoOy+bAsKNg0YyoU0naSRavpdxsyqaFGht3h4TC0WaRJEmS8rK4eDj2EYhPCot7U5+POpG0e2S0xtn/FCiUFG2WqJSqAme9DWXrwIq/YFgnmPdD1Kkk7QSL11JutfxPmPFuOG7aM9IokiRJUr6QUhvaXReOx10Hy/+KNo+U01bOh1/GheOC1jLkv0rsDWeNhQoNYfVCGN45XMxRUp5i8VrKrb5+BoJ0qNYq7NEnSZIkadcd0hsqNYX1y+HNPrYPUf7y7QuQvjFskVNuv6jTRK9oWejxZvh8rFsGTx8Lcz6NOpWkHWDxWsqN0jb+s1Cjs64lSZKknBNfaFP7kMTwm47fvhh1IilnBME/LUOaFPBZ1/+WXArOeDWcGLZhJTx7PMycEHUqSdlk8VrKjWa8888CG/sdHXUaSZIkKX8pVxfa9g3H466BFXOjzSPlhN8/g8W/QkJRaHB81Glyl6TicNpIqNkBNq6FESfD9LFRp5KUDRavpdxocsZCjacV3AU2JEmSpN2pxSVQsQmss32I8omvN826bnBcWKxVVgnJcMrz4QSxtA3w0unw/aioU0naDovXUm6zdE64+jnYMkSSJEnaXTLah8QlhAvcffdy1ImknbduOfw0Jhwf0CPSKLlaoSToNhz2PxmCNHil1z9Ff0m5ksVrKbf5+hkggOptoEyNqNNIkiRJ+Vf5etD2mnD89tWwcl60eaSd9cMrkLoGytYJFyfU1sUXgq6PQtOzgABe7w1fPB51KklbYfFayk3SUv9ZYKPZWdFmkSRJkgqCln1g70awbhm8ebntQ5Q3ZcwePuAMiMWizZIXxMVBl/vh4IvDy29fBR/fH20mSVtk8VrKTX5+G1bNh6IpUKdz1GkkSZKk/C8+4Z/2IT+/Fc5glfKSeT/A31+Hr+H9T4k6Td4Ri0HH26D11eHl8f1gwq1+gCXlMhavpdxizRJ457pw3OQMKJQYbR5JkiSpoKjQANpsKmCNvRJWLYg2j7QjMr69W6cTFEuJNkteE4tB++vh8H7h5Ul3wzvXW8DOrqVzYHx/+PFVWLci6jTKpwpFHUASkJ4Or54Py/+AvarDoX2iTiRJkiQVLIdeBtNeh3nfw5uXwcnP2X5BuV/qOvj2xXDsQo0779DLIKFo2D7k88GQuho63x+2F9GWzfkMXjoN1iwOL8clQPVWUOeo8IOUkvtEm0/5hu9CKTf4+D6Y8S4UKsz/2bvv8KjqtI3j3ynpDVJI6CWUEHrvIE0ERRERBQXsvXfX8squu65rWQsiKiBWFBWQJor03nsIJaFDQkggvU15/ziAsoJSkpzM5P5c11znzMmUO5Rk5pnfeR6Gfg7+YWYnEhERERGpWGw+MOhDsNohcRZsn2p2IpG/ljjL6NceWgNie5qdxrN1uAeu+wAsVlg/CabfB06H2anKp01fw+fXGoXrqDiIqA+uYkhaYJy98t8mMK4bLHwNjmzSSna5LFp5LWK25MWw8J/G/oA3oWpzc/OIiIiIiFRUMc2g21Ow+N8w+ymo011tGKR8O90ypNUtYLWZm8UbtLoVfAJg6j2w5VsozoMbJqqt52kuF8wfDcvfMa7HXweDxoFvIBzfDTvnGLO8Dq6GlC3GZfG/IbQ6NLzKWJVdtxvY/Uz9NsSzaOW1iJmyjsIPd4LbBS1vNSZDi4iIiIiIebo9CdFNIT8D5jxpdhqR8zuxD5IXARZoeYvJYbxI0xtg6Bdg84UdM+Gb4VCcb3Yq8xXmwJQRvxWuuz8NQyYZhWuAyAbQ5VG4Yy48tds4k6XxQKMdS9ZhWDcBvroB/lMPvh0BmyZDbrpZ3414EBWvRczidMD3d0BumvHieMAbZicSERERERG7LwwaCxYbJPxoDCITKY82fmVs6/WAyrXNzeJt4gbA8G/BHgB75sEX10PmYbNTmSfzEEy8ymhTY/ODwZ9ArxfP3xM8KBJaDjdmBzyTDLd8D21uh5CqUJRjzBeYfh+8WR8+HQAr3of0pLL9nsRjqHgtYpYFf4cDK8A3xOhzffrTShERERERMVfVFsYKbDDah+QeNzePyP9yOWHTqeJ165HmZvFWsb1gxFTjPfuBlfBh54r5Ydah9fBJL0jdCkFRcNssaD70wu/v4w8N+sLAd+DxBLh7IXR/BqKbGWeh718Ov7wI77eGMe1g3stwYJXxb1wEFa9FzJE4G5a/a+wP+gAiYs3NIyIiIiIiZ+v+NFRpAnnHYc7TZqcROVvSAqMVQ0BliLvG7DTeq3ZnuGcRVGtlDMb87jaYdj8UZJkcrIxs/R4mDYCcVOPn4d0LoGb7S388qxWqt4ZeL8D9y+CxrdD/DajXE6w+cHyXUSuZ2A/ebADTHzBatxTmlNz3JB5HxWuRspax1/hlB9DxQWPAgYiIiIiIlC92X2OhicUG26caLUREyosNnxvb5jdr+F1pi6wPd84zhrlarLD5axjXFQ6sNjtZ6XG7YeG/jBldjgJj2OKdP0OlWiX7PJVqQYd7YOR0eCYJhnwKzYaCfxjkpRtnF3x7q9En+6sbYd2nxuwwqVDsZgcQqVCKC2DKSCjMhBrtoe9osxOJiIiIiMj5VGsFXR+DpW/B7CehdlcIijA7lVR0OWmwc46x33qEuVkqCpsP9H4J6veGqffCyf3w6VXGGRrdnwGbF5XXivNh+v2/tUjp/Aj0eQWsttJ9Xv8waDrYuDiLjVYtO38yzlw/uR92/2JcAKq1hkYDoFF/iG4CFkvpZhNTaeW1SFma+yykbIHACLjxU+MXoIiIiIiIlF89noWoOGPQ+k/PmJ1GBLZ8Ay6HUcCLbmJ2moqldmej3UXzm4x+zYtfN1pceMuwwewUY4Di9mlGG49rx8CV/yj9wvX/svlA3e5w1Wvw6GZ4YBX0fhlqtAMscGQDLHwVxnWBd5rDnGcgaSE4iso2p5QJFa9Fysrmb2D9JMBiTOYNq2F2IhERERER+St2Pxg01mgXsO17o/+qiFnc7t9ahmhQozn8w2Dwx3DDBPALg8PrYFw32PCF8ffjqY5uho97GoXhgMpGK4/ysLLfYoEqjY0hunf9Ck/uhGvfN1Ze2wMg8wCs+Qi+GARvxBptWvNPmp1aSpCK1yJlITUBZj1u7F/xnHGqkYiIiIiIeIbqbaDLo8b+rCcgL8PcPFJxHVxjDLXzCYSmN5idpmJrNgTuX260EyrOhRkPwZQRnvnzYcdMmHgVZB+ByIbGYMY6Xc1OdW4h0cYHN8MmwzPJcPNkaDUCgqKgMMvoST7jYc/+IEHOouK1SGkrzDb6XBfnQWwvoyeWiIiIiIh4lh7PQWQjyD0Gc58zO41UVBtPrbpucj34h5qbRaBSTRg141RPaB+jCPxhZ0haYHayC+N2w9K3jaGIp2sWd/0K4fXMTnZhfAMhbgBcNwae3AW3fH/q72EGrP/U7HRSQlS8FilNbrfxiV/6bgitbrQLKeteUSIiIiIicvl8/H9rH7LlW0icY3YiqWgKs2HbqSF6rcpBOwcxWG3Q9XGj6BvRALKPwhfXw9y/QXGB2enOz1FoDGacP9q43v4eGP6d0RbFE1mt0KAv9Pk/4/rc542z4MXjqXgtUprWfHJq0IEdbpwEQZFmJxIRERERkUtVoy10esjYn/WYZ7YHEM+1barRniKiAdTqaHYa+V/VWsK9S6Dtncb1VR/AJ73KZwE1Jw0+uxY2TwaLDQa8CQPeAJvd7GSXr+ODENsbHAXw/R1QnG92IrlMKl6LlJZD6+Dnvxn7V74KNdubm0dERERERC5fz78ZxcOc1N9e74uUhTODGkcYQ+yk/PENhGvehmHfQmAkHNsOH18Bqz4El8vsdIbUBBjfCw6uMgZO3vo9tL/b7FQlx2qF68dBUBVI26Gf015AxWuR0pCXAVNGgasYGl8LHe4zO5GIiIiIiJQEnwCjfQgWY9Xirp/NTiQVQWoCHF5nnNXbYpjZaeSvNLoKHlgJDa4EZ6HRJ/+rGyA7xdxcu36BCVfCyQNQua7R6iS2l7mZSkNwFaOADbBuIiTMMDePXBYVr0VKmssFU++BrEMQHgvXfaBPxUVEREREvEnN9tDxAWN/+XvmZpGKYeMXxrbhVUZhTsq/4CowfIrRksPubwxxHNsJdswq+yxuN6z8ACbfBEXZUKcb3L0AohqWfZayUr83dHnU2J/xEJw8aG4euWQqXouUtGVvwZ55xi+noZ9rArSIiIiIiDfqeOrsyv3LITvV3Czi3RyFsPkbY7/1SHOzyMWxWIyWHPcugZjmkJ8B394CMx6GwpyyyeAogpmPGu0z3C7j39CtUyEwvGye30y9XoLqbaAgE364C5wOsxPJJVDxWqQkJS+Chf8y9q9+G2KamhpHRERERERKSaVaRlEENyTONDuNeLOdc4yiZ0hVYxCdeJ6oRnDXfOjyGGAx+pd/1A0OrS/d583LgC8Hw4bPwGKFfv+Cge+B3bd0n7e8sPnADRPAN8To8b3kP2Ynkkug4rVISck6YnyS53ZBqxHQ6hazE4mIiIiISGmKH2Rst083M4V4u9ODGlveAja7uVnk0tl9oe9oGDUTQqtDRjJM6AuL/1M6K4KP74bxvWHfUqN4O+wb6PRgxWtrGl4XBr5j7C95A/YtMzWOXDwVr0VKgrMYvr8DctMguhkMeMPsRCIiIiIiUtrirzW2+5dDTpq5WcQ7nTwASQuN/Va3mptFSkbdbnD/cmgyGNxOWPhPmHQ1nNhXcs+RtBA+6W0UyMNqwZ2/QMN+Jff4nqbZEGh5q7HY8Ie7jRXp4jFUvBYpCfNHw4GV4BcKQz8zJpCLiIiIiIh3q1wHqrUyCiI7ZpidRrzRxq8AN9TtbqwgFe8QUBmGTITrP/qtpcWHXWHTZGO44uVYOx6+vAEKM6FmB2MwY3R8yeT2ZP1fh4gGkH0Efnzw8v+cpcyoeC1yuXbMghXvG/uDxkJErLl5RERERESk7JxuHZIw3cwU4o1cTtj0lbHfSoMavY7FAi1uNlZh1+oERdkw/T74/nbIP3Hxj+d0wJynYfaTxoru5jcbLUqCo0o+uyfyC4YhE8Dma/SRX/OJ2YnkAql4LXI5MpJh+gPGfqeHoPFAc/OIiIiIiEjZajLI2O5bptYhUrKSF0HmQfCvpPea3qxybbhtNvR6Eax22D4NPuwCe5dc+GMUZMLXQ2HNx8b13i/D9ePA7lc6mT1V1RbQ9+/G/i8vQspWc/PIBbno4vWSJUsYOHAg1apVw2KxMH369L+8z6JFi2jdujV+fn7Ur1+fSZMmnfX1V155BYvFctYlLi7uYqOJlK3ifJgy8rdTcfq8YnYiEREREREpa5XrQNWWRuuQxJlmpxFvcnpQY/Oh4ONvbhYpXVYbdH/a6E0dHgtZh+Gza+GXl8BR+Of3zUiG8X0haT74BMLQL6DbkxVvMOOF6nAfNLwKnIXG7LKiXLMTyV+46OJ1bm4uLVq04IMPPrig2+/du5err76anj17smnTJh577DHuuusufv7557Nu16RJE44ePXrmsmyZpn9KOffTs8andIGRcOMksPmYnUhERERERMxwevX19ulmphBvkpsOibON/VYjzM0iZad6G7h3CbQeBbhhxXswvjek7Tz37fctNwYzHt8JIdXgjrm/DZKVc7NY4LqxEBwDx3fB3OfMTlRhWfZfWO3XfrEP3L9/f/r373/Btx83bhx169blrbfeAqBx48YsW7aM//73v/Tr99ukU7vdTkxMzAU/bmFhIYWFv336lJWVBUBxcTHFxcUX/Dgil8Ky5RvsGz7DjQXnoI9wB0SB/t2JiIiIyGU6/V5G72k8TMNr8Pn1Fdz7luI4eRSCIs1OJB7OuvErbK5iXDEtcEY21vvNisTqB/3fwlKvN7bZj2FJ2Yr7o+64eo/G1eaOMyuqLZu/xjbnSSyuYlxVW+K88QsIqap/KxfCNxTLdR9i+2owlg2f46jdDXf89WanqjjcLqyLX8e+4M0LuvlFF68v1sqVK+nTp89Zx/r168djjz121rHdu3dTrVo1/P396dSpE6+99hq1atU67+O+9tprjB49+g/HFy5cSGBgYIlkFzmXkPyDdN9p/NtLrHo9u3bkwo45JqcSEREREW8yb948syPIReoRUIdK+fvY/sPr7I/saXYc8WRuNz0TxxEKbLW3ZN8cvd+sqPxiX6HV/k+Izt6K7ednSVv5NZtq3UnssZ9ocOwnAA5Xas/GKnfjXLoR2GhuYA8TFz2QRqkzcP/4CIt2ZpHnp+GWpc3uzKf1/nFUzbzwf6sWt9vtvtQntFgsTJs2jUGDBp33Ng0bNuT222/n+eefP3Nszpw5XH311eTl5REQEMBPP/1ETk4OjRo14ujRo4wePZrDhw+zbds2QkJCzvm451p5XbNmTY4ePUpERMSlfksif64wG/vE3lgyknHV64Xz5m/AormnIiIiIlIyiouLmTdvHn379sXHR23pPIl1xbvYFv4DV90rcA7/3uw44sEsh9djn9QPtz0Ax6PbwD/M7EhiJrcL67rxWOePxuIsxG31weIyVlc7uz2Nq9sz6m99qZzF2L64Fuvhtbiqt8U5YqZawpamE3uxT7kVy/GduG1+HO8ymiq9HyAzM5PQ0NDz3q3UV15fiN+3IWnevDkdOnSgdu3aTJkyhTvvvPOc9/Hz88PP749TU318fPQiT0qH2w3THjeGIYTWwHrDeKy+mtwrIiIiIiVP72s8UNPrYeE/sO5birUoC4K0qEou0ZavALDEX4dPiFrQCND5QYjtCVPvxpK6DWx+MGgstmZDsJmdzZP5+MCQCTCuG9bD67AuewP6/J/ZqbxT0kL47jYoOAnBMVhu/hprQB3ggb+8a6kvGY2JiSE1NfWsY6mpqYSGhhIQEHDO+1SqVImGDRuyZ8+e0o4ncuHWfAwJ08HqYwxo1ItRERERERE5LSIWYpqB2wmJs8xOI56qMAe2TTX2W2tQo/xOdDzcvQCufR/uWQjNhpidyDtUrg3XvmvsL/svJC8yNY7Xcbth5Vj4crBRuK7eFu5ZBDXaXPBDlHrxulOnTsyfP/+sY/PmzaNTp07nvU9OTg5JSUlUrVq1tOOJXJiDa+HnF4z9K1+Fmu3MzSMiIiIiIuVP/CBjmzDdzBTiybZPg6IcCK8HtbuYnUbKG7sftB4J0U3MTuJdmlwPrUcBbph6L+QeNzuRd3AUwo8Pws/Pg9sFLYbDbbMh9OLqvRddvM7JyWHTpk1s2rQJgL1797Jp0yYOHDgAwPPPP8/IkSPP3P6+++4jOTmZZ555hsTERMaOHcuUKVN4/PHHz9zmqaeeYvHixezbt48VK1Zw/fXXY7PZGDZs2MXGEyl5uenGqQ2uYuPFaId7zU4kIiIiIiLlUZPrjW3yYsjLMDeLeKaNXxjbViPUx1ikLF31b4iKg5wUmH4/uFxmJ/Js2Skw6WrY9JUxK67fazBoLPj4X/RDXXTxet26dbRq1YpWrVoB8MQTT9CqVStefvllAI4ePXqmkA1Qt25dZs+ezbx582jRogVvvfUW48ePp1+/fmduc+jQIYYNG0ajRo0YOnQoERERrFq1iqgoTfkUk7lcMPVuyDoEEfWN03P0AkJERERERM4lIhai1TpELlHaTji4Giw2aDnc7DQiFYtvIAyZaPQT3/0LrB5ndiLPdWg9fHwFHFoL/pXg1h+g0wOXXE+76IGNV1xxBW63+7xfnzRp0jnvs3HjxvPe55tvvrnYGCJlY+mbkDQf7AEw9HPwP//0UxEREREREZpcB6lbYft04/R+kQu14XNj27AfhMSYm0WkIopuAv3+CXOegnkvQ+3OUK2l2ak8y+ZvYMYj4CyEyEYwbLLxwe5lKPWe1yIeK2khLPyXsX/Nf9VTSkRERERE/lr8qdYhe9U6RC6Co8go+oDRMkREzNHuLoi7xmgd+/0dxhBV+WtOhzErbtq9RuG60QC469fLLlyDitci55Z1BH64C3AbTftbqv+6iIiIiIhcgMj6EN0UXA7YOcfsNOIpdv0EecchOAYaXGl2GpGKy2IxWsaGVoeMJJjztNmJyr/8E/D1jbByjHG9+9Nw01cl1r1AxWuR/+Ushu9uN144xDSH/v8xO5GIiIiIiHiS+EHGdvt0M1OIJ9lwalBjy2Fgu+gOryJSkgLD4YbxxqDBzV/DlilmJyq/0nbCJ70haQH4BMKNk6DXi2AtuZKzitci/+vXV+DgKvALg6GfXdIkVBERERERqcCaDDK2yYuMFWkifybzEOz51dhXyxCR8qF2Z+jxrLE/63HISDY3T3m0c65RuM5IgrCacMfP0OT6En8aFa9Ffi9hxm+nOQwaC+H1zM0jIiIiIiKeJ7IBVIk3eqYmqnWI/IVNXwNuqN21RPrDikgJ6fYU1OoMRTlG/2tHkdmJyge3G5a+BZNvhqJsqN0F7lkEVZuXytOpeC1yWnoS/Pigsd/5YWh8jbl5RERERETEc51uHZIw3cwUUt65XLDxVMuQ1lp1LVKu2OxwwyfgXwmObIQF/zA7kfmK8uCHO2H+3wE3tL0DRkyHoMhSe0oVr0UAivNhyigozIJanaD3/5mdSEREREREPNnp1iFJCyH/pJlJpDzbuxhOHjDaVja+1uw0IvK/wmrAdR8Y+yve+63FT0V08iBM7AfbfgCrHa5+G675L9h9S/VpVbwWAWN6bOpWCIqCIZ+CzcfsRCIiIiIi4smiGkFUY6N1yE61DpHzOL3qutkQ8A00N4uInFvja6DdXcb+tPsgO9XcPGbYvxI+6QkpWyAwAkbOgHZ3lslTq3gtsvEr4wWDxWpMkw2tanYiERERERHxBqdXX2+fbmYKKa/yMmDHTGO/9Uhzs4jIn7vyVajSBHLTYPp9RsufimL9JPhsoPG9Rzcz+lvX6VJmT6/itVRse5fC7CeN/Z5/g3pXmBpHRKSsHcsq4MNFSRxIzzM7ioiIiPc53fc6aYFah8gfbZkCziKIaQbVWpqdRkT+jE8ADJkI9gDjZ/rK981OVPqcxTD7KZj5qHEWUfwguPNnqFSrTGOoeC0V14bP4YtB4MiHBldC1yfNTiQiUqaW7k5jwHtLeX1uIjd/vJJjWQVmRxIREfEuVeIgKs54079rrtlppDxxu433pACttOpaxCNUiYP+/zb25/8dDq83N09pyj0OX1wPaz8xrvd6EW6cBL5BZR5FxWupeFxO+PkFmPEwuBzQZDAM/Rys+u8gIhWDw+nirV92MnLiGo7nFGGxwJHMAu7+fB0FxU6z44mIiHiX06uv1TpEfu/IBji2HWx+0PxGs9OIyIVqPcr4ue5ywPd3QEGW2YlKXspW+Lgn7FsKvsFw82To/jRYLKbEUbVOKpbCbPhmOKwcY1zv8Zxx2odPgLm5RETKSGpWAcPHr+b9BXtwu2FY+1rMfbQ7lQJ92HwokyenbMblcpsdU0RExHuc7nudNB8KMk2NIuXIhlODGuOvhYDK5mYRkQtnscDAdyGsFpzYB7OfMM6k8BYJP8KEKyHzAFSuC3f9CnEDTI2k4rVUHCcPwIR+xul6dn+4YQL0fN60T45ERMra4l1p9H93KWv2ZhDka+Pdm1vy2uBmNIoJYdytbfCxWZi99Sjv/LrL7KgiIiLeIyoOIhsavY13qnWIAEW5sPV7Y1+DGkU8T0AluGE8WGyw9TvYPNnsRJfP5YIF/4QpI6E4z5gJd/cCqNLY7GQqXksFcWA1fNLLOC0rOBpumwPNhpidSkSkTDicLv4zN5FRE9eQkVtE46qhzHy4K9e1rH7mNh3rRfDP65sB8N6CPUzfeNisuCIiIt7FYvmtdUjCdDOTSHmR8CMUZUPlOlC7q9lpRORS1OpgLIgEY6jh8T3m5rkchdkwZQQs+Y9xveMDcMsPEBhubq5TVLwW77dlCnx2DeSmGVOc714ANdqYnUpEpEwczcxn2CerGLsoCYBbO9Zi2gOdqRcV/IfbDm1bk/t6xALwzPdbWLcvo0yzioiIeK3TrUP2zPfO/qhycc4MarxVs5dEPFnXJ6BONyjOhe9vB0eh2YkuXkYyjO8LibPA5gvXjYWrXgOb3exkZ+inpHgvlwvm/wOm3m2cohd3Ddw+F8JqmJ1MRKRMLNx5jAHvLmXtvhME+9kZM7wVrw5qhr+P7bz3eaZfI66Mj6bI6eLeL9ZzMCOvDBOLiIh4qSrxENEAnIVGG0OpuNJ2wYGVYLFCy1vNTiMil8Nqg8GfQEA4pGyBX18xO9HFSV5kdClI2wHBMXD7T9DqFrNT/YGK1+KdinLhu1Gw9E3jepfHYOgX4PfHlYYiIt6m2OnitZ92cPunazmRV0yTaqHMergr1zSv9pf3tVotvHNzS5pUCyU9t4g7Jq0lq6C4DFKLiIh4MYvlt9XX26ebmUTMtvHUqusG/SC0qrlZROTyhVaFQR8a+6vGwq6fzc1zIdxuWDUOvhgM+Segehu4ZxHUaGt2snNS8Vq8T9YR+LQ/7JgBVh/jh0jf0TodS0QqhCMn87n541V8tDgZgJGdavPD/Z2pExl0wY8R6Gtnwqh2RIf6sftYDg9/vRGH01VakUVERCqG032v9/yq1iEVlaMINp0a7KZBjSLeo9FV0OF+Y3/6/ZB11Nw8f8ZRCDMegrnPgtsJzW825sKV4w/TVM0T73Jko3HKw9HNEBgBo2ZCy+FmpxIRKRPzd6Qy4L2lrN9/ghA/O2Nvac3fr2v6p21CzicmzJ/xI9vh72Nl8a40Xp29oxQSi4iIVCDRTSCivtE6ZPcvZqcRM+z6CfKOG6fnN7jS7DQiUpL6jjbmrOWlw7R7wOU0O9EfZafCpGtg45dG66Ir/wnXjwMff7OT/any031b5HJtnw7T7gNHPkTFwbBvILyu2alEREpdsdPFf+Ym8snSvQA0qx7GmOGtqB1x4autz6VZjTDeuakl9325gUkr9lEvKoiRneqUQGIREZEKyGIxVl8vfRO2T4NmQ8xOJGXt9KDGlsPL1TA0ESkBdj8Y8il81AP2LoHl70C3J8vmuV0uo/1HTuqpy7Fz7B+DkweM4ZL+YTBkItTvUzb5LpN+Wornc7uNF4ALXjWu1+9j/Cf0DzM3l4hIGTh0Io+HJ29k44GTANzWuQ7PD4jDz37xq63P5aqmVXnmqkb8Z+5ORs9MoHZEED0aRpXIY4uIiFQ4TQYZ7132/AqFOZrJU5GcPAh75hv7rTSoUcQrRTaAAW/Ajw/Agn9CnW5Qs/2lPZbbDUU55ylE/09ROjcNXI4LzNgIhk2GiNhLy2UCFa/FsxUXwIyHYesU43rHB6DvP/QptohUCL9sT+Hp77eQmV9MiL+dN4a04KqmMSX+PPf3iCXpWC4/bDjEQ19t4IcHOtMwOqTEn0dERMTrRTeF8FjISIJdc7X6uiLZ9BXgNopZHlQ0EpGL1HI4JC2Abd/D93fCfUshoNJvX3cUnio8n6conZv227HivIt77oBwCI6G4Crn2J7aj2zkcTUzz0or8ns5x+Cb4XBoLVjtxqdbbe8wO5WISKkrcrh4fW4iE5YZbUJa1AhjzPDW1AwPLJXns1gs/GtwUw5m5LFmXwZ3TFrLjw92ISLYr1SeT0RExGtZLBB/HSx7GxKmq3hdUbicRo9ZgDa3mRpFREqZxQLX/BcOr4MT+4we0wGVfitQF5y8uMfzDT5PMToagn5XlA6KArtvKXxD5lPxWjxTyjaYfDNkHjTagwz9Aur1MDuViEipO5iRx0OTN7L54EkA7uxal2evisPXXrozmP3sNsaNaMP1Y5ezPz2Pe75Yz1d3dbikYZAiIiIVWpNBRvF69zy1Dqkokheeeu9aCeKuMTuNiJQ2/1C4YSJMvBJSt/7x61afP1kh/buV0kFV9DsCFa/FE+38CX64y+j9Ex4Lw6dAZH2zU4mIlLq521J4+vvNZBc4CPW38+aNLbiyScm3CTmf8CBfJoxqx/Vjl7N+/wmen7qVt4e2wGKxlFkGERERjxfTHCrXhRN7YffP0PQGsxNJaTs9qLHFzeDjb24WESkbNdrAiOmQsuW3gvTpldIBlY0V2nJBSneZlkhJcrthxfsweZhRuK7bHe76VYVrEfF6hQ4nr8zYzn1frie7wEHLmpWY82i3Mi1cn1a/SjAf3tIGm9XCtI2H+WDhnjLPICIi4tEsFmP1NcD26WYmkbKQkwaJc4z9ViPMzSIiZatuN+j0oNEiqm53qBIHgeEqXF8kFa/FMziKYMZD8MuLgBva3A63TjX+04uIeLED6XkM+XAlk1bsA+DubnWZcm8nalQunf7WF6Jrg0hGX9sEgDd/2cXsLUdNyyIiIuKR4gcZ293zoCjX1ChSyjZPBlcxVG8DMU3NTiMi4nFUvJbyLy8DvrjeGHBhscJV/zaa39t8zE4mIlKqftp6lKvfW8rWw5lUCvRhwqi2vHB1fKn3t74Qt3aszR1d6gLwxJRNZ3pwi4iIyAWo2gIq1wFHPuz+xew0Ulrc7t9ahrQeaW4WEREPZf67X5E/k7YLPukF+5eBb4jR37rj/TrFQkS8WkGxk5d/3Mb9X20gu9BBm9qVmfNIN3o3jjY72lleuLoxveKqUOhwcdfn6zhyMt/sSCIiIp7BYvlt9bVah3ivA6sgfTf4BKm3uYjIJVLxuqQ5Co1PVmc+CpsmQ/4JsxN5rj3zYXwfY5BJpVpw1zxo0NfsVCIipWrf8Vxu+HAFn6/cD8C9PerxzT0dqVYpwORkf2SzWnhvWCviYkJIyy7kzs/WkVvoMDuWiIiIZzjd93r3L1CUZ2oUKSWnV103vR78QszNIiLioexmB/Aa+Sdh/aew6kPISTWOrZ8EVjvU6QaNB0Lc1RBS9sO1PNLqj2Huc+B2Qq1OcNOXEBRpdioRkVI1a8sRnvthKzmFDioH+vD20Jb0jKtidqw/FexnZ/yotgz6YDk7jmbx6Deb+GiEMdBRRERE/kTVllCpNpzcbxSwTxezxTsUZML2acZ+61HmZhER8WBaeX25Mg8bQwT/2xR+fcUoXIdUg3Z3Q5Um4HJA8kKY/QS8FQcTroQV70PGXrOTl09OB8x+En562ihctxgOI39U4VpEvFpBsZMXp2/loa83klPooF2dysx5tFu5L1yfVqNyIB+PbIuv3cqvO1J5fW6i2ZFERETKP4sF4q8z9hOmmxpFSsHW742e5lFxUKOd2WlERDyWVl5fqmM7jCL0linG5GCAqMbQ5VGjl5Xd1ziWngSJs2DHTDi0Fg6uNi6/vAjRzYwV2Y0HQpXG6uOcfxK+u80o9mOBPv8HXR7Tn4uIeLW9x3N58KsNJBzNAuCBK2J5om9D7DbP+ny5da3KvHljCx6ZvJGPlyRTLzKIm9vXMjuWiIhI+dZkEKx4D3b9bLQO8Q00O5GUlN8PatR7WhGRS6bi9cVwu2H/Clj+Luz++bfjtbsaResGff/4Syki1vhal0ch6wgkzjYK2fuWQepW47LoXxBe71Qh+1qo1hqsnlW0uGzpSTD5Zji+C3wCYfAn0Pgas1OJiJSqHzcd5m9Tt5Jb5CQ8yJf/3tSSHg2jzI51ya5tUY3ktBze+XU3L07fRq2IQDrHeu6ZM8eyC/h0+T7yi5w8PyAOP7vN7EgiIuJtqrU25vucPAB75v22Els829HNcHQTWH2g+c1mpxER8WgqXl8Il9MoOi9/Fw6vO3XQYhSbuzwKNdpe2OOEVoP2dxuXvAzY+ZNRyE5aABnJxuMvfxdCqkLcNcbj1+4CNi//a9q7FKaMMIZbhlaHYZOhaguzU4mIlJqCYiejZyYwec0BANrXDee9m1sRE+ZvcrLL92jvBiSn5TJj8xHu/3ID0x7oTL2oYLNjXZSUzALGLU5i8poDFDpcAAT62njmqjiTk4mIiNc53TpkxfuwfbqK195iwxfGtvE1EBRhbhYREQ/n5VXRy1RcAJsnn+pRnWQcs/lBy+HQ+WFjVfWlCgyHVrcYl8Js2POrUcje9TNkH4W1nxiXgMrQ6Grjl169nuDj+YWNs2z4HGY9bvQGr94Gbv5aQy1FxKtlFRRz80erSDiahcUCD/Wsz6O9G3hcm5DzsVgs/GdIcw6eyGPjgZPc+dk6pj3QmUqBvmZH+0uHT+YzblES3649SJHTKFo3jA5mV2oO4xYncWWTGFrWrGRuSBER8T7x1xvvOXf9DMX54BNgdiK5HMX5RntRMFqGiIjIZVHx+lzyT8DaCbD6I8g9Zhzzr3Rq1fQ9EFzCA7T8