Notebooks/02_data_wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Data wrangling<a id='2_Data_wrangling'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Contents<a id='2.1_Contents'></a>\n",
    "* [2 Data wrangling](#2_Data_wrangling)\n",
    "  * [2.1 Contents](#2.1_Contents)\n",
    "  * [2.2 Introduction](#2.2_Introduction)\n",
    "    * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n",
    "    * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n",
    "  * [2.3 Imports](#2.3_Imports)\n",
    "  * [2.4 Objectives](#2.4_Objectives)\n",
    "  * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n",
    "  * [2.6 Explore The Data](#2.6_Explore_The_Data)\n",
    "    * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n",
    "    * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n",
    "    * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n",
    "      * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n",
    "      * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n",
    "      * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n",
    "      * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n",
    "      * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n",
    "        * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n",
    "        * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n",
    "    * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n",
    "      * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n",
    "      * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n",
    "        * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n",
    "        * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n",
    "        * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n",
    "        * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n",
    "  * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n",
    "  * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n",
    "  * [2.9 Review distributions](#2.9_Review_distributions)\n",
    "  * [2.10 Population data](#2.10_Population_data)\n",
    "  * [2.11 Target Feature](#2.11_Target_Feature)\n",
    "    * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n",
    "  * [2.12 Save data](#2.12_Save_data)\n",
    "  * [2.13 Summary](#2.13_Summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Introduction<a id='2.2_Introduction'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step focuses on collecting your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 Recap Of Data Science Problem<a id='2.2.1_Recap_Of_Data_Science_Problem'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n",
    "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 Introduction To Notebook<a id='2.2.2_Introduction_To_Notebook'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n",
    "\n",
    "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n",
    "\n",
    "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n",
    "\n",
    "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Imports<a id='2.3_Imports'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 1#\n",
    "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "from library.sb_utils import save_file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Objectives<a id='2.4_Objectives'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some fundamental questions to resolve in this notebook before you move on.\n",
    "\n",
    "* Do you think you may have the data you need to tackle the desired question?\n",
    "    * Have you identified the required target value?\n",
    "    * Do you have potentially useful features?\n",
    "* Do you have any fundamental issues with the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 Load The Ski Resort Data<a id='2.5_Load_The_Ski_Resort_Data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the supplied CSV data file is the raw_data directory\n",
    "ski_data = pd.read_csv('../raw_data/ski_resort_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good first steps in auditing the data are the info method and displaying the first few records with head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 330 entries, 0 to 329\n",
      "Data columns (total 27 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               330 non-null    object \n",
      " 1   Region             330 non-null    object \n",
      " 2   state              330 non-null    object \n",
      " 3   summit_elev        330 non-null    int64  \n",
      " 4   vertical_drop      330 non-null    int64  \n",
      " 5   base_elev          330 non-null    int64  \n",
      " 6   trams              330 non-null    int64  \n",
      " 7   fastEight          164 non-null    float64\n",
      " 8   fastSixes          330 non-null    int64  \n",
      " 9   fastQuads          330 non-null    int64  \n",
      " 10  quad               330 non-null    int64  \n",
      " 11  triple             330 non-null    int64  \n",
      " 12  double             330 non-null    int64  \n",
      " 13  surface            330 non-null    int64  \n",
      " 14  total_chairs       330 non-null    int64  \n",
      " 15  Runs               326 non-null    float64\n",
      " 16  TerrainParks       279 non-null    float64\n",
      " 17  LongestRun_mi      325 non-null    float64\n",
      " 18  SkiableTerrain_ac  327 non-null    float64\n",
      " 19  Snow Making_ac     284 non-null    float64\n",
      " 20  daysOpenLastYear   279 non-null    float64\n",
      " 21  yearsOpen          329 non-null    float64\n",
      " 22  averageSnowfall    316 non-null    float64\n",
      " 23  AdultWeekday       276 non-null    float64\n",
      " 24  AdultWeekend       279 non-null    float64\n",
      " 25  projectedDaysOpen  283 non-null    float64\n",
      " 26  NightSkiing_ac     187 non-null    float64\n",
      "dtypes: float64(13), int64(11), object(3)\n",
      "memory usage: 69.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Code task 2#\n",
    "#Call the info method on ski_data to see a summary of the data\n",
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "      <th>summit_elev</th>\n",
       "      <th>vertical_drop</th>\n",
       "      <th>base_elev</th>\n",
       "      <th>trams</th>\n",
       "      <th>fastEight</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>...</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
       "      <th>averageSnowfall</th>\n",
       "      <th>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
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       "      <th>0</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>3939</td>\n",
       "      <td>2500</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2600</td>\n",
       "      <td>1540</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>2090</td>\n",
       "      <td>294</td>\n",
       "      <td>1796</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11500</td>\n",
       "      <td>2300</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>11100</td>\n",
       "      <td>1800</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>800.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
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      "text/plain": [
       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
       "\n",
       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
       "0        250      1        0.0          0          2  ...            1.0   \n",
       "1       1200      0        0.0          0          0  ...            2.0   \n",
       "2       1796      0        0.0          0          0  ...            1.0   \n",
       "3       9200      0        0.0          1          0  ...            2.0   \n",
       "4       9200      0        NaN          0          1  ...            1.2   \n",
       "\n",
       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "0             1610.0           113.0             150.0       60.0   \n",
       "1              640.0            60.0              45.0       44.0   \n",
       "2               30.0            30.0             150.0       36.0   \n",
       "3              777.0           104.0             122.0       81.0   \n",
       "4              800.0            80.0             115.0       49.0   \n",
       "\n",
       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "0            669.0          65.0          85.0              150.0   \n",
       "1            350.0          47.0          53.0               90.0   \n",
       "2             69.0          30.0          34.0              152.0   \n",
       "3            260.0          89.0          89.0              122.0   \n",
       "4            250.0          74.0          78.0              104.0   \n",
       "\n",
       "   NightSkiing_ac  \n",
       "0           550.0  \n",
       "1             NaN  \n",
       "2            30.0  \n",
       "3             NaN  \n",
       "4            80.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 3#\n",
    "#Call the head method on ski_data to print the first several rows of the data\n",
    "ski_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.1 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Big Mountain Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Montana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>6817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>2353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>4464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   151\n",
       "Name               Big Mountain Resort\n",
       "Region                         Montana\n",
       "state                          Montana\n",
       "summit_elev                       6817\n",
       "vertical_drop                     2353\n",
       "base_elev                         4464\n",
       "trams                                0\n",
       "fastEight                          0.0\n",
       "fastSixes                            0\n",
       "fastQuads                            3\n",
       "quad                                 2\n",
       "triple                               6\n",
       "double                               0\n",
       "surface                              3\n",
       "total_chairs                        14\n",
       "Runs                             105.0\n",
       "TerrainParks                       4.0\n",
       "LongestRun_mi                      3.3\n",
       "SkiableTerrain_ac               3000.0\n",
       "Snow Making_ac                   600.0\n",
       "daysOpenLastYear                 123.0\n",
       "yearsOpen                         72.0\n",
       "averageSnowfall                  333.0\n",
       "AdultWeekday                      81.0\n",
       "AdultWeekend                      81.0\n",
       "projectedDaysOpen                123.0\n",
       "NightSkiing_ac                   600.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 4#\n",
    "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n",
    "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n",
    "#transpose method, but you can access this conveniently with the `T` property.\n",
    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's good that your resort doesn't appear to have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the number of missing values in each column and sort them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>3</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>4</td>\n",
       "      <td>1.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>5</td>\n",
       "      <td>1.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>14</td>\n",
       "      <td>4.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>46</td>\n",
       "      <td>13.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>47</td>\n",
       "      <td>14.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>51</td>\n",
       "      <td>15.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>54</td>\n",
       "      <td>16.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>143</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>166</td>\n",
       "      <td>50.303030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          %\n",
       "Name                   0   0.000000\n",
       "total_chairs           0   0.000000\n",
       "double                 0   0.000000\n",
       "triple                 0   0.000000\n",
       "quad                   0   0.000000\n",
       "fastQuads              0   0.000000\n",
       "fastSixes              0   0.000000\n",
       "surface                0   0.000000\n",
       "trams                  0   0.000000\n",
       "base_elev              0   0.000000\n",
       "vertical_drop          0   0.000000\n",
       "summit_elev            0   0.000000\n",
       "state                  0   0.000000\n",
       "Region                 0   0.000000\n",
       "yearsOpen              1   0.303030\n",
       "SkiableTerrain_ac      3   0.909091\n",
       "Runs                   4   1.212121\n",
       "LongestRun_mi          5   1.515152\n",
       "averageSnowfall       14   4.242424\n",
       "Snow Making_ac        46  13.939394\n",
       "projectedDaysOpen     47  14.242424\n",
       "TerrainParks          51  15.454545\n",
       "daysOpenLastYear      51  15.454545\n",
       "AdultWeekend          51  15.454545\n",
       "AdultWeekday          54  16.363636\n",
       "NightSkiing_ac       143  43.333333\n",
       "fastEight            166  50.303030"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 5#\n",
    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
    "#Order them (increasing or decreasing) using sort_values\n",
    "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n",
    "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n",
    "missing.columns=['count', '%']\n",
    "missing.sort_values(by='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alyeska Resort</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eaglecrest Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hilltop Ski Area</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona Snowbowl</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sunrise Park Resort</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Meadowlark Ski Lodge</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Sleeping Giant Ski Resort</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Snow King Resort</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Snowy Range Ski &amp; Recreation Area</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>White Pine Ski Area</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Wyoming</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Name   Region    state\n",
       "0                       Alyeska Resort   Alaska   Alaska\n",
       "1                  Eaglecrest Ski Area   Alaska   Alaska\n",
       "2                     Hilltop Ski Area   Alaska   Alaska\n",
       "3                     Arizona Snowbowl  Arizona  Arizona\n",
       "4                  Sunrise Park Resort  Arizona  Arizona\n",
       "..                                 ...      ...      ...\n",
       "325               Meadowlark Ski Lodge  Wyoming  Wyoming\n",
       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming\n",
       "327                   Snow King Resort  Wyoming  Wyoming\n",
       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming\n",
       "329                White Pine Ski Area  Wyoming  Wyoming\n",
       "\n",
       "[330 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 6#\n",
    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
    "ski_data.select_dtypes(include = 'object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
    "\n",
    "* Is `Name` (or at least a combination of Name/Region/State) unique?\n",
    "* Is `Region` always the same as `state`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crystal Mountain    2\n",
       "Alyeska Resort      1\n",
       "Brandywine          1\n",
       "Boston Mills        1\n",
       "Alpine Valley       1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 7#\n",
    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
    "ski_data['Name'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have a duplicated resort name: Crystal Mountain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alyeska Resort, Alaska    1\n",
       "Snow Trails, Ohio         1\n",
       "Brandywine, Ohio          1\n",
       "Boston Mills, Ohio        1\n",
       "Alpine Valley, Ohio       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 8#\n",
    "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alyeska Resort, Alaska    1\n",
       "Snow Trails, Ohio         1\n",
       "Brandywine, Ohio          1\n",
       "Boston Mills, Ohio        1\n",
       "Alpine Valley, Ohio       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 9#\n",
    "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 1** No, there are two unique 'Crystal Mountain' Resorts in two different regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "      <th>summit_elev</th>\n",
       "      <th>vertical_drop</th>\n",
       "      <th>base_elev</th>\n",
       "      <th>trams</th>\n",
       "      <th>fastEight</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>...</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
       "      <th>averageSnowfall</th>\n",
       "      <th>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Crystal Mountain</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1132</td>\n",
       "      <td>375</td>\n",
       "      <td>757</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>102.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Crystal Mountain</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Washington</td>\n",
       "      <td>7012</td>\n",
       "      <td>3100</td>\n",
       "      <td>4400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name      Region       state  summit_elev  vertical_drop  \\\n",
       "104  Crystal Mountain    Michigan    Michigan         1132            375   \n",
       "295  Crystal Mountain  Washington  Washington         7012           3100   \n",
       "\n",
       "     base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
       "104        757      0        0.0          0          1  ...            0.3   \n",
       "295       4400      1        NaN          2          2  ...            2.5   \n",
       "\n",
       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "104              102.0            96.0             120.0       63.0   \n",
       "295             2600.0            10.0               NaN       57.0   \n",
       "\n",
       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "104            132.0          54.0          64.0              135.0   \n",
       "295            486.0          99.0          99.0                NaN   \n",
       "\n",
       "     NightSkiing_ac  \n",
       "104            56.0  \n",
       "295             NaN  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.2 Region And State<a id='2.6.3.2_Region_And_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the relationship between region and state?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 10#\n",
    "#Calculate the number of times Region does not equal state\n",
    "(ski_data.Region != ski_data.state).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York               33\n",
       "Michigan               29\n",
       "Sierra Nevada          22\n",
       "Colorado               22\n",
       "Pennsylvania           19\n",
       "Wisconsin              16\n",
       "New Hampshire          16\n",
       "Vermont                15\n",
       "Minnesota              14\n",
       "Idaho                  12\n",
       "Montana                12\n",
       "Massachusetts          11\n",
       "Washington             10\n",
       "New Mexico              9\n",
       "Maine                   9\n",
       "Wyoming                 8\n",
       "Utah                    7\n",
       "Salt Lake City          6\n",
       "North Carolina          6\n",
       "Oregon                  6\n",
       "Connecticut             5\n",
       "Ohio                    5\n",
       "Virginia                4\n",
       "West Virginia           4\n",
       "Illinois                4\n",
       "Mt. Hood                4\n",
       "Alaska                  3\n",
       "Iowa                    3\n",
       "South Dakota            2\n",
       "Arizona                 2\n",
       "Nevada                  2\n",
       "Missouri                2\n",
       "Indiana                 2\n",
       "New Jersey              2\n",
       "Rhode Island            1\n",
       "Tennessee               1\n",
       "Maryland                1\n",
       "Northern California     1\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state       Region             \n",
       "California  Sierra Nevada          0.952381\n",
       "            Northern California    0.047619\n",
       "Nevada      Sierra Nevada          1.000000\n",
       "Oregon      Mt. Hood               1.000000\n",
       "Utah        Salt Lake City         1.000000\n",
       "Name: Region, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 11#\n",
    "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n",
    "#group that by 'state' and perform `value_counts` on the 'Region'\n",
    "(ski_data[ski_data.Region != ski_data.state]\n",
    " .groupby(ski_data.state)['Region']\n",
    " .value_counts('Region'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.3 Number of distinct regions and states<a id='2.6.3.3_Number_of_distinct_regions_and_states'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region    38\n",
       "state     35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 12#\n",
    "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n",
    "#the number of unique values in each\n",
    "ski_data[['Region', 'state']].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because a few states are split across multiple named regions, there are slightly more unique regions than states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.4 Distribution Of Resorts By Region And State<a id='2.6.3.4_Distribution_Of_Resorts_By_Region_And_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmDGEhYUBkJKSQkpKCgCLFi0iJCSE+fPnc/LkSXx9ffnggw+0+gkwfvx4RowYQWJiIr6+vgV6hk8rbPszMjJIS0vT2oQQQgjx9qhVqxb29vZa71jbt2/Hzs6OmjVrKseioqJo3LgxlpaWWFtb07ZtW2XkKuT9Aearr77C3NycrVu3at3zu+++w8TEhH///TdXe7Kzsxk4cCCOjo4YGRnh4uLCokWLtMoU5KPO9evXadeuHUZGRjg6OrJhw4YXflZCCCGKvv+UVLVjx454enoyderUPM8vWLCAFi1aMHnyZKpUqYJarWbYsGHMmzdPq1zz5s0ZM2YMzs7OODs7U7p0aQCsra2xsbGhZMmSSlkrKyuWLl1K1apVadu2LW3atGHfvn3Ao6klGzdu5Ouvv6ZJkyZUqlSJMWPG0LhxYyWoAI++XCxbtoyGDRvi4uKiBDRat27N0KFDcXZ2Zvz48ZQqVUprdMmTLly4gEajoWrVqs99Tv7+/jRr1gxHR0eaN2/O9OnT2bJly3Ovg0fTZx4HV2xsbLCxsQEejZIZP3483bt3x8XFhTlz5uDp6ZkrGaq/vz+dOnXC0dGRcuXKPfcZvoz2BwcHY2FhoWx2dnYF6qsQQgghXp/+/ftrvR+tWbOGAQMGaJW5d+8eo0aNIj4+nn379qGjo0PHjh1zTTd+8gNMx44d6d69u1bdAGFhYXz44Yd5Ln+Yk5NDhQoV2LJlC2fPnmXKlClMnDgx1/vG8z7qqNVqkpKSiI6OZuvWrSxbtozr168/8znIhxwhhBD/edndOXPm0Lx5c0aPHp3rXGJiIu3bt9c61qhRI0JDQ8nOzkZXVxeAOnXqFPh+1atXV64DsLW15dSpUwAcP34cjUZDlSpVtK7JyMjQyslRokQJPDw8ctX95DGVSoWNjU2+/4g+ThxbkCV8YmJimDVrFmfPniUtLY2srCzS09O5d+/ef0pempaWxtWrV2nUqJHW8UaNGvHrr79qHcvr2T7rGb6M9gcEBDBq1Cit9kpQRAghhHi79OnTh4CAAGWUx+MRtk9+DOrcubPWNatXr6ZMmTKcPXsWNzc35fjjDzCPffTRRzRs2JCrV69Srlw5bty4wc6dO9mzZ0+ebdHX1ycoKEjZd3R0JC4uji1bttC1a1fl+OOPOrq6ulStWlX5qDNo0CB+//13fvzxR3755Rfq16+vtLdatWrPfA7BwcFa9xZCCFH8/Odld728vPD19WXixIm5zmk0mlwBg7xWoClMUEBfX19rX6VSKV8pcnJy0NXV5dixYyQkJChbYmKi1rBLIyOjPAMZz6r7aZUrV0alUpGYmPjM9l6+fJnWrVvj5ubGtm3bOHbsGF988QXw4nNs83q2Tx/L69kWpp//pf0GBgaYm5trbUIIIYR4u5QqVYo2bdoQERFBWFgYbdq0oVSpUlplLl68SM+ePXFycsLc3FzJ85acnKxV7ukPMPXq1aN69eqsXbsWgHXr1mFvb4+Xl1e+7VmxYgV16tShdOnSmJqasnLlylz3yeujzuOPV4mJiejp6Wm1pWrVqlrTmPMSEBBAamqqsl25cuWZ5YUQQhQ9/3mECMDs2bPx9PTMNTLD1dWVgwcPah2Li4ujSpUqWv+YPa1EiRLAo/mkhVGzZk2ys7O5fv06TZo0KdS1hVWyZEl8fX354osvGDFiRK7Aw507d7C0tOTo0aNkZWUREhKCjs6juFNBp8vkx9zcnHLlynHw4EGtF4u4uDjq1av3QnU/7VW0XwghhBBvhwEDBjBs2DAA5YPHk9q1a4ednR0rV66kXLly5OTk4ObmlmtVwLw+wHz00UcsXbqUCRMmEBYWRv/+/fMdWbtlyxZGjhxJSEgIDRo0wMzMjHnz5nH48GGtcs/6qFOY0btPMjAw+M8r+AkhhCgaXigg4u7uTq9evViyZInW8dGjR1O3bl2mT59Ot27d+Pnnn1m6dCnLli17Zn1lypTByMiIqKgoKlSogKGhIRYWFs9tR5UqVejVqxd9+/YlJCSEmjVrcuPGDaKjo3F3d6d169Yv0s1cHuchqVevHtOmTcPDw4OsrCz27NnD8uXLSUxMpFKlSmRlZbFkyRLatWvHoUOHWLFixQvfe+zYsUydOpVKlSrh6elJWFgYCQkJLz152Mts/+kgXxktIoQQQrxF/Pz8lODG4+Trj928eZPExES+/PJL5UPT0x+6nqV3796MGzeOxYsXc+bMGfr165dv2QMHDtCwYUOGDh2qHHsyeWtBVKtWjaysLI4ePap8IDp37hx37twpVD1CCCGKnxcKiAB5JtqsVasWW7ZsYcqUKUyfPh1bW1umTZv23LXt9fT0WLx4MdOmTWPKlCk0adIk3+SmTwsLC2PGjBmMHj2av/76C2traxo0aPDSgyHwaH7r8ePHmTlzJqNHjyYlJYXSpUtTu3Ztli9fDoCnpycLFixgzpw5BAQE4OXlRXBwMH379n2he48YMYK0tDRGjx7N9evXcXV15dtvv6Vy5covo2uKV9V+IYQQQrx5urq6yvTfp0fvWllZYW1tzVdffYWtrS3JyclMmDChwHVbWVnRqVMnxo4dS6tWrahQoUK+ZZ2dnVm7di27du3C0dGRdevWER8fr0zRKQgXFxf8/PwYNGgQX331FXp6evj7+2NkZFTgOp4kH3KEEKL4UGnySu4hxEuQlpaGhYUFqamp8mIhhBDvMPl7XjSo1Wru3LlDZGRknuc7dOiApaUl4eHh7N27lxEjRvDHH3/g4uLC4sWL8fb2ZseOHXTo0IGkpCQcHR05ceIEnp6eueqKjo6mRYsWbNmyhS5duijHn74uIyODwYMHs2PHDlQqFT169MDCwoIff/yRhISEfNvt7+9PQkKC8uHs2rVrfPTRR+zdu5eyZcsyY8YMJk+ejL+/P/7+/gV6PvJ7LoQQRUNh/p5LQES8MvJiIYQQRYP8PReFtWHDBj777DOuXr2q5Ih728nvuRBCFA2F+Xv+wlNmhBBCCCGEALh//z6XLl0iODiYTz755J0JhgghhCie/vOyu0IIIYQQQjxp7ty5eHp6UrZsWQICAt50c4QQQohnkoCIEEIIIYR4KQIDA8nMzGTfvn2Ympq+6eYIIYQQzyQBkXdcUlISKpVKSTz2KlWsWJHQ0NBXfh8hhBBCCCGEEOJVkxwiL0CtVhMREcEnn3zCihUrtM4NHTqU5cuX069fP8LDw99MA98SblN3oWNg/NxySbPbvIbWCCGEEEIIIYQQMkLkhdnZ2bFp0yYePHigHEtPT2fjxo3Y29u/UN2ZmZkv2jwhhBBCFBEqlSrfJXNfhLe3d4GXpn0ZXteIU7VaTYcOHV75fYQQQry7JCDygmrVqoW9vT3bt29Xjm3fvh07Oztq1qypHIuKiqJx48ZYWlpibW1N27ZtuXjxonL+8dSXLVu24O3tjaGhIV999RXm5uZs3bpV657fffcdJiYm/Pvvv7nak52dzcCBA3F0dMTIyAgXFxcWLVqkVebxC8L8+fOxtbXF2tqaTz/9VCsAc/36ddq1a4eRkRGOjo5s2LDhhZ+VEEIIIXJTq9WoVCpUKhV6enrY29szZMgQbt++/aabVmBFaVqt29RdVJzw/XM3IYQQ7z4JiLwE/fv3JywsTNlfs2YNAwYM0Cpz7949Ro0aRXx8PPv27UNHR4eOHTuSk5OjVW78+PGMGDGCxMREOnbsSPfu3bXqBggLC+PDDz/EzMwsV1tycnKoUKECW7Zs4ezZs0yZMoWJEyeyZcsWrXIxMTFcvHiRmJgYIiIiCA8P15rao1arSUpKIjo6mq1bt7Js2TKuX7/+zOeQkZFBWlqa1iaEEEKI5/Pz8yMlJYWkpCRWrVrFd999x9ChQ990s4QQQogiTQIiL0GfPn04ePAgSUlJXL58mUOHDtG7d2+tMp07d6ZTp05UrlwZT09PVq9ezalTpzh79qxWOX9/fzp16oSjoyPlypXjo48+YteuXVy9ehWAGzdusHPnzlwBl8f09fUJCgqibt26ODo60qtXL9Rqda6AiJWVFUuXLqVq1aq0bduWNm3asG/fPgB+//13fvzxR1atWkWDBg2oXbs2q1ev1poWlJfg4GAsLCyUzc7OrlDPUQghhCiuDAwMsLGxoUKFCrRq1Ypu3bqxe/fuXOVu3LhBx44dMTY2pnLlynz77bda5/fv30+9evUwMDDA1taWCRMmkJWVpZy/d+8effv2xdTUFFtbW0JCQnLd4+HDh4wbN47y5ctjYmJC/fr1iY2NLVR/AgMDsbe3x8DAgHLlyjFixIh8yy5YsAB3d3dMTEyws7Nj6NCh3L17VzkfHh6OpaUlu3btolq1apiamioBpMeys7MZNWqUMhJ33LhxaDSaQrVZCCFE8SMBkZegVKlStGnThoiICMLCwmjTpg2lSpXSKnPx4kV69uyJk5MT5ubmODo6ApCcnKxVrk6dOlr79erVo3r16qxduxaAdevWYW9vj5eXV77tWbFiBXXq1KF06dKYmpqycuXKXPepXr06urq6yr6tra0yAiQxMRE9PT2ttlStWhVLS8tnPoeAgABSU1OV7cqVK88sL4QQQojc/vjjD6KiotDX1891LigoiK5du3Ly5Elat25Nr169uHXrFgB//fUXrVu3pm7duvz6668sX76c1atXM2PGDOX6sWPHEhMTw44dO9i9ezexsbEcO3ZM6x79+/fn0KFDbNq0iZMnT9KlSxf8/Pw4f/58gdq/detWFi5cyJdffsn58+eJjIzE3d093/I6OjosXryY06dPExERQXR0NOPGjdMqc//+febPn8+6dev46aefSE5OZsyYMcr5kJAQ1qxZw+rVqzl48CC3bt1ix44dz2ynjGwVQgghq8y8JAMGDGDYsGEAfPHFF7nOt2vXDjs7O1auXEm5cuXIycnBzc2Nhw8fapUzMTHJde1HH33E0qVLmTBhAmFhYfTv3x+VSpVnO7Zs2cLIkSMJCQmhQYMGmJmZMW/ePA4fPqxV7umXLJVKpUzfefxFJb975MfAwAADA4NCXSOEEEII2LlzJ6ampmRnZ5Oeng48GjnxNLVaTY8ePQCYNWsWS5Ys4ciRI/j5+bFs2TLs7OxYunQpKpWKqlWrcvXqVcaPH8+UKVO4f/8+q1evZu3atbRs2RKAiIgIKlSooNR/8eJFNm7cyJ9//km5cuUAGDNmDFFRUYSFhTFr1qzn9iU5ORkbGxt8fHzQ19fH3t6eevXq5Vv+yYSujo6OTJ8+nSFDhrBs2TLleGZmJitWrKBSpUoADBs2jGnTpinnQ0NDCQgIoHPnzsCjj0O7du16ZjuDg4MJCgp6bn+EEEIUXTJC5CXx8/Pj4cOHPHz4EF9fX61zN2/eJDExkc8//5wWLVpQrVq1QiVK6927N8nJySxevJgzZ87Qr1+/fMseOHCAhg0bMnToUGrWrImzs7NW8taCqFatGllZWRw9elQ5du7cOe7cuVOoeoQQQghRMM2aNSMhIYHDhw8zfPhwfH19GT58eK5yHh4eys8mJiaYmZlpjfBs0KCB1geNRo0acffuXf78808uXrzIw4cPadCggXK+ZMmSuLi4KPvHjx9Ho9FQpUoVTE1NlW3//v0Ffp/o0qULDx48wMnJiUGDBrFjxw6taTtPi4mJoWXLlpQvXx4zMzP69u3LzZs3uXfvnlLG2NhYCYaA9sjW1NRUUlJStPr19EjXvMjIViGEEDJC5CXR1dUlMTFR+flJVlZWWFtb89VXX2Fra0tycjITJkwocN1WVlZ06tSJsWPH0qpVK60vOU9zdnZm7dq17Nq1C0dHR9atW0d8fLwyRacgXFxc8PPzY9CgQXz11Vfo6enh7++PkZFRget40ukgX8zNzf/TtUIIIURxYGJigrOzMwCLFy+mWbNmBAUFMX36dK1yzxvh+fTozidHfRYkp0ZOTg66urocO3Ys1/uMqalpgfpiZ2fHuXPn2LNnD3v37mXo0KHMmzeP/fv352r/5cuXad26NYMHD2b69OmULFmSgwcPMnDgQK3V7/Lq94vmCJGRrUIIIWSEyEtkbm6e5//46+josGnTJo4dO4abmxsjR45k3rx5hap74MCBPHz4MN9kqo8NHjyYTp060a1bN+rXr8/Nmzf/U5b6sLAw7OzsaNq0KZ06deLjjz+mTJkyha5HCCGEEIU3depU5s+fryRVLwhXV1fi4uK0AgVxcXGYmZlRvnx5nJ2d0dfX55dfflHO3759m99//13Zr1mzJtnZ2Vy/fh1nZ2etzcbGpsBtMTIy4oMPPmDx4sXExsby888/c+rUqVzljh49SlZWFiEhIbz33ntUqVKlUH0GsLCwwNbWVqtfWVlZuXKjCCGEEE+TESIv4MllavMSGRmp/Ozj45NrRZknX1gqVqz4zC8dKSkpWFtb0759e63jT19nYGBAWFhYrqV6g4ODn9nu0NBQrX0bGxt27typdaxPnz75tk8IIYQQL4+3tzfVq1dn1qxZLF26tEDXDB06lNDQUIYPH86wYcM4d+4cU6dOZdSoUejo6GBqasrAgQMZO3Ys1tbWlC1blkmTJqGj83/fx6pUqUKvXr3o27cvISEh1KxZkxs3bhAdHY27uzutW7d+bjvCw8PJzs6mfv36GBsbs27dOoyMjHBwcMhVtlKlSmRlZbFkyRLatWvHoUOHWLFiRcEf1P/32WefMXv2bCpXrky1atVYsGCBTPUVQgjxXBIQecvdv3+fS5cuERwczCeffEKJEiXedJOEEEII8RqMGjWK/v37M378+AItZV++fHl++OEHxo4dS40aNShZsiQDBw7k888/V8rMmzePu3fv8sEHH2BmZsbo0aNJTU3VqicsLIwZM2YwevRo/vrrL6ytrWnQoEGBgiEAlpaWzJ49m1GjRpGdnY27uzvfffcd1tbWucp6enqyYMEC5syZQ0BAAF5eXgQHB9O3b98C3eux0aNHk5KSglqtRkdHhwEDBtCxY8dcfSsImeorhBDFh0oji7S/1QIDA5k5cyZeXl588803BZ6/+zZIS0vDwsKC1NRUebEQQoh3mPw9F8WB/J4LIUTRUJi/55JD5C0XGBhIZmYm+/bte6eCIUIIIYQQQgghxNtMAiJCCCGEEEIIIYQodiQgIoQQQgghhBBCiGJHkqrmQ6VSPfN8v379nrvKjHjEbeoudAyMn1suaXab19AaIYQQQgghhBBCAiL5SklJUX7evHkzU6ZM4dy5c8oxIyOjN9EsIYQQQgghhBBCvAQyZSYfNjY2ymZhYYFKpdI69tNPP1G7dm0MDQ1xcnIiKCiIrKws5XqVSsWqVavo2LEjxsbGVK5cmW+//VY5Hxsbi0qlYt++fdSpUwdjY2MaNmyoFXQB+O677555n8DAQOzt7TEwMKBcuXKMGDFCObds2TIqV66MoaEhZcuW5cMPP1TOaTQa5s6di5OTE0ZGRtSoUYOtW7dq3fvs2bO0bt0aU1NTypYtS58+fbhx48ZLe8ZCCCGEEEIIIcSbIiNE/oNdu3bRu3dvFi9eTJMmTbh48SIff/wxAFOnTlXKBQUFMXfuXObNm8eSJUvo1asXly9fpmTJkkqZSZMmERISQunSpRk8eDADBgzg0KFDBbrP1q1bWbhwIZs2baJ69epcu3aNX3/9FYCjR48yYsQI1q1bR8OGDbl16xYHDhxQ7vv555+zfft2li9fTuXKlfnpp5/o3bs3pUuXpmnTpqSkpNC0aVMGDRrEggULePDgAePHj6dr165ER0fn+VwyMjLIyMhQ9tPS0l7SExdCCCGEeD1kqq8QQhQfKo1Go3nTjXjbhYeH4+/vz507dwDw8vLi/fffJyAgQCmzfv16xo0bx9WrV4FHI0Q+//xzpk+fDsC9e/cwMzPjhx9+wM/Pj9jYWJo1a8bevXtp0aIFAD/88ANt2rThwYMHGBoaPvc+CxYs4Msvv+T06dPo6+trtXn79u3079+fP//8EzMzM61z9+7do1SpUkRHR9OgQQPl+EcffcT9+/f53//+x5QpUzh8+DC7du1Szv/555/Y2dlx7tw5qlSpkus5BQYGEhQUlOu4nf8WebEQQoh3WFpaGhYWFqSmpmJubv6mmyPyILnPXtzj33N5bxFCiHdbYd5bZITIf3Ds2DHi4+OZOXOmciw7O5v09HTu37+PsfGjf0Q9PDyU8yYmJpiZmXH9+nWtup4sY2trC8D169ext7d/7n26dOlCaGgoTk5O+Pn50bp1a9q1a4eenh4tW7bEwcFBOefn56dM3zl79izp6em0bNlSqy0PHz6kZs2aSh9jYmIwNTXN1f+LFy/mGRAJCAhg1KhRyn5aWhp2dnbPf6BCCCGEeCGS+0wIIYQoPMkh8h/k5OQQFBREQkKCsp06dYrz589jaGiolHt61IZKpSInJ0fr2JNlHn/deVzmefd5PFrjiy++wMjIiKFDh+Ll5UVmZiZmZmYcP36cjRs3Ymtry5QpU6hRowZ37txR6v/++++16j579qySRyQnJ4d27dppnU9ISOD8+fN4eXnl+VwMDAwwNzfX2oQQQgjx6knuM8l9JoQQovBkhMh/UKtWLc6dO4ezs/Mbv4+RkREffPABH3zwAZ9++ilVq1bl1KlT1KpVCz09PXx8fPDx8WHq1KlYWloSHR1Ny5YtMTAwIDk5maZNm+Z7723btlGxYkX09OTXRAghhHhXSe4zyX0mhBAib/J/uv/BlClTaNu2LXZ2dnTp0gUdHR1OnjzJqVOnmDFjxmu7T3h4ONnZ2dSvXx9jY2PWrVuHkZERDg4O7Ny5kz/++AMvLy+srKz44YcfyMnJwcXFBTMzM8aMGcPIkSPJycmhcePGpKWlERcXh6mpKf369ePTTz9l5cqV9OjRg7Fjx1KqVCkuXLjApk2bWLlyJbq6ugXux+kgXxktIoQQQrwhM2fOZMKECfTr1w8AJycnpk+fzrhx47QCImq1mh49egAwa9YslixZwpEjR/Dz89Oq6/HHlAkTJtCmTRvS09MxNDR87n2Sk5OxsbHBx8cHfX197O3tqVevHgDJycmYmJjQtm1bzMzMcHBwUKbx3rt3jwULFmjlPnNycuLgwYN8+eWXNG3alOXLl1OrVi1mzZqltHXNmjXY2dnx+++/5znVNzg4OM/cZ0IIIYoPmTLzH/j6+rJz50727NlD3bp1ee+991iwYAEODg6v9T6WlpasXLmSRo0a4eHhwb59+/juu++wtrbG0tKS7du307x5c6pVq8aKFSvYuHEj1atXB2D69OlMmTKF4OBgqlWrhq+vL9999x2Ojo4AlCtXjkOHDpGdnY2vry9ubm589tlnWFhYoKMjvzZCCCHEu+LYsWNMmzYNU1NTZRs0aBApKSncv39fKfciuc8Kcp8uXbrw4MEDnJycGDRoEDt27FCm0zyZ+6xPnz5s2LBBaduTuc+erHvt2rVcvHhRuffj3GePt6pVqwIoZZ4WEBBAamqqsl25cuWFnrMQQoh3j6wyI14ZWZVACCGKBvl7/m55enU8IyMjgoKC6NSpU66yTk5O6OjooFKp2LFjBx06dFDOWVpaEhoailqtVlbHu337NpaWlgAkJCRQs2ZNLl26RMWKFQt0nwcPHrBnzx727t3L119/jaOjI/v370dfX5+srCxiY2PZvXs327ZtQ0dHh/j4eM6dO8d7771HbGws5cuX16rXwMAAOzs73n//fYyNjZkzZ06ue9va2mJiYvLc5yarzAghRNEgq8wIIYQQQghAcp8JIYQQ+ZF/MYQQQgghijDJfSa5z4QQQuRNkkEIIYQQQhRhkvtMXneFEELkTXKIiFdG5pwLIUTRIH/PRXEgv+dCCFE0FObvuYTMhRBCCCGEEEIIUewU6xwieWVUfxm8vb3x9PQkNDT0pdabn4oVK+Lv74+/v/8rvY9arebOnTtERkYW6jq3qbskW7sQQgghhBBCiLdKkRwholarUalUqFQq9PT0sLe3Z8iQIdy+fftNN63AKlas+NoCKkIIIYQQQgghRHFTJAMiAH5+fqSkpJCUlMSqVav47rvvGDp06JtulhBCCCGEEEIIId4CRTYgYmBggI2NDRUqVKBVq1Z069aN3bt35yp348YNOnbsiLGxMZUrV+bbb7/VOr9//37q1auHgYEBtra2TJgwgaysLOX8vXv36Nu3L6amptja2hISEpLrHg8fPmTcuHGUL18eExMT6tevT2xsbKH6ExgYiL29PQYGBpQrV44RI0bkW3bBggW4u7tjYmKCnZ0dQ4cO5e7du8r58PBwLC0t2bVrF9WqVcPU1FQJID2WnZ3NqFGjsLS0xNramnHjxvG8/LsZGRmkpaVpbUIIIYQQQgghxNuoyAZEnvTHH38QFRWFvr5+rnNBQUF07dqVkydP0rp1a3r16sWtW7cA+Ouvv2jdujV169bl119/Zfny5axevZoZM2Yo148dO5aYmBh27NjB7t27iY2N5dixY1r36N+/P4cOHWLTpk2cPHmSLl264Ofnx/nz5wvU/q1bt7Jw4UK+/PJLzp8/T2RkJO7u7vmW19HRYfHixZw+fZqIiAiio6MZN26cVpn79+8zf/581q1bx08//URycjJjxoxRzoeEhLBmzRpWr17NwYMHuXXrFjt27HhmO4ODg7GwsFA2Ozu7AvVPCCGEEK9HbGwsKpWKO3fuvLE2eHt7v/K8Z0IIIURBFNmkqjt37sTU1JTs7GzS09OBRyMnnqZWq+nRowcAs2bNYsmSJRw5cgQ/Pz+WLVuGnZ0dS5cuRaVSUbVqVa5evcr48eOZMmUK9+/fZ/Xq1axdu5aWLVsCEBERQYUKFZT6L168yMaNG/nzzz8pV64cAGPGjCEqKoqwsDBmzZr13L4kJydjY2ODj48P+vr62NvbU69evXzLP/mS4ejoyPTp0xkyZAjLli1TjmdmZrJixQoqVaoEwLBhw5g2bZpyPjQ0lICAADp37gzAihUr2LVr1zPbGRAQwKhRo5T9tLQ0CYoIIYQQT7l+/TqTJ0/mxx9/5O+//8bKyooaNWoQGBhIgwYNXtp9XmaSd5VKpfxsbGxMuXLlaNSoEcOHD6d27dovXH9h/Nck7wVV0GTwhSGJ44UQ4u1UZAMizZo1Y/ny5dy/f59Vq1bx+++/M3z48FzlPDw8lJ9NTEwwMzPj+vXrACQmJtKgQQOtl4BGjRpx9+5d/vzzT27fvs3Dhw+1Xl5KliyJi4uLsn/8+HE0Gg1VqlTRum9GRgbW1tYF6kuXLl0IDQ3FyckJPz8/WrduTbt27dDTy/s/X0xMDLNmzeLs2bOkpaWRlZVFeno69+7dw8TEBHj0MvM4GAJga2ur9Ds1NZWUlBStfunp6VGnTp1nTpsxMDDAwMCgQH0SQgghiqvOnTuTmZlJREQETk5O/P333+zbt08Zofq2CgsLw8/Pj/T0dH7//Xe++uor6tevz5o1a+jbt++bbp4QQghRaEV2yoyJiQnOzs54eHiwePFiMjIyCAoKylXu6Wk0KpWKnJwcADQajVYw5PGxx+Wel1MDICcnB11dXY4dO0ZCQoKyJSYmsmjRogL1xc7OjnPnzvHFF19gZGTE0KFD8fLyIjMzM1fZy5cv07p1a9zc3Ni2bRvHjh3jiy++ANAqn1e/C9IfIYQQQvx3d+7c4eDBg8yZM4dmzZrh4OBAvXr1CAgIoE2b/xtFkJycTPv27TE1NcXc3JyuXbvy999/K+fVajUdOnTQqtvf3x9vb2/l/P79+1m0aJGy8l5SUpJS9tixY9SpUwdjY2MaNmzIuXPnntt2S0tLbGxsqFixIq1atWLr1q306tWLYcOGKSv53bx5kx49elChQgWMjY1xd3dn48aNz6w3KioKCwsL1q5dC8CpU6do3rw5RkZGWFtb8/HHHyu50AIDA4mIiOCbb75R+vU4L9v48eOpUqUKxsbGODk5MXny5DzflYQQQojHimxA5GlTp05l/vz5XL16tcDXuLq6EhcXpxUoiIuLw8zMjPLly+Ps7Iy+vj6//PKLcv727dv8/vvvyn7NmjXJzs7m+vXrODs7a202NjYFbouRkREffPABixcvJjY2lp9//plTp07lKnf06FGysrIICQnhvffeo0qVKoXqM4CFhQW2trZa/crKysqVG0UIIYQQhWNqaoqpqSmRkZFkZGTkWUaj0dChQwdu3brF/v372bNnDxcvXqRbt24Fvs+iRYto0KABgwYNIiUlhZSUFK1prJMmTSIkJISjR4+ip6fHgAED/lN/Ro4cyb///suePXsASE9Pp3bt2uzcuZPTp0/z8ccf06dPHw4fPpzn9Zs2baJr166sXbuWvn37cv/+ffz8/LCysiI+Pp6vv/6avXv3MmzYMODRtOOuXbsqyeBTUlJo2LAhAGZmZoSHh3P27FkWLVrEypUrWbhwYb5tl2TwQgghiuyUmad5e3tTvXp1Zs2axdKlSwt0zdChQwkNDWX48OEMGzaMc+fOMXXqVEaNGoWOjg6mpqYMHDiQsWPHYm1tTdmyZZk0aRI6Ov8XZ6pSpQq9evWib9++hISEULNmTW7cuEF0dDTu7u60bt36ue0IDw8nOzub+vXrY2xszLp16zAyMsLBwSFX2UqVKpGVlcWSJUto164dhw4dYsWKFQV/UP/fZ599xuzZs6lcuTLVqlVjwYIF/zkB2+kgX8zNzf/TtUIIIURRoqenR3h4OIMGDWLFihXUqlWLpk2b0r17d2Ua7969ezl58iSXLl1Sghjr1q2jevXqxMfHU7du3efex8LCghIlSmBsbJznB5iZM2fStGlTACZMmECbNm1IT0/H0NCwUP2pWrUqgDL6pHz58lpJ2ocPH05UVBRff/019evX17p22bJlTJw4kW+++YZmzZoBsGHDBh48eMDatWuVab5Lly6lXbt2zJkzh7Jly2JkZERGRkaufn3++efKzxUrVmT06NFs3rw5V2L5x4KDg/McPSyEEKL4KDYjRABGjRrFypUruXLlSoHKly9fnh9++IEjR45Qo0YNBg8ezMCBA7X+wZ03bx5eXl588MEH+Pj40Lhx41zJxcLCwujbty+jR4/GxcWFDz74gMOHDxc44ailpSUrV66kUaNGeHh4sG/fPr777rs8c5B4enqyYMEC5syZg5ubGxs2bCA4OLhA93nS6NGj6du3L2q1mgYNGmBmZkbHjh0LXY8QQgghtHXu3JmrV6/y7bff4uvrS2xsLLVq1SI8PBx4lMPMzs5O6z3B1dUVS0tLEhMTX0obnsyhZmtrC6DkEiuMJ6cSA2RnZzNz5kw8PDywtrbG1NSU3bt3k5ycrHXdtm3b8Pf3Z/fu3UowBB71vUaNGkowBB7lb8vJyXnutJ6tW7fSuHFjbGxsMDU1ZfLkybnu+6SAgABSU1OVraDvh0IIIYqOIjlC5PELxdN69uxJz549lf28cmY8PQqiadOmHDlyJN97mZqasm7dOtatW6ccGzt2rFYZfX19goKCCvUV4sl5vh06dMg1Tzi/svBo+OrIkSO1jvXp00f5Wa1Wo1artc536NBB63no6ekRGhr6UjLTCyGEEEKboaEhLVu2pGXLlkyZMoWPPvqIqVOnolar88xhBtq5zXR0dHK9xxQmX8aTucQe1/k4h1phPA7QODo6AhASEsLChQsJDQ3F3d0dExMT/P39efjwodZ1np6eHD9+nLCwMOrWrau0Ib++P9nOvPzyyy90796doKAgfH19sbCwYNOmTYSEhOR7jSSDF0IIUaxGiAghhBBCvI1cXV25d++e8nNycrLWiIWzZ8+SmppKtWrVAChdujQpKSladSQkJGjtlyhRguzs7Ffa7tDQUMzNzfHx8QHgwIEDtG/fnt69e1OjRg2cnJw4f/58rusqVapETEwM33zzjdYqgK6uriQkJCjPAuDQoUPo6OgoK/bl1a9Dhw7h4ODApEmTqFOnDpUrV+by5cuvostCCCGKEAmICCGEEEK8Jjdv3qR58+asX79eyRPy9ddfM3fuXNq3bw+Aj48PHh4e9OrVi+PHj3PkyBH69u1L06ZNqVOnDgDNmzfn6NGjrF27lvPnzzN16lROnz6tda+KFSty+PBhkpKSuHHjxn8aAfKkO3fucO3aNS5fvsyePXv48MMP+d///sfy5cuxtLQEwNnZmT179hAXF0diYiKffPIJ165dy7O+KlWqEBMTo0yfAejVqxeGhob069eP06dPExMTw/Dhw+nTpw9ly5ZV+nXy5EnOnTvHjRs3yMzMxNnZmeTkZDZt2sTFixdZvHgxO3bseKH+CiGEKPqK5JQZIYQQQoi3kampKfXr12fhwoVcvHiRzMxM7OzsGDRoEBMnTgQeTQ2JjIxk+PDheHl5oaOjg5+fH0uWLFHq8fX1ZfLkyYwbN4709HQGDBhA3759tVagGzNmDP369cPV1ZUHDx5w6dKlF2p7//79gUfTfcqXL0/jxo05cuQItWrVUspMnjyZS5cu4evri7GxMR9//DEdOnQgNTU1zzpdXFyIjo7G29sbXV1dQkJC2LVrF5999hl169bF2NiYzp07s2DBAuWaQYMGERsbS506dbh79y4xMTG0b9+ekSNHMmzYMDIyMmjTpg2TJ08mMDCw0P2UZPBCCFF8qDR5JdIQ4iVIS0vDwsKC1NRUebEQQoh3mPw9F8WB/J4LIUTRUJi/5zJlRgghhBBCCCGEEMWOTJl5glqtJiIiguDgYCZMmKAcj4yMpGPHjnmuSvOqqFQqduzY8czVZd4VblN3oWNg/FLrTJrd5qXWJ4QQQgghhBCieJERIk8xNDRkzpw53L59+0035YU8vbydEEIIIYQQQggh/o8ERJ7i4+ODjY0NwcHBzywXFxeHl5cXRkZG2NnZMWLECGWJuCVLluDu7q6UjYyMRKVS8cUXXyjHfH19CQgIKHC7/vrrL7p164aVlRXW1ta0b9+epKQk5bxaraZDhw4EBwdTrlw5ZWm6ZcuWUblyZQwNDSlbtiwffvihco1Go2Hu3Lk4OTlhZGREjRo12Lp1q3LO2dmZ+fPna7Xj9OnT6OjocPHixQK3XQghhBBCCCGEeNtIQOQpurq6zJo1iyVLlvDnn3/mWebUqVP4+vrSqVMnTp48yebNmzl48CDDhg0DwNvbmzNnznDjxg0A9u/fT6lSpdi/fz8AWVlZxMXF0bRp0wK16f79+zRr1gxTU1N++uknDh48iKmpKX5+flojQfbt20diYiJ79uxh586dHD16lBEjRjBt2jTOnTtHVFQUXl5eSvnPP/+csLAwli9fzpkzZxg5ciS9e/dm//79qFQqBgwYQFhYmFZb1qxZQ5MmTahUqVKudmZkZJCWlqa1CSGEEEIIIYQQbyMJiOShY8eOeHp6MnXq1DzPz5s3j549e+Lv70/lypVp2LAhixcvZu3ataSnp+Pm5oa1tbUSAImNjWX06NHKfnx8POnp6TRu3LhA7dm0aRM6OjqsWrUKd3d3qlWrRlhYGMnJycTGxirlTExMWLVqFdWrV8fNzY3k5GRMTExo27YtDg4O1KxZkxEjRgBw7949FixYwJo1a/D19cXJyQm1Wk3v3r358ssvgUfL6507d44jR44AkJmZyfr16xkwYECe7QwODsbCwkLZ7OzsCtQ/IYQQQgghhBDidZOASD7mzJlDREQEZ8+ezXXu2LFjhIeHY2pqqmy+vr7k5ORw6dIlVCoVXl5exMbGcufOHc6cOcPgwYPJzs4mMTGR2NhYatWqhampaYHacuzYMS5cuICZmZlyv5IlS5Kenq41dcXd3Z0SJUoo+y1btsTBwQEnJyf69OnDhg0buH//PgBnz54lPT2dli1bavVj7dq1Sp22tra0adOGNWvWALBz507S09Pp0qVLnu0MCAggNTVV2a5cuVKwhy2EEEKId4q3tzf+/v5vuhnP9Hg6sRBCCJEfWWUmH15eXvj6+jJx4kTUarXWuZycHD755BNltMWT7O3tgUcvCl999RUHDhygRo0aWFpa4uXlxf79+4mNjcXb27vAbcnJyaF27dps2LAh17nSpUsrP5uYmGidMzMz4/jx48TGxrJ7926mTJlCYGAg8fHx5OTkAPD9999Tvnx5resMDAyUnz/66CP69OnDwoULCQsLo1u3bhgb571ijIGBgda1QgghhHh3PF5t75NPPmHFihVa54YOHcry5cvp168f4eHhbN++HX19/TfU0oJZtGjRf1ohUFbHE0KI4kMCIs8we/ZsPD09lQSlj9WqVYszZ87g7Oyc77Xe3t589tlnbN26VQl+NG3alL179xIXF8dnn31W4HbUqlWLzZs3U6ZMGczNzQvVBz09PXx8fPDx8WHq1KlYWloSHR1Ny5YtMTAwIDk5+Zm5TFq3bo2JiQnLly/nxx9/5KeffirU/YUQQgjx7rCzs2PTpk0sXLgQIyMjANLT09m4caPy0QegZMmSb6qJz5WdnY1KpcLCwuJNN0UIIcRbTgIiz+Du7k6vXr1YsmSJ1vHx48fz3nvv8emnnzJo0CBMTEyUZKaPyz7OI7Jhwwa++eYb4FGQZPTo0QAFzh8C0KtXL+bNm0f79u2ZNm0aFSpUIDk5me3btzN27FgqVKiQ53U7d+7kjz/+wMvLCysrK3744QdycnJwcXHBzMyMMWPGMHLkSHJycmjcuDFpaWnExcVhampKv379gEdJZtVqNQEBATg7O9OgQYNCP8fTQb6FDuQIIYQQ4vWrVasWf/zxB9u3b6dXr14AbN++HTs7O5ycnJRy3t7eeHp6EhoaCjxa1W7hwoVcuXIFCwsLmjRpoqxct3XrVoKCgrhw4QLGxsbUrFmTb775BhMTE3JycpgxYwZfffUV//zzD9WqVWP27Nn4+fkBj/KwNWvWjNu3b2NpaQlAQkICNWvW5NKlS1SsWJHw8HD8/f1Zv34948aN4/fff+f8+fMEBQVx584dIiMjX9vzE0II8W6RHCLPMX369FzDLT08PNi/fz/nz5+nSZMm1KxZk8mTJ2Nra6uUUalUysiLJk2aKNdZWFhQs2bNZwYIHk9n0dN7FK8yNjbmp59+wt7enk6dOlGtWjUGDBjAgwcPnlmPpaUl27dvp3nz5lSrVo0VK1awceNGqlevrvRtypQpBAcHU61aNXx9ffnuu+9wdHTUqmfgwIE8fPgw32SqQgghhCg6+vfvr7XK3Jo1a575DvCsVe1SUlLo0aMHAwYMUPKoderUSXm3WrRoESEhIcyfP5+TJ0/i6+vLBx98wPnz5wvV5vv37xMcHMyqVas4c+YMZcqUee41sjqeEEIIGSHyhPDw8FzHHBwcSE9Pz3W8bt267N69+5n1Pf4y8phKpeLmzZvPbcf169cBsLGxUY7Z2NgQERGR7zV5tb1x48Zaq9A8TaVSMWLEiDxzoTwpJSUFPT09+vbt++yGCyGEEOKd16dPHwICAkhKSkKlUnHo0CE2bdqU7zvFk6vamZmZKSvbwaN3iKysLDp16oSDgwPwaATuY/Pnz2f8+PF0794deJTUPiYmhtDQUL744osCtzkzM5Nly5ZRo0aNAl8THBxMUFBQgcsLIYQoemSEyFtEo9GQlJTEjBkzKFu2LG5ubm+0PRkZGVy4cIHJkyfTtWtXypYt+0bbI4QQQohXr1SpUrRp04aIiAjCwsJo06YNpUqVyrf8s1a1q1GjBi1atMDd3Z0uXbqwcuVKbt++DUBaWhpXr16lUaNGWvU1atSIxMTEQrW5RIkSeHh4FOoaWR1PCCGEBETeIqmpqbi4uHDw4EE2bdqEoaHhG23Pxo0bcXFxITU1lblz577RtgghhBDi9RkwYADh4eFEREQ8d8rs41XtNm7ciK2tLVOmTKFGjRrcuXMHXV1d9uzZw48//oirqytLlizBxcWFS5cuKderVCqt+jQajXJMR0dHOfZYZmZmrjYYGRnlqud5DAwMMDc319qEEEIULxIQeYtYWlqSkZFBQkJCoZblfVXUajXZ2dkcO3Ys19K8QgghhCi6/Pz8ePjwIQ8fPsTX1/e55R+vajd37lxOnjxJUlIS0dHRwKOAR6NGjQgKCuLEiROUKFGCHTt2YG5uTrly5Th48KBWXXFxcVSrVg2A0qVLA4+m3jyWkJDwknophBCiuJMcIkIIIYQQQouurq4ybUVXV/eZZZ+1qt3hw4fZt28frVq1okyZMhw+fFhZTQZg7NixTJ06lUqVKuHp6UlYWBgJCQls2LABAGdnZ+zs7AgMDGTGjBmcP3+ekJCQV9p3WR1PCCGKDwmIvINUKhU7duygQ4cOJCUl4ejoyIkTJ/D09HzTTRNCCCFEEVHQoMDjVe0CAwNJT0+ncuXKyqp2iYmJ/PTTT4SGhpKWloaDgwMhISG8//77AIwYMYK0tDRGjx7N9evXcXV15dtvv6Vy5coA6Ovrs3HjRoYMGUKNGjWoW7cuM2bMoEuXLq+s30IIIYoPlebpNWXFK6VWq7lz5w6RkZH/uY4nAyLZ2dn8888/lCpVSlmm922RlpaGhYUFdv5b0DEwfql1J81u81LrE0IIkb/Hf89TU1Ply7kosuT3XAghiobC/D1/u/4PWhSarq6u1vK8QgghhBBCCCGEeD5JqvoGeXt7M2LECMaNG0fJkiWxsbEhMDBQq8z58+fx8vLC0NAQV1dX9uzZo3U+KSkJlUqlJBjLzs5m4MCBODo6YmRkhIuLC4sWLdK6Rq1W06FDB+bPn4+trS3W1tZ8+umnWlnb169fT506dTAzM8PGxoaePXty/fr1V/IchBBCCCGEEEKI101GiLxhERERjBo1isOHD/Pzzz+jVqtp1KgRLVu2JCcnh06dOlGqVCl++eUX0tLS8Pf3f2Z9OTk5VKhQgS1btlCqVCni4uL4+OOPsbW1pWvXrkq5mJgYbG1tiYmJ4cKFC3Tr1g1PT08GDRoEwMOHD5k+fTouLi5cv36dkSNHolar+eGHH/K9d0ZGBhkZGcp+Wlraiz0cIYQQQgghhBDiFZGAyBvm4eHB1KlTAahcuTJLly5l3759tGzZkr1795KYmEhSUhIVKlQAYNasWUoisrzo6+sTFBSk7Ds6OhIXF8eWLVu0AiJWVlYsXboUXV1dqlatSps2bdi3b58SEBkwYIBS1snJicWLF1OvXj3u3r2LqalpnvcODg7WurcQQgghhBBCCPG2kikzb5iHh4fWvq2trTI1JTExEXt7eyUYAtCgQYPn1rlixQrq1KlD6dKlMTU1ZeXKlSQnJ2uVqV69utYyek/eF+DEiRO0b98eBwcHzMzM8Pb2BshVz5MCAgJITU1VtitXrjy3rUIIIYQQQgghxJsgAZE3TF9fX2tfpVKRk5MDQF4LAKlUqmfWt2XLFkaOHMmAAQPYvXs3CQkJ9O/fn4cPHxb4vvfu3aNVq1aYmpqyfv164uPj2bFjB0Cuep5kYGCAubm51iaEEEIIIYQQQryNZMrMW8zV1ZXk5GSuXr1KuXLlAPj555+fec2BAwdo2LAhQ4cOVY5dvHixUPf97bffuHHjBrNnz8bOzg6Ao0ePFrL1QgghhCiOAgMDiYyMVBK+v2vcpu5Cx8D4pdaZNLvNS61PCCHEyyEBkbeYj48PLi4u9O3bl5CQENLS0pg0adIzr3F2dmbt2rXs2rULR0dH1q1bR3x8PI6OjgW+r729PSVKlGDJkiUMHjyY06dPM3369P/cj9NBvjJaRAghhHhHxcXF0aRJE1q2bElUVNRzy48ZM4bhw4e/hpYJIYQQL0amzLzFdHR02LFjBxkZGdSrV4+PPvqImTNnPvOawYMH06lTJ7p160b9+vW5efOm1miRgihdujTh4eF8/fXXuLq6Mnv2bObPn/8iXRFCCCHEO2rNmjUMHz6cgwcPPjOXmEajISsrC1NTU6ytrV9jC4UQQoj/RqXJK1GFEC9BWloaFhYWpKamyggRIYR4h8nf8+Lr3r172NraEh8fz9SpU3F1dWXKlCkAxMbG0qxZM6Kiopg0aRInT55k165d7N+/X2vKTF75zxwcHEhKSgJg//79jB07ll9//ZWSJUvSr18/ZsyYgZ7eo4HM3t7eeHh4YGhoyKpVqyhRogSDBw8mMDBQqW/BggWEhYXxxx9/ULJkSdq1a8fcuXPzXRkvL49/z+38t8iUGSGEeIcV5r1FRogIIYQQQog8bd68GRcXF1xcXOjduzdhYWG5kr6PGzeO4OBgEhMTc62eB5CSkqJsFy5cwNnZGS8vLwD++usvWrduTd26dfn1119Zvnw5q1evZsaMGVp1REREYGJiwuHDh5k7dy7Tpk1jz549ynkdHR0WL17M6dOniYiIIDo6mnHjxj2zbxkZGaSlpWltQgghihfJISKEEEIIIfK0evVqevfuDYCfnx93795l3759+Pj4KGWmTZtGy5Yt863DxsYGeDSlpnPnzlhYWPDll18CsGzZMuzs7Fi6dCkqlYqqVaty9epVxo8fz5QpU9DRefTtzsPDg6lTpwJQuXJlli5dyr59+5T7+vv7K/dzdHRk+vTpDBkyhGXLluXbruDgYIKCgv7DUxFCCFFUyAgRIYQQQgiRy7lz5zhy5Ajdu3cHQE9Pj27durFmzRqtcnXq1ClQfRMnTuTnn38mMjISIyMjABITE2nQoIHWtJpGjRpx9+5d/vzzT+XY0yNPbG1tuX79urIfExNDy5YtKV++PGZmZvTt25ebN29y7969fNsTEBBAamqqsl25cqVA/RBCCFF0yAgRIYQQQgiRy+rVq8nKyqJ8+fLKMY1Gg76+Prdv31aOmZiYPLeu9evXs3DhQmJjY6lQoYJWfU/nGHk8JefJ4/r6+lplVCoVOTk5AFy+fJnWrVszePBgpk+fTsmSJTl48CADBw4kMzMz3zYZGBhgYGDw3LYLIYQoumSEyFvC29tba7jn20itVtOhQ4c33QwhhBBCvGJZWVmsXbuWkJAQEhISlO3XX3/FwcGBDRs2FLiun3/+mY8++ogvv/yS9957T+ucq6srcXFxWnlJ4uLiMDMz0wrEPMvRo0fJysoiJCSE9957jypVqnD16tUCt08IIUTxJSNEXiG1Wk1ERASffPIJK1as0Do3dOhQli9fTr9+/QgPD2f79u25vn68bRYtWpQrkVpBuE3dJdnahRBCiHfIzp07uX37NgMHDsTCwkLr3Icffsjq1atZuHDhc+u5du0aHTt2pHv37vj6+nLt2jUAdHV1KV26NEOHDiU0NJThw4czbNgwzp07x9SpUxk1apSSP+R5KlWqRFZWFkuWLKFdu3YcOnQo13tXYZwO8pXVlIQQopiQESKvmJ2dHZs2beLBgwfKsfT0dDZu3Ii9vb1yrGTJkpiZmb2JJj5XdnY2OTk5WFhYYGlp+aabI4QQQohXbPXq1fj4+OQKhgB07tyZhIQEjh8//tx6fvvtN/7++28iIiKwtbVVtrp16wJQvnx5fvjhB44cOUKNGjUYPHgwAwcO5PPPPy9wWz09PVmwYAFz5szBzc2NDRs2EBwcXPDOCiGEKLYkIPKK1apVC3t7e7Zv364c2759O3Z2dtSsWVM59vSUmWXLllG5cmUMDQ0pW7YsH374oXJu69atuLu7Y2RkhLW1NT4+PkrSsJycHKZNm0aFChUwMDDA09OTqKgo5drY2FhUKhV37txRjiUkJKBSqUhKSgIgPDwcS0tLdu7ciaurKwYGBly+fFmmzAghhBDFxHfffcf333+f57latWqh0WgYNWoUGo0m18eSwMBAEhISgEfvNxqNJtf2+J0DoGnTphw5coSMjAxSUlKYPXs2enr/N4g5NjaW0NBQrXtERkYSHh6u7I8cOZKrV69y//59oqKi6NOnT55tE0IIIZ4kAZHXoH///oSFhSn7a9asYcCAAfmWP3r0KCNGjGDatGmcO3eOqKgovLy8AEhJSaFHjx4MGDCAxMREYmNj6dSpkzKVZdGiRYSEhDB//nxOnjyJr68vH3zwAefPny9Um+/fv09wcDCrVq3izJkzlClT5rnXZGRkkJaWprUJIYQQQgghhBBvI8kh8hr06dOHgIAAkpKSUKlUHDp0iE2bNhEbG5tn+eTkZExMTGjbti1mZmY4ODgoo0lSUlLIysqiU6dOODg4AODu7q5cO3/+fMaPH68skTdnzhxiYmIIDQ3liy++KHCbMzMzWbZsGTVq1CjwNcHBwQQFBRW4vBBCCCGEEEII8abICJHXoFSpUrRp04aIiAjCwsJo06YNpUqVyrd8y5YtcXBwwMnJiT59+rBhwwbu378PQI0aNWjRogXu7u506dKFlStXKkvfpaWlcfXqVRo1aqRVX6NGjUhMTCxUm0uUKIGHh0ehrgkICCA1NVXZrly5UqjrhRBCCCGEEEKI10UCIq/JgAEDCA8PJyIi4pnTZQDMzMw4fvw4GzduxNbWlilTplCjRg3u3LmDrq4ue/bs4ccff8TV1ZUlS5bg4uLCpUuXlOtVKpVWfRqNRjn2OGP7k6vFZGZm5mqDkZFRrnqex8DAAHNzc61NCCGEEEIIIYR4G0lA5DXx8/Pj4cOHPHz4EF9f3+eW19PTw8fHh7lz53Ly5EmSkpKIjo4GHgU8GjVqRFBQECdOnKBEiRLs2LEDc3NzypUrx8GDB7XqiouLo1q1agCULl0aeDT15rHHic+EEEIIIYQQQojiQnKIvCa6urrKtBVdXd1nlt25cyd//PEHXl5eWFlZ8cMPP5CTk4OLiwuHDx9m3759tGrVijJlynD48GH++ecfJeAxduxYpk6dSqVKlfD09CQsLIyEhAQ2bNgAgLOzM3Z2dgQGBjJjxgzOnz9PSEjIK+376SBfGS0ihBBCCCGEEOKtIgGR16igQQFLS0u2b99OYGAg6enpVK5cmY0bN1K9enUSExP56aefCA0NJS0tDQcHB0JCQnj//fcBGDFiBGlpaYwePZrr16/j6urKt99+S+XKlQHQ19dn48aNDBkyhBo1alC3bl1mzJhBly5dXlm/hRBCCPHyqdVqIiIiCA4OZsKECcrxyMhIOnbsqDU99lVTqVTs2LGDDh06vLZ7vipuU3ehY2D8UutMmt3mpdYnhBDi5VBpXue/lqJYSUtLw8LCgtTUVBkhIoQQ7zD5e/52UqvVbN68GUNDQ/744w+srKyAdzMg8vDhQ0qUKPFyG1VIj3/P7fy3SEBECCHeYYV5b5EcIkIIIYQQ7ygfHx9sbGwIDg5+Zrm4uDi8vLwwMjLCzs6OESNGcO/ePQCWLFmCu7u7UjYyMhKVSsUXX3yhHPP19SUgIKDA7frrr7/o1q0bVlZWWFtb0759e5KSkpTzarWaDh06EBwcTLly5ahSpQoAy5Yto3LlyhgaGlK2bFk+/PBD5RqNRsPcuXNxcnLCyMiIGjVqsHXrVuWcs7Mz8+fP12rH6dOn0dHR4eLFiwVuuxBCiOJDAiJCCCGEEO8oXV1dZs2axZIlS/jzzz/zLHPq1Cl8fX3p1KkTJ0+eZPPmzRw8eJBhw4YB4O3tzZkzZ7hx4wYA+/fvp1SpUuzfvx+ArKws4uLiaNq0aYHadP/+fZo1a4apqSk//fQTBw8exNTUVEkw/9i+fftITExkz5497Ny5k6NHjzJixAimTZvGuXPniIqKwsvLSyn/+eefExYWxvLlyzlz5gwjR46kd+/e7N+/H5VKxYABAwgLC9Nqy5o1a2jSpAmVKlXK1c6MjAzS0tK0NiGEEMWLBESEEEIIId5hHTt2xNPTk6lTp+Z5ft68efTs2RN/f38qV65Mw4YNWbx4MWvXriU9PR03Nzesra2VAEhsbCyjR49W9uPj40lPT6dx48YFas+mTZvQ0dFh1apVuLu7U61aNcLCwkhOTiY2NlYpZ2JiwqpVq6hevTpubm4kJydjYmJC27ZtcXBwoGbNmowYMQKAe/fusWDBAtasWYOvry9OTk6o1Wp69+7Nl19+CUD//v05d+4cR44cASAzM5P169czYMCAPNsZHByMhYWFstnZ2RWof0IIIYoOCYgIIYQQQrzj5syZQ0REBGfPns117tixY4SHh2Nqaqpsvr6+5OTkcOnSJVQqFV5eXsTGxnLnzh3OnDnD4MGDyc7OJjExkdjYWGrVqoWpqWmB2nLs2DEuXLiAmZmZcr+SJUuSnp6uNXXF3d1dK29Iy5YtcXBwwMnJiT59+rBhwwbu378PwNmzZ0lPT6dly5Za/Vi7dq1Sp62tLW3atGHNmjXAo1X70tPT800cHxAQQGpqqrJduXKlYA9bCCFEkSGrzAigaGWHF0IIIYobLy8vfH19mThxImq1WutcTk4On3zyiTLa4kn29vbAo2kzX331FQcOHKBGjRpYWlri5eXF/v37iY2Nxdvbu8BtycnJoXbt2mzYsCHXudKlSys/m5iYaJ0zMzPj+PHjxMbGsnv3bqZMmUJgYCDx8fHk5OQA8P3331O+fHmt6wwMDJSfP/roI/r06cPChQsJCwujW7duGBvnnSDVwMBA61ohhBDFjwREXpO3aWm8102WrxNCCCFevdmzZ+Pp6akkKH2sVq1anDlzBmdn53yv9fb25rPPPmPr1q1K8KNp06bs3buXuLg4PvvsswK3o1atWmzevJkyZcoUelUiPT09fHx88PHxYerUqVhaWhIdHU3Lli0xMDAgOTn5mblMWrdujYmJCcuXL+fHH3/kp59+KtT9hRBCFC8yZeY1MjQ0ZM6cOdy+fftNN0UIIYQQRYy7uzu9evViyZIlWsfHjx/Pzz//zKeffkpCQgLnz5/n22+/Zfjw4UqZx3lENmzYoAREvL29iYyM5MGDBwXOHwLQq1cvSpUqRfv27Tlw4ACXLl1i//79fPbZZ/kmfoVHU1wWL15MQkICly9fZu3ateTk5ODi4oKZmRljxoxh5MiRREREcPHiRU6cOMEXX3xBRESEUoeuri5qtZqAgACcnZ1p0KBBgdsthBCi+JERIq+Rj48PFy5cIDg4mLlz5+ZZJi4ujgkTJhAfH0+pUqXo2LEjwcHBmJiYEBAQQExMDL/88ovWNR4eHnTs2JGgoCDi4+OZOHEiJ06cIDMzE09PTxYuXEitWrWU8ufPn2fgwIEcOXIEJycnFi1alKsd48ePZ8eOHfz555/Y2NjQq1cvpkyZgr6+/st9KEIIIYR4aaZPn86WLVu0jnl4eLB//34mTZpEkyZN0Gg0VKpUiW7duillVCoVTZs2JTIykiZNmijXWVhY4OTk9MyRHo+ns+jpPXqtNDY25qeffmL8+PF06tSJf//9l/Lly9OiRYtn1mNpacn27dsJDAwkPT2dypUrs3HjRqpXr670rUyZMgQHB/PHH39gaWlJrVq1mDhxolY9AwcOZNasWfkmU32e00G+hR7ZIoQQ4t2k0hTluRpvEbVazZ07d+jXrx89e/bk/PnzVKhQQWvKzKlTp2jYsCHTp0+nTZs2/PPPPwwbNowaNWoQFhbG6dOncXd358KFC8rycWfOnMHNzY1z585RpUoVoqOjuXr1KrVr1wYgJCSEnTt3cv78eczMzMjJyaFGjRqUKlWKhQsXkpaWhr+/PydOnNDKITJjxgyaN29OuXLlOHXqFIMGDWLUqFGMGzcu3z5mZGSQkZGh7KelpWFnZ4ed/xaZMiOEEO+wtLQ0LCwsSE1Nlf9RFLlcu3YNW1tb4uPjqVOnzptuDocOHcLb25s///yTsmXLFvg6+T0XQoiioTB/z2XKzGv2rKXxCrIsnoeHB//73/+UazZs2EDdunWV+cLNmzend+/eVKtWjWrVqvHll19y//59Zem8vXv3kpiYyLp16/D09MTLy4tZs2blasvnn39Ow4YNqVixIu3atWP06NG5vjg9TZavE0IIIYoPjUZDUlISM2bMoGzZsri5ub3R9mRkZHDhwgUmT55M165dCxUMEUIIUTxJQOQNyG9pvOctiweP5uU+ztqu0WjYuHEjvXr1Uuq4fv06gwcPpkqVKkpg4u7duyQnJwOQmJiIvb09FSpUUK7Ja37t1q1bady4MTY2NpiamjJ58mSljvzI8nVCCCFE8ZGamoqLiwsHDx5k06ZNGBoavtH2bNy4ERcXF1JTU/OdmiyEEEI8SXKIvAH5LY1XkGXxevbsyYQJEzh+/DgPHjzgypUrdO/eXSmnVqv5559/CA0NxcHBAQMDAxo0aMDDhw8B8lzNRqVSae3/8ssvdO/enaCgIHx9fbGwsGDTpk2EhIQ8s1+yfJ0QQghRfFhaWmpNlX3T1Gp1riWHhRBCiGeRgMgbktfSeAVZFq9ChQp4eXmxYcMGHjx4gI+Pj9aQ0AMHDrBs2TJat24NwJUrV7hx44Zy3tXVleTkZK5evUq5cuUA+Pnnn7XucejQIRwcHJg0aZJy7PLlyy/WYSGEEEIIIYQQ4i0iAZE3JK+l8caPH897773Hp59+yqBBgzAxMSExMZE9e/ZolevVqxeBgYE8fPiQhQsXatXr7OzMunXrqFOnDmlpaYwdOxYjIyPlvI+PDy4uLvTt25eQkBDS0tK0Ah+P60hOTmbTpk3UrVuX77//nh07dvznvkq2diGEEEIIIYQQbxvJIfIGTZ8+XWsKy+Nl8c6fP0+TJk2oWbMmkydPxtbWVuu6Ll26cPPmTe7fv6+sCvPYmjVruH37NjVr1qRPnz6MGDGCMmXKKOd1dHTYsWMHGRkZ1KtXj48++oiZM2dq1dG+fXtGjhzJsGHD8PT0JC4ujsmTJ7/8ByCEEEIIIYQQQrwhsuyueGVk+TohhCga5O+5eBEqlYodO3bQoUMHkpKScHR05MSJE3h6er7ppml5/Htu578FHQPjl1p30uw2L7U+IYQQ+SvMe4tMmRFCCCGEEHlSq9XcuXOHyMjIl1KfnZ0dKSkplCpV6qXUJ4QQQrwICYgIIYQQQojXQldXFxsbmzfdDCGEEAKQHCJCCCGEEKIAvL29GTFiBOPGjaNkyZLY2NgQGBioVeb8+fN4eXlhaGiIq6sre/bs0TqflJSESqUiISEBgOzsbAYOHIijoyNGRka4uLiwaNEirWvUajUdOnRg/vz52NraYm1tzaeffkpmZqZSZv369dSpUwczMzNsbGzo2bMn169ffyXPQQghRNEhI0SEEEIIIUSBREREMGrUKA4fPszPP/+MWq2mUaNGtGzZkpycHDp16kSpUqX45ZdfSEtLw9/f/5n15eTkUKFCBbZs2UKpUqWIi4vj448/xtbWlq5duyrlYmJisLW1JSYmhgsXLtCtWzc8PT0ZNGgQAA8fPmT69Om4uLhw/fp1Ro4ciVqt5ocffsj33hkZGWRkZCj7aWlpL/ZwhBBCvHMkICKEEEIIIQrEw8ODqVOnAlC5cmWWLl3Kvn37aNmyJXv37iUxMZGkpCQqVKgAwKxZs3j//ffzrU9fX5+goCBl39HRkbi4OLZs2aIVELGysmLp0qXo6upStWpV2rRpw759+5SAyIABA5SyTk5OLF68mHr16nH37l1MTU3zvHdwcLDWvYUQQhQ/EhB5BwQGBhIZGakML33XuE3dJdnahRBCiCLAw8NDa9/W1laZmpKYmIi9vb0SDAFo0KDBc+tcsWIFq1at4vLlyzx48ICHDx/mWoGmevXq6Orqat331KlTyv6JEycIDAwkISGBW7dukZOTA0BycjKurq553jcgIIBRo0Yp+2lpadjZ2T23vUIIIYoOySHyBsTFxaGrq4ufn1+Byo8ZM4Z9+/a94lYJIYQQQjybvr6+1r5KpVKCDxqNJld5lUr1zPq2bNnCyJEjGTBgALt37yYhIYH+/fvz8OHDAt/33r17tGrVClNTU9avX098fDw7duwAyFXPkwwMDDA3N9fahBBCFC8yQuQNWLNmDcOHD2fVqlUkJydjb2+fZzmNRkN2djampqb5DvcUQgghhHgbuLq6kpyczNWrVylXrhwAP//88zOvOXDgAA0bNmTo0KHKsYsXLxbqvr/99hs3btxg9uzZygiPo0ePFrL1QgghiiMJiLxm9+7dY8uWLcTHx3Pt2jXCw8OZMmUKALGxsTRr1oyoqCgmTZrEyZMn2bVrF/v379eaMpPX1xYHBweSkpIA2L9/P2PHjuXXX3+lZMmS9OvXjxkzZqCn9+g/t7e3Nx4eHhgaGrJq1SpKlCjB4MGDtTLFL1iwgLCwMP744w9KlixJu3btmDt3rgRmhBBCCJEnHx8fXFxc6Nu3LyEhIaSlpTFp0qRnXuPs7MzatWvZtWsXjo6OrFu3jvj4eBwdHQt8X3t7e0qUKMGSJUsYPHgwp0+fZvr06f+5H6eDfGW0iBBCFBMyZeY127x5My4uLri4uNC7d2/CwsJyDTEdN24cwcHBJCYm5pqrC5CSkqJsFy5cwNnZGS8vLwD++usvWrduTd26dfn1119Zvnw5q1evZsaMGVp1REREYGJiwuHDh5k7dy7Tpk3TWhpPR0eHxYsXc/r0aSIiIoiOjmbcuHHP7FtGRgZpaWlamxBCCCGKBx0dHXbs2EFGRgb16tXjo48+YubMmc+8ZvDgwXTq1Ilu3bpRv359bt68qTVapCBKly5NeHg4X3/9Na6ursyePZv58+e/SFeEEEIUEypNXhM+xSvTqFEjunbtymeffUZWVha2trZs3LgRHx8fZYRIZGQk7du3V67JL6mqRqOhc+fOJCcnc+DAAYyMjJg0aRLbtm0jMTFRGUmybNkyxo8fT2pqKjo6Onh7e5Odnc2BAweUuurVq0fz5s2ZPXt2nu3++uuvGTJkCDdu3Mi3b4GBgXlma7fz3yJJVYUQ4h2WlpaGhYUFqamp8uVcFFnyey6EEEVDYf6eywiR1+jcuXMcOXKE7t27A6Cnp0e3bt1Ys2aNVrk6deoUqL6JEyfy888/ExkZiZGREfAow3uDBg20ptU0atSIu3fv8ueffyrHnpUlHiAmJoaWLVtSvnx5zMzM6Nu3Lzdv3uTevXv5ticgIIDU1FRlu3LlSoH6IYQQQgghhBBCvG6SQ+Q1Wr16NVlZWZQvX145ptFo0NfX5/bt28oxExOT59a1fv16Fi5cSGxsrNbydhqNJleOkceDgJ48/qxs7ZcvX6Z169YMHjyY6dOnU7JkSQ4ePMjAgQPJzMzMt00GBgYYGBg8t+1CCCGEEEIIIcSbJgGR1yQrK4u1a9cSEhJCq1attM517tyZDRs24ObmVqC6fv75Zz766CO+/PJL3nvvPa1zrq6ubNu2TSswEhcXh5mZmVYg5lmOHj1KVlYWISEh6Og8GkS0ZcuWAl0rhBBCCCGEEEK8CyQg8prs3LmT27dvM3DgQCwsLLTOffjhh6xevZqFCxc+t55r167RsWNHunfvjq+vL9euXQNAV1eX0qVLM3ToUEJDQxk+fDjDhg3j3LlzTJ06lVGjRinBjeepVKkSWVlZLFmyhHbt2nHo0CFWrFhR+E7/f5KtXQghhBBCCCHE20ZyiLwmq1evxsfHJ1cwBB6NEElISOD48ePPree3337j77//JiIiAltbW2WrW7cuAOXLl+eHH37gyJEj1KhRg8GDBzNw4EA+//zzArfV09OTBQsWMGfOHNzc3NiwYQPBwcEF76wQQgghhBBCCPGWk1VmxCsj2dqFEKJokL/nojiQ33MhhCgaCvP3XKbMCCGEEEKIV06tVnPnzh0iIyPfdFOeyW3qLnQMjN/IvZNmt3kj9xVCiOJKpswIIYQQQgghhBCi2JGAiBBCCCGEeK0yMjIYMWIEZcqUwdDQkMaNGxMfH6+cr127NiEhIcp+hw4d0NPTIy0tDXiUZF6lUnHu3DkA1q9fT506dTAzM8PGxoaePXty/fr119spIYQQ7xwJiAghhBBCiNdq3LhxbNu2jYiICI4fP46zszO+vr7cunULAG9vb2JjYwHQaDQcOHAAKysrDh48CEBMTAw2Nja4uLgA8PDhQ6ZPn86vv/5KZGQkly5dQq1WP7MNGRkZpKWlaW1CCCGKFwmIvKDY2FhUKhV37tx5Y23w9vbG39//jd1fCCGEEKKg7t27x/Lly5k3bx7vv/8+rq6urFy5EiMjI1avXg08erc5cOAAOTk5nDx5El1dXfr06aMESWJjY2natKlS54ABA3j//fdxcnLivffeY/Hixfz444/cvXs333YEBwdjYWGhbHZ2dq+030IIId4+RSKp6vXr15k8eTI//vgjf//9N1ZWVtSoUYPAwEAaNGjw0u7j7e2Np6cnoaGhL1yXSqVSfjY2NqZcuXI0atSI4cOHU7t27ReuvzBedZKzV5GcTJKOCSGEEO+mixcvkpmZSaNGjZRj+vr61KtXj8TERAC8vLz4999/OXHiBIcOHaJp06Y0a9aMGTNmAI8CIk9+DDpx4gSBgYEkJCRw69YtcnJyAEhOTsbV1TXPdgQEBDBq1ChlPy0tTYIiQghRzBSJESKdO3fm119/JSIigt9//51vv/0Wb29vZdjl2yosLIyUlBTOnDnDF198wd27d6lfvz5r1659000TQgghhHglNBoNoP1x6PHxx8csLCzw9PQkNjaW/fv34+3tTZMmTUhISOD8+fP8/vvveHt7A49GnLRq1QpTU1PWr19PfHw8O3bsAB5NpcmPgYEB5ubmWpsQQoji5Z0PiNy5c4eDBw8yZ84cmjVrhoODA/Xq1SMgIIA2bf5vFEFycjLt27fH1NQUc3Nzunbtyt9//62cV6vVdOjQQatuf39/5R9btVrN/v37WbRoESqVCpVKRVJSklL22LFj1KlTB2NjYxo2bKgk+XoWS0tLbGxsqFixIq1atWLr1q306tWLYcOGcfv2bQBu3rxJjx49qFChAsbGxri7u7Nx48Zn1hsVFYWFhYUSWDl16hTNmzfHyMgIa2trPv74Y2UIaWBgIBEREXzzzTdKvx4PRx0/fjxVqlTB2NgYJycnJk+eTGZm5nP7JYQQQgiRH2dnZ0qUKKHkAwHIzMzk6NGjVKtWTTnm7e1NTEwMP/30E97e3lhaWuLq6sqMGTMoU6aMUva3337jxo0bzJ49myZNmlC1alVJqCqEEKJA3vmAiKmpKaampkRGRpKRkZFnGY1GQ4cOHbh16xb79+9nz549XLx4kW7duhX4PosWLaJBgwYMGjSIlJQUUlJStIZVTpo0iZCQEI4ePYqenh4DBgz4T/0ZOXIk//77L3v27AEgPT2d2rVrs3PnTk6fPs3HH39Mnz59OHz4cJ7Xb9q0ia5du7J27Vr69u3L/fv38fPzw8rKivj4eL7++mv27t3LsGHDABgzZgxdu3bFz89P6VfDhg0BMDMzIzw8nLNnz7Jo0SJWrlzJwoUL8227JCcTQgghxPOYmJgwZMgQxo4dS1RUFGfPnmXQoEHcv3+fgQMHKuW8vb2JiopCpVIp0168vb3ZsGGDVv4Qe3t7SpQowZIlS/jjjz/49ttvmT59+mvvlxBCiHfPO59DRE9Pj/DwcAYNGsSKFSuoVasWTZs2pXv37nh4eACwd+9eTp48yaVLl5Qgxrp166hevTrx8fHUrVv3ufexsLCgRIkSGBsbY2Njk+v8zJkzlX+cJ0yYQJs2bUhPT8fQ0LBQ/alatSqAMvqkfPnyjBkzRjk/fPhwoqKi+Prrr6lfv77WtcuWLWPixIl88803NGvWDIANGzbw4MED1q5di4mJCQBLly6lXbt2zJkzh7Jly2JkZERGRkaufn3++efKzxUrVmT06NFs3ryZcePG5dn24OBggoKCCtVfIYQQQhQ/s2fPJicnhz59+vDvv/9Sp04ddu3ahZWVlVLGy8sLgKZNmypTaZo2bUpoaKhWQKR06dKEh4czceJEFi9eTK1atZg/fz4ffPDBf2rb6SBfmT4jhBDFxDsfEIFHOUTatGnDgQMH+Pnnn4mKimLu3LmsWrUKtVpNYmIidnZ2WiM6XF1dsbS0JDExsUABked5HHwBsLW1BR4le7W3ty9UPU/Pq83Ozmb27Nls3ryZv/76i4yMDDIyMpTgxmPbtm3j77//5uDBg9SrV085npiYSI0aNbTKN2rUiJycHM6dO0fZsmXzbcvWrVsJDQ3lwoUL3L17l6ysrGe+IEhyMiGEEELkJzw8XPnZ0NCQxYsXs3jx4nzLW1hYkJWVpXWsQ4cOyrvSk3r06EGPHj20juVVTgghhHjSOz9l5jFDQ0NatmzJlClTiIuLQ61WM3XqVEA7SdeTnjyuo6OT6x/OwuTL0NfXV35+XOfjDOeF8Ti7uqOjIwAhISEsXLiQcePGER0dTUJCAr6+vrmShHl6elK6dGnCwsK0+pFf359sZ15++eUXunfvzvvvv8/OnTs5ceIEkyZNkuRkQgghhBBCCCGKhCITEHmaq6sr9+7dU35OTk7mypUryvmzZ8+SmpqqJOQqXbo0KSkpWnUkJCRo7ZcoUYLs7OxX2u7Q0FDMzc3x8fEB4MCBA7Rv357evXtTo0YNnJycOH/+fK7rKlWqRExMDN988w3Dhw9Xjru6upKQkKA8C4BDhw6ho6NDlSpV8u3XoUOHcHBwYNKkSdSpU4fKlStz+fLlV9FlIYQQQgghhBDitXvnp8zcvHmTLl26MGDAADw8PDAzM+Po0aPMnTuX9u3bA+Dj44OHhwe9evUiNDSUrKwshg4dStOmTalTpw4AzZs3Z968eaxdu5YGDRqwfv16Tp8+Tc2aNZV7VaxYkcOHD5OUlISpqSklS5Z8obbfuXOHa9eukZGRwe+//86XX35JZGQka9euxdLSEniUiX3btm3ExcVhZWXFggULuHbtmlYW9seqVKlCTEwM3t7e6OnpERoaSq9evZg6dSr9+vUjMDCQf/75h+HDh9OnTx9lukzFihXZtWsX586dw9raGgsLC5ydnUlOTmbTpk3UrVuX77//XlnCrrBkLq4QQgghhBBCiLfNOz9CxNTUlPr167Nw4UK8vLxwc3Nj8uTJDBo0iKVLlwKPpoZERkZiZWWFl5cXPj4+ODk5sXnzZqUeX19fJk+ezLhx46hbty7//vsvffv21brXmDFj0NXVxdXVldKlS5OcnPxCbe/fvz+2trZUrVqVIUOGYGpqypEjR+jZs6dSZvLkydSqVQtfX1+8vb2xsbHJtTzwk1xcXIiOjmbjxo2MHj0aY2Njdu3axa1bt6hbty4ffvghLVq0UJ4NwKBBg3BxcaFOnTqULl2aQ4cO0b59e0aOHMmwYcPw9PQkLi6OyZMnv1B/hRBCCCGEEEKIt4VKIxmnxCuSlpaGhYUFqampMkJECCHeYfL3XBQH8nsuhBBFQ2H+nr/zI0SEEEIIIYQQQgghCksCIkIIIYQQ4qWKjY1FpVJx586dl1KfWq1+5pRhIYQQ4r9455OqCiGEEEKINyMuLo4mTZrQsmVLoqKi3nRzXgq3qbvQMTB+I/dOmt3mjdxXCCGKKxkhIoQQQggh/pM1a9YwfPhwDh48+MLJ5oUQQojXTQIiQgghhBCi0O7du8eWLVsYMmQIbdu2JTw8PN+yN2/epEePHlSoUAFjY2Pc3d3ZuHGjVpmtW7fi7u6OkZER1tbW+Pj4cO/evTzrO3bsGGXKlGHmzJkAREVF0bhxYywtLbG2tqZt27ZcvHjxpfVVCCFE0SRTZt5harWaO3fuEBkZ+aab8kwy9FQIIYQoejZv3oyLiwsuLi707t2b4cOHM3nyZFQqVa6y6enp1K5dm/Hjx2Nubs73339Pnz59cHJyon79+qSkpNCjRw/mzp1Lx44d+ffffzlw4AB5LYYYGxtLhw4dCA4OZsiQIcCj4MyoUaNwd3fn3r17TJkyhY4dO5KQkICOTt7f/zIyMsjIyFD209LSXtKTEUII8a6QgIgQQgghhCi01atX07t3bwD8/Py4e/cu+/btw8fHJ1fZ8uXLM2bMGGV/+PDhREVF8fXXXysBkaysLDp16oSDgwMA7u7uuer55ptv6NOnD19++SU9evRQjnfu3DlX28qUKcPZs2dxc3PLs/3BwcEEBQUVvuNCCCGKDJkyU0RkZGQwYsQIypQpg6GhIY0bNyY+Pl45X7t2bUJCQpT9Dh06oKenp3wNuXbtGiqVinPnzgGwfv166tSpg5mZGTY2NvTs2ZPr16+/3k4JIYQQ4q107tw5jhw5Qvfu3QHQ09OjW7durFmzJs/y2dnZzJw5Ew8PD6ytrTE1NWX37t1K3pEaNWrQokUL3N3d6dKlCytXruT27dtadRw+fJjOnTsTERGhFQwBuHjxIj179sTJyQlzc3McHR0BnpnXJCAggNTUVGW7cuXKf34eQggh3k0SECkixo0bx7Zt24iIiOD48eM4Ozvj6+vLrVu3APD29iY2NhYAjUbDgQMHsLKy4uDBgwDExMRgY2ODi4sLAA8fPmT69On8+uuvREZGcunSJdRq9TPbkJGRQVpamtYmhBBCiKJn9erVZGVlUb58efT09NDT02P58uVs3749VyADICQkhIULFzJu3Diio6NJSEjA19eXhw8fAqCrq8uePXv48ccfcXV1ZcmSJbi4uHDp0iWljkqVKlG1alXWrFmjXPdYu3btuHnzJitXruTw4cMcPnwYIFe5JxkYGGBubq61CSGEKF4kIFIE3Lt3j+XLlzNv3jzef/99XF1dWblyJUZGRqxevRp4FBA5cOAAOTk5nDx5El1dXfr06aMESWJjY2natKlS54ABA3j//fdxcnLivffeY/Hixfz444/cvXs333YEBwdjYWGhbHZ2dq+030IIIYR4/bKysli7di0hISEkJCQo26+//oqDgwMbNmzIdc2BAwdo3749vXv