BTC_EDA_Moving_Average.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Data\n",
    "\n",
    "I have uploaded to this [Kaggle Dataset](https://www.kaggle.com/paulrohan2020/crypto-data) which is a zipped file with 26,320 `.csv` files containing the top cryptocurrencies on https://coinmarketcap.com/ by market cap worldwide. After 20:45:05 on August 4, data was collected every five minutes for three months.\n",
    "\n",
    "Also I have uploaded the 9,432 .csv files based on which this below EDA analysis done into my [Github repository](https://github.com/rohan-paul/Cryptocurrency-Kaggle/tree/main/Notebooks/kaggle/input/crypto-data).\n",
    "\n",
    "This dataset is from **CoinMarketCap Data** From August 4 to November 4, 2017\n",
    "\n",
    "Filenames represent the date and time at which the data was collected: ymd-hms. The data in cr_20170804-210505.csv was collected on August 4, 2017 at 21:05:05.\n",
    "\n",
    "### The Columns in the Data\n",
    "\n",
    "symbol,ranking by market cap,name,market cap,price,circulating supply,volume,% 1h,% 24h,% 1wk\n",
    "\n",
    "# Some basics on Moving Average\n",
    "\n",
    "##### Moving averages are one of the most often-cited data-parameter in the space of Stock market trading, technical analysis of market and is extremely useful for forecasting long-term trends. And beyond its use in financial time series this is intensively used in signal processing to neural networks and it is being used quite extensively many other fields. Basically any data that is in a sequence.\n",
    "\n",
    "The most commonly used Moving Averages (MAs) are the simple and exponential moving average. Simple Moving Average (SMA) takes the average over some set number of time periods. So a 10 period SMA would be over 10 periods (usually meaning 10 trading days).\n",
    "\n",
    "Rolling mean/Moving Average (MA) smooths out price data by creating a constantly updated average price. This is useful to cut down “noise” in our price chart. Furthermore, this Moving Average could act as “Resistance” meaning from the downtrend and uptrend of stocks you could expect it will follow the trend and less likely to deviate outside its resistance point.\n",
    "\n",
    "### Factors to choose the Simple Moving Average (SMA) window or period\n",
    "\n",
    "In order to find the best period of an SMA, we first need to know how long we are going to keep the stock in our portfolio. If we are swing traders, we may want to keep it for 5–10 business days. If we are position traders, maybe we must raise this threshold to 40–60 days. If we are portfolio traders and use moving averages as a technical filter in our stock screening plan, maybe we can focus on 200–300 days.\n",
    "\n",
    "---\n",
    "\n",
    "### Now some real-world Exploratory Data Analysis with real Crypto-currency data from Coinbase\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import numpy as np\n",
    "import os # for accessing directory structure\n",
    "import pandas as pd\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [],
   "source": [
    "#for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#    for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "\n",
    "# Above code will print it all like below, this was just for the initial checking\n",
    "# I am commenting out as this folder has 26000+ file names to pring\n",
    "\n",
    "# /kaggle/input/crypto-data/train_BTC_combined.csv\n",
    "# /kaggle/input/crypto-data/Crypto-Coinmarketcap/cr_20170822-152505.csv\n",
    "# /kaggle/input/crypto-data/Crypto-Coinmarketcap/cr_20170812-020505.csv\n",
    "# /kaggle/input/crypto-data/Crypto-Coinmarketcap/cr_20170813-065506.csv\n",
    "# .....\n",
    "\n",
    "# Defining this input variable as I will be using this in few places\n",
    "file_dir = './kaggle/input/crypto-data/'\n",
    "\n",
    "# In Kaggle this file will be as below per Kaggle's file-tree-structure\n",
    "# file_dir = '/kaggle/input/crypto-data/Crypto-Coinmarketcap/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# First defining variables for the first 2 files to see their structure\n",
    "\n",
    "nRowsRead = 1000 # specify 'None' if want to read whole file\n",
    "# These .csv may have more rows in reality, but we are only loading/previewing the first 1000 rows\n",
    "df1 = pd.read_csv(file_dir+'cr_20170804-034052.csv', delimiter=',', nrows = nRowsRead)\n",
    "df2 = pd.read_csv(file_dir+'cr_20170804-035004.csv', delimiter=',', nrows = nRowsRead)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1000 rows and 18 columns\n"
     ]
    }
   ],
   "source": [
    "# Let's check 1st file: /kaggle/input/crypto-data/cr_20170804-034052.csv\n",
    "nRowsRead = 1000 # specify 'None' if want to read whole file\n",
    "# cr_20170804-034052.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows\n",
    "# df1.dataframeName = 'cr_20170804-034052.csv'\n",
    "nRow, nCol = df1.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking</th>\n",
       "      <th>by</th>\n",
       "      <th>market</th>\n",
       "      <th>cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market.1</th>\n",
       "      <th>cap.1</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating</th>\n",
       "      <th>supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>%</th>\n",
       "      <th>1h</th>\n",
       "      <th>%.1</th>\n",
       "      <th>24h</th>\n",
       "      <th>%.2</th>\n",
       "      <th>1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GMB</td>\n",
       "      <td>1011</td>\n",
       "      <td>Gambleo</td>\n",
       "      <td>?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$0.000895</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>Low</td>\n",
       "      <td>Vol</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FTC</td>\n",
       "      <td>164</td>\n",
       "      <td>Feathercoin</td>\n",
       "      <td>$9,481,236</td>\n",
       "      <td>$0.055794</td>\n",
       "      <td>169932600</td>\n",
       "      <td>$1,267,140</td>\n",
       "      <td>-0.05%</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>4.99%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HCC</td>\n",
       "      <td>953</td>\n",
       "      <td>Happy</td>\n",
       "      <td>Creator...</td>\n",
       "      <td>?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$0.000108</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>Low</td>\n",
       "      <td>Vol</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>8.39%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRX</td>\n",
       "      <td>288</td>\n",
       "      <td>Breakout</td>\n",
       "      <td>Stake</td>\n",
       "      <td>$1,793,819</td>\n",
       "      <td>$0.286183</td>\n",
       "      <td>6268082</td>\n",
       "      <td>*</td>\n",
       "      <td>$4,778</td>\n",
       "      <td>-4.53%</td>\n",
       "      <td>3.35%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNRC</td>\n",
       "      <td>872</td>\n",
       "      <td>UniversalRoya...</td>\n",
       "      <td>?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$0.005857</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>$664</td>\n",
       "      <td>3.98%</td>\n",
       "      <td>-7.83%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking                by       market          cap       name  \\\n",
       "0    GMB     1011           Gambleo            ?          NaN  $0.000895   \n",
       "1    FTC      164       Feathercoin  $9,481,236     $0.055794  169932600   \n",
       "2    HCC      953             Happy   Creator...            ?        NaN   \n",
       "3    BRX      288          Breakout        Stake  $1,793,819   $0.286183   \n",
       "4   UNRC      872  UniversalRoya...            ?          NaN  $0.005857   \n",
       "\n",
       "     market.1   cap.1   price circulating  supply  volume      %   1h  %.1  \\\n",
       "0           ?       *     Low         Vol       ?       ?    NaN  NaN  NaN   \n",
       "1  $1,267,140  -0.05%  10.49%       4.99%     NaN     NaN    NaN  NaN  NaN   \n",
       "2   $0.000108       ?       *         Low     Vol  -0.02%  8.39%  NaN  NaN   \n",
       "3     6268082       *  $4,778      -4.53%   3.35%     NaN    NaN  NaN  NaN   \n",
       "4           ?       *    $664       3.98%  -7.83%     NaN    NaN  NaN  NaN   \n",
       "\n",
       "   24h  %.2  1wk  \n",
       "0  NaN  NaN  NaN  \n",
       "1  NaN  NaN  NaN  \n",
       "2  NaN  NaN  NaN  \n",
       "3  NaN  NaN  NaN  \n",
       "4  NaN  NaN  NaN  "
      ]
     },
     "execution_count": 744,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a quick look at what the data looks like:\n",
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution graphs (histogram/bar graph) of sampled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# plot_per_column_distribution(df1, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1000 rows and 10 columns\n"
     ]
    }
   ],
   "source": [
    "# Let's check 2nd file: /kaggle/input/crypto-data/cr_20170804-035004.csv\n",
    "df2.dataframeName = 'cr_20170804-035004.csv'\n",
    "nRow, nCol = df2.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XBY</td>\n",
       "      <td>190</td>\n",
       "      <td>XtraBYtes</td>\n",
       "      <td>$7,422,935</td>\n",
       "      <td>$0.011420</td>\n",
       "      <td>650,000,000</td>\n",
       "      <td>*</td>\n",
       "      <td>$16,273</td>\n",
       "      <td>-1.36%</td>\n",
       "      <td>-0.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPN</td>\n",
       "      <td>637</td>\n",
       "      <td>CompuCoin</td>\n",
       "      <td>$57,962</td>\n",
       "      <td>$0.002955</td>\n",
       "      <td>19,615,019</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>-4.69%</td>\n",
       "      <td>-31.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEAP</td>\n",
       "      <td>976</td>\n",
       "      <td>Cheapcoin</td>\n",
       "      <td>?</td>\n",
       "      <td>$0.000112</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>105.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLAS</td>\n",
       "      <td>700</td>\n",
       "      <td>BlakeStar</td>\n",
       "      <td>$27,044</td>\n",
       "      <td>$0.000112</td>\n",
       "      <td>242,418,240</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>0.02%</td>\n",
       "      <td>-5.80%</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC</td>\n",
       "      <td>341</td>\n",
       "      <td>Capricoin</td>\n",
       "      <td>$848,890</td>\n",
       "      <td>$0.426568</td>\n",
       "      <td>1,990,045</td>\n",
       "      <td>*</td>\n",
       "      <td>$18,457</td>\n",
       "      <td>-0.49%</td>\n",
       "      <td>3.66%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking by market cap       name   market cap      price  \\\n",
       "0    XBY                    190  XtraBYtes  $7,422,935   $0.011420   \n",
       "1    CPN                    637  CompuCoin     $57,962   $0.002955   \n",
       "2  CHEAP                    976  Cheapcoin           ?   $0.000112   \n",
       "3   BLAS                    700  BlakeStar     $27,044   $0.000112   \n",
       "4    CPC                    341  Capricoin    $848,890   $0.426568   \n",
       "\n",
       "  circulating supply   volume     % 1h   % 24h    % 1wk  \n",
       "0        650,000,000        *  $16,273  -1.36%   -0.03%  \n",
       "1         19,615,019  Low Vol    0.34%  -4.69%  -31.49%  \n",
       "2                  ?        *  Low Vol   0.02%  105.40%  \n",
       "3        242,418,240  Low Vol    0.02%  -5.80%        ?  \n",
       "4          1,990,045        *  $18,457  -0.49%    3.66%  "
      ]
     },
     "execution_count": 747,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a quick look at what the data looks like:\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution graphs (histogram/bar graph) of sampled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {
    "_kg_hide-input": false,
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# plot_per_column_distribution(df2, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 18)\n",
      "symbol          object\n",
      "ranking          int64\n",
      "by              object\n",
      "market          object\n",
      "cap             object\n",
      "name            object\n",
      "market.1        object\n",
      "cap.1           object\n",
      "price           object\n",
      "circulating     object\n",
      "supply          object\n",
      "volume          object\n",
      "%               object\n",
      "1h              object\n",
      "%.1            float64\n",
      "24h            float64\n",
      "%.2            float64\n",
      "1wk            float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df1.shape)\n",
    "print(df1.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9432\r\n"
     ]
    }
   ],
   "source": [
    "# Given I have 26,000+ .csv files, I dont have enough GPU power to combine all of them\n",
    "# either in Kaggle Kernel or in my local Machine. Hence, I will take 9432 of those files.\n",
    "# There's no precise reason behind the number 9432 - I just could copy that many files at a time in my machine\n",
    "# combine those 9432 file's contents to a single pandas data-frame\n",
    "# So let's print all the files in the directory.\n",
    "\n",
    "!ls $file_dir | wc -l  # 9432\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XBY</td>\n",
       "      <td>224</td>\n",
       "      <td>XtraBYtes</td>\n",
       "      <td>$5,760,242</td>\n",
       "      <td>$0.008862</td>\n",
       "      <td>650,000,000</td>\n",
       "      <td>*</td>\n",
       "      <td>$10,278</td>\n",
       "      <td>-5.60%</td>\n",
       "      <td>-0.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CPN</td>\n",
       "      <td>698</td>\n",
       "      <td>CompuCoin</td>\n",
       "      <td>$59,253</td>\n",
       "      <td>$0.003021</td>\n",
       "      <td>19,615,019</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>-1.19%</td>\n",
       "      <td>-17.47%</td>\n",
       "      <td>14.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEAP</td>\n",
       "      <td>941</td>\n",
       "      <td>Cheapcoin</td>\n",
       "      <td>?</td>\n",
       "      <td>$0.000085</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>-2.59%</td>\n",
       "      <td>4.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BLAS</td>\n",
       "      <td>381</td>\n",
       "      <td>BlakeStar</td>\n",
       "      <td>$811,319</td>\n",
       "      <td>$0.003321</td>\n",
       "      <td>244,268,640</td>\n",
       "      <td>$81,686</td>\n",
       "      <td>-2.83%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>526.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CPC</td>\n",
       "      <td>386</td>\n",
       "      <td>Capricoin</td>\n",
       "      <td>$774,826</td>\n",
       "      <td>$0.389351</td>\n",
       "      <td>1,990,045</td>\n",
       "      <td>*</td>\n",
       "      <td>$24,731</td>\n",
       "      <td>-4.53%</td>\n",
       "      <td>-8.95%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking by market cap       name   market cap      price  \\\n",
       "0    XBY                    224  XtraBYtes  $5,760,242   $0.008862   \n",
       "1    CPN                    698  CompuCoin     $59,253   $0.003021   \n",
       "2  CHEAP                    941  Cheapcoin           ?   $0.000085   \n",
       "3   BLAS                    381  BlakeStar    $811,319   $0.003321   \n",
       "4    CPC                    386  Capricoin    $774,826   $0.389351   \n",
       "\n",
       "  circulating supply   volume     % 1h    % 24h    % 1wk  \n",
       "0        650,000,000        *  $10,278   -5.60%   -0.55%  \n",
       "1         19,615,019  Low Vol   -1.19%  -17.47%   14.35%  \n",
       "2                  ?        *  Low Vol   -2.59%    4.71%  \n",
       "3        244,268,640  $81,686   -2.83%   15.00%  526.99%  \n",
       "4          1,990,045        *  $24,731   -4.53%   -8.95%  "
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining a variable for to hold a Python-list of .csv files in that directory\n",
    "# files_list = glob.glob(os.path.join(file_dir, \"*.csv\"))\n",
    "all_files = glob.glob(os.path.join(file_dir, \"*.csv\"))\n",
    "\n",
    "# lets take the first 1400 .csv file (from which I shall create a combined-dataframe)\n",
    "# Note in the original .zipped folder (uploaded to Kaggle) there are 26,000+ files.\n",
    "# But for the sake of running this data in local file-system.\n",
    "files_list = all_files[:9432]\n",
    "\n",
    "# lets create dataframes and print them to see if it working\n",
    "\n",
    "df1 = pd.read_csv(files_list[0])\n",
    "df2 = pd.read_csv(files_list[1])\n",
    "\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MIU</td>\n",
       "      <td>1043</td>\n",
       "      <td>MIYUCoin</td>\n",
       "      <td>?</td>\n",
       "      <td>$0.000047</td>\n",
       "      <td>?</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>0.53%</td>\n",
       "      <td>2.81%</td>\n",
       "      <td>11.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XBY</td>\n",
       "      <td>196</td>\n",
       "      <td>XTRABYTES</td>\n",
       "      <td>$11,860,841</td>\n",
       "      <td>$0.028140</td>\n",
       "      <td>421,500,000</td>\n",
       "      <td>*</td>\n",
       "      <td>$146,279</td>\n",
       "      <td>2.54%</td>\n",
       "      <td>21.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CPN</td>\n",
       "      <td>704</td>\n",
       "      <td>CompuCoin</td>\n",
       "      <td>$65,942</td>\n",
       "      <td>$0.003362</td>\n",
       "      <td>19,615,019</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>0.74%</td>\n",
       "      <td>7.33%</td>\n",
       "      <td>-16.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEAP</td>\n",
       "      <td>1094</td>\n",
       "      <td>Cheapcoin</td>\n",
       "      <td>?</td>\n",
       "      <td>$0.000092</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>Low Vol</td>\n",
       "      <td>?</td>\n",
       "      <td>1.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BLAS</td>\n",
       "      <td>479</td>\n",
       "      <td>BlakeStar</td>\n",
       "      <td>$155,576</td>\n",
       "      <td>$0.000630</td>\n",
       "      <td>246,894,112</td>\n",
       "      <td>$4,413</td>\n",
       "      <td>15.64%</td>\n",
       "      <td>6.42%</td>\n",
       "      <td>69.75%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking by market cap       name    market cap      price  \\\n",
       "0    MIU                   1043   MIYUCoin            ?   $0.000047   \n",
       "1    XBY                    196  XTRABYTES  $11,860,841   $0.028140   \n",
       "2    CPN                    704  CompuCoin      $65,942   $0.003362   \n",
       "3  CHEAP                   1094  Cheapcoin            ?   $0.000092   \n",
       "4   BLAS                    479  BlakeStar     $155,576   $0.000630   \n",
       "\n",
       "  circulating supply   volume      % 1h  % 24h    % 1wk  \n",
       "0                  ?  Low Vol     0.53%  2.81%   11.19%  \n",
       "1        421,500,000        *  $146,279  2.54%   21.30%  \n",
       "2         19,615,019  Low Vol     0.74%  7.33%  -16.30%  \n",
       "3                  ?        *   Low Vol      ?    1.15%  \n",
       "4        246,894,112   $4,413    15.64%  6.42%   69.75%  "
      ]
     },
     "execution_count": 752,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Code to combine 9432 .csv files into a single dataframe and then\n",
    "### Filter data for 'Symbol' column == 'BTC'\n",
    "### generating a .csv file out that combined-single dataframe to work with.\n",
    "\n",
    "As we can see above, all these files have the same columns so it seems reasonable to concatenate everything into one dataframe. However, I want to keep track of the file names because that's the only reference to the date of the records.\n",
    "\n",
    "- First, creating a list of dataframes with the filenames in a \"file_name\" column\n",
    "- Then concatenate them all into one big dataframe\n",
    "\n",
    "#### The  below are the scripts for that, but I have commented-out all of these lines,\n",
    "#### as obviously I dont want to run this huge process-intensive steps every time\n",
    "#### of creating a single DataFrame out of 9432 .csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# dataframes = [pd.read_csv(file).assign(file_name=os.path.basename(file).strip(\".csv\")) for file in files_list]\n",
    "# combined_df = pd.concat(dataframes, ignore_index=True)\n",
    "# combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# combined_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The above dataframe has all the SYMBOLS of all the crypto-currencies as was in the individual .csv files.\n",
    "##### But now I want to extract ONLY the symbol 'BTC' for Bitcoin for the further analysis.\n",
    "\n",
    "Below is the code for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Creating a dataframe, by filtering only the rows where the column 'Symbol' is 'BTC'\n",
    "\n",
    "# btc_df = combined_df[combined_df['symbol'] == 'BTC']\n",
    "# btc_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Now generating a .csv file contain which will be used as a training dataset\n",
    "#### This file is created out that combined-single dataframe (that I earlier created from 9432 .csv files )\n",
    "as obviously I dont want to run this huge process-intensive step of creating a single\n",
    "Data-Frame out of 9432 .csv files.\n",
    "\n",
    "Below code is commented out as well, because I have run this script just once to create the file.\n",
    "And then I have saved the file (to be used as an input) both in Kaggle and also for my local machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# btc_df.to_csv(\"train_BTC_combined.csv\", index=False)\n",
    "\n",
    "# Passing index=False so as not to not write out an 'unnamed' index column to the combined dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "      <th>...</th>\n",
       "      <th>market.1</th>\n",
       "      <th>cap.1</th>\n",
       "      <th>circulating</th>\n",
       "      <th>supply</th>\n",
       "      <th>%</th>\n",
       "      <th>1h</th>\n",
       "      <th>%.1</th>\n",
       "      <th>24h</th>\n",
       "      <th>%.2</th>\n",
       "      <th>1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>$70,846,063,125</td>\n",
       "      <td>$4291.75</td>\n",
       "      <td>16,507,500</td>\n",
       "      <td>$2,814,510,000</td>\n",
       "      <td>-2.16%</td>\n",
       "      <td>5.57%</td>\n",
       "      <td>24.99%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>$76,715,293,809</td>\n",
       "      <td>$4639.63</td>\n",
       "      <td>16,534,787</td>\n",
       "      <td>$1,731,360,000</td>\n",
       "      <td>0.56%</td>\n",
       "      <td>2.73%</td>\n",
       "      <td>10.53%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>$71,411,773,702</td>\n",
       "      <td>$4316.74</td>\n",
       "      <td>16,542,987</td>\n",
       "      <td>$2,080,400,000</td>\n",
       "      <td>-2.51%</td>\n",
       "      <td>-6.91%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>$67,158,717,638</td>\n",
       "      <td>$4066.51</td>\n",
       "      <td>16,515,075</td>\n",
       "      <td>$2,796,620,000</td>\n",
       "      <td>-0.71%</td>\n",
       "      <td>-5.92%</td>\n",
       "      <td>9.26%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>$67,243,714,005</td>\n",
       "      <td>$4071.86</td>\n",
       "      <td>16,514,250</td>\n",
       "      <td>$2,611,450,000</td>\n",
       "      <td>-1.38%</td>\n",
       "      <td>-5.83%</td>\n",
       "      <td>13.93%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking by market cap     name        market cap     price  \\\n",
       "0    BTC                    1.0  Bitcoin  $70,846,063,125   $4291.75   \n",
       "1    BTC                    1.0  Bitcoin  $76,715,293,809   $4639.63   \n",
       "2    BTC                    1.0  Bitcoin  $71,411,773,702   $4316.74   \n",
       "3    BTC                    1.0  Bitcoin  $67,158,717,638   $4066.51   \n",
       "4    BTC                    1.0  Bitcoin  $67,243,714,005   $4071.86   \n",
       "\n",
       "  circulating supply          volume    % 1h   % 24h   % 1wk  ... market.1  \\\n",
       "0         16,507,500  $2,814,510,000  -2.16%   5.57%  24.99%  ...      NaN   \n",
       "1         16,534,787  $1,731,360,000   0.56%   2.73%  10.53%  ...      NaN   \n",
       "2         16,542,987  $2,080,400,000  -2.51%  -6.91%   0.54%  ...      NaN   \n",
       "3         16,515,075  $2,796,620,000  -0.71%  -5.92%   9.26%  ...      NaN   \n",
       "4         16,514,250  $2,611,450,000  -1.38%  -5.83%  13.93%  ...      NaN   \n",
       "\n",
       "   cap.1 circulating supply   %  1h %.1 24h  %.2  1wk  \n",
       "0    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "1    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "2    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "3    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "4    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now that I have already created a .csv with the combined dataframe for BTC, lets use that going forward.\n",
    "original_btc_train = pd.read_csv(\"train_BTC_combined.csv\")\n",
    "\n",
    "# The same code above will be as below in Kaggle because of the Kaggle's file-tree-structure for uploaded input-data\n",
    "# original_btc_train = pd.read_csv(\"/kaggle/input/crypto-data/train_BTC_combined.csv\")\n",
    "\n",
    "original_btc_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9431, 25)"
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_btc_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Lets analyze the dataframe with .info() method. This method prints a concise summary of the data frame, including the column names and their data types, the number of non-null values, the amount of memory used by the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9431 entries, 0 to 9430\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   symbol                 9431 non-null   object \n",
      " 1   ranking by market cap  9430 non-null   float64\n",
      " 2   name                   9431 non-null   object \n",
      " 3   market cap             9430 non-null   object \n",
      " 4   price                  9431 non-null   object \n",
      " 5   circulating supply     9430 non-null   object \n",
      " 6   volume                 9430 non-null   object \n",
      " 7   % 1h                   9430 non-null   object \n",
      " 8   % 24h                  9430 non-null   object \n",
      " 9   % 1wk                  9430 non-null   object \n",
      " 10  file_name              9431 non-null   object \n",
      " 11  ranking                1 non-null      float64\n",
      " 12  by                     1 non-null      object \n",
      " 13  market                 1 non-null      object \n",
      " 14  cap                    1 non-null      object \n",
      " 15  market.1               1 non-null      object \n",
      " 16  cap.1                  1 non-null      object \n",
      " 17  circulating            1 non-null      object \n",
      " 18  supply                 0 non-null      float64\n",
      " 19  %                      0 non-null      float64\n",
      " 20  1h                     0 non-null      float64\n",
      " 21  %.1                    0 non-null      float64\n",
      " 22  24h                    0 non-null      float64\n",
      " 23  %.2                    0 non-null      float64\n",
      " 24  1wk                    0 non-null      float64\n",
      "dtypes: float64(9), object(16)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "original_btc_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "As shown above, the data sets do not contain null values but some of the columns where I expected numerical or float values, instead contain object Dtype like the 'market cap' column.\n",
    "\n",
    "Also starting with 'file_name' upto '1wk' columns dont have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Features list ['symbol', 'ranking by market cap', 'name', 'market cap', 'price', 'circulating supply', 'volume', '% 1h', '% 24h', '% 1wk', 'file_name', 'ranking', 'by', 'market', 'cap', 'market.1', 'cap.1', 'circulating', 'supply', '%', '1h', '%.1', '24h', '%.2', '1wk']\n",
      "\n",
      "Missing Values symbol                   False\n",
      "ranking by market cap     True\n",
      "name                     False\n",
      "market cap                True\n",
      "price                    False\n",
      "circulating supply        True\n",
      "volume                    True\n",
      "% 1h                      True\n",
      "% 24h                     True\n",
      "% 1wk                     True\n",
      "file_name                False\n",
      "ranking                   True\n",
      "by                        True\n",
      "market                    True\n",
      "cap                       True\n",
      "market.1                  True\n",
      "cap.1                     True\n",
      "circulating               True\n",
      "supply                    True\n",
      "%                         True\n",
      "1h                        True\n",
      "%.1                       True\n",
      "24h                       True\n",
      "%.2                       True\n",
      "1wk                       True\n",
      "dtype: bool\n",
      "\n",
      "Unique Values  symbol                      1\n",
      "ranking by market cap       1\n",
      "name                        2\n",
      "market cap               9181\n",
      "price                    8789\n",
      "circulating supply       2001\n",
      "volume                   8969\n",
      "% 1h                      612\n",
      "% 24h                    2021\n",
      "% 1wk                    3370\n",
      "file_name                9431\n",
      "ranking                     1\n",
      "by                          1\n",
      "market                      1\n",
      "cap                         1\n",
      "market.1                    1\n",
      "cap.1                       1\n",
      "circulating                 1\n",
      "supply                      0\n",
      "%                           0\n",
      "1h                          0\n",
      "%.1                         0\n",
      "24h                         0\n",
      "%.2                         0\n",
      "1wk                         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# All Features List\n",
    "print(\"All Features list\", original_btc_train.columns.tolist())\n",
    "print(\"\\nMissing Values\", original_btc_train.isnull().any())\n",
    "print(\"\\nUnique Values \", original_btc_train.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7.084606e+10\n",
       "1       7.671529e+10\n",
       "2       7.141177e+10\n",
       "3       6.715872e+10\n",
       "4       6.724371e+10\n",
       "            ...     \n",
       "9426    7.130818e+10\n",
       "9427    7.047523e+10\n",
       "9428    7.234822e+10\n",
       "9429    6.852041e+10\n",
       "9430    6.749006e+10\n",
       "Name: market cap, Length: 9431, dtype: float64"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the '$' and the extra commas from the 'market cap' column\n",
    "\n",
    "# In the real world data set, to see if there are are non-numeric values in the column.\n",
    "# my first approach was to try to use astype() as below commented out line\n",
    "# original_btc_train['market cap'].astype('float')\n",
    "# The output would be - could not convert string to float: '$70,846,063,125 '\n",
    "\n",
    "# So now, let’s try removing the ‘$’ and ‘,’ using str.replace :\n",
    "original_btc_train['market cap'] =original_btc_train['market cap'].str.replace(',', '')\n",
    "original_btc_train['market cap'] =pd.to_numeric(original_btc_train['market cap'].str.replace('$', ''))\n",
    "# In above I using to_numeric() as otherwise the 'market cap' column will continue to be dtype object\n",
    "original_btc_train['market cap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "      <th>...</th>\n",
       "      <th>market.1</th>\n",
       "      <th>cap.1</th>\n",
       "      <th>circulating</th>\n",
       "      <th>supply</th>\n",
       "      <th>%</th>\n",
       "      <th>1h</th>\n",
       "      <th>%.1</th>\n",
       "      <th>24h</th>\n",
       "      <th>%.2</th>\n",
       "      <th>1wk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.084606e+10</td>\n",
       "      <td>$4291.75</td>\n",
       "      <td>16,507,500</td>\n",
       "      <td>$2,814,510,000</td>\n",
       "      <td>-2.16%</td>\n",
       "      <td>5.57%</td>\n",
       "      <td>24.99%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.671529e+10</td>\n",
       "      <td>$4639.63</td>\n",
       "      <td>16,534,787</td>\n",
       "      <td>$1,731,360,000</td>\n",
       "      <td>0.56%</td>\n",
       "      <td>2.73%</td>\n",
       "      <td>10.53%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.141177e+10</td>\n",
       "      <td>$4316.74</td>\n",
       "      <td>16,542,987</td>\n",
       "      <td>$2,080,400,000</td>\n",
       "      <td>-2.51%</td>\n",
       "      <td>-6.91%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.715872e+10</td>\n",
       "      <td>$4066.51</td>\n",
       "      <td>16,515,075</td>\n",
       "      <td>$2,796,620,000</td>\n",
       "      <td>-0.71%</td>\n",
       "      <td>-5.92%</td>\n",
       "      <td>9.26%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.724371e+10</td>\n",
       "      <td>$4071.86</td>\n",
       "      <td>16,514,250</td>\n",
       "      <td>$2,611,450,000</td>\n",
       "      <td>-1.38%</td>\n",
       "      <td>-5.83%</td>\n",
       "      <td>13.93%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  ranking by market cap     name    market cap     price  \\\n",
       "0    BTC                    1.0  Bitcoin  7.084606e+10  $4291.75   \n",
       "1    BTC                    1.0  Bitcoin  7.671529e+10  $4639.63   \n",
       "2    BTC                    1.0  Bitcoin  7.141177e+10  $4316.74   \n",
       "3    BTC                    1.0  Bitcoin  6.715872e+10  $4066.51   \n",
       "4    BTC                    1.0  Bitcoin  6.724371e+10  $4071.86   \n",
       "\n",
       "  circulating supply          volume    % 1h   % 24h   % 1wk  ... market.1  \\\n",
       "0         16,507,500  $2,814,510,000  -2.16%   5.57%  24.99%  ...      NaN   \n",
       "1         16,534,787  $1,731,360,000   0.56%   2.73%  10.53%  ...      NaN   \n",
       "2         16,542,987  $2,080,400,000  -2.51%  -6.91%   0.54%  ...      NaN   \n",
       "3         16,515,075  $2,796,620,000  -0.71%  -5.92%   9.26%  ...      NaN   \n",
       "4         16,514,250  $2,611,450,000  -1.38%  -5.83%  13.93%  ...      NaN   \n",
       "\n",
       "   cap.1 circulating supply   %  1h %.1 24h  %.2  1wk  \n",
       "0    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "1    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "2    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "3    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "4    NaN         NaN    NaN NaN NaN NaN NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_btc_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Basic statistics\n",
    "original_btc_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7.084606e+10\n",
       "1       7.671529e+10\n",
       "2       7.141177e+10\n",
       "3       6.715872e+10\n",
       "4       6.724371e+10\n",
       "            ...     \n",
       "9426    7.130818e+10\n",
       "9427    7.047523e+10\n",
       "9428    7.234822e+10\n",
       "9429    6.852041e+10\n",
       "9430    6.749006e+10\n",
       "Name: market cap, Length: 9431, dtype: float64"
      ]
     },
     "execution_count": 763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and now if I run below\n",
    "original_btc_train['market cap'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>symbol</th>\n",
       "      <th>ranking by market cap</th>\n",
       "      <th>name</th>\n",
       "      <th>market cap</th>\n",
       "      <th>price</th>\n",
       "      <th>circulating supply</th>\n",
       "      <th>volume</th>\n",
       "      <th>% 1h</th>\n",
       "      <th>% 24h</th>\n",
       "      <th>% 1wk</th>\n",
       "      <th>...</th>\n",
       "      <th>market.1</th>\n",
       "      <th>cap.1</th>\n",
       "      <th>circulating</th>\n",
       "      <th>supply</th>\n",
       "      <th>%</th>\n",
       "      <th>1h</th>\n",
       "      <th>%.1</th>\n",
       "      <th>24h</th>\n",
       "      <th>%.2</th>\n",
       "      <th>1wk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r_20170815-071005</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.084606e+10</td>\n",
       "      <td>$4291.75</td>\n",
       "      <td>16,507,500</td>\n",
       "      <td>$2,814,510,000</td>\n",
       "      <td>-2.16%</td>\n",
       "      <td>5.57%</td>\n",
       "      <td>24.99%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170831-114005</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.671529e+10</td>\n",
       "      <td>$4639.63</td>\n",
       "      <td>16,534,787</td>\n",
       "      <td>$1,731,360,000</td>\n",
       "      <td>0.56%</td>\n",
       "      <td>2.73%</td>\n",
       "      <td>10.53%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170904-085005</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>7.141177e+10</td>\n",
       "      <td>$4316.74</td>\n",
       "      <td>16,542,987</td>\n",
       "      <td>$2,080,400,000</td>\n",
       "      <td>-2.51%</td>\n",
       "      <td>-6.91%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170819-065505</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.715872e+10</td>\n",
       "      <td>$4066.51</td>\n",
       "      <td>16,515,075</td>\n",
       "      <td>$2,796,620,000</td>\n",
       "      <td>-0.71%</td>\n",
       "      <td>-5.92%</td>\n",
       "      <td>9.26%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170818-210005</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>6.724371e+10</td>\n",
       "      <td>$4071.86</td>\n",
       "      <td>16,514,250</td>\n",
       "      <td>$2,611,450,000</td>\n",
       "      <td>-1.38%</td>\n",
       "      <td>-5.83%</td>\n",
       "      <td>13.93%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  symbol  ranking by market cap     name    market cap  \\\n",
       "file_name                                                                \n",
       "r_20170815-071005    BTC                    1.0  Bitcoin  7.084606e+10   \n",
       "r_20170831-114005    BTC                    1.0  Bitcoin  7.671529e+10   \n",
       "r_20170904-085005    BTC                    1.0  Bitcoin  7.141177e+10   \n",
       "r_20170819-065505    BTC                    1.0  Bitcoin  6.715872e+10   \n",
       "r_20170818-210005    BTC                    1.0  Bitcoin  6.724371e+10   \n",
       "\n",
       "                      price circulating supply          volume    % 1h  \\\n",
       "file_name                                                                \n",
       "r_20170815-071005  $4291.75         16,507,500  $2,814,510,000  -2.16%   \n",
       "r_20170831-114005  $4639.63         16,534,787  $1,731,360,000   0.56%   \n",
       "r_20170904-085005  $4316.74         16,542,987  $2,080,400,000  -2.51%   \n",
       "r_20170819-065505  $4066.51         16,515,075  $2,796,620,000  -0.71%   \n",
       "r_20170818-210005  $4071.86         16,514,250  $2,611,450,000  -1.38%   \n",
       "\n",
       "                    % 24h   % 1wk  ...  market.1 cap.1 circulating supply   %  \\\n",
       "file_name                          ...                                          \n",
       "r_20170815-071005   5.57%  24.99%  ...       NaN   NaN         NaN    NaN NaN   \n",
       "r_20170831-114005   2.73%  10.53%  ...       NaN   NaN         NaN    NaN NaN   \n",
       "r_20170904-085005  -6.91%   0.54%  ...       NaN   NaN         NaN    NaN NaN   \n",
       "r_20170819-065505  -5.92%   9.26%  ...       NaN   NaN         NaN    NaN NaN   \n",
       "r_20170818-210005  -5.83%  13.93%  ...       NaN   NaN         NaN    NaN NaN   \n",
       "\n",
       "                   1h %.1  24h  %.2  1wk  \n",
       "file_name                                 \n",
       "r_20170815-071005 NaN NaN  NaN  NaN  NaN  \n",
       "r_20170831-114005 NaN NaN  NaN  NaN  NaN  \n",
       "r_20170904-085005 NaN NaN  NaN  NaN  NaN  \n",
       "r_20170819-065505 NaN NaN  NaN  NaN  NaN  \n",
       "r_20170818-210005 NaN NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_train = original_btc_train.set_index('file_name')\n",
    "btc_train.head()"
   ]
  },
  {
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       "                     market cap\n",
       "file_name                      \n",
       "r_20170815-071005  7.084606e+10\n",
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   "source": [
    "market_cap = btc_train[['market cap']]\n",
    "market_cap.head()"
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  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
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   "source": [
    "# Rolling Mean (Moving Average) — to determine trend\n",
    "\n",
    "A simple moving average, also called a rolling or running average is formed by computing the average price of a security over a specific number of periods. Most moving averages are based on closing prices; for example, a 5-day simple moving average is the five-day sum of closing prices divided by five. As its name implies, a moving average is an average that moves. Old data is dropped as new data becomes available, causing the average to move along the time scale. The example below shows a 5-day moving average evolving over three days.\n",
    "\n",
    "```\n",
    "Daily Closing Prices: 11,12,13,14,15,16,17\n",
    "\n",
    "First day of 5-day SMA: (11 + 12 + 13 + 14 + 15) / 5 = 13\n",
    "\n",
    "Second day of 5-day SMA: (12 + 13 + 14 + 15 + 16) / 5 = 14\n",
    "\n",
    "Third day of 5-day SMA: (13 + 14 + 15 + 16 + 17) / 5 = 15\n",
    "```\n",
    "\n",
    "![img](https://i.imgur.com/TpOZqYb.png)\n",
    "\n",
    "The first day of the moving average simply covers the last five days. The second day of the moving average drops the first data point (11) and adds the new data point (16).\n",
    "\n",
    "So the simple moving average is the unweighted mean of the previous M data points. The selection of M (sliding window) depends on the amount of smoothing desired since increasing the value of M improves the smoothing at the expense of accuracy.\n",
    "\n",
    "The moving average is used to analyze the time-series data by calculating averages of different subsets of the complete dataset. Since it involves taking the average of the dataset over time, it is also called a moving mean (MM) or rolling mean. Moving averages are widely used in finance to determine trends in the market and in environmental engineering to evaluate standards for environmental quality such as the concentration of pollutants.\n",
    "\n",
    "The easiest way to calculate the simple moving average is by using the pandas.Series.rolling method. This method provides rolling windows over the data. On the resulting windows, we can perform calculations using a statistical function (in this case the mean). The size of the window (number of periods) is specified in the argument window."
