{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "known-novel",
   "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=\"#Demo-Notebook-for-TimeSeries-Anomaly-Detection\" data-toc-modified-id=\"Demo-Notebook-for-TimeSeries-Anomaly-Detection-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Demo Notebook for TimeSeries Anomaly Detection</a></span></li><li><span><a href=\"#Installation-of-libraries-and-imports\" data-toc-modified-id=\"Installation-of-libraries-and-imports-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Installation of libraries and imports</a></span><ul class=\"toc-item\"><li><span><a href=\"#Installation-of-adcern-and-others-libraries\" data-toc-modified-id=\"Installation-of-adcern-and-others-libraries-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Installation of adcern and others libraries</a></span></li><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Imports</a></span></li></ul></li><li><span><a href=\"#Init-of-configuration-files---ETL\" data-toc-modified-id=\"Init-of-configuration-files---ETL-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Init of configuration files - ETL</a></span><ul class=\"toc-item\"><li><span><a href=\"#Creation\" data-toc-modified-id=\"Creation-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Creation</a></span></li><li><span><a href=\"#Reading-the-json\" data-toc-modified-id=\"Reading-the-json-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Reading the json</a></span></li></ul></li><li><span><a href=\"#ETL-steps-(Extract,-Transform,-Load)\" data-toc-modified-id=\"ETL-steps-(Extract,-Transform,-Load)-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>ETL steps (Extract, Transform, Load)</a></span><ul class=\"toc-item\"><li><span><a href=\"#Compute-Normalization\" data-toc-modified-id=\"Compute-Normalization-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Compute Normalization</a></span></li><li><span><a href=\"#Transform-Data\" data-toc-modified-id=\"Transform-Data-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Transform Data</a></span></li><li><span><a href=\"#Copy-Locally\" data-toc-modified-id=\"Copy-Locally-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Copy Locally</a></span></li></ul></li><li><span><a href=\"#Visualization-of-downloaded-time-series\" data-toc-modified-id=\"Visualization-of-downloaded-time-series-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Visualization of downloaded time series</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-time-series-with-pandas-and-host-definition\" data-toc-modified-id=\"Reading-time-series-with-pandas-and-host-definition-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Reading time series with pandas and host definition</a></span></li><li><span><a href=\"#Reconstruction-function\" data-toc-modified-id=\"Reconstruction-function-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Reconstruction function</a></span></li><li><span><a href=\"#Normalized-data-visualization\" data-toc-modified-id=\"Normalized-data-visualization-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Normalized data visualization</a></span></li><li><span><a href=\"#How-to-retrieve-the-original-data-without-normalization\" data-toc-modified-id=\"How-to-retrieve-the-original-data-without-normalization-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>How to retrieve the original data without normalization</a></span></li><li><span><a href=\"#Original-data-visualization\" data-toc-modified-id=\"Original-data-visualization-5.5\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>Original data visualization</a></span></li></ul></li><li><span><a href=\"#Init-of-configuration-file---ANALYSIS\" data-toc-modified-id=\"Init-of-configuration-file---ANALYSIS-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Init of configuration file - ANALYSIS</a></span><ul class=\"toc-item\"><li><span><a href=\"#Creation\" data-toc-modified-id=\"Creation-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Creation</a></span></li><li><span><a href=\"#Reading-the-json\" data-toc-modified-id=\"Reading-the-json-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Reading the json</a></span></li></ul></li><li><span><a href=\"#ANALYSIS-to-produce-anomaly-scores\" data-toc-modified-id=\"ANALYSIS-to-produce-anomaly-scores-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>ANALYSIS to produce anomaly scores</a></span><ul class=\"toc-item\"><li><span><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>Analysis</a></span></li></ul></li><li><span><a href=\"#Visualization-of-the-results\" data-toc-modified-id=\"Visualization-of-the-results-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Visualization of the results</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-the-scores\" data-toc-modified-id=\"Reading-the-scores-8.1\"><span class=\"toc-item-num\">8.1&nbsp;&nbsp;</span>Reading the scores</a></span></li><li><span><a href=\"#Visualization-of-both-downloaded-data-and-scores\" data-toc-modified-id=\"Visualization-of-both-downloaded-data-and-scores-8.2\"><span class=\"toc-item-num\">8.2&nbsp;&nbsp;</span>Visualization of both downloaded data and scores</a></span></li></ul></li><li><span><a href=\"#ETL-Steps-not-used\" data-toc-modified-id=\"ETL-Steps-not-used-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>ETL Steps not used</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-Presence\" data-toc-modified-id=\"Data-Presence-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span>Data Presence</a></span></li><li><span><a href=\"#Check-Normalization\" data-toc-modified-id=\"Check-Normalization-9.2\"><span class=\"toc-item-num\">9.2&nbsp;&nbsp;</span>Check Normalization</a></span></li></ul></li><li><span><a href=\"#Adding-a-model\" data-toc-modified-id=\"Adding-a-model-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Adding a model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Changing-config-file---Analysis\" data-toc-modified-id=\"Changing-config-file---Analysis-10.1\"><span class=\"toc-item-num\">10.1&nbsp;&nbsp;</span>Changing config file - Analysis</a></span></li><li><span><a href=\"#Reading-the-json\" data-toc-modified-id=\"Reading-the-json-10.2\"><span class=\"toc-item-num\">10.2&nbsp;&nbsp;</span>Reading the json</a></span></li><li><span><a href=\"#Analysis-of-multiple-models\" data-toc-modified-id=\"Analysis-of-multiple-models-10.3\"><span class=\"toc-item-num\">10.3&nbsp;&nbsp;</span>Analysis of multiple models</a></span></li><li><span><a href=\"#Checking-new-results\" data-toc-modified-id=\"Checking-new-results-10.4\"><span class=\"toc-item-num\">10.4&nbsp;&nbsp;</span>Checking new results</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "understood-cross",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Demo Notebook for TimeSeries Anomaly Detection\n",
    "This notebook shows the Anomaly Detection system for TimeSeries in action using the tools provided in this  repository.\n",
    "\n",
    "**Note that we recommend to run this notebook using swan004.cern.ch with the following configuration:**\n",
    "- Software stack: Other releases, **97a**\n",
    "- Spark cluster: **General Purpose (Analytix)**\n",
    "\n",
    "---\n",
    "**REMEMBER TO ACTIVATE THE ANALYTIX CLUSTER IN THE SWAN CONFIGURATION!!**\n",
    "Activating it means to activate spark (click the spark icon in the upper part of the notebook to enable spark before starting to run the notebook cells)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legendary-curve",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Installation of libraries and imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-teaching",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Installation of adcern and others libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "manual-accuracy",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:28:15.289755Z",
     "start_time": "2021-05-03T14:28:15.284123Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Set the variable to True the first time to download the libraries.\n",
    "# Note that with @branch you can install a specific branch\n",
    "\n",
    "first_time = False\n",
    "if first_time:\n",
    "    # If you install the libraries it takes some minutes\n",
    "    !pip install --user git+https://:@gitlab.cern.ch:8443/cloud-infrastructure/data-analytics.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "played-aluminum",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "romance-prevention",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:28:25.414952Z",
     "start_time": "2021-05-03T14:28:19.143582Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# AD System Libraries ----------------------------------------------------\n",
    "import adcern.cmd.data_mining as DM\n",
    "import etl.spark_etl.etl_pipeline as PL\n",
    "\n",
    "# To pass command line parameters and to use other functions -------------\n",
    "import sys, os, re, json\n",
    "\n",
    "# To run the visualization function --------------------------------------\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "from dateutil import tz\n",
    "\n",
    "# To read more parquet files with pandas ---------------------------------\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "superb-queens",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Init of configuration files - ETL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confused-navigation",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Our ETL and analysis methods work with json configuration files in input. In these files we save all the paths, the dates and the hyperparameters used then by the methods.\n",
    "\n",
    "Usually we have the files already saved for static tests or we create them in our production pipeline; **for the purpose of this notebook instead, we create the json files starting from a python dict.**\n",
    "\n",
    "Note that we need 2 config files: \n",
    "- A config file about the training part.\n",
    "- A config file about the data used by the trained model to infer the scores. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "possible-memory",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Creation\n",
    "Note that the 2 config files share most of the parameters so we will have:\n",
    "- json_data: containing all the main information\n",
    "- json_data_train, json_data_inference: containing specific paths for the 2 different purposes\n",
    "\n",
    "**Note also that you have to be sure that you have the writing rights for all the paths contained here, in particular *HDFS_folder_with_write_rights* should point to your hdfs personal folder to ensure that**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "spoken-finding",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:49:25.662860Z",
     "start_time": "2021-05-03T14:49:25.210257Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "demo_name = 'demo_AD_System'\n",
    "json_file_train = 'demo_config_train.json'\n",
    "json_file_inference = 'demo_config_inference.json'\n",
    "username = os.environ['USER']\n",
    "HDFS_folder_with_write_rights = '/user/' + username + '/'\n",
    "print(\"Folder to save data and results: \" + HDFS_folder_with_write_rights)\n",
    "\n",
    "collectd_path = \"/project/monitoring/collectd/\"\n",
    "                                  \n",
    "json_data = {}\n",
    "\n",
    "# Absolute path identifier of the cell/hostgroup that you want to mine.\n",
    "# Note that it is in a list format, but only one hostgroup is supported so far.\n",
    "json_data['hostgroups'] = []\n",
    "json_data['hostgroups'].append('cloud_compute/level2/main/gva_shared_017')\n",
    "\n",
    "# The pattern of the names of your data folders and \".metadata\" files.\n",
    "json_data['code_project_name'] = demo_name\n",
    "\n",
    "# Local area of your VM where to save your data and metadata data are saved in\n",
    "# folders with one parquet only. Metadata are saved in file with the same name\n",
    "# of the resepctive foler plus the \".metadata\" extension.\n",
    "json_data['local_cache_folder'] = '/eos/user/' + username[:1] + \\\n",
    "    '/' + username + '/' + demo_name + '/local_cache_'\n",
    "\n",
    "# HDFS Area where Spark saves the aggregated data of your cell.\n",
    "# Note that the saving can create multiple file depending on the number of\n",
    "# partitions that the workers were using.\n",
    "json_data['hdfs_out_folder'] = HDFS_folder_with_write_rights + \\\n",
    "    demo_name + '/raw_parquet_'\n",
    "\n",
    "# HDFS Area where Spark saves the aggregated data of your cell.\n",
    "# Note that here we force it to be one partiotion only.\n",
    "json_data['hdfs_cache_folder'] = HDFS_folder_with_write_rights + \\\n",
    "    demo_name + '/compressed_'\n",
    "\n",
    "# HDFS Area where Spark saves the normalization coefficients computed on the\n",
    "# normalziation chunk of data between the normalization dates.\n",
    "json_data['normalization_out_folder'] = HDFS_folder_with_write_rights + \\\n",
    "    demo_name + '/normalization/'\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# Wether you want to overwrite (true) or not (false) the raw data in HDFS.\n",
    "# If not sure leave true.\n",
    "json_data['overwrite_on_hdfs'] = True\n",
    "\n",
    "# Wether you want to overwrite (true) or not (false) the noramlization\n",
    "# coefficeints in HDFS. If not sure leave true.\n",
    "json_data['overwrite_normalization'] = True\n",
    "\n",
    "# The level of aggregation of your raw time series data.\n",
    "# The aggregator is typically the mean operator.\n",
    "# e.g. if 5 it means that we summarize the data every 5 min, and the values\n",
    "# with timestamp 7.45 will represent the mean of the previous 5 minutes from\n",
    "# 7.40 to 7.45 but that value will have 7.45 as timestamp\n",
    "json_data['aggregate_every_n_minutes'] = 10\n",
    "\n",
    "# The length of your windows of data.\n",
    "# e.g. if aggregate_every_n_minutes = 10 and history_steps = 6 it means that\n",
    "# every windows is summarizing 6 * 10 = 60 minutes\n",
    "json_data['history_steps'] = 48\n",
    "\n",
    "# The number of step you want to move your window.\n",
    "# e.g. if aggregate_every_n_minutes = 10 and history_steps = 2 it means that\n",
    "# you will get a window of data that is translated of 10 * 2 = 20 min with\n",
    "# respect to the previous.\n",
    "# Note that if slide_steps has the same value of history_steps you have non-\n",
    "# overlapping windows.\n",
    "json_data['slide_steps'] = 1\n",
    "\n",
    "# Used to create windows with future steps. If not sure keep this to 0.\n",
    "json_data['future_steps'] = 0\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# Dates representing the start/end of the data and noramlization chunks.\n",
    "# - start_date -> the starting date of data chunk of ETL\n",
    "# - end_date -> the ending date of data chunk of ETL\n",
    "# - start_date_normalization -> the starting date of the chunk of data used\n",
    "#   to learn noramlization coefficeints (typically this chunk preceeds the\n",
    "#   chunk of data)\n",
    "# - end_date_normalization -> the ending date of the chunk of data used\n",
    "#   to learn noramlization coefficeints\n",
    "# Note that the upper extremum is excluded (i.e. data will stop at the 23:59\n",
    "# of the day preeceeding the date_end_excluded)\n",
    "json_data['date_start'] = \"2021-03-01\"\n",
    "json_data['date_end_excluded'] = \"2021-03-20\"\n",
    "json_data['date_start_normalization'] = \"2021-03-01\"\n",
    "json_data['date_end_normalization_excluded'] = \"2021-03-20\"\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# List of plugins to mine.\n",
    "# Note that it is a dictionary where every key represents the name your plugin\n",
    "# have and the value is a dictionary with:\n",
    "# 'plugin_instance', 'type' 'type_instance', 'plugin_name'\n",
    "# the value asigned to these key is defining an and-filter.\n",
    "# you will get only the data that have all those attributes\n",
    "# ('plugin_instance', 'type' 'type_instance', 'plugin_name') in and with the\n",
    "# specified value.\n",
    "# Note that if you do not want to filter on one attribute do not express it.\n",
    "json_data['selected_plugins'] = {\n",
    "    'cpu__percent_idle': {\n",
    "        'plugin_data_path': collectd_path + 'cpu',\n",
    "        'plugin_filter': \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
    "    }, \n",
    "    'load_longterm': {\n",
    "        'plugin_data_path': collectd_path + 'load',\n",
    "        'plugin_filter': \"value_instance == 'longterm' and plugin == 'load'\"\n",
    "    },\n",
    "    'disk_io_time': {\n",
    "        'plugin_data_path': collectd_path + 'cloud',\n",
    "        'plugin_filter': \"value_instance == 'io_time' and plugin == 'disk'\"\n",
    "    }\n",
    "}\n",
    "\n",
    "json_data_train = json_data.copy()\n",
    "json_data_train['local_cache_folder'] += 'train/'\n",
    "json_data_train['hdfs_out_folder'] += 'train/'\n",
    "json_data_train['hdfs_cache_folder'] += 'train/'\n",
    "\n",
    "json_data_inference = json_data.copy()\n",
    "json_data_inference['local_cache_folder'] += 'inference/'\n",
    "json_data_inference['hdfs_out_folder'] += 'inference/'\n",
    "json_data_inference['hdfs_cache_folder'] += 'inference/'\n",
    "# The imporant change is that we want to have NON OVERLAPPING windows\n",
    "# in the inference!\n",
    "json_data_inference['slide_steps'] = 48\n",
    "# I want to infer only 24 hours!\n",
    "json_data_inference['date_start'] = \"2021-03-20\"\n",
    "json_data_inference['date_end_excluded'] = \"2021-04-20\"\n",
    "\n",
    "with open(json_file_train, 'w') as outfile:\n",
    "    json.dump(json_data_train, outfile, indent=4)\n",
    "\n",
    "with open(json_file_inference, 'w') as outfile:\n",
    "    json.dump(json_data_inference, outfile, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "anticipated-messenger",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Reading the json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "empty-september",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:49:30.976439Z",
     "start_time": "2021-05-03T14:49:30.953248Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_train.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_train/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_train/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_train/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 1,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-01\",\n",
      "    \"date_end_excluded\": \"2021-03-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_inference.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_inference/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_inference/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_inference/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 48,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-20\",\n",
      "    \"date_end_excluded\": \"2021-04-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Let's use the read_resource function in the data_mining library to be \n",
    "# sure that the format is correct and to print the files.\n",
    "\n",
    "_ = DM.read_resource(resource_file=json_file_train)\n",
    "_ = DM.read_resource(resource_file=json_file_inference)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-ballot",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# ETL steps (Extract, Transform, Load)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "figured-ground",
   "metadata": {
    "hidden": true
   },
   "source": [
    "What we want to achieve here is to reproduce the steps to download and normalize the data (following the order in the graph below).\n",
    "![](https://mattermost.web.cern.ch/files/skktqwaws7nkzpb16c73n11tac/public?h=KflwzsI6wpa58LJCUgTGH-r8dJHEskq_a4R_05QMOPg)\n",
    "\n",
    "For every step, we will call the specific function of the data_mining library (for both the json files in most of the cases). \n",
    "\n",
    "**Note that we use the click python library (it permits to use python functions through the command line but it has a default behavior that calls exit(0) at the end of them). To avoid the exit(0) problem just use the standalone_mode=False option.**\n",
    "\n",
    "Moreover, we will skip the first 2 steps of the pipeline that we show in the image above (data_presence and check_normalization) because they are used in production pipelines to check and avoid the re-processing of already processed time intervals. For the purpose of this example, we will force the reprocessing if data are available"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "played-faculty",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Compute Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "attempted-wrist",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:51:01.068276Z",
     "start_time": "2021-05-03T14:49:34.205805Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "PREPARING SPARK:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK CONTEXT: <SparkContext master=yarn appName=pyspark_shell_swan>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK OBJECT: <pyspark.sql.session.SparkSession object at 0x7f40f5cfdd90>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_train.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_train/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_train/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_train/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 1,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-01\",\n",
      "    \"date_end_excluded\": \"2021-03-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "COMPUTE NORMALIZATION COEFFICIENTS:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "Start: (2021, 3, 1) - End (2021, 3, 19)\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cpu', 'plugin_filter': \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/load', 'plugin_filter': \"value_instance == 'longterm' and plugin == 'load'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cloud', 'plugin_filter': \"value_instance == 'io_time' and plugin == 'disk'\"}\n",
      "/project/monitoring/collectd/cloud/2021/03/01/\n",
      "/project/monitoring/collectd/cloud/2021/03/02/\n",
      "/project/monitoring/collectd/cloud/2021/03/03/\n",
      "/project/monitoring/collectd/cloud/2021/03/04/\n",
      "/project/monitoring/collectd/cloud/2021/03/05/\n",
      "/project/monitoring/collectd/cloud/2021/03/06/\n",
      "/project/monitoring/collectd/cloud/2021/03/07/\n",
      "/project/monitoring/collectd/cloud/2021/03/08/\n",
      "/project/monitoring/collectd/cloud/2021/03/09/\n",
      "/project/monitoring/collectd/cloud/2021/03/10/\n",
      "/project/monitoring/collectd/cloud/2021/03/11/\n",
      "/project/monitoring/collectd/cloud/2021/03/12/\n",
      "/project/monitoring/collectd/cloud/2021/03/13/\n",
      "/project/monitoring/collectd/cloud/2021/03/14/\n",
      "/project/monitoring/collectd/cloud/2021/03/15/\n",
      "/project/monitoring/collectd/cloud/2021/03/16/\n",
      "/project/monitoring/collectd/cloud/2021/03/17/\n",
      "/project/monitoring/collectd/cloud/2021/03/18/\n",
      "/project/monitoring/collectd/cloud/2021/03/19/\n",
      "/project/monitoring/collectd/cpu/2021/03/01/\n",
      "/project/monitoring/collectd/cpu/2021/03/02/\n",
      "/project/monitoring/collectd/cpu/2021/03/03/\n",
      "/project/monitoring/collectd/cpu/2021/03/04/\n",
      "/project/monitoring/collectd/cpu/2021/03/05/\n",
      "/project/monitoring/collectd/cpu/2021/03/06/\n",
      "/project/monitoring/collectd/cpu/2021/03/07/\n",
      "/project/monitoring/collectd/cpu/2021/03/08/\n",
      "/project/monitoring/collectd/cpu/2021/03/09/\n",
      "/project/monitoring/collectd/cpu/2021/03/10/\n",
      "/project/monitoring/collectd/cpu/2021/03/11/\n",
      "/project/monitoring/collectd/cpu/2021/03/12/\n",
      "/project/monitoring/collectd/cpu/2021/03/13/\n",
      "/project/monitoring/collectd/cpu/2021/03/14/\n",
      "/project/monitoring/collectd/cpu/2021/03/15/\n",
      "/project/monitoring/collectd/cpu/2021/03/16/\n",
      "/project/monitoring/collectd/cpu/2021/03/17/\n",
      "/project/monitoring/collectd/cpu/2021/03/18/\n",
      "/project/monitoring/collectd/cpu/2021/03/19/\n",
      "/project/monitoring/collectd/load/2021/03/01/\n",
      "/project/monitoring/collectd/load/2021/03/02/\n",
      "/project/monitoring/collectd/load/2021/03/03/\n",
      "/project/monitoring/collectd/load/2021/03/04/\n",
      "/project/monitoring/collectd/load/2021/03/05/\n",
      "/project/monitoring/collectd/load/2021/03/06/\n",
      "/project/monitoring/collectd/load/2021/03/07/\n",
      "/project/monitoring/collectd/load/2021/03/08/\n",
      "/project/monitoring/collectd/load/2021/03/09/\n",
      "/project/monitoring/collectd/load/2021/03/10/\n",
      "/project/monitoring/collectd/load/2021/03/11/\n",
      "/project/monitoring/collectd/load/2021/03/12/\n",
      "/project/monitoring/collectd/load/2021/03/13/\n",
      "/project/monitoring/collectd/load/2021/03/14/\n",
      "/project/monitoring/collectd/load/2021/03/15/\n",
      "/project/monitoring/collectd/load/2021/03/16/\n",
      "/project/monitoring/collectd/load/2021/03/17/\n",
      "/project/monitoring/collectd/load/2021/03/18/\n",
      "/project/monitoring/collectd/load/2021/03/19/\n",
      "filter_str:  ( type == 'percent' and type_instance == 'idle' and plugin == 'cpu' ) or ( value_instance == 'longterm' and plugin == 'load' ) or ( value_instance == 'io_time' and plugin == 'disk' )\n",
      "NEW id_norm:  ef8bce\n",
      "Normalization Saved successfully\n",
      "Coefficient preparation shared:  0\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SUCCESS (inspect the first 40 rows):\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "                                  hostgroup             plugin    mean  stddev\n",
      "0  cloud_compute/level2/main/gva_shared_017  cpu__percent_idle  74.198  18.997\n",
      "1  cloud_compute/level2/main/gva_shared_017      load_longterm   0.302   0.294\n",
      "2  cloud_compute/level2/main/gva_shared_017       disk_io_time  60.421  69.629\n"
     ]
    }
   ],
   "source": [
    "# We must download firstly the normalization coefficients that we will use\n",
    "# to normalize the real data that we will download later.\n",
    "\n",
    "# Note that the function will use the same path for both both train and \n",
    "# inference (because the hostgroup, the dates and the plugins are the same).\n",
    "\n",
    "sys.argv = ['', '--resource_file', json_file_train]\n",
    "DM.compute_normalization(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-furniture",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T08:08:23.318591Z",
     "start_time": "2021-04-15T08:08:23.267079Z"
    },
    "hidden": true
   },
   "source": [
    "## Transform Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "middle-convention",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:52:57.741856Z",
     "start_time": "2021-05-03T14:51:01.075438Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "PREPARING SPARK:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK CONTEXT: <SparkContext master=yarn appName=pyspark_shell_swan>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK OBJECT: <pyspark.sql.session.SparkSession object at 0x7f4081d8a4d0>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_train.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_train/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_train/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_train/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 1,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-01\",\n",
      "    \"date_end_excluded\": \"2021-03-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "DOWNLOAD DATA - LONG MINING PROCESS...\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "Start: (2021, 3, 1) - End (2021, 3, 19)\n",
      "Reading normalization:  ef8bce\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cpu', 'plugin_filter': \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/load', 'plugin_filter': \"value_instance == 'longterm' and plugin == 'load'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cloud', 'plugin_filter': \"value_instance == 'io_time' and plugin == 'disk'\"}\n",
      "/project/monitoring/collectd/cloud/2021/03/01/\n",
      "/project/monitoring/collectd/cloud/2021/03/02/\n",
      "/project/monitoring/collectd/cloud/2021/03/03/\n",
      "/project/monitoring/collectd/cloud/2021/03/04/\n",
      "/project/monitoring/collectd/cloud/2021/03/05/\n",
      "/project/monitoring/collectd/cloud/2021/03/06/\n",
      "/project/monitoring/collectd/cloud/2021/03/07/\n",
      "/project/monitoring/collectd/cloud/2021/03/08/\n",
      "/project/monitoring/collectd/cloud/2021/03/09/\n",
      "/project/monitoring/collectd/cloud/2021/03/10/\n",
      "/project/monitoring/collectd/cloud/2021/03/11/\n",
      "/project/monitoring/collectd/cloud/2021/03/12/\n",
      "/project/monitoring/collectd/cloud/2021/03/13/\n",
      "/project/monitoring/collectd/cloud/2021/03/14/\n",
      "/project/monitoring/collectd/cloud/2021/03/15/\n",
      "/project/monitoring/collectd/cloud/2021/03/16/\n",
      "/project/monitoring/collectd/cloud/2021/03/17/\n",
      "/project/monitoring/collectd/cloud/2021/03/18/\n",
      "/project/monitoring/collectd/cloud/2021/03/19/\n",
      "/project/monitoring/collectd/cpu/2021/03/01/\n",
      "/project/monitoring/collectd/cpu/2021/03/02/\n",
      "/project/monitoring/collectd/cpu/2021/03/03/\n",
      "/project/monitoring/collectd/cpu/2021/03/04/\n",
      "/project/monitoring/collectd/cpu/2021/03/05/\n",
      "/project/monitoring/collectd/cpu/2021/03/06/\n",
      "/project/monitoring/collectd/cpu/2021/03/07/\n",
      "/project/monitoring/collectd/cpu/2021/03/08/\n",
      "/project/monitoring/collectd/cpu/2021/03/09/\n",
      "/project/monitoring/collectd/cpu/2021/03/10/\n",
      "/project/monitoring/collectd/cpu/2021/03/11/\n",
      "/project/monitoring/collectd/cpu/2021/03/12/\n",
      "/project/monitoring/collectd/cpu/2021/03/13/\n",
      "/project/monitoring/collectd/cpu/2021/03/14/\n",
      "/project/monitoring/collectd/cpu/2021/03/15/\n",
      "/project/monitoring/collectd/cpu/2021/03/16/\n",
      "/project/monitoring/collectd/cpu/2021/03/17/\n",
      "/project/monitoring/collectd/cpu/2021/03/18/\n",
      "/project/monitoring/collectd/cpu/2021/03/19/\n",
      "/project/monitoring/collectd/load/2021/03/01/\n",
      "/project/monitoring/collectd/load/2021/03/02/\n",
      "/project/monitoring/collectd/load/2021/03/03/\n",
      "/project/monitoring/collectd/load/2021/03/04/\n",
      "/project/monitoring/collectd/load/2021/03/05/\n",
      "/project/monitoring/collectd/load/2021/03/06/\n",
      "/project/monitoring/collectd/load/2021/03/07/\n",
      "/project/monitoring/collectd/load/2021/03/08/\n",
      "/project/monitoring/collectd/load/2021/03/09/\n",
      "/project/monitoring/collectd/load/2021/03/10/\n",
      "/project/monitoring/collectd/load/2021/03/11/\n",
      "/project/monitoring/collectd/load/2021/03/12/\n",
      "/project/monitoring/collectd/load/2021/03/13/\n",
      "/project/monitoring/collectd/load/2021/03/14/\n",
      "/project/monitoring/collectd/load/2021/03/15/\n",
      "/project/monitoring/collectd/load/2021/03/16/\n",
      "/project/monitoring/collectd/load/2021/03/17/\n",
      "/project/monitoring/collectd/load/2021/03/18/\n",
      "/project/monitoring/collectd/load/2021/03/19/\n",
      "filter_str:  ( type == 'percent' and type_instance == 'idle' and plugin == 'cpu' ) or ( value_instance == 'longterm' and plugin == 'load' ) or ( value_instance == 'io_time' and plugin == 'disk' )\n",
      "['cpu__percent_idle', 'load_longterm', 'disk_io_time']\n",
      "Deleting any previous/old remainders in /user/smetaj/demo_AD_System/raw_parquet_train//demo_AD_System ...\n",
      "Error while deleting directory:  [Errno 2] No such file or directory: '/user/smetaj/demo_AD_System/raw_parquet_train//demo_AD_System'\n",
      "Saving the big guy (window dataframe) in: /user/smetaj/demo_AD_System/raw_parquet_train//demo_AD_System ..\n",
      "Saved successfully: /user/smetaj/demo_AD_System/raw_parquet_train//demo_AD_System\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SUCCESS - DATA AGGREGATED IN HDFS.\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Here we download the data that we will normalize using the coefficients \n",
    "# produced by the previous step.\n",
    "\n",
    "# Note that we have to download both the train and the inference datasets,\n",
    "# so we will have 2 different download_data calls.\n",
    "    \n",
    "sys.argv = ['', '--resource_file', json_file_train]\n",
    "DM.transform_data(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "theoretical-visit",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:55:39.191564Z",
     "start_time": "2021-05-03T14:52:57.746059Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "PREPARING SPARK:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK CONTEXT: <SparkContext master=yarn appName=pyspark_shell_swan>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK OBJECT: <pyspark.sql.session.SparkSession object at 0x7f4081da5d10>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_inference.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_inference/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_inference/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_inference/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 48,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-20\",\n",
      "    \"date_end_excluded\": \"2021-04-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "DOWNLOAD DATA - LONG MINING PROCESS...\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "Start: (2021, 3, 20) - End (2021, 4, 19)\n",
      "Reading normalization:  ef8bce\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cpu', 'plugin_filter': \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/load', 'plugin_filter': \"value_instance == 'longterm' and plugin == 'load'\"}\n",
      "{'plugin_data_path': '/project/monitoring/collectd/cloud', 'plugin_filter': \"value_instance == 'io_time' and plugin == 'disk'\"}\n",
      "not found:  /project/monitoring/collectd/load/2021/04/14/\n",
      "not found:  /project/monitoring/collectd/cpu/2021/04/14/\n",
      "not found:  /project/monitoring/collectd/cloud/2021/04/14/\n",
      "/project/monitoring/collectd/cloud/2021/03/20/\n",
      "/project/monitoring/collectd/cloud/2021/03/21/\n",
      "/project/monitoring/collectd/cloud/2021/03/22/\n",
      "/project/monitoring/collectd/cloud/2021/03/23/\n",
      "/project/monitoring/collectd/cloud/2021/03/24/\n",
      "/project/monitoring/collectd/cloud/2021/03/25/\n",
      "/project/monitoring/collectd/cloud/2021/03/26/\n",
      "/project/monitoring/collectd/cloud/2021/03/27/\n",
      "/project/monitoring/collectd/cloud/2021/03/28/\n",
      "/project/monitoring/collectd/cloud/2021/03/29/\n",
      "/project/monitoring/collectd/cloud/2021/03/30/\n",
      "/project/monitoring/collectd/cloud/2021/03/31/\n",
      "/project/monitoring/collectd/cloud/2021/04/01/\n",
