5.02 Tick, Volume, Dollar Volume Bars- Exercised.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Chapter-1---Exploring-Tick,-Volume,-DV-Bars\" data-toc-modified-id=\"Chapter-1---Exploring-Tick,-Volume,-DV-Bars-1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Chapter 1 - Exploring Tick, Volume, DV Bars</a></span><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Read-and-Clean-Data\" data-toc-modified-id=\"Read-and-Clean-Data-1.2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Read and Clean Data</a></span></li><li><span><a href=\"#Remove-Obvious-Price-Errors-in-Tick-Data\" data-toc-modified-id=\"Remove-Obvious-Price-Errors-in-Tick-Data-1.3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Remove Obvious Price Errors in Tick Data</a></span></li></ul></li><li><span><a href=\"#Tick-Bars\" data-toc-modified-id=\"Tick-Bars-2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Tick Bars</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Bonus-Exercise:-Make-OHLC-Bars-from-Custom-Bars\" data-toc-modified-id=\"Bonus-Exercise:-Make-OHLC-Bars-from-Custom-Bars-2.0.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">2.0.1&nbsp;&nbsp;</span>Bonus Exercise: Make OHLC Bars from Custom Bars</a></span></li></ul></li></ul></li><li><span><a href=\"#Volume-Bars\" data-toc-modified-id=\"Volume-Bars-3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Volume Bars</a></span></li><li><span><a href=\"#Dollar-Value-Bars\" data-toc-modified-id=\"Dollar-Value-Bars-4\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Dollar Value Bars</a></span></li><li><span><a href=\"#Analyzing-the-Bars\" data-toc-modified-id=\"Analyzing-the-Bars-5\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Analyzing the Bars</a></span><ul class=\"toc-item\"><li><span><a href=\"#Count-Quantity-of-Bars-By-Each-Bar-Type-(Weekly)\" data-toc-modified-id=\"Count-Quantity-of-Bars-By-Each-Bar-Type-(Weekly)-5.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Count Quantity of Bars By Each Bar Type (Weekly)</a></span></li><li><span><a href=\"#Which-Bar-Type-Has-Most-Stable-Counts?\" data-toc-modified-id=\"Which-Bar-Type-Has-Most-Stable-Counts?-5.2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Which Bar Type Has Most Stable Counts?</a></span></li><li><span><a href=\"#Which-Bar-Type-Has-the-Lowest-Serial-Correlation?\" data-toc-modified-id=\"Which-Bar-Type-Has-the-Lowest-Serial-Correlation?-5.3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Which Bar Type Has the Lowest Serial Correlation?</a></span></li><li><span><a href=\"#Partition-Bar-Series-into-Monthly,-Compute-Variance-of-Returns,-and-Variance-of-Variance\" data-toc-modified-id=\"Partition-Bar-Series-into-Monthly,-Compute-Variance-of-Returns,-and-Variance-of-Variance-5.4\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Partition Bar Series into Monthly, Compute Variance of Returns, and Variance of Variance</a></span></li><li><span><a href=\"#Compute-Jarque-Bera-Test,-Which-Has-Lowest-Test-Statistic?\" data-toc-modified-id=\"Compute-Jarque-Bera-Test,-Which-Has-Lowest-Test-Statistic?-5.5\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>Compute Jarque-Bera Test, Which Has Lowest Test Statistic?</a></span></li><li><span><a href=\"#Compute-Shapiro-Wilk-Test\" data-toc-modified-id=\"Compute-Shapiro-Wilk-Test-5.6\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.6&nbsp;&nbsp;</span>Compute Shapiro-Wilk Test</a></span></li></ul></li><li><span><a href=\"#Compare-Serial-Correlation-between-Dollar-and-Dollar-Imbalance-Bars\" data-toc-modified-id=\"Compare-Serial-Correlation-between-Dollar-and-Dollar-Imbalance-Bars-6\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Compare Serial Correlation between Dollar and Dollar Imbalance Bars</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Update-[05.04.18]\" data-toc-modified-id=\"Update-[05.04.18]-6.0.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">6.0.1&nbsp;&nbsp;</span>Update [05.04.18]</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advances in Machine Learning\n",
    "\n",
    "time bars vs. tick bars, volume bars, dollar bars, and dollar imbalance bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1 - Exploring Tick, Volume, DV Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:06.523447Z",
     "start_time": "2018-05-05T00:36:02.825808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-10-08T15:26:20+08:00\n",
      "\n",
      "CPython 3.7.3\n",
      "IPython 7.4.0\n",
      "\n",
      "compiler   : MSC v.1915 64 bit (AMD64)\n",
      "system     : Windows\n",
      "release    : 10\n",
      "machine    : AMD64\n",
      "processor  : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel\n",
      "CPU cores  : 12\n",
      "interpreter: 64bit\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\IT\\lib\\site-packages\\dask\\config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n",
      "WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
      "C:\\IT\\lib\\site-packages\\theano\\configdefaults.py:560: UserWarning: DeprecationWarning: there is no c++ compiler.This is deprecated and with Theano 0.11 a c++ compiler will be mandatory\n",
      "  warnings.warn(\"DeprecationWarning: there is no c++ compiler.\"\n",
      "WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pandas 0.24.2\n",
      "pandas_datareader 0.7.4\n",
      "dask 1.1.4\n",
      "numpy 1.16.2\n",
      "pymc3 3.9.3\n",
      "theano 1.0.5\n",
      "sklearn 0.20.3\n",
      "statsmodels 0.12.0\n",
      "scipy 1.4.1\n",
      "matplotlib 3.0.3\n",
      "seaborn 0.9.0\n",
      "pyarrow 0.11.1\n",
      "fastparquet not installed\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# import standard libs\n",
    "from IPython.display import display\n",
    "from IPython.core.debugger import set_trace as bp\n",
    "from pathlib import PurePath, Path, PureWindowsPath\n",
    "import sys\n",
    "import time\n",
    "from collections import OrderedDict as od\n",
    "import re\n",
    "import os\n",
    "import json\n",
    "os.environ['THEANO_FLAGS'] = 'device=cpu,floatX=float32'\n",
    "\n",
    "# import python scientific stack\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "pd.set_option('display.max_rows', 100)\n",
    "from dask import dataframe as dd\n",
    "from dask.diagnostics import ProgressBar\n",
    "pbar = ProgressBar()\n",
    "pbar.register()\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.graphics.api as smg\n",
    "from numba import jit\n",
    "import math\n",
    "#import pymc3 as pm   install failed here, to be fixed..\n",
    "from theano import shared, theano as tt\n",
    "\n",
    "# import visual tools\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('seaborn-talk')\n",
    "plt.style.use('bmh')\n",
    "\n",
    "plt.rcParams['font.family'] = 'DejaVu Sans Mono'\n",
    "plt.rcParams['font.size'] = 9.5\n",
    "plt.rcParams['font.weight'] = 'medium'\n",
    "plt.rcParams['figure.figsize'] = 10,7\n",
    "blue, green, red, purple, gold, teal = sns.color_palette('colorblind', 6)\n",
    "\n",
    "# import util libs\n",
    "\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import missingno as msno\n",
    "\n",
    "from src.utils.utils import *\n",
    "from src.features.bars import get_imbalance\n",
    "import src.features.bars as brs\n",
    "import src.features.snippets as snp\n",
    "\n",
    "RANDOM_STATE = 777\n",
    "\n",
    "print()\n",
    "%watermark -p pandas,pandas_datareader,dask,numpy,pymc3,theano,sklearn,statsmodels,scipy,matplotlib,seaborn,pyarrow,fastparquet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook explores the idea of sampling prices as a function of something other than fixed time intervals. For example using the number of ticks, volume or dollar volume traded as the sampling interval. The rest of this notebook works through some of the exercises found in chapters 1 and 2 of the book. \n",
    "\n",
    "This notebook makes use of the following script found here: `./src/features/bars.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and Clean Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set used in this example is too large to be hosted on github. It is a sample of equity tick data, symbol `IVE`, provided by [kibot.com (caution: download link)](http://api.kibot.com/?action=history&symbol=IVE&interval=tickbidask&bp=1&user=guest). Download this data to the `./data/raw/` directory in your local repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:29.025681Z",
     "start_time": "2018-05-05T00:36:06.525588Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask   size      v        dv\n",
      "dates                                                              \n",
      "2017-03-21 15:41:44  103.93  103.92  103.93  114.0  114.0  11848.02\n",
      "2017-03-21 15:41:51  103.90  103.90  103.91  699.0  699.0  72626.10\n",
      "2017-03-21 15:41:51  103.90  103.90  103.91  401.0  401.0  41663.90\n",
      "2017-03-21 15:41:51  103.90  103.90  103.91  199.0  199.0  20676.10\n",
      "2017-03-21 15:41:53    1.00     NaN     NaN    NaN    NaN       NaN\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1129133 entries, 2009-09-28 09:30:00 to 2017-03-21 15:41:53\n",
      "Data columns (total 6 columns):\n",
      "price    1129133 non-null float64\n",
      "bid      1129132 non-null float64\n",
      "ask      1129132 non-null float64\n",
      "size     1129132 non-null float64\n",
      "v        1129132 non-null float64\n",
      "dv       1129132 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 60.3 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask   size      v        dv\n",
       "dates                                                           \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79  100.0  100.0   5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79  638.0  638.0  32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76  100.0  100.0   5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75  100.0  100.0   5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76  300.0  300.0  15225.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_kibot_ticks(fp):\n",
    "    # read tick data from http://www.kibot.com/support.aspx#data_format\n",
    "    cols = list(map(str.lower,['Date','Time','Price','Bid','Ask','Size']))\n",
    "    df = (pd.read_csv(fp, header=None)\n",
    "          .rename(columns=dict(zip(range(len(cols)),cols)))\n",
    "          .assign(dates=lambda df: (pd.to_datetime(df['date']+df['time'],\n",
    "                                                  format='%m/%d/%Y%H:%M:%S')))\n",
    "          .assign(v=lambda df: df['size']) # volume\n",
    "          .assign(dv=lambda df: df['price']*df['size']) # dollar volume\n",
    "          .drop(['date','time'],axis=1)\n",
    "          .set_index('dates')\n",
    "          .drop_duplicates())\n",
    "    return df\n",
    "\n",
    "infp = PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/raw/IVE_tickbidask.txt')\n",
    "\n",
    "df = read_kibot_ticks(infp)\n",
    "cprint(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2017-03-21 15:41:44</th>\n",
       "      <td>103.93</td>\n",
       "      <td>103.92</td>\n",
       "      <td>103.93</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>11848.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.90</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>699.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>72626.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.90</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>41663.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.90</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>199.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>20676.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:53</th>\n",
       "      <td>1.00</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": [
       "                      price     bid     ask   size      v        dv\n",
       "dates                                                              \n",
       "2017-03-21 15:41:44  103.93  103.92  103.93  114.0  114.0  11848.02\n",
       "2017-03-21 15:41:51  103.90  103.90  103.91  699.0  699.0  72626.10\n",
       "2017-03-21 15:41:51  103.90  103.90  103.91  401.0  401.0  41663.90\n",
       "2017-03-21 15:41:51  103.90  103.90  103.91  199.0  199.0  20676.10\n",
       "2017-03-21 15:41:53    1.00     NaN     NaN    NaN    NaN       NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1129132 entries, 2009-09-28 09:30:00 to 2017-03-21 15:41:51\n",
      "Data columns (total 6 columns):\n",
      "price    1129132 non-null float64\n",
      "bid      1129132 non-null float64\n",
      "ask      1129132 non-null float64\n",
      "size     1129132 non-null float64\n",
      "v        1129132 non-null float64\n",
      "dv       1129132 non-null float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 60.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save initial processed data as parquet in the `./data/interim/` folder and reload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:29.843915Z",
     "start_time": "2018-05-05T00:36:29.029886Z"
    }
   },
   "outputs": [],
   "source": [
    "outfp = PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/interim/IVE_tickbidask.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2017-03-21 15:41:15</th>\n",
       "      <td>103.9633</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>265.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>27550.2745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:44</th>\n",
       "      <td>103.9300</td>\n",
       "      <td>103.92</td>\n",
       "      <td>103.93</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>11848.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>699.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>72626.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>41663.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>199.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>20676.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        price     bid     ask   size      v          dv\n",
       "dates                                                                  \n",
       "2017-03-21 15:41:15  103.9633  103.96  103.97  265.0  265.0  27550.2745\n",
       "2017-03-21 15:41:44  103.9300  103.92  103.93  114.0  114.0  11848.0200\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91  699.0  699.0  72626.1000\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91  401.0  401.0  41663.9000\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91  199.0  199.0  20676.1000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:30.438528Z",
     "start_time": "2018-05-05T00:36:29.845701Z"
    }
   },
   "outputs": [],
   "source": [
    "infp=PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/interim/IVE_tickbidask.parq')\n",
    "df = pd.read_parquet(infp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask   size      v        dv\n",
       "dates                                                           \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79  100.0  100.0   5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79  638.0  638.0  32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76  100.0  100.0   5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75  100.0  100.0   5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76  300.0  300.0  15225.00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:32.711789Z",
     "start_time": "2018-05-05T00:36:30.440842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf6ba21748>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf0e07a278>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Obvious Price Errors in Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:33.141416Z",
     "start_time": "2018-05-05T00:36:32.713517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf6690a198>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:33.454840Z",
     "start_time": "2018-05-05T00:36:33.142719Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def mad_outlier(y, thresh=3.):\n",
    "    '''\n",
    "    compute outliers based on mad\n",
    "    # args\n",
    "        y: assumed to be array with shape (N,1)\n",
    "        thresh: float()\n",
    "    # returns\n",
    "        array index of outliers\n",
    "    '''\n",
    "    median = np.median(y)\n",
    "    diff = np.sum((y - median)**2, axis=-1)\n",
    "    diff = np.sqrt(diff)\n",
    "    med_abs_deviation = np.median(diff)\n",
    "\n",
    "    modified_z_score = 0.6745 * diff / med_abs_deviation\n",
    "\n",
    "    return modified_z_score > thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.073189Z",
     "start_time": "2018-05-05T00:36:33.458178Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "mad = mad_outlier(df.price.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.379824Z",
     "start_time": "2018-05-05T00:36:35.076483Z"
    }
   },
   "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2010-05-06 14:49:07</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>44.03</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:53:30</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>30.28</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>50.57</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>50.57</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price   bid    ask    size       v      dv\n",
       "dates                                                          \n",
       "2010-05-06 14:49:07   0.11  0.10  44.03   500.0   500.0    55.0\n",
       "2010-05-06 14:53:30   1.10  1.10  30.28  2600.0  2600.0  2860.0\n",
       "2010-05-06 14:55:32   1.10  1.10  50.57   300.0   300.0   330.0\n",
       "2010-05-06 14:55:32   1.10  1.10  50.57   100.0   100.0   110.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57   200.0   200.0   220.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57   700.0   700.0   770.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57  1200.0  1200.0  1320.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   500.0   500.0   550.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   100.0   100.0   110.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   200.0   200.0   220.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   800.0   800.0   880.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mad]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.827227Z",
     "start_time": "2018-05-05T00:36:35.381347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf0ac919e8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.loc[~mad].price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop outliers from dataset and save cleaned data in the `./data/processed/` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:36.824195Z",
     "start_time": "2018-05-05T00:36:35.829852Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.loc[~mad]\n",
    "#cprint(df)\n",
    "\n",
    "outfp = PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/processed/clean_IVE_fut_prices.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:37.434928Z",
     "start_time": "2018-05-05T00:36:36.826278Z"
    }
   },
   "outputs": [],
   "source": [
    "infp=PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/processed/clean_IVE_fut_prices.parq')\n",
    "df = pd.read_parquet(infp)\n",
    "#cprint(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf0ac369e8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf668e7b70>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEOCAYAAAAqksTuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFtFJREFUeJzt3WuM3OdVBvDneC+e9ezOemd3vS6OQ41DLqSIfmiCGkI/1CBAahGoKpW2EFWokai4Jd8qlUogIUQpUlEBpTKRSJuyAlRVoEq0oAZCVbVSUqVAadOkNnaSuvF675fxrPeSPx9mnG6t9drn1Pu++57z/CTLsSfaHD85/2f/u/N6RqqqAhERpXEg9wBERJGwdImIEmLpEhElxNIlIkqIpUtElFDvbg8+/fTT1cGDB1PNksXm5iZ6e3eNgXbA3PSYmU2JuV2+fHn21KlT4zs9tuuf5ODBg7j77rv3Zqp94sqVK/D+iWUvMDc9ZmZTYm7PPffcS9d7LPy3Fy5evJh7hCIxNz1mZuMtt/Cl29fXl3uEIjE3PWZm4y238KU7PDyce4QiMTc9ZmbjLbfwpTs7O5t7hCIxNz1mZuMtt/Cl6+2zaCrMTY+Z2XjLLXzprq+v5x6hSMxNj5nZeMstfOm22+3cIxSJuekxMxtvuYUv3aNHj+YeoUjMTY+Z2XjLLXzpejsDmApz02NmNt5yC1+6/f39uUcoEnPTY2Y23nILX7pDQ0O5RygSc9NjZjbecgtfunNzc7lHKBJz02NmNt5yC1+6IyMjuUcoEnPTY2Y23nILX7rejqOkwtz0mJmNt9zCl+7a2lruEYrE3PSYmY233MKXrrczgKkwNz1mZuMtt/Cl6+0MYCrMTY+Z2XjLLXzp1mq13CMUibnpMTMbb7mFL92BgYHcIxSJuekxMxtvuZX1bm97YGFhAY1GI/cYxbk2t8nJyYzTlOG+++7Ds88+m3uMmzI1NZV7hNd5u0bD3+mOjo7mHqFIzE3v/PnzuUcokrddC3+nu7KygsHBwdxjFOd6uW297f0ZpinD6MgBXFx4DQDQ86XHAey/vK7OtZ94u0bD3+l6e4HkVJibXr1Pco9QJG+7Fr50vZ0BTIW56X1zfiv3CEXytmvhS9fbGcBUmJvevc2e3CMUyduuhS9db8dRUmFueotXqtwjFMnbroUvXW8vkJwKc9NrbbJ0LbztWvjSXVpayj1CkZib3rF6+MvNxNuuhd+CsbGx3CMUibnpnV16LfcIRfK2a+FL19tn0VSYm96xOo+MWXjbtfClu7GxkXuEIjE3vVovS9fC266FL11vZwBTYW56PKdr423XwpeutzOAqTA3PZ7TtfG2a+FLt16v5x6hSMxNb26NT6RZeNu18KXb08O7DwvmprfBzjXxtmvhS3d5eTn3CEVibnpHD4W/3Ey87Vr4LRgfH889QpGYm96Li3wizcLbroUv3fn5+dwjFIm56Z0YCn+5mXjbtfBbUFX8+/AWzE2vJ/zVZuNt18KvgbcvXVJhbnovLPKZNAtvuxa+dKenp3OPUCTmpnfPiK9n4VPxtmvhS9fTey+lxNz0LrV5p2vhbdfCly4RUUrhS3d1dTX3CEVibnpHBsJfbibedi38FkxMTOQeoUjMTe/5BZ7TtfC2a+FLd2ZmJvcIRWJuencdDn+5mXjbtfBbIMLXOLVgbnpbfB7NxNuuhS/dZrOZe4QiMTe9cytsXQtvuxa+dL196ZIKc9O78zDP6Vp427XwpdtoNHKPUCTmpnfxMu90LbztWvjS3driM8oWzE2vL/zVZuNt18KvQavVyj1CkZib3mgt/OVm4m3Xwm+Btze9S4W56fGNKW287Vr40vX2pnepMDc9vjGljbddC1+6fX19uUcoEnPTW9v09bqwqXjbtfClOzw8nHuEIjE3vQstlq6Ft10LX7qzs7O5RygSc9M7ORz+cjPxtmvht8DbZ9FUmJvehRbP6Vp427Xwpbu+vp57hCIxN716r6/XEEjF266FL912u517hCIxN73DB1m6Ft52LXzpejsDmApz0+M5XRtvuxa+dL2dAUyFuenxnK6Nt13bk9KdnJzE5OTkXnzoW66/vz/3CEVibnqtDR4Zs8ixa3vZYeHvdIeGhnKPUCTmpnepzdK18LZr4Ut3bm4u9whFYm56JxrhLzcTb7sWfgtGRkZyj1Ak5qb3Mt85wsTbroUvXW/HUVJhbno8MmbjbdfCl+7a2lruEYrE3PQa/SxdC2+7Fr50vZ0BTIW56fGcro23XQtfut7OAKbC3PR4TtfG266FL91arZZ7hCIxN73ldR4Zs/C2a+FLd2BgIPcIRWJueotXWLoW3nYtfOkuLCzkHqFIzE3v9qHwl5uJt10LvwWjo6O5RygSc9M7t8xzuhbedi186a6srOQeoUjMTe/IAI+MWXjbtfCl6+0FklNhbnr1PpauhbddC1+63s4ApsLc9HhO18bbroUvXW9nAFNhbno8p2vjbdfCl6634yipMDc9Hhmz8bZr4UuXL8Ztw9z0WpssXQtvuxa+dJeWlnKPUCTmpnesHv5yM/G2a+G3YGxsLPcIRWJuemeXeE7XwtuuhS9db59FU2FuesfqPDJm4W3XwpfuxsZG7hGKxNz0ar0sXQtvuxa+dL2dAUyFuenxnK6Nt10LX7rezgCmwtz0eE7XxtuuhS/der2ee4QiMTe9uTU+kWbhbdfCl25PD+8+LJib3gY718TbroUv3eXl5dwjFIm56R09FP5yM/G2a+G3YHx8PPcIRWJuei8u8ok0C2+7Fr505+fnc49QJOamd4LvHGHibdfCb0FV8e/DWzA3vZ7wV5uNt10LvwbevnRJhbnpvbDIZ9IsvO1a+NKdnp7OPUKRmJvePSO+noVPxduuhS/dwcHB3CMUibnpXWrzTtfC266FL10iopTCl+7q6mruEYrE3PSODIS/3Ey87Vr4LZiYmMg9QpGYm97zCzyna+Ft18KX7szMTO4RisTc9O46HP5yM/G2a+G3QISvcWrB3PS2+DyaibddC1+6zWYz9whFYm5651bYuhbedi186Xr70iUV5qZ352Ge07XwtmvhS7fRaOQeoUjMTe/iZd7pWnjbtfClu7XFZ5QtmJteX/irzcbbroVfg1arlXuEIjE3vdFa+MvNxNuuhd8Cb296lwpz0+MbU9p427Xe3R7c3NzEmTNnXv91s9lUPZM4OTlpn4zImXubPXjmUhnFy2t37+x6pzszM4P777//9R+nT59ONReRO2ubvl4Xlmx2vdMdHx/HM8888/qvteflpqambFMl1Gq13L3baArX5sY7oxu70CqndPfTtZvjGt3Lfd61dHt7e3HHHXfs2X98P5idnWXpGjA3vZPDBzBXyLcX9hNvuxb+ibTh4eHcIxSJueldaPGcroW3XQtfuuvr67lHKBJz06v3+noNgVS87Vr40m2327lHKBJz0zt8kKVr4W3XwpeutzOAqTA3PZ7TtfG2a+FL9+LFi7lHKBJz07u3yRe8sfC2a+FLt7+/P/cIRWJueq2Nco6M7Sfedi186Q4NDeUeoUjMTe9Sm6Vr4W3Xwpfu3Nxc7hGKxNz0TjTCX24m3nYt/BaMjIzkHqFIzE3vZb5zhIm3XQtfut6Oo6TC3PR4ZMzG266FL921tbXcIxSJuek1+lm6Ft52LXzpejsDmApz0+M5XRtvuxa+dL2dAUyFuenxnK6Nt10LX7q1Wi33CEVibnrL6zwyZuFt18KX7sDAQO4RisTc9BavsHQtvO1a+NJdWFjIPUKRmJve7UPhLzcTb7sWfgtGR0dzj1Ak5qZ3bpnndC287Vr40l1ZWck9QpGYm96RAR4Zs/C2a+FL19sLJKfC3PTqfSxdC2+7Fr50vZ0BTIW56fGcro23XQtfut7OAKbC3PR4TtfG266FL11vx1FSYW56PDJm423XwpeutxdIToW56bU2WboW3nYtfOkuLS3lHqFIzE3vWD385WbibdfCb8HY2FjuEYrE3PTOLvGcroW3XQtfut4+i6bC3PSO1XlkzMLbroUv3Y2NjdwjFIm56dV6WboW3nYtfOl6OwOYCnPT4zldG2+7Fr50vZ0BTIW56fGcro23XQtfuvV6PfcIRWJuenNrfCLNwtuuhS/dnh7efVgwN70Ndq6Jt10LX7rLy8u5RygSc9M7eij85WbibdfCb8H4+HjuEYrE3PReXOQTaRbedi186c7Pz+ceoUjMTe8E3znCxNuuhd+CquLfh7dgbno94a82G2+7Fn4NvH3pkgpz03thkc+kWXjbtfClOz09nXuEIjE3vXtGfD0Ln4q3XQtfuoODg7lHKBJz07vU5p2uhbddC1+6REQphS/d1dXV3CMUibnpHRkIf7mZeNu18FswMTGRe4QiMTe95xd4TtfC26717sUHnZqa2osPuydmZmZw/Pjx3GMUh7np3XX4AL42w+/rauXYtb3ssPB3uiJ8jVML5qa3xb418bZr4Uu32WzmHqFIzE3v3Apb18LbroUv3ZmZmdwjFIm56d15mOd0LbztWvjSbTQauUcoEnPTu3iZd7oW3nYtfOlubfEZZQvmptcX/mqz8bZr4deg1WrlHqFIzE1vtBb+cjPxtmvht8Dbm96lwtz0+MaUNt52LXzpenvTu1SYmx7fmNLG266FL92+vr7cIxSJuemtbfp6XdhUvO1a+NIdHh7OPUKRmJvehRZL18LbroUv3dnZ2dwjFIm56Z0cDn+5mXjbtfBb4O2zaCrMTe9Ci+d0LbztWvjSXV9fzz1CkZibXr3X12sIpOJt18KXbrvdzj1CkZib3uGDLF0Lb7sWvnS9nQFMhbnp8ZyujbddC1+63s4ApsLc9HhO18bbroUv3f7+/twjFIm56bU2eGTMwtuuhS/doaGh3CMUibnpXWqzdC287Vr40p2bm8s9QpGYm96JRvjLzcTbroXfgpGRkdwjFIm56b3Md44w8bZr4UvX23GUVJibHo+M2XjbtfClu7a2lnuEIjE3vUY/S9fC266FL11vZwBTYW56PKdr423XwpeutzOAqTA3PZ7TtfG2a+FLt1ar5R6hSMxNb3mdR8YsvO1a+NIdGBjIPUKRmJve4hWWroW3XQtfugsLC7lHKBJz07t9KPzlZuJt18JvwejoaO4RisTc9M4t85yuhbddC1+6KysruUcoEnPTOzLAI2MW3nYtfOl6e4HkVJibXr2PpWvhbdfCl663M4CpMDc9ntO18bZr4UvX2xnAVJibHs/p2njbtfCl6+04SirMTY9Hxmy87Vr40vX2AsmpMDe91iZL18LbroUv3aWlpdwjFIm56R2rh7/cTLztWvgtGBsbyz1CkZib3tklntO18LZr4UvX22fRVJib3rE6j4xZeNu18KW7sbGRe4QiMTe9Wi9L18LbroUvXW9nAFNhbno8p2vjbdfCl663M4CpMDc9ntO18bZr4Uu3Xq/nHqFIzE1vbo1PpFl427Xe3APk1tPDuw+L6+XW86XHE09Sjq3jx9Hzyis/8HvM68a8XaPh73SXl5dzj1Ak5qbn7XuTqXjbtfB3uuPj47lHKNK1uU1NTWWapByXL1/Go48+mnuM4ni7RsPf6c7Pz+ceoUjMTY+Z2XjLLXzpVhX/PrwFc9NjZjbecgtfut6+dEmFuekxMxtvuYUv3enp6dwjFIm56TEzG2+5hS/dwcHB3CMUibnpMTMbb7mFL10iopTCl+7q6mruEYrE3PSYmY233MKX7sTERO4RisTc9JiZjbfcwpfuzMxM7hGKxNz0mJmNt9zCl64IX+PUgrnpMTMbb7mFL91ms5l7hCIxNz1mZuMtt/Cl6+1Ll1SYmx4zs/GWW/jSbTQauUcoEnPTY2Y23nILX7pbW3wLFQvmpsfMbLzlFr50W61W7hGKxNz0mJmNt9zCly5fWNqGuekxMxtvuYUvXW9vepcKc9NjZjbecgtduvPz8zh9+rS7F0nea8xNj5nZeMwtfOk+9thjrv6HpsDc9JiZjcfcQpcuEVFqsttbYTz11FMzAF5KN05am5ubPfPz8xPNZnO6t7fX17mUPcTc9JiZTcG5/eipU6d2fMuLXUuXiIhuLX57gYgoIZYuEVFCLF0iooRYukRECbF0iYgSYukSESXE0iUiSoilS0SUEEuXiCihcKUrIodE5KyInNn2e6Mi8lkRWRWRCyLyuzln3G9E5BER+T8RaXV/fqD7+8xtByJym4h8TkTmRWRaRB4XkYHuY8wMgIj8rIh8UUQWRaS65rFdMxKRN4rIv4vI5e4+vjvt9D+ccKUL4I8BXPsCnY8BOATgRwC8C8CfiMjbUw+2H4nIBwB8EMD7AAwBeBDffz0O5razvwbQA+A4gHsBvAXAh7qPMbOONoAnAPzeDo/dKKN/AHAOwBiARwA8KSJ37um0t1JVVWF+APhpAF8B8JsAznR/bxDAOoCf2fbvPQHgk7nn3Q8/ALwI4Ld3+H3mdv3M/gfAw9t+/WcA/omZ7ZjVg50aurm9AnAXgArAbdsefxrAH+X+s9zsjzB3uiLSD+BvAPwOgNe2PfTjAPoAfENEPikivw/gG+jcoYQmIg108hkSkW+LyKsiMiUio2Buu/kogHeIyJCIHAHw8+iULjO7sRtl9BMAFgB8T0S+ICK/hsIyDFO6AD4M4Omqqp675vcHuz+voPM//ASA5W2/H9lw9+d3A/g5AHcDaAL4OJjbbr6CzrdiFgFMA/gWgE+Dmd2MG2U02P11X/fxYygswxClKyJvAvAQgD/Y4eHV7s9DVVU9UFXVIwAa234/sqtvw3q6qqrvVlW1BOCvAPwCmNuOREQA/CuAr6NTBCMAegE8CWZ2M26U0SqARlVVV6qqOllV1cdQWIYhShedJzJuB7DUfab0bwGc7P7zRvfHT277998E4JvJp9xnqqqaBzCLzvfQrpLuz98Bc9vJGICT6HyialdVtYjOvr0TzOxm3CijbwEYEZHbrvP4/pf7m8qZvnn/PnSfSOv++h8BfB6dLwnvR+fLlVO559wPPwD8JYD/AvAGdO7c/gXA3zG3XTM7B+DPARwEUEfnWwtfZWY/kNEBADUAb0fnk3oNQO1mMgLwDIDHAQwAeAeANQB35f4z3eyPKHe6N/IBdP7HvQrgnwF8qKqqp/KOtG98EMB/A3gewHkAS/j+MR/mtrNfBvBmdI4mvozO98bf232MmXW8DZ1jY1f/7O3uD+DGGb0HwI8BmEfnpuChqqpeSDH0rcC36yEiSoh3ukRECbF0iYgSYukSESXE0iUiSoilS0SUEEuXiCghli4VR0TeKyL/lnsOIgue0yUiSoh3ukRECbF0KQsROS8iHxGR74nIyyLyG9see0JE/mLbW7a8JCINERnu/npNRL68w8c8JCIfF5HvisiCiHxq22NNEfl09+1zzonIw6n+rETbsXQppwfReY3edwH4hIgc3/bYQ+i8OtcwgF8BsFFV1VJVVYMAfus6H+9j6LzG6pvReauXL2x77El0Xr3qjei8NvAfishbbt0fhejm9OYegEI7XVXVMoBnReSrAH4RnXf3AID/qKrqc91//vqNPpCIHECnqO+rqmq2+9tT3cfeAOCXAIxWVdUGcFZEPgPgVwF87Zb9aYhuAkuXcrp0zT9PbPv1d5Qfaxydlwc8v8NjV++gz3VeYxxAZ/f/XvnfIPqhsXQpp+0lewSdNxi8alP5sWYAXEHn2wf/e81jr6DzUoGjVVVtKT8u0S3F7+lSTg93nyC7D8Bb0XmbG5Oqql4D8CkAHxWRMRGpich7uo+9CuA/AfypiNRFpE9EHhCRn7oVfwgiDZYu5fRlAN8G8Fl03ub9pd3+ZRH5sIisAvgEgLd2TzKc3favPArgLDovuv4qOu8qcNWvo3M3fQadu+KPgF/pUQb8yxGUhYicB/D+qqq+mHsWopR4p0tElBBLl4goIX57gYgoId7pEhElxNIlIkqIpUtElBBLl4goIZYuEVFC/w9cMF5menCb3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tick Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:37.778351Z",
     "start_time": "2018-05-05T00:36:37.437179Z"
    }
   },
   "outputs": [],
   "source": [
    "def tick_bars(df, price_column, m):\n",
    "    '''\n",
    "    compute tick bars\n",
    "\n",
    "    # args\n",
    "        df: pd.DataFrame()\n",
    "        column: name for price data\n",
    "        m: int(), threshold value for ticks\n",
    "    # returns\n",
    "        idx: list of indices\n",
    "    '''\n",
    "    t = df[price_column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    for i, x in enumerate(tqdm(t)):\n",
    "        ts += 1\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def tick_bar_df(df, price_column, m):\n",
    "    idx = tick_bars(df, price_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to choose `M`, or the threshold value for sampling prices. One way is based on ratios of total dollar value/volume traded vs number of ticks. The rest of the notebook uses an arbitrary but sensible `M` value. I leave it as an exercise for the reader to see how the results change based on different values of `M`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:38.140768Z",
     "start_time": "2018-05-05T00:36:37.782229Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num ticks: 1,129,121\n",
      "volume ratio: 534.0\n",
      "dollar ratio: 41416.0\n"
     ]
    }
   ],
   "source": [
    "n_ticks = df.shape[0]\n",
    "volume_ratio = (df.v.sum()/n_ticks).round()\n",
    "dollar_ratio = (df.dv.sum()/n_ticks).round()\n",
    "print(f'num ticks: {n_ticks:,}')\n",
    "print(f'volume ratio: {volume_ratio}')\n",
    "print(f'dollar ratio: {dollar_ratio}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:38.932231Z",
     "start_time": "2018-05-05T00:36:38.142830Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tick threshold: 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 2822463.68it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[99, 199, 299, 399, 499, 599, 699, 799, 899, 999]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_M = 100 # arbitrary\n",
    "print(f'tick threshold: {tick_M:,}')\n",
    "tidx = tick_bars(df, 'price', tick_M)\n",
    "tidx[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:39.266920Z",
     "start_time": "2018-05-05T00:36:38.933579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((11291, 6), (1129121, 6))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[tidx].shape, df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is large so select smaller example for quick exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:40.120356Z",
     "start_time": "2018-05-05T00:36:39.269233Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 2867527.63it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(11291, 6)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df = tick_bar_df(df, 'price', tick_M)\n",
    "tick_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:53:14</th>\n",
       "      <td>51.1500</td>\n",
       "      <td>51.15</td>\n",
       "      <td>51.18</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5115.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:02:52</th>\n",
       "      <td>51.2500</td>\n",
       "      <td>51.24</td>\n",
       "      <td>51.26</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>102500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:22:24</th>\n",
       "      <td>51.1961</td>\n",
       "      <td>51.19</td>\n",
       "      <td>51.20</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>56315.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:38:44</th>\n",
       "      <td>51.3650</td>\n",
       "      <td>51.36</td>\n",
       "      <td>51.36</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5136.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 11:24:29</th>\n",
       "      <td>51.3200</td>\n",
       "      <td>51.32</td>\n",
       "      <td>51.33</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>102640.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       price    bid    ask    size       v         dv\n",
       "dates                                                                \n",
       "2009-09-28 09:53:14  51.1500  51.15  51.18   100.0   100.0    5115.00\n",
       "2009-09-28 10:02:52  51.2500  51.24  51.26  2000.0  2000.0  102500.00\n",
       "2009-09-28 10:22:24  51.1961  51.19  51.20  1100.0  1100.0   56315.71\n",
       "2009-09-28 10:38:44  51.3650  51.36  51.36   100.0   100.0    5136.50\n",
       "2009-09-28 11:24:29  51.3200  51.32  51.33  2000.0  2000.0  102640.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:46.224121Z",
     "start_time": "2018-05-05T00:36:40.122783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1466,), (15,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_sample_data(ref, sub, price_col, date):\n",
    "    '''\n",
    "    select a sample of data based on date, assumes datetimeindex\n",
    "    \n",
    "    # args\n",
    "        ref: pd.DataFrame containing all ticks\n",
    "        sub: subordinated pd.DataFrame of prices\n",
    "        price_col: str(), price column\n",
    "        date: str(), date to select\n",
    "    # returns\n",
    "        xdf: ref pd.Series\n",
    "        xtdf: subordinated pd.Series\n",
    "    '''\n",
    "    xdf = ref[price_col].loc[date]\n",
    "    xtdf = sub[price_col].loc[date]\n",
    "    return xdf, xtdf\n",
    "\n",
    "## try different dates to see how the quantity of tick bars changes\n",
    "xDate ='2009-10-01' #'2017-10-4'\n",
    "xdf, xtdf = select_sample_data(df, tick_df, 'price', xDate)\n",
    "\n",
    "xdf.shape, xtdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.034945Z",
     "start_time": "2018-05-05T00:36:46.226037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_sample_data(ref, sub, bar_type, *args, **kwds):\n",
    "    f,axes=plt.subplots(3,sharex=True, sharey=True, figsize=(10,7))\n",
    "    ref.plot(*args, **kwds, ax=axes[0], label='price')\n",
    "    sub.plot(*args, **kwds, ax=axes[0], marker='X', ls='', label=bar_type)\n",
    "    axes[0].legend();\n",
    "    \n",
    "    ref.plot(*args, **kwds, ax=axes[1], label='price', marker='o')\n",
    "    sub.plot(*args, **kwds, ax=axes[2], ls='', marker='X',\n",
    "             color='r', label=bar_type)\n",
    "\n",
    "    for ax in axes[1:]: ax.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'tick bar', alpha=0.5, markersize=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tick threshold: 534.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 1903860.09it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[533, 1067, 1601, 2135, 2669, 3203, 3737, 4271, 4805, 5339]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_M = volume_ratio  # Exercise\n",
    "print(f'tick threshold: {tick_M:,}')\n",
    "tidx = tick_bars(df, 'price', tick_M)\n",
    "tidx[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2114, 6), (1129121, 6))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[tidx].shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 1893223.09it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2114, 6)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df2 = tick_bar_df(df, 'price', tick_M)\n",
    "tick_df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 11:32:58</th>\n",
       "      <td>51.3991</td>\n",
       "      <td>51.39</td>\n",
       "      <td>51.40</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>41119.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 15:39:46</th>\n",
       "      <td>51.4990</td>\n",
       "      <td>51.49</td>\n",
       "      <td>51.50</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>77248.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-29 11:32:13</th>\n",
       "      <td>51.5900</td>\n",
       "      <td>51.58</td>\n",
       "      <td>51.59</td>\n",
       "      <td>161.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>8305.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30 09:45:16</th>\n",
       "      <td>51.1800</td>\n",
       "      <td>51.17</td>\n",
       "      <td>51.19</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-30 14:33:39</th>\n",
       "      <td>51.3100</td>\n",
       "      <td>51.29</td>\n",
       "      <td>51.31</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>25655.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       price    bid    ask    size       v        dv\n",
       "dates                                                               \n",
       "2009-09-28 11:32:58  51.3991  51.39  51.40   800.0   800.0  41119.28\n",
       "2009-09-28 15:39:46  51.4990  51.49  51.50  1500.0  1500.0  77248.50\n",
       "2009-09-29 11:32:13  51.5900  51.58  51.59   161.0   161.0   8305.99\n",
       "2009-09-30 09:45:16  51.1800  51.17  51.19   100.0   100.0   5118.00\n",
       "2009-09-30 14:33:39  51.3100  51.29  51.31   500.0   500.0  25655.00"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1466,), (3,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xDate ='2009-10-01' #'2017-10-4'\n",
