task.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Event Data Analytics Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exercise is to undertake analysis of event data very similar to the data analysed within the Tobii ATEX project. \n",
    "The data is received as a stream of events related to an individuals browsing behaviour, with each event having a number\n",
    "of properties which describe the users browsing.\n",
    "<br>\n",
    "\n",
    "We would encourage you to work on three analytical questions and explore the data to try and answer them. When working\n",
    "on these questions we are particularly interested in your approach and conclusions along with your analysis. Suggestions\n",
    "of questions to analyse appear at the bottom of this document. Please choose the questions as follows:\n",
    "    \n",
    "    1. Answer one of the `Analytical Question Suggestions`\n",
    "    2. Answer one of the `Modeling Question Suggestions`\n",
    "    3. Answer one  question of your own design which you find interesting\n",
    "\n",
    "<br>\n",
    "The data itself can be found in the file `sample_data_190819.tar.gz`. As part of your interview with Tobii\n",
    "we will ask you to present your analysis and we will discuss this for a period of one hour.\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FAQ's\n",
    "\n",
    "1. Do I have to conduct this in Python or complete the analysis in a jupyter notebook?. <br> \n",
    "_No we would encourage you to undertake the analysis in a language, tool or environment you feel comfortable using_\n",
    "a jupyter notebook in python has only been used here to help illustrate the task<br> \n",
    "\n",
    "1. Should I just limit this analysis to 3 questions and how long should I spend?. <br> \n",
    "_We would encourage you to only complete 3 tasks as written above, the tasks are broad enough to allow you to explore\n",
    "the data and the analysis should be short enough to discuss within 1 hour_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Event Data Model\n",
    "\n",
    "The event data can be found within the file `sample_data_120819.tar.gz. It contains the following fields:\n",
    "    \n",
    "- `event_time_stamp` :- ISO formatted timestamp of when the event occured **[timestamp]**\n",
    "- `beacon_id` :- id for the beacon this event is related too **[str] **\n",
    "- `content_type` :- content_type of the event **[categorical str]**\n",
    "- `content`:- content object containing descriptors of the event  **[JSON object]**\n",
    "    \n",
    "<br>\n",
    "\n",
    "In order to understand these events it is important to note that they desrcibe the behaviour of **beacons** which appear\n",
    "within an individuals browsing sessions. A **beacon** is an area of the website that a user is browsing, which is of\n",
    "interest to the ATEX project, normally an advertisement. We monitor these advertisements and record the following events\n",
    "for them (which can be found as the variable `content-type` in the attached dataset):\n",
    "\n",
    "* `application/vnd.tobii.atex.beacon-detected-event-v1+json`:- initial rendering of the beacon on the webpage\n",
    "* `application/vnd.tobii.atex.beacon-updated-event-v1+json`:- the beacon is modified, normally html changes\n",
    "* `application/vnd.tobii.atex.beacon-removed-event-v1+json`:- the beacon is removed from the webpage\n",
    "* `application/vnd.tobii.atex.beacon-in-view-event-v1+json`:- the beacon becomes visible within the individuals screen\n",
    "\n",
    "\n",
    "There is also a final content-type `application/vnd.tobii.atex.session-started-event-v1+json` which is not associated\n",
    "with a beaco. This event is triggered when a new url being is loaded in the browser. This defines a new **session** within the data. These\n",
    "two concepts **sessions** and **beacons** are related by a hierarchy where every beacon should be assoicated with a \n",
    "session.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Initial Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cells contain a simple initial analysis of the data to illustrate how you might proceed to analyse the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some useful python libraries for analysis are imported and the data loaded into the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The commands in this cell should be run if you do not have the required python packages in your environment\n",
    "#import sys\n",
    "#!{sys.executable} -m pip install numpy\n",
    "#!{sys.executable} -m pip install matplotlib\n",
    "#!{sys.executable} -m pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('sample_data_190819.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceeding to analyse the data we view it to check it is loaded correctly and get a feel od the data we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_time_stamp</th>\n",
       "      <th>beacon_id</th>\n",
       "      <th>content_type</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-08-19 10:12:30.854 Europe/Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>application/vnd.tobii.atex.session-started-eve...</td>\n",
       "      <td>{\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-19 10:12:43.505 Europe/Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>application/vnd.tobii.atex.session-started-eve...</td>\n",
       "      <td>{\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-19 10:12:50.689 Europe/Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>application/vnd.tobii.atex.session-started-eve...</td>\n",
       "      <td>{\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-08-19 10:13:01.101 Europe/Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>application/vnd.tobii.atex.session-started-eve...</td>\n",
       "      <td>{\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-08-19 10:13:08.773 Europe/Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>application/vnd.tobii.atex.session-started-eve...</td>\n",
       "      <td>{\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           event_time_stamp beacon_id  \\\n",
       "0  2019-08-19 10:12:30.854 Europe/Stockholm       NaN   \n",
       "1  2019-08-19 10:12:43.505 Europe/Stockholm       NaN   \n",
       "2  2019-08-19 10:12:50.689 Europe/Stockholm       NaN   \n",
       "3  2019-08-19 10:13:01.101 Europe/Stockholm       NaN   \n",
       "4  2019-08-19 10:13:08.773 Europe/Stockholm       NaN   \n",
       "\n",
       "                                        content_type  \\\n",
       "0  application/vnd.tobii.atex.session-started-eve...   \n",
       "1  application/vnd.tobii.atex.session-started-eve...   \n",