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       "                     market cap\n",
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      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
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      "Try using .loc[row_indexer,col_indexer] = value instead\n",
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      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/home/paul/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
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      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
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       "      <td>7.052990e+10</td>\n",
       "      <td>6.752149e+10</td>\n",
       "      <td>6.724237e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170829-064005</th>\n",
       "      <td>7.234822e+10</td>\n",
       "      <td>6.962013e+10</td>\n",
       "      <td>6.799893e+10</td>\n",
       "      <td>6.726716e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170824-044005</th>\n",
       "      <td>6.852041e+10</td>\n",
       "      <td>7.137721e+10</td>\n",
       "      <td>6.867417e+10</td>\n",
       "      <td>6.740239e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170821-103506</th>\n",
       "      <td>6.749006e+10</td>\n",
       "      <td>7.044795e+10</td>\n",
       "      <td>6.869882e+10</td>\n",
       "      <td>6.738047e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9431 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     market cap  ma_rolling_3-Day  ma_rolling_30-Day  \\\n",
       "file_name                                                              \n",
       "r_20170815-071005  7.084606e+10               NaN                NaN   \n",
       "r_20170831-114005  7.671529e+10               NaN                NaN   \n",
       "r_20170904-085005  7.141177e+10               NaN                NaN   \n",
       "r_20170819-065505  6.715872e+10      7.299104e+10                NaN   \n",
       "r_20170818-210005  6.724371e+10      7.176193e+10                NaN   \n",
       "...                         ...               ...                ...   \n",
       "r_20170904-132006  7.130818e+10      6.985996e+10       6.701422e+10   \n",
       "r_20170817-190505  7.047523e+10      7.052990e+10       6.752149e+10   \n",
       "r_20170829-064005  7.234822e+10      6.962013e+10       6.799893e+10   \n",
       "r_20170824-044005  6.852041e+10      7.137721e+10       6.867417e+10   \n",
       "r_20170821-103506  6.749006e+10      7.044795e+10       6.869882e+10   \n",
       "\n",
       "                   ma_rolling_3-Months  \n",
       "file_name                               \n",
       "r_20170815-071005                  NaN  \n",
       "r_20170831-114005                  NaN  \n",
       "r_20170904-085005                  NaN  \n",
       "r_20170819-065505                  NaN  \n",
       "r_20170818-210005                  NaN  \n",
       "...                                ...  \n",
       "r_20170904-132006         6.704794e+10  \n",
       "r_20170817-190505         6.724237e+10  \n",
       "r_20170829-064005         6.726716e+10  \n",
       "r_20170824-044005         6.740239e+10  \n",
       "r_20170821-103506         6.738047e+10  \n",
       "\n",
       "[9431 rows x 4 columns]"
      ]
     },
     "execution_count": 767,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_cap['ma_rolling_3-Day'] = market_cap['market cap'].rolling(window=3).mean().shift(1)\n",
    "market_cap['ma_rolling_30-Day'] = market_cap['market cap'].rolling(window=30).mean().shift(1)\n",
    "market_cap['ma_rolling_3-Months'] = market_cap['market cap'].rolling(window=90).mean().shift(1)\n",
    "market_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2baad6c50>"
      ]
     },
     "execution_count": 768,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['steelblue', 'red', 'purple', 'black']\n",
    "\n",
    "market_cap.plot(color=colors, linewidth=2, figsize=(20,6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Weighted moving average\n",
    "\n",
    "Weighted moving average = (t weighting factor) + ((t-1) weighting factor-1) + ((t-n) * weighting factor-n)/n\n",
    "\n",
    "**weighted moving average assigns a specific weight or frequency to each observation, with the most recent observation being assigned a greater weight than those in the distant past to obtain the average.**\n",
    "\n",
    "**Example**\n",
    "\n",
    "Assume that the number of periods is 10, and we want a weighted moving average of four stock prices of $70, $66, $68, and $69, with the first price being the most recent.\n",
    "\n",
    "Using the information given, the most recent weighting will be 4/10, the previous period before that will be 3/10, and the next period before that will be 2/10, and the initial period weighting will be 1/10.\n",
    "\n",
    "The weighting average for the four different prices will be calculated as follows:\n",
    "\n",
    "#### WMA = [70 x (4/10)] + [66 x (3/10)] + [68 x (2/10)] + [69 x (1/10)]\n",
    "\n",
    "WMA = $28 + $19.80 + $13.60 + $6.90 = $68.30\n",
    "\n",
    "![img](https://i.imgur.com/MZO1bbC.png)\n",
    "\n",
    "The accuracy of this model depends largely on your choice of weighting factors. If the time series pattern changes, you must also adapt the weighting factors.\n",
    "\n",
    "When creating a weighting group, you enter the weighting factors as percentages. The sum of the weighting factors does not have to be 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2bad46b10>"
      ]
     },
     "execution_count": 769,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KXWJ7agWO5tWbXzgVEgoacNvH+xCXG9x8wxFac7BoABMFQYgGMAmAtObuBrDS+/dmALcII86JgzZOlzHJom9yKpYMUkYkrTR51rX3KU5aPpX7sqZu/H3WIe0EKb+6mXPkK6uS8e/5ccisbNW+WQO3x4O/zzqE+2YcBGDSaZ3Od913cnBg3ZYS2Zo/Dpcb07aeNM1ZYV17n6kChWD662jvtaHcq+335OJ4LIrNx6HsOsX7fRGVsquMnXrK1dfejGq8uTaV6pQ7kgZzM0yNxBBCVNvbqrjioC5ipJtQGtMO33TsH7XNWmj7qPht+u0uxBc0hJXA5J31aXhmSYKuceI7dSX4aPX7mLX4JTw074gFpRvZWNla5fqC3OHPMq+/wmWHzPVbyDEG6xajtq0X93wRizfWpFDd/+fPY5g1wF7/JgUr44qx80QVmjr7MXVZ4pBAM1LR0wcJIVhysBBfHyzQ8az870Z3lC+vTEZpYzf+qyGQicurR1e/A9tTK41lCCC/ZlC7MJIcjbOw1Dsm6vnOANDeY8NXMfn4ZCu7qwwjglZffkZddIwENIVAhJA6AF8CqAbQAKCLEBIrue0iADXe+10AugBMNreo4U8kyv4Xxebj4flx2GbCgBcqCmoH7cMTChqpn1GaUKSCvHD4pmbJU0saurAnvSrop1QxmbWIy6s3xVlhdWsvHp4fhwfnHjahZPLoqe11CaV4d32a5ib7bzMP4cnF8WgSmfnVBCH8udw3n70nB+llLTioIoTyYUYTJGTQd02f3TqhthHq2vqwPbUi4AT43fVp+PPnMQHahsBgn1oTX6JrEaMXojIqNXcNoHsgUJuBEIKtx8tx28d7LfOnIWbJoQL8+fMYpJcrm/O4PR4kFTaiZ2C4PXy2PRMfbc6gOoUN1jCWVtqC0sZudPWxm1Re0ViOh6cuxsf3v4F2FV9JHHlGskZFKM9Jzc7a6Hfq6nfglVXJisJ0pw6tHCm+9WEmpWa9w+VBTas+H0EL9ufhvQ0nUFDXabkWSLj3Ea3yFdR24O11qbLzq3865pTH5qQ/QDTa7sL80wSVlu4BPLk43q+P91Nq/0fSIWSkQWMOdhYGNX0uB3AhgFMEQfin9DaZRwOavyAIjwmCcEIQhBMtLaGN2BQOuD0E1a29moOklYO8T9q9OXlkOWYOq0EjTArzzJIEzNmbixMW+FlQw0x/Ur4IXt0D1gkS9PS2FUeKkFLSjOwqugVmTVv4OKCU2vzXt/fh3fVpKDJZWNDY2Y9/z4/D32ZSaBQGEd/4+vCCOHwVk48daZV+133RBeNlhMx6BVpmjujSDd00BYHUogODp3VLg6DRsNXrt2x9QqniPVuOV+D9TenoE2kM+U7N4wu0tQa16jCxsBFvr0tV1qRlnFfVhG9K1J99AQCg9pyLmZ8VszKuCE8sOjZiojDRwjdRo4M1x0qQXdUuK0yvaOrGHZ/sw6JYfzcLhBDszahGncJcmlbajH/OPhQ0X3RSgmXyGu5dREv5dOryJKSVtuDz7ZkB10L9bnd8ss+QSaoZwstIgGaL095rR3lTd1APzJQIky1ZWEBjDvY7ABWEkBZCiBPAVgA3S+6pBXAJAHhNxs4AEGC/QAhZTAi5kRBy47nnnmus5GEI64Jl5u5sPPrVUT/bdLNxuT04mF3rp3ngo7DO+hNhJWiqqqGjHzGZNdRq+OLNkNYTtN8qlOZgNGXwEII1x0qYnCTKnbiUNXbjxRVJlrSJMWPorE5pfC3InWA2dfbLtu9QsPpocaiLoIqsQFlSqZ9uy0RKSTOeW5oousX/nn0nq+EhhMm8yCdUsjvd2JtRPeRj41h+g99CPTarBtO2npR13FdY14EPN6WbGjkosdBfuOMz1wsk8F1D4R9Emqf0k2aUB5rFWik09cGq8p5W2kx1n83pRmZlK7Mjxw82pSOttEVVEKXGVzF5uvxCiCEmtY+18aWoaO5BgoJwLFy164bRNwmGu5YDZxCjWk1q2hlbvObwWyVm8bFZtZi9JwcPL4iTfe7tdWlo6bYNmf+MPgcVgxBC0NZjg8Plxtr4Eny+PVM1gmPg8zryVPmXEp1eX096hO0saL2PtJ2EKtiC3enGkoMFI9aUDODCmHCAZndWDeBngiBM8vr5uQWA1ABwJ4AHvX/fC+AwGYWzN6vjZJ8vh3n71BeaeifYovpO3P7JPnyxIwsPzYsLuP78ssTAhyzA4XLrWsw9NO8IZuzKNiWcaVppM/6jsFgAwuPEUc9nPppXj1VHi/HiCvrw5nIb93c3pCGvpgMvWxAmPTrKnKG+sK4jIMIaIQQPzD2CB+Za62+j3+6imozzanScOgah7REA2VVtuPvT/ZrO8Np7A8cx6RectTsHTyw6hgfmHqYW0opvm70nB69/k4KGjn58vCUDLywfduI8fWc24vLq/cw7fX3j+WVJSChsxD9nHzYt4lIcoz+f3Op23PrhHryyyvy+YhYlDV34xATzSxY+VTrhEwvndbT1j7dk4LXVKdiQqE9blVblXMr21EpVvxBF9Z3aGyqdc7fL7cGx/IYAHzUlDV0Bkf6WHSrEXz6PxYsrklAZQQ63Ae0NXxBdWIU1hBB8FZOHg9l0ayEtXzZarbJnwIkXlieZFkreCho66A5+jGxF5LqvFZHijKD1egtj8/H3WYfwrzmHsTKuGIdy6jBrd/AEG+Gwto5ENiWXY1NyOZ5ZkoCa1l48/XU89cGJmGD5vEwvb8GW42yOwM0sWfeAA4eya6kOJpX21C63Z9QdPND4BErBoLPnDAA53mcWC4LwgSAId3lvWwpgsiAIpQBeBPC6ReUNa9bpPHG0iq8PDMvqtAYCq9p9c9cA7py2H1/syNKdoS+ijBZqp/Jvr0vzM8EJx1MhPQ7xmrvYN8JumYx6vBsLK9RXoyk1gbR4fllSgNaGEmWN3Zi3L9fP34gRnl2SgGeWJFgSsjhYfLYtE3aXxzRneFUtvWjttmHAgJPuDpUofFqmL7tOVOrOVw2toeklr6A0u6odjTo10KxeaDyzJAFHTXLEbhW0hxupJYML3yMS4WVr9wCmLktU1I7RIuALMH6T55Ym4lBOHebty9WVvxpbjlfg4y0ZeEki3N+eWon7vUELfGxIGhSO5dV04PFFx0wviznom3CTI9yprlnk1XRge2pl4DpKhsyKVtw5bT++MaCVuimpDPm1Hfhyp3Z+gPHxTFWjMoz2ZNtSKnDntP3U65DgoF5BPkF2p8inWW0QzdFpP5/cdBAM/3UB5Qh6jvI0dAx/o+k7s1Da2I2317FFiV16qBB/+izGtAMzJQRBwJtrUrH4QAEK60JjfjnlywP4fEeWrFmhFmmlzVjmrauXLDgID2eodmeEkPcIIdcSQq4nhPyLEGInhLxLCNnpvW4jhEwhhFxFCLmJEBI+cQFHAJEsmTycM+h09lCOtvNZJahNt4TB6B6fb88cchbNCcQT5OPVMSZpArHw1Nfx2HWiCosO5GvfTEGt14QujVEI5CEESw8Vqp7gbEgqQ6nFKr9GhxAzhKasaWj5JHJ5CA5k1cpqLlmBXB0GM9pWpKK0wTM6r3UPOFFQ14kPN6trPCllo5Y9S9laVBbYRGfHyfA61K6VMd0NZjTDUQfF9wrFAdIAgzbbirgiAMDqYyVMedicbtz64R7c+uGeEeV7ari3SM1oCd5dn4YvVQRr0k+90OuXyKiZqJmE43AgLhPzOC+6/Y01qZbNsW4PQW51e9hpdsmhtz9uTCqD3enGHgtdjkjpZAigYGQoVWoVrJrdwKCSwIakMjjdHn3a/BGMOUf0nIinvdfO7HOBBqWOatW8tfhAAQ7l1FE5FhUTDppBesqg5xmWSbW5ayCiN7paESdYYa3u40VN2JhUpnmC8/SSBP2FCgKhiGSzJ71a9frm5HJ8uTMLU0WmZMFGr3xTqT4JIejuZ49CZSU2pxuxWTWa5aIZJcJhnFVEVLiyRiW/UBx9KEniAn+yWiAeUATKTWplSw/Vvc1d5vkr04XO6TpRtGaiDXAQDPT4iMkRld/3yaRjT/eAEyklzThAaWLnX6bwIZzKIodRIZXdIoHk2vgSvLQyWVUIGEpC4W8w2BhpGkbaVVivQ4IMFwJxhtDadAHaC6ZQ9i1BEEx1GMvCoexaHJIsJtwegtKGLtNObuXqVk9902oCnaxoxb/mHMa769lUUKWEUpONJmvpPWYWtzNIG/rK5h7M2ZuD9PKWgBDnANukJ379ps5+7EirhNOlIiCmTNvQpK2SSVNncPq8y0RhqFKfmLU7B1OmHxhymh0OLDlYgOk7s5lV0bUwU7BoysZb9E1eXGGOYFHNMXT3gAMzd2XLqs+H+8bOSoItEJeaGSrhcHmwhkK7Zn3isFuAtfElitGFrNqIULUdmbzFz7Ee/BjuyybXxSurjmveY2RdEsna+T7q2vuwNaUC3QMOS7VhqM3Bgrx78PXLcDebjjTEXYMLW8IfLgSKAIJ1Ck/je+ev0w9gpVfd2AjhNocaKQ8hZNAWVXKi8PXBAjy9JIFalVPZJ9Dg99cqIu0JqlzoUrm0fU4orQop7/Z4sO9ktWmRvZ5dmhDgNNXqSBNaKHVdsxeRzyxJwJ70ary5JhWfyvj8IUS5LqRF7BKp8z6xOB4L9udFlHmAVaNlMCK/7fcuTNUcEgcbX//XMs8LJX1KYeB1YlcTekpQm56JyrWlhwqxP7MGzy8LnSZbyLF4abPySJGmQ3EW84FNyfSeDpo6+7Eyrjhk0YVYGUn7NT3RqZjSD7P1qx4enh+HRbH5mPLlAdwn8TMmxvAairGyglW1SvsqK/dbNqcbSYWNzOupcG9vXNgTuXAhUBgSqJlgzgjAqlYpp8HSPeDE2njjDrCt2JyHYhxac6zEL7KRmG2SkKZyOFxuPL7wGBbFKvuuoX2vp5ckyIaHliKn6h2KSWZnWhVm7c7BIwuOAtA3kfSJnBIX13f5ncIC0LWiUC0HYxmD1SbFDr3jC7SdVqqZu4jT6jfg9DlUsJolGVMr5qsfH6EWuLISnNIqt48WGe0lK0yywwHFPmbhR+i3u7A2oRTLDhcyP2t3umUFiyzFNVNwzjJG0dzLEiKcLs/w7/s0I7X0NZTNdo2XxzRMKIz58/xwmWiVypS+j5F5pWfAiWlbTyKzcnhd7EstFFP37N3ZeH9TOubsydG+eZQtLdJKm5FVKW+GmlzchDoTnZmPsqpVhQuBRhGLNZzkStUx801wkBXUxYEFPVttoihp6MKqo8UoqBs+IWd93xNlLahs6cHWlArNSYnm9VJKtKOpBHvjqlQjPs0CvRHJdqRV4p4vYv1+k5ot0SxAxLdEwmJWD3anC209wwv/V1ePrggIUkL9mbWyV+uhrT02v2gfZrVZq9q+WQI39TzCY1n3VUweZosW+FGSyIiNHf1IL2/BhsQy2TaQXMQeApgjjxEz7Hu+iMVfvogNNJOhSNPXErVutcr8hWbT3KhhQhsm3QmA8THDijEnnJyz6ymJPYjOkFmFOGbOQ6uPFiMurx6vrU4JuBYl08gJIX7jt9kc9pqesgbKCfcDFr1jmfipt9el4dXVymacr36jbeLJYYcLgcIQqybgBK2wlpJ85XyLhDPBXrc8I+O7gHmoZjnhk/tRxzcbM0BnfmXGIrXX5rQsrPqC/doROvQsKNQeYa2RUKgcy5Fa6v8Nem3DJ3/G3TnoT8BNOcawlJH9G/n/m6XNWPUZtdJ9dqm5flPcHg+eWZJAHRJ6JGC2HwyX24PtqZXYm1ENm0I0p4fmHcGba1Kx7HAhcqraA643mmQayzG2HvAdTEg1Zogk3ZMVrfjbzIMB9xzOqcPU5YkGSuAP0zgT3vvF0EFRh7T1HE6HRXqKohbRMDAD9vR1PW/BXNrRp6zxJvetm0Lt1N0katt60any7pEIS5j7PpsTTyw6hi3HFcx3w0nKHWK4EIgTGpjUm+luzqlq99vcqhFRnvcZikrj0HFie/Cczr666riieVIwvgDNiV0EtYQRx8sUDjxZCWaUKqvGEa1U20WbU+Y1ukzi5U09KG3sxoEs9kg5Q+VQ8mmmO0Vr6TLRJGbRgXzc/sm+gN+lVSL+t5wG5KRGthPiSEHRNxpjOh5CUNLQZdkB1cojRShR8fMwNUMAACAASURBVK0nbeOvf5Pi1xd9fLY9EwOO0PhR07tnN/Ngwu0hWH20GPm1dNrk4TpGKGFELmJzurEqrhhVLYG+GUcKfiHiDSdmNAF5orS8o5tMz4C5fuuU6Oyz45EFR1X9PAHAkdw67DupHQwoEtmTUY2K5h4sPlAgez3Sxhsr4UIgi6lq6cFhieqflt0/jcyjz+7Ep9tOWqZlAYSPij0txQ1dKG+iC+1rlXol66kM1YbVO2TJRwfz/1Uawcjt8aB7QBqhyqCRtpcBhwu1bb2q95RRfo9QIq0NtWYfaX2Cww7LN951osqqQliTbhhh5htKnfsOKGjiWMXW40r+39jeckJh4KI11FoHHkIMC12UXqGxo18zspvb4xmqg01J5XhmSQKds2UdDSyhsFFWw5cFuWxn78lBey+D9oUB1NqLIFp7qplhG21yh3Jq8Y2Kv0SrKGnoQn27v+8QK3qPkfpZe6wEa+JL8NjCY+aUxZRUrEuf1nQu6DOeWeF2KVHUSqGApb3Vd9Bpk366LROzdueYMlfqXa5Y1XZpIyBzuBDIch5beAyfbc8cctg7betJ3P7xPsMhbTcklOFIbj3eXJsKACio7RgKUZ5V2YaFsfkBCzfWU2u1jq2kSq+2gcqtHlZ/D3UXnbs3F209Mosyw5svgt3pVdiRVmkwnWGcbuUTRWlxpeY1zy1NxJQvD5gTQlnCIwvi8MiCo9SCNx+VzT1Yl1AKp9v6ViCeC2Iya5BR3orsqsH+MbQIlsywZpqDRQLz9uVRO6T9aWEKfpGvf2F/Q3k27j6+U/fzNBjuwQwrLqsiZlU29+Dvsw7iSK4FmiHe16tr68O8fQrjoEVY1ePFDiUXHcjHnz6LYU5Db9kK6wLbgJo2iSrexitug3szgnta+/n2TMzdO+wX48lF8Zjy5QFZQZDd6R4aRxs7+zFvXy5TtMemrgH8a87hoX/LaaLdN+PgkK+IPRmDQldWnxpmQQjRdRCwN6MaMyWCq2DMJftOVmO5gmPse7+ItUzA2Kzhc8gKOvvseGZJAv49P072urlam/rrrapV/eCMmRAIiTPKW/HKqmS6vh7qxb4Ccj6BrOyTrL4vg7XWdDBEwqQhHPwXjYIzNNOIDnUBRgu1bb348RXnDJ1Yihc+UmgacGe/vwryVO+JyxXnna7qXEsNln6zI7USU26+kin9l1YmI+ad29kKBWvmkKSiJssiH83dm0t9L80irJbBK75UAF7qjQJ1smLYBEzuO+txzuxzNJxV2YYrzjud+rnHF5lzAkaDr34bOvoDTo8vOHMi7r7pcv/7NdLblFyOh/73GkSPoZOfR8pklFTYhP/33QtU7/njif2YunMeAOD2d7fBFT2WOZ+fFR1H7qXX4+raIhRffI2uslpNt1dtW++GzwzqvKfZn27LxP9ef5ElebyyOhltPXZUtfTg0d9dZ0kewUI8jipr5fhjpce7l1cdx4YXf2dKWgezgyfw6LM7hwQsz972fQBApddspbXHhvPPnDR0r8vtwV2f7sfpE8di08u/x3vrT6CypQe51e1Y+PivdOUv55OqZ8CJbBn/SVpYYapJZ2oqn29tu3nRbWiZtXtQmPd/N1yMiyef6jf525xueAgwRqa4wR72jI6znX12ZX9aljiGNj/NUJNU1IgbrzwX46LH+P0u92XeWDPoaHn23lx88vebAq6LNdKpXQLJtQFCRubJm4WwCnZDqTVj5NNmVbZhb0Y1XrrzB7g8YP+hnnKkrMuDAdcEChIs3ay7X2q+E4jSAqfNgJ8D6SCsFaEm0tF7iu8hBEsOytuaao2/D807goLaDngIQXe/g6pdmCGpZ5kYmrsGMHV5Io4Xa0caA8LroEfpPQNN4oDWHu2+IpfeN8dKqMuj1E/1nMBWNvfg9W9S9GsZqKAVLeTamsIhARAARBH2NllU34lFf3wMid+9GSSMVnbST9HU2Y9lhwvxj9mH0D3gwCUtNZi+5FV8r0rbEbnd6aZzOBwGncYnxK1qMedkmrVNs1ZBa7dNMfphfUe/pmlqYAGs/QgsjiyH8Zr+WrhKFX+nyuYePP11PDJ9hwQaVbIuoXRoXujsGxxTfUJTn7CoojnQ14kZNd3WY0OTTg0TubZpc7jxyZYMpJYoR2QrkGh50TQZK7RrjIaItztZtRDY29+cvTlDwRqM1IDdOTx+tvXYqA7B7ptxEM8vk9dQZdFMCAwRr3RfGAzgJvP+xnTcOW0/4gsa/N5P7U37bPJ+btaK1kjM84Lo9suaKhUL0NptQ6XMWEOLnCaQGTR29uOVVclIL7fOVYeZhDLSnZGc4/LqUdLQhQ83Z5hWntEIFwIFCS2nYGI75kUSZ1ZmqeuxjHlOtwdbUyowwT6AM/roN50lDV14a20qalp70dZjw9r4Utn7lMYdOcfGVo1RNI4bxY6mk4oaMWt3NhILGrEpWZ99b0NHP95dn4Z31qVhyvQDqGgKjnNAFmH/wth8FNR2WuLILi63XvsmE1Gb4HyLk6yqNv/fNaamdQnybdrH8eKmAB8lZvDk4nicrGjFSyuHw7v3DDix8kiR6XlJObfL35m4HiGOeDHvHhM8JVQPIWjs7EdxfaefSaoaGxLL0NZjx8GsWkzdPgd5374Ob278XPUZQgju+nQ/7v50P1P5+u0u5NW0B29jofHp1IRYyw4VoprVnEEhP7EJl5Remwtbj5fjnfVpQ7/1O1x4d/0J2fu7+h14ZMFRWZOl1h4bDmbXBlw7pdYif05iGLuJb44WtwXWvYrS2ON0e7DvZDX+Ov3A0Obp4y0ZKG3sxmvfBIZQllJY14kVR4rw3oYTusplFK1xVw2n24OZu7KRXDQsRNx8vBxH8xtUR/tpW09KftHfRwUI+Hx75nCaJtZfe68Nq48Wo73XpntTJ/6erN/W5SbYk17NbAafVNiIV1cf94tkdJdo/Pz7rEN+h3VKUfdoMNJec6ra8Pa61KF/DzjcaOuxMZvCA0BlM/0z03dm4bmliWjq7Jc9yALMP0/4aHMGZuzKRlVLD+ILGnSlcSB72KyTkMHxbMDhwvqEUuxJpx93J9nltbvi8urxj9mH8PiiY+jyHpp39Nrp5lDvLXLtgVX43m93SaLUEszZm4vsqna8uSbV795GSl89w2XRzvullckB5sJq70AIwbx9ucPfgBBThECh1KyR82mkVZ6ICgxkMdwczAScbg+io9Qb1eqjxTh1gnJ1K9kxA4Mq0r/9/kUYI85DxwkFS1/fnlqBpKIm7Pvkr4giBH96ayMGxk/SfM6npfDh5nSMHRMla2ZU2dwT4LwPGNQUeG5pIh77v+twz8+uGPo9GBJ1mtOi9zemA1A/OafRLvIQghNeh94swoIemUWAnqFMEDWEOXtzcPapE/yuMy+0GBrWtG3SRfUgrd02vxNAvQRE46EoGm1EOVp8m6Sffudbpqbrm6zF9XTvl7Gm5qEHPQsAD+NDPQNOVaFkfm0HvnvxWbLXBhc9wwulna//we+6VlE2/b97cPzan+HgD3+Lf+UrL4p9AmxW7eqXVyajrKkbr9x9A9uDXsyWHX2y5SRuvOpc3HzNeQFjw4akMmw+Xo69b91meTmkhyE0yAmBUkuakVrSjK5+h9+8Irhd8C2BWrot8mHCWCedfXbDoeuXHS7Eb753IYrqOzF9Zxae+eP1qGzuwXzRZuWNNSm4+ZrzmELS02goWwnrZqXPPjxeHMiqxf7MGuzPrBn6jSaSphRCtE2nlTZhAoZ9Gb3M2Ne1xqgPNqajoK7Tz+yb5Xmj6PEDUljXifc3pTM9oxXxSA5fs6GpAmk9+f4pF8Xy77MOAQC+ef63OPf0iarp2hwuTBgXjbLGbjQyaLPFen1kPTD3CADIulOw4uwgNqt2KO+P/vYTxfukecuZUKeWNmN9Yinq24fHmtv/51LlNCnaklg429I1gOL6Try9Lg2/+8FFeOXuH6o+2+9d35qhCfTNseIAwaeSdlRCoXyUXL3sSa9CbnU7cqvbcduPvz30u9oesLSxeyiYxR2pe/HwgRUY+F0ccLPyN6ZBPCaKfQxqDbGsX4BFgMihg2sCGaS914Y7Ptknc2IUyFcx+brzsTmHN6oeQnSdQLDgOync8dM78dUfH8O3uvwFMVoTT2efY8gfjRiHy43HFx3zW4z58JlYScP6KamAh0qNUelEBqD3R6EHsxwpuz0E/XYXWrtt2JNejdVHi/1vCIFY/x+zD+HtdWnaNzISSlVXp8ujuyrbe21hOeH9929v4c0HPjB8+kgYK6a1x4a3ps5VvP7C8iTFMVEsAALYNCvdhKDooqsBANXfuhQfb2FXPRafcvsQL3R9EfRozS/NpkuyuU8ubsLcvbl4RWbjA8hvnt0eD5oscD5vJnk1g+GqD+fUIae63W8O++dsZR99eiEA8yrX7nRj6/EKU8zB3lmXhqqWXryy6rifAAgA2nvt2J1ezdQXpF99VVyx7H1yhOLcdaooMpVeMzIpBNCMTCYX+nuCRKOBtT60pjGf2VoBZUj2oXIY+DBiYSXryXplcw+eX5aoeH3/yWrZ8dCm46BIrup0+3sjBL/IT8J5HcMb+jqvhqtacAWfyWReDbtvK80iWWxbXKOi+SnO++uDBbj3y9iA+W7Grmw/AZASRtriTq8ghsV/mhnL3ECBXvBGOj0WIuL+88v8RHx+z0twffARAOBEWYvfoYDb46E+EO7uHxZ8zds3PNfQal7TMofS36rmV+CKQENwTSCDxHtPho+qnBCbzYojRSiuZ/cLojXo7c2oxu9vuBjXXXzW0KS+8LbHAAAXtgZG7dCDmjNmn48BOZo6+3GeyCllQW0HXlyRhKf+cL0p5RpaxIhWW4ti2YV27H4xDE7gUj9OCt9YKgzZnFyOW394sXySMr+5PR6MiZKXGRc3dMHhcgc4FIxYiP+fRuYLvc8+MOeILmfdVpN83c8BAGOdwws9Pe+ox5ysSMOR9Aeb0rHimf+lyFu9z4m7ypKDhTjT4GpRyYfU+oRSpKj4I9HCQwiiBMEyQSeLQ3q1TXZQVa81vlV9ex8+254JAPjHucEoEBsEApq6BoyZBnofdeoxI2f4VHIHOT5SS5pxk0gT0owWqmWe7HJ7/Bz2i30ybUgqM6EEdMg5sRYscnDrGwOG/y3vk8l3h0AIiKgcRDTBsfbTB+YcYSztIC63B2WN6uvXmbtzVK8b5UhuPb51hrr2jhw/KTmBd9d/AgC49YPdAIbbdqxMZDspVpyvHS9uxpSfswVpYeEYpUnYZq+LhJjMGl3aSVUtvThe3ISrLzxD9KsQDu7zGLCmtHLzgVJbUhNuiq+8/tDHAIDoulNxb20H3vJGmvZpmz2y4CgaOvqx/bVbMXGcNaKCGoY1BhOa5mDqzN6Tg2dvu94yv1HhBNcEihDEE7RebZP2XjuKNcyVxKdnYkLtyPUjifOvBfvz4CGDph5WsTVFoZ5VxnnWKYBlspT7ArRfRZyPgGGVWNl8ZBLdmaaslXIktx4fMqp1W4VSfcrVU/eAI2BjYep+2kCXCUcBkBixSaHvr8rmHmoNRcECwYWcbbgeAqP4GBv75Mx7BAhYfqQI+aKT+/gCNnXxl1YM+obS54B4dNKpw6Qp2jX4jJ4my/oI7ZaHanwwe8oWVUCMigAIgJ8vJ7M4pnLQdrKiFbd/sk95zg5DWD6P/JxciTs+2ae5pgOU2yFViG8FOmQ0HLXYm1GN2z/Zh+Qgaz16CPGb63Oq2/HfjfL+xdS4qiHQF6SvW7SrBJpg2Ut29Nrx6baTKKyj0+oyW9tCSk2rykZdpmERov9w870NJ/zGWUJZb+EkKLLiTIYlSdYDhK6ocQHrtrLGbjR4fRgx+wAcAezNkNdEHIlwIVCEYJbK57NLlVVww5lQhFdVwqPyLcI3dCh9weTmXZ8phRKppZERCUFMTGat5