Spotify Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## More than Meets the Ear: The Data and Elements Behind Spotify Songs\n",
    "Since its humble beginnings in Sweden, **Spotify** has continually grown exponentially to become one of, if not, the world's biggest music streaming provider. Boasting an immense catalog of tracks from around the world, the music giant has gathered mounds of valuable data, both basic and complex, behind the songs deployed in its massive database. For this Notebook, the researchers **Anjelo Antioquia**, **Gabriel Minamedez**, and **Julian Sunpayco** will report their findings in this case study centered around a dataset gathered from online data science community Kaggle related to the songs in Spotify.\n",
    "\n",
    "This is in partial fulfillment of the course **Statistical Modelling and Simulation (CSMODEL)**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import\n",
    "Import Python modules. **IPython** is for an embedded HTML file in the Exploratory Data Analysis portion. **matplotlib**, **numpy**, **pandas**, **scipy**, **seaborn**, and **sklearn** are for data handling, analysis, and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import IFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import ttest_ind\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset: The Spotify Dataset\n",
    "The [Spotify dataset](https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks?select=data.csv) (titled **`data.csv`**) consists of 160,000+ tracks from 1921-2020 found in Spotify as of June 2020. Collected by Kaggle user and Turkish Data Scientist Yamaç Eren Ay, the data was retrieved and tabulated from the **Spotify Web API**. Each row in the dataset corresponds to a track, with variables such as the title, artist, and year located in their respective columns. Aside from the fundamental variables, musical elements of each track, such as the tempo, danceability, and key, were likewise extracted; the algorithm for these values were generated by Spotify based on a range of technical parameters.\n",
    "\n",
    "Let's bring in that dataset now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>artists</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>release_date</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6KbQ3uYMLKb5jDxLF7wYDD</td>\n",
       "      <td>Singende Bataillone 1. Teil</td>\n",
       "      <td>['Carl Woitschach']</td>\n",
       "      <td>158648</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.563</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>-12.428</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>118.469</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6KuQTIu1KoTTkLXKrwlLPV</td>\n",
       "      <td>Fantasiestücke, Op. 111: Più tosto lento</td>\n",
       "      <td>['Robert Schumann', 'Vladimir Horowitz']</td>\n",
       "      <td>282133</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.901</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>-28.454</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>83.972</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6L63VW0PibdM1HDSBoqnoM</td>\n",
       "      <td>Chapter 1.18 - Zamek kaniowski</td>\n",
       "      <td>['Seweryn Goszczyński']</td>\n",
       "      <td>104300</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-19.924</td>\n",
       "      <td>0.9290</td>\n",
       "      <td>107.177</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6M94FkXd15sOAOQYRnWPN8</td>\n",
       "      <td>Bebamos Juntos - Instrumental (Remasterizado)</td>\n",
       "      <td>['Francisco Canaro']</td>\n",
       "      <td>180760</td>\n",
       "      <td>9/25/28</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>-14.734</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>108.003</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6N6tiFZ9vLTSOIxkj8qKrd</td>\n",
       "      <td>Polonaise-Fantaisie in A-Flat Major, Op. 61</td>\n",
       "      <td>['Frédéric Chopin', 'Vladimir Horowitz']</td>\n",
       "      <td>687733</td>\n",
       "      <td>1928</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.0980</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>62.149</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       id                                           name  \\\n",
       "0  6KbQ3uYMLKb5jDxLF7wYDD                    Singende Bataillone 1. Teil   \n",
       "1  6KuQTIu1KoTTkLXKrwlLPV       Fantasiestücke, Op. 111: Più tosto lento   \n",
       "2  6L63VW0PibdM1HDSBoqnoM                 Chapter 1.18 - Zamek kaniowski   \n",
       "3  6M94FkXd15sOAOQYRnWPN8  Bebamos Juntos - Instrumental (Remasterizado)   \n",
       "4  6N6tiFZ9vLTSOIxkj8qKrd    Polonaise-Fantaisie in A-Flat Major, Op. 61   \n",
       "\n",
       "                                    artists  duration_ms release_date  year  \\\n",
       "0                       ['Carl Woitschach']       158648         1928  1928   \n",
       "1  ['Robert Schumann', 'Vladimir Horowitz']       282133         1928  1928   \n",
       "2                   ['Seweryn Goszczyński']       104300         1928  1928   \n",
       "3                      ['Francisco Canaro']       180760      9/25/28  1928   \n",
       "4  ['Frédéric Chopin', 'Vladimir Horowitz']       687733         1928  1928   \n",
       "\n",
       "   acousticness  danceability  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.995         0.708  0.1950             0.563    0.1510   -12.428   \n",
       "1         0.994         0.379  0.0135             0.901    0.0763   -28.454   \n",
       "2         0.604         0.749  0.2200             0.000    0.1190   -19.924   \n",
       "3         0.995         0.781  0.1300             0.887    0.1110   -14.734   \n",
       "4         0.990         0.210  0.2040             0.908    0.0980   -16.829   \n",
       "\n",
       "   speechiness    tempo  valence  mode  key  popularity  explicit  \n",
       "0       0.0506  118.469   0.7790     1   10           0         0  \n",
       "1       0.0462   83.972   0.0767     1    8           0         0  \n",
       "2       0.9290  107.177   0.8800     0    5           0         0  \n",
       "3       0.0926  108.003   0.7200     0    1           0         0  \n",
       "4       0.0424   62.149   0.0693     1   11           1         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df = pd.read_csv(\"./data.csv\")\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a huge array of variables present as seen above, it may seem overwhelming, but don't be! Together, these variables describe a track in each row, giving each some interesting numerical context across different parameters. Below is the list of variables and their descriptions:\n",
    "- **`id`**: A unique identifier for each track.\n",
    "- **`name`**: The track's title.\n",
    "- **`artists`**: Artist/s who participated in the track.\n",
    "- **`duration_ms`**: Length of the track in milliseconds (ms).\n",
    "- **`release_date`**: The track's release date in MM/DD/YYYY, or at the minimum, YYYY.\n",
    "- **`year`**: The year in which the track was released.\n",
    "- **`acousticness`***: A confidence measure from 0.0 to 1.0 of whether the track is acoustic.\n",
    "- **`danceability`***: Describes how suitable a track is for dancing. A value of 0.0 is least danceable and 1.0 is most danceable.\n",
    "- **`energy`***: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast and loud.\n",
    "- **`instrumentalness`***: Predicts whether a track contains no vocals.\n",
    "- **`liveness`***: Detects the presence of an audience in the recording.\n",
    "- **`loudness`***: The overall loudness of a track in decibels (dB).\n",
    "- **`speechiness`***: Detects the presence of spoken words in a track.\n",
    "- **`tempo`***: The overall estimated tempo of a track in beats per minute (BPM).\n",
    "- **`valence`***: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track.\n",
    "- **`mode`**: Melodic content from the modality of a track. 1 = Major; 0 = Minor.\n",
    "- **`key`**: The estimated overall key of a track from the [Pitch Class notation](https://en.wikipedia.org/wiki/Pitch_class). E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on.\n",
    "- **`popularity`**: A track's popularity, 0-100 where 100 is the most popular based on [Spotify's algorithm](https://developer.spotify.com/documentation/web-api/reference/tracks/get-track/).\n",
    "- **`explicit`**: Whether or not the track has explicit lyrics. 1 = True; 0 = False.\n",
    "\n",
    "*Based on the [audio features of a track from Spotify's Web API](https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are quite a lot of variables here to dissect, however, there are some columns here that will not be relevant to the following Exploratory Data Analysis and the Further Investigation and Inference on the Spotify data. For simplicity's sake, we'll drop those irrelevant variables. In this case, we'll drop the following columns, along with the reason for dropping:\n",
    "- **`id`**: No further analysis can be drawn from the track's unique identifier.\n",
    "- **`release_date`**: Since some of the data is incomplete (e.g. no date, no month), this will be dropped as it will be impossible to draw annual trends for time series, thus deeming this variable irrelevant. Filling in date information based on other complete dates will likewise harm the data. We will simply depend on the `year` variable for time analysis.\n",
    "- **`liveness`**: It is merely a measure of whether or not a track was performed live or through a recording studio, and so the researchers deem this as a non-fundamental musical element since this does not directly measure an element of music itself.\n",
    "\n",
    "Let's now materialize the deletion of these columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artists</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singende Bataillone 1. Teil</td>\n",
       "      <td>['Carl Woitschach']</td>\n",
       "      <td>158648</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.563</td>\n",
       "      <td>-12.428</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>118.469</td>\n",
       "      <td>0.7790</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fantasiestücke, Op. 111: Più tosto lento</td>\n",
       "      <td>['Robert Schumann', 'Vladimir Horowitz']</td>\n",
       "      <td>282133</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.901</td>\n",
       "      <td>-28.454</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>83.972</td>\n",
       "      <td>0.0767</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chapter 1.18 - Zamek kaniowski</td>\n",
       "      <td>['Seweryn Goszczyński']</td>\n",
       "      <td>104300</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-19.924</td>\n",
       "      <td>0.9290</td>\n",
       "      <td>107.177</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bebamos Juntos - Instrumental (Remasterizado)</td>\n",
       "      <td>['Francisco Canaro']</td>\n",
       "      <td>180760</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.887</td>\n",
       "      <td>-14.734</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>108.003</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Polonaise-Fantaisie in A-Flat Major, Op. 61</td>\n",
       "      <td>['Frédéric Chopin', 'Vladimir Horowitz']</td>\n",
       "      <td>687733</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.908</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>62.149</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                            name  \\\n",
       "0                    Singende Bataillone 1. Teil   \n",
       "1       Fantasiestücke, Op. 111: Più tosto lento   \n",
       "2                 Chapter 1.18 - Zamek kaniowski   \n",
       "3  Bebamos Juntos - Instrumental (Remasterizado)   \n",
       "4    Polonaise-Fantaisie in A-Flat Major, Op. 61   \n",
       "\n",
       "                                    artists  duration_ms  year  acousticness  \\\n",
       "0                       ['Carl Woitschach']       158648  1928         0.995   \n",
       "1  ['Robert Schumann', 'Vladimir Horowitz']       282133  1928         0.994   \n",
       "2                   ['Seweryn Goszczyński']       104300  1928         0.604   \n",
       "3                      ['Francisco Canaro']       180760  1928         0.995   \n",
       "4  ['Frédéric Chopin', 'Vladimir Horowitz']       687733  1928         0.990   \n",
       "\n",
       "   danceability  energy  instrumentalness  loudness  speechiness    tempo  \\\n",
       "0         0.708  0.1950             0.563   -12.428       0.0506  118.469   \n",
       "1         0.379  0.0135             0.901   -28.454       0.0462   83.972   \n",
       "2         0.749  0.2200             0.000   -19.924       0.9290  107.177   \n",
       "3         0.781  0.1300             0.887   -14.734       0.0926  108.003   \n",
       "4         0.210  0.2040             0.908   -16.829       0.0424   62.149   \n",
       "\n",
       "   valence  mode  key  popularity  explicit  \n",
       "0   0.7790     1   10           0         0  \n",
       "1   0.0767     1    8           0         0  \n",
       "2   0.8800     0    5           0         0  \n",
       "3   0.7200     0    1           0         0  \n",
       "4   0.0693     1   11           1         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.drop([\"id\", \"release_date\", \"liveness\"], axis=1, inplace=True)\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now check the specific information for the variables of this dataset and see if further cleaning is needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 169909 entries, 0 to 169908\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   name              169909 non-null  object \n",
      " 1   artists           169909 non-null  object \n",
      " 2   duration_ms       169909 non-null  int64  \n",
      " 3   year              169909 non-null  int64  \n",
      " 4   acousticness      169909 non-null  float64\n",
      " 5   danceability      169909 non-null  float64\n",
      " 6   energy            169909 non-null  float64\n",
      " 7   instrumentalness  169909 non-null  float64\n",
      " 8   loudness          169909 non-null  float64\n",
      " 9   speechiness       169909 non-null  float64\n",
      " 10  tempo             169909 non-null  float64\n",
      " 11  valence           169909 non-null  float64\n",
      " 12  mode              169909 non-null  int64  \n",
      " 13  key               169909 non-null  int64  \n",
      " 14  popularity        169909 non-null  int64  \n",
      " 15  explicit          169909 non-null  int64  \n",
      "dtypes: float64(8), int64(6), object(2)\n",
      "memory usage: 20.7+ MB\n"
     ]
    }
   ],
   "source": [
    "main_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With 169,909 tracks in total and every variable filled, it's looking good so far! However, let's dig in some more just in case there are anomalies with the data themselves. Let's use the `pd.Series.unique()` function to check the variables with limited choices of possible values (rules indicated in comments for each print statement):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0]\n",
      "[10  8  5  1 11  6  9  7  4  2  3  0]\n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "print(main_df[\"mode\"].unique()) # Should be either 0 or 1\n",
    "print(main_df[\"key\"].unique()) # Should be values from 0 to 11\n",
    "print(main_df[\"explicit\"].unique()) # Should be either 0 or 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the variables `mode`, `key`, and `explicit` are good to go. Let's now check the variables supposedly bound within a given range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1921 – 2020\n",
      "0.0 – 0.996\n",
      "0.0 – 0.988\n",
      "0.0 – 1.0\n",
      "0.0 – 1.0\n",
      "0.0 – 0.9690000000000001\n",
      "0.0 – 1.0\n",
      "0 – 100\n"
     ]
    }
   ],
   "source": [
    "print(str(main_df[\"year\"].min()) + \" – \" + str(main_df[\"year\"].max())) # Range: [1921 – 2020]\n",
    "print(str(main_df[\"acousticness\"].min()) + \" – \" + str(main_df[\"acousticness\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"danceability\"].min()) + \" – \" + str(main_df[\"danceability\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"energy\"].min()) + \" – \" + str(main_df[\"energy\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"instrumentalness\"].min()) + \" – \" + str(main_df[\"instrumentalness\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"speechiness\"].min()) + \" – \" + str(main_df[\"speechiness\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"valence\"].min()) + \" – \" + str(main_df[\"valence\"].max())) # Range: [0.0 – 1.0]\n",
    "print(str(main_df[\"popularity\"].min()) + \" – \" + str(main_df[\"popularity\"].max())) # Range: [0 – 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ranges are all as expected. However, let's check to see if there are any duplicate rows that might skew our data a certain way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>explicit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Rhapsody on a Theme of Paganini, Op.43: Variat...</td>\n",
       "      <td>['Sergei Rachmaninoff', 'William Kapell', 'Fri...</td>\n",
       "      <td>61467</td>\n",
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       "      <td>0.610</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>Interview with William Kapell</td>\n",
       "      <td>['William Kapell']</td>\n",
       "      <td>1325093</td>\n",
       "      <td>1945</td>\n",
       "      <td>0.91300</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.2230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-24.480</td>\n",
       "      <td>0.9350</td>\n",
       "      <td>112.485</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>Give Me Your Love (Love Song)</td>\n",
       "      <td>['Curtis Mayfield']</td>\n",
       "      <td>260333</td>\n",
       "      <td>1972</td>\n",
       "      <td>0.12800</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.6370</td>\n",
       "      <td>0.019500</td>\n",
       "      <td>-9.102</td>\n",
       "      <td>0.0887</td>\n",
       "      <td>172.452</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8094</th>\n",
       "      <td>Enamórate De Alguien Más</td>\n",
       "      <td>['Morat']</td>\n",
       "      <td>230040</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.6590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.464</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>145.997</td>\n",
       "      <td>0.622</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8169</th>\n",
       "      <td>Danse macabre, S. 555 (after Camille Saint-Saens)</td>\n",
       "      <td>['Franz Liszt', 'Vladimir Horowitz']</td>\n",
       "      <td>491333</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.99300</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.2260</td>\n",
       "      <td>0.826000</td>\n",
       "      <td>-21.483</td>\n",
       "      <td>0.0444</td>\n",
       "      <td>67.013</td>\n",
       "      <td>0.146</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164200</th>\n",
       "      <td>My Prayer</td>\n",
       "      <td>['Roy Orbison']</td>\n",
       "      <td>164467</td>\n",
       "      <td>1963</td>\n",
       "      <td>0.83800</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.004630</td>\n",
       "      <td>-11.501</td>\n",
       "      <td>0.0271</td>\n",
       "      <td>105.003</td>\n",
       "      <td>0.182</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166015</th>\n",
       "      <td>Bonzo's Montreux - 2012 Remaster</td>\n",
       "      <td>['Led Zeppelin']</td>\n",
       "      <td>262147</td>\n",
       "      <td>1982</td>\n",
       "      <td>0.21300</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>0.908000</td>\n",
       "      <td>-10.754</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>180.289</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168041</th>\n",
       "      <td>Shiver</td>\n",
       "      <td>['Maroon 5']</td>\n",
       "      <td>179773</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.04210</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.435</td>\n",
       "      <td>0.3420</td>\n",
       "      <td>172.017</td>\n",
       "      <td>0.515</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169035</th>\n",
       "      <td>Video Games - Remastered</td>\n",
       "      <td>['Lana Del Rey']</td>\n",
       "      <td>281947</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.80600</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.2550</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-9.676</td>\n",
       "      <td>0.0299</td>\n",
       "      <td>122.056</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169219</th>\n",
       "      <td>Style</td>\n",
       "      <td>['Taylor Swift']</td>\n",
       "      <td>231000</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.00245</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.7910</td>\n",
       "      <td>0.002580</td>\n",
       "      <td>-5.595</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>94.933</td>\n",
       "      <td>0.487</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     name  \\\n",
       "609     Rhapsody on a Theme of Paganini, Op.43: Variat...   \n",
       "652                         Interview with William Kapell   \n",
       "3344                        Give Me Your Love (Love Song)   \n",
       "8094                             Enamórate De Alguien Más   \n",
       "8169    Danse macabre, S. 555 (after Camille Saint-Saens)   \n",
       "...                                                   ...   \n",
       "164200                                          My Prayer   \n",
       "166015                   Bonzo's Montreux - 2012 Remaster   \n",
       "168041                                             Shiver   \n",
       "169035                           Video Games - Remastered   \n",
       "169219                                              Style   \n",
       "\n",
       "                                                  artists  duration_ms  year  \\\n",
       "609     ['Sergei Rachmaninoff', 'William Kapell', 'Fri...        61467  1945   \n",
       "652                                    ['William Kapell']      1325093  1945   \n",
       "3344                                  ['Curtis Mayfield']       260333  1972   \n",
       "8094                                            ['Morat']       230040  2020   \n",
       "8169                 ['Franz Liszt', 'Vladimir Horowitz']       491333  1928   \n",
       "...                                                   ...          ...   ...   \n",
       "164200                                    ['Roy Orbison']       164467  1963   \n",
       "166015                                   ['Led Zeppelin']       262147  1982   \n",
       "168041                                       ['Maroon 5']       179773  2002   \n",
       "169035                                   ['Lana Del Rey']       281947  2012   \n",
       "169219                                   ['Taylor Swift']       231000  2014   \n",
       "\n",
       "        acousticness  danceability  energy  instrumentalness  loudness  \\\n",
       "609          0.98300         0.635  0.0123          0.955000   -27.297   \n",
       "652          0.91300         0.704  0.2230          0.000000   -24.480   \n",
       "3344         0.12800         0.591  0.6370          0.019500    -9.102   \n",
       "8094         0.46000         0.591  0.6590          0.000000    -5.464   \n",
       "8169         0.99300         0.303  0.2260          0.826000   -21.483   \n",
       "...              ...           ...     ...               ...       ...   \n",
       "164200       0.83800         0.493  0.2170          0.004630   -11.501   \n",
       "166015       0.21300         0.291  0.9750          0.908000   -10.754   \n",
       "168041       0.04210         0.625  0.9250          0.000000    -4.435   \n",
       "169035       0.80600         0.390  0.2550          0.000001    -9.676   \n",
       "169219       0.00245         0.588  0.7910          0.002580    -5.595   \n",
       "\n",
       "        speechiness    tempo  valence  mode  key  popularity  explicit  \n",
       "609          0.0521   83.046    0.610     1    0           0         0  \n",
       "652          0.9350  112.485    0.403     0    2           0         0  \n",
       "3344         0.0887  172.452    0.962     0    5          26         0  \n",
       "8094         0.0332  145.997    0.622     1   11           2         0  \n",
       "8169         0.0444   67.013    0.146     1    3           0         0  \n",
       "...             ...      ...      ...   ...  ...         ...       ...  \n",
       "164200       0.0271  105.003    0.182     1    9          20         0  \n",
       "166015       0.0557  180.289    0.127     0    6          33         0  \n",
       "168041       0.3420  172.017    0.515     1    0          47         0  \n",
       "169035       0.0299  122.056    0.179     0    6          50         0  \n",
       "169219       0.0402   94.933    0.487     1    7          56         0  \n",
       "\n",
       "[607 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[main_df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the table above, there are 607 rows of duplicated data (not counting the first recorded instance for each). It's best to drop these rows since multiple instances of the same song will compromise our Exploratory Data Anaylsis and Research in searching for trends. We shall use `drop_duplicates` for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artists</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [name, artists, duration_ms, year, acousticness, danceability, energy, instrumentalness, loudness, speechiness, tempo, valence, mode, key, popularity, explicit]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.drop_duplicates(inplace=True)\n",
    "main_df[main_df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping the duplicates, the empty table above indicates that they have completely been removed from our dataset. Looks like this dataset is officially good to go for analysis! We can now move on to the **Exploratory Data Analysis** with the Spotify tracks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA) on Spotify Data\n",
    "Being datasets teeming with variables, the researchers have the opportunity to identify trends in the musical elements. It may be in the form of an evolutionary timeline of musical elements between decades. It may also be a comparison of musical elements among genres. From the data, the researchers may also determine assumptions on musical evolution.\n",
    "\n",
    "The researchers have already done a comprehensive data analysis of the dataset, with the help of the [Pandas Profiling Python module](https://towardsdatascience.com/exploratory-data-analysis-with-pandas-profiling-de3aae2ddff3) that aids specifically for exploratory data analysis. It gives a detailed exploratory approach at the data, offering visualizations of the summary statistics, correlations, possible warnings, and much more.\n",
    "\n",
    "However, with the interest of the project specifications, we will no longer be doing a descriptive deep dive in every summary statistic for this Notebook. Rather, embedded below is the **`spotify_eda.html`** file produced from the aforementioned Pandas Profiling module for you to explore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"900\"\n",
       "            height=\"500\"\n",
       "            src=\"./spotify_eda.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f91a5f44990>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src=\"./spotify_eda.html\", width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the explored data, the researchers have formulated three (3) of the most interesting EDA questions that could aid us in our further research, starting with..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA #1: Studying the correlations between the variables in the Spotify data.\n",
    "With all the musical elements, it will be intriguing to analyze the correlations between the variables. From those correlations, we can get an idea of the relationships between the variables among songs, as well as get an overview of the progression or regression of these elements spanning different variables, if any.\n",
    "\n",
    "Let's first visualize these correlations. This is a perfect time to use a **correlation heatmap**, where the variables are the ticks for both dimensions of a 2D graph, mapped with how much a variable correlates with another. For this, we will use **seaborn**'s heatmap tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(main_df.corr(), vmin=-1, vmax=1, annot=True, cmap=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The annotated values for each cell are the measure of the correlation for each cell's corresponding x and y variables. These values are also known as **Pearson's correlation coefficient (PCC)**, where values closer or equal to 1 have a positive linear relationship (i.e. when one variable increases, the other variable likewise increases). If the PCC are closer or equal to -1, this indicates a negative linear relationship (i.e. when one variable increases, the other variable decreases). When the PCC is closer or equal to 0, then there is nearly no linear relationship between the variables.\n",
    "\n",
    "To simplify our process for analysis, let's set the threshold for the PCC's that we will analyze to those greater than 0.5. We will apply the principle of absolute value for negative values as these likewise indicate the linearity of a relationship.\n",
    "\n",
    "Naturally, cells having the same x and y variables will have positive linear relationsips, thus the PCC values being equal to 1.\n",
    "\n",
    "There is a strong PCC value of 0.88 between `year` and `popularity`, which is most likely an effect of Spotify's algorithm on popularity. According to Spotify, a song's `popularity` is based on \"the total number of plays the track has had and how recent those plays are\". This means that people tend to play more recently released songs, probably due to the marketing efforts of the respective artists and their companies.\n",
    "\n",
    "There are a notable PCC's involving the `acousticness` of a song. [Acousticness](https://www.merriam-webster.com/dictionary/acoustic#:~:text=1%20%3A%20of%20or%20relating%20to,by%20or%20utilizing%20sound%20waves) is defined as the element of a song that strays away from being electronically modified. Based on the PCC of -0.62 between `year` and `acousticness`, this musical element has seen a dramatic decrease as the years go. This could very well be an effect of technological advancements in the music industry with the [rise of Digital Audio Workstations (DAWs)](https://techterms.com/definition/daw). The popularity in more acoustic songs have also dipped (-0.59 PCC value with `year`. \n",
    "\n",
    "With the decline of acousticness comes the rise of the `energy` element in songs, as indicated by a PCC of -0.75 between the two. According to Spotify, part of what defines energy is the `loudness` of a track, and this is seen in its PCC value of 0.78 with `energy` (adversely, a -0.57 PCC with `acousticness`). It seems that energetic tracks have prevailed through the years too, as it posed a 0.53 PCC value with `year`.\n",
    "\n",
    "A confirming PCC value is that of between `valence` and `danceability`. Valence is described as the positiveness (i.e. happiness) of a song, while danceability is as it is. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA #2: The evolution of different musical elements through the years.\n",
    "We've gotten an idea of the correlation of the variables between each other in the previous EDA question. For this, we will focus on the evolution of certain musical features by year. Given the different musical elements provided, it will be interesting to see how these facets have evolved through the years. Are songs faster in pace now?  Has autotune really taken over the industry? How about the general tone of the songs?\n",
    "\n",
    "The researchers made the call of handpicking 6 of the 8 musical elements, namely `acousticness`, `danceability`, `energy`, `instrumentalness`, `speechiness`, and `valence` given that these fields are measured at the same confidence range of 0.0 – 1.0. The `loudness` and `tempo`, meanwhile, are measured differently in decibels (dB) and beats per minute (BPM), respectively.\n",
    "\n",