       "2  application/vnd.tobii.atex.session-started-eve...   \n",
       "3  application/vnd.tobii.atex.session-started-eve...   \n",
       "4  application/vnd.tobii.atex.session-started-eve...   \n",
       "\n",
       "                                             content  \n",
       "0  {\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...  \n",
       "1  {\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...  \n",
       "2  {\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...  \n",
       "3  {\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...  \n",
       "4  {\"geo\":{\"country\":{\"name\":\"Sweden\",\"iso_3166_1...  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_time_stamp</th>\n",
       "      <th>beacon_id</th>\n",
       "      <th>content_type</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2910</td>\n",
       "      <td>2324</td>\n",
       "      <td>2910</td>\n",
       "      <td>2910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1495</td>\n",
       "      <td>1183</td>\n",
       "      <td>5</td>\n",
       "      <td>2910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2019-08-13 21:33:21.336 Europe/Stockholm</td>\n",
       "      <td>0be56815-1065-4d17-8c5d-aef1e0bd3b10</td>\n",
       "      <td>application/vnd.tobii.atex.beacon-detected-eve...</td>\n",
       "      <td>{\"request\":{\"headers\":{\"x-forward-cloudfront-f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>1182</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                event_time_stamp  \\\n",
       "count                                       2910   \n",
       "unique                                      1495   \n",
       "top     2019-08-13 21:33:21.336 Europe/Stockholm   \n",
       "freq                                          17   \n",
       "\n",
       "                                   beacon_id  \\\n",
       "count                                   2324   \n",
       "unique                                  1183   \n",
       "top     0be56815-1065-4d17-8c5d-aef1e0bd3b10   \n",
       "freq                                       9   \n",
       "\n",
       "                                             content_type  \\\n",
       "count                                                2910   \n",
       "unique                                                  5   \n",
       "top     application/vnd.tobii.atex.beacon-detected-eve...   \n",
       "freq                                                 1182   \n",
       "\n",
       "                                                  content  \n",
       "count                                                2910  \n",
       "unique                                               2910  \n",
       "top     {\"request\":{\"headers\":{\"x-forward-cloudfront-f...  \n",
       "freq                                                    1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple example analysis will look at the time distribution of events we have data for. \n",
    "\n",
    "The `event_time_stamp` column is currently formatted as an object and should be changed to a datetime? in order to\n",
    "allows us to manipulate and analyse it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "### convert the string timestamps into datetime objects for analysis\n",
    "def convert_to_datetime_hour(string, date_format='%Y-%m-%d %H:%M:%S.%f'):\n",
    "    tz_naive_string = string[:23]\n",
    "    datetime_string = datetime.strptime(tz_naive_string, date_format)\n",
    "    return datetime_string.replace(minute=0, second=0, microsecond=0)\n",
    "    \n",
    "df['datetime_hour'] = df['event_time_stamp'].apply(lambda x: convert_to_datetime_hour(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Hour  Number_Events\n",
      "0  2019-08-13 21:00:00            126\n",
      "1  2019-08-13 22:00:00              8\n",
      "2  2019-08-14 09:00:00            256\n",
      "3  2019-08-14 10:00:00             47\n",
      "4  2019-08-14 11:00:00              5\n",
      "5  2019-08-15 15:00:00              2\n",
      "6  2019-08-15 16:00:00             31\n",
      "7  2019-08-15 17:00:00            198\n",
      "8  2019-08-15 18:00:00             21\n",
      "9  2019-08-15 20:00:00            219\n",
      "10 2019-08-16 07:00:00             97\n",
      "11 2019-08-16 10:00:00             24\n",
      "12 2019-08-16 11:00:00             36\n",
      "13 2019-08-16 14:00:00            170\n",
      "14 2019-08-16 15:00:00            144\n",
      "15 2019-08-16 16:00:00            156\n",
      "16 2019-08-16 17:00:00             12\n",
      "17 2019-08-17 22:00:00             20\n",
      "18 2019-08-17 23:00:00             35\n",
      "19 2019-08-18 10:00:00             25\n",
      "20 2019-08-18 11:00:00             31\n",
      "21 2019-08-18 12:00:00            219\n",
      "22 2019-08-18 13:00:00            187\n",
      "23 2019-08-18 15:00:00            122\n",
      "24 2019-08-18 16:00:00            241\n",
      "25 2019-08-18 18:00:00             14\n",
      "26 2019-08-19 10:00:00             26\n",
      "27 2019-08-19 11:00:00             32\n",
      "28 2019-08-19 12:00:00             12\n",
      "29 2019-08-19 13:00:00            108\n",
      "30 2019-08-19 14:00:00              7\n",
      "31 2019-08-19 16:00:00             37\n",
      "32 2019-08-19 17:00:00             78\n",
      "33 2019-08-20 09:00:00              5\n",
      "34 2019-08-20 10:00:00              4\n",
      "35 2019-08-20 11:00:00              3\n",
      "36 2019-08-20 13:00:00             23\n",
      "37 2019-08-20 14:00:00             26\n",
      "38 2019-08-20 15:00:00            102\n",
      "39 2019-08-20 16:00:00              1\n"
     ]
    }
   ],
   "source": [
    "keep_columns = ['datetime_hour', 'content_type']\n",
    "# group the data set by 'datetime' object and count the number of events\n",
    "df_date_time = df.loc[:, keep_columns].groupby('datetime_hour').count().reset_index()\n",
    "df_date_time.columns = ['Hour', 'Number_Events']\n",