QqDJ9caluAD2LjYK2TvnQF46bPrSuPgGG61JGg+EBlca9/NULifMexlWjjGuNxkMg8bqhZqIeL135u0m4WgWEUG+vHNzS7o18Nw2Iefj72Pj4xFtGfTBcvYez+X+Lzfw2R3t8bWXzwL9wYw8xi7aw/frD1HsdAPQvk44j/RuQJf6ETz+7SambzrCk1M2MfuRbvj7qH2IiIiUoOqtIawWZB4wFjU1Hmh2IrkcCTOgMNP4O617hdlpREQ8norXv3fyIKz6ENZPguJc41hYTej0ILQaAX5lcNqzjz807GdcnA44sNIoZCfOgqzDsH2acbH5GiuxGw+ERgM861Skwmz44S7YNde43uM5uOI5DbEQEa+3OzWbz1buA+Dtm7yzcH1aVIgfE29rxw0frmBlcjovTd/Gv29ohqUc/azfezyXDxbuYdrGwzhdRtG6c2wEj/RuQMd6v/1efeXaJqxISicpLZe35+3ibwMamxVZRES8kcUC8dcaC3u2T1fx2tOdGdQ4ouLNshIRKQUqXgOkbDMmPG/7wWhfARDd1Ohn3eR6sPmYk8tmh7rdjEv/1+HIBqOQvWMmpO8xhkbu/hksVqM3duOBEHc1hNUo25xut1GQzs8wVq3n/X6b8dv29LGTByDvONj94boPoNmQss0rImICt9vNKzO343S56Rsf7dGDGS9Uo5gQ3h/Wijs/W8u36w4SWyWIe7pfRsutErLnWDZjFuxhxuYjnKpZ061BJI/0bkC7OuF/uH2lQF9eG9yMOz9bxydLk7kyPpq257idiIjIJWtyvVG83jVXrUM82fE9sH+Z8R695S1mpxER8QoVt3jtdsO+pcaAxD2//na8bnejaB3bu3ytBLZYjJ7Q1dtA7/+DtJ2nCtkzIGWL8b3sWwo/PWNMrG48EBpfC5H1L+55HEW/KzifOLv4fGZ74n++dgJcxRf3PMHRcPNkqNHm4u4nIuKh5m5LYfmedHztVl66Ot7sOGWmZ1wVXromntEzE3jtp0TqRARxZRNzZhvsTMnm/QW7mb31KO5TRetecVV4uFd9WtWq/Kf37d04miFtavD9+kM89d1mfnq0OwG+ah8iIiIlpHobCK0BWYdgz3xj5pF4no2nBjXW7wth1c3NIiLiJSpe8drlNAq+y9+FIxuNYxarMdW58yNGv7HyzmKBKnHGpcfTcGIfJM42itkHVhkrtI9sgPmjISrOKGRXbwMFWecuRuefOFWQzoCinEvPZfMzBlEGhJ/aVv7j9dP7Mc3AN6jE/khERMqz/CInr87eAcC93etRKyLQ5ERl67bOdUhKy+HLVQd49JtNfHdfJ5pWDyuz599+JJP35+9h7vaUM8eujI/m4V4NaFbjwnO8dE08y/ccZ196Hq/PTeSVa5uURlwREamILBbjPemqDyBhuorXnshZDJu+NvY1qFFEpMRUnOJ1cT5s+gpWjIETe41jdn9odSt0egjC65qb73JUrmP05e70IGSnws7ZsGOWMfgxLdG4XBTL7wrPvys4B4RD4KnrfyhMh4NvxSrGiIhcqHGLkzh8Mp9qYf48cMVFnhHjBSwWC/83sAn70/NYuvs4d322jh8f6kJ0qH+pPu/mgyd5f8Fuft1x7FQOGNC0Kg/1qk/jqqEX/XhhAT78+4bmjJq4hkkr9tGvSQydYj1o5oSIiJRvTQYZxeudc6G4wJiHJJ5j18+QewyCqhgzrEREpER4f/E6LwPWjofVHxl9lsEovLa/F9rfDUGR5uYraSHR0PYO45J/Anb9Aokz4cT+cxec/1Cgrgz+lTRYQkSkhBzMyGPc4iQAXrg6vsK2mvCxWRkzvDWDxy4nKS2Xuz9fx7f3dCqVP4/1+0/w/oLdLNqZBoDVAgNbVOOhnvVpEB1yWY/do2EUw9rXYvKaAzz9/WZ+fqw7QX7e/3JKRETKQPW2EFodsg5D0nxjnpF4jtODGlsON29uloiIF/Led1sn9sPKD4yeU8V5xrFKtaDTw9DqlorRsiKgMrS4ybiIiIgpXp2dQKHDRad6EQxoZk6v5/IiLMCHibe1Y9AHy9lyKJMnv9vEmGGtsVpLZsbE6uR03l+wh2V7jA+rbVYL17WsxoM96xMbFVwizwHwwtWNWbIrjUMn8vnXnB388/pmJfbYIiJSgVlPtbNcNRa2T1fx2pNkHoY984x9tQwRESlR3le8Tt0GCz+H7dPA7TSOxTQ3hjDGDwKb933LUnbcbjcOl5sih4tCh+vU1nnmeuH/XC86cxsXRQ7n7+7josjporDYeWrrovDU1tdu4dmr4qgdUQE+YBHxckt3p/Hz9lRsVguvXNsES3kaBGyS2hFBfDSiLbeMX8WcrSm8HbmLp/o1uuTHc7vdrExK5935u1m9NwMAu9XCDa1r8EDP2FL5WRrsZ+eNIc0ZPn41X60+wFVNY+jWIKrEn0dERCqg+EFG8XrnT2od4kk2fQVuF9TuChGxZqcREfEqXlfJ9fliIPidKg7U62kUretdYTS6FPkfKZkFvL9gN6lZBb8rPv+xKP376y536ecqdrr5ZGTb0n8iESk1xU4Xo2cmADCiY20axVxeuwpv0r5uOK8Nbs5T321mzMI91IsKYnDrGhf1GG63myW7j/Pe/N2s338CAB+bhaFta3L/FbHUqFy6cxg6149kZKfafL5yP89+v4W5j3cn1F+nCIuIyGWq0Q5CqkH2EUheCI36m51I/orLBRu+MPa16lpEpMR5XfHajQ2aDoYuj0DVFmbHkXJsd2o2oyau4UhmwSU/ht1qwc9uxdduxc9uO7W1/s/2f4/b8DvH7XxtVpxuo8XAvIRUEo5kEV/t4geKiUj58NmKfew5lkN4kC+P921odpxyZ0ibGiSn5TB2URLP/bCVmuGBtKsT/pf3c7vdLEg8xnsL9rD54EkAfO1Whrevxb096lE1LKCUk//muf5xLNqZxoGMPP45awevD2leZs8tIiJe6nTrkNUfGq1DVLwu//YugswD4BcG8deanUZExOt4XfHacfcCqNfK7BhSzq3Zm8Fdn60lq8BBvagg7u5WD38fK742258Wn/1+d93XbsVWQn1af2/TwZPM3HyEMQt3M/aWNiX++CJS+o5lF/DOr7sBeKZfI8ICtCL3XJ66shHJabnM3Z7CvV+sZ/oDXagVce4V0y6Xm18SUnl/wW62H8kCwN/Hyq0danNP93pUCS3706oDfe28eWMLbvp4Jd+uO8hVTWPoGVelzHOIiIiXaTLIKF7vnAOOQrD7mZ1I/szpQY3Nh4JP2X2ILiJSUXhd8ZqwWmYnkHJuztajPPbtJoocLlrXqsSEUe2oHORrdqwzHupZn5mbjzBnawq7UrNpGK1WAyKe5j9zd5JT6KB5jTCGtq1pdpxyy2q18PZNLTj8UT5bD2dyx2drmfpA57Pab7hcbn7alsL7C3aTmJINQKCvjRGdanN3t3pEBpv7hr593XDu6FKXCcv28tzULfzyWA/CAvVhhYiIXIYa7X9rHZK0EBpdZXYiOZ/cdNgxy9hXyxARkVJhNTuASFn6dPleHvx6A0UOF1fGR/P13R3LVeEaoFFMCP2bxgAwZsEek9OIyMXacOAE368/BMDoa5tgLYUzNLxJoK+d8aPaEhPqz55jOTz41QYcThdOl5sfNx3myneW8ODXG0hMySbYz85DPeuz7NlePN+/semF69Oe7teIepFBpGYVMnrmdrPjiIiIp7Naf2s/kTDd1CjyF7Z8A65iqNoSqqp9mIhIaVDxWioEl8vNa3N2MHpmAm63MTztw1vb4O9jMzvaOT3Uqz4AM7ccISktx+Q0InKhXC43r8wwipdD2tSgVa3KJifyDNGh/owf1ZYAHxtLdx/nvi830PftxTz6zSb2HMsh1N/OY30asPzZXjzVrxHh5exDR38fG28ObYHVAlM3HuaX7SlmRxIREU8Xf52xTTzVOkTKH7f7t5YhWnUtIlJqVLwWr1focPLYt5v4aEkyYKyQ+/t1TUqlX3VJaVItjD6No3G74YOFWn0t4immrDvIlkOZBPvZeeaqRmbH8ShNq4fxzs0tsVjg1x2pJB/PpVKgD0/3a8Sy53rxWJ+G5bodR+talbmneywAf5u2jRO5RSYnEhERj1azIwTHQGEmJC8yO42cy6G1kJYI9gBoNsTsNCIiXkvFa/FqWQXF3DZxLTM2H8FutfD20BY82LM+Fkv5LVyf9khvY/X1j5uOsD891+Q0IvJXMvOL+c/POwF4rE8DqoSU/QBBT9evSQyvDmpKXEwIz/WPY9mzvXiwZ/2zemCXZ4/1aUCDKsEczynk5RlqHyIiIpfh961Dtk83NYqcx4bPjG2T68E/zNwsIiJeTMVr8VopmQUMHbeSlcnpBPna+PT2dgxuXcPsWBeseY1KXNEoCqfLzdiFSWbHEZG/8N95u8jILaJ+lWBGda5jdhyPdUuH2sx9rDv39Ygl2M+z5kr7+9h4a2gLbFbLqcG7R82OJCIinix+kLHdORscOqOnXCnIgm1TjX21DBERKVUqXotX2pWazeCxy0lMySYqxI9v7+1EtwZRZse6aA/3agDADxsOcTAjz+Q0InI+O1Oy+WLVfgD+b2A8Pjb9eq2omteoxANXGO1DXpy+jeM56lMqIiKXqFZHCI6GgkzYu9jsNPJ726dCcR5ENjT+nkREpNTo3bV4ndXJ6Qz5cAVHMguoFxXE1Ps707S6Z57G1aZ2ZbrWj8ThcjNusVZfi5RHbreb/5uxDafLTb8m0R75QZmUrId7NSAuJoSM3CJenLYNt9ttdiQREfFEVhs0VuuQcun3gxo9oCWliIgnU/FavMrsLUcZMWENWQUO2tSuzA/3daZmeKDZsS7LI72N1dffrTvE0cx8k9OIyP+aszWFVckZ+NmtvHh1vNlxpBzwtVt5a2gL7FYLc7enMGPzEbMjiYiIp2oyyNgmzgJnsalR5JSUbXB4PVh9oPnNZqcREfF6Kl6L15iwbC8PTd5AkdPFlfHRfHVXByoH+Zod67K1rxtOh7rhFDldfLQ42ew4IvI7eUUO/jk7AYD7esR6/IdlUnKaVAs78+Hjyz9u51hWgcmJRETEI9XqBEFVoOAkJKt1SLmw8QtjGzcAgnXGnYhIaVPxWjyey+Xmn7MT+MesBNxuGNmpNh/e2gZ/H5vZ0UrMo6cKIF+vOaACiEg58uGiJI5kFlC9UgD3n+pzLHLa/VfE0qx6GJn5xfxt2la1DxERkYtntUH8qdYhCdPMzSJQXACbvzH2NahRRKRMqHgtHq3Q4eTRbzfxydK9ADx7VRyjr22Czepdfcc6xUbQpnZlihwuPl6i1dci5cGB9Dw+OvX/8cWrG3vVB2ZSMnxsVt68sQW+Niu/7jjGDxsOmx1JREQ8Ufx1xjZxtlqHmC1xlrEKPqwm1OtpdhoRkQpBxWvxWJn5xYyauIaZm49gt1r4700tuP+KWCxeODDDYrGcOf38y9X7OZ5TaHIiEfnH7ASKHC661I/gqqYxZseRcqpRTAiP9TV+fo+euV2zC0RE5OLV7gJBUZB/AvaqdYipNnxmbFvdaqyKFxGRUqfitXiko5n5DB23klXJGQT72fn09nZc36qG2bFKVfcGkbSoEUZBsYvxp1aai4g5Fu9KY15CKnarhVcGNvHKD82k5NzTrR4ta1Yiu8DBsz+ofYiIiFwkqw0aDzT2t083NUqFlpEMe5cAFmh5i9lpREQqDBWvxePsSs1m8NgV7EzNJirEj2/v7Ui3Bt4/KOP3q68/X7mPE7lFJicSqZiKHC5Gz9gOwKjOdWgQHWJyIinv7Kfah/jZrSzZlca3aw+aHUlERDxN/CBjmzhLrUPMsuHUoMb6vaFSTXOziIhUICpei0dZlZzODR+u4GhmAbFRQUy9vzNNqoWZHavM9IqrQpNqoeQVOZm4XKuvRczw6fK9JB/PJTLYl0f7NDA7jniI+lWCebpfIwBenb2DQyfyTE4kIiIepXYXCIw0WofsW2p2morH6YBNXxn7GtQoIlKmVLwWjzFryxFGTlhDdoGDtrUr88P9nakZHmh2rDJlsVh4uJdRLJu0fB+Z+Vp1IVKWjmUV8N783QA8c1Ucof4+JicST3J7l7q0rV2ZnEIHz3y/BZdL7UNEROQC2exqHWKm3b9ATqrxAULD/manERGpUFS8Fo8wfmkyD329kSKni6uaxPDlXR2oFOhrdixTXBkfTaPoELILHUxavs/sOCIVyr9/SiS3yEnLmpUY0tq7++xLybNZLbx5YwsCfGysSErnq9X7zY4kIiKepMkgY5s4y1gJLGVnw+fGtuUwsFfM96EiImZR8VrKNZfLzauzEnh19g4ARnWqzQe3tMbfp+JOdrZaLTzcuz4AE5Ylk12g1dciZWH9/gymbjwMwOhrm2C1akijXLw6kUE81z8OgH/NSWR/eq7JiURExGPU7gqBEZCXrtYhZSnrCOz+2dhvpZYhIiJlTcVrKbcKHU4e+WYj45cZvZ2f6x/HK9c2waaCEf2bViU2KoisAgefr9TKPZHS5nS5eflHY0jj0LY1aFGzkrmBxKON6FibTvUiyC928vR3ah8iIiIX6PetQxKmmxqlQtn0NbhdUKsTRDU0O42ISIWj4rWUS5n5xYyauIZZW47iY7Pwzk0tua9HLBaLCtdgnHp+uvf1+KXJ5BbqtEGR0vTt2oNsP5JFiL+dZ66KMzuOeDir1cJ/hjQnyNfGmn0ZfLpin9mRRETEU8QPMrY7Zqp1SFlwuWDjF8a+BjWKiJhCxWspd46czGfouJWsSs4g2M/Op7e1Z1Cr6mbHKneuaV6VOhGBnMgr5stVWn0tUlpO5hXxxs+JADzepyGRwX4mJxJvUDM8kL9d3RiA/8xNJDktx+REIiLiEep0g4Bwo3XI/mVmp/F++5bCiX3gFwrx15mdRkSkQlLxWsqVxJQsBo9dwc7UbKqE+PHtvR3p2iDS7Fjlkt1m5cGeRu/rT5Ymk1/kNDmRiHd6e94uTuQV0zA6mBGdapsdR7zI8Pa16NYgkkKHi6e+24xT7UNEROSv2OzQ+Bpjf/t0U6NUCKcHNTYbAr5B5mYREamgVLyWcmNlUjo3jltJSlYB9asEM/WBzjSpFmZ2rHJtUKvq1KgcwPGcIr5ec8DsOCJeJ+FI1pkzG14Z2AQfm35tSsmxWCy8fkNzQvzsbDhwkvFLk82OJCIinuD3rUNcWsBSavIyYMcMY18tQ0RETKN34VIuzNx8hFET15Bd4KBdncp8f18nalQONDtWuefzu9XXHy1OoqBYL15FSorb7eaVmdtxuWFAsxg619dZIFLyqlUK4KWB8QC8NW8Xu1OzTU4kIiLlXt3uEFAZ8o7D/uVmp/FeW6aAswhimkO1VmanERGpsFS8FtONX5rMw5M3UuR00b9pDF/c2YFKgb5mx/IYN7SuQbUwf45lFzJl3UGz44h4jZlbjrJmbwb+PlZeuDre7DjixW5sU4OejaIocrh48rvNOJwusyOJiEh5ZvOBOLUOKVVuN2z4zNjXqmsREVOpeF3Cip0u9hzLYf3+DIocevP5Z1wuN/+YlcCrs3cAcFvnOowZ3hp/H5vJyTyLr93K/VfEAvDhoiQKHVp9LXK5cgsd/OvUz6YHrqhP9UoBJicSb2axWPj3Dc0J9bez5VAm4xYnmR1JRETKuyaDjK1ah5SOwxvgWALY/aHZjWanERGp0OxmB/BU2QXFJKflsudYDklpOWe2+9PzcJwauFQp0If+TatyXctqtK8TjtVqMTl1+VFQ7OTJ7zYze8tRAJ7vH8c93ethsejP6FLc2LYm7y/Yw9HMAn5Yf5jhHWqZHUnEo32wcA8pWQXUqBzAPd3rmR1HKoDoUH9GX9eEx7/dzLvzd9O7cTSNq4aaHUtERMqruj2M1iG5x2D/CqjbzexE3uX0quv4QRBQycwkIiIVnorXf8LtdnMsu5CkYznsScv53TaXlKyC894vyNeGr93KibxiJq85wOQ1B4gJ9Wdgi6pc26I6TauHVugibWZeMXd/sY41ezPwsVl488YWXNeyutmxPJq/j437esTy91kJjF20hxvb1tBgOZFLtO94LuOX7gXgpWvidTaIlJlBLavz09YUfklI5ckpm5n+YBd87fpZLiIi52DzgbirYeOXkDBdxeuSVJgD234w9tUyRETEdCpeAw6ni/0ZeWcVp/ek5ZB8LIfsQsd57xcV4kf9qGBiqwSd2gZTv0owMaH+uNywKjmdGZuOMGfbUVKyCvhk6V4+WbqXepFBDGxRjWtbViM2KrgMv1PzFDqcrN93gsW70pi15SiHT+YT4mfnoxFtNASthAxrX4uxi/Zw6EQ+0zYeZmjbmmZHEvFI/5iVQJHTRbcGkVwZH212HKlALBYL/7y+GWv3ZZBwNIsPFu7h8b4NzY4lIiLlVfz1p4rXM6D/f8CqD9xLxPZpUJQD4bFQu7PZaUREKryLLl4vWbKEN954g/Xr13P06FGmTZvGoEGD/vQ+ixYt4oknnmD79u3UrFmTF198kdtuu+2s23zwwQe88cYbpKSk0KJFC95//33at29/sfH+VG6hg6S037X5OFWk3p+eS7HTfc77WC1QOyKI2Kggozh9qkgdGxVMWIDPeZ/LZoEu9SPpUj+Svw9qwuKdafy4+Qjzd6SSfDyXd+fv5t35u2laPZTrWlTnmhZVqRrmXT1VD2bksWhXGot3prEi6Th5Rb/1YosO9ePT29oTX02nRJeUAF8b93Svx7/mJDJ24R4Gt6qOXauvRS7KwsRjzE88ht1q4f8GNqnQZ8mIOaJC/PjHoKY89PVGPli4h77x0TStHmZ2LI/hdLmxqU2biFQUdbuDf5jROuTASqjT1exE3mHD58a29UjQa0EREdNddPE6NzeXFi1acMcddzB48OC/vP3evXu5+uqrue+++/jqq6+YP38+d911F1WrVqVfv34AfPvttzzxxBOMGzeODh068M4779CvXz927txJlSpVLiqf0eqj4FQP6lySfteT+mjm+Vt9BPjYiK0SRGxU8FmrqGtHBOJnv7xPsP3sNq5sEsOVTWLIKXQwLyGFGZuOsGT3cbYdzmLb4Sz+9dMO2tUJ57qW1RjQtCqVg3wv6znNUFDsZPXeDBbtPMbiXWkkp+We9fXIYD96NIziikbGJcT//MV/uTS3dKjNh4uS2Jeex8wtR7i+VQ2zI4l4jEKHk9EztwNwR9e61K9SMc6MkfLnmubV+GlrCrO3HuXJKZuZ8XCXy34t4u0y84sZPXM7MzcfYVDL6jzdrxFVQv3NjiUiUrrsvhB3DWz6CrZPV/G6JBzbAYfWgNUOLYaZnUZERACL2+0+95LjC7mzxfKXK6+fffZZZs+ezbZt284cu/nmmzl58iRz584FoEOHDrRr144xY8YA4HK5qFmzJg8//DDPPffcBWXJysoiLCyM+Od+INftd97bRQb7Enu6OP27InXVUP8yH6iYkVvEnK1HmbHpCGv2ZZw5brda6N4wimtbVKNvfDRBfuWzu4vb7WZfet6ZYvWq5HQKil1nvm6zWmhTuzI9GkbRo2EU8VVDNbSyDHywcA9v/LyT2Kggfnm8h1agiVygDxcl8frcRKJC/FjwZA99wCamysgt4sr/LuZ4ThEPXBHLM1fFmR2p3Fq6O41nvt9y1iKFQF9jFsTd3eoR4KvCv3iW4uJi5syZw4ABA/Dx0e8i+Qu7foGvb4TgaHgiEaw68/KyzH0eVo01PhS4+Suz04iIeLX09HQiIyPJzMwkNPT8nRlKvSq6cuVK+vTpc9axfv368dhjjwFQVFTE+vXref7558983Wq10qdPH1auXHnexy0sLKSwsPDM9aysLACyC5zY/aFG5QBio4KoF2mspj69Xynw3C8AnU4HTuc5v1RqQnwt3NSmGje1qcbRzAJmbT3KrC0pJBzNZkHiMRYkHsPfx0rvRlUY2DyGrg0i8TN5cFNekYNVe0+wZNdxluw+zsET+Wd9PSbUj+4NIuneIJLOseFnFX/M+DOuiIa1rc7HS5JISstl5qZDXN0sxuxIIuVeSlYB7y/YDcDTfRvgbzOKByJmCfG1MHpgYx6cvJlxi5Po1SiSFjXUPuT38oocvPHLbr5cfRCAOhGBPNCjHpPXHmTjwUzenreLr1fv56m+DRjYvKo+QBePcfr3j34PyQWp1QW7fxiWnFQce5fhrtXJ7ESey1GIffNkLICjxS249X9QRKRUXehrnVIvXqekpBAdffbAq+joaLKyssjPz+fEiRM4nc5z3iYxMfG8j/vaa68xevToPxx/rImDWhEOfKzZQDa4gFRISYWUkviGSlF14N46kBoN649b2XDcQlqBi9nbUpi9LYUAm5uWEW5aR7qpH+qmLN6Dud2Qkg87TlrYcdJCUpYFp/u3J7ZZ3MSGumlcybjEBDiwWHJx7t/P0v2ln0/OrXOEhZ8O2Xh95mbcBzaUyb8VEU/2+W4reUVW6gS78TmyiTlHN5kdSQSANpFW1h+38uDnq3iqmRMtIjbsy4Yv99hIKzB+wXWLdjGwdhZ+Rzcxqjo097cw84CVlKxCnvphG+/N3cqgOk5iNWpDPMi8efPMjiAeolVgM2oVLOPA3PfYWuOE2XE8VrUTq2iXf4J8n8r8srMQds0xO5KIiFfLy8u7oNuVz34UF+D555/niSeeOHM9KyuLmjVrMvyankRERJiYrGTcjtGWY9uRLGZtSWH21hRSswtZeczCymNQJcSPAU2juaZ5VZpXDy3RoWLZBQ5WJKWzdM9xluxO/0Ov8BqVA+jRIJJuDSLoWDe83LY1qci65hez9K2lHM134FOnDf2aRP/1nUQqqLX7TrB+5VosFvjvrZ1oWl3VLSk/OucVc/WYFaRmF5LoE8tzVzUyO5KpihwuxixM4qPte3G5jQHQ/76+KV3rn/3a72rg6WInk1Ye4MMlyRzIdfLedjtXNYnm6SsbUCs80JxvQOQCFBcXM2/ePPr27au2IXJBLLvtMGUZdfO3UrP/VWBR65BLYft6AgC+He5gQI9rTE4jIuL90tPTL+h2pV51jImJITU19axjqamphIaGEhAQgM1mw2aznfM2MTHnb3fg5+eHn98fe1v7+Ph41Yu81nUiaV0nkheuacKavRnM2HyYOVtTOJZdyKSVB5i08gC1IwK5rkU1rm1ZjfpVQi76OdxuNwlHs1i8K41FO9PYsP8EDtdvrdD97FY61os4M2yxbmRQiRbLpeRF+Phwe5c6vL9gD2MX7+XqFtX1dyZyDk6Xm3/M2QnAze1q0qqO53/4Kd4lKsyHf9/QjDsmrWPiiv30b1aNtnXCzY5lisSULB7/djM7jhqt4q5vVZ1XBjYh7Dwt4Xx8fHiod0Nual+b//66i2/WHGDu9lQWJKZxe5c6PNirPqHqbS/lmLe9r5FS1LAv+IVhyUnB5+gGqK3WIRftxD7YuxiwYGszCpv+74mIlLoLfZ1T6sXrTp06MWfO2afbzJs3j06djF+ovr6+tGnThvnz558Z/OhyuZg/fz4PPfRQacfzGDarhU6xEXSKjWD0tU1ZsiuNGZuPMC8hlf3peby3YA/vLdhD46qhXNeyGgNbVKN6pYDzPt7JvCKW7j7O4l1pLN6VRlp24VlfrxcZRI9GxqDFjvUi8PfRecqe5o4udZm4bC8JR7OYv+MYfeK1+lrkf3295gA7jmYR6m/nqSsr9opWKb96xUVzY5safLf+EE99t5mfHu1eoYYQOl1uPlmazNu/7KLI6aJyoA//ur4Z/ZtVvaD7R4X48a/rmzGyU23+OXsHS3cf56MlyXy3/hCP92nAsPa1sNu0SlFEPJjdF+IGwObJkDBdxetLsfFLY1vvCqhc29QoIiJytosuXufk5LBnz54z1/fu3cumTZsIDw+nVq1aPP/88xw+fJjPP/8cgPvuu48xY8bwzDPPcMcdd7BgwQKmTJnC7NmzzzzGE088wahRo2jbti3t27fnnXfeITc3l9tvv70EvkXv42u30ic+mj7x0eQVOZiXkMrMzUdYtDONHUez2HE0i3//lEi7OpW5tkU1BjSrSuVAX7YezmTRzjQW7zrGpoMn+d3iagJ9bXSONVZX92hYhVoROp3W01UO8mVEpzqMW5zE+wt207txFa2+FvmdE7lFvPWLser6ySsbERH8x7N5RMqLlwbGs2zPcfal5/G3aVv/dMWxN9mfnsuTUzazbr/Rw7VP4yq8Nrg5USEX//81LiaUz+9oz6Kdabw6O4GktFxe+nE7n63czwtXN6ZnoyolHV9EpOzEDzpVvP4R+r0GVn0od8GcDtj4lbHfeqS5WURE5A8sbrfb/dc3+82iRYvo2bPnH46PGjWKSZMmcdttt7Fv3z4WLVp01n0ef/xxEhISqFGjBi+99BK33XbbWfcfM2YMb7zxBikpKbRs2ZL33nuPDh06XHCurKwswsLCOH78uFf0vL4UJ/OK+GlbCj9uOszqvRmc/pu1WS2E+Ns5mXf2FM+G0cFc0agKPRpG0bZOZfzsFWcVV0VxPKeQrq8voKDYxaTb23GF3piLnPHCtK18tfoAcTEhzHq4q1ZeSrm3ZFcaIyeuASDI18atnWpzV9d6l1TILe/cbjdfrT7Av+bsIK/ISbCfnZcHxnNjmxol8kFssdPFN2sO8Pa8XZw49fqoe8MoXhjQmEYxF9+CTaQkFRcXM2fOHAYMGKC2IXLhHIXwRn0ozIJbf4D6fcxO5Dl2/QxfD4WAcHgyEeze93tVRKQ8Sk9PJzIykszMTEJDzz976qKL1+WVitdnS8ksYNaWI8zYfIQthzIBCPGz06V+JFc0iqJ7wyiq/UlbEfEer85KYPyyvbSuVYkf7u+s1dciwLbDmQwcswy3G765pyMd6+n3hniGX7an8Pa8XSSmZAPGXIqb29Xknh6xf9ouzJOkZBbw7A9bWLwrDYCO9cJ5Y0gLapbCkMXM/GI+WLiHT5fvpdjpxmqBm9vX4om+DYnU2RhiEhWv5ZLNeAQ2fAZ+oXDL91DrwheDVWjf3AKJs6Djg3DVv8xOIyJSYah4LWfsO57LibwimlYPw0crCyucY1kFdP3PQoocLr66qwNd6keaHUnEVG63m6EfrWTtvhNc07wqY4a3NjuSyEVxu93M33GMMQv3sOngSQDsVgvXt6rO/VfEUi8q2NyAl2HG5iO8NH0bmfnF+NmtPHNVHLd3roPVWrofvO5Pz+XfPyXy07YUAIL97DzYsz63d6mjuR9S5lS8lktWmANf3wT7l4FPENwyBep0NTtV+ZadCm83BrcTHlgNVeLMTiQiUmFcaPFalcwKoE5kEK1qVVbhuoKqEurP8Pa1AHhv/m6T04iY78dNR1i77wQBPjZeuLqx2XFELprFYqFPfDTTHujMV3d1oHNsBA6Xm+/WH6L324t58OsNJBzJMjvmRTmRW8SDX2/gkckbycwvpnmNMGY/0pU7u9Yt9cI1QO2IID68tQ3f3tORZtXDyCl08PrcRPq8vZhZW47gJWs9RMTb+QXDLd9BvZ5QnAtfDoGkhWanKt82f20Urmt2UOFaRKScUjVTpAK4t0c9fG1WVu/NYHVyutlxREyTU+jgX3N2APBQr/pUDfOONgtSMVksFrrUj+Truzsy9YHO9I6rgtsNs7ccZcB7S7lz0lrWnxp0WJ4tTDzGle8sYfaWo9isFh7r04Af7u9M/Spl33u6Q70IfnywC28PbUFMqD+HTuTz0NcbGTJu5ZlV7iIi5ZpvIAz7BhpcCY58YyX2rl/MTlU+ud2w4XNjX4MaRUTKLRWvRSqAqmEB3Ni2BgDvL9hjchoR84xZsIdj2YXUCg/kzq51zY4jUmJa16rMhNvaMeeRblzTvCoWC8xPPMYNH65g2MerWL7neLlbPZxT6OD5qVu4fdJa0rILqV8lmGkPdOaxPg1NPVvMarUwuHUNFjzVg8f7NCTAx8b6/ScY9MFyHv1mI4dP5puWTUTkgvj4w01fQtw14CyEb4ZD4myzU5U/+5dDRjL4hkD8ILPTiIjIeah4LVJB3H9FLHarhWV7jnvESjyRkpaclsOEZckAvHxNvPrYileKrxbKmOGtmf9ED4a2rYHdamFlcjq3jF/NoLErmJeQistlfhF7dXI6/d9dwuQ1B7FY4M6udZn1cFea16hkdrQzAn3tPNqnAYuevoIhbWpgsRhth3q9uYg3f95JTqHD7IgiIudn94