    "We then create a time series graph for each element in order to display the mean values (by year) of the elements present in all songs grouped through the years. Since we have 6 elements to explore, we should have 6 line graphs in total, plotted in a subplot `sp1` (a feature of the **matplotlib** module to show multiple graphs in one cell):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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hVccYo5igcJJeAAAA6PFIegEAgP0yZ8cPOuC7S3X72ucCHUq38knWAm2v2KXzm6jyqu+WEeeqX2gv3T7qfI2OHOzn6LA/DoubqBJvuRbmrQl0KECnYoxxSnpU0jGSUiSdaYxJaWDq69baCb7H0x0aZIDVrdm1d9JLql3XizW9AAAA0NOR9AIAAG1irdUjG97RZSsekrVWH+z4gcqVdvTMlo800N1bR/jWgGrOgLDemnvQ4zor6Sg/R4b9dUDsGAUbFy0OgX1Nk7TeWrvBWlsp6TVJJwY4pk4lv7pYkvZpbyjVJsKo9AIAAEBPR9ILAAC0WlVNta5b9YTuW/+qTupzoB4bf5UKq0v1Tc6yQIfWLSwvSNfi/LU6d8AxcpiW/7hmjPFjVGgvHpdb02NT9GXOkkCHAnQ2iZK21nue4Rvb26nGmBXGmLeMMQ32fzXGXGSMWWSMWZSdne2PWAMiv7LxpFdsMEkvAAAAgKQXAAA9jLV2v/YvqCrROUv+qtcyv9QVQ07Vw2Mv12FxExUbFKEPdvzQTlH2bM9u+UgeZ6hO73dooEOBnxwWN1HrSzK1tSwr0KEAXc0HkgZZa8dJ+kzS8w1NstY+aa2dYq2dEh8f36EB+lN+VeNJr+igCOX5tgMAAAA9FUkvAAB6kMKqEh0z7xpdufIR1diaVu+/tSxLpyy4WfPzVukfoy/R1UPPkDFGQQ6Xju09Q//LWqTS6nI/RN5zLMlfpw92/KDTEg9TZJAn0OHATw6LmyhJtDgE9pQpqX7lVpJvbDdr7S5rbYXv6dOSJndQbJ1CXSVXQ2t6xQSFK7+yaL8/3AIAAAB0ZSS9AADoIWpsja766VGtLtqit7d/o3vTXmnV/ssL1uuk+TdqZ0WuXpx0k05LPGyP7Sf0OUBlNRX6PGdxe4bdo6wvydR5S/+mRHe8rhhyaqDDgR8NCeunAe7e+jI7MEmvjLJsPb7xfeVV0goNncpCScOMMYONMcGSzpA0p/4EY0zfek9PkLS6A+MLuLpKr6gGPhQRExShSlutEi8fPkH7Kawq0QPr31B1jTfQoQAAALQISS8AAHqIxze9r/9lL9QtI87V2UlH6fFN7+uVjM9btO+X2Ut0+sLbFOoI1rvT7tYBvcbuM2dqzEj1DomhxWEb7SjP1W8X3yWXcerFSTerV3BUoEOCHxljdHjcRP2Qu1Ll3soOP/9727/X39JeUlF1aYefG2iMtbZa0mWSPlVtMusNa22qMeYOY8wJvmmXG2NSjTHLJV0u6bzARBsYeVVFCne6FewI2mdbbHDk7jlAe/l61zI9tOFNrShMD3QoAAAALeIKdAAAAMD/vt+1Un9Pe1W/6jNL5w84Vl5bo4yybN20+iklueN1cK/xje77ZuZXunbVExoVPlDPT7pR8SHRDc5zGqeO7z1LL279VIVVJbTma4XaddLuVn5Vsd6YersGhvUOdEjoAIfGTdRzWz/RgrzVOjiu8X+D/vD+ju80OXqEBnCtoZOx1n4k6aO9xm6p9/UNkm7o6Lg6i/yq4gbX85J+Xucrr6pI/d0JHRkWurHi6jJJUk5lQYAjAQAAaBkqvQAA6Oa2l+/SH1c8pGRPou5LuVjGGLkcTj06/koN9/TX/y3/h9YWbdlnP2utHtv4nq5OfUwzY0brjam3N5rwqnNCnwNUaav1adZCP72a7qfcW6kLl92r9JJMPTnhGo2NHBLokNBBZsaOVogjSF/mLOnQ864p2qy1xVt1Up//Z+++w+Mqr/Xvf58pkmZGvVnFsmy5N4yNMZjeCSEB0iHtpFeSnJz08qae9HbSE34JpBBCQgqY0DsYsMHY2MbGvcjqvY00oynP+4cKttVGtkYzku/PdenC2rNn7yUksKR71lrnTep9ReTktYU6Rwy9BvZ8aWypTCR/f+jV2NuW2EJEREREYqTQS0REZBrrjYb48NYfEYj28tvTP43P5Rl8LMPl5ZZVn8fjTONdW75DfbB18LGojfL13X/gu3v/wrVF5/KHVV8g/ajnjuT0rHmUpRVoxGGMIjbCJ7b/jI2tL/OTZR/j/LzTEl2STCKPM5VzcpfxWNPk7vW6s249TuPg6hlrJ/W+InLyRuv0yknpD7003lAmUFf/jrjm3o4EVyIiIiISm7iGXsaYVxljdhtj9hljPj/COW82xuzsn8t+WzzrEREROdV8c/cf2dy+lx8u/QjzfKVDHi9Jy+eWlZ+nNdTJe7Z8l+5wgGA0xMe3/4ybK+/lvbOu5qfLPz7s7pDhGGN4bfG5PNWyjRb9cmRU1lq+uusW7mvYyFcWvotris9NdEmSABfnr+Rgdy2Humsn5X5RG+Wu2vWcn3sa+anaGycy1bSGugbDrePlDnR6hbomsySZ5rr6dz82BdsSW4iIiIhIjOIWehljnMAvgauAJcANxpglx50zn7557Odaa5cC/x2vekRERKabcDSCtXbEx/9d+xR/PPIA7y9/DVcXjdzRsTyzgl+c9kl2dBzkxu3/x7s2f4d1dU/zhflv5ysL/wuHGd+3C9cUnUvERrm3fuO4nneq+fnBf/GnIw/wodnX143LLQABAABJREFU8r7yqxNdjiTIRfkrAXis6cVJud+mtt1UB5q4rvj8SbmfiEystlAX2a7hO72y3D4MRuMNZUJ1DY431E4vERERmRpccbz2GmCftfYAgDHmduBaYOdR57wf+KW1thXAWtsQx3pERESSmrWWllAHB7vrOOiv4UB3LQf8NbSGOumOBOmOBOiOBPCHA/REgvTaMB5HKrO9Rcz2FlHuLWKOt5jZ3iIAPrfjN6zJXszn579tzHtfVnAGX130br6662acxsGPl93IG0suPKGPY3F6OfN8pdxd9zRvL7v8hK4xnUVtlO/suZXfHr6b1xdfwOfnvzXRJUkCzfYWUeEt5rHGzbx71lVxv99dtetJc6RwReGZcb+XiEysqI3SPkqnl9M4yXR5aQ2p01omTlekL/Rq0k4vERERmSLiGXqVAkeOer8KOOu4cxYAGGOeBpzA16y198exJhERkaTz5yMPckf1YxzorqUj7B887jJOyjyFFKZmk5+SicdZiM+ZhteZiteVhseRSluoi0PddezpOsLDjZsI2cjg8wtSsvnlik/idsT21/27Z11FmiOF2d4i1uYuPeGPxxjDNUXn8pP9d1AXaKEoLfeErzXddIcDfOKln/FAw/O8s+xKvrbw3ePupJPp5+L8Vdxa9SA9kSAeZ2rc7hOKhvlP/bNcXrg6ph19IpJcOsLdRLEj7vSCvr1eGm8oE8nf3+mlnV4iIiIyVcQz9Ir1/vOBi4CZwJPGmOXW2rajTzLGfAD4AMCsWbMmuUQREZH4aQi28tVdNzPXV8K1Recyx1dChbeYCl8xpWkFMQdWABEboSbQzKHuOip76jk3dzkzUnPGVc8NMy8d74cwrNcWncOP9/+de+qf5b0a3QdAfbCV92z5Li91HOSrC9/Fe2a9GmNMosuSJHBx/kp+X3kPz7bs4JKCVXG7z1PN22gNdXJt0Xlxu4eIxE9bqG9sYbZ7+E4v6Nvr1RrSeEOZOF3hAACN2uklIiIiU0Q8Q69qoOyo92f2HztaFbDRWhsCDhpj9tAXgj1/9EnW2puAmwBWr1498vISERGRKebv1Y8TthF+s+JTzPWVntS1nP2dYWWewgmq7sTN9ZWyLGMOd9c9rdALeLnzMO/e8h3aQl387vTPcnnh6kSXJElkTc5iPI5UHmvaHNfQ66669WS5fFyUf3rc7iEi8dPW38E1WqdXtjuDhmDrZJUkp4CuSDcA7WE/vdEQKQ53gisSERERGV085+k8D8w3xswxxqQA1wPrjjvnTvq6vDDG5NM37vBAHGsSERFJGhEb4a9VD3FO7rKTDryS0WuLzmVz+14qu+uHfdxay9PN23myaStVPY1EbXSSK5wcjzZu5vXPfZmIjfKPM7+pwEuGSHOmcG7eMh5t2oK18Xl9V3c4wAMNz3F10Vr9wlJkimrt7Qu9ckYbb6hOL5lgA51eAC0acSgiIiJTQNw6vay1YWPMjcAD9O3rutlau8MY8w1gk7V2Xf9jVxhjdgIR4DPW2uZ41SQiIpJMnmzaxpFAI19Y8I5ElxIXrylay3f23srd9c/w0TmvGzwetVHub3iOnx34Bzs7Dw8eT3OkMMdbTIWvhLm+Eiq8JazNXUpxWl4iyj9ptYFm7qpdz3f3/oUlGbO5eeXnKJqiH4vE38X5q3i48QW+sfuP3FjxOvJSsib0+g81bqI7EuQ6jTYUmbLawmN3euWkpCv0kgnlD/dQkJJNY28bjb3t+l5GREREkl5cd3pZa+8F7j3u2FeO+rMF/qf/TURE5JRya9WDFKRkccU07fwp8xRyRtYC7q57mo/OeR0RG+Ge+g38bP8/2eM/QoW3mB8vu5GytAL2dVdzwF/Dfn8NOzoPcl/9BqL0dbysyV7MtcXn8uoZZ590ENATCZLmSBnXLq2IjfDdvbcRiAQpTSugxJPPzLQCSj0FFKRk4TAOeiJBtnccYEv7Xja37WFL+17qgi0AXFZwBj9f/gl8Ls9J1S7T25tKLmRr+z5uqbyX26sf4X3lV/P+8teS6faN+JzOcDePNm5mV1clH5597ajn3lm7nqLUXNbkLI5H+SIyCVp7+8KsnFF3emXSHQkSjIZIHaGrM2qj/N/+f/D6kvOZ7S2OS60yfXRFeliROY/G3jaagu2JLkdERERkTHENvURERGR4NYEmHml8gQ/PuW5ajxp7bdG5fG33Lfz64F3cUfMY+/zVzPOV8vPln+A1RWtxGicAZ+UuOeZ5wWiIA/4aHmrcxF216/nSy7/jK7tu5tzc5VxbdC5XFK4ha5Rf8A+nubedS5/+JNcWn8fXF70n5ufdeuQhfntoHT5nGv5I4JjHUoyLgtRs6oOthG0EgFmeGZyds4TTs+azKns+p2XOxWHiOVFapoM0Zyo/XPYRPjTnWn6872/89MA/+UPl/XxozrW8u+wqvK40oO/r+KGGTdzfsJH1zdvptWEAnml5iT+v+tKwwVdrbydPNL/Ie2e9Wl+LIlPYwE6vTNfIf/9l9wdirb2dFKXlDnvO3q4q/u/AHQSivXxxwdtjvr+1llurHuRVhWdRkJode+EyZYWiYYLRELO9RTzX9jJNvW2JLklERERkTAq9REREEuCvVY9ggRtKL010KXF1ddFavr77D3xn760sSp/Fr077H14946wxf/Ge6nCzOKOcxRnlfGzO69ndVclddU+zru5pPrXjV6TsvInvLv0Qbyy5MOZafn/4HlpCndxSeR9XFq7hnNxlYz6nLtDC9/bexvl5p3Hrqi/TGe6mJtBEdaCJ6p4mqgON1AVbKE3LZ2XWAlZmzSc/dWLH0smpZZ6vlF+t+B8+2nGQH+77G9/bexu/P3wPbyy5iK3t+9jYupMolrK0Av5r1qt4VeFZNPW289FtP+Edm781bPB1T/0GwjbCdcXnJ+ijEpGJ0BbqJNPlw+VwjnjOwL6v1tDIodfm9r0AbO8Y3zrtPf4qvvTy7whFI7yn/NXjeq5MTf5IDwCzvUUANGmnl4iIiEwBCr1EREQmWTga4fbqR7go/3RmeWckupy4mpGaw0+W3YjXmcoVhWeeUJeJMYZFGeUsyijns/Nu4MX2fXxt9y387+4/cmXhmWS4vGNeoy3UxR8q7+eygjPY21XFZ3f8hgfX/nCwe2YkX911MyEb5luL348xhky3j0y3j0UZ5eP+OETGY2nmHG5Z9XleaNvND/b+ld8cuosFvjI+VvF6XlV4FksyZh8zpvPXK/6HD2/98bDB1111TzHPV8qSjNkJ+EhEZKK0hboGQ62R5KT0d3qNstdrc/tuAF7qOIC1NuaRv9va9wF94+7k1NAZ7vtcF6Rm43GkqtNLREREpgTNNxEREZlkjzS9QH2wlbfNvDzRpUyK15dcwKti6O6KhTGGldnz+cai99IS6uSmQ3fH9LxbKu+jK9LDZ+bdwA+WfpjKnnp+sO/2UZ/zUMMm7mvYyCcq3jj4CmeRyXZG9kJuP/NrvHTxH3j43B/zqXnXszRzzpBfUl9ZuIZfr/gftncc4B2bv0VHyA9AdU8jG1tf5rri88e1y05Ekk9rqJPsMUKv3KPGG47khbY9ODC0h/1U9jTEfP9tHfuBvn2Ccmrw94de6U4P+alZNPVqp5eIiIgkP4VeIiIik+zWIw9SnJrHJfmrEl3KlLUiay5Xz1jL/zt8N43BtlHP7Qx3c/Phe7ii4EwWZ5Rzdu5S3ll2JTdX3svzrbuGfY4/3MP/t+t3LPCV8YHZr43DRyAyPsPt6jrecMHXurpnALim6Nx4lygicdbX6ZUx6jmDO736938drz3kZ5+/mssKVgPjG3G4tb0v9BoIQmT6G+j0Snd5KEjJGvN7LhEREZFkoNBLRERkEh3urueJ5q3cMPPSUXdyyNg+M+96gtEQPz/wz1HP+9ORB2gP+/lYxRsGj31+/tsoTcvnMzt+RSASHPKcH+3/GzWBZr679IOkONwTXrtIvBwffP2j5nFWZc1Xt6LINNAa6hqz0+uV8YbD7156sX+f11tnXobLOHmpM7bQqzca4uWuwwB0KfQ6ZQzs9PK5POSnZNOsnV4iIiIyBSj0EhERmUS3VT2E0zi4vvSSRJcy5VX4SnhL6SX8peohDnfXD3tOdzjA/zt0Nxflnc6KrLmDx9NdHr679IMc6K7lJ/vvOOY52zr2c/Phe3nbzMtZnb0wrh+DSDwcHXzt9VdxbfH5iS5JRCZAW6iL7JTRO71SHW68ztQRd3ptbt+LA8OanMUsTC+LudNrd9cRgtEQ8Er3j0x/XeEAABlOD3kpmdrpJSIiIlOCQi8REZFJEoyG+Hv1Y1xWcAZFaXmJLmda+O+KN+E0Tn68/2/DPn5b9cO0hDr5+Nw3DnnsgrwVXF96Kb89tI6t7fsACEcjfH7Hb8lLyeLz898W19pF4unKwjX8ZsWnOCd3GddqtKHIlBeORugI+8l2jd7pBZDrzqS1d/jxhpvbdrMwfRbpLg/LMyvY3nEAa+2Y1xwYbVicmjfY/SPTX1f//jafy0NBal+nV8RGElyViIiIyOgUeomIiEySB+qfoznUwdtnXpHoUqaNorRc3lP+au6sXc/OzkPHPBaI9PKbQ+s4J2fpiB1bX17wDgpTc/j0jl8RjIb4w5H7eanzIF9b9G6yYtihJJLMrig8k9tXf5XclMxElyIiJ6k93Bdi5YzR6QWQ7U4fttMraqNsad/Hyuz5ACzPrKAt1EVVoHHMa27r2E+2O53FGeUab3gK6Yr0dXqlu9LIT8kiih0xUBURERFJFgq9REREJsmtVQ9S5ink/LzTEl3KtPKh2deS4fLyvb23HXP8b9WP0hBsHbbLa0Cm28d3lnyA3V1H+MrLv+eH+/7Kxfkrec2MtfEuW0REJGZtob6gYaydXtAXjA0Xeu3319AR9nNG1gKgL/QCYhpxuK1jH6dlziXT5aWzv/tHpj9/f8Dpc3rIS8kCoLm3PZEliYiIiIxJoZeIiMgk2OevZkPrTt4283IcRn/9TqRsdzofnfM6HmvawoaWHQD0RkP8+tCdrM5eyNqcpaM+/9KCM3h98QX8tfoRrIX/Xfw+jDGTUbqIiEhMWvtDr5wYQq9c9/Ch1+b2PQCszO4LvRall+MyzjFDr0AkyO6uI6zInIvP5cHf3/0j019XuJtUhxu3w0VBf+jVqNBLREREkpx+6yYiIhJnERvhpkPrcBsnby65ONHlTEvvnvUqZqTm8N29f8Fayz9rnqQm0MzHK94QU4D1tUXvYlnGHL6y6L8o8xROQsUiIiKxG1enlzuD1t5hQq+2PWS5fFR4iwFIc6awIH3mmKHXjs5DRGyU07Lmku7yqNPrFNIVCZDu9ACQn5oNQFNvW+IKEhEREYmBK9EFiIiITFe90RD/rn2KXx28k4PdtVxfein5qVmJLmtaSnOm8sm5b+bzO3/LvfUb+OXBf3Na5lwuzDs9pudnuzO4d+3341ukiIjICRoIsbLdsez0yqA97CccjeByOAePb27fw8qs+cd0nC/PrODBhk1Ya0d8kci2/lBsReZcdncdIRgNEYqGcTv064Tpzh/uId3VH3r1d3o1qdNLREREkpw6vURERCZYIBLklsr7uGD9x/jMjl/jc6bxmxWf4rtLPpDo0qa1N5dczFxvCf/z0i+p7KmPuctLREQk2bWHx7fT6+jnAHSGu9nTVcWq/tGGA5ZnzKU11El1oGnE621t30dBSjYzUnPxOdMA8Ed6xv0xyNTTeVToleXy4TZOhV4iIiKS9BR6iYiITJDOcDe/PPhvznnqo3x1182UphXwp1Vf5J6zv8erZ5ytXV5x5nI4+cz8G+iJBlmcXs7lBasTXZKIiMiYrLV8bsdv2Nj68ojntPZ24sCQ6fKOeb3c/m6wgT1gAC+278NiWZV1XOiVWQEw6ojDbR37WZE1F2MMGf337wwr9JqKDvprx3W+P/JK6GWMIS8li6agQi8RERFJbppHICIiMgGebt7Of7/0c+qDrVyYt4Ib57yes3KXJLqsU85VhWfxodnXclnBGeryEhGRKaEt1MVfqx+hLtjCWTmLRzwny50e0wtocgZCr95O8PUd29y2B4Ph9Kx5x5y7OGMWTuNge8d+rppx1pBrdYV72O+v4ZqicwEGA5AuhV5TzoMNz/O+F7/Pw+f8mAXpZTE9pyvcQ2FqzuD7+SlZ2uklIiIiSU+hl4iIyEkIRcP8ZP/f+eXBO6nwFvPbNZ8eMjpIJo8xhi8ueHuiyxAREYlZTf9owaeat9HS20FuSuaQc1pDneTEMNoQIDslffA5A7a072Ger5RMt++Yc9OcqSzwlfFSx8Fhr7W94wAWy4rMvrAs3anQa7JU9zRS6imYsOvdW78BgNpAc+yhV6SHClfJ4PsFqdk0aryhiIiIJDnNWRIRETlBR3oaeNPzX+EXB//Nm0sv4p6zv6fAS0RERMalNtgCQNhGuK9h47DntIW6YtrnBUd1evWHXtZaNrfv5YwRvkdZnlnRF25ZO+SxbR37ATgtq28Mojq9JsfW9n2sfeojPNuyY0KuF7ERHmvaAkB7yB/z8/xH7fQCyEvJpFmhl4iIiCQ5hV4iIiIn4O66Z7jq2c+w11/FL5b/Nz9Y+hG8rrRElyUiIiJTTG1/p1eeO5O7654Z9py+0CsjpuvlHj3eEDjYXUtbqGvIPq8ByzMraA51UBtsHvLY1vZ9zEwrIC8lC3gl9OqMdMdUi5yYvf5qAO6ue3pCrvdC257BELQ93DXG2a/oDPcMdvcB5Kdk0xRsHzYgFREREUkWCr1ERETGoTsc4LM7fsNHt/2Eeb5S7jv7B1xTfG6iyxIREZEpqibQjMs4eevMy9jQsoOGYOuQc8bT6eV1ppFiXIMhxwttewBYOUKn17LMOUDfKMPjbes4wGlZcwffHwi9/OFATLXIiakL9AWQDzQ8T9RGT/p6Dze+gMs4gdg7vcLRCIFoL76jOr0KUrLotWE6wgo9RUREJHkp9BIREYmRtZb/2vJt/lb9KDfOeR13nPkNZnlnJLosERERmcJqA80UpeZyTfF5RLGDu5eO1rfTK7ZOL2MM2SkZg6HXlvY9ZLg8zPeVDnv+kozZODBDQq/W3k4qe+o5LfOo0Gtwp5dCj3iq7Q+9Gnvb2Ny+96Sv93DjJs7OWUKqw017KLZOL3+kL9jMOGa8YV/HX1Nv20nXJCIiIhIvCr1ERERidE/9Bja2vsz/Ln4fn53/VtwOV6JLEhERkSmuNtBMUVouC9PLWJhexrrjRtr1RkP4IwFyUmILvQBy3OmDodfm9j2cnjUfhxn+x3+PM5X56TOHhF4D7684KvTy9Y9y7oqo0yueagLNlHkKcRsnD9QPv+ctVoe6a9nnr+aygtVkudNpD8fW6TUQbPqOHm+YOhB6dZxUTSIiIiLxpNBLREQkBsFoiO/u/QuL0mfx1pmXJrocERERmSZqg82UpOUD8Nqic9nUtpua/j1f0DfaECDbFdt4Q4BcdyatvV10hXvY1Vk54j6vAcszK9jeceCYXU1bO/YBsCyzYvCY0zjxOlPV6RVndcFm5vtmcm7ucu5veO6kdmg93PgCAJcVnEGWyxfzeMOBYNN31M7agpRsAJqCbSdcj4iIiEi8KfQSERGJwZ+PPEBlTz1fXvBOnP07EUREREROhrWW2kAzxam5ALx2xjkA/Kfu2cFzBkOvlNhDr+z+Tq9tHfuJYmMIvebS1NtOXbBl8Ni2jv1UeIvJcvuOOTfd5aUz3BNzLTJ+tYFmitPyuHLGGg731LOrq/KEr/VwwyYW+MqY5Z1BltsX+3jD/s9xhtM7eCx/cLxh+wnXIyIiIhJvCr1ERETG0Bbq5Kf7/8GFeSu4IH9FossRERGRaaI11EkwGqK4v9Nrjq+YZRlzuPuoEYetA6GXO/bQK6d/p9fmtj0ArMyeN+r5y/u7uY4ecbi1ff8x+7wGZDg9+CMKveIlEOmlJdRJcVoeVxScicFwf8NzJ3St9pCf59p2cVnhGQBkucYz3rDvc3x0p1eOOwOD0U4vERERSWoKvURERMbw8wP/ojPczZcWvCPRpYiIiMg0UhNoBqA4LW/w2DVF57K1Yz+HuusABjtzctzj2emVQVuoixfa9jDXW0L2GM9dmjEbB4btHfsBqA+2Uhds4bSsoWGZz+UZDERk4g102xWn5lKQms3q7IU80HBie72eaHqRsI1wWcFqALLcPjpiHm/Y3+nleqXTy+VwkuvO0E4vERERSWoKvUREREZxuLueP1bez5tKL2JRRnmiyxEREZFppLY/9Co5KvR6TdGxIw5bQ53A+Dq9ct0ZhG2EDa07WJU9+mhDAI8zlXm+mWzvOAjAtva+8GvFMJ1e6S4PndrpFTd1/V8TRf1fE68qXMPOzsMc7q4f97UebtxEnjuTlf3hZZbbN/5OL2faMcfzU7PU6SUiIiJJTaGXiIjIKL6/7zacxsmn5r4l0aWIiIjINFN7XMABMNNTwBlZCwZHHLadQKfXQGeXPxKIKfSCvhGHA+MNt3Xsw4FhacbsIeelOz34w4GYa5Hxqe3v9BoIQq8sXAPAA+MccRiKhnm0aQuXFKwa3Eeb5UqnI9xNxEbGfP5A6JXu8hxzPD8lm6agdnqJiIhI8lLoJSIiMoLNbXu4u+4ZPjD7tcf8MkpERERkItQGm3EbJwUpWcccf03RObzcdZi9XVW0hjpxGeeQjpvR5KS8EpCtyoo99GrsbaMu0MK2jgMsSC/D6xp6z3SXhy51esXNYBCamgvALO8MlmbM5v5xjjjc1LabjrB/cLQh9HV6ATF16g3sbfMNCb0yaexV6CUiIiLJS6GXiIjIMKy1/O+eP1GQksWHZl+T6HJERERkGqoJNFGUmovDHPuj+dUz1mIw3F33DG2hLnLc6RhjYr5uTv8oRJ8zjQXpM2N6zvLMCgC2dxxga/s+ThtmtCFAhstDZ0Q7veKlNtBEpst3TNh0ZeEaXmjbQ0OwNebrPNy4iRTj4oK80waPZfV/XbTFsNerM9xDinGR6nAfczw/JZtmhV4iIiKSxBR6iYiIDOP+hufY1LabT827fsgrXEVEREQmQl2gZdhu8qK0XM7KWcx/6p+htbdzMKyIVa47E4AVWfMGR9uNZWnGbBwY7m/YSEuokxVZw4dePpcHf7gHa+24apLY1AZaKE7LPebYqwrXYLE82LAp5us83PgCa3OXHvN9bJarr9OrPYbQyx/uGfZ74PzULPyRAD2RYMy1iIiIiEwmhV4iIiLH6Y2G+M7eW1ngK+PNJRcnuhwRERGZpmoCTYO7m4732qJz2eevZlPbrnHt8wLITekLvVZnL4z5OV5XGnN9pdxd9wzAiJ1e6U4vIRshGA2NqyaJTW2wmeLUY78mFqbPYra3iAdiHHG431/Nwe7aY0YbwivjDdvDXWNeoyvSM2SfF0BBSjYAjcG2mGoRERERmWwKvUREZNp6ufMwOzoOjvt5tx55iEPddXxxwdtxOWJ7dbSIiIjIeFhrqQsO3+kF8OoZZ+E0Dhp728keZ6dXltvH707/LO8rf824nrc8s4JAtBe3cbIoo3zYczL6g5CusEYcxkNdoHnI14QxhisL1/BMy0sxdWk91N8RdlnBGcccH+gYjOUaXeEe0p1DQ6+8/kC1SSMORUREJEkp9BIRkWkpHI3wrs3f4brnvsTzrbtift7G1pf5zt5bOT/vNC7OXxnHCkVEROR4xphXGWN2G2P2GWM+P8p5bzDGWGPM6pHOSXYtoQ6C0RAlafnDPp6XksW5ucsBxt3pBXBF4ZnjDssG9notyigfsstpwMDIuy7t9ZpwvdEQjb3tFA8ThL6qcA0hG+HRps1jXufhxhdYklFOqafgmOPjH2+YNuR4fn+nl0IvERERSVYKvUREZFp6uHETtcFmUh0pvHvLd9nddWTM57zceZj3bvkuM9MK+PnyT4xrYbyIiIicHGOME/glcBWwBLjBGLNkmPMygE8Asc16S1I1gWYAilNzRzzntUXnAIw7vDpRy/pDrxUjjDYEdXrFU32wFRj+a2Jl1nwKU3O4v370L/vW3k42te0aMtoQIHtc4w0DZLi8Q44XpGYBCr1EREQkeSn0EhGRaemPRx6gNC2f/5z9XdKcKbzjhf+luqdxxPMru+t5x+Zv4XWm8eczvjy4C0NEREQmzRpgn7X2gLW2F7gduHaY874JfA8ITGZxE612IPQaodML4MrCNWS6vMz2Fk9KTadlVrDAV8YVhWeOeM7AyLuucPek1HQqGfiaGK77z2EcXFlwJo83vUggEhzxGo81bSGKHTLaECDNmUqqwx3jeMNufM6hnV65bo03FBERkeSm0EtERKadff5qnm7ZzttmXs5sbxF/XvUluiMB3rH5W7T2dg45vynYzjs2f4tgpJc/n/FlZh43CkZEREQmRSlwdGt2Vf+xQcaYVUCZtfae0S5kjPmAMWaTMWZTY+PIL3pJpFdCr5E7vbLd6Tx7wa9568xLJ6UmjzOVh8/9MReNMuI5fXC84ZTOHJNSXaAFgKIRviaunLGGnmiQJ5u3jXiNhxs3UZCSzWkjdOtlury0h2Lr9Br4XB8tzZlCpstLU7BtzGuIiIiIJIJCLxERmXb+fOQB3MbJ9aV9vyBanFHO70//HEd6GnjXlu/QHX7llzRd4R7+a8u3qQ00c8uqL7AwvSxRZYuIiMgojDEO4MfAp8Y611p7k7V2tbV2dUFBcr6YpTbYjNs4yU/JGvW8DJcXh0meH90HQy91ek242uBAEDp0pxfA2pylZLp83N8w/IjD3miIJ5pf5NKCVSN+zWS502kPx9bpNVzoBZCfkqVOLxEREUlarkQXICIiMpG6wwH+UfM4V89YS37qK79EOit3Cb847b/54Is/5MPbfszvTv8sUSzvf/EH7Ow8xO9O/yyrsxcmsHIREZFTXjVw9KtPZvYfG5ABLAMe79+7WQSsM8ZcY63dNGlVTpDaQDNFqblJFWjFIr1/z1OndnpNuJpAE+lOz7C7tADcDheXFqzi4cYXWFf7NEcCDVT1NFLV0//PQCPBaGjYfV4Dsly+MccbRm2U7kgQn3Ok0CtboZeIiIgkLYVeIiIyrdxZt57OcA/vnPWqIY9dWbiGby/5AJ/f+Vs+s+PX9EZDPN2ynR8vu5FLh9l7ICIiIpPqeWC+MWYOfWHX9cBbBx601rYDg8uOjDGPA5+eioEX9IVeo+3zSlbp/Xue/Aq9JlxdoGXE0YYDrp6xln/XPsWN2/8PgFx3BjM9hSzMmMVlBWdQ4Svl0oJVIz4/y+2jYYzRhP7+0ZUjhW95KZns9VeNeg0RERGRRFHoJSIi04a1lj8duZ8lGeWckbVg2HPeOvMymnrb+eG+2wH40oJ38MaSCyezTBERERmGtTZsjLkReABwAjdba3cYY74BbLLWrktshROrJtDMqqz5iS5j3LzONAyGrohCr4nWF4QOP9pwwOUFq/nHmd8gy53OzLR8fCOMIBxJljudvf7qUc/p6g80fa60YR8vSM3mmZaXxnVfERERkcmi0EtERKaNF9r3sLPzMN9Z8gH6xx4N62NzXo8Dg9vh4oOzr5nECkVERGQ01tp7gXuPO/aVEc69aDJqigdrLXWBZopmnJ3oUsbNGEOGyzMYjMjEqQ02s2CM/bLGGNbkLD7he8Qy3nDgc5s+4njDLNrDfnqjIVIc7hOuRURERCQeFHqJiMi08afK+8lweXhd0fmjnmeM4caK109SVSIiIiLHau7toNeGKRmjqydZ+ZwKvSZaKBqmIdhG8RjjDU9WlttHZ7ibqI2OuE/O39/Flz5CF1l+SjYALb0dFE3Rr2ERERGZvqbWxlwREZERNAXbuaf+Wd5YchHeEUaxiIiIyOQwxviM6fuNujFmgTHmGmOMWkL61QSbAMYcZZes0l0eOsPdiS5jWmnsbcNi477nLcudjsXSMcrnr3PMTq9MABp72ye+QBEREZGTpNBLRESmhdurHyFkI7yz7MpElyIiIiLwJJBmjCkFHgTeAfwhoRUlkbpACzB1Q68Mlwd/JJDoMqaV2kAzAMWp8f2ayHL5AOgYZcShf3Cn1wihV2o20PeiMxEREZFko9BLRESmvIiNcGvVg5ybu5y5vtJElyMiIiJgrLXdwOuBX1lr3wQsTXBNSaMm0N/pFeeAI158Lg9d6vSaULWDQWi8xxumA9AeHjn06uoPNEceb5gFQNMonV77/NW8/8Xv0x1WOCoiIiKTS6GXiIhMeY80bqYm0Mx/qctLREQkWRhjzFrgbcA9/cecCawnqdQGmkkxLvL6x8RNNelOz+AIPJkYtf1BaLx3ZA10erWP0uk1EGiONN6wIIbQ65bD9/JAw/Ns69h/oqWKiIiInBCFXiIiMuX96cgDFKfmcVnB6kSXIiIiIn3+G/gC8G9r7Q5jTAXwWGJLSh61gWZmpOXiMFPzR/J0lwd/RKHXRKoNNuNxpA6GUvGS5R4IvbpGPKcrPNDpNfyeXK8zjTRHCk29bcM+HoqG+U/9swDs7645iWpFRERExs+V6AJEREROxgF/DU82b+XT867H5dALyEVERJKBtfYJ4AkAY4wDaLLWfjyxVSWP2mAzJVN0nxf0hV5d6vSaULWBForTcjHGxPU+sY037MZlnKQ6UoZ93BhDQUrWiJ1eTzVvozXUCfR9ry4iIiIymabmy8pERESA7nCAH+//O27j5PrSSxJdjoiIiPQzxtxmjMk0xviAl4CdxpjPJLquZFETaJ6y+7wAMlxeusI9WGsTXcq0URdojvtoQ4htvKE/3IPPmTZqAJefmj1i6HVX3Xqy3enM85VywF97cgWLiIiIjJNCLxERmXJC0TC3HnmI89d/jHV1T/Pe8tdQmJqT6LJERETkFUustR3AdcB9wBzgHQmtKElEbZT6QAvFU7jTy+f0EMXSEwkmupRpozY4OUGox5mK2zhpD48+3jDD5R31OnkpmTQFh4Ze3eEADzQ8x9Uz1rIgvYwDGm8oIiIikyyuoZcx5lXGmN3GmH3GmM8P8/i7jDGNxpgX+9/eF896RERkarPWcm/9Bi5/5n/44ss3MdtbxL/X/C9fXPD2RJcmIiIix3IbY9z0hV7rrLUhQG1BQHNvB702TElafqJLOWHpLg8AndrrNSEiNkJ9sHVSRl4aY8hyp4/a6dUV6cY3wj6vAfkp2cPu9HqocRPdkSDXFZ1HhbeYyp56QtHwyZYtIiIiErO47fQyxjiBXwKXA1XA88aYddbanced+jdr7Y3xqkNERKaHjS07+fbeW9nSvpf5vpn8/vTPcVnBGXHfeyAiIiIn5LfAIWAr8KQxphzoSGhFSaI22AwwpTu9MvpDr65wNzPUbX/SGoPtRGx0UsYbQt+Iw/bQ6J1e6U7PqNcoSMmiubeDiI3gNK/s1b2zdj3FqXmcmbOII4FGIjZKZU89c32lE1a/iIiIyGjiFnoBa4B91toDAMaY24FrgeNDLxERkSGC0RBb2vbwdMt2nmrexub2vRSl5vKDpR/mDcUX4nI4x76IiIiIJIS19mfAz446dNgYc3Gi6kkmtYGpH3r5nAOhVyDBlUwPdZMchGa5fbSHR9/pleX2jXqN/NRsoljaQl3kpWQB0NrbyRPNL/Le8qtxGAcV3mIADvhrFXqJiIjIpIln6FUKHDnq/SrgrGHOe4Mx5gJgD/BJa+2R408wxnwA+ADArFmz4lCqiIgkWtRG2dl5mKdbtrO+eRvPte6iJxrEgWFF1jy+tOAd/FfZlaQ5UxNdqoiIiIzBGDMD+DZQYq29yhizBFgL/D6xlSXeYOg1Cfub4uXoTi+BXZ2HWZg+64QnENQMfk3kTmRZI8pyp9PcO3LjZVekhxLP6OM381IyAWgKtg+GXvfUbyBsI1xXdB4AFb4SAO31EhERkUkVz9ArFncDf7XWBo0xHwT+CFxy/EnW2puAmwBWr16tOfAiItPM1vb9fHbHr3m56zAA83ylvGXmJZyXu5yzcpaM+UpTERERSTp/AG4BvtT//h7gbyj0oibQTIpxDYYGU1G6ywv0hSNj6Qr3cPWGz/HNRe/lgvwV8S5t0j3Y8Dzve/H73HbGVzgvb/kJXWOyu/+yXD4O+EcOorrCPTGMN8wGoLG3nYX9x+6qe4p5vlKWZMwGINudTp47k/2j3EtERERkosUz9KoGyo56f2b/sUHW2uaj3v0d8P041iMiIkkmEAny4/13cNOhdRSkZvO9JR/i4vzTJ22fgYiIiMRNvrX278aYLwBYa8PGmEiii0oGtYEmitLypvRe0nRXGtAXjozlcHcdB7tr+dWhO6dd6GWt5VcH/w3Anq4jJxx61QWaSXW4yXFnTGR5IxpzvGGkh3TX6KFXfn93V1NvGwDVPY1sbH2Zz8y7/piv7Tm+Yg6q00tEREQmUTxDr+eB+caYOfSFXdcDbz36BGNMsbW2tv/da4CX41iPiIgkkY2tL/PZHb/mYHct15deypcWvEMdXSIiItOH3xiTB1gAY8zZQHtiS0oOtYEWSqb4C3zSnf2dXjGEXk29fZ/2Z1pe4qC/ljm+4rjWNpmea9vF5va9AFT21J/wdWqDLRSl5k5aEJrlTqcj5CdqoziM45jHrLV0hQNjhl4FqQOhV9+YxHV1zwBwTf9owwFzfSU82rh5okoXERERGZNj7FNOjLU2DNwIPEBfmPV3a+0OY8w3jDHX9J/2cWPMDmPMVuDjwLviVY+IiCSHrnAP/9/Lv+NNz3+FsI1w2xlf4ftLP6TAS0REZHr5H2AdMNcY8zTwJ+BjiS0pOdQGmiZtjF28DAQisYw3bOx9Jeu8rfrhuNWUCL8+eCe57gzmeIs53H0SoVegmZK00XdoTaRMl48odtjQsjsSwGLxjTHeMNPlw2Wcg51ed9U9xaqs+ZR7Zxxz3hxvCY297XSERu4sExEREZlIcd3pZa29F7j3uGNfOerPXwC+EM8aREQkeWxq283Htv0fNYFm3jPr1Xx23g14+8fjiIiIyPRhrd1sjLkQWAgYYLe1NpTgshIuaqPUBVumfOiV6nDjMs6YOr0ag20AnJ93GndUP8an511PqsMd5wrjb3dnJY82beZTc9/CS50HOOCvHftJI6gLNLM6Z9EEVje6gRebtYf9ZB73wrPO/s/pWJ1eDuMgLyWTpmA7u7uOsLPzMF9f9J4h5831lQBwsLuWFVnzJqJ8ERERkVHFrdNLRETkaNZaPrfjNxgM/1zzTb626N0KvERERKa3NcAKYBVwgzHmnQmuJ+GaetsJ2QjFqVM79DLGkO7y0BXuHvPcpt42PI5UPlD+WlpCnTzQ8NwkVBh/vzm0Do8jlf+adSWzPDOo7KknaqPjvk4igtAsV3/oNUz3lT8SW+gFUJCSTVNvG+tq1+PA8JoZa4ecM8fbN87yQPeJh4IiIiIi46HQS0REJsWOzkPs9Vfx4TnXsTp7YaLLERERkTgyxvwZ+CFwHnBm/9vqhBaVBGoDLQBTvtMLIMPpoSscGPO8xmAb+alZnJ93GmWeQm478tAkVBdf1T2N3FW3nhtmXkq2O4NyTxHBaIiG/q628Wju7Zj0IDTLnQ5Ae6hryGMDn9P0McYbAuSlZNLY286ddes5L+80ClKzh5xT7i3CgeGAv/rkihYRERGJUVzHG4qIiAy4s/YpXMY57CtARUREZNpZDSyx1tpEF5JMagNNAJO6vylefC4PXZGxO70ae9spTMnGYRzcUHoZ3993Gwf8NVT0j72bin53+B4A3l/+GgBmeQsBqOyppygtd1zXqg02A5MbhB493vB4A917sXR65adk81TzNqJYPjn3zcOek+pwU+YpZL+/5iQqFhEREYmdOr1ERCTuIjbCXXXruTh/JTkpGYkuR0REROLvJaAo0UUkm9rgQKfX+IKRZJTu8gzufxpNU29fpxfAm0svwmWc/LXq4XiXFzdtoU7+Wv0w1xSdS6mnAIByT9+X+uHu+nFfrzbQF3qNNyw7GQPjDTuGHW/Y1+nli2EMeX5qFlEsqQ43VxaeOeJ5Fb4SDmq8oYiIiEwShV4iIhJ3z7bspD7YynXF5ye6FBEREZkc+cBOY8wDxph1A2+JLirRagJNpDrc5LozE13KSctwefHHEHo1BtvIT8kGoDA1h8sLVnNHzeMEo6E4VxgffzryIN2RIB+afc3gsVJPPg4MlT0nHnolZLzhsJ1efZ/TDKd3zOsU9H9eLytYTYZr5PMrvCUc8Nee0M4zERERkfHSeEMREYm7O2ufIt3p4fKCMxJdioiIiEyOryW6gGRUF2imKDUXY0yiSzlpPmcaleHRQ55QNExrqGswHAF428zLua9hIw/UP8c1xefGucqJFYgEueXwvVySv5JFGeWDx1McbkrS8k849HIbJ3kpkxeE+pxpOI1jhJ1efaFXLJ1eAzu8ris+b9TzKnzF9ESD1Adbp8U+OxEREUluCr1ERCSuApEg99Zv4KoZZ5HmTE10OSIiIjIJrLVPJLqGZFQTaJ4W+7wA0l1eOiOjd3o193ZgsRT0jzcEOC9vObM8M/hL1UNTLvT6e/XjNIc6+PDs64Y8NstTSOUJjDesC7ZQlJaHw0zeIB5jDFkuH+3DjDfs6v+cpjvH3ul1RcFqvr34A1w2xgvbKrx9+9v2+2sUeomIiEjcabyhiIjE1cONm+mK9PC64gsSXYqIiIjEmTGm0xjTMcxbpzGmI9H1JVptoHlSdzfFU7orbczxhk297QDHdHo5jIMbZl7Ks607OOCviWeJEyocjXDT4btZlTWfNTmLhzw+y1vE4RPs9JrM0YYDstzpw4439Id7cGDwxPBiNZ/Lw9vLLsdpnKOeV+HrC72m0udbREREpi6FXiIiElf/rn2SwtQc1uYuSXQpIiIiEmfW2gxrbeYwbxnW2qm/yOokRG2UumDL9On0cnrxRwJEbGTEc5p62wDI7x+DN+BNJRfhMk5uq3o4jhVOrHsbNlDZU8+H51w37HjKWZ4ZNPW2x7Tn7Gi1gWaKExCE9nV6DR1v2BnpId3lmdARnEWpuXidqRzoVuglIiIi8RdT6GWM+ZgxJifexYiISPI44K9hc9uek7pGW6iTx5u2cG3RuWO+AlRERESmH2NMoTFm1sBboutJpMbedsI2kpCunnjIcPWNv/OHAyOe0xhsAyA/JeuY44WpOVxReCZ31DxOMBqKW40TxVrLbw7exVxvCZcXrB72nHLvDAAqexrGdd2B8YaTLcs9/HhDf7gHXwyjDcfDGEOFt0SdXiIiIjIpYu30mgE8b4z5uzHmVWY6bN0VEZERWWv5yLYfc8Omb1AXaD7h6/ynbgMhG9FoQxERkVOMMeYaY8xe4CDwBHAIuC+hRSXYwPdU02WnkW8g9IqMEnoNjjfMGvLYW2deRmuok/vrN8anwAn0XOvLvNR5kA/MvmbE3VuzPAOhV+wjDltDnQSjoaQab9gV7uv0mmhzfMUc6K6d8OuKiIiIHC+m0Mta+2VgPvB74F3AXmPMt40xc+NYm4iIJMiG1p3s7DxMTzTId/fedsLX+Xftk8zzlbI0Y/bEFSciIiJTwTeBs4E91to5wKXAhsSWlFg10yz0GghGOsPdI57TGGzD60wdDMiOdl7ucmZ5ZvCXqofiVuNE+Wv1I2S6vFxXfN6I55R7CwGo7I499Erk18RI4w27IvEJvSq8JVT1NEyJzj4RERGZ2mLe6WWttUBd/1sYyAH+YYz5fpxqExGRBPn94XvIdWfwnlmv5l+1T/Ji+95xX+NITwPPt+3i9cUXTOhOABEREZkSQtbaZsBhjHFYax8Dhp8Ld4qo7Q84SqZJ6JXh9AJ9nUEjaeptoyAle9jHHMbBDTMvZUPrTvb5q+NR4oRoD/m5p/5Zri06D48zdcTzst0ZZLp8HB5Hp1ftYOiVgJ1ebh/tYT99v+p5hT9OnV4VvhKiWA531034tUVERESOFutOr08YY14Avg88DSy31n4YOAN4QxzrExGRSXaou46HGjfx9rIr+PS86ylIyeLru/4w5AfisdxVux6Aa0d5RayIiIhMW23GmHTgSeAvxpifAkNnqZ1CagNNpDrc5LgzEl3KhPC50oC+zqCRNPa2k5+aPeLj1xX1fZ/4dPP2Ca1tIq2rW08wGuItMy8Z89xZnsJxdXrVBftCr6IEjTeM2OiQ8ZSd4R7SJ3inF/R1egHa6yUiIiJxF2unVy7wemvtldbaO6y1IQBrbRR4TdyqExGRSfeHyvtwGQdvn3kF6S4Pn53/Vl5o38Nddetjvoa1ln/VPsmZ2Yso8xTGsVoRERFJUtcC3cAngfuB/ZziPzvWBlsoTsubNh3wGa6xO70ag23D7vMaUJKWT6rDTVVPw4TXN1Fur36UJRnlLM+oGPPccm/RuDu9XMZJQerI/47iJcvlAxgy4tAf6RkMNCdSha8YgP3dCr1EREQkvmINvX4KdBpjco96cwNYa1+OX3kiIjKZOsPd/L36MV5TdA5F/WNW3lRyEcsy5vCdPX+hOzzyovKj7eg8xD5/NdcVnx/PckVERCR5fcVaG7XWhq21f7TW/gz4XKKLSqSaQBMlCejoiZeBEXijjzdsH3G8IYAxhplpBRwJNE50eRNiR8dBtncc4C2ll8YUVs7yFFLV00DERmK6fm2ghRmpOTiN82RLHbcsdzoA7eFjGzC7wgHS+wPNiZTh8lKQks1Bf+2EX1tERETkaLGGXpuBRmAPsLf/z4eMMZuNMWfEqzgREZlcf6t+jK5ID+8tv3rwmMM4+Oqid1MbbOY3h9bFdJ07a5/CZZy8ZsbaeJUqIiIiye3yYY5dNelVJJG6QAtF02SfFzA4Am+k0CsUDdMa6hx1vCFAmaeQqp7kDL3+Vv0oqQ43r4txXHe5t4iQjVAbaInp/Npgc8K+JrLcA51er4Re1lq6wt2kOye+0wtgrq9EnV4iIiISd7GGXg8Br7bW5ltr8+j7YeU/wEeAX8WrOBERmTwRG+GWyns5M3sRp2XOPeaxs3IW89qic/jNobuoCTSNeZ276tZzcf5KclKmx84KERERiY0x5sPGmO3AQmPMtqPeDgLbEl1fokRshLpgCyXTKPQaa6dXc287wKidXgAzPQUcScLxhoFIL/+ufYpXFa4hO8Y9bLM8MwCojHHEYV2gmeLU3BOu8WQMN94wEO0lisXnmvidXgBzvMUc1E4vERERibNYQ6+zrbUPDLxjrX0QWGut3QCkxqUyERGZVA83vsCRngbeVz78uo0vzn87Fst39tw66nWebdlJfbBVow1FREROTbcBrwXW9f9z4O0Ma+3bE1lYIkWt5QdLP8xVM85OdCkTJsXhJtXhHrHTq3Eg9BpjX9VMTyGtoc5RxyQmwgMNz9Ee9vOW0ktjfk65ty/0Otw9duhlraU20LfnLRGGG2848DnIcE78eEOAub5SWkKdtIU643J9EREREYg99Ko1xnzOGFPe//ZZoN4Y4wSicaxPREQmye8P30NZWgFXFK4e9vFSTwEfmn0Nd9U9zaa23UMe94d7+Hv1Y3xt1y2kOz1cXqDptyIiIqcaa227tfaQtfYGoAoIARZIN8bMSmx1ieN2uHhDyYUsz6xIdCkTKt3poSvcPexjjcE2APJTRg+9yjyFAFQn2YjD26sfpcxTyDm5S2N+TnFqHi7jjKnTqz3spycaTGDoNXS84UDoNdDFN9HmeIsBOKC9XiIiIhJHsYZebwVmAncC/wbK+o85gTfHpTIREZk0L3UcZEPrTt4166pRF2l/ePZ1FKXm8rVdtxC1Uay1bGzZyade+iVnPPF+Pr3jV4RtmO8t/RBpTjUCi4iInKqMMTcC9fSNyr+n/+0/CS1KJly6yzNih1ZTjOMNyzwFAFQm0YjDw931PN2ynTeXXIzDxPprE3A5nJSm5ccUetUGmgEoSk1M6OVzpuHAHDPecGBUZXqcxhvO9ZUAsF8jDkVERCSOXGOd0N/N9VNr7dtGOGXfxJYkIiKT7ebKe/A503hL6SWjnud1pfH5+W/jv1/6OZ/Y/jO2duznUHcdPmca1xSdx5tLL+aMrAUYYyapchEREUlS/w0stNY2J7oQiZ8Ml3fEnV6NvW0AFKRmj3qNmWl9nV5VgeTp9Lqj5jEcGN5UetG4n1vuLYppvOGOzkMAVPiKx32PieAwDjLdvmHHG6Y74xN6lXkKcRknB7oVeomIiEj8jBl6WWsj/SMNU6y1vZNRlIiITJ6GYCvrap/mrWWXk9k/5mQ01xWfx5+PPMBddU+zNmcpH694I68uPAtvnMagiIiIyJR0BGhPdBESXz5X2sg7vYJtpDs9eMbo/s9LycTjSKUqSTq9IjbCHdWPc2H+6ZSk5Y/7+bM8hWzr2D/meeubt5HnzmRReuKmfma5fHQMM94wXp1eboeLWZ5CDqjTS0REROJozNCr3wHgaWPMOmDwOyJr7Y/jUpWIiEyaW488RMhGeHfZVTGd7zAO/rjqi3RFek7oFwEiIiJySjgAPG6MuQcIDhzUz5DTS7rTS32wZdjHmnrbyU8dfZ8XgDGGmZ4CjiRJ6PVk0zZqg818ddG7Tuj55d4i2kJdtIf8g3uzjmetZX3Lds7NWz6u8YkTLcudfkynlz/O4w0BKnwlJ9zpZa2lOxLAF8f6REREZOqL9bur/fTNX3cAGUe9iYjIFBaI9HJr1YNcWrCKOeMYrZLp9inwEhERkdFU0rfPKwX9DDltZbg8I483DLaRnzJ26AV9e72qepJjvOHt1Y+Q587ksoIzTuj5szwzAEbd67XXX0VDsJXzcpef0D0mSpbbd8xOr87+Ti9fnMYbAlR4SzjUXUfURsf93HvqN3D64++NaXykiIiInLpi6vSy1n4dwBjjtdZ2x7ckERGZLHfXPU1TbzvvnXV1oksRERGRaUQ/Q54afC7PyOMNe9uY75sZ03XKPIW80LZnIks7IU3Bdh5q3MS7Z11FisN9Qtco9/aFXoe761meWTHsOeubtwNwXt5pJ1boBMly+ajpaRp83x/n8YbQ1+kVjIaoDjRR5ikc13Pva9hAMBrib9WP8Nn5b41ThSIiIjLVxdTpZYxZa4zZCezqf3+FMeZXca1MRETi7s669cz1lnBO7rJElyIiIiLTyIn+DGmMeZUxZrcxZp8x5vPDPP4hY8x2Y8yLxpj1xpglcShfYpTu8tAZHj7TbOptpyA1O6brzPQU0h72H7NfKhH+VfskYRvhLaWXnPA1Yun0Wt+yjdneImZ6Ck74PhNhuPGGBoN3jD1sJ6PC2zdd4qC/dlzPi9gITzVvA+DvNY8TjkYmvDYRERGZHmIdb/h/wJVAM4C1ditwQZxqEhGRSRCKhnmhbTfn552GMSbR5YiIiMj08n+M82dIY4wT+CVwFbAEuGGYUOs2a+1ya+3pwPcB7QhLoAyXl2A0RCgaPuZ4bzREW6iLgpTsmK4zM60v/KkKJG7EobWW26sf4YysBSxILzvh66S7POS5MzncXTfs46FomA0tOxM+2hBeGW9orQX6xhv6nGlx3TNW4SsBYP8493q91HGItlAXV89YS0OwlUebNsejPBEREZkGYv5Oxlp75LhDelmNiMgUtqPzIN2RIGty9AJpERERmXgn8DPkGmCftfaAtbYXuB249rhrdhz1rg+wJ12onDCfMw3o6xA6WnNv36cpPzXGnV7evjF3idzrtaurkn3+at5UevFJX2uWdwaVPQ3DPra1Yz9dkZ6EjzYEyHKlE7IReiJBoO/z6HOlxfWeBSnZpDs9HPBXj+t5TzVvBeCrC/+LwtQcbq9+JB7liYiIyDQQa+h1xBhzDmCNMW5jzKeBl+NYl4iIxNnG1r7/ja/JWZTgSkRERGQaOpGfIUuBo4Oyqv5jxzDGfNQYs5++Tq+PD3chY8wHjDGbjDGbGhsTF6RMdxkuL9DXIXS0hmAbQMydXmVpfaHXSEHRZNjReQiANTmLT/pa5Z4ZI443XN+8DYPhnNylJ32fk5Xl9gEMjjjsCgcGP6fxYoyhwlfCge7xjTd8snkrSzNmU5SWx5tKLuLRxs3UBZrjVKWIiIhMZbGGXh8CPkrfDxzVwOn974uIyBS1oWUnFd5iClNzEl2KiIiITD9x+xnSWvtLa+1c4HPAl0c45yZr7Wpr7eqCgsTuTZrOBju9jgu9mnrbgNhDr2x3Oj5nGlUJDL12dx4m1eFmtqfopK81yzuDmkDTkLGPAOubt7M8s4Jsd8ZJ3+dkZbn6Q69QFwBd4e7Bz2k8VXiLOeCPfbxhV7iHTW27uSBvBQBvKb2EKJY7ah6PU4UiIiIylcUUellrm6y1b7PWzrDWFlpr326t1UtqRESmqIiN8Hzby5yl0YYiIiISByf4M2Q1cPQypZn9x0ZyO3DdSZYqJ2GkTq+m3nYg9vGGxhjKPIUcSWDotaurknm+UlwO50lfa5ZnBhEbpTrQdMxxf7iHze17OC8v8fu8ALLc6QC0h/o7vSIBfC5P3O9b4SuhOtBEoH+s4liebdlB2EYGQ6/Z3iLOyV3G7dWPErXReJYqIiIiU1BMoZcxpsAY80VjzE3GmJsH3uJdnIiIxMfuriN0hLsVeomIiEhcGGPmGGN+bIz5lzFm3cDbGE97Hpjf/9wU4HrgmOcYY+Yf9e7VwN6JrVzGI70/IOk6LvRqHBxvGFvoBTDTU5DwnV4L02dNyLXKPTMAqOw+dsThxtaXCdsI5+Umfp8XDDfesJuMSQi9FmeUA/Bc666Yzn+qeStpjhRWHzWW/YbSSznS08DTLS/FpUYRERGZulwxnncX8BTwMGMvHxYRkSQ3sM/rrAnYWSAiIiIyjDuB3wN3AzG1Ylhrw8aYG4EHACdws7V2hzHmG8Ama+064EZjzGVACGgF/isexUtsBrqCuiLHhV69bWS4PKQ5U2O+1kxPIRtbd2KtxRgzoXWOpS3USX2wlUUTFHrN8vaFXoeP2+u1vmUbqQ43q7MXTsh9Ttbx4w394QA+Z/xDrwvzTifbnc7t1Y9wQf6KMc9/snkra3OXkupwDx67snDN4DXOzxs9RLyvfiM3HVrHj5Z9lApfyUnXLyIiIskt1tDLa639XFwrERGRSbOxdScz0woo9WjHhYiIiMRFwFr7s/E+yVp7L3Dvcce+ctSfPzEBtckEyRil0ys/xn1eA2Z5CukM99Ae7pr0fVe7O48AsChjYkKvGak5pDrcVHbXHXN8ffN2zsxeRJozZULuc7IGxxsOdHpFeial0yvNmcLriy/gz0ceoLm3nbxROgKP9DRwoLuWd5RdOew1bj3yIC29HeSmZA77/O0dB/jE9p8RiPbylk1f444zv85sb/GEfjwiIiKSXGIabwj8xxjz6rhWIiIik8Jay3OtL7NGXV4iIiISPz81xnzVGLPWGLNq4C3RRcnESu/vCvJHhu70Khhn6DUzre/FWEcSMOJwV1clwISNN3QYB2WeQiqP2lHWEGxlV1cl543RlTSZMlweDIb2UBfWWrrCPZOy0wvghpmXEbIR/lnz5KjnPdm8FWBwn9cx1yi9lF4b5l+1w1+jKdjO+1/8PrkpGdx2xlfojYa5ftPXh4ydFBERkekl1tDrE8DdxpgeY0yHMabTGNMRz8JERCQ+DnTX0NTbrn1eIiIiEk/LgfcD3wV+1P/2w4RWJBPO50oDoDM8NPTKT419nxf0jTcEErLXa1dXJZkuH0WpuRN2zVmeGRzueaXT65n+3VPn5S2fsHucLIdxkOny0h7yE4yGCNvIYJAZbwvTyzgjawG3Vz+CtXbE855s2kpxah7zfKVDr5Exi1VZ8/lr1dBr9EZDfGjrj2jp7eT/nf5Zzstbzm1n/H/4wwGu3/T1hO6PExERkfiKNfTKAt4FfMdamwksBS6PV1EiIhI/2uclIiIik+BNQIW19kJr7cX9b5ckuiiZWE7jxOtMpSvcfczxhmAbBaOMrBtOWX/odeSo7qjJsqurkkXpsyZ0l9gs7wwquxsGw5j1zdvJdqezNGP2hN1jImS502kP+we79dInqdML+rq99vmreb5t17CPh6MRnm55iQvyTxvxc3N96aXs9VexuX3PMce/uusWnmt7mR8u/QjLMysAWJo5h7+s/v/oCPu5ftPXqQ00T+wHJCIiIkkh1tDrl8DZwA3973cCv4hLRSIiElcbW3dSkJLNHM2yFxERkfh5CchOdBESf+ku7zE7vYLREB1hPwWpOeO6TpbbR6bLS9Ukh17WWvZ0VU7YPq8B5Z4ZdEV6aA11Yq1lfcs2zsldhtM4J/Q+JyvL7aM91DXYreebpE4vgNfMWEu608Nfqx4Z9vFtHfvpCPu5IO/0Ea/x2qJz8DnTjrnGn488yF+qHuIjc67jmuJzjzn/tMy53HrGl2kNdfCWTV+jLtAyIR+LiIiIJI9YQ6+zrLUfBQIA1tpWIDk2r4qISMystWxo2clZOYsn9JWsIiIiIsfJBnYZYx4wxqwbeEt0UTLx0p1pdB2106sp2AZA/jg7vQBmegomfadXTaCJznDPhO3zGjDLMwOAw931HOyupSbQzHm5ybPPa0CWy0d7yI8/PPmdXl5XGtcWn8c99c/SHvIPefyJ5q0YDOfljjwS0ufycE3Rudxd9wyd4W42tOzgq7tu5pL8VXxm3vXDPuf0rPn8adWXaAy2ccOmr9MQbJ2wj0lEREQSL9bQK2SMcQIWwBhTAETjVpWIiMRFVaCR2mCz9nmJiIhIvH0VeB3wbV7Z6fWjhFYkcXF8p1dTbzsABSnZ477WzLTCSR9vuKurEujbMTWRZnn7Qq/KnnrWN28Hkmuf14CB8YZdkQAwuaEXwA0zLyUQ7eWu2vVDHnuyeSunZVaQk5Ix5jV6okF+ffBOPrT1R5R7ZvCz5R8ftavujOyF/GHVF6kJNPPWTd+kNxo66Y9FREREkkOsodfPgH8DhcaYbwHr6fvhRUREphDt8xIREZHJYK19Yri3RNclEy/d5Tkm9GocCL1Sx9/pVeYppCrwyh6syfBK6DXRnV59O8oO99SzvmUbZWkFlPd3fyWTgfGGA3vZ0idxvCHA8owKlmbM5q/VDx/zeW8P+XmxfS8X5K0Y8xorMuexKH0Wvzj4b8I2wu9WfpZMt2/M552Vs5jvLPkAe/xHeLF930l9HCIiIpI8Ygq9rLV/AT4LfAeoBa6z1t4Rz8JERGTiPde6kyyXjwUT/EpWERERkaMZYzqNMR39bwFjTMQY05HoumTipTuPC70Gxxtmj/taMz0FdEeCtIY6T6qmx5u2EIqGYzp3d2clJWl5ZMUQkoyHx5lKYWoOB/21PNPyEuflnZaU48UzXT46wt34w32dXj5X2qTe3xjDDaWXsaPzENs7Dwwef7blJSI2GlPoZYzh3bNejdM4+Pny/2aurzTm+19SsBKDYUPrzhOqX0RERJJPrJ1eWGt3WWt/aa39hbX25XgWJSIi8bGx9WXW5CzGYWL+37+IiIjIuFlrM6y1mdbaTMADvAH4VYLLkjjo6/TqHny/qbcNOLGdXmX93VEnM+JwT9cR3rn52/y95rGYzt/VVcmiCe7yGlDumcFDjZvoCHdzXl7y7fOCvk6vYDQ0OJYyw+Wd9BquKz6PNEcKf616ZPDYk81b8TnTWJW9IKZr3DDzUl686GYuLlg5rntnuzNYnDGLZ1peGtfzREREJHnpt54iIqeI+mArB7trtc9LREREJpXtcydwZaJrkYmX4fIM7oOCvk6vTJeXNGfKuK/1SujVeML11AVagL5ur7GEomH2+6snfLThgFmeGXSE/QCcm7ssLvc4WVmudABqAk0A+JyT2+kFkOn28ZqitdxVux5/uAdrLU80b+Wc3GW4Ha6Yr3Oi3Xprc5bxQttugtrrJSIiMi0o9BIROUVs7B/ZoX1eIiIiEm/GmNcf9fZGY8x3gcCYT5Qpx9ff6TWwj6mxt52CExhtCFCalg9A1Ul0ejX0d5o93fzSmCMOD3TXErKRuIVe5d6+HV7LMuaQm5IZl3ucrIGgqCrQFzRO9njDAdeXXkpXpId76jdwuKeOIz0NMY02nAhrc5cSjIZ4sW3vpNxPRERE4iv2l8yIiMiU9lzry/icaSzNmJPoUkRERGT6e+1Rfw4Dh4BrE1OKxFO600vIRghGQ6Q5U2jqbSM/dfyjDaGv4yfL5Tup8YYDO8W6Ij1sbt876gu+dndWArAoI36dXgDn5i2Py/UnwkDoVdPThNeZitM4E1LHmdmLmOcr5a9VDxOI9AJMWui1JmcxBsOzrTs4K1dTMURERKY6hV4iIqeIja07WZ29EJcjMT/IioiIyKnBGOMEtllrf5LoWiT+MlweAPyRHtKcKTQG21icMfuEr1fmKRzsOjoRTb1tpBgXEaI80bRl1NBrV9dhnMbBXF/pCd9vNEsz5+DAcFnB6rhcfyIMjDesDjThc3oSVocxhhtKL+Wbe/5ER7ibMk8hs71Fk3LvbHc6SzNms6FlB8x906TcU0REROJH4w1FRE4Brb2d7O46on1eIiIiEnfW2ghwQ6LrkMnh6w+9OsM9ADT1tlOQmn3C1yvzFJ50p9eMtFxWZS3gieato567q6uSCm8JqQ73Cd9vNAvTy3jx4t8n9XjxgU6vxt62wQAzUV5fcgFu42Svv4oL8lZgjJm0e5+du4QX2vcMdpmJiIjI1KXQS0TkFPB82y6gb3SHiIiIyCR42hjzC2PM+caYVQNviS5KJt5AUNIV7iEQ6aUj3E1ByomNNwSY6SmgqqdxcEfYeDX2tlOYks2F+afzUsdBmnvbRzx3d9eRuI02HJDtzojr9U9Wdn/oBa8EmImSl5LFlYVrgMkbbTjg7Jz+vV7tU2+vV9RG6YkEE12GiIhI0ohr6GWMeZUxZrcxZp8x5vOjnPcGY4w1xiRvz7+IyBS2sXUnqQ43K7LmJboUEREROTWcDiwFvgH8qP/th4ksSOJjYCSeP9xDU3/AVJCSfcLXK/MUEoj2Dl5rvBqDfTvFLsxbgcXyVPO2Yc/rCvdwpKeBhenxDb2SXYbLO/jn9ASONxzwkTmv48K8FZyfd9qk3vesnCX9e712Tup9J8LNlfdx3lM3ErGRRJciIiKSFOIWevXPcf8lcBWwBLjBGDNkrpYxJgP4BLAxXrWIiJzqNra+zMqs+XEb3SIiIiJynPdaay8++g14X6KLkok30OnVGemhqbcNgPyTGG8401MIQFXPie31auptoyAlm2WZc8hxZ/BE0/AjDvd0HQFg0SkeejmNk8z+4Cs9wZ1eAMsy5/DnM7486bVkuX0szZjNsy0vTep9J8Kmtl009rZx5AT/mxEREZlu4tnptQbYZ609YK3tBW4Hrh3mvG8C3wMCcaxFROSU1RXu4aWOA0m9S0BERESmnX8Mc+yOSa9C4i59cLxhNw3BNoCTGm9YllYAcEJ7vULRMC2hTvJTsnEaJxfkncaTzS8StdEh5+7qqgQ45Tu9ALJcfSMOkyH0SqS1uUvZ0r53yu312tdVDcABf02CKxEREUkO8Qy9SoEjR71f1X9sUP9M9zJr7T1xrENE5JS2qW0XUSxrcoY024qIiIhMKGPMImPMG4AsY8zrj3p7F5CW4PIkDgbGG3aFA6+MNzypTq/+0Csw/tCrubfjmPtfkHc6jb3tvNx5eMi5u7oq8TpTKeu/36ksy50OgM95av8nujZ3GcFoiC1TaK9XOBrhYHdf2HWgW6GXiIgIgCtRNzbGOIAfA++K4dwPAB8AmDVLr8ISERmPDa07cRknZ2QtSHQpIiIiMv0tBF4DZAOvPep4J/D+RBQk8ZVxVKdXMBoCIO8kOr18Lg+57owTGm/Y2D9ecaDT7IL8FQA80byVpZlzjjl3d2clC9Nn4TBxXXU+JWS6Bzq9vGOcOb2dmb0IB4ZnWl5ibe7SRJcTk8qeekL9u7z2q9NLREQEiG/oVQ2UHfX+zP5jAzKAZcDjxhiAImCdMeYaa+2moy9krb0JuAlg9erVNo41i4hMK4FIkDuqH2dt7lK8rlP7lZsiIiISf9bau4C7jDFrrbXPJroeiT+vMw2DwR/poS3URZbLd9J7ZGd6Ck9ovGFT8NhOsxmpOSxOL+fJphf5yJzrBs+z1rKrq5IrC9ecVJ3ThcYb9sly+1iaOYcNrTsSXUrM9vqrAPA4UjXeUEREpF88X9L0PDDfGDPHGJMCXA+sG3jQWtturc231s621s4GNgBDAi8RETlxt1c/SmNvGzfOeX2iSxEREZFTy+uMMZnGGLcx5hFjTKMx5u2JLkomnjGGdFcaneEeGnvbT2q04YAyTyFVJxB6DXR65ae8UsMF+St4vm0X/nDPMee1hjpZmF6GaLzh0c7JWcqWtr0EIsFElxKTvf37vC7KP539Gm8oIiICxDH0staGgRuBB4CXgb9ba3cYY75hjLkmXvcVEZE+wWiIXx+8izOzF3G29nmJiIjI5LrCWttB36jDQ8A84DMJrUjiJt3ppSvcQ2OwjfyTGG04YKangKqeRqI2Oq7nHT/eEODCvNMJ2QjPtu4cPLarqxKARelanwB9HU4AGaf4eEOAs3OX0mvDvNC2J9GlxGSfv4qi1FyWZ86lIdhKZ7g70SWJiIgkXFyHV1tr77XWLrDWzrXWfqv/2FesteuGOfcidXmJiEycO6ofpzbYzCcq3kj/GFkRERGRyTIw3+5q4A5rbXsii5H4Snd56Ir00DRRnV5phfTaMA3BtnE9rzHYhs+ZdsxY7zNzFuFxpPJk04uDx3Z39odeGQq9ALJc6vQaMLDXa6qMONzrr2J++kwqfMUAHOyuTXBFIiIiiaeNrSIi01AoGubXB//N6ZnzOD/vtESXIyIiIqeeu40xu4AzgEeMMQVAIME1SZykuzwT2ulV5ikEoCrQOK7nDRe6pTrcrM1dyhPNWweP7eqqpCAli7wJqHU6UKfXKzLdPpZnVvBsy86xT06wqI2y31/NPF8pc70lANrrJSIigkIvEZFp6d+1T3Ek0Mgn5qrLS0RERCaftfbzwDnAamttCPAD1ya2KomXdJeHpt42uiI9FKTknPT1ZnoKADgyzr1eI4VuF+afzsHuWg531wOwu+sICzXacNBA6OVzqdML+kYcbmnfQ0+S7/WqDTTTHQkyzzeTcm8RBsN+hV4iIiIKvUREpptwNMIvDv6LZRlzuCR/VaLLERERkVPXIuAtxph3Am8ErkhwPRIn6U4Ph7rrAChInZidXgBV4w29etsoSMkecvzCvBUAPNn8IhEbYY9Cr2Ock7OMN5ZcyJKM2YkuJSmck7OMkI0k/V6vff5qABb4ZpLmTGGmp4AD3Qq9REREFHqJiEwzd9c/w6HuOj5e8QZ1eYmIiEhCGGP+DPwQOA84s/9tdUKLkrhJd3no7u+KmYjxhh5nKgUpWRzpGd94w8Zg27A7xeZ4iylLK+CJpq1UdjcQiPZqn9dR8lOz+PGyG/E4UxNdSlJYnbMQp3Ek/V6vvf4qAOallwIw11ui8YYiIiKAK9EFiIjIxInYCD8/8E8WppdxReGZiS5HRERETl2rgSXWWpvoQiT+0l2ewT8PFzqdiJmewnGNNwxGQ7SH/cN2ehljuCD/dO6qXc/2zgMALFKnl4wgw+VleUYFz7S8NOI5jzZuZnP7Hv5n7ptxmMS8nnxvVxU57ozB3XQVvhI2tr5M1EYTVpOIiEgy0N+CIiJThLWWsX5vdG/9Rvb5q/lYxRv0g46IiIgk0ktAUaKLkMmR4fIO/nm40OlEzPQUjGu8YXNve9/9RwjdLsxbQVekh79WPYLBsCC9bCLKlGlqbe5StrbvozscOOZ4R8jPp1/6Fe/a8h1+duCfbGnfm6AK+8YbzvOVDr4/11dCTzRIXbAlYTWJiIgkA/1GVERkivjO3r+w4vH38JuDdxEYZqly1Eb5+YF/MtdbwtUzzk5AhSIiIiKD8oGdxpgHjDHrBt4SXZTEh8/5SqdXXkrmhFyzzFNITaCJiI3EdH5jsC/0Gmm84jm5y3AaB0+3bKfcO0Oj/GRUa3OX9u31an9lr9cTTS9y+TOf4h81j/OB8tfiMk4ebHg+IfVZa9nrr2J++szBYxXeEgD2a8ShiIic4hR6iYhMAREb4R81jxO1lm/vvZXz13+MW488RCgaHjznocZN7Oqq5GMVb8BpnAmsVkRERISvAdcB3wZ+dNSbTEMD4w2z3emkONwTcs2ZaYWEbIT6YGtM5zf2tgFQOEKnV6bbx6qsBQAsVJeXjGF19iKcxsGzLS/RFe7hczt+wzs2fwufK407z/oWX174Ts7OWZKw0Ku5t4O2UBfzfK+EXnN9faGX9nqJiMipTju9RESmgE2tu2nqbeeXp32SwpRsvrvvNr748k3cdHgdn5r7Fl5TtJaf7v8H5Z4ZXFN0bqLLFRERkVOctfaJRNcgkyejP/QaqcvqRJR5CgA40tNISVr+mOc3Btv6a8ge8ZyL8k/n+bZd2uclY0p3eViROZe7657hztr1VAea+ODsa/jU3LeQ5kwB4IrCM/nKrpuHjBmcDPv8VQDMP+q+M1Jz8TnT2N+t0EtERE5t6vQSEZkC7mvYSKrDzSX5Kzkrdwn/OvOb3Lzy83gcqXxs+0+5YP3HeanzIDdWvB6XQ11eIiIikhjGmPX9/+w0xnQc9dZpjOlIdH0SHwPjDQsnaJ8X9I03BDgS416vpv5Or9GCt0sLzsBgOD1r/knXJ9Pf2blLOdxTj9vh4p9rvsmXFrxjMPCCvtALSEi3115/NcAxYZsxhjneYnV6iYjIKU+dXiIiSc5ay/0NG7kgbwW+/lfRGmO4rOAMLslfybq6p/nRvr8x11vC64svSHC1IiIiciqz1p7X/8+MRNcik2ew02uE0YInotRTgMFwuLsupvMbg21kurzHhBLHW5Ixm/Xn/4KZaQUTVaZMY+8vfw2laQW8seTCYXfAlaTlsyxjDg81PM9H5lw3qbXt9Vfhc6YN6YKc6yvhhbY9IzxLRETk1KBOLxGRJLe1Yx81gWaumnH2kMccxsF1xefzxHk/44FzfojbodcyiIiIiMjkGnhhVsEEdnqlOtyUpOVR2VMf0/mNve0x3b/MU4gx5iSrk1NBXkoW7yi7YtjAa8AVhWeyuX0vDTHunpso+7qqmOsrHfK1XOErpTrQRCASnNR6REREkolCLxGRJHdf/UZcxsllBWeMeI7DOCZsabiIiIiIyHi80uk1cTu9AMq9RePq9Jro+4uM5crCNVgsDze+MKn3HWmP2FxvCRbLoRj/uxEREZmOFHqJiCQxay33NWzknNxlZLvTE12OiIiIiMgQRam5LMuYw5nZiyb0uuWeGTH/8r6pt21CO81EYrEofRZlaQWTuterI+SnLtjC/PSZQx6b4ysGYL/2eomIyClMoZeISBLb1VXJoe46rppxVqJLEREREREZVpozlXvXfp81OYsn9Lrl3iJaQp10hrvHPLext21Cd4qJxMIYwxWFa3i6ZTv+cM+k3HMg0JrvGxp6VXj7Qq8D3Qq9RETk1KXQS0Qkid1XvxGD4cqCNYkuRURERERkUpV7ZgBwuHv0vV6BSJDOcA+F6vSSBLii8EyC0RBPNG+dlPvt81cBDDve0OfyUJSaq04vERE5pSn0EhFJYvc1bGBNziLtJxARERGRU065twhgzL1ejb3tAOSn6HtmmXxnZi8i253OAw3PTcr99vqrSTEuZvWHwser8JVwQKGXiIicwhR6iYgkqQP+GnZ3HeGqwrMTXYqIiIiIyKQr9/Z3evWM3unVFOwLvQo03lASwOVwcmn+GTzauJlQNBz3++3tqmKOrxiXwzns43O9JRzorsFaG/daREREkpFCLxGRJHVfw0YAXjVDow1FRERE5NST4fKS586ModOrDYB8jTeUBLmy8Ezaw36eb9sV93vt81cxb5h9XgMqfCV0hLtp6u+AFBEROdUo9BIRSVL31W/k9Mx5lKTlJ7oUEREREZGEKPfOiDn0KlSnlyTIBXkrSHW44z7iMBAJcqSnYdh9XgPm+koANOJQREROWQq9RESSUFVPI9s69nPVDI02FBERkVOHMeZVxpjdxph9xpjPD/P4/xhjdhpjthljHjHGlCeiTpk85d6iMccbNgbbAMhLyZyEikSG8rrSOD/vNB5q2BTXsYIHumuJYpk/WqeXty/02t+t0EtERE5NCr1ERJLQ/f2vELxKow1FRETkFGGMcQK/BK4ClgA3GGOWHHfaFmC1tfY04B/A9ye3SplsszwzqAk0E4yGRjynqbedbHc6KQ73JFYmcqwrCs6kKtDIzs5DcbvHvq5qAOanjxx6lXrySXW41eklIiKnLIVeIiJJ6L76DSxOL2e2tzjRpYiIiIhMljXAPmvtAWttL3A7cO3RJ1hrH7PWdve/uwEY+Te/Mi2Ue4uwWKp6GkY8pzHYRoH2eUmCXVpwBgbDg42b4naPvf4qHBjmjPJzotM4me0tYr9CLxEROUUp9BIRSTINwVY2te3mqhlnJboUERERkclUChw56v2q/mMjeS9wX1wrkoQr98wA4HD3yCMOG3vbKEjJmqySRIZVkJrNGdkLeKjh+bjdY5+/mjJPIWnOlFHPq/CWcFDjDUVE5BSl0EtEJMk80PA8Fqt9XiIiIiIjMMa8HVgN/GCExz9gjNlkjNnU2Ng4ucXJhJrtLQLgUHfdiOc0BtsoSM2epIpERnZFwZm81HmQqp74/H9nb1fVqKMNB8z1lVDZ00AoGo5LHSIiIslMoVeSiNgIP9j7V+qDrYkuRUTi7MmmrXxux294sOF5ApHgkMfvq99AhbeYBaMsJxYRERGZhqqBsqPen9l/7BjGmMuALwHXWGuHfjMFWGtvstauttauLigoiEuxMjnyU7LwOlM53DNy6NXU206+xhtKEriysG8n80NxGHEYjkY42F3DvBh+TqzwlRK2ESp7Ru6QFBERma4UeiWJPV1V/Pzgv7i77plElyIicWSt5X/3/Im/Vj/C+178Pqc//l4+svXH3F33DP5wD629nTzbuoOrZpyNMSbR5YqIiIhMpueB+caYOcaYFOB6YN3RJxhjVgK/pS/wGnnJk0wbxhjKPUUjjjfsDgfwRwLq9JKkMMdXzDxfKQ80PDfh167sqSdkI8z3jTb1tc9cbwmA9nqJiMgpyZXoAqRPdaAJgMPdtQmuRETiaVvHfnZ1VfKNRe+hwlfCffUbeaDhOf5T/yypDjfzfTOJ2Kj2eYmIiMgpx1obNsbcCDwAOIGbrbU7jDHfADZZa9fRN84wHbij/wVCldbaaxJWtEyKcu8M9vmHNP0B0NjbDqCdXpI0rio8i58f/BcffPGHfGj2tazMnj8h193rrwKIqdNrjq8YgAOjhF6BSC9Nve3M9KgbVkREpheFXkmipqcv9Do4ypxyEZn6bq9+lDRHCq8vvoBMt48L8lbwv4vfy/Otu7ivYSP31z/HwvQylmdUJLpUERERkUlnrb0XuPe4Y1856s+XTXpRknDlnhk81rSFqI3iMMcOrGnsbQNQp5ckjY/OeR0AfzzyAPc1bOSsnMV8cPa1XJK/csjX73js7eoLfuelj93ple1OJ8+dyYHu4UOvcDTCOzd/i20d+9lwwW/IdqefcF0iIiLJRqFXkqgO9C05HW05r4hMbT2RIOvq1nP1jLVkun2Dx53Gydm5Szk7dylfW/huAI02FBERERHpV+4tJhgNURdsoSQt/5jHmoJtANrpJUnD60rjM/Nv4CNzruP26kf53eH/8J4t32W+byYfmn0N1xafR4rDPe7r7vNXUZSaS4bLG9P5Fb6SEccb/nD/7Wxo3QnA3XXP8I6yK8Zdj4iISLLSTq8kUdM/3rCqp4HeaCjB1YhIPNxTv4HOcA/Xl14y4jnGGAVeIiIiIiJHKffOAIZ/kag6vSRZ+Vwe3lt+NU+e93N+uvzjuIyTT+34Fec/9TH+WfMEURsd1/X2+auZF8M+rwFzfSXDjjd8qGETvzp4J2+deRmL0mdxR/Vj46pDREQk2Sn0ShIDO72iWKp6GhNcjYjEw9+qH2GOt5g1OYsTXYqIiIiIyJRR7ukLvQ531w95rCHYhsGQ586c7LJEYuJ2uHhd8fncv/YH/HnVlyhIzeaTL/2CazZ+kedaX47pGlEbZZ+/innpY+/zGlDhLaE51EFbqGvw2OHuej750i9YljGHry18N28quYgXO/axp+vIuD8uERGRZKXQK0nUBJqY4+1bNKoRhyLTzwF/DRtbX+bNpRerk0tEREREZBxK0vJxGSeHe4b+rNzU206uOwOXw5mAykRiZ4zhwvzTWXfWt/m/ZR+jMdjGG5//Ch/a+qNhA92j1Qaa6Y4Eme8bR+jlKwEY7PYKRHr58NYfAfCbFZ8izZnCdcXn4zQO7qh5/MQ+KBERkSSk0CsJhKMR6gItnJO7DFDoJTId/a36UZzGwRtLLkp0KSIiIiIiU4rL4WSmp2DYYKAx2EZ+alYCqhI5MQ7j4PUlF/D4eT/lU3PfwmONW7j06f/m23tupSPkH/Y5+/zVAMwf53hDYHCv19d3/4GXOg/yk2U3Mqt/ZGhBajaX5K/iXzVPEo5GTubDEhERSRquRBcgUB9sIYpleWYFGS4Ph7prE12SiEygcDTCP2qe4OL8lcxIzUl0OSIiIiIiU065ZwaHh3mBaFNvGwUp2ZNfkMhJ8jhT+cTcN/KW0kv4wb6/8ttD6/jtoXWkOtykOFykONy4jQu3w0VPJAjAvHF0es3yzMBlnBzoruGfNU/wl6qH+Mic67i8cPUx572p5CIeatzEk81buaRg1YR+jCIiIomg0CsJDOzzmplWQLmnSJ1eItPMo02baext4/rSSxNdioiIiIjIlDTbW8Tm9j1Ya48ZF94YbGN1zqIEViZycorScvnRso/yrllX8WDDcwSiIXqjIULRML3RMCEbJhgNMcszg7yU2HfXuR0uZnkKebxpCzcfvpezc5bw6bnXDznvkoJV5Loz+HvNYwq9RERkWlDolQRq+kOvEk8+s71FbO84kOCKRGQi/a36UQpSsrg4f2WiSxERERERmZLKvUV0hntoDXWS2/+Lf2stjb3t6vSSaWF5ZgXLMysm9JoVvhIebnyBgpRsfnHafw+7+y7F4ea64vO59ciDtPZ2kpOSMaE1iIiITDbt9EoCVT39oVdqHnO8xVQFGglFwwmuanid4W4+svXH1AVaEl2KyJRQH2zl0abNvLHkItwOvc5AREREROREzPL07SA6eq9XV6SHQLSXgtTsBFUlktwWpc/CgeGXp/03haOM2n9z6cX02jB31a2fxOpERETiQ6FXEqgJNJLjzsDrSmO2t4iIjVIdaEx0WcPa1Lqb/9Q/y6NNmxNdikwBgUiQ3Z2ViS4jof5Z8wQRG+UtpZckuhQRERERkSmr3FsEwOGeV9YBNAXbAchPyUpITSLJ7sNzruO+tT/g7Nylo563JGM2SzNmc0fN45NTmIiISBwp9EoCNYEmStPygVe+kT/oT869XpU9fa+q2911agcZEps/HXmQV2/4LO0hf6JLSQhrLX+rfpQ12Yup8JUkuhwRERERkSmr3FMIHNvp1dDbBqBOL5ERZLi8LM4oj+ncN5ZcxPaOA+zqPBznqkREROJLoVcSqA40UdIfes3xFgNwqCc5Q6+BV9Xt7jqS4EpkKtjVdZiQjbDfX53oUhLiudaXOdhdqy4vEREREZGTlOZMZUZqznGdXm0A2uklMgFeV3w+buNUt5eIiEx5Cr2SQE2giRJPX+iVn5KFz5nGoe7aBFc1vIFX1e1R6CUxOOjv+zred4qGXrdXP0q608PVM85OdCkiIiIiIlPebG8Rh7pfCb0aBzq9FHqJnLTclEwuLTiDf9c+lbR75kVERGKh0CvBOkJ+OsM9g+MNjTFDvpFPJgOhV1Nv++D8dJGRHOgPb/f6qxJcyeTrCPm5p/5Zrik+F68rLdHliIiIiIhMeeWeomPGGzb1tuPAkJOSnsCqRKaPN5VcTFNvO481bUl0KSIiIidMoVeC1QSaAAbHGwLM9hYnZehlraWyp57F6X3zoPf41e0lI2vt7aQ11AnAvq5Tq9OrOxzgpwf+QSDay/UabSgiIiIiMiHKvTNo7G2jOxwAoCHYRl5KFk7jTHBlItPDRfmnk5+SxT804lBERKYwhV4JVt0fepUeE3oVcaSngXA0kqiyhtXQ20Yg2svlhasB7fWS0R3s7/LKcHnY331qhF5NwXZ+tO92zn7qw/y/w//hysIzWZE5L9FliYiIiIhMC+WeIgAqewYmkLRRkJqVyJJEphW3w8Xris/n4cYXaO7VdB8REZmaFHol2PCdXkWEbYSqQGOiyhrW4f7us9XZC8lxZ7C7qzLBFUkyGxhteFH+Siq76wlEehNcUfwc9NfyhZ03sfapD/OzA/9iTfZi/nnmN/l/p38WY0yiyxMRERERmRbKvTMAONwfejUG27TPS2SCvankIsI2wp216xNdioiIyAlR6JVg1YEm3MZJYWr24LHZ3mLglZApWVT2NAB9r65bmF6mTi8Z1aHuWpzGwcX5q4hiOdQfgk0nB/21fPDFH3LR05/gHzWP8/riC3j03J/wu5Wf5cycRYkuT0RERERkWin39nV6DawDaOptJ1+hl8iEWpRRzvLMiqQZcfiHyvv44Is/xFqb6FKmhYiN6N+liEx7Cr0SrKaniaK0PBzmlU/F7OO+kU8Wh7vrcGAo9eSzML2MPV2V+ovyKK29nYkuIakc8NdQ5ilkcfosAPb6p9eIw9beTt7+wjd5umU7H51zHU+f/0u+t/RDzPWVJro0EREREZFpKdudTpbLx+HuOqy1fZ1eR72AVEQmxnVF57Gj8xBVPYmfQPTHyvu5r2Ejz7XtSnQp08J7tnyPj23/aaLLEBGJK4VeCVYdaDpmnxdAYUo2Xmfq4E6kZFHZU09JWj4pDjcL0mfRGe6hNtic6LKSwk/238HKx9/L/mkW7JyMg921VHiLmesrwWCm1b+bcDTCR7f9hPpgK39e9WU+O/+tFKbmJLosEREREZFpr9xbxOGeejrC3fTaMAUp2uklMtEuLlgFwGNNWxJax0F/Lfu7awC4pfLehNYyHezqPMxjTVt4snmrXsQuItOaQq8Eqwk0HbPPC8AYw2xvUVJ2es3qn6G+IL0MgN2dyTniMGqjbGzZOSl/if+j5gl+sv/vRLE81vRi3O83FVhrOeCvZba3mDRnKjM9BexL4tCrpbeDjS07Yz7/e/tuY33Ldv538ftYmT0/jpWJiIiIiMjRyr0zONxdT2NvG4A6vUTiYK63hLK0Ah5r2pzQOh7pv//VM9byQMNz1ASaElrPVPfnIw8C0BbqSrrfOYqITCSFXgkUjkaoC7YM6fSCvr1ZybjTq9zTF3otTJ8JwO6uypieW9XTyJXPfJqt7fvjVt/RHm3awps2fZVbqx6M632ebdnB53b8mnNyl1HumcH65m1xvd9UUR9soScapMJbAsB8Xyn7/FUJrmp41lo+uPWHvGnTV/narlsIRyOjnr+u9ml+e2gd75h5BdfPvHSSqhQREREREYByzwyqA43UBvqmjminl8jEM8ZwUf5Knml5iWA0lLA6Hml8gQW+Mr644O1YawdDGxm/znA3/6p9ktMy5wKwpX1vgisSEYmfuIZexphXGWN2G2P2GWM+P8zjHzLGbDfGvGiMWW+MWRLPepJNQ28rERulxDM09JrjK6ayp37MX8BPlq5wD0297YOdXtnuDGak5rC7K7ZOr4cbN/Fy12E+veOX9E7CN0xb2/cB8L29f6Ux2BaXe+ztquL9L/6A2d5ifrvi01yQt4INrTsIRcNxud9UcqB/NOccXzEA83wz2e+vIWKT4+v5aP+oeYKNrS9zVs5ibq68l7dv/t8R97Pt7DzEp3f8itXZC/nqondNbqEiIiIiIkK5t4iIjfJi/y9s1eklEh8X5a+kOxLkudaXE3L/znA3G1t3cmnBKso8hVxReCa3VT1MIBJMSD1T3b9rnsIfCfD1Re/G60zlxY59iS5JRCRu4hZ6GWOcwC+Bq4AlwA3DhFq3WWuXW2tPB74P/Dhe9SSj6p6+tuzhOr1me4oI2UjStG5X9tQDfR1oAxamz4q50+vZlh14HKns7jrCrw7eGY8Sj7Gj8yAFKVn0RAJ8e8+tE379pmA779ryHVIdLv6w6gtkuX2cl3ca3ZGgXi0DHPD3hV4V3r7Qa66vlGA0NPg1nyzaQp18a8+fOCNrAX9b/TV+tPSjvNC2m9ds/Dy7Og8fc25rbyfvf/EHZLl9/GbFp0hxuBNUtYiIiIjIqWt2/8+kL7TtBtBOL5E4OTd3GSnGxeMJ2uv1RNNWwjbCpQVnAPCuWVfRGupkXd0zCalnKrPW8ueqB1ieWcGqrAWcljmXF9v0uysRmb7i2em1BthnrT1gre0FbgeuPfoEa23HUe/6gFNqi+JAoHX8Ti+A2d6+b+QP9STHiMPD3f2hV3+nF8DC9DL2+qvG7N6J2igbWnfymqK1XFt0Lj8/8M+YO8RO1I6OQ5ybu5wPzr6Gf9Y+wYaWHRN27UAkyHtf/B6NwTZ+v/JzlHkKAVibuxSDYX3z9gm711R1qLuWVIeb4rQ8AOb5SgGSbq/Xd/feRnvYz7eWvB+HcfCm0ov4++qvE4yGuO65L3Fv/QagbxTpjdv/j/pACzet+AyFqTkJrlxERERE5NQ08DPpC217cBkn2e70BFckMj15XWmclbuExxIUej3S+ALZ7nRWZS0AYG3OUhaml3FL5b2Tsr99OnmubRe7u47wzrIrMcZwetZ8dnYeGtfoyt8dvocv7ryJF9v36t+/iCS9eIZepcDRyUZV/7FjGGM+aozZT1+n18eHu5Ax5gPGmE3GmE2NjY1xKTYRqkcNvfo6ZA75kyP0Guj0muU5OvSaRTAaorK7YdTn7uk6Qmuok7NzlvK1Re8m3eXlszt+HbdRdy29HdQGm1maOYePVbyBsrQCvvTy7yZkrGLURvnE9p/zYvs+frb845yeNX/wsWx3OqdlVrC+RXu9DnTXMMdbjMP0/S9mfnryhV6b2/ZwW9XDvGfWq1mSMXvw+Mrs+fznrO+yKH0WH9r6I36073a+t/cvPNW8jf9d/D5WZs8f+aIiIiIiIhJXhak5pDrctIf95KdkDf7MISIT7+L8lezzV3OkZ/Tf+0y0iI3wWNNmLs5ficvhBPr2jL171qvZ0XmI59p2TWo9U92fjzxApsvHNUXnArAyaz69NszOjkMxPT8cjfCT/X/j1qqHuGbjF3nVs5/hlsr7aAsNvxpCRCTREv7dobX2l9baucDngC+PcM5N1trV1trVBQUFk1tgHNUEmshy+Uh3eYY8NiM1hzRHCod6ahNQ2VCHu+vIdqeT5fYNHluYXgYw5ojDZ/q7rNbmLiUvJYuvL3o3W9r3ckvl/XGp9aXOgwAszZiDx5nK1xe/l73+Kn53+J6TvvZ39tzKfQ0b+fKCd/CqGWcNefy8vNPY0r6XznD3Sd9rKjvor2VOf3ALfTvg8lOy2OevSmBVrwhHI3zx5ZsoSs3lk3PfPOTxorRc/nbm13lzycX89MA/+e3hu3n7zMu5fualCahWREREREQGOIxj8MWY+RptKBJXF+WvBJj0EYcvtu+jJdQ5ONpwwOuKzyfL5eOWynsntZ6prCHYyn31G3hTyUV4nKkAnJ41DyDmvV7bOvbTGe7hu0s+yLcXfwCXw8lXd93MmU98kE9s/xnPtuxQ95eIJJV4hl7VQNlR78/sPzaS24Hr4lhP0qnuaaLUM7TLC/pewTLbW8Sh7uTo9DrcU0/5UV1eAPN9MwHGHFW4oXUHZZ5CZnr6Astri87jkvxV/GDvX6nsH5s4kXb0v1JlWeZsAC4rOIMrCs7kp/v/QVXPiXcK/u7wf/jt4bt5Z9mVvK/8NcOec17uciI2ysYELXpNBuFohMM99czxFR9zfK6vJGk6vf5w5D52dh7u7zwcGjoDpDrc/GDph/nW4vdxfeklfG3Ruye5ShERERERGc7AiMPC1OzEFiIyzc31llCWVjDpIw4faXwBp3FwYd7pxxz3OFO5fualPNDw3ODKEBnd36ofJWQjvL3s8sFjxWl5zEjN4cUYd9Kvb+lb4/GqwjW8vexy7jn7e9x79ve5vvQSHml8gbds+hqf3fnruNQvInIi4hl6PQ/MN8bMMcakANcD644+wRhz9Jywq4FTaotiTaBp2NGGA+Z4i5Mm9KrsrmeW99jQy+tKY5ZnxqidXgP7vNbmLB08Zozh20vej9M4+PzO3074q0F2dB6kNC2fbHfG4LGBwOJru245oWv+sfJ+vrH7j7x6xtl8beG7McYMe94Z2QtJdbhZ33zqjjisCjQQthEqvCXHHJ/nm8k+f3XCX/1TG2jmR/v+xiX5K7mqcGi33tGMMbyj7Eq+v/TDpDjck1ShiIiIiIiMZmAHdn5KdmILEZnmjDFclL+SZ1peGtf+p5P1SONm1mQvPmba0IB3ll2JtZY/H3lw0uqZqiI2wl+qHuK83OXM9R27cWZl1ny2xBp6NW9jWcYcclMyB48ty5zDNxe/j+cvvIkbSi/ljurHORyHF7aLiJyIuIVe1towcCPwAPAy8Hdr7Q5jzDeMMdf0n3ajMWaHMeZF4H+A/4pXPcmoOtBE6SihV7m3iMru+rjtvopVOBqhOtA0pNML+kYcjtbptaurkrZQF2fnLj3meElaPl9Y8DbWt2znjprHJ7TenZ2HWJox55hjMz0FfGLuG3mw8Xkeatg0ruv9peoh/r9dv+eKgjP5+fJPDM6THk6aM4U1OYtZ37z9hGqfDg74+0ZyHj3eEGCer5S2UBfNvR2JKGvQ13f/gbCN8PVF7x0xvBQRERERkeRV7ukLvQrU6SUSdxflr6Q7EuS5SZpoU93TyMtdh4eMNhxQ5inkisIzua3qYQKR4KTUNFU90riZmkAz7yy7cshjp2fN51B3Ha29o+/l6g4HeKFtN+fmLR/2cY8zlU/OfRNO4+D3lSe/VkREZCLEdaeXtfZea+0Ca+1ca+23+o99xVq7rv/Pn7DWLrXWnm6tvdhauyOe9SSTznA3HWE/JWkj7yib7S2i14apDbRMYmVDVQeaCNsI5f2vpjvawvQyDnbXjviKn2cH9nnlLBny2NtmXs6a7MV8Y/cfqQ+2Tkit3eEA+/01LMmYPeSx95VfzXzfTL6262Z6YvzG6O/Vj/GFnTdxSf4qfrnik7gdrjGfc37uaezxH5mwj2mqOdDdF3pVHDfecGAcZiL3ej3etIV76zfw8Yo3DI5EERERERGRqWXge3nt9BKJv3Nzl5FiXJO21+uRxs0AXFqwasRz3jXrKlpDnayre2ZSapqq/nzkAYpSc7msYPWQxwb2em0dY6/X8227CNkI5+eeNuI5RWl5XFt8Hn+repS20OghmojIZIhr6CUjqwk0A4za6TXQKXOwP0RIlMM9fSMWZw3b6TWLsI1w0F8z7HM3tO5glmcGpZ6h4Z7DOPj+0g8RjPbylZd/PyG1vtx1GItlWeacIY+lONx8a/H7OBJo5OcH/jnmtf5V8ySf2fFrLshbwW9WfIrUGMfbnZfX943A06dot9eh7loyXT5y3ZnHHJ/r6xt3uG+Er5V4C0SCfPnl3zPXW8IHZl8z9hNERERERCQpLUovJ9XhZnFGeaJLEZn2vK40zspdMml7vR5peoHZ3qIhKxOOtjZnKQvTy7il8t6Er1BIVoe6a3mieStvm3n5sBOLTsuci8GwpX300Gt98zZSjIszcxaNet4Hyl9LTzTIrUceOqm6RUQmgkKvBKnp6Vu4WeIZOfQamFOe6L1eld0NAMN2xixMLwMYdsRh1EbZ0LKTtblDu7wGVPhK+OTcN3Nfw0aebN560rXu6DgEwNJhOr0Azs5dyhuKL+Q3h9bxsW3/xz9rnqC5t33IeXfXPcP/vPQL1uYu5Xenf4Y0Z0rMNSzJKCfHncH6llNzr9cBfw0V3uIhowNL0vLxOlMT1un1u8P3UtlTz7eWvD/mAFNERERERJJPUVouOy75I+fkLkt0KSKnhIvzV7LPX82Rnoa43qc7HOCZlpe4NP+MUdcRGGN496xXs6PzEM+17YprTVPVrUcewmWcXD/z0mEfT3d5WJA+kxfH2Ou1vmU7q7MX4nGmjnreooxyLso7nVsq753U/W8iIsNR6JUg1YG+0Gu0Tq8ZqTmkOtwcTnDodbinjlSHm6LU3CGPVfhKcBknu7sqhzz2cudh2sN+1uaM/oPIe8uvJj8li5sP33vStb7UeZBsdzolo/x7/eqi/+INJRfwdMtLfPKlX7Dq8ffz2g2f58f7/s6Wtr194++2/5TV2Yu4+fTPkTbGX+zHcxgH5+YuY33z9lPyFUcHu2uZc9xoQ+j7pnSur5R9/upJrykUDfPHI/dxYd4K/WAsIiIiIjINpOiFbCKT5qL8lQBxH3G4vmU7wWiIy0bY53W01xWfT5bLx9d33cKP9v2NWyrvY13t0zzVvI2dnYeoCzQTjkbiWu/J2tS2m/ds+S5f2XXzhF43EAny95rHuLJwDTNSc0Y87/SsebzYvm/E310197azo/PQ4ESjsXxg9jU09rZzZ+1TJ1S3iMhEGXtBkcRFTaARp3FQOMriXYdxMNtblPDxhpXd9ZR5CnGYoRlpisPNHG/xsJ1ez7buBODsUTq9AFIdbt4283J+duCfHOquZbZ3aGASqx2dB1maMWfUVwRluzP4wdKPELVRXuo8yGONW3isaTM/PfAP/u/AHQCckbWAP6z6Al5X2gnVcV7eafyn/ln2+auZnz7zhK4xFQUiQaoDTYOjOY83z1fKxv6vi8l0X8NG6oOtfHfJByf93iIiIiIiIiJT2VxvCWVpBTzWtIV3lF0Zt/s80vgCGS7PmKP0AP5/9u47vsr6/P/463NG9oYMCAkzIEtAGSq4t3XbWlerta2tVu1yttZZtdqf1g5rHXW0fltHXdg6WycuQBEQkA0hIQPIPklOTs75/P44JzGBjHOyTgLv5+ORB8l97vtzXwk3J9znOtd1xTtj+fH4r/PHTc/zh03PYdkzcTM6PpvHDrieCYm5/RFuj1hr+bDiC/646Tk+rFyFyzhptn7OyT2qw/n0PfFy6UdU+er4djd/VzNTC3i6+G22NpS1dptq64OKLwCYnzE9rPPOz5jG1OQxPLhlId8YeUSHryOKiAwEJb2ipLhxJyNih+E0e/bVbWtMQg6bPNGf6ZUfn9Xp45OS8lhRs2mP7R9VfMHo+Owuq65aXJB3HPdvfoEnCl/npv0u6lGcvkAza2sL+U7+iWHt7zAO9k8Zz/4p4/nx+K9T0VTDe7uWU9Swg2/nHU+SK75HcQAsCP2HYFHFyn0q6dXSirOz3tsTEnN5oeR9PM0NJPbi5xupxwtfZXR8NkeG3p0mIiIiIiIiIuExxnDE8Fk8V/Iu3oCvX0YGWGv5387POGzYzLArOb83+mS+N/pk/NZPla+OXU01VDbVsstXQ7m3kj9seo4zPvklf511LXPTJ/d5zJGw1vLOzs/5w+bn+LRqLVmx6dw46UJOzj6Yoz/8KX/c9DwPzPhZr89T2riL3218hgmJuRyU3vWb0GelFgDwefX6DpNei3atJMWVwPSUcWGd2xjDJWNO5ccr/8A7Oz/nqMwDIv8GRET6gFLuUbK9cWdYyaAx8TkUNpQRsIEBiGpP1loK68vJ7+CXX4tJSfkUNpRR39zYus1v/XxSuYaDw2wllx2bzsnZB/PM9reoa27oUawbPMU02Wampozt0fEZMSmcPuJQLh93JinuxB6t0SI/IZv8+GwW7dq35nptClUldtTeEGBCYjABuLF++4DF9EXNZpZWreXC/BP0LiMRERERERGRHjgy8wDq/V4WV67pl/VX1m6i3FvJ0WG0Ntyd0zgZFpPKxKQ85mVM4aTsg7go/0RemHs7w2JSOP/T2/h36Uf9EHV4Pq5YxcmfXMeFy+6gpHEnv97veyxa8Ce+N/pkcuKGcVHeibxS9jHr63o3A31XUzXnfXobVb46fjft8i67IAFMTMwj3hHLsg7mellrWbRrBQdnTMXl6PoN+22dnH0wI2KH8dCWhRHHLyLSV/QKcJRsb9hJbnwYSa/EEXgDPkoadw1AVHuq8NVQ529gdHx2p/tMSsoDYL3nq1/Oa2oLqWn2cHDG1LDPdVH+idQ2N/Dc9nd7FOuq2i0ATE3uWdKrry0YNp2PKlYN+h7SfamlKrGr9oYAG+oGbq7XE9teJd4RyzdGHjlg5xQRERERERHZmxySPpUY4+LtnZ/1y/r/2/EZBsORw2f22ZqjE7J5Ye7tTEsZx2Ur7uXhLS8P+Oz193Yu54JPf01VUy13T/kh7y34I9/OP544Z0zrPt8d/TXinTH8afPzPT5Pjc/Dtz69nW0N5Tw26zpmpE7o9hiXw8n0lHF8Xr1hj8e2NpRR1LiDBRnhzfNq4Xa4+O7or/Fh5SpW1GyM6FgRkb6ipFcU+K2fEm9F2JVe8FXbuIG2tb4MgPwuk175AO3men0U6vt7cDel1G3NSi1gRsp4Hi98tUeVbatqNhPniGFcJ1VGA21Bxv7U+RtYXrPnfx72VlvqS8iKTe+0NeSYhBxcxskGT+/evRSuyqZaXixZxJkjDyO1l9V7IiIiIiIiIvuqBFcc8zKm8M7Oz/tl/f/t+JQDUgsYFpPap+umxyTzzwN/xYlZ87ht3d+4ee1j+O3AvDn5g10r+e7ndzEhKZf/HHwX54w6usPWjRkxKXxr1HG8VLKILfWRjzipb27komV3sraukIdmXM28jEhei5vAqprNNAV87bYv2rUSCL6hO1LnjjqaZFc8D295OeJjh5pGvzdq3blEpHNKekXBDm8VzdYfXtIr1FZwS0N0kl6FDcGk1+iEzpNe+QlZxDrcrK0rbN32UeUqxiaMICduWNjnMsbwnfyT2Fi/vfWXayRW1W5mcvLobuekDZRDMqZiMD36XiD4n6NvLrmZbQ3lfRxZ/9lUv51xnVR5QfAdP6Pjs9noGZj2hv8s/h/egI+L8k4YkPOJiIiIiIiI7K2OHD6LDZ7iPn+dosxbyYqajT1qbRiOOGcsD8z4Gd8b/TUeK3yVS5ffS6Pf2y/navFJ5RouXnYXo+Nz+L8Df0WaO7nL/b8/5hTcDhd/3vxiROfxBnxcsvz/8VnVOn4//cccmRnZLPOZqQU02WbW1G5tt31RxQpGxA7rdGZ7V5JdCZyXewz/LvuIooYdHe6zqmYz16x6gL8Vvh7x+oNFZVMthy26kmM+/Blv71gW7XBEpA0lvaKguHEnALlhJL1GxA0j1uFmiyfyd3r0ha8qvbI63cdpnBQkjmqt9PJbP4sr13Q7MLMjX8s5mMyYVB4tfCWi46y1rK7dwtTkMRGfs79kxKQwNXkMiyoin+u1w1vFFSt/z0eVq/jusrt6POdsoG32lHTa2rDFhKTcdq0w+0tzwM/ft73OIelTmZSc3+/nExEREREREdmbHTE8mFB5Z2ffvsD/ZvlSAI7pp6QXgMM4uHHSRdw06SJeL1/CmYt/xRc1m/vlXJ9WreWiz+5gZNww/jn7xrCq17Ji0zl31DH8a/u7nSaKdtcc8HPFivt4b9dy7p56KSfnHBxxrLPSCgDazfUK2AAfVnzBgmHTu50L1pnvjD4Jg+HRwv+0brPW8s7OZZy79FZO/Pgani5+mxu+fISXSz/s0Tmi7da1T7CzqRpfoJkLl93Btz+9nXVtumCJSPQo6RUFkSS9HMZBfnx21Cq9tjaUkh2bTpwztsv9JiXlt1Z6rardQk1zfUTzvFrEOtycN+pY3tr5GZsjSPQVNpRT01zP1JTBMc+rxYJh+/NZ1To8ESStAjbAz774E7XN9dw06SLW1W3jJyv/OOjLpat8dezy1XSf9EocxZb6UnyB5n6N5387P6W4cScX5Z/Yr+cRERERERER2ReMTxhJXlwmr5Z9Qpm3stfzsXyBZv6w6Tlu+vKvFCSOah2f0Z++O/prPDzzakq9FZz88bXcuvbxbl+zCdgAr5R9zEkfXcPB713K3ev/0WkbwuXVG/j2Z7eTGZvGP2ffRGZsWtix/XDMqRjgL1te6nbfgA1w9aoHeK18MTdP+g5n5/ZsjvmI2GFkxqS1S3qtqt1Cla+OBcMim+fV1si44ZyScwj/LPofO7xVPF38Fsd++HO+/dkdbPAUcX3BBSw9/CFmp03iZ1/8ic/bnH8oeGfnMp4reZfLxp7O/+b/jhsnXchn1es4/qOr+NWaR6hoqol2iCL7NFe0A9gXbW8IJr1Gxnef9IJgi8NozvQaHZor1pVJSXk8V/IuVb5aPq5YDcBB6ZEnvQAuyDuO+ze/wBPbXuPm/b4T1jGraoPvzpmWPNiSXtP5y5aXWFz5Zdgl5n/d+h/e3bWc2yd/n2/lHQfALWsf554NT3N1wbn9GW6vtPyHb1xi16XvExJzabZ+tjaUMSExt9/ieazwVXLjhnNM5ux+O4eIiIiIiIjIvsIYwzFZs3ms8FXmvHsJya54xiWMZFziSMYn5jIuYSTTUsa2juroyprarfz8i/v5onYzp+bM59b9Lu5xVVGkjsuaw9z0ydy1/h88svU/vFL2Mbfu912Oy5rTbj+/9fNK2Sf8YdO/WFu3jbEJI5iYlMefN7/Inza/wLz0yZw98ii+ln0QCa44vqjZzPmf/po0dzJPzb6JnLiMiOIaGTecb+QeydPFb3H52DM7Pb7R38QNax7huZJ3uWrCOVw8+qQe/yyMMcxKLWB59Vfz6FvGdMzPmNbjdQEuGX0KL5S8zyHvX4Y34GNy0mh+N+1yTsk5pHW22cMzr+bUT37Bd5fdxcJ5d5Ibn9nlmruaqrl17RM0Wz9njzySBcOmRzzmxBdoZqOnmC9qt7C6djOrarawrm4bX8s5mFv3uxiH6bpGpK65getXP8SExFyuGHcWMQ433xt9MmeOOIzfbXyGJ4ve5IWSRfxk/Nc5IHUiO5qqKPdWUu6tpMxbxQ5vFbuaqrl49EmcPuLQiGIP2EC38YmIkl5Rsb1xJymuBJJdCWHtPzZhBO/tWh6VJ7bChjIOGzaj2/1a3o2ztq6IjypWMS5hRMS/3Ftkx6ZzcvbBPFP8NldNOIckV3y3x6yq2YzTOAbkXUGRmJu2H7EON4sqVoSV9FpRs5HfrP8/TsiaywWjjgXg4vyTWFtXyB83P8/EpDxOG7Ggv8PukU2hyrzuK72Cia6NnuJ+S3qtrdvGhxVfcG3Bebgcg2PGm4iIiIiIiMhQd33BBRw9/EA21m9nk6eYjZ7tfFK5mhdK3m/dZ1ryWE4bsYBTcg7ZY569L9DM/Ztf4I+bniPVncSDM67ixOx5A/1tkOZO4s4pl3DWyMO5fvWDfO/zuzk+aw637Hcx2bHp/Lv0I36/6V9sCL128YfpV3JKziE4jZPSxl08V/IezxS/zc9X3c9NXz7KSdkH8eaOpSS54nhq9k17fN/hunTM6Txd/BYPbV3IjZMu2uPxLfUlXLb8d3xRu5kfj/s6V4w9s5c/ieBcrzd2LKHKV0eaO4lFFSuYmJhHVmx6r9admjKWc3OPpsxbwXdHn8yCjD3bJQ6LSeWxWddxxuJfcvGyu3hu7m2dvg64tGotly2/l0pfLfHOWF4u/ZCRccP4xsgj+MbII8lPyN7jmIANsN5TzLKqdSyrXs8XtZtZV7cNb8AHQJwjhsnJo5mROp6/bXudWIebGyZ+u8sE7N3r/8H2xl08N/c2YkPJOwiOOblt8vf4Vt7x3Lb2CW5d+0S74wyG4TEpZMWmU+Wr46YvH+PI4QeQ6k4M6+dZ7fNw/Ec/Jzs2g6smnNPhz1NEgpT0ioLixp0R/fIbnZCDN+CjzFvJiLhh/RhZe41+L2XeSvLj9/ylsbtJyXkArK7dwuKq1ZySM79X5/5O/km8WLqI57a/y4X5J3S7/6razUxIzCXOGdOr8/a1OGcsB6ZNan2XTFc8zQ1cseL3DI9J4+6pP2z9xWWM4deTv8cmTwlXrfozYxJymJE6ob9Dj9jm+u04MB3+J6Ot8aFE1/q6Yo7vfFRcpzzNDWzwbGf/lHGd/nJ/ovBVYh1uzs09OvITiIiIiEjUGGNOAH4POIFHrLW/2e3xw4D7gP2Bc6y1/xrwIEVE9mFxzhgOGz6Dw2j/Bun65kY21ZfwceUqXipZxO3r/s7t6/7O3LTJnJozn6/lHERpYwVXrfozq2q3cFrOfG7Z72IyYlKi9J0EzU6bxCsH3c0jW//D7zY+w9Ef/JSs2HQ215cwMTGP+/f/KSdlz2tXTZQTN4wfjT2Dy8aczpKqL3mm+G3+XfYhKa5Enpp9M3nxPXixI2R0Qjan5xzKk9ve5LIxZzA89qt5YP8u/YhrVj2A0zh4dNZ1fTYDbWboNabl1RuYlz6FxZVrOD/0RuzeumvqD7vdZ2JSHn/e/2dctOxOrljxex6ZdXW7n7e1lr8WvsId6/7OyLjhvDD3dgqSRvFm+VKeLv4ff9j0PL/f9BzzM6bzzdwjSXTG83n1ej6rXs+Kmg3UhtpXproSmZ4yjovyT2Rq8himJo9lbMIIXA4n1lpu+vJRHt76b4bHpHHp2NM6jHVx5Rqe2PY6F+WfwOy0SZ1+P38/8AaWVH5JTbOHrNh0smLTGeZOaX1j9hc1m/nax9fy580vcP3EC8L6Wf5u4zOUNFZgLZz/6W0clD6Fqyecy5z0/cI6XmRfoqRXFGyPMOnVUha+pb50QJNeWxvKgeAv3O6MiB1GsiueF7a/R21zA4ek964EelZaATNSxvN44at8K++4bivcVtVuYX7G9F6ds78syNifuzf8g79ve51zco/G7ej4n91NXz7G1vpSnpp9E2nu5HaPxTjcPDjz55zy8fV87/O7eXneb3pcSddfNteXMio+q927XDqS5IpnROwwNniKIj5Ho7+Jb312O0ur1nJI+lSum3g+M1ML2u1T7fPw3Pb3OC1nQdT/8ywiIiIi4TPGOIH7gWOBImCJMWahtXZ1m90KgYuAqwY+QhER6UyCK45pKWOZljKW740+mS31JSws/ZCXShZxw5ePcNPaRzEY0t1JPDTjKk6IQnVXZ9wOF5eOPY2vZR/Er9f9jR3eKq6dcR4nZM3t8vUoYwxz0yczN30yt+53MQFsWN2KunP5uDN4vuQ9Hin8N9cVnI834OPXa5/giW2vc0BqAffv/9Nu2wBGYkbqeAyGZdXrcRkn3oCvV/O8euLw4TO5ZdLF3PDlI9yx7kl+NelCAGqb67l61QO8UvYxx2XO4Z5pP2qtjDo552BOzjmY4oYdPLv9HZ4tfpsrV/4BAKdxMDlpNGeMOIyZqQUckFrA2IQRnb552hjDzft9h0pfLXeuf5KMmGS+mXtUu30a/U1cu+ov5MYN55oJ3Y8f6SoZNS1lLGeMOJRHC1/h23nHd/v3ubZuG09se43zRx3DTft9h38U/Zc/bXqOs5b8iiOGzeSqgnPYP2V8tzGJ7CuU9IqC4sadHJA6Mez9JyXl4cDwzs5lHJzRszlZPVFYXwYQ1kwvYwwTk/L5tGotAPMypvT6/N/JP4mffPFH3t+1gsOHz+x0v53easq8lUxNHtPrc/aHc3KP4q2dn/LLNY/w4JaX+dn4szltxPx271pZWPIBz2x/mx+PO4uDOvk7HhaTyl9nXcsZi3/JJZ//lmfm3EycM3agvo1ubfJsZ1w3rQ1bTEjKZYOnOKL1rbVcverPLK1ay7fzjuc/pR9x6ie/4MSseVxTcG5rBdmz29+hIeDlovwTI/4eRERERCSq5gIbrLWbAIwxTwGnAa1JL2vtltBjgWgEKCIi4RmTMIIrx53FFWPP5Mu6Ql4qXURTwMcVY88iPSa5+wWiID8hm4dmXt2jYxNccX0Wx/jEXE7OOZgnCl/jpOyDuH71Q6ys2cT3R5/MtQXntc7D6ivJrgQmJObyefUGvAEfTuPgoPTev64XqW/nH8/G+mIe3vpvxiWO5MDUifxg+T0UNpTxi4IL+MGYUztMWuXGZ/KT8d/gynFnsST0uuT+KeOIj/A1M4dxcM+0H1Hlq+PaVX8hzZ3E8VlzWx//w6Z/sbF+O08ecAOJfZDcvHrCOfyn7CN+u+Ep7pt+Raf7tVShJTnjuXrCucQ63Hwn/0TOyT2Kxwtf44EtL3Lyx9dxQtZcfjXpwogrDZsDfpzGoVaJslfR5LsB5mluoMpXx8j48Cu9smLTOTH7IJ4seoMan6cfo2tva0Mo6RVGpRcEk3MA4xNGkt3Lvr8AX8s5mMyYVB4rfKXL/VbVbgaCvYIHo+GxqTw35zYen3U9ya54fvLFHzn+w6t5rewTrLUU1pdx/ZoHOTBtEj8e940u15qcPJrfT7+Sz2s2cO3qB7HWDtB30TVrLZvrSxiTGF7Sa3xiLhs9xRHFf+/Gp3mp9AOuLTiPX0/+Hu8f+id+Ov5s3tu1nGM+/BnXrvoL2xt38rdtrzE7bRLTBun1ICIiIiKdygW2tfm6KLRNRESGKGMMk5NHc13B+dw46aJBm/AabK4YeyYefyMnf3wdW+vLeGTmNfxq0oV9nvBqMSu1gM+r1/P+rhXMSi3ok4q1nvjVxAs5cvgsbljzCKd+8gvqmht46sCb+OHY07pNyjiMg3npk5mXPjnihFeLGIebB2dcxYzUCVy+4j4+rlgFwKqazTyw5SW+MfIIDhs+o5tVwpMbn8nF+SfxfMl7rKzZ1Ol+r5Z/wocVX3D1hHPb/fuJd8Zy6djT+ODQ+/np+LNZtGslZy6+gU2e7WHHsLRqLQe8+z2O/fDnPLzlZXZ6q3v1PcngstlTMmheOx5oSnoNsO2NuwDIjXCg5aVjT6O2uYH/K/pvf4TVocL6UpKc8aS7w/sPyaSkfIA+q0aLdbg5f9RxvLVzGatrt3S63xehpNeUQVrpBcH/5B2VeQD/Oegu/rz/z/BbP5cs/3+c+sn1/HD5PRgMf5z+49bevl05PmsuPx1/Ni+UvM+Sqi/7LMaADVDaWNGjY8ubqvD4G8Ou9CpIzMXjb6TUG975ni1+m99veo5zco/isjGnA8E2iT8d/w3eX/Anvp13PP/a/g4L3r+cLfWlXJSnKi8RERGRfZkx5hJjzFJjzNIdO3ZEOxwREZGI7Jc8mnNzj2Ze+mReOegujsua06/nm5E6gQpfLStqNrIgY2BbG7blcjj50/4/YVryWOak78erB9/dJ92kIpHgiuPxWdeTF5/Fdz+/i+XVG7l61QNkuFNa2y72lR+NPYN0dzK3r/t7h8mJBr+X29Y+weSk0Zw36pgO10h2JfDT8d/g+bm30Rzw840lN7G+rvuRIm/vWMZ5S28l1ZVIoiuO29b9jbnv/YBLPv8t/93xKc0Bf6+/P4me/5R+xOEfXMmiipXRDiUqlPQaYNsbdwJENNMLYP+U8SzImM4jW/9No7+pP0Lbw9aGMkYnZIdd3tqSdDqkD2drXZh/PJkxafxo+e+oCw2e3N2qms3kxWWS5k7qs/P2F4dxcHLOwbx5yL38v6mXsbOpmi9qN3PnlB8wKoJ+zJeMPpkkZzxPF7/VZ7E9U/w2c9/7Ad9YciOvly/Gb8P/5bY59C6ScQkjw9p/QuIoANaHMdfrg10ruXb1g8zPmM7tk7+/x/U4PDaVW/a7mHcW/IFTc+YzL30yJ2TP7WQ1ERERERnEioG8Nl+PCm2LmLX2IWvtbGvt7MzMvpt7IiIiMlDumvpDnp1zK/lhdmDqjVlt5qUvGNZ3r+v1RLIrgYXz7uT/DvwVWX3QSaon0mOSefLAG0h2JXDm4l/yRe1mfj35e33+2mOKO5GfjP8GH1Z8wds7l+3x+AObX6K4cSe37Pedbt8ov1/yaJ6eczNg+ebSm1lbW9jpvi+VLOK7n9/FhMRcXph7Oy/Nu4M3D7mXi/NPYknll1y87DfMe++H3LnuSXY1qfprqPEGfNyx/kkAFleuiXI00aGk1wArDiW9Iq30Arh07OnsaKri+ZL3wtrfG/Dxl80vUeWri/hcEJzplR8f/i/WuWn78eQBN3BSHw4jHRaTyv37/4TN9SVcs+qBDt/1sKp2y6BtbdgZl8PJ2blH8s6CP/DmwfdwSs4hER2f6Irn1BHz+XfpR33W8vLDii9IcSVQ1LCD73/+W45Y9GMe3fpKp8nGtjbVlwAwNsz2hhNC87c21HX9Gsb6uiJ+sPwexiaM4C8zfo7b0fkYwrz4LO6bfgXPzrm138r9RURERKRfLQEKjDFjjTExwDnAwijHJCIistfbLymfOEcMic64dgmwaBkM86VGxg3nyQN/RYorkVNyDuHEPny9s63zRx3DmIQcbl/393bVVdsaynlgy4ucmjOfg8LsqjUxKY9n5tyC0zg4e+nNHXbO+lvh61y58g8cmDaRp2bfxPDYVCA4tuaGSd9m8eEP8vDMq5mROp6Htr7M2UtuVuJriHmi8DW2NZST5IxnefWGaIcTFUp6DbDtjTtxYMiOzYj42AUZ05meMo4HtywMqwrn/63/J3esf5J/Fv0v4nP5rZ9tDeURvZvEGMNhw2fgMH17WR2UMZVrCs7j32Uf8Vjhq+0eq2tuYHN9CVOTh1bSq0Wsw82k5PweHXtu7tE0BLwsLP2wT2L5rHo98zOm8/6CP/Hn/X/G8JhUbl77GAe990N+vfZvFDd03hZms2c7sQ43I+OGhXWu4TGppLgS2dBFpddObzUXLbuTGIeLxw+4nlR3YsTfk4iIiIgMHdbaZuBy4HVgDfCMtXaVMeZWY8ypAMaYOcaYIuAbwIPGmFXRi1hERGTv4HI4OXTY/hyXNafLNxzvayYk5vLhYX/mj9N/3G/niHG4ua7gfNZ7inhm+9ut23+99m84cPCLiRdEtN74xFyenXML8c4YvrnkZlbUbATAWst9G5/lhi8f4ZjMA/n7Ab8kpYPX2twOF8dnzeXRWdfxjwNvpLChjAs+/TVVvtrefaMyICqbavnDpuc4YthMTs45mM9rNuyTc72U9BpgxQ07yYnLCGt20+6MMVw65nQ215fwWtniLvf9uGIVD239NwBv7FgS8blKGytoss2Mjs+J+Nj+8MMxp3Js5mx+ve5vfFq1tnV7yzsWhlqlV1/YP2U8k5NG81Rx5EnN3e30VlPYUMYBaRNxOZycnHMwL8y7nZfm3sHhw2fy18L/cPiiK/nr1v90+ES5ub6U0fE5OE1417UxhoLEXDZ2MFyzOeCnuGEH3/v8Lsq9lTw661ry4rN6/T2KiIiIyOBnrX3FWjvRWjveWnt7aNuN1tqFoc+XWGtHWWsTrbXDrLV9M1BYRERkH/fIzGv43bTLox3GoBPvjO3zN/jv7sSseRyYNol7NjyNp7mBRbtW8mr5J1w+7oyIR+QAjEkYwTOzbyHZlcB5S29lWdV6bl77GPdufIazRhzOgzOuIs4Z2+06B2dM5ZGZ17C+rohvfXp7n3Wbkv5z36ZnqWuu55eTvs2M1AlU+erY2lAW7bAGnJJeA2x7484ePVm1ODF7LmMScnhgy0udZmlrm+v52Rf3Mzohmx+MOZXPqtax0xtZGWrLP4bRA9A3OBwO4+CeaT9iRNwwLlv+OyqaaoCvkl7TQvPE9iXGGM4ZdTQrajayqmZzr9ZaVr0eYI8S9llpBdy//09ZtOBPHDZ8BresfZzvLLtzj+tpU/12xoXZ2rDFhKRcVtVu5sYvH+WSz3/LqR9fz5x3L2HCf8/l4PcvY1n1Bn4//UpmDoKyehERERERERGRvZkxpt+TO9IxYwy/nPgtdjRV8cCWl7jpy0fJj8/m+6NP6fGa+QnZPDPnFtLcyZyx+Jc8Vvgq383/GvdMuyyiYozDh8/kgRk/Z1XtFi5adieeMMagSHRs8mzn79ve4JxRRzMpKa/1dd59scWhnskGWHHjzh7N82rhNE5+MOZUVtRs5MOKLzrc55YvH2d7407um3YFp+cswGJ5a+dnEZ2nsD6Y9Ipkpld/S3Mn8ZcZP6fCV8OVK/+A3/pZVbuZDHdyj9pF7g3OGLGAWIebp4vf6tU6y6rX4TJOpqeM6/Dx3PhM/jrzWm7d72I+qPiC4z+6ivd2LQeCrTC31pcyJiGypNfctMnUNNfz/PZ32ejZToo7kcOHzeTycWdyx+RLWDjvDk7KPqhX35eIiIiIiIiIiMhgNzttEidmzeMPm55jvaeIGyddSJwzpldrjorP5Nk5tzA7bT+uKzifGydd2KPE5rFZs/nj9B/zWdU6Ll52Fw1+b6/iGspKG3dx38ZnKW3cFe1Q9nDn+ieJdbj52fizAZiYmEecI6a12GFfoiatAyhgA5Q07uRrvXwh/6wRh3Pvhqf585YXmT9servH3ihfwjPb3+aKsWdyQNpErLWMjBvGG+VLODv3yLDPsbWhDJdx9ipB1x+mp4zjlv0u5rrVD/KHTc+zqmYLU1PGDooBl9GQ5k7mxKx5PF/yPr+YeEFYpckd+ax6PZOTRxPfxfHGGC7KP5F56VO4fMV9XPDpr/nBmFP5Zu5R+KyfcREmvb6ReySnhpJ2IiIiIiIiIiIi+7LrJ57Pf3cs5ZCMaRybObtP1hwRN4x/zb211+t8LedgmmwzP1n5R77/+W/566xr96nX9Br9TTy89WX+tOkFGgJe3ihfwnNzb+vytdSB9EnFal4vX8JVE84hKzYdCM7qm54yTpVe0r9eKHkfn/UzqpfzieKcMXx39Mm8v2sFK2s2tW7f4a3i2lV/YVryWH48/utAMFFxbOZs3tu1nMYIsvBb60vJjRveo9lj/e3c3KP5+sjDuW/js6yp28rU5H1vnldb54w6mppmD6+Wdz3nrTN+62d59QYOCLON4OTk0fz7oN9wwahjeXDLQr655GYAxiaOjPjc+9IvRxERERERERERkc6MSRjBawf/Px6ccdWgfIP/GSMO5e6pP+S9Xcu5dPk9bPaUsL1xJ7uaqqlrbsAXaO7V+lW+WgI20EfR9g1rLa+VfcLRH/6U3254iiOGz+TOKZewqnYL16x6oNPxQwMpYAPctu5v5MRm8P3RJ7d7bGbqBL6o3dzrv5uhRpVeA8Bay+82Pst9m55lXvpkTh+xoNdrXjDqWO7f/DwPbH6RP8/4GdZarl39F+r8Ddw3/Qpi2iQTjs2awxPbXuf9XSs5Niu8dwkUNpSTP0jmee3OGMPtk7/PFzWb+bKukGn7eNLroPQpjI7P5qmi/3HGiEMjPn59XREefyOzUieGfUy8M5Y7plzCYcNmcPWqBwAYnxB50ktERERERERERESCCpJGRTuELn0z9yi8fh83fPkI/93x6R6PO42DBGcsl4w+lSvHnRV28u657e9y9aoHmJY8ll9P+R77p4zv69Ajtra2kJvXPs4HFSuZlJTHPw68kQWhrmuVTXXcveEfTEkey6VjT4tqnC+VfsCKmo3cO+3yPSrPZqROwLv133xZV9jpWJu9kZJe/azR7+WqVQ+wsPQDvjHyCO6cckm7hFRPpbgTOX/UcTy0ZSFb6kv5uGIV/93xKTdOupCJSXnt9j0ofQrJrnje3LE07KTX1vpSTsk5pNdx9pd4ZywPzriKuzf8k0OH7R/tcKLKYRx8M/do7t7wDzZ7ShibGFmbwc9CfV0PSAuv0qutE7LnMTN1Aus9xQyPTY34eBERERERERERERk6vp1/PFNTxlDYUI7X30RjoAlvwBf88Dexpm4r92x8mi31pdw19QddvhZureXBLQu5Y/2TzEotoKihnFM+vp7zRx3DNQXnkuZOHsDvLKgp4OP2dX/nb9teJ8kZz637XcwFo45r1xHtR2NPZ03dFn6z/v+YlJTHUZkHDHicEMw93LX+/5iWPJYzOyiGmJkyAYDPqzco6SV9o9xbyfc//y3LqtdzXcH5XDrmtD4tTf3u6K/x6Nb/cOvax/moYhWHpE/l4vyT9tgvxuHmiOGz+N+OpQRsoNuBhVW+OqqbPYxOyOmzWPvD2MQRPDDjZ9EOY1D4+sjDuWfjUzy9/S2uKzg/omOXVa8j3Z3M6Pie/X3nxA0jJ25Yj44VERERERERERGRoeXAtEkcmDapw8estfxh03Pcs/FpShp38uDMq0l1J+6xX8AGuG3t3/hr4X84JecQ7p12OV5/E/dufIbHC1/lP2Ufc33B+Zyde2S3r2f3lYANcPWqB3ih5H0uGHUsV084l/SYPRNvxhh+O+VSNnm2c+XK3/PSvDsYn5g7IDG29dfCV9jeuIt7p13e4c8oLz6LDHcyy6s38K2847pdr7KpljvXP8l1BeeTEZPSHyEPCM306idrardy2ie/4MvaQh6acRWXjT29z3uxZsemc9bIw/nvjk9xGMM9037U6RPAsZmz2dFUzedhDK5bUvklAGMTIqsYkujJicvgqOEH8GzxOxH3aP2saj2zUgsGZa9gERERERERERERGTqMMfx4/Nf53bTLWVL1JWcuvoFtDeXt9vEGfFyx8vf8tfA/XJx/En+c/mNiHW5S3IncvN93ePXg3zIhMZdrVv+FMxbfwMqaTQMS+53rnuSFkve5esI53DHlkg4TXi0SXHE8MvMa3MbF95bdTY3PMyAxtiht3MX9m17gmMwDOSRjWof7GGOYmTohrJwAwHMl7/JU8Vs8Xfx2X4Y64JT06gFrLU0BH/XNjVT56tjpraa0cReF9WVs8mzn5dIPOXPxDTRbP/+aeysnZM/rt1guHXsaI2KHcefkH5Abn9npfkcMn4XLOHljx5Iu1wvOH3uGvLhMjhg+s4+jlf50Tu7R7Giq4u2dy8I+psbnYYOnmFmpkbc2FBEREREREREREenIWSMP5+8H3kCZt4LTP/kFy6s3AlDbXM+Fn93By6Uf8ouCC7hp0kV7FHJMTh7Nv+bcyu+mXU5RQzknf3wdC96/nHOW3sLVq/7M7zf+i+e2v8snlWsobazok3gf3vIyD259mQvzjufysWeGdUxufCZ/mfFztjaUceXKP+C3/naP1/g8vFG+hJu+fJSzFv+KxZVr+iRWb8DHD5bfQ4AAN0z8dpf7zkwtYL2niLrmhm7XfaXsYwBeLHm/T+KMFrU37IHypirmvHtJl/tMSx7Lo7Ou7fe2b2MSRvDxYQ90W6WT5k5ibvpk3ixf2mX7u1fLP+GL2s3cM/VHfTJ7TAbOkcNnkRWbzj+L/sdxWXPCOmZ5zUYsllk9mOclIiIiIiIiIiIi0plDMqbx/Nxfc9Fnd3L2kpu4bfJ3ebTwP6yrK+J30y7nrJGHd3qsMYazRh7OsZmzeWLba6yr20ZRww7e3vk55d7Kdvt+Y+QR3D75e8Q5Y3sU5wsl73Pbur9xUvZB3LzfdyLqiDUvYwq37Hcxv1zzML9Z/w+OGn4Ai3at4IOKlSyv2YjfBohzxJDojOPiZb/hmTm3MCV5TI/ihGDRyg1rHmZZ9XoenHEV4xJHdrn/zNQJWCwrajZ2WhEGUOat5NOqdeTFZ7GmbitrawuZlJzf4zijSUmvHkh2xnP1hHNwGRduhwu3ceJyOIkxLlwOF/GOGA4fPpP4Hv4ji1S4/wiPy5zDzWsfY0t9CWM6aF3ot37u2fA0ExJzOXPknoPvZHBzOZycPfII/rz5RUobd4WVcF1WvR6DaR1qKCIiIiIiIiIiItJXJibl8eK827l42V1cterPJDhjeWzWdRweZpexFHciV4w7q922Rn8T2xt3UtSwg/d3LefBrS/zZV1wzFBX3dA68t7O5fz8i/s5KH0K9027AqdxRnQ8wLfyjmN17RYe3LKQB7csxGkczEgZz4/GnsH8jOkckDaRnd4qzlz8K7716a95bu6vGZOQE/F5AP627XWeLn6bK8edxYlhdJibEXrd9/PqDV0mvV4vW4zF8v+mXsZ5n97Ki6WLuDb5vB7FGG1KevVAgituj39oQ8GxWbO5ee1jvFm+lO+POWWPx18q+YD1niLu3/+nPfrHLdF3du5R/GnzCzy7/Z2wrtHPqtYxITGXlA6GSYqIiIiIiIiIiIj0VlZsOs/MvpkHtrzEcVlzmJ4yrlfrxTljGJc4knGJIzls+AzmpE/mp1/8ka99fC337/9T5g+bHtY6K2o28oPl/4+CxFE8MvMa4pwxPY7plv2+w4TEXPLjs5mXPnmP11tz4zN58sAb+PqSG7ng01/z3NzbyI5Nj+gcH1es4pa1j3P08AP52fizwzomPSaZ0fHZLO9mrtcr5R8zITGXgzOmcmjG/rxUsoirJ5yzR+vJoWDoRSw9lhefxeSk0R3O9fIFmrl34zNMSR7N17IPikJ00hfGJORwSPpUnip+i4ANdLmvtZZl1es5QK0NRUREREREREREpB8luOL4+YRv9jrh1ZHjsuawcN6dZMSkcP6nt/Hwlpex1nZ5zJb6Ei789A7S3ck8ccAvel0UEONw893RX+PYrNmdrlWQNIonDrienU1VfPvT26n2ecJev7hhB5cuv5fR8dn8fvoVESWjZqZO4PMukl67mqr5uGIVJ4XyAqePOJSixh0srVob9jkGEyW99jHHZs1mSeWXVDbVttv+7PZ3KGwo46oJ5w7J7K185bxRx7KtoZz/7fisy/22NpRS6atlVurEAYpMREREREREREREpO+NT8xl4bw7OT5rLret+xtXrPw99c2NrY/X+DwsrlzD37e9zi9XP8zZS24mgOXvB/6SnLiMAYtzZmoBD828mg2eIr677C4a/d5uj2n0e/n+57+lyfp4eNY1ESfoZqQWUOLdRWljRYePv1G+lACWE7OC7RKPy5pDnCOGF0vej+g8g4WyG/uYYzPnEMDy1s6vEiKN/iZ+v/FfzEot4OjhB0QxOukLJ2bPY2TcMB7csrDL/T6rWg/AAamq9BIREREREREREZGhLckVz19m/JxrJpzHy6Ufcson13PRZ3dw0Hs/ZNrbF/H1JTfyyzWP8GLp+4xOyOZvB/yS8Ym5Ax7nYcNmcN/0K1lS9SWXrfgdzQF/p/taa7l29YOsqt3C76f/mAk9iHdmanCu14qajR0+/mrZx+THZzMleQwQ/DkelzWHf5d9RFPAF/H5ok0zvfYx01PGkh2bzhvlSzhr5OEA/KPov5R4d3HPtB9hjIlyhNJbboeL740+mVvXPsGyqvXM6qR94bLqdSQ64yhIGjXAEYqIiIiIiIiIiIj0PWMMl487g+kpY/nFmocpbtzJnLT92C95NPsl5bNfUj4j44ZH/XXwU3IOocpXyy/XPMJVq/7M90afjNvhIsa4cDtcrZ8/XfwWL5S8z1UTzuGYzAN7dK5pyWNwGSefV6/nuKw57R6r9nn4oGIlF4/+WrufyekjDmVh6Qe8t2tFj88bLUp67WMcxsGxmbN5vuQ9Gv1NBGyAP21+nkPSp7IgzAF/Mvidk3s09218lge3LuQvaT/vcJ/PqtczI3UCTuMc4OhERERERERERERE+s/hw2fywaH3RzuMLn0r73h2NdVw78ZneL7kvU73OzFrHpePPaPH54lzxrJfUn6Hc73+u2MpPuvnpKyD2m0/fNgM0t3JvFjyvpJeMvgdmzmHJ4ve5KOKVayu28LOpmoemnl1tMOSPpTkiueCvOP4y+aX2FJfypiEnHaPN/i9rKndyg/HnBqlCEVERERERERERET2bT8e93UWDNufiqYamgLN+GwzvkBz6+du4+LMEYfiML2bVDUzdQIvlS4iYAPt1nq17BNGxA5jRur4dvu7HS5Ozj6YZ7e/Q11zA0mu+F6dfyAp6bUPOmTYNBKdcTxf8i7v7Pyco4bPYnbapGiHJX3sO/kn8fCWl3lk67/59eTvtXtsZc0mmq2fWakToxSdiIiIiIiIiIiIyL7NGDMgr83PTC3gyaI32VRf0joXrK65gXd3fc55o47pMKl22ogF/L3oDV4vX9w6Kmko6F16UIakWIebw4bN4KXSD6hu9vDzCedEOyTpB9mx6Zwx4jCeKX6biqaado8tq14PwKzUjud9iYiIiIiIiIiIiMjeYUbqBIB2LQ7f2vkZ3oCPk7IP7vCY2WmTGBWXyUsliwYkxr6ipNc+6tjQwLoTs+YxPWVclKOR/nLJmFNoDDTxt22vt9u+rHo9+fHZDI9NjVJkIiIiIiIiIiIiIjIQJiSOJNEZx/JQMQQEWxtmxqQyO63jbmAO4+C0EQt4v2IFO73VAxVqrynptY86IWsuZ444jF9M/Fa0Q5F+NDEpj6OGH8ATha/R6Pe2bl9WtZ5Zoey+iIiIiIiIiIiIiOy9nMbJ/injWyu9Gv1e3t75GcdnzcVpnJ0ed/qIQ/HbAC+XfThQofaakl77qCRXPPdNv4LRCdnRDkX62Q/HnMouXw3Pbn8XgJLGXZR4d2mel4iIiIiIiIiIiMg+YmbqBFbXbsEb8PHuruXU+72cmH1Ql8dMSspjSvJoXix5f4Ci7D0lvUT2cvPSpzAjZTwPb30Zv/W3zvM6IE3zvERERERERERERET2BTNTC/BZP2tqt/BK2cekuZM4KH1Kt8edlnMoy6rXs6W+ZACi7D0lvUT2csYYfjDmNLbUl/JG+VKWVa0j1uFmSvKYaIcmIiIiIiIiIiIiIgNgZmjczeLKL/nfjk85LnMOboer2+NOGzEfg+HFkkX9HWKfUNJLZB9wYvZc8uKzeHDLS3xavY5pyWOJcbijHZaIiIiIiIiIiIiIDICc2AyyYtN5ZOu/qWmu56RuWhu2GBk3nHnpk3mpZBHW2n6OsveU9BLZBziNk++PPpnPqtfzWdU6Zqm1oYiIiIiIiIiIiMg+wxjDzJQJlHorSHbFM3/Y9LCPPX3EoWys387K2k39GGHfUNJLZB9x9sgjSXMnEcAyK3VitMMRERERERERERERkQHU0uLwmMzZxEbQCeyk7IO4bOzpDHOn9FdofaZfk17GmBOMMWuNMRuMMdd18PjPjDGrjTErjDH/M8aM7s94RPZlCa44Lso7EQeGA9OU9BIRERERERERERHZl8xJ2w+Ak7MPiei4NHcS1xWcT258Zn+E1ae6n1LWQ8YYJ3A/cCxQBCwxxiy01q5us9syYLa1tt4YcylwN/DN/opJZF935bizODF7HiPjhkc7FBEREREREREREREZQPMypvDqQXczJXlMtEPpN/1Z6TUX2GCt3WStbQKeAk5ru4O19m1rbX3oy4+BUf0Yj8g+z+VwMjlZBZUiIiIiIiIiIiIi+6KpKWMxxkQ7jH7Tn0mvXGBbm6+LQts6813g1X6MR0RERERERERERERERPZS/dbeMBLGmAuA2cDhnTx+CXAJQH5+/gBGJiIiIiIiIiIiIiIiIkNBf1Z6FQN5bb4eFdrWjjHmGOCXwKnWWm9HC1lrH7LWzrbWzs7MHPyD0kRERERERERERERERGRg9WfSawlQYIwZa4yJAc4BFrbdwRgzC3iQYMKrvB9jERERERERERERERERkb1YvyW9rLXNwOXA68Aa4Blr7SpjzK3GmFNDu/0WSAKeNcZ8boxZ2MlyIiIiIiIiIiIiIiIiIp3q15le1tpXgFd223Zjm8+P6c/zi4iIiIiIiIiIiIiIyL6hP9sbioiIiIiIiIiIiIiIiAwIJb1ERERERERERERERERkyFPSS0RERERERERERERERIY8Jb1ERERERERERERERERkyFPSS0RERERERERERERERIY8Jb1ERERERERERERERERkyDPW2mjHEBFjzA5g6wCecjiwcwDPJ0OXrhWJhK4XiYSuF4mErhcJ12C6VkZbazOjHYTsnXQPKYOcrhcJl64ViYSuF4mErhcJ12C6Vjq9hxxySa+BZoxZaq2dHe04ZPDTtSKR0PUikdD1IpHQ9SLh0rUi0j/0b0sioetFwqVrRSKh60UioetFwjVUrhW1NxQREREREREREREREZEhT0kvERERERERERERERERGfKU9OreQ9EOQIYMXSsSCV0vEgldLxIJXS8SLl0rIv1D/7YkErpeJFy6ViQSul4kErpeJFxD4lrRTC8REREREREREREREREZ8lTpJSIiIiIiIiIiIiIiIkOekl4iIiIiIiIiIiIiIiIy5O1zSS9jzKPGmHJjzBdtts0wxnxkjFlpjHnZGJMS2n6sMebT0PZPjTFHtTnmwND2DcaYPxhjTDS+H+lfkVwvbR7PN8bUGWOuarPtBGPM2tD1ct1Afg8ycCK9Xowx+4ceWxV6PC60Xc8ve7kIfxe5jTFPhLavMcZc3+YYPbfsA4wxecaYt40xq0PPFz8Obc8wxrxpjFkf+jM9tN2Enjs2GGNWGGMOaLPWhaH91xtjLozW9yT9pwfXy/mh62SlMeZDY8yMNmvpOUYE3UNKZHQPKZHQPaSES/eQEgndQ0ok9sp7SGvtPvUBHAYcAHzRZtsS4PDQ5xcDt4U+nwWMDH0+DShuc8xi4CDAAK8CJ0b7e9NHdK+XNo//C3gWuCr0tRPYCIwDYoDlwJRof2/6iO71AriAFcCM0NfDAGfocz2/7OUfEV4r5wFPhT5PALYAY/Tcsu98ACOAA0KfJwPrgCnA3cB1oe3XAXeFPj8p9NxhQs8ln4S2ZwCbQn+mhz5Pj/b3p4+oXy+HtFwHwIltrhc9x+hDH6GPCH9v6x5yH/+I5Hpp87juIffRjwifX3QPuQ9/RHit6B5yH//owT2B7iH34Y8eXC+D/h5yn6v0sta+B1Tstnki8F7o8zeBs0L7LrPWbg9tXwXEG2NijTEjgBRr7cc2+Df6N+D0fg9eBlwk1wuAMeZ0YDPB66XFXGCDtXaTtbYJeAo4rb9iluiJ8Ho5DlhhrV0eOnaXtdav55d9Q4TXigUSjTEuIB5oAmrQc8s+w1pbYq39LPR5LbAGyCX49/1EaLcn+Oq54jTgbzboYyAt9NxyPPCmtbbCWltJ8Do7YeC+ExkIkV4v1toPQ9cDwMfAqNDneo4RCdE9pERC95ASCd1DSrh0DymR0D2kRGJvvIfc55JenVjFV38B3wDyOtjnLOAza62X4F96UZvHikLbZN/Q4fVijEkCrgVu2W3/XGBbm691vexbOnt+mQhYY8zrxpjPjDHXhLbr+WXf1dm18i/AA5QAhcD/s9ZWoOeWfZIxZgzBKoJPgGxrbUnooVIgO/R5Z9eGrpl9TJjXS1vfJfgOT9D1ItId3UNKJHQPKZHQPaSES/eQ0i3dQ0ok9pZ7SCW9gi4GLjPGfEqwhK+p7YPGmKnAXcAPohCbDD6dXS83A7+z1tZFKzAZlDq7XlzAAuD80J9nGGOOjk6IMkh0dq3MBfzASGAs8HNjzLjohCjRFHph7DngJ9bamraPhd7Va6MSmAxKkV4vxpgjCd6wXDtgQYoMbbqHlEjoHlIioXtICZfuIaVLuoeUSOxN95CuaAcwGFhrvyRYJo4xZiLwtZbHjDGjgBeAb1trN4Y2F/NV2R6hz4sHJlqJti6ul3nA140xdwNpQMAY0wh8Svt3fup62Yd0cb0UAe9Za3eGHnuFYH/uJ9Hzyz6pi2vlPOA1a60PKDfGfADMJvjuGT237COMMW6C//n8P2vt86HNZcaYEdbaklDrifLQ9mI6vjaKgSN22/5Of8Yt0RHh9YIxZn/gEYLzP3aFNnd2HYkIuoeUyOgeUiKhe0gJl+4hpSu6h5RI7G33kKr0AowxWaE/HcANwF9CX6cB/yE4sO2Dlv1DZX01xpiDjDEG+Dbw0kDHLdHR2fVirT3UWjvGWjsGuA+4w1r7J4KDRQuMMWONMTHAOcDCaMQuA6+z6wV4HZhujEkI9dk+HFit55d9VxfXSiFwVOixRIJDZb9Ezy37jNBzwV+BNdbae9s8tBC4MPT5hXz1XLEQ+LYJOgioDj23vA4cZ4xJN8akE7xBfn1AvgkZMJFeL8aYfOB54FvW2nVt9tdzjEgXdA8pkdA9pERC95ASLt1DSmd0DymR2BvvIfe5Si9jzD8JZqiHG2OKgJuAJGPMj0K7PA88Fvr8cmACcKMx5sbQtuOsteXAZcDjBAdCvspXvStlLxLh9dIha22zMeZygr8UnMCj1tpVXR0jQ1Mk14u1ttIYcy/BXwgWeMVa+5/Qfnp+2ctF+NxyP/CYMWYVYIDHrLUrQuvouWXfMB/4FrDSGPN5aNsvgN8AzxhjvgtsBc4OPfYKcBKwAagHvgNgra0wxtxG8HkH4NZQb3/Zu0R6vdwIDAP+HLzXodlaO1v/fxH5iu4hJRK6h5RI6B5SwqV7SImQ7iElEnvdPaQJtmMUERERERERERERERERGbrU3lBERERERERERERERESGPCW9REREREREREREREREZMhT0ktERERERERERERERESGPCW9REREREREREREREREZMhT0ktERERERERERERERESGPCW9RESk3xljbjbGXNVPa19kjPlTf6wtIiIiIiIiA0/3kCIi0lNKeomIiIiIiIiIiIiIiMiQp6SXiIj0C2PML40x64wxi4BJoW3jjTGvGWM+Nca8b4zZL7Q92xjzgjFmeejjkND2F0P7rjLGXNJm7e+E1l4MzG+zPdMY85wxZknoYz4iIiIiIiIy6OkeUkRE+oKx1kY7BhER2csYYw4EHgfmAS7gM+AvwInAD621640x84A7rbVHGWOeBj6y1t5njHECSdbaamNMhrW2whgTDywBDgdigE+AA4Fq4G1gmbX2cmPMP4A/W2sXGWPygdettZMH9JsXERERERGRiOgeUkRE+oor2gGIiMhe6VDgBWttPYAxZiEQBxwCPGuMadkvNvTnUcC3Aay1foI3IgBXGmPOCH2eBxQAOcA71todobWfBiaG9jkGmNJm/RRjTJK1tq7Pv0MRERERERHpK7qHFBGRPqGkl4iIDBQHUGWtnRnOzsaYIwjegBxsra03xrxD8Kanu3McZK1t7HmYIiIiIiIiMgjoHlJERCKmmV4iItIf3gNON8bEG2OSgVOAemCzMeYbACZoRmj//wGXhrY7jTGpQCpQGbpZ2Q84KLTvJ8Dhxphhxhg38I02530DuKLlC2PMzH77DkVERERERKSv6B5SRET6hJJeIiLS56y1nwFPA8uBVwn2Ugc4H/iuMWY5sAo4LbT9x8CRxpiVwKfAFOA1wGWMWQP8Bvg4tHYJcDPwEfABsKbNqa8EZhtjVhhjVgM/7K/vUURERERERPqG7iFFRKSvGGtttGMQERERERERERERERER6RVVeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQ54p2AJEaPny4HTNmTLTDEBERERGRPvLpp5/utNZmRjsO2TvpHlJEREREZO/S1T1kvya9jDEnAL8HnMAj1trf7Pb4RcBvgeLQpj9Zax/pas0xY8awdOnSfohWRERERESiwRizNdoxyN5L95AiIiIiInuXru4h+y3pZYxxAvcDxwJFwBJjzEJr7erddn3aWnt5f8UhIiIiIiIiIiIiIiIie7/+nOk1F9hgrd1krW0CngJO68fziYiIiIiIiIiIiIiIyD6qP5NeucC2Nl8Xhbbt7ixjzApjzL+MMXn9GI+IiIiIiIiIiIiIiIjspfoz6RWOl4Ex1tr9gTeBJzrayRhziTFmqTFm6Y4dOwY0QBERERERERERERERERn8+jPpVQy0rdwaFdrWylq7y1rrDX35CHBgRwtZax+y1s621s7OzMzsl2BFRERERERERERERERk6OrPpNcSoMAYM9YYEwOcAyxsu4MxZkSbL08F1vRjPCIiIiIiIiIiIiIiIrKXcvXXwtbaZmPM5cDrgBN41Fq7yhhzK7DUWrsQuNIYcyrQDFQAF/VXPCIiIiIiIiIiIiIiIrL36rekF4C19hXgld223djm8+uB6/szBhEREREREREREREREdn79Wd7QxEREREREREREREREZEBoaSXiIiIiIiIiIiIiIiIDHlKeomIiIiIiIiIiIiIiMiQp6SXdKjKVxftEERERERERKQLVb46/NYf7TBERERERAYNJb1kD0UNO5j1znf5tGpttEMRERERERGRDtQ3NzL//cv4Z9Fb0Q5FRERERGTQUNJL9lDmrcBvA5Q0VkQ7FBEREREREenAhvpiapsb+KJ2U7RDEREREREZNJT0kj14Az4AmkJ/ioiIiIiIyOCyvq4IgML6sihHIiIiIiIyeCjpJXvw+oPJLp9tjnIkIiIiIiIi0pENnmIAtjYo6SUiIiIi0kJJL9mDN9AEqNJLRERERERksNrgCVZ6bW/ciS+gNyyKiIiIiICSXtKBr9ob6sZJRERERERkMNpQV4zTOPDbAMWNO6MdjoiIiIjIoKCkl+xBSS8REREREZHBqyngY0tDKQemTgI010tEREREpIWSXrIHtTcUEREREREZvDbXl+K3AY7OPBCArQ2lfbb2yppNNPqb+mw9EREREZGBpKSX7KGl0stnVeklIiIiIiIy2GyoC87zmj9sGrEOd59VelX7PJz6yfU8X/Jen6wnIiIiIjLQlPSSPbQkvbyq9BIRERERERl01nuKMBgKEkeRF5/F1oa+SXrVNHvw2wA7m6r7ZD0RERERkYGmpJfswesPVXppppeIiIiIiMigs8FTzKi44cQ7Y8mPz2ZrH1V61fu9ANQ21/fJeiIiIiIiA01JL9mDZnqJiIiIiIgMXhs8RUxIGgXA6IRsChvKsNb2et0GfyMAdc0NvV5LRERERCQalPSSPbS0NWxSpZeIiIiIiMig4rd+Nnq2U5AYSnrF5+DxN7KrqabXa7dUenmU9BIRERGRIUpJL9lDS9LLZ5X0EhERERERGUy2NezAG/AxITEXgPyEbAC2NpT2eu2v2hsq6SUiIiIiQ5OSXrKHlvaGXrU3FBERERERGVQ2eIoBvmpvGB9MehU2lPd67fqW9oZ+zfQSERERkaFJSS/ZQ2ull9obioiIiIiIDCob6ooAWiu98uKzANha3/tKr4bW9oaNvV5LRERERCQalPSSPXj9LTO9VOklIiIiIiJ9zxhzgjFmrTFmgzHmuk72OdsYs9oYs8oY84822/3GmM9DHwsHLurBYYOnmMyYNNLcSQDEOWPIic1ga31Zr9f+qr2hKr1EREREZGhyRTsAGXxa2huq0ktERERERPqaMcYJ3A8cCxQBS4wxC621q9vsUwBcD8y31lYaY7LaLNFgrZ05kDEPJus9RRSEqrxajE7IprChL5JeofaGmuklIiIiIkOUKr1kDy3tDb1WSS8REREREelzc4EN1tpN1tom4CngtN32+T5wv7W2EsBa2/uBVXsBay0bPEWt87xajI7PYWsfJL1a2hvW+ZX0EhEREZGhSUkv2UNL0kvtDUVEREREpB/kAtvafF0U2tbWRGCiMeYDY8zHxpgT2jwWZ4xZGtp+ekcnMMZcEtpn6Y4dO/o0+Ggq81ZS29xAQWL7pFd+Qjbl3srWpFVPtVR6eQM+3Q+KiIiIyJCkpJfsQe0NRUREREQkylxAAXAEcC7wsDEmLfTYaGvtbOA84D5jzPjdD7bWPmStnW2tnZ2ZmTlAIfe/9Z4iACbs3t4wPhuAwobeFcTVt0maeZobe7WWiIiIiEg0KOkle/D6VeklIiIiIiL9phjIa/P1qNC2toqAhdZan7V2M7COYBIMa21x6M9NwDvArP4OeLDY4An+mPZob5gQTHptrS/t1fptK8XU4lBEREREhiIlvWQPLckuVXqJiIiIiEg/WAIUGGPGGmNigHOAhbvt8yLBKi+MMcMJtjvcZIxJN8bEttk+H1g9QHFH3Ya6IlJcCWTFpLXbnt9a6dW7uV4t7Q0B6pqV9BIRERGRoccV7QBk8GkMJb28VkkvERERERHpW9baZmPM5cDrgBN41Fq7yhhzK7DUWrsw9NhxxpjVgB+42lq7yxhzCPCgMSZA8E2cv7HW7jtJL08xBYmjMMa0257uTibZFU9hfW+TXl9VetU21/dqLRERERGRaFDSS/bgDai9oYiIiIiI9B9r7SvAK7ttu7HN5xb4Weij7T4fAtMHIsbBaL2niKMzD9hjuzGG0fE5bO11pZeXBGcs9X6vZnqJiIiIyJCk9oayB2+gCVB7QxERERERkcGiylfLzqZqJiSO6vDxvPisPpjp1UhmqHVirV+VXiIiIiIy9CjpJe1Ya1XpJSIiIiIiMsisrysGYEJiboePj07IoahhB37r7/E56v1esmLTAc30EhEREZGhSUkvaacpNMfLbZz4rJ9gVxERERERERGJpvWeIgAKOqn0Gp2QTZNtprSxosfnaJv08ijpJSIiIiJDkJJe0o7XH2xtmOxKAL5KgomIiIiIiEj0bPAUEeeIYVR8ZoeP58dnA/RqrlewvWEqALVKeomIiIjIEKSkl7TT0towqSXppRaHIiIiIiIiUbehrpjxiSNxmI5v40eHkl6F9T1Lellrqfd7SXTFk+iMoy7CmV4Nfm+PzisiIiIi0peU9JJ2WpJeKaGkly+gSi8REREREZFo2+Ap7rS1IcDIuOG4jLPHlV5Nthm/DZDgjCXRFU9dc2PYx9b4PMx8+7s8t/3dHp1bRERERKSvKOkl7XgDu7U3VKWXiIiIiIhIVHmaGyhq3MGEpM6TXi6Hk9y44WytL+3RORr8wSRXvDOOZGc8dc3hV3qVeStpCHj5R9F/e3RuEREREZG+oqSXtPNVe8N4AJpU6SUiIiIiIhJVG+u3AzAhMbfL/UYn5FDYUN6jc9SH2hMmOGNJcsVTF8FMr+pmDwBLqr7scXvFSKkriYiIiIh0REkvacfrDya9Wiu9rG4kREREREREomlDXTEQRtIrPrvHlV5tk16Jrnjq/OEnvWp8ntbPXypd1KPzR6K0cRdT37qQDyu+6PdziYiIiMjQoqSXtPNVe8NgpZfePSciIiIiIhJd6z1FuIyTMQk5Xe6Xn5BNdbOHKl9dxOeoD7U3THDGkRxhpVdNqNIrJzaDF0rex1ob8fkjsbxmI42BJlbXbunX84iIiIjI0KOkl7TT0s5QM71EREREREQGhw2eIsYk5BDjcHe53+j4bAAKGyJvMdjQHKz0infGkuRMiKy9YajS64K849jgKWZl7aaIzx+JdXXbgOAsMRERERGRtpT0knYaQ5VeSaGkl1dJLxERERERkajaUFfcbWtDCM70Atjag7la7Wd6xUXW3jBU6XVu7tHEGBcvbH8/4vNHYm0o6VWupJeIiIiI7EZJL2mnJcml9oYiIiIiIiLR1xTwsaWhNKykV158FtDTpNdX7Q2TXMFKr3DbFFb7PMQ5YsiMTePozANZWLqI5oA/4hjCpUovEREREemMkl7SzldJr5b2hkp6iYiIiIiIRMvm+lL8NkBBUl63+ya54hkek9qj9oYtlV7xzliSXPE0W3/rzOfu1DR7SHUnAnDGiEPZ0VTNBxUrI44hHL5AMxs9xYAqvURERERkT0p6STve3dob+qySXiIiIiIiItGyoa4IIKxKLwjO9SqsL434PA1t2xs6g50/6pobwzq22uchxRVMeh2ZeQAprkSeL3kv4hjCsaW+FJ/1k+pKVNJLRERERPagpJe04/W3r/TSTC8REREREZHo2RCqahqfMDKs/fMTstnag0qvhkBL0iuOpFC7+zp/fVjHVrep9Ip1uDk552BeK1uMpzn8uWDhapnnNX/YdGqa61uTdSIiIiIioKSX7Gb3mV5NSnqJiIiIiIhEzXpPEXlxmSS44sLaPz8+m+2NuyJ+A+NX7Q1jWt8EWRtm0qqmTaUXwBkjDqMh4OWNHUsjiiEc6+q2YTDMz5gGaK6XiIiIiLSnpJe009LesOUmx6eZXiIiIiIiIlGzwVPEhKRRYe8/OiEHi6W4YUdE56n3NxLrcOM0ThKdwQRbuJVabSu9AOakTWJUXCYvbO/7Fodr6woZnZDN6PgcQHO9RERERKQ9Jb2kHW/Ah8s4iXfEAtCkpJeIiIiIiEhUBGyAjZ7tYc/zAsiPzwJga31kLQ4b/F4SQsmupAgrvap9HlLbVHo5jIPTRyzgvV3L2eGtiiiO7qz3FDEpKY+s2DRAlV4iIiIi0p6SXtKON9BErMON2+ECoMkq6SUiIiIiIhIN1T4P3oCP3LjMsI8ZnRCsgNraUBrRuer9jSQ4g29+TG6d6dV90itgA9Q217er9AI4fcShBLAsLP0goji64g342FxfwsTEPLJjMwBVeomIiIhIe0p6STtev49Yh5vYlqSXZnqJiIiIiIhERYWvBoCMmOSwj8mKSSPOEUNhQ2SVXvV+L/GhpFdiS9IrjEqv2uYGLLbdTC+AiUl5TEseywsl70cUR1c2ebbjtwEmJeWT5k4ixrhU6SUiIiIi7SjpJe14Az5iHTFfVXop6SUiIiIiIhIVFU21AGS4U8I+xhhDfnx2r9obJjvDT3rVNHsASHUn7fHYmSMPY0XNRjZ4iiOKpTNr6woBmJg0CmMMWbHpqvQSERERkXaU9JJ2vIEmYp3B4cVO48CnmV4iIiIiIiJRUekLJr3SI6j0AhidEHnSq217w3hnLA4Mdc313R5X4wsmvVJCc8DaOiVnPg4ML/ZRtde6um24jJNxiSMByIpNU6WXiIiIiLSjpJe00xRoJtbhBiDGuFXpJSIiIiIiEiUVTcH2hunuyJNehQ1lWGvDPqbe721NehljSHLFhzXTq7qLSq/s2HQWDNufF7a/F1EsnVlbt40xCTnEhO5Zs2MzVOklIiIiIu0o6SXteANNrTcQboeLJqtKLxERERERkWio8LW0N4ws6ZUfn01joInypqqwj2nwe4kPtTcEgkmvcNobdlHpBXDGiEPZ1riDpVVrw46lM+vqtjEpKb/1a1V6iYiIiMjulPSSdhoDvtZKr1iHiya1NxQREREREYmKyqYaYh1u4kMVWOEaHZ8DwNb60rCPadveECDJmRBW0qurSi+AozMPBGBJ1Zdhx9KRBr+XwoZyJibltW7Ljs2gptlDo9/bq7VFREREZO+hpJe04w34iHXEAMFKL830EhERERERiY4KXy0Z7hSMMREdNzohG4DChvKwj2nb3hAgyRUXVnvD7iq90txJZMakscmzPexYOrLeU4TFMqlN0isrNh2AMm9Vr9aOlvrmxmiHICIiIrLXUdJL2vH6m76a6eXQTC8REREREZFoqWiqJSMmstaGACPjhgFQ2lgR9jF7tjcMv9LLQXAGWGfGJ45ko6c47Fg6srZ2G8BulV7BpNdQnOu1traQqW9fyNrawmiHIiIiIrJXUdJL2vG2aW/oNi68SnqJiIiIiIhERaWvhvQI53kBxDpicBsntaHWg93xWz/egG+3Sq94apvruz22xuch2ZWAw3T+8sL4xFw2eIqx1oYVT0fWebYRY1yMCbVuhLaVXkMv6bWydjN+G2BrQ1m0QxERERHZq/Rr0ssYc4IxZq0xZoMx5rou9jvLGGONMbP7Mx7pnjfgI84ZbG8Y63Djs2pvKCIiIiIiEg2VvjoyYlIiPs4YQ7IrIaykFQRbGwIktK30csaHXemV4k7scp/xiSOpbvZQ4asJK56OrKvbxvjEXFwOZ+u2oVzpVVgfTHaF8zMWERERkfD1W9LLGOME7gdOBKYA5xpjpnSwXzLwY+CT/opFwucNtG1v6KJJM71ERERERESioqKphoweVHoBPUp6xe9W6eUJY6ZXtc9DqqubpFdCLgAbezHXa13dtnbzvADS3cm4jZMyb/htHAeLwlCFVzg/YxEREREJX39Wes0FNlhrN1lrm4CngNM62O824C5AE1wHgXbtDR0ufEp6iYiIiIiIDLjmgJ/qZg/pPaj0gsiSXg3+4O142/aGya546pobCdhAl8eGU+k1LnEk0POkV21zPcWNO9vN84JgRVtWbDrl3qoerRtNLUmvWlV6iUgPLCz9gKeK/hftMEREBiVXP66dC2xr83URMK/tDsaYA4A8a+1/jDFX92MsEiav30esI9jeMMbhpr5ZuUgREREREZGBVuWrA+hxpVeKK4GaXrQ3THTGY7HU+70kueI7PbbG52FCYm6X64+KH06sw80mT3FY8exufV0RwB5JLwjO9RqKlV7bGsoB8CjpJSIR8jQ3cP3qB6ltbsAC5446OtohiYgMKv0606srxhgHcC/w8zD2vcQYs9QYs3THjh39H9w+ylrbrr2h27jwBnxRjkpERERERGTf0zL/Kj2mh+0N3T2Z6dW2vWEC0P3MqepmD6ndVHo5jZOxCSPY0MNKr7V1wffTdpT0yh6ClV6Nfi9loTlktWpvKCIRer7kfWqbG5icNJpfrHmId3Yui3ZIIiKDSn8mvYqBtv8jHRXa1iIZmAa8Y4zZAhwELDTGzN59IWvtQ9ba2dba2ZmZmf0Y8r6t2foJYIl1Biu9Yh1ufFbtDUVERERERAZaZVMtABnu/m9vWB9qbxi/W3tDgLpukjI1Ycz0gmCLw031PUt6ravbRpwjhvz4rD0eG4qVXtsavnozryq9RCQS1lr+tu01piWP5bm5t7FfUj6XLr+XL2o2Rzs0EZFBoz+TXkuAAmPMWGNMDHAOsLDlQWtttbV2uLV2jLV2DPAxcKq1dmk/xiRdaKnqaqn0inG4aNJMLxERERERkQFX4QslvXpa6RXRTK8O2hu2JL26WMMb8NEYaOp2phfAhMRcChvKaOpBN5G1dYUUJI3CYfZ8CSM7Np3qZg+N/qaI142Wlnle0H0lnYhIW59UrmZt3TYuzD+BJFc8j826nlR3Ihctu5PiBnXHEhGBfkx6WWubgcuB14E1wDPW2lXGmFuNMaf213ml55p2S3q5HS58SnqJiIiIiIgMuNb2hr2Y6VXX3EjABrrdt6P2hsnOlqRX53Oea3weAFJdSd2eY1zCSPw2wNb6sm733d06TxGTOmhtCMFKL4DyULvAoaBlnldefFa3lXQiIm09se110txJnJYzH4CcuAyemPULGvyNXPjZnVSHnpdFRPZl/TrTy1r7irV2orV2vLX29tC2G621CzvY9whVeUXXV5VewfaGMQ63ZnqJiIiIiIhEQUt7w3R39wmljiS7ErHYsCqJWtobtp/p1X2lV01z8MXVFHdCt+cYn5gLwEZPcTd7tlflq6XcW8nExI6TXtktSa+mqojWjabChjLiHbGMSchRpZeIhK20cRevlX/CN0ceSVyb5+tJyfk8NONqNtdv5wef/7ZHFbUiInuTfk16ydDiDQTbQcQ4XME/jUu/KEVERERERKKgwldDgjO23QubkUh2BRNR4bQ4bGlvGN9Be8PaLpIy1ZFUeiWOAGCDJ7K5XmvrigCYlJTf4eMtlV5ljUNnrldhfTl58VkkOeOV9BKRsP1f0X8JWMsFecfv8dj8YdO5e+qlfFi5imtW/QVrbRQiFBEZHFzRDkAGD69/z0ovn1V7QxERERERkYFW0VRLhjulx8dHkvTqsL1hKOnl6aL9XiSVXsmuBLJj09lUH1ml17q6QgAmJe9dlV75CVkkuZT0EpHwNAV8/KPoTY4cPovRCdkd7nPWyMMpbtzJ/9vwFOMTR3LFuLMGOEoRkcFBlV7SqnG3mV4xDhdNmuklIiIiIiJ9zBhzgjFmrTFmgzHmuk72OdsYs9oYs8oY84822y80xqwPfVw4cFEPrEpfLekxPZvnBcGZXgA1YSW9GnEbJ27HV++LTQod31VSprXSK8wWjOMTRrIxwkqvdXXbSHbFMyJ2WIePp7uTcRknZd6hUellrQ0mveKzSXLFd5lUFBFp8VrZYnY0VXNR/gld7nfF2DM5NnM2D215Wd2bRGSfpaSXtGppbxjrDCa93A43zdYf1uBjERERERGRcBhjnMD9wInAFOBcY8yU3fYpAK4H5ltrpwI/CW3PAG4C5gFzgZuMMekDF/3AqWiqIcPd86RXpJVebVsbQvDNkG7j7PL41kovV/eVXhCc67XRsz2itltr67YxMTEPY0yHjzuMg6zYNMq9lWGvGU0Vvhrq/d5g0ivU3lBtyESkO49ve5UxCTkcNmxGl/sZYzh/1LFUN3t4d+fyAYpORGRwUdJLWnkDu7c3DL7LT9VeIiIiIiLSh+YCG6y1m6y1TcBTwGm77fN94H5rbSWAtbY8tP144E1rbUXosTeBrt/2PkRV+urIiOlFe0N3JDO9Gtu1NmyR5ErA42/s9LiWSq8UV2JYMY1LHElNs4edTdVh7W+tZW3dNgqSOm5t2CIrJp2yIZL0KqwPXsr5CdkkuRIIYFtnqomIdGRVzWaWVq3l23nH4zDdv5R76LD9SXcn81LpogGITkRk8FHSS1q1JL3iWtobmmDSS3O9RERERESkD+UC29p8XRTa1tZEYKIx5gNjzMfGmBMiOBZjzCXGmKXGmKU7duzow9AHTqWvhvQBrfTqKOkV3+Xx1c0eYh1u4pwxYcU0ITH4V7UpzBaHO5uqqfTVMqmbpFd2XPqQqfQqbCgDIC8+iyRXsLquTi0ORaQLT2x7nThHDF8feURY+7sdLk7OPpg3ypfg0dxAEdkHKeklrbz+UHvD1plewT/VA1hERERERAaYCygAjgDOBR42xqSFe7C19iFr7Wxr7ezMzMz+ibAfNQV81DY39KrSK6KZXs3ejiu9Qu33OlPt84Q9zwuC7Q0BNtQXh7X/urpgfnPi3lTpFUp65cdnhTU3TUT2bVW+Ol4seZ8zRhxKWgTPt6eOWEBjoIk3dyztx+hERAYnJb2k1e7tDd1qbygiIiIiIn2vGGibxRgV2tZWEbDQWuuz1m4G1hFMgoVz7JBX6asD6FWlV5wjBlc3M7laNPgbO6306qpKoKbZE/Y8L4CRccOIc8SEXem1NpT06r7SK4MqX13rPe1gtq2hnMyYNOKdsSQ6VeklEg3FDTtYVbM52mGE5dnit2kMNHFhfmSdfOekTWJk3DBeLFGLQxHZ9yjpJa1ak17O3Su9lPQSEREREZE+swQoMMaMNcbEAOcAC3fb50WCVV4YY4YTbHe4CXgdOM4Yk26MSQeOC23bq1Q21QCQ0YuklzGGZFcCtb7w2hsmhBIwbSW74qntIiETaaWXwzgYlziCjZ4wK70820hzJ5EZk9blflmhx4dCi8PC+jLyE7KBr1pQqtJLZGBdu/pBzlh8Q2tifbAK2AB/2/Y6c9L2Y0rymIiOdRgHp+Ys4L1dy6kI/U4REdlXKOklrbyB3dsbtlR6Df53y4mIiIiIyNBgrW0GLieYrFoDPGOtXWWMudUYc2pot9eBXcaY1cDbwNXW2l3W2grgNoKJsyXAraFte5UKXy1Ar9obQjCpEu5Mr47aGya6um5vWNPsITWCSi+AcQkj2RhBpdekpDyMMV3ulx2bAQyRpFdDOfnxWQAktsz0UtJLZMBU+zx8WPEFjYEmLlt+Lw1+b7RD6tS7O5eztaGMC/Miq/JqcfqIBTRbP/8p+7iPIxMRGdyU9JJWu7c3jDHBpJfPqtJLRERERET6jrX2FWvtRGvteGvt7aFtN1prF4Y+t9ban1lrp1hrp1trn2pz7KPW2gmhj8ei9T30p4qmYNIrPYIqqo6kuBLCmunVaXvDMGZ6pbgSI4ppfGIu2xrKu21FaK1lfd02JiZ23doQICs2DYByb1VEsYSrzFvJMR/8LOwKtc40BXxsb9zZmvRKdqrSS2SgvbXzM5qtn6smnMMGTzE3fflotEPqUJWvlrs3/IPMmFROyJ7bozUmJ42mIHEUL6nFoYjsY5T0klZNrUmv3dsbqtJLRERERERkoFT6gq2o0ge00quj9oYJ3Vd6uSNNeo0kgGVLfWmX+xU17qCmuZ6J3czzgq8qvcq8/VP0t7TyS9Z5trGsekOv1tneuJMAlvz4YHvDlkovj2Z6iQyY18sXkxWbzuVjz+BHY0/nqeK3eKHk/WiH1U5pYwVfX3wTGzzF3DX10tbX5yJljOH0EQtYXLWG4oYd3e7f6G+icRBXvomIhEtJL2nlDfhwYHAZJwDuUHtDr2Z6iYiIiIiIDJi+qvQKN+nVEOi8vWFDwEtzwL/HY9ZaanpY6QV0WzX1evkSAA4dtn+3a2bEJOMyzn5rb7jeUwT0vn1iYUM5QGvSq2WmV60qvUQGRKO/iXd2LuO4zNk4jIOfjf8ms9Mm8YvVD7EpzLar/a2wvoyvL7mRosZyHp91PcdkHtir9U7JmQ/AwtIPu9yvxufh+I+u4ruf392r84mIDAZKekkrr7+JGIe7tV96yztJfEp6iYiIiIiIDJgKXw3Jrvgev7u/RThJL2stDf6mDpNeya54ADz+xj0eq/M3EMCSGmFiblzCCKD7pNe/Sz9gavIYxiWO7HZNh3GQGZNGWT8lvdbVbQP6IOlVH0x65SUE2xvGOWJwYPAo6SUyIBZVrKTe7+X4rGC7QJfDyZ/2/wluh4sfrfgdjf6mqMa3tm4bZy75FTXNHp6afRPzh03v9ZpjEnKYlVrAwtLOWxxaa7l29YNsri/h/V0rWF27pdfnFRGJJiW9pJU34GttbQgQG6r0UntDERERERGRgVPZVEu6u3etDQGS3d0nvRoDTVgs8R20N0xyBpNedR2sUePzAMG5YZFIdMUzInYYG7uoqihq2MFn1es5OeeQsNfNik3r90qvHU1VvVqnsKGMGOMiOzYdCLYeS3IlUKv2hiID4o3yxSS74jk4Y2rrtpFxw7ln6o9YVbuFO9b9PWqxLa/ewDeW3AjAs7NvYWZqQZ+tfVrOAlbVbmlN4O/uyaI3+U/ZR1w65jTiHDE8Ufhan51bRCQalPSSVo0BH7HOmNav3SaY9PJZVXqJiIiIiIgMlApfLRnu5F6vk+JKoLa5gYANdLpPfaiKq7P2hkCHc72qm4NJr0grvSA416urSq9/h9pwnZwdftIrOzajXyq9mgP+1rZnvW9vWMao+EycoZECAEmuOFV6iQwAv/XzZvlSjhp+wB5VtMdmzeb7o0/m8W2v8WrZJwMe24cVX3DO0ltIdiXw3JzbmJSc36frn5xzMA4MC0s/2OOx1bVbuHXt4xw+bAbXFpzH6SMO5YWS96ny1fVpDCIiA0lJL2nlDTS1q/Rq+U+AV5VeIiIiIiIiA6bSV0tGTO+TXsmuBCy2w/aELer9XgASOqj0amlvWNdBJVJPK70gmPTaVL8da22Hj79c9iEzUsYzOiE77DX7q9Jra0MpPusPzQyr6tVa2xrKW+d5tUhyJnSYVBSRvrW0ah27fDUcnzWvw8evLTiPGSnjuXrVnymsL6O2uZ6ihh18UbOZD3at5JWyj/lH0X9ZUbOxz2LyWz/PFr/DhZ/dQW5cJs/NuS2i571wZcWmM3/YdF4sWdTuedfT3MBly+8l1Z3E76ZdgcM4uDDveBoDTfxr+zt9HoeIyEBxRTsAGTx2b2/obm1vqEovERERERGRgVLZVMvExLxer5McSkjVNte3fr67lqRXfAeVXkmhY/q+0iuX2uYGypuqWlv9tdhSX8LKmk3cMPHbEa2ZHZtBha+WpoCv17PQ2lpfF2xtOCu1oNdzbgrry5g1on3LsiRXXIdJRRHpW2+ULybGuDhi+MwOH49xuLl//59y0sfXsGDR5Z2uE+tws3DenUxOHt3jWPzWz8ulH/KHTc+xwVPMAakFPDbretL74M0OnTk9ZwE/X/VnPq/ewKy0Aqy1/GLNw2ypL+Wp2TcxPDYVgKkpY5mTth9PFL7Gxfkn4TCqlxCRoUdJL2nl9fuIc3zV3rAlAeZT0ktERERERGTAVPhq+qzSC6DWVw97FnIB0NBFe8OkUPVXbQdJr95Ueo1LHAnAJs/2PZJe/y79CAi244pEVmidHd4qcuMzI46pMy3zvA7JmMqSqi/xNDe0tn2MRJWvjupmD3nxWe22J7m6n7smIr1jreW18sUsGDadpC7+/eYnZPPkgTfwZvlSUt1JpLoTSXUlkuZOItWdhMM4OG/prfxoxe94ed6dET8X+ALNvFiyiD9tfp7N9SVMSsrjT/v/hK9lH9Su7Wl/OD5rLr9Y8zAvlr7PrLQCnt3+Di+UvM/Pxp/NQW1mnAFcmH8Cl6+4j3d3LufIzFn9GpeISH9Q0kta7d7esGWmV5PaG4qIiIiIiAyIRr+Xer+XdHdKr9dqSUjVdJFU6aq9YUull6eDSqTeVHpNSMwFYIOnmIN3e7F1YekHHJg2iZFxwyNasyV5Vuat7NOk17q6bYyKy2R0wggAdjRV9Sjpta2hHGCP9oaJzjhKGnf1PlCRQWBd3TbKvVUsGDY92qG082VdIdsayrl87Jnd7jsztYCZqQWdPv7H/X/MuUtv5YY1f+V30zuvCGurKeDjue3v8afNz7OtoZypyWN4cMZVHJ81Z8AqqVLciRw1/AD+Xfoh5+YezQ1rHuGQjGlcMW7Pn8kJWXPJik3niW2vKuklIkOSalSl1e7tDWNC7Q19VpVeIiIiIiIiA6HSVwfQt5VeYSW9OmpvGB86voOkl8+DwbTO/YpETmwG8Y5YNnm2t9u+vq6IL+sKOSX7kIjXbKn06uu5Xus9RRQkjSIrNg2Ash7O9SqsLwMgf49Kr3jN9JK9wn9KP+Lkj6/jos/uoCH0vDJYvFa+GIPhmMwDe73WIRnT+PG4r/Ncybs8W/xOt/uXNlZw+ie/5NrVfyHdncxfZ17LKwfdzYnZ8wa8deBpIxawo6maby69hSRXPL+ffmWHFWYxDjfnjzqWt3d+zpb6kgGNUUSkLyjpJa28AR+xzq/aG7b0Qfeq0ktERERERGRAVDTVAJDhHqikV7C9YYczvZzBhFZdB8dXN3tIdiX06EVbh3EwPnEkGz3F7ba/XPohBsNJ2QdFvGbbSq++4rd+Nnq2MzFxFNkxvUuqtVR67dneML7DSjqRocJay30bn+XSFfcyLCaFJtvMksovox1WO2+UL2Z22iQyQ8nr3vrx+LM4OH0qN6x5pHXuX0e+rN3KaZ/8gk3123lg/5/x8rw7OTZrNsaYPokjUkcNn0WyK54qXx33Tb9ij/aybZ036hicxsHft70xgBGKiPQNJb2k1e7tDVsqvZo000tERERERGRAVPhqAUiP6X17w+Qw2hs2dNHe0OVwEueIwdPJTK/UHszzajEucSQb67+q9LLW8u+yD5mXPpmcuIyI1xsWk4LTOPq00mtbQznegI+CpLxeV5IVNpSR5k4ixZ3YbnuSM1jpZa3tdbwiA63B7+VHK37HvRuf4awRh/PqwXfjNk4WVayMdmittjWUs6p2C8dnze2zNZ3Gye+nX0mCM5bLVtxLYweVbe/tWs6Zi39FgAD/mnMbX8s5OGrJrhZxzlh+OfHb/Hry9zhs2Iwu982OTefE7Hk8Xfw29c2NAxShiEjfUNJLWu3e3tBhHLiME58qvURERERERAZEZVMo6dWDWVm7S+lle0OAZFc8tR3N9PJ5ejTPq8X4xFyKGna0vlj8ZV0hGzzFnJITeWtDCN6/Zsak9Wml17pQBUdB4ijS3Em4jbMXSa/yPeZ5QXBuWgA76NrBiXSntHEXX19yI/8p+5hfFFzAvdN+RJo7mQPSJvLBIEp6vV6+BIDjsub06bo5cRncN/0K1tZt4+a1j7d77Onit7joszsZFZ/JS/PuYFrK2D49d2+cN+oYvpV3XFj7Xph3AjXNHl4sXdTPUYmI9C0lvaRVU8BHrCOm3Ta3cQ3aSq8d3iou/OyO1ptCERERERGRoa7C19LesPeVXvHOWJzGEVZ7w86SXomdzJyqafa0JtV6YnzCSCyWLfWlALxc+gFO4+DErMhbG7bIik3v00qv9Z5g0mtCUi7GGDJj09jRVNWjtQobyvaY5wWQ5ApW2NWpxaEMIZ9Xr+fkT65nk2c7f511LT8ce1prFdMhGdP5omYzVb7B8VrNG+WL2S8pnzEJOX2+9uHDZ3LZ2NP5R9F/WVjyAdZafrv+n1y96gEOzpjKc3NvY2Tc8D4/70CZk7YfU5JH87dtr6kaVUSGFCW9pJXX377SC4ItDpvs4Ex6rajZyNs7l7GiZmO0QxEREREREekTFaE39aX1QaWXMYZkV0KXSa8GvxeD2eMNkC1a2u/trqa5t5VeIwHY4NmOtZaXSz/kkIxpDI9N7fGa2bHpfVzptY0RscNa20RmxaRT7q2KeB2/9VPcsIP8hI4rvYAOf8Yig9HbO5bxjSU3EWtcvDj3do7JPLDd4/MzpmGxfFSxOkoRfmVXUzWLK9dwfB9XebX18/Hf5MC0SVy3+kF+sPz/8cfNz3NO7tE8Puv61ueOocoYw4V5J7K6ditLqgbXnDYRka4o6SWtvAEfsc7dk15umgZpe8OWNhyVg+TdQyIiIiIiIr1V6ash1ZWIy+Hsk/W6S3rV+xtJcMZ2Omsm2ZXQYUKm2uchxd27mV4AGz3FrKzdxNaGMk7J7llrwxZ9XulVV0RB0qher1/aWIHP+snroNIr0alKLxk6anwerlr1Z8YljGThQXcyKTl/j31mpk4gwRk7KFoc/m/HZwSwHNeH87x253a4+NP0H+M0Dl4rX8w1E87jrik/wO1w9ds5B9LpIxaQ4krkiW2vRTsUEZGw7R3PwNInvIEmYjqo9PIN0vaGDa1Jr7ooRyIiIiIiItI3Kny1ZMT0vrVhixRXAjW+rmd6ddbaECDRFcf2xp17bK9p9pDq6nmlV7wzlty44Wyq305daQMu4+SE7N69MJ0Vm84uXw1NAd8e97aRCtgAGzzFXJAxpXVbZmwaS3tQ7VDYUA7Q4UyvZFV6yRByz8an2dlUzaOzrmNYTMdVmTEON/PSp/DBrugnvV4vX0xu3HCmJffvTK3c+Ez+OfsmKn21HDps/34910CLd8byzdwjeazwVUobK8iJy4h2SCIi3VKllwDBdgs+69+jpcVgrvRqTXppppeIiIiIiOwlKptqyXAn99l64bQ3jA9VG3V2fF1zY7ttvkAz9X4vKe7EXsU2PjGX9XVF/Lv0Qw4btj9pvfy+s2PTAdjZVN2rdQCKGnbQGGiiIDGvdVtWbDoVvtqI3xha2FAGdJz0SmyZ6aWklwxyX9Rs5onC1/hW3nHMSB3f5b7zM6azsX47pY27Bii6PdU3N/LeruUclzWn00rWvjQtZexel/Bq8a284/DbAE8WvRHtUEREwqJKLwGgKfSf9t1nermNq/Wxwear9oY1UY5ERERERESkb1T6ahkRN6zP1kt2JVDUsKPTx1vaG3YmONOrfdKsptkDBKvIemNc4kje27UcgJ9POKdXa0EwKQVQ1ljJyLjhvVprvacIYLf2hmlAMKkWyd9RYX0ZTuNgZAfHJDtV6SWDX8AGuGHNw2TEpHBVGP9W52dMA+CDii84a+Th/RbXipqNvFG+hCRnPMnuBJJdCaS4gn9+UbMZb8DHCVnz+u38+4oxCSM4MXsef9n8EsdkHsjM1IJohyQi0iUlvQQItjaEPZNeMY7BnPQKvttQ7Q1FRERERGRvUdFUy9Q+bMXV/Uyv7tobxu+RkKn2BZNeqe6etzcEmJCQC0CMcXFc5uxerQVfVXqVN/V+rtf6umDSa0Jibuu2rJjQ+t7KiJJe2xrKGRk7rMMZPy2VXh7N9JJB7Onit/msej2/m3Y5aWH8u5+cPJoMdzIfVKzs16TXDasf4fOaDZ0+nu5OZk7afv12/n3Jb6ZcwknVG7l0+b28ctDdpMf0XUWyiEhfU9JLAGj0B1sY7t7eMNbhpskOzvaGja2VXmpvKCIiIiIiQ5+1lgpfTZ++mJjiSqCmm6RX1+0N42myzXgDvtY3SfZlpRfAEcNn9bpVIrSv9OqtdZ5tZMWmt3uBv3V9b2TrFzaUkZewZ2tD+GqmV60qvWSQqmiq4c71TzIvfTJnjjgsrGMcxsHBGdP4YNcXWGv7pb3g2tpCPq/ZwI2TLuTc3KOpba6nprme2tBHTXM9YxNG4HI4+/zc+6I0dzIPzPg5Zy2+gStX/p7HD7gep9HPVkQGJ830EgC8gZak127tDR2uiPuVD5R6zfQSEREREZG9SIPfizfgI8Od0mdrBmdy1WOt7eSc3bQ3DCVlPG2SMn1V6TU1eQwZ7mTOG3VMr9ZpMTwmBQemzyq9JiaOaretpb1hubcqorUKG8o7nOcFEOeIwYFp9/MVGUx+s/7/qGtu4NeTvx9R8uqQjGmUeHexub6kX+J6ZvvbuI2TM0YcSqIrnpy4YUxMyuPAtEkcMXwWp+bMZ3rKuH45975qRup4bpl8Me/uWs4fNj0f7XBERDqlpJcAbdobOndvb+imKTA4K71akl5Vam8oIiIiIiJ7gYpQF4uMPqz0SnYlEMDiCbWH31137Q2TQlVgbSuRqvuo0is9JpnPj3yUozIP6NU6LZzGyfDYNMq8Fb1ax1rLek9Ru3leAMNjUjEYdjRVhb2Wp7mBnU3VnSa9jDEkuRKoVXtDGYSWVq3lqeK3+O7orzEpKS+iYxdkTAfgg4qVfR5XU8DH89vf45jM2QyLSe3z9aVz5+Uew9dHHs59G5/lnZ3Lut1/V1O1ZhaKyIBT0kuAryq94nZrb+g2g3emV0NA7Q1FRERERGTvUeGrAYJzaPpKsrulfV7HLQ67a2/YWunl7/tKr/6QHZsecSXW7rY37qTe76Ugsf2L/G6Hiwx3MuURtDcsatgBQH5CVqf7JLniVOklg05zwM8vVz/MiNhh/GTc1yM+fkxCDiPjhrFoV98nvd7a8Rm7fDV8M/eoPl9bumaM4fbJ32e/pHyuXPmH1ue43e3wVnHjl48y990fcNnyewc4ShHZ1ynpJUDn7Q1jHIM46RWq9PL4GwdtNZqIiIiIiEi4Wlq3Z8T0bXtDoNO5Xt23N4wH2ld6tcz0Su1lpVd/yIpJ73Wl13pPEcAelV4QnOsVSdJra0MZAPnxXSS9nAmqhJBB5/Ftr7Gmbis37/cdEkPPA5EwxjA/YzofVa4iYAN9GtvTxW+THZvOYcNm9Om6Ep54Zyx/mfFz/NbPpcvvaX1NEYLdmO5e/w8WvH85f9/2OhOT8nhn1+ds8myPYsQisq9R0ksA8PpD7Q07SnrZwZlQqm/TnqNSLQ5FRERERGSIa2lvmN6HFVQtLQg7qvSy1obR3jD4Ynddm+OrfR5iHW7iujguWrLjel/ptb4umPTafaYXBOd6RZL0KmwoB+i0vSEEK73q1N5QBpHSxl3cs+Epjhw+ixOy5vZ4nfkZ06ny1bGqdksfxlbB2zs/4+sjj8DlcPbZuhKZsYkjuHfa5Syv2citXz5OfXMjf9r0Agvev5w/bX6B47Jm89Yh9/HEAb/AZZz8X9Gb0Q5ZRPYhrmgHIINDSzVX7G7tDWMcbnyDttKrqfXzqqZasmPToxiNiIiIiIhI71Q0BdsbZrj7vtKr1rdn0qvJNuO3ARK6bG8YTHp5mr9602FNs6fX87z6S1ZMOjubqvEFmnE7evaSxzrPNjJjUknvYLZaZmwa60JJsXAUNpSR5IzvsmVlkiuh0/aTItHwh03P0Wz93LLfxRhjerzOIRnTAPhg10qmp4zrdL9tDeVsb9zFvPTJ3a75fMl7BLCcPfLIHsclfeP4rLn8cMxp/GXLS7xc9iFVvjqOGn4A1xScy5TkMa37nZA1l2eK3+bqCecMyjdLiMjeR5VeAoA3EKr0cu5e6eUetK0D6/2NrTcOmuslIiIiIiJDXaWvFgeGFHffJZSSu6j0agh1z4jv4kXI5Jb2hv72lV6DcZ4XBCu9AHY2Vfd4jfV1RRR0UOUFwfaGO5uqwm7Xtq2+nPyErC4TB4nOOLU3lEGjrrmBF0re59Sc+YxJyOnVWjlxGUxIzOWDis7neu30VvONJTdyztKbWV69ocv1rLU8U/wWc9MmMzZxRK9ik75xzYRzOS5zDlOTx/LcnNt4/IDr2yW8AL6VdzzVzR5eLv0oOkGKyD5HSS8BOp/p5Xa48A7aSi8vuXHDga/agIiIiIiIiAxVFU01pLmTcJq+a9mV4koEOp7pVR+ak9x1pVcwaVa320yvwVzpBfR4rpe1lvWeog7nebWs77N+qsJssV/YUEZeF/O8IJiYVNJLBosXSxbh8Tdy/qhj+2S9BRnTWVz5ZYdvqPYFmrl0xT3saqphWEwqP1n5RxpDz0sdWVq1lk31JXwzV1Veg4XL4eSRWdfwz9k3Mid9vw73OSh9ChMSc3my6I0Bjq5vlHsraejiuhSRwUdJLwHaJr3atzeMNa5B3N7Qy4i4YYAqvUREREREZOir9NWREdN3rQ2h65leXyW9Oq/0anmsXdJrCFR6bW/c1aPjy7wV1DY3UJCY1+HjWaG2+uHM9bLWUthQ1uU8L4BEVxwezfSSQcBay/8VvcGU5NHMSi3okzUPyZhGQ8DLsg6quG5b9zc+qVzD3VMv5b5pV7Cxfju/Wf+PTtd6ZvtbJDrjOCn7oD6JTQaGMYbzRx3Lsur1fFGzOdrhRGRrfRlHfvBjTvroGrbUl/b7+ay1nPrx9Txe+Gq/n0tkb6aklwBftTeM2a3nudvhoskO1vaGXka2JL2awnuX3b5gVc1mvv3p7a2JTBERERERGRoqm2q6nP3UE/HOWJzGQW2zZ4/H6sNob+gwDpKc8e2SXtWDuNJrYmIeic443t+1okfHr/ME53V1VumVGZsGQLm3qtu1ypuq8AZ83Sa9Wn6+1tqIYhXpa8trNrCqdgvnjzquV7O82jooYyoODB/sat/i8Nnit3m88FW+P/pkzhhxKAuGTec7+SfyaOEre+wL4Glu4OXSDzkl5xASQ21XZej4+sgjiHPEDKlqL1+gmStX/h6DYZevhtM/+QWfVq3t13OurSvk85oNPLhlIX7r79dziezNlPQSoPNKrxiHG78NDLon2oAN0BhoIt2dQpwjhipVerX6qHIV7+z6nNLGnrXzEBERERGR6Kjw1ZIR07dJL2MMya6Ejmd6NXff3hAgyRVPXZuZXoO50ivOGcMRw2fxRvniHt3Hrq8LJb06memVHUp6lYVR6bU1VBXQbdLLlUAAq/ZZEnVPbnuTBGcsp49Y0GdrprmTmJYyrt1cr+XVG/jFmoeZnzGd6wsuaN1+XcH5jE8Yyc9X3U+Nr32i/j9lH1Pv93J27lF9FpsMnFR3IqeNmM+LJe93+PtoMLp34zMsq17Pb6b8kBfn3k6KO5Fzlt7Cv/txNtmi0L+T4sadLOog+Ssi4VHSSwBo9Hc80ysm9PVga3HY6A9WpiU4Y0l3J6u9YRvVod7yLUOpRURERERkaKjw1ZLu7tv2hhCcGdX1TK/OK70gmPSqDVV6WWsHdaUXwAlZc9nRVM1nVesjPnZd3TYy3MkMj03t8PGWmWE7mqrCWKvrqrEWSa5g0rFOLQ6lH4RbQVjt87Cw9ANOH3EoyX3873t+xjSWVa/H09zADm8V3//8t2TFpHH//j/B5fhqhmG8M5bfTb+cMm8lN699rN0aTxe/xfiEkRyYOrFPY5OBc8Go46j3e3l++3vRDqVbi3at5M+bX+Tc3KM5OedgxiWO5MW5tzM9ZRyXrbiXP29+sV+qcxftWkl+fDYZ7mT+UfTfPl9fZF+hpJcAX7U33DPpFWx32DTIkl4tN2fxzljSY5KU9GqjOvRuqHq9S1BEREREZMiw1lLZVNPnlV4QnOtV4+so6RV8o1y3SS9nPJ5Q0qve34jfBgZtpRfAkcNn4TZOXi9fHPGx6z1FXSapElxxJDnjw5rptbZuK0nOeHLjhne5X1IowdC2haRIX/j7tjdY8P6P2Omt7nbf50veozHQxPmjju3zOOYPm06z9fNBxRf8cPk9VPnqeHjWNR3OMJyZWsDlY8/kX9vf5bWyTwDY5NnOkqov+UbukX3WdlEG3ozUCUxPGcfft70xqNu57mqq5idf/JHxiSO5eb/vtG7PiEnhHwfeyKk58/nN+v/jutUP9mmRgC/QzCeVqzl82AzOGvn/2bvv+LbKs//jn1t7ejvD2QmJM1iBsCmjtOzS3dJBobtP96+TPt3r6d6Ttk+f0skqLdAySikzzASSAEnsbDvL8baWNe/fH9JRHNuyjmzJkpLr/Xr5FSIdnXM7ZEjnuq/vdT73d68z9WdXCDGWFL0EkI43dFrsY948OFS6CFZ5Ra/DH87q7H764zLTyzCYkKKXEEIIIYQQ1SaYjBDXSRpK1Ok1XpzU4c2EJuINMwUZ4/NGjc1b5FUWT43dyzkNJ3DvoacLurGqtWZbcG/OaEPDDGedyaJXJ62+eXlv0nut0uklSmNt3/N0Dndz/eYbJvyzoLXmT533c1LNEk6oWVz0dZxWtxyHsvGJF3/OMwNb+e6q97PSvzDn8R9e/FqO9y/iM5t/RU90kFv2P4hVWXhty/lFX5uYXtfMvZj2UCdPD2wtyfljqTiJ1ORHtGit+fgLP2cwHuSnJ350zMxLl9XBj0/4MB9c9Gr+su8BrnvuG2OiOCdrw+B2Qslhzm08kavnXERCJ/nrgYeLcm4hjjVS9BJAutNrdJcXgN3o9NLx6V7ShCKZzjS31UW93c9ATDq9DEa8YVjiDYUQQgghhKgafZnPNPX24nd65ZzpNYl4Q+PmXq29coteAJfOPIOOSBdbgx2mX9MdG2AwEWKpb96ExzU76zgUHZjwGK01bcFOluU5F5CNkpNOL1Fs7cFOPFYn/+p+hlv3P5TzuGcGttIe6ixJlxekU3pOrWtlIB7kvQuv4qrZ50x4vN1i44cnfIhgMsKnNv+C2/Y/zAWNq5nprC/J+sT0uWrWOdTYPPyx876injepk/yh8z5OeehdfHlUNGYhfttxN//peZbPLrsmZ2HWoix8aumb+e6q9/NE34v8z7Y/Tvp6Iz3W9zwKxVkNK1nqm8uaulZu2vvAlLvitNb8Zs8/+Eb7H/nZrr/xx877uevg4zzSs5GNg9vZHT5Y0Z13QkyGrdwLEJXB6PQarVLjDY15VW6rg3q7xBuOdDjeUIpeQgghhBBCVIv++BBASeINcxa9UuaLXqFMF5LxeaOSO70AXt68hs/wK+499DQr/AtMvWabMYMrb6dXPc8P7ZzwmO7YAP3xAK2++Xmv6zVmeknRSxRRLBVnd/gg715wJc8NbuNLW/+PsxpWMc89Y8yxf9p7P36bm6tmTVyMmop3LriCZb65XL/0zaaOX+abx6eXvpmvtN0IwBuXX1iytYnp47G5eE3L+fyp8198MTqYc35iIZ4f2slnN/+aDUPb8Vid/OPgE3x5+TuwqMJ6PV4Y2sU32v/Iy5pP5dp5l9/lJEQAALmBSURBVOY9/g1zLmRt3/Pc3fUkX13+zmzjwGSt7X2eE2oWU5fZ/PKmORfx8Rd/ztMDWzmjfsWkz/tAz7N8pe1GrMpCUqfGPeaSGafxkxM+givP+wEhqoV0egkAosk4TotjzOPZTq9UZXV6HR647KLBUcNAPEgqx1/cxxqJNxRCCCGEEKL6GJ1epYg3rLF5GJog3tBlHftZcCSfzZPt9MrGG9o9RV5lcTU761hT18p9h54y/ZptoXTRa9kEM70AZjjq6c7T6WV0mC335y96+a3S6SWKb1foAAmdZLl/Ad8//oMAfOyFn5LUR0a/9ccC3N31JK+ZfR4e28RRp1Nx8YzT+OqKd2FVVtOvecf8yzmrfhUznPW8tPmUkq1NTK+3zn05cZ3klv0PTuk8gUSYL279La948nr2Dnfz4xM+zP+seA+98aG8GxNGCyUifHDTD6h3+Pnuqvebnh13+cwzGYgHebJ/82S+hSOu/9xgO+c0HJ997IqZZ+G3ublp7wOTPm8sFedrbTeyxNNC+0V/ou2iP/L0eTdw/9nf57bTvsJvV1/PRxe/nn8dWsdb1n+NARkfI44SUvQSQO54Q+OxYg5mLIbwiBiOOrufFJqhRHEydKvd4XhDKXoJIYQQQghRLfoy6RX1Dl/Rz+23eQgmwmPii8LJYZwWe96b0D6rK/t6o9Or1lb8dRbbpTNOZ3NgDx3hLlPHtwc7qbV5aXbUTXjcDGc9oeQwoQmKVO3BTgBaTcQbGp1eIZnpJYqoLZT+PbjMO5e57ma+vPztPNW/hd/s+ecRx922/yGiqXjJog2nwqIs/O6Uz3D3md/CMc49K1GdlvnmcWb9Sv609/5JbWDXWnPXwce5cO1H+V3Hvbx13sU8eM6PeNXsl3BB08koFP/pea6gc36l7UZ2hQ/yoxM+TIPD/OaT8xtPwmt18c+uJwr9No7wdP9W4jrJOY0nZB/z2Fy8cta5/LPriey/vYX6fee/2Bk+wOda34bdYsNtdTLL1UCrbx6n16/gZc2n8rHj3sBPT/woGwa38fpnvsDB4d4pfS9CVAIpegkgHV/otI4Tb6js2ecrSSS7I9FJvT39Yas/JrsRRn4IjUi8oRBCCCGEEFWjP5aON6wvQaeX3+YhhSY06jNCJBnFY83f2eHLvH44FctuNqyt8E4vgEtmnA7AfYeeNnX8ttBelvrm5t3hP8NZBzDhXK+2YAdNjloaHfmju4yZXgHp9BJFtC3YiQXFEu8cAF7XcgGXzDiN72z7C1sDe4D0PYQ/7b2fU+taWW4yBnS6ua1OZsgsr6POW+ddTGfkEA/3bCzodVprPvz8j/jAph8ww1HHHWd8na+teFd2zmSDo4bVtcfxYPezps/ZFxvilv0Pcu38Szl7RKeVGS6rk4uaT+W+rqdJpJL5X5DD2r7ncVrsnF63/IjHr557EcOpGHcceKzgc/bHAvxwx62c13gSL22auFPyFbPO5sZTPsu+SA+vfvpzbA/tK/h6QlQSKXoJwOj0qp54w5EDl41BzzLXC4ZTMWI6XaCUTi8hhBBCCCGqR188gFVZqLEVv5jktxtFlSMjDsPJ4bzzvAD8Nnf29cYmO38J1lls8z0zWelfwD0mIw63BffmnecF6ehEgEPR/pzHtAU6WWaiywvAZXFgQU3YOSZEodqDe1ngmZWNL1VK8c2V76XG7uWjL/yEaCrOE/0vsjN8gLdWYJeXOLpdOuN0mhy1/HTX7QV1e91x8DHuOLiWDy56NXee8Q1Orl065pgLm05h49AOeqKDps55z6GnSOoUb2yZ3Ny4y2eeSW98iKcHtkzq9QCP9W3i1NplY2ZqneBfzCr/Qm7aV3jE4Q923EIwEebzrdeaims8t/EEbj7tS0RTcV779Od5bmBbwdcUolJI0UsAEE3Fx403dBhFL11ZnV7hzA5FKXodaXBE9m5YOr2EEEIIIUSFUkpdqpRqU0ptV0pdP87z1ymlupVSGzJf7xrxXHLE43dO78pLpy82RIPdb3qOSCGMAtXouV7hZBS3iaKX15ouegUTEYYSIfw2d0FzecrpkhlnsH6gPe8Mrp7oIH3xAEtNFKpmOtJdJ12x8YteKZ2iPdTJcl/+eV6QLkb4bB4CEm/I7vABPrTph0QrbONtNWoPdo6ZT9foqOWbK9/L5sAefrDjFv7Y+S9qbV6umHlmmVYpjlUOi51PL30zzwxs5fed95l6zUA8yFfabuTEmiV8/Lg3YrOM/+/Qhc2r0Wge7t1g6rz/OPgEiz2zWelfaHL1o67XtBq3xcndXU9O6vW9sUE2B/ZwbuOJY55TSnH1nIt4IbCroDll7cFO/rD3X7x13sWmYnYNJ9Qs5vbTv4bf5uHqdV/mwe7CYiKFqBRS9BKAiaJXhb3hjCRjALgtzmzmfb8MWzwi4zcinV5CCCGEEKICKaWswM+Ay4CVwJuUUivHOfRmrfXJma/fjHg8MuLxq6ZjzdNhIB4sSbQhkO0eG93pZT7eMFP0SqaLXtUwz8tw6YzT0Wj+1f3MhMf9eOdtAJw2KlpqPEbUWq5CWmekm3AySqvJoheAz+aSTi/g0d7nuePgWnaHD5Z7KVUtmoqzO3KQZd6xN7svnnEab5xzIb/cdQf3Hnqa18+5YEx3iRDT4Q0tF3J+40l8c9ufTM1e/Pa2P9MXG+IbK98z4caL4/2LaHbU8qCJuV7d0QGe6HuBV8w6e9KbTtxWJxc2r+aerqdI6sIjDh/vexGAcxpOGPf5V81+CU6Lnb/s/bfpc36t7fd4rS4+tuQNBa9noWcWfzv9ayzxtvDODd/iG+1/lDlfoupI0UsA6XjD8YaCGo/FK2yml9HF5LI6qMt0eg1IpxcDicNFL4k3FEIIIYQQFep0YLvWeqfWOgbcBLyyzGsqu774EA0Of0nOnZ0ZFZ9svGH69cFEhMF4iJoqmOdlWO6bz3z3TO7ryj3X61+HnuF3nffy7gVXclLtkrznrLP7sCtrznjDtmAHQEG7631WD0EpemULs6MLtKIwO0P7SepUzojNL7RexxxXEwmd5M0SbSjKxIjctGDhU5t/idY657HPDrTzp73/5u3zL+eEmsUTnteiLFzQtJqHezbknbN1T9dTpNBcOfPsSX0Phstnnkl3bIB1A+0Fv/ax3k34be6c31et3csVM8/ijoOPEU7kT3V6sPs5HurdwEeXvJ4Gx+Q20zQ767j5tC9x+cwzuWH3nZz96Af40KYfsmFQIg9FdTBV9FJKfVspVaOUsiulHsjETLy11IsT0yeazNXplX4sVmFFr0gyitvixJLJvLeg6I9J0cuIN1QoiTcUQgghhBCVag7QOeLnezOPjfZapdQmpdRtSqmRd25dSql1SqknlVKvGu8CSqn3ZI5Z193dXbyVl1BfLJCNbi82f45Or3Ayaqro5bWlu8GMeMNq6vRSSnHpjNNZ2/c8QyOSMQwHh3v5xIs/53j/Ij619M2mz9nsrONQjk6v9mD6t7fZmV6Q7vQKSrxh9vfoeP+vhHn5fg/6bR5+d8p/86MTPsxx3vH++hViesxxN/Pfy67h8b4X+FOOTqZ4KsFnNv+KWc4GPn7cG02d98KmUxhMhHguT5HmH12Ps9Q7l1a/+c7c8VzUdApOi51/dj1R8GvX9r3AWfXH54xrBHjTnIsIJCL8M0+EYjyV4KvtN7LIM5u3zbuk4LWM5Ld5+OmJH+WRc3/C2+dfxn96nuWqp/6bVz/9Of5x8Im8BUUhyslsp9fFWush4EpgN3Ac8MlSLUqYl9IpfrrzdgamGO2Xjjd0jHncrmzZ5ytJJBnFnRnGalEW6uw+menF4XjDJketdHoJIYQQQohqdhewUGt9InA/cOOI5xZordcAbwZ+qJQa05qjtf6V1nqN1npNc3Pz9Kx4ivrjgUnvyM6nJsdMr/Tnqvzxhn5r9XZ6AVw68wziOjkm6iqpk3z0+Z8QS8X56YkfHXcjaC4zHPUcyjHTa2uwg3mu5mwspBk+W2k7vV4Y2sXqB99JZ+RQya5RDNLpVRztwU6sysJib0vOY5b65vLq2S+ZxlUJMb63zH0ZZzccz/+0/4F9kbEbVX7bcTdbgnv48vK3m/579SWNJ2JVFh7seTbnMQeH+3iqfwtXzppalxeA1+bmwqbV3NP1JCmdMv26jnAXHZEuzmkcP9rQcHr9ChZ7ZvOXfRNHHP5x7/1sD+3jc8veNm6i12TM98zk863X8tR5N/Cl1rfTEx3g/Zu+z0se+yDrBtqKcg0his1s0cuW+fEK4Fat9WCJ1iMK1B7s5Nvb/8K/u9dP6TzRVAyndexfhs7MTK+4rqxOr/Co7Pl6u19megGDmXjD2a5GmeklhBBCCCEq1T5gZPvB3MxjWVrrXq218Yb2N8CpI57bl/lxJ/AQsLqUi50OKZ1KF72mvdPLXLxhdqZXFXZ6AZxSu5RmRx33Hjoy4vAXu+7g8f4X+eryd05YHBjPDGf9hPGGywqY5wXgtbpKWvR6oHs9vfEh1lf4Dcpsp5cUvaakPdTJQvesggq5QpSLUopvr3wfKVJ8evMNR8Qc7o108/3tt/Cy5lO5ZMbpps9Za/eypq6Vh3o25DzmnkNPodFcOfOsqSw/6/KZZ9IV7efZAiIA1/a9AMC5OeZ5GZRSXD3nItYNtPH80M5xoyAH4gG+v/1mzm04gZc1nzrOWabGZ3PzjgWX89C5P+I3J38KgOtfvEE6vkRFMlv0+odSaivpDxoPKKWagbzZaUqpS5VSbUqp7Uqp68d5/n1KqeeVUhuUUo/lGF4sJhDIvCkOTvENYbrTa+ybIXum6BWrsE6vcHIY94gPZ/UOv3R6cTgCYqazXuINhRBCCCFEpXoGWKqUWqSUcgBXA3eOPEApNXvET68CtmQer1dKOTP/3QScA2yellWX0FAiTFKnqC9Rp5fH6sKqLAQSR0bGmY03NIpegWS4Kju9LMrCxTNO46Ge5xhOxoD0fJjv7biZq2adw+taLij4nDOcdXSPE28YS8XZEdpPq998tCGkC5OhEsYbGsWutmBnniPLayhuFL0k3nAq2oN7C4rXFKLc5ntm8pmlb+WR3o3csv9BALTWfGHr/wLwleXvRClV0DkvbDqFFwK7ODjcN+7zdx1cy3LffJb65k5t8RkXNZ+KQ9m4u4CIw7V9zzPDWW8qZvS1LefjUDauePLTrPjPNbx07Ue5Zv3XuH7zDfxk51/59Is3EEiE+ULrtQX/WhXCqqxcPOM0Pt96Le2hTm7d/1DJriXEZJkqemmtrwfOBtZoreNAiDyDhpVSVuBnwGXASuBN4xS1/qy1PkFrfTLwbeD7hS1fBLNFr6m9Oc4Vb2i0wsYrbKbXcCp2ZNHL7mdAil4MJoL4bW58NrfEGwohhBBCiIqktU4AHwTuI13MukVr/aJS6itKqasyh31YKfWiUmoj8GHguszjK4B1mccfBL6pta76opcxn7hUnV5KKXxW95hOL7Pxhi6LA6uyMBgPEUoOV12nF8ClM04nlBzmscxsrw9t+iEtzkb+Z8W7J3VzcIaznr54YMwG0V3hgyR0kuWFdnrZStfpldIpnh1sB9JdaJVM4g2nbjgZY0/4IMuKdCNfiOlyzbyLObN+JV9tu5GDw73cd+hp/t29nv+35PXMdRceVfzSpnQj+MO9G8Y8t3+4h3UDbbyiCNGGBr/Nw3lNJ3F315PjdmKNltIp1vY+z7kNJ5j6d6jZWcffz/gfvtB6HW+a8zKWeufSFw9wX9fTfGf7Tdxz6CneOu9ilvsXFOPbyeuyGWdwal0r39txM6ESdioLMRm2/IeAUur1wL1a66RS6nPAKcDXgIMTvOx0YHsmcgKl1E2kC2XZDySZOWEGL5D/bwRxhEBy6m8ItdY5O72Mole0wopeo2M46u1+nh/aWcYVVYbBeDpqxGN1EU5Ip5cQQgghhKhMWuu7gbtHPfaFEf/9GeAz47zucWDiDKAq1BdPfzRucJSm6AXpuV4jI+OSOkk0FTfV6WUUzfYP96TPZfeWbJ2lclbDKmpsHu7teoq/H3iU/dFe/nraVyf9vTQ76wDoiQ3S4mrKPt4WSBeVWgssevkzM7201kXfob8tuJehRBinxU57cG9Rz11s2aJXXIpek7UzvJ8UmqXS6SWqjEVZ+Paq93Hx45/gEy/+gm3BvSz3zeedC66Y1PlaffOZ7WzkP93P8sY5Lz3iubu7ngTgypnFK3oBXD7zLP7dvZ6NQ9s5uXbphMe2BTvpjQ9xTp5ow5GOr1nE8TWLxjweSUbpivYxxzV9c0yVUnx22TW85unP8as9/+D/LXn9tF1biHzMxht+XmsdUEqdC7wM+F/gF3leMwcY2Te/N/PYEZRSH1BK7SDd6fVhk+sRGcXo9IpmdqaN1+llV1agUmd6jSx6+STekEzRy+7FY3VKp5cQQgghhBBVoi/T6VVvL028IYDf7jlis6TxecFjotML0hGH+zJFr1pb9RW9HBY7L206hdsPPMKdB9fy8SVv5JS6ZZM+3wxHPcCYuV5twQ6sylLwjDCv1U0KXZLZzOsG09GGV8w8i45IV0VvkAwmJN5wqtozEZatXil6ieqz0DObTy19M4/0buRgtI9vrHxPdvRKoZRSXNi8mkd7N41JsLrr4OMc71/EIu/sHK+enJc3r8GurPzzYP6Iw8d6NwFwbuPU9/K4rU4WemZP+tdqstbUtXL5zDO5YfcdOedcClEOZotexkS6K4Bfaa3/CYytkEyC1vpnWuslwKeBz413jFLqPUqpdUqpdd3d3cW47FEjW/SaQva3UfRyWcd2elmUBbuyVtxMr0gyimtE0avO4SeaipfkA0I1GUwEqbWli16RVJSUTpV7SUIIIYQQQog8jA189fbSxQb6beMXvdwmOr0gXfTaH6neTi+AS2acTkInOat+Ff+1aMKJDXnNcBpFr4EjHm8LdrLIM3vcJJWJ+Gzp4uNUPtvnsn6gjUZ7DZfMOB2AbaHK7fYaknjDKWsPdmJT1qLfzBdiurx9/qVcOfMsPrL4dZxa1zqlc7206RSCyQjrMnMNATojh3hucBtXzDprqksdo9bu5SWNJ5qKOFzb9zxLPC3MdjUWfR3T6fqlbyaeSvD9HbeUeylCZJkteu1TSt0AvBG4OzM4ON9r9wEjt5XMzTyWy03Aq8Z7Qmv9K631Gq31mubm6WvTrAbFyLuOZTu9xn9TbrfYKrLoNTreEDjmu73SnV6+bC6/MaRZCCGEEEIIUbn6Yka8YQk7vUYVvSLJdLePmXhDAJ/NQ1e0D6jOTi+Al81Yw0cXv54fn/gRrJlUk8mamYk3HFv06ih4nhekf31h6vO6x7N+oJ1T61ppzcTdGZ1AlUZrnS36DUnRa9Lag3tZ6JmVHVchRLWxKis/P+ljfOy4N0z5XOc0HI9dWflPz7PZx4wurGJHGxoun3kWncPdPB/IPYYllorzZP9mzilCl1e5LfTM5q3zLuamvQ/QVqH/vohjj9mi1xtIDxm+RGs9ADQAn8zzmmeApUqpRUopB3A1cOfIA5RSI8NNrwC2mVyPyCh1vCGkYyBGtwGXWzre8HAMR7boFTvGi16JdLyhN/NrIxGHQgghhBBCVL6+eACHsmXfx5eC3+Y5Yk5SwfGGVhepzBjuau30clrsfOy4NzAz06U1FY2OWhTqiDincGKYjsihgud5Adn/98Xu9OqNDbIrfIBT61pZ4JmJ02Kv2JuS4eQwyUxayZDM9Jq09lAny2SelxAAeG1uzqhfyYPdz2Ufu6vrcU6sWcICz8ySXPPiGWuwKWt2bth4NgxuJ5yMcm4B87wq2UcWvw6fzc032v9Y7qUIAZgsemmtw8Ah4NzMQwnyFKi01gngg6SLZVuAW7TWLyqlvqKUuipz2AeVUi8qpTYAHwOuLfxbOLYFk+k3glMreqW7gXJ2eilbtjBWKSLJKO4RRTojBqQ/HizXkirCYPxwvCGkPzQIIYQQQgghKttAPEC9w49SqmTXqLF5juieOVz0Mt/pZajWTq9isltsNNj9HIodLnptC+1Fo7MdVYXwl6jTa/1AOwCn1i3Dqqws9c6lLdhR1GsUy8hORIk3nJzhZJQ94S6WyTwvIbJe2nwK7aFO9ka62R0+yPNDO3nFrNJ0eQHU2f2c3XD8hBGHa/tewILizIZVJVvHdGpw1PCBRa/hPz3Psrb3+XIvRwhzRS+l1BdJz9z6TOYhO5C3dKu1vltrvUxrvURr/fXMY1/QWt+Z+e+PaK1Xaa1P1lpfqLV+cXLfxrErkHlDPJU3hNE88YZOi524rpxOr5ROMZyKZSP8QOINIR1lGE3Fj4g3PNZnnAkhhBBCiNJSSp2rlHp75r+blVKLyr2matQXC9BgL120IaSLKsFkJHsDztggZ3aml9/mzv53bZV2ehXbDGf9EZ1eRgfVpDq9jJleRS96tWFXVk6sWQzAUt9c2oOVOdPLKMrOcNZL0WuStof2o9HS6SXECBc2rQbgwZ7n+Ecm2vCKmcWf5zXS5TPPZHf4IJuGdtIbG2RHaB/rB9p4oHs9f93/MP88+ATH1yymroSzPKfb2+dfxhxXE19v/wOpTNeuEOViM3ncq4HVwLMAWuv9Sil/yVYlTCtKvGEyX7yhjVgFxRtGxtmRWOdI/3YcOIaLXoOJEJD+AHq400uKXkIIIYQQojQymyPXAK3A/3F4c+Q55VxXNeqLD1HvKO1HbL/NS1KnCCeH8drcIz5XmYs39FrTRS+7suLK8dnxWDPDWUf3iJlebcEOXBYH8z0zCj6X31q6Tq/jaxbjynxGbPXN528HHs3Mg66s4qVR6JrjauK5wW0kdXLKs9eONca8tmW+uWVeiRCVY7GnhfnumTzY8ywHhns5pXYpc93NJb3mJTNO47Nbfs0rnro+5zGfXvrmkq5hurmsDj619M185Pkf8/cDj/GalvPKvSRxDDNb9IpprbVSSgMopSrrndExLJh5UziV3G8j3tBhGf+3g91iI1ZB8YaRZHq9I3ckSrxhOtoQoNbmk3hDIYQQQggxHWRzZJH0xwKs8C8o6TWM+LxAIozX5p5EvGG66FVr95U0hrGazHDWH9E1tTXYwVLf3EkVaoxOr1ARZ3rFUnE2Dm3nmnkXZx8zohfbg52cVr+8aNcqhtFFr0AiclR1QUyH9lAnNmVlkWd2uZciRMVQSvHSptX8ae/9xHWSL7ReV/JrNjpq+d6qD9AR6aLO7qPW7qPO5qPOfvir1JtdyuGVs87h17vv4jvb/8LlM8/IbrgQYrqZijcEblFK3QDUKaXeDfwb+HXpliXMMuINo6n4pOduZeMNrbk6vewV1ellFHJGfjhzWOz4rG76Y8dwp1f8cKeXEW8onV5CCCGEEKKEYjqdlSebI6eoLx6gwVHaeMOaTNHLiJAb73PVRIyiWc2I2V7HumZnHd2xgWyMU1uwc1LzvGBkUbJ4Ra/Ngd1EU3FOrW3NPmZELxpRjJVkaETRC2Su12S0BztZ5JmNPcemZiGOVRc2nUJcJwG4YuaZ03LN17Scx0eXvJ7r5l/Gq2e/hAubV7O6bimLvLOPyoIXgEVZ+Fzr29g33MN1z32T7aF95V6SOEaZKnpprb8L3Ab8lXR0xRe01j8p5cKEOSOjD4KTfEOYb6aXQ1Vap1e6kOMeFcNRb/fRFx8qx5IqghFvWGM7HG8oM72EEEIIIUQJyebIIkjqJAPxYDa9olRGdnpB7s9VuRidSLXSeZM1w1FPQifpjwfojwU4FO2f1DwvAJfFgQVFqIhFr3UD7QCcWne46DXH1YTX6qI9VHlFL+P3Zksmdmwos7FTmNce3CvzvIQYx1kNK3Fa7JxWt5zZrsZyL+eodnbD8Xxj5Xt4YWgXFz/+cb7e/gdT0b2JVJKNgzuIV1Djhaheprd+aK3vB+4v4VrEJASTYWpsXoYSIQKJCI2O2oLPMZyJN8xZ9LLYievK+QsnkjI+nB3ZmVbn8DMg8YbU2r3Z/5cSbyiEEEIIIUpFa/1dpdTLgSEOb46Uz4wFCiQiaDS19hLP9LIfWfQqNN7QmDklnV6HzXDWA3AoOpAt0CyfZNFLKYXP5iFQxHjD9QNtzHU1M8vVcMR1lvnm0R7oKNp1iiUQl06vqYgko3RGDvHalvPLvRQhKo7L6uQnJ3yEue7CZy6Kwr1l7su5pPl0vr39z9yw+07+duBRPrfsGl4569wjIpKTOsnT/Vu56+Ba7ul6it74EO+cfwVfXH5d+RYvjgqmil5KqdcA3wJmACrzpbXWpc1fEBNK6RTBxDDLfHMZCoYmPfA2mjQ6vXLFG9qyMQOVIJxj4HK93S/xhqSLXpZME6fEGwohhBBCiFJRSi0CHjUKXUopt1JqodZ6d3lXVl2y7+NtpU2H9I8Tb2hXVtMxaCNneom0Gc46ALqjA+wKHwCYdLwhgM/mKlqnl9aadQNbObN+5ZjnWn3zuP/QuqJcp5gCiTAWFDMzxcRKug9RDbaF9qLRLPPOLfdShKhIl848o9xLOKY0OWv59qr/4k1zXsbnt/4vH37+x/yx836+vPztBBMR/tH1OHd3PUl3bBC3xcnLZpxKKDHMjZ338tZ5L2eJd05R1vFwzwZOr1+BW+aLHVPMzvT6NnCV1rpWa12jtfZLwav8wskoGs1sZ7ott1TxhnZLZcUb5tqRWG/30R8/hoteI+INjb/IpdNLCCGEEEKU0K1AasTPk5nHRAEGE4cTG0qpxja208tstCGAN1P0kk6vw4xOr65oP23BDmpsXmY6G/K8Kjef1TPpzayj7R/uoSvaf0S0oWGZbx698SF6ooNFuVaxBBJhfDZP9s+CFL0K0x7cCyDxhkKIirK6bil3nvE/fHPle9kW2stlT36K16/7Ijfve5DT6lfw8xM/xnMX/Iafnfj/+O6q9+OyOPha2x+Kcu2n+jZzzbNf54bddxblfKJ6mI037NJabynpSkTBjCLXrEwW7WQH3kYz8YYua+54w1gF5aka2fOuMUUvP/3HeLyh1+rCbrFh0xoLSopeQgghhBCilGxa65jxE611TCk1fnyEyGkoE+k2XZ1eRoRcJDlsOtow/Xrp9BpthqMOgEOxftqCnSz3zT8itqlQPpuLYJHiDdcNtAHkLHoBtIc6aXIWPiKhVIYSYWpsnjHz54Q57cFO7MrKQs+sci9FCCGOYFEW3jz3ZVw+8wz+vPcB5rqauaj5lOyGGkOTs5YPL34t/7PtjzzSs5Hzmk6a0nX/t+OfANx+4BE+svh1U/o3WlQXs51e65RSNyul3qSUeo3xVdKVibyMIpcxgHGyb44Pd3rliDdUldXpFcnR6VVn9zOUCJFIJcuxrLIbjIeyO+KUUnisLok3FEIIIYQQpdStlLrK+IlS6pVATxnXU5VGzuYtJa/VhQV1RKdXIUUvn9Xo9CrtOquJx+bCZ3VzKJouek0l2hDAZytep9f6gTbcFicrfAvGPNeamTvWFuwsyrWKJZAI45ei16S1BztZ7G0xHVkqhBDTrc7u5/2LXsVVs88ZU/AyvH3B5Sxwz+QrbTdO6R5vZ+QQ/zr0DIs9s9kdPsiGwe2TPpeoPmaLXjVAGLgYeEXm68pSLUqYY7wZnp2JT5hsvKFR0HLkeGPksNiJ68rp9DK6l8bEGzrSg5+NeJBjzWAiSK3t8K5Lj02KXkIIIYQQoqTeB/y3UqpDKdUJfBp4b5nXVHWyMeUlLnoppfDZPIeLXonomDnJE5nhrKPVN4+TapeUaolVaYazjk1DOxlKhLLFpMnyWl1FK3qtG2hjde1x2CzWMc/NcNRRZ/fRXqFFL4fFjsviYCgz707Ao72beN/G72U3AY9nW7BTog2FEFXPabHzuda30R7q5M97/z3p89zYcS9KKX518idxWuzcfuDhIq5SVDpT2z+01m8v9UJE4QLJ9IeV2VOON4xjV1asauybYUgXwyor3jCdoOK2jJ3pBdAfD9LoqJyIhukystML0kXBiMQbCiGEEEKIEtFa7wDOVEr5Mj8/NnefTZFxY7/U8YaQnsdlzEmKJIcLGurusjq5/+zvl2ppVWuGs55nBrYCTLnTy2/zECpCvGEoEWFLcA8fWPTqcZ9XSrHMO4+2YMeUr1VMgUSYWZmZ5f4Rv1cF3HVwLXd3PUmNzcu3V71vzPOhRITO4W7e4H1pGVYnhBDFdXHzaZxdv4rv7biZq2afQ12B0cqhRISb9j3A5TPPZJlvHi9vXsOdBx/n863X4rCMP95ntGAigtfqkkjEKjVhp5dS6lOZH3+ilPrx6K/pWaLIxdgB1uysw6osk+70iqZiOaMNAewVVvQyOr1c1iPXXG9Pd3r1xwLTvqZKMJQYW/SSTi8hhBBCCFEqSimnUurNwIeBjymlvqCU+kK511VtBhMhbMpaUNfVZPlHdnoVGG8oxtfsrCOpUwC0+qdW9PLaitPptXFoB0md4tTasfO8DK2+ebQHO9FaT/l6xTKUCFNjT0cbpgu00ull2BrowKas3LTvAf5+4NExz28P7QOQTi8hxFFBKcUXll/HQDzIj3feVvDrb9v/MEOJMO+YfzkAr2k5n/54gId7Npp6/dbAHk5+8B38u3t9wdcWlSFfvOGWzI/rgPXjfIkyMt4M+20efFb3pN8cR5NxnBNUuR0We8XN9HJbnFjUkb9964yiV/zYLHoNxkNHxBu6ZaaXEEIIIYQorTuAVwIJIDTiSxRgMB6kxuaZlp3EY4tepS+0He1mOOoBmOmsz34mnSx/ZqbXVAtR6wfaAFhdtzTnMa2++QwlwnRF+6Z0rWIy4g0hHfcpM73SUjpFe6iTN899GWvqWvnM5l+xK3TgiGOMqMplvrnlWKIQQhTdSv9Crp7zUn7XcS87Q/tNvy6lU/xfx92cVLOEU2qXAXB+40k02P3cfuARU+f44c7biOkEG4dkDli1mrDopbW+K/PjjeN9Tc8SRS7GG0C/zZ358DL5eEOndYKil7JVXtHLOrYzrcEhRS+JNxRCCCGEENNortb6jVrrb2utv2d8lXtR1WYoEaa2wNieyRpZ9Co03lCMb4YzXfSa6jwvAK/VTQo94dwmM9YPtLHUO3fCOCijONJWIXO9tNZHFL38Ng+BuBS9ADoihwgno6zyL+KnJ34Uu8XG+zd9n+HM6AdIF70cysYC96wyrlQIIYrrE8ddjcvi4Kttvzf9mod7NrIzfIB3Lrgiu6HIbrFx1axz+Hf3urzzItsCHdzd9SQAOwootonKkq/TCwCl1DKl1K+UUv9SSv3H+Cr14sTEjM4ur9WNz+YmmCxNvKHDYieFJqmTkzp/seXakXgsxxvGUnEiqegRH5Yl3lAIIYQQQpTY40qpE8q9iGpndHpNhxr74TlJEm9YHDOcdcDU53kB+Gzpz7nBKcz1SukU6wfaObUud7QhHI7Bq5SiVyQZJalThzu9JN4wy5i9ttw3nxZXE99b9QFeDOzmf9r/kD1mW2gvS7xzsFnGn9UuhBDVqNlZx4cXv5YHetbziMlowt92/JMZznoun3nmEY+/puU8oql4tqCVy4923obP6uaU2qUFdZiJymKq6AXcCjwHfA745IgvUUbBZASXxYHdYsNnc0+p08thseV83p55rlLmeoVz7Ej0Wl3YlfWY7PQaHGf4tUfiDYUQQgghRGmdC6xXSrUppTYppZ5XSm0q96KqTTqxoQydXimJNywGo9NreRE6vXyZgs9U5nrtCO1nMBFiTZ6iV4OjhmZHXTYWr9yGskk2Izq9JN4QSM/zgsPdeS+fsYZ3L7iS33Xey71dTwHQHtwr0YZCiKPS2xdcznz3TL7SdiOJ1MQNGduCe3m4dyNvm3cJjlGjfE6qOY5Fntn8bZy5iIb2YCf/7HqS6+Zfyil1y9gVPkAqM7dTVBezRa+E1voXWuuntdbrja+SrkzkFUxE8NvcwOHs78mIpuJ5O72gcopew6nYuEUvpRR1dj/98WAZVlVeg5kdcCPjDd0SbyiEEEIIIUrrMmApcDHwCuDKzI+iAEOJI2PKS8koJKR0ikhy/M9VojCn1C7l6jkXcVHzqVM+l9c69U4vY57XqXXL8h67zDcv20VUbkaBq+aITi8pegFsDXYw3z0Tb+b+D8Cnl76Zk2qW8IkXf0FboIO9w93Z7j0hhDiaOC12PrfsGtpDnXxu62+IT3B/+v867sZpsfOWuS8b85xSitfMPo8n+l9kX6R73Nf/eOdf8VidvHvBK1jsmcNwKsaB4d6ifS9i+kxY9FJKNSilGoC7lFLvV0rNNh7LPC7KKJgIZ3eC+WxugpN8QxhNxnBacs/0cmY7vSpjrlc4OZwzhqPe7mfgmOz0Shf6am0SbyiEEEIIIaaH1noPMA94aea/w5jfWCkyBuMhamzTV/RK6hQD8SAaLZ1eReC1ufn2qvfR4KiZ8rn8Rej0Wj/YRp3dx2JPS95jW33zaA/urYhd7IFRnV41di/RVJxohdyHKKe2YMeYTkKHxc7PTvx/gOaaZ78OIEUvIcRR65IZp/O+ha/kz3v/zVvXf42+2NCYYwbiQf66/xFeOetcGh21457nVbNfAsDfDz425rltwb3cdfBxrp1/KfUOP4u9swHYEZaIw2qU7wPJemAdcC3pOMPHM48Zj4syCiQi+DI7fXzWqcUbTlT0sqtKK3rlzp6vd/iO7XhD+9h4Q611uZYlhBBCCCGOYkqpLwKfBj6TecgO/LF8K6o+Wutp7/QC6Ir2A8hMrwrjNWZ6TaXoNdDOqbWtKKXyHtvqm08kFaUzx4736TS66GX8GIgf291ew8kYu8IHWO4fG5853zOTb618HwejfQAs80rRSwhxdFJK8d/L3soPjv8gzw6284qnPsPWwJ4jjrlp7wNEUlHeseDynOdZ4JnJmrpWbt//yJj7pT/eeRtuq4P3LEiHFhibR2SuV3WasOiltV6ktV6c+XH01+LpWqQYXzARwW+dvnjDuK6MeMNIMoprgk6v/pjEG0I63lCjGU7FyrUsIYQQQghxdHs1cBUQAtBa7wf8ZV1RlYkko8R18ojEhlKqyRa90jfJpehVWfzWqXV69ccCbA/t49Q887wMrZnOoEqY6zU0Kt4wW/Q6xiMOt4f2kdQpWnPMjLti1lm8ff5lNDtqme+ZMc2rE0KI6fXalvO5Zc2XiabivOrpz3LfoacBSKSS3Nh5L2fVr2Klf+GE53j17PPYFtrLi4Hd2ce2h/Zx58HHedu8S7Od2zOd9XitLil6VSlT0RNKqQ8opepG/LxeKfX+kq1KmBJMHhlvGElF8w70G89wKobTOkGnVybeMFohM70iE3R6pWd6HYudXuPFG6Z3CYZlrpcQQgghhCiNmE5vk9UASqnpaVc6ihib12rsnmm53uhOL7fEG1YUo9MrNMmZXhsGtwPm5nkBLPXNBaCtAope2U4vuzHTK/3XyVDmz8ixamtm5troeMORvtT6dh4/7xdYlXW6liWEEGWzum4p/zjjmyz1zuXdG77Dj3f+lXsPPc2+4Z4Ju7wMr5h1FnZl5fYDD2cf+8nOv+KyOHjvwsOjaZVSLPa2SLxhlTKbt/5urfWA8ROtdT/w7pKsSJh2RLyhkf09iTfH+eINjecmGhQ4ndLxhuN/OKu3+xiIB465SL/x4w3ThUGZ6yWEEEIIIUrkFqXUDUCdUurdwL+BX5d5TVVlyHgfP02dXhJvWNkOdzdNrujVlimQrPAvMH29Oa4m2jOvK6cxM70yPw4d451ebYE9OC12Fnlm5zxGKTXhPR0hhDjazHI1cOtpX+bVs1/Cd7ffxMde+Cnz3DN4WfOpeV9bZ/fz0uZTuOPAWhKpJDtD+7njwGO8bf4lY2aBLfG0SKdXlTJb9LKqEYHQSikrkDsPT0yLYCKC32bEG7qzjxUqljfesLJmekWSUdw51ltv9xPXSULHWHfTYCKIx+rMduXByE4vKXoJIYQQQoji01p/F7gN+CvQCnxBa/2T8q6qugyME1NeStl4w2GJN6xELosDC4rQpItencx01lNnN19EbfXNoz1U/k6vYCKMQuHNfI41Or6qMd6wK9rP/YfWFeVcW4MdHOedg80iXVxCCDGSy+rkh8d/iP9e+laiqTjvWfAK0x2vr5l9Pt2xAdb2Pc+Pd/4Vp8XBexdcNea4xd4W9g33MCz3VquOLf8hANwL3JzZxQfw3sxjoky01gQTI+INrUbRq/A3hNFUHNcEu4LsqnKKXimdYjgVyxnDUe9IjxDojweyXXDHgsF4KBv/YDA+wEaOsQKgEEIIIYSYPlrr+4H7y72OamXElI9+L18qRiFB4g0rk1IKn81DYJLxhu2hTpZl5nSZtdQ3j8d6nyeRSpa1sDKUCOO3ubGo9N7sbLxhvLriDQfiQd607stsD+3j3rO+k3e2TD5bgx2c03BCcRYnhBBHGaUU71v0St4456UFbfh4afMp1Ni8/HjnX1k/0Ma7FlxJk7N2zHGLvS0A7AofNN1FLSqD2U6vTwMPAv+V+XoA+FSpFiXyi6bixHUyW9iZSgxCNDlxvKEj81xMlz/eMJKprOfakVhvTxe9+mLH1lyvwXhozO5Q6fQSQgghhBCloJQKKKWGxvkKKKWGyr2+amJEt01Xp9fheMN0p5fbKgEulcZnc02q0yulU2wL7qW1wKJXq28+MZ1gd+RgwdcspkAinP39CSPvcVRPp1c0Fec9G75DR7gLu7Jy2/6HpnS+gXiArmg/rRPM8xJCCJFughgRUpeX02Lnylln8czAVhwWO+9dOLbLC2CxJ1302iERh1XHVNFLa50Cfgd8Vmv9Oq31DVrrZElXJiZkxBj6rZlOr0nGG2qtiaZiOCf4sHM43rASil4xANx5il4D8WOs6JUIjpkDIDO9hBBCCCFEKWit/VrrmnG+/FrrmnKvr5oYnV5101T08lpdKNSImV7S6VVpfFbPpMYWdEQOMZyKscxbaNErfXx7sLwRh6OLXj5b+vdmtcz00lrzqRd/wZP9m/nu8e/nZc1r+PuBx6Y0G31rID1rbbkUvYQQouhePfs8AN467+U0O+vGPWZxZp7izrAUvaqNqaKXUuoqYAOZSEOl1MlKqTtLuC6RRzCZfuNnFLuMHwPJwt4QJnSSFNpUp9dU3qwVSzgT1Zez02tEvOGxZLxOL3e26CXxhkIIIYQQonSUUjOUUvONr3Kvp5oMZqLbRt7sLyWLsuC3uemODQAy06sS+WwugpOINzSKVoV2BR3nnYNC0Vbuolf8yKKXVVnx29wMJaoj3vC722/ibwce5VPHvYlXzX4Jr205n57YIA/3bpz0ObcGM0Uvv/y1KoQQxXZ63XJ+ffIn+cSSq3Me47G5mO1sZEdo3zSuTBSD2XjDLwKnAwMAWusNwKLSLEmYYcQYjo43LHRHWDQzp8tpyd3pZbdUzkwvI94w50yvTH5rf2bH5LFionjDiHR6CSGEEEKIElBKXaWU2gbsAh4GdgP3lHVRVWYoEcJvc5sevF4MfpuHpE4BUvSqRD7b5Dq9jKLXUt/cgl7ntjpZ4JlJW6bAUi5Dozq9IP17tRriDW/a+wA/2XU7V8+5iA8sejUAFzatptFew637Hpz0ebcGO6ixeZnlbCjWUoUQQmQopbhkxul4bBN3vS/2trBT4g2rjtmiV1xrPTjqMV3sxQjzsvGGtqnFG0ZT6bhAI8JwPA5VQfGGKaPoNX6Rzoj46z/WZnpJvKEQQgghhJh+XwXOBNq11ouAi4Any7uk6jIYD415H19qxmdIhZpw86MoD6/VNamiV1uwg7mu5uy9gUK0+uZVRLxhTRUWvR7p2chntvyK8xtP4usr3pWdKWO32HjV7JfwQPf6Sd+faAt2stw3v6A5NUIIIYpribeFneH9aC2lkGpituj1olLqzYBVKbVUKfUT4PESrkvkYbzxM97QejNdPYW+ITTT6WXEG8Z0+Tu9jAJOrux5m8VKjc17TM30iqcShJPRcTq9JN5QCCGEEEKUVFxr3QtYlFIWrfWDwJpyL6qaDCaC1NinJ9rQYBS9PFan3EyvQH6bh9Ck4g33sqzALi9Dq28+u8IHsvcHymH0TC+AGpuXoXjlFr02B3bzvo3fY5l3Hj8/6WPZlBzD61rOJ6YT3HlwbcHn1lrTHuyQaEMhhCizxZ4WAokIPbHR/UCikpkten0IWAVEgT8Dg8BHS7QmYYKx88tnTRe9LMqCz+ouvNMraRS9JprpVTmdXoeLXrljOOrtvmmf6aW1prdMf/kZGee19iN3iDotDhRK4g2FEEIIIUSpDCilfMCjwJ+UUj8CqmMAT4UYiofL1ukl0YaVyWsrvNMrkUqyI7SPZQXO8zIs9c4lqVPsDh0o6HXRVJx/HnxiyrvftdYEEuExXWqV3Ol1cLiP6579Bj6bm9+d8plx5/KtqlnESv8Cbtv/UMHn3zfcQyARKXhGmxBCiOJa4m0BkIjDKmOq6KW1DmutPwucr7U+TWv9Oa21tI+UUXDUTC/jv4PJQju90vGGExe90s/FK6DoZRRwXBMWvfzTPtNrbd8LrHn4Pewf7pnW68Lh4de1tiM7vZRSeKxOQgn5oyqEEEIIIUriQaAW+AhwL7ADeEVZV1RlBhPBMYkNpWZEyOWakyzKy5+Z6VVIIWl35CAxnWCZb96krtnoqAVgoMDP0f8+tI7/2vR9ng/snNR1DdFUjLhO4h/1mbbW7mWoQotef977bw5F+/ndKZ9htqsx53Gva7mAjUM7Co6P3JqZsbZcil5CCFFWizNFrx1hKXpVE1NFL6XU2UqpzcDWzM9PUkr9vKQrExMKZIpb/lFFr0DBM70ynV45ZmQB2Rb9WBmjDgwRM51eDv+0d3ptD+0lqVPsjXRP63VhRNFrnA/LHqtT4g2FEEIIIUSp2IB/AQ8BfuDmTNyhMGkwHqLGNr1FL+n0qmxeq5sUuqDEjrZAukDSOsmil3FfIVBgrOJAIl0k21Vgh9hoRmFrvJleRrJJpdk4tJ2lvrms9C+c8LhXzXoJNmXltv0PF3T+rVP8fyqEEKI4WlyNOC126fSqMmbjDX8AXAL0AmitNwLnlWpRIr9gIoJNWY+YxWXsCCvEcMpEvKGqpHjDdAFnog9odXYfA5McFDtZRq5roTvjimEw80FjdLwhpHdvSryhEEIIIYQoBa31l7XWq4APALOBh5VS/y7zsqrKUCI07Z1efrsUvSqZkeYSLKAA1R7ai0JxnHfOpK7pzVwzVOD9BOP+w+7wwUld12BEGPrtY4tegUR4yvGJxaa1ZuPgdk6qOS7vsU3OWi5oOpm/HXiEpE6avkZbcA9zXE3UTPPfD0IIIY5kVVYWemaxQ4peVcVs0Qut9ehebPP/WouiC2byrkcOHvbZ3AQLbP03E2+olMKhbMR0JXR6pdfrtkwcb9g3zZ1eRtFrsBxFrxzxhmB0eknRSwghhBBClNQh4CDpTZIzzLxAKXWpUqpNKbVdKXX9OM9fp5TqVkptyHy9a8Rz1yqltmW+ri3adzHN4qkE4WS0bJ1eEm9YmbJFrwIKUG3BDhZ4ZuKeZCHTmBVeaHKMscZd4al1emWLXqM6vWpsXpI6VXEbOfcOd9MXD3BS7RJTx7+u5QK6ov082rvJ9DW2BjtknpcQQlSIxZ4Wdkq8YVUxW/TqVEqdDWillF0p9QlgSwnXJfIIJCL4rUcOefVZC483NLq3RnaMjcdusVVEvKHR6eWaII6xwVFDKDk8revtiZax02uCeEOv1SXxhkIIIYQQoiSUUu9XSj0EPAA0Au/WWp9o4nVW4GfAZcBK4E1KqZXjHHqz1vrkzNdvMq9tAL4InAGcDnxRKVVflG9omk30Pr6UaiTesKJ5M8XIgjq9gp0s804+Bs+fLbQVtonWKHrtmWKn11COopfx80qLONw4uAOAE010egFc1HwqdXaf6YjDWCrOjtB+lvul6CWEEJVgibeFjkgX8QpIQRPmmC16vY90ZMUcYD9wcubnokyCiQi+cd4QFhpvGM10Trkm6PQCcFjsFfEHO5KM4rY4sajcv3XrMzF//dNYgOqJDQCVGG/orLhdcUIIIYQQ4qgxD/io1nqV1vpLWuvNJl93OrBda71Tax0DbgJeafK1lwD3a637tNb9wP3ApQWvvAIYN/LHex9fSjLTq7IZ/3/MfraPpuLsCh+Y0uwnj9WFQhEqcKZXMDNrfMrxhvHxZ3rVZItehRXjSm3j0HYcysYKk0Upp8XOK2edw32Hns4WuyeyM7SfhE6yXDq9hBCiIiz2ziGpU3REusq9FGGSqaKX1rpHa/0WrfVMrXWz1vqtMqC4vIx4w5F8Nnc2FsCsqDHTyzpx0ctusRGtlKLXBF1eAHV2P8C0zvXqNuINy7ADbTAewmVxjBtR6bG6JN5QCCGEEEKUhNb6M1rrDZN46RxgZHz+3sxjo71WKbVJKXWbUsq4o2/qtUqp9yil1iml1nV3d09iiaVn3PwefaO/1CTesLJ5bZlOL5NFr12h/SR1imVTKHoppfDZXAUnxxjH98aHGDJRzMl9nhzxhvbKLHptGtzBCv8CHHk2D4/0upYLiabi/KPr8bzHbg12AEjRSwghKsQSTwuAzPWqIqaKXkqpxUqpuzKZ6oeUUncopRaXenEit0Ayko0gMPhtboLJCCmdMn2ebNErT7yhU9nydnp1Rfu5aO3/Y/cU87wnEk5G8eT5cFafKXr1T+Ncr95YGeMNE8GckSjpmV4SbyiEEEIIIarOXcDCTFzi/cCNhbxYa/0rrfUarfWa5ubmkixwqqTTS4zHby2s06stmK4BT6XTC8BnLTw5JjTi+N2RyXd75Sp6GT83OsEqQUqneH5oJyfVmos2NJxYs5il3rnctu+hvMduDXZgU1YWe1smuUohhBDFtMg7G0h34orqYDbe8M/ALcBsoAW4FfhLqRYl8hsv3tD4eaiAIkc0lY43HK9LaCSHxU5MTzwjqy3YwbbQXtYNtJu+fqHCyeG8w3mnO94wnBjOdlOVa6ZXrW38D8puq1M6vYQQQgghRKXZRzoa0TA381iW1rpXa228kf0NcKrZ11aL7Ewvm8z0EocZnV5mowbbg51YlYVFUyyQeG2uguaIQfq+RLOjFphaxKHRyeWzHbnBNVv0qqBOrx2h/QSTEU6qWVLQ65RSvL7lAtYPtue9abo10MFiT0tBnWRCCCFKp87uo9Few86wFL2qhdmil0dr/QetdSLz9UdAshDKKFe8Yfo5829UD3d65Y83jKUmLnoZH9oODPeYvn6hhlOx/EUvx/R2ehnRhlDGolfOTi+XzPQSQgghhBCV5hlgqVJqkVLKAVwN3DnyAKXU7BE/vQrYkvnv+4CLlVL1Sql64OLMY1XHmM1bk+O9fKkc7vSSj/SV6HChx3yn1yLP7Lyf6c1ct9BOr0Aiwir/ImBqRa9AIozP6saqrEc8XpMpCJdjjEAuG4d2AHBigZ1eAK9uOQ8Litv2PzThcW3BDpabnBcmhBBieiz2tki8YRUxW/S6Ryl1vVJqoVJqgVLqU8DdSqkGpVRDKRcoxhdIRPBbR8UbWgvfBWU23tBhseeNNzQKPvuHSzfuLZwczrsjcbrjDXtiA0D6DXmlxhtqrad5VUIIIYQQQoxPa50APki6WLUFuEVr/aJS6itKqasyh31YKfWiUmoj8GHgusxr+4Cvki6cPQN8JfNY1RnMRLZNd6dXo6OGOruPhZ7Z+Q8W085lcWBBHREdOJH2UOeU5nkZfDY3wQI7qoLJMM3OOmY5G6Y05iCQCI+JNoTDXYmV1Om1aXA7HquT4ybRWTfTWc95jSfx1/2P5Pz/OxQPsW+4R+Z5CSFEhVnibZF4wypiM3ncGzI/vhcw7p4r0jvyNCDzvaZRPJVgOBUbJ95wEp1eyRgWFDaLdcLj7BYb0TxFr+no9Aono9n4hFzcVidOi52B2PQUvXpjQwAs9c5he2j6k1UG4yFW+BaO+5zb6iKFJpqK47JOXNgUQgghhBBiumit7wbuHvXYF0b892eAz+R47W+B35Z0gdNgMBHEabFP+/t0l9XJ+vN/jU1N/BlQlIdSCp/NQ8BE1OBwMsqecBevnn3elK/rs7rpGu4v6DXBxDBem5uFnllT7vQar+jltjqxKktFFb02Du3ghJrFY7rSzHrvwqt4y/qv8r6N3+O3q6/HbjnyttzhGW1S9BJCiEqy2NNCb3yIgXiQummexyoKZ7bT69PASVrrRcD/ARuB12qtF2mtpeA1zYxs79HxhsabxELjDfN1eQE4lS1vp5cRz1HKTq9IMorLRPZ8vd0/bTO9ujOdXsf55jCUCJPUyWm5rmEwHqLGPvYDAhzO6ZeIQyGEEEIIISrLYDyUjW+bbnaLDaVUWa4t8vPZXKY6vbaF9qHRLPPOLcI13QST5otLWmuCiTB+69SLXkOJMP5xPtMqpaixeRmKV0a8YSwVZ3NgNyfVFB5taDin8QS+sfK9PNy7kU+8+HNSOnXE81uDHQASbyiEEBVmcabDd1do8p3NYvqYLXp9Tms9pJQ6F3gp6UHCvyjdssREjGzv0Tuh/JkiWGHxhjGc1vzZ34XM9Cp10cvMwOV6u5+++FDJ1jFSTzQ902uxZw4aPa270BKpJMFkhFrb+DsMjF+rUHJ42tYkhBBCCCGEyG8okXs2rzi2+azm5mu1G11BRSiQpOMNzW+gjSSjpND4bB4WembTExuc9GfhQCKcjTIczW/zVEynV1uwk2gqzom1S6Z0njfNvYhPHHc1fzvwKN9o/+Ooa3Tgs7qZ62qe0jWEEEIU15JM0WtHWCIOq4HZopfRunIF8Gut9T8ByUorE+ON6OhOLyPuMGgiBsGQ7vTKX/RyWOzEdb54w3Rn1VAiZDp/vFDhZNTUwOV6x/R1evXGBqmxeWl21gFM61yvocxA39wzvdK/VmEpegkhhBBCCFFRBuMhaiUeR4zDZ3OZ+lzfFuzEoWwsdM+a8jX9tnShzew86MCIBJqFnvT194S7JnXtXPGGkJ7rNVQhRa+NgzsAptTpZfjQotdw7bxLuGHPXfxq913Zx7cGOmj1zZNOTCGEqDDz3TOxKSs7yzDaRhTObNFrn1LqBuCNwN1KKWcBrxVFZuxyGlv0Mjq9ih9v6LDYiebt9Dpc7ClVt1ckGcVtYr31dv+0zfTqjg3S7KjN5rlOZ9HL6K7L9WHZLfGGQgghhBBCVKTBeJDaHDf6xbHNZzPf6bXEOyfvjG4zvFY3KbTpz47BEfcljKLXZCMOA/F8Ra/KiDfcNLSderuf+e4ZUz6XUoovLX87V8w8i6+1/57b9z+C1pqtwY6idO4JIYQoLrvFxnz3DHaEpNOrGpgtXL0BuA+4RGs9ADQAnyzVosTEjDe/fuuRbwp9VnfmefO7oIaTMZOdXjZieWd6Hc6kP1CColdKpxhOxXCb6fSy++iPT0/Rqyc2QJPzcNFrcBrzxo05arU5ZgEY8YZhKXoJIYQQQghRUYYSYen0EuPyWl2mi17LfFOf5wUjNtGaTI4JJtJpIj6rO9tptjs8uTknE3V6+e2VE2+4cXAHJ9YsLloXllVZ+eEJH+LshuP5xIs/5+Z9/2EoEWK5T4peQghRiRZ5Wtgp8YZVwVTRS2sd1lrfrrXelvn5Aa31v0q7NJFLrnhDm8WK2+IsKIc7lkqYLnrF8xS9BuJBVvoXALB/uMf0GswydpyZnek1EA+OGQpbCj3RQRrL3uk1ftHLa5N4QyGEEEIIISpRutNLZnqJsfw2D6E8xadgIsLe4W5ai1QgMWaEm91Eaxznt7nx2FzMcNZPqtNrOBkjphM5Z3rV2LwVUfSKJKO0hzo5qXbq0YYjOS12fn3yJ2n1zeNTm38JIEUvIYSoUEu8LewOHySpk/kPFmUlEYVVKJA8/OZyNJ/NXdAbwmgqhtOaPy7QrmzE8sYbhljmm4dClSTeMJKMAYcj+yZS7/CTQk9L9nePEW9oq8R4Q6PoJZ1eQgghhBBCVIqUTjGUCFOTY/OaOLZ5bfk7vbYF9wKwzDevONfMJseY2zAZzM70SherFrpnTaroFcgWz3J0etk8DMXLX/R6YWgXSZ3ixJolRT+33+bhxlP+m3mZ2MRiFTKFEEIU12JvC9FUvGRjfUTxSNGrCh3u9Br7ptBvc5saeGtIz/Qy0+llJ65zd3rFUwmCyQhNjlqanXUciBa/08voVjLT6VVn9wMwUOKIw1gqzmAiRJOjLtttNa1Fr0y2eb54Q5npJYQQQgghROUIJiJotHR6iXH5MzO9tNY5j2kPdQLFK3oV2ukVGJVAs9Azi12TiDfMjm+YoOgVTEbKvqt+49AOgKJ3ehlmOOu5Zc2X+PmJH6Pe4S/JNYQQQkzNEk8LADtlrlfFk6JXFQomwijUuMUfswNvDdFUHJfJold0gk4vY7Bsrd1Hi7OxRJ1e6cKN2ZleAH2x0ha9emNDADQ5a7FbbHitrrJ0etXl2CF6eKaXxBsKIYQQQghRKQYTEyc2iGOb1+omhZ5w82JbsAOXxcH8THfQVBmbas1uog0ZRS+rUfSaTXdsIPu4WUN5Or2MzaVmO9BKZdPgdmY5G5jprC/ZNea4m7ly1lklO78QQoipWexNF712SNGr4knRqwoFEhF8NhcWNfZ/36TiDS354w0dFhuxVCLnTrNszJ7Ny2xXIwdKUfRKGUWv/OttdNQA0FfiTq/u2AAATY5aAOrsPgYT09npFcRpsePK0f3mkXhDIYQQQgghKo7x+SnXHCNxbDO6pyYqQLUH97LUN3fc+wKTu2b6s6PZTbTjdXoB7Il0FXRd4/5Frj8LRjHM2GhbLhuHdnBSbfGjDYUQQlSPJkctNTYPO8NS9Kp0UvSqQqFkBJ811xtCd8GdXmbjDTWapE6N+/zh2VJeWlxN7B/umTCKYTKMwo3HVKdXpuiV6cQqld5optNrRNFruju9aiaIRHFZHCgUEen0EkIIIYQQomIMSaeXmEC26DXBZ/u2YAfLvMWJNgSy9xjM3k8IJsPYlTV7P8Eoeu0KFRZxaBS9fOPMLIfDRa9CNvcW22A8xK7wAU6qKU20oRBCiOqglGKxp+WY7fSKpxL8u3t92SOHzZCiVxVKd3qN/4bQZy0w3jAZx2nNX/SyKxuQnmE1HqPQU2f30eJqJJyMZiM7iuVw0Sv/TC+j06u3xEWvnmynVx2Q/tA6vUWvYDbuYTxKKdxWh3R6CSGEEEIIUUGk00tMxJvZ6Jmr02sgHqQr2k9rkeZ5weGiU8Bs0SsRwWfzoJQCDhe9dkcOFnTdQJ54Q2OTZyBevqLXpsw8rxOl00sIIY55i7yzj9mZXv869AzveO6bPNyzsdxLyUuKXlUomIhkh8yO5i9hvCFATCfGfd6I9Ku1+5jtagIoesShkWeeK8pvJK/VhdNipy9e2qJXd2wQgOZMp1etzTu9Ra9EKO/uUI/VJUUvIYQQQgghKoh0eomJzHU3A/DDHbeOO9erPdgJwLIiFr2cFjt2ZSVkcqZXMBHBNyKFxWdz0+yoZU+4sKKXMdOrxj5xvGGxN9UWYuNgpuhVI0UvIYQ41i3xzuFAtJdwmWdNlsPvO+9jrquZ85tOKvdS8pKiVxUKJsLZIbOj+WxugsmI6WjBaCqeLWhNJFv0ytHpNXKmV4urEShd0ctMp5dSigZ7TcnjDXtiA7gtTjyZ/PM6u4/BaSx6DcVD1E4QbwjgtjoJS7yhEEIIIYQQFWNkPLwQo630L+Sry9/JA93P8uZ1X6E/duSs6vbgXoCidnoppfDa3ARNbqI1Or1GWuCZxe4Ci17ZeMMcIxxqKiDecNPQdhZ6ZlEnRWohhDjmLfa0ALArXFicb7VrC3TwRP+LXDPvEqzKWu7l5CVFryo0YbyhzUNSpxhOxUydKz3Ty0ynVzoCMZYav9PL6G5Kz/RKF732F7noZRRuzBS9IB1xWOp4w97YEE3O2uzP00WvUNHnmeWSL94Q0r9eUvQSQgghhBCicgzGg1hQ2Rg7IUa7dv6l/OKkj/FCYBevefpzdEYOZZ9rD3bgs7ppyaSsFIvf6i4g3jA85r7EQs+sgm8CBhIhPFYnNsv4N9CMDrByFr02Du7gJOnyEkIIASzzzQXghztvHbMp5Wj2+877cFrsvHHOheVeiiklLXoppS5VSrUppbYrpa4f5/mPKaU2K6U2KaUeUEotKOV6jhb54g3B3BvCRCpJQiezg2cnYhS94jmKXoOJIB6rE4fFTrOzDpuycmC4J+95CxFJpgt5bou5oleDo/SdXt3RgWy0IUCd3U9MJ8aNoCgFiTcUQgghhBCi+gwlQtTYvViU7EMVuV0+80z+eOrn6Y4N8uqnP8eLQ7uAdLzhMt/c7DytYvHZzM8IDyTH3pdY5JlNV7S/oMinQCKcc54XHI43HCpT0etQtJ8D0V5Oqj2uLNcXQghRWZb55vHppW/mP93P8rLHP8a/u9eXe0klNxQP8dcDD/OKWWfT4Kgp93JMKdk7bKWUFfgZcBmwEniTUmrlqMOeA9ZorU8EbgO+Xar1HE2CyXDO1n8jXsDMG1UjqtBMp5dd5Y83rLWliy9WZWWms75knV4ua/71AjTY/fSWeKZXT2yQJkdd9udG3MFAovQRh0mdZCgRNhlvKEUvIYQQQgghKsWgiZhyIQDOqF/BX0//KlYsvP6ZL7K293nagp1Fnedl8NpcBE3O9AolInhHFb0WeGYB0BHpMn3NofjERS+HxY7TYicQL89ML5nnJYQQYrQPLHo1d57xDZoctbzjuW/yiRd+zlCZ/p2aDn898AjhZJRr511W7qWYVsptZacD27XWO7XWMeAm4JUjD9BaP6i1NrbrPAnMLeF6jgopnSKYGM4db2g1Or3yv1GNZoteZjq9MkUvnTvecGS+9WxXYwk6vaK4LU7TuyEbHDUlbzPtiQ3SOKLCbUQNDkzDXC+jmy9/p5eTiMQbCiGEEEIIUTEGM51eQpjR6pvH38/4OnPcTbzt2a/TGx9imW9+0a/jL6TTKxHJ3n8wLMwUvXYVMNcrkAhn53blUmPzlq3Ta9PQdiwojvcvKsv1hRBCVKZVNYu468xv8KFFr+G2/Q9x8RMf59HeTeVeVtFprfl9x72cXHMcJ9VWzwaQUha95gCdI36+N/NYLu8E7inheo4K4WQUjc65E8qIFzAzfDZb9LIWUPSaqNNrxIe22a5G9keL2+kVSUZxm+zygvRMr2Aykv0+iy2pk/TFhmh21mUfy3Z6TUPRa8AYfp1nh6hX4g2FEEIIIYSoKEPS6SUKNNvVyG2nfZVT6pYBsNJf/OkQPpvb1L0EMMYuHHlfYqE7XfTaXWDRa6JOL4Aam6dsRa+NgztY5puHxybz94QQQhzJYbHzyaVv4m+nfx23xclb1n+Vz27+NcNH0X3Yx/qeZ0d4P2+bf2m5l1KQiggQV0q9FVgDfCfH8+9RSq1TSq3r7u6e3sVVGOMNaM5OLyPe0EQkQbSAeENjpldsgpleI4teLc5GDgz3orXOe26zwskongIGPRsZo6Wa69UfC5JC03TETK900WtwGlpajWvU5tkh6pailxBCCCGEEBXFzGxeIUartXv5wymf40+nfp6z6lcV/fxeq5ugiZSQRCpJJBUdc1+ixu6l0V7D7vAB09c0U/Ty2zym5pYXm9aajUM7qmpnuxBCiOm3um4p95z1bd694Er+uPd+3rPhuyVrwphuv++8lwa7nytnnlXupRSklEWvfcDIkOm5mceOoJR6GfBZ4Cqt9bh35rXWv9Jar9Far2lubi7JYquFEVuYu+hVSLxhDDAbb5g+Jp6j6DU23rCJaCpOXxFnaoWTw7itTtPHN9rTRa/eEhW9emID6euMU/Sajk6vwczcsHw7RCXeUAghhBBCiMoyGA/mjXQTYjwuq4OXNJ6IUqro5/ab7PQyNtmOd19ioWdW8Tu97N6yzErpjByiPx7gxJrjpv3aQgghqovL6uTzrdfyrZXv5aHeDXxw0w9y3kevFvsi3dx/aB1Xz70IVwHpa5WglEWvZ4ClSqlFSikHcDVw58gDlFKrgRtIF7wOlXAtRw0jX3t0drZhUvGGJopedmU74jWjpQcxHy56tbgaAdg/XLyIw+FUDE8BRa/6End69WTO2zyy6GWbxqKXyU4vj9WZjsUsYtedEEIIIYQQYvKGEmHp9BIVx2fzEE5GSerkhMeFJrgvsaDAoteQiZle5er02ji0A0A6vYQQQph29dyL+PLyd3DfoWf42As/zftvaiX74977AXjr3IvLvJLClazopbVOAB8E7gO2ALdorV9USn1FKXVV5rDvAD7gVqXUBqXUnTlOJzICSZPxhqY6vdIFLJeJeENnZqZXXI+tUA8nYwynYqNmejUBcKCIRa+CO72MolcRu81GMjq9mkbM9HJbndiVlYF4oCTXHOlw0WviD8seq4ukThEb5/+dEEIIIYQQYnoNJ6NEU/G8m9eEmG6+zDiBUGLipJDsZtxxilWLPLM5EO01Nc8klooTTcXx5/mz4C/TTK8n+l7AY3Wy3Dd/2q8thBCier19/mVcv/Qt3HFwLddvvoGUTpV7SQWLpuLctPcBXta8hrnu6kves5Xy5Frru4G7Rz32hRH//bJSXv9oZLy5zNX+77TYcSibqV1Q0aT5eEN7pugVG6fTazCRLr6MjDc83OnVk/fcZoWT0SO6qvIpfbzhIMARM72UUtTZfRUVb2gUCiPJYVP/r4UQQgghhBClM5DZvFaT5328ENNt5IzwmgkKUYGkcV9i/HhDgD3hLlr9ExeLjPsW+eINa20ehhLTG2+otebhng2c03BCdtyDEEIIYdb7F72KSHKYH+38K26Lky8vf0dJoolL5Z8Hn6A3PsTb5l9S7qVMSinjDUUJBPPM9DKeK6TTy2md2kyvwbhRfDlc9Gp01OBQtqLGG0aSUdyZnWdm1Nq9WJWlZPGG3dEB7Mo6puhUa/dlC4GlNBgPYVfWvN1vnsyvWdjETjshhBBCCCFEaRk376XTS1QaszPCjXEK3gmKXrsj+SMOzRa9/HYv0VR83E24pbIjvJ/O4W4uaFo9bdcUQghxdPnYkjfyngWv4Hed9/KNbX+qqtEzN3bey2LPbM5tOKHcS5mUknZ6ieI7/KZwoqKXJztYdiKHZ3rljzc0il7RcYteYz+0WZSFWa5GDhSx0ytd9DI/NM+iLNTb/fSWKN6wNzZEo6N2TJV+2jq94kFq7b68uwSMOWhS9BJCCCGEEKL8jM9PdTaZ6SUqi3GfIZS36JWOP/TnmOkFmJrrZUQW5pvpZTwfSIRpLCD9ZSoe6tkAwAVNJ0/L9YQQQhx9lFJ8dtk1RJJRfrn7DjxWJx9d8vpyLyuvTUM7eG5wG19svQ6Lqs6eKSl6VRmjg8s7zptLg9/mNhdvmErHGzos+X8bOFTueEOjwFM3arZUi6uxqJ1e4WQ027VkVr3dX7JOr57YwBHRhoY6u6+os8xyGUyE8kYbwuF4w3By4lx2IYQQQgghROkZqRA19olv9Asx3Yz7DPmSY4xOr/ESaOrsPurtflNFr0DcZKdX5vmh+HQWvZ7jOO8c5rlnTMv1hBBCHJ2UUnx1xTuJpKJ8f8ctPNq7iWvnXcqlM0+v2Pjc33fch8fq5HUtF5R7KZNWnaW6Y1gwEcZlcWRnbI2n4HhDU51e6evF9TidXjniOWa7Gota/Aknh/NG+Y3W6KihLxYo2hpG6o4N0uysG/N4rW16Or2G4iFTkSjZeMM8w4iFEEIIIYQQpTdkJGVIp5eoMP5svOHEm2iNZBlfjmLVQs8sdoUP5L2e6XhDo+hlYnNvMUSSUZ7q3yxdXkIIIYrCoix8Z9V/8YXWa+mK9vPB53/IWY+8n+9tv2laGicK0R8LcMfBx3j17POqOopbil5VJpCMTBhtCOCzeswVvZJG0St/Vdkoso3X6TXeTC+AFlcTB6N9JHUy7/nzSekU0VQct6WwoleDo4a+EsUb9kQHaXTUjHl82uINEyFq7fk/KEu8oRBCCCGEEJUj16ZBIcrNKGLlG5cQyM4aHz+JZaFnFnvMdHqZLHoZCSdmEm2K4Ym+F4mm4lzQKPO8hBBCFIdVWXnXgit55Nwfc+Pq/+aEmsX8eOftnP3o+3nvhu/yRN+L5V4iALcfeIRoKs418y4u91KmRIpeVSaYiOTcTWXw29ymdkAZ8Yamil7ZeMPcM71Gx3O0uBpJ6CTd0cG8588nkinYeArt9LLX0FuCeEOtNb2xQZocdWOeq7P7CCWHiY/za1VMg3Fz8YZS9BJCCCGEEKJyGJsG893oF2K6GUWsfJtoQ4kIbosTq7KO+/xCz2z2D/cynIxNeB6zM738dqPTKzThccXyUO8GXBYHp9evmJbrCSGEOHZYlIULm1fzu1M+wyPn/oR3LbiSJ/pf5I3rvsS/u9eXe3ncuv9BTqhZzEr/wnIvZUqk6FVlgonwuLnZI6XjDc0UvTKdXtb88YZKKRzKNv5Mr0SQGptnzBve2a4mgKK0aYazRa/CZno1OGoYiAeL0m020lAiTEwnaM4x0wsOFwNLZTAeLCjeMCIzvYQQQgghhCi7oUQIr9U1YWS9EOVgdqZXIM99iYWeWWg0HZGuvOeB8WeDjWQUiKer0+uhnuc4u2EVLhP3SoQQQojJWuCZyWeXXcPT5/2SZkctt+57sKzreXFoF5sDe3hDy4VlXUcxSNGrygQSEfzWfEUvk/GGmQKWQ5n7sOWw2HN0egXH7ThqcTUCsH+4x9T5J2J0erkLfNPZ6KhBo+mPFTdusCc2AEDTODO9jKLXQKJ0EYcpnWIoETYZb5iZ6SWdXkIIIYQQQpTdYDxEjYnEBiGmm91iw2Vx5N1EG8wzdmGhexYAu/NEHAYSYdwWZ94CcM00xhvuDh9gd/ggFzRJtKEQQojp4bI6uWLW2fyn59lp2+Axnlv2P4hD2Xjl7HPKtoZikaJXlTETb1hj8xDTiWxRK5doKo7TYkcpZeraDouNuB4/3nC84otR9DoQnXqn1+GiV4GdXvb0zK3eIs/16slENo4308vovirlXK9AIoJGS7yhEEIIIYQQVWYoEZJ5XqJi+WxugnlSQvLdl1jkTRe98s31CiTC2ejCideUvg9Q6jQVgId6NgBwQdPJJb+WEEIIYbhq1jlEU3H+deiZslw/lorz9wOP8fIZp1Fn95dlDcUkRa8qYzbe0Dh2ItFUDKfFfOeU3TJ+vGE6Zm9s0avW5sNtcRal0ys8yZleDZmiVH+R53r1xNJFr+YcM72gtEUvYw6AmU4vI5IhLPGGQgghhBBClF1606AUvURlMjMuIZAI45tgQ2qd3U+tzcuu8IEJzzOUCOed5wVgVVZ8Vve07H5/qGcDCz2zWOiZXfJrCSGEEIZTapcyx9XEXQfXluX6/+5eT388cFREG4IUvapOIE+MAJDdcRXIE3FodHqZlTPeMMdORaUULa5G9hdhplckZXR6FVb0MjqxektU9GqacKZXCYtemQG+Zj4sW5QFt8WZ7ZYTQgghhBBClI/EG4pKli4uTXwvIZQYzptAs9Azy1S8oS/P+AZDjd1T8qLXcDLG430vcEHjySW9jhBCCDGaRVl4xaxzeKR3E/2xwLRf/5Z9DzLTWc95TSdO+7VLQYpeVURrbSre0G8zN3w2mozjKqTopSbo9MrxoW22q5EDRSh6TbXTq6/Y8YaxARSKBsfYds/p6fTKFL1Mflj2WJ3S6SWEEEIIIUQFkHhDUcl8Njeh5MT3Eswk0Cz0zMofbxg3F28I4Ld5GEqUNt7w6YEtDKdiMs9LCCFEWVw16xwSOsndh56c1ut2Rft5qOc5XttyPlZlndZrl4oUvapINBUjoZP54w2tBcQbWs3HG47X6aW1ZiAezBZ6RmtxNRUp3jBdsCm006shk0Fa7E6v7tggDXb/uH8R+G0eFKpi4g3BKHpJp5cQQgghhBDlNhgPUWsz9z5eiOmWjjecuOhlJoFmoWc2+4Z7Jpw1HkxG8JuIN4T05+xAvLSdXg/1PIfTYueshlUlvY4QQggxnlX+hSzxtHDXgemNOLx9/yOk0EdNtCFI0auqGBEDfuvEbwpLF29oI66PLHpFklHiOpmz+DLb1cih6ADxcWIRCzGcjAGFF73sFhs1Ng99RS569cYGaXKOjTaEdN643+YpabyhEa9Yb3KwoNvqknhDIYQQQgghyiyRShJMRqiRTi9Rofw2z4T3EowEGm+eotdibwspNDtD+3MeY3amF0CNzctQieMNH+rZwBn1Kwu+7yCEEEIUg1KKV8w6hyf6N9MV7Z+Wa2qtuXX/g5xa18pib8u0XHM6SNGrihi7rfJ1emXjDfNEEhRa9LJbbGN2aeWbLdXiakKjp/wH1ej0KjTeENIRh33x4mah9kQHaXLU5Xy+zu4raadXW7CDGpuXmc56U8dLvKEQQgghhBDlZ8wkMhtTLsR081ndhCYoekVT8XQCTZ7NuGfUrwDg0d5NOY8JxMOmO71qbJ6SFr32RrrZHtrHBU0nl+waQgghRD5XzT4HjeYfBx+fluttGNzO9tC+o6rLC6ToVVUCyfQbvLzxhrYC4g0thcUbju7YMgo7dTniOVpcjQBTjjg8PNPLVfBrG+01JYk3bHaM3+kF6aLXYAnzxrcEO1jum49SytTxEm8ohBBCCCFE+Q0mjJhyKXqJyuS1uSfcQGtsxs0Xb9jiamKpdy4P924Y9/l4KkEkFS0s3rCERa+HejYAyDwvIYQQZXWcdw6r/Au56+D0RBzesv9BXBYHV846a1quN12k6FVFDr+5nPhNod9svGEyjsNiM319h7IRG93plWe21OxM0evAcK/p64zHiOYrpDPN0OCoKUm8YWOeolepOr201rQFOljun2/6NW6rSzq9hBBCCCFExVBKXaqUalNKbVdKXT/Bca9VSmml1JrMzxcqpSJKqQ2Zr19O36qnbjCe3hhXI51eokL5bW6iqfiYz/6GoMnNuAAXNJ3MU32bCSfGfhY1e3/DUGP3EkiE0VqbOr5QD/U8x1xXM0s8R0+0kxBCiOr0ilnn8OzgNjrCXSW9znAyyl0H13LZzDNM/3tcLaToVUXMxhu6LA6symKi0yteeKfXqJleZuINAfYXoejltjixqMJ/yzY6auiNF6/oFUlGCSWHc870gtIWvfYOdxNMRljuW2D6NR5bdXV6bQ3s4Yc7bi3ZBxohhBBCCFE+Sikr8DPgMmAl8Cal1MpxjvMDHwGeGvXUDq31yZmv95V8wUWU7/OTEOVmzAgPjVOogsOba80Uvc5vOpmYTvBk/+YxzxlRhWZnevltHhI6WZJZ1bFUnLV9z3NB08mm01SEEEKIUnnFrLMBuKurtBGH9x56mqFE+KiLNgQpelUVo5U/X4yAUgqf1Z0tkuUSTcVwWgub6RXLFW+Yo9PLZ3NTY/NwYMrxhsOTmucFUG/30x8bKloBpTs6AEDzBDO9am3ekhW9tgY6AArq9Kq2mV5/2ns/399xCz2xwXIvRQghhBBCFN/pwHat9U6tdQy4CXjlOMd9FfgWUD1vZPMwOr1yJWUIUW6+zEiBXFGCxrwvnzV/0ev0uhW4LI5xIw4P398wO9MrXSguxVyvdQNthJLDEm0ohBCiIsxzz+DU2mUljzi8Zd+DzHM1c1bDqpJepxyk6FVFsp1eeQbGgpF3na/oFS8oLtBhsREdE2+Y+dA2QTzHbFdjETq9YrgnWfRqdNQS18mi5X8bhZimfDO94kFSOlWUa460NbgHgFbfPNOv8VhdJdkRVyqbA+nvcWdof5lXIoQQQgghSmAO0Dni53szj2UppU4B5mmt/znO6xcppZ5TSj2slHpJCddZdIc/Px1dETLi6GF0euWa63W40yv/72GX1cFZDat4ODMv68jzFFr0MsY4FL/o9VDPBuzKytkNxxf93EIIIcRkvGLWOWwO7GFbcG9Jzr8v0s3avhd4bcsFk0pWq3RH33d0FCskRsBnc2eztnOJFRhv6LTYiY/q9BqMB7GgJlxTi6uJ/UXo9Jps0avBUQNAX5EiDo2iV76ZXil03m67ydga7GCeq7mgrFWP1VU18YZa62xhb0dYil5CCCGEEMcapZQF+D7w8XGePgDM11qvBj4G/FkpVTPOOd6jlFqnlFrX3d1d2gUXYCghnV6ishmf7XNtojWKYfkSaAznN57MzvCBMXNJskUvu/l4Qzj8Z6iYHup5jtPqlpu61yKEEEJMhytmnYUFxZ0l6vb664FH0Ghe13J+Sc5fblL0qiLBZBi7sprqzvLZ3EXv9LIr25hhtgPxILV234QV4dmuRg4UYabXZOMNG+3pz8C9seIWvZrzzPQCShJxuDXQwXK/+XlekI43TOhkzmHElWTvcHf29+4u6fQSQgghhDga7QNGxhbMzTxm8APHAw8ppXYDZwJ3KqXWaK2jWuteAK31emAHsGz0BbTWv9Jar9Far2lubi7Rt1G4wXgQh7LhKmDzoRDTyYgtDOUqemWKVV6TBaILmk4G4OHejUc8XuhMrxp7aTq9Dg73sjXYIdGGQgghKspMZz1nNqziroNrizayx6C15tZ9D3J2/Srme2YW9dyVQopeVSSYiOCzeUwNVvXbPCZmehUab2gnrkd1eiVCeYcwt7ia6I0PMZyMmb7WaJFUdMqdXsUuek3U6WXs3BxIFLfoFU3F2Rnez3Kf+XleAO5MLns1dHttyUQb2pVVOr2EEEIIIY5OzwBLlVKLlFIO4GrgTuNJrfWg1rpJa71Qa70QeBK4Smu9TinVrJSyAiilFgNLgZ3T/y1MzmAiRI3da+oznRDlYHQ75bqfEEykR+z5Tcz0Aljkmc08V/OYiMNC4w2znV7x4ha9HsysyyjOCSGEEJXiqlnnsDN8gBcCu4p63o1D29kT6eJ1cy4s6nkriRS9qkggETbdbu+zubM7sMajtc4UvczvMLRbbMTGiTecaJ4XwGxnIwAHo5Pv9gpPpdPLiDcsVtErOkCNzTthwbC2RJ1e24N7SerUpDq9gKqY67U5sBuF4uyG42WmlxBCCCHEUUhrnQA+CNwHbAFu0Vq/qJT6ilLqqjwvPw/YpJTaANwGvE9r3VfSBRfRUDyU9/OTEOVkxBYGcoxLCCbCWFCmN6UqpTi/6WTW9j1/RPLIZGd6DRW50+u+Q08z19VMa4EbS4UQQohSu3zmGdiUtegRh+sG2gA4r/HEop63kkjRq4qEEsOmc7N91onjDaOZN5tOq/lOL6fFRjQVP6KlciAezEb55dLiShe99k8h4jCSjGa7lQrVYPcD0FvEmV5NjjFjA45QqnjDLcEOgII7vYyiVzg5XNT1lMKWwB4WeGZyQs1iOiKHxsyRE0IIIYQQ1U9rfbfWepnWeonW+uuZx76gtb5znGMv0Fqvy/z3X7XWq7TWJ2utT9Fa3zXda58KM0kZQpSTEVsYSoz/2TGQNJ9AY7igaTWh5DDrB9oPnycewmmx4zCZPlOTKRYXM94wmIjwWO8mLplxunRfCiGEqDh1dj/nNZ7EXQfXktKpop33ucFtzHE1McNZX7RzVhopelWRYCKMz2q+9X+ieMNs0auATi/jzWhCJ7OPpT+05St6NQHFKHpNLvfeY3PhsjjojwUmff2R0kWvugmPMYpeg/HiDtltC+zBabGzyDO7oNd5qineMLiHFb4FLPbOIaGTdES68r9ICCGEEEKIKjAUD2Vv3gtRibyZz465ikvBRNj0ZlzDWQ2rsCkrD/duyD42lAibnucF4LY6sSoLQ4nifcZ+sOc5YjrBpTNOL9o5hRBCiGK6atY57B/u5cn+zUU754aBbZxcu7Ro56tEUvSqIukdVebjDSOpKIlUctzno6n0fK1CZnrZlQ3giEiCQRPxHLNdDQAcGO4xfa3R0vGGk+v0gnTEYTFnejU5c8/zArK/JoMl6PRa6p2LzWIt6HXubKdXZRe9QokIe8JdrPAvYImnBYAdEnEohBBCCCGOEtLpJSqdRVnwWd0TzPSK4DU5z8vgt3lYU9d6xFyvQCJsOtoQ0jGJfpunqJ1e9x16mkZ7DWvqW4t2TiGEEKKYLpt5Bs2OOr63/eYj0tcmqyc6SOdwN6ul6CUqRTARMR9vmHnzGEyO/0bV6PRyFVD0Mjq94jodN5fSKQZNxBu6rE4a7P4pdXqFk8OmM8PH0+Cooa9I8Ybd0QGaHBMXvVxWBy6Lo+jxhluDHZPKGj8806uy4w23BjvQaFb6F7LIm+5m2xU+UOZVCSGEEEIIURyD0uklqoDP5iaU415CIfclRjq/6WReDOzmULQfKLzoBemIw2IVvaKpOP/pfpaXzzgNqypsU6kQQggxXdxWJx9d8jqeGdjKAz3PTvl8zw1uA+Dk2uOmfK5KJkWvKhJMhLPFrHyMN6G5dmdNJt7QbrFlXpvInjuFzhtvCOmIw8l2eqV0imgqjtsy+aJXo704nV6xVJzBRChv0QvSEYfFLHr1xYY4FO1nhX9Bwa+tlnjDLYH0zLIV/gXU2X002muk00sIIYQQQhwVtNYMSaeXqAI+W+4Z4cGE+QSakS5oPBmAR3o3AZMtenmKNkLg8d4XCCYjEm0ohBCi4l095yIWembxrW1/IqnHT3Uza8PgNqzKwgk1i4u0usokRa8qEijgzaXPahS9xt8FtS/SDVDQBy4jCtGINxzMZGmbOcdsV+OkO70imUKNZwqdXvUOP31FKHoZ58g30wuKX/TaGkwXhJZPotPL6JLLNYy4UmwJ7MZvczPX1QzAYm8LO6XoJYQQQgghjgKh5DBJnTK1aVCIckrHG+aY6ZWMmN6MO9IK/wKaHbU83PMckJ7pVWjRq5jxhvceegqf1c05jScU5XxCCCFEqdgtNj5x3NW0BTv524HHpnSu5wa3scK3YEqJatVAil5VIp5KMJyKZYtZ+RhvHnPtzrrn0FN4rS7OqF9heg2OTKdXPNPpZeywyjfTC6BlCkWvcLboNbWZXsWIN+yODQLQnGemF6SLXoOJ4hW9tgT2ALDcP5V4w8ru9Noc3MNy3wKUUgAs8bawMyxFLyGEEKIURs5pFUKUnjHvV+INRaXz2dwEc0TjBxJhfLbCP5tblIXzGk/mkd5NJHWSQCJMjb3ATi977qJXPJVgb2Zzbz5JneT+7nVc2Ly6oDnnQgghRLlcOfMsjvcv4vvbb84muBUqpVNsHNp+1EcbghS9qoaRp22602uCeMNEKsl9XU9zUfOpuAqo6tpVuuhl3CAxupjyzfQCmO1qYigRIpSjCDcRo1DjtpqPYhytwV5LOBlleIpFn55ouujVWIZ4w7ZgBw12P80musxGOxxvWLmdXimdoi3QwcoR8Y2LPS30xAaLFmEhhBBCiLTH+17gvMc+xNbMphohROkNZW7WS7yhqHQ+W+5Or1BiGJ+18E4vgAuaTqY/HuD5oZ2Tijf02zwMJY78bKi15oHu9Vz8+Mc599EPsGloR97zrB9opyc2yCUSbSiEEKJKWJSF65e9hb3D3fyh81+TOseO0H4CiQira5cWeXWVR4peVcLo2DL7ptAoegWSY9+oPj2whd74EJfPPLOgNTiMeENtdHqlCzrmZno1Akyq2+tw0WtqnV7AlOd69WQ6vczM9Kq1e4ta9NoS2MNy/+EuqEIYBcNKnunVGekmmIywwr8w+9hibwuARBwKIYQQRaS15pvtf0JrWOiZXe7lCHHMyH5+kk4vUeHSRa+xG1ZTOpWJNyx8phfASxpPRKF4oPtZwsko/gL/LIyON2wLdnLNs1/n7c99E42mxu7lO9v+kvc89x16GoeycWHT6oK/ByGEEKJczms8iXMbTuCnO/86qbjfDYPbADhZil6iUhhvOM2+uTSKY+O9Ub2760ncFmfBb/CMeMOYEW9YwEyvOZkZTXsiXQVdE0bGG04+azRb9JpixKFR9Go20+llK16nV0qnaA/undQ8LwCrsuKyOIhUcKfXlsBuAFb4RnR6GUUviTgUQgghiuaeQ0+xYWg7Hz/ujbim0EkvhCiM8fmpRjq9RIXz2TzjjkoIZT5PFtqhZWhw1HBizWL+2fXEpM5TY/MSTAzTEx3kc1t+w6VPfIINg9v5Qut1/Ovs7/GBRa/m4d6NPNn3Ys5zaK2599DTnNt4wqS/DyGEEKJcrl/2FvriAX61+86CX/vc4DZqbB6WZO63Hs2k6FUljOrtVOMNkzrJPV1PcWHz6oIH1hmdXsZMr2y8oS1/p9dK/wKsypKtKBcikjI6vSZf9Kq3+wHom3Kn1wBuixOvif8PtXY/0VR8ypGKAB2RLiKpKCtGRP8VymN1VnSn1+bAHhSKVt+87GPz3TOwKot0egkhhBBFkkgl+fa2P7PUO5fXtpxX7uUIcUwZKmAmshDl5LO6CCUjaK2PeNy4v+CdxEwvwwVNJ7M9tA8Af4EdYzU2DxrNeY99iD/tvZ+3zn05j5z7Y9614AocFjvXzruEWc4GvrX9L2PWbtgc2E1n5BCXzDhj0t+DEEIIUS4n1izhypln8es9/6A7OlDQazcMbufEmiVY1NFfEjr6v8OjhPHm0m8yO9ubiQIc3eq4bqCd7thAwdGGAHbLkTO9BuNB7MpqqhjltblZ4VvAuv6tBV+3mJ1efbHApM8B6U6vpsy58jFmnQ0UYR7VlkAHwKQ7vSA916uSi15bg3tY5JmFZ8QHKIfFznz3THZIp5cQQghRFDfv/w87wwf49NI3Y1XWci9HiGPK4aSM/JsGhSgnn81DUqcYTsWOeLzQ+xLjOa/x5Ox/F9ppNcedTpA5pW4Z9571Xb664l00jPh87rI6+ciS17F+oI0Hep4d9xz3HnoaC4qXN68pfPFCCCFEBfjkcW8ilkrw4523mX5NJBllS3DPMTHPC6ToVTUKjTe0KAs+69gc7ru7nsBpsfPSplMKXoMRbxg1il6JELV2n+kZU2vql/Pc4HYSqWRB1w1nIhSm0ullvBHuK0K8YZOzztSxRtFrsAgRh1uDHSgUy0Z0QRXKbXVmfy0r0ebAniPmeRkWe1qk00sIIYQogkgyyg923Mqaula52SdEGQzGgyhUwd0tQkw34/fo6E20hd6XGM/q2qXUZIpdhRa9Lp1xOo+c+xP+cMpnj0gIGekNLRey0DOL72z7CymdGvP8fYeeZk3dcpqc+UcWCCGEEJVokXc2V895KX/a+292hw+aes3zQztJ6tQxMc8LpOhVNQLJ9JvNQj4g+WxugsnDb1JTOsU9XU9xQdPJk3qT6lRj4w3rCtiluKa2lUgqyubg7oKuO5xM7y6bStGr1ubFpqz0TjXeMDpIk4l5XnB41lkx5nptDexhoWfWlH4NPFYnkQrt9AokwnREusaNb1zibWFX+MC4H1iEEEIIYd7/7rmbQ9F+rl/6FtObloQQxTOYCOO3eY6JSBlR3bzW9P2CUOLITZPGfYmpFL1sFivnNp4IkC1+mWVRFhZ6Zk34b5jdYuPjS97IluAe7jy49ojndocPsjXYwaUzTy984UIIIUQF+ciS12NXNr63/SZTx28Y3A4gnV6ishzeUWX+TaHP5j5i+Oxzg9s4GO3j8plnTWoN2XhDnS56DcZDBeXRr6lvBWBdf1tB1zW6k6YSb6iUot7un3rRKzZguuiVjTdMFKfTayrRhlDZ8YZtwU4AVvjGFr0We1uIpuLsG+6Z7mUJIYQQR43+WIBf7v47L2s+ldPrV5R7OUIckwbjwezGOCEqmVHUCiSL3+kF8PLmNVhQzHTWT+k8ubxi1tms9C/ge9tvzm7ahXSXF8AlM6ToJYQQorrNdNbzzgWXc8fBtWwO7M57/HOD25jnaj5mOp2l6FUlgokwClVQ4cdv8xwRR3BP11PYlZWLJhFtCOn5SnC406vQD20triZmOxtZP1ho0cuY6TX5YbkADQ4/fVMoeqV0it7YUOFFryl2eoUTw+wOH2T5OF1QhajkeMMtmb+cV47zPS72zAZgV+jAdC5JCCGEOKr8bNffCCYifPq4N5d7KUIcswrdNChEuRixg6PHJWSLXtapFb1eM/s8Hjr3R8xyNU7pPLlYlIVPHvcm9kS6uHnfg9nH7z30NKv8C5nnnlGS6wohhBDT6b0LX4nH6uS3e+7Oe+yGwW2cVHvcNKyqMkjRq0oEEhF8NldBURg+2+GZXlpr7u56kpc0nkTNJHcX2kfP9IoHCx7CvKa+teBOLyOSz5kpuk1Wo71mSjO9+uMBUuhJzPQKTfqaANtCe9HoInR6VW684ebAHmpsXlpcTWOeW+xtAWBHWOZ6CSGEEJOxL9LNjZ338tqW82n1T+39hBBi8oYSoUl/FhNiOhmdXLmKXoXO4hpNKcXCzObGUnlp0ymcWtfKj3beynAyyqFoP88OtEuXlxBCiKNGrd3La2efzx0HH6M3NpjzuEPRfvYN9xwz0YYgRa+qEUxE8FkLe2M5stNr09BO9g53c8XMMye9BqcRb2gUvRKhwotedcs5EO1lfwFRdZFkFLfFOeXs+wZHzZQ6vXqi6b88zHZ6+axurMrCQDww6WsCbAnsAWDFFG9SVXK84ZbAblb454+bzd7sqMNvc7MztK8MKxNCCCEq31CeDTbf33ELAB9b8obpWI4QIgfp9BLVwujkGlP0SqZ/7rVNLYVlOiiluP64N9MV7ed3Hffyr0Pr0GgulaKXEEKIo8i18y8lmorzl70P5DzGmOd1shS9RKUJJMIF52b7R3R63d31BDZl5eUz1kx6DQ51ON4wqZMMJcIFf2g7tW4ZUNhcr3ByeErzvAyNjpopzfTqzlTMm00WvZRS1Nq8U4433BrswG1xMt89c0rn8VidhCow3jClU2wNdrDCv3Dc55VSLPa0sCMknV5CCCHEaLfue5DjH7yOSx7/BD/ccSvtmTmZhrZAB3/d/zDXzruUOe7mMq1SCAHGpkEpeonKl6vTK5AI47TYs6MPKt0ZDSu5oPFkfr7779x+4GEWuGfSOsUEFSGEEKKSLPPN4yWNJ/L7zvuOmGM50nOD27ApKyfULJrm1ZWPFL2qRDAZwV9g0ctn9RBMRLLRhuc0HE+d3T/pNRjxhjGdYCie7iCrK7DTa6VvIW6Lk3UDW02/JpKM4S5C0avBUctgIpTzL4B8jKr4As8s06+ps/umXvQK7KHVN2/KnW5uq5NIBRa9OiJdhJNRVvpyzyxb7G1hp8QbCiGEEEc4FO3ny203ssK3AK/NxQ923MrLHv8YL137Ub6z7S+8MLSLb23/Mz6bmw8senW5lyvEMW9IOr1ElTDuPRidXYZ0As3U5nlNt08ufRMD8SDrBtq4dOYZ46aLCCGEENXs7fMv52C0j3sPPT3u888NbmO5bz6uItxfrxZS9KoSwUQEX4G52T6bm2AywguBXeyJdHH5zLOmtAabsqJQxFNxBhPpQk6h8YY2i5XVtcexbqCwTq9iFL0a7TVAejZXobTW3H7gYU6vW8HsAobtTrXopbVmS7CD5UWYv+Gxuojr5KSLfqWyORvfmLvotcQ7h/3DvYQTlVe0E0IIIcrli1v/j2gqxi9O+hi3n/41nj7/Br62/F3McNbz891/5/InP8W/u9fzvoWvot4x+Y1PQoipS6SSzHU30yIdl6IKOC0OrMpCMDMuwRBKRApOoCm3E2oWc0XmXohEGwohhDgavbRpNQvcM/m/jrvHPJfUSTYN7jim5nkB2Mq9AGFOIBEuqNgCh4fL3rr/IazKwiUzTpvSGpRSOCw2oqkEg5nZEZPZqbimfjk/2/U3QokIXhNvmCPJaFHiDY2bPX2xIWY46wt67QuBXWwP7eMbK68o6HV1dl82FnEyDsUG6I8HihLBYOSuh5NRai2V80d/S2APFhStvnk5j1mUGXK8O3KQlTliEIUQQohjyf2H1vHPrif45HFXs9jbAsBMZz1vm38Jb5t/CX2xIf516Bm2hfbyzgWXl3m1Qgibxcp/zvlhuZchhClKKXxW9zjxhpHsfYZq8qXlb+eM+hWcUrus3EsRQgghis6iLFw7/zK+0vY7Ng3t4MSaJdnntof2E0xGjql5XiCdXlUjmJhEvGHm+L8feJQz61fR4KiZ8jocFhuxVDzbvVRovCHAmrpWkjqVjQvMJ5KKFqfTK/P9902i0+tvBx7BoWzZHWJm1U6x06st0AFM3AVllvFrWGkRh1sCe1jsbZmwxXZJ5maezPUSQggh0puhPrflN7T65vHehVeNe0yDo4ar517E51uvLcr7KCGEEMcWv81DYFTRK5gsfNZ4JZjprOe6+ZdJtKEQQoij1htaLsBjdfK7jnuOeHzD4DYAVtdJ0UtUoHR2duHxhgAD8SCXzzyzKOtwKDvxkZ1ekxjEvLp2GQplOuIwXKROLyPesDc2VNDrEqkkdxx4jIuaTy24yFdr9zE4haLX1mC66LW8CJ1eHuvhTq9KsiWwO29Rz+j02ilFLyGEEILvbPsLB6N9fHvlf+Gw2Mu9HCGEEEchr81FaJyZXt4qm+klhBBCHAtq7F5e13IBdx5YS0/0cOrYc4PbqLF5WJy5t3qskKJXFUjpFMFk4dnZ/kyRTKGKll1tt9iI6cnP9Eq/xssy31zWDWw1dXwkGcWdKdhMhdHp1ldg0Wtt3/N0xwZ59eyXFHzNOpuPoUSYRCpZ8Gsh3QU1w1lflC49o3A4nUWvjnAXN3bcS1KP//0PxUN0Dnezwjdx0cttdTLH1cTOsBS9hBBCHNvWD7RxY+d9XDv/0mNut54QQojp4xuv02sSCTRCCCGEmB7XzruUmE7w533/zj62YXAbJ9Ueh0UdW2WgY+u7rVKhTBxdodnZRpHsjPoVNDvrirIWh8VOLJXIRvZNZqYXwKl1rTw72E5Kp/Iemy56OSZ1nZHq7emZXoV2et1+4BFqbV4ubD6l4GsanWFDiVDBrwVoC3YUpcsLDscbhqcp3nA4GeWdG77F57f+L5944Rfj/r82OtnMxDcu9raYijc083tKCCGEqEaxVJxPv3gDs10NfOq4N5V7OUIIIY5ifqubYCJ8xGPBROGbcYUQQggxPZb65nJe40n8ofM+4qkE4cQwWwMdrD7G5nmBFL2KLqVT3LD7Tg4M9xbtnMbw2ELfXDY40kWeYkUbgjHTKx1v6LI4cE2yGLWmbjmBRIT2YGfeY9PxhlPv9LJZrNTavPTFzRe9QokI93Y9zZWzzsY5ifggo+hlxEEWIpFKsi20lxVFKnoZv4aRaer0+p9tf6It2MmVM8/irwce5vrNvxpTkNoS2APASv/CvOdb4mlhZ2g/Wuucx/xy1x2c/sj7eG5g25TWLoQQQlSiX+6+k/ZQJ19b8W656SiEEKKkfDZ3dgOuIZ1AU9hmXCGEEEJMn7fPv4yuaD/3HHqK54d2kkJzshS9xFRtGNzO19v/wMde+NmEN+cL8VT/ZgAWuGcW9Lol3jn8/pT/5i1zX16UdUC66BVPJRhMBCc1z8uwpm4ZAOsG2vMeG04OF20Ae6OjpqB4w/sOPUMkFZ1UtCEcLnoNTGKu167wAaKpOK0muqDMmM54wwe61/O7jnt45/wr+PlJH+PDi1/LTfse4Atbf3vEn4vNgd3U2rzMcjbkPedibwvBZITu2MC4z/fGBvnRzts4FO3nTeu/zCO9G4v17QghhBBltyO0jx/vuI0rZ57Fy5pPLfdyhBBCHOV8NjeBEZ1esVScaCoumy6EEEKICnZh02oWuGfyfx338Nxguilgde1xZV7V9JOiV5EZN9rX9j3P3w8+VpRz/qHzXyxwz+SshlUFv/aCptXYLbairAPArmzEUnEG4kFqbYXP8zIscM+iyVGbd65XSqeIpuK4LcUpejUUWPT624FHmOdqZk1d66SuZxQGJ1P0eiGwC6Bo8YaeaYo3PBTt5xMv/JyV/gVcv+wtAHx8yRt538JX8vvO+/hK243ZwtfWYAcr/QtRSuU972JPCwA7c0Qc/mLXHUSSUW5e8yUWembx9me/wV0HHy/SdyWEEEKUT0qnuH7zDbisTr60/O3lXo4QQohjgM/mzqbOAIQS6c+RPqsUvYQQQohKZVEWrpt/GesH2rh533+Y555Bo6O23MuadlL0KrJHejdyQs1iVtcu5attN06q2DHSlsAenhnYyjXzLqmIgXPpmV5xBuOhbBfTZCilWFPXyrqBtgmPM6L4PEXs9Oo1GW/YFe3n0d5NvLrlvEn/2mc7vRKF/z64t+spmh21RZzplY43LGWnV0qn+NgLPyOUHObHJ3wkGwmplOIzS9/CO+Zfzv92/JNvbPsTSZ1ka6DD1DwvSHd6AewIjy16HRzu5cbOe3lNy3mc1bCKm9d8mdW1y/jgph/yh877ivcNCiGEENNoOBnl5n3/4fInP8VT/Vv43LJrmOGsL/eyhBBCHAN8Vg+h5HA2ot7o+vJLp5cQQghR0V7fcgEeq5Md4f3H5DwvkKJXUQ3FQzw3uI0Lm1bzPyveTX88wDe3/WlK5/xD5304LXZe33JBcRY5RQ6LnZhOz/SaSrwhwKl1rXREujgU7c95TDhb9Jr6TC+ABrv5Tq+7Dq4lhZ50tCFMPt5wKB7iPz3PcuWss7FZrJO+/kjT0en12467eaR3I59vvZZlvnlHPKeU4out13HN3Iv55e47+PgLPyeSipqa5wXQ4mrEZXGM2+n1451/JaVTfHTx64F0h90fT/0sFzWfwme3/IYf7bitaHGjQgghRKntH+7hW9v+zBmP/BeffPEXaA3fW/V+3jjnpeVemhBCiGOEEWNozPUKJo1Z4zLTSwghhKhkNXZvtpZwLM7zAihe7p3gif4XSeoUL2k8iVU1i3jH/Mv59Z5/8LqWCyYVjxdIhLn9wCNcNesc6h3+Eqy4cA6LjUAizGAiyCr7wimdy/g1WT/QzmUzzxj3GKPTy211TOlahgZHDX3xACmdytu9dfv+RzipZglLvHMmfT0jAnKwwKLXfYeeJpqK88pZ50762qOVeqbXi0O7+Gb7n7i4+TTemmOOnFKKr654J3Gd5KZ9DwCw3G+uk82iLCzyzGbHqKLXnnAXN+37D2+e+zLmew7PvXNZnfzqpE/yqc2/4Hs7bqY3PsSXWq+riI5JIYQQYjzrBtr43z3/5N5DT6G15uIZp/GO+ZdzRv1KU1HAQgghRLEYRa9gIoLf5iGQiTr02oqzIVUIIYQQpfOuBa9g/UA7L2s+pdxLKQspehXRIz0b8VpdnJKpoH5syRv4Z9cTfGbzDdx95rcLnq11+/5HCCejvG3eJaVY7qQ4LDbiqQQD8eCU4g0Bjq9ZjNNiZ/1Am4miV3HeWDc6akjqFEOJ8ITrbw928kJgF19qndrcDJvFit/mLrjT646Da5nnnlHUFlSrsuK02EvS6RVJRvnQ8z+i3uHn26veN+GNOYuy8M2V7yGpk/zr0DMs887Leexoi70tvJiZdWb44Y5bsSoLH1z0mjHH2yxWvrvq/TTYa/jVnrsIJ4b57vHvN/+NCSGEENPkZ7v+xre2/Zkam5d3LbiSt827hHnuGeVelhBCiGPUyKJX+sdMvKFVOr2EEEKISrfAM5O7z/p2uZdRNtLyUESP9G7k7Ibjs8Utr83NV5a/k7ZgJ7/Z88+CzqW15ved93FizRJOqj2uFMudFLuyEUoOE05Gs11Mk+W02DmhZjHrBrbmPCZc5JleDfYagLwRh3878ChWZeGqWedM+Zq1Nl9BRa/u6ABr+57nlbPOKfqubrfVmS0kFtNX2m5kR2g/Pzj+QzQ4avIeb1EWvrvq/Txz/g24CujiW+JtoTNyiFgqDqSLk7cfeITr5l3KLFdDzmt9dtk1vGP+5dyy/0G6JojTFEIIIcrhln0P8q1tf+aqWefw9Hm/5LPLrpGClxBCiLLyWY8seoUS6c2TPpnpJYQQQogKV9Kil1LqUqVUm1Jqu1Lq+nGeP08p9axSKqGUel0p11Jqu8MH2RPp4rzGk454/OIZp3Fx82n8YMctdEYOmT7f0/1b2BbayzXzLi72UqfEYbHTHR0AmPJML4A1dct5fmgnw+MUYoaTUf6v424g3aFVDMZ5eicoeqV0ir8feJTzG0+iyVk75WvW2Qsrev2z6wmSOlXUaEOD1+oqeqfXvV1P8ae99/O+hVdxbuMJpl+nlMJVYDFzkWc2SZ2iI/Nn6Xvbb8ZrdfFfi16V91rGbLan+jcXdE0hhBCilP7dvZ5Pb/4lL2k8ke8f/wE8EhslhBCiAhjFrUAyfMSPfil6CSGEEKLClazopZSyAj8DLgNWAm9SSq0cdVgHcB3w51KtY7o82rsJYEzRC+ArK96BRSk+v+V/0VqbOt/v995Hjc1blE6jYnJY7ERS6QLVVOMNIT3XK66TbBraecTjHeEuXvP057nj4Fr+35I3cGLNkilfC8h2IfXFcxe9nu7fwr7hHl49+7yiXLPW7itoptcdB9fS6ptHq8lZV4VwW51Fnel1cLiXT23+JSfULObjx72xaOfNZYm3BYCdof08P7STew49xbsWXGmqu2yVfxE+q5un+qToJYQQojKsH2jj/Ru/z0r/Qm446RM4LPZyL0kIIYQAwG9LxxgaHV7B7EwvKXoJIYQQorKVstPrdGC71nqn1joG3AS8cuQBWuvdWutNQKqE65gWj/ZuZJ6rmYWeWWOea3E18YnjruY/Pc9yz6Gn8p7rULSfe7qe4g1zLsBdpFi/YnGMmEs21XhDgFPrlgHpwe2GR3o2cuVT19MR6eK3qz/N/1vy+qLF/Jnp9Lr9wKN4rS4unnFaUa5ZZ/cxmAiZOrYzcoj1A20l6fIC8FhdOeMN/3XoGd607iscGO41da6kTvLR539CLBXnJyd8ZFpu1C32pIteO0L7+e72m6iz+3j3witNvdZmsbKmrpUnpdNLCCFEBWgPdnLds99glquBG1f/t8RFCSGEqCjezFxtY5aX8aO3SPO2hRBCCCFKpZRFrzlA54if7808VjCl1HuUUuuUUuu6u7uLsrhiSqSSrO17npc0nZSzOHPdvMtY5V/IF7f+lqH4xAWQm/b9h4RO8ta5lRVtCKOKXkWIN2x01LLIM5v1A21orfnpzr9xzbNfZ6aznn+c+U0uaj51ytcYqcHuB3LP9BpOxri76wkum3lG0QqOhcQb3nlwLUDJOvw8Vue48YbPD+3kg5t+yNq+53nbs19nMM/vUYAbdt/F4/0v8uXl72BxpgOr1GrsXpodtdx+4GEe7HmO/1r4quwORDPOaFjJttBeemODJVylEEKUxnAyxs37/sMbnvkidx18vNzLEVNwYLiXa9Z/HYfFxh9O+VxR4pSFEEKIYjI+ZwUyHV7BxDA+qxuLktHwQgghhKhsVfFuRWv9K631Gq31mubm5nIvZ4wNQ9sJJCLjRhsabBYr31j5HrqjA7xx3ZdyzvdKpJL8qfN+XtJ44rQVEgphV4eLXsWIN4R0xOG6gTbeu/G7fHv7n3nFrLO54/T/YaFndlHOP5LL6sRjdeaMN/zF7jsYSoSLFm0I6eLgQDxoKtryzgNrOaV2KfM9M4t2/ZE848QbHor2867nvk2Dw89PT/goO0P7edeGbzGcjOU8z8bBHXx3+01cMfMs3tByYUnWmstibwttwU6aHXVcN//Sgl57Zn06YfWp/i2lWJoQQpREb2yQH+64lbMffT+ffPEXvBjYxQc3/ZD/67in3Es7ZnVGDvGJF37Ovkjhm7EG4gHeuv5rBBJhfn/KZ1lQon/zhRBCiKnwZmZMBpOZolcyLF3JQgghhKgKpSx67QPmjfj53MxjR51HejZiQXF2w/ETHndy7VL+b/Vn6Iwc4hVPXs/jfS+MOeaBnvUciPbytnmXlGq5UzIywq4YnV4Aa+qW0x8PcH/3Or7Qei0/OeEjJR3i3mivGTfe8NZ9D/GDHbfwupbzObfhhKJdr87mI6GThMbpsBqpPdjJluAeripRtCGAe1S84XAyxns2fIeBeJD/PfnTXDX7HH5w/Id4qn8LH33hJyR1csw5QokIH37+R8xw1vGNle8pWvSkWUbE4YcXv7bgbrwTahbjtjgl4lAIURXag5186sVfcuYj/8X3d9zCiTVL+POpX2D9+b/m5c1r+OLW3/Ld7TeZnhcqiiOWivP+jd/nlv0P8u4N38kZGzye4WSUdzz3LfaED/Kb1Z9iVc2iEq5UCCGEmDyHxY7TYs/O8gokIlL0EkIIIURVsOU/ZNKeAZYqpRaRLnZdDby5hNcrm0d7N3Jy7XGmOp8ubF7NXWd8k3du+BZvWf9VPr/sWt4+/7Js4eAPnf9itrORi5qKG+tXLCPjDWtsxSl6vbx5Df+ZcRrvmH8FZzWsKso5J9LgqBkTb/hY7/N8evMvObfhBL658r1FLeQYvy8ORvs4zpY74fPOg2uxoLhy1llFu/ZoI+MNtdZcv/kGnh3cxg0nfSJ74+2q2edwKNbPV9pu5Mtbf8eXl7/jiF+PL7X9H7vDB7l5zReL1u1XiEtmnE5XtJ+r515U8GsdFjun1C3jaSl6CSEqlNaaR/s28Zvd/+Ch3g04LXZe13IB71xwBcd5D/8b8suTPs5nttzAj3f+lZ7YIF9f8S6syjqta03pFIeiA0SSUWKpODGdSP+YSv9oVVbOqF+B3VLKt5vT79vb/sLGoR1cN/8ybuy4l+s338AP/3979x0fVZX+cfxzMuk9gRRqCj10CEUsKIplxS67trUXFAFZG/ZeV9wVxUVE1HV/KtYVV5TVtWChgyEk9E4gJKRAej2/PzJgVEomJJlM8n37uq/M3LnlueTxTs48c87pM+GofzuUV1dwU8pUluevZ3q/yUf9spSIiIi7BTsCas3pVUKwQ0UvERERaf4a7VMIa22lMeZWYD7gAGZba9OMMY8Cy6y1c40xQ4CPgQjgHGPMI9baxq96NKB9FUWs3LeBCYkX1XmfhKB2fDLsSSanvsTD615ndcFmnux1I7tLc1iQk8LtXf6Et1fTfnBVVwd6egU7AhrsQ6y2fmG8OuCuBjlWXbTxDSWrLP/g87UF27gp5a90DerAjP63/6o3W0NIjuiJv5cvk1Nf5N3khwg6xLfjrLX8e/cPjIjsQ7RfRIOev7bawxu+snUuH+1ewO1d/sRZMcN+td31cWPYU5rLK9s+JcY/kvEJFwDwn8yFzMn4hgkJFzK8CQqUhzIqahCjogbVe//hEUk8v+k98isK3VK0ExE5lPLqCuZm/sSrWz9lTeE2onzDubPrJVzecTSRvqG/297by8GzSTfT1jec6Vs+Jrd8P9P6TsLf4dvgsWWV5bGucAdbizPZVpzJVueyrSSTsuqKI+57etQQpvefjF8Dv7e6y9fZK5i57VP+3PF0Hu15LW19w3hu47v0DU3k+rgxh92vsrqKianT+GbvSp5OuqlRv+AiIiLSUIK9Aw729CpSTy8RERHxEI361Vtr7Txg3m/WPVjr8VJqhj30WD/lrqYae8T5vA4lxDuQmQPu4IXNH/K3Te+xoXAniUHt8TaOevVgaSoHCkINNbShO0T4hLK2cDsAmaU5XLXiKQId/rwx6B5CG+G6ugZ1YHq/ydzw87OMS5nK7IFTflcwTNm/ke0le5iQeGGDn7+2A8MbfpW9nKc2/B9jYo5j4mEKtvd0v4Ks8nye2fA20b4RjIjszZT0VxgY1o3buoxt1Dgb0/CIJCyWJXlrOD16iLvDEZFWLr+ikP/b+SVvbP+cPWV5dA/qxHO9b+G8dicctVBkjOHubpfR1jeMR9a9wZUrnmDWgLt+9V5WZavYVZrD9uI9ZJfnc3LbAYT7hNQpNmsts7fP47F1b1JNzRCKfl4+xAXEEh8Yy8ltB9A5IIZg7wB8vXzw9fJ2Lj74evmwLG8tT274Fzf9/Bwz+t/eKAW5ppRZmsNfVr9Er+A4HuhxFQC3JlxA2v4tPLH+LXoFx3F8m98Pj1xtq7k7fQbz9izige5XclnH05o6dJFmyRhzJvACNV+QnGWtffow210EfAAMsdYuc667B7gOqAImWmvnN03UIq1LiHfgwTm9CipLGvULmiIiIiINpWWNN+MGC3JSCPEOYEBYV5f39TJeTO4ylt4h8UxKnUbK/k2MiTmOmGb8h6SPqUkZTy56tfENJbe8gILKYq5a8RQFlcV8MPRR2vu3bbRzjo5O5qmkm7g7fQZ3pv2D5/uMx8v8MqXeJ7t/xNd4c2b0sCMc5dgFOvwoq65g4qoX6B0Sz9Q+4w87HJOX8eKvvW8muyyfu9L/QWJge6psFS/0nejRQ1X1D+uKn5cPi/PSVfQSEbfILstnWf46vs9J4aPdCyiuKuOEyL481/sWTmrT3+Uhdq+LO5s2vqH8ZfV0xi59iOTwHmwr2cP2kj3sLMmmstb8jO382jCt70SGRSYd8Zjl1RXcv+Y13s34H6dHDeG6uLOJD4wlxi/iV+9fR5Ic3oNg70DuXTOTG3/+KzMH3IG/i3MxNrZ1hTtYkpfOmdHDiPILP+x2VbaKSakvUlJVzvT+kw8W8LyMF1P7jOe8xfdyy6q/8Z/hT9MpIPrgftZaHln3Bu/v+pbbEsdyQ/w5jX1JIh7BGOMApgOjgZ3AUmPMXGtt+m+2CwEmAYtrrUuiZuj83kB74CtjTHdrDzEZrYgckyBv/4M9vQqritXTS0RERDyC535y3QxYa1mw92dGRPY5piLA6dFD+GTYk/x14zuH7XXTXPg5rzPc23OHhYv0DaW0upzrVj7D+qIdvDnwXpJC4hv9vJd2PJXs8nye2/gu0X4R3Nv9CqDmg7S5mT9yStTARi8mBjr8AQhw+PHawLsJOMqHj75ePswccCd/XPoQqwu28HyfW4kPjG3UGBubv8OXgWHdWKR5vUSkCVhr2VaSyZK8tSzNX8OSvLVsKd4N1PSaGhMzghvixxzz+9D57U4kwieEW1f9nbmZe+kcGEOfkAT+EDOcuIBYOgdEY4F718zkT8seZkLiRUxKvPiQwynnlu9nXMpUFuWlMyHhQm7v+qc6F7p+64pOo/E2Xtyd/grXrnymTu89jW1fRRFzM3/kvYyvSdm/CYBnNrzNnV0v5YpOow85N9qLmz9iYV4aU3vf8qu51aBm6KdZA+7inMVTuPHnv/LR0McPXuPUTXN4ffvnXB93NpM9uJe0SCMYCmy01m4GMMa8C5wH/PYPtMeAZ4A7a607D3jXWlsGbDHGbHQeb2GjRy3SygQ7AtlTlgvUzOl1qKH6RURERJobFb2OwbaSTHaUZjMu4bxjPlb34E5NOq9VfR0o7oV58FxIbZxzoyzKS+evvW/mpLauDU15LCYkXEhWWR4ztn5CtF8E18edzaLcdLLL8zk39oRGP398YCxBDn9eHXAn7fzb1GmfYO8A3k5+gJX5Gzm57YDGDbCJDItI4sXNH7K/oqhRhrQUESmrrmDOzq+ZsfUTdpZmAxDmHcSQiJ5c2uFUhkT0pE9oYoPOdTWy7QBSTpl9xALVvOHP8sCa13hh8wf8mJvKtL6T6BgQdfD1dYU7uG7lM+wpy2Va34mc3+7EY47rko6n4jAO7kh7mWtWPMXsgVMI9PY/5uPWll9RwGvbPsPbeBPrH0msX80S4x9JmHcQ1VTzY85q3t/1DV9kLaGsuoKewZ15sMdVDArrznMb3+WBta8xJ+Nrnky6gQFh3Q4ee1FuGn/f9D4XtDuRi9uffMjzJwS148W+t3H1yqe4K+0fTOs7iRlb5zJt84dc0uFUHuh+lcs9+ERauA7AjlrPdwK/GvLAGDMI6GSt/cwYc+dv9l30m31/XY2u2f9G4EaAzp07N1DYIq1LiHcAm4pLsNZSWFlCiENFLxEREWn+VPQ6Bgv2rgLgRBfn8/JkLWFOrwPFnkmJF/OnDqOa9NzGGB7peQ3ZZfk8uu4Non3D+SE3lSCHP6dFDW70858ePYSUU2Yf/D3WVbhPCKdEDWykqJre8IgkXuADluWvY1TUIHeHIyItyIFi1/QtH7O7LIfk8B7cknABQyJ60i2oQ717TNXV0Y4f7B3A3/reyklt+3Nf+qucufBOnkm6ibNjj+N/2cuZsOoFAhx+vJf8CAPDux3xWK4Y2+FkfLwc3Jb6IleueJI3Bt3TYEMkZZflc8Xyx1lbuB3rnHusNn8vX/wdvuRXFBLmHcQlHUYxtsMp9A1JPFiI+r/BD/Dpnp94bN2bnLf4Pi7teCpTul1GtbVMTJ1GXGAsT/S64YiFq1OiBnJn10t5duPbFFSW8PXeFZwTO4Knko68n4j8njHGC3geuLq+x7DWzgRmAiQnJ//+5iAiRxXkHUBhZQklVWVUYwn2DnR3SCIiIiJHpaLXMViQk0LngBiPH+7NFb8UvTy3p9fxkX34dNjT9AtNdMv5HcbBC30nkrvicSavfhEfL2/OiB7aZMM9uVrwaokGhXfHxzhYlJeuopeINIiy6grey/ialzb/Uuya2mc8x0f2aZYFjwvancjAsG5MXPUCN696nhMz+vFDTiq9Q+J5beDdde4N7Irz252Il/FiUuo0rlj+GGfHHIcFLBZraz6PtoCvlzfntzuBNr5hRz3m7tIcLlv2KLtKc3hr8H0Mi0hiT2kumWU1y4HH+yqKGNl2AKOjkg/Ox1WbMYZzY49nVNtB/G3Te8zePo8v9iymU0A0ueX7+fewJ+tUpBufcD6rCzYzb88iTm07mL/3mXDI4RJFhAygU63nHZ3rDggB+gDfOu+hscBcY8y5ddhXRBpIsHcABZXFFFSVHHwuIiIi0typ6FVPFdWV/JS7mvPbNf6QdM2Jr3EOb+jBc3o5jIP+YV3cGoO/w5dZA+5m7NIHWVu4nfOaYGhD+UWAw4/+YV1ZrHm9ROQYFVaW8NGuBby89WN2leYwOLwHz/W5hRMi+zbLYldt8YGxfDj0MaZumsM/tnzCWTHDeL73+AYferC2c2OPx9vU9PhasW/DYbd7cfOHPNLzWs6NPf6w/47bivdw2bJHyKso5K3B9zE0ohcAnQNj6BwYU6/4gr0DeKDHVVzc/mTuW/Mqy/LX8UjPa+kTmlCn/Y0xPN97PKOjkjk7Zvgxzfkq0sItBboZYxKoKVhdAlx24EVr7T6g7YHnxphvgTustcuMMSXA28aY54H2QDdgSRPGLtJqhHgHUlZdQX55AaCil4iIiHgGtcTraeW+DRRWlXBSKxraEGq+fQ2ePbxhcxHmE8S/Bt/P19krGNmE84pJjeERSczYOpciTcgsIvWQun8zb+/8in/v/p6iqlIGh3Xn2d43c2Jkv2Zf7KrNx8ubKd0u5/rOY2jjG9oksf8hZjij2g6iwlZiqDmfAQwGYwxbizOZkv4KE1Jf4N+7f+DJpBt+1/NsY1EGly57hLLqCt5JfqjBv8zSKySOD4Y8yubi3XQJbO/SvoHe/lzUfmSDxiPS0lhrK40xtwLzAQcw21qbZox5FFhmrZ17hH3TjDHvAelAJTDeWlvVJIGLtDJBjpovwmSW5QIQrDm9RERExAOo6FVP3+ek4DBeHBfZx92hNCkfFb0aVLRfBJd0PNXdYbRKwyN689KWj1m+b32rK16LSP0UVZYwN/Mn/m/nl6zavwl/L1/OiR3B5R1HMzCsm0cVu36rrd/RhxJsSP4OX/z5/TCDUFNw+mjoY7y+/XOe3fAOp/00mXu7/5nLOpyGMYa0/Vu4YvnjeBnDe8kP0zMkrlFi9DJedA3q0CjHFhGw1s4D5v1m3YOH2fbk3zx/Anii0YITEaCmpxf8UvQK0ZcFRURExAOo6FVPC3JWMSCsW6sr/nQP7sToqGSGhvd0dygix2RQeHccxovFuekqeom0cpmlOSzMS2dJ3hqKKkt+Vbwyzv/Kqsv5du/PFFaV0CO4E4/2vJYL2p3U6v4OaCoO4+D6uDGcFpXMlLQZ3JM+k093/8gVnc5gSvorBDn8eSf5QRKDXOuFJSIiInV3YDjD3aU5zueB7gxHREREpE5U9KqH/IoCUvZtZFKXse4OpcmFeAfy2sC73R2GyDEL9g6gb0giizSvl0irk1WWx6LcdH7KW83C3DS2FO8GINQ7kAifEAAsYLFgLZaa4ffOiB7K5Z1GMzisu0f36vIk8YGxvJP8EO9k/I8n1v+TW1Y9T1xADG8nP0ingGh3hyciItKiHSh6HRzesBHn/RQRERFpKCp61Yvhnu5XcHKbAe4ORESOwbDIJF7fNo/SqjL8HX7uDkdEGlhldRXbSjJZW7CddYU7WFe4nbWF2w8WuUK8AxgansQVHUdzXGQfeoV0xmEcbo5afssYw2UdT2NU24G8k/E1l3Y4lVj/SHeHJSIi0uIdmMMr80BPL4d6eomIiEjzp6JXPYT7BHNT/LnuDkNEjtHwiCRe2TqXFfs2MKKVzc8n0lLtLs1h+paPWZ6/jo1FGZRVVwA1wxTGB8bSI7gTl3Q4lRGRvekdkoC3l4pcniLWvw2TW2EvexEREXf5fU8vzeklIiIizZ+KXiLSag0J74kXhkW56Q1e9NpVupe1BdsZ2ba/eo6INIHiylL+sfUTXtk6l2pbzXGRvTkhsi/dgzvTI6QT3YI6EqAenSIiIiJ1FuKcwyuzNBcf48DPy8fNEYmIiIgcnYpeItJqhfoEkRQSz+IGnteruLKUK5Y/zsaiDLoEtueWhPM5v92J+HjplivS0KptNR/uWsCzG99mT1ke58SO4O6ul9E5MMbdoYmIiIh4tCBHzRxeuRUFhPsEa05TERER8Qhe7g5ARMSdhkcmsWLf+oNDoDWE+9fOYlPRLu7segn+Dl9uT3uZkT9M5J/b51NaVd5g55GWwVrLivz1bCve0yTnK60qY0neGrLL8pvkfI1pUW4aYxZN4fa06bTza8NHQx9ner/JKniJiIiINIAgb/+Dj0McGtpQREREPIO6HYhIqzY8ojeztn1Gyr6NDI3odczHez/jWz7Y9R2TEi9mQuJF3JpwId/sXcmLmz/k/rWzeGHzB9wYfw5XdBxNkMbEb9WqbBX/zVrGjK2fsHLfBoIdAbzYbxKnRg1u0PMUV5ayfN96FuWmsSgvnZR9Gym3lYR5B/Fs75s5K2ZYg57PFRXVlbyydS5VtpoT2/SjX2iXo86xtat0L9/tTeGLrMV8s3cl7f3bMK3vRM6NPR4vo+/yiIiIiDQUh3EQ5PCnqKqUYOdQhyIiIiLNnYpeItKqDY3oicGwOC/9d0WvwsoSNhVl4O/lS4+Qzkc91vrCHdy/ZhbHRfTmti4XA2CMYVTUIE5pO5CFeWm8uPkjnlj/Fi9t/ojLOp7G1Z3Pop1/m0a5NmmeSqvK+XD3d8zc+ilbinfTOSCGB3tczUe7vuPalc8wpdvljIs/95iGj6myVby+/XM+27OIlH0bqbRVOIwXfUMSuSbuD/QP7corWz/hppTnuLzjaB7scdUxz3f1U+5qOvpH1bmXVWFlCeNSprIgJwWDYeqmOYR6B3F8ZB9ObNOfk9r0o3NgDGXVFSzNW8u3e1fy7d6fWV+0A4B2fm24s+sl3BA3Bn/N1SUiIiLSKIK8A5xFL31hT0RERDyDil4i0qqF+4TQM7gz/81aSph3MBuLdrKxKIONRRlkluUe3O7WhAv4S5c/HbYXSklVGbek/I1Ahx8v9J2Iw/x6O2MMIyL7MCKyDyvy1/PK1rm8snUur277D+fEjuC6uLPpF9qlwa+v2lbz0NrZpBds4/Fe19MrJK7Bz9Fa7Crdy/yspXyVvYxgRwB/7nQGx0f2qXNxKr+ikH/t+JLXt39Gdvk++oYmMr3fZP4QMwyHcXB5x9O4ffV0ntrwL9YWbuOZpHH4O3xdjjO7LJ+JqdP4MTeV/qFduDH+HIZHJJEc3vNXH1acHp3McxvnMGPrJyzJW8NL/W6rV36UVVfw2Lo3+eeO+QQ6/His53Vc3P7kI/67ZJflc/XKp0gv2MozSeM4I3oIP+Sm8n1OCgv2ruLzrMUAdPKPYm/5fkqqy/A13gyN6MUfO5zCyLYD6B7UUfNKiIiIiDSyEEcAWeSp6CUiIiIew1hr3R2DS5KTk+2yZcvcHYaItCCPrnuDWds+AyDYEUCXoPZ0DepIl6AOdAvuwP+yl/NuxtcMj0hiWt9JxPpH/u4Yd6X9gzkZ3/DWoPs4qW3/Op13e/EeXt/+OXMyvqawqoRhEb24Pm4Mp0UN/l3RrD6qbTV3p89gTsY3BDsCKK0uZ0LiRYxPOB9fL59jPn5LZ61lQ9FO5mct4YusJaTu3wxA16AO5JbvJ7eigK5BHbiq05lc1H7kIT8I2Fu2jy+zl/FF1mJ+yFlFha1iZJv+jIs/jxGHKJhZa3lpy0f8deO7DAjtyswBdx4y3w5nUW4at6a+wL6KQh7vdT1/bH/KUQtDC3JSmJz6Evsri7i/+5Vc2emMOheTdpRkcXPK86zav4lrO/+BNQXbWJiXxpiY43gq6SbCfIJ+t8/W4t38efkT7CnL4+X+f+G03wznaK1lU/Euvs9ZxcLc1cT4RTCy7UCOi0jSkKAiLZgxZrm1NtndcUjLpDakSP2ds2gKKfs3cU7sCKb3m+zucERERESAI7chVfQSkVavsLKE1fs3Ex8YS4xf5CE/8P9g13fcl/4qQd7+vNB3Iie26XfwtY93f8+k1GncmnABd3W7zOXz768oYk7G17y+/XN2lmbTyT+KP3YYxdgOJ9Pev229rqnaVjMlfSbvZvyPSYkXc23nP/DQ2tn8O/MHkkLimNp7PL1DE+p1bHd4P+NbCqtK+HPH048659OxyCrLY1n+Opblr+Xr7BVsLt4NwMCwbpwRPZQzo4eSGNSe0qpy/rNnIW9s/5xV+zcR7Ajg4g4nc1WnMwhw+PHFniV8kbWYJXlrqMbSOSCGs6KHckH7k0gKiT9qHPOzljApdRoh3oHMGnAX/cO6HnH7alvNy1s+4bmN7xAXGMuM/re71Gtrb9k+7kibztd7VzI6KpnHe11/1GE3v8pezuTUF7FYpvYZzxnRQ6myVczYOpepG+cQ4xfB3/tOZFitYUNT9m3i6hVPUo3l9YFTGBTevc4xikjLpqKXNCa1IUXq75Jlj/BT7mou7XAqz/Qe5+5wRERERAAVvUREGsT6wh3cnPI8G4symJR4MZO6XMTW4kzOXnQ3fUISeTf5oWMqyFRWV/FF1hL+tfO//JS7GoNhZJv+/LHDKEZHJ+NXx95Z1baae9Jn8k7G/5iYeBG3d/nTwULef7OWck/6TPIqChifcAETEi9s8F5fC3PT2FS0i7NihtLGN+yYjlVRXckj697gnzvmA9A/tAvP9RlPj+BOxxxnta1mfeEOljqLXMvy17GjJAsAPy8fhoT35MzoYZwenUzsEQpAP+/bUDN/VuZCym3lwfU9gjtxZvQwzooZRq/gOJeH4ltbsI3rVj5DVnk+V3c+i/6hXekdEk9cYAxexuvgdnnlBdy2+kW+2buSc2OP5+mkm+o1/Iy1ltnb5/HU+n9RbisPFvrOiB5Cl6AOB7errK5i6qY5TN/yMX1CEni5/1+ID4z91bFS9m1kQuoLbC/ew4TEC5mUOJYfclcxLmUqbXxC+efg+351TBERFb2kMakNKVJ/N/z8LPOzlnJj3Dnc3+NKd4cjIiIiAqjoJSLSYIorS7lvzSw+3P0dIyL7kFdeQGZZLl8c99ej9oxxxbbiPby/6xvez/iW3WU5RPiEcGG7k/hjh1PoGdz5sAWUalvNvWte5e2dXzEh4ULu6HrJ77bNryjg4bVv8NHuBfQKjuOhnlczJLwnPl7HPs3jrG2f8di6N7FYvI2DU6MGMbb9KZzSdqDLx8+vKOTmlOf5MTeVm+PPo3dIAg+ufY3CyhJu6zKWcfHn1anIWG2rySjdy/rCHawv3Fnzs2gHGwszKKkuA6CtbxjJ4T1IDu9JcngP+oQmuFwMzC7L54Nd3wFwRvQQEoPau7T/oeSW7+cvq19iQc4qKm0VAIEOP3qFxNM7JJ74wHbM2vYpe8v28VDPq7mi4+nHPM/V1uJM/pP5E19kLWHV/k1AzZCOZ0QP5YTIvkzb/CEL89K4rONpPNzjmsPOO1ZYWcJDa2fz/q5v6RncmY1FGXQP7sibg+4jxi/imGIUkZZHRS9pTGpDitTf5NSX+HD3d0zu8kcmdxnr7nBEREREABW9REQa3HsZ33Dfmlcpq67gzYH3ckrUwEY5T5WtYsHeVczZ9TVfZi2lwlbRNagDY2JGMCb2OLrX6vFUbau5f80s/rXzS8YnXMBdXS89YgHky6xl3LNmJllleQQ6/EgO78nwiN4Mj0yiX2iiS0WfKlvFo+ve5PXtn3Nm9FDGJ1zIp5k/8vHuBWSX76ONTyjntTuBse1PrtOwipuLdnHtyqfZUZLF00njGNvhZAByyvfxwJrX+M+ehfQNTWRq71vo+Zth/Cqrq1i1fxPf56zih9xVpO7fTHFV2cHXY/wi6B7cie5BnegdmkByeA/iAmKOuVjUmEqrytlQtJM1BdtIK9hCesFW0gu2UlBZQqeAaP7R/y/0C+3S4OfdVbqX/2YtZX7WUhblpVFlq/H38uXJpBu5uP3IOh1jbuaP3Js+k76hXZg54A5CvAMbPE4R8XwqekljUhtSpP4eXPMab+z4ggd7XMX1cWPcHY6IiIgIoKKXiEij2FiUwfbiPYyKGtQk58sp38e8PYv4NPMnFuetwWLpEdzpYAFs9rZ5vLXzv9wcfx5Tul1epyJOQWUx3+1NYXFeGovy0llXuAOAAC8/Bod3Z5Szp1aYT9Bhj1FcWcqE1Bf4MnsZN8SN4d7uV+AwNT2wKqur+C7nZ97f9S1fZS2j3FbSLagjp0cP4fSoIfQP6/KrofoAfshJZVzKVLyNFzMH3MnQWnNCHfBZ5kLuXzOL/ZVFTEy8mDNjhrEwdzU/5KSyMG81BZUlGAx9QhMYHN6DHs4iV7fgjoT7BLvyz95sWWvJKN1LW9+ww/a2akj5FQX8kLOa3iHxJAS1c2nf0qoy/Lx8m3VhUUTcS0UvaUxqQ4rU3zMb3mb6lo95Nmkcl3Q81d3hiIiIiAAqeomItDh7yvL43FkAW5q/9uD6cfHncU8dC16HklO+jyV5a1mYl8ai3DTWFm4nyOHP2A6ncG3ns4gP/HWxI6ssj2tWPk3a/i083PMaru581mGPnV9RwNzdPzFvz0IW56+hylYT5RvOaVGDOT16CMdH9uWDXd/xwNpZdAnswOyBd9M5MOawx8st389Da2fzSeaPB9d1DojhhDZ9OSGyH8dH9iHCN6Re/w4iItK0VPSSxqQ2pEj9Td/yMc9seJuX+/2FMbHHuTscEREREUBFLxGRFi2zNIfP9izCYby4qtOZDdqbJnX/Zl7b9hmfZv5Ipa3mtKjBXB83huERSawv2sk1K54ip3w/L/W7jdHRdf+sMr+igK+zV/Jl9jK+3buSoqpS/Lx8KKuuYFTbgbzY77Y6D4O3YG8KGaV7GRHZh7gjFMlERKT5UtFLGpPakCL198/t87l/7SzeGnQfI9sOcHc4IiIiIsCR25DeTR2MiIg0rFj/NlwXd3ajHLtvaCJ/7zuBe7pfwVs75vPWjv/yZfYykkLi2FmSjZ+XL+8PfcTl+aTCfUK4sP1JXNj+JMqqK1iUm8ZX2cuI8WvDzQnnHhwesS5Oatvf1csSEREREZE6CPL2d/4McHMkIiIiInWjopeIiBxVjF8Ed3S9hFsTLuDj3T8we/tndPCP4rWBd9MxIOqYju3n5cPItgP0zVERERERkWYmKSSedn5tiA+IdXcoIiIiInWiopeIiNSZv8OPSzueyqUdT8Va26BDKYqIiIiISPPSKySOxSNnuDsMERERkTrzcncAIiLimVTwEhERERERERERkeZERS8RERERERERERERERHxeCp6iYiIiIiIiIiIiIiIiMdT0UtEREREREREREREREQ8nopeIiIiIiIiIiIiIiIi4vFU9BIRERERERERERERERGPp6KXiIiIiIiIiIiIiIiIeDwVvURERERERERERERERMTjqeglIiIiIiIiIiIiIiIiHk9FLxEREREREREREREREfF4KnqJiIiIiIiIiIiIiIiIx1PRS0RERERERERERERERDyeil4iIiIiIiIiIiIiIiLi8Yy11t0xuMQYkw1sa8JTtgX2NuH5xHMpV8QVyhdxhfJFXKF8kbpqTrkSZ62NcncQ0jKpDSnNnPJF6kq5Iq5QvogrlC9SV80pVw7bhvS4oldTM8Yss9YmuzsOaf6UK+IK5Yu4QvkirlC+SF0pV0Qah/7fElcoX6SulCviCuWLuEL5InXlKbmi4Q1FRERERERERERERETE46noJSIiIiIiIiIiIiIiIh5PRa+jm+nuAMRjKFfEFcoXcYXyRVyhfJG6Uq6INA79vyWuUL5IXSlXxBXKF3GF8kXqyiNyRXN6iYiIiIiIiIiIiIiIiMdTTy8RERERERERERERERHxeK2u6GWMmW2MyTLGrK61rr8xZqExJtUY86kxJtS5frQxZrlz/XJjzKha+wx2rt9ojJlmjDHuuB5pXK7kS63XOxtjCo0xd9Rad6YxZp0zX6Y05TVI03E1X4wx/ZyvpTlf93eu1/2lhXPxvcjHGPOmc/0aY8w9tfbRvaUVMMZ0MsZ8Y4xJd94vJjnXRxpjvjTGbHD+jHCuN857x0ZjzCpjzKBax7rKuf0GY8xV7romaTz1yJfLnXmSaoz5yRjTv9axdI8RQW1IcY3akOIKtSGlrtSGFFeoDSmuaJFtSGttq1qAk4BBwOpa65YCI52PrwUecz4eCLR3Pu4DZNTaZwkwHDDA58BZ7r42Le7Nl1qvfwC8D9zhfO4ANgGJgC+QAiS5+9q0uDdfAG9gFdDf+bwN4HA+1v2lhS8u5splwLvOx4HAViBe95bWswDtgEHOxyHAeiAJeBaY4lw/BXjG+fgPznuHcd5LFjvXRwKbnT8jnI8j3H19WtyeLyMO5AFwVq180T1Gixbn4uL7ttqQrXxxJV9qva42ZCtdXLy/qA3ZihcXc0VtyFa+1KNNoDZkK17qkS/Nvg3Z6np6WWsXALm/Wd0dWOB8/CVwkXPbldbaXc71aUCAMcbPGNMOCLXWLrI1v9F/Auc3evDS5FzJFwBjzPnAFmry5YChwEZr7WZrbTnwLnBeY8Us7uNivpwOrLLWpjj3zbHWVun+0jq4mCsWCDLGeAMBQDmwH91bWg1r7W5r7Qrn4wJgDdCBmt/3m87N3uSXe8V5wD9tjUVAuPPecgbwpbU211qbR02endl0VyJNwdV8sdb+5MwHgEVAR+dj3WNEnNSGFFeoDSmuUBtS6kptSHGF2pDiipbYhmx1Ra/DSOOXX8BYoNMhtrkIWGGtLaPml76z1ms7neukdThkvhhjgoG7gUd+s30HYEet58qX1uVw95fugDXGzDfGrDDG3OVcr/tL63W4XPkAKAJ2A9uB56y1ueje0ioZY+Kp6UWwGIix1u52vpQJxDgfHy43lDOtTB3zpbbrqPmGJyhfRI5GbUhxhdqQ4gq1IaWu1IaUo1IbUlzRUtqQKnrVuBa4xRiznJoufOW1XzTG9AaeAW5yQ2zS/BwuXx4G/matLXRXYNIsHS5fvIETgMudPy8wxpzqnhClmThcrgwFqoD2QAJwuzEm0T0hijs5Pxj7ELjNWru/9mvOb/VatwQmzZKr+WKMOYWaBsvdTRakiGdTG1JcoTakuEJtSKkrtSHliNSGFFe0pDakt7sDaA6stWup6SaOMaY7cPaB14wxHYGPgSuttZucqzP4pdsezscZTROtuNsR8mUYcLEx5lkgHKg2xpQCy/n1Nz+VL63IEfJlJ7DAWrvX+do8asbn/he6v7RKR8iVy4AvrLUVQJYx5kcgmZpvz+je0koYY3yo+ePz/6y1HzlX7zHGtLPW7nYOPZHlXJ/BoXMjAzj5N+u/bcy4xT1czBeMMf2AWdTM/5HjXH24PBIR1IYU16gNKa5QG1LqSm1IORK1IcUVLa0NqZ5egDEm2vnTC7gfmOF8Hg58Rs2EbT8e2N7ZrW+/MWa4McYAVwKfNHXc4h6Hyxdr7YnW2nhrbTzwd+BJa+1L1Ews2s0Yk2CM8QUuAea6I3ZpeofLF2A+0NcYE+gcZ3skkK77S+t1hFzZDoxyvhZEzaSya9G9pdVw3gteA9ZYa5+v9dJc4Crn46v45V4xF7jS1BgO7HPeW+YDpxtjIowxEdQ0kOc3yUVIk3E1X4wxnYGPgD9ba9fX2l73GJEjUBtSXKE2pLhCbUipK7Uh5XDUhhRXtMQ2ZKvr6WWMeYeaCnVbY8xO4CEg2Bgz3rnJR8Drzse3Al2BB40xDzrXnW6tzQJuAd6gZkLIz/ll7EppQVzMl0Oy1lYaY26l5k3BAcy21qYdaR/xTK7ki7U2zxjzPDVvCBaYZ639zLmd7i8tnIv3lunA68aYNMAAr1trVzmPo3tL63A88Gcg1Rjzs3PdvcDTwHvGmOuAbcAfna/NA/4AbASKgWsArLW5xpjHqLnvADzqHNtfWhZX8+VBoA3wck1bh0prbbL+fhH5hdqQ4gq1IcUVakNKXakNKS5SG1Jc0eLakKZmOEYRERERERERERERERERz6XhDUVERERERERERERERMTjqeglIiIiIiIiIiIiIiIiHk9FLxEREREREREREREREfF4KnqJiIiIiIiIiIiIiIiIx1PRS0RERERERERERERERDyeil4iItLojDEPG2PuaKRjX22Meakxji0iIiIiIiJNT21IERGpLxW9RERERERERERERERExOOp6CUiIo3CGHOfMWa9MeYHoIdzXRdjzBfGmOXGmO+NMT2d62OMMR8bY1Kcywjn+n87t00zxtxY69jXOI+9BDi+1vooY8yHxpilzuV4REREREREpNlTG1JERBqCsda6OwYREWlhjDGDgTeAYYA3sAKYAZwFjLPWbjDGDAOestaOMsbMARZaa/9ujHEAwdbafcaYSGttrjEmAFgKjAR8gcXAYGAf8A2w0lp7qzHmbeBla+0PxpjOwHxrba8mvXgRERERERFxidqQIiLSULzdHYCIiLRIJwIfW2uLAYwxcwF/YATwvjHmwHZ+zp+jgCsBrLVV1DREACYaYy5wPu4EdANigW+ttdnOY88Buju3OQ1IqnX8UGNMsLW2sMGvUERERERERBqK2pAiItIgVPQSEZGm4gXkW2sH1GVjY8zJ1DRAjrPWFhtjvqWm0XO0cwy31pbWP0wRERERERFpBtSGFBERl2lOLxERaQwLgPONMQHGmBDgHKAY2GKMGQtgavR3bv8/4GbneocxJgwIA/KcjZWewHDntouBkcaYNsYYH2BsrfP+F5hw4IkxZkCjXaGIiIiIiIg0FLUhRUSkQajoJSIiDc5auwKYA6QAn1MzljrA5cB1xpgUIA04z7l+EnCKMSYVWA4kAV8A3saYNcDTwCLnsXcDDwMLgR+BNbVOPRFINsasMsakA+Ma6xpFRERERESkYagNKSIiDcVYa90dg4iIiIiIiIiIiIiIiMgxUU8vERERERERERERERER8XgqeomIiIiIiIiIiIiIiIjHU9FLREREREREREREREREPJ6KXiIiIiIiIiIiIiIiIuLxVPQSERERERERERERERERj6eil4iIiIiIiIiIiIiIiHg8Fb1ERERERERERERERETE46noJSIiIiIiIiIiIiIiIh7v/wE/bsbUiRtFMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp1, elements = plt.subplots(3, 2) # Initiate the subplots\n",
    "\n",
    "arrElements = [\"acousticness\", \"danceability\", \"energy\", \"instrumentalness\", \"speechiness\", \"valence\"]\n",
    "row = 0\n",
    "col = 0\n",
    "\n",
    "for elem in arrElements: # Produce a line graph for each musical element\n",
    "    main_df.groupby(\"year\")[elem].mean().plot(ax=elements[row][col], figsize=(30,20), color=\"#1db954\") # Group rows by year and get the mean values for that particular element\n",
    "    elements[row][col].set_xlabel(\"decade\")\n",
    "    elements[row][col].set_ylabel(elem)\n",
    "    if col == 0:\n",
    "        col = 1\n",
    "    elif row == 0 and col == 1:\n",
    "        row = 1\n",
    "        col = 0\n",
    "    elif row == 1 and col == 1:\n",
    "        row = 2\n",
    "        col = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite a lot of graphs to unpack here, but let's run through them one by one and make some discoveries on the data.\n",
    "\n",
    "First and foremost, we can see a dramatic decrease in the areas of `acousticness` and `instrumentalness` through the years. These could be attributed to the **rise of electrical and electronic music in the mid-20th century**. For both elements, we see a gradual decrease in the 50's, followed by a sharp fall beginning in the 60's – a decade of musical breakthroughs with the rise of [synthesizers in music production](https://www.timetoast.com/timelines/history-of-electronic-music-timeline) and [rock music](https://www.rollingstone.com/music/music-features/bob-dylan-the-beatles-and-the-rock-of-the-sixties-176221/). This adversely affected the `energy` element, which saw a drastic upward trend in about the same time. This is because higher energy levels are typically attributed to having traits of being fast and noisy, scoring high in genres such as **death metal** and **electronic dance music (EDM)**.\n",
    "\n",
    "As shown by how the trend for synthesized increased, the `speechiness` of the songs have decreased since the synthetic melody of advance technology have grown into trend.\n",
    "\n",
    "`danceability` has sustained generally high levels in music through the years, however, there is a noticeable drop in the mid-40's to 50's. This was probably a side-effect of [post-war modernism](https://www.theguardian.com/music/tomserviceblog/2013/oct/04/music-in-the-post-second-war-world-tom-service), where music gravitated upon a more somber, heroic tone to fit the mood of the wake of **World War 2**, the deadliest war in history.\n",
    "\n",
    "Similar to danceability, `valence`, which is a measure of how positive (i.e. happy) a song is, likewise saw a sharp decrease in the post-war era. It saw an upward trend, particularly around the mid-50's to 70's that could have been because of the **hippie music** genre that had its [moment as an opposing force of peace to the United States' involvement in the Vietnam War](https://www.britannica.com/topic/hippie)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA #3: The divide between explicit and non-explicit songs through the years.\n",
    "There has been a significant uptick in songs containing explicit lyrics with the rise of more liberal and experimental approaches to music as a form of expression. For this analysis, we aim to compare the trend between explicit and non-explicit songs through the years.\n",
    "\n",
    "Let's start by creating a stacked bar plot to discern differences in the explicitness of the songs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bar plot of explicit vs. non-explicit songs on Spotify through the years.')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_df.groupby([\"year\", \"explicit\"]).size().unstack().plot(kind=\"bar\", stacked=\"True\", figsize=(25,10), color=[\"#1db954\", \"#191414\"])\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Bar plot of explicit vs. non-explicit songs on Spotify through the years.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the bar plot where '0' corresponds to non-explicit songs and vice versa, there's a noticeable development of the amount of explicit songs particularly in the entrance of the 90's, a trend that has sustained in favor of explicit songs until the present year. let's tabulate that data to see the actual numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1921</th>\n",
       "      <th>1922</th>\n",
       "      <th>1923</th>\n",
       "      <th>1924</th>\n",