    "xdf, xtdf = select_sample_data(df, tick_df2, 'price', xDate)\n",
    "\n",
    "xdf.shape, xtdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAHwCAYAAACcxBjDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXlwXNd95/s59/a+Ao2dIEBwFVeJEmVrtSSbtuzYlhdZsid5zjIzL/Z7cZ7L9sxzxeVkopdRzThVrlTGSSaJnbETV+LxEie2FceJaS0RtVkWSVFcxJ0AQWJroPf1dt973h8XaKIJgARAgA1cnk8VCkDf27fP6W8vv/u7v/P9CSklCoVCoVAoFAqFYmnQGj0AhUKhUCgUCoXCSagAW6FQKBQKhUKhWEJUgK1QKBQKhUKhUCwhKsBWKBQKhUKhUCiWEBVgKxQKhUKhUCgUS4ir0QO4Xp577jnp9XobPQzFElCtVnG5Vv1LUnEFSlfnoTR1JkpX56E0XV4KhcL43r1722bbtuqfda/Xy9atWxs9DMUSMD4+Tmtra6OHoVhilK7OQ2nqTJSuzkNpurwcPHhwYK5tqkREsWLQdb3RQ1AsA0pX56E0dSZKV+ehNG0cKsBWrBgymUyjh6BYBpSuzkNp6kyUrs5Dado4VICtWDG0tc1axqRY5ShdnYfS1JkoXZ2H0rRxqABbsWJIJBKNHoJiGVC6Og+lqTNRujoPpWnjUAG2YsUgpWz0EBTLgNLVeShNnYnS1XkoTRvHqncRUTgHdSnLmShdnYfS1JkoXW8M1WqVQqFwQx4rFAqpOuzrJBAILMrqUAXYihXD6Ogo69ata/QwFEuM0tV5KE2didJ1+alWq+RyOaLRKEKIZX+8crmM6hWyeKSUpNNpQqHQgoNsVSKiWDGEQqFGD0GxDChdnYfS1JkoXZefQqFww4JrUDZ914sQgmg0Sj6fX/B9VYCtUCgUCoVCcYO4UcG1YmkQQixKMxVgK1YMuVyu0UNQLANKV+ehNHUmSlfnYZpmo4dw06ICbMWKoaOjo9FDUCwDSlfnoTR1JkpX5+F2uxs9hJsWFWArVgzxeLzRQ1AsA0pX56E0dSZKV+dRrVYb+viPP/44X/nKVxo6hkahXEQUKwZVl+ZMlK7OQ2nqTJSuiqXme9/7XqOH0DBUBluxYojFYo0egmIZULo6D6WpM1G6Og/lItI4VICtWDGoy5POROnqPJSmzkTp6jyup0TkkUce4bOf/Sx79+5l3bp1/Mqv/AoTExO17bfddht/9md/xsc+9jF6e3vZsWMHJ0+eBOB3f/d36enpobW1lS9/+cszjm0YBl/60pe488476e3t5aGHHuLQoUO17U8//TR79+6lr6+PBx54gBdeeGHR82gU8yoREUI8B9wDVKbdvEVKOSSE+DDwOeAOIC6l7FvIAIQQtwNfBXYC54BPSSmfW8gxFM4gEok0egiKZUDp6jyUps5E6Xrj+eaB4WU9vmVZaNrlXOqv7ela0P1/+MMf8k//9E/09fXxiU98gt/5nd/ha1/7Wm37X/zFX/Bnf/ZnfOtb3+Ls2bNEo1EAnnzySZ588kkeeeSRWY/7xBNP8OKLL/Ltb3+bDRs28MYbb9QcT44cOcKv/uqv8vWvf52HH36Yffv28fGPf5zXXnuN1tbWhT4FDWMhGez/KqUMTfsZmrw9DXwF+P8W+uBCCC/wQ+CfgWbgT4EfCCHUdaqbEGUn5EyUrs5DaepMlK5ORF7XvR955BG2b99OIBDgt37rt3jqqaeQ8vIxH3vsMR544AF0XWfLli3zcqKRUvLNb36T//Jf/gubNm1C0zR2797Nnj17APjrv/5r3vOe9/Ce97wHTdN497vfTV9fH/v27buuudxornuRo5TyGQAhxMcXcfeHgBjw36SUZeDPhRCfBz4IfGM+B6hWq5w5c6b2fywWU3Vkq5R8Pr+qzk4V80Pp6jyUps5E6XrjWWhGeaFcb6v06QFze3s7hmGQTCZrcdbmzZsXfMzx8XEKhQLr16+fdfvg4CD79++nr6+vdlu1WmV0dHTBj9VIFhJgf0YI8TngIvA/pJT/awkefztwCvAJIZ4GvgAcBXbM9wBjY2M89thjtf8/+clP8sQTTzAyMkIwGETXdTKZDG1tbSQSCaSUtLW1MTo6WmsLm8vl6OjoIB6PI4QgFosRj8eJRCKYpkk+n6ezs5ORkRHcbjfRaJTx8XGi0SiGYVAsFmvbPR4P4XCYiYkJmpubKRaLlEql2nafz4ff7yeZTNLS0kI2m8UwjNp2v9+Px+MhnU7T2tpKOp2mUqnUtjt5TqZpUigUHDUnJ+q00DlFo1EGBgYcNScn6rSQObndbgYGBhw1JyfqtNA5uVwuMpmMo+a00nTK5/P4fD50XadaraLrOlJKLMvC7XZTqVQQQtS2u1wuLMuq265pGpqm1babpomUsm67EALTNNF1HcMw5tw+dX8Al8tFpVKpLYy0LIuRkREMwwBgeHgYj8dDKBSiUqkgpUTTNMrlct2Yp89JSkm1WsUwjNr25uZm/H4/p06doq+vb8ac1q5dy8c//nGefPLJGWMul8vXnPPV5mSaJm63u1abPl8dDMNgbGxsxmvvaojpqf45dxLiLuA4UALeCXwX+A0p5fen7fNx4MmF1GALIX4P2Av8CnAQ+BTwKJCVUv5f8znG/v37pc/nq/2vMtirl4GBAdatW9foYSiWGKWr81CaOhOl6/KTyWRuaK379WSwH3nkEY4dO1ZXg+3xePj6178O2Iscv/jFL/LRj370qsd48MEH+c//+T/X3f6FL3yBl156ia9//eusX7+eY8eOUalUuOOOO3j99dd59NFH+cY3vsH9999PqVRi//797Ny5k7Vr1y5qLtfLXLodPHjwwN69e++c7T7zqsGWUv5cSpmVUlaklD8B/gZ4/LpGa5MDIlLKISll52TAHpm8fV64XC42bdpU+1HB9epFdZxyJkpX56E0dSZKV+dxvd7mH/jAB/jUpz7Ftm3bME2TP/zDP7zmfTKZDD09PfT09PDyyy/z5S9/mZ6eHt797nfX9nniiSd4+OGHefzxx+nr6+O3f/u3a2PdvXs3X/va13jyySfZtGkTu3fv5pvf/Oaq82lfbA22BSzFTI8Dm4UQ3skabLDdRP5hCY6tWGVMrT5WOAulq/NQmjoTpavzuF4f7LVr1/LHf/zHs247fPjwrLdHIhEGBwevelyv18sXv/hFvvjFL866fe/evezdu3dhg11hXDODLYRoEkJ8QAgRFEJoQoi3A78O/P3kdl0I4QPc9r/CN+kOMv0Y3UKIM0KIT19x+OeAFPA7QgivEOIT2G4iP7z+qSlWG+Pj440egmIZULo6D6WpM1G6Oo9Gt0q/mZlPiYgb+H1gBNuS70+Az0opp/pf/ipQBL4O9E7+fXKWY2zEdgypMZm1/gDwCHag/RngQ1LKxGImo1jdqOyJM1G6Og+lqTNRujoP1cmxcVyzRERKGQf2XGX7XwN/fY1j9DNHSYmU8hAwa4G44uZiaqWywlkoXZ2H0tSZKF2dx3yMLObiqaeeWsKR3HyoVumKFUOxWGz0EBTLgNLVeShNnYnS1XlYltXoIdy0qABbsWLo7Oxs9BAUy4DS1XkoTZ2J0tV5KGeYxqECbMWKYWRkpNFDUCwDSlfnoTR1JkpX51GpVBo9hJsWFWArVgzX6oqkWJ0oXZ2H0tSZKF2dx2rzjnYSKsBWrBjC4XCjh6BYBpSuzkNp6kyUrs5DuYg0DhVgK1YMExMTjR6CYhlQujoPpakzUbo6jxvhg/25z32Oz3/+8wu+32233cZ3v/vdRT3mt771LfbsmdPgbkWw2E6OCsWS09zc3OghKJYBpavzUJo6E6Wr83C5ri/Me+GFF/jwhz9MPB6fc58/+qM/uq7HcCoqwFasGIrFIpFIpNHDUCwxSlfnoTR1JkrXlc3EiwcpDg4Tu/cOABIvHQQgdu8dBHq7Zr2PZVmqTKRBqBIRxYqhVCo1egiKZUDp6jyUps5E6bqyKQ4OUxwc5tJ3fsyl7/y49n9xcHjO+yzWB7u/v5+enh4++tGPYpomPT099PT08OlPf7q2z1/+5V/S09NDe3t73e3TH/trX/sad999N729vdx1113s27dv1sc7cuQIO3bs4Ac/+MG8xyil5Atf+MKsxz569Cjvf//72bBhQ20eFy9erLv/pz71KT71qU/xhS98gY0bN9LX18d3vvOdeT/+tVABtmLFoDxYnYnS1XkoTZ2J0nVlM5W5vvK2lvtm3j7FYn2w+/r6GBwc5Lvf/S66rjM4OMjg4CBf+cpXavt88pOfZHBwkMcff3zWY3z1q1/lT/7kT/iLv/gL+vv7+du//dtZnWpeffVVPvaxj/HHf/zHfOhDH5r3GPv7++ns7OTcuXN8/vOf5z/8h/9QW0dQLpf5zd/8Td544w1OnTpFJBLhN3/zN2cc46mnnmL9+vUcP36cAwcOsGvXrnk//rVQAbZixaA8WJ2J0tV5KE2didLVeTTSB/sb3/gGn/vc59i9ezeaprF582YefPDBun3279/PY489xpe//GXe9a53Lej4gUCA3/qt38LlcvGRj3yE1tZWnn76aQD27NnDBz/4QUKhEH6/n1/+5V/m9ddfn3GMW265hU984hN4vV5aWlrYvn374id8BaoGW7Fi8Pl8jR6CYhlQujoPpakzUbqubKZqrme7ba4stqY1Lo968eJF+vr6rrrPT3/6U3bv3s23v/1t3vve9y7o+M3NzXUZ+vb2dsbGxgAYGBjg937v9zhw4AD5fB7LsiiXyzOOsWXLlgU95kJwTAbbtCTD2TJSykYPRbFI/H5/o4egWAaUrs5DaepMlK6rg9i9d8xaLjIb1xtgX8/9165dS39//1X3+f3f/33+5m/+hoMHD/KXf/mXCzp+IpGoy9CPjY3R0dEBwGc+8xkCgQAvv/wy/f39/NVf/dWsx1jOBaCOCbD/7tAI+04l+NtDI5iWCrJXI8lkstFDUCwDSlfnoTR1JkrXlc1UYN1yn/0z/f+5uF4f7K6uLkzT5Pjx4wu+72/8xm/wR3/0R7z++utIKTl37hzPP/983T4ul4vm5ma++tWv8gd/8AccOnRo3scvFov8z//5P6lWq3z/+99nYmKCvXv3ApDNZolEIgSDQUZGRvjTP/3TBY//enFciYiUcHaiyJa2QKOHolggLS0tjR6CYhlQujoPpakzUbqubAK9XXV2fFcLrKe4Xh/s9evX8+lPf5pHH30UXdd55JFH+NKXvgTAjh07yGQytdKLf/zHfyQSiXDs2DHAXgQ59Xt4eJiuri6efPLJWR/n3nvv5bd/+7f5j//xP/Lcc8/Nyy6yr6+P4eFhNmzYQFdXF9/4xjeIxWIA/Pf//t/57Gc/S19fH729vXzsYx/jhRdeuK7nYqGI1V5S8fLLL8utW7fyzQOXbWq6o17esbEZIUQDR6ZYKFNvQIWzULo6D6WpM1G6Lj+ZTOaGeo0bhjGrc4diYcyl28GDBw/s3bv3ztnu45gSkelcSpd5sT/d6GEoFohhGI0egmIZULo6D6WpM1G6Oo/VnkRdzTgywNY0wblEkWLFbPRQFAtAebA6E6Wr81CaOhOlq/NYrA+24vpxXIDtdWmEPfaq0HJ1cR2MFI1BebA6E6Wr81CaOhOlq/NopA/2zY4jAuzpgbRLE3hd9rTyhgqwVxPKIsqZKF2dh9LUmShdnUcjfbBvdhzxzP/4xHjtb7cu6AjZBf1nE4VGDUmxCNRCDGeidHUeSlNnonR1HsrsoXE4IsDOlS/XWuuaYEtbAE3AhWSJvHHz1WEPJIucnbj6yYVRtXh9KEu6dH0emUtJOq0WpjoRpavzUJo6E6XrjeFGLjw0zZsvBlpqpJSL0mzVB9jGFXXWLk0Q9Oh0RbxYEkazN9eq6Ipp8W/nUrzYn+bYSI7SHHXoPzuT4I3hHD9+c3xGrfqp8QLPn0vOeG6Xm9bW1hv6eIobg9LVeShNnYnSdfkJBAKk0+kbFmRfrw/2zY6UknQ6TTAYXPB9V/0zny3Xn53t7LCfhKk67Bf6U/Q0eXHrK+tcQkq5LJduxnKXFzQcuJTl0FCWjS0B7lwbrj0H8bzBeN7er2pJ/uHoGFtaA2xtDxLPG7wyYGcxdE1wX1/Tko9xLhb7IlasbJSuzkNp6kyUrsuPy+UiFAqRyWRuSPlGoVAgEFCN9xaLlJJQKLSoE5VVH2BfSXfUB4Bbu/zCvZgusz62chZvjGTLPHs2yV09UTa02OMqVS1cmsClLf4NV6yYjGTtjko+l0Ys4GYkW+b0eIHRnMHb1jcR9uo1j/COsAddCIYyZY6N5jk2mq873tmJIrevCROYdGW5XqSUFCoWAbc26weLWu3sTJSuzkNp6kyUrjcGl8tFNBq9IY+VTCaV/WKDcFyAPYXHdTljfT1B63JwdCRPxZS80J9ivGBnk8fzFWIBN+/ftrhLdGfGC7w0cLl+7r6+KN1RH8lihf3nU6SKVX785uXFoD63xjs2NuPWNSbyFX4+mK5ltcNevXZl4I2RHHf3Xt8HQdWSnIoXODNRIFWssrU9wFt7Zh5TfQg4E6Wr81CaOhOlq/NQmjaOlVU3sYRMz2BXrRvbyShTqvIvJye4kCrN2DacLRPPX64LPzFWqAW2iUJl0c1xEsX6zEPbpJNKs9/Ne7e2cktb/SWinR2hWslIS9DNw1ta2Nwa4NauEB/e2c4Hd7QhhB24Z65zIeQL51O8djFDqmgf52S8wHh+Zm288mB1JkpX56E0dSZKV+ehNG0cjgqw926K1f6eHlBeWae93PzozXHGcga/GMzU3V61JM+dTVIxLwf8bl3w9o3NxAJ2t6XR3OIWZRameX63hzx49PoM/l1XZKG3ttcH3C5NcM+6KLvXhAGI+lxsaglgSXh9KDvvcZiW5HyiyL+emuDbh0c4NJTlQqqEWxc8tKGZbe1BpIRnziaZKNSfFKjaP2eidHUeSlNnonR1HkrTxuGoALs76q397dY17llnB5XXm4FdCKNZA2syY+6dVqZiWpJ9pyaomJJYwM37trVyz7ooH7utg54mHy2TAfbz51LsOz0x55gN0+IXgxnGpgXihmkxlLFrr9/aE+G+vtlLOh6/tZ2wV+dDO9rQ5rG44tauELom6E+WmMjPrM07FS/wyoU01rTV0K8OZth/PsVo1sCoSo4M52rH6m32sXtNiK6Ih1LF4qenJmrjBtD1pan1VqwslK7OQ2nqTJSuzkNp2jgcFWBfScRrl5hnyjcmwJZScnBatjdRqHB8NI8lJX93aIT4ZJC6scVPS8DN5tZALdC9vTvM9o4gbl0wnDF4sT81q43P4aEcb47lefZskkvpEv98YpxXBzNULUl7yMPW9iBh7+yl9X63zod3thPxza/0PujR2Tp5JeDgUH02/thIjlcupDkVLzAyaYV4PlHk9Ljtv725NYDPpeHWBRtifra22WfRbl3jHRtjrI/5qZiSZ84kODdRxJKSlPJgdSSZTObaOylWFUpTZ6J0dR5K08ax+hc5Viv4nn0GgELbOwj0dtU2RXz2mVuubCKlxDBlLav88kCaVLHCw1ta0JdoEeTFdJl4zsDn1tjWHuTQpSyvXczw2sXLL/A710Zm1EOD7fpx59oIOztDPHU8Tjxf4cxEkfUxf22RZqpY4WTcdvooVy2ePpMEqNVwr4/5lmQe09nZGeL0RIHhjMFItkxn2Mup8QIHLl0+kRjNGkgJL/anALijO8zOzhB390aAmZ2kdE1wf18Uv1vj+GieF/pTvNAPlYrGbleGPWsjSz4PReNoa2tr9BAUS4zS1JkoXZ2H0rRxrPoMtpbJoo/F8U+MUxwcrtvmc2nomqBUtZuvfOfwKIlCBSklp8cLxPMVjo/lZ80UFysmZycKXEqXZ2ybi5NxO3u7oyPIrs7QrKUa29oDVy3P8Lk0dnaGAPsk4O+PjHIpXUJKyWsXs1gS+mI+fK566TQB65qX3orQ69LY0mqfEBy4lOVHx+M1n+yw1z6BOZ8s8m/nklgStncE2THpRS6EmNPnUwjBnWsj3DktmC4Wi5waL9SVnChWP4lEotFDUCwxSlNnonR1HkrTxrH6M9iT3PnIfbTcd0fdbUIIQh6ddKlac/Q4NpqvZVYBDl3KMpQpszbiZX3Mz4VUiQupEqM5OysLdv3wbV2hGcGiJSVi8nGy5SrD2TK6JtjUYgekG1sCuHWNE2N53toTocnvntdcOiYdQACMquTMRJHzyRJDmTIel+AtayOkSlX2nbLfOJ1hD+0hz4yge6noCHk4Sr5Wh+3WBWsiXm7rCvFPb47XWtVvavWzpzu8IPP87R1BAh6NU/ECmYykYkp+cCzOozvbl2UuihvPjWwLrLgxKE2didLVeShNG8e8AmwhxHPAPcD0lW5bpJRDQogW4GvAw0Aa+JKU8k/medw+4DxQAKZeBc9KKR+Zz/3tY0Bfs6/ONWM6wckAe4pmv2uGbd9o1mA0a9SVPWiaoCPkZjRn8MZwjky5yn3rmmrlJIZp8aPj48T8Lh7c0MyZiSJSwrpmX93ixt4mH71NCyvdiAXc3Lk2wvlkkYl8hYHkZbu/27rC+N06frfOozvb8Li0Oee+VHRFvLWsdHfUS1vQU3seWoMexnIGPU0+7u6NLqozVV+zn75mP9VqhfGiJFc2KVetuudRsXpRlyidh9LUmShdnYfStHEsJIP9X6WUT85y+58DAWANsB3YJ4Q4JqV8ZgHHvkVKeXEB+9fwuTSCPheJlw4CzMhih7z1K2gN06qztLuSniYfvU1eeqI+PC6NS+kSz59P0Z8okTcSPLTBbh3+01MJCoZJwTD5u0OXfSY3tS5Nmcb2jiDNfhf7Ttdf3pkq1wAIzbGYcanRhJizLvqu3gjDGYNb2q5e+jIfet1Fxov2yUiiWKEr7L3GPRSrgdHRUdatW9foYSiWEKWpM1G6Og+laeO4rghNCBECPgS8XUqZAV4RQnwf+HVgIQH24sfg99VqjIqXLiESfcRil/2wQ1e0+S5VLX56emLWY71vaystwfoyju6oj/dsaeGZs0niOYOfnJzA59LqsuLTaQt6Zr19Mbj0ywFrW9DN3euiS7Ygc6lo9rtpnmfpy7XoikW4xeviZLxAoqACbKcQCoUaPQTFEqM0dSZKV+ehNG0cCwmwPyOE+BxwEfgfUsr/BWwG3MARIcTfAAeBI8AvL3AcrwghPMBrwP8rpTw23ztO5LP83le/AsCbssAnP/lJnnjiCUZGRggGg8iySTqdIhAIUiwWOZhJEwgEyedzeDx2MGwYBsFgiNzEMMWkRiwWIx6PE4lEME2TfD7P3vXtPHX4AsNZgc/npVAo0N0cIqhbnBovEAqF8FaLjI1COBxmYmKC5uZmisUipVKJzs5ORkZG8Pl8+P1+kskkLS0tZLNZDMOobff7/Xg8HtLpNCIQJZvNYlkmj21fT3xsCDMYRNd1MpkMbW1tJBIJpJS0tbUxOjpaezPlcjk6OjqIx+MIIWad09Rjut1uotEo4+PjRKNRDMOgWCzWtns8niWbU2trK+l0mkqlUtsenJxTPB7HH2onk8lw6kKejdG1dXPKZLO0tXeQnBhfNXNyok4LnZOmaQwMDDhqTk7UaSFzSqfT5HI5R83JiTotdE7VahW32+2oOTlRp4XMyeVyMTQ05Kg5rSSdroaYTwG8EOIu4DhQAt4JfBf4DWAMeB7bjeRF4FXgGPCfpJRb53HcEHALcBgIA08AjwHbJjPi12T//v3S57tc4xyLxeoy2MWKyffeGJv1vh/Z1c73j1ze9mt7umbdb4qqJXnlQpqxnMGDG5przWGy5SrnE0U2twbwu5fO1N20JD8+MU57yMPdvbM3j3ESAwMDhFrX8OMT47h1wS/v7qzb/qPjccpVi0d3tq+4TL5ibgYGBtQlSoehNHUmSlfnoTRdXg4ePHhg7969d862bV4ZbCnlz6f9+5PJbPXjwB9O3haWUt4LIIT4T0BunsfNAQcm/00KIT4L/HvgXuBf5nMMl8vFpk2b5tx+tUDMpdluGNO7CV71sTTB/X1NM24Pe13c2hWe1zEWgq4JPrD95lmg0NHRgdvjQgiomJJMqVpritOfLJIq2mU5xYpZqz+vTHax7I76an7hipVFR0dHo4egWGKUps5E6eo8lKaNY7E2DRYggNPYziK7pm3biZ3FXgxy2rGXBH3awrvp9dWasAPYe9dF2dji5/3bWpfqIRWLJB6Po2uiplmiaJvWjGYNnj+Xqu1XMe2rLpaUHLiU5d/OpWre3IqVRzweb/QQFEuM0tSZKF2dh9K0cVwzgy2EaAIeAJ4GisCD2IsY/08pZU4I8QPgd4UQHwW2AR8BPnzFMbqBfwO+IqX8yrTb78HOdh8HfMDvA3ngpeufmo2uCd62vgldCNY2eTk6kqNYsWgNunFpApdH575ZstKKG8+Uxd+29iBHRnL8/EKGdU0+ziYKdftdzJQ5ES8wmCpRqtqOMOcSRe5fr3RciSzGulGxslGaOhOlq/NQmjaO+ZSIuLED37+b/H8A+KyU8nuT///fwF8Bw0AW+KKU8ulZjrERiF1x+3rgvwFt2MH7q8C7pZRLmo5cH7tsnbccpRyKpWGqdt7nti+slKsWLw2ka02Cpjg0za9csfKZviZC4QyUps5E6eo8lKaN45olIlLKuJRyj5QyPPmzU0r59WnbJ6SUH5ZShqSUXbM1mZFS9ksphZTyiStu/5aUsk9KGZRStkop3yulPLokM1OsOqYuZa2b1pjn7ESRiinpDHu4vds+OYr6XOzqmmk9NJo1bsxAFQtCXaJ0HkpTZ6J0dR5K08bhmFbpitVPJGI3swl4dP7dbR0ki1Ve6E8R9bl42/omPLpgS2sAjy4QQtAV9vDTU5cb8fzrqYlrOsEsNcWKic+lqctwV2FKV4VzUJo6E6Wr81CaNg4VYCtWDKZp1v72uDQ6wh4e3dlWF7x6XZf/7gx7+bU9XXzzwHDttmfPJtjSGqQ7ujxNakpVi/G8QVfYy9mJIq9cSHPn2gjbJ1vJK2YyXdfVxMSLBykODhO71+4OO9UtNnbvHQR65z6RKxgm6XJ1RqOq8KVYAAAgAElEQVQkw7QYyRoYVQuPrtEWci+preeNZLVqqrg6SlfnoTRtHCrAVqwY8vk8ra31bi7zyQzfsy7Ky5MuIoOpMoOpMu+5pYX20LW7aparFulSlfaQh3LV4lyiSG+Tj6BnZuAzkCzy88EMpYpFX7OP/qRdG/7axQxhr45LF4Q9OhfTZTa3BpRX9ySz6boaKA4OUxwc5tJ3fjzj9rkCbMO0+PtJb/3eJh+3d4e5lC5zMV1iLF/Bsur7DjT5XXSGPXSGvLSHPfhcizV2urGsVk0VV0fp6jyUpo1DBdiKFUNnZ+e1d5qFTS1+SlWLqNfFqfECQ5kyI1ljzgA7WaxwerzArV1h9p2aIFms8vaNzTx7NgnApXSZd26ub1b0i8FMLaAWgtrfU0zdd4o3hnP80tYWwl71Flusro3G/5bbuHTyAoWKRZPfRcijE7v3DmL33k6xYlKsWJM/JuWqRXfUy2sXLy/AvZAq1S3QFQLaQx7CXp1CxSSeq5AqVkkVq5wYKyCEvf7g/vVNaCu85Gi1aqq4OkpX56E0bRzq21+xYhgZGVlUxykhBLs67UWPxmTjmXSpWrfPxXSJU/EChYpFomD7a58Yu2z/9+LAZZ/toUyZoUyZvGFydCRHtmxfYnPrgju6w0R9Lp4+k0RKiTVHI9RS1eLwcG7WxkQ3G4vV9UZiScmR4Rz5iollwXihQm5gAn/abkKVKlbwuTRK55Pk/SOz6n5gFncbr0ujO+KlO+qlK+Kty1CblmQ8X2E0Z58QjuUr9CdLbGyxGycdH81TrJjEAm5iATdhr75sgfebY3nGcgZv7YnMq2xlNWiqWDhKV+ehNG0cKsBWrBjcbve1d7oGUb/9kp4eYKdLVZ47m5wzGAYwqvUbf3Y6Ufd/2Kvzzs2xWkb6kcnGRBVT8q+nJmgLuVnX5CddqjKaM0gUKlRM67rn4wSWQtfl5lK6zOHh+ga0waPHiPhcVEw7U12oWHDwCHrFwn3HrfjdGn63hkvTGJzMVGsC3r4xRsWy7Ix3wD1nUKxrgo6wh46wh1u74MhIjkOXsrw6mKEna3B8NF+3v0sTtAbd3NfXNGsJ02IxLckvBjMATBQq3LMuStCt17qozsZq0FSxcJSuzkNp2jhUgK1YMUSj0es/xmRQkChUMC3JuUSRXwxmsCT43Rrv2GiXfgxny5xLFMkbZq0zJMBbeiK1YCPic5EpVQl4dN63tRXPtOzj9ODj0V3tuDVRq7meyFf48YlxksUqUsqb3mFkKXRdTjKlKsfH7GC2p8lLb5OPsNdF+XSEkijgu/M2XuhP4TlyjDURL5s2x2i77XL7YcO0+PbrdoC9Z21k0QtsN7X4ORUvkC2bteC62e8i5NVJFKrkDZORrMG5RLF2xWYpGMmWa3/nyib7Jp15mv0u+mJ++pp9M0qdVrqmisWhdHUeStPGoQJsxYphfHycYPD63Dg8ukbQo5M3TP7u0Ejt9vUxP3f1RGpBckvQzc7OEFJKvn9kzM5OAm1BN3d0hxlMlXhgQzNBj37NIPnKhWmxgAufSyNXNsmUzVrQfyMoVkw0IfBOjildqnJ2osC5iSIuXeP921oxqhY5w6Qt6L4hwf9S6Ho1pJRkyybJYgVL2llkTQj7RwNdCHQh7Ns1+7c+Oe8T8TzHxwpYlsStC3avCdPstzM+hXv3UBwcpuW+O7gnWWQw6mNje4C2+/fUPb5H19i9JowQsLUtsOh5+N06H9jeynDG4EKqRKJY4a09UTrC9lqCsxMFXuxPc3q8QHfESywwMzN15Ws1WaiQKVdrV2/CXp2w11V7fUgpOToZzO/sDJItm4zlDCqmJFmskryU5dClLG1BN30xP+uafAQ8+rJrqmgMSlfnoTRtHCrAVqwYlupMe/eaEC/2264ibl3w1p4IG1tmD3yEEBjTMtghr4udQQ87p2UIFxqECiHoing5nygylCnfsAC7Ylr86Pg4lpRsbg0QzxnE85VpO1h8/8gYpiWpWpK39ETY1j7/D95cuUrZlMT8rgU9J8uZQelPFnl5IF13FWIxbGr1c/uacF39caC3q+YW0tfsp+/Rt815/1tnaXy0GNy6Rm+zj95m34xtvU0+3gwUSBQq/OTkBLs6Q1Qti66Il/aghzdGcpyM59nWFmRbR5A3R/Mzyl6m8Lo0wl4dv1tnNGvgdWns7Azh0e3A27Qkw9ky5xMlLqZLxPMV4vkKr13M0BHy4LHcmBNF1sd8N/0VGifhxGynYVo8ezZJslihLeihM+ShO+rFlJAvm3SGPXVXJ52GEzVdLagAW7FiMIyl6cS4sSVAa9DDmfECm1sDV60lBehp8nE+UQRmZqMXS/dkgH0pXaY95OHcRJGdncE5F5CNZg0y5SqbWxeXAS1VLZ45k6BctTPxUyUGbl3Q1+wn4tU5NV6oLdgEOHAxQ7PfRWd49pIGS9qL8C6mS1xMl0kV7bp2n0tDE3D/+qY57zudpdJ1Nk7FC1RMScCt0Rxw49YFlmWP3ZL2b3Pqt3X5Nmvy7ya/izu6w7QGr23p2GjcusZ7bmnhF4MZTo8XeH3IXlR5dCRfK2cCODycqwus20Iegh4Ny4KcYZItVylXrcnXin0Ctr0jWAuuwa4PXxv1sTbqo2JaXMrYwfalSYeefL7AhbxgNBfgrt7Iinc9UcyP5XyvzkWyWGE8X6Gv2YdbX9pAt2pJnjubrHX5vZQucyldrluQrGmC7oiXt6wNE3Kg61MjNFXYOO/VpFi1FIvFJTtW1Odiz9r5dbDqafLWAuyloiviQYjLjiRgOzXc1xedNZv+r6cmAOgIea55QjCFJSWlSau4N0ZyjE9mq7siHsJeF21BN71Nl7+0trYHGc9XcGmCgVSRoyN5Dl3K8tBGF3nDJGeY5Mv275xhMpGvUKpeXqjp1gVuTdTKaX56KsGuzhC3doXm9Py+kCzxL28maB6RNPvdPLChqS6QWwyGaXExVebwcJZs2UQT8Mj2tlrZg5NxaYJ71kVpD7n5+YUM1cnaj0ypSpPfRV+zj8PDOaSEzrCHHR2hGTXhUkqKFYts2Q62TSnZNMcVHrAD+75mP33NfoyqHWyfHsgTR3B6vIBhWtzf16R83x3AUn4GX4tEocLR0RwDyRJSwutDWW5bE2ZTi3/JTtheGkgxkjUIuDUe2NBMrmxyMV2iP1nCrQua/G7G8waDqRKpYoX3b2td8iC/0dxITRX1qABbsWJolF9n22T2MuRdOmcGv1sn4nXNsAt8sT9Nb5OPfacTrI36alnIKX5wLM57bmmhJeCeM2CRUvLM2STDmXKdM4oQ8PaNzXRHvLNetp9yrQDbbeXUeIF4vsL33hibcx5hrz6ZyfTSHrJPGl4aSHNuwv7QPjKSYzhb5v6+phknBqfHC7xyIY3bH5z0iy7zD0fHeP/W1kVnik7FC/ziYgZz2sRvaQveFMH1dDa2BOgMe/nBsThSSnZ2htjVaZ/odEd96AKa/LO7BwghCHh0Ah699nqYLx6XxvqYnzXBHlKG4JmzCQaSJQwzwYMbmud18mSYFs+fS+F3a9ynbCzraPSi6Bv1GXwhWeL587azkxB2QiRdqvLKQJoTY3nu6A7P+Tk2XybyFfoTJVya4J2bYzT53bSHYEOLn12FCgGPjtelUTBM/unEONmyyVCmzLpm/xLOtPEoH+zGIaS8vtrFRvPyyy/LrVu3NnoYiiVgYGCgYX6dxYqJR9eWNAt3fDTPoaEsu9eEODCtAYkQMJ+3nc+tsanFz86OUK1G0DAt8mWTp94cr+0TcOsE3Hbgsz42/y+Hw0NZDg/n8LgEIY+LoEcn5NEJeXWCHp0mn4uwV5/xJSelJG+Y5A2LF/pT5A0Tty64qyfKhhb78fuTRZ4/Z3uLd2p5Nq/rZv/5VG3+a6M+trYF6Ax75v0lOpgq8dy5ZC07O5I1CHt13ret9bqz4qsVw7QwLXnDW65PvVcThQpPn0lQrFi0BN3s3RSbs8zKkpJ0qcrBS1kuTfqLv29rKy1BZSMGMJYz2Hf68lWhRrBUn8FVSzKRr1C1JC5N4Jq8+qVrAtOS/OBYHIDNrQF2dAQJe3UGUiUOXcrWytg6wx72dEdmfX1YUjKUKXNirECyWGFXZ4gtbYFa5tu0JP/78CiWJdneEeTOa1zNPHAxw7HRPLGAm1+6pWVe3wONPhmaL438Xr0ZOHjw4IG9e/feOds2lcFWrBg8nsbVwS5HgLK9I8gtbZdbpk8F2bMF15tbA5weL9TdVqpYHB3JczJeYGOLn61twdoXE0Br0M17ty6+Be5ta8Ls7Jy7vGMuhBCEvC5CXnj/tlZeuZBmIFnihf4UQ9kyuzpDvHrBtjq8oztMi4SuycB/MF3iQqrMYKrEYKpEk9/FppYAa6Peq5bGTBQqvNCfQkrYvSbMrV22A4yEm7r+16NrcGNja/txJ9+rsYCbd29p4ekzCSbyFf7x6Bj39TXR22TXbo9kDeJ5g/F8hYlCZcZi1FPjBe4JqkVYAAPJEqYleX0oS0vAPafd41TwapgWPreGaUk0IYj4XNe9hmSpPoOfOZNgJHv12l+vS+Pu3kgtSO1r9tMT9XFqvMAbwzlGsgY/PjHO+pif29eECHldlKoWZ8YLnBovkJu2nuTVwQyvDmZo8ru4uzdK1bLXWQBsn8dC7l1dIQZSJRKFCsdG81c9wUkVK7x2Mct4weAdG2NzdgxeKTTye/VmRwXYihVDOBxu9BCWnKngdUdHiLxh1rpH6pqgPeTmju4ILQE3UkoKFZNL6TK714R4fejyIrWKKTkxVqjrPAksKFt9rfEtFq9L44H1TZyJ2H7j5yaKtfKRtqCbHR1B8pP9UqYy7MWKyenxAqfiBVLFKq9dzHB4WPDYrvZZ6x8n8hV+cnIcS05e3u20vzCFENy8oXVjmf5ejfhcvHtLCz87kyBVtJs6BdwaZVPWlfKAXYbVGnDT0+Rj//kU5xNF9nSHHe3iMF/G8pcD0hf7U7xvW2utodB43mAgWWIsZzBRrNaCxysJeHS2tQfoa/YvqhnRUnwGy8nF0WAnAYQQVE2L6qR7UXFyDce29sCMDLCuCba1B9kY83NkJMeJeIHziSIDqRJdk1etpl5TYa/OlrYAAbfOLy5mKFUsUsUqPz2doHMy6N3VFSIwj+fBo2vcsy7KvlMJ3hjJ0RP10nyFDWbVkhy6lOVkPF8rzfv5hTTv29a6rCf5yWKFRKHC+tjiatOd+L26WlABtmLFMDExQSjUmEujN4LOsJcTYwU0Ab+yu6Puy0UIwd5NdhMc2/HC/nLqafKRLFQ4ES9wLlGsfbm8pSdyXZ7LS4kQgs2tAdqCbp56c7yWod/YYn+BXqmr361za1eYHR0hLqRK7D+fomJKXh5I87b1TbXnRUpJf7JUKy1pC7q5pze6Ki7LOp0rNQ14dB5Y38SPjtulS9N95TvDXlqDblqD7rorRafHC7XGOVsXYBfpNExLcmgoy0S+giagPWQHkj85Mc6etRGCHp1/PTVRe19NvfyjPhdu3S67qFqSTKlKwTA5cDHLgYtZWoJueqI+eqJemuZprbkUn8GvXcxStSQ+lzbrFbaxnO3zvr1j7sfxuDT2rI1wS1uA14dynJt0ZBICuqNetrYFWDOtRrs74uXoaI6jI3ksS9YWlvdGZ9pdzkVX2MstbQFOxgs89eY4/253B6fiBS6my+zqDHJ8LM9wxkAI+4rjcLZMsljlVLywLK/fvGHy+lCWs5MJC4+u0dN0eT5VS3IhWaI76r3qGhSnf6+uZFQNtmLFkMlkiETm5/yxGpHS7izZFpy/U8h0ihWTwXSZ9qB7zgVsjca0JC/0pwh6dHavCePSxDV1TZeq/POJcSqmZM9aO/A2LcmL/Sn6k6Xafo/tap9XNkqx/Myl6WCqxGjOIOpzsTbqvWrp1VSdfpPfxSPbWm/KEyfTkjx/Pslgyg4I24JuHtrYzHPnUsRz9SUWfc0+NrbYJ7KzZfyllAymy5ydKDKcKdccZsDO9t69LkrXNWw1r/czOFOq1srYHtzQtGQLBicKFcbzBl3hq5eSlaoWPzwWp1y1CHh0PrKzbUGvq4pp8b9fH51z+1Q34JagmwupEs+dTeJxCT64vW3JygzHcgbHRnMMZYy6K0CByXUxU7qOTXt9vLUnMmeQ7/Tv1UajarAVq4JisejoDwIhxJwNb+aD362zZZE+2TcKXRM8uKG57rZr6Rr1ubivr4nnziY5cNFeAOfWNQZTtpXWjo4gPVGfCq5XEHNp2tPkq8uyXY2eqA+/WyNVrDKWqyzY0WS1Y1qS/edTDKbKuDTBxhY/W1oD+N0679kS4/REkVcG0rX97+1rwnWVki4hBL1NPnqbfFQtyXCmzGC6xECyRLZssv98iocn3TTm4no+g8tVi/399tWmjrBnSd04WgJuWmbpXHolPpfGnu4wLw2k2bCIJkhXlqjpmt0Vt1Qx8bo03jXt+euJelkT8TKUKfP6UI571i1uLcHUYkkpJYOpMs+dS9a29TR5afa7eWM4R8EwKRjmrMd4dTBDxOdiTWTmCZTTv1dXMirAVqwYSqXStXdSrDrmo2vvtKBsanGUW7fttdpWQROYm42leK/qmmBTa4AjwzlOTxRmBNhOXsRqScmLAykupEp4XIJ3bW6pCyCFEGxpDRBwa/xiMMM966JXDa6vxKWJ2snOXT2SHx2Pky2b/NOJCXZ2BGuWjlcypatpyVoWeIqqJZFSzrpOolgxefpMkkShQtirc98ig82lYFNrgPaQZ1E16Ffywe22pWjeMHFrou7KgRCCt/REeOrNcU6PF9jc6q9rWFWZrDtPFaukSlXWNc1MEhwZyXFsNMfmlgAjOYOJ6Z13gYc2NGOY9smSW9fYEPPX1eVPb5jzs9MJYgE3e7rDdE0LtNX3auNQJSKKFUO5XMbrvXZnQMXqYr66DmfL7DuVAOwFkbd1hRZVSqNYfpbqvTqSLfPTSc3/j9s70QQYk4sjnz2XZCJf4e0bm9E1QdCjE/bqjgi4D17KcHQkf8NOIo2qxYFL2ZpTUSzg5u0bm2cEoelckYGsXVdcqFhs7whyW1eIk/ECR0Zy+FwaH9jeVrPbO3gpy5tj+dr9Iz4X79ocW5LgtlFUTIuT8QIbYv55XTWb0rIl6Oa9t7QghP3c/N2hkbr9trYHeGvP5ROPS+kST59J1u3jd2u0BT3E8wb3r2+6aklP1ZL8y8kJe0Fx1SI+LTh3aYJdXSHWNfnwClN9ry4jVysRUQG2YsWg/DqdidLVeSyVphP5Cj8+YS+MvLUrRLJYZTA1d8ZN0wQRr12LGvW5iPrt3xGvC10TFCvmDfcEXyh5w+Qfj44hgXdtjtF5jbropWQ0a/DSQIps2cTnth2A3LrGWM5gNGtw7MIo4cjVs88PbWymt8lX89Gfosnv4p2bYjddKVfFtOu+pxb29jR5aQl4ZjQRi/pcbGrxY2GX0xwftU9MNrT4KRgm3REvt7QHF3SlYoqqJcmWqlzKlHl9OFfnMnNntMT2TesXP0HFVVE12IpVgc83/xXfitWD0tV5LJWmzQEXmiawLMkb04K16bQE3bg1Qc4wyZVN+5J7sb5DqibApQuMquTedVE2reC1Cm+O2TZvvU2+Gxpcg10b/d6trew/n2IoU+ZnpxN13WA1XaenycvWtiAuXfDC+VSt8cuUTv2JIr1NPsYLlzOmfrddn7zST26WA7duO55MuR0Npsq1RauaJvjAtlZ+cCxOulStK+kAO+N/37rrd0ZyaYLmgJvmgJsNMT9HR3M1W9f9wyYb+qzr9khXLBwVYCtWDH6/s1rUKmyUrs5jqTTVhODe3igvTC6Om86aiJftHcG6hVsV0yJTMkmVKqRLVdIlO9jOGSZG1Y4UR7IGm1oDjOUMTo8X2L0mfN0lC3nD5PhonljAddWFyhdSJV44n6KnyTdr3XSqWOHEWB4hYFdnY6zTvC6Nd2xq5tClLMdGL5d3BD0693e3sKEzVrvt4S0tfP/IGAB7Nzaz73SCwXSZ4qRnP8C6Zh/3rovOWpt9s9DX7MO0olQmT0CmyjV+aUsLEZ+Le9dFSZXsk0JNgFfXcLs01iygk+18CXh03toTpTvi4+kzCTTdxbNnErxzcwxTglcXN6VjTyNQAbZixZBMJtVqZweidHUeS6npumYfmtZExZS1WuGgR+e+vuiMjKhb12gJajPaZ1ctydmJAj+/kCFdtgOZY6N5BlMlMqUqD2+ZX/vrK5FScmbCbqJUtSS6JljX7J/1Mv5YzuC5s3ZN7flEkTURD2siXl4eSNeCK8O0sKTto9zIFvGaEOxZGyEWcLP/fMq2tNvVzsDAAHA5wA56dHZ1hbAsSWfYQ0fYw2jW4KlJv3OAtVHvTR1cg73gceqqSVfYwz+fmCDk1YkF7BCrEVdUuqNePrKrnW+/coZ43ltnP7i1PdCQKyirDcO0eP5cipagm91doQWfmKgAW7FiaGlpafQQFMuA0tV5LKWmuibom7R0k1KyJmJ7HS8kIHZNBr4/v5AhU6oipSQ9GdTG8xVeu5hhR0eQoEef95dkuWrxyoU0A9O82E1Lsu90grt7IhwezlGqWrxrc4wDlzIzOq0eHs5xbDQ/o5zF79a4vXtldNdbH/PTOc29ZTZdb18Trtt/NGtQqtr1xp1hD2sX0MzlZqDJ7+aDO+yFoI3OFAc9Ou/d0cm+/vrX5omxAv3JEo9sa8UwJUGPTqZU5ekzCe5cG1mSLsHTMS2JNYcDzY1CSknFlAvqGns+UWQoU2YoU8atCXYu8KqTCrAVK4ZsNqs6TjkQpavzWC5NhRAzWlTPF59Lw+fWai2zM6UqQoAATsYLnIwX6G3y8bb1TdcM3keyZV7oT1MwTNy64K6eKGGfzrNnksRzBk+9eTmDeyKerwuuH7+1ne+9MUZusna5ye/ibeubcE8+ps+tL2oh23Ix/SrBtXTta/JxYixP2GuXPVytg+DNzIpyUTEK7OgIcilTJhZwc26yM2SpYvG9N+zyn4BbI+DRKVYs9p9P2VeVlvDk4GdnEsTzFT6ys42CYRELzK+z6FLyi4v2SfDDW+a/sPj0eLH298FLWTwujc0t/nmPXQXYihWDYRjX3kmx6lC6Oo+VqmmTz8VI5XIAHPLobGkLcOCivbjsQqrEM2cTPLihmeFMmYBHpzXgrn1hGlWLQ0NZTsbtgLkt6Ob+9U2EvfZX5Qd3tHF4OFsXUE8dG+CO7jB+t05n2FPzc19Ni/+upatn0qZPsXowDIM967rYg51JrpqSC9OceoSAQsWquaAAHBnOcUt7cMkWRo5OvheeenOcUsUi7NXZ2h4k5NFZG/Uue7CdLFRq79njo/l5BdgT+QqJQgWvS2N7R5BDl7K8MpBmJFvm/r6meZ2AqABbsWLo7Oxs9BAUy4DS1XmsVE0jPlctsAW7vnt7exBL2s1QBpIlhjMG355Wjxr26qyP+Ql7dQ5dylKoWGgCdnaGZjRk8bo03toTxbSoeUpP8fit7bVA+tauEFFfiVu7QqsmuIaVq6ti8UzXVNcED21srvOfv6UtwEjWqCtlOjyc4/Bwjl/b07Xox50qCzmXuJwFLk0G8dmyyS8GMwDc1xe96sLh4WyZJp9rUe+jUtXi0DT/d4CL6TKl6kxXFUtKTowVeO1ihg0xP9nJtRwbYn52dgTRBBweytGfKKGJ9LzcX1SArVgxjIyMKL9kB6J0dR4rVdPotMZEPrfG7WvCCCFqjh1b24LsO50gP63ldLZs1lkEtoU83N0bofkqLcVv6wqRKFZwa4Jkscr2jmBdANAZ9q7KBWQrVVfF4plN086wl3dujjGaNbilLcCuTuhPluiOePnBsXhtv38+Mc7ta+o7Q14Lo2pxbDTPm2N5qtbMPiuxgJv1zT6OjOYwqpIX+9P0NftnLdu6kCzx3LkkbSEPv3TLwtZ9nIoXeOVCetZt/3pygnde0RDp9aEsR0dsV52pkwIhYHOrXRKyoyNEW9DDT08nODdRZMtkx9CroQJsxYpB2bk5E6Wr81ipmk5vN76rc+aq/4jPxXu3tjCYKiORrI36yJSqnE8WSRarbGrxs6U1cM3MVMCj876trcsyh0ayUnVVLJ65NF0T8dZZYG5rDwK2K8zFSQvG8XyFfacTMzLZqWKF1y5m8bk0bp3WcXcsZ/Ds2STlqsWV3N4dJuzVWRP24nFp9Db7+MejdjA/ki3TfcViWaNq8dw525UnnjOwpJx3XfjUAmWwT5hdGmxqCdAR8vCzMwlSxSr/cnKC7R1BRrMGFctiNGdbK2rCfi4CHp2oz0XTtBPt9pCHbW0Bjo3mOTycvWYHVhVgK1YMHs/ytgtWNAalq/NYqZq2hzxsbg1wZqJA6xw2eH63XZc9RdCjLyhD52RWqq6KxbNQTR/Y0MwPj8XrrvIcuJhhz9oIlpS8OZbn9aEc5mR2uj9Z5NauMLs6gwymSpSrFi1BN2/tiRD1unDpdlB8ZXAc9rpoDboZz1fIV+oDcinljKY8R0dy3No1P/edM9NKQq7MfL97SwvPTC66nCpTmeLKdvazsb0jyKnxAsMZg+GMwc6rxPxqCbBixZBOz345R7G6Ubo6j5Ws6T3ronzsto5rZpcUM1nJuioWx0I1dWmCD++sX8h6bDRPulTl8FCOAxezmJZkU6ufza0BJHZ5xXPnkuQrdlC+qcVPW9CDx6WhCTFn5nnqxHY0a9RqtgeSRX5ycmLGGofDwzmGM+Vrjr9gmLXgfDa3Hq9L452bY2xs8eNxCTa3BnjHpmY+sL2Vt6y9tre/363Tfvokvn3PoI2OIY3KnB80KoOtWDG0tjrvkqtC6epEVrqmnpu88cliWem6KhbOYjTVhODBDU0cHs7VFj/+cFpt9vaOIHdOBqO9TV7296dq7eHBdu+ZDwG3/T49nyhyPlEk7NXJls26fR7Z1spAqsQbwzn296d4/9ZW/G6NoUyZlqCnbrGiaeoqdCMAACAASURBVEn2T+sK+87NMWbDrWvc19eElHJRDiausTj6WBz/z57FeveuOZ/geX0KCSGeE0KUhRC5aT9rJre1CCH+YfK2S0KI/2chAxVC3C6E+IUQoiiEOCaEeGgh91c4B5U9cSZKV+ehNHUmSlfnsVhN1zX7Z7VkbPK76poPdUd9vG9rK81+O1/rc2nXXPw3xZVNirJlk7BXJzZtLUXAo3NrV4jOsIdSxWJ/f4qT8QJPn0nyw2NxRrJlLCk5PV7g4KUso1mDgFvj8VvbrzmOxdoD7nzP3bX5Xo2FZLD/q5TyyVlu/3MgAKwBtgP7hBDHpJTPXOuAQggv8EPgG8DbgH8P/EAIsUFKmVjA2BQOoFKpNHoIimVA6eo8lKbOROnqPK5X03dtjvHSQLpWk713U2yG40fY6+KXtrZyKl6gLeSed8fGKxvy3LMuysYWu1vovtN2COjS7BKTt61v4sdvjjOaNWq+2uWqxbNnk3SEPLWFmQB3dEeW1R7T79bpafJRrBSuut91XUcTQoSAD2EH3xkp5SvA94Ffn+chHgJiwH+TUpaklH8OJIEPzncM1WqVM2fO1H4SCRWXr1aUB6szUbo6D6WpM1G6Oo/r1bQr4mVHR7D2v1ufPevr0gTbO4ILXvvwoR1tbGjx86EdbWxuDaAJUbPbdOuCqVje79Z52/rmuvuujXqpmLIuuNY1wbrm+sz4UpN46SAAG1qu7rqzkAz2Z4QQnwMuAv9DSvm/gM2AGzgihPgb4CBwBPjleR5zO3AK8Akhnga+ABwFdsx3UGNjYzz22GO1/z/5yU/yxBNPMDIyQjAYRNd1MpkMbW1tJBIJpJS0tbUxOjpaawmby+Xo6OggHo8jhCAWixGPx4lEIpimST6fp7Ozk5GREdxuN9FolPHxcaLRKIZhUCwWa9s9Hg/hcJiJiQmam5spFouUSqXadp/Ph9/vJ5lM0tLSQjabxTCM2na/34/H4yGdTtPa2ko6naZSqdS2O3lOg4ODbNq0yVFzcqJOC51TqVTC7XY7ak5O1GkhcxoaGiIYDDpqTk7UaaFzyufzrF271lFzcqJOC5mTYRgEg8HrmpMrP0EmU6Q1GmRo8ALRaHRJ59Qj0hTTZTAuz+ktMRNpVjAMozYnr8dDOp0iEAhQKpXpi5UZFW7GUxncbg+aJlgftCiXIsuqk6xWSafTRO+6FRHw1dudTENIOdMIfMZOQtwFHAdKwDuB7wK/AYwBz2Nnwl8EXgWOAf9JSrl1Hsf9Pfj/2XvzMLnu8s738zvn1Nb7rpZla7HkXcYLEMJmk1EAk0QkMYaEJZfcPFy4ycyTgeTOHWacmzg3PAzzhPtchoSbBJiEkAAZCGYRYR2DjQ2xwbskY8mSWrKW3qu61lN1tt/941SVeqnurpa6VdVH7+d5hOk6Vef8Tn1PVX3P+3t/78s+4B2E5vzfAncDea31/77qwICHH35YJ5Pn71YGBgYYGGic2C60NzMzM7LIJoKIrtFDNI0momv0WC9NK17Y4bTZ9I+NYqrg8MCxND+/vZddAylmig4/PZPn5Vd20xk3SVrGhrdeL704jn16nMFX386TTz75xL59+17W6HlNRbC11o/N+/Nb1Wj1W4H/Wn2sW2v9KgCl1B8CBZqjAPRorc8Bo9XX/zYw3uTrsSyLPXv2NPt0oY0xzc3TUlhoHtE1eoim0UR0jR7rpWnCao/KPCNdcX7zli11Ez3UufYujxdLx/atdGxfvY38hb5jAaCAFwAXuHnetr2EUexmeA64prrY8UJeL0SIXC63+pOETYfoGj1E02giukaPKGq60RHq9WJVg62U6lNKvVkp1amUMpRSv0C4iPGftdYF4KvAHymlupVSPwe8Bfjson1sU0odU0r9/qLdPwjMAR9USiWUUu8F+gkriwiXGcPDS0sCCZsf0TV6iKbRRHSNHqJp62gmgh0D/gSYALLAXwAf0Fp/qbr9dwlzs8cJjfG9WusHGuxjN2HFkDpa6wrwZmA/odF+P/BrUqLv8kQqwEQT0TV6iKbRRHSNHqJp61g1B1trPQ28dIXts8Cvr7KPk4QpJY22PQU0TBAXLi+aWXArbD5E1+ghmkYT0TV6iKatoz2y1gUBmcqKKqJr9BBNo4noGj1E09YhBltoGyYnJ1s9BGEDEF2jh2gaTUTX6CGatg4x2ELbUCsEL0QL0TV6iKbRRHSNHqJp6xCDLQiCIAiCIAjriBhsoW0oFJrtTyRsJkTX6CGaRhPRNXqIpq1DDLbQNmzZsqXVQxA2ANE1eoim0UR0jR6iaesQgy20DdPT060egrABiK7RQzSNJqJr9BBNW4cYbKFt2CztT4W1IbpGD9E0moiu0UM0bR1isIW2YWBgYPUnCZsO0TV6iKbRRHSNHqJp6xCDLbQNMpUVTUTX6CGaRhPRNXqIpq1DDLbQNvT09LR6CMIGILpGD9E0moiu0UM0bR1isIW2wff9Vg9B2ABE1+ghmkYT0TV6iKatQwy20DYUi8VWD0HYAETX6CGaRhPRNXqIpq1DDLbQNoyOjrZ6CMIGILpGD9E0moiu0UM0bR1isIW2YWJiotVDEDYA0TV6iKbRRHSNHqJp6xCDLbQF6XSaT37yk6TT6VYPRVhHRNfoIZpGE9E1eoimrUUMtrAmvv71r2/IftPpNH/1V38lXwQRQ3SNHpdC0436nhGWZ7N/VuWaWcpm13Sj2ehrRgy2sCYOHDjQ6iEIghBx5HtGWCtyzQhrZaOvGaW13tADbDQPPPDANHCq1eO4XMhms329vb1z671fz/PMdDq9ZWBgYNKyLKkrFBE8zzMnJiZ2jI6OnhJdo8Gl+Kxu1PeMsDyb/TtYrpmlbHZNN5p1umZ27Nu3b7jRhk1vsAVBEARBEAShnZAUEUEQBEEQBEFYR8RgC4IgCIIgCMI6IgZbEARBEARBENYRMdiCIAiCIAiCsI6IwRYEQRAEQRCEdUQMtiAIgiAIgiCsI2KwBUEQBEEQBGEdEYMtCIIgCIIgCOuIGGxBEARBEARBWEesVg/gYnnwwQd1IpFo9TCEdUBrjVKq1cMQ1hnRNXqIptFEdI0eounGUiqVZpZrlb7pDXYikeD6669v9TCEdSCTydDf39/qYQjrjOgaPUTTaCK6Rg/RdGN58sknTy23TVJEhLYhl8u1egjCBiC6Rg/RNJqIrtFDNG0dYrCFtmF4uOEsi7DJEV2jh2gaTUTX6CGatg4x2ELbkE6nWz0EYQMQXaOHaBpNRNfoIZq2DjHYQtugtW71EIQNQHSNHqJpNBFdo4do2jo2/SJHITrIVFY0EV2jh2gaTUTXaFAsFvF9H4Curi7Jw75IOjo6sKy122Ux2ELbMDk5yY4dO1o9DGGdEV2jh2gaTUTXzU+lUgGgp6en/reUMr5wtNZks1m6urrWbLIlRURoG7q6ulo9BGEDEF2jh2gaTUTXzU+5XKajo6P+t2maLRzN5kcpRW9vL8Vicc2vFYMtCIIgCIIQAZRS0lhmnbnQ91QMttA2FAqFVg9B2ABE1+ghmkYT0TV61HKxhUuPGGyhbdiyZUurhyBsAKJr9BBNo4noGj1isVirh3DZIgZbaBump6dbPQRhAxBdo4doGk1E1+jheV6rh7ChvPWtb+XjH/94q4fREKkiIrQNkjcWTUTX6CGaRhPRVdhsfOlLX2r1EJZFDLbQNgwMDLR6CMIGILpGD9E0moiu0WOtVUTG0jaHJgpkyx69SYu9o13sGkht0OiijaSICG2DTE9GE9E1eoim0UR0jR5rSREZS9s8PDZHxvYINGRsj4fH5hhL2xsytv379/OBD3yAffv2sWPHDt7xjncwOztb337LLbfwiU98gt/4jd9g+/bt3HTTTRw5cgSAP/qjP+Kqq65iaGiIj370o0v27TgOH/nIR3jZy17G9u3bed3rXsdTTz1V3/7AAw+wb98+du7cyR133MEjjzyy7ufXVARbKfUg8ErAnffwtVrrc0qpXwf+ALgdmNZa71zLAJRStwGfBPYCJ4B/q7V+cC37EKJBrTC+EC1E1+ghmkYT0TV6fOHZGQyjuVjq0ekStre06sjR6RLXDnc0eMVS/peXbl3T+L72ta/xjW98g507d/Le976XD37wg3zqU5+qb//rv/5rPvGJT/D5z3+e48eP09vbC8CHPvQhPvShD7F///6G+73vvvv40Y9+xD/90z9x9dVX8+yzz9Yrqhw8eJDf+q3f4m//9m95wxvewPe+9z3e9a538fjjjzM0NLSm8a/EWiLYf6a17pr371z18SzwceBP13pwpVQC+BrwTaAf+Evgq0opmae6DJFyQtFEdI0eomk0EV2jiG76mWUvWNPj68H+/fu58cYb6ejo4Pd+7/c4cOAAWp8f8z333MMdd9yBaZpce+21TVW60Vrz2c9+lj/+4z9mz549GIbBrbfeyktf+lIAPvOZz3DXXXdx1113YRgGb3zjG9m5cyff+9731vXcLjoHW2v9fQCl1Lsu4OWvAwaAD2utK8BfKaX+T+BXgb+72LEJm4tisbiud49CeyC6Rg/RNJqIrtHj7S8ZbrpV+oHnpsnYS1NK+lMW+28cXu+hAQtLQ46MjOA4DplMpr4e4JprrlnzPmdmZiiVSuzatavh9tOnT/Pwww+zc+fO+mOe5zE5ObnmY63EWgz2+5VSfwCcAf6b1vq/r8PxbwSOAkml1APAfwIOATc1uwPP8zh27Fj974GBAVmosUkZHR1t9RCEDUB0jR6iaTQRXaPHWupg7x3t4uGxuYaPbxTzTe3U1BTxeJy+vr76Y5a19jjw0NAQHR0djI2NcfXVVy/ZftVVV/Hud7+bj3zkIxc26CZpduT/EXgOKAO/CHxRKTWntf7yRR6/C8gBncAeYKT6d9NqTk1Ncc8999T/ft/73sd9993HxMQEnZ2dmKZJLpdjeHiYdDqN1prh4WEmJyfp6goPUygU2LJlC9PT0yilGBgYYHp6mp6eHnzfp1gsMjo6ysTEBLFYjN7eXmZmZujt7cVxHGzbrm+Px+N0d3czOztLf38/tm1TLpfr25PJJKlUikwmw+DgIPl8Hsdx6ttTqRTxeJxsNsvQ0BDZbBbXdevbo3xOp0+fZs+ePZE6pyjqtNZzKpfLxGKxSJ1TFHVayzmdO3eOzs7OSJ1TFHVa6zkVi0WuvPLKSJ1TFHVa6ZyKxSKpVAqlFL7vo7XGMAy01sRiMVzXxTCM+nbLsuqpQTv6ErjbOvjZTJls2aM7bnDLth62dZk4joNpmnieh2maaK0JgqC+T6VU09styyIIAoIg4MCBA/zO7/wOO3fu5BOf+AR33XUXWmscx6nvo1KpNByz1hqtNZ7n1Y9R2/7Od76TP/3TP2X79u1ceeWVPP/883iex0te8hLe+c53cs899/D617+e1772tRSLRX784x9z0003sXXr1oZjdhyHqampJTqthJqf69IsSqm/BIa01r8577F3AR9ayyJHpdQHgN/SWt8+77EDwBGt9f/RzD4efvhhnUwm639LBHvzcu7cOa644opWD0NYZ0TX6CGaRhPRdfOTy+UWLFZ1HGdVI9gq9u/fz+7du3nmmWc4ceIEr3rVq/j4xz/O8HCYjnLLLbdw77338ra3vW3B63K5HDfdFCY62LaNZVnEYjFuvPFGvvOd7wBQqVT46Ec/ype//GVmZmbYtWsXH/vYx7jtttuAsIrIRz7yEY4dO4ZlWbz85S/nz//8z9m2bVvDsS5+X2s8+eSTT+zbt+9ljV5zoTnYAbAeFemfA65RSiWqOdgQVhO5v9kdWJbFnj171mEoQquprQ4WooXoGj1E02giukaPtdbBvtRceeWVfOxjH2u47Zlnnmn4eE9PD6dPn15xv4lEgnvvvZd777234fZ9+/axb9++tQ12jaxaRUQp1aeUerNSqlMpZSilfgF4N/DP1e2mUioJxMI/VbJaHWT+PrYppY4ppX5/0e4fBOaADyqlEkqp9xJWE/naxZ+asNmYmZlp9RCEDUB0jR6iaTQRXaNH1FultzPNlOmLAX8CTBCW5PsL4ANa61p/yt8CbOBvge3V/3+kwT52E1YMqVONWr8Z2E9otN8P/JrWOn0hJyNsbiR6Ek1E1+ghmkYT0TV6tHsEO8qsmiKitZ4GXrrC9s8An1llHydZJqVEa/0U0DB/Rbi8cByn1UMQNgDRNXqIptFEdI0eF7LO7lJx4MCBVg9hQ5FW6ULbYNsb045VaC2ia/QQTaOJ6Bo9gmDjmsQIKyMGW2gbpAZrNBFdo4doGk1E1+ixljrYwvoiBltoGyYmJlo9BGEDEF2jh2gaTUTXzU+tNnQN13VbOJposPg9bRYx2ELb0K61OoWLQ3SNHqJpNBFdNz/JZJJSqVT/W6n1qKh8+aK1JpvN0tnZuebXXmgdbEFYd7q7u1s9BGEDEF2jh2gaTUTXzU8ikcDzPHK5HBBGsCVN5MLRWtPV1XVBLdvFYAttw+zsbL3trBAdRNfoIZpGE9E1GsyPtp46dYodO3a0cDSXL5IiIrQN/f39rR6CsAGIrtFDNI0momv0EE1bhxhsoW2QElHRRHSNHqJpNBFdo4do2jrEYAttQ7lcbvUQhA1AdI0eomk0EV2jh2jaOsRgC22D1GCNJqJr9BBNo4noGj1E09YhBltoG6QGazQRXaOHaBpNRNfoIZq2jkhUEXlkbI4fjmXI2B79KYs7dvXzml19rR6WsEaSyWSrhyBsAKJr9BBNo4noGj1E09ax6Q12vuLxlWNTlN2Aousznq9wdKbEVMHh7ptHWj08YQ2kUqlWD0HYAETX6CGaRhPRNXqIpq1j06eIFCo+p+fKHJu1OZdzmC64ZEou3zoyw1haVs9uJjKZTKuHIGwAomv0EE2jiegaPUTT1rHpDbbtBcyVfWpd4jVge5pzOYeHjsuFtZkYHBxs9RCEDUB0jR6iaTQRXaOHaNo6Nr3B9gPd8HE30Dw0luGRsblLPCLhQsnn860egrABiK7RQzSNJqJr9BBNW8emN9groTV8W1JFNg2O47R6CMIGILpGD9E0moiu0UM0bR2b3mCbhsJa5izcQDNnexyaKFzaQQkXhNTrjCaia/QQTaOJ6Bo9RNPWsekNdkfMQDV43FSgtabg+JzMSAR7MyD1OqOJ6Bo9RNNoIrpGD9G0dWx6gx0zDK7qSyww2aaChGWggc64Sb7it2p4whqQckLRRHSNHqJpNBFdo4do2jo2fR1sL9C4PnTGDWw3INDga7DdgIoXUHR84majGHc0GUvbHJookC179CYt9o52sWtg6Qes2eddSuLxeEuPL2wMomv0EE2jiegaPUTT1rHpDXa+4nE6uzRCrav/AE7Nlbn/4FTkG8+MpW0OPDfNZMGh7AWkLJOxtM3+G4cXmOfa88bzDq4fkJz3vLPZSsu6YmazWfr6pANn1BBdo4doGk1E1+ghmraOTW+wl6nSV99W9jQKzZcOTraNwd6o6PFDxzOcmiszW3QJtKYrYXEmV+ZUpszrdvfXj/PQ8QxHpktkyx6dcRPbDRjLlPjRyTlsL5wFUEDSMjhRrcByKUz20NDQhh9DuPSIrtFDNI0momv0EE1bx6Y32M2ggXTJa4so9lja5uF5tbkztseB56bpSVpYhrogwz2WtnnoeIZ/OTKL5wdowDIUuUqFmKGouAGPvZjj64encYKA2aKHrzUxU1H2AgKt8XxNxdcsvF8JcIsuXz44tS4Ge7Ubi2w2S2dn50UfR2gvRNfoIZpGE9E1eoimreOyMNgQmuwHjqdbbrAfOpHhsReznEyXqSW2KGAwZXHX9UNkbK9uwJsx2bV0j1NzZQKt8bXGCzSOH5rsiq+xPTg+WyRt+wsMtO9pXD8cRaBh8WSA42tShuJsrnLB5/v/PHSKH52ao+wFoKEvZXFFT5KkZSxJX3Fd94KPI7Qvomv0EE2jiegaPUTT1nHZGGyAqcKludCWi9Q+MjbHt4/Mkra9Bc/XwIzt8YNjaX5hzwAAhyYKTRnsQxMFTmbKzBQdyq6PF4SPKxUadw0YCmbtxpVUAh0+txEaCPTiqPbq1M7/m89PM5auoNR5Az9T8ig4RXqSYfpKEMD779gOSL3OqCK6Rg/RNJqIrtFDNG0dm75MX7MowA+CDT9OmAKS4WSmjOMH9Yh0zVzPLTLX8zmXdzgyXWTOdsmWl3/efE5mykwVHYqOX89H14RdLP1Ak7AMOmLmsq/XhO/NcpVW/EBzZU9zq5DH0jaf+ek5PvHj0/zgeJqxdIWAsKrLfJNe9jRag+drnpnI1zttSr3OaCK6Rg/RNJqIrtFDNG0dl00EWynoSWz86R6aKPDUuTxnshVA0Ze02NGf4KHjGSYKDitZ/DDC63I2V2EwFWuYp7w4Oj6Rr2AZCjfQKBXeMWnCWuCpmElHzMTXK8egO+MmlqFIBAGFSpjDXdtXKmby89t7l7xm8Tj6UzGePJvj4ESBfMWn5PornqvjBSSqLThr0XrJE4smomv0EE2jiegaPUTT1hFZg22qMHIKYYS2K25wRW8C2LgqHmNpm28emeFUpowiTM2YcD1OzZWb3sdMwSFuGuiUXpKPXVsg6fgBMVMxlrY5lSlT9gLc6skqBTGl6Iib7B3tJFPySNvLp8YYwDtuHeWRk2FpPssI00xipmJrd4KEpfjZdIm/f+IcA6kYe0e7OJut8O0jM5TccBzbehL8IJOh4gUUHJ+C4+OtVN6FsI299gK6kzF+cjrHyYzNbN5msNtmZ3+yLepyC+uDaS4/gyJsTkTTaCK6Rg/RtHVE0mBbCq7oSVBw/HBxHWEO8HTB5WMPv4ihoC8VA1jzosLlqJnfybxTP567xowUywijz26gOZOtkLEzAByZKvKeV2zj0ESBXMXjZNomV/bI2F49NaRmZxOmojthsaUrzva+FLdsDaPLf/7QSWxvqem9oifB3TePcNu27iU3HWjN3z0xTr7iMVVwUCgOPDfN89MlChWPguOjgFzZI1/xMQ2F64fpHyuRMBVmNTkpU/IoVvwwTSTwmLY1Woc3F3BxmgjtQS6Xo7+/v9XDENYR0TSaiK7RQzRtHZE02Lde0cVIV4JnJwrYro/ra+KWwUzJ4cETDr1Jk7gZdn6MmYr+VIwg0CumYqwWUT00UcD1g3rUfJUA7hJ6Eybb+5JkbJeJgoMfQL4SGtiZosNf/Og0Ax0xSo7PnO1xLl/BDxbmNltGWDmkL2mxsz8JUB/3VOEKPv/0OCXnfBm//g6L33n5FUBoZBef34HnptnaneCJMzlOz1WImwXKXoDjB1iGqp9j2vZw/QAFxEwDQwEoFHrBLIIGOmMGuwdTmIbixblyteMmmIbCD6CYq5Ate+zsTy3RRNicDA8Pt3oIwjojmkYT0TV6iKatI5IG+5U7+siWPe7c1cc3j8zgBj5aa/wAfK2ZKgT1POVAw7mcw+GpIt8+Ossv7hlg3zUD1ah26A4bRbkXG/CTmXK9LbuBgR0Eq1bfUIRl67Z2x7l9Ww9Hp0ucyZXrxtmvNnzxfc2JWZuxtE1H3KS0KGpdR4dGNWkZ7BpILbgpuPvmEUa64mvq0pgte8yWHDJlDz8Il0PaboAbaOJmeEAvgEq1OU3CUnQlDDpiBrMlFzeArpjB1u4EyZjBjr4kt2/rIVNdxDlbcjGUouT4lD0f1welwjKDGdvl0GQY2RaTvblJp9N0dHS0ehjCOiKaRhPRNXqIpq0jkgZ772gXhyYKZGwPu5qn4VbDrYEOK2xoFkWZdRiJ/eLBKb5zdIZbr+ghV/GwDMXO/hQJy6gvxlvcLGYsbXNwokDR8TEI85dtjwYOeCFvvXmE97xiW92sb+mO8exEgDEvf7y2i1I11aXohpHi+aX1VPWfoRTXDXVw/Ugn+29cetf6ml19a2oY05u0OJUJF1H6gcbxwzrbCnB9jaHOv5cAWmtSlkFXwqIvFQvbtcfCKia7B1PceXX/ArN8ZLpE0QlNdK28oNbhTVC27JGw4k2XKxTaF71azpCw6RBNo4noGj1E09bRlMFWSj0IvBKYv1ruWq31OaXUIPAp4A1AFviI1vovmtzvTmAMKHHep/1Aa72/mdc3ojdh1g1ZaIJryQkhBrBaAbxsJeCZ8TxX9SXxAs2x2RI7+1PV1IewWcyz4wUm8mHt6Yof1KPOKcugJ2lScn2cxqWnAeiOGbznFduAhekZj53OkbU9giVdFc9TK8NXM9q1WotKwUhXnN7k+tw37R3t4ovPTmEpcKqPhYU/FF5w3mADxK1wUaRpKm69oquptJrdAykOThboiJvhe9jghJstVyi0LzJFGT1E02giukYP0bR1rKUO9p9prbvm/TtXffyvgA7gCuAtwIeVUv9mjeO4bt5+L9hcG8AvXz8EhKb1tbv6cPwAL+D8vyZv5goVn2uHOuhJWHiB5njaZrrg8pmfnuM7R2Y5kbaZK7sU3AA3gIDQ+Ja8gFzF56q+JNt7E/Snlq7g3dGb4MvvvqXhcXf0JemMm00J050wMVW1qYyCLV1x+qqVPtaDXQMptvcliJkGnXGT3qTFaFeCrriJoRQx0yBhKRJVc92VCMsCvuv2rQs6NC7Hnbv72dGXJG4axE0DU4WVV+KmQVfCxFCs282C0DomJydbPQRhnRFNo4noGj1E09ZxUe5FKdUF/BrwC1rrHPCoUurLwLuB76/D+JoYRJhLHTMVN23p4s7d51fLfviBsTUvNqwRENaA3jmQ5Fyuwpm5CsdmS5iGqi/085apEmK7AQaK337Z1jXlPAO86bohvvD0xIoNaWr0p2LEDIUmNNuv3tnHa3f1rWtKxZuuG+Irh6cWPJawDBKmga81lqHojJskY0Z1TM1fUrsGUuy/cZiC43Mua5AtOyhlEGgwVZhLXrtZeGRsbs3vpdAedHWtzw2f0D6IptFEdI0eomnrWIvBfr9S6g+AM8B/01r/d+AaIAYcVEr9PfAkcBB4+xrH8ahSoB86wAAAIABJREFUKg48DvwHrfXhZl8YMxQjKYPdvQav2+rTiw2EBvNUtrLGYZwnYSr6UxbZssfeLV3EDIOJfCVsFrNKTpMGZm13zTnPQP35f/bA2IrPsxS8+cahda/lvdx4FptbYInxBurbmmXXQIq33zrKw2NzTMzleXaqQtr2SNsehUqMp87mOZutLDjWbMmt/y0mWxAEQRCEdqNZg/0fgeeAMvCLwBeVUnNAzfXkCc12BjgMNHvLNAO8DHgG6AbuA76rlLqhGhFflUQ5w4F/dw8AHwPe9773cd999110e9Dbhi1es9XC9xXFYpHZoolre0wFfpiWYbBsBBuAwCeXy2HbNuVymdHRUSYmJkgmk6RSKTKZDIODg+TzeRzHqW9PpVLs7Y+HixgbVQohzL2+adBi385O0uk0WnsMd3Rz6tSp+t1qoVBgy5YtTE9Po5RiYGCA6elpenp68H2fYrFYP2YsFqO3t5eZmRl6e3txHAfbtuvbdyfj3Ppzw8zOztLf318/p/3X9vO9I5MUPMVAR4yXDMCtwxbj4+NLzikej5PNZhkaGiKbzeK6bn17d2cntwwaPDNWZM6GIABDafIVj88/eRbTNCi5AZ4PpgG9cUV/yuSLz5zjW4fOUApMeuIGtw4pfuklO5o6p3g8Tnd395JzWotOK51TZ2cnpmmSy+UYHh6u6qQZHh5mcnJyQ3Rq13Mql8sUCoVInVMUdVrLOU1OTlIoFCJ1TlHUaa3nVCwWicVikTqnKOq0lnNyHIdKpRKpc2onnVZCXcgKU6XUXwJDwH8ljFr31gyxUuoPgbdrrV92Afs1gDngbVrrbzfzmocfflgnk8n63wMDAwwMDADwhk8/tdYh1Pnue25b8PeB56bJ2B7TRYfnp4rYbkC65DbM6TaB267s5sN37bng49/zD8+Sr4SrJBcf4lXbe7jvDbsveN/tyu988TBp28WprnaMm0Y9FceAeut1BSTMMJ8+VW23noyZ9CUt3n7rqES124xyucz8z6iw+RFNo4noGj1E043lySeffGLfvn0N/e5aFjnOJyD0OS8QVha5ed62vYRR7AtBz9t3U1iWxZ49e+r/auYaLvzkGlHLBR7ujHPzaBcjXXF6khbmonJ5poLh7hi/dN3QRR3vrmsHiZnVKiH1xX/wtptHImmuATIlF1BoHZZQnJ/nPn+yQANlP5xByDsBeSdguuhyeq7Mt47MtGDkwkpMT0+3egjCOiOaRhPRNXqIpq1j1RQRpVQfcAfwAGADdxIuYnyP1rqglPoq8EdKqbcBNxBWEvn1RfvYBjwEfFxr/fF5j78SKBCmnySBPwGKwI8v/tTg9m3dPH42v+bXjXbHljxWy28+NFHAUNQbuZzNVvjyoUnOZB0UsK0nwVtuHrnoKGqthN8PTmQouQEdMYNfuLq//ngUsUxFxdf1JjprXaBa9jVPXYDewsaiVNP3y8ImQTSNJqJr9BBNW0czOdgxQuP7uerfp4APaK2/VP37d4FPA+OEudj3aq0faLCP3cDAosd3AR8GhgnN+0+AN2qts2s8j4a8ckcvGnhhpkS+4tdTLWr1o2tNUgzCDoiGCitk/NqNIw3316id+K6B1IalJLznFdsibagXc+NIBz89W7iofXga/sM3jvLnv3LtOo1KuFjmzyoJ0UA0jSaia/QQTVvHqgZbaz0NvHSF7bMsilg3eM5JGqR9aK0/D3x+1VFeIHtHu8jYHjeMdPKzqQKHJgo41YVylqGqtZwVHZZJAPQkTfbtHuDumxsbbGFjeeOVBs9NmRQcv2HTmWZ5ZqLI/QenNlTH+w9O8cDxNLmyL9fNKkxPT7Njx45WD0NYR0TTaCK6Rg/RtHVEuovH/LSOm7Z0saUzwZHZIjNFb13TOYT14YYrBtg+5jNbdEnbLm7VZV+I2f70T88CrKvprdXiPjJdZDx/vqnpZAGOz5wlXXIvqxmHZunp6Wn1EC4ZK914jaVtHjqe4XjaBmD3YIo7r+7fkPKaG83lpOnlhOgaPUTT1hFpgw2N0zqE9sT3fW7e0sXByQIoyJW9sCV7oOvt4TXhgs8VSyQSbr//UFhFcjmTPZa2OTRRqNcSd33N89PFhs1sHhmbq9fenm+uawTAFw9O8cipOQY7Ytw82rVpzdN64/t+q4dwSbj/4BT/+NQ4JSdAA1MFODFzlv/xzCSGAjfQdMZNhjrDNR4HJwrkyl5TXU/bjctF08sN0TV6iKatI/IGW9g8FItF7tw9Qq7ioTWU3QAIiJsGlhFmGJmGoiNmkCt7ZMqrf3E8cDy9wGDXotDnchUqXsCO/hTb+5L8+NQcR6ZLxE2DpGVQcvwFzWx+OJZBayg4Kx/zXM7hXM7h8GSRrx6e5qXbunnX7Vs3nYFaT4rFIkNDF1dVp1Usvglr1NCp9pzPPjFOadGdnw9kyh5JS+H5mkLFp1Dx6EpYxEzFWMbmiTNhyf/VjtNObGZNheURXaOHaNo6xGALbcPo6CiJRIL9Nw5zaKLAyYzNeN6h4gWgIRkzGO1OsLM/SX8qxg+OpXn41MrrYXNlvz41/9MzOcYyNn5wvrb4qblK2DiIsGpJkQDTALOk8APN4YkxDEMRaE3cVMTN5oo/BhpKbsCjL+YouQHvfcW2tjZMG8no6Girh7AqNZP87Hiec3kHA0V30qQjZrClK0HJ8Xl+qsRDJ+bYO9pZn50YS9s8PDaHRmOvMK1Sq+2uCa+LVEzjB5qyG/Dg8QxPnM1T9nzmbB8v0PzzwSn2DIazbxttuJu5iVjMZtBUWDuia/QQTVuHGGyhbZiYmGDHjh1Np/W8Zlcfv3v/84ylbRZbm2rAm4Sl+NxT4xybCc16o3TuxTneXgDevGcG1XqBZU/jrHG6zQ80x2ZKPHQ8c9ka7Jqu7cr9B6f41pEZMrZH2fNJmAaWaTCeD/ADSMaKBIHGMBQKyJVdzmQrXD2YZCxdZiJfWVClqBFahzXyqf4b7Ijh+OFV62rNdMFhphSmHsVNRdHxmcg7/OR0js64yWh3nFuv6MYy1Loa7rG0zYHnphnPO6Rtl3zZo+wFdMRNdvQledN1Qw3XqLS7psKFIbpGD9G0dYjBFtqGWGxp/fHVeOdto3zm8XNMFipU5nlfrWG25NIRN8hXfDw/WNEANctaa3MDeIGuL2xrBRcSoVxPLkTXS8UjY3N8/WfTOF5AwfHRGlw/IGaGQhsKsrZH3DLqd2IlNyBmGpTdgDnbZa7srXocU4V13r1AYxkKy1RYZmhilYKnzhYwDfADKFT8eodYx9f4ZY+5ssd43uHK3gQKeOzFHHddN3jRC7QfOpHh1FyZiVyFbMXDD8ISpoGGU5kyX3h6AmDJcdpZU+HCEV2jh2jaOsRgC21Db2/vml9T++H/4ViGQxMFMiUPn3Aq3jRUuOBMB1jmxRXbrwUfAyBlKWyvOadtVEtCXgpq+eXzF2lu603wuafGOZWpUPYCDAXfOjLDDSNd7OxPXhKzfSG6rpVG576S+azddBz42Qz5cu2KOV9LNAg0cctAAZ46r7VZrZUftxRX9CawTDBNxWzRJW4onGXuwOJWmNtvuz7DnXE6Yma9isihiQI/OZ0nbhrYQcD8S6vW2jbQMGd7xAxFb8pCA98+MsO23kRdv+UW7R6ZLpKv+JiGwUhXbEFlk+OzNn4Qprd4QXhjqjRUdEDSMig4Pj8cyyx5Ly+FpsKlJ4q6tjrA0GqiqOlmQQy20DbMzMzQ2dm55te9Zlcfr9nVx4Hnpvnng5PMFN16tLrWFdK7mMLahMbLMCCmYM9QB1nb43S2smJUXBGa696Uxe7Bjf1Cr1U5KbsBRddnPF/h6EyJ3oTJTNHFCzRlL8DxNVrDRM7h9it7yNhh9HW1H5yL+ZG6UF1Xo2aqx9I2c7ZHb8qiN2kxW3IXLFBdTC1vGqDo+LhBgFc1xkpV/wGdMYOSG2AZCtNQBIEmYRn0p2KYSnFVbwI/0FXzquhOmJRcj+pbiqXCmvsx0yBhmQx0WOzbvaVhVZuvPzeD7epwga17PuHJIIyi63kXWsUNFxGczZb5T98+Rsoy6UuZDHbE2d6XBKgv2vUDTcXT1fMKmMzrenUdCCuZVLxgwcyMrv5P2QuoeAEn0mVOZmyu7E1yeq7MQ8czHDybpiOV2tSlBoWlbNRntVXM/6zX/n7sxRypmIFS0J2wLlmgoVVETdPNhBhsoW242DvtbNmj4AQopdBaE+iF9nctkefF1LrNvu7qAV6xvZcfjmUouT7pkhfmeytAh4a+NsUOYXrIVMHhweNh5ZJGkdX1iLB8+eAUJ9IlytXzi5uKpGUwkXeIm+EiTcc/H6EtuAFPnMlxKlPmTLbM228dXfaY83+kXpyzeWHG5jNPjNOdMLl1aze/etPyZebG0jbfOe1z9vnjwPrVfn5kbI4vPD1BwfGZKTrhAlXHJ1f2SMVMAL7wzARzZQ9TgVHt1GooxdPn8pTcMJ+otvhQEUana1eHoRR9qRgoj6RlUPECTEPRFTdJxsJo9N7RLgC0Do13xQvQOk7Z03iBZktXvKnz3TWQ4k3XDfL156ahOo7a9WOZKowqK41VFa/g+EwWKrhBaL4dKyBju5zLVjiVsSl7Qf0ms26cq5HpkuOjgC88PYFphJ+TWvlLCA29UuGNYe39UMAPT8xRcDxOz1XIVzyUNsDxN3WpQWEplzraudaZp7VyaCLsDJwte8wUw3UGJScgV/ZIWAZaa342FVuyeDlKSAS7dYjBFtoGx3Eu6vW9SQtDKSxD4Qfga41lhNE/w4CBjjhztkvFD0LTAqzkt2NGuODRUJCKGbx6Rx9/eGe4WKT2I9CosQiE5q5Q9gnXsWlmig62F/DcVJHHz+TqpnQsbfPH3znGVNGrm5mRTov/+417mv6iv//gFEdnSrjzwpAVX+MGYU5xEOj6ec4/XccPjVlvyawb6NqY5hv+ibxD0fE5kS5xKmNTC7A6XsCjL2YpOD7vvG2pQX9kbI6/eew0U4Xz5/bMeJ4j06WLrqryd4+f41yuUp+hoLr/ouPXK73kyj7p0tKa5eM5B119VW/CZE5rTCusU20oheMHDKRibOtNsG/PAAXHZ852mSq4lL0wdeKu6wbr48/YXmjG5/HaXX1rOr+7bx5hpCteb2Q0U3QxDYWpwpsjHcBgZ5zuhMnZbAXHp14b3naDallLyFV8DKUWvC81aobbDTS2G5CKGaEuCgxdTUUBkqaiI2ZiVaPyd17dT0/S4uh0ifF8Bc/X+L5PzAroT8WYKjgcmihEzphcjlzsd3CzjKVt/uZfz3B4qojWELcUAx0xZquf1/Uw2WNpm5+czpG2XYoVHzfQ9Rkn0Dh+OHOVKXskTIOxjM2PTmZ503WDkerKe6k0FZYiBltoG2z74hYC7h3tYrjT4lzOCc1JNV5rGYqrepO89MruJRHiX/rbpxo2rVHAq3f2rRpRufvmkSVfxv/528fq+dqa801xHNvDAL51ZJbvHp1taO41MFn0+N37n+eu6wYbRofnR31ipuJUpoyvl+5M6/DmwDINtK/DaOW87X416pste8zZLocmCgRa8/1jGVw/oOgGHJ0uMZax6UlYjOfDhaS1QwVobNfn2ExpicH69GNnuf/w1IL3VhNWYnnibJ6P/GCMv3nLjcu+r42oGf9/PTXHmQbpObXofK2RS1/K4pdvGMIPwtmMQBOaVQ3ZchjhTQ4ZTBcdTmUqeIHmmqHUEs1rxx3oiC25fuZ3i73YHM9aqhMsvXG7uj9Fxg4Nvrkop3/BTYY+X0Gn0XtjGdSj7ZYRGnEFVVOucQPNQEcMheKKnviCKiLjuQozJZcKPr4fvp9pO3wfs00s9ITw2v3mkRnGcw4xU3HjSOeKMyCXE+2QK3yx38HN8MjYHJ9/epwT6XL9wvUcjeNVQNMw578RK71f9333OD89k6Na4ZVEdUZv/vekF4SLigMgCAJipsJ2fb7+3DQjXfHIdHi+FJoKjRGDLbQNF1uvc9dAinfcupXPPzXBdMkhCKA7YbJnqKNhhBWgI2YuKbGmCF/3n//Nrgsax4uZcj31YDE1v7lapkoAfPPILN86OkvSNLhhpINfuWGYx0/nePR0FscPUIRR+rKnaeCvUUB/ykIpRb7ihUasGqk0qC5oU2GaxFPn8pxI2zx2OoftLixFaBnhD1NQNW/zT80LNHNlb4HBuv/gFN88MrNit82TmQqffuxs063la2kqrh9weLLYMPc9AGJK1Q3oL+4ZZLBj6Qr6O67uW5CXub0vxfa+1LJR59XKRm5Et9hGN241QzF7aJqS6xMEC6P3tS6nZnXWxl8UxVZAMmYSMxT9qXC2ZzG9KYtP/Nr1Dcc00hVnqCOG7RkESZO8E1B2A/IVD7+J8jqPjM3xmcfPkbZd/OpswaNlb9kZkMuJxbnCGdtbMKt0qViPmskr3UR9+rGzfOP5mXDWZd5rFKHhnSw4JGMGgdYNr88aK71f9x+c4tHTuQWpTxVfY6oA0zBImIAKK/r41dSpcHYnvBH3gpVNfjvcCK0FqYPdOsRgC23DetTrfM2uPrb1Jpr+Arx2qINnJ/J4PvUwn2WEj18oXiO3e4Ho6mKzZ8YLvDBTouwF9RSNMPJ4PqJswIJ64AlLcfVAitu39fDwyQwn02UU4Y+NJjTlqZiBH2h8oOgExMywc2bcVCQsg+6ExVW9mjPZMjHTwPU1ptIEnM9Z9gPNTMnF8QPOZit8/ukJis7Kvew18L1jaQoVn+emi7i+XhIxnc+hiQJ+tdxhZYUFq51xk8GO2IozD+sZdb6U1Iz8bNHlX56foej6de1ref+xanqHY4RpI14Q5oMbKozgbemO19OY5i92rFHb1oi9o12MpW1Ozfm4jkNvMomhPGKGQbbi8cVnJzk2U6rn014/3EnMVPX3+PvHMqRttz4mVU3UaTQDcrnx0IkMR2dKpIsuvSmLbT0J+lKxhu/L4hmskc44Q51LZ1cuhIv9Dq6tjajdcLs+PH4mR8HxGe2K89XD08tW2oHQ5FY8zTd+NsNtV3SHZSkbGO0wtzpM9yi7AacyNifnwrKSy3092J6mJwm9iRhlL6Dk+ji+rs/4mIbCDTSdMaO++Hsx7XIjtBakDnbrEIMttA3xeHxd9rOWiOIrd/SiCb84K15AwjLYNZDilTsufGHIYCrGWbeCZYC7ss9sCk0YjcwvMq21nFqzGlWuRX5qtYy39Sa5q2pY7755pP7DfHCigOdruhMmccvAUArTgF39KXYPdjSc7u9LWnWD6wcQN1Q9raAjZmIAn31inK8fnqKJDvZA+OP0r6ezWNUGLo3qLtfG/LOpEl4Q0Bk3MZVC6fCmAM6nP5gG/Jc3NZe7vhFR50vFnbv7eXaiwNlchZITzr7ETcVIV5yyGzDYGatXk/ECzcu29SybhrF4/cBKuae7BlLsv3GYh45nODzukYyZ3Lylk629CX56OscPT4QzDGGnVM2jL2a5aUsX1wylODVXZixj41Zv7qB6/VZzYptNMYkiYUS0SNEJo/m5isdE3qE7YdKfitGfipGx3XChXsHlhdkSyVhYhz1b9ngxU+aqviTZssdnn5wgYakVmwStxMV+B/9wLMNc2aPoeHi+RlVnlA5PFnjslL+iuTar6UvXj3QwZ3v84HiG0e44w50xzmQr9Ru164Y7ODZrM10Mu/yey1ZIL2OI4fz3g1Lw6u19zJQc5mwP04CKpwFdTR8B19ckUgb9qaXWaCxt84WnJ+rrOjrjJrsGUhhKbdgN4nosAl2v31Vh7YjBFtqG7u7uS37MvaNdZGyPG0Y6lzx+obz8qh4qJ+fI2h5Fx181HeRiqC1SS5phh7+C45OwFC8Z7eRdt29d8KU/v5zhWNpmquDUF+2NdMXZNZBi72jXgghNjTt393Pn7n4+99Q4L8yUKDjhYrrhzji/etMwrg+ffPR00+a69qNX8QIcwjz5hGVQnFd3+dOPneWbR2ZwPI2nNYqwCkZ3wiRX8TCrU7tmNcJ182jnpjXNa2HXQIq7947w7SMzC/TrS8Xoips8P10kY3ts602s+IPcKA2lmWPvGkhRKPTQ1XX+M/KVQ9MUHX9BuT8v0Dw7nidX9kjGwjQjX4cRw9psi681CSNcoHy58tCJDNNFh0wpjO6jIGGGNdjjpuIrh6fY0ZekK2Hy9ESeohN2Gy25YUUYP9A8M57HUAoD8AO1YpOglbjY7+CxdJls2auXRVUq1FxrTWUFc21U68v/0nVD/K8vv4KjMyWeHS/w/FSR78yV6UtZDKZivDhX5odjc/hBWHoyZhrkVumiWsNU8Kt7h4Hzs1djszbPz5SqKSqatO0xlikzlinz4ImniBuKG7Z04gUBFU9Xzy2sVBUuptdcO9y5ITeItdmA2o3ydNHhbLYChJo2m6rSit9VIeTy/VYT2o7Z2dkFP9qXgo1IF7jz6n5yZY+pQhgpmS46ZJp1nheAaSiuHuhouIizEbWbisWVL+a/brn34523bW24zQ80H//R6VXHqgjTGLSmWiUjjL67gcas/gBnbI/7D07xtXnTybUfUD/QmAqGO2NkbA+FIhULZx3e22Q+dxRYayrUerP4s5ouecyfyK/p5QVguz5DnTEGUzFmS054Q6QUbrW7al917JcjY2mbx8/kKHs+ng5TrwigrAPcalk5PwgbBg11xEIzqcGvVQginCXzA8AIX+97YUqQLns8eDy9puvkYr6Dx9I2mWoKUG2uzSS8AY5bBmXfb7hWBOD64U7ecvNI/WbghpFOdg+k+NRPzmIoxZztMTcvSt2VMBnujNOTNDk4UaivQaj9dzFKwcuv7FmyOBnOR4kfPrE0sOAEmmfGC1jV76laCcuYocAyOJursL0vydaexJrfr0aMpW3+8YlxDk8Xydpe9XsSFIoATRDAf/nBSQZ/YhE3wnKhFT+ceV2uu2srfleFEDHYQtvQ39/fkuOud7pAbSq99qPm+ZqvHJ668BrcNP7RqHFlb4Lff81VTZ/DaiZ6pfdjuW2LK1sspjdhELdMSm5AR8zgqt4EsyUP2/Vx/LBNea3BylTR5ZnxwpJ9KMKUmLKv2X/NIFR/7DZLDvV608o0l8Wf1XAmJUzdqRkdCA3JQEeM3YMdDHbEeH66SNEJ9VbKwEQx2BHjRyfnODRRuOx0PDRRoOD4VDyNZShcP4zMetVFqkZ1pZ4GZoouser6iHo5Uq2BgECHrw8CXa1cpPEcn8OTRf6/fz0drp0wFCnLZCxtL1u3/GK+gx86kQEWNkUKdDhL0Ze0UEC2vDTa3Jcw+X/ffO2S/cUto54SMpF3wtmrpMlQRzwsm7qzj0MTBWLVSHJt4faS6kIKbt7SyX1v2N1w3LWZvTeceGrZc6stGK6ta0haYSlQ19eczVV4/TXLr11YjsWVgm7b2s2PT2U4k1tYWjSolno9/7dmpuDWZ0ZrbRhSsQpnc2WOzZYW1PNu1e+qIAZbaCNs26anp6fVw1gXFpufc/kKJ2Zt0tVSa2EFD0Vn3OCXrx9iz2AHx2ZKHJwohM9xA/KOhxeEESDL0LhB+GUbcN7AdMYN3nHr1jWbkkttzv79a3YsKX33uafGeX6qhOvrFSuO1FCEP5aDHTF+++VXbNxghVVZ/Fm9sjfO0WkbI/QdYXRVQ9JSdTPSl4rxjlu31vOJbTfguy/M8uxEkYMTRWKmYmd/gn//mh2czVY2tAFJK5k/tf/MeIGiE85uGUoRN8PvBX/e4o3aDUttrULcNLAMRWfcJFv26ukVAIapiJuqflPuas14LqyDnLAMsngcmSnyk9M5btzSueR9tW2bWS+25OYblt6QL37s4ESBuKmImdQXjdfSXF69o4+epMU/PDkedg4lTBNKWAa/eevyVS56kxaB7dU7lM5/vPYdNpl3+PrPpsPI+Tx3HTNgZ3+KZMygv0E1obUwv1pP3Axr5Scti65EuKiaRQsxHxmb43NPj3M26xDo0JBfO9TBK3f0sne0i6fO5uuLjL1AM57zODFj0+w85/xYja7+K7oBbuDw7HihXoFl10AqUr+rmw0x2ELbUC6XWz2EDeOOXf3MlsIKAfP59ZvOT4vW/tsot+5stlIvceZUo1wdlslv3LKlbYzHjt4Ep6o5gvPpiy/NA901kOKdt23la4em+d6xdFP7D8vQKW4clra/rWbxZ/Ute7fwycfOkLE9Ah1GSy1DsbUnwZbuBP2ppbMMH/yXF8jOS51yA81zUzb/7qvPkzCNaqUahWUqDk8WeehEhpds7drUUe6xtM2B56aZKjjkKj7j+QquH1auiFnhwkXHD1BqXpWeWppA1TRdP9KB7QY4vqY3ZZGyDF7MlJm13br5hqo5rYaTawtJy16ApcIo6GzJ5SuHQ5NX+3wenylwrHze3Gdsj889NV7vWFvL939mPL/ksYm8g+NrLMOgM66IVe+2Yqbizt3nI6prWVi73JqQ+SlFtVKf3zuWZs4OU5USpuKKngTJmFE/j4ulVrXJUOENzM7+JK+/dpBnzuX5hyfG2TGQZCAVw/U1//jkOIV5N0mO7/PUuTxTBYextM0TZ8Jusp4f4AXhzVOz5nqlGU3Xh+OzJY7OFPnC0xMkLIMd3Qbvf13Ppv3MbGbEYAttQ5TrddZ+wJqJyjWKLtf+bueo3qfeeiP/25ee48VqExgFbO9N8Jdvbjw1u2sgxfvv2M4T5/JMV9udL0etlfnuwVR9oZLQOhZ/VmvX4VqayDw7uTQNCMKcYjc4b05qeftHZ0pc1ZdctizaZqhP/NCJDM9OFMiUwpksqKZXVWdmAMpuwOlseANjGef71WsNHXGTd962dMZqLG3ztUPTS0pefufoLCU3XJCczZbDZkLV3O3ZkkvW9vjw98fCSkKmojNmcP1IWOqx7AVkbZfnp0vVUo+hmZ7Ih11pqZb6LDkBWhXR1drVAymLgDAyaxmKW0a76+Nd68LaZtfIvOcV23jPK7bx4e+P1btBzqdRVZC1YqjviFSMAAAgAElEQVTwOlSEkfe7rhtiW0+c//lCmmMzJX56NocfLD8b52s4k6swV639bnA+9cRfofTo4jEYimWPoWFBKqLtBjyfDviz/3mC/+sXr267z0PUEYMttA1Rr9c5v1NfK15/KfjUW5d2Zzx16tSKuvYkTWaLC2t4z8dU4bT4y67sXlIZRWgNjT6ra70+m0kLgtA0OH4YcT2Xq5C0DB59Mcu23gRxM4xQPjI2t6SqSi1q2U7Xy09P55gpuvUFnrUKOF4QNr0CuHm0i9uu6ObBak7zfO7eO7JsM6T337F9yePHZsO0s2TMwFQKrcKFwjrQzBYdKtWwqapWeCm7AY+9mA0X7Wk4l6vU0xF8DZ4T4PgOSim0DhbWnNZh7f2+VKxed32kK8b+Gy/uhngt6Wx37OqvR+UXP74anTGD4gp1VXuSFjEznJl58w3D9YpM6ZJL2vYWrD1YjkATNonSC7/vVrPXYYrQ+bUufqBXfc38/Z7NOXzt0HTDa0TYOMRgC21DMplc/UnCpmM1XfftHuAfsuN4i37cFGGt2Z39Se7eO9L2NxeXE+vxWTWqzXGaJaiWKgM4k62mVsRNHD/gRyezeEGAF2i64iaZ6RIJy+CFmRI/d1XPRUezm6lHvFwUef7zZm2XYN5C0JphUgr+7I0LZ3oGOmJrSqdoxPyKRjFThdHPqguszIt01lJRIDT7JcevdzqcT+1mBxYavDCqq+hJmgx1xtjel2zJLMJaZgoX88vXD/Glg1NLjKsBbOtNMNARW7K/bNnjbK5S73DbzOXsL/LwNVO+XBUUS4X9GmzX59RchUBDwgqw3XBxa62ja+21jcaggX89PccLXy1d1PUkrA0x2ELbkEq1T6RJWD9W07X2Jf+lg5PM2WFL9zB/N85rdvYtWBEvtAfr8VntTVhk1lA/uDNuMtqdoOIF1eZIiqLjc3S6xFzZrTsLL9B4frWqhtYX3W3v/oNT/I9nJihV23vHTcXzU0UeP5Ord1A8Ol3ih2MZbC+s1x4zwvKEn3n8XP15nq+xHR+namwt83y802rQrfBC6pQvZn5Fo1TM4IUZm7IX5nhXvOXfe9NQVKprPerdQlnZwPmBpjthsb0vybtu33pR474YLnSmr5bLfeBn03UdU5bBr9wwVN+2mN6khVutya2aMNgG1bUkhHn1XrUpWC2YcM1Qiucmi9V1Noq+pMk9N29peB3cf3CKbx6ZYbboYhqKvqTFubwT1lJvQMb2ydg2AJMFODF7lsOThfqs4GZIsbrULK70stabEjHYQtuQyWRktXMEaUbX9TATwqVjPT6rv3HLFv7msbNNNwm5qi/Jlq6wK91rd/Wxoz9JoeLzD0+Ok694pG23nhMcNxUlN8x/OJEucXquwv2HpjCNMEf4TdcNNXW9jaVtvvTsJLlKOK1fa+xSdAK+c3SWZHVh4vwFahpwgnBhXb4SLiy8ZijFRN45f67zjGvSMtjev3Gzd7UUi/03DofNS56ZIFf2FpjnecPCUGE6RMVzw4WWTc4y+Dqsab6ZmwbVcrmbZe9oFzFD1Reer/ReGQo6YkY1n13RFTfr1WCKrs9gKsardvTx3ldc2ZSxrX1nzjfGh8YLPDVeaC6SruHhk1mOTJfoTVpowmuxlmu/UjnHtdAOxv1CjPL9B6e4/9BUvalR1tb1yi/N/lZt3k+CEDkGBwdbPQRhAxBdo8d6aHr3zSMcnizwo5PZZfPva2zrSbBrILWkGklPMoyY+oFGpcNa0UXHY666w7myz7m8U9+PAsquzxefnayPYTHzDcGRmRKZslc3TprzaS2+ZsWcXU24YDNdcjk6UyvxZuD5fr2snmWEdcB/6bqhVd6B9aHWoOjhsTkeeGGWyYJbf+9rMfRUzODtt4zy+Jkcj76YpeR6+EE1hWSZKK0i3DbSFb+smgbtGkjx2p19fP94ul6PGmoVj8J/Nd07q4a6VuXF02E6U18yxnXDHRdsZhfnqN/33eM8djq3JL1nOXIVj1zFqxpJRU/CpD8Vw/YCHjqRuSgzPJa267NHL87ZnMpU+OKzU2zvSyxJn9ooaka5Rtb2mjLKDxxP42uN7QXo6heAH8BnnxwnV/GamlkVgy20Dfl8XjpORRDRNXqsl6bvun0rSsHzUyWmikurP0BoUu7dt2vZH7NaZ9JdA3AqU2YFz1s3vbmKx5cPTVFy/QVRtZoh0Ggm8w4n0/b5FIk1RHMXH9PxA/pTFh1xg46YQcIKq3SkYgZvv3X0kq4vqL2PZ7JloMis7eIFYfm5Dgtes7O/bsQLjs8LM0UKjo/jh/Wc8xW/7rLr9aGrUc/1iHhuNv7wzh30Ji1+cCJT7Tap6UvG2DWQZKQ641JwfLoTJvmKT3fCojtublijrPvesLu+ZuCnZ/KUXX9Fs11e0ABNM1vyyFf8utYXk6J3aKLAk2dzHJ0p1Rc1mwoytsPhybCU4Muv6tnQNMCvHZ5mtujWZ5kMwpvIB46nVzTYubKP6+uw8o4fVswxjLC50MGJArmyt+oCXjHYQtvgOM7qTxI2HaJr9FgvTefXQ39oLE3RPf9jX4sIvn7PwIo/vvNLuX3nyGxTx/WqkeVHX8wSNxU/OB5G6vIVj5ipyJd9iq6PZYSNW1w/LFM3fzFgs9Qa7XQnwp/bkU6Da6u13PtTVksW7+4aSPH2W0eX1JjOZuf4lWoZzFCb0Xo0/8W5Ml1xk59NFzmVLtfDtWEZRYNfuX7osjPXNWqpJfMjtvO51DcetTz0D39/jMdPz1FofO/aEA1UfE3F98lW/Asq8VebBfrHJ8fJlBdW+PY1+D54fsCLc2WyFY9nxwsNF7JfbHrJfd89znhh4XdVQDjzVGvAVOPTj53l20dnKVQWdhutLY8IO6SGBv1E2uZstkIQwB0rxBnM++67r+nBtiNnzpy5b2jo0kyvCRtLKpXCsuSeL2qIrtFjPTXtT8X4+R29/Oato0zlHSYLYR31VMzgdVf384d3rl66sz8V47rhTv7+yYk1Hdt2fPJOQNnzcfyA/7+9O4+Tqy7zPf55uqq3dKc73UknzZINwiIBBUFRJiNqYBwdEVDQ6+iIKxmNosAMV65eZdSrzLyc0QFnorigXicqbiCOOnpRxkRxGQIjywwYSDoJSXd6S2/prap/94+qbpqkl6ruqj6nnnzfr1e/Qp1Tdeo5fLuqnzr1O7/TeXiUvT2ZmRqqKxKcWF/JSCrzNfH41fHymfmkOmnUViZJlkFttsE+oa6SquyUfM9bWUdD9fyuMjhXDdXl1FUl6RtOMZIeY0l1kvNXLWFdU+0z7nNaUw3PPm4xJ9ZXcbB/lJX1VYyk0/QNZ67IuLgyyZ+eujSvscteTfX/9Hkro7vIi5E5Ibd/eCyncdlT6R1Os21XF887sT6n39XxDxmPdwzweMfgtPcb/7A6MDLGwYFRfr3nEC9cvWTiOca3M5TK1D6UyjTkdVXJWevYvusQN/2/J3jk4OFp72PAn5+Tmc//C795ijsfPcjh0aOnPwxH/Dt+TYZ0gI7Dozx7SXrwtHUn3TLlc4S5fOcVI/fdd184/fTToy5DCmC2+ZKlNClXf+KY6bu/99883jn9H/QjZS7yl5kbozx7BcTaygSpdKC+Ksl5J9aRKDMODY6yq3uQgZExRtOBtr7hZ1yqeioGnFhfSVWyLHMC26JyzlpRW7RhAYUyW65xOGFN8vO93z3OL1oD/912OOerRU6losy4bH0Tbz//hImTBrsGRikrM1YvqeLUZYvA4InOQfqGM9MX9gzl94yNVQm+8cZns6trkL+7dzd7D2WGfCXLjOMWV/Ds42onTtidzvZdh/j6g6080TU444fhpGWmXzw8OkbHwOi0H0Amz++THT4/sV0DNp8ext50ycbElM8x/dOLLCxN0+eTcvUnjpn+IY/mevziRamxQMj+CQ0hM2QjPZaZim98juol1eVcM+kr/k9v28O/Pd457VARI3PF0UXlZVQly+Z1AttCmy3XfC76IvHwR6uXcPnzMmONd3UN8vF7dtHaP0J6LDAWpp968UgjY4E7HjrIjx/rYDg7ZArAzHi4rZ/H2gcYHQvZcctPD63IR9dQmu27DnH7fzzFvp6nZ90ZHQs81Ts8MSXiTH70WAdt/TNfGRgyr/f2SRd9mk5lMnNl077hNImyZ45ZD5ntlE33WDXYEhsVFRVRlyBFoFz9iWOmM/2RbKhKkgqB8uyY6vE/vuNT+VWXl9FQXc7Zxy2mZ/xS1saUR2kvXd/E7u5BHjv4zCOC4xdbuXBtA6c2LSrJo7xxzFXmZ3Kmaxurueq84/n6g630DKVIjQWGUmOkxsYYC7ldXbV3ZGxiTvQyy1xhMpUODGU/qk4MqZjj4IjvPnyQ1r6jjyiHAN2Do5mTbGfwROcgh0dmP3Juljn5+MgL/xypMlHG8XUVPNU7Qv9wasqLAU1HDbbERk9PD0uW6Gp93ihXf+KY6Ux/+L75xrMmxnQeGhzlsfbDDIymqRgrm7hIx6nLFmUu7lFdPuMR57WN1bx3wyruerid+/Yeom94jDKDpdXlXLq+qaTnc49jrjI/R2Y6fiLhDx/r4EDvCFXlZTRUlTOSHsv5W6Dx19lYIHM1ySOWz6QMZpyW878ODkz57dAYT1/MaLqhStt3HeLQUGrGo9cGLKlK0D/69PR700ka1FcnWdNQzYY1DXztwQOMjYzlfKKzGmyJDZ2s6pNy9SeOmTbVlE851d/ymswJUZNnGzFjXlOmrW2s5n0vWsX7WFX4HYlQHHOV+Zkq06mudrmra5CP3fMke3vymyEoc/Lv9B1nbUVZpuENUFOZoKE6SWvfML3DU7fZMzWvybLMBXrGPygf7B9lKDXGz5/opiJh7MueoDyd5x5fS6LMGEqN0dI9yEg68wFhqvndyyzzOp88jeA9O7t4oiv3oWg5Ndhmdi/wQmDyu9epIYT9ZrYU+DzwJ0APcHMI4dZcCzCzc4DbgDOBJ4HNIYR7c328+NHT00NNTU3UZUiBKVd/4pjpR192Mjf+aCddg09fAryxOslHX3byxG2NIZ5ZHHOV+ck107WN1bzlvBP41LYW+kZyGCtC5mhwMnslyyOXBzLDR15ycsPEh9k1DVUTH2BfefsD5DCS4xmWVCfZ3T1I9+Ao+3qGqcxewKfz8CiHp5gAf3yqz9rKBKc2LeLjf7qO7bsO8b1HDrKkqpyOw6MkywKj6cx5F8kyY1lNOfVVSS5ff/S0gc9bWce+nmEGcxlLQ35HsD8aQvjYFMu3AIuA44EzgJ+a2SMhhJ/NtkEzqwTuAm4H/hh4C3CnmZ0UQujKozZxYHQ0j8k6pWQoV3/imOnaxmo+8fJ1muViHuKYq8xPPpmON5RbH2xlZw7DRSoSmUu9p8fGGB8abWTGN5cBxy+u5D1/NPW3PMfXVbG7eyjn2gw4vq6S3+ztnThK3c8YnZM+UB95/2SZYcCK2oqJq6WO7+MvdnVjBoezQ0XMMiczrm2s4kVrG6acn/7Ckxr4+RPdDPbldpR/XkNEzKwWuAx4SQihF/i1mX0HuAqYtcEGXgw0Ah8PIQwDW8zsBuBSMk23HEOam5ujLkGKQLn6E9dMdYR6fuKaq8xdvpmOX8XzC795ivv398045OKWS09jbWM1X/7dfn72RBeHhlKkQ2aWniVVSTasmX48/+LKRF4nDJYBj7b15zwPfeYIembqzSOvljrVEJlcrG2s5tIzmviXB1rpH0nPWvu004tM4X1m1mVmvzezt2WXnQKUAw+Z2VfM7L3AQ8D6HLd5BvA4UGVm283sj4GH83g8qVSKnTt3Tvx0denAd6lqbc3vIhFSGpSrP8rUJ+Xqz1wyXdtYzdvPP4Fzjp/hMoU8fV7DhSc3cN6JdTy7eTHPaqrh2c2LOe/EOi48uWHax57VXMui8txb0GAwzcHqaTVUJ3nnC04s6NVSX33Wcv547RJWLalkRW05yTKmPdSf6xHs/wk8CgwBFwF3mNkh4GB2fR+ZZrsbeASYOZWn1QK9QA2wDlievZ3r4zl48CBXXHHFxO1NmzZx00030draSk1NDYlEgt7eXpqamujq6iKEQFNTE21tbdTWZp6mv7+fFStW0N7ejpnR2NhIe3s7dXV1pNNpBgYGaG5uprW1lfLycurr6+no6KC+vp6RkREGBwcn1ldUVLB48WI6OztpaGhgcHCQoaGhifVVVVVUV1fT3d3N0qVL6evrY2RkZGJ9dXU1FRUV9PT0sGzZMnp6ehgdHZ1Y73mfDh8+zOHDh13tk8ec8t2nZDJJS0uLq33ymFM++5RKpWhpaXG1Tx5zynefRkdH6e3tdbVPHnPKZ58SiQT79+/Pe5/qgEtWlXH/U9P3X+PvAWV9B3lBk7G3tooDXX0sr6/k5PoEZX0HGa6Zep/OrB/j8cYyHuvOXM1xfNq/yRdxCTx9oZfyMphlhr4J41dbPGERnHdcFS0tLQXN6RWnLCExOkAiUUFVYuTQtHXM5UqOZvYZYBnwt8AOoD47RAQzux54fQjhvBy2cy3wFyGE505adjfwWAjhr3KpZdu2baGqqmridmNjI42NjfnsjsREd3c3DQ3Tf+KV0qRc/VGmPilXf+ab6Su++MCUVy5NGvzwbefMo7LMzCX//kT3xMwcJy+tpnNglH/f1U16LEw02Iky44zlNezY3z/rNsfno1+/opYLVtfPeNXH+db+cGs/Nb1797/m5RtPmOo+cx2DPUZmP/5AZmaRs4BfZtedSeYodi4eBU4xs8rsGOzxx38310KSySTr1q3L9e4SY729vXpzd0i5+qNMfVKu/sw3043rGvnJH7qeMd7Yssvna7pzJuqrkvz8yW4Oj46xqLyMl5zUwMZTGvnQv+2kbeDocSJJe/qKlIuSZZy2vIZnLa/hzOacB0PMufYdO/YemO4+szbYZrYEeBFwDzAIXEjmJMa3hxD6zexO4INm9lrgWcBrgMuP2MYJwL8Dt4QQbpm06l7gEPB+M7s5u90GMjOLyDGmqak4nzQlWsrVH2Xqk3L1Z76ZXn/hagB+2XKI4XSgMmH80eolE8uL4e3nn8Dbzz/6oPBHXraOj/z0CfZPutJjfVWCNQ3VlCeM5TUVE9PsxWEGoVyOYJcDHwb+JXu7Bbg2hPCt7O13Al8ADpAZi/2BEMI9U2zjZDIzhkwIIQyb2avIzKP9fmAXcJmm6Ds2dXV1sWjRoqjLkAJTrv4oU5+Uqz+FyPT6C1dzPcVrqHO1trGa2193ZtRl5GzWBjuE0A6cO8P6To44Yj3FfXbz9Fj1I9c9AMw6Xlv8m8v5ABJ/ytUfZeqTcvVHmUYnn2n6RIpKX0/6pFz9UaY+KVd/lGl01GBLbLS1tUVdghSBcvVHmfqkXP1RptFRgy2xMT5PpfiiXP1Rpj4pV3+UaXTUYIuIiIiIFJAabImN/v7ZJ5GX0qNc/VGmPilXf5RpdNRgS2ysWLEi6hKkCJSrP8rUJ+XqjzKNjhpsiY329vaoS5AiUK7+KFOflKs/yjQ6arAlNsymnCpdSpxy9UeZ+qRc/VGm0VGDLbHR2Ng4+52k5ChXf5SpT8rVH2UaHTXYEhv6Kssn5eqPMvVJufqjTKOjBltio66uLuoSpAiUqz/K1Cfl6o8yjY4abImNdDoddQlSBMrVH2Xqk3L1R5lGRw22xMbAwEDUJUgRKFd/lKlPytUfZRodNdgSG83NzVGXIEWgXP1Rpj4pV3+UaXTUYEtstLa2Rl2CFIFy9UeZ+qRc/VGm0VGDLbHQ1dXFbbfdRldXV9SlSAEpV3+UqU/K1R9lGi012JKX73//+0XZbldXF1u2bNEbgTPK1Z+FyLRY7zMyvVJ/rep35milnmmxFft3Rg225OXuu++OugQRcU7vM5Iv/c5Ivor9O2MhhKI+QbHdc8897UBL1HUcK3p6epbU19cfKvR2U6lUoqura0VjY2NbMpnUvEJOpFKpRGtr6+rm5uYW5erDQrxWi/U+I9Mr9fdg/c4crdQzLbYC/c6s3rhxY9NUK0q+wRYRERERiRMNERERERERKSA12CIiIiIiBaQGW0RERESkgNRgi4iIiIgUkBpsEREREZECUoMtIiIiIlJAarBFRERERApIDbaIiIiISAGpwRYRERERKaBk1AXM17333hsqKyujLkMKIJVKkUyW/K+kHEG5+qNMfVKu/ijT4jp8+HDHdJdKL/n/65WVlZx++ulRlyEF0NHRwbJly6IuQwpMufqjTH1Srv4o0+LasWNHy3TrNEREYiORSERdghSBcvVHmfqkXP1RptFRgy2x0dvbG3UJUgTK1R9l6pNy9UeZRkcNtsRGU9OUw5ikxClXf5SpT8rVH2UaHTXYEhtdXV1RlyBFoFz9UaY+KVd/lGl01GBLbIQQoi5BikC5+qNMfVKu/ijT6JT8LCLih77K8km5+qNMfVKuC2N4eJihoSHMrOjPVVtbq3HY8xBCoKqqirlMB60GW2Kjra2N1atXR12GFJhy9UeZ+qRci29gYACAurq6BWmwh4eH59QcSkYIgcOHD5NKpaipqcnrsRoiIrFRW1sbdQlSBMrVH2Xqk3ItvnQ6TU1NzYI016Bp+ubLzKipqSGdTuf9WDXYIiIiIiIFpAZbYqO/vz/qEqQIlKs/ytQn5erPXI68SmGowZbYWLFiRdQlSBEoV3+UqU/K1Z/y8vKoSzhmqcGW2Ghvb4+6BCkC5eqPMvVJufqTSqWK/hzXXXcdN9xwQ96Pe85znsMdd9wxp+fcunUr55577pweu1A0i4jExkKd9CELS7n6o0x9Uq7xlty+nbI9e0ht2DBxGyC1YQNjq1YV5Tm3b9/O5ZdfPuOHr3/4h38oynOXOjXYEhuNjY1RlyBFoFz9UaY+Kdd4K9uzh7I9e6jYuvWo5dM12JpFJDoaIiKxoa8nfVKu/ihTn5RrvI0fuT5y2VTLJ9bPcYjI7t27WblyJa997WtJp9OsXLmSlStXcs0110zc53Of+xwrV65k+fLlz1g+bmxsjM9//vO84AUvYNWqVZx//vn89Kc/nfL5HnroIdavX8+dd96Zc40hBG688cYpt/3www/zyle+kpNOOmliP/bt2/eMx2/evJnNmzdz4403cvLJJ7NmzRq++c1v5vz8s8mpwTaze81s2Mz6J/0cn113uZltM7MBM9udbwFmdo6Z/c7MBs3sETN7cb7bEB/q6uqiLkGKQLn6o0x9Uq7+zPUI9po1a9i7dy933HEHiUSCvXv3snfvXm655ZaJ+2zatIm9e/dy5ZVXTrmN2267jVtvvZXPfvaz7N69m6997WtUVFQcdb/f/va3vO51r+PTn/40l112Wc417t69m+bmZp588kluuOEG3vrWt9LZ2QlkLrDzjne8g9///vc8/vjj1NXV8Y53vOOobdx9992sXbuWRx99lPvvv5+zzjor5+efTT5HsD8aQqid9LM/u7wHuAX4m3yf3MwqgbuAHwINwGeAO81M31MdgzSdkE/K1R9l6pNyjbfxMddHLptq+bgQQjFLmtHtt9/Oddddx9lnn01ZWRmnnHIKF1544TPus23bNq644go++clPcvHFF+e1/UWLFvGud72LZDLJa17zGpYtW8Y999wDwLnnnsull15KbW0t1dXVvP71r+fBBx88ahunnXYaV199NZWVlSxdupQzzjhj7jt8hHkPEQkh/CyE8C1g/6x3PtqLgUbg4yGEoRDCFqAbuDTXDaRSKXbu3Dnx09XVNYcyJA7GLyErvihXf5SpT8q1NMw2LGSysbGxIlczvX379rFmzZoZ7/OTn/yEs88+m2984xt5b7+hoeEZ0xAuX76cgwcPAtDS0sKb3vQm1q9fz5o1a3jLW97C8PDwUds49dRT837eXOVzkuP7zOw6YB/wjyGELxbg+c8AHgeqzOwe4EbgYWB9rhs4ePAgV1xxxcTtTZs2cdNNN9Ha2kpNTQ2JRILe3l6ampro6uoihEBTUxNtbW0Tl4Xt7+9nxYoVtLe3Y2Y0NjbS3t5OXV0d6XSagYEBmpubaW1tpby8nPr6ejo6Oqivr2dkZITBwcGJ9RUVFSxevJjOzk4aGhoYHBxkaGhoYn1VVRXV1dV0d3ezdOlS+vr6GBkZmVhfXV1NRUUFPT09LFu2jJ6eHkZHRyfWe96ndDrN4cOHXe2Tx5zy3af6+npaWlpc7ZPHnPLZp/LyclpaWlztk8ec8t2nZDJJb2+vq32KW04DAwNUVVWRSCRIpVIkEglCCIyNjVFeXs7o6ChmNrE+mUwyNjaWaZQvuIBw3HGkzj+fsrIyRkdHSSQSDD3/+YTh4YnHl5WVYWak02kSiQQjIyOEEKZcn0wmJ765SCaTE9uEZ36jMTIyMm3N6XR6YnuT159wwgk8+eSTvPCFL5xyn0IIfOhDH2Ljxo1cdNFFbNmyhbe+9a0TNc1UcyqVorOzc6JpLisro62tjcbGRtLpNO9973tZvnw5v/zlL6muruZnP/sZb3jDGxjO/n9KpVKk02nKy8sZHh6eNYeRkREOHjx41O/eTCyXrw/M7HzgUWAIuAi4A3hzCOE7k+7zRuBjIYQ1s27w6cf8b2Aj8OfADmAz8GqgL4Twl7lsY9u2baGqqmridmNjo86ELlEtLS2sXr066jKkwJSrP8rUJ+VafL29vQs61n14eJjKyso5P37Xrl2ce+65bN++fdrhE5s3byaRSDxjfDbAli1b2LJlC1/96ld5znOew65du9i3bx8vetGLgMw82B/4wAd47Wtfy69+9SuuvPJKfvCDH3DOOefMWtfWrVt597vfzYc//GE2b97MXXfdxbXXXsuDDz5IY2MjF110Ec997nP5xCc+QXt7O1dffTXbt29/xiiH6eqeynS57dix4/6NGzeeN9VjcjqCHUL4zaSbPzKzrwBXAt+Z5iG56gfqsuO5mwHM7M3AgVw3kEwmWbdu3TzLkDjQFad8Uq7+KFOflKs/853bfO3atVxzzcuueC4AAB7MSURBVDW8+tWvJpFIcMkll3DzzTcDsH79enp7eyeOIn/ve9+jrq6ORx55BMiMKBj/98CBAxx33HF87GMfm/J5LrjgAt797nfztre9jXvvvTenDyFr1qzhwIEDnHTSSRx33HHcfvvtEwdYP/GJT3DttdeyZs0aVq1axete9zq2zzBWvRhyOoJ91IPMbgFWhBBeN2nZXI5gvwz4NrAshDCcXbYL+EgI4fZctnHfffeF008/PZ/yJaYGBgaoqamJugwpMOXqjzL1SbkW30IfwR4fJiLzM5cj2LOe5GhmS8zsVWZWY2ZlZvYS4CoyjTFmljCzKqA8c9OqsrODTN7GCWa208yOnCjxXuAQ8H4zqzSzq8nMJnLXrHsr7nR0dERdghSBcvVHmfqkXP1ZiEuly9RymUWkHPgw0EpmSr5bgWuzM4cA/AUwCHwJWJX978em2MbJZGYMmZA9av0q4BIyjfb7gMtCCJoK5BhUX18fdQlSBMrVH2Xqk3L1R0evozPrGOwQQjtw7gzrvwx8eZZt7AamHAgUQngAmPLwuhxbRkZGoi5BikC5+qNMfVKu/kQ5D/axTpdKl9gYHByMugQpAuXqjzL1Sbn6E+U82Mc6NdgSG83NzVGXIEWgXP1Rpj4pV380M0x01GBLbLS2tkZdghSBcvVHmfqkXIsvkUgwMDCwYEM3RkdHF+R5vAohMDAwMKex7PlcyVGkqGa7KpKUJuXqjzL1SbkWX01NDcPDw/T29s57jupcHD58mEWLFhX9ebwKIVBVVTWni/WowZbYWLx4cdQlSBEoV3+UqU/KdWFUVlbO6+qK+SgrK5u4nLssLA0Rkdjo7OyMugQpAuXqjzL1Sbn6o0yjowZbYqOhoSHqEqQIlKs/ytQn5eqPMo2OGmyJDU0R5ZNy9UeZ+qRc/VGm0VGDLbExNDQUdQlSBMrVH2Xqk3L1R5lGRw22xIbmYPVJufqjTH1Srv4o0+iowZbY0BysPilXf5SpT8rVH2UaHTXYEhtVVVVRlyBFoFz9UaY+KVd/lGl01GBLbFRXV0ddghSBcvVHmfqkXP1RptFRgy2x0d3dHXUJUgTK1R9l6pNy9UeZRkcNtsTG0qVLoy5BikC5+qNMfVKu/ijT6KjBltjo6+uLugQpAuXqjzL1Sbn6o0yjowZbYmNkZCTqEqQIlKs/ytQn5eqPMo2OGmyJDc3X6ZNy9UeZ+qRc/VGm0VGDLbGh+Tp9Uq7+KFOflKs/yjQ6arAlNjSdkE/K1R9l6pNy9UeZRkcNtsRGRUVF1CVIEShXf5SpT8rVH2UaHTXYEhs9PT1RlyBFoFz9UaY+KVd/lGl01GBLbCxbtizqEqQIlKs/ytQn5eqPMo2OGmyJDX3S9km5+qNMfVKu/ijT6KjBltgYHR2NugQpAuXqjzL1Sbn6o0yjowZbYkPzdfqkXP1Rpj4pV3+UaXTUYEtsaL5On5SrP8rUJ+XqjzKNjhpsiY2ampqoS5AiUK7+KFOflKs/yjQ6arAlNhKJRNQlSBEoV3+UqU/K1R9lGh012BIbvb29UZcgRaBc/VGmPilXf5RpdJJRFzBvIyNUbN0KQGrDBsZWrYq4IJmrpqamqEuQIlCu/ihTn5SrP8o0OiV/BNs6Oynbs2fiR0pXV1dX1CVIEShXf5SpT8rVH2UanZJvsMelNmwgtWFD1GXIPIQQoi5BikC5+qNMfVKu/ijT6OTUYJvZvWY2bGb9k36Oz65bambfzS57yszek+uTm9kaMwtmNjBpu3fPdWektOmrLJ+Uqz/K1Cfl6o8yjU4+R7A/GkKonfSzP7t8C7AIOB54DfBxM3tpnnWcNmm7l+T5WACS27eT3L59Lg+VmGhra4u6BCkC5eqPMvVJufqjTKMzr5MczawWuAx4SQihF/i1mX0HuAr4WQHqm1V60aKJMUZ9Tz1FeVcXjY2NC/HUUmC1tbVRlyBFoFz9UaY+KVd/lGl08mmw32dm1wH7gH8MIXwROAUoBx4ys68AO4CHgNfnWcevzawC+A/gr0MIj+T6wNaBAT7whS8A8O9f+AKbNm3ipptuorW1lZqaGhKJBL29vTQ1NdHV1UUIgaamJtra2iZ+8fr7+1mxYgXt7e2YGY2NjbS3t1NXV0c6nWZgYIDm5mZaW1spLy+nvr6ejo4O6uvrGRkZYXBwcGJ9RUUFixcvprOzk4aGBgYHBxkaGppYX1VVRXV1Nd3d3SxdupS+vj5GRkYm1ldXV1NRUUFPTw/Lli2jp6eH0dHRifWe96m9vZ3q6mpX++Qxp3z3qaysjJaWFlf75DGnfPapp6eH/v5+V/vkMad89ymVSlFeXu5qnzzmlM8+JZNJ9u/f72qf4pTTTCyXAfBmdj7wKDAEXATcAbwZOAj8gsxQk18CvwUeAa4PIZyew3ZrgdOA/wQWAzcBVwDPyh4Rn9W2bdtCVVXVxO3GxkYdwS5RLS0trF69OuoypMCUqz/K1Cfl6o8yLa4dO3bcv3HjxvOmWpfTEewQwm8m3fxR9mj1lcDfZpctDiFcAGBm1wP9OW63H7g/e7PbzK4F3gJcAPw4l20kk0nWrVuXy10l5lasWBF1CVIEytUfZeqTcvVHmUZnrtP0jQEG/AEYBc6atO5MMkex5yJM2rYcY9rb26MuQYpAufqjTH1Srv4o0+jM2mCb2RIze5WZ1ZhZmZm9hMxJjN/OHoG+E/igmS02s+eTmUnkq0ds4wQz22lm1xyx/IVmdpaZJcyshswR8QHgVwXaPykhZvpc5ZFy9UeZ+qRc/VGm0cnlCHY58GGgFegBbgWuDSF8K7v+nWTGZh8A7gI+EEK4Z4ptnAwcOTh6LXA30Au0kDn6/bIQQk/+uyKlTmPnfVKu/ihTn5SrP8o0OrOOwQ4htAPnzrC+E7h8lm3sZophHyGErcDWWauUY0J7e7tOxnBIufqjTH1Srv4o0+i4uVS6lL66urqoS5AiUK7+KFOflKs/yjQ687rQjEghpdPpqEuQIijVXJPbt1O2Zw+pDRsmbgOkNmxgbNWqKEuLXKlmKjNTrv4o0+joCLbExsDAQNQlSBGUaq5le/ZQtmcPFVu3UrF168Ttsj17oi4tcqWaqcxMufqjTKOjBltio7m5OeoSpAhKNdfxI9dHLptq+bGmVDOVmSlXf5RpdNRgS2y0trZGXYIUgXL1R5n6pFz9UabRUYMtsVFeXh51CVIEpZrr+JjrI5dNtfxYU6qZysyUqz/KNDpqsCU26uvroy5BiqDUc9WwkKOVeqYyNeXqjzKNjmYRkdjo6OigpqYm6jKkwEo119SGDc+YRWTy8mNdqWYqM1Ou/ijT6KjBltjQJ22fSjXXsVWrnjEdnxrrp5VqpjIz5eqPMo2OhohIbIyMjERdghSBcvVHmfqkXP1RptFRgy2xMTg4GHUJUgTK1R9l6pNy9UeZRkcNtsSG5uv0Sbn6o0x9Uq7+KNPoqMGW2NB8nT4pV3+UqU/K1R9lGh012BIbFRUVUZcgRaBc/VGmPilXf5RpdNRgS2wsXrw46hKkCJSrP8rUJ+XqjzKNjhpsiY3Ozs6oS5AiUK7+KFOflKs/yjQ6arAlNhoaGqIuQYpAufqjTH1Srv4o0+iowZbY0HRCPilXf5SpT8rVH2UaHTXYEhtDQ0NRlyBFoFz9UaY+KVd/lGl01GBLbGi+Tp+Uqz/K1Cfl6o8yjY4abIkNzdfpk3L1R5n6pFz9UabRUYMtsVFVVRV1CVIEytUfZeqTcvVHmUZHDbbERnV1ddQlSBEoV3+UqU/K1R9lGh012BIb3d3dUZcgRaBc/VGmPilXf5RpdNRgS2wsXbo06hKkCJSrP8rUJ+XqjzKNjhpsiY2+vr6oS5AiUK7+KFOflKs/yjQ6arAlNkZGRqIuQYpAufqjTH1Srv4o0+iowZbY0HydPilXf5SpT8rVH2UaHTXYEhuar9Mn5eqPMvVJufqjTKOjBltiQ9MJ+aRc/VGmPilXf5RpdNRgS2xUVFREXYIUgXL1R5n6pFz9UabRUYMtsdHT0xN1CVIEytUfZeqTcvVHmRZecvt2KrZupWzPHspGRqb9BKMGW2Jj2bJlUZcgRaBc/VGmPilXf5Rp4ZXt2UPZnj1UbN1K+aFD0/4PzqnBNrN7zWzYzPon/RyfXbfUzL6bXfaUmb0nn0LN7Bwz+52ZDZrZI2b24nweL37ok7ZPytUfZeqTcvVHmRZeasOGnO6XzxHsj4YQaif97M8u3wIsAo4HXgN83MxemssGzawSuAv4IdAAfAa408wa86hLnBgdHY26BCkC5eqPMvVJufqjTKMzryEiZlYLXEam+e4NIfwa+A5wVY6beDHQCHw8hDAUQtgCdAOX5lpDKpVi586dEz9dXV357YTEhubr9Em5+qNMfVKu/ijTwktu357b/fLY5vvM7DpgH/CPIYQvAqcA5cBDZvYVYAfwEPD6HLd5BvA4UGVm9wA3Ag8D63Mt6uDBg1xxxRUTtzdt2sRNN91Ea2srNTU1JBIJent7aWpqoqurixACTU1NtLW1UVtbC0B/fz8rVqygvb0dM6OxsZH29nbq6upIp9MMDAzQ3NxMa2sr5eXl1NfX09HRQX19PSMjIwwODk6sr6ioYPHixXR2dtLQ0MDg4CBDQ0MT66uqqqiurqa7u5ulS5fS19fHyMjIxPrq6moqKiro6elh2bJl9PT0MDo6OrHe8z7t3buXdevWudonjznlu09DQ0OUl5e72iePOeWzT/v376empsbVPnnMKd99GhgY4MQTT3S1Tx5zymefRkZGqKmpcbVPUee0NpVisKeHoRe8gPSiRdNei95CCLM2sWZ2PvAoMARcBNwBvBk4CPyCzJHwXwK/BR4Brg8hnJ7Ddv83sBH4czLN+Wbg1UBfCOEvZy0M2LZtW6iqqpq43djYSGOjRpiUoo6ODp2Q4ZBy9UeZ+qRc/VGmhTd+kmNqwwZ27Nhx/8aNG8+b6n45HcEOIfxm0s0fZY9WXwn8bXbZ4hDCBQBmdj3Qn2Od/UBddjx3c/bxbwYO5Ph4kskk69aty/XuEmOJRCLqEqQIlKs/ytQn5eqPMi28sVWrGFu1atb7zXUM9hhgwB+AUeCsSevOJHMUOxePAqdkT3acy+PFkd7e3qhLkCJQrv4oU5+Uqz/KNDqzNthmtsTMXmVmNWZWZmYvIXMS47dDCP3AncAHzWyxmT2fzEwiXz1iGyeY2U4zu+aIzd8LHALeb2aVZnY1mdlE7pr/rkmpaWpqiroEKQLl6o8y9Um5+qNMo5PLEexy4MNAK9AD3ApcG0L4Vnb9O8mMzT5ApjH+QAjhnim2cTKZGUMmhBCGgVcBl5BptN8HXBZC0FQgxyDNAOOTcvVHmfqkXP1RptGZdQx2CKEdOHeG9Z3A5bNsYzeZISVTrXsAmHKAuBxbcjnhVkqPcvVHmfqkXP1RptHRpdIlNvRVlk/K1R9l6pNy9UeZRkcNtsRGW1tb1CVIEShXf5SpT8rVH2UaHTXYEhvjE8GLL8rVH2Xqk3L1R5lGRw22iIiIiEgBqcGW2Ojvz/X6RFJKlKs/ytQn5eqPMo2OGmyJjRUrVkRdghSBcvVHmfqkXP1RptFRgy2x0d7eHnUJUgTK1R9l6pNy9UeZRkcNtsSG2ZRTpUuJU67+KFOflKs/yjQ6arAlNhobG2e/k5Qc5eqPMvVJufqjTKOjBltiQ19l+aRc/VGmPilXf5RpdNRgS2zU1dVFXYIUgXL1R5n6pFz9UabRUYMtsZFOp6MuQYpAufqjTH1Srv4o0+iowZbYGBgYiLoEKQLl6o8y9Um5+qNMo6MGW2Kjubk56hKkCJSrP8rUJ+XqjzKNjhpsiY3W1taoS5AiUK7+KFOflKs/yjQ6arAlFrq6urjtttvo6uqKuhQpIOXqjzL1Sbn6o0yjpQZb8vL973+/KNvt6upiy5YteiNwRrn6sxCZFut9RqZX6q9V/c4crdQzLbZi/86owZa83H333VGXICLO6X1G8qXfGclXsX9nLIRQ1CcotnvuuacdaIm6jmNFT0/Pkvr6+kOF3m4qlUp0dXWtaGxsbEsmk5pXyIlUKpVobW1d3dzc3KJcfViI12qx3mdkeqX+HqzfmaOVeqbFVqDfmdUbN25smmpFyTfYIiIiIiJxoiEiIiIiIiIFpAZbRERERKSA1GCLiIiIiBSQGmwRERERkQJSgy0iIiIiUkBqsEVERERECkgNtoiIiIhIAanBFhEREREpIDXYIiIiIiIFpAZbRETEOTNLZv/V330nzKzCzJZEXYdMTS80KToze6uZPSfqOqSwzGyTmf2Nmb3czJqjrkfmz8zeZWafMrNLzWxt1PVIYZjZK4E+M3thCGHMzBJR1yTzY2Z/BfQC50ddi0xNDbYUjZmdZmY/B74AbIm6HikMM3u+mT0CvAOoBT4JfCTaqmQ+zGyDmf0X8FagDbgJ+HCkRUkhnQFUAn8PEEJIR1uOzJWZvcjMdgJXAi8PIfxb1DXJ1NRgS1Fkv4ZcDzxK5hP2WWb2xmirkvkys+XAO4H/G0I4L4RwPfBp4BQzOyPa6mQuzGwNsAm4JZvpzcDjQI2ZVZqZRVmfzE82v3rgOuBEM3tXdnky0sIkb2Z2CvBPwP4QwvkhhJ+bWaOZLYq6NjmahRCirkGcMrMVQG0I4Qkz+xhwFXB6CGEg4tJkHszsbcCPgQPZr5ufDfwr8KwQQn+01clcmNnJ2ddpGfBFMq/VH5D5lqI3hDAYaYEyL2Z2K/AQ0AC8P4TQkF1eFkIYi7Q4yYuZ3QScCnwZuAjYAAwAPwE+H0I4FFlx8gw6gi0FYWZvMLMrJ4/tCyG0hRCeyN78OGDAjdn763evBEyVK3B7COGpSX+Ya4CdwJCZlS98lZKPaV6r46/T/wEsBp4P3EpmqMiXFrxIyds0r9VxxwH/SebD0wEzu9nMXgqsXtAiJS/TZPp1YBnwXeB04L3AA8ArgJsXvkqZjr4iknkxsxOBfwZeCTwF/A7YfcR9LIRw2MzeD3zezL4cQti54MVKzmbKdbyxNrNEdiznOcBgCCEVTbWSi5kyzb5GA/C9EMLW8WXZh37LzM4JITyw8FXLbGbJdfw1OkLm28QOM/sumQMdm4B1k+4jMTHL++9jZvYl4LYQwrezD/mdmb0B2GxmJ4YQ9kVQthxBRxFlvo4D2oE1QBp455Fj+7J/uAkhfA24H/hfZrbSzL5sZo0LXK/kJpdcx/8o/xnwTQAzO87MPriAdUrups10/DUKDAGYWXV2WRLoAjSMIL5myjVtZjVAHZnPTHcD1wOPAL8JIXSibONotvffb5MZlsek8de1QBWgYXoxoQZb5utB4JMhhD3ADcBmYKYp+f4KeDPwJJk3/e5iFyhzklOuZlZPZvzfj8zsZuC/gRMXslDJ2ayZTvowPD7m+lXA74E/LGCdkp8Zc82e81JBZoxuJ5n33dcDF5vZGZM+XEl8zJZpavw1GkI4nF38XOBnGoMdHzrJUQrKzO4j85XWVUeezGhmrwW+QeaT93tCCLsXvkKZiyNzHR9SkJ3f/AEyR00eAN4eQlAzVgKme62a2V8A64C3kBlbf1UIYW80VUq+JuX65hBCf3aoz9lAx3iOZlYNVIcQuiIsVXI0w2v1pWRm6XoPmW8l3hRCOBBNlXIkHcGWgpj09dUm4DLgTyatGz9BYxB4XQjhEjXXpWG6XCcd9VoBPAa8KoRwoZrr+JvptZq1n0xDdnUI4aVqrkvDFLleDBOv1QdDCHvNrDz74XhQzXX85fBaNTJD9K4OIVys5jpedARbCmb8ZBkz+zJwMvAu4K+Bz4YQfhVpcTJn0+R6A/BPIYRfR1qczMkMmf5zCOG+SIuTOZvhPXiLci1Neq2WLjXYUjDjc6qaWRVwGAhkxv1dFkIYjrY6masZcr08hDAUbXUyF8rUJ70H+6PXaunSEBF5BjN7hZndamZnZW/nfBW37JvAxWTG4/4CWB9CeLne2KNXpFz15h4hZeqT3oP90Wv12KR5sAWYmOrnb4C3kRkrvdvMHp3D/Ki7yZzo9uXCVihzoVz9UaY+KVd/lOmxTQ22jAtkpnJ6BXApsJHM5Pa/yHUD2ZNn/oCm9IoT5eqPMvVJufqjTI9hGiJyDMuO6QIm5r39dPaktX8GFgGvMLMl2fvO+pWW5lONB+XqjzL1Sbn6o0xlnBrsY5CZXWxmDwJfM7NPTbrq167sv3uBO4EXAC+NrlLJh3L1R5n6pFz9UaZyJDXYxxgzuwD4Svbn68AbgVvN7LTs+vLsXT9H5qSKPzOz5uxFRWqiqFlmp1z9UaY+KVd/lKlMRQ32seciYFsI4VMhhO8AVwGnAVdlx3qNmlky+9XWl4DVwGvM7HPAR7MnbUj8KFd/lKlPytUfZSpHUYN9jJg01isJPCu77A6gGbgPeA6ZEzAAxgBCCN8FjgduzT7mkyGEwwtYtsxCufqjTH1Srv4oU5mJGuxjxKQTJX4BLMp+pbUfeCFwB1AHnGNmZUAws9Vm9jugGnhRCOFFIYT9UdQu01Ou/ihTn5SrP8pUZqIrOR5jzGwt8FGgHHhrCGEgu/yLwIoQwiuztyuBV2a/7pKYU67+KFOflKs/ylSmoiPYx4Dsp2dg4ozmfwVOAjZNutt/A/1mlsiOGRvWm0C8KVd/lKlPytUfZSqz0YVmHDCzE4COkL0cbvaFHMbfAEIIY9nl5SGEUeCHwDLgU2ZWD7QDfw1cH/K/wpQUiXL1R5n6pFz9UaYyXzqCXcLM7Awzux/4HvBjM3sxPD0uLIQwFkIYM7NzzOwHwJnZ5T0hhFuBdwFNwBuAvwwh/N8o9kOeSbn6o0x9Uq7+KFMpFI3BLlGWuRLUD8mcXPEPwD8BS4E7QgiftcyUQCkz+xLweuDvQwgfzD7WgoKPJeXqjzL1Sbn6o0ylkNRglygzey6Z+TQvCyHsNrNzgMuAVwMvDyHsy97vT4H7Qgg90VUruVKu/ihTn5SrP8pUCklDREqEmVVl/x2/IlSCzAkVwcyuBW4EngR2Au8ff1wI4cchhB4zS5pNzNkpMaFc/VGmPilXf5SpFJMa7JizzLyZvwB+ChAyV4SyEMLvgH1krhi1HagHHgC2AWea2ZrJ2wkhpPT1VXwoV3+UqU/K1R9lKgtBDXaMmdmzgX8BBoDnm9mrs6vGZ3+5GfgQsC+E8LIQwu/JTAvUBOhTdUwpV3+UqU/K1R9lKgtFDXa8jQC3Ae8APk3m0qpkpwSCzFnOPwO+bWZ12WX/NemxEk/K1R9l6pNy9UeZyoLQSY4xlh0XlgwhDJrZSuDXwOdCCB8xs0QIIW1my4HfAbvIvClcBfwc2Byy83dKvChXf5SpT8rVH2UqC0UNdonInkhxNXALmUuvHpo0ZdDZwAuAlwL/L4RwW5S1Su6Uqz/K1Cfl6o8ylWJSg11CLDNH54+BPSGE15pZdQhhMOq6ZH6Uqz/K1Cfl6o8ylWLRGOzS0gNcA1xuZn8PDJjZGyOuSeZPufqjTH1Srv4oUymK5Ox3kbgIIYTs2LAEma+tLg8h3BVxWTJPytUfZeqTcvVHmUqxaIhICTGzPyHzVdb7Qwh/F3U9UhjK1R9l6pNy9UeZSrGowS4h2bFih0MImirIEeXqjzL1Sbn6o0ylWNRgi4iIiIgUkE5yFBEREREpIDXYIiIiIiIFpAZbRERERKSA1GCLiIiIiBSQGmwRERERkQJSgy0iIiIiUkBqsEVEnDCzYGbroq5DRORYpwZbROQYpGZcRKR41GCLiIiIiBSQGmwRkRJlZmeb2QNm1mNmH5i0/OVm9qCZ9ZrZE2b2fjOz7LofmVl/9q7/aWb9ZvbpSY89y8zuNbPu7LZfOGndKWa2PbvdA2b2PxdsZ0VESogabBGREpRtmLcCXwWagKWTVpcB78kuuxi4DrgEIITw8hBCbfZ+zwkh1IYQ3pfd5mLgJ9ntLgM+AHzXzBZl7/8R4D+BJcAZwC+LtoMiIiVMDbaISGk6CTgZ+EwIYQT41PiKEMK/hhC2hRBGQwhPAj8Hzs5hm68EWkMIt4UQ0iGEHwIHgT/Krh8DmoHjQgjdIYTthdwhEREv1GCLiJSmJuBQCGE0e7ttfIWZPd/Mfm5m7WZ2CLgcKM9hmyuB9WZ2aPwHOAU4Lrv+BqAX+A8z22lmVxZsb0REHFGDLSJSmg4CS8xsvHFeMWnd14G7gONDCEuAHwB2xOPDFNvcC/w8hLBk0s+iEMJXAUIIT4UQ3gIcD/wdcFsB90dExA012CIipWkX8Adgc7bJvnbSusVAB5AysxcDL5vi8a3AmUcs+1fgTDO70sySZlZjZq82swYAM7vczI4PIQQyDXpfYXdJRMQHNdgiIiUo2+S+AXgzmWa6fdLqdwH/h8xwjncDP5xiE/8L+IyZPWVmn8husxd4OfCXZI6Q7wbeSGbsNcB5wP3ZWUjeDVxV0J0SEXHCMu/RIiIiIiJSCDqCLSIiIiJSQGqwRUREREQKSA22iIiIiEgBqcEWERERESkgNdgiIiIiIgWkBltEREREpIDUYIuIiIiIFJAabBERERGRAvr/G/fWFU8sNkIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample_data(xdf, xtdf, 'tick bar', alpha=0.5, markersize=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus Exercise: Make OHLC Bars from Custom Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract `tick_df.price` and `df.price` into two pandas series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.302701Z",
     "start_time": "2018-05-05T00:36:47.036224Z"
    }
   },
   "outputs": [],
   "source": [
    "sub = tick_df.price\n",
    "ref = df.price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below creates the OHLC dataframe by:\n",
    "    1. Iterating over the subordinated series' index extracting idx and idx+1 period\n",
    "    2. Selecting the same date period from the reference series\n",
    "    3. Extracting the max, min prices from the reference series.\n",
    "    4. Combining the o,h,l,c and start and end timestamps into a row\n",
    "    5. Returning the aggregated rows as a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.600145Z",
     "start_time": "2018-05-05T00:36:47.304265Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_ohlc(ref, sub):\n",
    "    '''\n",
    "    fn: get ohlc from custom bars\n",
    "    \n",
    "    # args\n",
    "        ref : reference pandas series with all prices\n",
    "        sub : custom tick pandas series\n",
    "    # returns\n",
    "        tick_df : dataframe with ohlc values\n",
    "    '''\n",
    "    ohlc = []\n",
    "    for i in tqdm(range(sub.index.shape[0]-1)):\n",
    "        start,end = sub.index[i], sub.index[i+1]\n",
    "        tmp_ref = ref.loc[start:end]\n",
    "        max_px, min_px = tmp_ref.max(), tmp_ref.min()\n",
    "        o,h,l,c = sub.iloc[i], max_px, min_px, sub.iloc[i+1]\n",
    "        ohlc.append((end,start,o,h,l,c))\n",
    "    cols = ['end','start','open','high','low','close']\n",
    "    return (pd.DataFrame(ohlc,columns=cols))\n",
    "\n",
    "## uncomment below to run (takes about 5-6 mins on my machine)\n",
    "#tick_bars_ohlc = get_ohlc(ref, sub)\n",
    "#cprint(tick_bars_ohlc)\n",
    "\n",
    "#outfp = PurePath(data_dir/'processed'/'tick_bars_ohlc.parq')\n",
    "#tick_bars_ohlc.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 11290/11290 [00:03<00:00, 3449.89it/s]\n"
     ]
    }
   ],
   "source": [
    "tick_bars_ohlc = get_ohlc(ref, sub)\n",
    "#cprint(tick_bars_ohlc)\n",
    "\n",
    "outfp = PurePath('c:/Users/MEC/Documents/AI/Adv_Fin_ML_Exercises-master/Adv_Fin_ML_Exercises-master/data/processed/tick_bars_ohlc.parq')\n",
    "tick_bars_ohlc.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>end</th>\n",
       "      <th>start</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11285</th>\n",
       "      <td>2017-03-21 13:52:38</td>\n",
       "      <td>2017-03-21 13:24:10</td>\n",
       "      <td>103.9700</td>\n",
       "      <td>104.1900</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11286</th>\n",
       "      <td>2017-03-21 14:20:31</td>\n",
       "      <td>2017-03-21 13:52:38</td>\n",
       "      <td>103.9900</td>\n",
       "      <td>104.2000</td>\n",
       "      <td>103.88</td>\n",
       "      <td>104.1725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11287</th>\n",
       "      <td>2017-03-21 14:47:55</td>\n",
       "      <td>2017-03-21 14:20:31</td>\n",
       "      <td>104.1725</td>\n",
       "      <td>104.2625</td>\n",
       "      <td>104.00</td>\n",
       "      <td>104.0663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11288</th>\n",
       "      <td>2017-03-21 15:10:52</td>\n",
       "      <td>2017-03-21 14:47:55</td>\n",
       "      <td>104.0663</td>\n",
       "      <td>104.2255</td>\n",
       "      <td>103.97</td>\n",
       "      <td>104.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11289</th>\n",
       "      <td>2017-03-21 15:38:27</td>\n",
       "      <td>2017-03-21 15:10:52</td>\n",
       "      <td>104.2000</td>\n",
       "      <td>104.2192</td>\n",
       "      <td>104.03</td>\n",
       "      <td>104.0300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      end               start      open      high     low  \\\n",
       "11285 2017-03-21 13:52:38 2017-03-21 13:24:10  103.9700  104.1900  103.96   \n",
       "11286 2017-03-21 14:20:31 2017-03-21 13:52:38  103.9900  104.2000  103.88   \n",
       "11287 2017-03-21 14:47:55 2017-03-21 14:20:31  104.1725  104.2625  104.00   \n",
       "11288 2017-03-21 15:10:52 2017-03-21 14:47:55  104.0663  104.2255  103.97   \n",
       "11289 2017-03-21 15:38:27 2017-03-21 15:10:52  104.2000  104.2192  104.03   \n",
       "\n",
       "          close  \n",
       "11285  103.9900  \n",
       "11286  104.1725  \n",
       "11287  104.0663  \n",
       "11288  104.2000  \n",
       "11289  104.0300  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_bars_ohlc.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Volume Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.898221Z",
     "start_time": "2018-05-05T00:36:47.601572Z"
    }
   },
   "outputs": [],
   "source": [
    "def volume_bars(df, volume_column, m):\n",
    "    '''\n",
    "    compute volume bars\n",
    "\n",
    "    # args\n",
    "        df: pd.DataFrame()\n",
    "        volume_column: name for volume data\n",
    "        m: int(), threshold value for volume\n",
    "    # returns\n",
    "        idx: list of indices\n",
    "    '''\n",
    "    t = df[volume_column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    for i, x in enumerate(tqdm(t)):\n",
    "        ts += x\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def volume_bar_df(df, volume_column, m):\n",
    "    idx = volume_bars(df, volume_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:48.774435Z",
     "start_time": "2018-05-05T00:36:47.900399Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volume threshold: 10,000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 2818319.99it/s]\n"
     ]
    }
   ],
   "source": [
    "volume_M =  10_000 # arbitrary \n",
    "print(f'volume threshold: {volume_M:,}')\n",
    "v_bar_df = volume_bar_df(df, 'v', volume_M)\n",
    "#cprint(v_bar_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:44:09</th>\n",
       "      <td>50.91</td>\n",
       "      <td>50.91</td>\n",
       "      <td>50.92</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>56001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:46:35</th>\n",
       "      <td>51.07</td>\n",
       "      <td>51.05</td>\n",
       "      <td>51.07</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>168531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:52:47</th>\n",
       "      <td>51.13</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.14</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>102260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:54:36</th>\n",
       "      <td>51.13</td>\n",
       "      <td>51.11</td>\n",
       "      <td>51.13</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>20452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:54:41</th>\n",
       "      <td>51.14</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.14</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>35798.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask    size       v        dv\n",
       "dates                                                             \n",
       "2009-09-28 09:44:09  50.91  50.91  50.92  1100.0  1100.0   56001.0\n",
       "2009-09-28 09:46:35  51.07  51.05  51.07  3300.0  3300.0  168531.0\n",
       "2009-09-28 09:52:47  51.13  51.13  51.14  2000.0  2000.0  102260.0\n",
       "2009-09-28 09:54:36  51.13  51.11  51.13   400.0   400.0   20452.0\n",
       "2009-09-28 09:54:41  51.14  51.13  51.14   700.0   700.0   35798.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_bar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:55.626890Z",
