{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f97a9b01",
   "metadata": {},
   "source": [
    "# Polar Vantage V2: Guide to data extraction and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf8409f",
   "metadata": {},
   "source": [
    "<img src='https://i.imgur.com/xqJvlna.jpeg' height=\"500\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a92f73a",
   "metadata": {},
   "source": [
    "A picture of the Polar Mobile Application"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb063207",
   "metadata": {},
   "source": [
    "Where do polar bears go to vote? <br>The North Pole."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30959560",
   "metadata": {},
   "source": [
    "In this notebook, we would definitely not be visiting the North Pole to watch Polar bears vote. Instead, we will be using the [Polar Vantage V2](https://www.polar.com/en/vantage/v2) which is a smart watch designed for carefully monitoring the most important muscle in your body -- your body. The watch is equipped with advanced wrist-based HR tracking, GPS, ultra-long battery life, running & cycling performance tests, FuelWise, route guidance, sleep tracking, & more. The Vantage V2 can also be utlized to record more than 130 popular sports. It also has an app that you can utilize to view reports on your data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f9f27e",
   "metadata": {},
   "source": [
    "This is a comprehensive, clear guide to extract your data from the Polar Flow App using the Wearipedia package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1845ce9e",
   "metadata": {},
   "source": [
    "We will be able to extract the following parameters:\n",
    "\n",
    "Parameter Name  | Sampling Frequency \n",
    "-------------------|-----------------\n",
    "Heart Rate |  Every 5 minutes\n",
    "Heart Rate Variability |  Every 5 minutes\n",
    "Breathing Rate |  Every 5 minutes\n",
    "Hypnogram |  For every change detected in sleeping phase \n",
    "Calories |  Per Activity\n",
    "Average and Maximum Heart Rate |  Per Activity\n",
    "Light Sleep Duration | Per Night\n",
    "Deep Sleep Duration | Per Night\n",
    "Interruption Duration | Per Night\n",
    "REM Duration | Per Night\n",
    "Sleep Score | Per Night\n",
    "Sleep Duration | Per Night\n",
    "Sleep Cycles Count | Per Night\n",
    "Sleep Regeneration Score | Per Night\n",
    "Beat to Beat Average | Per Night\n",
    "Heart Rate Variability Average | Per Night\n",
    "Breathing Rate Average | Per Night\n",
    "Heart Rate Average | Per Night"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07622639",
   "metadata": {},
   "source": [
    "In this guide, we sequentially cover the following **nine** topics to extract data from Cronometer servers:\n",
    "\n",
    "1. **Set up**<br>\n",
    "2. **Authentication/Authorization**<br>\n",
    "   - Requires only username and password, no OAuth.<br>\n",
    "3. **Data extraction**<br>\n",
    "  - We get data via wearipedia in a couple lines of code<br>\n",
    "4. **Data Exporting**\n",
    "    - We export all of this data to file formats compatible by R, Excel, and MatLab.\n",
    "5. **Adherence**\n",
    "    - We simulate non-adherence by dynamically removing datapoints from our simulated data.\n",
    "6. **Visualization**\n",
    "    - We create a simple plot to visualize our data.\n",
    "7. **Advanced visualization**\n",
    "    - 7.1 Visualizing Participants Heart Rate during Sleep! <br>\n",
    "    - 7.2 Visualizing Participants Sleep Breakdown!<br>\n",
    "    - 7.3 Visualizing Weekly Sleep Summary!<br>\n",
    "8. **Data Analysis** <br>\n",
    "  - 8.1  Analyzing correlation between Sleep Phase and Heart Rate! <br>\n",
    "9. **Outlier Detection** <br>\n",
    "  - 9.1 Highlighting Outliers!\n",
    "\n",
    "Disclaimer: this notebook is purely for educational purposes. All of the data currently stored in this notebook is purely *synthetic*, meaning randomly generated according to rules we created. Despite this, the end-to-end data extraction pipeline has been tested on our own data, meaning that if you enter your own email and password on your own Colab instance, you can visualize your own *real* data. That being said, we were unable to thoroughly test the timezone functionality, though, since we only have one account, so beware."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f69277",
   "metadata": {},
   "source": [
    "# 1. Setup\n",
    "\n",
    "## Participant Setup\n",
    "\n",
    "Dear Participant,\n",
    "\n",
    "Once you download the polar flow app, please set it up by following these resources:\n",
    "- Written guide: https://flow.polar.com/start\n",
    "- Video guide: https://www.youtube.com/watch?v=INEeXT8FC9I\n",
    "\n",
    "Make sure that your phone is logged to the polar flow app using the Polar login credentials (email and password) given to you by the data receiver.\n",
    "\n",
    "Best,\n",
    "\n",
    "Wearipedia\n",
    "\n",
    "## Data Receiver Setup\n",
    "\n",
    "Please follow the below steps:\n",
    "\n",
    "1. Create an email address for the participant, for example `foo@email.com`.\n",
    "2. Create a Polar account with the email `foo@email.com` and some random password.\n",
    "3. Keep `foo@email.com` and password stored somewhere safe.\n",
    "4. Request the participant to download the app and instruct them to follow the participant setup letter above.\n",
    "5. Install the `wearipedia` Python package to easily extract data from this device via the Polar API.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdcd1749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install wearipedia\n",
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6960f6",
   "metadata": {},
   "source": [
    "# 2. Authentication/Authorization\n",
    "\n",
    "To obtain access to data, authorization is required. All you'll need to do here is just put in your email and password for your Polar account. We'll use this username and password to extract the data in the sections below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abd3473e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Enter Cronometer login credentials\n",
    "email_address = \"test@stanford.edu\" #@param {type:\"string\"}\n",
    "password = \"putyourpasswordhere\" #@param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74394d52",
   "metadata": {},
   "source": [
    "# 3. Data Extraction\n",
    "\n",
    "Data can be extracted via [wearipedia](https://github.com/Stanford-Health/wearipedia/), our open-source Python package that unifies dozens of complex wearable device APIs into one simple, common interface.\n",
    "\n",
    "First, we'll set a date range and then extract all of the data within that date range. You can select whether you would like synthetic data or not with the checkbox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36527391",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Enter start and end dates (in the format yyyy-mm-dd)\n",
    "\n",
    "#set start and end dates - this will give you all the data from 2000-01-01 (January 1st, 2000) to 2100-02-03 (February 3rd, 2100), for example\n",
    "start_date='2022-01-01' #@param {type:\"string\"}\n",
    "end_date='2022-08-28' #@param {type:\"string\"}\n",
    "synthetic = False #@param {type:\"boolean\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f22756c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Login successful, user id is: 58933992\n"
     ]
    }
   ],
   "source": [
    "import wearipedia\n",
    "\n",
    "device = wearipedia.get_device(\"polar/vantage\")\n",
    "\n",
    "if not synthetic:\n",
    "    device.authenticate({\"email\": email_address, \"password\": password})\n",
    "\n",
    "params = {\"start_date\": start_date, \"end_date\": end_date, 'training_id':'7472390363'}\n",
    "\n",
    "sleep = device.get_data(\"sleep\", params=params)\n",
    "training_history = device.get_data(\"training_history\", params=params)\n",
    "training_by_id = device.get_data(\"training_by_id\", params=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd624c37",
   "metadata": {},
   "source": [
    "# 4. Data Exporting\n",
    "\n",
    "In this section, we export all of this data to formats compatible with popular scientific computing software (R, Excel, Google Sheets, Matlab). Specifically, we will first export to JSON, which can be read by R and Matlab. Then, we will export to CSV, which can be consumed by Excel, Google Sheets, and every other popular programming language.\n",
    "\n",
    "## Exporting to JSON (R, Matlab, etc.)\n",
    "\n",
    "Exporting to JSON is fairly simple. We export each datatype separately and also export a complete version that includes all simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ae6c889",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "json.dump(sleep, open(\"sleep.json\", \"w\"))\n",
    "json.dump(training_history, open(\"training_history.json\", \"w\"))\n",
    "json.dump(training_by_id, open(\"training_by_id.json\", \"w\"))\n",
    "\n",
    "complete = {\n",
    "    \"sleep\": sleep,\n",
    "    \"training_history\": training_history,\n",
    "    \"training_by_id\": training_by_id\n",
    "}\n",
    "\n",
    "json.dump(complete, open(\"complete.json\", \"w\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74fa198c",
   "metadata": {},
   "source": [
    "Feel free to open the file viewer (see left pane) to look at the outputs!\n",
    "\n",
    "## Exporting to CSV and XLSX (Excel, Google Sheets, R, Matlab, etc.)\n",
    "\n",
    "Exporting to CSV/XLSX requires a bit more processing, since they enforce a pretty restrictive schema.\n",
    "\n",
    "We will thus export steps, heart rates, and breath rates all as separate files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7282532b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "sleep_df = pd.DataFrame.from_dict(sleep)\n",
    "\n",
    "sleep_df.to_csv('sleep.csv')\n",
    "sleep_df.to_excel('sleep.xlsx')\n",
    "\n",
    "training_history_df = pd.DataFrame.from_dict(training_history)\n",
    "\n",
    "training_history_df.to_csv('training_history.csv', index=False)\n",
    "training_history_df.to_excel('training_history.xlsx', index=False)\n",
    "\n",
    "training_by_id_df = pd.DataFrame.from_dict(training_by_id)\n",
    "\n",
    "training_by_id_df.to_csv('exercises.csv', index=False)\n",
    "training_by_id_df.to_excel('exercises.xlsx', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32be33f",
   "metadata": {},
   "source": [
    "Again, feel free to look at the output files and download them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e237bf",
   "metadata": {},
   "source": [
    "# 5. Adherence\n",
    "\n",
    "The device simulator already automatically randomly deletes small chunks of the day. In this section, we will simulate non-adherence over longer periods of time from the participant (day-level and week-level).\n",
    "\n",
    "Then, we will detect this non-adherence and give a Pandas DataFrame that concisely describes when the participant has had their device on and off throughout the entirety of the time period, allowing you to calculate how long they've had it on/off etc.\n",
    "\n",
    "We will first delete a certain % of blocks either at the day level or week level, with user input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1497a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Non-adherence simulation\n",
    "block_level = \"day\" #@param [\"day\", \"week\"]\n",
    "adherence_percent = 0.89 #@param {type:\"slider\", min:0, max:1, step:0.01}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca1edca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "if block_level == \"day\":\n",
    "    block_length = 1\n",
    "elif block_level == \"week\":\n",
    "    block_length = 7\n",
    "\n",
    "\n",
    "\n",
    "# This function will randomly remove datapoints from the \n",
    "# data we have recieved from Polar Flow based on the\n",
    "# adherence_percent\n",
    "\n",
    "def AdherenceSimulator(data):\n",
    "\n",
    "    num_blocks = len(data) // block_length\n",
    "    num_blocks_to_keep = int(adherence_percent * num_blocks)\n",
    "    idxes = np.random.choice(np.arange(num_blocks), replace=False, \n",
    "        size=num_blocks_to_keep)\n",
    "\n",
    "    adhered_data = []\n",
    "\n",
    "    for i in range(len(data)):\n",
    "        if i in idxes:\n",
    "            start = i * block_length\n",
    "            end = (i + 1) * block_length\n",
    "            for j in range(i,i+1):\n",
    "                adhered_data.append(data[j])\n",
    "\n",
    "    return adhered_data\n",
    "\n",
    "\n",
    "# Adding adherence for sleep\n",
    "\n",
    "sleep = AdherenceSimulator(sleep)\n",
    "\n",
    "# Adding adherence for exercises\n",
    "\n",
    "training_history = AdherenceSimulator(training_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da17fcd0",
   "metadata": {},
   "source": [
    "And now we have significantly fewer datapoints! This will give us a more realistic situation, where participants may take off their device for days or weeks at a time.\n",
    "\n",