3pkaNGjg5OXH+/HmtMiqVikaNGhEUFMSJEycoUaIEO3bsUM6XKlWK6OhoLl68SLdu3cjMzAQeJWxNTEzk888/p0WLFlSrVi3PoIwQQgjxNMkhUgRcvHiRzMxMGjVqpBzT19enXr16JCYmAuDl5cW///7LiRMnOHToEE2bNqVZs2bMmDEDeBQQ8ff3V64/ceIEgYGBJCQkcOvWLXJycoBHQ09dXV3zbEdAQACjRo1S9tPS0iQoIoQQQhQxO3fu5Pbt2wwcOBALCwutcx9++CGrV69m4cKFWsednZ3Ztm0bcXFxWFlZsWDBAq5du0a1atWAR9Nh9u3bR6tWrShTpgyHDx/mn3/+Uc4/VqZMGaKjo2nWrBk9evRg06ZNWFlZYW1tzVdffYWtrS3JyclMmDDhP/fvdJCvjBYRQohiQkaIFAGPM7A/ndVdo9EoxywsLPD09CQ2Npb9+/fj7e1NkyZNSEhI4Pz58/z+++94e3sDj0actGrVClNTU9avX098fLzyhUaGngohhBDF2+rVq/Hx8ckVDIFHyU0TEhI4fvy41vHJkydTq1YtfH198fb2xsbGhg4dOijnzc3N+emnn2jdujVVqlTh888/JyQkhPfffz/XPWxsbIiOjubUqVP06tULjUbDpk2bOHbsGG5ubowcOZJ58+a99H4LIYQoemSESBHg7OxMiRIlOHjwID179gQgMzOTo0ePao368Pb2JiYmhsOHDzNt2jQsLS1xdXVlxowZlClTRvkK89tvv3Hjxg1mz56tjPA4evToa++XEEIIId4+3333Xb7natWqpXyoeXLUaMmSJYmMjMz3umrVqhEVFZXv+fDwcK19W1tbJRE8gI+PD2fPntUqk9eSvUIIIcSTJCBSBJiYmDBkyBDGjh1LyZIlsbe3Z+7cudy/f5+BAwcq5by9vVm0aBElS5ZUpr14e3uzZMkSOnXqpJSzt7enRIkSLFmyhMGDB3P69GmmT5/+n9snQ0+FEEIIIYQQQrxtZMpMETF79mw6d+5Mnz59qFWrFhcuXGDXrl1YWVkpZby8vABo2rSpMpWmadOmZGdnayVULV26NOHh4Xz99de4uroye/Zs5s+f/3o7JIQQQgghhBBCvEIqjYwnFK9IWloaFhYWpKamyggRIYR4h8nfc1EcyO+5EEIUDYX5ey4jRIQQQgghhBBCCFHsSEBECCGEEEIIIYQQxY4ERIQQQgghhBBCCFHsyCozQgghhBCi0AIDA4mMjCQhIeGF6omNjaVZs2bcvn0bS0vLAl2jVqu5c+fOM5fy/a/cpu5Cx8D4pdf7siXNbvOmmyCEEO88GSHyjoiNjUWlUnHnzp2XUp9araZDhw4vpS4hhBBCFC3t2rXDx8cnz3M///wzKpWK5s2bs2/fvhe+V8OGDUlJScHCwqLA1yxatIjw8PAXvrcQQojiTUaIvGXi4uJo0qQJLVu2JCoq6k0356V4k19a5OuJEEIIUXgDBw6kU6dOXL58GQcHB61za9aswdPTEy8vr2fW8fDhQ0qUKPHce5UoUQIbG5tCta8wwRMhhBAiPzJC5C2zZs0ahg8fzsGDB0lOTn7TzRFCCCFEMdS2bVvKlCmTaxTG/fv32bx5MwMHDiQwMBBPT0/l3OPRp8HBwZQrV44qVaoAjz72eHp6YmhoSJ06dYiMjESlUilTbZ4eBRseHo6lpSW7du2iWrVqmJqa4ufnR0pKSq57PRYVFUXjxo2xtLTE2tqatm3bcvHixVfxaIQQQhQhEhB5i9y7d48tW7YwZMgQ2rZt+8yhoDdv3qRHjx5UqFABY2Nj3N3d2bhxo1aZrVu34u7ujpGREdbW1vj4+HDv3r086zt27BhlypRh5syZgLxYCCGEEMWZnp4effv2JTw8HI1Goxz/+uuvefjwIb169crzun379pGYmMiePXvYuXMn//77L+3atcPd3Z3jx48zffp0xo8f/9z7379/n/nz57Nu3Tp++uknkpOTGTNmTL7l7927x6hRo4iPj2ffvn3o6OjQsWNHcnJy8r0mIyODtLQ0rU0IIUTxIgGRt8jmzZtxcXHBxcWF3r17ExYWpvUS8qT09HRq167Nzp07OX36NB9//DF9+vTh8OHDAKSkpNCjRw8GDBhAYmIisbGxdOrUKc/6YmNjadGiBUFBQUyaNAmQFwshhBCiuBswYABJSUnExsYqx9asWUOnTp2wsrLK8xoTExNWrVpF9erVcXNzY8OGDahUKlauXImrqyvvv/8+Y8eOfe69MzMzWbFiBXXq1KFWrVoMGzbsmflKOnfuTKdOnahcuTKenp6sXr2aU6dOcfbs2XyvCQ4OxsLCQtns7Oye2y4hhBBFiwRE3iKrV6+md+/eAPj5+XH37t18//EvX748Y8aMwdPTEycnJ4YPH46vry9ff/018CggkpWVRadOnahYsSLu7u4MHToUU1NTrXq++eYbPvjgA5YvX86QIUOU4/JiIYQQQhRvVatWpWHDhqxZswaAixcvcuDAAQYMGJDvNe7u7lp5Q86dO4eHhweGhobKsXr16j333sbGxlSqVEnZt7W15fr16/mWv3jxIj179sTJyQlzc3McHR0Bnjn9OCAggNTUVGW7cuXKc9slhBCiaJGAyFvi3LlzHDlyhO7duwOPhqp269ZNeQl5WnZ2NjNnzsTDwwNra2tMTU3ZvXu38g9/jRo1aNGiBe7u7nTp0oWVK1dy+/ZtrToOHz5M586diYiIoEePHlrn5MVCCCGEEAMHDmTbtm2kpaURFhaGg4MDLVq0yLe8iYmJ1r5Go0GlUuU69jz6+vpa+yqV6pnXtWvXjps3b7Jy5UoOHz6sjJh9+PBhvtcYGBhgbm6utQkhhCheJCDylli9ejVZWVmUL18ePT099PT0WL58Odu3b88VyAAICQlh4cKFjBs3jujoaBISEvD19VX+4dfV1WXPnj38+OOPuLq6smTJElxcXLh06ZJSR6VKlahatSpr1qzJ9cIgLxZCCCGE6Nq1K7q6uvzvf/8jIiKC/v375wpwPEvVqlU5efIkGRkZyrGjR4++1DbevHmTxMREPv/8c1q0aEG1atXyfHcSQgghnibL7r4FsrKyWLt2LSEhIbRq1UrrXOfOndmwYQNubm5axw8cOED79u2VKTY5OTmcP3+eatWqKWVUKhWNGjWiUaNGTJkyBQcHB3bs2MGoUaMAKFWqFNu3b8fb25tu3bqxZcsW9PX1lReLL7/8kiZNmgBw8ODBV/kIhBBCCPEWMjU1pVu3bkycOJHU1FTUanWhru/ZsyeTJk3i448/ZsKECSQnJzN//nyAQgVWnsXKygpra2u++uorbG1tSU5OZsKECf+5vtNBvvJRRwghigkJiLwFdu7cye3btxk4cCAWFhZa5z788ENWr17NwoULtY47Ozuzbds24uLisLKyYsGCBVy7dk0JiBw+fJh9+/bRqlUrypQpw+HDh/nnn3+0AiYAZcqUITo6mmbNmtGjRw82bdokLxZCCCGEUAwcOJDVq1fTqlUr7O3tC3Wtubk53333HUOGDMHT0xN3d3emTJlCz549tfKKvAgdHR02bdrEiBEjcHNzw8XFhcWLF+Pt7f1S6hdCCFF0qTQFmcgpXql27dqRk5PD999/n+vc8ePHqV27NiEhIYwePZrbt29jaWnJrVu3GDBgAPv27cPY2JiPP/6Y5ORkUlNTiYyMJDExkZEjR3L8+HHS0tJwcHBg+PDhDBs2DAC1Ws2dO3eIjIwEHiVh9fb2xtPTk//973/ExMQwYsQI/vjjD60Xix07dtChQ4cC9SstLQ0LCwtSU1MlICKEEO8w+XsuXqYNGzbQv39/UlNTMTIyetPNUcjvuRBCFA2F+XsuARHxysiLhRBCFA3y91y8iLVr1+Lk5ET58uX59ddfGTZsGN7e3qxfv/5NN02L/J4LIUTRUJi/5zJlRgghhBBC/D/27j2u5/N//PjjXXQ+yqFQOkmS5DjHDuSTiTns4zCGHGdYy5kZEuaUwzBjNtXMHDb02cwY6U3EJCI0SyTbMttQJNHh94dfr6+3DphI9bzfbq/brdf1ul7X9Xy91y3vXa/rel4vzLVr15g1axbXrl3DysqKPn36MH/+/LIOSwghhJAZIuLFkTctQghRMcjfc1EZyO+5EEJUDM/y91y23RVCCCGEEEIIIUSlIwMiQgghhBBCCCGEqHRkQEQ8tZSUFFQqFfHx8WUdihBCCCFeMV5eXgQGBirntra2rFixQjlXqVTK7nal5fE+hBBCiGchSVVLgb+/P+Hh4bzzzjusXbtW49qYMWP49NNPGTJkCGFhYQDK9rbP+g94WFgYgYGB3Lp1q9A1lUr1TFvivkyus/eipWtQ1mE8UcpCv7IOQQghhChX/P39uXXr1lMNdKSlpWFubl6q/cfGxmJoaFiqbcr3FiGEqDxkhkgpsba2ZsuWLWRlZSll9+7dY/PmzdjY2JRhZEIIIYQQZc/S0hJdXd1SbbNGjRoYGLz6gxdCCCFeTTIgUkqaNWuGjY0NO3bsUMp27NiBtbU1TZs2Vcr8/f05ePAgH3/8MSqVCpVKRUpKSqnHk5CQQMeOHdHX18fCwoJRo0Zx584d5XpeXh7BwcHUrVsXXV1d3N3d2bNnj0Ybx48fp2nTpujp6dGiRQtOnTpV6nEKIYQQonJ4dMlMwTLcHTt24O3tjYGBAU2aNOHo0aMa92zfvp1GjRqhq6uLra0tS5cu1bj++JKZoKAgbGxs0NXVpXbt2gQEBLzoxxJCCFGOyYBIKRo6dCihoaHK+YYNGxg2bJhGnY8//pg2bdowcuRI0tLSSEtLw9raulTjuHv3Ll26dMHc3JzY2Fi++eYb9u/fz7hx4zTiWLp0KSEhIZw5cwZfX1/eeOMNkpKSAMjMzKRbt240aNCAuLg4goKCmDRpUon9Zmdnk5GRoXEIIYQQQhRnxowZTJo0ifj4eJycnHjrrbfIyckBIC4ujr59+9K/f38SEhIICgpi5syZyhLkx3377bcsX76cdevWkZSUREREBI0bNy62b/neIoQQQgZEStGgQYM4fPgwKSkpXLlyhSNHjvD2229r1DE1NUVHRwcDAwMsLS2xtLREW1v7qftIT0/HyMio0PGoTZs2kZWVxZdffomrqysdO3Zk9erVbNy4kT///BOAkJAQpk6dSv/+/WnQoAGLFi3SyGuyadMmcnNz2bBhA40aNaJbt25Mnjy5xNgWLFiAqampcpT2QI8QQgghKpZJkybh5+eHk5MTc+bM4cqVK1y8eBGAZcuW0alTJ2bOnImTkxP+/v6MGzeOJUuWFNlWamoqlpaW+Pj4YGNjQ6tWrRg5cmSxfcv3FiGEEDIgUoqqV6+On58f4eHhhIaG4ufnR/Xq1Uu1D2NjY+Lj4wsdj0pMTKRJkyYaScbatWtHXl4eFy5cICMjgz/++IN27dpp3NeuXTsSExM12nh0XW6bNm1KjG369Omkp6crx9WrV5/zaYUQQghRkbm5uSk/W1lZAXD9+nXg4XeRor6rJCUlkZubW6itPn36kJWVhb29PSNHjmTnzp3KbJOiyPcWIYQQsstMKRs2bJiyNOWTTz4p9fa1tLRwdHQssU5+fj4qlarIa4+WP17n0fvy8/OfOTZdXd1ST5YmhBBCiIqratWqys8F30Hy8vKAor/PlPT9xNramgsXLrBv3z7279/PmDFjWLJkCQcPHtTop4B8bxFCCCEzREpZly5duH//Pvfv38fX17fIOjo6OkW+2SgtLi4uxMfHk5mZqZQdOXIELS0tnJycMDExoXbt2hw+fFjjvpiYGBo2bKi0cfr0aY1dc44dO/bCYhZCCCGEeJSLi0uR31WcnJyKXW6sr6/PG2+8wcqVK1Gr1Rw9epSEhISXEa4QQohySGaIlDJtbW1l2Ulx/1jb2try888/k5KSgpGREdWqVUNLSwtnZ2cWLFhAr169niuGgQMHMnv2bIYMGUJQUBB//fUX7733HoMGDaJWrVoATJ48mdmzZ+Pg4IC7uzuhoaHEx8ezadMmAAYMGMCMGTMYPnw4H374ISkpKYSEhPyreM7O8cXExOS5nkkIIYQQlcvEiRNp2bIlc+fOpV+/fhw9epTVq1ezZs2aIuuHhYWRm5vLa6+9hoGBARs3bkRfX5969eq95MiFEEKUFzIg8gI86X/+J02axJAhQ3BxcSErK4vLly9ja2vLhQsXSE9Pf+7+DQwM2Lt3L++//z4tW7bEwMCAN998k2XLlil1AgICyMjIYOLEiVy/fh0XFxe+++476tevD4CRkRHff/89o0ePpmnTpri4uLBo0SLefPPN545PCCGEEOJJmjVrxrZt25g1axZz587FysqK4OBg/P39i6xvZmbGwoULmTBhArm5uTRu3Jjvv/8eCwuLZ+pXXuQIIUTlocr/N8kihHgKGRkZmJqakp6eLl8shBCiHJO/56IykN9zIYSoGJ7l77nkEBFCCCGEEEIIIUSlIwMiQgghhBBCCCGEqHRkQEQIIYQQQgghhBCVjgyICCGEEEIIIYQQotKRAREhhBBCCCGEEEJUOjIg8grz8vIiMDBQObe1tWXFihXKuUqlIiIiolT7fLwPIYQQQgghhBCiIqpS1gEITf7+/ty6deupBjrS0tIwNzcv1f5jY2MxNDQs1TZdZ+9FS9egVNt8EVIW+pV1CEIIIUS5kZKSgp2dHadOncLd3f252rK1tSUwMFDjRVBJ1Go13t7e3Lx5EzMzs+fq+3HyvUUIISoPmSFSjllaWqKrq1uqbdaoUQMDg1f/S4AQQghR3q1duxZjY2NycnKUsjt37lC1alU6dOigUTc6OhqVSsWvv/76XH2q1WpUKhW3bt0qts727dvR1tYmNTW1yOvOzs4EBARgbW1NWloarq6uzxUTPHwhM2rUqKeu37ZtW9LS0jA1NX3uvoUQQlReMiBSjj26ZCYlJQWVSsWOHTvw9vbGwMCAJk2acPToUY17tm/fTqNGjdDV1cXW1palS5dqXH98yUxQUBA2Njbo6upSu3ZtAgICXvRjCSGEEJWCt7c3d+7c4cSJE0pZdHQ0lpaWxMbGcvfuXaVcrVZTu3ZtnJycXnhcb7zxBhYWFoSHhxe6duTIES5cuMDw4cPR1tbG0tKSKlWKnnCcn5+vMdhTkmd9IaOjo4OlpSUqleqp7xFCCCEeJwMiFcyMGTOYNGkS8fHxODk58dZbbylfRuLi4ujbty/9+/cnISGBoKAgZs6cSVhYWJFtffvttyxfvpx169aRlJREREQEjRs3Lrbv7OxsMjIyNA4hhBBCFK1BgwbUrl0btVqtlKnVanr06IGDgwMxMTEa5d7e3gDcv3+fKVOmUKdOHQwNDXnttdc02rhy5Qrdu3fH3NwcQ0NDGjVqxO7du0lJSVHaMDc3R6VS4e/vXyiuqlWrMmjQIMLCwsjPz9e4tmHDBpo3b06TJk2UlzHx8fFKjCqVir1799KiRQt0dXWJjo7m9u3bDBw4EENDQ6ysrFi+fPlT5Un7/PPP6dWrFwYGBtSvX5/vvvtO4/N4dKbLP//8w1tvvUXdunUxMDCgcePGbN68+Rn+awghhKiMZECkgpk0aRJ+fn44OTkxZ84crly5wsWLFwFYtmwZnTp1YubMmTg5OeHv78+4ceNYsmRJkW2lpqZiaWmJj48PNjY2tGrVipEjRxbb94IFCzA1NVUOa2vrF/KMQgghREXh5eVFVFSUch4VFYWXlxeenp5K+f379zl69KgymDF06FCOHDnCli1bOHPmDH369KFLly4kJSUBMHbsWLKzszl06BAJCQksWrQIIyMjrK2t2b59OwAXLlwgLS2Njz/+uMi4hg8fzqVLlzh48KBSlpmZybZt2xg+fHiJzzRlyhQWLFhAYmIibm5uTJgwgSNHjvDdd9+xb98+oqOjOXny5BM/mzlz5tC3b1/OnDlD165dGThwIDdu3Ciy7r1792jevDm7du3i7NmzjBo1ikGDBvHzzz8X2768yBFCCCEDIhWMm5ub8rOVlRUA169fByAxMZF27dpp1G/Xrh1JSUnk5uYWaqtPnz5kZWVhb2/PyJEj2blzZ4lTX6dPn056erpyXL16tTQeSQghhKiwvLy8OHLkCDk5Ody+fZtTp07h4eGBp6enMuvj2LFjZGVl4e3tTXJyMps3b+abb76hQ4cOODg4MGnSJNq3b09oaCjw8IVGu3btaNy4Mfb29nTr1g0PDw+0tbWpVq0aADVr1sTS0rLYHBwuLi689tprSpsA27ZtIzc3l7feeqvEZwoODqZz5844ODigo6NDeHg4ISEhdOrUCVdXV0JDQ4v83vE4f39/3nrrLRwdHfnoo4/IzMzk+PHjRdatU6cOkyZNwt3dHXt7e9577z18fX355ptvim1fXuQIIYSQAZEKpmrVqsrPBetq8/LygIdreR9fa/v4VNhHWVtbc+HCBT755BP09fUZM2YMHh4ePHjwoMj6urq6mJiYaBxCCCGEKJ63tzeZmZnExsYSHR2Nk5MTNWvWxNPTk9jYWDIzM1Gr1djY2GBvb8/JkyfJz8/HyckJIyMj5Th48CDJyckABAQEMG/ePNq1a8fs2bM5c+bMv4pt+PDhfPvtt9y+fRt4uFymd+/eT9zVpUWLFsrPly5d4sGDB7Rq1UopMzU1pUGDBk/s/9GXPIaGhhgbGysveR6Xm5vL/PnzcXNzw8LCAiMjI3766adiE8OCvMgRQgghAyKViouLC4cPH9Yoi4mJwcnJCW1t7SLv0dfX54033mDlypWo1WqOHj1KQkLCywhXCCGEqPAcHR2pW7cuUVFRREVF4enpCTzcSc7Ozo4jR44QFRVFx44dgYcvObS1tYmLiyM+Pl45EhMTleUvI0aM4NKlSwwaNIiEhARatGjBqlWrnjm2/v37o1Kp2Lp1KxcvXuTw4cNPXC4DDwcvChS8eHmWFzIFHn3JU9BGwUuexy1dupTly5czZcoUDhw4QHx8PL6+vty/f7/Y9uVFjhBCiKLTgosKaeLEibRs2ZK5c+fSr18/jh49yurVq1mzZk2R9cPCwsjNzeW1117DwMCAjRs3oq+vT7169Z6p37NzfOVLhhBCCFEMb29v1Go1N2/eZPLkyUq5p6cne/fu5dixYwwdOhSApk2bkpuby/Xr1wttzfsoa2trRo8ezejRo5k+fTrr16/nvffeQ0dHB+CplqwYGxvTp08fQkNDuXTpEvb29nh5eT3Tszk4OFC1alWOHz+uLEnJyMggKSlJGfwpDdHR0fTo0YO3334beDhwlJSURMOGDUutDyGEEBWPDIhUIs2aNWPbtm3MmjWLuXPnYmVlRXBwcJEZ5gHMzMxYuHAhEyZMIDc3l8aNG/P9999jYWHxcgMXQgghKjBvb2/Gjh3LgwcPNAYJPD09effdd7l3756SUNXJyYmBAwcyePBgli5dStOmTfn77785cOAAjRs3pmvXrgQGBvL666/j5OTEzZs3OXDggDIwUK9ePVQqFbt27aJr167o6+tjZGRUbGzDhw+nQ4cOnD9/nkmTJj3zNrfGxsYMGTKEyZMnU61aNWrWrMns2bPR0tIq1S1zHR0d2b59OzExMZibm7Ns2TKuXbv2rwZE5EWOEEJUHjIg8op5dAvcR7fQA0hJSdE4f3S6qa2tbaHpp2ZmZoXK3nzzTd58881i+3+0j549e9KzZ8+nilsIIYQQ/463tzdZWVk4OztTq1YtpdzT05Pbt2/j4OCgkfAzNDSUefPmMXHiRH7//XcsLCxo06YNXbt2BR7O/hg7diy//fYbJiYmdOnSheXLlwMPk4/OmTOHadOmMXToUAYPHqzx3eNx7du3p0GDBiQlJTFkyJB/9XzLli1j9OjRdOvWDRMTE6ZMmcLVq1fR09P7V+0VZebMmVy+fBlfX18MDAwYNWoUPXv2JD09vdT6EEIIUfGo8p9mEacQ/0JGRgampqakp6fLmxYhhCjH5O+5KE2ZmZnUqVOHpUuXPlVOkpdFfs+FEKJieJa/5zJDRAghhBBCvDCnTp3il19+oVWrVqSnpxMcHAxAjx49yjgyIYQQlZ0MiAghhBBCiBcqJCSECxcuoKOjQ/PmzYmOjqZ69eplHZYQQohKTgZEhBBCCCHEC9O0aVPi4uLKOgwhhBCiEK2yDkD8n5SUFFQqFfHx8c/dlq2tLStWrHjq+mq1GpVKxa1bt567byGEEEIIIYQQ4lVXoWaIrF27lsmTJ3Pz5k2qVHn4aHfu3MHc3JzWrVsTHR2t1I2OjsbDw4MLFy7g5OT0r/tUq9V4e3tz8+ZNzMzMiqyzfft2+vbty+XLl7GxsSl03dnZmf/85z8sX76ctLS0UplCGhsbi6Gh4VPXb9u2LWlpaZiamj53349znb0XLV2DUm+3tKUs9CvrEIQQQgghhBBCvCQVaoaIt7c3d+7c4cSJE0pZdHQ0lpaWxMbGcvfuXaVcrVZTu3bt5xoMeVpvvPEGFhYWhIeHF7p25MgRLly4wPDhw9HW1sbS0lIZzHlcfn4+OTk5T9VnjRo1MDB4+kEIHR0dLC0tUalUT32PEEIIIURZUalURERElHUYQgghyrEKNUOkQYMG1K5dG7VaTevWrYGHAx89evQgKiqKmJgYfHx8lHJvb28A7t+/z4cffsimTZu4desWrq6uLFq0CC8vLwCuXLnCuHHjOHz4MPfv38fW1pYlS5bg4uKitGFubg7AkCFDCAsL04iratWqDBo0iLCwMD788EONQYcNGzbQvHlzmjRpQkpKCnZ2dpw6dQp3d3clxj179jBjxgzOnDnD3r17adGiBaNHjyYiIgITExOmTJnC//73P9zd3ZVlMra2tgQGBhIYGAg8/NKwfv16fvjhB/bu3atsd/fGG29ofB4FM13++ecfxo0bR3R0NDdu3MDBwYEPPviAt956q9T/uwkhhBDi1ePv7094eDgLFixg2rRpSnlERAS9evUiPz+/DKN7cWRmqxBCVB4VaoYIgJeXF1FRUcp5VFQUXl5eeHp6KuX379/n6NGjymDG0KFDOXLkCFu2bOHMmTP06dOHLl26kJSUBMDYsWPJzs7m0KFDJCQksGjRIoyMjLC2tmb79u0AXLhwgbS0ND7++OMi4xo+fDiXLl3i4MGDSllmZibbtm1j+PDhJT7TlClTWLBgAYmJibi5uTFhwgSOHDnCd999x759+4iOjubkyZNP/GzmzJlD3759OXPmDF27dmXgwIHcuHGjyLr37t2jefPm7Nq1i7NnzzJq1CgGDRrEzz//XGz72dnZZGRkaBxCCCGEKL/09PRYtGgRN2/eLOtQhBBCiFJXIQdEjhw5Qk5ODrdv3+bUqVN4eHjg6emJWq0G4NixY2RlZeHt7U1ycjKbN2/mm2++oUOHDjg4ODBp0iTat29PaGgoAKmpqbRr147GjRtjb29Pt27d8PDwQFtbm2rVqgFQs2ZNLC0ti83B4eLiwmuvvaa0CbBt2zZyc3OfOOsiODiYzp074+DggI6ODuHh4YSEhNCpUydcXV0JDQ0lNzf3iZ+Nv78/b731Fo6Ojnz00UdkZmZy/PjxIuvWqVOHSZMm4e7ujr29Pe+99x6+vr588803xba/YMECTE1NlcPa2vqJMQkhhBDi1eXj44OlpSULFiwotk5MTAweHh7o6+tjbW1NQEAAmZmZAEyfPl2ZtfsoNzc3Zs+eDTzMe9a5c2eqV6+Oqakpnp6ehV70JCUl4eHhgZ6eHi4uLuzbt69Qm1OnTsXJyQkDAwPs7e2ZOXMmDx48eJ7HF0IIUcFVuAERb29vMjMziY2NJTo6GicnJ2rWrImnpyexsbFkZmaiVquxsbHB3t6ekydPkp+fj5OTE0ZGRspx8OBBkpOTAQgICGDevHm0a9eO2bNnc+bMmX8V2/Dhw/n222+5ffs28HC5TO/evYtNxlqgRYsWys+XLl3iwYMHtGrVSikzNTWlQYMGT+zfzc1N+dnQ0BBjY2OuX79eZN3c3Fzmz5+Pm5sbFhYWGBkZ8dNPP5Gamlps+9OnTyc9PV05rl69+sSYhBBCCPHq0tbW5qOPPmLVqlX89ttvha4nJCTg6+tL7969OXPmDFu3buXw4cOMGzcOgIEDB/Lzzz8r36kAzp07R0JCAgMHDgTg9u3bDBkyhOjoaI4dO0b9+vXp2rWr8n0pLy+P3r17o62tzbFjx1i7di1Tp04tFIuxsTFhYWGcP3+ejz/+mPXr17N8+fJin01mtgohhKhwAyKOjo7UrVuXqKgooqKi8PT0BMDS0hI7OzuOHDlCVFQUHTt2BB7+I6utrU1cXBzx8fHKkZiYqCx/GTFiBJcuXWLQoEEkJCTQokULVq1a9cyx9e/fH5VKxdatW7l48SKHDx9+4nIZQGO3mIL1uo8nP32adbxVq1bVOFepVOTl5RVZd+nSpSxfvpwpU6Zw4MAB4uPj8fX15f79+8W2r6uri4mJicYhhBBCiPKtV69euLu7KzM6HrVkyRIGDBhAYGAg9evXp23btqxcuZIvv/ySe/fu4erqipubG19//bVyz6ZNm2jZsqWS2L5jx468/fbbNGzYkIYNG7Ju3Tru3r2rLDPev38/iYmJbNy4EXd3dzw8PPjoo48KxfLhhx/Stm1bbG1t6d69OxMnTmTbtm3FPpfMbBVCCFHhBkTg4SwRtVqNWq1WEqMCeHp6snfvXo4dO6bkD2natCm5ublcv34dR0dHjcPS0lK519ramtGjR7Njxw4mTpzI+vXrgYe7swBPtWTF2NiYPn36EBoayoYNG7C3t9eI72k4ODhQtWpVjaUuGRkZSr6T0hIdHU2PHj14++23adKkCfb29qXehxBCCCHKh0WLFhEeHs758+c1yuPi4ggLC9OYZevr60teXh6XL18GHs4S2bRpE/DwBc7mzZuV2SEA169fZ/To0Tg5OSmDE3fu3FFmpSYmJmJjY0PdunWVe9q0aVMoxm+//Zb27dtjaWmJkZERM2fOlJmtQgghSlShdpkp4O3tzdixY3nw4IEyQwQeDoi8++673Lt3TxkQcXJyYuDAgQwePJilS5fStGlT/v77bw4cOEDjxo3p2rUrgYGBvP766zg5OXHz5k0OHDhAw4YNAahXrx4qlYpdu3bRtWtX9PX1MTIyKja24cOH06FDB86fP8+kSZOeeZtbY2NjhgwZwuTJk6lWrRo1a9Zk9uzZaGlpleqWuY6Ojmzfvp2YmBjMzc1ZtmwZ165dU577WZyd4yuzRYQQQohyzMPDA19fXz744AP8/f2V8ry8PN555x0CAgIK3WNjYwPAgAEDmDZtGidPniQrK4urV6/Sv39/pZ6/vz9//fUXK1asoF69eujq6tKmTRtlVmpRs2Af/85z7Ngx+vfvz5w5c/D19cXU1JQtW7awdOnSYp9JV1cXXV3dZ/ochBBCVCwVdkAkKysLZ2dnatWqpZR7enpy+/ZtHBwcNKZFhoaGMm/ePCZOnMjvv/+OhYUFbdq0oWvXrsDD2R9jx47lt99+w8TEhC5duihrUuvUqcOcOXOYNm0aQ4cOZfDgwYW23X1U+/btadCgAUlJSQwZMuRfPd+yZcsYPXo03bp1U7bdvXr1Knp6ev+qvaLMnDmTy5cv4+vri4GBAaNGjaJnz56kp6eXWh9CCCGEKD8WLlyIu7u7stQFoFmzZpw7dw5HR8di76tbty4eHh5s2rSJrKwsfHx8NL6fRUdHs2bNGuV719WrV/n777+V6y4uLqSmpvLHH39Qu3ZtAI4eParRx5EjR6hXrx4zZsxQyq5cufJ8DyyEEKLCU+VX1E3kK5HMzEzq1KnD0qVLnyonycuSkZGBqakp6enpMkNECCHKMfl7Xjn5+/tz69YtIiIilLLBgwfzzTffcO/ePfLz8zlz5gytW7dm6NChjBw5EkNDQxITE9m3b59GvrX169cTFBTE/fv3Wb58OW+//bZyrWnTptSoUYOPP/6YjIwMJk+ezIkTJ/joo48IDAwkLy+Pxo0bY2VlxdKlS8nIyGD8+PHExcWxc+dOevbsyf/+9z/++9//snHjRlq2bMkPP/zAnDlzyM3N5datW0/1vAW/59aB29DSNSitj/GFSVnoV9YhCCHEK+lZvrdUyBkiFd2pU6f45ZdfaNWqFenp6QQHBwPQo0ePMo5MCCGEEBXZ3LlzNRKVurm5cfDgQWbMmEGHDh3Iz8/HwcGBfv36adzXp08f3nvvPbS1tenZs6fGtQ0bNjBq1CiaNm2KjY0NH330EZMmTVKua2lpsXPnToYPH06rVq2wtbVl5cqVdOnSRanTo0cPxo8fz7hx48jOzsbPz4+ZM2cSFBT0zM8oS32FEKLykBki5dCpU6cYMWIEFy5cQEdHh+bNm7Ns2TIaN25c1qFpkDeKQghRMcjfc1EZyO+5EEJUDDJDpIJr2rQpcXFxZR2GEEIIIYQQQghRblXIbXeFEEIIIYQQQgghSiIDIq+QoKAg3N3dn7sdtVqNSqV66iRi8DBx2uNreoUQQgghhBBCiIpKlsy8JN27dycrK4v9+/cXunb06FHatm3LwYMHee+99567r7Zt25KWloapqelT3/Pxxx/zotLJuM7eK9nahRBCCCGEEEK8UmSGyEsyfPhwDhw4wJUrVwpd27BhA+7u7nh4eGBhYVFsG/fv33+qvnR0dLC0tESlUj11fKamppiZmT11fSGEEEIIIYQQojyTAZGXpFu3btSsWZOwsDCN8rt377J161aGDx9eaMlMwTKWBQsWULt2bZycnACIiYnB3d0dPT09WrRoQUREBCqVivj4eKDwkpmwsDDMzMzYu3cvDRs2xMjIiC5dupCWllaorwJ79uyhffv2mJmZYWFhQbdu3UhOTn4RH40QQgghhBBCCPHSyYDIS1KlShUGDx5MWFiYxtKUb775hvv37zNw4MAi74uMjCQxMZF9+/axa9cubt++Tffu3WncuDEnT55k7ty5TJ069Yn93717l5CQEDZu3MihQ4dITU1l0qRJxdbPzMxkwoQJxMbGEhkZiZaWFr169SIvL6/Ye7Kzs8nIyNA4hBBCCCFKUvDiRgghhHjZJIfISzRs2DCWLFmCWq3G29sbeLhcpnfv3pibmxd5j6GhIZ9//jk6OjoArF27FpVKxfr169HT08PFxYXff/+dkSNHltj3gwcPWLt2LQ4ODgCMGzeO4ODgYuu/+eabGudffPEFNWvW5Pz587i6uhZ5z4IFC5gzZ06JcQghhBDi2V27do358+fzww8/8Pvvv1OzZk3c3d0JDAykU6dOZR3eU7O1tSUwMJDAwEClrF+/fnTt2rXU+khJScHOzo5Tp079q2T15SX32bOQPGlCCFE0mSHyEjk7O9O2bVs2bNgAQHJyMtHR0QwbNqzYexo3bqwMhgBcuHABNzc39PT0lLJWrVo9sW8DAwNlMATAysqK69evF1s/OTmZAQMGYG9vj4mJCXZ2dgCkpqYWe8/06dNJT09XjqtXrz4xLiGEEEKULCUlhebNm3PgwAEWL15MQkICe/bswdvbm7Fjx5Z1eM9NX1+fmjVrlnUYQgghKiEZEHnJhg8fzvbt28nIyCA0NJR69eqV+GbH0NBQ4zw/P79QstSn2R2matWqGucqlarE+7p3784///zD+vXr+fnnn/n555+BkhO76urqYmJionEIIYQQ4vmMGTMGlUrF8ePH+e9//4uTkxONGjViwoQJHDt2DHj4wqJHjx4YGRlhYmJC3759+fPPP5U2CvKUbdy4EVtbW0xNTenfvz+3b99W6nh5eREQEMCUKVOoVq0alpaWBAUFacSSnp7OqFGjqFmzJiYmJnTs2JHTp09r1Pnuu+9o0aIFenp6VK9end69eyvtX7lyhfHjx6NSqZTvM0UtmSmuDXj4HSYiIkKjvpmZmZKnreAlTtOmTVGpVHh5eT3T5y2EEKLykAGRl6xv375oa2vz9ddfEx4eztChQ59pNxhnZ2fOnDlDdna2UnbixIlSjfGff/4hMTGRDz/8kE6dOtGwYUNu3rxZqn0IIYQQ4slu3LjBnj17GDt2bKGXJPBwICA/P5+ePXty48YNDh48yL59+0hOTqZfv34adZOTk4mIiGDXrl3s2rWLgwcPsnDhQo064eHhGBoa8vPPP7N48WKCg4PZt28f8PAFjJ+fH9euXWP37t3ExcXRrFkzOnXqxI0bNwD44Ycf6N27N35+fpw6dYrIyEhatGgBwI4dO6hbty7BwcGkpaVpJHd/VEltPI3jx48DsH//ftLS0tixY0eR9ST3mRBCCMkh8pIZGRnRr18/PvjgA9LT0/H393+m+wcMGMCMGTMYNWoU06ZNIzU1lZCQEIBnGlgpibm5ORYWFnz22WdYWVmRmprKtGnT/nV7Z+f4ymwRIYQQ4l+4ePEi+fn5ODs7F1tn//79nDlzhsuXL2NtbQ3Axo0badSoEbGxsbRs2RKAvLw8wsLCMDY2BmDQoEFERkYyf/58pS03Nzdmz54NQP369Vm9ejWRkZF07tyZqKgoEhISuH79Orq6ugCEhIQQERHBt99+y6hRo5g/fz79+/fXyCnWpEkTAKpVq4a2tjbGxsZYWloW+zwltfE0atSoAYCFhUWJ/UjuMyGEEDJDpAwMHz6cmzdv4uPjg42NzTPda2Jiwvfff098fDzu7u7MmDGDWbNmAWjkFXkeWlpabNmyhbi4OFxdXRk/fjxLliwplbaFEEII8fQKlreW9NIjMTERa2trZTAEwMXFBTMzMxITE5UyW1tbZTAEis4n5ubmpnH+aJ24uDju3LmDhYUFRkZGynH58mWSk5MBiI+Pf+4kr6XRxtOQ3GdCCCFkhkgZaNOmTZH5O4KCgjTW6hashX1c27ZtNdbrbtq0iapVqyqDK15eXhrt+/v7F5qJ0rNnT406j/fl4+PD+fPnNcqeJleJEEIIIUpP/fr1UalUJCYm0rNnzyLrFJVfrKjyovKJ5eXlaZSVVCcvLw8rKyvUanWhvgpygOjr6z/pkZ7oSW0UlQftwYMHz9yPrq6uMtNFCCFE5SQzRMqhL7/8ksOHD3P58mUiIiKYOnUqffv2LZUvIUIIIYR4dVSrVg1fX18++eQTMjMzC12/desWLi4upKamasxwOH/+POnp6TRs2LDUYmnWrBnXrl2jSpUqODo6ahzVq1cHHs4wiYyMLLYNHR0dcnNzS+znSW3UqFFDI/9IUlISd+/e1egDeGI/QgghhMwQKYeuXbvGrFmzuHbtGlZWVvTp00dj/a8QQgghKo41a9bQtm1bWrVqRXBwMG5ubuTk5LBv3z4+/fRTzp8/j5ubGwMHDmTFihXk5OQwZswYPD09nykZ6ZP4+PjQpk0bevbsyaJFi2jQoAF//PEHu3fvpmfPnrRo0YLZs2fTqVMnHBwc6N+/Pzk5Ofz4449MmTIFeLhs59ChQ/Tv3x9dXV1lIOVRT2qjY8eOrF69mtatW5OXl8fUqVM1ZrbUrFkTfX199uzZQ926ddHT08PU1PSpn1NynwkhROUhM0TKoSlTppCSksK9e/e4fPkyy5cvx8DAoKzDEkIIIcQLYGdnx8mTJ/H29mbixIm4urrSuXNnIiMj+fTTT5VtaM3NzfHw8MDHxwd7e3u2bt1aqnGoVCp2796Nh4cHw4YNw8nJif79+5OSkkKtWrWAh8t2v/nmG7777jvc3d3p2LEjP//8s9JGcHAwKSkpODg4KMlPH/ekNpYuXYq1tTUeHh4MGDCASZMmaXwPqlKlCitXrmTdunXUrl2bHj16lOrnIIQQouJQ5UtiCPGCZGRkYGpqSnp6urxpEUKIckz+novKQH7PhRCiYniWv+cyQ6SSsLW1ZcWKFcVeT0lJQaVSER8f/9JiEkIIIYQQQgghyorkEKkArl69SlBQED/++CN///03VlZW9OzZk1mzZmFhYfFUbVhbW5OWllbkWt7n5Tp7L1q6FWtJT8pCv7IOQQghhBBCCCHEc5AZIuXcpUuXaNGiBb/++iubN2/m4sWLrF27lsjISNq0acONGzeeqh1tbW0sLS2pUkXGyIQQQgghhBBCVHwyIFLOjR07Fh0dHX766Sc8PT2xsbHh9ddfZ//+/fz+++/MmDFDqXv37l2GDRuGsbExNjY2fPbZZ8q1opbMHDx4kFatWqGrq4uVlRXTpk0jJyfnZT6eEEIIIYQQQgjxQsiASDl248YN9u7dy5gxY9DX19e4ZmlpycCBA9m6dSsFeXOXLl1KixYtOHXqFGPGjOHdd9/ll19+KbLt33//na5du9KyZUtOnz7Np59+yhdffMG8efOKjSc7O5uMjAyNQwghhBBCCCGEeBXJgEg5lpSURH5+Pg0bNizyesOGDbl58yZ//fUXAF27dmXMmDE4OjoydepUqlevjlqtLvLeNWvWYG1tzerVq3F2dqZnz57MmTOHpUuXkpeXV+Q9CxYswNTUVDmsra1L5TmFEEIIIYQQQojSJgkjKrCCmSEqlQoANzc35ZpKpcLS0pLr168XeW9iYiJt2rRR7gVo164dd+7c4bfffsPGxqbQPdOnT2fChAnKeUZGhgyKCCGEEOKZ2draEhgYSGBgYJHXU1JSsLOz49SpU7i7u5dq35IMXgghKg+ZIVKOOTo6olKpOH/+fJHXf/nlF8zNzZWdY6pWrapxXaVSFTvbIz8/X2MwpKCs4L6i6OrqYmJionEIIYQQQjzq6tWrDB8+nNq1a6Ojo0O9evV4//33+eeff566jYLd8VxdXV9gpEIIISo6GRApxywsLOjcuTNr1qwhKytL49q1a9fYtGkT/fr1K3YAoyQuLi7ExMQogyAAMTExGBsbU6dOneeOXQghhBCVj+yOJ4QQ4lUi/4qUc6tXr6Zt27b4+voyb9487OzsOHfuHJMnT6ZOnTrMnz//X7U7ZswYVqxYwXvvvce4ceO4cOECs2fPZsKECWhpPds42tk5vjJbRAghhBAau+MVJIS3sbGhadOmODg4MGPGDD799FPg/3bH++abbzA3N+fDDz9k1KhRQNFLZg4ePMjkyZM5ffo01apVY8iQIcybN08GTYQQQhRLZoiUc/Xr1+fEiRM4ODjQr18/HBwcGDVqFN7e3hw9epRq1ar9q3br1KnD7t27OX78OE2aNGH06NEMHz6cDz/8sJSfQAghhBCVgeyOJ4QQ4lUjQ+YVQL169QgNDS2xTkpKSqGy+Ph45WdbW1uN5TEAnp6eHD9+vDRCFEIIIUQl9293xwOYOnUqy5cvR61W4+zsXOjeR3fHU6lUODs788cffzB16lRmzZpV5OzWBQsWMGfOnFJ8QiGEEOWNzBARQgghhBBl7kXujleU6dOnk56erhxXr14trUcRQghRTsiAiBBCCCGEeOFkdzwhhBCvGhkQEUIIIYQQL5zsjieEEOJVIzlEhBBCCCHESyG74wkhhHiVyAwRIYQQQgjxUsjueEIIIV4lqvzHtxYR5V5YWBiBgYHcunWrTOPIyMjA1NQU68BtaOkalGkspS1loV9ZhyCEEC9Nwd/z9PR0eXMuKiz5PRdCiIrhWf6eV9oZIteuXeO9997D3t4eXV1drK2t6d69O5GRkWUd2jOxtbVlxYoVGmX9+vXj119/LbU+UlJSUKlUGtv0CiGEEEIIIYQQ5VmlzCGSkpJCu3btMDMzY/Hixbi5ufHgwQP27t3L2LFj+eWXX8o6xOeir6+Pvr5+WYchhBBCCCGEEEK8sirlDJExY8agUqk4fvw4//3vf3FycqJRo0ZMmDCBY8eOAZCamkqPHj0wMjLCxMSEvn378ueffyptBAUF4e7uzsaNG7G1tcXU1JT+/ftz+/ZtpY6XlxcBAQFMmTKFatWqYWlpSVBQkEYs6enpjBo1ipo1a2JiYkLHjh05ffq0Rp3vvvuOFi1aoKenR/Xq1endu7fS/pUrVxg/fjwqlUrJyh4WFoaZmdlTtQEPt6OLiIjQqG9mZkZYWBgAdnZ2ADRt2hSVSoWXl1eRn2t2djYZGRkahxBCCCGEEEII8SqqdAMiN27cYM+ePYwdOxZDQ8NC183MzMjPz6dnz57cuHGDgwcPsm/fPpKTk+nXr59G3eTkZCIiIti1axe7du3i4MGDLFy4UKNOeHg4hoaG/PzzzyxevJjg4GD27dsHQH5+Pn5+fly7do3du3cTFxdHs2bN6NSpEzdu3ADghx9+oHfv3vj5+XHq1CkiIyNp0aIFADt27KBu3boEBweTlpZGWlpakc9cUhtP4/jx4wDs37+ftLQ0duzYUWS9BQsWYGpqqhzW1tZP3YcQQgghhBBCCPEyVbolMxcvXiQ/Px9nZ+di6+zfv58zZ85w+fJl5X/qN27cSKNGjYiNjaVly5YA5OXlERYWhrGxMQCDBg0iMjJSY8s4Nzc3Zs+eDTzMrL569WoiIyPp3LkzUVFRJCQkcP36dXR1dQEICQkhIiKCb7/9llGjRjF//nz69+/PnDlzlDabNGkCQLVq1dDW1sbY2BhLS8tin6ekNp5GjRo1ALCwsCixn+nTpzNhwgTlPCMjQwZFhBBCCCGEEEK8kirdDJGCTXUKlpcUJTExEWtra43/mXdxccHMzIzExESlzNbWVhkMAbCysuL69esabbm5uWmcP1onLi6OO3fuYGFhgZGRkXJcvnyZ5ORkAOLj4+nUqdO/fFpKrY2noauri4mJicYhhBBCiJfL39+fnj17lnUYz+TxBO5qtRqVSlXmO+YJIYSo2CrdDJH69eujUqlITEws9stCfn5+kQMmj5dXrVpV47pKpSIvL0+jrKQ6eXl5WFlZoVarC/VVkAOkNJKjPqkNlUrF47svP3jw4Ln7FUIIISozf39/wsPDWbBgAdOmTVPKIyIi6NWrV6F/e59VSkoKdnZ2nDp1Cnd39+eMFu7fv8+KFSvYtGkTSUlJGBgY0KBBA0aMGMHbb79d6DvNi9S2bVvS0tIwNTV9aX0WcJ29Fy1dg5fe76siZaFfWYcghBAvTaUbEKlWrRq+vr588sknBAQEFMojcuvWLVxcXEhNTeXq1avKLJHz58+Tnp5Ow4YNSy2WZs2ace3aNapUqYKtrW2Rddzc3IiMjGTo0KFFXtfR0SE3N7fEfp7URo0aNTTyjyQlJXH37l2NPoAn9lOcs3N8ZbaIEEKISklPT49FixbxzjvvYG5uXmrt3r9/v9TaKmjP19eX06dPM3fuXNq1a4eJiQnHjh0jJCSEpk2b/utBlwcPHjzzYIqOjk6Jy3SFEEKI0lDplswArFmzhtzcXFq1asX27dtJSkoiMTGRlStX0qZNG3x8fHBzc2PgwIGcPHmS48ePM3jwYDw9PZ8pGemT+Pj40KZNG3r27MnevXtJSUkhJiaGDz/8kBMnTgAwe/ZsNm/ezOzZs0lMTCQhIYHFixcrbdja2nLo0CF+//13/v777yL7eVIbHTt2ZPXq1Zw8eZITJ04wevRojS8uNWvWRF9fnz179vDnn3+Snp5eap+BEEIIUZH5+PhgaWnJggULSqy3fft2GjVqhK6uLra2tixdulTjuq2tLfPmzcPf3x9TU1NGjhz5xF3gQkJCsLKywsLCgrFjx5Y4+3PFihUcOnSIyMhIxo4di7u7O/b29gwYMICff/6Z+vXrA7Bnzx7at2+PmZkZFhYWdOvWTVnmC/+39GXbtm14eXmhp6fHV199RV5eHsHBwdStWxddXV3c3d3Zs2dPsfE8vmSmYAe9vXv30rBhQ4yMjOjSpYvGC53Y2Fg6d+5M9erVMTU1xdPTk5MnT5b4uQshhKjcKuWAiJ2dHSdPnsTb25uJEyfi6upK586diYyM5NNPP1W2oTU3N8fDwwMfHx/s7e3ZunVrqcahUqnYvXs3Hh4eDBs2DCcnJ/r3709KSgq1atUCHm6t+8033/Ddd9/h7u5Ox44d+fnnn5U2goODSUlJwcHBQUl++rgntbF06VKsra3x8PBgwIABTJo0CQOD/5sqWqVKFVauXMm6deuoXbs2PXr0KNXPQQghhKiotLW1+eijj1i1ahW//fZbkXXi4uLo27cv/fv3JyEhgaCgIGbOnElYWJhGvSVLluDq6kpcXBwzZ84scRe4qKgokpOTiYqKIjw8nLCwsELtPWrTpk34+PjQtGnTQteqVq2qzKjNzMxkwoQJxMbGEhkZiZaWFr169Sq0ZHjq1KkEBASQmJiIr68vH3/8MUuXLiUkJIQzZ87g6+vLG2+8QVJS0tN8jADcvXuXkJAQNm7cyKFDh0hNTWXSpEnK9du3bzNkyBCio6M5duwY9evXp2vXrty+fbvI9rKzs8nIyNA4hBBCVC6q/OddwCpEMTIyMjA1NSU9PV2WzAghRDkmf8//HX9/f27dukVERARt2rTBxcWFL774olAOkYEDB/LXX3/x008/KfdOmTKFH374gXPnzgEPZ4g0bdqUnTt3KnWKyyHi7++PWq0mOTkZbW1tAPr27YuWlhZbtmwpMlYDAwNGjhzJxx9//EzP+Ndff1GzZk0SEhJwdXVVYlqxYgXvv/++Uq9OnTqMHTuWDz74QClr1aoVLVu25JNPPin0LGq1Gm9vb27evImZmRlhYWEMHTqUixcv4uDgADyc8RscHMy1a9eKjC03Nxdzc3O+/vprunXrVuh6UFCQxg58BawDt0kOESGEKMee5XtLpZwhIoQQQgjxMi1atIjw8HDOnz9f6FpiYiLt2rXTKGvXrh1JSUka+bueZdluo0aNlMEQKHonvEcVl1D+ccnJyQwYMAB7e3tMTEyUZTupqaka9R6NNSMjgz/++KPIZ3x0974nMTAwUAZDoPAzXb9+ndGjR+Pk5ISpqSmmpqbcuXOnUGwFpk+fTnp6unJcvXr1qWMRQghRMVS6pKpCCCGEEC+bh4cHvr6+fPDBB/j7+2tcK2owoqgJvI8ngi/J0+yE9ygnJ6enGpzo3r071tbWrF+/ntq1a5OXl4erq2uhJK9FxVrUMz7NIEyBop7p0c/J39+fv/76ixUrVlCvXj10dXVp06ZNsQlodXV10dXVfer+hRBCVDwyQ0QIIYQQ4iVYuHAh33//PTExMRrlLi4uHD58WKMsJiYGJycnjVkej3veXeAeNWDAAPbv38+pU6cKXcvJySEzM5N//vmHxMREPvzwQzp16kTDhg25efPmE9s2MTGhdu3aRT5jae7eFx0dTUBAAF27dlUS1BaXcF4IIYQAGRARQgghhHgpGjduzMCBA1m1apVG+cSJE4mMjGTu3Ln8+uuvhIeHs3r1ao2EoUUpzV3gAgMDadeuHZ06deKTTz7h9OnTXLp0iW3btvHaa6+RlJSEubk5FhYWfPbZZ1y8eJEDBw4wYcKEp2p/8uTJLFq0iK1bt3LhwgWmTZtGfHy8Rp6R5+Xo6MjGjRtJTEzk559/ZuDAgejr65da+0IIISoeWTIjhBBCCPGSzJ07l23btmmUNWvWjG3btjFr1izmzp2LlZUVwcHBhZbWPK5gF7jg4GBmzZpFhw4dUKvV/youXV1d9u3bx/Lly1m3bp2y41zDhg0JCAjA1dVVScpacN6gQQNWrlxZaLvfogQEBJCRkcHEiRO5fv06Li4ufPfdd8p2vqVhw4YNjBo1iqZNm2JjY8NHH330xEGlopyd4yvJg4UQopKQXWbKyNWrVwkKCuLHH3/k77//xsrKip49ezJr1iwsLCzKOrxSUZDdV7K1S7Z2IUT5JrvMiMpAfs+FEKJikF1mXnGXLl2iRYsW/Prrr2zevJmLFy+ydu1aIiMjadOmDTdu3CjyvuKSggkhhBBCCCGEEOLZyIBIGRg7diw6Ojr89NNPeHp6YmNjw+uvv87+/fv5/fffmTFjBgC2trbMmzcPf39/TE1NGTlyJPAwCZmHhwf6+vpYW1sTEBBAZmam0n5aWhp+fn7o6+tjZ2fH119/ja2tLStWrFDqpKam0qNHD4yMjDAxMaFv3778+eefyvWgoCDc3d3ZuHEjtra2mJqa0r9/f27fvv1yPiQhhBBCCCGEEOIFkgGRl+zGjRvs3buXMWPGFEr0ZWlpycCBA9m6dauyjdySJUtwdXUlLi6OmTNnkpCQgK+vL7179+bMmTNs3bqVw4cPM27cOKWdwYMH88cff6BWq9m+fTufffYZ169fV67n5+fTs2dPbty4wcGDB9m3bx/Jycn069dPI57k5GQiIiLYtWsXu3bt4uDBgyxcuLDYZ8vOziYjI0PjEEIIIYQQQgghXkWSVPUlS0pKIj8/v9ht5gq2sPvrr78A6Nixo0ZCsMGDBzNgwAACAwMBqF+/PitXrsTT05NPP/2UlJQU9u/fT2xsLC1atADg888/10hatn//fs6cOcPly5extrYGYOPGjTRq1IjY2FhatmwJQF5eHmFhYRgbGwMwaNAgIiMjmT9/fpGxL1iwgDlz5jzHpyOEEEIIIYQQQrwcMkPkFVMwM0SlUgEogxoF4uLiCAsLw8jISDl8fX3Jy8vj8uXLXLhwgSpVqtCsWTPlHkdHR8zNzZXzxMRErK2tlcEQABcXF8zMzEhMTFTKbG1tlcEQACsrK42ZJo+bPn066enpynH16tV/+SkIIYQQQgghhBAvlswQeckcHR1RqVScP3+enj17Frr+yy+/YG5uTvXq1QEwNDTUuJ6Xl8c777xDQEBAoXttbGy4cOFCkf0+uplQfn6+MuDyeJ1Hy6tWrapxXaVSkZeXV+yz6erqoqurW+x1IYQQQgghhBDiVSEDIi+ZhYUFnTt3Zs2aNYwfP14jj8i1a9fYtGkTgwcPLnLAAqBZs2acO3cOR0fHIq87OzuTk5PDqVOnaN68OQAXL17k1q1bSh0XFxdSU1O5evWqMkvk/PnzpKenF7uURwghhBDiZVGpVOzcubPIl0cvmuvsvWjpGrz0fiuylIV+ZR2CEEIUSQZEysDq1atp27Ytvr6+zJs3Dzs7O86dO8fkyZOpU6dOsTk6AKZOnUrr1q0ZO3YsI0eOxNDQkMTERPbt28eqVatwdnbGx8eHUaNG8emnn1K1alUmTpyIvr6+Msji4+ODm5sbAwcOZMWKFeTk5DBmzBg8PT0LLdEpDWfn+D5x/2chhBBCvDhr165l8uTJ3Lx5kypVHn79u3PnDubm5rRu3Zro6GilbnR0NB4eHly4cAEnJ6cyiTctLU1jua8QQgjxIkgOkTJQv359Tpw4gYODA/369cPBwYFRo0bh7e3N0aNHqVatWrH3urm5cfDgQZKSkujQoQNNmzZl5syZWFlZKXW+/PJLatWqhYeHB7169WLkyJEYGxujp6cHPHzrEhERgbm5OR4eHvj4+GBvb8/WrVtf+LMLIYQQ4uXz9vbmzp07nDhxQimLjo7G0tKS2NhY7t69q5Sr1Wpq165dZoMh8HDnPVmGK4QQ4kWTAZEyUq9ePUJDQ0lLS+P+/fukpqaycuVKLCwslDopKSnKbjKPatmyJT/99BO3b9/mzp07nD59mg8++EC5bmVlxe7du7l37x4pKSl06NCB69evayyzsbGx4X//+x937twhIyODbdu2UatWLeV6UFAQ8fHxGv0GBgaSkpJSap+BEEIIIV6OBg0aULt2bdRqtVKmVqvp0aMHDg4OxMTEaJR7eXnh6OhISEiIRjtnz55FS0uL5ORkAFJTU+nRowdGRkaYmJjQt29f/vzzT6V+UFAQ7u7ubNiwARsbG4yMjHj33XfJzc1l8eLFWFpaUrNmzUKzYwte3sDD70MqlYodO3bg7e2NgYEBTZo04ejRoxr3rF+/HmtrawwMDOjVqxfLli3DzMysFD49IYQQFZUMiFRABw4c4LvvvuPy5cvExMTQv39/bG1t8fDwKOvQhBBCCFFGvLy8iIqKUs6joqLw8vLC09NTKb9//z5Hjx6lY8eODBs2jNDQUI02NmzYQIcOHXBwcCA/P5+ePXty48YNDh48yL59+0hOTqZfv34a9yQnJ/Pjjz+yZ88eNm/ezIYNG/Dz8+O3337j4MGDLFq0iA8//JBjx46VGP+MGTOYNGkS8fHxODk58dZbb5GTkwPAkSNHGD16NO+//z7x8fF07ty5xCXIANnZ2WRkZGgcQgghKhfJIVIBPXjwgA8++IBLly5hbGxM27Zt2bRpU6FdY4QQQghReXh5eTF+/HhycnLIysri1KlTeHh4kJuby8qVKwE4duwYWVlZeHt7o6+vz6xZszh+/DitWrXiwYMHfPXVVyxZsgSA/fv3c+bMGS5fvqwkad+4cSONGjUiNjaWli1bAg93yNuwYQPGxsa4uLjg7e3NhQsX2L17N1paWjRo0IBFixahVqtp3bp1sfFPmjQJP7+HyTnnzJlDo0aNuHjxIs7OzqxatYrXX3+dSZMmAeDk5ERMTAy7du0qtr0FCxYwZ86c5/9ghRBClFsyQ6QC8vX15ezZs9y9e5c///yTnTt3Uq9evbIOSwghhBBlyNvbm8zMTGJjY4mOjsbJyYmaNWvi6elJbGwsmZmZqNVqbGxssLe3x8rKCj8/PzZs2ADArl27uHfvHn369AEgMTERa2trZTAEHu5kZ2ZmRmJiolJma2uLsbGxcl6rVi1cXFzQ0tLSKLt+/XqJ8bu5uSk/F+ROK7jnwoULtGrVSqP+4+ePmz59Ounp6cpx9erVEusLIYSoeGRARAghhBCiEnB0dKRu3bpERUURFRWFp6cn8DCBqZ2dHUeOHCEqKoqOHTsq94wYMYItW7aQlZVFaGgo/fr1w8Dg4Za0+fn5yg52j3q8/PEZqiqVqsiyvLy8EuN/9J6C9gvuKSqW/Pz8EtvT1dXFxMRE4xBCCFG5yIDIU/D396dnz55lHcYzKUhAVpAYVa1Wo1KpuHXrVpnGJYQQQoiy4+3tjVqtVhKnFvD09GTv3r0cO3YMb29vpbxr164YGhry6aef8uOPPzJs2DDlmouLC6mpqRozK86fP096ejoNGzZ8Kc9TwNnZmePHj2uUPbqjjhBCCFGUcpVDxN/fn/DwcBYsWMC0adOU8oiICHr16vXENwFPkpKSgp2dHadOncLd3f05o32YmGzFihVs2rSJpKQkDAwMaNCgASNGjODtt99+qTk92rZtS1paGqampi+tzwKus/eipWvw0vt9VaQs9CvrEIQQQgjg4YDI2LFjefDggTJDBB4OiLz77rvcu3dPY0BEW1sbf39/pk+fjqOjI23atFGu+fj44ObmxsCBA1mxYgU5OTmMGTMGT09PWrRo8VKf67333sPDw4Nly5bRvXt3Dhw4wI8//ljkDJYnOTvHV2aLCCFEJVHuZojo6emxaNEibt68Wart3r9/v9Tb8/X1ZeHChYwaNYqYmBiOHz/O2LFjWbVqFefOnfvXbT948OCZ79HR0cHS0vJffTEQQgghRMXg7e1NVlYWjo6O1KpVSyn39PTk9u3bODg4aOQEARg+fDj379/XmB0C/7c1rrm5OR4eHvj4+GBvb8/WrVtfyrM8ql27dqxdu5Zly5bRpEkT9uzZw/jx49HT03vpsQghhCg/yt2AiI+PD5aWlixYsKDEetu3b6dRo0bo6upia2vL0qVLNa7b2toyb948/P39MTU1ZeTIkdjZ2QHQtGlTVCqVxlRSgJCQEKysrLCwsFDerhRnxYoVHDp0iMjISMaOHYu7uzv29vYMGDCAn3/+mfr16wOwZ88e2rdvj5mZGRYWFnTr1o3k5GSlnYKlL9u2bcPLyws9PT2++uor8vLyCA4Opm7duujq6uLu7s6ePXuKjefxJTNhYWGYmZmxd+9eGjZsiJGREV26dCEtLU25JzY2ls6dO1O9enVMTU3x9PTk5MmTJX7uQgghhHh12drakp+fr5H0FKBu3brk5+dz8eLFQvekpaVRpUoVBg8eXOiajY0N//vf/7hz5w4ZGRls27ZNY6AlKChIWb5bICwsjIiICI0ytVrNihUrlPOCLX0fjfnR2btmZmbk5+drfFcbOXIkv/32G3fv3mXnzp2kpKTg6OhY8gcihBCiUit3AyLa2tp89NFHrFq1it9++63IOnFxcfTt25f+/fuTkJBAUFAQM2fOJCwsTKPekiVLcHV1JS4ujpkzZyprT/fv309aWho7duxQ6kZFRZGcnExUVBTh4eGEhYUVau9RmzZtwsfHh6ZNmxa6VrVqVQwNDQHIzMxkwoQJxMbGEhkZiZaWFr169SqUWGzq1KkEBASQmJiIr68vH3/8MUuXLiUkJIQzZ87g6+vLG2+8QVJS0tN8jADcvXuXkJAQNm7cyKFDh0hNTVW2qwO4ffs2Q4YMITo6mmPHjlG/fn26du3K7du3i2wvOzubjIwMjUMIIYQQ5VN2djYXL15k5syZ9O3bV2Og41UUEhLC6dOnuXjxIqtWrSI8PJwhQ4aUdVhCCCFeYeUqh0iBXr164e7uzuzZs/niiy8KXV+2bBmdOnVi5syZwMO96M+fP8+SJUvw9/dX6nXs2FFjACAlJQUACwsLLC0tNdo0Nzdn9erVaGtr4+zsjJ+fH5GRkYwcObLIGJOSkgrNMCnKm2++qXH+xRdfULNmTc6fP4+rq6tSHhgYSO/evZXzkJAQpk6dSv/+/QFYtGgRUVFRrFixgk8++eSJ/cLDpTdr167FwcEBgHHjxhEcHKxcfzTLPMC6deswNzfn4MGDdOvWrVB7CxYsYM6cOU/VtxBCCCFebZs3b2b48OG4u7uzcePGsg7niY4fP87ixYu5ffs29vb2rFy5khEjRpR