      "/project/monitoring/collectd/cloud/2021/04/02/\n",
      "/project/monitoring/collectd/cloud/2021/04/03/\n",
      "/project/monitoring/collectd/cloud/2021/04/04/\n",
      "/project/monitoring/collectd/cloud/2021/04/05/\n",
      "/project/monitoring/collectd/cloud/2021/04/06/\n",
      "/project/monitoring/collectd/cloud/2021/04/07/\n",
      "/project/monitoring/collectd/cloud/2021/04/08/\n",
      "/project/monitoring/collectd/cloud/2021/04/09/\n",
      "/project/monitoring/collectd/cloud/2021/04/10/\n",
      "/project/monitoring/collectd/cloud/2021/04/11/\n",
      "/project/monitoring/collectd/cloud/2021/04/12/\n",
      "/project/monitoring/collectd/cloud/2021/04/13/\n",
      "/project/monitoring/collectd/cloud/2021/04/15/\n",
      "/project/monitoring/collectd/cloud/2021/04/16/\n",
      "/project/monitoring/collectd/cloud/2021/04/17/\n",
      "/project/monitoring/collectd/cloud/2021/04/18/\n",
      "/project/monitoring/collectd/cloud/2021/04/19/\n",
      "/project/monitoring/collectd/cpu/2021/03/20/\n",
      "/project/monitoring/collectd/cpu/2021/03/21/\n",
      "/project/monitoring/collectd/cpu/2021/03/22/\n",
      "/project/monitoring/collectd/cpu/2021/03/23/\n",
      "/project/monitoring/collectd/cpu/2021/03/24/\n",
      "/project/monitoring/collectd/cpu/2021/03/25/\n",
      "/project/monitoring/collectd/cpu/2021/03/26/\n",
      "/project/monitoring/collectd/cpu/2021/03/27/\n",
      "/project/monitoring/collectd/cpu/2021/03/28/\n",
      "/project/monitoring/collectd/cpu/2021/03/29/\n",
      "/project/monitoring/collectd/cpu/2021/03/30/\n",
      "/project/monitoring/collectd/cpu/2021/03/31/\n",
      "/project/monitoring/collectd/cpu/2021/04/01/\n",
      "/project/monitoring/collectd/cpu/2021/04/02/\n",
      "/project/monitoring/collectd/cpu/2021/04/03/\n",
      "/project/monitoring/collectd/cpu/2021/04/04/\n",
      "/project/monitoring/collectd/cpu/2021/04/05/\n",
      "/project/monitoring/collectd/cpu/2021/04/06/\n",
      "/project/monitoring/collectd/cpu/2021/04/07/\n",
      "/project/monitoring/collectd/cpu/2021/04/08/\n",
      "/project/monitoring/collectd/cpu/2021/04/09/\n",
      "/project/monitoring/collectd/cpu/2021/04/10/\n",
      "/project/monitoring/collectd/cpu/2021/04/11/\n",
      "/project/monitoring/collectd/cpu/2021/04/12/\n",
      "/project/monitoring/collectd/cpu/2021/04/13/\n",
      "/project/monitoring/collectd/cpu/2021/04/15/\n",
      "/project/monitoring/collectd/cpu/2021/04/16/\n",
      "/project/monitoring/collectd/cpu/2021/04/17/\n",
      "/project/monitoring/collectd/cpu/2021/04/18/\n",
      "/project/monitoring/collectd/cpu/2021/04/19/\n",
      "/project/monitoring/collectd/load/2021/03/20/\n",
      "/project/monitoring/collectd/load/2021/03/21/\n",
      "/project/monitoring/collectd/load/2021/03/22/\n",
      "/project/monitoring/collectd/load/2021/03/23/\n",
      "/project/monitoring/collectd/load/2021/03/24/\n",
      "/project/monitoring/collectd/load/2021/03/25/\n",
      "/project/monitoring/collectd/load/2021/03/26/\n",
      "/project/monitoring/collectd/load/2021/03/27/\n",
      "/project/monitoring/collectd/load/2021/03/28/\n",
      "/project/monitoring/collectd/load/2021/03/29/\n",
      "/project/monitoring/collectd/load/2021/03/30/\n",
      "/project/monitoring/collectd/load/2021/03/31/\n",
      "/project/monitoring/collectd/load/2021/04/01/\n",
      "/project/monitoring/collectd/load/2021/04/02/\n",
      "/project/monitoring/collectd/load/2021/04/03/\n",
      "/project/monitoring/collectd/load/2021/04/04/\n",
      "/project/monitoring/collectd/load/2021/04/05/\n",
      "/project/monitoring/collectd/load/2021/04/06/\n",
      "/project/monitoring/collectd/load/2021/04/07/\n",
      "/project/monitoring/collectd/load/2021/04/08/\n",
      "/project/monitoring/collectd/load/2021/04/09/\n",
      "/project/monitoring/collectd/load/2021/04/10/\n",
      "/project/monitoring/collectd/load/2021/04/11/\n",
      "/project/monitoring/collectd/load/2021/04/12/\n",
      "/project/monitoring/collectd/load/2021/04/13/\n",
      "/project/monitoring/collectd/load/2021/04/15/\n",
      "/project/monitoring/collectd/load/2021/04/16/\n",
      "/project/monitoring/collectd/load/2021/04/17/\n",
      "/project/monitoring/collectd/load/2021/04/18/\n",
      "/project/monitoring/collectd/load/2021/04/19/\n",
      "filter_str:  ( type == 'percent' and type_instance == 'idle' and plugin == 'cpu' ) or ( value_instance == 'longterm' and plugin == 'load' ) or ( value_instance == 'io_time' and plugin == 'disk' )\n",
      "['cpu__percent_idle', 'load_longterm', 'disk_io_time']\n",
      "Deleting any previous/old remainders in /user/smetaj/demo_AD_System/raw_parquet_inference//demo_AD_System ...\n",
      "Error while deleting directory:  [Errno 2] No such file or directory: '/user/smetaj/demo_AD_System/raw_parquet_inference//demo_AD_System'\n",
      "Saving the big guy (window dataframe) in: /user/smetaj/demo_AD_System/raw_parquet_inference//demo_AD_System ..\n",
      "Saved successfully: /user/smetaj/demo_AD_System/raw_parquet_inference//demo_AD_System\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SUCCESS - DATA AGGREGATED IN HDFS.\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sys.argv = ['', '--resource_file', json_file_inference]\n",
    "DM.transform_data(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sonic-mounting",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Copy Locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "desperate-pathology",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:27.466547Z",
     "start_time": "2021-05-03T14:55:39.195524Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "PREPARING SPARK:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK CONTEXT: <SparkContext master=yarn appName=pyspark_shell_swan>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK OBJECT: <pyspark.sql.session.SparkSession object at 0x7f40a81eda10>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_train.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_train/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_train/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_train/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 1,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-01\",\n",
      "    \"date_end_excluded\": \"2021-03-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "CACHE NEW DATA:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "ASSUMPTION: no data was in cache before and if anything is there, it will be deleted.\n",
      "Deleting any old remainders...\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "CACHE CREATION\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "1614553200\n",
      "SUCCESS\n",
      "Deleting the raw data saved in /user/smetaj/demo_AD_System/raw_parquet_train/demo_AD_System ...\n",
      "Save the config file locally: /eos/user/s/smetaj/demo_AD_System/local_cache_train/demo_AD_System.metadata\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SUCCESS - CACHED DATA AVAILABLE LOCALLY\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The final step of the ETL procedure is to save the HDFS downloaded \n",
    "# files into EOS, in this way the data are easier to be accessed and used.\n",
    "\n",
    "# As always we must do the same for both the datasets.\n",
    "\n",
    "sys.argv = ['', '--resource_file', json_file_train]\n",
    "DM.copy_locally(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "formal-enterprise",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:42.692287Z",
     "start_time": "2021-05-03T14:56:27.529326Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "PREPARING SPARK:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK CONTEXT: <SparkContext master=yarn appName=pyspark_shell_swan>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SPARK OBJECT: <pyspark.sql.session.SparkSession object at 0x7f40abc32d90>\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_inference.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"hostgroups\": [\n",
      "        \"cloud_compute/level2/main/gva_shared_017\"\n",
      "    ],\n",
      "    \"code_project_name\": \"demo_AD_System\",\n",
      "    \"local_cache_folder\": \"/eos/user/s/smetaj/demo_AD_System/local_cache_inference/\",\n",
      "    \"hdfs_out_folder\": \"/user/smetaj/demo_AD_System/raw_parquet_inference/\",\n",
      "    \"hdfs_cache_folder\": \"/user/smetaj/demo_AD_System/compressed_inference/\",\n",
      "    \"normalization_out_folder\": \"/user/smetaj/demo_AD_System/normalization/\",\n",
      "    \"overwrite_on_hdfs\": true,\n",
      "    \"overwrite_normalization\": true,\n",
      "    \"aggregate_every_n_minutes\": 10,\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 48,\n",
      "    \"future_steps\": 0,\n",
      "    \"date_start\": \"2021-03-20\",\n",
      "    \"date_end_excluded\": \"2021-04-20\",\n",
      "    \"date_start_normalization\": \"2021-03-01\",\n",
      "    \"date_end_normalization_excluded\": \"2021-03-20\",\n",
      "    \"selected_plugins\": {\n",
      "        \"cpu__percent_idle\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cpu\",\n",
      "            \"plugin_filter\": \"type == 'percent' and type_instance == 'idle' and plugin == 'cpu'\"\n",
      "        },\n",
      "        \"load_longterm\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/load\",\n",
      "            \"plugin_filter\": \"value_instance == 'longterm' and plugin == 'load'\"\n",
      "        },\n",
      "        \"disk_io_time\": {\n",
      "            \"plugin_data_path\": \"/project/monitoring/collectd/cloud\",\n",
      "            \"plugin_filter\": \"value_instance == 'io_time' and plugin == 'disk'\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "CACHE NEW DATA:\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "ASSUMPTION: no data was in cache before and if anything is there, it will be deleted.\n",
      "Deleting any old remainders...\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "CACHE CREATION\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "1616194800\n",
      "SUCCESS\n",
      "Deleting the raw data saved in /user/smetaj/demo_AD_System/raw_parquet_inference/demo_AD_System ...\n",
      "Save the config file locally: /eos/user/s/smetaj/demo_AD_System/local_cache_inference/demo_AD_System.metadata\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "SUCCESS - CACHED DATA AVAILABLE LOCALLY\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sys.argv = ['', '--resource_file', json_file_inference]\n",
    "DM.copy_locally(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-viking",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Visualization of downloaded time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decreased-conflict",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Reading time series with pandas and host definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "nonprofit-dragon",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:46.529305Z",
     "start_time": "2021-05-03T14:56:42.699288Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Now we have downloaded the data. \n",
    "# We also want some tools to visualize the time series.\n",
    "\n",
    "# in local_path we have our data saved in EOS\n",
    "local_path_train = json_data[\"local_cache_folder\"] + 'train/' + json_data[\"code_project_name\"]\n",
    "local_path_inference = json_data[\"local_cache_folder\"] + 'inference/' + json_data[\"code_project_name\"]\n",
    "\n",
    "# nr_timeseries will be equal to the number of plugins that we have downloaded\n",
    "nr_timeseries = len(json_data[\"selected_plugins\"])\n",
    "\n",
    "# finally df will be the pandas dataframe containing the data\n",
    "# (it is eaisier to print them with pandas)\n",
    "df_train = pd.read_parquet(local_path_train)\n",
    "df_inference = pd.read_parquet(local_path_inference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "operating-running",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:46.579295Z",
     "start_time": "2021-05-03T14:56:46.533965Z"
    },
    "hidden": true
   },
   "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>timestamp</th>\n",
       "      <th>hostname</th>\n",
       "      <th>hostgroup</th>\n",
       "      <th>cpu__percent_idle_h0</th>\n",
       "      <th>cpu__percent_idle_h1</th>\n",
       "      <th>cpu__percent_idle_h2</th>\n",
       "      <th>cpu__percent_idle_h3</th>\n",
       "      <th>cpu__percent_idle_h4</th>\n",
       "      <th>cpu__percent_idle_h5</th>\n",
       "      <th>cpu__percent_idle_h6</th>\n",
       "      <th>...</th>\n",
       "      <th>load_longterm_h39</th>\n",
       "      <th>load_longterm_h40</th>\n",
       "      <th>load_longterm_h41</th>\n",
       "      <th>load_longterm_h42</th>\n",
       "      <th>load_longterm_h43</th>\n",
       "      <th>load_longterm_h44</th>\n",
       "      <th>load_longterm_h45</th>\n",
       "      <th>load_longterm_h46</th>\n",
       "      <th>load_longterm_h47</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1617289200</td>\n",
       "      <td>p06428644w55120.cern.ch</td>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>0.236088</td>\n",
       "      <td>0.227848</td>\n",
       "      <td>0.243771</td>\n",
       "      <td>0.223976</td>\n",
       "      <td>0.198013</td>\n",
       "      <td>0.207111</td>\n",
       "      <td>0.193086</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.441156</td>\n",
       "      <td>-0.387925</td>\n",
       "      <td>-0.323980</td>\n",
       "      <td>-0.426701</td>\n",
       "      <td>-0.444898</td>\n",
       "      <td>-0.464626</td>\n",
       "      <td>-0.497619</td>\n",
       "      <td>-0.472619</td>\n",
       "      <td>-0.382993</td>\n",
       "      <td>2021-04-01 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1616770800</td>\n",
       "      <td>p06492044x27120.cern.ch</td>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>-0.607309</td>\n",
       "      <td>-0.593825</td>\n",
       "      <td>-0.539714</td>\n",
       "      <td>-0.609813</td>\n",
       "      <td>-0.832071</td>\n",
       "      <td>-0.663781</td>\n",
       "      <td>-0.510553</td>\n",
       "      <td>...</td>\n",
       "      <td>1.719558</td>\n",
       "      <td>1.712075</td>\n",
       "      <td>1.800340</td>\n",
       "      <td>1.461054</td>\n",
       "      <td>1.266837</td>\n",
       "      <td>0.829422</td>\n",
       "      <td>0.421259</td>\n",
       "      <td>0.274320</td>\n",
       "      <td>0.477721</td>\n",
       "      <td>2021-03-26 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1617836400</td>\n",
       "      <td>p06428644r12066.cern.ch</td>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>-2.064705</td>\n",
       "      <td>-1.436896</td>\n",
       "      <td>-0.930914</td>\n",
       "      <td>-1.901045</td>\n",
       "      <td>-0.657402</td>\n",
       "      <td>-0.541086</td>\n",
       "      <td>-0.013392</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.697704</td>\n",
       "      <td>-0.810091</td>\n",
       "      <td>-0.843537</td>\n",
       "      <td>-0.904762</td>\n",
       "      <td>-0.924320</td>\n",
       "      <td>-0.895408</td>\n",
       "      <td>-0.839002</td>\n",
       "      <td>-0.762755</td>\n",
       "      <td>-0.623639</td>\n",
       "      <td>2021-04-08 01:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp                 hostname  \\\n",
       "0  1617289200  p06428644w55120.cern.ch   \n",
       "1  1616770800  p06492044x27120.cern.ch   \n",
       "2  1617836400  p06428644r12066.cern.ch   \n",
       "\n",
       "                                  hostgroup  cpu__percent_idle_h0  \\\n",
       "0  cloud_compute/level2/main/gva_shared_017              0.236088   \n",
       "1  cloud_compute/level2/main/gva_shared_017             -0.607309   \n",
       "2  cloud_compute/level2/main/gva_shared_017             -2.064705   \n",
       "\n",
       "   cpu__percent_idle_h1  cpu__percent_idle_h2  cpu__percent_idle_h3  \\\n",
       "0              0.227848              0.243771              0.223976   \n",
       "1             -0.593825             -0.539714             -0.609813   \n",
       "2             -1.436896             -0.930914             -1.901045   \n",
       "\n",
       "   cpu__percent_idle_h4  cpu__percent_idle_h5  cpu__percent_idle_h6  ...  \\\n",
       "0              0.198013              0.207111              0.193086  ...   \n",
       "1             -0.832071             -0.663781             -0.510553  ...   \n",
       "2             -0.657402             -0.541086             -0.013392  ...   \n",
       "\n",
       "   load_longterm_h39  load_longterm_h40  load_longterm_h41  load_longterm_h42  \\\n",
       "0          -0.441156          -0.387925          -0.323980          -0.426701   \n",
       "1           1.719558           1.712075           1.800340           1.461054   \n",
       "2          -0.697704          -0.810091          -0.843537          -0.904762   \n",
       "\n",
       "   load_longterm_h43  load_longterm_h44  load_longterm_h45  load_longterm_h46  \\\n",
       "0          -0.444898          -0.464626          -0.497619          -0.472619   \n",
       "1           1.266837           0.829422           0.421259           0.274320   \n",
       "2          -0.924320          -0.895408          -0.839002          -0.762755   \n",
       "\n",
       "   load_longterm_h47                   ts  \n",
       "0          -0.382993  2021-04-01 17:00:00  \n",
       "1           0.477721  2021-03-26 16:00:00  \n",
       "2          -0.623639  2021-04-08 01:00:00  \n",
       "\n",
       "[3 rows x 148 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that each row of df will be a window of data following the granularity definition in the configuration file. \n",
    "# In this example it consists of 48 points representing 8 hours of time interval with an aggregation of 10 minutes.\n",
    "# So for every day, for every hostname, we will have 3 rows (3 x 8hours = 24),\n",
    "# each row will be composed by timestamp, hostname, hostgroup, ts and\n",
    "# for each plugin we will have 48 columns, 1 for each 10 minutes.\n",
    "df_inference.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "refined-chester",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:46.589975Z",
     "start_time": "2021-05-03T14:56:46.582582Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['p06428644y58212.cern.ch',\n",
       " 'p06428644w00836.cern.ch',\n",
       " 'p06428644y16173.cern.ch',\n",
       " 'p06428644u33510.cern.ch',\n",
       " 'p06428644w45688.cern.ch']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to visualize the data about 1 host in particular \n",
    "# (let's print 5 random hosts we have in our pandas dataframe).\n",
    "\n",
    "hostnames_in_df = list(set(list(df_inference['hostname'])))\n",
    "hostnames_in_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "mental-norfolk",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:46.595739Z",
     "start_time": "2021-05-03T14:56:46.592808Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# From previous studies we know that in this specific hostgroup there is a\n",
    "# particular host that was anomalous in the specific period of time we are\n",
    "# studying, so let's initizialize the host_to_viz variable.\n",
    "\n",
    "host_to_viz = 'p06428644y10205.cern.ch'  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "found-effectiveness",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Reconstruction function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "continuous-transition",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:46.950581Z",
     "start_time": "2021-05-03T14:56:46.598703Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Here the function to reconstruct the timeseries follows. Because we have\n",
    "# downloaded \"windows\" of data, we want to reconstruct, for each plugin,\n",
    "# the data points that we saved in the same row.\n",
    "\n",
    "# Note that hostnames will be a list of hosts and the function will return\n",
    "# a dictonary (each key a host and each value a dataframe with the data)\n",
    "\n",
    "\n",
    "def reconstruct_entire_timeseries(df, hostnames,\n",
    "                                  # To reconstruct the timestamps we need to\n",
    "                                  # know how data points we have per windows\n",
    "                                  points_per_windows=48,\n",
    "                                  # We also need the granularity of our\n",
    "                                  # aggregation (in minutes)\n",
    "                                  granularity=10,\n",
    "                                  overlapping_windows=False):\n",
    "    tmp_dict = {}\n",
    "    for hostname in hostnames:\n",
    "        tmp = df[df['hostname'] == hostname].sort_values(by='timestamp')\n",
    "        if overlapping_windows:\n",
    "            indexes_to_drop = list(range(tmp.shape[0]))\n",
    "            for i in indexes_to_drop:\n",
    "                if (i % points_per_windows) == 0:\n",
    "                    indexes_to_drop.remove(i)\n",
    "            tmp = tmp.reset_index(drop=True)\n",
    "            tmp.drop(index=indexes_to_drop, inplace=True)\n",
    "        data_columns = tmp.columns[3:-1]\n",
    "        unique_col_name = sorted(set(\n",
    "            [re.match(r'.*(?=_h[0-9]+)', c).group(0)\n",
    "             for c in data_columns]))\n",
    "\n",
    "        all_chunks = []\n",
    "        for row in tmp.iterrows():\n",
    "            content = row[1]\n",
    "            ts = content['timestamp']\n",
    "            artficial_ts = np.arange(ts,\n",
    "                                     ts - (points_per_windows *\n",
    "                                           granularity * 60),\n",
    "                                     - granularity * 60)\n",
    "            linear_data_chunk = np.array(content[data_columns])\n",
    "            linear_data_chunk = np.concatenate((linear_data_chunk,\n",
    "                                                artficial_ts))\n",
    "\n",
    "            # Note that the last row of square_data_chunk is the most\n",
    "            # recent last data and that the time increase towards the bottom\n",
    "            square_data_chunk = np.flipud(linear_data_chunk.\n",
    "                                          reshape(len(unique_col_name) + 1,\n",
    "                                                  -1).T)\n",
    "            all_chunks.append(square_data_chunk)\n",
    "        all_timeseries = np.vstack(all_chunks)\n",
    "        df_single_timeseries = pd.DataFrame(data=all_timeseries,\n",
    "                                           columns=unique_col_name +\n",
    "                                           ['timestamp'])\n",
    "        df_single_timeseries['timestamp'] = \\\n",
    "            pd.to_datetime(df_single_timeseries['timestamp'].astype(\n",
    "                int) * 10**9)\n",
    "        df_single_timeseries['timestamp'] = df_single_timeseries['timestamp']\\\n",
    "            .dt.tz_localize(\n",
    "            'UTC').dt.tz_convert('Europe/Oslo')\n",
    "        df_single_timeseries.set_index('timestamp',\n",
    "                                      inplace=True)\n",
    "        tmp_dict[hostname] = df_single_timeseries\n",
    "    return tmp_dict\n",
    "\n",
    "hots_to_visualize = [host_to_viz]\n",
    "df_ts_train_dict = reconstruct_entire_timeseries(df_train, hots_to_visualize, overlapping_windows=True)\n",
    "df_ts_inference_dict = reconstruct_entire_timeseries(df_inference, hots_to_visualize)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-cambridge",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Normalized data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "boxed-elizabeth",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:50.244680Z",
     "start_time": "2021-05-03T14:56:46.953563Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+gbnuFcBzMIU9toB5RugDc04HNWH3IBIB6cdhni02h7lfn2u3rRTA+xLdWfgjMM8k18PcS7znrMW+6c6D+S1/FMB+ML91B8AU+gCAs8Q0+ZcWEREAbyJxPAfB/E5sBfMc9j8wQbK5EYu5CcAvYQIMe9q0HQlTGAYwv1H7pJu2dsY9f6dGTHcczL4FgFcAG5FK3A9/LabJwqyp6iokfucvFJHtc7FcIiKilAodueHAgQMHDhz8A3w1Tux3FyGkNBkiapwA+Isz31kB47sjUUKzBsCmvvF9nfmXAdgqJM2lznT1AA4KmOZPzjQNAK4KmGZ3mMwXBXBZBvvul3beagAbBoz39lVZwLg+TvpKY66vCxKlM6sB/NQ3/jo7bhWA3UOW4ZYWPtg37mlnnx4Qsr+qnPn75uD86xN3ean2mTNOAZwSMP4Y3znxUMA0m8IE3RTAvQHjH0eitPePQtJ5obMft01zf2yO1jVF1sJkct0LEwDaIMUy3rfzfQOgV8g0d9ppVsBXY8o5bxUmkBNacwYRNU6y2VcwzVIpTI2TTaO2t1CD7xhdHzC+B0zH4t412dMZt5Ez74Mp1uOV6C73ff9rZxl/scdSQ4YRADaOWMendrr+Ice3LIv99LldRjOAfUKmEZgAdH1I+ufBdAQeVDNpPWe6x+15H7YfFgPYLyKtXq2bMt/3/b1lpNjWbkjUHOnrGzc37Fqx47eEuS8pgHNDpvmjsy9LY+z7Ps62p6px4taw/EnEdBs40/0jzXPhXGfeBwLGn+yMPzHFslbZ6QYFjBsQcQ54w38RUls0xnbc4izngojpuvo+H+TMF1hbB6agwjIE3FeQ/OzTDODYiHXPdaYNqpWzHhK/NYE1vVLsg3Oc5f8t7j6w37nHodV+sNeCV/P1/kyOUT4G5KfGydtI/AZuF7Gsl5H4LenmfP8TZx1XRMxfZqeZGzDOPa/OtN9tCvPsoXBqzgXsB9Y44cCBAwcOORtY44SIiNqLfyNRqjSdWid/sn9HqGqrUsGqWg9T+wMwL+1/jFjW/aq6NMY6X1fVbwO+/w/MSx1gMgce90+gqj8gUbLx4Bjr8s//KUxmZS+Y0pX5dg+AE2G263w1HSa7vJLi99pta0VVP4epBQCY2jwATH8uzudXVHVswLw/wNTgKXZfqeo7Ad9/iUT/CLUAbvdPoKrlAD6zH5POCVv69wL7sZ+qLg9Z/wswwYJuAE5LJ+GquhKmFPPnztcbwJTkvQGmU+RlYvqc2co/v6054JWmvlJVa0JWdTdMxtTmAI6PSNL1avqoSEsO9pXX7n41TMn0YrYAprZJElWtgzlmgMmEctug7+387/bxEWSd/esvfb6R8//ddh13wtS86wFgX5igCwD8HImaRklE5GyYWmizYIJzOSMif0Wi34ynVHVi0HSqqjA1wP4Ck4HotwnMdm0cMM7dDxfDXLdvwWx/DwA72GU3A9gawOCgPiScWjcNAK5ItW0hroepdTQC5nc0NlVdBlNjEwDOD5nM+75MVedmksAIcc/Jdc7/UTUikojIrgAesx8XwZyzmabBTUdQGpbCnMt9YI5/D5hjfxYSNU3/ABNgSYutafG/9uOHqvpi2LQB907vN/pDVf2vf3o7z2IknlfOCprG+kBVv4gY75kH4B8B61mHRF8xB8VYjp+3LZMQ8ZyY4vdjeNB+sNfCp1mkrRj1EpH17bCFiBwmIm/B1KZsAnCeqi4MmlFEegP4nf34pjr9D6nqJADf2Y856xfGPgv90378k63JSkRElFcMnBARUbtgX8rush9/KSI/TzWPiGwKk2EEJDLrgpY9HiaDDgBCO6gG8FGMpAKJl2v/eiqRyCT/wmbMBfHS0ioTGgBEZEMxnT5/KYmOrVs6Z4dpHgMwtTHyxmZuXm8/9vdnNojIbjAZRAAw1HlBbzUA+N5O53asvQ9MBj0ABAUdPG9ntyVtIuycUCQCgqM0vPPrsHPif2CCZAAwLGL/9kYiIyPtzstVdZ6q/gKmOZX7YErQ1zmTrAdTK2y8iOzlm/0YmNL75QCmRqSxBIlmcsLSuCIogBZTtvvqe5gA4YYAnhWRrTNMR1t4X1WbQsaVwTSbB5imfHLJfbfoDuCvqnqbqs5X1XobpDgNppYaAPxeRA5wF2CbRPMyVa9S02xWTojI8TDBXgCYCFMjLmza3WCO+aMw95//gWm2Z0eYPmTWwTT1NkJEtvXN7t8P7wI4TVUn2v2wQFVvR+L+6S3T7ymY/lYeVtUpAeMjOYGXRgCXR/zmRHnB/j3a3zyObUrxOPtxQAbLLhjbbN87MEGuRph+alZHz5U5Ve2nqjeq6lB7/OtVdamqvg6TEe81o3e6iPRJc/F7IfHb8HKa83pBxCEpfqMn2+n2iWhmL+4z0ueq2hwybob9u2XMZQEwz0VI3K9fjVh+Kp9EjMsobUVsMky/IZUAlsM0C/p7mGeSfVQ1qhnU3yERVHw1YLz33aE2QJkr/4CpddINpjYeERFRXjFwQkRE7clLSLQxHqfWyQ7O/6kynbxMgR0ippkTMc4VVStlXRrTrOcfYTOlJ8O0Sd4Hpt3ubiHL2Sjk+6yJyEEwbagDprThHQGTue1kf4PEC3rQcI2dbgtnnlLnf3+784g5rljk65xw9/ESRO9jr913dx+nRVXH2QzAQ2GCWgfDlJz3AoJbARhoS0D707hpivRVItEvRFga416DQbLaV6o6C4naTX8CsFBExojIwyJySoq2/0OJyHoRGZaSegmBQq8Jm3nu1f5yS+xWO//3TLF87zz0B/rcZaxEojS/f/39na9O9k1yH8x9bZCqxs2ITckGaP4DE6BbAOA3toR70LSbABgKE3x+QVXPUNVRqlpug0DPwvRJtc5O49/Oat/n/iFBi8eQ6BMnaT+IyFkwtW7mw1xjmXgS5lg+GlazJoYPYe5NXdC6T69zYPZnFSIKJ2Qh7jnp3hfDgs8tRKQHTMDd6zPqElUtyzINbjpSpsFla+Fd7HwVVasjyC7O/9+FTuVj71nb2I8PIvqe6B3fLjABxCBx789LIsZ5NRJbPf+kUIpE3kbsfRAg52kTka4R9/jIvp4KZCcA99r+lcJ4NUnmIBH0c72GRJ8/5+QqYTa4+Yj92FdESnO1bCIioiAMnBARUbthm1fwOmw+znZ4G8XNyEyVkVFp/7ZqLsVZf2AmW4Cwkt7pTpOUaWqbrnoLpsPWSpjMx8NgOqrfGInO2hfYWdLqwDsuW8r4HZhMpDEw/cAEySRw08P5320ixZ8R6Uork6pA8nJOIPt9nDFVbVDVb23J+b2QKI27D5JrbuUyjXGvwSC5SMfVAC6D6TC3C0yg5xqYTNjlIvJYUJNLKXyE8MzKTJsiibpegMQ1494jK5FokipVcM0bv8r3/Urn/2/c5lt8xjtpbKmhJCL7w9RcqkYimJo1EdkdZj9vABPk+6WqLoiY5WKYZpSAgObzAEBVpyHRCfLJIuI22eXuyyoAE0KWUQ8TVAaS90N3JGrdXKOqqY5nKyJyMkyfV4vCtiEO+7vr1WLwB068Zrr+k0kaY3DPp6hz0h3nPyeTiEgJzHE72n71l6imreKmwf4+e+dAZBqCqOpUADPtx/3TnN2951SGTtVapoUrsr0/x/mtS1em+8Avk9/hVM5B+D3+pjSXlUs7qaqoqsAEw44H4DUz+1uEFFCytS29mkrDAewnIvu7A8y14gVrcxY4sR6G6fetG0yNOiIiorxh4ISIiNqbV5DIXEhVCtfNUE9VGtwbn80Ld771AbCn/f8Pqvo3VR2pqotVdY2qVtmmnjbMVwJEpCdMJvE2MCUzT4kIKLn7f2fvBT3FUOrM42bEuUEUv4xK+ncQ3j5WACUx93GfXCfC9oPiZgD9NCCN82OmT1S1b67TiBzsKzWeUdW9YErlng3gGZgOvtcDcCWAz0Sk0M/YUdcLkLhmWq5R27SNF/zaKcX83vjpvu/dmi6hzR7Z2hdePzHu/aoUJlOyN4D5bhOETlOER9lpj3K+7x+2Ltu81GcwGXlrAfzKBj2ieE1BLteQNv4tr9m4LnBqNNl96dXqqUjRRJa3n9z90AuJ5oAGheyHlmCI832ZswzvGG0LoDJkGV5g7nzn+74BafSa69pdbDOZInIgEsGeqMBDNtzzK+qcdMf5z8kWtgbXv5CoUXaXqrbqayPDNOyIxLt1aBpS8PpdCuo3J4r73JJO4Nb9jf5TGvfnuWmmry1kug8Ipv8QNX3kHQNTMAAArrdNFvr9EYk+v86FCYQHDfvZaXYRkZz1uaeqFTDBE8Dcu3bO1bKJiIj8Cv1SR0RElBZfrZOjReSoiMnnI9EZ+49TLNprsmNeFsnLt33t39Wq+lnQBCKyHfLYRBdM81wHw3SSe7KqLoqYdrbzfyYvtnOd//cMmyjFuI7O28eC1Jnd+TbZ+b+X87+Xxq1FJN3mV3Ipp/tKVeeq6muqehlMpqmXkXMIoju39y+nTx4yKEOvCZt57PV/5L/fjbN/f2ZL0AfNvw1MrTd3egCAqq5C4roNa84HNrC0if24Jmy6bInIFjBBkx1gSsOfpKrjoucCEL8pHrfkub9PhTH27yYpmlzz9lPe9kO2bKDpa/vxfN/fWao6LE+rnoJEX0qHREznjos6vg/BNLMHAE+o6q2pEmCDwl7tpFykIYrXT0lF5FStzXT+3zd0Kh9VXQPT9xSQ2W90MZmLRG2R2PugLajqgIh7fP9Cp89lC99cZj+WILi2WiYdvuesk3jrEZjrpCuAlNcxERFRphg4ISKi9uhVJEpGh/Z1oqrlSPRt8oew6URkPwBe55XDc5HAPPGaxyiJmCbdttFjE5EbYErYA6Z06rdR0wOYhES/HadnsMqJSJQiPSViuqhxHd1XSDQJlMk+zqXtnP8XO/9/bv92Q2GPVd72lQ3oujXgCh3MO9E2SRTkKCSCFv626b1O23sD+HXI/Kc6/78bMH6w/Xuo7UsiyIFIBNfcZqy+hKmtFDV4tTzGOt897V+B7Sz6E5iaIA0ATlXVr0LS4+fdt35kg9Fh3I7t/U1/efuhNxKdVvvT2AOJDHd3P1Qi9X54xpne++4i57tXYyzD68/hPee7oGMKJGqdnGH37R/t53+HTJ81NX1/fGE/nhwWzEPinJysqrODJhCRWwFcaz++DOCqNJLiXRd9RCQsIOiloQLmXpMWEdkHieDF+DRnn4rEsUw3g9orhHFqEdSUy5iqrkWimamzs+gfqtOz98mP7cczRaSlDx0R2RuJpuT6paqdBOANO+3pKfpMSTeNa5BozvBcJJ7hiYiIcqrdPhwREVHnpapNSGRSHoXk/hT8vMyew0XkDP9I+yL3qP1YA+D1XKUzD7yOVzcMqmljm1TIS3vZInIigLvtx7+rasr9ZJun8UrhXygiJ6RYx4a27Wxv/kYk+hA4x3bu7J9nd5jmkTolm3nwvP14U9A+conIj2zH17GJyN0iclCKaXog0dZ4M4AhThqnwvQvAQAPperMVURKIzLcM5btvhKRnVJ05Ot20Jx2Hwc5tj2Av/i/tPv1fvuxHIkMYc97MH1iAMDf/BldIrIRgOvtxzGqOgatPQ1T8nsTANcFpKELErUGm2E6bAdgjpGqTogakGheqMr5fqlvHT2RCAY0AzhXVT8MSGuYL5z/A/sHEZE9kQgefOdPA0yn6t49+86QTOm/IhHEGuh9qapNMfbDUmd67/uZzncrYizDCySWO997NRD83oBpPnEjAM/C1JRR5DFwYj1l/24J4M/+kSJyKhJNAz7lH2+nuQKJZ4bBMIH/qObT/J6B2dbuMH2L+Zd/CICT7cfn7G+XO36riECm10n7s85Xr6aRNu+39gn78TciEho8CQg+eb/RewK4K2o9IlLiZqIXocft330Q0fdFRACOErxCSSUA+jnfe+eWwrlnRfCeFTcDEPkMmIF/wjR1WALgxhwvm4iICAADJ0RE1H69jkQ74lFNTDyJRK2Tl0TkNhHZVUQ2s8GHzwAcacffEpFpVAw+QaIGxusicpaIbCsi24nIxTC1ZWqRaHojJ0RkB5iMnC4wpRDvE5H1Iwa3pOcjAEbCvNi+KyJPicgRXqa0iOwuIqeKyPMwJbYP863+DiQ6Af1URC4WkW1sRsqI12wAACAASURBVNQ5AMpgOnvuzG6CqYG1PoBhInKviBwsIpvb83wvETlbRAbCNM2UbsbXLwGMFpFvReQ6Efm5PQYb22vpPJgOrr3r6Blt3fH25TCdLG8NYKyI3Cwi+4nIpiKyhf3/QhF5F6bZmXy1UZ/NvjofwDwReVhEThCRHe0+2FlEzgcwyE5XiUSgqFDmArhHRB4QkT2d+93nALwg2E2qWuvOZD/fYD/+FMBHzv45GqZGyLYwgZFWgRm7jGlIBKPvFJGHbBo2EZFDAbwPc04BprmkH4KWkymbQf0GEufjdQA+iLhf9QpYzEAkmj+6SERet/thExHZXkQuAjAUiSa9WtV8VNPx+7UwGYzHA3hfRA61y9hTRB5EIjN/LICXcrD5eaOqlQD+az96NbaGqOr8qPns79Oh3oBEvygAsLc7TkzTav71vo9ErYj7RORWe+1tIyL/i8R+m4zk4IO3/jMAPGY/jgRwIYCeEedDqwCHqn4P00wlAFwpIo+KyG4isqWInAXgA5jfxyUA7gvYDWcCmC4it4tIH/u7vbGI7CIiF8LUMPFqHr0SVDNKTEA5qk+fB5GoqTJARJ6x+3Qz+3t7uIjcBV+tWlX9xs4LADeKyCci8lt73DYSkR1E5Jcici+AWUjU2ikIZx8MCBj9GkzAEgDuEJE3ReRou/2bi8hBInIjEv0PUQhVHQXgU/vxPHvOChK1mr9W1ThN236ERNNzOW2uy9Yyesh+bO9NzRERUbFSVQ4cOHDgwKGoBpgSnQqgNsV0Z9npvGFayHQ7wnRarBHD/QAkYN6+3jQp0lLqLKtPxHRz7TT9I6YZYKcpCxh3HkymZdA2rIGpgRO6jhTL7uMsqzTk+zhDqW+5G8NklsaZ97cB6ToGpqRz0PQVAA51PvfNwfnXJ+7ywvaZMz7lcmCCPwpgQIxrYm7I+G0BjIq5j/dLc38MSePYvwigW8hy9oIJdqZaRiOATeKet+nuz0z3lXMMooZqACdmew5mce566bgUprmgsHQ+lmI5/WFqagTNWw/gwhTzl8BkYkbtq1fDzpWYxzfwXEDyvTjOEHZN7QETPEl1rv41RXqvhmkqLGwZ4wFsm8F+aDkfszhf5kZdKwHTH+lL+znppDPG0DdkGZvC9BkTNt9MADulOF/iDn1CltMDJhM4bL5lAA4MmfeamOt+AUD3GOd1/5BpfgTTF01a5ztM0OcuhD9buMM/ItIVuO8CzrfA9Ntp+kad1866As9ZmKbxBqfajojlBp6DvnM58J5RiAFpPK8EXI+lKab9uTPtIwCOdj5fmUYan7fz1ALYOODaDDon3fPqzIhlbwBTw9M9vocW+rhw4MCBA4eOM7DGCRERtWcDYdr2jqSmVNz+AP4PJlOhAibzb6FdxuGqer2qah7TmhOq+hKA42BKAq6F6Th3DkxTIj9T1aEFTF4gVa1Q1RNhSpm/CpPedTCZiUthSm7fBeCnqtqqfX1VHQLT2esAmL4z6mFqp7wI4CA1JSM7NVVdBJPJcRqAt2DO7To7LIIpsX0TgN1V9bs0F38cgMNhStV/AlMToxYm03g1TEfITwA4RFUvUNWGkDROgWlC5UKYEtpLYI5lLUyG2vswpZl3VNXVaaYxtiz21SMwJe3/BbPNS2D2QSVM/xQPANhTTQn5QqsDcCxMEyvfwwR01sJkVP1BVSP7d1DTYfExAN6GuUa9++UrMMf5+fC5ATVNTZ0F07fURzCZyg3272CYTtrPDjtXioGqTgewH0zg40uYmm2NMPtyGsw99wBVfSDFch6FqU3wEoD5MPuyAsAwAP8Lsz8XhS+heKipCeHVxFmLRC2rfK+3HOaa/T+YAMpamOMwEaYptZ+q6pw8p6EOpqmhP8Ecu3KY37EfYGps7KvBTdcB5jq6DuZ+Mw2m9l0jzHZMhrmnHKKqf1JTUynTNC6HqbV5DkzNC/e6Gwtzj2rVz5SqNqvqLQD2hqktNsmmrQnmHj8G5h7/KwQ0v1dMVLVaVU8GcBLM/l4Ec82tgrkXPono5l3JUtWRSPRRdjESNUaa4DSxGIPXXFcPmN/dnFFTE+7BlBMSERFlSNpBHhEREREREaUgIt6D/QWqOqCQaaGOSUQmwWSwP6+qF6WanoiIiIiovWKNEyIiIiIiIookIgfCBE0AU+OPiIiIiKjDYuCEiIiIiIiIUvGaeJuqqiMKmhIiIiIiojzrWugEEBERERERUfERka4AesH073OO/Zp9ChARERFRh8fACRERERERESURkVIA/k7XvwHw7zZPDBERERFRG2NTXURERERERBRGASwC8AyAE1S1qcDpISIiIiLKO1HVQqeBiIiIiIiIiIiIiIioKLDGCRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACRERERERERERERERkcXACREREVGBiMgOIlIlIiUh4/uLyCv2/1IRURHp2rapJCIiIiKizqo9vbOIyBEiMr0Q66aOh4ETIiIiohwTkbkiclyq6VR1vqqur6pNbZEuIiIiIiIioP29s4hIHxFZGPB9mYhcBACqOkxV94ixrJZgD1EYBk6IiIioU2LNDSIiIiIiKmZ8Z2l/eMw6DgZOiIiIKDYR2V5EBonIChFZJSKPi0hfERlh/18jItNE5FhnnqSSTNmU7nGqfl8iIotFZImIXOeM7yIi/URklk3fmyKyqW/eC0VkPoAh9vuLRWSqiFSKyBQR+Zn9fhsRectu6xwRudq3DW+KyEt2vskicqAd9zKAHQC8Z6u0Xx9je7razzuJyFC7zM8AbB4x70Yi8rzdB4tE5K6w6vNERERERJ1B0PuK/Z7vLJ3gnUV8tVJE5Aa73koRmS4ix4rIrwDcBOAMu+3f2Wm3EZF3RaRcRGaKyMXOctYTkX+LyGp7HK73rWeuXdf3AKpFpKtzjL1j9jtneu98fFhEKkRktoj83H6/QESWi8j5+dpPFA8DJ0RERBSLfcB9H8A8AKUAtgUw0I4+BMAsmIfm2wEM8h7+8+RoALsB+CWAG5yXnKsAnALgKADbAFgN4AnfvEcB+DGA40XkNAD9AZwHYEMAvwWwSkS6AHgPwHcw23ksgGtE5HhnOb+F2f6NAbwL4HEAUNVzAcwHcJKt0n5/Gtv1GoCxMPvxTgBRD8sDADQC2BXAT2H2xUVprIuIiIiIqMNI8b4C8J2lU72ziMgeAK4EcJCqbgDgeABzVfVjAHcDeMNu+352loEAFsIck1MB3C0ix9hxt8OcUzsD+AWAcwJW+UcAvwGwsao2wpxrRwDYCMDfALwiIls70x8C4HsAm8Hs04EADoLZV+cAeFxE1s92P1DmGDghIiKiuA6GeYj8q6pWq2qtqg6345YDeERVG1T1DQDTYR4a8+VvNg0TAbwI85AKAJcBuFlVF6pqHcwLxqmSXF26v513HcxD+/2q+q0aM1V1HswD6xaqeoeq1qvqbADPAjjTWc5wVf3QtvX7MoD9kAUR2cGu91ZVrVPVr2BehIKm3RLACQCusduyHMDDvvQREREREXUmUe8rAN9ZOsI7yza2hkbLAODwkGmbAPQAsJeIdFPVuao6KySt2wM4DMAN9ryZAOA5mGAVAJwO4G5VXa2qCwE8GrCYR1V1gT1mUNX/qOpiVW2259sMmHPUM0dVX7TH5g0A2wO4w+7XTwHUwwRRqEDY5hoRERHFtT2Aebb0jN8iVVXn8zyYl5Z8WeBb1z72/x0BvC0izc74JgBbhsy7PUxJIL8dYR/Kne9KAAxzPi91/q8B0FNEuobsnzi2AbBaVaud7+bZNAalrxuAJSLifdcFydtGRERERNSZRL2vAHxnKep3FhGpcj7uparzAyZbrKrb+eYrC1qeqs4UkWtgAlN7i8gnAP5PVRcHTL4NgHJVrXS+mwfgQGe8m+6gbUj6TkTOA/B/MDVVAGB9JDdrtsz53wu2+L9jjZMCYo0TIiIiimsBgB0kuLO7bcV5GoZpL9d7IK0G0MsZt1UO0uI+mLvrWgDg16q6sTP0VNVFzvTuy9ICALsELH8BTAkgdzkbqOoJMdOnqSdpZQmATUSkt/PdDiHTLgBQB2BzJ30bqureGayXiIiIiKgjiHpfAfjO4ldU7yy22SxvCAqapE1VX1PVw2GCOArgPm+Ub9LFADYVkQ2c73YA4B2TJQDcgE1QoKhlmSKyI0ztnysBbKaqGwOYBEAC5qMixcAJERERxTUa5oHxXhHpLSI9ReQwO+5HAK4WkW62Dd4fA/jQjpsA4Ew77kCY9mKzdauI9BKRvQFcAFO1GQCeBvB3+6AKEdlCRE6OWM5zAK4TkQPE2NXOOxpApe3gbz0RKRGRn4jIQTHTtwym/dvYbHX7MQD+JiLdReRwACeFTLsEwKcAHhKRDcV0MLmLiByVzjqJiIiIiDqQqPcVgO8sfh36nUVE9hCRY0SkB4BamBocXi2fZQBKbT8xUNUFAEYCuMeeN/sCuBDAK3b6NwHcKCKbiMi2MAGRKL1hAikrbFouAPCT3G0dtQUGToiIiCgW2/bqSTDtrM6H6TjvDDv6G5iOD1cC+DuAU1V1lR13K0wJqdUwneK9loPkDAUwE8AXAB60bcACwD9hOj38VEQqAYyC6XQvbJv+Y9P7GoBKAO8A2NRu64kA9gcwx27XczAd+8VxD4BbbLu716WxXWfZ9JbDdED4UsS05wHoDmAKzL79L4CtI6YnIiIiIuqwUryvAHxn8evo7yw9ANwLs1+WwgTObrTj/mP/rhKRcfb/P8I0q7UYwNsAblfVz+24O2DOpzkAPofZjrqwFavqFAAPAfgaJkizD4ARudgoajuS3LQfERERUXpEpC+Ai2wV6HyvqxTmYbVbFu3yEhERERFRJ8J3FsolEbkcwJmqyhr/HRhrnBARERERERERERERBRCRrUXkMNvc2B4A/gJTK4U6MAZOiIiIqCBE5GwRqQoY5oR8P7nQac5ExHa2y+0hIiIiIuos+M7SPrcnD7oDeAamqbQhAAYDeLKgKaK8Y1NdREREREREREREREREFmucEBERERERERERERERWV0LnYAom2++uZaWlhY6GRmrrq5G7969C50MCsHjU7x4bIoXj03x4rEpbjw+xYvHpnjx2BQvHpvixuNTXMaOHbtSVbcodDqo8Np7HhcRUUcV9ltd1IGT0tJSjBkzptDJyFhZWRn69OlT6