    "print(df_date_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Lr2QhtkQ0lfJViOaWz1IsRr9/t7DhzbGmxDSXcBmE6vVioEDB+If//gHbDYb\nrFYrEhISYDQa4XA4ohkjAVBV14acEJYM7UBF0bVFlmXoOSZol324BmZacaq2hcayEaKA7DQLqusD\nz7T3zrKn3ghCeiPordw//vEPrFu3DjzP+77UdDodDh48GPHgyFkyZDCUZMYtQZRhMrCKdtl3KBqU\nikMVjRg+OE3xbRMSryQ/N3HZaRbsOVqHovxUv6/pPMueEBKekBLS//3f/8XIkSOjEQ/xgxckcCzd\ndcczQZRgNnARWczgwsJ0bPzvMUpICQmDKHYfe51iNaCpxR3wNd5Z9pSQEtIbQbMcm81GyWiMnbA7\nMSgrtPqjRJt4QYLZyCneZQ8AjE6HzFRz0KLehJCzeEHu1hAQbCiUt4WUGg8I6Y2gZ86ll16K1157\nDXa7HU1NTb7/SPSUVTpQEMaEJqI9guhNSCPRZQ8AxRdkY+f+6ohsm5B4JIjh90xRCykhvRe0y/5P\nf/oTPB4PVq1a5XuMxpBGl7OdipvHO16QYDFy4IXITD4y6llwHANnmyci2yck3ngT0u4toiyjC5is\nilL3VlVCSGiCJqR79+6NRhyE9Gu8KMFkZHscQ9rXmfKTLszBf74+gUtHZPVpO4T0B4GSTluKGbVN\n7chJD73qCSEkuKC3cpIk4eWXX8bSpUvR0tKCF198EaJI6/NGS3OrB4nUOhr3fGNIe0hIPYLUp+7A\n1EQjmpzuiEycIiTe8KLsd0GJ7HRLyOOxGZ0OIq2YR0hIgiakTzzxBH788Ufs2bMHsizj888/71Io\nn0RWWWUzCnOTYx0GibCOWfY9jSF1e0Jbx74nk0YPwDeHavq0jVjZ8kV5rEMg/YgoSmCZ7l+RWakW\n2BvaQ9qGQc/C7aGElJBQBE1Id+7ciTVr1sBoNCIxMRF/+ctfsH379mjERgCctLdgUJY11mGQCAtl\nlr3LI/S5xuHQvBRU2J2aLJS/80A1JEl7cRNt4kUJeq77GFKzkYPLI4S0DaOBhZunHkVCQhE0IeU4\nDkynu0SDwQCOo6XRokWQqAZpf+CdZc/2uHSoy6PMsoTDBqbgyKnmPm8nmgRRgk6ng72RSleR6BAU\nqP9s1DPwUEJKSEiCnm3nnXceXn31VYiiiLKyMjz88MMoKiqKRmz9niTJ0IFWZ+oPOlpIe+qyd3lE\nmIx9LykzZmgG9hyt6/N2osnR6sGownScsLfEOhTSTwgKzJg36qmFlJBQBT3bVqxYgQMHDqC+vh53\n3HEH2trasHz58mjE1u9V1rVigI1mcvYHvCjBZGAhBktIFahxyDA6pCWZUNMU2jg4NWhwujE8P5WK\n+5Oo6amF1Gzk0Obig26DElJTUmQQAAAgAElEQVRCQhe0/89ut+Oxxx6LRizkHMcqmzGSlnvsF3hB\ngj5Ia4zLIyA9yajI/i65IBsffnUCN15WoMj2Iq3R6UZuugXHKh2xDoX0E4HqkAJAVpoF1Q3tKMjV\n97gNAyWkhIQsaAvpT3/6U8ydOxebN2+Gx0NFtaOp3uFCerIp1mGQKBAClJjpzK3QGFLA28LD6HRo\nc4U2OSPWGh0upCZ6zwUtTsgi2iOIgbvsc9IsqG5oDboNI82yJyRkQRPS//73v7jnnnuwbds2XHnl\nlVi1ahUOHToUjdgIgq+dTOIDL0jggiSkSo0h7XDphTnYvr9Kse1FUptbgNnIItVqQHMr3RiTyBPE\nwOdkRooJdU2uoNswGlia1ERIiIImpAzDYMqUKXj66afx97//Hfv378esWbOiEVu/1u4WFGsNI+on\niKF12fe17FNn6ckmNLW4NVO4W6fTIS8rESfszliHQvoBvodzkmUYSCG01NMYUkJCFzQhFQQBH330\nEe69917MmzcPo0aNwubNm6MRW79WXuXAkJykWIdBoiSUFtJIrJM9bpgN3x3Wzoz7gbYEnKzpPtOe\nvvSJ0kRRBhtgDCkAnJuPCqIElun6fEpICQld0G+3SZMm4ZVXXsF1112Hbdu2obS0FOeff340YuvX\nyqscGJydGOswSJTIsgwmBsMzCgck41ildmqSmgwcPHzXFt265na88cnRGEVE4lWwiYY6Hbq0knr4\n7kv7Gg0MJaSEhChoQvr666/jlVdewfXXXw+DIfw11VtaWjBz5kycOnUKALBs2TJMnToVN9xwA264\n4QZ8/PHHAIAdO3agpKQEU6dOxVNPPRX2fuKNmxdhNlKXPYm8glx1J6WSJHcZS31uR+muA3YYgrQu\nExIu7yz7wJ+rtCQTGh1u389uXuw2pIZlGFpdjJAQBTzb/vCHPwAABg8e3G2p0F/+8pchbXzPnj24\n/fbbcfz4cd9j+/fvx4YNG/Duu+/i3XffxTXXXAOXy4Xly5fj+eefxwcffID9+/fj008/7cXbiQ9K\nziKmGckkmHHnZeDbw7WxDiOg5lYPkhPO3gybDSza3d7qAIIowc2LMBmUG1tLCHCmC76HLvvsNAuq\nO60c5hFEGPR0Y0RIbwU8ez7//HPfv9etW9fld5WVlSFt/M0338QjjzyCzMxMAEBbWxsqKyuxcuVK\nlJSUYP369ZAkCXv37kV+fj7y8vLAcRxKSkqwdevW3ryfuFDb7IIt2dzn7bAsA5HuzkkQLMMgxWpE\nfXPwWcOx0Oh0IzXxbP3VvEwrTtV6x5F+d6QO486zxSo0Eud6GkaTlWZBdf3ZhNTt6d5CSggJXcA+\n4c4ta+e2soVaimj16tVdfq6vr8fEiROxatUqWCwWLFiwABs3boTFYoHNdvZLJTMzE3a7PaR9dEhP\nt4b1/Gix2cIfB3rgZDN+ckFOr17bWUqSCampCTDFQdd/X4+F2iUkGGGzJfr+39Nz+srfNm64fBje\n+6IMt12jvvHhR6qcKMxP88U9xqTH9j2VsNkSYd91AtdOKsCRSmfUPyPx/pmMFrUeR4ul5/MtI0PG\nrkM1vufUtfDI5Lhur1HqvA1GrcdRa+g4xk5ImYpStTDz8vLw3HPP+X6eN28eNm/ejOnTp/d5n/X1\nLaobq2OzJaK2NvwSNYfL6zF8YFKvXtuZq51HdY0DCaaeVxNRu94eRy1pbXWjttbp+39Pz+mLno6l\n0+nCydONqis3dvx0E3JSTF3iPm13Yu+haiSZOdTVtaCtre/HJhz94TMZDWo+jqF8ptpaPb7n2Ouc\n0LNMt9cocd4Go+bjqCV0HJXR26Q+YJd9JAqy//jjj/jwww99P8uyDI7jkJWVhbq6s6VnampqfN38\n/ZEMZWZcc6wOgqiuJJ2o1yUX5mDH/upYh9FNazuPBFP3JPnLH+yYMDwrBhER4tX5Mu2hLntC+iRg\nU0h1dTV+//vfd/s3gLC70zvIsozHHnsMEydOhMViwRtvvIFZs2Zh9OjRKC8vR0VFBQYOHIj3338f\nc+bM6dU+tI4XRMVqTbIsA1HURtFzEnuZKWbUN7sgSTIYRl0rhJ17g8wyOsiyDCNNZiIxxLEMPLzo\nXbOeJjUR0icBE9K5c+f6/TcA3HHHHb3aWVFREe655x7cfvvtEAQBU6dOxcyZMwEAa9asweLFi+F2\nuzFlyhS/3fj9wQl7CwZlKTOGxdtCSglpPIhWtYTRQzOw52gdxqp8otDw/FQkWrQ9FIVoX2aqGTVN\n7Rhos8LDS9RCSkgfBExIFy1apNhOPvnkE9+/586d2y3BBYDi4mJs2bJFsX1q1bFKByaOVKYbkmMZ\n6rKPE4IoQx+FWpvDBibjrf8eU31Cel5eSqxDIMRb+qm+DQNtVrjPtJQSQnqH+hdUpqXdgyRL+AsQ\n+MMyDLWQxgml17EPxLtevBUV1eoY2B/KeuGExEpWqgX2M7VIPbwII3XZE9JrdPbEMY7VUR3SOOHy\niFGb/X5RUSa+PlQTlX0F42zjkZSgzA0aIUozGljwgvemnxd6XtmJENKzgGdPx5KeHo8nasH0d80t\nbsVaR4GOLntqIdUSnU7nt1XQ7YneakQcy8Bq1qPR6Q7+5AhrdLq6FMUPRKfTqa7sG+kfOp+u/qrT\n0GeTkNAETEjXr18PALj11lujFkx/V1bpQOGAZMW2R2WftIdjdRCE7jcRrigmpAAwaVQOtu+ritr+\nAml0uJGWaAr6PAPH+FqqCFETg56BmxdjHQYhqhewDzAhIQHTpk2D3W5HSUlJt9+/9957EQ2sPzpZ\n04ILC9MV2x7LMuDPrPlNtEF/plX73MkRLo8Q1YL1VrMevOBdJz6WM4cbnG4UDgx+k6bnGHgEkcpA\nkaizWvRwtgXuSTTpWXh4EeY4WDGPkEgKeIa89NJLOHjwIFasWIGVK1dGM6Z+S5CUHYNELaTaw3EM\neD9/M5dHRIo1eNe1kiaOzMKuA9WYMmZAVPfbmbPNg0Rz8PJOemohJTGSnWZBdUNbwN8b9Cy1kBIS\ngoDZj9VqxUUXXYQXX3wRI0eOBAAIgoARI0bg4osvjlqA/YUkKbM6U2cczbLXhM41RjmWCdhlH+3W\nv5z0BNgb2mM+0z2UVeMMHEsJKYmJYAmpUc/CzdNnk5BggvYhOJ1OzJs3DxkZGRBFEXa7HX/84x8x\nbty4aMTXb1TWtSI3I0HRbbKsDoJEF0K1EyXZ1zKuDzARzdtlH/3u6AsL0rC/rAGjFBxKEgneLnv6\nrJPoS08yob7ZFfD3RgO1kBISiqD9w2vXrsW6deuwefNmvPfee3jmmWewZs2aaMTWrxyrbEZBbpKi\n2+RYBiJ12ate53Ix3i777omVm49e2afOivJTcaiiMer7DZdBT132JDYYRoeeOhGM1GVPSEiCJqQt\nLS2YOHGi7+fi4mK0t7dHNKj+qN7hQnpS8NnE4aCyT9rAi5JvFaZAy716k9bory+v0+kwwJaAUzUt\nUd93OMul6lkGvEBf+iQ2ehpVYtSz8Hjos0lIMEETUoZhcPr0ad/Pp06dAsv235msvCBGLMkLZaxc\nOFia1KQJgnA2Ie1pco7Sn49QXTw8C18etEd9vy3tPKwhTGgCAL2epS57EjOB6gcDVPaJkFAF7QNc\nuHAhbr31VhQXFwMAtm/fjkceeSTiganV0VPNYBgdzh+Uqtg221wCzBHojuUYBiK1kKoeL0rQdx5D\nqrLESs8xsBg5OFo9UV01qdHpRmoINUgBbx3S5hZ1HTeiXeG0zgPecaSBltulLntCQhM0C7r66qtR\nUFCAXbt2QZZl3HvvvSgsLIxGbKpkMLBobecV3WZ5tQNDcpQdPwqc6f6lFUJUjxckcL4uewatvPpq\nx156YQ6+2FeF6ybmR22fDU430pJCK3Wl5xjwIn3pE2WIkgw2jBJ82ekWVNa3+v2d0cDCTV32hAQV\nUrNcQUEBCgoKIh2LJhg5Fo28sksqHq9y4KrxAxXdJkBjSLVC6NRCqta/WVKCAe1uAbwgQs9FZ8hO\no9ONwdmJIT1XzzHgqbQOUQgvnD0nQ5GdZoElQOF7aiElJDTKVWHvJwwRKOERqRnUDENrKGsB32kM\nKcepMyEFgIkjsrDrh+iNJQ1niICBozGkRDneFtLQx2xbzXpcV+y/94BjGYh0HSYkKEpIw2Q8swyc\nUsIdq0Tij9BpBr2e1am2fNEAmxVVdW1R+8zKcuiLRdBKTURJ4baQAlB8YRNC+pugZ9xDDz0UjTg0\nw8Axiq66Udvsgi3ZrNj2iPZ4yz55u8HV2mXfYfjgVPygwrqk3oSUukWJMgRR2WWcCSHBBT3jDh06\nRK14nSjdQlp2uhkFA5IV2x7RHl6QfS2k3i579Z5vFwxJw4HyhliH0Q11ixIleRNSavEkJJqCDly0\n2WyYMWMGRo8ejYSEs0tblpaWRjQwtWKYwPXmeqOqvg0Xj8hSbHtEe3hRPFuHlFV317NOp0N2mgVV\n9a3ISVd2qdvO6CaYxJIgytRCSkiUBU1Ix44di7Fjx0Yjln5JRujj5Eh8EgS5x1n2giiBZdTzGZk4\nIgvv76zA7MmRq7zR5hZgMYVWFJ8QpQni2VJshJDoCJqQLlq0CC6XCxUVFRg2bBg8Hg9MJmWXuOyv\neEGku3ASdOlQlyc269gHYtCzMOqZsFZSClejw420xNBqkBKiNOqyJyT6gmZDe/bswdVXX40FCxag\npqYGU6ZMwbfffhuN2OJehb0F+Vmh1Vkk8UvoVBjf3/Kgbo8Io0Fdy/VeemEOvthbFbHtNzjdSKWE\nlMSI0ItZ9oSQvgl6xq1duxZ/+9vfkJKSguzsbDzxxBNYvXp1NGKLe2WVDgzJVX6FJqItnZcO9cfl\nEWBSWUKaYjWi1cVHrCJAo9NFCSmJGZ7GkBISdUHPOJfLhaFDh/p+njJlCkRaok8RLe0eJFmitzY4\nUafOLaT+qK3LvsNFRZn4+mBNRLbd3OJBspXODRIbIpV9iqh9ZfWxDoGoUNAzjuM4NDc3+7oSy8rK\nIh4UIf2JFKQAvIsXVddCCgCDshJxsqYlIjPiJVkGy1BCQGKDpzGkEfXd4dpYh0BUKOgV/7777sP/\n+T//B1VVVXjggQdw++2347777otGbHGtucVNraMkJC63OhNSADgvLwWHTzbFOgxCFKX0LHudTtly\ngVrX1OKJdQhEhYKecVdccQWeffZZ/OpXv8K4cePw6quvYtq0aSFtvKWlBTNnzsSpU6cAADt27EBJ\nSQmmTp2Kp556yve8gwcPYs6cOZg2bRpWrFgBQRB6+Xa0o6zSgUIqiE9CoMYxpB1GDU3HnmPU/Ubi\niyDK4BRsoTdwDHgFV/jTuqYWd6xDICoU0hknCAIkSQLHcdDrQyvzsmfPHtx+++04fvw4AO9Y1OXL\nl+P555/HBx98gP379+PTTz8FADz44INYuXIlPvzwQ8iyjDfffLN370ZDTta0IC/TGuswiAa4eXWO\nIQW863fbUsywN7bFOhRCFKN0C6lBz8Kt4Ap/Wtfc6qHFLyLsZE1LrEMIW9Az7u2338a8efOwb98+\n7N69G3PnzsWHH34YdMNvvvkmHnnkEWRmZgIA9u7di/z8fOTl5YHjOJSUlGDr1q04ffo0XC4XxowZ\nAwCYPXs2tm7d2se3pX6CRIPmSWhcKiz71FnxyCzs3F+t2Pba3YJqE3DSP3jLPik3htRICWkXbW5B\n1SvSxYNt35+OdQhhC3rV/9vf/obNmzf7EsvKykosWLAgaLf9uaWhampqYLPZfD9nZmbCbrd3e9xm\ns8Fut4f1JgAgPT16rY0WixEZGVa/NSPPZbN1rzMqSjISEox+f6e0aO0n0uLhPQRy7t/o3J/1Bg4D\ncpJD+ryFIhLHMimxBmarSZFC+SeqHRg8ICXsOKP9WY/nz2