1WEIIIV5h5W6GSIFFixYRHh7O+fPnC11LTEykXbt2GmXt2rUjKSmJ3NxcpexZEn41atQIbW1t5dzKyorr168XW7+4regel5yczIABA7C3t8fExERZtpOamqpR79FYMzIy+OOPP4p8xsenwJbEwMBAGQyBws90/fp1Ro8ejZOTE6amppiamnLnzp1CsRWYPn066enpyvFo1nkhhBBClC/+/v7k5uYSFxdHnTp1yjqcJ9q2bRvXr18nKyuLc+fOMXr06LIOSQghxCuuXM4QAfDw8MDX15cPPvhAY9YHPP1e9AXLVp7G4zvCqFSqQstaHuXk5PRUgxPdu3fH2tqa9evXU7t2bfLy8nB1dS2U5LWoWIt6xmdJmlrUMz36Ofn7+/PXX3+xYsUK6tWrh66uLm3atCk2Aa2uri66urpP3b8QQgghhBBCCFFWyu0MEYCFCxfy/fffExMTo1Hu4uLC4cOHNcpiYmJwcnLSmOXxOB0dHQCNWST/1oABA9i/fz+nTp0qdC0nJ4fMzEz++ecfEhMT+fDDD+nUqRMNGzZ8qt1zTExMqF27dpHP2LBhw+eOvUB0dDQBAQF07dpVSVD7999/l1r7QgghhBBCCCFEWSm3M0QAGjduzMCBA1m1apVG+cSJE2nZsiVz586lX79+HD16lNWrV7NmzZoS26tZsyb6+vrs2bOHunXroqenh6mp6b+KLTAwkB9++IFOnToxd+5c2rdvj7GxMSdOnGDRokV88cUXuLm5YWFhwWeffYaVlRWpqalMmzbtqdqfPHkys2fPxsHBAXd3d0JDQ4mPj2fTpk3/Kt6iODo6snHjRlq0aEFGRgaTJ09GX1//mds5O8cXExOTUotLCCGEEBVXSkoKdnZ2nDp1SmNnGSGEEKK0lesBEYC5c+eybds2jbJmzZqxbds2Zs2axdy5c7GysiI4OLjQ0prHValShZUrVxIcHMysWbPo0KEDarX6X8Wlq6vLvn37WL58OevWrWPSpEkYGBjQsGFDAgICcHV1RUtLiy1btijnDRo0YOXKlU+VjDUgIICMjAwmTpzI9evXcXFx4bvvvlO28y0NGzZsYNSoUTRt2hQbGxs++ugjjSS0QgghhBDwcJlteHg477zzDmvXrtW4NmbMGD799FOGDBlS4g59BaytrUlLS6N69eovKNqSuc7ei5auQZn0LSBloV9ZhyCEqERU+UUl1xCiFGRkZGBqakp6errMEBFCiHJM/p6LJ/H39+fAgQNkZGSQlpamzCi9d+8eVlZWmJiY4O3t/VQDImWl4PfcOnCbDIiUIRkQEUI8r2f53lKuc4gIIYQQQohXQ7NmzbCxsWHHjh1K2Y4dO7C2tqZp06ZK2Z49e2jfvj1mZmZYWFjQrVs3kpOTlespKSmoVCri4+MBUKvVqFQqIiMjadGiBQYGBrRt25YLFy5o9P/999/TvHlz9PT0sLe3Z86cOeTk5LzYhxZCCFGuyYCIEEIIIYQoFUOHDiU0NFQ537BhA8OGDdOok5mZyYQJE4iNjSUyMhItLS169epV4u59ADNmzGDp0qWcOHGCKlWqaLS7d+9e3n77bQICAjh//jzr1q0jLCyM+fPnF9tednY2GRkZGocQQojKRQZEhBBCCCFEqRg0aBCHDx8mJSWFK1eucOTIEd5++22NOm+++Sa9e/emfv36uLu788UXX5CQkMD58+dLbHv+/Pl4enri4uLCtGnTiImJ4d69e8q1adOmMWTIEOzt7encuTNz585l3bp1xba3YMECTE1NlcPa2vr5PwAhhBDligyICCGEEEKIUlG9enX8/PwIDw8nNDQUPz+/QslRk5OTGTBgAPb29piYmGBnZwdAampqiW27ubkpP1tZWQFw/fp1AOLi4ggODsbIyEg5Ro4cSVpaGnfv3i2yvenTp5Oenq4cV69e/dfPLYQQonwq97vMlIawsDACAwO5devWS+1XrVbj7e3NzZs3MTMze6l9q1Qqdu7cSc+ePV9qv0IIIYSo2IYNG8a4ceMA+OSTTwpd7969O9bW1qxfv57atWuTl5eHq6sr9+/fL7HdqlWrKj+rVCoAZZlNXl4ec+bMoXfv3oXu09PTK7I9XV1ddHV1n+6hhBBCVEjlfkDk+vXrzJw5kx9//JE///wTc3NzmjRpQlBQEG3atPlXbQYFBREREaEk83reei/btWvXmD9/Pj/88AO///47NWvWxN3dncDAQDp16gRAWloa5ubmwMPkZXZ2dpw6dQp3d/dSj6eyb18n2dKFEEJUJl26dFEGN3x9fTWu/fPPPyQmJrJu3To6dOgAwOHDh5+7z2bNmnHhwgUcHR2fuy0hhBCVR7kfEHnzzTd58OAB4eHh2Nvb8+effxIZGcmNGzfKOrQykZKSQrt27TAzM2Px4sW4ubnx4MED9u7dy9ixY/nll18AsLS0LONIhRBCCFERaWtrk5iYqPz8KHNzcywsLPjss8+wsrIiNTWVadOmPXefs2bNolu3blhbW9OnTx+0tLQ4c+YMCQkJzJs377nbF0IIUTGV6wGRW7ducfjwYdRqNZ6engDUq1ePVq1aadRbtmwZoaGhXLp0iWrVqtG9e3cWL16MkZFRoTbDwsKYM2cO8H/TMUNDQ/H393/m+L766itWrFjBhQsXMDQ0pGPHjqxYsYKaNWsWWT8rK4v//ve//PPPP+zevZtq1aoRGhrK4sWLuXz5Mra2tgQEBDBmzJhi+xwzZgwqlYrjx49jaGiolDdq1EgjG/ujS2YK1u4WbInn6elJcHAwnTp14urVqxqDJxMnTiQ2NpZDhw498+chhBBCiMrBxMSkyHItLS22bNlCQEAArq6uNGjQgJUrV+Ll5fVc/fn6+rJr1y6Cg4NZvHgxVatWxdnZmREjRjxzW2fn+BYbvxBCiIqlXA+IFCTNioiIoHXr1sWuA9XS0mLlypXY2tpy+fJlxowZw5QpU1izZk2huv369ePs2bPs2bOH/fv3A2Bqavqv4rt//z5z586lQYMGXL9+nfHjx+Pv78/u3bsL1U1PT6dbt27o6ekRGRmJoaEh69evZ/bs2axevZqmTZty6tQpRo4ciaGhIUOGDCnUxo0bN9izZw/z58/XGAwpUFyekuPHj9OqVSv2799Po0aN0NHRoVq1atjb27Nx40YmT54MQE5ODl999RULFy4ssp3s7Gyys7OVc9m+TgghhKgcwsLCSrweERGh/Ozj41NoR5n8/HzlZ1tbW41zLy8vjXMAd3f3QmW+vr6FlugIIYQQJSnXu8xUqVKFsLAwwsPDMTMzo127dnzwwQecOXNGo15gYCDe3t7Y2dnRsWNH5s6dy7Zt24psU19fHyMjI6pUqYKlpSWWlpbo6+v/q/iGDRvG66+/jr29Pa1bt2blypX8+OOP3LlzR6Pen3/+iaenJzVr1uSHH35QBjPmzp3L0qVL6d27N3Z2dvTu3Zvx48cXu4XcxYsXyc/Px9nZ+ZnirFGjBgAWFhZYWlpSrVo1AIYPH05oaKhS74cffuDu3bv07du3yHZk+zohhBBCCCGEEOVFuR4QgYc5RP744w++++47fH19UavVNGvWTONNRVRUFJ07d6ZOnToYGxszePBg/vnnHzIzM19obKdOnaJHjx7Uq1cPY2NjZTro49vK+fj4YG9vz7Zt29DR0QHgr7/+4urVqwwfPlxjC7l58+aRnJxcZH8Fb0oKlvo8L39/fy5evMixY8cA2LBhA3379i1y9gnI9nVCCCGEEEIIIcqPcj8gAg+3U+vcuTOzZs0iJiYGf39/Zs+eDcCVK1fo2rUrrq6ubN++nbi4OGULuAcPHrywmDIzM/nPf/6DkZERX331FbGxsezcuROg0LZyfn5+REdHa0wfLdhGbv369cTHxyvH2bNnlQGKx9WvXx+VSqUkMnteNWvWpHv37oSGhnL9+nV2796tkYfkcbq6upiYmGgcQgghhBBCCCHEq6hc5xApjouLi7JW9cSJE+Tk5LB06VK0tB6O/xS3XKaAjo4Oubm5zxXDL7/8wt9//83ChQuVpSMnTpwosu7ChQsxMjKiU6dOqNVqXFxcqFWrFnXq1OHSpUsMHDjwqfqsVq0avr6+fPLJJwQEBBSayXHr1q0i84gUzEop6plHjBhB//79qVu3Lg4ODrRr1+6pYhFCCCGEEEIIIV5l5XpA5J9//qFPnz4MGzYMNzc3jI2NOXHiBIsXL6ZHjx4AODg4kJOTw6pVq+jevTtHjhxh7dq1JbZbkHw1Pj6eunXrYmxsXGzC1qysLOLj4zXKjIyMsLGxQUdHh1WrVjF69GjOnj3L3Llzi+0zJCSE3NxcOnbsiFqtxtnZmaCgIAICAjAxMeH1118nOzubEydOcPPmTSZMmFBkO2vWrKFt27a0atWK4OBg3NzcyMnJYd++fXz66adFzh6pWbMm+vr67Nmzh7p166Knp6ckkvX19cXU1JR58+YRHBxc4udWHMnWLoQQQgghhBDiVVOul8wYGRnx2muvsXz5cjw8PHB1dWXmzJmMHDmS1atXAw+zkC9btoxFixbh6urKpk2bWLBgQYntvvnmm3Tp0gVvb29q1KjB5s2bi63766+/0rRpU41jxIgR1KhRg7CwML755htcXFxYuHAhISEhJfa7fPly+vbtS8eOHfn1118ZMWIEn3/+OWFhYTRu3BhPT0/CwsKUbXKLYmdnx8mTJ/H29mbixIm4urrSuXNnIiMj+fTTT4u8p0qVKqxcuZJ169ZRu3ZtZTAJHu7Q4+/vT25uLoMHDy4xfiGEEEIIIYQQorxQ5T++Z5kQjxk5ciR//vkn33333TPdl5GRgampKenp6TJDRAghyjH5ey7Kgr+/P7du3dLYsvdFkt9zIYSoGJ7l73m5XjIjXqz09HRiY2PZtGkT//vf/8o6HCGEEKLS8ff3Jzw8nAULFjBt2jSlPCIigl69evEy32sV7GJ39OhRWrdurZRnZ2dTu3Ztbty4QVRUlLKr3vP6+OOPX+rzFXCdvRctXYOX3q94KGWhX1mHIISoRMr1khnxYvXo0YM33niDd955h86dO5d1OEIIIUSlpKenx6JFi7h582ZZh4K1tTWhoaEaZTt37sTIyKjU+zI1NS0yGbwQQghRWmRARBRLrVZz9+5dli9fXtahCCGEEJWWj48PlpaWT8yBFhMTg4eHB/r6+lhbWxMQEEBmZiYAq1atonHjxkrdiIgIVCoVn3zyiVLm6+vL9OnTS+xjyJAhbNmyhaysLKVsw4YNDBkypFDd33//nX79+mFubo6FhQU9evQgJSUFeLgbn4GBAV9//bVSf8eOHejp6ZGQkAA8nB3Ts2dP5XpeXh6LFi3C0dERXV1dbGxsmD9/vnI9ISGBjh07oq+vj4WFBaNGjeLOnTslPo8QQojKTQZEhBBCCCFeYdra2nz00UesWrWK3377rcg6CQkJ+Pr60rt3b86cOcPWrVs5fPgw48aNA8DLy4tz587x999/A3Dw4EGqV6/OwYMHAcjJySEmJgZPT88SY2nevDl2dnZs374dgKtXr3Lo0CEGDRqkUe/u3bt4e3tjZGTEoUOHOHz4MEZGRnTp0oX79+/j7OxMSEgIY8aM4cqVK/zxxx+MHDmShQsXagzcPGr69OksWrSImTNncv78eb7++mtq1aql9NelSxfMzc2JjY3lm2++Yf/+/crzFyU7O5uMjAyNQwghROUiAyKikLCwMJmiKoQQQrxCevXqhbu7O7Nnzy7y+pIlSxgwYACBgYHUr1+ftm3bsnLlSr788kvu3buHq6srFhYWygCIWq1m4sSJynlsbCz37t2jffv2T4xl6NChbNiwAYDQ0FC6du1KjRo1NOps2bIFLS0tPv/8cxo3bkzDhg0JDQ0lNTUVtVoNwJgxY2jfvj2DBg1i8ODBNG/enPfff7/IPm/fvs3HH3/M4sWLGTJkCA4ODrRv354RI0YAsGnTJrKysvjyyy9xdXWlY8eOrF69mo0bN/Lnn38W2eaCBQswNTVVDmtr6yc+uxBCiIpFkqqWU15eXri7u7NixQqN8keTrAUFBREREUF8fHyZxFhAkpM9HUkiJoQQoiSLFi2iY8eOTJw4sdC1uLg4Ll68yKZNm5Sy/Px88vLyuHz5Mg0bNsTDwwO1Wk2nTp04d+4co0ePJiQkhMTERNRqNc2aNXuqXCBvv/0206ZN49KlS4SFhbFy5cpi4zE2NtYov3fvHsnJycr5hg0bcHJyQktLi7NnzyqJWx+XmJhIdnY2nTp1KvZ6kyZNMDQ0VMratWtHXl4eFy5cUGaSPGr69OlMmDBBOc/IyJBBESGEqGRkQEQIIYQQohzw8PDA19eXDz74AH9/f41reXl5vPPOOwQEBBS6z8bGBnj4MuWzzz4jOjqaJk2aYGZmhoeHBwcPHkStVj/17jAWFhZ069aN4cOHc+/ePV5//XVu375dKJ7mzZtrDNAUeHQ2yenTp8nMzERLS4tr165Ru3btIvvU19cvMab8/PxiB1OKK9fV1UVXV7fEdoUQQlRssmSmggoLC2POnDmcPn0alUqFSqUiLCwMgGXLltG4cWMMDQ2xtrZmzJgxRSYd27t3Lw0bNlTW/Kalpb3kpxBCCCHEoxYuXMj3339PTEyMRnmzZs04d+4cjo6OhQ4dHR3g//KIfPvtt8rgh6enJ/v373+q/CGPGjZsGGq1msGDB6OtrV3oerNmzUhKSqJmzZqF4jE1NQXgxo0b+Pv7M2PGDIYOHcrAgQM1krU+qn79+ujr6xMZGVnkdRcXF+Lj45UksgBHjhxBS0sLJyenp34uIYQQlYvMEKmg+vXrx9mzZ9mzZw/79+8HUL6AaGlpsXLlSmxtbbl8+TJjxoxhypQprFmzRrn/7t27hISEsHHjRrS0tHj77beZNGlSkW96CmRnZ5Odna2cS3IyIYQQonQ1btyYgQMHsmrVKo3yqVOn0rp1a8aOHcvIkSMxNDQkMTGRffv2KXUL8ohs2rSJ//3vf8DDQZKCJThPkz+kQJcuXfjrr78wMTEp8vrAgQNZsmQJPXr0IDg4mLp165KamsqOHTuYPHkydevWZfTo0VhbW/Phhx9y//59mjVrxqRJkzR2vimgp6fH1KlTmTJlCjo6OrRr146//vqLc+fOMXz4cAYOHMjs2bMZMmQIQUFB/PXXX7z33nsMGjSoyOUyJTk7x7fY5xJCCFGxyAyRCkpfXx8jIyOqVKmCpaUllpaWynTTwMBAvL29sbOzo2PHjsydO5dt27Zp3P/gwQPWrl1LixYtaNasGePGjSv2rUwBSU4mhBBCvHhz584lPz9fo8zNzY2DBw+SlJREhw4daNq0KTNnzsTKykqpo1KplFkgHTp0UO4zNTWladOmzzQIoFKpqF69ujL75HEGBgYcOnQIGxsbevfuTcOGDRk2bBhZWVmYmJjw5Zdfsnv3bjZu3EiVKlUwMDBg06ZNfP755+zevbvINmfOnMnEiROZNWsWDRs2pF+/fly/fl3pb+/evdy4cYOWLVvy3//+l06dOrF69eqnfiYhhBCVjyr/8X9RRbnwPElVo6Ki+Oijjzh//jwZGRnk5ORw79497ty5g6GhIWFhYYwdO1Zj2unOnTt58803ycvLKzamomaIWFtbYx24TZKqPgVJqiqEeFVlZGRgampKenq6vDkXFZb8ngshRMXwLH/PZYZIOWViYkJ6enqh8lu3bpX4H/3KlSt07doVV1dXtm/fTlxcnDI19cGDB0q9qlWratynUqkKvY16nK6uLiYmJhqHEEIIIYQQQgjxKpIcIuWUs7MzP/74Y6Hy2NhYGjRoAICOjg65ubka10+cOEFOTg5Lly5FS+vheNjjy2WEEEIIIYQQQoiKTgZEyqkxY8awevVqxo4dy6hRo9DX12ffvn188cUXbNy4EUBJmhofH0/dunUxNjbGwcGBnJwcVq1aRffu3Tly5Ahr1659obFKcjIhhBBCCCGEEK8aWTJTTtna2hIdHU1ycjL/+c9/aNmyJWFhYYSFhdGnTx8A3nzzTbp06YK3tzc1atRg8+bNuLu7s2zZMhYtWoSrqyubNm1iwYIFZfw0QgghhBBCCCHEyyVJVcULI8nJhBCiYpC/56IykN9zIYSoGCSpqhBCCCGEEEIIIUQJJIeIEEIIIYQQ/5/r7L1o6RqUdRjiCVIW+pV1CEKICkBmiAghhBBCiBJdvXqV4cOHU7t2bXR0dKhXrx7vv/8+//zzT1mHJoQQQvxrMiAihBBCCCGKdenSJVq0aMGvv/7K5s2buXjxImvXriUyMpI2bdpw48aNIu+7f//+S45UCCGEeDYyICKEEEIIIYo1duxYdHR0+Omnn/D09MTGxobXX3+d/fv38/vvvzNjxgzg4Q548+bNw9/fH1NTU0aOHAlATEwMHh4e6OvrY21tTUBAAJmZmUr7aWlp+Pn5oa+vj52dHV9//TW2trasWLFCqZOamkqPHj0wMjLCxMSEvn378ueffyrXg4KCcHd3Z+PGjdja2mJqakr//v25ffv2y/mQhBBClEuSQ6SSUKlU7Ny5k549e770vmUtbumTdbNCCCFehhs3brB3717mz5+Pvr6+xjVLS0sGDhzI1q1bWbNmDQBLlixh5syZfPjhhwAkJCTg6+vL3Llz+eKLL/jrr78YN24c48aNIzQ0FIDBgwfz999/o1arqVq1KhMmTOD69etKP/n5+fTs2RNDQ0MOHjxITk4OY8aMoV+/fqjVaqVecnIyERER7Nq1i5s3b9K3b18WLlzI/Pnzi3y27OxssrOzlfOMjIxS+cyEEEKUHzJD5F9au3YtxsbG5OTkKGV37tyhatWqdOjQQaNudHQ0KpWKX3/99WWHqUhLS+P1118vs/6FEEIIUf4kJSWRn59Pw4YNi7zesGFDbt68yV9//QVAx44dmTRpEo6Ojjg6OrJkyRIGDBhAYGAg9evXp23btqxcuZIvv/ySe/fu8csvv7B//37Wr1/Pa6+9RrNmzfj888/JyspS+ti/fz9nzpzh66+/pnnz5rz22mts3LiRgwcPEhsbq9TLy8sjLCwMV1dXOnTowKBBg4iMjCz22RYsWICpqalyWFtbl9KnJoQQoryQAZF/ydvbmzt37nDixAmlLDo6GktLS2JjY7l7965SrlarqV27Nk5OTmURKvDwLY6urm6Z9S+EEEKIiic/Px94OBMVoEWLFhrX4+LiCAsLw8jISDl8fX3Jy8vj8uXLXLhwgSpVqtCsWTPlHkdHR8zNzZXzxMRErK2tNQYsXFxcMDMzIzExUSmztbXF2NhYObeystKYafK46dOnk56erhxXr179l5+CEEKI8koGRP6lBg0aULt2bY2pmmq1mh49euDg4EBMTIxGuZeXF46OjoSEhGi0c/bsWbS0tEhOTgaefo3shg0bsLGxwcjIiHfffZfc3FwWL16MpaUlNWvWLDQ9VKVSERERAUBKSgoqlYodO3bg7e2NgYEBTZo04ejRoxr3rF+/HmtrawwMDOjVqxfLli3DzMys2M8kOzubjIwMjUMIIYQQ5ZejoyMqlYrz588Xef2XX37B3Nyc6tWrA2BoaKhxPS8vj3feeYf4+HjlOH36NElJSTg4OCgDKo97tDw/P18ZcHm8zqPlVatW1biuUqnIy8sr9tl0dXUxMTHROIQQQlQuMiDyHLy8vIiKilLOo6Ki8PLywtPTUym/f/8+R48epWPHjgwbNkxZL1tgw4YNdOjQQflS0LNnT27cuMHBgwfZt28fycnJ9OvXT+Oe5ORkfvzxR/bs2cPmzZvZsGEDfn5+/Pbbbxw8eJBFixbx4YcfcuzYsRLjnzFjBpMmTSI+Ph4nJyfeeustZQnQkSNHGD16NO+//z7x8fF07ty52DW4BWTqqRBCCFGxWFhY0LlzZ9asWaOxjAXg2rVrbNq0iX79+hU5YAHQrFkzzp07pyyhefTQ0dHB2dmZnJwcTp06pdxz8eJFbt26pZy7uLiQmpqqMYPj/PnzpKenF7uURwghhHgaklT1OXh5eTF+/HhycnLIysri1KlTeHh4kJuby8qVKwE4duwYWVlZeHt7o6+vz6xZszh+/DitWrXiwYMHfPXVVyxZsgT4vzWyly9fVgYTNm7cSKNGjYiNjaVly5bAw7ctGzZswNjYGBcXF7y9vblw4QK7d+9GS0uLBg0asGjRItRqNa1bty42/kmTJuHn9zA555w5c2jUqBEXL17E2dmZVatW8frrrzNp0iQAnJyciImJYdeuXcW2N336dCZMmKCcZ2RkyKCIEEIIUc6tXr2atm3b4uvry7x587Czs+PcuXNMnjyZOnXqlPjCZOrUqbRu3ZqxY8cycuRIDA0NSUxMZN++faxatQpnZ2d8fHwYNWoUn376KVWrVmXixIno6+srgyw+Pj64ubkxcOBAVqxYoSRV9fT0LLREpzScneMrs0WEEKKSkBkiz8Hb25vMzExiY2OJjo7GycmJmjVr4unpSWxsLJmZmajVamxsbLC3t8fKygo/Pz82bNgAwK5du7h37x59+vQB/v0a2Vq1auHi4oKWlpZGWUnrZgHc3NyUn62srACUey5cuECrVq006j9+/jiZeiqEEEJUPPXr1+fEiRM4ODjQr18/HBwcGDVqFN7e3hw9epRq1aoVe6+bmxsHDx4kKSmJDh060LRpU2bOnKl87wD48ssvqVWrFh4eHvTq1YuRI0dibGyMnp4e8H/Lfs3NzfHw8MDHxwd7e3u2bt36wp9dCCFExSYzRJ6Do6MjdevWJSoqips3b+Lp6Qk8TGBqZ2fHkSNHiIqKomPHjso9I0aMYNCgQSxfvpzQ0FD69euHgcHDLWmfZ43ss66bfbydgvYL7ikqluLW+QohhBCiYqtXr16hZb+PS0lJKbK8ZcuW/PTTT8XeZ2Vlxe7du5Xz3377jevXr+Po6KiU2djY8L///a/YNoKCgggKCtIoCwwMJDAwsMSYhRBCVG4yIPKcvL29UavV3Lx5k8mTJyvlnp6e7N27l2PHjjF06FClvGvXrhgaGvLpp5/y448/cujQIeXao2tkC2aJlNUaWWdnZ44fP65R9uiOOkIIIYQQpeHAgQPcuXOHxo0bk5aWxpQpU7C1tcXDw6OsQxNCCFHByYDIc/L29mbs2LE8ePBAmSECDwdE3n33Xe7du4e3t7dSrq2tjb+/P9OnT8fR0ZE2bdoo1172GtmSvPfee3h4eLBs2TK6d+/OgQMH+PHHH4tNmlYSWYsrhBBCiOI8ePCADz74gEuXLmFsbEzbtm3ZtGlTodmvQgghRGmTHCLPydvbm6ysLBwdHalVq5ZS7unpye3bt3FwcCiUWHT48OHcv3+fYcOGaZS/Smtk27Vrx9q1a1m2bBlNmjRhz549jB8/XlnPK4QQQghRGnx9fTl79ix3797lzz//ZOfOndSrV6+swxJCCFEJqPIlMcRLd+TIEby8vPjtt980BlFedSNHjuSXX34hOjr6qepnZGRgampKenq6zBARQohyTP6ei8pAfs+FEKJieJa/57Jk5iXKzs7m6tWrzJw5k759+77ygyEhISF07twZQ0NDfvzxR8LDw1mzZk1ZhyWEEEIIIYQQQjw3WTLzEm3evJkGDRqQnp7O4sWLyzqcJzp+/DidO3emcePGrF27lpUrVzJixIiyDksIIYQQL1BKSgoqlYr4+Phi64SFhWFmZvbSYhJCCCFeBFkyI14YmXoqhBAVg/w9L1tr165l8uTJ3Lx5kypVHk7uvXPnDubm5rRu3VpjKWt0dDQeHh5cuHABJyenf9VfSkoKdnZ2nDp1Cnd39yLrZGVlcfv2bWrWrPmv+iiOra1tmW2XW/B7bh24DS1dg5fev3g2KQv9yjoEIcQr6lm+t8gMESGEEEKIV5i3tzd37tzhxIkTSll0dDSWlpbExsZy9+5dpVytVlO7du1/PRjytPT19Ut9MEQIIYR42WRApBJ4mqmvQgghhHg1NWjQgNq1a6NWq5UytVpNjx49cHBwICYmRqPc29ubr776ihYtWmBsbIylpSUDBgzg+vXrSr2bN28ycOBAatSogb6+PvXr1yc0NFSj30uXLuHt7Y2BgQFNmjTh6NGjyrXHl8wEBQXh7u7Oxo0bsbW1xdTUlP79+3P79m2lzu3btxk4cCCGhoZYWVmxfPlyvLy8lNkgXl5eXLlyhfHjx6NSqVCpVMq927dvp1GjRujq6mJra8vSpUs1YrW1teWjjz5i2LBhGBsbY2Njw2efffavPm8hhBCVhyRVfUX5+/sTHh7OO++8w9q1azWujRkzhk8//ZQhQ4YQFhb2xLasra1JS0ujevXqLyjakrnO3itTT8uQTCkVQojyz8vLi6ioKKZNmwZAVFQUU6ZMIS8vj6ioKHx8fLh//z5Hjx5l1apV3L9/n7lz59KgQQOuX7/O+PHj8ff3Z/fu3QDMnDmT8+fP8+OPP1K9enUuXrxIVlaWRp8zZswgJCSE+vXrM2PGDN566y0uXryoLNt5XHJyMhEREezatYubN2/St29fFi5cyPz58wGYMGECR44c4bvvvqNWrVrMmjWLkydPKstyduzYQZMmTRg1ahQjR45U2o2Li6Nv374EBQXRr18/YmJiGDNmDBYWFvj7+yv1li5dyty5c/nggw/49ttveffdd/Hw8MDZ2bnIeLOzs8nOzlbOMzIynu0/ihBCiHJPBkReYdbW1mzZsoXly5ejr68PwL1799i8eTM2NjZP3Y62tjaWlpYvKkwhhBBCvGBeXl6MHz+enJwcsrKyOHXqFB4eHuTm5rJy5UoAjh07RlZWFt7e3tjb2yv32tvbs3LlSlq1asWdO3cwMjIiNTWVpk2b0qJFC+DhDIvHTZo0CT+/h4Pqc+bMoVGjRly8eLHYAYa8vDzCwsIwNjYGYNCgQURGRjJ//nxu375NeHg4X3/9NZ06dQIgNDSU2rVrK/dXq1YNbW1tZVZLgWXLltGpUydmzpwJgJOTE+fPn2fJkiUaAyJdu3ZlzJgxAEydOpXly5ejVquLjXfBggXMmTOn+A9dCCFEhSdLZl5hzZo1w8bGhh07dihlO3bswNramqZNmyple/bsoX379piZmWFhYUG3bt1ITk5Wrj++ZEatVqNSqYiMjKRFixYYGBjQtm1bLly4oNH/999/T/PmzdHT08Pe3p45c+aQk5PzYh9aCCGEEIV4e3uTmZlJbGws0dHRODk5UbNmTTw9PYmNjSUzMxO1Wo2NjQ329vacOnWKHj16UK9ePYyNjfHy8gIgNTUVgHfffZctW7bg7u7OlClTNJbdFHBzc1N+trKyAtBYdvM4W1tbZTCk4J6C+pcuXeLBgwe0atVKuW5qakqDBg2e+OyJiYm0a9dOo6xdu3YkJSWRm5tbZLwqlQpLS8sS450+fTrp6enKcfXq1SfGIoQQomKRAZFX3NChQzXW9G7YsIFhw4Zp1MnMzGTChAnExsYSGRmJlpYWvXr1Ii8vr8S2Z8yYwdKlSzlx4gRVqlTRaHfv3r28/fbbBAQEcP78edatW0dYWJgy7bUo2dnZZGRkaBxCCCGEeH6Ojo7UrVuXqKgooqKi8PT0BMDS0hI7OzuOHDlCVFQUHTt2JDMzk//85z8YGRnx1VdfERsby86dOwG4f/8+AK+//jpXrlwhMDCQP/74g06dOjFp0iSNPqtWrar8XJDPo6TvFo/WL7inoH7BpoaP5gV5tLwk+fn5T3VfSf0XRVdXFxMTE41DCCFE5SIDIq+4QYMGcfjwYVJSUrhy5QpHjhzh7bff1qjz5ptv0rt3b+rXr4+7uztffPEFCQkJnD9/vsS258+fj6enJy4uLkybNo2YmBju3bunXJs2bRpDhgzB3t6ezp07M3fuXNatW1dsewsWLMDU1FQ5rK2tn/8DEEIIIQTwcJaIWq1GrVYrMz4APD092bt3L8eOHcPb25tffvmFv//+m4ULF9KhQwecnZ2LnClRo0YN/P39+eqrr1ixYsULTULq4OBA1apVOX78uFKWkZFBUlKSRj0dHR2NWR8ALi4uHD58WKMsJiYGJycntLW1X1jMQgghKj7JIfKKq169On5+foSHh5Ofn4+fn1+h5KjJycnMnDmTY8eO8ffffytvQ1JTU3F1dS227eKmwtrY2BAXF0dsbKzGjJDc3Fzu3bvH3bt3MTAonCR1+vTpTJgwQTnPyMiQQREhhBCilHh7ezN27FgePHigzBCBhwMi7777Lvfu3cPb2xs9PT10dHRYtWoVo0eP5uzZs8ydO1ejrVmzZtG8eXMaNWpEdnY2u3btomHDhi8sdmNjY4YMGcLkyZOpVq0aNWvWZPbs2WhpaWnM/rC1teXQoUP0798fXV1dqlevzsSJE2nZsiVz586lX79+HD16lNWrV7NmzZoXFq8QQojKQQZEyoFhw4Yxbtw4AD755JNC17t37461tTXr16+ndu3a5OXl4erqqkyLLU5JU2Hz8vKYM2cOvXv3LnSfnp5eke3p6uqiq6v7dA8lhBBCiGfi7e1NVlYWzs7O1KpVSyn39PTk9u3bODg4KC8iwsLC+OCDD1i5ciXNmjUjJCSEN954Q7lHR0eH6dOnk5KSgr6+Ph06dGDLli0vNP5ly5YxevRounXrhomJCVOmTOHq1asa3yuCg4N55513cHBwIDs7m/z8fJo1a8a2bduYNWsWc+fOxcrKiuDgYI2EqqXp7BxfWT4jhBCVhCr/aRZvipfO39+fW7duERERQW5urrKrTGpqKtra2vTs2RMzMzOWLl1K9erVOXToEB06dADg8OHDdOjQgZ07d9KzZ09SUlKws7Pj1KlTuLu7o1ar8fb25ubNm5iZmQEQHx9P06ZNuXz5Mra2trRr1w5nZ2e++OKLf/0MGRkZD5fOBG6TbXfLkGy7K4R4XgV/z9PT0+V/FEWpyczMpE6dOixdupThw4eXdTjyey6EEBXEs/w9lxki5YC2tjaJiYnKz48yNzfHwsKCzz77DCsrK1JTU5k2bdpz9zlr1iy6deuGtbU1ffr0QUtLizNnzpCQkMC8efOeqS150yKEEEKIU6dO8csvv9CqVSvS09MJDg4GoEePHmUcmRBCiMpKkqqWE8VlP9fS0mLLli3ExcXh6urK+PHjWbJkyXP35+vry65du9i3bx8tW7akdevWLFu2jHr16j1320IIIYSonEJCQmjSpAk+Pj5kZmYSHR1dKDeaEEII8bLIkhnxwsjUUyGEqBjk77moDOT3XAghKoZn+XsuM0SEEEIIIYQQQghR6ciAiBBCCCGEEEIIISodGRARQgghhBBCCCFEpSO7zAghhBBCiEL8/f25desWERERZR3KS+U6ey9augZlHYYoRSkL/co6BCHEK0pmiFQi/v7+9OzZs6zDEEIIISo0f39/VCoVo0ePLnRtzJgxqFQq/P39X35g5VBQUBDu7u6FylUqVaUbqBFCCFH6Kt0MEX9/f8LDw1mwYAHTpk1TyiMiIujVqxcvc9MdlUoFwNGjR2ndurVSnp2dTe3atblx4wZRUVF4eXmVSn8ff/zxS32+AvKmpWzJWxEhhHj5rK2t2bJlC8uXL0dfXx+Ae/fusXnzZmxsbMo4OiGEEEJAJZ0hoqenx6JFi7h582ZZh4K1tTWhoaEaZTt37sTIyKjU+zI1NcXMzKzU2xVCCCGEpmbNmmFjY8OOHTuUsh07dmBtbU3Tpk2Vsj179tC+fXvMzMywsLCgW7duJCcnK9fv37/PuHHjsLKyQk9PD1tbWxYsWKBcDwoKwsbGBl1dXWrXrk1AQIBy7auvvqJFixYYGxtjaWnJgAEDuH79ukac586dw8/PDxMTE4yNjenQoYNG/wAhISFYWVlhYWHB2LFjefDggXKtqJkaZmZmhIWFPVX86enpjBo1ipo1a2JiYkLHjh05ffo0AGFhYcyZM4fTp0+jUqlQqVSEhYVha2sLQK9evVCpVMr56dOn8fb2xtjYGBMTE5o3b86JEyee9J9KCCFEJVYpB0R8fHywtLTU+Ae5KDExMXh4eKCvr4+1tTUBAQFkZmYCsGrVKho3bqzUjYiIQKVS8cknnyhlvr6+TJ8+vcQ+hgwZwpYtW8jKylLKNmzYwJAhQwrV/f333+nXrx/m5uZYWFjQo0cPUlJSAPjll18wMDDg66+/Vurv2LEDPT09EhISgMJLZvLy8li0aBGOjo7o6upiY2PD/PnzlesJCQl07NgRfX19LCwsGDVqFHfu3CnxeYQQQgjx0NChQzVeemzYsIFhw4Zp1MnMzGTChAnExsYSGRmJlpYWvXr1Ii8vD4CVK1fy3XffsW3bNi5cuMBXX32lDAB8++23LF++nHXr1pGUlERERITGd5P79+8zd+5cTp8+TUREBJcvX9ZYqvP777/j4eGBnp4eBw4cIC4ujmHDhpGTk6PUiYqKIjk5maioKMLDwwkLC1MGO55GSfHn5+fj5+fHtWvX2L17N3FxcTRr1oxOnTpx48YN+vXrx8SJE2nUqBFpaWmkpaXRr18/YmNjAQgNDSUtLU05HzhwIHXr1iU2Npa4uDimTZtG1apVi40tOzubjIwMjUMIIUTlUikHRLS1tfnoo49YtWoVv/32W5F1EhIS8PX1pXfv3pw5c4atW7dy+PBhxo0bB4CXlxfnzp3j77//BuDgwYNUr16dgwcPApCTk0NMTAyenp4lxtK8eXPs7OzYvn07AFevXuXQoUMMGjRIo97du3fx9vbGyMiIQ4cOcfjwYYyMjOjSpQv379/H2dmZkJAQxowZw5UrV/jjjz8YOXIkCxcu1Phy9Kjp06ezaNEiZs6cyfnz5/n666+pVauW0l+XLl0wNzcnNjaWb775hv379yvPXxT5YiGEEEL8n0GDBnH48GFSUlK4cuUKR44c4e2339ao8+abb9K7d2/q16+Pu7s7X3zxBQkJCZw/fx6A1NRU6tevT/v27alXrx7t27fnrbfeUq5ZWlri4+ODjY0NrVq1YuTIkUrbw4YN4/XXX8fe3p7WrVuzcuVKfvzxR+XlxieffIKpqSlbtmyhRYsWODk5MXToUBo0aKC0YW5uzurVq3F2dqZbt274+fkRGRn51J9BSfFHRUWRkJDAN998Q4sWLahfvz4hISGYmZnx7bffoq+vj5GREVWqVMHS0hJLS0v09fWpUaMG8HAmiqWlpXKempqKj48Pzs7O1K9fnz59+tCkSZNiY1uwYAGmpqbKYW1t/dTPJYQQomKolAMi8HCapbu7O7Nnzy7y+pIlSxgwYACBgYHUr1+ftm3bsnLlSr788kvu3buHq6srFhYWygCIWq1m4sSJynlsbCz37t2jffv2T4xl6NChbNiwAXj4tqNr167KP+4FtmzZgpaWFp9//jmNGzemYcOGhIaGkpqailqtBh4mamvfvj2DBg1i8ODBNG/enPfff7/IPm/fvs3HH3/M4sWLGTJkCA4ODrRv354RI0YAsGnTJrKysvjyyy9xdXWlY8eOrF69mo0bN/Lnn38W2aZ8sRBCCCH+T/Xq1fHz8yM8PJzQ0FD8/PyoXr26Rp3k5GQGDBiAvb09JiYm2NnZAQ//5x4ezu6Mj4+nQYMGBAQE8NNPPyn39unTh6ysLOzt7Rk5ciQ7d+7UmN1x6tQpevToQb169TA2NlZykhW0HR8fT4cOHUqcRdGoUSO0tbWVcysrq0LLbkpSUvxxcXHcuXMHCwsLjIyMlOPy5cuFlu08jQkTJjBixAh8fHxYuHDhE9uYPn066enpynH16tVn7lMIIUT5VmkHRAAWLVpEeHi48hbmUXFxcYSFhWn8A+3r60teXh6XL19GpVLh4eGBWq3m1q1bnDt3jtGjR5Obm0tiYiJqtZpmzZo9VS6Qt99+m6NHj3Lp0iXCwsIKTactiOfixYsYGxsr8VSrVo179+5p/IO/YcMGzpw5w8mTJwkLC1MStz4uMTGR7OxsOnXqVOz1Jk2aYGhoqJS1a9eOvLw8Lly4UOQ98sVCCCGE0DRs2DDCwsIIDw8v8t/37t27888//7B+/Xp+/vlnfv75Z+Dhchd4mIvk8uXLzJ07l6ysLPr27ct///tf4GEesgsXLvDJJ5+gr6/PmDFj8PDw4MGDB2RmZvKf//wHIyMjvvrqK2JjY9m5c6dG2wXJXkvy+GCJSqVSlvMUnD+esP3RHCMlxZ+Xl4eVlRXx8fEax4ULF5g8efITY3tcUFCQkhPlwIEDuLi4KM9cFF1dXUxMTDQOIYQQlUul22XmUR4eHvj6+vLBBx8U2v4uLy+Pd955RyM5WYGC7PBeXl589tlnREdH06RJE8zMzPDw8ODgwYOo1eqn3h2mIIna8OHDuXfvHq+//jq3b98uFE/z5s3ZtGlTofsfnU1y+vRpMjMz0dLS4tq1a9SuXbvIPp/0JSg/P7/YwZTiynV1ddHV1S2xXSGEEKIyKVjaCg9ziz3qn3/+ITExkXXr1tGhQwcADh8+XKgNExMT+vXrR79+/fjvf/9Lly5duHHjBtWqVUNfX5833niDN954g7Fjx+Ls7ExCQgL5+fn8/fffLFy4UJmx+XiCUTc3N8LDw3nw4EGJs0RKUqNGDdLS0pTzpKQk7t69+1TxN2vWjGvXrlGlShUlr8jjdHR0yM3NLVRetWrVIsudnJxwcnJi/PjxvPXWW4SGhtKrV69/9WxCCCEqvko9IAKwcOFC3N3dcXJy0ihv1qwZ586dw9HRsdh7vby8eP/99/n222+VwQ9PT0/2799PTExMsctVijJs2DC6du3K1KlTNaamPhrP1q1blSzsRblx4wb+/v7MmDGDa9euMXDgQE6ePFnk4Ef9+vXR19cnMjJSWSbzKBcXF8LDw8nMzFRmiRw5cgQtLa1Cn5UQQgghiqatrU1iYqLy86MKkqR/9tlnWFlZkZqayrRp0zTqLF++HCsrK9zd3dHS0uKbb77B0tJS2cklNzeX1157DQMDAzZu3Ii+vj716tUjLy8PHR0dVq1axejRozl79ixz587VaHvcuHGsWrWK/v37M336dExNTTl27BitWrXSyCNSkoIlta1btyYvL4+pU6dqDK6UFL+Pjw9t2rShZ8+eLFq0iAYNGvDHH3+we/duev4/9u48qqqqfeD49wKKzCAOYIKAAqKoqGQqyqAWJppTOWuEmYqGOKCSOaAZTjhA5vQqkJlDDmTmqzlwFccXSRzJgUSsLMsUEmWS+/vDxfl5BRQVB+T5rHXWunfvffbe58a6nvZ99nO6dcPd3R07OzsuXbpEcnIytWvXxsTEBH19fezs7NizZw8eHh7o6+tTpUoVQkJCePfdd7G3t+fXX38lMTGRnj17PvZ/s9NhvhItIoQQFYWmgnn//fc1Xbt21SobOHCgpkqVKpr7P44TJ05oDAwMNIGBgZrjx49rzp8/Vk1QAwAAjjZJREFUr/nuu+80I0eOVNoUFBRoqlWrptHV1dVs27ZNo9FoNMnJyRpdXV2Nrq6uJiMj46FzATRbtmxR+vrrr780OTk5Go1Go7lx44YG0MTHx2s0Go0mKytL4+joqPH29tbs379f88svv2jUarUmKChIc+XKFY1Go9G89957mjfeeEOTl5enycrK0jg7O2sCAwNLvPZp06ZpLCwsNLGxsZqLFy9qDh8+rPnPf/6jjGdtba3p2bOn5tSpU5q9e/dqHBwcNO+//36pP+uMjAwN8MjPQQghxMtNvs8fT3H3Gvfr2rWr8u/prl27NC4uLhp9fX1N48aNNWq1Wuv+YPny5Ro3NzeNkZGRxtTUVNO+fXvNTz/9pNFoNJotW7Zo3njjDY2pqanGyMhI07JlS83u3buVcb755huNnZ2dRl9fX9OqVSvN1q1bNYDm+PHjSpsTJ05o3nrrLY2hoaHGxMRE07ZtW01qamqJ1zFq1CiNl5eX8v63337TvPXWWxojIyONo6OjZvv27RozMzNNdHT0I+ev0Wg0mZmZmo8//lhTq1YtTaVKlTQ2Njaa/v37a9LT0zUajUaTnZ2t6dmzp8bc3FwDKP1u3bpVU69ePY2enp6mTp06mpycHE2fPn00NjY2msqVK2tq1aqlGTlypObOnTul/K8mf+dCCPGqeJzvc1kQ0Wg0aWlpGn19fc2D60P/+9//NG+++abG2NhYY2RkpGncuLFm5syZWm169uyptfhRUFCgqVq1qsbd3f2Rc7n/hudBDy6IaDQazdWrVzWDBg3SVKtWTaOvr69xcHDQDBkyRJORkaGJjY3VGBkZac6fP6+0P3bsmKZy5cqaH374odhrv3v3ruazzz7T1KlTR1OpUiWNra2t5vPPP1fqT548qfHx8dFUqVJFU7VqVc2QIUM0//777yOvq5DcWAghxKtBvs9FRSB/50II8Wp4nO9zlUbzQCYsIcpIZmYmZmZmZGRkSOipEEKUY/J9LioC+TsXQohXw+N8n1fop8wIIYQQQgghhBCiYpIFESGEEEIIIYQQQlQ4siAihBBCCCGEEEKICkcWRIQQQgghhBBCCFHhyILIc5CWloZKpSI5ObnENjExMZibmz+3OQkhhBBCCCGEEBWZ3ouewIuwdOlSQkJCuHHjBnp69z6CW7duYWFhQcuWLUlISFDaJiQk4Onpyblz53Bycnpmc+rduzedOnUq837t7OwIDg4mODi4zPsuLdepO9HRN3xh44vSSZvl96KnIIQQ4jny9/cnNjaWoUOHsnTpUq26wMBAlixZwvvvv09MTEyZjDdt2jTi4uIe+gPRy0DuWyo2uR8SomKpkBEiPj4+3Lp1i2PHjillCQkJWFlZkZiYyO3bt5VytVpNrVq1nuliCICBgQE1atR4pmMIIYQQQtzPxsaGdevWcefOHaUsOzubtWvXYmtr+wJnJoQQQjx7FXJBxNnZmVq1aqFWq5UytVpN165dqVu3LocOHdIq9/Hx4euvv8bd3R0TExOsrKzo168f165dU9rduHGD/v37U716dQwMDHB0dCQ6Olpr3F9++QUfHx8MDQ1p0qQJhw8fVuoe3DIzbdo03NzcWL16NXZ2dpiZmdGnTx/+/fdfpc2///5L//79MTIywtramgULFuDt7a1Eg3h7e3P58mVGjx6NSqVCpVIp527atImGDRuir6+PnZ0dERERWnO1s7Pj888/JyAgABMTE2xtbVm+fPkTfd5CCCGEeDk1a9YMW1tbNm/erJRt3rwZGxsbmjZtqpTl5OQQFBREjRo1qFKlCm3atCExMVGpV6vVqFQq9uzZg7u7O4aGhrRu3Zpz584B9+5zwsLCOHHihHJPUhh5Mn/+fBo1aoSRkRE2NjYEBgZy69Ytpe/Ce6SdO3fi4uKCsbExHTt25OrVq0qbxMRE3nzzTapVq4aZmRleXl789NNPz+pjE0II8YqokAsicG+xID4+XnkfHx+Pt7c3Xl5eSnlubi6HDx/Gx8eH3NxcZsyYwYkTJ4iLi+PSpUv4+/sr50+ePJmzZ8/y3//+l5SUFJYsWUK1atW0xpw0aRLjxo0jOTkZJycn+vbtS35+folzTE1NJS4ujm3btrFt2zb27dvHrFmzlPoxY8Zw8OBBtm7dyq5du0hISND6x3/z5s3Url2b6dOnc/XqVeXGISkpiV69etGnTx9OnTrFtGnTmDx5cpGQ2IiICNzd3Tl+/DiBgYEMHz6cn3/+ucT55uTkkJmZqXUIIYQQ4uX2wQcfaP2Is2rVKgICArTajB8/nk2bNhEbG8tPP/1EvXr18PX15Z9//tFqN2nSJCIiIjh27Bh6enpKP71792bs2LE0bNhQuSfp3bs3ADo6OkRGRnL69GliY2PZu3cv48eP1+r39u3bzJs3j9WrV7N//37S09MZN26cUv/vv//y/vvvk5CQwJEjR3B0dKRTp05aPyQ9SO5bhBBCVMgcInBvQWT06NHk5+dz584djh8/jqenJ3fv3iUyMhKAI0eOcOfOHXx8fHBwcFDOdXBwIDIykhYtWnDr1i2MjY1JT0+nadOmuLu7A/ciLB40btw4/Pzu7UsMCwujYcOGXLx4kfr16xc7x4KCAmJiYjAxMQFg4MCB7Nmzh5kzZ/Lvv/8SGxvLN998Q/v27QGIjo6mVq1ayvlVq1ZFV1dXiWopNH/+fNq3b8/kyZMBcHJy4uzZs8ydO1drkadTp04EBgYCMGHCBBYsWIBarS5xvuHh4YSFhZX8oQshhBDipTNw4EBCQ0OVJPAHDx5k3bp1SiRtVlYWS5YsISYmhrfffhuAFStWsGvXLlauXElISIjS18yZM/Hy8gJg4sSJ+Pn5kZ2djYGBAcbGxujp6WndkwBaec7s7e2ZMWMGw4cP58svv1TK8/LyWLp0KXXr1gVg5MiRTJ8+Xalv166dVp/Lli3DwsKCffv20blz52KvW+5bhBBCVNgIER8fH7KyskhMTCQhIQEnJydq1KiBl5cXiYmJZGVloVarsbW1xcHBgePHj9O1a1fq1KmDiYkJ3t7eAKSnpwMwfPhw1q1bh5ubG+PHj9fadlOocePGymtra2sArW03D7Kzs1MWQwrPKWz/yy+/kJeXR4sWLZR6MzMznJ2dH3ntKSkpeHh4aJV5eHhw4cIF7t69W+x8VSoVVlZWD51vaGgoGRkZynHlypVHzkUIIYQQL1a1atXw8/MjNjaW6Oho/Pz8tKJcU1NTycvL07p3qFSpEi1atCAlJUWrr8e914F7Ubpvvvkmr732GiYmJgwaNIjr16+TlZWltDE0NFQWQwr7vr/fa9euMWzYMJycnDAzM8PMzIxbt24p92nFkfsWIYQQFTZCpF69etSuXZv4+Hhu3Lih/JphZWWFvb09Bw8eJD4+nnbt2pGVlcVbb73FW2+9xddff0316tVJT0/H19eX3NxcAN5++20uX77MDz/8wO7du2nfvj0jRoxg3rx5ypiVKlVSXhfm8ygoKChxjve3LzynsL1Go9Hqp1Bh+cNoNJpSnfew8Yujr6+Pvr7+I8cXQgghxMslICCAkSNHArB48WKtuofdczxY9rj3OpcvX6ZTp04MGzaMGTNmULVqVQ4cOMDgwYPJy8srtt/Cvu+/d/H39+evv/5i4cKF1KlTB319fVq1aqXcpxVH7luEEEJU2AgRuBclolarUavVSsQHgJeXFzt37uTIkSP4+Pjw888/8/fffzNr1izatm1L/fr1i/21o3r16vj7+/P111+zcOHCZ5qEtG7dulSqVIn//e9/SllmZiYXLlzQale5cmWtqA+ABg0acODAAa2yQ4cO4eTkhK6u7jObsxBCCCFeTh07diQ3N5fc3Fx8fX216urVq0flypW17h3y8vI4duwYLi4upR6juHuSY8eOkZ+fT0REBC1btsTJyYnff//9seefkJBAUFAQnTp1UpLG//3334/djxBCiIqlwkaIwL0FkREjRpCXl6dEiMC9BZHhw4eTnZ2Nj48PVapUoXLlykRFRTFs2DBOnz7NjBkztPqaMmUKzZs3p2HDhuTk5LBt27bHukl4XCYmJrz//vuEhIRQtWpVatSowdSpU9HR0dH6tcbOzo79+/fTp08f9PX1qVatGmPHjuX1119nxowZ9O7dm8OHD/PFF19o7dUtS6fDfDE1NX0mfQshhBDi6enq6irbXx78ccTIyIjhw4cr9xy2trbMmTOH27dvM3jw4FKPYWdnx6VLl0hOTqZ27dqYmJhQt25d8vPziYqKokuXLhw8eJClS5c+9vzr1avH6tWrcXd3JzMzk5CQEAwMDB67HyGEEBVLhV8QuXPnDvXr16dmzZpKuZeXF//++y9169bFxsYGuPfIt08++YTIyEiaNWvGvHnzeOedd5RzKleurCQkMzAwoG3btqxbt+6Zzn/+/PkMGzaMzp07Y2pqyvjx47ly5QpVqlRR2kyfPp2hQ4dSt25dcnJy0Gg0NGvWjA0bNjBlyhRmzJiBtbU106dP10qoKoQQQoiK5WE/XsyaNYuCggIGDhzIv//+i7u7Ozt37sTCwqLU/ffs2ZPNmzfj4+PDzZs3iY6Oxt/fn/nz5zN79mxCQ0Px9PQkPDycQYMGPdbcV61axUcffUTTpk2xtbXl888/13oKzeOQH3KEEKLiUGlKk3RClAtZWVm89tprREREPNYvNs9KZmYmZmZmZGRkyI2FEEKUY/J9LioC+TsXQohXw+N8n1foCJHy7vjx4/z888+0aNGCjIwM5fFzXbt2fcEzE0IIIYQQQgghXm6yIFLOzZs3j3PnzlG5cmWaN29OQkKC1qPyhBBCCCGEEEIIUZQsiJRjTZs2JSkp6UVPQwghhBBCCCGEKHcq9GN3hRBCCCGEEEIIUTHJgshz5O/vT7du3V70NIQQQgghhBBCiAqvXGyZ8ff3JzY2lqFDhxZ5Nn1gYCBLlizh/fffJyYm5sVMsByZNm0acXFxJCcna5WrVCq2bNnyTBZsXKfuREffsMz7FS9O2iy/Fz0FIYQQQgghhHgq5SZCxMbGhnXr1nHnzh2lLDs7m7Vr12Jra/sCZyaEEEIIIUrL29ub4ODgUrdXq9WoVCpu3rz5zOYkhBCiYioXESIAzZo145dffmHz5s30798fgM2bN2NjY4ODg4PSbseOHXz22WecPn0aXV1dWrVqxaJFi6hbty4Aubm5jBkzhk2bNnHjxg2srKwYOnQooaGhwL0IilWrVvHnn39iaWnJu+++S2RkJABff/01Cxcu5Ny5cxgZGdGuXTsWLlxIjRo1lPHPnDnD+PHjSUhIQKPR4ObmRkxMjDI+3HsyTEREBLm5ufTp04eFCxdSqVIloPhIDXNzcxYuXIi/v/8j55+RkUFISAhxcXFkZ2fj7u7OggULaNKkCTExMYSFhSnjAERHRzNt2jQAunfvDkCdOnVIS0vjxIkTBAcHc+zYMVQqFY6Ojixbtgx3d/ey+Y8qhBBCiFeOv78/N2/eJC4u7kVP5YlIZGvFJlGwQlQs5WZBBOCDDz4gOjpaWRBZtWoVAQEBqNVqpU1WVhZjxoyhUaNGZGVlMWXKFLp3705ycjI6OjpERkaydetWNmzYgK2tLVeuXOHKlSsAbNy4kQULFrBu3ToaNmzIH3/8wYkTJ5S+c3NzmTFjBs7Ozly7do3Ro0fj7+/P9u3bAfjtt9/w9PTE29ubvXv3YmpqysGDB8nPz1f6iI+Px9ramvj4eC5evEjv3r1xc3NjyJAhpfoMHjZ/jUaDn58fVatWZfv27ZiZmbFs2TLat2/P+fPn6d27N6dPn2bHjh3s3r0bADMzM/z8/KhRowbR0dF07NgRXV1dAPr370/Tpk1ZsmQJurq6JCcnKws3xcnJySEnJ0d5n5mZWaprEkIIIYQQQgghnrdytSAycOBAQkNDSUtLQ6VScfDgQdatW6e1INKzZ0+tc1auXEmNGjU4e/Ysrq6upKen4+joSJs2bVCpVNSpU0dpm56ejpWVFR06dKBSpUrY2trSokULpT4gIEB57eDgQGRkJC1atODWrVsYGxuzePFizMzMWLdunbJw4OTkpDUfCwsLvvjiC3R1dalfvz5+fn7s2bOn1AsiD5t/fHw8p06d4tq1a+jr6wP3olHi4uLYuHEjH330EcbGxujp6WFlZaWcZ2BgANyLRLm/PD09nZCQEOrXrw+Ao6PjQ+cWHh6uRKAIIYQQQmRlZTF8+HA2b96MiYkJ48aNK9KmNBG4AElJSUyYMIGzZ8/i5uZGdHQ0zs7OSv2SJUuYN28eV65cwd7enk8//ZSBAwc+82sUQghRfpWbHCIA1apVw8/Pj9jYWKKjo/Hz86NatWpabVJTU+nXrx8ODg6Ymppib28P3Pufe7gXxpmcnIyzszNBQUH8+OOPyrnvvfced+7cwcHBgSFDhrBlyxat6I7jx4/TtWtX6tSpg4mJCd7e3lp9Jycn07Zt24dGUTRs2FCJwACwtrbm2rVrpf4MHjb/pKQkbt26haWlJcbGxspx6dIlUlNTSz1GoTFjxvDhhx/SoUMHZs2a9cg+QkNDycjIUI7CyBUhhBBCVEwhISHEx8ezZcsWfvzxR9RqNUlJSVptCiNwT5w4QVxcHJcuXcLf379IX5MmTSIiIoJjx46hp6en9UPVli1bGDVqFGPHjuX06dMMHTqUDz74gPj4+BLnlpOTQ2ZmptYhhBCiYilXESJwL0pj5MiRACxevLhIfZcuXbCxsWHFihXUqlWLgoICXF1dyc3NBe7lIrl06RL//e9/2b17N7169aJDhw5s3LgRGxsbzp07x65du9i9ezeBgYHMnTuXffv2kZuby1tvvcVbb73F119/TfXq1UlPT8fX11fpuzDS4mEeXCxRqVQUFBRovddoNFpt8vLylNcPm39BQQHW1tZaETOFzM3NHzm3B02bNo1+/frxww8/8N///pepU6eybt06JdfIg/T19ZXIFCGEEEJUbLdu3WLlypV89dVXvPnmmwDExsZSu3ZtrXaPisAtNHPmTLy8vACYOHEifn5+ZGdnU6VKFebNm4e/vz+BgYHAvR91jhw5wrx58/Dx8Sl2fhLZKoQQolxFiAB07NiR3NxccnNz8fX11aq7fv06KSkpfPrpp7Rv3x4XFxdu3LhRpA9TU1N69+7NihUrWL9+PZs2beKff/4B7i1qvPPOO0RGRqJWqzl8+DCnTp3i559/5u+//2bWrFm0bduW+vXrF4nsaNy4MQkJCVoLGI+revXqXL16VXl/4cIFbt++Xar5N2vWjD/++AM9PT3q1aundRRG0lSuXJm7d+8WGbdSpUrFljs5OTF69Gh+/PFHevToQXR09BNfmxBCCCEqjtTUVHJzc2nVqpVSVrVqVa1tLvDoCNxCjRs3Vl5bW1sDKPdiKSkpeHh4aLX38PAgJSWlxPlJZKsQQohyFyGiq6ur/ON2/9YTuJefw9LSkuXLl2NtbU16ejoTJ07UarNgwQKsra1xc3NDR0eHb7/9FisrK8zNzYmJieHu3bu88cYbGBoasnr1agwMDKhTpw4FBQVUrlyZqKgohg0bxunTp5kxY4ZW3yNHjiQqKoo+ffoQGhqKmZkZR44coUWLFkX+8S9Ju3bt+OKLL2jZsiUFBQVMmDBBK6rkYfPv0KEDrVq1olu3bsyePRtnZ2d+//13tm/fTrdu3XB3d8fOzo5Lly6RnJxM7dq1MTExQV9fHzs7O/bs2YOHhwf6+vpUqVKFkJAQ3n33Xezt7fn1119JTEwskqOlNE6H+WJqavrY5wkhhBCi/How4rU4WVlZj4zALXT//VDh0/IejLJ9cPwHy+4nka1CCCHKXYQI3IuQKO5/sHV0dFi3bh1JSUm4uroyevRo5s6dq9XG2NiY2bNn4+7uzuuvv05aWhrbt29HR0cHc3NzVqxYgYeHB40bN2bPnj18//33WFpaUr16dWJiYvj2229p0KABs2bNYt68eVp9W1pasnfvXm7duoWXlxfNmzdnxYoVD80p8qCIiAhsbGzw9PSkX79+jBs3DkPD/3/028Pmr1Kp2L59O56engQEBODk5ESfPn1IS0ujZs2awL2ksx07dsTHx4fq1auzdu1aZdxdu3ZhY2ND06ZN0dXV5fr16wwaNAgnJyd69erF22+/LaGlQgghhCiVevXqUalSJY4cOaKU3bhxg/PnzyvvSxOBWxouLi4cOHBAq+zQoUO4uLg8+QUIIYR45ak0pVm+F+IJZGZmYmZmRkZGhkSICCFEOSbf5+Jx+Pv7c/PmTeLi4hg+fDjbt29n1apV1KxZk0mTJrF3714GDx7MwoUL+euvv6hduzajRo1SInBDQkI4f/48x48fx83NDbVajY+PDzdu3FByoiUnJ9O0aVMuXbqEnZ0dcXFx9OrVi8jISNq3b8/333/P+PHj2b17t7IF51Hk71wIIV4Nj/N9Xi4jRIQQQgghxMtv7ty5eHp68s4779ChQwfatGlD8+bNlfrSROCWRrdu3Vi0aBFz586lYcOGLFu2jOjo6FIvhgghhKiYJEJEPDPyS4sQQrwa5PtcVATydy6EEK8GiRARQgghhBBCCCGEeAhZEBFCCCGEEEIIIUSFIwsiQgghhBBCCCGEqHD0XvQExKvPdepOdPQNH91QvJLSZvm96CkIIYQQQgghRBESIVKG/P39UalUDBs2rEhdYGAgKpUKf3//Mhtv2rRpuLm5lVl/QgghhBBCCCFERSELImXMxsaGdevWcefOHaUsOzubtWvXYmtr+wJnJoQQQgghhBBCiEKyZaaMNWvWjF9++YXNmzfTv39/ADZv3oyNjQ0ODg5Ku5ycHEJCQli3bh2ZmZm4u7uzYMECXn/9dQDUajU+Pj7s3r2bCRMmcPbsWdzc3IiOjsbZ2ZmYmBjCwsIAUKlUAERHR+Pv78/8+fOJjo7ml19+oWrVqnTp0oU5c+ZgbGwMQExMDMHBwaxfv57g4GCuXLlCmzZtiI6OxtraGoDExEQ++eQTjh8/Tl5eHm5ubixYsIBmzZo9t89SCCGEEKI0Cu9tbt68+dR9yVZfUVqyLViI8k8iRJ6BDz74gOjoaOX9qlWrCAgI0Gozfvx4Nm3aRGxsLD/99BP16tXD19eXf/75R6vdpEmTiIiI4NixY+jp6Sn99O7dm7Fjx9KwYUOuXr3K1atX6d27NwA6OjpERkZy+vRpYmNj2bt3L+PHj9fq9/bt28ybN4/Vq1ezf/9+0tPTGTdunFL/77//8v7775OQkMCRI0dwdHSkU6dO/PvvvyVed05ODpmZmVqHEEIIIQSAt7c3