       "      <th>1925</th>\n",
       "      <th>1926</th>\n",
       "      <th>1927</th>\n",
       "      <th>1928</th>\n",
       "      <th>1929</th>\n",
       "      <th>1930</th>\n",
       "      <th>1931</th>\n",
       "      <th>1932</th>\n",
       "      <th>1933</th>\n",
       "      <th>1934</th>\n",
       "      <th>1935</th>\n",
       "      <th>1936</th>\n",
       "      <th>1937</th>\n",
       "      <th>1938</th>\n",
       "      <th>1939</th>\n",
       "      <th>1940</th>\n",
       "      <th>1941</th>\n",
       "      <th>1942</th>\n",
       "      <th>1943</th>\n",
       "      <th>1944</th>\n",
       "      <th>1945</th>\n",
       "      <th>1946</th>\n",
       "      <th>1947</th>\n",
       "      <th>1948</th>\n",
       "      <th>1949</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1953</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>1956</th>\n",
       "      <th>1957</th>\n",
       "      <th>1958</th>\n",
       "      <th>1959</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>not explicit</th>\n",
       "      <td>121.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1601.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>1631.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>1579.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>1929.0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>1909.0</td>\n",
       "      <td>1877.0</td>\n",
       "      <td>1816.0</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>1677.0</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>1668.0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>1689.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>1591.0</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>881.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explicit</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>430.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>391.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>698.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>860.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year           1921  1922   1923   1924   1925   1926   1927    1928   1929    1930   1931   1932   1933   1934    1935    1936   1937   1938   1939    1940   1941    1942   1943   1944    1945    1946    1947    1948    1949    1950    1951    1952    1953    1954    1955    1956    1957    1958    1959    1960    1961    1962    1963    1964    1965    1966    1967    1968    1969    1970    1971    1972    1973    1974    1975    1976    1977    1978    1979    1980    1981    1982    1983    1984    1985    1986    1987    1988    1989    1990    1991    1992    1993    1994    1995    1996    1997    1998    1999    2000    2001    2002    2003    2004    2005    2006    2007    2008    2009    2010    2011    2012    2013    2014    2015    2016    2017    2018    2019   2020\n",
       "not explicit  121.0  72.0  169.0  237.0  263.0  655.0  584.0  1039.0  494.0  1803.0  630.0  479.0  620.0  554.0  1285.0  1054.0  589.0  576.0  999.0  1601.0  928.0  1631.0  634.0  768.0  1344.0  1493.0  1579.0  1730.0  1982.0  1890.0  1974.0  1934.0  1929.0  1996.0  1995.0  1999.0  2000.0  2000.0  2000.0  1999.0  2000.0  2000.0  1998.0  1997.0  1997.0  2000.0  1999.0  2000.0  1995.0  1997.0  1998.0  1992.0  1998.0  1998.0  1988.0  1998.0  1990.0  1984.0  1985.0  1986.0  1974.0  1945.0  1967.0  1966.0  1953.0  1965.0  1940.0  1885.0  1927.0  1909.0  1877.0  1816.0  1824.0  1730.0  1729.0  1677.0  1740.0  1653.0  1668.0  1684.0  1655.0  1770.0  1730.0  1690.0  1654.0  1753.0  1752.0  1765.0  1689.0  1698.0  1642.0  1591.0  1491.0  1540.0  1402.0  1313.0  1301.0  1063.0  1152.0  881.0\n",
       "explicit        7.0   NaN    NaN    NaN    NaN  219.0   12.0     NaN  430.0     8.0    NaN    NaN    2.0    NaN   277.0     NaN    7.0    NaN    NaN   391.0   10.0     NaN    NaN    1.0   536.0     1.0     NaN     NaN     NaN   105.0     5.0    54.0     6.0     NaN     2.0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     1.0     3.0     3.0     NaN     1.0     NaN     4.0     1.0     1.0     4.0     2.0     2.0    12.0     2.0    10.0    16.0    14.0    13.0    26.0    53.0    32.0    34.0    47.0    35.0    59.0   115.0    73.0    91.0   119.0   184.0   176.0   270.0   271.0   323.0   260.0   347.0   332.0   316.0   345.0   229.0   269.0   310.0   345.0   247.0   248.0   234.0   311.0   300.0   358.0   403.0   504.0   458.0   528.0   656.0   698.0   936.0   848.0  860.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None) # Jupyter display setting\n",
    "pd.set_option('display.expand_frame_repr', False) # Jupyter display setting\n",
    "pd.set_option('max_colwidth', None) # Jupyter display setting\n",
    "explicits = main_df.groupby(\"explicit\")[\"year\"].value_counts() # Get the count between explicit and non-explicit songs by year\n",
    "pd.DataFrame([explicits[0], explicits[1]], index=[\"not explicit\", \"explicit\"]) # Convert to DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the tabulated data, the presence of explicit songs can be seen as consistent and steadily increasing since the beginning of the 80's. This could be because of the advent of [Hip-Hop music](https://www.englishclub.com/vocabulary/music-hip-hop.html) that started in the Bronx, New York City in the late 70's. Being one of the more controversial yet artistic genres for its liberal expression, Hip-Hop has garnered its fair share of criticisms due to its **explicit mentions of drugs, vices, and sex**.\n",
    "\n",
    "Going back to the data, the notable outlier years are 1926, 1929, 1935, 1940, and 1945, being the lone years that have reached > 100 explicit tracks before the Hip-Hop era. The researchers have found that the genre of **dirty blues** rose around those times, being [earlier documents of predominantly male artists singing sexually suggestive lyrics](https://tonedeaf.thebrag.com/the-1930s-the-dirtiest-decade-in-music/). It is because of this that some musical experts have tagged the 1930's as one of the dirtiest music eras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Investigation and Inference (FII) on Spotify Data\n",
    "The Exploratory Data Analysis we just conducted gave us a good idea of what the Spotify data contains: what each observation represents, and given its variables, the context behind the data. These processes gave us insights on the trends and movements in different musical elements spanning various contexts such as time and correlation.\n",
    "\n",
    "As the music industry moves forward in its ever malleable state, **doing further investigation and drawing inferences** from the unpacked Spotify data could give us an even better idea on how music has evolved through the years, and how the shift will be moving forward.\n",
    "\n",
    "We have formulated three (3) research question centered around the data. The answers to these questions shall give the dataset more structure and validate (or invalidate) inferences on various musical variables, starting with..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FII #1: Determining if there is a significant difference in popularity between explicit and non-explicit songs.\n",
    "In the third EDA question, we visualized the growing trend of explicit songs that were particularly signficant and consistent in the 80's up until now. By this, we assume that songs with explicit content are produced because they are growing in popularity.\n",
    "\n",
    "Fortunately, with our data, we have the tools to determine **whether or not there is a significant difference in popularity** between explicit and non-explicit songs. Let us first visualize the summary statistics between the two categories of explicitness with box plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_df.groupby(\"explicit\").boxplot(column=\"popularity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the box plot for non-explicit tracks, the average popularity lies around the 30-35 range, quite lower than that of explicit tracks whose average popularity lies around the 45-50 range. There are also multiple outliers in the lower popularity spectrum of explicit songs. Let's tabulate the main summary statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>explicit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.255052</td>\n",
       "      <td>32</td>\n",
       "      <td>20.909213</td>\n",
       "      <td>154890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.735359</td>\n",
       "      <td>52</td>\n",
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       "      <td>14412</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         popularity                          \n",
       "               mean median        std     len\n",
       "explicit                                     \n",
       "0         30.255052     32  20.909213  154890\n",
       "1         46.735359     52  22.454995   14412"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.groupby(\"explicit\").agg({\"popularity\": [\"mean\", \"median\", \"std\", len]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the popularity of non-explicit songs might very well have been controlled by its dominant count over explicit songs, we can see that there is a tendency that explicit songs are more popular by looking at their average means.\n",
    "\n",
    "From this, we can determine if the claim that explicit songs are more popular is significant. First, let's get the difference between the mean popularity of the two categories of explicitness:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.576907"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "46.726749 - 30.149842"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set up our hypotheses as follows:\n",
    "\n",
    "$H_0$ (null hypothesis): The true difference is 0.\n",
    "\n",
    "$H_A$ (alternative hypothesis): The true difference is not 0.\n",
    "\n",
    "We can use a **t-test** to compare two means from unpaired groups (i.e. unrelated groups). In our case, we compare the popularity values between explicit and non-explicit tracks. This can be achieved with **scipy**'s ttest_ind() function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-84.75522692661204, pvalue=0.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(main_df[main_df[\"explicit\"] == 0][\"popularity\"], main_df[main_df[\"explicit\"] == 1][\"popularity\"], equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using a significance value of 0.05, meaning that we're setting a 5% chance of risk of claiming that there's a difference between the two means when in fact, there's none.\n",
    "\n",
    "However, the resulting **p-value** of the t-test is 0.0. Since it is considerably lower than the significance value, we can say that the p-value is statistically significant and that we can reject the null hypothesis. As a result, we can say that **there is a difference in popularity between explicit and non-explicit songs** with the variable being in favor of the former because of its higher mean popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FII #2: Finding the most frequent emotions in Spotify tracks and analyzing their musical elements based on the track's mode and key.\n",
    "A key in music is defined as [the minor or major scale around which a musical piece revolves](https://www.studybass.com/lessons/harmony/keys-in-music/#:~:text=In%20music%20a%20key%20is,based%20on%20a%20major%20scale.&text=For%20example%2C%20a%20song%20in,for%20a%20piece%20of%20music.). When combined with tonality (i.e. has a centered sound), a musical piece may exude a certain emotion. This is what makes music beautiful: its ability to speak to people in a melodic way.\n",
    "\n",
    "From the Spotify dataset, the `mode` and the `key` variables are the building blocks for these very emotions. `mode` corresponds to 0 for a minor key, 1 for a major key, in which both are applicable for each and every possible [pitch class](https://en.wikipedia.org/wiki/Pitch_class) that is defined in `key`. As a result, there are a total of 24 unique mode-key combinations.\n",
    "\n",
    "Our goal here is to **find the most frequent emotions emitted** from Spotify tracks. From the given mode-key combination from each track, we can achieve this by making use of the principles of **association rules**.\n",
    "\n",
    "From our dataset, let us first group by each key-mode combination and get the mean value for the numerical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>duration_ms</th>\n",
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       "      <td>38.292399</td>\n",
       "      <td>0.182414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>240858.487751</td>\n",
       "      <td>1972.359628</td>\n",
       "      <td>0.584859</td>\n",
       "      <td>0.506273</td>\n",
       "      <td>0.434457</td>\n",
       "      <td>0.225966</td>\n",
       "      <td>-12.413931</td>\n",
       "      <td>0.082830</td>\n",
       "      <td>115.283808</td>\n",
       "      <td>0.491589</td>\n",
       "      <td>27.635718</td>\n",
       "      <td>0.057080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>228943.479747</td>\n",
       "      <td>1973.689873</td>\n",
       "      <td>0.530995</td>\n",
       "      <td>0.535353</td>\n",
       "      <td>0.472575</td>\n",
       "      <td>0.193740</td>\n",
       "      <td>-11.637448</td>\n",
       "      <td>0.132086</td>\n",
       "      <td>114.478141</td>\n",
       "      <td>0.508471</td>\n",
       "      <td>28.826582</td>\n",
       "      <td>0.105696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>247547.561654</td>\n",
       "      <td>1980.417765</td>\n",
       "      <td>0.428680</td>\n",
       "      <td>0.526533</td>\n",
       "      <td>0.531111</td>\n",
       "      <td>0.174503</td>\n",
       "      <td>-11.023737</td>\n",
       "      <td>0.088047</td>\n",
       "      <td>116.483098</td>\n",
       "      <td>0.493201</td>\n",
       "      <td>34.279473</td>\n",
       "      <td>0.105976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>243497.709391</td>\n",
       "      <td>1975.588470</td>\n",
       "      <td>0.515191</td>\n",
       "      <td>0.559640</td>\n",
       "      <td>0.481804</td>\n",
       "      <td>0.184086</td>\n",
       "      <td>-11.512806</td>\n",
       "      <td>0.105762</td>\n",
       "      <td>115.896528</td>\n",
       "      <td>0.549500</td>\n",
       "      <td>30.514866</td>\n",
       "      <td>0.107324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>235829.785774</td>\n",
       "      <td>1985.138094</td>\n",
       "      <td>0.363730</td>\n",
       "      <td>0.578747</td>\n",
       "      <td>0.566290</td>\n",
       "      <td>0.133253</td>\n",
       "      <td>-10.067898</td>\n",
       "      <td>0.121760</td>\n",
       "      <td>116.705021</td>\n",
       "      <td>0.547359</td>\n",
       "      <td>37.762156</td>\n",
       "      <td>0.178383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>238988.799010</td>\n",
       "      <td>1973.489109</td>\n",
       "      <td>0.592813</td>\n",
       "      <td>0.530584</td>\n",
       "      <td>0.439758</td>\n",
       "      <td>0.218472</td>\n",
       "      <td>-11.996324</td>\n",
       "      <td>0.075107</td>\n",
       "      <td>115.717717</td>\n",
       "      <td>0.524027</td>\n",
       "      <td>28.863366</td>\n",
       "      <td>0.057921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>234322.671247</td>\n",
       "      <td>1981.824936</td>\n",
       "      <td>0.459851</td>\n",
       "      <td>0.567957</td>\n",
       "      <td>0.521672</td>\n",
       "      <td>0.183973</td>\n",
       "      <td>-10.761932</td>\n",
       "      <td>0.109672</td>\n",
       "      <td>115.389182</td>\n",
       "      <td>0.541422</td>\n",
       "      <td>34.658524</td>\n",
       "      <td>0.161323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>241023.910638</td>\n",
       "      <td>1978.332092</td>\n",
       "      <td>0.464635</td>\n",
       "      <td>0.539649</td>\n",
       "      <td>0.499157</td>\n",
       "      <td>0.179010</td>\n",
       "      <td>-11.417224</td>\n",
       "      <td>0.084846</td>\n",
       "      <td>117.539580</td>\n",
       "      <td>0.525749</td>\n",
       "      <td>33.020035</td>\n",
       "      <td>0.071454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>251929.951705</td>\n",
       "      <td>1980.149148</td>\n",
       "      <td>0.412953</td>\n",
       "      <td>0.588256</td>\n",
       "      <td>0.527620</td>\n",
       "      <td>0.169239</td>\n",
       "      <td>-11.054968</td>\n",
       "      <td>0.153072</td>\n",
       "      <td>113.643947</td>\n",
       "      <td>0.542612</td>\n",
       "      <td>33.792140</td>\n",
       "      <td>0.185843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>234912.597817</td>\n",
       "      <td>1982.582321</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.585091</td>\n",
       "      <td>0.570716</td>\n",
       "      <td>0.123371</td>\n",
       "      <td>-10.383177</td>\n",
       "      <td>0.157131</td>\n",
       "      <td>118.936029</td>\n",
       "      <td>0.561940</td>\n",
       "      <td>35.520690</td>\n",
       "      <td>0.196690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>228426.499163</td>\n",
       "      <td>1976.758412</td>\n",
       "      <td>0.490858</td>\n",
       "      <td>0.539482</td>\n",
       "      <td>0.477019</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>-11.485371</td>\n",
       "      <td>0.089762</td>\n",
       "      <td>117.592052</td>\n",
       "      <td>0.541185</td>\n",
       "      <td>31.305535</td>\n",
       "      <td>0.066428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233997.726221</td>\n",
       "      <td>1980.448620</td>\n",
       "      <td>0.443510</td>\n",
       "      <td>0.560136</td>\n",
       "      <td>0.498242</td>\n",
       "      <td>0.155241</td>\n",
       "      <td>-11.199068</td>\n",
       "      <td>0.137256</td>\n",
       "      <td>115.289928</td>\n",
       "      <td>0.502565</td>\n",
       "      <td>33.671338</td>\n",
       "      <td>0.182696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>233152.462393</td>\n",
       "      <td>1979.220943</td>\n",
       "      <td>0.439671</td>\n",
       "      <td>0.530731</td>\n",
       "      <td>0.523921</td>\n",
       "      <td>0.142815</td>\n",
       "      <td>-10.904767</td>\n",
       "      <td>0.082259</td>\n",
       "      <td>118.953871</td>\n",
       "      <td>0.537756</td>\n",
       "      <td>33.198392</td>\n",
       "      <td>0.067380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225865.283864</td>\n",
       "      <td>1967.269653</td>\n",
       "      <td>0.706653</td>\n",
       "      <td>0.491725</td>\n",
       "      <td>0.374665</td>\n",
       "      <td>0.216881</td>\n",
       "      <td>-12.828672</td>\n",
       "      <td>0.068454</td>\n",
       "      <td>115.455139</td>\n",
       "      <td>0.502987</td>\n",
       "      <td>23.467710</td>\n",
       "      <td>0.018349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>220930.931899</td>\n",
       "      <td>1978.018251</td>\n",
       "      <td>0.494722</td>\n",
       "      <td>0.526606</td>\n",
       "      <td>0.499166</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>-11.150494</td>\n",
       "      <td>0.073751</td>\n",
       "      <td>118.814658</td>\n",
       "      <td>0.544114</td>\n",
       "      <td>32.486516</td>\n",
       "      <td>0.042495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>226199.490941</td>\n",
       "      <td>1972.184893</td>\n",
       "      <td>0.602252</td>\n",
       "      <td>0.519730</td>\n",
       "      <td>0.426833</td>\n",
       "      <td>0.178848</td>\n",
       "      <td>-12.241256</td>\n",
       "      <td>0.076039</td>\n",
       "      <td>115.435182</td>\n",
       "      <td>0.531406</td>\n",
       "      <td>27.740128</td>\n",
       "      <td>0.032426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>225784.792853</td>\n",
       "      <td>1979.106602</td>\n",
       "      <td>0.479454</td>\n",
       "      <td>0.543712</td>\n",
       "      <td>0.495622</td>\n",
       "      <td>0.155419</td>\n",
       "      <td>-11.101155</td>\n",
       "      <td>0.115772</td>\n",
       "      <td>115.029623</td>\n",
       "      <td>0.516366</td>\n",
       "      <td>32.786594</td>\n",
       "      <td>0.105189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>225994.082848</td>\n",
       "      <td>1977.758240</td>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.543002</td>\n",
       "      <td>0.496858</td>\n",
       "      <td>0.141739</td>\n",
       "      <td>-11.315355</td>\n",
       "      <td>0.089427</td>\n",
       "      <td>117.687391</td>\n",
       "      <td>0.551867</td>\n",
       "      <td>31.986012</td>\n",
       "      <td>0.074803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>229788.320547</td>\n",
       "      <td>1975.131848</td>\n",
       "      <td>0.549227</td>\n",
       "      <td>0.531466</td>\n",
       "      <td>0.461458</td>\n",
       "      <td>0.169692</td>\n",
       "      <td>-11.567376</td>\n",
       "      <td>0.091156</td>\n",
       "      <td>116.254179</td>\n",
       "      <td>0.522423</td>\n",
       "      <td>29.805903</td>\n",
       "      <td>0.077409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>226481.263400</td>\n",
       "      <td>1979.177303</td>\n",
       "      <td>0.427484</td>\n",
       "      <td>0.532421</td>\n",
       "      <td>0.540750</td>\n",
       "      <td>0.125746</td>\n",
       "      <td>-10.567112</td>\n",
       "      <td>0.080006</td>\n",
       "      <td>120.751302</td>\n",
       "      <td>0.561848</td>\n",
       "      <td>33.335092</td>\n",
       "      <td>0.053099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>222297.211727</td>\n",
       "      <td>1971.612113</td>\n",
       "      <td>0.636595</td>\n",
       "      <td>0.520621</td>\n",
       "      <td>0.414111</td>\n",
       "      <td>0.171020</td>\n",
       "      <td>-12.153852</td>\n",
       "      <td>0.069923</td>\n",
       "      <td>114.990136</td>\n",
       "      <td>0.534338</td>\n",
       "      <td>27.051289</td>\n",
       "      <td>0.028222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>229311.971681</td>\n",
       "      <td>1981.226760</td>\n",
       "      <td>0.454805</td>\n",
       "      <td>0.542931</td>\n",
       "      <td>0.521446</td>\n",
       "      <td>0.147001</td>\n",
       "      <td>-10.757184</td>\n",
       "      <td>0.099427</td>\n",
       "      <td>116.658785</td>\n",
       "      <td>0.526451</td>\n",
       "      <td>34.112662</td>\n",
       "      <td>0.103222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            duration_ms         year  acousticness  danceability    energy  instrumentalness   loudness  speechiness       tempo   valence  popularity  explicit\n",
       "mode key                                                                                                                                                        \n",
       "0    0    241814.671296  1974.005604      0.575527      0.527936  0.433967          0.216107 -12.281273     0.082894  114.943113  0.502828   29.266569  0.073587\n",
       "     1    230019.658718  1985.972578      0.375685      0.569310  0.570446          0.150023 -10.053665     0.112924  117.853546  0.524113   38.292399  0.182414\n",
       "     2    240858.487751  1972.359628      0.584859      0.506273  0.434457          0.225966 -12.413931     0.082830  115.283808  0.491589   27.635718  0.057080\n",
       "     3    228943.479747  1973.689873      0.530995      0.535353  0.472575          0.193740 -11.637448     0.132086  114.478141  0.508471   28.826582  0.105696\n",
       "     4    247547.561654  1980.417765      0.428680      0.526533  0.531111          0.174503 -11.023737     0.088047  116.483098  0.493201   34.279473  0.105976\n",
       "     5    243497.709391  1975.588470      0.515191      0.559640  0.481804          0.184086 -11.512806     0.105762  115.896528  0.549500   30.514866  0.107324\n",
       "     6    235829.785774  1985.138094      0.363730      0.578747  0.566290          0.133253 -10.067898     0.121760  116.705021  0.547359   37.762156  0.178383\n",
       "     7    238988.799010  1973.489109      0.592813      0.530584  0.439758          0.218472 -11.996324     0.075107  115.717717  0.524027   28.863366  0.057921\n",
       "     8    234322.671247  1981.824936      0.459851      0.567957  0.521672          0.183973 -10.761932     0.109672  115.389182  0.541422   34.658524  0.161323\n",
       "     9    241023.910638  1978.332092      0.464635      0.539649  0.499157          0.179010 -11.417224     0.084846  117.539580  0.525749   33.020035  0.071454\n",
       "     10   251929.951705  1980.149148      0.412953      0.588256  0.527620          0.169239 -11.054968     0.153072  113.643947  0.542612   33.792140  0.185843\n",
       "     11   234912.597817  1982.582321      0.328703      0.585091  0.570716          0.123371 -10.383177     0.157131  118.936029  0.561940   35.520690  0.196690\n",
       "1    0    228426.499163  1976.758412      0.490858      0.539482  0.477019          0.148623 -11.485371     0.089762  117.592052  0.541185   31.305535  0.066428\n",
       "     1    233997.726221  1980.448620      0.443510      0.560136  0.498242          0.155241 -11.199068     0.137256  115.289928  0.502565   33.671338  0.182696\n",
       "     2    233152.462393  1979.220943      0.439671      0.530731  0.523921          0.142815 -10.904767     0.082259  118.953871  0.537756   33.198392  0.067380\n",
       "     3    225865.283864  1967.269653      0.706653      0.491725  0.374665          0.216881 -12.828672     0.068454  115.455139  0.502987   23.467710  0.018349\n",
       "     4    220930.931899  1978.018251      0.494722      0.526606  0.499166          0.136425 -11.150494     0.073751  118.814658  0.544114   32.486516  0.042495\n",
       "     5    226199.490941  1972.184893      0.602252      0.519730  0.426833          0.178848 -12.241256     0.076039  115.435182  0.531406   27.740128  0.032426\n",
       "     6    225784.792853  1979.106602      0.479454      0.543712  0.495622          0.155419 -11.101155     0.115772  115.029623  0.516366   32.786594  0.105189\n",
       "     7    225994.082848  1977.758240      0.465565      0.543002  0.496858          0.141739 -11.315355     0.089427  117.687391  0.551867   31.986012  0.074803\n",
       "     8    229788.320547  1975.131848      0.549227      0.531466  0.461458          0.169692 -11.567376     0.091156  116.254179  0.522423   29.805903  0.077409\n",
       "     9    226481.263400  1979.177303      0.427484      0.532421  0.540750          0.125746 -10.567112     0.080006  120.751302  0.561848   33.335092  0.053099\n",
       "     10   222297.211727  1971.612113      0.636595      0.520621  0.414111          0.171020 -12.153852     0.069923  114.990136  0.534338   27.051289  0.028222\n",
       "     11   229311.971681  1981.226760      0.454805      0.542931  0.521446          0.147001 -10.757184     0.099427  116.658785  0.526451   34.112662  0.103222"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df = main_df.groupby([\"mode\", \"key\"]).agg(\"mean\")\n",
    "emotions_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row represents a unique mode-key combination with corresponding mean musical elements. Let's add variables that will aid us in forming association rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>support_ratio</th>\n",
       "      <th>confidence_modekey</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>4104</td>\n",
       "      <td>0.024241</td>\n",
       "      <td>0.083207</td>\n",
       "      <td>241814.671296</td>\n",
       "      <td>1974.005604</td>\n",
       "      <td>0.575527</td>\n",
       "      <td>0.527936</td>\n",
       "      <td>0.433967</td>\n",
       "      <td>0.216107</td>\n",
       "      <td>-12.281273</td>\n",
       "      <td>0.082894</td>\n",
       "      <td>114.943113</td>\n",
       "      <td>0.502828</td>\n",
       "      <td>29.266569</td>\n",
       "      <td>0.073587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3355</td>\n",
       "      <td>0.019817</td>\n",
       "      <td>0.068021</td>\n",
       "      <td>230019.658718</td>\n",
       "      <td>1985.972578</td>\n",
       "      <td>0.375685</td>\n",
       "      <td>0.569310</td>\n",
       "      <td>0.570446</td>\n",
       "      <td>0.150023</td>\n",
       "      <td>-10.053665</td>\n",
       "      <td>0.112924</td>\n",
       "      <td>117.853546</td>\n",
       "      <td>0.524113</td>\n",
       "      <td>38.292399</td>\n",
       "      <td>0.182414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4082</td>\n",
       "      <td>0.024111</td>\n",
       "      <td>0.082761</td>\n",
       "      <td>240858.487751</td>\n",
       "      <td>1972.359628</td>\n",
       "      <td>0.584859</td>\n",
       "      <td>0.506273</td>\n",
       "      <td>0.434457</td>\n",
       "      <td>0.225966</td>\n",
       "      <td>-12.413931</td>\n",
       "      <td>0.082830</td>\n",
       "      <td>115.283808</td>\n",
       "      <td>0.491589</td>\n",
       "      <td>27.635718</td>\n",
       "      <td>0.057080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1580</td>\n",
       "      <td>0.009332</td>\n",
       "      <td>0.032034</td>\n",
       "      <td>228943.479747</td>\n",
       "      <td>1973.689873</td>\n",
       "      <td>0.530995</td>\n",
       "      <td>0.535353</td>\n",
       "      <td>0.472575</td>\n",
       "      <td>0.193740</td>\n",
       "      <td>-11.637448</td>\n",
       "      <td>0.132086</td>\n",
       "      <td>114.478141</td>\n",
       "      <td>0.508471</td>\n",
       "      <td>28.826582</td>\n",
       "      <td>0.105696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5539</td>\n",
       "      <td>0.032717</td>\n",
       "      <td>0.112301</td>\n",
       "      <td>247547.561654</td>\n",
       "      <td>1980.417765</td>\n",
       "      <td>0.428680</td>\n",
       "      <td>0.526533</td>\n",
       "      <td>0.531111</td>\n",
       "      <td>0.174503</td>\n",
       "      <td>-11.023737</td>\n",
       "      <td>0.088047</td>\n",
       "      <td>116.483098</td>\n",
       "      <td>0.493201</td>\n",
       "      <td>34.279473</td>\n",
       "      <td>0.105976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5516</td>\n",
       "      <td>0.032581</td>\n",
       "      <td>0.111834</td>\n",
       "      <td>243497.709391</td>\n",
       "      <td>1975.588470</td>\n",
       "      <td>0.515191</td>\n",
       "      <td>0.559640</td>\n",
       "      <td>0.481804</td>\n",
       "      <td>0.184086</td>\n",
       "      <td>-11.512806</td>\n",
       "      <td>0.105762</td>\n",
       "      <td>115.896528</td>\n",
       "      <td>0.549500</td>\n",
       "      <td>30.514866</td>\n",
       "      <td>0.107324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3599</td>\n",
       "      <td>0.021258</td>\n",
       "      <td>0.072968</td>\n",
       "      <td>235829.785774</td>\n",
       "      <td>1985.138094</td>\n",
       "      <td>0.363730</td>\n",
       "      <td>0.578747</td>\n",
       "      <td>0.566290</td>\n",
       "      <td>0.133253</td>\n",
       "      <td>-10.067898</td>\n",
       "      <td>0.121760</td>\n",
       "      <td>116.705021</td>\n",
       "      <td>0.547359</td>\n",
       "      <td>37.762156</td>\n",
       "      <td>0.178383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4040</td>\n",
       "      <td>0.023863</td>\n",
       "      <td>0.081909</td>\n",
       "      <td>238988.799010</td>\n",
       "      <td>1973.489109</td>\n",
       "      <td>0.592813</td>\n",
       "      <td>0.530584</td>\n",
       "      <td>0.439758</td>\n",
       "      <td>0.218472</td>\n",
       "      <td>-11.996324</td>\n",
       "      <td>0.075107</td>\n",
       "      <td>115.717717</td>\n",
       "      <td>0.524027</td>\n",
       "      <td>28.863366</td>\n",
       "      <td>0.057921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1965</td>\n",
       "      <td>0.011606</td>\n",
       "      <td>0.039839</td>\n",
       "      <td>234322.671247</td>\n",
       "      <td>1981.824936</td>\n",
       "      <td>0.459851</td>\n",
       "      <td>0.567957</td>\n",
       "      <td>0.521672</td>\n",
       "      <td>0.183973</td>\n",
       "      <td>-10.761932</td>\n",
       "      <td>0.109672</td>\n",
       "      <td>115.389182</td>\n",
       "      <td>0.541422</td>\n",
       "      <td>34.658524</td>\n",
       "      <td>0.161323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5640</td>\n",
       "      <td>0.033313</td>\n",
       "      <td>0.114348</td>\n",
       "      <td>241023.910638</td>\n",
       "      <td>1978.332092</td>\n",
       "      <td>0.464635</td>\n",
       "      <td>0.539649</td>\n",
       "      <td>0.499157</td>\n",
       "      <td>0.179010</td>\n",
       "      <td>-11.417224</td>\n",
       "      <td>0.084846</td>\n",
       "      <td>117.539580</td>\n",
       "      <td>0.525749</td>\n",
       "      <td>33.020035</td>\n",
       "      <td>0.071454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4224</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>0.085640</td>\n",
       "      <td>251929.951705</td>\n",
       "      <td>1980.149148</td>\n",
       "      <td>0.412953</td>\n",
       "      <td>0.588256</td>\n",
       "      <td>0.527620</td>\n",
       "      <td>0.169239</td>\n",
       "      <td>-11.054968</td>\n",
       "      <td>0.153072</td>\n",
       "      <td>113.643947</td>\n",
       "      <td>0.542612</td>\n",
       "      <td>33.792140</td>\n",
       "      <td>0.185843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5679</td>\n",
       "      <td>0.033544</td>\n",
       "      <td>0.115139</td>\n",
       "      <td>234912.597817</td>\n",
       "      <td>1982.582321</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.585091</td>\n",
       "      <td>0.570716</td>\n",
       "      <td>0.123371</td>\n",
       "      <td>-10.383177</td>\n",
       "      <td>0.157131</td>\n",
       "      <td>118.936029</td>\n",
       "      <td>0.561940</td>\n",
       "      <td>35.520690</td>\n",
       "      <td>0.196690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>17327</td>\n",
       "      <td>0.102344</td>\n",
       "      <td>0.144417</td>\n",
       "      <td>228426.499163</td>\n",
       "      <td>1976.758412</td>\n",
       "      <td>0.490858</td>\n",
       "      <td>0.539482</td>\n",
       "      <td>0.477019</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>-11.485371</td>\n",
       "      <td>0.089762</td>\n",
       "      <td>117.592052</td>\n",
       "      <td>0.541185</td>\n",
       "      <td>31.305535</td>\n",
       "      <td>0.066428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9420</td>\n",
       "      <td>0.055640</td>\n",
       "      <td>0.078514</td>\n",
       "      <td>233997.726221</td>\n",
       "      <td>1980.448620</td>\n",
       "      <td>0.443510</td>\n",
       "      <td>0.560136</td>\n",
       "      <td>0.498242</td>\n",
       "      <td>0.155241</td>\n",
       "      <td>-11.199068</td>\n",
       "      <td>0.137256</td>\n",
       "      <td>115.289928</td>\n",
       "      <td>0.502565</td>\n",
       "      <td>33.671338</td>\n",
       "      <td>0.182696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14678</td>\n",
       "      <td>0.086697</td>\n",
       "      <td>0.122338</td>\n",
       "      <td>233152.462393</td>\n",
       "      <td>1979.220943</td>\n",
       "      <td>0.439671</td>\n",
       "      <td>0.530731</td>\n",
       "      <td>0.523921</td>\n",
       "      <td>0.142815</td>\n",
       "      <td>-10.904767</td>\n",
       "      <td>0.082259</td>\n",
       "      <td>118.953871</td>\n",
       "      <td>0.537756</td>\n",
       "      <td>33.198392</td>\n",
       "      <td>0.067380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5559</td>\n",
       "      <td>0.032835</td>\n",
       "      <td>0.046333</td>\n",
       "      <td>225865.283864</td>\n",
       "      <td>1967.269653</td>\n",
       "      <td>0.706653</td>\n",
       "      <td>0.491725</td>\n",
       "      <td>0.374665</td>\n",
       "      <td>0.216881</td>\n",
       "      <td>-12.828672</td>\n",
       "      <td>0.068454</td>\n",
       "      <td>115.455139</td>\n",
       "      <td>0.502987</td>\n",
       "      <td>23.467710</td>\n",
       "      <td>0.018349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7342</td>\n",
       "      <td>0.043366</td>\n",
       "      <td>0.061194</td>\n",
       "      <td>220930.931899</td>\n",
       "      <td>1978.018251</td>\n",
       "      <td>0.494722</td>\n",
       "      <td>0.526606</td>\n",
       "      <td>0.499166</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>-11.150494</td>\n",
       "      <td>0.073751</td>\n",
       "      <td>118.814658</td>\n",
       "      <td>0.544114</td>\n",
       "      <td>32.486516</td>\n",
       "      <td>0.042495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10763</td>\n",
       "      <td>0.063573</td>\n",
       "      <td>0.089707</td>\n",
       "      <td>226199.490941</td>\n",
       "      <td>1972.184893</td>\n",
       "      <td>0.602252</td>\n",
       "      <td>0.519730</td>\n",
       "      <td>0.426833</td>\n",
       "      <td>0.178848</td>\n",