GRQCB6f4sVjU7x4bIoXj01x4/EpXjw2xYvHpnjx2BQ3Hp/iIiLzCp0GKg7tPY+LiKijCvutZlNdREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnREREREREREREREREFgMnlJWRs1airrGp0MkgIiIiIiIiIiIiIsqJroVOALVf05auxVnPfoNzD90Rd57yk0Inh4iIqM3NX1UDEWD7TXsVOilEREVpXX0TLnl5DP736F1x6M6bFTo5REQFM3HRGpT2+yDj+efe+5scpoaIiFJhjRPK2JqaBgAmgEJERNQZHfnAlzji/i8LnQwioqK1qGIdhs1YiTP/NarQSSEiIiIiio2BE8pYSRcBADQ1a4FTQkRERNRxzF9Vg/Lq+kIngygnmpXvCkRERETU/jBwQhnr4gVO+C7UKXwyeSm+nL680MnotJatrUVDU3Ohk0EdRH1jMyprGwqdDCIKceQDX+KI+4YUOhlEOdHIlwUiIiIiaocYOKGMlYgJnChLkXUKl748Fhe8+G2hk9EpratvwiF3f4GbBk0sdFKogzjn+W+wT/9PC50MIopQXd9U6CRQAX02ZRlqGzrGOeDWOGmOWVN9+dpa/Pqfw7C4Yl2+kkVEREREFCmrwImIbCoin4nIDPt3k4Bp9heRr0Vksoh8LyJnZLNOKj5sqosov7yMk8+mLitwSqijGD2nvNBJICKiEN8vrMDFL43B396bUuik5IT7rlBZ2xhrnjfHLMDUJWvxyqh5+UoWEVEg5nMREZEn2xon/QB8oaq7AfjCfvarAXCequ4N4FcAHhGRjbNcLxWBJlt6jIETovziFUaeK14di+eGzS50MoiIKI+q6kxwYfaKqgKnJDcanXeFppg11b15vD4ViYjaEPO5iIgIQPaBk5MB/Nv+/28Ap/gnUNUfVHWG/X8xgOUAtshyvVQEvKr27PCRqG0w64A+nLgUd30wtdDJICKiPOpeYl7ROkrfZu67QtwmfpsZOCGiwmE+FxERAQC6Zjn/lqq6xP6/FMCWUROLyMEAugOYFTHNJQAuAYAtt9wSZWVlWSaxcKqqqtp1+lOZVm6aD6qsqm6X29nRj0++tMU+6yjHpq5R0a0E6CLZvfSvrTeZBw0NDQXfLx3l2LR3Qccgk2PDY5k7qfYlr53iVczHpljT1VaK+djk0+wK84xdXrG2aLc/nWMzdVWir5YRI0diw+6pn4tmz60HAMyfNxdlZYszSmNn1lmvHaIcyWk+l5vHVbJhdrGV0n4fBH4/997fZLVcIiIKljJwIiKfA9gqYNTN7gdVVREJLUIkIlsDeBnA+aoaWnxKVf8F4F8AcOCBB2qfPn1SJbFolZWVoT2nP5VuM1cCo79Bz/V6tcvt7OjHJ+c+Ng9pbbHPOsKxqalvxF63fYLLjtoF/X69Z1bLWlVVBwz5HN27dy/4fknn2DQ1K2obmtC7R7YxemoRcR2mdd204fXc4cXclx3hvtZRFduxUVXg4w8B8BottmPTVjZdWAGMGoGevXqjT58jC52cQEHHZk1NAyrW1WPHzXonfd9t5krg228AAD//+c+x+fo9Ui5/1LppwOxZ2GXnndGnz645S3dn0VmvHaK42jKfy83j6rH1bmyug4ioHUmZm6Wqx4WNE5FlIrK1qi6xPxjLQ6bbEMAHAG5W1VEZp5aKitf2cH1jx2hGgCiXVlTWAQDe/35x7MBJc7OivqkZPbuVJH3fXp+ub357IgZ+uwBz7jkBkmWtGyKizoJ9x5F3DjS2s3Phl48MxbK1da1KPrvndNwWfr3mvdhUFxHlA/O5iIgojmz7OHkXwPn2//MBDPZPICLdAbwN4CVV/W+W66Mi4rU9vKhiHdbWNhQ4NZStlVV1+Mub32FdfVPqiSmlylrTsev6adS2+Nt7k7HnrR+HZpq1t6yDgd8uABA/k4SI2paqoq6R9/xi094yyyn3vKBBe+vjZNnausDvkwInMYuDeO8ZjJsQUQEwn4uIiABkHzi5F8AvRGQGgOPsZ4jIgSLynJ3mdABHAugrIhPssH+W66Ui4L7Y19Qx46W9e+Dj6Xhr3EIMnrCo0EnpEKrqTOBkw57dYs/zyjfzASR3opqNxqZmTF9amZNlZSPd7Vm+thaVDMZSO5CqxmV5dT1OfWoklqxZ10YpSs+TZbOwxy0fo6KmvtBJIUd9O8ssp4RcBToamzTpb3uXVCAk5iY1qRc4YeSEiNoc87mIiAhAloETVV2lqseq6m6qepyqltvvx6jqRfb/V1S1m6ru7wwTcpF4ahs/LKvET27/BHNWVid939TMF/uOaNTsVYVOQodQ5dU46Rm/xklYgCHTOMpNb0/E8Y98hZVVwSVA20q6hacPvvsL/PLhr/KTmHYsneZ7GpuaC37cO4Ob3p4YOf6tsQsxZt5qPD9sThulKD1vjV0IAFhVzcBJMekomeWdzYLyGux280d4c8yCrJfl3e87ShCtManGSTzePmBTXUTU1pjPRUREnmxrnFAn8OgXM1BV14jhM1Ykfe++BOWqhDy1jXmrqjF1ydrAce9MWNzGqemYKutMjYkN0gic5Poy8o5loZv6iNssh2vJmto8pKR9S+c4Pvz5Dzjwrs9RzgzxvPpsyrJCJyEr7a1E9zezV+GjiUsKnYy8a+wgmeWdzczlVQCAD77P/hxtaqdNdYXJ5D2hqbl93Z+IiIiIqOOJn6NHndaqKpPxtuF6yU0ONTFw0m4d9UAZACR13hn2XlrX2IQHP5neBqlq/8bOW4355dX43U+3a6lx0juNPk48g8YtxBkH7dDy2Qs8pJt34DUjVOiOhnl7yI2ofg/W1jZg7LJG9LGfJy0ygdGRs1bixH23yX/iOqn2XhC6uSVwUuCExHTGv0y/q/6OpzuaBvf5qlnRpb0coE4u09/qIN79vtC/37mSTefwHWUfEBHlU2m/DwqdhIx09Gc6Imr/WOOEUupaYt4A/Z2GZ/ISRIWXqk1+v7fHLcKzRdrMTLH5w1Mjce0b3wFINE/VNYMMrxve8jX/k+X1VehMBwZWcyOqFPqfXx+Px8bXYUF5DQBg203WAwCsrmFfMbmypqYBO934Ab76IVH7sqRL+36M8lrcZInuZLNXVOHlr+cWbP3utd7AZlE7paamjlXbIpPO4Zs6WPCIiIiIiNqf9v3GT22ie4k5TdY1JAdOOmpTXWPnrU7KGCtmq6rqsKgivU6H020vu6O8tLc1zeE1kVhS62Px5rcLMGVxcLNrnqiaCm2BeR650RDQ78GSNeswavYqzFtlAiZ1NjBaYq9bNvmTOz8sr4Sqab7SU9LOn6JaapywRkOSs5/7BrcOnoxa33OPquLO96eENnWZK27zTMw0bj+8n/1cXE1N7aw2WCqZFLbyLoNCP8MQERERUefVzl/5qS10szlDNRE1TtJ5p1m4ugaDJyzKSdry4Q9PjcR5L4wudDJiOeCuz3HYvUPSmicsQz8sPtK9K28TuZRJQMXL3Aw6Rte/9T1OeHRY5PyFznjLZRCpM2sMKHl+9INlONM2XwQkzhGvM910a5hRuDvemwIg+bfQDSyPnlPeap5M+vfJl4Gj5+NDX/8g7a2prrbiBUzWrkuusbW8sg7PD5+Dvi/m9xnBDZLmM9O4vrEZe9zyEd4evzBv6+hMWgInOShw0tE6Rm/KoHP4xLz8HSMiIiKiwmCOKKXkvbRFNdXl1jhRVfzz8xmYubwycHm/e3Ik/jxwQh5SGs+iinWoqW8s2PoLLd0XVgZOsuNmefxnzALsdOOHWLY2vY7P48QdZq2o8s3jZLwF1FTIpbrGJjz06fRWpbM9LCyavQXlNaiua33fqm0wGUr+Xexl6KfqWFhVsWZdA54fPqdTBLjGzitvda3ENXHRGgDJtS/dwMklL48JnbcYKu71GzQRV7w6Luk77/QoxKGva2zCPR9NDTyvC83r063CFzjxrqeSPB9Q957dlMf7d2VtA+oam3HX+1Pztg7KTGMH6xi9yfeekI6g2pZERERERG2BOaKUkpdR4G+qK7nafeL/Nesa8PDnP+CsZ78JXN6KyrpW87Slw+4dgr4vfluQdReDQ/7+RavvJi1ag9dHL2j53Owc2x5FEDipqmvEnweOx6qqukInJSvvfrcYADB9aXBQMUycK+UjX0lyt1R8UI2TsCBHJl7+eh4eGzITTw+dFTg+nWu9M2Tep6u2oQlH3P8lrnkjPODs7Tcvi80rpDx7ZXXkslWB2wZPwp3vT8FON36Yi+QWtUtfHovnh2fXZ5O/EIGnPWZveudNIa6617+Zj2eGzsbjX85sNW7GskrseetH+HLa8gKkDNigZ1cAQIWvjyAvoNE1x220LSivSbqHu/2a5LPGibcd6TbhScG8I5WLe0GzEziprmvEPR9NTdksZzHLpOard38qdK1ZIiIiIuq8Cp8jSkXPe2HxN9Xlvsy779ze96le9gtVwhUIblKlo6ttaMKNgya2CoABJuPU5ZYMLIYaJ2+NXYjBExbjn07fAsUu6PTu0bUEQOsgZMplxbhYtt5ovaTPlbWJUtz+Jp7e+24x9rz145QBnCPv/xIDR89PuW6vX42wZqHSyfNgBklr3rGctCg806wlw86WTvau4UHjFmHJmnU4+7lReLKsdQZ1syrKq+tzm+AiVtJFkgLDYSprG0L7sQirsZiL5nnamldbtBABSy+zviHgvvGLh79CbUMzbnjr+7ZOFgBggx6mxsmt7yT/Nnr30m4luT3WJzw6DJc7tYHcfRLURF+upaqZRtlbXV2PBz6ZFrvfqUSNE2Dc/NV4Zuhs9H93cj6TmFeZ9HES932CiIiIiChfuhY6AVTcauobWzI36lrVOEm8/LlNdTXHbF6gEK9Ba2zp0e557tF3eWUtenYrwYY9u+VtHc+ElO4PM3jCIrwekgnubwahqVnRzeTxo2uXwgdOenYzaQgr6V1opzwxoqVpF4+bMVBeXY9f//MrbGDPh3S3I06Hsxv3Sl6/FyQEWgcjvH4OZi6vwh5bbRCyTsX88hr0GzQRZx68ROyhbwAAIABJREFUQ4r0pQqSxr/amUHSmr92UFCT9/5zxA0OVNQ0YMTMVRgxcxWu6LNr8nxIvn93dF27dIl1jp33wmiMn1+Buff+ptU4N/DpntuZZqVPWFCBeauqcfL+22a4hMz8e+RcrLa/iW11CqyorMPIWSuTttX/qOAGYAt1O9h2ExOInr4sObhc32gS1C2HzxDDZ6xMCnQDyffBfDa16J2/+W7OsbOI6o/s5ncm4sOJS3HITpvhyN23aPl+eWUtNuzZDT29hy7Le8YWkZbf8DW+puPak0wKRXjzsI+TcG+PX4hr3/gO0+78VatziIioPSjt90Ghk1CUgt5BiKgwCp8jSkWrsakZe932CYbNWAmgdeaaW2huwoIKlPb7AJMWrWkp6dw1RYeWhSjh6mUS+TO5AdNx7lc/rMjJeg7++xc48v4v05pnyuK1aZX8vuejaSmn+XTyUtQ2NGHQuIWYt6omdDp/RmJjs+LlUfPw3LDZRdG5sfcyWFukHV1PWFDR6txJZKAIhs1YgWVr6zBzuelbIf0aJ+lP4x5S//H1ao/16h7+kp3J5RkWK00nv6SYAyd3vDclVg2cXKv21XCICmZ6x6DS6TciqnR8syrcPKls78s19Y2xanQUSkkXiZWBN35+Rei4sPb2M93qU54YUZB+v253Sq/n6+d43qpq3PLOxJZ9fsGA0fjzwAlYU9MQus7kPqASE7Vlzaj1e5hyRYftulnS915Bkq45rHFyzvOtmzV1a4DksxaedwyK+b7bniQCUK3Pj7krzTOY1wyc5+C/f4FzA84B7xQo6SItV0ExPI9lKrkvxHjzNLHGSUr3fzwdQNveH4mIiIg6EwZOikh5dX1SKfG21NjU3Koz97oUmdRuCbDPpywzf6cua3lxLEkVOMkkoVlaXWNeLFZW1WH2muR93W/QRJz3wuicrcvfNnoqJzw6DCc9Njwn6358yAw8M3QWLnl5LO76YAr+783v8GRZeA0Vf9MRTU2KW9+ZhLs+mFqQJtX8WgInAQGHz6csC+2vo6KmMC+S/sxnf9BxccU6/OGpkZixLF5fJ3EyS1oHNjXwfyDR1FBQ4OTLactx9evjk5pry1ZaNU6KoMmYi18a09IfjeuFEXPQb9DErJe/oLymJdAch7/z7KBMW/ccmbJ4LQaNW9TyOSrPSTX53Im671/52jgc+1BZ6Pg16xqw122f4JEiblKvaxfJaSacu6SgmjvFcP+MIxcZskHNPV39+ni8Mmp+y7k+e4Xtc0daNy/nCctgvTaij59c89LgL+ju1YbJd01MtwZIPjONM1ny9KWVWLqmNvWEnVBjSy2R1uOWV5p9FlQb+9u5qzFsxgosKK/BHNsvlfeMXdIlcbG05/iBex7Hvd80ttQ4accbTkRERETtGgMnReRnd36GCweMKci67/5wGo77x1dYUJ6oleDPOPW/trilbrvYIElDU3NLhkMRtPDUitscxuglwe3UuxqbmnHjoImYH1FbIxODJyzCrBVVrb5fVLEuJ8t/8NMfWmqktGRURWhd4ySRW1QMGX9eB/X+AMmkRWtw0UtjcPvg1u1+D56wCPvf8RkmLoyXOZ1LzZq83/yZb9/MKcfYeasjO/t2xTkG/nwFN/MxrMZJULMOFwz4Fu9+tzitWjGp0ucfXV5dj9e+mR8YUCmGkqWfTVmGq18fn7fll003HV6HNZ3nV12XfCw26dW91TSJproE3y9Mri0R1X/BYfcOSTp+bjNya2oacM3A8Vhba4LA73+/BLMi7ierquoAAO9OWBQ6TaGZGifxg3Opm6FL/B9V06bY+z8J2sxJi9bEDj4vrliH3W7+qFWNLO85wcss9u4rUU2cubvRvY+tqq6LlZZcaAmc+HaMV7gl132ceLz90nY1TtJf9vGPfIVD7/kiD6lp/6KaPFtZZa6lsN+4p4fOwhH3f4mjHywDkDjuIolAQy6aVVyzrgGLc/SsmY5MzmNve4vhuaBYLWEQk4iIiCivijBru3MbPnNlQdY7avYqAMAIZ/1NvhdA//ua+2JfYjNFGpu05eWoJFUfJwV4D0q3qYDxCyrw+uj5uPbN3JZ0/fPACfjFP4bmdJlhol447/loKkr7fdBSwtHTlEHJQFd9Y3NemmLzB0689r4XrG4d2PLO5cmL2z5wUt/YnNQWuT+zw6uMFTfzIqrd9ISoGifJGcVx+ljxt7kfh4T08uDf/psGTcRNb0/E5MWtO98udFv7bdGEYI+uJmCVqlafx1/jZLP1IwIn0jqIFta0FACsqq73ZU7X4/dPjjC11r6ahXcmLMZLI+cmzfPp5KWR6S3mIEFJF0nrHEvndCiGIHOmgpJ+4mPDcdrTX8eaf74tdPHWuIVJ33vn1rSl5lr39lFTs4buL/cadM/ltqyM5q3Ln0avxkku+zhxedvb4PZxkkagb01NQ1IfMXHX15EMm7ECH9l+vNpanGMVFkAor06updzoPEu3LDYHx+vYh8rw83uHZL+gNGXTObz/fYSMkc47WxH/7BIRERG1awycFIlC9Pfh8l5o3GZoUjXV42bIl7TUOFGnxknrp/gT/jms5f9CtNWclCETa3rzN0WrYxnJV4aF/1yKavromaGzA793j2266WxqVux+y0e464Op6c0YwTun/LUgWs61gDfGrjZjq6EAOUO3DZ6Ex7+c2fI5LAU1MTuJb2nSxglMqGpLTQCg9XFyMyn8GefeeqNKr1alEThJtYf9afPSHdQmdzqZhPnQFiVbe3Qz52aqwMmkRWvwzNBZqK6PU+MkEVzzH9d3xkfXAHGnnr2iCuPmV+DBT39oua/7byGXvDw2cnnFrKSLpFVqO9WUO2/Ru+X/XJQGLxR/2r3zacby1jUjg3i1Av2Z9t5i//rf733rc54BfLdvNyXu71lbPid5tYfmrqrGpS+PaQnae9ds17wFTmwJe+eiSyfQt98dn+KKV8fFnr4995kR5tznR+PyNPZBLnm/tf4nEvfcDfuN88/j/YbPWF6Fl0bNM8vJQRq9mi+5NmTassgOfpML5MTjXYf1RdCEZzFaUdV2tfCIiIiIOisGTopEoauhB73I+Zsd8afQzSDxgiSNzc1JpeT8pixJlDD354Gsrq7Hm98uSCfZoWavqMLcla2blHHXGa/5I+8luHiKcpX2+yCyrXd/acZMmkdILhmY3vzeufRvXyn1bHjvzLUNyeepl8ygknbdvHMyjRfuxqZmTF8ar9+RKIMnJPrHEGm9D739G7fGQdAh+PfIudi3/6ctn/0Zn6udJnYufXksPp6UKIHrdTYedWQra9ProweI6Bzedw56HS/7a1IAhatxUtvQhJnLq9pk/d1LvEzm6MDZiY8Nxz0fTWuVGR3UTJCXatXW59uAFNeie727NY28gGTc/m7aQxZsun2cpLr/9e6R6Og525/xU54Ykd0CsuDfzHSfSbw+P/z3NPe+5NZSjQoyJQdLEt+n+i076oEvcWMO+iACEulbXlmHTyYvw9AfVgBwapzkozQFnM7aM+jjxNtvn09dlsYKw0etrq4PbFK0mA2bsaKg6/eeN/y/hW7wO27ZAPe++5U9/4o5OPvoFzMjx2cSBPWeJ71+2ShZVG1SIiIiIsoNBk6KRFQb9G0hKEOiVR8nbnMuVXX4zmlH38tDaGjS2J3D+/31v9/j+re+x9QlrZvv8Xw9axVOf/rrlJnhxzw0FH1sO9EuDfk/jLfJo+eWR7Zf39beDilBPndlNS71lQbP5MXKLd2X7nt6S8ZPDvdXWIZZopR9eI2TdDLCbx08Ccc/8lVLJ66Z8nfe7U9++vum9fRDpidnELmLHD9/Nc59fnTS+MteSZTA9TLH/cfWvcbv/jB+jaF/fPYDgNYlZsN4gZPygP4TChVEvmbgBBz3j6FJTazlS9waJx5/YDvVNZlJLTGPW6vLu4ene+8rnjBza6aPk+jtScrgS7G88fMTv4PZZmpOWFCReqK8SU57us8kDfYcjQqcXO7cg5o10VSXv2BCUgEH5/tUAbx5q2pi9xuUSuvnH/PX6+PEf4/PFW9/1WfQx0nc+4krasknPjYcxz7UNk2K5or/d6+teb9f/nO6KSkQFnyc/N8HNU9VzIGTVE1Fpfv8DSTO/bi1czsb9z5dTAW8iIiIiDqSrqknobbQ0FjYl6EmX6nQbiVdWr2su59+8+hwLF2byFz2MtjWrmvAeJv5U9JFUF3XiHOf/wa3nbQ39t9+4+TlhTTfE5Vxee0bE7B0bS1WVNVh643Wi719QeusaVSU9vsAD562H049YLvg6Z2tnrR4DfbdbuPA6VxRmYwTF67BHlttED/Baeo36HuMml2e9F0mTR+5NQHSbcojH+/1bmbBmnUNGDh6Pi45cmcn4601L2MrnYz4t8aZgNSgcYtw2VG7ZJxe0/594kXfv0/SrQXkTe42beU1jZNYh3uuhgcf/5+9Nw/Qo6jTx59+37lzQyDhDiDIEUBOQa5B0XVF191Vf+u6h3gs67Hqd911xVUQRVZAQOUQD245FOUmJOR8cydkcpHMJJNrjkxmJnPf78z7vt31+6O7uquqq7qr3/edyYD9/DHzvv1WV1XX1VWf4/nw4OvBevRsadYT4nKWoArJiSjsoYoD2Vw/UlRda/fbXOETYdlK4yOMZfWelY1JM6uqFMt2d+DuRbvxPx89y71Om5iQ6OOLTT8qUZzoepzIvPwmG0oSiVBl6hs7vBguXrv673mroQddDFWKrJnyXQ6fWt+Ii0+ZhXOPn5FnDtHgi2Gm2JM8tb4RK3Z34PEvXMZdp22qouoCeE8IdsyJy4YqOPxEGi/4y6IKDfs/9bApdjmexwlD1aW5JtI4M5HKlwzavpEMCAEOOTG4CCGTOm7RZILKUIXtQ9X6PDzGKwdkexfaXX/e3ILOwTF8tTr/fUoUbG7qxVjWxAfeM1uZRkaZyiKqxzfgtZXMO3U8kc6Y+PuH1+GOv5uPi06eNaFlRwG7TrwbafdixIgR4y8ZQfSXMd65aLzzhiNdhRh5IPY4mSQ40vy9rGUbjW0QJHxjlSaAZ+m0YEcbbnl5JwBb6NbQNYwtzX348pObfHmIm/yqMjtg8t6OIVz7sxXSwNkuDVieNBlsmYeH7c+PrWlQpl+3rztyGVmFkKOxaxifeHANfrKgLnKeFGH0BjKBYD4W/Gx8C/Fx2vtHfUHaWYzH4Y2ORUIIbn1lJ366cDfW7Otyy5JTdVGPE/25RYV+dy7czV0fzZp4ZmOTtuCODRxswB9T4e0W/YD1hBA8stqORZMxLdcqvaI0KaTTzlJ5z1Aewon2/nDvHJ/HjTNOpeM1Dw+psZyJF7e0+OZHqr4D9y2u18vEuVUl+BqP+ApjIVRdFLwXiD22fpXaz6Whc4GARLZKZtOzVutUEKYz7vccHsSXnqyxv0xiGSvrcbKvY0i6lrEKPdqusiZYUtfOfT9qij/2DEXUJrn1lVrccP+aiHflD/HxVHuSW1+pxYp6Px0SXWfFMa0ai0FDlH2HcFRdmvReUfBwaj/+/fc1vutif7tB7Wm7ROzQgz0juOfNet9cEtvHjXHCpNNVhH7k56t81+5etBs3Pq72wpA124fuXYkLb1/ifi+mtb9pEfzX89uxu11XuT/+MC1SNKWcqdj/sX2o2pOJ9JiyVLS//vtP23HXot14NGD/GobvCnGHgvCph9fhc49sDEwjTonXtrdi4wFvD53P3nCiPE6erzmIV7Z5ntx1bf3Y1TaA21/Pf78+EcjksV/6q5+vKppnXowYMWLEiBEjxl8CYsXJJMERp+piTs+Uxsd3uAvYn8ssIhOG4QqQZcEo2QP7S1tbkHKEMY+vbUBT9wj+VNPir6dTjix+ig72HPb4ukedA0dlWVKatrUvzQX4DrOmo1AJXqllcq3EG0BX6BOmYJMFQs8nxskYZ8XG4/KfLvPRgbEYD6NgKkwisL2aAFvJQZtN1jdUuVaM4PA/X7oH339pJz750FqfoFXWd2IMikKE7jsO9eN5Zi681WALIioEj5Mfax7wg2iI8rHqZJWo9y/bi85Bf7BSUThIx7FonQ7kp+i7d/EefPv57VhR38Fdv/HxTbh/eTDvOgUtVaUUzGceqUCzCpvPdFizihN2bC3c4cWtYT1OghQnL2yWrasqjxP/7yoc6vUU3ZNYb4JkwkDOstAxOIrr71uJOxb4KemmlHvvBFdgLmmD3632hJZnzpmKY6aVF1y/ifSqYMeSz+Mk4p6EjmXRi0r1NKZFPKpF4Te2LrzHibr8fGiqAOCuRbvxZq0/JoiPqsu9bv/X3Q9QfOfP2/Hgin3Y2corzcVy6POy+4hC4i79KrXf3VvJIMu5e5jfr/UVkb6woWsIL2xpwdePUPB2GU7/3zfwr48Vh+LLDQ4vDI8gRViJs1cZzoTvLcRrhQj2/1gTPaZgg+NV+HZLH2qFsSw+8zee24p/+O0G953CV11vTNP5MTzOnqD/8+e38a0/eLEDoxgN5AtCSMG0sPlQddUfHixaLKgYMWLEiBEjRoy/BMSKk0kClZCibySDH71Wiz5JLIBiggsOPJb1XQOCrcVkB/tkwm9pr8Ktr9S6n0edw6PsXnr4pL/kTEu7bcZyJu5fttcrxzmHVSkUJ90SZY8OsgoBDm1PmdJHV64uBkf3lyERROchdPnC456HkOzwTgPlylAIB/e6fV2Yd/MCdIwoePIJH8/EDQ4v5JM1LfzS6etiKCV7HUHSjkP9eGwtb+EpeqcAfv77Qo7eokCQKiEp3RVFz3DGPeSzQuyg/MSuyie+x4Bwzw9f3elLI5ZDqadkfSNTpoSBer0MpPMXrtBxnlYoTvKxAFeB9lMYVRcVqI0yAjVWYPtVRvjY4SisCIKVl//1p+2+a6zwNi0pi/5+dJBHxWTWljCgHic0NkmThN5I9OYCwte1U2dP0fYgomjuHsFBoXxdWrRioLzEe07x+aKsm2M5031fGgbw/Zd2YJ1DfacycmDL81N1KTxOAgZ2usgW6SrPEPqOjTrcaVuLimXxlW0Ruz2f2djkXsuaFr7y+81ugPBiQkep3z8ify/I7l21p1OZfjJjzb6uouRDjYjEsRrkcUIkaQD5mkOgVu5PBK5zYgf+zYNrfR5xKjq3m1+wPVvYpwkadumM6a6ldP8q0piNN6LSVOaD36w6gMvuWIam7vwoLpu6h6X7zxgxYsSIESNGjBjFRaw4mSRYtLNdev3mF3bg8bWNWJsHZVQYNjf14sUttvVxTmJxHEXgLrOcThrBihP2l0pGUDXqCE4HR3M465aFWLHbsyKndaL5fufPb+N9P14Cy7Fe3dcxqCxPPJSOOhzulRIhGeA/MOkKBlUCJ5qfLB/dlg4SzB0eGMXOQ/l7s6gQVUZMCtBTvLKtFQBQ180/p0qGpwoOz/bBw6n9Lld7MSDOi9+sOuBLUyrw3wdZLYb1jzhuuxzBW0WJf9zSMfbrlft9v3nlqctetrsDKuzrGMKBziHfddHDSrZuEEIwPJZzva7oHJetG7SM42ZUcNcHR7O47I6l2NTY47uHlliI8J7moaM4oWtkJmflZZFK16/B0WBFDxXesHUKC0pNCImu2GGSj+ZYjxPe6nZahTosWr70iRONkoSB7S39+OMm29r6tNlTfGnYPnVjToS0aVVZiXLsqHDNz1bg6rtXcNfypQ36/YYmPL2hKTwhA7bHgjxO7loULJz70hM1rkIukTDwzMZmfO53Nq1PWYl8m8kGhxfBB4dnPE6YH6hihmKkyMJkn+GI63lk/4+61syeansjsTFxAP8+Y83eLjyyugEtjPI7nTWxqLYdX5JQnhYKne2BSqEu3tsznMG/PvYWvvGHrUWoWfGxr2MQy3fz3kXF9ihwaSiFfHnFuxgHSF4H2WWLEE7h/oHTj863qkWHakpQL2t+Xqtx9q2LcP19KwF4cz4fGtFioBgh19r603IDJMcTLMjQJQivOvtlCh0qtIn0aIwRI0aMGDFixHi3oGDFiWEYRxmGscQwjL3Of2UUPcMwphuG0WIYxoOFlvtuw08VVkP0wDoeVk+fengdvv38dhBC0MdYCKYzcou5oCrIlAXJhBHMY878yFr4UsvRvR2DGM1auPvNeryxo40TCA6PmegcHMNLW21O4tGcidfebsP1963iAmn9fn0jQwfCH+tGHTmLyuNEVProusGr6Hdo3WUUH7peGvTALBPa3Ph48YUqNqKNvbDDW2tfGg8u34s7FtT5DnGUNk00LqTpRItF1+NEaA9x7C6ulSsm80GF4+lBvWNk4GKcGMHKp7BzrDg26JogepwAep4QXPwA4bfeYbWX1fX3rcQH713pu64TuNgiNv/+JT9ZCsDzoJB5l1A6vRNnVXLXdx4aQMfgGH62yB+zJB8vp5xp4aanarC1uReAt76NqRQnzLymAvIzf7AQ/y14cNS3D3LW4jLQbjocQtNBFXAsv3tJiIKCIA8vKyZL1quNPueT65tw96LdrpJsVlWpJAsvk8kcSJoqeJY7SkKRVg/g6f08TwN+jInxCKrKkhgpglW0aRFturqxnOnS693y8k784GW/t1dgWRxtH19mhgkO/3BKrYgFeGt9cXyqFGrsI4rv1kbGAltF20UVMxQjAYJVyyL48Wt1keJqqJhK8/U4mT3N9tYSaUvFtevrz27xedHS/dmUcrXiMl/oKU7k7wXVu2l/h1/B7is3vNii4/r7VuGLT/DxbIaKQAHVNTTm7jPpGrl8dwdqGCU/qywRjQsIANk0kS0DhPB7TBlFXU1jDxq7/F4MDV3DkQXnP124Szs4rYq+jrj/vbLvXrQbf/ertcq8DvbYygS6Fk40nXFWMNLKF9sO9uGKny7H8xJaNNpc+ZbgoxPUyGgiPRpjxHinI5ZxxYgRI0YMimJ4nNwMYBkh5AwAy5zvKtwOwB+9MoYS1Fozqms+IQSLa9u1DkmtQnDnkQyNcSJaxanrp6M4ES2uVB4n1KuCHlh2tQ3ga89swbJdHW7g9evuSeGzv13P1NnEnna/t8ktr9Ri2S65FT1tmsoyWxghCtDEtktozhaVpw49AMry0T2c0XFQluQzIYSgpddPOQPkz/3u1a246T/98Drcs3gPfre6AbuFPqOKExrw0rQIDnQOcYc93kqaKqOEOgiPfMrRVfoPEAI6VhcqvMQA2+qagpDgg3FY34s/0/QyjxMdgSub35uCQimf4K3ZnEaZIJzXD11bZOvG0JjcupkqrEYDvK6iCOwbu0ewuO4wvv08r/hQ0eHxymUTPY6S6cWth7h0f/WLVfj+S8ECbDdmT0jTJZ01iV3/S0IWIkKi052xrcZ6TdzNKKl+ldrv9pus2u8QhxOfx05S0p4s3SJ9VvF98NWn+RgNU8pLpDz8tI+buuXrs4icpe8x9MkH1+LcH76plVYGFSUWkL+gUpyDqmcxLfVq8x/Peh4LbB2D2oVVLrb18xbc//vSDjy2tgE/EOZlkLefuC7TtJ7HSbjnFztvy5PyvdLqPX6KqDnTeW87qpCY4uxVnlzXiHk3L4hEiaVqO501X+VxImbZOyKnkeRRnIXihc0tuPz/lhXsUcu2YS6PMd8/ksUlP1nqUiax++ZP/3q9S8PEZk3H1otbWvBnJ+aUqGAkhEj7xiL8GBKpMmm51Q6lFsWBziFcd08KH7w35Uv/b0/V4Oq7l0uf7zcr/R61KqheTW4fMY+zdFeHS5cYBNdohhTuPR0FtI3zibnGYs9he4+7qbHX95sbRyXP5/LTKXuoax2QeuAHraHffG4rnlzXmFddYsR4lyKWccWIESNGDABAMczXPgmg2vn8JIAUgO+KiQzDuBjAHACLAFxShHL/IkAt16MqTl7d3opv/WEbbv34OfjiVacGphUPXlRwpiO8KS9JIJOzpMLThBDjRDwbsN8rmIN2wqH4EoXf/eksd8/+Ts+iLp0xOSt/Fr9K7cP158xRPgMVhIuPK37X9ThRCZzoAVBmlad7bqJKkPKSBKcQsYg6XoIY6DUqaN0uOWUW9ncOodcRNGRNC70jGRw7jRfyhB0CWUWdOMZoX1AZ2APL9+IXS/fiC1fOc+oiPyiKbSoq/cTfu4fGcPRU/UDObLHlTh2DLPdY61ICEnjgV9PVEBgSurtNjb1o7h5BqYQCxzSDaX4eWrEPn//APPf742sb8cNPnBtalyBkdTxOhCRUtyjSfAGe0F98DDq/x7IWDnQO4eMPrMGb/+8anHRUlTsQoojkuh3KHBq3gwqpVHRLrHXsaNZE+4A9jqcoPNaCQARhsMoqn1rvcx4nIVRdAClIWRoUdyXoncAKkiezDkVUlMg8eNg13BWYC5Njewsv9KsqS2I0a3H9aVrEHU+LatvxDyeGK3AtiyBn6PWfqHgGgP2dQzj9mKla9wdNXZXnZBjE1lSNGZaqi4Dg23/chi9dfSrOPX4Gl469W8zrUF8ab+5sxxeunMet+be8vBOPfP5S9/sfHFq2EwQvtqAtjlrhY5ezuLYdB3tG7PXHwb6OQVx/3yq8+h9Xoq51ADe/uANLv30tTp09Reom2j+Sxdef9QdJnyp4ltA92pRye62hHm2LattQVpLA3114oi+Ptfu6cOV7ZrvfR7Om1GMlaM2vLE0inTU5pTEL8d1EPWVEww4AGB7LYWC0eLFPfvx6HfrTWbT0prk+iAq2TqM5C1MV+0gVxkx7fv9u9QF872Nn+wTt+zuHcMrRU7jxSdOwSvtkwuDehzlLRWVH3PWpJGGE0j1S0H1bo0SBu6TOpi+bd/MCLPp/V+OsudO18gSAQ0Pec4V7nESHGFss/P1XHLi0wAUqTlTGPYC3JORbhJ8VwPv+sftXAwCe+OiUwHtYvLq9Fa9ub+X2iJMFL2xuwYMr9uHlr1+JGZV+j9cYMcYJsYwrRowYRYeuJ28MfTTeecO4l1EMxckcQkib87kd9ouDg2EYCQD3AvhnANcHZWYYxk0AbgKAOXPmIJWhKgBAAAAgAElEQVRKFaGKRwZDQ0Nc/U2L4P6tYxjOEvzg8krlfew9/b22cK52916kxhq1y97QaB+S1u/Yg9NyHm1MY7+JqlIDx1Z5h8O//uVq7t6at+vw/KqdOG82LxDs7Or09Ydh2YfG7l6/5diqPZ0YG/CoClYI965ZswZTSh2L6mHPQpRu/nt6eQut+no1z/qqtRtwsFN+gNzS3IdUKoUxhWV8W2sLUqkO34Fi8xaep7tm0ya0TQs/VDcNeEJOtr22H7br18s8F/2drVvQmN/ba+edILxwd0UqlbegKwy1tbUAgIGBfoxlvDJufGgJ1rbmcP8HqzC9zDsV9o15acLm71s1Neje542z1mZ73A6NZpBKpbB0qz3+t+yxBV8j6TS6um2B946dO0Bl3J2d/Nhk6wAA299+G2izl7v6HhM/fWsUX39fOS6d6y2B+/v4NmXza2/3eOkP7K1Hamg/Dh3iuepZDAx5Sr2WlhaMdKnHTWrlSpRJhAHLV6SQTBh4WxjXQ2M5XPOzFZhd6b9n5Zo1GMyoD8Y/e7Mep5k8ZQR9TosQrKiVc22zbSGua7uaeGFYV1eXr9831Xg0cqlUCv2ORfihtnZf2pY2u8/7+/u53w702/3TOziEe19ch5GMiV++tAYfPbUUNfvt/HbV1WFa757A+rt1arfblYwOIJVKwXTmz466eul9vYzwcM2Gt9x2Hs6YWL5iBVa15Li2p/cOZghe2JPBZ95b5q53O9q9Pl28PIXKErkwKJe1yzzc5a0ZI0PBNDgb39qEhmavridONdAyxI8JsT1GRjxh2tJdfAwAFukxWzCazWZ9edT3ePNneGR40r67uzp5D8uDzU1Ipdq4a3VM+61evQZVpQZ6R/k1ZUzwLmk7aL9n2f780bo0GgYY6/Ch8HZZvWYtJ2QT9xBrWnO4fG4Jypkxw6b5l1+vwv9dHSxIpulZYe6mmhp07aVGBASpgznpParvFNlMhkszOCT3tNm0qQaNnfaYeWiFTQW2pr4Vd1/D150Qr6wBQUj8b79Loa7bQnlvA0aYd2hPdzdXv6MqDPSMEmCQf0+wQllxXevu5sdJbd0uzOjbiwON9vMNZ0x88v4U7qv26ruwwR43D722EYeG7byvv28lLpmTxAlT7XdAQ0MjUik7NkHniPydvWs3T0dY32Cv2eboCFKpFIaG7Tb97gs7AACz+vcBAE6elkDzoJ3n62u2ItviCRiXrVzNvacp2ofV72vi7O921O9HiviphlauXMWNw3WH7OfPpP3j/BebR7Gt08RtV9iGFiMjI9prhNg3AHB0mYn+NPDC0nV437Hyo4wqf/Y63VMBwLLUaswo1xPMf2flCD58Sikum+sZ3qxYsQIHW3hDlZ07diDRvgsN/V45u3bvQSrdwKUzhOBwy1Mr0dzsN3oZG8tg7XonflCCYGR0TOs5Dw7Kx5p47wvLNuLKE+xxMzAm30uw93x/TRo5axlOmZ5Eb4+cenJ42O7r5mb/nmnezQt8wn22nJG0l+eKlStROg6ujamD3npPn227s+8ajjBOKe56K42ypIH/vLgCu5y829vbkUr14kCfiUNDFq4+sdRtr+3bt4O0Rj+ONzTx42P9+g04porfa/YP8HNnJBt+1hCv13aZKE0CZ86KbiQiQ1fawsxyI5R2lEVNYxYNXRnu3BgjxgRg3GRcyenHFLemMWLEiBFjXKG1UzMMYymAuZKfvs9+IYQQwzBkO+2vAXiDENKiQW/wWwC/BYBLLrmEVFdX61RxUiKVSoHWf1fbAKeg+MBV1/BBUxd5mkf2mV9u34pN7a04/qR5qK4+I7C8wdEszrttMR763EU4q2wM2F2LucedgOrq+W4aquFsvPMGrkwWu0emYFNrL+r6+L46+ujZqK6+hKvvtKoKDGTSqJw6Fej384dvbPcOi1dfcy2weKH7/corr8TMKtva+9H9G1Hfa1NWJBMJmKaF6TNmAIyS4dxzzgbe3iat8/z3XYR0Yw+wZ5f09+rqapsLfqmf1uTkk07CNdecBSx6g7t+/gUXAJs8HvXLLrsUZ8yZJs2fxfaDfcC6tW65FKM724CtWzD76KOALjso5NNNU/HI5y+xA186dQsa82X7uoCNGzGlsgJ9Y56Q+6qrrwEWLwqtWz4465xzgO1bcdSsmWgdGQBy9oFyg9O331w+wml5OwZGgRXLACiehRl377vwQlx8ylHu95aKJqB+J0iiFNXV1Xi6aRN2dndg6oyZQGc3KisrcczsqUBnB+bPP8/2xNq+FXPmHIvq6ovcfNr7vToAwPz556H6bHu/e2BNA4A6DFcdh+pqz9viRkH7z9Z9Qed24JBNqXH+/HNRfd5xeKNrO9DSIm2z8opKwBFunXDCiXagc4Xi7+qrr3Epyti2ueqaa1BekoS56zCwucZ3X1fav9RefvkVuOz/lvmus7jyqquApYt9z/nomga0DtVJ76murnbrNnXqVK5t9q0+AOzy5t3s2f514qKLLgHWrXHzqtq+GhgYwIyjZuPKqy/CSMZ0rQifbtoEHO7A1GnTUV19JUYyOfw6tR9HH1MOoBZGSRlOPuVEoGE/Tj3tNKwZHMPhkUYAwDnnnoPq84/3Ku6ULxuHBzc0Adt24sxTTkB19Xkwli4ELAsnzjsVcASX1157redJwfTNOee9z7b0dfqlsXQenqjl246W+fnH3sLKlhF8tvoCVM+3X6NDb7cC22zF7MWXXYFjBWoeiikblqNvLI3SyilAv+1dMHPmdGBATXFy6aWX4u3RBqDZFnT+x0fm4+YXd/jrxjzPlClTgOHwuAQwkgBMlJSU+tq0qqEHeMumT5w6ZQqqq68Nz+8I4LWO7UCrN29PO/VU37v1wJoGoM7uzyuvugozKkvR2pcGUh6dDauHf/wLl+JQbxp/rN/J9eeNwju2smoK326Sd/D7r7jCttxebscDYtNvbe7F44vXYX9mBp764mX8+HY+VwW1vTAfrEULMKUsieGMiYsuuhgXnDQTAHDbq7V4qq6Ru9Wth2xOMc9RWVGO3rFRN035phXAiF95cuFFF6OvvgPYt9e9Vl5R4RubNJ+m7mFgUYq7fvyxx6Cu+zBmzTsbZ04rBzba4+844X0wZ/tq9LQN4KSTT0F19Xvd6+mM6b43xXXtd/s2AN3d7vdHdozhm5+qxvr0XuCATV80lDVQXV2Ngz0jeHRNA/5Yb7fZnONPwFjPiPuerzls4sr5pwH79+KUefNQXX0mANtDBav8zB6nn3EGUFeLx268BF98ogZVM48GDh3Gccccherq96Nqy0qAUaDSeh9dtxbNg/bacPn7zkH1+05w2/Liyy7HCTP9Bjv7O4eA1SvdfCyLuHshEwYAgunHHIfq6vO8m5w8r7z6as47pnl9I7CjFtOm22s3iy8vdvZXs08DUIeqyipMmXc+tjb34qZrTvfViwW7l6Z4dP9GHOjvwqlnno1zTj8aMyvL7Ng4i7z29K37krE7pbHHHTcXXfp+Le8VQgg6F72BZ3dn8K2//xCQst+5r3bMwupDPHXjBRdcgGvPPAbTm3uB9esAAKed/h5UX3UqN87Ly0qRznkC/CuuvApvpfcCjbyCpaS0FO+76BJgzWpMrSxH1rS0nnPv4UFgrX+sifPt2ssuxFVn2J5KNY09wIr1vnuuvfZabr982tnnY/7xM5DbtgGA3wuusrIS1dXVWDNU53sesZ5i/UvWLgVGbYXLVVcxe6Ui4ovf85+/xmrbgc2bUUbXpAi4kan/oY1NQO1OnHD8caiuPt/dZ97yzx/GYwfeAro7cd7556P6vcdGrveaoTqgwWvPyy+/HCcdVWWfJxbZ5wmjgn/n9A5ngGVLuGcFHIM1p0/F572RPTcWCHpO/YdLTsJdnz5f+749q/YDu3fj2muu9nnkxYhRCI6UjKv8uDMmjnswRowYMWIUDK3dByFEqUE3DOOwYRjHEULaDMM4DoAsoMQVAK42DONrAKYCKDMMY4gQEsQV+a4CGyQSAHqGM5g7Qy4sY0FfwkG8/hSUQ/3BFfvwz5efDCA/ft4hJ8CteKcsp3JH+aOK68GCBmF282NuYSkfVO7rKhoAwKayGQoIDguoaQII5PRSvuDwmkZOKqoq2hep+k73GrXu1uU4pm0ickmPJ/Uz9QBKJgzOQllNwaKft+gkQ6m6xiyCsZyJpU58Ghp3QhXvY1jo+3yCZqowPJbDy9s8YQjNK8jBh22bTY09+DgrzBdgEYKuoTEf/z7N40tP+pUmKkSNcULR2DWM21+XK02CMJYzfWXuONSPzU09nEKM5Wpn3VOzpoVvPrcVC3e2u4dyGt+G3rGxoQf3L9+HKkdgwsYgsSyCtxrYALz6Hd3jBGmmVF20QDbAt4oaJJ01uTF2eEAd5H3HoX4APB0hW83hjHptL5HEOAlaBylY77MyCaWbCNMiOOPYqdgbEtSZ9rWMeo69pktreCQgWrn+fOkefKX6NJQzMYM4ukV3vvPPzAc392iUgvqTfU2q4imYFgFRNB+tw05nTMnAxgsLAu2v9xw7Fdtb+rn34yvbDslvYmBZhIvlpIJqbxAlSPGtr+xEVZl/u0rfn4d605jNUC+KczaniFfA1kEc07L+fmNHG0fdM91R9t70+83Y1eYZjmRNojVPVfGUaJvROFYDafv9tmZfF7qG/Fb7hBA8vbHZjbsE+MdBWjEuxW5g1w5KHTWquFfct9D02w/2Yeehfsw/waNdo20/ytAIfubXtlA+THEiA6V0JAS47I5luOG847BgR1vIXX6wjyCjxCWEoLF7BCfNqsT9y/fhy1efylGRsfe/tNU/b+gUYceT7D0lrks5kyj2pd76VFmWRHpYj8Y3Hzoo1S3iPGrsGsa/PPpWaD75bMO4djsCMU4O9qSRyVla71EZ3D27ZD1wuzzPxxL7gY6XzkFvjRCbTNWGMtrU8QAt5826dtwFfcUJXZaSEeLYxYihg1jGFSNGjBgxdFCM4PCvAvi88/nzAF4RExBC/okQcjIhZB6A/wbw1F/aC0XknBc3r1c7Fl4iNzQ9NKgOvSrQzaWpEX9ABA0OryOEpHEHdGii/uG3G7jvbO5sWZQfXjw0quIAAHadf7F0r/J3QB1YkhAiPUxEEcKyUN0WlJ/ueZC2iSiYfGGL3POhGCDMwU/1CB2DnuCY7bcXNgfXS4xFQp8jYwINXR7d1eYmf2BNwnDkp+o7Uc/w/ZvO4eyaM4/x1SkqFuxo4w6VdKzoxi2pbR3Auv3+AMAUFiF4OLUfi4RA7fmMv30hgm+7cv5LlDM/CjoGRvHeHyzyBRNt6x/Fpx7mrVRVz5I1LSzcaT83FUhmHCUxbd8xR5hF43ywwq17l+xBnSCwDMOutgF0D42he9gWLohCVlYRo1JEjWZN7pmCLCCpMNNSCGlFpR8LKkxj466ERjghfHB4VewnFjnLwnESa3R/Ol6pxWIihVqFIClRhC3cwc89tt+p0i9oDTEMwxVUj0gCxFPQ1+R9S/bgvbfIPQRNi/jWRa8u4dBlP6Hjl75X2THZqxF0XNUeusHh2fXbuyYv66n1Tfj1yv2BdWHrL+4VaB1EZZVpsv0s5ukv57gZFdzYmFllK04amXcVALT0jmD5bk++UsqOOaaeqth1Yt+w+6s/b27xvXtqWwdwy8s7XeMZNo+wssQnl8U4GguJ20bBKhw//sAa6T0ZSfyl0azpM6wB7DhUt76y07cON3ePYKOzTtPfVEqTP25qxmd/u14Zq4Idx7L4Vk+sa8R196Tw04W7cf+yvbhr4W4u5lQY2vpGMe/mBahhgoPL3ivimP2fP7+NxbV+2kRCvBgnlaXJwBgchBB84oE1eG17q/YeiDVyUN0iKkNlsZZYWO6eSasKHNj3Sr578nzAjuXmnmEM5hGf55mNTe5cFeX9hBD3XR4aF7AvjQOd/r2dP8aJ/Z+d62LeqjaMesbMF/RsKiuvpXdE+f6kzyEajcWIMc6IZVwxYsSIEQNAcRQndwL4sGEYe2FzO94JAIZhXGIYxiNFyP9dAVGxYAoHD1kwzZ8v2YNXt9tc2GMaHics6CEsH48TeijUEWpQi1Mdj5MgsHIi7zAhlBUgEGIDjqsQVEPZs+kKdfz3yRMGK070MncPD0Jb/ODlnZLUxQEVJiQMQ3lI//xjXgwL9qD2X3/aLkvupWX6fWA0i3X7u508gBLJCYkIvch+Z62k6WH7rLnT3PwoohqsjQnCFFWwaBbiYfVQnzx2CGAriJokAVvz0HniXx9TW31SiG0IACfOCqYnEQWDgCcsaVPMPdazRCUYYIVo1KuOtSQG/ErnoKmisuJn8de/XI2/+sUq13Kbjg3aLrWtnjW/as7SIOAU0yrCnTfZqoUJ6yjoHGAFdWHjlwjB4Ss0PBBMk0jj7PjSFUH5e6Qh41U3LYKDPSOuApgdl/S5gt6lBryg80HzNudkdv+yvQHBxwn325uMQpWuv0HvCx1PVsBbv+gYi9p97PrHjklxfKrazbQkXq0FjCH2VrGPs1a4x4lYTdm7rryEF1RTekFxDq/eyyvKSxIJ/HLZXl89VR4nbt8k/fs4mcCRFZSeNtuOF5EV6q/yyBUfU+bdnJUoO2T3qtJxaSRr9Pde3IG/+9U6m2KTwW2v1eGp9U3Y1sHX6ZqfrXA/hxkHffeFHdhwoAfDCoEs73Hiz4t6DNL/YznLFe6WlySk71MW6w/Ye5qnN3jGCbI6i/udpbsOS/cNBN55orw0GbguZU2CHYf68Y3ntuorTphk//LoRmka8TwjU4bJ8gxqq/6RrNSbyjSJu98tNFB7FGQZLsa//uVqnHfb4oDUcjywbJ9bZ9HjJGsSV8lM26dzcMym0hLwgTuX44P3rvRd93lWO//Z97/YYvTdIq7TQfuQILyy7RBn5BQGOgbEfR0AXHXXCtz4+CbfdYBRJsceJzEmFrGMK0aMGDFiACiC4oQQ0k0I+RAh5AxCyPWEkB7neg0h5MuS9E8QQv6j0HLfacgKgclFi1Li/vfSPbamwf0c1RqIWhnno9Cgwkf/Qcv+/gZj2Uf1Pc098gCwQWCFP2x70MOEeEgK4g5t6Q0vX3VuJEQuEAyiZglCPh4nunnTZwjjUS0mvufER7CpuuQV3dU24AoTogi/sky/s/1NiGCly1ynqjUCXkjJ1k202M1Zlh17hcETgqeELui8COozsQ2C5uHfPLhWGpBbZXVeKGT9IxNmsfjMb/w85+0B9FQiVNlnTOL2EV3jMsL6I3qRBAmAwp6Domsog0O9tlCKrkM0W5ZqSTXetx3swzee2+p+L9dRTnBj3bse5HFC22aU8zgJ41D2FO1nzZ3GUYSpkLP0qIW8Qpz7TAvv+/FiPLOxiVvfJrNsQ+a5aBKCq+9egcvusGMVUCE34L2bg4R2huEJ64MVquH1Mwnhxt32g148m6DbqcB8ioTSSloXZwxS4TydB0EeMyzYx2QVFez1XW0DUmEoUJgXYGh9BEMUuv6Kayr7XewbWT8SoW90adHSCgtwVVvTuUSF6ezakc6agd4xlFJIFM73KbyI2MccGM3iHyRrvcqjWEyrs/7SvNhnoF5+onKDWvkHsSSFeRpT/HbVAel1ds8tEx7TPqZ1TBieIruiNBm636FGUazxk47HiQqEeO/EytKE9vvwhvvlHkC+/J3/pkWkwm3Ab6CgSufmSfj/IiyL4H23L8YlP1nq+80kxD3PTKRXo4yyLh+oqLrSWdNVCNHcL71jKS68fYl23qIBnuily5bv3qNQ5KjWojBjlG/9YRtuuH91YBoWqncgrTvr8cvVQ9jTx4gxEYhlXDFixIgRgyJ2ep0giAdK8bAjCu8AcJwsKsvE5bsP4081B33X6eYyH9d2ukFVHci+9swWr5wCJGRs7uz+34txwpf/77/frMyrc0AunFEWKEAnxkmYZWFQXkHXw36TpTsSR4cwweqSOlv4H0lxojhwE+gIiPkeYRVxtJ3ogfu2V+tw2f8tQ386y+UaJOj5+rNb3LqwoLcECVJFpYKOJ4SI3pGstidSFMhyDKPaYzmzg66p8OUn5VaEmZzlCpao0Er0OBGtWYMFRQS1rf0+C8gtEhoYakFM10hZruz6Of+E6e5nUemm49mn8jgJon2hQu0oghtK1XXRyTPx+jeu0hLumhbJSyDR0ptG30gW339p5zuGqkvmcRJMu2f/FvZ8Cfedq55LOsuAJXicsEOLjpvekSyee6uZv8/5TSceGpveo+qyr3/16S2qWzjQOo5mTW58smP7nx6RW6wDwO/XN+H+ZbzQu5D1jn0blIrxIpy69g5nuTWf7SqxaNmUJuCfryRpoFuhGFKBxqg4+5ZFuEmxp6GKHtfjxGTXi5xvsWLbjSpOxDVjIK1QnDCZvbqtFY0S70fVe1KMiZTRWKdEIyLAUwyLM5Mq08sDvOFUHo8iHli+T3qd9zhRK04okgmDUZwkQneFtD+6hjxPAtneQbYuyetL3H1TRYjHSZgniCp/AOgb8Xs+UIhjK6pHvAhTQttHkbOI24YT6XGSz55NBIEXp0bcPtvKDT9NIqD/nFoeJwrFiW+uuZ7l/PWgmF20niMZOdWeDKo1Pkz5Zlm259FEGo3FiBEjRowYMd4ZmHfzAo7tZDwQK04mCKJgUjzsuFatik2lyo36i0/U4Dt/ftvOw7nVgGetn4/VOt1Yi7cS4t/0FrKJZbNiDwpUQB/ljNQtcW/3lac44hLI6VXEvlha5/cKkCEsOHyUe/zp7P9H4uwQFiqBKimiWBKrBPaEqCx+vc+mxZfFNi8VNFEBGrV6Fq3qgugJFrzd5ivTLscRpHJjVpmNXZ88Dvxt/elxCdgpzmFCSCQBCxWOqahfZBgYlacdSGdR7nhEXHXXCry4pcWti+dxIipO1OXkLAs33L8G192Twq2vePR1f/+rdb60NB+Xqotpl1lO7AJWeFJeksTpx9hW/ccLdEhKKhtFYFu2C4K8CctlptYaVF0Z08KU8hKUJBNaVF05Sy/Qt1eGjf0M7/oXFBQbkw1JCQVgoPeY8z/Ia8yA4XmcBEwlnemcswhXFjdfmY/UE9DL2/5RFqNCXhdBceJcp9RCMlicQsf+LCo/6FpnGMGKEFlMCosgEu0LBXH/2PB7nFhumV99ejO+/OQmtPaleY8TsS6SQUEIPw5KEgkltYwKlkXw2vbWwHdPryO0dj2HOaouf/+yNS13PU74+verFCdMMpX3m+77QUfYLFPCjLlrvnettrUfW5ttb6syPccebYjerRSi4iSdMfEI4/ltw8CI0062x0nwpJYFFc9axNdWuopriwCvOBS+lY7Hi0rQrhN7UATNqSdgTy32YThVV3AbBXpjW8Q1riiWcr4/ncW8mxfgkdVyLySg+MHSZR4n9JJYUn86q2WYoopxkgmi6nISifWhY7+8hJ9sQfs8dnytqO9EQ9dw6BqgalVavmoemCQ/444YMWLEiBEjRoxiIFacTBDEg4UohPE4gOVQB/Zk8mSEAHRTHIWqa97RdpwDN8YJU5uEYddNPLcUso+l+S/bdZgLak3387uYoM9h0KGICDpzyYPD89/vWbwHq/d25l1OcBDP0GztPFzrtYk/QIQdWvJRnLD9JsoHVYdp+ug5y+IO5JwijlCLXX6JE71YdOaVTNEA8GMmzBsnH8+v17e35RW0PQxiTVp602gNiMEi4qYltkXySATFiQq9IxkuvtNjaxt8a6VMKKNqbVbY8dR6vu0IIVI6CplQZ/bUcgC8wDJrWq4S4tjpvOJEJXDkYihIhM5A8NiQKT10g8PTdq2USB1veqqGr6dFkDQMfPicOSG525bRhBDcsaAOdy7cHZp+skGmAA7y+qFdFRwcnnnnFuhxIsY4YUsNWkZosdoeJy4dFO9xEvSe4sez/X9ACJrMWjTLlFRBICC47p5UpHu8ez2I1vvsPF66qwNLd3Xg3sV7OIMJmmTtvi48svoA9hz2B7wmIFwblCQM1EXYpwB2+4Tty6hHm8xzOJ3NSai6vCtU8ClS2/Sl5YJw9t5BhYJbx5ME0NuHydLQ9/DLWw+5tKs33L9GSutVDHBKbMjXZQBuzCMWLFVXWTIRuncrlSw4OdPyWdhrU3XBVrwBnjeMSqGgo/C658166fUgY6QV9R18OSH9ns6aGBxVe9DmOEUW/5n1ONHdRxFCfPFyWHQ6/Uq99tj9NF3/8lE6+evBUmPZ1+izjGYtj6pLaJeFO9tx6R1LsX6/WokNyNrDUZ4z/a6i6hK3rHTvJK6drGGHOJ7Ycg73j+K6e1KhewLVfKGsCrJ4nwD1OIkVJzFixIgRI0aMI4NYcTJBEA+LPksh+l+5qfRobFSCDZpnOmu6FotRLN1T37mO+87e6gYxFO4pyALIyexLT9Zw1pf55KhzoFKlIEQuFJMdRnsVPOEsonqcRAvc6QmlJhphyhqXK1+4HiSIowexkUwO19/nBb+UeTcBEChsiKBs8QujxTgpoqv/qMR6V4T/4Olcl3hJqZCPx8kfaw7iR6/VRb4vDGKzXn33Cjxf0xI5n6Gxwug5AFsAxa0zMFwhHZ0TUQQYQYK7P2w6iHNufdN3nZbDNstMx+OEHW+ZnCX3AAHQJ7HmJoQXkLJ5sWtL0NgQrT8BPW+zTM5yBTQy5ctiwXsuZ9lCnO9+9KzQvOk8/93qBh9VzzsBMmF+z7DaupeuK0HvGANeu+gEhw+CRQinfKEeYTWNPYF0kXQcr93Xjda+NObdvADLdh32KTYoxDhQhBC09aeVQth5Ny/gFEzuvBGSU4tjwzC06YcoCjEoZ+8V9yUyRUVZiSG0s/3/nx7ZiJ8s2CWnjiH8OEgmjchKcZMQrNQwwAA8ISZbz5GM6X83Ml/pGpUzbcMCWj+VBwGblYpySUcAnzMtPCkoq6V5SdZougY+uGIf/ubBtYF1VI3nKFBS4QlVk1HksrHedPa/Mo+TTM6vOCkJoCNjwdaXxtZSjdMJIXQAACAASURBVMEw+iPAbnMOTlZDCiUaANz6Si33PWx89I5kcd5ti5Wrl5qa0P7vGuVobgWe2diMy3+6DDsdOs4ooHH3ikGTSuCPcVLhKk5M15BHLKqmyY7zseNQH4Kg8jhh57Gu4sRSXGfz2nawT/lbp+PVvVERo8Sto2IUuB4vkphsWdPCb1Yd0BrPMWLEiBEjRowY44FYcTJBEA8WPm5a5vuVdy7HJx/iD4/0AHfmDxbiBw4FDWstf9urtXh2o2091dA1jO++YNN45GPpTsEK8w2njuJhohALIFXN8vGm0BFMv7T1kKIeRCrcl7mc68iBVFVR9cVr21sjeJzY/48IVVdIoaWOQFIcI+xYV3kT7Gob5CwcRcteCj4APN/WbHKazie4E74G0aVQiEotLzg8Mz9C+qOQeVhs6MbqCcL+ziG8sCW6skWGNOMFYhhAxjmMy2gnKF5/20/zAwR72C2ubZdet4htVcl284xKh6pL4XGiQ4NDCC/w5Kz1mduDYmLIhAh6weE9xYlujJNEwtBaV0qT4bz+kxkyYT4bOJuuX8dMK3cu2P8C3zG6HicaDZeTxDi5e9FufPrX61HbqvZuYNejpzfYAuwvPVmD829brEhv/6cByBfsaMMVP10e+C5i42TIFI6AR8tne5xEVJxESs3cJxg/iJ6GsvW3JMEH1tZZoomQlxhLRQfPbGh2aSDDQJ+DDQKdqvcrXdiqs9b5rAfeYY04cKq+1/EkWbpLj8rUi2ElL6xnOIPvv8TT0LF9c1cELzdVGfTyaNbkxrRJCNbu63Lj1sg8UlnzENF4QwaZsj1j+ukxdb2z2PtUtGyytLogzLNRTAnhStMVaKvaivU45wwMnM/umNbcKFNPjTDaP1ludA9RDFYwQrz2pOcaquwaGM0qqbrouyRs2onvGpqPTowT8exG328iZSeb1//3m/Xcb+z4EhXxSijalTIMyObLmr1dwXnGiBEjRowYMWKMM2LFyQQhzOOExaG+NLYLlj3prOnmQRUkP3rNs/p6Yl0jXpQoBvKJcULBbrgThmFTdQlpClKcEPmBPJ8cdRQnt78ut97vG8nilW2tWnmKzzvv5gWSQ75337ef3ya9LkLH46R3OIPDA3qBUMcDYQci+rPYbKwwWxQw00OZOIxEARWFRYg7PkzL4tpNFhxeFKCJzaxH1SV+ty+wQtCwaVDIPJyMuOH+1QXnMbW8BAAwyhy+h8ZyruCV9mG3xBuguccfwLi8JIFhCRUXhUoha1oEZ9+6iLs2o7LM+c2u22jWxP7OYVeAI64NUsUJBEtaBR1J0Nolo60IG2sEhKfq0oxxkjQMrfW8VIOeZjIHcJWtYzIBkEth5VwPEh6zMU6C1nLd4PA8dQ1Q79BGqdZ/27PA+64jyBSDw28IiG0i3gN4QkzV4xoGQGXBFRIFoAz5CivvWrQby3d79EFUobHxQDfuXVzvWpGzKE0muHa2NNQ2LPUO4Bcy6iCKB12p63EiGNpI6kVRzgSHZ9876rHjfabF/OkrV/B11hhPu9r81GZeGbwCOgzPOHtc934Aj61pwLybFwRSMIlQGrE49fnYL1fjG89t9dJbBP/0yEZ85te2gFiqODEMhr4v3BBB9HoF7Pbc3MQH047qnQUAJ86qBKB+h+QV44R5Noqwca4bV0nVVp962ItBJlWcJIMVRCLoOqV+n6mfx1WcON/fO2daYFnDY7nAfaTlKirs72fNtfNbtqvDfZeL7wxau7Bzgdi9rscJUx+xZ1TB4emcEK8HzX2eEkxPcSJ7ouGxHL76zBYAcuOXfDy2Y8SIESNGjBgxiolYcTJB8AWHV8Q4YcFuP0ezpm9zXt+uPqhSqAR3UWEHepXEOClwBAVxy0dBIc/20tZDuOONXb7rskOa7EzgO+QzjfTilkOup07Q5j/ofERpVy68fYlL3VQMa7ioCI3jIfHEAHilQd8ITxdCk4p5EyKnZeAP1fwhjEjSicIIixBO8JyfxwmtIyNAC6PqGocg73mjCFWRUZjkC1awf6DTthCtKku6ioenNzQr7uSRTBgYSKsVJ7K5O62iRGqVTKm6qMX2U+sbAXjW3qKXiNzjhLf4VsU7CRIG5UMXYnucmK63iopeTLwnmQjzZbGhI+CbvGoTuWBHRqPmUVjZ18PmME0flM4kwJK6YMv8T/96PZ7fdND9bhESmrdF7LEye2qZky58foprpM5aaAoKHeeTNG3WJDjYY8dOuuaMY3DTNadpKFDyX5xoXBDAFvQurTuMf350Ix5Yvk/6viwVaLZ0pxo3X8d5WU8oFCfimvHPj250P3seJxayOS9dbesAbnl5p6+Mf3pkg/uZgGBaRQlOceLdUfCxyOQPHfQOZG/JJ+i2RYBnnXgUUegBVWsrvX5A8Eigc59eH5UIjdn1Q2ffKa6qZSUJZE0LX392izJfHXzg9KPduat6Th0llQjZvA5b83UVNDpzTOalWVoSLX6eJ8TXSi4tnxZVVR5seHDuD9/EZXcsDaiL/T9hGEjVd2C14z0xOJpTUnV5HifBz5vO8vsd+ty8xwmfxz/+bgNXBgX1ahOvBynhM3koTmR9yJ4Du4czmHfzAnz5yU3KZ4gRI0aMGDFixJholBzpCvylIJMLPvjKLLHYK+ms6RNWitb0MqgEd1HhuZTzeRRG1SUP1hwluDjFeFgkyfK0CPCT1+vwteveg1mOcNWXRjhn3Pj4Jqzc04n//siZyrKCnvmFzX5KpCNxjAgbbrS9xOdnx7qoKKPPLTsgysarT8DJeZww+boxTvhKi8XoKU7kdeAsj8chxsl4YbLUhB6Gp1eUYEgIrj53egUIgUuZooNkwgjkvxe9IKaVl6A0mZBaJVOqrhX1HfjuC2/jE+cfz/3O9mdJwuDoXiio4oeCDw4vz0uWh/85lMnde4bHTExxPHp0reKNKB4nIaNoEjucSJU6PAUgv3ZoxTgxPIERXdPa+tO+dBbxB1aWQfQelcW6YGFaNrVhWULuEaW6B/DqndaI98TuJ+5dXI+7P32BVtwB0yIO3Wdwuq4hdUDqKCCE4MtP1QSmKU0mlLEVlPmCSJVs44WEYdgxNQTBdJDBSRnjcUI9bUqTBrImwe83NOFr152OY6aWu/vHASaWBSH2/CgVLGIyAdQ/QegeGkNpSQJVjHI8H2H+3ZtG8YHTj8a+jiEc7PV7G6qg2lepBLHsGv3N57aiqdtP9WTAW5d1qLrEOlSWJqVW/Pl4nCRDPDFk5Vw27yi81aiOQ0FzYqsdJgzX9ajVGTrc/HLWm/KIHid0iOl6PrKp3Od3PukYHgwo48EQxvuFpwsmdLLB3y50+oWdhZq6+bngepwEBIena4fYNKZE6QLw3isiOMWJ8zFsHMseSTZ+lu7y3pOTaPscI0aMGDFixCgSGu+84UhXIRJij5NxxsYD3TjtewvQOcjTC/hjnATnM5a1fB4nMhqXILCHjk88sCbSvS5Vl8IyKh8QIhcA5BMPIihOQL54Q8JD/mZtOx5Z04CfvrFLaWUnHnZW7rGt1IP6OOiRG7v9goJ8lEuFIqyvqdBBFKpSId7afV2+2D30ucUDPoH8GU3L8xgxTZ5chS3XjXEi0GRYFuEoS4IOhV4die/7ob40d2idzIJiEfkIrlSoCuE+DwJt1UpJHsmEAUIIfr50j3Z+yYSBwQDFiWjpP72yFAlDPs6o0PzuRfV4u6XfTUPjXrBrVEVpUi/GCSek9T7fuXA3vvr0ZmmdxaodN6MiNMZJ1rSQMS1MLYtmF5FM6I1jGfXMOwmyZ5T1k+hxEkzVxXicOPdf8dPlknKIFj0gi96RjCtEUlnrW8SO05UMsUBnQZ+L3qOzFrJt8HyNfoyjnGULCSfqraWzxD24Yh++9QeWSjP8HkL4vVu6SB6zKiQMO7aY2J9B5ZYkEkgYNMaJ3RBUiQrY4/KsW2xqwmVCXBJCCAzDQFKY4+xeR7Ze5kwLv1m133f94p8sxfvvWMa1rRvjRPkEchzrrL1Rtj6qORvmiQIAr25vxfYWf3DxRMKj6iLwP8e/XnEKtzcX9/oVpQnp3jGqxwngxZ2LojiZf8KMwDypUonNMXzvF/gzk3d4GplisrREf12zy3HWcB0rAwFu7Cbnt7IS9R7nV6l9IfXw8jEMQwh8T9x29SvyHCV8yPO29PLKeboHZoO2q3IQlUq0rKGxHP5U43k8BnmcsL+JnpoqcHt2555wD+ZYcxIjRowYMWLEOLKIFSfjjF+v3A+LwHcAEwX9sgPFIGPFlDEt9zvd7+oIsNg9LHvo2HHIfyAMzseQWunlcdZzQSAXAORjXTQeFv3rJZzvtL4Jw1AeKFRVCaqiKlYHAOzr8FOyUUqjYiOoP8Ms12kfiMOEjrvX3/bHkfGoBfhxYFN1eRkdP6OCywuwaW9UVvzugVtQLr66vRUPrfAEPDKPk9lTy4W6+JWcV965HIf6vENrIQrEiYZOkGBdTKvI32lRFFCwSCZsIcNzbx30/6hA0jAi0cBMryyFYRhS+qNzjp/Ofafj6YkvXAqAp0yqKE1IFSdiOrYY8ZkX7lQFrvcS/vkrV2D99z4Uqtyg3jussFQHSUMvOHyJRoyTyQyZ4okdN6LlLP0l6B1jOJ4BALCtuU9p0W4Sm74xCnYy72oVBVeqvhM5yxPE6cwDUdA1oqE4EQW+lkVCvY8AGptq4jQnuoYUbPBonaoR8O8gWaylYsJw5qQ49oIMJxKGgRInfgul6poiKFFpfl96kvfKIbD3AKLV+OBoDj94eYebRsQfNh1UesGksyZX33wV9zOryiLf8x/PbpVeV3kK6ewjE4aBLc12fBLTIr65fv6JM3H8zAr3u1hUeUlS2gZRPU4I8e5RPY9sj6qr+GazDPeo1e1TjXWJ6QO63lFFVFSqLhWVsCooOwBcdscydAyMur+VBbTX3YvqA+vBGgAlDEOIEeXtt/0GaTQNiURD7HqcZFlFpzxtfzqLeTcvcMcvO/bfrPUUquIYYsc773GiqThh7ze9GHJBiD1OYsSIESNGjBhHGrHiZJyhOoj5YpxoHCjaB2xBLT3ElGm4kLMHnkKUC6qtcD5WchSEkCJ6nEzMzppu9CtKEz5rvkfXNKC9f1RNAxFw6OMDm3vXd7T0S60ejwTCrPfowUl8TiokkPU1fW6Zx8mnnQCtT37xMrzxravx1/Pn+uJEcHTzzueRTM5VcInCiG0H+4Q6+59DPCj7qLqKrECcaLCBWAvFtAo5XZ0OaLvK2tMwDJ8yLQzJhKEVxJhiRmUJkkI51555DLbf+hE3KLNbV6eys6po0HivzuUlSfnYBh/XiFfyaVrOMp+Pm1mpdc/QKFWcRPMGSiQMLWoTm6orGJNZjyirW3CME79gSZYnXR8fWdOAV7b5lcR2HtHr28EoOrOKOnzl6c0YyZiMx4l+jJPSCDQ4WeEBTA2qIpq3Yejtc4qBfPY6eh4nPFVXVO+hqKAUcGLfjOUsTFUoRpMJ+72XMy2XqkvXM9ByPE5k3sw01pRs7QqLt8fewir1dGiQKPLZa1JvXxGq6aGzLqczOTy6pgGA41Eg/C4qJn72Ji9cLy9JSIOpJ/MIGEiNWUyFolTm2SJ64YqgObFtEUrVpWmwoLNWsHOXCu2jrFGAZ6Sgep+FjaT6w4NuZUXjGwrduBu0HQ3Dvweg9VBRIOcsoh0/hkWGi0kUnPZzv9vI1ZPWaF/HEC66fQmaBbo6VpGSMb31j+7zo1B10X162Dr6TjbUiBEjRowYMWK8OxArTsYZqg2fL8aJxsbwUJ9NMUQFNDoxTthzQyEB1F0uXp9lVGFUXTKL/3x4w8MObsUKLkgPvBVlSe4AcbBnBLe/XocvP7UpwOMkSHHifWat9zqHRiWpjwzCea6p4oS/Tse6TLhM0/qt2rzPZckEZlaV4Yxjpzpc+d6hUlQ/AsA5t76Jrz1jB14VD72ioEY21kSFpIyqS8Q7yeOkmPjspSflfa8rkJbM3WQCbqD3H3/yXNz68XMA2AFxZUg4AsYo1szTK2yqLpYm4t+uPg0zqkp9Y50qZCocrn5WuKNSYBMiF8gDESwonXQfOutYzJ1eEZzWwXCeHicJw9BSAL7TqbpkYPtzSZ3t/UMFQG399hocFnCdHTP7FAGsZYwk/3m9OvYVAAwy8X/C6kDrrFKwsNAN5stC9GQxBeW1CjnNGCdhOP2YKVrp8tlD6MU44cdKPoHOoyBhGEgahlQRpFp3EgnDVpwwVF1VmmsBcazgS5IJVJT688+ZlrQPW5i4I0dN8XuGsG9qWqem7pFAGiARhRjpiKBj/6y507jrOgq3DNPnRKI4LAlRgJSVJKSxuPJZV8M8TmTrRVj9aFZ7DnvKsDCdTjGDw5uc4sTeM5ZFDA6vTdWlwA9fqcWzbx2EYQBXnTHbly8g388OjeX8RkDOLRYhED3LqWJH5XFCSDQPLZnHSViLrT/QjUzO4vZhhADPbGxCz3DGF2+LZQlg904ZRXD5INDzXxhV10Qp3GPEiBEjRowYMVSIFSfjDCWXskj/o5FXq0MNRPelOjFOWFqSoENhZWmwRaJN1aW2jMoXsoNQPgqeMEu0YlksUTqTipKkNGBq30hWebgLOvRxNFPMlxmV0ekpxguhPNcu9RL/nHTcyWnZ7N9kHicUrsBE5GQmPE2GrHlF68oKYZwTYlvWPZzy6LvCAsrLytENQvpuweyp5fjc+0/GR+fPzTsPVqAgImEY7hppMAL9E2fJvS6SCTuweRSPk4rSJAzDwCjjcULjrYj9SQV85SUJGAZv0R8kz2OFHrIYJ+F84ARnHDsVj954qZs2bKzlTdWVMELjpwCOsO4dLMeQtR8rYPzuCzYlEW3vz/x6PfpHsoHKeTbGSRBkCo1rzpwtSSlHmIEAtUAPU7AA+SpOBI8TTaouOm8KHTa6dVVZ4AdBa9shCDL1KYryQ8JQU2SqvDWSLlWXJwyt0PTssMeEXd7Ucr834Xu+vxDt/X5jDlbwecVpfuW2LMZJVDy9oSmv+2RQ7Rd19p7snsN+R/H3hI3RspIEBiTUjmFUqDKEeZjJHiecEowgk7PwwHIvfkeYAkLb4yQiVVfGoZorcz1OtIrh6LEC66PYkx/oGkbX0BgShoHPXXYy/vGyk5x8vTR9kj6c/8M3cePjb3H50+exCL93tCzP80XsJ/qO0lVMu+VJYpx4xkkm9nfKFfq3vrITb7d43tgEXpsPjfGB71ljt57hjPvZ9TgJ82hinocqn0ZDvIsnilEgRowYMWLEiBFDhVhxMs5QWYL5NoIa+0KqOKGHAR0LNc7jJEBwv+F/P6SVj88yqoARRIj84JIPzUaYhWmxgqmnM/YhorIsyR1OaDsQoi4r6NDH3sM//+Q5MISdt+nhWeVxIvMu0vE4oXoM1wrPaRPT4mlTZM0uCjFEK1qLANfftxJ3LdrtXhMVJ+IYlQlX/sL0JiCEIGGoaSx0QMe87FCcMAy3nQ0AJx9dBQA4c840X1rAFjSUJI1ItBa2UJKniaD0VmJ/0jSlyYRtAc4IilSKDNHjxOLGKi8QUsGy/HUJG2qu4iRicHh9j5NEqABMRwFzpCCrmUzwx1pmp7NmKFWXTowC2fAsDwg+LCLMk4QKOHUEmdSjK0jQe/3Zx3LfxXXalHEVSXDPZy6AAXmctCjQNdSQ9dW6mz8YeI+e3oRgNGvh+rOPxdnHTZ8YjxNF/6g8TkocjxOTofnRXadNi7hrwFRnLRTLb+1Pi7eF7q/Y3/OhHgLkFv75wiIEo1nTJxTW2Xtywm/i33eUJg3c/ekLlPeXJeUxsaLGOAG8vlHVWzbfkhqCbVGQHR7frngeJ+yz0HzpWNem6rKo4kT+u66hi+GkPcGhyWTHcd9IRnrPuv18bERXcWLxHicmIe7LSJw/CUZxEuUI4HqcsLFHnGtfeHwTPnTvSul9f9h0EE+u9xSThBB3zRgOUJz0MooTqhDVNbACvLYZC6HqGu91NkaMGDFixIgRIwyx4mScobJgyyfGiT84fDSqrqBD4YzK4FgFCcOAzLa0UEv7Yhls0g349/76LOnvxdp2D49RjxOep5o96KgOh8EeJ8xhgg0oPb4GrZEQZklJ+8DnceI8T3CMEyE4PPOZti0VxtJhnLMIpzCTzSFRMF0hCCllB/HSEKqud3qMk2LAInYg6nyEPRS0FeUeJ147JwwDHzxrDv78lSvwhStPleaVMOBTaITB9mQxOGtpqmwQH8vjWjeQSPDUOaoWsGOcMB4nEqquMOU3AfEpIcKW3F8s3QsgeoyTZALhWhkUlzLnSEBWfZngj33O0qQRIhw0tCzGRWP7c4+fjnIJJZLy/pAXQiJAkCquy//++xpfGhbvP/UonDqbp8Z6ZPUB7rvlo0v047TZUzDHoZmbKMNh2bp+fEiMoAP94YJ5QmwhX0Vp0o0jMp4wDP94pbFNVEpXStWVNQn+/ld2PCtdGqicE4sGAKZWlEjvlcVWCVOcsHF6Wnr9ipeJhmkRXPjjJb66RPV2JkS2JwYuO/Uo5T3lpUkMS/ZC+ayrnseJXylvf/bfUxpG1QX/+STM40RXsK2Tin0W6sUQlaqLZiGuyQd7RrBeUGwEgT42Peew5cs8qEX0jmTxiBMPx6bq8n4jzh4K8I87j6pLz6PPzdP5zypO6DVRoRMEi3jnS7Fv71roGRl1yzxOQsbxV57ezJRD0DeSwZK6jsB7otCVxYgRI0aMGDFijAeimaTGiAxdjxOd8xr1cKBZsmcZWQBRgLf+KSTGScKQe4gUJjgleXGRy0AFGKrDZ9E8ThzLqGQy4VpO0