wcHBRcrj4uKUH3dkO7AQQojnSRZEnoGBAwdy4MAB0tLSuHz5MgcPHmTAgAFKfVZWFkuWLGHu3Lm8/fbbNGjQgBUrVmBgYMDKlSu1+po5cyZeXl40aNCAiRMncujQIbKzszEwMMDY2Bg9PT2srKywsrLCwMAAgODgYHx8fLC3t6ddu3bMmDGDDRs2aPWbl5fH0qVLcXd3p1mzZowcOZI9e/Yo9e3atWPAgAG4uLjg4uLCsmXLuH37Nvv27SvxusPDwzEzM1MOGxubsvg4hRBCCCGEEEKIMicLIs9AtWrV8PPzIzY2lujoaPz8/KhWrZpSn5qaSl5eHh4eHkpZpUqVaNGiBSkpKVp9NW7cWHlduJ3l2rVrDx0/Pj6eN998k9deew0TExMGDRrE9evXycrKUtoYGhpSt25drb7v7/fatWsMGzYMJycnZYHj1q1bpKenlzhuaGgoGRkZynHlypWHzlMIIYQQolDhduATJ06gUqlQqVTExMQAMH/+fBo1aoSRkRE2NjYEBgZy69atIn3s3LkTFxcXjI2N6dixI1evXn3OVyGEEKI8kRwiz0hAQAAjR44EYPHixVp1Go0G+P/cH/eXP1hWqVIl5XVhXUFBQYnjXr58mU6dOjFs2DBmzJhB1apVOXDgAIMHDyYvL6/Yfgv7LpwX3Htizl9//cXChQupU6cO+vr6tGrVitzc3BLH1tfXR19fv8R6IYQQQoiS9O7dm9OnT7Njxw52794NgJmZGfD/24Ht7Oy4dOkSgYGBjB8/ni+//FI5//7twDo6OgwYMIBx48axZs2aYsfLyckhJydHeS9bfYUQouKRCJFnpGPHjuTm5pKbm4uvr69WXb169ahcuTIHDhxQyvLy8jh27BguLi6lHqNy5crcvXtXq+zYsWPk5+cTERFBy5YtcXJy4vfff3/s+SckJBAUFESnTp1o2LAh+vr6/P3334/djxBCCCFEaTzr7cAPkq2+QgghJELkGdHV1VW2v+jq6mrVGRkZMXz4cEJCQqhatSq2trbMmTOH27dvM3jw4FKPUfgrSXJyMrVr18bExIS6deuSn59PVFQUXbp04eDBgyxduvSx51+vXj1Wr16Nu7s7mZmZhISEKDclj+t0mC+mpqZPdK4QQgghRHx8PJ9//jlnz54lMzOT/Px8srOzycrKwsjICHj0duAHhYaGMmbMGOV9ZmamLIoIIUQFIxEiz5CpqWmJCwGzZs2iZ8+eDBw4kGbNmnHx4kV27tyJhYVFqfvv2bMnHTt2xMfHh+rVq7N27Vrc3NyYP38+s2fPxtXVlTVr1hAeHv7Yc1+1ahU3btygadOmDBw4kKCgIGrUqPHY/QghhBBCwL37ooyMjCLlN2/efOgPJ4XbgV1dXdm0aRNJSUnKduTH2Q78IH19feVe7WH3bEIIIV5dEiFShgoTf5UkLi5OeV2lShUiIyOJjIwstq23t3eRf8Td3Ny0yvT19dm4cWORc0ePHs3o0aO1ygYOHKi89vf3x9/fX6u+W7duWn03bdqUxMRErTbvvvtu8RcmhBBCCPEI9evX57///W+R8sTERJydnYFHbwfW0bn3W96D22WEEEKIJyELIkIIIYQQ4pkLDAzkiy++YMSIEXz00UcYGBiwa9cuVq5cyerVq4Fnux24tGSrrxBCVByyZUYIIYQQQjxzdnZ2JCQkkJqayltvvcXrr79OTEwMMTExvPfee8Cz3Q4shBBCPEiledjmSiGeQmZmJmZmZmRkZMgvLUIIUY7J97moCOTvXAghXg2P830uESJCCCGEEEIIIYSocGRB5BXg7e1NcHBwqdur1WpUKhU3b958ZnMSQgghhBBCCCFeZpJUtRzw9/fn5s2bWk+pKU9cp+5ER9/wRU9DvCBps/xe9BSEEEIIIYQQogiJEBFCCCGEEEIIIUSFIwsi5UxWVhaDBg3C2NgYa2trIiIiirT5+uuvcXd3x8TEBCsrK/r168e1a9eKtEtKSsLd3R1DQ0Nat27NuXPntOqXLFlC3bp1qVy5Ms7Ozsoj8YQQQgghhBBCiPJOFkTKmZCQEOLj49myZQs//vgjarWapKQkrTa5ubnMmDGDEydOEBcXx6VLl/D39y/S16RJk4iIiODYsWPo6ekREBCg1G3ZsoVRo0YxduxYTp8+zdChQ/nggw+Ij48vcW45OTlkZmZqHUIIIYR4OT1uDjIhhBDiVSM5RMqRW7dusXLlSr766ivefPNNAGJjY6ldu7ZWu/sXNhwcHIiMjKRFixbcunULY2NjpW7mzJl4eXkBMHHiRPz8/MjOzqZKlSrMmzcPf39/AgMDARgzZgxHjhxh3rx5+Pj4FDu/8PBwwsLCyvSahRBCCFF6/v7+xMbGMnToUJYuXapVFxgYyJIlS3j//feJiYlh8+bNVKpU6QXN9Mmo1Wp8fHy4ceMG5ubmz2QMyX0myprkUxPi5SURIuVIamoqubm5tGrVSimrWrUqzs7OWu2OHz9O165dqVOnDiYmJnh7ewOQnp6u1a5x48bKa2trawBla01KSgoeHh5a7T08PEhJSSlxfqGhoWRkZCjHlStXHv8ihRBCCPFUbGxsWLduHXfu3FHKsrOzWbt2Lba2tkpZ1apVMTExeRFTFEIIIV4KsiBSjmg0mke2ycrK4q233sLY2Jivv/6axMREtmzZAtzbSnO/+38VUqlUABQUFBQpu3/8B8vup6+vj6mpqdYhhBBCiOerWbNm2NrasnnzZqVs8+bN2NjY0LRpU6XswS0zdnZ2fP755wQEBGBiYoKtrS3Lly9X6tPS0lCpVGzevBkfHx8MDQ1p0qQJhw8f1hr/0KFDeHp6YmBggI2NDUFBQWRlZSn1X375JY6OjlSpUoWaNWvy7rvvKnU5OTkEBQVRo0YNqlSpQps2bUhMTFTGL4xStbCwQKVSKVuCd+zYQZs2bTA3N8fS0pLOnTuTmpr69B+mEEKIV5osiJQj9erVo1KlShw5ckQpu3HjBufPn1fe//zzz/z999/MmjWLtm3bUr9+/WITqj6Ki4sLBw4c0Co7dOgQLi4uT34BQgghhHguPvjgA6Kjo5X3q1at0tpSW5KIiAjc3d05fvw4gYGBDB8+nJ9//lmrzaRJkxg3bhzJyck4OTnRt29f8vPzATh16hS+vr706NGDkydPsn79eg4cOMDIkSMBOHbsGEFBQUyfPp1z586xY8cOPD09lb7Hjx/Ppk2biI2N5aeffqJevXr4+vryzz//YGNjw6ZNmwA4d+4cV69eZdGiRcC9H4TGjBlDYmIie/bsQUdHh+7du2v90PMgyX0mhBBCcoiUI8bGxgwePJiQkBAsLS2pWbMmkyZNQkfn/9e1bG1tqVy5MlFRUQwbNozTp08zY8aMxx4rJCSEXr160axZM9q3b8/333/P5s2b2b1792P3dTrMV6JFhBBCiOdo4MCBhIaGKlEdBw8eZN26dajV6oee16lTJyV/2IQJE1iwYAFqtZr69esrbcaNG4ef372cCGFhYTRs2JCLFy9Sv3595s6dS79+/ZTIE0dHRyIjI/Hy8mLJkiWkp6djZGRE586dMTExoU6dOkrUSlZWFkuWLCEmJoa3334bgBUrVrBr1y5WrlxJSEgIVatWBaBGjRpaOUR69uypdR0rV66kRo0anD17FldX12KvVXKfCSGEkAiRcmbu3Ll4enryzjvv0KFDB9q0aUPz5s2V+urVqxMTE8O3335LgwYNmDVrFvPmzXvscbp168aiRYuYO3cuDRs2ZNmyZURHRyv5SIQQQgjx8qpWrRp+fn7ExsYSHR2Nn58f1apVe+R59+cXU6lUWFlZFYk0fVgOsqSkJGJiYjA2NlYOX19fCgoKuHTpEm+++SZ16tTBwcGBgQMHsmbNGm7fvg3cy5WWl5enlcOsUqVKtGjR4qE5zArP7devHw4ODpiammJvbw8UzZ92P8l9JoQQQiJEyoGYmBjltbGxMatXr2b16tVKWUhIiFb7vn370rdvX62y+/OPeHt7F8lH4ubmVqRs+PDhDB8+/GmnL4QQQogXICAgQNmqsnjx4lKd8+BTZ1QqVZFtJw/LQVZQUMDQoUMJCgoq0ndhFOtPP/2EWq3mxx9/ZMqUKUybNo3ExETlPuRxc5gBdOnSBRsbG1asWEGtWrUoKCjA1dW1SP60++nr66Ovr//QfoUQQrzaJEJECCGEEOIV1LFjR3Jzc8nNzcXX1/e5jNmsWTPOnDlDvXr1ihyVK1cGQE9Pjw4dOjBnzhxOnjxJWloae/fuVdrcn8MsLy+PY8eOKTnMCvu4e/eu0ub69eukpKTw6aef0r59e1xcXLhx48ZzuV4hhBDlm0SICCGEEEK8gnR1dZWtJrq6us9lzAkTJtCyZUtGjBjBkCFDMDIyIiUlhV27dhEVFcW2bdv45Zdf8PT0xMLCgu3bt1NQUICzszNGRkYMHz5cyRVia2vLnDlzuH37NoMHDwagTp06qFQqtm3bRqdOnTAwMMDCwgJLS0uWL1+OtbU16enpTJw48blcrxBCiPJNFkSEEEIIIV5RzzupeePGjdm3bx+TJk2ibdu2aDQa6tatS+/evQEwNzdn8+bNTJs2jezsbBwdHVm7di0NGzYEYNasWRQUFDBw4ED+/fdf3N3d2blzJxYWFgC89tprhIWFMXHiRD744AMGDRpETEwM69atIygoCFdXV5ydnYmMjHzivGeSDF4IISoOlebBxBFClJHMzEzMzMzIyMiQGwshhCjH5PtcVATydy6EEK+Gx/k+lxwiZcjb21t5zJwQQgghhBBCCCFeXrJl5hH8/f2JjY1l6NChLF26VKsuMDCQJUuW8P777xMTE8PmzZuLZGd/2anVanx8fLhx4wbm5ubPZAzXqTvR0Td8Jn2Liiltlt+LnoIQQgghhBCinJMIkVKwsbFh3bp13LlzRynLzs5m7dq12NraKmVVq1bFxMTkRUxRCCGEEEIIIYQQj0EWREqhWbNm2NrasnnzZqVs8+bN2NjY0LRpU6XswS0zdnZ2fP755wQEBGBiYoKtrS3Lly9X6tPS0lCpVGzevBkfHx8MDQ1p0qQJhw8f1hr/0KFDeHp6YmBggI2NDUFBQWRlZSn1X375JY6OjlSpUoWaNWvy7rvvKnU5OTkEBQVRo0YNqlSpQps2bUhMTFTG9/HxAcDCwgKVSoW/vz8AO3bsoE2bNpibm2NpaUnnzp1JTU19+g9TCCGEEEIIIYR4CciCSCl98MEHREdHK+9XrVpFQEDAI8+LiIjA3d2d48ePExgYyPDhw/n555+12kyaNIlx48aRnJyMk5MTffv2JT8/H4BTp07h6+tLjx49OHnyJOvXr+fAgQOMHDkSgGPHjhEUFMT06dM5d+4cO3bswNPTU+l7/PjxbNq0idjYWH766Sfq1auHr68v//zzDzY2NmzatAmAc+fOcfXqVRYtWgRAVlYWY8aMITExkT179qCjo0P37t0pKCgo8VpzcnLIzMzUOoQQQgghhBBCiJeRLIiU0sCBAzlw4ABpaWlcvnyZgwcPMmDAgEee16lTJwIDA6lXrx4TJkygWrVqqNVqrTbjxo3Dz88PJycnwsLCuHz5MhcvXgRg7ty59OvXj+DgYBwdHWndujWRkZF89dVXZGdnk56ejpGREZ07d6ZOnTo0bdqUoKAg4N6ixpIlS5g7dy5vv/02DRo0YMWKFRgYGLBy5Up0dXWpWrUqADVq1MDKygozMzMAevbsSY8ePXB0dMTNzY2VK1dy6tQpzp49W+K1hoeHY2Zmphw2NjZP8lELIYQQQgghhBDPnCRVLaVq1arh5+dHbGwsGo0GPz8/qlWr9sjzGjdurLxWqVRYWVlx7dq1EttYW1sDcO3aNerXr09SUhIXL15kzZo1ShuNRkNBQQGXLl3izTffpE6dOjg4ONCxY0c6duxI9+7dMTQ0JDU1lby8PDw8PJRzK1WqRIsWLUhJSXnovFNTU5k8eTJHjhzh77//ViJD0tPTcXV1Lfac0NBQxowZo7zPzMyURREhhBBClCuSDF68SJI4XojnSxZEHkNAQICyVWXx4sWlOufBp86oVKoi207ub6NSqQCUNgUFBQwdOlSJ+rifra0tlStX5qeffkKtVvPjjz8yZcoUpk2bRmJiIhqNRqvPQhqNpkjZg7p06YKNjQ0rVqygVq1aFBQU4OrqSm5ubonn6Ovro6+v/9B+hRBCCPHy6NKlC3fu3GH37t1F6g4fPkzr1q1JSkqiWbNmL2B2T8bf35+bN28SFxf3oqcihBDiJSdbZh5Dx44dyc3NJTc3F19f3+cyZrNmzThz5gz16tUrclSuXBkAPT09OnTowJw5czh58iRpaWns3btXaXPgwAGlv7y8PI4dO4aLiwuA0sfdu3eVNtevXyclJYVPP/2U9u3b4+Liwo0bN57L9QohhBDi+Rk8eDB79+7l8uXLRepWrVqFm5vbYy+GPOzHEyGEEOJlIhEij0FXV1fZaqKrq/tcxpwwYQItW7ZkxIgRDBkyBCMjI1JSUti1axdRUVFs27aNX375BU9PTywsLNi+fTsFBQU4OztjZGTE8OHDCQkJoWrVqtja2jJnzhxu377N4MGDAahTpw4qlYpt27bRqVMnDAwMsLCwwNLSkuXLl2NtbU16ejoTJ0584ms4HeaLqalpWX0kQgghhCgjnTt3pkaNGsTExDB16lSl/Pbt26xfv57PP/+cQ4cOMXHiRBITE6lWrRrdu3cnPDwcIyMj4N5T9T788EMuXrzIli1b6NatGz4+PgQHB/P1118zduxYrly5QqdOnYiNjWXjxo1MnTqVjIwMBgwYwMKFC5X7qhs3bjBq1Ci+//57cnJy8PLyIjIyEkdHRwBiYmIIDg5m/fr1BAcHc+XKFdq0aUN0dDTW1tZMmzaN2NhY4P8jZOPj4/H29n6On6oQQojyQiJEHpOpqelz/Z/7xo0bs2/fPi5cuEDbtm1p2rQpkydPVnKNmJubs3nzZtq1a4eLiwtLly5l7dq1NGzYEIBZs2bRs2dPBg4cSLNmzbh48SI7d+7EwsICgNdee42wsDAmTpxIzZo1GTlyJDo6Oqxbt46kpCRcXV0ZPXo0c+fOfW7XLIQQQojnQ09Pj0GDBhETE6NstQX49ttvyc3NpUmTJg992l2huXPn4urqSlJSEpMnTwbuLapERkaybt06duzYgVqtpkePHmzfvp3t27ezevVqli9fzsaNG5V+/P39OXbsGFu3buXw4cNoNBo6depEXl6e0ub27dvMmzeP1atXs3//ftLT0xk3bhxwL1F9r1696NixI1evXuXq1au0bt262GuXp+MJIYRQae7/10+IMpSZmYmZmRkZGRkSISKEEOWYfJ+/2n7++WdcXFzYu3cvPj4+AHh5efHaa6+hp6eHgYEBy5YtU9ofOHAALy8vsrKyqFKlCnZ2djRt2pQtW7YobWJiYvjggw+4ePEidevWBWDYsGGsXr2aP//8E2NjY+DedmQ7OzuWLl3KhQsXcHJy4uDBg8oixvXr17GxsSE2Npb33nuv2H6//PJLpk+fzh9//AGUPofItGnTCAsLK1JuE7xBkqqKF0aSqgrx9B7nvkUiRIQQQgghKrD69evTunVrVq1aBdx70lxCQgIBAQEkJSURExODsbGxcvj6+ipPuyvk7u5epF9DQ0Nl0QKgZs2a2NnZKYshhWWFT99LSUlBT0+PN954Q6m3tLTE2dlZ6+l4D/ZrbW1d5Al+pREaGkpGRoZyXLly5bH7EEIIUb5JDhEhhBBCiApu8ODBjBw5ksWLFxMdHU2dOnVo3779I592V6gwn8j9invS3sOevldS0PKDT8crro8nCXiWp+MJIYSQCBEhhBBCiAquV69e6Orq8s033xAbG8sHH3yASqUq1dPuykqDBg3Iz8/n6NGjStn169c5f/688nS80qhcubLW0/OEEEKIkkiEiBBCCCFEBWdsbEzv3r355JNPyMjIwN/fH3j00+7KkqOjI127dmXIkCEsW7YMExMTJk6cyGuvvUbXrl1L3Y+dnR07d+7k3LlzWFpaYmZmViSq5GHk6XhCCFFxSISIEEIIIYRg8ODB3Lhxgw4dOijbYR71tLuyFh0dTfPmzencuTOtWrVCo9Gwffv2x1rQGDJkCM7Ozri7u1O9enUOHjz4TOYqhBCi/JOnzJSxLl26cOfOHXbv3l2k7vDhw7Ru3ZqkpCSaNWv2Amb3ZEqbrf1Bhdl9JVu7eJEkW7sQT0+eMiMqAvk7F0KIV4M8ZeYFGjx4MHv37uXy5ctF6latWoWbm9tjL4bk5uaW1fSEEEIIIYQQQgiBLIiUuc6dO1OjRg1iYmK0ym/fvs369esZPHgwhw4dwtPTEwMDA2xsbAgKCiIrK0tpa2dnx2effYa/vz9mZmYMGTKEmJgYzM3N2bZtG87OzhgaGvLuu++SlZVFbGwsdnZ2WFhY8PHHH2slErtx4waDBg3CwsICQ0ND3n77bS5cuKDUF/a7c+dOXFxcMDY2pmPHjly9ehWAadOmERsby3fffYdKpUKlUqFWq5/pZyiEEEIIIYQQQjxrsiBSxvT09Bg0aBAxMTFaj4D79ttvyc3NpUmTJvj6+tKjRw9OnjzJ+vXrOXDgACNHjtTqZ+7cubi6upKUlMTkyZOBe4sqkZGRrFu3jh07dqBWq+nRowfbt29n+/btrF69muXLl7Nx40alH39/f44dO8bWrVs5fPgwGo2GTp06kZeXp7S5ffs28+bNY/Xq1ezfv5/09HTGjRsHwLhx4+jVq5eySHL16lVat25d7LXn5OSQmZmpdQghhBBCCCGEEC8jWRB5BgICAkhLS9OKpFi1ahU9evRgxYoV9OvXj+DgYBwdHWndujWRkZF89dVXZGdnK+3btWvHuHHjlEfbAeTl5bFkyRKaNm2Kp6cn7777LgcOHGDlypU0aNCAzp074+PjQ3x8PAAXLlxg69at/Oc//6Ft27Y0adKENWvW8Ntvv2nlA8nLy2Pp0qW4u7vTrFkzRo4cyZ49e4B7WecNDAzQ19fHysoKKyurEh+zFx4ejpmZmXLY2NiU8ScrhBBCCCGEEEKUDVkQeQbq169P69atWbVqFQCpqakkJCQQEBBAUlISMTExGBsbK4evry8FBQVcunRJ6cPd3b1Iv4aGhtStW1d5X7NmTezs7DA2NtYqu3btGgApKSno6enxxhtvKPWWlpY4OzuTkpJSYr/W1tZKH48jNDSUjIwM5bhy5cpj9yGEEEIIIYQQQjwPsiDyjAwePJhNmzaRmZlJdHQ0derUoX379hQUFDB06FCSk5OV48SJE1y4cEFrUcLIyKhInw8+ck6lUhVbVlBQAEBJDxDSaDSoVKqH9vskDx/S19fH1NRU6xBCCCFE+aBWq1GpVNy8efNFT+WRytNchRBCvLz0XvQEXlW9evVi1KhRfPPNN8TGxjJkyBBUKhXNmjXjzJkzyjaYZ6lBgwbk5+dz9OhRJe/H9evXOX/+PC4uLqXup3LlylqJWoUQQgjxclu6dCkhISHcuHEDPb17t3u3bt3CwsKCli1bkpCQoLRNSEjA09OTc+fOcfXqVczMzF7UtEutdevWz2yurlN3oqNvWOb9ClHW0mb5vegpCFHuyYLIM2JsbEzv3r355JNPyMjIwN/fH4AJEybQsmVLRowYwZAhQzAyMiIlJYVdu3YRFRVVpnNwdHSka9euDBkyhGXLlmFiYsLEiRN57bXX6Nq1a6n7sbOzY+fOnZw7dw5LS0vMzMyKRJU8zOkwX4kWEUIIIZ4jHx8fbt26xbFjx2jZsiVwb+HDysqKxMREbt++jaHhvf/pV6vV1KpVCycnpxc55cdSuXJlrKysXvQ0hBBClHOyZeYZGjx4MDdu3KBDhw7Y2toC0LhxY/bt28eFCxdo27YtTZs2ZfLkyVhbWz+TOURHR9O8eXM6d+5Mq1at0Gg0bN++/bEWNIYMGYKzszPu7u5Ur16dgwcPPpO5CiGEEKJsODs7U6tWLa0E72q1mq5du1K3bl0OHTqkVe7j41NkG8rly5fp0qULFhYWGBkZ0bBhQ7Zv366cd+bMGfz8/DA1NcXExIS2bduSmpoKQEFBAdOnT6d27dro6+vj5ubGjh07lHPT0tJQqVRs3rwZHx8fDA0NadKkCYcPH1baPGz8B+caExODubk5O3fuxMXFBWNjY+UJeUIIIURJVJonSRYhRClkZmZiZmZGRkaGRIgIIUQ5Jt/n5VP//v35+++/2blzJwAtWrRg/Pjx7N27FwsLC2bOnElubi7m5uZERUVRt25dfHx8uHHjBubm5nTu3Jnc3FwiIiIwMjLi7NmzmJqa4unpyW+//Ubjxo3x9vYmNDQUU1NTDh48SOvWrXF2dmbBggVMmzaNZcuW0bRpU1atWsWCBQs4c+YMjo6OpKWlYW9vT/369Zk3bx6Ojo5MmjSJxMRELl68iJ6e3kPHL1zEKZxrTEwMH330EV5eXoSHh6Ojo8OAAQNo2rQpa9asKfbzycnJIScnR3mfmZmJjY0NNsEbZMuMKBdky4wQxXuc+xbZMiOEEEII8Qry9vZm9OjR5Ofnc+fOHY4fP46npyd3794lMjISgCNHjnDnzh18fHxIT0/XOj89PZ2ePXvSqFEjABwcHJS6xYsXY2Zmxrp165So0/u33MybN48JEybQp08fAGbPnk18fDwLFy5k8eLFSrtx48bh53fvf+rCwsJo2LAhFy9epH79+g8dvzh5eXksXbpUSVI/cuRIpk+fXmL78PBwwsLCHtqnEEKIV5tsmRFCCCGEeAX5+PiQlZVFYmIiCQkJODk5UaNGDby8vEhMTCQrKwu1Wo2trW2xiw1BQUF89tlneHh4MHXqVE6ePKnUJScn07Zt22K34GZmZvL777/j4eGhVe7h4UFKSopWWePGjZXXhduHr1279sjxi2NoaKj1xD5ra2ulr+KEhoaSkZGhHFeuXHlo/0IIIV49siAihBBCCPEKqlevHrVr1yY+Pp74+Hi8vLwAsLKywt7enoMHDxIfH0+7du2KPf/DDz/kl19+YeDAgZw6dQp3d3clAbyBgcEjx1epVFrvNRpNkbL7F1QK6woKCh45fnEeXJxRqVQ8bGe4vr4+pqamWocQQoiKRRZEhBBCCCFeUYXJUtVqNd7e3kq5l5cXO3fu5MiRI/j4+JR4vo2NDcOGDWPz5s2MHTuWFStWAPciOxISEsjLyytyjqmpKbVq1eLAgQNa5YcOHcLFxeWx5l/S+EIIIURZkAWRV5C/vz/dunV7aBs7OzsWLlz4XOYjhBBCiBfDx8eHAwcOkJycrESIwL0FkRUrVpCdnV3igkhwcDA7d+7k0qVL/PTTT+zdu1dZ0Bg5ciSZmZn06dOHY8eOceHCBVavXs25c+cACAkJYfbs2axfv55z584xceJEkpOTGTVqVKnn/rDxhRBCiLLw0iRV9ff3JzY2lvDwcCZOnKiUx8XF0b1794eGPJY1lUrFli1biiwq+Pv7c/PmTeLi4p7bXJ6VxMREjIyMnstYrlN3SrZ2US5ItnYhxKvGx8eHO3fuUL9+fWrWrKmUe3l58e+//1K3bl1sbGyKPffu3buMGDGCX3/9FVNTUzp27MiCBQsAsLS0ZO/evYSEhODl5YWuri5ubm5K3pCgoCAyMzMZO3Ys165do0GDBmzduhVHR8dSz/1h4z9Lp8N8ZfuMEEJUEC/NY3f9/f1Zv349VapU4ZdffsHCwgKQBZEnURbzzMvLKzZR2uMofNyRPL5OlBeyICJE8eSxu6IikL9zIYR4NTzO9/lLtWWmQ4cOWFlZER4e/tB2hw4dwtPTEwMDA2xsbAgKCiIrKwuAqKgo5fFscG9BRaVSaT3izdfXl9DQ0Kee744dO2jTpg3m5uZYWlrSuXNnUlNTlfq0tDRUKhUbNmygbdu2GBgY8Prrr3P+/HkSExNxd3fH2NiYjh078tdffynnFW55CQsLo0aNGpiamjJ06FByc3OVNhs3bqRRo0YYGBhgaWlJhw4dlM+g0Lx587C2tsbS0pIRI0Zo7fN9cMuMSqVi6dKldO3aFSMjIz777DMAvv/+e5o3b06VKlVwcHAgLCyM/Pz8p/7shBBCCCGEEEKIF+mlWhDR1dXl888/Jyoqil9//bXYNqdOncLX15cePXpw8uRJ1q9fz4EDBxg5ciQA3t7enDlzhr///huAffv2Ua1aNfbt2wdAfn4+hw4d0tpH+6SysrIYM2YMiYmJ7NmzBx0dHbp3765kRy80depUPv30U3766Sf09PTo27cv48ePZ9GiRSQkJJCamsqUKVO0ztmzZw8pKSnEx8ezdu1atmzZQlhYGABXr16lb9++BAQEkJKSglqtpkePHlpRNPHx8aSmphIfH09sbCwxMTHExMQ89HqmTp1K165dOXXqFAEBAezcuZMBAwYQFBTE2bNnWbZsGTExMcycObPY83NycsjMzNQ6hBBCCCGEEEKIl9FLk0OkUPfu3XFzc2Pq1KmsXLmySP3cuXPp168fwcHBADg6OhIZGYmXlxdLlizB1dUVS0tL9u3bR8+ePVGr1YwdO1bZc5qYmEh2djZt2rR56Dz69u2Lrq6uVllOTg5+fv8fUt+zZ0+t+pUrV1KjRg3Onj2Lq6urUj5u3Dh8fX0BGDVqFH379mXPnj3KPtvBgwcXWayoXLkyq1atwtDQkIYNGzJ9+nRCQkKYMWMGV69eJT8/nx49elCnTh0AragYAAsLC7744gt0dXWpX78+fn5+7NmzhyFDhpR4zf369SMgIEB5P3DgQCZOnMj7778PgIODAzNmzGD8+PFMnTq1yPnh4eHKoo0QQgghhBBCCPEye6kiRArNnj2b2NhYzp49W6QuKSmJmJgYjI2NlcPX15eCggIuXbqESqXC09MTtVrNzZs3OXPmDMOGDePu3btKNEWzZs0wNjZ+6BwWLFhAcnKy1vHOO+9otUlNTaVfv344ODhgamqKvb09AOnp6VrtGjdurLwuTGh2/wJGzZo1uXbtmtY5TZo0wdDw//NutGrVilu3bnHlyhWaNGlC+/btadSoEe+99x4rVqzgxo0bWuc3bNhQa0HH2tq6yBgPcnd313qflJTE9OnTtT7rIUOGcPXqVW7fvl3k/NDQUDIyMpTjypUrDx1PCCGEEEIIIYR4UV66CBEAT09PfH19+eSTT/D399eqKygoYOjQoQQFBRU5z9bWFri3bWb58uUkJCTQpEkTzM3N8fT0ZN++fajVary9vR85BysrK+rVq6dVZmJiws2bN5X3Xbp0wcbGhhUrVlCrVi0KCgpwdXXVyvUBaCUnValUxZY9uM2mJCqVCl1dXXbt2sWhQ4f48ccfiYqKYtKkSRw9elRZlHkwIWppxnjwqTMFBQWEhYXRo0ePIm2rVKlSpExfXx99ff1SXYcQQgghhBBCCPEivZQLIgCzZs3Czc0NJycnrfJmzZpx5syZIosV9/P29mbUqFFs3LhRWfzw8vJi9+7dHDp0iFGjRj31/K5fv05KSgrLli2jbdu2ABw4cOCp+y104sQJ7ty5g4GBAQBHjhzB2NiY2rVrA/cWODw8PPDw8GDKlCnUqVOHLVu2MGbMmDKbQ7NmzTh37txDP2shhBBCCCGEEKI8emkXRBo1akT//v2JiorSKp8wYQItW7ZkxIgRDBkyBCMjI1JSUti1a5fStjCPyJo1a/juu++Ae4skY8eOBXhk/pDSsLCwwNLSkuXLl2NtbU16ejoTJ0586n4L5ebmMnjwYD799FMuX77M1KlTGTlyJDo6Ohw9epQ9e/bw1ltvUaNGDY4ePcpff/2Fi4tLmY0PMGXKFDp37oyNjQ3vvfceOjo6nDx5klOnTilPoSmN02G+8vg6IYQQQhTL39+fmzdvEhcXV2IbOzs7goODlRxyQgghRFl4aRdEAGbMmMGGDRu0yho3bsy+ffuYNGkSbdu2RaPRULduXXr37q20UalUeHl5ERcXp0RvNG7cGDMzMyXfx9PS0dFh3bp1BAUF4erqirOzM5GRkaXajlMa7du3x9HREU9PT3JycujTpw/Tpk0DwNTUlP3797Nw4UIyMzOpU6cOERERvP3222UydiFfX1+2bdvG9OnTmTNnDpUqVaJ+/fp8+OGHZTqOEEIIUd74+/sTGxtLeHi41g8icXFxdO/eXevJb8+aSqViy5YtdOvWrcgcH7XQUF4kJiYW2dr7rLhO3YmOvuGjGwrxgqXN8nt0IyHEQ6k0z/NfbFEqr8oNTGZmJmZmZmRkZEiEiBBClGPyfV6Uv78/69evp0qVKvzyyy9YWFgAsiDyJMpinnl5eUXypz2uwr9zm+ANsiAiygVZEBGieI9z3/JSPmVGCCGEEOJl16FDB6ysrAgPD39ou0OHDuHp6YmBgQE2NjYEBQWRlZUFQFRUlNaT5+Li4lCpVCxevFgp8/X1JTQ09Knnu2PHDtq0aYO5uTmWlpZ07tyZ1NRUpT4tLQ2VSsWGDRto27YtBgYGvP7665w/f57ExETc3d0xNjamY8eO/PXXX8p5/v7+dOvWjbCwMGrUqIGpqSlDhw7VSjK/ceNGGjVqhIGBAZaWlnTo0EH5DArNmzcPa2trLC0tGTFiBHl5eUqdnZ0dCxcuVN6rVCqWLl1K165dMTIyUrbyfv/99zRv3pwqVarg4OBAWFgY+fn5T/3ZCSGEeDXJgogQQgghxBPQ1dXl888/Jyoqil9//bXYNqdOncLX15cePXpw8uRJ1q9fz4EDBxg5ciRwL8fZmTNn+PvvvwHYt28f1apVY9++fQDk5+dz6NAhvLy8nnq+WVlZjBkzhsTERPbs2YOOjg7du3cv8hS6qVOn8umnn/LTTz+hp6dH3759GT9+PIsWLSIhIYHU1FSmTJmidc6ePXtISUkhPj6etWvXsmXLFsLCwgC4evUqffv2JSAggJSUFNRqNT169NCKoomPjyc1NZX4+HhiY2OJiYkhJibmodczdepUunbtyqlTpwgICGDnzp0MGDCAoKAgzp49y7Jly4iJiWHmzJnFnp+Tk0NmZqbWIYQQomJ5qXOIVFSPugEQQgghxMuhe/fuuLm5MXXqVFauXFmkfu7cufTr109JBuro6EhkZCReXl4sWbJESQS/b98+evbsiVqtZuzYsSxYsAC4lzsjOzv7kQnh+/bti66urlZZTk4Ofn7/H1Lfs2dPrfqVK1dSo0YNzp49i6urq1I+btw4fH19ARg1ahR9+/Zlz549eHh4ADB48OAi9yqVK1dm1apVGBoa0rBhQ6ZPn05ISAgzZszg6tWr5Ofn06NHD+rUqQOgFRUD95LVf/HFF+jq6lK/fn38/PzYs2cPQ4YMKfGa+/XrR0BAgPJ+4MCBTJw4kffffx8ABwcHZsyYwfjx45k6dWqR88PDw5VFGyGEEBWTRIgIIYQQQjyF2bNnExsby9mzZ4vUJSUlERMTg7GxsXL4+vpSUFDApUuXUKlUeHp6olaruXnzJmfOnGHYsGHcvXtXiaZo1qwZxsbGD53DggULSE5O1jreeecdrTapqan069dPSTBvb28PQHp6ula7xo0bK69r1qwJaC9g1KxZk2vXrmmd06RJEwwN/z/vRqtWrbh16xZXrlyhSZMmtG/fnkaNGvHee++xYsUKbty4oXV+w4YNtRZ0rK2ti4zxIHd3d633SUlJTJ8+XeuzHjJkCFevXuX27dtFzg8NDSUjI0M5rly58tDxhBBCvHokQkQIIYQQ4il4enri6+vLJ598gr+/v1ZdQUEBQ4cOJSgoqMh5tra2wL1tM8uXLychIYEmTZpgbm6Op6cn+/btQ61Wl+oJdlZWVtSrV0+rzMTEhJs3byrvu3Tpgo2NDStWrKBWrVoUFBTg6uqqlesD0EpOqlKpii17cJtNSVQqFbq6uuzatYtDhw7x448/EhUVxaRJkzh69KiyKPNgQtTSjPHgU2cKCgoICwujR48eRdpWqVKlSJm+vj76+vqlug4hhBCvJokQeYbUajUqlUrrZuRlVZ7mKoQQQrxsZs2axffff8+hQ4e0yps1a8aZM2eoV69ekaNy5crA/+cR2bhxo7L44eXlxe7du8ssf8j169dJSUnh008/pX379ri4uBSJ0ngaJ06c4M6dO8r7I0eOYGxsTO3atYF7CxweHh6EhYVx/PhxKleuzJYtW8psfLj3WZ87d67Yz1pHR255hRBCFCURIqW0dOlSQkJCuHHjBnp69z62W7duYWFhQcuWLUlISFDaJiQk4Onpyblz57h69SpmZmYvatql1rp162c2V9epO+XxdaJckMfXCSGeVKNGjejfvz9RUVFa5RMmTKBly5aMGDGCIUOGYGRkREpKCrt27VLaFuYRWbNmDd999x1wb5Fk7NixAI/MH1IaFhYWWFpasnz5cqytrUlPT2fixIlP3W+h3NxcBg8ezKeffsrly5eZOnUqI0eOREdHh6NHj7Jnzx7eeustatSowdGjR/nrr79wcXEps/EBpkyZQufOnbGxseG9995DR0eHkydPcurUKeUpNKVxOsxXHi8thBAVhCyXl5KPjw+3bt3i2LFjSllCQgJWVlYkJiZq7U1Vq9XUqlULJycnrKyslHDTl1nlypXLzVyFEEKIl9GMGTO0npwC9/Jx7Nu3jwsXLtC2bVuaNm3K5MmTsba2VtqoVColCqRt27bKeWZmZjRt2rRM/udcR0eHdevWkZSUhKurK6NHj2bu3LlP3W+h9u3b4+joiKenJ7169aJLly5MmzYNAFNTU/bv30+nTp1wcnLi008/JSIigrfffrvMxod7jyfetm0bu3bt4vXXX6dly5bMnz9fSeQqhBBCPEilefBfblGi1157jY8//lj5RWXChAlkZWURHx/PokWL6NChA3DvpsDa2poPP/wQHx8fbty4gbm5OZcvX2bkyJEcOHCA3Nxc7OzsmDt3Lp06dQLgzJkzjB8/noSEBDQaDW5ubsTExFC3bl0KCgr47LPPWL58ufKryqxZs+jYsSMAaWlp2Nvbs2nTJqKiojh69CiOjo4sXbqUVq1aATx0fLVarTXXmJgYgoODWb9+PcHBwVy5coU2bdoQHR2tdRP3MJmZmZiZmWETvEEiRES5IBEiQhSv8Ps8IyNDfjkXRfj7+3Pz5k3i4uJe9FSeivydCyHEq+Fxvs8lQuQxeHt7Ex8fr7yPj4/H29sbLy8vpTw3N5fDhw/j4+NT5PwRI0aQk5PD/v37OXXqFLNnz1ayxv/22294enpSpUoV9u7dS1JSEgEBAeTn5wOwaNEiIiIimDdvHidPnsTX15d33nmHCxcuaI0xadIkxo0bR3JyMk5OTvTt21fp42HjF+f27dvMmzeP1atXs3//ftLT0xk3blyJ7XNycsjMzNQ6hBBCCCGEEEKIl5HkEHkM3t7ejB49mvz8fO7cucPx48fx9PTk7t27REZGAveSiN25cwcfH58ij7FLT0+nZ8+eyqPrHBwclLrFixdjZmbGunXrlEzrTk5OSv28efOYMGECffr0Ae494i8+Pp6FCxeyePFipd24cePw87v3K3dYWBgNGzbk4sWL1K9f/6HjFycvL4+lS5dSt25dAEaOHMn06dNLbB8eHk5YWNhD+xRCCCGEEEIIIV4GEiHyGHx8fMjKyiIxMZGEhAScnJyoUaMGXl5eJCYmkpWVhVqtxtbWttjFhqCgID777DM8PDyYOnUqJ0+eVOqSk5Np27ZtkcfOwb2Qn99//x0PDw+tcg8PD1JSUrTKGjdurLwu3Npy7dq1R45fHENDQ2UxpLC/wr6KExoaSkZGhnJcuXLlof0LIYQQovyLiYkp99tlhBBCVEyyIPIY6tWrR+3atYmPjyc+Pl5JgGZlZYW9vT0HDx4kPj6edu3aFXv+hx9+yC+//MLAgQM5deoU7u7uSoZ5AwODR47/YMJTjUZTpOz+BZXCuoKCgkeOX5wHF2dUKlWRZHH309fXx9TUVOsQQgghhBBCCCFeRrIg8ph8fHxQq9Wo1Wq8vb2Vci8vL3bu3MmRI0eKzR9SyMbGhmHDhrF582bGjh3LihUrgHuRHQkJCeTl5RU5x9TUlFq1anHgwAGt8kOHDj32I+tKGl8IIYQQQgghhKhIJIfIY/Lx8WHEiBHk5eUpESJwb0Fk+PDhZGdnl7ggEhwczNtvv42TkxM3btxg7969yoLGyJEjiYqKok+fPoSGhmJmZsaRI0do0aIFzs7OhISEMHXqVOrWrYubmxvR0dEkJyezZs2aUs/9YeM/S6fDfCVaRAghhBBCCCHES0UWRB6Tj48Pd+7coX79+tSsWVMp9/Ly4t9//6Vu3brY2NgUe+7du3cZMWIEv/76K6ampnTs2JEFCxYAYGlpyd69ewkJCcHLywtdXV3c3NyUvCFBQUFkZmYyduxYrl27RoMGDdi6dSuOjo6lnvvDxhdCCCGEeFwxMTEEBwdz8+bN5zqunZ0dwcHBBAcHl3nfrlN3oqNvWOb9CvGqSZvl96KnIMRTU2kelhRCiKfwOM9/FkII8fKS7/Nnx9/fn9jYWAD09PSwsbGhR48ehIWFYWRk9IJn92gvakHkr7/+wsjICEPDslu4KPw7twneIAsiQpSCLIiIl9Xj3LdIhIgQQgghxAvUsWNHoqOjycvLIyEhgQ8//JCsrCyWLFnyoqf20qpevfqLnoIQQohXgCRVFUIIIYR4gfT19bGyssLGxoZ+/frRv39/4uLi0Gg0zJkzBwcHBwwMDGjSpAkbN25UzlOr1ahUKvbs2YO7uzuGhoa0bt2ac+fOKW2mTZuGm5sbq1evxs7ODjMzM/r06cO///6rtNm4cSONGjXCwMAAS0tLOnToQFZWFvv376dSpUr88ccfWvMdO3Ysnp6eRa7j3LlzqFQqfv75Z63y+fPnY2dnh0aj4e7duwwePBh7e3sMDAxwdnZm0aJFWu39/f3p1q0b8+bNw9raGktLSyV/WyE7OzsWLlyoNUajRo0wMjLCxsaGwMBAbt269Xj/IYQQQlQ4siAihBBCCPESMTAwIC8vj08//ZTo6GiWLFnCmTNnGD16NAMGDGDfvn1a7SdNmkRERATHjh1DT0+PgIAArfrU1FTi4uLYtm0b27ZtY9++fcyaNQuAq1ev0rdvXwICAkhJSUGtVtOjRw80Gg2enp44ODiwevVqpa/8/Hy+/vprPvjggyLzdnZ2pnnz5kUSvn/zzTf069cPlUpFQUEBtWvXZsOGDZw9e5YpU6bwySefsGHDBq1z4uPjSU1NJT4+ntjYWGJiYoiJiSnxM9PR0SEyMpLTp08TGxvL3r17GT9+/EM/55ycHDIzM7UOIYQQFYssiAghhBBCvCT+97//8c033+Dj48P8+fNZtWoVvr6+ODg44O/vz4ABA1i2bJnWOTNnzsTLy4sGDRowceJEDh06RHZ2tlJfUFBATEwMrq6utG3bloEDB7Jnzx7g3oJIfn4+PXr0wM7OjkaNGhEYGIixsTEAgwcPJjo6Wunrhx9+4Pbt2/Tq1avY+ffv359vvvlGeX/+/HmSkpIYMGAAAJUqVSIsLIzXX38de3t7+vfvj7+/f5EFEQsLC7744gvq169P586d8fPzU+ZcnODgYHx8fLC3t6ddu3bMmDGjSJ8PCg8Px8zMTDlKSoovhBDi1SULIi+ZmJgYzM3Nn/u4D4aeCiGEEOL52LZtG8bGxlSpUoVWrVrh6enJuHHjyM7O5s0338TY2Fg5vvrqK1JTU7XOb9y4sfLa2toagGvXrilldnZ2mJiYaLUprG/SpAnt27enUaNGvPfee6xYsYIbN24obf39/bl48SJHjhwBYNWqVfTq1avEhK99+vTh8uXLSvs1a9bg5uZGgwYNlDZLly7F3d2d6tWrY2xszIoVK0hPT9fqp2HDhujq6hY75+LEx8fz5ptv8tprr2FiYsKgQYO4fv06WVlZJZ4TGhpKRkaGcly5cqXEtkIIIV5Nr1xS1fKerf1FSUxMfGafjzy+TojSkWztQlRMPj4+LFmyhEqVKlGrVi0qVarE0aNHgXsRGa+99ppWe319fa33lSpVUl6rVCrgXlRIcfWFbQrrdXV12bVrF4cOHeLHH38kKiqKSZMmcfToUezt7alRowZdunQhOjoaBwcHtm/fjlqtLvFarK2t8fHx4ZtvvqFly5asXbuWoUOHKvUbNmxg9OjRRERE0KpVK0xMTJg7d65yvaWZ84MuX75Mp06dGDZsGDNmzKBq1aocOHCAwYMHa+UdeZC+vn6Rz1IIIUTF8sotiIBka38Skq1dCCGEeDGMjIyoV6+eVlmDBg3Q19cnPT0dLy+vZzq+SqXCw8MDDw8PpkyZQp06ddiyZQtjxowB4MMPP6RPnz7Url2bunXr4uHh8dD++vfvz4QJE+jbty+pqan06dNHqUtISKB169YEBgYqZQ9GvDyuY8eOkZ+fT0REBDo694KfH7VdRgghhIBXdEGkMFs7QL9+/YiPjycuLo4vv/ySuXPnsnTpUq5evYqTkxOTJ0/m3XffBe5la/fx8WH37t1MmDCBs2fP4ubmRnR0NM7OzsC9bO1xcXGMHTuWyZMnc+PGDd5++21WrFihhKNu3LiRsLAwLl68iKGhIU2bNuW7774jKSmJ9u3bc+XKFWV+cC9be2JiIvv379e6jnPnzlG/fn1SUlKoX7++Uj5//nwiIyO5dOkSBQUFfPTRR+zdu5c//vgDW1tbAgMDGTVqlNLe39+fmzdv0qZNGyIiIsjNzaVPnz4sXLhQ+QXGzs6O4OBggoODlTGio6P55ZdfqFq1Kl26dGHOnDnKnmIhhBBCPDsmJiaMGzeO0aNHU1BQQJs2bcjMzOTQoUMYGxvz/vvvl8k4R48eZc+ePbz11lvUqFGDo0eP8tdff+Hi4qK08fX1xczMjM8++4zp06c/ss8ePXowfPhwhg8fjo+Pj1aES7169fjqq6/YuXMn9vb2rF69msTEROzt7Z/4GurWrUt+fj5RUVF06dKFgwcPsnTp0ifu73SYL6ampk98vhBCiPKjQuQQkWztkq1dCCGEKG9mzJjBlClTCA8Px8XFBV9fX77//vunWjx4kKmpKfv376dTp044OTnx6aefEhERwdtvv6200dHRwd/fn7t37zJo0KBS9dmlSxdOnDhB//79teqGDRtGjx496N27N2+88QbXr1/XihZ5Em5ubsyfP5/Zs2fj6urKmjVrCA8Pf6o+hRBCVAwqjUajedGTKEuF0RBxcXHAvWztnTp1wsfHh23btrF3715atWqltP/www+5ffs233zzjVaESPv27QHYvn07fn5+3LlzhypVqjBt2jTmzp3LH3/8oUSEjB8/nv3793PkyBF++uknmjdvTlpaGnXq1Ckyvzlz5hATE8PZs2cB+O677xgwYAB//PEHRkZGxMTEEBwczM2bNwFYsGABX3zxhRJOev78eZydnTlz5oxWgrL7jRgxgj///JONGzcqn4larSY1NVVJUNarVy90dHRYt24dUDRC5EHffvstw4cP5++//y7xs582bRphYWFFym2CN0gOESFKQXKIiJdVZmYmZmZmZGRkyC/nFdSQIUP4888/2bp164ueyjMjf+dCCPFqeJzv81cyQkSytUu2diGEEEI8vYyMDHbv3s2aNWv4+OOPX/R0hBBCiDL1Si6I+Pj4kJyczLlz58jOzmbz5s1K3Q8//EBycrJynD17VomkKFQW2dr/+9//0qBBA6KionB2dubSpUsAWtnar127xvbt24tsybnf/dnaAdauXcuAAQOU+sJs7QEBAfz4448kJyfzwQcfkJubW+I1PTjnBxVma3d1dWXTpk0kJSWxePFigEdmazc1NdU6hBBCCFF+de3alXfeeYehQ4fy5ptvvujpCCGEEGXqlUyqKtnaJVu7EEIIIZ7ewx6xK4QQQpR3r+SCSHEkW3vpSbZ2IYQQQgghhBCvuldyy0xJJFt76Ui2diGEEEIIIYQQr7pX7ikz5YVkaxdCCFFeyPe5AIo8CW/atGnExcWRnJystJk2bRpLlizh2rVrbNmyhW7duj2z+TzqCXmPS/7OhRDi1fA43+cVZsvMyyIjI4PExETWrFnDd99996KnI4QQQogK4o8//mDmzJn88MMP/Pbbb9SoUQM3NzeCg4Np3779Y/c3btw4rSfPpKSkEBYWxpYtW2jZsiUWFhZlOf0iEhMTS3xK39NwnboTHX3DMu9XCFE6abP8XvQURAUiCyLPWdeuXfnf//4n2dqFEEII8dykpaXh4eGBubk5c+bMoXHjxuTl5bFz505GjBjBzz///Nh9GhsbY2xsrLwvTOretWtX5Sl9TyIvL6/I0/GKU7169SceQwghhIAKlkPkZaBWq7l9+zYLFix40VMRQgghRAURGBiISqXif//7H++++y5OTk40bNiQMWPGcOTIEQDmz59Po0aNMDIywsbGhsDAQG7dulVin9OmTcPNzU153aVLF+BerrTCBZGCggKmT59O7dq10dfXx83NjR07dih9pKWloVKp2LBhA97e3lSpUoWvv/4af39/unXrxrx587C2tsbS0pIRI0aQl5ennGtnZ8fChQuV9487fyGEEEIWRIQQQgghXmH//PMPO3bsYMSIEcVuMTE3NwfuLWRERkZy+vRpYmNj2bt3L+PHjy/VGOPGjSM6OhqAq1evcvXqVQAWLVpEREQE8+bN4+TJk/j6+vLOO+9w4cIFrfMnTJhAUFAQKSkp+Pr6AhAfH09qairx8fHExsYSExNDTExMiXN43Pnn5OSQmZmpdQghhKhYZEHkJXL/Ly0vgre3d5klJhNCCCHEy+HixYtoNBrq16//0HbBwcH4+Phgb29Pu3btmDFjBhs2bCjVGMbGxsrCipWVFVZWVgDMmzePCRMm0KdPH5ydnZk9ezZubm5akR2FY/fo0QN7e3tq1aoFgIWFBV988QX169enc+fO+Pn5sWfPnjKbf3h4OGZmZsphY2NTqmsVQgjx6pAcImWorJOVvSokOZkQ5YMkMRPi1VT4QMFH5fWIj4/n888/5+zZs2RmZpKfn092djZZWVlPlLw0MzOT33//HQ8PD61yDw8PTpw4oVXm7u5e5PyGDRuiq6urvLe2tubUqVNlNv/Q0FDGjBmjNV9ZFBFCiIpFIkTKSFpaGs2bN2fv3r3MmTOHU6dOsWPHDnx8fBgxYsRzm8f9e2uFEEIIIRwdHVGpVKSkpJTY5vLly3Tq1AlXV1c2bdpEUlISixcvBp7+3uLBhRiNRlOkrLgFiwcTq6pUKgoKCood40nmr6+vj6mpqdYhhBCiYpEFkTJSmmRl6enpdO3aFWNjY0xNTenVqxd//vlniX0+aSKy69ev07dvX2rXro2hoSGNGjVi7dq1Wn1nZWUxaNAgjI2Nsba2JiIiosj4N27cYNCgQVhYWGBoaMjbb79dZM+vEEIIIV5uVatWxdfXl8WLF5OVlVWk/ubNmxw7doz8/HwiIiJo2bIlTk5O/P777081rqmpKbVq1eLAgQNa5YcOHcLFxeWp+n7Qs5i/EEKIV58siJSB0iQr02g0dOvWjX/++Yd9+/axa9cuUlNT6d27d4n9PmkisuzsbJo3b862bds4ffo0H330EQMHDuTo0aPKOSEhIcTHx7NlyxZ+/PFH1Go1SUlJWv36+/tz7Ngxtm7dyuHDh9FoNHTq1KnEX1okOZkQQgjxcvryyy+5e/cuLVq0YNOmTVy4cIGUlBQiIyNp1aoVdevWJT8/n6ioKH755RdWr17N0qVLn3rckJAQZs+ezfr16zl37hwTJ04kOTmZUaNGlcFV/b9nNX8hhBCvNskhUgZKk6xs9+7dnDx5kkuXLin7U1evXk3Dhg1JTEzk9ddfL3LO/YnIAGbPnk18fDwLFy5UwkDh/xOR3W/cuHHK648//pgdO3bw7bff8sYbb3Dr1i1WrlzJV199xZtvvglAbGwstWvXVs65cOECW7du5eDBg7Ru3RqANWvWYGNjQ1xcHO+9916R+YaHhxMWFvbIz0sIIYQQz5e9vT0//fQTM2fOZOzYsVy9epXq1avTvHlzlixZgpubG/Pnz2f27NmEhobi6elJeHg4gwYNeqpxg4KCyMzMZOzYsVy7do0GDRqwdetWHB0dy+jK7inL+Z8O85XtM0IIUUGoNIWZtsQTO3r0KC1btmTLli1069at2DaRkZEsWLCAS5cuaZVbWFiwaNEiBg0axLRp04iLiyM5OZnMzEzMzMxQq9V4eXkp7UePHs2JEyfYu3cvaWlp2Nvbc+DAAa2EZXfv3mXWrFmsX7+e3377jZycHHJycujevTsbNmzgxIkTuLm5cfnyZWxtbZXzmjZtipeXFwsXLmTr1q307NmT7OxsrYRmTZs2pXv37kyZMqXINRaOU6gwOZlN8AZJqipEOSBJVUVJCv9NysjIkP9RFK8s+TsXQohXw+N8n8uWmTJQmmRlxSUQe1h5oSdJRBYREcGCBQsYP348e/fuJTk5GV9fX3Jzc5U+HqWkNg+bryQnE0IIIYQQQghRXsiCSBkoTbKyBg0akJ6ezpUrV5Tys2fPkpGRUWxisadJRJaQkEDXrl0ZMGAATZo0wcHBQSvvSL169ahUqZKS7BXuJVA9f/688r5Bgwbk5+dr5R25fv0658+fL/NEaEIIIYQQQgghxPMmOUTKyJdffknr1q1p0aIF06dPp3HjxuTn57Nr1y6WLFnC2bNnady4Mf3792fhwoXk5+cTGBiIl5cX7u7uxfYZEhLC1KlTqVu3Lm5ubkRHR5OcnMyaNWseOpd69eqxadMmDh06hIWFBfPnz+ePP/5QFjKMjY0ZPHgwISEhWFpaUrNmTSZNmoSOzv+vjzk6OtK1a1eGDBnCsmXLMDExYeLEibz22mt07dr1sT4b2YsrhBBCCCGEEOJlIwsiZeRRycpUKhVxcXF8/PHHeHp6oqOjQ8eOHYmKiiqxzydNRDZ58mQuXbqEr68vhoaGfPTRR3Tr1o2MjAylzdy5c7l16xbvvPMOJiYmjB07VqseIDo6mlGjRtG5c2dyc3Px9PRk+/btVKpU6ek+LCGEEEIIIYQQ4gWTpKrimZHkZEII8WqQ73NREcjfuRBCvBokqaoQQgghhBBCCCHEQ8iWGSGEEEII8dimTZtGXFwcycnJL2R8b29v3NzcWLhwYZn26zp1Jzr6hmXapxCi7KXN8nvRUxCvAIkQEUIIIYSogP744w8+/vhjHBwc0NfXx8bGhi5durBnz54XPTUhhBDiuZAIESGEEEKICiYtLQ0PDw/Mzc2ZM2cOjRs3Ji8vj507dzJixAh+/vnn5zKPvLw8SdYuhBDihXllI0QKn+oiSs/f359u3bq96GkIIYQQ4hkLDAxEpVLxv//9j3fffRcnJycaNmzImDFjOHLkCADp6el07doVY2NjTE1N6dWrF3/++WeJfRYUFDB9+nRq166Nvr4+bm5u7NixQ6lPS0tDpVKxYcMGvL29qVKlCl9//TXXr1+nb9++1K5dG0NDQxo1asTatWu1+s7KymLQoEEYGxtjbW1NREREkfFv3LjBoEGDsLCwwNDQkLfffpsLFy6U0ScmhBDiVVQuI0SuXbvG5MmT+e9//8uff/6JhYUFTZo0Ydq0abRq1QqAq1evYmFh8YJn+v/UajU+Pj40bNiQEydOoKurq9SZm5uzcOFC/P39X9wEnyHZiytE+SB7cYWoGP755x927NjBzJkzMTIyKlJvbm6ORqOhW7duGBkZsW/fPvLz8wkMDKR3796o1epi+120aBEREREsW7aMpk2bsmrVKt555x3OnDmDo6Oj0m7ChAlEREQQHR2Nvr4+2dnZNG/enAkTJmBqasoPP/zAwIEDcXBw4I033gAgJCSE+Ph4tmzZgpWVFZ988glJSUm4ubkp/fr7+3PhwgW2bt2KqakpEyZMoFOnTpw9e7bYKJScnBxycnKU95mZmU/4iQohhCivyuWCSM+ePcnLyyM2NhYHBwf+/PNP9uzZwz///KO0sbKyeqoxcnNzqVy5cpHypw3tTE1N5auvvuKDDz54mukJIYQQQjyRixcvotFoqF+/foltdu/ezcmTJ7l06RI2NjYArF69moYNG5KYmMjrr79e5Jx58+YxYcIE+vTpA8Ds2bOJj49n4cKFLF68WGkXHBxMjx49tM4dN26c8vrjjz9mx44dfPvtt7zxxhvcunWLlStX8tVXX/Hmm28CEBsbS+3atZVzChdCDh48SOvWrQFYs2YNNjY2xMXF8d577xWZb3h4OGFhYY/8vIQQQry6yt2WmZs3b3LgwAFmz56Nj48PderUoUWLFoSGhuLn9/+/bj64Zea3336jd+/eWFhYYGlpSdeuXUlLS1PqC7eLhIeHU6tWLZycnJ4qtLMkH3/8MVOnTiU7O7vENhkZGXz00UfUqFEDU1NT2rVrx4kTJwA4d+4cKpWqyN7e+fPnY2dnh0aj4e7duwwePBh7e3sMDAxwdnZm0aJFWu3v3r3LmDFjMDc3x9LSkvHjx6PRaLTa7NixgzZt2ihtOnfuTGpqaqmuUwghhBAvp8J/71UqVYltUlJSsLGxURZDABo0aIC5uTkpKSlF2mdmZvL777/j4eGhVe7h4VGkvbu7u9b7u3fvMnPmTBo3boylpSXGxsb8+OOPpKenA/d+TMrNzVWigAGqVq2Ks7Oz1nz19PSUiBIAS0tLnJ2di50vQGhoKBkZGcpx5cqVEj8PIYQQr6ZytyBibGyMsbExcXFxWmGOD3P79m18fHwwNjZm//79HDhwAGNjYzp27Ehubq7Sbs+ePaSkpLBr1y62bdumlE+YMIGgoCBSUlLw9fVVQju3bdvG6dOn+eijjxg4cCBHjx595FyCg4PJz8/niy++KLZeo9Hg5+fHH3/8wfbt20lKSqJZs2a0b9+ef/75B2dnZ5o3b86aNWu0zvvmm2/o168fKpWKgoICateuzYYNGzh79ixTpkzhk08+YcOGDUr7iIgIVq1axcqVKzlw4AD//PMPW7Zs0eozKyuLMWPGkJiYyJ49e9DR0aF79+4UFBQUO/ecnBwyMzO1DiGEEEK8XBwdHVGpVCUuFMC9+5HiFkxKKi/0YF1x7R/cphMREcGCBQsYP348e/fuJTk5GV9fX+Ue7cEfbEqa7+NcB4C+vj6mpqZahxBCiIql3C2I6OnpERMTQ2xsLObm5nh4ePDJJ59w8uTJEs9Zt24dOjo6/Oc//6FRo0a4uLgQHR1Nenq61j5YIyMj/vOf/9CwYUNcXV2V8sLQTnt7e2rVqsVrr73GuHHjcHNzw8HBgY8//hhfX1++/fbbR87f0NCQqVOnEh4eTkZGRpH6+Ph4Tp06xbfffou7uzuOjo7MmzcPc3NzNm7cCED//v355ptvlHPOnz9PUlISAwYMAKBSpUqEhYXx+uuvY29vT//+/fH399daEFm4cCGhoaH07NkTFxcXli5dipmZmdZcevbsSY8ePXB0dMTNzY2VK1dy6tQpzp49W+y1hYeHY2Zmphz3/6okhBBCiJdD1apV8fX1ZfHixWRlZRWpv3nzJg0aNCA9PV0rauLs2bNkZGTg4uJS5BxTU1Nq1arFgQMHtMoPHTpUbPv7JSQk0LVrVwYMGECTJk1wcHDQSoZar149KlWqpCR7hXsJVM+fP6+8b9CgAfn5+Vo/Tl2/fp3z588/cnwhhBAVV7nNIeLn50dCQgKHDx9mx44dzJkzh//85z/FJiZNSkri4sWLmJiYaJVnZ2drbQFp1KhRsXlDigvtnDVrFuvXr+e3335TknIVl5isOIMHD2b+/PnMnj2bzz//vMhcb926haWlpVb5nTt3lLn26dOHkJAQjhw5QsuWLVmzZg1ubm40aNBAab906VL+85//cPnyZe7cuUNubq6SeCwjI4OrV69qhZ7q6enh7u6u9QtLamoqkydP5siRI/z9999KZEh6errWglGh0NBQxowZo7zPzMyURREhhBDiJfTll1/SunVrWrRowfTp02ncuDH5+fns2rWLJUuWcPbsWRo3bkz//v1ZuHChklTVy8uryH1RoZCQEKZOnUrdunVxc3MjOjqa5OTkIlGtD6pXrx6bNm3i0KFDWFhYMH/+fP744w9lIcPY2JjBgwcTEhKCpaUlNWvWZNKkSejo/P/veo6OjnTt2pUhQ4awbNkyTExMmDhxIq+99hpdu3Z9rM/mdJivRIsIIUQFUS4XRACqVKnCm2++yZtvvsmUKVP48MMPmTp1arELIgUFBcVuMwGoXr268rqkBY2SQjsXLlxIo0aNMDIyIjg4WGv7zcPo6enx2Wef4e/vz8iRI4vM1drautgM7ubm5gBYW1vj4+PDN998Q8uWLVm7di1Dhw5V2m3YsIHRo0cTERFBq1atMDExYe7cuaXa0nO/Ll26YGNjw4oVK6hVqxYFBQW4urqWeJ36+vro6+s/1hhCCCGEeP7s7e356aefmDlzJmPHjuXq1atUr16d5s2bs2TJEiUX28cff4ynpyc6Ojp07NiRqKioEvsMCgoiMzOTsWPHcu3aNRo0aMDWrVu1njBTnMmTJ3Pp0iV8fX0xNDTko48+olu3blqRtHPnzuXWrVu88847mJiYMHbs2CKRttHR0YwaNYrOnTuTm5uLp6cn27dvf6pk+EIIIV5t5XZB5EENGjTQSqJ6v2bNmrF+/XolSenTuj+0