Q0qfE4Gs0GZGUlKXZjZMtwwGI1RfS9\nqvE4+iNKMswGDtYkM5Kt6ivtppXjGEyrS9Tcewl6tun1el8yCgC5ubkhd9t35q95XqfTBXw8XPX1\nLZCk6HQB8B4elVXNMOh7brWy2RJRW+vs9vjJmhakWvR+f6e01lZ3VPYTSYGOY7w492/k7+e6OmW6\nXyJ1LMcUpGHLf4/gmovy+rytYycaYNSzYccZzc96vH8mo0Wtx7G5uR3NjW1wKrRkr7vdg2q7Awlc\nZGbuq/U4+tPmEpBo0eNUVTM8KeZYh9OFlo5jMNV1LTF7L71NhAMmpAcOHAAAnH/++Vi1ahVuvfVW\nsCyLd955B+PGjQt7R1lZWairq/P9XFNTg8zMzG6P19bWdkmA1aij+yVYQhpIWWUzRg5JUzgqQmIn\nLckER5syM2cbHW4MH5zaq9fKsqxYSzLpvyRZBqNQMgpQl31nLo+AZKsBbg8dj0hqcLo1dz0MmJAu\nXry4y8/btm3z/Vun06G0tDSsHY0ePRrl5eWoqKjAwIED8f7772POnDkYMGAAjEYjdu/ejfHjx2Pz\n5s2YPHlyeO8iygx6Fp4+zJisd7iQnmRSMCJCYs9kYBVZ5rSpxY2UXnTl6TkGgihBz6l3vC3pnwx6\nlmaWn9HuEZFiNVJCGkGSJMPDi5q7HgZMSD/55BNFd2Q0GrFmzRosXrwYbrcbU6ZMwfTp0wEA69at\nQ2lpKVpbWzFixAjceeediu5baUrc7WrproWQUAzKSsSJGieGDUzp03ZEqXfLNuo5FrygrQsw6R+o\nhfQsl0dAitUAlyf+yzvGSptbQEqiES6PqKnrYdAxpLW1tdi0aROamroWv37ooYdC2kHnxLa4uBhb\ntmzp9pyioiJs3LgxpO2pgVHPwiP07uLS5hJgphnEJA7lZyfiywP2PiekvaXnGHgECZaY7J2QwIx9\n7FWLJ64zLaQuaiGNmDYXj7REbyt0ooYuiCGt1LR3717Istzlv/7MoGd63d1QXu3AkJwkhSMKjFYI\nIdGSZDHA2c7HbP+GMwkpIWpjPDOchXhXnktOoIQ0ktrcAtKSTJo7xkGb6niex7PPPhuNWDTDqGfR\n6updd8PxKgeuGj9Q4YgC41gdRFEGE6HZnYSohZ5jqLYhUSXqsj/L5RGQnW6hMbUR1OoSkJZkhEtj\nn7mgLaQjR47E4cOHoxGLZni7X3r3h3bzUlSLfrOMd6IHIdFgMrB9Ghvm9ogw9rJ6hYFjwfdyKA0h\nkcSxOroOn+HyiEhJMNIY0ghqdwlITTRp7hgHzYzGjRuHG2+8ETabDRx39un/+c9/IhqYmhn0TK/u\ndr1DHaLbfc6xOohRqs9KlNExzILR4MS3QVlWnLC34Ly83o0jbXC6kJrYu2LZeo6hcXpElWgS61ku\nj7cOKfVmRE5rpzGkWhI0IX322Wexbt06DBo0KBrxaEJvW0hrm9phi3IhYI6lFlK1O/fLyjvMQgLD\nsZAkbSWmg7OTsGN/da8T0kanu08JKX3JEaJu3koYtGx2JLW5BRTkJuOEXVtF/oMmpMnJybjuuuui\nEYtmGPQs3L1oiTlW6UBhbnIEIgqMpa4iVfM3SZBjGfCCDD2n/nXsz2U169HSh4lNjU43hvUymTXo\nGbT1cmw3ISR6qMU4stweEckJhvib1HT55Zdj7dq1mDp1KgwGg+/xkSNHRjQwNettC2l1fRsmjMiK\nQESBcSwDUaQue7XyV3Ozc6u2yyPApKGEtK8anW6kWg3Bn+iHnmV6XY6NEELiicmgvYl0QRPS9957\nDwDw4Ycf+h7T6XT9egwpw/SulJKM6He/Upe9uvGC1C0h7VhxCADcvBjVSXBKMBtYtLsFmI3hx92X\nlUX0epa67AkhBB1j6uMsIVV6xab+ihdE6Hux+kxfcYwOArWQqhYvdh9PxbE68L4WUm112QPAoOxE\nnLA7cf6g3q1H31sGGkNKiOpRd310aPE4B01I//rXv/p9/O6771Y8mHhWYW/BoKzEqO+XZRkIEn1J\nq5XgZ4B/xxKYAOByCzBrLCHNz0rE9n1VMUlIqTA+IerW24V1BFHCv3ZVYMYlgzU10ZOELmhC2rkG\nqcfjwe7duzFhwoSIBhWPyk43o/iC7Kjvt6MwPlEnXpS6tZx3rlno8ohISzLFIrRes5r1vV44oi+8\ns+y11UVFCAlNo9MNZzuPt7cdw+wpBWAZmqkfb4ImpI8//niXnxsaGkJex56c1eLikWjp3WSNvqAx\npOomCBK4c1tIWQaC4L2JcPHa67LvLV4Q+1QOxludgD7rhMSj+mYXxg7NQGqSCW98chQ3X17Y6/Hm\nRJ3CvvqnpaXh9OnTkYiFRIC37BO1kKqV/xZSpssYUi3Osjcb2bBLMPWlBimgzTFThJDQ1DtcSE82\nITvNgukXD8IbnxxFu5vKvJ2rt0Mi1CCsMaSyLGP//v1IT0+PaFDxpqnFjeSE3n/R9gXHMBCphVS1\nBEEGx51TGJ9jfBdarZZ9GpyViAq7E8PzQx9H2uBwIzVRW8MTCAmHLMv9+sapc7Kk0+m8C38woR2P\nBufZ60Nakgk3TBqCjZ8ew6zLCmA16yMSrxZ5BAmGXi6/HGtBW0gPHz7s++/IkSPIycnBunXrohFb\n3CirdKAgNykm++ZYHQRaOlS1/FVf8HbZnyn75BE1eXHJz05ERXV4q4Q0Ot1I60MLKSFqpqdJd2fK\nunmvd0Z9eHUyxXMqkiRaDLhpSiE2fV6GRqdb8Vi1qs0lwGLSVqnADmGPISXhO1nTglGFsWlVpjGk\n6saLMhLOaQHtXPYJgCZnlFpMerSF2Z3W4HRhXKItQhEREppwWu3C0ZGAGTV4g6mUds/ZusomAwuX\nR+xVveIOZiOHW68Yio3bjuGqnwxEVqpFqVA1q83Fw9KHYxpLAaNetmxZwBfpdDo89thjEQkoHolS\n9+Ln0UJLh6obL/gZQ8r1z5sIXpD69Zc1UQdejMz12mhg4fGIQD/OmTqPiVdqJSGDnsUtVw7FO5+V\nYeKIrJiUV1STVpeABJM2hzAETEiHDRvW7bHGxkb8/e9/x4ABAyIaVDyRpOivztQZLR2qboKfwvh6\nlomLiWgWI4dWF6/ZiyPpn4RIJaRhdlHHI5f77Jh4o4GFyxNaL0qwiTocy+Cmywux5YtyuHkRwwam\n9DlWrWpzC0hO8Fb00em8q0pqpZctYEL605/+tMvPO3bswJIlS1BSUoLS0tKIB6YFoQxQP13XityM\nhChF1B112aub3xbSOClf1DGOdMTgtFiHQkjIBFGGno1Ul732z+u+cHXustezcHtCS9CdbcHLJjI6\nHW6YNARbvzwBl0fEhQX9c/J1m4tHTrq3Gd545hj3ZVhENAW9DRQEAWvXrsUDDzyAFStWYPXq1TCb\nzdGITdX0IXarHqtsRmFuchQi8o+lpUNVTRC71yGNl5uI/DMz7QnREkGQwFILaUR06bI3cmgPMSGt\nd7iQHsICITqdDtdOzEddUzu+PlTTp1i1qs0l+MaQGhUaFhEtPZ51FRUVuOWWW7Bv3z5s2rQJ06dP\nj1ZcqmcI8W63weFCWlLsZg5zLAORlg5VLd7v0qHx0UJqMXFod4d2MRTEyCQBhIRL8FMbWAlaSw4i\nweURYOpIlsJoIa1v9tYgDdUV4wZCECR8vqeyV3FqWZv77Cz7joljWhHwrNu4cSNuvvlmXHPNNdiw\nYQNycnKiGZfqGfUsPCFeXGJZd44K46ubv/FqXD+ciNbkdCPVSiWfSOz567VQgiGM74x45fKIMHea\n1BTqGNJQW0g7K74gG1azHh9/fTLsOLVMkmTfsqrhDItQg4ADC0pLS8EwDP70pz/hz3/+s+/xjnGT\n3377bVQCVCuDngl6t9vSzsNsiO3YDUan0/TKDfHO3xjSeCqcnWDi0NLOBy1c3eB0IzWGPQmEdBBE\nGVxEyj4xmkoOIqHbLPsQj0e7u3e1NceeZ8PBika8t+M4Zhbnx9W1NRThJP1qEPAv/J///CeacWiO\nt4W051asIycaY1YQn2iDJEem5qFaDM72jiMdGWRiU4PThfx+Xq6FqAMfoRZSGkPq7bI3dp5lH4Xj\nMTw/FSYDi3c+K8Osywri+np7LqOBg7PNE+swQhYwIaXSTj0zhHBxOXqqCROLqNA36R0pDpYZHJSV\niG3fnQ6akDY63RhdmBGlqAgJTKSyTxHTuTuZZRhIUVpFcEhOEgx6Fm/+9yhuurwwZnXBo81kYFHX\nrJ3PXP/4q0RAKBeXziUuCAmX26P9VV3MIc6k1VJpEhLf+AhNaoqXyYpaNSAjAVePH4g3Pjnab4ZO\naG1SU0y+Ae68807U19eD47y7X7VqFU6cOIEXXngBPM9j/vz5mDt3bixCC5lRz6K5JfD6uTRuk/SV\nmz873oqEhmV0ECXJ1wpDSLgEQQZrUr5nQuu9HbGi5I15RooZM4vz8ea2o5g9uSDuF+0wUkLaM1mW\nUVZWhm3btvkSUrvdjvvvvx/vvPMODAYDbrvtNkyYMAFDhw6NdnghCzapqbap3VeclpDecHlE33gr\nLbOa9SFNbFKCnvOO7TYbKSElvROpsk+kd+oc4ZV8CibZasScyQV4+7MyXH/JYCTHcXUPo15bk5qi\nftaVlZVBp9PhF7/4Ba6//nps2LABO3bswMSJE5GSkgKLxYJp06Zh69at0Q4tLMFW3ThW6cD5+bRC\nDek9l0eIixbSwdmJOF7tiMq+qFuU9FWkyj6R3vHW8lYuIQUAi0mPWy4fivd3VqCuqV3RbcfSuUuV\nc2z0xukqIeotpA6HA8XFxXj00Ufhcrlw55134tprr4XNdnbyT2ZmJvbu3RvWdtPTrUqH2qOERBMO\nnGiCzeZ/ZrDDdQoDbFZVzOhLSDAGjFMrtB5/INYAf5uEBCNMFiOyzQbF33u0j6U1yYx/bi8PuF9B\nlJCYaFIkrvRUCxKTzLClRb53Il4/k9GmtuNotjQg05YYkc+QxRK5a7HajqM/534XhXI83EfrMXpI\nOtKTlV8hcuEtY/GPD37ANRcPwqBsb0UcLRzHQJxtHtjSE7q8By19/0c9IR07dizGjh0LALBYLLjp\nppvw+OOP49577+3yvLy5flUAACAASURBVHDH29TXt0T1TkCUJNQ3tqG21v/SiK2tbjCMLuDvo6m1\n1a2KOHrLZkvUdPw9aW3z+H1vra1uVNc4kZxgUPS9x+pY9nSuNDhc0OugSFwulwfVNQ7oxMiOm4rn\nz2Q0qfE4Nja1w9HcBiYCn6G2tshci9V4HP0597solONxqqoZ4wrTUFsbma7nmRMGYdPnZRg7zIYJ\nowdo4jgGUtPYBkkQu7yHWHz/9zYBjnq/xDfffIOdO3f6fpZlGQMGDEBdXZ3vsZqaGmRmZkY7tLD0\nVLLCw4v9pqwEiZx46bIPpsHpRlqiMuO4DBxLXfakT/gIlX0ivcdEcEIYw+gwe3IBfjjegD1HaiO2\nn2ho6+UCAmoR9bPO6XTiiSeegNvtRktLCzZt2oQnn3wSO3fuRENDA9rb2/HRRx9h8uTJ0Q5NMSfs\nLRicrY0mcqJe8VQ2LNGshyNAgeZGpxupCiWkeo6BhxJS0gfe5XxjP9SKRI9Op8PMSwajotqB7w5r\nNyltdQmarhwQ9YT0iiuuwJQpU3DjjTdizpw5mDNnDsaPH4/7778fd955J2688UbMnDkTo0aNinZo\niimrbMaQHPWs0MScKYVDtCUe6pB2GJyTiIrqwF32qYnKTFow0KQm0keCKFMLaQRIshzRlk4lXH9Z\nIVpcPHbur451KL3S7hJg0XA955hE/pvf/Aa/+c1vujxWUlKCkpKSWISjuBYXj0SLIdZh+KRYjWhu\n8Sg+U5FEltsTP3VIB2Um4t+7T+LCgvRuv2t3CzAblXmfeo5FS7t2lsoj6iOKElgVTEaNN26PCMM5\nN9gsy5xpkfZ/AyBKUtQnBl82KhffHKrBf789hSvGDYzqvvuq1cVTlz1Rt7QkI+odrliHQcIUT+vc\nGw09l0lTqmi4gWPg6WE/hISCitgrz+URYTrnxjPYSkKNTjdSY1An9CdFmbClmPGvXRWaWuSGxpCS\nLppa3EhOUFeh3fQkEyWkpF+gOqRE7bSU4CjJ3yRNU5DC7Q0ON9IULIofjgsK0jFsYAre/aIckkb+\nZlof5kUJqcLKKh0oyFXP+FEASEs0ocEReJlTQqIhyaJHc2tku9P1HANepISUqJOe83ZR90f+Jmma\njFyP68rXN7uQEcOhZkMHJmP8+ZnYuO2YZv5uWm7dp4RUYSdrWpCXGd0i/cEYDSw8PSxzSkg0DM5J\nQsU5KzaJkqToBdSgp886US9DkBX+4pnLLcB8Tgupd2nLwOervbFN0WVDeyMv04opY3Lx5idHNXlt\n6RinqwWUkCpMlKiGHQlNsK47jfQShSwv04oT9pYuj+05Wo8Rg1MV24eepS57NXtvezne216O3T/W\noqapvd91Xxv1bI8tgvHMbwupgYUrQJInyzJESR0VD7JSLbh2Yj7e+O9RtLu1szY80DEsQhufOe2O\nflUhUZJUX9aCqIcoBZ60xLGMZi4ioTLq2W41Qo+easbNVxQqtg+9nhJStapvdsFq1mPSqBxU1rXh\nxxON+O5YPdrODONITzYhL9OKARkJ3WZjxwujnoVbg61sSnD5qRpiMrBwNvF+n19e5cTgbPUMf0tN\nNGLWZQXYuO0YbrhsCJJUVEmnJyaD9ybIalZ/fVJKSBV0urYVA2zq6q4n6iWIEvQB7v45loEg+r9Q\nx4uaxjbYUs2KdtkzOh36V5ubdnx7pBYXFWVCz7HIz05Efnaib8lLWZZR73DhZE0LDpQ3+G4qTEYW\neTYr8jKtSI7BbGul9aeEdNcP1ZgwPMt3fvub1OStvuG/xXF/WT2mTxgU8TjDYTXrcdPlhXj702O4\nbmK+JkopGg09TxxTE0pI+0iWZd8JV1blwCg/dRbVwGTgztR7pD+5WrS5hG5lUDrE62zx5AQDmlrc\nSLEasesHO675SZ7i++hv3cBa0dziQUqApFKn0yEj2YyMZDPGDrP5Hm93CzhZ04Jvfqz1TYhjdEB2\nmgV5WYnITjODZZTr0m1z8RE974x6RpPjEMO1+8cafHe4DnmZiRiQkQAgUJe9/0lNsizDI0iqbCk3\nGznceuVQvLXtGK4cNxDZaZZYh9SjnoZFqA1lJ33gnTEpQ895E1LvijPqvItPSzKiwenGAEpIVaOq\nvg256Ql+f6fnGLBxuHzh4JxEHK92YuRgPQRRohukfqLR6UayNfwuTrORw3l5KTgvL8X3mChJsDe0\n40SNE98cqoEkeW9AkhIMyMu0YqDN2qtajJIkY9Pn5bhh0pCwXxsqg4FFS1t893ycrGlBdUMbbr1y\nKPYcq/clpG5ehEHf9eYh0PjGE/YWDMpSb2+jnmNx65VD8c5nZbi4KAv5KlgqPNCNuMnAaWb4F30b\n9IGB83a/6LmzJ5laSy6kJZnQ4HD5Lg4k9k7XtuCSC3P8/o5jdXGzjn1neTYrPvz6JNrdAn5yfmas\nwyFR8t2RWow/zxb8iSFgGQa5GQnIPeda1tzqwckaJz7bU4m2MxNP9ByDARkJGJRpRXqyqcfr8z93\nHsflYwdEdKydUc+ino/fmtDONg++2FuFW68aCkanQ6Oz63s99/gb9IzfIQx7j9XhmouU7z1REssw\nmDOlEO9tPw43L3a5aYqFQC3KRj2LlnZt3ATF3zdeFPnKKZn1aHPxqm7tSU8yYc+xuliHQTppdQkB\nv/w4lombZUM7M+hZ8IKEkzUtKB6ZHetwSJQ0ONwRH2+XnGBA8pB0XDDk7LApXhBxuq4VP1Q0or75\nbHKUkWxCXpZ3ApWeY7HzQHWX7uVIiccxpKIkoa7Zher6Nuw9Vo+bLi/sMrm387C2c/l7XJZluPju\n3ftqxOh0uP7Swfjwq5NweQSMKsyIWSy1je1+ewZMRu1UdlD/X1zFDNzZu7vyKicKctQzI/BcyQkG\nNLfQGt9aoY/ThBTwjgscrIIuLhIdjlYPkiyxmeGr51gMzk7qMltblmXUN7twoqYF+8q8E6jSEo1R\nuUEy6lnNLm3b0s6juqEN1fVtqHe4fF3E3vG/JmSnWXDzFYVdEsnMFAtqGtuRlWYJuffwdG0rBmpo\ncrBOp8P0CYOw7fvT+OqgHRcPz4rq/gVRwn+/PQ2PIOJqP2Pyg62GpSaUkPZB54vL8WoHrh6v3i4G\nhtHRZA8VCfa34Lj4TUgnXZiDnHR1TwQgyvn2SC3GKtRdrwSdToeMFDMyUswYF+W4vLPK1dtaJYgS\napvavYlnQxtcbhE6nbcmcoKJQ3a6BeflJSMjOTtgybrOzh+UgoMVjcgKY+LPnmN1uGLswL68jZi4\nfMwA7PqhGp/tqcTk0blR2Wd5lQM79lfjirEDug1h6WAycDSpqT8wdLq4uHkJxjhNIIjyvF2YgSfA\ncSwDoz4+T89ITwBQ6zju/qquyQVbijnWYaiCgVPHLHtnmwdV9d6ks8Hh8i3CwTI62FLMyE63oGhQ\nap+HoWUkm1Db1B7Wa9rdYq8mpanBxBHZ+P5oHT766gSuuSgvotciSZKx64Add1w9rMf9GPQMddn3\nB0bO2xROLY8kXKfreq5Zm51moTXZe4nOx9gSRAkuj4h2twBHqwcJZvqa6fD/27vzqKjO+w3gz50Z\nZoZ92EFAECGAihuxpJq41N2KiUn96alNNB5rY4MmetLT5CR/mKQ2VXuOTU5at9JqamJdY9RSY4wB\njGvN0YpGBcEFXEAZZR2Y7f7+AEY0VhBevXeY5/OPzjBwvjzcmfnOe9/7vo/zw5Ld4UT5LQuuV9aj\n/FY9Gq1No50A4OvthahgH/SKC0JwoPGRbegiSRIe5kdfq6xz+7Mn/RNDce7yLew8eBGZg+Mf2d/8\n2LkKDEoNb/Pnu9MHdL5SdILeS4OqOicqblsQ7gYjAJIkwfmA3YHo8bl6s+6Bp3XUunwYdV1Op4wG\nqx2WxqZm0vKD/9vbNdKi1UjwNujgbdDBqNdh2GM6femJZFlGdZ0V16sbcbbkJm5VNwIAJKl5tDPI\nG1HBvujdI0ixi4RCAoy42c5tYk8U3cTQ/u5/vCR3D4JBr8XWvBI8PzRB+HuuU5ZRcrX6sc9XfdTY\nkHZCy1X2JVer0TNavRc0tQjw1aOqzspmRwUsjXa3PS1F6iLLMhqsDteo5L3NZEOjHQ1WB5xtNAQa\nSYJRr3U1k94GHXy9dQgNNDbf1sLgpXWrEZeuwmZ3oNzcNLez/Fa969oFSQL8ffRISQhB34QQmPwN\nqtu+Orl7EM6V3m7XY+sa7PA1qn+Ly/aIjwyAwUuLTd+cxwvDet61PGRnHS+88djnPz8OfEfshJZ1\nSM01jcjopf5PKiEBBphr1Lt4P5EI7tIwtexG09Boh6V1M3nPqGTLwu//iyRJMHhpYTRo4dM8Kulj\n0MHkZ4C3vmWkUsszIyonyzJu11pxvbIO1831uN1qVRQvnQbhQd6IDPZBWs8QGO5Zb7JlC1Y1igjy\nxoGCa9C2cfyV36pHeJD6zzQ+jKgQX4x+Mhab9p3HC8MThIxSy7KMorIqTBuZJKBCdWFD2glNV9k7\nAMiq+1R6P8EBRlyrrEdP9z8j4tacsvxQ86ro4TyOOaQ2uxPV9damZvKeBrLltr0dc4D1Xlp4N49K\nGg06eOu1CDMZW53y1kKnFTeyQsqzO2RcLq9pHu20wN5qq1KTvwGRwT7onxQGk5/ebT5cPYgkSXA4\n5Ta3AT1eeBOD07re2sQhgUZkDonH5m+KMXloQqc3Xvjv+Ur066nOLco7iw1pJ+i9NKhtsMHLTd4w\nQgKMOFViVroMj3ezqgEhgY92kXC6P4fTCUuj4z6jkk23Gxrt7VqWJyDACNnuhFHfPCpp0CHI34Bu\nob6uUUmRp+io6+gZHYCKWxZ0C/XFgKRQeOm6/uosoYFGOB4w0i/LMmotNgT4PPz2su4gwFePF4b1\nxNb8YmQOjofJr2NnKWVZxplLtzBtZKLgCtWBDWkn6LQaXLharfotzlp4G3Rus0BuV3bVzRZ+djcm\nfwO27y+579c0Ggneeh2MBm1T42jUwd9Hj/Ag76ZRSb0Oei9NmyNTaj5FSuo2IKnrzf1rS6/4YFyv\nrL/v1wxeWlytrEfIA5bB6wp8jDr834hEbMktxuhBsR26EPrUBTPSEoK7xMj5/bAh7aTSG3VI6Bao\ndBnkRq5W1iElTtl9j7uy4f2jlS6BiFqJDPZB5P9YHN+o1+LQqesY/aT7LYb/sAxeWkz9SSK25hVj\nSFrUQw1MyLKMUyXmLjs6CgA8p9RJ3UJ8Oj0nhDxLo9U99mkmInrUDHotblZZENjB09juRqfVYMqI\nRBw9U4HiK1Xt/r4zl24hNT6oy46OAmxIO81dTtcTERGpjVGvQ89ozzrLqJEkTH6mB85evoXTF9t3\nXcfJ4s5dzOQOG4awIe0kd5sP5KXTqHov5a6OGxMQEd2RFBOIIX2ilC7jsZMkCT/9cTyuV9bju3M3\nHvjYc5dvISnG1OHR0aYVgdS/8x/PG3qYhG6B2HHgQrtXBtBIErRaCVqNBjqtBK1WA51Ggk6rcd2v\n1UrQaSXoXP/XQKtpfqxWglZz577W/3piY1Zx24IwE6+wJyICmhb292Qj02Nw8NQ1bM0rhp+3F+Ij\n/dE9wh/ehjvt2fGim/i/n3R87qhBr0WDzQGDXt0rOqiqId25cydWrFgBm82GmTNnYvr06UqX1OWk\nxgUhNS6o3Y93OmU4nE7YHTIcThl2hxN2h7P5/zIcrv874XDIaLQ5UN9gb35803321o9p9di2do5p\n4etrQF1d40P9nprmplfX3BhrWzXGd5riuxvtuxvmuxtrbasmvDNrzl65UYfoUF5hT0RETQY3jxDX\nWmy4VF6D3ONX0NC8Ta8MGT2jAzv1vmPUa9FotQO+6m7+VdOQlpeXY/ny5di2bRv0ej2mTZuGjIwM\nJCZ23SvK3IFGI0Gj0cJLwSPlYZfYkeWmZrepGZZhb26MWzfPrRtsh1OGrfFOE333v3ca6Jb7OjMX\np7KqAS+OTe7w9xMRUdfk5+2F3vHB6B0f7Lqv1mKDbye3mTZ46VwNrpqppiE9ePAgnnrqKZhMTcvh\njB07Frt370ZWVpbClZG7kSQJWkmCVgPouQACERG5KRGr+MRF+LnFRh2qaUgrKioQFnbnAqHw8HCc\nPHmy3d8fEqLO06BhYf5Kl9AlMEdxmKUYzFEM5igGcxSjK+boLr+TahrS+50GfZgryiora+F8wNZk\nSuBuLmIwR3GYpRjMUQzmKAZzFIM5itHRBlg1Y7gRERG4efOm63ZFRQXCw8MVrIiIiIiIHgfVNKSD\nBw/GoUOHYDabYbFYsGfPHgwdOlTpsoiIiIjoEVPNKfuIiAgsWLAAL730Emw2G372s5+hb9++SpdF\nRERERI+YahpSAMjMzERmZqbSZRARERHRY6SaU/ZERERE5JlUNULaGWrdhlKtdbkb5igOsxSDOYrB\nHMVgjmIwR+VIcme2nSEiIiIi6iSesiciIiIiRbEhJSIiIiJFsSElIiIiIkWxISUiIiIiRbEhJSIi\nIiJFsSElIiIiIkWxISUiIiIiRbEhJSIiIiJFsSElIiIiIkWxISXV4KZhREREnokNKamGw+FQugQi\nAE0fji5cuKB0GV0KP3CKwRypq2JD2gGyLOPEiRNYsmQJqqqqlC7H7e3cuRPPPfcccnNzlS7FrW3d\nuhWrV6/Gnj17APCNq6P27t2LH//4x/jjH/+odClu7fPPP8df//pXfPvttwB4PHbUF198gc8++wyH\nDh0CAEiSpHBF7mn79u3YsmULzp49C4DHY2ccP34cX331FQCxObIh7QBJknD16lV89tlnOHHiBEf2\nOqi8vByZmZk4cOAAli9fjlGjRrm+xheL9pFlGVarFUuWLMHOnTvh4+ODNWvWwGw2843rIZ08eRJz\n5szBpk2bMHr0aPTr1w8A4HQ6Fa7MfciyDIvFgrfffhvbt2+Hv78/Xn31VZw+fRoajYbP64dQW1uL\nhQsX4osvvoCvry/efvtt7NixAxaLRenS3IYsy2hoaMA777yDLVu2oKqqCvPnz8ehQ4f4+tgJu3bt\nwj/+8Q80NjYKzVEn7Cd5mPLyckRERCA/Px8JCQmIjY1VuiS3I8syTCYTZsyYgX//+98wm80IDw/H\nnDlz+GLRTpIkweFw4OLFi1iyZAkiIiIwbtw4mEwmpUtzKw0NDVi7di3Gjx+PyZMnY82aNbhy5QoA\nQKPh5/b2kiQJkiShpqYGixcvRkxMDE6dOoXKykrX16l9ampqYLPZ8OGHH8Lf3x9OpxMff/wxIiIi\nkJGRoXR5bqHleKuursby5csRFhYGHx8frF69GpGRkejRo4fCFbqfc+fO4ejRowgODsaaNWuQlZUF\nWZaFPLe1ixYtWtT5Eru23NxcyLKMoKAg2Gw2aLValJSUYNiwYTh8+DBCQ0MRHx8Pu90OrVardLmq\nlpubC6fTiaCgINTU1KCsrAybNm1CZGQkhg8fjpUrV6KxsRFpaWnM8gFaH5NFRUU4fPgwxowZg08+\n+QQffvgh/vOf/8BisSAlJUXpUlUtNzcXdrsd4eHhGDlyJHr37g0AOHv2LIxGI9LT04W92HZlrY/H\nK1eu4Pjx4zh58iSOHz+O9evXQ6fToaKiAomJifDy8lK6XNVq/fr43Xff4fDhwxg3bhyMRiOCg4Ox\ne/dueHt7IykpCd7e3kqXq1rr1q1DeXk5JEmCl5cX9u3bh/T0dAQFBSEtLQ35+fmorq7GwIED+dxu\nQ0uWTqcTISEhOHPmDKxWK1588UVs3boV6enpMJlMQl4n+dG/DWazGdnZ2fjyyy8BwPVimp+fjwED\nBuDll1/GypUr8fLLL6OkpETJUlXv3iyjoqIQGBiIXr16Yd68ecjIyMDSpUuxe/duzs19gJYcd+/e\nDQDo1asXrl69ijVr1sBisWDlypUYOXIk1q9fj+LiYoWrVa+WHFvmQul0OtjtdtfXWuaa0YPdezzG\nxcVhzpw50Ov1OHbsGPbv34/x48cjLy8PR48eVbha9bo3x+HDh0OWZSxbtgyrVq3C+++/j1GjRmH/\n/v2w2WwKV6tO58+fx9SpU/Hdd9+hsLAQs2bNgslkgsPhwJEjR1yPmz59Onbt2oX6+noFq1W31lkW\nFRVh1qxZsFqtGDhwIF577TX06tULffr0QXZ2NgAxZz/YkLYhLy8Ply9fxrlz51yTyq1WK8LCwnD5\n8mVs3LgRlZWViI6O5mhUG1qyLCwsdF3o8Oyzz2Lu3LnQ6XSwWq1IS