T51lE5lehXsxsOZmsnqocofX3QMTy6O1+VrwyrkHJGznnjTLkSZ0mToj24PkW0vPgHShE7jm4A+FQ3NkUqPjU4AfG0tvdmTFciNIQ/O1TZfM10GJPpbyyQqGOxMqxzy5eTT2tWjvRspnN/egMtFQoj5+/Pg9pAA83kWWP2U5Fa3Y0NiqS5NNZvDhdl7BjV8WAXeRnlrbSrWShyLUwW+kPY9mfdmW5tr98oFMXk4kluvGO0s2OiJ+miWIESPHyda5DRow0nxwwoNWlln2DLt94UVw23PV01ujydiouKZUXdqVisjCS4E4sij4CBv6N9a6nY6+qCa+YHVY7NpdtXM4TDNHVRtChHPNLPxUxUKo5kwBJj1RQQom+p8ui3Tm1nwJtUxbuVJTes0sXfCKfjP84uG/q2myUPTpscQ407AadgoYwYi7etaxSWU0+Ryi6K0Pb8sEfd8oSysnOjQf5rvY76CRqWSxsnGpDI/B5CRgHRUyyj3d6Kr9K4C0a+VJ87zgnY6swol57Ri2lS0DpTSCSVWbB7m78/DC17NZTN8KAUTpeI6XG7EZNYE9C2X24PGsy9AxQVXAO3sQl9B5i89vLQyGcsOFwVq8viZmMl/6+mhEhwSEtDXVW61BN+mlKaZhtu4qlYnVq9eqIJ96bS0FPux8TEao0aNlQlqJA3SYnO48OfPY/GWBX47pZhxiCjuZ24PQVJRo5+vqgibLiyFC4FCyBiXE7emx+C6Kv0nOVZhtI/oGUzVfFsEq9PSDD9qExPddkE5eoiOx/zIVXA86Bdy2Pu3+DUm9wyHa5bPJjJOAMzG6KLwix1Zite+U1eC9Z//C7/KjTeWCSUXq/j12ndS3byj65Qz/P4daZb6RqCNZObziQCYu+n1RVxUwoyFq5JtvltBMLL0UOGQDwEg8jbhcvgiNpqJ2Ix6xaxHcW6nug8oEsbbkHA8BG7xmkJqBGcNAwILKFefa46VYMaubLy0Mtnvd/HGiLTSCTRo8CsDQx1KTVDVTPUOeyOiqd2jh/EOOmGJqc1WbpyjEVR4bwr7ZioD6wGpVdoiqpGPTU6PIRGZspivBsXin485G4p6KKjrhN3pRroFc6SUI7n0zr1p2HeyGu9vTPezgjE7InAkw4VAFkETevXHZZmI+Z9bUXDpddoJBnEB1tXvQFZlm/aNnABSqRy9yn/MgF8pBmfpmqSXRsVZhrN71M29hlVCCfPkKXj8TxWiPMHRAgl3Xtk6A2f1deKtjZ8FJb+xLiMmaGqaPwaSDRFMi3ELBBxm1lmo/LXJOaCVI5jyIa2sxNdDUW9XNqr7rDGzREYFc9JxPtjd/JSB3sgcXHz4lT1Q+1DOxDOranDd5fPHIYdTR5UMtwWVgzbZ++kQaw5IzYF8jtjN5hKTgpWoIml/rqgxePNf7yP2h7cM38KQnFK99gw4kVvdrj9qmQyEkKHIZUbwMGpmEGKiIIiiKuwuN9JMckcQqWcZtH4B5dqWXv+MM3dlY4LdeNTFO1N2Y87CFzDJNjjmme3jMLNiUGjeHObRS0MFFwIZJL922Cnf5K7hE5rHFx7TfNY+brwlZTLKE4uOBYT7lZ78h3xtFsLB2syTDrWkoiRmCGPb9Z8Ayi3nlapQKWKR3enGlC9jmUO5T3D4my5Eu80RAv0+Ixa/zEswJS0xwWrbURZmJLf4M9RlJEUVDBbd0g0440pO63Zdp4yiv5VMNM0ilENxXk07MivN00ywGiOLfDMcjwfzW+l6VSL7p/cHY6UnhKC8qVs1lLaPG8qzsHXa/Xh6z0KqtMMyiouM9q3fZX1JweH2f9KMV9+dXoWvD5qjkR6s+TNaxbxZjFx59mZUy94rrctHFx7z2zyWXnAl0r/zP5j+lxdE6VMc1nl7o9K9Tyw+hpdWJiOlpNm0mXHpoUI8vCDOcDpqMiCl9yEAbsk8jPkLnsPZ3YEHyl/F5AU+pIGScGx9onLkP6tGBQ/xHoaGcNwR18c/Zh/Sn45kn0EbbXR/Zg1OtRl3Gv3MnoW4vKkCd6TuAWBOcJ0wnA3CFi4EMkhc3rDj0QnOYQmmzUmx8A+1IEWB9l72qA9SzB8bw6dbB+uzSYVAUY7A70JbK3ITOdMiFIOh4PvsLsNmE3oc7UoXG5NsfXhp+xy8s+FT1fvYBXbB65RGfI1oIb/40/9uMsYM7ImIN0VW1rPJuxAiGsyuqSlkfr5e5VTfKHocjJtpcPTiimS8tjol7KPZAYDD7UF6mX6B1SMLjlIvkPUTmH6pQjhtrUh8wTbR08pty/EKPLk4HrMown/fk7gNAHCXd2OgmXc4CoEE2T+HkJ2bKJqXx4K10Ny9uUOmdQCFRp1F1X1jURpuS9uLKJMOimhZFVfs5zNEzLbUYe09d9QY3XkU1nVizl5532s+7YfMyjbT6nZTcmAkMz2IxzyaaHI+TbBXt87AVY3lePDwNwH3bE+tBAC09wZqffh3C0FzOm8JsoYHIQSPLzw26LjbW7gojxt3pO7FRa3mmjLJ0d3vQJ/OwCKyEYYlFVxQK7UKIJYKdr/48wu4873tGBg3wbpMvNS196GqhU6DebQQHeoCjCSsCHvsI0zlRZYRL7Eft3qNN3xSY20+UlhM8gVB/wKkW0doZNMIMCtg/5jSzzLORfc+XTLO/9hz1tf4NJ2nyzQ2t4fg483puvLTLI+hti05fdYVL1t0chakfkabTaWKaZNYCFR0ybV0+epvNkw8OPcIvs/oEMU0nwUiXBEgBEotaaY011XG5fZgXLT+cZhmIpMKNAYUNMkK6zpxxXmn6y+LDH4GTJI2bLTV7DxRCQCIzTLfjKdIFCa9qbMfMykETZYjDJ+xGl4bCgp/M+IJEGLIl0tro6T+Ovrf9cQ1PwEA3FCWhawrb1Avg4kD7Jr4EhTVd2Lyaera+XojRQoCsGA/u/ZLOCAWViqNRZIHIG4DamboK+PUIy/6j0fy7SrYWoADDvdQ2PRzrjkPAPCL/CTkXPo9PLJ/KdZeP50qnZ4BJ06bOJY5/ynTDwAA/v2/1/j9XlTfiY5eO3Oa0rGJxTGzGX3w4I8GTSsrz7tsME0zNmGC/ID58Pw442mPMLgmUEiJHNGOlQIuOT7akhHU/Jg2Rmb682Cp1zGiBRzj91gnCo/qGxKPSyN8qGHm9zdh0iYAXnvwI0y79xW/351uj1/oV5+mhOUnxTrqR65PZVW2IbGI4bswYd439PetQpeu+H2tNIXTw+OL1Mx32dtOr82JmMwa9Ntdg09L3nf6LmWH4az5hMonEA3ZVcbD17PA1KqCXG0bfnmPphaHGd/SSHQV/2ZqXh/VinhECLuvOSVm78nByYrwMlH0CILf+0n95tidbvTbXYo1TsSOoYPQcF2mtSGd+Y+hELRSVgNtuxILEv0T0Eqf0iSMorysPoFO6x/UBrSpmKoa1TgWNwVp0dSU2Q7/4Df4eMqrcAvK20xpFKrB58Uaw8CjXx1FS7eytk+UTq/wcgIt5mWi94HSC65E1XmX4r//eJf60fZem2q7YdXGe25pIt7bcALtFJEjxUhLIGteFZS50pr1qV7CbIlqGVwTKISwNFS1BunnlG+UNFw5xjtssOtUKfR4CD7ekoGrLzhD814zq5go/F3X1hew2POI7iAuF4SxY/WNzt5G4u/8m/hfFwQIHg9IlHE5sRUDskeIQuaVPwy472B2HQ5m61fJXRibr8uZ4un93egOiKClTu3kC/HOv/6LR2OWDf3mDLIavF6kAiw6vwji5Z2FA5WoT9C0Pa2i00YHE/PR5nRkV7UPOSWcZO9H/4RThq4X1CpsOPQQvjKgEUHPxNOG/u63uzB2TBT9Rk1027Lf/xvXVedr3D7YGPvsotNzBd8Tc/bk4MdXnBPwe2uPDRuTypinhvUJpfjt9y/yy5YWrf6/O70a0SqbtdyaDnxhkgPhHp3BEaykf+KpGCPSSnW4/Mf5uz7dr/q8v4UMe4dPLGxEQoF50bnUitDSbcOa+BJj6dPcZMEUomf9/MnWDHz7nNNw01Xnqt5H+9Vo77szZTee2bMQX//+Yey8RVlDdZzTaZr/Udr5lBDgs3tfBjA49xlJr6PPjpVHlDWG9Ex/eTXtiO5oA07RXu8H5Od3AjbYYDyidTKtyfBjC4/hlu9fhFf/FLiONUKnita/bHA7ivFE8ZVMlA4xpSSaEzv67Io+vjjacE0gC9Gynx3DECVJvH2S+l+Yv1/ezjgcKazTt/mhGSC+1RXoq+aG8iw8v2OuZjjR1NIWHMtvwJJD2j4/jHuZl38bcYSDT7bKaEKJBmKPw8KFrndwPaN/2BcFAfxmgmKlUzO55GTdUrNh5JTY5nRjxq5sqnt3plUhvVz7FDmvxl/DYSyFk8rbP97r9+9Ff3wUtedcjPcYTo9CRaAPH+XvoXRFrP1jqdwiDCThPg2Yw7n1gEBvvqiGclS90EuBgl3l53S1Bs1vSOX5lw/9fd+Mg/jvxnTq8UgqyHdTmPQKAtAjEhgo+c8iAN7fKG86uvRQIbOZxPIjRZivYrKi9so0taGmXfLyyuSAYBR6KW6Q96EUanpEfjw2JZWDZSa0u/yjb7Gy6mgxVh8LFMxY0W8/Nzka2ESbwjo6SMOeS8OR+YDDjaL6TtX2y+S7jfK9Lm6tReek0/Fo7DJUqPoHM/Ej1/kfsMmlTCS6wQPjJurOzpeO2+NRFLzLaQLZHC5VjcjYrFp8u9lfcDB3bw7m7ZMf/wghiM2qQW2bvCPkKFEb6eijn+sP5dShRDReEUJktaOMMuzHk927Y2efA/12c/Ybp/d1YazT2FponNOO6UtexX/2LRn6bfYef/PfcHQRF65wIZCFPLE4XvX6WDd9x3K7lbtqTGYQQmUyotQJ39twQmd6+nr15yvewm3pMfhz8k7V+xwWDLzKyH9LsTpyaaPMpC5ejHg3hObZmQfW7ymixZd0sfjs0kTdOdGcPEhZGCs5QZckcVlTJT5e+Q4ulwnBvPV4uaYTVSWUFsnF9f4bDT2bIPvYwNM5M532BqKedmlDl3LkHjPWGIAtmAAAIABJREFUkeLKpNx9KAs+jJSD8XaDK4rBtmL8u55iU9BQY0zaijZmcyoLQevajYcpFnNtTSHWTH8In694M+CaUZ8/NBwvbsLHW05S3SvdiERRNL6A76PySE2r8egsYsQmwoFiX+WCzN2bK2NWEHphbLhS0dSN8xvpT69n7BQdYpiwwxG843xMVo0pYcTFNKuY7tAibjmXN1Uq3het4m/GhxHTNgBIKhSbZyvXvVoum5PLqcfpli71A0ufpslXtz8xZH6k9o6mjvY2/7IpOjcXH1gyapMrJqkwnkgPlvtsTtz9WQyeUDXxluZJsDu9GomFjbLX4wsaMX1nNh5ZcFT2ujiIC6t/vJmiA8ppW0/irk/3o6Gj34ALg8B6UhOMSw8qpOP8extO4MPNw4fSsVk1eHDuYYWclDm9rwubPvsHvpnxb6pyKPG9qny89OgXyPjOjxXv4TIgergQyEL67eraAR4GR7/5AR7bQ0ufSZJhWnoGHNhyvFwxggMA1Y3lWb3q9RfwpMYCdqKtDxe11uEMjXQ1UcnGPcbfWnN9wvBJnkdhoiEAdp2o0lGOwIKIJ7bk4ibmxeen23wbJWna2umMkSzupO9EJEPXJyvfwY1lJzFt5TsBaXWotRkN+hVs7bOr2tAo1vTTMWG7xtA78JOGWZWLqmGUp5ckYM4eOq3CgfGT/P7t8ZPvyDdqcXuijQ42QUODT8zQ5ln0LWhy0QwRb8KmS49JmZQoxRPp0C95tqhEoulkOBml4Ve5g4cr368KFIB/HCRfcvF6TWs05hW5hbBek1yjUT47JGNMaYOyIF1OHd+IWa4eJne34tQBc4ViVqKlnSzGqDmYFN/4W9/eryuMeGqpSoRQmSa+NSXwcIYWx9hxitmc2WeiWS3kq7ZVw5+VjwMqDs9dbg+18F3Lx16Z6ICwwBuowIgfMBbUtFzO7RwUwHeUVvm9g5oQyApfjSXeaIrVWgJyhnWCWNPqs23Dmm4+/43iuVmP1sy2lAq8sioZR70BcQ7n0Iyd7HWn1xxMzPSd2WjsZBf0XtVQBgA4s09eIFV2wRVU6TSfOWh2mXGVshBIDNcKUocLgUyE1WGfTUYTQAnFE3qdsJ7SSTdtWtGmzD5t9hBg8YECRfX3wTz10zMw/D6PxC7Hiln/wUQVW+azejtRd85F6JloLDqL0oQ/uTNwkSU2pyEuBQ0JQjBvXy4Kajvw8spk2Vvk6knuN+VNpzbdAw4cya0fTFvqP4biQ53XyXaiP8FpQ+wPbzHF7EbMlC8P+Jtfet8lqagJD849MvyzDjVbKueXXn5Y7u9I2O60JhrT/swa2d+lbycWULZ02XDvF7FM+dA6mmfym+arcSonncMpN2ksaDwqji2DiZLgTElQqYbZoc57dIas1UNghKPwRG6OlS64BY/Hr72mXfMTOJXG9iAj3fDF5dUzPf/lziyMd9jwu8xDOK2fLSwvS+tcFVeMSbZ+rP3yIWyZdj9TPobR2Y8K6jrh0Oi3yUVN6JJZZ9lcoYnEJ9Y6U9O4k1vTLJJq8orWFlrCMDUBupkBS6xUXGMRdmitneWuqgqBTHyxVSrRvFrO/BYAIKm0xc/pshnzZzgpFSbLBFQRr82dKlYbSiyMzQ8IoqBkeqYFc11J7rdWG10Zn89ELZ9uVH2eob9FQmRTKwmP1e0ohfY0nIWqVrrF1n91mmXR4rHoZMKnEcU6ULWePln1erdo4PlrwhZc0NGEX+fGAwqRFWxeB9Qeho28j17b8OJPaTw7s19OWi4SAimYyfj8TE1dnoScavXIPFo1GGUgqsTGxDLFnAjFoqD+HLGDUu22NP1PUzH9Ly/gi7+8QF1GWrpEmyGlUww5wVaGhm8ht8xmlnbuoqkTWt9VZ/TSnKYq57c5uZxKECGevJvPPI+maLId5PLGCry39iOcOsC2sRTTZ3PikpYa/KAiG7agmoLqR1D4BLXt6r7npBAQ3Wa54YDcyeW3m3RoP4aA/vHDzsEFjwdfz30SM5f4RzjMqWm3PG4ojZml24TF8eP7l+CVrTPx3rqPmJ4rF/lh0mJNfAnO6dbw42byIZqPiQ59pk+Dwh31r/zfjSfw7NKEgN8XxphgAs7QwDr77HhgzmH85yt5UxgpbRTRiSZp1ZvYub9CWQXJfZrokCREG/RfIoamqNtTK1UF9PH5DbI+cDwq7dvMfUarjAay2+NBVcvwXBzoH1H+xbUOktWeVX+C/Zn0MhXNNlgaxkIRrciaejRc5B6Rrl2ZIiVr5adjD6FlvkkjBMqvobfQeGtdKvW9IxEuBAolDJMSaaULebrkoLZjYwA4brHvhK4B9QGexpbbTPb/z++ZnxEIwfkd1vuYkENOs4RKE0gBufrWan0BmkAa7TWzcriNblIxD5FFJe2vYtQj6gBA4vd+AQBIvu7nbPlS4OeUXWKit+ZYCfJq2mW/l5qfFIBNDVePSdK/5hymuu/SZmORFegWc/4LkI7TzqJLXOa9v1j+Bm4uPI6fFSpM3hp15XB5sOhAAZbMfRJfLH8TZ7fJ+wHwoVb3kwbM9aehhpmLeWrfOZTtzgq1fiXkTpb/mrAlaPmzIF1g13zrkqG/T7X14pK2Ony3xn/O7u63fm4c79TeqGudaAseDy5sq1cdu39aNNhH5Uz31OibcCrT/Vp+R65qZJyPKFGLfKQFTY+R01Iskvijc+jQDGIZS7Yer5B1enx1XTHe/+Z9XNDObhopXlvIOxfWhjCOiGPFWsIybVZuCLu2TlnzxSoaOpTb1EdbMmQdvhv1e0SL3Drn8+1ZeGzhsP8dacAbpZK9uVZ78y0eP7t1RP1TrRVRG1iXUMqcdiSiNE+b2XrEe41oioAprND0+qMirVXxG/+oLBNP7lmEMaJyZVYMu1qYYB/ARa3BNWMONVwIZCJWaPb4iD58yLK0FTGgg6llo3xhO5tqOQ3jnXZcU1vk91vraZORcN3PmfyviDnNgLaBIWRF9qI/PR50awjaxJwripwmEAKbw6X5eVnb82urU5Bd1Rbwu99pgI42tSOtMvDHIGqsJhcNq//2TfTfnKw6WowXVyTLriB9r3pJSw0uVXFuqQc9C38lTh/w9/UxjmKDKEYarVAJPadCbpnN3Wlevx9Sf0FyJ4ByzUTqT+ns9kD1bjFqZr5P712o+qyZmGX6YIW6txHnsmNlBNQ/Lj2J32YdkblbXghk5dw7lAeNNgnTN1Ixc6FMgbbvSZHTQgy4p1pdODx151wsn/0Y/pDBZgpKg2tMNLb+/C40nEWnMaj1PhN0auxoY6AvmdSf9UZcNcrcRS/iZ8VpeH3TF8zPiucCuXFN6qpWPhG2PMXjhlJftsrsyJGfr+2jhhI5TSAlJtgHMMbCKIqEBJqJ0mqQlzBE8ethWOuKoXV7kaehOTKgw+Rayv8qzGdmw9yEAzz6Mzwrs+69pFXeNxb1+kXjPpp0iMLfn658G39K2YU/psvPWUvnPI5lcx7HZSav18MZTSGQIAjXCIKQKfqvWxCEqZJ7fiMIQpfonvCPeRwi/MJdsmgChEIfURzSWerXxWDSNKFy9fDUnkV+//731MX48G9vYWA8e5hKM+3NlVDOQk4TaHhybevux5QvD1DnM0YyMe87WSNr22yUV1YdD/jNT51b6YUZ6zqY/UEtbLKPrlPOCPjN172XzH0Si+c/43f6ACj4EVKqHslYsSquSP5GHZSdf6Xfvy+TM61RczlAPRqwb5hsCuFl+8dNDEhOrhxlFFHhPAY2cqx+4IwQjPEoAEKoFtK0zlPl+E5dYOjqaavewWtbpgc4RAeGtT5Kzx92JGm0bmh81lxbq93nxIcGO9Oq1PuNwvwvwHrzAweFP0LSq75p/UPG4Pxz9/FdppRJzLHrf4lFf3wMTz41l+p+txCF2Xc+jU2/+IvsdaVe+p+YZXhh+2ydpYShiUh37B8ThpwoE8eSyT3qZita+WsLcJW0FwSm8Vd8Z7RncC6e3N2KnxckW7KgEEQaMS0V9bK+ThYfyMe8fXTBGHzIRbJVKv4/jq5nFpBf0ViOq1U0oN7WMJ+RagKx2i1tT60I+I1FgEa9HmEolxmHbq9vmS77u9wahaZorL5segac2J0euLazufxXQLvTjWmGR4uiXovnZQJg2r2vYMvNf1J9/nCuuoKAXFRdVpQCBZ3jHct+UJEje30kEq11AyGkCMAPAUAQhDEA6gBsk7k1nhByh7nFiyxoTDYub65E/re/C4DNYVooZECCaDiVLrKdBgdFOfXtKLdbl48dMQnfvdnv3zSLXSl7bvwDms48Lyiny0rILW7E5SnW8PcTmKDWu5jrbM3vMci3FcHjGYp8IzCIEkyHEKZ3G28fgF2HUHG804F+kTmZdLwghOCd9WlUaeVKTq7O6O1E16lnMpcJABomX+D3b9l2r1I90qal5GjZNs745A0AO356Bxbc/gQmiUOmG9Fa1FaJCwuUBB1Wj1N9MlEubypKQ+sZk1F+Pl1ED1VU+t5ptl60S/y5eQQBnZNOx/FrbzKet5cLOhrQM+k01XtOl/XT5s8Z/d3omTQYLOBAdi2uknm3cU47HGPHgwBIvuYmKtOsUFBS2YLvUZyAG/Edp4Tvm0ujECrhHBONvT/54+A/5MZzhS4yJXErAGDmn57XVU5DsIxZoncyRfBsotBD3/cXHzCqXaVLg5VotwvOseOxbPbjmOC045MpryLjplt0pyfHZU1VqNCIeLTleKDAQ4svd2Zp3+Tl4tY69E04RftGEV8teA4AcOsHuwOuEQFIE0WGq2wJFJ5TaUz67hX814GAfJ20dKscMoj7OyF4bXWKZr77T9bgehPnTTMEs6ysVHDSrVSUe79U0NiUPCCnza+EXA0q+fDqnXAqMr7zP4j7wa9V0+zVCDLRePYFqtcDyiP6+/APfoPDP/gNrqzXMP8LkzVfMNAUAkm4BUAZISQyvDAGmZ8WpWLrORer3iOewP02gRQb0br24PmfAOBvfiTpFXKDPwty6tvfrSlAw9nnY5J9ADXnXiLzlDp9E06Bawxrkw5kzl3PAAD+L+MgTu/X1iQwgqLoQ6Yp+EnVFRZeyhtF/1wpsjNMtMsJV/RYv7T9BIsQaRnIFFtVhV/pFF2yoKCCUbgV7XGDZtsm/RRa5lBGzPovbG/QFALRLlSMbjLEQoPTRJtiYlJUpwW3PwFgOJoEAFzcWotagX3MAIC2XgdwtvJ1GkfmwUBR2GOhDGj5kaKAE+wL2+rx4Zr3AchvFFhRiv5jGzseDWedH/B774RTcN/raw3nK643R7R8GGoxHhrtVYrN9Sm2vqHDif/+Q0ZpWjBpPPZ4AJ3h5QHgWHYNqvZon4jSaGE5TJiXVcsg+jvK44bH4vyGM9b/pZieZDyoMBWNbPWY+bJoIjmjlb6lAJfMtVP7u9E7SSZqqyjPsW4XBgBM8Apgr6spRPpPbjG1iic4AwUXdx/fia5JZ2huhNWoaKZfe1d+69ua0RTHO2ywewOdAEDnKWdQmYsqIj3c0rh9nMsJu4EDomiXE66xg+O3QAi9/8QgS24GRHVsJazLAbO1mZXWKYTBjNEqPrv3ZQDa5sPB1PAONawrhPsBrFO49nNBELIEQdgnCML35G4QBOExQRBOCIJwoqVF3Rt7JHJzobYEWnGA0pgUu/vseHh+HB6eH6ejZPoQd2bpaY8VmkC5l12PttPPwZK5T+pKs37yhdimoWrIwp2pexSvSU+n9aIURU1ewj7865FsebtbRcw4+WNI4+7jO7Hngz9jclcrOk8VOQGWGOtOX/oaZix5VXbiuLquBHAo2IJrlcVC0xnalHedGJSVH/7+rxHzo98F9KGAcNFq2jaSoVpqoqPmGPWM3k78pDgNtNsO+U05/aTY0TssIvt1zjGVO/3R8iOmhh6TBDXO7KGPLuFjgo3eSazUNFCKbPSmEGgCbU+tDPhN7F/MDJQWXMe+90tZTc4mmchySRQO4aUn1OJ6o1Exl/NPJUW6uVX7Mkqb5+5+J5rVTr4pOa/LWFADAUBiobLT9MKLrsZHf30dnRJT2PEOG6K8fkic0WMx7/YncOd72w2VRRNRRcsJGJT2e7E/vAUpV99oRraWIm6rZvR3pjRUbu0bPxGJOoIx+AsOZVc7Q39Vf+tS+bwnnIKuUwIPPpQiePrnH/yw0Gf3tOOpvYvxxmZ2H0pqdE88TfEwMem6n2k+f5YkOuh9r63B319ZpXC39jqAvXUaa8/jVILMsKwpfliWiYcOKr33IBPs8geTStovYh5+bhEm2fox3qE9thtZvi6gcF9gKQwuLoKBnDNsl4LFSe63v4u8S67VFYglUqE+LhEEYRyAuwC8IXM5A8ClhJBeQRBuA7AdwHekNxFCFgNYDAA33nhj6GxtLGIGhUqxnyaQ6HeBDA/jp/V1oUeysMqqagd+YEIhGRBP1NJFg2DQ2ZyaKRxNwwhGH40iHvrFEiH4dksN6s++AK5oeifUejtBdVMXcBa7SZISRRdfY1padW19eGrvYgBA2xnn+F0TD64CCK6vHoz8FUVIgNGYQAhgC5wwr6ovwc0FyVh1ywMB13yaRhMdA9SmBKzQThA+x4efTRkMAX26ZHEaaA6mlqd6Xmr9afH8p3FmXxfev/9NlFwUMCzLYKxz/XfjcPjxiRQLHh9/+TwGa6YaU8m3OVw4ruHriqbPdVKa1n15z4tDf/+gKgepV9+EcS6Hphnq3vf/hD03/gEXt9Zh8R8eQemFV/ldP72/2194iuA4P6ZBIATuqCiMkQhVxjodmNzTjsazA7V31BMM/MkVNUbRj5ucwDPBGx1wCBnNiQs6GlE/+UJRtmJ/BRSbGyqNMOk3UnH+rPA51UJE+4hyuzS1XU7v70aTjCYVC2olef7xGQCA+Ot/OfTbBPsAdnw8BfVnnY9/v7AE3/zm7zj4I+NmNhPs/bCpjOd+QhKZDb5SXU//ywuGymVF9BspXf0O06VNZvgX2/SLv2DJrQ/rfFr8vfSVRdyX/VNW8rUlPtSU5mmtAZqAwUAG6Vf+CKfYzNXo//i+1xWv0Wj0sMwrVGsfhsW5AMHwhtvPB41EHKZkmu67V8xnK98GAKz4XeC60nfodmZfFxpl5iWad2g/fTJi3h30mHLHO1vhHKusfRrMmZ42r1/mJeKGimwsuO1xdm37EHJWQzVoRR0v/edzAMBtafssLFF4wfIl/wgggxASsMImhHQTQnq9f+8FMFYQhHOk9400pKfxSpOS3zN+Y4VIICSSTEoFQIByRz3L5BNwJaQT9RkaYZW1UR56pGG4ZZ8Owig5MH4iTnyH7pTw17nx+HreU3hv3UdsmTC8h983MOKQklaupTP9hxfEofqci7HgtscCLypMHrKaDwrcXJCCG0tOqt4zUeHERg0t7QyjREk2zSwqp4USIZ3026gJgXwno2Np34+2gfjyNqkz2pxu+tDlChzMGQzveW1NIa5SsPuW1rps6XUsTB3R4/DftR9i14f3YIJG6OjHn56H8zqbcUNlDqatfCfg+liX9ZvLgDwpfdM4x0Tjzne2YpbXbNbHnMUvYuWs/6g6FZVDbgH94AtLhsz+pGj50rugvQGbp92PexK3+mn/BGzYRR+eZiMkl+/P85P9/t1wFr2/AiVfaTRIxxI5tOpJvHmShzBPM99urQEAXNgxuD448Z0fK947ydZHNY99p7YYtvGTcDWFY25AXqggm4sZ41YQDqP+Pe+If/sMtgMShez0C4BozMH0fZso2jlOkr9AaW3HVvPiuwk8goA3H/xwSHhqFplX3KB4zR3FaBYp811OE7lEoBKWM3cKY+3ZTyAhKb5qYAgd2ZrV9WYsfVX1+uqjbHOoISjXOu9smIa7Uvfg54Xi4C/qvkvF9DL6pjKLkip2zWWz/FdGAixCoL9BwRRMEITzBa/OlSAIN3nTpfcuFaHom6aGOw2THaxCR+04TcWZhUHcYo0WycuOodyknKkgpFLbZz7w4nKqtK3mqz/KCDEUuCXzMADgppJ0pjyU1Hjl6sdPM0t0/Ve58Zr5BFu5ceqjX2LHz+5Svcc9Jnro7eVOVHWVecjhEPvTk3QIjqRc2KYc2SDAgSZDEZs1QiWrmYP5MLTlUVm0Z1cFRxCtird4bg9BtMuJ2V+/jPkLp6o+0jPhlEFVepW28q1OeqGUa8xY/LxoMHKK1ian8rzL8NYDH+CJp+ai5pyLKHNQOOE2SQh3SWsd1X0dp54F95ho7LvxD36/X+ENq3pTEZ1zcx9ym4bWM85VvF/Jh5CPfx5Zi9NsfXgsZhkmi6KLqQp6aOpQMiif1d2G5O/6m8IEnO7K+XaTT44JGme8WppLNBHRAODSpko8tu9rnDqgHaEn8FvKf6traoqwedrf8NwO7ehfLWcOtoViFW1VsfaPbN1YNAEGw3dEn90VmsiAKqgd2tCYWgkiMx1fDfqb2uir1/M6mxGluKYKXD/tuOkOPPLcQvR7tTsC/fiZV+8eZu8bDCgUk9W3j9y3U4qkpFgUE9X0adLqEfl/YvlaTJHlNMpxBoUJopirtRwTBxW2Nn6KxgGXEg0UShJW0D0g41JC45VdrMLTCIZqVBIEYRKA/wOwVfTbE4Ig+I7q7gWQKwhCFoA5AO4nNHFlRyFiSXLZBVcq32gSV9cW4ZxO4z4c1E7P3Sqnkud2tyqkp9w8Ok47S/Ha0PNBkGqUa0R2EEOzCTeKknneHal72dKR1r1iV9VfyX0TT6W67+j1vwIgE1ZUBZrTGGsHH51R0gL8hVDppMgi/WQ0iyVDizOdz7KYgwGB2pUMTw79NVZTw2GQe9/cgClvrFMNEf/itlnUJdASTshRcf7leONBOe3B4E+fSnbyUrQEEKztjKbexjntQ41ey0Gz1OR0+IL09H/438rOZ5WJZhiz5NDTXnzQ1LGWs2vpeHRaf7ef5pRABh33L57/DO5J3oHH9i9RKdBgWj8qy0T3xFPh0DCJvjNtD559Yibm/Ok5jbdQZnJ3K87tbMaZvR0Qj8m+9xrrHF78O6JlDtwiyO+Dmmm+0fRkUbg+ySsIVHM2HE3hKiAK/u0MAHZ8PEVUwMH/nckogBjvdFAJdN3Rg+VfcMcTqD3nYqRccxN6BpyIzfL3tVhx3mVM+Stjlrd3NqjGYlF9aWoYUq29JGbuGmUwU7GNzdcV/b17M5TDpl/aXDXkYFyLltMno0PGj1UkMPOuZzH10S/95i3Zb+cnbA29GEAr0rIcLJG7Ix2qNyWE9BNCJhNCukS/LSSELPT+PY8Q8j1CyA2EkJ8RQpKsKnA44Wvf36vKw+fL5FwlyRG8meD8tgYUX3yNom8FNQTJAld6QkAEAcX1nSCEwOVW7lBKpx+Gog+EIXojCCnWnOwJsjUDajD8GihR5PVREyXnwI8QhYlaxc+GKPaYOJmQIV5gSfqQ21AEGy2Bkhy0Y495Y1SHzrD1VhLgDUJlkUrjZJQGtRq1G40aYlEDn6jg6FpbC4Wt/bRTaLPu+vAevLxtJgDAoxFlhMgIBKR/B14b/n1K/GbcSKHRqVcLxPe+rjH0vuOkOEU+p8YpbEC0hHriOevyhnKcNtCLP2TEDP0mfbuLVTTFrqwvw1X1pbg/fiOmvLEed767zZuHQt4QDB2CjXfYsPbLh9By5rfQeepZfpswnxbef2KHNYr1rIHCCbPnfq30xJGtxJ+Q5rCLyrRbUwY1mOvZjA76BdD5ceyZeJrqv330TvL/nUVgIS2FlT7dtMJqqyGo/EsPTEIdk7dDRoQOa399H554SkszMbDA57c1Io3Sufw/X16J+1/7BkUXKvhmDMliVf0jnOIV/O6/8VYUXHItOk5RP6SPHNG6Mv+fvfOOk6SoHvirmdkcbu9u93YvZy7B3cEBkoSTHAVEJINIPsIPUEDJoCBKkqRkA4KKJ+KBiKCACJLhCJfgctzbvbA57/Tvj9vZ7VDdXVVd1dU9876fz8HOTHfV6+qKr169x7ohlg3kjrpLIfc8cQ3MWu0fShVArqmkH419voVYrTLMjPQ41gKwI5z1pU+8Df9d4u0byM2BmMpjbDoQtQRy6/M3DnGaTroPcMEGDldH1gRgpw1fwhRGXwxB6M5jXxA9deBp8OKeR1J/y5SRWrcJ7uXt9SZk7orwWAJ15BVAbcUwZQNbmYei5IWvHc2VlmgJ9T+9p3dt+m9ebffZ/U5glqHVdObdbtkm493Lqj1uVcV+hG1Kn28fu985t2d5ddaBcM1ZP4FOSn9S3troGlmFdSw4pO/Ira/C3cUSyPHYBBzXJXt7gIABtz11kzNZ22emPsbjpclaFLo5Ia8d4u2fyLyjW9rRChuHjoB/7H6E5RpWCQe1NcHYurWwfugoxjuCYY8GaFFa9CkpDzAfkxYs6pQt8pCzDgsONJz9gSx/gAPpef+eZ3pu86Usik+7w3gAZ7RFYkvVzobKEX1p8W1QEYOanC8sfihZGLF1Q3/0J9bgAjJY6eX7hgO/Polt/eJ9jb0uOOqUJkuS3x50BqyqGe9zlVOehlKnD1c/LrvwXur3I7Zt4k4rKH5tet5Lj1g+txcMbFpR3VZEJIBFBpHNmlyKDoZKoEDwV3Z7OEZnkv5pljKe5W8vlBcdabXNLDbTSN5eWmstBXvoatHGFAEzQh5kmQ+O3LIBRmzd6Osvym9wTPV0w4n/nd//uc20E8o8sBoADzxyJdz/6Pcd4ZVl00ZxGuc1mLyy2yGe6VmsAZirIFu58ExMiMUSyHuHlKfKO3YZDcNy/MHMOZc9DGdd+SS84xLKN2i43GGSQ4b3I7kPcFNseymBXpt9IHP6a6rHwUcTdwUAq0Jo7yXvMPlwMcNVx7hSBvhyI30McstzCGPwgbtOuBIWTpztiECX39UBf/7ZadbjHpxsNUVK8+trzU/hVY40K6HeZAqe/TpdTvtkks3pqnv+tD4PYIclxZAm9S4VzfWePum1Oob26o+TRi8YhEBLkXznn/R84Q54AAAgAElEQVRJvFWWQtOx04wy06z0FnXCPXOVdXNv1FarNZToZkNJRyvnQkP2osS7f8lzCbvNssCj9XXj+/yF9adjqlgd+U4rraaSQUCMNNPRMjOp3h4499VfU3/z7FM9indIs3dbzFjNp3q64df3XdBv2WlRwgoqp1jJJD24aRv1e7a7wX/MZUjQXq/tRWtW2NKuN8vg1kb2XfQWTKYEIOBqJdKalLy2WeG3PlSCixV+H1Ntm8DmY9C0/i/fZR7KyghG34TMCL0eVAIhDIisUdp9TP/tDa4/L1NnWLPdOwSyCuzWIm7a1bJ2q4JK9NiX36Ipak3UrOyq4nAma+6AUz3d8OT9F7jWAeuuNv0oQ4Zj310A+y4ZiFxjdrTK6uvI3MHr0u4vW89rDu5kwQdr5AjjkYf7td7vSZS2zh7o6R1oI3m93XDoJ/+iXuvlZBeA9Sgj22KaxrS1S+Dex37AkIe1bAsYz9mzklFmOKyogPTv3gbl44mzAcDqM6OsrZnpyJMZrjpGK3+Pd9LSQd9ddyoDd6RhPqo5iGGSau/z3fzC8XDq1U/Bhr5FlZ/VpdAunq24Ppg8x/eWBgbfdSIMa6jztcSVgX+7J8CuGE9DZyofrrX5t1LlONlutWZ2Vpr5qcdk3VHVKFYH7QtWJ2LP11pUSrX2dcXiEyg4vptIpiPi5vxYxrAWiuW511FMN0tk0SP2YriXaq+PD7Ix9Tv8COX3dEGPx3w3yBzKL3JjxpdPoW0cY/FbZvax5uZUmwd7/2tRyqfTNv9WTryO8GZ4e8Z+8NXInRzfD9dhSSPRaoSljozcst73Gr5MnbmWdLa6Xp7X7X30sKhrwFpSpGQqWtUrwvz6MfQJhCjji3E7e/7eyhBGb31VXzQZjdYyGQXBG4s2QlvnQKdgD0Nt7iCTLrtL9Ax8zFLZUwoF88Lkd/eew3yf+TEzfg1o1lO96TTXhk13Mg8uP/9uZjnoDMgxe+WnAdOi89y+x8Ndx9OjOBHDgB/P54u2FgSeAevQj1+Fy/92v9NCyj4BMr0nP4Uob53+dHV4ARi9Bk2/cvvF41fB9HVLmfIxZ+O2G+1/J53Px+/i+tup//kTR17urBg+AV6ac5jlu1fmHColbTeTNkJ5OSyRnBzpUMImA9gs2Ajxfd+dNofEshQBmVDIS8ZM87zOnJ9blLaxdVbFsP3Zrz/jFkrC/KOOyJPzOlMXxeLg08XahvWRk+k0bFOkFKNBDAN6TWOu+e+JtSsBDMPic4nXEs8Nh9PbAGl12iPJgbs/negcsRCTw24xwPo8Irm59TfmPL0cW9tpMkWfos3PMwvm7mQKjrr5b9xyseB3TIg2t5656jPI7/G3yqgdUtP/N9WJLudLsKexyWQRNWrLBse7d1oOiSs8H/nlJczXimzKydzIo6fvn8GGSrlHbu05Gobhac1j+Iwb5jLqEPB3KN/m0ZminwUnV+TumINKoAD4NleBSeO6qtG+1/Sbz2o8t2huWH96e0X/3/Ydb/PCl8fJalrSGW3Z5HVZd2QyC9UekoTfHngaLBozzTc8tBnzlT2JJBx264vwxsy5juuO+9k/rYOjz6tfPmISswws/PR3N0pNz8yrux7s+ltbJ6fT6gCKUVbtPzEM+P7z98ERH70Cs1d9xpy+7Im8PbW/u/hJUpPbAPWDKtVkKXBcwgCWiZpzEirr+M0nE3eF+469lPs+NyscM20uzm1rBzt9v4gseh39FnF+35mX79vGemx99xEfvswtSyDM1cZF1kcfvNiya2z4OJsWJyqLdyd+O9hy/HG45MHTtmnXGgZ855pn+j9+OHnAMeuP5