     "start_time": "2018-05-05T00:36:48.776371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (488,), xtdf shape: (29,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xDate = '2010-10-1'\n",
    "xdf, xtdf = select_sample_data(df, v_bar_df, 'price', xDate)\n",
    "print(f'xdf shape: {xdf.shape}, xtdf shape: {xtdf.shape}')\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'volume bar', alpha=0.5, markersize=7)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dollar Value Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:55.921723Z",
     "start_time": "2018-05-05T00:36:55.628100Z"
    }
   },
   "outputs": [],
   "source": [
    "def dollar_bars(df, dv_column, m):\n",
    "    '''\n",
    "    compute dollar bars\n",
    "\n",
    "    # args\n",
    "        df: pd.DataFrame()\n",
    "        dv_column: name for dollar volume data\n",
    "        m: int(), threshold value for dollars\n",
    "    # returns\n",
    "        idx: list of indices\n",
    "    '''\n",
    "    t = df[dv_column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    for i, x in enumerate(tqdm(t)):\n",
    "        ts += x\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def dollar_bar_df(df, dv_column, m):\n",
    "    idx = dollar_bars(df, dv_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:56.779280Z",
     "start_time": "2018-05-05T00:36:55.923791Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dollar threshold: 1,000,000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████| 1129121/1129121 [00:00<00:00, 2854626.49it/s]\n"
     ]
    }
   ],
   "source": [
    "dollar_M = 1_000_000 # arbitrary \n",
    "print(f'dollar threshold: {dollar_M:,}')\n",
    "dv_bar_df = dollar_bar_df(df, 'dv', dollar_M)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2017-03-21 15:21:50</th>\n",
       "      <td>104.1100</td>\n",
       "      <td>104.11</td>\n",
       "      <td>104.12</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>312330.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:26:40</th>\n",
       "      <td>104.1701</td>\n",
       "      <td>104.17</td>\n",
       "      <td>104.18</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>9400.0</td>\n",
       "      <td>979198.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:30:38</th>\n",
       "      <td>104.1400</td>\n",
       "      <td>104.13</td>\n",
       "      <td>104.14</td>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>93726.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:37:43</th>\n",
       "      <td>104.0909</td>\n",
       "      <td>104.09</td>\n",
       "      <td>104.10</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>11449.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:53</th>\n",
       "      <td>103.9700</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>207940.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        price     bid     ask    size       v          dv\n",
       "dates                                                                    \n",
       "2017-03-21 15:21:50  104.1100  104.11  104.12  3000.0  3000.0  312330.000\n",
       "2017-03-21 15:26:40  104.1701  104.17  104.18  9400.0  9400.0  979198.940\n",
       "2017-03-21 15:30:38  104.1400  104.13  104.14   900.0   900.0   93726.000\n",
       "2017-03-21 15:37:43  104.0909  104.09  104.10   110.0   110.0   11449.999\n",
       "2017-03-21 15:40:53  103.9700  103.96  103.97  2000.0  2000.0  207940.000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv_bar_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2017-03-21 15:31:32</th>\n",
       "      <td>104.1175</td>\n",
       "      <td>104.12</td>\n",
       "      <td>104.13</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10411.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:31:47</th>\n",
       "      <td>104.1300</td>\n",
       "      <td>104.13</td>\n",
       "      <td>104.14</td>\n",
       "      <td>489.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>50919.5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:31:47</th>\n",
       "      <td>104.1300</td>\n",
       "      <td>104.13</td>\n",
       "      <td>104.14</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11558.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:32:20</th>\n",
       "      <td>104.1300</td>\n",
       "      <td>104.12</td>\n",
       "      <td>104.13</td>\n",
       "      <td>199.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>20721.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:32:24</th>\n",
       "      <td>104.1300</td>\n",
       "      <td>104.13</td>\n",
       "      <td>104.14</td>\n",
       "      <td>2301.0</td>\n",
       "      <td>2301.0</td>\n",
       "      <td>239603.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:32:37</th>\n",
       "      <td>104.1400</td>\n",
       "      <td>104.14</td>\n",
       "      <td>104.15</td>\n",
       "      <td>340.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>35407.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:32:37</th>\n",
       "      <td>104.1400</td>\n",
       "      <td>104.14</td>\n",
       "      <td>104.15</td>\n",
       "      <td>160.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>16662.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:34:16</th>\n",
       "      <td>104.0900</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>548.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>57041.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:34:19</th>\n",
       "      <td>104.0900</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>15613.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:35:03</th>\n",
       "      <td>104.0831</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>575.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>59847.7825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:35:08</th>\n",
       "      <td>104.0845</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10408.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:36:00</th>\n",
       "      <td>104.0700</td>\n",
       "      <td>104.07</td>\n",
       "      <td>104.08</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>124884.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:36:03</th>\n",
       "      <td>104.0800</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>114488.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:36:03</th>\n",
       "      <td>104.0750</td>\n",
       "      <td>104.08</td>\n",
       "      <td>104.09</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10407.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:37:00</th>\n",
       "      <td>104.1200</td>\n",
       "      <td>104.12</td>\n",
       "      <td>104.13</td>\n",
       "      <td>215.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>22385.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:37:43</th>\n",
       "      <td>104.0909</td>\n",
       "      <td>104.09</td>\n",
       "      <td>104.10</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>11449.9990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:37:49</th>\n",
       "      <td>104.1000</td>\n",
       "      <td>104.09</td>\n",
       "      <td>104.10</td>\n",
       "      <td>899.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>93585.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:38:11</th>\n",
       "      <td>104.0800</td>\n",
       "      <td>104.07</td>\n",
       "      <td>104.08</td>\n",
       "      <td>125.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>13010.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:38:27</th>\n",
       "      <td>104.0300</td>\n",
       "      <td>104.03</td>\n",
       "      <td>104.04</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>52015.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:38:29</th>\n",
       "      <td>104.0400</td>\n",
       "      <td>104.03</td>\n",
       "      <td>104.04</td>\n",
       "      <td>850.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>88434.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:38:31</th>\n",
       "      <td>104.0600</td>\n",
       "      <td>104.05</td>\n",
       "      <td>104.06</td>\n",
       "      <td>142.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>14776.5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:38:43</th>\n",
       "      <td>104.0600</td>\n",
       "      <td>104.06</td>\n",
       "      <td>104.07</td>\n",
       "      <td>198.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>20603.8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:39:12</th>\n",
       "      <td>104.0400</td>\n",
       "      <td>104.04</td>\n",
       "      <td>104.05</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>104040.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:39:22</th>\n",
       "      <td>104.0100</td>\n",
       "      <td>104.01</td>\n",
       "      <td>104.02</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>104010.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:39:28</th>\n",
       "      <td>104.0100</td>\n",
       "      <td>104.00</td>\n",
       "      <td>104.01</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>31203.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:39:50</th>\n",
       "      <td>103.9900</td>\n",
       "      <td>103.99</td>\n",
       "      <td>103.99</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>207980.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:00</th>\n",
       "      <td>103.9700</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>219.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>22769.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:05</th>\n",
       "      <td>104.0000</td>\n",
       "      <td>104.00</td>\n",
       "      <td>104.01</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>15600.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:05</th>\n",
       "      <td>104.0000</td>\n",
       "      <td>104.00</td>\n",
       "      <td>104.01</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>41600.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:05</th>\n",
       "      <td>104.0000</td>\n",
       "      <td>103.98</td>\n",
       "      <td>103.99</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10400.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:05</th>\n",
       "      <td>104.0000</td>\n",
       "      <td>103.98</td>\n",
       "      <td>103.99</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>41600.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:32</th>\n",
       "      <td>103.9700</td>\n",
       "      <td>103.97</td>\n",
       "      <td>103.98</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>103970.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:47</th>\n",
       "      <td>103.9575</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10395.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:53</th>\n",
       "      <td>103.9700</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>207940.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:40:53</th>\n",
       "      <td>103.9800</td>\n",
       "      <td>103.97</td>\n",
       "      <td>103.97</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>51990.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:15</th>\n",
       "      <td>103.9633</td>\n",
       "      <td>103.96</td>\n",
       "      <td>103.97</td>\n",
       "      <td>265.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>27550.2745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:44</th>\n",
       "      <td>103.9300</td>\n",
       "      <td>103.92</td>\n",
       "      <td>103.93</td>\n",
       "      <td>114.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>11848.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>699.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>72626.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>401.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>41663.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21 15:41:51</th>\n",
       "      <td>103.9000</td>\n",
       "      <td>103.90</td>\n",
       "      <td>103.91</td>\n",
       "      <td>199.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>20676.1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        price     bid     ask    size       v           dv\n",
       "dates                                                                     \n",
       "2017-03-21 15:31:32  104.1175  104.12  104.13   100.0   100.0   10411.7500\n",
       "2017-03-21 15:31:47  104.1300  104.13  104.14   489.0   489.0   50919.5700\n",
       "2017-03-21 15:31:47  104.1300  104.13  104.14   111.0   111.0   11558.4300\n",
       "2017-03-21 15:32:20  104.1300  104.12  104.13   199.0   199.0   20721.8700\n",
       "2017-03-21 15:32:24  104.1300  104.13  104.14  2301.0  2301.0  239603.1300\n",
       "2017-03-21 15:32:37  104.1400  104.14  104.15   340.0   340.0   35407.6000\n",
       "2017-03-21 15:32:37  104.1400  104.14  104.15   160.0   160.0   16662.4000\n",
       "2017-03-21 15:34:16  104.0900  104.08  104.09   548.0   548.0   57041.3200\n",
       "2017-03-21 15:34:19  104.0900  104.08  104.09   150.0   150.0   15613.5000\n",
       "2017-03-21 15:35:03  104.0831  104.08  104.09   575.0   575.0   59847.7825\n",
       "2017-03-21 15:35:08  104.0845  104.08  104.09   100.0   100.0   10408.4500\n",
       "2017-03-21 15:36:00  104.0700  104.07  104.08  1200.0  1200.0  124884.0000\n",
       "2017-03-21 15:36:03  104.0800  104.08  104.09  1100.0  1100.0  114488.0000\n",
       "2017-03-21 15:36:03  104.0750  104.08  104.09   100.0   100.0   10407.5000\n",
       "2017-03-21 15:37:00  104.1200  104.12  104.13   215.0   215.0   22385.8000\n",
       "2017-03-21 15:37:43  104.0909  104.09  104.10   110.0   110.0   11449.9990\n",
       "2017-03-21 15:37:49  104.1000  104.09  104.10   899.0   899.0   93585.9000\n",
       "2017-03-21 15:38:11  104.0800  104.07  104.08   125.0   125.0   13010.0000\n",
       "2017-03-21 15:38:27  104.0300  104.03  104.04   500.0   500.0   52015.0000\n",
       "2017-03-21 15:38:29  104.0400  104.03  104.04   850.0   850.0   88434.0000\n",
       "2017-03-21 15:38:31  104.0600  104.05  104.06   142.0   142.0   14776.5200\n",
       "2017-03-21 15:38:43  104.0600  104.06  104.07   198.0   198.0   20603.8800\n",
       "2017-03-21 15:39:12  104.0400  104.04  104.05  1000.0  1000.0  104040.0000\n",
       "2017-03-21 15:39:22  104.0100  104.01  104.02  1000.0  1000.0  104010.0000\n",
       "2017-03-21 15:39:28  104.0100  104.00  104.01   300.0   300.0   31203.0000\n",
       "2017-03-21 15:39:50  103.9900  103.99  103.99  2000.0  2000.0  207980.0000\n",
       "2017-03-21 15:40:00  103.9700  103.96  103.97   219.0   219.0   22769.4300\n",
       "2017-03-21 15:40:05  104.0000  104.00  104.01   150.0   150.0   15600.0000\n",
       "2017-03-21 15:40:05  104.0000  104.00  104.01   400.0   400.0   41600.0000\n",
       "2017-03-21 15:40:05  104.0000  103.98  103.99   100.0   100.0   10400.0000\n",
       "2017-03-21 15:40:05  104.0000  103.98  103.99   400.0   400.0   41600.0000\n",
       "2017-03-21 15:40:32  103.9700  103.97  103.98  1000.0  1000.0  103970.0000\n",
       "2017-03-21 15:40:47  103.9575  103.96  103.97   100.0   100.0   10395.7500\n",
       "2017-03-21 15:40:53  103.9700  103.96  103.97  2000.0  2000.0  207940.0000\n",
       "2017-03-21 15:40:53  103.9800  103.97  103.97   500.0   500.0   51990.0000\n",
       "2017-03-21 15:41:15  103.9633  103.96  103.97   265.0   265.0   27550.2745\n",
       "2017-03-21 15:41:44  103.9300  103.92  103.93   114.0   114.0   11848.0200\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91   699.0   699.0   72626.1000\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91   401.0   401.0   41663.9000\n",
       "2017-03-21 15:41:51  103.9000  103.90  103.91   199.0   199.0   20676.1000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:03.626421Z",
     "start_time": "2018-05-05T00:36:56.782573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (1466,), xtdf shape: (26,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xDate = '2009-10-1'\n",
    "xdf, xtdf = select_sample_data(df, dv_bar_df, 'price', xDate)\n",
    "print(f'xdf shape: {xdf.shape}, xtdf shape: {xtdf.shape}')\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'dollar bar', alpha=0.5, markersize=7)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count Quantity of Bars By Each Bar Type (Weekly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:03.903498Z",
     "start_time": "2018-05-05T00:37:03.627930Z"
    }
   },
   "outputs": [],
   "source": [
    "def count_bars(df, price_col='price'):\n",
    "    return df.groupby(pd.TimeGrouper('1W'))[price_col].count()\n",
    "\n",
    "def scale(s):\n",
    "    return (s-s.min())/(s.max()-s.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:04.239680Z",
     "start_time": "2018-05-05T00:37:03.906214Z"
    }
   },
   "outputs": [],
   "source": [
    "# count series\n",
    "# scale to compare 'apples to apples'\n",
    "tc = scale(count_bars(tick_df))\n",
    "vc = scale(count_bars(v_bar_df))\n",
    "dc = scale(count_bars(dv_bar_df))\n",
    "dfc = scale(count_bars(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:31.294452Z",
     "start_time": "2018-05-05T01:08:30.655597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bf00296710>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHDCAYAAAAeBq+FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXl8HMWZv781l0a3LUu2hD2+sI2xDAabxYC5wrEhEExMMATCYUhYh3UWNgH2RzbshiQbEq6sw2bZmIRwH4sJdzgT1gFz28aAbS6BLeRDt6zRMWd3/f6YntGMLFnVM12trtb7fD4Cabq7uvqZ8vQ7dbzNOOcgCIIgCIIg5OIZ7QoQBEEQBEGMBSjoIgiCIAiCsAEKugiCIAiCIGyAgi6CIAiCIAgboKCLIAiCIAjCBijoIgiCIAiCsAEKugiCKAjGWC9jjBs/Kywsd4eZ8hhjJzLGKAcOQRCOhYIugiAKZSaAOgA7R7sixNAwxu5hjN0z2vUgiLGOb7QrQBCE2nDOWwGAMaaNdl0IgiCcDPV0EYSLYYz9G2OskTEWNf5/8xD7nMUYe8fYp5kxdjdjLGBsO4gx9pTxeowx9hFj7PI86jGZMfa/jLEuxlgHY+xxxtiUrO0exthNxrZOxtj1BVzzZYyxXYyxPsbYo4yxcVnbzmSMvWbUI8IYW88YO3aIMnYwxn7OGPslY6yNMdbPGPu9yXrMZ4w9yxjrNn5eYIzNyto+jzH2F6MenYb38qztNzDG1g1RrxsG/f3/GGPPGXXcxhg7Jmv7OmPI9RIAl2QNA98zqNwR2wlBEIVDQRdBuBTG2DIAPwFwNYCDAFwAYM+gfc4E8ASAlwAsBPB1AP0AgsYuEwG8C+BMAHMB/BrAGsbYV03UoxTAOqR61k8EcJyx6WnGWPoz6DsAVgH4B2P7YgBTkB+XG/U9FcDhAG7P2lYH4F7jHAsAfAjgBcbYhCHKuQTABAAnAzgKwFuiFWCMhQC8BkAHcIpx/EsAphrbfQCeBpAAcCSAswF8BcBvRM+RxZUAfofU+9cG4K6sbWcjdc2PGj91xs9VWXUdsZ0QBGERnHP6oR/6ceEPUjfRvQCK9rPPGwCeMFnuRgC3D/H6DgArhnj9uwC6AASyXqsFwAEsNv7eAOB3WdunIBWw7FPefup1olHmMVmvXQAgCaBymGPKjWPOHuJaNgNgebq/EanAJTjM9jOM807Jeu1io64Vxt83AFg3RL1uGPT3zVl/f9Mot3zQcfcAuCffdkI/9EM/1vxQTxdBuJfHAfQBaDAmUv8DY6xi0D6HAfjbcAUwxkqMIbatxpBcr3FMmYl6LARQAaDTWOnYC6DB2Hag8f/ZSPU6AQA45zsBdJo4RzYfDPrdC2AGADDGZjDG7meMfc4YC2OgR2eo63mVc57vasjDALzNOY8Os302gA7jOtO8l11XE3ya9XuH8f8qE8eLtBOCICyAJtIThEvhnG835hCdiNRw2g0AfsgYO2w/wcBgbkFqeOxapG7uSQCPwdzUBAbgYwBnDbGtJV3dYY4rlHQZ6fKfBbAdwKVIBVx+AFsx9PXkG/Rln9fM9sGvDeVkqHom8zj/wEmsaScEQQhAPV0E4WI45xHO+fOc838F8A2k5uzUZ+2yGcAJ+ynieAC/5Zw/zTn/GMBuANOG2bcXQPEQr28CMB3AXs55w6CfHmOfzwAckj6AMTYZwPiRr3BIDs36/RCkgpIvjHlb8wD8iHP+Kuf8MwCVeZ5jJN4DcCRjLDjM9s8ATMheTIBU75iGVFAIpIb8Mj1wjLEiAJPyrE8c+/mSLdBOCIKwAAq6CMKlMMYuNoaK5hs9GZchFRh9nrXbLwGcxRj7GWPsYMbYYYyxW7OGlz4BcK5RxqEA7sfwN+93AXybMXYgY6zWmCwOAA8BaAbwOGPsWGP71xljj2Sd53cALmSMfZMxNg/AHUjN6cqHWxljC41VfD8FcJ8R3HUhNdF8JWNsJmPsRACrMXSPUqH8D1IB6MOMsSNYahXoPxrnBIAXkQqufs8YO4QxdoJR1wc452Fjn3cBHGq4ZwB+hPxHJz4DsIQxNpsxFmSM+dMbBNsJQRAWQEEXQbiXLgArALyOVM/LoQC+zjnfm96Bc/4MgGUAvmbs8xKAGgDpYaUfIDXf5y0AzwN4E8Dbw5zvegAxANuQGrqbb5yjD6kesyYATwLYAuBWAK1Z5/kjgN8C+L1R33eRf7LVPwD4M4CXkZqg/wOjHjqA5UgNoW0D8N8A/g35B3fDwjlvMs7jB/AKUtfzDRjXxDlPAFgKoAjAOwCeAvAqslYVcs7fQCoo/BtSAVAEKYf58Huk5rdtMMrJTn8xYjshCMIaWP7zRAmCIAiCIAhRqKeLIAiCIAjCBijoIgiCIAiCsAEKugiCIAiCIGyAgi6CIAiCIAgboKCLIAiCIAjCBhyfkX7dunW8qKjIkrJ0XYfHQ3HmSJAnMciTOciXOORKDPIkBnkSxwpX/f397SeffHLNUNscH3QBwNy5cy0pp7GxEdOmDZdMm0hDnsQgT+YgX+KQKzHIkxjkSRwrXG3atKlxuG2OD329Xq9lZVVU0DNcRSBPYpAnc5AvcciVGORJDPIkjmxXjg+6rETTtNGughKQJzHIkznIlzjkSgzyJAZ5Eke2K8cHXVYK6Ovrs6wsN0OexCBP5iBf4pArMciTGORJHNmuHB90+f3+kXcSpLa21rKy3Ax5EoM8mYN8iUOuxCBPYpAncWS7cnzQlUgkLCurubnZsrLcDHkSgzyZg3yJQ67EIE9ikCdxZLty/OpFxtiw22KxGKLR6H73yaaoqAjhcNiqqrkWN3rinCMYDMKq9COAtb2wYwHyJQ65EoM8iUGexJHtyvFB13CrF9PjrhUVFcJBV2lpqaWrId2KGz1xztHf349kMonS0lJLyqysrLSknLEC+RKHXIlBnsQgT+LIduX44cVkMjnk65qmobS0VDjg2l9ZRC5u9MQYQ2lpqaULM9rb2y0rayxAvsQhV2KQJzHIkziyXTk+6LKyx8VtvTeyIE9i0LdHc5AvcciVGORJDPIkzpjv6eKcO7IsN0OexIjH46NdBaUgX+KQKzHIkxjkSRzZrhwfdOm67siyZPDDH/4Q//Iv/2L6uAULFuDRRx+1rB5O9+QUIpHIaFdBKciXOORKDPIkBnkSR7YroaCLMXYcY+wvjLG9jDFT3SCMsemMsVcYY/2MsS8YY8vNHG/lSoLRXMGxfv161NQM+fzLDL/+9a9x880321Sj4bHb00MPPYRFixbZek4roNw35iBf4pArMciTGORJHKfk6YoAuAfAlXmc438BbAdQDeCfAdzPGJsjerCVebqsLMvNkCcxKPeNOciXOORKDPIkBnkSR7YroaCLc76Bc/4AgC/MFM4YOwjAkQB+wjnv55w/DeAtAN8WrqDHuhFQMysdrWLHjh0IhUI499xzoWkaQqEQQqEQrrxyIH5ds2YNQqEQJk6cmPN6Gl3X8fvf/x5HHXUUpk6disWLF+Pll18e8nwffvgh6uvr8eSTTwrXsaWlBVdccQXq6+sxffp0nH/++Zk8XZqm4cYbb8Shhx6KAw88EOeffz527tyZOXbVqlU5dR7ca3XmmWfi2muvxVlnnYVQKITTTjst06jXr1+PUCiEq6++OuMpFArhlltuEa77aBIIBEa7CkpBvsQhV2KQJzHIkziyXcnO0zUPQBeA3YyxFwD8EcCHAOpFC+Cco6GhIfN3VVUVqqqqhtz37//wXkGVNctL3z18xH2mT5+OpqYmrF+/HsuWLUNTU9M++6xcuRIrV67EqlWrhizjzjvvxB133IH77rsPhx56KD7//HPs3r17n/3eeecdrFixAr/5zW9w6qmnCl2Druu44IILMGvWLKxfvx4lJSV46aWXMr1d9957Lx599FE89dRTqK2txdVXX43LL78czz//vFD5APDCCy/g6aefRnV1NZYuXYr/+Z//wU9/+lMce+yxaGpqwkMPPYTbbrsNGzduFC7TCZSXl492FZSCfIlDrsRQ2VM0qeOlTztwzLRKVJfKvdGr7MluZLuSHXSVAQgD8AOYDWCy8fds0QJaWlpw3nnnZf5euXIlbrjhBvT19aG4uBiMMWiaBp/P/jyvmqZB13Xoug6/349EIgGPxwOPx4NkMgmfzwdN08A5z0xOTyQSOXVOb/f7/dA0DYwxJJPJnO133303/vmf/xkHH3wwdF3HjBkzMHXqVOi6jmQyCc45Xn31VVx99dW44447cOKJJyIWi2XqxBiD1+tFMpmE1+vN1Mfv9+Ptt9/Gli1b8PjjjyMYDMLn8+GUU06Bx+OBrut4/PHHcf7552Pq1KlIJpO45pprcMQRR6CxsRGhUGifOuu6Ds555vy6ruOMM87I7HvCCSfgo48+Qjweh8/nQyKR2OeYdJ6w4eo80jUN3u7z+TLvk6ZpaGxsRDAYRHFxMbq6ujBhwgT09PQgHo+jtrYWzc3NKC4uRiAQQHd3N6qrq9Hd3Y1EIpHZXlpaivb2dhQXF6OmpgadnZ3gnKOmpgYtLS0oKysDAPT29mLSpEloa2sDYwxVVVVoa2tDRUUFNE1DX19fpky/34/Kykq0t7ejsrIS8XgckUgksz0QCKC8vBwdHR0YP348IpEIotFoZrsV1+T1ehEOh6VcUzQazdTbLdck633q6upCMBh01TXJeJ+i0Sj8fr+S1/T2Kw347J3taJxcicuvOVPq+6RpGjo6Osbsvycz17R9+3aMGzeuoGvaH8xMegDG2LEAXuOcC43TMcaWAbiLc16V9dp/AajjnJ8jUsa6det4dgbxdE9XOBxGRUWFcN2BVJA0Wjmo0j1dbW1tw+6zatUqeL1e3H777TmvT548GQ8++CBOPPHEIY9bsGABotEoDjroIIwbNw733XefcL2eeOIJXH/99di6dWvmtWxPixcvxj/90z/hwgsvBJB69FJdXR1eeeUVHHbYYfvUeXCv1ZlnnokTTjgB11xzDQDgV7/6Fd5++2088cQTmfPZ2dOVT7uxo6yxAPkSh1yJobKnB362Fs3RcpT0NuMfb18h9Vwqe7IbK1xt2rRp48knn3zEUNtkp4zYBmA8Y2xK1mvzAWwdZv998Hg8mDVrVuZnuKFFEUYzFUIhc9OmTJmCHTt27Hefn/zkJ7j33nuxadMmrFmzRrjsUCiE9vZ29Pb2Zl7L9jRp0qScyD39+6RJkwAAwWAwJ4N9Ps9stHLenp3QMmxzkC9xyJUYKnuyMx+iyp7sxikpIzyMsSCAgPF30Pg7e58GxthN2a9xzj8B8C6AGxhjxYyxrwM4GsBDohV0S56uuro6aJqGbdu2mT52xYoV+PWvf43NmzeDc44vvvgCr776as4+Pp8P48ePx5133omf/exneO89sflthx9+OObNm4err74aXV1diMfjePHFF9HR0QEAWLp0KR544AFs374dkUgEN998M/7u7/4OdXV1AIDZs2fjvffeQzweRywWy+nBEqWurg7Nzc1obW01fexoEo1GR7sKSkG+xCFXYqjsKdETAwAkufxJ7ip7shvZrkS7GI5HKm3EX42/I8ZPNgcCmDTEsecBmAmgE8B/AbjYCMaEcEuerhkzZuDKK6/E2Wefjfr6elx33XWZbfX19QiFQli7di0eeeQRhEIh1NcPrDVYuXIlrrjiCqxcuRLTpk3D+eefj1gsNuR5jjnmGHz/+9/Hd77zHaFeJ6/Xi4cffhgAsGTJEsyePRt//OMfM65WrFiB5cuXY+nSpaivr0d7ezv+8Ic/ZI6/4IILMGXKFCxZsgQrVqzA0UcfbdrNcccdh6VLl+K4445DfX39PsOrToVy35iDfIlDrsRQ2VN/LHX7jZfnP3ojisqe7Ea2K1NzukaDdevW8QULFuzzej7jrrFYDEVFRVZVzbW42ZOVcxsaGxsxbdo0S8oaC5AvcciVGCp7WvMPa9BTPQ0smcTVN39d6rlU9mQ3VrgazTldBWPlfB9V5w7ZDXkSIxgMjrwTkYF8iUOuxFDZU0m4HQBQ9fEG6edS2ZPdyHbl+LsrBV32Q57EKC4uHu0qKAX5EodciaGyJ27jdBeVPdmNbFeOv7tmr4xzUlluhjyJ0dXVNdpVUAryJQ65EkNlT4mDDwYA9IemSz+Xyp7sRrYrxwddViY9HY0EqipCnsSYMGHCaFdBKciXOORKDJU9pSfSRyrlT3JX2ZPdyHbl+KDLyjQPmqZZVpabIU9i9PT0jHYVlIJ8iUOuxFDbk33PAlbbk73IdjWmgi6nr9R0CuRJjHg8PtpVUAryJQ65EkNpT8bzbe1AaU82I9uV44Mut+TpUgnyJAblvjEH+RKHXImhtCcbv9wq7clmZLtyfNCVsPDbgJVluRnyJMZIDzYlciFf4pArMVT2VKanhrE82tCJrq1EZU92I9uV44OusZ4y4s0330QoFEJdXR0WLVpkyzlV9DQa0DJsc5AvcciVGCp7KmraDgAo2f2l9HOp7MluxnzKCMasm2xoZVl2cfTRR6OpqQm33XabbedUzdOqVatw5ZVX2n7eQED+M9PcBPkSh1yJobIn1t9v/CL/81ZlT3Yj25Xjgy4rV9LRqjwxyJMY3d3do10FpSBf4pArMVT2FKmYCADoq5X/eB6VPdmNbFeOD7pUz9O1detWTJ48OWcZ6vvvv48pU6agt7cXANDU1ITzzjsPM2fOxIIFC3DTTTcJr9r88ssvUVVVhV27dmVeW7BgAR599FEAwJlnnonzzjsPs2fPxo033ohTTjkF9fX1+PjjjzP7r127FscccwymT5+O0047DZ9++qmpa9y8eTOWLVuGWbNmYfbs2TkP8+7s7MTll1+OOXPmZB70HYkMPCs9u67Avr1WVVVVWL16NY466ihMnToVV1xxRcbNLbfcss+DwkOhENavX2+q/vlSXV1ty3ncAvkSh1yJobKn/spU0MV98hcuqezJbmS7cnzQpXpPV319PWbMmIE///nPmdcee+wxnH766SgrKwMAXHbZZairq8O2bdvwpz/9Cffddx/uv/9+y+pw6qmn4o477sCtt96K3/72t1iyZAn+9Kc/AQBeeOEFXHvttVi9ejU+//xznHvuufj2t7+NWExscufu3buxdOlSnHbaadiyZQs2btyIww8/PLP9mmuuQTgcxoYNG/DXv/4Vr7/+uumh0vXr1+Pll1/GunXr8Mwzz+Cvf/0rAODaa69FU1MTli9fjm9961toampCU1MTjj32WFPl5wt9ezQH+RKHXImhsqeiImP1ooVpkYZDZU92I9uV41OPm80Z9ULtMcNuO+iXP8SMS88BADTd/yS2XnvzsPue1vxG5vc3/v5ShD/4ZNjtI7F8+XI89thj+Na3vgVd1/H4449j9erVqXo0NWHjxo245557EAwGMWvWLFx44YV46qmncMkllwifY3/Mnj0boVAo8/u0adPQ2toKALjrrruwYsUKHHnkkQBSAeB//Md/YMOGDViyZMmIZa9duxZz587FypUrAaQeFnreeecBSAW5zz77LJ5++mlUVFSgoqIC3/ve97B69Wpcf/31wvW/7LLLUF5ejvLycsybNw+ffPIJTj31VFMOZECrPM1BvsQhV2Ko7Mk/rQbYAwR62qWfS2VPdiPbleODLitzRnm9XsvKMsM555yDX/7yl2hra8PHH3+MZDKJr3zlKwAGlqdm5wapq6tDS0uLZef3er2ZoVWv1wuv15t5vmJTUxPeeOMN3HPPPZn9E4mE8LLZpqYmzJgxY8ht7e3tSCaTOddWW1tr+tqqqqoyvxcVFSEajZo6XhaU+8Yc5EscciWGyp50xgDYk6tLZU92I9uV44Mus1Hn/nqgsofMQhd9A6GLviFU5jEv3W2qDoOZPHkyjjzySDz55JP48MMPsWzZskwQlH6Dm5ubMXnyZADAnj17MGnSpJwyAoHAkMOjwWAQQO5DqsPhsHDdQqEQLrroIqxatSrzWiwWQ1FRkfDx2UOn2VRXV8Pn86G5uRnTp08HkLrO7GsLBoM573E4HMb48eOF6w+M3mrL5uZmTJsmfxKsWyBf4pArMZT21BeBN6qBcfm9UEp7shnZrhw/p8vK3qnRzD+1fPlyPPLII3jmmWdw7rnnZl4PhUJYuHAhbrnlFkSjUTQ0NODBBx/E0qVLc46fO3cudu/ejYaGhpzXq6urUVVVhbfffhsA8Pzzz5sak16xYgXWrFmD9957D5xzhMNhPPXUU8LPnzrnnHPw8ccfY82aNYjFYujp6cFjjz0GIPXenX766Vi9ejV6enrQ3NyMNWvW5FzbnDlzMnXftWsXXn31VeG6p6mrq8NHH32UE3jaQWlpqa3nUx3yJQ65EkNlT8mWMLRgKZJB+RPpVfZkN7JdOT7ospLRzD911lln4aOPPkJNTQ0WLlyYs+2uu+7Crl27MG/ePCxbtgwXXHABLrroopx95s+fj6uuugpf/epXMyv2gFQgedNNN+HnP/85zjzzTGzevDkzf0uEM844A//2b/+GK6+8EtOnT8fixYvxwgsvCAeokydPxpNPPonnnnsO8+bNw8KFC7Fhw4bM9ltvvRWlpaVYuHAhTjrpJCxevBhXX311ZvuPfvQjbNiwASeddBJ+8Ytf4IQTThCue5pLL70Ufr8f9fX1qK+vx1tvvWW6jHwYreFqVSFf4pArMVT2ZOcTblX2ZDeyXTGnP9x43bp1fMGCBfu8Hg6HUVFRYaosM8NmYxk3e8qn3QxHY2MjddmbgHyJQ67EUNnT7/75XvSWTII/vAdX/fZSqedS2ZPdWOFq06ZNG08++eQjhtrm+J4uKyfSj0aeLhUhT2LU1NSMdhWUgnyJQ67EUNlTMpnqUUlU1Ek/l8qe7Ea2K8cHXVbO06FM62KQJzE6OztHuwpKQb7EIVdiqOyJcfs+Z1X2ZDeyXTk+6CIIp+L0oXmnQb7EIVdiqOwp0J9aZV735vPSz6WyJ7uR7crxQZfqjwFSEfIkBnXZm4N8iUOuxFDZU+bWbkNApLInuxnzw4tWZoelrLxikCcxrExgOxYgX+KQKzFU9hSbMxsAEDceBycTlT3ZjWxXjg+6rFy+SctmxSBPYpTZ8GHpJsiXOORKDJU9JbXU7bfj0OOln0tlT3Yj25Xjgy6CIAiCIAg34Pigy8qVdLQqTwzyJEZvb+9oV0EpyJc45EoMlT15evtsO5fKnuxGtivHB11W5umysiw3Q57EGPx8TGL/kC9xyJUYKnticfvmzqrsyW5ku3J80GVlni67n803HFdeeWXOA6b3x5tvvolQKIS6ujosWrRon+1r165FKBTCpEmTsGzZMkvq5xRPTqetrW20q6AU5EscciWGyp6C/e22nUtlT3Yj25Xjg66xztFHH42mpibcdtttQ25fvnw5mpqa8IMf/MDmmjmHVatW4corr7T9vKP5LE8VIV/ikCsxVPZU3rIDAODr65Z+LpU92Y1sV44PuqzMGUWr8sQgT2JUVVWNdhWUgnyJQ67EUNmTp78fAMB0Xfq5VPZkN7JdOT7osjJn1GgNm33++ec444wzMHXqVCxbtgzd3bnfbJqamnDeeedh5syZWLBgAW666SboFv1D7O/vx8UXX4y5c+di8uTJOPbYY/HGG2/k7PPQQw9h0aJFeOCBB3DIIYdg2rRpuOaaa4TPsXnzZixbtgyzZs3C7Nmzcd1112W2dXZ24vLLL8ecOXNQX1+P6667DpFIJLN9wYIFePTRRzN/D+61qqqqwurVq3HUUUdh6tSpuOKKKzJubrnlFoRCIaxduxaPPPIIQqEQQqEQ1q9fb9pTPlCXvTnIlzjkSgyVPcVLKgEAidIK6edS2ZPdyHbl+NTjZntdbv3XF0ztP+mAClz0/WP2Of6aG0/LvHb/b99Ay+5wznHZ20fiu9/9LhYvXownnngC7777LpYtW4bly5dntl922WWor6/Htm3bsHPnTpx11lmora3FJZdcYupahiKZTOLII4/EbbfdhgkTJuAPf/gDzj33XGzbtg0VFQP/2FtbW/Hmm2/itddeg8/nw7Zt24TK3717N5YuXYof//jHePjhhxGPx/H88wOPtbjmmmvQ19eHDRs2oL+/H8uXL8dtt92G66+/Xvga1q9fj5dffhltbW04/vjjcfbZZ+PUU0/Ftddei2uvvRarVq2C1+vF7bffLi7GArL9ESNDvsQhV2Ko7Gnv5LmpXzzyRxZU9mQ3sl05vqdLdb788ku8//77uOqqqxAIBLBkyRIsWbIks72pqQkbN27Etddei2AwiFmzZuHCCy/EU089Zcn5Kyoq8P3vfx81NTXweDy4+OKL0d/fj08++SRnv1gshl/96lcYN24cioqKcOSRRwqVv3btWsydOxcrV65EMBhERUUFzjvvPACp1BPPPvssfvCDH6CiogK1tbX43ve+Z/raLrvsMpSXl2PmzJmYN2/ePnUfLSi1hjnIlzjkSgyVPXm98ocV06jsyW5ku3J8T5dZAfvrgYrFYigqKjJ9fHZPmFnSjxTIXoZaW1ub+b25uXmf1+rq6ix7FEE0GsUNN9yAF198EV1dXZnX4/F4zn51dXUoLy8HAFNDm01NTZgxY8aQ29rb25FMJnOurba21vS1ZY+xFxUVIRqNmjpeFn19faiurh7taigD+RKHXImhsqfoMfXA++3wRuTn61LZk93IduX4ni7V83SlA47sQCMdaGVvz35tz549++QKCQQC+w1Ah9v+3//933j11Vfx9NNPY8eOHdiyZQuAfZ+knr1gwYynUCiE7du3D7mturoaPp8v59qam5tzri0YDObM2wuHc4dxRRitlTnZwSQxMuRLHHIlhsqeePpjy4bPL5U92Y1sV44PulR/4HUoFMLChQvxm9/8BvF4HK+//jpef/31fbbfcsstiEajaGhowIMPPoilS5fmlDN37lzs3r0bDQ0NQ55n7ty52LZtG9rbc3O/9PT0oKSkBOPGjUMkEsGNN944Yp3NeDrnnHPw8ccfY82aNYjFYujp6cFjjz0GIDUf7/TTT8fq1avR09OD5uZmrFmzJufa5syZg7fffhsAsGvXLrz66qvC505TV1eHjz76yPaFEtnBJDEy5EscciWGyp48/VF4o33wxORK7B1MAAAgAElEQVT3dKnsyW5ku3J80GVlL8Zo9Yjceeed2LJlC2bNmoVbb70VX/va13K233XXXdi1axfmzZuHZcuW4YILLsBFF12Us8/8+fNx1VVX4atf/WpmxV42p512Gk4//XQcccQRCIVCePPNNwGkVgNWVlZi3rx5OPLIIzFnzpwRFyeY8TR58mQ8+eSTeO655zBv3jwsXLgQGzZsyGy/9dZbUVpaioULF+Kkk07C4sWLcfXVV2e2/+hHP8KGDRtw0kkn4Re/+AVOOOEE4XOnufTSS+H3+1FfX4/6+nq89dZbpsvIB8rcbw7yJQ65EkNlT/4Nn0MLlqKzTP4sH5U92Y1sV2zwMJPTWL9+Pa+vr9/n9XA4bHqVgaZplINKADd7yqfdDEdfXx9KS0stKWssQL7EIVdiqOzpt9+/F9GKSQhre/Gzm74l9Vwqe7IbK1xt2rRp48knn3zEUNsc39PlxscAOR3yJMbgoVxi/5AvcciVGG7wpNswAOMGT3Yh25Xjgy4re1zc2ntjNeRJjMrKytGuglKQL3HIlRgqe+LG7XecZ5z0c6nsyW5ku3J80GXl8KfTh1KdAnkSY3DaDWL/kC9xyJUY5EkM8iSObFeOD7qsehyO1WW5GfIkRvbjjIiRIV/ikCsxVPbEjBQ/U//yiPRzqezJbmS7cnzQpXqeLhUhT2JQ7htzkC9xyJUYSnsy5nIxG+bQKu3JZihP135yRpkdBhuNPF0q4lZPnHNLh04p9405yJc45EoMlT1FJ6aSRNuRyEhlT3Yz5vN0eTxDV7GkpATd3d2mbqKjladLNdzoiXOO7u5uS5dNBwIBy8oaC5AvcciVGCp70oqLAQA7TrtQ+rlU9mQ3sl05/tmLwwVdPp8PZWVlCIfDwkFCIpGgoTMB3OiJc46ysrKcxx0VSvpZlYQY5EscciWGyp7sXK+ksie7ke3K8UHX/nJG+Xw+U8s7GxsbMW3aNCuq5WrIkxgdHR0oKysb7WooA/kSh1yJobInf7gbHEUAUl8KZY4wqOzJbmS7cvzwopU9E+PHj7esLDdDnsQgT+YgX+KQKzFU9uQP9wz8IbnXS2VPdiPbleODLivTF9CyWTHIkxjkyRzkSxxyJYbKngI9HZnfZedGVNmT3Yz5lBFWBl3RaNSystwMeRKDPJmDfIlDrsRQ2dO4He8DeipXl+zciCp7shvZrhwfdFk5oZtylYhBnsQgT+YgX+KQKzGU9sQ5mNHDpSflBl1Ke7IZytNlYc4oylUiBnkSgzyZg3yJQ67EUNmT7vWDe1NzlrmRnV4WKnuyG8rTNUzKiHwIBoOWleVmyJMY5Mkc5EscciWGyp7a5p+Q+Z1rcud0qezJbmS7GlNBV7GRjI7YP+RJDPJkDvIlDrkSQ2VPjA8MKeqSe7pU9mQ3sl05PujaX54us3R1dVlWlpshT2KQJ3OQL3HIlRgqe2r62lHwxGMA5AddKnuyG9muHB90WZmna8KECZaV5WbIkxjkyRzkSxxyJYbKnjhHJi29pssdXlTZk93IduX4oMvKpbQ9PT0j70SQJ0HIkznIlzjkSgyVPfGs/+pJuT1dKnuyG9muxlTQFY/HLSvLzZAnMciTOciXOORKDJU9TXv+TTDO8Zm3HyUTx0k9l8qe7Ea2K8cHXZSny37IkxjkyRzkSxxyJYbKnnRvAFqwBEmvD4zJvRWr7MluHJGnizEWZIzdxRjrZoy1M8Z+IXoCxtjxjLF3GWNhxtgOxtgPzFSQ8nTZD3kSgzyZg3yJQ67EUNqT8XxrzqQ/elFtTzbjlDxdPwOwCMBsAEcDWMEYu2ykgxhjXgBPAHgGwDgAZwH4D8bYKcIVpJQRtkOexCBP5iBf4pArMdT2lIq65oc17N3ZJvVManuyF6ekjLgIwC2c81bO+WcAfg/gEoHjKgFUAXiYc65zzt8HsBXAfNEKMsZEdx2RQCBgWVluhjyJQZ7MQb7EIVdiuMGTXlSMRL/ceURu8GQXsl2NGHQxxsYDqAXwIWPsRsbYzQA+BFA/0rGc804AdwI4nzHmY4wdDmAGgBdFKxiPx9HQ0JD56ezsFD10H7q7u/M+dixBnsQgT+YgX+KQKzGU9mR0KEx+9UkUBeXO6VLak83IdiWSBKvM+H8YqYApCODlrNdHYi2ANQD+3fj7h5zzj0Qr2NXVhVNOGRiNXLlyJW644QY0NzejtLQUXq8X4XAYNTU16OzsBOccNTU1aGlpQVlZqoq9vb2YNGkSkskkdu7ciaqqKrS1taGiogKapqGvrw+1tbVobm6G3+9HZWUl2tvbUVlZiXg8jkgkktkeCARQXl6Ojo4OjB8/HpFIBNFoNLM9GAyiuLgYXV1dmDBhAnp6ehCPxzPbi4uLEQgE0N3djerqanR3dyORSGS2m72mtrY2MMYsvSbGGBobG111TTLep0AggMbGRlddk8z3qaioCK2tra66JlnvU1FRERobG111TTLep7KyMjQ2Nip5Tel5XKUtXyIej6OpqUna+1RZWYnGxsYx++/JzDVpmoampqaCrml/MM73P4XP6OnqBLCAc/6B8do3AazhnFePcOyBALYB+DZSc7umAXgWwG8452v2e2KDV155hZeXl2f+rqqqQlVVlcih+7B7924ccMABeR07liBPYpAnc5AvcciVGCp7uuW6Z8A8fsx59HYc9fwaTJwdknYulT3ZjRWuNm3atPHkk08+YqhtI/Z0cc67GGPNAA4B8IHx8nyk5maNxEIAnZzzx4y/v2CMPQ3gTKR6v0bE4/Fg1qxZIruOiJUrId0MeRKDPJmDfIlDrsRQ2VMyGIQ/rmHP4q8iGpV7HSp7shvZrkQHku8HcA1jrIYxNgvA5QDuzd6BMdbAGLtp0HGbAVQxxr7JUkwBcAZSc8KEoDxd9kOexCBP5iBf4pArMZT2ZAwy9Uw7CNGIdc8YHgqlPdmMI/J0ITUf6z0ADQDeBnAfgLsH7XMggEnZLxgrHc83ju8G8A6A15BKQSEE5emyH/IkBnkyB/kSh1yJobInbzLr3iZ5xZzKnuxGtiuhp0lzzqMALjN+httnyNwOnPPHATyeV+0AeL3efA/dh9LSUsvKcjPkSQzyZA7yJQ65EkNlT/5IBFowVX/v+Eqp51LZk93IduX4xwBZiZUBnJshT2KQJ3OQL3HIlRgqe/LEo5nfR1jPVjAqe7Ib2a4cH3RpmnVPXw+Hw5aV5WbIkxjkyRzkSxxyJYbKnkJ/fRhFnS0AgGRMbnJUlT3ZjWxXjg+6rJxIX1NTY1lZboY8iUGezEG+xCFXYijtiXMwo4srtrNF6qmU9mQzsl05PuhKJq1b1VFINvuxBHkSgzyZg3yJQ67EUNoT55lxRV3TpZ5KaU82I9uV0ER6tzBSIlgiBXkSgzyZg3yJQ67EUNlT0ynfRqJyAgBA0+UGXSp7shvZrhzf0+XzWRcXUherGORJDPJkDvIlDrkSQ2VPWsnAk/Rk3+hV9mQ3Y3540co8XS0tcsfN3QJ5EoM8mYN8iUOuxFDZU8OsangjfQAAXZMbdKnsyW5ku3J80GXl8s30gyuJ/UOexCBP5iBf4pArMVT21DtuHFgsFXRx3bpV+kOhsie7ke3K8UEXQRAEQbgNzpF5FJDsni7COTg+6LIyT1dvb69lZbkZ8iQGeTIH+RKHXImhsqeFf3sH8AewK9GF6kNmSD2Xyp7sRrYrx69etDJP16RJk0beiSBPgpAnc5AvccaCK03niCQ0lBXlfxtS2ZNWMRHwevF5bRkCEyqknktlT3Yj25Xje7qszNPV1tZmWVluhjyJQZ7MQb7EGQuufv3alzjvoS3o6M9/sZTSnoynFXPGpD8GSGlPNiPbleODLithbMhnchODIE9ikCdzkC9xxoKrxq4oEhpHc08s7zLU9pSq+/EfbkPHh9vlnklpT/Yi25Xjgy4r83RVVVVZVpabIU9ikCdzkC9xxoIr3ejeKaSXR2lPntTtl1WH0LNTbu+K0p5sRrYrxwddVubpoi5WMciTGOTJHORLnLHgKh106QVEXap6yk6GWvfWCyguktu7oqqn0WDMDy9amaerokLuZEW3QJ7EIE/mIF/ijAVXOs/9fz4o6yl9zZxjwrZ3EAzIDbqU9TQKyHbl+KDLSqxMP+FmyJMY5Mkc5EucseBqIOjKP+pS1VOmpys9xCo5OaqqnkYD2a4cH3RZKaCvr8+ystwMeRKDPJmDfIkzFlwNDC/mX4aqnjJxpseDrlmHIhKR+8BrVT2NBrJdOT7osjJPV21trWVluRnyJAZ5Mgf5EmcsuLJiTpeqnrKveNfx30BPn9ygS1VPo4FsV44PuqycSN/c3GxZWW6GPIlBnsxBvsQZC640I84oZPWiqp74oIvmfrl5ylX1NBrIduX4oMvKnBlW9pq5GfIkBnkyB/kSZyy4smJ4UVVPg4Mu34HTpZ5PVU+jgWxXjg+6rFy9WFlZaVlZboY8iUGezEG+xBkLrtJxh1ZAV5eqnvigSLOQwFMEVT2NBrJdOT7osvIxQO3t7ZaV5WbIkxjkyRzkS5yx4MqK5KiqevJ7Geb872oUt+5MvSD5OUCqehoNZLtyfNBFPV32Q57EIE/mIF/ijAVXmgUpI1T2FOgLI9C7FwAQ+/ATqedS2ZPdjPmersFj34UQj8ctK8vNkCcxyJM5yJc4Y8GVFXO6lPU0OE9XUm5uKGU9jQKyXcldMmEBum7dUtpIJGJZWW6GPIlBnsxBvsQZC664xhFMagX1dKnqKRqJo/Hkc9EzbS4AgEPu8KKqnkYD2a4cH3RRni77IU9ikCdzkC9xxoKrw7a3oiShIdJVl3cZqnrSEnom4AIAXfJMelU9jQaUp4vydNkOeRKDPJmDfIkzFlxpnlQ6oGQ8/8VSqnoKlgexJ9kNT6wfwL6rGa1GVU+jwZjP0+XxWFfFQCBgWVluhjyJQZ7MQb7EGUuueAG5GFX15C/yY+tBc6D3dAKwdu7yUKjqaTSQ7WpMBV3l5eWWleVmyJMY5Mkc5EucMeGK5/wvL9ziSXbQ5RZPdiDbleODLivzdHV0dFhWlpshT2KQJ3OQL3HGgqtyY1ixv7M/7zJU9dTf3Yfj3t0IHwf6unej4qApUs+nqqfRQLYrxwddPp91c/3Hjx9vWVluhjyJQZ7MQb7Ecbur7J4dLZF/ugRVPUX29qGoagpQUoHXDz8c5QtmSz3fYE8dfYmCVo26GdltyvFBF6WMsB/yJAZ5Mgf5EsftrrLnjRdNKM27HFU9afs8Bsi+lBHvNHXj/Ie34D9f+1LqOVVFdpsaU0FXNBq1rCw3Q57EIE/mIF/iuN2Vzjn6famnjXj8+d+GVPXEtdR9LVlagUVbt6F32w6p58v29NiHrQCAFz/tlHpOVZHdphwfdFGeLvshT2KQJ3OQL3Hc7krnADOm0GvIf/Wiqp6y83JNKJ6Ark0NUs+X7SmepGHF/UF5uihPl+2QJzHIkznIlzhud6VzjuKk0duz7Yu8y1HVk64NzGMb99n7KPJYt2BsKLI9xTTrRo/cCOXpsjBlRDAYtKwsN0OexCBP5iBf4rjdVfaUJq1xT97lqOop3dMV6O7AlNeeQrknJvV82Z5iSQq69ofsNjWmgq7i4mLLynIz5EkM8mQO8iWO211lTyQvZLBLVU88M6RqPPCayw2Esj1R0LV/ZLcpxwddVubp6urqsqysodB0jo4+64ZDRwvZntwCeTIH+RLH7a6sWq2nqidfVSUAIFZWiei4GiS1/Oe1iZDtKa7RnK79IbtNOT7osjJP14QJEywrayh++0YTLnh4C77cq+aKmjSyPbkF8mQO8iWO211xDnhi6aX5+fe8qOopPbzIvH40nH0FOuJye1eyPVFP1/6R3aYcH3RZmTKip6fHsrKGYnc4Bg6guUfu+LxsZHtyC+TJHORLHLe70jlQvutzAAAroONFVU+DH3At+zFA2Z4o6No/stvUmAq64vG4ZWUNRfrfkeQHxktHtie3QJ7MQb7EcbsrjXPA+Gwv5BNeVU/R5vbcF+bPlXq+bE/p25PPI3dIU1VktynHB10q5ekaCLrUjrpUzX1jN+TJHORLHLe70jlH96xDAQCc5X8bUtWTnsx99BH3eKWebyhPAe9A0PXAe8149qP2ffYZi1CeLoXydKW7iC3snBsVVM19YzfkyRzkSxy3u8oeDYjOOzjvclT1VFLixdS//C8CHal0GYOHG61mKE9BX+r23xfXcN/GPbh7w26pdVAFytOlUMoIt/R0qboM227IkznIlzhud5U9h4mz/Ie5VPUU8HtR8eUnGNf4SeqFz/JPECvCUJ4CRtCVMJKlJmhVIwBKGQFWwD/IwQQCAcvKGop0sKV625XtyS2QJ3OQL3Hc7krjwM7y1M2tkO+oqnrig4ZDeF+/1POlPWXnR/Mbc7rS9yvZk/lVQXabcnzQpWnayDsJ0t3dbVlZQ+GWni7ZntwCeTIH+RLH7a50znHoO68BAPwNn+ddjqqeevviaF1wHLqMeW0FRZ4CpD1Fs1Yups+YDsRUXwBmFbLblHVJsCRhZZ6u6upqy8oainSwpXrQJduTWyBP5iBf4rjdlaZxhEOzAQAsmn+KHVU99fUm0LroK5m/ZfcypT31JwY6MdLBVub/it+3rEJ2m6KeLgvRM920Uk8jHVW/PdoNeTIH+RLH7a6SSR3JslRW9iLkP7Smqqey2irE9u6Bty+cekHyPSPtKRIf6OlK36+SevpRRHLroAqy25Tjgy4rvwFYuRJyKLhL5nTJ9uQWyJM5yJc4bneV7l1hyQRYAZm6VPU0bvok/G3hYeiNpxJxyr5lpD0N2dNl3Lc4aF4XIL9NOT7oUipPV/r/ijdcVXPf2A15Mgf5EsftrjRjxRzjekHJUVX1pKcfdG2sE5N9y0h7iiQGbKeDrezJ9TSvi/J0KZWnK/08LV3xlqtq7hu7IU/mIF/iuN1V5rPSX4QYL8q7HFU99TZ34ZBPPoI/qYG170RRdZnU86U9DT2na2A/1TsMrGDM5+nyeq3L1FtaWmpZWUORbq6Kx1zSPbkF8mQO8iWOFa6iSR0dfc4cfsvuXdF4/kv0VW1TbR81oc5fhRLmx4tHLkLw9GOkni/tKbunK/0WZE+gp5hLfptyfNBlJVYGcEPhltWLsj25BfJkDvIljhWufvryF7j40a0IR5MW1MhatKw8VXoBvTyqtildG5SnS/I9I+0pp6driOFFWsEov005PuiycvViOBy2rKyhGPjmIPU00pHtyS2QJ3OQL3GscLWnJ46ExtEVcV5vl2Z8SMa8HvQeXp93Oaq2qUyQ5fWhrqUFyT1yn3uY9jTU8GJSp56ubGS3KccHXVZOpK+pqbGsrKFwS0+XbE9ugTyZg3yJY4WrzGpqBz4LNh10FbpiTtU2lZ7TlhhXg0P6gN4XN0o9X9pTdsqIwXm6APXvXVYgu005PuhKJq3rGu/s7LSsrKFwS0Z62Z7cAnkyB/kSxwpXQ83ZcQq6MYIR1HR4dnfkXY6qbSr7AddFXa3wanJ7I9Oe+keY06X6fGQrkN2mHB90WYnscfNMT5cDv1magXK1iEGezEG+xLHClT7EnB2noEXjmd9LPvgs73JUbVPpnq6ynQ2Y/cTvUNW3S+r50p4iWcOLHKk2QqsXc5HdphwfdFn5GCDZ3YbcJT1dqnbZ2w15Mgf5EscKV07u6dKybv6FTCRStU1lHnidvnbJ71HaU3ZPF5AKyClPVy6OGF5kjAUZY3cxxroZY+2MsV+InoAxVsIY+x/GWCtjrIcx9h5jrFz0eCvzdLW0tFhW1lAMDC9KPY10ZHtyC+TJHORLHCtcOXlOl57M6nEp4PNS1TYVmBYCAHRNmAggd7hRBmlP2T1dQGrRF02kz0V2mxLtRvoZgEUAZgOoBLCOMfY55/yPAsfeDSAE4CgA2wHUA4jv94gsrFy+WVYmNwFdpjtf8ZYr25NbIE/mIF/iWOFKc3BPl8fP4InHoAeKCrrTq9qmuDfV3+EvqcTWi/8VZbH857WJkPYUSegA56hr2o622inQdZ7TPpzYVuxGdpsSHV68CMAtnPNWzvlnAH4P4JKRDmKMTQawHMDlnPMveIotnPP8HyvvYNzywGuCINTHyXO6vD6Gie/9LfWHieo9fu9GPLzmLUQdmAbDDNmXzH0+cDBbzhvXdMzf+AbOv/M2/P2TD0LjucOLdO+Sz4hBF2NsPIBaAB8yxm5kjN0M4EOkeqxGYhGAHgBnMMZ2M8a2M8Z+zhgTnksWj8fR0NCQ+SlkZUFvb2/ex4qQ7s5XfU6XbE9ugTyZg3yJY4UrJ88x1ZM6wNPjnuL1++KTNuxq3IuevVEA6rap2Jd7cv5OTp0i9XxpT0md45i//hkAMPeDDUPM6XJeW7Eb2W1KZHgx3dcWBjADQBDAy1mv749KACUADkFqaLIOwP8B+BKp3rIR6erqwimnnJL5e+XKlbjhhhvQ3NyM0tJSeL1ehMNh1NTUoLOzE5xz1NTUoKWlJdNN2Nvbi0mTJkHXdezcuRNVVVVoa2tDRUUFNE1DX18famtr0dzcDL/fj8rKSrS3t6OyshLxeByRSCSzPRAIoLy8HB0dHRg/fjwikQii0Shqa2uRNCZPRKIxNDY2YsKECejp6UE8Hs8cX1xcjEAggO7ublRXV6O7uxuJRCKz3ew1tbW1gTFm6TV5vV40NjZmtgeDQRQXF6Orq0vZa8p+n6y6pmAwiMbGRlddk8z3qbi4GK2tra66JlnvU3FxMRobGwu6pqSRlqGltQ07Wc+oX1P2+9Te2oGe0BwAqYdft7e3C11Tmrb2FsS0blRWVqKxsdER12TmfepubgYw8PgjLRhEU1OTtLY3fvx4NDY2IhZPImnkvvy/08/BtOYWtLb3ZXntQLFe4rp/T2beJ845mpqaCrqm/cFGWh5p9HR1AljAOf/AeO2bANZwzqtHOPZsAH8CMJdz/onx2q0ApnPOz9nviQ1eeeUVXl4+MO++qqoKVVVVIofuQ1NTE0KhUF7HivD1uzcjrnEsm1+DK46S+81FJrI9uQXyZA7yJY4Vrpbe8z6iSR3/fsoMHDt9nEU1s4Yn1+9Aw3MfAwBi4wL48b+cJHTc72/9G7o7I/ju1cdj3IQSZdvU64+sQ8N9L6FzyoHQqicjMKUSV/7j0dLOl/a04sHNOOf/XQlfMon//vEt+ONlf4fXd3Tjjjd3AgDuOudghMYFpdVDBaxoU5s2bdp48sknHzHUthF7ujjnXYyxZqR6qz4wXp4PYKvAuT9JF5P1mqnBa6/Xi1mzZpk5ZFgYkztunlm96MDVQmaQ7cktkCdzkC9xrHA1kDfQeUNGLDBw60kWid/k017S16ZqmyoLApM2rQPrC6O1ejI8LW1Sz5f2VN7UBF8yiY6aWsSKS2h4cQhktynRuVX3A7iGMVbDGJsF4HIA92bvwBhrYIzdlP0a53wrgC0AfmyknZiO1MT6F0UraGWernx7yERxy2OAZHtyC+TJHORLHCtccQevXgxUBLG12hjBMFG/vR39qUOMQEHVNpWuf1EsAgDwdHVJPV/a04TtX2DvzPmoDHdjwVt/g6ZTRvrByG5TokHXvwN4D0ADgLcB3IdUKohsDgQwaYhjvwlgKoAOAOuNY+8SraCVebra2uR+m3DLY4Bke3IL5Mkc5EscK1xpmdWLBRdlOVpzG0594kEAQLBd7GHP2VNh0r+r2qaiMR09U2YhWpW+ZcrtXUl72jNnPnaeeDY+Oe8HmPXJ1n1WL6p+77IC2W1KqBuJcx4FcJnxM9w+Q7YazvmnAL6SV+1gbZ6uiooKy8oaDHfRtwWZntwEeTIH+RLHCldO7unSevuQLEn1dAXCYaFj+BCpDVRtU51hDY1/fwFY0uhUkPwepT11l1UCHe3gPh+icxcbjwGilBHZyG5Tjn8MkJVomjbyTnmSHWip/m1Bpic3QZ7MQb7EKdQV5zwzkdaJebr6u2JoXZT6Lu7rbRU6JvsyMtn2FW1TwapyeLqa4QunUyDJ7elKe+rx+7DDGzVOyVJzunLuXVKroQSy25Tjgy4rBfT19Y28U55kB1qa4g1Xpic3QZ7MQb7EKdRV9s3TiUGXbox5lu1sgLd/r9AxOT1dxu+qtqm6Ew7FC4sWYGepdXOW90dfXx8455j79nrM/Ci9Ho7t8xgg1TsMrEB2m3J80OU3copYQW1trWVlDSanp8uBH3JmkOnJTZAnc5AvcQp1pTt8ukM66ALnwsu99SGGwVRtU+n3hxsr5WTHOrW1tdA5cPimt1GbnrPEWOoxQPTA6xxktynHB11WTqQfKWlZIeR+yKndcmV6chPkyRzkS5xCXeX0dDnw84gbgVZvaDa0YKXYMUNMpFe1TSXC/Rjf1QnoOnzhTng8clc7NDc3I6FztC1Zit1Lzki9yNgQjwFyXluxG9ltyp6+zQKwMmeGlb1mg8mdbyDtNLYg05ObIE/mIF/iFOoqZ7qDA7sveNYcjGRlndAx+hDBgaptqvH5Dfi7Lg884T48d9SROPPg/eYZLxi/3w9N5+BZC9M0f2CflBGqT42xAtltyvE9XVauXqysFPtGlQ/cRQ1Xpic3QZ7MQb7EKdSV03u69OBAQlRNiwodkz2nK1icujGq2qbSbwkf9LcsKisrkdB0cO9AP8veCRMpZcQQyG5Tjg+6ksmkZWW1C+aDyQc3rV6U6clNkCdzkC9xCnWV8yXQgXm6kuPHAwCaS4uw9cQlQsekr6m0vAhVNann4Snbpgb1PuomHvqdD+3t7UhqHNwzEHR5OIeu85yJ9IrfuixBdptyfNClSk+Xm+Z0qfrt0W7IkznIlziW9nQ5cXjRqBMHE/68TA8vejwDU05UbVM6T0XCvqIyfPWzXfD979+knq+yshKxWBzwDNzyGVK9oJrDe0XtZsz3dFk5sS8ej1tW1mBye7qkncYWZHpyE+TJHORLnEJd5aawcd4HEo+lrq8iGoMvKvOl/pwAACAASURBVLZYKh109XRHEY+ljlG1TQ28JRzc65Pcz5XyFO3PdVXZ1Zma00U9XTnIblOOD7p0C58eHYlELCtrMNxFw4syPbkJ8mQO8iVOoa6cnsLGs2s3AKBU46jfsEXomOwv4Fvf+gKAum0qfS3Btl2Yd+8vUPvJeqnni0QiSHoGjRoNsXpR9XuXFchuU44PulTJ06U5fA6FGVTNfWM35Mkc5EscK/N0OXFhD8/+Mi1Yv5LSoszvjKWOV7ZNpS+fc3g0DUxyFvTa2lrE4qlzZCbvezz7PAbIgfG57VCeLkXydA3u6eqJJbE7HJN2PpmomvvGbsiTOciXOO7P0zXwu2hSoKKgD7MfugXz//gzTDxwIgB125Rn6gEAgNYDQqkXJL9Hzc3NiMdT0nWvByXNjShu3QVNp4z0g5HdphwfdHk81lUxEAhYVtZgBmeA/snLX+Dyxz5CT8y61Zd2IdOTmyBP5iBf4hTqKufzyIHdFx6mwRNPpYowUztm9JDpRv5GZdtUWWr1pVZaiS/OuBTtMxZJPV0gEEB0VyqYKOrpwszn7sXUVx41nr1Ic7qykd2mxlTQVV5ebllZgxmcMqKtN4GEzrE3ol7QJdOTmyBP5iBf4hTqKvvm6cTpDn5fEtNefsT4S6yvq783jp4DD0X39IORjKRGEVRtU+k5XQxA/6QQ4iVyV8yVl5cj1pMKcj3xlDsP59A0Lad9UE+X/Dbl+KDLyjxdHR0dlpU1mMEpI9Jdtio2Ypme3AR5Mgf5EqdQV45fvahrA5Gh4Phid1c/9hz9NTSdtBxNW1MT8VVtU1pz6vmHPiMASkpOU9DR0YGEseKTcY5kURBaoAh6UsuZ0+XE+X92I7tNOf4xQD6fdVUcbyTkk0HunK6BZbhO/JY5EjI9uQnyZA7yJU6hrhyfpyuhI1lcavwlFnUVlQwM+2hGdKBqm9Lb2gGUIBCLAoFSJEtLRzymEMaPHw8v/xLjPn0P3O/Bx9/+FwDAQRyDhhed11bsRnabcnxPlyopI3JXLw6MkzvxW+ZIqLoM227IkznIlziFp4xwdk9X9/ipaDr5XABApExsOKdsXBDB3Z8DAHQjUFO1TQWYjrKmz8Bj/akXJL9FkUgEfo+GKeufQeWeTwCvBwnGoDMPrV4cxJhPGWFl0BWNij3jKx+yP9c4snu61GvFMj25CfJkDvIlTqGunN7TpZeVZH6PB4uFjtF0nglO0vcFVdtUVSCK6S8/jMCeVL4xr+QbfTQahWYML3K/H8Vfm4f/mzEROmj14mBktynHB12q5OkavFpIy8zpknZKaSib+8ZmyJM5yJc4rs/TFRqPNydXGX+IHdPfn0B8fCpVhG5clKptKv3+lPZ0AwB83d1Sz1dbW4tYfxzR8ROhlZSh/J13cPB7b0OLxPZZeT/WoTxdiuTpyu6P0/nAB50Tu/ZHQtXcN3ZDnsxBvsSxMk+XE1NGFH36GY55+WkASM1rEmD39nboJamhSG4kE1W1TWkakAyWQPelOxVEs5XlR3NzM/Z6K9Gw7HtoP2QJqu68C1/7033g3WEks/PUKni/shrZbcrxE+mtTBkRDAYtK2swup47hyKp8PCiTE9ugjyZg3yJU6irnJQRDryRFjU2IvTxh2iYdyyK+3qFjtHiA1nb05+rqraplmgxmi+4Bv6WRlvOFwwGgbqJ6GmKonL6NDQefw6SviLU9Meh6QOZ/p3YK2o3stuU43u6rAy6iovF5g7kQ3ZbzR4jVzHokunJTZAnc5AvcQp15fTHkkW1UjScfQUAINDaJNTDkp0+yFNbA0DdNuXxeeGJ9kNP3zmY3J6u4uJilEwbjzenTEDRnIlIlFQgUVEFLZ6g1YuDkN2mHB90WZmnq6ury7KyBpPd05XQ1B4jl+nJTZAnc5AvcQp15fSeLvBUkDFx4yso+Xyz0Oeklhzo6eLG8aq2qcnnn4gX5s3AtplTAUhfvIiuri5ou5sR+vxjBFtaMs9h0pP6oIn0kiuiALLblOODLivzdE2YMMGysgaT/WUykfXV0pEfeCMg05ObIE/mIF/iFOpK5w7vbU9nZOccjOtCq+aSiYGgK72/qm0q85akR3Ik93RNmDAB7NU3sfzu/0Ll8y+CGf70RDKnw4BWL8pvU44PuqxMGdHT02NZWYPJbrhxzeEfeCMg05ObIE/mIF/iFOrK8Q+8NqoUHT8RWmml0DP/tKwZ34k9qYzu6rYpI+j0yA220vT09KA9UYGtl/wYPaW1YJmeLi1nHpeCtyvLkd2mHD+R3sqgKx6PW1bWYNzU0yXTk5sgT+YgX+IU6io3hU2htbGe9Nyh7gMPgeeAGUKfk9nDi3okteJR1Ta184FX8LUujmisC3+rn49DD5gi9XzxeBy6zsF9XjCfFyye7unSkPS6v6fr845+1JYXoTTg3Wdb094oSgNeVJWkVpLKblOO7+lSJk/XMM+vcuIH3kiomvvGbsiTOciXOIW64hw4oCeCUHc/9IR182KtgmdNG/H17hXqYdEDA48BSo/KqdqmtIQGraQcXg7EgsVISl4xV1tbC25I9ng94JPrAACxyZNzRmPcGHNtbe7FFU98glVPfrzPtra+OL7z2Ef47mMfZV6jPF2K5Okarq2q2NOlau4buyFP5iBf4hTqSuMcM7v6cHBHD5CVasEp9Bk9O5+MK8FzRxwmtGqOG0FXZcMHqBmfGpZTtk3tc7ly7xPNzc2ZDgCPzwOPN9XjowODHnit3v1qJN7bk0pJsju8bw/Whp2pocTerH8jlKdLkZQRw3XLqjinS9Vl2HZDnsxBvsQp1JXOOVj6Ru7AnBHpIEv3elM/InO6tPTkez3zu6ptKn39fk3H1zZtgXczB06fLe18xcXFmbjO4/dm5pJpmg5PNIJ/+M+fIen3Q3/wTml1GC2KfcPHEJ939O+7/1hPGcEsXNURyOqetprhPjQUjLmkenIT5Mkc5EucQl1xDsSM3gyedG7QZWR+EFu92B9NH5zJSK96m2KcQxtXAy1QKvU8gUAgcy/y+r3wGL05vsYm6JzBEyyHzxNw5ZyuYv/+gq59n3kpu005PujSNOu6xrslPt9q2J4uBRuxTE9ugjyZg3yJU6grnQ8ENNyB3/zKv/wSADCvvQd//2mT0JfTRFfKyd45h2NPa+q+oGqbSt8WmJ7EjGfvRt2bz0o9X3d3N2LGXCXvwvmA8YBt3tuP8r4+fHHmZdh5ynlKdhKMRHCYni7OORqGCLpktynHB11W5umqrq62rKzBDNdYVRxelOnJTZAnc5AvcQp1pXOO8lhqAj134PBi9gojxrnQBO7i4oHbFS8pAeCCNsWA0tYmBDtbcl7uaO3FS09swcbXd1hymurqaujG7d5fXpa58etJHaX9qTlP3ON1ZU+X3zvQbrIzC7T1JRAzeoF9Wak7ZLcpxwddyvd0KRh0tX/RiD1PvuzIVU9OQtVv2aMF+RLHip4un/GZ5Ek6byJ97rxxJjQiUF7uxbx7f4Hql+/HxBPrAajbpnh1KgFnb80kAMjkzUrz0ebd+ODdnfi/P++74i4furu7Mz0DgYAHHmjwxKPQNQ0+IxDRgiWOfDh6oWTfm/uyJsx/2p6az3VATwTj+gYeui67TTl+Ir2Vz4KyciXkYIarpoptuOGSf0VidysO+vc2zPjHC0a7Oo5FZntyI+RLnEJdZX9uOjJtjZaAJxaBXlQMMCacHNWjaakLMnomVG1TfEIV0NGGvokTsXvxV6H7cvNHJbv2Wnq+RCIBX3cYurcIbNceTO/8GNpfXsXWf/gefMliID23zsLH7jmF3KBLxzhjnvwXHRGUxpOY3xbO7OdhTHqbcnxPlzJ5ulw0p6t8zgwAgKdI7UmqslE1R9BoQb7EKdRVdq7AiuY9BdbGegKdOzD3kV8DADhjQsNakb44+msmozQSgdaTGhJTtk2l53SBobN+McIzD8nZXJxMzTWa2GvNe1dbWwtmBFS+/l4wI8jTEhp8xmhSScuXruzpyh5d78t6lFRfXENp1mhO+tIpT5ciebrcNKdLK099FfAEKejaH8rmCBolyJc4hbrK7uliTuzq0nWw9GejYNDV3hbHF2d+B43f+B46/rIZgLptiodT+aECsVRwpXtzB50ibamervie3Lle+dLc3AzOjDldwQCYcT4tmcwEXUxLSn/w9mgw3PBiQuMIZEVk6Qd/y25Tjg+6Wvqt+8AoLZW3LHf4ni5pp5RGwMiOzGlO136R2Z7ciBN8fd7Rj+ueb8D2zn1XLTmJQl1lf9eLOzGtgq5joLvHIzQNw5s1Ahczvos7oU3lA9u1GwBQ3pK6weuDFozF9qaCMqsC5tLSUvj7ulHU1YpASQAtMw/Dlm9fg2jZpEzQ1VdStk/w5way78E5QZeu5wRd6Q4S2W3K8UFX3MKoxevd97lLVjHsnC4Fe7ra1r4AANi7Ycso18TZyGxPbsQJvtbv6MamXT14bbu1c2asplBXOV8CHbh6ce+co/DhRf8v9Qdj0ASCi3HHz4d/x4cABtJgOKFN5QPTdXgjfUB6Av2g20Svlgp+ug5aaMn5vF4vat/+M2Y/8TsES4Pg06YDRSUIV08aeKbSuInQEg5cdFEg2f8W+rOCrvigni7Npjbl+KDLSsLhsLSyh4sNVZzTlSa6u3W0q+BoZLYnN+IEX+khhJgDE4ZmU6ir7GkNHgtXgFtFvLQMHt9AD5xIh47u9SHhTx2THj51QpvKh0nx3Tj44dvgTaTqzwbdJ6y+bYTDYXiMqTr+YAAHHjkV60MT0FIRRHNo5sB5Y2o+QHx/ZP9b2P/wYup32W3K8UGXlW2vpqbGwtJyGW6VpYo9XWm4E+eCOAiZ7cmNOMFX+gM47sDen2wKdZX9ueOPOm8otWlqNdZNHciHJNLTlUzqgPGEkvTnrRPaVD6kP1u9yVSQw/TcwFi3eHZVdXU1kJ5IHwygKNGPYFc7WG8vvNnz/4wFCm4i/U9hZlcvmv7yKTTj335C07GrfOCRP1rSnjbl+KDLSjo7O6WVPexEegVjLm9lOQBg4mnHjXJNnI3M9uRGnOAr3fNs5bQFGRTqKjvo6i0fZ2nqHSuobPoCM7duGngGo0AM3Pry+8ABcwAMjMo5oU3lRfrtyAxl5T7uzhu09vl/zXvasH3Z97H129fCX1qM/gcex4rbf45Zb70GT1bT0J2Y061AdM4RSGqY1dWHeGsv2ltSgWVC5+goKcKbk6vwxpQqpK9cdptyfNBl5WeFzA8eN6WMCM6ZBgAoP/jAUa6Js3HajczpOMFX+ubu9OHFQl1lD6nsrix3XL7A2ev/htPX3gOPlup9EVnlzSMDCSwzz250QJvKh5aiA/DxuVdBD1SmXvDk3oqDUw+w9Hya0d4jJaUIThiHDm8Fvjzxm/AGKjC9YSABK3fgUHShaDpHPCsPWrrNJIwvXj1FfvQG/NAHbZeF44MuQOxhqCLI7DYc9oHXTvu0E8BvrGBhXiWax6ih6tDGaOEEX5quo7KjDXGHf6Mv1FV6orknEUfE73Vc6pr4pFnYcer5YFxHUUez0Gd89jMk9dpUJncntKl80JgPybJKIOszNudm72FDHJU/U6cfgM2HTMb6UDV8Hg+irBjhmfXw+org7cnq2XH4l5F8SDeb7qLUfU03gq1kLIlQdz+q+2MABgJ/Gl6EdVndW1qsyXkyFMNFxw6fOjIk4Q9S33y63v5glGvibGS2JzfiBF/j/vQEvvOfN2DiE0+NdlX2S6GuMkEW11HV3uK4Hne9pBK9odmY9tLDmPnMH+DxjbxiLDvo4oEiAM5oU/lQsfRIvFNVhI5jFmW6X7PfImZhUnAglXvK29MLbyIGn4eBGUEd44B3byv84VTg5dbhRQDQjSFcPX1TjsRxcEcPFjbvRX1rNyL9qfl1stuUEkk5NJ3nPJAyX8rKyiyozdC4afWi3p/qxu96a/Mo18TZyGxPbsQJvoq+2A4A8Le3jXJN9k+hrjhjCLbvRrT6ABzQvstxw4uZOUycg3FuvqfL2N8JbSofvOPKsXfcOPCyEqQneKW+uKe8VH72PoBJlp2vNFiM8//9augeD3zfWQevlwGJ1Nm8yWTm2Y/ceQ2lYCIdfVi0pwvjjeRu6dGnOBiaKooRCkcwuTeKaDQ11C27TSnR0+W0rvGhGHb1ooJB19QfXQ4AKJ09fXQrQhAWE5uUupH1/n/2zjs8srO6/5/33ukjjXrb3vuuvW5rsI2NbWwgNqaZTiA4hJIKof4CDhBCSEhogSQQsOmm2NiY5oK713W967W3d2m16tJI08u99/39ccuMtNJqZkcjzSh8n2eflUZz79z7zrnve95zvud7mqozLVUwVEHd8X0A+Nz+ip1DhTRQpCyM05VfZTdkRWYMyVB/tOq4XQ6PXlEcuQgjLy3SoJtkbzFDVYwjvVGOXn8Tpy65HpciUK20pgBU1U2mzqwklfOw96Ke1GhK5qQw7OrFhFtlf3OIAx317GkJ4fLNTgyqOpyuGXqgYrHylcPOpzZAGYtMOR8fwJlEOe1pPqISxssu1c9W+NRX6lgZEjJaipbnHyWdSVTgPGRGdI5dfxN73nMzyVhqmvePj8IIS0vpwd8c5ntf286enafKc5llQvz2h3nVs7uo27mH42qKEV/KcYQAtJEwMHOFZKPDYyRbFpKub8KlCBTVSi8CsiNXMJVunn+bEaXBz472etLW+NqRLptIH2+qoafW7/SjLPc8Vdkzj4WZmjDa2mYuXDsR86kNUMBl8gn01PwTyptJlNOe5iMqYbw8PWYD4aWPPzy3FzINSh0rXdMZbF9IqPMgejpaeTSHCWwRmZ2e/KrnySjYt9N1xHS+nnqxurhd6biO3rQAVyTKoRUr6V67GiXP6RoUVopLiBlJ+YWs1jZSgksRBDesAmCofRFqMudk6Ep1KvyfER4XIwEvY15zXbOJ9EoqSyiVJWhFE20/o9zzVFU4XTO1SRscLB+PYz5Fuo594VsARF48OMdXUtkopz3NR1TCeHmGhwGoHazsbguljpWW0nAt30rXVW9C0fWKK+iJ1jcBkKzN8KIax1XrnfYYrb4egI4nf0+jbjpZfQFTof6QVmXzrOU1CqsJ9USfuNvV6vw8ExSVkUG7QlGiCPA2mlIVWa+Xo/VB9FTc+rAKM5QZgB3ZMixHP2tVaLYPRri4Z4QlXcMsHkuQiJpVjOWep6rC6ZqpXZoQM1uGm4/5xOlyMA8fwJlEOe1pPqISxkv3Tr+4VwJKHSupZXFHw2i+IB7FU4GRLvP+oitW0Lt0BYjplyJ7jlUzKacRtH1UpsqmKmmF+hQBL9/xHOc+9gypvBRrY8ZMLwpdm5EUo5EXSRQix+lSJOzfug0hNdAyuAfmfmM008iEE6wejtIaN52qZMaizVgRr0AkxfrhKPExs3NDueep6nC6Ziha1NjYOCPnmQxTR7rK9pFlh+KZ2bLl+YZy2tN8RCWM1+j6DXN9CQWh1LFSVEHrrkeQLhee2tbKi7hbnoTL4hYVVL1oL5bSQFoTq9v63xudnhNWWbDuVxH4fPXIug7SeU7X4mQvm275HBu//wVcrtKXabdr/Fyu9fYBUD8yhGJImp/8LZt+8EXUTLzkz6o06GMplo8lHGcnmdaRUiImPBOa5YSVe56qEqdrZs5T3vTi/FGk9yw11ZC3fPMzc3shFY5KSJdVEyphvLquuRaAtNc3x1dyZpQ6VoauO/38wi1tFTcPBeImj6j18T28Yu8REkPRaY9xWfpJ3Ve8gTGXmZ4MZM17bB2e+yKN4mAR2YWg6YXH6Hjy97hcuQjL0j+/0fl5Jnrgxq3I1miryVcyrHRjMBbh0uOnGLnybSSbOuZlv13DuicDOFnrxxvykTXkuJ6TYPX2BB67fz+//dluRgbLY1PV4XTN0IQRCoVm5DyTYT5xuhyjmGFV5PmGctrTfEQljFfGcrY8mTRaBQtBljpWelZ3UnaKrCymgJQS1Wr/o/lDCH8tWmL6SJVq5Ap7Mm6TGJ61nImku7oI4M6SpghCx/fStP9Z3GpuvtWWriDVshDd5Z4RwVJPyORwJWtNu1LdtmSEoNaqBBW6BvOwDZDN6TpV62d/S4i6BSGyukSxW0lZ65xmRXeGetPs391LdKw80dPqcLpmyHHRy2hQ84nTZYfuhVpdE9lso5z2NB9RCePlGh0DQEhJYrRyoyOljpUeSdF9+esAU+upkiJdkhynyY7GFdIuLZAZZN1P/p3UqX3Iy7cAkLUWzD3Nc+/QFwMZMCsxjVAIw+q7mC/R87tfvMCR629i/59+kkSi9CryrOW42Ur0LrsDgBBIq2LxyOs/gEyX/FEVB7s5urR8Wk2XZHUjF+my0re2Tlpvl+mEJhPZslxPVThdM+W4xOPly1dPqUhfQTvMQpHuNvP9ez70z3N8JZWNctrTfEQljNfqn/7E+TlVQHRlrlDqWOl5E0/j0EBFRdwNmSMr206XLCCaI3UdVyqBlopBjVkQYW8LZZUF5fVWUw9LW7GM2KLVhFef47ShAUiH89KtovTNb6xvCIBg2Ewrtjd7WH37N/Ae2gH5m+tqXLCmgbS8Lo9uEEpliUVSZnrRGO906bocFzzJZsqzSawKp2um7KC9vX1mTjQJpuy9WEE7zEKx5ANvBUBPzMNtzwyinPY0H1ER42Xl2X79lpuQDQ1zfDFTo9SxknmRMsUwKmoeMgxJ3ZO/Zu1tX8YdM6MKWgGTvKFLpBBIRTjpOdtd8Gk6mUz1iDnb168ogvCml3LqshuI5aWzMmNmFPbGq1uprSudf+ixFNlrh0xenN/nxhsZwZWMI1VTiX3F3d8ByhPdmUvYqfX2eJqLe0bo3t9vpRetN1hRP10zxlWKamVq/l0dTtcMTRh9fX0zcp7JMNVGshrTi+K8deb/VcaTmG2U057mIypivOxuC0IhU8G7+lLHSuZN7QJRUQEMXUrUdAp3MoaQ1vdRQDo1FljC3j/7NC11i/H9/B4Ah5ezZSBCT+do+S56pmGlElVNQ1dP1+oyLD7eyMNPYaRLTy/Gombk1A4IKm7T0fJk0hiW09XT0kIqVFvyZ1UanC4UQjDmceHyucelF+11TtMliiJYs8WsXsyky+PEV4XTVUi+vxC4Z7hzez7mk2SE22eG7icL+Xff9hseu+xtHP/mj2f7sioO5bSn+YhKGK9ssJFTl1xH0OUnXcFE+lLHSstr2iuorIi7IUGxiTa2cGUhrTuse0g1tpN2mxyu/AUsHqueyLzrVA8A3oMHGW00U43u5lzk1ea8nfrBnWQjpXMPJ379CTwcufZtjG3YhrTkJO5+23sJr1hV8mdVGmxF/666AE8vaqJ5bYsZ6ZqYXrScM6/PHI+p0ovJjM5n/+cZbn/k+FldT1U4XTM1YdTV1c3IeSaDISVvuPU/+fCn/pKlh/c7r1fSZFcoInc+AIAWPZ1XsudDXyB++ASRPYdm+7IqDuW0p/mIShgvwxMgvPY81h07Rvg3D8315UyJUsdqHN1BiArjdEl6Lr2Bfa9/v9PaxyiAkxXZvADvnseAXMPoVDAndhuNVC5H73TkJCNsPlp+ux870nX0+psYHUkUffZUMkt4OE4ibkbJFjYpbLzlc9ScfBEAz/KlpBauIrJ0nXPMloEx3JZA6HzCaUR6Q46TjFCsSJehGWQyGomo6WxNFel6ZPsJgl0jnLj37Dq2FOR0CSF8QojvCiHGhBBDQoiiGdZCiM1CiIwQ4jvFHjtT0aKhoaGZOdEkkBKWHj0AwPJDe53XK2myKxQDv7wfAD02/mHPn8in4rD9X0I57Wk+ohLGK9W8GIDw2vPIzEAEoVwodazyswMCUVE0BymBUDNGfSvZNrPdjQxN72TGW9vpW7jEOon5X+eCEIcazaje2Fj1RLraup9nzc++itebi2rlf2d2RWG2tp5suviI7K4nu/jufzzGzu0nAIhFoghAWJWSdY1+XmgNcbgxl05sj6dxxeaj02U5V0KAlGSzBumMhoJpRiKPSD88EOfofvPZmyrSFe4vbd4oNNL1OeB8YDXwEuDdQoj3FPohQggV+Bawu+grpDoiXeOvMW/HUjlzXdGYKJSXHc1V1LhDNRPf/n8OlRC5qSZUwnhJl8f5WYtWrtNV6lip/Xm9JUXlcbp2t9VxcHEj2HymAiZK3TCJ9JCbYaVQyFjniEarx+lSsyk88QiqImju6wYgdqzb+buR1xZJ14rnFimW5pdupW0dRXprrPwBD301fsJ+z7jj6k4cK/qzKh12gGBhJME1xwfoerqTrC7pDPlJddShKDmnN5sX3Zoq0pXKk5KQUtLfE0HLFu4YF+p0vRP4kpRyQEp5GPhf4F0Ffwp8GNgO7J3ujZNhpqJFmUzphMSpMC6an/dLNUa6bCx863Xjfk929Tg/G9nqqRQqF8ppT/MRlTBefa0mF8gVj6BF5l7CYiqUOlYyr/Iq4/VXFM3BkJBNRSETdVagQubJlqf301i/ZNxrMq1jWI5YNXG6nIbXquL0kdStOdUwJCi5pflsxFEfu9ekfxx4oReAwZjK4RveS7x9JQCxQyf4689+iDf84L/GH1jF69VUsB16WwTV0CW6EBxsDpFZ04pnYT17WkJ42mrJ5jlPmSkiXelkzuna93wPP/zGE9z9k+cLvh7XdG8QQjQA7cCLQogvWMc8DfxVIR8ghFgN/DlmpOwbBV9ZHrp7emjKmG0xGhsbz7o3UjJZvtDpfGoDJLweZDrDyr9797jX850u+Uenq6z2NB9RCeM10NFGy+F+FC07KWexUlDyWOVFqY+2NnJJBS2mhpS88db/pD48xLHr/oxE62KM3j5g+RmPcycyGN7xkZnNR/pxWfeWjFeP3MFw21oGr1pBSBeOA2ZYjrIxISypl1DwYeu1JRcsJD3iJ7l4PQAZzWB07fnImvERVVGF69V0SC0IsUeHxYaBbyCCrhtk7QigKvA2BumpjaGGfONSudkpgyhqHQAAIABJREFUIl3ZVJ7TtctcE48dLLxt17ROF2DnkSKYT4UPuD/v9SkhTAW87wCflFLGzrZ790c/9glGLQLl+973Pj7zmc/Q19dHMBhEVVUikQgtLS2MjIwgpaSlpYX+/n5qrAqeWCxGW1sbhmHQ3d1NY2Mjg4ODhEIhdF0nHo/T3t5OX18fbreburo6hoaGqKurI5PJkEwmnb97PB5qa2sZHh6moaGBZDJJKpVCm6LkOZPV6OzsdI73+/14PB7GxsZobm5mbGyMbDbr/L3YexocHEQIMaP3ZE/YGU3jVGcnPp8Pv99Pz+5coDI2OkY6nR53T8OdJxn+0veoe91V+C7YWFH3ZH9P9t/tewqHwzQ1NRGNRslkMkV9T36/n87Oznl1T+X8ngKBAAMDA3N6TymLVat7fCR7RhgeHq7I7ykQCNDZ2XnW35OWyQAC98BJulZcwMDQMIl2b0XYnuGvI7L+QtLJZK4iMZkiHA6f8Z7y/YFMsIbu7m40RcFlCaxmM9lxc20lP0/JQCOZlga8mSR2snRkeJju7m6CgfHq+oN9AyxOryjqns55SSu7nxygbVGQzs5OOtZ38OsRWNnsIxKJ0N07QP9FrwAg0H+SRJvJdTR0g9HR0Ypen4r9nuICRgJe2jJmJDSdSnPqVB/1qQzaSISM1xz/4fAoMW+uajidMu1p4j1l8kRshWo6ZooixtnemSCmI0Rbka4R4Bwp5QvWa28AviWlbJ7m2L8AXiulfLX1+/cATUr552f80Dx8/+4HZEL1c0GrORilRLo6OztZunTpWR07Hb740AnOfevbAHh+28t48Po3A9AUcHPb2zaV5TPLhXvaXwrAhXd8g6ZLznNe3/uxL3HyB3cCsPlrn2Lhm1897rg9H/1Xun/4KwBe2ffErFzr/YeH+f3BYT5z9QpCvkL2EDOHctrTfEQljNfn/+ZWfDUdAKw7cA/X/fKrc3o9U6HUsfrKV+5DHzSo7TzIEysX8c53vpyrV5/dvDnT6BlL8ZN/fRiAdT/7D9R4nPS3vsJrb9h2xuO+/Pe3Y3jNBVCv9fHxT17Ba27dhZLUuLR7GE+tl7/55MvLffkzgv/57wc4dGyQi6/ZQs/Pn0BrWcTVF9Zz7usuJhHL8F9feNB575vecz5LVrUUdf4Xd3Rz7y/3sPG8hbzqjZv51bOH+ebuGC9ZUsdnr1nB8MEubv3+PtRkjPW3fZmebdcysnEbemqQj3/5nTN9u3OKj/72MLt7Y5yLTuuxIQJLGujY0sHR3+yDkI+mbUt4fGcvF29s5bxaNw/99gCLVzTy6hu3TCpM+50HjzH6BzN9e97LV7LzoaMsPGcBb33zFuc9O3fufO6qq666YLLrmZbTJaUMA33A5ryXN1EYP+ulwKuEEFIIITF5YDcJIU4UcKyD1tY2Vq1axapVq87a4QLweDzTv+kske+6aq6ct1yNnC7hMp2Xne/62LjXa9Yso+nyC7nw9v88zeECyA7Pvjjhlx7pYk9fnO8+2zP9m2cY5bSn+YhKGK/6SK4iN3XNK+bwSs6M0sfKwD94ikxtPS995smKojk4c6JhmJwmCiPSS3KZEjtYoEsccn1BWl8VAu2cNexZuxZXRy5uYVjZEkWF1lM52aH8+y4Uqjq+n6D6yFPc9O83s+SOX5i/2xEdq0rSSStWUmf0GYL31DBbunqoDZvdDwzDQAdGvW5EyEd2IMb64SipU2NOxWLH4ropOwGkfW52t9bxQkc93SMmDaArUjifsFAi/Q+BjwghWoQQq4D3At/Pf4MQ4ogQ4l/zX5NSvltKKex/1jHflVIuK/gKmTleVG1t+dR2DUPyjU/9O9/+5Bd54uocAb2SJrtCcfHOO8wfJnALlv75jVz4s6/RdOn5kx43lzIS+/pnn59TTnuaj6iM8cotYLHNm8/wvrlFqWMl3AbL7vkh6cY2Ylsur6jNn11RJ6R0WtAYRTb4VlOmJpcucxpfRhU5DPbXoQiBvWW3nUaPKmi99xfU9JiVhGcjDn7fnXsAGAubTkE8rhBfvw1DN8dbtRTpDdWFoboQjpjVWd1ORaP25CDtmkrDUVPM1NAlasjHMwsb8W9dhK8xQFfIj9IYcJwuj2fqrElKM+iv8dHn9zrvN4rQiCs0H3Mz0AwcATRM+YdbJ7xnJdBW8CcXgZkqdx4eHnbyszMNA8j4/AiXMq5nUyVNdoUiHLF2BJOUKmcjMWL7j6IG/YQ2rRn3N29HcSHwmUTn6OwLI5bTnuYjKmK88oIGmVTlEq9LHatkaxt7tm6jrvsIWVVBNy6ewasrDbbyN9Kgf8tlJOtaqR8r5LvIfXnuaAwtq3PF4W7cmo70+AqKllUK3Pc9zeX9oxgd0F3rp2asl4UbrgDAyFpjYTmiiZN9sKa4udVeg3Tr/0haYWz9BQQjXUDO6ZIuN/ve9f+c40bbF5z1PVUqdJFiwY5HQUpSjQuR6WyOSK8Igu0hDjRHWd8eIjNkrn2P33+Y/p4I17/1XEdSAmCwL4q2s5sluqSrLkjK4ne5hwvf9BfkdEkpU8B7rH9TvWfaGKiU8t0FX1keZipa1FDGBreGIQnEIvg9LtIun1PyW0XzgIOG5ibArFCUUmIXQCQ6TzH63F5e+OBnaLj4XLbdNb7cOPX2t/BD32ou2bxw1q51UZ2XbksUMasbuNXZa7JQTnuaj6iM8cpNU+qTz8FVK+fwWqZGqWOVrqvnwMVX8K7//DzhphYMeeMMXVnpcAJSUpKpaSDVvKCg5sIyT0ZBAumsjktxIz1ujMgQ6obqaWGTGdPxNi5C9gzywoaNANy8aRkA6ViaRMtCMiGTSpONFa9I78DaOCtCAZmzfk+t33mL0DSkRSlJ+4Nn/1kVCoM0LS9sJ7rQetYzGdKagZASl5B4fvQz1sU96BtePU4Q9fDefrIZHW8eV3hkMI46EGUd4DIkWqB4HnF1tAGaIc+lrJIRwPu/+Ene9bmPcv4TORJktaUX9WSa567Mk2CzZkgpJY9uu5EXPvgZ8/dJomCjXj+DCxYzVDt7i6vfnTPhzvDsRrsqQQKhmjDX4yWlhLwK6prf3TuHV3NmlDpWal+EC2KCU5e+BkXXK2oesiNdhttNcHUDI8PHcS9tnfa4jD8AwKJH7iTU9QIZy1FT0ikCe7eTXFuWREt5IRTsQIr9FY0Mxjh2/U1k6szN70QJiWKQONppnsMac2F9mOrLtU+6ov9RVjea8/l8lIzAktwQVpWrNCSxY0O84vgAqUf2I356F1c88ge0SMpxulaua+H6t56L6hrvIi1YUs/oatNWV4XjaClz3HqXNhV8Of+nnK5UqnyLcj6fqWEopwZdbelFqWtkenKaI7YI6sRO95OJo2qxBK09Xbi7Z4/Unj8fHRoqYUd4FiinPc1HzPV4GRLH6Wp/6l6CPSfm9HrOhFLHSo1FkdJA93iRbh9aBUnSa1Zqx1BVjKsvZceFFyNbC1i0rKk02HMMdzzsRMeEobNq/27iU4hZViQsOxQKrDp+jAv27qVnzwnzNUPHP3jKeWuxTte4Ho6W827zxeyshRDCUa0/97tfxNNgylQEh+e+VddMQ+gqY8vWo3vN6J40DLSsVWAQixFfsJxj19+EdmjQEUTdcuFi1m5uxzXB6aqt8zHaEORQYw1H64POZiZdhLNaHU5X3v2EE1k+9OtDbD9RfKVce3v7DF7VeEz0rTbt2M5Vv7oNw5BV1adwIi/CjmhNdLrkJIJ9xg9/zjv+61/Z+omPnfa3ciF/B39wcHadrnLa03zEXI+XLiWa29zh+0YHUTOVq2Be6lh5I/1svvXzxBas4NR1NxXVpqTccAjvwiaSF0bDsGOUQkqQBhnrnoQ0yNTUkx5LVc1ca1ckKopgRd8Qjf5mjjxpyhA0hlys/PV32XjL59h0y+dY1uo+06lOQ76qer3Xigbaqdk8fpId1dJ1nbaho6z+xX8S7DyrpjEVDZcIcvLKG0k1WH0+Delw3VzxWC6bYxisWNvCxvMWUNfon/J8yazOifogRxtr0C37zWqy4IKHqnC68m/m/iMj7O2P89k/HC86ijSdaFkpyK+cEVJyzV0/4ZxnH2dh59Gq4nXJvF3VZdt/iuo3y2b11PgFajJFemmFsmcTuiGpGxnivf/2D3hu/9WsfnY57Wk+Yq7HSzckAwtMEUgMA2EYp/UXrRSUOlbSqQa0F9bKmYSGLeV4VzpN9qFdXLL/MNljvdMe506b0b++C64m3raKrGZFbwydQ2/8Kxa80F09ZHqRc7pEYpT6w89T4zavXQ0GCLzzDY6TOdkG90ywnS41GafXiphlrKIMfUHOmben+lv+4zEG9ADeaBiyc9+qa8ZhmYQrlcA31INQpKPyr8Rj49KOWy9ewoYL6wgPJ3j6kWOMjozfyD/xwBGC/RFUa96Ih8z1cXVPmMHeSEGXUxVOV340I+hRnZ93dBd2kzZ8vsl1N2YC+Y6hU34LxGrrKopPMS2sydrdWEdw5RKEao63kZqQXpyE0yXnoN5YNyTX3vEDaiOjrPzZbbP62eW0p/mIuR4vI49I3P2yGwiv2lKx7axKHqsJzqReAFF9tnB0yEx5qekU2uFugt4Q8mT/tMfZPQpH15xLsnkpWSe9aOCJjqCpZyevMBewtcVQFZTRPhY9djdNPmvubWrA+1c3cXSNSbCXRaYXbV6SomWdSkij0eTZZpfmeld6xsxUore/mw6/+T5RoZuQUpAOmg6nSzVYdfd3cAdTTssl15WX4P7YX5tvtGzH5/Px4rPdPHbvIYYHYs55dN3gqYePsqRnlFBaozmRZijkZ8RnRiKTycLmkipxuvJ+znuofrO/uPyz3z91yLBUCC1X8qzoOsmAWQWS8fmqitdlh+eFMt407PSi8JgGZmROL/Gei9C+LiWLOo/O+udCee1pvqE/mqFzjlsd6oZEWCaq1dSRbFmYK8+vMJRqWzLr5vDr3o+00qlaBc1Bx4et6IFhOBGfQiKOoSfuYv0P/5UhLcrg6oV5TpdO6Knf0Nnhw+VWpzlLZUBaoqSK2+1UuuvWRvbIvn4e+9ZTJC99A/vf+nf0ZIpzwO1UsuYPIrxm8YGwthtqXnpx2b0/YeVd3+ayxVkihpeuK94ANXMn+1Mu2GOtvfV13PWO9xNZtsJxutw+N25L6kgakp6uUWJjukOgz+T1XwwPJTB0SdKlsiIc57y+UYik0K0xjScLm0uqwunK371k8zywZ05GSBRBngyHwzN6XfmQeTtJVddRrQdIV11Vll40xzMzFOb5999MenAEACNtpheDyxZxyUM/5KLbv376sXPhdBnw4J+Y5fCRBbMnVQHltaf5hn956ASffrCH4cTcOTm6IWnt7QagYd8zNBx6HqOCuE75KNm2DEg35CoCjQqJdEkpHadLyJzW+nQRKiklwjBQs2kG3JLR9vocd8kweOD6tzDWcMaudBUFPWhuypV1K5EeH6mGVhIpa+6N5HYnuj8ELcU5QnakS7rcZFeanWj0iBmxccWizvs8sVH8I300bDuHhOEismIjwjv/JCPsslB11XKOrd1EKliTc7pcKi6X7fhL7vjeDu783gtOSjKT1wB7qN8cu6hHdQR53ZEUAcsOY4nCUrNV4XTlR4qyeTsiCSSLmEyamgov6ywWmuri2ctegRSCvkVL8Vgk3dDoSFVFutSAn0XvfwsAfXf9Ac3SiAksW8QFP/sqG/7to9SuX0lg2aLTjp2LLKpuSPoWmT3qdPfstpkppz3NN4STWSQwWuBusBzQ8xb5By59Gb+7+TO46ytBJf90lGxbEx5GvUheULlw7FSEYDjBkvt/ysInfpNT8JhmjjRkLvVlCIGUkqyei3QZqlJV86z9/ShCYLQs5cjr3k/nqBmRiXWZfD413M+xte2s2VRcUUU+kV63KhTdg1Yq8fAR52/LP/h22q+/ksaXbqUtqLPooTtg5BTzDW6rglPs3s81xwcIPnXCSdlq3/sJiff+vfl3TadtQYjmtiCBWnMtyWZyka6hftNxjXlcGJbhLh2OEbTGO1HghrI6nK68CUSbQAgt5kGLRqPTv+ksoasqj137Wh78zvd47pKrnNf9iXhVcbrcoRqa3ncjwVVm7t/mvLhqgzRffhGNF5875bFz4nRJSdbjJVLXQKpmdhfQctrTfINTWq3N3bNgSOjY/muW//ZWon4fKak4JfSVhpJta8K8OJnEy1zgvl+8yNpwnETbYmoHuhyS3XRRcl1KItv+hENv+EuuefgBNvz6HkcGI+MPsCEuOH9vN5HRKtHOs9sAqTn706370a3vyh0bRUTDeIziNip2pKvm5GHidXZlnk0byX3e2pv/knP/9/MIVaWh0Y9r8CQZWRl2MpNwWRQC9X5TP1PoWo4nd7zTIdJjGLzpzy/i2jetwh8wna78SNdwvtOlnD5vJFPzidM1RXoRiuMqZDLlq8yw5wxVEePIiMLQq66HaCaTcZpeTxRB1VNpXvirz/Hi337+tOPS69cCcPCV1532t3Jh3eMPsem5J7j9z/6aR973N7P2uVBee5pvsOe4zBzqRemGxD/chy88QHsiS91gcYU4s4lSbWtsydJxv8tMZSym8UFz4RrecBFCUXIVetPM45pmoNc2kKlroudlryXbvNzR6UqFQtTEYyiIqml6rVgcWXmsk4F1GwDwbjTnT92KnARiEa7//D/Qf8+jRZ3b1qBStCzn3vYDIOeDiyk6dix806v59if+hcevfW1xN1JFcB0+zMZbPocr1ekQxYWWza3X1iBlMhk8XjPqmM/pstOLMbcL3TLcvc21HGkwU7KpAp2u4jXs5wD5z1HWOHunq5w6QSIe55L772ZhjZuDm17K8TUbWH5oH2qFqUFPBy2exH2wi9gBq9mqteuK7jtCzy/vo2bNcnpuvwfhcbP5a58ad+zoG1/Pd9ZeyvKG2atSW/7iThYdO8yxdVtIz7J3O9e6U9UE+zmdU6dLSlTDIOvysDYcR0knSHT1EljSMWfXNBVKta2JU45WIQUD0udGpLJIl5vIxS/P+8M06UWgf+wkS7qOk978MkCgS8goAulSnWiFXkEisGeCXb2oGAbSIv/bV65ZqeDo4tWcUlQaRwyKYauu3tDK+h/9GwDxBaZtN40dx/eHO9He8ppJjxkZiLFsNE6iSgoRioGj72YYCMwNiB3pUnQNYcurGJJsRqetrY2RXlPg3Ha6slmd8EgCISCel15UJI5WV7rAXq7V4XSdKdJVxM6mr6+PpUuXTv/Gs4A7FmPbI2ZbkT/Zs5+01bKi0lpwTIdUTz+73vMpUm2LCfSfdIj10QPHOP6NH9F+g5k6nazU3v6eZnOzqVgPj6EoRdnCTKCc9jTfYFRAelHTdPq3XkGmtt56RUGPz66gbqEo2bYmzDmVEukyAm5Ua3Hq2XAJgcV1cGSY5DT3qkvYvfUChpqa2KgBQhDqCLHfnWSzNoawmM3VEulqe/FhvENDKJd8wGmobM9ftrxHtqae8NrzCBcoRWBDURWEoXPgzR9yPA4RHyMw1ENyCqdquD/KmpEYvUHvuH678wN2DttKtWqaE9VStCxCWrpmmuRrn7mf2noPl12zDoC0Fb0KD8VBgr/ejxTCcbpchiRjpYjT6cJ4k9XhdOVzuiZEM7QiHJpylvjnp+Fc2SzejFl9pOpa1WjHgKkJ03nt20m0LWHhY3c7zpVhiaP2ZIWpIi0lUtcdHS8AGYly9V0/wVsbgDd+dlau196lvPk7X2GsuRXeftesfC78UTKiGJxtpCurG+zqibJ1QW3Jzcz1jE549TloNXUASFWtGK7TRJRqWzU9PUCuEk3Pc7oOvthHqN5Hx+L6SY4sL/Kn66UrmxiyHI3pZkh7Q2cI2wYEuiF5/fe/iT8R58gN7wUgWyEFA9MhOHiSQFcXqqLQ0HkM1HpSB48BG62K2pytF+tIGoYBho7h9Tl6bbZUijLFMxTdfQCA9lMnMSSo88nnsqEqHHnNeyFYT8rnxt/fh5JJnaZp5/ao+AOmNFLKKvwZGTQrSn31ZhbHdrpWh3M6XvmpyDOhKjhd+WMyMdJVDJHe4yljdVveYlIfHqLFKk1XdJ0qiXibkJJEm0miT2y9GDVoTv66JY76wnAGzdI9mbhgtX75a2zZsZ21D90/K5dqWGXkNryJ2RWCKqs9zTPYz2mmSOmCT993jE/de4x7D42Ufg1CMJqNQtJMHUhFRZ9Eb64SUKptuSZ0kNAsZyU8FOfXtz3Pj//7qZLOf7awuVsbX72OG96xFduHmo5In85obDvSybrhXIGBZkgU3W5mbEWHKkQao2CoAo81b+lWpbiTIs1TSi8Gu57p5pl3f8L8RVGQUhKtX0zPS15Ndqo4izWOiqHPgcR1uWFplLlUUs0d4PLTubCB2h334Y2GUTNpGvc/i+Yy79zjceHzT+50eUK203X6p2gFyldVhdOVH+kqhdM1NjY2Y9d0GvIjXXkk2LQ/UFXpxVz7EAidu57QpjVATqdLc7kwrN3SRJL9bEMzJKqRu15llq+nrPY0z2A7Xekidu3JrM7OU+Yia/9fCgxF4Ynzz6fz8ovMkIuikEpXZjFE6bZljbOUDAY8pGvNyt50gbvxssGaCxMfvplHL74Rsd/sN+jpPrNUQSatU6f48ATM6JwUgoFjwxy/4S/o3Xatw+mqBqdL1w3Cy7YwuOUShFCcikI7I+JfaaVarXSYUSRXdWgwRk2ec2UYktGl6xlZfwH6pk2THiPUXCPsasrMFIJUbICaA8/ga6hxIjiabtC1ci2Nf3IlLTfdyKF6P+EmixLkkvisSFcyYTtdZkRLrR0f6cqHVmAKvzrSizPE6WpuLqN4Xl44y6Wbgz+2YBHH125y+CzVgPyWE/kPXyphOl26y43hRLrGe/az3fdMN2Su3BczqjibfISy2tM8gpTS4fkVk1588GhOIHRhqPSoor35URXFXNCESrZAQcPZRsm2Zd1rc6aX+1Zu5YKgOX6qpX7e3FZT2vnPEooVgROpFDopRCwGNfUoydQZj7PneWHoSNxIRSGb1tBq6tG8/qqKdOm6QXjVeYhshiWKcFT57elTKlbluKUrV2x90NLNHdz53FFWZdzO57G6jX2noqxumzylrLpUwKKSFH1HlQspJY+dbwrEfjm1H9FlIBUFmdV59vJr+dg7tzCa1Nh5x342Z7NAklBd0JGMSCXGR7oIeoDEaU7XmNdFa21hBWTVF+maMGlXTKRLn6QXodt8eKoqvZj3hBua7qQQUzFT/0ZzuelZsoL4+vVM9G1mu/eiISHS0MxYk6nYrBr6rAok/jHSVRjyv5L89KJuSP7fPUe4dUfPpMf9Nq/NV2oGCPhaMsN5e/fSseNFJxA055GfKVCybdkSNppGIBZx0qi2tpXd5mS2ETq+n9CJ/aipBKgKTcl+Vtz9HVycuaDB5vI6lWZCoWZRHWt+/nXan72fgcXLzPdVgdOlKAouY4xkbBClscGJdNmbVju9mLbEnn0rTheiPhP0wQFe+z//YvKVMDlhqTo33aEA3sDkmxdFyXHl5lOky74VRcCC99xI1m06ohcc7ucVxwdQBShCUpfKEBwzbbB75z5+ee1fcPX167juLecAcONNF/K2929D1phttfS8tS/uVnl6YROuLQsKuqbqcLryniPbybKJfsU4Xdkylk0bTBJdEaLqqhfze6CNPr2bAUsjRvP5GKtvJBUIcteffpDDN38ad31o/LGzfJu6IbnrnR/g55/4JwxFQUhJdhaJ0eW0p/mEfEc4P73YF82wozvKfVPwtY6N5IQuZ0JqQovGaHHXoWpBpL3QNcw+mbwQlGxb0kBoGuw/zE1f/wL+Y51ATgOq/9TcaJR5B4+x5MFf4I2MIBQFHxqBoR6kcebn1ol0WSk3ASAlntgoaiaFZjUIr4ZIl8ulMHLlBTx6/nm4Gutz6UW7721fD67kGGgWL88XKOr8SauNkP2k6bpB692/5rrbvoM4dGTSY2yCvRBiXnG6dClpi6Voi6dRGM/F0pHoJ3tI7NzDtp4woYjppAZ3PU/joYOQGmHFWnND7w94WLCkgZQ1OFrQy6DfdGBtyYhUtjDbqw6na1yky/zZZ5W+FhPZKKeu0tDS5Xz9H7/C0M2f5MmXvwqA+hPHOX/7A1W1cwhtWsOCDvMhb9n9uMPxcr3jRr77kX9i97aXAZMvgnORXgRQhUC3qiiz6dlzhP6o01UY8jdG+ZEu24YmsyXNkOMiZKkZWEx1TcdmbuuW+K+ydEnJ5y0HStbpGj3Fxh98gVRDKwff8iHUxOk6VnPRK1XkVRcKVbXSWrkmzVPBvu78whnN5uO5XGYog+qIdMH4DaoT6bJeWzp2gnW3fQ052g+AXmR+sfPoGH0XXIX0WPwjXeIZTtOcBTk8eU9PJS/yWVXtlKaBbkjO6QuzuW+U8G8fcvTRHlnSzIYf/AtPXPoWhr/7c/wD3bljLLczFao77XxJy7ES9X6OWaKohhAIKUkmMgU9UxXvdNWPDI5zWuwJ3G8ZyURi/ZnQ19c3sxeXB0OC5vaQveh8+hYtc16vtkiX4vWQzXWhzYmjWiRBtypQtSxaNDGOdA+Mm0lmY0LXHI6OYOc11/PYNTcwmzJQ5bSn+YR8+893sGxHajKHKj3htWKrHie9Dk1DWmkUO9I1UxIDBwbiHB+ZuRY0pdrWyMJF7Nl6MWoqgSsRxdBtpyvvGZ2DxVUKF5lgHVIIhKoQ89Zz6qWvJiu9ZzzOTosiDYcCMdqf4LH3fopjH/k0jSdPAON75VUqslmduh0vsvHgAUQmg9FchxjqJthsbnYNK8pZP2xW2Sa6+4s6/+BgmqEtlzi/G4bB2IJ19Fx6PbqcnMbt7zAjOomaEDu6I/RF05O+r9pgSFBGeggd38fIfY8SiJppe0XXcWsaKAqu2gArf3MLGc1ML3riZhS4uy/N9j8c5okHjnDnD57j0J4+UtZ8EfK6UK2qOFR4AAAgAElEQVTHxxBw5YkBPI8ccTTWzoSKd7o86dSkk7bPbe1Yi5g4gsHydVC3Q8NuRdC7eBmDK1cDZgluVXG6gCuvX81liRcIDHYjrR1o1BJ+awq4eft/fZGXfuAviB06Me64rle+ivtveBu3fOILs0Jm1w3Je//tH3j7J/6K4xe9hGdfdg2GlbOfDZTTnuYTtCnSi7ZjldXlacUmEx2x9Aw8RHpWN0nLUmInX5LDpfPysrrBR393hH+452jJ57JRqm11bdrKfW94J0tlP+t++hXSmFGhqFUCH6v3T6nZVE6MbriMQ2/+W7I1dUjDIOmuIbzuAnR55uc2R6Q3EJatpMbSNOgKmj9EqKcLz9gwUlb+ZJuIZSDuY1lagViM1EXncO9F59P4ussA03GUQCg8DEBmoDi5lInRPl2XToRnKi6ff0ErAKlgDV98uJM//dm+oj6zUmFIiefQsyx56HYUKVEs7rXHcmxdQT+KJc8iLIqQJ25WKg7u7eHJB4/y4o5ujh4YJDKaYiBmPkf1qqA1bqYjdSHQFAXDpYzr1TgVKr96UcpJOV1+y+kqhtOlquVrcdCxfy+v//EtBONRzrv8lQxu3EzL0cNV1wYouu8Iu7/6S04orYQ2vQRpGan+uX/nA089x773vJdUy2KMocHTJCMGN23hxaYVTNILtCzQDXBnM7gyGVSXG2Rxkc9SUU57mk/Iz45Mll4E0wHz56llT4xszUR60SnplgbeaBzDV0PkiZ1w/rKSzpvSDNKaQVY3Zqx6tlTb6ugbZc1Ykkj9Qvw8j9TMRcZ2ujJz5JsomSSqriN0nfbXXIXa0sTJR/cSWb3sjMfZm+tkYyMLt99HKhDA2HSjeU5V0LrrYdxP/p7mj7yi3LdQMpwsgDRAVbB9X/sed2QXMPSemwl0HQQomp5iTAj3G7qBrVWluCa3K1sVP5/QNR+U6Q2JIyuk+n1Otfu2gRinLrmOFUeewuV1I4VAEebYqBkzYu0f62PLldtoW1iHoRvIWi/P338Cn6Fxzu2/oHPh+eZnCMEjS5pZ2xokUDN9lXXFR7rMktncDOFwumwuQBEGGYmUjzyqplME46aW0EWP3Ye0OSO6XlWSEemBYSIHu0g1tpOtCTmyEHokij8Rx6/7Gd32KvovuOo0yQj7uzB0o2htmbOBnieOuuDIAda8+BzpsdkTSC2nPc0nTJdehNPTiROdrMwM5I11K1UupMQ/fJzFD92OyyidA2hHYQxZ3Hx0JpRqW/54kpqsbvLYyLXtylgCjr5kqqBUyEyjYdcfWPvzr+FORGncdg5r334t911xJUdWrz3jcXZ6Uaoqzfuepu7QLoyEtTju2YO0nFQtj9N5/NBgme6iNNhOlDAMFJcLdyzOgr4+Esd7gTynzHIQijUp+xFTspbMjy4dWYqpIl16wkyteTO5tGK0wLY2lQxNN5CeAJmaOsvpyuME+mtQg34Uj5uDb/4QqmrJqqRNu/JG+7nk6tWsWt/Kmk3tPD6QRAI3HNmB+6mnnfNIS/ZjXhHp85XPS4l0tbS0zOyF5SOP36QYBuvvusP6ubrSi1I3SLQuxjM2TP2RF5xolmEp0htpMw0QXnPuaZGulmee5sOf+ks+fPNfk4mU3/nRDYlqjfvWO3/GdT+7hVTf7E20ZbWneYRx6UVN8u+PdPIvD50Y50hlJujt2c6Zx+5rNkOcLgCkgTsboe74PoRWutOVHXd/M/Owl2pb9XufZNUv/5toVOfAWz6EVzc5U6kR05kLZCWx6Jm1scqB45u3Oj/Hj3Y53NzktER6a4wt50FIibTmJHdnF4bldNntjg7v6+eO7z1HvAK5SUZeqhRVxbdjD5sSgoH7XgDgguRBNt7yOWoOPsvKX32bJXpxnC5DNTf83tEhPGPDKIrIpRfdkye33KPDLHroDtqe/YPzWk+k8sauWKRTGomLXs2R174P1e8dr+uoZXAF/aget+Pger0KLsvp0qO5NUwzJPccNNO9a1JhfGNDLHj814BZjQqQLJAfWh1OV97ibut0+YWBK5MuyukaGSm9lchUEBMGXLG+RLMNUPVEuqRukGxqJ1PXRPcNN9D88m0AGBOUu12J2GltgFb+/jfOz/os9EDLF0fVveaiohXQ1uXYN37Ejrf9/Ti7OhuU057mE/LtP6Xp3Hd4hIeOhonkaWSdxuGyfg95zUViRjhdC029I8PnQ1iR6JloAzQVZ60UlGxbmRS+0UF8KxahBWqJ1TcCkBrMnVefg+bQL156pVPd3XXrHYwc6WVtdw+hgZEzFt/YFXyBwQHGlq0nvniNE2kXAoY2X8q+d3yc3dtP8sjvD/KrH+0CIJ2qPFkXp4BBGqguFVUVeEf6cVtprcDKJfStWE08EMQ/3IdHL9z50bI6UjcVE5f/7vusueObNLfVOI6Yq3ZyrqDP76b++F68g7kqvt4KdFiLRT4XUA34x0W6lGwaNeBH9XocKZLzXruR5y6+HIBsPMOe57r54Tef4LFHjzOW0mitcROMmRktNZ1kzOtCBD0sG42zbn8vLzx7ctprqgqnS+btguxd5eaPfJQP/vPHnJ6ABZ2nnGk+43QnQwsE2H/uRVXF6ZKGTscz9+E5+DQnzzuPGptrYTldi1aYFTbBvs7TIl35judsOF2aYaBaD5FhkSELcboOff6/GHrwSeKHO0v6/Lkoua9GaIbksnvv4qq7byM+NOq8PpbM2c9EDlfaioKFfKr1e+lOl3MGIcgGmxnc/FIS6RkQXS1DpKtU27IXF7sqLW3JB9QtyGnraXPQHPr8Q30El2zFUF0kTpxizy8eZWlGZU1X92ndRvKhWfagplP0XHIdgxe/Ct0u3xcC6VIxPF7GhpI8+9hxFi1v4N1/ewmhhuI0rmYDjh6XNBCqC58fVt/1LVoHzKbTK//mT7nrfR/m+JqN5vuL2BwO9EZAmoKddlGBzGrO/OhdvnjS4+xNiJJndz2RyuzWAGaKtpAopm3jmsdDcM1SVvz6u/gOmw752IUXsP7zf8fKv/lTRhuaAEgYCr2LlwOQVf3cc8ce+k9FOLTbFHBeVOdztMwOLVvOrrZ69OVNqIbEl9WJjk0fPa54pyvl85M/NWR1aYriDQzg0jWMkcl1RyZDOdNBEyNdAENXXUXvkhVVxemSuoErGUdvaMfbl8crsfpJBoJmRMlspTDB6cpzPPVZECm1J10pBNKqWtQK2Nn6FrYB4KopbUL+Y3qxMBhScuFj93POM48ju3ud10dTORuZ6KzYv9faka6zcGae7hpzqo0Ah8MkFEE62EL/hVeTyJZOFNYmqcgsFaXalmxYQPdlNxDTrJYy1jW6lnXgGzK/g8wcsOnduo6iuqi7cDMbv/Qxp+hGSEnyDGPnDXlYdef/sOiROxn2qowKzSKIm5EuT3aY0KO307bMdCrbF9bR3FbrpH4qCUae5phwKQ653Zbg+em3n+ac44NQ08jxK99Ib03hivS9J61q3MQYqmGQCgTNjIRloi7X5Pae0SSDm15CYvGa3LkqOL1414928t//8hA9XaNnfJ+t76Z5vTRcsJk911zndC+QrU2ENq/FFfA5BQPRRJa0zw9gtqiyIH3m+rKg1kvrKy/De+MN+FdeyEtOjVDjUcladhaPTe+oVp5FTsBYYwtpb66nUVY3QAj0JjNcXkyEvL+/uNx4MRCTRLrwWL2vqojThWGQ9degty6l/uhJRp563nzdKrENWW0QTi1fTe3G1eMOFXk3qs2G04XkoT95Iwff9BawCysy0xu9YUXDhKc0eYly2tN8Qr79+0aGnZ/H8p2uCQ+J/XvId3ZO185TET593zH+7Oe50ne900ydqIk4Xj1K8wtPoGql85rKEekq2baCDYyuPoeE1dokGBnFkJKMcDlzVXoaHtVMw+wlaC5uG7/4ERa/87UIS6xWcGZFb11KfOEB3PExdq1ewjPLFzqRHKEIjn3gA9z+7r/EsKQ2KtHZsmET6fs7FqEoCopdqWpt3Ad6IoQyGn1LVxFftoHo4tVTneo09J40nZA1zzzMs2//Ow689SN0dcccDUV1Cr35jA79F72C+Koc566ngtOLxw6Y3N19uyZvIWbDfjalAP/iDvZe91pG2hYC4Pbk+G0B61k4/ru9vPy3vzBfzOR091LWeztCHhbe+CoWfPIDALgMg5qRYYRlb7HY9GNW+ZIR5DghRl7jXOzIRhGcjJqa8jV57V+whKeueBUXP/x757WmBx9kub8V/WWVqXo9GTzNDXSfb+a0/ZEogw88SePF57LzZdfgSevUPmmKNkbrm3C3jW/KOy7SNVE4tQwwhMKul7wco6OGthd3m59bgD1kLF5Lun8YX9vZNxYupz3NJ+Q7JSIv6jvO6ZpQnWg7LwG3giLMzZVmSFwF6pHs7jV3qfkkdyNiviayWTKXbeXJ2nrefPXGIu/mdGTzeCIz5XSVbFt2AdzIKLSFqIlG0AxJ+PggyVYzcpKeZSFRRw3fMHBbEUyR5xudiYg8MJaiAUBVnEiB2bJMQSg4dmFHv/fu6iEylmLrxUvoWFxZrZ6cHouqCyEgq6vsfdf/w5syMwvZVBYUBd9wL3ubO3jr5sK7E9iRLv/gKaTiwiUlumag6DooKrKnD5Y0nHacz++m6cUnySoCNq0CoK+C04uFwuYtutIpsmNRFp4K02BtRGqfepL+5ghCyRmhYmg0D/Ty1OWv5MWrXs1LTpj9XyOKAB06Ql6klIT/7b8hcA6qhNZv3Yba3Eh25QXzI9Kl6hpYURM7jO9WhBPZmEjmniv0LV7GE1dfx5Xdj7PsfW8BwB0eZcuO7VVFpG98yVb0FjPvr3n9yKyGZkieO/8Sjm+5yHmfIk9fYPIjXfos7KLtxVwRwmwFQp4WUwFInfqjovxsIJ/TqOQ5KGMFpBd9LhWvtYssxqFJTho1Meh48nc0nNqPaGuhd8kKsk1NBZ9zKoxLL1ZKWNvyTRWLySakRNMlkb05ou9s9ym0eXvCMFCsTXN+pGvy78xEb88Y3ZfdwPD6i7j0vt9w+QO/x8BcA0RTA57eUS7sHqJvj5k6jY6l2Lerh/DwmRtpzwVsuokUJh9NcbmQqguJMPWgFAWkJOl2cSoUILSoMKcxEcswFk6CruELDzCgjfHQ8hZWrmsBxRwr1xTVi16/h45n76f+wLPOa0OJ7IxtIuYKtlCsb2yMRGcPNcO5QhL/zp2M7thDZihMoK8LgLqwKXCc9vtJytwGb8BaztqMDIP3Pc7oT3/l/E0YOktf2AFAMj4PnK7m/h4C/ebiaO9a1+zbhdpthhWNIiJdsbwc7UzD0V4R4K6vdV43dbrK9rHlhaIgdZ2YVWUWbwxw7es3AVA/MkR43/jmqfZtbr/qOpSG0/tWzTT0VJoNO5+i7blnOPZXf803/+FLcMHW6Q+cIZTTnuYT9CkiXaPJ6dOLXpfAa6lHFtMKKJE53ekX0qBp/w7qwt2oyQyNyTTJGUihZCdIYswESrWtsQaTE+ZrMp9DBYFmSLxqbr7MFKgrNFPI2sR3qSOsxd8JMsgzC+CGw0lGV59DbMEKfC2r8S4/D81lUh3UV19N8+/upSGjO9w1G9P1dJwLaAkzpd3U34MAhEVuzzY1OU6ComVZfmgPf/lPf0/sc18u6Ly93WZqUY2FEVJy3W3/y7v+/WaS9uZSSlxTUCp8C1q45wv/wY8/+PFxr1d6O6D0NBkVe+4R0iA7MsqGn/0PasJ8tpSsKRkh3LmUu5E07zft86PlRdV7LdsNnuxk57s+bn5vVoFDw6FdqClTXiKVmN4fqXinC3D4RLZcRCiSI88VQ9hua2ub2evKQ91QP8sP7iF5vJuVH/ozLvj51wAzUldNkS49lf+QCWRWI5rWWLt7B+sOvkBdg0kybAgPM7ZzfKuIH3/qi3z5n77B01e8EmrK3yJHi8R45S9/yKof/RClNkjaH0BTzmzSMi/SUmrvuXLa03xCNt+hynO6YnmO0emRLvN9XpfiRLpSRUSREpM4FE4VmKognzvKBb2jJB949rT3FQu9DJyuUmxLSoluRZK8fiuihFmE5FVS1HaaVXKzH+myJB4MI8djsnW3xJk5XackqEd3sdSb4yfZDpaqKkwUTvd4zfNnJ3G+5xq2gKsvEUMIcIXMVLJ0u8naMhhaFunyEl+2gTCFpZqXrGzifR+/nJoXHnVeC42FyaYytOx8gI4nfzelOCpCQVF9+BTTkbWHcyRRGZmkqRBOnvn68hulq34f3mgYV9xMwarZDGowgOLx5JwuSw3hmrt+wnu+8hkWr21iwYpGoopCg9+F0ZPjWgqrW4tUXXRfeSUAmVR22nWlOpwua7K000m28vuerReTWFh4ZcfgYHmEM6WUrNm9g9f98L954tK38MC6ax05gmpreN37y/udn6UQGJpGNJriT35xKy/9/v86D62hqGQnRBl1aSkfC1F064qzgS2EiKI4nI5CdNsUv1WBmS1Nw6dc9jTfYOTtRl1TlL9PlV70qspZpRcTk0Q4shmD8OpziNd34LJ2u+RVU54txkW6Zii9WIptGTK3aLotcVkFQdYw0NMZp/9cejRa6mUWBcfJMwwn0tV44WYARprbnGbCE5HRDE7h4d5rXsnVX/87s30OMLqske0LGqhfVHdauxpfwJRIyFZgpCtU66Zl16MEuw4iEKhWJFcaEs1yPBVdwwjU0HPJdQyEJpd5mAi3W0Xxe3Bb6+PQxm0ce9W7OHZslNDRF2k68NyU4qiaZrDgYD9b+8yARkuN6azHK9Bpzcd0S6ujRScNFL9ZkCdd5r2ZOl0+FI+L2CKTx5auyXF860eGWHNuKxtevR6EoKPWS7I7R0mxnyNDdaEGfCYfTkIyeeZ1pSqcLmk9jLaOS8BqSNm7ZDlZV+G1AOXqIyUBJW+yzY5GncoadQpxVCkl20+MMlRADng2IfOrMBUFqelEoiYvovuK1/P0w8fwdj/PsvtvO62IQTMkq/Y9z5ZnHiM9VLiUx9nCVhiXLhetv/sdb7jl62iPP3PGY4SiUPsKs7GsUSKRuNr7kpUbw/Esj58YRUPh+W0vQ1NdzoI5ERNFRR2ny6U4qvTFtALKVzjXDMmjx8NEYxqnLruBkfa1Dnl2JrTW8jldxaRAz4RSbMuQkmDUJGXLcNg6n4JmSJJJSbx9GQCZxOymjrIOp0t3dKE8jabEg+bxTsnp2rW7l7VDETr8Ltxul5Oirt+zm/d9/kPwxa8iJhRY+AN2oVXlOQ31tR7adj1MTddBhADVkj1SYjEnHSr0LFmXnQos3BZGkxrurLmmpOuaSXQsJRpJI6yNjzrFemkkzUo9l+VINFtOa7wCnVaAwfXt7G2upXVzxxnfp+VHunxewqu2kGkwm3sr2YzVBsiDO2o6m56R8VXDsfCYI52xIOQhcSInHqsFzAhkvGMZvnSKjOU8T8frqgqny86d2jvKQMycUBLB2qJSd42NjTN/cZg7S2WCZMSBT30VMF+f7BIPDCb47B+O8z9PnSr588ee38+Bf/w6Wjw5/ZungcxzHqUQCLeLuNUuJN6+jGMHB1GlhppJkU2Pd1pec/vPWKU2sX4oSupk+UnqdhGFVBV8PT0sPXYQY2D6CMGLw+ZDFImWNl7lsqf5gm8/c4rP/eE4L/TFePC6N/H1z36NFy+6bNL3ToxiOW2AXAo+O71YFKcr996nu8b4/AMneDouEIMn8fsliuXIzURANj+6OhONuaE025ISvCnLtq2InlRVsroklg06i0U2Pbtq7bbTlawNoVrRZjvKoxpySqdrxwOHWRJJsnHPKQ48eSSXXrR7abpcpzldLs1c+Cox0uW0VlMUBKBaDrKSSudxujQ0K0VcqIkefLGPB36+m/vf/WHu/fI3SPtNKkg6pRFZsZnwys2oUzS8dkJG1n8twcqOdMVqfZwKBVADZ24wbYxLL3pJWJW77qPP442M4AqYvRdX/OYW/Hu3E81E6V20jHStycuOj0TpsyoS292Swfu2m+fL48YZbi+td//ajHQBifngdE3kdPmiZvj0yt/8HM/RYwWfplzpIEPK05wuG1KISR1Dm0ScX8F1tnjylTdx4ls/5fg3f1zyufKdrozXz6YvfZyURfw07LCstWBpeek5Q0pqoxEMtwepKONSSuWCowWmqI6EyHTRq2wkxpLtjwGQkqVFqv6YXjwzfPfczzu+8QUivUPjCPSTYWKEyHZePM/v5pJ//AfaujvHNcueDvmLhc37OLl4Cfduu4Dgm65AcdkpnYJPOSXy04sTe0ieLUqxLV1KJzbS9pJz0FwK4cYmNEM6fBM1Gcftnt1Ire10GS6XE2nUT1hVY9HIlA5rOp6bZx768ZPYnoEqQnS9/I2kfTWnOV36STMiUYmcrmgkRXTRalINrShCOCk/KYQT6Rqrq3ciXbLASNfIYJyB4yMEdEmwqc5x1lIpjYGLrqHrZa/BXeOf9Fgn7Wh9Ly01VqSrAscPclmv6egkis9N7Yl9BHtPjGt4nQoEca9ahruxHm9bM70bNtJbG+C+172D297/UVJLlwKQHI06RTn1Tz+NFo1Td/5GOl5zVe5DfOZY6dZkMl2kqyp0umzROHuAfdbOoDYyijo0POVhExEKhaZ/01nAjHSdPmGE/+nT/ES0885JFhxb26eY3pHTIdlVetQs3+kSSKSUpOIp/IrilB2nmlbR/bIbWJ23U85qOrGFKwEYXbN1ViQjDE03pyOXimJNGsY04qjZsEmiHG1oYvkN15T0+eWyp/mC9d+/FQDl1u/zoWeeJhqq538/9s+TvnfigmunErN/+ymCwCvv+D6pd19R0OdKKRnLi8JOXDhUJY9HMxORrrxnZqaI9KXYlnlP5kLdXO+m5/wFHBhMoBmSdH0DSjTD4odup/ZVfz8j11oo7ChOvg+hnzoFBPBqOqkp5gzpdUFWB8Og/eReupaeY57GFSCyfAMycRAx4Tv2CGt+neUKzULQ3R2l85q34u8+DAJcTvQp53TFakIYcXOzW6jTtW5LO126ZPvhMMt9LhB2wYFBf32AlMGU1Yu2Kj5CoBoG/kgSpKxYp2v5C92syeqMLAjA2qllX/z1fu644nLOaw/iqq91nK4jG7fy9q++l4Y6k+e186a/4NhITijZVWdGulKjUacoxzM6iuLzsPjtN7Dobdfx7S9vpyGSZMOoue5qUkd43dPymSs+0hVuaqFr4TI+eOcBfrTTTFl1XpVbLCe2ojkTyiXYKaU0xecmQDiK9Kd/CVqBnnpRUKYIHRcBI895FFYZdyaZQrpyYVzNW0d04Sq0PPPR0llk3ufPRqTLcexUFWFHuqZx9myJEUN1jePinNXnz8I9VjNObT0fgPDSZYC5Sbrw0fsmfe/EKNZEJ0zV9YL5UinNGNfHz5Y88ScSLOjrx+gdctIskzld/7n9JJ/8/ZGC23eVQ5G+FNsy7IIWQKgKbsvBzOoGhvXMKrqOlp5dPqmW0SCdwD8y4LwW9ELHU/dg9B2Zsg2Q7aytvuObNA93Em0xKzuz8QEWP/gL/KqOfp7piCl6hmX3/Ah97x4A0rHSKRczjdr6AO6xfmIuU59ftZXRheI4iYYCY1aTchksrBK8oTmIevIIN/7XP7P47l8x1r4AAFddkKNtdRxsrkWdQlxYqKrTP/iSk8P0P3SEYFavWKfLbc3z8cEzS6sYUoKioHhcKC4Xw1dcAcDa/8/eeQfaVVZp/7fL6eX2flNJQgqQ0JHeQQEBARFFR0VGR1FHHfvMOJ+jYx3L6KjYCyoqTZReEkIihCSQkJB6k9t7P/3s+v2xy9nn3HbSGHRm/ZPc3ffZb3neZ631rLGUG7YAttajYRDMpBB1nYCdUapMFpiu8Dtv5qLtf6Lp+ssAGKyPY7ZtRuy02NohM0PwsuNZftLscWavedClBEJkozHaRrNs6rYYrr4rr8R/uaWafijiqOl0+pg840xMl2iDrulwleOSOBqgyxFjjS49cuV7L1gSgZ0f/RJqNo/us0BXKOxDW93CpkUtmLfe5B6rqRphu0J99a4XXhXRWuO4hXz7c9+i54ufL/zWc2QkOqBLlySUI/ztj1V7+lsxzQbhZrawgowmpq+VNlNMl1hnTTypWEXZgGaixGXvSFMsb9vHCRkYf3S7G1Okhae6W55sG2Nrb7JIR2w2Oxag60jalrdZJ7bt4rxPfpQr7vklqm66kilKrJJsGerZR9NCUR8n/PrrLHvgR+42nyxSs+sFhMnhGSUjnGcWDN1yS9q4Qc8mqejYTcAnulpXOTVPtO8g8pC1QFeyr61EJYClZy5h9yVn8tTZ5yAKVtYhWO5FNZ1FwCQ6MUay0lKON4LB2S5XZNqOPVSPDBHKpt3FDpEoq59+hNPXP444Q4SYIIkuCxS0+55sGKT/B+pzlmMDtVEm/TKheVPV9b2mKjohVUNyFuOBgLsvZP/uhqISHxqgpesg7/+PT/Ler34G/1mnsPXsi5lsanGZrpBPwlcRc8cOv2CyZPd293rBbGZaqZpSe82DrunMJwlIDk16CJN7Y2P55RQOxQzTZP2V1/Prz3yJlf/5GXd7/COf5s0/+sa0TFe5PulyTLInjiPNxgOoveRs9/9L7vs+2b5BJhcex113fBaAYNiHv6mCsXCgiJ1QFc2lwUVVcTMLj6XpCOg+H2Io6LoX55KBMG3QVTfYR/Indx/R/Y9Ve/pbMVW0V/DZAtsgzSgZMX32YvUH3gnARE3dlAzHmWwyOz3oEk2PgPECiwVILltedKzuCehWy7xfEeg6SpIRR9K2DNMk8vIzLLnv+4QEnY5L3w6rzrfKKKUsMNdz4ZsYHH91Y7qcbGdTKjDigs3CzVbwWnAqT+iafbz13E6BaMknu+5i5xuLdgbfazGmC4qzZiVfwbU3v1Zi1U//nSUP/dIdT0szbGdyX7XtHiI5AdmaJgInrXDj3PJ5lZO2bOb0TRtnZrpEsSjusnF5PZNB/7TSK5PSxtgAACAASURBVK8Fa6+Ls6m1Bl/N7CzgaMco53WP0vhnK45XsHXrop272bj0EpTxBLm+QS783Ge4+cffBEANhYldeSHPvOEGhhctIavq+HNZQkKhfe74yH/w9o+9j8beLndbMJspypqeyV7zoCuamKCpu939O5KYoGHrFvR2q5yFeQiT+/rd3ewbOfplIQwTlGCIWlXinj0+pBMK9dxqhgam1elykgKOhnDqojtu5dK2JzjuH//uiK8VWmAVA9VNk+DEMIaikTNB91vo3ueX8duV6r1Bw7qqu4OpYOgYRymgeDZzJjtJABbMY9+qk8m1zq5po+YKK1/Nju86XBsY+L8yQrPZks1/ASDSX9DCmhl0Te9eDNZVoTY3kY3Eio7pmcxx744h/vjKMBMlujilySmOi0Twgi57ki6dwLzuFHUa9no684Kzo8V0HUnbMk1IhYJkgj4idZVokThmIISiG4hOxq9pIjROrTu6dzjNzoFDU8NPJ/Pky0gIUl3Xvgd0CSLJ1iUINa3kZioW7KiK6zqCJFFpT3RK42I2vuUDBC94HbLt4pFDcQZOKwQ5vxYV6bOJNOGhYSLJBKIg4A8HkRNjSLkUuq2Irvt8+G2Vczxu4H07B/jW5x7n4d+/TCpRXKy9bdcgk03LydY0Ej95pRv7nByY5MC1t9N16ZuRZhGPNjwyJUG70Pyxci/mNYOn28Zc1/+hWtmkhWHiT4wRHLGkICo2WZJCwYlRjLyCFAog+oszILVQkIjfaqNpRSerGpz3+AP0nHUNPXc/BOAu8r0WN0QCGw7wxAOvzPpIr3nQFUklaOjtdP9u7m7n+O/8F8ore4HyY7oU3eDbLyb518cPHPVndGI/WkZTFti45joWf+gdgCUZMW1M11FyL5qmyQvX/QNb3voxpsgyH4bJssT2hgp6RatD66pGVjWQ7ef0+STkJ7fypkcfhp8WsiUNWWZw8QoAhk++APmc04/4Wea0Pfu4+Yf/SfVPf4Fwwdn8+Zb3MHnuObOe4hV0PZQSUtOZzzd9UOr/WbGJucLkIOkzgC59qntR1HUiC5qZ+Pp/8OwV1xUBmq8/08Wdm3r57+d6+OWLxQClFHQl8w7TZf0tCCD7CoKUXvPqEpWrC6YpKjWDfcTGR49aGaAjaVuGCY/d8A4e/PjnqD5rNct+9y1qnvyNFcNojxEDiW4qltdPOfefHzvIpx9pKztTNJ9T+f6X1vKjrz0z57GjfUl2ve3j9J39RnebIImMnHAWtC7HSOamPc8Fy4aGIEkIdpZYJX4qwjX4lyxG3r3HPX7kpHMITAxz3AN3cuHqY18Z41Bt60PbaenOcPGLLwEQntfIw2tWcODS0zDsiiCa7CM+YdUJFDzu+d7OcQzdZNe2Pu79xdai6yqTFlhWfH4qFzbTsMea/PNjFvjCMGdkugBUD/jQnnuBsKKROQrZ9dPZnZt6+fK6Tr62vmvug0tMVXWW9oxw7sFeMv2JWY+NN4RZds93aXjxaQB8/Vb5QFOUEPw+pGDAFep1TI9ECCpZmjvaCOzZS0bVqRkaAE0naC9UxGCg6JzKT32AweYFSIpOZg63/WsedEGx8KjfM4B3LV5Gpswsn7xmoBgwntGOiiCi1wwTzlj3KJEd6zi/JcfSeUE32E7StaKUcsdcpH6EjJCRzZPYsY/Ey3uKqqUfrk1s3s6CzeuoS6ToOf9aFEMgtG0bFzx6PwA+v0Q6FyC17FT00UJ8jhkOMdpYqA5QbhDykZiZSNHSdRB/dw+yI2MxB4j1/tz6EYKuiopjX1/yr9W833+wuRBrKGuF3zySnOSOz3+U27/62WnLAMXHR9l79W3UfPRT9rbCMQMeVqRjvDhYujSmK13qXkRAHBoBoGrnzqJjM4fBdJkjY1z70APc/PtfoeSOTgzRkbQt3TQ5fiTJot5xFEHGn04QyCTttm/1Ezmfc2sAuucZJpM5jbxuksyVx3CMDFoTfW4OFW4ANadiBEIYHkDpq4pTlR1BnegnI0wdv0zDdMHykh99iRVf/AjhgU4qDuwoXEMWifg0VvzqKygjXbRHffQ2zyM0NkiYV1eLDCxdrOfXHbCKT09jjnaUiRUq462msXPPBPuvfx/pRavcrEbTs5j2uksnx4q9NjkbXE1WVlEV8rkA260LbJrMgrmKmK6eUCvn9oxCYnogfKT22F5LdeC5zkP3NmQzKk0ZlTAy+S37Zz3WjfG12dVcleW2Hz3xdfjsYHmxJKMz3lANe9t4y4+/yfL77yGrGlTbMYLRZYusy5WArtrrLqe7vpYDJ7bw+ptOnPWZ/ipAl3d1LNu++sq3vJF73v1hek46paxrOODG5Ohp6ThmmiaNvZ0s2rye9J0/Yc/7/4WxjS8CVpbQdLEhR4vp0tJWxzNyCvu+9IMp+0c3bKHr5/eR2tdR1vWG12/h3Cf/RMgXZmLJajTFQBoYombU0g3y+SUEOxVZ90yCpe9xlEJbZjU3WF+SkHM5KsZGMMdmV8IPn7GGx65/m3X+EYKukZGRIzr/b9kU3US1ky9ePP1c9q+0ssskTUNyAqElGb+Sx6fkpzArec0gYosgU10JpumyYaZpkvCAgv5EMdCZEtNlM10CBfdivCJAzY7nCA90FB3rdaeUO07o6Rw9F95A12W3oOWOziR1JG3LNKEhnaNmIuOWPJFVBdUwMe2F2fmP3Q9rNxSd5wW1yTLjQx0QMH/x3GKukQofK+76Kg1bn3S31V9+Lufc+QnWnrKGxDQuGxPoq4txsDJMzQVnUnX6iVTv3sS8Z+5HN20poc4eJFlGUvOok/3sr68mF7EmVD17bEDDbPbUn3ax4fH9PPCrF6fdb3jauiwK2J5udMMknVLIV9Wj+0P4nKxGj2SEkteL/u9lap1sbiUQoCIo4yAs93amYWXpzWTTiNZpx0hA1yEinDJVh2KKp1yUMDE7aNPt9umEvqiRmLtPdkFXsXuxpj5GRYuVIRucnMCcmCCcSSFFwwSarELypUxXJB5GFwWSgoA/MLsS118F6BKLardZjUAKWy89HYs0nRXVRzvKhV4N03rGbE0jmfpWDEmm/4/WwCKaJso0LlA3pusIGSEtVVjtJHdNdZ323/8Euz71dcae31bW9fKqydDqczHVPC3rH0BIjGNkshj2BOrzS+4Q4B081EyOYK6wsstvnL0cz9EwwymgK0uIzz7Hbd/4HA2/ml0gVtVNdMkOuj8GTNfktt2MPffSEV33b8Fyqo5k99XJyho2XvpGtp15PgeWn0i1XaJFtwdCn6oUSUSYpkleMwinEqQaF3Bw6QXc8osfuP02rehohklAEpAEGM2oRX261L2YcmO6rL8FESrrwjRtfoJY156iY73ZWmqZKwfdU05Hz5Q3ySuawQf/uJefb+ljNK1y2x928cieAtA6EqbLME3qNz9B87MPYgwN0XfWlQyffQ2Zyaz18sDuWz/BYLI4K877G6by5TFdTlxRNF5Ghp1Ppr+phdz8lqLNQd/MFQdEUaCzNkZbdawwQdvvINnzgvLyLjfGxpkrJmrq6Xr9W3mu89Uv1eXEt1XVhqfd7zJdJvhEESGZ4op9PSzd0s6yBoEl930fcaQLn1+meeOfqduzyT1XLQHDijfmKmLdb6h1ASGf6PxMHtA1+1zT9JIHhLuq/wbffLaLf/rz/qMSf+xYNK9yVs8oLYeR5Z73vPNcGmYj7ePsuvUTDK621A6qeqz+Xtm2HTlmuZ5FfzFIqmppoHKe5XqPTE5QNVhguZzyXF6my19bxfjPfs+qrc/NWFXBa38V4qiSB3T5bPFLUZIIZDPgO/QMo5xmcDRlLS1FeoMD1/49AM15AXFzAeRMt1pQjhLTpacKqeV6ZiqdrdpFbf2VZbphdRg69WJMNUdHbRUrl58AA0kM2QO6REAvBl353kGautvdwqHGxOy+9qNhbhKFLCEFfOjgFkefyVTDQLfrj80lLzGXKdMIsT535W0AXLz7UfxV/3vFU3NZxXXnVYyPMFbfyNPX3AzAioiP4bRKgx0QLel6kdtENUxMIJZK0PEGKzlEX32BCwocUFUV9iEKAn2JPP3JPAurrCzeRElwrtPHBloWsiSjI81vxWevbgVtelckHALTlfeArlx59Qy7J3PsHc6QyuscVxOmezLPxs5JXr/cihmZrm2Va4YJ4b4DVA8NEKqKkVx2IqocQsmqBS0/QZiS7OIFPclyQVfSet9AUMY0zCnK8EXPVV/H72//KOctqizaLmoG1ekMoh6Y9jzNMKkcHaL7K3dSsXQ+ajiKgAT2mCT7ZTQ5wIGr302ovpU3/vEeNq85hYgeITGozflcR9tkW/9p6cqGafcXxk0TSQTZJ1kMpCDil0x8ShpVFAgEZar3vkiuudk9NxoPUl0bYWzEGveVvEbADnrXgyEgR2LJEgRBwKdMsuJXX0a95Rba5AVFbsrprGLPZpSaRlKtSwiiksOPzzB5xHYFDqYUmuPTf6NDtTWDk4Q1nXjHoTO6igfYzAW6dE3H8Acx7TG/YriD6L078SfG8J1r6QgKkvX7C4ZB45c/RXRpE4F4FCUQxJ/P0dRlVb2JLlvoXtdhuub93fU0XXcpL1z/AVYetwJ/ywr+/LvtNC+d+Zn+Spiuqe7F0V8/wAe++HFWPv5QWdfwxk4drfpojjlMl3v9WkscLXDzG3nxdRdOqwd1tMRRvUyXk/nitcFH1gPQd99jZV1P0FXqtj9LLj3G2qvfzORNNyBkM1Tv2crFdSOcf8Uyl6L2DtqaouJLF4CWoZU3aB+JuUyXJCM7FPEcK6fRPz7JNXf/BADl7LOO6P7ZbDHI9cYKpvd3HNG1/9ot52F8zn/0fk7e8jyrDrYBEAvIViyLJ77L6wZywFU8kyIwYbm1RTXvSkY4MVsVQZnmuPXdvS5Gh6WpCBavKZWwxQRI8QgIAqmmheQr6oqOKcpeLBN0mbnCe5hl6kI5/T6vG+77eseC0rZ1KGYtAu3M3lAQo95S7M7nNYyhdvwT1kRXmkRQDLrKYyBSdjHgl57vIjlH/M94zwSn9o/j7xwr3HNwhKev+hCnDSZpHU5MibdVFZ2KRJaG8QTd3/81/fc/wdCpl7H/hg+4x/gCPkRZJmvX1dMXnkgwl6FvfjVX37y67NqFR8sSE9a3i1VMz/65TJedRSvJEit/+WWOv+vLNL3hAvb8/Cc8ctM7CQQsRljwzC2XXbeKd3/0PKrrLJbGmzWq2O7FiO3eEiQJSVUwbG2q4dbZdRxNUcTwWceGRetaslHMQB8NM02T4BHMD152T62cXafLEexWGyzmSvD5CEyOMnjhhcy/7Ub3uDPv+Q5nPPgDVr/jami1js3abPOCNosdc+K5AGrOOYVVX/8kzTddic8mNAL5HHWZPB37ZweSr3nQZUgSSqDQeF33Yszyx87FbDh2LIrSOmZOqb1ogZLKT36AdVfdhCJOJRSPViC97ilyPW38gt1p0m2dU/dNY7Ku0rB1LbquMH8yQyqRQ8zkEEyDeGWEQNDn1l70lgzSVZ3GzbZLNZ97VUCX6XUvOlo32uzslZqwAn+3n34umQunL75crpVqKRme1O70gUPPyvlbMsUouA+bu9ppiDTSQoz53Z1E/BIhWUT2MI2morjuCydrMJpKUL17CwChkf4pTFdlUKYpZk0S/cnCgsNhaapDxf3OcS+KokAuZ9Dx+ncwsuYC9/zN3YkSpqu8ccLwMNnmHIH0L/en2D2Udvu9ohnueOQdC0rb1lBK4Z4dQ9y7Y4ihkuyoRE7jyf1j5DWDoZTC0wfGSS9YwejKMzDArTOZUwyePOssEhkbdJUMPYfDdKU9v7s2x7iaS+aoySqIqcI45YvHkCatOEx/SSUBgORklpP6J2gSbFedJJKtrULTCvf1+2WCYR91L1jVDkRdRZN97PX7WLKyFvFVZLkA+rutOKP+nunjjZzAdhMrpsvnkxENHVE3ePEvnejbe4nnVQI+ibHjT2Vi/oop1/AHZERRKCrordjq+9VpO8HJdrnqzlg8x8+Qq2kk02BL7vRYkkxBTwjG0crMzaiGCzwOp/ytN6ZLn0M41mL/ILfS+g3Nk1bRtvxExq56Aw1Xnu8eV332yVSfcRKCKLp9L28Duv0nnMwL7/p76i4vZMZHj19E6y1XE1uxxI0NC6Ws7z2XNtxrHnSNN7XwwoVXun9vuOxaRn/9E5redi0w1T0wkx0L1WjHdKNYkd5pSH67s08fSF/IYDkSX3mgqY5qmyadzr3omBgojxZ2VmGBaC3LR5OYW3bit/VegnFrdeUI046vKOiRaZrmlpF4tQpe5+vq2LXmDDhxRdlMlyPQqMtyUc28wzFHS6nnt39m+/v/Dd3DOma7/3dreKmBAE/Z7kS/ksefsiaC8x9/kMqQTMgnuaw1gKyqLshxJv9IKuGKXCLJrsaaEyhfEZRpst0d/QkP6LLjXpzYMcdqh2y9sOERQtEgkb52QiN9vPyhL3DXl3/LZx87wK6hgru+bKbLMzEJijojI5DTDP7pof18+MF9boxpXjPc9/aOUaU6XXdu6uWHm3q5c1MvP3qhuMbqvTuH+Ooznaw9MM6PNvXy65cGSKw8i/6zrsRAQE7bGYY22HGy1ErjpotiusrUZ3Jium5575lU184uz2D0WL9/fHtBxVsKBfBbgQH4db1IssM6QGQ45CdnWt9eECUGz1vN2qU2ODAM5KCf2A2v58Hrrcocgqax+oX1fPAzH2Dn579f1nscLdM9Y8rBvdMXLfc3Wuxqz8IlyKKAbMcUCYZO14FRxP4EAU1Ham6k75yrGD7xfJeVdNrWLe89k4/8++U0tRZi/zQ7lKR+724Ahs+/lI1//zn6YzZDM4d7cfS0S93/h0SrD0Uyhf5wKAXnZ7MRT0FoUzp0CFLkXpzjmZzfzYlvC374dh689X0EFi+YcuyB//ol7T/4LX3tHQCoVRboUn0+xs8+m+iS4nMSr7Tx5HGXsPnNHwYglEpY8/kc+OI1D7pK24nm9+OrrcZfbTW2ckGXN/17pnITh2tpVadv/mLPFtv91naQ5o42tGniPLwD+pGArviqpZz03X+17jeNe9Gx0rTYmUzzBxldvArZZ60Q6n/3B5IVVey+7EbWd/tp3zeMFLZWF4Or17jnGZrmlpFAlFwW6lhaevnxPHrj38G1b0AOTqXipzPNnrjnte9H2LHriO7vt4Herk99nf77HifT1ceyz76PZZ/9B5Z+4j1HdO2/dstrhpuwIGsqot1Pw0qeRde9mbf94+2EPAN6PhhyJ33n3x033UL8HRZwM3x+VNtl6RSz9roX+zzuRZfpKgFd8XErNoWxcepaquiQsuSTA/T9/mFO/MkPkRWFvcMZohPjVA/1lz3JpBy3jWmQDIVmBGteEVfnmLxuuuORd4zyl2RUeUsSlSYKOPtGMiojGfsebu1FCf/efQBo4wnqk2mEkJXBVQoO85oBpolgmuW7F22mKxKbe1FXYL+L7xsKW+3Er5skSjJPQ7EAe6Mi+USf/T4iouBJijAMZL+MTxSQHEkQXUMJBJlctJK2XHRO3aSjaSkP+J+pBp9ge25SFVVIooAsS3RcdgvtV7+bsYMW2F60ezt18SDdsRD55gr3F/vvLzzNt//tCVRFd4O6HXM+p1PLUQiGqNBMFPv9a7s7Zn32eLLgFgvbsdI+Tx9QdANDN6a4pQ/VRjMercTDAF2qZ/4W58hONezYSMnO8j9nYQXvj0xy4c7nSO4uJJ4d+ObP2P8fP2Dvv33H1cjrvvlmfviJL7J79RmEfMXPmRsYpuPO3wLgr65A8PuQNQ29DObutQ+6puFEZcmzOiiTUfFS90eb6UorOuuvvL6wQbDSUYf+8f/xlh9/E3l0bMo53mzKI43rkmMRGq+5mPrXn1+0vaj+YZkUu/8tN9B/4Q2ITt08UWLnqa9jYNWp9A3lyKQVN1BUUz3voGp0XfJm6xxJKhSjPoamG1bsip5RCmWh5gDhjjZX3UAvlT/62RHdPxazJi/Hpx9sqmfxB9/B4g++/Yiu+7dguYkUTT0d7t+CZg1+pmfIccQf95x7IUow5LovHGkIo76e3R22SK8vgGkzuS7TFZrqXszbLiqfJBANlBSAz6Wp3LeNqKQRCPp46XUXsffEU1GiFRiSzNJXXmIyp/Gu//4yb//B11Amk2W9a6a6gh11cbY2VTNa3zhjKaCEx2WXm4ZV8i6+nLblmKIbNKRynNc1TLB9tPi17GtllEKBYtNe2ouy5Epl6Kk8a4ZThCqtwGw1Fp9ynTN7xzi7Z5RkGaKYpmGStkHG5MNPzXm8wwCULqSDFVEETUUCRpPFAEnVTa7/5fe56D4rK1mQJWq3b2PFy5bbWdBVJJ8PwTRZPmJ9L1HX0KNRhk88mw5fsxtj9WpYctJqr03zKlh9xvTVMXSnlqQ9JvskkWxtE9m6FvI2oPDl8zRE/eyui5M8rg5RFDBNEyWvoSq6W6/Ra07zEezxWaoIsrmpilirHcuozg4+F7c9h6hYzy/aTKvsAeY5xeBn397Ar3/w/Nw/xCw2mlFdcNKzYvbi0NOZ4plb5gJdpq0lGbWV6OVMhgVPPEb3v36T0We3uMd1/uQe9/8V9RYT6WuoI5DNcNHD99Dy4ha8lmnvof9ey50thYP4a6zkkHKQxWsedFX2dnPZ/QUZgHMfe4DgP3+BzL52wGK6ygnwU49hTFdG0YugoSnKyLEIguz41KfGGXlXw0cCurLd/Tzx5Mt8/6xrqP3nDxftE30yZz/1C2B2Fmym5wILQPnzeUYa49z4rtOYv7gGyV5mBnv73OMM1XA1gQD0hbOX4zkqNjHJ6p4B9t/7MslANfe88w5evumts56ie+Kuyo0HnMlGR63JT01Yg73j2/8/g1xbB2s2rXf/TrccB4AWKriggg7TZbu+HbDiLIoCguDGoYzXN5GyZUuKYrps92LPZJ6b7trhii3GApLr3nctNUbrhgepDynIgoBkGAQVnX033kHbtX/PCVs2ArD/xjvYfesnUYenL87t2Lc3dPHRP+0jJ4n0x0KM2jI2zvMPJPO88/ev8JAtBeHVD/PWaEvY7+Pte07bckzVDWKZDCHNINLTU7RP8UhpuKDL1uPyV8UQHV09RSPU305wzJpQVUnmtj/s4t4dQwDkFI0KRSOi6qTSc7NDmw6OufFJf/zL5IzuNMfcxJeS7YGaCuSs1RZGbYD05P4x3vn7VzgwnEaQZHT724+s20R03xAtcYtdTEci1F58FsrmbcRtt7KoqUiVcUR73FVfxVJASTeIfmohdceyg9Zio2bQGj8lUUCxy6xpdmyKJonUoXDjT7/Nqm99AwBBEPjHz1/O+z9xHts2dfHz/9rAjq2FtuBk8kl2LFd8107Ovf8XBHfa7tw55skTvvJPrLzrq6z62b8jZS2XtOQ5JZNVGR/JMNAzWeRGPVQbSeSRTAug5A8j3k4t8qLMfr7rfrQP2/HhLzD4sFU9wZGMANCSBdZ9bMz6PmG/RFO3NY7VvFSsueaVjJBCQcLzm8nW1WGUkbbxmgddgmHgzxdWKs3d7YjPb3HjKERdp5zQC/0Ygq60ohNNFIIm6954Kec8/UtX/0OfhqrXPK6EckGXoWoMPvIMqmcF3vnTexHe+1Fqn1nHrqGpNdOcYtizxXt5TS8JljREicaeDmLZBPMXVRKrCCIqFoBb8Mxa9zjz+CVsWFTQ39FOml2V92hYxfpnOeE5q7xDV9s4XUtWMNo61VfvtSJBVDuQW0ulGXz4mbLT/R2rqqrCyCsuoJ3ctpuuXz7A+nPewvqzbyaxY+8hXe9vydSZ9Ko8quM9i5aSvPhCAqpCdHLcBSuKbuDL51j1259zUcyqmaaEo6Rkyy3jzV4MyiKntVqs0GRO46k2a8CM+WW3vqJ7azuISZQklKzCJR3D1EebQZRQKmtp6emkcmTQ1aTT5pgQ1h4YZ+dgGmHLS1zx5GOct30n1YmEy9ht7U3Sl1BY2zbuPp9jXj0fR+LCOw5UVRVnZSm6ybzNT7H0nu+y8nc/KtrngFUXdJmm63ryRyOIoiNmrNL69B9oXXefdU1Ft6UqLHCZ9rh9Mtm5FyRbDxQYfKWylnRi9jGmdAJ0zFddgWzXGZywmaKvPtNJX0Lh92vbGbr87XRfZGWaLfnou9yMNABdEhEEoYjFSUei+OpqCqDrVSx6nbCff3I8Q2/n9ELNuRFruwO6ZFFACTigy2ap/DJBSaBuIkFgNOGCnME/Pc365Zcx9OIeRgZSpCYL/axxbB/zn7wb2W9dIzg8zNJd2wi2H6Bq71YCo4VF8nQWO2MNwZNWAQLhKmsBKXkggukTCUetvnEkLtvRyRxZWSTrkzicSB/Vq+k3R5yaaRSzq6K3GkJFgU12kqAEWXL7Xnx8lMsfsAgfY36h2goUi6NK4RBn/vH7vPKtb5Evo3zXax50QbFOlxN8W3PeaTx6y7t56eyLygItx5LpSis6b/pVIWBzbMtOun/1R1ewz5iGUTkcpuvgt3/BS+/6NC+9+9PuNid7MZhJk9tzoNilCPhiEarOWkPlaSeUdY/x3/2p6G9TlDj/sQe48v99GnXcCqh33IteM4JBhptb0ezWfSQrobJN06nav41GbYiz3rAcmFss1zh1Da+sORMAwf6tXr7j87z07k+z/0t3HtLts9msmw0J0Hv3Q+z6xFfJHOgic7Cb1N72Q7re35IpNhCdqKphyzmXULXXWil6J8zJqhp8iQSL1q+lrr/HZWxymkEkmaDx2WcZfcQC9pJpkrMZi0kP6AL44hXH8eFzLWa1Y9wW6wxIU9SuRVEmV1WPJslIdokVMZ9j77I6RvpfYcs5F+Nz2BhVmdKXvKYbJhl7xqjq7sNcvIZQrI7j2g+44NEJ7u+zXZ/e8kSZIqbL+r+37ZZKRii6QSSbo+eC6+k/84qifQ7IS+b1IjBnYrmwnOw9Q9WRNM0NyfDb+mJOFDcNWwAAIABJREFUtmjWA7qyZZT1yQoCyVSB3cqnZ1+0OBIzpVi25earMO2adpPJ4mtIThUBXef853/P4g++w903JuoMNlouUl+gEAPXO38RweZ6F3S9mkWvHffiYG+CR+7ZMe0xUUkj2r0fdFtzUhBclsqwFyX+kB/ZJ9Nx5a10XX6rKw2x59/+CwDtm9/kHXeczeozCzIQweQI8a59SLbUhCn7GTj9UjKxOlo2PkSkb9+sz37fz7ey5bQbyDTMI1JfYT9bYazPayaRqAU2MjMVJy/Dxgx4obmavCTScnB2dnQ688Z0MU3pKK+ZdptzFiGCRwhVjk9N/JDjUbfvhf2ewuwLi+U2pJAXdFmLwZBPRC+DufurAF1FivQ2OxFZsoCONafTP29RWYHoxzSmSzWKFm+5iRQvtefZvepSTEHEUFQSOY0n9o+6gM87wJYbSN93/xMAbokhAC1trRBP3/Akwm0fRhkprK6G1z7P89e8l4rVyznpu58r6x5aopgtM+1g6OGTzuH5TQOkk3lWnVDNgsd/Q2C4kEXlAMct1X5e9ucwx2cvx3M0zNR15FyGJmGcoJ7n3Mf/yPGP/XnWc/RTVrPhCivz1XEvDj36LAB99z1+SPfP5XJFtLQ6VuyOSrf3lJ7yv8a0bJ6xZSfTe/mtPH/ZtWw73Uq3NjwuaJ+qINgrRq8qvaKZhDIpDNmH316NBhSVwMiIWx8QrJgusAbUBnsFPmivwGMBaQrTJVY103b9+xjIBZHsfaYkMZjI8uI5F7Ph8mtJVVqaVqbPT0qeXlEcirP7RM9xZi7jMk9OcP9oWkXRDNeNCDMwXV4twZJyQopmkovEyNa1kGwuZnOd8Ww0o1pAy30YE0NRC0WOVR18Abckis8GJc752bRHHqCMQPqMKMFwBzU7ngMgPUPBaseczOhScqL2/NOR5zfb18gXjYdh+zsJhoYctSZJJ0Zt6fNPcsGdXyO1vwPZkyhkCgKxeBjhf9C9CDNnsbWGsix84reIuYJ3xKcUg5hQ2I8sS25ykmmYjI2k2XvxW+k59434MknGBZNwpAA2TXtudJOmZJmRE88m4Sh1ilPjwLw23G0xl90XXE/rG84lTJaEbLpuSUU3XKYrfSRMlw3uq3Mqkcyhg7dwXZjmZx8kfvAVAqMdsx7rMF1OpxBlD+iKTQ0H8cWjbt8L2QsBAN/CWZiukAW6wj4JfQ7mDf5KQNd0TJcUChQVCp3LjqVOV1rREbw0rOSjPdBCMlpHunEBhqpx744hvvZMF+sOWGDEy3SVW8pIT2embksVb/O6EfODo2Tae1DGyi8qWhof5zATY8tOYfNfulDyGs3NcWI9bciZpHu8tnsfl2/dxoW727jxVz/A/8gTZd/zcM3UNHrOv5b90jwyQwnOWP84Szc+M+s5qmG6+lFCCZMxV/pxqTU2NqJ5XL3KaDHoUkdnjwn6WzYtl6fv3GswIxUsnMyg2zFGsTMLGa9LX9lGYIM1Ycuq6gKmnGYQTicZWnMBW064CrBiSxp6O8lrhhsbVekRP60pyVSMBmT8JUyXKgrIiVFCkQCSozUn+3jLz7/Hmt5umpJZQob3eB9dD2+kb93WKe+X8oASITNJzSubmP/E3Qj5dIHpslkbExhIKjO6F51sS29JsFKdLkU3EOdbbLXuLynf47ie7N/P1SPTdQxFQbIzrwL42fPWj9F94Q3OkxNWNLdMmREPkrTZ+Xx+7ljZtKITTiUR7SSJTGp20KVVW/UZ9eOnynU7k3luPMMjD7xCS8Ia15xMOVHXEIPFGZ2hTJrKvl5MTXfZHQC/qhBZ1Ep/i8VOHIp78XAy8zRVZ2zYWqxe8saVXHXzScDMoEt3RZ0LICicLB6jw9EAsk9ydT0MwySXUUgFKshXWYHe3/3Nc0Xn9Fx5Mw/d8XmMM0+zrm8DDM0fIlvThBaaPebUYUy1SJyGy85lzXsvYtOiVhclT/Yl6GyzYg2PiOnKaCiSyOamKna3zF2zs9Qkv0j1/m3MX3cvvsnZXaZusW/RYbq87sVpQFd1pdv3IpEAHUtXMNTUSqAUdHkkmJquv4y+ex9jwd+9h5qBuRfafyWgS8PnZHrYoEsZm2TNusdZvu2FQ3YvHovsRS/ANSXvisLE1DTGbLq+EDTrERYts6M7wXuheYWMD60UdHmyOVS7FI/ol8kPjxVQ/yxWesiL51xsbffUXgzbg5+g6+5EovcN4JcC5OrnYb5KkhGmppOpayFFmA3rehg49WLEOe6r7drP/ANWrJXjfmi64XIAYiuPO6T7DwwMEGxtJGCviBzQJdqujvzIsWf7XqumeRI35nV3snjCYgQjK5e520948TlSzYvIVdYia6pbbiSr6oTSKbSwxWxUa5PMW3sPPiXPeFYjr5v4JYGgx81dCrqmY7qeOeMMHl6zkhPffrE1CNugou/Kd1Cf93PicIKmcWvyy0siuqhz75OD/ObxYbrvKa7o4M1ElCaGadr0GPHufa7emGmaRdphfcl8kXvRG0jvyDN4F2KlOl26aoAj9VCymi6V2nCYIEwDQZQIXWkJwGKzjJKSt1TpZR/n9oyysM1y8eQNk4ydFSfqBffpTKaNpwn6o+hBi+nLzqHGr4es+FKhubg8Tq5viGC7lb5vjGfZs6WHFps1czLVBF2fUph46OQLSDfMR/DJRUzXcTtfJNZYw1CjxZ6V617UdYMffGUd9/xsyyG5JP/w0y389Jsb6DowSrwyxKJlde71prNsXkcNRdzSNMCUIPfQ4vnIPtmNQzQM0y12Lap5HnnrPyArQZ5fd8Ddn28fZ35GQ7bdXU5AvSn7OHDt7Qyeccms7+FIRjQ+/xiiT+b01jgfeF0rFx1nxTgpnoXGkTBd8d5xLugcJpZXGfdPFQ6fy4pA9By6jKJp4E+MIdnF0WdiulpuuZroskUs/9wdbt+L+CXue8cHuOv9nyJUAviduViOR6k8ZRWiz4eYSCDnphIjU55pziNeAybqulsU1XEvKsNjnPbQfZy0eUNZqu5eIcyjrtOl6Mj5LJH+DqDYhfLUDbfSP2+RO0hPJ4RYbkxXwzUXE1kynxVf/GjhXBt0GTaT4C2464Cunl//ibUnXj3FdTidGSXPottlIQqgS6ZnIMPgKReiVDXSb6d4m6rusmIdr38HqvkqNC3dwLTV/sdGs6RaFheVjJrO5N/fx9W/+ynPXvZGnvvilwGou8gqBxSoO7RVVzAYJFBXzYLbbgIKoCvYaq2UlP/FoMubJVo1VtD/ESriXNa+lsu71xO/5go6rnw7bW96PxFDY+9whv0jGbKqQSidQrVBxmJhjHjXXnyKwqC9wq4IykU6RbGSGK6YX5rCdDkmiQKCICB4q0jY/28csgL3IyP9SB3dLqDY+K37ixIjvAWhZVUhV1HL5MIVyKJMTjNI5PUi0NKfyBdnL3oWfk6X844DQY/StmGaSJ5Cx1LJwkIpWUQ6by2YJoIk0njiPNYtqGNvtQVi1VCAZfd9j/D2ddb1bE9CXjPcmBS5DK2uYH8C9fgzyNRb8XT5OeLAdHuclkrAcLq9B+MhC9T67O/r1w38mu66BkdXrETwlZR1qqgh1bwI0Scje/aZgiUX4ojAlst0DfUlSCetMi7Prz0w9wm2OQHz+16x2k7qZUucVJvh99ubjrP3lo9hRmvcbYJHqdYEKlcuxidL7irY0A33PQJnn8rAwqW0JHO07SnEREnL6jlYGcFvL0ZEj6xEwi8zuWD2MkCibn2/QDaBMjJO/4NrOalrP/Ni9iK7NsoZF1hCq4fLdGVVHUnV8RsmCAKaYTIxlmHDE/vJlRFHCJBIatz3sa/w6Ae/yL4rbpyVnZRet4zNtWGCN1rCr05M17J/fj++6oKw7Inf/Aznrv81VWeudvtexCdZLJ8gEPEXu2bFoJ8r+jZw6T4rJMVfb80dvtzcCWuvedCVilWw/YzzCNgNqf34EzDOOs1FqZKuH7J7cSYdncO1tKLjTyWY/8RvueqK+SzuKrgjsjV1aP7AlNXs4QTSH//Zf+C8DXdT7ylH4LgcMxFrgipiusaLi07rM2WUeazUpSBrGiaF2C6fX6K3N8XwmvNRahrps1fzhqYV6lxFKzBejTAKTStiFU1JLor/m86crNfxugZScWsFV3vx6zj7yZ9z/OfuOKTbh+yVe2mGaGieDbr+F7oX/9I5wa1372Tz8ScXNnra1OC6LagTCUSfTEYvfLvjK6z29fCeETKqTjidQgtbfTxsx275VIUBG+SX1lUUBKGI7XLqOzoW8PxfcsCap5H6Deu6ARvQBNNJzERh1Tq64nQOPFOIpfQCEtEXpP91r6f74pvwhSvJa4bbLxzrS5S6F6e2U80w3f7ntC2wxoqiWnWCUNRPp4RLOLtMA0GWCAZkFElEt/tnrt5iZiU7jsgRvFE7x2i2XYSyYRYBy+lsMiAT6j1AcNSWoIjEpj3upb4kt969k8kBi8mUSlg8f3UFwbFBIm3bSdsix2FNpz6Td0sL5RobXJCdsjOjpUySSH8ngiwTqi8AmOBIH1HRpLbPKsVVbkxXd3shGzNThmRGqYmCwFMP7uKh71ihFbrne3pNlgTEXAazSLDaE+MrCNRG/ciSiGqze4amo9jAWxMENBu45uy6n6IoUL/+IU788y+Ruy0Xl1RX6fa9TS3VpObNvqiUbJB23MfeTaqtkz//qY17n02Q3NLFkrEUimlSU2/1yXTy8JiuyZzG3poYyvBuTtv8F163cy93/3ATz689wNN/3l3WNRI9kywbT9OaztPUPT5r0pYeiTDYugDmW5n1rbdcw6l3fZ3Gay6eIi7rmNP3vECrVBxVEARGnn6ejh/9jtT+DgJ2+6vZsRH1giWzPn9ZoEsQhKAgCD8RBGFSEIQRQRC+WOZ5tYIg3C0IQr8gCAlBEF4QBOHics51LB2rYPfJZxKURW5e3cDmd/095/3uG0gR64cRDb28QHpvTNcxYLpEw0DSVJadcRxXPvhNd5/f/q7JEqbrcMRRDU1DGU+geMDUyb/4Kr9938cZabCodG9Ml8N0ObbzY19iy1s/RnJX28z3KPlpjn/lJQtwCQKSLCKKAotWNRFuCvLsORe4LhRd010ANP+JuxHVYy9I2HnRJeTCBYrYkH1I+uyxKKZNR+uS7P7uI08/x8TmHUXUczk2Pj7OxIuv0HfPo0XbI4vm0fr2a2l585UznPm3a5u7EwylVHbnRBS77TuZiwDp3iE6f/x7cn1Drlo0wPygdXD7WI6sapCOxdGiVmbaqBRnaPV5BDSDYTvYOxaY+q28oCsakPB7GJXKkI/LN23m8v197PjNOoBCBQUg5HgPbGY31bqETFrlhSYLmGfrWtjXUQDR3kB6ZcEq0s0WAyCZMJ7R3H7hLBb7k/ki0JVRCveWDMMq+2IYbpsc9ySiKLpB0AusJLmI3VJKmP6VtbYbzzRBFFHWbuS6X32fee37rdPtfirZQpiSYaIZJoZd0icviUwEfHPWX+yIhpDathJIWjpfijI9s7O1x2oT2Y7dLL/rq9Ts2ly0319TSXB8iIatTzHuic0yBAHNHqsFLztmZ55WvfI80YEORL+PSHMdOaf8WmqMqE9kmS2gqinljffd7dZvft4Vy7jwquVlneM1QRLY/kI3w/Pt8miCMMVzALD6vZfx6MpF9Fx2QWGjd8wydKrQkEWBVMxiY0xBcN2LKnDVb34IFBe8Du3Zw+J9ryDnre8orDgexf7dFrXtobJn9nqwo1FrsdjRl0OOhvFlkshqjnTXBIsn0uST+SPOXpzMaZiCQPXEMJmWJURCla6Kf09HeZ4BX3WYzniYrCwyWj1H6SknvtFmcGPLF1N36dmEFzTPeI7T97ygKzyNGG3vPY+y51++TWLHPgI20xUbHyEzx3Re7izzeeBUYClQAawTBOGAaZo/neO8CLAF+BjQD9wI/EkQhONN0zyk1K6gLHLb6c28+7QmBEFwAz4Ph+k66uKoqs5Dt9zO205oYCKRp6YhwJKV9bTtGuKE9U+RX7KUZNiKZXHAljemq1zQNfaXl9jy5g9TddYaznzgewD4F7bS3zpKLmS5QXRPLE0p6BpZ9wKYJks/ftuM9zBXLIVxICAwr8rE/8cXydod3283woXL6lloiDywodt1Lxqa7goyRvrbSZ+0eNrrH01L19QSb0u5A5Yp+ZB0C4TLM7iWHKbrsgd+Q3LbX9Au+wYHv3sXqT0HqTprDf7a2avWe62mpoa+3zzC5Eu7WPKJ2y0NIcPA1A1XLuRILdPZi57JETluftmlnP4nzdu3nGnS1cdScjQ99wjt67K0//evSTUtgvlnA9DwoXfBn/aTVnUyqs7Wcy/lsvYhEKBPizB+6kXIndvdum2ldD9QwnRJBUYLqAzJiKaJIUlue1EjYSQ7eLgiLDGZAiNYyERUFZ2JkJ9MdoJwqJJkyptxWAAko02t1GWs5xpsXYQvmUex+/RJjVE29yTonsgVATUv07V8JEmLzTBpholPstqWY4puEssUx4rkVYOAT0KzAZPXWqsCNDxzPwImgvAmMn2jiAtOoipqxVIFx0fZ9baPYwQscBbWdPKaQbIhzi4djEiABIJbw3I6U3UDRTd54N0f5PJKP2ztntGN57hZRV1DVnJQMoH5qqzxJZhJ47fvua2+gqFokEqbLY4fPADYUi9O+r9T9keW7QxNK3fTCPiJx0IItrtMUeZ2W5mGSa896a9Y3YS/JNZIyWt0HRwjXhGkvjk+3SUQBYFL3riSHZ/6TwZPuwRT9qHrxhR3qhMO4x2jnHdpeeZ+guNDRG9ZitxQ6cbvGYaJav82gbXPUNPfxRiQz6gM9ydRVY10ZTNGk0mVzY5JooAiifgNnSv+9AfSrY3w/kuZy8aSOnIswrx196GedAp7T7saALVjjEe2WMDtcGO6nIVHJJ0kp6luGwRYfXrrTKcVWTA3wVXf+hQAu867GNl33ozHqmt3ctmkRnasA064nvTBbl58x8eJrVzKmh/++7TnOH1vNqYLYOCBJwFLWFWKhCEYwJfLo6QyUD/z85frXnw78DXTNIdM09wP/Aj4u7lOMk2z0zTNr5um2WuapmGa5u+BHHBamfclkMuwaM8Ogj4RQ9XIdvWRHx5z/ftiCeh65uA4XeNT3WjqUZSMyGsGj+8bdQfOtGKQaD2O5/fq/Ozbf+GZR/e6Wlb1HQepHB0uMF1OhshhSEZssQtremOznHfZdOGVvPDJz1J7wRnuvsZrLmbBe27C78Qq2R17ZFOh4Gypmavsauw1cRYtihEa7WekwaJmZU8jLC00bHrci4INPI61aYaJ6Fkh6j4f4zX1M664rZOsfdHkJE07tmNkc25x6n2HqNOVTCbRJq1v4QjtCaJ41AAXwAtvuoONF72d/ODI3Ae/BszJylvx0iZkR5jQ/kaB8WHkfJZMXQvp+nlFsY8Be3JJK5bWlM9w3Gw+FlYZ1G17FjmXdtPNI/6pQ1epe9GJbYmPjbDyiYeJT9qJDo5chKfbVcWtcw1PZmBoaJjbv/pZ5u952Xo3qZCx5M1eFD1NXQsE6U/k3X5xSovVLvpL3DHemC7Tsz5wxoVk0pMVqxtUpAvSJABZe/KabiyLBiSqDuygqmsPAFIwSKq14PIIDA+TDxbLYSiaQdovMxwJEqsK2e84M9OVUQ3CqkaVYFL93F+sZ5/Bne6Mk6LdHkoZZdEnI1fEyDbMp94Oxs85Omp2rdT4vj3u8eF9lt7U+OKVqFddjhQMIGgaQfuHVGPVxAIypg32lVlihRxWPJtVUfIagaBMvLJYTd40TLZu7OCBX73I9he6Z7yWKAqsPmMei+VJBDu2dLoMRqfGpjyNppMvnSQ0NkhVZRRZFNzraHnNZbr8Y6Pk7PqNhmbwi+9s5O4fvsDgmkvoPfcaV7NMNg00O5X1wHXvJdm6asZn95rS1YscsdrHZKQgm2BMZEnbGbmHy3RNZDVO6R8nXbPMXaSH7MzVpvmVZV1D9YDoyv7eWY4EIZVBkGTETuu7DT32LOm2LgYenLlsldP3wnMwXa4ZBoIgINdWk2pcQHzXwMzHUgboEgShCmgEdgiC8B+CIHwV2AGU9wWLr7UMiymbXjVuGqscG+HSB+9mfkBl719eYP2ZN/H8G253JzZR19yBqmsixxef7uBbG6fSqJph4s9lkZX8ETNdD+0Z4evru7jjgb2Ypkla0dFEkYYWawW0eX07e162fnhDkpF0vRDLZViZTYca0+VVSxfsAUnP5tn9oc9zwcP3MNrQzMDC49waUADzbr2WFV/4yJSsvO7umSdwuSLIwcowUn2Mqhq7tqANoJx6X5PjWSbuf4Zr7vst4vNWTSs1Gnfjvp657ZOMvP6qOd/pSK3pueeKGrDuC/Czj3yuJHu0xEpWvYaquXFxw49vOKT7K4riiqOWph+n9nUwvPb5Ilfw4Viu1wrOPVS1/P8pc0BXc0+7K1w4fKLFZomGjgkcvOY22q9+F7q/AGJ8ZqF+YEbVCdvMTijs56L3v56hj7yTp668jtG0A7okUvs6ePa8W9j5T18m2zNATaSY6XIynuMTYyy65w/uIO8siLyjQCxWnJ0EIIl+Eieew/iy1QD0Ly64nLyuN8mD3kTTpC+h0Dtpfa/FNSGqw1NBuFcyYkRy9MnyLguieFyvima4sWbu+XnV3Vdq4VCAK3qf5bID1sQSiflZ8PhvCPUdtJ5RhPUL6tgwr8CmZVSdnGZw+YO/44x77qUym59SWNtraUXn1L5xTm0bQrYFmo05mK5QtI72K28l7Zsa++WvrnBrtwLkZBGfbiCV1CkEkFJWn9NqWzHedhNSKFBUpSNf04QkCvgnhmh95n6WLZ/KXg8PJPn6Zx7le1+0Klo4YMIwTB741Ys8dt9O61o5jR9/Yz29XRagPLhvuCh8wRvEXVFtgbWzH/8ZwRqLvZsOdL38vcd5/c42GtdtdLftOf9k1rZWIiaswHifLYlU19sJQHYihWIDUkHXSEeiRTGJrjSCriPZcXHSwQ4W2AsGa+eURymyoGxdoz4uuCVyklUF6RIjVxg7W5fUzBrGsXNrD3u290/ZPpFVqc4qpOYtdd3bOZu9DkwTMjCdKYks6Yb5pBvmowfjs2aaxhhlxa++Qtyw2szIuk1zX9/ue7IoEA9IyKJApLSOq9fscaXu9lvYfs4lBHKz44ty3tKZTRLAIiAIPOHZXpYJghAC7gK+Yppm+akhgJlJ8u9vvZifGgJf9S1GCPjpGxpCDYZQ/QF6+/tZGGumrcsCOmNplc7OTqJR6xFTqRT5tMYdX/gnVJ+Pe77233R2dhKPx9F1nXQ6TWNjIwMDA/h8PioqKhgZGaGiogJFUchms+5+v9/PvkFrQuiezLOnsx9D1Vj53BOsXB6n6XUXMNyfobfDSj0PJMaKMuqS6Sxj4xNFFZr6B4bINwQZGBggEokgSRKJRIK6ujrGxsYwTZO4JxtQzeXp7OykUpAZ++OTLI/EeOYNN5LM5hkfH0fXdRKDw7QsWczAwABqyYoqoWh0dnYWvVMsFrPqvXW3o472oTb76O33kWxdQuXkOGOAiU4qlWL7lg629spUhSqJHOygu2+AsVPWwGNWzEidLjNpSOTz+Vnfqa6ujsHBwaLv1NDQwPDwMIIgUF1dzfDw8IzfqWbPK6RXNSFgYtpKaYJp0t7ZRU006L5TVVUV2WyWXC7nCgg61t1erBrvPHMoFMLv9zM5OUltbS2Tk5OoqurePxKJEAqF6Bqw4llevuPzvHzH54mddDyV73kTE3c9RPKFl5n/nc+w+JpLy34nb9vLe+Lzeju6iEcD076Tc34wGCQUCjE+Pk5NTQ3JZBJFUdz95bzTkX6nlA0OJa8OXcxaCKSbFtJz/nXu9upljTjr1D1f/zEcfyEZ1WA8leOGu+6k+4pbkSSDVC6LoqsgCAzZK+ygCJ1bt5Np7yW9v5OBxzcQ/fl33Wv70RkdssaDhW1WgK5TBFrJW7+bL6+4Mgo5NQeUZCj5gowff6r7t55VOdjeQTDgZyKTZ/5EmrCmUzs8gBa3A2lHhnilvoJxm12p92mcWh/giY5iAON1L564+VlYeiaxxAQd3d2oVVHC4TCdnZ3U1tbSOzCCryTYcqh/GEEZxwhVUGpGJsOzT+3BROPM848nq2aJ9bSRbJxPtnkxsmCiiwIZUUYTBGTTpKurn4quUYKNy5iobWbxWIrOwTHy+aopY0RVVRXd/ZP4NQ0EkdDGjfCG4zDR6ezsnNL2xm0BYUkOkm5uRjV66evrK2p7QjxCaKib1DwrDOOc3jF8usG45Mi9moyOjpJKpZC0wgKkoS5OZ2cnYVOgou1l0k0LSZ12Ap2dnYhqjviBHUwmhpiYqC4ayzc9azFn2YzKwQPt5FK2YKxfpG33EOGYj8HBOrrbkkyOZQlHfARDMsmJHAO94yh6klAohCz7WHN2A4FAiHgtPP3INhqbanEg/djYBKPj+aL+pOqgh2NI+Un3ncxQgKqRLEOvewNVB1+hd2iQCgmX6cqksowNWYH+hmmQC0eQlLybXeuYYOjohvUdNNNAyhYYUtM0GRwcnHGMONnfRftf9rLo1Ca6B/oZW3UG6caFhWvb7O6u2hhVzSGy2ey0Y4SS13n0Xgu0hqtURFF0x4jBUcUap1XFTeRwsNvGtbu5+Orl08653rY30Z9j+Kp3us/VfqCHhuaqad9JSaeQ1DyqadDZ2Yma9uhYzjA/AXR3d1NXV8ftJ4TBFyA9Oc5gybjX+JY3MPTg0wTPO5mRkRG48FReMkdpNHRmC1wvB3Q5vqy4aZq3AAiCcINn+5wmCIIfuAfYDfxzuec5VhkKsmndRnK7D9Jz+78hh0MsOeMUfvi9H7CtL8VX6hoIh8NUVNcCk2Q1gwULCqrNNTU1iDssxO9TVRTdLNpfW2tRqN5tkcjUAD1nf0NvHxYGhY1DJpKmcubTDzO2Kcxl//ohxkYl3tOwAAAgAElEQVTS7N7WR+rp9ZiDXUhGweUn+vyEY8UxAVU1tQQCgaL7O/WfwmGrUyX3HCxcw7Dezykzkw+FmXdgLyfs34lSeRmVp53Ajrd9EumOt7PgPTcR/8d3kbj0XHZ/1iqc6hdE917ee0ajUXb+w5e54cXtdHz8n9kyKhJfuobqPVvt/WGi0SjVNVVAD4YkUzHWixasQJaLwUwgEJzznUrv7/jS582bN+3+0u/kikCKltCgpuqIpkljcwu1tlKzAxTices3/81nPsPmAyO8+cffpHJ8lKa6eg4IBc0mv89XdM/KSgsweNuDs7+zsxNfySor+fJelkTjqE11JIFKyU8wGCz7nbz38opgNtTUUNnUNO07lZ7vbHeOK90/2zvBkX0nzbSAjpzPsuTXX+Op624lIos4/OvkkpPcc6rVDKcu9tN1/1MEaiF8gkhGNcim8yBb3y9eHcNQZapUkZCqkbaHrHjYz6pbrmXB6WvY+raPke3so6anByuMFKoCPrjzdxznb6ZudIx0wzw3G7KyqoJgMIiUnsSoqMXEZNG5p7Ll4DYq9RTyrpcZOfFshHya5o1/Jnnc8SQbl+LTdUK1TTTFAmS37mP5mM24xAuMUSydxMSKw2qK+Wmpq+KmU4M80VFwj2HvFw0TQwBfLoeBpe/X0NRCfTxAZ2en+5tWVJtIJWy4Xw6wYEErneMlCSumSYUvwAvr9lNZHeaCy4M0tDSRAASbiQ7oGrd9/V/IB0N0X3M7sm4SiVZSN7KXXG0z4f5OxlesICyFpvThaDSKquj4oyDa1El4uJcTfv5F+m+8kQULrnOPcx9J8gMa6ngPSzc8TMXVF9DcXAhkXrBgAfMf/jHPPtyG/7l2MrJERcSHOpnDNA2i3QeQtRw1NTXU1NRQNXyARM0ijEDw/1N33mGSlWXa/51QuTrnnp7p7umenJhhAgzRAUGysqIEFUVlXXdd1/gZcI2suhjWVQzrKosIKCCC5CEMMMAkZobJeXo6x+rq6sonfn+cUKc6DDOoe63PdXk5VFedU+fUe973fu/nfu4H/dBBmt/9drR0lpkvP4Lq83P8/t/Q3DyLrbaBZSRc6o55J2rrqjiIVU1ZUVZL37jFZNU2lHLiSAxDg7q6Ompra6lvrESURN7Y0sX+nX30dCSIlAR45cljLFjWyMVXWpW6saEUOzbuo6JyDMku2IlGolTWWGyR8zyZ5mYAfH6fe03+XYcpzWukmhcQjg3SMncOok92XfV1DWTRToFjkguFkdTJoEvUNaLlpTQ3z+TQkTjjHt8oURKpq7N0fVPNEbU+keTezZReegvNzc28WFUQJj3fUsOy0RTV41l0QeDVQZ1bQiGamgo6LGecxDw9gBvqZ+Cz03TNzc3kD1lFXJKSQ5jQHu/4/jGufV/x7zRx7AEIZjGJUF5eSWlpyZTX5LclL75QkObmZrKrljL++l5CMxumXZ+gMLdduryQlp847zX9x20Y3/uCmy4Pl1agbBxjSPYB07Nvb5peNE0zDgwA3g7Gi4F9b/ZZAEEQfMADWCDtFvPNbI6nOoZu0N7eTmOVhUKlsG1MZjM4To7cqeKZytTP8PgGqbm3buwGxZT+A7uHkHQdQxQx/X5M06SyOsI5F8+h2WftMrw2BopenFqEU0svekXxTk8457VsOELNQA8LXnmB0S1vMPTsq+QHRuh76Gm2nojzj8clRtoLppTeezEx8lKA5Iw2BEFg2cpGSnqOutob5+GRHU2MKFEWj9E7nkebkLaLbN9Bcv9RzDexcPizwk57igL4bKHjR+/4V9K9Q9N+JBeOkiqrQLVTW3o6WyTuOdXG4GCVFjuaLm+IwYAryFdib92ry1vdd7Lf7C8VD+we5Obf7yOeOTW/nKnC0SrJmoqcz5KXoHN2YezVbt8AWGX9ZjbLkjlR6rZvwJdJuhoKbTThLibhiJ9tT+8hs+kE529+zT2OI3KNtjdT+3bLQkXes9/6jE+k48Fn2RRZxpldg+Taz6Djig+RqbcmWNFOL1bstY4nGHlCZdb55MZ66m+9HoCRymoqD+0gOm6lSWpGhukdsBbnpKLD2CByvHisCWJhSp1bYx2zpaJYIwQg6wYXnxhixcAY43b6VSmrdtOLXssIRTddgGNiYgKqDUrzExrUzxlNcejRvZSND1B+zNJu+qNhBpdfQKLVUoTIskh89TsYuvzDBOxnKJlWkEzAMGjb/zwdlSWMTjEOejpG+dHXnuXQ5i7L60xT3UIJr12NNzJ5jWhexVBzBIe6CQQnLzuCJJGxDVFzskhllXXv0oZCy7P3E0kW/KhMUcCwn9/kZ6xCeq/cxKkY1aKlDC8+i6OHClYQTqw+fzZNrdYzmogXtEoVVRZoVxSrCloQBJpaK2mcVY5ga70OdMQ5cXiE/u4E/d3WeDAN060kzB7uIPzKc1T37CMQnFz84vxi3orMtqdfpm0kTsXB7ZR0H3avJzjUTeX+rUQisqtN0wUspkudLDnIB4L4bQsb2edzG4mfShz74V0AxF62qksv/86HCYV9LLpwNrooYtjPduVQP+O9o2zvsBulJ/PER9JsePIgLz51kNR4YRzkc8VjKGlrG2W/5PqCOTFn1cxJnlup8dykY0w0+Z7O+R9g3NdIx6U3odvPS/tnP8yCb32KNY/+bNrPeJ+9NwuvPlHv7GbBzi1UdJw8kXeqQvp7gM8KglAjCEI78FHgbu8bBEE4KgjCdye85gAuHbjJNM23tAIbdkrI0bU4fY8c0OVotr3OzBPF6Y5uJ1FeRebPtCfLTwBNgmEwumA1u9/1z/zPj17lwK4+xkYzJKQwSqS0CHSp+uRqI/0UcKgXdDm2B4rtnp0LhdFs81I9nXOF9uWrl7KtL81IWuVgsrCrME6iD0qWNdJ56U3oqsDF71pCxZE3MHzFoEuSC33rykeH2dgxRskjxY2yK599jlfXfYBD35p+cP/5Yf3+oiBYLTMA2TDcZstThQPQdadFRjKN4emjpaVPHXT5/X7MKQzJpFAAf5UNuv4Mg1TDA2T/N0DXf2/toz+p8OCe6UHrm4Vjx+KYGAtmobFyY1sZoZgFYKR8lq5jMZ752r3kKmrRszkXSIXSKbSAteiGwj6cwqGwp4Iv4pPIdvdjKCoVZ1mthbSde2irCnHWrDK6xyUQJcbmLEOQixe+UJ3FLobtHmvlYyP4bT1JPJax3N/BLdLwiQbB0QHCQz0MdFr3JplVWPzwL5j/x58j5T1jRiikKOdWF1iIOy5vpzIk02Brx6psfU51VilqIebMDX6P+7qqG64+Llei8OzsOkKV1rEneg62Jqx7JHefoPLlpwAI1lYSr2vCxCQwOki1nigS1gMkx2z7iHyWkK0LjU0Buhwvq2TM3lCqeUT7fk4Husp7x1jbO0rX6ov4z6/9ByUffM+U7zurylrs2mrChEvsnpyOVslzPwzJD4KAqOSR7WfXKaySDIOyzZZu59jnPs/g6kvo7p9am1Zmg+Fxj0C8tCKEJIsYujlpMf/jQYsZ29Mzzv43rPYzB3b18/BvtvP9257hT/fttO6JrlKz5zXkN14jUhJgYjjTvegBXaJmYsp+on3HCaYLBQnR7kM0bn6aysqAu7HVBYHeOQtIhyb3Bh2prcdfYr0uBnyUdB0mPGhpnKfzpZoY4WarcKpmdh3/eNtFzF9pMTt++5luzpqcNZRi7x6L1X7t+aP86gcb2f7KCba/2snoSOE5zWWL733GHlOBuko3dQpWMlZpqy7S7gG88uwRfvLN59m7oyCYNyesv+pJirZUKUx6Rpu7ZsmREM0fuY5g4/TlhX7/ZH3nqURswxYu+dPvWdh38ibep4o+/hXYCRwFtgC/Ae6a8J42oG7Ca2uBdwKXAWOCIKTs/33pFM8LWCDDNE33oZZCQQxFZcVnP8MtP/iqO1EpXtf5iejXnrBTpWVo5qnbNEwVE39kySh4VMWGUjzx+93c/4stbMw10X/OO0iXFHQXim68NabLFmSLoQCLvveFotdy4QiqDbrUTM51qZcjYRI5lYau40gbXqHzTCvN6WVQJoYvnyLacww5ICIIAo3vvpRo73EuajdZc6FlA+GCLtlHJJVk88EBchNEtE4lY/pwsWbqLxlOVZwoguxzvpNMLjU9cJr70zu5+rc/Z6S2kY65C/HXVrHrrrsYq7QXjtSbt3FwIpFIcN4rv+Pio8V9JqVQsMB0/TmgK/+/y3Q5kT6NXnUTw63KC5ey95Z/pTXUwLIua8I0dI2Fv/g36xwz2hg68230n30ZAysvImNIlgM0EE4n8WXGKc/GqKyN4o/YC6sH0IRlgY3n3sD6lrdRtswSuCe27+Unl8/mC29rAc8YFyekMYK2p44asa1QJJBt8KxrBgdet9xsAqKP0Xkr0KPlNB/YwMyX/kis15pQ5aE4mdomtEAIQ/KoNDxNhefVFBbFZY0l/O6mJZzTYgEaw7u2eBZDZy5IJAq9+BTNdItUhJAfUdPIq1NXL25oruGyG+bTsGW929uvrL2ZJ89dS4c+RvsTv6I8OHm+GU9Yz4yk5Egc7GRmd5fVQHvChrCyJsrseTUIYduWQMnjq67k6NUfJdW43G1q7Y2ozWDVpfPMiSVRxieDs94Hn2LwIUvUXhqQCdmGuH5BZqymAd2THhxpsCqsDX8AwQYYXkAR3m75woWjQTpLw5S2FdK/TpiG6VYpjnuYrkg04Frj/PZnm/ifH73i9lVMO4AYqKwppOaP267wTpscJ20ma1MzxqazWfSArtLuvcx+7FdsPnMV/OpHhfc6nUY0nXdeO49l6gGOzZyJefYqpNBk6wpDENwCEtkv48umCNjmtcnly6b8Pk6c9eR/M+P6K5j/jU8WvT5yYpSVfaOU25uR4OgAmq66FiiCKBAMWRsb0zCpbShxi8qUfPE9yNpsXSjkc9uwAeiiQFa1isyyGYWsbcGiqQaCKFBdW0gZasHi3qPqNL6bumZvVgwDufrUu414n73TCav4wLQY45O971QOZppmDrjF/t9075kEo03TfIk3rZk4tTA1HcMBXcGA5bQ8NESQAgjymgRmVL3IZ0PXDNKRErIR68fLa0aRBcKbnt80efF4nEV1UXeiq4v6GUwpCLpePPFSqIYZe9t57Nc9InjdRJtAj55KG6OK1UtZdMfnCTXPoPr8VdaxHNAViqDZ6FxNZ6yUGSBFQiRyGjOPH6b8ucfQZjZzfO4i6k/SDiI63EXlwecYvehb9HXFiecE5HyWxoYwtQ3Wg+T4zoh2WXEkNsJAbJyVf7qdjituJlvd5E4Wp+N7dbohZFIsvPvfaP/pt9jUZf2WhuQjm5weOFXt2U19NsudX76DfCjMR9qbyZzoYKCpBaGmyq1EOZVw9FhSJGwtnPYCJYWCBP4SoMtbGr365BPmXzIyp9F3zhuGaXL9979O1fAAyaZ2V/SplFmL3sCJNC8MFLRNcsZ6R2rmHGK5WBHTVXF0NwuX1bN4xU3sGLMq07ypu+B4AiOv4K8qJzSzgYqzluErL0VLJJFqq1AzOazlEXS70a+cS6MFI65R4vBZbwcgPLeVQMRP/db1DKy+pHAOX5i+c66kNKJRlejHOASJ/hHymkFdPMnxK2+hau9mTA+T5tqmAG1Vk5kIZ0F0LBEAQtkMBK3v6DCxztgCa6P2/JwZnD+rlPm33cbKnm5y7XfCzOWTQJcqiQRErMouO2Uq2ec8uGwVH/nSDSypj/Lcl4t7STpMl2DoHLr+U8xTDLoNk/G8XuT+376ojs6Aj75jo8AgoponesZCsnINgiSjqjoBD5gwTZPd1aVU5BRmJTK0ZhXUzOQNhKlq1K1/kOpr30P07vsYbVsK9YupFAL0XPVRys8r+P7lSgtzSqB8MvCQ7HtbVuLnUHUJa+fUTHrPL7/3EuP2NY+P5YiUBKisjkBQRrPH2ciAXZnsl9EN023aHhbhknct5nf/tQWfX6Jtfi1VtRFq6kt45Lc7EXWVXEUtSmUdmbRCOFLMnDizvegZAz4lQ7qhBV9JDeWtM9zXtWCYTM0MRmNZatfOJ/3B9/HGph4uqQwRC/ldxtQJUdfcDJDsgBMbkJrhyePRG+UrFlK+YqH730e++0tir24nd/37qPSk+Oq3Pcfg0uWY77AURxdfvZCLr17I47/bxcHd/cRH0oRsUD6R6XJShb50kpinqbvPMEl0jPL9P1gp8bPXtXHOxXO4+N1LYFE9gqcyWZf9SBSOq0wzXzlthRRZInjt5Se9dm94n73TCV9ZCZKqEDy2E1g17fv+z7cBCs5t5qJDzyDIElXnr2Ll7/+D1o/fiCCK7sKu2bSndwLKTnAhHps3n4PLVhLIZakcGjhtV/rf7Bjg2xs6ueOlTje9uLje2u1YTNdk/Pq2K+YjtxX/gI6poDdOhemKtM1i5vvf6QIusJyczUXzGa2udZkuLZNDs1OpcjRMIqe7+qVcKMIT772F9NVXcPjbP2ff5+8gN8H/ycmpG7rAfT/fwv6opccRfYVB7zBdUkU5/svWoUsSoqEj6jqCYT0MzuIzsSH3XzLSZRWMVlUTrozyzvetoH77EwTGhsif5JyCXXqv27+XqluWH0++50O8+C//j0jrqRn0QWFHJAgCUsia4HrPuYInN/QhV1o78z+n6bUDuqJzW/FXTG3I+NeI7Fvs2JDXDKqGrV21nkmwr9qyBgj3n2DRXd9E1FVX9xKJ+qh7/XkaXnvSYlZTCdd7K2Cn63xl1jUHSmyNhYdFkgcsK43QLEuQveaRn7Hif77rtuPIedq46GHre0QqLWDjbErmLJtBMCiz/NIl+EIBag5vZ/Gvv0Hjq48XXZfc0kSwtho1XEJ2KE4qrxO2mabY4rOK3uuM+9bK4JQGro4hpjw+6jIiXpbG2YAVMV26teD7w35G56/m6DW3EhtMu/ccLF0jpklY1UgNZ+m44oN0Lnmb9TdMwskEpfEYbVXhovOJSo6mp+9BtxdJXyphH08E03TNaJ3YcHiEDb/fRW6jpVuRVIWadWfxWnMdO2bX4ptgKpqz/b96SsOEklbaTN0x2THIV1mGnM9Ssek51ENH0Q4XumYoYkE+ADC8xLLACQ31QKSgvzmx1jLJlEqt37vqgYd4/49vR3tp06TzpTzmnol4lrddsYBbPn0ez43miU1gesMRP92JHJp93yTDJGgzcSVlQa68fhlnr2t33fNFTWVg1cUMnf8uBnomsyYO+DGaCsUEpiQxdMYF1IoRKkOFuXbfNTdx/KoPc7jLGrPO79EoaSSzw5wgTb3H32rWkf3uGJNKIjz1dx8gvsBaM8QpfMFOFunjXYxt3U2NmeL1hgLQFTSV8tjwJM1fhZ1Oj8cyBGyg7tVjKbqBad+j9GvbEO1KqN5okJRPIr+j4IFm2M/Bk9t7OXD/Tu79WcHqwVmjdPtyVI+diq4ZhdZRNuhSJdHdeJxKvGWmq9TCA4E36b/4l3Nx/CvFWFxxjScDtVXupApWKklQFHTbDNObXpy4W9cMkxm9XTR1HiOcTp62V9e9O63FZFd/ijNtw8NFdVGePxrHRECPTnbQ8Pkl/CkFSVPR7d2wMpWm6xRTnaZh0HHnbzE1nbZPfYgZ772cLYtWsmdzLzNOWJOUms5OYrrCNgvWdHi/1SB8dTP9jzxHtrOPln+4oegcBiJaIOQCK8UUiLcvZXu3yRm9CepnlLl/8zfWsfbr3+Df797ltlRx5nOnPD++dTd/rXjp2hvJqAbvWrOUiF9CFg1Ewzgp6JJsSlvSNfypHKPbdnP2bd+kqWEWm983vVP/VJE+0cPL7/4UJfNn0/T+qznxi98Tn3cm8a4k8bUtnPvK/afdRNsb0XmtXHzk2b9uMcIU8VaZrnS6oKWTNJWU7aBvyj4E00RSchj2grKwQSSbjBPpO8iSxVX4W1vpskHKifaFbP3ARznzmjMxTZNgNFQ4jm6n8h3QZbfzSMSz+PySyypk8zpMkNMk7P4cWmwUmMmV7y1mD+VoGHU0gTDhfvt9IserFjJy/SoY7WA8r6GHS8Er8DUNmjrfwC/Cv178dprKJmt5ALcfZPnoMKIWRpdlBKmwyLptwlR10ms+SUQLlZCrqidvs0XOBrA8KDOeVji3O8Zz3bGicxo5hY9990sQCBD5XLEjeWSgi/K+Dgbs1FimrBRRVTB8fiTTJJZR8WbneobSReyKMLOB6qVzSff3kxWKMqXW8VSDsKKxIJZECtoGwrnJ6UW/vUnJnLBS0bJddTcS8rOjoYJbPemlvA2K5FwGIVoAXbkK61mTw9YGyB+PEzYlRoYzGIbpgg5N1V1ROFiaLieGX95G1cA4WqXFwoTCPiRZ5PBwxm0Gjma4zbtzWSsFq44l3R6PgqYRTA6gyz6CoTWTrtUIhiCVR5hZYLTSta2Yjij7qWfhlmsBSDQ0IPaPEaqI8NsfvcxYLEV51EftSJYrf/0jBmY0I378U4WDm4bLpvokkQNnrGZmh6VDDB05Apwx6ftMF3LUAlGBVIKEXIlu6kiChKQqlI8Os3s876afBUFwixC2vFiotM97mK7xnOYaJktKDtM2cU37ZVRJJGrrERec0cB5l84lNZ4jrhgIgO41I3bGj66DKKGqdkcS0+SuH72CoRt89LMXuIBPEyc3rD5ZqOrUaeE3C6cf9JuBrv/zTJdpTm7C7P7NMQm1GQEvgzRxt64apsv4+JQ8Oe3UF5bDw4VFvK0q5J5npj2xJqpqGD3/gkmfS27aTvuNH+DCJx7yfI+3pukaemYjPff+icO3/5wj3/2le0+cnW42HGG0uhaxroaai9fSfOt7ic6bTSKnuSJ7AFE30EfHyHZaQtDknsNF5+m6/sMcvOlzpA1bs1Begf/669l7JMmYLZCU7IVD1wyCskhLRRBRDnL8ig+SrmmxT2T9Nsrw5Mqhv1QEswqrekd55SkrZSXYbJM6jabL1HUE0yRTUctFLz3Px77zRQYfeoaSwQFKxuKkEynyp9AU3IlyOUDmeDfZnkEWfP2TXNy90f3bsaNxou3N7obhrYQgisRe3c6RO/6b2MbX3/JxTjfeKtOV8lSNloyPuW1YHL2jqBYW6/5f3k+2qgGlbR7zv/9l5n/9n92JMV5Thy8yk7sftQpS/Pau2ZD9+B1vn74BEq0LOVG3kN/91xZ+ecdL7Hn1mJvONf1Tgx4AX2ByRRlActZcRuetKHLKB/DnckTDMnImiZDJkFJ0QhPmj7EZFVx/35e49rdf4tyW8ikrFgFkh533B9FD9jMmF55PZ5de70m9ZNMKl7++i8CvHqOq4w3aHvkF4YB1fuf5rwj7XBNj91h2MYIYClhoKJ/nhSVX0vnrP7jvCdjVlzmHtRBMRLsqTjZMYpni9NDIS8WVWf7mRkJV5cwY7KGyr2fS2Hnt6UPMG00WAbWJbBhYTJd1fpulsdnO0uQ4f//tLxC86173vULcSjeLSo6SukLqULbHmxy0WbtwkM5LbyRpVBW1KMra16qIAiaQTFgVcnnN4JwffY9QqlC05Ajhj4xk0BzQpRvcc6dV+ZpJKbzwyf/gZ1953DVUFXWV+u0v0LL+XhpnTZZXOEyNs3kFyFYWGHbjRIHxyVSE2TKjijmrZjIwmAZNZOH2TdQ2Wkg4mE1jetsMmSY++79lsRgF+8ZOj3WXo9b4PPrVH/HhH3zVraAVNIWxtdewYE8P3cdH+dFXn+Whu153mS5veJmuRE5Ddq5dyVG9ZzPx2HEGQj63RyRAdZ01Zz7wq23E1x+yXlT1wn2zK8xDNivrPDNKXmMslmF8LIdpmm5qc8axg2h/fOKUr9v77J1OyKUW6PL/rYMuTNhy3SfJD48y/NxrHLr9Z4y+ZlWJ4FSg5SenFyfu1huffJLWI1ZJuU/Ju6XW/ck8N96/lz/tn77i4ImDhRScaZruztPveWjUKZizTQNBOt9+Az7F0w9xKk3XKYCuzl8/xL7P/Xvhe6ga6tg4uUyeykyeMl8JD3/sS5Tc/gXyF57H1+ev4wWxAs0w3U71AOc9+yhV3/2++9/Dz79WdB7HJVwOBxBFAVU1WHbRQi64bB61M4o1XZpmkO7oYen4IKIgkKkraMXM09BGvdWQNIOKvMroUIodr51gYOk6Xr36JnKtLVO+37DHyfF3fQxt7mpyFbWo49YEPvPEET7xzc9w7L5TfziHTlhVQY4bveLZje3c2cvePuvYb8ElxY2xrXvo+tVDJHYdfPM3/4XirYKuZHfBgXq8eR7n77MmzFxlPceu+jB5j5/VeMsCjl3zUfYvv4K+rjGyGaVoNypIVkopFPa7wuZ0mbWA+SSBfPcA483zOaoWUitv3P0M+20vuhu/fT3LhV6iPcXN3Zte/AM11VMDor65a+g750r6117OnAd/TPlhuxrt2DEuuWYhsfFODsycyUAiV9SAWtXypGZNFmtPFX57w1KhT34+BE3lwECKG+/by5O7C101lJyGVllPLlyOLOqERgddk1/HvqYy5MM3YTOnBK1FUxAEqi+0imiU4VHUeIK+BfWETuwnFOund+0V0GOl/sq7T0wAXRPK9fPFICwQlEkf62TdSy9z9rFuevoS3LWtjw8+sJ+0onN874Dbl9IJaYo2WQ7TJZdGabzuMiSb6ZIQiaSTiJ45tLHbYjnHZi8m/J6CBq/96Setz9hMiC8acn2uvK7ljkhbkURy9hz+4288z10/fBnAvX4AX9jH9ffu4dH9Iy7TJeoGXnfrndEFqKUFRrtn1mx+9sXv8OgPfjrpOq0TW+f3pwsu+lqksDkL1heA5OzNr3DBk38ge7STC/zdtD5+F2ogQGOTNd5C6RTd2zzuTabharokUaBt/y7Kjlo6qdOdkSWb6dJ9flLzVyHY2QtRU103+WNHRtA0g119ST7yZAGQp5oqyM+qKAKdY1nN3RiI+RyVO7Zy5qYXaR9NUeUZI9V1UXJZldHhtFVAJ1gayZwN4FxNnL2JU+xUvyiJXIBSuOoAACAASURBVHPTci579xJESXTTi5KSIzQF0J8uBgZO3sZnuvDZLv5+ZfrqefhbAF1AbOse9EyW2Cvb6fjxPSR27mfLi8foX3ExJl6my6PpmrBwyOOF3YtPUVyma/+gZamwqXP6PO6u/sLDkdMMF9wFJJEvXNhMRUimtXzqnbUhyUU/gqKbb0nTpcaTRf9tqCpbr/0nGq69gVnDMebE05TnFLKqwRt9SUYzGk/aJc6qb8J38wi0tWSxj4vTTkKUChUpDU1lrDqv1RKaUtihaTmFjWe/h9af/ISDcxcwMt6LHLUGt2IbwNZcvPZNr+2txrrf/pRZT9/DyvYgA73jqIpE94LlZBtnTPl+YwJtbMg+1LHi+5qccJ9PFo4xqlwaJXWog+O/fIiAz9bs6CabHtnClmv+gX2f/c6bHis3MEx8626yPYUHfmzHPjp+au3w/zerF3NvMb2YtVsWAWQr68lVFzQr2Zri32S8ZYH774fuep0n/2czERtzzT6wm/L4IT76gXkEQz7XziFbUk4+FLaqHA2D8sNvcEZ7iNUXWCLrTN0s1PHCeG6t91G1f1vRefVA2G2jNTGqUoV770/G3ZS57JcoXTSHjkveQW/rHDr6ktae3/57KJ+nbv8eju4fZN/OXrSTsOjOgugtl3di/n3f46EDMUYyKj/blXLBui5Cy5N3M+PVx8H2ENLt8ZDTDMpyCjWH+mk9SQZl4Xc+W7gH6SxGWZjGTU+RL6smPv9MZHtBC8UG3cVMMkxG0540p6IhTJirpM5OBEki1dBKvnE2o6NZXutM0Dee51gsgzpFK6Epma6yKIgiWiJJ2bL51J9jpX4l2c/B93wSPVBgUeoE6zeuiWiUt7dMOlalrQEMhENuhZzqGdMOq1fV14U5XthsazaD4gVdWVFkNKvhz2UpyYyzt7qUnsaT9wjsnzGLXDiKqppF53VCsM/j7+5xX/OanAbqCzrgmbt2sGLTBpLHupGSo0SGuiEcoqLGYgYD+RwtO18lNNxLpO84/qFuN70oiQLz9u7AnxwjONKPJJzec+20AkIQGVmyFtM0qXvmbnyXrWOopQWAzmPWGpMSBFRJdLufvC5KvCT7aWwpgK6JTBdArqKWxqxKRC2MkxceP8BPvmm1sFLDfvI2U+6ysXbNnqjZFY72Ztrnk5izqI5FK2bY77fHcT5HcAq/tOliKmP0UwlfVTlH7rmbn3/h5PP93wToMiSf1SPPrl4UgkE2rj/C2OwlbHvbFW7lnuIxCpzIdInZAuXnUwtMl1MeP12fsfGcRt94YcHLaQUhvF8WWNdeyXcSe0i/YIk1/RN6NJmSjC/vZbqs9OLsg3v456/9C5c/cNcpga78QDETZ6qa69NVq1vnjKg62ZxCZtsuZnQcoXc4SVUmz2yxjLSHhRI890IZLQab4b3WrknKZAjaDYSzE3aqbu86e+cjDAzRN7OFHWesQLYFl46319zbPv6m1/ZWwjBNfLkMpX0d1FUHWX7WLCrXtjAa9Bc1Ey4KQaBj0TKwJ2J/IlbUsw0gNXbKjRYwU47gu4Td//R1er97J3N+8XWG6yzAmcxAfMsuhp/b9KZsV8dP72PL1R9j4LEX3NeU4VFMQcCQ5L866DI83++tWkbkPOlFQy6u2Jr92K8oL7deW7isYdJnhzftImQv9ot2bqHl3t+S3G+xVA7ocvQgEb/E4h98kQUXLuKCv1tB69xqwkERPRgmodiVWoaBv6YKLVQ8gWrBcJEppTcWNNoGm6piNZ5xHNxtkOBU8fXam7DwsKU/0iKlzHx+A4/dtZmnHtxDqtu6D/3dY+zc3MXwgM14GiaDWzpZPJQgqyQp6TxUdH5RU4sqBffY1XNqPk90oJNIYpBMRSO951zJ+Lj1Gym6waq+ONpgirru6VP54eYZLP7hl/BXV1B7+fnUHtpPKJumpOcoja8+4abO5GwGvyy493vE8+xnUpPHoPDY44gBv7sAprNqYU61jTA1QShqReYYGRcdR5Jo/fiNhFub8FWVseInt7mMhhYtQ/SkhJeKQ7Q8dQ8XNptInj6rkTktALT8nVWV6o96QNcU6cVAepwzHr+XSEWIi65eSGmHBdAH6wsbhLw9Vm598L/48I+/xZigMBKaPnUNlm3DvFiSRft62fN6z6S/S1oeKZsqZvw838/LdGkVNez70Fd4/ZBKeswCm+HyKJLP56azyo/vpe2xX9H69G/xj/S4onFZEMhEotTtfIn2P/2SsHl6RU2OpssLQu/52Oeouv3/IZZbz9VIr11Bb29kMo7ti73+eueSRE5jV10Zcx/5KSU9R0g3zCLRspCkBL0lBfbZW+2ZDPlRbXY4O4F1dZmunOZmGbqPj7L15eMM9iZI26lxUcnh95866JJO1rv3JCEIAsHS8GRh44T42wBdsg9TUdFt00vFw9xsP/9SVMcG4iRCesmTZ/UyXc6gGJsGdB2xdUzN5ZZeKKcaLqXvtx/I/PCoa6Q30YHYlIuZLsO00qD+fM7ycTHNIiH9WFblsT0DZDyDVc/kyA/FQJbx2VVshp1e9C7lC97YinLBNVR8/du891f/QSid5MyBMfyIdL3t7wr3Il1gA/JDxaJbX8yauEVdI2SDrtdfOcHB3f3uNfqDMmsvamfNBbMJ1FWDphFNWHoBX3MlWxvKKTtu6Ruk8Km7+55O6IaJ6Ij3ZZmGmeVUylla9u/A3H9oys/4SqM88b6/dzU7oqZOAl3ZxKk7OKeH7V2eP8i4pwHyoP3PtBQkUFdNfnDEBRBO7O5PsbnLAryv94wTV6xfUvU0yDbyKkeu/Qf23/wllD+zi8KbhVdnmNfNot6AU4VumDx1KEbfuGdsz25h74qzSbW2Ftko6Jg8+u6bCJfZ912ePO0YvgBh2zvZl7etYRwhb7AAuoLpFBG/hOj3sfgHX8RXVmL5yTVYi8CYVIKWzvKTf7yXR14YYnSu1aLFYZaGl1/A2PjUQtmWf/mQ/V387L3xk4zNsdgWf0BmsHeM1q3HuPDgceIjFhjyJwrPTmzBWiqGT1B2bI8LkI8eGOL5P+3n8F6LQRNEgdGjIzSmcsiqhi8zjpS1jiVoqpVC8dz3xw5Ysgbd0RkGAmQWLSU+bwXpcsvcMa8ZpzyJN91wJW/b8zgVK5ew5Od3AhCK9VN5aDuKXUkm5dJUr7Sd683i9GIqWZw2qd+ynrLOg/gqylzmIp1WSdvXkLD7ZGqigOKRG8jTpHrm3fZxlNEEu//haxjZHOPRgOczhfEkigLR/g4Ofv67DG3bVXjdBjEOox0qCbuga6r0opTP4k+NsfS6pSw/axbBN6yinyNVpYyUWvNWCoHKoQH8hw5jpLMYooSmTN8IHKC6v4vWg1aFpjKFfU3TrvUsuP8HrlbR+jLW//lSiSKmS7DvVUYRORxsZnTeCkoqrFTkVHpRQ/IVQJcokA17CrymYXinC39lOQgCgmkiKnkEu09nxC8RLCv2ysrJIkvro27afX7M2vAfO1KQ5jhrrC8RR9R1cg2zSM+YjYLBifIIGZ/E0lVNrmktQMwno9pjJ53KM9ibQEwNUbVvMyXdR6g4tIP9W3v4+Xc2cOCNPn7/31t5+enDdHfESdmFPVI+i+A79Wsf92TFTjdCpyDY/5sAXabs49V1H6DvAStnnxU8wnDTJG8/UN72PBMtI0SbJTMkiXh1rZt+zHiYrqnYiMM26FpSX/D3csCd025CHR1zfboCoeIJxZDkIk0XWIDQeU31B1A9oOvJO/8Il1/P8w+8WLgWWysTL6tAd6whxlMY2TymLLOvynr4XDBhaxqUgOfB8JaJ53NE5lgtUfKDxaDLMe6TZNFNL+7d3svjv9vlAjy/3wJdZ57TQrjF2hUu3/k65+49SHDrLm65/dNE+60d3vieQ27bor9k6KbV865/zSUc7bKAUuD1nVz+4P8QenHjlJ8xTRPNs0jpgRCGnV5VbLY0nzx10BWwh9immMZASnHvj6MTMbIq5assL5uUp3cmwGefOMK/rj/O4ZEMtz1zjPH/eQCA9LGClsdQFJQyawJOn1wm8GeHMkGEPR3z68SegRQ/3NjFf28tOEWra85k/bXvo+MrX3aZTgABgdisFiI2iEpPwZgYPh8hw3axj5Rz4IZP89pBW1vkkxBNAxGB9oN7qEzGGeqMsWNTJwO9FnCtrreegSx+UoOj5CtqyFU3kK2z3LTDmqcH3TSLz3jKc83BgqFxMOjDH/QBEhFFwzdi6Z98FACJrGRpP76VmS/9EZ9pHad+RhlzZ4UJ+QW2v3oCKHhIHV22CrljN3P++AvAmuMO3PhZhGShkOO1Ewl0w0SJpehf/XbGWhchzLBEvpqt15rYBgigat8WIn0dzDm+edLfHLsItbbgyJ0LhugSFZqfuRd/Mk7ANqOVJmi6hkeLBcJaKIIUDSOFg8hZ6/4mk3l3bk2mC9VjqoddDDdN9NC2Qk0kLZ81uyAmGci5HR9kD9Pl9WuLmh4doA26nI4dFYvaXdd2L9PlpKmkfBZDEIiN51B0g7tv/SwvXPVe5uzdQemgPefqJnP2Wdq+xuuvoNyUmDUyvQShfsszLN7wOBG7l6OanTzWnWpkrw1GU89OwgOdND97v7WRtcPL8MXLmkg1tFJRaY11V3PlDxJvX8aGD36GLVcW3P4lETKRAugST5PBqX3HeSz98Veofcd5btulJUMJzG1v0PzIg0Xv1fwyZ/sy5O2xX6ponDkwxvYNBZ1XIqeBIKA9fj8XvvEoQTVF/eanqd23lUsfvIujpLjwqoWuaS3AoCS5Ivv1D+/lnjs3QT5Hw5b1VB3YxoxXH0dTdJS8zotPFTbbqWSeVLqg6fK263mzcJpev5Uo/+GPed+d3z7pe/4mQNfEaqKMYacBJKjpPMzQkDXAi5muCa7xNhA5+NnPcXThGa6bbtoGZ6puTikgdioXF9ZFkARrsXeAmiOKVeLjboXWxNyxKfnw54sr4jKKTsWIpX9Zsv21IqYrdN8D+FQFflYw/M/YlYaJ8iry0RL81RVuWlCNRFDsRV4PFrNKqqeCKxOK8MR11k5e9wc49+X7EIN+9