E+4tYly4cOGxQjLfe+895s6dWyS7ebNmzfjjjz/Q09PDzs6uxPP79+/PhAkT6Nu3L6mpqUpG98L5tW7dmsDAQKXs/kgYMzMzrK2tOXLkCJ6engDk5+cr+UrgXphpSkoKy5Yto23btgBFwmCFEEIIUX5ZW1vzxRdflJjXzNbWlu+++67E86dNm8a0adOU9zo6OkyZMoUpU6YU274w+fuDqlatWuI9XCFjY2NWr17N6tWrlbKQkBCtNhYWFnz11VcP7UcIIYS4X7lbELl+/TrvvfceAQEBNG7cGBMTE44dO8acOXNKDIns378/c+fOpWvXrkyfPp3atWuTnp7O5s2bCQkJ0XpsW2k8KrSztGbNmoWvr69WWYcOHWjVqhXdunVj9uzZODs78/vvv7N9+3a6deumhKn26NGD4cOHM3z4cHx8fHjttde05vfVV1+xc+dO7O3tWb16NYmJidjb2yttRo0axaxZs3B0dMTFxYX58+dz8+ZNpb7waTzLly/H2tqa9PR0Jk6c+FjXV0hCT4UQQgghhBBCvGzKXVJVY2Nj3njjDRYsWICnpyeurq5MnjyZIUOGlPgLh6GhIfv378fW1pYePXrg4uJCQEAAd+7ceaL/UZ88eTLNmjXD19cXb29vrKys6Nat22P3065dO9q1a0d+fr5SplKp2L59O56engQEBODk5ESfPn1IS0ujZs2aSjtTU1O6dOnCiRMn6N+/v1a/w4YNo0ePHvTu3Zs33niD69eva0WLAIwdO5ZBgwbh7++vbKvp3r27Uq+jo8O6detISkrC1dWV0aNHM3fu3Me+RiGEEEIIIYQQ4mWk0pTmWWZCPIHMzEzMzMzIyMiQCBEhhCjH5PtcVATydy6EEK+Gx/k+L3cRIkIIIYQQQgghhBBPSxZEhBBCCCGEEEIIUeHIgogQQgghhCiWt7c3wcHBpWqrVqtRqVRaSdofNG3aNNzc3MpkbkIIIcTTKndPmRFCCCGEEE/O39+f2NhYhg4dytKlS7XqAgMDWbJkCe+//z4xMTFs3ryZSpUqldnY48aN4+OPPy6z/p4F16k70dE3fNHTEEKIMpM2y+9FT+GlJREiQgghhBAVjI2NDevWrePOnTtKWXZ2NmvXrsXW1lYpq1q1KiYmJmU2rrGxMZaWlmXWnxBCCPE0JELkOfP29sbNzY2FCxc+sq1arcbHx4cbN25gbm5ebJtp06YRFxdHcnJymc6zLMkvLUKIV4380iLKu2bNmvHLL7+wefNm+vfvD8DmzZuxsbHBwcFBaffgfUtOTg6TJ09m7dq1XLt2DVtbWyZOnMjgwYOVc5KSkpgwYQJnz57Fzc2N6OhonJ2dgaL3Lfn5+YwZM4avvvoKXV1dPvzwQ/744w8yMjKIi4sDYMeOHXz22WecPn0aXV1dWrVqxaJFi6hbty4AaWlp2Nvbs2nTJqKiojh69CiOjo4sXbqUVq1aPeNPUgghRHkmESJlwN/fH5VKxbBhw4rUBQYGolKp8Pf3B+7dbMyYMaPMxh43bhx79uwps/6EEEIIUTF88MEHREdHK+9XrVpFQEDAQ88ZNGgQ69atIzIykpSUFJYuXYqxsbFWm0mTJhEREcGxY8fQ09N7aJ+zZ89mzZo1REdHc/DgQTIzM5WFkEJZWVmMGTOGxMRE9uzZg46ODt27d6egoKDIuOPGjSM5ORknJyf69u1Lfn5+iWPn5OSQmZmpdQghhKhYJEKkjBSGni5YsAADAwOg5NDTsmRsbFzkRkQIIYQQ4lEGDhxIaGgoaWlpqFQqDh48yLp161Cr1cW2P3/+PBs2bGDXrl106NABQCuapNDMmTPx8vICYOLEifj5+ZGdnU2VKlWKtI2KiiI0NJTu3bsD8MUXX7B9+3atNj179tR6v3LlSmrUqMHZs2dxdXVVyseNG4ef373orbCwMBo2bMjFixepX79+sdcTHh5OWFhYsXVCCCEqBokQKSPNmjXD1taWzZs3K2WFoadNmzZVyh7M1p6Tk8P48eOxsbFBX18fR0dHVq5cqdV3UlIS7u7uGBoa0rp1a86dO6fUPZitPT8/n6CgIMzNzbG0tGTChAm8//77dOvWTWmzY8cO2rRpo7Tp3LkzqampSn3hjdHmzZvx8fHB0NCQJk2acPjw4Yd+BvJLixBCCFF+VKtWDT8/P2JjY4mOjsbPz49q1aqV2D45ORldXV1lsaMkjRs3Vl5bW1sDcO3atSLtMjIy+PPPP2nRooVSpqurS/PmzbXapaam0q9fPxwcHDA1NcXe3h6A9PT0Jxq3UGhoKBkZGcpx5cqVh16XEEKIV48siJShih56Gh4ejpmZmXLY2Ng89NqFEEII8WIFBAQQExNDbGzsI+9ZCiNgH+X+p9KoVCqAIvcY9ytsU0ij0Wi979KlC9evX2fFihUcPXqUo0ePApCbm/tU4+rr62Nqaqp1CCGEqFhky0wZquihp6GhoYwZM0Z5n5mZKYsiQgghxEusY8eOysKCr6/vQ9s2atSIgoIC9u3bp9y3PA0zMzNq1qzJ//73P9q2bQvA3bt3OX78uBL9ev36dVJSUli2bJnS5sCBA089thBCCAGyIFKm7g891Wg0zzz09P7cJPDw0NP7fyFJTU1l8uTJHDlyhL///lupS09P11oQKWnckhZE9PX10dfXf+i1CCGEEOLloaurS0pKivL6Yezs7Hj//fcJCAggMjKSJk2acPnyZa5du0avXr2eaPyPP/6Y8PBw6tWrR/369YmKiuLGjRtKhIeFhQWWlpYsX74ca2tr0tPTmThx4hONJYQQQjxIFkTKWEBAACNHjgRg8eLFD237IkNPbWxsWLFiBbVq1aKgoABXV9enDj0VQgghRPnzOFtFlixZwieffEJgYCDXr1/H1taWTz755InHnjBhAn/88QeDBg1CV1eXjz76CF9fX2VxRkdHh3Xr1hEUFISrqyvOzs5ERkbi7e39xGM+yukwX9k+I4QQFYQsiJQxCT0tSm4shBBCiJdHTEzMQ+vvzz324LbfKlWqMH/+fObPn1/kPG9v7yI/wri5uWmVTZs2jWnTpinv9fT0iIqKIioqCrj3w4uLi4tWxEmHDh04e/asVr/392lnZ1dkXHNz8yJlQgghxINkQaSMSeipEEIIIUTpXL58mR9//BEvLy9ycnL44osvuHTpEv369XvRUxNCCFEByFNmnoHHyVS+ZMkS3n33XQIDA6lfvz5DhgwhKyvriceeMGECffv2ZdCgQbRq1QpjY2N8fX2VBKyFoadJSUm4uroyevRo5s6d+8TjCSGEEEI8KR0dHWJiYnj99dfx8PDg1KlT7N69GxcXlxc9NSGEEBWASiPxhK+0+0NPZ8yY8VzHzszMxMzMjIyMDNkyI4QQ5Zh8n4uKQP7OhRDi1fA43+eyZeYVI6GnQgghhBBCCCHEo8mWmVeMhJ4KIYQQQgghhBCPJhEirxgbGxsOHjz4oqchhBBCCCGEEEK81CRCRAghhBBClHsxMTGYm5u/6GkIIYQoRyRCRJQoJiaG4OBgbt68+aKnIoQQQogy4O/vT2xsLOHh4UycOFEpj4uLo3v37jyPXPvnz5/Hzc2N//znP1o5zgoKCmjTpg01a9Zky5Ytz3weJXGduhMdfcMXNr4QQlRkabP8nut4siDylOTG4tHkxkIIIV6c531jIV5+VapUYfbs2QwdOhQLC4vnPr6TkxOzZs3i448/xsfHB2trawAiIiK4ePEicXFxj91nXl5eGc9SCCFERSBbZspA4Y3FjRs3Xsj4999YXL16VSkvvLFYtmzZY/cpNxZCCCHEq6lDhw5YWVkRHh7+0HaHDh3C09MTAwMDbGxsCAoKIisrC4CoqCgaNWqktI2Li0OlUrF48WKlzNfXl9DQ0GL7/vjjj3Fzc2PIkCEA/Pzzz0yZMoXly5dTrVo1pk+fTu3atdHX18fNzY0dO3Yo56alpaFSqdiwYQPe3t5UqVKFr7/+usgY169fp0WLFrzzzjtkZ2eX/gMSQghRYciCSBmQGwshhBBClBe6urp8/vnnREVF8euvvxbb5tSpU/j6+tKjRw9OnjzJ+vXrOXDgACNHjgTA29ubM2fO8PfffwOwb98+qlWrxr59+wDIz8/n0KFDeHl5Fdu/SqUiOjqahIQEVqxYgb+/P71796Zbt24sWrSIiIgI5s2bx8mTJ/H19eWdd97hwoULWn1MmDCBoKAgUlJS8PX11ar79ddfadu2LfXr12fz5s1UqVKlyBxycnLIzMzUOoQQQlQssiBSBuTG4h65sRBCCCHKh+7du+Pm5sbUqVOLrZ87dy79+vUjODgYR0dHWrduTWRkJF999RXZ2dm4urpiaWmp3Keo1WrGjh2rvE9MTCQ7O5s2bdqUOAdbW1sWLlzIsGHD+P3331m0aBEA8+bNY8KECfTp0wdnZ2dmz56Nm5sbCxcu1Do/ODiYHj16YG9vT61atZTy8+fP4+HhQYcOHYiNjUVPr/gd4uHh4ZiZmSmHjY1NqT8/IYQQrwZZECkjcmMhNxZCCCFEeTJ79mxiY2M5e/ZskbqkpCRiYmIwNjZWDl9fXwoKCrh06RIqlQpPT0/UajU3b97kzJkzDBs2jLt375KSkoJaraZZs2YYGxs/dA4ffPAB1tbWBAUFYWZmRmZmJr///jseHh5a7Tw8PEhJSdEqc3d3L9LfnTt3aNOmDd26dSMyMhKVSlXi2KGhoWRkZCjHlStXHjpXIYQQrx5ZEClDcmMhNxZCCCFEeeHp6Ymvry+ffPJJkbqCggKGDh1KcnKycpw4cYILFy5Qt25d4F50q1qtJiEhgSZNmmBubo6npyf79u1DrVbj7e1dqnno6ekV+bHlwfsNjUZTpMzIyKhIX/r6+nTo0IEffvihxKjd+9uamppqHUIIISoWWRApQ3JjITcWQgghRHkya9Ysvv/+ew4dOqRV3qxZM86cOUO9evWKHJUrVwb+f7vvxo0blXsULy8vdu/e/dBtvg9jampKrVq1OHDggFb5oUOHcHFxeeT5Ojo6rF69mubNm9OuXTt+//33x56DEEKIikMeu1vGZs2ahZubG05OTlrl999YlMTb25tRo0aVeGMxatSox57P/TcWnp6eSvmhQ4do0aLFI88vvLHo168f7dq1Q61Wa22nEUIIIUT51ahRI/r3709UVJRW+YQJE2jZsiUjRoxgyJAhGBkZkZKSwq5du5S2hdt916xZw3fffQfcu5cZO3YswEO3+T5MSEgIU6dOpW7duri5uREdHU1ycjJr1qwp1fm6urqsWbOGvn37KvcuVlZWpR7/dJiv/KgjhBAVhCyIlDG5sShKbiyEEEKIl9eMGTPYsGGDVlnjxo3Zt28fkyZNom3btmg0GurWrUvv3r2VNiqVCi8vL+Li4mjbtq1ynpmZGQ4ODk/8b39QUBCZmZmMHTuWa9eu0aBBA7Zu3Yqjo2Op+9DT02Pt2rX07t1buXepUaPGE81HCCHEq0ul0Wg0L3oS5Zm/vz83b94kLi5OKbt8+TLOzs7k5ORw/8ebmJjIpEmTOHz4sNaNxf1bbN59913i4uL4559/MDU1RaPRUK1aNRwcHEhMTCzVnOzs7AgODiY4OBi4t13ns88+Y/ny5cqNxaxZs+jYsSNw77G79v/X3v3HVFX/cRx/XQqQUFAMBRIVf6WokAkW6tcfqKgtBzkXNZc4t5o/G5NNs2a4tvLHpk2HWpkzbTVd88faLA0RsHIkJk5UdKQY/gGyNH6EhQaf7x+Nm9ffGHg95zwf290859x7z/u9N5zz9s2550ZFqaioSM8884z7fT777DOlp6erurpa0j/fdJOamuq+p8m9Gova2loFBwerpqaGgQgAWBjHczgBP+cAYA8tOZ4zEEGbobEAAHvgeA4n4OccAOyhJcdzPjKDNtM8a6utrfVyJACA/6L5OM7fUGBn9C0AYA8t6VsYiKDNXL58WZIUGRnp5UgAAK2hrq5OwcHB3g4DaBP0LQBgL/fTtzAQQZsJCQmRJJWXl9uqga6trVVkZKQuXrxom0tq7ZiTRF5WYsecJPvkZYxRXV0d3zIGW6NvsQ475iSRl5XYMSfJPnm1pG9hIII24+PjI0kKDg629C/UnQQFBdkuLzvmJJGXldgxJ8keednpP4jA7dC3WI8dc5LIy0rsmJNkj7zut2/xaeM4AAAAAAAAHjkMRAAAAAAAgOMwEEGb8ff3V2Zmpvz9/b0dSquyY152zEkiLyuxY06SffMC7Miuv692zMuOOUnkZSV2zEmyb1534zJ8hx4AAAAAAHAYrhABAAAAAACOw0AEAAAAAAA4DgMRAAAAAADgOAxEAAAAAACA4zAQQZvZsGGDoqKi1K5dOw0dOlTff/+9t0N6YMuWLZPL5fJ4hIWFeTusFjt06JCmTJmiiIgIuVwu7dmzx2O7MUbLli1TRESEAgICNGbMGJ06dco7wbbAvfKaOXPmLfV7/vnnvRPsfVq+fLni4+PVoUMHdenSRSkpKTp79qzHc6xYr/vJy2r12rhxo2JiYhQUFKSgoCAlJCTo22+/dW+3Yp0AJ6JvefTQt1jjPCjRt1ipXvQtnhiIoE3s2LFD6enpeuedd1RUVKT//e9/mjx5ssrLy70d2gMbOHCgKioq3I/i4mJvh9Ri9fX1io2NVVZW1m23r1q1SmvWrFFWVpYKCwsVFhamCRMmqK6u7iFH2jL3ykuSJk2a5FG/b7755iFG2HL5+fmaN2+eCgoKlJ2drb///ltJSUmqr693P8eK9bqfvCRr1atbt25asWKFjh49qqNHjyoxMVHJycnu5sGKdQKchr7l0UTfYo3zoETfYqV60bfcxABtYNiwYWb27Nke6/r372/eeustL0X032RmZprY2Fhvh9GqJJndu3e7l5uamkxYWJhZsWKFe91ff/1lgoODzUcffeSFCB/MzXkZY0xaWppJTk72Sjytpaqqykgy+fn5xhj71OvmvIyxR706depkPv30U9vUCbA7+pZHH32LtdC3WIuT+xauEEGru3btmn7++WclJSV5rE9KStLhw4e9FNV/V1paqoiICEVFRemVV17R+fPnvR1SqyorK1NlZaVH3fz9/TV69GhL161ZXl6eunTpon79+un1119XVVWVt0NqkZqaGklSSEiIJPvU6+a8mlm1Xo2Njdq+fbvq6+uVkJBgmzoBdkbfYk12P75a9TzYjL7FGvWib+EjM2gDv/32mxobG9W1a1eP9V27dlVlZaWXovpvnnvuOW3btk379+/Xpk2bVFlZqeHDh+vy5cveDq3VNNfGTnVrNnnyZH3xxRc6ePCgVq9ercLCQiUmJqqhocHbod0XY4wWLlyokSNHatCgQZLsUa/b5SVZs17FxcVq3769/P39NXv2bO3evVvR0dG2qBNgd/Qt1mTn46sVz4M3om959OtF3/Kvx70dAOzL5XJ5LBtjbllnFZMnT3b/e/DgwUpISFDv3r21detWLVy40IuRtT471a1Zamqq+9+DBg1SXFycevToob1792rq1KlejOz+zJ8/XydOnNAPP/xwyzYr1+tOeVmxXk8//bSOHz+u6upq7dy5U2lpacrPz3dvt3KdAKew0+8pfYs169bMiufBG9G3PPr1om/5F1eIoNU9+eSTeuyxx26ZIlZVVd0ybbSqwMBADR48WKWlpd4OpdU0333eznVrFh4erh49eliifgsWLNDXX3+t3NxcdevWzb3e6vW6U163Y4V6+fn5qU+fPoqLi9Py5csVGxurtWvXWr5OgBPQt1iTk46vVjgPNqNvsUa96Fv+xUAErc7Pz09Dhw5Vdna2x/rs7GwNHz7cS1G1roaGBpWUlCg8PNzbobSaqKgohYWFedTt2rVrys/Pt03dml2+fFkXL158pOtnjNH8+fO1a9cuHTx4UFFRUR7brVqve+V1O1ao182MMWpoaLBsnQAnoW+xJicdX61wHqRv+ZcV6nUzR/ctD/surnCG7du3G19fX7N582Zz+vRpk56ebgIDA82FCxe8HdoDycjIMHl5eeb8+fOmoKDAvPjii6ZDhw6Wy6eurs4UFRWZoqIiI8msWbPGFBUVmV9//dUYY8yKFStMcHCw2bVrlykuLjavvvqqCQ8PN7W1tV6O/O7ullddXZ3JyMgwhw8fNmVlZSY3N9ckJCSYp5566pHOa86cOSY4ONjk5eWZiooK9+Pq1avu51ixXvfKy4r1WrJkiTl06JApKyszJ06cMG+//bbx8fEx3333nTHGmnUCnIa+5dFE32KN86Ax9C1Wqhd9iycGImgz69evNz169DB+fn7m2Wef9fh6KqtJTU014eHhxtfX10RERJipU6eaU6dOeTusFsvNzTWSbnmkpaUZY/75SrTMzEwTFhZm/P39zahRo0xxcbF3g74Pd8vr6tWrJikpyYSGhhpfX1/TvXt3k5aWZsrLy70d9l3dLh9JZsuWLe7nWLFe98rLivWaNWuW+1gXGhpqxo0b524qjLFmnQAnom959NC3WOM8aAx9i5XqRd/iyWWMMa1/3QkAAAAAAMCji3uIAAAAAAAAx2EgAgAAAAAAHIeBCAAAAAAAcBwGIgAAAAAAwHEYiAAAAAAAAMdhIAIAAAAAAByHgQgAAAAAAHAcBiIAAAAAAMBxGIgAAAAAAADHYSACwLEqKyu1YMEC9erVS/7+/oqMjNSUKVOUk5PzUONwuVzas2fPQ90nAABAS1y4cEEul0vHjx/3dihAq3nc2wEAgDdcuHBBI0aMUMeOHbVq1SrFxMTo+vXr2r9/v+bNm6czZ854O0QAAAAAbYgrRAA40ty5c+VyuXTkyBFNmzZN/fr108CBA7Vw4UIVFBRIksrLy5WcnKz27dsrKChIL7/8si5duuR+j5kzZyolJcXjfdPT0zVmzBj38pgxY/Tmm29q0aJFCgkJUVhYmJYtW+be3rNnT0nSSy+9JJfL5V4GAAC4UVNTk1auXKk+ffrI399f3bt31/vvvy9JKi4uVmJiogICAtS5c2e98cYb+uOPP9yvHTNmjNLT0z3eLyUlRTNnznQv9+zZUx988IFmzZqlDh06qHv37vrkk0/c26OioiRJQ4YMkcvl8uh3AKtiIALAca5cuaJ9+/Zp3rx5CgwMvGV7x44dZYxRSkqKrly5ovz8fGVnZ+vcuXNKTU1t8f62bt2qwMBA/fTTT1q1apXee+89ZWdnS5IKCwslSVu2bFFFRYV7GQAA4EZLlizRypUrtXTpUp0+fVpffvmlunbtqqtXr2rSpEnq1KmTCgsL9dVXX+nAgQOaP39+i/exevVqxcXFqaioSHPnztWcOXPcV80eOXJEknTgwAFVVFRo165drZof4A18ZAaA4/zyyy8yxqh///53fM6BAwd04sQJlZWVKTIyUpL0+eefa+DAgSosLFR8fPx97y8mJkaZmZmSpL59+yorK0s5OTmaMGGCQkNDJf0zhAkLC/sPWQEAALuqq6vT2rVrlZWVpbS0NElS7969NXLkSG3atEl//vmntm3b5v5DT1ZWlqZMmaKVK1eqa9eu972fF154QXPnzpUkLV68WB9++KHy8vLUv39/d8/SuXNnehbYBleIAHAcY4ykf25meiclJSWKjIx0D0MkKTo6Wh07dlRJSUmL9hcTE+OxHB4erqqqqha9BwAAcK6SkhI1NDRo3Lhxt90WGxvrcdXriBEj1NTUpLNnz7ZoPzf2LC6XS2FhYfQssDUGIgAcp2/fvnK5XHcdbBhjbjswuXG9j4+Pe7jS7Pr167e8xtfX12PZ5XKpqanpQUIHAAAOFBAQcMdtd+pZJNGzAPfAQASA44SEhGjixIlav3696uvrb9leXV2t6OholZeX6+LFi+71p0+fVk1NjQYMGCBJCg0NVUVFhcdrH+Sr6Hx9fdXY2Nji1wEAAGfo27evAgIClJOTc8u26OhoHT9+3KOn+fHHH+Xj46N+/fpJurVnaWxs1MmTJ1sUg5+fn/u1gF0wEAHgSBs2bFBjY6OGDRumnTt3qrS0VCUlJVq3bp0SEhI0fvx4xcTEaPr06Tp27JiOHDmiGTNmaPTo0YqLi5MkJSYm6ujRo9q2bZtKS0uVmZnZ4uZC+ueu7jk5OaqsrNTvv//e2qkCAACLa9eunRYvXqxFixZp27ZtOnfunAoKCrR582ZNnz5d7dq1U1pamk6ePKnc3FwtWLBAr732mvv+IYmJidq7d6/27t2rM2fOaO7cuaqurm5RDF26dFFAQID27dunS5cuqaampg0yBR4uBiIAHCkqKkrHjh3T2LFjlZGRoUGDBmnChAnKycnRxo0b5XK5tGfPHnXq1EmjRo3S+PHj1atXL+3YscP9HhMnTtTSpUu1aNEixcfHq66uTjNmzGhxLKtXr1Z2drYiIyM1ZMiQ1kwTAADYxNKlS5WRkaF3331XAwYMUGpqqqqqqvTEE09o//79unLliuLj4zVt2jSNGzdOWVlZ7tfOmjVLaWlp7j/uREVFaezYsS3a/+OPP65169bp448/VkREhJKTk1s7ReChc5mbP0wGAAAAAABgc1whAgAAAAAAHIeBCAAAAAAAcBwGIgAAAAAAwHEYiAAAAAAAAMdhIAIAAAAAAByHgQgAAAAAAHAcBiIAAAAAAMBxGIgAAAAAAADHYSACAAAAAAAch4EIAAAAAABwHAYiAAAAAADAcf4P3v6n3EDVdDsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 13#\n",
    "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n",
    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
    "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n",
    "#Give the plot a helpful title of 'Region'\n",
    "ax[0].set_title('Region')\n",
    "#Label the xaxis 'Count'\n",
    "ax[0].set_xlabel('Count')\n",
    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
    "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n",
    "#Give the plot a helpful title of 'state'\n",
    "ax[1].set_title('state')\n",
    "#Label the xaxis 'Count'\n",
    "ax[1].set_xlabel('count')\n",
    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
    "plt.subplots_adjust(wspace=0.5);\n",
    "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n",
    "# as the importance of easy-to-read and informative figures is frequently understated\n",
    "# and you will find the ability to tweak figures invaluable later on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>47.333333</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>81.500000</td>\n",
       "      <td>83.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>78.214286</td>\n",
       "      <td>81.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>90.714286</td>\n",
       "      <td>90.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>47.800000</td>\n",
       "      <td>56.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AdultWeekday  AdultWeekend\n",
       "state                                  \n",
       "Alaska          47.333333     57.333333\n",
       "Arizona         81.500000     83.500000\n",
       "California      78.214286     81.416667\n",
       "Colorado        90.714286     90.714286\n",
       "Connecticut     47.800000     56.800000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 14#\n",
    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
    "# Hint: use the pattern dataframe.groupby(<grouping variable>)[<list of columns>].mean()\n",
    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
    "state_price_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
    "# Compare the index order you get from\n",
    "# state_price_means.index\n",
    "# with\n",
    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
    "# See how this expression simply sits within the reindex()\n",
    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
    "    .sort_values(ascending=False)\n",
    "    .index)\n",
    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
    "plt.xlabel('Price ($)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 15#\n",
    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
    "#specify 'state' for `id_vars`\n",
    "#gather the ticket prices from the 'AdultWeekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
    "#call the resultant price column 'Price' via the `value_name` argument,\n",
    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
    "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n",
    "                        id_vars='state', \n",
    "                        var_name='Ticket', \n",
    "                        value_vars=['AdultWeekday', 'AdultWeekend'], \n",
    "                        value_name='Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>state</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AdultWeekday</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state        Ticket  Price\n",
       "0   Alaska  AdultWeekday   65.0\n",
       "1   Alaska  AdultWeekday   47.0\n",
       "2   Alaska  AdultWeekday   30.0\n",
       "3  Arizona  AdultWeekday   89.0\n",
       "4  Arizona  AdultWeekday   74.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket_prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 16#\n",
    "#Create a seaborn boxplot of the ticket price dataframe we created above,\n",
    "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n",
    "#This will use boxplot's x, y, hue, and data arguments.\n",
    "plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('Price ($)')\n",
    "plt.xlabel('State');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
    "\n",
    "* disregard State completely\n",
    "* retain all State information\n",
    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
    "\n",
    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we currently have two main questions you want to resolve:\n",
    "\n",
    "* What do you do about the two types of ticket price?\n",
    "* What do you do about the state information?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>330.0</td>\n",
       "      <td>4591.818182</td>\n",
       "      <td>3735.535934</td>\n",
       "      <td>315.0</td>\n",
       "      <td>1403.75</td>\n",
       "      <td>3127.5</td>\n",
       "      <td>7806.00</td>\n",
       "      <td>13487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1215.427273</td>\n",
       "      <td>947.864557</td>\n",
       "      <td>60.0</td>\n",
       "      <td>461.25</td>\n",
       "      <td>964.5</td>\n",
       "      <td>1800.00</td>\n",
       "      <td>4425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>330.0</td>\n",
       "      <td>3374.000000</td>\n",
       "      <td>3117.121621</td>\n",
       "      <td>70.0</td>\n",
       "      <td>869.00</td>\n",
       "      <td>1561.5</td>\n",
       "      <td>6325.25</td>\n",
       "      <td>10800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.172727</td>\n",
       "      <td>0.559946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>164.0</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.078087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.184848</td>\n",
       "      <td>0.651685</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.018182</td>\n",
       "      <td>2.198294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>330.0</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.312245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.619130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>330.0</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.815028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>330.0</td>\n",
       "      <td>2.621212</td>\n",
       "      <td>2.059636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>330.0</td>\n",
       "      <td>8.266667</td>\n",
       "      <td>5.798683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>326.0</td>\n",
       "      <td>48.214724</td>\n",
       "      <td>46.364077</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>33.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>279.0</td>\n",
       "      <td>2.820789</td>\n",
       "      <td>2.008113</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>325.0</td>\n",
       "      <td>1.433231</td>\n",
       "      <td>1.156171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>327.0</td>\n",
       "      <td>739.801223</td>\n",
       "      <td>1816.167441</td>\n",
       "      <td>8.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>200.0</td>\n",
       "      <td>690.00</td>\n",
       "      <td>26819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>284.0</td>\n",
       "      <td>174.873239</td>\n",
       "      <td>261.336125</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.50</td>\n",
       "      <td>3379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>279.0</td>\n",
       "      <td>115.103943</td>\n",
       "      <td>35.063251</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.00</td>\n",
       "      <td>114.0</td>\n",
       "      <td>135.00</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>329.0</td>\n",
       "      <td>63.656535</td>\n",
       "      <td>109.429928</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>316.0</td>\n",
       "      <td>185.316456</td>\n",
       "      <td>136.356842</td>\n",
       "      <td>18.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>276.0</td>\n",
       "      <td>57.916957</td>\n",
       "      <td>26.140126</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>279.0</td>\n",
       "      <td>64.166810</td>\n",
       "      <td>24.554584</td>\n",
       "      <td>17.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>77.50</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>283.0</td>\n",
       "      <td>120.053004</td>\n",
       "      <td>31.045963</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>139.50</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>187.0</td>\n",
       "      <td>100.395722</td>\n",
       "      <td>105.169620</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40.00</td>\n",
       "      <td>72.0</td>\n",
       "      <td>114.00</td>\n",
       "      <td>650.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean          std    min      25%     50%  \\\n",
       "summit_elev        330.0  4591.818182  3735.535934  315.0  1403.75  3127.5   \n",
       "vertical_drop      330.0  1215.427273   947.864557   60.0   461.25   964.5   \n",
       "base_elev          330.0  3374.000000  3117.121621   70.0   869.00  1561.5   \n",
       "trams              330.0     0.172727     0.559946    0.0     0.00     0.0   \n",
       "fastEight          164.0     0.006098     0.078087    0.0     0.00     0.0   \n",
       "fastSixes          330.0     0.184848     0.651685    0.0     0.00     0.0   \n",
       "fastQuads          330.0     1.018182     2.198294    0.0     0.00     0.0   \n",
       "quad               330.0     0.933333     1.312245    0.0     0.00     0.0   \n",
       "triple             330.0     1.500000     1.619130    0.0     0.00     1.0   \n",
       "double             330.0     1.833333     1.815028    0.0     1.00     1.0   \n",
       "surface            330.0     2.621212     2.059636    0.0     1.00     2.0   \n",
       "total_chairs       330.0     8.266667     5.798683    0.0     5.00     7.0   \n",
       "Runs               326.0    48.214724    46.364077    3.0    19.00    33.0   \n",
       "TerrainParks       279.0     2.820789     2.008113    1.0     1.00     2.0   \n",
       "LongestRun_mi      325.0     1.433231     1.156171    0.0     0.50     1.0   \n",
       "SkiableTerrain_ac  327.0   739.801223  1816.167441    8.0    85.00   200.0   \n",
       "Snow Making_ac     284.0   174.873239   261.336125    2.0    50.00   100.0   \n",
       "daysOpenLastYear   279.0   115.103943    35.063251    3.0    97.00   114.0   \n",
       "yearsOpen          329.0    63.656535   109.429928    6.0    50.00    58.0   \n",
       "averageSnowfall    316.0   185.316456   136.356842   18.0    69.00   150.0   \n",
       "AdultWeekday       276.0    57.916957    26.140126   15.0    40.00    50.0   \n",
       "AdultWeekend       279.0    64.166810    24.554584   17.0    47.00    60.0   \n",
       "projectedDaysOpen  283.0   120.053004    31.045963   30.0   100.00   120.0   \n",
       "NightSkiing_ac     187.0   100.395722   105.169620    2.0    40.00    72.0   \n",
       "\n",
       "                       75%      max  \n",
       "summit_elev        7806.00  13487.0  \n",
       "vertical_drop      1800.00   4425.0  \n",
       "base_elev          6325.25  10800.0  \n",
       "trams                 0.00      4.0  \n",
       "fastEight             0.00      1.0  \n",
       "fastSixes             0.00      6.0  \n",
       "fastQuads             1.00     15.0  \n",
       "quad                  1.00      8.0  \n",
       "triple                2.00      8.0  \n",
       "double                3.00     14.0  \n",
       "surface               3.00     15.0  \n",
       "total_chairs         10.00     41.0  \n",
       "Runs                 60.00    341.0  \n",
       "TerrainParks          4.00     14.0  \n",
       "LongestRun_mi         2.00      6.0  \n",
       "SkiableTerrain_ac   690.00  26819.0  \n",
       "Snow Making_ac      200.50   3379.0  \n",
       "daysOpenLastYear    135.00    305.0  \n",
       "yearsOpen            69.00   2019.0  \n",
       "averageSnowfall     300.00    669.0  \n",
       "AdultWeekday         71.00    179.0  \n",
       "AdultWeekend         77.50    179.0  \n",
       "projectedDaysOpen   139.50    305.0  \n",
       "NightSkiing_ac      114.00    650.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 17#\n",
    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
    "#will be useful again\n",
    "ski_data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.424242\n",
       "2    14.242424\n",
       "1     3.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
    "missing_price.value_counts()/len(missing_price) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 18#\n",
    "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
    "#Try passing it an argument figsize=(15,10)\n",
    "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
    "#It's important you create legible and easy-to-read plots\n",
    "ski_data.hist(figsize=(15,10))\n",
    "plt.subplots_adjust(hspace=0.5);\n",
    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What features do we have possible cause for concern about and why?\n",
    "\n",
    "* SkiableTerrain_ac because values are clustered down the low end,\n",
    "* Snow Making_ac for the same reason,\n",
    "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
    "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
    "* trams also may get an amber flag for the same reason,\n",
    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "      <th>summit_elev</th>\n",
       "      <th>vertical_drop</th>\n",
       "      <th>base_elev</th>\n",
       "      <th>trams</th>\n",
       "      <th>fastEight</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>...</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
       "      <th>averageSnowfall</th>\n",
       "      <th>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Silverton Mountain</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>13487</td>\n",
       "      <td>3087</td>\n",
       "      <td>10400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26819.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name    Region     state  summit_elev  vertical_drop  \\\n",
       "39  Silverton Mountain  Colorado  Colorado        13487           3087   \n",
       "\n",
       "    base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
       "39      10400      0        0.0          0          0  ...            1.5   \n",
       "\n",
       "    SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "39            26819.0             NaN             175.0       17.0   \n",
       "\n",
       "    averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "39            400.0          79.0          79.0              181.0   \n",
       "\n",
       "    NightSkiing_ac  \n",
       "39             NaN  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 19#\n",
    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
    "ski_data.loc[ski_data.SkiableTerrain_ac > 10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>39</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Silverton Mountain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>13487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>3087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>26819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   39\n",
       "Name               Silverton Mountain\n",
       "Region                       Colorado\n",
       "state                        Colorado\n",
       "summit_elev                     13487\n",
       "vertical_drop                    3087\n",
       "base_elev                       10400\n",
       "trams                               0\n",
       "fastEight                         0.0\n",
       "fastSixes                           0\n",
       "fastQuads                           0\n",
       "quad                                0\n",
       "triple                              0\n",
       "double                              1\n",
       "surface                             0\n",
       "total_chairs                        1\n",
       "Runs                              NaN\n",
       "TerrainParks                      NaN\n",
       "LongestRun_mi                     1.5\n",
       "SkiableTerrain_ac             26819.0\n",
       "Snow Making_ac                    NaN\n",
       "daysOpenLastYear                175.0\n",
       "yearsOpen                        17.0\n",
       "averageSnowfall                 400.0\n",
       "AdultWeekday                     79.0\n",
       "AdultWeekend                     79.0\n",
       "projectedDaysOpen               181.0\n",
       "NightSkiing_ac                    NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 20#\n",
    "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n",
    "#Hint: don't forget the transpose will be helpful here\n",
    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 2** Silverton Mountain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what can you do when you have one record that seems highly suspicious?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Silverton Mountain information](images/silverton_mountain_info.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26819.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 21#\n",
    "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 22#\n",
    "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1819.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 23#\n",
    "#Use the .loc accessor a final time to verify that the value has been modified\n",
    "ski_data.loc[39, 'SkiableTerrain_ac']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the distribution of skiable area look like now?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.SkiableTerrain_ac.hist(bins=30)\n",