    "Now let's detect non-adherence. We will return a Pandas DataFrame sampled at every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5a1c9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sleep_df = pd.DataFrame.from_dict(sleep)\n",
    "training_history_df = pd.DataFrame.from_dict(training_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c87aca1",
   "metadata": {},
   "source": [
    "We can plot this out, and we get adherence at one-day frequency throughout the entirety of the data collection period. For this chart we will plot Energy consumed over the time period from the dailySummary dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23c2697e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "\n",
    "dates = pd.date_range(start_date,end_date)\n",
    "\n",
    "sleep_score = []\n",
    "\n",
    "for d in dates:\n",
    "  res = sleep_df[sleep_df.date == datetime.datetime.strftime(d,\n",
    "  '%Y-%m-%d')]['sleepScore']\n",
    "  if len(res) == 0:\n",
    "    sleep_score.append(None)\n",
    "  else:\n",
    "    sleep_score.append(res.iloc[0])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(dates, sleep_score)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe5e3aa",
   "metadata": {},
   "source": [
    "## 6. Visualization\n",
    "\n",
    "We've extracted lots of data, but what does it look like?\n",
    "\n",
    "In this section, we will be visualizing our three kinds of data in a simple, customizable plot! This plot is intended to provide a starter example for plotting, whereas later examples emphasize deep control and aesthetics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6558104d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Basic Plot\n",
    "feature = \"Training Recovery Time\" #@param ['Training Calories', 'Training Average Heart Rate','Training Distance','Training Recovery Time']\n",
    "start_date = \"2022-03-04\" #@param {type:\"date\"}\n",
    "time_interval = \"full time\" #@param [\"one week\", \"full time\"]\n",
    "smoothness = 0.02 #@param {type:\"slider\", min:0, max:1, step:0.01}\n",
    "smooth_plot = True #@param {type:\"boolean\"}\n",
    "\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "start_date = datetime.strptime(start_date, '%Y-%m-%d')\n",
    "\n",
    "if time_interval == \"one week\":\n",
    "    day_idxes = [i for i,d in enumerate(dates) if d >= start_date and d <= start_date + timedelta(days=7)]\n",
    "    end_date = start_date + timedelta(days=7)\n",
    "elif time_interval == \"full time\":\n",
    "    day_idxes = [i for i,d in enumerate(dates) if d >= start_date]\n",
    "    end_date = dates[-1]\n",
    "  \n",
    "if feature == 'Training Calories':\n",
    "    data = training_history_df.get(['startDate','calories']).assign(startDate = training_history_df.get('startDate').apply(lambda x: x[:10]))\n",
    "    concat_data = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          datapoint = data[data['startDate']==day]\n",
    "          if len(datapoint) != 0:\n",
    "              concat_data += [(day,datapoint.iloc[0].calories)]\n",
    "          else:\n",
    "              concat_data += [(day,None)]\n",
    "    ts = [x[0] for x in concat_data]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_data]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Calories\"\n",
    "\n",
    "if feature == 'Training Average Heart Rate':\n",
    "    data = training_history_df.get(['startDate','hrAvg']).assign(startDate = training_history_df.get('startDate').apply(lambda x: x[:10]))\n",
    "    concat_data = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          datapoint = data[data['startDate']==day]\n",
    "          if len(datapoint) != 0:\n",
    "              concat_data += [(day,datapoint.iloc[0].hrAvg)]\n",
    "          else:\n",
    "              concat_data += [(day,None)]\n",
    "    ts = [x[0] for x in concat_data]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_data]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Heart Rate (bpm)\"\n",
    "\n",
    "\n",
    "if feature == 'Training Distance':\n",
    "    data = training_history_df.get(['startDate','distance']).assign(startDate = training_history_df.get('startDate').apply(lambda x: x[:10]))\n",
    "    concat_data = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          datapoint = data[data['startDate']==day]\n",
    "          if len(datapoint) != 0:\n",
    "              concat_data += [(day,datapoint.iloc[0].distance)]\n",
    "          else:\n",
    "              concat_data += [(day,None)]\n",
    "    ts = [x[0] for x in concat_data]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_data]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Training Distance (m)\"\n",
    "\n",
    "if feature == 'Training Recovery Time':\n",
    "    data = training_history_df.get(['startDate','recoveryTime']).assign(startDate = training_history_df.get('startDate').apply(lambda x: x[:10]))\n",
    "    concat_data = []\n",
    "    for i,d in enumerate(dates):\n",
    "        day = d.strftime('%Y-%m-%d')\n",
    "        if i in day_idxes:\n",
    "          datapoint = data[data['startDate']==day]\n",
    "          if len(datapoint) != 0:\n",
    "              concat_data += [(day,datapoint.iloc[0].recoveryTime)]\n",
    "          else:\n",
    "              concat_data += [(day,None)]\n",
    "    ts = [x[0] for x in concat_data]\n",
    "\n",
    "    day_arr = [x[1] for x in concat_data]\n",
    "\n",
    "    sigma = 200 * smoothness\n",
    "\n",
    "    title_fillin = \"Training Recovery Time (s)\"\n",
    "\n",
    "with plt.style.context('ggplot'):\n",
    "    fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "    if smooth_plot:\n",
    "        def to_numpy(day_arr):\n",
    "            arr_nonone = [x for x in day_arr if x is not None]\n",
    "            mean_val = int(np.mean(arr_nonone))\n",
    "            for i,x in enumerate(day_arr):\n",
    "                if x is None:\n",
    "                    day_arr[i] = mean_val\n",
    "\n",
    "            return np.array(day_arr)\n",
    "\n",
    "        none_idxes = [i for i,x in enumerate(day_arr) if x is None]\n",
    "        day_arr = to_numpy(day_arr)\n",
    "        from scipy.ndimage import gaussian_filter\n",
    "        day_arr = list(gaussian_filter(day_arr, sigma=sigma))\n",
    "        for i, x in enumerate(day_arr):\n",
    "            if i in none_idxes:\n",
    "                day_arr[i] = None\n",
    "\n",
    "    plt.plot(ts, day_arr)\n",
    "    start_date_str = start_date.strftime('%Y-%m-%d')\n",
    "    end_date_str = end_date.strftime('%Y-%m-%d')\n",
    "    plt.title(f\"{title_fillin} from {start_date_str} to {end_date_str}\",\n",
    "              fontsize=20)\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.xticks(ts[::int(len(ts)/8)])\n",
    "    plt.ylabel(title_fillin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791b825c",
   "metadata": {},
   "source": [
    "This plot allows you to quickly scan your data at many different time scales (week and full) and for different kinds of measurements (heart rate and weight), which enables easy and fast data exploration.\n",
    "\n",
    "Furthermore, the smoothness parameter makes it easy to look for patterns in long-term trends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d877b852",
   "metadata": {},
   "source": [
    "# 7. Advanced Visualization\n",
    "\n",
    "Now we'll do some more advanced plotting that at times features hardcore matplotlib hacking with the benefit of aesthetic quality."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ffcb2eb",
   "metadata": {},
   "source": [
    "## 7.1 Visualizing Participants Sleep Breakdown"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aae4700",
   "metadata": {},
   "source": [
    "Polar Vantage V2 has an inbuilt sleep/recovery tracker which allows the participant to track their Sleep into different stages. On the polar flow app, a user can go into their nightly recharge breakdown to check their Hypnograms. The plar flow app should show you the following chart:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83455989",
   "metadata": {},
   "source": [
    "<img src='https://i.imgur.com/HNXVQar.jpg' width='750px'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1803b3b",
   "metadata": {},
   "source": [
    "Before getting started with data wrangling, in the box below input the date for which you want to draw the specific plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e95c8859",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aBfjetr29vcRiseBe6+rq8n6zbDbLoUOHWLFiBevXr6erqwuA1157jUwmg5SSyspKpk+fHiwiHTp0iPb2dsaPH8/EiRMHGG6VUnR0dHDkyBGamppIpVLBu1FWVsbYsWOZPHly3hxIo5Sit7eXtWvXMmnSJMaNG0dzc3Pw23d1dZHNZikqKqKqqipIzVBUVDTA8yWVSgXvXGNjI729vQghKCkpoby8PMh1V11dTXFx8QDPfUM/JkOewWAwGAwGg8FgMBgMBsMwMJ4oo5Bo/KIQIs8FX+8T9k6Jut27rssrr7zCvffeS3l5OV/84heD6jza8rpz504efPBBnn32WQ4cOBBYvYUQQcJKnaRSu9++5z3vYe7cuYFHiVKKrVu38vTTTwOwfv169u3bR0dHBzU1NZx77rlBPgZtsU6lUqxcuZKf/exnvPTSS0H85c9+9jMeeuihILb6ox/9KGeccUbBZ6JX04zl1TBaCK9WRBPLhWOnNfX19VxxxRVs2LAhCJn7n//5H+bNm0cikQj27ejoAODVV1/l4Ycf5rXXXuPAgQPBKtDll18OwFVXXcXcuXMpLi4OrqnP0d3dzd69e7n//vt57rnnaGhoIJ1OB6tHc+bM4aMf/SinnXZaUGFHH3vgwAF+/OMfs2vXLv793/+dp59+mhUrVgTuud3d3YEnyw033MDy5cuDFS29ch3OqWJkgmE0Ucg9PFxpA/r7B5CXzFKvhI8ZM4bS0lK6urqCpLOacDhxOGS4UGhxtB3hkN+qqipOP/10nnrqKbZs2QIQ5GEJr/zr8+3bt4/NmzdTXl7OGWecwebNmwdUOzEYjgfhinXaS1u/621tbfzyl78E4JlnnuGrX/0qDQ0NrFixgg0bNgQeH5lMhurqam688Uauv/566uvricViHDx4EID777+fe++9lwMHDlBaWsq//uu/5oV7/NEf/RHvec97GDNmTJ5XA/jhdrt27aK5uRnLspg4cWKgk99yyy1Mnz4d6B//X375Ze677z7A1wM+9rGP8dhjj/Hoo4+yefNmWltbAfjqV7/KDTfcwM6dO/nFL35BR0cHd955ZxDmd//997N69Wo6Ozv5yEc+wqc//WkOHToEwDe+8Q22bNnCb37zG+rr6wMZEQ55aWxs5O677+aFF17gzjvvDLxghRCk02lWrlzJr371q+Aefv7znwO+l8uhQ4eIxWLU19dz/vnn8973vheAGTNm4Hkevb29PPTQQ9x7772sW7cukGt33XUXlZWV2LbNokWL+NSnPhV4tP/2t79l1apVvPvd7+YjH/lI4JmnZU5DQwPPP/88Tz31FBs2bKCtrS3wvKmrq2PhwoXcfPPNLFiwIKhIqu/V8zwOHTrEX/3VX/He976Xc845hzVr1gDw3HPPsWfPHpqbmykrK+O0007jox/9KADnnHMOFRUVgdzs6upi9erV/OY3vwH8OVVLSwtSSmpqaqivr2fZsmUAXHrppcybN8/MiYbAGFFGIVoIacVH1ysPEw3liZYWtSyLnp4edu/eTVlZGel0Oi9cJ5VK8fWvf51HHnmEmpoaLrzwwqB88s6dO3n00UfZvXs3ixcv5rzzzuPss88GYOzYsXn107u6urjjjjuC+M7p06dz3nnnsX//ftauXcvPfvYzHnnkEQBWrFjBmDFjSKfTZLNZysvLGTt2bODyNnnyZGbOnInneUyePJnS0tIBiplWDE1iWcNoIywXID9DfzT2WveTRYsWcc011/DYY48B8PzzzweTIZ2VfsWKFQB85zvfYd26dSxatIhrr72Wvr4+XnnlFb797W8D/mD+qU99KsiZEI7b3bFjB1/4whdYu3YtM2fO5KKLLiIejwdKxK9+9StWrlzJd7/7Xc4999y8SVo6nWbnzp088cQT3HPPPfz2t79l0qRJgczxPI/Nmzfz4IMP8uSTT/LNb36T6667DhiYqd8oEIbRhn7no3kYwjkbCrnaQ3+ywnQ6TSwWY9KkSZSWluYleA0bTnTeAq2X6OsJ4Sej1GNzoaSN2WyWCy+8kK1btwZGlE2bNnH++ecHx4VlyvPPP8+OHTs466yzOOuss9iwYcOA6hfGaGo4HoTz+4QXJ3Wf2r17NwCrVq3i7rvvZsuWLcRiMU477bRAb96wYQPbt2/nC1/4Am1tbXz84x9n6tSpdHd3A77xcubMmWzdupWSkhLmzp0bvMuxWIwJEyYE42JHRwcvv/wyH/7whwH/XV++fDnnnnsuLS0trFmzhu9///sArF27lh/+8IdBBT4hBO3t7XmGypUrV/JP//RPFBcXM2vWrCAsV4e6dHR0sGXLFg4ePMiaNWv4zGc+A/gyZfr06cyePZv6+nqSyWSw2Lpnzx42bdpEX18fSqmg/4blguu67N69mzVr1tDd3R1s0+FHzc3NrF69mkQiwcGDB4PnPWvWLBYtWsTu3bt59dVXWbNmDdu3bwfgm9/8JslkEsdx8DyPqqoqKioqgmc5e/Zs6urqSCaTTJo0iUQiEYQn7dmzhy1btrB8+fJAhtm2HdzTz3/+c37yk5/Q1dXF/PnzWbRoUSArN2/ezC9+8QteeOEF/uEf/oFLLrkkMELrMtO9vb289tpr1NTU8OCDDwZhU5MmTeL888+no6ODZ555hkcffZTnnnsO8A1VZ511FrZtk81mWbVqFXfccQf79u0D/JDHd7zjHcRiMbZu3cqqVauCFAinnXZanmHKMBAzUxzF6E4eziMQro0erVKTzWYDIayVGp0pPDwguK7LI488wksvvcTUqVP5zGc+w7XXXht0wlQqxYc+9CEuu+wyUqkUF110ERdeeCHgCz+tcKXTaZ5++mmuuuoqvvSlLwEwf/78QKCsWbOGf/iHf+Dll18GfE+T2267jaqqKm6++WamTp3K9773vWDF+dOf/jRnn3124HUTtqqGlb9MJhPkTjGCwzBaiCYpC6/K6slQOPs7QElJCbW1tUHf379/P47jBHG427dv5/777wd8RfGjH/0on//85ykqKkIpRVNTE3/1V38F+KWVp06dyqJFi6ioqMjre1//+td55ZVX+PjHP87tt9/O+PHjEUIEeU2+8Y1vcM899/DAAw8wfvx4Zs2alde3Lcuir6+PO++8kx/96Ed5ZZk9z+PgwYPcd999fO1rX+OOO+7goosuAshLgFsoCa7B8HanUO6SsKcq9MsHx3HyjBz79+8HfGOGlJKlS5fmGSX1+cK5VfTkJbywocfrcAlYfb1sNouUkkwmw5VXXsnjjz/OCy+8AMDq1au5+OKLg1XUcEn15557Ds/zWLhwIWPGjMmTcdF2GQxvhng8DvTn9gonWhVCBNullNx333188Ytf5JZbbsmrRNXd3c1zzz3He9/7Xu6++26uu+466uvrWbx4MQALFy7ktNNO49FHH2XOnDn8/d//feAhoc+t++ahQ4f40pe+FHiM/PrXv2b58uWUl5ejlOLFF1/kO9/5DuD3oR/96Ef8+Z//eVAQIryo8vTTT3PkyBHe97738YUvfCFvEVJfT4+bO3fu5OMf/ziXXHIJAH/zN3/DhAkTgn3C46tehNHnCJd9Du8TNrCEF310321ububJJ5/k6quv5pvf/Cbg5/oQwi/F/tBDD/HNb36TJ598EoD//d//5f3vfz81NTV84hOfYNKkSUHOEIA///M/5+yzzw7aE4/HA6ND2ONI35PneYE8+vWvf00sFuPLX/4yN910U+A1C/786j/+4z/4/ve/zw9+8APKysoCPUQ/c/1cn3zySZYvXx7Miy644ILgng8cOMDnPvc5nnjiCQDuu+8+ampqmDVrFs3Nzaxdu5YjR45w6623AvDP//zPxOPx4Bl3dHQE3vu6uln43gz5GCPKKCfqnhXNjh2m0N/RBLT6+23bttHR0cFll13G/Pnz8xSr0tJSZs6cycKFC/MSNulzuK5LJpPBtm0sy+ITn/gEc+bMAfwkSVpwTp8+nSuuuCIQUDt27AgswrZtE4/HB5RQKy4uHuASGCa80mUUKMNoIjxpicViSCnJZrN5yk20X+g+NWbMGMB3FW1sbCQWi5FIJHjssccC193p06dz3XXXUVZWFoTq1NXVBSF1a9euZePGjbzyyitcdtllZDKZIExozZo1TJw4kaVLlwaJ7rT7KcBNN93Ez3/+cx544AGuvPJK5syZE7RTe7aVlJRw/fXXs2zZMsrKygKFxLIs6uvrufzyy3n++ed58skn+f3vfw/44YXaoGMSThtGI+Fx3XXdvKSyYSNrU1NTYMzo6upi48aNPPDAA4A/ETvjjDO48sorB+gRWrasWrWKbdu28fnPfz5PrwB/Euq6Ll/4whf4wAc+kGeA0RMoy7JIJpMsXbqUdevWAf7q/eHDh5kwYUIw7uuyy7t27QpWWpVSwcTBYDjeaANduCJL2CMlvP2iiy7i4osvZsKECXlh9rZts3DhQhYtWsSWLVuCxKQ6qallWcECh07erLeF+9LRo0d5/vnn2blzZxCyc8YZZ1BTUxP0q9NPP51LL70UgEcffZT//u//5rbbbgvCdMOeYolEggULFvDpT3868O4utDipjR2TJ0/mjjvuAMjzjokS9QiLzjf0eR3HGWC8DR+jE8D+3d/9HWPHjg3apZNXX3zxxRw+fJgvf/nLgB/adMsttxCLxbBtO++e9e+gE2MX0gW0wTeciPuee+4BfE+VT33qU7zrXe+ipKQk+N3A9xb6yEc+wquvvsqqVatYvXo1Z555JuDPmbLZbJ6H7/vf/37OOussgCAEGvzQyeuuuy7wAD