t8Jey9916IYqmwUbN+2vFeMsDmzDlBtaL5z3JQT8h1DC94nWCcci0HmZPgkJWBInxeb/VrS5giZMul28c3Vl0qgd8iqGG4oG/BDdPDC17itVLopiklei3D7AtuypiEAG4eO9LzfPN5c8PLjMKbOPTKXnRtOu8nyOcWgBIsSQWsu4Tw+mcnV6e/O5E7BHtVW9bpTRlfn00/59WP2jaxsBpVAEvh4wiyYd9F9ju+ruY4FxQvzIsMrMoFZCWRfHARBl/qr20VD/Pm4neGZuafAlefeyZegWWvuEcGkqyfNZCbbn6xHR81qJaDbOZpI7v2TFI9z5m7QzNjpGLBxcA0sHD/TYX7tKg+4v7OgPnm80pbBzqu/gHwPq5yGUnkWABb/pxz32R2NvjTnMHh11oEu791Zs96buidHbvIpb7U69qQpslpcItjQEGo7trJqLdix620tWf+UV9mOm9Y0hjsOWvyBmaS3F+k40+Jic0W1f8LmPoWxvYkchyaGEUrfa17Q0nZGE4YRORVWYV/EuvaCItf+2iAJ2G/x/2B5zUA9FD2S6P/84u+JRynAM/ZTcZyB5cjbUhfFnnfJmOmWzwlWH00CphdmC7dHH7gITnnjj773RK2e85Lp89oLBvyAyjvSwvvO3a+3b3LTXZFZ+2xzlD8/PrId4x1d7350itWfV75pjke7R2oQkgDzuOrttWBICP6xo70PPNQim+WtZQ5AeYHBFdYSWqPlyKFIelFzOKIOVAIFwOiraD/67m3UcKeVgufQqXlJS4kNv4mG+XdPf3vms98Rte6RwXaJC2E33LolWifntYhIagwJz0vYXTGLbxhiGHD2FY/DNWffDm22HU77m2CZvGe+/9Nby7lktU7N1fUQP5x/Jxz28aue1zCFBQ6B1z7fCGAYcN+xl8JdJ1zpKPOizjag9aZby4aGJCEdFrN91djL6stRTp8LIgvQ2076kbBMIjSUDILrT78ZFo2e6u1/wtKAOHsaxutX1bD5X7OjojXbj67KVDS5ySs7SmOyT2Hu5ecsTQgQw4BHjjiv/zs/p/zueD9AkPfEo/wPWoxj6tfZ0uOwVhZQQJX7WX0zZl87uMb/IhPLRk2xHD8aW78Ovvva7wHAW59kLx8VhK5oInKO4hoen6jXc2Zpt35z1E2POudXz/J73TevWPWLUzZ8ZbpH7VsMMo/bzNlWMtDf14AcT889xeN6p7z15cEsw8Pweeb7HmMWmCgIqARSyKJxM+QlFrWQdQLyyLB4iCq9Jg28cEQ0P1zMJEo6nE7cvAb//oEm6h2dYcgbdBnTud127O2Mfz/lqRiym7l7hWV2G+Azk6Cn3vyK+jsVhucpbnd37ueF3VKMGP4LRj7/PWyILFKXbWzwVI7RIl0ZfYvGKBFUqddYwh+2lgWdvkk+mrQb03X/2WV/+GCn3eHK8+6yKWFtSpBovfJ+Vg6fAIt8fAeKMNrmUNTseHfxWMpcpa+AFo+eCredeDU0eVmi2XaP+5OQrMbvYjHTJwQShtVydunIKVLlMOclfKtglEkRdcIg22I5k97Yzavh/Jcfh2LbHMLVNyXDynmn9V/Cse++4HlNPUWJ10aJEiZiZfro4edy3xMkCAHLe6S3DolQ6mFXMsUcBIQjI98rtgZVAnC0i1SADU3WXKxKD7V9nCpH+t54P1OvbfPez2E7bfNIFjMZ3S8kTWM97Wh8p+fRzR3NKR3VCYJkUAmEOCDptO8CzKzo8LrUvGgwa9SzDbPZP9NEtY8Ckyd9P6w+gQb+/qcpXHrmVbAsoHmVcuYusXpbLRz8yb9DWEnJSZ918brz2sWWz7NWfwHHv7PA8p19UHTjsI9esQyv5W3NrtdyQ4jN94nzkhlrFwklPdgWAeq2k66Br3x9TOlVUluOAHmcKXNzqqjb8ap2JZTh4bPCbP2tUc63p+/DdJ25/zVPAO3rV93HXcPGrgjw26zIHD+84ry74M1d9ofn9jlOmWxe8Na407//a2gpLIVUemCB+O60vZTkHcwSSPBuCdU2k8SjD10CJ/zveceRm9a+o3b2qFQsMifTvfCyjzN82tH346//s2/awVDTdyWMNIxkCGutsu+kpbxwwmzX6/2ijVmwHINlv40paR/Hwtee+WP4cLK78t/XLYKwZOZEcmucsGNX2Crb5O7DKzLmnb++lvq93ReSZW1DqQSLx053fmnGMOCeF9j9fcYZVAIFwDAYzF4lUdgZTsQQAIDbnrqJofckcP8jV8DOq79gTnd19dhAckWR7r7wpuajbjxOWQs62N+rmxXm+zvt4fjaa4GTGWQNhmhVmX92Ng+pgTNefxqGb6v1TCMIH02ewz1xcr1ecDZgEAKlHVbLmDbTLqnXuft/zjnUckyqoEtuyHMztN0XmuNRERaPmQ4vfO1oz2t0z5PcLD5ocuWtsB67MyB6lkBhm6lMX7cE3BuJVcHm96p1Hzc1v8uEZxvIrcn9qprxls9+PkPWDRsNKZOFnzlKkh1XFaLLOCTaM7Eoa9OJJLy5836QEnKSaoXNxkMMvz7H3I6s/Rt/Xo75gC1vt4XXxNqVFp9ALE+7ZMw0qK8YxiuiVlgVwrR3NrRpGzx5/wX+eQSoKyL32oO0mKlp2MyR9wDtLn4rZfb5xNQ7dBQUwYK9vgnJ3h7p4wprifodfwpMxOYehmF47uwnDAN2WfU55HV3KbFcGrnVX6Fqx3ycc1hDne0YeLTKN2qgEigglZKiyvix51cfhpIPAMCcFZ/4XpMmBErbW+CuJ3/o7RPI1ACDmonSYPHhEgaLTOb0PB0jV/fk41MGAKCnTynllS7zgp0QuOH0m+HaM2/dEXXJ9vPiMdOhrF2idYtEZHX7frseq2vGMaflpRw8+9XfMKfDiqhiIyoWEqJyWAykKIrOYbde77xB90SM81mnrV0CBy18TSwryncVNusvz3t9ysprJ081Fc3bLZ/NilD7ZHDa+qVcaUfOpx1nnbU7Uu6PeuSRTmY86buQKz+vdJdTfCi6JiPUDxDYPJjB2bdvMj4+gQJ0lX63/vS3N5gyGihLkYXXFz7HC93elEORFsP1VG+fslNGeO9CigUNAaM/Dy+YF6OGAae/9jQc/f5LzHLxthGSFiuMVooz9pFb1jNbR7NA27y6bMFDgnm4l0tU5jm7L/944IMGkWjl4FU7dln9BRT0dMJpb/xBiTxvzdjX9bdrzvqJ7/1P3fM967gvMLeLRs0IB1QCBWDHwpivgomGIg69w/LJrr5iGHzv8sfg0cPO8bxO5drqyA9eglufvkVdBoKstUVA8Ia9gApMzmPNHduGSmfITa/6wjOJ/GCn3eHjSbvBxiEjHL+ZQ/CqQtbCS3w3wNtKpNvn6B/rjsSf9z2BXzQJO912qrfVSjf3Fe8C2OUY2rx1QCHM2ekYQCK3W+T35L94/Cq4+rl7hNJ2i4a+rtLZbxV2tkFDyUDYYZaybSpmj2Imm3kvPWJpcymLY9CB7y+c94Alkg4Li2h+c7IAVn9enk6uKU5PvPpNR6h1yXw2fhel6Q+gzhJo9JaB3W3z8W8ZfZUjDeY+P1r9pB+/n3syHHnLAt/rmJ+K8s5em/UN+Na1f/K8ram43OE/0I38ni4Yvr0WxtSzh0anzem8xnGZlq+B/AFSRKTJdu/x/yeeh2I2DnXOjXkYX7uq/289PoG8mbHG6lbgmW+cAh9O3h1+c8hZwRJ2qYNfjnT3KbRwovsRR7e0Rep6vHq5YKASKCgh1ZaoaK3tPLfv8dryXlUzAa45+6fa8nfjkovuY76Wp9M3O1v0V454m3OysLp6XP/fF1z8oOP3nkQKVDeANjcHlS74ReDixSDBFAReIarNtBSXAeGMsFX39HyfzPn7jOPeWwBv7rI/931e9fiWU67jTo+XEdtq4c5f74hC5fe+eonTOkh372qvnyqPpxFqvSDUXd7ZKz+D2iE1pqu8TcUBxJy5yuLXh5xliQSVZzpCYK6jq2rGw6uzDwxVNvnIqSPdeew+7Nygtf9Uujf2k+mvRkz2/D3Y83nffbVpbiPDisVMJr23p+0NF1z8INzx7R8w3efnFDZqPHXg6UzXMRtHu7wzmo8jM+urRsF2j+NZZvJ6uuHOE74PvzzqQmYB6VbGXnNAngOZPpkHWJukBS2SZMA8xvoch+Sdo9qZun5ZoPv9KGTwO2ouCUKI5Zm9jtU2BQlAITM6pS2thOmJWNc7ZnTPB8MkXj161AjxCIHXefwoo7IxLRk9VWHq4dDJMRbzTMCGtGx3/U3E0qM7Lx+2t1rDWPcmko6IM1GF9Yk/mrir5bMBEKidmyfvCZ9kkr29XHn9Z7v3tX4+P2h8PlZwB93iPNIq1zvT9hZKkveoRcbxvN+C6fPBVosXg5BI7sClFERcc6O1kG4Vs2Go1cqQ5TgYja2lg+HS88Usl3jYNGQ4JEwLi6RFsWp9xx8zRhuLLJHTsHj7nZGWbohsK/dZuAcQzW9xss7FopgYBlz4yJviGQNApvLcesp1sLp6nLvi1iZji0YrP5Ww9//q66J5Qcu6MUSTSlbro47DAY8nZnjlU+f8Mcm5GaYa1f4C913yjtL0hzY5I6Ka8Xs61Y6gVWANeiOiBIrc4KoMVAIFhrOyCDYoy66AYu444QdcEa5aO90dtqnqQJdsYPNhETkClAdPZ+x5rWAdvO0vH1s+9yaSUKjQ2bEIrp03Y7lfe9aPLZ+/GDsj0LTPOhh5a/xS6R6uULU9Pd7pGQID2cah7iHuvfMaQJZDaoNjeOIZtNsofVvULC2JkYavL3rb9fcVNePhZkELqx7K7uunE2ZRr7UvAJqKy6GN8xgVAMCTh3xXaehYM5Z3qdqpp0aiVGNrB1fDpE0rHN+7tSseBbXZaiFqC8RAymOeuYDt2lV1wXzxsVtBBMomOhgGpNIeSnUCkGByPKy+DzG3mbyebpi2dglUNm7xu8szHTt883JnOmPqBo6qrZEc8IVnLPeNDhaD+nvnt64Uui/BqiD0tfoisNrUnxiGdeYYRyWQNSoof5sd1lAvU5xIg0qgAIgMB14e+8PihQ9Xe/7++qy5UMfhVPHdL9kjDeQ6KdtEg2eQ4lmofjR5jutvjUFMOE30JpORWzzLIGmywHj6G6fCdrNPFE7MPoPa871NxlO9vVDS2db/efraJZ7Xd3XLXxCJDvjWeuDsGa/yOLZJTEqjtGFAXncX3P+ryyHN4XPqtwedAe9mouT5DPpdyZR+R9A27BO6ZDrt2Tf84Hs/E7awCmphtNl0POyAz/8DVY3+E6Z/7XpQoDx5MPsqMy/Ss6+nig5rh42FF/c8ivn6pYJWvH672mFjH895YFnsDm3aAuNrV0FVk1kJIMMnEPuV2dBuxtatgYd/eZn7BQbARJNvFjfCKAtzn5VOJOEXj1/lu3FHX+gGdwngRn5PN5S0t/hf6AdFjlKOdLdUVPlfFJgBGTcNEdsk8+LfgseSuYI5+FzwwodrYOzm1fD95+6FoQ11ljpY2NPlcXM0MVuDi1j17Lf4bZi6biks2xhTYwMOUAkUgB39VzyGSHPUlAf/scjjSrm0uRwzCMJXmxqlpxkW1mgrAPsueZf5XouZcAQWsb0+IeZ14HYUiGcg6LW9oy3lQ4XlMfvbWOzjWJZA2rJrc+/jV/mk7vdMYlF1gkJTDHo5ah1tCu/52hcbYPevPoLLz78bum3vwY+bTr8JAPzfdZNt3RaFtsRLkH61vK2J42rv+nDUBy/DLx75vrAsKkib+qUDvvhv/9/xe8t+ZN8T0TBvWiTTcsNEB2VbgLFhyyD/BWxpeyusqhkPK0ZM6v9ORrCErQHkjiOTNq30vYbHClclZv9CPak86FYQlTDocZeRWzfA/DtOCS4IZewNoljlypp1rtMnouwQ9UFhtYr0e9OZcrj38avg0IX/hkv/8POAkmnAEZTAZP1kAExdtxSmcPhe+uvex8IlL/4K3l2W/QYOqAQKSFzODjaU6XHWqcJJ6Px3/Ad0XsL0v2HmmPf/znyt5YheBHSPjx/2PWjzcYioDdu5+iALfV5lhCh2BZafVY5fFRAxhRa17LLvXvJQaYqYWNfYDp15BZBOiisY/Z6gtj3t2FnNRos2N6wRs7zxq0NXf++ncMX5dwWUSC5mSyDzsSPZznV1k+rtgdNefybUPLmc1xP57UpHK01qmhsAAGwvdVqhbiuvDFGC7Gk0nov+CPf/aZLwVVhQ5zcet7QEdGa8x1cfSfGjN2rrBsd3x7/zt2CJMs71mNdufXVjGIPFa1ASRppd2cRZZw/89HXP30s6dziQHrZlkyXtZkrAiKgxYusmy2fzcbCEkYYfzr8T7nuUfbNq0dgZO5Rs0e0WpIFKoAAYQl43kKC8sWij9DRPfvNZ6WnK5p7jLx/4EJGK98bMubpFsOEsmPG1qwOlKOJgWRiT+Efc8oL7dQx8MGVP7nvcnJH6EshB6sCAbRgAa4aNEU8MALp8oh2FpdTjwT4hVbm5kMe1o+n/YusqhokLowCzhaLFWjHLtED7LnkHzgxZCaR9VqzhFaY0+iEKFH1HCgRe+8K5UM82DGBTWOoIIPDq7IPgf9O9j/7Sjnd5ybqJI6w5rVwMAlK6AtoRvMJuecePZLyv6oY6OOefT0JxRyv3vcO3bfK/yMRn42fC8e/8jcnfD4fnJAAAuOYvd3v9LCMjbXznrYFIuYtGT7UM9RsqR8J3r3gCnt/rm1xp8kXQiy+oBMpBijrb4JxXfg3jGc5A5wp7LXtfS76v7Hqw0H2DW7P/rKoItB2xPb/6UIMk/PCHo/e/NiwLtyCTLbuPqqCREFuKvCPYRNPqJzyZkh4hX+1Es6y8Mfveqh9kspqIg5dQDvJ1WKhwF6FsS6DwVyRxsfZWRW2Df4jpOODX/Fneso7+8IFvXsxwlVP6LkmbHXSLZDkRNf+zy/6O7376nasDpclq9c36LndZ/QV85+3nICmgFBDpOebvdwIUdcprc/71nqLkk5Z7+Fx53l2WOpCZDz4992SudBKSApxEHVQCBSGmLeWUN/4Ic776GO5/5ArdokSGUK09TCzY6xi2C20D28xVnyuQJjsZvm1ToONgYU37movLmfxEZFg6eprvNdPWLQ0iUih8NXKy9QsNa/XOvILwM/VgdfU4+GLszrrFiCWtJvP1+fud0P/38pGTaJfHFgMAVlSP1y2GK92pfGiP6nFhDjp8HPpnMzXbB3xi7G/yrxVHvJQWrAoNHZZALNDmN+sCWtRmqHVxhhzHDQIzrHPCjDWp2LsXKyM2SyDWtHdc9+aMfZnzN7/bWOyd2OoizYqnmXNzcVtpRZa4xfcGlUABMEC7cTQ3RZ1t0FBaAfMufgB+cvK1usWJDJddeK9uETyxD1j5MfTYHwa09rhk9NRAu7kx1fUCAECxKdqYSgyOSF6+aSnuVe3pbx5cDctH6FUQbKSY528ZFKbvDzpxrvvZztvT94V5Fz8QbqaaK0QcnbjHmYSRhuKOHWPIdc/+TLM04vjVm/en7snmyy6iE/6wxXp19kHaNk79YJ3r9TD6HWSzxKIjqkBh6udY/Vr3XXfbST+i/r657zh3U1EpvDrrQOhMWY/TRzEAjB8yxolrvncHtHdFyxm4CqLZihFltBcUwycTdwUAgPcEfIbQOPWNP0hJB3HH3qm1BnTsl620UJzYbSkfmm3uQJgZvj1e0Q3aOnuU7zDa0+9OefsQymlivtubzaiIvBl9crQj18Qrux0SmahZQfFT9G8cwu4nJ2qEPR/8bMJMuOPbftFL9WCdKruPX81F7JYhl15wj9NiWSEJpn5Oztjc1ld3bj71BrjrhCvh6bnWqG+9CfnR6WRzyynXWz7LOsL78Yo6KelEGVQCBSCum1IyQoyaOeu1p6WmhzixmzfWcxwbynWMiO5YIU4a29RbuEV1BzM0OOaOMR3iEARBLNxy6vWev7NYbSwbNUWSNPHHz1m1Nhg3LniiZH45cidRYYTuYvFHwzo2G4zL/EVjZwAAwMIJMy3fs1pM6cTuV1KWU+fUhvVS0okyOT4bDk4czZNlO7z6++6HS00PcWKvZ7TQscgAtFgZwqAxRKioPg6Wlnh0LduJqg8MJDdZOXyibhFylo8mztYtAoL4YiQSUMjgWDnFFSVTUBZBS1qWdaUqf2sErBuncdw0I2k56/KChm1S0oky8Xu7ESKuAeKTkkOe3v/NS6Smhzixa7YXjZmuSZK4Ir6YnbL+S4lyhMtqSc4hwyQBaqMyxN2hZVDK2lvYL87tokIQpI9rz/qJbhGE+deuB/leE8cNXYROB4OCZHkIx7tEaxRhsGRh3aDhPxplQK9po2wTxV9h1JEW0TEHugRUAuUgcQ95WrNtk24RQsc+QZGtyMt2guyayDpOVtbWJCUdHhbGZffWVL9V+296Z1pEzdhDYvi2Wo6rUQuEDFCuoQ9DkHDAvi6b2HXFQt0iCPvUY5nfq9rMSpNkLK1/zMhT6MZ7rcxCvN+0bgyIp2OgOMpswi1kZTaTsL2zwS0NmiSJKQEGzPd32l2KCLwhKnOJYQ0DDvjSOW6po5qVNeOYr433SIHIprC7Q7cISMhgH4DEkY78Qsjj8PujAmFLIJa0Fc2TDBL/I/No1cdOvN+0Zgghsdw7wGg48aOwy3rGuaqxXpMkuUfGYR6ijmqTEijXj2uphsuvCr4LBMltcqYLwIVjNrFk9FRYMmaaZinEGo/ujbB0DMPCm5GmBMoBZRIqgQJQVpSnWwQh1sbQT0iuc/2f7rB8xoUyOx9P3FW3CIgP5iOqcZ+AZBPZPwVC+MBxB8lOUnjEHpEMS8Q5Ov43sh/Z4hvFVR/HDwNZ0cFyAVQCBSTu/nWQeNCmKBJAtmIeQo2Ym7bmArIjFiIIgiAIK3GOhFiJluGRZGt5pdB9LHWRdSOYV6nD4pQ66ozaukFKOgVtHEE0Yorv6ogQMoUQstD0r4kQcrntGkIIuZ8QspwQ8hkhZDd1IkeLOA8cSHy4/sxbLZ+x3nlDUKkQK3rR+ieSoMUhgiC5cCxC3GpDP1sGVekWAZEJQ11kHZtz0SpmVfU4Kenkgm8hXyWQYRjLDMOYbRjGbACYAwBtAPBX22VHAMDkvn/nA8CvZAsaXbK/kiDRI84TFgSxY57QzE8gmv0AACAASURBVFn+sUZJECvY0SAD4LiTe7Tn54YVMm6s5S7mwBRRgOWECdZXdzryC6Wk01pVIyWdKMN7TuIgAFhhGMYa2/fHAsDvjB28CwAVhJCcCOGEzRDRwWJ0VoxkKdPXLtYtAtIHbnEgSG7TKWlBFXXQ6jF3qasYplsEKwwDL6ubA15rlmxoBbIs4xI50CfwKoFOBoA/UL4fCQDrTJ/X932X/eAsGUEQJCDYkUaSHJgEIezg7jOSrbA72kWQ7AWP5g8wtblWtwjKYe71CCH5APBNAPgz7WfKd45ZPSHkfELIh4SQD+vrs8ORWU8ypVsEBEGQLAIXmggSTbBt5iQ5oAxuKS7TLQKCaGHElgFHyg2lFRoliRZ7bl+tWwTl8Ki+jwCAjw3D2Ez5bT0AjDZ9HgUAG+0XGYbxqGEYuxuGsXtVVXY4MttWNli3CAiCILEmTRIwYusGAMOApw48Tbc4SB9on4WYQZ9ACIIgaqkbXB1qfhsrc+PgDi+54FSbRwl0CtCPggEALACAM/uihO0FAI2GYWwKLB2CIIgIObBzmU0sHjsDfn3fBXDTMz/RLQpiAv1kIAiCIEiE4PDzs3bYGIWCZDe9ZYN0i6AcJiUQIaQYAA4BgOdM311ICLmw7+NLALASAJYDwGMAME+ynJEliaGoEQRBpHDLaTfoFgFBEFdQKYggCKKTXAhdHgU2HnW8bhGUw+TQxjCMNgAYavvuYdPfBgBcLFe0eJDX06VbBARBEARRAC76EQRBECQqEDDwqHYI9BYU6BZBOegOH0EQBNFOGoejyIE+YBAzWwZV6hYBQRAkp0m1dOsWISfIhePwOOsOSPZXEQRBEPXUW41NkUiAIxyCIAiCRIWSunbdIuQGJPtVJNn/hIrBs5kIEj2wVcaPV8mhukVAbGA7QhAEQZAIgQMzIglUAgUGWyOCRI32/CLdIiCc/LuqQ7cICIIgCIIgkSW/GX3RhoGRyH4VSfY/oWLQWB5Bokd3Xr5uERBONl5ytm4REDs5cCYeQZD4sOqtv+gWAUG0MuK/dbpFyAm2/GudbhGUg0qgoKAhEIIgCJKFpFEJhCBIlEAXDEiO0zy2RLcIOUFPc/Y74EYlUEAIaoEQBEGQLKS5uFy3CAiCICZwzo3kNkYebs6EAclL6hZBOagEQhAEQRAEQRAk0qAhEJLrkK60bhFygkQ+KoEQP3BEQhAEQRAEQRCljMwv1C0Cgmhl++QK3SLkBqgEQvxAozwEQRAEQRAEUcvueEQVyXF6ilK6RcgJ0BIIQZCsxOhs1y0CgiAIgiAIM6WJ7F+YIYgXJI0nUEIBlUCIHwSPgyExpBedKyIIgiAIEiMSGLEQyXEa8DhYKJD87FeRZP8TKsbA8QiJI6gDQhAEQRAEQRAEsUAKsv/YHSqBAoNaICR+GKgFQhAE0cq0p77SLQKCxAq0vkcQJAwS+agEQnzBAQmJITiRQhAE0Qr6dkAQPgjOuREECYE0+gRC/CA4HiExoWhDnW4REARBkAyojEcQBEGQyPHCS0/qFkE5qAQKCp4GQ2JCvdGsWwQEQRDp5PV06RYBQZAQwONgCIKEQRFp0S2CclAJFBAcjpC4UGgUDHzAiRSCIFnC+NrVukVAECQEcN8VQZAwuOKyK3SLoBxUAgUGhyQkJpgUP+gYGkEk0o2WKLoYX7sK/YQgSM6AbR1BEPWMHT9WtwjKQSVQQHoT2e84CskOWo3W/r+LNMoRhIKGTt0iIIgD3ArQi4FvAEFyA9QBIQgSAlUjq3SLoBxUAgVkQ+VI3SIgCBNlIyf2/10c0+Ngu65ZqFsEBHGQRB2ERuLZlwFgYAkE4QWt/hAECQNCsn9ih0ogBMkREqm8/r/j2rUVknbdIiCIg7i2p2wgzoqUrmq0JEYQHmoGxdWOGUEQJFqgEghBcpC4RtiI84IPQRDETOueuKBFEB5Q4Y4gCCIHVALFiHG1q3SLgGQJcZ1IoSk4EkmwWmojrgptBEH4ievcBUEQJGqgEihG4GQXkQfWJQRB4k+sFcO4okUQTmLc3hEEQSIEKoFiRAKVQIgk8FgVgsgj1oqILMCIqQNHjGqGIHzkgrNWBEGQMEAlUIxIGGndIiBZAy5aEQRBtILrWQThBBsNgiCIDFAJFCPwOBgii7halWEbQBDEgmHEVqVtGLigRRAe0BAIQRBEDindAiDs4AIYkUVc69Kn42fqFgFBkAgR6zVhrIVHEA2gFghBEEQKaAkUI/A4GCILEtO6FE/VFYIgiBPszxCED1QBIQiCyAGVQDEClUCILGI7kYqt4AiCqIBAfI+DhWHVMKyhTnkeCBIW6BgaQRBEDkxKIEJIBSFkPiFkKSFkCSFkb9vvcwkhjYSQhX3/blQjbm6TSKMSCJFDXI+D9STwBCuCIAPEtS8DCCc6WJzLB0EcoBIIQRBECqwrqvsA4GXDML5NCMkHgGLKNf81DONoeaIhdtASCJFFa2GJbhGE6Mgv1C0CgiCIFOIa2h5BdEHia/eHIAgSKXwtgQgh5QCwPwA8AQBgGEaXYRgNqgVDnHTk4QIYkUNLUaluERAEQeQQU2XKnpOG6RYBQWIFHgdDEASRA8txsAkAUA8AvyaEfEIIeZwQQjMj2JsQ8ikh5B+EkBlyxUQQBEGQaIJHbjQS57LH9SyCcNFbVqZbBARBkKyARQmUAoDdAOBXhmHsCgCtAPBD2zUfA8BYwzBmAcADAPA8LSFCyPmEkA8JIR/W19cHEDs3QTNYBEEQBBmAAMQ2zFYatUAIwkXb1+fqFgFBco687i7dIiAKYFECrQeA9YZhvNf3eT7sUAr1YxhGk2EYLX1/vwQAeYSQSntChmE8ahjG7oZh7F5VVRVQdARBeEike3WLgCAIIh3cIEGQHCG/QLcECJJzoE/a7MRXCWQYRi0ArCOETOn76iAAWGy+hhBSQ/oO6hJC9uxLd6tkWREECQKukxAEyTKIYcT3SBj6N0FiztCmkKf62GQQJHRQCZSdMIWIB4BLAeBpQshnADAbAG4nhFxICLmw7/dvA8AXhJBPAeB+ADjZMOI6K4suYYSTRbKXdDKpW4TAYAtAogjWS30QMCARUw13PKVGkAFmrFkUan7Y1yJI+BAcrLISphDxhmEsBIDdbV8/bPr9QQB4UKJcCAUDRz8k58GRCFFH9fZa2Dy4RrcYCC9x7RZCsQSKa+EgcaBm+2bYb9Hb8NaMfcPJEK3nECR0CFoCZSWslkBIJMDBD8lt0BoOUQnudsUPYhix8Ak0qn6d4zvsz5C4s720Amat+iy0/FAHhCDhgxFQsxNUAsUIA0c/BEEQhQhOdHCCpJU4TFDzerod3+GYnv3kdXfqFkEpX42YBFvKhoaWH7YYBAkf9AmUnaASKEakkvi6zORn+eQKcRKHxR6CIOGi2xKosnGL1vwRRCefjd9ZtwgIgigE597ZCWoVkNjy1N1nw+wVC3WLgSBIloC7zDHEAEik9e5STln/pdB96OcvB8jCtVNed1f/35sG14R6jJZgL40goaN7owVRAyqBYsSwJtxtNFPR1gQ/++31usVAQgQHIkQpuNsVOwgYEdilFM0fF7RZT5a/4s6ColDzwxOUCBI+ujdaEDWgEihGlHS16xYBQbSif7GHZDO4vogfxDAgGdMJahiOodHZuV5ywe9TmBZtxQVMQY0RBJFIAufeWQn2pjGCxHSiq4q3p+0F8/c9QbcYSIhgNB1EJeL2HDhB0smXIyfpFoEBSt8VwsQaj5zpJSvHLJtiK0xF4y5jh4SXWQ4yaeNyWD4iDv0pEia5oMzORdASKEbgQsPKradcD4vHTNMtBhIiPUnUW8tkUGujbhEiBU5z4gcBA9oKS3SLIYSBY3oOgL2KTBK4GFXKoLYm3SIgEUSXFf53/vtnLfnmCqgEihFoCYTkOt15+bpFQLIZNHmOHXE+IoobO0gcwVqbxcS4P3WjHDe7AkM0hYgfX7taS765AiqBYkRjzSjdIiBIVjGkaZtuERAGZq0MJwpgWHvM+d2dIeWERAXasawsXG8hOQAeDcleslExffDC13SLEHt0tfh7jrtMU865ASqBYsSGGbvpFgFBsoqxdWt0i4AwEJ7jX/UT4Bmrv4Cqhnrl+eQKUViO9iaSvtfoWlyhY2i9oE8mJE70JvDIPeJEl8Vtd16BlnxzBVQCxYh0Sk/nPKyhTku+CKKabNz1QqLN6Pr1QHBlnlV8Nn4X32uoDoKxGmQ9UXAMneztkZpeFJ4JUUNbfpFuEZAIous4GKIWVAIpIL+7S0m6uobdSRuXa8oZQdSCk9m4EM57CkM3k0z3Yr3LMlgcU7dTF1fx1wKN3LJetwjRJgJNvaSjVW6CjmeKfz1GosvYzWixrZs4+95D3EElkAKmrVuiWwQEQRAkJHiUOgQMVALJJCaT03SCMt0KKHuqpzvQ/TJI9vbqFqGf8bWrdIvgIAptPbssXvWXZ7ZADzYTbl2Z+9kbvtckAlqhsFhqIt5Qxy8k9uBbVUE2jbeAQy6CILlBGIslYmBgcJnEeYEbVPK8Xv1KIHQSHH1kWzjqVWyF396rstQlgkFZ2If9ZidsXu17TWArlJhsFEQZtATKTlAJpAJlvWg43fOo+nXWL3Ko8e+64hPdIiAIoosc6usQfoL4VqEtnDsqhgYRJxK4KeHoVgbZA/PzZaGSjKY8yGZmrPW27h8XQQu0bGLjkOG6Rch5UAmUneRWTx5zkgRg+LZNoeebrU1/p/VfOr4LusMV94nvOIZdGQQJm9D6oJAWbFFaF8Z9chem/CO3bJCaXhiLaV2WUkObt4aSz95L3h34EOKjzlnxMduFkWjsagtG5ROWtzYqTJ2NlTXjPX+PwhuWRszHAxpZ9X40Efd5AkIHlUAKUNVUEgkCm4YMh9K2JkU57KA7lW/5nEsd6KLR0wLt9pa36Z+wBAE7eiSaZFe9jIKfkH5i3uaDlOTUdUu5rq9q2gIAAINaGrjzoipj4l30nuio46EqvGL07uI8rjtrUfj1au2wMd4XxLh87YRdupEaCxFXdL4ljEymDlQCKUBVYyktzAMAgJbickU57KAjr0Bp+tHBOXB35xfAxE0rAqSIAxqCyCesdhXOZD5KflRUSlLU2aYw9T5CXICN2LoRfvK7G+HRhy4OLc+ok9Y8jYxSW4oqsS6hGChYYl2+DsIr772WvAsGQ+HF2e8bgkQZVALFiMqyQt0iZBkuo0+A8Sa7JgMIEg3CmgKGESI+ahNalVYCBd2dytKWAa8CYUXNBNhj+cdQIXBEhfbes8FFOAmj0XjwqSXyjxxZ5ixnPOqlkJL2Ft0iMIOKuPi346AMaeI//lnY3aFAEhrq3k++hDEuHpYu/mVYvb02BDkQmaASKAYk+kKwkpwfaEMiQDHHfzKEkxnEG2KkobStOdw8Q82Njertm32vGRsDH1tRiDAVBD9/HVLzGj4erjjn53DqD37DfW9VQ73zyyzobtMkAYWd7dryby8o1pa3DPZc9j71+zkKg1Q8cd/5UtPbMHSk1PTMNJZW2L6JXqPRrAeViuizjNy60fW3/O7OQFahwS3soziDGCBbThC05xd5/j4eHahHDlQCKUD2eFDc1daXbjgjTXZ0R/6o2JGP+1wgrMlMdBxo50ptl0cinYZiCcd8EuleCdLIJngDKG/19tlmAInUpG90/VplaYfRn2wvG6I+kz4MILB47HTYWl7JfW+KUt/jPl4A7HiGFMWPXqw3RCJwBKnT5puRCquctutGeSzYRWCSNavRX19kITov9mrtkzYuh1SPc7OhuagM3th5f6H8YkME+hIZ9CZSvtc0lQySmueYur65iYYiLFPsezcqoBIoIEUdzsWQ7KlPS1GZ5BT9MDw+IV5EaXGnCtpgzgMx0jCN0yFrhsEluT7ZzB7Gc1nJhKQAl5CN3yQ6ak5aVSpqonb0TScqlJ5MdUl5fSORUfhErW2x4Cbz5sHVvveyjsXReDuyiN7ThCnRuNpVgYKX+KKkCdFLqCeRgtUMlpyFXQPHxqavWcSfe0j9wghK9EiWo15xGCdrh9RISIX3OfWVSzFlbZ+NoBIoIL3JpG4R5BP9/kgKasa66E1QZFMeUENe0t4C+T1dQvfikciIIOM19DXAYQ11DPmF894Ht2wXu9Eknt9iX2TCl1J6ZEthhx/DRbkXQZQd9GNL0Suf4ds2cV3vVibh+fGSb1Xam9A/r5O5cLWndfJVT0lLe0cGcpOLG1vKh4aWV1l7CxR2qTt+qUohQasirHmZfcutGD5RkkTyiYMyRy/x6SgSsfDTFBxUAgWky2QGO3HjjqhS2dYNqGy2id5eSAa0LJGP+BNn07ufIbDjwoLo5HbSxuWSJeEkyxa0UYFFCRSWhd2SUVOYr20rMJ9/55OPJSKKmUECjohlk9/dKeT8Uwej69cxXRem5ebaqtHO/CPYpfD67jCIy9InJMXt2Dr5xxlbCkulpwkAMGvlpwAAsMvqz/0vZqkcgmW8vWyw5+953XybNLlgAe1FY7H1GIz6o87qyltc+eh+n0Ho6bLWG7Pfus786AbI2VA5CoZvtSrRc71tBEGHr63TX38GHnnwYkhEcXBWACqBJLJJirmcfsLssoiR1mZKriTXCPf30fLBwt/BLh8xCQyNHXOUIzjs/tWH8P3n7mW69qq/3B0oLwKGlIlNZunI8kbdrsnnXKz45sPRFzUzHNOlpddYMgjMHUW02qU7s1d+CoNbGrju0dUdRnH6FuXFgNWxM6dCU/NxMPNREVmkE2qe56KXHoUfPHcP3PiH23yvlbkIOfO133Ndn60WDTuv/iKUfNKqLcli9npcWxNjM5Phg9DM3M/+IzU9M5uGDrd+kcBltiitheE7/a/evhlG16+DuophoeetA6ydEkmT7ChO3l3qYHnpc5KqYhw1Yl8HBkqlS5Gzx7hOMKO8iLvtqZvh0IX/1i2GEGyl6rxqUGsDDGrlU0pIxbLwdavTTrnfnbKnZdHMsvPKuiMmEn0jyaiEitP41pPMU5JuoB4gut2HpS7zjv/ux8HCemD548m6ylHS0wQAaC0sgUMWvsbkZFXmcbBZqz6Hs/7NfgQsk3eUQz4f+cE/uO9J+ASkqOE8Cpkh3DmzWqfrwvMzj9sMA9gs29yQ/Lw0R/ZhUUBTWsdzSuxLvukYnwhby3YcswxzzfDiHkfAsTf8BbpTauYQUSM+s7oYkFa8GxaeEYT9OdRlHOWFtQhR7st5J5Wq6nPCMFwXtXE5bmJmkKgfGR+qWHzl6EByJWeyBKLUxeKONum9h+zJNe3Z0iShrN/jaeNn/Pv3ML52FQxt3sZ0fa/AjqZsh5ysETs2K9rFo9UPauh36s2ShQGAtsISKemY+3ruukn6/0P5PgzkZ9SdVyA9TQCAhw8/F17Y40j4wTl3+F7L4pOCp0oJORMWqLNDQxrD5730CPz8yR+JJ0Dpm+IxG1W8zhBM32uhbhACnYraFBtRnplnL0GVQIYGKyqDJHJGAQSASiCpZItCI8zukhDQNvK6Zev1/L+/6yzPNKMSJYUGmzbdX/6g9aMr6W5hVNLR6nlvfiqYmfWU9cvEb3Z5t6oiT9z15A/hlP/8UUna0YKhzYTUrKT34RHuD579+rehoLsT3pq+L9P1LNYLPBCfXXkaQ5vYFFask0fe/ppWP8ZtZrS+omSVDUuTNBCqJURYz2Z+J15vc8KmleqF8aGxpBwePGYerGewNBINniAVxuZhbkcdIS3283p7YBaLbyUTlvYus28OtSFHs9fwkoqAAR0BjvYEHZdVjsJxjEgYFqrWQ5kobCo2SsO06osCqASSiGoFgK6uhthaxcxVn0lL2wB9x8Hc8FKW+JkTx78HGXh2VU/SVlTiWsZ+dSE/FazLKrDtTEh5XYrafU1DHXz33/6+HL629D2udN+avo+oSAAgzzR3YPLkn154RpBi79KyEDXM37vlY/4ziFckcTrzC2Hp6KnMz7yUw2l2hoaSQf4XxZwoLgJ4Hfta/Opwt4EIjXkRfBeisBxZ4Zpzclw7UKdZ7xm4rrVIjVNtOzecdhO3wknZHD3EJjC0WY3lsVr0RhC0s7V8iKac48uey97nv8n2gmWdLsj4gsxTEFQoautR1aASSCJRtgLxY7DnwGJtyTInvdEsMzGZUpGLcmaDJdiIeikCI+L3pJ8gVTdk3xe9jD5YxtWt4Uq3Q0J0DRnPzPcqojWJdOBWNxj6t9L2FtnSKKErv4C/vD0eX0SZOG39Eu57vOAeywKMV7Q2E1Sp7QbrEb8M5aZjdrxvxd2vX0ijiSmbXJvAq4etNujoh9+fsgcce/18DTnrJU3olneyEN3kMYs0c+WnjJaebA/SK9nRdljWaqxEcSPBjpiM1ve7achwl+t4U1VXXlsGVSlLO4qgEigopklhnBxnOhloVEmfs+itBcE8tg9tNPlQIMRzYn39H28Xzmf6msWCd7p3MF6dD9Xhm0bsPgCkdZwqnRIyecCVNwDICEGpakJ26YX3wpsz9pOebtBHljVhYTvEES6iT5Z2fQbn9/ZFc7ElMlMOwVHYB3z+Jpzw1l/gPzsfIFcEzZsQ1YOKAABgCKfSxg/uSIZBlFvaHUPHnegtAAfmCtF7h5bqxllvO/2CXQiPbeGVU3TXGbbNYtNn176AsdhWVY8TlEkPo7asd/2NZuGn01E1KzLWD10Z5RtnO7PP09OZ494Kml