    "plt.xlabel('SkiableTerrain_ac')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of skiable area (acres) after replacing erroneous value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.2 Snow Making_ac<a id='2.6.4.2.2_Snow_Making_ac'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    3379.0\n",
       "18    1500.0\n",
       "Name: Snow Making_ac, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Heavenly Mountain Resort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>Sierra Nevada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summit_elev</th>\n",
       "      <td>10067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_drop</th>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>base_elev</th>\n",
       "      <td>7170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trams</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastEight</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastSixes</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fastQuads</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quad</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>triple</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>double</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surface</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_chairs</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs</th>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TerrainParks</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <td>4800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <td>3379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearsOpen</th>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>averageSnowfall</th>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekday</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdultWeekend</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NightSkiing_ac</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         11\n",
       "Name               Heavenly Mountain Resort\n",
       "Region                        Sierra Nevada\n",
       "state                            California\n",
       "summit_elev                           10067\n",
       "vertical_drop                          3500\n",
       "base_elev                              7170\n",
       "trams                                     2\n",
       "fastEight                               0.0\n",
       "fastSixes                                 2\n",
       "fastQuads                                 7\n",
       "quad                                      1\n",
       "triple                                    5\n",
       "double                                    3\n",
       "surface                                   8\n",
       "total_chairs                             28\n",
       "Runs                                   97.0\n",
       "TerrainParks                            3.0\n",
       "LongestRun_mi                           5.5\n",
       "SkiableTerrain_ac                    4800.0\n",
       "Snow Making_ac                       3379.0\n",
       "daysOpenLastYear                      155.0\n",
       "yearsOpen                              64.0\n",
       "averageSnowfall                       360.0\n",
       "AdultWeekday                            NaN\n",
       "AdultWeekend                            NaN\n",
       "projectedDaysOpen                     157.0\n",
       "NightSkiing_ac                          NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[ski_data['Snow Making_ac'] > 3000].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What, then, is your rough guess for the area covered by snowmaking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2880.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".6 * 4800"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.3 fastEight<a id='2.6.4.2.3_fastEight'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the different fastEight values more closely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    163\n",
       "1.0      1\n",
       "Name: fastEight, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.fastEight.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 24#\n",
    "#Drop the 'fastEight' column from ski_data. Use inplace=True\n",
    "ski_data.drop(columns='fastEight', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Name</th>\n",
       "      <th>Region</th>\n",
       "      <th>state</th>\n",
       "      <th>summit_elev</th>\n",
       "      <th>vertical_drop</th>\n",
       "      <th>base_elev</th>\n",
       "      <th>trams</th>\n",
       "      <th>fastSixes</th>\n",
       "      <th>fastQuads</th>\n",
       "      <th>quad</th>\n",
       "      <th>...</th>\n",
       "      <th>LongestRun_mi</th>\n",
       "      <th>SkiableTerrain_ac</th>\n",
       "      <th>Snow Making_ac</th>\n",
       "      <th>daysOpenLastYear</th>\n",
       "      <th>yearsOpen</th>\n",
       "      <th>averageSnowfall</th>\n",
       "      <th>AdultWeekday</th>\n",
       "      <th>AdultWeekend</th>\n",
       "      <th>projectedDaysOpen</th>\n",
       "      <th>NightSkiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Howelsen Hill</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>7136</td>\n",
       "      <td>440</td>\n",
       "      <td>6696</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Pine Knob Ski Resort</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>1308</td>\n",
       "      <td>300</td>\n",
       "      <td>1009</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name    Region     state  summit_elev  vertical_drop  \\\n",
       "34          Howelsen Hill  Colorado  Colorado         7136            440   \n",
       "115  Pine Knob Ski Resort  Michigan  Michigan         1308            300   \n",
       "\n",
       "     base_elev  trams  fastSixes  fastQuads  quad  ...  LongestRun_mi  \\\n",
       "34        6696      0          0          0     0  ...            6.0   \n",
       "115       1009      0          0          0     0  ...            1.0   \n",
       "\n",
       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
       "34                50.0            25.0             100.0      104.0   \n",
       "115               80.0            80.0               NaN     2019.0   \n",
       "\n",
       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
       "34             150.0          25.0          25.0              100.0   \n",
       "115              NaN          49.0          57.0                NaN   \n",
       "\n",
       "     NightSkiing_ac  \n",
       "34             10.0  \n",
       "115             NaN  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 25#\n",
    "#Filter the 'yearsOpen' column for values greater than 100\n",
    "ski_data.loc[ski_data.yearsOpen > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 26#\n",
    "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n",
    "#Pass the argument bins=30 to hist(), but feel free to explore other values\n",
    "ski_data.loc[ski_data.yearsOpen < 1000].hist(bins=30,figsize=(15,10))\n",
    "plt.subplots_adjust(hspace=0.5);\n",
    "plt.xlabel('Years open')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of years open excluding 2019');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above distribution of years seems entirely plausible, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review the summary statistics for the years under 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    328.000000\n",
       "mean      57.695122\n",
       "std       16.841182\n",
       "min        6.000000\n",
       "25%       50.000000\n",
       "50%       58.000000\n",
       "75%       68.250000\n",
       "max      104.000000\n",
       "Name: yearsOpen, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data = ski_data[ski_data.yearsOpen < 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2.6.4.2.4 fastSixes and Trams<a id='2.6.4.2.4_fastSixes_and_Trams'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 Derive State-wide Summary Statistics For Our Market Segment<a id='2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n",
    "\n",
    "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n",
    "\n",
    "* TerrainParks\n",
    "* SkiableTerrain_ac\n",
    "* daysOpenLastYear\n",
    "* NightSkiing_ac\n",
    "\n",
    "When you think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>state</th>\n",
       "      <th>resorts_per_state</th>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <th>state_total_days_open</th>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <th>state_total_nightskiing_ac</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>21</td>\n",
       "      <td>25948.0</td>\n",
       "      <td>2738.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>22</td>\n",
       "      <td>43682.0</td>\n",
       "      <td>3258.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
       "0       Alaska                  3                       2280.0   \n",
       "1      Arizona                  2                       1577.0   \n",
       "2   California                 21                      25948.0   \n",
       "3     Colorado                 22                      43682.0   \n",
       "4  Connecticut                  5                        358.0   \n",
       "\n",
       "   state_total_days_open  state_total_terrain_parks  \\\n",
       "0                  345.0                        4.0   \n",
       "1                  237.0                        6.0   \n",
       "2                 2738.0                       81.0   \n",
       "3                 3258.0                       74.0   \n",
       "4                  353.0                       10.0   \n",
       "\n",
       "   state_total_nightskiing_ac  \n",
       "0                       580.0  \n",
       "1                        80.0  \n",
       "2                       587.0  \n",
       "3                       428.0  \n",
       "4                       256.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 27#\n",
    "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
    "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n",
    "#respectively\n",
    "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n",
    "#what it does)\n",
    "state_summary = ski_data.groupby('state').agg(\n",
    "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
    "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
    "    state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n",
    ").reset_index()\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.8 Drop Rows With No Price Data<a id='2.8_Drop_Rows_With_No_Price_Data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82.317073\n",
       "2    14.329268\n",
       "1     3.353659\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
    "missing_price.value_counts()/len(missing_price) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 28#\n",
    "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
    "ski_data = ski_data[missing_price != 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ski_data.hist(figsize=(15, 10))\n",
    "plt.subplots_adjust(hspace=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values.  Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.10 Population data<a id='2.10_Population_data'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#added to resolve an [SSL: CERTIFICATE_VERIFY_FAILED] error in the code below, found at \n",
    "# https://stackoverflow.com/questions/44629631/while-using-pandas-got-error-urlopen-error-ssl-certificate-verify-failed-cert\n",
    "\n",
    "import ssl\n",
    "\n",
    "ssl._create_default_https_context = ssl._create_unverified_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 29#\n",
    "#Use pandas' `read_html` method to read the table from the URL below\n",
    "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
    "usa_states = pd.read_html(states_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(usa_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
       "      <th>Established[A]</th>\n",
       "      <th>Population [B][3]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Total area[4]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Land area[4]</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Water area[4]</th>\n",
       "      <th>Number of Reps.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Name &amp; postal abbs. [1]</th>\n",
       "      <th>Name &amp; postal abbs. [1].1</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Largest[5]</th>\n",
       "      <th>Established[A]</th>\n",
       "      <th>Population [B][3]</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>mi2</th>\n",
       "      <th>km2</th>\n",
       "      <th>Number of Reps.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Dec 14, 1819</td>\n",
       "      <td>4903185</td>\n",
       "      <td>52420</td>\n",
       "      <td>135767</td>\n",
       "      <td>50645</td>\n",
       "      <td>131171</td>\n",
       "      <td>1775</td>\n",
       "      <td>4597</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>Juneau</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Jan 3, 1959</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "      <td>1723337</td>\n",
       "      <td>570641</td>\n",
       "      <td>1477953</td>\n",
       "      <td>94743</td>\n",
       "      <td>245384</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Feb 14, 1912</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "      <td>295234</td>\n",
       "      <td>113594</td>\n",
       "      <td>294207</td>\n",
       "      <td>396</td>\n",
       "      <td>1026</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Jun 15, 1836</td>\n",
       "      <td>3017804</td>\n",
       "      <td>53179</td>\n",
       "      <td>137732</td>\n",
       "      <td>52035</td>\n",
       "      <td>134771</td>\n",
       "      <td>1143</td>\n",
       "      <td>2961</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Sep 9, 1850</td>\n",
       "      <td>39512223</td>\n",
       "      <td>163695</td>\n",
       "      <td>423967</td>\n",
       "      <td>155779</td>\n",
       "      <td>403466</td>\n",
       "      <td>7916</td>\n",
       "      <td>20501</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name & postal abbs. [1]                                 Cities               \\\n",
       "  Name & postal abbs. [1] Name & postal abbs. [1].1      Capital   Largest[5]   \n",
       "0                 Alabama                        AL   Montgomery   Birmingham   \n",
       "1                  Alaska                        AK       Juneau    Anchorage   \n",
       "2                 Arizona                        AZ      Phoenix      Phoenix   \n",
       "3                Arkansas                        AR  Little Rock  Little Rock   \n",
       "4              California                        CA   Sacramento  Los Angeles   \n",
       "\n",
       "  Established[A] Population [B][3] Total area[4]          Land area[4]  \\\n",
       "  Established[A] Population [B][3]           mi2      km2          mi2   \n",
       "0   Dec 14, 1819           4903185         52420   135767        50645   \n",
       "1    Jan 3, 1959            731545        665384  1723337       570641   \n",
       "2   Feb 14, 1912           7278717        113990   295234       113594   \n",
       "3   Jun 15, 1836           3017804         53179   137732        52035   \n",
       "4    Sep 9, 1850          39512223        163695   423967       155779   \n",
       "\n",
       "           Water area[4]         Number of Reps.  \n",
       "       km2           mi2     km2 Number of Reps.  \n",
       "0   131171          1775    4597               7  \n",
       "1  1477953         94743  245384               1  \n",
       "2   294207           396    1026               9  \n",
       "3   134771          1143    2961               4  \n",
       "4   403466          7916   20501              53  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states = usa_states[0]\n",
    "usa_states.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code task 30#\n",
    "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
    "#It should be a column of dates\n",
    "established = usa_states.iloc[:, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Dec 14, 1819\n",
       "1      Jan 3, 1959\n",
       "2     Feb 14, 1912\n",
       "3     Jun 15, 1836\n",
       "4      Sep 9, 1850\n",
       "5      Aug 1, 1876\n",
       "6      Jan 9, 1788\n",
       "7      Dec 7, 1787\n",
       "8      Mar 3, 1845\n",
       "9      Jan 2, 1788\n",
       "10    Aug 21, 1959\n",
       "11     Jul 3, 1890\n",
       "12     Dec 3, 1818\n",
       "13    Dec 11, 1816\n",
       "14    Dec 28, 1846\n",
       "15    Jan 29, 1861\n",
       "16     Jun 1, 1792\n",
       "17    Apr 30, 1812\n",
       "18    Mar 15, 1820\n",
       "19    Apr 28, 1788\n",
       "20     Feb 6, 1788\n",
       "21    Jan 26, 1837\n",
       "22    May 11, 1858\n",
       "23    Dec 10, 1817\n",
       "24    Aug 10, 1821\n",
       "25     Nov 8, 1889\n",
       "26     Mar 1, 1867\n",
       "27    Oct 31, 1864\n",
       "28    Jun 21, 1788\n",
       "29    Dec 18, 1787\n",
       "30     Jan 6, 1912\n",
       "31    Jul 26, 1788\n",
       "32    Nov 21, 1789\n",
       "33     Nov 2, 1889\n",
       "34     Mar 1, 1803\n",
       "35    Nov 16, 1907\n",
       "36    Feb 14, 1859\n",
       "37    Dec 12, 1787\n",
       "38    May 29, 1790\n",
       "39    May 23, 1788\n",
       "40     Nov 2, 1889\n",
       "41     Jun 1, 1796\n",
       "42    Dec 29, 1845\n",
       "43     Jan 4, 1896\n",
       "44     Mar 4, 1791\n",
       "45    Jun 25, 1788\n",
       "46    Nov 11, 1889\n",
       "47    Jun 20, 1863\n",
       "48    May 29, 1848\n",
       "49    Jul 10, 1890\n",
       "Name: (Established[A], Established[A]), dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "established"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the state name, population, and total area (square miles) columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>state</th>\n",
       "      <th>state_population</th>\n",
       "      <th>state_area_sq_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4903185</td>\n",
       "      <td>52420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>3017804</td>\n",
       "      <td>53179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>39512223</td>\n",
       "      <td>163695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  state_population  state_area_sq_miles\n",
       "0     Alabama           4903185                52420\n",
       "1      Alaska            731545               665384\n",
       "2     Arizona           7278717               113990\n",
       "3    Arkansas           3017804                53179\n",
       "4  California          39512223               163695"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 31#\n",
    "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
    "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n",
    "#respectively.\n",
    "usa_states_sub = usa_states.iloc[:, [0,5,6]].copy()\n",
    "usa_states_sub.columns = ['state','state_population','state_area_sq_miles']\n",
    "usa_states_sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you have all the ski data states accounted for?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 32#\n",
    "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
    "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No?? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    Massachusetts[C]\n",
       "37     Pennsylvania[C]\n",
       "38     Rhode Island[D]\n",
       "45         Virginia[C]\n",
       "47       West Virginia\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete square brackets and their contents and try again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    Massachusetts\n",
       "37     Pennsylvania\n",
       "38     Rhode Island\n",
       "45         Virginia\n",
       "47    West Virginia\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 33#\n",
    "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n",
    "#with the empty string. Do this inplace, so you need to specify the arguments:\n",
    "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n",
    "#value='' #empty string as replacement\n",
    "#regex=True #we used a regex in our `to_replace` argument\n",
    "#inplace=True #Do this \"in place\"\n",
    "usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n",
    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 34#\n",
    "#And now verify none of our states are missing by checking that there are no states in\n",
    "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n",
    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
    "missing_states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>state</th>\n",
       "      <th>resorts_per_state</th>\n",
       "      <th>state_total_skiable_area_ac</th>\n",
       "      <th>state_total_days_open</th>\n",
       "      <th>state_total_terrain_parks</th>\n",
       "      <th>state_total_nightskiing_ac</th>\n",
       "      <th>state_population</th>\n",
       "      <th>state_area_sq_miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>3</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>731545</td>\n",
       "      <td>665384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2</td>\n",
       "      <td>1577.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7278717</td>\n",
       "      <td>113990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>21</td>\n",
       "      <td>25948.0</td>\n",
       "      <td>2738.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>39512223</td>\n",
       "      <td>163695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>22</td>\n",
       "      <td>43682.0</td>\n",
       "      <td>3258.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>5758736</td>\n",
       "      <td>104094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>3565278</td>\n",
       "      <td>5543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
       "0       Alaska                  3                       2280.0   \n",
       "1      Arizona                  2                       1577.0   \n",
       "2   California                 21                      25948.0   \n",
       "3     Colorado                 22                      43682.0   \n",
       "4  Connecticut                  5                        358.0   \n",
       "\n",
       "   state_total_days_open  state_total_terrain_parks  \\\n",
       "0                  345.0                        4.0   \n",
       "1                  237.0                        6.0   \n",
       "2                 2738.0                       81.0   \n",
       "3                 3258.0                       74.0   \n",
       "4                  353.0                       10.0   \n",
       "\n",
       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
       "0                       580.0            731545               665384  \n",
       "1                        80.0           7278717               113990  \n",
       "2                       587.0          39512223               163695  \n",
       "3                       428.0           5758736               104094  \n",
       "4                       256.0           3565278                 5543  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 35#\n",
    "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n",
    "#specify the arguments how='left' and on='state'\n",
    "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n",
    "state_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkyZh6NCh8PDwwFdffQVnZ2dzhU1ERBa0aUokhnbz09g2tJsfNk2JtFJEZG9kgtBk1lcrVF5eDrlcjrKyMs7fISKyICm1c3JuV+JacSXr7JCa2N/fNl1nh4iIHJMxtXPC/JjkkHFsdoIyERE5LtbOIUtiskNERBbF2jlkaUx2iIjIolg7hyyNyQ4REVkUa+eQpTHZISIii1LVznFu0tbHWSbDiHB/TkImk2OyQ0REFsfaOWRJXHpOREQWJ/dwxa64gaydQxbBZIeIiKyGtXPIEvgYi4iIiBwakx0iIiJyaEx2iIiIyKEx2SEiIiKHxmSHiIiIHBqTHSIiInJoTHaIiIjIoTHZISIiIofGZIeIiIgcGpMdIiIicmhMdoiIiMihsTcWERFpSMkqRGZ+Kfp29sHwcH9rh0PUYkx2iIgIAJBbXInYLSdQUlWn3ubj4YqD84Yh2NfDipERtQwfYxEREQA0S3QAoKSqDuO3pFopIiLTYLJDRERIySpsluiolFTV4fjlIgtHRGQ6THaIiAiZ+aV696fnlVgmECIzYLJDRET4Q6d2evf37exjmUCIzIDJDhERIapHAHw8XLXu8/Fw5aossmtMdoiICABwcN6wZgmPajUWkT3j0nMiIgIABPt6IOONMTh+uQjpeSWss0MOg8kOERFpGB7uzySHHAofYxEREZFDs2qyc+zYMTzxxBNQKBSQyWQ4cOCAel9dXR2WL1+O3r17w9PTEwqFAlOnTsXNmzc1rlFTU4P58+fDz88Pnp6eGD9+PPLz8y38ToiIiMhWWTXZqaysRJ8+fbB58+Zm+6qqqpCeno7XX38d6enp2L9/Py5duoTx48drHLdo0SIkJiZi3759SE1NRUVFBcaNG4eGhgZLvQ0iIr2yiyqQlFWInNuVZr9XSlYh3v/uEosAEjUiEwRBsHYQACCTyZCYmIjY2Fidx5w5cwYDBw5Ebm4uOnfujLKyMvj7+2P37t2YPHkyAODmzZsIDg7G119/jbFjx2q9Tk1NDWpqatSvy8vLERwcjLKyMnh7e5v0fRFR61VaVYsFezNxrFHiMSLcH5umREKuY5m3sdjXilqj8vJyyOVyg7+/RU1QXrx4segbb9iwQfSxUpWVlUEmk6Fdu3YAgHPnzqGurg5jxoxRH6NQKBAREYG0tDSdyU58fDzefPNNs8VJRAQAC/Zm4sSV2xrbTly5jfl7M7ArbqBJ76Wvr1XGG2N0nEXUOohKdjIyMjRenzt3Dg0NDejRowcA4NKlS3B2dka/fv1MH+H/3L17F6+++iqeffZZdfZWUFCANm3awMdHs7JnYGAgCgoKdF5rxYoVGgmcamSHiMhUsosqNEZ0VBoEAccuFyHndiXC/DxNci8xfa24uopaM1HJTlJSkvrPGzZsgJeXF3bu3KlOMkpKSjBjxgwMHz7cLEHW1dXhmWeegVKpxNatWw0eLwgCZDKZzv1ubm5wc3MzZYhERBpyf6vSu/9asemSHTF9rZjsUGsmeYLy+vXrER8frzGa4uPjg9WrV2P9+vUmDQ64l+hMmjQJOTk5OHLkiMYzuaCgINTW1qKkRLNBXWFhIQIDA00eCxGRWCHt9c+TCfU1TaIDsK8VkSGSk53y8nL8+uuvzbYXFhbizp07JglKRZXoXL58Gd9++y18fX019vfr1w+urq44cuSIetutW7dw4cIFDBkyxKSxEBFJ0cX/PowI94dzk1FmZ5kMI8L9TTaqA7CvFZEhkpOdJ598EjNmzMC///1v5OfnIz8/H//+978RFxeHiRMnSrpWRUUFMjMzkZmZCQDIyclBZmYm8vLyUF9fjz/+8Y84e/YsPv/8czQ0NKCgoAAFBQWora0FAMjlcsTFxWHJkiX47rvvkJGRgeeffx69e/fGqFGjpL41IiKT2jQlEkO7+WlsG9rND5umRBp9TV3L2NnXikg3yUvPq6qqsHTpUmzbtg11dfcmxLm4uCAuLg5///vf4ekp/v9WkpOTERMT02z7tGnTsGrVKoSFhWk9LykpCdHR0QDuTVxetmwZ9uzZg+rqajz88MPYunWrpAnHYpeuEREZI+d2Ja4VVyLU19PoER2xy9jZ14paE7G/v42us1NZWYmrV69CEAR069ZNUpJja5jsEJEtyy6qwIK9GfjpZjmUjbY7y2QY2s3P5MvYieyFSevsaOPp6YkHH3zQ2NOJiMgAbaM5jZljGTuRI5Kc7FRWVuKdd97Bd999h8LCQiiVSo392dnZJguOiKg101aUUBtTLmMnckSSk51Zs2YhJSUFL7zwAjp06KC3ng0RERlHV1FCbUy5jJ3IEUlOdv7zn//g0KFDGDp0qDniISIiGC5KCPw+Z4ejOkT6SV567uPjg/bt25sjFiIi+h9DRQmBli9jJ2otJCc7b731Ft544w1UVRn+vw4iIjKOrqKETjIgoqM3kpZGY1fcQJN3TydyRJKXnkdGRqqXnIeGhsLVVfMvWnp6ukkDtAQuPSciW1RWVYf5ezMM1tYhaq3MtvQ8Nja2JXEREZFIcg9X7IobaJKihEStmdFFBR0JR3aIiIjsj9jf35Ln7ABAaWkpPv30U6xYsQK//fYbgHuPr27cuGFctERErYSu3lZEZD6SH2P9+OOPGDVqFORyOa5du4bZs2ejffv2SExMRG5uLnbt2mWOOImI7JrY3lZEZHqSR3YWL16M6dOn4/Lly2jbtq16+6OPPopjx46ZNDgiIkehrRryiSu3MX9vhpUiImo9JCc7Z86cwZw5c5pt79ixIwoKCkwSFBGRI1FVQ25oMkWycW8rIjIfyclO27ZtUV5e3mx7VlYW/P39TRIUEZEjMVQN+Voxkx0ic5Kc7EyYMAF/+9vfUFdXBwCQyWTIy8vDq6++iqeeesrkARIR2TtD1ZDZ24rIvCQnO++++y6KiooQEBCA6upqREVFoVu3bvDy8sLbb79tjhiJiOyarmrIzjIZRoT7s3YOkZkZXWfn6NGjSE9Ph1KpRN++fTFq1CgIgmCXXdBZZ4eIzI3VkIlMT+zvb8nJTnx8PFasWNFse0NDA55//nns3btXerRWxmSHiCyF1ZCJTMds7SI2btwIX19f/OlPf1Jva2howDPPPIMLFy4YFy0RUSsR5sckh8jSJCc7X3/9NUaNGoV27dph0qRJqKurw+TJk/HLL78gKSnJHDESEdmc7KIK5P5WxREaIjsgOdnp168fEhMTMWHCBLi5uSEhIQFXr15FUlISAgMDzREjEZHNYCVkIvtjVG+s6Oho7N69G3/84x9x7do1pKSkMNEholaBlZCJ7I+okZ2JEydq3e7v74927dppzN/Zv3+/aSIjIrIxqkrITTWuhMxHWkS2R1SyI5fLtW4fO3asSYMhIrJlYiohM9khsj2ikp3t27ebOw4iIpvHSshE9smoOTv19fX49ttv8Y9//AN37twBANy8eRMVFRUmDY6IyJawEjKRfZKc7OTm5qJ3796YMGEC5s2bh6Kie8+v161bh6VLl5o8QCIiW7JpSiSGdvPT2Da0mx82TYm0UkREZIjkpecLFy5E//798cMPP8DX11e9/cknn8SsWbNMGhwRka2Re7hiV9xAVkImsiOSk53U1FScOHECbdq00dgeEhKCGzdumCwwIiJbxkrIRPZD8mMspVKJhoaGZtvz8/Ph5eVlkqCIiIiITEVysjN69Ghs3LhR/Vomk6GiogIrV67EY489Julax44dwxNPPAGFQgGZTIYDBw5o7BcEAatWrYJCoYC7uzuio6Nx8eJFjWNqamowf/58+Pn5wdPTE+PHj0d+fr7Ut0VEREQOSnKy89577yElJQU9e/bE3bt38eyzzyI0NBQ3btzA2rVrJV2rsrISffr0webNm7XuX7duHTZs2IDNmzfjzJkzCAoKwujRo9UrwABg0aJFSExMxL59+5CamoqKigqMGzdO6+gTERERtT4yQRAEqSdVV1dj7969SE9Ph1KpRN++ffHcc8/B3d3d+EBkMiQmJiI2NhbAvVEdhUKBRYsWYfny5QDujeIEBgZi7dq1mDNnDsrKyuDv74/du3dj8uTJAO4tgQ8ODsbXX38tuuih2BbxRCSNlGaZlmys+cXpPHyfU4yhXf3wdP9gs96LiMxH7O9vyROUAcDd3R0zZ87EzJkzjQ7QkJycHBQUFGDMmDHqbW5uboiKikJaWhrmzJmDc+fOoa6uTuMYhUKBiIgIpKWl6Ux2ampqUFNTo35dXl5utvdB1BpJaZZpycaa5/NL8eTWNNQr7/0/3oGMm1ix/zwOzhuKnh21V4onIvtnVFHB3bt3Y9iwYVAoFMjNzQVw7/HWl19+abLACgoKAKBZg9HAwED1voKCArRp0wY+Pj46j9EmPj4ecrlc/RMczP+zI1LJLqpAUlYhcm5XGn0NKc0yLdlYs3Gio1KvFDB+ywmT34uIbIfkZOfDDz/E4sWL8eijj6KkpEQ9N8bHx0dj4rKpyJpUKhUEodm2pgwds2LFCpSVlal/rl