0uD0WjEgQMHFK5WvVrnuH//\nfgDA3Llz8fe//x1hYWEICwvDhAkTEBgYiLy8PIWrVa/WObY8t1tMmDABlZWVqKio4AhKG1rn2PK8\nTUxMRFxcHH76058iNDQUQ4cORWBgIAoKChSuVr1aciwqKkJ+fj4A4N1338WIESNw48YNTJkyBfPm\nzYNGo/nB8UpNzpw5g2eeeQYfffQR5s2bh4SEBOTk5GDmzJnYsGEDysvLYbfbkZ6eDj8/P3z99ddK\nl6xarbPMyspCSkoK/vnPf8LX1xeBgYEwGAwYP348ysrKXMdjZ+fbsyFtQ01NDWbMmIH09HTs2bMH\nDocDer0eJ0+exG9/+1uMHDkSq1atQmFhIY4fP85J+w/QOsu9e/fC4XAgJiYGdXV12LdvH6xWK6xW\nK4KDgzFw4ECly1Wt1jl+/fXXsNvtmDBhAgYNGoTi4mKUl5cDAAICAtC3b1+Fq1Wv+z23dbqmafUO\nhwPJycm4dOmSwlWq3/2e1wBQWFiIsrIy19kOSZKQlpamZKmq1jrHffv2weFwICEhAaNGjcLs2bMx\nbNgwWCwWhIaG4kc/+pHS5arS6dOnERgY6LodEBAAPz8/ZGRkIDExEWvXrkVxcTFkWUZoaKjrwkX6\noXuz9PPzQ1xcHICm6z80Gg2SkpIwZMgQ/PnPfwbQ+fn2nEN6j9bzIOx2O3x8fDB69GhYrVYUFBSg\nvr4eKSkp6NOnD7KystC7d2+EhYUhKCgIGRkZnPfYSltZWiwWJCcno66uDitWrMC+ffuwevVqPPHE\nE5gwYQIvJmnWVo51dXVITU3FgAEDcPDgQeTm5mL16tWIiYnB1KlTeUw2a+/xCDS9kW3evBne3t5I\nTU1lhq2053hMSUlB9+7dkZOTg7y8PKxYsQKxsbGYPn06s2zW3uPx9u3bWLVqFTZs2IDs7GykpqZi\n7NixfH1sJsuyK8sBAwYgKCgIJpMJ1dXVyM7OxogRIxATE4N+/fqhsLAQu3btQnZ2NhISEjBx4kTm\n2EpbWY4ePRrdunUDANf83KCgIAQEBCAlJcV1UWNHSbKHD+nl5+ejuLgYAwYMQP/+/V33O53Ouw7U\nmpoa7NixA6dOncIbb7yBkJAQAE2n7/V6/WOvW406kuWCBQsQHh6O27dv4+LFizCZTIiPj1egevXo\nSI4LFy5EWFgYrFYrSktLYTAYEBMTo0T5qtHZ5/axY8cQGxuLiIiIx167mnTmeV1bW4uLFy8iICAA\n3bt3V6J81ejM87qurg7FxcUICAjg62M7c/zmm2+wYsUKbNq0CQBQV1cHX19fnD9/Ht7e3oiOjn7s\ntatNR7O8ffv2I1nJxWNHSG02GxYvXoycnBxER0cjOzsbkZGRP7ha3uFwQKPRwGAwQK/X4/Tp07hw\n4YLrlAk/7Xcuy4sXLyIjIwNGoxGRkZEevVxRZ4/JlhH64OBgBAQEKPzbKEfUc7tbt27w8/NT8ldR\nlIjntV6vR3h4+F2n/jyNiOe1Xq9HREQEXx8fIsfNmzdj0KBBSEhIwKuvvopDhw5hzJgxHv/6CHQu\ny6ysLBw9ehRjxowRXpdHr0NaX1+P999/H0lJSaipqXENNbdcYLN06VJERkZi9uzZAJom6j/11FPw\n9fVVsmxVYpZiMEcxmKMYzFEM5ihGWzkuW7YMYWFhmDlzJkpKSvDtt99iy5YtePbZZzFz5kxli1cZ\nNWbpUQ3p7t27ERoail69eqGhoQG1tbX417/+BZPJhHXr1qGxsRHFxcXIzMzE/PnzkZSUhClTpgBo\nmluh0+kwYcIEXnULZikKcxSDOYrBHMVgjmJ0JMepU6dCr9ejvLwcffr0wZtvvunxI6KAe2TZ5eeQ\nyrIMs9mM119/HRqNxnVV96JFi3Djxg1s3rwZBw4cwJIlS2CxWPDee+/h3XffRXR0tGvuGBfGbsIs\nxWCOYjBHMZijGMxRDBE5Ak1br3rylBvA/bLs8nNIJUlCaWkpSktL8dFHH+Hpp59GQUEBPvnkE8ye\nPRulpaVIT0/HiBEjEB0djaKiInz//ffIzMyE0+l0LW9AzFIU5igGcxSDOYrBHMUQkaMkSbzYGO6X\npUcc/QUFBSgrKwMAGAwGvPXWWzh37hyOHTuGmpoa5Obmuh6r1WoxbNgwAE1ravEF4m7MUgzmKAZz\nFIM5isEcxWCO4rhTlh7xl5s8eTLOnj2LY8eOue77zW9+gz/96U+YNWsWrl27hjfffBPPPfcczGYz\nMjIyFKxW3ZilGMxRDOYoBnMUgzmKwRzFcass5S7i9OnT8saNG+XLly+77nM6nbLVapVlWZZXr14t\n//znP5dlWZYdDod88+ZNedasWXJ5ebl869Yt+b///a988uRJRWpXG2YpBnMUgzmKwRzFYI5iMEdx\nukqWbj+H1Ol04g9/+APWr18PPz8/bN++HaWlpRg0aBBsNptr7kNaWho2btyI6upqPPnkkygrK8OJ\nEyfw/PPPw9vbGxERER6/ADazFIM5isEcxWCOYjBHMZijOF0tS7df9un27du4fv061q1bB39/fxQU\nFGDKlCkYPXo0kpKSAAAff/wx/Pz88MEHH2Dx4sUoLCzE6dOnkZmZCY1GwysbmzFLMZijGMxRDOYo\nBnMUgzmK0+WyVG5w9uE5nU5ZlmX5/Pnz8qVLl2RZluXi4mJ54MCBssVikWVZlq9fvy6PHTtWfv31\n12WHwyFPnDhRXrhwoVxSUiLLsixXVVXJBw8evGto2xMxSzGYoxjMUQzmKAZzFIM5iuMJWbrdOqQ2\nmw0zZ87ExIkTtC0K1gAABeVJREFU8fzzz8NgMODXv/41HA4HXnrpJeTk5CA9PR2///3v8fnnn0On\n0yEqKgrAD/dn9XTMUgzmKAZzFIM5isEcxWCO4nT1LN1uDunBgwexdu1a+Pn5ISIiAlFRURg2bBgq\nKytx6NAhJCQk4Be/+AVKS0tRV1eHwYMHA3CPP8bjxizFYI5iMEcxmKMYzFEM5ihOV89S9XNIjx49\nitjYWFeXf/XqVSxYsAClpaU4ceIE4uLiEBwcjDlz5uDKlSuIjo4GAFRWViI9Pd31c9zhj/GoMUsx\nmKMYzFEM5igGcxSDOYrjaVmqtiEtKSnBvHnzEBERAY1Gg/Hjx2PSpEmIjY1FRkYG8vPzkZOTgyee\neAJDhgwBAHzwwQcIDg5GQUEB4uPjERkZqa4JuwphlmIwRzGYoxjMUQzmKAZzFMdTs1TdKXur1Qqt\nVou9e/ciMTERixYtgp+fH44cOYLi4mJMnjwZGo0GPXr0wLFjx1BZWYno6GgEBASgf//+iI2NRb9+\n/fDKK6/AaDS61R9DNGYpBnMUgzmKwRzFYI5iMEdxPD1L1TSkdrsdy5cvx86dO6HX65GTkwNZljF0\n6FB069YNPj4+2LFjB3r37o2QkBAAgMlkQl5eHry8vJCcnIyAgABERUWhR48eCv82ymKWYjBHMZij\nGMxRDOYoBnMUh1k2UcXEArPZjPnz56O+vh7Dhw/Hjh07kJKSgmvXrqGsrAwGgwGpqakYMGAAdu3a\n5fq+tLQ0xMXFQavVws0WC3hkmKUYzFEM5igGcxSDOYrBHMVhlneoYg6p2WyG2WzGX/7yFwDAhQsX\ncOLECaSmpmL79u3IyspCYGAgIiMjceHCBTQ2NkKn00Gr1WLu3Lnw8vJS+DdQD2YpBnMUgzmKwRzF\nYI5iMEdxmOUdqhgh9ff3x7hx41BRUQEAiI6ORnh4OPr164dz584hLy8PWq0Wer0eVqsVBoMBWq0W\nALrUH0MEZikGcxSDOYrBHMVgjmIwR3GY5R2qmEPq6+uLhIQE19yIpUuX4plnnsG4cePgcDiwbNky\nlJWVYePGjXjhhReQlJTkdlePPS7MUgzmKAZzFIM5isEcxWCO4jDLO1S3U1NpaSlefPFF7Nq1C35+\nfrh06RIuXbqE2tpa9O3bFzExMUqX6DaYpRjMUQzmKAZzFIM5isEcxfH0LFUxh7S10tJSjBw5Ejdu\n3MBrr72GwMBAvPXWWwgLC1O6NLfDLMVgjmIwRzGYoxjMUQzmKI6nZ6m6hvTs2bP49NNPUVBQgClT\npmDKlClKl+S2mKUYzFEM5igGcxSDOYrBHMXx9CxVd8p+69atuH79On75y19Cr9crXY5bY5ZiMEcx\nmKMYzFEM5igGcxTH07NUXUPaVSfrKoFZisEcxWCOYjBHMZijGMxRHE/PUnUNKRERERF5FlWsQ0pE\nREREnosNKREREREpig0pERERESmKDSkRERERKYoNKRHRI5KcnAyz2XzXfdu2bcOvfvUrhSoiIlIn\nNqREREREpCg2pERECqmpqcEbb7yBiRMnIjMzE0uXLoXdbgfww9HVlttHjhzBpEmTMG3aNEyaNAlW\nq1Wp8omIhFHd1qFERF3JjBkzoNHc+exfVVWF5ORkAMDvfvc7mEwm7Ny5EzabDXPnzsXf/vY3zJkz\n54E/s6ioCHv37kV0dPQjrZ2I6HFhQ0pE9AitW7cOwcHBrtvbtm3Dl19+CQDIz8/Hhg0bIEkS9Ho9\npk2bhnXr1rXZkEZFRbEZJaIuhafsiYgU4nQ6f3C75ZR9a/eelvfx8XmkdRERPW5sSImIFPL000/j\n008/hSzLsFqt2LRpEwYPHgwACA4ORkFBAQDgq6++UrJMIqJHjg0pEZFC3nnnHZjNZmRmZiIzMxM9\nevTAK6+84vrae++9h8mTJ+P7779HWFiYwtUSET06kizLstJFEBEREZHn4ggpERERESmKDSkRERER\nKYoNKREREREpig0pERERESmKDSkRERERKYoNKREREREpig0pERERESnq/wEf89+1R7CJ/QAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Will allow us to embed images in the notebook\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(rc={'figure.figsize':(11, 4)})\n",
    "df_date_time.plot(x='Hour', y='Number_Events', linewidth=0.5);\n",
    "plt.title(\"Number of events per hour\")\n",
    "plt.ylabel(\"Number of Events\")\n",
    "plt.xlabel(\"Hour\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example JSON analysis\n",
    "\n",
    "This second example analysis shows how we might interogate the JSON `content` field in order to extract properties for\n",
    "analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2910 entries, 0 to 2909\n",
      "Data columns (total 5 columns):\n",
      "event_time_stamp    2910 non-null object\n",
      "beacon_id           2324 non-null object\n",
      "content_type        2910 non-null object\n",
      "content             2910 non-null object\n",
      "datetime_hour       2910 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), object(4)\n",
      "memory usage: 113.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "df['content_json'] = df['content'].apply(json.loads) #Creating a column with the JSON content\n",
    "df_content = pd.io.json.json_normalize(df['content_json']) #Helper function to create flat table from nested JSON\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove NA's from the browser hostname data\n",
    "df_hostname = df_content.loc[:,['browser.page.url.hostname', 'time.stamp']].dropna()\n",
    "# create an hourly column\n",
    "date_format='%Y-%m-%dT%H:%M:%S.%f'\n",
    "df_hostname['datetime_hour'] = df_hostname['time.stamp'].apply(lambda x: convert_to_datetime_hour(x, date_format))\n",
    "# renaming columns\n",