27hE3SOMHUmxBCKggh8wkhSwkhSwghe9t+J4SQ+wkhywkhnxFCdlMjbrTROaEMGsVhSPM2uOL5+2DWyk9h0sblntcuHz4xUF4F9rPuHuUWZHLM4liRl1tOvs71N4MkInUazP78TIt3lqMsMgiUuLxClvG+VC12VgyfCLed9EPf61j8S1iRIC9nEhM3rgiUFtXniIL+VjhNl9vYjkPJtKyUlpQvp7/+DAAA1GxzH3tkPVlzUSn8Zb8ToE1DyFgztOdZMmaq8L2kr76Nrl8nLpRkePsztxDxYdVFq0+g6ClUrDgLhWcjYkzdWpi84cv+z+oVbYzChTj3DbIH0ZtUHLo9BAydjjQ9aCoqH/hgtw50Eder/laaNosDOwgO2dLm2HcWwOwVC6m/1WzfDAAAZa2mIAfEsLRrN357zzlS5BOhSGCzym0+NXYzn/W6nYwiVEX/p3tjKGxYW9Z9APCyYRhTAWAWANhtso8AgMl9/84HgF9JkzBOZCqPqkrk0Y8FDYk7fNsmOPzjV+Hnv7kOKhu3eF8cosf2v+9xpPC94t2++/tbOmaae34K3rtOZ5bujrPVdZIsaQexRFGhGNS97nh72t7wwNHz4MJ5D+gVhBvD9F+/K519jpIJgGCa1rY/8EQ0/xiGfaka9XWrC2e8/gwsuPVbMMvDR5ysNxTlImoudir6Rmzd4LyQpiih/CVFpqKygWylpuwkShsf0cf5nrnnDcIRvHl8AvHlleqN8FF403zBT+EWh6psqLYEEqxfa4eN4b/Jo8CnBgnioZmHjpkHCyfMov6WsdK0OH43AFhqn06LoVKfoC08BJ2H55qiRiW+vQkhpBwA9geAJwAADMPoMgyjwXbZsQDwO2MH7wJABSFEzuG/OMFaMRVopZtMkz4R3tr56/1/V7Q1Wn6T3dx4JiOLR7PtslLzCbmjSBMivayUKC08CG9CL3juPOA7VXL2WveARAi8uOeRsKpmfGhZ8pZiYIeAMs7tKYQ/rDbfYTj2ehtuXXRYddqQp6gLls6oenfzfB5YxxTd1bWlmG8+YBaXe9zE42AA4GPt6MEKbstqfwsLO83czpr7FPSMdSEKkdfYUNQwQ5wDqPYJJIq5vbcUstW3sPoIey6h9M++fgLNQhBgqZtrua2+JRIhv0Uqj0Tm2rjFUpITAKAeAH5NCPmEEPI4IaTEds1IADDbM6/v+84CIeR8QsiHhJAP6+vr7T9nDz6NRfb5fwC5Co+6imHS0oo0kju1Hb4+JCNRRhbnepbBUUOn71ePDYUymc2PeYjO0MhK+IMczQx/oK4xqUFcvpL8LKzJ2ZSz5no7uMW+R2LPQ11ExDAnMOdf8hDM3+d4WF09Vuh+rt4y4GNlLBXybEor3nGTVRnHGzJdt9JIRLGQYceilHLMKbfm0p71uaTP+Xsy3ev4rYlDYcdoMODgpydezX8TB8Vd2eLXTHtD9CUdoiW+KCuHT7B8di9Vr1+yswMZ6Cv5n+/BYy6SKwwHCZ+2USzRUsiXvqJTstGfndXOFZbeJAUAuwHArwzD2BUAWgHA7qiCVmyOGmMYxqOGYexuGMbuVVXZ7IE7nrXo/867C3574Gnw2sy5wmmc9vozcMx7L8oTihG6XwrR9yA2EVChnZZZk3itMVyPg4XvUmYg78D5uqdQ1tYsmGg827soey17X+CZ3YcIlvrUnXIedy3o7uSUwR/2iaf1OvN9lc1bJUq0g55kakeuPpaBYVo/rhk2Fh47/BzoFjyKzBPxJWi7X91nJTdDOFiADNzfTbR82Qj4BIrpnEcqHq/wjz8/HZ677USuUqKWqWD73lA5Et6etrf/hZls+pSdrJbItRXVQnKFT/zraWTbm4dI7kpuTc+hsfio6wRGeRooR4+DMG3tEth30dtM1/ptgJzx+tOO7+RZrFnzzvRLSvxCRrFtKYRl1boeANYbhvFe3+f5sEMpZL9mtOnzKADYGFy8mBLTOrR09FR4Zu4pkA4QjvHM15/x3QnnaWTshyD4z9p35rl5gRd7gVHfoTEkhNlkC/spjt9Gu/yjiQPUVVgV06Pr1krOLXwKKc78ggzMs1Z+CnM/fUPovtH16+Ca+XeZvs1s5/jfv7zGeWQir4fdD8W0tXJDi9sxO670exwD+N9BI+PkL1Zn5TXI+tm4XeCyBeK+s3ooykg6bpZrLlebOr6vLXsfLl3wEJ9gEuEOEe/yYDrUWmFaVHU5Ik25Z57f2wMlLo5VeeZDjcXl1qN7iiebVX7+IftYJ+IPzryRowAAIABJREFURpA4dXFxRFQhLXKXNtW3xqNNmTHaPlaz9F2yNwuGNG+DJYwuN/yUQO9N3kOGSEyQ9A5ZOjLRxhBhfFethmHUAsA6QsiUvq8OAgD7dtoCADizL0rYXgDQaBjGJrmiZg8qxrDy9ib/iwQxODueqJxXri8f6vn7uiq387WCg6CLWXwwAnT6ysY5dS+Y7WBQAJfftoHMnF+rza9WglHhFeWFd36P01omyMKhsKsDfnHcZdz3lXW0wOMPXAQHfvaG80cGcdIcUV0O/egVx3cVfke0+mWJ5rtkff4o18VgyHmudDIJ93/zUilpScXUL9369K1w9If/0CgMHwYhdKfXIdVF1tFA9rxkW9lg5mvfmbInrK0cBbWDa9gzoMjbVFweSh/Vf2AlQn5AZFBu83dpR/+xzOgxrC+alT9eWm6370MaryJUj3uJ+Gas7PrZm0jANp910kDe3pkvnDibdpeAVOyyNJsj0kkie+dQdFhNFy4FgKcJIZ8BwGwAuJ0QciEh5MK+318CgJUAsBwAHgOAedIlzSJEB1YvZUx+t7eDTi92Xv2F8L00ZDWioo425kGCZhWwxce3UW/fEQtZqIja4NXpD2uok5+fz5SaVQFzwOdvigrgSZhDOeuzRmd64YSm8AmiRHtv6tegqWQQtyLJO3qh3EH3lTmHOr57fwrjLlVQx+PpNLVs8rs6+v92Lprl1aDADrj90DQ/YqlvpaLHORVAe6OePoFUCcKIWUHSk+AbF93HvZCeyuJk3b2UZS+gimx+cLye9uZTb4DzLv0Vl5U1VVxCoLWg2PJZBd2ZuZHuiimZfF9H9vqYsIndsXiYC1XWI4EiMkV57qSKDZUjAMDmFppxw7GL2RKVDZ4+0XfeyPH+t5WyK9BpZOokz+YgQodp1WoYxsI+Xz4zDcM4zjCM7YZhPGwYxsN9vxuGYVxsGMZEwzB2MQzjQ7Vix51odX15PuE9ebt22tENEYiRZlaYnf3qb2B87So5+QrfGO6MqdtHiUVzQikH73dy8Yu/gmv//HNFeXtTYFpsI0Ctkzp2O1/c40hYPGoKPHC0eX+APUQ8HbaIGgDhTZoJGC5lbpVTmWNoDbtY0RrN+FB3nIaW7sB3ZW1NkGfyaRUli4vawbz+XXhthbMFjrrjYi0lAo8vLTMJjvlAZ98xi+x7r9HVao3c6u5B457HfhCiJFaYxxT7daaPyjcnYgTNLYPfGixDZ0GRVFl43kpFq7cVHQ3z5sJQ09HSRg4rShqthfbYVPJAn0CIcpRM+BROIj+ZQDPzo3PjaTfC+zvt7n2RgjY2YnstPPxLOWb+tJJM9KpSqIjjNzgP3+Z+InPklg0sOdC/9sh3xupFcNQH/kcZ3HYV/Dpgv6ozYqv3KVSetsdqXRdl81H70xZ2tkNPMni331zEF4K6rbAYrjj/bnhxzyMD5x0JXN45MfwXxPY6vq1sCHu+vtU3/Loo26KSRlSOGMuiuagMul38GTx41IVw2fl3S8uLe77B6dvO7Rh0lPtFGVAin0hN3/UEjWA+PSmOdtr37qKknMwQQZGUM2Pd0v6/47lIda3Nrndke/9hnUPoMrFlb0wHf/KvQFkFcVli74fqFUawLuuIjkVxGKASSANRHFi9aOEIYfrelD0hLWlRoGsQoFlK+EXl0YGfRUeqtwcmr//K5Td2p7oWDPBciOb1dkNSdVl5tB+/xaJd+eSW0jN3ngkp5p3T8OrpjX+4LXAarOeoR9Wvc/2tO8/uFJWf/vor3M7Zp8Oqe9wJm1YCAMAuaxZRq4O9LzPX0/Z8ebt7OnZc1VkcelPRst3yOUoRtnopZuqWvsnuFNQk+wtfOxqWjZoC4SJeb9zGaR1vo6A7PEtQafMTl2SYyo9DhpXVE/wvspGI4FyVPzCl9YbzX37c/VKNfQjzuiAuuhHD/CddaB6/WkGw5x7FItRR93jyzOvt4U4/jgrL4T4bydkGKoE0IOx9X1EfEd2Gyi6XzCMuNEVCNHcl/B/aTSmSzxBZSaRMF06YBQ8deQFcfu6dPoK55RkNZdvQ5m3M14ZppbDvkne4rne0bQKwhjGSS5FLNBvpBOnYmO+lv6Spph3WIJz/8mNw1r9+Bz969mdg+A2rJLuOg81esRBmr1ioNhPKc5W3qQuGEJQtVGebtIGl75cILrZZiVLI6mKPPkt6P+1Q5IWD6PxRxH9GrwI/h0EJUs5fjJkBJ/zvebj597dIkycMbj7lOstnXfPRmas+E7rPbXPC01F6jPtEXrRteHP0JUElNGJizhtFxbdKotfDIxoIt9JHZcLoBk35YZb5bz8+AZ6941TqvSyd+f/9jT1EMWsnXdlYDxUUxUUg5ZjrDqVXFAgCC/Y6BpaMmSaUZVfKP+RjkNrDs9jaUDmS6bpo12eKbKY6qnWS3yfG4Fb3yF353c7oZrbb+/E+sqm2j/vf9H3g1Defhc/Gz4RWpnP72aMEKulsg5/99nqleUS7jTnhcQAMEHMlECEu+q1w3pm55DYOHRFKnvZ8g6UT3bq9aehw3SI4CFLurcWlcPQNz8HNp90oTR5WCn02VbwWn+9M21u2OO54yMFltcpy2imSm6t8xPoJOMYdkTHKMh8JuaBmrfw03AxjCiqBFLClvNLzdy4fEIzEqSNiVWx05PsrBNRAOw828OdPT7waHjvkbOHUhzZvFb7XjPmdV7Q0QANHvepl8ftAU4YpHrTrB3uf9eX1t2KHdSA748onobFkENO10bQS68Mmmt2paJrT/4dMVg8bCwAAw7e5O8T0wm41NqnWI7qKm+WZUM5OFux5NFw0736449v+Djyd1lny6o+KCIW5ziABh5g06NGepCQdmCALa/f+T+3DZSJQ7vnlQBySdVWjpeZRs63W9TfnEZNwFHmsY5gMS7ktg6oCpxE1uvPytSgfOn2OUPMsslUqDgkAFHQNbL6Y81pRM14oTZE5Up1Cvy8yEY3YZTaM0bUFwPNW3pyxnzI5VCDqQD/XwBmjAjYO8d496U4F96chl3AHRNYBjOZFXx8DMr879Wvw+qy58PUv3nJcJXtw9pwYBBg5RCfLRiIhxaxeeMJsQChmwnGZgPhhL6luW6h2nQNlZ34hAAAkGcOj2iGGrefyqBauVVZWXSIEVtZMgN5kipqXwyeQnFwdpKOiVYgJTIsTlf1NNhwHIwn6uKe4Kv5w/p3w1N1nw4y1i5XlUdm0xf+iDLJfoUvdTDD2l/GtUcEZu3mN0H0qq6zffLa9bzz0TUeGMIK5iPv75C/Z5SMnC+YVLrxWn1GC56TAL4+6IFBeYW+W9oQQsCIbQCWQCqJsGUABwzf6Q+vALnvhIaG05PkvMkx/ubxDl6/ZFJERnEb6VdUsrMpj6sQmtFRs9VinJVAGUSUQV/10qRcNpRWCeQcgQH/rpjydtnYJAAB847P/CKcdZehPHVJjx/HRE12jRMIwYFhjPccdkt+jMzyY3PQRYbbr6Nc5GWI7vr+qZhzTfQSi5QTfDfOcVL+1dPTKy1E+GjYCeHxwdrlEs/TC/N6jfOzVjP66Gi76VwCIFNx2EllCgS8dPVW2OIGo3u5ugq2D2397g+M7Ano6C6uPFJbztpwDS1jjkGg+dusPx88+x8HsGUt4h371YMTWjUzt0I3Bze4+c4KS9jo+FFL1Fo0utXHICOvESUDeTUOGhzL5stdL2X3HXU/+EB781f/BUR/8Q2q6Tpxyh1JNQu5rZ64Uc4AqSkK3Q/wA5ZsmCer99lY1vnaVcB40mgtLAABg2aidGO+Q287bmXx/KSC31ihCNDEe5daJPUIrzyI5bkdd0EKVQgT0UmG+lTgoLnMRVAIhQhpelciM9CWUv62zmrPiE8ck19WnDsNk+t5jLxWWrXr7ZuZrDUIEynLgBldPD6yRTIUtPALgV/4K6pbf5C2/uytQtkEGTz9lwyGf/Es4bVl0MjgDp9FSXGb9wvPkpLczc5lQ35fFQaL8qVcq3QuTN62AhIaJ1scTZ4eeZ6iEoCSM83GwHaYJFCWQ7btbf38LXPCPx6CgS04Y93nzHoCHjrwA/rD/SUzXy55X1NqODMtu1S2FpZJTRHwJoR3e8vtboGZbLcz7+yNC9xuEBDiWFR6WcVCzDsje9tsKihnuUTx/tfSPhLuIUgwRfn1FUFzfWa3BpvlEaQ2qQJJqTZ9loBIoRGQ0WjfipGXtTabgxLf+AhcJDoI0Xps5V1paLCNWOpGEW06+zvc6Gtuo4YPd8Hqv3u+8J5ni999j6qg3uYTvZB0cR251t34Rrq9+z+MzqClpJz6LeoO4K1oHeUTG6k9e4UB96MevwuP3BTvrHZT/zthXeR6hWu0p7Ir960L448CisTPgoovuV5pH6FaXWbRxzdLnBTHVZ723J5mCb73zNyjpbBPOy0xdxTBYsNcxOxz9akDaQtxWfMUdrQAAsKrPcb4oVY0c/owiSF53F/X7uDfNvb78AH77i3NhvKDfojB6eBnzpNL2lv6/o3YUaHXAthUFegQdUluJz7oxLHKtRFAJFCJBK5cR591CE1sGVUJeTxesdIk0IDIAPfv1E+Cqs28PKtoObNmfe+mvqJetYwwhLlMW98ucg6yYY7SBDDtcHBUmgU0J5BVda71g2QWdSjQVlwdMYYDR9esAgOUVEdjiovgr7HIPgd5/d6B273c8DmC0m7IupO6mO8BkJlpTSzXIspxQycrhE6jf2/ulipbtymQQsfSQ4eSeFS//W3HaxHHAqKD70Vk/huf2PhZ6OSLY7bR+mahUUtDRv7T1HXMzksGO/BRLUrbpIr9X3aZpJIloF1AnGCGuuWjAUjdqflaMCPhCtFrJqM/PfvwQQPwoPjMRee+6T5dEGf0tAcka9lj2AfO1z8w9Bf4551Df61gXwOlEEj4bP5M5f+9MrR95Imn5SZvs7eGXh4N5Lz7c/3dPMiUwsfDvtEmaLdFmD4ULizkuDV87CJ9BR6Y13oitG5nyBADXwXAzQxSyYL4noj/6hSJhqJMRuU+085pFUtPTSXu+PD8q9eWVElIJr154RQeSYu2na5PIpW3Z+8XawTXwyBHncTmjL+pqDySaTSKJadGSF0s/alYSSNRRW18cCmmG9nrZggcpCZmVHLqXmtGeB4XRB0zcuKL/73P/+STMXvkpjBO0RhPB6xnXVo4KTQ5fIqK4CgvdLTOnCKoNF+nGwtzpzJa5jNmM1YsuATP0Xolnua0ninfUjmPff7H/OxYFg1C+EhYbJR0Bdik98vd3DC0fvzw92yBDn7B01BROieQQ2u6dgmhZ2YpvcDzFioCgfhI6GcMg26GFT24vFFMk8xJGHZPR0oK8exXzBEe/KNTOs2RSEZAhTVt1ixA+OqIlhZiXrDYn2/ciMQzmxUb19lp44Zbj4KgPX/a8ztWHZmjQC5tmHdN/h2p/OcT8d7j93IlvPwc/+811UF8hZuXFimH52/0ZW4vQ/5kudLfMnCKotlfk7nB3mSTlpfnYW0EP/Sy6nQ4fa5b8EI9x0N6zkUjyvxKGok8YwU1Iy9qbxW70k8/neTu1+I+wClWzjS/6nZFIwPjalXDWv34XOG87XtZaKiZBlxwxw/FdoD7KQ8aMbw1ZjN28Wmp6cYQ3ipWsye3KavrRYVWEPgJJaGtRO3LhRpycYPNIqrL0+fwIquOID1+Gm575iVYZ4lN7vBE9omJv516KDNX0JPMgn8G63TMKaQAqGf1euSryI1KZ0iQRar946QX3wNMHnAwv7nFkaHkGIXjZRORFRxBUAoWI0imaATCqz0eJLmQ1s7aQdnhVYB6gZXTq3uHQB+jMd4mwxCuCeXfCJXfRaE4yKC3Ks0h13DsLrBf4NLIlo6dJk6X/XfsGJLNeUN3AHuEtw5XP3w+nvvks930EAPb//E3X3x84Zh58MGkO9TcVi8pjdh/HdX2QULgsTrd5yHdxVGqHpdR4SjZKx0USnLvO0nwvKNpJdu8ewy3zOClGmImYUgr9QgTn8gUPwj5L39UqQ7RqlTxELQ6ljw8K2gnPEVAe4qL49iXk5/hy5E7wu4NOl3oygYbl/YTwiNPXLDbnrj7DLACVQGESsBH4Ven1VaMhqTACmR+ymlxjsbtDYVaCOCCV9RyqByhz6htknallWIw0lg2Wk5cAo4dazUYnb/jS8tl3QmR7J4FcLveVlX+e9i/4c/3piVfD+5PnwGXn3811X2dePlz355+7/l4/qAquP/MWbnlk4mUW76cEiuIUsJFiXdUTQJnF0o9knO1uLRsinA8LvJZAiDeZnkBKPQ403shvSTLGUd3TeN35U4mkUOFBsmXhbydCj8UuCqPPTu0+gejoPU4+UMrpiNbpUVvW6xaBk4H3iUp/NqLZMrMUlTu6mc7MsWAIsXMRyen5aw6DX5y9jy0hkzWNYCc9pm6t0H1IxCEA5o7+vmMvtfzM28bCWHw5ZeLPdePQEXDDGbfAMk7/QL3JFFw47wHu/MLFozy4+q9ojPrUyHcBdkIP+MLdkivDZRfcA29N3wfuOPEq4XzMTNi0EgAADvn4Vcv3PUkZYWmjg3vtMk8mw6hX6vIQje4TFBmbINqXRiHMn3jHLFZFbDR6Qyd5jMft3eZ+SqPkarTIY2+n7nP8hIRoTyoUIyL+M1nZ40v2gDR2zH27zKAhvETJ2tfMoNbGQPeH/VyGy9oxfrPI8EAlUIyIusW4yKSvKD8F+Sn51TBI17Nwwmzhe+V3el4vXUWFGJA/lVYXyUyW5F15+o6mDRwH837nVY31IUjjzqoaNn8qO6/+QrEkLkRz/iOOxF3P4//3PFQ2bfO9bvmISfDjk6+FzYNrpOR79V/uhj/8/HQ42+aHarlLKHgapW2Cfr8AYPraxf4XScC9vw6nUmZyUaloYukjIz61kIJ5UTCWEhWnooS2UGWvB8LHeTjnTVl5dJBCZZ8z7OIgQSQ40TkUeUUP9LzP9DfvcV03WOsY63zXy/dgUD4NEBU4yHHzwGh0DM2KzL4mnGeU644jF0AlUIgEbQRlRf67sPZOOaoa5igTJZ9Enm+PoY/jffvmiSyrDxQxonV0QQTW9nzSm3+23hcR+e2USHakzIoRIExKtg/0jjoW0vMSABjS0gAdtqhcPAuVoc05GNGIk8zbbPMJMhBH3Po5Xbuy5rRogQkSlP6cq2sSdfTLaSWYKxERr/zrL2C/RW/D3U9crVuUSOF5fFrCBgRPlW8orYBVw8bCI4edY/k+rGh2aUL6ldw79bkG4DnCpFUJZIaQSLbrwBJZfIyqR45P1mjOz1WBSqAwCagEOmD6cNff+sOB29cMIdbnqGqzY4fZ3FvjuBBVZcXqYWNdfwuzDnYzHI35xqdvQFlHi/XLaBYrTFu3FI5752+6xYguEt9bVyrax6ruPP5yWDZyMtx6ynXiiQTpu4TulblrOfB3GIrGTyaKW59GFgntJcyuMpGgKIEkSEC3MBInEXfFt4v4ebbjOMO3b4Yb/vRTmGCLylhToU5hqrtkWY7KOTZ6TXMeKY74DYN5HpVOJOHCSx6C5/Y93vL9GA1Bau5+4hp47P4LYVS9Uwnk2oeH7LjYglmmqK6doiqXC+Z6222eY3H0mWGumaMAKoFiBG2SkqGb41jMjNWLZIjDRfV2r4hIWdTqJJh4si46VOwcmFNU2hkKpP29A3f4w+lyi4QmgEgJTt7wFQCI+52KqvVK0kjDRf94rP9zVJWA8158GE5467nQ8qto3uFkvlbSUSsAgC6bhY07et7B8pGT4bIL7oXV1eOE08BNAXZ0HmsFgEhN9oeZ5grNRWXS0jU/Ia0PTnrMr9gIyUqP2Tl7dN6pGTepWErvxhPnQGG+OuuNMErs9V0OAACAl3c7xPHbmDp+5QlRoEyI40I4v6cbxsTIkXHYRazjlVrmkCGMMWYH216bxcgAqATKMlic0Krq4L0XjYK+bUTnVdFcZwvg/iAdeawLScGcIxTRgQDASftO4r5vcLOfPxX+xnD3E9fAIw/Mg9FbNzBd/79pe1k+R1W5EhfmrPgYCrs7+j4NtA9V9bWkc8cxOTe/Bqfsx18vWTnkk38pS1s5Aap5WN13HBc7cUG0n0ualByrAigh7fjVKepxMI70XZUbkitzR16R3ARDx8XhM8MisawoL/bRwe46/nL4v/Pugj/tfyLlV/4z/mrCfAcr41j2q2GvGez5CeYvwxl4GIQx7zXn0cm80ZbbRGeVh8hB5wAZx45fIaKdnvk+rxRaikod3701bW/L5yBWJyrDVoY13hZ3yncqWdDTBePq1zKVrUEA/rj/d6xfYjuxwDthrK2ohvn7fsvx/ZphYyRJFA3m334STKpdCZ0KI6uoJFuUnVF+isLOdt0iuOM2fvCY5ss43sII1RKIZwxkOHEig01D5Fkk8vLCHkfCjafeoCTtbOkv/OhJ5cHS0VMhLcsfjeQKRsAIPD8LLzy3vGcP23JVliX/sIY6KekoQVKZMjsqd8kvN3oWMVAJlOWE6hPIM+BubjTDUJ+T0uH95KQfBUvS9HduHOdQO1vZWl6paKcuBEJ6/bzZPHnwWdRdHlVHanSF2L67zy/Pw0ec1/9dFJ1HqoC3TlSVy971i0ffN3nj8lDz4wqjHLHxw29so0nLN57T2yaRXJfCVIzZefCYefDe1K8FSqMn4TIeRqC6RMZRsAfK55hShpgIvMyAKD+270hffn4z1gRz/WGWaPeJeuZBPOTKOlMmqATKNmyTm3g0iujLWFIQ4kI+wOTZPkHkVeRYfQJFzCmQEgKUdcQWObJJ9faElBPn5MdU7uNtTkNZ+Po0dwf7NHj8rcnknWl7w2UX3Av1Gaf/cSNQ82CvE+cePBUKUnIXb5Zxk2ExMHv8UKn5AwD86NmfSU/TDdbSHkU5AvuDb86SK0wI0BZ4uaFeFWfkFrbjz36kk/S2GoW56qbB1bpF8EX1vIOAAT0MQS+8iOVxsJCRtaHD3G4CZifk4D7kObIR2K9b7oFKoBghdhY6exvFAdOHw2MX7k/9TfaO+V47MU4OLNEGJJR9wMcwT3Znjh3CdW9ak08g2m4zazGEaSkRVQfPQbnjN9fBpQsegqKuDv+LJRBk8n/828/Duf98Ap66+2zme67/9m6uv40Ywh95xsjSehCUKCzqRFk2aqf+v1me4ltfGy9dhrlf/BeGhhRqOcjCsmawe5u55elbHd/59dGqWpM5XZoMg0ucyl6exay7w2M9/YOshfglL/wSjnnvRbj9d2qOgWWwb2Ddc9xlO/5/7GX936nuUdJ9VrsFfWPfmLo1QukMKVW3cRDG5lOvi6IuyszfZ0eEssVjpjl/DDhGqzhyJevInFd/uqW8Uk4mIN+3mQoMVGlwgyWGSMNzcPIcuNx7F68O7toTdoMxVfKih4TJjNGDoabCxcGjOeRnwAE/yERQ5WTDS66C7s4ACVs/ZquiRgUfTpoDAAC7rvwUjv7wH7BpCJ/FTFiY605zSTmc+PZfYVhjvZS0zzxgJ/+LNBKer4XgZPqPoWXqLamCFMv1JziVgj0p753w8w6mLDQkc+oPfuN7TYI5UpQedln9uW4R+jEPOQ3Fgxy/V1CUQDLUDnnJeE+zR29ZD5f8/WGocVkIC1kIMPDOtL3hyJueh3/OObT/u7C6v5PffBYeu/9CeOCRK0PKUS07j2HfAJQxZ1L5nopNVvnmucBjh58DR970PDSVONs2C17zXRW+Jdmj/HnjNQZstlgRB3srcTB8l6P4jsGDSiSmziriQ6qn23dCqZSI+ATKRby63LvP2hsMAPjLuyvh8X8tdU+D2D+rNgU256Vu8uq1mG2lOLxmfeowF0Us74L2mFFtJ59OmAkXzbsf0iQBRZ3tsNnHNH7quiWwdLT6xbCTgfK7/5iLoSO/CJ7b51iPq9hJavS3kW2Era8SswIk8PXpwwH+4nWNNd1hg4pg8nDnQqMglYDOHnn9z9bySt/ohvk9XXDGa7+H4s42eOSI86nXFOQlobNbTwSZZDqaSqq1VaMBYIdj7Y6CHZsxtO7crUYNKs6HxrYuprwK8+JnVWHGzyfaby75Bhz3s38qyVuXP71kOh3ZcOPJXr62PHfGCPjh8bOhvqkDzrj/Nd/rVwyfICpaKJxxwE7wyCuLqb8Fqi/2DUTzFwo2E/3jODOm4yVbHDQ3DLA+hdvcWtephjiAJaMazW0wKhvH3nLILyRtz03pdA+Y7rSoIIRAghA4cjfviEb2Tk21ZYs5daXRwSIyNk2soYf9DgPe43lhsLJmAqyuHgdLaCbVdjQ1MnO228qHwh0nXgVfjpRjwUONEORD3EMWq0bEMW5YJcrSD5kV1i9eewT85pK51OvmX3Uo9XvP/CX056e/8Uf41jsLXH9PMdVpthKnSUtTiGUo6O6ESRuXw/S19EUbjU5FPrgssvcpezMKIACASppzcZe2fe7BU2H8MKsVspsSMu79w2OHfQ9emX0QXHbe3dTfw7R0IhBO31BXET0nuNXbN+/4P8/RJMOAwaUFQAiBYYNcLM9tbBP0Qfe1yeb7olXn6yLoV6/RbrFE6T7yevwVzXk93VDa3gIzV8m3ulxbFbOIqy59Lc9aJipr5rBAJZBiIrnrr2pS4pFstjvRpZF55mspxw0y+C6QJJZbPoPz1KJ8k6mt0rqrJm27kswvl4QGR3IZa6W9p+gL9cvCSftODCcjRX2DyGCeEJAFfQK5wV6W+bYjoGGNm90pvqMseckEJBMJqp8Xlv6VlziMmoV5SZg7YwT1t3uOuwwefPhyuOfxq5nTcyyOJOFXlqfvP5krPbvflxU1aiwoqt2OjftQ1t4iJf+mkkFw97eugGWjp1B/D9p9yzoWI5N/zDkMnt3vBPj+9+7QLUo/jzx0MTxx3/lQ2sH+Xos627j7kGLBICijhpb0/826ycdzTC0D8fjkRh2Lw28vbxZMudA5ZNYo6veOEyKCmSTAgGd/dir8/NfBIgPTaCmuZS9AAAAgAElEQVSW524jjDWgm7InjRbermDJKEfvNI7W8Io75J9vBQgycacvosqLxI/RRUHpJOdstS3aW4DnuuSInX2vMStFolCGGZgdQ9vK3G8HwPwrq9n+29P2AgCAf80+kFEqK5WNW4Tuo3HlMTOlpWXnewdOVZZ2hhd+dHjubb0EQm5hPXjuflLTs9DXuFgc4+636H+2b8KpFJsHCe4Qy3Lq6dfH+owhUdlkcrOgW1852mG94efXKq0hTPeu4yuhpMA53+DZQW4rLKF+H0RJfMzuY7nv+cH8u+Hux6+GpaPoSpuoIdO6eZ8pcqJ79SZT8MShZ8MX4/znTGFR1NUBo7Zu5FrQtheWcC9B8lJifYrIW/zGziNg2sgKofxkkeyLgprQfXQ1QDNIptMRGQn04rZm8TsOpiK6Z1xg6k0IIasJIZ8TQhYSQj6k/D6XENLY9/tCQsiN8kWNJ3aNeGUZxeTYhYK8JEwZMdBBijXygbt+c+85cPtvb4Ayjp0Erpw8B/OQQwVmyRlQmYqY4R5RXKhESAmUwU8iu1PpjUPoO9Q07Ob9bvz4pGvhxGuehmUMk2xai3h95lxmmfwYW+X0nxQn8lNJahnxHB9BxLjq2FmeR3l4MDt/Hl+7CvK6u3x9V4wc4r5zzHaEyZsz9p8Mk3yOe5I8ll1vdeHEs8WKrDft8hyRej5+WTry3a1wjp7Dr6DhZd7hM7jvueuEK+GHZ/0EGkv1Lq5ZkanIvOk7u0tLSwcsCrGmYr4j7LylWz2IP0ImAEDa0gfInTuybGDaKWttYrquN5kCkk57+24N0o8x3hpmZNtspbXARRHvsx4UsQDPFnhWyt8wDGO2YRhuvex/+36fbRiGMy5ojmJfxB82ezTzvYOK82HuDLYIPUX5/rtnw7dvhjkrPmHOnxcvp7wNnibe9AYY5Cx9FHzOMDkO5hxcNiqO2GSRWKVPIAn3VVF8OOT3dkOV6cy8PewsD2Mq6QoWI5FwRKB44UeHQ2khmxl1OobhV2nIqh20yc+Q5u2SUudDVzhnHRw8k26mLsIzlx/c//cvf3UZ/PX270BbofdiwmbjaPk0fdRg6j08Ss/TD9gJHjrv657XDCr2Pw5Gq+cydBsH7syuoHZDlsNL9vGSfmFeSt+my6Euxy2c8PdYmwZXQ0VLA4ypW+v4bZ+p6o/zJgiBCw+dzncTIdCdx3bMccEPDxeQSh5V5YX9io9EWo/zchWomDrdfMr18MIeR8J/dvbu05yy8AkjuiA294ms3aNhABTm+8+bxrls0LUVuCtp/cYfixw+88Qgr7OU8URDc5HY0autpdHzLSkdxgG3oYw+b+A5EZBh1rjcsA7KDnOJCGPXQLJaG/BSVkQf9GlNp5ESHlUGBd3uTszCjpAmurv0nX2cflBKBM9IAwDstZO3eXKvT+fWaxuc0pqiZciG94wu7W3eeebezusMA4b3OVBkQZalVX4qye0AV6WecheBs/asVJTkQ1mAo5oZajK+LgiBcZtXM98XlSMwGWQ5fg3Sz0SJG0+7ER44eh5sLa/0vtBUbH5HhDLI3rFjmltSLpKhLBxcGtwBck8qBQ8ffh7ce+ylAVMKVq7H7jHOJVV6fMSo4Vat0skUNJRWwNph6h2kHjJrFFRXFDkUk/tMqYHnBJyOs1CgOXLZIxfu378pouMYoAqOmjMGpo6kL0b98JqPvDNtL3jwmHnc5RTWaJk295OMmRbkJQL5ZezycCIfOLKcpG5q752q4bDZA4rqUUNK4Kg5zv4kE7GQl8bSCvi/8+6C7132iOd1QeuBSICHqMCz3si0wVtPirdVISusJWMAwCuEkI8IIfQ4pAB7E0I+JYT8gxDCb8OaI+w3jW/3yH7W/vbT9oT9OHagpq0bCD9+3ek3w9MHnAwfT5zNJYMf1//xdjho4WswvnalYAryJ4Wii3uaX5hHLtxfWI6rj5vl+XvazYw+y+E9rkcrJdrxNt4Q8TKHNd4qp/LN7zFJXTSM8w+eBvtTIt7xYraK3HnNIstvMvxEVNOi/SggW470yOKDnfaAf85hWLR6FZvtt7xkAoaWFdAjOIEGCy5bduOq1GzusHQpf93nWHh5zmFK8mdFpwLTr989959PAgDA4R+9Ii1PvyYv4k/t8qN2gd9deiB89xvOY8Ylhf5K90uP3Bmeufwg7nx1UsRgBWIhBsc2Lj58xv+zd97hUVTdH//O7KYnm056SEJ6L6SQhEASSOjNhA5JaCoiggJiwS7SlPdVfFVs2FB+FBUUXxVFeaMgitIURNQoKE2U0Fsyvz822czuzszOzM7szm7m8zw8ZHdn7r1z55Zzzz33HMwalCHqXrmjv8qJkRKIx8hVlBiC8vQIXmlL8daZovTyR/x7iQ/T4ZZ+HcfZkiP9EOhtPo9ZY7V+MCoZfwRx12WzwGOEzoRFi1mGcYWPhZozwLfVlVAUlQugP4BbCIIwXRV/C6ArRVFZAJ4C8A5TIgRBTCMI4huCIL45deqU6EI7MkJ2jQkAVSbHx/LigrGgNg/3rHkMHle4HTyP/exN1H3ymuHzN4nd8WrleMET6cOj8zl/7/nDl5i34QlF6InT2haTQWelc74brBMXnQMAo7NJOpZ2QWxdpzbbNZLJZ5M9hShGQd0BhFahCBbcLXDe3ZvhvbG/x3YFbz8LR2sTQjt8YogJ/S4ncSHmAhmfpnJNY1uLSlvC9YoG5EbhtZmVijm7T2+dz91YhhA/cX40LGdk+/FMboWO1AEHLFVR7RcbsPmBIYj86yjrNVK3KjGRFbVWhlpPDvdDoACfk/YmIsALJEHgtZniAiwoFZIgoGOxzBdDsJCQ8AxwrTl6ppgrRsR2T6FD1YOjusNFQwrue6blS4n0Qxdfbhn9rhE5uPuGXLOIfrbAy81FEWLgnxaURM6MpU1nBbweu8Fr1qEo6s+2/08CeBtAgcnvZymKOt/292YALgRBmNmCUxS1kqKo7hRFdQ8ODra68M4OBfYFV9n3X+CV5VMA6M9S58aZm94P3bEJnlcvW12OggR+VgXinTFL1wWXvTQfGx6thbuJg2Au5FpY8FF06DxcZY3CpNSdJSFRV4RwnE8oUJkQ2o4IAP5ethdK+FCZwSEwGKI+WUd703R3dzHzc+B3odli/mLykosQC0KoKWNL40XlY81uoZIhQFnwKUFYVOSJOSbYxVfcoplu/cXmr8KRsFYxw7bQZJp/hDq2lQJNa6vdFPJCj4va2rLw/pF5VqYgrl5D2yx5u/h6GIUX50LoOOvIeF86j9r/rcOyl6wL/c31dpjGP7HNj95ubXlke3l9MZ63YK1f0GYZLfbZ+B5X5pUWS934eZkrDr0uXZAuYytxZH+JQo6DNXP6r3U+LNYMQRBeBEH4tP8NoArAfpNrQom2mY4giIK2dE9LX9zOjaks4XvxLMZ89hZaSQ2m9U1hvOezDPFHmYRy0d3+EzRJUfC6cskwaKdFWT6bTXcALKWc2ItrIU2Dc9fQ5koc2+R3lafjSqGc8A/Fz6GxvK/vzDsAXMwbZvnIqFR1lxMbZCQ0pv7+A6r5HN2Q4+WJbP5CotK8dEtvuLowTb2drDXSHtd80UCxXSopaSwOqI3zVq7we10hflSEvB9LPk0yu4r3Z7agJtcs3O/akhEAgLd61rLeF8USBKAzUJwUijyGTUQlMaY0HssmFlm0+HAmLrh5IuTMSSwcOc+6hAQOnmJlYPpxsB8jE8UlIgKCICTzy2eULm3c/5vF4bCU6Bj8LNKj3dKjadqbRBFRRa1RDFo7B1vakKc3n1N+8rlSUCJ81GMhABoJgtgDYCeA9ymK+i9BEDcRBHFT2zU1APa3XfMkgNGU6ijBJrhf01v6MPmyeXrgTVhVOYFXOkyWRHxZVDMHjanF+CBXrF8C+ZrKI2MKLF8kE7MGijsXTsdZl4X/eAubVIXUwwUP/gK9lPUrdKdE3gHSsYZfuiVCyfdf4uExd/O4SVAOgsskBC4jFdOxVUnCnHMhvDdreBzBYdoFjgpUhtLgp/AEydMU01PY1mB8LFG9L50HAEz58EUAQACDvwy+lKaE4Y7Bxn74XqiehGH3/B92M/hCHJAbjZHF3VDX29wPj1QIFYXtYllm5/MqlnKPCPBCRld+0Xqm9JHPspqLt2b3wVuz+1ilkLjg3jE3UCSJFYOn48dIedomSUg7KxYndfgqtXUgGEu4MW668EeovDqsIAbuLhpMqjB/d2xyIsmgqKArP16Y3ktQGaSGbsHk4WBBLK5Y2HR21nUWHyy+SYqifgFg5t2WoqhnaX+vALDC2sJcu3YNR48exeXL1h9hshV39RHmbOzAgQO879GQhNH1V//+EwfOHtf/+MEHAIBu7l64y80LPx48aJYuWVmLEp5HtHSeLqiO099PkkArzb+uxTL3GYPrGIM5vHIy5sCBA7jW0sqYPkkCZJEvrpPGzZQCheNnr+HatWtwcVHWZEPHVSvFLq1jLeb5whXVgYn2WpBqsE46+iN+jExCdusZ/B/ki/xCLzfTm7RW9nZkVXv7s1OUsbnuG73H4KKHuaJkwNcf4HBYNxBtzr8tKt3oliZ2qqcVU0rx07FmfPuLZR9lSvAbYC+ERguUCrHNItTfE09NLpEkwhcXlnZA+YYCF4SISmFb+P7DYwe9x4HtSD1yAC/2rW9LS1jeZt7EGDr7JTdP3D0iB91CdZj8n88N38d08WGNbGYv0qIC8NDo7ogOsqEyyM4TiZS51/bohu0/nsD3R/6RMFXLSDEWHAsMl6Ak/JDaDYKlSLjWwFbUlEg/5h9M0Fg7vwisqzB/T7xzZ7WkFkp294dnJE85luB51cVNP8bZuw4ViKLUeUePHoWPjw9iYmJkMe+TA82fZwRdnxjux/serYZEXIjOcH1UkLfBR9Cfrj7QXTqH4/4h8CVIJIT5QntM70cj6OxptJAkmr18eTvgDfXzxPEzekfTfl5uOHOhwwwxMdwP3n+dx6Wr13k/J18Sw/1w+VoLfj91zuw3DUlCc/UyrmqNBV2KohB44SyOHj2K2Fjuoz/29AerhCYs2OmeY43tZsQE+6CJoS2ZsvyFuTjrocPVW2dKljfXvKjVkLjWYhy5TM7mIeccHR/qi29+PgWpRHcKMFL0svknuW3T0/r/py7jlS49FSElXfzyXTgUkYgXqxoE3MVMQpgvfjrG4d+IhgKGC7thOsabweGHyprQtdYIs4nh/BYg1mCzNkEQ8HZ3QXFSCBoPHJcs2aNBkVh0wx044c8e0fTj3L74OLev6DzK08Px8Z6jBos7tjfqoiERaWLBZYv6FSPLFibYz7edzXCwhaSzQZL2C/q9cJw0FvpBbY7QpZK1iw7swI6UIknSEtrvmS63pW8li9ixu0qyLuF4H46ib5ADRSmBLl++7FAKIHty3sMb51mOvVxw9wJBtfJSAAX6uON6ayvcaMfJmKrfx8NFFiWQJZgWhARBwM1Lh8tn2YVVAhQaypMcPsyfoytlbE0rT8FS09oK/wtncMLBxxpbF/+lW3ob/DJIFuWHonBdowzfJgCQ/es+JB89JIkSSAidbt6jddVzHt6iZcwOizB5Bku5fALp3zd32rZcBKyd0xckQeB/B44Jvper6W7NKheUllDdQEKoL9bc3gc+7RGZ1DlTcdR/tApnfAJQfGA75k16TPD9nW1otAVsVZoW5S+7FZU1x2mNNnco4/+tpXzfNsmUQHRM53ZHO04FGFteO5us4lxPIwzFhRpxtsZlDy65uuOiG7+Qtb6ergjxNb42wMtNMe+BLSQyn/KNFhmBR1moEi0d09qID7Uu0gyXzwq5w4p7WhAECuKD4cPgLNCeGEdnEVY/T9T3MPpMr3pLzmJFISaKmIhs/L2VM146EvS63tc1nedNzG9IdEQbcbfZjGYbRtLic9yAUmgz9/NyM4zXrO+UcaddBZC/HjSgcPMHK5HVtE/mnFRMYeuyBMGs3ranz7rJlcL8ORnU/zwnACF+G62JrGsW5oCWFgECA3Plc0FgT56eUop/Tyq2dzFUBKA4JZCK/dFoSKOIWXLjpiUVb7HTNVgZjkDlxp6RcMTU8c3VaUaf+VoC2Q69OMCkzKmwED3uodH5WHO7+GMSckBfJ3JZAjWUJ+GZacZh39OijKP+uGr10w9lkvA1DfdYwGYBCQDLJjLv4vl66q0EAiT04RLg7YYh+V0xMK+r4HvtZ4ivUEx9Nph0Yz47x5JZpplwzt36sX9G/3QUJRhHHeGzcJHC5w8fBaW1oyaTAun+WnGhx631CaQ0pBxzbEnPFPYjfHSk6HaWkihNEeZ7U6UDtv5Bsmxy2bo/0duPUP9c7WOoVGX+OiHX8HfRwa8AAL33fs52OStc6xkKFNxcNJhQZnmT2lTpXtMjTnBZpIJNZlk1o8PSMz7MF8kRzH7gTJ9Fy2OTtV9OFP8CWkMn3sRTlUAy8tWXjbhx4iiz79mEsgP79+HzT3iERnZwTJ+fIAibKJ08L19k/J7PwDq8kH/Y8Xak0EdYsxshhjMx0keb4UtGtPDQwKZdSWh1XY/gVsRIwcOj8w0Kj3aGFcTAxUJkIoIgZLdGEg5zefpkRhgtBEeXxiMuhJ8Vg+kru86w8F02sQj3jbsPTw+4EUeDIlnTSmNpQ4+NK0RuXBAeHStdtMCsmEDc0i/d4nsM6URhjYUgpGUnR1j2vyOqp/AYL857+kDTct2qAT0hzNdugmZGtLDINlJNOcXJoaKsCqzNn025Zi/F65D8GIwQIT8IQUsSgsPcPzw6n/P3e2vyEOJneeyyhRUeU/RbJSFGPrQ3JOH4x2DG9tTLq3zboKUxYEtOH8PfjWkliPvzZ2xLK+VdnjlDslCSHGq2wSfWUtjUHcTUPikY3F34ppPU0J+GKbQ9H/iMVzMH8LQQFki7LykVVQkkG9evs/vPiQz0YpzUDny/D59/+jHrfY4+YLfjprW+2WktLaAZZoUuzSeNPj84qjvW3N4HJcmWd7xctRoUmOzkOgJC5bNr7tIuWHun2S7axb035Fo0901pW0zOnrwEr5aPxYXhtbKXq4uvh82PC8mlOjSyBKJ9f9vADPh5ibRa4CHBebhq8VVSATYWDRaVRbdQHR4bV8hfMSXh+8qNC+I1xjBlOaVPssVjg2JQ+lxi2iIs9R/CJNyxpQUuWz5stGi0DrtbaPCVIyOmVRN16ggAYccvROdt8lkplkG3D85EZUYEihJDcGNVKqIC5TlmQxIE3pnfz8ivIx/4yDOxwfJFKJOiN0UGePG2WBIDSQCVFix2Acj2buUkwNt+C2EjOcKKDpsQ5tuWBnMiAd5umNY3xfD5vpF5vBUAraQGv4R3QyuHv8IHRnY3+tw3KxL31eZZ3BgCIHo+4XNbbBd5IwtaO8YOyI2Gn5dlC8mOqG7Sjer/mVpqrqSTLHXHQ1UC0WhqakJycjKmTJmC9PR0jBs3Dlu2bEFJSQkSEhKwc+dOAMDOnTtRXFyMnJwcjBvWD78c/gkAsGHNasycVo+b6kZj0pgRRmnv3f0thlWV4ZdffkHrtSt44M7bcMOACgyrKsOWDzfj6tWreHLZQmze+DaG9u2Jze9uMLq/paUF8++ch4yMDGRmZuK1l1YCAFYsX4IbBlQgIyMDC+bNMgyGE2oG4dH77sLoIVUYVNEDe7/bxfjMbAOKUgQpNoJ11k1gc4ZkoTChC6+BqB2hAwWbua09SQz3NRyNYcKeJbZWUdIzNcyiQJHeZinyQ9dUvFE+FoTcoaklrtBIic/rh/nz8x3WjpFTRp6RB6WA6bUyvWvl9Th9ux6SL273LtjHAy/c3Iv39aYWZ2zIHdqcD8LmGAsOlE1+jqAtyu65IRdimVSRJPpeuRC62OdHR88RM/eb7rAvXnWP+JJY24kFPICc40V1dhTmDcs2WHMGc1gEjusp3gKXJMC56Bxgjf8RhpfBa4Frwvvd+wMANhYMNHynEZEOYL4BKKexdO+0cMwblm3xOiX7hGMrmburRnBff3BUd8sX2QF3V+YxcfWsStxQ1GHpnxrpjzdmVUqWb48kflH8+LQOpjYk1gcbnzYrFBcW2UJM97ttYIZ1hbGCbqG+Zt8puPvKjmIdsVQ//L4s6X64YCDn74cPH8batWuxcuVK5OfnY/Xq1WhsbMTGjRuxcOFCvPPOO0hOTsa2bdug1Wqx+YMPsXzxw3jq+VcBAPt2f4NNWxrhrfPDV182AgC+/forPLLgTvzn5TcQFxeHu+++GxUVFZj/yBM429yM2oGVKO7ZCzPn3I39e7/DfY8uNSvXmtdXoampCd999x20Wi12fv8rAGB8/VTMmD0PCWG+GFYzGls//i8qqvQT7qVLF/HWxo/w9Y4vcPcdM/Dep9sZnpit9dtfDUQSQAvD92H+nlbvbvbNYj9SwodQPw8cP3OJ8xo+jjYtYW0KphZRT04qQStFYcCjH0ia4aiSbljzxc/ibhaApeJZ8rMxoVci1m7/xfCZy/Hy9H5pmPvqDt5lY8s7Ksi+u4Rje8Zj9f8OM/72eF0P7Dh0Ak9u3s8rLbqgYuq7Z1JFMpZt3IO63oniCysAiyOUGMfQdvQpxWSqToFCoI87/u+Ovnhk3S7s/e1vw2/dQnT4+cRZo+sfGNUdd7+xU1C+99yQi0fXfyuu0DzomRKGd3Y2SZIWd4h45hfOpfS29L5HlcTjpU9/5Fk6YbBFB5tSmYwXPjkoS57WQyAy0AtHT18w/pZW9dPfexaB5/6GrTDdbGGzPopp2x3fMLcKI5Zad+xezDhx++BMjP/3p2bf98uJwoRe0h3Dvrk6Fc98+AMK4oMxqSIZMV18sPnb3znvMX0csUc82DgeEIoB97+jt6hrQwr5CIR8kmpNjziMLuEXXIRveyhPD8fW/X9aUyzMG5qFJe/u4X09W8nEHI/kGkuFQK8uejNg2zg1st5haDcakmSsW6Uo5+jFYPMhyFTSi67CNunaiQvRYc6QLCzbyN1OhhXEICrIG/5ebnhoLbOhQDv58cH4+/wVZMUE4qtDJ0SVi44trET5ooxWYh9USyATYmNjkZGRAZIkkZaWhsrKShAEgYyMDDQ1NQEAmpubUVtbi/T0dMybewd++rFDYKvq2xddgoMMn38+fAj33TkLz6x6E+EReidXH330ERYtWoShfXtiQs0gXLlyGcf+OMpZru2Nn2Pq1GnQavWTqJ+//pz/V1/+D7WD+iAzMxM7vvwfDh/qKMvAoTcAAPKLSnD+3DmcbW4GAEOIZ4Cj8cvYP/nuAIUFMA+AXJPXSd9gAMDeGHk1zUzaZGVi7n9Jw2H94u0uTvir652E+0eKcwTKBzbzVtNJ3pIs5uaigSfNaV+gjzvuHpGDxRMKza7N7Bpo+HtEkfgz/xqSNBMSxQonPZJCJBXQA33cRTk2BoCrWmOBsG9WJNbO6Ws4p0+H62mVIwp0YMvQ3O1wNQlfT1csnWgcXW3hOHP/Rq5ajWBBPTVSmM8YIXQN9maN+NK9W7CAlNgrR0phkm5JZS9Dztribpy/y1Esox1nC4MoU/7tmzIRfx1F8h8/Yu1Dz/FJyirqeieipkec2ZxlmudzN5bh6SmlBqtHL5FzHBNCjogH65gtgYJ83CVdrA4riMWHCwbi4TEFiA3RMaZtyaJjcPcYANK2tRaTTQPJ/N0xNDIh7a6LrwdjRKO63omGTSKp6kEKxVdlZiTev7s/BnfvilIex4zZYBs3uepObp2Ki4Y0cwa8bGIRgnhY/gux6rc1GpLEyOJuGJLfVdB8e95LfHRIPhvdN1enYVBeV5Qkhxo5sJ/YKxE5sUEIp63BNCSBB0d1N/NvxtQkpFAWSqVw5IVClIX2QLGWQJYsduTCza2jI5AkafhMkqTBz8+CBQtQXl6Ot99+Gz8eOozKygrDPV5exrv+wV1CcOXKFRzYvxchofoIBxRFYf369SB8jE0Jj/38PVy1zGaNFEWZTeZXLl/Gg3fPwfrNn6KsexpmzrkLV65cMfxu7oBZ/7/O0xUnmy8ZfSd2Pg7WeeDUWW6LmFA/Txw/0+GUuYuvB07B8pEEdxfhzXPyzGfhe6EZp/wcz38PM7ZdIqdE+OH7I/+w/p4eHYD9v5vv8mpIAknh5s5bmcZWoTtZg7t3RS3PqAi8aotWJoIg0Mvgt4h9x7+uVyI27PjV+rzNiyAIL3cXrLmjL/o/spm7LDawaLlGUwK1jzU6ERZ6loo6vox5l5zJios+5pFWiO7aVnafblLAV+bgiorFJPTyfWJbyTzFSaFmc9qDo7qjtZVCbIgO737dxHyjWaOgmL82u0KP8kQ6+6o6F48vxJ2vfyVJWt1CfXHExBKofUH/R1AkZt64HP1tENWFSdkMmFsTBHi7QceyoBD7VtrHmeKkEOz86aSFq+VFjAKpKJH7GEt7knzqR+xYIpUSyNqe5aYlWSMaKRWthsSM/un44Lvf0XjwOOe1XLUs/A3IP7LOHpSJ/353xPA5o2sgWlpbZc/XWiz5v2TbDFEifl6uWDS+EM9+9APe/opB7mXxDdnO/SPzcPsq05MnwtrO67dVWLwmOcIPB/84IyhdFWNUSyARNDc3I6ItqtBrr71q+N6vTalBF1R1Ol+sfHUNnlj0MA7t0ZvpV1dX46mnnjIs1H7YvxcA4OvriwvnzzPmWVJWjuefX2lQRJ355x+Dwsc/IBDnz5/Hh++/a3TP5o16v0Lf7NwOH50OPro26xWGXuui1cDf283ISogPfJyWmgplLhoS4QFe8BAbFp5jLLnq4oZTfl3g7S4ubS6Byi5ivJWZnvQTstvOfu43Ny4I6+ZUQauRZieLiUgW54oz+qcjxI+fWSyfEPH0J+AtwFq5apay7QjdTWSznPv3pBKrynFaJzyaGxN9Mtkdb8aF6DChVyKjUmto9xj0TAnD7YMz0Ss1DIvHG1tzXddZjijFxBNDZ2JLVoW0Zgwik4oPM7Y4lCqKoj1D1Os8XVGcHBgxjX8AACAASURBVCp55Ds2Z95c3aX9FU9oUzRO7EU/ymi/OpI6ekl2bJDli3jC57XZ0x9euL8ninn66hBL+3ikPGWjODK7Go/lfH0uumhIi/MRW1OQov8TYB6mrZVTuvh6iPJ9JJb4UPEWH9agRCtcR4VNduaLvY1R6Plba5mYFhWABTW5Vvn+oW8esUVJ5usD0UuG4BrOgqoEEsG8efNw1113oaSkBC0tHV5r2MJYBgV3wbOvvIk758zGV199hQULFuDatWsYUd0Tgyp64N9LHgUAlJeX49CPBxgdQ9eOnYioqChkZmYiKysL772zFjpfX9SOnYjBfUowfPhwZGQZO8D09fPD6CFVeGD+7Xh02VOMZaN39mCdh6JNKoXw1u19zb477i9cMOQTGUJOSCsXon/rAi1fRINt6H9sXCGn/xzA+jXzIB7HkixlwccCxpYWA/94sSgiRGbM9za6gtXXk/m90cNujy3l5/uATispzkFtYrhesVHYdpSCawetvc0xvVU3Fw3urclFdXYU7r4h12yhe81fnJLqw7wq/HvorWZSWbDOHffWMDsZ9hKodBa7wJAqxH0KzSTdXmfzhVirtV+aEMZ+DPeSm/DIhu3PPq4sAa/NrDA5imVcPq3Eig2uDZf/TOuJR8bwi3AmBfTjj1K0BrHHXhaNL7T6yAxBEFY5A28nRsbIWNYgxzGJ2uJumDs0C/fekIvRJd0MR0mY3gTdMXlKpH4O4RrP3p3fz+hzexTJrBh22cSSpVI7JEkwjiPh/vz98Jk6mh5RGItVM8oV409GxTEQ2lqU1ry0IoOksE3jpSlhskTNe+fOasH3/MvKDU9nRlWP0YiJicH+/R0OUletWsX4W48ePXDo0CEAwOWr19EwYy4AoL6+HvX19Th6Wm/NU1hcisLiUgBAeEQUvv/+e0N6zz2nPzP/+6lzuHxNr0gKCAjAh1sbceZCx5GudrRaLZYsXQb3tsXdoT/1JnCz77wXs++8FwlhvvjpWLPRPVUDhuCOu+43f1Aeg4+riwYkQcgUicRKeEipTELJZVfhiwT6WWR6tZUmh+ILmhkuk7NMabDtAs1iKGaZ1CbhAZ5mwhgfQv2M3ymvdaWRU0J5Z+JjAaFmeVqDpWTGlyXglxNnMSA3GhEBXvji4HH0z43Gfz78gfO+uvIkrG5kdh4tNf9qKMa5S9cMCmeCIOCiIXGtRVqTb6Eh69Oi/FnrN7aLD569sYz13mCdByIDvHD0b5YxwKSZZcYEomdKKFIi/bHy4wP6S3g0RUu753zSaChPEu0HSiyvzChH3YqtANinnxn907HiA24H5WNK4/EmSzulKyXZIjFVpIfjU5rz0JwYveKQIAiLVrBixqcOjEdOX09XDMyNxvEzFxmdZvt6uiI/nv1I883Vqdj962lst9JBp+u1K7jq4oaAC/xN6vmMmWKtPHJigxAR4IVP9v0h6n5GOIqisHUYL/juftOZXJmMFzmcjbtoSPRps9DtmRrGmRbTu/33pGK82XgYqZH+eO7jA+ibGYmP9x5tK6+xDPnMtJ74/sg/KOHwZ5MTG4gdHG37wVHd8dzHP+DOYTl45TOGY9y0IpYmh5odmSpOCkF1dhSe33LAEEnprhE5eH/XbxjTM97sGZnmBW93F5y/fI21jEqBHg470McNp8+Zry/4wtb1Q/yEy9ZKZ0ButEWH6oBeNvj15Dn04Km47KCjMocVxODHP/mNwWWp4dj49W9IjfTHD0fZXTcIZUFtHma8oA9m1G6JKuX4OCgvGi/9wq/t0X0RlaWG4ZtvO+Z8MSdILFkC0Q046suTUMAx9zobqiWQDNgxwAwAyztFJEEgMtAbURxHC0iCQLdQHesRHSGI2fWW0nmjJbhk2hoWXzTl6eFG1hPLTJy22gI5FHRSKkUCJAhDban9BXi745lpPQ3nh0dacKoKsB8Z8+aydOLo1LMGtZm8MoYuYm77wpzi8md8WQLuH9kdbi4aFCR0wezBmax+xqTEUquhNysNSdrE4lCq/rFiSin+1WDuONSU8QIi/JAEgXtr8ozC19rqiNbo0ngjqz5L81UIi3Jk3ZwqADA43GWifZc+lHZNe3amY01JMrsQ3X4P0ztlqjf6nEMv3+zBmVg6sQhrbu+DJROKeIf45cv9I/PQl/G4q7G9VX15ErQaEpEcPp/o0Od0gtA7/32AZ7hmrte7/Pm56HFgO+a9t6LjetoNL93S2zw9HgKONUd9mJRxbFbWzoBwCwLhdTuyuBuenlKK+FAdlk0sEpAXv+u6hfri3po8jCiKw9o5fTG1bwrrtQHe7uiZEmaVxVdRYghevqUc3UJ1hj5sdKSKpYmmRPrhrhE5mDs0G0WJIXhxem/Ehejv650WjqUTe/D2a8dXrp3GUReWiAvRYUb/NMPnp6eUGv1uqSv6ebmiK82i7T9Te4ouC8DeVtm+Dw/wxON15rKxNWKmXLOkqYN5vkeZljcU46nJJYLnEnqAkrSoANw+OIvXfRnRAXjl1nIspfXjGf3TMNzEYTMXb86uxNo7jE9LJIT54vG6HphSmYz8eL18KuVSNtRUTuBIfHl9h7x194gc1qPeUpAa6W/kr2lYQQy62el4pj1QlUAKhs2ZIRvdQnUI9fdEsK8HXlv3HjKycliv9XTTWtSoEgRhtVIgzN9T8GJM5+mKcI6FhSkD86KFFosV+tN+uGAgq0BAEASSaMdp/L3d8ER9D6RHS+MnhQ+WlH0fLhjIaXItNTrasaORxd3weF0PqybsMaXxWDzeWGBlSi8uRGeIvDKsIMZiumyLmJkDxJ1f7p/D3v7YzvrTj2JZgnlRaU6QTpooM1yLenshRrEulc+ZhDBfgwUmF2Wp4RhdYlkJKSdcjqT5wFRlmTGBuL82Dy/f0ttwlA/QH9XbdFc/vDi9t1V5MiGlY/NgnQeenFyCV28th6tWg8yugfDzckNWTKCg/sKnRMVJoZgz1LIwrxSlxv6uaXjgzUdxpt9gxt/dTBTIfIVxrgiU7fh7u7Ier1QiK6aUGnxyZceI87H00i29JTmqJob4MF88PbUnMrrylwlCefrioyMmOABXP7QUsbQ6OwpLJhRhCQ/lVmwXHXqnhfPyZSkVmQLqm46rlsQz03oaIrUBQHiAsE1Z03ch1+YL2/t7anKpTWViobT7vqrtEYd5w/gpYUzxcNUiMdxPsOxl6kNIiL+/UD9jy3l3Fy08BMwpAd7ujOvL9OgA1BZ3E/Qsbi7SqBEq0jvcAtCNAAiCYI3Warq2LLNgxdgOXb5Y3lCMQJoPPnsbcdgaVQmkYAIFWlJoSBI6D1dJwlDyxVJ/aQ8dy7abzAQBYbtdt/ZP532tJQJ8xE+SaVEBVu362JK63omM32tEtJ0bq1IB6M2+35rdB+vmVGFyZTKnwDKmzYqq3SHlpArzyAn15Um8QoPS4dNu2NpsF18PzODRlvLighjDbzKlK0VflMIZtxDoC9QnJ5dg/vBs3DEkE3eNYFcqs9E+sVdY7VtL+MzMZyEqJRqSQENFMroGi1TEEHqBRCyrZ1VC5+kqaEcQMN6V/eDegfjvvQPQUJ5kdE1xcihjf3bVagQr29h3kzt+aWkV4C+IR9tICvfj7VzelCl99GOTlGM7X2G1HYqikN2m0O/eTZipOtfbebb/VEye+SwOjJ7MrxzgtzHEp014uGrRMyWM10YB/Q0nhPliah/+70IqCzsXjX5Rvub2Pua72jyJCPAS/O7tyYReiRhWEIOnJtvWpwb9jY3rye2vjiQIZMUEwsuNWVlEAfjP1FIMzY8RHaGJqQWVp3NHg7KWQd3Nj+xK6b+Nb79YQbM+Eqro4HO1PY9k3lSVitWzKjGlTwqyRCp2OyP0tuOq1eC5G8vw4vReZtfRWyvXBgJJAHcOz8H6uVVYc3sfcys7k1sfGNkdiWG+mNHPWF6Xwpm7vXwk2gvVJ5AMeLu74NJV60MMO4JjOt7OMmV8FCnrqTIjAk0nz3H6Y3Ak2HbU2RysWqpLU2XeO3dWG1mU+ZsoLtnSqy9PwuiSbnB31eLiles23ZmTQtMvNNqAXNOKEAeYYkgK90NSuGWrJbZmc9ugTPRMDUNeHPfxN7b729tvu6UXHbYIRI+OLQBFUQalhanfKDoji7vh/778mbNstoKA3jRZLO27WSMKY7FqK4OfDBZM+wNBEBhdGo+X29MwkuREF89haPfx0H5MpLZHN1RnRQmyzA3z98Sxfy6y/k7fxe2bFYmP9xzlTI8CcE9NLv73wzH0lnDxSZEkjgZF8rbwoQiCl5WuEOvfIJ07Tp29zPv6FSZHYqyBqzlXZITjpU+N+5FWY3yUVazssXBsAe5evVPUvabIKSZ6umlxc3Wa5QtNkNKST5RFp8kt3UJ9Mb0fu1N5oeTGBaGuPAkbmMJncxeFPzyqkOndT69OtegDUJ88v3ckJHLZi9N7YdfPpyzmL7x5WK5FrnmeNVWCMMyb7i4aDMqLxnu7LPsBUjEmpotwJ/o3V6fhxue2GX3HZvVHmbhV6JEUIvlR7o7M5ElWqaiWQDIg1CGpHAR4u9tkYd3u16CrLSNpSCz00CckDUliWt9U5PAIqWsbs0HbKgItCZST+yQbCWVinLS1037ExpYKINEYx8/EuJ4JyIkNwgIrjzPQk90wrwqb7uqHofkxeGxcIfP17dfOrcJrMysEHxm1Ne4uGhQnhVrtnyfQxx0Lx3VExhqaH8N6HLJ7t2Dkx3dBRIAXXr+tAs/fbL5DxQqPPt1+lj+FRWEj1vJAKmW20vcOmMpXnc3vyCMALJtYhMEMu+RS8eCo7hhWEIP7R3b43BHazx6v64HZg4yPl7K9FrrvCW+OKHM6D1cMzOvKavHAhpTT1GVXd9QWd8OQfO76H5ofo8+bY5Js7ydMvjc2zu+HRNpGhTVNWkx/mFSRzOsIqFg6k88Ja1HCmoxehjlDsvDo2ALeRzptPR6LtVLjA9ujtPfzyEBvDC3osES15bsblNeVd1Q5Ntg2lhLDfXH3iBwzC1lrkLpZ2LKua4v1vgzbx3k+mE4FQhRHfJ+tSoAcoaJHVQLJAEEQos/8c2rnabNJXIiOU/ESpHPn7XTSWjzdtLaNIqYEqUAByCFbWBJYdB6uvB3mAUBGV+WdB2dzDM0J/R6Kgs7TFYvGF6I0RfgZZDa83FzgqtVger805MZ1KCGZ7vRydzE4UG0/S87HiiSY43hdcdvOCtNRNyWQGNZhkTS9H7/d6WCdh0XH2DP6p0FDErip7VijJVbeXIZ5Q7MwSIAvMi7F0B1DMgHQHIwrDYLlbz63Mgwo7i7mi2tzp5js/SWja6DZsU0pLQ9C/Dxxc3WaxWhhXAT6uKMfzVcY0zO3Qzdhz4k1tphrb+e38GzvYqG/JU7FTXgY3Fw06Mkx7qVG+gtS7DMpRNxcNFg8oQi+nq6oyooUPOXzXXhzXmZSD0pRrpoWo3eavMeSxGDa7wUHGFFKZTPg5a61qcsFOnw2GOjXWCrmgLYxql92FHt6EjzrDUXsR5SDGKx8xaDVkJjYi9nNgSnt6zM2i3im63vZ2JeUksmLC8b6uVW4uZpbZqIPoUz+xdrbBT1ABmM6HO0+kObCI7aLeOW6W5uPJjmV/0qkcz2tgxLo446r11sMjRTQD3gkYVk0igjwwt/nr0hyPE3FMtKLBvJovNgmdradELFUZ0dB5+GKYJ07bn3xC0nTFgvXIkdqKz42pa6UkaAeG1eIT/b9gQG5lpUSpSlhSAzzZbRiuWtEDn45cRaJPI5/mWPd87DdLVcUNTqDu8dgYF5XkASBE2cuWbw+WOeBSp7OutvhUu5XZUWhMiNSMkfWsiJwOKL3tTuHZePo6Qu8dgC5hD5H4s5h2fjzb37PDJgv2obmx6A6O8oqR9JSWtEQVvjZeuHmXvjr3GXMf/0ro3IFeLvjkTH5ZpZ9nm5avHV7H5AEgc9/OCY6X3GYN3QpW6SR9bFQPxa0gtxXm2eTMdJa7hqeg1vawk87I/bal7RW9x0V5I1Nd/WDq1aD/+4+Ik2hGJjWNxX15UkY/Nh/zX5rqEjCtZZWXvILwK3Y4quvenN2H5y9dNUqRb+10GVwKcYWpjQiArzwx98XJEjdHEsO203xTYrDy8nxBj+xADC5MgWVGRGIDbGgvOF4sQ3lyVi2cY+gsjCxbm4VWinpAoo4CqoSSCaEjs1Hj/yOEVWl+PSLr/HVl4146dmnsHXLh9i4cSN++OEHzJ8/X1Q5vNxd4OXugsPHm9Ha5mxz8uTJ+Oabb0BRFBITE7Fq1Sp4e5tbDcXExMDHRy+8Xrt2HeXVA3HLrLlwdZM/vLOKNAgVEng58hMwRpIEgZLkUJxstry4thVcdVKSHIraHnGioqpxKZfYfvFw0QK4wpmupYhPQTp3jOIZlYoAu5NmV60GyRH8fNKYRg2SC0s7RFIh5c6umKQ6g+AhyDm4SV+ydKxIqf7z6M8sdr0mRAGUFO6HG6tS8H9f/oIdh06Iys/IBZSpvyhe9zM/aVSQN6JYIuCw+eCTol+KTsFMA8adkqeb3r8dH661tHYkK7hgHZQkh1pxt/Xw7XeCo1I5VYge245NQh3bmlrKco61LI/Cpx3Q86FfrvNwxZwh4iJzicXTTata9dgagjQLLqEhCXQLtWyNxbdFW9PTLFmMOyvqcTAbcv26uYBg2rhNB/AhQ4aIVgDRaW/gWg2J5cuXY8+ePdi7dy+io6OxYsUK1vu2bt2Kffv2YcvnjTj6exMWzJslKn9vdxeQBGExrLkjodB1By/YnHsyTeZcpv98CdK5Iz5Uh6IEbofbtjCz5ppQSILAlD4psjsGb+/n99TkIj5UhyUT2MPbDqWFvVeKaDy6lDtii1TQHejK0TQcrQvb2weUrdqfo70Xe5FpctzW212LtKgA4+NdAtMMaXOwGsgQKbN9fuBco8vQSPrn6I+r9Mm0NsogX8xbIJe/JqFcu95q+SIVAOLGAnX8kAtl1yx9XLqvNk/w/VJaaNsaOeZma49ZSxVpi3fgAkly61woVwlEEPL8s0BTUxOSk5MxZcoUpKenY9y4cdiyZQtKSkqQkJCAnTt3YufOnSguLkZOTg569+qJXw7/ZJZOF18PEASBd9a+iZnT6nFT3WhUVVWBoijMnTsX6enpyMjIwKZ31nOWZ9WqVZgxYwYAoL6+HjNnzkRxcTHi4uKwbt06AEBrayumT5+OtLQ0DBo0CAMGDDD81k6Ynyd8PV0RGegFnU5vekdRFC5dusRLi+/t7Y0HFz2BLR++jzP//IMLF86jbuRQ5ObmIiMjA++++y4A4F9LHsUrLzxruO+ee+7Bk08+CQ1JoluoTnSYXiXCND7adBDiMS5Gs4SrDg/wwrK6HqjtYWxpwWSRMG+Y8S6NmHmBJAismFKKB0fnc143upSfRYsSEfPu40J0eHpqT07LIynCXkqNr6erYbEIOK5C1FwJr2xcNCTWzanCO3dW2yxPJbxabp0DJalPIHviKdAfgRxjw8Oj89EzJZTROb29+vmUPilYNL5QkD86PrhoOeqP1qbuuSEXAd7s/tSE4u7q3LvOVjcTKxua1KPBlD7iQssD4h9F7DPwVWTYQuFhz7lDjJWcLcOD22IsdZZw51xKIDFW+yodKG91oQAOHz6M2267DXv37sXBgwexevVqNDY2YtmyZVi4cCGSk5Oxbds2fPfdd7jvvgewfPHDZml4uGoRH6qDh5sWu3d9jUX/egaffvopNmzYgN27d2PPnj3YsmULHntwAU6eOM67bMeOHUNjYyPee+893H33XQCAjzZvwm+//YZ9+/bhhRdewPbt283uc9GSCPHzNFgENTQ0IDQ0FAcPHsStt97KK29vHx0io7rit19/hpubO55+8TV8++232Lp1K+644w5QFIWaMRPwzto3AeiVU2+99RbGjRsHQLroN/YimsWUXUo8XDWYXCle4DBlckUyRhZ3w8qbysx+y4gOQJiJRZCGweeDVGaSjvz+Xeh1IOA5pJ6CrReupSiFDDhw25CLHiyRTnw8XKyKyseX2wZmIDzAExN783O2aS30JuAcoqsxXC38wVHdkR0biIYK6aLPiCUy0Bv31uQxBp5oXzgydde8Nt805bQQ9lK9RxcNiZzYIHFzEUNZZ/RPR25cECp5HlEsS7XeGpZOe1hqZ0VR/VcCBXFtD+7NKWtnL76+aVxNlJaWpk2pFT1s+cmlhFfaMekCC5bszoaUMrs1Ueu4lED0vqOs1uIYKPdQpB139mJjY5GRod9xSktLQ2VlJQiCQEZGBpqamtDc3Iy6ujr89JPeAuji5auM6bR3oJKy3vDz1/vaaGxsxJgxY6DRaBASEoLikp7Yt+dbpKXx2+EaNmwYSJJEamoqTpzQn/nftXMHampqQJIkQkNDUV5ebjGdl19+GS0tLbj11luxZs0aNDQ08Mq/fbCnKApPLHoYe3d9BZIk8ccff+CvUycRGRUNP/8A/LB/L/46dRI5OTkIDFS2ppavtnx8WQJcNCR6cUTj4DMI9eVwKjuqJB4B3h1m+Fzp8cnLy92Ft1KpIj2cl6JL1vW6jN0+JVK4w+O1c/qitZVSnDDidDiJJQcbYoRxDztbCwzIjebtrFNqrOltvp7CHFYqgaLEEHHhjVkGY/rXbHXp6+mK5ovMsgt7fuw/3V+bh19OnEVSBM9x1o5D6uDuXTG4O3eYe8GIHMJ8PByvvdoSpc0MQsdyPu935oAMeLppMawgFjMYHGi/MqMcLa2U0fFoueAKDmKvLksfzzKi9euJIBkUqZbebaifJ9Ki/PH9kX8kz9spoXVea1w9UIRqryIXas0y4EZzfEySpOEzSZK4fv06FixYgPLycuzfvx/rN7yDq1cuc6bn4dmhATXVmLu5aODr4YYwf34hNOllMyhkRE6TGo0Go0aNwvr169HS0oLs7GxkZ2fjvvvuY7z+/Plz+OPo70hPS8GmDWvx9+nT2LVrF3bv3o2QkBBcuaJ3cFs7ZgI2/N9qbFizGpMmTRJVNiXi7qpFXXkS70gvTAzNj8HswcoMBX3n8BynNsh4or4YAIyUbJbQebgKd2ppcj8A5UmyDogceiIlN3dbNRkhu7hCd3zt0eydye+cWOivie0dPDW5xGI6pvOBrm1By9QM3Fw0SIn0t1v4bDbEWUPwa7nWPOqDo7qjX3YUqjjCc3dWrG1B8u5TmbcNptayZEIR5g/PRjCPEOj+3m6YOzSbNWR5qL8nIgLN1wjWzImmzzG5MhlJ4X4oTxfud4vNYkRs8Qri9VaFhQyWNxGBXnhtZgVeuqW3yNStw0dgVCw2lDVKOjcKm5IUhaoEEkFzczMiIvQD5WuvvSro3rKyMqxZswYtLS04deoU/ve/bajoVcJ9Nt0CeflF2LBhA1pbW3HixAl89tlnrNdSFIXDhw8b/t60aROSk5Oh0Wiwe/du7N69Gw899JDZfefPn8eDd81Bn+qB8PPzx7lzZxEYFAQXFxds3boVv/32m+HaPv0H4X9bP8G+Pd+iutp2viscgZguPoxHrugo3ShC6eVjo31xIjS0JW8Y6iU/Xvnhe6XA3j4gxOKgTdnpETLGmC7yHfnYqS3xFaHcltL/grxvyTZtwJq5sCgxBLMHZwr266S2bmHYa4zPigkUpVCREiFD4cjibnhycglnNEJbja13Ds/BrEEZrFHDuvh6wE1A1ES+2M2HjkzVau1xQKX52vO3tIHLelxR+rI4C8o9DqZg5s2bh7q6OjzxxBMo69Vb0L3Dhw/H9u3bkZWVBYIgsGTJEoSGhqKpqUl0eaoHDsGB73YgPT0diYmJKCwshK8v844CRVGoq6vD2bNnQVEUsrKy8Mwzz7CmXV5eDoqicP16C8qrBmD6rLkAgMEjanFz3Rh0794d2dnZSE7uOHLk6uqKwuJS6Hx9odE4gANEGw4QSluf8LVAU6EhYEZxaWv/ZsKFE0xKUjZlPmnZxJGiTNKCGOFSYUOFXXEGIU6WR7BxxQj2yeOw7025vc8RlJy2nhucEaUtwAG9b9FHxxbgntU7Zc3H290F/XPscxTZVjhCP1YaubFB3Bcor8soHlUJZEJMTAz2799v+Lxq1SrG3w4dOgQAuHz1OhpmzGVNb8y4CageUmv4TBAEli5diqVLlzLme6L5IgqLS1FYXApAHxGsvr7erCwAcPbcORw+1gySJLFs2TJ4e3vj9OnTKCgoMPg0MoUkSXzxxRfcldAGXTH1z4UrONV8yfA5ICAQazZ9hMTwjvP/Zy9exaWr13HlWgv2fPsN/v2ccXlVLGPriT8nNhCzBmUgIZRZaciEOncx4yyRGDoTalOWV24SUr8KXPM4LHx8AnWW0NtyzledfS50hMeXuoyOHEZcLLf0S8PxMxcREeCFiADn3TjsjO9WTqSe0uWw/ursqEogJ2HQoEE4c+YMrl69igULFiA0VHh4RGvRebriaNNh9B8wEJXVAxET5yDhvm047gvNimsQlWIngSAIox0Xe0+BliYNRQjdIgoh1QLX2ueXq/qsbYvOtP5Xd/jEYaxENW0R7C3EUZSv9moV1tSOqpjjj63aoRItRFTsA9NUI8f0MyQ/hvN3tjapzoS2Q45xQWmyjMKK4xSoSiCJ+N9nn2DkEuNQ8bGxsXj+1Tdtkj+TH6Dhw4fj119/Nfpu8eLFovz08O17qamp+HLXfly4ck1wHnZDwrHT0qBp7e+dHceTf1kKLPI1K+n5pSyKUlq9XNVryx1GZxhDuELCMl4vUzkUj0Tv2h5NRunttNO2KQmwd93R25a9y6JUVKsX+2KT4+1Wtn6HUziz1KnCpxq7wksJRBBEE4BzAFoAXKcoqrvJ7wSAfwMYAOAigHqKor6VtqjKpmfvSkwee4PZ93+d444cZoaEfe7tt9+WLC0HGwocFtNBl+hkFe9wkw4NBy66KCT1+6BO0k6J6C5hRWfqbP1QCpy5zuQcWuyxkFaU8swmC1kRgNkgBQAAIABJREFU9zhzg6bB9Jj2eHRbtkkFtX4VC3SSbujQCLEEKqco6i+W3/oDSGj7Vwjgmbb/VVQ6FZ1F+FBxTBS1gFBxOOQc3sSmre5oc8PqE4hHtanDhQon0hq6dtyvtjsVGmpzEIc6NzKjrtI6kCpE/FAAr1J6dgDwIwgiTKK0VVQ6FY7i40LFCuz0ilUlpYoZDtAk1GbLAo+K4VN1QhfdfOcoRbw2kRoFdfmk4ogYOYXnaPu2kDNt3/8VMeLwQlXQqCgBvkogCsBHBEHsIghiGsPvEQCO0D4fbftORUXxiJ02RIV9Fjjuk1Sr4DxUlIO6eOWHKhCZ06mttpyw4yj5dTpbdUtR10KrxFZ1qOR2JAfWPq7U76Wz1b+K/H3bWZuU1IpGZ60ne8L3OFgJRVF/EgTRBcDHBEEcpChqG+13pndj9vbbFEjTACA6OtrsBhUF42RCor0Qutg9GJmIgh93gmxtxY6UIuO0nHBEdIRmJqbaFRMdzBkbjTWYVocE70mtYXHQ+wj/2GCOg7MpWlRUlDrYqfOcbXEWC2M+zcY5nrRzo44OHfCyBKIo6s+2/08CeBtAgcklRwFE0T5HAviTIZ2VFEV1pyiqe3BwsLgSOylNTU1IT08HAHz1ZSNunDgKALBx40YsWrRI0rwmT56MrKwsZGZmoqamBufPnwcAXLlyBaNGjUJ8fDwKCwvR1NTEeP+sW6ahoigLQ/qUIjExERMnTsQff/whaRkdASaFjtTCxzUXN+xMKjBTALHlryI/qhDgRCj4ZdpKsFZKFYjdNVSPz1qPXGtmrjZsq3W6uGzUNqViX/gM/0rXddnBbbpVd9tSl6X0d+eI8F0TqaN7BxaVQARBeBEE4dP+N4AqAPtNLtsIYCKhpwhAM0VRxyQvrYNz/fp1wfcMGTIE8+fPl7Qcy5cvx549e7B3715ER0djxYoVAIAXX3wR/v7+OHz4MGbPno0777yz4yaTvjXv3oewcUsjfvzxR+Tk5KC8vBxXr16VtJydFbET0fiyBGkLYg8sPLuSJ06u9+aMC1VJn4iPk1op83Ni5Kwn52vFDo6SB0QVFZ44ejO2lyGMpXy5qlXKTUTlWF7J8yKM/CzJkoP1KHJuVmShVOjwOQ4WAuDttk6uBbCaoqj/EgRxEwBQFPUsgM3Qh4c/DH2I+AZrC/Yg8aC1STByP3U/5+9NTU3o168fSktLsWPHDmRlZaGhoQH3338/Tp48iTfeeAMAMGvWLFy6dAlu7u54YPGTiItnXoC/+cZreP+993H1ymWg5So++eQTzJs3Dx988AEIgsC9996LUaNGsZZn1apV+Oabb7BixQrU19dDp9Phm2++wfHjx7Fo8WJkl/RFa2srpk+fjs8//xyxsbFobW3FpEmTUFNTw5imTqcDoN+lu3TpkmEAf/fdd/HAAw8AAGpqajBjxgxQFKX/nS0KBEFg9uzZePvtt/HBBx9g6NChuGvObfj22124cvkyxo4eiQcffBCffPIJVqxYYQhb//HHH+OZZ57Bhg0bON+HsyHXXPne3f1BEsDr236SJwMVUShtDlSqAMOnomSpSxkqRKo+bivBWoltQmviC82iVZTSOpoKAO42bCtLVnHdSIm9Qo+SLIClKgv3BoqKSgdyTou21GUppxc7P0oaM5WGRSUQRVG/AMhi+P5Z2t8UgFukLZr9OHz4MNauXYuVK1ciPz8fq1evRmNjIzZu3IiFCxfi1VdfxbZt26DVarH5gw+xfPHDeOr5V1nT273ra2zc0oiCtFisX78eu3fvxp49e/DXX38hPz8fZWVlvMt27NgxNDY24uDBgxgyZAg2b+uLjzZvQlNTE/bt24eTJ08iJSUFkyZN4kynoaEBmzdvRmpqKh5//HEAwB9//IGoKP2pPq1WC19fX5w+fRpBQUEWy5Wbm4uDBw9i6NChmHfP/XD19EFLSwtunnAD9u7di4qKCtxyyy04deoUgoOD8fLLL6OhgVlXqE76wnHRkGhpVZ1IKw6Jtwid5Oi9U+No70hIcaU8omYqcFMUcNu7T+GjnD4ozg/H+hM80+nMAp6tG5vAVZKz+ArhQ+d5UlvgWH1aMYYwKioqKgLg6xja5liy2JGT2NhYZGRkAADS0tJQWVkJgiCQkZGBpqYmNDc3o66uDj/9pLe6uHiZ+xhUSVlv+Pn7AwAaGxsxZswYaDQahISEoFevXvj666+RmZnJq2zDhg0DSZJITU3FiRN6KXnXzh2ora0FSZIIDQ1FeXm5xXRefvlltLS04NZbb8WaNWvQ0NDAKLDx3Y2m3/veOxvw2isv4XrLdfx96iR++OEHZGZmYsKECXj99dfR0NCA7du349VX2RVnjoCo6GAylKMz0YnWFJ0LhXQMZzy250is7j0a3Y79gquVdcDqnfYujqSoC0UVZ4Dejm0xXqrdRkXFMmo/URED3xDxnQo3NzfD3yRJGj6TJInr169jwYIFKC8vx/79+7Fuwzv6o14ceHh6Gv62dmeMXrb2tMROxBqNBqNGjcL69esBAJGRkThy5AgAvf+i5uZmBAQEoKGhAWXFBZg6oZY1re+++w4pKSn49ddf8ex/nsSqNe9i05YvMHDgQFy+rK+fhoYGvP7663jzzTdRW1sLrVYZOkhHVSyog76ysIlArL50xePM70jKI2pM4+4p32DsSC4UXolOXOdi4FMdyvHjoeJIOJq85GDFNeAMGxJSPYG9xyquyJVioT+TFM/n+K3FMlK1g85koWoJVQkkgubmZkRERAAAXntNmDVLWVkZ1qxZg5aWFpw6dQrbtm1DQYFpsDVh5OUXYf369WhtbcWJEyfw2WefsV5LURQOHz5s+HvTpk1ITk4GoHdC/corrwAA1q1bh4qKChAEgZdffhnbvtyJ519by5jek08+iWPHjqFfv344e/YsPD094aPT4a9TJ/HBBx8Yrg0PD0d4eDgeeeQR1NfXs5ZRQ5p3dFethk9VKB57T2YW4VE+OY9fOMLQbM8Q8SqOiZK7vRCBSE7hiZ624AWQA/QvpxoDeD4L12VK7hNdfN3tXQRWlFRtrLKAUB2ukh7KieCqV3sqmTr1Ed5OhDNNec6KMkwxHIx58+ahrq4OTzzxBHr26i3o3uHDh2P79u3IysoCQRBYsmQJQkNDWcOx86F64BAc+G4H0tPTkZiYiMLCQvj6+jJeS1EU6urqcPbsWVAUhaysLDzzzDMA9KHjJ0yYgPj4eAQEBOCtt95izXPJI/fhP/9aipZrV1BUVIStW7fC1dUVWVlZSM/IwsDyHoiK7oqSkhKj+8aNG4dTp04hNTXVLM3wAC+cvXgVAd5uZr9FBHji15PnhFSL7HTWicwZdqhshTPX1LD8GDz38QH0y46yOi1elgtW56Jiczg6gJlPINFZOHMvUxGD2LHiifoe2P3rafRKC8e7O5ss50PPyKm0fFZiZVU4mlJIffXMONhrdFjUOVAYit+MtyGqEsiEmJgY7N+/3/B51apVjL8dOnQIAHDp6nVMmjGXNb0x4yagekjHMSqCILB06VIsXbqUMd8TZy6isLgUhcWlAID6+nqD1Qy9LABw9tw5HD7WDJIksWzZMnh7e+P06dMoKCgw+DQyhSRJfPHFF4y/ubu7Y+1ac2sfU/719Eqcu6T3g5QY7mf2+/IVz+HClWuMvzc2NmLq1KmM6Xq7u8Db3YXxNzk7rYvGdgMCyeM5eO+2myXl+AObI5tpOnDRRTG8MBa5ccGICvK2d1EUjy3bhuCTVEoUiEzqq7P1Ld4o8d0pDCHtOy0qAGlRATKWRoUPkh6/cdDBQ+5i23MTU1VaqEO3ijJQlUBOwqBBg3DmzBlcvXoVCxYsQGhoqL2LZEZeXh68vLwM0ciUgrurFrcPzoSbjEfOxpclYOdPJ1GcHCJbHp0BJUycUogvziACEQSBmC4+NsvPFnXmoOsFqwj180Bet2CE+HrYuyii6KwWmQCsarBW1Rrfmzthf5IdBTV3JczHKp0N9kYn1/yt9HZOgFDkWKvUTV2llsseqEogifjfZ59g5JKHjb6LjY3F86++aZP8mfwADR8+HL/++qvRd4sXL0Z1dbVNymTKrl277JIvH6pFHGkRspsxoVciJvRKtJymA4xNci66bKlUsCXqpKOiVAiCwMKx1vmlY09cwLVW7P47Qu9S+kJCEI5Q4W2IrXanel+2xsq6o9e9+hpUlIQjtUdHKqsUqGO2cFQlkET07F2JyWNvMPv+r3PckcPk5O2335YlXbWfKQN7vQc5TXkrMiJw5VoLMroGypaHLZFe92PdW1fk0R+ok7eKsV5BVZo6CQ7cr9UmyIzDjdUOV2AVpSFLdDCpB0e1mbOiDgHsqEogFRUF0dkHK5IgMDCvq72LwYmo6GCS5W5dSspdXPOJSmePXCXKp5P3a7lRq1ehcDoGd663ptSRtdNjgzlPSU3Z2fqVORTYRnxnfPTEcF9M65Mi6l51TFKxhKoEUlFREBTFf+DOjg3CkdMX0DXYto557epQUJ3VVCREbU7CENz/BFzPpaB0hvdk07HLGVdDIlGrQkVulCSX0MdReyuE2KpFLhlSSe/BIjyPPD41uZRfeo707HZGraoOVCWQimDUDqQMJlcmIy5Ehx6JqrNpJcC0iGUXguyDvYVCdpxnVFFuHSsbeguID/UVcJ8aa8YMh1oNOS726On2Hl1s0bTs/YxcOMvwro6a9sVJmhEn6jSkfEh7F0BFT1NTE9LT0wEAX33ZiBsnjgIAbNy4EYsWLZI0r8mTJyMrKwuZmZmoqanB+fPnGa+LiYlBz549jb4b2rcnyou7i8574cKFhr/pz6zSAd+B091FgwG50fD3dpO3QCaowoOK9FgWieRodZ1BEJMSW/V9f283kPSX4wTSpLMsHgH+fZGrvchZHVIoYuNDdRKURHpUJbMKX+zdUuTJ395PpeJIhPqZRz1VW1AHqhLIhly/fl3wPUOGDMH8+fMlLcfy5cuxZ88e7N27F9HR0VixYgXrtefOncORI0cAAD//9KPVedOVQCrS0hlkQyU/I9fCyHQN6/hLWufE000e41gxCzclt3VZMOkUr86s4HVbpw4Rb2Mcs6bFlTqjayAeGt0dr9xaLnF5Oh/urhpB10up6JJ6rvX1tO2mm1D41p2U4ybp5JOVHI9nC2Wus70VIVVW2yMOc4ZkYXlDsXwFcgJUJZAJTU1NSE5OxpQpU5Ceno5x48Zhy5YtKCkpQUJCAnbu3ImdO3eiuLgYOTk5qOxdhj9//wV+XswTw5tvvIaZ0+pxU91oVFVVgaIozJ07F+np6cjIyMCaNWuMrjedsFatWoUZM2YAAOrr6zFz5kwUFxcjLi4O69atAwC0trZi+vTpSEtLw6BBgzBgwADDb0zodPodLoqicOnSJc7BaOTIkYYyvvfOegwc1hEB7crly2hoaEBGRgZycnKwdetWAMD/vfk6ZkyZgMnjapCQkIB58+YBAObPn49Lly4hOzsb48aNAwC0tLRg6tSpSEtLQ1VVFS5dugQAePLJJ5GamorBfUow++ZJrOVzRpQ0n3bxZdKiK6iAdsDPy9WOuVsZHUyiUtgDW5S9IKELBuRGo29WpOg0mMqZGumH9OgA3FAUK75wDkJciDgLCp2ncb8K1nWMParSVCAyTSLt78GRxxEhFCaEINTPk/V3uuwkt7FaZKAXSIJAMMOcbEvEhG/3dnfBgppcPDaukDlNWkrxoTpUpIdbUUJ5mv9j4wpxY98UJEf4SZ84jVHF3QAAwwuEzxWkyXMPL5R3vokI8EJ4gCfuHpEjaz58cXURpmzkC71vx3bxkSTN7t2CJUmnnd5tfSZP4nStwdanBujN30VDom9WJAK83W1aBkdDsT6B5NKS8omOc/jwYaxduxYrV65Efn4+Vq9ejcbGRmzcuBELFy7Eq6++im3btkGr1WLLli14aumjWL9+PWt6u3d9jY1bGlGQFov169dj9+7d2LNnD/766y/k5+ejrKyMd/mPHTuGxsZGHDx4EEOGDMHmbX3x0eZNaGpqwr59+3Dy5EmkpKRg0iRuxUlDQwM2b96M1NRUPP7446zX1dTUoL6+HpNvuhVbP/4vlq1Yifc2/B8A4I1VLwAA9u3bh4MHD6KqqgqHDh0CABz4fh/e+fBzpMWEICkpCbfeeisWLVqEFStWYPfu3QD0CreffvoJb775Jp5//nmMHDkS69evx/jx47Fo0SL8+uuv+O30JZxtbuZdP46Ol7tWtDAptRD65uxKeLu7SJuoA7N0YhHe3dmEqUIiNRheirqMVRIpEX448McZlCSHGn1PEgRuG5iBXT+fwsd7jkqWn4Yk8XhdD0H3iBVe7H1yqiixC345cVbwfT4eLvhXQ7Eoayx3mvCv3Ah49mF8WYLd8nbR2H+fUUmbKtaw8qZeaGltxc/Hz2LXz6dQ2yPO3kUSRGlKGK/rnp7a0/JFdiA3Lgi5cUGi7u2RGILth06gKisSn+z7g/PaSZXJqCtPgsZUo8ODlTf1wunzlw2fKzIiBKchhPz4YNxcnSb4PrmUA9FB3hhRFItwf3bFrbWM7ZkAD1etmewgFD7zXGIYf994cSE6rL2jL7w9VJndEqqE0IH9Z2gFEhsbi4yMDJAkibS0NFRWVoIgCGRkZKCpqQnNzc2ora1Feno6Zs+eje+//541LU9XDUp7laNbtH4CbGxsxJgxY6DRaBASEoJevXrh66+/5l22YcOGgSRJpKam4sSJEwCAXTt3oLa2FiRJIjQ0FOXlls2XX375Zfz5559ISUkxs0aiExAQAH9/f6xf93/olpAId4+OwXXX1zswYcIEAEBycjK6du1qUAL1KO0FH50v3N3dkZqait9++40x/djYWGRnZwMA8vLy0NTUBADIzMzEuHHj8O76NdBopdXuRwZ6SZqeFCyoyUXvtHAMyI22aznoYkeAtztcGeq+s/oEyuwaiAW1eaxWf45AQjh/ocKW2HqhtqyuB16/rQKJ4dLv6qZ3DQDAfBadD0snFqE6OxJjSuMtXuvbZj1jK2UtH/0KAQIBbX7KsmIDBaWfEumPrsHCd1rvuSFX8D2dBS5LFrHwnQHurclDVKAXFo4tMPvNGZQz3eP1u+4lSfIHZ9CQBFy1GqRE+mPTXf0wRWTYaCkxtd5TIr42KGOwjlthf9/IPKyeVYnsWH5KJL4KoEAfY1kkKsgbGdGBKEkKwZTKZF5pMHFjX/u3LbHc2DcVg7vHyJa+m4sGo0vjERUkf1Te+0cK87+q83RlPZpXJCKATFaMsPnb3ri5aAzPWW6lNWFnQbGWQPbczXNz6xhYSZI0fCZJEtevX8eCBQtQXl6Ot99+G01NTejduzdrWhqSREigLwJ99JOEtc9FL1t7WmIX5RqNBqNGjcLSpUsxceJE5OXlAdD7IXrooYcM140aNQqzZs3CwieMfQdxPYura8fEq9FoWP0h0Z9Ho9EYjoO9//772LZtG15ZvRb/+dcyHPiBXdHGl+ggb2TFBKKmSHk7aKUpYYw7ZU9OLsFzH/2AuBAdNn3zGyZVJOGlTy37ZrpjSKYcxXQ43F00uHytxW75tysZ7G2c8NrMCvz4xxmUWrl75SxoNaTRcSMpqe+dhMgAL1FCF6BXNmZ25Sd8PTauAM9+9ANu7JsqKi+5WHlTLxw9fR4pkf5WpZMS6YcDR88gwcKOaAzNRF+pjnNTIv3x5Y8n4CWh36mIQC/s+uUvs+89BPpgYcPaRX5CmC9emN5bkrIoEZ2HK96/uz80JIHBj/3XZvkybc7YkiUTivDOzl8VN+4wMdkKZYglHq/rge+P/IPChC6c15EEYVgDSElBfBd4uWlx4UqHfK0hCdwnUHlgSt+sKDz38QFri6fCF5YpK8iCctESdLEzNzYQOw6dEHR/j8QQJIT5ovniVeTGKeeYmSmrZ1WipZWCVkPigZF5uHjlOrzUUwy8UKwSSMk0NzcjIkJvZrlq1SqL19OF0rKyMjz33HOoq6vD33//jW3btmHp0qW4fPkyRwrc5OUXYf36dairq8OpU6fw2WefYezYsYzXUhSFn3/+GfHx8aAoCps2bUJycjI0Go3hmJYpw4cPxy9NR1DauxInTxw3fJ9fWIw33ngDFRUVOHToEH7//XckJSXhq6+/AcB8dt3FxQXXrl2Diwt7B21tbcWRI0dQXl6OLnHpeO+ddW0RzKwzXAvWuWNGf+mikfl4yL/DlBTuhyfq9Y7NJvZKhM7T1aAEosB+Jr8qK0q2MjmST6CkCD/saTptE+svuqJn9axKHPnrPNKjA2TPlw9dfD0Y/TupsGBFE3dz0WBgXlfpysJBt1BfLJ3IfszM002Li1eEBySwBk83LXw8XKxWAAHA8vpiXGtp5Vz0mrZrpR4HG1EYC52Hi6TCdH15Ely1Gqzb/ovR95MqknH8zCXcwOITJMDbjZe1gYuGxLo5VahZ9hEAx/QFJHeZtQo48mZrsmICFW0lQFfwy2m5mx4dYNc5niAIjCmNxwufHLRbGbhwY/HRI0aG9PNyRUsrZeb3yJnolRqGz384JkvaA/O6ggIEzT8EQWDFlFLReSaG6TdBA2SOYExXsBIEoSqABKAqgUQwb9481NXV4YknnkBFBb8IJu0MHz4c27dvR1ZWFgiCwJIlSxAaGmo4BiWG6oFDcOC7HUhPT0diYiIKCwvh68u8c0pRFOrq6nD27FlQFIWsrCw888wznOn7+Pjgttvn4NRZvZWORkNCqyFx28wZmH/HbcjIyIBWq8WqVavg5uYGFw0JX08X+DNMvtOmTUNmZiZyc3Px6KOPMubX0tKC8ePHo7m5GVeuXUf91Jvh5+eHvzn8TDw4qjue3LwPp89dYb2GlHj2SI/yx9jSeIu71FLRvis7pU8yKMr5IzJIwfzh2diw41cM7m6bRXk7gT7unDt/QtepY3vG46PdR9E/Rz7lnj2ZVJGMf7+/j/MaDzctAn3c4OGqTltc0Me55fXFuPG5bYbPaVHWKWaGFsTgqc37UcXgOPvemlx8/v2fkvY1giBYFUD/aijG3t9OozTFMazbtBoS/XKkPe7r5eaCqX1SzJRAgT7urD6o7h+Zh8KELrwtpnxoPiYiApR3lNoSXJslKsKRw89T7/RwrNr6I/JoPnfC/D1x7J+LvI9Q0RlRGIszF66gLJWfHyJHRqzloz9HgAsvdy2CdO6irRbX3N7HYJkhFatnVYKilGvpKQXzR+RgWt9U/HLiLFxd+NcdH4W+VkNimAhn49bg7+2GNbf3kS3qqlhC1A1RA8p6MwogJiYG+/fvN3ymW/rQf2v3fQMADz/8MGt69fX1qK+vN3wmCAJLly7F0qVLGfM9fuYiCotLUVhcana/qdXRuXPn8NOxZpAkiWXLlsHb2xunT59GQUEBMjIyGMtDkiS++OIL1vLSYVJMRUZF4/v9+0FRFAiCYLSEMn3m9957z/D34sWLsXjxYsNnel3PmTPH8HdjYyMA4NCfZ8zSZ5oDihJD4OGqxbzXdpj9Nn94Nl7f9pMoB3ZcEASBuvIkSdPkQ22PbjbPk45URw3EkBMbhA93H+U0k63rnYhXPtP3zwBvdxv6TpDPAqGudxIm9kp0WgGoNCXUohKIJAi8NrPSKXyJyAlJEHjplt4gAIQHeGHzPf1x7tI1/Pe7I6jOtk6JODA3+v/bu/coqaoz7+Pfp5u+QDcgggjaQjfECMqtmxav3QJRQEEIXmYp0XiJr8ZLNCY6yqizNA4xGm/jInlZxjckJppkjKMvie/EcQzMiEacJoKgqKCi8bLUkNEARuTyvH+cXVC0VdXV3VVdt99nrbO6ap9TZ+/TVU+dc3btCxPqB3JAgsqAltFDaUlz8FfofovC0XUDMtLaqFTccFoTa976C0d+cf9O/4Dww//Vwtq3NtF6WPT+DszyL7uZcMyoIeCuH0sy5HtnHcEPf/cif//lCRnf998dPYLRB+6z18xb9369lY+2ftalFqxVFeUZv97LVyc2HsTjq/7El9IcBPrOc4/i4T+8ziUzkreKLzPj/m9M7fK5Nhutr8rLst/iLtn1VbZbUcdm1CwzY1C/6rS7gV10wmhWbPgg6Q8h+fDNl8nPQlevGdpXQs2cOJy/bN7GkT0wllu+UyVQATMzhoZR8GedOI2PPvqIzz77jBtuuIEhQ7L762gubkb369+bv23bkbRWOdnYSFPGHMiUMdmdJSEml1+6PfWWHHXIEI4fdyAT6rs2U0Z3TD7sAPr1qeTgIclbX5157BfY/Ol2RmVh0N9M6cp7VawVQBCNrfHdeZNwoF/vCvr1qaSy1+cv+royY0pXjAwXZZkcv6UnxbfYKC8rY5+aKs5IY6DpjpgZdQMzMyBmqQ4wnyuJxp2LdT+Y2sH5ceSQfowc0m/382H79WX+3EaGDMjfX1T/8fSJuS5CUWlsGMSPLj4uK/suLyv7XIufyl7lRdmFef4pjby9aSs7d+7iweUbut06s6a6olPvy2EH7cthB3Xcha2nzrX5pH1X4oUXHMtvV77J16ZmZ1yph646gY+2ftblwftPOXIEp+ThGKf5prJXOT+6+DgqQ8u0ivIyzs/iWGGFpDCvcPPQ448/zjXXXLNXWkNDA4888khW842NS7Ns2bLPrZs7dy5vvPHGXmm33nor06dPz2qZsmVATVXCLmb5JBO3NV0d06LMjMtOPCzrlQXlZcbVczL/a2A6zIyJHfRpNrOcDFh5zKghPL7qbQ7/wufLp9vdjk0cmT8DD+5TU8Uvr8y/ZswimTT/lEa+PXt80rE7UpnczdlXirlSu9QV0riBPW3yYVHcbN+5i4MP6M/4NCcBkJ538ND+XDkre5Os9OtdSb8eGFtUoomB5PN0hZsh06dPz7vKlUxWQOkmtjBkc2pMSe2SGWNobBjEpA5mCpHCMKAAuryIdIeZdakCSES6p6K8jKMPKYzxzEqFKqal1JTetAZStLrapDLfqMIB+08UAAAWWUlEQVStMFVXlDNlzIHUVCWYmUBvqsheMt1aIDbz46UZnAFSsqfYbrfydVa6XFBXT0klUVdvEel5agkkRWPIPn24/Zyjsj4dYTErhR9Chg2q5a0/b8npL+C6XxDJrJObh3NS00E9MoCoiIh0zvxTGtn8t+27Z7rNN6U4DlIhKYX7k56mSqA807uynL9+kvnpzEvF2GEdD3gnyZVC5cQ3ThrDoH7VzGzK7HTNqeiXUZG9ZaOuRhVAIiL5KTYeUr76u6NHsvK1D5nVPDzXRcmIqWMP5P7/fJUji2SIgvjJCSQzVAmUZ/r1rqTMjN6V+fXWVKn5ZlpUdZf/9qmpKpnpY0Xy1b611UwbX5f3g/0Xkr4aZFTygAaGLm4T6geyauMmjsvzSp3O6t+nkkUXtea6GBlzxrEjGTNsXw45MH9nyk3HTy6bwpq3NnH8uLpcF6Xo6M4+z5gZfXtX0qv8829NbW00uvnGjRsZMyYa96CtrY3LL788o2W44YYbGDduHBMmTGDatGm8++679KnqxdABfagf3DejeYmUgsZ209+KCHx79nhN1ZoB3zvrCCYdPJiLpo3OdVHSp3oCkYL03a9M4oErvsShdd2b3l6yq7ysjPH1A6ku8MH/hw7ow7TxB1Gm/mAZp0qgPLJz585Ov6a5uZl77rkno+W4+uqreeGFF1i1ahWzZs3iO9/5zu7Kqcpehf1lUghy2SVL37HZ8a2Tx3PRtJ6ftl5Eil9jwyBuPuNw9q2tznVRSpY6/EqpKC8rY1A/fdeIFDpVArVz22237a5UufLKK5k6dSoATz75JGeddRa1tbVcc801TJw4keOPP57nnnuOyZMnM2LECJYsWQJELXVaWlpoamqiqamJZ555Jml+y5YtY8qUKcybN4+xY8cCcOeddzJmzBjGjBnD3XffnbK8y5YtY9asWQDceOONnH/++bvLE185dPPNNzNq1ChOOOEEzjzzTG6//fak++zXb0+/y61bt2raRJFu6tu7glOOaMh1MURE8oK6DImIiOROfg08E2f6zY9lZb+P3zAz5frW1lbuuOMOLr/8ctra2ti2bRvbt29n+fLltLS08MADDzB58mRuvfVW5s6dy/XXX88TTzzBSy+9xDnnnMPs2bMZPHgwTzzxBNXV1axfv54zzzyTtra2pHk+99xzrF27loaGBlauXMnixYtZsWIF7s4RRxzBcccdR2NjY1rH9/LLL7N06VI2b97MIYccwsUXX8zq1at5+OGHef7559mxYwdNTU1MnDgx5X6uu+467r//fvr378/SpUvTylsyo36/2pzlfcCAmpzlXUp6V6pFnYiUrgFFNovn5MMO4Mk179Ayemiui5JztdW9mHTwYGqq8vYWQ0Sk5Okbup2JEyeycuVKNm/eTFVVFU1NTbS1tfHUU09xzz33UFlZyYwZMwAYO3YsVVVVVFRUMHbsWDZu3AjA9u3bueyyy1i1ahXl5eW8+uqrKfOcNGkSDQ1RK4Hly5czd+5camqim/FTTjmFp556Ku1KoJkzZ1JVVUVVVRWDBw/m/fffZ/ny5cyZM4fevXsDcPLJJ3e4nwULFrBgwQJuueUWFi5cyE033ZRW/pnWu7IXO3d5wUzdWDcwet8G9e16U9mxwwdy/WlNNORg/KUBtVXcd/Fx1FZX9HjepeChq05g1y5POOaXiEixu+u8o3n4D69zyYziGpz/8pljOeqL+9P8hf1yXZScMzNuPuPwXBdDRERSSLsSyMzKgTbgHXef1W7ducD3gXdC0kJ3v687BeuoxU62VFRUUF9fz+LFizn66KMZN24cS5cu5bXXXmP06NFUVFTs7h5VVlZGVVXV7sc7duwA4K677mL//fdn9erV7Nq1i+rq1BUCsQofAO/mgDCx8gCUl5ezY8eObu1z3rx5zJw5M2eVQLFKlULpklZd2YtHr5lORTdv8nP5a+JBg3LXEqnY9dPsPSJSwg6tG8Chp6duiVyIqivKaTlUrYBERKQwdOZO9QpgXYr1v3L3CWHpVgVQrrW2tnL77bfT2tpKS0sLixYtYsKECWlXRHz88ccMHTqUsrIyfvazn3VqwOfW1lYeffRRPvnkE7Zu3cojjzxCS0tLVw8FgGOPPZbf/OY3fPrpp2zZsoXHHkvd1W79+vW7Hy9ZsoRRo3I3e4uZFUwFUEzvyl5q6SEiIiIiIiJ5J62WQGZWB8wEFgDfymqJ8kBLSwsLFizgqKOOoqamhurq6k5VxFxyySWceuqpPPTQQ0yZMmWvlj4daWpq4txzz2XSpEkAXHDBBWl3BUvm8MMPZ/bs2YwfP57hw4fT3NxM//79k25/7bXX8sorr1BWVsbw4cNZtGhRt/IXERERERERkdyzdLoKmdmvgVuAvsBVSbqD3QJ8CLwKXOnuf0qwnwuBCwGGDRs28c0339xr/bp16xg9enSXDkRS27JlC7W1tXzyySe0trZy77330tTUlOtidYs+LyIiIiIiIiJ7M7OV7t6caF2HfVbMbBbwgbuvTLHZb4B6dx8H/Afw00Qbufu97t7s7s377afB83rShRdeyIQJE2hqauLUU08t+AogEREREREREemcdLqDHQPMNrOTgGqgn5n93N3Pim3g7pvitv8RcGtmi1n41qxZw9lnn71XWlVVFStWrOiR/B988MHPpV166aU8/fTTe6VdccUVnHfeeT1SJhERERERERHpOR1WArn7fGA+gJlNJuoOdlb8NmY21N3fC09nk3oA6ZI0duxYVq1aleti7OUHP/hBrosgIiIiIiIiIj0k7Sni2zOz7wBt7r4EuNzMZgM7gL8A53Z1v+5ecLNBSc/rzrT3IiIiIiIiIqWoU5VA7r4MWBYe/2Nc+u7WQt1RXV3Npk2bGDhwoCqCJCl3Z9OmTVRXV+e6KCIiIiIiIiIFo8stgbKhrq6Ot99+mw8//DDXRZE8V11dTV1dXa6LISIiIiIiIlIw8qoSqKKigoaGhlwXQ0RERERERESk6HQ4RbyIiIiIiIiIiBQ+VQKJiIiIiIiIiJQAVQKJiIiIiIiIiJQAy9VU22b2IfBmTjLPvEHAn3NdCJEipNgSyQ7Flkh2KLZEskfxJZK+4e6+X6IVOasEKiZm1ubuzbkuh0ixUWyJZIdiSyQ7FFsi2aP4EskMdQcTERERERERESkBqgQSERERERERESkBqgTKjHtzXQCRIqXYEskOxZZIdii2RLJH8SWSARoTSERERERERESkBKglkIiIiIiIiIhICVAlkIiIiIiIiIhICcjrSiAz62Nmj5nZy2b2opl9L25dlZn9ysw2mNkKM6sP6QPNbKmZbTGzhXHb9zWzVXHLn83s7g72VWFmPzWzNWa2zszmx+3vx2b2gZmtbVfmG83snbh8TkpybPua2RNmtj78HRDSr4577Voz22lm+3aQZ7J9mZndE47rBTNrinvNzrh8lnTl/ZHCla+xZWYHhTzWhXJdEZdPws95gmNrCHmtD3lXtlt/mpm5mTWH5yeY2cpQlpVmNjVu22Vm9krcsQ3u4Ljqzexvcdsv6vq7JIUqD+Kr0swWh8/0ajObHLe/iSF9Qzg/WEj/fijvC2b2iJntk+TYTg/HtCsWQyE91flyRoijDWZ2bVx6wlhVfEkyeRBbXbkuTBgzCY4tWWwlLH9Yt8DM/mRmW9qlJyx/WDc/pL9iZtPj0jeG41plZm2p3wkpNgUaW906b8WtHxaO4ap26eVm9ryZ/TYu7Sdm9kbcsU0I6Wa655JC4u55sQAGlLVL6wNMCY8rgaeAE8PzS4BF4fEZwK/C4xrgWODrwMIU+a0EWjvY1zzgl3Fl2QjUh+etQBOwtt1+bwSuSuN4bwOuDY+vBW5NsM3JwO/jnifLM+G+gJOAfwv/2yOBFXGv2ZLr91xLzyyFFFvAUKAppPcFXgUODc87jJmw7l+AM8LjRcDFcev6Av8FPAs0h7RG4IDweAzwTtz2y2Lbtcsj2XHVt49PLcW95Gl8XQosDo8Hh9eUhefPAUeFcv9bXLmmAb3C41tTxNdo4JD2sZEipsuB14AR4X+xOi6mE8aq4ktLeL/zMba6cl2YMGYS5J8stpKWn+jabijtrulSlP/QEINVQEOIzfKwbiMwKNfvu5bsL0UUW906b8Wtfxh4iHb3b8C3gAeB38al/QQ4LcE+dM+lpaCWnLYEsuhXvXVm9kPgj8BB8evd/RN3Xxoefxa2qQur5wA/DY9/DXzJzMzdt7r7cuDTFPkeTHRh/FSqfQEO1JhZL6A38Bnw11Ce/wL+0uWD3zvPnwJfTrDNmcAvYk9S5JlsX3OA+z3yLLCPmQ3tRpmlQBRqbLn7e+7+x1CuzcA64MAE+0oYM2HfU0Neiba7magyafcxuPvz7v5uePoiUG1mVcmOsYPjkhJQAPF1KPBkyP8D4COgOXz/93P3P7i7A/cT4sPd/93dd4R9PRtX3r24+zp3fyXRKhKfLycBG9z99fC/+CUwp4NYVXyVqAKIrU5fF6aImbS2S1V+d3/W3d9LsLtk5Z9DdKO9zd3fADYQxagUuSKNre6etzCzLwOvE13/xafXATOB+5IdWzu655KCkg/dwQ4hCppGd38z2Uahid/JhAtbohvDPwGEL4CPgYFp5nkmUc1zbGq0ZPv6NbAVeA94C7jd3dOp+LksNAX8sSXpsgLsHztxh7+D41eaWR9gBlHtdEeS7Wv3cQVvs+eGutrM2szs2fAFKMWnoGMrNBFuBFaEpJQxEwwEPoq7KNj9mTezRuAgd/9tgtfFnAo87+7b4tIWhya8N8TdiKb6HzWE5sP/aWYtKfKSwpbP8bWaqKKll5k1ABOJLvgPJIqJmPhzQrzziX7R7IxkMZ3sPJQ0VlMcFyi+SkE+x1ZXrwt7WrLyp7oudODfLeoWfWEPllV6TjHHVqfPW2ZWA1wD3JRg9d3A3wO7EqxbEO7z7or70VD3XFJQ8qES6M1QY5pUqBX+BXCPu78eS06wabrz3Z9BXAubFPuaBOwEDiBqNvttMxvRwb7/NzASmED0RXZHmmVq72Tg6W5eXKT6Hw1z92ai5pd3m9nIbuQj+algY8vMaokqQL/p7n9NM++k+ZlZGXAX8O2kLzQ7jKg58UVxyV9x97FAS1jOTpUPUcwPc/dGQjNiM+vXifJL4cjn+Pox0QVoG9GF7DPAjnTyNrPrwrYPpFmmmGQxnSzPVGVRfJW2fI6trlwX5kJX4u4Yd28CTgQuNbPWbBVOcqYoY6sb562bgLvcvf2YWrOAD9x9ZYLXzAdGAYcD+xJVIoHuuaTA5EMl0NY0trkXWO/ud8elvU1oyhi+sPqTRvcsMxtP1H80PrCT7Wse8Dt33x6a1D8NJB3UD8Dd33f3ne6+C/gRoZmtRYN0rjKz/xc2fT/WTDD8/aDdrtp/aaaSbF+7jyuoA94N5Yz9fZ2oj2xjmnlJ4SjI2DKzCqIKoAfc/V/j9pXwc25mj4fYug/4M1ET3F7hNbHPfF+i8X6WmdlGov7aS2zP4NB1wCPAV939tViG7v5O+LuZqF94rNl8wuMKTew3hdesJBpv4Ysd/e+kIOVtfLn7Dne/0t0nuPscYB9gfdg+vrn87nNC2Mc5wCyiyk8Pae3PXckki+lk56FksZrquBRfpSFvY4suXBemyDfd2OqKZOVP57rwA6LzobqJFZ+ii61unreOAG4L14XfBP7BzC4DjgFmh/RfAlPN7OcQtUQPXb62AYtJcF0Y6J5L8lo+VAKlZGb/RPQF8c12q5YA54THpxENoJxOrfRe4+x0sK+3iALfQpPBI4GXOyhvfP/PucBaAHc/L1yQx2YLi8/zHOD/xu2jP3BcfFoHku1rCfDVUP4jgY/d/T0zGxBrvmhmg4i+7F5KMy8pEvkYW6G71f8B1rn7nSn2tftz7u7TQ2xdEPa9NOS1ezt3/9jdB7l7vbvXE/Udn+3ubaHZ82PAfHd/Ou7/0yvER6xiahYhnpMdl5ntZ2bl4TUjgIOJ+ppLicllfFk0y0tNKMcJwA53f8mjbpSbzezIEGtfJcSRmc0g+kVztrt/EssgwbkrmWTny/8GDrZoJrBKoh84liSL1Q6OS/EleXnu6vxRdCq2uiJZ+ZcAZ1g0Q1MDUQw9Z2Y1ZtYXdneRmcae852UiEKLre6et9y9Je668G7gu+6+0N3nu3tdSD8jlPGskGfsx0gjGscu/rpQ91xSODyHo1LTwUwfRLWoTjQ47KqwXBDWVRON5L6BaLaTEXGv20hUq7yFqGb20Lh1rwOj2uWTcF9AbUh/kShgr457zS+ImqZvD3l8LaT/DFgDvED0hTA0ybENJOpruz783Tdu3bmEEfLbvSZZngn3RdQ08QdEv5auYc9sSEeH56vD36/l8nOgJfNLocYW0SwTHuInVq6TwrqkMdMuzxEhrw0hj6oE2yyLi4friX4dWxW3DCaa9WJlKMuLwD+zZxaVZMd1ath2NdGgiifn+rOgJfNLAcRXPfBKyP8/gOFxr2kmumh9DVgIWEjfQDSeQay8i5Ic29xQtm3A+8DjIT3V+fIkopn+XgOui0tPGKuKr9JdCiC2unJdmDBmEhxb0u2SlZ9oooO3icYteRu4MY3/xXUhFl9hz+xPI0JcrQ7Hdl2y90BLYS5FGlvdOm+12+ZGEszuDExm79nBfk9077QW+DlQG9J1z6WloJbYxZ+IiIiIiIiIiBSxvO8OJiIiIiIiIiIi3der4016hpmtAKraJZ/t7mtyUR6RYqHYEskexZdIdii2RLJDsSUi6g4mIiIiIiIiIlIC1B1MRERERERERKQEqBJIRERERERERKQEqBJIRERERERERKQEqBJIREREioKZXW5m68zsf8zs2pB2o5ldleuyiYiIiOSDvJkdTERERKSbLgFOdPc3cl0QERERkXyklkAiIiJS8MxsETACWGJmV5rZwgTbjDSz35nZSjN7ysxGpdjfT8zsHjN7xsxeN7PTQnqtmT1pZn80szVmNiek15vZy2Z2n5mtNbMHzOx4M3vazNab2aSwXY2Z/djM/tvMno+9XkRERKQnqBJIRERECp67fx14F5gC/E+Sze4FvuHuE4GrgB92sNuhwLHALOB7Ie1TYK67N4W87jAzC+u+APwzMA4YBcwLr78K+IewzXXA79398PD675tZTScOVURERKTL1B1MREREip6Z1QJHAw/tqbOhqoOXPeruu4CXzGz/2K6A75pZK7ALOBCIrXvD3deE/F4EnnR3N7M1QH3YZhowO26compgGLCuO8cnIiIikg5VAomIiEgpKAM+cvcJnXjNtrjHsZqjrwD7ARPdfbuZbSSqyGm//a6457vYc81lwKnu/konyiEiIiKSEeoOJiIiIkXP3f8KvGFmpwNYZHwXdtUf+CBUAE0Bhnfy9Y8D34h1ITOzxi6UQURERKRLVAkkIiIipeIrwNfMbDXwItCVQZkfAJrNrC3s7+VOvv5moAJ4wczWhuciIiIiPcLcPddlEBERERERERGRLFNLIBERERERERGREqCBoUVERKRkmdl1wOntkh9y9wW5KI+IiIhINqk7mIiIiIiIiIhICVB3MBERERERERGREqBKIBERERERERGREqBKIBERERERERGREqBKIBERERERERGREqBKIBERERERERGREvD/AZwMxVTurfBbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def weighted_mov_avg(weights):\n",
