FinalProject_group041.ipynb

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   "source": [
    "![ChronoChord](chrono.png \"Title\")"
   ]
  },
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   "source": [
    "# Overview\n",
    ">Music has been at the center of popular culture for hundreds of years and some songs and genres have grown to define specific eras due to their popularity and relevance to a given time period. This leads to various questions arising pertaining to the aspects of songs which were popular at a given time, including lyrics, chords, rhythmic elements, and instruments used. Particularly, from the 20th century onwards, chords have been heavily utilized across all genres in order to construct songs in contrast to the baroque and classical eras which were dependent on counterpoint techniques.**With this analysis we wanted to expand this notion look at a core aspect of popular music, chords, and observe trends over the past 70 years in terms of their frequency, usage in larger progressions, and whether they are strongly correlated with year.**"
   ]
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    }
   },
   "source": [
    "# Names"
   ]
  },
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    "*   Ardel Alegre \n",
    "*   Oscar Arias \n",
    "*   Ian Carrasco\n",
    "*   Saarthak Mehra \n",
    "*   Alex Tov"
   ]
  },
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    }
   },
   "source": [
    "# Group Members IDs"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Research Question\n",
    ">Music, like other artforms, has the ability to reflect the times in which it was produced. With this notion in mind, is there a statistically significant relationship between the chords used in a song and the year that song was produced? Additionally, how has the usage of chords (frequency of specific chords, progressions) changed from 1950 to the present day?"
   ]
  },
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   "cell_type": "markdown",
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    }
   },
   "source": [
    "# Background and Prior Work"
   ]
  },
  {
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    ">Popular music tends to be very similar terms of chord sequences, as there are well known progressions that persist due to their sonic qualities such as the I–V–vi–IV. Previous analyses of chord patterns have been conducted **[1][2]**, which have found interesting results in looking at chords most likely to follow specific chords, time signature, key, as well as chord distributions across popular songs. They have found that the most popular key has been C and the most popular chords are those found in C major including G, F, C. However, they don’t analyze such information throughout time, which would add a unique angle as pop music in 2019 is radically different from that in previous decades. Additionally, it has been presented that modern pop music tends to utilize very basic chord resolution patterns and keys **[3][4][5]**. Another aspect to note however is that pop music is a general term to describe popular music of a given time, thus pop music of the 60s consisted primarily of rock and folk genres, whereas currently it consists of hip-hop and electronic. Overall, we believe the analysis of chordal structure through time should bring up compelling findings and provides interesting questions in which to guide our project.\n",
    "\n",
    "Sources Noted in the Background:\n",
    "\n",
    "1. http://www.hooktheory.com/blog/i-analyzed-the-chords-of-1300-popular-songs-for-patterns-this-is-what-i-found/\n",
    "\n",
    "2. https://flypaper.soundfly.com/write/chartmania-breaking-down-billboard-top-40-songs-2017/\n",
    "\n",
    "3. https://www.youtube.com/watch?v=nuGt-ZG39cU\n",
    "\n",
    "4. https://www.youtube.com/watch?v=yAm1UWQSriI\n",
    "\n",
    "5. https://pudding.cool/2017/05/song-repetition/\n"
   ]
  },
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   },
   "source": [
    "# Hypotheses"
   ]
  },
  {
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   "source": [
    ">We believe the number of unique chords and progressions used in song will have a negative correlation with a medium effect (0.3-0.5) on the year a song was released.\n",
    "\n",
    ">We believe the major quality of songs (ratio of major to minor chords) will decreases through time as music has become more melancholy, and is usually associated with the usage of minor chords.  \n",
    "Study On Sadness in Modern Pop Music: https://apnews.com/7feecbf68ecb4cc382eea998fea75650\n"
   ]
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    }
   },
   "source": [
    "# Dataset(s)"
   ]
  },
  {
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   "source": [
    ">**Dataset Name**: Billboard Hot 100 Charts API  \n",
    "**Link to the dataset**: https://www.billboard.com/archive/charts    \n",
    "**Number of observations**: 6900\n",
    "The source contains the Hot 100 lists for popular music from 1950 - Present.\n",
    "\n",
    ">**Dataset Name**: UltimateGuitar Chords Dataset  \n",
    "**Link to the dataset**: https://www.ultimate-guitar.com/explore?type[]=Chords  \n",
    "**Number of observations**: 580,641\n",
    "The dataset contains guitar chord diagrams and lyrics for popular songs.\n",
    "\n",
    ">**Data Combination**: In order to build our dataset to perform our analysis we first compiled all artist and song names that were present on the Billboard Hot 100 charts. Then, using that list we queried the UltimateGuitar source for said song and artist and scraped the corresponding chord chart for the song if it was present. Since not all songs on the Hot 100 had chord charts on UltimateGuitar the resulting dataset size is less than the total number of observations in the Billboard Charts.\n"
   ]
  },
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   },
   "source": [
    "# Part 0: Import Necessary Libraries"
   ]
  },
  {
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "#Import Pandas to Load CSV and Perform Data Reading\n",
    "import pandas as pd \n",
    "\n",
    "#Import tqdm for use of progress bars when performing long operations\n",
    "from tqdm import tqdm, tqdm_pandas\n",
    "\n",
    "#Import Matplotlib for visualizing data\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "#Import numpy to perform numerical analysis and formatting\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "#Import scipy to gather statistics\n",
    "from scipy import stats\n",
    "\n",
    "#Imprt statsmodels for regression analysis\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#Import BeautfulSoup4 for parsing html from webpages\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#Import requests to create web requests\n",
    "import requests\n",
    "\n",
    "#Import selenium for browser automation and more scraping utils\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "\n",
    "#Import urllib for creating web-safe urls\n",
    "import urllib\n",
    "import ast\n",
    "import json\n",
    "\n",
    "#Import wrapper for billboard charts to extract name and year of popular songs\n",
    "#Source: https://github.com/guoguo12/billboard-charts\n",
    "import billboard\n",
    "\n",
    "#Import defaultdict for merging of dictionaries\n",
    "from collections import defaultdict\n",
    "\n",
    "#Import musthe for music theory helpers\n",
    "from musthe import *"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Set global style for Seaborn\n",
    "sns.set(style=\"dark\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "# Part 1: Scraping Song and Chord Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "##### First we utilized a wrapper for the Billboard Music API in order to get the Hot 100 charts for every year since 1950. Then, we scraped the chords for the songs on these charts from the popular guitar chord website UltimateGuitar (https://ultimate-guitar.com)."
   ]
  },
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   "cell_type": "code",
   "execution_count": 74,
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   },
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   "source": [
    "#Function to get the parsed webpage for a given song by an artist\n",
    "def get_chord_url(song, artist):\n",
    "    \n",
    "    #Create a google search query string\n",
    "    strRequest = song + \" \" + artist + \" chords ultimate guitar\"\n",
    "\n",
    "    #Create a websafe string from the query\n",
    "    strRequest = urllib.parse.quote_plus(strRequest)\n",
    "\n",
    "    #Create a query to get ultimate guitar page\n",
    "    base_url = 'http://www.google.com/search?q=' + strRequest\n",
    "\n",
    "    #Perform proxy rotation to limit bandwidth\n",
    "    proxies = {'http': 'http://hhprox:qweazsxdc@us-wa.proxymesh.com:31280',\n",
    "               'https': 'http://hhprox:qweazsxdc@us-wa.proxymesh.com:31280'}\n",
    "\n",
    "    #Get List of Top Search Results from Google\n",
    "    search_results = requests.get(base_url, proxies=proxies)\n",
    "\n",
    "    #Use beautiful soup to parse the page\n",
    "    soup = BeautifulSoup(search_results.text, \"html.parser\")\n",
    "\n",
    "    #Extract the url of the top ultimate guitar chord sheet\n",
    "    return soup.find('cite').text"
   ]
  },
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    }
   },
   "source": [
    "### Process Each Song from Billboard Charts"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "#Helper function to generate (song, year) tuples\n",
    "def process_songs(chart, year):\n",
    "    processed = []\n",
    "    \n",
    "    for song in chart:\n",
    "        processed.append((song, year))\n",
    "    \n",
    "    return processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "scrolled": false
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   "outputs": [],
   "source": [
    "#Build a List of Songs from the Billboard Top 100\n",
    "songs = []\n",
    "\n",
    "#Extract Hot 100 Songs from Billboard from All Available Years\n",
    "for year in tqdm(range(1950, 2020)):\n",
    "    chart = billboard.ChartData('hot-100', date=str(year) + \"-06-06\")\n",
    "    \n",
    "    #Append to Songs List\n",
    "    songs += process_songs(chart, year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "### Run scraper to extract chords for a given list of songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
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   "source": [
    "def get_chords(songs, outpath):\n",
    "    \n",
    "    #Initialize Headless Chrome Instance\n",
    "    options = webdriver.ChromeOptions()\n",
    "    options.add_argument('headless')\n",
    "    browser = webdriver.Chrome(executable_path='/usr/local/bin/chromedriver', options=options)\n",
    "    \n",
    "    dataframe = []\n",
    "\n",
    "    #Iterate over a set of song objects to scrape\n",
    "    for idx, song in tqdm(enumerate(songs)):\n",
    "        try:\n",
    "            #Fetch url to chord sheet\n",
    "            chord_url = get_chord_url(song[0].title, song[0].artist)\n",
    "            \n",
    "            if chord_url:\n",
    "                #Navigate to the given url\n",
    "                browser.get(chord_url)\n",
    "                \n",
    "                #Extract Page Source and Parse With BS4\n",
    "                html = browser.page_source\n",
    "                soup = BeautifulSoup(html)\n",
    "\n",
    "                #Build List of Chords from Parsed HTML\n",
    "                chords = []\n",
    "                \n",
    "                #Find all chord elements on page\n",
    "                for match in soup.findAll('span', attrs={'class':'B24oE _1r_2U'}):\n",
    "                    chords.append(match.string)\n",
    "\n",
    "                #Append List of Chords and Song Info to DataFrame Row\n",
    "                if len(chords) > 0:\n",
    "                    dataframe.append([song[0].artist, song[0].title, song[1], chords])\n",
    "        \n",
    "        except Exception as e:\n",
    "            print(str(e))\n",
    "    \n",
    "    #Create a pandas dataframe with titled columns\n",
    "    df = pd.DataFrame(dataframe, columns=[\"Artist\", \"Track\", \"Year\", \"Chords\"])\n",
    "    \n",
    "    #Write the dataframe to a csv file\n",
    "    df.to_csv(outpath, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "e64368f39e6f5",
     "named_versions": [],
     "output_hidden": false,
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   },
   "outputs": [],
   "source": [
    "#Run scraper and output csv\n",
    "get_chords(songs, './chords.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2bT51Kji16gY",
    "janus": {
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     "cell_hidden": false,
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     "named_versions": [],
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    }
   },
   "source": [
    "# Part 2: Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
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    "colab_type": "code",
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    },
    "outputId": "ddd20a46-eb60-4cc3-bcce-bdf28887887e",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Load in dataframe with chord data\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/IanCarrasco/chord-scraper/master/chords.csv?token=AD2SIKN6AWGXP5YVX4FYQQS47DQ6U')"
   ]
  },
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   },
   "source": [
    "### The Base Dataframe after Scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Artist</th>\n",
       "      <th>Track</th>\n",
       "      <th>Year</th>\n",
       "      <th>Chords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Perez Prado And His Orchestra</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>1950</td>\n",
       "      <td>['Am', 'Am', 'C', 'Dm', 'Em', 'Am', 'C', 'Dm',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bobby Darin</td>\n",
       "      <td>Splish Splash</td>\n",
       "      <td>1950</td>\n",
       "      <td>['C', 'G7', 'C', 'F', 'C', 'G7', 'C', 'C', 'F'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kalin Twins</td>\n",
       "      <td>When</td>\n",
       "      <td>1950</td>\n",
       "      <td>['G', 'C', 'G', 'Em', 'G', 'G', 'D', 'C', 'C',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Coasters</td>\n",
       "      <td>Yakety Yak</td>\n",
       "      <td>1950</td>\n",
       "      <td>['G', 'C', 'D7', 'G', 'G', 'G', 'C', 'D7', 'G'...</td>\n",
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       "      <th>4</th>\n",
       "      <td>Jack Scott</td>\n",
       "      <td>My True Love</td>\n",
       "      <td>1950</td>\n",
       "      <td>['G', 'Em', 'C', 'D7', 'D7', 'G', 'Em', 'C', '...</td>\n",
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      "text/plain": [
       "                          Artist          Track  Year  \\\n",
       "0  Perez Prado And His Orchestra       Patricia  1950   \n",
       "1                    Bobby Darin  Splish Splash  1950   \n",
       "2                    Kalin Twins           When  1950   \n",
       "3                   The Coasters     Yakety Yak  1950   \n",
       "4                     Jack Scott   My True Love  1950   \n",
       "\n",
       "                                              Chords  \n",
       "0  ['Am', 'Am', 'C', 'Dm', 'Em', 'Am', 'C', 'Dm',...  \n",
       "1  ['C', 'G7', 'C', 'F', 'C', 'G7', 'C', 'C', 'F'...  \n",
       "2  ['G', 'C', 'G', 'Em', 'G', 'G', 'D', 'C', 'C',...  \n",
       "3  ['G', 'C', 'D7', 'G', 'G', 'G', 'C', 'D7', 'G'...  \n",
       "4  ['G', 'Em', 'C', 'D7', 'D7', 'G', 'Em', 'C', '...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "named_versions": [],
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    }
   },
   "source": [
    "### Calculate various properties of chord data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "e7371191fef96",
     "named_versions": [],
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     "source_hidden": false,
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    }
   },
   "source": [
    "#### Helper function to calculate the ratio of major to minor chords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "janus": {
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   },
   "outputs": [],
   "source": [
    "# Compare the major and minor chords of a song\n",
    "# Return ratio of major chords to minor chords ('Majorness' of a song)\n",
    "def major_to_minor(chords):\n",
    "    majorCount = 1 \n",
    "    minorCount = 1 \n",
    "    for chord in chords:\n",
    "        if 'm' in chord:\n",
    "            minorCount += 1\n",
    "        else:\n",
    "            majorCount += 1\n",
    "    return majorCount/minorCount\n",
    "\n",
    "# Count number of unique chords given a list of all chords or progressions in a song\n",
    "def num_unique(chords):\n",
    "    if isinstance(chords[0], list):\n",
    "        return len(set(tuple(row) for row in chords))\n",
    "    return len(set(chords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "81ab9fc9e087",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### Create new columns and add them to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "286f084bd130a",
     "named_versions": [],
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    }
   },
   "outputs": [],
   "source": [
    "# Adding two new columns to quantify 'majorness' of a song and number of chords \n",
    "df['Majorness'] = df['Chords'].apply(major_to_minor)\n",
    "df['Chords'] = df['Chords'].apply(ast.literal_eval)\n",
    "df['Song Length'] = df['Chords'].apply(len)\n",
    "df['Unique Chords'] = df['Chords'].apply(num_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "adb4215eb44af",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### Additionally Include Chord Progression Data to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
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     "current_version": 0,
     "id": "a6238ef024158",
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   },
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   "source": [
    "#Splits a sequence of chords into n-chord progressions\n",
    "def chords_to_progressions(chords, progression_length=4):\n",
    "    progressions = []\n",
    "    \n",
    "    #Perform a sliding window operation over a chord sequence\n",
    "    for i in range(0, len(chords), progression_length):\n",
    "        progressions.append(chords[i:i+progression_length])\n",