+/bpJYiexZaVUtpiacxsj1KZix/Qxi3k3G1ITTKKuqk3QdVbPMhiZPyBs3yzTm2Jb64nRes0RHpV4p4F9n+e8AkaOSnOxs2rQJn3zyCV577TW4uPz+FKx///44f/68yQILCgoCgGYjNIWFherRnqCgINTW1qKkpETnMdq4ubnB29tb44eotTPVCIuYZpnGHNtS3+cU691/4uptvfuJyH5JTnZycnIQGdm8LLqbmxsqK033D1NYWBiCgoJw5MgR9bba2lqkpKRgyJAhAIB+/frB1dVV45hbt27hwoUL6mOIyDBTjrBIaZZpycaag8N89e4f2tVP734isl+Sk52wsDBkZmY22/6f//wHPXv2lHStiooKZGZmqq+Xk5ODzMxM5OXlQSaTYdGiRVizZg0SExNx4cIFTJ8+HR4eHnj22WcBAHK5HHFxcViyZAm+++47ZGRk4Pnnn0fv3r0xatQoqW+NqNUy5QiLlGaZ5mqsqW3e0eSBneHipP3xtouTjKuyiByY5NVYy5Ytw7x583D37l0IgoDTp09j7969iI+Px6effirpWmfPnkVMTIz69eLFiwEA06ZNw44dO/DKK6+guroac+fORUlJCQYNGoTDhw9rVGp+77334OLigkmTJqG6uhoPP/wwduzYAWdnZ6lvjajVMvUIy6YpkZi/N0NjhZWuZplSjjXE0Mqug/OGYvyWExpzd1ycZDg4b6jkexGR/TCqzs4nn3yC1atXqyf2duzYEatWrUJcXJzJA7QE1tkhAqYmnMaJK7c1HmU5y2QY2s0Pu+IGGnXNL87k4ftscfVsTNFYU+x7+NfZ6zhx9Tbr7BDZObG/v0UnO59++ilGjhyJLl26qLfdvn0bSqUSAQEBLY/YipjsEAFlVXXNRliMrXeTW1yJ2C0nUNJoJZePhysOzhuGYF/9o0jGyi6qwMj1KTr3Jy2NZuNOIgdj8qKCCxcuxN27d9GxY0fExMRg5MiRGDlyJGvUEDkIuYcrdsUNNMkIS9NEBwBKquowfksqMt4Yo+OslhEz74jJDlHrJDrZKS0txcmTJ5GSkoKkpCTMnTsXd+/eRUhICEaOHImYmBjExMRAoVCYM14iMrMwv5a1a0jJKmyW6KiUVNXh+OUiDA/3N/r6ulhyZRcR2RfRyY6rqyuGDx+O4cOH469//Svq6upw8uRJJCUlITk5GXv37kVNTQ3q6+vNGS8RWYixfa0y80v1HpueV2KWZEe1skvXnB2O6hC1Xkb1xgKAhoYG1NbWqvtM1dfXIywszJSxEZEVtLSvVYRC/7y3vp199O5vCVOu7CIixyF6gvLdu3eRlpaG5ORkHD16FGfPnkWXLl0wYsQIREVFISoqym4fYXGCMjkKU3QO17aiyUkG9FR4Y9OUvhrX1bX6SSaD1tYMPh6uRs/ZkfLeTDHviIhsn8knKLdr1w6BgYEYP348Fi5ciKioKLtfhUXkKEzVOVxVSbkppQBcuFGOmHeT1dctrqzRemyDIAAC4N3WBeV3f3+srVqNJZUx762l846IyLGITnb69OmDzMxMpKSkQCaTwcnJCdHR0fD11V+CnYjMT19fKyk1cgytaGp83RnDQvUe9/6USLg4yZCeV4K+nX3U83Skjj6Z6r0RUeslOtk5deoUKisrcfz4cSQlJWHdunWYMmUKunfvjujoaPWjLI72EFmWrtGYxn2txI5yGFrR1Pi6s4brn6OnSmZUSY4xIzSmfG9E1HpJ6o3l6emJRx55BGvXrsWpU6dQXFyMdevWwdXVFbNnz7bbOTtE9swSfa20aRAESX2tjOmqbsmu6ETkuCQ3AgUApVKJU6dOYevWrfjggw/w6aefory8nAUGiazAHH2thnYz3AE81NdT67HaVj9J7aquauTpbCDnYu0cIhJD9GOsM2fOqGvqpKamoqKiAp06dUJ0dDQ++OADxMTEIDQ01IyhEpE2pq4v07iS8vw96fjpZjmUjfY3va6Yqstiqxtre9Tl4+GKsqo6vTEQEekjOtkZNGgQOnTogOjoaGzYsAHR0dHo1q2bOWMjIpHMUV/mdHYxOvt6oLahAZd+/X3kRdt1Da1+EjP6tPm7y/gkNVtjBRcAlFfXQe7hqlGVmbVziEgK0XV2srKy0KNHD3PHYxWss0OOwhT1Zc7nl+LJrWkadXKcnYA3Hu+JET0CTN6RvKfCC+dvlBs8f3fcQNQrBdbOISI1sb+/Rc/ZUSU6Xbp0QXFxcbP9paWlGh3Ricjywvw8EdOChARAs0QHABqUwN/+76cWxaZtfk/fkHaiEh3gXpHClr43ImqdJLeLuHbtGhoaGpptr6mpwY0bN0wSFBFZxxen87RWPgaABgEaRQWlFCsENOcCXbxRhp1p13DmWono8zkZmYiMJTrZOXjwoPrP33zzDeRyufp1Q0MDvvvuO05QJjIRMYX3TNEaoqnvc5qP2jbV0oJ+YX6eWPnlRaTnlYo6npORiailRCc7sbGxAACZTIZp06Zp7HN1dUVoaCjWr19v0uCIWhsxhfdM1RpCm8FhvjiQcVPvMS0t6KerUKAunIxMRC0les6OUqmEUqlE586dUVhYqH6tVCpRU1ODrKwsjBs3zpyxEjk8MYX3jCnOJ9bkgZ3h4mS4oCBgfEE/MS0pVJKWRmNX3MAWJ3FE1LpJLiqYk5MDPz/DBceISBoxhfekFuczxsF5Q0UlPMbOoRHTkgIAvpj9EB9dEZFJiHqM9cEHH4i+4IIFC4wOhqg1SskqRGZ+qcEWDWJGUlTF+VqiZ0c5rqx5DP86ex1//yYLRXdq0Di1kjKHRvXeGjcC1VUEEQDk7i6YM6Ir5sawhhcRmY6oZOe9994TdTGZTMZkh0ik3OJKxG45oVEsT59QX08YKotlyhVLT/cPxpieQUYVK9T23nw8XHFw3jAE+3poLYJoqnlHRERNiS4q6MhYVJCsIfJvh0UlOqqRFNXqJ13F+Rof01RLV25JKVb4xek8vP7lBdQ2NP+nxcfDFRlvjDHqukRETYn9/S25zg4RtVxKVqHoEZ2mIylSWkOYauWWoXYQgPbKy02VVNXh+OUi9SMtMdclImopycnOzJkz9e7ftm2b0cEQtRaZ+aV6908fEoKoHgFaRzwaF+czNCqib+WWsXVytMkuqkDslhPQMpjTTHpeiTrZISKyBMnJTkmJZsXTuro6XLhwAaWlpRg5cqTJAiNyZH/o1E7v/ocfCDSYEBgaFdFVz6aldXIa0zZyZEjfzj4tuicRkVSSk53ExMRm25RKJebOncveWEQiRfUIgE+TTt4qPh6uJhn5MFTPxhQrt7SNHOljqvdGRCSF5Do7Wi/i5ISXX35Z9KotIgIOzhsGnybzZlQrlkzBUD2blq7c0lXzRxdTvjciIilMNkH56tWrqK+vN9XliBxesK8HMt4Yg+OXi5CeV6JRi8YUdNWzaWmvqc3fXcaJq7ehaOcu6ngnADvjBnJEh4isRnKys3jxYo3XgiDg1q1bOHToULOeWURk2PBwf7MlAlJWbhmSdqUIz356WtI5Lk4yHJw3FD07yg0fTERkJpLr7MTExGi8dnJygr+/P0aOHImZM2fCxcX+VrOzzg45upbWs/nidB6W7z8v+vgArzZYNvZ+PN0/WPK9iIjEMludnaSkpBYFJkV9fT1WrVqFzz//HAUFBejQoQOmT5+Ov/71r3ByujfdSBAEvPnmm/j4449RUlKCQYMGYcuWLejVq5fF4iSydcbWsxFTO6cpVkImIltj08Mwa9euxUcffYSdO3eiV69eOHv2LGbMmAG5XI6FCxcCANatW4cNGzZgx44d6N69O1avXo3Ro0cjKysLXl5eVn4H5OhaWplYKm29pszlbwcvYlvaNVHHRgbLsWBUd1ZCJiKbJCrZiYyMhMxAk0KV9PT0FgXU2Pfff48JEybg8ccfBwCEhoZi7969OHv2LIB7ozobN27Ea6+9hokTJwIAdu7cicDAQOzZswdz5szRet2amhrU1NSoX5eXl5ssZmodTFWZWCxDvaZM6fCFW/jTZ9L+Ho/uGYSYHgEmjYOIyFRELT2PjY3FhAkTMGHCBIwdOxZXr16Fm5sboqOjER0djbZt2+Lq1asYO3asSYMbNmwYvvvuO1y6dAkA8MMPPyA1NRWPPfYYACAnJwcFBQUYM+b3Xjtubm6IiopCWlqazuvGx8dDLperf4KDOa+ApJm18yxSmxTSU1Umliq7qAJJWYXIua27q7m2hqElVXUYvyXVZPdQkZroAGCXciKyaaJGdlauXKn+86xZs7BgwQK89dZbzY65fv26SYNbvnw5ysrKcP/998PZ2RkNDQ14++23MWXKFABAQUEBACAwMFDjvMDAQOTm5uq87ooVKzRWlZWXlzPhIVFKq2oxe9dZnM0tabZPamVisaND+vpoNe01Zew9VMc+9v4xg3E39cXshySfQ0RkSZKLCv7rX//C1KlTm21//vnn8f/+3/8zSVAqX3zxBT777DPs2bMH6enp2LlzJ959913s3LlT47imj9gEQdD72M3NzQ3e3t4aP0RiLNibiXNaEp3GrhUbHj1RXUtX36rGIzGG+mil5+mOR989GssuqsDzn57CzbIaiPXK2B649s7jGNTVV/Q5RETWIHmCsru7O1JTUxEeHq6xPTU1FW3btjVZYACwbNkyvPrqq3jmmWcAAL1790Zubi7i4+Mxbdo0BAUFAYB6pZZKYWFhs9EeopbS1WuqKTGViQ31rRq5PkW9rXdH/cm4rl5TYnpj+Xi4Su5txdo5RGRvJCc7ixYtwosvvohz587hoYfuDV+fPHkSCQkJGo+7TKGqqkq9xFzF2dkZSqUSABAWFoagoCAcOXIEkZH3iqTV1tYiJSUFa9euNWksRIZ6TTkBGBbuL+oRlqFrNfbTzTtwcZJpXf6tr9eUmN5YK7+8Jqm31d//+CBr5xCR3ZGc7Lz66qvo0qUL3n//fezZswcA0LNnT+zatavZaE9LPfHEE3j77bfRuXNn9OrVCxkZGdiwYQNmzpwJ4N7jq0WLFmHNmjUIDw9HeHg41qxZAw8PDzz77LMmjYXIUK+pfiE+oisTG7pWYw2CAAiAd1sXlN/9vSWLoV5Thu7hLJNJGtFJmNofD/fkiCkR2R+j6uxMmjQJkyZNAgCUlpbi888/R3x8PH744Qc0NDSYLLhNmzbh9ddfx9y5c1FYWAiFQoE5c+bgjTfeUB/zyiuvoLq6GnPnzlUXFTx8+DBr7JDJ6eo15SS7l+j8689DJF1rSFdfpF0tFn3O+1Mi4eIkE91Hy1BvLLENPDvK3XBixSjRcRIR2RrJ7SJUjh49im3btmH//v0ICQnBU089haeeekr9OMmesF2E7bNkMT19hQLLquqa9ZoyVF9H1/WmfHwS32eLT3aSlkZLLtinLd4IhTfWPNkb97V10ZgbpA2rIRORLRP7+1tSspOfn48dO3Zg27ZtqKysxKRJk/DRRx/hhx9+QM+ePU0SuDUw2bFdliymJ2WZtpheU/quV1xZYzDRUFGNxOyKG2jEu7rnh+uleO3AeVy48XsBzRHh/qhXKnEq+7dmI1U9Fd7YNKUvqyETkU0T+/tb9NLzxx57DD179sRPP/2ETZs24ebNm9i0aZNJgiXSpaXF9KQQu0wbuNdrKqZHgN5kQN/1pExQNrZLeWOvJZ7HxRualcJPXLkNQbh3/caGdfPH53EPMdEhIoches7O4cOHsWDBArz44osmn4hMpE1LiulJJWaZtpRf/oauN2t4qN7zd8cNRL1SaHGvqdziSjyxKVVjYnPjWL7PLkbS0mgAaFFXdCIiWyZ6ZOf48eO4c+cO+vfvj0GDBmHz5s0oKhK/koNIqpYU05NKzDJtU16vQbj3GMm5SfFLZ5kMI8L9MTzc3+DIkSHZRRV4/IPjWhOdxq4VV4oaqSIisleik53Bgwfjk08+wa1btzBnzhzs27cPHTt2hFKpxJEjR3Dnzh1zxkmt0B86tdO7X1cxPWMYWqYtplCglOttTbqCt2Mjmj1CMsUjq9KqWkxNOI2R61NQUWN4daTU90ZEZG8kLz338PDAzJkzMXPmTGRlZSEhIQHvvPMOXn31VYwePRoHDx40R5zUCkX1CICPh6vWR1n6iukZQ+eyctybrNuUthVWjbfpup5Kem4pXjtwAbviBoqa7CxWdlEFFuzLwE83yw0eKwMwXGQRRCIie2b00vPGGhoa8NVXX2Hbtm12mexwNZbtul5chfFbUi2yGkvbMu3GRoT7Y3VsL/z1wEWNYwZ38YVMBo2aOSPC/fF2bARe/mem1qahKsYsJ9dG28ovQ3orvPHZrIe4rJyI7JZZlp47KiY7tu/45SLRxfRaKud2JebvTcdPN8vRuEODs0wGb3cXlFfXGyzIp1ouPmNYKGZsP6PzuO0zBiCkvYfOuj5NaRtRSskqxBsHLyCvuBpi/zJ7t3XBj6vGijyaiMg2if39bVQFZSJLG/6/SbuWIAiCRj0alQZB0Lk6TNux91Zdhek9buvRKzjTaORHV10fbSM3A0N9kPXrHZRV65+A3JShNhNERI5G9ARlIkvILqpAUlYhcm5LW/1kSqdyxFc1NqRBEHSuuvLxcEV6XqnGdl11fbTV7Dl9rUR0oiMDEOLrjt1xA5HxxhiTPwIkIrJlHNkhmyClerElY2ipUF9PbJoS2WwuUN+QdjhzrflcHm11fXTV7JFiONs+EFErxmSHzEZfj6mm9FUbbkmbBCm0xdCYMXN2VO+76aqra8WVeufyqGrfAIZr9uiiWkm26Vm2fSCi1o3JDpmc1FEaU1cvNoaY0ZOh3fzwdmwEXjtwweBqLG31csL8fk/6DK0LaFz7xlDNHl2GcTSHiAgAkx0yA7GjNKqRn4Kyu3qv13iUQxcpo0jajjU0ehI/sTemDOwMoPkojeoaUurl6KrD03REyBj3uTnjq/nDOZpDRPQ/THbIpMSM0vh4uEqaG6Ovwq+UUSR9xxYaSLhcnDQnGDcepdG3TR9tc3kajwgZM4fI3dUJ/1kwghOQiYgaYbJDJiWmx9TKL6/pnRvTmIuTzOjO4rviBmqM4qz88qLOY/uFttMbx82yalHxSiH3cNU5SpRdVIEFe8VVQlZxlgE/v/WoyeMkIrJ3THbIpAzNL3GWySSNVNQrBZ3dzQ2NIj39UZrWFU/ajn28d5De40zZh6upxiNCxq4Ic3GS4eC8oeYIj4jI7rHODpmUai6Krm7ehlYxaaOru7mhUaRzeto0NBUgbwsfHRN573NzRicfyzwWMrQiTJu///FBXFnzGHp2lJspKiIi+8Zkh0xu05RInd28jVlZpGtUxdC1lBLyqlBfTxycN0xrwlNR04CYd5MxNeE0ykRWUDaGaqRKSkL4xeyH8HT/YLPFRETkCPgYiyStZBJzvr65KHIPV73dwJtSdTfXFqOuFU0yQHSPqKarnzLeGIMtR6/g4+NXUX63Ho1DNFfdH9V7+9XAJGkVubsL5ozoirkx3UwaBxGRo2IjULTeRqAtrVps7PmGuour+Hi44rOZg7D2myyd9xB7LV0aXyu3uBKxW04Y7H9lzU7llq4qTURky9j1XILWmuxMTTits86LmNELY85vPEIDQGt3cRmACIU3vlowXPQ9dHUqb8rFSYYji6O01sN5cNU3KL9ruNfU9hkDENMjoNn7kZIANV5tpTRwrJPsf5WQp7ASMhFRY+x6Tnq1tGqx1PO1jWL0D/HR2l1cAHD+ZjmOXSoSfQ9dncqbqlcKyC+pUicrqtie//SUqEQHuJcwGTuqZcxozrBuHM0hImoJJjutlJh6OPqSHanna1tllG5gtVTGdf37je0flZ5XorGUfcHeTFyUUM+mXikY3ctL7Gqr+Im9ESRva/Q8KiIi+h2TnVbK0EomfVWLpZ6vaxTI0OObyGD9tW2M7R/VeHWXMR3FddUKMjQqlpJVKPpeD3XxZZJDRGQiXHreShmqhyO2t5OY8w2NujTpxKC+xoju/qLv0cX/PsjdDefuqtVdYmPTdl9Dq8iuFVdqvC6tqsXUhNOYpqfLedN7MNEhIjIdJjutmL56OKY839CoS78QzRGcxtcQe4/sogqUVeufc+Pj4YqD84ZJiq2xviHtMGlAp2bJV1NNR8VeSDiF4yJHdKR8/kREJA4fY7Vi+urhmPJ8Qx2+9V1D7D1O5fxmMN7eHdvB211zkq++Wj0RCm988GxfXLxRhp1p13DmWom6/YSPhyvKq+vQ0GiQp2nNntziSjyxKdXgxGeutiIiMi8uPUfrXXpuSdrq4ZiyZsze07lYsf+C3mN0LYs3FJu25e9OuJeINa7J0/T9RP7tsMGaPdrOIyIicbj0nGxKS0eRDBkU5mvwGF0TiA11H9c1ubqkqg674waiXik0ez8pWYWiEp3dcQO1NjklIiLTsfk5Ozdu3MDzzz8PX19feHh44A9/+APOnTun3i8IAlatWgWFQgF3d3dER0fj4sWLVozYerKLKpCUVYic25WGD7YSYwcSDb23Lv73YUhXwwkP0HwCser6J7Nvo6DsLq7/Vqm+l6EJzPVKATE9AhDm54kvTudh0RcZ+NfZ68jML9V7ngz3RnSY6BARmZ9Nj+yUlJRg6NChiImJwX/+8x8EBATg6tWraNeunfqYdevWYcOGDdixYwe6d++O1atXY/To0cjKyoKXl5f1greglrZ9sARTFuHTdd6Hz/UT1Tqi8QTi0qpazP08HWlXi7UeOyDU8PL38/mleHJrGur/V7r5QMbNZivMmoro6M2JyEREFmLTc3ZeffVVnDhxAsePH9e6XxAEKBQKLFq0CMuXLwcA1NTUIDAwEGvXrsWcOXNE3cfe5+y0tO2DJRiKUVfbBWPe2+ajl7Hx20uo11LIx8fDFf/vxSHqe6388qLe5MhZJoO3uwvKq+u1xtDN3xPb0q5J+CQA77Yu+HHVWEnnEBFRcw7RG6tnz54YO3Ys8vPzkZKSgo4dO2Lu3LmYPXs2ACA7Oxtdu3ZFeno6IiN//7/kCRMmoF27dti5c6fW69bU1KCmpkb9ury8HMHBwXaZ7GQXVWDk+hSd+03VtLIlDMU4INRHvcoJ+H3kpriyRtJ7azrCYkoDQnxwplHF5/uD7sMvBRUGz/NwdUJV3e9Zl2r5e7Cv+CXvRESknUNMUM7OzsaHH36IxYsX4y9/+QtOnz6NBQsWwM3NDVOnTkVBQQEAIDAwUOO8wMBA5Obm6rxufHw83nzzTbPGbiktbftgCYZiPNekbYSq7cKMYaF6z2v83lKyCjFj+xmDVZmNNXdkN4T6eqonMMe8myzqvDERQXiqbyek55Wgb2cfztEhIrICm052lEol+vfvjzVr1gAAIiMjcfHiRXz44YeYOnWq+jhZkyJvgiA029bYihUrsHjxYvVr1ciOPWpp2wdLMBRj04EY1aqpgWH658vcvlOD3OJKxG45IWrlU0uoHq/5eLji8Q+0P1bVZmhXPwznRGQiIquy6dVYHTp0QM+ePTW2PfDAA8jLywMABAUFAYB6hEelsLCw2WhPY25ubvD29tb4sVctbftgCbpiNPTlu1Kk/zHRzbJqsyc6TT/HBXszcaP0rqhzXZxkeLq/fSbRRESOxKaTnaFDhyIrK0tj26VLlxASEgIACAsLQ1BQEI4cOaLeX1tbi5SUFAwZMsSisVpTS9s+WIK2GHt11J9khvvfp3e/i0xm9hGdxp+jlKahLk4yHJw31JyhERGRSDb9GOvll1/GkCFDsGbNGkyaNAmnT5/Gxx9/jI8//hjAvcdXixYtwpo1axAeHo7w8HCsWbMGHh4eePbZZ60cveWYu2CfKWiL8VpxJWboaY7Zs6McPk2qFKv4eLiiXsLc+viJvfGf8wU6V3a9OaEXTmUXQwDQycddXSgwr7gSO77PQd/OPqInPv/9jw9yRIeIyIbYdLIzYMAAJCYmYsWKFfjb3/6GsLAwbNy4Ec8995z6mFdeeQXV1dWYO3cuSkpKMGjQIBw+fLjV1NhpLMzP9pKcphrHaGghYKivJw7OG4bxW1I1Eh7Viqbs24ZXQ6k81MUXj0V0aFaHRzVyI/dw1ezUrmUukHdbw39dEqb2x8M9dT9CJSIiy7PppeeWYu91doyhq66Nua/bdL/YOjr/PJOHtOxiDO3qpzFqYqj/lLZrGRoB++J0Hl7/8gJqG5r/1XBxkkEQoBEvAHRq1xaprz6sMw4iIjI9h6izYymtKdkxV7VlQ9fVtf/t2Ai8duCC5PNU+68XVzUb+WlMynsTW6end0dvnL9RbtQ9iIjIdJjsSNCakh1TVVuWOkJjaL+u0Rax8R6/XKSuZdPJx8OouUtdVxyClsGcZl4eHY7xfTra7PwoIqLWwiGKCpJp6VpNpKsbuDbaRlqaVkBuet1jlwpF3bfpvaXE27SWjZQEpLSqFpM++l5UogMAfTv72MX8KCIiuseml56TaYmptmzIgr2ZOHHltsa2phWQm8q4XmrUfQ3FezL7tkm6vC/Ym4nLheImO/t4uLJAIBGRneHITivS0mrLukZaDK3IjgxuZ9R9DcW7Yv8F9Z+NnTcjpXaOahUYERHZF47stCItrbZsaKSl6ZdJdd0R3QN0VlCOUOh+xqorXm1U/bTEyC6qUI8IGXpPqjh3xw1Exhtj2MCTiMgOMdlpZVpSbdnQSEu/EM1eVo2vq+2+SgAXbpYj5t1kTE04jTItK6pWx0bA293wAGTjeTy6lFbVYmrCaYxcn4IZ288g5t1kbDl6Re91nWXA/80fxkdXRER2jKux0LpWY6kYW23Z2FVVje87f286frpZrvH4S9eKMG3302f7jAGI6REgKXZvdxeUV9drbJcBCA+8D4dfjhJ1XyIisjyxv785stNKhfl5IqZHgOQVRYZGhgxdVxAEXLhRrrPTeeORGdV8GrGJDqB7/o+uazUIAkqq6tA3pJ3G9uHh/vjXnNbTX42IyJFxgjJJoupxdexSITKul6JvZx9Jj3jErAhTJUpi5tOoqEaGmiZZfzt4EWnZtxHSXn9SNzemm7pfF2vnEBE5FiY7JElLKzBLWRFm6NjGms47OnzhFv70Wbr69S8F+peWqxIcJjlERI6HyQ5Joq3OjmollJgKzKoVVrrm/TRONrr436e36/n+uUN1jsQ0TnT00TUiREREjoNzdhxI4yXVhvYbOlbX+brmvehbCZWSVYj3v7uE4/8bDRK7Iiy7qEJnzyvVdm3zg4a/853o9yR2JRoREdkvjuw4AGOacDYm9jGUlPk2AJBbXInYLSc0EhZVYT4xK7ek3u+z73Pw1y9/0nsOAHTx88DrT/Ti3BwiolaCIztWYMyoij76Hi3p2q/rWH0MzaFxcdIs/vf4B8ebjcyUVNVh/JZUAIZXbkmZ35OSVSgq0QGAkfcHGrUSjYiI7BNHdiyopZN7tTHULPPYpSKD7RDENgLVNd9G5YWE0xgR7o9XxnbH5I9PorK2Qet1SqrqcPxykcFVXGLm92gbPTLkr+N6ij6WiIjsH0d2LMjQCIwxDD3qybiuv0lnY2IagS4ZE44HFF4696deLsKELSd0Jjoq6XmacWkb7couqsCkAZ2a1cBRzbPZ/N1lPLw+WVKikzC1v+hjiYjIMXBkx0IMjcAYGlXRxdCjnshgH737G9PXCNTQvB8VJQCIqAHYt7OPzusO6eoLQQC+zy5WbxsQ4oPpQ0LRs6Mct0qr0Odvhw3fpJEQH3ekLB8p6RwiInIMHNmxEDGTbY1hqLnniO7+BptpimkEamjejxQuTjL1Iyxt1027WqyR6ABAel4pvjibjzA/Tzz76WnJ92SiQ0TUejHZsRApk22lMrSUW9t+XcdqY0zbBn3qlYJ6+bvY66pGwHr89T+S7/fOkxHGhElERA6Cj7EsREoxveyiCuT+ViV6abSqhYOupdza9gNQ/1kQBKRfLzF6CbgxDv14E706yiWfV1OvFH3sI70C8dELnKNDRNTaMdmxoE1TIjF/b4bG/JTGoyotXa1lqN1B0/0+Hq6i7meO4b93D1+Cd1vzfP1UtXyCfcW3myAiIsclEwQTPZuwY2JbxJuKrhGYqQmndY78iGnFIJXY++09nYcV+8/rvI4MgJMMaDDim+TiJIMgwGSPyHbHDZTUmJSIiOyX2N/fHNmxAm0jMOZaraWLmPtpG/nRRoBxiQ5wb/5O747eOH+jXL3twU5y/JhfJvlaX8x+CIO6+hoXCBEROSwmOzZCamsES9xv5ZfXTLYCS59RPQPxwZS+uFZcicKyu1iRqHsUSZu2rk745a1HzRQdERHZOyY7NsKcq7W0MTQPp+jOXYMjOqbSt7MPnGTA4i8yJRUIBH6fn0NERKQLkx0bIWW1lhS6VnYZWtP0081yA0f8zlkmg7e7C8qr6yXPvVHV3On5+n9QVSd+pZUTgJ2cn0NERCIw2bEhhlZrSWFoZZehkaQdabmi7zW0mx/ejo3AawcuSB4NqlcK6LLiEJQSciQXJxkOzhuKnkYsXSciotaHq7Fg+dVYhuharSWFmJVW2o4RK35ibwTJ2zaLsXHsr/z7B5zLLZGUyOgjA7Dujw/i6f7BprkgERHZNbG/v1lB2QaF+XkipkdAix5daatM3HilFWC4srI+D3Xx1Rpj49g/nToAw7qZ5jGTi5MMh+YPY6JDRESS8TGWDZJaQbkpsSu7mlZWLii7q7eeDiBtDlHj68/fk46fbpYbnCvUlKuTDGsm9maSQ0RERrOrkZ34+HjIZDIsWrRIvU0QBKxatQoKhQLu7u6Ijo7GxYsXrRdkC5RW1WJqwmmMXJ+CGdvPIObdZExNOI0yiSuUpK7sUo3GDAprb/DaxswhCvPzxMKR4ZITHQ9XJxxdEs1Eh4iIWsRukp0zZ87g448/xoMPPqixfd26ddiwYQM2b96MM2fOICgoCKNHj8adO3esFKnxtHUAP3HlNubvzZB0HUOd0MP8PJFdVIGkrEL1Iy2VCIV3sy+FkwyI6OiNpKXR2BU3UFTrisa+OJ2H2Z+dk3SOkwz46a1H2fKBiIhazC4eY1VUVOC5557DJ598gtWrV6u3C4KAjRs34rXXXsPEiRMBADt37kRgYCD27NmDOXPmaL1eTU0Nampq1K/Ly8UvszYXU1dQ1rWya3VsL0xNOK2xfXAXX8hkQNrVYq3XGtZNfH+uxs7nl+LJrWmoNzBD2QmaS+FVq62IiIhMwS6SnXnz5uHxxx/HqFGjNJKdnJwcFBQUYMyYMeptbm5uiIqKQlpams5kJz4+Hm+++abZ45bC1BWUiytrMGNYKGaPCEO9UlB3N5/7eXqzGjrfZzdPcpxkQE+FNzZN6Wv0RGkxiQ4ADOrSHhP7dsKJq7cxtKsfH1sREZFJ2Xyys2/fPqSnp+PMmTPN9hUUFAAAAgMDNbYHBgYiN1d3nZgVK1Zg8eLF6tfl5eUIDrbuL1hTVVDWVl/H0MiNNkoBuHDD+BGvL07niUp0AGB4uD+e7h/MJIeIiMzCppOd69evY+HChTh8+DDatm2r8zhZk7kpgiA029aYm5sb3NzcTBanKUipoKxvtZa2eT/aRm7EMrYn1/c54u85N6ab5OsTERGJZdPJzrlz51BYWIh+/fqptzU0NODYsWPYvHkzsrKyANwb4enQoYP6mMLCwmajPfbAUAVlQ1WRdc37aQlje3INDvPFgYybBo/7YvZDRl2fiIhILJtOdh5++GGcP69Z92XGjBm4//77sXz5cnTp0gVBQUE4cuQIIiPvJQS1tbVISUnB2rVrrRFyizSte9N05Ebfaq1dcQMNzvuRoqU9uSYP7IzXDlzQ+SjrlbE9OKJDREQWYdPJjpeXFyIiIjS2eXp6wtfXV7190aJFWLNmDcLDwxEeHo41a9bAw8MDzz77rDVCNokwv+aPp8Ss1jI070eKviHtjOrJ1djBeUMxfssJjYSHfa2IiMjSbDrZEeOVV15BdXU15s6di5KSEgwaNAiHDx+Gl5eXtUMzKTGrtWJ6BGid96ONTAboO2RuTDeDS80NVXru2VGOK2sew7/OXudKKyIisho2AoXtNQLVJruoAiPXp+jcn7Q0GoIg4Kdb5diZdg1nrpWo92lbjTUg1EfjGG3X0/UIq7SqFrN3ndU4v/HcISIiIksQ+/vb7kd2Wgt9q7UGdWmPlV9e1HjMNSDEB9OHhKJnR7k6aWk6F2jKxye1rtQa3MVXb6IT824ySpq0sDhxpUg9d4iIiMiWMNmxI0vGhOO3qhqN+jdDu/mhrkHZbOJyel4p3NvkY1cfhXpb07lAulbn61m1j1k7zzZLdACgQYBRlZ6JiIjMjcmOHdC25DxC4Y01T/bGfW1dtD7eMtRmIruoQmeRwbSrxVrPyy6qwNlc3Y++AOPr8hAREZmL3TQCbc20LTn/+dYdvHv4kqiJy9oYc56Ype3G1uUhIiIyFyY7Nk615Lzp6irVyI2znkdOgO7kw9B/eBen5hc2tLR9QKgPR3WIiMjmMNmxcYZGUxqEeyuhnJtMtHGWyTAi3F9n8qHUuvV3R38uxKIvMvCvs9fV21STpLV9aXw8XPHp1AEGrkpERGR5THZsnJgGoZumRGJoNz+N7Y3bTGhj6D/89rRrOJBxE8v+/SO6/eVr/HSjDMC9lhbDwv01jh0Q4oPkpTFcdk5ERDaJE5RtnNgGofraTGhjaGSnsXqlgPFbTuDKmscMtrQgIiKyNRzZsQNiR27C/DwR0yNAVPIhtbVEvVLQeKQl5V5ERETWxJEdG9S0DYM5RlN0jRjpc+LqbbZ7ICIiu8Nkx4Zoq6fTuA2DtgahLbE6thcmbDmhtUigNkO7+hk+iIiIyMbwMZYN0VZP58SV25i/N8Ms9/vrgYsor64XdayLk4yjOkREZJeY7NgIQ/V0cm5rLw5o6vtp4+Ikw8F5Q016fyIiIkvhYywbIaaisSkfYRm6X9zQUBRX1WJoVz+O6BARkV1jsmMjxNTTseT9nh8cypVWRETkEPgYy0aoVkdJrYRsL/cjIiKyFiY7NsSYSsj2dD8iIiJrkAmCyCIrDqy8vBxyuRxlZWXw9va2djgWr07MashERGSPxP7+5pwdG2Tqejq2dj8iIiJLYrLjQJpWXrb16xIREVkCkx0HYKjysq1dl4iIyJI4QdkBmKvysqUrOhMREZkDkx07Z67Ky5au6ExERGQuTHbsnJjKy7Z0XSIiIktjsmPnzFV52dIVnYmIiMyFyY6dM1clZFZYJiIiR8FkxwGYqxIyKywTEZEjYAVl2F4FZWOZqxIyKywTEZEtYgXlVshclZBZYZmIiOwZH2MRERGRQ7PpZCc+Ph4DBgyAl5cXAgICEBsbi6ysLI1jBEHAqlWroFAo4O7ujujoaFy8eNFKEWvKLqpAUlah5Jo0KVmFeP+7SzjeqHIxERERGcemH2OlpKRg3rx5GDBgAOrr6/Haa69hzJgx+Omnn+Dpee+xyrp167Bhwwbs2LED3bt3x+rVqzF69GhkZWXBy8vLKnEb22Yht7gSsVtOoKSqTr3Nx8MVB+cNQ7Cv/qXgREREpJ1dTVAuKipCQEAAUlJSMGLECAiCAIVCgUWLFmH58uUAgJqaGgQGBmLt2rWYM2eOqOuaeoLy1ITTOHHltkb1YWeZDEO7+WFX3ECd50X+7bBGoqPi4+GKjDfGtDguIiIiRyL297dNP8ZqqqysDADQvn17AEBOTg4KCgowZszviYCbmxuioqKQlpam8zo1NTUoLy/X+DEVY9sspGQVak10AKCkqo6PtIiIiIxkN8mOIAhYvHgxhg0bhoiICABAQUEBACAwMFDj2MDAQPU+beLj4yGXy9U/wcHBJovT2DYLmfmles9LzysxNiQiIqJWzW6SnZdeegk//vgj9u7d22yfrEmVX0EQmm1rbMWKFSgrK1P/XL9+3WRxGttm4Q+d2uk9r29nH2NDIiIiatXsItmZP38+Dh48iKSkJHTq1Em9PSgoCACajeIUFhY2G+1pzM3NDd7e3ho/piKmzYK2VVpRPQLgo2Pyso+HK4aH+5ssRiIiotbEppMdQRDw0ksvYf/+/Th69CjCwsI09oeFhSEoKAhHjhxRb6utrUVKSgqGDBli6XDVdLVZWB0bgakJpzFyfQpmbD+DmHeTMTXhNMr+N1fn4LxhzRIe1WosIiIiMo5Nr8aaO3cu9uzZgy+//BI9evRQb5fL5XB3dwcArF27FvHx8di+fTvCw8OxZs0aJCcnS1p6bq52EU3bLIhdpXX8chHS80rQt7MPR3SIiIh0EPv726aTHV3zbrZv347p06cDuDf68+abb+If//gHSkpKMGjQIGzZskU9iVkMS/TGyi6qwMj1KTr3Jy2NZksGIiIiCRyiN5aYPEwmk2HVqlVYtWqV+QNqATGrtJjsEBERmZ5Nz9lxJMau0iIiIqKWYbJjIWJWaREREZHpMdmxIF2rtDZNibRSRERERI7PpufsOBq5hyt2xQ1stkqLiIiIzIfJjhWE+THJISIishQ+xiIiIiKHxmSHiIiIHBqTHSIiInJoTHaIiIjIoTHZISIiIofGZIeIiIgcGpMdIiIicmhMdoiIiMihMdkhIiIih8Zkh4iIiBwa20UAEAQBAFBeXm7lSIiIiEgs1e9t1e9xXZjsALhz5w4AIDg42MqREBERkVR37tyBXC7XuV8mGEqHWgGlUombN2/Cy8sLMpnM2uEYrby8HMHBwbh+/Tq8vb2tHY5N4GeiiZ9Hc/xMmuNn0hw/E0228nkIgoA7d+5AoVDAyUn3zByO7ABwcnJCp06drB2GyXh7e/MvYxP8TDTx82iOn0lz/Eya42eiyRY+D30jOiqcoExEREQOjckOEREROTQmOw7Ezc0NK1euhJubm7VDsRn8TDTx82iOn0lz/Eya42eiyd4+D05QJiIiIofGkR0iIiJyaEx2iIiIyKEx2SEiIiKHxmSHiIiIHBqTHTsTHx+PAQMGwMvLCwEBAYiNjUVWVpbGMdOnT4dMJtP4eeihh6wUsfmtWrWq2fsNCgpS7xcEAatWrYJCoYC7uzuio6Nx8eJFK0ZsfqGhoc0+E5lMhnnz5gFw/O/IsWPH8MQTT0ChUEAmk+HAgQMa+8V8J2pqajB//nz4+fnB09MT48ePR35+vgXfhWnp+0zq6uqwfPly9O7dG56enlAoFJg6dSpu3rypcY3o6Ohm35tnnnnGwu/EdAx9T8T8PWlN3xMAWv9dkclk+Pvf/64+xha/J0x27ExKSgrmzZuHkydP4siRI6ivr8eYMWNQWVmpcdwjjzyCW7duqX++/vprK0VsGb169dJ4v+fPn1fvW7duHTZs2IDNmzfjzJkzCAoKwujRo9U90RzRmTNnND6PI0eOAACefvpp9TGO/B2prKxEnz59sHnzZq37xXwnFi1ahMTEROzbtw+pqamoqKjAuHHj0NDQYKm3YVL6PpOqqiqkp6fj9ddfR3p6Ovbv349Lly5h/PjxzY6dPXu2xvfmH//4hyXCNwtD3xPA8N+T1vQ9AaDxWdy6dQvbtm2DTCbDU089pXGczX1PBLJrhYWFAgAhJSVFvW3atGnChAkTrBeUha1cuVLo06eP1n1KpVIICgoS3nnnHfW2u3fvCnK5XPjoo48sFKH1LVy4UOjataugVCoFQWhd3xEAQmJiovq1mO9EaWmp4OrqKuzbt099zI0bNwQnJyfhv//9r8ViN5emn4k2p0+fFgAIubm56m1RUVHCwoULzRuclWj7TAz9PeH3RBAmTJggjBw5UmObLX5POLJj58rKygAA7du319ienJyMgIAAdO/eHbNnz0ZhYaE1wrOYy5cvQ6FQICwsDM888wyys7MBADk5OSgoKMCYMWPUx7q5uSEqKgppaWnWCteiamtr8dlnn2HmzJkajW5b23dERcx34ty5c6irq9M4RqFQICIiotV8b8rKyiCTydCuXTuN7Z9//jn8/PzQq1cvLF261KFHSAH9f09a+/fk119/xaFDhxAXF9dsn619T9gI1I4JgoDFixdj2LBhiIiIUG9/9NFH8fTTTyMkJAQ5OTl4/fXXMXLkSJw7d85uql1KMWjQIOzatQvdu3fHr7/+itWrV2PIkCG4ePEiCgoKAACBgYEa5wQGBiI3N9ca4VrcgQMHUFpaiunTp6u3tbbvSGNivhMFBQVo06YNfHx8mh2jOt+R3b17F6+++iqeffZZjSaPzz33HMLCwhAUFIQLFy5gxYoV+OGHH9SPSR2Nob8nrf17snPnTnh5eWHixIka223xe8Jkx4699NJL+PHHH5GamqqxffLkyeo/R0REoH///ggJCcGhQ4eafSkdwaOPPqr+c+/evTF48GB07doVO3fuVE8mbDyiAdxLFJtuc1QJCQl49NFHoVAo1Nta23dEG2O+E63he1NXV4dnnnkGSqUSW7du1dg3e/Zs9Z8jIiIQHh6O/v37Iz09HX379rV0qGZn7N+T1vA9AYBt27bhueeeQ9u2bTW22+L3hI+x7NT8+fNx8OBBJCUloVOnTnqP7dChA0JCQnD58mULRWddnp6e6N27Ny5fvqxeldX0/7IKCwub/Z+9I8rNzcW3336LWbNm6T2uNX1HxHwngoKCUFtbi5KSEp3HOKK6ujpMmjQJOTk5OHLkiMaojjZ9+/aFq6trq/jeAM3/nrTW7wkAHD9+HFlZWQb/bQFs43vCZMfOCIKAl156Cfv378fRo0cRFhZm8Jzi4mJcv34dHTp0sECE1ldTU4Off/4ZHTp0UA+lNh4+ra2tRUpKCoYMGWLFKC1j+/btCAgIwOOPP673uNb0HRHznejXrx9cXV01jrl16xYuXLjgsN8bVaJz+fJlfPvtt/D19TV4zsWLF1FXV9cqvjdA878nrfF7opKQkIB+/fqhT58+Bo+1ie+JNWdHk3QvvviiIJfLheTkZOHWrVvqn6qqKkEQBOHOnTvCkiVLhLS0NCEnJ0dISkoSBg8eLHTs2FEoLy+3cvTmsWTJEiE5OVnIzs4WTp48KYwbN07w8vISrl27JgiCILzzzjuCXC4X9u/fL5w/f16YMmWK0KFDB4f9PFQaGhqEzp07C8uXL9fY3hq+I3fu3BEyMjKEjIwMAYCwYcMGISMjQ72ySMx34s9//rPQqVMn4dtvvxXS09OFkSNHCn369BHq6+ut9bZaRN9nUldXJ4wfP17o1KmTkJmZqfFvS01NjSAIgnDlyhXhzTffFM6cOSPk5OQIhw4dEu6//34hMjLSIT8TsX9PWtP3RKWsrEzw8PAQPvzww2bn2+r3hMmOnQGg9Wf79u2CIAhCVVWVMGbMGMHf319wdXUVOnfuLEybNk3Iy8uzbuBmNHnyZKFDhw6Cq6uroFAohIkTJwoXL15U71cqlcLKlSuFoKAgwc3NTRgxYoRw/vx5K0ZsGd98840AQMjKytLY3hq+I0lJSVr/nkybNk0QBHHfierqauGll14S2rdvL7i7uwvjxo2z689I32eSk5Oj89+WpKQkQRAEIS8vTxgxYoTQvn17oU2bNkLXrl2FBQsWCMXFxdZ9Yy2g7zMR+/ekNX1PVP7xj38I7u7uQmlpabPzbfV7IhMEQTDr0BERERGRFXHODhERETk0JjtERETk0JjsEBERkUNjskNEREQOjckOEREROTQmO0REROTQmOwQERGRQ2OyQ0RERA6NyQ4RmdyqVavwhz/8QdI5oaGh2Lhxo1niaanp06cjNjZW0jnGfAZEZB5MdohIlLS0NDg7O+ORRx6xyP1kMhkOHDigfv3LL79AJpPh1KlTGscNGjQIbm5uqKqqUm+rra2Fh4cHPv74Y4vESkS2jckOEYmybds2zJ8/H6mpqcjLy7P4/e+//3506NABSUlJ6m0VFRXIyMhAQEAA0tLS1NtPnTqF6upqxMTEWDxOIrI9THaIyKDKykr885//xIsvvohx48Zhx44dGvvfeecdBAYGwsvLC3Fxcbh7967G/ujoaCxatEhjW2xsLKZPn671fqGhoQCAJ598EjKZTP06OjoaycnJ6uOOHz+O7t27Y/z48Rrbk5OT0bFjR4SHhwMAtm/fjgceeABt27bF/fffj61bt2rc78aNG5g8eTJ8fHzg6+uLCRMm4Nq1azo/j3PnziEgIABvv/226M/gzJkzGD16NPz8/CCXyxEVFYX09HT1/pkzZ2LcuHEa59TX1yMoKAjbtm3TGQsRGcZkh4gM+uKLL9CjRw/06NEDzz//PLZv3w5VD+F//vOfWLlyJd5++22cPXsWHTp0aJZMSHXmzBkA95KUW7duqV/HxMQgNTUV9fX1AICkpCRER0cjKipKY8QnKSlJParzySef4LXXXsPbb7+Nn3/+GWvWrMHrr7+OnTt3AgCqqqoQExOD++67D8eOHUNqairuu+8+PPLII6itrW0WW3JyMh5++GG8+eabeO2110R/Bnfu3MG0adNw/PhxnDx5EuHh4Xjsscdw584dAMCsWbPw3//+F7du3VKf8/XXX6OiogKTJk1q0edJ1OpZtec6EdmFIUOGCBs3bhQEQRDq6uoEPz8/4ciRI4IgCMLgwYOFP//5zxrHDxo0SOjTp4/6dVRUlLBw4UKNYyZMmCBMmzZN/TokJER477331K8BCImJiRrnXLp0SQAgpKWlCYIgCAMGDBD++c9/CgUFBUKbNm2EyspKoaamRnB3dxcSEhIEQRCE4OBgYc+ePRrXeeutt4TBgwcLgiAICQkJQo8ePQSlUqner7rGN998IwiCIEybNk2YMGGCcODAAcHLy6vZ9cR8Bk3V19cLXl5ewldffaXe1rNnT2Ht2rXq17GxscL06dN1XoOIxOHIDhHplZWVhdOnT+OZZ54BALi4uGDy5MnqRys///wzBg8erHFO09emEh4ejk6dOiE5ORnl5eXIyMhAVFQUAgMDERYWhhMnTuDkyZOorq7GyJEjUVRUhOvXryMuLg733Xef+mf16tW4evUqgHuPpK5cuQIvLy/1/vbt2+Pu3bvqY4B784Ceeuop7Ny5E1OmTNGIS8xnUFhYiD//+c/o3r075HI55HI5KioqNOY/zZo1C9u3b1cff+jQIcycOdOknyFRa+Ri7QCIyLYlJCSgvr4eHTt2VG8TBAGurq4oKSkRdQ0nJyf1Yy+Vuro6o+KJjo5GUlISHnzwQYSHhyMgIAAA1I+y3NzcEBISgtDQUPz6668A7j3KGjRokMZ1nJ2dAQBKpRL9+vXD559/3uxe/v7+6j937doVvr6+2LZtGx5//HG0adNGUtzTp09HUVERNm7ciJCQELi5uWHw4MEaj8qmTp2KV199Fd9//z2+//57hIaGYvjw4ZLuQ0TNcWSHiHSqr6/Hrl27sH79emRmZqp/fvjhB4SEhODzzz/HAw88gJMnT2qc1/S1v7+/xlyUhoYGXLhwQe+9XV1d0dDQ0Gx7TEwM0tLScOTIEURHR6u3R0VFITk5GcnJyRg5ciQAIDAwEB07dkR2dja6deum8RMWFgYA6Nu3Ly5fvoyAgIBmx8jlcvX1/fz8cPToUVy9ehWTJ0/WSNbEfAbHjx/HggUL8Nhjj6FXr15wc3PD7du3NY7x9fVFbGwstm/fju3bt2PGjBl6PyMiEofJDhHp9H//938oKSlBXFwcIiIiNH7++Mc/IiEhAQsXLsS2bduwbds2XLp0CStXrsTFixc1rjNy5EgcOnQIhw4dwi+//IK5c+eitLRU771DQ0Px3XffoaCgQGMEKSYmBpWVldi2bRuioqLU26OionD27FmcPHlSY8n5qlWrEB8fj/fffx+XLl3C+fPnsX37dmzYsAEA8Nxzz8HPzw8TJkzA8ePHkZOTg5SUFCxcuBD5+fkaMQUEBODo0aP45ZdfMGXKFPVEaTGfQbdu3bB79278/PPPOHXqFJ577jm4u7s3e9+zZs3Czp078fPPP2PatGl6PyMiEofJDhHplJCQgFGjRmmMcKg89dRTyMzMRHh4ON544w0sX74c/fr1Q25uLl588UWNY2fOnIlp06Zh6tSpiIqKQlhYmMEaOOvXr8eRI0cQHByMyMhI9fawsDCEhITgzp07GslOx44d0blzZ9y9e1fj2rNmzcKnn36KHTt2oHfv3oiKisKOHTvUIzseHh44duwYOnfujIkTJ+KBBx7AzJkzUV1dDW9v72ZxBQUF4ejRozh//jyee+45NDQ0YPLkyQY/g23btqGkpASRkZF44YUXsGDBAvUjuMZGjRqFDh06YOzYsVAoFHo/IyISRyY0fZBORERWU1VVBYVCgW3btmHixInWDofIIXCCMhGRDVAqlSgoKMD69eshl8sxfvx4a4dE5DCY7BAR2YC8vDyEhYWhU6dO2LFjB1xc+M8zkanwMRYRERE5NE5QJiIiIofGZIeIiIgcGpMdIiIicmhMdoiIiMihMdkhIiIih8Zkh4iIiBwakx0iIiJyaEx2iIiIyKH9fxUg4a0X1cr8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Code task 36#\n",
    "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n",
    "#'AdultWeekend' on the y-axis\n",
    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>AdultWeekend</th>\n",
       "      <th>AdultWeekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AdultWeekend  AdultWeekday\n",
       "141          42.0          42.0\n",
       "142          63.0          63.0\n",
       "143          49.0          49.0\n",
       "144          48.0          48.0\n",
       "145          46.0          46.0\n",
       "146          39.0          39.0\n",
       "147          50.0          50.0\n",
       "148          67.0          67.0\n",
       "149          47.0          47.0\n",
       "150          39.0          39.0\n",
       "151          81.0          81.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code task 37#\n",
    "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there any reason to prefer weekend or weekday prices? Which is missing the least?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdultWeekend    4\n",
       "AdultWeekday    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 25)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a final quick check on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.11.1 Number Of Missing Values By Row - Resort<a id='2.11.1_Number_Of_Missing_Values_By_Row_-_Resort'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count     %\n",
       "329      5  20.0\n",
       "62       5  20.0\n",
       "141      5  20.0\n",
       "86       5  20.0\n",
       "74       5  20.0\n",
       "146      5  20.0\n",
       "184      4  16.0\n",
       "108      4  16.0\n",
       "198      4  16.0\n",
       "39       4  16.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n",
    "missing.columns=['count', '%']\n",
    "missing.sort_values(by='count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These seem possibly curiously quantized..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  4.,  8., 12., 16., 20.])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing['%'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the percentage of missing values per row appear in multiples of 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     107\n",
       "4.0      94\n",
       "8.0      45\n",
       "12.0     15\n",
       "16.0     10\n",
       "20.0      6\n",
       "Name: %, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing['%'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 277 entries, 0 to 329\n",
      "Data columns (total 25 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               277 non-null    object \n",
      " 1   Region             277 non-null    object \n",
      " 2   state              277 non-null    object \n",
      " 3   summit_elev        277 non-null    int64  \n",
      " 4   vertical_drop      277 non-null    int64  \n",
      " 5   base_elev          277 non-null    int64  \n",
      " 6   trams              277 non-null    int64  \n",
      " 7   fastSixes          277 non-null    int64  \n",
      " 8   fastQuads          277 non-null    int64  \n",
      " 9   quad               277 non-null    int64  \n",
      " 10  triple             277 non-null    int64  \n",
      " 11  double             277 non-null    int64  \n",
      " 12  surface            277 non-null    int64  \n",
      " 13  total_chairs       277 non-null    int64  \n",
      " 14  Runs               274 non-null    float64\n",
      " 15  TerrainParks       233 non-null    float64\n",
      " 16  LongestRun_mi      272 non-null    float64\n",
      " 17  SkiableTerrain_ac  275 non-null    float64\n",
      " 18  Snow Making_ac     240 non-null    float64\n",
      " 19  daysOpenLastYear   233 non-null    float64\n",
      " 20  yearsOpen          277 non-null    float64\n",
      " 21  averageSnowfall    268 non-null    float64\n",
      " 22  AdultWeekend       277 non-null    float64\n",
      " 23  projectedDaysOpen  236 non-null    float64\n",
      " 24  NightSkiing_ac     163 non-null    float64\n",
      "dtypes: float64(11), int64(11), object(3)\n",
      "memory usage: 56.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ski_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are still some missing values, and it's good to be aware of this, but leave them as is for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.12 Save data<a id='2.12_Save_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(277, 25)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ski_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory ../data was created.\n",
      "Writing file.  \"../data\\ski_data_cleaned.csv\"\n"
     ]
    }
   ],
   "source": [
    "# save the data to a new csv file\n",
    "datapath = '../data'\n",
    "save_file(ski_data, 'ski_data_cleaned.csv', datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing file.  \"../data\\state_summary.csv\"\n"
     ]
    }
   ],
   "source": [
    "# save the state_summary separately.\n",
    "datapath = '../data'\n",
    "save_file(state_summary, 'state_summary.csv', datapath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.13 Summary<a id='2.13_Summary'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A: 3** The initial dataset was (330,27) and included the resort in question, Big Mountain. In order to clean up the data, certain columns and entries were removed, such as the 'fastEights' column which was dropped because the values were almost all missing or 0, and a specific entry in the 'yearsOpen' column was dropped due to the provided number being impossibly high. The AdultWeekend is our target feature because fewer rows were missing it and for all other resorts in the same state as our resort, the weekend and weekday prices are the same. Rows which were missing the AdultWeekend value were dropped from the table. The statewide summaries were made by pulling in population data from wikipedia using the read_html function and were then merged in with the states in question to get a table with relevant information on states with resorts."
   ]
  }
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