vgKtBwlVVdA+7O38ALW8HqXJo0JscgKEwzSeouPmbMs3PNmPerbB3z30OfzeZwwnz2KIucexoJPtP4UIcYBEq10ZRaZYcHhZeVMphgnHztvLt7Y0T6uZVjEfmadOEsq0KaXCXmTCcO3zl0yTy2MopbZUa2Zk4bBeZx4QZT9VF1lyYRLG8bOUVXXE8ILoGTB4Vnld+9wBq39aZx7/Awuj6geJxT5eZz4cz9r7jTsFoI/hy1Lk2g6+CBT9svenSzlSMIIXmNFjxOVsCsnNNwFJ82MxMMfphgskQhS3ToJl3pHbKv3wxLqJRVqmnq577ahQHCdEsL6XQh019l89hWP78zghyFpCLG9zypKk0gY+XnHRoEB/5ycNaUUQ2M5pcKNUnWxY0DX48RiYojR9aM0kcAovHVV5k0W5glw/X1yS3cWs6pK3TE53rCI3ANWy+ME7Drun1dhVGQqhVdeHieGf76zj7BgR5u/HA2PE1FQXSxledQYJ3TNjRocnr5rP/vbDWi88wb3+tV3rwAArPjvai69qt/pWKdzkK1/1JlvEX4/bxHvXS6uV3TKmyQiVZe7n+apug72jKhukecD9ThZXHfY/cxTdTmGLs5Y2XCgG8dMK8fpx0zl7mf3EqZF8MUnNmFLcx+CECtOYsSIESNGjHcm2H3YOx2xx8k4QyUs98U40ThR0I0pPTxUMYIxpfUhc+gu5JBvuDFO1PlHhYqqKx/QNmEPeKcxgWTb+sKFQxWlCXz8/OMC01AFjWUR1+MkmTA8oX7AQSdImCNzXxevixCt4cYboTzXCo+Tlt40HlyxT9ourPcIB+ar53HCpzUJCW0rURghppDVyR/jJHyuTibFSSH0WbqwCNGmJwqDTBjCjmmdIqjiJBuBP9+m4eCVdnRNFbtzLGe6ltdJg1dSNyuEEgTBweENgxdOfPKhtbjh/jW+PPJFPsHhZU09s4r3RtQZ6pNnNvghq79MKZLkFCHqOAIU+XiciOWEIatZB5mgSXzCv7vwBADApy4+UZqXztgzNTxOksL6XQh0l1kxxsmNH5indZ/OfmQ0Z6Gi1FaiRvFwywesUQYFjT0S7nHijQHdd0KOoeqi64fogSnbxxSjGY6dVlF4JppQ0qlq9D/7umLzof0UFvBe5f2TjxECpd1SeZys3NOZVzmi8qdY+xud+bWi3vM+ED1OdBWi2kHkAWwWlMEcnMemc4IP7q5VBFcncex45wY+Le/Brl+QS32btVwjEEII5xmiA0II3mroCU3Xy9CV0XpmTYKxnInP/naDkhrMra8F7O8cDi0n37hIMWLEiBEjRowYxUKsOBlnqITorKC4qXs41OIG8IT2WctC19AYfrvKo81Qc/l6nwsJsEezEffwhRynMqaFFbv9B7t8QNuTFeSyh73qe1KheXz+inmhfPP08W3+cNNXjmnl53HC/pSz9A5nVU6g8ygWy4UgTDB4y8s7sffwoG/Mbz+oHtu0rcS2YY9JruKEWuFRxYngcSJrKzEYvFiOlKrLF+OE/10u6PeXfaQwEUocm57bUAYoDsJ7juUtEGXzgh3TOvEybEqb6B4nCcHjhMbAEUscy1qu8CYhePep5h8hhItJkTMJBkaz7m821ZnXfjIFDCH+/tRVlMoodYKQTMg9TsT5YMCYRJGXigOpxwmz3lmEBApvDCH9Qyv2K8qR3BthuoYJ6pPCGknxyOoDeGD5Xu5aRWkCx0wrD6TpDBtrJlFTU1K4740irEv5xDg5YWYlbvubc7XuC/PcJHA8Tkpsj5MwRVahkHmBUYGoKsZJeUnC9r7LsYoTTU8di7h9TsezeK/cmyn/FeGsudOw5D+vQWnJxL1EVXthUeEmA+flanlUShVOf7AUrjKoFKVR93EEROphFrbNL1WU/+/XnubmO14eJy9uPRSa5mdv1rufc4LHia7XfFgy9mnYoOiqdFzMkTxhWYSrv8l4d/mouph+jVYk9TixUOnsfZ/fkw1V+osYHM1JlW4iuof8CpnXtrfivT9YpFWOTfkani7fuEgxYsSIESNGjBjFQqw4GWeoD2gW9nUMontoDP/5x21aeXkW/cABwUpHZ28tBsKMAsNwKArEggo4T929qB5/rDmYfwYMPI8Tb0hHPcwnEhrBkZ0sLeJ5nJQkDPd6kIVY0AGIvYePZ3nh0wAAIABJREFUcaK+qVJhHV8IDMPA7X87X/qbzgHntbfbfM//G0bB54PbnvxlVuDsUXXxh1fT4vmfxbY6a+40zJ3BW7GKni0yJRcrKH5g2V6/UkfSJZPJ42QiqkIPvCXJRGQXTFFAJI0Zw7yZdJ4nmYgeHB7wK04Mgx9rFKM50x0XohApSLjMCix+s+oAzr9tMfrTWTcIc5hAijBWqW55gXd4qJBQ+Rw/Q23VzXrOsThr7jRpvd6pkPVXWIyTz/52A+5ZvCcwT501oGCPk5DxrQoW/ZMFu9zYNwDw4PK9eO6tgwUr3bU8TpxhWIxlSbep2HkXSTEVImBs708zVF1GZKvzqDAkawQViKqMPI6ZVo6SZAJdQ15ckWRIXAsK0/KvN35DAv9DF0KFesXpR+OMOdO0FOTFgnJfrvEc7Ii3vabtz5efdjQAYJ4QF0iEykszrxgnhn++/3ypep0C1B4nNC9C/IJqHW+68QCtR3nE4PBh41F3tNK5QJ9fjFESBSJVFyHePkd8LoO5hx1vOw/1B5ZBsx/Lma7R0MFBK7Jn3P7OIa10LFVXPmuAycR5CUI2987db8SIESNGjBgx3h2IFSfjDNVGfzhj4vr7VuE6DU8ICtYa0hewWsXRy2xK//ahtdpliaBCgiiBCsOguznXAQ1qWsj5LmGEC1lou9seJ06Mk4RngT0wmlNarQZTdXmfWeFN0GGEWp4WQ2jPZvHx8+R0ZTpCNgPRAgC7HifCc7L04+I5n+0D2XWKE2dV+UQx4j2y5i1jLF/vXbLH9zyjEm70yRQvO5/xwNLa6cAiJG9hiiggCovNpONhQS2zI53dnflOLbP/eNPl7E8cWI8TXWEzgdxD4PDAqEPVZYRSnRFIYpxoNntUCj/DkN9z4cmzML2ihEunldkkhaxmqjg7FPs6gt9VoseJCmKMEyCapXkYZYnu3KdKoLB3XlhuOlQyLlVXEYaE7phmp12UckUBneiNc8srtbCIrZScCGGy4VAQspjmzEWVEu3oKeUoSRhoZ2LXyBTKHYN++tL1B7p97SVSgsnW60IMwkWP0omAasjqCOZrGj1qJ4vxuPrMJSdi1XeuCwwMD6gVF/nQXsqClj+cknu7hZVDxxmBjKorctWKgkdW20Y31OtJ3ONlcpZ0TQxVnGhSbnkxTiT3BZbgx2NrG7gA7XYd5c/lUngJXtUff4Cn8hRBADxfcxB7Dg+hssxTrEb1lBkcDTeyI4SgZ4RVnKjT/ttTNTjrloXSPILW9Pr2QeRMK1QZGCNGjBgxYsSIMd6IFSfjDNUG/sl1jQBsQbvulnZXmxdcW1dxUqwDz3icm4pttZww5BbTukhqWA3TGlsEXIwT9lHW7uuS3hscHN7vOZE1LaQzfiE9BY2PUOygu6r8dARFrmcSg/knTFemp00ijoUMcxYWBSs0pUjVJc6JqrKkrz/FebJdQtMQFuPkCWfuspioODM6yGfOf/jsOZHS0xgd+SCq4kQvxkmeFrsJwxWas/f7Y5xY7rhQ9fWZc3gKMkLk6//QWM6h6tL0OPGtvvmPta4ArvNkwPp54qwqAMDsqeU4/8QZ72iqLtkzyhRcUea0zDNABpneI4oA/kBXMB+8552nl5+up4wKeh4nTp2KsIvQjnHCepxEKFek3ppVJacxK3eouiYC4jOffLQ9F1WxlWZPK0NJ0kDbgKcYocJ8dox+7JerffcOjuZ840EMEi0zAClkL+cyueWdQ3SojFh04gCy7c57BBhu3wRBpbjIJ8aJm1WE5lcF/XYVJ0RC1XWE9jebHCVVmRscnv/9yruW48q7lvvuC2OmIorPIug4SbgKKua+Al+CrHeXuE/oGc665UcphxDgf/78NgBe4VlIfEsVRrO80kp11m3oGsaSusMYlVDYmVbw/u6vfrFKK9ZKjBgxYsSIESPGeKMgxYlhGEcZhrHEMIy9zv9ZinQnG4ax2DCMXYZh1BmGMa+Qct9JkB3QTjqqkgu4m88GfEiwCFJtjN0Nf4EbZ8OwvSomM0NLwuCFJFGrmlBQ1bCgz28ROwAiYB+EdTxxgg4v7E90zHz64XX46jNblPfQw2wx+Kd1ctARsBkwfJZnQYFpPY8T/jrrteMFh/cO9oBzqGTuSWdM3L/Mo6OpLE36+lPsg9ffbvPVSayvjrXeJNKb5CUIjWo9TWN06OCSU/jXgliWrHmDlBgyyCyzQ++BLTSm/csHpOfz6hwcc+vBDo8F37zKHY8+Ohwif7bfr2/Cb1YdACEaihMUy1Lf/q/yJgP8MU4+eu5c+154a+ltf3OOtufZZIVMkC6d4xFfIDqKFlFx8umLTywqzV/UOaCiZwPgDL7g+22LexIY6yiqMicIulnkuPeHfv6iAm1mVZk0XUVpYsLoGcX5Rimhzj1ebpAwvaLU3pM4j3Lrx8/Bv1x+Crbe8mF8dP5cN12XJD4B4LUxLbZEWNdkQmlu/kRsFrcdJ/AlmlO4yETdJ7MeJ7rVV83RwjxO9O9RepwwDyAGuC9WjBMVTpxVGfh7qSI4fOfgGDoHx3zpZZ4p825e4H6n4zXsPEMVSPTxL7p9CRbtbA++SRN2/DJaH/63595qBuCn6grDN57zzgtsDKRCYrOoIPaFqoxbXt6pzsMK30eW5hFHL0aMYiGWc8WIESNGDIpo0WP9uBnAMkLInYZh3Ox8/64k3VMA7iCELDEMYyr42M/vasgOYuedMAN7DnvUH/lsaW/6/WatdHRLKh6EoiKRAOQhTvI/UBV7K58wjIIkdrbFdZgg0xHaW8RtU3HjrzoIqA7rAH/Qo8Kb7S3BfMaeJW9xoepTHYNIw/AfWoP4lYdGc+gcHPMfdJmmEgVvdEo9u7EZX6s+3U23or4TK+q9gJaVEo8THcGIqDgJo8cBJluMkzwUJxFvYQ/9YRCFLqrgtKr66Cns8rOKTRieMi2oWu0Do1KqrtNme14mokCKgEitMF9yAuSO5Syl9a+bBymONxNVfnzk3LkoTSaksaUShno1d5VDDA/+OxVSj5MCg3zrvgfFpfCik2ehfyRbUNksogo4RWMDEWHPZVr2WJhWUYqxIb8A0y6jeO8p3XWWFeJFmT+ipX2wx8kEKU6EPn3vnGnY8L0PYX/nEN6sPexLX5IwOGXHpy4+EQAwa0qZlnDejfPk9JgouJStaTqxQcLLmzio4v1FtcxnH1u3/ipDknyo31wKKdgKzB++Wht6j6gIE8snBD662fFWnIRNJdpmKs+m/pEsZjBzVRyjj61t4L5/5Oer3M9B3lKeUsyr4GvbW/HR+XMLpi22GG9SlbeGFTE4/OEBbw0eb48T3X3+cEZN+0U9bwF7LMuqOR5KnxgxIiCWc8WIESMQUWO9xnjnolBTjk8CeNL5/CSAvxUTGIZxDoASQsgSACCEDBFC5BwD70JIAx8bBi/ADTl0XnTyTK2y/vGykzhBMuAdSNKSuAxRIFr7Tya4ARwLFGTo6F1ot7GKE5GqS1WNbMABgD2E6B4UPAqrAMGXZpNw6RT36AqwxTESFND4T5tbcOkdS30HJlZXQQ/0nnWel/j5mhZl3pVlfo8TcT7KBAJlgjCbehYFYXLFOMnnnmg3WRE8TkTlwMWnzMK/X3Ma7v70+UWrD5CfcEflZSIrnirQWAEXm04UfBESPpdVQiwKW7hSOOhzJgTF5g2MB0pJUk3bRG9J6ASCmuSQ1V4WlyqKYEy3SWQCLM243Vqgii1dWZOOl2UQVu3pxMKd7RhIq5U/RfU40cyDXeejlCvGnBNjnFAU2m5RIM5JwwDmzqgIpNRk11xWgKqzRor5isoWOVVXaLah5U3ksjI8Jn+nF+ZxovcAqjU2P48TWg97j//U+qbQe8KougC/sch4K07CQGOc0LYeHM3i68963hUixR7bjZZFcOfC3QWVz/bZgh1ttuKtwKOQRTyqLtU+IWdZyjNX2FmM9zgprgzXsgiIkKXK+GCYUVL2C+8Jk4lxUqbwLMkn6HyMGEVELOeKESNGjBgACleczCGEUK6bdgAyovwzAfQZhvGiYRhbDcP4mWEYSUm6dyVke8lkwuA2g0HbwqXfvgZPf/n9WmXNqirDMdPKuWt0U1qo4iRhOFZtBeXCo6m7OPuKBCMU5JBHZdkD0uypNk3Hi1/7gHuNHqwJIZ4g1TC4ovLxOGHPBrrWYTqHWV0BtI7FtG5e4jlHRxEUdAh0gwtLlHdBSo2q0vAYJzMlgjHRIlTHW2syeZzkRdUVWXGiLygSKawMw8D3PnY2jptREXCPXKGhQiIfqi5DXU7QfFCVI8qjCMIFipWl4a9C/+PLMz3pKDXdCc3CMPxUehRVZSVS4SWBp0QI8krhyps808GHT1xwvO+abI0qsqwJgCLGSREby7Ma11e+F1L6gyv2AZAHH6fwFCeFP6d2cHjW4yRC/uK7VyXMSyYmTpgslkN7TOkZKqyFbJNpeZwI38VbZMoFdj/7P3/13tAyZPlP5JLRzsR/YUH7/6vVp+M9x06VpmFhEW9XrFt/leIizPtQBjofSIRYGCplvetNCCIJDn+kFSd2nVv70/jxa3V4dmMzFjA0q+KYZL+bhODoKXLKPV2ITkIvbmnBakUsQ4rjA/Y3dr28MaPqO+rRJ0NYf7PjqVCPE5GKMWf5vWlVRbDsChf8aLHvHrpWqTyxaDG3/+18LP+va6NUO0aMYiCWc8WIESNGDAAaVF2GYSwFMFfy0/fZL4QQYhiGbOtUAuBqABcCaAbwRwA3AnhUUd5NAG4CgDlz5iCVSoVVcdJiaGgII2n/ZrCj4zCGR7yDycDAoDKPhp01aNY8szQ3N2Okkk/c3dWFVCqF1qFgKVBYO4+m0+gzR7F27Tru+uHDxeH7LQg0ToZlon63Z1k2PBJNMdPQ0IC+MW8I/9s5Cbz3qCnYttWzbKMHkAONTe5BIT06ivXr17tpOjv99BkA0N6hPmht27bd/VyzeQsGG8L3XMND9rjJZtQBn3VP08QxHyOEYO2aNdI0+/fv81278dwyTCk18NA2myKgobEBuW5+zA8O2QGNLzw2ia0dckVHU3Ozsm4bNqzDzPIE9jfY1mr9zHyxTDUNQOvBRqxayXuktLXz49XM+S2l2w7x97Qe7vSlETE8HBy0eSLR2noo8j3NTY153ZNKtYam6+/lg3sebG5GKtWO2i610qu7y2vzXXV1mNa7J7CMTGYMfb3RKI/a29sxyKzDW7bUoHufPe8ODsrXy1QqhbF02v2+evUqmKb9HEODA1zaNWvXorZTPT4BYGSwT1oGRU9PGmMmf62zSy70G03z19l7qCC9ducOtLZ7dero7HA/N+7djdX9XoygTqcPamtrMeQEla/duQON/eEahcGBgUn97v7Xc8rwVJ23bmay/n7q7Aqf9xSbN9ega2/4mj2azYEVsaZSKWQCqAyjorfbfsf0B+wpWKRHhrFp0ybpb339fTjYHJxPOhM+5/p6e5FKpdBwIOA9pYn/n733DrOjuNOF3+pzZs7kJM1olKVRGuUcUDySZUAIJ4zB9hqDccCAvcsaB9ZxvU6s7bvr9eMNDne9Xt+939rr9V1nY9KAEMKIDJIQkpAEEqCc06T+/uhT3VXVVdVVfc4ZSbje55lnTndXzvWLx4/F54sMBw9Fa86Z06cTx+LEZg8vHImP64OK883WLc/jyGH93C4Furq6cOrkGe7dhg0Po7nKw9bD8vXz4YfX49iRyGTPQ+seDBkm+16Tm1NjceZM0F50nTl+jDcZ+syzz8bLeDoq485nN2LxsCwefsWsfV4q7AfHj59JDlwi3P34Vun71/YFZ7ezB1/G0lZg+35psBD9PvDY44HZ3OeeexbZ/VsS835lT9xMIgDs3rlT+l6FI0eO4skngrPp0888g9MvmdHpnnv2aen7F1/cAQDYvHlLjEF59Eh5HXSLe5eI5zdvBgB8/fdBv81o5eu6/uFgTlCwZ//7ux5ArdeLQ5J0z5w5g+3bd2jz7urqwgt7+HXu87+Im0QT15hKX7/eHT92HHtIwFTYuWsnurri57YDBw9iwyOPSPNJ0sRgz13PPrdZGzYJGcHq0P0PPACF0pYVHn/yaRyhPmr65evFk4W70dlXt+Olszuhvik4OKTDQNK5WBpXpqG1qHI7ODg4OAwsEhknvu+vVn0jhOwjhAz1ff9VQshQALJrxh4AT/m+/2Ihzv8AWAQF48T3/e8B+B4AzJs3z8/n84mVuFDR1dWFbGUvcI6/rA5rH4qXTh8CCgS4uvo64PhxWRLIr1gRSOLc9RvpdxZjx4zGmEG1wKboYjRo8GDk8/Pw3N5jgIIgDgBhO/9enk9NbQ0aaipxyeI5wP33hu/b29uBvWpzSQOBjOehr68flRUVmDSpE3juGQBATU0NcNqcoN3R0RE4mnxpF6YOa8BNVy0DAAzacwzYwLfdiJGjAqm8nTvhZSuxcOEi4IH7AQBD29sBCfG6sbkFOCAnxk2fMQN47FEAwKm6Efjmswk3dgBNjQ3AsaPIVlQAPXICFm2bJGQyHnr6+0EIwbJlS4F7/xALM2niBGALf2FcMGsarpg+FP/4VDBuxnV0BJKaT0Q+eCqrqoHTp3H9yul48idPSfMfPmIksEtOOFi2ZAkG1eWwPfMisHULauui+ZKrrMSpHvkFdWrnRKxcNJqbO4MGtwGvRpKKXrYCOMfH7xg7GtgZMYmePpB8Q6yvqwNOHFfaaR5IZOoGAZAz71ToGDsW2K5nTsji5PMTggfFugEAQ1oHAwwzcdToUcjnO1Gx/SDw2B/lcYYMAV4LmDLTpk1FnnVqLskrl8uhdXAjl08S2tvb0X34NFAgMMyfNx9TCg6Xn3/tOLB+XSxOPp9H7eNd4bqyYvkKZLr+APT1oaW5KUwLABYvXozurQcABaFq+vBGjGipwdMHXuXes3ve97c/grM9/cjnI623f9+1ETgQXx9qa6px4ExEMGLTydz9O/T19WPmjBnY2b8XeCVo2yFtbcBrQf4L5swMHE//4XcAgLbWVmDfa5g6dSrueuUF4ORJzJo5A3j5KLAjYrDI0NDQgHx+iTbM+cT+x14GNj8TPveHXmAiDBo0GNhnNp7CsaOZBwAALwsgWk/y+Xywj9z9O9OiazFkSBuw71XU1KrPFCzq6+qwcOEc4KEHYt+aGpswanQzsFNNWDRxndY6eBDy+fnYjO3ANjnB2hQtzc3A4UPIeESpyUgIUN/QBBwO5mI2V514vrlm8SR8+TdxoveIEcOBl+Pmj6ZNm4LtPXul87CUyOfz+M6Wh/HisSPhuyWLF6OtoQo1Ow8Dj26IxVm+bCl+9dozwP6A6bMynw+luu85+iywR096rKutRT6/Av/58uPAvteCNj8SkZ0nT5nG7e/5fB6VG+4FCsTvfD6PX+57SnoG6myvx/Ov8cy4sWPGIJ+fiO9seRg4eiQWpxzYcZKfhxQtg1qB117D5EmdGFxfiX/b9FhiWnPnzAU2rMeMGdOR74yEkb/VuBe3Sc47HWNGAy/GhVAmThgPbDUncDc1NWHu3MnAI+sxffp0zBnVDNx7d2K8+XNnS8dN58QJwPOb0Nk5OSDKPxOtj62DB1vtrSwaqrI4flbPRKuurgbOqAWdZs6YDjwV9UVLC3+WXrDoEgxvijQuKx+9Dygw8w7WjcNLJ3hmH0VVdRXGjRsDbFUzvPL5PA4+vgd4Tr6Ps+HY9aW5qQG7jqsZvbX1dRg+vBl4aTdGjQ7mgLg+Nbe0YMGCqcCDXXw+KGiw36XeN4YOacWThTVg4qRO4Jmg/DWVGZzutuN61FTlcJrxYbV4yVKc7ekH7rvHKh0R//R0d7iHVOVyONEdZ+zSu9GcOXMwd7TUL7eDQ1EYSDoXS+PKDZ3g7NA5ODg4XEQo1lTXLwFcX/h9PYBfSMJsBNBECKGs9VUAihN/uYggM2uQ8Xi/DzrBIRsVeYK4I0+a9tmifZzQBBXvzyMiHyf8e1t/LGxTL50wWPqeot/3QzNRMfvBimxNncN/+77teG5vMtGLmlzQ1tKwgyLzH2pzKLL3MnMlbLtnPBLWW+f81MSOMc2fnVI6yyOieQEgbodZ1icqkwE6UOsX59ukBZC2/GnMe5mFE02QmERjoxiZhkI6G/GcE3qm2UxNdbHxZXOBrv+VQp9MHtqAn9y0CFUJprp837ydjXwdCYw9Nk5tLqvMi87pwLSTgbmfC2Ae6FAhjEmZKZNy3GhlxP5SWnzKMKZ7TJBkqiupaCZM4tBUVwlOC3RYZTTjK0MI54fjpcPJmqcqk1wqeIQM2BiPrSvUtJUi+4zg44SNnuRTKQhPTavJ8/nWPXEGe8zviaJssjbzEupTDhw4Ide8CetBgCENelNLFKop0CaYzaUQTVdSFLN/+b65OaYk5/D9vo+v/JZnJKQ4UoQoxTwR20ZMMW6qK/r96f8nZ5pQmBggTrNGJ53DODNcivW6a+sBXP/DR6XfkkrNnkXZfcd0XLMQ98u+fr8k/i7ZMatq4+v/9VHtdweHMsPRuRwcHBwcABTPOLkTwBsJIdsArC48gxAyjxDyAwDwfb8PwMcB3EsIeRbBmff7ReZ70UDlHL7bmHFikRkhqFBEKN7HCQnsKBeVSnkQElNKcLIOiQYJRJ7+/sgOtHhflfU5APRqTLKkuYOYOBU2J7qmS6umMk74pWYM/vNDizB7ZFN4OdKVRVf//oiWUQjLOv5VJyojhol8EpHYMHtUk5HfCRGRn53y3O7Ei6sOaWZpmnKn9XESxjcsj0k+JI2PEyEf/rd92cQ29P2IISgyj0a31KCmMovqShPn8GK6Ckl7TToRc5lwRCY2Tl0uy9WHzYb+PN9OgksF1ZhkYUMYMncOLytL6do0wxA/TeB55WdyZQRCfDEIGfyatDIesbbpb8ts9ggZMEKeWDZWyEEGj5CQ0EyIfo3SQcU4ETVGgPgZSOl/RdLMpmeugUB/eFYhaJT4P5OBrhOx8iuqY+Kc3RQ0z988+6qRLzlt/oV+2PTKcRw9zWswy8o2taCdmVjGEnRrkmCHuN6VgqjP5Z+iEklnNt+P/IToui6tL0i2z4r1cSKOmcDHSXI8G4Z0UhtfCEJJDn+ScHQuBwcHBwcABqa6dPB9/xCAN0jePwbgA8zz3QBmFJPXxQqphKlHYs4XVbAhahDICABB/icTVOWTQB2gl/g+UhJEEpKlk2hlk5Il2+f7odPw/n7eMafqAtuj8TbcY2BOS4TJRWLu6Gas3y6z7syDKH4n5VdTyS8hW149jv0Fac6W2kp4JCJi6QgjOkJfdYE5Q7Nnw+ru0zJimKgdJDKz5o5qTnXRJyUkDsqQ9Tz09KmZn5++ohNf/W3g38eUgMIiDRHQ9CKrlKTVMScUWh3a8qSohEp7JIkwG4Zj3ov19BERF8RvlKCQxKTzfWjbiYUp81M1PmoqM9K2JojW/UDS3qw8FzJMpLvHtdXhni1mppiKYZyUknFB56TpEpAp0jm8DAvGtuDRnZHJOsqjKkU+LANQhaxH4lqgCbBlnGQ8e0ZtWsScwysYGmz4bKE+omaOiQNycTyarPMioVrVNDJNIRL7cf4QnVXMx0RY8xjfxI5BkopxUojy8yf24uOXTjKKo6oXXRNFrdwgH0m/me5LJmESAsX3Vh6mwksiTO8zJnUVtV6Sxk8/I4iWRtglqexskf/nqb3S96YQtZT6GOfwn7x8Uuh7RkRzTQX2HU/2qwQkrzOvF6ENh4sLjs7l4HDxYteda893ERxeZyhW48QhAbLDbYbwjJPNryabZTIBIfGLMc3/xLniGCf0THsh6pzQGsdMdVmnE0mRsknJDvT9/X5oJ7jP97l2UTEBdBonrx3XO8eUgV4kdBeor73N7BzHXoyVBBnJh9ocT/hli+KRgGgWmurS3HtUxNy/v3Ym6nIBc4ZGP3YmkoZMIqCJECXvZMwsG0LmpCH1XNnKJRWXRPBqZcyCpODBpbqUmkZRpa0j2fJMjCIZNAqI0th8PvG0aPoc44QJJtbzvx7bgzt/F5g8ESUv6XOiqS5JSVTTXWuqq5CKRwi3PrFRRI0TLs/QVJe2uEx+FzaSxvvkoQ2YP7rFOD1T9kNvmbdPSqszlbgmCYywNMuZOKZNtERMQcenrv88j2j3WhlsTXVlBkBTh0JtpkieP6txIu5HJuu8GMRkTxP3b1UcGXObvrsQ1gy6Nnqe+Z7IWPfioGo21T6Vpv5sHvsV5sdEqOpF+0F2FpOd/UzPOqWYJ2Je4vwWy2wjO2KyVJqMBZFZk2QWr9+P1uk0CiFJdzH2K8vITjPQxDH7jbu24vjZ4Bw+uC4XM0VK0VRdaZxH0jB5PQhsODg4ODg4OFy8cIyTMkN6CUlhSsIEBCR2gKW5UI0TWwJBmDYh8P3iNU7GDKopLgEJ6KVKvNzZlpUlpPLmeOJh+3wfp7uDNu0z1TjRULSpeSsVZGr/Jpe5qgRTQBQ0JVsiWq2gcSKSeFgnvroL9H/8Ue6wdlhj5PCTxmcl2HSXd9HfDwCs23aQe5aNERv6eyQBHfwvl1RcElOAJdyamunh4qe4labVOEmSmAZ4aU2TJiWEL8/4tjrue35SqxgllnbSnPckxEidGZyAuBCsESIRRcc4+deHdkYPfrwsqu7VNVPY5hB8nDBh6qvUCqisqa7XA/0iaT6NbqmxItSYa5zIO2/miEasVIxRG1ATZKbHC4+Uhtmr0sJiX5Ri5Jj4w8h4xHoNrLQwhRiUgwyY6ZiYj6gE7UaPRP0h0m51vmGi9OPpyTC8qTr8dk5QpVLRjKUaJwZ9OhDIMgw3AmLBiE8+34j5yJCm/uwYfOs/ri8qf/padWcRYVrfUnSrOAdEjX2RUWzKOPZ9MwGrNMzDyqw+DqupnuZelRSnlHfM9kbeL8rPHt8TapmwWqit9Tl8cNnYMFyuwvy+mdTEzlSXg4ODg4ODw/mEY5yUGTKV8XKd/zyC0ESDiBMFAh6V3k+TtuwYblsnhW+IAAAgAElEQVSXDyzrSJW/FuHFu7iGJVCY6pJc/fp94FRBi4dVuQfUEv/FXGRk0msmRBCPEHx4xThcPXeENlyoUeSrCVxyU11qiXlKlOsx8HGigsqUUvhOs4KppOCSkGYUiQ51Sw3VvJZhoEx1mdY1jbN6lsFrmg9LEHr/0rHct+YaueSjyq+JLEsihBPLpTKpA8QJP3Rsykx1/c2vN2PzK4EWog8/7jtFUragXOqGooRkkajMrpm6MRaa6vJeH6a6khicge+P0uerstD5i48sxVevml50+rQLTRkHunZgd7U109q16WiZdvR/Cdoz8oehRoa8vnyciHs/Ef6LIISE9UmncUK0zxSBjwbgXG9fqH0bL6WQtkzjJOzT87uw8EIe5ntuT8hs4aGqTYZJt51x1J2m/mnmVJKpMJm2lmwMmGZdynlPcU44ZIv3LJszkMlSaTLXxTUnWeOEZZyUXpBOdT9I0x01lRn82/vmc+92HzoFgGe+B6ZAozA2Z/Ck8e8YJw4ODg4ODg7nE45xUmaIdm8BM4J3GhAS10ygB/KT53pQXZFJLRFPi8ypfOP8X3YB5tBegtEsc7wq667+fh+nCsQCVuU+eFaZ6kq2oaSS+paZajLx6UAA3LGmE998x0x9OBNTXZL8ajWMOCqV2xcyTuzHCldHS3MRtqabomzM44kaSmkvdw1V2dDslwy35McllCP6nUbjJJ2pLkMJW1FiOsG5MSAyTsyIfWw+Yn3qclls/8qaWDnUGifqPGnaYggxzzbGfJpImM0V6qdyIHvFt9dhz5HT6JdonKigC0cldGsqsxxRybTXKRHd3DyLYcLnCYmME0Ks9jbTkDoLUqXYS+nZwtx2f4KpLpitazptrVKatIqYMPr5acs8TuPjRGyTllq9WZqk7yrENU7o/+Q1KubjxPDMEPwnXFoiKLH68Knu2DdVNlLhB4ER3TG4NrGM5cC53n48uutwoSzmGidne/oKcfj3qv5h0y1GQ9WH/Xnjs2snK4nZIeNEqnESD2++LxU//8W+EDVORIurpVboN6lDn6V5wH4/Oqv5AJ7Zc9QqftIa31afQ3NNRex9mv6QMRF3HKCMk8jfk2gK1M45fHHfHRwcHBwcHBzKCcc4KTNkGiflMufDShpS0NxPnO1FncYUS2LaINh//Cxu+8lTqdNIS8hOAk123zHexnMafyxhERWS6BR9/X6ocSISaVREGxM/Jg1V8YsOICfsREQydT1LSeiUhckJFyPRdBHrjDqVdCRLkEv4LkJmqisJATHCPLxI9Ek7xFdPGYIhgjkEFm+eOSwxjW9cHfizsbXvD6Q11WUWTkWo0eXJxjHJJmAaR3Fka41cglhO8NXlGWkX8aFiGidMKiKDhBIUdMzPezbvw8GT52IEddV817UnXZKqKz2esWbYhzRKhhCjsXIhMNR1MGGc2FTBdProNCFKIpmt8VMgzRP6cWNsRkkzd2TMjk9d3omHPrUSb5s9XJvsW2Ylr3siSsk4UVVbZqprRHM1fnjDfHxk5XhpnJ/etCimCWcC1bqi6xLKbBHXFyNhi7DTg3+rJw+RhqNNfPCEjHEiz0dq8knwgXMhMF09Yn5epWbKTNc8lY+sNEumSRSPBPPtJx9ahA8s61COAXqWkt1ZZP1pfLY0CqWHOG66e3kNJ1FYJI3wiA4mde0VuDdJJWA11fv7fbz5O2am1kzhA7h67giISuFp+kO37xPCnn8J1w42DOmkc4XJ2uXg4ODg4ODgUC44xkmZITu/l9OpaIxxUsj/VHefkZmuu/9yufS9RyAxx2CHjEfK4lr+yOnASWG3oNFx+VS9eRERbLfwZ/R4f/X5EeME4CXcVJe2sz3JGieqPpJd4kPn8Jr02AtfrcasFoVuaErNNQjvnmWk5ggRTW2lIc4riAwGSGuqyyajUS2Bzx7qUyO1OQHfjFivAiEEI5qDssgIH0lIcyc1vciKa5IJgayS83Filg/LxDMxsyImy5vtiufJ+vkAJBonQhyWcSu2ATXjoWP8/fWvNmP3odPmkr0GYaoqMuD5JjzRUoXIVJdZWS5wvglqKvV7oWfHN4FpaJWpLvMU9DBhptvkS7/ZrGviniDQ4QEAw5qqMKK5Bn9/7SxtWmn3DJmmrw5J/ghEBBon/DvfB1Z2tuHjl02SxiGCVpwpKkRTXR5NTx2nMjTVxb830jgRglRmPXS2x7Uh6RjbfuBEYhoU8jNE4b+hdtNAgIAYCziFjBMhuKoaKo2TNLU21ca8OT8OCzsGhc8yRKa64ouULB/T8hoJ5SR8jzFOhDLGGCeG83/v0TPYtj8+fkWYDIWv/GYL95y0Bp/t6cPPHt8DoDzO4YHgjCZ2ZyoBJo1vM5aJ7HmCqS4L4aWkcl0I64KDg4ODg4PDny4c4+Q8IC09NwmB1LX8cHm2pw+5rJeo3j1BYS4oyaSHCcqlaSPDFdPb8f6ldv5U2KsB91tSbNZUF8BL+KbxMUGhNNUldc4Z/f7VR5biy2+dFo/IhLlLwRQTghmb6rpZYj5q16HTXDomJsB0YGlGsqubrqXT+NbwfTsmwurJQ/CzD1+CGxaPAZCeKXryXK8230TGCaL+sSUaAunMBxo7w01BLLT1cUKEOCqH9DqYMulYsxRcfCFPthtERg59tJIATwyXHKa6IsOb6irEqcrGmarLJg4GAHS01oXvBnINLydmjmjEF988VfndU0jY3rGmE4PrcrH3xhonuqlZgqY1YaaLUK0tC8a2xNJNg2Kk1XX+NVQgxJ4QmVFwBH0A37tuLm5bPUEolx1RMIhDUjHzMzFTh/S/ug0pE1lsB5N+FEOouoqmff/zB2JmyNIQOS80jRPTdfccNdUlvFfFZvcCTqO2iDazCaPa62mfyc6vchNrhvtSKUwQJpnqEopsM/9//sTexDAm+/TPn+TTSSrCwZORplYa7Xi6BH5Ao8XmEaI1D2kKVqtElgd7JlrUEe0bqvuoKh39d+OkHBwcHBwcHBxKjvS2mxxSo1w+TjwSmepqq89haFN1eBw/29OHnMQRsSlURbapykAS3Wors0U5vGbrJUtm/4lz6Ov3Mbguh4Mnz3Hq6cUwTlQ+Q2Rmp9iLxvQRjTh48pwkTPSbaiTIwDE4JDX+5z+bE5eoVKYWIOt53FhPQ0xRmrUoQNfWaQj2gN1F3/OAeWNasH3/ySBuyiHe7+vbhyTQ2wLtnuB3Oo0T+4Kbzi9bMzgAT5QUy/Z318zEsTM9+OKvNkdpCUTJNGYBTaOE5Sn8CzVRhHKyDKzKmJ8CIo0jgxhC1b0mXVhTmcWgurivBdna/O4Fo3D51HYMqsuFhGqvBAz0CwGEEFy/eAy+8MtNyu+yamY9gpbaCulaawK9xknpCIw2e5CsP6+aPRwfe+Mk/MM9LyjDcNBYfwsJ/cwHc8aJMhtNHII+P1m7k4Vuvbh0ajsmD23At+7ZFuXhEbTVq00rykCQ7DDapGx07dA1Ya7ACO0RxM1N1kWxb1QEfSrlf7q7D03VFZyvk2LWCHEeNNdUhFrFAwWb8p/TTWoJeGYJk6dVKuZxYhpgCUNQZk5QOl+N90uzcPo09IyTmLnc82CqKwaLIqQpLo3S1hBn5NM0ZeeLNPtMhqh9fgXO4elvgrfMGo6/+M/ApLPNepdUKqdx4uDg4ODAYteda893ERz+xOA0Ts4DymWqiyAiUvb7Pggiycxzvf2oynqwk0WN4BFi7fxQxIBLK2uyW9XZZhxV1l9PvRyYpJo5ohEAcOpcpH1SjH3lpxUOImUEj9irIqQCk7pm3pgW63HrebzGSCrGCcfQiUPX1mlNddn5OKGSdqmyYuBbm0ljETg6D8Kk0ThJpQ1kGEmUOpQRUkVwGifCt6vmjMD7lsSlLNk4RpLVohQl4b+pEPq1Ed8LebLEG5GAQNM3LScLlXSqCUEkl/XwlbdNZ+Koy0EIwaCCdgUdUgNp1/58wtNI2MpgGlQ3NUtxLAi1ziyWAFm2Ha21yHgErfVB/7fUmDs2j9VDMl9qcmZCHOK4pGv6zJFNyjgesV8Dbc8mGUIwREGwVMEjBBUFk2AmZlMpYmuHQRy6For+rkz8fsnGoWybpW3c09ev9e+UlE6Ur5whZGJ20RQ66XyhNMZphowTIYqJ5m6xZ2KT9VjMQsWsp69lTFdZFNOiG/nESgiTpHEiaqDZmCo0QZpustEiSVNeGke378vKnfaspxaei7QzY2PNouGSzdEaJ+Xg4ODg4ODgUHI4xsl5QPmcw0dS9r39PCH2XE8fqgw1Tl748pqQYELhEaCn306yTkS5NG1U0Erwy94pgsu6i/p7GdFcDQC45rsbwm8bdx0xLqOIMwo/MjLJrfCyWbhz2dRJmZYiDiH2jI+s5xXlowTgidGy+DoCWRqNEz+BgaFCscxQ39enkbRkEBKNEZ0DahVM1qQJbXXcc7EaJzriFOcc3lArw9a8lwh+rKoTCH2cxIgE/DNLgBLHIs0rjekcFUz6w/MIGqsrcM28EUHahThJkuiUCBTYOufDUj8/rwewJkdsJHNLIQxRit2RjitTQpyvyJjW590LR+Mf3jkL75g3MnWZIj86UUYNVRVGcUVmZE1lBv9z6xL883vmauPYSpwnjf/4XCcY0mCpcUIifyXs2jB7VBN2fPUKddli2mryMrGgprpEXxAmwgQhA6PwrBpL9HV3b3+MuaFqTh0xWYxC15U02oMqfPbKKUbhbLI810tNdSk4hgJUft/SnTsMwgjlUJ3jtIwTg3TLCXEMiONaLLNYBbrfpUUaoR+bJSidj5MAWiEPpt2mDW+wz4RJR5WNR6K7Xcx8qUW7JQV1GicODg4ODg4O5xOOcVJGdL0sNy9QNsYJCHcxZ3M519vPOU/WoTLrxZgcBCQmvQjYXfYyHkmnk54SuqJR4sDUYQ1435IxCemoU0rjR0MHlQZFhcR5rdj2MuJd0mWjqkKUZpXXlsDeN0+GkERTWyZpsGUTobtwpmXU2RBBRY2TYmZ2sT5OKG8tjak4k0upbry9aeYwZbzYHDEwNWPNBCFiHPue4IhYBuHE8agz1SW2Ae1rM40TY+6nWTg2CswYOKFzeBLPpr3RjoB8ISNknHhyCVtVX5RiR5elLTIrkxBpnNg4h1fvGxkvML1it6YkE/obq800LmRalbNGNmk1NjxCrNfAJH8lYt94hChN5CjL5UXO4VnGTrC3qkdQzFQX5OsPi1yFvD4mflloqknrDh1jPX39sTKqfELoNU7455BBrS1FgHmjmw1CmcNm/zjbI3cOr0K2VIyTBPOeUR7CcwpTXVLn8Ibl7WitNQuogTg/evr0jBJx/hdLdE8T3eaqk8bHiUrIik2TPZOMaIoEHP7hnbOs8vIIlBMxcA4f/WZhw/RM6qPXi381BwcHBwcHh4sTjnFSRvxiu5xxUi7BGUKiSxEl2tHD+9mCxonpYT52kCfpJNlZ6A6+YwaVXmpZd/klkoM+UUgZ6/pLZ/riE5dNSi6kANaMDgt6AbppRQcWjAmcL9bnshjXWou/vXpGUE5JvKSxRk2w0GBKM0CMOr5p2p7Ht2+xPk5kFdQRCNMQz33fjgiqIqKb4J6PLcfXrgr6209II6kqhKQjmpqmD+glV//hWvVF3ETKWTTnlWOclZuOGzafYn226KJHWgn8e3G8scRRce2LGG72hC+ljxNNGp+8fJKUuEj3i0TGSVgWGZFdUpaLlMaRZfpWVoU3Th4ibf9S1FeWxKT2eqs0WFNdHa21WDJ+UHK+kozFPh0zuAaD68wYBTEGq/AfsNA4SbNnEKKcIw/fsUr6XtTGnTiEZ1iJpfA82GucgDGlanGWivs44f/LwK6fLMw0TszKRde3c739Sq0YG8Ta2CKNUkuj2+RN/cjEeHwqQrPCVFe5NDhMtQBo/uamupLL29lej9svtT8Hi0jan5LOPMUOj1QaJxbMkDRXq6V/e58+f58fa/SuQgjBwrHJ+wILXf09j9FSizHpzNvN5Izr4ODg4ODg4HC+4BgnZcRZhU+Qcpqsqi4QAG5cOhaEkPDwbqNxIoPNRXLXnWulEqG6y0/XJ1Yap798Yiu+e13cVMf/ef9C7rKiK3MkVSn/bnqPqdBksqjD7nICANcoTKJ4IUGPvXR7uPf2PK6YPhSAvC7iZVylNcC+l0oXwv7yKJrqSkXMVkhnUugunDWVZqbpYnlaaZwE/02IWSLGt9WH8X3f10qCysr0nkWjUF9F5xkJ15U0GicmmlOxS7GC0SBKmIraUjJCqrg25Cw1Tgj0flFUpCLuKWGs0jd0TIrzRKwD2w2qNd9MipIPo6IR6ZK6JT8eP7t5sTJtU42TjMRkx+vJhAZrqoutaFt9DvPHNGNUGRj8FLJmtNVoDP0chT7OzOOwEMdDTWUWj312tVEZRjbzbRQmz+TTUB0xTnQMmTRjixD1GjisqVr6XmSc1FTy55eYNgQhVn5fgIKPE4nGiQrfK5xxVD4+9IwTeRyT8SRrcxkRmJ41D5/qVmrFxNIx8HFCQU1PmuxmFj6ojWAz7CgTLCZYogif4faZdHmG8Q32jxPnevk4qowKr2UCUjIzvyblXTGx1ejekZRU0jqQzDiJ4ndaMqPTwkZ+ZdPeY9bpU60bvYnX6BtlmhLYjzWZiU4KQiLNcnHfKKXGyevpnOHg4ODg4OBw8cExTsqIs73y9+VSOfYIQTbjYdeda3H7pZM4wkmocWKYVozgXgJpuKxHUrqm59FSUyGV9hQdzuqdJhaIn0i+4OgupxnNjb2U/RzZEEZ4y4xL9sbzSyoCLT4bVxaFEHuGX6BxYl4WGdI6h//s2smoTXDAK0oVA0E9baoZai6l7Gt66fVhfgGmYLVjCImIG82WBD3AjMkkM1cj+zZcIE6qiHVsnAphHlVw2iOJRQMhxFrjRKCN80QsTbwMs3Zw70XGCUOA2rjrMPctMoWUWMzYeKREzOaaCiFcFHDyUL09c3GtSCZwBHnKNU5kTKaLk8jBa5xEdej3o+f8pNZYPFV9V3W2GectS8PWvwMdT76hGZ8gX8m7IohUn1rDS5hHpqUisMRUHUMmneaYvY+TmMnKhL3V8wg8j8TWOn25onXNpFaXTm0HEF9Xwj1H0zYqjRMTv1+mTT6voPm69+iZmA+2ND5OovwLc9Bi7Jf6TG0z/vtCxolZGhOYc0exvpHSxFY1FX3d2xf3ZShjbprOzVL0jcxEJAuWUbroq/fGvrNRv/XOWYzAiRnS1MBmBXp6jz3jhEJVthUTW7m+riwIsIjnHqM8NO2vM9Vl5xw+6fvFeaZwcHBwcHBweH3A7vToYAXVwblcB8D4xS36fbanP0Yc0EG84Kr5A/K6yByKpiUum+YaM0OhyS7SENAT5hOSkfoeoUhb3c+unYwv/2YLnxbntNg8fVspLqVUGUgsvyTiaCl8nKgkMiuzHrp7+5WSxeMMfAOomBF2Di3lRHRTRM6c9WnI+rpfcCg/sqUGX33bdKye0oYFX4kTD3RIYjIBEi0Ow0qLRDVZeuLawNviNyD2obTO4XVZhkXVrLcAL1V+4qwo9Rv81zFek4oSn7sR/vODi/RpCokmm0Ip5Onp9xndu4sBVLo/ME3Ifokm6Kcu78T31+3k4umISsaQBFVpG6hAx1O/7xszgWVhTJ3LyyAS7WXaeKYE41j1mWL98Ib5eN+/bZTEIVamsID42SFpraPM03tvX4GnXz6Ka7/3SCJTgBQEW+hvU5F0tY8TNVS+TGw0TnTpLxjbgs9fOQV3b94XlFFgyKjGva7Kop+wYk11TR7agFEt1bhr0z7zhMCXwQR0nTeNwwr9FMtUSKeRpY+z6ZXjsXet9XHGiSqZVZ1tuO/5/eFzKe47AXFezRBlX792/GzsO1sE9gxtirRmXwcCsqL9982LMXd0M3YcOBm+Y9c42/ro2swj7P2E/2bF+E+8q5gn5eDg4OBwYWPXnWvPdxEcHKzhNE7OA8p1AJQl6/sBEeRcb59SCtEEppcf2UJIzVOY2jVPQiCdH38vEgt0RWaJOZRIpCI06dIRJeVZpL0wzhrZFC9DWBaWESEQVFIQMEXn5so4xP6ylfGItrwm8BSMF/rz2BmFHyHm9w2Lx8jTVpTHppghkSkk+theSIP/vqY8QfrS2c3kG+DdC0ehrd7eWXdtpQHjRCiCKWGgUsFcZNMTCUjsXDbTOCkt40TWj6GfD4XTYlEji2sfITBNw0SLS9XuOg2gxhr9Wnv5tECaff6YwO+JKQEvIKCI687rh6KRlTCoAapxUghjwcyw4XvImlH0/ZOYXyF4v+/DI2pfHyF8ef+l8ZOkQsRcVtflhzfMl77XrYkrFdo8HtGXX+YYOb6X8nM8NtcL46SqIhMz66UCIZGmjQ1jSnWu0U07lXmkNIT64AzJv7t0yhA0MWuMSismlpYmn3fOHwUAmD68kUvTpKlk9fq7a2Zi+cS4dpgJbM5uSo0To3yY8Io8B9VGGqTtDVX44fuiuTJQS297gdlzCWN+VkdIZ2E05hKCEKI/BySZJ+X3dnvo2lmtdTYwnBNZ0agGMTumKkMfJ/bjxtOc/ylTC4gLwNisN5dOGaIvg+OcODg4ODg4OJxHOMbJeUCpzAps+uJl+L8fXBg+x20sBz5OfD8g/GQzJLUkaTHEsVkjm/DJyyfhn98zJ3UasfJIrguiNKXu8ttbsA+8//g57j21G2zqtFNn+iIt40RvsgkxYnkYzzItNk4Sg4MQ+3Eb2D6OntM6+g3TY2pok9Y4wedGmIZi9bMZ65GPkvi35790uUF8ShzyjRmqX3rrNABAP2NRo1jidW3OxFQX/2zqS0Wcl1IGn/DMascZmd0C4QiYaUxF8RLx6nB0TB6PaZHo5y2fFyU0JJdLTJe2eowob1Hl/KQ27LpzLSYOqZfmIYLuG1J/GK8jegZdz0VCEdXgUEFJKLbYbmVJqLS1VGCdw5tCJhUssdaTGiaE/pWdbZg3utkgMZP8iLb+b5k13D6b2PyNfps6gfYIiRgnRjECrJk2VFEUdWPkFNrFJow4URgAiJeXEMKt6+I4tXU+DQCrpwzBrjvXhn5o2Pn33TfqfQvZmBG9eu4I6fuvv31G+NtmO42YdGbMIy6M0Jv/9r44A5E1K0UIUFMwyelDvybpcPsbJ8beDapV+xqa1F6Pv1rTif91zUymLKrMRYZ+qiLyKRKiPeMk+jgR0rLOX/Pt+sWjpe8HTuMkXrpsKJgRvdP5gDPJQ9VsLFNLPB/YMDtuyY/TfnemuhwcHBwcHBzOJxzj5DygVJIztbks54Q9dq4kBWlBmm8RB0/lFckgyerKLG7Jj8fQRnN74Dr4vqHGiSaN9dsPAgjscy8ZPxgAMHNkE04VHGnWMj4fdN2lkz5O67BUxqBg+zA01ZFGxFGACVGLJl2sj5I08ZUaJ4kSismBVcQWm2LGuoB5ljlUVcX3FZLfMuQylPgW3cyLXVFqKrNYO32oNozIjDD1IyAS1SJTM+pSN1VHUramy9bQxkjTRsWoYNExmGeomUqlykyBAPp1XWVWK43GCe32GJEixSCgBKck3weUEE0k5ZH6OLlIaRy0PwJtueh9f7+fihlnQzuTEsAU/SIjsALRGPR9X5reyJb4Hizbb1SEyJvz47B6clzTQ8c8IMJ/G5g4lhZRiuNV0lkpjfCLR6I9gW3epHW/sVruy0ivcSLfe4xMAxpUzSM8w01kvi2dMFgeUTJMVJqz7NtcwvqkWntlc5ZlkLBoZjQ77DROjINqQYhqDVALEZTCl9TsUU343V8sw6gWNXOKEOCmFeNCplZQFlVodnCXhuCdpHHy9J6jSu3jIL78HGmTvwqqOTVAfBMp6PrEzovKTEELBXomlAqqGJzGiRDIxlRXUpmcwomDg4ODg4PD+YRjnJwHlFJyRuWgGYgOuv2htHD6w3wxRa5mpB/LKYUlSlPqysxeRi+d2o5n//pSzBnVjFPdBcYJ6/NBk06FznE8UwCZc02TeCFCYilTrBjfJA0xJ5kIE3yPX7aMJCo5xol9+VQEqqS0THJSEltSjPW0wzqVD5hCONZ8ULFLSm0ugzvfPl0bRiSOmmqvKZ3RasrMEgtNHb23MXbYk5jT//juObhxyViuDCZmU4BIW02EjgkifomcwxvUTYhN+6FYM3EA0FvgiCQxcFhzhrHyXaxcEgGEMGbYCN+aKvOQUVz1x/s/no+9+8wVk/HTmy7h05DEVWkITCuYMxJB+7HfL+z3vvw7CxlxSzXGP3V5J35wvZxpo4Ixc174ft2i0akWtpKcrxL21qT5ctPyDkmSJBUjSGSemTCiVPnoCJmUcG60dxLef5mJ03nATDsnkl43j/PhFeOwoOCsPjF95Zki+m0zgvoKqp+lOI/JYnz/vfO45/bGyGxWWmIyW9aKjIfJQxu0lWY/RRq8fIT6wplZ1PYqjXN4op3X/3j/DvzZDx5Rfuf3+TTl0Z3z5e+L8RNFIQp3yCCrDhVWYetawTiHt+0SX6NxyfaNznxosXAaJw4ODg4ODg7nE45xUiac7elTfrMxK5AE3qRUHD4ixomKuNPekOwTwfbQyl4Zqg0k723TlhWHzYckSLqJ5oPqC/5XQo2TnJnZH52zVba9qyvNp5qcbxL1oUwiUxUvCbFLrYrILQubJn3b+ArGoE2qqrCyvvN938oXUMzfg0W5AMZUF8wl2lmH8mG+RS4pVdmMNQFcJWkr0gtMGG5jBAKBKSGOjxP055LxgxJbcumEwTECGrtepNKO0sRRaYcYaR8KQShhis6tYphn1Il20jyNjNHEw8milkISeqDhERKuN2KdVFqOFLrajpUQvz64vAMLxiYTelsU5nMG1+Xwmz9fiu9dN5d7T8eTyrSYrJ9l70y1yUwgapjZjNM0o6gUGr1JeyunCSkppUzjgnhqTRAK2Vks5hw+ZESp62nr4+Rf3jMXDdXBmSduGtCPEYGpdgRl7JlIlldkCN40c29p/9sAACAASURBVFhiuNCMocVAGdZUhZ9++BI0VGk0sBPgSTgnMn84Iug+aHMeu2pO3FxcoAkQD8uuH6e6ezF6UC3WfXIl/mL1xNRMa5U5VhXYvnjvJWNi4T935RR87k1TUJn1eKEjlIZxQpA8Hp7bG3dqTyFqk9q2m1bjRHEHKMUK2tMvP2T97PE94W8CoEtgzmcy8TnEOYcv4f4cOIcv5Cs0lJVz+AQ4vomDg4ODg4PD+YRjnJQJOrXxNBd7leQpr3HCfyMEAOPYU3bx+MbVM/D725Yl5q8qsdoxYoRqQ+eppvB9OZHZxDQSRaWiPU93BwyvWp0JNAZ6HyfRb5FIryOaSU11SfpQZf7HBiaOe4NwxROT00iMsVYQ2Ng20suqsCriUl2utONVB9ZHimn70mCsOZ5iLsL/9Gdz4HnJKYi0VFPiqkm3i1K1bBSbcbPpi5fhhzcsSCSMyOYYN2bSaG9pOlBsKVonE8JCdy9PPAnNazHaEWyaNtOM+qlJ8qWxenLguDVX4ZVVqvR8wiNRXTxCuD7rV+w5FKomsPJxIklDJ9QwdVhjICnOgI4J35ePYVk/E0Ji47DfxklKAsJ6Eb6MsXCS8optcsPiMYn5lcNUl5gkSyCk/piSBFA8QqS+RyhTYv0dq3DXXy6PfU9yXC+DaLaUQnWOzHisCVAkpk/9rtDxpDNZSvH3186yMl9ps6ykXYPuWNMpTYP+NvGHkySYBMT3m29ePRPbvrImFi5p3zl6OrhXjGypKfRZ6aBLS6ppWPjflCN4/9KxuGbeSLzw5TWx+V2K/cEjcqaSiK6t+6Xv054nTKDSOi8F71ml+fcff9wdPUjaJvRxwkzLXAU11QV9ZyuhWLdZgQNhGSilQ/dSChw6ODg4ODg4ONhi4CiEf2I416M2fJzmLFmZ8dDTF9diyXCEZfGyT+CjnzfVJZzDO1pr0VRTiSR0S8TLl44fjA+vGIdv3LVVG5fVBCmF+jogv8iZOKEOwyoICyelPk7UCek0TtjLsnjoXzp+MB7deTgxHgAsHNsSmtThnMPHGGXJA6vAS+OeaboAlGJyBHpTCSqwF6c0Pl8yEmIGkFzXJJLCl94yFfdvPSD9loZxwo7r77x7Nsa11hnFo/UIGCfxMl8+tR1feds07h1tR66rirhTXlHwbWLbvyri6jvmjcBDBR9CgJq5ymbXUlspfFMzhOVpBYEowzMpjox4y/vF0eUlf6+TrO0R1k+ahkmb3715H/dM250ybUO6tEWaFH2GGid/+/YZ+MRlk6SET5rd7W+ciP/c+DL2Hj1jnP+FBFabT9wrE53DKweM+X4nS6O9UW/iUSRMsf0o61Kxn6kmY8Yj4R4DlFrjhP+vHJ/Cax8+F/ZHNy7AjBFNifmVyqcC/6wmBk8YUo+/v3YmVnUOicIXKtPeUBX6RPKI3meLiRCKrowsVJotKj9zuunP+smjaC2YRcxmCNBjxgAmibtyFA6w0wpQaeEkJfHhFeNw5++eB5DeVBedN7o4GY+Eay0QzFvPgFGYhJKMdaMw6fIhiEy65bIezvXK70VJqQeCO8ll+NXTr0rfq3zlmSKpb2UoxQqqWoY5bWPE+yf0ccJUlvrGAyEpTHWp2y24k8TzA0qrcfJ6EdBwcHBweD1h151rz3cRHBwGDEVrnBBCWgghdxNCthX+NyvCfZ0QsokQsoUQ8m3yejGMrsCoQTX44mK5BGIa1XWVBGGixgki0y5yJ75mZXls15HYu9WT24zqsnyiwlFoSiTZm6fQHbRVX0Y2B3a+WZ8kaS5NYv6mfhqCsNG7yqyHnzC28AlhCAuJKSZDRXCQhUuj4WLbBiJUPkBsppDs0n/dJWM40wUUPoC6KnvGCa1bTS6LK2cMi0mCJ8XzISfMVmQ9DBL849D6sA6cSzkWVJgzil/aVQ6kRSndjEewUKJhpZXgZ36nYtglxEn0kWORJW0GnURkn8BkovMtzV4QmeqK2zEH0hH9kspRmfVCx8BiyHDdusi9t1ZlPc5WO8sMTVLAKJfGSZJQgxiFGwuSBFXFEQlc4ngtBoRpU0A91mRv2Xe9hl64TdaLCW16xnbsLCXmIWwdb5s9gvPLJJfQJ1ZmIJOgNyEq/1aby+Kpz78x9t7zCJ7dewwAcM+WQGrf5GxIBUeyGYL/vnkx7vnYCnV5iTzN+Bjm35vMIdoWxQjmcEIaFmsZZWSrzt8AMMKAKRaY69Rj9eQ2IRODAhpC19+yT6YtXQpNgeD8mRzuv5/YI33PRk3FONFEUmmdl0pILAnBvOLfZSWME3qHJFDX50PLO5T+GFUt4DGMGDHdUphpC/O/uI8XDhcpHI3LwcHBwYGiFKa67gBwr+/7EwDcW3jmQAhZDGAJgBkApgGYD0B9w3qdIKvYN/ccsZfINWKcSL77PmtKIH6YVxEZxDO/aLfYFP9+4wLkJ0WXvVKdJVREg5WT2lBflcUNi8dq75QqutBXr5qOH924gPO5oCuy1iE088nMCXQAWZ/4odYQy0gQCKapLoR83rpGK15jxD6+yq+JDeFbFVSldVSbsyBuFdIe2liFT14+CT+8wc5xMmuqS0qckFy+CRsnfFf+M/rnrpyCH79/QfhsSlwlkK9TWgl+g/5jYeiqJ0TkH8SciEuhYhjpiG29/eK6W8gnRbdF5rX4yZvGNwDVLLAhcJgkb+LQ+UIDu8eJprrgp5tjxbaCjLnLQuxr0eeZ2A8xXxWFwSOaWiol44QtD2BHSGWDntH4jWNhQvT+71sW499vXIARzSoNDP3emjRfpEwgAqmprrTQr586Qm+8DGmluekalPU8zB3djPEahhSBoXYDZcRalKkU9Nm0PtT6Qo0TYcwwz//3g4sMC6H//Lkrp3DPpXAOHzsDWsQ1gRcpOqRG4P+lqASin8TM7JciOgDgL94wIfydSXMwNoTpPqpan9h1imq7BcJXcgxvqsaVM4Yq8pDH8kikVSTykEqpJeLo0A7nCY7G5eDg4OAAoDSMk7cA+FHh948AvFUSxgdQBaASQA5ABYB9knCvK6jOedOGNwKAkaNMChXjhDfNEb/s+4iYILKDp+nFS0ZMpkf6zwuXOT4ez3ARCTe1lRncLzg2NIGqbdsaqvDsX1+GKcMatAdtFfGzLpfFiomtfF6aK6WOgMKZ6rK44ZqGTZKKNUojpLom5xW7BBlcZPiq8OGbaipgAxtiuklbyKRyCYD6XLxcw5uqcUt+HGaMaFSUjeCW/HiMbKkxyDlCpHFCc+chG6Z0XLPmg0zq+93r5nLOc+Pp6uNXZj0sm9CKy8Zkw/yNQORMN1PCn5H/dMsLe1KaOslohb/WGNFAn7+ecaNDv8DsCBlREH8kg0pCLxk/yLocUXaFcjD5DpCwbUnBmpSUmurSxFV9s5E6lmqcJTJO+OckU10iRg+qicUDysQ4KWSRUUwUqZYG8/JMtxnjxGQeNlRVYPnEVtzzsRXY9MXLAACVGn65OE8TCYKSzx4hWlNdAwWZ+ZxEZpZiOIQaJyaCIcSUCR5fT0zjcPlZrq68dqt5zD5GMEmF9ka9/5sw34QSi/O0lMRkU0EGW9Ay65bCpHoE/qfSl8ETzhO2SYnFoz6/APXYL9ce+K8P7eTWQhlTifoeaqiOznvsHVIrDCbzsQh1m7FpnTjby5fD5lDEIEkj0MFhAOFoXA4ODg4OAErj42SI7/vUsOxrAIaIAXzf30AIuR/AqwjOX9/xfX+LLDFCyIcAfAgAhgwZgq6urhIU8fzgzJnTkBJDX9mEH1xag7t3H4xHEkDrP7GuBy8fjn87dCai4m3d+jy6Tu0In48eOYNzfcC6dQ8BAF7cvh29vfzB9onHH8fh7XGKQXd3N/fcczauJbN923Z09exG7mRUBlreDIKD/VNPPoETO6P0t+3u4dKoyfRj93MbsTuWuhr79+/Hxo1HY+/FsaIj7J45c1YZT8TpHnU6zz37jPLbIxs2hL9PnjzBfdu1a2csvO8HZTlwOmrP/r5+dHV14fjxoP137NiBw0eCPty9axe6ul4Jw247EicsJdXt5ImgXGdOnw7y6++Xxln34IPYf5pvBzF/Wd67d0fj6LGNj3Lf+3p7xCghPtjp4/HDWa4sm/ZFY7enRx0XAJ56+ml07wnG3Qsvx8N2dXXh0P5zsfd79u7FI+vjvk/Onj2LBVWv4aXKbrA9/uwzz4C8mm4Z7erqwrMHgjodO3oUr/Yej4XZt39/rD+e37wZQDAPuruDsfLMM0+j/xV9OXIHnkdfX2/sPU1f1IpQhevr6QFAsH37DnT1v6wMR7Hh4Q04eiSabzt37URX1168fCK+bsieH310I/bU6QmOJ06c5OLsPs7PhT179qCrK+rXBx54AACwvLkfv5fkebY33hZ0bhxmfHh0dXWhrz/Ia/v27cryNVQSHO+O0nx+yxZ0HduGvSfMzA+xZTt5Mpirp04E48UvcHKOHDkcPtvsm99cUY1BfS+hq4vvS1Ua24U1/MCBwLTPrhdfxNkC4eTIkSMX3d7dffY0+rqD/XrXrp3IHn0p/Nbb7+PQ4cPKOj284WE05eJj9NAheRzZO9n8e/SRDbF3H5heGcY/eo4fP5s3PRf+Pn78OEQ6/cmTJ7nnDu8gurq60CPs9y8L80WFj8/L4ZuPneP8HYl127s3SOv5V4Kx0dfbI63/UcE3zit7X8HZQ9H56dnNz6P15A4xWgyHD8d9h5mMxbqsj8N9QX5HjgSmSffs3YuuroM4JZwBHlr3oJZZsOVQsCZ0d0d7zLoHHwiJv1kPaK8h2HPSx/Hjx43HCPv+4Jn42vGFS6q0aykgZ4o988zTsTiv7QvW7C1btuDU6Z7YdwDo6Q7CvLJ3D7q65I65KTZt2oRzEt7X9u3b0dUbnQB37AryOnY06INz3d04efIsdOTu9evXo66ScOfbjRs3YsdRPkNa7k/Mq0J1lm+bp596Mvz9pHBu1WH/geAc//jjj+HgtiiOan9jfx89Fo35zZs2oyEXryMb/o+PPILt1dGkPifZp2QQx8DOF6P5fuzoUXR1deGcwhE5EJz/6Hh/obD+HzwY1Nv3+f2GjhsAePmll/DQuuB62KeSOABw+tQpbfkffPCBxDOfDi+/FK3lj2zYYJVWV1cXdh3jx9ETTzwW/t6yeZM03uEjch+GNjh3rjv27m9+vZl7fuGFrcgd5s8eDz30ICo8giPMGvF8YW84cfw4Hlq3Tprftm3bcPBMfBzsefllPNEn9x/zxOOPYdOeoM8f283v+9u3bZPGkYGNd9PkPnzuMHCmV/7dwWEAUTYaV6ahVRbEwcHBweEChRHFjxByD4B2yafPsA++7/uEkNipixAyHsBkACMKr+4mhCzzfT92evN9/3sAvgcA8+bN8/P5vEkRL0js+819AOIMB1qnXeteBLZK99ZY2KXL+jH+M7+LfXv12BnggfsAAJ2dncjPGxl+/987/ohT53qxeMl84L67MXHiBGR2bQWYy+X8+fMwdVhcir5y/T3AuejSX1NbC5ziiS7jJ4xHfslYbN9/AnjoQa68LRvvx4lDpzF37lzMGhk5dN25fiewJTr4V1dXh3Hw+99o24KirbUNCxdMBB56INYeLHzfB+76bSz+Z9dOxg/W7QTOnpXGE3H8bA9w7x+k32bMnAk8/qj025Ili4GuewEATY0NwLGI2TN2zFhg2wtceM8jyOfzgXPlB4M+JYV33968Hjh6FBPGj8funn3AoUPoGDsW+XxkMqB+9xHgjw9zaYp1I3/4LScK19BQDxw/htpC/3qeF8QR+mLFihV4+fBprs3HjBmDfH5i8CDpu3w+j6d7twE7gnouWrgQWNcVfq/K5XCiO868AIAlY+rwmRv4sp/b9Brw5OMAgFxlJTc+RcyaNROLxwW+dfZtfAnY9GysbPcdew7Yw7Pshg8fjpUrp2HBCxvw6M7o4ltVVYV8Po8nurcCL0aX1OkzZnCm6GKQtMsvbl2CQ6fOId85BN4LB4DHH0VjYxOGDakDXn6JCzt4cCvy+blcWjesXYp/evpe3P7m+bj1/z4JdJ/DzJkzsWwCcwhX9Eem6y6gpzf2Hig4MP/D72LxAGDlpFbk84GZrp9u/QOAHowe24F8fnwsT3H8LF2yGD/b8xRwKCC00HG79bUTwPoHpXHY50ULF6CjVSKByIRvqK9HPr80fN70yjHg4YfC5xEjRiCfn8qXsYBPPxR/d6a7D7jn92BBCnPjW5uCuUjjZO79PdDXh85JE4Etz0GG39yWx7Kv3x8+T506BfmZw4K1s9AGOrBlq37iAeDkSbQ0NwFHDyOTyQD9fRg8aBBwYD+ymUzimqbD/+s4guaaSs5cIYtdwhre2tYGvPYqxo0bh4cP7AbOnkFDYxPy+Uuk8S8oMGNoUFNDID1+/Dg6OjowY2QTsPGP4fdtR33lXrVk8ZLAYbbwvrmlJZg3wntZ/8jm34rlS4H7+b1ncudk5OcGR6mDJ88B998Tfps1YwbwxEYAQFNjYyDxyzASamvrgAIT/00zh+Hmt88GAFSsuxvoiYh07UOHIZ+fHitjiEJ9li6ch28+th6eR9BXILyKc3n48GDuHX1yL/DMU6jK5aT1/5cXNnBlHTpsGIY3VwPbtgIARo4Zh/zyDmk5WFTVNQIHeKKlyXz41Kn78Kl1wXltUEsLcPAARhbWDfEMsCqf15oEy+04BGx8BFW5HHfOyHgEn6/YiSXjB+PYmR5c890NaGxsRD6/OFYnrszi2ggE54TC2Q8INDjf95Y36NNB4Vz0B/5cNGf2LGDjI1wev9z3FPDKXnR2TsY9r24HTp/ivgNA/eNd2H/6FMaOGYV8vpNvBKFvpk2bFqyrzzzFvR8/fjzyS8eGzy8+tBN4fjNaWlqAQwdRWVmJurosADVxfdmypWisrkC2667wfDt//nxkXzrC7f203FyLFMo5d+6c8Pw0jz23JpxLm1tagAMHMG8ef5Z+/rXjwPp1Ub6S/vin5zcABQL7lKlT0FqXAx6N+kGMu2TxYk57RbZPySCOgS3YAbzwPACgqakZ+fwinO3pA+6Wp7VixYpQu2j3w7uALZvQOngwsH8fCPG49O85+iw2vBKcY0aNHoUVKyYAd/8ejdWVOHQqzggA5PcLFivzeVRtuBfHNGc+HcaMHh2e25YsXoyKRx8EDJkn+Xwez+09BmyIzhPz580HHg76dtaM6cCTj8XiNTU1A4cOpSovRS6nP+cCQOekSVg8qZVbC1YV1hrf93H7A8Fcnz93NvDYBjQ0NGD58kXSvp44cQLqj50FdvEM6hEjR2LurGHAhvWxOAsWzEfFk48AvT1YO30o8vk54Xid0jkJ2PxsLI4M7Di/es0qvJTZim/ft53/7uBQBpwvGldu6ISLUDfbwcHB4U8XRnYDfN9f7fv+NMnfLwDsI4QMBYDCf5nY2dsAPOL7/knf908C+B2Ai4CqUhyS1NupSrTOLjSFzC41wJtYkKm7+4g0L2T3/FLaoGVRW1kKZSY5VI60RajU/+eMbjY3MwTA1wiFnz4Xl+CnYPvGxq57kt+UyPRa/BsLlmGlTRDJpgsI0jrpZn/z8W2dhrKhky2lJKctdQ5faNuYuTZqZkawOZVm9swc2YRVnYHQEuccXhJWJoXe1lCFXXeuxcKOyLySqVESaiZQBl3/zhsTOXenZvpNu48wNrD59+bxk8Pwz8Wua7roqrVDZ2JPNOEWmlhLUU5qGoaawRDNtRW7pM8e1axkmgTpyzPwGNvxF+NtMFeR4cwEid2sG1Ol2EZlScjWKDYvsUweZ6qL//b1q2dwNvPrNWb7TPdH1kfTt981G//1YfWxLlxDFY0lW8PYYkxXmEkUYbO3sxhS62H+mMDva1J/JvlRkcWnUW5cOhaT2uuVefzwhvn4/nvnJRU31lqmQ5AQgnWfXCmUTR1b15o0nonTewKzeZJmbQzNQRnHiEPlL1BsKxGlMmvnS/wo7bpzLfcc8+WVct0p1scHoG7rO9ZMRg1j9y6XzeDzV07Rrg2J+RNS1J7ORS3BWs0eA1X7/tevnlF8RgYIfJYI+4BkDrHzSmuqS3aP9NVnTI+QcM29eu4I/lsR9tWcTxOHgYKjcTk4ODg4mKAUBpd/CeD6wu/rAfxCEuYlACsIIVlCSAUCp1l6VYvXAWqy+oMfPXAXZbvXk1/2KHjn8CR22ykN4ySeRl2BKHO6W81YEPH4Z1dj3uhm7t2PblwgDcvm+H/evxA/vcn8jOL7UTPcsHhMcnjNdfyUxu4627Y6IlUsnmZWEqY8MefwzO8/WzgK/3PrEvM8ExgoaS+u3PiMEbft0uKdt5pHVoVVOYcHJE6UCY1T2sucyjk89bewff8JSax4uUy75l+um6tOyywJrBlbgesvGW00d2i6srRN80uzPpbyzl0rOD9QOodPyHMYIymcNN90oGaRKBMvYpikT9MGYttSQgv3/iLknOSyHuMvhsTW/TT22m2aQUYokvlhUhF4AYHoJTB/CNR298X17orpcgfBIti19c0zh2E+w2BVwcbfF8V1i0ZjUYeZH540foPEuGmZErrwuv2axcrONrxxSswaiCQ9/lm2P1cpnNE3VPF+vKRyOUxyKl89lImr8sHHJadimCnqYdPmpXAOz6bBlinJb5mJc3hTJAqEiGMoZb1teYsyAnxTdTCGLhvDj6W6XBa3rhzPvbtx6Vi51qgG4jpRHOOEZyiL7fjRVeNx123LNfH5Z7YsWcmB/Q2dbRjRbOfvTgaTfiIg4TwM3ymYH0F4fVuq1mdVFC+4lEjjmvg+UqFcQn0ODpZwNC4HBwcHBwCl8XFyJ4CfEkLeD2A3gGsAgBAyD8CHfd//AICfAVgF4FkER6zf+77/qxLkfUGjrpLge9fNxYd+/Lj0Oz0YFnNA5JweCud3QojgHF4WP3XWTD7xd9QJ9UnBWaDuIjCoLofBdTnunax8omTe0gmDzQsbphEU5Nr5IxNC6i/AOsYQ2zdrpg9FflIbvvBLuT3kIJ94vFgYyQVWxNDGKnzpLdOUacjyTNbgkI0vg/QVxIjg2W7wcUKDiQQGRUQGNpc62icVOq5WCtA2CJxvRuWZNrwBG3cdSSRgEuF/EhqqKjCiuRp7jsRNCJp2R1WW4IuG44umyxF7ZYR2XXyD2okhyqlx0qfQQEvK8+G/egPe84M/4qHtB6USoaagAs7i+C1GiyUNZo9qwlVzRuDFAyfDfGlfpZX6P5+ozHhhI3okamfuuwKqFrdyDi95l6SpFdc40aenKo34njP7p4HJUBPbQMWAEtO6csYwPLYrMGPUWF0hiVE+iO1qvVcNwByMEeqFLJ/83BuRUbW15Kwo4sYlY/HrZ17F8gmD8Z375L4KaNeaOL0ninxi4QpBWKGCJEidw1t2Ae9A3DxyyDiJnW/s8geS93FxOSglcVmXlOxTVUUGu+5cW1LfE//rHTNx+38F/nY8ArBiScVUNek42FBVoWQyysD2g3yNLk2/mO4ePb3JvtJ8PxqnqtIR2DO2CaNxIp5J0jDJo3RTR3VwKCUcjcuh7BC1Sx0cHC5MFE0F9H3/kO/7b/B9f0JB3fFw4f1jhQ0Fvu/3+b5/k+/7k33fn+L7/seKzfdiwbCmauU3esikh2xWsvnXH12KzX9zGRf+61fPwLXzeEI/Z6orJvEWIDLVFT+JmkgKAnoCkOx8O7Zg6qVCQ2wCgJbaSu5ZNMehNutRHPoVElIyNNZU4AtvmoLhkr5cqfFvwRKxsh7B9QkS+rSqbJ3FZieElRyTE3eqKzPGKvLGxGuS7jKoI0aw7fO1q6bjf1+vN0+iIxjGwhqU7dVjZ2PvqJR5rN0L/9NInetAu6nf583PZT0PL3x5DT5x2SRt/HB+WRSL5nPdotH4w18uZ96X56ZKQHDTinHp45sUK0Ga27Zq7NwS26VfYZpFNiYvn9qOX31kaew9TdNkmr5vyRg+f59qnPD7hykTtFjQ5Ic1VuO6RaM5SVaa98XHNuHndrDOmmuclGLuyJKQpcsxz4XtlZV+9gqCEyxsGDlJsJ5ThQjKPZ15/bkrp+CScYNCSepSCHjYIGRoU2KjdXzzsGn7JKb5JTRSc21lTLOEwsRs5rThjXjhy2vQ1lAV+0ZBmQZGpro0BFs+HF0baejk9lGdB2z6IS0Tot+XM05s4RukYaK1dNdty63KEjLcNb2Ttm6m0Wi4qcMbmDz52DJhD1MkMcWCsampv+KsDcj3hbTr1cpJKZxFk4J/LOPgcY0bFrI7kWge+ee3LOa+q+5TxQkFpo7q4FAyOBqXg4ODgwNFacWnHaxAidv0fJiryISMjPbGKtQIfkKumTcSH1rBO0flJExlB03fDwlbHolfQZMYG0wyyneyQ/jHL5uEv7tmJvKT5L4iKL73Xt500M0rxuHXH13KhJcc4v3iL6l9/TzxMQnvWzIWoyQmG0a21ODdC0dJ46SVYKzNxRXBaPNTLaIgTT5MMU2SRPgLpMntYerj5F0LRuENk/XmSWzakDfrJcdrBcYJa0IpKT3R11CxBNPQNrsfL2dl1ktMP/RzkcJ02TvmjcDEIfXmhU0LAiwY24IPx5gnSWOO/2+VZQk1TsSURLMYFLK1ZGxrrdQ3Ax3L4hovg2gSjTJuROnOYsx/WUFoW19COCwlgX6gwM7twFSX8F2jbaZqc9oMv7h1SaKvBNMxSxS/Ad7kkiy5UvXKXbctxyN/9QY7Am3hv9IUjKQV+zVnDBV0pjVNUaz2QAperzXE6LrxmRQ3aW9VtSg9R5mZ6jKrcxoGsDik5oxqwgQD34EA8MAn8vjRjQu4cWlz1uhVmupKA32suI+TePi2+py1Ka4gLd03OTFdhdTMQKb+pj7wlhlonHP7uSRZ1j9XUnwankK2nqVlGKQ5txDoBfT+fHYOn7q8k+stXS4m6/OcUc3oKAjHBaaPeb9rFMWY6nI+ThwcHBwcHBwuJDjGSZmhO/vRiwF7380V+oYuzgAAIABJREFUKB8q0xTiZSLJYS3rHF52EFVdeIslPeSyGVw1Z0QsT/Y+NayxCm31PNHa8wjnwFop/VQUedCPpLYtDueqCwX1RyGCN6MW/F42YTCWjNfbatcRITiiWQriji3BhEWaMcEzj4Rv1k5O2LgW0RR1HDM4YISxjs9V932aQkWJxeBYU138e7P4kdZairzLSGL/+2tnRvkoxmlSmcP1URGQnXfxcW1UTCV080ypcSIloOjTb63PYVGH3i+EWP8+X870jdp1gAgOhWwiRi5RroUXA7Ie4Ym2oj8wne+pQsR1n1yJn3xoUfieEpRmjmxK9JVgCp3mHW8Sz5x4a0vnnNRejyENVeEaYhKdFs3GfIsfapzYxDEOqkRcKMFuTg3EFBTnucwfjgo67U8b0P4xM9VFrNoxjXN4ik9e3mkcf/SgWqyY2Kr0cZKE/hKZ6vIFrVMZxDrJppJJvjKmRzmGbDHzoJRHLZ7ZHB+FSUy9OOMk+i0b+6bzqTSmkglqc1mlqZc5Q7K4OT8OU4Y1oLYyg79YPUFdV0KMNAKB6Lzj+z6jceJJw6SB45s4ODg4ODg4XEhwjJMyQ3dRjJzDRwfQ/7r5Enx27WSlJoh4QWSfxUtVd28/ntlzDLsPnebyYWFsqkvzLe351uRiqyQ8GmZ65QyFjwgLU11JeapsI7N3CJrNj9+/EP/xgUXS8FUSoqOUoK4y1ZWiJ4jw3y5uciyuj4XgNkwrMXpS3iZJf3btFPzyI0swojkurRcb74X0RI2TYsFLQtr3Al0nbGIWo8lhirfNHhHlpypHQhqesD6K+MNfLsct+UCLJU60Kq5yMp8sFKsVjptlS0m3wvY4uzYsHKtnpIrEB0qkEPeIyGeWNrmiISbPmqr5/nsDc3ujB9WWtxBlQMYjnGaf6KfFhCA/sqUGCxkn5uVQvBGZIyxEqflY9obO4Y3LYsPAhn4+y15HZkbN8ylFkyf5DzFOp0SL7I9uXCBJm38u5iyTqHGiEigoxDNhnIAhTs8c2YTrLxmdFNwYYTszZgOD/xbMpJQaJ5SRXYqeTtwTDfa5tEw+e59z6vDvWjAKC8e2aM3TqrRAqCZDKf23sH0rmjekeenqL9aVLZtMa9S07DFfSsJ3M+fwZmioqsCmv7kcS8YPVtaVQG56TKblT5u0z/fD/UM805fCOfzqyUPw4levSJ2Og4ODg4ODg0Mp4Bgn5xEywmBnewM+sKxDFSVGROOIKELYh3ccAoDQIblH4gQSlcPb+AFe4+MkJVHD5KIvk1iyofGofESopLZ1oGFFZpPKRAanzm/QSD+/eXFimMCETAmdkRbiFOvz/Pe3LeOen/7CpUG6TJl00tEmYC97Sd1GFL9ZVFVkMGNEk/SbyseJjVSvCcLUDAb1rJFNsfxDU10WbZmKSVZEtdMSD5O0aUa21GBVZ+BjSAyiGh9t9TmjvFVEHQD41OWd0jiyOd7TJ+9XzpRcQvOI6cpMYwUv4mmXEzQX1mTjyJYajGqpuThNdbHENdjtM+XU3ornFSFOeFOXwweMzbWYl4UYp5VO4yT4X4zkshVKlk1yQjbja8XEuO8DMQdTs6uAxGxmyvbNFYRGjEx1QS6oocq5mBWkWH9sNtGpe4l4HPsyJJXb6MyUlsmXLpoUg+py+MlNl8Q0ylmo6vLfNy/Grz+6tGyCHbJkE8+Tmu8y5oDp2bYUdSx1O5muI7SO/f3RWhbzcVKMxgn9TwZw/XdwcHBwcHBwUMAxTsoM3aGWngXpf5OLYszckYYwTaEzd6FinMScymrKlJZoZMQ4kZS5Npc1viyo2kR0sGyT1urJbdL3uryTLsRzRjVhAuNvYul4ud3mYjUUKFYL/kRM+lBHDB0jSJjXVgbaM3rn8JaME/a3DaMgIaiZmZkgEZFJVux1TmWqS4b/uXUJtn2Fl7yj5RHruGZae2KeA2UKISSwC7VM6sOMQTmp+aMrZwwT8pRHuvf2FXj0M2/Q5iuWTUxJtWbIxnNvv0LjhEs/gVgmLNHUrwBNY+HYwNTXqklthffa5IqG2B91BZ9MNQWNuYvVxEbG8zhiTdyxuiayYZ3/akEV/l2iPWADFdPtjjWdnPaj3D9YVIl6heNwu7Ikhwm1eAr/bfbcyMeJVbFKDtv8bcKn3cfjprpsGCfic7oyVBWcwps5h5f7kVBNK5Exa4QixglnqssiXl+4xgtMTKO5IeyJzO+ZI+NCHSb9ZD1WUzZasb6EVMtAc20lpg1vTM3Mk+elPocCyXNQ/Jp0dtV9/vH7F+DrV88wynegQYiaCSqOE6oh3+9Hpo9FbRVbrXIWpdQ4cnBwcHBwcHAoFsmeaR2KgsnZz+aAKB5EeVNd8jg+81286pgSr3WX1/RmLJLDiPX9+KUTcd2iMTjZ3WuUh6p+/QoJKW1aISFXYAAokmDfq/wRRM/8i6++bTqWf+P+GLOCgJHwFvIrTisgOY3BdYG0fmt9DgdOnEtIL0iIZ+zxYWytXum0AHRhKRqqsjh+1mzcxNIr/JeZMdDhgU/k8S8P7MD/9+jL2nR9P91FkTI+Rb8b//juOej49G+1eaZxKJ8GsbFuKKWeMXB8P6ShCs9/6fKYqRhVU9ZXVcQIxj9+/wJsfe2EMg9T4oas//qEfqHEJht7+uIaSJOkaUwd3ogfXD+PmZPlJTiE/Vco122rJ6KpphJvnT08DHPx6ZvENU5GWfgkEfvwlvw4/FPXjti+Oaklg+US7QEbcOsg09cfXjEO2/ZF49gj4DpCHEfNNdE8SNtfNiMtncZJGh8npR991uaPSl6C5DzsTHWZnWEoVERyyqjT7YuN1RU4dqaHW/XLRhMtgtHGjrGzPXKGtwwq5l6aKtI0pg5rwC9uXaL8rk0jRb6maReTfjw/YYECP85KSThPOjsm5SV+Ttq/dekNaajCkdM9sXRUaSWh1HNJZXZPzOc7756NHz+yG1OGNoS9FtM4KaJwjm/i4OBQDqj8QTk4ODgkwWmclBla0xkhAYxwzzqIjACdZHSUUTysLYqVLgvT8e0uRqK09UdWTUBjTYXx5U1GEPB9KG3ymqRlKn2mMy2lMgUVxZWXgWV+JTmeVsXnw5oRsYFAwmzXnWvxzvkjY2kpiQaC1s3PPnxJaHbE3seJfqz/+qNLZSUIizBndLM0Xdm8i2tHBP9tTXWNHlSLL71lGr7ytmnS7zRdH8mOYWWgBCvRJBQ7Jq+cMRQzRjQymfJ5lxthv4lj3pJhoEJVRSa2ttnUbdmEVq15RNOkZIzAXoWpLpv0xXWyX9A4IQjmVtK6UGrQbKorM7g5Py4kmhCUx7dHuSH6C5vUXo+vv31G+E5rrlJ4XlLQGCzVvsnC0+wrPFMlwvThjXjTzGFcaZprKsPfaW3RE/nU5hA1WxBY7Xw4/j6Nj5NiQLOJzGGmbZfyF1jMws45PP/Mjv33LBoVC68a+lTT5JyG0cAKZbD5tjUEZpxaaiul4cO8lSmrQZMw9eEH8HvmiXM9xvFEoQUbxM+B+j400ziJwvzmz5di42dWJ4SPxxsIsOuArN6lLI+oma9jhMihZghI/cxoyl6Z8Zi7ROzkn1QQScmiOP/ynrn49BVyU6KmMDXfPKK5Bn+1ZjI8j4TjWNxHbLvwX2+Yh9tWTyjEdZwTBwcHBwcHhwsHjnFyAcDmfKgjJqo+9ZWA+CAnLhcHE8Ko0ixOEVLgACO1ncLHiYmvjiXjeYfPYj6DBV8LcQ0U/pn1IxD5OBAuKWlkAWmUIi8pqrzZaueyHuaNacFNywMitbWpLuHyK2La8MbYO13ZZFCbDQnSUPmz0SGb8fBnC+WOcGm64vwyJTzTMdmjMAkFAN959xz88iMRU6lEXW4M1dhO6hdaN9FJtwnKJa0qQ6RFEg8oapzICUT69OPO4XkfJ/S/+L5cSNb2ujgJHizRh/4c1lSdKq2QoVACvsl9t6/g02Z+x8ecnKD3mbWTUZn1uLnUzBCsqyuSTS3JkMZkompPXzAmztiOtKv0+fz8lsg/WDl4drZDmg3+kw8twicvl/taKwbiWmKzN8U1TqLnT18x2TidkS3B/NBpnEQMXt4B94eWd+Bb187Cm2cOU0VNDZrNlTOG4taV44zisOfRBWNajPPq7Zc7h0+zDiatG7ZJDqrNodXQr5cpSjW/kjSkSsksLQVDimJkS3Xynl34LtPeqMx64VhL1jgxELxg4lw+rR0fWm423qVpgShN/pk0kU6zXmZ6TsSqziG4bfVEAGpBNQcHBwcHBweH8wHHOCkzjFTrw8tS8iFZT2yWf9t96HQQlxBjQo6pE2XA7jJn4xQZUF9ojM0KKMKFNnlTmeoS38fD/vCGBUIYPtC180biW9fOwrsWBNKd4sVOWW/2d+KlKxlE+G8C2RhSXQDZelBJNioF2lLDS5omQVfGb79rtjR/E5hIhdP0ipWoU6Xr++kuiPSSa6LZEOUZEbLM49iVSwZbggu9hPemkOi1cZqdDLO4MkKQWHbZWEv09SKk2ycwTkWNxYFyVK4r9kWocBKahgMQVo6to9bFiWK8laIdOlrrhLzkv0X09EXMVBqMjpFVnW3IT4pMhlVVpmScpAirIpjekh+PlZN4M2amTNM5o+TahKVC2hlFCLCwYxBuyY8vaXmCxPlHWzOSLDyOaWiezhfeNBVfu2p66GdJmjbD4GXPGxUZD2+dPbxMjNaCoEPGwycuM5PAp+VsqMoia2FHlDLH42tAeqhGvZHGCftbEfxC0AiUlY0tl7hOiGe8tHlJ2zBRGCD6/ds/Xyb4KIuDlv2hT62Kfct6JLZ3mxRj9qhkxkMpoNbSSh57IvO2KFNdqWM6ODg4ODg4OJQejnFSZpgc/mwOlzo6v6kUlAl+eMN8fONq1kyJLl/zhK1NdRkwEHRQabXQYlj5OLHQOBEvH2I5PI/grbOHR+Z9YswYOeGNNclTjHkiMU4xTBd5WeISdbT95o5uxuevnIK/ZcaXUX6aQopSqybEAxnouFCNd5GG314wN1Is4v1sFo8ycnr7zG2y2+ZRLMQxRoT3KrxhcuDsvDYFUbeUdTNNS7bW9CkGkg1zI8nHCf0/UKa6kpInKI+fiXJD9HHC/gcS9kDxOeRUlKBgAnRmYtjHY2fU5ob++k1TOcnigdE4KewJKi1Sj2DM4FrunWjOdKAQM6Fkmf9AFFfMI625NUDvjw1Qj/3aXBbvWjBK2z4Ro96uXagvqg8sG2seqQjQKuYs5kLGI5GmXwnKUIozPMdYLa448vRLlE7SnBa/sz6ZbJEksBUIlZmZYvQIEdZgdX6t9Tl85orJuGpO5P/L8wjHTDSFWoistL2sdA5vkE0pfZzYaqQ7ODg4ODg4OJQTjnFSZphI5aqI5DLILrWXThkSxE9IICC6m1Fy2hqq8I55I8NnG/vuOoxri6RnTZgWyiCGmcoO7mxN7JzDy7M2OeCr/ZFERAVZeJkvFKVzeDu9kVi6xUDZTZL2J4TgxqVjORv7RnmU6x5lQdzsLZjEWjp+MO69fQUmDKkvKmtT6XEVKPGzx0IrI6TpWtS7mKan4/LWlePxrgUjcd0lcrNlIr7wpqlYf8cqNFmOE6DEjBPDcLJ1oM/Ex0lCBuL30FQfXTsKARqqsgCAtzFEmnJAZIDFA5Q1+7Lh0int4e+wzZm66PbOmPafQZzU0LQv++nQye5Y/pHJRz5easZJirAmihF0jKfxcSKua1fPHWEeWQHbIW1lHjIlk1HMwUZLQgTLnLX1PZYEj5lLERM9OY/KrIddd65Npa2TpgrdvcHernKOLUOGEEbjpPgyJMGk3ThfcAPMcLRB0pwWi27jGykWhsuXQJw9yWWJAgSMEzZtgqGNvAAN+/2Dyzvwd9fMCp8zDOPFRvjJ5q6SFoTIfSX5vm+0ouk0sq3X0At47Do4ODg4ODj86cExTsoO9eGPXphtJGtkjABqoqFX4+dAFbcYpLnwr5zUhr99+/SgPAb1Vl0WTAkTSXW2IRJkFJcdk+5LqmusGCpGi4bQblIV0acGfS52bKii69K1vQdaEelkDBuL+DJNHyAi5FVkCMYJJnSKge/7XJnfPseM2Efnfl/C3GdBsykLUVeCyARKBb521QzUVGa5cqhQkfEwPK2PiRJS702nhmysq8yMsW1v6uuFoq+fJybTbOurKvDd1TW4/Y2l96cgg46wcbHpm+y6cy1mjmximEJ240cMX06ij+lafeJspHGSVJ7qtKa6LKpJta8yFr44+lOckcSx9813zDSOq4JtdxrtxUUOEbFPi9E44QjDJSbQRsIhRCkoIof9KhJq41rHBIY3VeNdC0bi+++dZxzH8xhTXeIaYFmKUinpFSuMMVDIZaM1R6UFwqKYNZVndCTnJYL9Ski8LOJZUOe3x/MI4y9RzId/wZkuUzGOlDmlQzF3gUxGnAPpcQEPXQcHBwcHB4c/QWTPdwEcmAOiwcVJxkigdmVFR8TKfArQ2aUWobfvbpwMAGB8QevEiOGglDIzy4ul0cwb3YzHdh8RvpsXPjQ5IUQpyuSYJcOBkLimUvit8N/m/h1JgJrHsfHToDctZ0mctDELYxFP9EIBAPU5fmmkKVBfIqWS/pMRV+5Y04m3G0pJ07nfY+PjRGCelRuq9i8vcbmUaZklJhP4NmFoJSWvMtUls5Oey5Kym7hIIggS4KLhnDz0qZU4090XPkemoYL/psRPVR+WY47pSsTOKZ1vILG875w/Cuu2HbQuS+RfR50X3S/6++namZxuxKjm8yk3VNmUY60qdmyIJSpK46SMawbH4B0gaqjYX7+/bVniXPY8gq9dZWc+NEMI43PKrowi2HNVqUwdlrO5iynirSvH4coZw7DmH9Yp04ybfUqfX5KpLkKA+pzaFJjIjPKE52+9cxbmffkeAMDX3z4DCzsGKdPKeCRML2ldY5tY5cOo1Nq1op8SAFgxqdVoDYxrnBTD7HKsEwcHBzl23bn2fBfBwcHhTxBO46TMKLXkoUzqiB5Wk4innsdfTN4629yki+ySlMbJNGBHDFERAU1zVOXxi1uX4M3j7GwmU7pEnUBUN7nQqWgaJPwvXhJV9SZhZ4gh0twz0kpYm+Zne/lZf8cq3POxFanzk8Yr/FfNDpmk7vWLx+CzayeHz2umBWZ8ZhecEL9/aUe6wgigd1TWObwNQWL0oBoAQFO1+VgONU4k+TTXVOCjq+KmUYq5w56P628pCZ3FrDUq4jVnTiUpXdUaGM7dAUZChheTiY0RzTVSc3syhrKVaTs6x4ooW1La0m/M717JeUBFkF07Yyge++zqIkumR6RxYj4+Qq3ci2hMAXbrZdr5IkYb2ZxOOw8or18kzsdJ+K58+dG8WHS2N2BSe3FmNWXwCFEKLKU6j5VgNeeJ/BfmvPnEZZ0YM6hWG0YsejHMPS+hTTxC0FhTgfV3rMI18+JCK/x+Tbg0CIDBdTm8oyDskqTJy5rqEpkhuu4qVvveBITwwmYAsPXLl2NV5xCjXHTMrnJo7Tk4ODg4ODg4DBScxkmZoTv7ib4qTIgsssMkPawmSTfH7Oka5Echuwz4KSXtQslTg4jF2DUG1ASXmSObcNUESx8bhRYbXMfHM5HwTpK6j2ux8M+RQ3g2rqqcyVDHLe1txXZsUNNMezZL0kqZb1IZblk5Hj94aGdo4xwI7Kt/YFkHvvybLQAC/xxA4OyzlJIutL37Gc6JjQmt/5+9+46To7zvB/75Xj/dSTqdeu+ooV4QSMBJ9GYMBhswGLANtgEH94CJHXBJ+MWOE+M4DtjYOIkd4hgTOwHT7JwESDQhUYQQVUICCfUuVO6e3x8zszs7O7M7vex+3q+XXtrbnfLMPM/MPPPU6zrG4piBPXGaPs+RF3b7WfnN0wEAP/rzG5635yRIOvUrzIJAu2v3158+DgdMPRW0fRYvV64XoLZ9n+EKaZg9v0o+27LS5cSB3ZBCnnryGetE0OXEbXzbDd05rL0H3tv9ge0EwH5SUb6Cqfza+SHm3O/JOARPSTyurnQlhP0cLbePf/74LF/PAEO5PEyQdGwUxIoI3DS2iePchaWmRnCkS0ukaSno9VIpD4RXcRf2+tb7RKBGVNZ3n6L8tvbF0LZm2/mezMvXiCV/YVPJXkpNTf6dza53hxOnYUvDTnfWyg9jSDU3+7EejzkOveZTrEOREhERESWJPU4iFnaLL7vt5ec4KdPjRKSg8MfT8EwOk8r60e1hW07vFW6DYbcPv2UAxjjw1kInNy8E5SqJrD87FR6IKQ6DpC3rHCdRvZzkWtY5vPR+89zJeOgLJ7ralqdWvDbnz2n11sY63HrelJLbC2P4oxPG9sUlc4cXhilXWZIPs5f0WVdbgzOPHeRtGDPxvp8gnCsN499nWE4Y1w+nWgoq7VqEWu/JYZzzgb0atW0hnCFivCq3uyyXc+TOpU2FQKm4K7p/m67rsJU6v+ZwdKviMN95+Wz8y+WzMKBn4WTGvsPiIvEZYTAqTrzMxeFncvgwhBVv0d7j8p/PnjoY9QGG6vIyZJBX+QreYPeGOy6diX+5fLa7fcaUXmokn0aTCkOp/Wal0PmaE7UevENNvaas17xTHtpP2rRuybxpu0rlwmWl5PVS7hlfK/mhutz2OPn1p49Dv9ZG+/CU3p1nLQ327SndVGha42x4ew/T+nlj+pXubQTEn08lIiIiKoU9ThJkFLQZLXr8turL9zgpV3FS+LeXwkVjy7UiOKqH8xh9iBOvGfduD8NvBJ3jxFyYGfQlsr1F62liPc1uNlvuOIomFi1RYWTtqRSE3ZA05XhJpk5DEhg+uXC0+42Zjthb6+90lB78+pr5Rd+ZW6bHVcjhq2W5y7UmD+4V+jb9cCrIDrStMonObh+uepx4PA/3X7cAL27chafe2gEgwWGMSuw2q4Udfu+txXEYXaFPyfnGHCvctf/7tDTgzGMHOyzjPR15WSPX48RFLYiRF/LTSMBYp3/PRjT4rEwIPP9IOh45rkU7VFf+f2vFpBcfmj4E+w8dLblMrhd0TM/8GhFTr6hg+wxtcnjzZxdh8htqP+GdMbwNc0b2Kfr+I7OHFc3rVtTjJMD57eoq3RvfnP6v6xiHLXsP4fer3nNctqByKtc70V34amukbIMiQ26owhpBvUMeOkwCwfD2HvjJx2fhc796vvA3F7u3prem+hqcPXUQHnxpc8H34wa04q1t+0tuyzhep4pJIiIiojix4iRipfKaZx47CNeeNAafOH4kHlq9ucSSpRndo+3GNDdzmkzcjdykuVoTO/z9xdNx0jH9vW8I+YywmzGLnZZw+5ISZqHipxaOxs4Dh3H5/JE469hBuYKgYD1nxHYbbiqMwmxV7+c8udm/EcX1HoYkcN6fv2XNnxdPHIDlb253XC/ud7TCF/B49+n2WHs21eHsafaFrWYv33ZGLC/3boR53fuppDVYe5zYbctrUIe0NWNIW3MuHSdXb+J8j8p6WYeRfgrPrfvJ1qPscXKkRCFgoApCH+u4mRzekHvuexmqy0Mji/uvOwEvv7cHv3l2AwDg7y6ahkUTBrjeV5jiuCbD3Ee58ztlSC9s2v1BwG2LpwoNuyTVWKY3gCG2Hic1khsSr7gXQzI35nIToQMBhxAMcFz/ff0C15uxnj83vc9bG+uwz6ZyrVxvfPO++rQ04IeXzCyoOLHmu2tKnONyZ9a8vtteYgKg1uEERJHMzppaPr9nduXxI/HL5euLvheI7XXsJvUZDQoPl3mvJSIiIooDK04iVipTW19bg6+fPQld3Qp1NYJbzpnsax9GqyXrmObWAiwBCnKsfl7sjLLBUaau1l5b93mqcHBayOUuw2xJ2dJYh7/Wh3QyTxDvrgu7t4CUqjAyhujxdWxOpzOid3wjjTn1OPG0rcBbAH5+1Vzb74e3a0NEjBvQGsJevDO/GkYxL4JZvlDXeT+/uHourv7FswCAl249w9V2zdeEl3BEIcxNu25JanNAt5w9qeDvMKPW2FTcPU7KFgylpIeXH9YeeOYj8TQ5vJ+VXHLTiykuXpKeUXjp5llgHabF6Vn348tmoVezdt+ZOaIPZo7ok6s48dUYwEXa7dlU/j4X9xwnUfvHS2ZixfqduPLnz3i+b9v1OPEb8rraGqy7/RyMuukBn1sIl96OCIBN5amP7YVxKxeHz1Hu049y6ddap+DmenbKO7kZxriUokocc8VJbhkjDKXDCOSPzW2+WERK9E6JJwJLnaLbzj8Wt51/bMl1zJ/dnCOjkvTw0a4ySxIRERFFjxUnEXPzcltbI3jjb872vY9ahzlOrJnTIJPDA9aWn/mNe33xOn5sX5w3fQi+evqEsssGHarLfMxfPWMibrx3JSYPcT+ckLudlF/EqXdNvlDB+cXMunyY881E3SrS2LxTazkv/BYQu1nrxPH9cd/nTsDM4W2+9uGX0apucO8mzz1B/HIzl0ocLbWjTHlhFGIVbctjC9n5Y9oxb3S7i+37C53RGj+xgq8S+8385PCWChSg3BBZ8TkSUQtcP+nIyypGhY+bnqaGcj1OzinREy6qOHnypsVll4njmgzaKGT1bWfgsTXv4yedb5Yd0qy1sQ7zRpW/l9nJ9d5CcSFz1hUWoKfvoJyeLWnoEVguDViv+SG97SdHd8PrMMZW1p/DapBlnUy926EzoUg4jY/KGeIwAb1fQa7zpnq9x8nR0sOsEVH6rbv9nKSDQEQUGCtOIhbHC6LREqmr7FBdhQVaXsJmvHcYraqDvHg11tXiR5fOdLewKYw3nTUx99lPIfq80e1YfvMpntcrx01IHCtCHLZRqqONdWL3IPL7d78tL1HvdixnN8IYqquU2TZjb0dteHsP3HHpTJw4rh/ufuLtWPYZ5TBCnkTa4yS8jbvdkrVA2G0Y/IY039I55gI7F3O9pKFgLgixuTOX6gkWZxxYe5YWhqPw78jngnKxihGGXMWJp6G69N14CJoqauQRrl5N9WWX8fJc9dvDMGh9F/ZMAAAgAElEQVSaa2msw/kzhuL8GUMBaMPtPPrK+yX2p//vcT/GbdFteMNpEBJ8G26UGrIpDRXaQYPwyBdPwptb9gXcir1yYbNev7171PvubWQdxtgaN+V7nFj/lqLPbnryWsNjHdp0aB/7igtBqTx08Idt35YG3HnFbMwpUTnqbw4s/ynQ6HESVUMBIiIiIi8CNQMXkYtFZLWIdIvInBLLnSkia0XkDRG5Kcg+qVh7SyMAoNUyhETRy4G1YM9HntbYhjkrG+X7oRHkloZafPbksbHsMwpOcz/YtWwGyvQ4saxr8FL+Yt1GVC/5Rvx5aWXsRCyFmDNK9A6xe2FLaszxcj40fQj6tDTk/o76NdE4C1EPCVY+HOmMDyu36cZaIOy2k5XfZJmbODb2ehNjAubK5Xk4oqL1i5+TYfEzVJerw/FVb+J+JT+Tw+d7VXmo2C8zvFecsjIcIaANt7MskoYl+R4nyvJdVGKbHN50jy+6B3jt0+3zZvHDS2bgDzeY5w5xrsyx+75UKI8Z2LNovote+ntGuynP4ke5a/ofPzYj0PbNSlU2a2EpvX6puBSbT+UY90Jrj5Mrjx9V8Hc+ny6OvbbDyMaJSMlKE8Df/aamIJ1520JujhP2OKEEsZyLiIgMQXucvAzgQgB3Oi0gIrUAfgzgNAAbATwrIn9QSr0ScN9VbYxpjpErjx+JhroaXDp3eMl1rAUJXlpkWgvpCuZOSaBAOk1l4G7OY53DUBi51mpF31u+sClAcjrvpeLD6Zeo4tB+kmV/rNv4j2vmY9I3H3KzZvCdx8Du2opEhIW6aRFmci63Kaeh89zeX/0GNV9AnEz6Lj1UV7ZZWxEDZYbqsrZINtbxeSKu6xiLt7ftt/3N2gL31vMmY+qw3no4CgNy8exhWLF+J0b07eEvIGXk7lkuls0XFvrPd3iS4G3fW+9IfwGN+7LPP8u97diucUbUYU+ix0mY6c3LfcPoMWTH6dkQJI9x3rQhOHC4Cx+ZNcz/Rlwwz6NYipu49jI5vNd9+GnAdMRhvqdSFV1hD9X1F6eMx6EjXbhz6VuuzqGvoRwdVyp/khrr9TlOulhxQoliORcREQEIWHGilFoDlM10zgPwhlLqLX3ZewGcD6AqHihRvMAtu2kxejXnh4uoq63BFfNHugiL+H5hsk5EbG6tnkT5RJpaq7uJ43qnOU4cli81tFeYLc3LVtjY8DRJsr69KAp33b7MpqmSraRchUbEk8Pr/yc9nFKU8dJYV4PPnDwG7+/+AP+96r1A23IbTmvBRqk0X5g+/Z2IXGt8X2v7Vy7dBHnOpIVY/gf83ff8XstfO3Oi429HLQVJVy0Ynd+vZdlL5o3AJfNGuNpnuAVjecZ56zKeWyUeXNbn+vgBPQFsxuDeTZ7DllSFoqb8vo0ewiPb3VVq9Wqqw54Pjub3EPPx+d2dOR489YrNwD2k1Bwn/q6noCHys09vO62pEVzq8p5Scr+Bt1DMKcmEPceJ7TIeDsi4h9fVCD42Zzj+87kNAKwN0gr3H8Zwt2ZfOu0YPLtuh1Zx4mJ5P+9cNWI6Jsvqn188Dk+8sQ0r39llu24Te5xQCrCci4iIDHHMcTIUwAbT3xsBHOe0sIhcC+BaABg4cCA6OzsjDVyU9u3bh6efeqro+9iOyfKusOr55wsqPNasWYPeu153takjR44U/L9q1SocfEfL2O47nN9mWMfWWg/sOwI88cQTAICurq6CbR/u8r5P63L79u0LJbxr3j1Sdl9PP7UcPRuKM17vbDgMANixY0fBOuZ46uzsxJ69BwEAq1e/jH37tHVeeeUVtO54Lbfcu/u0F4wD+/c7Hle3PmTB9u3bc/sFgF36/9bzbHc877yj7f/tt99Cp2y03Y+x/MvbtIKeQx8c9HSu7eJm/Z6u3OcDBw5g6dKltuEDgOeeew7v99RarL38vhaG7du3+47vOK7Z9euM87oOnZ3BCvvNrGGf2OMIVgF4Z81K7H27/HhSQa8bp2X3lLhvhHG+j28GXjmgpZmGve+hs3OLr+0cPHAAgHbt2IXLuKaeXr684PudO3cULb9zp3Ydv/DCCziyUbt/vr6++P4BAM11wI9P6eF4LjZtOgQAeO21teg88BaA8O5ppazdoIV38+bN6OzcWfT7/n0Hsb3L+R6UZnt2a/Gz5pVX0HPna3hrV/6ec+TIkdwx3bG4B7q6Fb7YqS2/ZMmSgu2s262tt3dvYXy4jZ9Sy2xa91ouvq22HywsZPISBwePen+m5p79ynmdTe+9h87O7XjjTe3+9u6GDejstJ9LY8NGLU2/+eab6Ox6B9PrFG45rgl7334RnS6ngNqrPytfWLUKH+h5FFfHsm9fLv63b98GANi4cSM6O7cWLOfm3GzSn8UHD5Z+7t04qxGT23e62ubfLmjAoa76ss/nqBit9pVSnva3f782R8aKFSuwX7/V7drlfMzG/WXT5k3o7NTyJeWuG+O3ri7tujM//6P0wcEDuc/Lly1Dr8Z8Hm/XB/lr0Rx28+ddenoDgDWvrsGB97T0un+//fF6vXc8/vhS20Y7b+t5DaB0vsjN/vw+c6x53FJK/b5j+47cZyP+rdatf6dgW4cPHS74/aUXX0TXu/b3is7OTuw6ZB+XAPDkk0+ipV7w3nvG8/g1dH7gfLPq7OzEKj1PumvnDpw3uAH/qf/21NP598VvndCM25/R0sfK55/HloP2lT8vr16N5u1rHfdXKn5e26mdr8OHD5dNAzsd0nMpS5YswdZtHwAAdu/KV5Bs3bYdl4/cj9mTgavesd+msb99Bz/IZF6Cqorrci5zGVdtr/7Rh4yIiEJTtuJERB4DMMjmp1uUUr8PO0BKqbsA3AUAc+bMUR0dHWHvIjadnZ2YOeM4YMmfC76P7ZgefqCg8mT27Nn4Uu9t+N7DWiZ7yuTJ6Jg+pPQ2HtImYqytrQOOHEVzUwP2HD6E6TNmYP6YvgCAXQcOA39+FEB4x/bIjINY+/5ezB3VDjz2MGprawu2/cGRLuDRh9ztUz8G63KdnZ2hhHfnyo3ASy8UfJfbrr7vk05ciN7NxZPKLj+4Bnj7LfTr2xcdHXMLf3z4AdTVCDo6OvAPLz8B7N6NqcdOxcPvrQX27cWxU6agwzT+9Ovv7wWeWIoeLS3o6DjZNqw1j/0R6O5Ge3s7sHUr+vXtC2zdgr5924FtW/Pn+aHCCTjN5+mpg68Cb7+JMWPGoKNjXH4h0zrG8nWvbwOeexotLT08nWu7uHlp425gmVaR1qNHD3ScfBLw6B8Lw6eHYe7cOZg4qBcA4PDqzcDKFehrd47LcUg7UVh55DXgzdcxctQodHQcE3yDDmE/+WSFbx7pQo+GMrf/oNdNmXO3Y7/lvmGTfoLqAHD+KQcxtM1+0tWS9PC0tLQA+/ehpqbGNlzGNbVgwQKg87Hc9/1t0tvrNW/huw+uwTkd8zGyrzYUyYbl64A1q4u2W19Xh8WLFjkG73+2vAC8uxGTJk5ExxxtmMaw7mmlbH7mHWD1Sxg8aDA6OqYV/d76wuPo29bk/VpLgTteeRLYvQtTpkxBx7TBaNuwC3jqSQBAXV1d0bn9Yqd9Gn/53d3A8ifQ0tKKjo4Tc9+XjR+na0b//t8+NQ8Lx/VzbP24affBgvyGl7Sw94MjwGOPeFrPePaLWNYxXcuDhwxBR8dU9D9mN373+hO49ux5mDbMfn6qJXtXA+vXYdy4cehYqPWkWez6CDStLzwO7N2DWbNmavkHlzo7O3H3tfPxL0vexLA+zVj5wBoMGzYMHR1TCo7Jzbl5a+s+4IklaG5uLrl8+S2VEePzqbtboe6xP+Kb501Gh2UuBlt62Hr17Ans2Y3Zs2dj98EjwHPPoL1POzo67NtPbbK5vxRdNw75k5o/PwR0dWHe3LmYMKin10MseyxWrfqzAQBOWHAC+rU25n7bsucDoPNP+fDZxNWP1iwDdmmVzxMnTsKxQ3sBTz6u3zdOKtq/63uH/vnkk05GQ11xBdJLXa8Dr2uNbgryRSXyfU4CPXMeLn1cS6cewN5DRzBlSO/8l5Ywtut5VwCoq63FIZvKk8FDhgLr1+f21bjsT8ChD3K/z5gxHSeM7Ve4kul8btn7AfB/prg0/b5woZa3f2TnS8CGdzD+mGPQYTcCgGl7w7bsxY9WLsWlJ03BCeP759LJvHnHAUs7MaK9B644bxG+//wjwJEjmD17Nt7ddRB44fmizU6xvAdYlYqf1nU7gKeXo7GxwfGZY3xflJ5LMa1774YVwPub0butDdipVXL1s0lz1m3u2H8Y6HwUUlv83CUKU5zlXOYyrsbB4zPQr5KIiAxlK06UUqcG3Me7AMyTbwzTv6sKaRolqEYE1y8ah+VvbscTb2zz1LW8eKiu/G9RDJs1pK0ZQ9qaceio9hJ00jGFLTPSNPySm+N3nBzemDjV5uebzpqIjgnFx52bU8FbMO33nxt/PKo5Toz/g2+/yzJ2R8mhusxTdqYpsaSAiJSvNIkjHDHtx1eliUku+ZR5xbE27LVLd58+cTQumDW0oIDNKSGXS7dGi92403e5Nz3zPSpr8s857X+x+c3P9sJy4vjSLRTN973ffOZ4T9v2k468PPunDOmNdbef43kfXlnj0ItBvZtw64em4OdPuOze4qASnzk1NYI3/uZs3+sr5e6+EGa+JmqFQ3VZA+FtWyqCm6bTecjK7dlubqbHv7YIuw8ewbk/esL1dsrNcVIuf1pycngfaW3cgJ548dbT0aupXquQsDCG5coPyyuoDXmoLsBj2H3u3hiu0hx8N+nPqPDjUF0UNZZzERGRG3GUnj0LYLyIjIb2ILkEwGUx7DcV0vT+XDyBrfeak1zFCQpqTiLTWFeLJV/twMBehWOcZ22Ok7oap8nhc5+KfvvsyWNtl+92MVZ8OXNHt+P/1m7FaH0CTi8v7cYLTYPDhPdmRgFSbRgVJ5aX31JbtNtdelKMvXwBfTjFGr/5zPFobynu5UTelLvXOFU+2l2eIlJYaQLndFnukglSQBxEruLWYb9peub5ZTehtZfSxjScg3mj3fe2APzdH0V/BIRdEBukEDmpCkWyZ44GZfOdH9+7aBqmDWvDGf+41Pb3uGLenAezpjc3edQ2m17IYarEK2B4e4+CEko3jnYFrDhxcSJzi7i8d/VqqresCIzs2wPXnDi6aA4ZEefGV8HoDbdiSCnmfbi5v7c01OJD04fg48cFn0+HKGJVWc4VR0MYIqI0CVRxIiIXAPgRgP4AHhCRVUqpM0RkCICfKaXOVkodFZEbADwMoBbAz5VSxeOSVKgkC/gFhQUa1koPPwVuuVZPDpMYRsEY0sYsa2Uizj1O9P9dHo9Ainr/GLwUNX3i+FE4b9oQPP9O4RwFboLx2ZPH4PDRblxuNxyBRa7ldgjx1V3U46REK0Cbz2lvZRn2vcJrwWncsnINu782C7ntZeX3POQmh4+74gTl95v2a6284gIlL8fUqE9s279nY5klwxV3WnC3O/dnLsx7YBpuL3GE4cTx/covlAJh3hMunuO16Dwa5jy0Na7dXIvfv3g6Zn770aLvVUhny80zKA3XSdSOdJfutWB3miYM7IlJg7Xh3ko20imxDTcKei2J4JZzJtsuV+vQ+CpIOxvbBgJOywZMKV7Pj4jgjktnBtonUVAs5yIiIkOgihOl1P0A7rf5/j0AZ5v+fhDAg0H2lVVJFg6KZcwUI99drsWwHWMrRsWJOa+exCFm7WXPqZA/9+JSZn1zS818932HfbkJD7SWe29s0cbnHtCzqWA/pfRoqMNNZ010sWS+RWYoQ3V56HGSZdkvdHYnTb3GSnHbct26mEM5R/F6Dueh7D3B0gswfk7hzka82rEWgvk9teMGtOLvLpqG0yYNDCVcbgU5836ONc29OpK7LuKz/ObF6NOjIelglFTQactDKW+QyoNEhurysc8+LQ34yKxhuO/5jdo2Qr53VsEl4Go4xcG9LT3Wrc9qm/P08Bfzc8yUbKQToAET4DK/LkB9hF1L3YbBD7tLvlryuJR9LOciIiKDy6IdyiLrS2pxQYL7nLCytG5Oegz7NBXYBAlLqTlOnPYVpMDUWiDYMaE/vn/xdPyly4oQr4x3vTDGZ+62VpyU2GQWh+qqScm1FZu0R4jObTAFghsWjcNVJ4zS/o6px0kaRTFef5zsosTrMX10znD0aYm5UDvANeWn0DbsSzislvZAsIqTQXpB67A+weZHitrg3s1oqq9NOhieRZ9/i+fhUtjjpHCfaXi8uTnPKcpK+2LcMe6+co7jMjeeckzJbZQ7T+4aI/k7ka7iCIL6OoceJ0EqGCNa1s4ZU+zm3iYiIiLKBlacRCxN7yTFkxe7X7docnhTZj2JSgwve/z1Nceh8ysdUQUlnMlMXW5FEN444do2BBfNHpabtyRsRtoIo7Fc8eTwbgZQyI5cpSTb46XCnJF9AHi4zgT4yhkTMHNEGwD38/o4LVV+cnjt/7hb1perPxDJfotSu3Oa9WOKgpuk56cOLYw8RZBNnHXsIPzi6rn45ILRgcNBGqWif7L56U0dRE2Jsbq8puE47y8Zr9e2Vep0l8vflntWl2ykY/nb67ktlTfOvXfVAPUu5hT0ykij7irYgl1UA3s14RdXzQVQmemPiIiIKhsrTqKW9FBdhd8AML1cetiWsY7xgmHO+CYyVJeHnZ4wth9G9SueJyWJsDit677HiWlyeD89TspNdO15i6XlCzLCH6rLdRgC7zlelfJS2aOhdEvotLd0/fSJYwq/KBNe43i6ywyl57Re0fdl1gtyHwiD33Bngd19OQvXZZChfvwkoxqb/EDSwig8FxEsmjCgsGDcUxhSdEIS8scbT8S3zp9iuYi0/yqjv0nwobqCrEcayf3v/0Q21pd+FS617aDDOroJt0DQEEXFSUTLRrsRIiIiovgFmuOEykvTeO/FPU48DNVlTChvN8dJAoeYpqG6gsi99LktZEWwobqcGC9lF80eFto2gXzhbhhDdQ1tcz9sShaTh5GmK6XI7U9fPhkbdhx0/N0piv7tU/OiCZBHnicz1f+fM7IdgPsJjI86VAh+8bTSw4vk7wOudhMaN+kz6+XG+bmn8ic3Cz3BsnjfsxNGxUOSc5zke4VWSIT4MGlwL0wa3Av3Pf9u0W9hnZavnjEBvZrrw9mYDyUnh3e5jbOOHYTfrtiImcPbYDwKgib/7188HXctfdPl0tWXRq1HHKRSwvqO5/ne5eL0iwANdeHHk5frMMxbWfqfpERERESFWHFSRazDbHkpcCtVSJemyqEkBDp+o6u826G6xF/8ldNQV4PVt50R+njpxrwkboctKmX8wJ64+8o5+NQvnyv74iUOnyk+g3s3Y3Bv58oup0LFE8f3jypIvniZfwgAhrf3wLrbz3G9/UNHugv+vnDWUPzgozPKrmdUSsZeNmvs1+l3kcwXjHide6pahX1+wshLGM/HNMRdCoKQGgruKh+9xNv1i8Y5bCOeM2+unKurKSx8dxuEUyYNzD0vDh7uQs+mOnztzGBzzl00e1jojWCywFxncdUJo3DPsnU4dmivsus1lsv3uqncMMJQftECbvLxAuehusJopOAmrYZxb86do6y3rCCqcF7eYYiIqgUrTiKWhpd3g3VYjWBDczDjawg0JIiP5bu7jf0Wrm28gPkdC7mlMfzbQVfIhbsj+/bQPnhIfllLqdVyaaXo1mjLa88uv8dz6Gh3+YVsJN2qvRqG6jLLwnUZ5Nz7SUZprmCq9gYdaWGOBTfDxIZSEBx8E66Ynw1hzBPX3FCLl249I/B2yimcozDy3SXi5An98aXTj0Gji3gp1+Ok5BwnAc+fu/lFwklfRduN4UrJwGOTiIiIqCzOcRKxNL2TWPPnXjLN+cnhC/+22261ibWwSvKVVtaWamP7t+KGReNw5xWzHVe/YNZQAMWtI6OSmxsntO4xbnvmFC+X9nTKyeHTKZdsXEyK7sdhS8WJ2/uySmiOE3dDdWU7Decnzc1/l4UjClKJ5qcQLarJ4YPIeNKrWAXz4pVIOAN6NQIAhvfp4X0fMfc2MrJRtTVSlMdhxV2yBECvpno01pXvRV2uUsJNTIqlYZpbJbPGpjkCHXuceNtdAU/zswSuIKruoQuJiIgo29jjJGJJZhStey6a6NTTUF2Fc5xkohQpJqFEcZltmFu/G6e+6EVdBF85Y0LJ7Xz7/GNx01kTI2m9ZifsCazdzwVTLO0FatVW0GKNy+9ecCy6HOb7SJTrHif+4u/Q0S5f63Xn7gm+Vg/M6XgroWxEcv9nrOYkgDRMDh9GpXG+J1bgTRWYM7IPTp8yMNyNVoGCykcX0btowgD84uq5OCnAkI1xPUuN9F9fazuGbSZkJJiO8nPDKdvv7fz0yjn4xZPr8NsVGwGgbK+UUtsK3OPE1eTw0fQ48RSGrCcUIiIiogBYcRKxNOU1rXUefsLWpk/EWWd6UWSG2j+vL/gC89wG3k98bY2gV5P7yVQ/uWA0Vm7Y6Xk/BqMgPKtp5H8/vxA9GsKd98VJ7hxVeAGtwZr2P37cyIRCUlq5pBu00NjvUF1JzXFS7ngzeqkXcNsS939uWIhn1u2IPkAuxX3uo9pfGlsm//ZzJyQdhMxzk/cUESyaMMDX9uNufGBUnNgN9RRmEr7vcyfgwOGj4W3QpJKyG+ZzXur0TxnSG9+/eHqu4qTsUF0u9v2xucNxz7J1OG2yfeXqc391qv22XWxcRAJNYF9++y6W8bnttDdYIiIiInKDFSdVxPpS6aVwwsj8fueCqfjDqvewcFw/x+1Wm1Ln8T+umY89Hxwpsa73fakYW5p/87zJgdYPf6gud+zOq5+CjGOH9g4eGJeM4HVXyZtmCstGbbmfHN7f9v32OFGmYTzipFxU2GQ1CeeG6LJ5ptn1hpg6rDemDovvHlFOnPNtmfe3eKJzQbefXiRZH+qN7EQfp1EN1fWDj07HCxt24ZfL1xd8b2RrGlwMBxXE7JF9It2+4dxpgzFvdDu++fvVsewvbIXDwblfr6g3vkXJOU70O+ekwb1KTqjcr7XR87bNFY1+5y4sxcs5CprPEFRGowoiIiKqTqw4iViShYPWfefeDXwUvBsZ+LbmenyuY2zQoFWUUqfx+LF9fa9ru7yYh+hJ/2tIU71WoDCgp/1LY1SyWJmXtugc278Fm3Z/kHQwEhTPHCKHjvjrcWJcU62NyTzGnc6KiGR2nh7r/FFS8Fv84TEs/eoidEUcAD8FYyKCx7+2CP1Dur+Hcd/OVewF3lKQMCS485QpvIay2QP1wlnDcOGsYTYVJ9qB2A31lOZDLKhgMH3/T5fNAoDMVpyYhfncLnVfimWoLnFufBSkktnL/dbLYf6/j0xF59qtRd+3NGrvA/0dKpGIiIiI0ooVJxFLUwGutWDE0+TwJeaqyNpLcJp47nECIK4C3TDMH9OO2y+civOmD0ksDFlrvZyW4D72pZOTDkIqlLvKBvduwrrtB3xv3+9QXbd+aArmj+mLOTG1RjaUS57pvyu5YDNUV5KX5Yi+7ibKTiK/Mbzd+yTecUjF4zENYUilaE9MfJPD60N12VWcpCIBVi/r2f/iqcfgza37/G2rZI+TYEr33Iw2r58bktLDsm58bO4IfGzuCP2v/JNz1og++N5F03DW1MHuN0ZEnpTq+UZERP6x4iRqCb47WQtg83OceG/1V2rC1Wp/PQzygux2XXOc5Yfo8b3b2IgILpk3ovyCoe839l0GJgXVYsmLuuAnK3FULpz3Xns8nn57e653VVxaGuvwkdnDYt2nWan0kZbKP6+Kh+rKH2MmKmBjHqorjTIQS1VJqfjiJq5KCyNPbTs5fEZk5TlcjlLAbedPwdd++6L2heW4bjx1fPyBciHJBlD5ubxc9HoJ+IQQEYgILp4zPNB2iIiIiJIQ3WxzlDrWDLqXbHCpwvpqb1k3tK05xr1Jbg6MLPQ4SYOspM+MBDM0aeqNV0q5cA7q3YTzZwz1vf1vnDs5lvmK4lIJ6djt5PBpE2iOkxQca7hDvKXggKjg+ZulRh9u5CaHz9hQXZXqo3OGY/rwNgDx5S/CmPuj/D4C7SKUMBARERFVM1acRCxdc5xYvvARNhbWF5s8pFfgbZQ7q+YJ4Y2ipRpeva5koqW4ScaC61vmbiURhbd/z0Z849zJ0Ww8Am7SZ9bTcNaSZpr5SQtZqex2lvELIERXLxgFABjTvxWj+mnDus0fU3rut6DiSj25obpsJu5OcxIulzqbY+49GUSub6D19cbF+X/ypsV48C9OLL+PhIbqyi+jLXTHpTNDjRtPc5ykOD0TERERRY1DdUUsybymlikungUy1+rPRej+54aF2LbvEK6+51nzJiz7oSG9m/Cej4m0jZcRt8UsImKqROGZd2J/arJxvrI6sXbFiiHZZCNlakoN2whoz5Wsp2GjQNR8iFk4oiDpKE2VFYEqu1MQUcazubWRWexzpw3BudO0Oc7aWxrw5E2LMaR3UyT7irtHS6keJ1lh9x6w8punJRCSYKy3DDdJYGhbs6se45FODu9qmCzNh6YPwT1Pvo3n39kVbKe5fVt2QERERES2+FYXsSQLI6yFVzWWQno3w8NMHdbbso3ilVJU3pIYv/HsdTgBAUxDdfnaZUUoO0E1E2XqpT2GzL28yIMKOF/5Vsz5g2mqS38r7Kzf98IYXqdcxV4cRvdrwdfOnIAPBxjCr1LFMbRpXMM0Gc+GBpt7Q1aGorQT93xdUQjzXpjUvST/riZF3+X+DlBR7OWwMv5oISIiIgqEFScRS1Nes2iOEx85Yc5xEi6vp848OTx7nFQWIz6zPsyRW1m5b+QKwCKMF/O5SPtpcdMboFLSsDkq/vv6BZHv798+NQ9tzQ2R7ydOcScFI30m2ttXBNd1jEswBNUt7h4n9Ta162m/jxuyEk7XImhYVGpTceRjzLsw96pSKpweyq7mWfF5R3mCsFUAACAASURBVK2UvAARERFVt+z2L6eyrBndfMGsXrDgIR988jH99XUq7S0rfHNH9XG9rNezKVU+ObzbI87imam26MzK4cYRL1mMe6eCFMsAkZlk7bXQr7UBEwb1jHy/J47vX9TL04sMJiOizDLyYHW1vPLSJsxnatLvPXZDRoaZ/3c1XFjQIcmCrU5ERESUKPY4iViaCsSCzA1/5xWzsXXvoVDDE9QPL5mBSYODT8wepp9+Yg7mj2l3vTx7nEQjy6cma5PZ+5WVOMpKONPCfI/KmhIj2ccYCv+YVoHBvZuxbvuBTM87QcHEdRkYvRpqszaeo+kGXbn3jBArFkLbUggBMPWo6Qq4WS+P6cTPAREBANbdfk7SQSAiqkqsOIlYmsY5Lh6qy/26TfW1GN7eI+QQBXN+CscPnzioJ3o21bte3k/6MLrmV+4Lb7iyUoZrRGdWwlst4riHZ+lSdjUBc0YTsTXYRtzzXuvPxEE9cV3HWNfLhzHszI8/PguPv74Vw/qkK79CMYp5qC7O/Zc+4fY4CW9bvvZvStD53pDh9e1kUiUiIiIqLVCTPBG5WERWi0i3iMxxWGa4iPyfiLyiL3tjkH1mTZIZ7nKTw1Py3KYPcwvubqPHSdZaOcbIrrA76ZffcpIeDiJuWTnejAQzdk6nRSChFIAnKT85fKLB8CxNDTUA4KEvnIQx/Vtj3Wd7S0MqG1VQfGKbHF7Pg9n1OEnbtZhG3/nwsbj/uhMCbcPpHl0JZz/fu7z0d0G374bf/Fq2cwJU7VjORUREhqA9Tl4GcCGAO0sscxTAl5VSz4tITwArRORRpdQrAfdNHhkZ33xmuRJeLbIt9zLi5e0ixBenrCo3nJXdO17ahw8a2KsRADC0rTnhkBBgbdlJhnKVIpVwuop7nmRD1s89C5spSzI7VFdKXD5/ZGTbDvO5nVQeIN+7XIq+qyl6nwuy/fLLBj0DWX82UdViORcREQEIWHGilFoDlM5UKqU2Adikf94rImsADAVQdQ+Ux750Eloa4xsdzZqhtr7bMSMbnlz9h8eXGD9RYH1xqiaVXIh9xpRBuPvKOeiYMCDpoJBJHCku5XV6tkpdimmvpHRiPSTjGCv4tkNUMfKV3fHsz8iD1WZsqK6M3p5Lyuozxw27pFQbQsVJfvvFe5gwsCfWvr83v4zP9Fwtc/ZRZWI5FxERGWKd40RERgGYCeDpEstcC+BaABg4cCA6OzvjCFok9u3bh8cfX5r7e+MrKwAAa2Paf7clw7pkyRIAwJ49BwEAzz//PPa8VRvqPrMUX/v27QstvIc++AAA8NTTT+GtHu5HwHtz/REAwPtbtpQMy759WpytWPEcuvSxupYvX4a2xmgnwPV7foKeV6e42by/GwBw8ODBgt+tyy5ftgxtTdq5efn9owCA7du3pT591gJ4/P01SQejpDCvG0Ma42X1Zi3d7NyxAwDQ3d0dWThf0+8DALB582Z0du70tZ0o4sbqjbcPAwA2bNiAzs4tRb/v2nUQh7vSGafl7Nqt3WdXrlyJ/etqse2gdr85fOhwKMcTdfwc7so/84PsJ2gYb57XhE37vV8vGzceAgC8+eab6Ox6J1AYvIrj2iF/3MZNd7d2vS5btgy9GqKvudj03rsAgPffL75nG/k0oPB6ijqNudn+uvWHc5+3bt0aKExJXzfbtm0HALz08kuo27LG9H6zArveDPf9Big+v2Eeu3Vb3V1aen7yySfRqqfnvXu14+vq0vInr766Bp1733DcZqn42bBX2/7+/cXLfHW6QpfqYbuul2PevkN7N3rppZdQWyJvy3svVYJy5VzmMq7aXv1jCxcREQVXtuJERB4DMMjmp1uUUr93uyMRaQVwH4AvKKX2OC2nlLoLwF0AMGfOHNXR0eF2F6nT2dmJExaeBDzyRwBA3McijzxY0BzJ2P8/rH4S2L0Ls2bNwqwRfcLZ2UMP4OPHjUBHx9RwtheDzs7O0OKk6Zk/AwcPYv5x8zGir/tJad9Zvg5Ysxr9+/dHR8dsx+VaX3gc2LsHs2fPAZY/AQBYuGAB+rU2Bgy5g4ceAOAxzerreF7PhlPcvL1tP/B4J5qbm7XfreHU/z5hwQkY0LMJAHBo9WZg5Qr069cPHR22Q9SSB2FeN77SWUwOvLQJWPU8+vZtB7ZtRU1NTWThXL9sHbBmNQBg0KBB6OiY7ms7ocaNg7XyJrD2VYwYMQIdHZOKfv/ZG0/j4JEudHQEG7s+CT9aswzYtRMzZ87E3FHteHfXQWDJn9HU1BjKeY06fg4d7QIefQiAz2sqpOvR79pL974CrH8bY8eORceJYwKFwas4rh3yx23c1Dz2R6C7GwsXLEB7S0P4ATHlcQBg+PBhwDvrMGzokKK879Gu7sK8f9TPOg/bX3F4LfCmVtg+oP8AdHTM8r3bxK4b/Xj79esLbN2CqcdORcfkgfj7l54A9uzGnNmzMW1YW+j7s+Y1Qzl2h23V/ElLzycuXIjePeoBAC0vPA7s2YOGhnocOHoEEydOQsfsYY6bLhU/azbtAZ58HK2trejoOMl3OEv55dvPAFu3Ytq0qeiYOLDo97848hqGtjWhY+4I19skClOc5VzmMq7GwePZHYuIKEPKVpwopU4NuhMRqYf2MPmVUup3QbeXJYlODl9uHogQ97Xu9nNC3Fr1MOLAS2/23OTwaR4LIiINdVovkoG9mkouZx56gCMFUBBxDA+XpeEs3IQ0S8djlvU7atbnCCk3fw6RG3FdBUYerM5ucvis5M8yEkxnmT8AfPm0YzBvdHvR98ZjVEwdy407ZBj5/zQ8pr902jFJB4GqXNbKuUbdVFiBz/IXIqJ4RD5Ul2hvD3cDWKOU+kHU+yMX0pBbJgD5l2s/BUZ242pXuqFtzfjhJTNw4nh2caZo5Qotkg1G6pQ7L5V0WzIqgCrokIgoJEZ9iV0hNu8Zccn++8znTxlf8ndzWjKeSc312jBktTaVdm7ZTT5PRN6wnIuIqDoEmiBBRC4QkY0AjgfwgIg8rH8/REQe1BdbAOAKAItFZJX+7+xAoc6QJLOjTq8T+Qk0mVkOm9cKkJoAEzxKoKs3u86fMbTsMBzmpM1kTkHEkX4yWfRTanL4+EIRCeN+nKskyshNJCPBdHTJ3BEQAc6YYjdqBpE7sU0OrxdaBym8TgLbTmWL3fPn62dPwmdPHotzpg32vV2v6eBTC0fjvz57vO/9EWUNy7mIiMgQqMeJUup+APfbfP8egLP1z0+gihtfJVngUi5TXLWREgG/Q6QY79vl3l/sfo9yqK7ffvZ4NNV7m1jzMyePwZ1L3oooRN4UttBLLBhEnlXCfZnXXDKynnYmDOqJt/+Ww06QT7necPFcCUYezK7iJCuVmBkJpqPjRvfFY2u2YFh7c9JBiYxdfratRz1uOmti6Nsv5RvnTva8bWYFKMtYzkVERIbIh+qqdnySVoe/PHMivvSbVWXn3rDy+nJtXj7KRo5zRhWPt1zOzWdNSk3FiR1ei5RWWapoKNerLiu9M0qpgEMgql4xXb91JXqcWO+DK79xGu8rEfj0iaNx5rGDMLy9R9JBCZ3dfCb54bUSCFAAWZ9/i4iIiKobK06qUJYK6bLinGmDfXWZlwBDdVXj5PBuVULhbbUY3a8l6SCUZBSKXT5/ZGT7yNItWblo1Z2l4yklP1RXsuFwi/c9oviu1/pa9+Ol9ikzvGhSsn7PEJGCShM/8wWmnTmKjGdSmJPDZzwJEBEREUWOFScRS3OGNM1hqxb5lx/vL3uMP2d2p6byXqez77+vX4ARKW0p2lCnFYq1Ntbjje+elblx7KPmdP8RIPO187k5TjLWujcjwSSqCHW12hXX3Z2t+10lVi5YVWoPB7teKEFl5flGRERElBRWnEQsja25quGlKSuCpA72OKGsmzG8LekgODpl4gB8+bRj8IkTRqHOQ8viauF098nybckadje9a4goXeK6Wo2hurpKVJz0a01nTxPKJqU/lMJox8F3QaLsWnc754MjIooTK06qEAuD0qNGL4/lUF3hsjs1PFvkRU2N4POnjI9lXyrjPTTMvnrGBBztyubxOEVDVm61WQknUZTiarBk5MGc6k1+dOnMVDcOAJgvyhojqYWZxqN8F6ygrA0RERFVMVacVDEWsiTPePH2827BkYOcsVKQKBrlKnmmDOkdU0iil7UynzT2cCWKS25ovZj2l684sb9TnDd9SEwhoUpUVyM4bP0yN8dJ8O0b6be5oTb4xhzkrgw+moiIiCjDWHESky+fdkzSQaAMsyusZCGZW1kr/qRqk8VWmZV4+ykeqiveglgiCi6ue1Oti6G60i6r9/EfXzYLg9uakg5GpO773An440ub0FSfr9gIc46TKUN64SunH4OPzhkeeFvlZDSZEREREQFgxUksOA4lOcn1OHFZcsqeFIV+dOlMDOjZWPwDTxNRJKppqMcohkUhomho96T4KjFqjIqTjNV8Zyy4ts6ZNjjpIERu0uBemDS4V8F3+TlOgj+TRAQ3LI5nOFIiIiKiLGPFSRWqhJemShFkqC5yHgqj8J2ShZ5EYclXJiQajFgMbWtGnx71+PrZk5IOChGVkR+qK56bU63Hhi8UvUqPimp6/hIRERGlBStOKlhjXQ0OHe0u+r7C3ysyxRinOMMjPWQATy6lm8pQGh3R3gMAMFz/v5K09WgAANTXajfmpvparPzm6UkGiYg8iqtQ2ci/ZXmoLsoWlZvjhDUnRERERHGpSToAFJ3//fzCkr8z3508P3Fw+fwR4QekwtidVqZ3SitzK9m0p9PzZwzBvdfOx8WzhyUdlNB976JpuPW8yZgxvC3poBCRR3H3NsgN1VXcPokSlvbnqF9GI4savr0TERERxYY9TirY+IE9bb/nsALpIT6GevjOh6fiOx+eGlWQKgLnJKAsMV/9ab89iwjmj+mbdDAi0dajAVctGJ10MIgogNgmh9d31J32m7aFObTMKWVL1nqc8H2TKDjOlUtElDy2Wali1TC5b9q5ffm5btE4AMDw9uYog0NERESUOXGXJdfWZLPihLIrX3GSbDi8YmMmIiIiyjJWnBAlyO2rxIemD8G6289Bz6b6SMNTKfiKRlnF8gUiIu+MQuW4GgVJBcxxwgLtbMpKvH1s7nAAwASHERCIiIiIsoBDdRElyBinmA0Ww5WRd0oiALz+iYjCEtXz/w83LEBrYx0W//0SAOxxQvEzhr7KylBd504bgnOnDUk6GERERESBsOKkCuVa5WUj313RcnOcgC/eUWGZBqUdr38ionSbNqyt4O+Jg3oBABZPHJhEcMhGpef3jMPj6xsRERFRfFhxUsVYcZI8RkE0OH8PEVWjUycNwIWzhiUdDKKKN25AK1bfdgZaGrP7KsWckj//c8NCvLp5T2L7z0qPEyIiIqJKwDlOqhBbN6eH8fJT6a3k4mZ+p+T7JaUdr38Ky8+unIuzpw5OOhhEsfvW+ceitbEOtTE+9LNcaVKJhrQ1AwCa6msj3c/UYb1x8Zzhke7DDkcMICIiIoofc/xVjK3yk8eKk+jx3BIRlff9i6dzEl/KrMuOG4HLjhuRdDBSryBPVGGvAT/42HQsWbsV4wa0Jh2USBgN32pqKiziiKjIutvPSToIRESkY8UJUYKMdx/2AooeKwqJiJxdNJtDfBGVs/zmxdh/qCvpYJCNXk31OG965U5GblR6sd6EiIiIKD6sOKlCbIGfIkbFCeMkVOZhDHroQ2n0aWlIKDREpSneAIiIMmFw7+akgxCIuaEOG5RkixFznOOEiIiIKD6B5jgRkYtFZLWIdIvInDLL1orIShH53yD7pOB+dNlMfHjGEIzt35J0UKqe8dLKYtPonDS+H77z4WPxjXMnJR0UorJYkEVERHFgb+dsyc1xkmwwiKoCy7mIiMgQdHL4lwFcCGCpi2VvBLAm4P4oBBMH9cI/XjITdbVBo5+CyjUa47trqMyFzyKCy+ePRI8GdrCjdDJ3OGFBFhERERVjzQlRjFjORUREAAJWnCil1iil1pZbTkSGATgHwM+C7I+o0vDdJxocxYCIiIjIGXs4Zku+xwnjjShqLOciIiJDXE2w/xHA1wD0jGl/ZPGvn5yXdBCoBLYyDxdfKSlLzFc/C0SIiCgqvZrqkw5C5txz9VwMbUt+bhsjr8DGQUSpwnIuIqIKV7biREQeAzDI5qdblFK/d7H+uQC2KKVWiEiHi+WvBXAtAAwcOBCdnZ3lVkmtffv2pSb83e+tRud7SYciXdIQP2t3dAEAdu3anXhYwhT0WILGzZIlS1BbwzfLKKThuqk0b799OPd50+ZN6Ozc4Ws7jJt0Y/ykF+MmvRg37rk5TxOUwikj6vCnd45ibO22QOe2muLm3U3AuwkPxHP4sJZXWLZsGXo1lM/jVlP8EPkRZzmXuYyrtlf/gt/W3X6O2yATEVECylacKKVODbiPBQA+JCJnA2gC0EtE/l0pdbnD/u4CcBcAzJkzR3V0dATcfXI6OzuRePgfegAAkg9HCqUhflrW7QCeWY7evXujo+OERMMSipDSm++4Me2fFSfRSMN1U2leOPo68MZrAIDBgwajo2Oar+0wbtKN8ZNejJv0Yty44DHvdericHbLuIlX3dJHgCNHsHDBArS3NJRdnvFDVFqc5VzmMq7GweM51AQRUYZEPju4UupmpdQwpdQoAJcA+LNTpQlRtWLuKVysMqEs4VB9REREVIri3PBEqcJyLiKi6hCo4kRELhCRjQCOB/CAiDysfz9ERB4MI4BElcx4+VGKBadERERE5M2/f+o4zmVYBYx3Bc5xQhQ9lnMREZEh0OTwSqn7Adxv8/17AM62+b4TQGeQfRJVEuPlh9Um4eJLJWUV0y4REXmxcHy/pINAMbhu0Tjc/sdX0aMh0Os7EbnAci4iIjIw50WUKK2UlB1OwiUsfaYMMV//vBcQERGR1WdPHovPnjw26WAQERERVZXI5zghImfscUJEvP6JiIiIiIiIiNKFFSdECcr1i2Azc6LqZbr+2VmKiIiIiKjyrbv9nKSDQEREZbDihChBHFKKiMxYh0pERERERERElDxWnBClAMtKiYiIiIiIiIiIiNKBFSdECTL6m7CVOVH1Ml/+7IRGRERERERERJQ8VpwQJYiFpERkrjhtbaxLLiBERERERERERASAFSdEqaA4WBdR1TKu/56Ndfjy6RMSDg0REREREREREbFpK1GCRB+sq1KG6urRUItxA1qTDgZRJn3m5DFobqhNOhhERERERERERFWPFSdECTKG6qqUipNXvnVm0kEgIiIiIiIiIiIiCoRDdRERESWoUipOiYiIiIiIiIgqBStOiBKU63GSbDCIKEHG9S/GDYGIiIiIiIiIiBLFihOiBOXnOGHVCREREREREREREVEasOKEKEFsYE5ErDclIiIiIiIiIkoXVpwQpQALTomIFalEREREREREROnAihOiBA3t0wwAuHrBqGQDQkSJUZzliIiIiIiIiIgoVeqSDgBRNevVVI91t5+TdDAqxg8vmYH7V76bdDCIvNHrTYw5j4iIiIiIiIiIKFmsOCGiinH+jKE4f8bQpINB5AuH6iIiIiIiIiIiSgcO1UVERJQgDtRFRERERERERJQurDghIiJK0PFj+gIAZo3ok3BIiIiIiIiIiIgI4FBdREREiVo0cQBevu0MtDbykUxERERERERElAbscUJERJQwVpoQEREREREREaVHoIoTEblYRFaLSLeIzCmxXJuI/FZEXhWRNSJyfJD9EhERERERERERhYnlXEREZAja4+RlABcCWFpmuR8CeEgpNRHAdABrAu6XiIiIiIiIiIgoTCznIiIiAAHnOFFKrQEAEXFcRkR6AzgJwFX6OocBHA6yX/JmaFtz0kEgIiIiIiIiIkq1KMu5pg7tjeduPyeUcBIRUfREKRV8IyKdAL6ilHrO5rcZAO4C8Aq0WvgVAG5USu132Na1AK4FgIEDB86+9957A4cvKfv27UNra2uiYTjarSAAamucH/rVKg3xQ/YYN+nFuEkvxk26MX7Si3GTXoyb9GLcpBvjJ10WLVq0QinlOOQTpVNY5VzmMq4RI0bMXr9+fZTBJiIiH0TE9lldtseJiDwGYJDNT7copX7vYt91AGYB+LxS6mkR+SGAmwB8w25hpdRd0B5AmDNnjuro6HCxi3Tq7OxElsNf6Rg/6cW4SS/GTXoxbtKN8ZNejJv0YtykF+Mm3Rg/RKXFWc5lLePyH2oiIopb2YoTpdSpAfexEcBGpdTT+t+/hfZAISIiIiIiIiIiig3LuYiIyI2gk8OXpZTaDGCDiEzQvzoFWndGIiIiIiIiIiKizGA5FxFRdQhUcSIiF4jIRgDHA3hARB7Wvx8iIg+aFv08gF+JyIsAZgD4myD7JSIiIiIiIiIiChPLuYiIyFB2qK5SlFL3A7jf5vv3AJxt+nsVAE6GRkREREREREREqcRyLiIiMkQ+VBcREREREREREREREVFWsOKEiIiIiIiIiIiIiIhIx4oTIiIiIiIiIiIiIiIiHStOiIiIiIiIiIiIiIiIdKKUSjoMjkRkK4D1SYcjgH4AtiUdCHLE+Ekvxk16MW7Si3GTboyf9GLcpBfjJr0YN+nG+EmXkUqp/kkHgpInInsBrE06HAnj/YnnAOA5AHgO0nb8ts/qVFecZJ2IPKeUmpN0OMge4ye9GDfpxbhJL8ZNujF+0otxk16Mm/Ri3KQb44conXht8hwAPAcAzwHAc5CV4+dQXURERERERERERERERDpWnBAREREREREREREREelYcRKtu5IOAJXE+Ekvxk16MW7Si3GTboyf9GLcpBfjJr0YN+nG+CFKJ16bPAcAzwHAcwDwHGTi+DnHCRERERERERERERERkY49ToiIiIiIiIiIiIiIiHSsOCEiIiIiIiIiIiIiItKx4oQqlohI0mEgyjJeQ+nDOCEiorjx2ZM+jBOi7BGRM0VkrYi8ISI3JR2euIjIOhF5SURWichz+nftIvKoiLyu/98n6XCGSUR+LiJbRORl03e2xyyaO/R08aKIzEou5OFwOP5bReRdPR2sEpGzTb/drB//WhE5I5lQh0tEhovI/4nIKyKyWkRu1L+vpnTgdA4ylRZYcUKVrDbpAJAzEZklIvVJh4OKichXRWSM4iRYqcM4IfJHRFpNn1ngmCIislhEWpIOBxUTke+KyCQ+e1Ipl4fmPY0o/USkFsCPAZwFYDKAS0VkcrKhitUipdQMpdQc/e+bAPxJKTUewJ/0vyvJPQDOtHzndMxnARiv/7sWwE9iCmOU7kHx8QPAP+jpYIZS6kEA0K+DSwBM0df5Z/16ybqjAL6slJoMYD6A6/VjraZ04HQOgAylBVac+CQi14rIt0WkOemwUCER+biILAPwdyJyTdLhoUIicpmIvADgDADdSYeH8kTkUhF5GsCXAZyadHgoT0QuF5EnRORbInJh0uGhPBG5RkT+WUTGJh0WKqbnCZ4D8D0R+RbACsi00ONmBYBFAI4kHR7K0/NqSwFcB+DypMNDeXpebQWA7xotN3lPI8qEeQDeUEq9pZQ6DOBeAOcnHKYknQ/gl/rnXwL4cIJhCZ1SaimAHZavnY75fAD/qjRPAWgTkcHxhDQaDsfv5HwA9yqlDiml3gbwBrTrJdOUUpuUUs/rn/cCWANgKKorHTidAyepTAt1SQcgS/TWPHUAPg3gLwF8AOARAI8nGS7KxU0LgO8AOBbA1wH0BXC1iDyqlFqXYPCqnh4/TQC+CeBSAJcppZaZf+dLXzJEpAZAG4A7oV1DXwFwHoADxu9KKVZwJUhEOqAVXn0VWmXjt0QESqnfiUitUqor0QBWKf3auRjA1wBsAnCciLyrlPog2ZARAIhIE7RrZjGALwHYDuAeEfmNUurlkitTpESkDsAXANwC4Cz95ZBSQER6AfgegFEAbgYwCUBv/Tfm1RImInMAfB7A9dAKE/4kInuVUj9n/BCl3lAAG0x/bwRwXEJhiZsC8IiIKAB3KqXuAjBQKbVJ/30zgIGJhS4+TsdslzaGQnu/qDQ3iMgnADwHrSfCTmjHas4LGsdfMURkFICZAJ5GlaYDyzlYgAylBfY4cUlEGvSavyMAnof2InEntIL5vsmGrrqZ4mYftNrJU5VSnQB2QrsRbSi5AYqUKX4OAtgC4F8BPC0izSJyuoj05IteMvS46VZK7QDwT0qps5VSj0OLp6sBgJUmyRCRBtOfJwC4Tyn1pFJqOYCXANwOAKw0SY5+bawCMBdaV+qToOUNKAX0Cqz/Vkot0lu9NQB4HcC7yYaMlFJHocXFvwNYLyINIvIRERmScNCqnlJqD4CfKqXOUEo9Ca2w66P6b8yrJW8KgMeUUk8ppbYB+BWAvxGR3owfIkqxhUqpWdCGIrpeRE4y/6jfv6rqHlaNxwztfWksgBnQKgP+PtngxEMfLvg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\n",
      "text/plain": [
       "<Figure size 1800x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the following cell we visualize the plugin reconstructed data. \n",
    "# Note that we save in EOS the normalized data, so every time series \n",
    "# here will have mean equal to 0 and variance equal to 1.\n",
    "\n",
    "for host in df_ts_train_dict:\n",
    "    df_ts = df_ts_train_dict[host]\n",
    "    n_rows = len(df_ts.columns)\n",
    "\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(25, n_rows*6), gridspec_kw={'width_ratios': [3, 1]})\n",
    "    fig.suptitle('Normalized Time Series - ' + host + \" - TRAIN\", fontsize=26)\n",
    "    fig.tight_layout(pad=10.0)\n",
    "\n",
    "    for i, c in enumerate(df_ts.columns):\n",
    "        c_ax = axes[i, 0]\n",
    "        df_ts[c].plot(ax=c_ax, grid=True)\n",
    "        c_ax.grid('on', which='minor', axis='x')\n",
    "        c_ax.set_title(c)\n",
    "\n",
    "        c_ax = axes[i, 1]\n",
    "        df_ts[c].plot.hist(ax=c_ax, orientation=\"horizontal\", bins=30)\n",
    "        c_ax.set_title(c + ' - Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "honest-morris",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:53.599065Z",