    "    return progressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "d5d717a242bce",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "df['Progressions'] = df['Chords'].apply(chords_to_progressions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "85ac56415f7b5",
     "named_versions": [],
     "output_hidden": false,
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   },
   "outputs": [],
   "source": [
    "df['Unique Progressions'] = df['Progressions'].apply(num_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
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     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
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    }
   },
   "source": [
    "### The Final Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    {
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       "      <td>1950</td>\n",
       "      <td>[C, G7, C, F, C, G7, C, C, F, C, G7, C, F, C, ...</td>\n",
       "      <td>126.000000</td>\n",
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       "      <td>3</td>\n",
       "      <td>[[C, G7, C, F], [C, G7, C, C], [F, C, G7, C], ...</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1950</td>\n",
       "      <td>[G, C, G, Em, G, G, D, C, C, G, C, G, Em, G, D...</td>\n",
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       "      <td>7</td>\n",
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       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Coasters</td>\n",
       "      <td>Yakety Yak</td>\n",
       "      <td>1950</td>\n",
       "      <td>[G, C, D7, G, G, G, C, D7, G, G, G, C, D7, G, ...</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>[[G, C, D7, G], [G, G, C, D7], [G, G, G, C], [...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jack Scott</td>\n",
       "      <td>My True Love</td>\n",
       "      <td>1950</td>\n",
       "      <td>[G, Em, C, D7, D7, G, Em, C, D7, D7, G, Em, C,...</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>[[G, Em, C, D7], [D7, G, Em, C], [D7, D7, G, E...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                          Artist          Track  Year  \\\n",
       "0  Perez Prado And His Orchestra       Patricia  1950   \n",
       "1                    Bobby Darin  Splish Splash  1950   \n",
       "2                    Kalin Twins           When  1950   \n",
       "3                   The Coasters     Yakety Yak  1950   \n",
       "4                     Jack Scott   My True Love  1950   \n",
       "\n",
       "                                              Chords   Majorness  Song Length  \\\n",
       "0  [Am, Am, C, Dm, Em, Am, C, Dm, Em, Am, G, F, G...   13.555556           73   \n",
       "1  [C, G7, C, F, C, G7, C, C, F, C, G7, C, F, C, ...  126.000000           24   \n",
       "2  [G, C, G, Em, G, G, D, C, C, G, C, G, Em, G, D...   46.555556           80   \n",
       "3  [G, C, D7, G, G, G, C, D7, G, G, G, C, D7, G, ...  131.000000           25   \n",
       "4  [G, Em, C, D7, D7, G, Em, C, D7, D7, G, Em, C,...   31.800000           59   \n",
       "\n",
       "   Unique Chords                                       Progressions  \\\n",
       "0              7  [[Am, Am, C, Dm], [Em, Am, C, Dm], [Em, Am, G,...   \n",
       "1              3  [[C, G7, C, F], [C, G7, C, C], [F, C, G7, C], ...   \n",
       "2              7  [[G, C, G, Em], [G, G, D, C], [C, G, C, G], [E...   \n",
       "3              3  [[G, C, D7, G], [G, G, C, D7], [G, G, G, C], [...   \n",
       "4              5  [[G, Em, C, D7], [D7, G, Em, C], [D7, D7, G, E...   \n",
       "\n",
       "   Unique Progressions  \n",
       "0                   12  \n",
       "1                    5  \n",
       "2                   18  \n",
       "3                    6  \n",
       "4                   11  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
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    }
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   "source": [
    "df.head()"
   ]
  },
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   "execution_count": 16,
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Majorness</th>\n",
       "      <th>Song Length</th>\n",
       "      <th>Unique Chords</th>\n",
       "      <th>Unique Progressions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2317.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "      <td>2317.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1984.343979</td>\n",
       "      <td>80.100879</td>\n",
       "      <td>83.428571</td>\n",
       "      <td>7.139836</td>\n",
       "      <td>11.655589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.144393</td>\n",
       "      <td>153.204022</td>\n",
       "      <td>43.911718</td>\n",
       "      <td>4.544954</td>\n",
       "      <td>6.578513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1967.000000</td>\n",
       "      <td>14.038462</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1985.000000</td>\n",
       "      <td>20.354839</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1318.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year    Majorness  Song Length  Unique Chords  \\\n",
       "count  2317.000000  2317.000000  2317.000000    2317.000000   \n",
       "mean   1984.343979    80.100879    83.428571       7.139836   \n",
       "std      20.144393   153.204022    43.911718       4.544954   \n",
       "min    1950.000000     3.666667     2.000000       1.000000   \n",
       "25%    1967.000000    14.038462    53.000000       4.000000   \n",
       "50%    1985.000000    20.354839    78.000000       6.000000   \n",
       "75%    2001.000000    52.500000   106.000000       8.000000   \n",
       "max    2018.000000  1318.000000   399.000000      49.000000   \n",
       "\n",
       "       Unique Progressions  \n",
       "count          2317.000000  \n",
       "mean             11.655589  \n",
       "std               6.578513  \n",
       "min               1.000000  \n",
       "25%               7.000000  \n",
       "50%              11.000000  \n",
       "75%              15.000000  \n",
       "max              54.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "88e97aaaf2f66",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "### Populate Dictionary of Chord and Progression Frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "2135044e4f8c6",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2317/2317 [00:00<00:00, 5022.43it/s]\n"
     ]
    }
   ],
   "source": [
    "#Store frequency of each chord in a dictionary\n",
    "chords = {}\n",
    "progressions = {}\n",
    "for idx, row in tqdm(df.iterrows(), total=df.shape[0]):\n",
    "    \n",
    "    #Split by comma to create chord list\n",
    "    for chord in row['Chords']:\n",
    "        \n",
    "        #Strip quotation marks and remove trailing and leading whitespace\n",
    "        chord = chord.replace(\"'\", '').lstrip().rstrip()\n",
    "        \n",
    "        #Update chord count\n",
    "        if chord in chords:\n",
    "            chords[chord] += 1\n",
    "        else:\n",
    "            chords[chord] = 1\n",
    "    \n",
    "    #Split by comma to create chord list\n",
    "    for progression in row['Progressions']:\n",
    "        \n",
    "        if len(progression) is 4:\n",
    "            #Strip quotation marks and remove trailing and leading whitespace\n",
    "            progression = ','.join(progression)\n",
    "\n",
    "            #Update chord count\n",
    "            if progression in progressions:\n",
    "                progressions[progression] += 1\n",
    "            else:\n",
    "                progressions[progression] = 1\n",
    "                \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "7026c4afac44e",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "# Part 3: Data Analysis and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "fb39a2ee3b3c8",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "### Total Number of Years Represented and Unique Artists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "7c12c682d9522",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Years: 69 (1950,2018)\n",
      "Unique Artists: 1430\n"
     ]
    }
   ],
   "source": [
    "out = 'Total Years: {} ({},{})'.format(len(df['Year'].unique()), df['Year'].min(), df['Year'].max())\n",
    "\n",
    "print(out)\n",
    "\n",
    "print('Unique Artists:', len(df['Artist'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "763e7b750b3be",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "### Total Number of Chords "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "bd1e371e6ebc5",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Chords: 193304\n"
     ]
    }
   ],
   "source": [
    "total_chords = sum(df['Song Length'])\n",
    "print('Total Chords: {}'.format(total_chords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "b647f956d5b3f",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "### Distribution of Songs Per Year\n",
    "The distribution of songs in the dataset per year is fairly uniform with the minimum number of songs in a year being 20 and a maximum of 47."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "a31d94178d912",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=69, minmax=(20, 47), mean=33.57971014492754, variance=31.158994032395565, skewness=0.08292716239664653, kurtosis=-0.18114051101438333)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the bin counts for number of songs\n",
    "bin_counts = np.histogram(df['Year'], bins=69)[0]\n",
    "stats.describe(bin_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "17eb4c7180bcf",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Number of Songs Scraped per Year: 33.57971014492754\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot number of songs scraped for each year\n",
    "sns.distplot(df['Year'], bins=69, label=\"Songs\", kde=False)\n",
    "plt.title(\"Distribution of Songs per Year\")\n",
    "plt.xlabel('Year')\n",
    "plt.axhline(mean, color='r', linestyle=\"--\")\n",
    "plt.xlim(1948, 2020)\n",
    "plt.ylabel('Number of Songs')\n",
    "\n",
    "mean = np.mean(df['Year'].value_counts().tolist())\n",
    "plt.legend()\n",
    "plt.grid(False)\n",
    "\n",
    "print('Average Number of Songs Scraped per Year:', mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "492878b0c3223",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### When looking at the top chords used throught the time spanned in the dataset, 98.9% of the chords appear less than 5000 times, which shows that most popular songs use a select set of chords *predominantly* over others. The top 5 most used chords (G, C, D, A, F) are all formed from only two scales: the C scale and D scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e89qiv1yJuZP",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "c675e2d9a2854",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.89 percent of chords appear less than 5000 times\n"
     ]
    }
   ],
   "source": [
    "#Sort the chords in descending order of frequency (high -> low frequency)\n",
    "ranked_chords = sorted(chords.items(), key=lambda x: x[1])[::-1]\n",
    "\n",
    "#Extract top twenty chord names and counts\n",
    "chord_names = [x[0] for x in ranked_chords][:20]\n",
    "chord_counts = [x[1] for x in ranked_chords][:20]\n",
    "\n",
    "chords_less = [i[1] for i in ranked_chords if i[1] <= 5000]\n",
    "print('%.2f'%(len(chords_less)/len(ranked_chords) * 100), 'percent of chords appear less than 5000 times')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "d470314997914",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### This notion of predominant chords is further seen with the proportion of total chords represented by those belonging in the top 20 most frequent chords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "da85ec145713e",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of total chords represented by top 20 chords:  0.8189794313619998\n"
     ]
    }
   ],
   "source": [
    "print('Proportion of total chords represented by top 20 chords: ', sum(chord_counts)/total_chords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "4e63e4aa3a4e4",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### Plot the accumulation of total chords in the dataset as a function of chord rank\n",
    "We note that once the top 50 chords have been counted the growth rate of the aggregation tends to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "792bc68f1a5ec",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12570e748>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hlt7suP7Ya2Vl6yo99vrtZeqmErFdd77rt1+vb0Azwvu2s8K/psqR5FOL3TVkWE2HIG6DyaSgN5rQG0r+GK57rTeaMFz9WW670YTRqGA0mTBc/Wk0KtdemxQMxpKfJeVXX5sUjEYThqvl5uOulhuv+2lSriYC09Vn22r5rV+VCuxUKlQqFXaqkt/Qr5VRptzO7moZ1+1jV/Y4FSrs7ChXX8n7q2V2duay6+uDsq0Ec5lKherqRtXVDaqbbae03tIylXmbSvX37WWPvX5f/h6byvL2q3uUic2/jacV/8Ysk+RTiznf29byTqJSTIqC3mCiWG+kSG+kWG+i2HD1p95Ikfl9SVmR3mjeXiZZXJ8wrv68UbnBUHKRtwaVCtR2dmjUKtR2KtTq616by+1Qq1Vo7FTYq+1wslehUdtd3b/sdrurx9nZldwstlOV1HWj13bm/UvfV+WY0m3c+Jir78slkevKRP0jyacWK/zjFHBnJiGTolBYZKBQZ6Cw2Iiu2ICu2Iiu2EhhUenrqz+Lrm3TFRsoLDKWSR7FeiPFBtMtx6BSgYNGjb3G7toftR2a6143cNRgry55r7laVm7f68s0dmjU17aXeX/ddrWdypxU7Ozk4ivqH0k+tVjmlxuAuj3U2qQoFOgMXMkvRltQjLbQQL5OT4Gu5Ge+zkCBTk9+Ycnr0m0FOkO5PukbcdDY4eSgxslBU/LTUUNDVwcc7dU42qtxsLfDwV6Ng8bu6vurrx3UOGgq2G5vh4NGjUatkt+6hagmknxEleiKDWTlFpGTpyM3v5jcfH3Jz4Liq++LuVJQjLZAX2H3k9pORQMnDS5O9rg4aXBr4EATrwa4ONqXlDvb4+yoxtlBg5PjtQRz7b0a9S3M2yeEqD0k+YgbytfpScsuJP1yAdm5RWTn6sjOLSIrV0d2ro58naHcMRq1HQ1d7HF3caCRmyMtm7jh7uKAu4sDDV0ccHO2x8XZHhenkuTi5KCWloUQdyhJPncwg9HEX9kFXMzIJy2noCTZ5BSQllOItlBfZl8XJw2N3Jzwcnfk3mYN8XJ3wtPdEU83JxpeTTCSTIQQlSXJ5w5hMJo4l5bH2Ut5nE/L43y6losZ+RiM127EN3JzpHEjZ7rc503jRg1o3MgZn0bOeLo74ewo/1SEENYjc7v9TW2a2610RmunFi1v+dgivZHfUy5z8lwOf1y8wplLeeZE4+psT4vGrrTwcaN5Y1eaebvi08gZR3u1VeMXQtR/MrGoldSm5HOr0i8X8v9+y+DYmSx+S7mCwWhCbaeiVRM37vFtyL2+DWlztzuN3Byle0wIYRVVTT7Sl1KL5R8/BnDTBeUyrxSy/2Q6+0+kc/avPACaejWgd2df/Nt40q65h7RohBC1jiSfWix7y1dA+eRjMJo4fCqTnUdSOXYmG4BWTdwY3vseAu7zwdvD2eaxCiHErZDkU4cYjCZ+OpLK13vPcllbTCM3Rwb1aMWjDzTFRxKOEKIOkeRTR5y6cJkPt5wg/XIhbZs1ZEz/9jzYxkumXhFC1EmSfGo5Bfhqzxk27z7DXQ2dmDr8QR5o4yUDBoQQdZokn1rMYFRIzcon4aczdPdrzOjg++R5GyFEvSBXslrqfFoe/7N/kCJPE88P7MCj/k2ktSOEqDck+dRCZy7lEvf5YZycGzF1TEea+9z6GHohhKjNqj35aLVaRo4cyfvvv8/p06dZvHixeVtaWhodO3bkgw8+YNmyZWzcuBF3d3cAnn76aSIiIjhx4gQzZ85Eq9USEBDA3Llz0Wg0pKamEhkZSVZWFq1btyYuLg4XFxdyc3N59dVXSUlJwdPTk6VLl+Lt7V3dH9Nqzv2Vx6LPD+PipGFaRw1uqafAp3NNhyWEEFZVrfPRHzlyhFGjRnH27FkAgoKC2Lx5M5s3b2b16tW4uroyY0bJWjXJycksXrzYvD0iIgKAyMhIZs2axbZt21AUhfj4eADmzp1LeHg4SUlJ+Pv7s2LFCgCWLl1KQEAAW7duZfjw4cTExFTnR7Sq7Fwdi+MP4+yoZvqozhh2byfn26SaDksIIayuWpNPfHw8c+bMwcfHp9y2hQsXMnLkSFq1agWUJJ9Vq1YRFhbGvHnzKCoq4uLFi+h0Ojp16gTAkCFDSEpKQq/Xs3//foKDg8uUA+zYsYOwsDAAQkND2bVrF3q9vtz5axuD0cT7m49RbDDxzxGduEue2xFC1GPVmnxiYmIICAgoV3727Fl++eUXxowZA0B+fj4dOnTgtddeY9OmTeTm5rJixQrS09PLdJl5e3uTlpZGTk4Orq6uaDSaMuVAmWM0Gg2urq5kZ2dX58e0ik0//ckfF68wNqQ9Tb1cajocIYSoVjWyDOT69esJDw/HwcEBABcXF1atWkXLli3RaDSMGzeOnTt3cqM5T1UqVYXlFbGr5atd/pmaS9LP5wns2JRuHRrXdDhCCFHtauSqvH37dgYMGGB+n5qayoYNG8zvFUVBo9HQuHFjMjMzzeUZGRn4+Pjg6emJVqvFaDSWKQfw8fExH2MwGNBqtXh4eNjiY1WJyaTw360n8XB15OnebWs6HCGEsAmbJ5/s7Gx0Oh3Nmzc3lzk5OfHOO++QkpKCoiisXbuWfv364evri6OjIwcPHgQgISGBwMBA7O3tCQgIIDExsUw5lAxqSEhIACAxMZGAgADs7e1t/Ckr75eTaVzI0PJ073tp4FR28GGTf7xIk3+8WEORCSFE9bH5cz4XLlygSZMmZco8PT2ZN28eEyZMQK/X89BDDzF27FgA4uLimDlzJvn5+fj5+ZnvE82ZM4eoqChWrlxJ06ZNzUO4p0yZQlRUFAMHDsTNzY24uDjbfsBbYDSZ2PzTGZp5u9C1Q/lBGfaeXjUQlRBCVD9ZTO5vbLmY3E9HU/k48SSThjxA53bln0XK++VnANy6PWyTeIQQ4lbJYnJ1jMFo4us9Z2nVxI1Obe+64T6Xd/wASPIRQtQ/tXsYWD22/2Q6mVd0DOrZWuZsE0LccST51ABFUdj6f+e5+y4XHrxH7usIIe48knxqwLEz2VzI0NK/WwvspNUjhLgDSfKpAd8duICHqwPd75cHSoUQdyYZcGBjOXlFJJ/JYuAjrdCob577757wio2iEkII25LkY2N7ky+hKNDzgSYW91W7udkgIiGEsD3pdrMhRVHY/etftGvugU+jBhb3v7LnJ67s+ckGkQkhhG1J8rGhi5n5pGUX0N2vcvd6cvfsJnfP7mqOSgghbE+Sjw0dOlUy4WlFD5UKIcSdwmLyWb9+fbmyjz76qFqCqe8O/Z7BPXe74+HqWNOhCCFEjapwwEF8fDw6nY4PP/yQoqIic7nBYODjjz9m3LhxNgmwvsjO1XH2rzyGBrWp6VCEEKLGVZh8FEXh119/RafT8euvv147QKNhxowZNgmuPjn8R0mX20M3mEBUCCHuNBZntd62bRvBwcG2iqfGVdes1ovWHybzio7YF7tX+hjT1RannaN00wkhaqeqzmpt8Z5Pz549eeutt3j++ee5cuUKc+fOpbCwsEpB3qkKiwycPJdD51scaGDn6CiJRwhRL1lMPvPnz8fR0ZG0tDTs7e3Jyspi1qxZtoit3jh5LgejSaHjLU4ievnH7Vz+cXs1RSWEEDXHYvI5duwYkZGRaDQaGjRowJIlSzh+/LgtYqs3ks9k4+ig5h7fhrd0XN7+X8jb/0s1RSWEEDXHYvKxsyu7i6Io5crEzSWfyaJDi0YW53ITQog7hcWrYZcuXVi8eDE6nY59+/YxefJkunXrZovY6oW0nAIyLuu4v7VnTYcihBC1hsXkc32X24IFC2jdujXTp0+3RWz1wslzOQD4tWpUw5EIIUTtYXFWawcHByZPnszkyZPJz89Hq9Xi5ORki9jqhT8uXMGtgT1NPC1PJCqEEHcKiy2f7du3ExMTQ35+PmFhYYSEhPDpp59W+gRarZbQ0FAuXLgAwIwZM3jiiScYPHgwgwcP5rvvvgNg7969hIWF8cQTT7BkyRLz8SdOnGDo0KEEBwfz+uuvYzAYAEhNTSUiIoL+/fszYcIE8vPzAcjNzeXFF18kJCSEiIgIMjIyKv9tVINTF69wr29DVFVYsbT59Bk0ny4P9Aoh6h+LyWflypUMGTKEb7/9lo4dO7Jz5042bdpUqcqPHDnCqFGjOHv2rLksOTmZTz/9lM2bN7N582b69euHTqcjOjqaFStWkJiYSHJyMjt37gRKuv1mzZrFtm3bUBSF+Ph4AObOnUt4eDhJSUn4+/uzYsUKAJYuXUpAQABbt25l+PDhxMTE3Op3YjW5+cWk5xRyb7NbG+UmhBD1XaWGX3Xo0IG9e/cSGBiIm5sbFiZFMIuPj2fOnDn4+PgAUFBQQGpqKrNmzSIsLIz33nsPk8nE0aNHadmyJc2bN0ej0RAWFkZSUhIXL15Ep9PRqVMnAIYMGUJSUhJ6vZ79+/ebZ14oLQfYsWMHYWFhAISGhrJr1y70ev2tfStW8sfFKwDce4tDrEtlb9tK9rat1gxJCCFqhUoln2+//Zbdu3fTo0cPdu+u/PoyMTExBAQEmN9nZWXRvXt35s+fT3x8PAcOHGDDhg2kp6fj7X1tzjMfHx/S0tLKlXt7e5OWlkZOTg6urq5oNJoy5UCZYzQaDa6urmRnZ1c6Zmv64+IVNGoVrZpUbUXS/COHyT9y2MpRCSFEzbOYfKZPn86aNWt45ZVX8PHx4d133yU6OrpKJ2vevDnLly/Hy8sLZ2dnRo8ezc6dO2/YklKpVLdcXpGaei4pJS0P37tcsdeoa+T8QghRW1kc7datWzfWrFljfv/FF19U+WS//fYbZ8+eNXeXKYqCRqOhcePGZGZmmvdLT0/Hx8enXHlGRgY+Pj54enqi1WoxGo2o1WpzOZS0mjIzM2nSpAkGgwGtVouHh0eVY74dFzLy8Zfne4QQohyLTYKjR48yadIknn/+ecaNG8ezzz7L448/XqWTKYrC/PnzuXLlCnq9nvXr19OvXz86duzImTNnOHfuHEajkS1bthAYGIivry+Ojo4cPHgQgISEBAIDA7G3tycgIIDExMQy5QBBQUEkJCQAkJiYSEBAAPb29lWK93bkFRRzJb8YX+9bn+1VCCHqO4vJJzo6Gj8/P3JycggODsbBwYE+ffpU6WTt27fnxRdfZNSoUQwcOJAOHToQGhqKo6MjCxYsYNKkSQwYMIA2bdrQv39/AOLi4oiNjSUkJITCwkLGjBkDwJw5c4iPj2fAgAEcOHCAqVOnAjBlyhQOHz7MwIED+eyzz5g9e3aVYr1dFzJKhn4383Gpch0qBwdUDg7WCkkIIWoNi+v5hIaGsmXLFt5++2369euHn58f4eHhfPnll7aK0aastZ7P9oMXWPvd7yya2INGbrIsghCifqq29XxcXUsqbdGiBb///jtOTk4YjcZbj/AOk5WrQ6NW4eEqLRchhPg7iwMO7r//fv71r38xceJEJkyYQEpKSpWe1r/TXM4rwsPV8ba+q6yvNwPgFTbYWmEJIUStYLHl8/rrrxMeHk6bNm2IjIxEp9OxaNEiW8RWp2XnFeF5m91tBSeOU3BC1k4SQtQ/FpPPnDlz6NKlCwB9+/Zl1qxZ/Pvf/672wOq6y9oiPORejxBC3FCF3W5z584lIyODn3/+mZycHHO5wWDg999/t0lwdZm2QI97A7nfI4QQN1Jh8nnyySf5/fffOXbsGEFBQdcO0GiIioqySXB1lcFooqDIgGsD2z9fJIQQdUGFyadjx4507NiRRx99FF9fX1vGVOfl60qWfXBzvr3ko3aVB1SFEPWTxdFuWVlZLFiwgIKCAhRFwWg0kpKSwg8//GCL+OokbUExAK632e1298uTrBGOEELUOjad4eBOoS0sWcLB9TZbPkIIUV9ZbPkATJgwgdzcXNq2bcvgwYMJDw+v7rjqtLyCkuRzu91uGRtLJnH1Hjr8tmMSQojaRGY4qAbmls9tDjjQnf4D3ek/rBGSEELUKhZbPv7+/jLDwS3Kk243IYS4qUrd8/n7DAdxcXG2iK3O0hbocXJQo1HXzCJ2QghR21ls+Vy8eJGcnBz27NnDAw88QN++fW0RV52mLSyWVo8QQtxEhcmnoKCA6dOns3fvXlq2bIm+1qeDAAAgAElEQVRKpeLMmTP07duXmJgYHGSdmQrlFepxs8IDpppGsgqqEKJ+qjD5LFq0CE9PT/bs2YOzszMA+fn5zJ8/n6VLlzJ9+nSbBVnXaAv0uFlhap2mL4y3QjRCCFH7VJh8fv75ZzZv3oxarTaXubi4MHPmTEaOHGmT4OoqbaGepl5VX8FUCCHquwrviGs0mjKJp5Szs/MNy8U11up2S/98Lemfr7VCREIIUbtU2PK52XBqCytv39H0BiNFxUarDDgoOn/eChEJIUTtU2Hyyc3N5YcffrhhosnLy6vWoOoybWHJpKIy2k0IISpWYfLx8fFh9erVFW4TN6YrLkk+To7SNSmEEBWpMPmsW7fOKifQarWMHDmS999/n2bNmrF+/XrWrFmDSqXC39+fuXPn4uDgwLJly9i4cSPu7u4APP3000RERHDixAlmzpyJVqslICCAuXPnotFoSE1NJTIykqysLFq3bk1cXBwuLi7k5uby6quvkpKSgqenJ0uXLsXb29sqn6UyivQlUw852kvyEUKIilTrI/hHjhxh1KhRnD17FoAzZ87w4Ycf8vnnn/PVV19hMpn47LPPAEhOTmbx4sVs3ryZzZs3ExERAUBkZCSzZs1i27ZtKIpCfHw8ULLSanh4OElJSfj7+7NixQoAli5dSkBAAFu3bmX48OHExMRU50csp6jYesnHoUkTHJo0ue16hBCitqnW5BMfH8+cOXPM3XQODg688cYbuLq6olKpaNeuHampqUBJ8lm1ahVhYWHMmzePoqIiLl68iE6no1OnTgAMGTKEpKQk9Ho9+/fvJzg4uEw5wI4dOwgLCwMgNDSUXbt2odfrq/NjllGkNwHWST6Nx4yl8Zixt12PEELUNtWafGJiYggICDC/9/X15dFHHwUgOzubtWvX0qdPH/Lz8+nQoQOvvfYamzZtIjc3lxUrVpCenl6my8zb25u0tDRycnJwdXVFo9GUKQfKHKPRaHB1dSU7O7s6P2YZxdLtJoQQFlVqPZ+0tDQuX75cZuRb+/btq3zStLQ0/vGPfzB06FAefvhhAFatWmXePm7cOKKjowkKCip3rEqluuEIvJsNDbezs90En6X3fBwcbj/5pP3vYwBp/Qgh6h2LyWfx4sV8/PHHNGrUyHyBV6lU7Nixo0onPH36NC+88ALPPPMM48aNAyA1NZW9e/cybNgwoOQ5Io1GQ+PGjcnMzDQfm5GRgY+PD56enmi1WoxGI2q12lwOJSPxMjMzadKkCQaDAa1Wi4eHR5VirQprDjgo/uuv265DCCFqI4tNgi1btvDdd9+xa9cudu7cyc6dO6uceLRaLc8//zxTpkwxJx4AJycn3nnnHVJSUlAUhbVr19KvXz98fX1xdHTk4MGDACQkJBAYGIi9vT0BAQEkJiaWKQcICgoiISEBgMTERAICArC3t90zN+aWj0aWUxBCiIpYbPk0bdqUJlYacbVhwwYyMzP56KOP+OijjwB4/PHHmTJlCvPmzWPChAno9Xoeeughxo4t6WqKi4tj5syZ5Ofn4+fnx5gxYwCYM2cOUVFRrFy5kqZNm7J48WIApkyZQlRUFAMHDsTNzc3maw9Zc7SbEELUVyrFwlw5y5Yto7i4mD59+uDo6Gguv517PrVZVpYWk6nq0wfF//gH2w9e4INXH7vtWFIWxgLQfPqM265LCCGqg52dCi8v11s+zmLL54svvgBg8+bN5rLbuedT3+n1Jqt1uTm2aGGVeoQQoraxmHx27txpizjqDb3RiMZKycdnZIRV6hFCiNqmwuTz0UcfMW7cOGJjY2+4fcYM6Qq6Eb1BwV4tgw2EEOJmKkw+pfd3SlcxFZWjN5qwt1LL59KqDwBZ0VQIUf9UmHxK51abOnWqzYKpDwwG6yUfQ47tZmYQQghbkv4hK9MbjNLtJoQQFshV0sr0Vmz5CCFEfSVXSSvTG01WG+0mhBD1lcWr5JkzZ9i4cSMAkydPJjg4mF9++aXaA6urrDnazemee3G6516r1CWEELWJxavk7NmzUavV7Nixg/T0dObMmWOeykaUZ83Rbt5Dh+M9dLhV6hJCiNrE4lVSp9Px5JNPsnv3bkJCQnj00UcpLi62RWx1ksFglHs+QghhgcWrZHFxMTk5OezYsYNHH32U7OxsdDqdLWKrk/QGk9W63VJX/JvUFf+2Sl1CCFGbWLxKDh8+nF69etGxY0fatm3L0KFDGT16tC1iq5OsOeDAqNVi1GqtUpcQQtQmFud2e+aZZxg5cqR5yeoNGzbg5eVV7YHVVTLUWgghLKsw+bz//vs3PfCll16yejB1nUlRMBhlbjchhLCkwuRz6dIlAM6dO8fp06fp27cvarWaH3/8kfvuu89mAdYlRqMJQFo+QghhQYXJZ+7cuQA8++yzJCQkmLvaXnnlFV555RXbRFfH6A2lycc6q5g26OBnlXqEEKK2sXjPJyMjo8w9noYNG5KZmVmtQdVV15KPdVo+XmGDrVKPEELUNhaTz7333svrr7/OU089haIobNiwgQceeMAWsdU5pclHo1bVcCRCCFG7WfwVff78+Tg7OzN79mzeeOMNGjVqZO6SE2XprXzP58LSRVxYusgqdQkhRG1iseUTExNT4Wqmoixzt5vaOvd8FJlJQghRT1n8FT05ObnKlWu1WkJDQ7lw4QIAe/fuJSwsjCeeeIIlS5aY9ztx4gRDhw4lODiY119/HYPBAEBqaioRERH079+fCRMmkJ+fD0Bubi4vvvgiISEhREREkJGRAZTMxhAZGUlISAhPPfUUp0+frnLsVWHtlo8QQtRXFq+SjRs3JiwsjFmzZhEbG2v+Y8mRI0cYNWoUZ8+eBUrmiIuOjmbFihUkJiaSnJzMzp07AYiMjGTWrFls27YNRVGIj48HSkbchYeHk5SUhL+/PytWrABg6dKlBAQEsHXrVoYPH05MTAwAa9aswdnZma1btxIdHU1UVFSVvpSqMlh5wIEQQtRXFq+S/v7+9OnTBy8vL5ydnc1/LImPj2fOnDn4+PgAcPToUVq2bEnz5s3RaDSEhYWRlJTExYsX0el0dOrUCYAhQ4aQlJSEXq9n//79BAcHlykH2LFjB2FhYQCEhoaya9cu9Ho9O3bsYNCgQQB07dqVnJwcUlNTq/C1VI21R7sJIUR9ZfGez9SpUyksLOT48eMYDAYeeOABGjRoYLHi0tZIqfT0dLy9vc3vfXx8SEtLK1fu7e1NWloaOTk5uLq6mqf1KS3/e10ajQZXV1eys7NvWNdff/3F3XffbTFea7h2z8c6ycelYyer1COEELWNxeSTnJzMhAkT8PDwwGg0kpWVxQcffGBuqVSWoijlylQq1S2XV8TO7sYX/IrKq0PpPR9rTSzqGRxilXqEEKK2sZh8FixYwIIFC+jRowcAu3fvJjY2lvXr19/SiRo3blzm4dT09HR8fHzKlWdkZODj44OnpydarRaj0YharTaXQ0mrKTMzkyZNmmAwGNBqtXh4eODj40NGRgYtW7YsU5etSLebEEJUjsWrZF5enjnxAPTs2ZOCgoJbPlHHjh05c+YM586dw2g0smXLFgIDA/H19cXR0ZGDBw8CkJCQQGBgIPb29gQEBJCYmFimHCAoKIiEhAQAEhMTCQgIwN7enqCgIDZv3gzAgQMHcHR0tFmXG1i/2y1lYSwpC2WYuxCi/rHY8rGzs+PSpUs0bdoUKBn+rK7CcyyOjo4sWLCASZMmUVRURFBQEP379wcgLi6OmTNnkp+fj5+fH2PGjAFgzpw5REVFsXLlSpo2bWpevnvKlClERUUxcOBA3NzciIuLA2D06NHMnj2bgQMH4uDgwMKFC285ztth7naTGQ6EEOKmVMqNbq5cZ9u2bbz55pv06tULRVH46aefeP311xkwYICtYrSprCwtJtNNv5IKJf18nvgf/2D5tECcHS3mdYtKWz3Np8+47bqEEKI62Nmp8PJyveXjLF4hg4ODueeee9i3bx8mk4lx48bRrl27KgVZ38lDpkIIUTmVukrm5+fj6+tLs2bNSElJYfv27dUdV51U+pCp2k663YQQ4mYstnxmz57N9u3badGihXmos0qlok+fPtUeXF1jMJrQqO1uOiT8Vrh17WaVeoQQoraxmHx2797N999/X6lZDe50eqMJe431Wj0evSXBCyHqJ4vdbqXT6gjLDEYFjZWGWQOYioowFRVZrT4hhKgtKmz5lN7XadOmDZMnT2bgwIHmqW4A6Xa7AYPBZNXkc/HdkqHlMtpNCFHfVJh8PvzwwzLvP/nkE/NruedzYwajyWoPmAohRH1WYfL57LPPbBlHvaA3mqw2r5sQQtRnN71SLl++nH379pnfz5gxg5UrV1Z7UHWV0ajI7AZCCFEJFSafDz/8kB07dpin1QEYMWIE27dvL9MFJ67RS7ebEEJUSoVXys2bN7Nq1SpatWplLuvUqRMrV65kw4YNtoitzjEYTKitmHzce/TEvUdPq9UnhBC1RYX3fNRqNR4eHuXKvb29qzSx6J3AYDTh5GC976Zhj15Wq0sIIWqTCn9NVxSF4uLicuXFxcXo9fpqDaqu0hutO9TamJeHMS/PavUJIURtUeGVMigoiCVLlpQrX7JkCd27d6/WoOoqg1Gx6mi31JXLSF25zGr1CSFEbVFht9vLL7/MSy+9RL9+/ejYsSOKonD06FGaNWvG8uXLbRljnWEwyIADIYSojAqTj6OjIx9//DH79u0jOTkZOzs7Ro0aRUBAgC3jq1Os3e0mhBD1lcWJRR955BEeeeQRW8RS5xnkIVMhhKgUuVJakUEeMhVCiEq5/bWehZm153bzeOxxq9UlhBC1iSQfK1EUxeqzWrt1e9hqdQkhRG1SYfKZOHHiTVfkXLZMhgBfz2hSUMCq93z02VkA2Ht6Wa1OIYSoDSpMPo899li1nPCLL77g008/Nb+/cOECgwcPprCwkIMHD5oXrnvllVfo168fe/fuJTY2lqKiIkJCQpg2bRoAJ06cYObMmWi1WgICApg7dy4ajYbU1FQiIyPJysqidevWxMXF4eLiUi2f5XoGownAqt1uf63+DyDr+Qgh6h+VoijKrR6UkpJC8+bNb/vkp06dYuLEiXz++ec8++yzfPjhh/j4+Ji363Q6+vfvz5o1a2jatCnjx49nzJgxBAUFERoayltvvUWnTp2Ijo7G39+f8PBwxo8fz6BBgxg4cCDLly+noKCAyMjISseUlaXFZLrlrwRtoZ7J7/5EeN+29A24/e8GIGVhLCDJRwhRe9nZqfDycr314yzt8MUXX9CtWzf8/f3x9/fHz8+P4cOHVynIv3vjjTeYNm0aTk5OpKamMmvWLMLCwnjvvfcwmUwcPXqUli1b0rx5czQaDWFhYSQlJXHx4kV0Oh2dOnUCYMiQISQlJaHX69m/fz/BwcFlym1Bbyhp+chzPkIIYZnFAQcrV65k5cqV/Oc//2HKlCn88MMPZGVl3faJ9+7di06nIyQkhJSUFLp37868efNo0KAB48ePZ8OGDTRo0ABvb2/zMT4+PqSlpZGenl6m3Nvbm7S0NHJycnB1dTUv911abgul3W6SfIQQwjKLV0oPDw+6dOlChw4dyMnJ4ZVXXuHIkSO3feLPP/+csWPHAtC8eXOWL1+Ol5cXzs7OjB49mp07d3KjHkGVSnXL5bZgTj4aec5HCCEssZh8NBoNubm5tGrViuTkZADy8/Nv66TFxcXs37+fxx8veY7lt99+Y9u2bebtiqKg0Who3LgxmZmZ5vL09HR8fHzKlWdkZODj44OnpydarRaj0Vim3BYMxpLEZ80BB42e6E+jJ/pbrT4hhKgtLF4phw0bxvjx4wkMDGTdunU8/fTTZRaYq4rffvuNVq1a0aBBA6Ak2cyfP58rV66g1+tZv369eULTM2fOcO7cOYxGI1u2bCEwMBBfX18cHR05ePAgAAkJCQQGBmJvb09AQACJiYllym2hOrrdXDt1xrVTZ6vVJ4QQtYXFez5PP/00AwYMwNXVlbVr15KcnEyPHj1u66QpKSk0adLE/L59+/a8+OKLjBo1CoPBwBNPPEFoaCgACxYsYNKkSRQVFREUFET//iUtgbi4OGbOnEl+fj5+fn6MGTMGgDlz5hAVFcXKlStp2rQpixcvvq1YK8s84MCKz/kU/3UJAIcmTS3sKYQQdYvFodZPPfUUmzZtKlM2cOBAvvnmm2oNrKZUdaj1yXM5LFx3iNfCO3Nfi0ZWiUWGWgsharuqDrWusOUzduxYjh07hlarpVu3buZyg8FAu3btqhZlPWa8mrDs7GTAgRBCWFJh8nn33XfJyckhOjqa2NhYc7laraZx48Y2Ca4ukeQjhBCVV+ENCnd3d1q2bMnatWtxcHDg8OHDHDhwAMD8HI24prSrTmMnz/kIIYQlFq+Ue/bs4cknn+Sbb75h69atDB48mB9//NEWsdUp0vIRQojKs9iEWbp0Kf/73//M93l+++03oqKi6N27d7UHV5eYFOsnH8/QQVarSwghahOLyUev15cZYHDfffeZH+IU1xhNJUOt1VZMPi5+91utLiGEqE0sdrs5ODhw/Phx8/tjx47h4OBQrUHVRUaj9Vs+uvPn0J0/Z7X6hBCitrDY8nn11Vd54YUXaNOmDQB//PEHS5YsqfbA6prSAQdqK84ll/H5Z4A85yOEqH8qTD7FxcU4ODjQrVs3vv76aw4fPozJZKJz5854ecnKmn9nvHrPR62WAQdCCGFJhclnxIgR5pkNPD09zZOAihszyWg3IYSotArv+VRhgdM7WulQa2sOOBBCiPqqwpZPUVERx48frzAJ3X+/jMS6nnnAgY3WDxJCiLqswuSTkpLCpEmTKlygbfv27dUaWF1T+pyPNVs+dw0ZZrW6hBCiNqkw+dx7770kJCTYMpY6rTpmOHC+t63V6hJCiNpEJiKzElM13PMp/OMUhX+cslp9QghRW1SYfAICAmwZR51nNCnYqVSorHjPJ/PLDWR+ucFq9QkhRG1RYfKZOXOmLeOo84wmkwyzFkKISpJuNysxmRQZZi2EEJUkycdKjCZFWj5CCFFJknysRFo+QghRebIkqZVUR/LxHhlu1fqEEKK2qJHkM2bMGLKysszLcc+bN4/z58+zcuVK9Ho9zz33HBEREQDs3buX2NhYioqKCAkJYdq0aQCcOHGCmTNnotVqCQgIYO7cuWg0GlJTU4mMjCQrK4vWrVsTFxeHi4tLtX8mQzV0uzm1aGnV+oS4XYqioNVeobBQi8kk63rdaTQaBxo18katvv3UYfPkoygKf/75Jzt27DAnn7S0NKZNm8aXX36Jg4MDI0eO5OGHH6ZZs2ZER0ezZs0amjZtyvjx49m5cydBQUFERkby1ltv0alTJ6Kjo4mPjyc8PJy5c+cSHh7OwIEDWb58OStWrCAyMrLaP1d1tHzyjx8DZFE5UXvk5GSgUqnw9GyMWq2x6qMFonZTFIX8/FxycjK4666mt12fze/5/Pnnn6hUKl544QUGDRrEp59+yt69e+nevTseHh40aNCA4OBgkpKSOHr0KC1btqR58+ZoNBrCwsJISkri4sWL6HQ6OnXqBMCQIUNISkpCr9ezf/9+goODy5TbgqkaWj7ZW74ie8tXVq1TiNtRXKzDw8MLjcZeEs8dRqVS4eLijsFQbJX6bJ58cnNzeeSRR1i+fDn//e9/+fzzz0lNTcXb29u8j4+PD2lpaaSnp1eq3Nvbm7S0NHJycnB1dTW3qErLbcEoAw7EHUFBpZJxSncqa/7CYfNut86dO9O5c2cAGjRowLBhw4iNjeWll14qs59KpapwUtNbLbeF6mj5CCEsMxgMrF37Cd9+uxWVSoXRaCQkJJTRo8da9f//zZu/pEGDBvTr15+YmDfo3LkLAwaE3fSY8+fPsnz5u1y6lApAmzb3MnVqJB4eHlaLq66y+a8wBw4cYN++feb3iqLg6+tLZmamuSw9PR0fHx8aN25cqfKMjAx8fHzw9PREq9ViNBrLlNuCtHyEqBmLFr3NiRPHeP/9j/n00y9Yvfp/HDy4ny+//MKq50lOPoper6/0/pmZGUya9BJhYU/xv/+t55NPPqdNm3uIjn7VqnHVVTZPPnl5eSxcuJCioiK0Wi2bNm3inXfeYd++fWRnZ1NYWMi3335LYGAgHTt25MyZM5w7dw6j0ciWLVsIDAzE19cXR0dHDh48CEBCQgKBgYHY29sTEBBAYmJimXJbkOQjhO2lp6fx7beJREe/gZubGwAuLq7885+v4eXlBUBMzBtMnz6NiIhh7N69i+TkX3nhhWd59tlRTJkygQsXUtixYzuzZ88AICXlPD17BpCdnQXAP/85ibVrP2H37l2sXv0+P/9c8svz3r27eeGFMQwdGsrmzV+Wi23Tpg1069adnj1LrkEqlYqIiGd56qlhGAwGPvzwAz788APz/sOGhXHpUiqJiV8zadJ4xowZQVxcLIMGBWMwGAD4888/ePbZkQBs3bqFceMieO65cGJj51FUVFQdX3G1sXm3W+/evTly5AhPPvkkJpOJ8PBwunTpwrRp0xgzZgx6vZ5hw4bx4IMPArBgwQImTZpEUVERQUFB9O/fH4C4uDhmzpxJfn4+fn5+jBkzBoA5c+YQFRXFypUradq0KYsXL7bJ5zJVw9xujcc8Z9X6hLC2Pb9eYvfRS9VSd88Hm9LjgZuPqjpx4hitWrXB3d29THnLlq1o2bKV+X3Dhg1ZuHAJer2eUaOG8OabC+jQ4X5++OF73njjdd59dwVLl8ahKAoHD/5Co0aeHDr0/+jZsxcpKedYtOg9zp49Q+fOXXj44Uf4/vttFBcX85//fMKZM6eZPPklBg8eUiaGU6d+45FHepYpU6vV9OvX3+Jnz8hI59NPv0Cj0ZCZmcHPP++jR49efPfdNp54IoQ//zzN118nsHLlRzg6OvL++8tYt24Nzz33D4t11xY18pzP1KlTmTp1apmysLAwwsLK958+8sgjfPVV+RFf7du3Z8OG8jM++/r6smbNGusFW0lGk4LayveXHJrc/nBGIeq76+/r/Pjj93zyyUeYTEYcHBxZvfp/APj5+QOQknIONzc3OnQoeXzh8cf7snBhDIpSkrD++OMUBw8e4OmnR3H48P+jQQNnOnfucsN7R716BaFSqWjd+h4uX758w7gqWgnaknbt2psHTgUHD2D79m/p0aMXP/74Pe+99z4//bSDCxdSGD9+LAAGg5527dpX6Vw1RWY4sJLqGHCgPXwIANdOna1arxDW0uMBy62T6nTffR04e/ZP8vO1uLi40rt3X3r37sulS6lMmjTevJ+joyNwbd2tshRMJiPdu/dg//6fOX/+LK++GsXkyS9hZ6eiR49eNzy3Wq0GKh7U1L69HydPHi9TZjKZmDnzNV59NapccirtWrs+XoAePQL597+XcPjw/8PHpzE+Po0xGk08/nhfpk4teYaxoKDAfK+7rpAxk1ZiVKx/zyfn2yRyvrXNc0pC1EVNmjQlOHgAb731Bnl5eQAYjUb27v0JO7vyl7cWLVpy5coVTpwoeYB7+/bvaNy4Ke7uDXn00Z5s3ryRVq1a07ChB2q1hj17fqJr1+5ASbK5lQv8oEFPsW/fHvbt2w2UDK76739Xk5OTjaenFw0benDmzJ8AHD+eTFZW5g3rcXBw4OGHH+G99xbxxBMhAHTu3IVdu3aQk5ONoigsWhRLfPxnlY6tNpCWj5UYjQpqJ8nlQtjav/4Vxeefr2Xy5PEoCuj1xdx//wPExb1Xbl8HBwfmzYtl8eKF6HSFuLs3ZN68WKCk201RFDp37gKUXOD//PMPGjRoAEBAQDc++GAFrq6ulYrLy+su4uLeY8WKd1m58t+YTCbatWtPbOwiAPr2fYKdO3/gmWeGc9997Wnb9r4K6woOHsC2bVvp3bsPAG3btmPs2BeYPPklFEWhbdv7eOaZ5yr9ndUGKqWqnZL1VFaWtoKm+c298dEveLo7MXnYg1aLJWVhyf8UzafPsFqdQtyOv/46R5MmMufgnezv/wbs7FR4eVUuIV9PflW3kurodhNCiPpKko+VyAwHQghReXLPx0qq4yHTJv940ar1CSFEbSHJx0qMRuu3fOw9vaxanxBC1BbS7WYlpmq455P3y8/k/fKzVesUQojaQFo+VlId3W6Xd/wAgFu3h61arxBC1DRp+ViJDDgQQojKk+RjJUZJPkLUiMmTX+KZZ57muefCee65cI4dSwbg22+TeOaZ4YwY8SQbN8ab979+2p2ePQOqPb7S2aqrIibmDRITv7b6vpVR3d+NdLtZiUmWVBDC5hRF4dy5s2zcuMU8ESeUzAq9atUKPvxwDfb2Drz00jgeeiiA1q3bcOjQwRqMWJSS5GMlRpMJ9Q3mkhJCVJ/z58+hUql49dXJ5OTkMGjQkwwdOoIDB37hoYcCcHdvCEDv3n3YsWM7mzdvBOCFF55l1apPAHjnnfkkJ/8KQEzMQpo1a17mHMOGheHn58+pU7+xYsVq4uPXcfDgfnJzc/Hw8CAmZiFeXncxeHAwjz3Wh6NHD6NWa5g3L5a77/YtE+v06VOZOXMeHTr4sWLFuxw6dBCj0cSAAaGMGBGBoigsW7aEPXt2c9ddd2EymczT/Vxv/fq1JCRsRK1W8+ijvXj55clAyRpDmzZ9QXZ2NmPGjGPw4CHodDrefvst/vjjd+zs7Bg58hlCQkJJTPyarVu3cOXKZXr0CGTQoKeYN28WhYWF3H+/v/X/sv5Gko+VVEe3290TXrFqfUJUh9JpoK7n1rUbHr37YCoq4uK75dfUcu/Rk4Y9emHMyyN15bJy2z0ee7xSA23y8nLp0qUrr746g6KiIiZNepEWLVqSmZmBl9dd5v28vO7i+PFjvPba62zYsN6ceKBkzrbIyGiWLVvK5s1fMnHilHLn6d79UebNi+XChRTOnz/L++9/hJ2dHW++OZtvv01i1NisdegAABGSSURBVKhnyMrKokuXbkybNp1//3sJGzfGM2nSNADS0v4iLm4B0dFz8Pd/gISEkuVgPvpoLcXFxfzzn6/Qvr0f2dlZ/P77b3z6aTx5eXk899zIcrEcP57Mpk0bWL16DU5OTvzrX5M5efIEwA3XGProow9o2LAha9bEc/nyZV544VnzPHLXrxs0ffpUBgwIIyzsSZKSvrnhAnnWJMnHCkyKgqJg9W439dWVGYUQN+bv/yD+/iXzKTo7OxMaOph9+/bg4dGo3L4V/XLYq9djALRu3YYjRw7dcJ/S9YCaNWvOK69M4+uvEzh//hzHjv2Kr28z834PP/wIAG3a3FOmrtmzZ9C+vR8PPtgJgAMHfuHUqd85ePAAAIWFBZw+/Qdnz/5JUFBvNBoNjRo1onv3HuViOXz4ED169DJPcPruuyuu+yzl1xg6ePAAUVGzAPDw8KBXr0AOHTqIi4tLmXWDDh06yBtvxADwxBMhLFjw5g2/C2uR5GMFpRORWrvlc2XPTwA0rGA9ESFqg5tNfGvn6HjT7Wo3t9uaOPfIkcPo9cUEBHQDSu4BaTQavL19ylz8s7Iyuesu7xvWUXrxvdnib6Xr65w8eYI33nidkSPD6d27D2q13f9v796joirXB45/R25CoIghulxeskjTkqOiRSgsUi4x4LhIT2DKUTQ0/Ynh0Z+ImpdIDDXSFJeVmaYuU1MIJdRfRguEI+oyL2lGKYFi4B25zjCzf3+wmCMKBkrDdM7z+ct53ZfH/e69Hvfe736feuvULXf/tmbOnM2mTR+Tk5OFh8dQ9HoD06ZF4e39CgC3b9/G1rYtSUlr6k1sXFczqKF461y/fg0bm7b1lr+3xpCiGOotryig19fUi7eWyrhvlUrVYEmKliQvKVpAXYe19J1P6ZEsSo9kteg2hfhPUlZ2l6Sk1VRXV1NRUc433+zHy8sHd/chnDhxjFu3blFVVUVGxmHjXYmFhUW9wm3N8cMPJxgwYBCjRo2mZ89e5OYexWAw/OF6ffv245//jGHVqveprKxk0CB3vv46mZqaGioqKpg2bRI//ngWd/chfPfd/6HVaiktLeXo0ZwHtuXmNoB//SubiooKampqWLx4/gNF6+41cOBg9u9PAWqTXGZmBgMGPDiSzd19CAcOpAHw/feH0Wq1TT0sj0TufFqAvu7Op4XLaAshHs7Tcxjnzp0lIuIN9HoDISFjjI/h3nxzGlFRU9DpaggO1hgfnQ0d6sWECWPZuPGLZu9v+HA/YmPn8I9/hGJhYcnTTz/T5GHUAwYMYuBAdz75JIlp02Zy+XIhEyeORa/XExgYzMCBtQnh/PlzhIe/jpNTR3r27PXAdnr37kNIyN+ZOnUiBoOCt7cPgwe/yMGD3zS434kTJ7Nq1fuEh7+OwWAgPDyC3r378OuvefWWmzXrf3n33Xf4+us99OnTFzu7J5p5dJpH6vnc51Hq+ZRV6ohanUnYCFd83bv98QpNJPV8hLmRej5C6vmYESvLNrS1tsDJweaPFxZCCNE6yWft2rWo1WrUajUJCQkAzJs3Dz8/PzQaDRqNhkOHDgGQnZ1NcHAwfn5+JCYmGrdx/vx5XnvtNfz9/Zk/f77xGW5RURFvvPEGAQEBvPXWW5SXl//p/x4bKws++B9PBj7b8AtNIYQQ9Zk8+WRnZ5OVlcXevXtJTk7mxx9/5NChQ5w9e5atW7eSkpJCSkoKvr6+VFVVERsbS1JSEmlpaZw9e5bvv/8egDlz5rBw4UIOHDiAoijs3Fk7fcaSJUsYO3Ys6enpPP/88yQlJT0snBbT1tqy3giTltB15iy6zpzVotsUQghzYPLk4+zsTExMDNbW1lhZWfH0009TVFREUVERCxcuJDg4mDVr1mAwGDh9+jQ9evSgW7duWFpaEhwcTHp6OleuXKGqqoq//a12zHxISAjp6enodDqOHTuGv79/vfa/qjY2NrSxkUd5wpyoHhi6K/57tOQQAZOPdnN1dTX+OT8/n7S0NLZv305ubi5Lly7Fzs6OKVOmsHv3buzs7HB2/vejrE6dOlFcXExJSUm9dmdnZ4qLi7l16xb29vbGcfB17X9Vt7/7FgBHn+GtHIkQtayt23L79nUcHDpgYdHyd/vCfCmKQnl5KZaW1i2yvVYbap2Xl8eUKVOYO3cuvXr1Yt26dca/Gz9+PMnJyQQEBDywXmMfgj2s/a/q7rFcQJKPMB8dOjhTVnaHmzeLMRj0rR2OMDFLS2s6dGiZd9utknxOnDhBVFQUsbGxqNVqLly4QH5+vvFxWd1Xyi4uLly/ft24XklJCZ06dXqg/dq1a3Tq1AknJyfKysrQ6/VYWFgY24UQLUOlUuHg4IiDg2NrhyL+4kz+zufq1atMnz6dlStXolargdpks2zZMu7cuYNOp+PLL7/E19cXNzc3Ll26xG+//YZer2ffvn14eXnRtWtXbGxsOHGidmr05ORkvLy8sLKywt3dnbS0tHrtQgghzIvJPzKNi4vjq6++onv37sa20NBQDAYD27Zto6amBj8/P2bPng1ATk4O8fHxVFdX4+3tzbx581CpVPz0008sWLCA8vJy+vbtS3x8PNbW1ly5coWYmBhu3LhBly5d+OCDD2jfvn2T43uUj0z/LPKRqRDC3D3qR6Yyw8F9bt0qN5vkc3VD7TDxLlOmtXIkQgjRsDZtVHTo0PypeCT5CCGEMDmZXkcIIYTJSfIRQghhcpJ8hBBCmJwkHyGEECYnyUcIIYTJSfIRQghhcpJ8hBBCmJwkHyGEECYnyUcIIYTJSfJpAampqQQGBuLr68u2bdtaOxwAysrKCAoK4vLly0Dzy5GbQkPl1M0xToDVq1cTGBiIWq1m06ZNZh0rwPvvv09MTMxD42mNkvN1wsPDUavVaDQaNBoNp06davQ6auw4m8Lhw4cJCQkhICCAuLi4h8bTmv2+a9cu47HUaDQMGjSIpUuXmmWsRop4LL///rvi4+Oj3Lp1SykvL1eCg4OVvLy8Vo3phx9+UIKCgpR+/fophYWFSmVlpeLt7a0UFBQoOp1OiYiIUDIyMhRFURS1Wq2cPHlSURRFmTdvnrJt2zaTxHjkyBHl9ddfV6qrqxWtVquEh4crqampZhenoijK0aNHldDQUEWn0ymVlZWKj4+Pcv78ebOMVVEUJTs7W3nxxReVuXPnPjSeyMhIZd++fYqiKMratWuVhIQEk8RnMBgUT09PRafTGdsau44edu7+2QoKCpShQ4cqV69eVbRarRIWFqZkZGSYbb/X+fnnnxVfX1+lqKjIrGOVO5/HlJ2dzUsvvYSjoyN2dnb4+/u3eununTt3smjRImMto+aWIzeFhsqp5+fnm12cAEOGDGHLli1YWlpy48YN9Ho9paWlZhnr7du3SUxMZOrUqQBmWXL+4sWLqFQq3nzzTUaOHMnWrVsbvY4aO3dN4dChQwQGBtK5c2esrKxITEzE1tbWLPv9XosXLyY6OprCwkKzjlWSz2O6v6R3Xanv1vTee+/h7u5u/N1YjI2VIzcFV1dX48lfV05dpVKZXZx1rKysWLNmDWq1Gg8PD7M8pgDvvPMO0dHRtGvXDniw782h5HxpaSkeHh6sW7eOzz//nB07dlBUVNSk42nK66uujtikSZMYOXIk27dvN9t+r5OdnU1VVRWvvvqq2ccqyecxKX+B0t2NxWgOsefl5REREcHcuXPr1Xi6Nx5ziBMgKiqKnJwcrl69Sn5+foMxtWasu3btokuXLnh4eBjbzLHvBwwYQEJCAnZ2djg5OTF69GjWrFnTYDytGaderycnJ4cVK1awc+dOzpw5Y3yHen885nKO7tixg4kTJwLm2ff3apUy2v9JXFxcOH78uPF3Xalvc9LccuSmcn859dzcXLOM89dff0Wr1fLcc89ha2uLn58f6enpWFhYmFWsaWlpXLt2DY1Gw507d6ioqEClUpldyfnjx4+j0+mMSVJRFLp27dqkvjfl9fXkk0/i4eGBk5MTAMOHDzfLfq+j1Wo5duwYy5cvB8z3uq8jdz6P6eWXXyYnJ4ebN29SWVnJwYMHza50d3PLkZtCQ+XUzTFOgMuXL7NgwQK0Wi1arZZvv/2W0NBQs4t106ZN7Nu3j5SUFKKionjllVeIj483u5Lzd+/eJSEhgerqasrKyti7dy8rVqxo8Dpq7JwwBR8fH7KysigtLUWv15OZmUlAQIDZ9XudCxcu0LNnT+zs7ADzvZ7qyJ3PY3JxcSE6Oprw8HB0Oh2jR4+mf//+rR1WPTY2NixfvpwZM2YYy5EHBAQAsHLlynrlyMPDw00S08aNG6murjb+Lw1qy6mbW5wA3t7enDp1ilGjRmFhYYGfnx9qtRonJyezi7UhjcWzaNEiYmJiWL9+vbHkvCn4+PgYj6fBYGDs2LEMGjSo0euosXPiz+bm5sbkyZMZO3YsOp0OT09PwsLC6NWrl1n2e2FhIZ07dzb+Nsfr/l5SyVQIIYTJyWM3IYQQJifJRwghhMlJ8hFCCGFyknyEEEKYnCQfIYQQJidDrYVoBr1ez5YtW0hNTUWv16PT6fDx8WHmzJlYW1sTExODq6srkyZNarF9bty4kby8vHrD0qH2+yNfX1+effZZY1tFRQWdO3dm2bJldOvW7ZH2t2fPHg4cOMCGDRseK24hHkaSjxDNsHjxYu7cucPmzZtxcHCgoqKC2bNnM3/+fFasWGHyeNq2bUtKSorxt6IoxMXFkZiYaLLvdoR4FJJ8hGiiwsJCUlNTycrKwt7eHgA7OzuWLFnCyZMnjcudPHmS0NBQrl+/jqurK6tWrcLOzo7jx4+TkJBAZWUlVlZWvP3223h5ebFnzx52795NZWUl9vb2fPbZZ8TFxZGdnU3Hjh3p2LEjDg4OTYqxurqaa9eu0bFjRwAuXbrE0qVLqaiooKSkhD59+vDhhx9iY2PDCy+8QGRkJEeOHKGkpITw8HAmTJhQb3vp6emsXLmSjz/+mF69erXMgRQCST5CNNm5c+d45plnjImnjrOzM35+fsbfxcXFbNmyBWtra8aMGcPBgwfx9vYmKiqK9evX4+bmRl5eHuPGjWP37t0A/PLLLxw+fBh7e3s2b95Mfn4++/fvp6amhnHjxjWafKqqqtBoNBgMBm7cuEH79u3x8/MjMjISqC2vMWrUKDQaDTqdjpCQEDIyMvD390er1dKhQwd27NjB2bNnCQsLIywszLjt1NRUNmzYwBdffEGXLl1a+nCK/3KSfIRoojZt2mAwGP5wuREjRmBrawvUlo64efMmp0+fpnv37ri5uRnbBw4cSG5uLiqVit69exuTWk5ODkFBQVhbW2NtbU1wcDAXLlxocF/3PnbLzMxkzpw5eHp68sQTTwAwZ84cjhw5wieffEJ+fj4lJSVUVFQY1x8+fDgA/fr1Q6vVGv/uzJkzZGZmEhsbK4lH/ClktJsQTdS/f38uXrxIWVlZvfbi4mIiIyOpqqoCMNbIgX9PYd9Q0lIUxVi+uG4yyIbcO4vywwwbNoyJEycya9Ys7t69C8CsWbPYuXMnXbt2ZcKECfTr16/elPo2NjbGOOtiAnBwcGDjxo189NFHDZYREOJxSfIRoolcXFwIDg4mNjbWmIDKyspYvHgxjo6OtG3bttF162YYPn36NFBbx+jYsWMMGTLkgWWHDRtGcnIy1dXVVFdXG2eeboqIiAjatWtnrI+TlZXF9OnTCQwMRKVScerUKfR6/R9up2fPnnh4eDB+/Hjmzp3bpDs+IZpDHrsJ0QyLFi0iKSmJ0NBQLCws0Gq1jBgxghkzZjx0PScnJ1avXs27775LVVUVKpWK+Ph4nnrqqXqDFaB2du+CggKCgoJwdHSkR48eTY7PysqKhQsXMnnyZMaMGUN0dDTTp0+nffv22NraMnjwYAoKCpq8valTp3L48GE+/fRT43skIVqCzGothBDC5OSxmxBCCJOT5COEEMLkJPkIIYQwOUk+QgghTE6SjxBCCJOT5COEEMLkJPkIIYQwOUk+QgghTO7/AXIs+gyJryg5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(len(ranked_chords)), np.cumsum([x[1] for x in ranked_chords]), label='Growth Curve')\n",
    "plt.axvline(x=50, color='r', linestyle='--', label='50th ranked chord')\n",
    "plt.title('Aggregation of Total Chords')\n",
    "plt.xlabel('Chord Rank')\n",
    "plt.ylabel('Total Chords in Dataset')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 310
    },
    "colab_type": "code",
    "id": "-s75Vpw8JuZX",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "e882618e31668",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "outputId": "b998c3f7-9fb7-4ae6-b5fb-501ca111de31",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot distribution of popular chords\n",
    "indexes = np.arange(len(chord_names))\n",
    "width = 0.7\n",
    "plt.figure(figsize=(15, 7))\n",
    "plt.title('Top Chords Used from 1950-2018')\n",
    "plt.bar(indexes, chord_counts, 0.5)\n",
    "plt.xticks(indexes, chord_names, rotation=\"vertical\")\n",
    "plt.xlabel('Chords'),# fontdict=None, labelpad=None, **kwargs\n",
    "plt.ylabel('Instances')#could be renamed to 'Indexes' \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "9cf48f7bc9ae",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### The majorness variable (ratio of major to minor chords) follows a normal distribution a strong positive skew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "a2928b2333a69",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Majorness'].loc[df.Majorness < 200], kde=True)\n",
    "plt.title('Distribution of Major Quality of a Song')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "9d43d86d3ed81",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### The distribution of unique chords has a lower variance than those of unique progressions as progressions take into account ordering of chords not just the count of them appearing. This notion is more representative of musical style as chords played in different order have different functional purpose in the scope of a song."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "6045918ed3506",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1265869e8>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Unique Chords'], kde=True, label='Unique Chords')\n",