HQGLeUFGgW3ZCe9CJbjuzwFQ+H4JM3pOEo4XIE5gUV448NfQhkdm/S5Pzd0w0QvqSC26Cx6+zP0d48xSgWpxlbXRHdiqHmVUk8FT7KpHW3WTLZfcBlHL7oOLRhBPQ3QJc6dxayPXMd4zXxGLr2Z/jWXcui6TzBz3NaG5DV8dgrA2yLFC+4f2z+M6dEAefuKFbUBUqZmZ/5SMVFn+Gaga8yuDPIuyo73XcjUi9KLIrC0s5+eE9a97zg0uWjF8AUI2tVnoiihh6LoQmG8tSj91G7fwMKdW1j7uU+z9ccP88JjB9izzQL3UduDSxF9kEzS/oefsnjfevfzjSHr2JGRXmrnTq35e+3ZQiXvyLw6q8swEAz5KCmzni01XEJZ2mKnQgGR8KBVaVbescdtT+a02aqM9yB/48u89Ng+NjxxkJefOYRmp0GWDCUYevv7ic9ZhmIzfHowjOTReqmGSSKnoY/niC0+m1RDqwvadHvM5POTx4V/fJTWp++hanD6HnA1FXbq6D+/y6P//A3MYJiS3mOIQR+ywzgaJmNZzZ2fRsaKQVembhZSaQliwI+Ut56b8USWykyettEUKdtTShVFNK/7evvU5swjGyyQaOg6G5Zcydu/f7vb/sg3DejKCB7rB7vYZOSlrQDUnbWUWE2dfZ8K99V1O8dANE3GugYYzaio/gCZOe2s2LSBiP28DmkF0FV/xYW0JjLU2zug+qYyZjQX67vKj+xCyiTsDpmgTsWK2euU5AECcwI5Zj95N8H4ELLHe0wKFafpJSVLpa3nWnnv9zl7x6O8+J5b6T3/GmrECGJlofJOEgWyHoG+0TA12J0uBFGk8d3vIDK78HvVjqdJPv4c5RtfQOw/SrIsRE/Ez/nPP0Tk5n9gxe9/wkBQpPmZe4n0HiPisfpIZDVW9o3S/2oXYmUFASNHIBGjdjRG64njzDx+iLSie1vhkpULOjEnvWhUFo+fC97Zxmduv5Sq+sK1JsZzrvu9lM9Oq+OcKkZH33rFvdTTR23/5JSyN/4mQVdKs25gTec+rr37TtJHLHZAz+W59q4fc8amFyelR2Qb+ITLrB1i0k4HpdXinPPEcJiuuTVhl9ly1icnvajGEwTjw9RU+AhNdB8uKeGFK99DdX8PC3ZuoXqgl4xqWMDKDq+m6/hFVjVOxuMar44nUfwBEpXV7P+321m39wn3wVQiUddBWfcXmC1NljE8O5vjVaWcmGu5DYuqiiAIBGqrkUsiKKOevo42ISZJkst0weS0aWwoxb4dvWhNFmPmmEKOE6J/zaX0n3EOYdtodKJY/y8RXqAqyRI9J+L0Z0Ikm9phGtA1duAY7/v5He5/b19zHgO3fgR93lkYDW30nnvVm5q55gZHSOw6iGkYRM9ZTt0X/4l0OAqCSL68GrWkAl0UGA75GQr5Me2FWPdYUXh/75c7xghlCjoyr7Bf8yyoUsXJxbt/bija1EzXdFo0RzeX9KRVlc07qBrsI6Rrk57ZSkUgm54eyOk+PwEbcIh2u59gpLBpmO8fo3bXRspGLcCWDVppdqcdS7TC+n/VF0QZjBFMjFDl17h49DUW//obtDbb4FfyTct0ZY4XWMby3qOEhq3Nji/gQ5ZFJCULokjIsJ63aGkQyW4obCKil5SSL6sil7J+68QbBxANnaB9nMN7CkL9gKaDz48vPY7SsYP5932P+ffeQU4ovt+xjOo2t5bra/CN2sy03UhdTU6mQB27BUeTM1U4Ghc9p7NoJEmTzQoI6Qz99zxsXZ9oySEcgB23HdwDces3yNTNJNvQYpkD25quzHiOlQNjtI2lSXVai5cmCogerat/Ck0XwP4vWlXVpqISqK9GEESXmQvarZ4A2wnWCsFzjQ5DVrbcnucEAcGes9M5jZHBFLu3ddN13PpekYT1/+nuAXfzEK4sJVMzA9UGL/rwMLUDvcglEUxdJ9pzhIy92Q6Ffa5Y2wlT9qH6/S7oUnKTQXHXmms4+N5/KUqzB23R+sJvf6bovb5IEC8K2bd0JQ1L5lifaawlWl9NV3Or+3fFA06t9KJ13L6Zrejrzp/0XU4lSpfOQ8pZ81LtjhcpmdWAAJTv3cSmqlK0oePM3PSq9Z2SY2gDRynpPUbrM/dSt6JQTJPIqlTmVMb7xpFlCSEQYHTeCvrXXkGuuoGzNzxFWtWZPc9imirqS0AoZkmtmxLg7i/cwcZPfZGHb/0cSiiCIAgkE4U5Njmep+2idg4ZcUbOW0XJwjmnfL1/TrW5FH1zEf7fFOiKLmgj3DYLxyQ5Ylo3eTRpTTKR/QdoOXaQ85/+4ySm69Dqc9hx9tsokwWiY3FS9s7HK/SbqOuKZ1SG0yohn0hTWYCgZ7KQRcHNnavxBI2bnuTvrmljycpiR3NDljm+YCnzd7/OZX/4Da2H9pJW9aIqIO8iPGz30xKGC7nwqndewk++8n1evOLddMbt1KGt/Uk2tLB8wGKSvKDLYbkcF+W+kiCKP8CjN97K/o99HEPVOG/jfVx85FnCs7zC5kJ60WnlABQBMIDdr/fw1EN7iEWsNIVjEaEgE1u0BqWi1p30T6ef4amGbpjuLliSCyycKclTgq4/vHSIn//7Q67WSA/52DN/PsOVNYRt1mCsqY29695x0vP2P/QMmy69hQNf+gE1NTV0xTLUZuxmud1HmPvgf3KkMsqeGZUcqi4l7zQizxRYAq8xb1Y1CKc9oCteKGzwgq6mDxY0eX+NmJheHMtq7BtM8d579/JKx2Sm0tEeOaDLUDUiX/o67/vJv+ELhzF8/kmfqW20dqLLzy5sKGTsz8t+/Lb+RrABfMij6XFSKaF8nkPXfYJOw9rtO6DL8VMKLJqPmrCeDV9lGYkdlk1MxE7JUF1dxCZ6Q/c0Sj/jP3/opkgrzrAWfL9hTTyC3VaoekaFK+bVQ1EOzlrNkb/7R4aGM+RzGie6kuRLKwnaXljNlYIrSg7buq+eC69lVs5EzmX45ae+Sn4CWx3LqOTt59q/aA7yiRPW/bD1hLqtmyprKKWxxu6HaIOu6QoGAOKb3wDAfMVil0oMgaEla5Ea6/BlrAXW6YHgiOmTNsALjBUKJvx+23D0/dcBkM8X7q3Dnlb3d9FwvMAiivmpc+V+u9G2XFZCqKmenvOucf8WXjzP/XfzRwoptLrWwlg6b/MDrPz9f1B9gWWPoWdyhGzriXRW5cSREdb/cR/D/Y7Pl/U9MvFxhp7fxNX3/hdte99A92iGz17/CAD1V62j69d/YMlj95BKW3Ozzy8X+VABxBauJh8td3GSw3Tteb2HDU8cQMlr6P4wWqQUn2dj4jAx5oTnUK6qcP3uAFLRKI21BUZHEgXS0QCvRw3SRzdTfXxP0d8y0VIykSj5YAjxFBteT4yGd15MxmbJAokRSmxpStROoZbHCsz17lXnErQLtbpmz8WbsR/LaRyW88w7tpGj3/kF6re+QtKuYhaVPLKmcvfr/dy2rZ933HwmS69aCIZB6chkC4czh5KIJbXM1UIc296HoRukPKArk8qTUgw62+eTvupyws2Nk44xXfw56UXHlf5k8TcBukx/AESR2Z94P5G2WWTsnmojVS3kKmrJ51SSeQ3bmJbe5rZJTNf+NefR09JOxedvY90TDxSYLmV6pqvL3tm1VAQRBYGgZ2fipBahUAHoqyilqaWC8y6Zw9qL2q3vbk88mj2B+1SFjKK7InAAzX7QDE2z3bJBinnSYHkrF67LPjpGsyiaQXRuC2f88lt0rlyLbD/hpmehU/xBJMNAtn1OdEHAlCSOLVyGIvlYP/N8Xr/h05PvtWNOKE9kuopz4jNmlTNvST1NK+dy9vq7CLZY2plSn0b95mcI93e4qL84ffmXCd00OTF3MQC+cMDVRxiSD6ZwvO66/U5Wb3jKTXs5C5J67ARZe+E/Vl3GsabZkz7rhJZKM2ynQCrOWkbHsxvp3viG+3dDkvEnx1B9EnNsLyhj6WJmf/IDlK9a6r5vYr+8cKrAbqmealL/zMJEoak6HXfeS8/vTt1H7HRiqvTi7v4UYzmN7b2ThaU5l+kqbkYfW7CKoRc6XM+2mthht7WMINoePR6mImCXsZs+v9sgWAtY9y7oqWhKB8sYnzWP8ZYFqCVWhWxpeZCGmdZC7TDMiuijpz9Dz3nXMFI5i2yXxTJFyi3QltNF8vniucEJn1FgLQaXX4AhSvQ3VVBpWwQEZNtOZWwI4j3MOHsRyXaLVYnNW4Vks1RqTmW4f5wdciuJj3+e8og11mLdI4QHe+x74OkLGwihBENkoyVu78+akP2ZjOq6p/t8kltZ5RhFmvYOtLy+xE2/yLptJ9E3vfego/FUX9vqvnT43MuYdf9PCMaHKOs6hGwLmh0WyNHiBW2my5dOUC7Zrarm2Yy3DUIMIGfPGRWDfZTZoCs40ofSU/Bz88bSn36dijXLWPW7HxJsqkdSC8+xzzPfhpsb3e8fyxTmlmBdtQu4ADKdvczcvd36d1ajsibCohWNlFaGELNpIoPd3H/rZzg8bymp/cdoP7CL0vExQrF+Zq6/D2NxDaHkGGo0ypwv/j0VZ1lu7hVj1hx9bHcPqaSdwspa32Nk6TkopZVuxkBRNN7Y3MUzD+9l+6udxEfSJM+oZ2tVEL8NXgAUe76fOFdKV7ydnEebq4oi1ROyKWds3cg77/kZa15eT8vuHe7rUb9Eqq6eX3366zx13c1Ib9FpHUCwCQfDHyQy25rro8lxBMOgLmV994Xf+Sy7b/oAmp02TVTXEx/NuDZEibyOnBzF99IGUoeOg+c5lOzNyEsdY/QnFV4ezZMx4YKnH+bMl60+ofVzql1pctonIdnr09DhEX7wlfWonhRyLq24a3xJ4PS0bIODg2/+pmnCVzK5M83E+JsAXYYgUbKgjcZrL+HM3/w7CadRabiKXEUtoq7Rn1RchkMNBCY70usFk1CfopwS6OqzS54bS+1dtL1Q1/V2cuMdX2X4OctYtP0zH6b5X25BLIni80usubCNs9e1WT2QDZNVL69n7QtWEUD1YD8Z1eDAskIPRcdzZ+TEABf/4bcA+OOFvHLS870uv/eXvLTineR6B6m/ah2JqkIO3/S2EwkECDjaARPq0x4gYmtOxMBkNkIptRgEX9BH3YxSKuymvYEJTNfcxfVcdcMZLDhrNmVL5yEErHtUFjCo3r+FmoNvEN9kaSH+Gv0XdQOGZ8yyv6sf2ZkUZN+UO2mncMFhYERdY9XLz7LmK1+mpqcDgJRfdkHExBha/yob117P6CvWJL7rY1+l8++/jnDb7Rh2KmFozVruv/UzlIdkagMS0bxKfM585n7xY1Sds8I91sQWVOU5i+mKV9ey+olfFl4/Z6X7b208yaFv3snef7n9dG7TKcdUQvr8vsN86IdfQ9yyfdL7nefLMK1/O0ze0FnFTGFASyPbzEn7whquuWk5S1fNdP9u5vL4bIuCkqULCMoi+1ZZPfRCJYXF5nCuhK6L38t4s8U6tVSLfPRzF7gbg3DUbv+TUoiNqYzNWUYyWM6ax37BvK99grp1hXYs06UP2kaPEOnroOnFhxlefgFatAxlRjkRm3ELhazJO69m6Lj0XGZctAb/+fOIB2T857QRsQXix5N+Du+1Ju6qxjKq7UbxY0ndtVZwItx3hOSsuRy+8XOs6I9TaoOWxlLremJpFcHppZrLINkLiHsJTquTn/2S9LAFbiUbxDuAc6o454XfWEDiknPd17rKQtTUVxAZ7GLmc7+ntLXS/Q4Aii0Kd1zsfbU1rPzdDwEoLbV+q4C98OVkCTVnC8bzWQy70i4U65/SHBWgbOk81jz6M8qWLyRQU0ntTsusVDN1iBfYVi2ZBtNEioYpKS+b8lhgVU471YuZvMbseTVc9u6lXPX3Z9Gw/jfIuTS5rB8mnwAAIABJREFUUISRjEp+0AKSgRl1CIJJWc9RDqkm93/scwze/jUCNZUE62tQomX4QtY5Ta0wL8t2FWogPoShqy44UFWdzqOFYqV4LEO2tISxsjL8nvl34FHLl+ro935VdA0hn4TuYaiWbnvJ9XpzYv72TUSzOfKlVUUMs08SmV0V4vxn/sjH/+3/4X/muWnv1cnCyCv47BY9Y21L8FWW46soRTR0/vSuVtbItkP8rEYaSgKUxe3rnX8Ow08eIJtR0A2TZF4naBdc+MpL8Xkq/RzG2KlcfvbIKIMphXm7t+NLjaPm0wTKgjQ2Wxuuui3rmfPwbzCGTkz5nZWsRs/TB3jbgeNUvvTyaaUMo1O09DvVcIyyTxb/50GXiIGoKUTntgBWFUraprlbEscJxocQdZ3+8TzYE3/7gd2kU1n+tH+YgaTVH6p59w4au6wKMp+SJ6VMTi8mJnRE77fL4Rvs1IWTXjzv6UcoHRli+/ssw8HmD7+bF802fvSNFxjoSXBwdz89HXGXfXEAF0AwkyKj6HS3zUNxur7bIGj4eKESzJfNoqWzmKbJ0fOv4wP/+S1kJU8onUQfGilohDyMniDJbLv2eh7+wMd57oP/QMCzuC+wK25WbnyWeQ/eD1jC1ZfPuZ7D3/65+76xFovp8UXDNMws56x1VnPZYOjk1R/Omi3Jkylsr6arM57lN1t7ePDf7qHzQOe0x+say/HkwZEi/6iiYxomoumcU3TvtSnLU4KuP73vYzz0wU+QjdhJk6xOa8YGDvZE1XZoDys3PD1lQUV+aGSSvQaGgS+boiPssA9hagKVNOo6kc5R1vaO0jOFaHwi09UqWN/3RPsChiKFRUSSRdpnhak69DqdN3/CfvGv88hOxXRFHniYitgwLT+d7KztBY7JvF6kWfOGhOHq/SprwsxZVEeVR1xrmuC3xcKm32oS7fhxednV6lKpyNcqHPEXNW72+2Uk0eqSoEate1gxp4mKVUto/dgNCILAO969hPPfMa8obe6Npbd9lDmbHuGMq+0NkSix6r/udA1rS20xvSTKVNlFJuGqCNtmVOGvKyPcYFWdnehMsmOTNbYrqiIs/8LNAKT8JUWmsABKNIJhp7OqswoVCQtc1EWsa49lVIJDtnXEth3IcjHo6muqpCfZbfn2ha2xXXfOMnwVpbR//qNTXidAyYI22j55M0Hbb8nUNYZKQpSHfK4soFooFEuYpoluM10Vn/4Qf3/rUj7+5XWuqN3/p+Im4QFdR7QrMSUlR6jrBGAVTAjTgC5v+CvL3OKkUGoc/XCh+nfo6Y32dz65rYkUDrqO9DnPxjWZ1wlmrMVfiURI5jSUAeseRxprMG2Gfmw4gSFJ1Cy29EByaYTYwtVQZkkqSk8cKJzLTunWb3kGQ1dIz7BYakP2sfbidtd+Jz6ScZuayx72bvmvv40UDXPmvYVuIQBhn1hoPwTUdRS3bQNQSss5eONnOPLuf0SrLE6jzakOu1KW6bSMbxbj+44g2oxqqqkdORomYHuJ5QeGWXrnV1n9yE8pXTKX1tEBFr5hNacO2JutfE5jPKdRklepCVUwOnc5cnkJ4mOFddFJ9TZ0n2De7tdJ5zVeeO0w0dQ4vrEhXpk9g7ozZlBZa2s4VZ2qbVupnKK1kSoKYJpo8Sw+X4iSn/930Vzx14zgKYCu//MNr0PpOGUnDhC8ytr153MqM2y0O3tzF0PxISRdp288j+DR8sT6Y/wkYzCzLMAvLm/livsKDIJPnZrpmqjpckCXy3TZ6cU9K89hVsdh6q++yH2vKAmIokDH4RFee/4o0dIAsiyiKrqlI7Iffl2S3XMagQBkMpj29457QNeOiy7nYsNAHU1gxOJEgyE0nx/dtqYYfu41xrbvITxWhWvyAmw94xzyosiiugjprsKAHLQFmStfea5IQ5Q51sXxH/2G2Evb8FWUYSy7jJGQn5DNHDgNSycK6cFqFDsWy9A4qxzFZrO0YIR9F15Ny8FdRAashcer6frppl6EBx/hwqceZueDj9C88w+Tjgvwi829bOsZZ2Z50LXr8IZumFQP9kGgDEEoGLZ2tS/k4EXnc8OE95cFZbra5/NMXTPLhq10maPZyZdbE8jMkQSzXnqMxPgnCFcXn7Pppqvpf+Q5l+nyRi4QcAF/fTpPRoSSyjDdvQmCqQzDGzbjLy91Bb65CaCuat1ajpWVcdCMcHw0S2ultbjL2TRr6hV2vfokeSedUnvq2oTTCXUKTVd4xQpKtmxlcNGSSe/PJ9Nc/sBd7D9jNSllHiFHs2YYbooMYKxkBrmwxcbm85N7kgXqqhBkiTTQcc9jrN7XzWCjlcbxjrnzbzib/eEg4cdfASBSNtn/bUaym8yxLvIXnguYVM4p1lcuXjF11aITJfNnc9GBpyzvtjs2YUoyclJHSecIATPPW8qhP+5HrGqi7kQX0EaFvRkpCUiUlAcnHTP36iaiF71/2nOGszEa//RbutddR7qxFb+dZgkeH6FtVCOWKaVUN0C0xrgvIEMKTDt/peYU3vHgXYzNtry1KoY6qFm5kNV33V5U5TddhEpCkANBELn5P79FbNankaJhsrpIcCyFYHt16brJaFUUMZ5h9roVlNQUa1fMTdsQ51Vh2KkwyYTShF3Fq+Qp6TlK5f6tSPncKS3+vspyDNG6t4Kuu/0UAdJ2wUPbJ28mlUpRVTV1CyY5EiYy2I2+dzO+S26m4/Aw5ZVhkhmFoK31uvEX3yMvyWi2n2Dp/+fuvQOkKs/2/89p08vOzPa+C0sHAakCiqLYFXvsUdOLNe1NfaNJ/OZNN6abGEuMvbdYwAIiiCBIr7vssr1Or+f8/jhnzsywCyyo36/v7/5Hl2lnnnnO89zPdV/3dVWX0ddQR7dswxUcJOQposrw2JU9rgL/UHtfJ4Po4ptiOgVqhiev/RpRl5XJIvh3dZOxOSgpd7Nw6ThefXoLvV0h6lZ9SE0ijrCgBIy1reyskzj1jEXDfjN5xy5K25qJlxpzeYRDl5Zn84ZUmFyML3GgrdNJ7uL+w3fVHSoUr5vA1jX0TluAf9s6ZOcplJx6Ap7JTYg2K7ayYmxlxUT2tdH4zW+brxMN7bZEPE1U07W7FLufaFkNiteD1DoAxm2cLS9e/M/fAxDy+vAe0H/nfeMmcVxfhA0Pb2BMk36wiZXo62DKORzpFONRsNihxknDn/+AeBg0dKQ43Jw6UninT+DV409gyWGe8+lPuuqrmfR/vkH1lecBesfQ5V/USwUfvK+frkQ1Q0cwWaC0botFCXt9tA4liB/UPackE4QTGVStUCZieHnRyL4NqD/L6YobCFVqMEi8s4f+le9z6SlV+GZNpas9yDuv76aqzkcinqJnQ+HJJCPLRFMqY7d8AJLE1ulzSBqJVNjQ41p74lJWnXw233E5CG/QT1NBXwBBEMyOxM7nlhNr7cBxxc1g85jvL2X0Ta/IJnPl0rF0tQ3xVluIrQbBNZ1H3qy8+AwUv5fW+55i6AP9c8oSCi8tPofPV3rJZFR2b9NLCTbb8KnyxD/fp687zNVfO4HKS8/iwPoegkkZrXE6reVjUEURocnPkgtzZbLOUIJAlrPTMTK3A6DbOFn1HKTpFGvtAFEkbnFTt2cHA5PmIIsCSlbzRhCJqnoJKf90oye2EgN2Cy0eO3XBGCnDeiNjXE+oTifrDnX0UJGXdHW/uor+le9Td/3FbB0/jf73tzDpA50LE/WX4zzowO1xWGmYWsYjgynO697N+//1G0qWLuT4+3XvzCxKNKnUyZkTAixu9HFPoJSaPz5A7DsrCd95I66mevb89p/s/7thlm5AGzGLlWgyg2MUInxHE1k3B49VIpjIEE5miBoITLa8lR/O11ZQu2kdNXt3ELz5fPyxhN7IIYp6V5aqoaoacUtubpbkmROLooCqatSNL6OzTS+L7fnTw0zs68ByQYCYr6wAXbUU+/jMZQt5Lgj0JPFW5bSMsjFR7OHAutfpmT8fEE1y/dGEIIqIFgtCJoMmSbQvPNf0eC0tyxGYlTXb4Asnc/q4AHZFYkG9F9ukEkrFKMH7HuLD8pkMWbzY2g+N5gLEbRW0LzzXLE9JGQ0loxJvGWIMsC8Yx2MgFYpFRrYW3odWwyfOdWAvE995nCUv/WlY08vhwuZxQA96V2Y8BZpudLx30RWk3u3EVlNMXzSFJAlsddpI2Kx832uj5e+Ps/+fT1J3/UXUXncRitOOHI8UaAO2emwsvu+3KLEwGgL9k+YgphIIo9BMKl26kNZeO/RBsqgYxZb7LbOJiZZRKSs7tAyCaLfi7NiHq30vUfsNPPHP97E7FCafPRZB01AVBXdfDw5JIhrXf1t/TRmRX9/O4y9u4St3fpuY08W0G14GdG+9bHIQ2PwuRXs2EfeXgaZS+c6LdEycQfLia3HYFCRDXyxrCZWlafR2hUl5ShCTcRPRPfh75YfdbkFO5snNjCDyqdnyk67Cx8cVO8jiw0Jo9G4b+aF43ZS9vwJ36y56T16AYFEY/70vD7/WqjIQBDRBQDlziSmYm4ilWP/ufnzxFEI6Sen6N1DO/DxKT+569oyfzLgPcwfaZFUlTa8+C0BleyvxoUFSVicevz6OQ41TcHa1EKnSKzMLlzax8pVdlK19lVDNOKIVdaRjEZxd+5Emjr5zETjsnDpSVC9dQGnpWODQe9unvryoClBz7QWm2nA2BvuiWK+7FsuDf2Xv+Cl0hBKmKCiALY9gmVUFzhL85GSScDJDJJnBERpi5qrlTFm3iuAwpMvgdLkLka64YS2SGgwS3LSTTV+7nb2/+ScAZZUevvr9UzjnM8dx8XWzmdKyCjme27Q0QSCazLDgtWex9fay9sSlppZKolVfQINFfjR0Pa9kVvXcU0TAoZAxbqpU0FCyNnhcc09qpKPEyuKnHuDGH91E7co3mdUU4OyTGxGynomqSrSkyhTxVHweJt5+Eyete5Ixt3wWgIZ33uYrP/s2UiJBJq2y3/DWOpjTBVBkLCSDfVEkj6GSbCQDaYcb1eYk05owlZM149TcVaVzeoYqq4e9Zzaypd78RFjLZHhz9kW8efwFxFJp04BVskhmeVFCI6NRIDiraRrX3v5Nbv7h19EyKbqd+saQzFrEGIufd5+ubj10oLAk2PfmWpr/8jCDe1p5omkOQ77cKahv0mzqg4UIjsdtMctPgwYKWdC9mEwx542XGbdyBUub/FhlEVswRqlmJbGr3SxnpVIq+0++iM3X/5DeyfMAqG7ZQ/vG4SWGjxpZTpffuO5wIkPK4AspeQrNWW5E0igZ7h0/lXAiQyYaI+nWCecOr43rb13EtTcuoGRerp19aCiHvJ57+XTOvGQqC05roqreR+OEErM0nbWXsuS1v2fSKvaMisXo5HMFhqOfWU20PkPmJb+MeTQhWhUE1RB53LdFb9uHAtFGp1ESdlgkzhgfwG2VUSwS7p5WBpe/Q/W//8CEh35FSUMZyYEgjvjIKtdJbzGR8joEY5OW1AwpUcBqlBfj/THT4ddilUy9Kk3UDX3r+5K0LLkUORFD2rndtPAZbdg8uTGKVDZgLQsw6c5bKW8oxlvsQNQ0+iIp+qNplL5+rr/rDt6uXcS27/2ayK5mU9fO4rRT98pDRAwR2u0BF3udCvaBbjKaimS3Urb2VUrXvzkqzSRbRQnMmoAUCVK8cSVKnl5VxnBnUFMpenoO3SwgCIIp2RI00G2v30G4L4gmCKT9PlRfEVImg9voxCutL8drk83OPDngM6WB5CIPmnHtqmJBSiZ0P8y27QjAwPFLmX+gH7ckYE/o49DfMsCKF7cTNqom/UaiIagq8igOTnaPswBdE6zD12HBmScNIhXuk7V56GvK4+FYQva6ETQVe3cr4QvOLTjMDm3YyobPfY/99z+NaFGwVZYiqCq2TAopkUO6evYbnY4HPsQSCaIUuVFsFpzte0lHBth+/oW8suwKAJJ1tZw0pZLytmYAXAN9uPq7UUpcNB1fhRQawL9tXYHC/vS5tVin+inevBpB09eyjKFqkHVxGW0cbk4dKRRJ5Nrjh9uc5cenPuka6Imx/Plc7TyVzJDJqOzY3MkLz+0mFhFIW6y0DsZZP28xIa9eeswS9nx22VQZj7n0wbcYk7g7nMQRCrL4pSeYsfqNgvJiMJ4mnMxgk0WKjBN3Fula/KKOPgQ37TCFPxVfDsK0O3J8EznvhsgoCs9e+UWiqQwWg3eUtNjIZI1tO3TybUaSqd29je5Nu8zOyJjdScCpmMr3acO+RjD+nji9gkZtkAmb1iFn0njaWs3PtWUyuBMpZrcP0HvihSavJCvmaC3x092kozxpi4201Y4gCAVER4t1+Ok0e3ob6IuQGYGncHCEkxmSGc1MWuXIyCcvVdMoW7eGcx/6K0N5GmL5AqOxcMxE/WRFNon0Rf19XP7nXxDLF0NMpFAMP72UxWpyJLJdclljY3tUR/WiHYU3XbRZL/vusHhIpFWieYKDScdwKw6f20rAqYCmMZSRdFP2vGuPB6MsfO05Gu+/j/bHXtYXrHc2kqybRKS81uxg7EtbCDboZaN8gmxfXhn644ospyubLIYSaRqf1dvlS1ubyaga8bTK9Y9t466VrTCo/y4xp4tQIk3RrKms+6x++k1Ekqx8ZSf79/Thm1Jr+tdJed2/TZPLmDyjCofTwinnTOTCa47HazHuAyOxyke6tryzm7/8/A26jE7Kg/XwAFJOD0MNk0jbXYjpJN6i4SXI0YQoy2iGsXTl6pdQDK89u9MCRnJU7hzZlqnq0rPwzZuOoGnIyTjOMTXIbgfVLz6Aq20PpUOtw16TsTnMBgFRVUEQqKrT55UYiuutgIDFplC0SEeNo75iYtEkFtFCPCuIqarDPFqPFBZ3bn0S0ymsZQECC2dx2S1LuPjL84lYZKL9Ud5+fReBoUGKugvb97Prmy3gxRoaQDJ4VtNWvYaoZlhz4lI+mHsiVr+Hks2rKd7y7qjVwa02hdLXH6L8/eVY8pCuzmd10vne3953RK5O2lNEuKKBkCHMuyOc4q7mNL/58V30/O4X2GZOMZ/bPG4yRV4HHpuE2zgg+OtyjUrOhmp6r78GgKS7CM1AdZ694gtsmT7XRJnGrV1Jxdtv4Gzfh6bB+yubGeyPIitirgFC01BGwW1zeJ1YQrnDiugaYU7nWa2pB9muSaLAv778LT6Ys4j2ZcuO+HkjhajIaDYboqbhMjpjM/EE4d0tdDy7nK7nVzC4Trd9s9foCYcQCudMrWMpEoa0iVhXTvl5S3A2VKM4rNS//CC2batxVJXgDOn3tjxzKkukEIKqMugrRhNFGl56gHd8Tm54cgfFbz1B5eoXSdtzc/dLz+0kGA4jgNm4Ixjei1b/0WkcfhT+VyaWILj58IfiT33SpWmQzisBrnp9F7/5wStsXKsvXtm8vz+WJuZy09akL142o4VXECAZ1P8/6i1i1sO/4Y0v3wxAZyiJd1BHckq62gtQlY5sadGdS6CySZeWBwOnBoZQJZlVnuk8+MfVw65fdDlMGYZEkQ9NFHVxVIM4WNJ1ACFoTJJufcGsaN3Hxf+8m85/P2tuwDGnk4AikqqfSrwor0xjCEnaHRYqKvzmv1vcTja918q6lftwbmhj/oF+PEbzQKimiZprllF25knm8zc4y3jsuq+z/9TL2H7lNxnoiSIIAmMnlRrvP/yEZSZdvVEyBlIyTLU+HqblHt0yojeSwhkcZOGaFfo1RkeWkggnMpz98D9o2roRywOPmP+eT9aO9QVNQ2JJyhHpJU2joq2ZaDiHLIUHs1Y/VsahssRQsc4oVjRBRJNlvdvISJxjnTmNNIBos86FeCOuL2jxvO6Wgw2VAYo9FkqdFuYd6KcsbCXp8RdcezKYx6lrOUDzH/+Ftmkr0mA3zq5W08hcSKVQwoN4926mZGPO2ijYW2hS/nHEwUhXKJkp6JqKJDMcGIpzIJjg3dYhxKB+Dc7QEOFEBtlpJ27Tx8Vikdm+qZN9O3tQRAHR2GkCgdz8HOiL0Lq3v0DQ0FKiI7zZpCuf05VfVZEFrcAUNxvN+Gg9+WL9ulIRBPHYF8/sIUOVZLPLV5JE7D3t2Lta8fpG5omIFoXjH/yFnnhZFIpmT0OUZTwuhfpX/kXtvvcZ8/Rfhr1ONb6zpGpYJYGaer186s5TNLfaFIoCTlZX+WltKEZxWGDHu9S88aT5nDdnX8TQhq2j/p7OMTmdK8tQD5Zin/m31yYjCSCFE+x+dz9VI8hrZUn31hId/VUMnb/q3VuZ0dbD4PTFbDztXOyl+uOT7rwNuWj4QeXgyETj+O+9n0Cv0SmZZzKd33Xt9/uHvTY/It+4jeYzrybRrt9zfcb1IYo4fW7qlunsmwO1jaz+6s0IgkDm7n9wwYN6c5G3przg/bIH0UhlI+HKRnZNOo6Y08X2Uy8y1yNnNISFJA0vP4Bb0ue3v8RJkT+XJAiaekgPyvxweV0EtujEdDkaRh7Bb1Hz536zwRkzhz1+xWcWsfnyqzlndt2wx0YbWfRo8mZdkqLvzbWsXHg5zX96CMDUegxt2aX/d9U6ZEP0eag/SiaeRgOEC85k+l/vwDtjErLdigBM2LSOWX//K9X79NeKT71A8zW30vzQfWz8xrfA40YAEgNDpFUN2egaTefxudqDSd6V/ewbN4nSDW+SlhIkB/UDgj1wdJyuI82pw0VkTwvvnPrZwz7nU590+UpsLL1gsvl3JqUiCOA0iN49b7zHhN1bzMfThtdUFumKJFWT5J202ShePJf4+HEAdIWTlLflOBfBPHXhdqO0WOHJ3ezZpEs1ki5HfRXJ/iE0SSaChYHewiTikb+t5e3iE0zNovAEQ+BP00xroAse+BO+nXrVffWNt/HYdTfS1ajXoOMdPeYGHLc78ezpQSurZ/eyzyN73WiCgGRsjJ0HhshEZSKleunO5nawYfV+3nhxR4EQK+gE7YoLl1I0c5L5b22qTOuYCYipBFI8itXYdKbPreWUcydSWjkcoi0KGF0+fVFUAymxGC3tgoEm2kND9Bjk875oiorWZia+vRwAJZUiHhlOrs5HHC1bt5v/n857biIcNbviREkwk72skG50KC+xMf4/Y7NxUaWTaJuOTmqyTNrgoGgaxMsbUSWZVF6XopbJmDZM2xU3frtMyXHjWX3ymWy79DMES4ZDySVeGxZZRDQSg0h5XUF5MZFnghvb30G8d4D+CceTKSrF3tNGykD3vJFexj96l76pWix8MFdXlI70fvy2Slmky60mOfG1Z/HvbzZLiBlJMsvxoCfFsiFAOnnDGkJGgisav1vTtHKWXjCZk86YQPDl1SjG/BvIk0H5z5ObeeSetSx/TkexB/ujvDf1HHZd+BWUSBCLmjTRSwCH0WVn72rl+Dfvw1c8PNn1eXKbsdc6vAP1aELJUgkCFYh5ZstjXriXMS/cS+W0Q+u5yS4nc568m1M2v2CKMtqrDJ5IVxdWwyhbGyEnlDSVsf1h3ntLlzHxJFJmGd3utOKwyYSsChFFJq5q+PfvwtHTRu0NF5vvIdqHE/oPFa6mej7wylSveAJ7KoIoy3S/soodP/0Tg+s+pNgmEVUk1Ooi1MjwTrGsaK2l2EewdhySVV9/955zPS7FSVU4TkORHWupvpEp/qJRIV2CIuN6+jnzb2te0jX9L7fjnTGJuc/95YilIFdTDf15yXs0L3t3W2XqzlxIWlGo2r+XyqS+Tsh5z7dWFAplWvMQf1VTWXna+ZR0tOGM5zJSxW4z50wio/92voADXyA3ZwVVRVGOzL1zenSuXNNjv0fZthqxqWHYc8Jnn2GKYOMd7kJw5vgAPz3BTbV39PNiWBiyRtFf6cmorbK04GG7Mc8Di3L8XUtYX6faWgybPkmkKN9aLm+e2sMh6vbmupPToQg3jPdw5w0LEb363mM3DulNF5xC5cWnYzH075KG32MioxF1uBESEWKpAdKxKBlRxFGSS0pHEx+lvCj//0GnS5KkArhvyXmTuPWO05k+V09koh29VAzqgzT7rVeY+uarAFjj+kaQSKvEgxE0IHzcKTzz4Abcxo3TGUoipXOCcdpQiGRaZXXLEC/t0NGOyrykK8vpkg2Rual3/YBU36BZ5pIO6iyRZAFBgMevv4m/fuMnOHt7OOff9yCnUubpH0Awbtg2exGtY8bjNCZwqquX4sVzaV52IW0NTebnhwMenA3VZCw2BAQ0WaR1Xz8te0JEDYNfm9fJ1NnVzJxfh2TcbNkyT1qAjlCSFXv6TY5ORzABmkb9qw8z8aFfUl6rQ7L1TcXMnF9XcErLhq84V16ctaiez3x+DuMMZCzLQ0hb7cQNpLE/mqKsPWe1ErfZCfYNN4/NRxxtbblSmpZKYyn24Z05mZjbQ9n7K6h7/d/UNPoLxVGBaCiX5EQG9cVUtVppHF/K7EX6wiUIEnd/739YU6lvCAOWCiK+kgIPz9iBbr0d3+cjbbFyxvgAjsZaVi85h3fnLEYyTs7pvIW63BCV9Bj6THrSNTLSFd65j2QaECVUNYOYyZiq9GmDu7K/cRxd115N3IDTY4Ybwb7+GC9s7/1IthXZyHYvVtx+J7Pe+A8nP/8YGSPpeuz6mwgl0gS37+Pq3/+Ukx75J1Io97tF+4N0v7KK8hb9AFNVV8S02TWUVLgZaMkliEV56FDW6mP8VB1JsNkVkpKVtMPNuCf+wBJ1e8F9bzeSLtViQ/aMjJSMH+vVuR6A3/8RNhggZqCj+0+9rIDgPPe5v9D07c9TsezUw75eEEWTYwYQ2robgPiBLv2wEA+jCcMTw5TFhi+aIGpIeTtTGTZWFrPLlmHMuQtJ7W7m6rt/xgn3/plQIoPPQIIab7wGq4EmKaNAkvJDjIUo2rcFwUAie99cw8Zn1nLvUwcY2zHEoM3Ch343Srd+77rG5zb+LNLlqKskWJtLRLPK+91qlPFaiKrLzmLiT2/FM3Xc6K7/7S/vAAAgAElEQVRJkU3phvu//j1seRunZ+p45r90D77ZU/EcgadUUullXYXPNE2Ou6xMWv8uV/zx5yjPvIjschIdqwtZV7fqiW7+xnlwcuH79e/0f+/rQAwPcMLyF7j6j/8H//YcCVx22BCsFjIWG0lDyLso4KSoOG8N1VSUUSBdVotCSlZQoiFePucirBecNew5Nlnk7dpiVlf5kQ+RWB1pnI4UTd/W5Ueyv597chN1X7zMfNw9UZcWmvTzb9J44zXMfe4vbJ13AgAdBp8rIYu497cQbW5DU1Wc4/LmkTF307JC0dzjAH1tBJANTpY9EqY4GeX4O77OtLt/ROqLV/BupZ9Y21YuuP8PyMkEr1xwJXf/8De8P346a09cyt9+djdjb772qL7rRxkra4mfqb/7/mGf86lPukYKQRQK4H+HsXg17NDrys9f/3VWnXZe7vmL5nHvN39GxuVn19YuJj78ILZImK5Q0uRWATjDQXoiSe54fR8fGHB0nS83ibNIV9Y3UXLYiHd058pcB5XWLrx2FrfcsZSY145dBue27ZS3NZulRfP6Egn6oin6YykkAfy1+kakdvcRWHg8m884l/a6MVgNaDvotiE57KQC+sYlWGUamoqZv6gOZ5e+MHp9bmbOr+OUcyciG+KOgrE575s8g1deXs+fHn+Pta1B0qpGVzjJcWtzJSxlFHpQbo8NWRaJhpPYHRaqG/x4jdNcxqEvXCmPn4SRZPRFU5R26GXhd677In/8/i+Ju4dP8KFYmhcv1m8UOREn3q5vLK5x9Zyy+QXmv/g3Esk01mAfzv4OnC4rFovEuZdPp2SjXrqM56NJBqcvY7dTNzbAoqU6kihpukNa3GWlpELfqB7+8rdpvzRnM5ItLfZ4A4gCnDWhGK/RyTkYS5soTtKA7tOCYD5eP0bfxKLldaTzkq5UnoRGcON20oatjChKBGvHmzzBVCJJpLyOjpMuoq1yipl0JQ1/xj+808bvVrayo+eji88mMxo1e7Zj3aYji/ZoGDmdIlJeR1qxEEpkiPUNUtLVTlFfD0pe0hXvHyK4dReKAd0U56FQ2VvCu+dDNC2XZMw5sZGvfv8UJhynI4VWm8xSbycTHtK9MSVnYZJvd+n3YcZiLUCe8qPk1BNIGzpzpbXDuxuPJg6lAeWbPZUxt1w3KjmG/MiW8ZLuItZeeRMlOzYw7Z6fDHtexOOjqzbAnMlWBFk35UrYnbTXVuOuCCCkMyTHTEctHcv+vX3sPu0ymk9bhrU0QNLg2Sne0W8aaipNoKuDjup6AhfrwrYWfxFSIoaGgN0Yh65wEqdBLvdMy/kgZpOukiXz2X/VJWwo85LckystLXjyXuqef5bSpQsZXPche35976gPCZrxPQRFPiQ6ljmCVpf26DOc8dzjWDIqcUnkvFmVVO/bRXn7flzGNVqvv5yUYiFg09fXfIFLW0Vh0iUZrxksruC+G7+vy9YA01fr6L0cHkJx2NFsTrZd9S1A15Sz2mSTjgF6cjGapAtg04XXsfXa79IYSuAegXxvlUXmvfoMV/3qe5SuWDHiexxpnI4U2QTJUqKvaYIoMvHHN7F4/dPMffbPeKboyZi1xM+4734J3+yp9BynS81kf++4JJK64WbemncpmqrinTmJdxefCehyLfd9/Xv85/af4zIM0ddddrMuzGoc1uaveJFrbv8mPa/qEhiKJBK0KSx49Wkadm7lxttv5cv/5zvU79xiIvceuzJqDmE2PspYSQ4bVZcNT4zz41OfdA32xXjp8Q+H/Xu+ArndsN+wGMnMzedM5r9OrqfMKEEOZYQC7k3J8hU4IiE6QwmzzAe6HUvzQJy0quGySNy4oIaTG3MnrCzSpBiJWnhXM5G9rSa6cjCfSRQFRFHkxwvKuW2WfvPKqZR5nebzEglWLfsqpz/6Txb5BNyVxobRP4CWyZhCroJBDne27Wfyr77DwM/uQAVkl5W6scWccPp4HN1tqKJIsT/3fRUjQbUYCUKgq5OJ/7qfko42nt/WS084iapB/c5cmVYaBR9GEAW8/mwHo57YyCO0NGeTjL5oCn+33iyQrteTlFAyPez5g/E026fP4fHPfo2H/+snwyB+gJjNwW9/fBcDD+j6a6IkMn5qOfawjnoWJF1DhimxQY4WJRFJFhGAa2eU85OljaYQp2IoJ2fDVlZMeuJ4+gOlzK3xUuqy4LXLLH3iAb7+028jAIpNxmN0jWlSzqR3WlOAlCiQcnmZ/p8HzPfMHCRhkrLnFvnWhefiMBa44IVXsu+sa3EKCq7N21n8ks7dyRiba2dYRyd7I8du75GNZFrl+FX6xrHr/Au495b/Jl43mX1nXUtNSiYYTRIP6mNatX8vb52+jIyYM3yPhWI0Pv8P4l078OeVURzGuNj7OolECr93vkipIAhM+/ZnmfvYXTR85Ur8J8woeG7290k7PWytncNIoWkaIau+QFdNqRnxOaOPj1dMcerdP6T4lPlM+duduEUbQ3UTCh63GkKkoqYh2kWit30P7y79fvQkUqb8ktVhZWD88cSqxtLbHUYorqZ/zkJi+9vRkoYljX30UhmZWJzTn3qQiv4u5tx8FaCjVkpEn2OWRJrSSBxXIoXT4PF5puXQqvykxOG10+O0EZNFpHhWt89BUYkXLZOh46lXaX/iP6MmKgtG0uWKHdpG7OA5dXCkNnxIidGFO2S3MPmpx5iyQbfyqqzR19mzr1yCffkTnPu1CwEKkNSDkTnJ6B6UNI0zH7/PTLqybiBiJo3FYUPKS6i8RVaGNm7Hm1f+Dnm8WEZBpAcYMOgmzmQK1wjdqc49e5i98jXs0QiSNnJZ/UjjdKTIai3KzkKivq2yFN+caSO9BJvLRn56nUIDVUVyORBlGUUUsBrzxF/i5WtXLuDWZcch5MleiFYLjsYaesqrqDE4X+uv+RbhHfuQ0BAPSpBssSiqPXeNWc3Do4mPOlZHik990pVOqfR05k7V99/9Dvff/Y7ZLadJCk7jh8smUBXlPk4e48Np0b/eUDyNklcyUi02lGSSrnCyIOmSMmnTb7HMbeGcicVYsqKb/3wS5ZbvMu7D91EM6HzTl/+biT+5hamP3A2AwzV8set8djkdCy+g++IvACCnkwz5i/nN7b/He6XeTeLs6sSyaTNjtm3i9Jk1uFx2ok4Xgqqy/76n8W3YgJxMEurWT1kNW7bT9fxy4l47rzWUUrlQP91nT99DjVNYvkugu8NYOA/qPBQEgVD1WLxWH2v3D/FBuz6+QV+AfWdczdbLbyXUPzr0JHt6e+s/O1n+/DYG+oa/LmmYYvYNRPAM9aNarTgTCt54kvAIPnjZ8uL+sRNpd/rIp6Rt/sbPebXhFMQ3VlITihPZ0cvQQJ4kh82Qg8hDk5JVVbx00TX0nHs2Lbv72LWlyyT+L/JZ6X5vP237dL6KrKqmGTqAvameR75wG2+eeSFnT9Qh8CKbzJQN75qJvM1pwWXsikJe92a938GQoaHTHszNv1Q6QzzP2DjtyCVdGZsD/5f0zS/tyZXjrHnIUndxOaqm0fTMU3zulz9gsPMgpfxjiGRGwxnW50t0hi5OGjUSg0BGJLJnP/G8MRU0vdsLID0YIh6KIidiiIpagPh6mvRTq6W6gvLyQlLySBEuq+UVxrO2r5Abkd9Jm5ZGVpSPDMVy6vfjPmLS9TELWLvG1jHroV9ROXcS+yqLKFn/BgCzI1so3rSKcsU4tKTT+Hv1g4kvqSNLE/tCLHzrHcJ7W7E5rFSueh7/+hVEje4sxSoT3tF8TNdlbqLRmGkEbq+tRAkPoqRipONppncNMbtjgK0z51Fx0en45k4nsGgW1VefX1B+c8sCNXt3oqSSpEr0hKZv8lx8JV4TreYoSuGiUeZb9udfHfI5R5pTgstD1yydLB+o9uIpzyGgFgNBkUWBU5sCOI05ljUtDpw4G2tJIak6K12hIJhSPwCRCv0QmfQGsLps2GwSlat0HUlbIszq06+n8ye/5tTzdR6thjCqgy1AtNyDtWUbcx7+A/KWbcMet+TpdB0KBR7NvXe4yHaMDqzZNKrnq6k0E1b8By3P9ipjyLBkJYTEeJz6XXrTh93nYUF9EQ1+O9WXn6Nf87mnADDme1/hga99t+D9V550Jc57H+TmH9047LPPv/f3nP3wPVx4393M/uH3iOwd3jF8uPioY3Wk+NQnXQBpg8CrqRo9HUG624M4nPrkUmUZpyGymS3bvX/FrWy44bumgGT80WdZsOIljDdBSsZRkgmiKdVMunb88AfsGz+FlgH9b+9BYqBDH2yDjVtwBQdZdeq5uWsbCpHQ9Oe6PIVJ15o39/L8BwkGG3NtyXIqBZqGIIl4yvVNvHaLrg/VM24iM+r8uK0SYY/Oqdr23V9x+r1/xBqPmuMQqhmLmkzryYkg4PdYiUWT7N7axfg3nqLxJ9+kczDDtg/0cbEcZOHjikbonn4ifslBcTTBA+tz+mAppxvV7mK0jV9ZnkLL7j7Wv9Ni6tFYB3pIZ9uLjaQr3nIAQdPoWXgW1s4Qc9sHGHph+bD3jLS0M3PV6ybyltVP63huOW0PPkMmFicTjlAejhPc0UPUQHrWv9NC76yT2TzzBJLu3IIY8xaxbcZc0nNmseq1XTzzrw2m9lFXe5CNa3I35eKXn2bRl7+AanTIrGsL0hVJ4S/xMqtaP3ln50Y26XK5rETf199DzkvYZFHAZpDpW3typ6feOXP54/d+Abd8SX+fPNkJEegb0scwkeeWoBkNAo987hbeXnAqQ/E0c1e8hGdogOSjz4700xxVJDMqVkNc2GpwKCLOXOITHQgVJLKu4AAx4/tnBoMkjCYH+aCWdaVU3+T2V06hs7NQbuDgeH9VM4/9Yx2JeNpMirMhCAKioWg/MT2ysraryMEFV8/k8i/MRjrE5jPaKPtQ91W1Dh47qXakSO1rofaDNbSfuIzmpVdQlAxSvu51Uu/qXLSKoRCe9hChykaq/HmocVkDmVAExW7Fv2M9nr2biRrIoy2TJHDiLNyTxlJ73UVHdT35qELM0Am011YgAO72feZjUUVi24y5HPeHH+GdNp7Zj93FlF/k1Mc1TaPxqs9yyT9+x5y3XgFDFDTYMAnF4ya86/AisSOF6NAPUMJhErUjzal8aZF5Yn+BRpjsHt6MAbmkIB0cLmmTj/RMfv8d8//j3lwyZ6utRD5lIRsXng7Azh36d9/s8FNaaSQcambUSVdFy26aXn8M22AP7pLh8gdWT+57HCrpOtI4HSkSvfqhVBilDZkgidQ9+gglO3IG3JJm6P4Z60ty7358ffr95cjT0vLOmMSJax5n6l0/ANCTYU1j88x5hZ9hfNeYo/B3VJJJfH09BLo6kPe1HHJMDhUfdayOFP8rkq6U4S8Yj6d0rzarbAonarKCpVVfhLP8rGjzAYY+2IbTKHVlduwhYHROVYYOIKZTJi8LdMFSh3GjZZGug5OuvrfeA2DxS0+yedYCEufqtejkYJBQUH+N+yASYzScYDAumJwd0MsHUiaN36GgGDewK6TD9u5JjYiCgMsq8cwVX+D9P/7J1H6R8sidJRtXsvsX9zD+pltAVfHaZAZ6ozz94AZWvr6HqKHmnl1wbAbJO7uNqbKCvecAGhrWjEpv1DCF9QfQsoKjo7y5sh05NrvCyWdPoNTgRmmiaPb5B8eNR02liQyF6S0tRyjWF6jijStH1BVK79jN4peeZPZbr3LG4/ex5Us6MTHWlyNlC837qVy3goqB3XgMAcD33t5HVA7w1rmfIVaXa4+OGfPHrui2TADFZS7qxgYoq/Sw5NyJZqOAiICcSBDctIP1132HV9/ST2JnTyhGNMoi2bmRLQt6vTbsB0tlGOE0DgcDj75gejfGUyrHdQ2xT2lg6YGVVN14XcFrune0omka0Q9yrf/ZEnbCZieV0WgeyCG06Z6PA+lSaWtoQpg5jbE/vp0bfvlDUnnaYImhMKm87tHTn/qXWaYhGCIcV9l/yiWItsJScDrPo/FI3Vp9+/XmlZKNb7P4pOFIVbYc6Z8xYdhj2RgzsZSq+mOz8Cj4rAEdbcpq/nxcEdqxlzHr16LJCpoooRpdYYHefUx46JckY4NoqoNIZQNFVQGKivSDnKttN5LdhmgYy8vpFMlufU3zvv02ks3KguX3M+nO24752nqX65I31tIAotWCc29u/olOK5+fc2gLKkEQ0PLKcva8xhLZ68JylAKVAHLVkRGHI80ph8uOEhpA0NJ431rB9h/+jspLzqTm2gsoXjx35NfUVTHhJzfTeNM1wz/P7WDco3cx7tG7CsBQNW9jd01oxFpegmasD64ufX+qeO45tj2hI0b+7kObkR8cTf/JeRR6R0i6LHkNG4J1ZBR4NJ2Sh4vpf/0JlZeexcwHfzmq5wuiiOZwULHmFbT6IjqdVpwYptbGXmtx5PZL36zJBa931FWaZXKLLDK/1kvoq19AyjvUiYb90ZaZ83hl2ZWFrw+HzG5H5RDyLoeKjzpWR4r/FUlXOpt0GcmB3aHk2skdTqS5MxEzmYJEKjUYMuHiTChC2qYnBxbJsNQwULFHvnAb8pvPYje6L1qNpMsnZOhfvcEkASa6Cjc20Zg427//W1pf1xMyl6cw6cqa06oHqQSP2baJ8377MxLNbdz/te+yc7JeznEb3CW3VSbkCxDOaJDJkLRY8RoTLJNO4jdOD10zTmNuxwBSPInD4K9Fw0liBvKTRQOzIpPZH1uVLZRuXMlGewa5Lgefl1cXg5F0iaM8hWXLi0UBB8cvqDdb+ZPeAFa7zJ4iJ5s/cznIEjtLa7n/xh8gTdTFWZ2dLSQHhit1J/v0JDRU5GPixvcIv7YKNZkq4GkJnd00rFtByaZ3cBpl3enzarFPLCMjCgX+hqlN25jxzgo8+/aZSdf5V83gkutnUzsmwIz5dUycrhO644aA7qav/pjul97Cdd9DKKLA6eNy4+S1y2yeOc9EqJweK9d+dT5f/PZivvjtxQXfJWvJonb2kTII8Il0hrJogkhvhL7uCJFQodBmz40/Ih0Mk96XQ3RUWSFeVIJFBdfgALt6orx8oe7plxnBpudoI5nReOXCq7DffSdSOIJ3sI98rCkxFCqQ7ABI2u088rlb2Dh1FtGERrB+IoJUiPZm0V+LVcbrPfzi5zZ4iKqkkMiTCjHfq0znV/qXnjTssY87TDmS9HDO4UcJ2WEn6fZTsuFNqlY/T9eLb+qf19aOHI/mTNwTMZr//G8829bj6dlPycaVSHa9iWCwcQrBpumkDNeGkUzmjyWyCIQginimjafSb+jXAQu9aU7q2Kn7Uh4ixEAuIVB7clp3isdlErFtVaO3WHFdeQEAMc+h582R5pTitNP01J8529dJzJB+qblmGZN//s1DIiCWQBH1n7u0QMcwG7LbhSU8iCU8SNqam+vurj0owX46hThORcIiCJRGDS3G/XsAXfh253Y9UU4EDq9aXhB50gru4uHfNx/pkg6RMBxpnI4UjrpKpt31fVxjR6/1JRjJ4J5klE1lRdTv+EB/r3rdA1XOig7XVGArO3TjS8/rqznhc9dx3sN/Z+JPb9HfW5YQ83hzwQr9QBCp0t/bFRpCTqcQbRYkx9F1Mn/UsTpS/K9IurJIVyybdDktptee6CvC99+3IqoZ2urGEGlqQpAkMpEoToNgr0UiZAyfvXZ7BbFAeUEHoW3rNgLXfpHz/vVXEgZXrPz+B1h7wVdpve8pkgPBgm6mKetWIW/O1dYjUf2xg5GuLLdFy3ZPlJWweeY8nKEhilqa0eJJesurTPjcV1lMJJSge3MnkqrS067foDGHE0/2u6i5TSAeKMebSON3Wc3EIxpODEO6HAdZ+KiKhbi/DIdi4ewJOVSgcUKNKfwqjrI7q6reR22jn6XL9JOKw2nB2qGXJRRRYo/fRY+qdySqGnitEr0GR8/e10lmhEVcHdSTLi3gJ+z2gqoS7+gmGY4Rqh5LqLIRa7fOEZHyTjFzT2okUOfC09tJIg/9Ede+z8kvPo5z/QaSBnFfOYjwnxXiTDr1jSe6r42Uw8GbZ13EwoYiivLGsMgm88qyK3n/JL3bS++elHF7bcPmQHbsVYvN1Oqqfjgn+BocihEx1JqzNd20zUmydwA17yydtjvZfeGXmRKXuf63/83O3ih9pfrCrbQdYGN7iBV7husojTaSBiIlCwIDE2eRdBfhGchtnMlQhIGqakKe3MY60DiW4PjxRN1eMj4Xro1vYispPGln5+W4KWX09haKzh4c3lJ97NMO14j+fF3teoK+a2vXMXzDo4vwdB0FiQeO3YdtpJCcdjpnLaFnxkloFoVJ//MtvNMnErhGJ3GLWaTZKKUWrXiOxree0D3kHHYkm4W2E86ke/ZpZIz+idF2wR0qmr7zBXzzjqP0jBPNf5v33F9Y9MKfqarTE934W6tYf+23TeXxkULOE7+1dulSL0ImjexxITvtnLrrVRatenjU16UYCa96GOThSHNKcth0M+pYXPdtBVM77VjCs+QE004uXa7PjZjTjWBTGP/43dhatuEgg7w/J43j7snJ3gj9Bjq5c8OoPzPfIPzg8j2APa9xQhZGLsUeaZw+iVA8LlRJIt0Twh1LULxL1+Gq+5zeHZ7Vk8uivYcKyWlHS6XpeXWV2WAje1ymSO6sla8zq1lHZVPl5STy/D8t/qKjVpj/pMfqf0XSlU6paKpGzBAvtTkUs0suncrgtcmkFQuPfv5WWn56h6na604aJ/NwhLSRdCVFheSkaWTyjJ+9XgdCTy/uoZx4o+3FVwDY/Yt7iO7N3UAAS59+COGDXEdlyqq/t9tbeMrPXmNWUkL58bd45cKryR4fZZcDUcC0PyitLeOFRzfx4Rt7mNl8gLN/eycAcYcTn/FLSYqNYK0uslq2/BHWVPooDzhQLBKKRSKT0Rg0yOxZ1W5HHtSfHjqAf9s6ms+4irFBlVObAjhkAUUSmD9/IgljnEY7TyVJ5JIbZpviqRarTJmhoOyx6bX4UPcAHe19WGNRykXNRJvaTlxGZgTehGYIb9qLiwgW6Qt5rLWTeDhGy9IraDnjKpx9vQw2TmaoepyZlAO4n36Oa3//U9LP/Mf8tywSpLgd5mdLsu5d17K7jw/XtRExHAj6axvZMn0uW4+bzdOX3UDE7eXciYWnMJssYlEkOrxuEg0BahoOLb7nNH6DjMVqanXJwRy/a7Avan52VmMnY3PoSZeQuz3zbYCkTIbtrQP0l+iLvr2jg+Vfv5Od19xGd9uxcZBSiRSe/l72vbGT9uNPpX3emXi7c5tFOhjhwLjJfHDpV9lz7g1ogkDG48FtiOHuveginrzgUuzzpxS8b/bgkUmrRzxBZpHiocYp7G8f3kGUNbA+GFH+JKKvSPcFTTk/3lOv5LCbGlZdN99E7TXLmP/y34kVldG89Aosbh3tlpIJrGXFqPGkafkl2W0IkkS0yLgmTR97q/2jJV1jbv4sc5/+E5JteCPQknMnMffEehSjs9U7Y9Kw52TDUZZLusreX07xh+9wySXjKZqlSwfIbueIn3GocIn6QUBSDy10e6Q5Za+txDf3OJQiN6n+IUS71ZQ9OJYInDiHTbMXAWCt1g890bIy+q7XJW4WvvYcllAIqaeXqrefoXLlc0ipJG5DUqFs81pqVjyOd/foky45ryNxpATClkdtEA5hefNJozcjhc3vIVw9lgkpK9N6Q/T87hfMfuJuU9MrOxcS3X2oyUN3YFvyvlPXCzoyLCpKAW+zQkoT9PoQxtSjFeW+q8/Q/Dqa+KTH6qPdrf8XIjvH0mk1h3Q5FBRFZP4pY5BlAWtHB4GudvrKKrHKAilD58gZCwMKUjRKxigvVtR4WfSlm6jKCLT3hfF88SZ6DdTLETZQF1VFqK9Ba24lNRQivGMfh4u4aIHM8M0g23GVMTZMxeuG3owpORFv7+K0Dffj7+1i88x5nDiuhsF1elmlVLZgN1qlAxUBaitcvL8VBEFk/ymXMOWfP0HLJEm4bNiMz3G6rAz2Rxk0Og+zSJcnD30RUoO4DhhwN/DAr9/i5kUNSA4L7zy2EclIEEeLdMHwhUBs0m8qq9POknffZty7b7L7wBKuf/hpYrXjaF+gNyKEq8fg2lDoU6VpGqJhsOwp9dPjC1C1fy+xtk5ikSQYUjdKKknHvDPJ2PRESlEkBnojWANlRDx+OnuC9EVTBBwKmlEWU1wOUn160vX6s1vZvqkTi1UmmUhT06AvxJrDyX8u1rkcpzX5+WytlynlebY/qQwvPf4h44JRNjvteCYWU1FzaG8vjyubdOWQLvKSxIG+qFledPgdBAdipG0OEr0DJr8OdAVr95uPUTHQjaBpODdvZtkz/wZgzennc8ILTwCw+cpbWPTMH1GKjo5DYzlwgM/9+kcMLTwVxp1AuKaJKf+4Hf/W95CSMfbNn08kpVKCTKykio45SxlqnMzxr79BVTRES0DvxHVZCxHEuHFQ2rWli9knH76kkt+IojTUD3v8yi/PY//efiZNP3akYrRRGZBp603jSoxsVH2sITnspF2G3VQmVxbKyBbC1WPNv/3zp1AU6qHr+Zzukmh0qUl2BaLJnD2ZY/SJzNGElsngc4lMqxRYHY3jGFOLxX/oDSlQWUx2tKRUkvL3XqN66vcLbHuOJux79XXXludkcHAkk4dHSSrOX0LF+UsIbtnFrjv/gr264iN569lkEYuBQvrnTiN54ZnU1VfwluQjbndgi0VxeRwM2Cz4dm00X1e8eA6D7T3Y+zux93cS9Y7eD1BSD68bZVNElp9zCYKmMa9h5PLfkcbpkwiH34t1czMAsqrhd9sITM3ZFBUk4Iehs+QbVluN7tNEVy9iHm1n7rXnsGnpScw6bRbbrtxOqEtHw8vOXnzU1/1Jj9WnH+kybpBUKmMu4Ha7BVESWXBqE3XhVrYvvYaTXn4SOZnAIgr45ukcKedbuoiaHIsR2AAjDM0AACAASURBVLyaZJmV86+cQWWtj4UNRSxrdMOBDtJhPbmxR8J6S7MoUvX033GOrUVLZ0j2DQ7rChKryrEU+9DQS1WyLJon8Wxk/QqF0jI8U8ehiFDc0Wa25quJFJM3rKGvpJz1V12Po6qM04yW4pJADkauqS1BzOtkQxTRBJGUYi0g/Gd5XebfRtLldllYWR3gnYYSsCioltx1hoMJwq1DrHlsE23NecaqhzGuPlyoGZXYfL1Fu6MtiFQ2nr6Jsxn6cCf2WISE4RvpdEnUvfIQ0mDhghpNqdgMI2x3mS8P6eogGTN0iKIhuitr0A5yAtj0XhsfRAIE6ydiiUd5eYdht2IkO7LLCZqup2Zz6IKFSaPb0G2Q8dW8hOhrJ1SzqEFfHJOJNMlEmpY9fez4sJPKriBoGsXOw5Mui4sdiD2tuNr3konGUTUNIa9U3dMRIplII0kCbp+eUaZtTpJ9g6hiLoHRZIX1J5xsqo37e7twhYZoHjMBOU9OIrljL/vvf/qw1zRihIwxV/WE3TpgdBX1tvHGKWewe/J03HtasRr6HcH6iWSsLrwtrUzYtI5ISw+NfSGkwUJ+WdDwVkynVWKx4ZZP+ZF/aHH4h6uqe4rsTJlZNWq+4UeJUou+1nhix16yHSnyicBaKNcMEXBA3SsP5T7/qjMpPmm2+bdv/gxTEsal5l4H4HB//Mhf7xtreKXhFDZ++UcEN+kHQe9xh25gAJ0LlQ3FQCeyNmbHEhXnn0rpGYuY/MtvH/I5R5pT5vMMPtdHKS0CiAMDTFmvN5B46ytZdOVSJi6Yil2RkA1OscvjxGJIS3RU1/P+//ySuhsuwToup9ivSdLwNz9ECPW67MrQ2KYRH7fKIh/MW8yG+ScXuCDkx2jH6eMMi99LRoTWeA/vVPoIHOTfK1ot1H3+Uhq+cuVhBUzzk67SpQsoP38J43/0NcJNufHw1Jaz9NLF+H0uai47C9+84yhZMp/ik+eN9JaHjU96rD71SFd2fU2nMibSZcv78VwGQbN+1zZuvP1WklMnM+FX36DrhRU0r93KWZv2srdpMo5ICMuESnNhV5Mp0kYLvMVfpBNEw1Gs8RgJu0M3ez1lPpHd+0mHI0y68zaGtuxmaK1+epHKSrClEiR7B/jMuTV4pk8cdoIyy3szp3HCnz/P62d9kWvWf0ivwcUJl9WStm5ETqUocenfKUtED0YyZEHwSXfehuh2MfOEOv7y8zcAGBw7laHxM6nMUzp35umEybJoIm1WWSRqkXFbRGJVY9my1IcF3ftNEgS2b+oYPu7HuLGpqmZyb8qrvbS39WAb6Ma7Ux+3eKW+gEyeXcXb62oQKgvRj8FYGjRI2l0Ido+ZdMXbOsnMnAktEHY6SVbVIYuFTgDZeZGx2PF3t/DSjl6umF4G0RzSBRkUi8Sp503i1PMm8cyDG9i1tQu3MS8kQ6qg0W/Hni0PqxoP/fldxk4qY+FpuRt9zEAEuTeMqmqHHK+aKi9a61aKt6zhvR80M3jHPciuHI9uaCDKOZcdR09niKAkcgCjvNjTjyYULszRmiaorkNt6zTtXxJ2O5PX611nH8w9kWmhLpRDtMIfLgTj4GE1OgQzWV7ElElsnTkfqyxy2ksvkByjn1SLt7xL55yldM9cTN0Tm1n4xnKGJs0lua8bTszZe8xe1MCerd1Mn1d7RP2bg/Xk/l/G4OZd4B+PxX304oqHi3zJAakvl9BVn3kCf2hJUidoWDSByupyfOOq2f7Du8jE4kzJSzx8b79J95SF5t8u78d7jaB7KWrJFNGWdoKbdC6OZ9r4w76m5LQF2CpKsddW8uFNPyHVP8i6y29lwev3HbWCP+jyBzP/+fPDPudIc0rTNNR4knhnL5LLgb32oyVdie27zf8PTMjNc9e7a0wjZovDitXgKwmqiq+pFltFCcWTx9D5rm4XpImjT7qkYn0NTDSNHfHx/PKifIh16JPWnhopJv/Pt3hoyUXsbO7nq3fcRvreMrQ1jxbskxPvuPmI7yPKMtP//jMEQUB2OZn+lzsAWL3uAFZRRFJVrHlE/NprL6D22guO+bo/6bH69Kxyhwrj90klMwXdi6BrQ8WiCjgcEI2iAUOVTcQD5dR/8XL2/u5M6u0OXRMJOCmZZOtrm9j5X7+gtEgyPZJktxNBkYmFo3qraSSEWxiPY+kContb8UzW6/ENX72CR6Ii4zdv0C2AtunaTNbK0hEh62x5L0tsl+xWUoAjEiJUPZbNjglYzilh3BN/oDrYh6ZqOrAnQDicRHC50MJhEEUkScTttZGRRaS0yuC8E0kpPlx5yIzDbSn47Ow1BRwKx1W4qPZaSW7vQDB4I0mPnfNOHcN/nhhOjj1WCD5faqJpchkZT5LAX9eY/5bw6p9dO6maN8+6mKKDpDn2D8Z56tqvclJ/EOuWCImSGponTGHMxCaSkyZBy3bsmkhH/XgqTKTLKLMYpPWM1UZJ1wG6Q0kG42lE4+SiOBxACDlPaDNLrC8ud3Hy2RPYHU7RHcnwo9NyC2pvd5jerjC+YieaplHd4KNt3wBjBiO0vrEHYcmYQ46H1ybTXVWDIx6hYftmSoCUow9rRsV61RXMO30SVXU+JhxXwcaNHWxDV/EeOtBNxl64QUzrDrJ53ClUdoXNpMsdDiJmVGJ2J8vPuZTyGeXUzjr6jUWIROictQRLiX4aT1sdbLviG2RsDqb3DNHstmPpbMEmWgk2TGagrAZR1FBVARkBDKj/YNTF6bJyw206QbulpYW6utF1P9ls/2+XJr9XYXDbe1QuGVlt+1gjHw0S8pSv3fVV7Bs/hca9ukZQ797dlMw/nvJlp3Lg4Rfof/cD00rImi5EuhrO/fi7OZ2NtQgWhcjuFlSj3OKZevikyzW2zuxuk4xSaHjbnmNKuEYbnZ2dh51TA2s2snbZV/DNPY5Td72Klvpo3ahWA0kKjx1LSZ4yv3VLTl5DEATToFvOpKkw1uWKBTNY/fZmQh4fe2bM5mJGF1O+dCl3jJ3N1fNGFvy15iVdyiEqFEcap08iBEGgwm1l19AgciaNmEkf875SPkKZcEmJxAZVRSvyHhVX8EjxSY/Vp768mP2RdKTLKC8ap/FXnt7M849+iGWKXpKLlNfRVTWNB/6wWvfPEkVssShyKk3tUBRLS5AXl7fTMWkeid4BE+mSXQ5zMbRHQlx2z29ZM/VMHPXVHP/gLyk/T1fGLT99Ec1TZ9A3cTbrxp3J1pOvQBMErMUjE6mzSFfMSLpEQy29q6oWzyK9dJAyZAfG3fM3IuEEf/vFW6Zwp9agb4Dx9m5TukIwSJVJwZCDyEP98pEuR54ooCQK/OLsJm5aWEuiyGqKTAqKyNTjqznjoimmH2E2jhXpEkTB5NxY7QrFdTkkK2V3kRYVrDYZNZpkTH8YZTBa4MW2s9cobw3q11jp8PLkVV9mx0lLiOcZYcuy/l1FSTDnSDYZV90enVsRHKQjmCSDQEYUUYzTpyWv2ytbXvQU2Tl+QT2Xnd7Eny+cQEVeqdjuUFh0+jgqa/VOmEBJDsKfOP3wHBGbLLB39om8fd5V5r8p0RD+nRs46/zxZncYQKlhWdFbVc/2xacRqawe8T3TdheBHh2djI5tYt/4yTx7xedBEOiLjt4SSFNVU4VcjETpnziLdrvxe0mS2fFbGoozds9uLNEw3r26YG3bxOMQPPrjmsWOYJzcnYdBXSyWI3N7Lr5uFgtPa6Kq/tDNCf83olSOU7n6JSqt8SM/+VgjLxlJRJKM6wvpBuqqSspQ/h5zy3WctPZxaq7MecnaLbn5pgHeuo//ZC45bPjnHgeappfmRBHPlJHLWyOF9TASAB9nHGlOZcu56UhUF9j9iKK5WUHVMqEweZMP0jW0Gjy74q52iv5+n/6as07izS/dxGvLrmBf3ejHsr7Mw9+vPI7FY0ZuAMhHupRDrNujufc+iajwWPEO6DQPx1HIhYwqtuoIrDB47CXskeKTHqv/PUlXWmXnZp0cZzd4NPVNxYybUobHgHlTrrxTpCQhGV2MNW37mdAXQjDsblSLjWTfIGnDA09yOai5ehkfLruEpM2OMxxElOURNWXsmTQD43TOWNxTjOv/a+/M4+O4qnz/vb2vaqlbu2RZsizv+5J4yWLHTkJ2ErKRnQw8CCQwMAxvIAzkDbMwgXnwHgzzCAOEwDAMS5gkkDi7sy9O4jiJE8drZNmWrH1v9XrfH1Xd6pbUUsnullrO/X4++kjqqrpd9evbXafPOfec797Nj779DK9sPzB6X90ICAYjxOMSoVvje5au4bz/fTuf//J6Fv/y2wBYiosYCkbT8sJ6ejVj7eBjO/jJd57h+cf3JY0uqed4OVNKGaTmdKVWYk7T0xmi/NXHATDp4bMlq6s5fVN9WiLzyeTNDOm5Vw6HheLSIoI+LZw2FNBuDiUVXloOd1PfPcDsw00M6PW3YsEQLc/uwBQZNhxs/SGElPzpvTbCe4Y1XvnsNiDds5bwdFGo3bBLWo5yrDfEQ7f9Ff/nf/1fXHWap8BqM3Pw/TZ+8t1naG7S3rDjhbY8BQ5OP3sOa8/U5pm/RPvgXbSqnAuuHN8TIoTgtMPtrGofTEuM33nmVoLSwrY/vM2BPZrXqjDgon5dDXsqivlTv52wzcKA1UzBS3/Cc2Q4rBHespmjs3XvWmUZ2668maN12od4Z2+QyBgrQkciYzEe33QzT1x8GwCWgQHKX32cBmtXcg6WPfFr5hx6marnHqD+tWewhkPY+rSQWMlgmJhuGEdcHmI2zaAtKMhsdHm9o/O0RlLbUMy6zfUnleycDRIhtbYnX8r62BaL9r4u7BsO63e+10htj/Y+MIeCeHWjxVVTgXNWegjel7IwZn/92F72bJDIh6m8+kK27ns8WdTSCPO+/lkA3A259a5MNKcS4dxQc3Y6C1h0T9fAvg+Ip3jNLCNeA2dq7ax9w59bc06gF+CE52QSyTQcSwZPl5H3XrYJd/XiveFTXHnvDwBwVWf3y0EiVBxIyX3MBrnWKu+NLneBFYfTyktPHaBqdhFWm5niMk2Ucy9bzKXXraRsqR4K0g2Shcu1DymLvtLmot/8BEfje5hKtW/mcbsTGY4w1Kzd7CxuF9XXXsTxiy8mpoes7OXFoz7Met9+H188TKdf+0C85OMrkMXFSU/WSExmk2YISHjt+UO0FNYSKvBjiUYQQmBOKXpXVFVCcZmH2766mVXrtQ+qoo9fgWdeHUMNi+ntHqK3O5ioXZrM9/GkNA3O5OlKxX9gH3H9BmkeUasqUZUdNI/VidLcpK0etdrMbP/ZDg5c8VniJhNDehJ9WWVB0iis3/8+7fu0MO3+f/kpy//529z+7a+mjVfX10ff7n3w6HYAiva8jqddu2GlGV26kWuurKDlnh9yqGERLX0hrSK9ENgTtd1MguraomQRWQCb3cyxw93seO5QsmdlJhJGV1vLxCvbhBCEXVb6LIKXN2tdDPaedg5Hz7iAF57+gHdeP8pTf9JqvjldNj56yUIsFT46BiM87/fywqxiyg6+mzRqANzlpYDg+MqzCVmrsel5aKVHD3PmJ29lx5V3THhere83Et97gNgbbxMLR7AMDuB//w0WlknsVu21337J1ay780aK9u3C1dlKuKKe9iXrR40VdXmJW7W5V+jN7Obv6Dj5yvlTRc9OLVzUsf3VrI8979/vpv6Bn1Dcsi/5WPjYcOsRcyjIoMhcJqGwVL8pxOMse+x+gkdzU7eseLNWq6z96Zcn1UQbSH6htWRI7M4WE82phKcr3NF9YgtMRpCaqB4fGq716KnTvNL9c7W8K09VKU9dfBUAvhTD85xYJ9f++LtsfvmJkz6XBEKIpLcrU07XdLz3rIVeSPFCpfbpzAYFixvY9OYDrP6PzL05T4Rca5X/OV1SEApFCQ6GueGz64lG4mnNb2E41yARDkmEH+3+QkIHDmMb6MV2aBfr/vYatt/3FjG92bDF42b2p6/Bu1B7o/icFjr1mln20tGu3I7nXmf573/DwVu+zKCw4vU5mLekjFXrZ2c0UlweG1JKdr50mD57FbWeQi7va2TX428RdRUQ9hRi6++mpHq4fUqR3s9Q1tRyxrP/gYxLapbOwmIx8d7uo0ggpreYcLtHe7q8PgdnfWQ43yANYU42ZzWP8O587JY1WKwm+vpOrvVJYsFDIg+tt3uIl8+7Al91MZ/+600AHDusGWYxh4vOpuPUrl5A1z6tHlrEnV7uYNnb7zDnkf+gbekGbdxwKJmImlqNO/G6h2KC2nk10NpEU0+IcExiElBR7qUo4GL+0nJsdgtL11Tx+gtaXzSb3cKOZz/gzVcOs+nCBZRWaOfQ0zXIuzuPUdswXBoiaXQdGyQ0FEkWVs1EQfc+Gn51H3sXr+R3n7gDvyNA7fEezJVuCgMuPvKxpcl946EwZz27jd73DzH3vbfoDpRgjkWTfR4BTOEYq2+8gBf3aQb97O4B9gW8hPXk97F6xo3kwGvDFd972rrZedpZ7K9fyHcvOAvTr94CTFzWuh9/iebtsA0F6Q5UMDB79Aq2gaISYvqKWG8GYx+gqGh6Q4aToeraizjyqwepvOqCrI9tisdxdjRTOG/4m7/ZbgU0z6E5PERhZebwk6vUj/nYADGnm5pXXyU2kJvVVp4Fc3BUlSHjcQYPHUnmlBkhWVssi7k2YzHRnEqtRp6oD3UymFMXQqT8XV3lpwuYtag2+dhl3jB9QEGK0SXv+CqVg0Eqmw4Bf33S55PAYTExGIljzZA/Nx3vPSEErppKBvZ9AIAz2+FFwFFeMvFOkyTXWuW90WW2CG776mb6e4cQQqQZXMHBMKGhKI76WvwbV3F8SF9yH41pjwcKk3VjbD4PFV7tBh2z6D0JK0tZ+NGt2lhHWqh6fQfW3VrROlvxaKPL4tMMHcnwajkhxLjFGm/5whmYTIJ7vqMXdQsP0f/Ui7xjr6e/og6u/jwL7/snHOXDORCJFYxd7dq3RWESVFRrXjthFsiU8VNvckUBN2d9ZB6FflfGc+oPzEM49arsI4zXxP/BYJCCgsn3ShuJu8COz++it3uIfas2sG5VZbJieyJEHHW46T6ieRx7j2vlI5rP1kpOeH0O+nqGGPJrN6dEqYv+ylocnZpnwJxSrTkRXhwKRijXk1cPtfVz6798g5Yt1/LbYJDrP7s+ud+KdTXDRpfNwuyGAAiS/SMBPtjXwQtP7Kf9eD+XfFwLKxek5C29+OR+Nl+0cFwd7B5t/0hBEXF/JbKthUGXh5XrarjwqvTwZHNzP7Z3D1N7rBFLNII5GmXX9XdgNw+fU3dTGws7d0LBaQC0eBzMIYbEggQifaMLi47k+K69JK6io6WTwQI/TpuHXuHCbjdBEPZ6F2DdfoTOBWsgFkkmyydwFbsZbB+gcflaylq1Ks72cRLgszWvpoKF3/oipeedQeCMNTl7jtRGzpaUL0CVLz1C9FOrxjoE0PKl4m3a/sfXbMnYuPmkz08Iqq65iGO/e2TSz3Hsj1px6c4X3phgz5Njojll8brxzK8Dk4nC1Ysz7mcUYTKx4Yl7k38nSBhgtmiK97xZ88a764cT4N1za5Kh62zisGrv2Uzhxel677lmDxtdk2kBNZ3kWqu8N7ri8Tgut23McNlj9+9m37vHufS6Fcz/5h0cfewgbZ1avaZZc/wUnb6M1m3PEirw46mqZqB/ELNZEMNM3Gwm3D68ZLvzxZ1U/u/vk1j3ZS9Nb5objcR4s8NBpKSacESCHR741U7OvWwR1XWZKxybTIJ4LE5vl/ZtNHTDrbi+//fEPcMvaszuxF4aYNsf3qZxfwdr9SX3iSKnaeOZTcRTuuIVpORxuTw2TjtrzqhjUpFOO2JQX9qcIY/pZOuUfPSGlfT2DFEUcFPod9F0sBNnNEZFwejwZ9ThYuCYXv6gowsrYFm+FA4HqW0oprW5l573jyOFiZgewhoqrqRt2UZAK42RwGI1YbaYiEXjhO7+V657fhdPX3QVvu5Ojnj9tB7rxZZiaBYF3Fx8zXIkErPFRMOiMhoWpX8wHDmkGYKpr3GqVzM0NPFqKLvXhRQmnIXVLO4KMmfbHwiJGBV3XTRq3907m2lZ9xEqXnyYA5d9WguZF3mhdzihezAY5cgfn4CbT0OYBANWC6c3tmGKSTqWrKNk7+sTnlP/4WacwMubLqDYX0zhUAeL2vp48+UmfeWgdl3732kmtOFCHO3HaC8pwyOhZ7af431hLltTzXvb9mAzWwgWFGKGcb1+Q0M5TErPMmanndLzzph4x5Mgra6VnuBtCg3h7GgmOk5rn/KLNiH3aLWiuhuWY/G6Mu57ssz98q3Uf+GmSRc4nfe12xg62sqc22+YeOeTYKI5JUwmNj79S2QsnrVVlAVLRkcRhJ7mkAj1yng8GZp2zRk2ulx11Tkxuuzm8cOL0/XeS+Rd2QKFeBcbXzwwneRaq7w3usbr+G2xaRMtEonhWzkf09uD0KlN+nhMUnfbdbzc1Etw93E6S1dQ3RzH7rAyOBBm7j/8NSa7jdd+9xL7WuPUjPjgGhlefOHJ/bzfEodLbtUKqAIdrf385ievsnJ9DVsuydweo79vOPZvc2uenohj+Jtj3ObAGiik53CQvp4hCv0uHE4rTreNPbua2bWjiUUrKlm6phqTxQQpRlfRiIKo2x/ZQ1fbABu3NiRb86RiSjFSRnol2pr7+MUPtIKyX/7H2ozXMxFzUwwXn544WjcYovuJ93mxrY8NW+Ymc7piDhehFj3RtFvzS85bM4sNn6ghFovjdNn45RdeQch4Mm8IIOTXniO116EQAqfLSn9vCLPVTvnRw1zwu3tBSmof/QVnPPRjTCNWGS1YnrlKupSSJt3oGtnqp25eMYf2tjN/6cTJoYMDTnZ/4uugrxQMeYsoOLR7zH1n1wc49uCT2Pq6tAKKcQGz/fD2MaRZYJIQs9qJ6FXNXR4bs90W9I4ptKw9F0dHC8d+v43937uXVT//Np55taOeZ9sVN9J3xsXELBbWhaF+73tQVIXdFMfitpMwutxWSSgmiNmdWl9ACUsXl9FyfJAFswp4D7BLEHqOoW0cT9d01ArKRwKbTqNj+6tUp6xI1MKLw0UzK+dm/vJkCxSy6vwG9t3zEAWH92J2nZezcxUmE+IEKso7q8o4/b9/lIMzSsfInBImU07LVgCUbN3AvDs/k1x8kPp8rtlVyb+rr7+UlgeeHLed0omwqb4I86FuaovGjnBM13svUYy2/NItOQkv5oJca5X3ifSRSOYl8BZLov+idsdJJLRf/RdrWbxKm+hrCi3JXK9wJJg0NAIXnkPro8+z+2cPcvRoHx90xJFoiZ+r7rt7VPuAoynV2kc2JuxqH+2RSrDrlcPcc7cWWqyY5WNdeQgpBFHLsAFhrqrEu7CePt2b4StycvvfbuHmOzbSdryPpoOd9OmVvU32dCO00JOeM7HrlSYO7GkbbqI8AqEbXWGTwFWUbmgWFruw2c2UVGTvm3Oh/hzOwTC97YPDzbhdNkASc7iItmqGzVNf+iq/+dSXqJhVgs1uSeZomUu10GvhgbfxNO5JG99XlL4aKBE6rPr0dQz6iijs6kAAhQtqKK+auKdWa3Mve95qpqt9gM62Afp7QzhdVgKl6QnBl163knOvrKO2YeKl8Vb9XRZ4bweVj/8nltAQoUAFsejoZOkFyytYED2C9+gBFv/8W/if/S0OXQfhcQyXIdFDrgO9Ia4ttHLT7euJVBeCEPTVzOOt2/+OwQOH2XPXD0Y9R9dghLaBCFGHi7jVRmt/mLJWLRRit8KqlaWU7NTmrNepnXzU4caiV6M/b2EJP/nYQir00hnWvgFMaMV2Uz2PI2lpacm47cNEomlxsi0UYNUrmEuLlbrf/5TWzvGTec9cWcbsJ39LScu+nBsU+Uy+zCmTxcKcO25K84Kd9eofOOuV36WVqSg+ay3rt/2UNf/1/aw+/8dXlPOjyxckCzqPZLp0ctbo/Sn1jgAzgVxrlfeervF6AFpTml7DcAJ3wosSD4Xpe3cfUYeWyOgpcGJ3avkuoaEo0X5txVbbyrNpbgvhc7hxelyUnncG4VCUQ3vbqG3QVjEO9I1txAAESjPnO0RTbqxOIrx9x7eIOVzIFMNt/ve/Qd+QpE9fgp+aj5UISxbobWpcc8sJ73yMeGUDcRnHN6K1wpozajmwpy1jnSOTxUQMOFDkYXVJ+nlbrWY+d+cWWttaM17PZEl4ugKlHj7ysSXJkKbJJLBYTESjEtk3gJSS/c4iQrN9VBWmtEqRElMgwED5bDzHDhKRUfr1ZG5fx34WLEtfLpwwuqImKw9feysrtz/K3qWr+Zfv/YWh8/3jfW8kDdyEgV5d6x+1ktVqM1NeZayDvaO2Et5t5eiCpdQf3cORs68mZncSCkVxWUZ7ESrOOZ2eJ17Qcn4E2GMxhgCz04JLwEBfKFl+A+D1Fxrx+hy4Am4iR7qJpnhRTXqYKhKL88fdbayr8dHSF8Icl2z64DimoQGGeg8hzJpuniI3nmIvJj03xe2yYOmPE8WGLR4H03BY2umyYjIL4mjXYIrHxq9Z5sh9o+qZQCJUl5oAb0lZHdiHk8JxtJJS8uYn7wRIWwH9YSSf55SrZmwvum/F+DmguWC6dCpYtoD537gd7+Kxq+nnI7nWKu+/Io1ndCXCi0mjK+lFsSKlpOO512jd9hwxfeWXr8idvJEe+fOzNN//mFao0qN7f7xFhDu6kVLy3KN7eempA8lk9jVn1pEJf3Fmo2vh8srhYqERzSMWdaZ7TXq7h7j/3teJxSQNi8qS5yilpKtDO6ZA9+gU1BRhP6LlBMSROK3p+mzc2sBNt29IKwCaikn3DpqlTOYBpGK2mHC7s+fp8ullKPp6hiiv9qV5jNy6cfXSTZ+hMxglFI3jtZv5831v8Nt/f5XB/jADfSGOHDbRuOUaBlwe/vvjn9LOMziAxxKkFLD+fAAAIABJREFUpj49927p2mrO+sh8CoqcrDz/dB648Tbqtp7Fw797h7dfOzLh+RaXDyesJ/K1quvGNmCdTmM1dwr1xHuH2UX/5dclc9PGqrou4xLT8uX06c2PI5gwJUpT9A7h1sPJQd3o8he72bh1LjVzAnj1cg3BBQuTjWEb/uf/AODVpl7+/dVj3PvaMZp+/Seu+eWPtXCh00vs3X1I/Zw8fg/WAncylGt3WIYXDujvxUQIUZgEnpQSEZaB8Ve9GtXrVCexok5Yh1//RCV3gMqawnG1EkLQ8ewOAOw5WL01k1BzyhjTpZOzqoy6z15H8dmnTcvznwi51irvPV3RaOZE5UTSbl9vCBmXBPWinPf94EWsNjNXbdE+kOIe7UYaDg8mjzl034Mkgk0FXiud/SEigWLibUd4/1s/Yo9pAcHBSDJ0uXJdDW++3EhHq2aEOWwmhsLaNn9J5lo0Lo8t2RvwvRawXP0Fqp9/KG2fnS830tMZpKTcywVXaeUDXn/hA557bF/SoCzQDRS7kLxyzsUs64eozTbpwohmm5kIUDIYwpbBnu3q6sra6g2ny4rVZiYcijIUjCRDhqDVBetpH2QwGKPxtfe44Lc/J7ZwPke8c5Fxic1h0RqJF7tobY/y9C1fwml1AhGiThdDW0fnsixeOZw/ccsaO8sqPDiOdvPMw41Jg2U8ztg6l5JyD7Pq/PzpN7sIDUWZPTcw5r5GdarwO0kERftDdjBpZU9G5peBVmH89388SPy863B0ttDpdrGm1MO7R3qorfPj0L1ModkNEI2zeFUlp2/SlsJ79nfSida3MlFDyF6i5SYe79e+kDR1h6g7dITyfW/TedpHiNudRNp6sOghXJfXQXdQ0rbiTABsThteYaUzsZI2HksLIdYvKOXAbx/H1dKIvbsduDajDtmcVzOZxXd/hTl33IirNr3jgNUqiEQk3f/1RyJXnzOuVt5Fc+l7dz+Lv/OVXJ9uXqPmlDGUTsbJtVZ57+myjNN9PNFCpXFfO+FwDJvNjMVqYnAgTE9XkAefaaOnbhERPX+qoqokWSU+onub+q65hQ+atRtUrFhL9Nvzn48THIzg9topLvcSGooQj8XxppQKCBQNf8P3l4y/nLq3eziMEPX4iFVUpW3v6dS2L1pZmQzdWG3mpMElRErCeGcvFz34Oyqe/j2H500+MTFxwywaimDLsNIlEBjbyDgRhBBE9Or5Lz65P22bP6B5wcRQhGO7D1J/YC+VHxzk5js2ctWta5Pnuu6albwwp4qKiIklbZrXRyCIDJrHzItK4LaZObOukCG9fZTLgNFVVuXjrPPnUzevhOs+s47Lb1qVLMY7EqM62VO6BiTON7X1USomk0i+1kP+cqJeP1svXchF1yzjoiuXUl1XRN38Ekz60nB3Sijap/8tQxEi3X1gsWDVCwR3DEQQ8TjNfSEix7Xq3Dbd8+oIDhFN1Lhz24hGh8/N7rZhGxqu+2WJp38J2nLpIs6sh+J3Xmbu8rHbFiXI5ryayQizeZTBBWDSi8H07N4/oVb2Mm176PjMKTibC9ScMobSyTi51irvja54PPNNtXKWD7vDQlfHIIMDIe74xlY+/42tye2tbUFa1p6LNJmxWk0EhwapX1jKhi1zWX37x1j/2M9pCQyvEvJeqHlOBuZp3qa6ecUca+ziVz96iQN72obbzAD+Ug+BUg+lFd5xb+ahoSiN+9M/GKNV2hLikU6q1KTwBcsquPFzWvVvT4EjWXl9oG2IpnOuIlhZh8Mz+cKDqVXoXRlWmp1scdRMtB5LHzdxvct2vUbXC29w5OzLObb4XAb7Q2neJY/dTNmRRtyH3yc80InUjcXenUdGFaXt6Rpkz1vNHG0cXvgwqHt5MlXpz0Sg1EP9gsxVlI3qZB7DuE14UMeiuCyl6rUQ2GwWFi7XDPKlq6v52M2rKdMXBaSG9wp92t9iKELH6jN59+N/xbbP/DMAA+8f5PZvfYkVTz5CTL9R253a6+8eGja6XB4b/hI3iWpw7tIiPCkFeC0MN1hPMP9rt7Hinr9nyfe+Nq4OuZpXpwLRvgFCiTVDBb4JtUoY0yG9PtqHFTWnjKF0Mk6utZrRRpfJbEquHjv0fnvysdT+hYml9Q6biXA4zJz5JWzYMpeFV5yJZ1FDWp2l3l7N49Wv59NU1Rbx1J/3MNAX5rnH9ia9ZCazYMHa2dx0xwZu+Oz6cUN85pRQjNet3eSCVs0zNjIs6Utpw2OzW5LnlmqMeQJOvI17cLU34w9lXjWZCYtep6rZ48i40iUcHrut0Yny0RtWUlLu5SNXLkl7vGFxGUV7nqf2+YcRe/YlX6uCwvSYutduZsPTDzP3mQeof/Q/seuvQ8ks36gekW3NffzpN7t45pHhWjiJFZOuEzBSx8OoTqnzw4i3bV5KGYpZ/rGTOgf0uZo614s8dmQ8BsJE8/LNxK12Dlm0ZN7Sn92LNRLhjCceIlY1n3ev/wr9Fs2FHnd5idudICUOhxW7w0rNXO195S724SsfXvVpYfT78ciRPrZ9YOONneMvwMj2vDqVEGYzxW+/iKu5kYIC24RadTyj5XTt/vI/T8Xp5S1qThlD6WScXGuV90bXeHW6AOrma3lbh/YONzTdcslCGhanh95cXvuo+huDI3om9vaGiJvN9BWUIAQ0LCrjpts38PlvbuXWL55JWZV2k5q/pJzZc4sxm01j5uWkYrGYWH9OPevPqcft0L1VZs2oGBmWLBzRDLUnuXJx+PGy+hKK9u6kb9Y83F2TL+Jm0Q0tc1xiz7C8P9t1SuYuKuPmz2+kKJB+vb4iFza7xBrsp6j5KBFXASDTam8BtB7oYGDjFRxbfyEFPZ2U6OOcc/78Uc9VUx+gZo6fhsVlyRDeoF4+w4jBMxmM6jR3cRkLV1Rw8bXL03LaMu6/cHjujmVyRcIxVq6v4ZxLFqb1y/TYzchYevgvsPc1AKztwx4RYbYQtzso0D1jfaVaqMvutiU9hwmD32Y3468eLothEaPDojueO0R7Sz9NB8cPdak6XZkRNgvlO55gziO/wObzTKjV7E9qff0qrzx/Kk4vb1FzyhhKJ+OoOl3j1OkCqGsoBgGH9rbzf+56nBef3M+8JeVcdv3KNC+Ay+dK1t/4YF87O19qpF+vi+UvcWuVvYNxwgUBECZ8Ra60cKK2n4ea+kDaCjcjbNzawMatDbgD2nERj1aJOpBidDmc1lHVvB+9/x3tjxRnjsNqJuwrpq9mHtbY2HlB45EIU1pj8WST1JFMZU0Xi175P+Z0g8mEw5LuHQQI6J6+3rpFRNwFOPSw2NDQ6Llhs1u4+pOnsfbMuqSHKenpmmR4cSKM6mSxmLjo6uUsWFbB4lXaStaq2YUZ908tWtvWPNrV/dyje3nyofcID0XTWjl57WbiMj38Z2s9RmwohKtrONxas/1+5t7/Q1au03rpDZZqOYapocqWI4lGtQJfyfB8X+FMqVenc8l1K9i4tYHzr1g6alsq+VJTKR8R5uHX0eJ1T6hV3e03sPYPP2Txd/4m16eW16g5ZQylk3FyrVXeG13jlYwALbySyLuJhGOEQsPf9FM9Ry63NbkU9NE/vsOTD71Hs35j8focFPgcIAT9lVqOl88/etloZU0hqzfOxlfoHOUlM0IiB2v1xtlceNWypJcu0/MlSI1eWpAcrK4mcnw/3gWTt8idek9Cazye0dM1lcuLQ7MW0nT25QwlKsy7R+eZFQaGvTkHLryZXr2OVneHsfBqMqcry56uE9FpzcZaLr52OZddn7m3HsCFV2s9GU/fNLoyeeXsQkrKvWkJ+qAtHOgf7KDquQcBEJEw1q4OOvZ8gBTQ6yviX+/8Di+dcxEDWzdTXKWXwnBoYe6SlAUDCY9ood+FV68RJ6JR5l5x1qjzsdksrD+nfpSHciRqeX9mUkPQZpdzQq1MFguBjaswO7MbMp9pqDllDKWTcT70JSOMlEQ4/ew6qmYXMbven5a34/O7ONrYDWirsmw27aY7f0m5VsJAL6bqctswmQQ9XUHiZ2+GIdLCNqm89NQBWo70cP1t6ybtOUl4zjwFDhat1Dwed3xjC81NPWPuv/6cenY8eyitn2J/6wCuyoV02a3ML81cqiITnmI3z80KEDKbM3q6EjpNBXGzm576pViCWkkC3xi6p+oc9PtZua6Gxx94l7Ix2hyBtjKw5Wgve3Ydw1/sJhKOYTaLjL0mT5QT0clkNrFgWebWQwkWraikclYhnjEMmQXLKsYcwyQEERs4OrTq8tJqo23VZoKVFfzrnd/F09dDyOni5XMuZNlplXj0XpgiGqHstcdZctOdybFu++omhoLRpKG6sHUXwdfeZGjr5/AtG/XUhpjKeTWTcVSUKK0MonQyhtLJOLnWKu+Nrlhs9GqpkVTWFFFZM7qAZSIB3WQW1MwJ0NPTQ2FhIZsu1Cqa73juEKB5QFweG4f2ttM6pBlGmTxPtQ3FFBQ600KXRkmED0PBSNpjmVrJbNzawOmb6tPqIjn10JNZSjy2sRPhx8NiMhG0WrCaxJir6oCkTlPBus1zafzFf9OsezQDNaOLPaZ5AaRg2dpZLFpZNW7LmZee2s/BPcN5fi6PfdI1zSYi1zqleviMYva4iLiHE9/7qubS3tqD2WRmnsmB6WgHJglBu8QzX7eepMR3ZC+1C4ZzyeyO9HB3YN1yGmvm0WYp5EQ7qE3lvJqJWP2FRDq78a1YSIvSyhBqThlD6WScXGuV90bXeHW6JmLd5no2bJmbvNkODAykbU/N9XGMSHD2FY19wzvj3BPvlJ7wdL28/SCzG4qZVacVrkwkfI9lFIw0LJy6t6YgHMUyRk7TRCS60DusmQ2W4uKJ+wlmi0UrKinrP40nHj1IZ2y48n4mTHrtsvEMLiEEl9+wit07j/LuzmPEYpIlq6sy7n+iTKVORrG5vRzbcKH2TzRMz2A7R/qjRIWgqLOfRIJgrGsAm92CKRIibrUjwpmLEAOEqutoOniIWvOJdyvIR73yiTl33EA8EsXstFPs+nCHDY2i5pQxlE7GybVWeW90GfF0ZcI8YmVhT08PbrebWCxOV/sAO55NeLrslFf7WHNmLa899wEweiVhNkhNzG891susOj/f/do2AG743HpDDZkd9mHvlukkjK6xWgAlSOg0VQTOWAPvxeFQZ7LH5EikSSDiEqO+KmESLFldzZLV4xfsPBmmWicjyPJqon1a6YajDhO7N5zJrG//nIudBYTrF5OowOIJaiHtyhf+TFfDCgbnLck0JAALl1UQKPVQVnHilZrzUa98ou6265J/tx47prQygJpTxlA6GSfXWuV9In2myt1GadzfQdPBTiKRWHIl5GB/mF/96KXkPi63jZJyL5suWJAMG46VW3SyJIpRAsmegYkQktH8sNSCpr4TSAy3mif2dE20YjTbNDd103SoE0ivSZaKVS+jEKvNn8rKU62TEewp+vU7Hbj7e+kvrWOobgnmlHChz699qFj9Pgaq5hBcPn5vtNLKApasqqKkYnIrd1PJR73yFaWVMZROxlA6GSfXWuW9p2uiOl3jIaXkdz/Tigh+4i/PSNbf8PocXP0Xa3nyoffoah9Iu5F85m82ERyIjCoXkQ2cLht2h4WaOQFK9LITN92+geBgeFRB0EykGV0nkFeW9HSNE56b6pouqQsJvBl08M0r4cGeCGctzFwhfqrJx9o3niIXpvZjDBVXUhQMU3e4haK9b9LT1Uzgho/S9MphAHy6sX/aNz+J/YFXmXPlxpyfWz7qla8orYyhdDKG0sk4udYq742uk7E6hRCs3jibjtYBiordNDUdZvbs2YCWfH/j5zYQi8aTdaHaWvo4frSHqtqirJcX0J6zkDtS2hSBVldqMqvqrLbhfYtOwOgq1L1tAVdmo7KlpSWp01Sw/PRZNB3q1JpjZ6iSX17gYMBmoTjLtbZOhqnWyQgel5WW0ipscUlJfxBRVIm9+wmi5hh180uSRlfpAq1G16y185m1dnSR2VyQj3rlK0orYyidjKF0Mk6utcp7o8tsnvwKvVQ2X7Qw+fdYcdrUQpyvv/AB77x+lK2XLRpVPT1fSG174x7HcMpEbZGTfzi/ntlFmWsqTXXs32w2cdn1K8fdZ8vcIgrsZlZWnnh4K9vkY46Ex2Zin9+DIxqn/mgH4CbmdBEtLWFVQ4AdLivBwQiBqtGrfXNNPuqVryitjKF0MobSyTi51irvja5sMpEBlwjxRSMnnrw/FVx2w0qikRg224m9fGtnjZ8MfbKGbi6wmU1srM2vJc/5qJPXYeVogRY6XHBcEAGOr9pMZXEUi9nEJdetYKA3NGEh01yQj3rlK0orYyidjKF0Mk6utTKUSC+EcAghfiqE6BFCtAsh/sHoEwghaoUQTwkhBoUQB4UQV03mBE9m9eJIent7x92+btMcLr9xFctPr8nac+aChkVlLFxembPxJ9JJoZGPOnlTVrcmarqFfQH8lVrJjJo5ARauyN3cGY981CtfUVoZQ+lkDKWTcXKtldHVi38HrAYagPXALUKIWw0e+1/AIaAY+Evgl0KIeUZP8GQS6UdSUjK68GYqJrOJ+oWlGfOKPixMpJNCIx918qYUzC1JKXtSs2jWdJxOGvmoV76itDKG0skYSifj5Foro0bXjcB3pJStUsp9wE+Amyc6SAgxHzgN+KaUclBK+SDwMnC90ROMRscv2jgZOjs7szbWqYzSyRj5qJM3ZVFGYcpK0Iol02905aNe+YrSyhhKJ2MonYyTa60mTAoSQhQB5cDbQoh/1I95BbjdwPiLgC7gmBBiG/Az4G1gsdETjMVi7N+/P/m/3+/H7/cbPTyNk6359WFB6WSMfNTJkxJe9PhcgOYqL/BNf8PbfNQrX1FaGUPpZAylk3FyrZWRTOxEV+VeoA5wAI+nPD7Rsb2AFS00WaX/b7iXTmdnJ1u2bEn+/+lPf5q77rqLlpYW3G43ZrOZ3t5eSkpK6OzsREpJSUkJx48fx+PRTrG/v5+ysjKi0ShHjhzB7/fT1tZGQUEBsViMgYEBysvLaWlpwWq14vP5aG9vx+fzEQ6HCQaDye02mw2v10tHRwdFRUUEg0GGhoaS2x0OB06nk66uLgKBAH19fYTD4eR2p9OJzWajp6eH4uJienp6iEQiye2Tvaa2tjaEEFm9JiEEjY2Np9Q15eJ1stlsNDY25tU1mW12bCZw28y46gLwhmZ0HW46PO2vk9PppLW19UP3fjqRa7Lb7TQ2Np5S15SL18nr9dLY2HhKXVMuXqeioiIaGxtPqWvK1esUi8Voamo6qWsaDzGRVad7ujqB5VLKt/THPgb8WEo5bpMiIcTlwE+llP6Ux34AVEgprxz3iXWefPJJWVAwvNruZDxdjY2NqlaJAZROxshXnd5vG8BmNlHnd3L4QAc+vzNjL9GpJF/1ykeUVsZQOhlD6WScbGj1xhtvvL5ly5Y1Y22b0NMlpewSQrQAS4G39IeXALsNPPe7QJEQolpKeSTl2GcNHAuAzWZj7ty5Rncfl4TFqhgfpZMx8lWn+SXDdWYS7abygXzVKx9RWhlD6WQMpZNxcq2V0UT6XwJfFkKUCCHmAp8CfpG6gxBivxDin1Mfk1K+D+wA7hJCOIUQF6Otfvz1yZ+6QqFQKBQKxczBqNH1DWAnsB8tif4+4Ocj9qkHysY49hpgDlqI8gfATboxZohs1unq7+/P2linMkonYyidJofSyzhKK2MonYyhdDJOrrUyVNJcSjkE3Kr/ZNpHZHj8EHDOCZ0d2a3TVVY2lk2oGInSyRhKp8mh9DKO0soYSidjKJ2Mk2utjHq6po1s1ulqa2vL2linMkonYyidJofSyzhKK2MonYyhdDJOrrXKe6MrmwgxpjNOMQKlkzGUTpND6WUcpZUxlE7GUDoZJ9da5b3RZbFkryf3iZaa+LChdDKG0mlyKL2Mo7QyhtLJGEon4+Raq7w3uiKRSNbGUi5WYyidjKF0mhxKL+MorYyhdDKG0sk4H/rwotmcvebTqUVWFZlROhlD6TQ5lF7GUVoZQ+lkDKWTcXKtVd4bXdkkm+UnTmWUTsZQOk0OpZdxlFbGUDoZQ+lknFxrlfdGVzYFGBgYyNpYpzJKJ2MonSaH0ss4SitjKJ2MoXQyTq61ynujK5t1usrLy7M21qmM0skYSqfJofQyjtLKGEonYyidjJNrrfLe6MpmIv1E3b8VGkonYyidJofSyzhKK2MonYyhdDJOrrXKa6NLCBFob2+ns7PzpMfq7OzknnvuycpYpzJKJ2MonSaH0ss4SitjKJ2MoXQyTja06uzspLW1tVIIERhre14bXUCgtbU1a0bXv/3bv2VlrAcffPCkx8j2WNkaJ5s6Qf5dX7bGyledsj1WtsbLV73yUXf1WWUMNaeMka86ZXOsfJpTnZ2ddHV1VQAz0ujKSx566KG8Gyub55RN8vH68lGrfL6+U1mvfNY9G+Tj9eWjTpCf15ePWuXj9eWjTpkQUsrpPoeMCCECDzzwwFG73f6exWI5qWWM0WjU3NnZWeb3+4+f7Fg9PT2FPp+v+2TGyPZY2Ronmzpl87zyTfN81SnbY2VrvHzVKx91V59VxlBzyhj5qlM2x8qnORWNRs3RaHTexRdfXCOl7Bi5Pa+NLoVCoVAoFIpTBRVeVCgUCoVCoZgClNGlUCgUCoVCMQUoo0uhUCgUCoViClBGl0KhUCgUCsUUoIwuhUKhUCgUiilAGV0KhUKhUCgUU4AyuhQKhUKhUCimAGV0KRQKhUKhUEwBM87oEkJ8RwhxQAjRL4TYJ4S4fcT2gBDifn37USHEHSO2f1oI8aoQIiSE2D7G+CuFEDuEEEEhxG4hxKbcXlFumAKd/lYI8aYQIiqEuDe3V5NbcqmVEGKuEOIhIUSrEKJLCPG0EGLlFFxWTpgCrV4SQnTqWr0ghDhzCi4rJ+T6PZiy3/lCCCmE+HqOLiWn5HhO1eraDOjH9wshZk7PmBFMwed6qRDi1/r7r2e8eZfP5HhOfS1lLiV+pBDiS0bOzXLSVzf1DAKXAu8Bi4CnhRDNUso/6Nv/DXABlfr2x4UQu6WUT+nbjwN/D2wG0m5+Qgg78ADwc+BM4BPAfwsh5kgpZ1qL9pzppPMB8DdoGs10cqlVIfAQmk5dwBeBbUKIWillMIfXlCtyqVU78BfAXillVAhxKfBnIUS1lLI3p1eVG3L9HkQI4QHu1p9jppJznYD5UsojubqAKSSX9z+Bdv9rBRbqv1fk9nJyRs50klL+I/CPif+FEIuAXcB/GTozKeWM/gH+AHxf/9sDhIGNKdvvBX4xxnFfB7aPeOx8oB+wpzx2CPjEdF9nPuk0Yvu/A/dO9/XNBK30fbyABFZM93Xms1aAGbhC12r5dF9nvmoF/CvwOWA78PXpvsZ80wmo1edQ9XRf1wzQagMQBALTfV35rNMY+/wAeMDoucy48GIqQggrsBp4U3+oAbACbwshfiGE+ALwNrDY4JCLgL2AQwjxvB7aeGcSx+clOdDplGUKtFoP9AEHTvZcp5tcaSWEeAsYQvugfFQfY0aTC630z6e1aN/aTwly+P57WWgh/oeFEKfE51wOtFoD7APuFEK0CSH2jgy7zURy+ZkuhHADNwE/NnrMjDa6gO8Bh4Ff6v979N99aMLWAb0pj0+ER9/fDcwFSid5fL6SbZ1OZXKmlRCiBPh/wF9JKftO/lSnnZxoJaVcBhSghWTvk1LGs3K200tWtRJCOIB7gM+eIvokyPacakczJmqB+WhGxWNCiIIsne90km2tfGhhxTBQDVwH/JMQ4rxsnfA0kcv73/VAN7DN6AEzMacLAKEljZ4JbJZSxvSH+/XfXinlBn2/v0p5fCL6gQIp5TGgXD/+FqA5W+c91eRIp1OSXGolhPABjwD/IaX8SZZOedrI9bySWr7bvUKId4UQTVLK57Jx3tNBjrT6BvC0lPK1rJ7sNJILnaSU/cDr+r9dQogvohnzG5jEjTLfyNGcGkAL6/8vKWUIeE0IsQ04D3gsayc/hUzB/e824KeT+eIzIz1dQoivoFnhW2V6gvs+IAIsTXlsCbDb4NDvAg16Qv2JHJ9X5FCnU45caqV/q96Glhvwt1k43WlliueVAFadxPHTSg612gDcpq+aksDZwLdm8GqzqZpTEoijzasZSQ61eh9NH5n6dCdxqtNKrueUEGK9PsZPJ3Vi053gdgIJcV8G9gDlGbb/Fs2j4AVOQ3MbbknZbgEcwF3As/rfNn2bHWgCvqn//T/QXIf+6b7ufNJJ327VH/s5mtvWAVin+7rzTSv9mBfRkzhn+k+OtboE2KS/9+zAF4AosHq6rzvftBpjrO3M0ET6HM+pxI3RjJY2cjdwFPBN93XnoVYuhlftWYDlaCG4c6f7uvNJp5R97mMSCfTJ46ZbnBMQUwIhNFdg4ueRlO0B4I/6483AHSOOv4thaz7xsz1l+0rgNbRVHO8Cm6b7mvNUp3vH2H7vdF93vmkF3Kz/3z/i5/rpvu481OpKtGTXbv1D8HXgY9N9zfmo1RjPtZ2Za3Tlck5dh1beZgAtv+thYMl0X3M+aqVvPx3Yoet1aOTxM+VnCnQKoNkIF0323IQ+gEKhUCgUCoUih8zInC6FQqFQKBSKmYYyuhQKhUKhUCimAGV0KRQKhUKhUEwByuhSKBQKhUKhmAKU0aVQKBQKhUIxBSijS6FQKBQKhWIKUEaXQqE4JdCrs8+d7vNQKBSKTCijS6FQfOhQBppCoZgOlNGlUCgUCoVCMQUoo0uhUMxIhBArhBA7hRA9Qog7Ux6/QAjxphCiVwhxQAjxN0IIoW97RAjRr++6SwjRL4T4fsqxS4UQ24UQXfrY61O2NQghntfHbRZC/M8pu1iFQnFKoIwuhUIx49CNqF+jNZ0tQeuFlsAE3KE/di7wJbRm2kgpL5BSevT9lkspPVLKv9TH9AKP6eMWA3duT9QHAAAB8klEQVQC9wshXPr+fwfsAgqBRcALObtAhUJxSqKMLoVCMROZA9QDP5RShoHvJTZIKf8spXxOShmRUh4EngZWGBjzYqBFSnmPlDImpXwYaAU26tvjQDlQIaXsklI+n80LUigUpz7K6FIoFDOREqBbShnR/z+e2CCEOE0I8bQQok0I0Q1cDlgNjDkLWCyE6E78AA1Ahb79K0Av8JoQYr8Q4qqsXY1CofhQoIwuhUIxE2kFCoUQCWOqLGXbfwIPAJVSykLgT4AYcbwcY8wm4GkpZWHKj0tKeR+AlPKolPITQCVwN3BPFq9HoVB8CFBGl0KhmIkcAvYBn9MNry+mbPMC7UBUCLEJOH+M41uAJSMe+zOwRAhxlRDCIoRwCyGuEEIUAQghLhdCVEopJZrR1pfdS1IoFKc6yuhSKBQzDt3wuR64Bc3AakvZ/FngH9BCgbcDD48xxNeAHwohjgoh/kkfsxe4APgMmiftA+AGtFwugDXA6/rqx9uBm7N6UQqF4pRHaJ9dCoVCoVAoFIpcojxdCoVCoVAoFFOAMroUCoVCoVAopgBldCkUCoVCoVBMAcroUigUCoVCoZgClNGlUCgUCoVCMQUoo0uhUCgUCoViClBGl0KhUCgUCsUUoIwuhUKhUCgUiilAGV0KhUKhUCgUU8D/B/IdhHipBS6JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot time series of count\n",
    "\n",
    "f,ax=plt.subplots(figsize=(10,7))\n",
    "\n",
    "tc.plot(ax=ax, ls='-', label='tick count')\n",
    "vc.plot(ax=ax, ls='--', label='volume count')\n",
    "dc.plot(ax=ax, ls='-.', label='dollar count')\n",
    "\n",
    "ax.set_title('scaled bar counts')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which Bar Type Has Most Stable Counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:15:38.670995Z",
     "start_time": "2018-05-05T01:15:38.332653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tc std: 12.07%, vc std: 10.92%, dc std: 11.83%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "volume    0.109205\n",
       "dollar    0.118333\n",
       "tick      0.120695\n",
       "df        0.121785\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'tc std: {tc.std():.2%}, vc std: {vc.std():.2%}, dc std: {dc.std():.2%}')\n",
    "\n",
    "bar_types = ['tick','volume','dollar','df']\n",
    "bar_std = [tc.std(),vc.std(),dc.std(),dfc.std()]\n",
    "counts = (pd.Series(bar_std,index=bar_types))\n",
    "counts.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which Bar Type Has the Lowest Serial Correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:31.845911Z",
     "start_time": "2018-05-05T01:08:31.582441Z"
    }
   },
   "outputs": [],
   "source": [
    "def returns(s):\n",
    "    arr = np.diff(np.log(s))\n",
    "    return (pd.Series(arr, index=s.index[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:15:49.217691Z",
     "start_time": "2018-05-05T01:15:48.886957Z"
    }
   },
   "outputs": [],
   "source": [
    "tr = returns(tick_df.price)\n",
    "vr = returns(v_bar_df.price)\n",
    "dr = returns(dv_bar_df.price)\n",
    "df_ret = returns(df.price)   \n",
    "\n",
    "bar_returns = [tr, vr, dr, df_ret]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:17:45.890539Z",
     "start_time": "2018-05-05T01:17:45.516855Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>37782.0</td>\n",
       "      <td>-0.127882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1129120.0</td>\n",
       "      <td>-0.092525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>-0.017763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>0.063133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  autocorr_stat\n",
       "dollar      37782.0      -0.127882\n",
       "df        1129120.0      -0.092525\n",
       "volume      48436.0      -0.017763\n",
       "tick        11290.0       0.063133"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>0.017763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>0.063133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1129120.0</td>\n",
       "      <td>0.092525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>37782.0</td>\n",
       "      <td>0.127882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  autocorr_stat\n",
       "volume      48436.0       0.017763\n",
       "tick        11290.0       0.063133\n",
       "df        1129120.0       0.092525\n",
       "dollar      37782.0       0.127882"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_test_stats(bar_types,bar_returns,test_func,*args,**kwds):\n",
    "    \n",
    "    dct = {bar:(int(bar_ret.shape[0]), test_func(bar_ret,*args,**kwds)) \n",
    "           for bar,bar_ret in zip(bar_types,bar_returns)}\n",
    "    df = (pd.DataFrame.from_dict(dct)\n",
    "          .rename(index={0:'sample_size',1:f'{test_func.__name__}_stat'})\n",
    "          .T)\n",
    "    return df\n",
    "\n",
    "autocorrs = get_test_stats(bar_types,bar_returns,pd.Series.autocorr)\n",
    "display(autocorrs.sort_values('autocorr_stat'),\n",
    "        autocorrs.abs().sort_values('autocorr_stat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:32.817899Z",
     "start_time": "2018-05-05T01:08:32.477309Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_autocorr(bar_types,bar_returns):\n",
    "    f,axes=plt.subplots(len(bar_types),figsize=(10,7))\n",
    "\n",
    "    for i, (bar, typ) in enumerate(zip(bar_returns, bar_types)):\n",
    "        smg.tsa.plot_acf(bar, lags=120, ax=axes[i],\n",
    "                                 alpha=0.05, unbiased=True, fft=True,\n",
    "                                 zero=False,\n",
    "                                 title=f'{typ} AutoCorr')\n",
    "    plt.tight_layout()   \n",
    "    \n",
    "def plot_hist(bar_types,bar_rets):\n",
    "    f,axes=plt.subplots(len(bar_types),figsize=(10,6))\n",
    "    for i, (bar, typ) in enumerate(zip(bar_returns, bar_types)):\n",
    "        g = sns.distplot(bar, ax=axes[i], kde=False, label=typ)\n",
    "        g.set(yscale='log')\n",
    "        axes[i].legend()\n",
    "    plt.tight_layout()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:34.001599Z",
     "start_time": "2018-05-05T01:08:32.819419Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plot_autocorr(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:35.507998Z",
     "start_time": "2018-05-05T01:08:34.002850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partition Bar Series into Monthly, Compute Variance of Returns, and Variance of Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:35.783601Z",
     "start_time": "2018-05-05T01:08:35.509227Z"
    }
   },
   "outputs": [],
   "source": [
    "def partition_monthly(s):\n",
    "    return s.resample('1M').var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:16:13.455019Z",
     "start_time": "2018-05-05T01:16:13.021434Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tr_rs = partition_monthly(tr)\n",
    "vr_rs = partition_monthly(vr)\n",
    "dr_rs = partition_monthly(dr)\n",
    "df_ret_rs = partition_monthly(df_ret)\n",
    "monthly_vars = [tr_rs, vr_rs, dr_rs, df_ret_rs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:16:19.562679Z",
     "start_time": "2018-05-05T01:16:19.220785Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>var_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>91.0</td>\n",
       "      <td>6.369022e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>91.0</td>\n",
       "      <td>2.249338e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>91.0</td>\n",
       "      <td>2.520678e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>91.0</td>\n",
       "      <td>3.262948e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size      var_stat\n",
       "df             91.0  6.369022e-12\n",
       "tick           91.0  2.249338e-09\n",
       "dollar         91.0  2.520678e-09\n",
       "volume         91.0  3.262948e-09"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_test_stats(bar_types,monthly_vars,np.var).sort_values('var_stat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Jarque-Bera Test, Which Has Lowest Test Statistic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:36.802000Z",
     "start_time": "2018-05-05T01:08:36.406004Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>jb_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>8.326276e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>37782.0</td>\n",
       "      <td>2.628628e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>1.896350e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1129120.0</td>\n",
       "      <td>1.156081e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size       jb_stat\n",
       "tick        11290.0  8.326276e+07\n",
       "dollar      37782.0  2.628628e+10\n",
       "volume      48436.0  1.896350e+11\n",
       "df        1129120.0  1.156081e+14"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def jb(x,test=True): \n",
    "    np.random.seed(12345678)\n",
    "    if test: return stats.jarque_bera(x)[0]\n",
    "    return stats.jarque_bera(x)[1]\n",
    "\n",
    "get_test_stats(bar_types,bar_returns,jb).sort_values('jb_stat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Shapiro-Wilk Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapiro-Wilk test statistic > larger is better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:37.132288Z",
     "start_time": "2018-05-05T01:08:36.803870Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>shapiro_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>0.648796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>37782.0</td>\n",
       "      <td>0.399860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>0.271589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1129120.0</td>\n",
       "      <td>0.169642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  shapiro_stat\n",
       "tick        11290.0      0.648796\n",
       "dollar      37782.0      0.399860\n",
       "volume      48436.0      0.271589\n",
       "df        1129120.0      0.169642"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def shapiro(x,test=True): \n",
    "    np.random.seed(12345678)\n",
    "    if test: return stats.shapiro(x)[0]\n",
    "    return stats.shapiro(x)[1]\n",
    "\n",
    "(get_test_stats(bar_types,bar_returns,shapiro)\n",
    " .sort_values('shapiro_stat')[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Serial Correlation between Dollar and Dollar Imbalance Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update [05.04.18]\n",
    "\n",
    "Earlier version was missing some additional code. Before we can compare we must compute the Dollar Imbalance Bar. This is my initial implementation of this concept but is experimental and may need some adjustments. \n",
    "\n",
    "   1. Compute the sequence ${bt}_{t=1,...,T}$.\n",
    "   2. Compute the imbalance at time $T$ defined as $\\theta_T = \\sum_{t=1}^{T}b_tv_t$.\n",
    "   3. Compute the expected value of $T$ as ewma of previous $T$ values.\n",
    "   4. Compute the expected value of $\\theta_T$ as ewma of $b_tv_t$ values.\n",
    "   5. for each index:\n",
    "        - compute $\\lvert\\theta_t\\rvert >= E_0[T] * \\lvert2v^+-E_0[v_t]\\rvert$\n",
    "        - if the condition is met capture the quantity of ticks\n",
    "        - reset tick count\n",
    "        - continue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:37.402694Z",
     "start_time": "2018-05-05T01:08:37.134093Z"
    }
   },
   "outputs": [],
   "source": [
    "tidx = get_imbalance(df.price.values)*df.dv.iloc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:41.492275Z",
     "start_time": "2018-05-05T01:08:37.404111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "window size: 1,129.00\n",
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                             bs        E_T          E_bs        absMul  \\\n",
      "dates                                                                    \n",
      "2017-03-21 15:41:15 -27550.2745  1127986.0 -11716.780451  1.321636e+10   \n",
      "2017-03-21 15:41:44 -11848.0200  1127987.0 -11716.896592  1.321651e+10   \n",
      "2017-03-21 15:41:51 -72626.1000  1127988.0 -11770.798542  1.327732e+10   \n",
      "2017-03-21 15:41:51 -41663.9000  1127989.0 -11797.252614  1.330717e+10   \n",
      "2017-03-21 15:41:51 -20676.1000  1127990.0 -11805.110001  1.331605e+10   \n",
      "\n",
      "                         absTheta  \n",
      "dates                              \n",
      "2017-03-21 15:41:15  4.163981e+08  \n",
      "2017-03-21 15:41:44  4.164100e+08  \n",
      "2017-03-21 15:41:51  4.164826e+08  \n",
      "2017-03-21 15:41:51  4.165243e+08  \n",
      "2017-03-21 15:41:51  4.165450e+08  \n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1129120 entries, 2009-09-28 09:30:00 to 2017-03-21 15:41:51\n",
      "Data columns (total 5 columns):\n",
      "bs          1129120 non-null float64\n",
      "E_T         1129120 non-null float64\n",
      "E_bs        1129120 non-null float64\n",
      "absMul      1129120 non-null float64\n",
      "absTheta    1129120 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 91.7 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wndo = tidx.shape[0]//1000\n",
    "print(f'window size: {wndo:,.2f}')\n",
    "\n",
    "## Expected value of bs approximated by ewm\n",
    "E_bs = tidx.ewm(wndo).mean() # expected `bs`\n",
    "\n",
    "## what is E_T??? \n",
    "## in this implementation E_T is ewm of index values\n",
    "E_T = pd.Series(range(tidx.shape[0]), index=tidx.index).ewm(wndo).mean()\n",
    "\n",
    "df0 =(pd.DataFrame().assign(bs=tidx)\n",
    "      .assign(E_T=E_T).assign(E_bs=E_bs)\n",
    "      .assign(absMul=lambda df: df.E_T*np.abs(df.E_bs))\n",
    "      .assign(absTheta=tidx.cumsum().abs()))\n",
    "cprint(df0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:44.211934Z",
     "start_time": "2018-05-05T01:08:41.493878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df0[['E_T','E_bs']].plot(subplots=True, figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:44.710274Z",
     "start_time": "2018-05-05T01:08:44.214754Z"
    }
   },
   "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>bs</th>\n",
       "      <th>E_T</th>\n",
       "      <th>E_bs</th>\n",
       "      <th>absMul</th>\n",
       "      <th>absTheta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1129.120000</td>\n",
       "      <td>1129.120000</td>\n",
       "      <td>1129.120000</td>\n",
       "      <td>1.129120e+03</td>\n",
       "      <td>1129.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.368911</td>\n",
       "      <td>563.432358</td>\n",
       "      <td>-0.353120</td>\n",
       "      <td>3.387870e+06</td>\n",
       "      <td>215214.083153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>424.866740</td>\n",
       "      <td>325.945805</td>\n",
       "      <td>9.939266</td>\n",
       "      <td>7.267299e+06</td>\n",
       "      <td>230644.953492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-122720.979510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-142.279615</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.353707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-17.922287</td>\n",
       "      <td>281.150750</td>\n",
       "      <td>-2.466245</td>\n",
       "      <td>3.381084e+05</td>\n",
       "      <td>40936.497246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.004000</td>\n",
       "      <td>563.430500</td>\n",
       "      <td>-0.006400</td>\n",
       "      <td>1.243607e+06</td>\n",
       "      <td>95291.692023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.876000</td>\n",
       "      <td>845.710250</td>\n",
       "      <td>2.602636</td>\n",
       "      <td>3.226507e+06</td>\n",
       "      <td>469891.026034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>103881.575980</td>\n",
       "      <td>1127.990000</td>\n",
       "      <td>73.198231</td>\n",
       "      <td>1.161008e+08</td>\n",
       "      <td>779639.569647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bs          E_T         E_bs        absMul       absTheta\n",
       "count    1129.120000  1129.120000  1129.120000  1.129120e+03    1129.120000\n",
       "mean       -0.368911   563.432358    -0.353120  3.387870e+06  215214.083153\n",
       "std       424.866740   325.945805     9.939266  7.267299e+06  230644.953492\n",
       "min   -122720.979510     0.000000  -142.279615  0.000000e+00       0.353707\n",
       "25%       -17.922287   281.150750    -2.466245  3.381084e+05   40936.497246\n",
       "50%        -4.004000   563.430500    -0.006400  1.243607e+06   95291.692023\n",
       "75%        17.876000   845.710250     2.602636  3.226507e+06  469891.026034\n",
       "max    103881.575980  1127.990000    73.198231  1.161008e+08  779639.569647"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df0.describe()/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:28:13.856302Z",
     "start_time": "2018-05-05T01:28:07.710605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf00378b70>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df0.loc['2010-06',['absMul','absTheta']]\n",
    " .reset_index(drop=True)\n",
    " .plot(figsize=(10,5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:51.342504Z",
     "start_time": "2018-05-05T01:08:51.034248Z"
    }
   },
   "outputs": [],
   "source": [
    "def test_t_abs(absTheta,t,E_bs):\n",
    "    \"\"\"\n",
    "    Bool function to test inequality\n",
    "    *row is assumed to come from df.itertuples()\n",
    "    -absTheta: float(), row.absTheta\n",
    "    -t: pd.Timestamp()\n",
    "    -E_bs: float(), row.E_bs\n",
    "    \"\"\"\n",
    "    return (absTheta >= t*E_bs)\n",
    "\n",
    "def agg_imbalance_bars(df):\n",
    "    \"\"\"\n",
    "    Implements the accumulation logic\n",
    "    \"\"\"\n",
    "    start = df.index[0]  \n",
    "    bars = []\n",
    "    for row in df.itertuples():\n",
    "        t_abs = row.absTheta\n",
    "        rowIdx = row.Index\n",
    "        E_bs = row.E_bs\n",
    "        \n",
    "        t = df.loc[start:rowIdx].shape[0]\n",
    "        if t<1: t=1 # if t lt 1 set equal to 1\n",
    "        if test_t_abs(t_abs,t,E_bs):\n",
    "            bars.append((start,rowIdx,t))\n",
    "            start = rowIdx\n",
    "    return bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:26.553865Z",
     "start_time": "2018-05-05T01:08:51.343906Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      start                stop  Ts\n",
      "1129083 2017-03-21 15:40:53 2017-03-21 15:40:53   2\n",
      "1129084 2017-03-21 15:40:53 2017-03-21 15:41:15   3\n",
      "1129085 2017-03-21 15:41:15 2017-03-21 15:41:44   2\n",
      "1129086 2017-03-21 15:41:44 2017-03-21 15:41:51   4\n",
      "1129087 2017-03-21 15:41:51 2017-03-21 15:41:51   3\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 971250 entries, 0 to 1129087\n",
      "Data columns (total 3 columns):\n",
      "start    971250 non-null datetime64[ns]\n",
      "stop     971250 non-null datetime64[ns]\n",
      "Ts       971250 non-null int64\n",
      "dtypes: datetime64[ns](2), int64(1)\n",
      "memory usage: 29.6 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bars = agg_imbalance_bars(df0)\n",
    "test_imb_bars = (pd.DataFrame(bars,columns=['start','stop','Ts'])\n",
    "                 .drop_duplicates())\n",
    "cprint(test_imb_bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:26.900421Z",
     "start_time": "2018-05-05T01:11:26.555120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    971250.0\n",
       "mean          3.0\n",
       "std           2.0\n",
       "min           1.0\n",
       "25%           2.0\n",
       "50%           2.0\n",
       "75%           3.0\n",
       "max         139.0\n",
       "Name: Ts, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_imb_bars.Ts.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:28.038734Z",
     "start_time": "2018-05-05T01:11:26.901749Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf00a8f630>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_imb_bars.set_index('stop')['Ts'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.101336Z",
     "start_time": "2018-05-05T01:11:28.040300Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                        price\n",
      "dates                        \n",
      "2017-03-21 15:27:22  104.1422\n",
      "2017-03-21 15:35:03  104.0831\n",
      "2017-03-21 15:35:08  104.0845\n",
      "2017-03-21 15:37:43  104.0909\n",
      "2017-03-21 15:40:47  103.9575\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 208564 entries, 2009-09-28 09:30:00 to 2017-03-21 15:40:47\n",
      "Data columns (total 1 columns):\n",
      "price    208564 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.2 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dvImbBars = df.price.loc[test_imb_bars.stop].drop_duplicates()\n",
    "cprint(dvImbBars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.391117Z",
     "start_time": "2018-05-05T01:11:37.103211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                        price\n",
      "dates                        \n",
      "2017-03-21 15:21:50  104.1100\n",
      "2017-03-21 15:26:40  104.1701\n",
      "2017-03-21 15:30:38  104.1400\n",
      "2017-03-21 15:37:43  104.0909\n",
      "2017-03-21 15:40:53  103.9700\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 37783 entries, 2009-09-28 09:46:35 to 2017-03-21 15:40:53\n",
      "Data columns (total 1 columns):\n",
      "price    37783 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 1.8 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dvBar = dv_bar_df.price\n",
    "cprint(dvBar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.706922Z",
     "start_time": "2018-05-05T01:11:37.392565Z"
    }
   },
   "outputs": [],
   "source": [
    "dr = returns(dv_bar_df.price)\n",
    "drImb = returns(dvImbBars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:38.000919Z",
     "start_time": "2018-05-05T01:11:37.708594Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>37782.0</td>\n",
       "      <td>-0.127882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>208563.0</td>\n",
       "      <td>-0.077302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_size  autocorr_stat\n",
       "dvBar      37782.0      -0.127882\n",
       "dvImb     208563.0      -0.077302"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_types = ['dvBar','dvImb']\n",
    "bar_rets = [dr, drImb]\n",
    "\n",
    "get_test_stats(bar_types,bar_rets,pd.Series.autocorr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:38.602438Z",
     "start_time": "2018-05-05T01:11:38.002187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorr(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:39.787193Z",
     "start_time": "2018-05-05T01:11:38.606456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:40.146218Z",
     "start_time": "2018-05-05T01:11:39.789951Z"
    }
   },
   "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>sample_size</th>\n",
       "      <th>jb_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>8.326276e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>1.896350e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_size       jb_stat\n",
       "dvBar      11290.0  8.326276e+07\n",
       "dvImb      48436.0  1.896350e+11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_size</th>\n",
       "      <th>shapiro_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>11290.0</td>\n",
       "      <td>0.648796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>48436.0</td>\n",
       "      <td>0.271589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_size  shapiro_stat\n",
       "dvBar      11290.0      0.648796\n",
       "dvImb      48436.0      0.271589"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jbs = get_test_stats(bar_types,bar_returns,jb).sort_values('jb_stat')\n",
    "shaps = (get_test_stats(bar_types,bar_returns,shapiro)\n",
    "         .sort_values('shapiro_stat')[::-1])\n",
    "\n",
    "display(jbs,shaps)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "257px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}