     "start_time": "2021-05-03T14:56:50.247503Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7euQu7Tg84p/b6bnXC/sEpBYrtN7olQoAAACgEZE4AUJdPeVlB8rplZEsslFjsGUfCT1OAAC1rhbOoJmny7f94FElvbzhNipx9i1ypK6cy/U/x0m5+I0BAAAAoPYwVBcQKjdxkihjrK5kuG6pPVYGG3kTAECtGugcFdU9CeUM4VmIp15do7GXjtP8VZv7XS49HGiRv/orMb9KQfthjhMAAAAANYjECRDa1p0oa/1EWT1Oyh9/vJKyh92gxwkAoFale1TkyZDU8ymsv2Gwxr24QpI0fcn6/rcR/l/onDWpeszcdX9JlErVb630ugUAAAAAicQJkDbQHCeZciVZEonyh+qq3cnhoy4BAGAwJZOupevaoy5GSQptuK/LyeH7fa2w+dHSPToKPP6iq6nMg+YnBgAAAIBaROIECO0+anjBy3761uf6PFdOj5P17V2SpB2GNZe8jUpijhMA2K69q0ezl22MuhiD6oYnFuj0nz2hhWu29Hmt1s8AqQRCLdx68NSyHq3Z3FnSuhvbu/VSW+Hvs+1DXPV/5KWewzPX6n+Ok/LeIQzVBQBAb2MvHZf+BwCIDokTILTj8MKTFk+/9kaf55JldMt4bc3WoAy1kjjx7Me13mwGAIUr9ivtK3+eqfN/PVmbtnUPToFqQOq8tnLjtohLUryB4lmtuTok6YmlPfr8nTNKWvc/fv+MzvvV5F7PVXJS9kKHwkotVujnpHJDdVVmOwAAAABQCSROgFC5w1GV0+MklZio1fxEqm5Sd710dJU3HwwARKHUhtlZSzdIkto74/ndV+vt2bV27nxjS2k9Tl5Zsamo5dPHPdBQXYUtlqHvkpVM4PTddo0FEAAAAAAkDYm6AECtKHc4qkQZmZfUqrXSdJB9d2523Wzo6NLqzUk1mWm/XUdWs2gAULJSv+aHNgf3mXQnkhUsDSolPVRXjWR4KjlfWSG9ZQbcW5FznBSzb6lyiY9CJ68HAAAAgGogcQKEyu1xkiyjPa2aw4iUIrtuhjQ16eRrJkqSFl99bgQlAoDqGdIcNOj2lHuiwKAoeHL4KjXMVzSBU4G5RbbPAVPcUF2F7qvsHidlrg8AAAAAg4GhulC3vnbPTH3hzhkVGzaq3DsmyxmqK5V0qZXhKnLNcVKtst086TXNX7W5KvsCEC+lNmg3NwUrJsrJkNex2jgzDaxWeiyU2+Ok0PNtarHM/T356hpt6ezptVzqbVt0j5NqB742wgcAAAAAkkicoI498HybHnlppQ6/bLz+NrOt7O2V20BQ3lBd4Rwn5RVh0CTd1Z3wXo8Hg7vrynFz9IEbpgzK9gHEW6lfXcPSQ3XV6rd05dRI/r4oA04OX+VjKrfHSWZ5+yt6KsGS2t/Sde266A/T9I17ZvVeLt3jpDC5lut3jpMy67ce33MAAAAAGh9DdaEhPPbyKn3gmDeXtY1ykwEVSSbUaONB0qVtPYmMx4NT0FTuqZ3J5wFU0PINHZowZ1XJ66eH6opB4iSXWu8IUOgcJ9WaA6XsHidFLp/aXercuXDNlt7bK3SOk6weLAX3fKnQj5daf58BABCFsZeO6/Mcw2UDQHXQ4wQNoaM7oR/9/RW9saWz5G0kXTr94N0187L3pJ/73YXHFbx+OT1Oau1uy+zyJN21LSOZUc6x9mewEjIA4u3iW6fpsr+9rDe2dkkqvqG3uSnscRKDobpyz29R2wo9dVTyFLM+fC/lYhUcqqvfnh6Fbi/91yClJsrtcRLBO6yrJ6ml69qrvl8AAAAA9YPECRrC5AVrdcvkRbr0gdklb8PdNWJos3YZOSz93MhhzQWvX5mhumqzecpd2tjR3evxYCBxAmAwrG8Pvr/S37VFftUMbYp3j5NaV+2oPDJ7hY790eOavnhdztebysxPZP6cyPxdsK07oW3d229iSPckyUqIZOdtBupxsnrzNn357hfU0V1ab8+y67/QHjEV9L2/ztbpP3ui128bAAAAAMhE4gR1JZF0JXMkKLp6gruAF6/dqrYNHWrv6umzzEDc+w6vscPQZk365hm64v1HDrh+OY3+pTbmDZbsBE4y6emGR2nwepzUyvEDaEylfsdsH6qr8Xuc1KOBhpRKvVqphvlnXntDkvRS28acr5c/VFfu4znq8kd1+GXjJUmvrtqsyQvWStp+XPlvvuh/jpNrxs/Tw7OW65GXVvZey3P/3WfrFTp3ZyeABtOTr66RJHUwNCgAAACAPEicoK4c+J1/6IM3Ts37uks69eqJuuDmZ4vedtJdTVmfiB2GNGu/XUfqsL1GD7h+JYbqqpW8Qd+huqQN7V0Zj0mcAKgf2c2xxX7VDE1NDj9ISeNakvk9XM0eAOUYKCqFztVRKeX2OMmXsOhOePrxe3/xlNblGS4sOwGxvcdJ7oJlP709EVPYZ6VWe8v2h98bAAAAAAZC4gR1Z9bSDXlfSzVWPP96/mXySbr3aVQYMSz4iJz01t0KWr9UtX79nnRXZ0+y1+PB2g8ADLZSG9Kr3QAftXo53IESA+keJ9UpTtkZp2Lr3dKTuefZXmq5fOtnD/WVoxyDmRypdI8gAAAAAKgEEidoKOUM85BrqK5hzdvnOBnoDtJCx76fsWSd/u+xeVn7ro2humYt3aB/zlmVbsQ4dv9dZBYkNDJ71AzWaDUkTgDUonIn+64n9XioqXNovqJX+9RSdo+TjCRFIUXP3l3fOU485/Mp2b1tc73fB3OoroHmYBkM9fg+BwAAAFBdJE7QUMq5EE6692l82Hnk0IzX+18/EV75b+tOaOXGbXmX+9CNT+tXExdk7Tv4P+rhLj5wwxR99vbp6cfnvG1vNZspkXT1ZFRAdoLD3XtNWFuK+2cs07gXV5S1DQDIJd9k2YWKW0+TlHppXK61HgsvvL5BM5bknji+EL0mhy/gvTfQcQ9cP+XOyQIAAAAAjWdItXdoZs2Spktqc/fzqr1/NLbM9oWNHd0aOaw5PTb9gOuq712iO48YmnPZXJJhL4zP/XG6Js1fq8VXn1v4ujXS4yQls6GmuSlInDw8a3n6uez5XH7x+Ku6fuICvXTF+zRqeGlfK1+/d1ZphQUAVEytnIeKMXCZK3tQhWztQzc+Xfr2iwxCqrdt3qG6ipzjJL2ett9Q0l+Jyk0sRn3TCAAA5YiijWvspeP6fb2YtggAQH5R9Dj5iqQ5EewXMZDZE+LoKx7T5++YUdS62UN1Zfrdhcf1u36qx8mk+WsL3mdKrTVUZd6dOrS5Sd0J15Ovrtn+elZ573r2dUlSe1dPlUoIAKUrdQ4J1KaBGu5r7Rw7kMziFlL2gd6eLy/fGCyXb/082yu03io2VFf1ZqGpu/cEAKCm0cYFAA2qqokTM9tX0rmSbq7mfhEf2UNI/XPu6sLXTfbfOPa+I/fS199zSK/ndskcyiurF0b24/6k5zgpeI3Ku+bRuTmfH9Js6kn2ntTkugmvpv8+9eqJemNrl6TKNnrcP2OZlq5rr9j2AKBccWhrrccc0UBDUQ123HoqPPGXD7C5eSs393p8xcMva0N7V86eG3c8s0Rrt4Tn6DwVlH3TSDpxklmmKmQaonjv1eP7HQBQO2jjAoDGVu0eJ9dJ+qakQZpaGnGX67q+0It9dx/wAnpI1rBfmcNSZQ9f1Z3s/22eWS7f/uSA5RwsNzyxMP13ZjGGNDWpO6tRKDMh1bahI/13sZO7f+a253TSTybkfO3r987SR39X+lAnAJBPyUMDxSFzUocGOvUMNFRVuQ767iMV3V7m+3NjR3ef19933VO9Hq/a1Kmfjt9+80PmcX7/ry9tfz7P/sqdzH7p+vbYzgMEAIg92rgAoIFVbY4TMztP0mp3n2FmLf0sd4mkSyRpzJgxam1trU4BG9CWLVsatv7yHVdHR98eCo9PbFWTSQ/M79a5bx2qHYcGLQSz1/Ron1FN2m1EkAzZ1tmpVStXqrV1fd79vL64dwPGaOtM/z1r9mwNWb29h+4TrU9pWHPfOzm3v96afq1jW7Cdea/OV2vn4pzLF6uY+Gc3eEyePFmStHDhQiV7unX3tKUFbWfylKnp+ly6OandR5hGDMnfIjNx7lZJ+eO5cuO2hn0Pl6u/emnkz349oP5rT2dnV6/HzzzzrBbtWPi9I+vXbZMkzXrxRdnK/n861Wv8168PkuAzZ85S97JmSdLiJUG9LVq0SK2tbZGVbSBz3khIkrZszl33SzcHbRnt7e0ViU1bW3DOnr9ggZ7oWjzg8sXuc/LkKRo1zLSly/WliYX1vFzatkLTpwdDauZ7D7744iwll/d9/6aOJ2VbR/B+X7J4sdrbgyE4Zzz/vDa+1pxz35/4/bP63FHDdOqbC58XLtPsNcE+1q9bX7XPTldX8N6eOnWq3rRD8F1wzIYNkqSZZZShXj//qAziH2/EP35KaeNq3mmPKpUOAFAJ1Zwc/lRJ7zezcyTtIGknM7vT3S/IXMjdb5J0kyQdf/zx3tLSUsUiNpbW1lY1XP2NDyZBa2lp0Y4Tx2trV6LXy7vsNFortm7q9dxxJ52iZ15bp3889rx23HWMLj51rNyl/xs/WW8aOVQvXPZeSdLQqRP05n32VEvL25omJz4AACAASURBVHvtJ9OSqYuluS9Lkt59yB467i1v0pzHg2GrjjjiSLUctXd63e89k9CIYU2a9M0zch7Du9717nQPlqFTJkidnTrkkIPVcvLY0usnQzHxTyZdevQf6cennXqa9M/HdNBBB2ny6kVat62jn7W3O+HEd2r/3UYqkXQd+J1/6OS37qa7L3lnr2W2dSe0pbNHu48a3ruex/ed4K6pyRrvPVyuPO/NTA352a8j1H/t2WHqP6XObenHJ510ksbuvmPB69/62jRp7RodddRRajl8TL/L1mv8f7/gGWndGzr66KN12sG7S5JmdM2TFi7Q2LEHqKXl4IhLmN+wBWul557V6NGj1dJyWp/X567cJE2ZpJEjR1YkNhM3viS9vkQHH3SQTjlpf+nR8f0uX/A+w+/3k085RbuNGq6Fa7ZIE58saNUxY/bScceNlaZO1qhRo9TScnqvbUrSMUcfk45tptZNL0uvL04/HjFihNTRrre85S2avXGF1L5V73jHO/SO/d+Us7yS1LHjXmppOaqw48w2b7U04zl1No/Qn17fUdd9/BiNHDa4lyjDpwa/u04++RTttfMOwZO77CKpiHjlUK+ff1QG8Y834h9LRbdxDd/7YLpoAkAdqdpQXe7+bXff193HSvq4pInZJxSgGMOH9r3zcc6KTX2ea+9KpIe92NrVo3Ovn6zzfhX0qFjfvr0HSdIHHsZjaJjo+I8T99PtnzlRe44enn4t4a7xL61IP167pVNL1+VPOOSa/DWqkS6yd5uqL1P+HjO5pIYn27wtqNdpi9f1WeaiP0zT8VfmHp4LAKqBK9a+6nmkpQHnOBnEY9vWVfmROcotbt5J4PO8UNgcJwPt0/SHyYv0rp89UVAZM6U2PX/1Fj32yio99vKqordR9D7r+P0OAKgNtHEBQOOrZo8ToKISSddFJ79FHz5uP53/68l5l9va1ZNOeHT15L9SdvcBx/k+Zr/gbsTTDw662DZnrPDoy6v08KzlhRa/13wg6cnhI7qSzzc3iZm05I3CJ2h/z7VP6rWrztWmjmDYjWHNfXOzzy7qm0zJJ3veGACohFK/a+PQ2Jqrcb3WJ9AudI6TwdDRnRh4oSK5S999cLbuevb1wtcpI92SHd/Uw2LqzUz64d9fKa0Anv2weh+0Wn9vAwBQirGX9h3NoRIWX33uoGwXAGpVtSeHlyS5e6u7nxfFvtE4kklXU5NpSHP/V73tXYl0A372JOeSdMMTCzT20nFhj5PguU+etL8uP/+IPssesc9OeuWH79M5R+0taXsPFElFJU2k3g0SqcRFVG1y2YmTUhuZUnmOTWGPk4FiAwBRKPW7NgZ5k5yqlTBau6VT/3n7dG1s7zshej5dPUl96/4X+10msxdlJV3+8Cs5J2/vs/8iK9Ddi0qaBCsVst3czxdWL/3voJjeqX23XJnfIAAARIU2LgBoTJEkToBKSLir2UxDBugm0t2TTDfg9yT7Jk6ueXSepFSPk2C5H3/wKF186gE5t5c57nYhiYF8DSa9epwMuJXBVV5jSl/bBuEOXADoj7vrlxPma/XmbX1eq9Rd5VH1CixXIuk655eT9NjLKwdcNspD/P1Tr2nCnFW6+7nCkwYT5qxS24ZgWMx8Yc51TImka/mGwubvynT5Qy/rjmeWpB+/1LZxwHWK7TxZSgjKCVtTU/ZQXRZus/CtlvMZy/5pRmdTAAAAALWAxAnqkrurozuhEcOaew2XlUt3wjWkKexx0s9QXYmkF33HZGq7/Ul6UN7s3i6ZDTmRz3HSZ5iM/IYOkCyatmidZixZL0nq7Cl/7Pd6baiMwiOzV+h9v3gq79BrQCN7YekG/WLCq/raX2YNuGyxH5Fccz7Uky3bevTKik36xr3566YmhiwKy1CN77DrJryqU66eqBdeX1/UerdNXVz0+6fY89gjs1cMvFAOqXrLO9dLnndwIXPDDHQIt05ZPEDp8qvXzxUAAACAxkbiBHWpsycpd2nEsGbtv+tInXvU3vrESfvnXDYzYdGZY6iulETSi244GiiJIAW9XH4/6TUd/N1HtKG9K/18ZsNQpYbq2tad0NbOnqLX6ztMRqrxxTT78vemn//tBe/Q987tO4RZpo/+7mld9chcScEQKuXKnOfkt08u1NhLx9GjJY+v3TNL81ZtVhfVgxjqSQTfFZ09fT8Afb+pmeMkW65jq3YuxcI9psoyZ8UmzVq6oYgNFF7ip+avlSR98DdTC99+ibss9m1z+cPFzxXi7umeGsX+lrGqR7q37MRSNW+YaOTPNAAAAIDykDhBXWoPW4ZHDm3WkOYm3fDJd+i4/d+Uc9nOnmQ6MbF47da82+xOetGNB9nDW+SSSLrum7FMkrRy0/YhZJI57uQcqLFgxpJ1/R7DuddP0pE/eHTAMmXLNyyGmTR6h6Hpx2cctmdBx1z0/vsZl6M7sf21myctkiRtKmBM+Tiq97vigXKk77Yf1Ebgxv90ZTa6V/to73p2Sa/H//rLSfrADVMKXr/Jgt4a67Z29Xp+oMbx/s5BAykkSZF5o8RtUxZp1aa+w8mVyzXwb4i8w3Lm7aGS++9Ky67+gfb1UttGHX/l433iXIpqTkQPAAAAoL6QOEFdSvWqyJxvZNiQ3G/n7sT2xEl/k7gGQ3UVV45CGlved91T6cZ/z9MKUeiwJB+68Wm1/Lw17+sL12xPqlw8fquum/BqQdvtMzl8nuWGD2kecE6ZUnTnmHsmpatXL6HU7bQVL0JDoFoQZ+mvsUJ6ABQ7VFeJ69WjzGOcu3JzVfe9eVtwbi81kbF+a5e+cNfz+q87pvd6fqDG8UQZgS0kUZfa/NJ17br84Vd0yR0zSt5ff/tIVdvKjZ05e2bmO8pKnzuK7zFSXObkxtaFWrulS1MWrC1yPzn2HIPPNAAAAIDSkDhBXUo1CIwY1px+Ll/ipKsnqecWDzyGeSLpRfemyOwNcevFJ+RcZum6Di1b3y5JeuD5Zenne00OP0gX7tdNmF/QcsVMDr+hvTK9PTIbVnoS+Ssg53BfNHSktW3o0HcenK2eRHL7hL7UD2Io1Tg+bdG6ApYtdtuNL1evg8dfWVX9gqhvD4REP4mUzGKnzslt63tP+p4rqZa5Xn/bH0gxw2KlbgTYPGi9JoPjWLulU5+57bmcS/QkkvrlhPm9hvXMN79brjlOJs5dpVun/H/2zjtMiipr42/15EhmSANDziBRMoOigJhzzrvmXXXVxZwVw66ucQ2rrqJ+6poFyTQ5Sg7DDGFgYGCGNDl31/dH9a2ucCt1mhnm/J6Hh+mqW/feulV1q/uce96z37QXTofT7jvrWGk1Hv1hqyzH5zQvHbftoGsgCIIgiKZB7szp9d0FgiCIiEOOE6JRwowPMVH+WzhJEX2ipNbjxVuL7DkQnP4GZ/04f1B7TOrT1rAc6+dHy/3GhvziStkBJOc4CfAX/NuLcjB3+9HADgZPX1y9v2PzBPnvosrgpTG0bdSZSnX5HSfsGLPyTY2//28rvlp7EGv2nfSviq/XHhFE/fPz5sOmsoaBzrXherY8XhFdH52NWWsOWBcOEw3J4aqNEPl63UFHxwual7nVqYU7Gb3/HR++qEkR0kINxqq9J/RlRBG/bMnHGwuz8dq83ajzeHHPVxsNI4t4kTq3frYBz1rkYHEacaItbRQh9PxvO/H1ujws3FUIwPl3NrM2CYIgCIIgCIIgtPAtzQTRwGGrQ5WyUWmpcdyyP2w6bLveGJczX2KtLxoiNsr8uChOJMv0t1ZgfM/W+OK2M/05TgL8Kf+PBWpJrrdtOooYWhuH3A+fVWL+AxPkyI+K6tBkHveqIk6MpbpUjhPf/8GsDj7dYNdKee80JAMoQUQMxX3/1//bDCB0K+PCLdVVVeuBKAIvzt6F60d1CU8jNgnBIv6g0U7xx8uqbR1nmKtDzn/Dx+idsmBnAeo8Xkwb2N6wTTtOF38eM+n/UERK6NsQcf83my3LVfve5VW1HuQUlmH21iPm9Srqt4vTV7ROLtTgeG057SjWerxYnnMMZ/VJs912JBPREwRBEARBEATRuKCIE6JRwowcSodE25R4blm7BhcAiI5yZswY2bUlAODqkZ1Nyxn9Ll+eI+lzhzo5qdaRYoXWGDFzThYAv1EiKS4aLZJiAYCrmw4A43u25m43MkooDStmESQ8qS6KOPHjUshzMVucsRuKIE5fzGYFfQRCYHNIuBJJy0pSDcBp0SDsyJpOLM0+BvfuQsvD2PgZrYEwOjWjNFt/+nwD7vpyo2mbJn5/Xbvs1RWOy2znsmnLWDl9Fu4swH6DyC2zPDSs3nX7T+LVuVnW/XKYHJ4hCMDR4iqUVEnSZ28syMatn23AKhu5T5pS3iKCIAiCIAiCIAKDHCdEo4T9KFfmJEmO1wdQ9e+Qim6tk2zXG2MROaIlvWUicmdOlx0oRlhFSGhXo4aSHfnFlmW03TOL0nl4Sm/dtlZJsbgrszu3PNOczy+qRGFJlbxdaYCsNbE8VSocNcwJYxah0lTxiiLlOCGaNE7klgKW6grzsxUuv4kdh099O22WZh+T/9a+kzYdLMLNn/JzdvD6rU3Yzjt75XHBJIe3k8ie3Zvyd5dwDLbDU1iec9zyft57zO800RYtq6mDEazeKz9Yjffce3ULKA6cKFdtC3T0BUHAqJcXYcoby6R6T0r55E6Uh0ZSlCAIgiAIgiCIpg05TohGCUsmrpTq4slhxUa7ZFkKO0Q7TA5vFzPHAKDQPw9D29PfWgFAct4YRX8YGdV4tp22qfrInitHpBvKlTHHx5iZizHypUXy9qwjfk11M8fSzvwSAEBxZS1O+RLTf7Bsn2H5pobsLFFsI+kRoikSztue95yFknA/s5GaEtbtP4mMGbOxOa/I8bE3fbJO/jvQyB7mkNC+u6xSiwST48SO04UVYe3Um5NK0dXDRZWG523nfmTfw/jNqPcpF0Cs3nsCE19z439/HDJsz6h57WY2jEeKq7RFbUOvS4IgCIIgCIIgjCDHCdEo8dhctRkb5UJljf2cHDznSygwk5a6//82yatrw/kDvvtjc/Dkz9u5+4Jtt6bOazh2RtJeF727Uv671sQAM+OHbfh05X5c/v4qeZvS4NLUkUedpLqIJo6TaSzwiJPwSnWJACpMVvIHW79pGZNCdh0Zi7MkOa2VNqSSzAhUjVHQ/O/HvEI7USOGx9q5JyKR48TmNVK2bHTavM3a0/SKIjo2T+Aez+qNj5F+ZpRV++/pPYXSogmlc80wz5oFWgk+J5C/hCAIgiAIgiAIK8hxQjRKmB651lh/x4Ruqs+x0S6UVtk3QjmV6goFP23Oj1hbs9Yc5G43Mvxo5U60LH9kEqJcAq4Y3slw7LILSrnbldQZCcz7+H37UeQUllnW0xRhj4AIkTTbiSaNE6dGuHKVBArrekWNB/2emmfocA68/sicb6j8AUbdLa6sxSkbMkzMoF5cWYuiihrLOTFSUl1y5EsY1mhYSYIC+vveOOJEv23BzgLdsb3bpXCPZ/Umx0kSqmWK72HcKElNv+xejlCsdWlocwFBEARBEARBEA0HfVIIgmgEMEO71nHy6Hl9kXuiHPN2SD/w46JdctJQOzhNDq/lwxuGYdGuQnyzIS+g48OWeJhjhXh93m6c3bcthnRuYWiksDLupLdMxN6XzgMAZB0t4Za54T/rsPmpc0zrMZP8AMKn+386wIxQXq/ib7IDEU2QRn3bazpfXetFfExUyKsvqarDjZ+sw5tXnYGWSbGqMmbzvZUTPdQYOXoGPzsfAJA7c7piq79vslSXpvx3d46WtitOUnlGdpwORthzWLD/9dGyX6zOxZZDxXj9isEB90Hqh3UZXWSHYcSJfscnK/fjyfP7qo6truM7+Fi9CbHSPVyhiPx1ybm4RF15p+juWYt6Pl25HwkxUbh6ZOeg2yYIgiCIpkbGjNn12r76+x9BEERkoIgTolHCVjPy5KGSfCscLx/WCbHRLkfa1zGu4B6Jc/u3w92T/EnSv7z9TEfH837Abzx4Cv9amBNUv5T1MiPPO0v24JL3JPmrYPTdGWaGNZabxAgzKTPAOkdMU8YfceI3BJIdKPQcDUJDn4gQJje+Uc4LLYUlVZjyxjIcOlXBb8IqcsEr4ua55fh8dS53f3l1HZ74aZtKuggI/6p3Zb+XZR/DrDUHDMvWciIAw92/d5fsUX22+07yekX1+8FAq4tXXZHivWQR9GiKhd/f1z5LDi99Vt6PT/68IyTyk4G8x40cVEbjUaMYa68oYuWeE6b1xkVLjhOlg0WWlFS0of0KYPdMjL53GDkBn/11J2b8sM13rLO2CIIgCIIgCIJoepDjhGiUsN/uvGTuFwzqAAC4bVxXw4TlRgQbcSLV4W/Tac6UjQdO4coPVqNGkdD+0vdW4Y2F2apyN/xnraN6lQaVc95YajsRq1Hv/3vrSLx62SDVNjO5LaNVqQzl+fKPJ8eJMSzKRPQbpMgSFFJ+2nQYo15ehPW5J+u7K4QJoXAAf7/xMHYXlOILA8eClQOBzWUvzt7F3f/pyv2YteYgPly2T11vCJ/ZLXlFjh0z+46VyUbwWz5dzy2zOa8I5/xzKcqrQ5+D5bV5u1Wf7Y7H07/swH1fb9Jtt5NDZN/xcvnv8Et1Sf+zd28wuTmMsBX5ostT4qyN8mr/u9zsWLYvLlr6PlRV63+HM9kudk+OfnkRHvtxm3lH5e2az0EMI70mCYIgCIIgCIKwghwnRKPkjwOnAPCNI5P6tMWeF6ehb/tUxEY7dZwE/0goe8Rz7JixKKsQ6/afxIET5ablluc4S7yrNHB4vaJ+dadDC8LEXm1w5Yh01TYzo43S2MKjrNo8IkWr958QQgmbSBJsnoGth4p029gjIFUtKP4mQgVzmGQdtc7XQ9QfTu57p8+I3fxBv2w5LJU3mPrr/BZ01fZQOH0AoLLGg4veXYm7Zv2h3mEx51/5wRrLul+cvRM5hWXYdrhY3lZYUmUYnRMMdkdD6+BSBpwo50urudeuVBevHjvXjjkJ5IgTW605Y2n2McfHGOc44W8vrVJG6Rift1eOOJG+UykXT7w4Z5evjPT5SHGVbvGE3etv5CBz8jgVV9qXcyUIgiAIgiAIomlBjhOiUfLvpXsBGEd0MAeIlePk4Sm91ceFINMoM+rfNq6r44gTRqgjBn7c5JcB8Yp6Y4lhcngH3e/cMtFwn3b1s5bSKvP9e4+pHUkdmsfb71g9U+fxoqKmDvlFlej66JyAJVl25BfjwndW6rb7L5E/4iTUfpOKmjq8NGdXyBNWNyTmbj+CjBmzVYbBhs6Xaw9g48FT9d2NBkNI73ujBe8Wjfz9e2nlvHKFPfd4zeQaqr4zA/TmPLWT1ar+ihrrKJJanyZVjGKBwciXFmHcK0ucddIGgTqSWCSHIEA1X1rVZrc9noPFVrSKqP4jFEnN7cAWmei64cPI+WF0RhNfc8t/5xQaO5LZkMRyIk7ktk3Gze7l1w1jAON68bv69ypBEARBEARBEARAjhOikWPlmGD62gDQIjFGt7+5ZlucwwgVHi2SYrH2sbPx2Hl9ER1gzhS7evJVtR488dM2y3Ibcv3GE68o6ow/Rq05SQjcPDEWuTOn4/JhnXT76ixylJQoHCfxMeZj1rllYlCJfCPNbf/dgH5PzcOewjIAkuxTIBSWVnO3+xPt+reFOuLkffdefLhsH/o8ORffbcgLbeUNhLcWSTkWDpwI/er5cPHMLzuwcGdBfXejweAkostojrVyFgf7aLHjta+uUEWcsPPS5cwOQfXMGRPDkbT844AUlTV/x9HgG0IQycLl/zWOKYtID7vvFF4xJ1Jd/hwnkfGcXPb+Kt02ZdNGTh8743/rZxsM9/FynFTWeNT5aEJwTxoNI0++jSAIgiAIgiAIwinkOCEaNVaOE2XESTxH3ql5Qqzqc0JsaCSg0lLjEeUSAo44KanUr/7dxFlZ/uOmw5i15qBlfVqpLr3OeXgdEbd/bmxgAYBKxWrn5y4cYFp2eEYL5JoYt3/fdgQPfbfFWQfDCJNPkR0cIY4HYYajoyVVOOZzrgSbEUYURdX9ppRRefh/W4OsPTysyDmOoc8vCDj/AnsGImTPDAmCIFhGpz303RZMeDX0EQENESdPligCj/24DRkzZtsq75fEC+75lXNc6LKXB1Wton7pf61hXjvvaD/byQnCOFFeo9t22fursftoqS46MFACHWd2GlqHgNU7zr7jRF/u9fnZnJJq2HizwyMVcaLrh6b/dQaZ7YP9TsDLcdL3qbm4XOHIMY84MYqEUW93sriDIAiCIAiCIAjCKdH13QGCCIYoC2OPUnrrjPTmOFKsXg3bLEEdceI0mbwVgSabv/KD1cidOV21bcb3+sgSu7V/v9FcqsvQfhFA91ldd0zshg+W7jOv3wdzFN06tisuHdoR0we1R9bREtzxxUYcL1NHWvywUYrY2HaoGAM7NdPVddeXGwEAr18x2Hnnwwi7Fb0BejWMLgV7BJR5B4I1en33xyE88r+teP+6oZg2sH1QdUWKV+dl4WR5DfYUlmFwenPHx7MxC9TZWR+4BGsDc6DScI0Rs6HgvSq+WmvsdNZWFSrfst+xod6utdvvLijFlR+sxu9/HY++7VPt1w9+/Vb9t7rrf9mcL/99y6frde8nADjJcah8ve4gnv11B3Y+OxUuB89WoMPNDOkswo+hdYxooyCDkeqyQ3l1Hd5YkI2RXVuq+qlEm+cjHHy7IQ8Te7WRP9cZnE+w7xDm4GDzKWtnyyHle8r4eKN92m41oumaIAiCIIggsbvgKRTwvusSBNE0oYgTolETZeGYWKCQsWmTEmdZXyiSwysJpRG2TmNxL6uuc2SIYnhEUbcaN9hV1DxaJcVaF/LxzhJJJumx8/ogOsqFpLhoDOvS0lQ67XBRpWW9w19YgGd+2WG7H1p+33YEu0OVEJw5TkQR1360BmNnLg5RtfrEJsFezr3HJKOjWWTP6QYz1PFW3jdUYTiXIIQ9WqwxwZvH1uw7gRv+s1bnsAzUVxy8VJeBlJam5jnbjgAAft/uTPrKH9GirT849h1XR5Lw5Kl40XQvzt6Fqlovym3kUFHVH2TEiZYbP1mn+lylcVLYdYjk23jv8HhzYQ7+tShHljoUBGBPYSkKSqrkMs/8Gvi7yi4LdxXiyZ/97Ridt1GOHrto51Pes2k24vUxr/V4bI7O4UYQBEEQBEEQRNOGHCdEo8Yq4qSixp/Mune7FN3+ds3UzpS2NpwrTghFsnmGdmXogKfnYXnOccf1iKIIUWMTMbIZBdJ7ZjxzCQLeu26oo2O1jqbPbhlhWNbjFfH9H4d0SZAZj/+4DcfLavDZqlxHfVBy15cbMeXNZQEfr4Q5OEQRWLX3hC3HT3FFLarrzBOy8xLCB2Ny8nhF5PqMpB6vF8WV9ZMsvaKmDjd9sg4HNc4bj1cMy8psZqgze2Qb2uJmlw2prqYEbyju+3oTlucc10WuOXUWy6+aIMfbbsRJwPWD1a/N8RFkyIkGriOE0wSTvyyvNp/HtHy7wTxSao9JYnIzWBcDjThZn6uXzLRDtW/OKvNJCboEAZP/uQxnvrRILrNm34mA6g6GO2f9wd1e4dDRpUV2rAmaz8oyJmNu93roSgXxHNV5Rd08QRAEQRAEQRBE04YcJ0SjJjHOPCdJUpxfje7qEZ11+3u09TtT3A9lokPzhNB1DvyIkwROrhU78LTIf92Sr/rc0Ub/PV5OxIlhouTATcUuQcAgjpSWGdr2eqbpnV0f3jAMAFBWXYu/fbcFF7+7Ut6nNM58aSbDI4pYuLPAVlLfUMEMl2YGoW/WH8TWQ35H0ODn5uO6j9aa1itwVvQanZZ7dyEOnDDPQfCvRTmYt0OK1Hp9fjYGPzu/Xlb/LtpViKXZx/DK3CzV9ls+W49eT/zuuL4/DpzE1DeXoaqWb8A1lXliZRy3Gjw5BaXImDEby3OO6fYJqJ+V2Q0V3lAYDc+hU+aOS7s5Fpxi5NjQzkVOHTt1Hi++WJ0rvyecJod3OtOXVdepE30bkORznJRVO3PAWjlHJ/9zWUDRgIdPVaKgpEo3R1qdytLsY1i0qyDgxRVskQdzoPBerUb5RuqDYF+Notpvwq3P7B5n16OoogavzM3SOboYdue//cfL8bsviosgCIIgCIIgCMIu5DhpZMzZdgSr9jqPMjhdSY41T9Pz9jVD5L+VToynL+iHCwZ3UJXNaJ0U2s4BiHbpH7Ex3VthaGfrHAy6JK42kmPY8XPwcpwEmneDR7zPMeQSgJgQS58BUq4aAKjlGJlemZel28Y4942luO7jNQCAnzYfxu2fb8CstQdC3j8jWMSQmZnn799vw4XvrFRt23DAfIUzz6hvZEu6+dP1mPia27S+9ftP6rbZsI8GzaFTFXjht52ydIw/kkZ9Msuy9Q4EOzz32y5kHS1FloGx1Ss7tvT7ImHOXJJViO2KPDWMFXuk+V4pO8gQhNDl3jgd4Ds11PcT476vN3HrsJpDgx1vo4gTqwNyj5ebRn99u+EQnvx5Bz5YttdXvzNXiLb8HAsjc2lVHXo+bu3ATPS9o8tMIk4ClYrkRQMaPd+MyloPznxpkU6iykqq66ZP1uG2/24I2FHJZEWZs4knCRiJHCeRQpakY459Tplth4tx71cbucezcX5h9i68794rO/O1w2/3cpz1D7ec/4zbX5pICYIgCIIgCILgEDHHiSAI8YIgrBMEYYsgCDsEQXg2Um2fTtz95UZca7EC/XTit635WMcx5DKscnx0M3CG3DK2q+xUWf7IJCx/ZFLgnTSBF3GSd6pCXnVqhvZ3fEGJtYSELceJV9SvbjaKOLGuTkeiz3FSXecNSY4XrdwZy0PDW4H67fo8w3qyC8qwco8khcLG8rDFqvNQwgxzgRjelDr4Sj5cthcbck/66vVvFwH8vPkwlgbgZOBds0VZeqN9EwATEgAAIABJREFUqJnx/TZ8vGI/NudJjiKltBkPpZHx4IkKbD2kdzooYfdRUUUNlmQV6vaz6+PxioZRKQIkCZvnf9spS7pkHS1BflElDp2q4CbHtsstn63H+W+v0G2v9PWFSR4pcbkEiKKIB7/ZjL99uyXgtq1Ymn0MGTNmyxJuDRWzR4tnqHaGsQGYwXNsdH10Nh79Yav82Z+DRBNxoosCVJP5uhvnv73cspd7j0nXyCo5vPaztvzdX240TcBZWqWXcuKNTSKLOOGUZ9SH3JzWUM7G/53FOXjN54A/wZFtCtRxwuafNfuk+Zp3O9qJ4GksaJS6uI6JvJOV+G0r30HH5mP2Xclo4Yjdq2EVjXb/N5tt1kQQBEEQasjORRAEcXoTyYiTagBniaI4GMAZAKYKgjAqgu03KIoqatDr8d+xem9gmtbv+pJph5q3FclLGwL3frUJV36wGiVVtfh582HHxzPHiplkVHrLRKS3TAy4j2bwcpxkF5RhYEdrCatADDR5J60dAV5R1BmqQrnYUta0r/GEJMdLSrw6qogZ9lfs0T87dhP8sm6Zlbdbl11qfEaxQKo986VF3BXkL83JQn6x5FQpr/YbJr0i8Nf/24ybNAmRzTheVo3dR0u5zsgDEUgSz8anulYtZWN0byqN1J+s3C//vSz7GD5evg+AJM9VWCqND7sXb/50PW75bD0n54X0/0fL96HPk3Pl47R8tfYg/rNiP75cI0nBTX1zOcbMXIxxryzB2JmL7Z6uIcdKqzH65UX4cZOU46HSl6cpMUYfXcdynPyw6TC+32ieEyIYfvTVvfFgYPkdIgU33kRjwLXLR8v3c7cb3Y+r9hzH4Gfnc8t/vc7/TmWHe0URGTNm4x/zd3Pr5bVjNr+3TIoFABwvle5rAcBr87KQdbTE1675xON0fI4U6/vC63NiHIs4MY6WqQ+5Oa1cJZvvX5+fjXeXSFE7w15YqDsu0NeCNh8b75xrTivHiTrSy+k1tp3jJET3zs+b860LEQRBEAQfsnMRBEGcxpjrHIUQUfp1U+b7GOP712Rj43fkl6DG48Xbi3MwunsreXtxZS1iogRZ3sKI1+btxj2TeoS8X/9YkA0AuGJ4esjrDoZHvtuKuTuOqrbdNLqLrWMX/20i2oQ46btdmDyHlmcu7I8WSbF4373X8NhwrcL1iKJeqsvA+BDIIm3m6OjWOkmODgmGlPgYnKrwG91ifGO6cJc/CkIURQgOEmWz1eda45mSO77gJ821Q0FJFW76ZB3+ceVgeRtbORuoocfquD2FZfLfgdw7k/+5FEUVtZjUu41l2Ss/WI23rh6Cds3inTdkALuuJb6V6ezW0z73TJ6quLJGfq6V9ymbw24f3w2Xvb8abVPisO7xyYjWPIvMIcFghlMmUbT/WDnapujPjznu8ov0huNKg0gVJ7w6NwtHiqvw2aoD8Hj9DqKEWOlZOlpchVEvL8L/7hwNl2D87BZX1IZc1pGN84myavxrUQ6emN4PsdHGz/jc7UcwqFPzkOeOMoL3jBjlFDFCGQly1utu/HVyT1x0RkdFffzxXmMSGcnrI4su+Gj5Pvzt3N6ciBNnDzF7ftjK/MoaD95dshefrz6Abc9MsXSOO03e/s5i/eKN6/+jj4b15zgxk+py1HRI0CZ5t2uoD1iqS+OQ5slyNaQcJ8Hij6yScPpO0kbFbs4rwkPfbcGgTmqZU+Xl2JJX5Oi5CToIjSAIgiBAdq7TFbPI68ZG7szp9d0FgmjURMxxAgCCIEQB+ANADwDviqKo+5UtCMKfAfwZANLS0uB2uyPZxYiQddKD4mrpXXr0+Cn8OHcxck55MbJ9NG6eW46kGODds63zbbjdbjy0tAIdkl14cJjewFdWVhbw+DW0cc8+pJfWyc8/DLfbnmFQmyY8UudXXaf/zjQ1IxprVi5HVJGxdAkAuJcuRayB4yUY6jxerFy12t+O2419xXyjVlZWFtwlzqKbMjwi7j4jDs2KsrFqRbajY3nXRaz1r/x/ZXwCVq3Qy9U8/+VCTOgUg6oa/pgq6337u4U4Wi5dl4N5h+B28+WsFu7yyxI5vV9+3VuDrKO1mP6WX3ppy7YdAICSEr8Gv9vtRmWdiNX5dRjY2i/F9Pr/LUT7ZLVB+o/NW1WftX0qr/Ab8isqK8FMVry+87YV+ZxTp05aG4DX7T+Jez5x474hoXOcHDsh9X/j1u34ffVWpMb57/3FS5bIzq7EaKC8Fli8ch0OtZDG7NAhvaQOO8fC0mq43W4UF6kjSFauXoN2Sf4xrqqW6oiBF1UAVm/YhMqD0isyP1/al52dDV9ADA7lH4HbrR8rt9sd1Nz73R9SdMeWvCJsySuSt2/etRdubx7+KJDu8ef+tw61tV4cPuxfKa1s8/UNVdh+3MPdp4UZOT0iP0ruaIE0dhu37UKL4j34aGs1VubXIb78KMZ0MP4acefccjSPE/DmpPBE9GnZflgf1VBbI8mneTzG8+0HPyxC6wQBrRJc2LPfX8e+4+V48JvNaFaUg2PHpTHI3p0Nd6U+GmXPPr1Mm3LM2d8H86R76UBurq9fXrjdbhwpUxvS2XXNPXAAbvcRXT1athVK51daJs1bXq/0ubK6Dm63G0fL1fXn5ubC7fbfO06jHQqLykz379+3D27hEEpOSue7afsutC7lv0tqDBwGf/1oPprHCSiqDr3t4ZeV21SfP1u4Ca//4m/HaJy379gZUHuFBWoH8PFTemnBUDheGwo3fbgcM0YmoKBQuv579hovEuGxL1e67wt9c8+nK3MBAH9ocn5t2ep/L1707koMS/O/R83mPLfbjZoavrRiUVERNgfxHTGY+Z9o/ND1b9rQ9W+6WNm5lDauqFTrRWoEQRBEwyGijhNRFD0AzhAEoTmAHwVBGCCK4nZNmQ8BfAgAw4cPFzMzMyPZxYhws8J7HZuQhHd3ithTWIn7LpsEzJ2L8lpgwoSJOsmcoooaYO4C+XNmZiaOz52N45Ue8MbJ7XZzt5syd7ZcN4+qWg8EAYiL1uvth4riilokxEZJK5l9/WnWLBUoLlKV65KejszMfs4qtzi/UFNT5wUWqhPoDu/fE5nju8GbVQBs3mB47PjxEyTZq7mhXe1Q5wU+yYkBIBmqMzMz0ezgKWD1Kl3Zvn37IHNIJ8dtnOv7v9bjBRZYJxBm8K5Ly+0rkFcqGZkmjhuDtilxwPw5qjIxrTohM7Mv6gzGKjMzUx7H3w7F4tozOwNZO9ChQ0dkZg6Qy2UdLcHUN5fj13vHAVihPt4B2a69QI46UX3XHr2A7duRlJwMlJTI9T74zWb8sFMtQ/fOZr0jIKNHH2CTP49Fh77DgLn+5MhRMbFAlXRcfHwCgCp9331jUNOmD87t307dgG9ft07tsPmYtSxe69atMXHiMAD61fxl1XV4fd5uzJjWB/Ex1nPFxoOnkD1Xuv+E5h3x89Z9qv0TJkxEWXUdmifGouW6xSg/WYmoNt1w82878cnNw5FeegI4oDZmK695ZmYmPs9dDxz3O2CHDh+BXmkp8ufoFQuA6hqkJiWgtKgSnXv0QeZQ6d6ff2obkHcQyW3TkZoQA2RlIa1dGjIzz9A9n2PGTcCqFcsCnnuNmLO/FrdNHYGRHeuATesQk5SK+JoKtGvfFjiU5z9nH89ucAPwO/+M+vOfFfvx/G87kRgbhYoaD7Y8fS6aJcSoyvxwZBNwJB9f7KzBQ5dnIq1gB5B/GL1690HmMP78IIoiMHcOiqrFkM23x0qr0SwhxjDKpWD9QWCb2iAeHRMD1NYiOioaqOM7T15eJz0rv/91PHq4jgO7d8n7BEFAcfOe2HJMyoHQs1cvZI7SRzsuLd0B7M9VbdPegwAw68AGAAXo1rUrsCcbLpcLmZmZ2FNYCqzwP88dOnQA8g6iS5cuyMzsbfnu8mYVABs3ID4hESgvR4zvvOtE6Zi9x8qA5Uvl8l0yMpCZ2ctfgcP3TI0YBcDYGdW1WzdkZvbAvJNbsTI/D+kZ3ZA5sTu3bGWNB1gwV7f9573G8l7Bkta+A3DQv6TCnac+F+W1U9KnT19gi/N8GJ06dgAO+duLS0gCis0T2TM+uXk4Kmo8uPerTY7brS8Ol4l4e4cLAzq0A/IPo2vXbkD2btvHd0rvjMzMPvj+yCbgqLGMVv8BA4GN/u9RLVu1BgqkaFTeu4+RmZmJmBULgWr9u7Z58+ZBzVkBffcmThvo+jdt6Po3XazsXEobV1z7nhSNQhAE0YiIZI4TGVEUiwAsATC1PtpvSFTUeGTJF6Vs0OM/qY0/+46V4YznFqi2BZOIOFD6PDkXvZ+Yy03MHSoGPzcff/pc7VDgxV2EIvF4uOGt3mZG5s4tzaOKwqn7vutIiaYtfjltAmOnhCLHifI6R7kEbg6O3OPleGOBcXSLMjmzRxTlOrRSXQt3SgaX37fzE9baJdqln1qZVJd2rI/bfI7LNdE0576xTPVZmZNFJ/sjinh9nt9o9WcTGbJUjdHcCAECuj46h5tU970le/DZqlzMWnPAsp53Fufg0vf8Truyar0xdu6OozjjuQVYt/8k0ltI0QssF9P3Gw/bklyxksphnzv78h1tO6xfEf6eey8On/JF9hg8M3sKy/CfbdVw79ZHyb2zOAeb84o4R9ljZ36xLDEWG+Xy5TgJLhfAN+slY26Fr15eQmwlpypq5OfH6xXx4LebkfnaEl055X3+wm/8VfoLdhbgaDE/l4wWURQx4sWFuP8bY+NxLSdyQdT8b4ZRLp9fFPkPjOqp5kgv8VBKDCrR5TgxOL64ohb3fb1JWkjBweh+0G4+XlaNOo8XL8/ZhWOl5tech/3oCOleeXfxHqzdx8/pVh85TgKVxfqnyXvGDO278IjN+x4A2jdLQPOE2IDarU+yC8rkL29a6S0r7EpaanORhTo3GUEQBEE4gexcBEEQpx8Rc5wIgtDG54GHIAgJAM4BkGV+1OmP8kfe24tz5L+/26BO9Lv/eDm0XPexXk+cx6nyGsdJ6PceM5fheGNhYMYDQJKg0SZm1rI0Wy2f5OJYRnkG9IYGr49sS4+2yabHRtKYFK62BEFArzTz87RC6YSIMZAum7ejAP9alMPdB6iTM3u8opyoV2vMYU4ts9H4ePk+TH1zGQY9Mw/T39LLhhn185MVUkSE1iBk9y62MvTVKpyZ2pIllXV4Z4k9yTW7BmD2SPKS6rJ5TWnILq+uw+yteofU6/PVc0k5x3Gyco80f135wWok+CJYrOYQ5hBgaA2XtRrnL+sry4Wi3c9gSeONroZXFLH8cB1u/nS9bt/r87Nx8bsrTfttRo1HlA3WMdEuCDB2emrvM6NyVtGDh05V4Jct6mvMHAk/bDqEHzYeRi7H4aCcUz5eoZe2EkURf/p8Ay57Xx/pxoP1f862o4ZljK4ZwM8pocUl8PMeqIaOM1dW13nkpOzqosbPLGvHnyxeeyz/uC/W5OLXLfn4aPk+bnljw7F6+1drD+L37UfxwbJ9ePqX7QbHGGPHQP3dhjx8vU56Dkur63DVh2tUuYXyTlagps5bL46TWm9giz8OnuQ716yI0jjTWe4iOwiCdG82RtjiC6f+DLsOEO0zVkeOE4IgCCLCkJ2LIAji9CaSESftASwRBGErgPUAFoii+FsE268XBjw9Dxe8vcJwv/JH3wdL/YaQhFi1MYtnzNFGDRhx4yfrcM1Ha7hRImv2ncDOfH09VitQs47Yk5jQIooiLnp3Jc5/a4XlisIvVufKf3MdJ43UkKDsd6sk41Wkkfz9b7QaNBTJU7XJXJ2ijTgJFo9XBMtZH8jq1Bdm70LW0VKUVNVhB+fZAYCYKP3UepiTTBywfx9bGdqU51JYoXHOcGZ6o3M/dMqeYXDrIX1Ehr9B6T8RIk6V12DY8wtw/tsrcM9XG7nzjRJeEmmPwsi51RcJonTw8Ibw799rJJs010RrYGNGd/Z/ZY0Xf/t2C/73xyF8tdbvhGHjZmSg40XMaCkosb/aXNtHf8SJAEEQVAb2Z3/dIedF0fauzsBQHB+jHhftcbPWqB1QJytq5HwYa/YZ58OxMoaz3ey5OHSqAu+79xq+F+wY13mOE3aYnRweRgnklW3zenHTJ+swf6c+ksTW9CKy/7SF+Qez+1jrCGJdNBom3nbmBDBzRgWK1yvqFoAAQN+nJEmu0qpajH91CR79YVtE33WMSPtqgn51NdLvO+yRcuoc00aD2i3nJLLFyvlOEARBEDZpknYugiCIpkLEHCeiKG4VRXGIKIqDRFEcIIric5Fquz4pq67DtsPFyDNYpegV+b+HtduMDDp2YJIzeacqdUapqz9cg/PeWo57vtqI1+b5F0ZY/WYNdFUfk4M5WlKFro/OwUtzdhmWffLnHfLfPKNXVCis+hHg4Sm9VZ+V19Is/4NdqYpg8XhFbApCPsiKJ6f3wy1jMzD/gQkBHR+tiN4wym3gBI9XlK8BS8TNkFeBGwx9dZ09eRqe40TZvhKeUxDQn6tZRA2gfiZn7VLL+PBaMJLaWZ5z3LQdBs8RNGvNAWw7VCyvMhZF4G/fbcGJ8ho5aq6y1tyxwJMy+lZhgGXnIhvJbT4m2iCgtzTjycaPGaR/3ZKP7zcewkPfbVGVk6NpDCIYrv5wDXe78nk+86VF9jqtobbOK8+hsdEuuFzqej9dmYuLDCJajBbY8yJOPli6V3Zw/XupOqnzShv3xy2frsMCjiMBkPqbMWM2Xv5dPfff+tl6vDI3C/kGEkZWTs7y6jq8NEe/uM/JPFpWzXdOKqvgVWfkQFL2+dCpCimPibZu3w2svT5G3Y6L5jtOmGGa/W9H+studFkgeETR1FjO2l6yu9D2MxxKIh3lEoxzqFfbFMP3BI/U+ODSFz5+Xt+gjlfC5nOn480cIFZnHahUV05BYIt/CIIgCEJLU7VzEQRBNBUimhy+qaGUihn/6hLkzpyuKyNC5DpFtNuc/GhWsl2h0z/pdTeemN4Xt4/vpiunldCxMjYZrV62QpuX5cNl+/CYjR/pvLwAgUh1fXTjcCm5eAS5Z1IPvKbIL6G8lMlx/kdwaOfm2HjQf56RWoU7a80BzPw9fNHEzRJj8PQF/bkSTHZQSizFmjgk7OLxiob3t2zw11jyiitr0SwhBl+s1ufsEEX9Mxxj4uDJKVTL4Bk92h2bJ3Al+owwMxjx9lTU1Knuv1DwxE+S5M9dmf4k0Iuz9Lk+goE5fJSGYzvTo3YONXIQMckuozmuRhOZYpdQaO/Xerx+qS6THCcDnp6ni3yRzkfvJInT3Ku1Hi9e/j0Lby/eg+3PTtGVP2oQLfPx8n24fXw31Hm8WLL7GJbsVsstZh0tQZ92qbLB/KPlfvmuf87fLUc5LskqxKtzs7ByxllIiffn27EyvP5jfuDykYwHvtmCsT1a6barIk5EEVW1HizJKsS0ge1N61Ne83GvLDEtq89LxC/HnLI1Guk+1pTRfcarz64jOBC8Xn0MjRL2PHq85g6WcGFHui2UBHOOLpezbGOvXDYI2/OL8e6SvdaFOVx4Rge8aLKoxQlFFZIz0uliG7vFteOqvP8f+GYz3rjqDO5xJVX2pdIIgiAIgiAaMxkzZtd3Fxo1PDsu0bSol+TwTQVlxIQRZj8OC0qqZG15qx/NSt3wNxdmY9UeySiold1ZY5CcVYvVb9ZAE6tqE1wnaiTJnMgsBBJxck6/NAxOD046KhAGdEyV/1b2+qMbh8t/90pLUR0TKWOSmVxFMJFOWgKNFlFqw4eiP0dLqrDLQGqOVV9dq1nNzaIROMbyO2f9gad/dp4jQNEqd6s2J4cVRoapmz9dh5Nl+kTSyjnjji82OGrLivfdksHup02HQ1ov4JfCUp4u777Q5pnhlSnlGM+YQdVoKmLjZiX9dMHgDqrP2uvDog+GPr8A93y10bQuuW8eyWgPSPOf5DjRl+PJhWn9QGv2ncAPGw+pIroAcO8VJUYOoxdmS4bWKgOD9NQ3l6v6pry931q8B6d8BtZPV+5HSVWdLs+VlePJSP6spMqZw3atJnpEENROh2d+3YlX5mbhri83WuYOM1tgYCmtBbWzhsHeC/uPlyFjxmxkyyvn1REnZvUxlPKgocYjGjuolXjryXESzmgbHsE6TrULRW4ek2FYtrC0OuDFNkB4VMGcnr9HFHHPlxt1+ZW0aB+xOIX04I++9w/PQfi/P/QycgRBEARBEARBEFrIcRJG7KxINsolIgjA5f9ehb98vQk1dV7LH8Gb8k7Jf7+5MAfX+hLH3zlLbZCz++PVypCxdr9kXPp2fR4yZsy2rRWtbb+ixoOMGbNxyheJYlfXGmgcyeEZP949FpcN7SR9UFzLzq0SAQBXDU/H0C4tVMdEypjUItE4z0ooceoIYFx3ZucQ9wT4bFUudzvr4fwdas1/diV4zrp5OwrwX00kihMHoNGwhOrqu3cfw6w1+kiZCoXjZN4OvqySXYxkmbTRNRLBPbfax2L/8XJ8uExvAO7ZVu2I5I1z5mtu3TYrhwgbN6v5PUqQ5js252nnvkf+txWAFIWnjfgzokYh1eURRQiC/XmizutV9eHqD9fgwW+3QLuWfZkvEscoGslqpX6VgQQcIK3yrvDlsNHmnGH0aS85mfcdU0db1WfOZ63T4ahPTuxUhbmTyex9q5XK015HZc4UZT1P+RZkMHkwdu8EEnESTryi8Rz28pxdcvSpJOkVuX4xrO7jUESIqeoL4AK8cPEArJxxFgD9/DWuR2vD446WVAW1yKA2DBfE6WKb8uo6zN7mnxeNTkc7rt3bJOvK3PvVJt227zeG3qlPEARBEARBEMTpBzlOwoj2d3JVrQcHTuild4xWpeadlHIIlFbVoqLGfNWsSxBw9Yer8fnqXHnbmnzOqmObv11FESiuqMUfBySHzPbDxaoV6gCw+2gpPlgmrSw/aJDDRde+ga3igndWYEtekSNjRaRXjAZDTJRLjrjQ/v7PnTkdr1w+SOccc2pnCdQxYXZYKF1TPEOOnS5P6tM2hL0wh3VRm2eBGTXtruK1cx+zc6+vVD0VNaGT6fnT5xuwI98kWXwA2B2XnUf4yeY7tkhQfeZduxPlNXjmF3VkoF3HQK2FIfCnzfno/tgcjHhxIQBOEuMAbJPHy6plg3udV4RLkxzejH8v3Yvuj81BcaU6ykY7LCd8TvAhnfmReVaOJe17QsmgZ+bjy7WSE89onBN8eZ+00YlKZ+S7S/YY5g0LB9ox/n275Fj9aPk+fLchz/A4M3mi93xRWayEtg0mcQSY3yvsvmbHs7LK63qqvAalDiNvgsXrFQ3vzQ+W7cOzv0rPnZl0YjjZdPCU6f6Zv4dGqorhxJnO6Ns+BR2bs3lM/aBGaZM2Kbh5TEZQOeDCIWPmcSjvWlhibzEOc0AzeN+needTH/ccQRAEQRAEQRCND3KcRJAHvtmMiZwVzlZG1mEvLMSfv/jDtIwAaQXqUwp5sH9v1f/wVK5qrTMxgIkAbvp0HS57fxVOltfg/LdX4MFvN6vKTHlzGfb6VgXzjJK7j5YiY8Zs/F3xw/Z4Of/H8KFTlbhr1h+OoizW7bcnO9ZwMDe+a50ITiNOogJ0nDzz607DfeE26q/4+1ny39/eMTq8jRlgV04GsI5yOnSqAgOenocth/Q5ebSw62WkXh9Kww6vpnxOcvdgsGuYVd5TgRgT7aKNCDG6dp+tylWNtaVjoNZexAnjZHkNvll/EB6No0UrU2iHX7bk46u1Uu4sj0eEAPvzxJe+4wY/O1+lc7vhgNqAzBxqzRNjwEMrY6dEFEXLvBksWrFbmyTufvZeUo7X2n0nMOT5BfLn1+btxiXvrURxRejzFPCcHUZjvOlgER7WGG5Vx9m4vz1eEXd/+QdOGkSvvL14j6EMGeB/b8jJ4TltDnl+AW78ZK1lX0KJxyLHCYs48dZTxEm5heNYmYMnFFg5Wvn45yzt9GW2UCIhNkpX/pqR6bZavHlMBjJ8kbChxGmOk9JqjYPX5nHa79PzdxzVyf4BQGo8f34jCIIgCIIgCIJQQo6TCMJWqWrh/aB0aq+eOddecm+l/efv328zLOcVRTkhe4lvhbJR/wF+f897S9K0/8a3IrfO48Utn643adOZPEageVbqC7YQ0sgZoXWoODUmBaNpXl8ojT8ju7aslz4o7zkjJwZ7Rp//zdjJ5PWKWJp9DGXVdfick0ReS61HlGT46mkWvu9rvXxJMAQiLfekSW6YYP1Gbk1ycjOf1/bD/qgVbUSGlpJKyUHkxBD69++3YXeBOq+ONim7U2o8XnhF0XY/jK6PVi6y1JeD5Ot1edzcN2aRfh6viPJqc4N0n3aShFovjZQag+UfUjqweM/T8bIanPPGUtO2QkGtxzhywgq7xuI5247ikxXGhvrth42juZhDkLXEIpv0Ea8RzukhiqYP8Y586ZmTHCyN610eCHYdrUqUr3Tt+91soYRLEHSO4mkD2ttqc8a0PiHNbcZwKn2mzbVk93Dt90KjRUdn1EOuO4IgCIIgCIIgGh/kOGkA8GwLTn+4bjpovcIdAPYocg58v9E4OaZy1arVCmyA7wzQ5TMx0b4HfCtPbdoWOrVIwOtXDLZXuIHQp71kKOzckr+aUzuGTlfjh8NvYuRICJQ3rhqMH+8eI382ynOgpYVm9XuPtnod80BRGjcNddS9opzM24j3l+5FWkq8o7Y/W7XfcIxDrbFvxBec/CeBYPfZ9XhFOaE1i4IIN79tzTd1LF7wzgr5byuZGmXEiZNntEwTkVPnFS2dNGYszirE3mPlWLjLXn4au10tVySW5+W+MYsoqfOK2Gpi5Af8jhkjQzJzMKmug8GlKzTIERYKlE7dQB0nvGc4LTWOW3a5L7cMtx6TDrDbmkVNsYihYO6tUOC1iDiRy4nAzny+3F5Dx0nUWLDyV9rpK8bk3RklCLryTiNS75/c03ZZO04Iu99RGSfKzfMHGWH3vWnney1BEATjarYwAAAgAElEQVRBEARBEAQ5TpoYR0uqcLK8xjSBLwD89f/8slxm0iwMO9EOVkZGr2g/gepD5/ZGRmu+1EtD5eYxGfjl3rEYa5DUNdgcJ+z4aQPaKXTRGxaXDOmEIZ1byJ/tGp6W//0sbHryHPnz93eNwfMX9Q9Jn2atOYCDJ6R8CUYOy6paDyb/c5lpPav3nnC8blprTFcSmLQLH969dMmQjjh4ogJP/mQc9eEEu8/u24v34Nw3luGJn4wj3kLNvV9tkp01oaLW43WU8LlK43BYnnMcg5+dH9I+mWFX+s3sngTMDcBeUURplbnBnkm6WRkule3URyyd0jAdaESEU3kiI5zkOGkoeET7kTq3/Vcf2aSkTYre2fTSJQMD6VZIMcoDxCNYQ72TiBPea0x7/M7npiB35nTDY++f3Mv2AoX+HVIty2gj7qwINIeddp41IpAIIIIgCIIgCIIgmh7R9d2B05Vg8xOE01A09PkFGNO9lWmZMsWq4xpPaJJIW60EFEXRttxPI1SlgiAIGNTJ2NCil+oKLOJEEIAYk8SxgdQZLuJjonB3ZndMHdDOtFxyXDSgsJ01S4hBWqqz6A4jXpi9Cy/M3oWtz5yLWQbRF3YSqW88eAoXDu7gqO13luwxNIoGIn1lxCcr9TJAO/NLMOG1JSFr4+ZP19kqt9mXlHnWmshEmzDsXEMj0lLj8NT5/XHPVxvlbQdOVDhyxjCJr/rCrg3fyvBoZtB8eU4WWiTFmh5f5nOsWK3Ar1YYNsMhHWSF0lmyPtc8kbgRvITYgTzWZt8nWBLwUM4XocDjDdzhpGVk15aYvfWIaps2CrE+cCKPaeWQ5KGsXduUWY4TXr+0xRNjrb/+a++77m2S5Lx2DZU524wlZZWEcmECQRAEQRAEcfqizA9KqOEtxDodoYiTMBGqlabhYtVe+4nVA9FG1+rmA9aGO0mqy964NcZ8HlYEmxy+WYJkSBIgIDbI/AmMcI3y8C4tJGcIgEem9jF1KBnhVHrEikHPzMf+43yj0L8W5VgeX1HjwSPfGyeK5mF2u4fbsLPnWJl1IQfYvV071FM0VDBG3LP6pOG8ge10xsrpb60wOEKPWX4cLR/fONx2WbvYlbCxijA0c3h8seYA9lrcV8yBZbXiu74jTkIB73tAIHeh2bvAL9UVQMVhxOu1L71pRQxnrq8PZ5oWJ99D1uWeNN1v5XTXSjqatc17Nyq3DezYzHY7Sh6e0oe7vVVSLNpyooIaMnUUcUIQBEEQBEEQhA3IcRImgk1cHqi+cziotLFSW2vY4cnwWDkCTlXU4jfNqlIjTkfHifaULnlvFS5/f5WtY28Zm4Gp/dvJ9fRuZy2dUZ98c8dobH7qHOuCJmiT34aTxVmFEWuLwVutHtr668fS2r2NJP+S2btNRNu1MizfP7knuhrI/8XHuCAIApoHscq90kIeUcm4nnw5v0hgJSlkJaFjGUni2+9EqsvOox7qBONRIXjH7C3UO2IDcXAUlhjncpm/owBVtZ6GF3Eihu6KRLn0X1UjOP0bEso+/LIl37wtzRCYfQfi9Uv5vvz2jtGGx5rd9rzI0FHdWuLes3pi1YyzjA9sgNRQxAlBEARBEARBEDYgx0mYqAuz0TOS2DH4ebwico+Xy/r22qTCZdV1mLPN2inynM1V2aE2kjUEtMleiytrseGAPYmYa0Z2lhOtC4KAdr4ExI9O468QtUu4/FNRLsF2YnjDOnydS4ixn6C3MaFcrT4io4VJycYFM5i7dx+LaLtWjqL7J/dCSrwUBTW6m1rKkEXCNU80l6EKBcsenoT4erynqy3me6v8WJaOE9/xVuVUESeN1FGulHbz4/zd9fLvWYb71uWexFUfrml4ESeiGLRkKYMnPdkQ7olwL+BQnqMu4sTk9SkIgu5+UL4vE0xyizk9o6tHdEZstCvo97mStNTwR69QxAlBEARBEARBEHYgx0mYCDbipCFhx3HiFUVkvu7GFf9ejcLSKt3+x3/chmd/tS9Vw2NwJ7+8hNbJcDqQmhD4anaXAFmeS4DfwHuqwjxRc2OGSY+cke6X+Xpiet+I9mFAx9BG9qTE+XXnlbJ10S4XhnUxd54kmRjDgqVjCOW16stglXXUOh9JfLQ0hj3T1EmRc09UAAhvXoXsF6Zh/gMT0LlVIgCgc8tEeV/r5MjJ4FhFglhFQ1q9L1jEiZUUnbIf9WEi94TJExGOarfkFeHXreYRC6HghYsH2C7r9Yoor3Ge1+P+yT3x233jVNuiFY6Tib2kSDWXEJrnYnzP1nj50sASzUfSeaOMIrlwcAfHEVHMyRPqLgdan9Y5rSTazCsUIig5PEEQBEEQBEEQdjj9rM8NhNrTKOLEaoUxICWCBSTj5MgXF+n2HzpVGXQ/RnZtiXE9JAmb+lyRHS4yWvFlguwhINZnXPJ4RVkGaYJDyZ9nL+yPDU9MVhjK639VrxHMEKQ0cN4ytiteuWwgEsPoRFDy5PR+hvuaBeAIG6DQni9XSOS5XNZXIsNAZioUeEURfduHxklkZDB/5TJj4+XTFxiPcyhgxj8mxRWrccyeqpCcBSyh8hXDOoW0/SuGdUJstAu90lLkbXPvH4/ZfxmHib3aYNGDE3V9ChfB5tYprzY3lstSXTYjTtbtP4kfNh0Oqk+BEK5XeLiWVGw9VBymmv1cNSIdFw7uYOv+/2lzPvJOOn/v905LUc2DAN+QLgj65OWB8MVtZ2JKf70ElR0iuX5D66Rx6rRhQ2gVJRNKZ9DlJvfJ3ZO6G+6L5kQYhRpKDk8QBEEQBEEQhB3IcRImGnrEyciuLW2XDSTHiZZQ6MW7BEF24pyO8kxtgkiuKigiTmo8XvTv0AyfTknEmB6tcceEbrbrmdK/HVonx6FD83i53oZK97aSo0BpxItyCbhqROeIONb+enZPxJm0Ex/jfHpt1yyeu90lCJbXIpRSKQBU+TyuHJ6uS4weKEYRDVeN6Mzd/uplg3DzmAzcObE7zu2XFpI+aGHTF4vyiIlWj+W0Ae0BQB6DAR2b4arh6bp63r5mSEDt926XotuWGBuN/h2a4b+3jkSzxBjEBXA/1Qf7junzeigp8zlWDheZG9WZ4+SBbzaHpmMOCZfc5skw5S+z854OFpcg4K1rhuDes3qErQ3ePKece9ifXq/19w67tEwKTIIv7FJdyr8F9d9W07HWqSRHnDhoM1iuO5M/pwPmCepD9a4xwyzipK6e8n8RBEEQBEEQBNHwiLYuQgRCfSVetosTmQKrFcRm9Q3p3Bw78ksCzknyjysGY9eREny8Yj+S46IxoVcbbDhwCp1ahE46qCFxx4Ru+GDZPsfHCfDLlzEpJLZy9NHz+tquk9krwm0QCgVtU+KRO3M6AOCsPm1RpVjB/t51Q/HB0r0Y1qUFXp+fHZb2B3VqZuocyezVFmXVdZhtI7cPw6g+QRB0GvdalMam2Cjgkal98cLsXbbbBqTnddPBIgzomContv7tvnHo3yEV7uzQ5CQxm3t+umcsauq8qKipw82frgcgJUoXBAEzpvXBy79L5zNjWh+8vShHFZUDANeMTMfX6/IC7lu6z3FSpJC4W/pwpuxQYTJUrZJjuauiowI0+Nk5Li46CqWQ5uKhnZtj48GigNoKN3akHe2wu6AUFTV1skM40jTwV7gOK4k1J1w/qjNmrTmo287u0i5BRUeawxv3jor3PXMQ85LPJ8VG6eaEcBLJ96S2Lau2tcZ/eY5x0GU7j4CZ70obOaTEZTLnRUKKVfseapYQg+LK01falCAIgiAIgiBCTcaM2fXdBRlmGwwHjWMJKxFyqmrtG1mOluhzlmipNqgvMTYKNXXegB1JfdqnyPkG+rRPxb2TemDd42ejQwhzLjQkHj0vsBwdLkGQDYzBrJZkxgy7q1MbCq2S41R5OEZ1a4VPbxmpizx5+oJ+eO+6obbrnf/ABMN9gzo1R1y0ccRJy+RYvHr5IPlztzbWxkaj+lwCLC+G0vjuQmCrsR/33X+vXDZIjuhJS42HIAgIlXqKmePkjPTmGNm1JSb2aoMZ0/rgrWuGqJ51lvclShBwBSfiI9hIIxbxU6CY89o1i5edkLeO7QpAvVr6vIHt8M2fR+HrP40K2HFix1DInGrvXTcUz11knmvigsEdAupHQ+PZX3ZyE4Mr+WLNAWTMmB2RiIumAssdktEqUbU9En4C7bz1r6vPwIWK+5k5iOs8oioPFABcP6pLwO0G8uxajce3d4zGwgcnoA8nosxOXcpt2u5Z9Vf7nct2xInDYTBbFGPm3NHuY3MrYH1uoZCNVEaFD+/SAmf1aRt0nQRBEARBEARBnH6Q4yRMpLdMxLrHzw66HmVCdC3ByBnsOlJiu+yewjLLMlV1fKNVQowU1ORkNSzLz9EyKRb92qfiyuHp+PHuMTinXxpcLgFtU/hyRk0ZQdDnZQgEZswI1ADc0EiNV+cZuWVsV5w3sL3lcU+d3w8f3jAM3dskG5ZpkxJnGnFy1fB0lRHq53vGWrZrlBelziNaGrwuGOQ/r04pLkvJJB7DM1oid+Z09O/QDP+88gy8d91QWUKO5d+49szOOsP8qG72pf/syBgKgoA7J3ZXGUwB/2p0QQCePL8fvrz9TNX+Sb0l49fIjJb49/X2HWSM9j7HyYmyatnYqXRmTe6Xhn0vnYcurZJkc+Ho7q1xZrdWGN29VcBzsh2Hz+S+kkzZyK4t5cgYI9qlhieZ/OD05mGp14j9x8stI04+Xi5F0x0tqQ64nUYQYBcRzu2XhqfO74cz0ptDEPSSnpFIhq71+4/r0VolncfeTXVeL0RR/a66coTemWqXrU+fi2cv7O/omI4Wka8ju7ZEj7YptsaN52RQRhlqIw6tqjSKONH2pa1GIjSU19isJu2+OzO7yU5Sq3l0vMPcbTxKFZHUocqXQxAEQRAEQRDE6Qc5TsJIKPJ6TBlgnLS0X4fQJGu24mixdcRJSSVfzosl6ba7Gnhgx2aysfqOCd0kiSJBwJDOLWz2tvHz233jHB8jwB9xEoxDTVbzkP9v3BbFlHjnaoSD05vj1nFdcW7/dohyCdzE5GxFttZZpYwwyWidJCc1vmFUF6TEGyeLj4t2Yf/L5+FPE7rhxtFddP2uqKmzNJRdP6oLdj43BZ/dMgL3D43HdWfaX33drU0SFv1tompbr7QUlZPpmQv749ObR+ClSwbiH1cMRj/fql/3Q5n47JaRttsKRlLozxO6YWyPVrh8WCdEuQRZCqZj8wR8cdtITOjVBtkvTMO3d47G1AHmDjJegut+7VNxxbBOePnSQfjfXWOw/vHJujIsKislTrpGcYp7IMm3LT3FhdHdWllGSzDs5Gx6YnpfLH9kElonx6FZQgxmTOtjWDY+Jso0v0C7VHPn89DOegdJt9ZJ6N46fBJNPBJioyyjcdjYlVUHLrPT2Gymt43ral0oAD68cThuHdcVmb3bIueFaZjYK/Kr8LVRJC5BUDkV2PvN4xXhFUVVJIZLENAqwHwlSXHRSHPgcHzn2iH42zm9bZW1MwtYfV/U7raU6tLMs6x+7deDXwP4vmEXsy7ypMfYNqt8Xdp7RIvTfHHsPU0QBEEQBEEQBKGFfi2EETs/xqxWanc10RJvk6z/cfjOtYElJzbrT74Nx8ljP27jbk+Kk4xadqXB/nnlYHmVa1Nyligx0wU3QhlxEozuOnO+NDapLiPiY80N0jwdRG1kiFZa6pGpvbHi75MAqJOIv37FYFw5PB0r/j4J6x6Tos1io13Y/uwUPONbyZxk0J/YKBcEQUByXDSeu2iA7HBkRLmsc5wIgoDE2Ghk9m6L5FgBA02i1bRMH9jeNLoGkIzxk3xyJrHRLnx752h8fONwZLROQnxMFJ6Ybk9mLhhJpbTUeHx5+yg0T5SMo80SYrD04UwseSgT43u2kftmxZldW8orsC8d2hFLHsoEIBnsXrtiMPp1SEVyXLSpAe62cV1x76QeKuc2M+K7BODrP4/CF7dJETEjM1rizK7Gc71Z5BIjOsqlijQxM7TGRbtM58+3LJLYK1fxb3n6XPx23zj8fO9YpPoioq4ekW7qFBqRoW57+qD2OsecGSzaZ2n2MWyyyOXCnpW8k+bJ5k8nkuPCn54uOsplmYA8HGilugRBfa9HufySlCL0zv0/njwn4LaTHIzr+YM62M6/Y8cub/Xa1uYEsYoK1UacGC2GSLNwogaD2cILniOInVOwEa/aKBor7My/BEEQBEEQBEE0TejXQhhJTYjGeQONI0YAa217M0kWpscPSEnUNz91DoYG6WyYdduZ1oUcwKS6DhfZM2q1To7DlP7tsOGJyTqZEMIYQZBWZwPBrehnskSni1TXiIyWuH6U8cp7APj1Xv+K223PnKvbn3uiQvU5NsolSyslx0ajR9tkvHfdUFzui2Do1CIRbRXGqOS4aHk8f7jbWq4L0K+Aj4+J4hrWurVJwn9vHSk7cgIlEGdbclw0JvdLkz9fP6oLnjzfH53D0/V3CcARjiM2GM36Lq2SHCUQz505Hd/cMVpOAD+5bxq6BhBJ0TY1Hg9N6a2SV4vzGeBqfUZL5kiprPXgnWuHGua5CSQ3i9kzKorm0jNWhnel0bVZQgwGdGyGlPgY2VmS3jIRo7sby+V0aZWEh6f4V+N3ap5g6ZhTMrp7K53zUIkyMqApJnSe0KtNRNqpj4hDbTCBFHXq/8zuQV7ESSB5nVRthWmpgJ35lfc8Kw/T7rWqUpfjRM5fZtmVsKB1HOsjTvznaDdSzwjnjhP1XNPYItAIgiAIgiAIgggf5DgJI4Ig4L3rhuHSIR0Ny7x86UDdtqsUSY87mWhot0iMRe7M6cidOR2XDeuE5omxQSdNj45yoWdb+wYuK8yMXzyYEbI1J5qGMIZFGwBAeTVfNs0OURrjSrCGqPomOS4aL1ysf8b+c9NwvHqZJKvVXyF5xzMo3zOph+ozG2dAMkYtfHCirbwpANCllXluCiMuH9YJPTjP5eK/ZWJirzbo1CKwehmhcJTFx0SpcpL8ePdYuH2RHAyeI/jSoR3x6732HEpO+ejG4arPypxRlbVS5IsdmSy7sOswNUOax5jkGoteuWl0BgB98vaqWudRONEa4+LDU3rL2v8ul6Az/qW39L8bYqPVx142VC1b1qVlIu47q4fO2MnqbJ4YgxNl/pwiyXHR2PDEZFm+zeMVcc+kHnjlMunZyzuldj7akRPkrf5nz0C64n7fG0AuHwAY1qXxRjS2DFCOyilOpoWp/c0XidhFL9WlNrL7c5yI8IrqfVYSTox/XX0Gd3uf9ilBG+15BJrjxGy/pVSX4VgEd37NE40lJ81I1eTv0t5bTJYV0EdrO82txPv++NIl+u8BjDgHjneCIAiCIAiCIJoW4dd7IDC0Swv8sOmwbvvc+8ejU4tE7HvpPHy0fB9e/j0LAHD/OT3xzYY8APpk0Z/dMgI3f7oeADCmR6uw9Pe7O0fjjOcWGO6/YVQXfLHmgK26Ehw6TrRyFIQ9BPgNo+N6Br8amRllbNqhGjyCoDYOn93XHynhcgn48IZhOF5WwzVwdWyegNyZ0/Hgt5vxw8bDQRkt46JdmDagHS4Y3AHr9p/EZ6tyueWUw77s4Uno3CoRUwe0w+6jpVi7/6Tt9n65dywufGelbnvnlok4eNJvzF7noE4zlMl9E2KjdMmTWyTG4oAmgqdf+1RLTftA6a6J8Lh9fDf5b+Y4CSTaw4jkuGjkzpwOt9sNAOjWJhnPXzwAU3yROTV1UjRY25Q4LPrbRJz9j6UApNxOTrlqRDouGdIR415Zgkm92+CeST1w4+gu+HbDIVwzMh2/bM5Xla+o9jtntJGOymn339cPw9gerbg5ee7K7A6vKD1LbVPi8afPNwAAhnRujtbJcbh5bAYe+d9WVNdJbTGps3Jf209M74sXZu9CYmwUSqokB+/XfxqFaz5ao2pHFPlGe5a3YWCnZthw4JTlGJkxtHNz/BFkHUZc1jMG3+eELxImco4T++/jUAWn6KW6BNW9wJxudR4voHWc2HxfaXNTMVonxyHnxfOQMWO2s04ruGRIR/yo+b5n52uNVRnt+FpFjWnzGLHDg/2KNecv47G7oBS3+L6H2l1bwXOUaPcbRZwM7tQM/7hiMP76f5uwI7/Esq0YjSMkd+Z0VNZ4DCVl42OiNM7r0+SLD0EQBEEQjRqerDhBEJGHllnVI33aSatzXS4Bd0zsjucukvIgtE6OwyW+KBXtj8ve7VKw58Vp2PbMuRhjIJXy/MUDAACJAbrFWP4AI5zoQRsZKBg3jvYnsHaigU+oEQSgfbMErHn0bPz17J66/R0dRiIxY5SZ3E9jYv/L0/H6FYMN95/bvx2uNUmmDQCPTuuLOyZ2w+S+gSdMFgQB718/DOcNbK8y+H126whVOeWwt0iSDNhx0VGY2NuZU2xQp+bInTkdF50hRTjERrswtkcrzP7LOAzu1Ew2xrH9wcIcpY+dJ+U7iYly4e1rhmBy37b4y1k9uAbfXml6Sa9Qoc1RopSvYUmkA11BbZcbRnWRpdtKfdFgyXHR6N4mWY4YbBtAnoG46CikxMdgy9Pn4s2rpZwlKfExuG1cVyTGRutMf+U1dXj+4gHolZasc5x4RBFXDu+EXmnJmDqgHddpAgCtkuPw1AX9EB8ThXP6peHbO0YD8OetSfUdV+3LacWk/5hRkr2zlO+1NilxWPpwJr68/UzcndkdggCM6d6KO2dNHyRFdj10rnlS7hcvGYDpFlFgTmSotBFZqfEWRuuk8H61smo/VDhxhoTKceIR9REnyvFP80mUpsTH6KS6tPJURoRTgoxFMin7HKhUlxJtFVYO37syu+P964ZiuCayKthT79A8AZN6O38Has9Pe7pRCs+Jsuw1I9Px8JTe6NE2GRefIX0vtrrKvPxPZucdSuc5QRAEQRAEQRCnF+Q4iQDa5NJG3Dg6A7kzpyMmyoU3rjpD9jBvVeRdaJYQg+gol6FhCwASfT8CKwJXbJL5y1lqmaIJvdrgRp/cDK9MzovTZKPO+J6t0SNNLy+kTEz8zAX95b+daOA3FbSrRo1ghpl2zeK5Bph5D0zAXZndbbfLlDK0RqymTJuUODw6rW/IoiNYNdee2RnDuhjn81FJgwVo9WL3xIsXD8CXt49CSnwMfr53HPa/LBnur1DIAwZDXHQUcmdOx63jusrbLhjcAR/fNAIPntubK9HEy4USKlLiY5D9wjTZEa00rM68dBD+eeXgoPKrOKXMF2WREgHDt/bRfe6iAbhhVBfMf2AikjXtHy+rwauXD8b8B5w5rzNaS5JZ03xOikRNniUmgVPti7Rh8mKx0S45L01ctAtdWiVhbI/WeGRqH+S8MA3n9m+HD25Qy6wBksMk58VpSIqLxtKHMw37dd2ZXUyjMmKjXY4Ei7ROHCv7fFyY7bDBGv5vUixYCBXMSRYs2rF1KSScAODP47vh9SsG49IhHSXHiWJOsSstGe4cXq9NSMC6x86WP9uLOHEm1QUAM6b1wQc3DOOWj4lyYdrA9vjvrSPhfijTnxw+THlcrIjSyG/pI078PVO+Yx89r6/8fdfube8SgCuGddJsMz743rN6UIwJQRAEQRAEQRBcIuY4EQQhXRCEJYIg7BQEYYcgCH+NVNv1TbByR6kKJ4kdPf6xPaRVvX8eFCdr3hvx673jMJOTZ4XxwDm98OIlA+B+KBNz/jIen986EmkaY35rxarumCgXlj0yCVePSMe/rx/G1Y5unRSL964bio1PniMbPS4cHJoV76cbdg0FVsWS46LRxiJvjNKAnRIn3XOBGuoJa9jYxnCtav5JQ71yObC2YnxGK7srssNFnGL++u2+cdj30nkBRVs4ITbaJY+h8vybJcbgUk1uj3BzoS+yZ7JCKi5cjOqmdsZdqXCOpcbHYNnDk7D8kUkAgMwAk423TYnHruem4taxGQAgO0NYxMng9Oa4ZEhHvH6FlE+IXYe4aBfifWVjNe8IZjRtkxKn2ycIghwt06VVEu7WOIN/vXcc5t4/HoB5ZGRctMtRqgetob3SIidNJObNP43val3IAK0ROxQoz/jrP40KuB6rPCXRUS5cPqyTlMcHWqku9bFGeS2s5tFg89+0SXShleJ9a8fRZVVGGUXB/rpzYndMscgtkxQXjYzWSbIjtb5e6X3bmzvIBcE/BjEOo3W0dGqRiNc0EaZm1WhzopAThSAIgnBCU7ZzEQRBNAUiGXFSB+Bvoij2AzAKwD2CIPSLYPv1Bk/uaPuzUxzVwVbP2vkB3q5ZPHJnTseYDtH47JaRpmUHdmqGq0caSxQJgoDrzuyCjNZJ6OdLoq01ZgFQrdpunhiLmZcNQlJcNNdxkl9chfMGtpfPKev5qXjjKn6y1qaObZOBjYLM+DfrtjO5+3+7b5z8923ju+KGUV1wdhCyVIQ5zGnIe56MFk4rVwu/4JPks0NUlD+hcn2ilO4b0LFZxHIayXkR6vn8z0iXpNMyWidZFw6Sbj4psH9fPwy/3jtOt79zq0Skt0zEruem4haf4yMQEmKj5PcSy7vAImpY9GSPtpLRlBnF42Oi0NGX4N3sDnjv2qHy369ePkjftsIRN6Z7Kwzs1EyWwDR7V8bHRDlaea80yJ8/qD3etHhfRUJJ6/Hp/eS8Vk6JDkMCdCWdWyUGfKzWuWtkOBdFEaIIVCucWNrHu6vBc2ZljL/YF6EGSFJRTuDNMHZGmzcVKruZZJHTBABeu3wQbldE+ylh9zCvL6sfPctGD4PjzgnmEa9SZJH0t9Kx5+QVMbFXG/z7+mGqiEdl/YxIRPwRBEEQTYoma+ciCIJoCkTs14MoikcAHPH9XSoIwi4AHQHsjFQf6guefIRVYk8tc+8fj2Ol1Y7bjnIJePqCfnj2V2fD/PzFA7B673FbZUUR+P6u0bLOvZLYKHWEzKhuLXGOZrU16UsbY1eSxY4RkBmpe7dLwQ93j8F7Syd+4HsAACAASURBVPZg4a5Cblu90lLkXDlEeGAriLmOE9//7103VLVdeTtoV8maoUqoXI/E+aIAHjynV0TbZfd+U5SemzrAfEU6y0sTCvp3SMWzF/bH+YP4+UWYhFdibBQ+uGEY5u8oMI046t9RcoI8dX4/VcQMg91Pk/u2xdvXaJ4VTn0vXjIAj/+4HbFRLkcr72vqvHj32qE4XlaNm8ZkAAC6tErEhe+s5JZPjA6NY6J/h1TTZNhKY/BVw9PxzYY8W/XaXcUf6OOizTHRr30q9hSWydffDO33JSPDOStWVeevs1mCWsLU6Fir01cu+NDmAwqEUEQg8d4TWswkF/0RJ/q+tG8WmAPOCS6XgF5pycguKOPvV/RLmRyeN3Zm96XRfKe8F9okx6G0KgRatgRBEASBpm3nIgiCaArUy7IrQRAyAAwBsJaz788A/gwAaWlpcLvdkexaWNizr0a3LdDzcmfbL1tWVga3240uoog7B8VhZPso3DqvQt5/c/9YuR8vjUtAYYVX/pwOIL2jcT/TU1zIK5UMFjk5OVhXk8std7hUbSi5s1c1Nq3jG5sINVECUFVVZavs6tWrkBqrNjCw68/Ym1cLAFixciVaxLtwfRdg4S5/+WVL3WFNmkuoOXBAmhcO5x2E231Uta+2RtpXlrcL7hO75e37cmvlv3fs2I7441ncurXX/ki+5HTdnbMH7toDIel/IJQfl87rZH4u3O7DEWu38oTUbt6+HLir9oe9Pe34NyW6ANi2IZe7zyuKOLtzNKZk1GD3prXoAsDt5pdlvD85EfG1uXC79fdtarkXyTHAsOQSrF21XLUvL0//3q3I3wMAqKmuQt7Bg3ZOBwBQXlmNpJO7kaTp79SMGMxVPJMMb3UFHGmBGTCudTV25Ku3CfC/l6sqK+XtfWKO2a73cB7/3LX37LbCOtP9SgoKCuS/165ZrdpXWVEGr2jPaZuzZy/cnoMQIDmQly1bqjKesz6wyJQoQQRbsnFg+3oo75ItWzZz29i