506FDgseI4TrBIrEOhi4qK8uaB0UX1cJEPE+o4EGNEGeVo97ioIIu6HWolJcxghga9kgS+wClklNDXCytl4evqfSdNmsScOXMCgRM+R1VVFfPnzw/+PnToULAqFd43ulo2mDDWK83GgGIYjRQK2dGrt5rwipDuI7ZtB+677e3twapvNptl9+7d9PX1Ab4XWbi/6mNPP/10wK8C0NLSwqFDhwK59OqrrwbnveGGG5g5c2YgT8JKzKxZs6ivr2fr1q00NjaSTqfzFEjtQXPhhRdSVlYWVPIKt2PSpElcfvnlPPLII2zbtg2Anp6eYDUmLKcMhtFCVC5EvVXBN4DedNNNwX6ZTIbOzs5g0vL+97+fa665JqgYEUb39ZKSEiZOnMiYMWPyVl3D43ZlZWVeG6J52izLYtmyZTzzzDOAv/L94osvcuONNwb76BXa1tZWrrjiCmbOnMnevXsDY7GpwmU4kRTSLcPv3BlnnMH48eMHVKcCvwrd/Pnz2blzJ01NTfT19QUhH/rcUQOn/l6fp7W1NfAMu/766wHyPD91X5wyZQrgT+APHjxIV1cX5eXlAyq1TJ48mYsuuihY0Bjq/ioqKrjkkksCDxs9DkfTB+jP4RLE4cpGersOC9TVbKJ5lDzPIx6PM23atDzZE9ZfampqmDt3biCrduzYQXd3d7AIG86nFD23bosO1wrPl/QxrusG1cJqamqYN28eY8eOLVgqura2luXLl7N+/Xr27t1LQ0MD4Os3lmUFsrCuro5p06blhVjp9hUXF7Ns2bLAu+nw4cOkUimE8Kug6vw22kO4traW6667jqqqqmDhOjp3chzHeKIMgjGijGLC1uRwrHCYsCElKuxc1x3gmgu+YjNv3jzKysrYunUr+/fvZ+bMmcGx2WyWffv2sX//fmpraykrK8vrtFLKwM1t/PjxeYNEWFAmEglqa2uDv8N5WcLJMcOCQN+D4ziDJpHTx5gJk2G0Eu7z+m/oVyDC36fTadra2gBfKaqqqsKyLFpaWmhrawsSyz733HN85jOfyVuNA39lDPxwn2QySVNTE0L4uUh27twJ+JOyVatW0dbWFiR9DSuXjuNw6NAhUqkU7e3t9PX1BYYdbQwSQjB16tRAOQrLPiH88n7Tp09HKRWUVM9kMgXdiA2G0UZ4NTjqfp9MJoOJged5bNu2jb179wZG00suuYSFCxfmyY/wuKyUor6+nptuuomzzjorr7+FE0rW19cH34Xbpc8JMHfuXObOnQv4YUQvvPAC7373u4M8bjpXQHV1NfPnz6e6upp9+/YNmByahRTDyWDKlCl5Om9YN7csi9ra2sDjS0+qob+fhD0gwsfqftvR0cHu3btJpVI8+uijAHmJU/U1dULbvr4+bNumtbWVurq6AePgmDFjWLBgQd51oguWun0lJSWcc845BRdk9T7Rvh3W06N9Up8/bNCNnlPnLinkYaGfaUVFBRMnTgR877XGxkYqKiry9IRwmwq1Jdx+rTO4rktDQ0PgbV9bWxuct9D5lFJBvsaWlpYgn+PMmTPzZGJdXR1lZWUFdZJYLJb3O2kdBnyvk7PPPpsPf/jD3HvvvQB897vf5cknn2TJkiWcc845zJ8/PyhxDPnhjYaBGK3QYDAYDAaDwWAwGAwGg2EYGE+UUYy2bIY9UjRRi3A0oZResSmUW0RKyZlnnsmZZ57Jq6++yk9/+lP27t0bJLlqampi7dq1dHd385GPfIT6+voBVk4dWlNeXp4XUhC2WOt46KHcb8Pnjbo3Rq9pLK2G0Ux01Socxhd1sdVks1na29uDCgH19fWUlJSglF9ZK5VKBfun02kOHz6cl2NBe4eBv4JcV1fHxIkTg1UcnThWe761tLTQ0dEReJmFXf4XL16MECI4Pnwd7YlSXFw8IJRPY1lWkPBWe9ZEQ5dMThTDaGOwPGnhfjF58mT+8i//Muhba9eu5Uc/+lHgZabd0rUOEdUthBCUlZUxe/Zszj///AE6Sfi60W3hFWXwkyHqEMEnn3ySDRs2sHv3bmbMmMErr7zCrl27AL+k+uzZs/PaM5hHrsHwVlFaWlowlCccPgsM8LQO6+SFvAe013gmk6GjoyPwIoX+/hn2HNH/veCCC0gkEnnJcaOhsNorIuq5GvY61R4l48aNC47V8iDq8a6vr73GYfDEpvp+o2GHepsO99F/h9HtjMViQUlhKSXt7e15nqrh3yHsZRNuv25zdF7U3t4eHF9aWpr3HPU8J4z+/bu7uwMPluh8q6KiYkBIclTnCX8Ot6++vp4PfvCDwe+wbt061q1bF3jtnXXWWUHi3zPPPDPvnTLeuAMxRpRRju4c2hgRdi2LuhFGXea0gAq7r2nq6uq46aabOHToEI899hjbtm0LYiw7Ozvp7Ozkpptu4k/+5E+YPHlycJy+ru6w2iU/aqzR++nEl+Hjw+0IT/q0O592RQ4rY9FwIoNhtHKsnEBhOXH48GE2b94c9JnZs2fnKXu2bQfG0+XLlwe5CfQ1dNlSTXFxMZMnTw76pnZrllJy8cUXB1V1oiF3YWVk5syZee7Q+npKKXp6egIlKKqAeJ5HX18fUsq8HEyDKXkGw2ginA8J8kscV1RUcN555wV9ctasWezYsYM//OEPADz++OMsXrw4WDCJ6hZajwi7w0cNnYP1vWioMRAYURYtWsSzzz7LM888w9SpU7n//vuD/r906VImTZqUd03btvNCJAyGt5pwHg5ggD57LEPfUBNevfBQUlJCSUlJkMdo6tSpQRLbwRYZxo8fP6C8ePi8ul8XCr8ZLBw2eq3odfVCS3RRV+N5Ht3d3YOOyXoxJly8Ql8nahjRZYiFEHn51LSuEg3jCacOiJZ/D8u4ioqK4Hw6OWyhOZPeX4ff2LadVw01LBN14v/ocyukp0SfbywWY8aMGfzZn/0Z4CfYXrVqFS+++CJr1qwJSlEDlJWVMXfu3EHfCYMxohhyFEqoFu6kOoFT2CrseV6gcITjKcEXCNOnT2fatGmAnxhJJ24qLS2lvr6eq666KsgMrYkaScJJpPR/o8IhmsVbf9ZGn/B34XssVBLNTJIMo5VwP9R9Izwoh/fRBoc1a9bw5JNPBjG0l156aaAw1dTU5OU7GjNmTLCqBQNji6OrKLZtB/LDsiyqqqpYunRpYIzV3il6e3giFlU89YrWvn37gpwLUeWit7eXffv2YVkWU6dOBQiy8GuMbDCMNsKThrBMGGpBpaamhne+851s2bIFgBdffJHf//73fPzjHx9QalifJ5xjJeqpovcrtEii2xQ2fsyYMQOAxYsX8+ijj/LEE09w+eWXB2XUwddJysvLB6yqhyulGAxvNYMZMsKTbu2FXWghQBsd9DHh/ZVSFBcXU1dXh23bQR6Q888/P/DEKETYYBBtm/b0DBtBC03ww+h2h+WA7ttRj5u+vr48j9jwPafTafbv3x/o+uHz6ms7jkNDQ0Mwhwm3S99LZ2dnkAPGtu0BuV/CcwV9jOu6wcJsuDCGvqY+/6RJk4Kku42NjUHyfX2t6G914MABenp6qK2tpba2dsBzLGREi+oyYSOcLt4R9abVbZ4+fTqzZs3i+uuv58EHH+See+4JKvvMmDGD+fPnGy+UITBGlFFMWBhpt/uoQUILjGw2SyKRGBAeE3ZfC3fkeDzO3Xffzfr16/nwhz/M7bffnlcmFfInQVH09aOKVvh4ncE/k8kA/cJCk0gkKCoqCkoXNjc35w1Q0TZHr2+EhmE0ETZURl2DdR/Sfaa3t5dNmzbx6KOPsnXrVpYvXw7ABz/4QeLxOI7jUFxczLhx44K+u2fPHo4ePcqkSZPyZEFUKdB9Lx6PM2/ePABKSkp47bXX2Lt3LxMnTgzaEa4qFjX6RFfyHMdh/fr1XH311UGVgfA1W1paWLNmDUqpYBJWXFyc5x5tZIJhtKHlguu6QTlh3Q/0oooee8MGkQsuuICXX34ZgLvvvpuHHnqISy65hNNPPz3wBoX80EF9rfDkImzAja7I6olTuBqGECJIPj1jxgwmTpzI6tWruffee9mzZw/ve9/7AL/ynz5/OEGlqUJhOJkUMoDo78PGQm0wCY9N2pukq6sr8HoA8vpaRUUF06ZNI5vN8vDDDwOwZMkSSkpKCi6m6r4Zi8VIp9OBcSNsVNDn1x7rYY+McP8WQgxI8BpFh7toj9NNmzYxa9Ysqqqq8owsqVSKhoaGIGQwjF5EdV2Xnp4eDh06xNGjR4OFXF2FBnzP+P3793Po0CEA5syZQ01NTZ4XiBAiCH3q7OwM7l/fV7g6j/5vWI7oRNePPPII+/btC6r+hfUUKSWpVIqNGzfS3d3N+PHjg/bqZxs1AhVaiNIeguHfPmoID//O+j6Li4t573vfy/79+7nrrrsAP8munv/F43GziFQAoxGOcqKCLZvNks1mA8uu/q/OBh4t+aWFui5jCr5wU0qxffv2wDjT09NDX19f8C+TyQTuc0DedcPGnKiVOmq4Cf+dTCbzhExxcTEVFRV0d3fT3d3NunXrSKfTOI5Db29vXoWeqBeKmSwZRht64A0bT7Rrqe732WyWrq4ufve73/G5z32On/70p0ybNo3bb7+d22+/nUQikbeifNlll7FkyRKWLFnCCy+8wF133UU2mw36oK4w4Lou6XSaVCqVt4pz7bXXcu2113L66afzzDPP8Nhjj7F///7AqOM4Do7jBO1KpVIFV7aklKTTaR544AGOHDlCT09PIHMcx6Gjo4PXXnuNBx98kMrKSq6++mquvvpqysrKglhyIxMMoxHdh7SOoJV3y7JIJBJ5+RL0hEopRVlZGddccw3XXHMNl1xyCRs3bgyU86hHaDi8L5VKBf/V/Tmsm0B/qKD+p7+LuunPmjWLiy++mIMHD/Ktb30LIQQXXXQRF110EbW1tTiOkycvstlscE7T3w0ng/ACZvhf1HihDX76nXddl2QySXV1Nbt27Qo8NPQ/Pa5OnTqVq666ioqKCu6++27uvvtuNmzYQHd3d6AfO44T9D+tK3ueF4zvUW+wcKh/eCEj2s/Dx4QXO8Ln0zJl4sSJxONxVqxYQUNDQ9BXtRzYu3cvDz74IN3d3XneN5BvcFBK0dHRwQ9+8IMglYCe2ziOwyuvvMLvfve7IMTpxhtvzLvH6upqampqaG5uprm5mddeey2QE5lMpmAulrC3ilKKyy+/nMsvv5wxY8bw0EMP8eyzzwbPNPz7rFixgqeffppx48axZMmSwBtFny+TyeQtAoefXdRgpdHX0R4yWr5qfU/rT9qI09raGvz2Op1CNP+KoR/jiTIKibri6e++/e1v8+tf/zpvH73dtm2Kior48z//c8BPJBd2u9NKFBBYLK+44gruvvtu7rjjDr71rW8FuRG0MK6trWXp0qVcf/31zJo1KzhXJpMZ4LYXtbTq1ad0Op3n7quFlmVZjB8/nhkzZtDT0wPAvffeS3FxMUuWLAlWuevq6vLOD5hJk2FUEl452blzJ7/4xS9YvXo12Ww2SPC6Z88eDh48SGtrK4lEghtvvJHbbruN8847D8gP6xNCcN555wVlinfs2ME999zDxo0bOe+886ivr2fnzp2sX78egG3btnHeeefx13/919TX12NZVrBK8olPfIKGhgb+8z//k5dffpkLLriAurq6oG9v2LCBDRs2MGXKFD7/+c+zbNmyvBU9bZytrq7mtttu413veleQN8FxHF5++WV+//vfI6Xks5/9bOB+G55MFQonMBje7kRXjS3LCiY0els2m81z49d9ZOHChQBccsklvPjii6xYsYInn3ySyy67bMCEY/v27dxzzz08/vjjgyapPPPMM7nooovySh1rI0jYi0Sfe9KkSZxzzjkkEgk6Ozu5/PLLAw+U8MqqPk94pd+E8xhOBuEFvUKha7FYLC9kIzzmVldXs2zZMu6//36+8IUvcMMNNwAwYcIEJkyYwOTJk6mqqmL69Ol89rOf5Utf+hIAt956K1dddRVLliyhtLSUw4cPs3HjRgCOHDnCnDlzuPPOOykuLg50cN3XtPHyWMlHtfdoOI9h+Dx6H53w9IYbbuDuu+/md7/7HY2NjVx66aVMmDCBw4cPA/D888/z2muvsWDBAjZs2JBXjjg8VhcXF1NTU8N3vvOdwNvkjDPOYMyYMezYsYPHHnuMLVu2sGjRIgD++I//OHjeQghmzpzJ5MmTeeihhwD47W9/i2VZLFiwACEEZ599dp6MCi9C6TZdf/31AOzbt49f/OIXfOUrX+H5559n0aJFwfNat24dv/rVr+jq6uLjH/943nnDslfL32gIj77nsGFZH6Pfo8OHD/Pb3/6WRx55hCuuuALwiwGUlpZy4MABXnrpJVauXBmEPF544YWk02mSyeSAOZnBxxhRRjHRTvjss8/mWbvDQlzXUv/Qhz4UbA/nHIlmgG5qagpWhuPxOCUlJcG5pZT09fWxfv16Vq9ezXPPPcdnPvMZAC6//PI875Co1TtqeY0KznCITmVlJZdccgm33norAD/72c/43ve+R1lZGfX19fzt3/5tILCjg8CxkncZDG83wkaHo0eP8vTTT/Piiy+ilArc4ysqKpg5cybTpk1j+fLlnH322UyYMCFvJVobLITwE7S9+93vBqC2tpbf/e53PPvss6xevZpEIkEmkwnC8erq6pg0aVJQQSesjJ1//vl84xvf4Je//CUrV67krrvuCkILALq7u6murubyyy8P3ICjK0RlZWX8y7/8C3/4wx946qmn+O1vfwv4idwcx2HChAl84AMf4Oabb6a8vHzA8zEGFMNoR/fraKL3wXKKabmwfPlyrrvuOn70ox/x3e9+l3PPPZfi4mKgf+W9oaGB9vZ24vE4tm3nhRDqft7X18f8+fMDI0rU6yysC+hjp0yZwvnnn8+KFSu4/vrrqa6uDo4JT37CeRcMhhNNeLwNh6WGvSiiyUfDIayFki9PnTqV22+/nUOHDrFq1aogJ1EsFuPWW2/l1ltvpbq6murqat73vvcxYcIEAL7//e+zcuVKnnnmmWAhU4eo1NfXs3Tp0jy9W3s1AIEHeaGxsZAxKKyjFwrx1znTFi1axLe+9S1++MMf8sorr7Bx48a8KjpLlizh//2//8fGjRvZunXrgJwxmpKSEpYvX85ll13GPffcA8CKFStwXZdMJkNpaSk33HADH/3oRwFfTwnnOqmsrOSaa64JDDCPPfYY3/72tykpKWHmzJl8+9vfDhZdwvcRzpOiPTo++MEPUlJSwhNPPMFjjz3Gr3/96zyv++nTp/PhD3+YSy+9lDFjxhR8puH3JWyI1jI4OncJy+aioiIqKytpbGzkO9/5DuB7/oQjDRYsWBCEPF5xxRXE43GT3mAIzFMxGAwGg8FgMBgMBoPBYBgGQhmz+6hFx9g988wzpFKpIN4O+q3F2pLseR7FxcWcf/75gJ/06eDBg+zcuZN4PM6iRYsCa6tSik9+8pMcOHCAZcuWsXDhQqqqqvJWq7PZLH19fXzrW99iy5YtQbmt22+/nZqaGhobG3n55ZcZP348CxYsCCyv4SocnufR0dHB008/DfjVP5YuXUpRUVHQ5p6ensD9b/PmzTQ0NOB5HvX19SxZsiTwRAnfb6Es4AbD253m5mbA7ydNTU15sbe67xYVFVFSUkJpaSnjxo2jsrISyF9RCnuEhYeX7u5u9u7dy759+2hpaaG7uxvbtoOVpdraWqZNm8bkyZNJJBIDEqf19PSwb98+Dh48SHNzM+3t7cG5y8vLqampYfr06UyePDmvxPHatWv553/+Z1asWMGKFStQSnH06NFgZSmTyVBeXs64ceOYM2cO48ePL5hcD0yuJMPoQ7/7juMEHiJSSvbu3cv27dsB30Pt3HPPHbCCro/t6+tj//79bNmyhaqqKs4///xAZjQ3N7NlyxZaWlryVqjDlQB1/rLJkyczffr0oBpYNpvlyJEjvPbaayxcuJC6urpg/Nft6OrqYvv27Rw5coSlS5fmjfngy5bW1la2bdtGR0cHZ599NuDLlGjVQYPhjaB10E2bNlFeXs6MGTMYM2YMSvlJ2rdu3QrA/v37WbZsGePGjRvgpQH++75lyxb279/PvHnzmDRpUl61O/2+v/rqq3lJVxOJBGeeeSbz5s2jpKQkCKvRXqAbN26ktbWVo0eP0tvbSzweD8Lv6+rqmDlzJuPHjw/atH//fnbt2hWce8GCBZSWlg4I9dE0NjaydetWMpkM55xzDiUlJQADPNrCnx3Hoa2tja1bt3Lo0CFaW1spKSlhzJgxgF9yedq0aTQ1NbF161bOPffcYJtlWXR1dfGjH/2Ir33ta7z//e/njjvuYNOmTQA0NTXR2tpKPB5n3LhxTJ06lZkzZwZtilYE6+rqYu/evYAfdnz06FEsy2LatGmcd955wW+wadMmmpqamDZtGnPmzAnOEfaMO3LkCIcOHaKpqYnGxsbgNxg3bhw1NTXMmzePsrKyPI8krVd1dnayYsUKqqqqWLhwYeAxG/aA0bqSrrCTTCZZtGgRNTU1ZDIZjh49ytatW4NqROl0Okh0W1lZycSJE4OqiGEPGzMfKowxooxywhOV8OfwZCE8MQoT7lQ6CSXA6tWr+cAHPsBFF13EJz/5SRYvXpwnEMJlwL75zW9y9913c+WVVwLwqU99itmzZwP9rrph979oR46G3oRLpkXbqQcYx3GoqKjIKxEXToalS5cZDKOJQiFsURf36GAd/ayPKWRsCMuZvr4+UqkUsVhs0ORl0X4dJpPJBPlQwE8iHc0er6+3bt06vvrVr/LUU0/xxBNPMHfuXBKJRJAM23Vd4vE4sVhswHWiyp1RJgyjDd0HstkssVgsL1wvuk80rEYTlRXRfaL5T6IhvOFrhsMYwgsehRJVRr8Ly6Zw3oSwbApP7ExfNxwPwmEX0fetUN6ToYz1hYwU+nt9rE5WqivJxGIxiouLByx26L91OE5vb29QgUeH24Wr6RQK5YsaTgqF1YSrwoT7YPhZhM8F/WXT9dyit7eXRCIR6Avh80c/K6Xo6uriJz/5Cf/yL//CjTfeyLe+9a1Ar3cch76+PmzbJplMDjg2/DtFp8iO49Dd3Y1SioqKiqAiUfj5ROXYYLpVOGyqvLx8QDqBcMn36O8V/ju66B3ODxVuS3gfXW1IJ5vVKRfCRiR9LZ33xsjDgRgz+ygmKsjDwiAsyKO5R/R30ZViLQzWrl1Lb28v8+bNY/z48cTj8QHGGt0ZdafVlumioqIByZkGy36t/w4L9Wipw7CQllJSUVFRMJa0UOypERiG0U60D4UnMdHJSvi/hSZYGsuyKCoqoqioaFBjy2CGGX39WCxGVVXVAKNv+Frhc4cNq3ofrSSGr1fouvpcRh4YRjNhhT3a7yG/lHg0wWyUQivP0QWNwbxaov1Ty4TwBGYoo2+hCU1YnzHrioa3gkLvZHTxMvouhvV0bZSI9rGwN7X2zAhvCxsgw+cFggo14e+i/WWo/qH7baH+HTYORO+3UD8NL4jqij2FjE5hg2whvUNX9go/K9u2A6NFVJfRz72QkRUIdA/HcQYYiwrpROH7DM8zLMsKdBBN1Au+0AJ3odxNYQOavr/BdDH9T3vrKuXnvNPGlyhG7xkaY0Qx5FmvNYU8NAodFxV6AFVVVcRiMfbs2RO4KYZdYzOZDJ2dnezdu5cVK1ZQVFTEaaedBhC4OIazUUct14XaoLcdy2NGCzldTaDQfWnLuxEehtFEOMFZeEISXb3VFFICwwZX3dcKKTbh0oyaqAJSyJgRPqaQcTV8rrACUqjiRqEJYHgyFt7PeKEYRjuFDBSF+n94hTTa3wr172g/jBoztD4QXc0Pnyc6iSg0+dTb9WLPYIbXQpMPg+HNMNQ7FR0Ho97VhfTb6PsePrf2HCi0WBj2yAq3JTweF/KWiXq+RPX+sCEjugAbHbtt2w76YHScj/btwe4xfGz4v9F7DT/TQotBgz3n6N9huaDbpPWc6PwkaiAq5HE3lKdu9DeG/go7+jvP8/JCDcNzt0Lew2H5V+i+h2qX8cwbGmNEGcXojqWtljrmWRN1Hwu79YXPoTuWzptw4YUXsnjxYp555hlaWlpYsmQJU6dODSzcHR0d7Nq1ixUrVtDU1MTVV1/N0qVLAfLimcNCpdBErNAELTr5GUzo6jJxUUty+N7NpMkwmijk1aEH3PBkKZyXKHpM2BBTyOVYn6PQxCfcBq2gRI0u0fYNtioW/V63RZdPH2wSp9seXi3SbQ57wxgMo4Xw5Az6DQ3hyYPuFzo0bjDPzrAyHz5feD8gzytW9/3oRC/cFsuygjCEQhOeqLdK+L4KeQGEtxsMb5awUTG6+BA12uvv9Xsbfg8dxwlCTzWFFj+BoB+G0eeLLmJEjRDhz9ogo8f+qMdC+N4KGW/CY7neXkg3iBpPdTnfqKEkapTQBoWot4Y+JiyvwnJjMO81/dy1jNKfh5prhL+PerdEPXOihpfBdJhC6HsNe8HoNoefod5XP8fwsxJC5L0/4fsvZOwLy1JjSBmIMaKMYsIdQsc7684YthRr4QeFPUH09/rYqVOn8pWvfIVf/OIXPPnkkzz77LMDVptisRinn346n/nMZ7jyyisZP378gPZpQRcuZRpWxLQgiq4sRduj0XHV4fOH7yP8PAq5SRoMb2cKuX1qg4nepuWA53mBghHOLaSP1f2nUB9yHCcwZArRHwYYNmroyVD4nLr/hleoBjOC6jaC38914lgd9xuWBWElTytu4QleIUXIYBhthJO9RldZdX/USWez2WwgG/SxhQwvUHj1Njqhi67UR7cJ4ZdTdxwnzwhaSEaEr6EnI9GJot5udADD8cRxnLyxUfcLnU8wXHwBBnpeRvOORD1G9Jga7X+DeWpE+5XuA1FdOJrDJBzerz1f9LWklEG+jfA4rs+fSqWC+wwbScLGE30/emE36nmmzxddCA2fQ+dbKy8vL5gkOvz3YB5CeltYRulQqng8HsybwkYxfb/ZbDa4z7B8i8q2sAw9VpvCzzjcJn3v+reI3l/4nFEjS/j+ojqbDoWybXvQRbHRjik1YDAYDAaDwWAwGAwGg8EwDEx1nlFINPYubOUNWyfDKzbpdJp4PF7Q9VafK2yp1Jbw1tZWGhsb6erqCraVlZUxceJEkslkcM6oi164Qk7UPVBbSwuF4IRXwcJxhOCvAmircaHkctAftpTJZAZUCzEY3s5ks1mgf8VLrw7p1TPILzca9goJy5DoanKhlZZCuYuGciUt9L1eEQKC1apwiJDeX4cPNjc3s2zZsqB8YHSlabBrhl2BC7nwGgyjifCKZbhvh0N/ozkI9HE63Ecfo/8bTRwNDFj5LCQfNNq7LTzuR4/V4/9guVWinq1RDzuD4Y1S6J2Dgd5Ouu+EPRei7+hQ49BgfSl87bDXZfS64X11f9OhueGxPao/h71So/cbDRkq5EkWDn+JhicNJgf0Mbp90WNd1+XgwYPs3LmTiRMnMnv27AHhh+G5RdgLX+s3UZ1Ct6nQcw+3KexdU8i7Ixo6FGYwjxD9G4Wfx3Deq2gC3HB4E/SHToaPL+ThW0hnMxgjyqgmLMDCySCHolCHjwrkaFyi4zgDki9pN79CiZm0wBoqVrnQ5Co6EBRyAx5MEYu6FA4WimAwvF0Jv/u6hF40jC+a5V4fFzachPOhRBW5cCxyISUvqtCE26SPK9SHw0bcsGKh/3Ych0wmQ1FR0aBuqcdy6TUYRiPh8Vwr3IWMJIV0A000DC+qkBcK1w3Lgug1w9sKhSmE2x7NPzDYZEO3LZyLxWA4nhR6dwsZFjTRPhR+VwcjPO4NNW5FFz6jxo5C4SzRfEPRa2rdoVCOF21ADfe5QmFJhe6nUFn1cPhdoXxleu6hF4HCRtGhjEbh9gz2/KJyY6h5RZhwjhV9bNgQDfl52PTx0dClqKElbOgp1O5Cci6qJ4XPpQkbzcycqDDGiDLKiQrO19tJhppk6PMda59CHOu4wdpSSHi8nnO+kWdgMLzdGKyfHGuAHmyfY10nfK3Bjo8qAkNNhoxBxGA4voTH8+H0oeM5lhbq7zC8vvx6JkNvRO8wGF4Pr2e8HMpIebzbMdi138icoJCOEDUWDacNx9rv9cikaF+H4/dMh3u+Y81RwucY6rcfSh4Ox4hiZNvxwxhRDAaDwWAwGAwGg8FgMBiGgQlwMhgMBoPBYDAYDAaDwWAYBsaIYjAYDAaDwWAwGAwGg8EwDIwRxWAwGAwGg8FgMBgMBoNhGBgjisFgMBgMBoPBYDAYDAbDMDBGFIPBYDAYDAaDwWAwGAyGYWCMKAaDwWAwGAwGg8FgMBgMw8AYUQwGg8FgMBgMBoPBYDAYhoExohgMBoPBYDAYDAaDwWAwDANjRDEYDAaDwWAwGAwGg8FgGAbGiGIwGAwGg8FgMBgMBoPBMAyMEcVgMBgMBoPBYDAYDAaDYRgYI4rBYDAYDAaDwWAwGAwGwzAwRhSDwWAwGAwGg8FgMBgMhmFgjCgGg8FgMBgMBoPBYDAYDMPAGFEMBoPBYDAYDAaDwWAwGIaBMaIYDAaDwWAwGAwGg8FgMAwDY0QxGAwGg8FgMBgMBoPBYBgGxohiMBgMBoPBYDAYDAaDwTAMjBHFYDAYDAaDwWAwGAwGg2EY2Ce7AcNFKQ+EyH0WKBRSKAQe+H8B4HkSJQRSgFQAHuCEzhQHhf9PuCAUCv+8npAoBCq3pwCUAkv/LU7wTRoMbwTlASBEziaq+v+jlIsQHgiFUBb9L7cLAhQWvi1V9y3/XC69IBQxr8w/k8iAqw+Og5R4wj9spFhilfLbJwI5oYLPAHjguC6WrRBC4SiF7t1SyFzP9yWA5ymk0OJRhGSGPpdCWUYgGEY+0XEr102C7xXZ0JhnIZC+SFG5fYQCkc0dayGEwHUVQoCUMuh3nuchLSt3npHXN6LyAcDBQ6GwkEil5Qa5/fSBgAAXhas8crshhQQ8ZE44SBWSwSNGKhoMYXIdO4cKfYZwv5WgZGg/XzcQiPx+ERwocsOkQimQon8HX6UQCHlyZcIAfWCo7z38sV4oFF5OK+h/Hvr2JL68EMrXsfo36tP48xELg2Eko469S7CbCsZSJQQIqXs60D/y+X1E5L53fdlArP9KAgRpJDacwj3klDGioLxAuXGRIIT/YykBISEoEOCBEApXuICHpaQvFHNH9+KRFjbtbYKODmjr9Le0tDu0dCq6+sBxBZ6vQaK0cBzui2YwvIVIARIBysPz+icBCIVlQTwmsGOCimJBdZm/qaZSMH4M1JYLknZunkS/UiU8CSg86eB4YAmJlZtIQRalYjkpmbOknEQCgR5RhgopTEIolMqiEAgsVG5G5Pk2JSwhUBlFU0Mnrc29AHS2Z+lsS9HVkSHd56FcgVAiMDgNr5HD3G/kzT0NpyLCn+R7ykN5Xq5v5DYJgWXbxOMxYrEY5bUJSip8VWDsuFJqaksoKrURlsotXkhEnlFAT6QU/sDq5s6r8FyJkOKUWXDQQ7u/1KKQiH6lSPk7OAKyuPR5aTqyfbRnc3LBzdKl0nR6GdK4uEIEBpZ+IWwwjCREbsTOGQyjRhQhEEJQTpxxsoiKeBEANfESykWcIiwsJfOMpEoRrDZ6CIRQ4GorBIEBEsA+iQPcYAYU/d/wdk84kJtL5A6OtFwhlALVjZvqQfW2QaoNAJluRaY7EJkuhJvGl49xfaLje1MGw1tJIolKlOMlawBQJXWI4lqIV4CIBeMpgJubVATyRoEQLkqk/R2U7RsnB/StU4tTx4gSsmwLPH/FTBtHlNSmb/8/SuHh4eKihEApC9fxt3d2u+xo99jd0cPuPTEaGy1aOvxLNLYqGjs8unoFWdcfXpRQgRw1apFhJCKFQAp/wFdKhPR3hR0TxGMQi0FJaYqacn/jxKokM8dZzJ6gmDbOZcIYQXWpJGn7/cRWSVDgCIeMFMSEjZJ+T5DKQ+CGJlYnTwQq1W8Vh8KKUm5PPOFg2cL3VlPSt4B7/v6ZXkV3a5bmox007+tj764jHD7oK0Vd7Q4dbRk6W/vo63ZQnsQSFsJxh9lI8gaXIXc9+TYpw9sEKRWe5+F5KjAIgD9G+kaUOIlEgrK6akqr/GNqJ8WYMK2U8dPKqZ1UTuWYUorL41h2bqVIuHiehxASIQRKuegRUkiJckEoccq8w1bOnc6VgShAj/QZXLq8FK1umiPZTo5mO2lN99CU6QGg1U3RqdK0uRn6hIsrBV7uWSjlDLyYwXCSEcpCYPX3z8i4JIVASkm5jDHeSlJl+UaUifEyJscqmRgvp9ouoUwkSeY8Nf0FHF/G+F1foFT/xEhICcL35xjpgkEblTIiS0xZueeljUa58d7thVQ79HVA52vQ04zqboSeJv8cfa146TZId4ObBekFizUGw4hEDc9zUhVXoeJlUOQrDKJkAqJsKpROg5JJyJJxUFTh7yyE1gwQWEhAKgcpMv65AI8kp7rXplDqVFkyUeSbM4S/Wu7mhFNOx1PSBc/FQ+AKSdoVdHR7HG7099u6W7Jmt8f6gx0cbIjRlYpp0YiLxM2t4FmA7Xl4nue7LIGJ5zGMSLycR4QlLKQIucXljIngoIQiK1KBkhCjmGLbYlwFLJyqWDoHFk2zmVLrC7SaEkXCVnhS4Fi+34bn+cdaeNhCIXUo0EnsF/6EbqD3yQCxJhQuafAsLBkDF5yUR1drHwBHdneya0MbOzY2sn9LD62tbaT6fGEfs+JIaaFcB89zkRIsKRHOqeuCaHh7o1B4yvcck0LmXOm1e70X9A8pJS7FeNrLLJamqEJQN7WC6fPrmLVgHFPnVFM73ndhS5QpPE8gkH4oj/BQys1dx0IImesjFiNtwlQonEeHLSHxA3uEIuX5/b7B6WB3Xyt7Mm1sSbdwMNNF2nPIaNVQCKTIuf4phS1EsOKeFa/DS81geMuQMMSEXuY8UZC+F5rK+u9zQtnUxsuYmqxhZqKaaYlqxifKAaiWRZQpG4XClb5rv/BynhqE1AO92DPCCHuhaP2olwxFykaqXKizl8ZLNwPgdexGNG9FtuxDtbyYM5b0QmA4VbmUAzZKxkHGsTzf8CowcsEwAhmmEcWRNUgvg3S7ARBkIVaCKpmKGnM6qm4xVE8FQBZPRMXLcKwEHhKpFDGlEDkjCkKSFZYfSjvCdIXXw6ljRPFAacVEKy6e9PM8CIJVciVSKE8AMXqzFvubPV7elmXlOv8212yL09ruDxSudMgKD1fp1SMPmZscxrGIKYlU/a6I7nCXkw1vKWqQzzASgk1OPJ6Xm8RIKxeTr5MdKLzcSrHCIyZiIH1h2SPTdIksQiWodBLUFWVZNEOxbFEMgLPmucwe71GaSGDjKxpOLt7fEwobF+nl3PxPoiG5UDxzQZEmBK7ycDJpEnaSvu4MR3a2s+mlgwC8+txedm9pxUsnsVUCJ9u/kiyk8JUf5SKkh8xNuNJefOB1BmG45pb8iHWD4U0gcjl+9MQoh+flQnzwvVJslcUS/rusRAxXSVwcRCJN9QSbBcsmcfZ5cwGYuriC8uokCA8h8XOghCZlQnq4Xg9SFnMqZALw0DkNFI7K0q1S7Mq0ALC2+zBbeps46PTQJbI5XUEnR/LlbRyF5Sls5WeXyuSM2Gl5aq+uGd6eaF1gMHxZAdJzwXXI5t7nlPRzpBRloI4E04uqmV5ZC8C84nGcbo0hIW0sJXEQuKJ/HLZdD1uBkBacInnElPJASYSnUG4Kr3svbtNqAJwjLyFbthHvacfFBk8hlIclc0YjKQLDqp9HxUIoHQptRnfDSGR4Y7VLKVJlQfXkvulGiQxYCbxEDV7xOCiuBMCedDly7GLc0klk42UoFLYHtsrlTZMCR2R9r+5T2Bvl1G25wWAwGAwGg8FgMBgMBsNbyCnjieJlIYhUkB7g+bHXWL4boZcCQFkeUiXp6pOs3wWPvOjy9DqHPV0JAFqEQmXSlDoWpRKkFLg5y5hvQRf0CUhJhaIPRJpSz1+dT3inTgqZ0cTgeTB8TpFX/I2jPaR0ktTQ/UohENJ/Rp5HEJIjhIctBLa0cD1FT7qPRFIydqwfA33OaYJrzlIsP00y1lbIrA3+JtIyhVI2Mc/CEmJEmWILuewH21DgCrJdHi8+tZVn/7CVfZt9i3qmK4bKgOtmyDopYraNbeVW55F4jp/YWkh/5d31HNxkdsA1DIaRgEBgybifs0RF0kfmMuwLKbEsIOPhurr6jsKSMSxp47oerspSXBZn3Dg/BnrJVRO54IoZ1NYXYyUdv7Kd8vuJEAIhFR49SIoZUYJhEFTO07RPZDmcbWNDxwHWdR4BYLfq4qDI0EyWKiyqEGSVF9QyyghBVkBWqSD5fInn33OJO/K9cAyjEKF8T+5Cm8IJHqWfJDkI4VWQsGw8z8NxXYSQxGxfH56dqOGdJTOZVzaOcRQjHAvHAieXp9BSYHvKV1PskS8TAFTWQUiBSveSadyAOvgEdtMzANjd2yHr4TpjsGIC8MALeQAL6U9WlAAlUZYIVe0yGEYiLsPzkmpBCXCFPxlwRREeNsJzsN1ebDcNwp+Lu9VnIMZdiJx4Pl7tXNxkKUp45IJGkMqfV4uTmxHgTXPKGFHCv68upyRy8diu56KsJOC/Cn09gmfW9HLfSoeVO+IczsRA+rkP4l4bUhYhRSXS60MqByFyPyY2KhfXLZTEUwqFixUp3WQYGQyYHBwDwbENLqciYcNBXry/Uri5/Ad+6UGJLX3Fx5IWWU+R8RxitkssJhEK3KwfGhQTDgvrJX90YRFXLraoHQNIP8QlrTyEsLDJJZU7iQpCXjxzLsls9Dn4uKhsH90NpTz5f7t4+qGNHN7TisjmEukKC88B5QniRbmQB525P1cGVeTKNAohkCiUZySCYQSjrH4Dcqg7KE/5Y2auYo+yk8RiuY3CwXP8cuaWiGGLhJ8wMjeZciqynP+OGVxy/XSmLywjVgpK5RYXVLK/tOlJlLOFVJrBQv6EUnSSYYvXwrOd+1nZtpcj6S4AypIxZEzQ4TrYjkcxsj9nBH4+GYlf4tlTnq8v5IwpnjS5D6KI0P+/baIa8l4rVeDTyMIvYe4b+IZacLCVQHoKR/UnT7dsv6S38lykUsRzw19cJKm1q7mkeAKXlNRTaZUhlYXKhe54UuD1B7ac0PsbiqhcKHTfegEqLQTJ7t2IAytQex7Fa1wNrr/gYsXGgiqBlMItjiHww3kEuRBglc3lR8kAuUokFOurHv8bMxjeLCIFw8jX41hJfz9P7ysRxBDKBi/hz6Vz3g7KcXCLq1DjTkdOXo49/kxU6bggPYaF5ec1PcVzLpw6rhXCDZLfeK5AIbBtv2SStGwyuV/hSCM8+XKGh1/uZvV+l2ZXUFwisHr9wSCeqUImk7S6DkIk/dX4XGrZOFlsL01MCWIqhqXiWMrmqO0LxzZrmNU43mJE6L+6VG102+slr6izX51qyNwjbxUCf2WjSInX/fIqIIMiJZQfs3sC2ndSyJXi9svyeP03JkFJgRAWQghKMxIrZzTI2Iq+uEfa9hWAZDZDqWtR7OVioFWcjfssfv0ktHX3cfUlFlMq/ZlWTFl+jhDpMdJMi4XyPwBk+rK0H+hlxe+38twjjbQdFiRUGcr2J0xKpbDiRUiVQFkt/vuudMUvC6TMhTkr36NHCOJyZMoDgwH8scDz+qvnBEiwLD/hrJQSR1o4WV/Z9zwHKWwsy08M6ygHcCA39qnuIlY/tZve7m6Wt89i/tkTKav2JbESWbKOJGZZJ1UpihqS9X/9akL51bzaRIotfUd5rn0fK/sa2GlnSMR9z5psNksipRhjSYpEjLgnUJ7CyQlYB5csjp94XvilGmO5bfFRZkPp1xdEoCt4uc/6+/B+bxtCN5Svd/l3LCP/PenoXB15Cw7+JqE3hxYMvJw3SZ9QpHJZmBNKUOoJ7Nzw1yUzHFYteB1psq7H0oopTJIVJHLGVakknhC4DD832IkgKhcKLcDoJNmxVAPZXU8gd/8Sq3MjkiSePRmAjFeK8HqJJVpBVYCwfa8dtBy0UMpBKQuFDThY0l/kPZVzPxjeziQZjnS2XJnL9RNanBG+IcSTLoIsMreoKmQRVt9uvIYDkG1AZboRE87HqpqUO5sMTVhHhHR8Q5w6RhSyQM5jRPTbsz0UrrJp9+dCrF59lHtXl7HuUBlO2qOcGHavjeP6P6wbUyjlUIKHpXyhpl3thIrjCUUfHq1SgUiD8IjnVpyr3ZH4Q2tF5a1qW/Rqb4VaFL3aG7/XmPL/ibeROudFnkq/siD9VWRy1TqsDOmcN4mlFJWeRKZjuE4ClzhZG3pjuYoeriLtSV5oynJ4dZZ0TPG+c3xxManKJSYlrmf7E7K38F6jFKrMo/E8j0zGzwTe0tDLc79r5PlnttDSkEaqpG9MV1rgx1HCxaUX0sUIKfPfOld7peCXa1SQJYYBojLg9ebfzi8WMRJl7ClILmTHU34FLZEL69MbVa4kqfQEIutALrm6LQXSAg8Xx/VVJcuWgYeFlc6S7pBsWt2Kw35cEiw8z08wWVapsCwnFHd78tGGEwhPlPoNKa+kG3ix+wBHUm2IbJqxyoVcmXdLCBICijzokh6NMeGPHSEvFpS/H/iBxW5OnvSOgsmSMIn288hfZPLfCbeAPDuZ5W59b1zfY8pTCinyncb0QoEjPDwBidzGEiUoVYASZBH0WZK2QOHwSCiHDV4nLb0H6IhZXFRqUy/86j1JTyCF8iNeRoh8jxpU/cURD7K9AFgbf4fXuo1UCoQaT1zFsHOJ5AV9eFYrvdZhkhk758luIYIKJxIlBUragABhIQOPnlFmXTWcGiib4VXoSQAKkatEJVQWRBaFwvI8sGQQ2eFYbUg3jdWbQbnb8VQxrpXASvgGRatoDErJU92GcgoZUVwbJ3CVdZBKIl2BUjY9CB5f7wunh1YVseOQRSYtiAkLqQSe6/WPFMIXnDYSSS5Pgk4pAXhK4vkFXXMCVqAjRY+nK6LQjgPkBtWQt0fOnyCoRqSv6ooUSVWCp7zQgP1Wv33RFSWBJwBcYoHi6OUGaT9vjYX0XbeUtvp7SBQeFq7IrVxFFLKQrfOE3EMogjVyP+HvLDL4k3ApBBIb5SlsyyXmxXA93+BgqziudPFOklIp9WWDVzzsMq30f3Ck70Gh9xGeQCjfl0QJ3+VWKwIKD+E5eGk4fDTG71+IkcwZYN53gWRcqe4VIggX6jdi9Dtu6zCA/gnc8XxfcytHufwDwgKFiycsfI8cj84Gv81rHj3Kqsf30NKY8T1thdvvZqXbpXJVv4iUZ1X9H6S+57zvRzbKy2JJ2x8oAaX8SbHjZYnFYriOQogYKueO7PnZofwyzkg8V2FZuffCc3JWkuMoCwcm7OB4GId1mIlCIOxePE8hVBGW8MNZgl9e5qSuGjmT/+OBQg97uZlS3nPW+ZNA4gZ919/NH5x0+KNf5EfnDQNLZOnrTLHj1QbiIklp3M83tvCSClx6UMLGCptW1YDL4jgOtn3iyiB7wWQl12d1qVIFjvB4JetX5VrRs4ttqRYcsniWRzHgev3voCcEafwwYZFbo9e6xFDhocdTNAhAKhXkYsnil4qNIRCePxn2cs3ISoEjBdVZX6532oJeoSjOGYFLlT9eZ1DEBQhXIS2/n2Rdzy/1rDx/LJADx0nDm0cbnxRv/VKOL7lzOoHwC+7mtUL4Rm3tYT+g6wrfq0bi9evDSiGVQuDS5nWxsnMXtvSIFU8DYLKsIKakX1SzwDxNhxWKE/ymeYD09BzCRemw/dzzsFJtsOdxf+f9j2M5vcTcDIhkbtz3dUEhHCQ2tqpBBAZjLzSQ+TMIoXKr7MrqV/ZHuNIgglmRl9OjABVHYiE835cbmfUVukDP9HNUkitv7bkSmZtaCmGhyIJ0/TAy1wVl93sDWhKFgxDegPftrcT3k/LHDI9KhJWGtIewmlCiDJeioJ9Y2Kjcu/Bmrjii3gXlS4JjossT555V3ixN+qOF/m0lLkKWI0ggnAxey1osN9M/P5l2MX3xSuJIbOX5OnnQBhvdffzr6m0jb3Hi1DGiKBtP+Eq+Kzx00qZUWvLqvj4efN53R35lXwmprCKRE2hOJL4l56CPAvqd8fPXECSQUDpEov9Hczh+eDlBC/krAZp+xS/0nSrKxZONLLXGn8Rboedp5Rmmcg7h/YOMCA/RJ0eQFFohGoiHrbtIbtYsBLiehYsH0h9kssGdn6TfZdDLhgZu4RtOLNXf5bPg/yY5V32pIOFqI5lESoghSKc9Nh9I88Qqf9u0MTbnznepLrWRwcSz/7fUDsxC5XQHoU6IgqTwSzgHEzbPN8blpjp0tqTYtOowAKse3UHjoXbImfqUivQ7pdsoYJB8BmLA51NjVUlIByEVSsexKgFCIaWDtMD1XPxkg75RUOD1T56UQkrf8JnbyFvznh+HAIBA3gDCATykSCCFNtJqM41Wdk+N33O45P9UauA2jewPNVCI3GstAg8LvP79pZ1EiU6k8Oht6WPbmoOUlvnnrp44k7rZRSjloYRAu66HbJAF2nQC3iUV0u/1OOv5RoGM63LQa2dV9z4Adqaa6HT9RHhajPSPun6pVjf3XdGA1yO/7SFfteOq6vnvZ3hZQWIJiCtyixEqGIE8IVBSUKw8pIKe3HsuQ+3ylEDlfnNP0H+0ldtH5HJdGU4ogpMX4jPUdXOjt/9H7pVzgs7rY6Eozm1z8d+ppPKQyqEx087GzgOMzyWfLCmOMVaUYqn+CVZk8EUpUVAPPq4oCMZsRS4MT4HnoNLtqKYNiH3P+Nt7DyFRxPWxWmUN/kwQ8xKDdPSQXqv6j8j/78hET419nU2P+ZnA6843Ndg5HSEnNzyJELbv0S9dhHQCz108Xwn0F0o932BiSYT2zJF27qJhPfKtx9cl9V8JhMyAFwMr4y/wKkkwY1SS45MHcAS9C2K4c7GwHqiJPAttRFExlIjhWRLhech0B6p5C45d4+9WPh5ZezrCLvWvrVTIEJmvG/ghySczo9LgjDyzjsFgMBgMBoPBYDAYDAbDCOSU8URRVr+lzHejhbQn2N8qePi5FGu3+FbCrowL4kQ7Br555LDiz6IMflev/EecKWMF965w+dQPjqfPjEHzv1+KsXye5PnNHu/5p7d3eVspFJaQKGwcpxdFH7v3+THOT6zyqKzKsHi6oszOhdIICNwhdXSEkAh5AntirsQ5ugqAqxBCIoUi0wN7NnXw8jO7ANi78zAiyJA/uvCI5RaGdG05DyH86iKu4/hx8WT7l+9H0LDw3r9YxA1/sQiA98/+2es8WnvWKIQbRyrfL87LrXoGo4SKhroYBsOWFlnXX3UUQtLR2sm61X7lu5rJNu8cdwaJcnLuiTrxdNhHw3/qlmXl3PgjsYjHg7CntJB+yEIuD0ST18MrXQfY3dMCQK/3Zt2yTzwevienl1v9jOHnZpHK95qVoeSgScBzPbLCQwmIeVAl/PAfgExuvxiCH09+H3WxMh7r2sE3mla+1bc1Kvj6+KtYVDSedX1H+OsjD5/s5pwQLPzxP+N5xJXARtLY18Vm6ZcKr7JKKU4mKBfx3IKzQmq9IOeaFiR4xfd4OBFVFHNZEf3PKpYLd1QIpwdat+IeehrRfQAYSSPgW4zIJSJXEFQbEo7/z/J9czxRhCAbhP8KmUAoG+EK/1jpIKSfW0aRBTcBqtiv0mRbYGWJXXYPYtzZqIaXEI99GOElcqGSJ8kTRdgoXaVVOQgnBbFysCoRKoGFF+QB8T1RTmBjxi1FXPlD/1KP3A5HXzmBFzuRxFD04SkHKSyESOC5WVJtmwGwD00kUVqFLK1HyUQujLDfO1iEQpAV1gkP93ujnDKyQgkvcLe2cq74zX2Kl7YrXt4Sp6XXd7yTMYHMZRE/VVg0TXD5YsnZcySzJwpqysFxoaFN8fJ2xa+ecXlp28i/nw9dLvm3j/Qn2/yL/8ryP88e20V+2SzBh99hcfYcydgK6OyFTfsV965w+d8X314u9qcKnosfsmR7xKRFUsboSPl97MVtHpNmpBhf41Fao8M+CAZAgR+upnLK0YkSff657SD/geeBZSmkguaDfWx6qYmdG9sBcDO2zhc56nDcBEp5WHZOfkoFHowdX8o7/2QOiy+eRHVdMU7G4+iBLl76wz4e/+VWMqlTu/pQf9TIqeFKfSqQzab9HF6WhSUtPA9aGvxQ2rUvHGbGaVM5ffkYsHSmAchzy82N3UIIvH7/6ePuyq+ljsqF9gjph13udtt4qe8wh11fyc9GwhRGIh5+bhY795DiSjEzVsWykinMT9YxJV5JhZXEVR4tTi+b0kd5pHMHm9JHgZPvbhxDclbxZOYkxjA7MZZau4RyK0mxjNHrZTiQ7WBt32H+0LmN5tzvUohxdik/n3LTsK5pDENvLQJwLAsPSCjocDJszhkqK2PljLVLKY1VInVIj+qXDQPKj3OCuqTqL2Esc0l1kS70tiCPvoZqXInKdodaMRqJ4YdXOXi5+ZYUMZQsQkiBbZdgTboAVT0fUTMXimshWQVWEjJd0LYbdfA5vF2/hHQ7QvYibOnn4JNFIBM4Tg+26g8YFAqU5/iLbicypstKwmm3QP07oGwSyDj0NsDBlbD1f6Cvzb9fLwNuBmyFS3ku7M4NvbNmTjIshJ/vBpFGiSIQMYTrQN8eALINrxCvrIVYBapoHC5WEEJmKx1Eq0NOR26A6aljRAGsXP4FKTzSrsf+Rnjm1Sz7OkpQMV/ht3JTuFNFBD7wdzHOnTtQzUnEYMZ4wYzx8EcXWfzPsy6fvdshO0LnNeMq4Uu3vP7X6a9vsPjseyyskMdCMg61lYJLFkpuWO5y2384pN/ejh8jjpiwyaLoJU2RsBBeCX05+9jOXosXNtssmiKZUgUxkcs9IHRMvZWreKUzh/THfh9/k4qH4/qTuFg8gUDgplz2bG5gyytH6G7025Swi3DdEdp5TjDxmPATqQr//oUnOeOSyfzZN86nuCzev2MxlFYmmLFgDBe/bybf+NhTHN3fdZJabRiJCOEgsFDKw3Ed38PB83MfHNndx/NPbmbmgvNIVPmGEikV+aVFvVz+A5E734nJiSJzhjOBn0jVEx5dTg870o2s9TqJ5fJAhRxcRyxCgC187xOAf6u7mgVFdQV2tJgUr2BSvIJ3ls3m8a4d/HvT8zgnWekfa5fw5brLCm4rt5LMt5LMT47jhorT+W7zizzevfMtbqHhTZEzithxG+G64EE2JjmQS0K5KdVMfaKC8bESSkQir2JWsNoc8h47YfNoocjkpjxxBEJ44GWh6zCiaT129y5cq9Lf13t7JRkfLlmvCIFFTGYRueSpSilw/d9MjF2AuOBrhbW4ompEUTVMWIpa+EF49h9RR9aA7EGRRmVdhATbSSJzufkENpDAk1l8c/EJeu5lk+Gy7yIqpuZ/XzHN/zfrPVgrv+gbVAAli8FLhVLhCJQ01RhfF8oFbJAx/ESxDtLKUpLz6El3HUDsfx7KpkG8EuyiULUsD6H8aj8eHhJxAuYOx4dTyIhiBSsqQmVp74SNO2HddmjqU/R57QCMtSrI9+cd2dRV+S/GkVbF/73k8tJWxcEWhSVh6SzJJ662mFAjuPlCC9uCT3xvZIbqfO1DNuXFgqYOxdiK4b3sf3Kp5K9v8F/BPQ2Kbz/gsOWAoq5K8NErLS6YL7liicW3PzYy7vvtHsKThxRY0ncDdxwX103SFvNXaVwkG3a5rN1mM3ciTKoBlPRLBAKW1D3Q147ECe6PKqesKQRCJWg80Mr21/ZzdE87Ku2/X24sffKXZE8SQjm+apIzQk+dW81ffPtCEkU2fd1Z/u+uDWxe1UA8aXPuu+q59ObZTJhewV/ddSl/d8NDpHpOXt/73Z3r+N2d607a9Q35SNtDeH5iPcfNknUchOsbUURXjO1r9nN4wzzqz6nBLtahPHrCBKDwPIWUJ3jVMZRTWCpFr8qyv7eZ7Z1H2Oj1MS23W/WJa8FxQyoQSgWmkGrbD0tsdnp4tmcvG1MNNDo9SATzkrXcUHE6Y+0S3lE2C0tIvta44uQ1Pkeb08e61BG2pZtpdLppcXpx8RhjlXBW8SQuLZ1BkYzxubEX0O6mWN13cMjz/bh1DS/27Bt0e9cICdN6u4bwRBFC4ObCcLK4ZOMJenL9e7/Tza6+JmYUVzPNTiAsSSiLp0/enydCLnggUni5ZLcKF6kUblc7TuNOaNlHLA1uwtfxrBM1mR/hJNQhUDZ45ahcdR4lPD8EGIWSCrqPQMMr0LLZ9+TobfZd/YrHQf3lMOVSRLIKddnXEA/dCm3bEEgsmQEyYIMSbkhHVEhxAqeidjFcdmdgQFHb74M9j4CbhrplsOAjiHgZ6sJ/g4c/BG3b6DednASOvoL66eKTceXjTAblJUEVg8zgqXYs5SJFJQDxdAoaXkWNX4Isn4ZnFQVV5nxVQfp5iYXnGzUjyZ1HCqeMEUW4/eFynoxxpM1l426Pxi4bIR0qcsJPut4A+TyS2XHY45//x+P3Lw9s95qdLr95zuX3X44xc4LkhuUWP33SZdXWkXWDV54peddZFk0dijsfdPnKB479WlWWwN/9kb/fgSbFVV/O0Boseisee9XjJ5+1ufJMixuWW/z8KZcXtoys+347k/UcUJIiK44nUqh4btUGX4719Nis2yk5YzaMH+P7ywsVttSLXGxzOJPR8UXl0i7EciU6PVyEq9i7tZX92ztI97nEErl3UfYbeUYbyssPWfiTLy0jUWTjZD2+9pHH2flac7Bt86oGGvZ28cefP5MJ0yu4+iPzjRHDEJB1MtiWX67esiyEFLiezknk0Xy0hzUv7KH29DIqiotyi9T9q86+d8oJ8kDJkef5pgSeB93KYXu6mX3ZTsZZimQQay1GpGIWJTzyHci08+PWNTzXs3eAz+3WdBNPdO3k3ydcw+R4BZeWzuChzq1sSB19axsc4ojTxc37/7vgtm0083zvPv7QtY1vTXgXMWHxoeolrD40tBGl2elhb7b9BLTW8HoRMrc6kckiLUkKRZ/n5ErHQ8ZT7Mu0sSvdwkSrGkvIwPNYeV7OazV3LnHiyj4rnGAdxcFDqCyy6yCybQcq242warEYRQtlBVB2GlScoGwxEfHYsBruu2rwE+x7DCZfApf+O8KKoxZ9DJ75XPQsby2nfxBRUQ+AeuXfYdNP+7c1rYeGV1BX/hARK0Kd9dfw6O0np51vN2wL4cRA2Ujh+iWwXQfh+fMES0hw2n2j1ZgzITEG7P7qPApwhf8WWicsxu/Nc8qszUoJwnIQlkOv47L7qGLjAY8OBUnLo0SWUCJL6FQO2VMoZu0D33D4v5cGN/y0dsGXf9kfhnDtWSPrJytJwr98yJ+o/r9fObR1D28I/MAlFhUlfq/4x3udkAHFx1Pw+R87OLlSaZ+8ZnSuDJwsVG6yYwOuJ+gTHnHXJu7ajMkkKFJx9jVbbD7g0dXjohAo4f/zPECJoGqaNiAfbxkoFEhPIInn/gncFBza0UvrYRcv4+B6vbheL54anaE8QF6ytukLazht2TgAVvx2R54BRfOHH23i0M52AK78k9OwRmsyGcNApIUSgqzn4riAsJBCIYXCy2ZwUjY7N7bTdLSLbMZFhHq/b0sZmAPheOMIhSv9fwg/F8pR1c3GbDstVpZxQLGQFJ/oGPwTxN8ffYJne/YMGrTc6aW5q/Xl4O8LSurfopYVZjgawbZ0M6/1+YlIZyXGkDyRK9OG44qHn6415oHwBI4lkJ5HseNS7LhI5XDY6WZ7qpVeJ4ub298l926o/jcknIL6eCM837/Ewk+2rrJd0L4Vu2sXMTIIK4Yl/H+jFU/V+Z4Dg6GGMbc68DSqw897wbgzjk/D3ijChrl/DIBq3wWbCiSnb1oHO+73d69bCjXz38IGvo1RAikdpOxDiCxCxBEkQKX8f5YD0sZr24Xq2Idw+nL5ZxTa79LX2r1cyODJvJnBMSPVKcDzm/sFV/244Sl9M8YL/uxdFhctkIyr9JO1rt7uceeDLmt2Hr+38Uu32EysETy3yePXKz1uvnB4Rp6rlvr7dfYqHnq5sGA+0grPblRcukhwwXxJSRJ6Uvn7NP4qAcDX73P4+n0uy+cJPnalxRkzJBUlvpfL717w+K8/uPSm+4+7bLHktiskp0/199vfqPjNcx7/+ZA7aN6ZoarzTB4Da77jt0Un1L3odMHtV1qcMd2/RkMbPL3e49/vdzjSOqzHZDAcV5ZePiX4vOK+XQX3UQpW3r+bW/5qCSUVCeadXceG548E2//251cw7+w6Nr/UwD/f+hjjppTxrtvmseD8CVSOLaKjOcWmF4/wv99bT/PhnuC4SbMqufoj85h71jgqa4vpak2x9umD3PeddXS2pgo15ZjVeb791HsZO6mUZ3+3kx/c8QLjp5Xzrtvmc/p546msLaK3M8P2tU38/q6N7Fw30GBkMLwdWdfX31/Hx8qHdcykWDk3VixgSdEEqu1ietwMm9ON/E/7eramm05UUwP6vP4xNSYsUurEhBE+Nv0jAPy8bS0/b1vLomQd76mYz5zEWEplnKNON0937+K+jk15bVhWNInrK+YyI15DqYzT4HTxZPcuftu+cdC8M0NV5wknyv1647M83r2TJUUTeHf5PGYnxlBqJWh1elndd5D/bls3ZNJdg2FEkc29q1ZiePsX18K8W2HyRf5nJ+WHC235JRx6/o23Y/wyRLzM/7zrQQadie/8P5jzPv/zlEuhZVP+9kV/ilj8pwB+qE2sxG/v1MugdGJ/e9ff7RtlNMkq34gz5VIomeCHEDWuhXX/Ba3bCrflWNV5ln8FMfM6VPdhuO9qiJXB/A/A1Mv9aygH2rbDtvtgzx+G/6wMb4hTxojieQoh/TjXlk6L7QcEe5qyZGI2pQgyfX5nzcRdYqdWcZ5jEg/9Su4wDMFXL5V8789sSpL9BpexFXD1Mosrlkg+8T2HB1a9eW+dM2cKPnS5JJ1V/M2Ph6/wxCw4Y4bftld2qCGT5b6wxePSRZJkXLB4uuD5zYP/sH9xrcXf3mz1l88DZk8U3PE+yaULJTd9LUtvGv7pTyw+dmX+qz9nkuBLt0jOPU3y/q9n33RI2N/ebPGX1+dfY2otfOhyi3ctk7z7H7PsODyCX1Ih8XCRbhpLldAjJOUxP564rE9iSY/97Sm2NmRp77BJJkuJWboihtdfhxzlr14EK77HuZSpEJCLX3bcNA0Huti1pZ2uZhcLC0/qfCmxkeoN+JYy58xaAFI9WfZsahl0vy0vNwSfZ59Zm2dECTP/vPF85rsXUVTan6B27KRSLn7fLM64ZBL/+IFHObK7k3PfVc/H/3U5sXi/R1nN+BIu/+M5LLpwIv9wy8O0N/a9qXtb+o7JfOLr55Ms7l9JrBhTxLJ3TGHJJZP4/l89x6o/7H1T1xj1CBvH9RMA+t27v0yxwkN6cfbsaOPQwWbGTiqjrLxoQJlzv7TxiQ3nUbnzSwEZx6Eh3cXmbAddUjEm4+LlbP2uODXCeV4vsVCIhKeOPc4sL57K52svJBlKnhi3i1huT+Wc4sl8rXEFK3r2nJC2AlTIJIuLJgDQ7vbR5aWPccTx4eaKhXy4+sygBDTAlHglH6w+k6XFk/jCkUdJKYdP1JzNeyryV6inxqv4SPVSFiTr+LuGx990OYOPVJ3JLVWL8r6ri5VxbWwuF5TU87nDf+BAtuNNXeNE4Cnf9b7Usn33e0tiKyhx/OdhK49mkWJvtps+N0M8FkPknOAtotV4VOTv44SS4CX9ynRAVklUqh3atkLXPjyVxhV9xD0714q3oVAYBnIYsuKYlE+F6tn+5469x95/7GK49FuIZChDlZ2EiefBxPNQr3yrsAfJcKhd3P+5Yc3g+7VsRmX7ELEiqF00+H7g53654gdBiBAAsWKYdD5qwjnw7Bdg3+NQNctPZlsyLrRfEUy5BDXxPHjik35umTdD+VS4/PuIson53487E8adiapdCC997c1d4w3iOhJp9yFEBjwbKEEh8UQnAJYsxnXjeJ2HkN1HwMkgVM7olnsPPcBi5HqhwClkRBEoRE7AHWh32dEE2T6LYstDeglSuUEwKTJYwgqVtTz1OS9UvWfHoaHfprlTBNefa9PYDv/431le260QAi5ZKPmL6yyK4oJv3m7z3KYMLW+i8IZtwTdvt7Gk4D8ecNj5OgwCM8YL7NyEe8fhoY054fPOnjC4EeXSRZIzZ0pWb/f44aMOuxoUNWXw0SstLl9scdYcyaeus2jvgY9dafPEay6/fNpjf5NiQrXgL6+3WDpLctliya2XSn765Bs3Mt16iX+95zd7/OxJl10NiopiuOkCi5svtBhbIfiPj9tc/eWRG38b5B9CIgXEPS8oMpa1FBkUfWk43GKzt1Uwqa4X6fluoEJI8LK4EgQyVykjnDHq+KDwu7nMKWRSSA7v6qatsRfXySIlQXI0T0lOXLT1qcOEGRUAHN3fhecO/jwO7+7sP2Z6RcF9qmqL+dS3L6SnM8v/fGstu9Y3Y8cszrpiCu/84FwqxhTx0X8+j198dTV/+m/nc3RfJw/9aDMHtrWRKLK56MaZXPDuGYydVMoHvrCU737mjZcknTy7inOurqe9qY97v/4quze0IAQsvGAC137sdOJJm9v+8Rw2vXiErra3ZoL2dkQoP8mR1LaH0CskEAgl6O3KcHSHQ3pBH2UVNioXmqGUQHh+aK4/U/LoFwjHTzDYyFClD0WXSLPTbaMn20eRpZAeZHOX8+Tb0obCwuT44PP+Y+QOmRav4qKSabS6ffy29RV2pH2PraVFE7m5ciEJafPpsct5re8IHV5hj7E3QgxJjV3MGUUTuKlyIeW5Vev/7dh8zGPfXTGPP65azFirmCweTU4PG1NH+UPnNnZmBjcOh1lWNInTkmPZlDrKAx1bOJTtoNxK8p6KeZxVPJn5yXHcUrmQLi/Neyrm83LvAR7u3M5Rp5sxdjG3VC5iXrKWZcWTuKpsNg91DbKyPAyuLp/D/OQ41vUd4aHObRzKdlAi47yjbCbvKJtFpVXE58ZewKcP/96vXJHLL6RCpYJPFnp0d6TAxUObSHSSyIyADB7Nbg9b3WbOd5NYOX1dCT/sV+mFFiUQSh3/RAMCEJZf8QP/9KLjEF73Uf/aMoZyUyByi4GjOKTnDWElfQ+SyRfC6R9CaGPs5l8OfVzRGLj0W6AUas234ehrfu69cWfAwo8hEuWw5C98b5T2wp6zQ1Ixvf/zUAYd5ULXfqiek39MIS7+OhSPQ62/Bw4/73uh1J4Bi//UT1B73pd9T5ZLvwNWAvXqnb4BRzkw4TxYeDvCSqCWfwX+9zrw3qDHnZX0r5GoQK27C4685HsA1ZwGiz6OKKlDnHYL6sAKOPziG7vGm0H4Yz7I3HzcH+9FoA8ohFJ+zr6uXdCzB1Xih395Io4E4gpfLvQX+xxxnDJGFCk9cP3mHu12aehUWF6cBGmUEqhc3H7Mc5FqZOUNeTMIAX9xXf/q7QMvDT25XzRN8tpuj/f+c5bu0MLump0ue44q/vOTMcqLBTeeb/GDh994nohPXmMxb4pk71HFv9//+s4zPmRwPlZYy6GWfi19Qs3gvejMmZIHX3L56HecPC+SFRscfv8PgqWzJB99p4Vtw3/9weHvf9Hf5g17Fc9u9Fj59ThTxgo+dLn1powoZ82R/Owpl7/6Yb5wXLnJIePArZf6BpvTpwo27huZE3uBB0qgiINwSbg6PhH6cq+jVDadXRYHW11iohfcXCytFICLh0AqC6XkCStbGLaV2tKi+XAfmR4HcH0jS64izch8ym8tsbikvDoJQGvD0K7hvZ0ZUj1ZkiUxasaXFNxn/LRyjuzp5P/d8nCeYWL7mkZcV3HN7fOZc2Ytf3XXpexe38y/fPhxMqn+frfl5aPE4hbnXF3PsiumUlb18hs2cEw7vYbdG5r56p88Tl9Pv3Fy57pmGvZ18clvXkBxWZzl10/nkZ9seUPXMPiVYnxlZqB81CUIJdCy1yHTm0UIFw+9uqv/X68vnZiSlnYuqprc9frIctjrQiqXYhc8KfF0YktGrG72hhHAzZULg7+f7R7ag2RWYgzb0838zeGH6VX9fWdruonDTid31F5MiYxzWdkMftexaYgzHZuFyTq+MeHqQbc/3rWD37RvOOZ5ZiXGBJ/jQEk8Tn28imvKT+P3nVv5z+ZVx8yNd1pyLCu79/DPjc/keZGs7TvMtya8i3nJWq6vmIeN5HcdG/mvlv48MzszLaztO8zdk95LXayMa8rnvikjyvzkOP7QuY1vN+eHLryWOkJWeVxdPod5yVpmxKvZmW5GKQ8p5Iny23hdaG07k3vedu6xZyNqeEpl2O22cYmaGAzIWUtnTfITqPXnUDoxDdU2J0uC6GnCS/egLBsBWG7Mn+hy4prwtmLGdYjzvzLoZrXhnmOGk4iKej8s5eEPQW9j/4aWTdC8EXXljxAyhpp1A6z+t9ffxpwXiMr2QvYYq8Y9R6F6DqKo2i9n7A2yyFk9Bx65DZo3htq7GTr3w+V3IuKlqKt/4b9Df/gAdIWSZDdvhHQ7nPNFROkE1KQLYP/Tr/++wG+nFYOHP5hvYGrd4ifLve43CDsJc246KUYUKZRf6Qk7T2cQ+B7tePhlxmUc+o5A70FQZ+YO9oWHrUAJiZICNUKdRk8ha4P0LQpC0NMt6OoWOLn0M55y/FUt4aE8+23lhfKnV1mcOdP/mX7/ssv6PceeDn76B06eAUVz3/MeR1r948+Z88af0bRx8Nn3+Mrv53+cJf06HSpKi/qv3ZMa+n7CeUzC4UlRelKKz/3QGRCG4yn42VP+xK2sWNDSCV/574FGn74M/PpZ//v5UyVlRce6i8FpaFN88SeFrcvff6j/2uecdgp1v0Ho7VW0tecStYlc1xP4Je+EBUIEpU2PtylDiJyg1ihJa0sHfX1ZPFfgeSK47imYP/K4kyzpX11L9R6706b7/Hc4WTy4rf1n/1TY8PHEr/onFGVVSe7+0ot5BhTNk//t72fHJLPOGHvMNg3FXV98Ic+AonnhwT20HvWNRqctrX1T1zAMj6amTtK9HkJZ/allcwlotTKlOBFSIUdOVwBwPZe+TAYlwFUqp5CJIOTn7cZ7K07ntKTfl1b27GXHMDwzvtm0Ms+AonmqezfNjp/X6PTkuAHbjxcN2S7uOPIIX29aOWh+EYAuN80jXdv5t8Zn+ctDD/KJg/fzt0ce43cdm+jNTXquKT+Nz4294JjXTHlZvt38/IAwHA/FHzp9uVQi43R4Ke5uWT3g+LRyebx7JwAzEtUUvwnvhRanl+81F57o/Laj36i0IFmXq2YjT7mFAcfz6HWy/qwj988LZ50/0f0xdHqJQmR6kU4P0ksjlJt7rrlJn+ENo1q2on7/fnj1zuEd8NLX8g0omsbXoCn37r/RBLWx3AKQM4x8Qk5o0hQbIrnu5l/mG1A0h1b6BiF8Awdrv59vQNHsfADl5Dz6apccu11DsfZ7hT10ug7AgZxxpvYkJ/c9BtJzENleZDYdiAYgbyFkJHPKSItwKcKuLouOTg9PKkCiRBYr9+Slk8A3cY3sBz8czj1N8KVbfGNFU4fib350bLevzfs9Nh8Y/N437PUYX20xtfaND1hfvy1GUVzwwCqXp9e//uecDOkamWPcUsbpP38yPvh+z270aO8pvG1TyNvjodW5qhKF9tvfv9/U2jfuJfLgS+6g97XriKK7T1FaJJj6NpjP9fRAa7vle53k+qCfI8FCaR884YUUmOOpKKlcP8+dU1m0NHWQTrkIZfl10YP173DowOgkluhf9Xeyx/a0ymb8fWLJwt4CPR1p1q88XHBb08Fu+rozFJXGObCtjcO7Csfy79vaFnyunVx2zDYNxv6tbRzY1j7o9n2bW6keV/ymrmEYPs2NHaRSWg7oPqj7a+7deyuWlgRkPZeudB+eFHiOh7SsINTgbaAm5LEgWcdt1UsBaHP6uLPphWMeszvdyp5M26Dbd6ZbGGOXMN5+831ne7qZjx34HeAnj621SzmnZDKXl87kr8deyE/b1vBI146Cx7Y4vfzx/ntJRyqt7aKV1X0H+b+OLfzr+CsZFyvl0rIZPNOzm1W9BwZty6t9h+nyMgW37c70u8g+17Mv59dYYL90/351sbK8414PK3v2DOo5czDbSa+XpVjGqIv5v4EQws9HdAoFqTqeS082nevzfgfU6QtFWBCcIJng0V9aXeBCqhOR7UKoPhS2vyLu6rHuxCQ1flux/2nUAzf4n60klE2C+isQUy9DXfgvsPrrcHDoEF2V7hx6n9Ytfo6SsklvrI1WbtLgDmOlNywLhkqIu+fRwbe1bYfSCX6I2N5B9nPTvtdK9ew3fl/kQuD2PDz4Di1bYNpViGQlKlZ2bE+ck4RUGUSqGzIpZCADRc647XuVCqHyZcQI4tRZChcCJT2U9OjuEXR2y5wVOwbSI5H7F8MKcjecysyZKPjJZ2PEbEFfRnHbf2Rp7jz2ccdKVqoNDWFvkNfDzRdKLjxd0tmr+NLP3thAkwrJs/gxzHjxUHnVVGF9B/CNE4PR2du/bfcQ+3X09G8rSQ7drqE4Vn6Y4DcYwrPmVCGVFnT32CgRxxMKTygUjp9PVmn3WV0E8USUGfZQSuViwyUd7T24WZAyhhRWqLbyKC5xnCOb7n8GduzYoj8W9/fJFvAgAWjYN/Sg3NPpd/QjewYXXL2d/Z06WfLGbfqHdw+dcLG7I527hol1fyvobE+TzQLIoH96yp8yaaOKQPp5VI7zeK3NNHqC5rgePZkMWcv3XvUnUicwbOAkMTVWyZfHXYYtJGnP4Z8an6J9GDlMjpWsVBsaiuSb7zsp5bA3287ebDs7Mi0837uPbzY9xxeOPEqZjPPZsRfw/srFBY918AYYUMIcdjr516YVwd/vLp83ZFsODnHf3aEJ1aEh9usJ7Vf8Jp7PsX6D7lyi3bC3i5crCHqqvMdZz6PLyYS8VUOrzQI/L8YJMgmFTLe5yyncVC9etheh0gjl4C/IWkEeNcMxyHb5XhDtu/zwm72PwjOfQ638W984cMm3YcZ1Q5+jaz9Dehqkc/3CHsIzZCjcXP+0htE3ZWiV1h0irLhz3+DbMjmdKNXe/7kQ2qAxlMfLsUi19z+fQoS3vZnrnGCEclCZHlSoxLHfYXVy2RMlFY4Pp44RZRQxZSz8+gsxqkoFjqv4+J0Oq7YO7zXqO0ZKAS83ksg38MvXlME/vN+f6HztNy5H21//OQC6+8LGiqEVgOKQQXio0J++IQwsXmj07B1qv9DprTfRM4a6Rvg6b+YaBsPrJRUKdQlXsBmMRJHf11O9hY2lOtxnMFTuRc+kBt8vXAxAvokOMdQ1wm2R8tSYcBgMr4c6u5R/Gf9Oyq0ErvL4auMzbEgdHdax6WOUEtYq7IlcnHotdYT/7fQTyn6gajGTY4WTWR+Ljamj7M151cxPjhuyxUMZZMJqe2qIxI/hUKA383yGugb0V1iSb9MQNMPbiN0Pwd7HEdKCs++A+BAl1p1jGHlz772Qb9Cwlc2tWA7HCGOHYvizQ4T/uEO0WSs0Q+0T3u/NGOyGe403ex3DkJwy4Tz+qpI/G85kIZsBnYhKonBz2yylThXDfEHGVcJvvxhnfLXA8xSfvsvhkTVvvhzx8eD9l1jUlAnaexRt3Yp3nztw0qPzt+jPOl/Kc5u8wJMmnEw2nGS2EBNDyWQPt4xke+ToJOMoejOKsGOxwAUhEUFXPGGZDwaQTmdwXYVE5saQXJuEGOmhlSecbMajqy1FWVWS6rqhlYri8njgtdFyZJA4OYNhEDJ9aTwnOm7pDvjWjWcKP8V1xnNxhMAT4IZEwdshnKfaKuJr469kjF2CpxTfbHqOF3v3n+xmvW5e7NnPzZULsYRkeclU7m1f/4bOsz/TTn28ioS0KZfJ41pRaKRh5dzdR4aGODSeUmS9QQpBh9MfnED9PejvwkO5WYSbBbJ+gmsJSmgdxvCmOPAMTHsnIlaMmrh86LCTE0nPURiL345jhbToJLR9rYMnlTUcdwQOnpf2qxTpMNtc9bFTgVPGiKKUzrXgJ5MUfgEQlPJQliSbS3RxKi/uV5fBb74Yo36c//J88acOv145cobHRG7xurJE8J+fPPZK9ocut/jQ5b4F9N3/mKG50//9dh1ROK7CtgSzJkiGCrWYOaG/I21/HWWUDW8VHko5uCpGLBB6fnJnEUp5EOgux/36sl/Yeh4IF9fLolzpZ/4+ASVUT2UO7ezgtGVJxk0pQ1pi0DLHE6b3rx4dK1TGYIii8MBzQalg9VwEo7NfglgFoTXHl3CAg863IAPXS4E8tddZ8iiXCb42/komxPz++r2WVTyRS3Z6qtEeWlkdZ5e+4fOMFi3BT5t+Ct2t8CdHKpctzf9Ky4bc73aCopMEYNG/ECsQIC2UtEHFQNihLC2GN00qlGOpZPzg+51oOnb3f66oh+ZBKn8JC8omDzzGcOLJ5XZC9NcxVoCQEjy/346AImSDcsrYHCQglUQqSSIOybjKRVRDnBiuq3BdhRKnZlLZsiL4nztinDbJ/0n+8b8dfvT421OgZ11Yu8v/jZbOEsSG8DQ7b67/PFIZxWu7T73f9e2ObSsSCc9PCKXL8yhfJeqfyITS8R9XIhqX8ihKxrAsf/0ZHVsZaGeGbWv8LPjJkhjT5tcMut/cs+qCz9vXFMicbzAMQVGJjWX7mQh03hMhLPygB8vPCX0CV5rCWU+klMRsO7cQk9PHlEKoU3s8KRYx/mX8O6mPVwHww5bVPNh56pbvHhNyue87RnjLUEyNVwKQ8Rw638ZeKKcaUkjilkXgIKqNmTqy4S0pzqMVAom0E0irCETCL6OqHL9KzxBhXoZhUhyqmjCcyjgnisbX+j/XnTn4fjXzEDpvSOO6E9okQwQhEVbML2WNRCFxyZkzAxkRJFgccZwynih+BTR/EhaPOyTiilSfRAqBLSXK8RNRSOn5z/sktvX1UhSHX/1NjEXT/Pv71v863PngyBPkX7/P5ev3Dd2umy+U3PmnvpfKX/xXlv95trAh6OFXPJbNlpQXC951luT+FwfuN74aLjzdH/pWbvLoMfrQiCOREBQXSyxUv+1SSRBuv4IkTkw0vVJ+fhkrtMBdXJokHreCJKqBKUcFa12jmlee2M/1f7oAgItumMGu9c0D9hECLnj3dMCvwLP5pYa3tI2GU5+ysiI/MXGoelb/hEno/51A+t3ypRDYto1yfa8YqUKy6hS1rSaExT+Nv4JZiTEA/KrtNX7dMcgq6ynChSXTgs973mCVm3mJ2sCotDHVaCT+CEIKgSVl3hpnsPascj41J9KSEvJAU0og7ARYcZRn51xhPP8f5FbFDW+Y+nf0f247iZ5xDatRmS5EvAxmXAsbf1J4v5mhBLj7n3pLmmbwUUiEjIG0g6p5gaHVUyPe1WOEN68f/3kKPCWoLLIpLwZHONhIPCHISpustLHVSC2EVJiYBT/5bIyz5/g/xQ8edvjab0aeAeV484un3aAazpdusamKeO9KAf/6YRvb8n/N7/3+7f9MRhKWABsBnkQIi6xyA2cSgcTKVcEqj3uMLVE4AhAZ/1+udKkrXN8z7ASj8Hw3XSGpHFNMLJn0M+xLB0QWRNYveWxg9/oWtq72E05edOMsZi4eM2Cfqz8yn4kzKwF45GdbcR0zFTH4COF7nCnhlwlWws2VMPdAgWVZuCJLWU2MmI7/FG6u3LgvC/z+euLkuQcoV6BcgeuCJySlwqZSSeIKUp4b+MWdilLBRvLlcZdxetKP4f9dxyZ+0vbqSW7V4FxcMj2vqkwhLiyZxrvK5wDQ7aZZVSCny3nFU4Y8xwS7jC/UXhz8fSp75Qyfk6/tSqWQSpGRkBEKC0Us5JSaxbellqkY1RSTRqI9QixP9YfyKIH2IT3+KDyy4Elfp1EKJ1GEl4iBVLgKsib55rGZcV1+FZtCzPsAYtIFAKiug9B4EmWT58CWXwEgKmfA/A8O3GfsQpj1bgBUwyt+pSHDm0Yh/TAppfwcM8rNGSf1RELij9YWbqIUN54AV4GrEJ5CChdleYFhZSTIukKcMp4oPv5DHFNmU1XusLkhSzkx0q4C4Xds6bp+7pSR+bwH8IO/sLlkoW9AeXajx6+e8Tht0uCNzziwu+HUn9S09/ghS9+4PcaUsYJHvhLn3x9w2LJfUVcl+NhVFhfM95/Lfc+7vLDl1L/nUwkh8HMHKL8coKe8oI/5TvoeSnlUJKGuLEj5nDs6nitU5oY8QUInPm6NJJfONhfnrCRjxpURS7bikUEoFy/nmmsJyy+9bOBn/7SaL997JYkimzt+9A7+7wcb2LyqgXjS4px3TeOyW2YDfi6UP/zIKBSGfoTwUNgoLDzhIlFYyu/TrgvYAg+HitoYsUQ854KrjSdWLicCgIdQ8sSN0zl54ymBJWzG2EU0pQUOgl48inPrR9JPgz2i8chf7frCuItZWjwJgLV9h3mkczv1scpBj8/icSg7eJnxE827yufw6bHLeaFnHxtSDRzMdtDjZUkKm0mxCi4orefsYj8fgacU/9nyUlBWOcw/1F3OoWwHz/XsY1u6iWanl4xyqbGKObN4IleWzQ7KDD/TvZvne4coRfo2QWf4OJlelgI/PC6TK1NcpgRSQF9ue0ZATAnKlU2dLCMjJEW6WppSfqnI3OKnUuCJ42/cVOQMvnrKo0AVVaISxdDroLBwseEYlapGPYv/FJZ+FvY/CUfXQtdBP1wnVgxVs2Da1YhxZwCg3Ay8+I/93j0ni40/RdW/E1FRj1j6GVTZZNj7CDhpqFsGC2/zQ0mcPnj56ye3rW8jdD4ygZczoFgoYePpvi8UQglAooqqEImKIMRWCIHEQ0m/6PFITjJ7ihlR3n5cc1b/cHHh6ZIV/zq0lXd/k2LpXx6jhu4pws+e8qircvjseyym1Qm+8/GBq1WPr3X59F1mYDMYjhf7trRy56ef5c++cT7FZXFu/tySAfsc3t3BNz72FKke0/cMhpHEBSX1wecziiZw1+T3DLl/Q7aLPznwmxPcqqEpljEuL5vJ5WUzB92n003zveYXebpn8MSOE2MV3Fy5cMhrPdixhf9qeekNt9VgMAyOSFbC7Bv8f4Ogehrg+X+AIyOgHzq98ORfoC77LqJiKmLOjTDnxrxdVKYLVn4R2radpEYaTlVOGSOKDGWgGlMF1dWSLJK05eGmoSTpGyOyIoUbuAwZRjr/dp/L0+s9PnKFxdlzJGMroLMXNu1T/PcKl/8tkCvFcOJxPN+XRFjgiSwJywLP72MZMiDSSGVRWhpnbI30M98rv8/pBG4WuX6rTpAVWYHnKaxcyJenPGrrykmW5DwF3Tgqt6IpYx6ueZUC1j59kC9c+yDv/OBcFl80keq6YtysR8P+Ll5+eB+P/WIrmdRIX6M3vNUoz6904ynfHV8I0V91R1k4WQ9pS+omVlJUEot4mujcKFbe38cbP3+a/9kSUC5i1CXKWNvpkY5b2EoGskDpbLMjGCVy1QlPdkPeIP/W+CznlExmfnIck2MVVFlFVFhJHOXR4abYm2ljdd8hnu7eRXcBDxTN3zc8ztxELaclx1Jrl1IhEyRljF4vw5FsFxtTR3m0azt7s+1v3c2dZPSQdlK1XSX891OB8DykkigPsrkX1rUkngJbSMbGivInHSK30hyqMyROQCkOgUCqmC8QAE9J7JLxYNeAq7BEBlQpQeyAiewpzOOfQE26AGoX+9VsimogUeF7daRaoXUbHFwJex8DdwQlMew6AL+/GTXnFj9fS9lkkDHobYCDz/khPz1HTnYr31YIHL9XCw+sGEImUcrC9bTnOAglfM+z+BhEogYdde+nKJK+YFMWb0EStTeMUGqEprwdgBdk593TJrjr8Sx3P+ziUYTsdokn/SecVj1IZYcUNYPB8HrxxZ9HTDgIXGxRQl/WV3+ytoOUfSSlxbuXJvn0uy1mTnKJOXrSnQA7iysyCOIIFeuXf8dRECql8DyFzGWW9dwMBzb18Itvv8iOV7rwUhLX8zPDS9ufABoMhjeOv5gRI+t5KJHBEh628j0IhZck42VIVio+9KWzWXLxFEor7aBanhIWKOnbLJTrj9EnoEsq6J9dKkUnKV7MHOSuAy/RkHQp9RSO68sqAVgj3Iji5ZJ2j2SXZsNbjwCcnAHiZBpRpB8rQ5+tUI5DtbBwUbTkXldPWlQqi6V2LbeOWcq0omriuVmHQuAJ30oYLMEIxQnJbOiBkv6FM0oQ69iLt/5nsPc32KoDmABZ3/NSxYwHpsHwZlAiC3h4SiJEEiHiKE/h5eYJMeHrBqlYHGvWe4jNeS9U+GGqColyPYQtUEr6C7ojdPw7ddw1VH8J1apSwczxiilVLpYSxGIC281guxnSwsp5ohgMhjeKKyQZAZ5yiXk2EhtHZXFUlpi0SYoixpZZTB2v+P/t3fmTHMd14PHvy6zqY6bnwA0QGBwEDwC8JJGSqGMtecMrybFh/eT/cH/YiN0f9vhhY7XrlfdwyDTtlSyZli2SFmhQIgUQAHEMMN1dlfn2h8yq7hmQ1HCJwXRPv09EB+boGXT3VGdlvXz53skjkYIAUqQbTCZFexiiFRGcmwys4oTjp5c5dWGZzgDUOXzZxZddqnqGVkXMYnAH7zwk4gghAhEnaZGJkG7OCa6InDp9hDMXDuVMlLTnGVyqgZJXrHWvstOyttu6Qj8UnPGrHOuuEGIqWodz6TajE7NpBUKZLytn/9GaJyHVD2na9e6vdB52CI4lX1Jo00kzHbE+worr8FR/lZPlMinkOslk01w9LUrOQtmLjNFcmqlmnG4SiUvHYO089A8BVSqKXxbpZoz5QhypTqFoiapPCydxTMmIkhH4EbiKODiCrj0DvWNNegppUtEsjgLU7M3A8MXNzyyv6VussFQIz56EFzegHCniKqqYbtBhnp6WMbMoOEfwHrRAdDm1BCyHSDlEK6U3Fp474bl8wTPok6f4Pt0cua1x09pY2asrgKZwlRAREXprJRdfOM6h4wXjsEUVlSoqKvs/2TSLobm0abIdDhJVpU1eVUnp+JLeX8PwAC0f8tKrZzlyaomiA5M3/s7ily5HYPbkUeZ+XankdCkFx1yfy4PjHKkdOg5pOqHMRRAFFPcEupyZ+dFmT83EaS2dhV1UCu+pUIKks79D6NVwSvo8vXSE1aLTjgTNQ3ea2h8D7FEOSqYoNUqdpihFn2L9LKxeZBwHqXaG05xaY4z5QqJHYgdHF4eDWOF0C9xmuvGAyJDi0Dn8+hlC0aUmUhOBOo8JjgB5s99svi/nKNpQUOOoSenAZ48UfO3pkhO9iootgoPgwEeXK/4aY/7/RUQFTwdUqDTguoLrChrHrHVGvHxeef4MFBLT8JZjKGkfoyMFNPd6W11kMiXzuEJ4+tIxzj63QmdpTAgVIVR4393jx2FMs8UstreDJsaQEjhc7rIjDnXp7R5kyPrJkhdfP01/4HNlAzcJo0hAJBCbt2z7vn28k6OmJopIepwisCwdXlw+zkW3QqkCIUKIbTeAWRZVqYN+YijKLB6v4Jv3kLCnYYddUSXGQIiBMYGhRkYx0lVHVx0n6HGxPMRGZx00dcOKkrbxqE66ALYjwV48HVGQGiiBEo+kLp5rp9BDL6GdjTR2MyZyMBo3GLOvAnn7rscJOK1BRjQrGCPtMO4cwR26gls+jjpH1HSD1B5UNdc3nOE8zPkJoqhQC9QCKsrJZcdrFwpeej5Q9IToPdF7SpU5elLGzCYh4Ih4LcEplRtRa4daOyx34fmzypefgdOHBNEUPVEJ7Q0RiDmFP9dX0T1Lx0v7CSIQFY6dHnD5Syc5e3GdsiuUXcHJo52fjHmsJCIOfOHxhacoDl5aeMr40maDTvpa3sW3erTPldc22Li8jO9N15NOl0dKAAltgGPvwgG5KDaadxMJPUqe6xzm8vJRlrt9OuLoiJuLRWcRhysKRFzaTjWjk0mzd5oAQ2hSqFTz6uwsFFzU1KpYlHEIVF4Yo3Qp6FKw0TvEpaXjnHLLoKneWt4BmMS2bFIeM/boCbmAI21NLjQFUXT5BO7oK/j1y1AsEXIwyBjzBbmISrrR3CgIskSQJarOCfzRF/CHX0R760QXabb+anRAnYKsArN8VT+7j8wYY4wxxhhjjDFmhsxPECVCpCJSERjTKZSzJxyvvVIwWOkzdI6hcxRE3BwkvNqOIzPLUhZKLubsxwz9kFFVMqpK1gclL13u8uwZz6BUchGUvJsx3YC2XaDuVSZKm/+b1rWaT7vLnmeunOS5F55iedBledAlBHvDmcenOdZEXKoXlIuYRo1ovh1ELhd/TAvGjgiIF8QLJ04f4SvfuMzgmKIuQk7Rjxrz65I6fUlbz3Wv3pOKaLopSi3gxXGMPhdXj7LWX6brPF3n52ICpCIEhBDTbWv2pzfmMRBSC1DVyfYXmDrt5e7c+72dJyd74bwwjjV4j4rQcZ6O85xcXmejf4RV7TX5aJNMlPxkUrL+Hj8PARcl39L4EIpldPU8/vBz0F1P7VTVOnuaJyHV/JCgSDh4g7r6iBaBIDVB6/xW7xBZIbICvdP4ky8hK2eovaOmotkbnDLYc0bY9jS1mTM/+cYOfDPIqqAusL6mvH6p4GfvFvyXWw8AGBWeTlBEY67Qr1MdAlKTHwBxqebDfv1hKomUOdjj1RH3PyfTfE51/pOVgFNHdDUxRhw+J5NPOCa1pffrL+2n9hWG/HiiCLVrKv0HnKSBqxDF5cClw+HHHQa5uOEL5+ArVwpOHU77s9siBJMmhc35IWUb71XxRmmmk8XUpzFd0J1d58prG1y7eg+An795lYJeaqlIfrxtXYb2dJZeh2aYmaqX4HKnleZrzrkDWfPC/B6SWs42b27nXAqYTJX2aI+bOSlm7CS250UFxKU3bqoRkIovNMd/0E56j1EjpNdi+VAHgCuvbnDpK6eQ3hjE07yRJqm4BdvXbRx7MxruqBySLzQ7WvJ07zgvLN8lDFPdg9vhPkMXGOXxqzNVlKGWdMFXNJ3Gfs84tlf1SlRzWb1m7MldTbxqew4y86/OR08QoVTBR0mLD6LttjMhTVlj/kyUPRlnnKYtOs25OyLtx6rbZzdBoRLHUkwlJMeq9PGc664B8Fz/KCeKVYroUacUTAVM8jxBZCqEskfHtGoxVS8xEomoeGT5OHLiy8TNq4yv/3V+UncpJOSuoB2QzuSBxbxI5Dqojj8hILyz1tPs1nN41BilzGP2juNK04gvEtDoIW+RVq+ohlR25ok/3sdl8shT5ziPSJWfs+4Y+yOf75k2C43Nx1U+9voUKnlb3vbHMKuUmDtvueYLTF4PTzrHN0HICNHlhYyAE6XQAleuAtA5/Ax64jV06TBOfPrJZlhwDmT6imUWti1+sjkKokQ6+Y8TKRCp6PcCl0/Cn34Vrv8mnVbevKXUo0jRXt+kyuE+tz2uEVQjEc1TuP35y3TiZDJpl2LzqWgumsgVOWKqR/9JQ6Ky/2OAE2knEVEjQQKVQJ2LFPjpi8OYPh8XY4Iuszoueflcmjz88VcjL1+A1aVcRTZfpHh21B3Jh/ie7mcU0Fg0H7YTyt5awcVXjvKl350G4Lcfvs+Da0LMQZame0fzN2nGB0f63qOBn+Z108mgbhaPTmqBAIRQN19ONSv26WF9EbJtAknOrHG52mPqvBPz+ypGh0NTQFYjy2ueZ18+CsBX/+VZDm0U1BIo6ExdKDWTKj/9n7J37yHZ9pHL1zMOx2lZ4+v904w7WwD8NA55QE1Jyqqh6doDFLl4bq2Kl/TopwMl00FWyWOofML3Hs+z0VxgD5ahzbAxB0eR/9JFDu6ra4JmU39oycckzeKEPnKt+zhEUo0TaQ46BNVmISE0S5L5vsJYARVG4unUcKGzwuvLTwHwQucYa3TbBZXO9H8ktFdOe5sVli/ympdSUktlBaS7AseuoKOPCMPr6Znd/ltUXJohRMkNB/OzdgVoheh9NC2hbZsv6FTRbCFVxtzrlu6PT6cdbx6RInYoRf5jpXOfxIMwG5oey5X2qqwNUE6/Hp/32e4Mr5fp5dOmIcM85ENmqvk8l87lSkhBRYk5qCYpwAaIFkBMK62Se2j6kuLweQD8mVcJ689DudyOfZP3pwAlrpknzHAXvfkJolAh+eF6PBpL0JJ+V3nlxZof3u8BMPyR8subwlgEHx0uOAr1dHw6iGsClQqh8nu3EGbMDKpjm4cBboxjhA+BTvAUrkeh3XYLTtDAqBgzLKCMNecPw/e+mQbHP3zVc2q9wmlEpWgH1P2gKmjMk03v0iqd1jjxrB/v8OVvbABw/+6QP//3V7l39y5d75EQIEa8z2OKQIyK8566qvDe78g+UWLUNvtkz7JrjHnCgnYmgQE0VWcWwDnEpbBiDPm414egS6gsUS5XXHhhmW997xwAF19YTxNQLfb/vDr9/my2DCh0RThTrvLqWgquvuuH3BrdYr1WKg3cl9C2Qz+iHkW5LRAkTZamV8wjO4Ilc9Dpx5jdGDvHyE3O7KWmYIKootKsCqd3Qk+FAuEfisiNqPxQB3xr6TRf7acgyoliue2+s5/DgkhM2wWANPn3xLwIpN0l3LHnWRp/G4Di/geE2KPWSCH3EB4SczBYyy7EIW50DfFPt4GmZiyYbOWUWb72M+Zzc1EgOrbHPAKwhVKh6iHmDCVdJhYB5wtKLVAK4upp9Px30s8+9TV80WH/R4YvZn6CKKKTlI02u0rxUnN8cJ/vvb4OwPi2o3oD3r09pJYHOCnwLDPOK4aVbkJR0KOfq4RbHohZDGEqdbAQx5LvUsQOseoSRoBXpKiAtGo7jktQVZw8vMX3vuX4V99Mk4VTy45SPVUcglSUbrAvz0cVNAc+thFFpcb3hVPPrgDwbZ5n86byxv/5Wx7eDnSlT+EjTpqgUgqSbI1C6j6ibmpSlLf8iFB4Z9dK5kCpp8+BojgHThQNStSIF0cndxpSHROjx3WFM5fW+OYPzvPl76Ygih+ktF6JYd9X1/IaH5rzYXxeXPTOc0oG9JfSmHE3jPnLCt7RO3wYx3QLWM+PfVQHUGWdgjEwZnsAdXqLZvq/jDkYBJCouLxAISr0nMN7R4Uw0sA4d7HxmrI0bkflGb/M6/0NXh+cZ6NI23m6ucbI/iZjpG0UNHkw6nA52BsEpNOnPPQ0RX7v65176I1/pPPwKuLHRJQ6DgGIo4cU4onl06l7IXnL3VQdrGbOEHE4AvNUftKYT6PtsdykmRfg+mnrjatzslLO2naCp4/Uo5S5tfos/uwfw/k/SL+rv4aqQ3S+g42ijzPvdE+N24JPIXiQgHd1WjWLUJcpE+U31+Hf/m/40U/uc/U3gZHvEpYit4Z3AXCs8FR3wJ379yh8QeHnJ45kzBcRRdoAgEQo1OGkoPBpIlFRs0lKcy/qgtW6x1NHAj/4o4o//X7g0qALQCekXc3qNe/m2acRME9cJK8QRQVcTCvnmoKmTWvjMILfvXuH//xv/oY3/+w644+XoA6EsJl+lw8U3W4a1H1EdFLzJMaIOId3LtfAUKuHYg6M6AMhpOCphogXofQlzvmcfaU0h3uMkdqPeOn1s3z3h5d4+dunOXyqn74pkMIKY6DLvq0uaWzHOZVUy0EiqXiDk20ZqHfHW/x06wP+48Or/Gj0IXd1i7M5E0VCoBcda9IhOmGUt/9NZ6HFqenTI9t7jJlTXqFUKHLkQyRtaaljZChK7YRYNJt6FKkDJ2XAN1c2+M76s5z16/RzHouXVLtFlH0MrirKEInd/HlaTa9J6+iSt+u5PG94uPke5S//E+Wv/wPy8O/AeZDD+Wd7qC4R/ApO75FCteFTi4nbWGAOitoNUUpUUzByUuEoUiC4WCAhvcfH3U18FZFYENfOwYUfUFz8E1hL23+j1Iy1SyluH3PZv7i5CaJEDSmNEIjqgIpCxqnGQ90nb56i5j5X7xf8+CeB//Y/hZ9d63C9LHjYSVFzCZFV9SxpJAbdNgky5iALrm6jxKV0iQFGYchYNtGOw8sSQdMkYz0orxyL/NHrju9/q+bZU45OXsVRkRRZxuHEt3v1n7wIGvJ4QK7jkLJtVGMufpd3+MaAjkbcuub4r//uLd74s6tsfhTxpOcbYqAONb4sUN169L/RHRdPMbbbfYyZZ4ERpWu2tblUZqGpqZgXndrTZK/mlW+c4g/+5FkuvfYUS4f6bYZ8Wbq8XbAppLdPFw9xEkRBBHGSa72kCzmZqpkYRfk4bPHW1nV+/OA9fjz6gH+sU2D1onhWxPHACT4qvWZVnsnvNuYgKgRKIiFHT1VBvCPiGKOMVduKYj0859wy3x2c5V+sXeRosYZEwefzYykOp82euv05Z6a6dSFtRwBcM86JokwWXJrLoUqHcO8q8t7/wv/zf8fdeWuqoOxhkBINW6khhLjcJanJWJ38rylD5ZNqrBkzfypfAYLkDCyn+ZyvIdUeCz2abK8hIyg9xZEX8Oe/j258Gx0cQ6TIPwtjJ4+Um5838/zYjTHGGGOMMcYYY56YuclEqVVBcjVoBFGPkKoCi3bbltL4B4y05MY9z9/8Cn70BvzFL+HDzdydR8ZQKyt0CBIIYmn5ZkEIhJxyWqGo93SkgFATQ2Cp03TcgZfOKT/4muM7X/KcXqno0iGW6T1USUA0UqB4XNoXuS/S+tJkBJPUc0sE2VmhQBWJSqgdtz94wJt//g5v/I+rvP/2QwBGm54YNLXvC6n9qc+1VrxzxEgqril5FcuYg0I17ecDHD4f90oVxgQ/prfSYXVtGYArr5/iO//6ac5eXqO/1gXn2mxO7yMahVg7fLmP+5ybtBOgWR9W17SuzEmr+dtV7tI1DEP+eXiTv3rwAT8Z/g6AD+IDHmrNWGt6QG+qxWvDVpjNQRQltSUd5UyUOrc59+roRUefgp5Pq9GnO6t8Y+k0Xx+cYcOv46QgTnUxc5p+XxRt+pI8cXmmMPnfY6pWkjp/VECd5jGaaz+FKl0bDG+jv/2/uN/+Be7mz9PPbl6D8DHi+kS3nuouyWS8k6n2xqrRMlHMgaFNm+v2KwFhC/QhUIJbB7cEQOwdg+NXkI1vIydehf5xgiuomu53gJMawe9fSYDHYH6CKJCCJqTBWXLl/Kbfu29r5AZQz1jg1gN4+zfKX71V89e/SBdGv/5AubPlGdWRynnULojMgvD4tuBiRQ2F0NGScqisdUZcuuB5+XJ6H33livDShcip9UAnehiX1J30/osuvfMKQgqibG9a+MQ0qbLNrhpVpQ6Kdx7nhBgnreqcc2iEqh5TesftG1u8/fOP+Ps3U0vDX/3sQz547xZxrBS6RFVV7cTHOweafp/mz0XE6qKYA8E7T2hmNpq2v0QZE8stVo91eO6Vs7z4pecAeP6rq5w8N6C37BGf6hBpfh84n9qgxiB4v8+7XbbVKslFLSVtNvJMOlfWeUQUoAoVN8eb/P3DFET5y/F1fj2+w2j8gJEPjH2qgTJdcNoujsxBFFBGEqnayIBQRqVfC8e1x0ZvnTODIwA80z/OlfIox4ol+vj01st1UCBtfY0o4v2+1j5I40BuSa81jhJRD4S0QCsOzdsUJEbUOZRItXUHbv8T5fU30/euv0G89y6jAD2NSNxeVBaY2tY4F5dXxuyKaJmLD1b5KxVITXQQynXoP4XrpZon7vgl5OTXiYcvEbqrgKOgJJdMITKi1CJ3t5rf8+jcBFEiINq0WdS0YdsJiicScM2IHYrU/z137xnWyvXbgbevplSVn/2T8Itrnnc/GnL9tmc0nueSNsbsntPJKT2K0unA4QGcPyK8fA5efc5z6Xy6x1NHHIOe4BgTtcbHZdQ1E4WASoQoODyyT8WZ04Lz9KqwEmNIdVrEpVWgnKImktq0Rop0sYdneL/i5m/vAXD1rY9456c3+PDX97n5fuTe3XuMxinw6kTy7yN3BpvfAd+YRxSRWOdzK56lQZdDJ/qcvDDg/AuHefZLx9m4eAyAwTEQKXDStAxVYswFJmPKQIk6xkm5b6tLdduTBzyaC1CT5wt5D3Nouvco6gQVRyRd8D2oHgBwrb7Lr8Y3uDq6xTvhDjfikBCn60RMjT3Y5ZI5ODyASnup5MVxqOhx2g+44Nd5rneEs/0URDlerrJKNxVdRtM8XYQ4lbnlNGdw7l9d2bS+WqQOO4EKoYfTMrdtjkSJKLnbDorXVDA2KGg1Qh7cAEDuvI3c/AXj2+/Qvfs2Uo/RUE8tqqSJQnNNIiLMyWWWMZ8puCJlcbUdKgT1fWL/CLp6AXfsMrKWuvXp+jlk+SkoV1PxZo2UIm0NolS1sEyFp/fp+TwOcxNEASazFA2k6LEAZfqy5O4CMQVRJk2YYqrLXacLvfdvK//4vvLeR2Pees/x4U1h82Ea/O5tBjYfKg9HQh0cKj6tdsv8vERm8WwbgHT7N5xAUTiKQljvBNZ76Q4rK55jR+H8KeXyBeHShrBxSBiUTdvCtE1HJRDiEO+WUhFnAK1BAjEUgMftY4Or6UBKykhpl53zcnN6PlEDGgPOL6WLJQVPbOd0o/uRm9e2eP/du7z/D5t8+OFNPrr+MQCbd0dsPagZPghUw0ioFVGZBG6NmWmaszYnx6tzQlmWdLol5eGK/lLKJls/PODkmXXOPnOUjecOc/LcgMFRh5Tt5RSQAiRCQIAYc9e8Wig6oIxwdNiv1aW6LXnJZOU7VYae3Cm0k4m0JUkkZ7umriT5TtyI97ka7vKr8W1+O77Hw2rMvTpdiN2vR9zXii2tGaKMNKagDdDFmNmTLlfyggCTi/z2+7mb1Tolx7SXtvsCa2WX0/1DnO8d4lSxynG3xIqko7xk0sJYUZymba91bvYgCD5K+u/2a82yDaKkhZFIADo49SkAKkpo89RyEIWYngs0n6XfVT/E378GH78DN/8OHd0jDu+gW2m+oON7uOoBLgwhRkRJi07tAzFmPtX9FSj6uGIVAOkdRZdOwOAsrJ/HHzqPDo6n+/qldB2uks7Iojhiun4AoKQWN90weS7NVxBllz4rzTbGSIxpn+KvbzqufQQ3bqULrd/dDFz/OHL7PozqApUyrTq1EeUn9hSM2bUcMsgTo6mDVNJqSK/n6HYdp5cjG4fS908c82ycipw8ogz6EU9ANAVF2t+6gMe7RhjfU25cv8cH124D8NGHm3x8Y8i9WyMe3htTj5WUg3Pghk5z4DQBlNimnCuRovAs9XssDfqsnBYOH18B4OTZdU48tcLK4Q5lT3PFkJzBCxSyj62L99EDHXMnbnG32uKjcerec6Pa5KM45E4cc5eKzRjp5jFhsJ8P1phPIw7Bt1thYRJEcSK4vFX1ED3OyApHytS+/GR3wPFimRXfyxdF28MBi9moTtGtO+joDvHhddj8Tfryg98hw1u48SZSBzQo6sJn/ypj9tXu5rK6fBTXOwxLJ9Png9Po8inoH4ViKS9IpPsWc13pZPcObBAFPjntvjl5qCqOkFLt2kh6OgAmi1ZK0Iqi7XdvW3/M7NGd6SfN1zXv/ZUUCBwhhBzz9YDXSEEglWFNWV1NTHiRA4ZK2qrULh61L0beRkhaaVMp9+kRGrM7TVxVtn0tB1RE0racyk3ukBaqaUYVzTVSmiFmYccFbV8RYl5UiQQ8zculOdKUtzBYrTUzi0QRScfxzpl/em+n43eEMMJR5M9LUivSZq5QM+nlsH9V0WZAWwolMKkTkXPaxBOlKb9/4C6zzIGyy+MzVCiTa+YgnpD6e+eRIW2BAygXJIxiZ3pjjDHGGGOMMcaYXVi4TJTp+4im4jbbdisKSEzbIJyGVH+lyUCxTBQzk5pK8JKP0anjPgbIrcBVRuljAC1Sa3AtU2u/1ICmfS+0C9ILRlFCrAGHyznKktffVNNqk0hEESqW9vWxGvNZdOq2872cRgzFIXSp2qLtqYNFfvfrVOvB5pzqD/7K0ieJMa3c58YjQJpfOGgLTqM6eaHdYr5O5oBQTQ0ctn+RiBAElEnh2FQlaTFprZNi89PZfDl5L0gqbd+J0n7LmNmzuzDASGi36zSENAYUaVRgkqM2yWw/yA5kEGW3lHFK3c8mw1xqoYy6lMbbtFC1EdDMoEjeopaaf7dn6u2Hq+Ji6tCTP83/5irMuaNVlNj+rFuAAfBRijKCSaI+TYSpKcbXXkQ1ASljZpFIDgpGBJe6Y5C7RUzdTZmuj+By0LDpNxNRIiGkY73wPRbyUqAJlMhkHlDT1qtsv13m17EzNa8wZna4T53IbrsS0JAWDfLJTlxaoBHyfnekvX8Tc11ED5lcRPr8erhmS4OG1PBCI+qaKkkL+kKZGbfL81U1TMd1s5jiXGpnnhddiH5ybeEXo67iQgdRUu+yqdWjXAcltRzIJ5umaicsxAFh5k9bE2VXUb5JFEVy+0+avdDq2l+xyG1800WnTL0G29soq0ZUQ+5AYsysyrUPaLpyyPZv5RwV9ZPjW9sOXOlYF4lAheaK+iLLLOKJMLYVUSbNjFPseXIxmV6zHIQWC6KYWfQZvTB0+yfTxWM1zxEcuc9CnPpmE0VYQGOGOBxCgTavazO2SjNuaNNDdAFHTjMfdne+ktqnqGl7TZyObxUh4iYdPEnJmItwvO9jc9IZ4JpSOEnqE99s65H2grIpCmUdTc0smp4W7SwyK9IUmCWl4bbHdMyFlWOOHfqclu6aH13YRRO3bTLEZKdUDrKmC6SmGK8xsyjlSGzPKpkKlqS+m2zbCkgOnrafCqoOKBFppgqLecxHmtbJOjXWgqjiVCbbedrJ5YJeVZoZl4ujT9IpP0Wzka9ZoEljRsyZruIWdnowRSnz1t/tL2QKWE+C13m8ZdflO415wnb3bg7ePTJkCKk0httRRnZRZgqLnYmiY8hRNGjX5lPlYXWEXG3fy2TlyZhZI6p5G89n1QBKexnbTJPm4klT/Z/JNrY2FWVBW3Eo6Hj7BEhl+55nbS5RFzsGbWabSNN5C3bEVrd94Ji031TNQRedZK7IIs6MdohRiVHzsDi12rbjfpMFmQV9ocyM2znd/+TjVDRv/Z3Kwm5ihZM160l2hV/QgaGOVR4P8pYGgKmPgDxfsCCKmWW7OzKjq6fKXgA5+0Saa4fpcMKCpKIs7FVAuwd8+sJRJa0quWZyObkPLOg1pZl52yc209koOknjF3ASpu7p0Ogm6ek5ZqIyXSNoMQ/4yQXQjtW6qQvL9GVL2TezS3ZmVLVfz1SnLoPy96ZS0SfLCsLknouZYSGA2zku8GiRvXZ8te08ZiblFNPfc800XWQ+fSGNDSI5cCjbd/MsKpEyD5o6VV8xfdRcXCLCAi9Vm7mwuwPUN1nZU9t22rrqko58zUXqvbU4NsYYY4wxxhhjjDGNA5uJoqq/vzimdPJ9p36OqUSlpk1hu5/x4EfVzPzRnd02tlc6bL+eqnjE5oeaBJUd75NFP8YFpTPpYpS+9An3Ai/h0W8YMxOmDlrR7Se2qe+l8+R0dkmz5Jw/bmsiLPZSqjr5lLW67evxbT8vtUwUM4t2vNc/keROfVNfidomqLV1k3Zmai6g6Y7vky07k/mXimN6q/Vij6Jmdu0un0LUP1IbtMlCgSaDLd3Bb590HFiLXRPFGGOMMcYYY4wxZpdsO48xxhhjjDHGGGPMLlgQxRhjjDHGGGOMMWYXLIhijDHGGGOMMcYYswsWRDHGGGOMMcYYY4zZBQuiGGOMMcYYY4wxxuyCBVGMMcYYY4wxxhhjdsGCKMYYY4wxxhhjjDG7YEEUY4wxxhhjjDHGmF2wIIoxxhhjjDHGGGPMLlgQxRhjjDHGGGOMMWYXis9zZ1Xdq8dhjDHGGGOMMcYY88SJyK7va5koxhhjjDHGGGOMMbvwuTJRPk90xhhjjDHGGGOMMeYgsUwUY4wxxhhjjDHGmF2wmijGGGOMMcYYY4xZWJ9n141t5zHGGGOMMcYYY4zZBdvOY4wxxhhjjDHGGLMLFkQxxhhjjDHGGGOM2QULohhjjDHGGGOMMcbsggVRjDHGGGOMMcYYY3bBgijGGGOMMcYYY4wxu2BBFGOMMcYYY4wxxphdsCCKMcYYY4wxxhhjzC5YEMUYY4wxxhhjjDFmFyyIYowxxhhjjDHGGLML/w8aCT0zHYBisAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Set date for the chart below\n",
    "\n",
    "plot_date = \"2022-05-12\" #@param {type:\"date\"}\n",
    "\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "req_sleep = sleep_df[sleep_df.date==plot_date].iloc[0]\n",
    "start_date = datetime.fromisoformat(req_sleep['sleepStartTime'])\n",
    "hypnodata = req_sleep['sleepWakeStates']\n",
    "sleep_hypnogram_altered = {}\n",
    "sleep_hypnogram_altered_color = {}\n",
    "master_color_key = {'1':'#F8772E','2':'#F8772E','-1':'#45DD9D','-2':'#1559F8','-3':'#5A25A1'}\n",
    "\n",
    "time_dict = {0:0,\n",
    "            1:0,\n",
    "            2:0,\n",
    "            3:0}\n",
    "\n",
    "# Iterating through each key in timestamps except last\n",
    "for i in range(len(hypnodata)-1):\n",
    "\n",
    "    # Getting the current timestamp\n",
    "    key = hypnodata[i]['offsetFromStart']\n",
    "    \n",
    "    key_str = str(start_date.timestamp()+int(key))\n",
    "\n",
    "    # Getting the next timestamp\n",
    "    next_key = hypnodata[i+1]['offsetFromStart']\n",
    "\n",
    "\n",
    "    # Getting the current marking for the hypnogram\n",
    "    value = hypnodata[i]['sleepWakeState']\n",
    "    \n",
    "    milli_seconds = int((int(next_key) - int(key)))\n",
    "    time_dict[value] = time_dict[value] + milli_seconds \n",
    "        \n",
    "    # t_val is the hypnogram value changed to suit our plot\n",
    "    t_val = None\n",
    "\n",
    "    # Value for Light Sleep\n",
    "    if value == 2:\n",
    "        sleep_hypnogram_altered[key_str] = -2\n",
    "        t_val = -2\n",
    "\n",
    "    # Value for Deep Sleep\n",
    "    elif value == 3:\n",
    "        sleep_hypnogram_altered[key_str] = -3\n",
    "        t_val = -3\n",
    "\n",
    "    # Value for REM\n",
    "    elif value == 1:\n",
    "        sleep_hypnogram_altered[key_str] = -1\n",
    "        t_val = -1\n",
    "\n",
    "    # Value for Interruptions\n",
    "    else:\n",
    "        if hypnodata[i]['longInterruption'] == True:\n",
    "            # Longer interruptions\n",
    "            sleep_hypnogram_altered[key_str] = 2\n",
    "            t_val = 2\n",
    "        else:\n",
    "            sleep_hypnogram_altered[key_str] = 1\n",
    "            t_val = 1\n",
    "\n",
    "    # Key to be changed in order to get all the \n",
    "    # keys between the concurrent timestamps\n",
    "    test_key = key\n",
    "\n",
    "    # Setting color for the main timestamp\n",
    "    sleep_hypnogram_altered_color[key_str] = master_color_key[str(t_val)]\n",
    "\n",
    "    # Setting the color and t_val for the test_key\n",
    "    sleep_hypnogram_altered[key_str] = t_val\n",
    "    sleep_hypnogram_altered_color[key_str] = master_color_key[str(t_val)]\n",
    "    for s in range(milli_seconds):\n",
    "        key_str = str(start_date.timestamp()+int(key)+s)\n",
    "         # Setting color for the main timestamp\n",
    "        sleep_hypnogram_altered_color[key_str] = master_color_key[str(t_val)]\n",
    "        # Setting the color and t_val for the test_key\n",
    "        sleep_hypnogram_altered[key_str] = t_val\n",
    "        sleep_hypnogram_altered_color[key_str] = master_color_key[str(t_val)]\n",
    "\n",
    "from urllib.request import urlopen\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "# Creating the header figure\n",
    "# Creating the header\n",
    "header = plt.figure(figsize=(14,8), facecolor='white')\n",