    "df_hostname.columns = ['Hostname', 'Datetime', 'Hour'] # grouping and counting events by hostname by hour\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_df = df_hostname.groupby(['Hostname']).count().reset_index().sort_values(by=['Datetime'], ascending=False)\n",
    "sorted_df = sorted_df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f86b550>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ObdSqVYe6desxZ84sgoKapIpRu3Zdtm79HrvdTmxsDO+88xZXrkRQrlx5tm79HoAjR37h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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar chart over time\n",
    "df_hostname_popular = df_hostname[df_hostname.Hostname.isin(sorted_df['Hostname'].unique())]\n",
    "df_hostname_popular.groupby(['Hour','Hostname']).size().unstack().plot(kind='bar', stacked='True')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code prints out the content fields for a single `beacon-in-view-event` to illustrate the fields it contains. It should be noted that `beacon.meta.html`contains a large amount of html data to process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'beacon': {'height': '94',\n",
      "            'id': 'cff73bae-7abc-44e3-ab35-3cd9eb6e64b6',\n",
      "            'in_view_ratio_percentile': '100',\n",
      "            'meta': {'ad': {'video': {'subtype': 'Overlay', 'type': 'Instream'},\n",
      "                            'youtubeformat': 'Overlay'},\n",
      "                     'html': '<div class=\"ytp-ad-image-overlay\" '\n",
      "                             'style=\"max-width: 728px;\"><div '\n",
      "                             'class=\"ytp-ad-overlay-ad-info-button-container\"><span '\n",
      "                             'class=\"ytp-ad-hover-text-button '\n",
      "                             'ytp-ad-info-hover-text-button\" '\n",
      "                             'id=\"ad-info-hover-text-button:1q\" '\n",
      "                             'style=\"\"><button class=\"ytp-ad-button '\n",
      "                             'ytp-ad-button-link ytp-ad-clickable\" '\n",
      "                             'id=\"button:1r\" style=\"\"><span '\n",
      "                             'class=\"ytp-ad-button-icon\"><svg fill=\"#fff\" '\n",
      "                             'height=\"100%\" version=\"1.1\" viewBox=\"0 0 48 48\" '\n",
      "                             'width=\"100%\"><path d=\"M0 0h48v48H0z\" '\n",
      "                             'fill=\"none\"></path><path d=\"M22 '\n",
      "                             '34h4V22h-4v12zm2-30C12.95 4 4 12.95 4 24s8.95 20 '\n",
      "                             '20 20 20-8.95 20-20S35.05 4 24 4zm0 36c-8.82 '\n",
      "                             '0-16-7.18-16-16S15.18 8 24 8s16 7.18 16 16-7.18 '\n",
      "                             '16-16 '\n",
      "                             '16zm-2-22h4v-4h-4v4z\"></path></svg></span></button><div '\n",
      "                             'class=\"ytp-ad-hover-text-container '\n",
      "                             'ytp-ad-info-hover-text-short\">Why this '\n",
      "                             'ad?</div></span></div><div '\n",
      "                             'class=\"ytp-ad-overlay-close-container\"><button '\n",
      "                             'class=\"ytp-ad-overlay-close-button\"><svg '\n",
      "                             'height=\"100%\" viewBox=\"0 0 24 24\" '\n",
      "                             'width=\"100%\"><path d=\"M19 6.41L17.59 5 12 10.59 '\n",
      "                             '6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 '\n",
      "                             '17.59 19 19 17.59 13.41 12z\" '\n",
      "                             'fill=\"#fff\"></path></svg></button></div><div '\n",
      "                             'class=\"ytp-ad-overlay-image\"><img '\n",
      "                             'src=\"https://pagead2.googlesyndication.com/pagead/imgad?id=CICAgKDb4u_G5AEQ2AUYWjIIFp0QLMSTlBk\" '\n",
      "                             'width=\"728\" height=\"90\"></div></div>',\n",
      "                     'htmlbaseurl': 'https://www.youtube.com/',\n",
      "                     'text': '',\n",
      "                     'view_id': '6183ccae-e6e2-4e45-bbec-7484cc9af637'},\n",
      "            'parent_ids': [],\n",
      "            'tags': [],\n",
      "            'type': 'ad/youtube.com-watch-player-overlay-v1',\n",
      "            'width': '728',\n",
      "            'x': '143',\n",
      "            'y': '417'},\n",
      " 'browser': {'adblock_detected': False,\n",
      "             'client': {'height': '684',\n",
      "                        'scroll': {'x': '0', 'y': '0'},\n",
      "                        'width': '1440',\n",
      "                        'x': '0',\n",
      "                        'y': '79'},\n",
      "             'page': {'height': '2250',\n",
      "                      'url': {'host': 'www.youtube.com',\n",
      "                              'hostname': 'www.youtube.com',\n",
      "                              'href': 'https://www.youtube.com',\n",
      "                              'origin': 'https://www.youtube.com',\n",
      "                              'protocol': 'https:'},\n",
      "                      'width': '1440',\n",
      "                      'x': '0',\n",
      "                      'y': '79',\n",
      "                      'zoom_factor_percentile': '100'},\n",
      "             'screen': {'available': {'height': '812',\n",
      "                                      'left': '0',\n",
      "                                      'top': '23',\n",
      "                                      'width': '1440'},\n",
      "                        'height': '900',\n",
      "                        'orientation': {'angle': '0',\n",
      "                                        'type': 'landscape-primary'},\n",
      "                        'os_zoom_factor_percentile': '100',\n",
      "                        'pixel_ratio_percentile': '200',\n",
      "                        'width': '1440'},\n",
      "             'window': {'height': '812',\n",
      "                        'viewport': {'x': '0', 'y': '79'},\n",
      "                        'width': '1440',\n",
      "                        'x': '0',\n",
      "                        'y': '23'}},\n",
      " 'client': {'id': 'chrome-extension://jlinlkoiaifccncicmegnchnjiemnokg',\n",
      "            'installation_id': 'c74c5cfe-647b-42f0-8bd2-1f9222c68698',\n",
      "            'name': 'Tobii Pro Attention Panel (local)',\n",
      "            'version': '0.1.151.7928099'},\n",
      " 'duration': 'PT1S',\n",
      " 'experimental': {'http_block_detected': False,\n",
      "                  'http_block_urls': '[\"http://freegeoip.net\"]',\n",
      "                  'permissions_block_detected': False},\n",