+bZuuPiUlp/yLQI7k8+cpo7HIyc5Gi5bVqv1FRZXcsoyb+sXi5736+3XLxj9wLFvvMCkoKOC2b3Z9Ciuk8a+urjItp9xXUV5huI+xa9cuuEv2WNbldrtRrqhPW9eypW54aqVn6XjBEXn78uXLZOmuvb5n7dAhvoPw5MmTtsalqlJ/XgUF/u9aJcUl2B7E/N2U53+Crn9Th64/YWTnUtq4olIDk8glCIIg6oeIO04EQUgG8D2A+0VR1C2lFEXxQwAfAsDw4cPFzMzMyHYwDOzEHiB7N1olxeJEufTjOBLn5Xa75XZkIYZ5swEASx/ORJdWgcvFpGxZDpRKl69Hjx7IHMuXh8gvqgRWLpY/nw7XMxIs6l+G1PgYXPLeSqDK3OgCAOPGjtUlQ1ZefwAoWH8Q2LENZ44ajQ4s2fHc2fL+SZMmhaTvhD3WVmUB+/aiZ/duyMzsodoXvWw+UFuL/2fvvsPcqK6/gX/Panu1vbYX93U3xtjG3eCybjRDIAQIJPRiSIBQEhIICaGHhAQI+ZECIbRQQn1DgNAxBmwwBlzAxgX33su6bD3vHzPSjqSRVm01mp3v53n8WBpJM3fmSLPSPXPPHTtmDLq1ayp7s/rjVcA3xsVLRxwxCFURrq4Njf3H+xcDa1ahsmcvVE2MXjKlJY05pgH9P16FS8f3SsmV3LE6elwjxny5HmcM75aW8mChx5+aTE7xaeas6fbLPz30DbDq26Blw4YNAz6djcLCAlT26AystO/wDXVk93aoqgqft2Pc+Eb0uel/YcvLSooAHAhbHq+BAw8HFgZ3/vuyJPDeKpo3EziwHwAwfPhwYE5sFyX06lkJrFwetjz0PduwZAvwxTz7xy1/OwCg4rAKwEwyTBh3DPD+24HHykpLsaF6L4DmkyeXnDgGfStK8GinrfjXJ2sxqWq4kdA3t+dvQ11DI/DW/5CbnY3ahvrg9pnPHW7GO9RRQ4cA8z613WcAqAJwx6fGOnp06wasCU+2+l/3SMUWXPx40zHq268fig+tClrvwys+AXbuiLjPt543DW/c9Q5QYyS4Jw/oiJMGdwqfg8ncr4qKClRVHRVY/ELlTrQrykWvDsURt7Fmx35g1kwUFhTYn5tCji8AFFreX6GP+Z8/YMDhqPJPxB7ynqiqqgpab+EXHwDV1WGPAcb3j9yP3gbqatGvVw+8s9b47FZNnBj4W7Hiw5XAN0vQtat9TNq2a4eqKkt52tB9Mu8XFxcB+5tGvlRVVeHlzV8Cm4wsZWlZaVLnb57/vY3x9zbG39ui9XNZ+7jyOvX13hB4IiIXS2viRERyYPwxeUpVX0rntp3kLytw5shuaFOQE9bB7YRkkiZAcKmLaH/5S0NKZ1BsepsdILEOAAktjWLHf9Wu0xOEkyFwxbRNR74/QqGd/ImOCPLXjHd6jo/8HB9+HJIkSofc7Cx8f2TkuZyo9bH7pPg/Ptb5FJpz2rAu+PV0+zLV2RGSf6nKzdl93q3nBOvteOZsyY6xgfGU6rJuP/y8Fds65tw4OVA2qqp/R1T1jzzHVmDOjijrTrRUl5Xd+dlqSkjpUbuDFkupLuupuU1BTnjSBMC9Zw7Bdc8tCFs+orJdzOtPdFBpeYTvrfH8RYn1udb5XFJR5ixUs+vkVyQiIopTJvdzrb47wlVGREQUk7QlTsToAXgEwBJVvTdd280EjYGSEoLLHLza2wlFKayf70XWzqjXfjIOs5Ztx+/eCC/PVBzDZKf+DiC7OXco/eobjDhE68QMTYgl2iE7Y0IvrNxWjXNG90hsBUQuY9c36u8wFYQnVm4+aSBuezW8FPWl43uhbRwXO+T6slJQpMtgtx7rOcH6eDz9y3ajrpLtn7a+PnT1sa46WvLnxhMGBCUIAnN2RFlfpA7yeDrj0zFCDQi+oKG5ZE0i1DxgiSQinr5kNI7oUpayNrx97QTbx/1Ns35vjOdQaIzfbewSktaX8hsSERHFw8v9XEREXpC+WinAMQDOBTBZROab/05M4/Ydk+yVfqmU4xMM6Zr8D+CgESdRfmVyzozkWA/fEZ3LcPE4+5JosXS0+J9j7aCZHmESZ2p5DY1G2RrbESdmiMI6IK2dpnF8tNoV5eKh80agrJAjwMgb7DqIxXoj5PGLxvW0PR8m0tGcqj97duv51yWjA7fjadvxRzSV9bNL1kYboROv0HbF2s5oT7tsYm8M79E2cD/aZOfNbTeexEQqQhnL9yD/3+WJ/TrglycenoKtBvP/1U9kfyrK8lGWgtHDzcfMWG4dcRLPd8hYR1KlKRdGRETe4dl+LiIiL0hb4kRVP1JVUdXBqjrU/Pd6urbvpKaSEs7/Wlt+54n4z5Xjkl5PPGVB/puC7XlV6FGOtWPAjr8TyTri5MEfDEt4fZQcf9ksu5gGrg5OsOQNkdfZfVSsI05s2VwEkMgpN1VfrOz+zlqTB/GMhvjbucMxvm97APaJg+auwo9HaMIidNWREhfxHOrGwAiKKO2IEIh4tpOK0QexbM+fOLnj1EFxjXCKVSKx/P7IbgCA9sV5qWmD+X+kv2OBESd5iQ2Gj1Q6L1RLjOghIiLv8nI/FxGRF6R9cngv+v7IbvjP/A2BH6GtQTwduEemYISLV4V2ZoV2lL161Thsq66JaV1NI05S0zZKTtMcJ+GdPYE5ThK8cpvI8+xGnFjnOLEs/86QzlFWE+dnTlp2xImV9c9BPNuMdcRJPKKVDWuJi0ZiuSAlFefLVFS2jKUZ9eYIxJwonf/J7U70OWHm3Dg5LFE3Y0IvXDq+V9QE3TF9ypNpFADg2qn9jLaZ9xNNnOTGmDjJhIuYiIiIiIjIHZg4SYPObQow8/pJTjcjpaw/O2PpV/jTWUOxYN2elmpOq3VMn3Ks2r4/4uOD4qg7XmjWDd99oDbpdlHyGqKMOPGLNscJu36IIotWekokuFN90oAOEdeT0IiTNH04rfsQzyhQn00Hc7x9yX88Ywj2HarDLf8NnxcmdF0xH4842pDjy8KZI7pi7c4D+GTlTvyoKnz+uJQkTlIw5iSeESc5vsjPTiaJk59j/P3v07HY9vFOZQVhy0Sk2feF3esi8Y+kDF3l1VP7mtsz7ic6N152lGNnZX3auWM47xcREREREUWWzjlOyMNOGdoFN5880OlmuM5vTj4Cpw3rguuP6x9Ytvru6Qmty59k+Wrj3qDl541lx4ET6gMjTmw6eyIUpI+nc5TIy+znOJHAYyLhy+06yROa4yTuV9hrLuGQaIImJ8ZSXX69OhThiYtGBS373vCuuOCYpjm3gieHDynVFeMRief8lp/jw+9PH4LxfSMnvfKyU/AVNyUjTmKf4yTWclPx6tq2EI9fNAp/PHNoi6w/Fk2luiKVajOWFzSTOImUzMqOVJsthPX9efupg2zWT0REREREZOCIE0qM5YenpqKWBdnK8WXh3hR1dLQvzsOHP5+Erm2DrxC97ZRBuO2U8M4DalmBOU5srpKNVAueFUaIYmP3WbF2uEYrLWWVSOIk2REnxw6swFuLt6C5FIy1AzquUl12I06iPL9neREm9IucoDBe37QGuzlOYhm5kerzm3+URUtvx8puL2PZnPnnIOZyU4mY2EwMW9qAw0qwZseBZkeUFOba/zRpLgEVbbSOFUteEhFRa5boRZZERGSPiRNKCH92ulO3doVON4FMjdFGnJhCH2GHD1Fs7D4p/hx/2IgT847dNQCJfOSSnUMh1ksREp3jxK6D2e71TaXN4tuf0FNarOetVJ3dxvdtj74dS5Cfk3wSIiWTw8dx/GIpN5Xpc3T0bF+E7TZzr9175lAs2rAHHUvzbV/n363cOEcKZWcJ6hs15lJdzR4+XgtEREREREQmJk4oIRn+u50o4/knA7ab46Qoz4fqmvqwDrIYK5EQeZ7dhNb+xIhIyGiNKOuJlthsaSLAP84bgUuemGf7eKKJVLt9siuTNaFfB5w7pgeunNwnrvWHnrdibWYyCQHrK5+8eDQA4FBdQ2DZuz+diMuf/BzLt1YDAA7vVIp9h+qaXa/diNpjB1bE17Z4RgM5+H5LlYfOHY6+FSVhy4vysjGmV+TJ5K17PveXU1BWmBPT9vpVlGDxpr0YWdkupufzAgQiIiIiIooVu+EoIUGTw/PqPKK4NQRGnISfhp+dMRa/mn44ivOCc9uc44Qocf5yUaEdp9E6Uluyk/W+7w+xXR5I8ACYOrAiYsd7pMnhP/z5JJw5omvE9drNBWHXX5/jy8Ltpw5CRYQRAlbRDpOIxPQ9IZEjHa1UqHWOk94dilGc33Q+/d/V4/HRLybHsP7wZUV5ka85qurXMWxZLPv1wNlHYWRl24wfTRJN7w5FABK/sMa/742Nio6l+cjLti/pFRqTUT3bYc6Nk3HK0C4xbcfuAgSNco+IiIiIiLyLI04oIZdP7I0ZT34OgKNPnPDOdROxc3+t082gJDRNDh/+WM/2RbhkfK+w5XblhYgonO0cJ0EjTpqW+5MGdp3k8Q4AiOfp3z2qK67994Kw5f5kQLQSYsbj9re7tSvEYVGSHXajGpI9n0RNnITcj5TsSKYJ9qXGjIXhpcla5tzprym+Mqwdzb/2O0M64ztDOqe+US1k7i+nhB3Glk43RDuMncoKojwajCNOiIiIiIgoVhxxQgk59ojDsPCWY/HD0d3xw9E9nG6O5/TpWIxRPWMrS0GZ6Yzh3QAYJWNixQ4fotjYjc7SwGPBjzc3WiIepw2L7ar3WDS35eARJ8HOGdsDw7q3CV6f+SS7uSCSObMYIzuizNUkofdTdx5rbiTLgz8YhjevmRDTc23Xn0CbQkU6b58/tgd6maM03KZjaT46loQk5wIHK7b4/ueKY/DRLyYF7k8eYIzWKcm3v6artMAo3dW2MDeutobKa2YOFY43ISIiIiIiP444oYSV5ufgzu8e6XQziFxp+uBOmD54elyvYd6EKDaHdwqfY6HRMuTEOuqiqr/RYas2XaaxjjjJ8Qk+//U0FOVm48NZH8TfYItAgqeZbUeb86hjST5e+vExqLzhtfDX2Q7PiL19Vk9fMhrdywvx4PvfAgD6VRQ3u71ubQuwescBmyYkM8eJ/WunD+4U/tw4NmOXbIlWHsxOpO3desqguNaT6WVR7d63V0zqjRy7YZUAhnQLTuz95uSB+FFVb7SJkBg57aguqK1vxOnDu+K+d5Yl3M5IJcCIiIiIiIhCccQJEZFLcMQJUWyq+nfE708fHLg/rHubQMdzlgDnjOmBc8f0wKJbjkV+TuSO1Hg+c6X5OSmZTL6pVFf05xXmNl37EkszI3W8dyzJwx2nxteJ73d0n/bo2rYwcP+8sZVhz7EekoW3HIuOkcqIJXDo4sklJJJ3sEumkb3A+9ay7PrjBuCaqf1ien22Lwud20QuuZWVJfjB6O7IbWbEiN/cm6Zg5s+qAveLco3P+XeGuqckGhERUTz8ZUOJiCh1mDghInKJFPTJEnlGZXlTGaR/XzY2qGO3INeH208dhJL8nKjrcCJZGWtX/d2nWUd8xt7O0F2ae9PUmCfWjnWdVmeN7B64XZDji7iD6TrUsWxmYKdSdG1bkNJRHq097/2XHw7HaUd1QY/yli8/dsbwrs0+p2NJPirbN7WlwkzY9e4QPiqKiIiIiIjIDkt1ERG5RuQ5DYgomL+jekSPtsjxZQX66yMlQ+w6ycWBy0tyzdJGvmi1uACUF+chPycLh+oa41p/Oks++a98rCjNx4bdBwEAl07ohbmrd+KYPuXoUV6Epz9dCyCzzmmvXz0eAHDLK183+9zHLxqF5Vv2Nfu8Xxw/ANOP7ITxv38/6fZlooGdS3Hv94emZVv3nDEEh3cqxW2vLo75NT3KC7Fy+36UFmTj7WsnIDtCCTEiIiIiIiI/Jk6IiFyCI06I4ufPE/SrMOY9uWJyn9hfm8Ykw5QBHfGHM4agvlHRs0MRxvVpDwCYMaEXHpq1MuprRYDK8kLbuUPSwX9qinS4nrt8LD5btRM5vixMG1gRVEoikDhJYkhGLC+9clIfXPrEPPTumNoRBxP7dcDEfh0iPu6ffyU7S9CtXWHE51HLuv+so7Bg3W50LAmf2D7eeWuIiIiIiMgbmDghInIJznFCFLvQT0tZQU7U2s99K4rx1uIt6NWhCCu37cfATqUozU/sa9LgrmU4dmAF/vBW7JNYT+zfAW2LjImxbzzh8MDyX554OKYf2Qn7a+ojvlYAvHLVOOw5UNfsdkSMUS21DfGNVGlunQCCMk2je7YL3O7SpgBdjopeDiyRs1s8/d2hCZvY1p95Her8K5CYsoIcTIiS4PLLwJATEREREZFDmDghInIJa+Ue5lCIoisrMOYv6RvjCINrp/bDuD4dMKRbGf4zfyPOGtkt5lEQEtKd/cqV4wAAJw3ujNcWbcI9by4NPPbSj4/GovV7wtaRE6V00JBubZrdbml+DkqbmbPF7+g+5Zi5dBuGRlhvsubfPA0F5mTcsUrmnNZSp8MrJ/fF43PWpGRdyYyosWK/PhERERERUXowcUJE5BKhnbNEFFnfihI8fcloDOvRNqbnZ/uyMLZ3OQDg7FHdm3l2sKun9rVdXtm+CAM7lwYtG9a9LYZ1D29TbgJzLqjZjR6tU37q4R0BGKXKPlqxHW0Lc/GXHw7Dup0H0f+wkri3Gb09hjaFuXG/NpHzm7ZwGqFDSZ7NNhPDszcREREREZG7MHFCROQSHGVCFJ+jzXlCWsp10/ph/a4DuGJS5HlT/B/bo7q3wV9+OCzoscFdy7DQHH2Sk534ZNWRTg3L7zwhUOLvhhMG4LgjKjCoSxkApDRp4nhSN5NPjiluWgbvaVpx5A0REREREbU0Jk6IiFyCc5wQZZafTLEfaWLlHw1SlJuNTmUFQY+9cuU4zHhiHt5avAW5vtRPjm4t/5WbnYXRvcoT3kZLS+T0lo75KP54xhD89PkF6FSWj017DiW8Hp6+iYiIyCreudeIiCj9mDghInIJdrwRuU9zH9se5YUAgI6l+QmsOzNOCv5zU2ucWPu0YV1QkOtDdU09fv7CwoTXkxmRaj1a6ni2wrcwEREREREliIkTIiKX4IgTIvdp7mP7s+P6Y3zfDrbznsS8DYe75f1b1yQyJ5l6ehMRnHhkJ6zavh8AcPLgzvG93rIeykxMlhARERERkR0mToiIXIL9bkStT162DxP6dUhqHa3h3JBM8icdu9+zfVFCJTWYMCEiIiIiInKnxGciJSKitHL6qnIiomiSuXI/oTlOktheuiWbP3HTvrobjzQRERERERmYOCEicoksS8cbL2ImokyRilEVyazBDedDFzSRgLC8yaMfr0LlDa/hUF2DM+0hIiIiIiLHMHFCROQSWVnseiOicJmSOEhmcviEki9umo0+U4JEcfnLzG8BAHsO1jncEiIiIiIiSre0JU5E5J8islVEvkrXNomIWhPmTYjITqbMo5FUqa6kXpsZ+28n5S3L3F1tFULfwzzcRM6prW/E7gO1TjeDKKpk+rkqb3gNlTe81hLNIiKiFEnniJPHAByfxu0REbUyYrnF7hwiygwZkrfJaDxE7uamwU1ErcXl//ocQ2972+lmEDXnMbCfi4io1Upb4kRVZwHYma7tERG1NhxxQkR2WsOpobUnX1r7/rVWjBuRc977ZqvTTSBqFvu5iIhat2ynGxBKRGYAmAEAFRUVmDlzprMNcrHq6moePw9j/FuflXuaJqdduGghZLP9KZyxdxaPv7eFxv+r7cbndteunSl/XzQ0Guv+5JM5aJfv3LR169fXAABWrFiBmfVrElrHBx98EPdrVq8xSrisXr0KM2duSGi7qRYa/61bDwEAli9bhpkHVwWWx/te+GaDMcfG5s1beH4BsGK1cTw2bFiPmTO3JbWubWaMAGDfvn2YZzm+NTXGe2zOnNloG8NnjOf/+L2+qhYLtjbgxtEFTjclaYx/6qhlmJdbjinjT5FY+7h8pR0cbg0REcUj4xInqvoQgIcAYMSIEVpVVeVsg1xs5syZ4PHzLsa/9Wm3fjcw52MAwOAjB6NqQEfb5zH2zuLx97bQ+PuWbwPmzUXbtu1QVTU6pdvyvfsG0NCAsWPHolOZc52Os/YtBtasQu/evVE1vld8L37DqO2dyGfm89qlwLcrUFnZE1VVfeN+fUsIjf/Lm78ENm1Ev379UTW6e8L7u/3z9cCiBTisogJVVUNT2GJ3WvnRKuCbxejSpSuqqo5Ial3Pb/gC2LwJAFBSUhIUm7zZ7wA1NRgT42eM5//4XZDEOSDTtET899fU49NVOzB5QEVK15vpzvvnXAAHALjnvcHPP0Vi7ePK69SXxR+JiFzEucsTiYgoLlmsGUJENpye88jpU5PT249Fytrogn11s/DJ4Y0DzjlOWoevN+7Bc/PWpXWb//5sLSpveA279ic2yfnPX1yIix6bh1Xb96e4ZZlt1rKmkWQHaxuiPJOIiIio5TBxQkTkEm7oHCQiexrWJZs6Tp8b/JtPd+eymzqzefp2t4ZGF73ZKKK3vt6Cn7+wMKgMVEt78hOjfOH6XQcTev2qbUbCZH9Nfcra5AYdS/ICt4+7f5aDLSEiIiIvS1viRESeATAHQH8RWS8iF6dr20RErUHQVeXshSNyhXSMBvH66SCT99/fNqeTW5QYf9zclKSjyHxZRkCdSIS1ZPLczV5ZsBEPz1oZttwao9r6xnQ2iSguqejnqrzhNVTe8FrqG0dERElL2xwnqnp2urZFRNQaseONiKwy7ZyQ7o5BN3RESqYFiaKL8JZqYOakVQgkTlQzb6JPj3rr681YvHEvLp3QND/Wu0u2YIeltFlNPUt1UeZiPxcRUevGUl1ERC7BOU6IyCrQl+t0qS6emprl9Dw0lBh/1BqZOHHE24u34JUFG1O2Pv/3qHSGM5F5cnZU12DOtzuM17fyU0ejKrKygndy7uqdQfc54oSIiIicwsQJEZFLCCt1EZENdsq7QJIhSuecDNTEP2KoMYnSTh8t3455IR3BFJtn5q61LeOUKH//fKbPWXPG3+fg7Ic/cboZadHQqPCFZIeyQxIpNUycEBERkUOYOCEicgl2jRKRVaZciSwOXMUdvH1nthsPFzTRs6wl3yK9hZPpZz/nkU9x+t/mJL4Cj3nko1V46Yv1AIDCXB/216ZuUnRrqa5MttKcEN4LCdOGRoSNOMnOCu6iqM/wRBcRERG1XkycEBG5BGvlE7lPOubhcPrU4N98uru23NCnyLN268BSXS3PnyR4ft46vPHVZgBG4uRATfT5Lf67YCNueeXrmLYRKNWVxgEM/vNzIu8ga76gtb4FG1XhC+mRCB1xQkREROQUJk6IiFzC6c5RIsos/o40r58a3JBUTlUbWZbNGTOenOd0E1qV95duxWsLNwUta1Rg7Y4D+GbzvkAprcLc7GZHnFz1zJd4bPbqmLYbKNWVAVmIh2etxP8WbYr6nIZGDfvu98Ln6/H+0q0t2LLIfvu/Jai84bWUrtOuVJfPx/McERERZYZspxtARESx4c9IIrKTKYmDdPdFOt/1GbvMiBA1L/hd5f9ordt5MOIrvt64B4vW78FZo7q3ZMMi2rrvEHKystC2KNeR7Sfiwkc/AwBMHzw9sKyhUQPzeviTJUV5PhysbYCqpuQ8FyjVlWTppz++tRR/fm8FVt89vfknR3Dn60sAIOo67EY6/eX9FTiiSxkm9e+Y8LYT9fcPwuebaWhU7D5Qi/LivIivq2toxIwn5uG6af1xZNeyoMfsJofPypC/aUTplurEZLolc04kIspUHHFCROQS1k6DTOkoJaLoWnKEQMacBjKlHRnMTUkeshdpgvjpD3yEG15alObWNBl157s46va3Hdt+c56ftw4/eebLZp931+tLsGG3kaDab5bnKsjxob5RUduQmtpa/g76ZEuv/fm9FcZ6YkjAJHN6tEvw5PiyUFsfvXxZS1NVzF215NwUSwAAIABJREFUE9/9y8e4+39LMPyOd7Cjuibi85dvqcb7S7fh+hcWBJY1NCo27zmE+gZFdpbg6me/xITfvw/A/ph5Yb4XIiIiyjxMnBARuQRLPhORHa+eGlzRj+bV4LhU6HvKmpw81EKd1ac++DEua8WlwK5/YSFeWbCx2eflWMozLdqwBxt3H0Retg8AUFvffOJk675DuPzJz/HZ6p0Rn+MvCRVP4mTh+t0R2x9Pya9EOv6t6/e3OSdbUNfQtPyJOatRecNr2H2gNu71J6qhUXHDSwvx5drdePjDVQCAnfsjb98uyX/X60sw5rfvYsf+GmSJ4D/zN2LtzgMRn88J4omIiMgJTJwQEbkEa9sTkR2nR574z03KcRUR8Wpp9ztY2zKJk/nrduPNr7e0yLozid1nwFqWpnt5UdBjv3nla+TlGD9Va+sbMW/1TvzrkzWBx7/asAdfb9wTuL91bw3e+Hpz1A58fwko60iO/y7YiNtfXYwd1TX4cu2usFEe3/m/jyOOmIlW8mvtjgNYsmlv4H6jAq8u3IiaOBJw//hwVeB916CKHdU1yPVloc4yAsd/TLbsjTziw2/e6p1BiaXGRsWu/bVxtQkwkhj+cMZzUY/1LfDm15sBALsP1AVKqPnZfd9NtrwaERERUSI4xwkRkUs43TlKRGTHf25ibiAcE96Zz/q+3bG/FnKoDqX5OWHPO1DbgC0b92Jg59KUbPerDXvw78/WRWmXolER1qmcyRobw+er8Kupb0R+ji/ia8tD5mjZX1OP3/3vGwDAhN+/j/1mAuGHo7tDRHDdc/MDJb0AYJc54qJtobGe1dv344H3luN33xuMHF8W9h2qw95DdWY7jde89MV6XPecUT7qkY+MkRN3ffdI/GB0+Hw1//xoFTqV5WNc3/aBZdFGQTzw3nLMXrEdHUrzAQBvLd6Mv3+wEldM6o3rjxsQ8XVB63h3eeD2/LW7cdpfZgMARvdsF9YGX5Zgyaa9GHBYSVg51x3VxqiO0/82B0DTPAS7D9Zh2O1v49bvHIHzj66M2pZt+5oSMw2NGkiE+bIEjQ2R0+bbq2sCI0msrGXOwhInNm+huobo7x8iIiKilsARJ0RELsHECRHZcbpzfkLfDgCAsb3LHdm+G86NzCm5w9a9h3DvW8sC962frfveXoYTH/gQX23Yg/eXbsWhuuCr9GOZ78KvoVFx0p8/wpOWERSqGpjjAwB+8ux89P7l64nshiNWb9+PXr98HW98tcn28f019VFf3y4kcTL72x2BZMl+y2ifvYeM9dQ1BB+vXQeMpEjbwhw0Nip+8uyXeOmLDZi/bjcA4M7XluCO14wJ2f1lr/xJE6vqmrrA7ac+bYrPba8uxo+e+gJXWUafRBsFUd/QiGxf009t/8Tq2/clVlJr7qqmkSI19Y34zX++woqt+wJt+GLNLpzwpw/xxJw1Ya+98ukvceFjn9m2EYgtOXfPm980va6xKVESGHEY4VCM/e27uOzJz8OW+w9dXUNj0GTwkUYMccQJEREROYEjToiIXCLLDb2DRJR+Dp8axvYux8q7Tox4pXlLcUNpMJ623cffifzm15uDrpR/e7FRTuukP38EADhjeFfcc8aQwOP7a+sxb3M9JkQZdeFXZzPZ+d9nrcTd5giLm08aiP+a82pc/Nhn6Fiah9+eNhiAkaBZsa0afToUp/0zF82s5dsAAG8v3oqK0nwc1b1t0OOb9hzCc/PW44IIIxsKc2MbTfD5mp2YPKACq7bvD1q+zoxVWWEObn9tMRauN8p4+Tv031myNfDcaHOT5JjJjrteX4KHZq0Me3zm0m1N64nSmV/XqMj2hcfHX34sXjv2N434+HZrNeav242Zy7ah3pzv5Ntt1QAQVL4s0JaGxkACyco6Ymbp5n0oL85F++I82+1bR/c0NGog+VRvDt+JdD62zsdifY4/BjX1jUGJmz+/txyHmaN0/F7+8dEosRkFRkRERNTSmDghInIJawdc5nSVEFEsWrKMVSZ0zjvZgev0iJt0yPwUUevxyEer8MqCjUGliQBgX8iIiaVb9gXdn/SHD7C9ugaf7JqD0oIc/O2c4cjNbuokf3vxFrz3zVb89rQjbRMn7y5pmufktlcXNy3/xujwv2pyX2zcfRCzlm3DA++tAGCUrfJ7Zu5aDOnaJmWlxOLlT1y8+MV6vPjFerx17QT0qygJPO5POEWaxDzWMkw7qu1ff8+bSwEYnfqPfrw6sFzNuUG2VzfFM9rooAPm6JbZ325vti31jY2orqnHxY99hjtOHYSaBsXtry7G2aO6ob6hETlZ8SVJ9hysQ1mBfYLAmrioM5MVa3YcQKcyI8lQU28s819ks3XvIbz85QbMmNDL9v0GNCV+9tfU47j7ZwEAHr1wJPpXlKBdUW5QTKxJj/rGxsDftMDIkXrFup3GvC7XPbcAE/t3wML14ckaP38MDtQ24L1vmpJaAgmMHvILTcIRERERpQsTJ0RELuGFzkGi1iYTkhqtFrMJntXSoQ9NmtixXkkPINAxP2/NLgDArGXbMHVgReDxS5+YBwA4eXAnHN4pPLlRkBv9Z9mZf5+D9bsOBi176tO1gds3vrQI1x/X37HEyfaQhMax983CijtPQK4vC7WWjvuNew7Zvj4/OzxxkiVNHfN+1TX1WB6StPIrzssOjMDw+3rjXqzeETw6JVrVp/019aipb8BXG/ZGfpKpoVExc+lWfLpqJ6bdN8tcugpvLd6M/hUlyPaFf3MryguO87It+/Dxiu0Y0q0NTvvLbPzprKG227IeQ+s++t+r/uSIf36TK5/+EnNX78TRvdtj5bbg/ffzv6Y4v6lN/1u0CRc++hmO6VOOpy4ZE1jeaMm9NFgmh/c7/9G5QWW2XlsYXrJt2ZZq1NQ3IC/bF3F+GF8WcOn4njhQW29bdoyIMlflDa853QTyMP/8XUSpxjlOiIhcIoMqchBRBvH6qcEVySkmeVqdg7X1uO65+REf94WUaRrUxUhorNhWjYMh86MAgE1VpyChSRM7ednO/bSzG9XwzpKtQR3+ADDgsJKw50Uy5fAKPH3J6KBlT85ZY0lSBCsryAmbK+Wx2avxixcXBS1raFRs2WufwFmxtRq79tfZPhbqsic/xy9fWhS2fN3Og9i6rwbZli9u93/fSIiElsKat3oXbv3vYtzzhjFi5upn7d9TK7ZWB25bkw7+2/7jv/eg0fat+4z9e27eurDRUoAxEmed+Z4qtiRz/EmWj1fsCHp+oyVTUt/QNDm8X6S5SUL1/9UbeHz26oijftoU5qK8OA+3nTIopvURERERtSQmToiI3MJaqssNHYVElBbi0ROCG3IR3oxMy8uE47p6xwG89MWGiI9nh1zt4O8w33uwDhc8Ojfs+aEjWBKR62DixG7y98v/FT4peKSJyPfV1KEkZDRGWUEOju7TPqbXA8CG3QdxxG/ebLZdjaoYfde7tut4a/EWjPmt/WOhFq7fE5isPtSaHQeCJof3z53y2OxVQc/zj/aYszI4UREv//pfW2SM9PC/nyLN5/LzFxbi/H8a78O87Czkmq/fXxue1DPW15QAM+Y4Sbytj89ZbZvMAYACS3mwHuWFOLJLWeIbIiIiIkoSEydERC7BUl1EZMUzAlHm+mJN8PwOgREBh+qxbEt12PP319p3JMcj1+dk4sS+wz3Uog3hk5cDwJa9h7Do1uOCloUmnwBg+dbwYxfNDpuREJc9GZ7QCTWyMrl5NfYcrMPna3YFJmX3h2bdzoNBozWK82Kb26U5px7VpWnbB+qwYbcxmqTBJiH378/W4vnP1wfu+7KykG9OWr9kU1OJspe+WI/dB2qx91Ad3l+6LbB8f219xMngYxGpdBjQNFcLAHxw/ST896pxCW+HiIiIKFlMnBARuYS1/6Bb20LnGkJEMWvJSeHdMOKCUsujg4tc6b53luHGlxYG7lebiYWHZq20ff6BGBMP0Tg64iTGxI/d3BcAcNFjxhwwk/p3CCyLdcL4ePmTCgBwwwkDbJ9z7tjKlG7TOjLwg2VNSYgic26bSJPCx+r+d5YFbt/9xjeB26Gl0gDgsdnBc4cs2bQXBbnhx/q65xZg6G1vY832A0HLb3r5q6RGnETzuze+wWerd7bMyomIiIjixMQJEZFLWH90V7YvcrAlRBQvdni3HDcc2mSuzqaW1ZLJzWfmrgvcXrBud5RnpmbESZ7NBOvpMmNCL/zjvBFJreNvH3yLh88bgWun9gMAdCwNng8kWpmuRH1nSGfb5V3bFsS9ri7FkdvXYMk0XPDoZ4Hb/sni9xyMbV6VSKxzksz5dnvgtt3cI9ZRJQBw79vLgkpkhXr2s7VB9+ev292in5sL/hleyo6IiIjICdnNP4WIiDKB/+d4slclElHr4IaEQUsKnZw4E/kTZi5oque1K8oLSnAO6VqGU4Z2wW2vLg4s+/u5w2Mq85QIu47zolxfxDkn7Dg54uSUoV2af5KNjiV52LqvBgBw4qBOyPZlYcaEXqhtaMBFx/QEADx+0Sjsr6lHRWkevvfXOQCM+S8eu3AUrn9+Aeat2RX3dqcNrEB5UW4gcTGsexucf3RlYHL2Ph2L8dxlY3GorgE92xehMNeHpZv3ob5RcV6Ejv0N1ZE/6DX19nEszov8czzXl2U7YqQ5q3c0jRDZXl0T02v+cf5ITL33A9vHGkNOYGN7lWPZln0xrbdXh6Kopbns7K9twIbdB9GlTfzJKyIiIqJUYuKEiMglsn1Gj04iV0ESkbPYcd5yMnk0D+emco/y4tygDuL/XGnMrXDRuJ74asMePPXpGkw9vAJHdC7F1xv3RlpNwvaZk4yPrGyLHF8WZn+7A1/cPA39f/VG2HMvOqYn/vnxqrDloR3cbjCyZ7tA+a7u5UYZ0oJcH64/rqmE1sR+TeW7enUowu4DdXjm0jHo3KYAL/zoaNTWN+LY+z7Ar6YPRNuiXJQVZGPqvbOibjfXl4W7vzcYAPDJjVNQVpCDglxfUAJoVM92Qa85uo8xAuaqyX3w5/dWxLWfD80KjxcQfaRRWWEOtu2LLfERyYwJvQLJoGj2HAwfmeLXGJK7iTSR/dG9yzH72+DHThnSBfdZyoiFGtGjLa6c3AevLtyEFyzzrnQuy2+2zUREmWD13dOdbgIRtSCW6iIicomS/Bz87ZxheOzCUU43hYhi1JKd+u7rIvWuZGN17MAKDOxUiism9UlJe8he++I82+WDupTht6cNhi9L8NpPxmPKgI5Bjy+/84SgjpM+HYtj2t6RXcqCSiQdO7ACz19+NJ6+dAxW3z0dedk+PDtjDHJ8goIcH66a3AcPnTscN588EN/edSJeuHws3rhmPNoV5aJX+yIc2aUsgb1OracvGR24/c51E2w7wH8y2Xgfnz2qG35nJi9K82O7nu/qKX1x3/eHorNlNEJudhZmXj8JUwdWYHiPtujTsaTZ9Vgnfz+sLN92jo9IzhzRDX06FuPyib2Dll91lP37BzDKYw3uGh6f6Q98FPE1z84Y02xbOpZE3uYdpw5CX/NY3PXdI7H0juORHaHcWW195LPUSDOBdPWUvkHLj+5dHnT/4fNG4MtfTwvcf+zCkc2OeHnhR0ejqn9H/OGMIYH5bW4/dVBQeVoiIiIip3DECRGRixw/qJPTTSAiyghuuLi+zrxU299Z+fQlo9GuODfu9bQpzMXrV49PadvIUGx22GeJUf7unesmIscXvdP2j2cOwedrduGYPu1R36jI8QVfizZtYAVOHtwZ972zDBP7dcDB2gbMXb0TAzuV4oRBh+G8sZX46wff4idT+qAwNxtrdxzArf/9Gnd8d1DYtsb0KsfyO08MW+7LEoyoNDq0v7B0Vjvt6D7t8eHPJ6GmvhF9OhZj9o1T8Kv/twj/+mQtrpvWDxeP64mivGxcd2z/wGv+PWMMurUrjGn9sZYEu+nEw/HIR6uwee+hoOU/P74/jh14GHp3SHyuuG7tCvHOdRPxp3eWB5Ytue14fDr7Q/zhjAH42fMLbF/Xp0MxFq7fE3G9fTsW44pJfVBRmo+xZlKiIMeHg3VGma/2xbnYXh08MmRkZTu8tsgYsdOlTUFg4vvJAzrinDE9ABgjag4zE1hHdC7FgpA2PHfZ2LDRNQBQXpSLId3aoJP52qN7l0MEuN/c7xxfFp68eBTe+GozurQtQFFeNooseZwxvcoxuGsb5OdkYef+Orz4xfqg9d/13SOD7hfmGp9FlqQlIiKiTMHECREREZELef163BxzPofsrMwdQH3I7PD0X81+dJ/2TjaHbNx88kD0bF+ENp8YCa1YRou0KczFlMMrwpZfNCgX//yqFu2L83DxuJ44ZWhntC3MRVlhDt5evAVDupWhY4nRCX3DCU2lqLqXF+KRC0amaI+cF5oE6dHOSFIc06c8MKeI1ehe5WHLknXphF64dEIvrNq+H4W5Poy+610AwI+rUjdqq21RUwe//zNeWR45ARRpAnhflqChUbHvUD1OPSo4MfTVrceh9y9fBwB0aVsYljhpU9jUhumDO+GhWSsBAIM6lwaWH2YZ9dOhJB9AcOLEnzQZ2KkUizftxU+n9cObizfjiYtGo11RLt7/ZisAY2TP1VP6BiVOxvftgPF9OwStr3u7QqzdeQB52VnIz/HhpukDASAscRKarBlR2RavLdrEuU2IiIgoY6Q1cSIixwP4EwAfgH+o6t3p3D4RERFROlWUGh1WI3q0beaZ8Tuyaxk+WbkzYukVN3jvpxMDV0jH64pJfVBX34gfjO6e4lalzqE6Y8RJfnbsZYCoeUO6GSWPxvZOvsO9ND/HKIF2a9Krwrgu2RgyaCBOGtwZAFDZvmlUw7SB4YkWr7jgmEoce0QFepQnPsojUT3NGPz57KNQnsBor2h+OLoHbv7P10HLauubJgQpyvWhrlEDy0ITJ4d3KkW7ohz8/LgBOOXBj5GbHZ4E9mUJnrpkNBoaFX/74Fsc3qkUew/WYcPug+hclo8bTzwcT326NmzbPwkpq+U3orIt3lmyBQBw2cReuODoysBjT186Gut3HcSgLmW4yvL6fTXGPCxFedkQEay48wQcd/8s5Gbb/+154fKxWLGtOqzc1rMzxiBLBG0KcyAIT1JecHQlxvVpj74VzZdaI8ok7OciImq90pY4EREfgAcBTAOwHsBnIvKKqi5OVxuIiIiI0qlPx2K8+9OJqGyBDsOHzxuB5VurkZ/j3k75Xh2K0atDbPNBhCrOy8avThqY4hal1sHa4BEnlBrDe7TDgt8cm3ElfbJEYi4l5SU5vixHkiZWJw/pnPJ1+myS1tU1TZO933bKIMxctg1XT+mLl79cj817ajBvzS78/nuDcebIbkGv619Rgsr29qNVjjFHqnVtW4AsEWzccxA/ePhTPHHxKBTnZeOd6yZAFYEECgBk++xH4l0+sTe+XLsL3xnSBdMHB5d/bVOYizaF4cmlXfuNUS7tinID626wKVHn17E0Hx1Lw+e2GdPMyCIRYdKEXIf9XERErVs6R5yMArBCVVcCgIg8C+AUAPyDQkRERK1W7wQTA80pyc/BsO6pH8lCqeOfm8DNya1MlWlJE/KmH47ujrcXbwnc722OovjTWUNxytAu+N7wrgCA648bgEN1DRhwWElgmVVdY2PEZIefP8lc2b4Iq++eHljex5wA/tqp/bBpz0Hcfmr4XDlWfz93RAx71mTH/lqIAG0sn7nvDOkc87w0RK0c+7mIiFqxdCZOugBYZ7m/HsDoNG6fiIiIiChtzh7VDfPX7Uav9s5ebU9ELePO7x6JOy2TnPfuUIxvbj/eNlman+PDpRN62a7nx1V9UF6UXCmxssKcuJMisehXUYzTh3UNSuxcd2z/lG+HyKXi6uc6sksZ5lkSn0RElNlEVdOzIZHTARyvqpeY988FMFpVrwx53gwAMwCgoqJi+LPPPpuW9rVG1dXVKC5umatcKfMx/t7F2DuLx9/bGH9vY/zda+g11wAA5t9/f8LrYPy9jfH3tmjxnzRp0ueqmvqsFjkuln4uax9X9+7dh69Zs8aRthIRUWQiYvu3Op0jTjYAsBZz7WouC6KqDwF4CABGjBihVVVVaWlcazRz5kzw+HkX4+9djL2zePy9jfH3Nsbfxdq0AYCk4sf4exvj722Mv2c1288V2seVvqYREVGyohdSTa3PAPQVkZ4ikgvgLACvpHH7REREREREREREqcB+LiKiVixtI05UtV5ErgTwJgAfgH+q6tfp2j4REREREREREVEqsJ+LiKh1S2epLqjq6wBeT+c2iYiIiIiIiIiIUo39XERErVc6S3URERERERERERERERFlNCZOiIiIiIiIiIiIiIiITEycEBERERERERERERERmZg4ISIiIiIiIiIiIiIiMjFxQkREREREREREREREZBJVdboNEYnINgBrnG6Hi7UHsN3pRpBjGH/vYuydxePvbYy/tzH+3sb4exvj723R4t9DVTukszGUmURkH4ClTrcjSW4/17H9znP7PrD9zmuJfbD9W53RiRNKjojMU9URTreDnMH4exdj7ywef29j/L2N8fc2xt/bGH9vY/wpFq3hfeL2fWD7nef2fWD7nZfOfWCpLiIiIiIiIiIiIiIiIhMTJ0RERERERERERERERCYmTlq3h5xuADmK8fcuxt5ZPP7exvh7G+PvbYy/tzH+3sb4Uyxaw/vE7fvA9jvP7fvA9jsvbfvAOU6IiIiIiIiIiIiIiIhMHHFCRERERERERERERERkYuKEiIiIiIiIiIiIiIjIxMQJEZELiYg43QYiIi/i+ZfI23gOIKJEiMjxIrJURFaIyA1Ot8eOiPxTRLaKyFeWZe1E5G0RWW7+39ZcLiLygLk/C0VkmHMtD7S1m4i8LyKLReRrEbnaXO6mfcgXkbkissDch1vN5T1F5FOzrf8WkVxzeZ55f4X5eKWT7fcTEZ+IfCkir5r3XdN+EVktIotEZL6IzDOXueY9BAAi0kZEXhCRb0RkiYiMdcs+iEh/89j7/+0VkWucaj8TJ0QuJiKTRaTI6XaQI3L8N/gDnii9RGSYiOQ0/0xqjZQTBHqWiBRbbvNvr8eIyJ0icjjPAUQULxHxAXgQwAkABgI4W0QGOtsqW48BOD5k2Q0A3lXVvgDeNe8Dxr70Nf/NAPDXNLUxmnoAP1XVgQDGALjCPM5u2ocaAJNVdQiAoQCOF5ExAH4H4D5V7QNgF4CLzedfDGCXufw+83mZ4GoASyz33db+Sao6VFVHmPfd9B4CgD8BeENVBwAYAiMWrtgHVV1qHvuhAIYDOADgZTjUfiZOXEpELhWRv4hIb6fbQuknIj8Ukc8BTAJQ53R7KH1E5Gwz9nf6r6DhD/j0EZEZInK7iBQ43RZKPxH5gYgsAHAcgEan20PpJSLniMhHInKbiJzmdHsofczvXfMA3CMitwH82+sl5rl/FoAfAzjH6fZQevF3N6XIKAArVHWlqtYCeBbAKQ63KYyqzgKwM2TxKQAeN28/DuBUy/In1PAJgDYi0ik9LbWnqptU9Qvz9j4YncVd4K59UFWtNu/mmP8UwGQAL5jLQ/fBv28vAJji9MUdItIVwHQA/zDvC1zU/ghc8x4SkTIAEwA8AgCqWququ+GifbCYAuBbVV0Dh9qfnaoVUXqYVyqcDuDnADYBGC0iG1T1kLMto5Zm/vHIhpG5vwnACeZJgTxCREYAuArAFQBWAHhXRPap6j9FRNiJ0zIsn71LAPwCwCEAbwH40Ml2UXqY8c8HcDOAswH8QFVnWx/nZ6/1E5EqGJ2m18NImt0mIlDVl0TEp6oNjjaQWoSI5MOI+WQA1wHYAeAxEXlOVb+K+mJyPREpBXAPgEoANwI4HECZ+RjP/a0cf3dTinUBsM5yfz2A0Q61JV4VqrrJvL0ZQIV5226fusD4vDhOjJJPRwH4FC7bB/P88zmAPjBGKn0LYLeq1ptP8bcTsOyDqtaLyB4A5QC2p7XRwe6Hce4sMe+Xw13tVwBviYgC+LuqPgR3vYd6AtgG4FERGQLjvXQ13LUPfmcBeMa87Uj7OeLEJfwlQcwf5l/CuGLhrzCyiIc72DRKAxHJMbOndQCWAXgKwBoRyRWR74lIZ4ebSC0kpBzQQBhDEz9R1e0w3gd3iUgZf7y3DBHJtXz2voBxvv07gAtFpNzZ1lFLs8T/IICtAJ4A8KmIFIjIsSJSws9e6yVm7WXT0QBeVNWPVXUOgEUA7gYC382oFTI7SP+fqk4yr8LNBbAcwAZnW0bpoKp7ATysqsep6scwOlLONB/jub+V4u9uosjMc1/Gn//M0povArjGPJcHuGEfVLXBLFPUFcY5aIDDTYqZiJwEYKuqfu50W5IwTlWHwSgBdYWITLA+6IL3UDaAYQD+qqpHAdiPprJWAFyxD/7fYt8B8HzoY+lsPxMnLiAiNwJ4REQuEJF2qrpMVXfBGMYmAMb7J8Wh1scS/wtFpATA+wDWAvgfjI7c7wJ4XERuMp/Pz3UrERL7bACrYdQ49dfDbQSwF8C15vMZ+xQSkd8AeNpy7v3U7ED/K4wvsVN5zFsvS/wvMkuzPQugGMAbAObCqJ/6mIjMMJ/P90IrYon/heai+QCuMkcgAEYizWeepxn/VkREfikio83bWaq6yLw9BcC/AHQEcK+I/Mz/HMcaSykXEn+fqs6zPPwigHoRGexM66il8Xc3taANALpZ7neFe5LwW/xlb8z/t5rLM3KfzOTniwCeUtWXzMWu2gc/s7zS+wDGwig/5K8aZG1nYB/Mx8tgjJB1yjEAviMiq2H8fpoMY74Nt7QfqrrB/H8rjLk1RsFd76H1ANar6qfm/RdgJFLctA+Akbj6QlW3mPcdaT+/6GcwERkgIrMBHAEjw3Y6jEnEcgHAvAL6RRiT5QwLeW0m1gSkONjE/3sAzjdrdX4II3FyvKqeA6Pj/GciUq6qrLvvchE++5ebV7u+B+AFtoTiAAAgAElEQVQ6MeY56QjgBwBOEpEixj51RORaGF/6/gqjruZv/H+kzSuQH4Vx7CudaiO1nJD4T4IxsuAAgHcAfANgiqqebj7+Y3PUFz9/rUTo519EHgDwJoz4PyzGPDfFAC4FcJSI5DH+7icinUTkRRilJf4FAKraaPlOvQ7AeFWdCuOccIuItGfsW4cI8Q8dTdYWwCrwN3Srw9/dlAafAegrIj3N99VZAF5xuE2xegXA+ebt8wH8x7L8PDGMAbDHUkbHEebn8REAS1T1XstDbtqHDiLSxrxdAGAajLla3odxbgLC98G/b6cDeM/JUZGqeqOqdlXVShjv8/dU9YdwSftFpMi8YBkiUgTgWABfwUXvIVXdDGCdiPQ3F00BsBgu2gfT2Wgq0wU41H7OcZLZ9gF4TlXvB4wTKIDJqvqgeQVco6q+JSLHADjSvAqyUlUf5PDxVsEu/lMB/B+MGoVfqGoNAKjqVyLyBoD2cDg7TylhF/tpMGL/axjzLfRU1QVi1G6dDaBWhPW2U0GMmrJHAbhVVT8WkXUALgRwDYw5TqCqz4jIiQAmilGyq5+qPuVYoyllIsT/EgA/VdWbReR9c+QRYHwBXQigAMAeZ1pMqRQh/jMA3Kiql4hRGrOrqs41RyCsUdUann9bhT0AnlfV74nIZyJyndnpkg2gTlWX+Z+oqktF5L8wLmBwsgY3pY5t/EUkW82a7Kq6SkR6ABgKYL7/95ijraZ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\n",
      "text/plain": [
       "<Figure size 1800x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for host in df_ts_inference_dict:\n",
    "    df_ts = df_ts_inference_dict[host]\n",
    "    n_rows = len(df_ts.columns)\n",
    "\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(25, n_rows*6), gridspec_kw={'width_ratios': [3, 1]})\n",
    "    fig.suptitle('Normalized Time Series - ' + host + \" - INFERENCE\", fontsize=26)\n",
    "    fig.tight_layout(pad=10.0)\n",
    "\n",
    "    for i, c in enumerate(df_ts.columns):\n",
    "        c_ax = axes[i, 0]\n",
    "        df_ts[c].plot(ax=c_ax, grid=True)\n",
    "        c_ax.grid('on', which='minor', axis='x')\n",
    "        c_ax.set_title(c)\n",
    "        c_ax.axvline(x='2021-04-12 12:00:00+02:00', c='r')\n",
    "        \n",
    "        c_ax = axes[i, 1]\n",
    "        df_ts[c].plot.hist(ax=c_ax, orientation=\"horizontal\", bins=30)\n",
    "        c_ax.set_title(c + ' - Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wound-protein",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## How to retrieve the original data without normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "marked-buffalo",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:57.072402Z",
     "start_time": "2021-05-03T14:56:53.602233Z"
    },
    "hidden": true
   },
   "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>hostgroup</th>\n",
       "      <th>plugin</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>cpu__percent_idle</td>\n",
       "      <td>74.198</td>\n",
       "      <td>18.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>load_longterm</td>\n",
       "      <td>0.302</td>\n",
       "      <td>0.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>disk_io_time</td>\n",
       "      <td>60.421</td>\n",
       "      <td>69.629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  hostgroup             plugin    mean  stddev\n",
       "0  cloud_compute/level2/main/gva_shared_017  cpu__percent_idle  74.198  18.997\n",
       "1  cloud_compute/level2/main/gva_shared_017      load_longterm   0.302   0.294\n",
       "2  cloud_compute/level2/main/gva_shared_017       disk_io_time  60.421  69.629"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to accomplish this we have to save the normalization coefficient \n",
    "# and to use them to have the original data.\n",
    "\n",
    "# We retrieve the normalization pandas df\n",
    "# Note that we have a row FOR EACH HOSTGROUP\n",
    "norm_df = PL.get_normalization_pandas(spark=spark, \n",
    "                                      config_filepath=json_file_train)\n",
    "norm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "clean-harvard",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:56:57.092541Z",
     "start_time": "2021-05-03T14:56:57.075655Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'load_longterm': {'mean': 0.302, 'stddev': 0.294},\n",
       " 'disk_io_time': {'mean': 60.421, 'stddev': 69.629},\n",
       " 'cpu__percent_idle': {'mean': 74.198, 'stddev': 18.997}}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will save in a dictonary the coefficient to use them \n",
    "# later for plotting the data \n",
    "\n",
    "norm_coeffs = {}\n",
    "for plugin in list(set(list(norm_df['plugin']))):\n",
    "    norm_coeffs[plugin] = {}\n",
    "    norm_coeffs[plugin]['mean'] = list(norm_df.loc[norm_df['plugin'] == plugin]['mean'])[0]\n",
    "    norm_coeffs[plugin]['stddev'] = list(norm_df.loc[norm_df['plugin'] == plugin]['stddev'])[0]\n",
    "norm_coeffs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lightweight-prerequisite",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Original data visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "covered-nashville",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:57:01.629144Z",
     "start_time": "2021-05-03T14:56:57.095466Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1296 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for host in df_ts_inference_dict:\n",
    "    df_ts = df_ts_inference_dict[host]\n",
    "    df_original = df_ts.copy()\n",
    "    n_rows = len(df_ts.columns)\n",
    "\n",
    "    fig, axes = plt.subplots(n_rows, 2, figsize=(\n",
    "        25, n_rows*6), gridspec_kw={'width_ratios': [3, 1]})\n",
    "    fig.suptitle('Original Time Series - ' +\n",
    "                 host + \" - INFERENCE\", fontsize=26)\n",
    "    fig.tight_layout(pad=10.0)\n",
    "\n",
    "    for i, c in enumerate(df_ts.columns):\n",
    "        mean = norm_coeffs[c]['mean']\n",
    "        stddev = norm_coeffs[c]['stddev']\n",
    "\n",
    "        c_ax = axes[i, 0]\n",
    "        df_original[c] = (df_original[c] * stddev) + mean\n",
    "        df_original[c].plot(ax=c_ax, grid=True)\n",
    "        c_ax.grid('on', which='minor', axis='x')\n",
    "        c_ax.set_title(c)\n",
    "        \n",
    "        c_ax.axvline(x='2021-04-12 12:00:00+02:00', color='r')\n",
    "        c_ax.axhline(y=mean, linestyle='--', color='b')\n",
    "        \n",
    "        c_ax.axhline(y=mean + (2*stddev), linestyle='-', \n",
    "                     color=(0.0, 0.0, 0.0, 0.5))\n",
    "        c_ax.axhline(y=mean - (2*stddev), linestyle='-', \n",
    "                     color=(0.0, 0.0, 0.0, 0.5))\n",
    "        \n",
    "        c_ax.fill_between(df_original.index,\n",
    "                          mean - (2 * stddev),\n",
    "                          mean + (2 * stddev),\n",
    "                          color=(1.0, 0.6, 0.8, 0.2))\n",
    "\n",
    "        c_ax=axes[i, 1]\n",
    "        df_original[c].plot.hist(ax=c_ax, orientation=\"horizontal\", bins=30,\n",
    "                                range=[min(mean - (2 * stddev),\n",
    "                                           np.nanmin(df_original[c])),\n",
    "                                       max(mean + (2 * stddev),\n",
    "                                           np.nanmax(df_original[c]))])\n",
    "        c_ax.set_title(c + ' - Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changing-console",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Init of configuration file - ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aware-ladder",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Creation\n",
    "For the analysis part we need another json configuration file in input. This time in the file we save:\n",
    "- information about the window size we want to study\n",
    "- the algorithms we will use to elaborate scores\n",
    "- for each algo the hyperparameters and some useful metadata\n",
    "\n",
    "Here we will have only 1 algorithm, the IForest. This because the purpose of the notebook is to show a simple pipeline; in production we train different models in order to evaluate them later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dramatic-welsh",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:58:41.184425Z",
     "start_time": "2021-05-03T14:58:40.895826Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "demo_name = 'demo_AD_System'\n",
    "json_file_exp = 'demo_config_exp.json'\n",
    "\n",
    "json_data_exp = {}\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# The length of every window in terms of steps.\n",
    "# This number is dependent on the data we feed to the algorithm. This\n",
    "# value should typically match the corresponding ETL config file for the test.\n",
    "json_data_exp['history_steps'] = 48\n",
    "\n",
    "# The number of step between two subsequent window.\n",
    "# This number is dependent on the data we feed to the algorithm. This\n",
    "# value should typically match the corresponding ETL config file for the test.\n",
    "# Note that slide_steps has the same value of history_steps we have a non\n",
    "# overlapping scenario.\n",
    "json_data_exp['slide_steps'] = 48\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# Algorithms to use for the analysis\n",
    "# The keys correspond to the identifier of the algorithm.\n",
    "# Note that it should be unique since ths will be used on all your plots.\n",
    "# Every element will have the following sub-keys:\n",
    "# - import_path: to indicate the path to the single algorithms classes.\n",
    "# e.g. path.of.module.classname.\n",
    "# e.g \"adcern.analyser.AEDenseTF2\"\n",
    "# e.g \"pyod.models.pca.PCA\"\n",
    "# then the last token will be separated by the \".\" and we do this:\n",
    "# from pyod.models.pca import PCA\n",
    "# and instantiate the object PCA()\n",
    "# - parameters: dictionary of parameters to pass to the class of the algo\n",
    "# during its initialization\n",
    "# - train_on_test: default False, if True there won't be any training phase,\n",
    "# every algorithm will operate on the current windows at test time. The only\n",
    "# thing that comes from the train data is that the normalization, in fact the\n",
    "# test data on which we perform direct prediction are normalized with mu and\n",
    "# sigma of the data extracted as etl for the train period.\n",
    "# - subsample_for_train: default 0. It defines thenumebr of training samples\n",
    "# to extract to train your model. If -1 all the available data will be used.\n",
    "# If 0 it uses the deafault values in the max_samples_for_train field.\n",
    "# Note that subsample_for_train is used in combination with\n",
    "# train_on_test = False because this allow for a bigger training set where\n",
    "# subsampling makes sense\n",
    "json_data_exp['algo_and_params'] = {\n",
    "    'IForest_S1000': {\n",
    "        'import_path': \"pyod.models.iforest.IForest\",\n",
    "        'family': 'Traditional',\n",
    "        'train_on_test': False,\n",
    "        'subsample_for_train': -1,\n",
    "        'parameters': {\n",
    "            'n_estimators': 100\n",
    "        }\n",
    "    }\n",
    "\n",
    "}\n",
    "\n",
    "# ----------------------------------------------------------------------------\n",
    "# ----------------------------------------------------------------------------\n",
    "\n",
    "# Max number of sample we want to limit for traditional algortihms\n",
    "json_data_exp['max_samples_for_train'] = 1000\n",
    "\n",
    "# Max number of sample we want to limit for deep algortihms\n",
    "json_data_exp['max_samples_for_train_deep'] = 10000\n",
    "\n",
    "# Random seed used to subsample the data to feed to the algorithm.\n",
    "# This is used only if max_samples_for_train or max_samples_for_train_deep\n",
    "# are set, otherwise no subsampling is performed.\n",
    "json_data_exp['random_seed'] = 42\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "#                           ANALYSIS OUTPUT\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "# Path to save the traing time of your algo.\n",
    "# Note that this path hould be accessible to the executor of the analysis\n",
    "# (e.g. your VM or your container). Typically this is in a local folder if\n",
    "# we work with our VM as main executor or in a shared volume if we work on\n",
    "# a cluster.\n",
    "json_data_exp['folder_training_time'] = '/eos/user/' + username[:1] + \\\n",
    "    '/' + username + '/' + demo_name + '/demo-fluentd/time'\n",
    "\n",
    "# Path to save the anomaly scores created by your algo.\n",
    "# Note that this path hould be accessible to the executor of the analysis\n",
    "# (e.g. your VM or your container). Typically this is in a local folder if\n",
    "# we work with our VM as main executor or in a shared volume if we work on\n",
    "# a cluster.\n",
    "json_data_exp['local_scores_folder'] = '/eos/user/' + username[:1] + \\\n",
    "    '/' + username + '/' + demo_name + '/demo-fluentd/scores'\n",
    "\n",
    "# Number of most serious anomalies to send to MONIT for every temporal\n",
    "# window of analysis.\n",
    "# Note that this apllys only if your analysis executor is properly connected\n",
    "# to the MONIT infrastructure via Fluentd (--log-driver docekr option)\n",
    "# If not sure ignore this\n",
    "json_data_exp['publish_per_windows'] = 0\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "#                     ANNOTATION EVALUATION\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "# Hostgroup name in the form of absolute path.\n",
    "# Note that this will be used to retrieve the annotations from grafana also.\n",
    "#json_exp_data['hostgroup_abs_path'] = \"cloud_compute/level2/batch/gva_project_013\"\n",
    "json_data_exp['hostgroup_abs_path'] = json_data['hostgroups'][0]\n",
    "\n",
    "# Start and End date of the benchmark you are running.\n",
    "# Only annotations in this range will be considered.\n",
    "# Note that the underscore between the date and time is fundamental\n",
    "# because this will be passed as a parameter on the command line\n",
    "# Note that if the DAGs experiment has produced less scores than the interval\n",
    "# specified here, only the intersection will be considered.\n",
    "json_data_exp['start_benchmark'] = \"2021-04-01_00:00:00\"\n",
    "json_data_exp['end_benchmark'] = \"2021-04-15_23:59:99\"\n",
    "\n",
    "# Path to save the artifacts of the evaluation section\n",
    "# Note that it might contain: annotations, plots, summarized table etc.\n",
    "json_data_exp['evaluation_artifact_path'] = '/eos/user/' + username[:1] + \\\n",
    "    '/' + username + '/' + demo_name + '/demo-fluentd/results'\n",
    "\n",
    "with open(json_file_exp, 'w') as outfile:\n",
    "    json.dump(json_data_exp, outfile, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-liechtenstein",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Reading the json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "tired-fellow",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T14:58:41.986481Z",
     "start_time": "2021-05-03T14:58:41.975059Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "RESOURCE DETAILS: demo_config_exp.json\n",