    "header_ax = header.gca()\n",
    "header_ax.set_facecolor('white')\n",
    "\n",
    "# Removing x and y ticks for the header\n",
    "plt.xticks([],[])\n",
    "plt.yticks([],[])\n",
    "\n",
    "# Plotting header background image\n",
    "header_img = Image.open(urlopen('https://i.imgur.com/2I4TxYH.png'))\n",
    "plt.imshow(header_img,  interpolation='nearest')\n",
    "\n",
    "# Removing the spines on top, left and right\n",
    "header_ax.spines['top'].set_visible(False)\n",
    "header_ax.spines['right'].set_visible(False)\n",
    "header_ax.spines['left'].set_visible(False)\n",
    "header_ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Function to convert seconds into the time format used in Polar Flow headers\n",
    "def secondsconverter(secs):\n",
    "    if secs//3600 == 0:\n",
    "        return str(int(np.ceil(secs/60)))+'min'\n",
    "    else:\n",
    "        return (str(int(np.floor(secs/3600)))+'h '+\n",
    "            str(int(np.ceil((secs%3600)/60)))+'min')\n",
    "\n",
    "def time_setter(test_date):\n",
    "    if int(test_date.split(':')[0]) > 12:\n",
    "        return test_date + ' PM'\n",
    "    else:\n",
    "        return test_date+' AM'\n",
    "\n",
    "# Displaying header information\n",
    "plt.text(0.765,0.4575,secondsconverter(time_dict[0]),\n",
    "        transform=header.transFigure,fontsize=20,color='white')\n",
    "plt.text(0.56,0.4575,secondsconverter(time_dict[1]),\n",
    "        transform=header.transFigure,fontsize=20,color='white')\n",
    "plt.text(0.3875,0.4575,secondsconverter(time_dict[3]),\n",
    "        transform=header.transFigure,fontsize=20,color='white')\n",
    "plt.text(0.185,0.4575,secondsconverter(time_dict[2]),\n",
    "        transform=header.transFigure,fontsize=20,color='white')\n",
    "\n",
    "# Creating the plot figure\n",
    "plt2 = plt.figure(figsize=(14,8), facecolor='#F1F1F1')\n",
    "ax = plt2.gca()\n",
    "ax.set_facecolor('#F1F1F1')\n",
    "\n",
    "# Removing xticks and yticks\n",
    "plt.xticks([],[])\n",
    "plt.yticks([],[])\n",
    "\n",
    "# Removing the spines on top, left and right\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Creating the sleep cycle text\n",
    "plt.text(0,2.5,'Sleep Cycles: '+str(req_sleep['sleepCycles']), fontsize=20,\n",
    "     color='#B5B5B5')\n",
    "#Setting x-axis to black\n",
    "plt.axhline(y = 0, color = 'black', linestyle = '-',xmin = 0.046, xmax = 0.953)\n",
    "\n",
    "# Plotting the bar chart\n",
    "plt.bar(list(sleep_hypnogram_altered.keys()),\n",
    "        list(sleep_hypnogram_altered.values()),width=1,\n",
    "        color=sleep_hypnogram_altered_color.values())\n",
    "\n",
    "# Adding bottom bar to plot in a seprate chart\n",
    "# insert path of the image.\n",
    "b_bar_img = Image.open(urlopen('https://i.imgur.com/RMsObxL.png'))\n",
    "b_bar = plt.figure(figsize = (14,2), facecolor='#F1F1F1')\n",
    "ax2 = b_bar.gca()\n",
    "ax2.set_facecolor('#F1F1F1')\n",
    "\n",
    "# Plotting the image\n",
    "plt.imshow(b_bar_img,  interpolation='nearest')\n",
    "\n",
    "# Removing xticks and yticks\n",
    "plt.xticks([],[])\n",
    "plt.yticks([],[])\n",
    "\n",
    "# Adding text for start and end time\n",
    "start_time =  req_sleep['sleepStartTime'].split('T')[1][:5]\n",
    "plt.text(0.19,0.45,time_setter(start_time),transform=b_bar.transFigure,\n",
    "         color='white',fontsize=20)\n",
    "end_time =  req_sleep['sleepEndTime'].split('T')[1][:5]\n",
    "plt.text(0.735,0.45,time_setter(end_time),transform=b_bar.transFigure,\n",
    "         color='white',fontsize=20)\n",
    "\n",
    "# Adding text for total time\n",
    "total_sleep_secs = sum(list(time_dict.values()))\n",
    "total_time_str = (str(int(np.floor(total_sleep_secs/3600)))+'h '+\n",
    "                  str(int(np.ceil((total_sleep_secs%3600)/60)))+'min')\n",
    "plt.text(0.45,0.45,total_time_str,transform=b_bar.transFigure,\n",
    "         color='white',fontsize=20,fontweight=800)\n",
    "\n",
    "# Removing the spines on top, left and right\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.spines['bottom'].set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6abdc0",
   "metadata": {},
   "source": [
    "*^ Above is the plot that we created ourselves!*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d839f1",
   "metadata": {},
   "source": [
    "## 7.2 Visualizing Weekly Sleep Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394b6c1c",
   "metadata": {},
   "source": [
    "Along with the detailed sleep breakdown, our Polar Vantage also provides information on weekly sleep breakdown. For our next plot, let's recreate the following plot from the Polar Flow app."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c93e847",
   "metadata": {},
   "source": [
    "<img src='https://i.imgur.com/jog0iGJ.jpg' width='600px'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cff5aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3mx7rkcffdTp9nPmzCEIQpV0zc2ipfwGgU2aNFbz5s0UHBxcZHn+LeOTdeLECR05ckRZWSU3ALxawV3JQkJC5OXlvESLxaJq1apJktLT7RuBlcc+rhYcHOy0q77FYlHNmqGS8huWZWZmOt0PgPLn5eWlTz/9VMuXL9fIkSPl4+Njt/z8+fOaM2eORo8erV69eunw4cPX/BwLFy6UJN1yyy3y9/d3up6Pj4/69u0rSdqwYUO57+Nqw4cPV6NGjRwu8/Ly0pQpUyRJly9f1vbt253ux5WGDRumWrVqOVzWtWtX28/jxo1zes69er2jR4/aLSv4Hbdr106dO3cutpaBAwdKkrZs2ULj6EqGY7oojmnXH9MJCQk6ePCgdu3aZfuvbt26kqQdO3YUWb9evXoaNmyYJGnOnDmyWq1F1vnqq69sP9955512y1atWmVrHH3PPfc4ratr1652vzNUPq+88opdA+bg4GANGjTI1ui4Xr16WrBggcPzxsSJE+Xt7e1wv8nJybZ9TJgwodgaOnTooDp16kgq/rzjTHR0tJKSkkr1XAXHXk5OjrZt2+Z0vbvuuqvUz1/cuj/++KPy8vJUrVo13XTTTaWq7cyZMzpx4oRt/Oom/vfdd5/T7ceOHavQ0NBSVg24D5+SV3EuODhYwcHBysvLU3p6ujIyMpSRkam0tDTbH7+MjEwdPXpUrVq1LNXtCQ3DUEZGfpBy5cqVYrvQX+3qP/jlsY/CAgMDit02MDDQ9nNmZqYtXALgOsOGDdOwYcOUnJysdevWacuWLdq6davWrFljezOzdetWDRgwQNu2bVPDhg1Ltd+8vDzFxcVJkj766CN99NFHpdru3Llz5bqPwnr27FnstlffmSQ+Pl59+vQp1XNWpLZt2zpddvUbrdKuVxD6F9i6daskaf/+/aW+m05OTo4uX76sevXqlWp9uA7HtD2O6Yo/ptetW6f3339fK1as0OXLl52ud/HiRYfjd911l5YuXaqzZ89q5cqVGjp0qN3ygiCod+/eRe6Kt2vXLtvP3bt3L7bOHj16OAyjULm1aNFCEyZM0NNPP+3032eXLl2cbh8bG2v7jDV58mRNnjy5VM9b3HnHmYJjT1Kpz63FPVdwcLBatmxZqn2UtG5Bbenp6UW+KCiptqZNm0rKP4dK+XfBLi5Y9fX1VUREhO2OfUBVcV1BUAFvb2+FhIQoJCREkmS1WpWUlGS7rXpubq4uXLigxo0bl7ivst6G/epvXcpjH4WVdJK5ejm3kgfMVb16dd100022b4mysrL01Vdf6amnntKVK1d09uxZ/elPf9KMGTNKtb/Lly+X6dvl9PT0ct1HYSV9yKlfv77d81cGxYXkV8/eLO16hc+3Fy5cKFNdxf2eYT6O6Xwc06VXlmP65Zdf1iuvvFKqdZ3dknrMmDGqVq2a0tPT9b///c8uCNq+fbv27dsnyfFsh6u/uCyYeeRMScthrunTp+vhhx+WlH/lQEBAgOrUqaMaNWqUuG3NmjWdLnPl8VDez3Uts2pKWrc8ais4h9aqVcvpDKwCV597gaqiXIKgwry8vFSzZk35+Pjq+PHjkqTk5BQ1amSU+G2OYRi2n2vWDFXt2rVL9ZxX77c89gGg6vD399d9992nRo0aaeTIkZKk+fPn6+OPPy720tECV38wmTZtmh577LFSPa+fn1+57qMwzllFFfyeu3btqlmzZpV6u9J8UYHKg2Pac7jqmP75559tIVDLli319NNPKzIyUk2bNlVQUJDtC78///nP+stf/uJ0P8HBwbrtttv09ddfa/78+frggw8UEJA/q7xgNpC3t7cmTpx4TfXBvdSrV0+dOnUq07bFhRJXn3c++ugj9evXr1T7LC5cKs1zbd++vVRXdkhSkyZNHI6XFLZcy7oFtdWpU+eaZuq0aNGiyBjnXXiqCgmCCoSE5PfVycnJUV5envLy8kqcWVP4wC/443ktymMfhZX0rd/Vy6/lRAfAdUaMGKGwsDCdPHlSV65c0aVLl0r1rerV/S8MwyjTm7vy2Edh58+fL/VyZz08qpqC4D81NbVcfseo3Dimqz5XHdOffPKJpPwPzBs3bnT676g0M7Huuusuff3110pOTtYPP/ygCRMmyGq1avbs2ZLyL3l0NPvr6g/rCQkJxYZZCQkJJdaBqufqL7erVatWocfE1c9Vt25dpwGPGQpqS0lJUYcOHcr02avgeLt06ZLy8vKK3UdJ52bAHZWpWfS1uJbrNqX82UQFjdPKOlW/PPZRWEHPIefLf50iXB7BE4CKcXUj1tJ+C+Tn56eOHTtKyu8fURblsY/CtmzZUurlZXmz6I7fkkVEREiSjhw5UqaeCHA/HNOlxzHt3O7duyVJgwcPLjZMvLpvijMjRoywNektmAUUHR2t06dPS3LeBLfg35OkYhvulrYOVD3h4eG247i8zjvOFBx7rniua1VQW1ZWVpmPhYLm89nZ2cX228rNzbX1gwOqkgoNgqxWq7KysvKfyMur1GltQa+hrKxspaSklum5y2MfV0tNTVVOTo7DZYZh6MqVREn5s4EIgoDKKT09XXv27JGU33OktJeNStLo0aMlSfv27dOyZcvK9PzlsY+rLV++XGfPnnW4zGq1aubMmZLyv/Xq1q3bNe//6nNZwbm8siv4HRuGoffee8/kalDROKavDce0cwUzu9PS0pyuExsbq02bNpW4Lx8fH91xxx2SpMWLFysxMdEWCFWrVk1jxoxxuN2gQYNslzb+97//dbr/HTt20CjaQ9WtW9fWJP6rr76q0JlhQ4cOtfX2ev/99+1ab5jt1ltvtQVi7777bpn2cXX/roJzqyPfffddqW88BLiTaw6C8vLydPjwESUnpxR7QjAMQ2fOnLU1YA4JCSn1N1F16tS2/SE8ffp0ibdjT0lJKbJOeezjagWvx9FrTki4aHtDFRoaWqr+BADKR2pqqnr37q0ffvih2IbvVqtVv/vd72x3pBk9evQ1fTv+2GOPKTg4WFL+bUYLvj125scff9TOnTvLfR9Xy8rK0kMPPeSwQf3rr79uuyPG/fffX+ytrZ25+i4hZbk9txmGDx9uu7PSm2++qW+++abY9ePj47Vo0SJXlIZS4pjmmL6aq47pNm3aSJJiYmJ06NChIssTEhKKvaV7YQWzfgoam8+bN0+SdNttt9n+zRTWpEkT3XzzzZKkuXPnasGCBUXWycjI0IMPPljqOlD1vPjii5LybyU/YcIEJSYmOl03KytL//73v0v8HORIaGioHn30UUnS+vXr9cQTTxR7Tj5//nypG/Zfr3bt2un222+XJM2ePVvvvPNOsesfPXpUX3/9td1Yr169bIH6Bx98oJiYmCLbnT17Vk8//XQ5VQ1ULmXqEZSRkaETJ07Ix8dH1atXV7VqgfL19ZWXl7es1jxlZGTqypUrdrOB6tcv/S08fXx81LhxY508eVK5ubk6fPiIatYMVXBwfs8hwzCUk5OrjIx0JSUlKycnR02bNrX7pqs89nG1wMAApaSk6MiRo6pTp7b8/PyUm5urxMREJSUl256zXj3u4gC42ubNm3XrrbeqcePGGjNmjPr27atmzZopJCREiYmJio2N1WeffWb7EFWjRo1im306Ur9+fc2cOVMTJkzQ2bNn1aNHD02dOlU33XSTmjRpopycHJ06dUqbN2/W3LlzdeTIES1atMjuNrDlsY+r9ejRQ4sWLVL//v31xBNPqE2bNrpw4YJmzpxp60XRpEkT/elPfyrT7/XqJpRPPPGEXnjhBTVs2ND2Ybt58+bXfPmvK3z11Vfq1auXLl++rIkTJ2rWrFmaOHGi2rRpI29vb124cEGxsbFatGiRNm7cqKeeekq33nqr2WXjKhzTHNNXc8Uxfe+992rRokVKS0tTVFSUnnvuOdst3NevX6933nlH586dU9++fbVhw4YS99evXz+1aNFCR48e1QsvvGD7sO7ssrAC77zzjn7++Welp6fr9ttv1/Tp0zV27FhVr15du3bt0t///nft2bNHPXv2LPFSQlRNo0aN0mOPPab33ntPa9asUYcOHfTb3/5WkZGRql27ttLS0nTo0CGtXbtW8+fP15UrVzRlypQyPderr76q6Ohobdq0Se+9955Wr16tBx54QOHh4QoKCtKVK1e0e/durVixQkuWLFHnzp01bdq0cn7Fjn3wwQfaunWrjhw5oqeeekrff/+97r33XnXs2FH+/v66dOmSduzYoaVLl2rlypUaO3asJk+ebLeP//znP4qMjFROTo6GDRumJ554QqNGjZK/v782bdqkv/3tb7p48aK6du3KLDxUPcY1ysvLM/bu3WfEx+8q1X/79x8w0tPTi+zn8OEjRnz8LuPw4SNOnyspKdnYs2dvqZ4nJSW13PeRlZVlW3b58mXj5MlTTrfdu3efkZGRca2/TgDXKSMjw2jQoIEhqVT/tWnTxti6davDfUVFRRmSjKioKKfPt3DhQqNWrVolPo+Xl5excuXKct/H0aNHbcs///xzY+rUqU63b9iwobF79+4y/24NwzDuuOMOp/s/evSobb0pU6YYkoxmzZo53E/BNi+99JLT5yr82pxZtWqVbb1Vq1Y5XGf//v1Gp06dSvVv4pVXXin5FwGX4ZjmmHbEFcf0fffd53Sf3t7exrvvvmu89NJLtrGSvPDCC3b7qFOnjpGTk1PidsuXLzeCgoKc1vLSSy8Zf/rTnwxJRkBAQJleK8rf1f+OizsuStrW2TFwNavVarzyyiuGj49PicdDUFCQw89izZo1MyQZU6ZMKfa5kpOTjXHjxpXq2Bs8eHCR7Us6l5R1XcMwjLNnzxoDBgwoVW333Xefw3189dVXhp+fn8NtfHx8jI8//via6wLcwTV/7ePl5aV27doqIyNDqampSk/PUFZWlvLy8mS1WuXl5SVfXx8FBAQoJKS6qlcPKfOlUtWrhygoqI2uXLmilJRU2/NI+bNvAgL8FRQUpOrVa8jPz/EtDctjHwWaNGms4OB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\n",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#@title Set date for the chart below\n",
    "\n",
    "plot_date = \"2022-05-15\" #@param {type:\"date\"}\n",
    "\n",
    "start_date = datetime.strptime(plot_date, '%Y-%m-%d')\n",
    "end_date = start_date + timedelta(6)\n",
    "end_date_str = str(end_date.year)+'-'+str(end_date.month)+'-'+str(end_date.day)\n",
    "weekly_dates_list = []\n",
    "xticks = []\n",
    "for i in pd.date_range(start_date,end_date,freq='d'):\n",
    "    weekly_dates_list.append(str(i)[:10])\n",
    "    test_date = datetime.strptime(str(i)[:10], '%Y-%m-%d')\n",
    "    xticks.append(str(test_date.day)+'        \\n'+\n",
    "                test_date.strftime(\"%A\")[0]+'         ')\n",
    "value_dict = {}\n",
    "goals = []\n",
    "for date in weekly_dates_list:\n",
    "    res = sleep_df[sleep_df['date'] == date]\n",
    "    if len(res)>0:\n",
    "        duration = float(datetime.fromisoformat(res.iloc[0]['sleepEndTime'][:19]).timestamp()) - float(datetime.fromisoformat(res.iloc[0]['sleepStartTime'][:19]).timestamp())\n",
    "        value_dict[date] = duration/3600\n",
    "    else:\n",
    "        value_dict[date] = 0\n",
    "    goals.append(8)\n",
    "# Creating the figure\n",
    "plt3 = plt.figure(figsize=(14,8))\n",
    "ax = plt3.gca()\n",
    "\n",
    "# Adding grid to the plot\n",
    "plt.grid(axis = 'y',color=\"#7F7F7F\", linestyle='-', linewidth=2,alpha=0.8)\n",
    "\n",
    "# Adding the color lines to grid\n",
    "for (x,y) in [(0,2),(4,6),(8,10),(12,14)]:\n",
    "    for i in np.arange(x, y):\n",
    "        plt.axhspan(i, i+1, facecolor='#F2F2F2', alpha=1,\n",
    "                xmin = 0.025, xmax = 0.975)\n",
    "\n",
    "\n",
    "# Plotting line for preferred sleep\n",
    "plt.axhline(np.mean(goals),color='#0C9AE0',lw=3)\n",
    "\n",
    "# Plotting line for average sleep\n",
    "plt.axhline(np.mean(list(value_dict.values())), linestyle='--',\n",
    "            color='#969696',lw=3)\n",
    "\n",
    "# Plotting the values\n",
    "plt.plot(list(value_dict.keys()),list(value_dict.values()), \n",
    "         color='#064C75',lw=3)\n",
    "\n",
    "# Removing margins on left and right of the plot\n",
    "plt.margins(y=0,x=0.1)\n",
    "\n",
    "\n",
    "\n",
    "# Highlighting single data points\n",
    "for x,y in value_dict.items():\n",
    "    plt.plot(x, y, marker=\"o\", markersize=10,\n",
    "    markeredgecolor=\"#2474AA\",markerfacecolor=\"white\", markeredgewidth=2)\n",
    "\n",
    "\n",
    "# Removing the spines on left and right\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Modifying our x and y ticks\n",
    "plt.yticks([0,2,4,6,8,10,12,14,16],fontsize=14)\n",
    "plt.xticks(list(value_dict.keys()),xticks,ha='right',fontsize=14)\n",
    "ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "\n",
    "# Adding header text\n",
    "plt.text(0.09,0.96,'Sleep', transform=plt3.transFigure,\n",
    "         color='#D3D3D4',fontsize=20)\n",
    "plt.text(0.109,0.92,'h', transform=plt3.transFigure,\n",
    "         color='black',fontsize=14)\n",
    "\n",
    "plt.text(0.3,0.96,'Sleep time', transform=plt3.transFigure,\n",
    "         color='black',fontsize=20)\n",
    "plt.text(0.325,0.9,'—', transform=plt3.transFigure,\n",