       "      <td>-12.241256</td>\n",
       "      <td>0.076039</td>\n",
       "      <td>115.435182</td>\n",
       "      <td>0.531406</td>\n",
       "      <td>27.740128</td>\n",
       "      <td>0.032426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4953</td>\n",
       "      <td>0.029255</td>\n",
       "      <td>0.041282</td>\n",
       "      <td>225784.792853</td>\n",
       "      <td>1979.106602</td>\n",
       "      <td>0.479454</td>\n",
       "      <td>0.543712</td>\n",
       "      <td>0.495622</td>\n",
       "      <td>0.155419</td>\n",
       "      <td>-11.101155</td>\n",
       "      <td>0.115772</td>\n",
       "      <td>115.029623</td>\n",
       "      <td>0.516366</td>\n",
       "      <td>32.786594</td>\n",
       "      <td>0.105189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16657</td>\n",
       "      <td>0.098386</td>\n",
       "      <td>0.138833</td>\n",
       "      <td>225994.082848</td>\n",
       "      <td>1977.758240</td>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.543002</td>\n",
       "      <td>0.496858</td>\n",
       "      <td>0.141739</td>\n",
       "      <td>-11.315355</td>\n",
       "      <td>0.089427</td>\n",
       "      <td>117.687391</td>\n",
       "      <td>0.551867</td>\n",
       "      <td>31.986012</td>\n",
       "      <td>0.074803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8707</td>\n",
       "      <td>0.051429</td>\n",
       "      <td>0.072571</td>\n",
       "      <td>229788.320547</td>\n",
       "      <td>1975.131848</td>\n",
       "      <td>0.549227</td>\n",
       "      <td>0.531466</td>\n",
       "      <td>0.461458</td>\n",
       "      <td>0.169692</td>\n",
       "      <td>-11.567376</td>\n",
       "      <td>0.091156</td>\n",
       "      <td>116.254179</td>\n",
       "      <td>0.522423</td>\n",
       "      <td>29.805903</td>\n",
       "      <td>0.077409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11940</td>\n",
       "      <td>0.070525</td>\n",
       "      <td>0.099517</td>\n",
       "      <td>226481.263400</td>\n",
       "      <td>1979.177303</td>\n",
       "      <td>0.427484</td>\n",
       "      <td>0.532421</td>\n",
       "      <td>0.540750</td>\n",
       "      <td>0.125746</td>\n",
       "      <td>-10.567112</td>\n",
       "      <td>0.080006</td>\n",
       "      <td>120.751302</td>\n",
       "      <td>0.561848</td>\n",
       "      <td>33.335092</td>\n",
       "      <td>0.053099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7760</td>\n",
       "      <td>0.045835</td>\n",
       "      <td>0.064678</td>\n",
       "      <td>222297.211727</td>\n",
       "      <td>1971.612113</td>\n",
       "      <td>0.636595</td>\n",
       "      <td>0.520621</td>\n",
       "      <td>0.414111</td>\n",
       "      <td>0.171020</td>\n",
       "      <td>-12.153852</td>\n",
       "      <td>0.069923</td>\n",
       "      <td>114.990136</td>\n",
       "      <td>0.534338</td>\n",
       "      <td>27.051289</td>\n",
       "      <td>0.028222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4873</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.040615</td>\n",
       "      <td>229311.971681</td>\n",
       "      <td>1981.226760</td>\n",
       "      <td>0.454805</td>\n",
       "      <td>0.542931</td>\n",
       "      <td>0.521446</td>\n",
       "      <td>0.147001</td>\n",
       "      <td>-10.757184</td>\n",
       "      <td>0.099427</td>\n",
       "      <td>116.658785</td>\n",
       "      <td>0.526451</td>\n",
       "      <td>34.112662</td>\n",
       "      <td>0.103222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          support  support_ratio  confidence_modekey    duration_ms         year  acousticness  danceability    energy  instrumentalness   loudness  speechiness       tempo   valence  popularity  explicit\n",
       "mode key                                                                                                                                                                                                    \n",
       "0    0       4104       0.024241            0.083207  241814.671296  1974.005604      0.575527      0.527936  0.433967          0.216107 -12.281273     0.082894  114.943113  0.502828   29.266569  0.073587\n",
       "     1       3355       0.019817            0.068021  230019.658718  1985.972578      0.375685      0.569310  0.570446          0.150023 -10.053665     0.112924  117.853546  0.524113   38.292399  0.182414\n",
       "     2       4082       0.024111            0.082761  240858.487751  1972.359628      0.584859      0.506273  0.434457          0.225966 -12.413931     0.082830  115.283808  0.491589   27.635718  0.057080\n",
       "     3       1580       0.009332            0.032034  228943.479747  1973.689873      0.530995      0.535353  0.472575          0.193740 -11.637448     0.132086  114.478141  0.508471   28.826582  0.105696\n",
       "     4       5539       0.032717            0.112301  247547.561654  1980.417765      0.428680      0.526533  0.531111          0.174503 -11.023737     0.088047  116.483098  0.493201   34.279473  0.105976\n",
       "     5       5516       0.032581            0.111834  243497.709391  1975.588470      0.515191      0.559640  0.481804          0.184086 -11.512806     0.105762  115.896528  0.549500   30.514866  0.107324\n",
       "     6       3599       0.021258            0.072968  235829.785774  1985.138094      0.363730      0.578747  0.566290          0.133253 -10.067898     0.121760  116.705021  0.547359   37.762156  0.178383\n",
       "     7       4040       0.023863            0.081909  238988.799010  1973.489109      0.592813      0.530584  0.439758          0.218472 -11.996324     0.075107  115.717717  0.524027   28.863366  0.057921\n",
       "     8       1965       0.011606            0.039839  234322.671247  1981.824936      0.459851      0.567957  0.521672          0.183973 -10.761932     0.109672  115.389182  0.541422   34.658524  0.161323\n",
       "     9       5640       0.033313            0.114348  241023.910638  1978.332092      0.464635      0.539649  0.499157          0.179010 -11.417224     0.084846  117.539580  0.525749   33.020035  0.071454\n",
       "     10      4224       0.024949            0.085640  251929.951705  1980.149148      0.412953      0.588256  0.527620          0.169239 -11.054968     0.153072  113.643947  0.542612   33.792140  0.185843\n",
       "     11      5679       0.033544            0.115139  234912.597817  1982.582321      0.328703      0.585091  0.570716          0.123371 -10.383177     0.157131  118.936029  0.561940   35.520690  0.196690\n",
       "1    0      17327       0.102344            0.144417  228426.499163  1976.758412      0.490858      0.539482  0.477019          0.148623 -11.485371     0.089762  117.592052  0.541185   31.305535  0.066428\n",
       "     1       9420       0.055640            0.078514  233997.726221  1980.448620      0.443510      0.560136  0.498242          0.155241 -11.199068     0.137256  115.289928  0.502565   33.671338  0.182696\n",
       "     2      14678       0.086697            0.122338  233152.462393  1979.220943      0.439671      0.530731  0.523921          0.142815 -10.904767     0.082259  118.953871  0.537756   33.198392  0.067380\n",
       "     3       5559       0.032835            0.046333  225865.283864  1967.269653      0.706653      0.491725  0.374665          0.216881 -12.828672     0.068454  115.455139  0.502987   23.467710  0.018349\n",
       "     4       7342       0.043366            0.061194  220930.931899  1978.018251      0.494722      0.526606  0.499166          0.136425 -11.150494     0.073751  118.814658  0.544114   32.486516  0.042495\n",
       "     5      10763       0.063573            0.089707  226199.490941  1972.184893      0.602252      0.519730  0.426833          0.178848 -12.241256     0.076039  115.435182  0.531406   27.740128  0.032426\n",
       "     6       4953       0.029255            0.041282  225784.792853  1979.106602      0.479454      0.543712  0.495622          0.155419 -11.101155     0.115772  115.029623  0.516366   32.786594  0.105189\n",
       "     7      16657       0.098386            0.138833  225994.082848  1977.758240      0.465565      0.543002  0.496858          0.141739 -11.315355     0.089427  117.687391  0.551867   31.986012  0.074803\n",
       "     8       8707       0.051429            0.072571  229788.320547  1975.131848      0.549227      0.531466  0.461458          0.169692 -11.567376     0.091156  116.254179  0.522423   29.805903  0.077409\n",
       "     9      11940       0.070525            0.099517  226481.263400  1979.177303      0.427484      0.532421  0.540750          0.125746 -10.567112     0.080006  120.751302  0.561848   33.335092  0.053099\n",
       "     10      7760       0.045835            0.064678  222297.211727  1971.612113      0.636595      0.520621  0.414111          0.171020 -12.153852     0.069923  114.990136  0.534338   27.051289  0.028222\n",
       "     11      4873       0.028783            0.040615  229311.971681  1981.226760      0.454805      0.542931  0.521446          0.147001 -10.757184     0.099427  116.658785  0.526451   34.112662  0.103222"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidences = []\n",
    "\n",
    "for i in range(2): # Loop through each unique value for the mode variable\n",
    "    mode_df = main_df.query(\"mode == @i\") # Filter df to only contain tracks of the present mode\n",
    "    for j in range (12): # Loop through each unique value for key variable\n",
    "        modekey_df = mode_df.query(\"key == @j\") # Filter df to only contain tracks of the present key, partnered with the present mode acquired in the parent loop\n",
    "        confidences.append(len(modekey_df)/len(mode_df))\n",
    "\n",
    "emotions_df.insert(loc=0, column=\"support\", value=main_df.groupby([\"mode\", \"key\"]).size())\n",
    "emotions_df.insert(loc=1, column=\"support_ratio\", value=emotions_df[\"support\"]/len(main_df))\n",
    "emotions_df.insert(loc=2, column=\"confidence_modekey\", value=confidences)\n",
    "emotions_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new variables are defined as follows:\n",
    "- **`support`**: The number of tracks from `main_df` bearing the specified mode-key combination.\n",
    "- **`support_ratio`**: The ratio of tracks from `main_df` as to the total number observations in said dataset.\n",
    "- **`confidence_modekey`**: The confidence that a specified key will follow, given a specified mode.\n",
    "\n",
    "We will also be adding an `emotion` variable that will indicate the mood a specified combination gives off (**NOTE: The researchers have cross checked and assure the compability of the values in this column with the [corresponding emotions indicated here](https://ledgernote.com/blog/interesting/musical-key-characteristics-emotions/)**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>support_ratio</th>\n",
       "      <th>confidence_modekey</th>\n",
       "      <th>emotion</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>17327</td>\n",
       "      <td>0.102344</td>\n",
       "      <td>0.144417</td>\n",
       "      <td>Innocently Happy</td>\n",
       "      <td>228426.499163</td>\n",
       "      <td>1976.758412</td>\n",
       "      <td>0.490858</td>\n",
       "      <td>0.539482</td>\n",
       "      <td>0.477019</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>-11.485371</td>\n",
       "      <td>0.089762</td>\n",
       "      <td>117.592052</td>\n",
       "      <td>0.541185</td>\n",
       "      <td>31.305535</td>\n",
       "      <td>0.066428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16657</td>\n",
       "      <td>0.098386</td>\n",
       "      <td>0.138833</td>\n",
       "      <td>Serious, Magnificent, Fantasy</td>\n",
       "      <td>225994.082848</td>\n",
       "      <td>1977.758240</td>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.543002</td>\n",
       "      <td>0.496858</td>\n",
       "      <td>0.141739</td>\n",
       "      <td>-11.315355</td>\n",
       "      <td>0.089427</td>\n",
       "      <td>117.687391</td>\n",
       "      <td>0.551867</td>\n",
       "      <td>31.986012</td>\n",
       "      <td>0.074803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14678</td>\n",
       "      <td>0.086697</td>\n",
       "      <td>0.122338</td>\n",
       "      <td>Triumphant, Victorious War-Cries</td>\n",
       "      <td>233152.462393</td>\n",
       "      <td>1979.220943</td>\n",
       "      <td>0.439671</td>\n",
       "      <td>0.530731</td>\n",
       "      <td>0.523921</td>\n",
       "      <td>0.142815</td>\n",
       "      <td>-10.904767</td>\n",
       "      <td>0.082259</td>\n",
       "      <td>118.953871</td>\n",
       "      <td>0.537756</td>\n",
       "      <td>33.198392</td>\n",
       "      <td>0.067380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11940</td>\n",
       "      <td>0.070525</td>\n",
       "      <td>0.099517</td>\n",
       "      <td>Joyful, Pastoral, Declaration of Love</td>\n",
       "      <td>226481.263400</td>\n",
       "      <td>1979.177303</td>\n",
       "      <td>0.427484</td>\n",
       "      <td>0.532421</td>\n",
       "      <td>0.540750</td>\n",
       "      <td>0.125746</td>\n",
       "      <td>-10.567112</td>\n",
       "      <td>0.080006</td>\n",
       "      <td>120.751302</td>\n",
       "      <td>0.561848</td>\n",
       "      <td>33.335092</td>\n",
       "      <td>0.053099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10763</td>\n",
       "      <td>0.063573</td>\n",
       "      <td>0.089707</td>\n",
       "      <td>Furious, Quick-Tempered, Passing Regret</td>\n",
       "      <td>226199.490941</td>\n",
       "      <td>1972.184893</td>\n",
       "      <td>0.602252</td>\n",
       "      <td>0.519730</td>\n",
       "      <td>0.426833</td>\n",
       "      <td>0.178848</td>\n",
       "      <td>-12.241256</td>\n",
       "      <td>0.076039</td>\n",
       "      <td>115.435182</td>\n",
       "      <td>0.531406</td>\n",
       "      <td>27.740128</td>\n",
       "      <td>0.032426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9420</td>\n",
       "      <td>0.055640</td>\n",
       "      <td>0.078514</td>\n",
       "      <td>Grief, Depressive</td>\n",
       "      <td>233997.726221</td>\n",
       "      <td>1980.448620</td>\n",
       "      <td>0.443510</td>\n",
       "      <td>0.560136</td>\n",
       "      <td>0.498242</td>\n",
       "      <td>0.155241</td>\n",
       "      <td>-11.199068</td>\n",
       "      <td>0.137256</td>\n",
       "      <td>115.289928</td>\n",
       "      <td>0.502565</td>\n",
       "      <td>33.671338</td>\n",
       "      <td>0.182696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8707</td>\n",
       "      <td>0.051429</td>\n",
       "      <td>0.072571</td>\n",
       "      <td>Death, Eternity, Judgement</td>\n",
       "      <td>229788.320547</td>\n",
       "      <td>1975.131848</td>\n",
       "      <td>0.549227</td>\n",
       "      <td>0.531466</td>\n",
       "      <td>0.461458</td>\n",
       "      <td>0.169692</td>\n",
       "      <td>-11.567376</td>\n",
       "      <td>0.091156</td>\n",
       "      <td>116.254179</td>\n",
       "      <td>0.522423</td>\n",
       "      <td>29.805903</td>\n",
       "      <td>0.077409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7760</td>\n",
       "      <td>0.045835</td>\n",
       "      <td>0.064678</td>\n",
       "      <td>Joyful, Quaint, Cheerful</td>\n",
       "      <td>222297.211727</td>\n",
       "      <td>1971.612113</td>\n",
       "      <td>0.636595</td>\n",
       "      <td>0.520621</td>\n",
       "      <td>0.414111</td>\n",
       "      <td>0.171020</td>\n",
       "      <td>-12.153852</td>\n",
       "      <td>0.069923</td>\n",
       "      <td>114.990136</td>\n",
       "      <td>0.534338</td>\n",
       "      <td>27.051289</td>\n",
       "      <td>0.028222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7342</td>\n",
       "      <td>0.043366</td>\n",
       "      <td>0.061194</td>\n",
       "      <td>Quarrelsome, Boisterous, Incomplete Pleasure</td>\n",
       "      <td>220930.931899</td>\n",
       "      <td>1978.018251</td>\n",
       "      <td>0.494722</td>\n",
       "      <td>0.526606</td>\n",
       "      <td>0.499166</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>-11.150494</td>\n",
       "      <td>0.073751</td>\n",
       "      <td>118.814658</td>\n",
       "      <td>0.544114</td>\n",
       "      <td>32.486516</td>\n",
       "      <td>0.042495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>11</th>\n",
       "      <td>5679</td>\n",
       "      <td>0.033544</td>\n",
       "      <td>0.115139</td>\n",
       "      <td>Solitary, Melancholic, Patience</td>\n",
       "      <td>234912.597817</td>\n",
       "      <td>1982.582321</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.585091</td>\n",
       "      <td>0.570716</td>\n",
       "      <td>0.123371</td>\n",
       "      <td>-10.383177</td>\n",
       "      <td>0.157131</td>\n",
       "      <td>118.936029</td>\n",
       "      <td>0.561940</td>\n",
       "      <td>35.520690</td>\n",
       "      <td>0.196690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5640</td>\n",
       "      <td>0.033313</td>\n",
       "      <td>0.114348</td>\n",
       "      <td>Tender, Plaintive, Pious</td>\n",
       "      <td>241023.910638</td>\n",
       "      <td>1978.332092</td>\n",
       "      <td>0.464635</td>\n",
       "      <td>0.539649</td>\n",
       "      <td>0.499157</td>\n",
       "      <td>0.179010</td>\n",
       "      <td>-11.417224</td>\n",
       "      <td>0.084846</td>\n",
       "      <td>117.539580</td>\n",
       "      <td>0.525749</td>\n",
       "      <td>33.020035</td>\n",
       "      <td>0.071454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <td>5559</td>\n",
       "      <td>0.032835</td>\n",
       "      <td>0.046333</td>\n",
       "      <td>Cruel, Hard, Yet Full of Devotion</td>\n",
       "      <td>225865.283864</td>\n",
       "      <td>1967.269653</td>\n",
       "      <td>0.706653</td>\n",
       "      <td>0.491725</td>\n",
       "      <td>0.374665</td>\n",
       "      <td>0.216881</td>\n",
       "      <td>-12.828672</td>\n",
       "      <td>0.068454</td>\n",
       "      <td>115.455139</td>\n",
       "      <td>0.502987</td>\n",
       "      <td>23.467710</td>\n",
       "      <td>0.018349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>4</th>\n",
       "      <td>5539</td>\n",
       "      <td>0.032717</td>\n",
       "      <td>0.112301</td>\n",
       "      <td>Effeminate, Amorous, Restless</td>\n",
       "      <td>247547.561654</td>\n",
       "      <td>1980.417765</td>\n",
       "      <td>0.428680</td>\n",
       "      <td>0.526533</td>\n",
       "      <td>0.531111</td>\n",
       "      <td>0.174503</td>\n",
       "      <td>-11.023737</td>\n",
       "      <td>0.088047</td>\n",
       "      <td>116.483098</td>\n",
       "      <td>0.493201</td>\n",
       "      <td>34.279473</td>\n",
       "      <td>0.105976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5516</td>\n",
       "      <td>0.032581</td>\n",
       "      <td>0.111834</td>\n",
       "      <td>Obscure, Plaintive, Funereal</td>\n",
       "      <td>243497.709391</td>\n",
       "      <td>1975.588470</td>\n",
       "      <td>0.515191</td>\n",
       "      <td>0.559640</td>\n",
       "      <td>0.481804</td>\n",
       "      <td>0.184086</td>\n",
       "      <td>-11.512806</td>\n",
       "      <td>0.105762</td>\n",
       "      <td>115.896528</td>\n",
       "      <td>0.549500</td>\n",
       "      <td>30.514866</td>\n",
       "      <td>0.107324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>6</th>\n",
       "      <td>4953</td>\n",
       "      <td>0.029255</td>\n",
       "      <td>0.041282</td>\n",
       "      <td>Conquering Difficulties, Sighs of Relief</td>\n",
       "      <td>225784.792853</td>\n",
       "      <td>1979.106602</td>\n",
       "      <td>0.479454</td>\n",
       "      <td>0.543712</td>\n",
       "      <td>0.495622</td>\n",
       "      <td>0.155419</td>\n",
       "      <td>-11.101155</td>\n",
       "      <td>0.115772</td>\n",
       "      <td>115.029623</td>\n",
       "      <td>0.516366</td>\n",
       "      <td>32.786594</td>\n",
       "      <td>0.105189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4873</td>\n",
       "      <td>0.028783</td>\n",
       "      <td>0.040615</td>\n",
       "      <td>Harsh, Strong, Wild, Rage</td>\n",
       "      <td>229311.971681</td>\n",
       "      <td>1981.226760</td>\n",
       "      <td>0.454805</td>\n",
       "      <td>0.542931</td>\n",
       "      <td>0.521446</td>\n",
       "      <td>0.147001</td>\n",
       "      <td>-10.757184</td>\n",
       "      <td>0.099427</td>\n",
       "      <td>116.658785</td>\n",
       "      <td>0.526451</td>\n",
       "      <td>34.112662</td>\n",
       "      <td>0.103222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">0</th>\n",
       "      <th>10</th>\n",
       "      <td>4224</td>\n",
       "      <td>0.024949</td>\n",
       "      <td>0.085640</td>\n",
       "      <td>Terrible, the Night, Mocking</td>\n",
       "      <td>251929.951705</td>\n",
       "      <td>1980.149148</td>\n",
       "      <td>0.412953</td>\n",
       "      <td>0.588256</td>\n",
       "      <td>0.527620</td>\n",
       "      <td>0.169239</td>\n",
       "      <td>-11.054968</td>\n",
       "      <td>0.153072</td>\n",
       "      <td>113.643947</td>\n",
       "      <td>0.542612</td>\n",
       "      <td>33.792140</td>\n",
       "      <td>0.185843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4104</td>\n",
       "      <td>0.024241</td>\n",
       "      <td>0.083207</td>\n",
       "      <td>Innocently Sad, Love-Sick</td>\n",
       "      <td>241814.671296</td>\n",
       "      <td>1974.005604</td>\n",
       "      <td>0.575527</td>\n",
       "      <td>0.527936</td>\n",
       "      <td>0.433967</td>\n",
       "      <td>0.216107</td>\n",
       "      <td>-12.281273</td>\n",
       "      <td>0.082894</td>\n",
       "      <td>114.943113</td>\n",
       "      <td>0.502828</td>\n",
       "      <td>29.266569</td>\n",
       "      <td>0.073587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4082</td>\n",
       "      <td>0.024111</td>\n",
       "      <td>0.082761</td>\n",
       "      <td>Serious, Pious, Ruminating</td>\n",
       "      <td>240858.487751</td>\n",
       "      <td>1972.359628</td>\n",
       "      <td>0.584859</td>\n",
       "      <td>0.506273</td>\n",
       "      <td>0.434457</td>\n",
       "      <td>0.225966</td>\n",
       "      <td>-12.413931</td>\n",
       "      <td>0.082830</td>\n",
       "      <td>115.283808</td>\n",
       "      <td>0.491589</td>\n",
       "      <td>27.635718</td>\n",
       "      <td>0.057080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4040</td>\n",
       "      <td>0.023863</td>\n",
       "      <td>0.081909</td>\n",
       "      <td>Discontent, Uneasiness</td>\n",
       "      <td>238988.799010</td>\n",
       "      <td>1973.489109</td>\n",
       "      <td>0.592813</td>\n",
       "      <td>0.530584</td>\n",
       "      <td>0.439758</td>\n",
       "      <td>0.218472</td>\n",
       "      <td>-11.996324</td>\n",
       "      <td>0.075107</td>\n",
       "      <td>115.717717</td>\n",
       "      <td>0.524027</td>\n",
       "      <td>28.863366</td>\n",
       "      <td>0.057921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3599</td>\n",
       "      <td>0.021258</td>\n",
       "      <td>0.072968</td>\n",
       "      <td>Gloomy, Passionate Resentment</td>\n",
       "      <td>235829.785774</td>\n",
       "      <td>1985.138094</td>\n",
       "      <td>0.363730</td>\n",
       "      <td>0.578747</td>\n",
       "      <td>0.566290</td>\n",
       "      <td>0.133253</td>\n",
       "      <td>-10.067898</td>\n",
       "      <td>0.121760</td>\n",
       "      <td>116.705021</td>\n",
       "      <td>0.547359</td>\n",
       "      <td>37.762156</td>\n",
       "      <td>0.178383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3355</td>\n",
       "      <td>0.019817</td>\n",
       "      <td>0.068021</td>\n",
       "      <td>Despair, Wailing, Weeping</td>\n",
       "      <td>230019.658718</td>\n",
       "      <td>1985.972578</td>\n",
       "      <td>0.375685</td>\n",
       "      <td>0.569310</td>\n",
       "      <td>0.570446</td>\n",
       "      <td>0.150023</td>\n",
       "      <td>-10.053665</td>\n",
       "      <td>0.112924</td>\n",
       "      <td>117.853546</td>\n",
       "      <td>0.524113</td>\n",
       "      <td>38.292399</td>\n",
       "      <td>0.182414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1965</td>\n",
       "      <td>0.011606</td>\n",
       "      <td>0.039839</td>\n",
       "      <td>Grumbling, Moaning, Wailing</td>\n",
       "      <td>234322.671247</td>\n",
       "      <td>1981.824936</td>\n",
       "      <td>0.459851</td>\n",
       "      <td>0.567957</td>\n",
       "      <td>0.521672</td>\n",
       "      <td>0.183973</td>\n",
       "      <td>-10.761932</td>\n",
       "      <td>0.109672</td>\n",
       "      <td>115.389182</td>\n",
       "      <td>0.541422</td>\n",
       "      <td>34.658524</td>\n",
       "      <td>0.161323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1580</td>\n",
       "      <td>0.009332</td>\n",
       "      <td>0.032034</td>\n",
       "      <td>Deep Distress, Existential Angst</td>\n",
       "      <td>228943.479747</td>\n",
       "      <td>1973.689873</td>\n",
       "      <td>0.530995</td>\n",
       "      <td>0.535353</td>\n",
       "      <td>0.472575</td>\n",
       "      <td>0.193740</td>\n",
       "      <td>-11.637448</td>\n",
       "      <td>0.132086</td>\n",
       "      <td>114.478141</td>\n",
       "      <td>0.508471</td>\n",
       "      <td>28.826582</td>\n",
       "      <td>0.105696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          support  support_ratio  confidence_modekey                                       emotion    duration_ms         year  acousticness  danceability    energy  instrumentalness   loudness  speechiness       tempo   valence  popularity  explicit\n",
       "mode key                                                                                                                                                                                                                                                  \n",
       "1    0      17327       0.102344            0.144417                              Innocently Happy  228426.499163  1976.758412      0.490858      0.539482  0.477019          0.148623 -11.485371     0.089762  117.592052  0.541185   31.305535  0.066428\n",
       "     7      16657       0.098386            0.138833                 Serious, Magnificent, Fantasy  225994.082848  1977.758240      0.465565      0.543002  0.496858          0.141739 -11.315355     0.089427  117.687391  0.551867   31.986012  0.074803\n",
       "     2      14678       0.086697            0.122338              Triumphant, Victorious War-Cries  233152.462393  1979.220943      0.439671      0.530731  0.523921          0.142815 -10.904767     0.082259  118.953871  0.537756   33.198392  0.067380\n",
       "     9      11940       0.070525            0.099517         Joyful, Pastoral, Declaration of Love  226481.263400  1979.177303      0.427484      0.532421  0.540750          0.125746 -10.567112     0.080006  120.751302  0.561848   33.335092  0.053099\n",
       "     5      10763       0.063573            0.089707       Furious, Quick-Tempered, Passing Regret  226199.490941  1972.184893      0.602252      0.519730  0.426833          0.178848 -12.241256     0.076039  115.435182  0.531406   27.740128  0.032426\n",
       "     1       9420       0.055640            0.078514                             Grief, Depressive  233997.726221  1980.448620      0.443510      0.560136  0.498242          0.155241 -11.199068     0.137256  115.289928  0.502565   33.671338  0.182696\n",
       "     8       8707       0.051429            0.072571                    Death, Eternity, Judgement  229788.320547  1975.131848      0.549227      0.531466  0.461458          0.169692 -11.567376     0.091156  116.254179  0.522423   29.805903  0.077409\n",
       "     10      7760       0.045835            0.064678                      Joyful, Quaint, Cheerful  222297.211727  1971.612113      0.636595      0.520621  0.414111          0.171020 -12.153852     0.069923  114.990136  0.534338   27.051289  0.028222\n",
       "     4       7342       0.043366            0.061194  Quarrelsome, Boisterous, Incomplete Pleasure  220930.931899  1978.018251      0.494722      0.526606  0.499166          0.136425 -11.150494     0.073751  118.814658  0.544114   32.486516  0.042495\n",
       "0    11      5679       0.033544            0.115139               Solitary, Melancholic, Patience  234912.597817  1982.582321      0.328703      0.585091  0.570716          0.123371 -10.383177     0.157131  118.936029  0.561940   35.520690  0.196690\n",
       "     9       5640       0.033313            0.114348                      Tender, Plaintive, Pious  241023.910638  1978.332092      0.464635      0.539649  0.499157          0.179010 -11.417224     0.084846  117.539580  0.525749   33.020035  0.071454\n",
       "1    3       5559       0.032835            0.046333             Cruel, Hard, Yet Full of Devotion  225865.283864  1967.269653      0.706653      0.491725  0.374665          0.216881 -12.828672     0.068454  115.455139  0.502987   23.467710  0.018349\n",
       "0    4       5539       0.032717            0.112301                 Effeminate, Amorous, Restless  247547.561654  1980.417765      0.428680      0.526533  0.531111          0.174503 -11.023737     0.088047  116.483098  0.493201   34.279473  0.105976\n",
       "     5       5516       0.032581            0.111834                  Obscure, Plaintive, Funereal  243497.709391  1975.588470      0.515191      0.559640  0.481804          0.184086 -11.512806     0.105762  115.896528  0.549500   30.514866  0.107324\n",
       "1    6       4953       0.029255            0.041282      Conquering Difficulties, Sighs of Relief  225784.792853  1979.106602      0.479454      0.543712  0.495622          0.155419 -11.101155     0.115772  115.029623  0.516366   32.786594  0.105189\n",
       "     11      4873       0.028783            0.040615                     Harsh, Strong, Wild, Rage  229311.971681  1981.226760      0.454805      0.542931  0.521446          0.147001 -10.757184     0.099427  116.658785  0.526451   34.112662  0.103222\n",
       "0    10      4224       0.024949            0.085640                  Terrible, the Night, Mocking  251929.951705  1980.149148      0.412953      0.588256  0.527620          0.169239 -11.054968     0.153072  113.643947  0.542612   33.792140  0.185843\n",
       "     0       4104       0.024241            0.083207                     Innocently Sad, Love-Sick  241814.671296  1974.005604      0.575527      0.527936  0.433967          0.216107 -12.281273     0.082894  114.943113  0.502828   29.266569  0.073587\n",
       "     2       4082       0.024111            0.082761                    Serious, Pious, Ruminating  240858.487751  1972.359628      0.584859      0.506273  0.434457          0.225966 -12.413931     0.082830  115.283808  0.491589   27.635718  0.057080\n",
       "     7       4040       0.023863            0.081909                        Discontent, Uneasiness  238988.799010  1973.489109      0.592813      0.530584  0.439758          0.218472 -11.996324     0.075107  115.717717  0.524027   28.863366  0.057921\n",
       "     6       3599       0.021258            0.072968                 Gloomy, Passionate Resentment  235829.785774  1985.138094      0.363730      0.578747  0.566290          0.133253 -10.067898     0.121760  116.705021  0.547359   37.762156  0.178383\n",
       "     1       3355       0.019817            0.068021                     Despair, Wailing, Weeping  230019.658718  1985.972578      0.375685      0.569310  0.570446          0.150023 -10.053665     0.112924  117.853546  0.524113   38.292399  0.182414\n",
       "     8       1965       0.011606            0.039839                   Grumbling, Moaning, Wailing  234322.671247  1981.824936      0.459851      0.567957  0.521672          0.183973 -10.761932     0.109672  115.389182  0.541422   34.658524  0.161323\n",
       "     3       1580       0.009332            0.032034              Deep Distress, Existential Angst  228943.479747  1973.689873      0.530995      0.535353  0.472575          0.193740 -11.637448     0.132086  114.478141  0.508471   28.826582  0.105696"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions = [\"Innocently Sad, Love-Sick\", \"Despair, Wailing, Weeping\", \"Serious, Pious, Ruminating\", \"Deep Distress, Existential Angst\", \"Effeminate, Amorous, Restless\", \"Obscure, Plaintive, Funereal\", \"Gloomy, Passionate Resentment\", \"Discontent, Uneasiness\", \"Grumbling, Moaning, Wailing\", \"Tender, Plaintive, Pious\", \"Terrible, the Night, Mocking\", \"Solitary, Melancholic, Patience\", \"Innocently Happy\", \"Grief, Depressive\", \"Triumphant, Victorious War-Cries\", \"Cruel, Hard, Yet Full of Devotion\", \"Quarrelsome, Boisterous, Incomplete Pleasure\", \"Furious, Quick-Tempered, Passing Regret\", \"Conquering Difficulties, Sighs of Relief\", \"Serious, Magnificent, Fantasy\", \"Death, Eternity, Judgement\", \"Joyful, Pastoral, Declaration of Love\", \"Joyful, Quaint, Cheerful\", \"Harsh, Strong, Wild, Rage\"]\n",
    "emotions_df.insert(loc=3, column=\"emotion\", value=emotions)\n",
    "emotions_df = emotions_df.sort_values(\"support\", ascending=False)\n",
    "emotions_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The most frequent mode-key combination is 1-0 (Major C) with a support of 17,327**. Major C depicts an **innocently happy tone**, exuding elements of simplicity, naivety, and pureness. Closely behind is 1-7 (Major G) at 16,657, depicting elements of being serious, magnificent, and semblances of fantasy. These type of songs are usually poetic and lyrical. Both of these mode-key combinations have nearly identical mean values for their musical elements, scoring relatively high in danceability, valence, and tempo.  \n",
    "\n",
    "**The least frequent combination is that of 0-3 (Minor D#) at 1,580**. These songs usually deal with deep distress and anxiety, going even as far as being labelled \"the language of ghosts\". There's a noticeably higher level of speechiness and a lower tempo.\n",
    "\n",
    "From the sorted data, we can see that a **majority of the combinations are in the Major scale**. The most frequent of the Minor scale is that of 0-11 (Minor B), or songs that exude solice and patience. The least frequent of the Major scale is that of the 1-11 (Major B), or songs that exude wildness and rage.\n",
    "\n",
    "We can determine the significant mode-key combinations with **support and confidence thresholds**. This way, we can discern the most frequent combinations given a specified mode.\n",
    "\n",
    "For this, we will set our support threshold at >= 0.05 (based on `support_ratio`) and our confidence threshold at >= 0.10 (based on `confidence_modekey`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>support_ratio</th>\n",
       "      <th>confidence_modekey</th>\n",
       "      <th>emotion</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>17327</td>\n",
       "      <td>0.102344</td>\n",
       "      <td>0.144417</td>\n",
       "      <td>Innocently Happy</td>\n",
       "      <td>228426.499163</td>\n",
       "      <td>1976.758412</td>\n",
       "      <td>0.490858</td>\n",
       "      <td>0.539482</td>\n",
       "      <td>0.477019</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>-11.485371</td>\n",
       "      <td>0.089762</td>\n",
       "      <td>117.592052</td>\n",
       "      <td>0.541185</td>\n",
       "      <td>31.305535</td>\n",
       "      <td>0.066428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16657</td>\n",
       "      <td>0.098386</td>\n",
       "      <td>0.138833</td>\n",
       "      <td>Serious, Magnificent, Fantasy</td>\n",
       "      <td>225994.082848</td>\n",
       "      <td>1977.758240</td>\n",
       "      <td>0.465565</td>\n",
       "      <td>0.543002</td>\n",
       "      <td>0.496858</td>\n",
       "      <td>0.141739</td>\n",
       "      <td>-11.315355</td>\n",
       "      <td>0.089427</td>\n",
       "      <td>117.687391</td>\n",
       "      <td>0.551867</td>\n",
       "      <td>31.986012</td>\n",
       "      <td>0.074803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14678</td>\n",
       "      <td>0.086697</td>\n",
       "      <td>0.122338</td>\n",
       "      <td>Triumphant, Victorious War-Cries</td>\n",
       "      <td>233152.462393</td>\n",
       "      <td>1979.220943</td>\n",
       "      <td>0.439671</td>\n",
       "      <td>0.530731</td>\n",
       "      <td>0.523921</td>\n",
       "      <td>0.142815</td>\n",
       "      <td>-10.904767</td>\n",
       "      <td>0.082259</td>\n",
       "      <td>118.953871</td>\n",
       "      <td>0.537756</td>\n",
       "      <td>33.198392</td>\n",
       "      <td>0.067380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          support  support_ratio  confidence_modekey                           emotion    duration_ms         year  acousticness  danceability    energy  instrumentalness   loudness  speechiness       tempo   valence  popularity  explicit\n",