      "\n",
      "pppppppppppppppppppppppppppppppppppppppppppppppppp\n",
      "\n",
      "{\n",
      "    \"history_steps\": 48,\n",
      "    \"slide_steps\": 48,\n",
      "    \"algo_and_params\": {\n",
      "        \"IForest_S1000\": {\n",
      "            \"import_path\": \"pyod.models.iforest.IForest\",\n",
      "            \"family\": \"Traditional\",\n",
      "            \"train_on_test\": false,\n",
      "            \"subsample_for_train\": -1,\n",
      "            \"parameters\": {\n",
      "                \"n_estimators\": 100\n",
      "            }\n",
      "        }\n",
      "    },\n",
      "    \"max_samples_for_train\": 1000,\n",
      "    \"max_samples_for_train_deep\": 10000,\n",
      "    \"random_seed\": 42,\n",
      "    \"folder_training_time\": \"/eos/user/s/smetaj/demo_AD_System/demo-fluentd/time\",\n",
      "    \"local_scores_folder\": \"/eos/user/s/smetaj/demo_AD_System/demo-fluentd/scores\",\n",
      "    \"publish_per_windows\": 0,\n",
      "    \"hostgroup_abs_path\": \"cloud_compute/level2/main/gva_shared_017\",\n",
      "    \"start_benchmark\": \"2021-04-01_00:00:00\",\n",
      "    \"end_benchmark\": \"2021-04-15_23:59:99\",\n",
      "    \"evaluation_artifact_path\": \"/eos/user/s/smetaj/demo_AD_System/demo-fluentd/results\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Let's use the read_resource function in the data_mining library to be sure \n",
    "# that the format is correct and to print the file.\n",
    "_exp_ = DM.read_resource(resource_file=json_file_exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifth-aaron",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# ANALYSIS to produce anomaly scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fiscal-radiation",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "altered-nursery",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:01:56.762990Z",
     "start_time": "2021-05-03T14:58:43.807493Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start IForest\n",
      "Hyperparameters Analyser:\n",
      "type(hyperparameters) ->  <class 'dict'>\n",
      "hyperparameters: {'n_estimators': 100}\n",
      "train_on_test:  False\n",
      "subsample_for_train:  -1\n",
      "Analyser instance created:  IForest(behaviour='old', bootstrap=False, contamination=0.1, max_features=1.0,\n",
      "    max_samples='auto', n_estimators=100, n_jobs=1, random_state=None,\n",
      "    verbose=0)\n",
      "Opening in Pandas -> parquet file:  /eos/user/s/smetaj/demo_AD_System/local_cache_train/demo_AD_System\n",
      "Opening in Pandas -> parquet file:  /eos/user/s/smetaj/demo_AD_System/local_cache_inference/demo_AD_System\n",
      "Analyser instance before train:  IForest(behaviour='old', bootstrap=False, contamination=0.1, max_features=1.0,\n",
      "    max_samples='auto', n_estimators=100, n_jobs=1, random_state=None,\n",
      "    verbose=0)\n",
      "Algorithm name:  IForest_S1000\n",
      "Algo str representation:  IForest(behaviour='old', bootstrap=False, contamination=0.1, max_features=1.0,\n",
      "    max_samples='auto', n_estimators=100, n_jobs=1, random_state=None,\n",
      "    verbose=0)\n",
      "Full model training (e.g. on the entire prev week)\n",
      "Fitting the methods...\n",
      "Sqlite3 execute successful, breaking the retry cycle.\n",
      "Sqlite3 commit successful, breaking the retry cycle.\n",
      "Sqlite3 execute unsuccessful, retrying after 1 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 2 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 3 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 4 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 5 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 6 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 7 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 8 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 9 sec....\n",
      "Sqlite3 execute unsuccessful, retrying after 10 sec....\n",
      "Sqlite3 commit successful, breaking the retry cycle.\n",
      "IForest(behaviour='old', bootstrap=False, contamination=0.1, max_features=1.0,\n",
      "    max_samples='auto', n_estimators=100, n_jobs=1, random_state=None,\n",
      "    verbose=0)\n",
      "Current window raw ts:  1616194800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-20 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616223600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-20 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616252400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-20 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616281200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-21 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616310000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-21 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616338800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-21 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616367600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-22 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616396400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-22 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616425200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-22 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616454000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-23 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616482800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-23 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616511600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-23 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616540400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-24 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616569200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-24 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616598000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-24 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616626800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-25 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616655600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-25 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616684400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-25 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616713200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-26 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616742000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-26 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616770800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-26 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616799600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-27 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616828400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-27 08:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616857200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-27 16:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616886000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-28 00:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616914800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-28 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616943600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-28 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1616972400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-29 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617001200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-29 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617030000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-29 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617058800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-30 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617087600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-30 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617116400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-30 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617145200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-31 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617174000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-31 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617202800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-03-31 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617231600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-01 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617260400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-01 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617289200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-01 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617318000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-02 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617346800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-02 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617375600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-02 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617404400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-03 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617433200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-03 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617462000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-03 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617490800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-04 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617519600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-04 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617548400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-04 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617577200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-05 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617606000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-05 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617634800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-05 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617663600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-06 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617692400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-06 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617721200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-06 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617750000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-07 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617778800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-07 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617807600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-07 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617836400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-08 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617865200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-08 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617894000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-08 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617922800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-09 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617951600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-09 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1617980400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-09 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618009200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-10 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618038000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-10 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618066800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-10 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618095600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-11 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618124400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-11 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618153200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-11 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618182000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-12 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618210800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-12 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618239600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-12 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618268400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-13 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618297200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-13 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618326000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-13 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618354800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-14 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618383600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-14 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618412400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-14 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618441200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-15 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618470000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-15 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618498800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-15 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618527600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-16 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618556400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-16 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618585200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-16 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618614000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-17 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618642800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-17 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618671600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-17 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618700400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-18 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618729200\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-18 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618758000\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-18 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618786800\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-19 01:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618815600\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-19 09:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Current window raw ts:  1618844400\n",
      "Enough data: ok\n",
      "------------------------------------------------------------\n",
      "Timestamp:  2021-04-19 17:00:00\n",
      "============================================================\n",
      "Predict with the trained model:\n",
      "hostname | anomaly score\n",
      "Save to Parquet...\n",
      "Iteration on windows finished\n"
     ]
    }
   ],
   "source": [
    "# Finally we will use the analysis function to produce the anomaly scores.\n",
    "\n",
    "# Note that we need a for loop to call the analysis function for every model \n",
    "# that we have in the \"algo_and_params\" parameters (here obviously we have \n",
    "# only 1 model but in this way you can easily add models in the configuration).\n",
    "\n",
    "algo_and_params = json_data_exp['algo_and_params']\n",
    "\n",
    "for alias_name in algo_and_params.keys():\n",
    "    algo_dict = algo_and_params[alias_name]\n",
    "    \n",
    "    module_and_class = str(algo_dict[\"import_path\"])\n",
    "    module_name = str(module_and_class[:module_and_class.rfind(\".\")])\n",
    "    class_name = str(module_and_class[module_and_class.rfind(\".\") + 1:])\n",
    "    \n",
    "    train_on_test = str(algo_dict[\"train_on_test\"])\n",
    "        \n",
    "    subsample_for_train = str(algo_dict[\"subsample_for_train\"])\n",
    "\n",
    "    parameters = algo_dict[\"parameters\"]\n",
    "    \n",
    "    sys.argv = ['', \n",
    "                '--alias_name', alias_name,\n",
    "                '--class_name', class_name, \n",
    "                '--module_name', module_name, \n",
    "                '--analysis_path', json_file_exp, \n",
    "                '--train_path', json_file_train, \n",
    "                '--test_path', json_file_inference,\n",
    "                '--subsample_for_train', subsample_for_train, \n",
    "                '--train_on_test', train_on_test, \n",
    "                '--hyperparameters', str(parameters)]\n",
    "    DM.analysis(standalone_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electoral-orientation",
   "metadata": {
    "hidden": true
   },
   "source": [
    "---\n",
    "Note that the output of the cell is composed by 3 parts:\n",
    "- In the first we can see the parameters of the model.\n",
    "- In the middle part we can see all the points that the models has computed (every dictonary is a score computed by the algo).\n",
    "- In the last part we can see, for each timestamp considerated, the 4 most anomalous hosts.\n",
    "\n",
    "Inspect the list of hosts of the last part and choose a host to visualize in the next section! In my case I found the host 'p06253927b11147.cern.ch' and I will show the scores computed for this host!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classified-plant",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Visualization of the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mighty-brain",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The last part of the notebook is to show what are the result of the analysis above. Obviously we don't care now if the results are good or not, we only want to show them in order to understand what is the output of the model.\n",
    "\n",
    "In particular, as said above, we are interested in the data about some specific anomalous host."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cardiac-seeking",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Reading the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "sized-software",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:01:57.980409Z",
     "start_time": "2021-05-03T15:01:56.767310Z"
    },
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores_IForest_S1000_ID_24bff9fe65_W_1616194800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616223600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616252400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616281200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616310000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616338800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616367600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616396400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616425200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616454000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616482800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616511600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616540400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616569200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616598000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616626800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616655600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616684400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616713200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616742000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616770800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616799600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616828400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616857200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616886000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616914800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616943600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1616972400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617001200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617030000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617058800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617087600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617116400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617145200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617174000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617202800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617231600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617260400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617289200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617318000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617346800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617375600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617404400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617433200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617462000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617490800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617519600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617548400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617577200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617606000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617634800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617663600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617692400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617721200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617750000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617778800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617807600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617836400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617865200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617894000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617922800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617951600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1617980400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618009200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618038000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618066800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618095600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618124400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618153200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618178400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618182000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618207200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618210800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618236000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618239600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618264800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618268400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618297200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618326000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618354800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618383600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618412400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618441200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618470000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618498800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618527600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618556400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618585200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618614000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618642800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618671600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618700400.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618729200.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618758000.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618786800.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618815600.parquet\r\n",
      "scores_IForest_S1000_ID_24bff9fe65_W_1618844400.parquet\r\n",
      "scores_LOF_200_S1000_ID_91ae160398_W_1618178400.parquet\r\n",
      "scores_LOF_200_S1000_ID_91ae160398_W_1618207200.parquet\r\n",
      "scores_LOF_200_S1000_ID_91ae160398_W_1618236000.parquet\r\n",
      "scores_LOF_200_S1000_ID_91ae160398_W_1618264800.parquet\r\n",
      "scores_LOF_200_S2000_ID_7e9f568fbf_W_1618178400.parquet\r\n",
      "scores_LOF_200_S2000_ID_7e9f568fbf_W_1618207200.parquet\r\n",
      "scores_LOF_200_S2000_ID_7e9f568fbf_W_1618236000.parquet\r\n",
      "scores_LOF_200_S2000_ID_7e9f568fbf_W_1618264800.parquet\r\n",
      "scores_PCA_S1000_ID_9148169a2c_W_1618178400.parquet\r\n",
      "scores_PCA_S1000_ID_9148169a2c_W_1618207200.parquet\r\n",
      "scores_PCA_S1000_ID_9148169a2c_W_1618236000.parquet\r\n",
      "scores_PCA_S1000_ID_9148169a2c_W_1618264800.parquet\r\n",
      "scores_PCA_S2000_ID_bfe2ae1092_W_1618178400.parquet\r\n",
      "scores_PCA_S2000_ID_bfe2ae1092_W_1618207200.parquet\r\n",
      "scores_PCA_S2000_ID_bfe2ae1092_W_1618236000.parquet\r\n",
      "scores_PCA_S2000_ID_bfe2ae1092_W_1618264800.parquet\r\n"
     ]
    }
   ],
   "source": [
    "# Here we look with a bash command what we have inside the scores folder, \n",
    "# we retrieve the information using pandas and we build the dataframe used by \n",
    "# the visualization function.\n",
    "folder = json_data_exp['local_scores_folder']\n",
    "\n",
    "!ls $folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "apparent-draft",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:01:59.797479Z",
     "start_time": "2021-05-03T15:01:57.983863Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hostgroup</th>\n",
       "      <th>hostname</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>score</th>\n",
       "      <th>end_window</th>\n",
       "      <th>noramlization_id</th>\n",
       "      <th>param_behaviour</th>\n",
       "      <th>param_bootstrap</th>\n",
       "      <th>param_contamination</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_max_samples</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_n_jobs</th>\n",
       "      <th>param_random_state</th>\n",
       "      <th>param_verbose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>p06428644v89170.cern.ch</td>\n",
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       "      <th>1</th>\n",
       "      <td>cloud_compute/level2/main/gva_shared_017</td>\n",
       "      <td>p06492044s78258.cern.ch</td>\n",
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       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  hostgroup                 hostname  \\\n",
       "0  cloud_compute/level2/main/gva_shared_017  p06428644v89170.cern.ch   \n",
       "1  cloud_compute/level2/main/gva_shared_017  p06492044s78258.cern.ch   \n",
       "\n",
       "       algorithm     score  end_window noramlization_id param_behaviour  \\\n",
       "0  IForest_S1000 -0.769135  1616194800           ef8bce             old   \n",
       "1  IForest_S1000 -0.769135  1616194800           ef8bce             old   \n",
       "\n",
       "  param_bootstrap param_contamination param_max_features param_max_samples  \\\n",
       "0           False                 0.1                1.0              auto   \n",
       "1           False                 0.1                1.0              auto   \n",
       "\n",
       "  param_n_estimators param_n_jobs param_random_state param_verbose  \n",
       "0                100            1               None             0  \n",
       "1                100            1               None             0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob(folder + '/scores_IForest_S1000_ID_24bff9fe65*.parquet')\n",
    "tmp = pd.concat([pd.read_parquet(fp) for fp in files])\n",
    "\n",
    "tmp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "unexpected-print",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:01:59.835957Z",
     "start_time": "2021-05-03T15:01:59.800270Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8536 entries, 0 to 87\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   hostgroup            8536 non-null   object \n",
      " 1   hostname             8536 non-null   object \n",
      " 2   algorithm            8536 non-null   object \n",
      " 3   score                8536 non-null   float64\n",
      " 4   end_window           8536 non-null   object \n",
      " 5   noramlization_id     8536 non-null   object \n",
      " 6   param_behaviour      8536 non-null   object \n",
      " 7   param_bootstrap      8536 non-null   object \n",
      " 8   param_contamination  8536 non-null   object \n",
      " 9   param_max_features   8536 non-null   object \n",
      " 10  param_max_samples    8536 non-null   object \n",
      " 11  param_n_estimators   8536 non-null   object \n",
      " 12  param_n_jobs         8536 non-null   object \n",
      " 13  param_random_state   8536 non-null   object \n",
      " 14  param_verbose        8536 non-null   object \n",
      "dtypes: float64(1), object(14)\n",
      "memory usage: 7.7 MB\n"
     ]
    }
   ],
   "source": [
    "tmp.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "excellent-situation",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:02:31.461278Z",
     "start_time": "2021-05-03T15:02:31.408241Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "little = (tmp.copy()[['hostname', 'score', 'end_window']].\n",
    "          rename(columns={'end_window': 'timestamp'})).\\\n",
    "    astype({\"timestamp\": int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "particular-polls",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:02:32.121838Z",
     "start_time": "2021-05-03T15:02:32.104541Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hostname</th>\n",
       "      <th>score</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>p06428644v89170.cern.ch</td>\n",
       "      <td>-0.769135</td>\n",
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      "text/plain": [
       "                   hostname     score   timestamp\n",
       "0   p06428644v89170.cern.ch -0.769135  1616194800\n",
       "1   p06492044s78258.cern.ch -0.769135  1616194800\n",
       "2   p06428644v69757.cern.ch -0.769135  1616194800\n",
       "3   p06428644u81072.cern.ch -0.769135  1616194800\n",
       "4   p06428644w45688.cern.ch -0.769135  1616194800\n",
       "..                      ...       ...         ...\n",
       "19  p06492044s69327.cern.ch  0.636994  1616252400\n",
       "20  p06428644u38463.cern.ch  0.524381  1616252400\n",
       "21  p06428644y59324.cern.ch  0.486742  1616252400\n",
       "22  p06428644w12028.cern.ch  0.438262  1616252400\n",
       "23  p06428644w10206.cern.ch  0.299992  1616252400\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now in 'little' we have only hostname, score and timestamp. \n",
    "# In this way we can merge the dataframe with the original downloaded \n",
    "# one or we can just use these data to plot the scores!\n",
    "little.head(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electronic-harris",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Visualization of both downloaded data and scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "resistant-discrimination",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:02:35.178655Z",
     "start_time": "2021-05-03T15:02:33.813092Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Here we will visualize the data regarding a specific (maybe) anoalous host.\n",
    "# We want to visualize both the data the we downloaded in the ETL part and\n",
    "# the data about the scores that the IForest generated.\n",
    "\n",
    "def visualize_plugins_and_scores_for_one_host(df, df_scores, host_to_viz):\n",
    "    df_ts = reconstruct_entire_timeseries(df, [host_to_viz])[host_to_viz]\n",
    "\n",
    "    df_scores = df_scores[df_scores['hostname'] ==\n",
    "                          host_to_viz].sort_values(by='timestamp')\n",
    "    scores = list(df_scores['score'])\n",
    "\n",
    "    all_timestamps = list(df_ts[list(df_ts.columns)[0]])\n",
    "    to_plot = [np.nan for x in all_timestamps]\n",
    "\n",
    "    count = 0\n",
    "    while str(all_timestamps[count]) == 'nan':\n",
    "        count += 1\n",
    "    tmp = int((len(all_timestamps) - count)/len(scores))\n",
    "    for s in scores:\n",
    "        for i in range(tmp):\n",
    "            to_plot[count] = s\n",
    "            count += 1\n",
    "\n",
    "    fig = make_subplots(rows=4, cols=1, shared_xaxes=True)\n",
    "\n",
    "    for i, plugin in enumerate(df_ts.columns):\n",
    "        fig.add_trace(go.Scatter(x=df_ts.index, y=df_ts[plugin],\n",
    "                                 mode='lines',\n",
    "                                 line_width=0.4,\n",
    "                                 name=plugin),\n",
    "                      row=i+1, col=1)\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=df_ts.index, y=to_plot,\n",
    "                             mode='lines',\n",
    "                             line_width=2.5,\n",
    "                             name='Anomaly scores'),\n",
    "                  row=4, col=1)\n",
    "\n",
    "    fig.update_traces(marker=dict(size=2,\n",
    "                                  line=dict(width=0.1)),\n",
    "                      selector=dict(mode='markers'))\n",
    "\n",
    "    fig.update_xaxes(showline=True, showgrid=True, zerolinewidth=0.3,\n",
    "                     zerolinecolor='rgba(0, 0, 0, 0.1)', linewidth=0.3,\n",
    "                     linecolor='black', gridcolor='rgba(0, 0, 0, 0.1)')\n",
    "    fig.update_yaxes(showline=True, showgrid=True, zerolinewidth=0.3,\n",
    "                     zerolinecolor='rgba(0, 0, 0, 0.1)', linewidth=0.3,\n",
    "                     linecolor='black', gridcolor='rgba(0, 0, 0, 0.1)')\n",
    "    fig.update_layout(autosize=False,\n",
    "                      width=1000,\n",
    "                      height=800)\n",
    "\n",
    "    fig.update_layout(plot_bgcolor='rgb(254,254,254)')\n",
    "\n",
    "    fig['layout']['xaxis' + str(4)]['title'] = 'Timestamp'\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "lucky-palestine",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-03T15:02:38.634060Z",
     "start_time": "2021-05-03T15:02:35.184338Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
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       "data": [
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