    "sns.distplot(df['Unique Progressions'], kde=True, label='Unique Progressions')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "54d37f1332dc6",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "## Comparison of Top Ranked Chords by Decade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4GATHvQMJuZ1",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "57b32194d40d4",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "#Gets the top chords from a given year\n",
    "#Returns chord names and counts\n",
    "def top_chords_year(year):\n",
    "    #Create a dataframe containing all songs from a given year\n",
    "    df_year = df[df['Year'] == year]\n",
    "    \n",
    "    chords_year = {}\n",
    "    #Iterate over all songs in the year\n",
    "    for idx, row in df_year.iterrows():\n",
    "        \n",
    "        #Iterate over all chords in a song\n",
    "        for chord in row['Chords'].split(','):\n",
    "            chord = chord.replace(\"'\", '').lstrip().rstrip()\n",
    "            \n",
    "            #Update chord frequency\n",
    "            if chord in chords_year:\n",
    "                chords_year[chord] += 1\n",
    "            else:\n",
    "                chords_year[chord] = 1\n",
    "    return chords_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "c217e44006737",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "#Gets the top chords from a given decade\n",
    "def top_chords_decade(decade):\n",
    "    \n",
    "    #Check last-two of decade to create proper year\n",
    "    if decade < 50:\n",
    "        decade += 2000\n",
    "    else:\n",
    "        decade += 1900\n",
    "        \n",
    "    #Create a list of dicts for each year in decade\n",
    "    dicts = []\n",
    "    for year in range(decade, decade + 11):\n",
    "        dicts.append(top_chords_year(year))\n",
    "    \n",
    "    #Merge the values in the year dictionaries into a superdict\n",
    "    super_dict = defaultdict(list)\n",
    "    for d in dicts:\n",
    "        for k, v in d.items():\n",
    "            super_dict[k].append(v)\n",
    "   \n",
    "    return super_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "a333d52f9ad46",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "#Get the frequencies of each top chord and return a unified list\n",
    "def get_freqs(year, decade=False):\n",
    "    \n",
    "    #Vector to store chord frequencies\n",
    "    freqs = []\n",
    "    \n",
    "    #Check if decade was specified and get total chord instances for normalization\n",
    "    if decade:\n",
    "        chords = top_chords_decade(year)\n",
    "        total = sum([sum(val) for val in chords.values()])\n",
    "    else:\n",
    "        chords = top_chords_year(year)\n",
    "        total = sum(chords.values())\n",
    "    \n",
    "    #Iterate over each chord in the top ranked chords and append the frequency to the vector\n",
    "    for chord in chord_names:\n",
    "        if chord in chords:\n",
    "            freqs.append(sum(chords[chord]))\n",
    "        else:\n",
    "            freqs.append(0)\n",
    "    \n",
    "    #Normalize each frequency value\n",
    "    return [freq / total for freq in freqs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "1b27a6171c781",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Calculate decade frequencies for 1960s, 1980s, and 2000s\n",
    "freqs60 = get_freqs(60, decade=True)\n",
    "freqs80 = get_freqs(80, decade=True)\n",
    "freqs0 = get_freqs(0, decade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "colab_type": "code",
    "id": "mgM1F2NTJuaA",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "68c374dd34fc8",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "outputId": "d9d15544-4716-4f6c-8d38-fb981c146de2",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Define a new figure\n",
    "indexes = np.arange(len(chord_names))\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "width = 0.25\n",
    "\n",
    "#Build a multibar chart showing difference across each of the three decades\n",
    "plt.bar(indexes, freqs60, width, label=\"1960s\")\n",
    "plt.bar(indexes + width, freqs80, width, label=\"1980s\")\n",
    "plt.bar(indexes + (2*width), freqs0, width, label=\"2000s\")\n",
    "\n",
    "#Add plot title, legend, and axis labels \n",
    "plt.xticks(indexes + width , chord_names, rotation=\"vertical\")\n",
    "plt.title('Distribution of Top Chords across Decades')\n",
    "plt.xlabel('Chords')\n",
    "plt.ylabel('Normalized Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "ed51c700774cd",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "### Have songs become more minor over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "5a2a5b78e0452",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "#Generate a time series for chord uniqueness\n",
    "def generate_major_ts(start, end):\n",
    "    series = []\n",
    "    years = list(range(start, end))\n",
    "    \n",
    "    #Append unique chord count to series for given year\n",
    "    for year in years:\n",
    "        songs = df.loc[df.Year == year]\n",
    "        mean = songs['Majorness'].mean()\n",
    "        series.append(mean)\n",
    "\n",
    "    return years, series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "7570bb40977e8",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1100a3da0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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az6UREREREREREZEKBLmvgUklyOsNIZUaEV8KEREREREREVFBGQwCqqpcQ3usymshIiIiIiIiIqIjGItNRERERERERESkGhabiIiIiIiIiIhINSw2ERERERERERGRalhsIiIiIiIiIiIi1bDYREREBfPejlZs2d1R6GUQEREREZGKTIVeABERHbleeHMPqsptmDm5utBLISIiIiIilbDYREREBeEPxdAVjMEgCIVeChERERERqYhtdEREVBBNrUEAwMFQDKmUXODVEBERERGRWlhsIiKiglCKTVJKRiASL/BqiIiIiIhILSw2ERFRQTS1BLK/7grGCrgSIiIiIiJSE4tNRERUEE2tQTRUOwEAvoBY4NUQEREREZFaWGwiIiLddQVj8Ifj2VPofEw2ERERERGNGCw2ERGR7ppa0y10046uhNlkYBsdEREREdEIwmITERHprqklCEEAxtW64XFb2UZHRERERDSCsNhERES6a2oNon6UE1azEZVuK5NNREREREQjCItNRESkK1mW0dQawITRZQAAD4tNREREREQjCotNRESkK18ghmAkgfGj3QCAyjIbuoIxpGS5wCsjIiIiIiI1sNhERES6UoaDT6hLF5s8biuklIxgOF7IZRERERERkUpYbCIiIl01tQZhNAgYW+0CkC42AYCPrXRERERERCMCi01ERKQrZTi4xWwEAFS6bQDS7XVERERERFT6WGwiIiLdyLKMppZAtoUOADxl6WRTV1As1LKIiIiIiEhFLDYREZFuOv0iwmIyexIdALjtZpiMBrbRERERERGNECw2ERGRbppagwDQI9kkCAIq3VZ0sdhERERUktb/sxl+HvRBRIdgsYmIiHTT1BKAySigfpSrx+0etxVdAbbRERERlZqDoRj+97VdeP+TtkIvhYiKCItNRESkm6bWIOqrXTCbev748ZRZ2UZHRERUgqKxJAAgLCYKvBIiKiYsNhERkS5kWUZTaxBHjXb3+rNKtw1dwRhSslyAlREREdFQiXEJABCOJgu8EiIqJiw2ERGRLtoPRhGNJTGhrqzXn3ncVkgpGcEIr4oSERGVkmyyKcaf4UTUjcUmIiLSRVNLZjh4n8kmKwDAx7lNREREJUVJNkVEJpuIqBuLTUREpIum1gBMRgPGjHL2+rPKMhsA8EQ6IiKiEiPGObOJiHpjsYmIiHTR1BLEuFoXTMbeP3o8mWQTi01ERESlhckmIuoLi01ERKS5lCyjqS3YZwsdALgcZpiMAtvoiIiISkz3aXQsNhFRNxabiIhIc22+CGJxCRNG9x4ODgAGQYDHbWWyiYiIqMR0J5sSkHmqLBFlsNhERESaa2rtfzi4wuO2wcdiExERUUlRik1JSUY8mSrwaoioWLDYREREmmtqCcJiMqBulKPf+1S6rWyjIyIiKjHKgHCAc5uIqBuLTUREpLmm1gDG1bphNPT/Y8dTZsXBUAwpRvCJiIhKhhiTsr/miXREpGCxiYiINJVKyfhygOHgikq3DUlJRijCjSoREVGpYLKJiPrCYhMREWmqxRtGPJHChLqBi00etxUA4AuylY6IiKhUiHEJTpsJAJNNRNSNxSYiItJU93Dwvk+iU1SWpYtNXQEOCSciIioVYlxCVbkNAJNNRNSNxSYiItJUU0sQVosRoyv7Hw4OpE+jA8AT6YiIiEpINJ5EVVn6Z3iYxSYiymCxiYiINNXUFsD4WjcMBmHA+7kdZhgNAtvoiIiISogYk1DhtkIAEGEbHRFlsNhERESakVIp7G0LDTocHAAMggCP24ouJpuIiIhKgizLEOMSHFYT7FYTk01ElMViExERaeZAZwSJZCqnYhMAVLqtnNlERERUIuLJFFKyDJvFCIfNxAHhRJTFYhMREWmmqSUAAJhQN/BwcIWnzMY2OiIiohIhxiUAgM1igtNm5oBwIsrStNgUCoWwYMECNDc397j9mWeewZIlS7K/P3DgAK666irMnz8f3/72txEOh7VcFhER6aSpNQi71Ygajz2n+1dm2uhkWdZ4ZURERDRcYjxdXLJbmWwiop40KzZt27YNV1xxBZqamnrc/tlnn+G3v/1tj9uWL1+OK6+8EmvXrsW0adPwm9/8RqtlERGRjppaM8PBhYGHgys8biuSkoxglJtVIiKiYifGDk02mZhsIqIszYpNq1atwrJly1BTU5O9LR6P4+6778b3v//97G2JRAIffPABzjvvPADAJZdcgrVr12q1LCIi0klSSmFfeyjnFjoA8LjTRydzbhMREVHxU5JNNosRTruZA8KJKMuk1ROvXLmy122//OUvsXjxYjQ0NGRv6+rqgsvlgsmUXkp1dTXa2tq0WhYREelkf0cYSUnOeTg4AFSWWQEAvqCI8Xk8joiIiPQXPWRmk8NmQkRMQJZlCDkmmolo5NJtQPjGjRvR0tKCxYsX97i9r7kc/MeJiKj0NbXmNxwcSM9sAoCuIJNNRERExe7QmU1OmxlJSUY8mSrwqoioGGiWbDrcyy+/jN27d2PRokWIRCLo7OzED37wA9x///0IhUKQJAlGoxEdHR09Wu+IiKg0NbUG4bSZUF1uy/kxbqcFRoMAH9voiIiIip54WLIJACJiElazsZDLIqIioFux6d57783+evPmzXjkkUfwwAMPAABmzZqFV155BRdeeCHWrFmD008/Xa9lERGRRppaghg/2p1XWtUgCPC4regKihqujIiIiNTQPSA8nWwCgLCYgCeTVCaiI5dubXQDWbZsGVatWoULLrgAH374IX7wgx8UeklERDQMiaSE5o4QJozOvYVOkS42MdlERERU7JQ2OqvF2CPZRESkebJp/fr1vW6bPXs2Zs+enf19fX09nn76aa2XQkREOmnuCENK5TccXOFxW9HUEtRgVURERKQmMS7BajHCIAhwZopNYTFR4FURUTEoimQTERGNLE0tynDw/ItNlWU2+IKxPg+QICIiouIhxpOwWdLzmRyZNjomm4gIYLGJiIg00NQahMtuRlVZ7sPBFR63FUkphVCUV0aJiIiKWTQmwWZJJ5q6k00sNhERi01ERKSBptYgJtTlNxxcUZkZKsoT6YiIiIqbGJeyySa71QQBQIRtdEQEFpuIiEhl8YSE/R3hIQ0HB9JtdAA4JJyIiKjIifEk7Jlik0EQYLeamGwiIgAsNhERkcr2tYeQkoc2HBxA9rjkrqCo5rKIiIhIZelkU/eZUw6bickmIgLAYhMREamsqTV9ktxQi01lTguMBgE+JpuIiIiKWjSWhM1qzP7eaTMz2UREAFhsIiIilTW1BFDmtGQTSvkyCAIqXFbObCIiIipyYlyC/bBkU5jJJiICi01ERKSyptYgJowe2nBwhafMyjY6IiKiInfogHAgfSJdhMkmIgKLTUREpKJYXMIBb3jILXSKSreVbXRERERFLCmlkJRSPYpNDrbREVEGi01ERKSave1ByDIwoW5oJ9EpKt02dAVjkGVZpZURERGRmsS4BAA9BoQ7MwPC+fObiFhsIiIi1TS1DG84uMLjtiKRTPHqKBERUZESY+mf0YcOCHfYTEhKMuLJVKGWRURFgsUmIqIRQowXvjDT1BqAx21FhWtow8EVlWXpx/sCnNtERERUjJRk06EDwp12MwBwbhMRsdhERDQStPoiuPlXG/BFS6Cg62hqDWJ87fBSTQDgcdsAgHObiIiIilQ0c5Gr54DwdLGJJ9IREYtNREQjQHN7CClZRltXpGBriMaSaPVGMKFOjWJTOtnUxWITERFRUeprZpPDlv41k01ExGITEdEI4M20mxVyc7e3LQgZwITRwxsODgDlTguMBoFtdEREREUqW2yyHppsShebmGwiIhabiIhGAKXYFI4WbnPX1KrOcHAAMBgEVLgsTDYREREVqeyAcMuhA8I5s4mI0lhsIiIaAbz+TLGpgJu7ptYgqsqsKHNaVHk+j9vGZBMREVGRivbRRtedbGKxiehIx2ITEdEI4AukE0CFvJLY1BJQpYVOUVlmZbKJiIioSIl9DAi3W00QAETYRkd0xGOxiYhoBMi20RVocxcRE2jriqoyHFzhcaeLTbIsq/acREREpA4xLsFsMsBk7P5IaRAE2K0mJpuIiMUmIqJSF4tLCGVmNRUq2fRldl6Teskmj9uGeDLFDSsREVEREuNSj1STwmEzMdlERCw2ERGVOl+we65RoZJNynDw8SoMB1dUuq0AwLlNRERERUiMJfssNjltZl4oIiIWm4iISp3SQlfjsRdsc/dFaxCjym1w2c2qPaenLF1s4twmIiKi4iPGJdgPGQ6uSCebWGwiOtKx2EREVOKUk+jG1bgK2EYXwIQ69VroAKDSbQMA+FhsIiIiKjpivL9kk6lgSWsiKh4sNhERlThvIAZBAMaMciKWkJCUUrq+fiiaQMdBEUep2EIHAOVOCwyCgK4g2+iIiIiKTTQmwWbtK9nENjoiYrGJiKjk+QIiPG4r3A4LAP2HhHcPB1e32GQwCKhwW9AVYLKJiIio2AyUbIqICZ4mS3SEY7GJiKjEef0iqspscNrSVxf1jq43tQYAqDscXOFxW9lGR0REVITSp9H1PbMpKcmIJ/VNWhNRcWGxiYioxHkDmWJTZji33tH1ppYgaj12OGzqDQdXVLptLDYREREVoXSxqe/T6AD9k9ZEVFxYbCIiKmGplIyuYAyVZTY4MsmmiO7JpqAmqSYgnWzqCoqM4hMRERWRVEpGLNF3sclRoKQ1ERUXFpuIiEqYPxyHlJJRVW7LXknUM9kkyzIOhmKorrBr8vyVbiviiRQHjRIRERURMS4BQJ9tdErSmskmoiMbi01ERCXMG0if1FZVZj0k2aTf5k6MS5BScrbQpbbKMhsAoIutdEREREVDjKf3GnZr3wPCASabiI50LDYREZUwX6bYVFlmgyNz/HA4qt/mTnktp733lU01eNxWAEBXUNTk+YmIiCh/AyWbHJzZRERgsYmIqKR5/UqyyQaT0QCbxahry1koc9XSpVGySSk2+QJMNhERERWLaCbZ1PeAcCXZxGIT0ZGMxSYiohLmDYhwWE2wZ1JNTptJ1wHh4Wh6I6nMZ1BbhcsKgyDwRDoiIqIioiSblP3HoexWEwTof2AJERUXFpuIiEqYLxDLzjUC0tF1Pa8kKvMYtCo2GQwCyl0WttEREREVETGmtNH1TjYZBAF2q4nJJqIjHItNREQlrNMvYlR5d7FJ72RTKDOzyaVRsQlIn0jHNjoiIqLiIQ7QRgcADp33I0RUfFhsIiIqYb6AiMoya/b3uieblAHhNm0GhAOAp8zG0+iIiIiKyEADwgHAqfN+hIiKD4tNREQlKhpLIhJLoqqsZ7JJz6OGQ9EkrBYjTEbtfpxUuq3oCsYgy7Jmr0FERES5U5JNdutAySYWm4iOZCw2ERGVKG8gcxJdjzY6s66bu7CY0OwkOoXHbUUsISES46aViIioGIhxCUaD0O/FJr0vfhFR8WGxiYioRPkyxaaeA8JNiCdTSCRTuqwhFE3AadeuhQ7o/vq6OLeJiIioKIgxCTaLEYIg9PnnDp0vfhFR8WGxiYioRHkzxZfD2+gA/Y4bDosJTYeDA+lkEwD4OLeJiIioKETjyX6HgwNKsinJFniiIxiLTUREJcrrF2E0CCh3WbK3OTItbSGdriaGokk4NW6jq8wWm0RNX4eIiIhyI8Yl2Kz9J5sdNhOSUgpxnZLWRFR8WGwiIipRvoAIj9sKwyERdqWlTbdkU1T7ZFO5ywJBYBsdERFRsRAHTTal9wZspSM6crHYRERUorwBsUcLHdC9udPjuOGULCMsaj+zyWgwoMKVPpGOiIiICi8ak2CzDJxsAsAh4UVESqXw+Is7sK89VOil0BGCxSYiohLlDYg9TqIDujd3eiSbxFgSsgzNT6MD0nOb2EZHRERUHJhsKj1dgRje+6QNH3/uLfRS6AjBYhMRUQmSUil0BWM9TqID9E02KXOhnBq30QHpuU1MNhERERUHMS7BzmRTSVH2hqEo/05IH5oXm0KhEBYsWIDm5mYAwJ/+9CcsWLAAF154IX70ox8hHo8DAHbu3InFixfjvPPOw49//GMkk6yCExH1pysYgywDVWXWHrc7MsM6wzpsJJTX0HpAOAB43Db4AjGaYS3GAAAgAElEQVSeakNERFQExLg06Gl0AJNNxSSUKfwFWWwinWhabNq2bRuuuOIKNDU1AQC++OIL/O53v8Pzzz+PF198EalUCs8++ywA4NZbb8Vdd92FV199FbIsY9WqVVoujYiopPkyw7IPb6MzGATYrSZdNndKsUnrAeFAuo0ulpAQjXHTSkREVEiyLKfb6KwDFJvs+iWtKTfK3lCPC5JEgMbFplWrVmHZsmWoqakBAFgsFvzkJz+By+WCIAj4yle+ggMHDmD//v0QRREzZswAAFxyySVYu3atlksjIipp3kB6ftHhA8KB9NVEXdrolGSTxgPCAaAyk+DysZWOiIiooOKJFGQZA7bR2a0mCNDvdFwanFJkYrKJ9KLpJ4SVK1f2+H19fT3q6+sBAD6fD8888wzuvfdetLe3o7q6Onu/6upqtLW1abk0IqKS5vWni02V7t7FJofNpMvmLqzrzKb019kVjKGh2qX56xEREVHfxHj65/9AbXQGIZ20ZrKpeCjzs5hsIr0UZEB4W1sbrrnmGixevBizZ8/ucwaHIAgFWBkRUWnwBUS47GZY+9joOW1mhHVoN8smm2zaJ5s87kyyKcAT6YiIiApJjEsAANsAySZAv4tflBul8BeM8O+E9KF7sWnPnj244oorcPHFF+Omm24CANTW1qKzszN7n46OjmzrHRER9eYNxPpsoQPSmzu9BoTbrSYYDdr/KCl3WSAI4Il0REREBRbNIdkEZC5+MdlUNJS9YVhMIMUDV0gHuhabQqEQrr/+enz/+9/HN77xjezt9fX1sFqt+OijjwAAa9aswemnn67n0oiISoo3IGbnGB3OaTPrMyBcTOiSagIAk9GAcqeFM5uIiIgKTIxlkk3WXJJNLDYVC+XvQpZ5SiDpQ9di0+rVq9HZ2Ynf//73WLRoERYtWoQHH3wQAPCLX/wC9957L84//3xEo1FcffXVei6NiKhkyLIMb0DsdRKdQr8B4UldTqJTeNw2dLGNjoiIqKC62+gGSzaZsnOCqPAO/bvg3CbSgy6XpNevXw8AuPbaa3Httdf2eZ+pU6di9erVeiyHiHQST0j46VMf4pLTj8bMydWDP4ByEoklEYtLA7bRJaUU4gkJFvPAG8HhCIsJXYaDKyrLrDjQGdbt9YiIiKi3XAaEA4BDp6Q15SYsJmEyGpCUUghGE6gt9IJoxCvIgHAiOjK0eCPY3xHG2s17C72UEUU5ia6/YpPTli4AaZ1uCkX1a6MD0kPCObOJiIiosKI5DgjXK2lNuQmLCdR47AC6D3kh0hKLTUSkmRZvOoWyu9mf/fVI17inEw+tbtR08KI300rWXxudI1MA0jq6Ho4mdG2jq3TbIMYlXiUlIiIqICXZZLcOlmzqTlpT4YXFJGqVYhNPpCMdsNhERJpp8UYgCIBBEPDO9pZCL0cXH+xsx9bPOrPpIy34Aul0T2V/yaZMAUjLokwqJSMiJrMpKj0oA9G7gpzbREREVChiTIIAwDpIq75eSWsaXFJKIRaXUOtxAGCyifTBYhMRaabFG0Z1hR3TJ1Zh0/ZWSKlUoZekueaOdIKruT2k2Wt4/SJMRgPKHH0Xepw6JJsisSRkQOcB4Uqxia10REREhRKNJ2G1GCEIwoD30ytpTYNTCn5V5TYYDQKLTaQLFpuISDMtvgjGVDkxd3od/OE4tu/xFXpJmpJSKezPDLBu7tCw2BQQUVVm7XeT57Bpn2xSTjFx2vWd2QQAPhabiIiICkaMS4MOBwe6k01sfy+8iNi9b3PazSw2kS5YbCIiTaRSMtp8EYyucuC4iVUoc1qwofFAoZelqTZfFEkpnd7a16HdjCpfQOy3hQ44NNmk3eYulNm06JlsqnBZISD99RMREVFhiHEJduvgF5uYbCoe4Wh6T+iymeG2m7MXDYm0xGITEWmiwx9FUpJRV+mAyWjAnGmj0bjHC384XuilaUZJM1VX2DRto+sMiP0OBwcAu9UEAdB0I6FsWvSc2WQyGlDmsrCNjoiIqIDEeDLHZFO62MRkU+EpBT+HzQyn3Ywgi02kAxabiEgTLd4IAKBulBMAMHd6HaSUjHc/bi3ksjTV3BGCQRBw4tRatHVFENPg9JVEMgV/KI6qAZJNBkGAw2bSqY1Ov2ITAFS6rWyjIyIiKiAxJsFmySXZxAHhxSJ8SBsdk02kFxabiEgTrZli0+jK9KkXdVVOTKovx4bGA5BluZBL00xzexh1VQ5MGO2GLAMHOtVvpesKKSfRWQe8n8NmQjim3UaiEG10AFDptjHZREREVEC5JpscViXZxMJGoR2aSGeyifTCYhPRESAly2jxajdDqC8HvGGUOcw9ihFzp9ehxRvBngMBXdeil33tITTUuDC2xgVAmxPpvP70vKJRAySbgPTVRK2TTQK6N5J68bitnNlERERUQOkB4YP//DcYBDisJiabikC2jc5qgiuTbBqpF3+peLDYRHQE+Oe/OrD0ic1o64ro9pqt3gjqqpw9bps1tQZWsxEbto28QeERMQlvQERDtRPVFXZYzAbs0+BEOqXQUjnAzCYgPSdBy4Gc4WgSDpsJBsPAxx6rzVNmhRiXEI1x40pERFQIYlyCzTp4sglApq2fKZpCC4tJOKzpfZvLboaUkhGNqT/ugehQLDYRHQG+bAtCBvBla1CX15MzSaq6KkeP2+1WE078ag3e/7QdYnxkFQv2d6YLSw3VLhgMAupHubRJNinFJvdgbXTmbGRaCyExoetwcEWlO11k49wmIiKiwojGcmujA9JtW0w2FV5ETGRPB1S6DkIsApLGWGwiOgK0+tKJpn0anpB2qGAkgbCY7JVsAtKtdLG4hA8+bddlLXpRCktKC93YGieaO8KqR5S9fhHlTgvMpoE3eS6NrySGowndh4MD6TY6AOhiK13efvH8FryxZX+hl0FERCUskUxBSsmw59BGB0DzA0soN2Exmd23uRyZYlOExSbSFotNREeAtkyxaX+HPnOblPlQhyebAGBSfTlGVzrwTmOLLmvRy76OMOxWU7YY0lDtQiiagD8cV/V1fAERlYPMawIyySYxqVk/flhMwGnXd14T0J3oYrIpP1IqhZ1NXdi172Chl0JERCVMSabnnmzStq2fchMWE3AenmzikHDS2KDFpqeffhqhkD5pCCJSX0qW0dYVBaBfsqlFOYmuj2KTIAiYO70Ou5v9ug8t11JzRwhjq50QhPQMI62GhHsDMVQNchIdkN7cSSkZ8URK1ddXhKIJ3U+iA4AKtxUCwBPp8hSMJCAD8If4vhER0dCJ8fScn1wGhAPaH1hCuQlHk9nxB+5ssUndC6JEhxu02LRr1y6cd955+PGPf4zt27frsSYiUpHPLyKRTGFUuQ3egKjLYOUWbwQWs6HfBM6caaNhEAS8s31kpJtkWUZz5iQ6RX11+tdqDgmXZRm+gIiqQYaDA8j25Wt1NfHQTYueTEYDypwWnkiXp0AmYXcwdORsLFMpGc0aDOknIjqSKfvI/JJNLDYVWjqRnt63ObPFJv69kLYGLTb99Kc/xauvvopp06Zh+fLlWLx4MVavXo1YjFdHiUpBa+YEullTagDo00rX4gtjdKUDBqHvk8rKXVZMn1iFTdtbIaW0Sd7oyesXIcalHsUml90Mj9uqarIpGE0gnkzl1EanFIK02OBJqRQisWQ2jq23yjIrk0158meLTUfO+/bcut24+3fvszBJRKQiJdlkt+Y+sykppRBP8OSzQpFlGRGxe9/msJkgCEw2kfZymtnkcrkwf/58LFiwAAcPHsSzzz6L+fPnY+3atVqvj4iGqTXT0jZrarrYpGbSpj8tnZE+h4Mfau70OvjDcWzf49N8PVprzhTwGqpdPW5vqHZhX7t6xT3lQ3NVTsWm9IZCiyHhShy+EG10AOBx21hsypM/k2gS49KIOwmyLx/9qx2v/7MZAOAL8HuFiEgt3W10uZ9GB2hz8YtyI8YlSCk5+3dhEAQ4bWYmm0hzgxabNm3ahB/84AeYP38+Pv/8c/z617/Gn//8Zzz55JO455579FgjEQ1Dqy8Cu9WIo+rcsFuNmreVxBISvAGxz+HghzpuYhXKnBZsaDyg6Xr0oBTw6kf1LLA11DjR4g0jKamT3vL6cy82OTTc3CkDJQtxGh2QPpHOF2RaJR+BSPfVS/8Ib6XrOBjF71/5FOUuC4CeXzsREQ1PvgPCHRpe/KLcKBcJHYck0t0OM0L8+UgaG7TYtGLFCpxwwglYt24dVqxYgalTpwIAxo0bh8suu0zzBRLR8LT5Iqj1OCAIAuqrXaoPrD6ckqQaLNlkMhowZ9pobPvMW/JDi5vbQ6iusPWKlI+tdkFKydn3ZLi8mYRGLjObnBrObAoXONlUWWZFNCbpMn9spDi0wDSSW+mSUgqP/fVjAMC3F00DAAS5mSYiUk2+A8KZbCo8ZS946KxNp93M0+hIc4MWm5YsWYKrr74abrc7e9vjjz8OAPje976n3cqISBWtvkj2VLix1S40d4Qhy7Jmr9fiS7eNDZZsAtKtdClZxqYdrZqtRw/NHaFeLXQAsjOc1Gpd9AVEWMyGnGYlKckmLU6AySabCjAgHEgnmwCeSJePQCQOZYTaSB4SvvrNPfiiJYhvXDAVR9WVAQACEW6miYjUImYu9Nit+SWbtDqwhAYXzuzbXPZDkk0sNpEO+v3E8txzz0EURfzxj39EPN69MU0kEnj66adxww036LJAIhq6dEtbDHMr04WfhmonorEkfIFYTumYoWj1RiAIQK3HPuh966qcmFRfjncaWzD/pHEQ+hkoXswSSQmtvkh2APuhRlc6YDQI6TTZscN/La9fRFWZLaf3yWY1QhA0Sjb1sWnRU6U7/b3rC4oYM2rgBB2l+UMx1FU5caAzXPJJwv5s2d2B1z7Yh7NPaMDXMv8/2ixGJpuIiFSkJJuseZxGB2hz8YtyE8620fVMNn3REijUkugI0W+yyWQyYdeuXRBFEbt27cr+t3fvXtx11116rpGIhqi9KwogXfQAgPpM+kbLuU0HvBFUl9thNuW2CZk7vQ4t3gj2HCjNH3gHOiOQZWBsTe9kk8lowJhRzuwA8eHyBsSc5jUB3cMftYitK89ZqJlNlUqyiYOfc+YPx1FX6YDJaMDB8MgrvnT6o/j933ZifK0bl501KXt7mcOCEJNNRESqicaTsJgMMBpyOmdK0xmSlJvuNrreySYtux2I+r0sfemll+LSSy/FunXr8G//9m96romIVNLqS88KGp1NNnUXm46fNEqb1/SGc2qhU8yaWoNn1+3Ghm0HMKm+XJM1aWlfZgZWQx/FJiD9nn+6t0uV1/IFRIyrdQ9+xwyHzaRZG50g5H7ssdoq2EaXt0A4jqnjPahwWUbczKaklMJv/7oDUkrGty46FmZT9wcgt8PMAeFERCoS4xJsefz8d1g5ILzQlL3goeMPXHYzkpKMWELKef4WUb76/c564okn8M1vfhPvvvsu3nvvvV5/vnTpUk0XRkTD1+pNJ2pqPenij8NmQlWZVbWkzeFSKRmtviimHVWV82PsVhNO/GoN3v+0HVf82+SS+4HX3BGCxWRATUXfbYMNNU68u6MVoWhiWAO14wkJgUgir/ZHp82k0YDwBJw2MwwFans0GQ0oc1p4Il2OEskUwmIS5U4LKlzWEXca3V/e/hx7DgTwrUXHZv+tU7gdFngD/D4hIlKLGJdyPokOAAwGAXaricmmAgqJCZiMAizm7osxyp40FEmU3N6bSke/+UdlILjH40FFRUWv/4io+LX6Iqgss/boq2+odmnWRtfpjyIppbIDyXM1d3odYnEJH3zarsm6tNTcEcKYUU4YDH0XXsYqabJhngLoy6R4qsqsOT/GYTNrkmwKRxM5DSnXUqXbmn1PaGDKzKJypwXlIyzZ1LinE3/fvBdnzBiDk75a2+vPmWwiIlKXGEvmVWwC0he/mGwqnIiYhNNm7jHzM1ts4t8LaajfTwuXX345AODmm2/WbTFEpK5WX7TXlf6GGhc+/sKHpJSCyZhbv32uWrzptr0xVfkNbZ5UX47RlQ6809iCudPHqLomrTW3hzB9gJbEQ0+kmzreM+TXUdIZuc5sAtKbu86D0SG/Zn/Cw0xpqcHjtqJdg69tJPJnZjSVZZJNnzSp09ZZaL6AiP/v5Z1oqHbh8rMn93mfMmd6ZpMsyyV5AAERUbGJxvNvu3LYmGwqpHA0kT0VUOFydCebiLTS778UM2fOHHBj9s9//lOTBRGROmRZRqsvgpOP7Xm1v6HaBSklo8Ub6XOo9XAoxaZ8k02CIGDu9Dq88OYetHjDqMuzWFUo/nAcgUgiOwurL+VOC1x287CTTV7/UIpN2gwID0WTKHdZVH/efFS6bfh078GCrqFUKMWmcqcVFS4LorEkYgkJVnN+V6aLiZRK4bcv7kAimcK3LzoWln6+FrfdDCklIxJL9phVQUREQyPGk/C4ck9ZA+n9CE+jK5ywmOx1qEs22RRlsYm002+x6eWXX9ZzHUSkskAkgWgsmR0OrmioThdymttDGhSbwihzmIeUepkzbTT+31uf453tLbj0jEmDP6AIKO2IY6v7L44JgoCxNcNvXfQFRAjoHo6dC2VAuNqpjrCYwJhRhS0IVpZZEY0lEY0lCzaovFQEwt1tdBWZDwj+UAw1nvyKwsVkzYYvsLvZj29eeMyAxWm3I10UDUYSLDYREakg3wHhQDppvb9Tm3mhNLiwmOhVIFT26kEWm0hD/fbQ1NfXZ//z+/1oaWnBgQMHsG/fPmzcuFHPNRLRELQddhKdorbSAZNR0GRuU4svgtFDTCWVu6yYPrEKm7a3QkqlVF6ZNpS0Uv0gRbuGahf2d4SRSg39eFlvQESF25pX66PTZkZKliHGpSG/bl9C0QSc9sIWeDyZottImj+kle42OnM2kXawhIeEf/yFF6+8+yXmTq/DKceOHvC+bmdmM825TUREqsh3QDig3QxJyk042jvZ5LSZISDdYkeklUE/LSxduhSvv/46RFFEbW0t9u7di6997Wu47LLL9FgfEQ1Raz/FJpPRgLoqJ/apXGySZRktnWHMmloz5OeYO70OWz/rxPY9PsyY3P8cpGLR3B5CucuCMsfALWUNNU7Ekyl0HIyitnJoaRKvX8yrhQ5Atj8/IqqX/klKKYhxqShmNgGALxArmbbLQgmE4nBYTTCbjKhwlnaRrisYwxMvfYIx1U5cec5XBr2/257+fzMQ5maaiEgNQx0QzplNhROJ9Z7ZZDAIcNhMTDaRpga9RL5p0ya8/vrrOPfcc/H444/jj3/8I2y2/D7wEJH+Wr0RmIyGPgsUDdVO7O9QN84cjCYQFpPD+uB/3MQqlDkt2NB4QMWVaae5IzzgvCaF0q64bxhzm3yBGCrzOIkOQPbEuLCKJ40oVyYL3ZJUmfm+9gV5rP1g/OFYNtGktGH6SzDZlErJeOKlHYglJHxr0bScZk6VOTNtdNHS+3qJiIqNlEohnkzBPoQB4UkphXhC3aQ1DS4ppRCNSXD1sW9z2c1MNpGmBi02VVdXw+Fw4Oijj8auXbtw0kknoatrZJxkQzSStfoiqPXYYTD0ntXTUONCVzCm6lDA1sxw8Lo8h4MfymQ0YM600dj2mRf+Ik9eSKkU9neGMTaHYtOYKicEAUNuXUzJMnxBEVXl+RX6lYKQmlcTle+ZQrfRKbOHuoLF/X1SDALheDZ957SZYDIKJZlsenHjF/h070H833OmoD7HmWHZmRRhFpuIiIYrlmnLzz/ZpP5+hHITiWUuEvaRSHc5zAjyNDrS0KDFJrPZjA8++AATJ07E22+/jWAwyGITUQlo9UV6tdAplDTOfhVb6Q5400mpuiG2iSnmTq9DSpaxaUerGsvSTJsviqSUQkPN4B96LWYjaj2OISebAuE4kpI8jDY69TYSSkqq0G10ZpMBZQ4zfIHSK5rozR+OZ5NNgiCg3GktuZlNO5t8eGljE+ZMG43Tptfl/DizyQC71cTNNBGRCpQZkPkOCNdiP0K5URLph7fRAYDLxmQTaWvQYtMPf/hDPP/885g3bx527tyJk08+GQsXLtRjbUQ0REkpPR9odD8pI6XY1KxiK12rNwKLyYDKPNM3h6urcmJSfTneaWyBLA99oLbWlJRSLm10QDpNNtRkkzeQbhWrzLPYpGmyqQhO9vKU2ZhsykEgEu8xV6zCZYE/XDrvmz8cx+MvfYLRVQ7833MHn9N0OLfDjAAHhBMRDVs0k5Jhsql0hAfYt7kcZs5sIk0NWpaeMWMGZsyYAQB44YUXEAgEUFZWpvnCiGjovH4RUkruN9lU4bLAaTMNa4bQ4Q54wxhd6YBB6N22l6+50+vwh79/ij37A5jUUK7C6tTX3BGCQRBynlE1ttqJDz9tRzSW/7BuJb0zahgDwtUSjqafq9DJJgCodFvRkmnfpL7FEhKiMSmbbALSLYgtvtJ435Q5TZFYEv/1f2bAluecECBdbGKyiYho+LLJpiHMbALU3Y9QbsLZWZt9JJs4s4k0Nui/FPfcc0+fty9dulT1xRCROpQPkv2dfCYIAsbWuFRto2v1RjCxXp3C0KypNXh23W5saDxQvMWm9jDqqhwwmwYNiAJIJ5sAYH9nGJPyfJ+8/qElm2wWIwyCoOqA8GJKNh0zoRJbdndid/NBTG6oKPRyilIgM6tIGZQNAOUuCz7dWxrt8O9sb8EnTV249vyp2f+H8lXmsKDjIAfJExENlzjkmU3qH1hCuVHe8z5nNtnNiCdTiCWknA7dIMrXoJ+SKioqsv85nU5s2bJFj3UR0TAow7r7SzYBQH21C80dYaRUaFWLJSR4/eKw5zUp7FYTTvxqDd7/tB1ivDivgu1rD+X14VcZJN48hDSZNyDCbjX22W8/EEEQ4LSre9xwWEzAIAiwWwu/KTntuDo4bSas3by30EspWkqxqbxHscmKsJgsiVOB9neEYbUYMTePOU2HSyeb2EZHRDRcQ22jc7CNrmAigySbADDdRJoZ9JPLzTff3OP3N954I2688UbNFkREw9fqi8BlNw/Y6jS2xoVYQkKnX0RNhX1Yr9fmi0AG+p0RNRRzp9fhncYWfLCzHXOPH6Pa86ohIibhDYg4ozr3dVWV22CzGLFvCGkyX0DMezi4wmEzqzsgPJqA026CoEK75HBZLUacdUIDXt7UhBZvOOeWxiOJP1tssmZvq8i01PnDcVQP8/99rYWiCbhs5mF9v7kdFoSiCaRkWZU2XyKiI5WSbMp3HIDDygHhhaIUkvocEG5P7wdC0UTe6XmiXOTW/3EIh8OB9vZ2LdZCR5Dm9hCefu1fuPU3m/C7v32Czw8EinoYdKlpG+AkOkV9dfqD+X4V5jYpc3PGqPhhf1J9OcqdFvxr30HVnlMt+zvzGw4OpFNGDTWuIb3fXr845E2A06ZusikkJotiXpPi7K81wGQy4NX3mW7qi7+PNroKV7rw5C+BE+nCYmLY329uhwVSSuasECKiYVLS5vkmmwwGAXaruvsRyk1ITMBuNcJo6P2x32VPF6A4JJy0ktfMJlmWsWPHDhx99NGaLopGpkRSwoefduCNLfvx2X4/TEYDpo6vwIefdmDj9laMq3XhzJn1mH1M7ZCGwFK3Vl8Exx1dNeB96kelC0P7OkKY+ZXqYb1eizcMAUBtpXopCUEQUOOxZ+cVFROlFW5snjNkxla78N4nbZBlOa+khjcgDnkelsNmUjUeHY4mimJek6LMacGpx9XhncYDuHju0Sh3WQd/0BFEaaNzO7r/zpRi08FQ8Z9IpyTphqMs87UHI/GiKpQSEZWaoQ4IB9IXv5hs0l9ETMJh7ftnnytzUi3b6Egrg/5LUVHRc+jqwoULsXDhQs0WRCNPmy+CN7fuxzuNLQiLSdR67Pg/Z03CqcfVwWU3IxpL4r0drXhjy348ufZf+NP6z3DKtNE4c0b9kAfCHsmisST84figLW02iwk1FXY0d4SH/Zot3ghGVdhgNqk7x2dUuQ279vlVfU417OsIw241wePOr7DRUO1ENJaELxBDVXluSSUxnkRYTOZ8/8M5bWa0+6JDemxfwtFE3l+31s47aSze2rIf6z5qxuJ5Ewu9nKLiD6cLLCZj9xVN5WS6Uig2haKJIX/vK9yZzXQwkkDdwDV4IiIaQDSehNEg5Hw4yqEcKietKTcDXbRRLsDwxFbSSk4zm4LBICRJyhaeAoEAYrEYrNbi+sBBxSMppbB1dyfe3LofnzR1wWgQMHPyKJwxsx5fHe/pkeqwW00484QGnDGzHnv2B/DGlv3YsK0Fb/xzPyY1lOPMmfWYNaVa9ULGSNWqnETnGXx+Un21c0gDqw/X4o1oMi+nqtyOrk/aIaVSfcZ/C6W5I4Sx1c6858goxdN9HaGcP0B7A+mCQGXZ0P69TW/uVEw2iYm8E11aq/U48LUp1Vj/z/244OTxec+SGMn8oVi2uKRw2c0wGgQcLIk2umSfJ+jkw31IsomIiIZOjEtD/hnrtJnZzlwA4Viy30R69pRAJptIIwP+a/HWW2/h5z//OT7//HMAwLhx43Dbbbdh06ZNOOOMMzB37lxdFkmlwxcQ8dbWA3i78QD8oTgqy6y4eO5RmHv8mGzrRn8EQcCkhnJMaijH5WdPwsbtrXhz63488dIneG6dGadNr8O8GWNyKqIcyZRiUy7DusfWuLD1s07EExIsQzzyNJWS0eqL4NijPEN6/EBGlduQkmV0BWIYVSSDjGVZRnN7CHOmjc77sfWjuk+kmzFpVE6P8QXSbYRDHRDutJkRiSVVG44cig7/w78W5s8ejw//1YEN2w7g3JPGFXo5RSMQiaPM0bPYZBAElLss8Bd5siklywiLw2/bVJJNAV65JSIaFjEm5T2vSeGwmXCgc/hpespPOJrIjs44nMlogN1q4swm0ky/xaYPP/wQy5cvx5133ok5c+YAAN59912sXLkS9fX1uPFzSsUAACAASURBVOuuu3J6gVAohMsvvxyPPfYYGhoasGnTJtx7772IxWI4//zzccsttwAAdu7ciaVLlyIUCmHWrFlYvnw5TCZenS4FKVnGx5/78OaW/di2pxOQgeMmVuGM+fWYfnQVDIb8P+C6HRbMnz0O5540Fju/7MKbW/bjtff3Ye3mvTh2ggdnzGzAjMlVRZV2KRat3ggEATmdMNdQ7YIsAwe8YUwYXTak1+sMiEhKKU2STaMy6Z9Ov1g0xSavX4QYl4bU4umwmTCq3IbmPE6k8w672GSCLKc3iH2dRJKPRDKFWEIqymLT0WPKMGVsBV77cB/O+lpDj7YxtSlF9QtPnaDp66jBH4pjUkPveV/lTmvRt9FFxCRkGSoMCGeyiYhIDWI8OeRiU3pmE5NNehssIey2m5lsIs30+8nj0Ucfxa9+9Sscf/zx2dvOPvts/OEPf8j5ybdt24alS5eiqakJACCKIu688048/fTTqKurw4033oi33noL8+bNw6233op77rkHM2bMwJ133olVq1bhyiuvHPpXVoLe3LIfGz9uKfQy8tYVjMEXiKHMYcYFJ4/HvOPHqFYYMAgCjp1QiWMnVKIrGMOGxgN4a+sB/Pov21HutGBURekd0zl1nEfTuTJtXRFUl9tz6qdXCibN7UMvNrV601ep6nJIUuVL+T7q8EcxFeonp4ZCmXGVz0l0h2qodmFfHq2LXr8IgyAMmgzsj1JgioiJYReblHY81zCfRyvzZ4/Dg6sb8cHOdpwyhORZLmRZxu/+thM7v+zCV8d7MHV8cXxf9kWW5T6TTQBQ4bKg/aB6s7y0oHy/OYf5/WYyGuCwmjiTgohomMS4NORDfJw2M2c26UyW5UH3f067mckm0ky/33ler7dHoQkA2tvbUVtbi507d+b05KtWrcKyZctw2223AQAaGxsxfvx4jB07FgBw4YUXYu3atZg0aRJEUcSMGTMAAJdccgkeeuihI67YZDYZYBtiK1Mhja9147IzJ+GEr1RrepXf47Zi4alH4d9PGY/GPV68u6MN0RI71aLTL2Lt5r24cM6EIbetDabVG0FtZW6Fn5oKOywmQ15Jm8Md6Ey37WmRbKp0WyEIKKoT6fZl3qv+IsmDaahxYdueTiSSUk5zyHwBER63dUgJQQDZFqSwmERujXv9U658FWOyCUgnKutHOfH3zV/i5GNr856plYtNH7di55ddAIDdzQeLutgkxiXEE6leM5uA9Il0u5uLb/j+oUKZ7zc1TpBzO8xMNhERDZMYH3orvcNmQlJKDWt0A+UnnkghKclwDdCO7naY4Q/z5yNpo99iUzze+5uupqYGv/zlL/Hv//7vOT35ypUre/y+vb0d1dXdR6zX1NSgra2t1+3V1dVoa2vL6TVGklOPq8Opx9UVehlFz2gwYObkasycXD34nYvM1t2deOj/NaKpNYivjK0Y/AF5SskyWrsimDIutw/ABoOAMaOcwyo2tfrCcDvMmhwpbjIa4HFb0VlExabm9hCqK2xDHpA5tibTutgZwfjR7kHv783j5Lq+ZIc/qlCYVa5IFmuxySAIOO+kcfj9Kzux4wsfph2t7tFjgXAcz7++G5MayhEVk9hV5MWaQGbzWO7sO9kUiiaQSKaGdKqQHsLR9PebKsUmp4XJJiKiYRLjEqrKh9a9cOjFLxab9KHs/QZMNtnM2K/CydREfel3hzl69Gg0Njb2ur2xsRG1tbVDejFZlnvdJghCv7cTjTTK7JTdzQc1ef6DwRjiiVROw8EVDdWubGvYULR4I/j/2Xvz4Dbu+/z/2cV9AyRI8ABFSiJ02vIRRUdsS/EVX6nbOkl/Tn+uv53O1HHauE2TpjOdHG5zNI4n306bJp2kTVv/muabbx27kVOnceIrh+z4jiNFIkWJ9wHwwI0FFsBi9/cHsCAlkQSwFxbg5zWjGcsiF0tyufjs83me591bp5NKCn63VV9i03JGcoQOAIJdjupx6iGaZNEpcRIdsLq4U6Inoeo0kVnYrCZH9gfgdZrxw1dnFD/2d54/j3yxhN+/fQ92DXhxYT6JEs8r/jpKIe5UutcRmzyVWGaS0W9vk5JOOpfNhBRxNhEIBIIsyjE66QXhQDnWT9CG6iZhDWdThvxMCCqxodj0oQ99CB/96Efx3HPPIZlMIplM4tlnn8Wf/dmf4Y/+6I8kvVggEMDKykr170tLS+ju7r7s/y8vL6O7u1vSaxAIesZpM6HP71AtvlKdROerf9cp2OVAiilUXRCNEo5m0aNChE6k02NDNKmPbpkiV0IklpUlNgV8dpiMdF29TTwvIJ7Oo0NiOTiwurhTxNlUffjXZ2cTUHbD3frO8mCBqUhKseOeGl/Bq2cX8d6jQ+jzOxAKepAvlDC3pN/dwFVn0+VipbcSrUtk9CvAKBuj2xrOpp+8PY+Tp1qv+5FAILQGubycgvBVZxNBG0Rhb7NNG4fNhHyhhCKn380zQuuyodh0+PBhPPzww/i7v/s7HD58GEeOHMHf//3f4zOf+QwOHjwo6cWuuuoqTE5OYnp6GqVSCU8//TSOHTuG/v5+WCwWvPnmmwCAEydO4NixY9K+IgJB54SCHpyfS4Jfx9Enl6rY1ID4Uy0JlxClS2cLyOSK6FOhHFzE77Eils6DKzX/TXBhJQtBKEfhpNJIdDGRyYMXBJkxOgWdTdXCZv06mwDg+FX9sFkMeEYhd1Muz+Hff3QOfX4H7jw6CADVGOyYSi5FJUhuGqOrOJt0PJGOYYugANglRlbX4naYkMkWVbnv6gWuxOO7L47j3344grFZ/V6XBAKhNREEAflCCTaJBeGrziYiNmlFJic6mzb+mYkbOhlSEk5QgU2LGo4fP46nn34ar732Gl599VU8/fTTOH78uOQXs1gseOSRR/DQQw/hzjvvxI4dO3D77bcDAL785S/ji1/8Iu644w7kcjncf//9kl+HQNAzoaAHuTyHBRXy0ZFoFhaToepaqAfRpTPXwIQ0kXC0cXGrUfweKwShPPWw2YhupKAMsQkABrqcdX2/o6lyfLBThrPJbKJhoClFLNJMjoOBpiTvamqF3WrE8av78froEpYVmLj2vZ9NIJ7K4/fv2FMdgtDhtqLTre+S7SRTAEWt7wwSY3R6dzbZrUbJ5fhrcdnM4AWhrR9yxueTyOU5GA00vvH9M+TBgUAgKEq+WIIAwGqR6mxSzmlNqA+mjk1CFxGbCCpSlzTtdksbiS7ywgsvVP/76NGj+P73v3/Zx+zZswdPPPGErNchEFqBULDsiDg/l5AtWlxKJJ5FoMPWUOeZ22GG22GW1NsUjpY/R1Vnk7ccCVxJ5NDllVZKqRRzyxmYjTS6ZZ5HsNuJk6fDSDKFdV0nIqLYJCdGR1EUHFajYp1NDpupJTr1bj04gGdfn8WPX5vF//ueXZKPM76QxPNvzuHGa/sx3O+56N9CQS9GZuIQBEGX35MUk4fbbl5XrHHZTaApCgkdO5vE600JXI7ycdLZgirDDPTAqYkoDDSFj37gKvztf76Nf/ufEXzknit1eW0SCITWI5cvAQCskp1NJEanNeLab9OCcCI2EVREnyNoCIQ2xu+xwuM0q+KIiESz6JFQ1h3skjaRLhzNwmSk0SEj5lULf+XYeigJn1vOoM/vkO20GBBLwmu4m6JJ0dkkvSAcKC8klFjcMWyxZR7UfS4LjuwP4OenFiSPvOdKPB774Si8Lgved3znZf8eGvAimSko4p5Sg2RmYzGTpih4nGYkdexsYlhOscimy17+PkjtpmsFTo9HEQp6sHfQhw+8eyd+eX4FL7w13+zTIhAIbQJbKK8jbFILwi2kIFxrGLZY05FOnE0ENakpNhWL5MIjEJSEoiiEgl7FxaYiV0I0yUoUm5yYX2HA8431mYQr4hat4s65z2UBRelEbFrKKOJG668co1ZJeCyVh8NqlLyLKGK3GhVZ3DG54qa5f71x+6FtKHA8XpT4wP3DV2cwv8zg996zG7Z1eoNC1emS+ozSpbKFdSfRiXgcZt07m5QSN90VsaldS8JjKRZzywyu3NkJALj1nQM4sLMT//nCecwsppt8dgQCoR1gC/KcTTRNwWYxEmeThjAsB7vVuKnDlTibCGpSU2z6wAc+oMV5EAhbilDQg2iKRSylnICyFM9BACSLTUWOx1KDDo1wlEGvihE6oDxdrMNlabrYlGQKSGWLsibRibjtZnic5ppusmiKldXXJOKwKuNsyuS4lnE2AUB/lxNX7ezEc2/OoVAsNfS54SiD/35pEu/c042rQ/51P6bP74DDasR5nZaE14ppep0WXXc2MbkinApNPnTZV2N07cjpiSgA4MCOsthEURT+4K69cNhM+PpTZ6qOBAKBQJDKqtgkvbfRodDmF6E+ypuEm6/bqgXhbfr+SGguNcUmq9WKSCSixbkQCFuGXdXeJuUcEauT6BoXf8Tpao2UhBeKZSdVr4rl4CKdHhuiyeZGlURhSIzAyaWekvBoipU1iU7EbjWCUWDHimFrL1r0xu2HtyGTK+Kl0/WPg+cFAf/fM+dgNhrwu7eENvw4mqIw3O/B2Kz+nE2CICDFbO5s8jr17WxS8noTF9Pt6mw6NR5Fp9uCPv/q/cltN+OB39iPxVgW3352rIlnRyAQ2gE2XxatpRaEA5X1CHE2aQbDFuGosWljMtKwmA3VyXUEgpLUFJtyuRxuvvlm3HzzzfiN3/iN6h8CgSCdYLcDFrNBUUeEKDYFfI2LTb2ddlAUGuptisSyECqfqzZ+jxUrCrrApCAKQ/0KlboHu5xYiDIo8fyGHxNLsbLKwUUcFpMiBeFMrvaiRW/sGvBiR58bP3pttu6Y6M9+tYCx2QT+n5uGq1PbNiI04EUklkVKZzuC2TwHriTUdDZlckVwpY2vwWbBlXjk8iXFnHRGAw2H1ai7n5MSFDkeZ6fjuHKn/7KoxN5BH977riG8dDqCX5whG4d6gS1wDbstCYRmIzqbbDKi/Q6rMusRQn3U233ospmQybXf+yOh+dS8W3zyk5/U4jwIhC2FgaYx3OdW3NnkdZrX7ZaphdlkQMBnr9khdOnrAdDE2eT3WBFP58GV+Oroea2ZW8rA4zRXu1/kEux2gCsJiMRy6Pdf/j3Mshxy+ZIyMTqbEbk8B14QJPdrFYolFDi+pWJ0QDlOdPuhbfjHE7/GW2PLOLine9OPj6fz+O6L49izzYvrD/TWPL7Y23RhLolrd3Upcs5KIBZ/u52bdDY5V0uzlRA1lUR8GFFqGh0AOO3mtnQ2nZ9LIF8oVSN0l3L39UMYnYnj3390Djv63JI2JAjK8neP/wpuhxl/9NtXNvtUCIS6EeO4cmJ0dqsRCyuNTz8mSIPJFeuaGO2wmYiziaAKNZ/aDh06BIvFgtdeew0vvfRS9f8RCAR5hIJezC1lFNvhicSkTaITCXY7Mb9c/wIgHM2CAhDw2SS/Zr34PTYIAhTtuGqUuWVGkb4mEfFYG0XpopWvVZkYnQkCgFxe+rUm2t5bLUYHANfu6kK3z4YfvjoNQdjc3fR/nh0DV+Lxv27fU9fI+KEeN4wGGmOz+uptEqeueTYRR70V11Zch1E6sahUSSed225qy86mU+NRGA0U9g761v13A03jQ3fvh5Gm8PUTZ1Dk9Odk20pkckWcn0vizFSs4aEcBEIzkVsQDoidTUTU0Iosy8FOnE2EJlJTbDpx4gT+5E/+BMlkEgzD4OMf/zgef/xxLc6NQGhrQkEPBADjC8q4myJRmWJTlwPLiVzdRbLhKAO/1wqzSfoOV734K4JLs0rCSzyP+RUGAwqKTb2dDhhoasPooig2dbg3j3HVgzhBTk5Pgtj51GrOJqA8Aee2Q9swGU5vKgq9eW4Zb44t4+7rhhCo83fJZKSxo9elu4l0SUZ0Nm18/YhiU1KHJeEMq/z15mpTZ9PpiSh2D3hh2cRt0OG24g/u3IvpxTSe/Om4hmdHuJRzM4mK+F9qKLpOIDSbXIEDRQFmk3SHuV2hgSWE2vC8gGyeq2uKsNNmItPoCKpQ827x2GOP4bvf/S4++clP4lOf+hSeeOIJ/Pu//7sW50YgtDU7+jygKUqR3qZMrgiG5WSJTQNdTggA5uu0N4ejWU0idEDzxabFWA5ciUewW7mv12Sk0dO5cXRRdHH5FYg32UWxScZCouo0qWPRokeuu6IHLrsJP3x1Zt1/z7JF/Mez5zDQ7cRth7Y1dOzQgBczi2nkC/rpYBHFps06m8QYnR5LwlevN+XEpnZ0Ni0ncghHs7hy5/oTE9dyza4u3HxtED9+fRZvX1jR4OwI6zE6HYeBLrsm9SZStyOP/XAEb55bavZptAVsvgSr2ViX63cjHFYjuBJPOss0IJuvP45OxCaCWtQUm3ieR3f3asdFIBAATTenM4VAaCcsZgMGe5w4r8Akq0i0Ug4uQ2wSi6/ridLxvCA7ttcIPrcFNEU1TWwSd5+VjNEBlYl0GzmbkiyMBgquTcSCehEf2OVY10WniZIdOlpiNhlw8zuCODUeXfd7/sRPxpFiCvj9O/Y03AsWCnpR4gVMKORSVIIUU4CBpjYVB912MygKSOjQ2ZRRwUnntJuRzhXB14hSthKnJ6IAgAM71+9rupTfuWknBrqd+NcfjCCe1p/IuBUYmYljz6APPpdF0SEhhMvJ5Tn87FdhvHSalOMrAVsoyeprAlbXI8TdpD7VdVudzqZcvqTLgSGE1qbmitrr9eK5556r/v25556Dx+NR9aQIhK1CKOjFRDgl++YejpUFoh4Zk+H8HissZkNdJeHRFIsix180ZltNDDQNn8uCaDKnyetdytxyBjRFKe7kCnY7EUvlqwuCtURTLDpcVsmF3mtZjdHJdza1YoxO5KZrgzCbaPzoEnfT2GwCP3l7AbceHMD2XnfDxx3ud4OCvlwKSSYPt8O86Q40TVNwO8xI6tDZxFSKSpW83tx2EwRBnsNPb5waj6Lba6u7O89kNODB39yPIsfjn75/hnQGaUySKWBhhcG+QR9CQQ/GZhM1e+QI0hGLqKcX000+k/aALXCyxSbRaZ2VsR4h1If4PlqPQ9hpr4iAbfT+SNAHNcWmT3/60/jSl76E48eP49ixY3jkkUfIhDoCQSFCQQ+KHI/piLyFUCSWhYGmqnEzKdAUhaDfgfk6OiTCFSeVVs4moCyGNc3ZtMSgt9MOk1FZV+dmJeGxVF6RcnAA1XJIec4m5aeDaY3TZsINB/rwytnFakyxyJXw2A9H4fdY8ds37JB0XLvVhP4up65cCkmmsGmETsTrtOjS2cSwRRhoSvaDzVpclbL0VJv0NhWKJYxOx3Hlzs6GYi29nQ7c955dODebwNO/mFLt/AiXMzodBwDsGfRh14AXiUyhae9rWwGxFiCezleHJhCkkyuUJE08XgtxNmlHlq0/ji5u7JAoHUFpaj45hUIhPPPMM/jXf/1X/Mu//AueeeYZ7NmzR4tzIxDanuGgF4B8R8RiLIdunw0GmRHXYLcTs0uZmjut4Wh5Adcrw0nVKM0Um2aXMujvUt7FNVCJLs6tE12MplhFysEBZZxNTK4Io4GGWWHBTWtue+cABAF47o05AMDTL08jEsvi/tt2b1qwXItdAx5cWEihxOvDgp5iCnDXIzbp1NmUyRXhsMrrBrkUV2XnNtMmvU3nZhMocHzdEbq1vOuKHhzdH8BTJyd1N0mxnRmZjsNmMWIw4EKo8v5Pvv/qsbYWgLib5KOss4mITWqTYeuf6krEJoJabHj1/fM//zP+8A//EJ/73OfWXezZbDbcfvvt2L9/v6onSCC0Mx6HGQGfDefnErj9cGOlxGtRqj8p2OXET99eQCJTgM+1sdARjmbhtJmqTgEt6PRYkUjnUeR4xR1Gm5FlOURTLN59TZ/ix/Y6zXBYjZd1CHElHol0Hp0KlIMD5b4io4GWtZOYyRXhsCn78N8M/F4b3rm3Gz95ex7X7PLjf16ZxtH9AVyxo/EH9rWEgl688NY8ZpcyGOppPIqnNEmmgMGAq+bHeZwWTIZTGpxRYzC5ouIuOnflftUuE+lOjUdhNtLYPeBt+HMpisJ979mN8YUUvvH9M/jrPzjU0hHZVmF0Oo7dA17QNIX+LgfsFiPOzyVw3ZW9zT61tmR+JYNAhx2LsSymImlcKfM+v9VhCyV4HPI2wZTY/CLUhyjo2YmzidBENnxic7nKi1Sfzwev13vZn2KxiI985COanSiB0K6Egl6cn0tK7m3geQFLcaXEprJ7p1aULhJlNHU1AUCX1wYBQCytrbtpfkWdcnCg/MAX7HJeFqNLpPMQAMXEJqC8wJPTkcCwXNs8jN5+aBvYQgn/+/++DZvFiHtvDsk+ZihY7jJUovBfLrwgIM0U63M2Oc1IZ4u6KwXNqCA2ic6mVBs4mwRBwKnxFewZ9MFskuY0sFmMePA39yPFFPCvPxgh3UEqs5LMYSmRw95BH4BydH046NFV11u7Mb/CYLjfjYDPhhmZdQUEcRqdXGeT/Fg/oT6YBqYIi+u7NBGbCAqz4dV37733AsC6glI2m4XdbkcsFlPvzAiELcJw0IOTp8OIxLKSCqhXUiy4kqCI2NRfEVRmlzObOj0Wollcu6tL9us1gthHtZJkEfBpJ3SJQpAYeVOaYLcTJ0+FwQtCtQw8WukT6lCoswkody3JdjYpOIa+mQz2uLBvyIezU3H8r9v3KOLQ63Bb4fdYMTaXwK3vHFDgLKWTqUxcq7ezSUA5dtehoLgpF4blFBVbgdUC1HZwNi3Gc1hOsHjPO6U7YgFgqMeND9w4jP/7/Hk8/+YcbjnY3Gu3nRmdLsflRLEJKIvUp8ajSGULVeedXlmKZ/HfL0/h996zW7LAqSWZXBHJTAH9fieKHI+JBf05OFsNtsDBZpbX2WS3EGeTVjAsB4vZUNeEXVFsIgXhBKWpefU999xzuPvuu3HLLbfg5ptvxo033ojrrrsOAPDoo4+qfoIEQrtTdURI3N2MVMq6AwqITU6bCT6XBXNLl3cIiaSzBWRyRc2dTWJZdlTj3qbZZQY2i3HTWKEcBrqdyBdLWEmsTtoTxSYlH7btVqPMgvBi2zibAOD+23bj996zC0f2BxQ7ZqjiUmi2QyRVKfz2OGtfs97KxyR1Vp4rxjaVxEDTcFiNbeFsOj0eBQBcKaGv6VJuPRjEVTs78fiLFzDThF4brsTj3Exc89fVmpHpOFx2E/rW9P/tqkQgL7SAu+ml0xG8dDqC0Rb5WYmT6Pr8DgwGXFhJsiQiJANBEMAWSrBa5AmNNE3BZjGSgnANYNhiXa4moFy3YDbRbbEZQ9AXNcWmRx99FA8++CB6e3vx8MMP44Ybbqi6nggEgnx6Ouxw2kw4L7EkNBKrTIZTSPwJdjkv6xBaiziJTmuxyeeygKYorCRztT9YQeaWMxjocqjWVSTG82bXCHzRVLmwuUNBgcthMcraSRQLm9uFbp8dN14bVPTnGgp6kWIKWEpoe41eSrIiprjttcVBj7PspkjorCScyakjbrod5rZYTJ+aiKK3045ur032sSiKwh/ctRdOmwn/9N9nNS+5P/HzSXzp//yyOniiHREEAaMzcezZ5qs6WIGys8xooFuiJFw8x7NTrSE2iXUAwS4HBnvK1SCkJFw6XIlHiRcUmRAqN9ZPqA8mxzXkSHfaTMTZRFCcmmKTzWbDnXfeiauvvhoWiwV/9Vd/hVdeeUWLcyMQtgQURZUdEfPSdjYXY1k4rEa4FHowC3Y5EI4yG3a4iOKWlMifHAw0jQ63RdOJdIIgYG4pg6BKEToA6Pc7QAEXCXzRJAu33aRoVMFuNYHJSdtJFAQBTK59OpvUIlRxKTS7t0mKsymR0Y/bp1AsocDxqsQ2XTYT0jpzcTVKvlDCuZm4omXHLrsZ971nNxZWGPz8V2HFjluLWIrFs2/MArh4cli7sRTPIZ7OXxShAwCTkcaOXpfue5uKHI/xSgzt7FRrVGjMrzCwWQzwuSzYVhmWME16mySTy5cAAFaZMTqg7LQmzib1yTbgbALKYhPpbCIoTU2xyWw2o1AoYNu2bRgZGQFN0ygUWnuhRiDojVDQi6V4TtII8kgsi0CHXTGHRrDbCa4kYLEiKl1KOMrAZKQV71OpB7/HqqnYFE2yYAslVcrBRSxmA7p9totKwmMpVvH+HIfViGxe2iKiUOTBlXjFC5vbjd5OOxxWI8bmmutSECNx9XQ2uR0mUICke49aiA8haoibLru55RfTI9NxcCUBBxSI0K3lmpAfoaAHJ05Ogi1o8yB44uQkBEEAhdXYUzsyMl12A+25RGwCyiL1zGIa+UJJ69Oqm6lIClyJx44+N+aWGd3FbtdjfplBn7/sSnbaTPB7rERskoF4T1DG2WQiBeEawLDE2URoPjXFpptvvhkPPPAAbrjhBjz22GN46KGH4PU2PmaXQCBsTGhAem9TJKbMJDoRUViZ22CXORzNIuCzg6bViZVtRqfHelG3kdqI3wM1nU3i8WfXOptSbLWjSikcNhNy+RJ4vvE+ITF+R5xNm0NTVHW6ZDNJMQWYjHRdDwUGmobLYdZVjE5c7KoiNjnMSLXAg/JmnJqIwmI2IBRUdi1GURR+58ZhpJgCnnl1RtFjr8f8cgYvnQ7jpmuD6PRYsdDGMbqR6Th8LgsCvstjj6GgFyVewPiCft1NYoTuN6/fDgAYmda3u0kQBMyvMOj3rzqwB3tcRGySAVtQ2tlERA21YdjGug+dNhPpNSMoTk2x6cEHH8Tf/M3foKenB//4j/+IgwcP4itf+YoW50YgbBkGAy6YjXTDD6lsgUM8nVdUbOrttMNAUxv2NoWjDPr82vY1iXR5bEhkCihy2nSKiALQ2gWrGgS7nFiO55AvlCAIQllsUtjZZK9YqbP5xncTMw2Mz93qhAY8WIxlmypoJJk8PA5z3W5Hr9Osqxidmtebq7JzK0V01QOCIOD0+Ar2DfpgMtaeMNQoIVGpCAAAIABJREFUO/s9OLi7Cz96bVZ1AfLJn07Aajbive8aQp/fgYWV9d20rQ6/pq9pvd/J4X4PKEgfEqIF52YT6Pc7sH+oA3aLUfe9TalsEZlcEf3+1Y2ioR4XlhI50hUkkarYJLMgHBA7m4izSW0YloO9QWcTEZsISrPhSuXMmTPVP/F4HGfOnIEgCDh48CAikYiW50ggtD1GA40dfW6cbzB+sxgru3yUFJuMBho9nfaLYl0iRa6ElQSr6Os1guj2iaW0idLNLWXQ5bXCZlFXZAl2OSGg3DHBsBwKRV6VGB0gbdwwU334J86mWohuk0Z/l5UkyRTqitCJeJ0WJPUoNqlUEC4AyLToA+fCCoNoKq/IFLqNeN+7d4Ir8fj+yUnVXmNsNoG3L6zgziPb4LSZ0NfpQCSW1bycXAsWlhmks8XL+ppE7FYjgt1O3ZaE87yAC3NJhAa8oGkKewd9GJmKNX3q5mYsVDaK1k7+G6z0Ns0sbjwAhbAxubxyMTq71UQ6m1SmUCyhyPENdzZlWa4t78OE5rHhFfi+970PHo8HTqfzsjcUiqLw/PPPq35yBMJWYjjoxf/8YhpsgavbplydRKew+BPscq47ijkSy0GA9uXgIv6K2LSSZBHQQPCaW86o2tckMtDtqL6eoRJPVN7ZVH5wZ3IcsP4zz4ao2aHTbgwGXDBVXIrv2N3dlHNIMQV0NTClzOs06ypeomZs01WZ0JdmCnDb6xfk9MLpiXJ86YCC5eCXEvDZ8e5r+vHiW/O45eAA+hR2dgqCgO++eAE+lwW3HhwAAPT67eBKPFYS2tzbtWRkRuxr2jj2uCvoxcnTYXAlHkaD8o41OcwuZcAWSthVifvvHfLhzbFlLCVyCPj0+bOaq/R/Bddcu9sqE+mmIul1u7MImyM6m2wKxOgcViO4Eo9CsaTIIJSfvD0Pq9mAI/t6ZB+rXRDXbY12NgmVz23F90eCPtnwHe0jH/kI/H4/9u/fj4cffhjPPfccXnjhBbzwwgtEaCIQVGBX0ANeEDBRmfhSD5FYFhSA7nV6IOQQ7HIgmmIvszmLo6l7O5uzwPR7yl/nSlL93qYiV0IkltVEbPJ7bbCYDJhdyiBacW11empPEmsEcXdLSoRATadJu2Ey0tje27hLUUkadTZ5HBaksgXd7Gaqeb25KgvodLY1nU2nxlcQ7HIo7ny8lN+4bggWM40nfjKu+LHfPLeM8YUUfuv67dUHTVHQasfeptHpOLq9tur713qEBjzIF0uYXcdR3GzOVRxXuyquzX1DHQCg6yjdwgoDh9UI95r7oNtuRofbgplF/QjrrYSSBeHVzS8F3E2CIOC/fjqB/3z+QsvGo9VA3LRp5H3UaRc3JVvz/ZGgTzYVm37wgx/g/vvvx7PPPou77roLjz76KC5cuKDl+REIW4ad/R5QVGO9DYuxLDo9VkV2htYiCizzKxcvfMPRsrjVrBid12WGgaY0mUi3sJKFIAADKpeDA+Vi6WCXA3NrxCblY3TSF3fVRQvpbKqLXQMeTEcymk30WkuJ55HJFi96yKqF12mGIAApRh8LTCbHwWSkYVH4vgasOptSWf3EBusll+dwfi6paoROxG03484jg3j7wgrOzSgnKnAlHk/+dBx9fgfedeWqC6G3oyI2tdlEOp4XMDqT2NTVBKyJ3+owSjc2m0CX11p9Twr4bOhwWzAypd+S8PkVBv1dzss6sgYDLkzpyMXZSihZEC5n8+tSFuM5ZHJFJJkCRhW8V7U64maxvcEYHdC6mzEEfVLTq3vw4EF8/vOfx4kTJ7Bv3z58+tOfxvvf/34tzo1A2FLYLEYMdDkbckSEY1lVIgeiwHJpb1M4yqgibtWLgabhc1kQ1UBsEneY+7u0iQwGu52YW84gmmRhNtJwKezqkOtsMhvppv3cW41Q0NuwS1Ep0tkiBKDhziagXCyuBzJsUTVhs5WdTWenYijxgqoRurXccnAAPpcFj784rlg/z89PhbEYz+H9x3fCQK8uQe1WI3wuS9uVhE8vppHLczVjWz6XBV1eK8Z0VhIuCALGZhNVVxNQrtLYO+jDyHRcl04SQRAwv8ysO9hjsMeFxVi22j9EqB9lO5vEDkn5Pwex8oGmKLxydlH28dqF6lTXBmJ0Lpv5os8lEJSgrmB4sVjEz372MzzzzDOYmZnB3r171T4vAmFLEgp6MT6fqivOIggCIrGsKi4jn8sCm8WIueWLd5kj0WzT+ppE/B4rljWI0c0tZ2Ay0pp1UgS7nGBYDuPzSXS4rXVPEqsXObZ1JseRCF0D7Oxr3nQpsejb7ag/humpiE2JtD7cPkyuqFo/mNNmBAUg3YLOplPjUdgsRuzs92jyehaTAb91w3ZMhlN4fXRJ9vHYAoenTk5iV9CDq4YvF8z6Ou1tF6MbnS47LfZuq90RFAp6cX4uoavi7YVoFplcEbsGLnZm7RvqAMNymFnSn0sokSkgl+fW3SgaDLggALqMK+odtlCCxWQATctfm4hOayUm0o0vJGGzGHF4XwBvnltCkSvJPmY7kJHgSHfYyh+bJmITQUE2FZveeOMNfOYzn8GxY8fw5JNP4s4778SLL76Iz33uc1qdH4GwpWiktyHJFJAvlFQRmyiKwkCXA3PLq+fBV8StZvU1ifi9Nk1idHPLGfT7HYosrOpBdJNNLKTQ6Va2rwkodwmZjbSkaXSZXJFMomsAu9WIgSZNl0oyZRGlMWdT+WMTOnE2qSk2GWgaDpup5ZxNgiDg1EQU+4d8mhZIX3dFL4JdDjz503FwJXmdXj9+bRYppoAP3Di8rpje63cgEs2C15HYIpeR6Tj6/I6qoLsZuwa8SGeL1cEfekCM9e3adonYVHFq6bG3ab6ybtnI2QRAVwMRWgW2UFLE1QTIm457KRfmk9jZ58a7ruhBLl/CqfGo7GO2A6sxOuJsIjSXDVcsN998M/7iL/4CHR0d+I//+A888sgjeNe73oVsNotEQn+ZcgKhHRiu7Fifn63tiIhE1ZlEJ9Lf7cTcMlPdZY0lWRQ4vvlik8eKZKag+u7V3FIGQQ36mkTEXVgBQKdHnfJfu9UoubPJaSN9TY0QCnoxsZCS/YDeKKmK2OR21i82uR1mUFh1RTWbDMupKm667KaWczbNLmWQzBQ06WtaC01T+MCNw1hOsHjxl/OSj5NiCvjhazN4x66uDZ1ZfZ0O5IslxFLqbyZoAVficX4uWZerCQBCwcr7v46idGOzCXicZnRfMt3S47Sg3+/QZW/TfKX3a70pil6nBR6nGdOkJLxhypOSlRGb7Ao5m7Ish4VlBsP9HuwZ9MLtMJMoXQWGLYKmKNgs9f/MzCYaRgNNnE0ERdlQbJqfn8fCwgK+/vWv473vfS+OHj2Ko0eP4siRIzh69KiW50ggbBk63Fb4Pda6epvE3U+1xKaBLidyeQ6xVNntEK68nh5idAAQTannwkgyBaSyRU0m0Yk4rCZ0VBxNak2acthMkhZ3DEtidI3SrOlSYu+Sp4GxxUYDDZfdhERGH86mTK6o6vXmspuRajFn0+mJ8m79lRr1Na3liu0d2Dvow3+/NCW50Pf7L02iWORxz/EdG35MdSJdm/Q2TYZTyBdLNfuaRHo67HDZTbopCRcEAecqfU3rOdH2DvkwNpfUXWxpfpmB22Gu9rNdymDARZxNEmALJVgtymw62S3KOJsmwkkIKA/YMdA0Du3pxq8uRBWJ57U6DMvBbjU2VMlAURRcdlN1IiyBoAQbik2jo6PVPyMjI9U/4t8JBII6DAc9OD+XrNnbEIllYTbS8KkQuQJWJ9LNVizp4cpuYU/TnU3lHdYVFXubxPjggEbl4CLi97xTLbHJYpRcEE5idI3RrOlSKaYIi9kAS4M70B6nRRfOJkEQwOSK1e4INXC3oLPp1HgUgwFXtcxdSyiKwu/cOIxMroj/eWWm4c9fjGfx07cXcOzqvk03K1bFpvbobRqZjoMCsHvb5pPoRCiKQijoxVgDQ0LUZCXJIp7OX9bXJLJvqANFjq8WNOuF+ZX1y8FFhnpcWIgyyBf1JZLpHTbPwaaQs4mmKdgsRtmi0Ph8ChSAHX1uAMCR/T3gSjzeHJPfMdfqMDlpgzYcVhMyLbYZQ9A32gX/CQRCXYSCXiSZApYTm4spkVgW3T47aIWLpEXEWJfYfxCOZeG0meBuwDGhBqKzSc3eJnEKX7+GMTpgtbdJLbHJbjU1HKMTH/7V6tBpV3wuS8WlqO2DWJLJN9TXJOJxmhHXgbOJLZRQ4gVVrzeX3dxSnU0MW8SF+aTmEbq1DPa4cHR/AM++MdtwzO2/fjoBg4HCb143tOnHld9fTAi3SUn46HQcAwFnQ9dyKOjBcqIs8jQbsXNu9wZi0+4BL2iKwtlp/fQ28YKAhRpi02DABUEgJeGNUu5sUm4TwCEx1r+WC/NJ9Hc5YKs4pbb3utDtteFVEqVDVqIj3WU3VcvFCQQlIGITgaAz6u1tiMSyqrqMbBYj/B5rdUEWXmGa7moCyp0LBppCVEWxaXYpA4/DrLmwtmebD2Yjjd5NFspyKC/uGltEiA//ajpN2pVmTJdKMQVJYpPXaUFSB2KTeH02Mq65UVx2E5hcsa6pn3rgzGQMggAcaKLYBAC/fWwHBEHA9342UffniJPsbnvntrpKsns7HW0xka5QLOHCfBJ764zQiYguonqi9GozNpuAw2pE3wYOX5vFiB19bl2VhMeSLPLF0obnDJCScKnkFOxsAsQOSemiBi8ImFhIVrtOgbI78PC+AEam47qJhTcLhi3CLsXZZCPOJoKyELGJQNAZfX4H7BbjpmITV+KxkmBV62sSCXY5Mb9cXviHY1n06UBsomkKHW5LTeeXHKYi6eqCVEv2b+/A1z52TJJYUA9SnE1aPPy3K7sGPEhli1iMq3etXkqSKcAtSWwyI8kUwPPNnQTG5MrXp9qdTQKATK41ej1OjUfhsBqxo9fd1PPwe2y45R0DePnXEczUUbAsCAK+++IFuOwm3H54W12v0ed3YGElq6lAqwYX5pPgSkLDYtNAtxNmE13XkBC1GZtNIBT0buqe3jfkw1QkJbnLS2nmKhHMoH9jV7LPZYHLbiJiU4Mo2dkElONacmJ0CysMcvnSZQMHjuwPQBCA10a2dpSOyXGS1m0uG+lsIihLXWITy7I4d+4cBEEAy7bHlBACQa/QFFXpbdp4Z3M5kQMvCOhVW2zqdiASyyKRySOdLaKno7nl4CJ+j001Z1MuzyG8wmB7kx7sDLR6ewAOmxH5QqmhCWlaPPy3K83obZLjbBIENL3LSFzkqhujKx+72V9rPfCCgNMTUVyxoxM0rU5kuhHuetcg7FYjnvjJeM2PPT0RxehMAndft70ac6lFn9+BXJ5DQgf9YXIYmY6DrnQwNYLRQGNn3+bv/1qQzOSxGM9t2Ncksm+oA4IAjM4034kFrPZ99fk3XhtRFFUuCdfxRLpMrojx+eYLjmspx+j042wSvz/Dl4hNvZ0ODAZcePVsRNb5tTpynE0MWwTf4oI/QT/UfKp5++23ccstt+BDH/oQFhcXcfz4cbz11ltanBuBsGUJBT0IR7MbPgxFouVpPQENnE0lXsAvx5YBAL06cDYB5d4mtTqbZhbTEFDO/rcbYsl3Nl//bqKY3SedTY3T22mH02bSrLepyPFgWE5aZ5OjHHFq9kO++PChprgpxmNbobdpOpJGOlvEgSZMoVsPh9WE975rCL+ejOHM5MZj73lewBM/GUe314bjV/fVfXzRPdvqvU2jM3Fs73XVLbKtJRT0YHYp09SJWmOVe1YtsWlHnxtmE42zUxtfC1oyv5yBz2WBvYajY7DHhYUVRneT9ER+/PoMHvn2W8g18F6tJlyJR5HjFRWbHFZ5BeEX5pNw2kzo9tku+7fD+wKYDKexGGuPyZaNwgtCubNJorNJEEAm+hEUo6bY9Oijj+Kxxx6D1+tFT08PHn30UXzhC1/Q4twIhC2LuBt6YYOdrUjlDVSLGB0AvD5atiOr1SXUKH6PFUmmgIIK02Qmw+XdzqEmR1bUQNzlamQRwVScJlKmmmx1ytOlPJpNlxLFaUkxOlf5c5rdc1F1Nql4vbWSs+nUeBQUgP07Opp9KlVuujYIv8eK7754YcPd71+ciWBumcE9x3fAaKjfrdnbBhPpcnkOkwtp7GkwQieya8ALAcD4QvOcLWMzCVhMBmwLbD4kw2igsXvAp5veplqT6EQGAy6UeAFzy/q8zpbiOZR4oa64qhawhfJaS8mCcCmx/rWMz6cw3O8BtU7M8/C+ACgAr2zRonA2z0GAtHWbuLFIonQEpai5AmBZFsPDw9W/Hz9+HKWSPncCCIR2YXuvC0YDtaEjIhLLwu0wS7LINkKgwwajgca5mQSMBhp+laakNYrfU97JijY4FakeJsMpdLqtTZ+6pwbiwoNpYBFRFZuIs0kSoaAXS/GcJuXbSaYsnogupUbwVj5HPEazyGhwvbkcreNsOj0RxfY+t67uRyYjjXuO78DMUgavnLk8qlLkSvjezycw1OPCwT3dDR3b4zDDbjFiIdq6joTzcwnwQuN9TSI7+zygKao6Da4ZnJtNYLjfXZdQuG/Ih0gs2/CUQqXheQHhaLY6SXczhnReEi6ubSbCqSafSRm2UBaFbAo7m7gSL2nTMJMrIhLLYmf/+puCPpcFu7d58crZxZbvf5NChpVef+C0E7GJoCw130WMRiOSyWRVOZ6YqH8KCYFAkIbJaMBQr3vD3oZILIuedazDSmOgafT57RAA9HTYdNEZAgCdnrLopUZv02Q41ZYROgDVaEEju4nVRQspCJdEvdMllSCZke5s8jgrzqYmj1xnchwsZkNDbphGcVpNoFDut9IzqWwBkwsp3UTo1nJobwCDPS78188mLosiPffmHGKpPD5w4/Cm5dLrQVFUpSRcn46TehiZjsNooC7rkqkXi9mAwR6npl1va2HYIuaXMwjViNCJiKLayHRz3U3LiRyKHI++OpxNnR4rHFYjpnQqNsVS5fuw6LRuNlVnk4IF4VLWIyIb9TWt5cj+HizGsrru5lKLVUe6BLFJdDa1wGYMoTWouZp78MEHcd999yESieBjH/sYPvjBD+LDH/6wFudGIGxpQkEPpsLpdXd9IrEsejTqTxKjdL2d+ojQAeUYHQDFe5vS2QJWkmzTysHVxlGN0TXmbLKYDDAZyfBSKQz2uGA20ppE6VJZ0dnUuNhkNNBw2kxI6MDZpPbkQ5qm4LCZkNb5zu2ZiRgEAFfu1J/YRFMUfufGYcRSeTz3xlz1/2dyRfzg5WlcuaNTsrOnz29v6c6mkek4hvs9MJuku0BCQS8mwmkUufqHOSjF+bkkBAC76xSbgt1OuOympvc2zVcEyv5NJtGJUBSFwR59loRzJb4q+k8u6MXZJMbolHU2AY2tR0QuzCdBUxSGejZeq71jdxeMBgqvnNl6UTqxKkFqQThAnE0E5aj59HDTTTfhq1/9Kh566CFce+21+Pa3v43bbrtNi3MjELY0oaAXJV7A5CU2aoYtajoZblVs0kc5OFCenGWgKSwnlR0pL+5ytmNfE7C6y9XITiKTK8JpI31NUjEaaOzoc2vkbCo/oEhxNgGA12nWJO63GQxb1KSM3u0wI61zZ9OpiSjcdhMGe/TptNw76MOBnZ14+hfT1QeT//nFNHJ5Du9/907Jx+3tdCCdLVbF01YikytidjEjua9JJBT0givxmIpoLzaMzSZgNFB1b7rQFIW9g+XepmZGluaXMwA2n0S3lsGAC/PLmYams2pBPJ2HAKDf70A0xTY92gyUO4AAwKZoZ1Ml1i/R2TQQcMKyifjlsJpw5Y5OvDqyCJ7fWlE6OYM2XERsIihMTbHp9ddfx9LSEkKhEHbv3o1EIoFf//rXyGQyWpwfgbBlEe3Blz6kiuXggQ71Y3QAEOwui1p6cjbRNIVOj1XxGN1kOAUKq30O7YZdwk5iJlckETqZDAe9mFlMqz5ZKMUUYbcYJbvQvE5L0wvCmVwRDg3ETZfNpOuCcJ4X8OuJKK7c0dlwFE1L3v/unWALHJ5+eQrRJIvn3pzD0St6MNBd212yEWIMKtyCUbpzMwkIAPZskyk2DWgXv72UsdkEtve6G3Jm7RvqQJIpNLVra36Fgd9jrbvEerDHBa4kYF5nJeFi95XYd3bphmMzUMfZVJmO26DYVOJ5TIRTGO6rHVM9sr8HyUwB52b0UWCvFUy1/qDx91Kr2QADTRGxiaAYNa/CL37xixgdHUUoFAJN0xgbG0NXVxdyuRy+8IUv4JZbbtHiPAmELYfTZkK/33G52BTVZhKdyN5BHz54SwjXhPyavF69+D1WxWN0U+E0ejrtksZVtwJGAw2LydCYs4nlSDm4THYNePD0y+Wy1/1D6k0VSzL5aveSFDxOczWK0iwyuWK1k01NXA5z1QmhRyYWUmBYTpcRurUEu5y4/spePP/mXPXa+e0bdsg6Zl9lYyMczWK3TNFGa0an4zCbym5GObjtZvR22jE2m8CdRwYVOrvasAUO05E0bj+8raHP21dxcp2ditU1DU4N6p1EJyI6BqcX07pyD4rl4NeE/Pj+S5OYXEjh6uHmrr9ylYJwJcWmVWdTY6LG3BKDQpHHzmDt37GrdnbCajbglbOL2Kvie6/ekDNFmKIoOG0mZHL63YwhtBY1tz/7+vrwb//2b3jqqafwve99D9/5zndw7bXX4qmnnsLXvvY1Lc6RQNiyhIIeXJhPXmQBXoxnQVMUurzaOJsMNI1bDw7I6p9QA6XFJkEQMBFObdoB0A7YrcaGFneZXJGITTLZ2ecBRUH1wt8kU5DU1yTidVqQYgobjrPXAoblNHHSuewmXReEn5pYAUUB+7fr/wHpt27YAQNN4cxkDLe8IyhbLOxwW2AxG1qyJHxkJo5dQa8iBfehoAcX5pKa/j6OL6RQ4gXsqrOvScTvtaHba8PIVHMcJFyJRySaRV8dk+hEurw22CwG3U2kEx3bvZ129Pud+nI2KbgRJ9XZdEEsB6/D2WQ2GfCOXV1449zyZYMM2pksy8FsomEySlu3l8UmdZ3YhK1DzXfD2dlZHD58uPr3AwcOYGpqCj09PZJf9KmnnsJdd92Fu+66C1/60pcAACMjI3jf+96H2267DZ/85CfBceQiJxBCQS9yee4it0EkmkWX16rqtKZWoNNjQ4opSBqbux7xdB4pptC2k+hEHFZTQ4s7rTp02hmbxYiBbqfqkZgUU5Dc1wSUxaYSLzRtCg0vCGBYbcRNt90MhuVQ4vXV1yJyejyG4X5PS0RYfS4LfvOG7fB7rLjzqHwXDkVR6O2wY6HFSsKTmTwWVhjJxeiXEgp6kc1zWNAw5nV+NgGK2nzK10bsHfJhdCbelN+pxXgOJV5AsI5ycBGaojAY0F9JeDSVh9thhslowI4+FybDqaZ2YQGrnU2KOpss0pxN4wtJeJzmukXtw/sDyOU5nBpvboG9lmRYefUHTpsJGR3HzAmtRc2nVaPRiJMnT1b/fvLkSZhMJsRiMUmCkBi/+9a3voWnnnoKb7zxBl5++WV84hOfwKc//Wn86Ec/giAIePzxxxs+NoHQbqyOTV91RERiWc0idHpGnEgnWs7lIu4etuskOhGH1Vh3jE4QBDA5TpIVm3Axu4JejC8kVS2jTcoUm0RXVLN6m3J5DoIATcRNl12/450TmTymF9M4oPMI3VruODyIRx48qtjPrs/vaDln00ilF0ZuObhIqOIu0mKSpcjYbALbAi5JUfJ9Qx1gCyVMhrUXb8Rrpa/BCN+2gAuzSxldic6xFItOtwVAeT3CsByWEsoOQ2kUtlCC0UAruslJ0xRsFmPjzqa5JIb7PKDq7LLbO+iD227Cq2cjUk6zJWFyRVnrNqfNhIyE4nYCYT1q3jUefvhhfOpTn8KNN96I48eP47Of/Sz++q//Gt/85jdx7733NvyCpVIJPM8jl8uB4zhwHAej0QiWZXH11VcDAO655x4888wzjX81BEKb0emxwus0Vx0RvCBgMZ5Dj44mwzULUWxSKko3GU7DQFPYFpBebNsK2K3GugvCc/kSeEFoCXeF3gkNeFEo8phZVKcnKF8sgS2U5MXoXOUHnESmOTuaGRk9E43ispe/T2kdik2nJ6IAgCt3tI7YBEDRIvM+vwOJTKHhB9FmMjodh81ixGBAGXds1yXv/2pT5HiML6SwK9hYhE5kzzYvKJR7m7RmfjkDimp8au5gjwtFjkd4pXnF5pcSTbHocJfXN+Lm1+RCc6N0bKGkqKtJpJHNL6DsHlxJstjZgPPOQNM4tDeAty9EW+p+Iocsy8Eux9lkJ84mgnLUXNEdOHAAzz//PMbGxmAwGLBz504YDAbs2bNH0gs6nU786Z/+Ke644w5YrVYcOnQIJpMJXV1d1Y/p6urC4uKipOMTCO0ERVEIBb1VZ1MsxaLI8cTZBMDvKXdWrSi04zcZTqG/yyE5494qOKwmTLH17TyL9nYSo5PPWpei3PLg9RD7h2TF6Cqfm2ySs0kUm7S43twVZ1NKhwvq0+NReJ1mWRPdWp3VknCmoQfLZjI6ncDuAS9oWhnRjaIo7BrwYmw2AUEQ6nZySGUqkkKR4xvuaxJx2c0YCDgxMhXH3ddtV/jsNmd+hUG319Zwt6Q4eXYqkkZQB79vgiAgmmKrQnN/lwNmI42JcApH9kuvL5FLrsCpIjY1svkFABfmy6JbozHPw/sDeO7NObw1tozrD/Q29LmtCMMWZfW6ip1NWtx3CO1PTWdTLBbDt771Lbzwwgv48Y9/jK997Wv4+Mc/LvkFR0dH8eSTT+LFF1/EyZMnQdM0Xnrppcs+jlzcBEKZUNCDWCqPaJJFJKbtJDo943GaYTRQijibeEHAVCSNHW0eoQMaKwivOk00GEXf7nidFnR7baq5FJIVscnjsEg+hscpOpuaIzYxlUJSLcQmp06dTfliCb80e0OhAAAgAElEQVSejOHAzs4tvQ7q9Zff41olSreSzGEpkVOsr0kkFPQins4rFhffjLHKAIPQgHRxb99QBy7MJ5EvaFvGPL/MoL+rcbEo4LPDYjLopreJYTkUijw6K84mA01jsMeFqSZEE9fC5kuqTOl1WE1g8vW7jcYXkjAaKAz2NPaz3tHrRrfXtmWidHIHbThtJvCCgFwDPxsCYSNqik0f/ehH8fLLL+PJJ59EJBLBiRMnQNPSM7snT57E0aNH0dnZCbPZjHvuuQevvvoqVlZWqh+zvLyM7u5uya9BILQToYql/fx8ApEoEZtEaIpCp1uZiXRL8RxyeQ5DW0BscthMKBT5urqDiLNJWUJBD87PJVQpe01mRLFJurPJZKThsBqRaNKUtuq45i3sbHprbBlsoYTD+5rnYtADXR4bjAa6ZUrCR6fLQo3yYlPFETmrfpRubDaJPr8Dbrv0e8i+IR9KvKBpz1SRK2Epnmu4rwko9wZtCzh1IzaJk+jEGB1QjtJNL6ZV7furBauis0m879fDhfkkBgOuhh3oFEXh8L4Azk7Hm+bc1ZLyoA15nU3A6oYjgSCHmqrRwsIC/umf/gnHjh3Dfffdh+985zuYmZmR/IJ79uzByy+/jGw2C0EQ8MILL+DQoUOwWCx48803AQAnTpzAsWPHJL8GgdBOBLsdsJoNOD+XxGIsB5vFICsq0074PcqITVulHBxY7cOppydhtUOHiE1KEBrwIp0tVh2KSiKKJnLvDV6nBYl0+8foHDYTKEp/zqaTp8Lwe6zYvU1alKldoGkKvZ12hKP66dLZjJHpOFx2E/q6Ghc8NiPY5YTNYrhoSIga8LyA83MJ7ArKiyyGgl4YDZSmvU3haBa8ICAo8Xs/GHBhZjENnm/uxDdgdeCJf82ktR19bhQ5HvMaTiW8lHJnkxrOpvoLwrkSj6lwWnKs9vC+AAQBeG1kSdLntwpFjkehyMt2NgFAmohNBAWoKTb5/X4AwNDQEMbGxhAIBCRNoRO5/vrrcdddd+Gee+7B3XffDY7j8MADD+DLX/4yvvjFL+KOO+5ALpfD/fffL/k1CIR2wkDT2NnvwfnZJCIxBgGffUvHK9bS6bEhmpTf2TQZTsFspNHnb3/HmL0iNtXTk6BlrGkrsNrbpLxLIZnJg8LqlDWpeJ3maiRPaxi2CAqrI7HVhKYouHQ23nklmcPodBzXXdmraNl2q9IqE+kEQcDoTBx7tvkU/7nRNIXhfi/GVC4Jn13KgC2UJPc1iVhMBgz3ezAyFVfozGojdRKdyGCPC4Uir8omQKOIYlOHezUOXS0JDzevJDynUkG43WqquyBcdHc12tck0ud3YFvAiVfOtncnsLi2kzuNDkBDrjMCYSNqXomdnZ345je/iauvvhr/8A//AKfTiUxG3jSdBx54AA888MBF/2/Pnj144oknZB2XQGhXQkEPnvr5JBIZE67Y0dHs09ENfo8VqWwR+WIJlgaLQdcyFU5jW48LBhkR4VZB3O0ShaTNEBcadg2mg20FejrscNpMOD+bwLGr+hQ9doopwGEzyR5N7XFaEJnR7kFxLZlcEXarUbGC5Vq47GakdORsevl0BAKA667Y2hE6kd5OO149u4h8oQSLCg+6SrEYzyGeziseoRMJBT04/bMoMrmiasK/2NckV2wCgL1DHfjezyaQyhZkRfLqZX6FgYGmJNcLDFZKwqcjacmClVJEkyzMRvqin7PfY4XTZsJEOIV3X9PflPNiCxxsFnWm0XElHoViqWa5+3ilHFzOwIAj+3rw+IsXsBjPIuBrz83FTEW8kxNHd9pJjI6gHDVXpZ/97GdhNptx8OBBXHHFFfjKV76CP//zP9fi3AgEQoVQ0AsB5Rs/6WtaRbSaR2VE6Uo8j5nFNLb3tH+EDlgVjuopCc+wRdgsBtkCBqFMebqkRx1nE1OAxyn/wc7rtCCRKajSK1ULhuU06WsScdlNSOvE2cQLAk6eDmPvoA9+GVOE2onqRLqYvt1No9NlcXaPSmKTKACpGaUbm03A77Fe1BUklX1D5e+D+H1Rm/llBoEOu+T3qd5OO8xGWhe9TbEUi06P9SL3OkVR2N7rbqqzSa0YnV3c/KrD3XRhPolOtwU+l/QhGIf2doMC8OqZ9nU3ic4mOZuE1c4mHW3GEFqXmnfmL33pS9VI2yc+8QmcOHECt956q+onRiAQVtnR64ahsttPxKZV/J7yQ5mc3qb5ZQYFjsf2XpdSp6VrnJXFXT09CUxO3kQTwuXsGvBiKZFTfOJbiinIKgcX8TjNKPFCU3Y0M7miptebnpxNYzMJrCRZXH9l+4/lrhfRZRJeaX68aTNGpuPwuSwI+NQRCbf3umA0UKqVhAuCgHOzCUVcTQAw1OOCzWLAWY2idAsrjCxHkoGmMdDtxHSk+WJTNMWuK/ht73VhYZlpynQwXhCQVylG52gg1j8+n5TlagLKxeu7t3nxytnFpmyoaIHoWpfzXmqzGEFTFDJ1Ti4mEDajptg0Ojratr+QBEKrYDEbsC1QFkOI2LSK31telK3I6G2aqiwwt0I5ONCYs4lhtX343wqI0yUvKOxuSjIFRQYHeJ3lXeNERnvHj5oxofVw2fXT2XTydBg2iwHX7u5q9qnohm6fDQaa0vVEOn5NX5NaXYomowFDvW7VnE3haBaZXFExsclA09izzadJSXi+WMJyIoegzPjbth4XphfT4Jv8vBNN5dHpvty5s6PPDQHATBPcV/lCCQBUcjbVN7AklmIRT+dli00AcGR/DyKxLGYW5VXC6BVxbSfHJUxTFBw2I3E2ERShptjU1dWFu+66C3/5l3+Jz3/+89U/BAJBW3YPeGGgqbbNmUvB7TDDaKBlOZsmwynYLUZ0q7QrrTdWC8Lr62xyyhifS7icbQEnTEYaF+aVE5sEQVDM2eStRPGaMR6ayckb19wobrsZDMs1daQ4AOTyHN44t4R37gnI6p5rN4wGGt0+m65LwheWGaSzRdX6mkR2Bb2YiqSRL5YUP7bY17RbIbEJAPYNdWAlyWIpIX+Ax2aEowwESC8HFxkKuMAWSliOq3u+m1HkSkgxBXSu42waqmyGTTQhSseKYpMqnU31Oa3F90up5eBrecfuLhhoCr84E5F9LD0iCndyCsKBcpSOdDYRlKCm2HTNNdfgzjvvRH9/P7xeb/UPgUDQlrveNYhPfPAaXRelag1NUej0WGWLTUO9ri0z4c9A07CaDXXZozO5oqYdOlsBo4HGUI8L4wqKTWyhhALHw+OQ3mUh4qk4m+LNEJvYYjXmqQUunZSgvj66hEKRx/UHSITuUvQ+kW6k2tek7ro4FPSgxAuYWFBebBibS8DjMCu64SKKbyMqu5vml8vXRn+XPLFJLAmfamKULpYq33PXi9G57Wb4PVZMqvDzrwVbKIsX6kyjq89pfWE+CbOxHHeUi8NqwoGdnXhtZBE8337JHSZXnupqkznVlYhNBKWoeSV+5CMfAcuymJ6eRigUQqFQgNUqv0CQQCA0hsNqUszm3k74PVZEJcboilwJ88sMbj+8TeGz0jcOq7E+Z5PGhc1bheF+D378+iyKXAkmo/wFfIopR8HcDvk/K69DdDZpGy/jSjxy+ZLGMbry15rOFqvxwWZw8nQYPR127OzbGlHeRujrdOCtsWUUOR4mo/4GFYxMx9HttVX7A9UiFPSAQrkkXEkXlSAIODeTQGjAq+iGS2+nHV6nGWen4jh+tXoT1OZXGBgNlGyhrM/vgNFAYXoxjcP7AgqdXWNEU+VNs/WcTUA5SidOZNMSVsUYXb3OpvH5FIZ63YoNKzm8L4Bfnl/BuVllf5/0QJblylNdZf4+O20mLKvsTCRsDWr+1v7qV7/CLbfcgg996ENYWlrC8ePH8dZbb2lxbgQCgVATvwxn08xiBiVewNAWmUQn4rCaai7ueEEgnU0qsbO/7FKYjijTGZGsiE1KOJvMJgPsFqPmYlNWgXHNjSI6m1JN7G2KxLK4MJfE9Qd6t4y7shF6/XYIArAY019JOM+Xi7XVdjUB5ald/V1OnJ9Vtrcpmix34SgZoQPKE9T2DXVgZDquag/S/DKD3k4HDLQ8EcJooBHsam5JeFVs8qwvNm3vdSOaYqv3e60QS8ltajibLLWdTYViCTOLaezsV26ddvWwHxazAa+ebb8onVLrNqfNhDRxNhEUoK5pdI899hi8Xi96enrw6KOP4gtf+IIW50YgEAg18XusSGeL1RLLRhBHCW+VSXQidquxpm09l+cgCNDUabJVEEtOleptSladTfI7mwDA67IoPi2vFqJdX8vOplVnU/PEppdOh0FRwNH9PU07Bz3T11mOR+mxJHx6MY1cnsMejZwRoQEPLiykUOKV6xg7VxGv1HBN7x30IZMrYm5JvSLmhZUM+mX2NYkM9rgwHUk3bShSNMmCAuBzrb9pIA4xmdS4t0lNZxNNU7BZDJtufk1F0ijxgiJ9TSJmkwHv2NWFN0bLrsl2gqk4m+TitJvA5IpkSBhBNjXFJpZlMTw8XP378ePHUSopX1BIIBAIUhB3AVdSjbubJsNpeBzmDRd37Uo9zqbqw78CixbCxXgcZnR5rYr1NqWqziZlxCaPw4wEo63YJIqfWoqbojiXZpqze8vzAl7+dQRX7ujccvegeunpsIOioMveptFKX9PebdqITbuCXuQLJcwqKN6cn0vAbjHK7jxaj31DHQCAs1NxxY8NlDdEoqm8Yuc+2ONCNs/J6oCUQyyVh8dp3jAqNhhwgaYozXubqp1NKhSEA+X1yGbT6MT3yZ19yolNAHBkXwDZPIfTE1FFj9tsGFaZrk2nzQSuJFTFRgJBKjXFJqPRiGQyWbV3T0xMqH5SBAKBUC9iV8aKhGz5VCSF7b3uLRdfsVuNNQvCmZz2saatxHC/Bxfmk4rsGiaZPGiKUkyo8TrNSKS1dfusipvaXW9ir0U61xxn05mpGOLpPK6/khSDb4TZZECX14aFqP5idCPTcfT5HdVSfbUJBcsP2+dnlRsucG42iV0DXtn9Luvhc1nQ22nHWZVKwkUBUu4kOpHBQNnh3KwoXTTFbhihAwCL2YA+v6OtnE1A+T6c3WQ9cmE+iW6fTTHnrsjeIR/cdhNeObuo6HGbDZMrKrJJKK4nGBKlI8ikptj04IMP4r777kMkEsHHPvYxfPCDH8SHP/xhLc6NQCAQatIlOpsa3I3Mshwi0SyGtliEDqjP2dQMp8lWYme/B0mmgKgCu+jJTAEuuwk0rcwDo9dpQZLJa2qfF8UmLa83mqLgtJuQapKz6eSpMBxWI64a9jfl9VuFvk4HwjpzNjFsUfNy4Q63FX6PFWNzyvQ2JTN5LMayCA0o6xhZy77BDozNJlSJKs1XrgmlYnTBLicMdLkkvBlEU+yG5eAiO/pcmAynNL03q9nZBFScTfn11yOCIGB8Pqm4qwkoT+Z9594AfnVhpfo1tgMMyynW2QSA9DYRZFNTbLrpppvw1a9+FQ899BCuvfZafPvb38Ztt92mxbkRCARCTdwOM0xGuuGH9ulICgJWexC2Eg6bEUWOR5Hb2B5NYnTqMqxgb1OKKSgWoQMAj9MCriRsGm1QmqqTTuNCepfd1JTOpkyuiF+eX8aR/T26nLKmJ3r9dkRiWXAl/XSrvHQ6giLH44YD2rrSQkEPzs8mFBEbxubK9x41p9zuG/KhwPGYWFDOjSUyv8zAbKTh9yozCdBkpNHvd2CqCc4mXhAQS+XRUUNs2t7rBsNyWNJwShhbKIGmKNXuU/ZNpuMuJ1mkskUMB9URRI/sC6DI8XhrbFmV42uNIAjIspwi3YcuW3lNQZxNBLnUvHN87GMfQzgcxu/+7u/ivvvuu6i/iUAgEJoNRVHodFuxkmxs8TVZWVAO9Ww9Z5O98kC/mZjANMFpspXo73LAYjYoMso6yRTgdionNnkrx9KyJJxhizBUymK1xG03N2Xn9tWzi+BKAonQ1UFfpwMlXtDNGG5eEPDiW3PY2e/GtoC27x+hAS9S2SKW4vK/F2OzCZhNdDU+pga7t/lAUcAZFXqb5lcy6PU7FI0AbmtSSXg6WwRX4ms6m6ol4Rr2NrGFEqxmg2p1Aw6rcUNBY3xO7GtSZ1NwR58bXV5r20Tp2EIJvCDAbpG/bhMFK+JsIsilpth06NAh/O3f/i1uvfVWfOMb38DycnuovwQCoX3we6wNx+gmwyn4PdbqRKqthOhW2kxsEp1NSkw1IVyOgaaxo9etjLMpW4BHwevYW+mgSWa0c/yIPRNa96e57CakNR4lDgAnT4cx0O3E4BYUuxtF7ORZWNFHb9PIVByL8Rxuuiao+WvvCpZdSGOz8qN0Y7MJDPd7NiykVgK71YjtvW6MqNDbNL/CIKhQhE5kqMeFTK6IWErbAQmiM7uW2NTf5YDZSGNCw94mNs+pVg4OlDe/NlqLXFhIwmI2INjlVOW1KYrCNaEujM0mdOWclAqj4FRXcW2cIWITQSY132HuvfdePP744/j617+OZDKJe++9F3/8x3+sxbkRCARCXUgRm6bCqS0ZoQNWBaTN7NEMy8FuMcJAk4iPWuzs92B2KYO8jGkvgiAgmVHW2eRpgrMpk1Nmgk6juGxmpLPaLqbnljKYjqSJq6lOejvtAICFqD56m154aw5OmwkH93Rr/tq9nXY4bSacn5MnUjPs/8/enYfHVdh3o/+e2ffRMtJIGi3WYmOwMbYLAYpZE3Zss6VJAyRNSKBN3yTN9mQvSUvf3Kbppfcmzfs27ZtmJbckLAkOgUBZjSFsZrExXmRZsrWvM5rRnFnP/WPmjGSsZWZ0Nmm+n+fJ8wRbHh3bxzPn/M7v9/2lcGIkWiheqemMNdXoGZxeMiewFNF4CuFoEk0Kb9ErhIRrnNs0kd+mW+NbPGzebDKhrcGraUi4mMzAqVI4OJB7+JXOZJFMnfo52H0ijI5Gn2J5hPPpCvmRSmd1y+pSkly08ygwju6yWyAAiGr8+UirT9F3EaIoIplMQpIkmHjzQUQGUut3IBpPFVb0LiUSS2I8kqjYYpOci7PYxX8snlLk6RgtrCvkQ1aSlnXjEBPTyGQl+N3KbcSqyr9WRRSb3FbMJNKaPtXe/dYgzCYB520IavY9VzKHzYJan90QIeETERGvHxnDRWc16ZK1JQgCTmutwksHhvHIi71ln7eHT4QhQd28JtkZbTXIShIOHldulG6gEA6ubMdLS70HJkHQfCOd/LAssMg2Oll7ow99w1HN3rPEZBoOlcLBgYXH+sVkGsdHo4V8Q7XIWx6PLLOAawTyYhclOtJNJgFup3XJzcVES1nyk/JHP/oRtm/fjs997nMIBoO477778L3vfU+LYyMiKkrAnwsILba7Sb65b6/ATXTA3DG6hS8iomKKeU0q62hafkh4JD8C5nMr93dlt5nhtJsxpeUYnZhW5GlsqeRRAa26m9KZLF7YP4TNXYGKHOEtV2PAbYjOpqdfHwAk4JLNTbodw4fetw4b2mvw66e7cdePXsI7vaUXcQ4fn4LZJKBDpSycuTpDftgsJrytYG6T0pvoZDarGY0Bly6dTQ6bGU770kWCjiYfUuks+ke1+fcgZzapRb4emXnX9UjPQASSlDt/1OT32FFX5Vglxab8og2Frt3cTis7m2jZliw27d+/H1//+tfx2GOP4WMf+xhefPFFvP/979fi2IiIihKoyj0NLKXYJACah7sahauoziZl1ufSwjxOKxprXeheRrEpnC82KdnZBORym8KadzZp30nnc+XXO2u0ke7N7nFMz6RwgcZbzFa6plo3BsdnkM1qG9w8VzqTxbNvDGBTZ61iG9DKUe2141M3bcKnb96EVDqL7/xyL3748P6S/r0eOj6F9iYfbFb1A/mtFhPWtlThQBlFsYX0j0bhsJmXHDsrx5qgV/POpvGIiFq/o6jMukJIuEajdPFkBo4iimDlci2QIXkkH4LeGVK/INoVqsKR/rDmwfBKkx8gKnXt5nVamdlEy7Zksemf//mfsX79evzwhz/EZZddhm9+85vYtm2bFsdGRFQUubNpvMhi07GhaTQG3EU9RVyNXPalO5tiOo01VZrOkB/dA5GyL3LDsdwNps+tbJeM323DlIbB2bG4Pp10Wnc27X5zEH63DWd21Gjy/VaLpoAbqXQWY5HSsvmU9OrBUURiSVz2J9oHg89nc1cAd3/8XGz/0zV45Z0RfPXfX8QTrxxHJrv4eFUimcGxoWlN8ppkZ6ypxsBYDJPTyhSwB8ZiCAXcqiwUaG3wIhxLKnasxRiPiEuGg8sCfgc8TqtmIeFqj9EtNNbf3R9GU8CtyUOvrmY/wrEkRkvM/jSaQkC4QotdPCw2kQIWLTYdPXoUd911Fy655BL89re/hSiKeOqpp/CZz3xGq+MjIlqSz2WF1WLCWHjpddBSPiOnUkfogNwsvtNuWXQbXUxM6TLWVGm6Qn5E4ykMl7nKPBLLXQj6FS42VXntmNLoZiuZyiCZzurSSefNdzZFNOhsCseSeLN7HOdvbGDwfomaauWNdPqN0j312gnUVTmwod04hUKb1YwbLurA391+Ljoafbj3icP4+5+8smi3ZPdAGJmspElek2xTRy0A4PGXjyvyeidGYwgpHA4u0yMkfCKSQE2RxSZBENDe6NOss0lMZOBQMSDcNc9Yf1aS0N0fRqcGY54AsDYk5zYtf8ujnmbENCxmk2Idiyw2kRIWvNr5xCc+gVtvvRVWqxU//elPsWvXLrjdbni9lXuDRkTGJAhC0RvpxiMipmdSFRsOLnM7LKdkJMiyWQkzYpoB4RroDC0vnDQcS8BsEhR7kimrctsRjiU1GSsobNBZ5Z1NL+wbQlaScAG30JWsMZDbSDeoU27TiZEoDp0I49ItzTCp0E2zXA01LnzuA5vxV9dvRCSWxD/87FX8+PfvzHujeOj4FAQBqgcvzxWq8+DCTY34w8vHcWI0uqzXisSSiMZTaFI4HFzWGvRAADQbpUskM4jGU6gtYSSwvdGLgdEY4gnlNvzNR5IkDTKbTu1sGp6YQUxMa3aONtXlOt1Xem5TTFR2HJ3FJlLCgsWmAwcO4IwzzsDatWuxZs0aAFClXZWISAm1RRabjg3mLiBZbLIu2Nk0k0hDgnIhk7SwxloXXHYLugfKu8iNRJPwuW2Kfz77PTak0lnMqHwzA8xp/dfhfHM5LDCbBNUzmyRJwvNvDaK90ad4qHElcDus8HtsunU2Pbm3H1aLCdsMnLUlCALOWV+Pf/jEebjinBbsfnMQX/3hi3j2jQFk5xSNDx2fQmu9V5GNVaV4/6VdcDks+NljB5dVxO7PF6vU6mxy2CxoqHVpVmwaz4+GFjtGB+RCwiUAfSp3XyXTWWQlSdXIgfnG+uWlGWqHg8tMgoDOkA+Hl5GfaASxuLKLNjwuK1LpLBKpjGKvSZVnwWLT008/jZtuugm7du3Ctm3b8OlPfxqJhHbzy0REpQj4nUVlNvUMRmA2CWiuU+ep6ErhclgWDAiXn2RxjE59JkFAR8hX9ka68ExS8RE6IBcQDkCTjXSz55v2nXQmQYDHaVW92HRsaBr9YzFDFyuMrqnWjYGxGc2/bzyRxgv7hvCe0+tXxIZOp92CD753Lb750XPQUOvCj3//Dr7981fRNzyNdCaL7oEI1rZo19Uk8zituPmSThw+EcaefUNlv45am+jmamvwajZGN5EvNhU7RgcAa/IPy9TObRKTuSKDmp1NubF+80nXI939YbgduaKfVtaG/BgYjS3Y8b0SxMSUokVk+f2OG+loORYsNlksFlx99dX42c9+hvvvvx/19fUQRRFXXHEFfvnLX2p5jERESwr4HYjGU0u2lfcMRtBS74HVUtmZKW6HZcGAcD07TSpRV+Eit/QuokhUrWJT7jW12EgX1fl887qsqo/R7X5zEFaLCeeeXq/q91nNchvpYppvjNqzbwiJVAaXbTVGMHixmus9+PItW/Gxa07HyGQc3/rxy/hfD+1DKp3FaRrmNc21bVMjOkM+3PfUkbLHc/rHYnA7LKq878nagl5MTicQ0WBJgtzZFPAXX2zyuWwI+B3oGVC72JT7TFKz2AQALvvJndbd/RF0NPk1HVntCvkhAehW+c9UTTFR2S3ChWITR+loGYq62+rq6sLXv/51PPfcc7j99ttx3333qX1cREQlkS/UFutuykoSeoenK36EDgBci4zRzd78M7NJC535i9yjg6V3N4VjScU30QFzO5vULzbJRU+9uka8LpuqAeGpdAZ/fHsYW9fVwcVuwbI1BVwQkxlNt4RJkoSn9vZjTYN3RX5umAQB2zY14n/ecR4u2RzC64fHAABrNdxE9+7jue2K0xCNp/DAs0fLeo1+FTfRybQMCR+PiDAJAvye0t7HO5rUDwkXE3Jnk7rXAnMzJGfEFPrHYugKafvvTS5uHV7BuU0zYkrR/EYWm0gJJT3adzqd+MAHPoAHH3xQreMhIipLwO8EgEVzm4YnZhBPZLCmgjfRyeSLu/m6BPS++a80HY0+CELpIeHZrITpmVTJNynF8Bc6m7Qbo1utnU2vHRrDTCKNbQwGX5am/NjUgIYh4Qf7pjAwFsOlW0OafU81uB1W3Hblafj6R87GJ6/fqEqBulitQS/e9ycteGZvP46W2EUiSRL6R2NoUnkMvjVfbDqmQW7TeDiBaq+t5A2V7Y0+jEcSCKvYfSV3NjnV7mxyWBDLd6XL54RWeU0yu82MlqBnRW+ki4ppRT9HWWwiJVT2HAkRrRpyZ9NYeOEV8vJTwJX4hFppbqcV6YyEZDp7ys/F4rmLPj1W0Vcip92CUMBTcvt+NJ5CVpLgdxe/xahYDpsFdpsZk5p0NqVhtZhgV2hdc6l8LpuqmU273xpEjc+O09uqVfselaCxNl9s0jC36cm9/XA7LDj39KBm31NN7Y0+nL1e/1HO6y9sh99jw88eO4hstvixyKloEvFEWk2hyxcAACAASURBVPWQfZfDgvpqJ/o0KDZNRMSSwsFl8nWMmt1NcTmzScWAcCB3PSKPkR/pD0MQ9LlOWxvy4+hgBOnMqddFRpfOZJFIZtjZRIbDYhMRrQpelxU2i2nRzqaewWnYrWY01XIblBwiOV9OUDSegoDZLTGkvq5mP44OhE/aGrUUOU9ErS6FKo9ds84mJS+QS+V1WRFPZJCap/C6XBMREW/3TOBPNzbCZOJG3+XwuqzwOK2abaSbnE5g76FRbNvUCJtOhdDVSg4x7x2exlN7+4v+dYVNdBpsdFzT4NWmsykioqaEvCZZW9ALkyComtukVWbT3AzJ7v4wmus8qm7AW0hXsx/JVBYn8ufZSiJfyyk5qi1HKbDYRMvBYhMRrQqCIKDW71g0s+nYYARtQQ9v+jDbtTRfSLi80YR/TtrpbPIhnsiUdCMtj0+oFZRb5bZpk9kUT+k6sunN//mpcUG9Z98QJADbzmxQ/LUrjSAIaKp1YVCjMbrn3hhAJivhki0re4TOqM5ZX48Na6rxwLPdRS8ikDfRNdWpX2xqC3oxHhFVvdHOZiVMTifK6myy28xoCrhV7WzSKrPJ5ch1NmWzEroHIujSeIROJn/flZjbJF/LKZm1aTaZ4LJbuI2OloXFJiJaNQJ+54KdTelMFn0j0cLK4EondzbF5rmQjsZT3ESnsa7m3EXukf7iL3LDsdwNmmqdTV5tOpti8ZSuI5teZ+7PT+nNU5IkYfdbg1jXUoX6au1WeK9mTQE3BsbU30iXzmTx9Ov92NhegyD/7lQhCAJuueI0pNJZ/NdTR4r6Nf1jMfhcVvhc6mdOtTWoHxI+FU0gk5XKKjYBQEeTFz2DEdX+PYjyGJ0GnU2pdBbHhqYhJjPo1DgcXFbjc6DWZy85P9EI5IUvSn+WepxWRBfYXExUDBabiGjVCPgdC2Y29Y/GkEpnmdeU515kjC4mphkOrrH6Kic8Tiu6Syg2RWK5C0C1Opv8bhumYgnVb+yjOp9vXlfue0/HlS02HT4RxshknMHgCmqsdSMmphFR+Un764fHMBVN4rKtzap+n0rXUOPC1ee24cX9wzjQO7nk1/ePxhBSORxcJoeE96o4SjcRyT0wqCmz2NTe6ENMTGNkauGsyuWQx+jsqgeE596D3+zObUvUq7MJALqaq3CkP6z6557S5AeHihebXFZEVcw0pNWPxSYiWjUCVQ7ExPS8BZTZcHBuogPmjtHNn9nEcHBtCYKArpAfR/qLH4kIxxKwWUyqPXWu8tiRTGURz49SqEXvTjq5M2w6pmwBY/dbg7BbzTh7fZ2ir1vJChvpVM5tempvP2p9dmzqrFX1+xBw7fltCPgd+PkfDi4azJyVJAyMxwrngNo8TisCfoequU3jkVwndq2vvCUPhZBwlXKbxGQGdpsZJkHdkXr54deb3ePwuqyoq3Kq+v0W0xXyY3I6Ufi7WSnk614lx+iAfGdT/NTrRKJisdhERKtGwJ+7QJnvIqFnMAK3w6LrRYyRzHY2zZPZFE/Bo/AFCy2tM+TD8MRM0ZvRwrEkfG4bBJVuBKo8tvz3US+3SZKk3BidjudbobNJwae3iWQGL78zgnPW16ued1JJ5EKDmrlNA2MxHOidxCVbQsyt04DNasatV6zD4PgMHnupb8GvmwiLSCQzCGmQ1yRrC3rRp+IYnXytUm5nU6jODZvFhKMq5TbFE2k4Ve5qAmbH+o8NTaMr5FftM60YcldVKSPtRiCPuqkyRqdw12+5Vlq3GeWw2EREq0Ygv9FlvlG6nsFprGn06XoRYyQOuwUC5u9sionsbNKDfJHbXeRT6kgsqdoIHQD4Pbmn7VPT6hWbxGQGmayk6xidy26B2SQoOpr1ysERJJIZbNvEETolVXlscNrNqnY2PbW3HxazgAs3Nan2PehkmzoD+JN1dXj4+WMLj8Ln/8612EQna23wYmQyjnhCnc6O8YgIt8NS9uY1s8mEtgavaiHhYjKjSbF87vVGp44jdADQXO+G3WZecblN8hid0luEjdDZlM5k8a8PvoV//MVrJW3sJWNgsYmIVo3aQrHp5M6mRCq35YsjdLNMggDXnHXDsnQmNzbFgHDtrWn0wWwSis5tkjub1CJ3Nk0pHJw9V0ylp7GlEAQBHpdV0c6m3W8Oor7aibXN+t44rTaCIKCx1q1asUlMprFn3yDOXl+v6r8tOtWfv28tBEHAvY8fnvfn9Sg2tQVz+VBqdTdNhMWyu5pk7Y0+9A5FFx1BLFeu2KRdZxOgb14TkCvgdTb5VlyxaUZMw2lXfouwx2lFIpVBKq3uOP1CJEnCT37/Dl49OIpDJ8J45Z0RXY6DysdiExGtGl6nFTarCePvKjb1DU8jK0lob2A4+Fwuh+WUfKuZ/BNcBoRrz241o6XeU3yxKapuZ1NVvrNJzY10sbgxzjefy4ZphTqbJqcTOHh8ChdsbGAnpQqaat0YGJ9R5bVffHsY8UQGl21hMLjWanwO7Ni2Bq8fGcPew6On/Hz/aAzVXnshTFoLbfmQ8L7hqCqvPx4Ry95EJ+to8iGdyaJ/VPkCrJhMa1Jskh82mE0C1jTo/1CwK+TH8dGoah1tash1pCvfhSZ/NuvV3fTAs0fx/L4h7LhgDUIBNx56rgeZrPKFVVIPi01EtGoIgoCA33lKZ1PPYO6p5BpuojuJ22E9ZYxudqMJc2b00BXy4+hgZMmLqXQmi1g8pWr3hcNmht1qxlRUvTG6aP5807vY5FWws2lfzzgAYPNaBoOroSngRiSWLJw7SpEkCU++2o+Weo9uq9cr3eVnt6Ap4Ma9jx9GInVyJ0X/WFSzcHCZ32OH321Dr0qdTeORxLKLTYWQcBVG6eKJTNkjfqWQR79agx7YrOoXt5aytrkKkgQcVSl4XQ0xMa1Kh/BssUndDaDzeeKV4/jdC724ZHMTdm5rx/UXtmNoYgYv7h/W/FiofCw2EdGqEvA7MPauNcDHBiOo8thQ7S1v48tq5XZYTgkIN0qnSaXqDPmRTGVxYmTxp9TTMylImM1VUoMgCPB7bKoWm2bH6PQtbnoV7Gza3zMBv8eGZg2DjCtJY60LgPIh4Uf6wzgxGsWlW0PsSNOJxWzCbVesw3hExK49xwo/ns1KGByf0XSETtbWoE5I+IyYRjyRRo1/ee/hAb8DHqdVlZBwrTqbTCYBAb8DZ6ypUf17FaOjyQdBWFkh4TFRnUUbhWKTgmPmxXjpwDB++cRhbFkbwK1XnAZBELB1XR1agx78ZnePKmOjpA4Wm4hoVQn4HfN0NkUKT/9olmuezib56RUzm/RR7CacSD5HyedSN1emymPHlIpjdEbqbIoocDGdzUrY3zOBjWtqWLBQidzdonRu01Ov9cNpN+P8MxoUfV0qzWmt1bhgYwMe/WNfoaA4OhVHKp3VpdjUGvRgYGwGyZSymTUT+U10y+1sEgQB7Y0+VTqbtAoIB4BvfvQc7NzWrsn3WorTbkFznQdHTkzpfShFm1Grsym/rTU6zzIZtRzoncR/7HobXc1+3LljQyGHShAE3HBhB8bCIna/NajZ8dDysNhERKtKwO/ETCJd6NiZEVMYnoxzhG4e7nkCwgudJiw26aLGZ0eVx7ZkblM4X2zye9QuNtkQVrOzySDFTa/LBjG5/BDU3uFpxMQ0NrQb4wn9alTrd8BmMWFgTLncpkgsiZffGcEFGxth16CTgxb3/ku7YLea8fM/HIIkSbPh4HUezY+lLehFds4xKGVcoWITALQ3ejEwGlM8Y0irgHAg9/DLYjbObWlXsx/dAxFksytj+1ksrnJmk0adTX3D0/j+A28iWO3Cp2/edMpY5abOWnQ2+fDw88d0Cy2n0hjnXzURkQIC79pI1zOUa3/nJrpTuRxWzIhpSHNWyRY6TZjZpAtBENAV8i/Z2RSO5QpAam/M8rvV7mxKw24z636T4cs/vV3uKN2+o+MQAJzBYpNqTPmNdEqO0T335gAyWQmXbg0p9ppUPp/bhpsu6cSB3kn88cAw+kdzAd1NAZfmx9KaDwnvHVJ2lE7ubFruNjogN/YlQdmteelMFulMFg4NMpuMaG3IDzGZwYlRdcLhlSRJUi6zSYWHNlpmNo1NxXHPr96Aw2bBZ//srHk7tQRBwPUXdWByOoFnXh9Q/Zho+VhsIqJVpTZfbJI30h3Lt5av4Sa6U7idFmSy0klBrDExBZMgaBIKSvPrDPkxFhYXzUqSx+j8ao/ReW1IpDKqbeWJxlPwaLhdaiHe/J/jsotNPRNobfCqPt5Y6RoDLgwoVGzKZiU8vbcfp7dVo7GWOVtGcfFZTWhv9OK//vsIjvRHEPA7NBvpmivgd8BltygeEj4WEWE2CYp0p8qd20rmNonJ3HWBVp1NRtPVXNxIuxEkUhlkshJcKjwktJhNcNjMmFa52DQ9k8T/fd8bSKWy+NyfnbVoEfaMtmqc1lKFXS/0nrJIgIyHxSYiWlVO6WwanEZ9lVP3TBgjkp8azcyZxY/G03A5LMyb0ZGc27TYKF04lsxti1P5RqDKbS98PzWoFWpaKl+h2FT+7zOeSKO7P4KN7GpSXVOtGxORhCJF0De6xzAeSeAydjUZiskk4LYrT0MklsRbR8c130QnEwQBrUGP4iHhE5EEanx2mBT4rPW5bAj4HehRcHuamP+3VanFplqfA1UeG46cMH6xSV7sokZmE5DrboqpWGxKJDP4f379JsYjIj5986Ylx2UFQcANF3UgEkviqdf6VTsuUgaLTUS0qnicVtit5jnFpgjam9jVNB953fDckPBYPKV7fk6law16YTELiz5RjcSSqo/QAbnMJgCYmlYntykWTxmiEOzNj9EtJyT8QO8kspLEYpMG5MLD0MTyc5ueeq0fVR4bNq8NLPu1SFlrGnyF0caQjtsd2xq8OD4SU3QD1nhYVCSvSdbRpGxIuNzZ5NShm8wIBEFAV3MVDq+EYlNhq6t6xSa1Opsy2Sz+12/2oWcwgjt3bMC6lqqift26lipsaK/BIy/2qtZ5TcpgsYmIVhVBEPIb6eIIRxOYnE6gvYF5TfORwyTnPrGKiSl4DNBpUsmsFhPWNPjQ3b/wjUMkloRfg2KT35PrbJqKqVNsihqs2LScMbp9PROw28zozHemkXqU2kg3PDmDfT0TuGRzCGYTL4mN6MaLOrCxvQZb1tbpdgytQS/SmSyGxpULpR+PKFtsam/0YTySUGyhQzyZ72yyV2ZnE5DLbRqPiJhU6WGLUuQHhmoEhAO5jXRqdDZJkoSfPHoQb3aP49YrTsPWdaX9G7/hwg5E4yk88cpxxY+NlKPLJ+uTTz6JG2+8EVdddRXuvvtuAMCePXuwfft2XHHFFbjnnnv0OCwiWiVyxSYRPYO5tnduopufK/8ULHbSGF1KtadjVLyukB/HhiJIped/kh7WqNg029mk1hidOuuaS+W0W2A2CWV3NkmShH1Hx3F6a7XuYeeVoK7KAYtZWHax6anX+mE2Cbhoc5NCR0ZKczms+NwHNhfGi/XQJoeEKzRKl85kMRVNKBIOLmvPX+fI1z3LNZvZVLkPn+TcpsMnpnQ+ksWpvdXV47QuO89wPg8+dxS73xzEjgvW4NItpY8xdzT5sLkrgEdfOn7KZmUyDs2viI4fP4677roLP/jBD/Dwww/j7bffxjPPPIOvfvWr+MEPfoBHHnkE+/btwzPPPKP1oRHRKhHwO/PFpggEYfZCkU4mZ+XMzPmQjsXThug0qXSdIR/SGWnBnJBwVJsxOqfdApvFVNh+p6SsJOUzm/Q/3wRBgNdV/gX1yFQcY2ERGzhCpwmzyYRgjWtZxaaDfZPY/eYgtq6rQ1W+g49oPg01LtisJsWKTVPTCUjS7EITJbQFvTAJgmKjdJUeEA4ALfUe2Kwmw4eEzyRU7mxyWhUv5vz3qyewa08vLjqrCTu3tZf9Otdf2I54Io3HXmJ3k1FpXmx6/PHHcc0116ChoQFWqxX33HMPnE4n2tra0NLSAovFgu3bt+PRRx/V+tCIaJWo9TsQT6Tx9rEJhAJu1UOUVyr3fJ1NIjubjEAexZrvIjeVzmImkdaks0kQctuSwlHlO5viiTQkCfCodIFcKp/Lhukyg9D3HZ0AAGzsYLFJK021bgyWMdYUmUni//zubfzjvXvhtFuwYxk3OlQZTCYBLfUe9A0pU2waj+QyJZUco7PbzAjVuRUrNsk5OJWa2QTkNrF1NPoMHxJe6GxSMbMpnsgolln2yjsjuPfxQ9jcFcBtV65b1kKa1qAXZ6+vx+OvHF/Wgg9Sj+bFpt7eXmQyGdx+++3YsWMH7r33XoyMjKCubnZOs76+HsPDw1ofGhGtEvJGuu6BCEfoFuGwmWESBMwkchcq6UwWiWSGmU0GUOWxI+B3zLuRLpIviPg16sao8tgxpVAOyFxRlVv/S+V1lR+Cur9nAgG/A/VVToWPihbSFHBjdCqOZJGrr7OShGffGMDXfvgiXtw/jGvOa8PdnzgXIZ22nNHK0hr0om8kiqwkLfu1JiK599Man7Lv4e2NXvQMRiApcIyFzqYKzmwCcqN0fcNRJJLFvc/oISamYTYJsFnVua335j+jlchtOtg3iR8+vB+dIT/u3LlBkay867e1I5nK4Pd/7Fv2a5HyNC82ZTIZvPDCC/inf/on3HfffXjrrbdw4sSJU76Oa7eJqFyBqtmnhe0sNi1IEAS4HJbC2ly15/6pNF0hP470h0+5cQjni00+l/qdTUCuqDWlQmeTXGwyytim120rFPJKkc5kcaBvEhs7anntoqHGWhckFLeR7sRIFP/Xz1/Dj3//DkJ1Hnzzo+fg5ks6YbdW9o00Fa8t6IWYzGB0Mr7s1xpTobMJyF3vxMQ0RqaWf4yiHBBe4Z3hXaEqZCUJRxXc9Kc0eRxdrc8f+ZpwuRvpToxE8f/e/xbqqpz49M2bFHv/bQq4cd4ZQTz56glVHozR8mhebAoEAjj//PNRU1MDh8OB9773vXj++ecxNjZW+JqRkRHU19drfWhEtEoE/LPdBe2NzGtajMthKcziR/PjdEa5+a90nSE/pqLJwlNw2WxnkzbFpiqPTZULOLnIaZTiptdpK+tiurs/jEQyg43Ma9JUMRvpxGQa9z15BN/8z5cxNDGD2689HV/60BaE6jxaHSatEkqGhE9ERHhdVtgULnYWQsIHll8YEZMZWC2mit/S2BXK/ZkeMXBIeG7Rhnod6Up0NmWyWfzrQ/tgt5rwuT/brPh15o5t7UhnJPzuhV5FX5eWT/N3kEsvvRS7d+9GJBJBJpPBc889h6uuugo9PT2FEbtdu3bhoosu0vrQiGiVcDsssNvMsJgFNPOmYlFuhxUz4rs6m5jZZAhdC+Q2yWHdWnU2VXnsEJOZwpNupcSM1tnksiKRzBQ9liXb1zMBkyDg9LZqlY6M5hOsdsEkCBhYILdp76FRfP0//ohHX+rDtk0N+J93nIcLzmxk9xmVJVTnhtkkKFJsGo+Iim6ik4Xq3LBZTIp04YiJNJwV3tUE5LYhhgJuHOk3cGeTyluEC51Ny9hI98e3hzE8MYNbLj9N0WB8WbDahW2bGvDM6/2YyHcOkjFoHsxx1lln4eMf/zg+9KEPIZVK4YILLsCf//mfo6OjA5/61KeQSCRw8cUX46qrrtL60IholRAEAXV+J6wWE9eQL8HtsBQCwmfH6JjZZATN9W7YrCZ094dx7hnBwo/LnU1abKMDUAgiD0eTcNQod25ERWMVm+Q/z+mZFGr9xd9k7euZQGfIB6ed/260ZLWYUF/txOC7OpvGwnHc+/hhvH5kDM11btx56wasba7S6ShptbCYTQjVudE3HF32a42HRTTVKp8VZjaZ0NbgVSQkXExm4KjgcPC5upr9ePnACLKSBJMBi9UxMYVqFTMcvfkHW9EyN9Jlslk8/PwxtNR7sGVdQMlDO8l1f7oGz781hIf3HMNHrlqv2veh0ujyLnLzzTfj5ptvPunHzj//fPz2t7/V43CIaBX6yFWnsdBUBJfDgtF8vkPh5p+dTYZgNuU34ZzS2ZSE22GB1aLN+V3lzV3ETkUTCNa4FHvdWDwFAYDLIEUabyGXIln0k9fITBJ9Q9O4/kJuNNNDY60LA+O5YlM6k8XjLx/Hb57vAQD82aVdeN/ZzfwcIMW0Br14/fAYJEkqu0NOkiRMRBLY2F6r8NHltDf68ORr/Uhnsss693PFJnY2Abku42deH8DAWMyQ3fIzYhqhgHrHJS+NiZbZ2fTHt4cxPBnHX99wpqrFuoDfiYs3N+GZ1wdw9XltXNhhEPwEJqJVqTPkR1sD85qW4nZY53Q2GStDh3Ln8fGRKBJzRrvCsaRmXU0AUCV3NpURnr2YaDwFl8MCk8kYT4q9+d9nJFb8BfXbPROQAGzsUOfGkRbXFHBjeCKOA72T+NaPX8avnu7GhjU1+IePn4erzm1loYkU1Rb0IhpPYXK6/Ay7mJhGIpVBrcKb6GRdIT/SmSyOLjO3SUymWWzKW9ucH2k/cep2WCPIBYSr99DGajHDbjUXlnqUQquuJtm156+BySTg4d09qn8vKg4/hYmIKpjLYcGMmIYkSYiJKZhNAi8wDaQz5EcmK+HYnLGISCxZGG3TQqGzaRk3WPPJhZoap7Dpdcm5FMUX1fb3TMDtsBTCg0lbTQE3spKEf/rlXoiJND5105n41E2bVMkEIVIiJHw8nN9Ep9I5uqG9BhazgNcOjS7rdeKJDBwG6TrVW12VEz63DYcNWGzKZLOIJzKqd6R7nJayik1yV9OOC9o1GUGs9tpx6ZYQ9uwfwuD4wssjSDssNhERVTC3w4qsJEFMZhCNp+B2WBigayCdTflNOHNG6bTubHLZLbCYTZhSobPJSF10cuB6sSGokiRh37EJbGivMUx3VqVZ2+xHjc+Oq89rxd0fPw9b1tbpfUi0irXUeyAAy8ptksOL1QgIBwCn3YIz1tTgtUOjkCSp7NdhZ9MsQRDQFfKju994xSZ5wYtLxW10AOBx2kouNmnd1SS75rw22Cxm/IbdTYbAYhMRUQWT1+XGxFRuo4mBbv4pF8wZrHGhe84mHK2LTYIgoMpjw1RU2c6maDxlmHBwAHDkN1gW29l0YjSGcDSJDe01Kh8ZLSTgd+K7n7wA77+kC3beGJPK7DYzGmpd6B1aRmdTvthUq1KxCQC2rA1gLCzi+Ej5RTEGhJ+sK+THyFRc8XHy5ZJjENS+diuns0nrriaZz23D+85uxksHRpb1b4CUwWITEVEFc+Vbr2fEtOE6TSinK5QLCZckCYlkBolkRtMxOiD3FF4OkldKrrhpnJsZQRDgddmK7mza1zMOANiwhsUmokrRFvSib2R5xSarxVQY21XD5rV1EADsPTxW9muIyQycdhZwZbO5TVM6H8nJYvnFLm61O5tcpXU26dXVJLvyPa1w2s146Lmjmn9vOhmLTUREFazQ2RRPISamuYnOgDpDfkTjqdxT1XzXjd+t3prj+bTUe3BiJIbsMsYy3i0mpgx3vnldVkSK7Gza3zOBUMCt2jgMERlPa9CLiUiipGy3ucYjCdT4HKqOq/vdNnQ1+8vObcpmJSRS7Gyaq63BC6vFZLjcpsJiF7UzmxzWkrbR6dXVJPM4rbjynFbsPTyGnsHlheXT8rDYRERUwVyFMbq06htNqDxdodlNOJFo7gZHyzE6AGit9yCRymBkUpnupnQmF2pqtE66YjubEqkMDh0Pc4SOqMK0BXMr5svNbRoPi6ptoptry9o6HB+JltWRKiZz20+Z2TTLYjahvcF7Un6iEczkO5tUz2xyWTGTSCOTzS75tXp3NckuP6cFbocFDz3H7CY9sdhERFTB5MycmUR+jM5gnSaU27jltJvRPRAp5EVoPUbXmt/C1LeMLUxzyaGmRspsAgCfy1pUx8LBvimkM1lsZLGJqKK0LHMj3UREVDWvSbY1f5O/t4zuJjGZe39mselknc1+9A5NI5nK6H0oBdplNuVeX+6kWozeXU0yp92Ca85rw1tHx3HYYOOPlYTFJiKiCiY/DQtHE0imsoa7+SfAJAjoaPLnOptiuZBurTubmgJumE3CsrYwzSVnPxitk67Yzqb9PROwWkxY11KlwVERkVF4nFYE/I6yCu+pdBbhWFKTYlN9tQvNde6yRuni+c4mp91Y7896WxuqQiYr4dgyAuKVpllmU/7acKncJqN0Ncku29oMn8uKB545uqztjFQ+FpuIiCqY3WqG2SQUxqOMNtZEOZ1NPvSPRjE8GYcAqBouOx+rxYSmgFuxzib5AtmImU2JVAaJJZ5c7+sZx7qWKtisfPJPVGlag96yNtJNTOc20WmV87Z1XR0O94cRKXGDGjub5teVDwk3UpdMLJ6Gw2aG2aTuLX2xxSajdDXJ7DYzdl7YgYPHp7DrhV69D6cisdhERFTBBEGAy2HBcD7XgZ1NxtTV7IcE4PXDY/C4rLCYtf/4bg160KfQGuHZziZjnW9eV65jbLFRuomIiMHxGW6hI6pQbUEPhifjiCeWHimaayKcKzbV+rUrNkkS8PqR0rbSzWY2sbNpLo/TisZaF44YKCQ8JmoTf1BMscloXU2ySzY34bwNQTz07FG8vowNjVQeFpuIiCqcy2HFqNzZpHIrNpWno9EPAcDIVFzzETpZa70XkVgSU9HEsl9Lzn0wWnFT7hhbbJRuX88EAGBjB4tNRJVIzrA7XmLxfTySe+/UIiAcyG0RrfU5Ss5tEhMMCF9IV8iPI/1hRTezLseMmNbkuq2YYpPRuppkgiDgL65aj9YGL3748H4MjMX0PqSKwmITEVGF8zgsheBpo938U47LYUFTnRuA9uHgstbCFqblj9IVOpsMNkbnK6KzaV/PBKo8NoQCbq0Oi4gMpK2hvJDw8Uius6naq01nkyAI2LquDvuPTZbUhVUYo2Nm0ym6Qn7ExDSGJ2b0PhQAQFRMadIh7HEtXmwyaleTzGY141M3JU8uFwAAIABJREFUngmbxYTv3f9mYYsfqY/FJiKiCueac8NvtJt/mtUVyuVF6FVsaqmXN9Itf5QuJqZgEgQ47cZ6cr5UZ1M2K+HAsQlsaK+BYKAnt0SknSqPHT63DX0l5jaNR0T4PTZYLdrdfm1dF0A6ky10ZBZjdozOWO/PRjCb22SMUTqtOpvsVjOsFtOCxSajdjXNVeNz4JM3nImxsIj//dv9yGaN0Z222rHYRERU4eZeqBhtOxjN6mzKXeTqNUbnclhQV1XeFqZ3i8VTcDsthivYyJlNkQU6m3qGIoiJaWxsr9XysIjIYNqCXvSWWHifiIiabKKbq6vZD4/TWtIondzZ5GSx6RQNNS54nFbD5DbF4qmTHhiqyeO0IjrPgxijdzXNta6lCrdcvg77jk7g/me79T6cisBiExFRhXPli00WswA7t2sZlvxEtcqjTd7HfFqDXkVCwqPxlCFHNh02Myxm04KdTfuPTkAAcMaaam0PjIgMpTXowcBYDKn04psr5xoPi5ptopOZTSZs7grgje5xpDPZon6NmMzAbBJ0WURhdIIgoCvkx+F+/YtNkiTlAsI1ekjocVrn7WxaCV1Nc12yJYRLtoTw+xf78OLbQ3ofzqrHdxEiogonPxVzO6yG6zShWQ01Lvzlzg244MxG3Y6htd6DkTK2ML1bNK5NzkSpBEGAz23F9AKrwvf1TKCtwVvogCKiytQW9CIrSTgxWlzYsCRJGI8kENC42ATkttLFE2m80zdZ1NfHE2k4bGZeDyygq9mP4YmZBTtgtZJMZ5HOSJrFH8xXbFpJXU1zfeh9a7G22Y8fP/IOeksch6XSsNhERFTh5DE6I3aa0Mnec3pQ17+ncrcwvVtMTMNj0Hwwr9OG6Xme3s6IKRwdiHALHRGhtcSQ8OmZFNKZLGo02kQ31xlrqmG3mvHaoeLWvovJDBw2jtQvRM5P7Na5uylWWLShX2fTSutqklnMJnzyhjPhcVnxvQfeRGSBB0y0fCw2ERFVOHehs4kXl7Q4udi03NymaFy71v9SeV3WeS88D/ROIitJzGsiItT5HXDaLUUvTJA30Wmd2QTkNnFt7KjB3sOjyEpLhyKLyQwcBlveYCTtjV5YzILuuU0zYq7DWK/OppXa1STzu234HzeeiemZFH7w4FtFj5lSaVhsIiKqcHKRyYhjTWQsVR4bvC7rsjfSxcSUYTcfel22eTOb9vVMwGEzo6PJp8NREZGRCIKAtqCn6BGc8XC+2OTXvtgE5EbpwtEkegYiS36tmEzDyc6mBVktZrQ1eHXPbYqJ2nc2xcRUYYvbSu1qmmtNgw8fvXo9Dp0I45dPHNb7cFYlFpuIiCqci8UmKpIgCLmQ8GV0NqXSGSRTWcOObXpdVkzHT+5skiQJ+45O4PS2aobmEhGAXKfnidEoMtmlOyLkziatA8JlmzprYTYJeK2IrXTxRAYObqJb1NpQFY4NTiOV1q8bJiZ3Nmn0WepxWiFJwEwiveK7muY6b0MDrjq3FU/t7cfTr/frfTirDq+YiIgqnNxhYtSbfzKW1qAH/WOxslvOo/HcBbJRzzef24ZkKotEcnbL1PBkHOMRERvbmddERDltQS9S6SwGx2eW/NrxiAi71azbuLrbYcX61iq8dmgU0hKjdGIyzWLTEjpDfqQzWV3DpeXMJpdWnU2u3Gd2NJ5aFV1Nc918cSc2ttfgF384hMMnpvQ+nFWFxSYiogpX6GxiZhMVobXei0xWwsBYcVuY3q0QamrQYpM3f1zTczYN7Ts6DgDY0MG8JiLKkUPCi+n0nIgkUOt36Lrhbeu6OgxPxjGwRHGMAeFL62rOhYQf0XGULqZDZhMARGLJVdPVJDOZBNy5cwNq/Q7864P7MJHvRKTlY7GJiKjCVXntuHRLCJu7VsdFA6mrNegBgLJzm+SAUY9Bi5tetw0AEJmT27SvZwL1VU7UVzn1OiwiMpjGGhdsFhN6h5Z+LxwPi7psoptr89o6AFhylI4B4Uvzu22or3bq2gUTE1MwCYJmXWhysemJV0+sqq4mmdthxadu2oREKoPvP/AWkqnM0r+IlsRiExFRhTMJAm678jSE6jx6HwqtAMFqF2xWU9m5TYVQU6N2NrlO7mxKpbN4p28SGzo4QkdEs0wmAS31nqLeC8cjIgI65TXJqr12dDT5sHeRYpMkSfkxOmM+DDCStSE/9vVM4J//63X86JEDePDZo3h6bz/eODKGvuFpTM8klxxZXI6YmIbbadGsW04uNr3yzsiq6mqaKxRw447tZ+DY0DR+8ug7qv79VQq+kxAREVHRSrnBmk+hs8mwxSa5sylXbDrSH0YylWVeExGdojXoxYtvDyErSQt2eSRSGUTjKd3CwefasjaA+585iomIOO/xJFNZSBLgZGbTkt57djPiyQwmp0X0j0YRjiXx7tqExWxCtdeGao8d1T4Hqj12VHntqPHaEapzo7HWXfb3n9F4q+vcz+zV1tU015a1dbj+wnY89FwPWoNeXPmeVr0PaUVjsYmIiIhK0hr04oV9i99gLSRq8MwmnxyCmh+j29czDrNJwPrWaj0Pi4gMqK3Bi6f29mNsKo76ate8XyPnv9QaoNi0dV0d7n/mKPYeHsN7/6T5lJ8Xk7kcIAaEL21Ngw//48YzC/+dyWYRjiYxOZ2Y/V909v8fHQhjcjpZWK4hAPjsn52FjWVmAcbiKU2zNh02MyxmAY217lXZ1TTXdX+6BsdHorjvqSMI1bmxsZ15jeVisYmIiIhK0lrvwVPJzKI3WAuJiWlYzCbYLMac5LdbzbBaTJjOF5v290ygM+SH085LJiI6mZxh1zscXfC9cFwuNvn1LzY11rrRWOvCa4dG5y02xfNbOB18vyuZ2WRCjc+xaAebJEmIxlOYnE7ghw+/jf/zyAH8/e3nltXpGxXT8OczBrUgCAJuveI0rGnwrtquJplJEHD7tadjeGIG//ab/fi7289FtVffzLWVyphXekRERGRYrUF5C1PpIeHReAoeDXMmSiUIAnwuKyIzSYRjSfQNRzlCR0TzCgU8MJuERceKJyIJANA9IFy2dV0dDvZNFbpM52Jnk7oEQYDXZUNr0ItPXHcGojMp/Oyxg2VlA82IqcI2Ya1cdFZT4fN/tXPYLPjrG89EMp3FvY8f0vtwViwWm4iIiKgkzXVumAQBfSOl5zbF4inD5jXJPC4bpmdSeLtnAgCwgcUmIpqH1WJCU8CN3qGF3wvHwiIEAajyGKfYlJUkvHFk7JSfExP5ziYGhKuurcGLHdva8fI7I/jj28Ml//pYPK1pZlMlCla7sOOCNXj10Cj2Hl58iyPNj8UmIiIiKonVYkZjwFVWZ1MuZ8LYF8g+lw2RmST29UzA47SiraEynuQSUenagl70Dk8v2J0yERFR7bXDYjbGbVdbgxfVXjtem2crnSiP0bGzSRPXnNeKzpAPP//DoUK2VzGyWQnxRFrTzKZKdeV7WhGqc+PnfziEeCKt9+GsOMZ41yMiIqIVpbXeW9ZGuqiYNnxnk9dlRSSWxP5jE9jQXrPq8ymIqHytQQ+mZ1KYiibn/fmFNr/pxSQI2LI2gP09E0ikMif9XDw/RseMOm2YTSZ8/LozkMlK+NEjB5AtcpxuJpGGBBj+wc1qYDGb8JGr1mNqOoGHnuvR+3BWHBabiIiIqGStQQ+moklEYvPfYC0kFk/B7TT2jYzPZcPkdAKRWBIb1nCEjogWJnc+LjRKNxYWDbGJbq6t6+qQTGexPz8qLGNnk/aC1S584LIuvH1sEk++eqKoXzMjyltdjf1Zulp0hfy4ZEsIT7x6HMeGInofzorCYhMRERGVbDYkvPjuJnkTj3sFdDbJmNdERItpqfdAwPzvhdmshMnphOGKTetaquB2WE4ZpWNAuD4u3tyETZ21+NXT3Rgcjy359TEx9/fkYmeTZm66uAM+lw0/+f1BZLJZvQ9nxWCxiYiIiEomr/zuGyk+t0lMZpDJSoYfo/Pki03NdW6uOyaiRTlsFgRrXOidp9gUjiWRyUqoNcgmOpnFbMKmzgDeODJ20o2zmMhAAGC3stikJUEQ8BdXr4fdasa/P/w20pnFixmx/CZBD4tNmnE5rPjQ5evQOzyN/36luA40YrGJiIiIyuB2WFHrc5TU2RSTW/8NfoHsc9kAsKuJiIrTGvTM+144ng99NlJmk2zrugBiYhqH+qYKPxZPpuGwmyEwp05zVR47PnzlaTg2NI1de44t+rWznU0co9PS2afVYVNnLR58rgfj4eID3SsZi01ERERUltagB70lbKSLxXMXyEbvbGoMuGExm3D2afV6HwoRrQBtDV6MRxKI5jtOZPKGsVq/8YpNG9trYbWY8NrhscKPickMHDYWMPRy9vp6nL+hAbv29OLowMLZQIUHNwb/LF1tBEHArZevgwQJP//DwQU3UNIsFpuIiIioLK1BL0YmZgo5H0uJ5i+QjV5sqq9y4n9//mJ0hvx6HwoRrQByht27R+nk7gejZTYBgN1mxsb2Guw9PFq4ac4VmzhCp6dbLl+HKq8N/77r7VO2BcrkziY3O5s0F6hy4vptHXijexyvHhxd+hdUOBabiIiIqCytQQ8kACdGlg40BWZzJlbCBbLJxDESIipOm7ww4V0b6cYjIpx2C5x2Y77nbVlbh4lIAsfyxy0m0+xs0pnLYcHt15yO4YkZ/OqpI/N+TSyegt1qhsXMW3k9XH5OM1rrPfjFE4cwIxb3sK1S8QwlIiKishRusEaKy22SR0yM3tlERFQKj9OKWp/9lM6miYjxNtHNtXltAIIA7D2c69AQE+xsMoLT19Tg8rNb8ORr/djXM37Kz8fEFNxOFgX1YjaZ8JGr1yMSS+L+Z7v1PhxDY7GJiIiIylLttcPtsBQdEl7obGKxiYhWmdag95QMu7GwaLhNdHN5nFac1lKF1w7lcptynU0sNhnBTRd3oLHWhR/97sApWWAzYhouOz9H9dTe6MN7tzbj6df60d0f1vtwDIvFJiIiIiqLIAjz3mAtJBpPw25j6z8RrT5tDbkMu3hidqxmIiKixoDh4HNtWVeHgbEYhiZmGBBuIDarGXds34DpmRR+/oeDJ/1cLJ6Ch51Nurvhog5Uee34yaPvIJ3J6n04hsSrPSIiIipba9CD/tFYURda0XgKHgefxhLR6tMa9EICcHwkV3yPJ9KYSaQRMPAYHQBsWRsAAOw9NIp4Ig2nnZ1NRtHW4MWObe146cAIXnx7qPDjsUQaLn6W6s5pt+CWy9fhxGgMf3j5uN6HY0gsNhEREVHZWoNepDNZDI3PLPm1zJkgotWqkGGXHysej+Q20dUYvNgU8DvRFvTitUOj7GwyoGvOa0Vnkw8/f+wQJvLnVCyeWhGLNirB1nV12LI2gN/u7sHIVFzvwzEcXYtN//iP/4gvf/nLAIADBw7gpptuwpVXXomvfe1rSKeZ7E5ERGR0C638nk+u9Z9PY4lo9any2OBzWQvvhXJhwMgB4bKt6wLoHoggk5WY2WQwZpMJH99+BtLZLP7zkQPIShJmxDSzDw3klsvXQTAJ+PljByFJkt6HYyi6FZteeOEFPPjgg4X//uIXv4hvfOMbeOyxxyBJEu677z69Do2IiIiK1FDjhNViKoyOLCYqpuFm6z8RrUKCIKC1wYu+fIbdeCQBAKg1eGYTkMttkrHYZDzBahc+cNla7D82iT+8dBzJdJadTQZS43Pgxos6sK9nAn88MKz34RiKLsWmqakp3HPPPfjLv/xLAEB/fz9EUcTmzZsBADfeeCMeffRRPQ6NiIiISmA2mdBc5ylqIx07m4hoNWsLejEwFkMqncV4WITZJMDvtul9WEsKBdyor3YCyOXQkPFcsrkJZ3bU4tdPdwMAH9wYzHu3NqO90Yv/74nDp2wPrGS6FJv+9m//Fp/97Gfh8/kAACMjI6irm62o19XVYXiYVUEiIqKVoC3oQd9wdNH28awk5TObeIFMRKtTW9CLTFZC/1gUExER1V47TCZB78NakiAI2Lo2dy/GziZjEgQBH71mfSHA3cXOJkMxmQR85Kr1iMbThYIg6VBs+tWvfoXGxkacf/75hR+b7+JUEIz/xkxERERAS9CLmUQa42Fxwa+JJ9KQJMDDC2QiWqVagx4AQO/QNMYi4orIa5Kde0YQZpOAuiqn3odCC6jy2PGRq9ZDAPj3ZECtQS8uP6cZz74xgEPHp/Q+HEPQ/IrvkUcewejoKHbu3IlwOIyZmRkIgoCxsbHC14yOjqK+vl7rQyMiIqIyFG6whqMILHABLLeVs7OJiFaruionnHYL+oZznU2ntVTrfUhFa2vw4l8/exFsVnY2GdnZ6+vxvb+5EC6O0RnS9ds68Mo7o/jJo+/gmx99D6wWXfex6U7z3/1//ud/YteuXfjNb36DT3/607jsssvw7W9/G3a7Ha+++ioA4KGHHsJFF12k9aERERFRGZrrPBAE4PjIwrlNsXhuyywzm4hotRIEAa31HhwdjGByOoFav13vQyoJC00rAwtNxmW3mXHbleswOD6DR//Yq/fh6M4wpbbvfve7+Pa3v42rr74a8XgcH/7wh/U+JCIiIiqC3WpGQ42rsIVpPuxsIqJK0NbgRe/QNCQJK2qMjoiUsakzgLPX1+PhPb0IRxN6H46udA1OuPHGG3HjjTcCANavX49f//rXeh4OERERlakt6MXBRTIKYvliEzubiGg1k8eKARabiCrVLZevg99tg9lsmN4eXVT2756IiIgU0RL0YHI6gemZ5Lw/HxXznU0MCCeiVawt6C38/xoWm4gqkt9twy2Xr6v4B2wsNhEREdGyteZvsPpG5h+li8VTEAC4mTVBRKtYQ62rEArMziYiqmQsNhEREdGytdbnRkeOL5DbFI2n4HJYYDIJWh4WEZGmzCYTWuo98DitsNsYuE1ElYu97ERERLRsXpcN1V47+obn30gXE9PsaiKiinDJ5hBGpmb0PgwiIl2x2ERERESKaAt60btAsSkaT3ETHRFVhG2bGvU+BCIi3XGMjoiIiBTRUu/B0MQMEqnMKT8Xi6fgdvIZFxEREVElYLGJiIiIFNEa9EKSgBOjp+Y2ReOpit/KQkRERFQpWGwiIiIiRbQFcyHhffOEhMfEFDzMbCIiIiKqCCw2ERERkSJq/Q647BYcf1duUzqTRTyRYWYTERERUYVgsYmIiIgUIQgCWoMe9L6rs2lGTAMAx+iIiIiIKgSLTURERKSYlnovToxGkclmCz8WjacAgAHhRERERBWCxSYiIiJSTGvQg1Q6i6GJeOHHYmKu2MTMJiIiIqLKwGITERERKaYt6AUA9M3JbZrtbGKxiYiIiKgSsNhEREREimmodcFiNuH4nNymWDyX2cRiExEREVFlYLGJiIiIFGMxmxCqc6N3ns4mjtERERERVQYWm4iIiEhRbUEPjo9EIUkSgFxmk0kQ4LSbdT4yIiIiItICi01ERESkqJZ6L6LxFCanEwCAWDwFt9MCQRB0PjIiIiIi0gKLTURERKQoOSRcHqWLxlPwMK+JiIiIqGKw2ERERESKaq53QwAKIeExMQ0385qIiIiIKgaLTURERKQoh82C+hoXO5uIiIiIKhSLTURERKS4tqAHffnOpmg+s4mIiIiIKgOLTURERKS4lnoPxiMiYmIKMTHFMToiIiKiCsJiExERESlODgnv7o8gmcpyjI6IiIiogrDYRERERIpryRebDvROAADcLDYRERERVQwWm4iIiEhxfrcNfo8NB45NAgA7m4iIiIgqCItNREREpIq2oBd9I7mQcI+DAeFERERElYLFJiIiIlJFS72n8P85RkdERERUOVhsIiIiIlXIIeEAx+iIiIiIKgmLTURERKSK1iA7m4iIiIgqEQMUiIiISBWBKiccNjPSGQk2C59vEREREVUKXvkRERGRKkyCgNZ6DzxOCwRB0PtwiIiIiEgj7GwiIiIi1Vx+TgtGpuJ6HwYRERERaYjFJiIiIlLNn5xWr/chEBEREZHGOEZHRERERERERESKYbGJiIiIiIiIiIgUw2ITEREREREREREphsUmIiIiIiIiIiJSDItNRERERERERESkGBabiIiIiIiIiIhIMSw2ERERERERERGRYlhsIiIiIiIiIiIixbDYREREREREREREimGxiYiIiIiIiIiIFMNiExERERERERERKYbFJiIiIiIiIiIiUowuxabvf//7uPbaa3HttdfiO9/5DgBgz5492L59O6644grcc889ehwWEREREREREREtk+bFpj179mD37t148MEH8dBDD2H//v3YtWsXvvrVr+IHP/gBHnnkEezbtw/PPPOM1odGRERERERERETLZNH6G9bV1eHLX/4ybDYbAKCzsxPHjh1DW1sbWlpaAADbt2/Ho48+iosvvrjo1zWZBFWOl4iIiIiIiIio0iynzqJ5sWnt2rWF/3/s2DE88sgjuO2221BXV1f48fr6egwPD5f0utXVbsWOkYiIiIiIiIiIyqNbQPjhw4fxsY99DF/60pfQ2tp6ys8LAjuViIiIiIiIiIhWGl2KTa+++ir+4i/+Ap///Odxww03IBgMYmxsrPDzIyMjqK+v1+PQiIiIiIiIiIhoGTQvNg0ODuKv//qv8d3vfhfXXnstAOCss85CT08Pent7kclksGvXLlx00UVaHxoRERERERERES2TIEmSpOU3vPvuu3H//fefNDr3wQ9+EGvWrMG3v/1tJBIJXHzxxfjKV77CUToiIiIiIiIiohVG82ITERERERERERGtXroFhBMRERERERER0erDYhMRERERERERESmGxSYiIiIiIiIiIlIMi01ERERERERERKQYFps0Eo1Gcd111+HEiRMAgAceeADXXHMNtm/fjrvvvhvpdBoA8NBDD2Hbtm3YuXMndu7ciXvuuQcAMDAwgFtuuQVXXXUV/uqv/gqxWEy33wsZS7Hn1sjICO644w5cf/31+OAHP1j4+kgkgjvuuANXX301brnlFoyOjur2eyHjKeb8Gh8fL7xn7dy5E5dddhm2bNkCgOcXLazY964TJ07glltuwc6dO3Hbbbehv78fAJBMJvHFL34RV199NW644QZ0d3fr9nshYyn23HrzzTdx0003Yfv27bjzzjsL7088t2gh3//+