MbJ0GT68FVDFNGwvZpZqcqX06vum52g9FyRUREyiUVr0UqiBqVA7mh9enV1+p9LRWL2+3m77MSKHa6uaJRFL0bVzE7kkipqhcVzH+GtGDxMz0Z1ak2fnYrmw+e5O7P1zHgvaX8uOkwThOK2AXFTv41Zwc3f7KKgxn5VK8UwOS7O/JSOf5AKTrUnzdvbMHMh7rSoW44hQ4XYxbuoeebi/h27QFT/hxFRC6IzQcGj4fmN4HLAd/fAVummJ2qfMg/AQk/GvtqGSIiUq6peC1SgTzQMxab1cKinWlsVu9OqUDm7zjGop1p+NgsvHxNPBZL6Q+AEykPqlcKYPR1TVn2bC/u6xFLsJ+dxJRsHv1mE73fMoqvRQ5XmWTZdjiTa8cs4+MlybjdcFPbmsx9rBsd60WUyfNfrqbVw/jmno58NKINdSICScsu5NkftnL1e0tZvue42fFERM7NZodBH0KrW8Htgqn3wIYvzE5lvq3fg6MAoptCtdZmpxERkT+h4rVIBVI7IojrWlYD1PtaKo6TeUW8MnM7AHd0rUu9KJ0WKhVPVIgfz/WPY/mzvXiib0MqBfqwLz2PZ3/YSo83FvLp8r3kFzlL5bkdThfvzd/NoA+Wsys1h8hgP8aPbMvrQ5oT4u9TKs9ZWiwWC/2axPDL4z146Zp4Qv2NDwNuGb+auz5bS1JajtkRRUT+yGqDge9D2zsBN8x4CNZOMDuVedxuWP+Zsd96JGhRg4hIuabitUgF82DP+lgt8OuOVLYfyTQ7jkipKna6uP/LDRw6YbQneLhXA7MjiZgqLNCHR3o3YPmzvXhhQGOqhPhxNLOA0TMT6Pr6Aj5YuIesguISe76ktBxuGLeSt+ftwuFyM6BZDL883p0+8dEl9hxm8LVbubNrXRY/3ZPbOtfBbrXw645j9PvvEl6ZsZ0TuUVmRxQROZvVCle/BR3uN67PfgJWfWhuJrMc3QSpW8HmB81uNDuNiIj8BRWvRSqY2KhgrmlurL4eo9XX4sXcbjf/N2M7K5PTCfK1MX5UW4L97GbHEikXgvzs3N29Hkue6cmrg5pSo3IA6blFvPHzTrr8ewFv/bKTjMsowLpcbj5dvpcB7y5l88GThPrbeffmlnwwvDXhQb4l+J2Yq3KQL69c24SfH+9On8ZVcLjcTFqxjx5vLGT80uQya8kiInJBLBa46jXo8phxfe5zsOwdMxOZ4/Sq6/hrITDc3CwiIvKXLG632yumzGRlZREWFsbx48eJiPCM3okiZtmVmk2/d5bgdsPPj3WnUUyI2ZFEStyk5Xt5ZWYCFgt8MqKtx6/0FClNxU4XMzcfYeyiJPYcM1pfBPjYGN6hFnd3q0dMmP8FP9bhk/k8/d1mViSlA9CtQST/GdKcqmEBpZK9PFm+5zj/mJVAYko2ANXC/OnRKIr2dcPpUDeCapW8/89ALl9xcTFz5sxhwIAB+Ph4Vmsd8RBuNyx6DRa/blzv+QL0eMbcTGWlKBfebARF2TBqJtTtbnYiEZEKKz09ncjISDIzMwkNDT3v7VS8FqmgHvhqPXO2pjCwRTXeH9bK7DgiJWrxrjRu/3QNLjc83z+Oe3vEmh1JxCO4XG5+3p7CmIV72H4kCwBfm5Ub2tTg/h6x1IoIPO993W43P2w4zOgZ28kudBDgY+NvVzfm1g61KtSQVKfLzffrD/LGz7s4nlN41tdqVA6gQ90IOtQNp0O9cGqFB1aoPxu5MCpeS5lZ8iYs+Iex3+0p6PWi9/d/3vgV/PgAVK4LD28w2qmIiIgpVLwWkT+VcCSLAe8txWKBeY/3oH4VDbET77DnWDbXj11BdoGDIW1q8MaQ5ioOiVwkt9vN4l1pfLBwD2v3nQDAZrVwbYtq3H9FLA2jzz5j53hOIc9P3cq8hFQAWteqxFtDW1I3MqjMs5cXeUUOVials3pvBqv3ZrDtcCZO19kvu6ND/ehQN4L2dcPpWC+c2Khg/bwSFa+lbK14H3550djv/DD0/Yd3F7An9IODq6D3y9DtSbPTiIhUaCpei8hfuvvzdcxLSOX6VtX5700tzY4jctlO5BYxaOxy9qfn0a5OZb68qwN+dpvZsUQ82pq9GYxZuIclu9LOHOvXJJqHejagWY0w5m5L4YVpW0nPLcLHZuHxvg25t3ssNqsXFz8uQU6hgw37T7B6bzpr9maw+WAmRc6ze2JHBPnSro6xKrt93XAax4Ri1Z9jhaPitZS51R/DT08b++3vhf6ve2cBO20nfNAeLDZ4fDuEVjU7kYhIhXahxWtNrhKpwB7p1YB5Can8uOkwj/ZuQJ0KvEJOPF+Rw8X9X61nf3oeNSoHMO7WNipci5SA9nXD+bxue7YcOsnYhUnM3Z7Cz9tT+Xl7Ko2iQ9iZavR3josJ4e2hLYmvdv4XnhVZsJ+d7g2j6N4wCoCCYicbD5w8U8zecOAE6blFzN2ewtztKQCE+tt/V8yOoGm1UOw2neIuIiWswz1g84FZj8Oaj8BZCFf/1/taamz43Ng27KfCtYiIB1HxWqQCa1YjjJ6Noli40zg1/I0bW5gdSeSSuN1u/m/GNlYlZxDka2PCqHZEBPuZHUvEqzSvUYlxI9qwOzWbsYuSmLH5CDtTs7Fa4N4esTzWp4E+MLoI/j42OsVG0CnWOGOwyOFi6+GTrErOYM3eDNbtyyCrwMH8xGPMTzwGQJCvjda1K9OxntFqpHmNMP2Zi0jJaHs72P3gxwdh/SRwFMF1Y8DqBT9j3G7YuwQ2fW1cbz3S3DwiInJR1DZEpILbcOAEg8euwG61sPCpK6gZfv5hXCLl1cRle/n7rAQsFhg/si29G0ebHUnE6x1Iz2P6psN0bRBJ61qVzY7jdRxOFwlHs1idbPTMXrsvg8z84rNu42e30qpWpTNDIFvVqkyArxcUmio4tQ0RU239HqbeA24nNLsRBo0Dm4eueXO7IXkRLH4dDqw0joXHwoNrPPd7EhHxImobIiIXpHWtynRrEMnS3ccZuyiJ1wY3MzuSyEVZtPMYr85OAOBv/RurcC1SRmpFBPJI7wZmx/BadpuV5jUq0bxGJe7uXg+Xy83O1GxWJ6ezZl8Gq5MzSM8tYlVyBquSMwDwsVloXqMS7euG06FuOG1qVybEX8VPEbkIzYYYLUS+vwO2fgeOQrhhAth9zU524dxuSFpgFK0PrjaO2fygzShjSKMK1yIiHkUrr0WEtfsyuHHcSnxsFhY/3ZNqlQLMjiRyQXanZjN47AqyCx0MbVuD129ojsUbBwyJiPwPt9tNUlrumZ7Zq5MzSMkqOOs2Vgs0rR5G+zrhdKhntCgJ9lPRprzTymspF3b+BFNGgrMIGvaHoZ8ZbUXKM7cb9syHxf+GQ2uNY3Z/aHMbdHlMfa5FRMqZC115reK1iABw88crWZWcwchOtfn7dU3NjiPylzJyixj0wXIOZOTRvk44X97VAV+7lw0WEhG5QG63m4MZ+aw6Xczem87BjPyzbhMZ7Mdnd7SjSbUwk1LKhVDxWsqN3b/Ct7eAowDq94GbvgSfcrjIxe2G3fOMldaH1xnH7P7Q9g7o8iiExJibT0REzknFaxG5KCuSjjP8k9X42q0sfaYn0aH+ZkcSOa8ih4sRE1azem8GNcMDmP5AFw1oFBH5H0dO5p8qZGeweOcxjmQWEOJnZ8Jt7WhfN9zseHIeKl5LuZK8GCbfDMV5ULc7DPsGfIPMTmVwu2HXz0bR+sgG45g9ANrdCZ0fgRC1khMRKc8utHitJWoiAkCnehG0rV2ZIoeLjxYnmx1H5Lzcbjcv/7iN1XszCPazM2FUOxWuRUTOoVqlAAa1qs5rg5sx9/HutK8TTnahgxETVrMgMdXseCLiCer1gFt/AN9g2LsEvhwChdnmZnK7IXEOfHwFTL7JKFz7BELnh+GxLdDvnypci4h4ERWvRQQAi8VyZvDW12v2k5ZdaHIikXObuHwf36w9iNUC7w9rRcPoELMjiYiUe6H+Pnx+Z3t6x1Wh0OHins/X8+Omw2bHEhFPULszjJgOfmFwYAV8cT3knyz7HG437JgFH3WHb4bB0U3gE2S0Bnl0C1z5KgRXKftcIiJSqlS8FpEzujWIpEXNShQUuxi/VKuvpfxZmHiMf85OAOBvAxrTM05vUERELpS/j41xI9owqGU1HC43j327ic9X7jM7loh4gprtYNSP4F/JGIb4+XWQl1E2z+1yQcIMGNfN6MGdssVYCd71cWOldd+/Q3BU2WQREZEyp+K1iJxhsVh4tHd9AL5YtZ+M3CKTE4n8ZldqNg9P3ojLDTe1rcmdXeuaHUlExOP42Ky8PbQlt3Wug9sNL/+4nffm78ZLxuCISGmq1gpumwWBEcaq588GQu7x0ns+lwu2T4ePusGUEZC61Shad3vSWGnd5xUIiiy95xcRkXJBxWsROUvPRlVoWj2UvCInE5Zp9bWUDxm5Rdz52VpyCh20rxvOPwY1xWKxmB1LRMQjWa0W/m9gPI+eahf29rxd/H1WAi6XCtgi8hdimsFtcyA4GlK3waSrIbuEe+i7XLBtKozrAt+NMp7HNwS6Pw2PbYXeL0NQRMk+p4iIlFsqXovIWSwWCw/3Mt7MfrZiP5l5xSYnkoquyOHivi/WczAjn1rhgYy7tQ2+dv36EhG5HBaLhcf7NuTla+IB+HT5Pp76fjMOp8vkZCJS7lWJMwrYIdUgLREmDYCsI5f/uC4nbP0ePuwE398OxxLALxR6PAuPb4VeL0Jg+OU/j4iIeBS9+xeRP+jbOJq4mBByCh1MXL7X7DhSgbndbl6cvpU1+zII8bMzYVRbwoN8zY4lIuI17uhal7dubIHNamHqhsPc9+UGCoqdZscSkfIusj7cPgfCakH6Hvi0P5w8cGmP5XLClu9gbCf44U6jIO4XBj2eM3pa9/wbBFQu2fwiIuIxVLwWkT+wWn9bff3p8r2cUO9rMcmEZXuZsu4QVgu8N7wVDaJDzI4kIuJ1bmhT48xZLb/uSOX2T9eSXaAzr0TkL4TXhdtnQ+U6cGIffDoAMi6i7aDTAZu/hQ86wNS74PhO8A+DK/52qmj9vIrWIiKi4rWInFv/pjE0jA4mq8DB/V+tp8ih04ilbC1ITOWfc3YA8MLV8fRsVMXkRCIi3qtvfDSf3d6eYD87K5PTGf7JatJzCs2OJSLlXaVacPtPEFEfMg/Cp1fD8d1/fh+nAzZNhg/aw7R7IH03+FeCni8aPa2veBYCKpVFehER8QAqXovIOVmtFt4b1opgPzurkjN4YdpW3G4NcpKysTMlm0cmb8LthmHta3JHlzpmRxIR8XqdYiOYfHdHwoN82Xo4k6EfreTIyXyzY4lIeRdazeiBHRUH2UeMFdjHdvzxdk4HbPwKPmgH0++DjCRjZXWvl4yidY+njZXXIiIiv6PitYicV1xMKO8Pb4XVAt+tP8SHi5PMjiQVQHpOIXd+tpacQgcd64Uz+tqmWCwWs2OJiFQIzWqEMeXeTlQL8ycpLZchH64gKS3H7FgiUt6FRMNtsyG6GeQeg0lXQ8pW42vOYtjwBYxpAz8+YLQWCQiH3v9nFK27PwX+oebmFxGRckvFaxH5Uz0bVeGVa5sA8J+5O5mz9ajJicSbFTqc3Pfleg6dyKd2RCAf3mL0YBURkbJTv0ow393fmXpRQRzJLGDouJVsO5xpdiwRKe+CImHUDKjaEvLSYdI1sPRteL8NzHjI6IsdGAF9RhtF625PgJ/mmYiIyJ9TRUBE/tLITnW4rXMdAB7/dhObDp40NY94J7fbzYvTtrF23wlC/OxMGNWWykG+ZscSEamQqlcK4Lt7O9G0eijpuUXc/PEqViWnmx1LRMq7wHCjgF2jHRSchPmj4eR+CIqCvv8witZdHwO/YLOTioiIh1DxWkQuyEvXxNMrrgqFDhd3fbaOw+qBKSXsk6XJfLf+EFYLvD+8FfWraCWOiIiZIoL9mHx3RzrUDSen0MGoiWv4NSHV7FgiUt75h8GIaVC/D4TWgCv/CY9ugS6PgG+Q2elERMTDqHgtIhfEdmqAY1xMCMdzCrlz0lqyC4rNjiVeYv6OVF77KREwPii5olEVkxOJiAhAiL8Pn93Rnj6Noyl0uLj3y/VM23jI7FgiUt75hcCtP8AT26HzQ+AbaHYiERHxUCpei8gFC/azM+G2dkSF+JGYks3DkzficLrMjiUeLjEli0cmb8TthuEdap1pUSMiIuWDv4+Ncbe2ZnDr6jhdbh7/djOfLt9rdiwRERERqQBUvBaRi1K9UgDjR7bF38fKop1pvDp7h9mRxIMZq/jXkVvkpFO9CEZf2wSLxWJ2LBER+R92m5U3h7Tg9i51ABg9M4H/ztuF2+02N5iIiIiIeDUVr0XkorWoWYl3bmoJwKQV+5ik1VdyCQodTu77Yj2HT+ZTJyKQsbe0xsemX0siIuWV1Wrh5WvieaJvQwDenb+b0TMTcLlUwBYRERGR0qEqgYhckquaVuW5/nEA/H1WAgsTj5mcSDyJ2+3mb1O3sW7/CUL87Ywf1Y7KQb5mxxIRkb9gsVh4pHcD/n5dE8D4EPvJ7zZTrDZiIiIiIlIKVLwWkUt2b/d6DG1bA5cbHvp6AzuOZpkdSTzEx0uS+WHDIWxWCx8Mb039KsFmRxIRkYswslMd3r25JXarhWkbD3PfF+spKHaaHUtEREREvIyK1yJyySwWC68OakanehHkFjm5c9JajmUVmB1Lyrl5Can8e24iAC9fE0/3hlEmJxIRkUtxXcvqfDyyDX52K/MTjzFy4hqyCorNjiUiIiIiXkTFaxG5LL52K+NubUO9qCCOZBZw9+fryC/Syis5tx1Hs3j0m4243XBLh1qM7FTb7EgiInIZesVF88WdHQjxs7NmbwbDPl7F8ZxCs2OJiIiIiJdQ8VpELltYoA8TR7WjcqAPmw9l8sSUTRreJH+Qll3IXZ+tI6/ISefYCF65tgkWi8XsWCIicpna1w1n8j0diQz2ZfuRLIaOW8nhk/lmxxIRERERL6DitYiUiDqRQXw0oi0+Ngs/bUvhjV92mh1JypFCh5P7vlzP4ZP51I0MYuwtrfGx6VeQiIi3aFo9jO/u60z1SgEkH89lyIcr2HMsx+xYIiIiIuLhVDkQkRLTvm44r9/QHIAPFyUxZe1BkxNJeeB2u3l+6lbW7z9BqL+d8aPaUinQ1+xYIiJSwupGBvH9/Z2oXyWYo5kFDP1oJVsOnTQ7loiIiIh4MBWvRaREDW5dg0d61Qfgb9O2siLpuMmJxGzjFiczdcNhbFYLH9zSmtioYLMjiYhIKakaFsCUezvRokYYGblFDPt4lV4LiIj8j4JiJ3uO5VDo0KwgEZG/Yjc7gIh4n8f7NmRveh4zNx/h/i83MPWBzipYVlC/bE/hPz8nAvB/A+Pp1iDK5EQiIlLawoN8+erujtzz+TpWJKVz26drGTOsFVc2iTE7mohImXK73Rw+mU/i0WwSU7LYkZLNjqNZ7Duei8sN1SsF8MxVjRjYvBpWq2bBiIici8XtdnvFVLWsrCzCwsI4fvw4ERERZscRqfAKip0M/2QVGw6cpHZEINMe6EJ4kFpFVCQJR7IYMm4FeUVORnSszT8GNTU7koiIlKGCYiePTN7ILwmp2KwWXr+hOUPa1DA7VrlXXFzMnDlzGDBgAD4+PmbHEZELlFfkYGdKNjtOFaoTj2azIyWL7ALHOW/vY7NQ7DTKMc1rhPHCgMZ0qKdahohUHOnp6URGRpKZmUloaOh5b6fitYiUmuM5hQz6YDmHTuTTvk44X9zVHj+7zexYUgbSsgu5bswyjmQW0LV+JJ/e3k4DGkVEKiCH08VzU7fy/fpDALx0TTx3dq1rcqryTcVrkfLN5XJz6EQ+O04XqI9mkZiSxf6MPM5VXfGxWYiNCqZx1VDiYkKIqxpK46ohhPj5MHH5XsYu3ENukdE+pG98NM/3j6OezloVkQpAxWsRKRd2pWZzw9gVZBc6GNyqOm8NbYHFolPivFlKZgG3fbqGxJRs6kUGMe2BLoQF6s23iEhF5XK5+decHYxftheAh3vV54m+DfV64DxUvBYpP7ILitmVmk3C0WwSj2aRmJLNzpRscgrPvZo6KsSPuJgQ4quGElc1hLiYUGKjgvG1n38RR1p2Ie/O38XkNQdxutzYrRZu6VCLR3o3ICLYr7S+NRER06l4LSLlxpJdadw+aS1Ol5sn+zbk4d4NzI4kpWRXajajJq7haGYBkcF+TLm3o1aOiIgIbrebsYuSeOPnnQCM6Fib0dc2UY/Xc1DxWqTsuVxu9mfkkXj0t77UiSlZHMzIP+ftfW1WGkQHExdjrKJuXDWURjEhRF5GsXnPsWz+/VMiv+44BkCIn50Hetbn9i518PfR2asi4n1UvBaRcuWr1ft5Ydo2AN4f1oqBLaqZnEhK2qrkdO75fB1ZBQ7qRQXx2e3tqRkeaHYsEREpR75YtZ+Xf9yG2w2DW1XnjRtbYFMB+ywqXouUrsz84lO9qY0C9Y6jxmrq/GLnOW8fE+pP46pGu4+4GKNQXTcyqNRa4q1IOs6/5uxg2+EswBjq+HS/RlzbQkMdRcS7XGjx2l6GmUSkArulQ22S03KZsGwvT363mWqVAmhTu7LZsaSEzNpyhCe+3UyR00Wb2pUZP7ItlTWgU0RE/seIjrUJC/DhiW83MXXjYXxsVl4b3EwFGREpVUUOF/+YlcCCxGMcPnnu1dR+diuNYkJoHPNby4+4mJAyf03bOTaSGQ925cfNh3lj7k4On8znsW83MWHZXv42oDGdYrVYT0QqFhWvRaTM/G1AY/an5/LrjmPc8/k6pj/YRStzvcD4pcm8OnsHAP2aRPPuza10aqOIiJzXtS2qYbNYeHjyBr5ddxA/Hyujr22iHtgiUmremreTL1btP3O9eqUAYzV1TKgxSLFqCHUigsrNmSBWq4XrW9Wgf9OqTFi2lw8XJbH1cCbDPllFn8bRPNc/jvpV1JpPRCoGtQ0RkTKVW+jgxnErSTiaRYMqwfzwQGdC/XVKrCdyudz8c84OJpwawDWqU21eHtik3LzoFxGR8m3qhkM8+d1m3G64p3s9nu8fpwI2ahsiUtKW7T7OrRNWA/Da4GYMaFaVsADP+r91PKeQd3/dzddrDuB0ubFZLQxvX4tH+zS4rD7bIiJmutC2IaXTpElE5DyC/OxMuK0t0aF+7D6Ww4NfbcDhdJkdSy5SocPJw99sPFO4fq5/HK9cq8K1iIhcuMGta/Cv65sB8PGSZP47b5fJiUTE26TnFPLElE0ADO9Qi2Hta3lc4RogMtiPfwxqys+PdadP42icLjdfrNrPFW8s4oOFeyg4T79uERFvoOK1iJS5qmEBTBjVjgAfG0t3H+f/ZmzHS04CqRAy84oZOWENs7ccxcdm4Z2bWnJfj1itlhMRkYs2rH0tXhkYD8B7C/bwwcI9JicSEW/hdrt59octHMsupH6VYF66Ot7sSJetfpVgxo9qy+S7O9Ksehg5hQ7e+Hknvd5cxNQNh3C59J5KRLyPitciYoqm1cN49+aWWCzw1eoDTFy+z+xIcgGOnMznxo9WsHpvBsF+dibd3p5BraqbHUtERDzYbV3q8nz/OADe+Hkn45cmm5xIRLzBF6v28+uOY/jarLx3cysCfL1nJkun2Ah+fLAL79zUkuqVAjiSWcATUzZz7QfLWJF03Ox4IiIlSsVrETHNlU1i+Fv/xgC8OjuBXxNSTU4kfyYxJYvBY1ewKzWH6FA/ptzbiS71I82OJSIiXuDeHrE83qchAK/O3nHWYDURkYu1MyX7zEDx5/rHEV/t/L1UPZXVamFQq+rMf7IHz14VR4ifnW2Hsxj+yWru+mwte45lmx1RRKREqHgtIqa6q1tdhrWvhdsNj3yzkW2HM82OJOewIuk4N364kpSsAupXCWbqA1288k2AiIiY55He9bn/ilgAXpq+jSnrDpqcSEQ8UUGxk4cnb6DI4eKKRlHc3qWO2ZFKlb+PjfuviGXR01cwqlNtbFYLv+44Rr93lvLi9K0czyk0O6KIyGVR8VpETGWxWPj7dU3oWj+SvCInd322jpTMArNjye/8uOkwoyauIbvQQfs64fxwX2eqVwowO5aIiHgZi8XCM/0anSk0PfvDFn7cdNjcUCLicf41Zwe7UnOIDPbjzRtbVJi5LBHBfoy+rim/PN6dvvHGUMcvVx04M9Qxv0hDHUXEM6l4LSKm87FZ+eCW1tSvEkxKVgF3fb6WvCKH2bEqPLfbzcdLknj0m00UO91c3awqn9/ZnrBAz5vQLiIinsFisfDyNfEM72CclfXElM3M3XbU7Fgi4iF+TUjl85VG26G3hrYgMtjP5ERlLzYqmE9GtuXbezrSvMbvhjq+tYgf1muoo4h4HhWvRaRcCAvwYeKodoQH+bLtcBaPfrMJp15YmcbpcjN6ZgL/mpMIwB1d6vL+sFb4+3jPoBsRESmfLBYLr17XlBta18DpcvPw5I0sTDxmdiwRKedSswp4+vvNANzVtS49GkaZnMhcHepFMP2BLrx7szHU8WhmAU9+t5mBY5axYo+GOoqI51DxWkTKjVoRgXwysg2+divzElJ5fW6i2ZEqpIJiJw99vYFJK/YB8OLVjXl5YDxWa8U45VJERMxntVr4z5DmXNO8KsVON/d+uZ5lu1VsEZFzc7ncPDFlEyfyimlSLZSnr2pkdqRywWq1cF1LY6jjc/2NoY7bj2QxfPxq7pi0lt2pGuooIuWfitciUq60qR3OG0OaA/DxkmQmrzlgcqKK5WReESMmrOanbSn42qy8N6wVd3WrZ3YsERGpgGxWC/+9qSVXxkdT5HBx1+drWZ2cbnYsESmHPl6azPI96QT42HhvWCv87Dpb8Pf8fWzc1yOWxc/05LbOdbBbLSxIPEa/d5bwt2lbScvWUEcRKb9UvBaRcue6ltV5vE9DAF6avk0rrcrIoRN5DBm3krX7ThDib+ezO9pzbYtqZscSEZEKzMdm5f3hrbiiURQFxS7umLSWDQdOmB1LRMqRLYdO8ubPOwH4v4HxxEYFm5yo/AoP8uWVa5vwy+Pd6dckGpcbvl59gCveWMj783eTU6i5QyJS/qh4LSLl0iO96zOoZTUcLjf3f7WePcd0Sltp2n4kk8FjV7DnWA4xof58f19nOsVGmB1LREQEP7uNcbe2oXNsBLlFTkZNXMO2w5lmxxKRciCn0MEjkzficLkZ0CyGm9rVNDuSR6gXFcxHI9oy5d5OtKgRRm6Rk7fm7aLTa/N57acdpGQWmB1RROQMFa9FpFyyWCz8+4bmtK1dmewCB7dPWkt6jk5nKw1Ld6dx00erOJZdSKPoEKY92JlGMSFmxxIRETnD38fG+FFtaVfHeF0wYsJqdqbog22Riu6VGdvZl55HtTB/Xru+ORaLZrRcjPZ1w5l2aqhjbFQQ2QUOPlqcTNfXF/DEt5tIOJJldkQRERWvRaT88vex8dGINtQKD+RgRj73fLGegmKn2bG8ytQNh7j907XkFDroWC+cKfd1ompYgNmxRERE/iDQ187E29rRomYlTuQVc8v4VSSl5ZgdS0RMMmPzEb5ffwirBf57U0vCAn3MjuSRTg91nPd4DyaMakuHuuE4XG6mbjzMgPeWMmLCapbsSsPtdpsdVUQqKBWvRaRciwj2Y+Jt7Qjxt7N+/wke+2YTWQXFZsfyeG63m7GL9vDElM04XG4GtqjGZ3e0JyxAL/pFRKT8CvH34fPb2xNfNZTjOUUM/2QV+9NzzY4lImXsYEYeL0zdCsBDPevToZ7a3V0uq9VC78bRfHtvJ358sAvXNK+K1QJLdx9n5MQ19H93KT+sP0SRw2V2VBGpYFS8FpFyr36VYMbd2ga71cLc7Sn0fXsxc7elmB3LYzldbl7+cTv/mWsMtrmnez3evamlprKLiIhHCAv04Ys729OgSjCpWYUM/2Q1h0/mmx1LRMqIw+nisW83kV3ooHWtSjzSu4HZkbxOi5qVGDO8NYuf7skdXeoS6GsjMSWbJ7/bTLf/LODDRUlk5mtBkYiUDRWvRcQjdKkfyZd3daBORCCpWYXc9+V67vl8HUcz9Wb1YhQUO7n/y/V8sWo/Fgu8fE08fxvQGKtV/QFFRMRzRAT78dXdHagXGcThk/kM/2QVqVkaMCZSEby3YA/r958gxM/Ouze3wm5TWaO01AwP5OWB8ax8rjfPXhVHlRA/UrMKeX1uIp1fm8/fZyZw6ESe2TFFxMtZ3F7SuCgrK4uwsDCOHz9ORIROGRLxVgXFTsYs2MO4xUk4XG6C/ew8c1UjbulQG5sKsH/qRG4Rd362lg0HTuJrt/LOTS0Z0Kyq2bFEREQu2dHMfIZ+tJKDGfnERgXx7b2diAz2MzvWZSkuLmbOnDkMGDAAHx+18xL5vTV7M7j545W43PDuzS25rmV1syNVKEUOFzM2H2H80mQSTw3NtVktDGhWlbu71aV5jUrmBhQRj5Kenk5kZCSZmZmEhoae93YqXouIR9qZks1zU7ew8cBJAFrVqsRrg5sRF3P+H3gV2cGMPEZNXEPy8VxC/e2MH9WO9nXDzY4lIiJy2Q5m5DH0o5UczSwgLiaEb+7pSKVAX7NjXTIVr0XOLTOvmP7vLuFIZgGDW1fn7aEtzY5UYbndbpbuPs4nS5NZuvv4meMd64Vzd7d69GxURWd2ishfUvFaRLyey+Xmq9X7eX3uTnIKHditFu7tUY+HezXA30f9m0/bdjiT2z5dy/GcQqpXCmDS7e1oEB1idiwREZESs/d4LkM/WkladiHNqofx1d0dCPX3zMKvitcif+R2u3no643M3nqU2hGBzH6kG8F+drNjCZBwJIvxS5OZsfkIDpdRXoqNCuLubvUY1Kq63peJyHldaPFazaFExGNZrRZGdKrDr0/04Mr4aBwuNx8sTOKqd5awYs/xv36ACmDxrjRu+mglx3MKiYsJYeoDnVW4FhERr1M3Moiv7+pAeJAvWw9nctvENeQUOsyOJSIl5Lt1h5i99Sh2q4X3bm6lwnU5El8tlLdvasnSZ3tyb/d6hPjZSUrL5bmpW+n6+gLen7+bE7lFZscUEQ+mldci4jXmbkvh/2ZsIzWrEIAhbWrwwoDGVA7y3FOHL8d36w7y/NStOFxuutSPYNytbQjx0FVoIiIiF2L7kUyGfbyKrAIHHeqGM+n29gT4etaqP628FjlbUloO17y3jPxiJ89eFcf9V8SaHUn+RHZBMd+uPciny/dx+GQ+AP4+Vm5sU5M7u9alTmSQyQlFpLxQ2xARqZCyCop5Y+5Ovly9H7cbwoN8efmaeK5rWQ2LpWL0XXO73YxZsIe35u0CYFDLavxnSAt87TrZRkREvN/mgye5ZfxqcgoddGsQyScj23rUaesqXov8ptDhZPDYFWw/kkXn2Ai+vLODeil7iGKnizlbj/LJ0mS2Hc4CwGKBfvEx3N29Hm1qVzY5oYiYTcVrEanQ1u/P4PmpW9mVmgNAtwaR/HNQM2pFBJqcrHQ5nC5e+nE7k9ccAOD+K2J5+spGepEvIiIVyrp9GYycuIa8Iid9Gldh7C1tPOZDXBWvRX7zz9kJfLJ0L5UDffjp0e7EhPmbHUkuktvtZlVyBp8sTWZB4rEzx9vUrszd3erRNz4am96riFRIpdrz+oMPPqBOnTr4+/vToUMH1qxZc97bFhcX8/e//53Y2Fj8/f1p0aIFc+fOPes2r7zyChaL5axLXFzcpUQTEQGgTe1wZj3cjaeubIiv3crS3ce58p3FfLQ4CYfTZXa8UpFX5OC+L9czec0BLBb4+3VNePaqOBWuRUSkwmlbJ5zxo9riZ7fy645jPPbtRq/9/S/irZbsSuOTpXsBeP2G5ipceyiLxUKn2Agm3taOeY9356a2NfG1WVm//wT3fbme3m8t4ouV+8gvcpodVUTKqYteef3tt98ycuRIxo0bR4cOHXjnnXf47rvv2LlzJ1WqVPnD7Z999lm+/PJLPvnkE+Li4vj555954oknWLFiBa1atQKM4vX333/Pr7/+euZ+drudyMjIC86lldcicj7JaTn8bdpWViVnABBfNZR/39CM5jUqmRushLhcbjYfOsnomQlsOngSP7uVd29uxVVNY8yOJiIiYqpFO49xz+frKXK6GNSyGm8NbVnuV/hp5bUIHM8p5Kp3lnI8p5BbO9bi1UHNzI4kJehYdgGfr9jPl6v3czKvGIDKgT6M6FibEZ3qEBXiZ3JCEe9zID2PL1fvJz2nCF+7Fb/fXYzrtjPHfc9z7Lf7GMd9bVb8fIyt3Xbx66NLrW1Ihw4daNeuHWPGjAHA5XJRs2ZNHn74YZ577rk/3L5atWq88MILPPjgg2eO3XDDDQQEBPDll18CRvF6+vTpbNq06WKinEXFaxH5M263m+/WH+Kfs3eQmV+M1QK3da7Lk1c2JMgDp5U7nC7W7M3g5+0p/Lw9lZSsAgAqBfowYVRb2tQONzmhiIhI+fDL9hQe+GoDDpebm9rW5LXBzcr1WUkqXktF53a7ufOzdSxIPEaDKsHMfLirR/WtlwuXV+Tg+/WHGL90Lwcy8gDwtVu5oXV17uxaj/pVgk1OKOL5ktJyGLswiembDuN0lV7naJvVclYx+/TW1247b/HbVZDDe7d1+8vi9UVVbIqKili/fj3PP//8mWNWq5U+ffqwcuXKc96nsLAQf/+zT+8JCAhg2bJlZx3bvXs31apVw9/fn06dOvHaa69Rq1at82YpLCyksLDwzPWsLGMAQHFxMcXFxRfzbYlIBXF9ixi6x1bmnz/tZOaWFCYu38vcbUd5ZWBjejaKMjveXyosdrIsKZ1fEo6xcGcaJ/J++1kX5GvjioZRPNo7lrqRQfo5KCIickrPhhG8NaQZj3+3hW/XHcTHBi9fHVduBzmf/h2u3+VSUX2+6gALEo/ha7fy3xubYcNFcbHa/ngjHwsMa1udoa2rMW/HMSYs38emg5lMXnOQb9YeZFCLqjzSqz41KgeYHVXE4+xKzWbs4r38tC2F0zXrbvUj6FA3nCKni2KHiyKni0KHiyLH/2yd5z9W9Lv7/b4Y7nS5yXc5yS++8BZArsK8C7rdRa28PnLkCNWrV2fFihV06tTpzPFnnnmGxYsXs3r16j/cZ/jw4WzevJnp06cTGxvL/Pnzue6663A6nWeKzz/99BM5OTk0atSIo0ePMnr0aA4fPsy2bdsICQk5Z5ZXXnmF0aNH/+H4119/TWCgdw9kE5HLt+OEhSl7rWQUGm9cW0W4GFzHRaivycH+R4EDEk5a2JxhYccJC4Wu395oB9ndNK3spkWEm4Zhbnw8Yw6ViIiIKdakWfh6jxU3FnpVdXFtbRfltH4tUmEdzoW3t9pwuC3cUMdJ96qlt0pQyqe92TD/sJWtJ4w3NzaLmy7Rbq6s4SJEJ6OI/KVDufDzIStbMn4rEDSt7OLK6i5qn7vEesmcbnC4Tl3cv22LXb8/bjGu/89ti12Ql5vH+KeHlWzbkEspXqelpXH33Xczc+ZMLBYLsbGx9OnTh4kTJ5Kfn3/O5zl58iS1a9fm7bff5s477zznbc618rpmzZocPXpUbUNE5ILkFTl4b0ESn67Yj8sNof52nu3XkCGtq5t6OnF6bhELEo/xc8IxViSlU+z87cd0TKgffeOj6RdfhTa1Kl1SXykREZGK6pu1h3hpRgIAD15Rj8d61zc50R8VFxczb948+vbtq7YhUqHkFzkZPG4Ve9JyuaJhJB/f2qrcniEhpW/r4UzemreH5UnpwP+3d+dhUdWLH8c/M8Mum+yCqICCu5j7mrtm1zIty66VZrtaad3Sm+2366+6lblU95ZpZXptUSsz03Bfct9NlMVdUFBAQGBg5veHxY1cMeAM8H49D88j55w5fMacmPOZ7/l+L9xpen+nurq/Uz15VsJpH4HytvNYpqavTNSK+LTibX0bB+mxbpFqXOvyxbCR0tPTVatWrbKdNiQgIEAWi0Wpqakltqempiok5NILgwUGBmrhwoXKy8tTenq6QkNDNX78eEVGRl725/j6+io6OloJCQmXPcbV1VWurhdP4u/s7MybPADXxMfZWc8PaKrbbgjX+Pm7tOd4lp77Zp++2ZWif97WrELnWDuRcV4/7k3Rkj0p2nzojH4/FVVkQA31bRqifk1C1Ly2D2/iAQC4Tvd0jFChXXr5u32avjJJHq7OGtXd8QpsiesaVD8vLdqvhNM5CvRy1VtDYuXi4mC3RKJC3VAvQJ8/GKB1CWl6fcl+7TqWqakrkvT5pmMa3b2+/tq+jlydmAsd2HzojKbEHdSagxdKa7NJGtAiVKO611d0cBkPtS5j1/o+p1TltYuLi1q1aqW4uDgNHDhQ0oUFG+Pi4jR69OgrPtbNzU1hYWGyWq36+uuvNWTIkMsem52drcTERN1zzz2liQcA16VpmI8WPtZJs9Yf0ltLD2hT8hn1f3eNRnWvr0e6RZbbm6KEU9m/LriYol3HMv+QyVv9moSob5MQ1Q/ypLAGAKCMjOgUofxCm/7vh/1688d4uTqZ9UCXyw+sAVD+ftybos83HpEkvT2khfw9Lx6ohuqpU/0AfTOqk37Yk6J//RivpLQcvbJon2asTda43tEa2DJMFgdehBcoD3a7XesT0zUl7qA2Jp+RdGHBxNtahumxblGKDKxai52WatoQSZo3b57uu+8+/fvf/1bbtm01efJkffHFF9q/f7+Cg4N17733KiwsTJMmTZIkbdy4UcePH1dsbKyOHz+ul156ScnJydq2bZt8fX0lSU8//bQGDBigunXr6sSJE3rxxRe1Y8cO7du3T4GB17aIWlZWlnx8fJSWlsa0IQCu29EzuXr+mz1aGX9aklQ/yFOTBjVTm3p+f/rcdrtde45nXRhhvTdFCaeyi/eZTFKbun7q0yRYfZuEKNyPufsBAChPk386oMk/HZQkvTqwqe5pX9fgRBdYrVYtXrxY/fv3Z+Q1qoWUzDz1e3e1MnKteqhrpP7ev5HRkeCgCots+nLrMU3+6YBSsy5MIxsT7KW/9Y1Rz0ZBDPhBlWe327XywGlNjTuobUcyJEnOFpNubxWux7pFVboeIT09XQEBAWU7bYgk3XnnnTp9+rReeOEFpaSkKDY2VkuWLFFwcLAk6ciRIzKb/zcHa15eniZOnKikpCR5enqqf//++uyzz4qLa0k6duyYhg4dqvT0dAUGBqpz5876+eefr7m4BoCyEu7noZnD2+i7XSf1ynd7lXAqW3d8sEF3t6ujZ/s1lI976S4ii2x2bTl0Rj/uTdWPe1N0PON/c/07W0zqGBWgfk1D1KtRsAK9GGECAEBFeaJnA+VZbfpgVaKeX7hHv5zM0sjOEYqqYqOVAEdWZLNr7Lwdysi1qmmYt57uE2N0JDgwJ4tZQ9vW0W0twzRr/SG9tyJB8ann9MCnW9S6bk09e1PDMhl0BDgam82un35J1bQVCcV3bbs4mTW0TbgevjFKob7uBicsX6Ueee2oGHkNoKxl5BZo0uL9mrflqCQp0MtVL9/SRDc1Dbnip/r5hUVan5iupXtTtHRvqtJzCor3uTtb1C0mUP2ahqh7wyB5uzGiCgAAo9jtdr266Bd9vC65eFuPhkF6oHOEOkT5GzKKj5HXqE7eW5mgN5bEy8PFokVjOle5W91RvjJzrfpgdaJmrktWntUmSerZMEhP941RIwddoA4oDZvNrh/2pGjq8oPan3JO0oVO4a/t6uihrpEK8nYzOOGfc60jrymvAeAqNiSm67kFu5WUliNJ6tUoSK/c2rTEp5s5+YVadeC0luxJ0Yr9p3Quv7B4n4+7s3o2ClK/JiHqGh0oN2cWFgEAwFHY7XZtSErXx2uTFbf/lH67OmoY4qWRnSN0S2xohS4KRnmN6mLH0Qzd/v56FdrseuP25hrSOtzoSKikUrPy9G7cQc3bfFRFNrtMJmlgbJjG9Y6udNMoANKFKXK+23VC01ckFk836unqpHs71NXIzhFVZl0AymsAKEN51iK9tyJB769KlLXIrhouFo3rEyMfd2ct2ZOiNQdPK7/QVnx8kJer+jQJVr8mtdQu0k/OFvMVzg4AABxB0ulszVx3SF9tPabz1iJJUoCnq+7tUFd/bVenQi4WKa9RHWTnF6r/u2t05Eyubm5eS9OGtmS+YvxpyWk5+tfSeH2/66SkC9M0/rVdXY3uUV8BVaTsQ9VmLbJpwbbjem9lgg6l50qSvN2cNKJThEZ0qidfDxeDE5YtymsAKAcHUs9pwvzd2nr47EX76vh5qF/TEPVtEqKW4b4ys+o1AACVUkZugeZuOqpP1h9SSlaeJMnVyaxBN4Tp/k4RahDsVW4/m/Ia1cG4eTs0f/txhfm6a/ETXUq9rgxwJbuPZeqNH/drzcE0SZKHi0UPdInUg10i5MW0jXBA+YVF+nLLMb2/MrF4nayaHs56oEuk7ulQt8pON0p5DQDlxGaza86mI3p/ZaK83JzUt0mI+jUNUcMQL0aMAABQhViLbFq8+6Q+WpOs3cczi7ffGB2okZ0j1KVBQJn/7qe8RlW3cPtxPTlvh8wmad7DHVhgD+VmXUKa3liyXzt/XeDOZhU+ogAAMdtJREFUr4aLRnWvr2Ht61TodFDA5eRZizR30xH9e1VS8YflAZ6ueqhrhP7arq5quDoZnLB8UV4DAAAAQBmw2+3afOisZqxN0tJ9qcXzYkcHe2pk5wjdGhtWZmtaUF6jKjuSnqv+U9YoO79QT/RsoLG9o42OhCrObrdryZ4Uvbk0XkmnL6xhFObrrrG9o3VbyzBZuFsWBsjJL9TnGw/rP6uTlZadL0kK8XbTwzdGamjbOtVmnSzKawAAAAAoY4fTczRz3SF9ueWocgouzIvtX8NFw9rX1bD2dRXo9efmVaW8RlVlLbJpyL83aPuRDLWuW1P/fai9nFgXBhWksMimr7Ye0+SfDhaPcI0O9tTf+jZUr0ZB3EGLCpGVZ9Wn6w9pxtpknc21SrrwYcpj3aN0e6va1e6OAMprAAAAACgnmeetmrf5iGatO6QTmReKEBeLWQNbhmpk50jFhFzfvNiU16iq3loar6nLE+Tl5qQfnuii2jU9jI6EaijPWqRP1h/SeysTlXn+QnnYqm5NPduvodpGMIUNykdGboE+XndIs9YlKyuvUJJUz99Dj3Wvr9tahsm5mn6QR3kNAAAAAOWssMimH/akaMbaZO04mlG8vUuDAN3fOUI3Nggs1SLOlNeoin5OStfQD3+W3S5NHdpSA1qEGh0J1Vzmeav+vSpRH69LVp7VJknqHhOoZ/o1VKNaly/RgNJIz87XR2uT9en6Q8V3a9UP8tTo7vX1l+a1qv3dJ5TXAAAAAFCBth6+MC/2kj0psv16lRUVWEMjO0dq0A3XNi825TWqmozcAt307hqdzMzTHa1q6807WhgdCSiWmpWnKXEH9d/NR1Vks8tkkm5tEapxvWNUx5+7A3B97Ha7vthyVK99/0vxSOuGIV4a06OBbmoaUqoPtasyymsAAAAAMMDRM7matf6Q5m0+quz8CxetNT2cNax9Xd3Tvq6CvN0u+1jKa1Qldrtdj87epiV7UxQRUEOLxnRWDVcno2MBF0lOy9FbS+O1aNdJSZKzxaS729bR6B4N/vRaBqheDqXlaML83dqQlC5JalzLW0/2aqBejYIprf+A8hoAAAAADHQuz6ovthzTzHXJOnb2vKQLhciAFqEa2TlCTUJ9LnoM5TWqkrmbjmjC/N1ytpg0/9FOalb74n/zgCPZfSxTb/y4X2sOpkmSPFwseqBzhB7sGikvN/6fjMsrLLLpwzXJmvzTAeUX2uTmbNbTfWI0vGO9aj89yOVQXgMAAACAAygssmnZvlTNWJusLYfPFm/vEOmvkZ0j1KNhUPFoLMprVBUJp7L1l6lrlGe1acJNDfXwjVFGRwKu2fqENL2+ZL92HsuUdOHumVHd62tY+7rXNAUUqpc9xzP17Ne7tPdElqQL6168NrAZU89cBeU1AAAAADiYHUczNGNtshbvPqmiXyfGjgyooRGd6mlwq9pyNtkpr1HpZeZadce/1+tAarY61w/Qp/e35XZ5VDp2u10/7k3RGz/GK+l0jiQp1MdNT/aO1uAbasvCv+lq73xBkSb/dEAfrU1Wkc0uH3dnPf+Xxhp8Q5hMJv59XA3lNQAAAAA4qOMZ5/Xp+kOas+mIzv26mJOPu7Pual1boTkHdfdtlNeonHLyCzVsxkZtP5KhQC9XLRrTWcFXmOcdcHSFRTZ9ve2Y3ll2UClZeZKkBkGeerpvjPo0DqakrKbWJ6RpwoLdOpyeK0ka0CJUL/ylMXOklwLlNQAAAAA4uJz8Qn255ahmrj9UfAHsbLJr5og26hwdbHA6oHTyrEUa+clmrUtIl4+7s754uINiQryMjgWUiTxrkT7dcEjTVyQq87xVktSyjq+e7ddQ7SPpoaqLzFyrXlu8T19sOSZJquXjpn8MbKqejfidXVqU1wAAAABQSRTZ7Ir7JVXTVhzUrmNZCvJy1eInuijAkxFcqBwKi2x67PNtWrovVTVcLPr8wfaKDfc1OhZQ5jLPW/Wf1Yn6eO0hnbcWSZJujA7U3/rGqGkYi5JWVXa7XYt3p+jFb/cqLTtfJpN0T/u6+lvfGBbzvE6U1wAAAABQyWTmnFeff8Up9bxJN0YHaubwNswVDIdns9n19Jc7NX/7cbk4mTVrRBt1jAowOhZQrk5l5Wnq8gTN3XREhb+uYXBLi1CN6x2tegE1DE6HspSSmafnv9mjZftSJUn1gzz1f4OaqXU9P4OTVW7XWl6bKzATAAAAAOAKPFycNDy6SK5OZq06cFofrkkyOhJwRXa7XS9/t1fztx+XxWzSe3ffQHGNaiHI202vDmyquKdu1C0tQiVJ3+48oV5vr9LEhbt16tf5sVF52Wx2zf75sHq/vUrL9qXK2WLS4z0b6PvHO1NcVyBGXgMAAACAg7BarVq8eLGyApvr+W/3ycls0hePdNANdWoaHQ24pLeWxmvq8gSZTNLkO2N1a2yY0ZEAQ+w9kak3lsRr1YHTkiR3Z4vu71xPD3WNko8700pUNgmnsjVh/i5tPnRWkhQb7qvXBzdnHv8yxMhrAAAAAKik7mwdppub11Khza7H524vXhwMcCT/WZ2oqcsTJEmv3NqU4hrVWpNQH31yf1vNfbC9Wtbx1XlrkaavSFTXN1boP6sTlffr/NhwbAWFNk2NO6j+767R5kNn5eFi0UsDGuvrRztSXBuE8hoAAAAAHIzJZNKkQc1Ux89Dx86e1/ivd6mK3DSLKmLupiP65+L9kqRn+sXonvZ1DU4EOIYOUf6a/2hH/eeeVmoQ5KnM81b9c/F+dXtzpf676YgKi2xGR8Rl7DiaoVumrdVbyw6ooMimbjGBWjq2q4Z3ipCF9ScMQ3kNAAAAAA7I281ZU4e2lLPFpB/2pGj2xiNGRwIkSd/tPKG/L9gtSXrkxig91q2+wYkAx2IymdSnSYiWPNlVb97eXKE+bkrJytP4+bvVZ/Jq/bD7JB9IOpCc/EK98t0+3fbeOu1POSe/Gi56965YzRzeRrVrehgdr9qjvAYAAAAAB9Ui3FfP9msoSXp10T79cjLL4ESo7lbsP6Wx83bIbpf+2q6Onu0XY3QkwGFZzCbd0Tpcy5/upok3N1JND2clnc7Ro59v08Dp67Q+Ic3oiNXeqgOn1eed1fp4XbLsdum2lmH6adyNujU2TCYTo60dAeU1AAAAADiwkZ0j1KNhkAoKbRo9Z5tyCwqNjoRqamNSuh6ZvVWFNrtujQ3Vq7c2pdwBroGbs0UPdInU6me66/GeDeThYtHOY5m6+6ONumfGRu0+lml0xGrnTE6Bxs3bofs+3qTjGecV5uuuWSPa6J07Y+VXw8XoePgdymsAAAAAcGAmk0n/uqOFQrzdlHg6Ry98s9foSKiGdh/L1MhPtii/0KaeDYP0rztayMwcsECpeLk5a1zvaK36W3cN71hPzhaT1hxM04BpazVqzjYlnc42OmKVZ7fb9c2O4+r19irN335cJpN0f6cILR3bVd1igoyOh0ugvAYAAAAAB/fb/Jtmk/TV1mNasP2Y0ZFQjRxMPad7P96o7PxCdYj01/S/3iBnC3UCcL0CvVz10i1NtPypbhrUMkwmk/T9rpPq/c5qTZi/W6lZeUZHrJKOZ5zX/bM264n/7tCZnALFBHtp/qMd9cKAxqrh6mR0PFwGv20AAAAAoBJoF+mvJ3pGS5KeW7CHEXqoEEfP5GrYjI06m2tVi9o++vC+1nJzthgdC6gSwv089PadsVr8eBf1bBikIptdczcd0Y1vrtD//bBfmblWoyNWCUU2u2atS1bvt1dpRfxpuVjMeqp3tL4b01kt69Q0Oh6ugvIaAAAAACqJ0T3qq0Okv3ILijR6znblWYuMjoQq7FRWnv760UalZuUrOthTs0a0lSejE4Ey16iWt2YMb6MvH+mg1nVrKs9q0werEtXljeV6f2Wizhfw//rrdSD1nG7/YL1e+m6fcguK1KZeTS1+oovG9GwgFydq0cqA/0oAAAAAUElYzCZNvitW/jVctO9kliYt/sXoSKiizuYUaNiMjTpyJld1/Dz02ch2qskiZkC5alPPT18+0kEz7mutmGAvZeUV6vUl+3Xjmyv0+cbDshbZjI5YaeQXFumdZQd085Q12n4kQ56uTvrHwKaa91AH1Q/yNDoeSoHyGgAAAAAqkWBvN701pIUk6ZMNh7VkT4rBiVDVZOcXaviszTqQmq1gb1d9/kA7BXu7GR0LqBZMJpN6NgrW4ie66O0hLVS7prtOncvXcwv2qM87q7Vo1wkV2exGx3RoWw+f0c1T1urduIOyFtnVq1Gwlo3rqmHt67LQbCXE/T4AAAAAUMl0iwnSw10j9e/VSXrmq51qGuat2jU9jI6FKiDPWqQHP9minUczVNPDWbNHtlO4H/+2gIpmMZs06Ibaurl5Lc3deERTlycoOS1Ho+dsl8m0Xf41XBTg6frr169/9rrwfaDXhW2Bnq7yq+Eipyq8wKrNZte5/EJlnbcq87xVX2w5qs9+Piy7XQrwdNHLtzRV/2YhMpkorSsrymsAAAAAqISe6hOjn5PPaOfRDD0+d7vmPdxBzlW4oED5sxbZNHrONm1ISpenq5M+ub+tGgR7GR0LqNZcnSwa3ilCt7cO14w1yfpoTZLO5RcqLbtAadkFks5d8fEmk1TTw6W44A70ci1Zenu5KvDX7/09XQz5PWK325VbUKSM81Zl5l4ooS98FRT/OeN327POWy8c++ufLzUQfUjr2vp7/0by9WC6o8qO8hoAAAAAKiEXJ7OmDW2p/lPWaNuRDL297ICe7dfQ6FiopGw2u57+cqd++uWUXJ3M+ui+1mpe29foWAB+5enqpCd6NdDoHvV1JqdAp8/lKy37918FSjuXr9PZ+b/uK9CZnHzZ7NKZnAKdySnQgdTsq/6cmh7O/yu3vVxLlN6Bxdtd5F/D9aIFD/OsRRcVzRm5BcUlc+bvSufMPxTVhX9yKhQ3Z7N83J0V5uuup/rEqFP9gD91PjgOymsAAAAAqKTC/Tz0+uDmeuzzbXp/ZaI6RPqra3Sg0bFQydjtdr3w7R59s+OEnMwmvT/sBrWP9Dc6FoBLsJhNF4pkL9erHltks+tsbsGFcvtcgU5n5ynt3IXvT/+u8E7Lzld6TsGvx1t1Nteqg6euXnT7uDurpoezcgoulNYFhX9uQUlni0k+7i7ycXeSj7uzfD1c5OPufMkvX4///dnb3VluzpY/9bPhuCivAQAAAKAS69+slv7aro4+33hE477YocVPdFGQF4vr4dq9+WO8Zv98RCaT9PadserRMNjoSADKgMVsKh5FrZArH2srLroLikdz/zaC+4+jvNOzC1RosxePmv49s0n/K5l/Vz77/r589rh0Ce3ubGFualyE8hoAAAAAKrnn/9JYWw+f1f6Ucxo7b4c+vb+dLGYKAFzd+ysT9d7KREnSawOb6ZYWoQYnAmAEs9kkf09X+Xu6KkZXnuve9mtxfTo7Xxm5Vnm4WIqLaE9XJwpolClW8wAAAACASs7N2aJpd98gd2eL1iWk6/2VCUZHQiUw++fDen3JfknS3/s31N3t6hicCEBlYDabVLOGi6KDvdQ2wk9Nw3wU7uchLzdnimuUOcprAAAAAKgC6gd56pVbm0iS3l52QJsPnTE4ERzZNzuO6/lv9kiSRnevr4e6RhmcCACAi1FeAwAAAEAVcXur2rqtZZhsdunxudt1NqfA6EhwQD/tS9W4L3bKbpfu7VBXT/WJNjoSAACXRHkNAAAAAFWEyWTSqwObKiKghk5m5ulvX+2S3W43OhYcyIbEdD02Z5uKbHbd1jJMLw1owm3+AACHRXkNAAAAAFWIp6uTpt3dUi4Ws376JVUz1x0yOhIcxI6jGXrgk80qKLSpd+NgvXl7c5lZ2BMA4MAorwEAAACgimkS6qPnbm4kSZr0wy/afSzT4EQwWnzKOQ2fuUk5BUXqVN9fU4e2lJOFSgAA4Nj4TQUAAAAAVdC9Heqqb5NgWYvsGj13m87lWY2OBIMcTs/RPTM2KiPXqthwX/3nntZyc7YYHQsAgKuivAYAAACAKshkMumNwS0U5uuuw+m5em7BHua/roZSMvM0bMZGnTqXr4YhXpo1oo1quDoZHQsAgGtCeQ0AAAAAVZSPh7OmDI2VxWzStztP6IstR42OhAp0JqdA98zYqKNnzquuv4c+HdlWvh4uRscCAOCaUV4DAAAAQBXWqq6fnuoTLUl68du9OpB6zuBEqAjn8qy67+NNOngqW7V83DR7ZDsFebkZHQsAgFKhvAYAAACAKu6RrlHq0iBAeVabRs/ZpvMFRUZHQjnKsxZp5CdbtPt4pvxquOizke0U7udhdCwAAEqN8hoAAAAAqjiz2aS3h8QqwNNVB1Kz9cqivUZHQjkpKLTp0dlbtSn5jLxcnfTp/W1VP8jT6FgAAFwXymsAAAAAqAYCvVw1+c5YmUzS3E1H9d3OE0ZHQhkrstk17osdWhF/Wm7OZs0Y3kZNw3yMjgUAwHWjvAYAAACAaqJzgwA91i1KkjRh/m4dTs8xOBHKit1u18SFe7Ro10k5W0z6YFgrtY3wMzoWAAB/CuU1AAAAAFQjY3tFq3XdmsrOL9SYudtVUGgzOhL+pFNZeRozd7vmbjois0mafGdLdYsJMjoWAAB/GuU1AAAAAFQjThaz3h3aUj7uztp1LFNvLNlvdCRcJ2uRTR+tSVKPt1Zp0a6TMpmk/xvUXDc3r2V0NAAAygTlNQAAAABUM2G+7vrXHS0kSR+tTdby/akGJ0JpbUhM181T1ugf3/+i7PxCtQj31TejOmlIm3CjowEAUGYorwEAAACgGurdOFjDO9aTJD31xU6dzDxvbCBck5TMC1OEDP3wZx1IzZZfDRe9PriZFjzaUc1r+xodDwCAMuVkdAAAAAAAgDEm9G+oLYfPaM/xLD3x3x2a80A7OVkY4+SICgpt+nhdsqbEHVRuQZHMJumv7erqqT7R8vVwMToeAADlgnclAAAAAFBNuTpZNHXoDarhYtGm5DOasjzB6Ei4hLUH03TTu6v1fz/sV25BkW6o46tvR3fWqwObUlwDAKo0Rl4DAAAAQDUWEVBD/xzUTE/8d4emLj+o9pF+6hgVYHQsSDqecV7/WLRPP+xJkSQFeLpo/E2NNKhlmMxmk8HpAAAof4y8BgAAAIBq7tbYMA1pXVt2u/Tkf3coLTvf6EjVWn5hkaavSFCvt1bphz0pMpuk4R3rKe6pbrq9VW2KawBAtcHIawAAAACAXrqlibYdyVDCqWw99cVOzRzehpLUACvjT+nl7/YpOS1HktS2np9evrWJGtXyNjgZAAAVj5HXAAAAAAB5uDhp+t03yNXJrFUHTuvDNUlGR6pWjp7J1YOfbtHwmZuVnJajQC9XTb4zVvMebk9xDQCotiivAQAAAACSpJgQL704oIkk6c0f47XtyFmDE1V9edYivfvTQfV6e5WW7UuVxWzSA50jtPy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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_return_df_one = test_return_df.copy()\n",
    "\n",
    "# process the order of the weights_df_solution_one and test_return_df_one are the same\n",
    "weights_df_solution_one = weights_df_solution_one.T.sort_index().T\n",
    "test_return_df_one = test_return_df_one.T.sort_index().T.drop(benchmark_token, axis=1)\n",
    "# test_return_df_one = test_return_df_one.T.sort_index().T\n",
    "\n",
    "portfolio_ret = (weights_df_solution_one.shift(1) * test_return_df_one).dropna().sum(axis=1)\n",
    "(1 + portfolio_ret).cumprod().plot(figsize=(18, 8), label=\"Mkt Neutral Portfolio\")\n",
    "(1 + full_return_df.loc[portfolio_ret.index][benchmark_token]).cumprod().plot()\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title(\"Backtest of Market Neutral Portfolio One Return\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "69a64fdb-3d1e-4268-8478-e397145aff68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Backtest of Market Neutral Portfolio Two Return')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_return_df_two = test_return_df.copy()\n",
    "\n",
    "# process the order of the weights_df_solution_two and test_return_df_two are the same\n",
    "weights_df_solution_two = weights_df_solution_two.T.sort_index().T\n",
    "test_return_df_two = test_return_df_two.T.sort_index().T.drop(benchmark_token, axis=1)\n",
    "# test_return_df_two = test_return_df_two.T.sort_index().T\n",
    "\n",
    "portfolio_ret = (weights_df_solution_two.shift(1) * test_return_df_two).dropna().sum(axis=1)\n",
    "(1 + portfolio_ret).cumprod().plot(figsize=(18, 8), label=\"Mkt Neutral Portfolio\")\n",
    "(1 + full_return_df.loc[portfolio_ret.index][benchmark_token]).cumprod().plot()\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title(\"Backtest of Market Neutral Portfolio Two Return\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fe200e-18d7-4529-a7f6-363745a00395",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "crypto-trading-analysis",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 34.659839,
   "end_time": "2023-11-01T02:04:21.309361",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-11-01T02:03:46.649522",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}