       "mode key                                                                                                                                                                                                                                      \n",
       "1    0      17327       0.102344            0.144417                  Innocently Happy  228426.499163  1976.758412      0.490858      0.539482  0.477019          0.148623 -11.485371     0.089762  117.592052  0.541185   31.305535  0.066428\n",
       "     7      16657       0.098386            0.138833     Serious, Magnificent, Fantasy  225994.082848  1977.758240      0.465565      0.543002  0.496858          0.141739 -11.315355     0.089427  117.687391  0.551867   31.986012  0.074803\n",
       "     2      14678       0.086697            0.122338  Triumphant, Victorious War-Cries  233152.462393  1979.220943      0.439671      0.530731  0.523921          0.142815 -10.904767     0.082259  118.953871  0.537756   33.198392  0.067380"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_df.query(\"support_ratio >= 0.05 and confidence_modekey >= 0.10\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These three combinations remain as the most significant combinations given a specified mode. For added context, let's view the top 10 most popular songs for each combination:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artists</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
       "      <th>popularity</th>\n",
       "      <th>explicit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87945</th>\n",
       "      <td>Blueberry Faygo</td>\n",
       "      <td>['Lil Mosey']</td>\n",
       "      <td>162547</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.20700</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.909</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>99.034</td>\n",
       "      <td>0.349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87851</th>\n",
       "      <td>Watermelon Sugar</td>\n",
       "      <td>['Harry Styles']</td>\n",
       "      <td>174000</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.209</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>95.390</td>\n",
       "      <td>0.557</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87950</th>\n",
       "      <td>Righteous</td>\n",
       "      <td>['Juice WRLD']</td>\n",
       "      <td>243357</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.03970</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.951</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>142.942</td>\n",
       "      <td>0.223</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87976</th>\n",
       "      <td>Blueberry Faygo</td>\n",
       "      <td>['Lil Mosey']</td>\n",
       "      <td>162547</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.20700</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.909</td>\n",
       "      <td>0.0383</td>\n",
       "      <td>99.034</td>\n",
       "      <td>0.349</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87743</th>\n",
       "      <td>All Girls Are The Same</td>\n",
       "      <td>['Juice WRLD']</td>\n",
       "      <td>165820</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.07690</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>-7.226</td>\n",
       "      <td>0.3070</td>\n",
       "      <td>161.989</td>\n",
       "      <td>0.203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97818</th>\n",
       "      <td>Physical</td>\n",
       "      <td>['Dua Lipa']</td>\n",
       "      <td>193829</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.01370</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>-3.756</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>146.962</td>\n",
       "      <td>0.746</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87669</th>\n",
       "      <td>Thunder</td>\n",
       "      <td>['Imagine Dragons']</td>\n",
       "      <td>187147</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.00671</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.134000</td>\n",
       "      <td>-4.833</td>\n",
       "      <td>0.0438</td>\n",
       "      <td>167.997</td>\n",
       "      <td>0.288</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87911</th>\n",
       "      <td>Watermelon Sugar</td>\n",
       "      <td>['Harry Styles']</td>\n",
       "      <td>174000</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.209</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>95.390</td>\n",
       "      <td>0.557</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87967</th>\n",
       "      <td>DOLLAZ ON MY HEAD (feat. Young Thug)</td>\n",
       "      <td>['Gunna', 'Young Thug']</td>\n",
       "      <td>197760</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.470</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>145.115</td>\n",
       "      <td>0.161</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84540</th>\n",
       "      <td>Livin' On A Prayer</td>\n",
       "      <td>['Bon Jovi']</td>\n",
       "      <td>249293</td>\n",
       "      <td>1986</td>\n",
       "      <td>0.07780</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>-3.757</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>122.511</td>\n",
       "      <td>0.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name                  artists  duration_ms  year  acousticness  danceability  energy  instrumentalness  loudness  speechiness    tempo  valence  mode  key  popularity  explicit\n",
       "87945                       Blueberry Faygo            ['Lil Mosey']       162547  2020       0.20700         0.774   0.554          0.000000    -7.909       0.0383   99.034    0.349     1    0          92         1\n",
       "87851                      Watermelon Sugar         ['Harry Styles']       174000  2019       0.12200         0.548   0.816          0.000000    -4.209       0.0465   95.390    0.557     1    0          90         0\n",
       "87950                             Righteous           ['Juice WRLD']       243357  2020       0.03970         0.707   0.622          0.000000    -6.951       0.0310  142.942    0.223     1    0          89         1\n",
       "87976                       Blueberry Faygo            ['Lil Mosey']       162547  2020       0.20700         0.774   0.554          0.000000    -7.909       0.0383   99.034    0.349     1    0          88         1\n",
       "87743                All Girls Are The Same           ['Juice WRLD']       165820  2018       0.07690         0.671   0.529          0.000335    -7.226       0.3070  161.989    0.203     1    0          85         1\n",
       "97818                              Physical             ['Dua Lipa']       193829  2020       0.01370         0.647   0.844          0.000658    -3.756       0.0457  146.962    0.746     1    0          85         0\n",
       "87669                               Thunder      ['Imagine Dragons']       187147  2017       0.00671         0.605   0.822          0.134000    -4.833       0.0438  167.997    0.288     1    0          84         0\n",
       "87911                      Watermelon Sugar         ['Harry Styles']       174000  2019       0.12200         0.548   0.816          0.000000    -4.209       0.0465   95.390    0.557     1    0          84         0\n",
       "87967  DOLLAZ ON MY HEAD (feat. Young Thug)  ['Gunna', 'Young Thug']       197760  2020       0.11400         0.825   0.458          0.000000    -7.470       0.0577  145.115    0.161     1    0          84         1\n",
       "84540                    Livin' On A Prayer             ['Bon Jovi']       249293  1986       0.07780         0.532   0.887          0.000214    -3.757       0.0335  122.511    0.795     1    0          83         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.query(\"mode == 1 and key == 0\").sort_values(\"popularity\", ascending=False).head(10) # Top 10 Major C songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artists</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>year</th>\n",
       "      <th>acousticness</th>\n",
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       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "      <th>mode</th>\n",
       "      <th>key</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87844</th>\n",
       "      <td>Supalonely</td>\n",
       "      <td>['BENEE', 'Gus Dapperton']</td>\n",
       "      <td>223480</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>-4.689</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>128.977</td>\n",
       "      <td>0.817</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87540</th>\n",
       "      <td>goosebumps</td>\n",
       "      <td>['Travis Scott']</td>\n",
       "      <td>243837</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0847</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.370</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>130.049</td>\n",
       "      <td>0.430</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87863</th>\n",
       "      <td>bad guy</td>\n",
       "      <td>['Billie Eilish']</td>\n",
       "      <td>194088</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.3280</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>-10.965</td>\n",
       "      <td>0.3750</td>\n",
       "      <td>135.128</td>\n",
       "      <td>0.562</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87848</th>\n",
       "      <td>Ballin' (with Roddy Ricch)</td>\n",
       "      <td>['Mustard', 'Roddy Ricch']</td>\n",
       "      <td>180435</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.3920</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.678</td>\n",
       "      <td>0.0735</td>\n",
       "      <td>97.008</td>\n",
       "      <td>0.244</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97780</th>\n",
       "      <td>PA' ROMPERLA</td>\n",
       "      <td>['Bad Bunny', 'Don Omar']</td>\n",
       "      <td>194200</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.019</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>94.011</td>\n",
       "      <td>0.697</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87813</th>\n",
       "      <td>Shallow</td>\n",
       "      <td>['Lady Gaga', 'Bradley Cooper']</td>\n",
       "      <td>215733</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.362</td>\n",
       "      <td>0.0308</td>\n",
       "      <td>95.799</td>\n",
       "      <td>0.323</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87939</th>\n",
       "      <td>LA CANCIÓN</td>\n",
       "      <td>['J Balvin', 'Bad Bunny']</td>\n",
       "      <td>242573</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.1520</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>-5.795</td>\n",
       "      <td>0.3170</td>\n",
       "      <td>176.089</td>\n",
       "      <td>0.429</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107145</th>\n",
       "      <td>China</td>\n",
       "      <td>['Anuel AA', 'Daddy Yankee', 'KAROL G', 'J Balvin', 'Ozuna']</td>\n",
       "      <td>301714</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0846</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>-3.702</td>\n",
       "      <td>0.0882</td>\n",
       "      <td>105.027</td>\n",
       "      <td>0.609</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87777</th>\n",
       "      <td>God's Plan</td>\n",
       "      <td>['Drake']</td>\n",
       "      <td>198973</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>-9.211</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>77.169</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87547</th>\n",
       "      <td>Starboy</td>\n",
       "      <td>['The Weeknd', 'Daft Punk']</td>\n",
       "      <td>230453</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>-7.015</td>\n",
       "      <td>0.2760</td>\n",
       "      <td>186.003</td>\n",
       "      <td>0.486</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              name                                                       artists  duration_ms  year  acousticness  danceability  energy  instrumentalness  loudness  speechiness    tempo  valence  mode  key  popularity  explicit\n",
       "87844                   Supalonely                                    ['BENEE', 'Gus Dapperton']       223480  2019        0.3050         0.863   0.631          0.000030    -4.689       0.0534  128.977    0.817     1    7          95         1\n",
       "87540                   goosebumps                                              ['Travis Scott']       243837  2016        0.0847         0.841   0.728          0.000000    -3.370       0.0484  130.049    0.430     1    7          92         1\n",
       "87863                      bad guy                                             ['Billie Eilish']       194088  2019        0.3280         0.701   0.425          0.130000   -10.965       0.3750  135.128    0.562     1    7          91         0\n",
       "87848   Ballin' (with Roddy Ricch)                                    ['Mustard', 'Roddy Ricch']       180435  2019        0.3920         0.809   0.545          0.000000    -6.678       0.0735   97.008    0.244     1    7          87         1\n",
       "97780                 PA' ROMPERLA                                     ['Bad Bunny', 'Don Omar']       194200  2020        0.0160         0.772   0.830          0.000000    -5.019       0.0599   94.011    0.697     1    7          86         1\n",
       "87813                      Shallow                               ['Lady Gaga', 'Bradley Cooper']       215733  2018        0.3710         0.572   0.385          0.000000    -6.362       0.0308   95.799    0.323     1    7          86         0\n",
       "87939                   LA CANCIÓN                                     ['J Balvin', 'Bad Bunny']       242573  2019        0.1520         0.754   0.646          0.000018    -5.795       0.3170  176.089    0.429     1    7          85         0\n",
       "107145                       China  ['Anuel AA', 'Daddy Yankee', 'KAROL G', 'J Balvin', 'Ozuna']       301714  2019        0.0846         0.786   0.808          0.000289    -3.702       0.0882  105.027    0.609     1    7          85         0\n",
       "87777                   God's Plan                                                     ['Drake']       198973  2018        0.0332         0.754   0.449          0.000083    -9.211       0.1090   77.169    0.357     1    7          84         1\n",
       "87547                      Starboy                                   ['The Weeknd', 'Daft Punk']       230453  2016        0.1410         0.679   0.587          0.000006    -7.015       0.2760  186.003    0.486     1    7          84         1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.query(\"mode == 1 and key == 7\").sort_values(\"popularity\", ascending=False).head(10) # Top 10 Major G songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>87909</th>\n",
       "      <td>Tusa</td>\n",
       "      <td>['KAROL G', 'Nicki Minaj']</td>\n",
       "      <td>200960</td>\n",
       "      <td>2019</td>\n",
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       "      <th>87977</th>\n",
       "      <td>Safaera</td>\n",
       "      <td>['Bad Bunny', 'Jowell &amp; Randy', 'Nengo Flow']</td>\n",
       "      <td>295177</td>\n",
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       "      <th>88036</th>\n",
       "      <td>MAMACITA</td>\n",
       "      <td>['Black Eyed Peas', 'Ozuna', 'J. Rey Soul']</td>\n",
       "      <td>249143</td>\n",
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       "      <td>0.1930</td>\n",
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       "      <th>88007</th>\n",
       "      <td>Si Veo a Tu Mamá</td>\n",
       "      <td>['Bad Bunny']</td>\n",
       "      <td>170972</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.4020</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-5.313</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>129.928</td>\n",
       "      <td>0.824</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>88003</th>\n",
       "      <td>You should be sad</td>\n",
       "      <td>['Halsey']</td>\n",
       "      <td>205473</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.350</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>110.940</td>\n",
       "      <td>0.324</td>\n",
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       "      <th>87746</th>\n",
       "      <td>Sunflower - Spider-Man: Into the Spider-Verse</td>\n",
       "      <td>['Post Malone', 'Swae Lee']</td>\n",
       "      <td>158040</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.574</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>89.911</td>\n",
       "      <td>0.913</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
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       "      <th>87854</th>\n",
       "      <td>Robbery</td>\n",
       "      <td>['Juice WRLD']</td>\n",
       "      <td>240051</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.3280</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.122</td>\n",
       "      <td>0.0457</td>\n",
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       "      <td>Lalala</td>\n",
       "      <td>['Y2K', 'bbno$']</td>\n",
       "      <td>160627</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.1810</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-7.899</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>129.972</td>\n",
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       "      <th>87928</th>\n",
       "      <td>Callaita</td>\n",
       "      <td>['Bad Bunny', 'Tainy']</td>\n",
       "      <td>250534</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-4.773</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>176.169</td>\n",
       "      <td>0.244</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>87972</th>\n",
       "      <td>Tell Me U Luv Me (with Trippie Redd)</td>\n",
       "      <td>['Juice WRLD', 'Trippie Redd']</td>\n",
       "      <td>180129</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.2990</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.605</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>128.022</td>\n",
       "      <td>0.379</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                                                name                                        artists  duration_ms  year  acousticness  danceability  energy  instrumentalness  loudness  speechiness    tempo  valence  mode  key  popularity  explicit\n",
       "87909                                           Tusa                     ['KAROL G', 'Nicki Minaj']       200960  2019        0.2950         0.803   0.715          0.000134    -3.280       0.2980  101.085    0.574     1    2          92         0\n",
       "87977                                        Safaera  ['Bad Bunny', 'Jowell & Randy', 'Nengo Flow']       295177  2020        0.0103         0.607   0.829          0.000000    -4.074       0.3790   96.172    0.685     1    2          92         1\n",
       "88036                                       MAMACITA    ['Black Eyed Peas', 'Ozuna', 'J. Rey Soul']       249143  2020        0.1930         0.894   0.635          0.001050    -6.929       0.0473  105.013    0.428     1    2          90         1\n",
       "88007                               Si Veo a Tu Mamá                                  ['Bad Bunny']       170972  2020        0.4020         0.900   0.603          0.000005    -5.313       0.0646  129.928    0.824     1    2          87         0\n",
       "88003                              You should be sad                                     ['Halsey']       205473  2020        0.1430         0.591   0.585          0.000000    -6.350       0.0277  110.940    0.324     1    2          86         1\n",
       "87746  Sunflower - Spider-Man: Into the Spider-Verse                    ['Post Malone', 'Swae Lee']       158040  2018        0.5560         0.760   0.479          0.000000    -5.574       0.0466   89.911    0.913     1    2          86         0\n",
       "87854                                        Robbery                                 ['Juice WRLD']       240051  2019        0.3280         0.685   0.692          0.000000    -5.122       0.0457  159.966    0.578     1    2          86         1\n",
       "87896                                         Lalala                               ['Y2K', 'bbno$']       160627  2019        0.1810         0.843   0.391          0.000000    -7.899       0.0845  129.972    0.496     1    2          86         1\n",
       "87928                                       Callaita                         ['Bad Bunny', 'Tainy']       250534  2019        0.6000         0.610   0.624          0.000002    -4.773       0.3090  176.169    0.244     1    2          85         1\n",
       "87972           Tell Me U Luv Me (with Trippie Redd)                 ['Juice WRLD', 'Trippie Redd']       180129  2020        0.2990         0.609   0.757          0.000000    -5.605       0.1190  128.022    0.379     1    2          85         1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.query(\"mode == 1 and key == 2\").sort_values(\"popularity\", ascending=False).head(10) # Top 10 Major D songs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This study on musical tonalities, their frequencies, and their popularities could be of help for musicians looking to strike a creative block with the different emotions exuded by a specified mode-key combination. Defined emotions for each mode-key combination, along with their musical attributes, should be of big help **for musicians to know what elements they will revolve a song around to induce a certain feel**.\n",
    "\n",
    "Additionally, **smarter digital audio workstations (DAWs) may be produced** with this type of data mining. When modified, it could be a possiblity that users may simply describe the emotion they want to emit from their music, and the DAW can configure preset instruments and synthesizers for artists to tinker around with to make music creation easier.\n",
    "\n",
    "With the right combination of musical scale and pitch class, this specific research on the data just might be a crucial factor to the formula of the next big hit!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FII #3: Determining the classifications of the Spotify tracks through K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Spotify data, by all means, is as comprehensive as it gets when it comes to the details on the musical elements. It gives us a look on the different audio features behind, as well as the prominent key combinations that gave us an idea on how and why songs sound the way they are, based on our previous EDA and FII questions.\n",
    "\n",
    "However, one glaring omission from the data is the genre for each track. A while back, particularly in the EDA, we were only able to contextualize musical elements without taking into account all of these variables as a whole. When combined, we could predict what the genre of the song is.\n",
    "\n",
    "Now, how exactly are we going to do that? Fortunately, with the musical elements at our disposal, we can factor in all of them and perform a data mining technique called **clustering**. Clustering allows us to give the data more structure, which is perfect in our situation given that again, no genres are defined.\n",
    "\n",
    "There are an immense possibility of genres to include, however, we will filter it down to the [ten fundamental music genres](https://www.blisshq.com/music-library-management-blog/2011/01/25/fundamental-music-genre-list/) (Blues, Classical, Country, Electronic, Folk, Hip-Hop, Jazz, New Age, Reggae, Rock), thus, we could group the data into 10 clusters.\n",
    "\n",
    "Let's begin by using **scikit**'s KMeans(), a method specialized for vector quantization that clusters items to their nearest mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Singende Bataillone 1. Teil</td>\n",
       "      <td>['Carl Woitschach']</td>\n",
       "      <td>158648</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.1950</td>\n",
       "      <td>0.563</td>\n",
       "      <td>-12.428</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>118.469</td>\n",
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       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Fantasiestücke, Op. 111: Più tosto lento</td>\n",
       "      <td>['Robert Schumann', 'Vladimir Horowitz']</td>\n",
       "      <td>282133</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.901</td>\n",
       "      <td>-28.454</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>83.972</td>\n",
       "      <td>0.0767</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chapter 1.18 - Zamek kaniowski</td>\n",
       "      <td>['Seweryn Goszczyński']</td>\n",
       "      <td>104300</td>\n",
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       "      <td>-19.924</td>\n",
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       "      <th>3</th>\n",
       "      <td>Bebamos Juntos - Instrumental (Remasterizado)</td>\n",
       "      <td>['Francisco Canaro']</td>\n",
       "      <td>180760</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.781</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.887</td>\n",
       "      <td>-14.734</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>108.003</td>\n",
       "      <td>0.7200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Polonaise-Fantaisie in A-Flat Major, Op. 61</td>\n",
       "      <td>['Frédéric Chopin', 'Vladimir Horowitz']</td>\n",
       "      <td>687733</td>\n",
       "      <td>1928</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.908</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>62.149</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            name                                   artists  duration_ms  year  acousticness  danceability  energy  instrumentalness  loudness  speechiness    tempo  valence  mode  key  popularity  explicit  cluster\n",
       "0                    Singende Bataillone 1. Teil                       ['Carl Woitschach']       158648  1928         0.995         0.708  0.1950             0.563   -12.428       0.0506  118.469   0.7790     1   10           0         0        5\n",
       "1       Fantasiestücke, Op. 111: Più tosto lento  ['Robert Schumann', 'Vladimir Horowitz']       282133  1928         0.994         0.379  0.0135             0.901   -28.454       0.0462   83.972   0.0767     1    8           0         0        4\n",
       "2                 Chapter 1.18 - Zamek kaniowski                   ['Seweryn Goszczyński']       104300  1928         0.604         0.749  0.2200             0.000   -19.924       0.9290  107.177   0.8800     0    5           0         0        7\n",
       "3  Bebamos Juntos - Instrumental (Remasterizado)                      ['Francisco Canaro']       180760  1928         0.995         0.781  0.1300             0.887   -14.734       0.0926  108.003   0.7200     0    1           0         0        5\n",
       "4    Polonaise-Fantaisie in A-Flat Major, Op. 61  ['Frédéric Chopin', 'Vladimir Horowitz']       687733  1928         0.990         0.210  0.2040             0.908   -16.829       0.0424   62.149   0.0693     1   11           1         0        4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=10, random_state=1)\n",
    "clusters = kmeans.fit_predict(main_df[[\"acousticness\", \"danceability\", \"energy\", \"instrumentalness\", \"speechiness\", \"valence\"]])\n",
    "main_df[\"cluster\"] = clusters\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the code above, we first initiated a KMeans process that will divide the data into `n_clusters` = 10. In the second line of code, we used the fit_predict() function from KMeans that computes the cluster centers and predict the cluster index for each sample. It takes in the metrics of our variables – `acousticness`, `danceability`, `energy`, `instrumentalness`, `speechiness`, `valence` – and draws cluster from their values.\n",
    "\n",
    "For the third line, we added a `cluster` column to `main_df` to give each track their respective clusters.\n",
    "\n",
    "Let's visualize the mean statistics of the important musical elements for each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>valence</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6646</td>\n",
       "      <td>0.124687</td>\n",
       "      <td>0.496422</td>\n",
       "      <td>0.692551</td>\n",
       "      <td>0.734079</td>\n",
       "      <td>-10.134035</td>\n",
       "      <td>0.060748</td>\n",
       "      <td>122.431447</td>\n",
       "      <td>0.509037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22374</td>\n",
       "      <td>0.867860</td>\n",
       "      <td>0.389454</td>\n",
       "      <td>0.213163</td>\n",
       "      <td>0.036244</td>\n",
       "      <td>-15.032943</td>\n",
       "      <td>0.048695</td>\n",
       "      <td>106.970097</td>\n",
       "      <td>0.252358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21861</td>\n",
       "      <td>0.050423</td>\n",
       "      <td>0.469511</td>\n",
       "      <td>0.822242</td>\n",
       "      <td>0.024632</td>\n",
       "      <td>-6.275095</td>\n",
       "      <td>0.089364</td>\n",
       "      <td>128.689150</td>\n",
       "      <td>0.412310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18425</td>\n",
       "      <td>0.566220</td>\n",
       "      <td>0.623094</td>\n",
       "      <td>0.591894</td>\n",
       "      <td>0.017554</td>\n",
       "      <td>-9.275587</td>\n",
       "      <td>0.072053</td>\n",
       "      <td>121.540533</td>\n",
       "      <td>0.794201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13054</td>\n",
       "      <td>0.923179</td>\n",
       "      <td>0.322282</td>\n",
       "      <td>0.159399</td>\n",
       "      <td>0.832784</td>\n",
       "      <td>-20.048982</td>\n",
       "      <td>0.046637</td>\n",
       "      <td>99.938566</td>\n",
       "      <td>0.183295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10125</td>\n",
       "      <td>0.916151</td>\n",
       "      <td>0.554024</td>\n",
       "      <td>0.331815</td>\n",
       "      <td>0.794956</td>\n",
       "      <td>-14.550489</td>\n",
       "      <td>0.078988</td>\n",
       "      <td>116.262533</td>\n",
       "      <td>0.702986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31713</td>\n",
       "      <td>0.112588</td>\n",
       "      <td>0.676404</td>\n",
       "      <td>0.709249</td>\n",
       "      <td>0.017264</td>\n",
       "      <td>-7.959882</td>\n",
       "      <td>0.090180</td>\n",
       "      <td>121.316249</td>\n",
       "      <td>0.774302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4202</td>\n",
       "      <td>0.480906</td>\n",
       "      <td>0.671804</td>\n",
       "      <td>0.260920</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>-18.380994</td>\n",
       "      <td>0.893191</td>\n",
       "      <td>107.718031</td>\n",
       "      <td>0.544079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18999</td>\n",
       "      <td>0.294897</td>\n",
       "      <td>0.553017</td>\n",
       "      <td>0.462509</td>\n",
       "      <td>0.018527</td>\n",
       "      <td>-10.215267</td>\n",
       "      <td>0.067528</td>\n",
       "      <td>117.131337</td>\n",
       "      <td>0.358160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21903</td>\n",
       "      <td>0.865319</td>\n",
       "      <td>0.586578</td>\n",
       "      <td>0.311398</td>\n",
       "      <td>0.035786</td>\n",
       "      <td>-12.585990</td>\n",
       "      <td>0.085273</td>\n",
       "      <td>115.964328</td>\n",
       "      <td>0.657243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  acousticness  danceability    energy  instrumentalness   loudness  speechiness       tempo   valence\n",
       "cluster                                                                                                             \n",
       "0         6646      0.124687      0.496422  0.692551          0.734079 -10.134035     0.060748  122.431447  0.509037\n",
       "1        22374      0.867860      0.389454  0.213163          0.036244 -15.032943     0.048695  106.970097  0.252358\n",
       "2        21861      0.050423      0.469511  0.822242          0.024632  -6.275095     0.089364  128.689150  0.412310\n",
       "3        18425      0.566220      0.623094  0.591894          0.017554  -9.275587     0.072053  121.540533  0.794201\n",
       "4        13054      0.923179      0.322282  0.159399          0.832784 -20.048982     0.046637   99.938566  0.183295\n",
       "5        10125      0.916151      0.554024  0.331815          0.794956 -14.550489     0.078988  116.262533  0.702986\n",
       "6        31713      0.112588      0.676404  0.709249          0.017264  -7.959882     0.090180  121.316249  0.774302\n",
       "7         4202      0.480906      0.671804  0.260920          0.003271 -18.380994     0.893191  107.718031  0.544079\n",
       "8        18999      0.294897      0.553017  0.462509          0.018527 -10.215267     0.067528  117.131337  0.358160\n",
       "9        21903      0.865319      0.586578  0.311398          0.035786 -12.585990     0.085273  115.964328  0.657243"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustersfinal_df = main_df.groupby(\"cluster\").agg(\"mean\")\n",
    "# clustersfinal_df[\"count\"] = main_df.groupby(\"cluster\").size()\n",
    "clustersfinal_df.insert(loc=0, column=\"count\", value=main_df.groupby(\"cluster\").size())\n",
    "clustersfinal_df[[\"count\", \"acousticness\", \"danceability\", \"energy\", \"instrumentalness\", \"loudness\", \"speechiness\", \"tempo\", \"valence\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The researchers looked up the most popular songs under each cluster (by way of filtering `main_df` by a specified cluster and sorting by popularity). We made best-guess interpretations of what genre the clusters could contain, judging from the mean musical value elements and the songs we are familiar with for each cluster:\n",
    "\n",
    "- **Cluster 0: Heavy bass and fast-paced.** (high energy, high instrumentalness)\n",
    "    - Smooth Criminal - Michael Jackson\n",
    "    - Seven Nation Army - The White Stripes\n",
    "    - She - Harry Styles\n",
    "   \n",
    "\n",
    "- **Cluster 1: Acoustic melancholy.** (high acousticness, low valence)\n",
    "    - lovely - Billie Eilish, Khalid\t\n",
    "    - when the party's over - Billie Eilish\n",
    "    - All of Me - John Legend\t\n",
    "    \n",
    "    \n",
    "- **Cluster 2: Upbeat, energetic pop.** (low acousticness, high energy, high tempo)\n",
    "    - Blinding Lights - The Weeknd\n",
    "    - ROXANNE - Arizona Zervas\n",
    "    - Thunder - Imagine Dragons\n",
    "\n",
    "\n",
    "- **Cluster 3: Feel-good, easy on the ears pop.** (high tempo, high valence)\n",
    "    - Toosie Slide - Drake\n",
    "    - Sunflower - Post Malone, Swae Lee\n",
    "    - Shape of You - Ed Sheeran\n",
    "    \n",
    "    \n",
    "- **Cluster 4: Laid-back, instrument-heavy.** (high acousticness, high instrumentalness)\n",
    "    - Coffee - beabadoobee\n",
    "    - Ribs - Lorde\n",
    "    - River Flows In You - Yiruma\n",
    "    \n",
    "    \n",
    "- **Cluster 5: Feel-good instrumental.** (high acousticness, high instrumentalness, high valence)\n",
    "    - Buttercup - Jack Stauber\n",
    "    - In My Room - Frank Ocean\n",
    "    - walk but in a garden - Llusion\n",
    "    \n",
    "    \n",
    "- **Cluster 6: Hip-hop beats galore.** (high danceability, high energy, high tempo)\n",
    "    - Intentions - Justin Bieber, Quavo\t\n",
    "    - Life Is Good - Future, Drake\n",
    "    - SICKO MODE - Travis Scott, Drake\n",
    "    \n",
    "- **Cluster 7: Heavy on the rap.** (high danceability, high speechiness)\n",
    "    - 21 - Polo G\n",
    "    - Caroline - Aminé\n",
    "    - RNP - YBN Cordae, Anderson .Paak\n",
    "    \n",
    "    \n",
    "- **Cluster 8: Danceable sound, melancholy lyrics.** (high danceability, low valence)\n",
    "    - Before You Go - Lewis Capaldi\n",
    "    - Lucid Dreams - Juice WRLD\n",
    "    - bad guy - Billie Eilish\n",
    "\n",
    "\n",
    "- **Cluster 9: Emotional tracks.** (high acousticness, high valence)\n",
    "    - Best Part - Daniel Caesar, H.E.R.\n",
    "    - Someone You Loved - Lewis Capaldi\n",
    "    - Say You Won't Let Go - James Arthur\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the Spotify data unfortunately does not have a genre variable that we can compare our findings from the clusterings to, however, we managed to craft our own descriptions of the clusters based on context of the mean musical elements and the most popular songs under each cluster.\n",
    "\n",
    "In the end, **we were able to grasp an idea of possible classifications for the tracks in the Spotify dataset**.\n",
    "\n",
    "In a way, the clusters we have made along with their fitting descriptions is our own scratching-the-surface method of creating a playlist based on the elements the tracks are prominent in. It gives our data more structure, and likewise, introduces us to how Spotify possibly gathers similar tracks to recommend users and create playlists based on our recent music activity, given the defined musical elements."
   ]
  }
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