93Httdfi2muvxXe+8x0AwJ49e7B9+3ZcccUVhet2ADhw4ABuuukmXHnllfja175WOO94TU8LKeX8kn3pS1/CAw88UPhvnl80n1LOrSeeeAI7d+7Ejh078MlPfhLhcBhAmeeWRKp7/fXXpeuuu07asGGDdPz4cam7u1u68MILpeHhYUmSJOmuu+6SfvSjH0mSJEl/93d/Jz388MOnvMYdd9wh7dq1S5IkSfr+978vfec739HuN0CGVcq59ZGPfES69957JUmSpHvvvVf6zGc+I0mSJH3rW9+S/u3f/k2SJEl68MEHCz9OVMr5JctkMtKtt94q/fa3v5UkiecXza+Uc+sLX/iC9Itf/EKSJEn66U9/Kn3+85+XJEmS/uM//kP6xje+IUmSJL300kvSzTffrMPvhIym2HMrm81KF198sfTCCy9IkiRJv/vd76Q777xTkiSeWzS/559/XvrABz4gJRIJKZlMSh/+8Ielhx9+WLr44oulvr4+KZVKSR/72Mekp59+WpIkSbr22mulvXv3SpIkSV/5ylcK72O8pqf5lHp+DQ0NSXfeeae0adMm6f777y+8Ds8verdSzq3p6WnpggsukIaGhiRJkqR/+Zd/kf7+7/9ekqTyzi12Nmngvvvuw1133YX6+noAwMGDB7F58+bCf1966aV44oknAABvvfUWHnroIezYsQNf+MIXEA6HkUql8PLLL+PKK68EANx444149NFH9fnNkKEUe25NTEzgnXfewQc/+EEAwE033YS/+Zu/AQA8/fTT2L59OwDguuuuw7PPPotUKqXD74aMppT3Ltn9998Pp9NZOKd4ftF8Sjm3stksotEoACAej8PhcADInVs7duwAAJxzzjmYnJzEwMCA1r8VMphiz63JyUmIoojzzjuv8OO7d+9GMpnkuUXzqqurw5e//GXYbDZYrVZ0dnbi2LFjaGtrQ0tLCywWC7Zv345HH/3/27v70KrLPo7j72NnDt3EbeCGc41JxIZFC1wPRO2QGqVFuaCwpLSaPWCzZMROD0xbnpSwwqCHRdhoFUFIs4dhMImaEZRCSsQifEpkbSsXbNlO7pxz/zE8tdud3ZuePN73/X79d6799uO64MP1u/Y91/XbDo4ePcrQ0BCXXnop8Nfa3TW9UplMvgA++ugjFi5cyOLFi5P3MF8ay2SydeLECdavX09RUREA5eXldHd3n3a2LDadBZFIhKqqquTniooK9u7dS3d3N7FYjB07dvDLL78AI2Goq6tj+/btzJ49m6amJvr7+8nNzSUYDCav6enpychYdG6ZaLaOHDlCcXExzz77LDfffDNr1qwhKysLGDleN2vWLACCwSC5ubkcO3YsI+PRuWUycxdALBbj1Vdfpb6+PtlmvjSWyWTrkUceoaWlhWuuuYatW7eyatUqYHS2YOTZ+PPPP5/dgeicM9Fs5efnM336dHbt2gXAJ598wokTJ+jv7zdbGtOFF16YLB4dOnSI9vZ2AoHAqKwUFhbS09MzZoZ6enpc0yulyeQLoLa2lttuu23UPcyXxjKZbOXn57No0SIAhoaGeP3111m0aNFpZ8tiUwbMnTuX+vp6HnroIZYvX055eXnyD/+XX36ZyspKAoEAtbW1fPHFFyQSiVPuEQgEzna39V8gVbaGh4f5/vvvueqqq/jwww9ZuHAh4XA45X2mTHFq0KnGm7sAOjs7mTt3LuXl5ePex3zp342XrYaGBpqamujs7OTpp5/m4YcfHvO5CGZLp0qVrUAgwEsvvURzczNLly5lYGCAvLy8UXPa35ktnfTjjz9y77330tDSLZDqAAAGFUlEQVTQQGlp6Sk/DwQCKdfurun1n0wkX6mYL41nMtkaGBhg1apVVFRUUFNTc9rZ8smZAdFolEsuuYS2tjbee+89iouLOf/88xkYGKClpSV5XSKRIBgMUlBQwODgILFYDIC+vr7kdnDp71Jla9asWeTk5HDttdcCI8eZ9u3bB4xUsk/uIBgeHmZwcJC8vLyMjUHnrlT5Oqmjo4MlS5aM+h3zpYlIla1jx45x4MCB5Lds119/PX19ffT391NYWDjqhfM+GzWW8eatYDBIa2srbW1t1NTUEI/HycvLM1tKac+ePaxcuZL6+npqamooKioatcO3t7eXwsLCU9pPZsg1vcYz0XylYr6UymSy1dvby5133klFRQWRSAQ4/WxZbMqA48ePs2LFCgYHB/nzzz9pbW1lyZIlTJ8+nTfeeIO9e/cC8Pbbb3PdddeRlZVFVVUV7e3twMh/rKuurs7kEHSOSpWt0tJSioqK+PzzzwH47LPPuOiiiwAIhUK0tbUB0N7eTlVVVcpvdvX/LVW+Tvr2229HHV8B86WJSZWt/Px8srOz2b17NzCyWMrJyaGgoIBQKMT27dsB2L17N9nZ2RQXF2dyGDoHjTdvPfHEE8kvXrZu3coNN9zAlClTzJbG1N3dzerVq9m8eTM33ngjAJWVlRw8eJDDhw8Ti8X4+OOPqa6uZs6cOWRnZ7Nnzx7gr7W7a3qlMpl8pWK+NJbJZCsWi/Hggw+yePFinnzyyeTupdPNViCRai+60m7BggW89dZblJSU8P7779PS0sLw8DA33XQTdXV1wMiiJhKJMDQ0RFlZGc899xwzZszg6NGjhMNhfv31V2bPns0LL7zAzJkzMzwinSsmkq0DBw6wbt265JnbTZs2UVZWxm+//UY4HObIkSPMmDGDzZs3U1JSkuER6VwykXzByIPr66+/Jjs7O9lmvjSeiWRr3759PPPMMwwNDZGTk0NjYyPz5s0jGo3S2NjId999x9SpU9mwYUOyiC5NNFvr1q3jjz/+oLy8nEgkQm5urtnSmDZs2MC2bdtGHT9ZtmwZZWVlbNy4kWg0SigU4vHHHycQCNDV1cVTTz3F77//zrx589i4cSNTp051Ta8xTTZfJ4XDYS6//HJuvfVWAPOlU0wmWx0dHdTV1Y16JcbFF19MJBI5rWxZbJIkSZIkSVLaeIxOkiRJkiRJaWOxSZIkSZIkSWljsUmSJEmSJElpY7FJkiRJkiRJaWOxSZIkSZIkSWljsUmSJOkMNDU1cfvttxOLxZJtsViMZcuW8eKLL2awZ5IkSZlhsUmSJOkMNDQ0cPz4cZqbm5Ntzc3NnHfeeaxZsyaDPZMkScqMQCKRSGS6E5IkSf/NfvjhB+644w7eeecdEokE999/P9u2baOoqIiOjg5ee+01hoeHmTZtGuFwmMrKSnp7e2lsbKS/v5++vj7mzJnDli1bKCgooLq6mvnz59PV1cVjjz3GggULMj1ESZKkCbPYJEmSlAatra188MEHxONx1q5dSygUYv/+/axdu5bW1lZmzpxJV1cXtbW17Ny5k3fffZd4PM59991HPB6ntraWUCjEihUrqK6uZvny5TzwwAOZHpYkSdKkBTPdAUmSpP8Fd911F59++ikXXHABoVAIgC+//JKenh7uvvvu5HWBQICffvqJe+65h2+++YY333yTQ4cOsX//fi677LLkdfPnzz/rY5AkSUoHi02SJElpUlJSQmlpafJzPB7n6quv5vnnn0+2dXd3U1RUxKZNm+jq6qKmpoYrrriCaDTK3zec5+TknNW+S5IkpYsvCJckSfqHXHnllXR2dnLw4EEAdu7cydKlS4lGo+zatYuVK1dyyy23UFBQwFdffUU8Hs9wjyVJks6cO5skSZL+IRUVFaxfv55HH32URCJBMBjklVdeYdq0aaxevZpIJMKWLVvIysqiqqqKw4cPZ7rLkiRJZ8wXhEuSJEmSJCltPEYnSZIkSZKktLHYJEmSJEmSpLSx2CRJkiRJkqS0sdgkSZIkSZKktLHYJEmSJEmSpLSx2CRJkiRJkqS0sdgkSZIkSZKktLHYJEmSJEmSpLT5F9A+PuUVSgCbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate a time series of unique chords\n",
    "X, y = generate_major_ts(1950, 2018)\n",
    "plt.figure(figsize=(20, 7))\n",
    "plt.title('Average Major Quality over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Major Quality')\n",
    "sns.lineplot(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "a5e55c643b0d8",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R: - 0.10101030365374941\n",
      "                  Results: Ordinary least squares\n",
      "===================================================================\n",
      "Model:              OLS              Adj. R-squared:     0.010     \n",
      "Dependent Variable: Year             AIC:                20470.1580\n",
      "Date:               2019-06-12 23:09 BIC:                20481.6540\n",
      "No. Observations:   2317             Log-Likelihood:     -10233.   \n",
      "Df Model:           1                F-statistic:        23.86     \n",
      "Df Residuals:       2315             Prob (F-statistic): 1.10e-06  \n",
      "R-squared:          0.010            Scale:              401.83    \n",
      "-------------------------------------------------------------------\n",
      "              Coef.   Std.Err.     t     P>|t|    [0.025    0.975] \n",
      "-------------------------------------------------------------------\n",
      "const       1985.4078   0.4700 4224.7021 0.0000 1984.4863 1986.3294\n",
      "Majorness     -0.0133   0.0027   -4.8850 0.0000   -0.0186   -0.0079\n",
      "-------------------------------------------------------------------\n",
      "Omnibus:             1545.230       Durbin-Watson:          0.020  \n",
      "Prob(Omnibus):       0.000          Jarque-Bera (JB):       134.046\n",
      "Skew:                -0.039         Prob(JB):               0.000  \n",
      "Kurtosis:            1.824          Condition No.:          195    \n",
      "===================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/icpc/.pyenv/versions/3.6.3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#Perform an Ordinary Least Square (OLS) Regression of Majorness vs Year\n",
    "Y = df['Year']\n",
    "X = df['Majorness']\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X)\n",
    "results = model.fit()\n",
    "print('Pearson R: -', math.sqrt(results.rsquared))\n",
    "print(results.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "60b59e04a51a6",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115cd8390>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df['Year'], df['Majorness'],scatter_kws={'s':1,'alpha':0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "e407e67413abd",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "## Analysis of Number of Unique Chords and Progressions over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qAtfW0xZJuaR",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "d1bc8194b1e5c",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "# Counts the number of chords in songs with respect to year\n",
    "def chord_count(year):\n",
    "    \n",
    "    # Select all songs from a given year\n",
    "    songs = df.loc[df.Year == year]\n",
    "    count = 0\n",
    "    \n",
    "    #Count number of times chord appears in a\n",
    "    for index, row in songs.iterrows():\n",
    "        \n",
    "        #Append ratio of unique chord count to total amount of chords in a song\n",
    "        count += len(set(row['Chords']))/len(row['Chords'])\n",
    "\n",
    "    #Get the average count for a given year\n",
    "    return count/len(songs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "chp08NiOKpKq",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "f15c42812f34a",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": [
    "#Generate a time series for chord uniqueness\n",
    "def generate_chord_ts(start, end):\n",
    "    series = []\n",
    "    years = list(range(start, end))\n",
    "    \n",
    "    #Append unique chord count to series for given year\n",
    "    for year in years:\n",
    "        series.append(chord_count(year))\n",
    "\n",
    "    return years, series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "f500cbd25a25c",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "#### We can observe from the line chart that there has been a large decline in unique chords after 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cv2RHxrDLyLP",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "9ebe23d4c964c",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fe220f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASxA8AQAAAAAAwBIETwAAAAAAALDE/wfAnpP4aRcqqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generate a time series of unique chords\n",
    "X, y = generate_chord_ts(1950, 2018)\n",
    "plt.figure(figsize=(20, 7))\n",
    "plt.title('Average Number of Unique Chords in a Song')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Percentage')\n",
    "sns.lineplot(X, y, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "b52ef5724c832",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R: - 0.16764331884296005\n",
      "                   Results: Ordinary least squares\n",
      "=====================================================================\n",
      "Model:                OLS              Adj. R-squared:     0.028     \n",
      "Dependent Variable:   Year             AIC:                20427.8698\n",
      "Date:                 2019-06-12 23:09 BIC:                20439.3658\n",
      "No. Observations:     2317             Log-Likelihood:     -10212.   \n",
      "Df Model:             1                F-statistic:        66.94     \n",
      "Df Residuals:         2315             Prob (F-statistic): 4.56e-16  \n",
      "R-squared:            0.028            Scale:              394.56    \n",
      "---------------------------------------------------------------------\n",
      "                Coef.   Std.Err.     t     P>|t|    [0.025    0.975] \n",
      "---------------------------------------------------------------------\n",
      "const         1989.6491   0.7686 2588.7212 0.0000 1988.1420 1991.1563\n",
      "Unique Chords   -0.7430   0.0908   -8.1819 0.0000   -0.9211   -0.5650\n",
      "---------------------------------------------------------------------\n",
      "Omnibus:               1622.287       Durbin-Watson:          0.056  \n",
      "Prob(Omnibus):         0.000          Jarque-Bera (JB):       140.024\n",
      "Skew:                  -0.116         Prob(JB):               0.000  \n",
      "Kurtosis:              1.818          Condition No.:          16     \n",
      "=====================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/icpc/.pyenv/versions/3.6.3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#Perform an Ordinary Least Square (OLS) Regression of Unique Chords vs Year\n",
    "Y = df['Year']\n",
    "X = df['Unique Chords']\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X)\n",
    "results = model.fit()\n",
    "print('Pearson R: -', math.sqrt(results.rsquared))\n",
    "print(results.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "378abfde0338",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1154fb710>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Year', y='Unique Chords', data=df,  scatter_kws={'s':2, 'alpha':0.5})\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "8be18b713ed7f",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R: - 0.12414933969550612\n",
      "                      Results: Ordinary least squares\n",
      "===========================================================================\n",
      "Model:                  OLS                Adj. R-squared:       0.015     \n",
      "Dependent Variable:     Year               AIC:                  20457.9298\n",
      "Date:                   2019-06-12 23:09   BIC:                  20469.4259\n",
      "No. Observations:       2317               Log-Likelihood:       -10227.   \n",
      "Df Model:               1                  F-statistic:          36.24     \n",
      "Df Residuals:           2315               Prob (F-statistic):   2.02e-09  \n",
      "R-squared:              0.015              Scale:                399.71    \n",
      "---------------------------------------------------------------------------\n",
      "                      Coef.   Std.Err.     t     P>|t|    [0.025    0.975] \n",
      "---------------------------------------------------------------------------\n",
      "const               1988.7750   0.8452 2353.1341 0.0000 1987.1177 1990.4324\n",
      "Unique Progressions   -0.3802   0.0632   -6.0199 0.0000   -0.5040   -0.2563\n",
      "---------------------------------------------------------------------------\n",
      "Omnibus:                 1729.608         Durbin-Watson:            0.030  \n",
      "Prob(Omnibus):           0.000            Jarque-Bera (JB):         139.514\n",
      "Skew:                    -0.086           Prob(JB):                 0.000  \n",
      "Kurtosis:                1.810            Condition No.:            27     \n",
      "===========================================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/icpc/.pyenv/versions/3.6.3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "#Perform an Ordinary Least Square (OLS) Regression of Unique Progressions vs Year\n",
    "Y = df['Year']\n",
    "X = df['Unique Progressions']\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X)\n",
    "results = model.fit()\n",
    "print('Pearson R: -', math.sqrt(results.rsquared))\n",
    "print(results.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "70008bcd1735",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115057b70>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='Year', y='Unique Progressions', data=df,  scatter_kws={'s':2, 'alpha':0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wp__OAkI2oAW",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "84b8daa023831",
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   },
   "source": [
    "## Ethics and Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "7bc2e5e9d2327",
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   },
   "source": [
    ">When looking at the ethics of the analysis in a legal sense, since we are not using raw audio of the songs being analyzed we do not have consider royalties and fair use in this instance. Additionally, Ultimate Guitar, which is the site that we are scraping for the chords data has only explicitly noted that the chord sheets posted on their website cannot be used in a commercial setting or sold. Hence, since we are only analyzing the data in an academic setting and non-commercially and not dealing with private information the analysis has no apparent ethical issues in that regard.  \n",
    ">However, ethical issues arise in a cultural sense leading to an inherent bias in the data, and in turn, the analysis. Firstly, all the songs on the Billboard Top 100, are those released in the United States, thus the analysis cannot necessarily generalize to top songs in other countries which might have varying stylistic elements and chord usage. Secondly, we weren't able to obtain chords for all songs in the Hot 100 for a given year due to them not being present on UltimateGuitar. As a result, this could have altered the analysis slightly if the missing songs were consistently from a similar genre or year. With our regression models, we relied on using proxy variables which were representative of the chords in the song such as chord frequency and number of unique chords to predict year. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8VV7TPGW2slR",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "52fe719146fec",
     "named_versions": [],
     "output_hidden": false,
     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "source": [
    "## Conclusion and Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NSxACpjw2v8m",
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "5c8ff2212879a",
     "named_versions": [],
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     "show_versions": false,
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    }
   },
   "source": [
    ">Because music can often depict themes from the time period it stems from, we wanted to discover if there was any significant relationship between the chords used in a song and the year of that song’s production. We had also realized that popular songs from today’s time tend to have similar chord progressions, and so combining those observations, we hypothesized that a song’s number of unique chords and the type of progressions used in it will have some correlation on that song’s production year. After running an ordinary least squares regression analysis on Majorness, Unique Chords, and Unique Progressions with respect to time we found that they all had small effects (|r|<=0.2). First, the hypothesis that unique chords and unique progressions would be negatively correlated time was correct. This, follows our original intuition that songs are becoming more repetitive and would have correspondingly lower uniqueness metrics. However, we thought the strength of this correlation would be much more prevalent, but this could be due to having a relatively small sample when looking at the total number of songs produced over the last 70 years. Secondly, the hypothesis that the majorness of songs would decrease over time also held as we obtained an r=-0.1 which represents a weak negative correlation. As with the first hypothesis, it is possible that the correlation could change given a larger sample size. Overall, the results stayed fairly consistent with out original hypotheses and believe the analysis was successful in providing evidence to support them.\n",
    "\n",
    ">When at potential next steps for the analysis, we think building sequence classification model (LSTM) could yield interesting results as it could eliminate the need for proxy variables and learn inter-sequence dependencies between chords. We intially tried to use a TF-IDF approach where we considered documents labelled by year and chord sequence as a string, but this method completely ignored ordering of chords which lead to poor results (F1:0.16). Additionally, we could extend the analysis to include genre and look at various measures pertaining to chords across them, whereas this analysis only considered measures over time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "janus": {
     "all_versions_showing": false,
     "cell_hidden": false,
     "current_version": 0,
     "id": "5b86fc342bed3",
     "named_versions": [],
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     "show_versions": false,
     "source_hidden": false,
     "versions": []
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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