    "    def calc(x):\n",
    "        return (weights*x).mean()\n",
    "    return calc\n",
    "\n",
    "market_cap['market cap'].rolling(window=3).apply(weighted_mov_avg(np.array([0.5,1,1.5]))).shift(1)\n",
    "\n",
    "market_cap['wma_rolling_3'] = market_cap['market cap'].rolling(window=3).apply(weighted_mov_avg(np.array([0.5,1,1.5]))).shift(1)\n",
    "market_cap.plot(color=colors, linewidth=2, figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Exponentially weighted moving average (EMA)\n",
    "\n",
    "![img](https://i.imgur.com/bFRUfN3.png)\n",
    "\n",
    "The formula states that the value of the moving average(S) at time t is a mix between the value of raw signal(x) at time t and the previous value of the moving average itself i.e. t-1. It is basically a value between the previous EMA and the current price The degree of mixing is controlled by the parameter a (value between 0–1).\n",
    "\n",
    "The 'a' in the above is called the smoothing factor and sometime also denonted as  **𝛼** ( alpha ) is defined as:\n",
    "\n",
    "![img](https://i.imgur.com/DCXU7Vc.jpg)\n",
    "\n",
    "where 𝑛 is the number of days in our span. Therefore, a 10-day EMA will have a smoothing factor:\n",
    "\n",
    "So the above Formulae can also be written as by simpley re-arranging the terms in the above formulae\n",
    "\n",
    "### Exponential moving average = (Closing Price - Previous EMA) * (2/(Alpha + 1)) + Previous EMA\n",
    "\n",
    "So,\n",
    "- if a = 10%(small), most of the contribution will come from the previous value of the signal. In this case, “smoothing” will be very strong.\n",
    "- if a = 90%(large), most of the contribution will come from the current value of the signal. In this case, “smoothing” will be minimum.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "By looking at the documentation, we can note that the .ewm() method has an adjust parameter that defaults to True. This parameter adjusts the weights to account for the imbalance in the beginning periods (if you need more detail, see the Exponentially weighted windows section in the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#exponentially-weighted-windows))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_name\n",
       "r_20170815-071005    7.084606e+10\n",
       "r_20170831-114005    7.378068e+10\n",
       "r_20170904-085005    7.259623e+10\n",
       "r_20170819-065505    6.987747e+10\n",
       "r_20170818-210005    6.856059e+10\n",
       "                         ...     \n",
       "r_20170904-132006    6.978009e+10\n",
       "r_20170817-190505    7.012766e+10\n",
       "r_20170829-064005    7.123794e+10\n",
       "r_20170824-044005    6.987917e+10\n",
       "r_20170821-103506    6.868462e+10\n",
       "Name: market cap, Length: 9431, dtype: float64"
      ]
     },
     "execution_count": 770,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now lets implement the ewm() function of Pandas\n",
    "market_cap['market cap'].ewm(span=3, adjust=False, min_periods=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>market cap</th>\n",
       "      <th>ma_rolling_3-Day</th>\n",
       "      <th>ma_rolling_30-Day</th>\n",
       "      <th>ma_rolling_3-Months</th>\n",
       "      <th>wma_rolling_3</th>\n",
       "      <th>ewm_window_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r_20170815-071005</th>\n",
       "      <td>7.084606e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170831-114005</th>\n",
       "      <td>7.671529e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.084606e+10</td>\n",
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       "    <tr>\n",
       "      <th>r_20170904-085005</th>\n",
       "      <td>7.141177e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.378068e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170819-065505</th>\n",
       "      <td>6.715872e+10</td>\n",
       "      <td>7.299104e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.308533e+10</td>\n",
       "      <td>7.259623e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170818-210005</th>\n",
       "      <td>6.724371e+10</td>\n",
       "      <td>7.176193e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.016917e+10</td>\n",
       "      <td>6.987747e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>r_20170904-132006</th>\n",
       "      <td>7.130818e+10</td>\n",
       "      <td>6.985996e+10</td>\n",
       "      <td>6.701422e+10</td>\n",
       "      <td>6.704794e+10</td>\n",
       "      <td>6.948973e+10</td>\n",
       "      <td>6.825201e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170817-190505</th>\n",
       "      <td>7.047523e+10</td>\n",
       "      <td>7.052990e+10</td>\n",
       "      <td>6.752149e+10</td>\n",
       "      <td>6.724237e+10</td>\n",
       "      <td>7.021384e+10</td>\n",
       "      <td>6.978009e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170829-064005</th>\n",
       "      <td>7.234822e+10</td>\n",
       "      <td>6.962013e+10</td>\n",
       "      <td>6.799893e+10</td>\n",
       "      <td>6.726716e+10</td>\n",
       "      <td>7.018650e+10</td>\n",
       "      <td>7.012766e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170824-044005</th>\n",
       "      <td>6.852041e+10</td>\n",
       "      <td>7.137721e+10</td>\n",
       "      <td>6.867417e+10</td>\n",
       "      <td>6.740239e+10</td>\n",
       "      <td>7.155055e+10</td>\n",
       "      <td>7.123794e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170821-103506</th>\n",
       "      <td>6.749006e+10</td>\n",
       "      <td>7.044795e+10</td>\n",
       "      <td>6.869882e+10</td>\n",
       "      <td>6.738047e+10</td>\n",
       "      <td>7.012215e+10</td>\n",
       "      <td>6.987917e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9431 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     market cap  ma_rolling_3-Day  ma_rolling_30-Day  \\\n",
       "file_name                                                              \n",
       "r_20170815-071005  7.084606e+10               NaN                NaN   \n",
       "r_20170831-114005  7.671529e+10               NaN                NaN   \n",
       "r_20170904-085005  7.141177e+10               NaN                NaN   \n",
       "r_20170819-065505  6.715872e+10      7.299104e+10                NaN   \n",
       "r_20170818-210005  6.724371e+10      7.176193e+10                NaN   \n",
       "...                         ...               ...                ...   \n",
       "r_20170904-132006  7.130818e+10      6.985996e+10       6.701422e+10   \n",
       "r_20170817-190505  7.047523e+10      7.052990e+10       6.752149e+10   \n",
       "r_20170829-064005  7.234822e+10      6.962013e+10       6.799893e+10   \n",
       "r_20170824-044005  6.852041e+10      7.137721e+10       6.867417e+10   \n",
       "r_20170821-103506  6.749006e+10      7.044795e+10       6.869882e+10   \n",
       "\n",
       "                   ma_rolling_3-Months  wma_rolling_3  ewm_window_3  \n",
       "file_name                                                            \n",
       "r_20170815-071005                  NaN            NaN           NaN  \n",
       "r_20170831-114005                  NaN            NaN  7.084606e+10  \n",
       "r_20170904-085005                  NaN            NaN  7.378068e+10  \n",
       "r_20170819-065505                  NaN   7.308533e+10  7.259623e+10  \n",
       "r_20170818-210005                  NaN   7.016917e+10  6.987747e+10  \n",
       "...                                ...            ...           ...  \n",
       "r_20170904-132006         6.704794e+10   6.948973e+10  6.825201e+10  \n",
       "r_20170817-190505         6.724237e+10   7.021384e+10  6.978009e+10  \n",
       "r_20170829-064005         6.726716e+10   7.018650e+10  7.012766e+10  \n",
       "r_20170824-044005         6.740239e+10   7.155055e+10  7.123794e+10  \n",
       "r_20170821-103506         6.738047e+10   7.012215e+10  6.987917e+10  \n",
       "\n",
       "[9431 rows x 6 columns]"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_cap['ewm_window_3'] = market_cap['market cap'].ewm(span=3, adjust=False, min_periods=0).mean().shift(1)\n",
    "market_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2baa65150>"
      ]
     },
     "execution_count": 772,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# market_cap.plot(color=colors, linewidth=2, figsize=(20,6))\n",
    "# Now that we have quite a few columns in the 'market_cap' dataframe\n",
    "# if I plot the moving averages of all of them, it would become so dense\n",
    "# and can not separately visible\n",
    "# so lets plot only the column 'ewm_window_3'\n",
    "market_cap[['ewm_window_3']].plot(color=colors, linewidth=2, figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# What Is Exponential Smoothing?\n",
    "\n",
    "Exponential smoothing is a time series forecasting method for univariate data. Exponential smoothing forecasting is a weighted sum of past observations, but the model explicitly uses an exponentially decreasing weight for past observations. Specifically, past observations are weighted with a geometrically decreasing ratio.\n",
    "\n",
    "The underlying idea of an exponential smoothing model is that, at each period, the model will learn a bit from the most recent demand observation and remember a bit of the last forecast it did.\n",
    "\n",
    "The smoothing parameter (or learning rate) alpha will determine how much importance is given to the most recent demand observation.\n",
    "\n",
    "![img](https://i.imgur.com/eL82Ugp.jpg)\n",
    "\n",
    "Where 0 <= alpha <= 1\n",
    "\n",
    "alpha is a ratio (or a percentage) and  of how much importance the model will allocate to the most recent observation compared to the importance of demand history. The one-step-ahead forecast for time  T+1 is a weighted average of all of the observations in the series  y1,…,yT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_name\n",
       "r_20170815-071005             NaN\n",
       "r_20170831-114005             NaN\n",
       "r_20170904-085005    7.247460e+10\n",
       "r_20170819-065505    6.875348e+10\n",
       "r_20170818-210005    6.769664e+10\n",
       "                         ...     \n",
       "r_20170904-132006    7.040298e+10\n",
       "r_20170817-190505    7.045355e+10\n",
       "r_20170829-064005    7.177982e+10\n",
       "r_20170824-044005    6.949823e+10\n",
       "r_20170821-103506    6.809251e+10\n",
       "Name: market cap, Length: 9431, dtype: float64"
      ]
     },
     "execution_count": 773,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_cap['market cap'].ewm(alpha=0.7, adjust=False, min_periods=3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/paul/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
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       "      <th>market cap</th>\n",
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       "      <td>6.704794e+10</td>\n",
       "      <td>6.948973e+10</td>\n",
       "      <td>6.825201e+10</td>\n",
       "      <td>7.040298e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170817-190505</th>\n",
       "      <td>7.047523e+10</td>\n",
       "      <td>7.052990e+10</td>\n",
       "      <td>6.752149e+10</td>\n",
       "      <td>6.724237e+10</td>\n",
       "      <td>7.021384e+10</td>\n",
       "      <td>6.978009e+10</td>\n",
       "      <td>7.045355e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170829-064005</th>\n",
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       "      <td>6.962013e+10</td>\n",
       "      <td>6.799893e+10</td>\n",
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       "      <td>7.177982e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170824-044005</th>\n",
       "      <td>6.852041e+10</td>\n",
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       "      <td>6.867417e+10</td>\n",
       "      <td>6.740239e+10</td>\n",
       "      <td>7.155055e+10</td>\n",
       "      <td>7.123794e+10</td>\n",
       "      <td>6.949823e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r_20170821-103506</th>\n",
       "      <td>6.749006e+10</td>\n",
       "      <td>7.044795e+10</td>\n",
       "      <td>6.869882e+10</td>\n",
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       "      <td>6.987917e+10</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9431 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     market cap  ma_rolling_3-Day  ma_rolling_30-Day  \\\n",
       "file_name                                                              \n",
       "r_20170815-071005  7.084606e+10               NaN                NaN   \n",
       "r_20170831-114005  7.671529e+10               NaN                NaN   \n",
       "r_20170904-085005  7.141177e+10               NaN                NaN   \n",
       "r_20170819-065505  6.715872e+10      7.299104e+10                NaN   \n",
       "r_20170818-210005  6.724371e+10      7.176193e+10                NaN   \n",
       "...                         ...               ...                ...   \n",
       "r_20170904-132006  7.130818e+10      6.985996e+10       6.701422e+10   \n",
       "r_20170817-190505  7.047523e+10      7.052990e+10       6.752149e+10   \n",
       "r_20170829-064005  7.234822e+10      6.962013e+10       6.799893e+10   \n",
       "r_20170824-044005  6.852041e+10      7.137721e+10       6.867417e+10   \n",
       "r_20170821-103506  6.749006e+10      7.044795e+10       6.869882e+10   \n",
       "\n",
       "                   ma_rolling_3-Months  wma_rolling_3  ewm_window_3  \\\n",
       "file_name                                                             \n",
       "r_20170815-071005                  NaN            NaN           NaN   \n",
       "r_20170831-114005                  NaN            NaN  7.084606e+10   \n",
       "r_20170904-085005                  NaN            NaN  7.378068e+10   \n",
       "r_20170819-065505                  NaN   7.308533e+10  7.259623e+10   \n",
       "r_20170818-210005                  NaN   7.016917e+10  6.987747e+10   \n",
       "...                                ...            ...           ...   \n",
       "r_20170904-132006         6.704794e+10   6.948973e+10  6.825201e+10   \n",
       "r_20170817-190505         6.724237e+10   7.021384e+10  6.978009e+10   \n",
       "r_20170829-064005         6.726716e+10   7.018650e+10  7.012766e+10   \n",
       "r_20170824-044005         6.740239e+10   7.155055e+10  7.123794e+10   \n",
       "r_20170821-103506         6.738047e+10   7.012215e+10  6.987917e+10   \n",
       "\n",
       "                   esm_window_3_7  \n",
       "file_name                          \n",
       "r_20170815-071005             NaN  \n",
       "r_20170831-114005             NaN  \n",
       "r_20170904-085005    7.247460e+10  \n",
       "r_20170819-065505    6.875348e+10  \n",
       "r_20170818-210005    6.769664e+10  \n",
       "...                           ...  \n",
       "r_20170904-132006    7.040298e+10  \n",
       "r_20170817-190505    7.045355e+10  \n",
       "r_20170829-064005    7.177982e+10  \n",
       "r_20170824-044005    6.949823e+10  \n",
       "r_20170821-103506    6.809251e+10  \n",
       "\n",
       "[9431 rows x 7 columns]"
      ]
     },
     "execution_count": 774,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_cap['esm_window_3_7'] = market_cap['market cap'].ewm(alpha=0.7, adjust=False, min_periods=3).mean()\n",
    "market_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2baa04610>"
      ]
     },
     "execution_count": 775,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "market_cap[['esm_window_3_7']].plot(color=colors, linewidth=2, figsize=(20,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2ba946610>"
      ]
     },
     "execution_count": 776,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now also plot it along with 'market cap'\n",
    "market_cap[['market cap','esm_window_3_7']].plot(color=colors, linewidth=2, figsize=(20,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Trading Strategy based on Moving Average\n",
    "\n",
    "There are quite a few very popular strategies that Traders regularly executes based on Moving Averages. Lets checkout couple of them.\n",
    "\n",
    "First note the fact  that a moving average timeseries (for both SMA or EMA) lags the actual price behaviour. And also the assumption that when a change in the long term behaviour of the asset occurs, the actual price timeseries will react faster than the EMA one. Therefore, we will consider the crossing of the two as potential trading signals.\n",
    "\n",
    "- When the price of an asset crosses the EMA timeseries of the same from below, we will close any existing short position and go long (buy) one unit of the asset.\n",
    "\n",
    "- And when the price crosses the EMA timeseries from above, we will close any existing long position and go short (sell) one unit of the asset.\n",
    "\n",
    "For some more of these strategies have a look at [this article](https://blackwellglobal.com/3-simple-moving-average-strategies-for-day-trading/)"
   ]
  }
 ],
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