    "         color='#064C75',fontsize=50)\n",
    "\n",
    "plt.text(0.475,0.96,'Sleep time avg', transform=plt3.transFigure,\n",
    "         color='black',fontsize=20)\n",
    "plt.text(0.525,0.9,'--', transform=plt3.transFigure,\n",
    "         color='#969696',fontsize=50)\n",
    "\n",
    "plt.text(0.69,0.96,'Preferred', transform=plt3.transFigure,\n",
    "         color='black',fontsize=20)\n",
    "plt.text(0.71,0.9,'—', transform=plt3.transFigure,\n",
    "         color='#0C9AE0',fontsize=50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf7ac02",
   "metadata": {},
   "source": [
    "# 8. Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb4b600",
   "metadata": {},
   "source": [
    "According to a [course](https://www.uh.edu/class/ctr-public-history/tobearfruit/__docs/curriculum/ms/science/conxnbetwrespandhrtrate/lessonplan_conxnbetwrespandhrtrate.pdf) taught at the University of Huston, \"The more the heart beats, the more breathing occurs. As the heart beats faster, it uses more energy and sends more oxygen to the body. If a person is exercising the oxygen is used very quickly in order to provide the muscles with needed energy to move.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "041f8a02",
   "metadata": {},
   "source": [
    "**Our test hypothesis will be that Heart Rate and Calories Burned are correlated.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63a7e802",
   "metadata": {},
   "source": [
    "In this portion of the notebook, we will be testing this exact hypothesis using the data that we fetched from the Polar Vantage 3."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03746695",
   "metadata": {},
   "source": [
    "First, let's get the required data from the training_history_df dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e0b764a",
   "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>calories</th>\n",
       "      <th>hrAvg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>475</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>312</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  hrAvg\n",
       "0       475    123\n",
       "1       169     68\n",
       "2       387     84\n",
       "3        95    136\n",
       "4       312    140"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = training_history_df.get(['calories','hrAvg'])\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c6bd22",
   "metadata": {},
   "source": [
    "Let's create a quick plot on this to see if there is a correlation between these two values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ea7bc87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Setting Figure Size in Seaborn\n",
    "sns.set(rc={'figure.figsize':(16,8)})\n",
    "\n",
    "# Setting Seaborn plot style\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "#Plotting our data\n",
    "plot = sns.regplot(data=test_data, x='calories', y=\"hrAvg\")\n",
    "\n",
    "#Renaming x and y labels\n",
    "plot.set_ylabel(\"Breathing Rate\", fontsize = 16)\n",
    "plot.set_xlabel(\"Heart Rate\", fontsize = 16)\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdab791f",
   "metadata": {},
   "source": [
    "Looking at the graph, we can see a line of regression which hints that Breathing and Heart Rate are directly correlated. However, there seems to be too much variability in the data. In the next line, we will prove this statistically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd752a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.0692\n",
      "Coefficient of determination: 0.09\n",
      "p-value: 0.00299\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(\n",
    "    test_data.get('calories'), test_data.get('hrAvg'))\n",
    "\n",
    "print(f'Slope: {slope:.3g}')\n",
    "print(f'Coefficient of determination: {r_value**2:.3g}')\n",
    "print(f'p-value: {p_value:.3g}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74275d51",
   "metadata": {},
   "source": [
    "The p-value for this is 0.285% which is much smaller than the 5% cutoff. This means that there is enough evidence to convincingly conclude that that there is a correlation between Avg Heart Rate and Calories Burned."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165f69fd",
   "metadata": {},
   "source": [
    "# 9. Outlier Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "595a531c",
   "metadata": {},
   "source": [
    "However, even though our P value seems to provide enough statistical significance that there is a correlation between Avg Heart Rate and Calories Burned, there might be outliers that are not following this correlation. In this section of our analysis, we will find if there are outliers like that and if they exist, we will visually highlight them in our plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c48324a",
   "metadata": {},
   "source": [
    "Before finding the individual outlier values, it would be interesting to see the summary of our Avg heart Rate and Calories Consumed. It will give us a clear idea of what values are typical and which values can be considered atypical based on the data that we recieved from Cronometer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71f32e75",
   "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>calories</th>\n",
       "      <th>hrAvg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>341.697917</td>\n",
       "      <td>113.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>147.164507</td>\n",
       "      <td>33.977850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>257.000000</td>\n",
       "      <td>84.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>354.000000</td>\n",
       "      <td>112.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>462.000000</td>\n",
       "      <td>141.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>597.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         calories       hrAvg\n",
       "count   96.000000   96.000000\n",
       "mean   341.697917  113.729167\n",
       "std    147.164507   33.977850\n",
       "min     76.000000   60.000000\n",
       "25%    257.000000   84.750000\n",
       "50%    354.000000  112.500000\n",
       "75%    462.000000  141.250000\n",
       "max    597.000000  178.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d204f9",
   "metadata": {},
   "source": [
    "To locate the outliers we will be using a supervised as well as unsupervised algorithm called the Elliptic Envelope. In statistical studies, Elliptic Envelope created an imaginary elliptical area around a given dataset where values inside that imaginary area is considered to be normal data, and anything else is assumed to be outliers. It assumes that the given Data follows a gaussian distribution.\n",
    "\n",
    "\"The main idea is to define the shape of the data and anomalies are those observations that lie far outside the shape. First a robust estimate of covariance of data is fitted into an ellipse around the central mode. Then, the Mahalanobis distance that is obtained from this estimate is used to define the threshold for determining outliers or anomalies.\" [(S. Shriram and E. Sivasankar ,2019, pp. 221-225)](https://ieeexplore.ieee.org/document/9004325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ad6893c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     94\n",
      "-1     2\n",
      "Name: outlier, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/saarth/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but EllipticEnvelope was fitted with feature names\n",
      "  warnings.warn(\n",
      "/Users/saarth/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but EllipticEnvelope was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.covariance import EllipticEnvelope\n",
    "import copy\n",
    "\n",
    "# Sometimes EllipticEnvelope shows slicing based copy warnings\n",
    "# The next line changes a setting that prevents the error from happening\n",
    "\n",
    "pd.set_option('mode.chained_assignment', None)\n",
    "\n",
    "#create the model, set the contamination as 0.02\n",
    "EE_model = EllipticEnvelope(contamination = 0.02)\n",
    "\n",
    "#implement the model on the data\n",
    "outliers = EE_model.fit_predict(test_data.get(['calories','hrAvg']))\n",
    "\n",
    "#extract the labels\n",
    "test_data[\"outlier\"] = copy.deepcopy(outliers)\n",
    "\n",
    "#change the labels\n",
    "# We use -1 to mark an outlier and +1 for an inliner\n",
    "test_data[\"outlier\"] = test_data[\"outlier\"].apply(\n",
    "                                    lambda x: str(-1) if x == -1 else str(1))\n",
    "\n",
    "#extract the score\n",
    "test_data[\"EE_scores\"] = EE_model.score_samples(\n",
    "                      test_data.get(['calories','hrAvg']))\n",
    "\n",
    "#print the value counts for inlier and outliers\n",
    "print(test_data[\"outlier\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206a44c7",
   "metadata": {},
   "source": [
    "Below we will replot the test_data dataframe to see how the two new columns were applied to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cac9e1a6",
   "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>calories</th>\n",
       "      <th>hrAvg</th>\n",
       "      <th>outlier</th>\n",
       "      <th>EE_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>475</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.769426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>169</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.452778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>387</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.111575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.888862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>312</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.129723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calories  hrAvg outlier  EE_scores\n",
       "0       475    123       1  -0.769426\n",
       "1       169     68       1  -2.452778\n",
       "2       387     84       1  -1.111575\n",
       "3        95    136       1  -5.888862\n",
       "4       312    140       1  -1.129723"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ee509c",
   "metadata": {},
   "source": [
    "\n",
    "Now that we have labeled the outliers as -1, let's try to see which values of average heart rate and calories are being identified as outliers by our Elliptic Envelope Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29a1f13b",
   "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>calories</th>\n",
       "      <th>hrAvg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>109</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>77</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    calories  hrAvg\n",
       "88       109    156\n",
       "92        77    148"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_df = test_data[test_data.get('outlier')=='-1'].get(\n",
    "    ['calories','hrAvg'])\n",
    "outlier_df_cleaned = outlier_df.drop_duplicates()\n",
    "outlier_df_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17972bdd",
   "metadata": {},
   "source": [
    "Sweet, now that we know that there were outliers in our dataset, let's try to visually see which pair of values are being identified as outliers using a plot. Highlighting these outliers in a bright red color will make it super easy for us to identify them in our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "396d97cb",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting Figure Size in Seaborn\n",
    "sns.set(rc={'figure.figsize':(16,8)})\n",
    "\n",
    "# Setting Seaborn plot style\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "#Plotting our data\n",
    "plot = sns.regplot(x='calories', y='hrAvg', data=test_data.drop(\n",
    "    outlier_df.index))\n",
    "\n",
    "plt.scatter(outlier_df_cleaned.get('calories'),outlier_df_cleaned.get('hrAvg'))\n",
    "plt.scatter(outlier_df_cleaned.get('calories'),outlier_df_cleaned.get('hrAvg'),\n",
    "            facecolors='red',alpha=.35, s=500)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c119051",
   "metadata": {},
   "source": [
    "Thus, the points highlighted in red are ones that seem to not be following the general trend of our dataset. Lastly, let's see what the new p-value is after outlier removal!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20133edb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.0838\n",
      "Coefficient of determination: 0.127\n",
      "p-value: 0.00042\n"
     ]
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(\n",
    "    test_data.drop(outlier_df.index).get('calories'),\n",
    "     test_data.drop(outlier_df.index).get('hrAvg'))\n",
    "\n",
    "print(f'Slope: {slope:.3g}')\n",
    "print(f'Coefficient of determination: {r_value**2:.3g}')\n",
    "print(f'p-value: {p_value:.3g}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6835502c",
   "metadata": {},
   "source": [
    "The p-value for this is 0.0214% which is much smaller than the 5% cutoff. This means that there is enough evidence to convincingly conclude that that there is a correlation between Heart Rate and Calories Burned."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}