      " 'eyetracker': {'configuration_valid': False,\n",
      "                'connected': False,\n",
      "                'detected': False},\n",
      " 'geo': {'city': {'name': 'Stockholm'},\n",
      "         'country': {'iso_3166_1_alpha_2': 'SE', 'name': 'Sweden'},\n",
      "         'region': {'name': 'Stockholm'}},\n",
      " 'id': 'c86365b1-ef70-4ab7-bf72-613ebc2ecee7',\n",
      " 'intersection': {'ratio_percentile': '100', 'threshold_percentile': '50'},\n",
      " 'ntp': {'in_sync': True, 'offset': '0'},\n",
      " 'platform': {'description': 'Chrome 76.0.3809.100 on OS X 10.14.0 64-bit',\n",
      "              'layout': 'Blink',\n",
      "              'name': 'Chrome',\n",
      "              'os': {'architecture': '64',\n",
      "                     'family': 'OS X',\n",
      "                     'version': '10.14.0'},\n",
      "              'ua': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) '\n",
      "                    'AppleWebKit/537.36 (KHTML, like Gecko) '\n",
      "                    'Chrome/76.0.3809.100 Safari/537.36',\n",
      "              'version': '76.0.3809.100'},\n",
      " 'request': {'headers': {'accept': '*/*',\n",
      "                         'accept-encoding': 'gzip, deflate, br',\n",
      "                         'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',\n",
      "                         'cloudfront-forwarded-proto': 'https',\n",
      "                         'cloudfront-is-desktop-viewer': 'true',\n",
      "                         'cloudfront-is-mobile-viewer': 'false',\n",
      "                         'cloudfront-is-smarttv-viewer': 'false',\n",
      "                         'cloudfront-is-tablet-viewer': 'false',\n",
      "                         'cloudfront-viewer-country': 'SE',\n",
      "                         'content-length': '3457',\n",
      "                         'content-type': 'application/vnd.tobii.atex.beacon-in-view-event-v1+json',\n",
      "                         'host': 'i0r8i4mqd4.execute-api.eu-west-1.amazonaws.com',\n",
      "                         'origin': 'chrome-extension://jlinlkoiaifccncicmegnchnjiemnokg',\n",
      "                         'sec-fetch-mode': 'cors',\n",
      "                         'sec-fetch-site': 'cross-site',\n",
      "                         'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X '\n",
      "                                       '10_14_0) AppleWebKit/537.36 (KHTML, '\n",
      "                                       'like Gecko) Chrome/76.0.3809.100 '\n",
      "                                       'Safari/537.36',\n",
      "                         'via': '2.0 '\n",
      "                                '05c02ade53b3395a9e9f2e8f66c7e4d1.cloudfront.net '\n",
      "                                '(CloudFront), 1.1 '\n",
      "                                'bb45ea5b3a4c19db9fecccf1bc9e803d.cloudfront.net '\n",
      "                                '(CloudFront)',\n",
      "                         'x-amz-cf-id': 'Gdfp1g6GAmcEOyHivF9QoVOtHiyAq6S21NxJ2cv8W099x_hCFvlIUg==',\n",
      "                         'x-amzn-apigateway-api-id': 'bkfx4gphe3',\n",
      "                         'x-amzn-trace-id': 'Self=1-5d556fa2-40569cac1ea0890090005acc;Root=1-5d556fa2-97fbe064b79b6e48c3744918',\n",
      "                         'x-forward-cloudfront-forwarded-proto': 'https',\n",
      "                         'x-forward-cloudfront-is-desktop-viewer': 'true',\n",
      "                         'x-forward-cloudfront-is-mobile-viewer': 'false',\n",
      "                         'x-forward-cloudfront-is-smarttv-viewer': 'false',\n",
      "                         'x-forward-cloudfront-is-tablet-viewer': 'false',\n",
      "                         'x-forward-cloudfront-viewer-country': 'SE',\n",
      "                         'x-forwarded-for': '84.217.178.135, 205.251.218.152, '\n",
      "                                            '108.128.162.40, 52.46.34.101',\n",
      "                         'x-forwarded-port': '443',\n",
      "                         'x-forwarded-proto': 'https'},\n",
      "             'method': 'POST'},\n",
      " 'session': {'id': 'b1fa013f-8063-4420-bd8f-255d602c9b95'},\n",
      " 'time': {'stamp': '2019-08-15T14:43:45.703Z',\n",
      "          'utc_offset': '120',\n",
      "          'zone': 'Europe/Stockholm'}}\n"
     ]
    }
   ],
   "source": [
    "#from pprint import pprint\n",
    "#pprint(json.loads(df[df['content_type'].isin([\"application/vnd.tobii.atex.beacon-in-view-event-v1+json\"])].iloc[0]['content']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analytical Question Suggestions\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Within the content JSON object there are a large number of interesting fields to extract and visualise such as the\n",
    "`beacon.height`, the `beacon.width` or the `browser.page.url.hostname`. Explore some of these variables graphically to\n",
    "understand the ads.\n",
    "\n",
    "1. There is a specific field within content `beacon.meta.html`, which contains the html code found within the beacon.\n",
    "You could explore using regular expressions or text mining to try and identify url's that are contained here.\n",
    "\n",
    "1. Given the different events it is possible to try and calculate some metrics which might be interesting. Each\n",
    "beacon_id should have a `beacon-detected-event-v1+json`, but only a fraction of these beacon_ids have a\n",
    "`atex.beacon-in-view-event-v1+json`. It is possible to calculate this fraction and possibly intersting to see how it \n",
    "varies for different websites.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling Question Suggestions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although each event is quite large, there are not a huge number of distinct events to undertake prediction. However it\n",
    "is interesting to model which variables may be correlated and could act as predictors of others. Suggested models\n",
    "include.\n",
    "\n",
    "1. `Regression model`:- Counting the number of distinct beacons per session and predicting this number from a combination\n",
    "    of the website and time of day.\n",
    "    \n",
    "1. `Classification Model`:- Using the beacon_width or beacon_height, and the time of day to predict which website a beacon\n",
    "    appears on.\n",
    "    \n",
    "1. `Time Series Model`:- Calculating the number of beacons or number of sessions per hour and trying to see any periodicity\n",
    "    in this or predict this value for the next period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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